diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_VariableFunctions.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_VariableFunctions.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c1f499fff0d0857807396b74e1dcd323a0e47c68 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_VariableFunctions.pyi @@ -0,0 +1,25821 @@ +# @generated by tools/pyi/gen_pyi.py from torch/_C/_VariableFunctions.pyi.in +# mypy: disable-error-code="type-arg" +# mypy: allow-untyped-defs + +import builtins +from typing import ( + Any, + Callable, + ContextManager, + Iterator, + Literal, + NamedTuple, + Optional, + overload, + Sequence, + TypeVar, + Union, +) + +import torch +from torch import contiguous_format, Generator, inf, memory_format, strided, SymInt, Tensor +from torch.types import ( + _bool, + _complex, + _device, + _dtype, + _float, + _int, + _layout, + _qscheme, + _size, + Device, + Number, +) + +from torch._prims_common import DeviceLikeType + +@overload +def __and__(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def __and__(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +@overload +def __lshift__(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def __lshift__(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +@overload +def __or__(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def __or__(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +@overload +def __rshift__(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def __rshift__(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +@overload +def __xor__(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def __xor__(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +def _adaptive_avg_pool2d(input: Tensor, output_size: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]]) -> Tensor: ... +def _adaptive_avg_pool3d(input: Tensor, output_size: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]]) -> Tensor: ... +def _add_batch_dim(input: Tensor, batch_dim: _int, level: _int) -> Tensor: ... +@overload +def _add_relu(input: Tensor, other: Tensor, *, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def _add_relu(input: Tensor, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: ... +@overload +def _add_relu_(input: Tensor, other: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: ... +@overload +def _add_relu_(input: Tensor, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: ... +def _addmm_activation(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, use_gelu: _bool = False, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def _aminmax(input: Tensor) -> tuple[Tensor, Tensor]: ... +@overload +def _aminmax(input: Tensor, dim: _int, keepdim: _bool = False) -> tuple[Tensor, Tensor]: ... +def _amp_foreach_non_finite_check_and_unscale_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], found_inf: Tensor, inv_scale: Tensor) -> None: ... +def _amp_update_scale_(input: Tensor, growth_tracker: Tensor, found_inf: Tensor, scale_growth_factor: _float, scale_backoff_factor: _float, growth_interval: _int) -> Tensor: ... +@overload +def _assert_async(input: Tensor) -> None: + r""" + _assert_async(tensor) -> void + + Asynchronously assert that the contents of tensor are nonzero. For CPU tensors, + this is equivalent to ``assert tensor`` or ``assert tensor.is_nonzero()``; for + CUDA tensors, we DO NOT synchronize and you may only find out the assertion + failed at a later CUDA kernel launch. Asynchronous assertion can be helpful for + testing invariants in CUDA tensors without giving up performance. This function + is NOT intended to be used for regular error checking, as it will trash your CUDA + context if the assert fails (forcing you to restart your PyTorch process.) + + Args: + tensor (Tensor): a one element tensor to test to see if it is nonzero. Zero + elements (including False for boolean tensors) cause an assertion failure + to be raised. + """ + ... +@overload +def _assert_async(input: Tensor, assert_msg: str) -> None: + r""" + _assert_async(tensor) -> void + + Asynchronously assert that the contents of tensor are nonzero. For CPU tensors, + this is equivalent to ``assert tensor`` or ``assert tensor.is_nonzero()``; for + CUDA tensors, we DO NOT synchronize and you may only find out the assertion + failed at a later CUDA kernel launch. Asynchronous assertion can be helpful for + testing invariants in CUDA tensors without giving up performance. This function + is NOT intended to be used for regular error checking, as it will trash your CUDA + context if the assert fails (forcing you to restart your PyTorch process.) + + Args: + tensor (Tensor): a one element tensor to test to see if it is nonzero. Zero + elements (including False for boolean tensors) cause an assertion failure + to be raised. + """ + ... +def _assert_scalar(self: Union[Number, _complex], assert_msg: str) -> None: ... +def _assert_tensor_metadata(a: Tensor, size: Optional[Sequence[Union[_int, SymInt]]] = None, stride: Optional[Sequence[Union[_int, SymInt]]] = None, dtype: Optional[_dtype] = None, *, device: Optional[Optional[DeviceLikeType]] = None, layout: Optional[_layout] = None) -> None: ... +def _batch_norm_impl_index(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, momentum: _float, eps: _float, cudnn_enabled: _bool) -> tuple[Tensor, Tensor, Tensor, Tensor, _int]: ... +def _cast_Byte(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Char(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Double(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Float(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Half(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Int(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Long(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _cast_Short(input: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _choose_qparams_per_tensor(input: Tensor, reduce_range: _bool = False) -> tuple[_float, _int]: ... +def _chunk_cat(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: _int, num_chunks: _int, *, out: Optional[Tensor] = None) -> Tensor: ... +def _coalesce(input: Tensor) -> Tensor: ... +def _compute_linear_combination(input: Tensor, coefficients: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _conj(input: Tensor) -> Tensor: ... +def _conj_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _conj_physical(input: Tensor) -> Tensor: ... +def _convert_indices_from_coo_to_csr(input: Tensor, size: _int, *, out_int32: _bool = False, out: Optional[Tensor] = None) -> Tensor: ... +def _convert_indices_from_csr_to_coo(crow_indices: Tensor, col_indices: Tensor, *, out_int32: _bool = False, transpose: _bool = False, out: Optional[Tensor] = None) -> Tensor: ... +def _convert_weight_to_int4pack(input: Tensor, innerKTiles: _int) -> Tensor: ... +def _convert_weight_to_int4pack_for_cpu(input: Tensor, innerKTiles: _int) -> Tensor: ... +@overload +def _convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], transposed: _bool, output_padding: _size, groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool, cudnn_enabled: _bool) -> Tensor: ... +@overload +def _convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], transposed: _bool, output_padding: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool, cudnn_enabled: _bool, allow_tf32: _bool) -> Tensor: ... +def _convolution_mode(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: str, dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def _copy_from(input: Tensor, dst: Tensor, non_blocking: _bool = False) -> Tensor: ... +def _copy_from_and_resize(input: Tensor, dst: Tensor) -> Tensor: ... +def _cslt_compress(input: Tensor) -> Tensor: ... +def _cslt_sparse_mm(compressed_A: Tensor, dense_B: Tensor, bias: Optional[Tensor] = None, alpha: Optional[Tensor] = None, out_dtype: Optional[_dtype] = None, transpose_result: _bool = False, alg_id: _int = 0, split_k: _int = 1, split_k_one_kernel: _bool = True) -> Tensor: ... +def _cslt_sparse_mm_search(compressed_A: Tensor, dense_B: Tensor, bias: Optional[Tensor] = None, alpha: Optional[Tensor] = None, out_dtype: Optional[_dtype] = None, transpose_result: _bool = False) -> _int: ... +@overload +def _ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int = 0, zero_infinity: _bool = False) -> tuple[Tensor, Tensor]: ... +@overload +def _ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: _int = 0, zero_infinity: _bool = False) -> tuple[Tensor, Tensor]: ... +@overload +def _cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int, deterministic: _bool, zero_infinity: _bool) -> tuple[Tensor, Tensor]: ... +@overload +def _cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: _int, deterministic: _bool, zero_infinity: _bool) -> tuple[Tensor, Tensor]: ... +def _cudnn_init_dropout_state(dropout: _float, train: _bool, dropout_seed: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +def _cudnn_rnn(input: Tensor, weight: Optional[Union[tuple[Tensor, ...], list[Tensor]]], weight_stride0: _int, weight_buf: Optional[Tensor], hx: Tensor, cx: Optional[Tensor], mode: _int, hidden_size: Union[_int, SymInt], proj_size: Union[_int, SymInt], num_layers: _int, batch_first: _bool, dropout: _float, train: _bool, bidirectional: _bool, batch_sizes: Sequence[Union[_int, SymInt]], dropout_state: Optional[Tensor]) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: ... +def _cudnn_rnn_flatten_weight(weight_arr: Optional[Union[tuple[Tensor, ...], list[Tensor]]], weight_stride0: _int, input_size: Union[_int, SymInt], mode: _int, hidden_size: Union[_int, SymInt], proj_size: Union[_int, SymInt], num_layers: _int, batch_first: _bool, bidirectional: _bool) -> Tensor: ... +def _cufft_clear_plan_cache(device_index: _int) -> None: ... +def _cufft_get_plan_cache_max_size(device_index: _int) -> _int: ... +def _cufft_get_plan_cache_size(device_index: _int) -> _int: ... +def _cufft_set_plan_cache_max_size(device_index: _int, max_size: _int) -> None: ... +def _cummax_helper(input: Tensor, values: Tensor, indices: Tensor, dim: _int) -> None: ... +def _cummin_helper(input: Tensor, values: Tensor, indices: Tensor, dim: _int) -> None: ... +def _debug_has_internal_overlap(input: Tensor) -> _int: ... +def _dim_arange(like: Tensor, dim: _int) -> Tensor: ... +def _dirichlet_grad(x: Tensor, alpha: Tensor, total: Tensor) -> Tensor: ... +def _disable_functionalization(): ... +def _dyn_quant_matmul_4bit(inp: Tensor, packed_weights: Tensor, block_size: _int, in_features: _int, out_features: _int) -> Tensor: ... +def _dyn_quant_pack_4bit_weight(weights: Tensor, scales_zeros: Tensor, bias: Optional[Tensor], block_size: _int, in_features: _int, out_features: _int) -> Tensor: ... +@overload +def _efficientzerotensor(size: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +@overload +def _efficientzerotensor(*size: Union[_int, SymInt], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +def _embedding_bag(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool = False, mode: _int = 0, sparse: _bool = False, per_sample_weights: Optional[Tensor] = None, include_last_offset: _bool = False, padding_idx: _int = -1) -> tuple[Tensor, Tensor, Tensor, Tensor]: ... +def _embedding_bag_forward_only(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool = False, mode: _int = 0, sparse: _bool = False, per_sample_weights: Optional[Tensor] = None, include_last_offset: _bool = False, padding_idx: _int = -1) -> tuple[Tensor, Tensor, Tensor, Tensor]: ... +@overload +def _empty_affine_quantized(size: Sequence[Union[_int, SymInt]], *, scale: _float = 1, zero_point: _int = 0, memory_format: Optional[memory_format] = contiguous_format, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +@overload +def _empty_affine_quantized(*size: Union[_int, SymInt], scale: _float = 1, zero_point: _int = 0, memory_format: Optional[memory_format] = contiguous_format, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +@overload +def _empty_per_channel_affine_quantized(size: Sequence[Union[_int, SymInt]], *, scales: Tensor, zero_points: Tensor, axis: _int, memory_format: Optional[memory_format] = contiguous_format, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +@overload +def _empty_per_channel_affine_quantized(*size: Union[_int, SymInt], scales: Tensor, zero_points: Tensor, axis: _int, memory_format: Optional[memory_format] = contiguous_format, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +def _enable_functionalization(*, reapply_views: _bool = False): ... +def _euclidean_dist(x1: Tensor, x2: Tensor) -> Tensor: ... +def _fake_quantize_learnable_per_channel_affine(input: Tensor, scale: Tensor, zero_point: Tensor, axis: _int, quant_min: _int, quant_max: _int, grad_factor: _float = 1.0) -> Tensor: ... +def _fake_quantize_learnable_per_tensor_affine(input: Tensor, scale: Tensor, zero_point: Tensor, quant_min: _int, quant_max: _int, grad_factor: _float = 1.0) -> Tensor: ... +def _fake_quantize_per_tensor_affine_cachemask_tensor_qparams(input: Tensor, scale: Tensor, zero_point: Tensor, fake_quant_enabled: Tensor, quant_min: _int, quant_max: _int) -> torch.return_types._fake_quantize_per_tensor_affine_cachemask_tensor_qparams: ... +def _fft_c2c(input: Tensor, dim: Sequence[Union[_int, SymInt]], normalization: _int, forward: _bool, *, out: Optional[Tensor] = None) -> Tensor: ... +def _fft_c2r(input: Tensor, dim: _size, normalization: _int, last_dim_size: Union[_int, SymInt], *, out: Optional[Tensor] = None) -> Tensor: ... +def _fft_r2c(input: Tensor, dim: _size, normalization: _int, onesided: _bool, *, out: Optional[Tensor] = None) -> Tensor: ... +def _fill_mem_eff_dropout_mask_(input: Tensor, dropout_p: _float, seed: _int, offset: _int) -> Tensor: ... +def _foobar(input: Tensor, arg1: _bool = True, arg2: _bool = True, *, arg3: _bool = True) -> Tensor: ... +def _foreach_abs(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_abs(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.abs` to each Tensor of the input list. + """ + ... +def _foreach_abs_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_abs_(self: List[Tensor]) -> None + + Apply :func:`torch.abs` to each Tensor of the input list. + """ + ... +def _foreach_acos(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_acos(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.acos` to each Tensor of the input list. + """ + ... +def _foreach_acos_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_acos_(self: List[Tensor]) -> None + + Apply :func:`torch.acos` to each Tensor of the input list. + """ + ... +@overload +def _foreach_add(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_add(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, alpha: Union[Number, _complex] = 1) -> tuple[Tensor, ...]: ... +@overload +def _foreach_add(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Tensor, *, alpha: Union[Number, _complex] = 1) -> tuple[Tensor, ...]: ... +@overload +def _foreach_add(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_add_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_add_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, alpha: Union[Number, _complex] = 1) -> None: ... +@overload +def _foreach_add_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Tensor, *, alpha: Union[Number, _complex] = 1) -> None: ... +@overload +def _foreach_add_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_addcdiv(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_addcdiv(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Tensor) -> tuple[Tensor, ...]: ... +@overload +def _foreach_addcdiv(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], value: Union[Number, _complex] = 1) -> tuple[Tensor, ...]: ... +@overload +def _foreach_addcdiv_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_addcdiv_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Tensor) -> None: ... +@overload +def _foreach_addcdiv_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], value: Union[Number, _complex] = 1) -> None: ... +@overload +def _foreach_addcmul(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_addcmul(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Tensor) -> tuple[Tensor, ...]: ... +@overload +def _foreach_addcmul(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], value: Union[Number, _complex] = 1) -> tuple[Tensor, ...]: ... +@overload +def _foreach_addcmul_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_addcmul_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Tensor) -> None: ... +@overload +def _foreach_addcmul_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensor2: Optional[Union[tuple[Tensor, ...], list[Tensor]]], value: Union[Number, _complex] = 1) -> None: ... +def _foreach_asin(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_asin(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.asin` to each Tensor of the input list. + """ + ... +def _foreach_asin_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_asin_(self: List[Tensor]) -> None + + Apply :func:`torch.asin` to each Tensor of the input list. + """ + ... +def _foreach_atan(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_atan(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.atan` to each Tensor of the input list. + """ + ... +def _foreach_atan_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_atan_(self: List[Tensor]) -> None + + Apply :func:`torch.atan` to each Tensor of the input list. + """ + ... +def _foreach_ceil(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_ceil(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.ceil` to each Tensor of the input list. + """ + ... +def _foreach_ceil_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_ceil_(self: List[Tensor]) -> None + + Apply :func:`torch.ceil` to each Tensor of the input list. + """ + ... +@overload +def _foreach_clamp_max(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_max(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_max(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_max_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_clamp_max_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_clamp_max_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: ... +@overload +def _foreach_clamp_min(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_min(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_min(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_clamp_min_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_clamp_min_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_clamp_min_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: ... +def _foreach_copy_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], src: Optional[Union[tuple[Tensor, ...], list[Tensor]]], non_blocking: _bool = False) -> None: ... +def _foreach_cos(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_cos(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.cos` to each Tensor of the input list. + """ + ... +def _foreach_cos_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_cos_(self: List[Tensor]) -> None + + Apply :func:`torch.cos` to each Tensor of the input list. + """ + ... +def _foreach_cosh(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_cosh(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.cosh` to each Tensor of the input list. + """ + ... +def _foreach_cosh_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_cosh_(self: List[Tensor]) -> None + + Apply :func:`torch.cosh` to each Tensor of the input list. + """ + ... +@overload +def _foreach_div(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_div(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Tensor) -> tuple[Tensor, ...]: ... +@overload +def _foreach_div(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_div(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_div_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_div_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Tensor) -> None: ... +@overload +def _foreach_div_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_div_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: ... +def _foreach_erf(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_erf(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.erf` to each Tensor of the input list. + """ + ... +def _foreach_erf_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_erf_(self: List[Tensor]) -> None + + Apply :func:`torch.erf` to each Tensor of the input list. + """ + ... +def _foreach_erfc(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_erfc(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.erfc` to each Tensor of the input list. + """ + ... +def _foreach_erfc_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_erfc_(self: List[Tensor]) -> None + + Apply :func:`torch.erfc` to each Tensor of the input list. + """ + ... +def _foreach_exp(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_exp(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.exp` to each Tensor of the input list. + """ + ... +def _foreach_exp_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_exp_(self: List[Tensor]) -> None + + Apply :func:`torch.exp` to each Tensor of the input list. + """ + ... +def _foreach_expm1(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_expm1(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.expm1` to each Tensor of the input list. + """ + ... +def _foreach_expm1_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_expm1_(self: List[Tensor]) -> None + + Apply :func:`torch.expm1` to each Tensor of the input list. + """ + ... +def _foreach_floor(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_floor(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.floor` to each Tensor of the input list. + """ + ... +def _foreach_floor_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_floor_(self: List[Tensor]) -> None + + Apply :func:`torch.floor` to each Tensor of the input list. + """ + ... +def _foreach_frac(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_frac(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.frac` to each Tensor of the input list. + """ + ... +def _foreach_frac_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_frac_(self: List[Tensor]) -> None + + Apply :func:`torch.frac` to each Tensor of the input list. + """ + ... +@overload +def _foreach_lerp(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensors1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], weight: Union[Number, _complex]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_lerp(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensors1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], weight: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_lerp(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensors1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], weights: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_lerp_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensors1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], weight: Union[Number, _complex]) -> None: ... +@overload +def _foreach_lerp_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensors1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], weight: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_lerp_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], tensors1: Optional[Union[tuple[Tensor, ...], list[Tensor]]], weights: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: ... +def _foreach_lgamma(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_lgamma(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.lgamma` to each Tensor of the input list. + """ + ... +def _foreach_lgamma_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_lgamma_(self: List[Tensor]) -> None + + Apply :func:`torch.lgamma` to each Tensor of the input list. + """ + ... +def _foreach_log(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_log(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.log` to each Tensor of the input list. + """ + ... +def _foreach_log10(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_log10(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.log10` to each Tensor of the input list. + """ + ... +def _foreach_log10_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_log10_(self: List[Tensor]) -> None + + Apply :func:`torch.log10` to each Tensor of the input list. + """ + ... +def _foreach_log1p(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_log1p(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.log1p` to each Tensor of the input list. + """ + ... +def _foreach_log1p_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_log1p_(self: List[Tensor]) -> None + + Apply :func:`torch.log1p` to each Tensor of the input list. + """ + ... +def _foreach_log2(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_log2(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.log2` to each Tensor of the input list. + """ + ... +def _foreach_log2_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_log2_(self: List[Tensor]) -> None + + Apply :func:`torch.log2` to each Tensor of the input list. + """ + ... +def _foreach_log_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_log_(self: List[Tensor]) -> None + + Apply :func:`torch.log` to each Tensor of the input list. + """ + ... +def _foreach_max(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_maximum(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_maximum(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_maximum(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_maximum_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_maximum_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_maximum_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: ... +@overload +def _foreach_minimum(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_minimum(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_minimum(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_minimum_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_minimum_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_minimum_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: ... +@overload +def _foreach_mul(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_mul(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Tensor) -> tuple[Tensor, ...]: ... +@overload +def _foreach_mul(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_mul(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_mul_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_mul_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Tensor) -> None: ... +@overload +def _foreach_mul_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> None: ... +@overload +def _foreach_mul_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: ... +def _foreach_neg(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_neg(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.neg` to each Tensor of the input list. + """ + ... +def _foreach_neg_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_neg_(self: List[Tensor]) -> None + + Apply :func:`torch.neg` to each Tensor of the input list. + """ + ... +def _foreach_norm(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], ord: Union[Number, _complex] = 2, dtype: Optional[_dtype] = None) -> tuple[Tensor, ...]: ... +@overload +def _foreach_pow(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exponent: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_pow(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exponent: Union[Number, _complex]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_pow(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exponent: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_pow(self: Union[Number, _complex], exponent: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_pow_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exponent: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_pow_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exponent: Union[Number, _complex]) -> None: ... +@overload +def _foreach_pow_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exponent: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: ... +def _foreach_reciprocal(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_reciprocal(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.reciprocal` to each Tensor of the input list. + """ + ... +def _foreach_reciprocal_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_reciprocal_(self: List[Tensor]) -> None + + Apply :func:`torch.reciprocal` to each Tensor of the input list. + """ + ... +def _foreach_round(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_round(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.round` to each Tensor of the input list. + """ + ... +def _foreach_round_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_round_(self: List[Tensor]) -> None + + Apply :func:`torch.round` to each Tensor of the input list. + """ + ... +def _foreach_rsqrt(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +def _foreach_rsqrt_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: ... +def _foreach_sigmoid(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_sigmoid(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.sigmoid` to each Tensor of the input list. + """ + ... +def _foreach_sigmoid_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_sigmoid_(self: List[Tensor]) -> None + + Apply :func:`torch.sigmoid` to each Tensor of the input list. + """ + ... +def _foreach_sign(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +def _foreach_sign_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: ... +def _foreach_sin(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_sin(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.sin` to each Tensor of the input list. + """ + ... +def _foreach_sin_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_sin_(self: List[Tensor]) -> None + + Apply :func:`torch.sin` to each Tensor of the input list. + """ + ... +def _foreach_sinh(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_sinh(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.sinh` to each Tensor of the input list. + """ + ... +def _foreach_sinh_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_sinh_(self: List[Tensor]) -> None + + Apply :func:`torch.sinh` to each Tensor of the input list. + """ + ... +def _foreach_sqrt(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_sqrt(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.sqrt` to each Tensor of the input list. + """ + ... +def _foreach_sqrt_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_sqrt_(self: List[Tensor]) -> None + + Apply :func:`torch.sqrt` to each Tensor of the input list. + """ + ... +@overload +def _foreach_sub(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_sub(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, alpha: Union[Number, _complex] = 1) -> tuple[Tensor, ...]: ... +@overload +def _foreach_sub(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> tuple[Tensor, ...]: ... +@overload +def _foreach_sub_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalars: Sequence[Union[Number, _complex]]) -> None: ... +@overload +def _foreach_sub_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], other: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, alpha: Union[Number, _complex] = 1) -> None: ... +@overload +def _foreach_sub_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scalar: Union[Number, _complex]) -> None: ... +def _foreach_tan(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_tan(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.tan` to each Tensor of the input list. + """ + ... +def _foreach_tan_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_tan_(self: List[Tensor]) -> None + + Apply :func:`torch.tan` to each Tensor of the input list. + """ + ... +def _foreach_tanh(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_tanh(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.tanh` to each Tensor of the input list. + """ + ... +def _foreach_tanh_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_tanh_(self: List[Tensor]) -> None + + Apply :func:`torch.tanh` to each Tensor of the input list. + """ + ... +def _foreach_trunc(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + _foreach_trunc(self: List[Tensor]) -> List[Tensor] + + Apply :func:`torch.trunc` to each Tensor of the input list. + """ + ... +def _foreach_trunc_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_trunc_(self: List[Tensor]) -> None + + Apply :func:`torch.trunc` to each Tensor of the input list. + """ + ... +def _foreach_zero_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> None: + r""" + _foreach_zero_(self: List[Tensor]) -> None + + Apply :func:`torch.zero` to each Tensor of the input list. + """ + ... +def _from_functional_tensor(t: Tensor) -> Tensor: ... +def _functional_assert_async(input: Tensor, assert_msg: str, dep_token: Tensor) -> Tensor: ... +def _functional_assert_scalar(self: Union[Number, _complex], assert_msg: str, dep_token: Tensor) -> Tensor: ... +def _functional_sym_constrain_range(size: Union[Number, _complex], min: Optional[_int], max: Optional[_int], dep_token: Tensor) -> Tensor: ... +def _functional_sym_constrain_range_for_size(size: Union[Number, _complex], min: Optional[_int], max: Optional[_int], dep_token: Tensor) -> Tensor: ... +def _functionalize_apply_view_metas(tensor: Tensor, base: Tensor) -> Tensor: ... +def _functionalize_are_all_mutations_hidden_from_autograd(t: Tensor) -> _bool: ... +def _functionalize_are_all_mutations_under_no_grad_or_inference_mode(t: Tensor) -> _bool: ... +def _functionalize_commit_update(t: Tensor) -> None: ... +def _functionalize_has_metadata_mutation(tensor: Tensor) -> _bool: ... +def _functionalize_is_symbolic(tensor: Tensor) -> _bool: ... +def _functionalize_mark_mutation_hidden_from_autograd(t: Tensor) -> None: ... +def _functionalize_replace(self_: Tensor, other: Tensor) -> None: ... +def _functionalize_set_storage_changed(tensor: Tensor) -> _bool: ... +def _functionalize_sync(t: Tensor) -> None: ... +def _functionalize_unsafe_set(dst: Tensor, src: Tensor) -> None: ... +def _functionalize_was_inductor_storage_resized(t: Tensor) -> _bool: ... +def _functionalize_was_storage_changed(tensor: Tensor) -> _bool: ... +def _fused_adagrad_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], grads: Optional[Union[tuple[Tensor, ...], list[Tensor]]], state_sums: Optional[Union[tuple[Tensor, ...], list[Tensor]]], state_steps: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, lr: _float, lr_decay: _float, weight_decay: _float, eps: _float, maximize: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +@overload +def _fused_adam_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], grads: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exp_avgs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exp_avg_sqs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], max_exp_avg_sqs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], state_steps: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, lr: Tensor, beta1: _float, beta2: _float, weight_decay: _float, eps: _float, amsgrad: _bool, maximize: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +@overload +def _fused_adam_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], grads: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exp_avgs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exp_avg_sqs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], max_exp_avg_sqs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], state_steps: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, lr: _float, beta1: _float, beta2: _float, weight_decay: _float, eps: _float, amsgrad: _bool, maximize: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +@overload +def _fused_adamw_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], grads: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exp_avgs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exp_avg_sqs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], max_exp_avg_sqs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], state_steps: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, lr: Tensor, beta1: _float, beta2: _float, weight_decay: _float, eps: _float, amsgrad: _bool, maximize: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +@overload +def _fused_adamw_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], grads: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exp_avgs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], exp_avg_sqs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], max_exp_avg_sqs: Optional[Union[tuple[Tensor, ...], list[Tensor]]], state_steps: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, lr: _float, beta1: _float, beta2: _float, weight_decay: _float, eps: _float, amsgrad: _bool, maximize: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +def _fused_dropout(input: Tensor, p: _float, generator: Optional[Generator] = None) -> tuple[Tensor, Tensor]: ... +def _fused_moving_avg_obs_fq_helper(input: Tensor, observer_on: Tensor, fake_quant_on: Tensor, running_min: Tensor, running_max: Tensor, scale: Tensor, zero_point: Tensor, averaging_const: _float, quant_min: _int, quant_max: _int, ch_axis: _int, per_row_fake_quant: _bool = False, symmetric_quant: _bool = False) -> torch.return_types._fused_moving_avg_obs_fq_helper: ... +def _fused_sdp_choice(query: Tensor, key: Tensor, value: Tensor, attn_mask: Optional[Tensor] = None, dropout_p: _float = 0.0, is_causal: _bool = False, *, scale: Optional[_float] = None, enable_gqa: _bool = False) -> _int: ... +@overload +def _fused_sgd_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], grads: Optional[Union[tuple[Tensor, ...], list[Tensor]]], momentum_buffer_list: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, weight_decay: _float, momentum: _float, lr: Tensor, dampening: _float, nesterov: _bool, maximize: _bool, is_first_step: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +@overload +def _fused_sgd_(self: Optional[Union[tuple[Tensor, ...], list[Tensor]]], grads: Optional[Union[tuple[Tensor, ...], list[Tensor]]], momentum_buffer_list: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, weight_decay: _float, momentum: _float, lr: _float, dampening: _float, nesterov: _bool, maximize: _bool, is_first_step: _bool, grad_scale: Optional[Tensor] = None, found_inf: Optional[Tensor] = None) -> None: ... +def _fw_primal_copy(input: Tensor, level: _int, *, out: Optional[Tensor] = None) -> Tensor: ... +def _grid_sampler_2d_cpu_fallback(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ... +def _has_compatible_shallow_copy_type(input: Tensor, from_: Tensor) -> _bool: ... +def _histogramdd_bin_edges(input: Tensor, bins: _size, *, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> tuple[Tensor, ...]: ... +def _histogramdd_from_bin_cts(input: Tensor, bins: _size, *, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> Tensor: ... +def _histogramdd_from_bin_tensors(input: Tensor, bins: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, weight: Optional[Tensor] = None, density: _bool = False) -> Tensor: ... +def _index_put_impl_(input: Tensor, indices: Optional[Union[tuple[Tensor, ...], list[Tensor]]], values: Tensor, accumulate: _bool = False, unsafe: _bool = False) -> Tensor: ... +def _indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _int_mm(input: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _is_all_true(input: Tensor) -> Tensor: ... +def _is_any_true(input: Tensor) -> Tensor: ... +def _is_functional_tensor(t: Tensor) -> _bool: ... +def _is_functional_tensor_base(t: Tensor) -> _bool: ... +def _is_zerotensor(input: Tensor) -> _bool: ... +def _lazy_clone(input: Tensor) -> Tensor: ... +def _linalg_check_errors(info: Tensor, api_name: str, *, is_matrix: _bool) -> None: ... +def _linalg_det(A: Tensor, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types._linalg_det: ... +def _linalg_eigh(A: Tensor, UPLO: str = "L", compute_v: _bool = True, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types._linalg_eigh: ... +def _linalg_slogdet(A: Tensor, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types._linalg_slogdet: ... +def _linalg_solve_ex(A: Tensor, B: Tensor, *, left: _bool = True, check_errors: _bool = False, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types._linalg_solve_ex: ... +def _linalg_svd(A: Tensor, full_matrices: _bool = False, compute_uv: _bool = True, *, driver: Optional[str] = None, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types._linalg_svd: ... +def _log_softmax(input: Tensor, dim: _int, half_to_float: _bool, *, out: Optional[Tensor] = None) -> Tensor: ... +def _log_softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input_dtype: _dtype, *, out: Optional[Tensor] = None) -> Tensor: ... +def _logcumsumexp(input: Tensor, dim: _int, *, out: Optional[Tensor] = None) -> Tensor: ... +def _lstm_mps(input: Tensor, hx: Optional[Union[tuple[Tensor, ...], list[Tensor]]], params: Optional[Union[tuple[Tensor, ...], list[Tensor]]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]: ... +def _lu_with_info(input: Tensor, pivot: _bool = True, check_errors: _bool = True) -> torch.return_types._lu_with_info: ... +def _make_dep_token(*, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +def _make_dual(primal: Tensor, tangent: Tensor, level: _int) -> Tensor: ... +def _make_dual_copy(primal: Tensor, tangent: Tensor, level: _int, *, out: Optional[Tensor] = None) -> Tensor: ... +def _make_per_channel_quantized_tensor(input: Tensor, scale: Tensor, zero_point: Tensor, axis: _int) -> Tensor: ... +def _make_per_tensor_quantized_tensor(input: Tensor, scale: _float, zero_point: _int) -> Tensor: ... +def _masked_scale(input: Tensor, mask: Tensor, scale: _float) -> Tensor: ... +def _masked_softmax(input: Tensor, mask: Tensor, dim: Optional[_int] = None, mask_type: Optional[_int] = None) -> Tensor: ... +def _mixed_dtypes_linear(input: Tensor, weight: Tensor, scale: Tensor, *, bias: Optional[Tensor] = None, activation: Optional[str] = None) -> Tensor: ... +def _mkldnn_reshape(input: Tensor, shape: _size) -> Tensor: ... +def _mkldnn_transpose(input: Tensor, dim0: _int, dim1: _int) -> Tensor: ... +def _mkldnn_transpose_(input: Tensor, dim0: _int, dim1: _int) -> Tensor: ... +def _mps_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def _mps_convolution_transpose(input: Tensor, weight: Tensor, padding: Sequence[Union[_int, SymInt]], output_padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +@overload +def _native_batch_norm_legit(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Tensor, running_var: Tensor, training: _bool, momentum: _float, eps: _float, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> tuple[Tensor, Tensor, Tensor]: ... +@overload +def _native_batch_norm_legit(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], training: _bool, momentum: _float, eps: _float, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> tuple[Tensor, Tensor, Tensor]: ... +def _native_batch_norm_legit_no_training(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Tensor, running_var: Tensor, momentum: _float, eps: _float) -> tuple[Tensor, Tensor, Tensor]: ... +def _native_multi_head_attention(query: Tensor, key: Tensor, value: Tensor, embed_dim: _int, num_head: _int, qkv_weight: Tensor, qkv_bias: Tensor, proj_weight: Tensor, proj_bias: Tensor, mask: Optional[Tensor] = None, need_weights: _bool = True, average_attn_weights: _bool = True, mask_type: Optional[_int] = None) -> tuple[Tensor, Tensor]: ... +def _neg_view(input: Tensor) -> Tensor: ... +def _neg_view_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _nested_compute_contiguous_strides_offsets(nested_size: Tensor) -> tuple[Tensor, Tensor]: ... +def _nested_from_padded(padded: Tensor, cpu_nested_shape_example: Tensor, fuse_transform_0213: _bool = False) -> Tensor: ... +def _nested_from_padded_and_nested_example(padded: Tensor, nt_example: Tensor) -> Tensor: ... +def _nested_from_padded_tensor(padded: Tensor, offsets: Tensor, dummy: Tensor, ragged_idx: _int = 1, min_seqlen: Optional[Tensor] = None, max_seqlen: Optional[Tensor] = None, sum_S: Optional[Union[_int, SymInt]] = None) -> Tensor: ... +def _nested_get_jagged_dummy(any: Tensor) -> Tensor: ... +def _nested_get_lengths(input: Tensor) -> Tensor: ... +def _nested_get_max_seqlen(input: Tensor) -> Tensor: ... +def _nested_get_min_seqlen(input: Tensor) -> Tensor: ... +def _nested_get_offsets(input: Tensor) -> Tensor: ... +def _nested_get_ragged_idx(input: Tensor) -> _int: ... +def _nested_get_values(input: Tensor) -> Tensor: ... +def _nested_get_values_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _nested_tensor_from_mask(t: Tensor, mask: Tensor, mask_check: _bool = True) -> Tensor: ... +def _nested_tensor_from_mask_left_aligned(t: Tensor, mask: Tensor) -> _bool: ... +def _nested_tensor_from_tensor_list(list: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = None) -> Tensor: ... +def _nested_tensor_softmax_with_shape(input: Tensor, query: Tensor) -> Tensor: ... +def _nested_view_from_buffer(input: Tensor, nested_size: Tensor, nested_strides: Tensor, offsets: Tensor) -> Tensor: ... +def _nested_view_from_buffer_copy(input: Tensor, nested_size: Tensor, nested_strides: Tensor, offsets: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _nested_view_from_jagged(input: Tensor, offsets: Tensor, dummy: Tensor, lengths: Optional[Tensor] = None, ragged_idx: _int = 1, min_seqlen: Optional[Tensor] = None, max_seqlen: Optional[Tensor] = None) -> Tensor: ... +def _nested_view_from_jagged_copy(input: Tensor, offsets: Tensor, dummy: Tensor, lengths: Optional[Tensor] = None, ragged_idx: _int = 1, min_seqlen: Optional[Tensor] = None, max_seqlen: Optional[Tensor] = None, *, out: Optional[Tensor] = None) -> Tensor: ... +def _nnpack_available() -> _bool: ... +def _nnpack_spatial_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]], stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1) -> Tensor: ... +def _pack_padded_sequence(input: Tensor, lengths: Tensor, batch_first: _bool) -> tuple[Tensor, Tensor]: ... +def _pad_packed_sequence(data: Tensor, batch_sizes: Tensor, batch_first: _bool, padding_value: Union[Number, _complex], total_length: _int) -> tuple[Tensor, Tensor]: ... +def _pin_memory(input: Tensor, device: Optional[Optional[DeviceLikeType]] = None) -> Tensor: ... +def _prelu_kernel(input: Tensor, weight: Tensor) -> Tensor: ... +def _print(s: str) -> None: ... +def _propagate_xla_data(input: Tensor, output: Tensor) -> None: ... +def _remove_batch_dim(input: Tensor, level: _int, batch_size: Union[_int, SymInt], out_dim: _int) -> Tensor: ... +def _reshape_alias_copy(input: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None) -> Tensor: ... +def _reshape_from_tensor(input: Tensor, shape: Tensor) -> Tensor: ... +def _resize_output_(input: Tensor, size: Sequence[Union[_int, SymInt]], device: Optional[DeviceLikeType]) -> Tensor: ... +def _rowwise_prune(weight: Tensor, mask: Tensor, compressed_indices_dtype: _dtype) -> tuple[Tensor, Tensor]: ... +def _safe_softmax(input: Tensor, dim: _int, dtype: Optional[_dtype] = None) -> Tensor: ... +def _sample_dirichlet(input: Tensor, generator: Optional[Generator] = None) -> Tensor: ... +def _saturate_weight_to_fp16(weight: Tensor) -> Tensor: ... +def _scaled_dot_product_attention_math(query: Tensor, key: Tensor, value: Tensor, attn_mask: Optional[Tensor] = None, dropout_p: _float = 0.0, is_causal: _bool = False, dropout_mask: Optional[Tensor] = None, *, scale: Optional[_float] = None, enable_gqa: _bool = False) -> tuple[Tensor, Tensor]: ... +def _scaled_dot_product_attention_math_for_mps(query: Tensor, key: Tensor, value: Tensor, attn_mask: Optional[Tensor] = None, dropout_p: _float = 0.0, is_causal: _bool = False, dropout_mask: Optional[Tensor] = None, *, scale: Optional[_float] = None) -> tuple[Tensor, Tensor]: ... +def _scaled_dot_product_cudnn_attention(query: Tensor, key: Tensor, value: Tensor, attn_bias: Optional[Tensor], compute_log_sumexp: _bool, dropout_p: _float = 0.0, is_causal: _bool = False, return_debug_mask: _bool = False, *, scale: Optional[_float] = None) -> torch.return_types._scaled_dot_product_cudnn_attention: ... +def _scaled_dot_product_efficient_attention(query: Tensor, key: Tensor, value: Tensor, attn_bias: Optional[Tensor], compute_log_sumexp: _bool, dropout_p: _float = 0.0, is_causal: _bool = False, *, scale: Optional[_float] = None) -> torch.return_types._scaled_dot_product_efficient_attention: ... +def _scaled_dot_product_flash_attention(query: Tensor, key: Tensor, value: Tensor, dropout_p: _float = 0.0, is_causal: _bool = False, return_debug_mask: _bool = False, *, scale: Optional[_float] = None) -> torch.return_types._scaled_dot_product_flash_attention: ... +def _scaled_dot_product_flash_attention_for_cpu(query: Tensor, key: Tensor, value: Tensor, dropout_p: _float = 0.0, is_causal: _bool = False, *, attn_mask: Optional[Tensor] = None, scale: Optional[_float] = None) -> torch.return_types._scaled_dot_product_flash_attention_for_cpu: ... +def _scaled_grouped_mm(input: Tensor, mat2: Tensor, scale_a: Tensor, scale_b: Tensor, offs: Optional[Tensor] = None, bias: Optional[Tensor] = None, scale_result: Optional[Tensor] = None, out_dtype: Optional[_dtype] = None, use_fast_accum: _bool = False) -> Tensor: ... +def _scaled_mm(input: Tensor, mat2: Tensor, scale_a: Tensor, scale_b: Tensor, bias: Optional[Tensor] = None, scale_result: Optional[Tensor] = None, out_dtype: Optional[_dtype] = None, use_fast_accum: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: ... +def _shape_as_tensor(input: Tensor) -> Tensor: ... +def _sobol_engine_draw(quasi: Tensor, n: _int, sobolstate: Tensor, dimension: _int, num_generated: _int, dtype: Optional[_dtype]) -> tuple[Tensor, Tensor]: ... +def _sobol_engine_ff_(input: Tensor, n: _int, sobolstate: Tensor, dimension: _int, num_generated: _int) -> Tensor: ... +def _sobol_engine_initialize_state_(input: Tensor, dimension: _int) -> Tensor: ... +def _sobol_engine_scramble_(input: Tensor, ltm: Tensor, dimension: _int) -> Tensor: ... +def _softmax(input: Tensor, dim: _int, half_to_float: _bool, *, out: Optional[Tensor] = None) -> Tensor: ... +def _softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input_dtype: _dtype, *, grad_input: Optional[Tensor] = None) -> Tensor: ... +def _sparse_broadcast_to(input: Tensor, size: _size) -> Tensor: ... +def _sparse_broadcast_to_copy(input: Tensor, size: _size, *, out: Optional[Tensor] = None) -> Tensor: ... +def _sparse_csr_prod(input: Tensor, dim: Union[_int, _size], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... +def _sparse_csr_sum(input: Tensor, dim: Union[_int, _size], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... +def _sparse_log_softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input: Tensor) -> Tensor: ... +def _sparse_semi_structured_addmm(input: Tensor, mat1: Tensor, mat1_meta: Tensor, mat2: Tensor, *, alpha: Union[Number, _complex] = 1, beta: Union[Number, _complex] = 1, out_dtype: Optional[_dtype] = None) -> Tensor: ... +def _sparse_semi_structured_apply(input: Tensor, thread_masks: Tensor) -> tuple[Tensor, Tensor]: ... +def _sparse_semi_structured_apply_dense(input: Tensor, thread_masks: Tensor) -> Tensor: ... +def _sparse_semi_structured_linear(input: Tensor, weight: Tensor, meta: Tensor, *, bias: Optional[Tensor] = None, activation: Optional[str] = None, out_dtype: Optional[_dtype] = None) -> Tensor: ... +def _sparse_semi_structured_mm(mat1: Tensor, mat1_meta: Tensor, mat2: Tensor, *, out_dtype: Optional[_dtype] = None) -> Tensor: ... +def _sparse_semi_structured_tile(input: Tensor, algorithm: str = "", use_cutlass: _bool = True) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: ... +def _sparse_softmax_backward_data(grad_output: Tensor, output: Tensor, dim: _int, input: Tensor) -> Tensor: ... +def _sparse_sparse_matmul(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def _sparse_sum(input: Tensor) -> Tensor: ... +@overload +def _sparse_sum(input: Tensor, *, dtype: _dtype) -> Tensor: ... +@overload +def _sparse_sum(input: Tensor, dim: Union[_int, _size]) -> Tensor: ... +@overload +def _sparse_sum(input: Tensor, dim: Union[_int, _size], *, dtype: _dtype) -> Tensor: ... +def _stack(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: ... +def _standard_gamma(input: Tensor, generator: Optional[Generator] = None) -> Tensor: ... +def _standard_gamma_grad(input: Tensor, output: Tensor) -> Tensor: ... +def _sync(t: Tensor) -> None: ... +@overload +def _test_autograd_multiple_dispatch(input: Tensor) -> Tensor: ... +@overload +def _test_autograd_multiple_dispatch(input: Tensor, b: _bool) -> Tensor: ... +def _test_autograd_multiple_dispatch_view(input: Tensor) -> Tensor: ... +def _test_autograd_multiple_dispatch_view_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _test_check_tensor(input: Tensor) -> Tensor: ... +def _test_functorch_fallback(input: Tensor, other: Tensor) -> Tensor: ... +def _test_parallel_materialize(input: Tensor, num_parallel: _int, skip_first: _bool = False) -> Tensor: ... +def _test_serialization_subcmul(input: Tensor, other: Tensor, alpha: Union[Number, _complex] = 1) -> Tensor: ... +def _to_cpu(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: ... +def _to_functional_tensor(t: Tensor) -> Tensor: ... +def _to_sparse_semi_structured(dense: Tensor) -> tuple[Tensor, Tensor]: ... +def _transform_bias_rescale_qkv(qkv: Tensor, qkv_bias: Tensor, num_heads: _int) -> tuple[Tensor, Tensor, Tensor]: ... +def _transformer_encoder_layer_fwd(src: Tensor, embed_dim: _int, num_heads: _int, qkv_weight: Tensor, qkv_bias: Tensor, proj_weight: Tensor, proj_bias: Tensor, use_gelu: _bool, norm_first: _bool, eps: _float, norm_weight_1: Tensor, norm_bias_1: Tensor, norm_weight_2: Tensor, norm_bias_2: Tensor, ffn_weight_1: Tensor, ffn_bias_1: Tensor, ffn_weight_2: Tensor, ffn_bias_2: Tensor, mask: Optional[Tensor] = None, mask_type: Optional[_int] = None) -> Tensor: ... +def _trilinear(i1: Tensor, i2: Tensor, i3: Tensor, expand1: _size, expand2: _size, expand3: _size, sumdim: _size, unroll_dim: _int = 1) -> Tensor: ... +def _triton_multi_head_attention(query: Tensor, key: Tensor, value: Tensor, embed_dim: _int, num_head: _int, qkv_weight: Tensor, qkv_bias: Tensor, proj_weight: Tensor, proj_bias: Tensor, mask: Optional[Tensor] = None) -> Tensor: ... +def _triton_scaled_dot_attention(q: Tensor, k: Tensor, v: Tensor, dropout_p: _float = 0.0) -> Tensor: ... +def _unique(input: Tensor, sorted: _bool = True, return_inverse: _bool = False) -> tuple[Tensor, Tensor]: ... +def _unique2(input: Tensor, sorted: _bool = True, return_inverse: _bool = False, return_counts: _bool = False) -> tuple[Tensor, Tensor, Tensor]: ... +def _unpack_dual(dual: Tensor, level: _int) -> torch.return_types._unpack_dual: ... +def _unsafe_index(input: Tensor, indices: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> Tensor: ... +def _unsafe_index_put(input: Tensor, indices: Optional[Union[tuple[Tensor, ...], list[Tensor]]], values: Tensor, accumulate: _bool = False) -> Tensor: ... +def _unsafe_masked_index(input: Tensor, mask: Tensor, indices: Optional[Union[tuple[Tensor, ...], list[Tensor]]], fill: Union[Number, _complex]) -> Tensor: ... +def _unsafe_masked_index_put_accumulate(input: Tensor, mask: Tensor, indices: Optional[Union[tuple[Tensor, ...], list[Tensor]]], values: Tensor) -> Tensor: ... +@overload +def _use_cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: _int) -> _bool: ... +@overload +def _use_cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int) -> _bool: ... +def _use_cudnn_rnn_flatten_weight() -> _bool: ... +def _validate_compressed_sparse_indices(is_crow: _bool, compressed_idx: Tensor, plain_idx: Tensor, cdim: _int, dim: _int, nnz: _int) -> None: ... +def _validate_sparse_bsc_tensor_args(ccol_indices: Tensor, row_indices: Tensor, values: Tensor, size: _size) -> None: ... +def _validate_sparse_bsr_tensor_args(crow_indices: Tensor, col_indices: Tensor, values: Tensor, size: _size) -> None: ... +def _validate_sparse_compressed_tensor_args(compressed_indices: Tensor, plain_indices: Tensor, values: Tensor, size: _size, layout: _layout) -> None: ... +def _validate_sparse_coo_tensor_args(indices: Tensor, values: Tensor, size: _size, is_coalesced: Optional[_bool] = None) -> None: ... +def _validate_sparse_csc_tensor_args(ccol_indices: Tensor, row_indices: Tensor, values: Tensor, size: _size) -> None: ... +def _validate_sparse_csr_tensor_args(crow_indices: Tensor, col_indices: Tensor, values: Tensor, size: _size) -> None: ... +def _values_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def _weight_int4pack_mm(input: Tensor, mat2: Tensor, qGroupSize: _int, qScaleAndZeros: Tensor) -> Tensor: ... +def _weight_int4pack_mm_for_cpu(input: Tensor, mat2: Tensor, qGroupSize: _int, qScaleAndZeros: Tensor) -> Tensor: ... +def _weight_int8pack_mm(input: Tensor, mat2: Tensor, scales: Tensor) -> Tensor: ... +def _weight_norm(v: Tensor, g: Tensor, dim: _int = 0) -> Tensor: ... +def _weight_norm_interface(v: Tensor, g: Tensor, dim: _int = 0) -> tuple[Tensor, Tensor]: ... +def _wrapped_linear_prepack(weight: Tensor, weight_scale: Tensor, weight_zero_point: Tensor, bias: Tensor) -> Tensor: ... +def _wrapped_quantized_linear_prepacked(input: Tensor, input_scale: Tensor, input_zero_point: Tensor, packed_weight: Tensor, output_scale: Tensor, output_zero_point: Tensor, out_channel: _int) -> Tensor: ... +def abs(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + abs(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Computes the absolute value of each element in :attr:`input`. + + .. math:: + \text{out}_{i} = |\text{input}_{i}| + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.abs(torch.tensor([-1, -2, 3])) + tensor([ 1, 2, 3]) + """ + ... +def abs_(input: Tensor) -> Tensor: ... +def absolute(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + absolute(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Alias for :func:`torch.abs` + """ + ... +def acos(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + acos(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Computes the inverse cosine of each element in :attr:`input`. + + .. math:: + \text{out}_{i} = \cos^{-1}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.3348, -0.5889, 0.2005, -0.1584]) + >>> torch.acos(a) + tensor([ 1.2294, 2.2004, 1.3690, 1.7298]) + """ + ... +def acos_(input: Tensor) -> Tensor: ... +def acosh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + acosh(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Returns a new tensor with the inverse hyperbolic cosine of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \cosh^{-1}(\text{input}_{i}) + + Note: + The domain of the inverse hyperbolic cosine is `[1, inf)` and values outside this range + will be mapped to ``NaN``, except for `+ INF` for which the output is mapped to `+ INF`. + + Args: + input (Tensor): the input tensor. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4).uniform_(1, 2) + >>> a + tensor([ 1.3192, 1.9915, 1.9674, 1.7151 ]) + >>> torch.acosh(a) + tensor([ 0.7791, 1.3120, 1.2979, 1.1341 ]) + """ + ... +def acosh_(input: Tensor) -> Tensor: ... +def adaptive_avg_pool1d(input: Tensor, output_size: Union[_int, _size]) -> Tensor: ... +def adaptive_max_pool1d(input: Tensor, output_size: Union[_int, _size]) -> tuple[Tensor, Tensor]: ... +@overload +def add(input: Union[Tensor, Number, _complex], other: Union[Tensor, Number, _complex], *, alpha: Optional[Union[Number, _complex]] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + add(input, other, *, alpha=1, out=None) -> Tensor + + Adds :attr:`other`, scaled by :attr:`alpha`, to :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i + \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to add to :attr:`input`. + + Keyword arguments: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Examples:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.0202, 1.0985, 1.3506, -0.6056]) + >>> torch.add(a, 20) + tensor([ 20.0202, 21.0985, 21.3506, 19.3944]) + + >>> b = torch.randn(4) + >>> b + tensor([-0.9732, -0.3497, 0.6245, 0.4022]) + >>> c = torch.randn(4, 1) + >>> c + tensor([[ 0.3743], + [-1.7724], + [-0.5811], + [-0.8017]]) + >>> torch.add(b, c, alpha=10) + tensor([[ 2.7695, 3.3930, 4.3672, 4.1450], + [-18.6971, -18.0736, -17.0994, -17.3216], + [ -6.7845, -6.1610, -5.1868, -5.4090], + [ -8.9902, -8.3667, -7.3925, -7.6147]]) + """ + ... +@overload +def add(self: Tensor, alpha: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + add(input, other, *, alpha=1, out=None) -> Tensor + + Adds :attr:`other`, scaled by :attr:`alpha`, to :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i + \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to add to :attr:`input`. + + Keyword arguments: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Examples:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.0202, 1.0985, 1.3506, -0.6056]) + >>> torch.add(a, 20) + tensor([ 20.0202, 21.0985, 21.3506, 19.3944]) + + >>> b = torch.randn(4) + >>> b + tensor([-0.9732, -0.3497, 0.6245, 0.4022]) + >>> c = torch.randn(4, 1) + >>> c + tensor([[ 0.3743], + [-1.7724], + [-0.5811], + [-0.8017]]) + >>> torch.add(b, c, alpha=10) + tensor([[ 2.7695, 3.3930, 4.3672, 4.1450], + [-18.6971, -18.0736, -17.0994, -17.3216], + [ -6.7845, -6.1610, -5.1868, -5.4090], + [ -8.9902, -8.3667, -7.3925, -7.6147]]) + """ + ... +@overload +def add(self: Tensor, alpha: Union[Number, _complex], other: Tensor, *, out: Tensor) -> Tensor: + r""" + add(input, other, *, alpha=1, out=None) -> Tensor + + Adds :attr:`other`, scaled by :attr:`alpha`, to :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i + \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to add to :attr:`input`. + + Keyword arguments: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Examples:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.0202, 1.0985, 1.3506, -0.6056]) + >>> torch.add(a, 20) + tensor([ 20.0202, 21.0985, 21.3506, 19.3944]) + + >>> b = torch.randn(4) + >>> b + tensor([-0.9732, -0.3497, 0.6245, 0.4022]) + >>> c = torch.randn(4, 1) + >>> c + tensor([[ 0.3743], + [-1.7724], + [-0.5811], + [-0.8017]]) + >>> torch.add(b, c, alpha=10) + tensor([[ 2.7695, 3.3930, 4.3672, 4.1450], + [-18.6971, -18.0736, -17.0994, -17.3216], + [ -6.7845, -6.1610, -5.1868, -5.4090], + [ -8.9902, -8.3667, -7.3925, -7.6147]]) + """ + ... +@overload +def addbmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], batch1: Tensor, batch2: Tensor) -> Tensor: + r""" + addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored + in :attr:`batch1` and :attr:`batch2`, + with a reduced add step (all matrix multiplications get accumulated + along the first dimension). + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the + same number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + .. math:: + out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` + must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.addbmm(M, batch1, batch2) + tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], + [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], + [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) + """ + ... +@overload +def addbmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: + r""" + addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored + in :attr:`batch1` and :attr:`batch2`, + with a reduced add step (all matrix multiplications get accumulated + along the first dimension). + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the + same number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + .. math:: + out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` + must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.addbmm(M, batch1, batch2) + tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], + [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], + [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) + """ + ... +@overload +def addbmm(input: Tensor, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored + in :attr:`batch1` and :attr:`batch2`, + with a reduced add step (all matrix multiplications get accumulated + along the first dimension). + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the + same number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + .. math:: + out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` + must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.addbmm(M, batch1, batch2) + tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], + [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], + [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) + """ + ... +@overload +def addbmm(beta: Union[Number, _complex], self: Tensor, batch1: Tensor, batch2: Tensor) -> Tensor: + r""" + addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored + in :attr:`batch1` and :attr:`batch2`, + with a reduced add step (all matrix multiplications get accumulated + along the first dimension). + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the + same number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + .. math:: + out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` + must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.addbmm(M, batch1, batch2) + tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], + [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], + [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) + """ + ... +@overload +def addbmm(beta: Union[Number, _complex], self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: + r""" + addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored + in :attr:`batch1` and :attr:`batch2`, + with a reduced add step (all matrix multiplications get accumulated + along the first dimension). + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the + same number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + .. math:: + out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and :attr:`alpha` + must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for `batch1 @ batch2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.addbmm(M, batch1, batch2) + tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653], + [ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743], + [ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) + """ + ... +@overload +def addcdiv(self: Tensor, value: Union[Number, _complex], tensor1: Tensor, tensor2: Tensor) -> Tensor: + r""" + addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise division of :attr:`tensor1` by :attr:`tensor2`, + multiplies the result by the scalar :attr:`value` and adds it to :attr:`input`. + + .. warning:: + Integer division with addcdiv is no longer supported, and in a future + release addcdiv will perform a true division of tensor1 and tensor2. + The historic addcdiv behavior can be implemented as + (input + value * torch.trunc(tensor1 / tensor2)).to(input.dtype) + for integer inputs and as (input + value * tensor1 / tensor2) for float inputs. + The future addcdiv behavior is just the latter implementation: + (input + value * tensor1 / tensor2), for all dtypes. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i} + + + The shapes of :attr:`input`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the numerator tensor + tensor2 (Tensor): the denominator tensor + + Keyword args: + value (Number, optional): multiplier for :math:`\text{tensor1} / \text{tensor2}` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcdiv(t, t1, t2, value=0.1) + tensor([[-0.2312, -3.6496, 0.1312], + [-1.0428, 3.4292, -0.1030], + [-0.5369, -0.9829, 0.0430]]) + """ + ... +@overload +def addcdiv(self: Tensor, value: Union[Number, _complex], tensor1: Tensor, tensor2: Tensor, *, out: Tensor) -> Tensor: + r""" + addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise division of :attr:`tensor1` by :attr:`tensor2`, + multiplies the result by the scalar :attr:`value` and adds it to :attr:`input`. + + .. warning:: + Integer division with addcdiv is no longer supported, and in a future + release addcdiv will perform a true division of tensor1 and tensor2. + The historic addcdiv behavior can be implemented as + (input + value * torch.trunc(tensor1 / tensor2)).to(input.dtype) + for integer inputs and as (input + value * tensor1 / tensor2) for float inputs. + The future addcdiv behavior is just the latter implementation: + (input + value * tensor1 / tensor2), for all dtypes. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i} + + + The shapes of :attr:`input`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the numerator tensor + tensor2 (Tensor): the denominator tensor + + Keyword args: + value (Number, optional): multiplier for :math:`\text{tensor1} / \text{tensor2}` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcdiv(t, t1, t2, value=0.1) + tensor([[-0.2312, -3.6496, 0.1312], + [-1.0428, 3.4292, -0.1030], + [-0.5369, -0.9829, 0.0430]]) + """ + ... +@overload +def addcdiv(input: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addcdiv(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise division of :attr:`tensor1` by :attr:`tensor2`, + multiplies the result by the scalar :attr:`value` and adds it to :attr:`input`. + + .. warning:: + Integer division with addcdiv is no longer supported, and in a future + release addcdiv will perform a true division of tensor1 and tensor2. + The historic addcdiv behavior can be implemented as + (input + value * torch.trunc(tensor1 / tensor2)).to(input.dtype) + for integer inputs and as (input + value * tensor1 / tensor2) for float inputs. + The future addcdiv behavior is just the latter implementation: + (input + value * tensor1 / tensor2), for all dtypes. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i} + + + The shapes of :attr:`input`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the numerator tensor + tensor2 (Tensor): the denominator tensor + + Keyword args: + value (Number, optional): multiplier for :math:`\text{tensor1} / \text{tensor2}` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcdiv(t, t1, t2, value=0.1) + tensor([[-0.2312, -3.6496, 0.1312], + [-1.0428, 3.4292, -0.1030], + [-0.5369, -0.9829, 0.0430]]) + """ + ... +@overload +def addcmul(self: Tensor, value: Union[Number, _complex], tensor1: Tensor, tensor2: Tensor) -> Tensor: + r""" + addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise multiplication of :attr:`tensor1` + by :attr:`tensor2`, multiplies the result by the scalar :attr:`value` + and adds it to :attr:`input`. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i + + The shapes of :attr:`tensor`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the tensor to be multiplied + tensor2 (Tensor): the tensor to be multiplied + + Keyword args: + value (Number, optional): multiplier for :math:`tensor1 .* tensor2` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcmul(t, t1, t2, value=0.1) + tensor([[-0.8635, -0.6391, 1.6174], + [-0.7617, -0.5879, 1.7388], + [-0.8353, -0.6249, 1.6511]]) + """ + ... +@overload +def addcmul(self: Tensor, value: Union[Number, _complex], tensor1: Tensor, tensor2: Tensor, *, out: Tensor) -> Tensor: + r""" + addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise multiplication of :attr:`tensor1` + by :attr:`tensor2`, multiplies the result by the scalar :attr:`value` + and adds it to :attr:`input`. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i + + The shapes of :attr:`tensor`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the tensor to be multiplied + tensor2 (Tensor): the tensor to be multiplied + + Keyword args: + value (Number, optional): multiplier for :math:`tensor1 .* tensor2` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcmul(t, t1, t2, value=0.1) + tensor([[-0.8635, -0.6391, 1.6174], + [-0.7617, -0.5879, 1.7388], + [-0.8353, -0.6249, 1.6511]]) + """ + ... +@overload +def addcmul(input: Tensor, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addcmul(input, tensor1, tensor2, *, value=1, out=None) -> Tensor + + Performs the element-wise multiplication of :attr:`tensor1` + by :attr:`tensor2`, multiplies the result by the scalar :attr:`value` + and adds it to :attr:`input`. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i + + The shapes of :attr:`tensor`, :attr:`tensor1`, and :attr:`tensor2` must be + :ref:`broadcastable `. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the tensor to be multiplied + tensor2 (Tensor): the tensor to be multiplied + + Keyword args: + value (Number, optional): multiplier for :math:`tensor1 .* tensor2` + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(1, 3) + >>> t1 = torch.randn(3, 1) + >>> t2 = torch.randn(1, 3) + >>> torch.addcmul(t, t1, t2, value=0.1) + tensor([[-0.8635, -0.6391, 1.6174], + [-0.7617, -0.5879, 1.7388], + [-0.8353, -0.6249, 1.6511]]) + """ + ... +@overload +def addmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat1: Tensor, mat2: Tensor) -> Tensor: + r""" + addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. + The matrix :attr:`input` is added to the final result. + + If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operation has support for arguments with :ref:`sparse layouts`. If + :attr:`input` is sparse the result will have the same layout and if :attr:`out` + is provided it must have the same layout as :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + mat1 (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2, 3) + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.addmm(M, mat1, mat2) + tensor([[-4.8716, 1.4671, -1.3746], + [ 0.7573, -3.9555, -2.8681]]) + """ + ... +@overload +def addmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat1: Tensor, mat2: Tensor, *, out: Tensor) -> Tensor: + r""" + addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. + The matrix :attr:`input` is added to the final result. + + If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operation has support for arguments with :ref:`sparse layouts`. If + :attr:`input` is sparse the result will have the same layout and if :attr:`out` + is provided it must have the same layout as :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + mat1 (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2, 3) + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.addmm(M, mat1, mat2) + tensor([[-4.8716, 1.4671, -1.3746], + [ 0.7573, -3.9555, -2.8681]]) + """ + ... +@overload +def addmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. + The matrix :attr:`input` is added to the final result. + + If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operation has support for arguments with :ref:`sparse layouts`. If + :attr:`input` is sparse the result will have the same layout and if :attr:`out` + is provided it must have the same layout as :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + mat1 (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2, 3) + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.addmm(M, mat1, mat2) + tensor([[-4.8716, 1.4671, -1.3746], + [ 0.7573, -3.9555, -2.8681]]) + """ + ... +@overload +def addmm(beta: Union[Number, _complex], self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: + r""" + addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. + The matrix :attr:`input` is added to the final result. + + If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operation has support for arguments with :ref:`sparse layouts`. If + :attr:`input` is sparse the result will have the same layout and if :attr:`out` + is provided it must have the same layout as :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + mat1 (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2, 3) + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.addmm(M, mat1, mat2) + tensor([[-4.8716, 1.4671, -1.3746], + [ 0.7573, -3.9555, -2.8681]]) + """ + ... +@overload +def addmm(beta: Union[Number, _complex], self: Tensor, mat1: Tensor, mat2: Tensor, *, out: Tensor) -> Tensor: + r""" + addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`mat1` and :attr:`mat2`. + The matrix :attr:`input` is added to the final result. + + If :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a :math:`(n \times p)` tensor + and :attr:`out` will be a :math:`(n \times p)` tensor. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat1` and :attr:`mat2` and the added matrix :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operation has support for arguments with :ref:`sparse layouts`. If + :attr:`input` is sparse the result will have the same layout and if :attr:`out` + is provided it must have the same layout as :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): matrix to be added + mat1 (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2, 3) + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.addmm(M, mat1, mat2) + tensor([[-4.8716, 1.4671, -1.3746], + [ 0.7573, -3.9555, -2.8681]]) + """ + ... +@overload +def addmv(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat: Tensor, vec: Tensor) -> Tensor: + r""" + addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`mat` and + the vector :attr:`vec`. + The vector :attr:`input` is added to the final result. + + If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a 1-D tensor of size `n` and + :attr:`out` will be 1-D tensor of size `n`. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): vector to be added + mat (Tensor): matrix to be matrix multiplied + vec (Tensor): vector to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2) + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.addmv(M, mat, vec) + tensor([-0.3768, -5.5565]) + """ + ... +@overload +def addmv(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat: Tensor, vec: Tensor, *, out: Tensor) -> Tensor: + r""" + addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`mat` and + the vector :attr:`vec`. + The vector :attr:`input` is added to the final result. + + If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a 1-D tensor of size `n` and + :attr:`out` will be 1-D tensor of size `n`. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): vector to be added + mat (Tensor): matrix to be matrix multiplied + vec (Tensor): vector to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2) + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.addmv(M, mat, vec) + tensor([-0.3768, -5.5565]) + """ + ... +@overload +def addmv(input: Tensor, mat: Tensor, vec: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`mat` and + the vector :attr:`vec`. + The vector :attr:`input` is added to the final result. + + If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a 1-D tensor of size `n` and + :attr:`out` will be 1-D tensor of size `n`. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): vector to be added + mat (Tensor): matrix to be matrix multiplied + vec (Tensor): vector to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2) + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.addmv(M, mat, vec) + tensor([-0.3768, -5.5565]) + """ + ... +@overload +def addmv(beta: Union[Number, _complex], self: Tensor, mat: Tensor, vec: Tensor) -> Tensor: + r""" + addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`mat` and + the vector :attr:`vec`. + The vector :attr:`input` is added to the final result. + + If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a 1-D tensor of size `n` and + :attr:`out` will be 1-D tensor of size `n`. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): vector to be added + mat (Tensor): matrix to be matrix multiplied + vec (Tensor): vector to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2) + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.addmv(M, mat, vec) + tensor([-0.3768, -5.5565]) + """ + ... +@overload +def addmv(beta: Union[Number, _complex], self: Tensor, mat: Tensor, vec: Tensor, *, out: Tensor) -> Tensor: + r""" + addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`mat` and + the vector :attr:`vec`. + The vector :attr:`input` is added to the final result. + + If :attr:`mat` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a 1-D tensor of size `n` and + :attr:`out` will be 1-D tensor of size `n`. + + :attr:`alpha` and :attr:`beta` are scaling factors on matrix-vector product between + :attr:`mat` and :attr:`vec` and the added tensor :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): vector to be added + mat (Tensor): matrix to be matrix multiplied + vec (Tensor): vector to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat @ vec` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(2) + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.addmv(M, mat, vec) + tensor([-0.3768, -5.5565]) + """ + ... +@overload +def addmv_(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat: Tensor, vec: Tensor) -> Tensor: ... +@overload +def addmv_(input: Tensor, mat: Tensor, vec: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... +@overload +def addmv_(beta: Union[Number, _complex], self: Tensor, mat: Tensor, vec: Tensor) -> Tensor: ... +@overload +def addr(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], vec1: Tensor, vec2: Tensor) -> Tensor: + r""" + addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` + and adds it to the matrix :attr:`input`. + + Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the + outer product between :attr:`vec1` and :attr:`vec2` and the added matrix + :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector + of size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a matrix of size + :math:`(n \times m)` and :attr:`out` will be a matrix of size + :math:`(n \times m)`. + + Args: + input (Tensor): matrix to be added + vec1 (Tensor): the first vector of the outer product + vec2 (Tensor): the second vector of the outer product + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{vec1} \otimes \text{vec2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> vec1 = torch.arange(1., 4.) + >>> vec2 = torch.arange(1., 3.) + >>> M = torch.zeros(3, 2) + >>> torch.addr(M, vec1, vec2) + tensor([[ 1., 2.], + [ 2., 4.], + [ 3., 6.]]) + """ + ... +@overload +def addr(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], vec1: Tensor, vec2: Tensor, *, out: Tensor) -> Tensor: + r""" + addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` + and adds it to the matrix :attr:`input`. + + Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the + outer product between :attr:`vec1` and :attr:`vec2` and the added matrix + :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector + of size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a matrix of size + :math:`(n \times m)` and :attr:`out` will be a matrix of size + :math:`(n \times m)`. + + Args: + input (Tensor): matrix to be added + vec1 (Tensor): the first vector of the outer product + vec2 (Tensor): the second vector of the outer product + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{vec1} \otimes \text{vec2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> vec1 = torch.arange(1., 4.) + >>> vec2 = torch.arange(1., 3.) + >>> M = torch.zeros(3, 2) + >>> torch.addr(M, vec1, vec2) + tensor([[ 1., 2.], + [ 2., 4.], + [ 3., 6.]]) + """ + ... +@overload +def addr(input: Tensor, vec1: Tensor, vec2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` + and adds it to the matrix :attr:`input`. + + Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the + outer product between :attr:`vec1` and :attr:`vec2` and the added matrix + :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector + of size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a matrix of size + :math:`(n \times m)` and :attr:`out` will be a matrix of size + :math:`(n \times m)`. + + Args: + input (Tensor): matrix to be added + vec1 (Tensor): the first vector of the outer product + vec2 (Tensor): the second vector of the outer product + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{vec1} \otimes \text{vec2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> vec1 = torch.arange(1., 4.) + >>> vec2 = torch.arange(1., 3.) + >>> M = torch.zeros(3, 2) + >>> torch.addr(M, vec1, vec2) + tensor([[ 1., 2.], + [ 2., 4.], + [ 3., 6.]]) + """ + ... +@overload +def addr(beta: Union[Number, _complex], self: Tensor, vec1: Tensor, vec2: Tensor) -> Tensor: + r""" + addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` + and adds it to the matrix :attr:`input`. + + Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the + outer product between :attr:`vec1` and :attr:`vec2` and the added matrix + :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector + of size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a matrix of size + :math:`(n \times m)` and :attr:`out` will be a matrix of size + :math:`(n \times m)`. + + Args: + input (Tensor): matrix to be added + vec1 (Tensor): the first vector of the outer product + vec2 (Tensor): the second vector of the outer product + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{vec1} \otimes \text{vec2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> vec1 = torch.arange(1., 4.) + >>> vec2 = torch.arange(1., 3.) + >>> M = torch.zeros(3, 2) + >>> torch.addr(M, vec1, vec2) + tensor([[ 1., 2.], + [ 2., 4.], + [ 3., 6.]]) + """ + ... +@overload +def addr(beta: Union[Number, _complex], self: Tensor, vec1: Tensor, vec2: Tensor, *, out: Tensor) -> Tensor: + r""" + addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs the outer-product of vectors :attr:`vec1` and :attr:`vec2` + and adds it to the matrix :attr:`input`. + + Optional values :attr:`beta` and :attr:`alpha` are scaling factors on the + outer product between :attr:`vec1` and :attr:`vec2` and the added matrix + :attr:`input` respectively. + + .. math:: + \text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + If :attr:`vec1` is a vector of size `n` and :attr:`vec2` is a vector + of size `m`, then :attr:`input` must be + :ref:`broadcastable ` with a matrix of size + :math:`(n \times m)` and :attr:`out` will be a matrix of size + :math:`(n \times m)`. + + Args: + input (Tensor): matrix to be added + vec1 (Tensor): the first vector of the outer product + vec2 (Tensor): the second vector of the outer product + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{vec1} \otimes \text{vec2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> vec1 = torch.arange(1., 4.) + >>> vec2 = torch.arange(1., 3.) + >>> M = torch.zeros(3, 2) + >>> torch.addr(M, vec1, vec2) + tensor([[ 1., 2.], + [ 2., 4.], + [ 3., 6.]]) + """ + ... +def adjoint(input: Tensor) -> Tensor: + r""" + adjoint(input: Tensor) -> Tensor + Returns a view of the tensor conjugated and with the last two dimensions transposed. + + ``x.adjoint()`` is equivalent to ``x.transpose(-2, -1).conj()`` for complex tensors and + to ``x.transpose(-2, -1)`` for real tensors. + + Args: + {input} + + Example:: + >>> x = torch.arange(4, dtype=torch.float) + >>> A = torch.complex(x, x).reshape(2, 2) + >>> A + tensor([[0.+0.j, 1.+1.j], + [2.+2.j, 3.+3.j]]) + >>> A.adjoint() + tensor([[0.-0.j, 2.-2.j], + [1.-1.j, 3.-3.j]]) + >>> (A.adjoint() == A.mH).all() + tensor(True) + """ + ... +def affine_grid_generator(theta: Tensor, size: Sequence[Union[_int, SymInt]], align_corners: _bool) -> Tensor: ... +def alias_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.alias`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def all(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + all(input: Tensor) -> Tensor + + Tests if all elements in :attr:`input` evaluate to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.all(a) + tensor(False, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.all(a) + tensor(False) + + .. function:: all(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if all elements in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(4, 2).bool() + >>> a + tensor([[True, True], + [True, False], + [True, True], + [True, True]], dtype=torch.bool) + >>> torch.all(a, dim=1) + tensor([ True, False, True, True], dtype=torch.bool) + >>> torch.all(a, dim=0) + tensor([ True, False], dtype=torch.bool) + """ + ... +@overload +def all(input: Tensor, dim: Optional[_size] = None, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + all(input: Tensor) -> Tensor + + Tests if all elements in :attr:`input` evaluate to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.all(a) + tensor(False, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.all(a) + tensor(False) + + .. function:: all(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if all elements in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(4, 2).bool() + >>> a + tensor([[True, True], + [True, False], + [True, True], + [True, True]], dtype=torch.bool) + >>> torch.all(a, dim=1) + tensor([ True, False, True, True], dtype=torch.bool) + >>> torch.all(a, dim=0) + tensor([ True, False], dtype=torch.bool) + """ + ... +@overload +def all(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + all(input: Tensor) -> Tensor + + Tests if all elements in :attr:`input` evaluate to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.all(a) + tensor(False, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.all(a) + tensor(False) + + .. function:: all(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if all elements in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(4, 2).bool() + >>> a + tensor([[True, True], + [True, False], + [True, True], + [True, True]], dtype=torch.bool) + >>> torch.all(a, dim=1) + tensor([ True, False, True, True], dtype=torch.bool) + >>> torch.all(a, dim=0) + tensor([ True, False], dtype=torch.bool) + """ + ... +@overload +def all(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + all(input: Tensor) -> Tensor + + Tests if all elements in :attr:`input` evaluate to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.all(a) + tensor(False, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.all(a) + tensor(False) + + .. function:: all(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if all elements in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(4, 2).bool() + >>> a + tensor([[True, True], + [True, False], + [True, True], + [True, True]], dtype=torch.bool) + >>> torch.all(a, dim=1) + tensor([ True, False, True, True], dtype=torch.bool) + >>> torch.all(a, dim=0) + tensor([ True, False], dtype=torch.bool) + """ + ... +def allclose(input: Tensor, other: Tensor, rtol: _float = 1e-05, atol: _float = 1e-08, equal_nan: _bool = False) -> _bool: + r""" + allclose(input: Tensor, other: Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> bool + + This function checks if :attr:`input` and :attr:`other` satisfy the condition: + + .. math:: + \lvert \text{input}_i - \text{other}_i \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other}_i \rvert + + elementwise, for all elements of :attr:`input` and :attr:`other`. The behaviour of this function is analogous to + `numpy.allclose `_ + + Args: + input (Tensor): first tensor to compare + other (Tensor): second tensor to compare + atol (float, optional): absolute tolerance. Default: 1e-08 + rtol (float, optional): relative tolerance. Default: 1e-05 + equal_nan (bool, optional): if ``True``, then two ``NaN`` s will be considered equal. Default: ``False`` + + Example:: + + >>> torch.allclose(torch.tensor([10000., 1e-07]), torch.tensor([10000.1, 1e-08])) + False + >>> torch.allclose(torch.tensor([10000., 1e-08]), torch.tensor([10000.1, 1e-09])) + True + >>> torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan')])) + False + >>> torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan')]), equal_nan=True) + True + """ + ... +def alpha_dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def alpha_dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def amax(input: Tensor, dim: Union[_int, _size] = (), keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + amax(input, dim, keepdim=False, *, out=None) -> Tensor + + Returns the maximum value of each slice of the :attr:`input` tensor in the given + dimension(s) :attr:`dim`. + + .. note:: + The difference between ``max``/``min`` and ``amax``/``amin`` is: + - ``amax``/``amin`` supports reducing on multiple dimensions, + - ``amax``/``amin`` does not return indices, + - ``amax``/``amin`` evenly distributes gradient between equal values, + while ``max(dim)``/``min(dim)`` propagates gradient only to a single + index in the source tensor. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.8177, 1.4878, -0.2491, 0.9130], + [-0.7158, 1.1775, 2.0992, 0.4817], + [-0.0053, 0.0164, -1.3738, -0.0507], + [ 1.9700, 1.1106, -1.0318, -1.0816]]) + >>> torch.amax(a, 1) + tensor([1.4878, 2.0992, 0.0164, 1.9700]) + """ + ... +def amin(input: Tensor, dim: Union[_int, _size] = (), keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + amin(input, dim, keepdim=False, *, out=None) -> Tensor + + Returns the minimum value of each slice of the :attr:`input` tensor in the given + dimension(s) :attr:`dim`. + + .. note:: + The difference between ``max``/``min`` and ``amax``/``amin`` is: + - ``amax``/``amin`` supports reducing on multiple dimensions, + - ``amax``/``amin`` does not return indices, + - ``amax``/``amin`` evenly distributes gradient between equal values, + while ``max(dim)``/``min(dim)`` propagates gradient only to a single + index in the source tensor. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.6451, -0.4866, 0.2987, -1.3312], + [-0.5744, 1.2980, 1.8397, -0.2713], + [ 0.9128, 0.9214, -1.7268, -0.2995], + [ 0.9023, 0.4853, 0.9075, -1.6165]]) + >>> torch.amin(a, 1) + tensor([-1.3312, -0.5744, -1.7268, -1.6165]) + """ + ... +def aminmax(input: Tensor, *, dim: Optional[_int] = None, keepdim: _bool = False, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.aminmax: + r""" + aminmax(input, *, dim=None, keepdim=False, out=None) -> (Tensor min, Tensor max) + + Computes the minimum and maximum values of the :attr:`input` tensor. + + Args: + input (Tensor): + The input tensor + + Keyword Args: + dim (Optional[int]): + The dimension along which to compute the values. If `None`, + computes the values over the entire :attr:`input` tensor. + Default is `None`. + keepdim (bool): + If `True`, the reduced dimensions will be kept in the output + tensor as dimensions with size 1 for broadcasting, otherwise + they will be removed, as if calling (:func:`torch.squeeze`). + Default is `False`. + out (Optional[Tuple[Tensor, Tensor]]): + Optional tensors on which to write the result. Must have the same + shape and dtype as the expected output. + Default is `None`. + + Returns: + A named tuple `(min, max)` containing the minimum and maximum values. + + Raises: + RuntimeError + If any of the dimensions to compute the values over has size 0. + + .. note:: + NaN values are propagated to the output if at least one value is NaN. + + .. seealso:: + :func:`torch.amin` computes just the minimum value + :func:`torch.amax` computes just the maximum value + + Example:: + + >>> torch.aminmax(torch.tensor([1, -3, 5])) + torch.return_types.aminmax( + min=tensor(-3), + max=tensor(5)) + + >>> # aminmax propagates NaNs + >>> torch.aminmax(torch.tensor([1, -3, 5, torch.nan])) + torch.return_types.aminmax( + min=tensor(nan), + max=tensor(nan)) + + >>> t = torch.arange(10).view(2, 5) + >>> t + tensor([[0, 1, 2, 3, 4], + [5, 6, 7, 8, 9]]) + >>> t.aminmax(dim=0, keepdim=True) + torch.return_types.aminmax( + min=tensor([[0, 1, 2, 3, 4]]), + max=tensor([[5, 6, 7, 8, 9]])) + """ + ... +def angle(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + angle(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Computes the element-wise angle (in radians) of the given :attr:`input` tensor. + + .. math:: + \text{out}_{i} = angle(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + .. note:: Starting in PyTorch 1.8, angle returns pi for negative real numbers, + zero for non-negative real numbers, and propagates NaNs. Previously + the function would return zero for all real numbers and not propagate + floating-point NaNs. + + Example:: + + >>> torch.angle(torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]))*180/3.14159 + tensor([ 135., 135, -45]) + """ + ... +@overload +def any(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + any(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Tests if any element in :attr:`input` evaluates to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.any(a) + tensor(True, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.any(a) + tensor(True) + + .. function:: any(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if any element in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 2) < 0 + >>> a + tensor([[ True, True], + [False, True], + [ True, True], + [False, False]]) + >>> torch.any(a, 1) + tensor([ True, True, True, False]) + >>> torch.any(a, 0) + tensor([True, True]) + """ + ... +@overload +def any(input: Tensor, dim: Optional[_size] = None, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + any(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Tests if any element in :attr:`input` evaluates to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.any(a) + tensor(True, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.any(a) + tensor(True) + + .. function:: any(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if any element in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 2) < 0 + >>> a + tensor([[ True, True], + [False, True], + [ True, True], + [False, False]]) + >>> torch.any(a, 1) + tensor([ True, True, True, False]) + >>> torch.any(a, 0) + tensor([True, True]) + """ + ... +@overload +def any(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + any(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Tests if any element in :attr:`input` evaluates to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.any(a) + tensor(True, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.any(a) + tensor(True) + + .. function:: any(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if any element in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 2) < 0 + >>> a + tensor([[ True, True], + [False, True], + [ True, True], + [False, False]]) + >>> torch.any(a, 1) + tensor([ True, True, True, False]) + >>> torch.any(a, 0) + tensor([True, True]) + """ + ... +@overload +def any(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + any(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Tests if any element in :attr:`input` evaluates to `True`. + + .. note:: This function matches the behaviour of NumPy in returning + output of dtype `bool` for all supported dtypes except `uint8`. + For `uint8` the dtype of output is `uint8` itself. + + Example:: + + >>> a = torch.rand(1, 2).bool() + >>> a + tensor([[False, True]], dtype=torch.bool) + >>> torch.any(a) + tensor(True, dtype=torch.bool) + >>> a = torch.arange(0, 3) + >>> a + tensor([0, 1, 2]) + >>> torch.any(a) + tensor(True) + + .. function:: any(input, dim, keepdim=False, *, out=None) -> Tensor + :noindex: + + For each row of :attr:`input` in the given dimension :attr:`dim`, + returns `True` if any element in the row evaluate to `True` and `False` otherwise. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 2) < 0 + >>> a + tensor([[ True, True], + [False, True], + [ True, True], + [False, False]]) + >>> torch.any(a, 1) + tensor([ True, True, True, False]) + >>> torch.any(a, 0) + tensor([True, True]) + """ + ... +@overload +def arange(start: Number, end: Number, step: Number, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note: When using floating-point dtypes (especially reduced precision types like ``bfloat16``), + the results may be affected by floating-point rounding behavior. Some values in the sequence + might not be exactly representable in certain floating-point formats, which can lead to + repeated values or unexpected rounding. For precise sequences, it is recommended to use + integer dtypes instead of floating-point dtypes. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number, optional): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number, optional): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +@overload +def arange(start: Number, end: Number, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note: When using floating-point dtypes (especially reduced precision types like ``bfloat16``), + the results may be affected by floating-point rounding behavior. Some values in the sequence + might not be exactly representable in certain floating-point formats, which can lead to + repeated values or unexpected rounding. For precise sequences, it is recommended to use + integer dtypes instead of floating-point dtypes. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number, optional): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number, optional): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +@overload +def arange(end: Number, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note: When using floating-point dtypes (especially reduced precision types like ``bfloat16``), + the results may be affected by floating-point rounding behavior. Some values in the sequence + might not be exactly representable in certain floating-point formats, which can lead to + repeated values or unexpected rounding. For precise sequences, it is recommended to use + integer dtypes instead of floating-point dtypes. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number, optional): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number, optional): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +@overload +def arange(end: Union[Number, _complex], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note: When using floating-point dtypes (especially reduced precision types like ``bfloat16``), + the results may be affected by floating-point rounding behavior. Some values in the sequence + might not be exactly representable in certain floating-point formats, which can lead to + repeated values or unexpected rounding. For precise sequences, it is recommended to use + integer dtypes instead of floating-point dtypes. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number, optional): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number, optional): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +@overload +def arange(start: Union[Number, _complex], end: Union[Number, _complex], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note: When using floating-point dtypes (especially reduced precision types like ``bfloat16``), + the results may be affected by floating-point rounding behavior. Some values in the sequence + might not be exactly representable in certain floating-point formats, which can lead to + repeated values or unexpected rounding. For precise sequences, it is recommended to use + integer dtypes instead of floating-point dtypes. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number, optional): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number, optional): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +@overload +def arange(start: Union[Number, _complex], end: Union[Number, _complex], step: Union[Number, _complex] = 1, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil` + with values from the interval ``[start, end)`` taken with common difference + :attr:`step` beginning from `start`. + + Note: When using floating-point dtypes (especially reduced precision types like ``bfloat16``), + the results may be affected by floating-point rounding behavior. Some values in the sequence + might not be exactly representable in certain floating-point formats, which can lead to + repeated values or unexpected rounding. For precise sequences, it is recommended to use + integer dtypes instead of floating-point dtypes. + + Note that non-integer :attr:`step` is subject to floating point rounding errors when + comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end` + in such cases. + + .. math:: + \text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + Args: + start (Number, optional): the starting value for the set of points. Default: ``0``. + end (Number): the ending value for the set of points + step (Number, optional): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `stop` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.arange(5) + tensor([ 0, 1, 2, 3, 4]) + >>> torch.arange(1, 4) + tensor([ 1, 2, 3]) + >>> torch.arange(1, 2.5, 0.5) + tensor([ 1.0000, 1.5000, 2.0000]) + """ + ... +def arccos(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arccos(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Alias for :func:`torch.acos`. + """ + ... +def arccos_(input: Tensor) -> Tensor: ... +def arccosh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arccosh(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Alias for :func:`torch.acosh`. + """ + ... +def arccosh_(input: Tensor) -> Tensor: ... +def arcsin(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arcsin(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Alias for :func:`torch.asin`. + """ + ... +def arcsin_(input: Tensor) -> Tensor: ... +def arcsinh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arcsinh(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Alias for :func:`torch.asinh`. + """ + ... +def arcsinh_(input: Tensor) -> Tensor: ... +def arctan(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arctan(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Alias for :func:`torch.atan`. + """ + ... +def arctan2(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arctan2(input: Tensor, other: Tensor, *, out: Optional[Tensor]) -> Tensor + Alias for :func:`torch.atan2`. + """ + ... +def arctan_(input: Tensor) -> Tensor: ... +def arctanh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + arctanh(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Alias for :func:`torch.atanh`. + """ + ... +def arctanh_(input: Tensor) -> Tensor: ... +def argmax(input: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + argmax(input) -> LongTensor + + Returns the indices of the maximum value of all elements in the :attr:`input` tensor. + + This is the second value returned by :meth:`torch.max`. See its + documentation for the exact semantics of this method. + + .. note:: If there are multiple maximal values then the indices of the first maximal value are returned. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 1.3398, 0.2663, -0.2686, 0.2450], + [-0.7401, -0.8805, -0.3402, -1.1936], + [ 0.4907, -1.3948, -1.0691, -0.3132], + [-1.6092, 0.5419, -0.2993, 0.3195]]) + >>> torch.argmax(a) + tensor(0) + + .. function:: argmax(input, dim, keepdim=False) -> LongTensor + :noindex: + + Returns the indices of the maximum values of a tensor across a dimension. + + This is the second value returned by :meth:`torch.max`. See its + documentation for the exact semantics of this method. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. If ``None``, the argmax of the flattened input is returned. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 1.3398, 0.2663, -0.2686, 0.2450], + [-0.7401, -0.8805, -0.3402, -1.1936], + [ 0.4907, -1.3948, -1.0691, -0.3132], + [-1.6092, 0.5419, -0.2993, 0.3195]]) + >>> torch.argmax(a, dim=1) + tensor([ 0, 2, 0, 1]) + """ + ... +def argmin(input: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + argmin(input, dim=None, keepdim=False) -> LongTensor + + Returns the indices of the minimum value(s) of the flattened tensor or along a dimension + + This is the second value returned by :meth:`torch.min`. See its + documentation for the exact semantics of this method. + + .. note:: If there are multiple minimal values then the indices of the first minimal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. If ``None``, the argmin of the flattened input is returned. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.1139, 0.2254, -0.1381, 0.3687], + [ 1.0100, -1.1975, -0.0102, -0.4732], + [-0.9240, 0.1207, -0.7506, -1.0213], + [ 1.7809, -1.2960, 0.9384, 0.1438]]) + >>> torch.argmin(a) + tensor(13) + >>> torch.argmin(a, dim=1) + tensor([ 2, 1, 3, 1]) + >>> torch.argmin(a, dim=1, keepdim=True) + tensor([[2], + [1], + [3], + [1]]) + """ + ... +@overload +def argsort(input: Tensor, *, stable: _bool, dim: _int = -1, descending: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + argsort(input, dim=-1, descending=False, stable=False) -> Tensor + + Returns the indices that sort a tensor along a given dimension in ascending + order by value. + + This is the second value returned by :meth:`torch.sort`. See its documentation + for the exact semantics of this method. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. If ``False``, the relative order of values + which compare equal is not guaranteed. ``True`` is slower. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): controls the relative order of equivalent elements + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0785, 1.5267, -0.8521, 0.4065], + [ 0.1598, 0.0788, -0.0745, -1.2700], + [ 1.2208, 1.0722, -0.7064, 1.2564], + [ 0.0669, -0.2318, -0.8229, -0.9280]]) + + + >>> torch.argsort(a, dim=1) + tensor([[2, 0, 3, 1], + [3, 2, 1, 0], + [2, 1, 0, 3], + [3, 2, 1, 0]]) + """ + ... +@overload +def argsort(input: Tensor, dim: _int = -1, descending: _bool = False) -> Tensor: + r""" + argsort(input, dim=-1, descending=False, stable=False) -> Tensor + + Returns the indices that sort a tensor along a given dimension in ascending + order by value. + + This is the second value returned by :meth:`torch.sort`. See its documentation + for the exact semantics of this method. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. If ``False``, the relative order of values + which compare equal is not guaranteed. ``True`` is slower. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): controls the relative order of equivalent elements + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0785, 1.5267, -0.8521, 0.4065], + [ 0.1598, 0.0788, -0.0745, -1.2700], + [ 1.2208, 1.0722, -0.7064, 1.2564], + [ 0.0669, -0.2318, -0.8229, -0.9280]]) + + + >>> torch.argsort(a, dim=1) + tensor([[2, 0, 3, 1], + [3, 2, 1, 0], + [2, 1, 0, 3], + [3, 2, 1, 0]]) + """ + ... +@overload +def argsort(input: Tensor, dim: Union[str, ellipsis, None], descending: _bool = False) -> Tensor: + r""" + argsort(input, dim=-1, descending=False, stable=False) -> Tensor + + Returns the indices that sort a tensor along a given dimension in ascending + order by value. + + This is the second value returned by :meth:`torch.sort`. See its documentation + for the exact semantics of this method. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. If ``False``, the relative order of values + which compare equal is not guaranteed. ``True`` is slower. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): controls the relative order of equivalent elements + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0785, 1.5267, -0.8521, 0.4065], + [ 0.1598, 0.0788, -0.0745, -1.2700], + [ 1.2208, 1.0722, -0.7064, 1.2564], + [ 0.0669, -0.2318, -0.8229, -0.9280]]) + + + >>> torch.argsort(a, dim=1) + tensor([[2, 0, 3, 1], + [3, 2, 1, 0], + [2, 1, 0, 3], + [3, 2, 1, 0]]) + """ + ... +def argwhere(input: Tensor) -> Tensor: + r""" + argwhere(input) -> Tensor + + Returns a tensor containing the indices of all non-zero elements of + :attr:`input`. Each row in the result contains the indices of a non-zero + element in :attr:`input`. The result is sorted lexicographically, with + the last index changing the fastest (C-style). + + If :attr:`input` has :math:`n` dimensions, then the resulting indices tensor + :attr:`out` is of size :math:`(z \times n)`, where :math:`z` is the total number of + non-zero elements in the :attr:`input` tensor. + + .. note:: + This function is similar to NumPy's `argwhere`. + + When :attr:`input` is on CUDA, this function causes host-device synchronization. + + Args: + {input} + + Example:: + + >>> t = torch.tensor([1, 0, 1]) + >>> torch.argwhere(t) + tensor([[0], + [2]]) + >>> t = torch.tensor([[1, 0, 1], [0, 1, 1]]) + >>> torch.argwhere(t) + tensor([[0, 0], + [0, 2], + [1, 1], + [1, 2]]) + """ + ... +def as_strided(input: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + as_strided(input, size, stride, storage_offset=None) -> Tensor + + Create a view of an existing `torch.Tensor` :attr:`input` with specified + :attr:`size`, :attr:`stride` and :attr:`storage_offset`. + + .. warning:: + Prefer using other view functions, like :meth:`torch.Tensor.expand`, + to setting a view's strides manually with `as_strided`, as this + function's behavior depends on the implementation of a tensor's storage. + The constructed view of the storage must only refer to elements within + the storage or a runtime error will be thrown, and if the view is + "overlapped" (with multiple indices referring to the same element in + memory) its behavior is undefined. + + Args: + input (Tensor): the input tensor. + size (tuple or ints): the shape of the output tensor + stride (tuple or ints): the stride of the output tensor + storage_offset (int, optional): the offset in the underlying storage of the output tensor. + If ``None``, the storage_offset of the output tensor will match the input tensor. + + Example:: + + >>> x = torch.randn(3, 3) + >>> x + tensor([[ 0.9039, 0.6291, 1.0795], + [ 0.1586, 2.1939, -0.4900], + [-0.1909, -0.7503, 1.9355]]) + >>> t = torch.as_strided(x, (2, 2), (1, 2)) + >>> t + tensor([[0.9039, 1.0795], + [0.6291, 0.1586]]) + >>> t = torch.as_strided(x, (2, 2), (1, 2), 1) + tensor([[0.6291, 0.1586], + [1.0795, 2.1939]]) + """ + ... +def as_strided_(input: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: ... +def as_strided_copy(input: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.as_strided`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def as_strided_scatter(input: Tensor, src: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + as_strided_scatter(input, src, size, stride, storage_offset=None) -> Tensor + + Embeds the values of the :attr:`src` tensor into :attr:`input` along + the elements corresponding to the result of calling + input.as_strided(size, stride, storage_offset). + + This function returns a tensor with fresh storage; it does not + return a view. + + Args: + input (Tensor): the input tensor. + size (tuple or ints): the shape of the output tensor + stride (tuple or ints): the stride of the output tensor + storage_offset (int, optional): the offset in the underlying storage of the output tensor + + .. note:: + + :attr:`src` must be of the proper size in order to be embedded + into :attr:`input`. Specifically, it should have the same shape as + `torch.as_strided(input, size, stride, storage_offset)` + + Example:: + + >>> a = torch.arange(4).reshape(2, 2) + 1 + >>> a + tensor([[1, 2], + [3, 4]]) + >>> b = torch.zeros(3, 3) + >>> b + tensor([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]) + >>> torch.as_strided_scatter(b, a, (2, 2), (1, 2)) + tensor([[1., 3., 2.], + [4., 0., 0.], + [0., 0., 0.]]) + """ + ... +def as_tensor(data: Any, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None) -> Tensor: + r""" + as_tensor(data: Any, dtype: Optional[dtype] = None, device: Optional[DeviceLikeType]) -> Tensor + + Converts :attr:`data` into a tensor, sharing data and preserving autograd + history if possible. + + If :attr:`data` is already a tensor with the requested dtype and device + then :attr:`data` itself is returned, but if :attr:`data` is a + tensor with a different dtype or device then it's copied as if using + `data.to(dtype=dtype, device=device)`. + + If :attr:`data` is a NumPy array (an ndarray) with the same dtype and device then a + tensor is constructed using :func:`torch.from_numpy`. + + If :attr:`data` is a CuPy array, the returned tensor will be located on the same device as the CuPy array unless + specifically overwritten by :attr:`device` or a default device. + + .. seealso:: + + :func:`torch.tensor` never shares its data and creates a new "leaf tensor" (see :doc:`/notes/autograd`). + + + Args: + data (array_like): Initial data for the tensor. Can be a list, tuple, + NumPy ``ndarray``, scalar, and other types. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, infers data type from :attr:`data`. + device (:class:`torch.device`, optional): the device of the constructed tensor. If None and data is a tensor + then the device of data is used. If None and data is not a tensor then + the result tensor is constructed on the current device. + + + Example:: + + >>> a = numpy.array([1, 2, 3]) + >>> t = torch.as_tensor(a) + >>> t + tensor([ 1, 2, 3]) + >>> t[0] = -1 + >>> a + array([-1, 2, 3]) + + >>> a = numpy.array([1, 2, 3]) + >>> t = torch.as_tensor(a, device=torch.device('cuda')) + >>> t + tensor([ 1, 2, 3]) + >>> t[0] = -1 + >>> a + array([1, 2, 3]) + """ + ... +def asarray(obj: Any, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, copy: Optional[_bool] = None, requires_grad: _bool = False) -> Tensor: + r""" + asarray(obj: Any, *, dtype: Optional[dtype], device: Optional[DeviceLikeType], copy: Optional[bool] = None, requires_grad: bool = False) -> Tensor # noqa: B950 + + Converts :attr:`obj` to a tensor. + + :attr:`obj` can be one of: + + 1. a tensor + 2. a NumPy array or a NumPy scalar + 3. a DLPack capsule + 4. an object that implements Python's buffer protocol + 5. a scalar + 6. a sequence of scalars + + When :attr:`obj` is a tensor, NumPy array, or DLPack capsule the returned tensor will, + by default, not require a gradient, have the same datatype as :attr:`obj`, be on the + same device, and share memory with it. These properties can be controlled with the + :attr:`dtype`, :attr:`device`, :attr:`copy`, and :attr:`requires_grad` keyword arguments. + If the returned tensor is of a different datatype, on a different device, or a copy is + requested then it will not share its memory with :attr:`obj`. If :attr:`requires_grad` + is ``True`` then the returned tensor will require a gradient, and if :attr:`obj` is + also a tensor with an autograd history then the returned tensor will have the same history. + + When :attr:`obj` is not a tensor, NumPy array, or DLPack capsule but implements Python's + buffer protocol then the buffer is interpreted as an array of bytes grouped according to + the size of the datatype passed to the :attr:`dtype` keyword argument. (If no datatype is + passed then the default floating point datatype is used, instead.) The returned tensor + will have the specified datatype (or default floating point datatype if none is specified) + and, by default, be on the CPU device and share memory with the buffer. + + When :attr:`obj` is a NumPy scalar, the returned tensor will be a 0-dimensional tensor on + the CPU and that doesn't share its memory (i.e. ``copy=True``). By default datatype will + be the PyTorch datatype corresponding to the NumPy's scalar's datatype. + + When :attr:`obj` is none of the above but a scalar, or a sequence of scalars then the + returned tensor will, by default, infer its datatype from the scalar values, be on the + current default device, and not share its memory. + + .. seealso:: + + :func:`torch.tensor` creates a tensor that always copies the data from the input object. + :func:`torch.from_numpy` creates a tensor that always shares memory from NumPy arrays. + :func:`torch.frombuffer` creates a tensor that always shares memory from objects that + implement the buffer protocol. + :func:`torch.from_dlpack` creates a tensor that always shares memory from + DLPack capsules. + + Args: + obj (object): a tensor, NumPy array, DLPack Capsule, object that implements Python's + buffer protocol, scalar, or sequence of scalars. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the datatype of the returned tensor. + Default: ``None``, which causes the datatype of the returned tensor to be + inferred from :attr:`obj`. + copy (bool, optional): controls whether the returned tensor shares memory with :attr:`obj`. + Default: ``None``, which causes the returned tensor to share memory with :attr:`obj` + whenever possible. If ``True`` then the returned tensor does not share its memory. + If ``False`` then the returned tensor shares its memory with :attr:`obj` and an + error is thrown if it cannot. + device (:class:`torch.device`, optional): the device of the returned tensor. + Default: ``None``, which causes the device of :attr:`obj` to be used. Or, if + :attr:`obj` is a Python sequence, the current default device will be used. + requires_grad (bool, optional): whether the returned tensor requires grad. + Default: ``False``, which causes the returned tensor not to require a gradient. + If ``True``, then the returned tensor will require a gradient, and if :attr:`obj` + is also a tensor with an autograd history then the returned tensor will have + the same history. + + Example:: + + >>> a = torch.tensor([1, 2, 3]) + >>> # Shares memory with tensor 'a' + >>> b = torch.asarray(a) + >>> a.data_ptr() == b.data_ptr() + True + >>> # Forces memory copy + >>> c = torch.asarray(a, copy=True) + >>> a.data_ptr() == c.data_ptr() + False + + >>> a = torch.tensor([1., 2., 3.], requires_grad=True) + >>> b = a + 2 + >>> b + tensor([3., 4., 5.], grad_fn=) + >>> # Shares memory with tensor 'b', with no grad + >>> c = torch.asarray(b) + >>> c + tensor([3., 4., 5.]) + >>> # Shares memory with tensor 'b', retaining autograd history + >>> d = torch.asarray(b, requires_grad=True) + >>> d + tensor([3., 4., 5.], grad_fn=) + + >>> array = numpy.array([1, 2, 3]) + >>> # Shares memory with array 'array' + >>> t1 = torch.asarray(array) + >>> array.__array_interface__['data'][0] == t1.data_ptr() + True + >>> # Copies memory due to dtype mismatch + >>> t2 = torch.asarray(array, dtype=torch.float32) + >>> array.__array_interface__['data'][0] == t2.data_ptr() + False + + >>> scalar = numpy.float64(0.5) + >>> torch.asarray(scalar) + tensor(0.5000, dtype=torch.float64) + """ + ... +def asin(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + asin(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Returns a new tensor with the arcsine of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \sin^{-1}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.5962, 1.4985, -0.4396, 1.4525]) + >>> torch.asin(a) + tensor([-0.6387, nan, -0.4552, nan]) + """ + ... +def asin_(input: Tensor) -> Tensor: ... +def asinh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + asinh(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Returns a new tensor with the inverse hyperbolic sine of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \sinh^{-1}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.1606, -1.4267, -1.0899, -1.0250 ]) + >>> torch.asinh(a) + tensor([ 0.1599, -1.1534, -0.9435, -0.8990 ]) + """ + ... +def asinh_(input: Tensor) -> Tensor: ... +def atan(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + atan(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Returns a new tensor with the arctangent of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \tan^{-1}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.2341, 0.2539, -0.6256, -0.6448]) + >>> torch.atan(a) + tensor([ 0.2299, 0.2487, -0.5591, -0.5727]) + """ + ... +def atan2(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + atan2(input: Tensor, other: Tensor, *, out: Optional[Tensor]) -> Tensor + + Element-wise arctangent of :math:`\text{input}_{i} / \text{other}_{i}` + with consideration of the quadrant. Returns a new tensor with the signed angles + in radians between vector :math:`(\text{other}_{i}, \text{input}_{i})` + and vector :math:`(1, 0)`. (Note that :math:`\text{other}_{i}`, the second + parameter, is the x-coordinate, while :math:`\text{input}_{i}`, the first + parameter, is the y-coordinate.) + + The shapes of ``input`` and ``other`` must be + :ref:`broadcastable `. + + Args: + input (Tensor): the first input tensor + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.9041, 0.0196, -0.3108, -2.4423]) + >>> torch.atan2(a, torch.randn(4)) + tensor([ 0.9833, 0.0811, -1.9743, -1.4151]) + """ + ... +def atan_(input: Tensor) -> Tensor: ... +def atanh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + atanh(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Returns a new tensor with the inverse hyperbolic tangent of the elements of :attr:`input`. + + Note: + The domain of the inverse hyperbolic tangent is `(-1, 1)` and values outside this range + will be mapped to ``NaN``, except for the values `1` and `-1` for which the output is + mapped to `+/-INF` respectively. + + .. math:: + \text{out}_{i} = \tanh^{-1}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4).uniform_(-1, 1) + >>> a + tensor([ -0.9385, 0.2968, -0.8591, -0.1871 ]) + >>> torch.atanh(a) + tensor([ -1.7253, 0.3060, -1.2899, -0.1893 ]) + """ + ... +def atanh_(input: Tensor) -> Tensor: ... +def avg_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, ceil_mode: _bool = False, count_include_pad: _bool = True) -> Tensor: ... +@overload +def baddbmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], batch1: Tensor, batch2: Tensor) -> Tensor: + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` + and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the + same as the scaling factors used in :meth:`torch.addbmm`. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{batch1} \mathbin{@} \text{batch2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(10, 3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.baddbmm(M, batch1, batch2).size() + torch.Size([10, 3, 5]) + """ + ... +@overload +def baddbmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` + and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the + same as the scaling factors used in :meth:`torch.addbmm`. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{batch1} \mathbin{@} \text{batch2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(10, 3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.baddbmm(M, batch1, batch2).size() + torch.Size([10, 3, 5]) + """ + ... +@overload +def baddbmm(input: Tensor, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` + and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the + same as the scaling factors used in :meth:`torch.addbmm`. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{batch1} \mathbin{@} \text{batch2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(10, 3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.baddbmm(M, batch1, batch2).size() + torch.Size([10, 3, 5]) + """ + ... +@overload +def baddbmm(beta: Union[Number, _complex], self: Tensor, batch1: Tensor, batch2: Tensor) -> Tensor: + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` + and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the + same as the scaling factors used in :meth:`torch.addbmm`. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{batch1} \mathbin{@} \text{batch2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(10, 3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.baddbmm(M, batch1, batch2).size() + torch.Size([10, 3, 5]) + """ + ... +@overload +def baddbmm(beta: Union[Number, _complex], self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` + and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + :ref:`broadcastable ` with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. Both :attr:`alpha` and :attr:`beta` mean the + same as the scaling factors used in :meth:`torch.addbmm`. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then the content of :attr:`input` will be ignored, and `nan` and `inf` in + it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{batch1} \mathbin{@} \text{batch2}` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + + Example:: + + >>> M = torch.randn(10, 3, 5) + >>> batch1 = torch.randn(10, 3, 4) + >>> batch2 = torch.randn(10, 4, 5) + >>> torch.baddbmm(M, batch1, batch2).size() + torch.Size([10, 3, 5]) + """ + ... +@overload +def bartlett_window(window_length: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + bartlett_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Bartlett window function. + + .. math:: + w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \begin{cases} + \frac{2n}{N - 1} & \text{if } 0 \leq n \leq \frac{N - 1}{2} \\ + 2 - \frac{2n}{N - 1} & \text{if } \frac{N - 1}{2} < n < N \\ + \end{cases}, + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.bartlett_window(L, periodic=True)`` equal to + ``torch.bartlett_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +@overload +def bartlett_window(window_length: _int, periodic: _bool, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + bartlett_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Bartlett window function. + + .. math:: + w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \begin{cases} + \frac{2n}{N - 1} & \text{if } 0 \leq n \leq \frac{N - 1}{2} \\ + 2 - \frac{2n}{N - 1} & \text{if } \frac{N - 1}{2} < n < N \\ + \end{cases}, + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.bartlett_window(L, periodic=True)`` equal to + ``torch.bartlett_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +def batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, momentum: _float, eps: _float, cudnn_enabled: _bool) -> Tensor: ... +def batch_norm_backward_elemt(grad_out: Tensor, input: Tensor, mean: Tensor, invstd: Tensor, weight: Optional[Tensor], sum_dy: Tensor, sum_dy_xmu: Tensor, count: Tensor) -> Tensor: ... +def batch_norm_backward_reduce(grad_out: Tensor, input: Tensor, mean: Tensor, invstd: Tensor, weight: Optional[Tensor], input_g: _bool, weight_g: _bool, bias_g: _bool) -> tuple[Tensor, Tensor, Tensor, Tensor]: ... +def batch_norm_elemt(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], mean: Tensor, invstd: Tensor, eps: _float, *, out: Optional[Tensor] = None) -> Tensor: ... +def batch_norm_gather_stats(input: Tensor, mean: Tensor, invstd: Tensor, running_mean: Optional[Tensor], running_var: Optional[Tensor], momentum: _float, eps: _float, count: _int) -> tuple[Tensor, Tensor]: ... +def batch_norm_gather_stats_with_counts(input: Tensor, mean: Tensor, invstd: Tensor, running_mean: Optional[Tensor], running_var: Optional[Tensor], momentum: _float, eps: _float, counts: Tensor) -> tuple[Tensor, Tensor]: ... +def batch_norm_stats(input: Tensor, eps: _float) -> tuple[Tensor, Tensor]: ... +def batch_norm_update_stats(input: Tensor, running_mean: Optional[Tensor], running_var: Optional[Tensor], momentum: _float) -> tuple[Tensor, Tensor]: ... +@overload +def bernoulli(input: Tensor, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + bernoulli(input: Tensor, *, generator: Optional[Generator], out: Optional[Tensor]) -> Tensor + + Draws binary random numbers (0 or 1) from a Bernoulli distribution. + + The :attr:`input` tensor should be a tensor containing probabilities + to be used for drawing the binary random number. + Hence, all values in :attr:`input` have to be in the range: + :math:`0 \leq \text{input}_i \leq 1`. + + The :math:`\text{i}^{th}` element of the output tensor will draw a + value :math:`1` according to the :math:`\text{i}^{th}` probability value given + in :attr:`input`. + + .. math:: + \text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i}) + + The returned :attr:`out` tensor only has values 0 or 1 and is of the same + shape as :attr:`input`. + + :attr:`out` can have integral ``dtype``, but :attr:`input` must have floating + point ``dtype``. + + Args: + input (Tensor): the input tensor of probability values for the Bernoulli distribution + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.empty(3, 3).uniform_(0, 1) # generate a uniform random matrix with range [0, 1] + >>> a + tensor([[ 0.1737, 0.0950, 0.3609], + [ 0.7148, 0.0289, 0.2676], + [ 0.9456, 0.8937, 0.7202]]) + >>> torch.bernoulli(a) + tensor([[ 1., 0., 0.], + [ 0., 0., 0.], + [ 1., 1., 1.]]) + + >>> a = torch.ones(3, 3) # probability of drawing "1" is 1 + >>> torch.bernoulli(a) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.], + [ 1., 1., 1.]]) + >>> a = torch.zeros(3, 3) # probability of drawing "1" is 0 + >>> torch.bernoulli(a) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.], + [ 0., 0., 0.]]) + """ + ... +@overload +def bernoulli(input: Tensor, p: _float, *, generator: Optional[Generator] = None) -> Tensor: + r""" + bernoulli(input: Tensor, *, generator: Optional[Generator], out: Optional[Tensor]) -> Tensor + + Draws binary random numbers (0 or 1) from a Bernoulli distribution. + + The :attr:`input` tensor should be a tensor containing probabilities + to be used for drawing the binary random number. + Hence, all values in :attr:`input` have to be in the range: + :math:`0 \leq \text{input}_i \leq 1`. + + The :math:`\text{i}^{th}` element of the output tensor will draw a + value :math:`1` according to the :math:`\text{i}^{th}` probability value given + in :attr:`input`. + + .. math:: + \text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i}) + + The returned :attr:`out` tensor only has values 0 or 1 and is of the same + shape as :attr:`input`. + + :attr:`out` can have integral ``dtype``, but :attr:`input` must have floating + point ``dtype``. + + Args: + input (Tensor): the input tensor of probability values for the Bernoulli distribution + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.empty(3, 3).uniform_(0, 1) # generate a uniform random matrix with range [0, 1] + >>> a + tensor([[ 0.1737, 0.0950, 0.3609], + [ 0.7148, 0.0289, 0.2676], + [ 0.9456, 0.8937, 0.7202]]) + >>> torch.bernoulli(a) + tensor([[ 1., 0., 0.], + [ 0., 0., 0.], + [ 1., 1., 1.]]) + + >>> a = torch.ones(3, 3) # probability of drawing "1" is 1 + >>> torch.bernoulli(a) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.], + [ 1., 1., 1.]]) + >>> a = torch.zeros(3, 3) # probability of drawing "1" is 0 + >>> torch.bernoulli(a) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.], + [ 0., 0., 0.]]) + """ + ... +def bilinear(input1: Tensor, input2: Tensor, weight: Tensor, bias: Optional[Tensor] = None) -> Tensor: ... +def binary_cross_entropy_with_logits(input: Tensor, target: Tensor, weight: Optional[Tensor] = None, pos_weight: Optional[Tensor] = None, reduction: _int = 1) -> Tensor: ... +def bincount(input: Tensor, weights: Optional[Tensor] = None, minlength: _int = 0) -> Tensor: + r""" + bincount(input, weights=None, minlength=0) -> Tensor + + Count the frequency of each value in an array of non-negative ints. + + The number of bins (size 1) is one larger than the largest value in + :attr:`input` unless :attr:`input` is empty, in which case the result is a + tensor of size 0. If :attr:`minlength` is specified, the number of bins is at least + :attr:`minlength` and if :attr:`input` is empty, then the result is tensor of size + :attr:`minlength` filled with zeros. If ``n`` is the value at position ``i``, + ``out[n] += weights[i]`` if :attr:`weights` is specified else + ``out[n] += 1``. + + Note: + This operation may produce nondeterministic gradients when given tensors on a CUDA device. See :doc:`/notes/randomness` for more information. + + Arguments: + input (Tensor): 1-d int tensor + weights (Tensor): optional, weight for each value in the input tensor. + Should be of same size as input tensor. + minlength (int): optional, minimum number of bins. Should be non-negative. + + Returns: + output (Tensor): a tensor of shape ``Size([max(input) + 1])`` if + :attr:`input` is non-empty, else ``Size(0)`` + + Example:: + + >>> input = torch.randint(0, 8, (5,), dtype=torch.int64) + >>> weights = torch.linspace(0, 1, steps=5) + >>> input, weights + (tensor([4, 3, 6, 3, 4]), + tensor([ 0.0000, 0.2500, 0.5000, 0.7500, 1.0000]) + + >>> torch.bincount(input) + tensor([0, 0, 0, 2, 2, 0, 1]) + + >>> input.bincount(weights) + tensor([0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 0.0000, 0.5000]) + """ + ... +def binomial(count: Tensor, prob: Tensor, generator: Optional[Generator] = None) -> Tensor: ... +@overload +def bitwise_and(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_and(input, other, *, out=None) -> Tensor + + Computes the bitwise AND of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical AND. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_and(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([1, 0, 3], dtype=torch.int8) + >>> torch.bitwise_and(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ False, True, False]) + """ + ... +@overload +def bitwise_and(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + bitwise_and(input, other, *, out=None) -> Tensor + + Computes the bitwise AND of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical AND. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_and(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([1, 0, 3], dtype=torch.int8) + >>> torch.bitwise_and(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ False, True, False]) + """ + ... +@overload +def bitwise_and(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_and(input, other, *, out=None) -> Tensor + + Computes the bitwise AND of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical AND. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_and(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([1, 0, 3], dtype=torch.int8) + >>> torch.bitwise_and(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ False, True, False]) + """ + ... +@overload +def bitwise_left_shift(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_left_shift(input, other, *, out=None) -> Tensor + + Computes the left arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i << \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_left_shift(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 24], dtype=torch.int8) + """ + ... +@overload +def bitwise_left_shift(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + bitwise_left_shift(input, other, *, out=None) -> Tensor + + Computes the left arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i << \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_left_shift(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 24], dtype=torch.int8) + """ + ... +@overload +def bitwise_left_shift(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_left_shift(input, other, *, out=None) -> Tensor + + Computes the left arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i << \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_left_shift(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 24], dtype=torch.int8) + """ + ... +def bitwise_not(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_not(input, *, out=None) -> Tensor + + Computes the bitwise NOT of the given input tensor. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical NOT. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_not(torch.tensor([-1, -2, 3], dtype=torch.int8)) + tensor([ 0, 1, -4], dtype=torch.int8) + """ + ... +@overload +def bitwise_or(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_or(input: Tensor, other: Tensor, *, out: Optional[Tensor]) -> Tensor + + Computes the bitwise OR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical OR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_or(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -2, 3], dtype=torch.int8) + >>> torch.bitwise_or(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, True, False]) + """ + ... +@overload +def bitwise_or(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + bitwise_or(input: Tensor, other: Tensor, *, out: Optional[Tensor]) -> Tensor + + Computes the bitwise OR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical OR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_or(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -2, 3], dtype=torch.int8) + >>> torch.bitwise_or(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, True, False]) + """ + ... +@overload +def bitwise_or(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_or(input: Tensor, other: Tensor, *, out: Optional[Tensor]) -> Tensor + + Computes the bitwise OR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical OR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_or(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -2, 3], dtype=torch.int8) + >>> torch.bitwise_or(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, True, False]) + """ + ... +@overload +def bitwise_right_shift(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_right_shift(input, other, *, out=None) -> Tensor + + Computes the right arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + In any case, if the value of the right operand is negative or is greater + or equal to the number of bits in the promoted left operand, the behavior is undefined. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i >> \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_right_shift(torch.tensor([-2, -7, 31], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -7, 3], dtype=torch.int8) + """ + ... +@overload +def bitwise_right_shift(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + bitwise_right_shift(input, other, *, out=None) -> Tensor + + Computes the right arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + In any case, if the value of the right operand is negative or is greater + or equal to the number of bits in the promoted left operand, the behavior is undefined. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i >> \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_right_shift(torch.tensor([-2, -7, 31], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -7, 3], dtype=torch.int8) + """ + ... +@overload +def bitwise_right_shift(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_right_shift(input, other, *, out=None) -> Tensor + + Computes the right arithmetic shift of :attr:`input` by :attr:`other` bits. + The input tensor must be of integral type. This operator supports + :ref:`broadcasting to a common shape ` and + :ref:`type promotion `. + In any case, if the value of the right operand is negative or is greater + or equal to the number of bits in the promoted left operand, the behavior is undefined. + + The operation applied is: + + .. math:: + \text{out}_i = \text{input}_i >> \text{other}_i + + Args: + input (Tensor or Scalar): the first input tensor + other (Tensor or Scalar): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_right_shift(torch.tensor([-2, -7, 31], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-1, -7, 3], dtype=torch.int8) + """ + ... +@overload +def bitwise_xor(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_xor(input, other, *, out=None) -> Tensor + + Computes the bitwise XOR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical XOR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_xor(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 0], dtype=torch.int8) + >>> torch.bitwise_xor(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, False, False]) + """ + ... +@overload +def bitwise_xor(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + bitwise_xor(input, other, *, out=None) -> Tensor + + Computes the bitwise XOR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical XOR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_xor(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 0], dtype=torch.int8) + >>> torch.bitwise_xor(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, False, False]) + """ + ... +@overload +def bitwise_xor(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + bitwise_xor(input, other, *, out=None) -> Tensor + + Computes the bitwise XOR of :attr:`input` and :attr:`other`. The input tensor must be of + integral or Boolean types. For bool tensors, it computes the logical XOR. + + Args: + input: the first input tensor + other: the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.bitwise_xor(torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8)) + tensor([-2, -2, 0], dtype=torch.int8) + >>> torch.bitwise_xor(torch.tensor([True, True, False]), torch.tensor([False, True, False])) + tensor([ True, False, False]) + """ + ... +@overload +def blackman_window(window_length: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + blackman_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Blackman window function. + + .. math:: + w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right) + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.blackman_window(L, periodic=True)`` equal to + ``torch.blackman_window(L + 1, periodic=False)[:-1]``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +@overload +def blackman_window(window_length: _int, periodic: _bool, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + blackman_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Blackman window function. + + .. math:: + w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right) + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.blackman_window(L, periodic=True)`` equal to + ``torch.blackman_window(L + 1, periodic=False)[:-1]``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +def bmm(input: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + bmm(input, mat2, *, out=None) -> Tensor + + Performs a batch matrix-matrix product of matrices stored in :attr:`input` + and :attr:`mat2`. + + :attr:`input` and :attr:`mat2` must be 3-D tensors each containing + the same number of matrices. + + If :attr:`input` is a :math:`(b \times n \times m)` tensor, :attr:`mat2` is a + :math:`(b \times m \times p)` tensor, :attr:`out` will be a + :math:`(b \times n \times p)` tensor. + + .. math:: + \text{out}_i = \text{input}_i \mathbin{@} \text{mat2}_i + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + .. note:: This function does not :ref:`broadcast `. + For broadcasting matrix products, see :func:`torch.matmul`. + + Args: + input (Tensor): the first batch of matrices to be multiplied + mat2 (Tensor): the second batch of matrices to be multiplied + + Keyword Args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> input = torch.randn(10, 3, 4) + >>> mat2 = torch.randn(10, 4, 5) + >>> res = torch.bmm(input, mat2) + >>> res.size() + torch.Size([10, 3, 5]) + """ + ... +def broadcast_to(input: Tensor, size: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + broadcast_to(input, shape) -> Tensor + + Broadcasts :attr:`input` to the shape :attr:`\shape`. + Equivalent to calling ``input.expand(shape)``. See :meth:`~Tensor.expand` for details. + + Args: + input (Tensor): the input tensor. + shape (list, tuple, or :class:`torch.Size`): the new shape. + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> torch.broadcast_to(x, (3, 3)) + tensor([[1, 2, 3], + [1, 2, 3], + [1, 2, 3]]) + """ + ... +@overload +def bucketize(input: Tensor, boundaries: Tensor, *, out_int32: _bool = False, right: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + bucketize(input, boundaries, *, out_int32=False, right=False, out=None) -> Tensor + + Returns the indices of the buckets to which each value in the :attr:`input` belongs, where the + boundaries of the buckets are set by :attr:`boundaries`. Return a new tensor with the same size + as :attr:`input`. If :attr:`right` is False (default), then the left boundary is open. Note that + this behavior is opposite the behavior of + `numpy.digitize `_. + More formally, the returned index satisfies the following rules: + + .. list-table:: + :widths: 15 85 + :header-rows: 1 + + * - :attr:`right` + - *returned index satisfies* + * - False + - ``boundaries[i-1] < input[m][n]...[l][x] <= boundaries[i]`` + * - True + - ``boundaries[i-1] <= input[m][n]...[l][x] < boundaries[i]`` + + Args: + input (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s). + boundaries (Tensor): 1-D tensor, must contain a strictly increasing sequence, or the return value is undefined. + + Keyword args: + out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise. + Default value is False, i.e. default output data type is torch.int64. + right (bool, optional): determines the behavior for values in :attr:`boundaries`. See the table above. + out (Tensor, optional): the output tensor, must be the same size as :attr:`input` if provided. + + + Example:: + + >>> boundaries = torch.tensor([1, 3, 5, 7, 9]) + >>> boundaries + tensor([1, 3, 5, 7, 9]) + >>> v = torch.tensor([[3, 6, 9], [3, 6, 9]]) + >>> v + tensor([[3, 6, 9], + [3, 6, 9]]) + >>> torch.bucketize(v, boundaries) + tensor([[1, 3, 4], + [1, 3, 4]]) + >>> torch.bucketize(v, boundaries, right=True) + tensor([[2, 3, 5], + [2, 3, 5]]) + """ + ... +@overload +def bucketize(self: Union[Number, _complex], boundaries: Tensor, *, out_int32: _bool = False, right: _bool = False) -> Tensor: + r""" + bucketize(input, boundaries, *, out_int32=False, right=False, out=None) -> Tensor + + Returns the indices of the buckets to which each value in the :attr:`input` belongs, where the + boundaries of the buckets are set by :attr:`boundaries`. Return a new tensor with the same size + as :attr:`input`. If :attr:`right` is False (default), then the left boundary is open. Note that + this behavior is opposite the behavior of + `numpy.digitize `_. + More formally, the returned index satisfies the following rules: + + .. list-table:: + :widths: 15 85 + :header-rows: 1 + + * - :attr:`right` + - *returned index satisfies* + * - False + - ``boundaries[i-1] < input[m][n]...[l][x] <= boundaries[i]`` + * - True + - ``boundaries[i-1] <= input[m][n]...[l][x] < boundaries[i]`` + + Args: + input (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s). + boundaries (Tensor): 1-D tensor, must contain a strictly increasing sequence, or the return value is undefined. + + Keyword args: + out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise. + Default value is False, i.e. default output data type is torch.int64. + right (bool, optional): determines the behavior for values in :attr:`boundaries`. See the table above. + out (Tensor, optional): the output tensor, must be the same size as :attr:`input` if provided. + + + Example:: + + >>> boundaries = torch.tensor([1, 3, 5, 7, 9]) + >>> boundaries + tensor([1, 3, 5, 7, 9]) + >>> v = torch.tensor([[3, 6, 9], [3, 6, 9]]) + >>> v + tensor([[3, 6, 9], + [3, 6, 9]]) + >>> torch.bucketize(v, boundaries) + tensor([[1, 3, 4], + [1, 3, 4]]) + >>> torch.bucketize(v, boundaries, right=True) + tensor([[2, 3, 5], + [2, 3, 5]]) + """ + ... +def can_cast(from_: _dtype, to: _dtype) -> _bool: + r""" + can_cast(from_, to) -> bool + + Determines if a type conversion is allowed under PyTorch casting rules + described in the type promotion :ref:`documentation `. + + Args: + from\_ (dtype): The original :class:`torch.dtype`. + to (dtype): The target :class:`torch.dtype`. + + Example:: + + >>> torch.can_cast(torch.double, torch.float) + True + >>> torch.can_cast(torch.float, torch.int) + False + """ + ... +@overload +def cat(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cat(tensors, dim=0, *, out=None) -> Tensor + + Concatenates the given sequence of tensors in :attr:`tensors` in the given dimension. + All tensors must either have the same shape (except in the concatenating + dimension) or be a 1-D empty tensor with size ``(0,)``. + + :func:`torch.cat` can be seen as an inverse operation for :func:`torch.split` + and :func:`torch.chunk`. + + :func:`torch.cat` can be best understood via examples. + + .. seealso:: + + :func:`torch.stack` concatenates the given sequence along a new dimension. + + Args: + tensors (sequence of Tensors): Non-empty tensors provided must have the same shape, + except in the cat dimension. + + dim (int, optional): the dimension over which the tensors are concatenated + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497]]) + >>> torch.cat((x, x, x), 0) + tensor([[ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497], + [ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497], + [ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497]]) + >>> torch.cat((x, x, x), 1) + tensor([[ 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614, 0.6580, + -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497, -0.1034, + -0.5790, 0.1497]]) + """ + ... +@overload +def cat(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: Union[str, ellipsis, None], *, out: Optional[Tensor] = None) -> Tensor: + r""" + cat(tensors, dim=0, *, out=None) -> Tensor + + Concatenates the given sequence of tensors in :attr:`tensors` in the given dimension. + All tensors must either have the same shape (except in the concatenating + dimension) or be a 1-D empty tensor with size ``(0,)``. + + :func:`torch.cat` can be seen as an inverse operation for :func:`torch.split` + and :func:`torch.chunk`. + + :func:`torch.cat` can be best understood via examples. + + .. seealso:: + + :func:`torch.stack` concatenates the given sequence along a new dimension. + + Args: + tensors (sequence of Tensors): Non-empty tensors provided must have the same shape, + except in the cat dimension. + + dim (int, optional): the dimension over which the tensors are concatenated + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497]]) + >>> torch.cat((x, x, x), 0) + tensor([[ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497], + [ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497], + [ 0.6580, -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497]]) + >>> torch.cat((x, x, x), 1) + tensor([[ 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614, 0.6580, + -1.0969, -0.4614], + [-0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497, -0.1034, + -0.5790, 0.1497]]) + """ + ... +def ccol_indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def ceil(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ceil(input, *, out=None) -> Tensor + + Returns a new tensor with the ceil of the elements of :attr:`input`, + the smallest integer greater than or equal to each element. + + For integer inputs, follows the array-api convention of returning a + copy of the input tensor. + + .. math:: + \text{out}_{i} = \left\lceil \text{input}_{i} \right\rceil + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.6341, -1.4208, -1.0900, 0.5826]) + >>> torch.ceil(a) + tensor([-0., -1., -1., 1.]) + """ + ... +def ceil_(input: Tensor) -> Tensor: ... +def celu(input: Tensor, alpha: Union[Number, _complex] = 1.0) -> Tensor: ... +def celu_(input: Tensor, alpha: Union[Number, _complex] = 1.0) -> Tensor: ... +def channel_shuffle(input: Tensor, groups: Union[_int, SymInt]) -> Tensor: ... +def cholesky(input: Tensor, upper: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cholesky(input, upper=False, *, out=None) -> Tensor + + Computes the Cholesky decomposition of a symmetric positive-definite + matrix :math:`A` or for batches of symmetric positive-definite matrices. + + If :attr:`upper` is ``True``, the returned matrix ``U`` is upper-triangular, and + the decomposition has the form: + + .. math:: + + A = U^TU + + If :attr:`upper` is ``False``, the returned matrix ``L`` is lower-triangular, and + the decomposition has the form: + + .. math:: + + A = LL^T + + If :attr:`upper` is ``True``, and :math:`A` is a batch of symmetric positive-definite + matrices, then the returned tensor will be composed of upper-triangular Cholesky factors + of each of the individual matrices. Similarly, when :attr:`upper` is ``False``, the returned + tensor will be composed of lower-triangular Cholesky factors of each of the individual + matrices. + + .. warning:: + + :func:`torch.cholesky` is deprecated in favor of :func:`torch.linalg.cholesky` + and will be removed in a future PyTorch release. + + ``L = torch.cholesky(A)`` should be replaced with + + .. code:: python + + L = torch.linalg.cholesky(A) + + ``U = torch.cholesky(A, upper=True)`` should be replaced with + + .. code:: python + + U = torch.linalg.cholesky(A).mH + + This transform will produce equivalent results for all valid (symmetric positive definite) inputs. + + Args: + input (Tensor): the input tensor :math:`A` of size :math:`(*, n, n)` where `*` is zero or more + batch dimensions consisting of symmetric positive-definite matrices. + upper (bool, optional): flag that indicates whether to return a + upper or lower triangular matrix. Default: ``False`` + + Keyword args: + out (Tensor, optional): the output matrix + + Example:: + + >>> a = torch.randn(3, 3) + >>> a = a @ a.mT + 1e-3 # make symmetric positive-definite + >>> l = torch.cholesky(a) + >>> a + tensor([[ 2.4112, -0.7486, 1.4551], + [-0.7486, 1.3544, 0.1294], + [ 1.4551, 0.1294, 1.6724]]) + >>> l + tensor([[ 1.5528, 0.0000, 0.0000], + [-0.4821, 1.0592, 0.0000], + [ 0.9371, 0.5487, 0.7023]]) + >>> l @ l.mT + tensor([[ 2.4112, -0.7486, 1.4551], + [-0.7486, 1.3544, 0.1294], + [ 1.4551, 0.1294, 1.6724]]) + >>> a = torch.randn(3, 2, 2) # Example for batched input + >>> a = a @ a.mT + 1e-03 # make symmetric positive-definite + >>> l = torch.cholesky(a) + >>> z = l @ l.mT + >>> torch.dist(z, a) + tensor(2.3842e-07) + """ + ... +def cholesky_inverse(input: Tensor, upper: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cholesky_inverse(L, upper=False, *, out=None) -> Tensor + + Computes the inverse of a complex Hermitian or real symmetric + positive-definite matrix given its Cholesky decomposition. + + Let :math:`A` be a complex Hermitian or real symmetric positive-definite matrix, + and :math:`L` its Cholesky decomposition such that: + + .. math:: + + A = LL^{\text{H}} + + where :math:`L^{\text{H}}` is the conjugate transpose when :math:`L` is complex, + and the transpose when :math:`L` is real-valued. + + Computes the inverse matrix :math:`A^{-1}`. + + Supports input of float, double, cfloat and cdouble dtypes. + Also supports batches of matrices, and if :math:`A` is a batch of matrices + then the output has the same batch dimensions. + + Args: + L (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions + consisting of lower or upper triangular Cholesky decompositions of + symmetric or Hermitian positive-definite matrices. + upper (bool, optional): flag that indicates whether :math:`L` is lower triangular + or upper triangular. Default: ``False`` + + Keyword args: + out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`. + + Example:: + + >>> A = torch.randn(3, 3) + >>> A = A @ A.T + torch.eye(3) * 1e-3 # Creates a symmetric positive-definite matrix + >>> L = torch.linalg.cholesky(A) # Extract Cholesky decomposition + >>> torch.cholesky_inverse(L) + tensor([[ 1.9314, 1.2251, -0.0889], + [ 1.2251, 2.4439, 0.2122], + [-0.0889, 0.2122, 0.1412]]) + >>> A.inverse() + tensor([[ 1.9314, 1.2251, -0.0889], + [ 1.2251, 2.4439, 0.2122], + [-0.0889, 0.2122, 0.1412]]) + + >>> A = torch.randn(3, 2, 2, dtype=torch.complex64) + >>> A = A @ A.mH + torch.eye(2) * 1e-3 # Batch of Hermitian positive-definite matrices + >>> L = torch.linalg.cholesky(A) + >>> torch.dist(torch.inverse(A), torch.cholesky_inverse(L)) + tensor(5.6358e-7) + """ + ... +def cholesky_solve(input: Tensor, input2: Tensor, upper: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cholesky_solve(B, L, upper=False, *, out=None) -> Tensor + + Computes the solution of a system of linear equations with complex Hermitian + or real symmetric positive-definite lhs given its Cholesky decomposition. + + Let :math:`A` be a complex Hermitian or real symmetric positive-definite matrix, + and :math:`L` its Cholesky decomposition such that: + + .. math:: + + A = LL^{\text{H}} + + where :math:`L^{\text{H}}` is the conjugate transpose when :math:`L` is complex, + and the transpose when :math:`L` is real-valued. + + Returns the solution :math:`X` of the following linear system: + + .. math:: + + AX = B + + Supports inputs of float, double, cfloat and cdouble dtypes. + Also supports batches of matrices, and if :math:`A` or :math:`B` is a batch of matrices + then the output has the same batch dimensions. + + Args: + B (Tensor): right-hand side tensor of shape `(*, n, k)` + where :math:`*` is zero or more batch dimensions + L (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions + consisting of lower or upper triangular Cholesky decompositions of + symmetric or Hermitian positive-definite matrices. + upper (bool, optional): flag that indicates whether :math:`L` is lower triangular + or upper triangular. Default: ``False``. + + Keyword args: + out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`. + + Example:: + + >>> A = torch.randn(3, 3) + >>> A = A @ A.T + torch.eye(3) * 1e-3 # Creates a symmetric positive-definite matrix + >>> L = torch.linalg.cholesky(A) # Extract Cholesky decomposition + >>> B = torch.randn(3, 2) + >>> torch.cholesky_solve(B, L) + tensor([[ -8.1625, 19.6097], + [ -5.8398, 14.2387], + [ -4.3771, 10.4173]]) + >>> A.inverse() @ B + tensor([[ -8.1626, 19.6097], + [ -5.8398, 14.2387], + [ -4.3771, 10.4173]]) + + >>> A = torch.randn(3, 2, 2, dtype=torch.complex64) + >>> A = A @ A.mH + torch.eye(2) * 1e-3 # Batch of Hermitian positive-definite matrices + >>> L = torch.linalg.cholesky(A) + >>> B = torch.randn(2, 1, dtype=torch.complex64) + >>> X = torch.cholesky_solve(B, L) + >>> torch.dist(X, A.inverse() @ B) + tensor(1.6881e-5) + """ + ... +def choose_qparams_optimized(input: Tensor, numel: _int, n_bins: _int, ratio: _float, bit_width: _int) -> tuple[Tensor, Tensor]: ... +def chunk(input: Tensor, chunks: _int, dim: _int = 0) -> tuple[Tensor, ...]: + r""" + chunk(input: Tensor, chunks: int, dim: int = 0) -> Tuple[Tensor, ...] + + Attempts to split a tensor into the specified number of chunks. Each chunk is a view of + the input tensor. + + + .. note:: + + This function may return fewer than the specified number of chunks! + + .. seealso:: + + :func:`torch.tensor_split` a function that always returns exactly the specified number of chunks + + If the tensor size along the given dimension :attr:`dim` is divisible by :attr:`chunks`, + all returned chunks will be the same size. + If the tensor size along the given dimension :attr:`dim` is not divisible by :attr:`chunks`, + all returned chunks will be the same size, except the last one. + If such division is not possible, this function may return fewer + than the specified number of chunks. + + Arguments: + input (Tensor): the tensor to split + chunks (int): number of chunks to return + dim (int): dimension along which to split the tensor + + Example: + >>> torch.arange(11).chunk(6) + (tensor([0, 1]), + tensor([2, 3]), + tensor([4, 5]), + tensor([6, 7]), + tensor([8, 9]), + tensor([10])) + >>> torch.arange(12).chunk(6) + (tensor([0, 1]), + tensor([2, 3]), + tensor([4, 5]), + tensor([6, 7]), + tensor([8, 9]), + tensor([10, 11])) + >>> torch.arange(13).chunk(6) + (tensor([0, 1, 2]), + tensor([3, 4, 5]), + tensor([6, 7, 8]), + tensor([ 9, 10, 11]), + tensor([12])) + """ + ... +@overload +def clamp(input: Tensor, min: Optional[Tensor] = None, max: Optional[Tensor] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + clamp(input, min=None, max=None, *, out=None) -> Tensor + + Clamps all elements in :attr:`input` into the range `[` :attr:`min`, :attr:`max` `]`. + Letting min_value and max_value be :attr:`min` and :attr:`max`, respectively, this returns: + + .. math:: + y_i = \min(\max(x_i, \text{min\_value}_i), \text{max\_value}_i) + + If :attr:`min` is ``None``, there is no lower bound. + Or, if :attr:`max` is ``None`` there is no upper bound. + + + .. note:: + If :attr:`min` is greater than :attr:`max` :func:`torch.clamp(..., min, max) ` + sets all elements in :attr:`input` to the value of :attr:`max`. + + Args: + input (Tensor): the input tensor. + min (Number or Tensor, optional): lower-bound of the range to be clamped to + max (Number or Tensor, optional): upper-bound of the range to be clamped to + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-1.7120, 0.1734, -0.0478, -0.0922]) + >>> torch.clamp(a, min=-0.5, max=0.5) + tensor([-0.5000, 0.1734, -0.0478, -0.0922]) + + >>> min = torch.linspace(-1, 1, steps=4) + >>> torch.clamp(a, min=min) + tensor([-1.0000, 0.1734, 0.3333, 1.0000]) + """ + ... +@overload +def clamp(input: Tensor, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + clamp(input, min=None, max=None, *, out=None) -> Tensor + + Clamps all elements in :attr:`input` into the range `[` :attr:`min`, :attr:`max` `]`. + Letting min_value and max_value be :attr:`min` and :attr:`max`, respectively, this returns: + + .. math:: + y_i = \min(\max(x_i, \text{min\_value}_i), \text{max\_value}_i) + + If :attr:`min` is ``None``, there is no lower bound. + Or, if :attr:`max` is ``None`` there is no upper bound. + + + .. note:: + If :attr:`min` is greater than :attr:`max` :func:`torch.clamp(..., min, max) ` + sets all elements in :attr:`input` to the value of :attr:`max`. + + Args: + input (Tensor): the input tensor. + min (Number or Tensor, optional): lower-bound of the range to be clamped to + max (Number or Tensor, optional): upper-bound of the range to be clamped to + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-1.7120, 0.1734, -0.0478, -0.0922]) + >>> torch.clamp(a, min=-0.5, max=0.5) + tensor([-0.5000, 0.1734, -0.0478, -0.0922]) + + >>> min = torch.linspace(-1, 1, steps=4) + >>> torch.clamp(a, min=min) + tensor([-1.0000, 0.1734, 0.3333, 1.0000]) + """ + ... +@overload +def clamp_(input: Tensor, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: ... +@overload +def clamp_(input: Tensor, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: ... +@overload +def clamp_max(input: Tensor, max: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def clamp_max(input: Tensor, max: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def clamp_max_(input: Tensor, max: Tensor) -> Tensor: ... +@overload +def clamp_max_(input: Tensor, max: Union[Number, _complex]) -> Tensor: ... +@overload +def clamp_min(input: Tensor, min: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def clamp_min(input: Tensor, min: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def clamp_min_(input: Tensor, min: Tensor) -> Tensor: ... +@overload +def clamp_min_(input: Tensor, min: Union[Number, _complex]) -> Tensor: ... +@overload +def clip(input: Tensor, min: Optional[Tensor] = None, max: Optional[Tensor] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + clip(input, min=None, max=None, *, out=None) -> Tensor + + Alias for :func:`torch.clamp`. + """ + ... +@overload +def clip(input: Tensor, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + clip(input, min=None, max=None, *, out=None) -> Tensor + + Alias for :func:`torch.clamp`. + """ + ... +@overload +def clip_(input: Tensor, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: ... +@overload +def clip_(input: Tensor, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: ... +def clone(input: Tensor, *, memory_format: Optional[memory_format] = None) -> Tensor: + r""" + clone(input, *, memory_format=torch.preserve_format) -> Tensor + + Returns a copy of :attr:`input`. + + .. note:: + + This function is differentiable, so gradients will flow back from the + result of this operation to :attr:`input`. To create a tensor without an + autograd relationship to :attr:`input` see :meth:`~Tensor.detach`. + + Args: + input (Tensor): the input tensor. + + Keyword args: + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned tensor. Default: ``torch.preserve_format``. + """ + ... +def col_indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.col_indices`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def column_stack(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + column_stack(tensors, *, out=None) -> Tensor + + Creates a new tensor by horizontally stacking the tensors in :attr:`tensors`. + + Equivalent to ``torch.hstack(tensors)``, except each zero or one dimensional tensor ``t`` + in :attr:`tensors` is first reshaped into a ``(t.numel(), 1)`` column before being stacked horizontally. + + Args: + tensors (sequence of Tensors): sequence of tensors to concatenate + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([1, 2, 3]) + >>> b = torch.tensor([4, 5, 6]) + >>> torch.column_stack((a, b)) + tensor([[1, 4], + [2, 5], + [3, 6]]) + >>> a = torch.arange(5) + >>> b = torch.arange(10).reshape(5, 2) + >>> torch.column_stack((a, b, b)) + tensor([[0, 0, 1, 0, 1], + [1, 2, 3, 2, 3], + [2, 4, 5, 4, 5], + [3, 6, 7, 6, 7], + [4, 8, 9, 8, 9]]) + """ + ... +def combinations(input: Tensor, r: _int = 2, with_replacement: _bool = False) -> Tensor: + r""" + combinations(input: Tensor, r: int = 2, with_replacement: bool = False) -> seq + + Compute combinations of length :math:`r` of the given tensor. The behavior is similar to + python's `itertools.combinations` when `with_replacement` is set to `False`, and + `itertools.combinations_with_replacement` when `with_replacement` is set to `True`. + + Arguments: + input (Tensor): 1D vector. + r (int, optional): number of elements to combine + with_replacement (bool, optional): whether to allow duplication in combination + + Returns: + Tensor: A tensor equivalent to converting all the input tensors into lists, do + `itertools.combinations` or `itertools.combinations_with_replacement` on these + lists, and finally convert the resulting list into tensor. + + Example:: + + >>> a = [1, 2, 3] + >>> list(itertools.combinations(a, r=2)) + [(1, 2), (1, 3), (2, 3)] + >>> list(itertools.combinations(a, r=3)) + [(1, 2, 3)] + >>> list(itertools.combinations_with_replacement(a, r=2)) + [(1, 1), (1, 2), (1, 3), (2, 2), (2, 3), (3, 3)] + >>> tensor_a = torch.tensor(a) + >>> torch.combinations(tensor_a) + tensor([[1, 2], + [1, 3], + [2, 3]]) + >>> torch.combinations(tensor_a, r=3) + tensor([[1, 2, 3]]) + >>> torch.combinations(tensor_a, with_replacement=True) + tensor([[1, 1], + [1, 2], + [1, 3], + [2, 2], + [2, 3], + [3, 3]]) + """ + ... +def complex(real: Tensor, imag: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + complex(real, imag, *, out=None) -> Tensor + + Constructs a complex tensor with its real part equal to :attr:`real` and its + imaginary part equal to :attr:`imag`. + + Args: + real (Tensor): The real part of the complex tensor. Must be half, float or double. + imag (Tensor): The imaginary part of the complex tensor. Must be same dtype + as :attr:`real`. + + Keyword args: + out (Tensor): If the inputs are ``torch.float32``, must be + ``torch.complex64``. If the inputs are ``torch.float64``, must be + ``torch.complex128``. + + Example:: + + >>> real = torch.tensor([1, 2], dtype=torch.float32) + >>> imag = torch.tensor([3, 4], dtype=torch.float32) + >>> z = torch.complex(real, imag) + >>> z + tensor([(1.+3.j), (2.+4.j)]) + >>> z.dtype + torch.complex64 + """ + ... +@overload +def concat(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + concat(tensors, dim=0, *, out=None) -> Tensor + + Alias of :func:`torch.cat`. + """ + ... +@overload +def concat(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: Union[str, ellipsis, None], *, out: Optional[Tensor] = None) -> Tensor: + r""" + concat(tensors, dim=0, *, out=None) -> Tensor + + Alias of :func:`torch.cat`. + """ + ... +@overload +def concatenate(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + concatenate(tensors, axis=0, out=None) -> Tensor + + Alias of :func:`torch.cat`. + """ + ... +@overload +def concatenate(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: Union[str, ellipsis, None], *, out: Optional[Tensor] = None) -> Tensor: + r""" + concatenate(tensors, axis=0, out=None) -> Tensor + + Alias of :func:`torch.cat`. + """ + ... +def conj(input: Tensor) -> Tensor: + r""" + conj(input) -> Tensor + + Returns a view of :attr:`input` with a flipped conjugate bit. If :attr:`input` has a non-complex dtype, + this function just returns :attr:`input`. + + .. note:: + :func:`torch.conj` performs a lazy conjugation, but the actual conjugated tensor can be materialized + at any time using :func:`torch.resolve_conj`. + + .. warning:: In the future, :func:`torch.conj` may return a non-writeable view for an :attr:`input` of + non-complex dtype. It's recommended that programs not modify the tensor returned by :func:`torch.conj_physical` + when :attr:`input` is of non-complex dtype to be compatible with this change. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x = torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]) + >>> x.is_conj() + False + >>> y = torch.conj(x) + >>> y.is_conj() + True + """ + ... +def conj_physical(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + conj_physical(input, *, out=None) -> Tensor + + Computes the element-wise conjugate of the given :attr:`input` tensor. + If :attr:`input` has a non-complex dtype, this function just returns :attr:`input`. + + .. note:: + This performs the conjugate operation regardless of the fact conjugate bit is set or not. + + .. warning:: In the future, :func:`torch.conj_physical` may return a non-writeable view for an :attr:`input` of + non-complex dtype. It's recommended that programs not modify the tensor returned by :func:`torch.conj_physical` + when :attr:`input` is of non-complex dtype to be compatible with this change. + + .. math:: + \text{out}_{i} = conj(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.conj_physical(torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j])) + tensor([-1 - 1j, -2 - 2j, 3 + 3j]) + """ + ... +def conj_physical_(input: Tensor) -> Tensor: ... +def constant_pad_nd(input: Tensor, pad: Sequence[Union[_int, SymInt]], value: Union[Number, _complex] = 0) -> Tensor: ... +@overload +def conv1d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +@overload +def conv1d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: str = "valid", dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +@overload +def conv2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +@overload +def conv2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: str = "valid", dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +@overload +def conv3d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +@overload +def conv3d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: str = "valid", dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, groups: Union[_int, SymInt] = 1) -> Tensor: ... +def conv_tbc(input: Tensor, weight: Tensor, bias: Tensor, pad: _int = 0) -> Tensor: ... +def conv_transpose1d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, output_padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, groups: Union[_int, SymInt] = 1, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1) -> Tensor: ... +def conv_transpose2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, output_padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, groups: Union[_int, SymInt] = 1, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1) -> Tensor: ... +def conv_transpose3d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, stride: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1, padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, output_padding: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 0, groups: Union[_int, SymInt] = 1, dilation: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]] = 1) -> Tensor: ... +def convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], transposed: _bool, output_padding: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +@overload +def copysign(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + copysign(input, other, *, out=None) -> Tensor + + Create a new floating-point tensor with the magnitude of :attr:`input` and the sign of :attr:`other`, elementwise. + + .. math:: + \text{out}_{i} = \begin{cases} + -|\text{input}_{i}| & \text{if } \text{other}_{i} \leq -0.0 \\ + |\text{input}_{i}| & \text{if } \text{other}_{i} \geq 0.0 \\ + \end{cases} + + + Supports :ref:`broadcasting to a common shape `, + and integer and float inputs. + + Args: + input (Tensor): magnitudes. + other (Tensor or Number): contains value(s) whose signbit(s) are + applied to the magnitudes in :attr:`input`. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(5) + >>> a + tensor([-1.2557, -0.0026, -0.5387, 0.4740, -0.9244]) + >>> torch.copysign(a, 1) + tensor([1.2557, 0.0026, 0.5387, 0.4740, 0.9244]) + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.7079, 0.2778, -1.0249, 0.5719], + [-0.0059, -0.2600, -0.4475, -1.3948], + [ 0.3667, -0.9567, -2.5757, -0.1751], + [ 0.2046, -0.0742, 0.2998, -0.1054]]) + >>> b = torch.randn(4) + tensor([ 0.2373, 0.3120, 0.3190, -1.1128]) + >>> torch.copysign(a, b) + tensor([[ 0.7079, 0.2778, 1.0249, -0.5719], + [ 0.0059, 0.2600, 0.4475, -1.3948], + [ 0.3667, 0.9567, 2.5757, -0.1751], + [ 0.2046, 0.0742, 0.2998, -0.1054]]) + >>> a = torch.tensor([1.]) + >>> b = torch.tensor([-0.]) + >>> torch.copysign(a, b) + tensor([-1.]) + + .. note:: + copysign handles signed zeros. If the other argument has a negative zero (-0), + the corresponding output value will be negative. + """ + ... +@overload +def copysign(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + copysign(input, other, *, out=None) -> Tensor + + Create a new floating-point tensor with the magnitude of :attr:`input` and the sign of :attr:`other`, elementwise. + + .. math:: + \text{out}_{i} = \begin{cases} + -|\text{input}_{i}| & \text{if } \text{other}_{i} \leq -0.0 \\ + |\text{input}_{i}| & \text{if } \text{other}_{i} \geq 0.0 \\ + \end{cases} + + + Supports :ref:`broadcasting to a common shape `, + and integer and float inputs. + + Args: + input (Tensor): magnitudes. + other (Tensor or Number): contains value(s) whose signbit(s) are + applied to the magnitudes in :attr:`input`. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(5) + >>> a + tensor([-1.2557, -0.0026, -0.5387, 0.4740, -0.9244]) + >>> torch.copysign(a, 1) + tensor([1.2557, 0.0026, 0.5387, 0.4740, 0.9244]) + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.7079, 0.2778, -1.0249, 0.5719], + [-0.0059, -0.2600, -0.4475, -1.3948], + [ 0.3667, -0.9567, -2.5757, -0.1751], + [ 0.2046, -0.0742, 0.2998, -0.1054]]) + >>> b = torch.randn(4) + tensor([ 0.2373, 0.3120, 0.3190, -1.1128]) + >>> torch.copysign(a, b) + tensor([[ 0.7079, 0.2778, 1.0249, -0.5719], + [ 0.0059, 0.2600, 0.4475, -1.3948], + [ 0.3667, 0.9567, 2.5757, -0.1751], + [ 0.2046, 0.0742, 0.2998, -0.1054]]) + >>> a = torch.tensor([1.]) + >>> b = torch.tensor([-0.]) + >>> torch.copysign(a, b) + tensor([-1.]) + + .. note:: + copysign handles signed zeros. If the other argument has a negative zero (-0), + the corresponding output value will be negative. + """ + ... +def corrcoef(input: Tensor) -> Tensor: + r""" + corrcoef(input) -> Tensor + + Estimates the Pearson product-moment correlation coefficient matrix of the variables given by the :attr:`input` matrix, + where rows are the variables and columns are the observations. + + .. note:: + + The correlation coefficient matrix R is computed using the covariance matrix C as given by + :math:`R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }` + + .. note:: + + Due to floating point rounding, the resulting array may not be Hermitian and its diagonal elements may not be 1. + The real and imaginary values are clipped to the interval [-1, 1] in an attempt to improve this situation. + + Args: + input (Tensor): A 2D matrix containing multiple variables and observations, or a + Scalar or 1D vector representing a single variable. + + Returns: + (Tensor) The correlation coefficient matrix of the variables. + + .. seealso:: + + :func:`torch.cov` covariance matrix. + + Example:: + + >>> x = torch.tensor([[0, 1, 2], [2, 1, 0]]) + >>> torch.corrcoef(x) + tensor([[ 1., -1.], + [-1., 1.]]) + >>> x = torch.randn(2, 4) + >>> x + tensor([[-0.2678, -0.0908, -0.3766, 0.2780], + [-0.5812, 0.1535, 0.2387, 0.2350]]) + >>> torch.corrcoef(x) + tensor([[1.0000, 0.3582], + [0.3582, 1.0000]]) + >>> torch.corrcoef(x[0]) + tensor(1.) + """ + ... +def cos(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cos(input, *, out=None) -> Tensor + + Returns a new tensor with the cosine of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \cos(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 1.4309, 1.2706, -0.8562, 0.9796]) + >>> torch.cos(a) + tensor([ 0.1395, 0.2957, 0.6553, 0.5574]) + """ + ... +def cos_(input: Tensor) -> Tensor: ... +def cosh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cosh(input, *, out=None) -> Tensor + + Returns a new tensor with the hyperbolic cosine of the elements of + :attr:`input`. + + .. math:: + \text{out}_{i} = \cosh(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.1632, 1.1835, -0.6979, -0.7325]) + >>> torch.cosh(a) + tensor([ 1.0133, 1.7860, 1.2536, 1.2805]) + + .. note:: + When :attr:`input` is on the CPU, the implementation of torch.cosh may use + the Sleef library, which rounds very large results to infinity or negative + infinity. See `here `_ for details. + """ + ... +def cosh_(input: Tensor) -> Tensor: ... +def cosine_embedding_loss(input1: Tensor, input2: Tensor, target: Tensor, margin: _float = 0.0, reduction: _int = 1) -> Tensor: ... +def cosine_similarity(x1: Tensor, x2: Tensor, dim: _int = 1, eps: _float = 1e-08) -> Tensor: ... +@overload +def count_nonzero(input: Tensor, dim: Optional[_int] = None) -> Tensor: + r""" + count_nonzero(input, dim=None) -> Tensor + + Counts the number of non-zero values in the tensor :attr:`input` along the given :attr:`dim`. + If no dim is specified then all non-zeros in the tensor are counted. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): Dim or tuple of dims along which to count non-zeros. + + Example:: + + >>> x = torch.zeros(3,3) + >>> x[torch.randn(3,3) > 0.5] = 1 + >>> x + tensor([[0., 1., 1.], + [0., 0., 0.], + [0., 0., 1.]]) + >>> torch.count_nonzero(x) + tensor(3) + >>> torch.count_nonzero(x, dim=0) + tensor([0, 1, 2]) + """ + ... +@overload +def count_nonzero(input: Tensor, dim: _size) -> Tensor: + r""" + count_nonzero(input, dim=None) -> Tensor + + Counts the number of non-zero values in the tensor :attr:`input` along the given :attr:`dim`. + If no dim is specified then all non-zeros in the tensor are counted. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): Dim or tuple of dims along which to count non-zeros. + + Example:: + + >>> x = torch.zeros(3,3) + >>> x[torch.randn(3,3) > 0.5] = 1 + >>> x + tensor([[0., 1., 1.], + [0., 0., 0.], + [0., 0., 1.]]) + >>> torch.count_nonzero(x) + tensor(3) + >>> torch.count_nonzero(x, dim=0) + tensor([0, 1, 2]) + """ + ... +def cov(input: Tensor, *, correction: _int = 1, fweights: Optional[Tensor] = None, aweights: Optional[Tensor] = None) -> Tensor: + r""" + cov(input, *, correction=1, fweights=None, aweights=None) -> Tensor + + Estimates the covariance matrix of the variables given by the :attr:`input` matrix, where rows are + the variables and columns are the observations. + + A covariance matrix is a square matrix giving the covariance of each pair of variables. The diagonal contains + the variance of each variable (covariance of a variable with itself). By definition, if :attr:`input` represents + a single variable (Scalar or 1D) then its variance is returned. + + The sample covariance of the variables :math:`x` and :math:`y` is given by: + + .. math:: + \text{cov}(x,y) = \frac{\sum^{N}_{i = 1}(x_{i} - \bar{x})(y_{i} - \bar{y})}{\max(0,~N~-~\delta N)} + + where :math:`\bar{x}` and :math:`\bar{y}` are the simple means of the :math:`x` and :math:`y` respectively, and + :math:`\delta N` is the :attr:`correction`. + + If :attr:`fweights` and/or :attr:`aweights` are provided, the weighted covariance + is calculated, which is given by: + + .. math:: + \text{cov}_w(x,y) = \frac{\sum^{N}_{i = 1}w_i(x_{i} - \mu_x^*)(y_{i} - \mu_y^*)} + {\max(0,~\sum^{N}_{i = 1}w_i~-~\frac{\sum^{N}_{i = 1}w_ia_i}{\sum^{N}_{i = 1}w_i}~\delta N)} + + where :math:`w` denotes :attr:`fweights` or :attr:`aweights` (``f`` and ``a`` for brevity) based on whichever is + provided, or :math:`w = f \times a` if both are provided, and + :math:`\mu_x^* = \frac{\sum^{N}_{i = 1}w_ix_{i} }{\sum^{N}_{i = 1}w_i}` is the weighted mean of the variable. If not + provided, ``f`` and/or ``a`` can be seen as a :math:`\mathbb{1}` vector of appropriate size. + + Args: + input (Tensor): A 2D matrix containing multiple variables and observations, or a + Scalar or 1D vector representing a single variable. + + Keyword Args: + correction (int, optional): difference between the sample size and sample degrees of freedom. + Defaults to Bessel's correction, ``correction = 1`` which returns the unbiased estimate, + even if both :attr:`fweights` and :attr:`aweights` are specified. ``correction = 0`` + will return the simple average. Defaults to ``1``. + fweights (tensor, optional): A Scalar or 1D tensor of observation vector frequencies representing the number of + times each observation should be repeated. Its numel must equal the number of columns of :attr:`input`. + Must have integral dtype. Ignored if ``None``. Defaults to ``None``. + aweights (tensor, optional): A Scalar or 1D array of observation vector weights. + These relative weights are typically large for observations considered "important" and smaller for + observations considered less "important". Its numel must equal the number of columns of :attr:`input`. + Must have floating point dtype. Ignored if ``None``. Defaults to ``None``. + + Returns: + (Tensor) The covariance matrix of the variables. + + .. seealso:: + + :func:`torch.corrcoef` normalized covariance matrix. + + Example:: + >>> x = torch.tensor([[0, 2], [1, 1], [2, 0]]).T + >>> x + tensor([[0, 1, 2], + [2, 1, 0]]) + >>> torch.cov(x) + tensor([[ 1., -1.], + [-1., 1.]]) + >>> torch.cov(x, correction=0) + tensor([[ 0.6667, -0.6667], + [-0.6667, 0.6667]]) + >>> fw = torch.randint(1, 10, (3,)) + >>> fw + tensor([1, 6, 9]) + >>> aw = torch.rand(3) + >>> aw + tensor([0.4282, 0.0255, 0.4144]) + >>> torch.cov(x, fweights=fw, aweights=aw) + tensor([[ 0.4169, -0.4169], + [-0.4169, 0.4169]]) + """ + ... +def cross(input: Tensor, other: Tensor, dim: Optional[_int] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + cross(input, other, dim=None, *, out=None) -> Tensor + + + Returns the cross product of vectors in dimension :attr:`dim` of :attr:`input` + and :attr:`other`. + + Supports input of float, double, cfloat and cdouble dtypes. Also supports batches + of vectors, for which it computes the product along the dimension :attr:`dim`. + In this case, the output has the same batch dimensions as the inputs. + + .. warning:: + If :attr:`dim` is not given, it defaults to the first dimension found + with the size 3. Note that this might be unexpected. + + This behavior is deprecated and will be changed to match that of :func:`torch.linalg.cross` + in a future release. + + .. seealso:: + :func:`torch.linalg.cross` which has dim=-1 as default. + + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + dim (int, optional): the dimension to take the cross-product in. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4, 3) + >>> a + tensor([[-0.3956, 1.1455, 1.6895], + [-0.5849, 1.3672, 0.3599], + [-1.1626, 0.7180, -0.0521], + [-0.1339, 0.9902, -2.0225]]) + >>> b = torch.randn(4, 3) + >>> b + tensor([[-0.0257, -1.4725, -1.2251], + [-1.1479, -0.7005, -1.9757], + [-1.3904, 0.3726, -1.1836], + [-0.9688, -0.7153, 0.2159]]) + >>> torch.cross(a, b, dim=1) + tensor([[ 1.0844, -0.5281, 0.6120], + [-2.4490, -1.5687, 1.9792], + [-0.8304, -1.3037, 0.5650], + [-1.2329, 1.9883, 1.0551]]) + >>> torch.cross(a, b) + tensor([[ 1.0844, -0.5281, 0.6120], + [-2.4490, -1.5687, 1.9792], + [-0.8304, -1.3037, 0.5650], + [-1.2329, 1.9883, 1.0551]]) + """ + ... +def crow_indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.crow_indices`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int = 0, reduction: _int = 1, zero_infinity: _bool = False) -> Tensor: ... +@overload +def ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: Tensor, target_lengths: Tensor, blank: _int = 0, reduction: _int = 1, zero_infinity: _bool = False) -> Tensor: ... +def cudnn_affine_grid_generator(theta: Tensor, N: _int, C: _int, H: _int, W: _int) -> Tensor: ... +def cudnn_batch_norm(input: Tensor, weight: Tensor, bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, exponential_average_factor: _float, epsilon: _float) -> tuple[Tensor, Tensor, Tensor, Tensor]: ... +def cudnn_convolution(input: Tensor, weight: Tensor, padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool, allow_tf32: _bool, *, out: Optional[Tensor] = None) -> Tensor: ... +def cudnn_convolution_add_relu(input: Tensor, weight: Tensor, z: Tensor, alpha: Optional[Union[Number, _complex]], bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def cudnn_convolution_relu(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def cudnn_convolution_transpose(input: Tensor, weight: Tensor, padding: Sequence[Union[_int, SymInt]], output_padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool, allow_tf32: _bool) -> Tensor: ... +def cudnn_grid_sampler(input: Tensor, grid: Tensor) -> Tensor: ... +def cudnn_is_acceptable(input: Tensor) -> _bool: ... +@overload +def cummax(input: Tensor, dim: _int, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.cummax: + r""" + cummax(input, dim, *, out=None) -> (Tensor, LongTensor) + Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative maximum of + elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index + location of each maximum value found in the dimension :attr:`dim`. + + .. math:: + y_i = max(x_1, x_2, x_3, \dots, x_i) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([-0.3449, -1.5447, 0.0685, -1.5104, -1.1706, 0.2259, 1.4696, -1.3284, + 1.9946, -0.8209]) + >>> torch.cummax(a, dim=0) + torch.return_types.cummax( + values=tensor([-0.3449, -0.3449, 0.0685, 0.0685, 0.0685, 0.2259, 1.4696, 1.4696, + 1.9946, 1.9946]), + indices=tensor([0, 0, 2, 2, 2, 5, 6, 6, 8, 8])) + """ + ... +@overload +def cummax(input: Tensor, dim: Union[str, ellipsis, None], *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.cummax: + r""" + cummax(input, dim, *, out=None) -> (Tensor, LongTensor) + Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative maximum of + elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index + location of each maximum value found in the dimension :attr:`dim`. + + .. math:: + y_i = max(x_1, x_2, x_3, \dots, x_i) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([-0.3449, -1.5447, 0.0685, -1.5104, -1.1706, 0.2259, 1.4696, -1.3284, + 1.9946, -0.8209]) + >>> torch.cummax(a, dim=0) + torch.return_types.cummax( + values=tensor([-0.3449, -0.3449, 0.0685, 0.0685, 0.0685, 0.2259, 1.4696, 1.4696, + 1.9946, 1.9946]), + indices=tensor([0, 0, 2, 2, 2, 5, 6, 6, 8, 8])) + """ + ... +@overload +def cummin(input: Tensor, dim: _int, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.cummin: + r""" + cummin(input, dim, *, out=None) -> (Tensor, LongTensor) + Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative minimum of + elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index + location of each maximum value found in the dimension :attr:`dim`. + + .. math:: + y_i = min(x_1, x_2, x_3, \dots, x_i) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([-0.2284, -0.6628, 0.0975, 0.2680, -1.3298, -0.4220, -0.3885, 1.1762, + 0.9165, 1.6684]) + >>> torch.cummin(a, dim=0) + torch.return_types.cummin( + values=tensor([-0.2284, -0.6628, -0.6628, -0.6628, -1.3298, -1.3298, -1.3298, -1.3298, + -1.3298, -1.3298]), + indices=tensor([0, 1, 1, 1, 4, 4, 4, 4, 4, 4])) + """ + ... +@overload +def cummin(input: Tensor, dim: Union[str, ellipsis, None], *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.cummin: + r""" + cummin(input, dim, *, out=None) -> (Tensor, LongTensor) + Returns a namedtuple ``(values, indices)`` where ``values`` is the cumulative minimum of + elements of :attr:`input` in the dimension :attr:`dim`. And ``indices`` is the index + location of each maximum value found in the dimension :attr:`dim`. + + .. math:: + y_i = min(x_1, x_2, x_3, \dots, x_i) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([-0.2284, -0.6628, 0.0975, 0.2680, -1.3298, -0.4220, -0.3885, 1.1762, + 0.9165, 1.6684]) + >>> torch.cummin(a, dim=0) + torch.return_types.cummin( + values=tensor([-0.2284, -0.6628, -0.6628, -0.6628, -1.3298, -1.3298, -1.3298, -1.3298, + -1.3298, -1.3298]), + indices=tensor([0, 1, 1, 1, 4, 4, 4, 4, 4, 4])) + """ + ... +@overload +def cumprod(input: Tensor, dim: _int, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + cumprod(input, dim, *, dtype=None, out=None) -> Tensor + + Returns the cumulative product of elements of :attr:`input` in the dimension + :attr:`dim`. + + For example, if :attr:`input` is a vector of size N, the result will also be + a vector of size N, with elements. + + .. math:: + y_i = x_1 \times x_2\times x_3\times \dots \times x_i + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([ 0.6001, 0.2069, -0.1919, 0.9792, 0.6727, 1.0062, 0.4126, + -0.2129, -0.4206, 0.1968]) + >>> torch.cumprod(a, dim=0) + tensor([ 0.6001, 0.1241, -0.0238, -0.0233, -0.0157, -0.0158, -0.0065, + 0.0014, -0.0006, -0.0001]) + + >>> a[5] = 0.0 + >>> torch.cumprod(a, dim=0) + tensor([ 0.6001, 0.1241, -0.0238, -0.0233, -0.0157, -0.0000, -0.0000, + 0.0000, -0.0000, -0.0000]) + """ + ... +@overload +def cumprod(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + cumprod(input, dim, *, dtype=None, out=None) -> Tensor + + Returns the cumulative product of elements of :attr:`input` in the dimension + :attr:`dim`. + + For example, if :attr:`input` is a vector of size N, the result will also be + a vector of size N, with elements. + + .. math:: + y_i = x_1 \times x_2\times x_3\times \dots \times x_i + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(10) + >>> a + tensor([ 0.6001, 0.2069, -0.1919, 0.9792, 0.6727, 1.0062, 0.4126, + -0.2129, -0.4206, 0.1968]) + >>> torch.cumprod(a, dim=0) + tensor([ 0.6001, 0.1241, -0.0238, -0.0233, -0.0157, -0.0158, -0.0065, + 0.0014, -0.0006, -0.0001]) + + >>> a[5] = 0.0 + >>> torch.cumprod(a, dim=0) + tensor([ 0.6001, 0.1241, -0.0238, -0.0233, -0.0157, -0.0000, -0.0000, + 0.0000, -0.0000, -0.0000]) + """ + ... +@overload +def cumsum(input: Tensor, dim: _int, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + cumsum(input, dim, *, dtype=None, out=None) -> Tensor + + Returns the cumulative sum of elements of :attr:`input` in the dimension + :attr:`dim`. + + For example, if :attr:`input` is a vector of size N, the result will also be + a vector of size N, with elements. + + .. math:: + y_i = x_1 + x_2 + x_3 + \dots + x_i + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randint(1, 20, (10,)) + >>> a + tensor([13, 7, 3, 10, 13, 3, 15, 10, 9, 10]) + >>> torch.cumsum(a, dim=0) + tensor([13, 20, 23, 33, 46, 49, 64, 74, 83, 93]) + """ + ... +@overload +def cumsum(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + cumsum(input, dim, *, dtype=None, out=None) -> Tensor + + Returns the cumulative sum of elements of :attr:`input` in the dimension + :attr:`dim`. + + For example, if :attr:`input` is a vector of size N, the result will also be + a vector of size N, with elements. + + .. math:: + y_i = x_1 + x_2 + x_3 + \dots + x_i + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randint(1, 20, (10,)) + >>> a + tensor([13, 7, 3, 10, 13, 3, 15, 10, 9, 10]) + >>> torch.cumsum(a, dim=0) + tensor([13, 20, 23, 33, 46, 49, 64, 74, 83, 93]) + """ + ... +@overload +def cumulative_trapezoid(y: Tensor, x: Tensor, *, dim: _int = -1) -> Tensor: + r""" + cumulative_trapezoid(y, x=None, *, dx=None, dim=-1) -> Tensor + + Cumulatively computes the `trapezoidal rule `_ + along :attr:`dim`. By default the spacing between elements is assumed to be 1, but + :attr:`dx` can be used to specify a different constant spacing, and :attr:`x` can be + used to specify arbitrary spacing along :attr:`dim`. + + For more details, please read :func:`torch.trapezoid`. The difference between :func:`torch.trapezoid` + and this function is that, :func:`torch.trapezoid` returns a value for each integration, + where as this function returns a cumulative value for every spacing within the integration. This + is analogous to how `.sum` returns a value and `.cumsum` returns a cumulative sum. + + Arguments: + y (Tensor): Values to use when computing the trapezoidal rule. + x (Tensor): If specified, defines spacing between values as specified above. + + Keyword arguments: + dx (float): constant spacing between values. If neither :attr:`x` or :attr:`dx` + are specified then this defaults to 1. Effectively multiplies the result by its value. + dim (int): The dimension along which to compute the trapezoidal rule. + The last (inner-most) dimension by default. + + Examples:: + + >>> # Cumulatively computes the trapezoidal rule in 1D, spacing is implicitly 1. + >>> y = torch.tensor([1, 5, 10]) + >>> torch.cumulative_trapezoid(y) + tensor([3., 10.5]) + + >>> # Computes the same trapezoidal rule directly up to each element to verify + >>> (1 + 5) / 2 + 3.0 + >>> (1 + 10 + 10) / 2 + 10.5 + + >>> # Cumulatively computes the trapezoidal rule in 1D with constant spacing of 2 + >>> # NOTE: the result is the same as before, but multiplied by 2 + >>> torch.cumulative_trapezoid(y, dx=2) + tensor([6., 21.]) + + >>> # Cumulatively computes the trapezoidal rule in 1D with arbitrary spacing + >>> x = torch.tensor([1, 3, 6]) + >>> torch.cumulative_trapezoid(y, x) + tensor([6., 28.5]) + + >>> # Computes the same trapezoidal rule directly up to each element to verify + >>> ((3 - 1) * (1 + 5)) / 2 + 6.0 + >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2 + 28.5 + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 matrix + >>> y = torch.arange(9).reshape(3, 3) + tensor([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> torch.cumulative_trapezoid(y) + tensor([[ 0.5, 2.], + [ 3.5, 8.], + [ 6.5, 14.]]) + + >>> # Cumulatively computes the trapezoidal rule for each column of the matrix + >>> torch.cumulative_trapezoid(y, dim=0) + tensor([[ 1.5, 2.5, 3.5], + [ 6.0, 8.0, 10.0]]) + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with the same arbitrary spacing + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([1, 3, 6]) + >>> torch.cumulative_trapezoid(y, x) + tensor([[2., 5.], + [2., 5.], + [2., 5.]]) + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with different arbitrary spacing per row + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]]) + >>> torch.cumulative_trapezoid(y, x) + tensor([[1., 2.], + [2., 4.], + [3., 6.]]) + """ + ... +@overload +def cumulative_trapezoid(y: Tensor, *, dx: Union[Number, _complex] = 1, dim: _int = -1) -> Tensor: + r""" + cumulative_trapezoid(y, x=None, *, dx=None, dim=-1) -> Tensor + + Cumulatively computes the `trapezoidal rule `_ + along :attr:`dim`. By default the spacing between elements is assumed to be 1, but + :attr:`dx` can be used to specify a different constant spacing, and :attr:`x` can be + used to specify arbitrary spacing along :attr:`dim`. + + For more details, please read :func:`torch.trapezoid`. The difference between :func:`torch.trapezoid` + and this function is that, :func:`torch.trapezoid` returns a value for each integration, + where as this function returns a cumulative value for every spacing within the integration. This + is analogous to how `.sum` returns a value and `.cumsum` returns a cumulative sum. + + Arguments: + y (Tensor): Values to use when computing the trapezoidal rule. + x (Tensor): If specified, defines spacing between values as specified above. + + Keyword arguments: + dx (float): constant spacing between values. If neither :attr:`x` or :attr:`dx` + are specified then this defaults to 1. Effectively multiplies the result by its value. + dim (int): The dimension along which to compute the trapezoidal rule. + The last (inner-most) dimension by default. + + Examples:: + + >>> # Cumulatively computes the trapezoidal rule in 1D, spacing is implicitly 1. + >>> y = torch.tensor([1, 5, 10]) + >>> torch.cumulative_trapezoid(y) + tensor([3., 10.5]) + + >>> # Computes the same trapezoidal rule directly up to each element to verify + >>> (1 + 5) / 2 + 3.0 + >>> (1 + 10 + 10) / 2 + 10.5 + + >>> # Cumulatively computes the trapezoidal rule in 1D with constant spacing of 2 + >>> # NOTE: the result is the same as before, but multiplied by 2 + >>> torch.cumulative_trapezoid(y, dx=2) + tensor([6., 21.]) + + >>> # Cumulatively computes the trapezoidal rule in 1D with arbitrary spacing + >>> x = torch.tensor([1, 3, 6]) + >>> torch.cumulative_trapezoid(y, x) + tensor([6., 28.5]) + + >>> # Computes the same trapezoidal rule directly up to each element to verify + >>> ((3 - 1) * (1 + 5)) / 2 + 6.0 + >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2 + 28.5 + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 matrix + >>> y = torch.arange(9).reshape(3, 3) + tensor([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> torch.cumulative_trapezoid(y) + tensor([[ 0.5, 2.], + [ 3.5, 8.], + [ 6.5, 14.]]) + + >>> # Cumulatively computes the trapezoidal rule for each column of the matrix + >>> torch.cumulative_trapezoid(y, dim=0) + tensor([[ 1.5, 2.5, 3.5], + [ 6.0, 8.0, 10.0]]) + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with the same arbitrary spacing + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([1, 3, 6]) + >>> torch.cumulative_trapezoid(y, x) + tensor([[2., 5.], + [2., 5.], + [2., 5.]]) + + >>> # Cumulatively computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with different arbitrary spacing per row + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]]) + >>> torch.cumulative_trapezoid(y, x) + tensor([[1., 2.], + [2., 4.], + [3., 6.]]) + """ + ... +def deg2rad(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + deg2rad(input, *, out=None) -> Tensor + + Returns a new tensor with each of the elements of :attr:`input` + converted from angles in degrees to radians. + + Args: + input (Tensor): the input tensor. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([[180.0, -180.0], [360.0, -360.0], [90.0, -90.0]]) + >>> torch.deg2rad(a) + tensor([[ 3.1416, -3.1416], + [ 6.2832, -6.2832], + [ 1.5708, -1.5708]]) + """ + ... +def deg2rad_(input: Tensor) -> Tensor: ... +@overload +def dequantize(input: Tensor) -> Tensor: + r""" + dequantize(tensor) -> Tensor + + Returns an fp32 Tensor by dequantizing a quantized Tensor + + Args: + tensor (Tensor): A quantized Tensor + + .. function:: dequantize(tensors) -> sequence of Tensors + :noindex: + + Given a list of quantized Tensors, dequantize them and return a list of fp32 Tensors + + Args: + tensors (sequence of Tensors): A list of quantized Tensors + """ + ... +@overload +def dequantize(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]]) -> tuple[Tensor, ...]: + r""" + dequantize(tensor) -> Tensor + + Returns an fp32 Tensor by dequantizing a quantized Tensor + + Args: + tensor (Tensor): A quantized Tensor + + .. function:: dequantize(tensors) -> sequence of Tensors + :noindex: + + Given a list of quantized Tensors, dequantize them and return a list of fp32 Tensors + + Args: + tensors (sequence of Tensors): A list of quantized Tensors + """ + ... +def det(input: Tensor) -> Tensor: + r""" + det(input) -> Tensor + + Alias for :func:`torch.linalg.det` + """ + ... +def detach(input: Tensor) -> Tensor: ... +def detach_(input: Tensor) -> Tensor: ... +def detach_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.detach`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def diag(input: Tensor, diagonal: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + diag(input, diagonal=0, *, out=None) -> Tensor + + - If :attr:`input` is a vector (1-D tensor), then returns a 2-D square tensor + with the elements of :attr:`input` as the diagonal. + - If :attr:`input` is a matrix (2-D tensor), then returns a 1-D tensor with + the diagonal elements of :attr:`input`. + + The argument :attr:`diagonal` controls which diagonal to consider: + + - If :attr:`diagonal` = 0, it is the main diagonal. + - If :attr:`diagonal` > 0, it is above the main diagonal. + - If :attr:`diagonal` < 0, it is below the main diagonal. + + Args: + input (Tensor): the input tensor. + diagonal (int, optional): the diagonal to consider + + Keyword args: + out (Tensor, optional): the output tensor. + + .. seealso:: + + :func:`torch.diagonal` always returns the diagonal of its input. + + :func:`torch.diagflat` always constructs a tensor with diagonal elements + specified by the input. + + Examples: + + Get the square matrix where the input vector is the diagonal:: + + >>> a = torch.randn(3) + >>> a + tensor([ 0.5950,-0.0872, 2.3298]) + >>> torch.diag(a) + tensor([[ 0.5950, 0.0000, 0.0000], + [ 0.0000,-0.0872, 0.0000], + [ 0.0000, 0.0000, 2.3298]]) + >>> torch.diag(a, 1) + tensor([[ 0.0000, 0.5950, 0.0000, 0.0000], + [ 0.0000, 0.0000,-0.0872, 0.0000], + [ 0.0000, 0.0000, 0.0000, 2.3298], + [ 0.0000, 0.0000, 0.0000, 0.0000]]) + + Get the k-th diagonal of a given matrix:: + + >>> a = torch.randn(3, 3) + >>> a + tensor([[-0.4264, 0.0255,-0.1064], + [ 0.8795,-0.2429, 0.1374], + [ 0.1029,-0.6482,-1.6300]]) + >>> torch.diag(a, 0) + tensor([-0.4264,-0.2429,-1.6300]) + >>> torch.diag(a, 1) + tensor([ 0.0255, 0.1374]) + """ + ... +def diag_embed(input: Tensor, offset: _int = 0, dim1: _int = -2, dim2: _int = -1) -> Tensor: + r""" + diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor + + Creates a tensor whose diagonals of certain 2D planes (specified by + :attr:`dim1` and :attr:`dim2`) are filled by :attr:`input`. + To facilitate creating batched diagonal matrices, the 2D planes formed by + the last two dimensions of the returned tensor are chosen by default. + + The argument :attr:`offset` controls which diagonal to consider: + + - If :attr:`offset` = 0, it is the main diagonal. + - If :attr:`offset` > 0, it is above the main diagonal. + - If :attr:`offset` < 0, it is below the main diagonal. + + The size of the new matrix will be calculated to make the specified diagonal + of the size of the last input dimension. + Note that for :attr:`offset` other than :math:`0`, the order of :attr:`dim1` + and :attr:`dim2` matters. Exchanging them is equivalent to changing the + sign of :attr:`offset`. + + Applying :meth:`torch.diagonal` to the output of this function with + the same arguments yields a matrix identical to input. However, + :meth:`torch.diagonal` has different default dimensions, so those + need to be explicitly specified. + + Args: + input (Tensor): the input tensor. Must be at least 1-dimensional. + offset (int, optional): which diagonal to consider. Default: 0 + (main diagonal). + dim1 (int, optional): first dimension with respect to which to + take diagonal. Default: -2. + dim2 (int, optional): second dimension with respect to which to + take diagonal. Default: -1. + + Example:: + + >>> a = torch.randn(2, 3) + >>> torch.diag_embed(a) + tensor([[[ 1.5410, 0.0000, 0.0000], + [ 0.0000, -0.2934, 0.0000], + [ 0.0000, 0.0000, -2.1788]], + + [[ 0.5684, 0.0000, 0.0000], + [ 0.0000, -1.0845, 0.0000], + [ 0.0000, 0.0000, -1.3986]]]) + + >>> torch.diag_embed(a, offset=1, dim1=0, dim2=2) + tensor([[[ 0.0000, 1.5410, 0.0000, 0.0000], + [ 0.0000, 0.5684, 0.0000, 0.0000]], + + [[ 0.0000, 0.0000, -0.2934, 0.0000], + [ 0.0000, 0.0000, -1.0845, 0.0000]], + + [[ 0.0000, 0.0000, 0.0000, -2.1788], + [ 0.0000, 0.0000, 0.0000, -1.3986]], + + [[ 0.0000, 0.0000, 0.0000, 0.0000], + [ 0.0000, 0.0000, 0.0000, 0.0000]]]) + """ + ... +def diagflat(input: Tensor, offset: _int = 0) -> Tensor: + r""" + diagflat(input, offset=0) -> Tensor + + - If :attr:`input` is a vector (1-D tensor), then returns a 2-D square tensor + with the elements of :attr:`input` as the diagonal. + - If :attr:`input` is a tensor with more than one dimension, then returns a + 2-D tensor with diagonal elements equal to a flattened :attr:`input`. + + The argument :attr:`offset` controls which diagonal to consider: + + - If :attr:`offset` = 0, it is the main diagonal. + - If :attr:`offset` > 0, it is above the main diagonal. + - If :attr:`offset` < 0, it is below the main diagonal. + + Args: + input (Tensor): the input tensor. + offset (int, optional): the diagonal to consider. Default: 0 (main + diagonal). + + Examples:: + + >>> a = torch.randn(3) + >>> a + tensor([-0.2956, -0.9068, 0.1695]) + >>> torch.diagflat(a) + tensor([[-0.2956, 0.0000, 0.0000], + [ 0.0000, -0.9068, 0.0000], + [ 0.0000, 0.0000, 0.1695]]) + >>> torch.diagflat(a, 1) + tensor([[ 0.0000, -0.2956, 0.0000, 0.0000], + [ 0.0000, 0.0000, -0.9068, 0.0000], + [ 0.0000, 0.0000, 0.0000, 0.1695], + [ 0.0000, 0.0000, 0.0000, 0.0000]]) + + >>> a = torch.randn(2, 2) + >>> a + tensor([[ 0.2094, -0.3018], + [-0.1516, 1.9342]]) + >>> torch.diagflat(a) + tensor([[ 0.2094, 0.0000, 0.0000, 0.0000], + [ 0.0000, -0.3018, 0.0000, 0.0000], + [ 0.0000, 0.0000, -0.1516, 0.0000], + [ 0.0000, 0.0000, 0.0000, 1.9342]]) + """ + ... +@overload +def diagonal(input: Tensor, offset: _int = 0, dim1: _int = 0, dim2: _int = 1) -> Tensor: + r""" + diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor + + Returns a partial view of :attr:`input` with the its diagonal elements + with respect to :attr:`dim1` and :attr:`dim2` appended as a dimension + at the end of the shape. + + The argument :attr:`offset` controls which diagonal to consider: + + - If :attr:`offset` = 0, it is the main diagonal. + - If :attr:`offset` > 0, it is above the main diagonal. + - If :attr:`offset` < 0, it is below the main diagonal. + + Applying :meth:`torch.diag_embed` to the output of this function with + the same arguments yields a diagonal matrix with the diagonal entries + of the input. However, :meth:`torch.diag_embed` has different default + dimensions, so those need to be explicitly specified. + + Args: + input (Tensor): the input tensor. Must be at least 2-dimensional. + offset (int, optional): which diagonal to consider. Default: 0 + (main diagonal). + dim1 (int, optional): first dimension with respect to which to + take diagonal. Default: 0. + dim2 (int, optional): second dimension with respect to which to + take diagonal. Default: 1. + + .. note:: To take a batch diagonal, pass in dim1=-2, dim2=-1. + + Examples:: + + >>> a = torch.randn(3, 3) + >>> a + tensor([[-1.0854, 1.1431, -0.1752], + [ 0.8536, -0.0905, 0.0360], + [ 0.6927, -0.3735, -0.4945]]) + + + >>> torch.diagonal(a) + tensor([-1.0854, -0.0905, -0.4945]) + + + >>> torch.diagonal(a, 1) + tensor([ 1.1431, 0.0360]) + + >>> b = torch.randn(2, 5) + >>> b + tensor([[-1.7948, -1.2731, -0.3181, 2.0200, -1.6745], + [ 1.8262, -1.5049, 0.4114, 1.0704, -1.2607]]) + + >>> torch.diagonal(b, 1, 1, 0) + tensor([1.8262]) + + >>> x = torch.randn(2, 5, 4, 2) + >>> torch.diagonal(x, offset=-1, dim1=1, dim2=2) + tensor([[[-1.2631, 0.3755, -1.5977, -1.8172], + [-1.1065, 1.0401, -0.2235, -0.7938]], + + [[-1.7325, -0.3081, 0.6166, 0.2335], + [ 1.0500, 0.7336, -0.3836, -1.1015]]]) + """ + ... +@overload +def diagonal(input: Tensor, *, outdim: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None], dim2: Union[str, ellipsis, None], offset: _int = 0) -> Tensor: + r""" + diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor + + Returns a partial view of :attr:`input` with the its diagonal elements + with respect to :attr:`dim1` and :attr:`dim2` appended as a dimension + at the end of the shape. + + The argument :attr:`offset` controls which diagonal to consider: + + - If :attr:`offset` = 0, it is the main diagonal. + - If :attr:`offset` > 0, it is above the main diagonal. + - If :attr:`offset` < 0, it is below the main diagonal. + + Applying :meth:`torch.diag_embed` to the output of this function with + the same arguments yields a diagonal matrix with the diagonal entries + of the input. However, :meth:`torch.diag_embed` has different default + dimensions, so those need to be explicitly specified. + + Args: + input (Tensor): the input tensor. Must be at least 2-dimensional. + offset (int, optional): which diagonal to consider. Default: 0 + (main diagonal). + dim1 (int, optional): first dimension with respect to which to + take diagonal. Default: 0. + dim2 (int, optional): second dimension with respect to which to + take diagonal. Default: 1. + + .. note:: To take a batch diagonal, pass in dim1=-2, dim2=-1. + + Examples:: + + >>> a = torch.randn(3, 3) + >>> a + tensor([[-1.0854, 1.1431, -0.1752], + [ 0.8536, -0.0905, 0.0360], + [ 0.6927, -0.3735, -0.4945]]) + + + >>> torch.diagonal(a) + tensor([-1.0854, -0.0905, -0.4945]) + + + >>> torch.diagonal(a, 1) + tensor([ 1.1431, 0.0360]) + + >>> b = torch.randn(2, 5) + >>> b + tensor([[-1.7948, -1.2731, -0.3181, 2.0200, -1.6745], + [ 1.8262, -1.5049, 0.4114, 1.0704, -1.2607]]) + + >>> torch.diagonal(b, 1, 1, 0) + tensor([1.8262]) + + >>> x = torch.randn(2, 5, 4, 2) + >>> torch.diagonal(x, offset=-1, dim1=1, dim2=2) + tensor([[[-1.2631, 0.3755, -1.5977, -1.8172], + [-1.1065, 1.0401, -0.2235, -0.7938]], + + [[-1.7325, -0.3081, 0.6166, 0.2335], + [ 1.0500, 0.7336, -0.3836, -1.1015]]]) + """ + ... +def diagonal_copy(input: Tensor, offset: _int = 0, dim1: _int = 0, dim2: _int = 1, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.diagonal`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def diagonal_scatter(input: Tensor, src: Tensor, offset: _int = 0, dim1: _int = 0, dim2: _int = 1) -> Tensor: + r""" + diagonal_scatter(input, src, offset=0, dim1=0, dim2=1) -> Tensor + + Embeds the values of the :attr:`src` tensor into :attr:`input` along + the diagonal elements of :attr:`input`, with respect to :attr:`dim1` + and :attr:`dim2`. + + This function returns a tensor with fresh storage; it does not + return a view. + + The argument :attr:`offset` controls which diagonal to consider: + + - If :attr:`offset` = 0, it is the main diagonal. + - If :attr:`offset` > 0, it is above the main diagonal. + - If :attr:`offset` < 0, it is below the main diagonal. + + Args: + input (Tensor): the input tensor. Must be at least 2-dimensional. + src (Tensor): the tensor to embed into :attr:`input`. + offset (int, optional): which diagonal to consider. Default: 0 + (main diagonal). + dim1 (int, optional): first dimension with respect to which to + take diagonal. Default: 0. + dim2 (int, optional): second dimension with respect to which to + take diagonal. Default: 1. + + .. note:: + + :attr:`src` must be of the proper size in order to be embedded + into :attr:`input`. Specifically, it should have the same shape as + ``torch.diagonal(input, offset, dim1, dim2)`` + + Examples:: + + >>> a = torch.zeros(3, 3) + >>> a + tensor([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]) + + >>> torch.diagonal_scatter(a, torch.ones(3), 0) + tensor([[1., 0., 0.], + [0., 1., 0.], + [0., 0., 1.]]) + + >>> torch.diagonal_scatter(a, torch.ones(2), 1) + tensor([[0., 1., 0.], + [0., 0., 1.], + [0., 0., 0.]]) + """ + ... +def diff(input: Tensor, n: _int = 1, dim: _int = -1, prepend: Optional[Tensor] = None, append: Optional[Tensor] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + diff(input, n=1, dim=-1, prepend=None, append=None) -> Tensor + + Computes the n-th forward difference along the given dimension. + + The first-order differences are given by `out[i] = input[i + 1] - input[i]`. Higher-order + differences are calculated by using :func:`torch.diff` recursively. + + Args: + input (Tensor): the tensor to compute the differences on + n (int, optional): the number of times to recursively compute the difference + dim (int, optional): the dimension to compute the difference along. + Default is the last dimension. + prepend, append (Tensor, optional): values to prepend or append to + :attr:`input` along :attr:`dim` before computing the difference. + Their dimensions must be equivalent to that of input, and their shapes + must match input's shape except on :attr:`dim`. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([1, 3, 2]) + >>> torch.diff(a) + tensor([ 2, -1]) + >>> b = torch.tensor([4, 5]) + >>> torch.diff(a, append=b) + tensor([ 2, -1, 2, 1]) + >>> c = torch.tensor([[1, 2, 3], [3, 4, 5]]) + >>> torch.diff(c, dim=0) + tensor([[2, 2, 2]]) + >>> torch.diff(c, dim=1) + tensor([[1, 1], + [1, 1]]) + """ + ... +def digamma(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + digamma(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.digamma`. + """ + ... +def dist(input: Tensor, other: Tensor, p: Union[Number, _complex] = 2) -> Tensor: + r""" + dist(input, other, p=2) -> Tensor + + Returns the p-norm of (:attr:`input` - :attr:`other`) + + The shapes of :attr:`input` and :attr:`other` must be + :ref:`broadcastable `. + + Args: + input (Tensor): the input tensor. + other (Tensor): the Right-hand-side input tensor + p (float, optional): the norm to be computed + + Example:: + + >>> x = torch.randn(4) + >>> x + tensor([-1.5393, -0.8675, 0.5916, 1.6321]) + >>> y = torch.randn(4) + >>> y + tensor([ 0.0967, -1.0511, 0.6295, 0.8360]) + >>> torch.dist(x, y, 3.5) + tensor(1.6727) + >>> torch.dist(x, y, 3) + tensor(1.6973) + >>> torch.dist(x, y, 0) + tensor(4.) + >>> torch.dist(x, y, 1) + tensor(2.6537) + """ + ... +def div(input: Union[Tensor, Number], other: Union[Tensor, Number], *, rounding_mode: Optional[str] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + div(input, other, *, rounding_mode=None, out=None) -> Tensor + + Divides each element of the input ``input`` by the corresponding element of + :attr:`other`. + + .. math:: + \text{out}_i = \frac{\text{input}_i}{\text{other}_i} + + .. note:: + By default, this performs a "true" division like Python 3. + See the :attr:`rounding_mode` argument for floor division. + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + Always promotes integer types to the default scalar type. + + Args: + input (Tensor): the dividend + other (Tensor or Number): the divisor + + Keyword args: + rounding_mode (str, optional): Type of rounding applied to the result: + + * None - default behavior. Performs no rounding and, if both :attr:`input` and + :attr:`other` are integer types, promotes the inputs to the default scalar type. + Equivalent to true division in Python (the ``/`` operator) and NumPy's ``np.true_divide``. + * ``"trunc"`` - rounds the results of the division towards zero. + Equivalent to C-style integer division. + * ``"floor"`` - rounds the results of the division down. + Equivalent to floor division in Python (the ``//`` operator) and NumPy's ``np.floor_divide``. + + out (Tensor, optional): the output tensor. + + Examples:: + + >>> x = torch.tensor([ 0.3810, 1.2774, -0.2972, -0.3719, 0.4637]) + >>> torch.div(x, 0.5) + tensor([ 0.7620, 2.5548, -0.5944, -0.7438, 0.9274]) + + >>> a = torch.tensor([[-0.3711, -1.9353, -0.4605, -0.2917], + ... [ 0.1815, -1.0111, 0.9805, -1.5923], + ... [ 0.1062, 1.4581, 0.7759, -1.2344], + ... [-0.1830, -0.0313, 1.1908, -1.4757]]) + >>> b = torch.tensor([ 0.8032, 0.2930, -0.8113, -0.2308]) + >>> torch.div(a, b) + tensor([[-0.4620, -6.6051, 0.5676, 1.2639], + [ 0.2260, -3.4509, -1.2086, 6.8990], + [ 0.1322, 4.9764, -0.9564, 5.3484], + [-0.2278, -0.1068, -1.4678, 6.3938]]) + + >>> torch.div(a, b, rounding_mode='trunc') + tensor([[-0., -6., 0., 1.], + [ 0., -3., -1., 6.], + [ 0., 4., -0., 5.], + [-0., -0., -1., 6.]]) + + >>> torch.div(a, b, rounding_mode='floor') + tensor([[-1., -7., 0., 1.], + [ 0., -4., -2., 6.], + [ 0., 4., -1., 5.], + [-1., -1., -2., 6.]]) + """ + ... +@overload +def divide(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + divide(input, other, *, rounding_mode=None, out=None) -> Tensor + + Alias for :func:`torch.div`. + """ + ... +@overload +def divide(input: Tensor, other: Tensor, *, rounding_mode: Optional[str], out: Optional[Tensor] = None) -> Tensor: + r""" + divide(input, other, *, rounding_mode=None, out=None) -> Tensor + + Alias for :func:`torch.div`. + """ + ... +@overload +def divide(input: Tensor, other: Union[Number, _complex], *, rounding_mode: Optional[str]) -> Tensor: + r""" + divide(input, other, *, rounding_mode=None, out=None) -> Tensor + + Alias for :func:`torch.div`. + """ + ... +@overload +def divide(input: Tensor, other: Union[Number, _complex]) -> Tensor: + r""" + divide(input, other, *, rounding_mode=None, out=None) -> Tensor + + Alias for :func:`torch.div`. + """ + ... +def dot(input: Tensor, tensor: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + dot(input, tensor, *, out=None) -> Tensor + + Computes the dot product of two 1D tensors. + + .. note:: + + Unlike NumPy's dot, torch.dot intentionally only supports computing the dot product + of two 1D tensors with the same number of elements. + + Args: + input (Tensor): first tensor in the dot product, must be 1D. + tensor (Tensor): second tensor in the dot product, must be 1D. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.dot(torch.tensor([2, 3]), torch.tensor([2, 1])) + tensor(7) + + >>> t1, t2 = torch.tensor([0, 1]), torch.tensor([2, 3]) + >>> torch.dot(t1, t2) + tensor(3) + """ + ... +def dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def dsmm(input: Tensor, mat2: Tensor) -> Tensor: ... +@overload +def dsplit(input: Tensor, sections: _int) -> tuple[Tensor, ...]: + r""" + dsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with three or more dimensions, into multiple tensors + depthwise according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=2) + (the split dimension is 2), except that if :attr:`indices_or_sections` is an integer + it must evenly divide the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.dsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(2, 2, 4) + >>> t + tensor([[[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.]], + [[ 8., 9., 10., 11.], + [12., 13., 14., 15.]]]) + >>> torch.dsplit(t, 2) + (tensor([[[ 0., 1.], + [ 4., 5.]], + [[ 8., 9.], + [12., 13.]]]), + tensor([[[ 2., 3.], + [ 6., 7.]], + [[10., 11.], + [14., 15.]]])) + + >>> torch.dsplit(t, [3, 6]) + (tensor([[[ 0., 1., 2.], + [ 4., 5., 6.]], + [[ 8., 9., 10.], + [12., 13., 14.]]]), + tensor([[[ 3.], + [ 7.]], + [[11.], + [15.]]]), + tensor([], size=(2, 2, 0))) + """ + ... +@overload +def dsplit(input: Tensor, indices: _size) -> tuple[Tensor, ...]: + r""" + dsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with three or more dimensions, into multiple tensors + depthwise according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=2) + (the split dimension is 2), except that if :attr:`indices_or_sections` is an integer + it must evenly divide the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.dsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(2, 2, 4) + >>> t + tensor([[[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.]], + [[ 8., 9., 10., 11.], + [12., 13., 14., 15.]]]) + >>> torch.dsplit(t, 2) + (tensor([[[ 0., 1.], + [ 4., 5.]], + [[ 8., 9.], + [12., 13.]]]), + tensor([[[ 2., 3.], + [ 6., 7.]], + [[10., 11.], + [14., 15.]]])) + + >>> torch.dsplit(t, [3, 6]) + (tensor([[[ 0., 1., 2.], + [ 4., 5., 6.]], + [[ 8., 9., 10.], + [12., 13., 14.]]]), + tensor([[[ 3.], + [ 7.]], + [[11.], + [15.]]]), + tensor([], size=(2, 2, 0))) + """ + ... +def dstack(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + dstack(tensors, *, out=None) -> Tensor + + Stack tensors in sequence depthwise (along third axis). + + This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by :func:`torch.atleast_3d`. + + Args: + tensors (sequence of Tensors): sequence of tensors to concatenate + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([1, 2, 3]) + >>> b = torch.tensor([4, 5, 6]) + >>> torch.dstack((a,b)) + tensor([[[1, 4], + [2, 5], + [3, 6]]]) + >>> a = torch.tensor([[1],[2],[3]]) + >>> b = torch.tensor([[4],[5],[6]]) + >>> torch.dstack((a,b)) + tensor([[[1, 4]], + [[2, 5]], + [[3, 6]]]) + """ + ... +def embedding(weight: Tensor, indices: Tensor, padding_idx: Union[_int, SymInt] = -1, scale_grad_by_freq: _bool = False, sparse: _bool = False) -> Tensor: ... +@overload +def embedding_bag(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool, mode: _int, sparse: _bool, per_sample_weights: Optional[Tensor], include_last_offset: _bool, padding_idx: Optional[_int]) -> tuple[Tensor, Tensor, Tensor, Tensor]: ... +@overload +def embedding_bag(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool = False, mode: _int = 0, sparse: _bool = False, per_sample_weights: Optional[Tensor] = None, include_last_offset: _bool = False) -> tuple[Tensor, Tensor, Tensor, Tensor]: ... +def embedding_renorm_(input: Tensor, indices: Tensor, max_norm: _float, norm_type: _float) -> Tensor: ... +@overload +def empty(size: Sequence[Union[_int, SymInt]], *, memory_format: Optional[memory_format] = None, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, memory_format=torch.contiguous_format) -> Tensor + + Returns a tensor filled with uninitialized data. The shape of the tensor is + defined by the variable argument :attr:`size`. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.contiguous_format``. + + Example:: + + >>> torch.empty((2,3), dtype=torch.int64) + tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], + [ 7.5751e+18, 7.1428e+18, 7.5955e+18]]) + """ + ... +@overload +def empty(*size: Union[_int, SymInt], memory_format: Optional[memory_format] = None, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, memory_format=torch.contiguous_format) -> Tensor + + Returns a tensor filled with uninitialized data. The shape of the tensor is + defined by the variable argument :attr:`size`. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.contiguous_format``. + + Example:: + + >>> torch.empty((2,3), dtype=torch.int64) + tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], + [ 7.5751e+18, 7.1428e+18, 7.5955e+18]]) + """ + ... +@overload +def empty(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, memory_format=torch.contiguous_format) -> Tensor + + Returns a tensor filled with uninitialized data. The shape of the tensor is + defined by the variable argument :attr:`size`. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.contiguous_format``. + + Example:: + + >>> torch.empty((2,3), dtype=torch.int64) + tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], + [ 7.5751e+18, 7.1428e+18, 7.5955e+18]]) + """ + ... +@overload +def empty(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, memory_format=torch.contiguous_format) -> Tensor + + Returns a tensor filled with uninitialized data. The shape of the tensor is + defined by the variable argument :attr:`size`. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.contiguous_format``. + + Example:: + + >>> torch.empty((2,3), dtype=torch.int64) + tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], + [ 7.5751e+18, 7.1428e+18, 7.5955e+18]]) + """ + ... +def empty_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns an uninitialized tensor with the same size as :attr:`input`. + ``torch.empty_like(input)`` is equivalent to + ``torch.empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + Example:: + + >>> a=torch.empty((2,3), dtype=torch.int32, device = 'cuda') + >>> torch.empty_like(a) + tensor([[0, 0, 0], + [0, 0, 0]], device='cuda:0', dtype=torch.int32) + """ + ... +def empty_permuted(size: Sequence[Union[_int, SymInt]], physical_layout: _size, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty_permuted(size, physical_layout, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Creates an uninitialized, non-overlapping and dense tensor with the + specified :attr:`size`, with :attr:`physical_layout` specifying how the + dimensions are physically laid out in memory (each logical dimension is listed + from outermost to innermost). :attr:`physical_layout` is a generalization + of NCHW/NHWC notation: if each dimension is assigned a number according to + what order they occur in size (N=0, C=1, H=2, W=3), then NCHW is ``(0, 1, 2, 3)`` + while NHWC is ``(0, 2, 3, 1)``. Equivalently, the strides of the output + tensor ``t`` are such that ``t.stride(physical_layout[i]) == contiguous_strides[i]`` + (notably, this function is *not* equivalent to ``torch.empty(size).permute(physical_layout)``). + + Unlike :func:`torch.empty_strided`, this is guaranteed to produce a dense + tensor with no overlaps. If possible, prefer using this function over + :func:`torch.empty_strided` or manual use of :func:`torch.as_strided`. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (tuple of int): the shape of the output tensor + physical_layout (tuple of int): the ordering of dimensions physically in memory + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Examples: + + >>> torch.empty((2, 3, 5, 7)).stride() + (105, 35, 7, 1) + >>> torch.empty_permuted((2, 3, 5, 7), (0, 1, 2, 3)).stride() + (105, 35, 7, 1) + >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).stride() + (105, 1, 21, 3) + >>> torch.empty_permuted((2, 3, 5, 7), (0, 2, 3, 1)).stride() + (105, 1, 21, 3) + >>> torch.empty_permuted((2, 3, 5, 7), (0, 2, 3, 1)).dim_order() + (0, 2, 3, 1) + """ + ... +def empty_quantized(size: _size, qtensor: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +def empty_strided(size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + empty_strided(size, stride, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Creates a tensor with the specified :attr:`size` and :attr:`stride` and filled with undefined data. + + .. warning:: + If the constructed tensor is "overlapped" (with multiple indices referring to the same element + in memory) its behavior is undefined. + + .. note:: + If :func:`torch.use_deterministic_algorithms()` and + :attr:`torch.utils.deterministic.fill_uninitialized_memory` are both set to + ``True``, the output tensor is initialized to prevent any possible + nondeterministic behavior from using the data as an input to an operation. + Floating point and complex tensors are filled with NaN, and integer tensors + are filled with the maximum value. + + Args: + size (tuple of int): the shape of the output tensor + stride (tuple of int): the strides of the output tensor + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> a = torch.empty_strided((2, 3), (1, 2)) + >>> a + tensor([[8.9683e-44, 4.4842e-44, 5.1239e+07], + [0.0000e+00, 0.0000e+00, 3.0705e-41]]) + >>> a.stride() + (1, 2) + >>> a.size() + torch.Size([2, 3]) + """ + ... +@overload +def eq(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + eq(input, other, *, out=None) -> Tensor + + Computes element-wise equality + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.eq(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[ True, False], + [False, True]]) + """ + ... +@overload +def eq(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + eq(input, other, *, out=None) -> Tensor + + Computes element-wise equality + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.eq(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[ True, False], + [False, True]]) + """ + ... +def equal(input: Tensor, other: Tensor) -> _bool: + r""" + equal(input, other) -> bool + + ``True`` if two tensors have the same size and elements, ``False`` otherwise. + + Note that tensors containing NaNs are never equal to each other. + + Example:: + + >>> torch.equal(torch.tensor([1, 2]), torch.tensor([1, 2])) + True + >>> torch.equal(torch.tensor([3, torch.nan]), torch.tensor([3, torch.nan])) + False + """ + ... +def erf(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + erf(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.erf`. + """ + ... +def erf_(input: Tensor) -> Tensor: ... +def erfc(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + erfc(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.erfc`. + """ + ... +def erfc_(input: Tensor) -> Tensor: ... +def erfinv(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + erfinv(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.erfinv`. + """ + ... +def exp(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + exp(input, *, out=None) -> Tensor + + Returns a new tensor with the exponential of the elements + of the input tensor :attr:`input`. + + .. math:: + y_{i} = e^{x_{i}} + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.exp(torch.tensor([0, math.log(2.)])) + tensor([ 1., 2.]) + """ + ... +def exp2(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + exp2(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.exp2`. + """ + ... +def exp2_(input: Tensor) -> Tensor: ... +def exp_(input: Tensor) -> Tensor: ... +def expand_copy(input: Tensor, size: Sequence[Union[_int, SymInt]], *, implicit: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.Tensor.expand`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def expm1(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + expm1(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.expm1`. + """ + ... +def expm1_(input: Tensor) -> Tensor: ... +@overload +def eye(n: Union[_int, SymInt], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + eye(n, m=None, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 2-D tensor with ones on the diagonal and zeros elsewhere. + + Args: + n (int): the number of rows + m (int, optional): the number of columns with default being :attr:`n` + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 2-D tensor with ones on the diagonal and zeros elsewhere + + Example:: + + >>> torch.eye(3) + tensor([[ 1., 0., 0.], + [ 0., 1., 0.], + [ 0., 0., 1.]]) + """ + ... +@overload +def eye(n: Union[_int, SymInt], m: Union[_int, SymInt], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + eye(n, m=None, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 2-D tensor with ones on the diagonal and zeros elsewhere. + + Args: + n (int): the number of rows + m (int, optional): the number of columns with default being :attr:`n` + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 2-D tensor with ones on the diagonal and zeros elsewhere + + Example:: + + >>> torch.eye(3) + tensor([[ 1., 0., 0.], + [ 0., 1., 0.], + [ 0., 0., 1.]]) + """ + ... +def fake_quantize_per_channel_affine(input: Tensor, scale: Tensor, zero_point: Tensor, axis: _int, quant_min: _int, quant_max: _int) -> Tensor: + r""" + fake_quantize_per_channel_affine(input, scale, zero_point, axis, quant_min, quant_max) -> Tensor + + Returns a new tensor with the data in :attr:`input` fake quantized per channel using :attr:`scale`, + :attr:`zero_point`, :attr:`quant_min` and :attr:`quant_max`, across the channel specified by :attr:`axis`. + + .. math:: + \text{output} = ( + min( + \text{quant\_max}, + max( + \text{quant\_min}, + \text{std::nearby\_int}(\text{input} / \text{scale}) + \text{zero\_point} + ) + ) - \text{zero\_point} + ) \times \text{scale} + + Args: + input (Tensor): the input value(s), in ``torch.float32`` + scale (Tensor): quantization scale, per channel in ``torch.float32`` + zero_point (Tensor): quantization zero_point, per channel in ``torch.int32`` or ``torch.half`` or ``torch.float32`` + axis (int32): channel axis + quant_min (int64): lower bound of the quantized domain + quant_max (int64): upper bound of the quantized domain + + Returns: + Tensor: A newly fake_quantized per channel ``torch.float32`` tensor + + Example:: + + >>> x = torch.randn(2, 2, 2) + >>> x + tensor([[[-0.2525, -0.0466], + [ 0.3491, -0.2168]], + + [[-0.5906, 1.6258], + [ 0.6444, -0.0542]]]) + >>> scales = (torch.randn(2) + 1) * 0.05 + >>> scales + tensor([0.0475, 0.0486]) + >>> zero_points = torch.zeros(2).to(torch.int32) + >>> zero_points + tensor([0, 0]) + >>> torch.fake_quantize_per_channel_affine(x, scales, zero_points, 1, 0, 255) + tensor([[[0.0000, 0.0000], + [0.3405, 0.0000]], + + [[0.0000, 1.6134], + [0.6323, 0.0000]]]) + """ + ... +@overload +def fake_quantize_per_tensor_affine(input: Tensor, scale: _float, zero_point: _int, quant_min: _int, quant_max: _int) -> Tensor: + r""" + fake_quantize_per_tensor_affine(input, scale, zero_point, quant_min, quant_max) -> Tensor + + Returns a new tensor with the data in :attr:`input` fake quantized using :attr:`scale`, + :attr:`zero_point`, :attr:`quant_min` and :attr:`quant_max`. + + .. math:: + \text{output} = ( + min( + \text{quant\_max}, + max( + \text{quant\_min}, + \text{std::nearby\_int}(\text{input} / \text{scale}) + \text{zero\_point} + ) + ) - \text{zero\_point} + ) \times \text{scale} + + Args: + input (Tensor): the input value(s), ``torch.float32`` tensor + scale (double scalar or ``float32`` Tensor): quantization scale + zero_point (int64 scalar or ``int32`` Tensor): quantization zero_point + quant_min (int64): lower bound of the quantized domain + quant_max (int64): upper bound of the quantized domain + + Returns: + Tensor: A newly fake_quantized ``torch.float32`` tensor + + Example:: + + >>> x = torch.randn(4) + >>> x + tensor([ 0.0552, 0.9730, 0.3973, -1.0780]) + >>> torch.fake_quantize_per_tensor_affine(x, 0.1, 0, 0, 255) + tensor([0.1000, 1.0000, 0.4000, 0.0000]) + >>> torch.fake_quantize_per_tensor_affine(x, torch.tensor(0.1), torch.tensor(0), 0, 255) + tensor([0.1000, 1.0000, 0.4000, 0.0000]) + """ + ... +@overload +def fake_quantize_per_tensor_affine(input: Tensor, scale: Tensor, zero_point: Tensor, quant_min: _int, quant_max: _int) -> Tensor: + r""" + fake_quantize_per_tensor_affine(input, scale, zero_point, quant_min, quant_max) -> Tensor + + Returns a new tensor with the data in :attr:`input` fake quantized using :attr:`scale`, + :attr:`zero_point`, :attr:`quant_min` and :attr:`quant_max`. + + .. math:: + \text{output} = ( + min( + \text{quant\_max}, + max( + \text{quant\_min}, + \text{std::nearby\_int}(\text{input} / \text{scale}) + \text{zero\_point} + ) + ) - \text{zero\_point} + ) \times \text{scale} + + Args: + input (Tensor): the input value(s), ``torch.float32`` tensor + scale (double scalar or ``float32`` Tensor): quantization scale + zero_point (int64 scalar or ``int32`` Tensor): quantization zero_point + quant_min (int64): lower bound of the quantized domain + quant_max (int64): upper bound of the quantized domain + + Returns: + Tensor: A newly fake_quantized ``torch.float32`` tensor + + Example:: + + >>> x = torch.randn(4) + >>> x + tensor([ 0.0552, 0.9730, 0.3973, -1.0780]) + >>> torch.fake_quantize_per_tensor_affine(x, 0.1, 0, 0, 255) + tensor([0.1000, 1.0000, 0.4000, 0.0000]) + >>> torch.fake_quantize_per_tensor_affine(x, torch.tensor(0.1), torch.tensor(0), 0, 255) + tensor([0.1000, 1.0000, 0.4000, 0.0000]) + """ + ... +def fbgemm_linear_fp16_weight(input: Tensor, packed_weight: Tensor, bias: Tensor) -> Tensor: ... +def fbgemm_linear_fp16_weight_fp32_activation(input: Tensor, packed_weight: Tensor, bias: Tensor) -> Tensor: ... +def fbgemm_linear_int8_weight(input: Tensor, weight: Tensor, packed: Tensor, col_offsets: Tensor, weight_scale: Union[Number, _complex], weight_zero_point: Union[Number, _complex], bias: Tensor) -> Tensor: ... +def fbgemm_linear_int8_weight_fp32_activation(input: Tensor, weight: Tensor, packed: Tensor, col_offsets: Tensor, weight_scale: Union[Number, _complex], weight_zero_point: Union[Number, _complex], bias: Tensor) -> Tensor: ... +def fbgemm_linear_quantize_weight(input: Tensor) -> tuple[Tensor, Tensor, _float, _int]: ... +def fbgemm_pack_gemm_matrix_fp16(input: Tensor) -> Tensor: ... +@overload +def fbgemm_pack_quantized_matrix(input: Tensor) -> Tensor: ... +@overload +def fbgemm_pack_quantized_matrix(input: Tensor, K: _int, N: _int) -> Tensor: ... +def feature_alpha_dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def feature_alpha_dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def feature_dropout(input: Tensor, p: _float, train: _bool) -> Tensor: ... +def feature_dropout_(input: Tensor, p: _float, train: _bool) -> Tensor: ... +@overload +def fill(input: Tensor, value: Tensor) -> Tensor: ... +@overload +def fill(input: Tensor, value: Union[Number, _complex]) -> Tensor: ... +@overload +def fill_(input: Tensor, value: Tensor) -> Tensor: ... +@overload +def fill_(input: Tensor, value: Union[Number, _complex]) -> Tensor: ... +def fix(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + fix(input, *, out=None) -> Tensor + + Alias for :func:`torch.trunc` + """ + ... +def fix_(input: Tensor) -> Tensor: ... +@overload +def flatten(input: Tensor, start_dim: _int = 0, end_dim: _int = -1) -> Tensor: + r""" + flatten(input, start_dim=0, end_dim=-1) -> Tensor + + Flattens :attr:`input` by reshaping it into a one-dimensional tensor. If :attr:`start_dim` or :attr:`end_dim` + are passed, only dimensions starting with :attr:`start_dim` and ending with :attr:`end_dim` are flattened. + The order of elements in :attr:`input` is unchanged. + + Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view, + or copy. If no dimensions are flattened, then the original object :attr:`input` is returned. Otherwise, if input can + be viewed as the flattened shape, then that view is returned. Finally, only if the input cannot be viewed as the + flattened shape is input's data copied. See :meth:`torch.Tensor.view` for details on when a view will be returned. + + .. note:: + Flattening a zero-dimensional tensor will return a one-dimensional view. + + Args: + input (Tensor): the input tensor. + start_dim (int): the first dim to flatten + end_dim (int): the last dim to flatten + + Example:: + + >>> t = torch.tensor([[[1, 2], + ... [3, 4]], + ... [[5, 6], + ... [7, 8]]]) + >>> torch.flatten(t) + tensor([1, 2, 3, 4, 5, 6, 7, 8]) + >>> torch.flatten(t, start_dim=1) + tensor([[1, 2, 3, 4], + [5, 6, 7, 8]]) + """ + ... +@overload +def flatten(input: Tensor, start_dim: _int, end_dim: _int, out_dim: Union[str, ellipsis, None]) -> Tensor: + r""" + flatten(input, start_dim=0, end_dim=-1) -> Tensor + + Flattens :attr:`input` by reshaping it into a one-dimensional tensor. If :attr:`start_dim` or :attr:`end_dim` + are passed, only dimensions starting with :attr:`start_dim` and ending with :attr:`end_dim` are flattened. + The order of elements in :attr:`input` is unchanged. + + Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view, + or copy. If no dimensions are flattened, then the original object :attr:`input` is returned. Otherwise, if input can + be viewed as the flattened shape, then that view is returned. Finally, only if the input cannot be viewed as the + flattened shape is input's data copied. See :meth:`torch.Tensor.view` for details on when a view will be returned. + + .. note:: + Flattening a zero-dimensional tensor will return a one-dimensional view. + + Args: + input (Tensor): the input tensor. + start_dim (int): the first dim to flatten + end_dim (int): the last dim to flatten + + Example:: + + >>> t = torch.tensor([[[1, 2], + ... [3, 4]], + ... [[5, 6], + ... [7, 8]]]) + >>> torch.flatten(t) + tensor([1, 2, 3, 4, 5, 6, 7, 8]) + >>> torch.flatten(t, start_dim=1) + tensor([[1, 2, 3, 4], + [5, 6, 7, 8]]) + """ + ... +@overload +def flatten(input: Tensor, start_dim: Union[str, ellipsis, None], end_dim: Union[str, ellipsis, None], out_dim: Union[str, ellipsis, None]) -> Tensor: + r""" + flatten(input, start_dim=0, end_dim=-1) -> Tensor + + Flattens :attr:`input` by reshaping it into a one-dimensional tensor. If :attr:`start_dim` or :attr:`end_dim` + are passed, only dimensions starting with :attr:`start_dim` and ending with :attr:`end_dim` are flattened. + The order of elements in :attr:`input` is unchanged. + + Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view, + or copy. If no dimensions are flattened, then the original object :attr:`input` is returned. Otherwise, if input can + be viewed as the flattened shape, then that view is returned. Finally, only if the input cannot be viewed as the + flattened shape is input's data copied. See :meth:`torch.Tensor.view` for details on when a view will be returned. + + .. note:: + Flattening a zero-dimensional tensor will return a one-dimensional view. + + Args: + input (Tensor): the input tensor. + start_dim (int): the first dim to flatten + end_dim (int): the last dim to flatten + + Example:: + + >>> t = torch.tensor([[[1, 2], + ... [3, 4]], + ... [[5, 6], + ... [7, 8]]]) + >>> torch.flatten(t) + tensor([1, 2, 3, 4, 5, 6, 7, 8]) + >>> torch.flatten(t, start_dim=1) + tensor([[1, 2, 3, 4], + [5, 6, 7, 8]]) + """ + ... +@overload +def flatten(input: Tensor, dims: Sequence[Union[str, ellipsis, None]], out_dim: Union[str, ellipsis, None]) -> Tensor: + r""" + flatten(input, start_dim=0, end_dim=-1) -> Tensor + + Flattens :attr:`input` by reshaping it into a one-dimensional tensor. If :attr:`start_dim` or :attr:`end_dim` + are passed, only dimensions starting with :attr:`start_dim` and ending with :attr:`end_dim` are flattened. + The order of elements in :attr:`input` is unchanged. + + Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view, + or copy. If no dimensions are flattened, then the original object :attr:`input` is returned. Otherwise, if input can + be viewed as the flattened shape, then that view is returned. Finally, only if the input cannot be viewed as the + flattened shape is input's data copied. See :meth:`torch.Tensor.view` for details on when a view will be returned. + + .. note:: + Flattening a zero-dimensional tensor will return a one-dimensional view. + + Args: + input (Tensor): the input tensor. + start_dim (int): the first dim to flatten + end_dim (int): the last dim to flatten + + Example:: + + >>> t = torch.tensor([[[1, 2], + ... [3, 4]], + ... [[5, 6], + ... [7, 8]]]) + >>> torch.flatten(t) + tensor([1, 2, 3, 4, 5, 6, 7, 8]) + >>> torch.flatten(t, start_dim=1) + tensor([[1, 2, 3, 4], + [5, 6, 7, 8]]) + """ + ... +def flip(input: Tensor, dims: _size) -> Tensor: + r""" + flip(input, dims) -> Tensor + + Reverse the order of an n-D tensor along given axis in dims. + + .. note:: + `torch.flip` makes a copy of :attr:`input`'s data. This is different from NumPy's `np.flip`, + which returns a view in constant time. Since copying a tensor's data is more work than viewing that data, + `torch.flip` is expected to be slower than `np.flip`. + + Args: + input (Tensor): the input tensor. + dims (a list or tuple): axis to flip on + + Example:: + + >>> x = torch.arange(8).view(2, 2, 2) + >>> x + tensor([[[ 0, 1], + [ 2, 3]], + + [[ 4, 5], + [ 6, 7]]]) + >>> torch.flip(x, [0, 1]) + tensor([[[ 6, 7], + [ 4, 5]], + + [[ 2, 3], + [ 0, 1]]]) + """ + ... +def fliplr(input: Tensor) -> Tensor: + r""" + fliplr(input) -> Tensor + + Flip tensor in the left/right direction, returning a new tensor. + + Flip the entries in each row in the left/right direction. + Columns are preserved, but appear in a different order than before. + + Note: + Requires the tensor to be at least 2-D. + + .. note:: + `torch.fliplr` makes a copy of :attr:`input`'s data. This is different from NumPy's `np.fliplr`, + which returns a view in constant time. Since copying a tensor's data is more work than viewing that data, + `torch.fliplr` is expected to be slower than `np.fliplr`. + + Args: + input (Tensor): Must be at least 2-dimensional. + + Example:: + + >>> x = torch.arange(4).view(2, 2) + >>> x + tensor([[0, 1], + [2, 3]]) + >>> torch.fliplr(x) + tensor([[1, 0], + [3, 2]]) + """ + ... +def flipud(input: Tensor) -> Tensor: + r""" + flipud(input) -> Tensor + + Flip tensor in the up/down direction, returning a new tensor. + + Flip the entries in each column in the up/down direction. + Rows are preserved, but appear in a different order than before. + + Note: + Requires the tensor to be at least 1-D. + + .. note:: + `torch.flipud` makes a copy of :attr:`input`'s data. This is different from NumPy's `np.flipud`, + which returns a view in constant time. Since copying a tensor's data is more work than viewing that data, + `torch.flipud` is expected to be slower than `np.flipud`. + + Args: + input (Tensor): Must be at least 1-dimensional. + + Example:: + + >>> x = torch.arange(4).view(2, 2) + >>> x + tensor([[0, 1], + [2, 3]]) + >>> torch.flipud(x) + tensor([[2, 3], + [0, 1]]) + """ + ... +@overload +def float_power(input: Tensor, exponent: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + float_power(input, exponent, *, out=None) -> Tensor + + Raises :attr:`input` to the power of :attr:`exponent`, elementwise, in double precision. + If neither input is complex returns a ``torch.float64`` tensor, + and if one or more inputs is complex returns a ``torch.complex128`` tensor. + + .. note:: + This function always computes in double precision, unlike :func:`torch.pow`, + which implements more typical :ref:`type promotion `. + This is useful when the computation needs to be performed in a wider or more precise dtype, + or the results of the computation may contain fractional values not representable in the input dtypes, + like when an integer base is raised to a negative integer exponent. + + Args: + input (Tensor or Number): the base value(s) + exponent (Tensor or Number): the exponent value(s) + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randint(10, (4,)) + >>> a + tensor([6, 4, 7, 1]) + >>> torch.float_power(a, 2) + tensor([36., 16., 49., 1.], dtype=torch.float64) + + >>> a = torch.arange(1, 5) + >>> a + tensor([ 1, 2, 3, 4]) + >>> exp = torch.tensor([2, -3, 4, -5]) + >>> exp + tensor([ 2, -3, 4, -5]) + >>> torch.float_power(a, exp) + tensor([1.0000e+00, 1.2500e-01, 8.1000e+01, 9.7656e-04], dtype=torch.float64) + """ + ... +@overload +def float_power(self: Union[Number, _complex], exponent: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + float_power(input, exponent, *, out=None) -> Tensor + + Raises :attr:`input` to the power of :attr:`exponent`, elementwise, in double precision. + If neither input is complex returns a ``torch.float64`` tensor, + and if one or more inputs is complex returns a ``torch.complex128`` tensor. + + .. note:: + This function always computes in double precision, unlike :func:`torch.pow`, + which implements more typical :ref:`type promotion `. + This is useful when the computation needs to be performed in a wider or more precise dtype, + or the results of the computation may contain fractional values not representable in the input dtypes, + like when an integer base is raised to a negative integer exponent. + + Args: + input (Tensor or Number): the base value(s) + exponent (Tensor or Number): the exponent value(s) + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randint(10, (4,)) + >>> a + tensor([6, 4, 7, 1]) + >>> torch.float_power(a, 2) + tensor([36., 16., 49., 1.], dtype=torch.float64) + + >>> a = torch.arange(1, 5) + >>> a + tensor([ 1, 2, 3, 4]) + >>> exp = torch.tensor([2, -3, 4, -5]) + >>> exp + tensor([ 2, -3, 4, -5]) + >>> torch.float_power(a, exp) + tensor([1.0000e+00, 1.2500e-01, 8.1000e+01, 9.7656e-04], dtype=torch.float64) + """ + ... +@overload +def float_power(input: Tensor, exponent: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + float_power(input, exponent, *, out=None) -> Tensor + + Raises :attr:`input` to the power of :attr:`exponent`, elementwise, in double precision. + If neither input is complex returns a ``torch.float64`` tensor, + and if one or more inputs is complex returns a ``torch.complex128`` tensor. + + .. note:: + This function always computes in double precision, unlike :func:`torch.pow`, + which implements more typical :ref:`type promotion `. + This is useful when the computation needs to be performed in a wider or more precise dtype, + or the results of the computation may contain fractional values not representable in the input dtypes, + like when an integer base is raised to a negative integer exponent. + + Args: + input (Tensor or Number): the base value(s) + exponent (Tensor or Number): the exponent value(s) + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randint(10, (4,)) + >>> a + tensor([6, 4, 7, 1]) + >>> torch.float_power(a, 2) + tensor([36., 16., 49., 1.], dtype=torch.float64) + + >>> a = torch.arange(1, 5) + >>> a + tensor([ 1, 2, 3, 4]) + >>> exp = torch.tensor([2, -3, 4, -5]) + >>> exp + tensor([ 2, -3, 4, -5]) + >>> torch.float_power(a, exp) + tensor([1.0000e+00, 1.2500e-01, 8.1000e+01, 9.7656e-04], dtype=torch.float64) + """ + ... +def floor(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + floor(input, *, out=None) -> Tensor + + Returns a new tensor with the floor of the elements of :attr:`input`, + the largest integer less than or equal to each element. + + For integer inputs, follows the array-api convention of returning a + copy of the input tensor. + + .. math:: + \text{out}_{i} = \left\lfloor \text{input}_{i} \right\rfloor + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.8166, 1.5308, -0.2530, -0.2091]) + >>> torch.floor(a) + tensor([-1., 1., -1., -1.]) + """ + ... +def floor_(input: Tensor) -> Tensor: ... +def floor_divide(input: Union[Tensor, Number], other: Union[Tensor, Number], *, out: Optional[Tensor] = None) -> Tensor: + r""" + floor_divide(input, other, *, out=None) -> Tensor + + .. note:: + + Before PyTorch 1.13 :func:`torch.floor_divide` incorrectly performed + truncation division. To restore the previous behavior use + :func:`torch.div` with ``rounding_mode='trunc'``. + + Computes :attr:`input` divided by :attr:`other`, elementwise, and floors + the result. + + .. math:: + \text{{out}}_i = \text{floor} \left( \frac{{\text{{input}}_i}}{{\text{{other}}_i}} \right) + + + + Supports broadcasting to a common shape, type promotion, and integer and float inputs. + + Args: + input (Tensor or Number): the dividend + other (Tensor or Number): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([4.0, 3.0]) + >>> b = torch.tensor([2.0, 2.0]) + >>> torch.floor_divide(a, b) + tensor([2.0, 1.0]) + >>> torch.floor_divide(a, 1.4) + tensor([2.0, 2.0]) + """ + ... +def fmax(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + fmax(input, other, *, out=None) -> Tensor + + Computes the element-wise maximum of :attr:`input` and :attr:`other`. + + This is like :func:`torch.maximum` except it handles NaNs differently: + if exactly one of the two elements being compared is a NaN then the non-NaN element is taken as the maximum. + Only if both elements are NaN is NaN propagated. + + This function is a wrapper around C++'s ``std::fmax`` and is similar to NumPy's ``fmax`` function. + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and floating-point inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([9.7, float('nan'), 3.1, float('nan')]) + >>> b = torch.tensor([-2.2, 0.5, float('nan'), float('nan')]) + >>> torch.fmax(a, b) + tensor([9.7000, 0.5000, 3.1000, nan]) + """ + ... +def fmin(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + fmin(input, other, *, out=None) -> Tensor + + Computes the element-wise minimum of :attr:`input` and :attr:`other`. + + This is like :func:`torch.minimum` except it handles NaNs differently: + if exactly one of the two elements being compared is a NaN then the non-NaN element is taken as the minimum. + Only if both elements are NaN is NaN propagated. + + This function is a wrapper around C++'s ``std::fmin`` and is similar to NumPy's ``fmin`` function. + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and floating-point inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([2.2, float('nan'), 2.1, float('nan')]) + >>> b = torch.tensor([-9.3, 0.1, float('nan'), float('nan')]) + >>> torch.fmin(a, b) + tensor([-9.3000, 0.1000, 2.1000, nan]) + """ + ... +@overload +def fmod(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + fmod(input, other, *, out=None) -> Tensor + + Applies C++'s `std::fmod `_ entrywise. + The result has the same sign as the dividend :attr:`input` and its absolute value + is less than that of :attr:`other`. + + This function may be defined in terms of :func:`torch.div` as + + .. code:: python + + torch.fmod(a, b) == a - a.div(b, rounding_mode="trunc") * b + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and float inputs. + + .. note:: + + When the divisor is zero, returns ``NaN`` for floating point dtypes + on both CPU and GPU; raises ``RuntimeError`` for integer division by + zero on CPU; Integer division by zero on GPU may return any value. + + .. note:: + + Complex inputs are not supported. In some cases, it is not mathematically + possible to satisfy the definition of a modulo operation with complex numbers. + + .. seealso:: + + :func:`torch.remainder` which implements Python's modulus operator. + This one is defined using division rounding down the result. + + Args: + input (Tensor): the dividend + other (Tensor or Scalar): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.fmod(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) + tensor([-1., -0., -1., 1., 0., 1.]) + >>> torch.fmod(torch.tensor([1, 2, 3, 4, 5]), -1.5) + tensor([1.0000, 0.5000, 0.0000, 1.0000, 0.5000]) + """ + ... +@overload +def fmod(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + fmod(input, other, *, out=None) -> Tensor + + Applies C++'s `std::fmod `_ entrywise. + The result has the same sign as the dividend :attr:`input` and its absolute value + is less than that of :attr:`other`. + + This function may be defined in terms of :func:`torch.div` as + + .. code:: python + + torch.fmod(a, b) == a - a.div(b, rounding_mode="trunc") * b + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and float inputs. + + .. note:: + + When the divisor is zero, returns ``NaN`` for floating point dtypes + on both CPU and GPU; raises ``RuntimeError`` for integer division by + zero on CPU; Integer division by zero on GPU may return any value. + + .. note:: + + Complex inputs are not supported. In some cases, it is not mathematically + possible to satisfy the definition of a modulo operation with complex numbers. + + .. seealso:: + + :func:`torch.remainder` which implements Python's modulus operator. + This one is defined using division rounding down the result. + + Args: + input (Tensor): the dividend + other (Tensor or Scalar): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.fmod(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) + tensor([-1., -0., -1., 1., 0., 1.]) + >>> torch.fmod(torch.tensor([1, 2, 3, 4, 5]), -1.5) + tensor([1.0000, 0.5000, 0.0000, 1.0000, 0.5000]) + """ + ... +def frac(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + frac(input, *, out=None) -> Tensor + + Computes the fractional portion of each element in :attr:`input`. + + .. math:: + \text{out}_{i} = \text{input}_{i} - \left\lfloor |\text{input}_{i}| \right\rfloor * \operatorname{sgn}(\text{input}_{i}) + + Example:: + + >>> torch.frac(torch.tensor([1, 2.5, -3.2])) + tensor([ 0.0000, 0.5000, -0.2000]) + """ + ... +def frac_(input: Tensor) -> Tensor: ... +def frexp(input: Tensor, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.frexp: + r""" + frexp(input, *, out=None) -> (Tensor mantissa, Tensor exponent) + + Decomposes :attr:`input` into mantissa and exponent tensors + such that :math:`\text{input} = \text{mantissa} \times 2^{\text{exponent}}`. + + The range of mantissa is the open interval (-1, 1). + + Supports float inputs. + + Args: + input (Tensor): the input tensor + + + Keyword args: + out (tuple, optional): the output tensors + + Example:: + + >>> x = torch.arange(9.) + >>> mantissa, exponent = torch.frexp(x) + >>> mantissa + tensor([0.0000, 0.5000, 0.5000, 0.7500, 0.5000, 0.6250, 0.7500, 0.8750, 0.5000]) + >>> exponent + tensor([0, 1, 2, 2, 3, 3, 3, 3, 4], dtype=torch.int32) + >>> torch.ldexp(mantissa, exponent) + tensor([0., 1., 2., 3., 4., 5., 6., 7., 8.]) + """ + ... +def frobenius_norm(input: Tensor, dim: Union[_int, _size], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: ... +def from_file(filename: str, shared: Optional[_bool] = None, size: Optional[_int] = 0, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + from_file(filename, shared=None, size=0, *, dtype=None, layout=None, device=None, pin_memory=False) + + Creates a CPU tensor with a storage backed by a memory-mapped file. + + If ``shared`` is True, then memory is shared between processes. All changes are written to the file. + If ``shared`` is False, then changes to the tensor do not affect the file. + + ``size`` is the number of elements in the Tensor. If ``shared`` is ``False``, then the file must contain + at least ``size * sizeof(dtype)`` bytes. If ``shared`` is ``True`` the file will be created if needed. + + .. note:: + Only CPU tensors can be mapped to files. + + .. note:: + For now, tensors with storages backed by a memory-mapped file cannot be created in pinned memory. + + + Args: + filename (str): file name to map + shared (bool): whether to share memory (whether ``MAP_SHARED`` or ``MAP_PRIVATE`` is passed to the + underlying `mmap(2) call `_) + size (int): number of elements in the tensor + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + >>> t = torch.randn(2, 5, dtype=torch.float64) + >>> t.numpy().tofile('storage.pt') + >>> t_mapped = torch.from_file('storage.pt', shared=False, size=10, dtype=torch.float64) + """ + ... +def from_numpy(ndarray) -> Tensor: + r""" + from_numpy(ndarray) -> Tensor + + Creates a :class:`Tensor` from a :class:`numpy.ndarray`. + + The returned tensor and :attr:`ndarray` share the same memory. Modifications to + the tensor will be reflected in the :attr:`ndarray` and vice versa. The returned + tensor is not resizable. + + It currently accepts :attr:`ndarray` with dtypes of ``numpy.float64``, + ``numpy.float32``, ``numpy.float16``, ``numpy.complex64``, ``numpy.complex128``, + ``numpy.int64``, ``numpy.int32``, ``numpy.int16``, ``numpy.int8``, ``numpy.uint8``, + and ``bool``. + + .. warning:: + Writing to a tensor created from a read-only NumPy array is not supported and will result in undefined behavior. + + Example:: + + >>> a = numpy.array([1, 2, 3]) + >>> t = torch.from_numpy(a) + >>> t + tensor([ 1, 2, 3]) + >>> t[0] = -1 + >>> a + array([-1, 2, 3]) + """ + ... +def frombuffer(buffer: Any, *, dtype: _dtype, count: int = -1, offset: int = 0, requires_grad: _bool = False) -> Tensor: + r""" + frombuffer(buffer, *, dtype, count=-1, offset=0, requires_grad=False) -> Tensor + + Creates a 1-dimensional :class:`Tensor` from an object that implements + the Python buffer protocol. + + Skips the first :attr:`offset` bytes in the buffer, and interprets the rest of + the raw bytes as a 1-dimensional tensor of type :attr:`dtype` with :attr:`count` + elements. + + Note that either of the following must be true: + + 1. :attr:`count` is a positive non-zero number, and the total number of bytes + in the buffer is more than :attr:`offset` plus :attr:`count` times the size + (in bytes) of :attr:`dtype`. + + 2. :attr:`count` is negative, and the length (number of bytes) of the buffer + subtracted by the :attr:`offset` is a multiple of the size (in bytes) of + :attr:`dtype`. + + The returned tensor and buffer share the same memory. Modifications to + the tensor will be reflected in the buffer and vice versa. The returned + tensor is not resizable. + + .. note:: + This function increments the reference count for the object that + owns the shared memory. Therefore, such memory will not be deallocated + before the returned tensor goes out of scope. + + .. warning:: + This function's behavior is undefined when passed an object implementing + the buffer protocol whose data is not on the CPU. Doing so is likely to + cause a segmentation fault. + + .. warning:: + This function does not try to infer the :attr:`dtype` (hence, it is not + optional). Passing a different :attr:`dtype` than its source may result + in unexpected behavior. + + Args: + buffer (object): a Python object that exposes the buffer interface. + + Keyword args: + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + count (int, optional): the number of desired elements to be read. + If negative, all the elements (until the end of the buffer) will be + read. Default: -1. + offset (int, optional): the number of bytes to skip at the start of + the buffer. Default: 0. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> import array + >>> a = array.array('i', [1, 2, 3]) + >>> t = torch.frombuffer(a, dtype=torch.int32) + >>> t + tensor([ 1, 2, 3]) + >>> t[0] = -1 + >>> a + array([-1, 2, 3]) + + >>> # Interprets the signed char bytes as 32-bit integers. + >>> # Each 4 signed char elements will be interpreted as + >>> # 1 signed 32-bit integer. + >>> import array + >>> a = array.array('b', [-1, 0, 0, 0]) + >>> torch.frombuffer(a, dtype=torch.int32) + tensor([255], dtype=torch.int32) + """ + ... +@overload +def full(size: _size, fill_value: Union[Number, _complex], *, out: Optional[Tensor] = None, layout: _layout = strided, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a tensor of size :attr:`size` filled with :attr:`fill_value`. The + tensor's dtype is inferred from :attr:`fill_value`. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + fill_value (Scalar): the value to fill the output tensor with. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.full((2, 3), 3.141592) + tensor([[ 3.1416, 3.1416, 3.1416], + [ 3.1416, 3.1416, 3.1416]]) + """ + ... +@overload +def full(size: _size, fill_value: Union[Number, _complex], *, names: list[Union[str, None]], layout: _layout = strided, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a tensor of size :attr:`size` filled with :attr:`fill_value`. The + tensor's dtype is inferred from :attr:`fill_value`. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + fill_value (Scalar): the value to fill the output tensor with. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.full((2, 3), 3.141592) + tensor([[ 3.1416, 3.1416, 3.1416], + [ 3.1416, 3.1416, 3.1416]]) + """ + ... +@overload +def full(size: Sequence[Union[_int, SymInt]], fill_value: Union[Number, _complex], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a tensor of size :attr:`size` filled with :attr:`fill_value`. The + tensor's dtype is inferred from :attr:`fill_value`. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + fill_value (Scalar): the value to fill the output tensor with. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.full((2, 3), 3.141592) + tensor([[ 3.1416, 3.1416, 3.1416], + [ 3.1416, 3.1416, 3.1416]]) + """ + ... +@overload +def full(size: _size, fill_value: Union[Number, _complex], *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a tensor of size :attr:`size` filled with :attr:`fill_value`. The + tensor's dtype is inferred from :attr:`fill_value`. + + Args: + size (int...): a list, tuple, or :class:`torch.Size` of integers defining the + shape of the output tensor. + fill_value (Scalar): the value to fill the output tensor with. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.full((2, 3), 3.141592) + tensor([[ 3.1416, 3.1416, 3.1416], + [ 3.1416, 3.1416, 3.1416]]) + """ + ... +def full_like(input: Tensor, fill_value: Union[Number, _complex], *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + full_like(input, fill_value, \*, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor with the same size as :attr:`input` filled with :attr:`fill_value`. + ``torch.full_like(input, fill_value)`` is equivalent to + ``torch.full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + fill_value: the number to fill the output tensor with. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... +def fused_moving_avg_obs_fake_quant(input: Tensor, observer_on: Tensor, fake_quant_on: Tensor, running_min: Tensor, running_max: Tensor, scale: Tensor, zero_point: Tensor, averaging_const: _float, quant_min: _int, quant_max: _int, ch_axis: _int, per_row_fake_quant: _bool = False, symmetric_quant: _bool = False) -> Tensor: ... +@overload +def gather(input: Tensor, dim: _int, index: Tensor, *, sparse_grad: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + gather(input, dim, index, *, sparse_grad=False, out=None) -> Tensor + + Gathers values along an axis specified by `dim`. + + For a 3-D tensor the output is specified by:: + + out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0 + out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1 + out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2 + + :attr:`input` and :attr:`index` must have the same number of dimensions. + It is also required that ``index.size(d) <= input.size(d)`` for all + dimensions ``d != dim``. :attr:`out` will have the same shape as :attr:`index`. + Note that ``input`` and ``index`` do not broadcast against each other. + + Args: + input (Tensor): the source tensor + dim (int): the axis along which to index + index (LongTensor): the indices of elements to gather + + Keyword arguments: + sparse_grad (bool, optional): If ``True``, gradient w.r.t. :attr:`input` will be a sparse tensor. + out (Tensor, optional): the destination tensor + + Example:: + + >>> t = torch.tensor([[1, 2], [3, 4]]) + >>> torch.gather(t, 1, torch.tensor([[0, 0], [1, 0]])) + tensor([[ 1, 1], + [ 4, 3]]) + """ + ... +@overload +def gather(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, *, sparse_grad: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + gather(input, dim, index, *, sparse_grad=False, out=None) -> Tensor + + Gathers values along an axis specified by `dim`. + + For a 3-D tensor the output is specified by:: + + out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0 + out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1 + out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2 + + :attr:`input` and :attr:`index` must have the same number of dimensions. + It is also required that ``index.size(d) <= input.size(d)`` for all + dimensions ``d != dim``. :attr:`out` will have the same shape as :attr:`index`. + Note that ``input`` and ``index`` do not broadcast against each other. + + Args: + input (Tensor): the source tensor + dim (int): the axis along which to index + index (LongTensor): the indices of elements to gather + + Keyword arguments: + sparse_grad (bool, optional): If ``True``, gradient w.r.t. :attr:`input` will be a sparse tensor. + out (Tensor, optional): the destination tensor + + Example:: + + >>> t = torch.tensor([[1, 2], [3, 4]]) + >>> torch.gather(t, 1, torch.tensor([[0, 0], [1, 0]])) + tensor([[ 1, 1], + [ 4, 3]]) + """ + ... +def gcd(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + gcd(input, other, *, out=None) -> Tensor + + Computes the element-wise greatest common divisor (GCD) of :attr:`input` and :attr:`other`. + + Both :attr:`input` and :attr:`other` must have integer types. + + .. note:: + This defines :math:`gcd(0, 0) = 0`. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([5, 10, 15]) + >>> b = torch.tensor([3, 4, 5]) + >>> torch.gcd(a, b) + tensor([1, 2, 5]) + >>> c = torch.tensor([3]) + >>> torch.gcd(a, c) + tensor([1, 1, 3]) + """ + ... +def gcd_(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def ge(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ge(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \geq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is greater than or equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.ge(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[True, True], [False, True]]) + """ + ... +@overload +def ge(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + ge(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \geq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is greater than or equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.ge(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[True, True], [False, True]]) + """ + ... +def geqrf(input: Tensor, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.geqrf: + r""" + geqrf(input, *, out=None) -> (Tensor, Tensor) + + This is a low-level function for calling LAPACK's geqrf directly. This function + returns a namedtuple (a, tau) as defined in `LAPACK documentation for geqrf`_ . + + Computes a QR decomposition of :attr:`input`. + Both `Q` and `R` matrices are stored in the same output tensor `a`. + The elements of `R` are stored on and above the diagonal. + Elementary reflectors (or Householder vectors) implicitly defining matrix `Q` + are stored below the diagonal. + The results of this function can be used together with :func:`torch.linalg.householder_product` + to obtain the `Q` matrix or + with :func:`torch.ormqr`, which uses an implicit representation of the `Q` matrix, + for an efficient matrix-matrix multiplication. + + See `LAPACK documentation for geqrf`_ for further details. + + .. note:: + See also :func:`torch.linalg.qr`, which computes Q and R matrices, and :func:`torch.linalg.lstsq` + with the ``driver="gels"`` option for a function that can solve matrix equations using a QR decomposition. + + Args: + input (Tensor): the input matrix + + Keyword args: + out (tuple, optional): the output tuple of (Tensor, Tensor). Ignored if `None`. Default: `None`. + + .. _LAPACK documentation for geqrf: + http://www.netlib.org/lapack/explore-html/df/dc5/group__variants_g_ecomputational_ga3766ea903391b5cf9008132f7440ec7b.html + """ + ... +def ger(input: Tensor, vec2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ger(input, vec2, *, out=None) -> Tensor + + Alias of :func:`torch.outer`. + + .. warning:: + This function is deprecated and will be removed in a future PyTorch release. + Use :func:`torch.outer` instead. + """ + ... +def get_default_dtype() -> _dtype: + r""" + get_default_dtype() -> torch.dtype + + Get the current default floating point :class:`torch.dtype`. + + Example:: + + >>> torch.get_default_dtype() # initial default for floating point is torch.float32 + torch.float32 + >>> torch.set_default_dtype(torch.float64) + >>> torch.get_default_dtype() # default is now changed to torch.float64 + torch.float64 + """ + ... +def get_num_interop_threads() -> _int: + r""" + get_num_interop_threads() -> int + + Returns the number of threads used for inter-op parallelism on CPU + (e.g. in JIT interpreter) + """ + ... +def get_num_threads() -> _int: + r""" + get_num_threads() -> int + + Returns the number of threads used for parallelizing CPU operations + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Optional[Union[Number, _complex]] = None, dim: Optional[_int] = None, edge_order: _int = 1) -> tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Sequence[Union[Number, _complex]], dim: Optional[_int] = None, edge_order: _int = 1) -> tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Sequence[Union[Number, _complex]], dim: _size, edge_order: _int = 1) -> tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: Optional[_int] = None, edge_order: _int = 1) -> tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Union[Number, _complex], dim: _size, edge_order: _int = 1) -> tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, spacing: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: _size, edge_order: _int = 1) -> tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def gradient(input: Tensor, *, dim: _size, edge_order: _int = 1) -> tuple[Tensor, ...]: + r""" + gradient(input, *, spacing=1, dim=None, edge_order=1) -> List of Tensors + + Estimates the gradient of a function :math:`g : \mathbb{R}^n \rightarrow \mathbb{R}` in + one or more dimensions using the `second-order accurate central differences method + `_ and + either first or second order estimates at the boundaries. + + The gradient of :math:`g` is estimated using samples. By default, when :attr:`spacing` is not + specified, the samples are entirely described by :attr:`input`, and the mapping of input coordinates + to an output is the same as the tensor's mapping of indices to values. For example, for a three-dimensional + :attr:`input` the function described is :math:`g : \mathbb{R}^3 \rightarrow \mathbb{R}`, and + :math:`g(1, 2, 3)\ == input[1, 2, 3]`. + + When :attr:`spacing` is specified, it modifies the relationship between :attr:`input` and input coordinates. + This is detailed in the "Keyword Arguments" section below. + + The gradient is estimated by estimating each partial derivative of :math:`g` independently. This estimation is + accurate if :math:`g` is in :math:`C^3` (it has at least 3 continuous derivatives), and the estimation can be + improved by providing closer samples. Mathematically, the value at each interior point of a partial derivative + is estimated using `Taylor's theorem with remainder `_. + Letting :math:`x` be an interior point with :math:`x-h_l` and :math:`x+h_r` be points neighboring + it to the left and right respectively, :math:`f(x+h_r)` and :math:`f(x-h_l)` can be estimated using: + + .. math:: + \begin{aligned} + f(x+h_r) = f(x) + h_r f'(x) + {h_r}^2 \frac{f''(x)}{2} + {h_r}^3 \frac{f'''(\xi_1)}{6}, \xi_1 \in (x, x+h_r) \\ + f(x-h_l) = f(x) - h_l f'(x) + {h_l}^2 \frac{f''(x)}{2} - {h_l}^3 \frac{f'''(\xi_2)}{6}, \xi_2 \in (x, x-h_l) \\ + \end{aligned} + + Using the fact that :math:`f \in C^3` and solving the linear system, we derive: + + .. math:: + f'(x) \approx \frac{ {h_l}^2 f(x+h_r) - {h_r}^2 f(x-h_l) + + ({h_r}^2-{h_l}^2 ) f(x) }{ {h_r} {h_l}^2 + {h_r}^2 {h_l} } + + .. note:: + We estimate the gradient of functions in complex domain + :math:`g : \mathbb{C}^n \rightarrow \mathbb{C}` in the same way. + + The value of each partial derivative at the boundary points is computed differently. See edge_order below. + + Args: + input (``Tensor``): the tensor that represents the values of the function + + Keyword args: + spacing (``scalar``, ``list of scalar``, ``list of Tensor``, optional): :attr:`spacing` can be used to modify + how the :attr:`input` tensor's indices relate to sample coordinates. If :attr:`spacing` is a scalar then + the indices are multiplied by the scalar to produce the coordinates. For example, if :attr:`spacing=2` the + indices (1, 2, 3) become coordinates (2, 4, 6). If :attr:`spacing` is a list of scalars then the corresponding + indices are multiplied. For example, if :attr:`spacing=(2, -1, 3)` the indices (1, 2, 3) become coordinates (2, -2, 9). + Finally, if :attr:`spacing` is a list of one-dimensional tensors then each tensor specifies the coordinates for + the corresponding dimension. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then + the coordinates are (t0[1], t1[2], t2[3]) + + dim (``int``, ``list of int``, optional): the dimension or dimensions to approximate the gradient over. By default + the partial gradient in every dimension is computed. Note that when :attr:`dim` is specified the elements of + the :attr:`spacing` argument must correspond with the specified dims." + + edge_order (``int``, optional): 1 or 2, for `first-order + `_ or + `second-order `_ + estimation of the boundary ("edge") values, respectively. + + Examples:: + + >>> # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4] + >>> coordinates = (torch.tensor([-2., -1., 1., 4.]),) + >>> values = torch.tensor([4., 1., 1., 16.], ) + >>> torch.gradient(values, spacing = coordinates) + (tensor([-3., -2., 2., 5.]),) + + >>> # Estimates the gradient of the R^2 -> R function whose samples are + >>> # described by the tensor t. Implicit coordinates are [0, 1] for the outermost + >>> # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates + >>> # partial derivative for both dimensions. + >>> t = torch.tensor([[1, 2, 4, 8], [10, 20, 40, 80]]) + >>> torch.gradient(t) + (tensor([[ 9., 18., 36., 72.], + [ 9., 18., 36., 72.]]), + tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]])) + + >>> # A scalar value for spacing modifies the relationship between tensor indices + >>> # and input coordinates by multiplying the indices to find the + >>> # coordinates. For example, below the indices of the innermost + >>> # 0, 1, 2, 3 translate to coordinates of [0, 2, 4, 6], and the indices of + >>> # the outermost dimension 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = 2.0) # dim = None (implicitly [0, 1]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.5000, 0.7500, 1.5000, 2.0000], + [ 5.0000, 7.5000, 15.0000, 20.0000]])) + >>> # doubling the spacing between samples halves the estimated partial gradients. + + >>> + >>> # Estimates only the partial derivative for dimension 1 + >>> torch.gradient(t, dim = 1) # spacing = None (implicitly 1.) + (tensor([[ 1.0000, 1.5000, 3.0000, 4.0000], + [10.0000, 15.0000, 30.0000, 40.0000]]),) + + >>> # When spacing is a list of scalars, the relationship between the tensor + >>> # indices and input coordinates changes based on dimension. + >>> # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate + >>> # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension + >>> # 0, 1 translate to coordinates of [0, 2]. + >>> torch.gradient(t, spacing = [3., 2.]) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + + >>> # The following example is a replication of the previous one with explicit + >>> # coordinates. + >>> coords = (torch.tensor([0, 2]), torch.tensor([0, 3, 6, 9])) + >>> torch.gradient(t, spacing = coords) + (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000], + [ 4.5000, 9.0000, 18.0000, 36.0000]]), + tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], + [ 3.3333, 5.0000, 10.0000, 13.3333]])) + """ + ... +@overload +def greater(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + greater(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.gt`. + """ + ... +@overload +def greater(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + greater(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.gt`. + """ + ... +@overload +def greater_equal(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + greater_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.ge`. + """ + ... +@overload +def greater_equal(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + greater_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.ge`. + """ + ... +def grid_sampler(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ... +def grid_sampler_2d(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ... +def grid_sampler_3d(input: Tensor, grid: Tensor, interpolation_mode: _int, padding_mode: _int, align_corners: _bool) -> Tensor: ... +def group_norm(input: Tensor, num_groups: _int, weight: Optional[Tensor] = None, bias: Optional[Tensor] = None, eps: _float = 1e-05, cudnn_enabled: _bool = True) -> Tensor: ... +@overload +def gru(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: Optional[Union[tuple[Tensor, ...], list[Tensor]]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> tuple[Tensor, Tensor]: ... +@overload +def gru(input: Tensor, hx: Tensor, params: Optional[Union[tuple[Tensor, ...], list[Tensor]]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> tuple[Tensor, Tensor]: ... +def gru_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None, b_hh: Optional[Tensor] = None) -> Tensor: ... +@overload +def gt(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + gt(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} > \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is greater than :attr:`other` and False elsewhere + + Example:: + + >>> torch.gt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, True], [False, False]]) + """ + ... +@overload +def gt(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + gt(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} > \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is greater than :attr:`other` and False elsewhere + + Example:: + + >>> torch.gt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, True], [False, False]]) + """ + ... +@overload +def hamming_window(window_length: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hamming window function. + + .. math:: + w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hamming_window(L, periodic=True)`` equal to + ``torch.hamming_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + .. note:: + This is a generalized version of :meth:`torch.hann_window`. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + alpha (float, optional): The coefficient :math:`\alpha` in the equation above + beta (float, optional): The coefficient :math:`\beta` in the equation above + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window. + """ + ... +@overload +def hamming_window(window_length: _int, periodic: _bool, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hamming window function. + + .. math:: + w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hamming_window(L, periodic=True)`` equal to + ``torch.hamming_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + .. note:: + This is a generalized version of :meth:`torch.hann_window`. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + alpha (float, optional): The coefficient :math:`\alpha` in the equation above + beta (float, optional): The coefficient :math:`\beta` in the equation above + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window. + """ + ... +@overload +def hamming_window(window_length: _int, periodic: _bool, alpha: _float, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hamming window function. + + .. math:: + w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hamming_window(L, periodic=True)`` equal to + ``torch.hamming_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + .. note:: + This is a generalized version of :meth:`torch.hann_window`. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + alpha (float, optional): The coefficient :math:`\alpha` in the equation above + beta (float, optional): The coefficient :math:`\beta` in the equation above + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window. + """ + ... +@overload +def hamming_window(window_length: _int, periodic: _bool, alpha: _float, beta: _float, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hamming window function. + + .. math:: + w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hamming_window(L, periodic=True)`` equal to + ``torch.hamming_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + .. note:: + This is a generalized version of :meth:`torch.hann_window`. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + alpha (float, optional): The coefficient :math:`\alpha` in the equation above + beta (float, optional): The coefficient :math:`\beta` in the equation above + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window. + """ + ... +@overload +def hann_window(window_length: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hann_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hann window function. + + .. math:: + w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] = + \sin^2 \left( \frac{\pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hann_window(L, periodic=True)`` equal to + ``torch.hann_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +@overload +def hann_window(window_length: _int, periodic: _bool, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + hann_window(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Hann window function. + + .. math:: + w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] = + \sin^2 \left( \frac{\pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window and is + ready to be used as a periodic window with functions like + :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have + ``torch.hann_window(L, periodic=True)`` equal to + ``torch.hann_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported. + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window + """ + ... +def hardshrink(input: Tensor, lambd: Union[Number, _complex] = 0.5, *, out: Optional[Tensor] = None) -> Tensor: ... +def heaviside(input: Tensor, values: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + heaviside(input, values, *, out=None) -> Tensor + + Computes the Heaviside step function for each element in :attr:`input`. + The Heaviside step function is defined as: + + .. math:: + \text{{heaviside}}(input, values) = \begin{cases} + 0, & \text{if input < 0}\\ + values, & \text{if input == 0}\\ + 1, & \text{if input > 0} + \end{cases} + + + Args: + input (Tensor): the input tensor. + values (Tensor): The values to use where :attr:`input` is zero. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> input = torch.tensor([-1.5, 0, 2.0]) + >>> values = torch.tensor([0.5]) + >>> torch.heaviside(input, values) + tensor([0.0000, 0.5000, 1.0000]) + >>> values = torch.tensor([1.2, -2.0, 3.5]) + >>> torch.heaviside(input, values) + tensor([0., -2., 1.]) + """ + ... +def hinge_embedding_loss(input: Tensor, target: Tensor, margin: _float = 1.0, reduction: _int = 1) -> Tensor: ... +def histc(input: Tensor, bins: _int = 100, min: Union[Number, _complex] = 0, max: Union[Number, _complex] = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + histc(input, bins=100, min=0, max=0, *, out=None) -> Tensor + + Computes the histogram of a tensor. + + The elements are sorted into equal width bins between :attr:`min` and + :attr:`max`. If :attr:`min` and :attr:`max` are both zero, the minimum and + maximum values of the data are used. + + Elements lower than min and higher than max and ``NaN`` elements are ignored. + + Args: + input (Tensor): the input tensor. + bins (int): number of histogram bins + min (Scalar): lower end of the range (inclusive) + max (Scalar): upper end of the range (inclusive) + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: Histogram represented as a tensor + + Example:: + + >>> torch.histc(torch.tensor([1., 2, 1]), bins=4, min=0, max=3) + tensor([ 0., 2., 1., 0.]) + """ + ... +@overload +def histogram(input: Tensor, bins: Tensor, *, weight: Optional[Tensor] = None, density: _bool = False, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.histogram: + r""" + histogram(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor) + + Computes a histogram of the values in a tensor. + + :attr:`bins` can be an integer or a 1D tensor. + + If :attr:`bins` is an int, it specifies the number of equal-width bins. + By default, the lower and upper range of the bins is determined by the + minimum and maximum elements of the input tensor. The :attr:`range` + argument can be provided to specify a range for the bins. + + If :attr:`bins` is a 1D tensor, it specifies the sequence of bin edges + including the rightmost edge. It should contain at least 2 elements + and its elements should be increasing. + + Args: + input (Tensor): the input tensor. + bins: int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, + defines the sequence of bin edges including the rightmost edge. + + Keyword args: + range (tuple of float): Defines the range of the bins. + weight (Tensor): If provided, weight should have the same shape as input. Each value in + input contributes its associated weight towards its bin's result. + density (bool): If False, the result will contain the count (or total weight) in each bin. + If True, the result is the value of the probability density function over the bins, + normalized such that the integral over the range of the bins is 1. + out (Tensor, optional): the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). + + Returns: + hist (Tensor): 1D Tensor containing the values of the histogram. + bin_edges(Tensor): 1D Tensor containing the edges of the histogram bins. + + Example:: + + >>> torch.histogram(torch.tensor([1., 2, 1]), bins=4, range=(0., 3.), weight=torch.tensor([1., 2., 4.])) + (tensor([ 0., 5., 2., 0.]), tensor([0., 0.75, 1.5, 2.25, 3.])) + >>> torch.histogram(torch.tensor([1., 2, 1]), bins=4, range=(0., 3.), weight=torch.tensor([1., 2., 4.]), density=True) + (tensor([ 0., 0.9524, 0.3810, 0.]), tensor([0., 0.75, 1.5, 2.25, 3.])) + """ + ... +@overload +def histogram(input: Tensor, bins: _int = 100, *, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.histogram: + r""" + histogram(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor) + + Computes a histogram of the values in a tensor. + + :attr:`bins` can be an integer or a 1D tensor. + + If :attr:`bins` is an int, it specifies the number of equal-width bins. + By default, the lower and upper range of the bins is determined by the + minimum and maximum elements of the input tensor. The :attr:`range` + argument can be provided to specify a range for the bins. + + If :attr:`bins` is a 1D tensor, it specifies the sequence of bin edges + including the rightmost edge. It should contain at least 2 elements + and its elements should be increasing. + + Args: + input (Tensor): the input tensor. + bins: int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, + defines the sequence of bin edges including the rightmost edge. + + Keyword args: + range (tuple of float): Defines the range of the bins. + weight (Tensor): If provided, weight should have the same shape as input. Each value in + input contributes its associated weight towards its bin's result. + density (bool): If False, the result will contain the count (or total weight) in each bin. + If True, the result is the value of the probability density function over the bins, + normalized such that the integral over the range of the bins is 1. + out (Tensor, optional): the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). + + Returns: + hist (Tensor): 1D Tensor containing the values of the histogram. + bin_edges(Tensor): 1D Tensor containing the edges of the histogram bins. + + Example:: + + >>> torch.histogram(torch.tensor([1., 2, 1]), bins=4, range=(0., 3.), weight=torch.tensor([1., 2., 4.])) + (tensor([ 0., 5., 2., 0.]), tensor([0., 0.75, 1.5, 2.25, 3.])) + >>> torch.histogram(torch.tensor([1., 2, 1]), bins=4, range=(0., 3.), weight=torch.tensor([1., 2., 4.]), density=True) + (tensor([ 0., 0.9524, 0.3810, 0.]), tensor([0., 0.75, 1.5, 2.25, 3.])) + """ + ... +@overload +def histogramdd(input: Tensor, bins: _int, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> torch.return_types.histogramdd: + r""" + histogramdd(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor[]) + + Computes a multi-dimensional histogram of the values in a tensor. + + Interprets the elements of an input tensor whose innermost dimension has size N + as a collection of N-dimensional points. Maps each of the points into a set of + N-dimensional bins and returns the number of points (or total weight) in each bin. + + :attr:`input` must be a tensor with at least 2 dimensions. + If input has shape (M, N), each of its M rows defines a point in N-dimensional space. + If input has three or more dimensions, all but the last dimension are flattened. + + Each dimension is independently associated with its own strictly increasing sequence + of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D + tensors. Alternatively, bin edges may be constructed automatically by passing a + sequence of integers specifying the number of equal-width bins in each dimension. + + For each N-dimensional point in input: + - Each of its coordinates is binned independently among the bin edges + corresponding to its dimension + - Binning results are combined to identify the N-dimensional bin (if any) + into which the point falls + - If the point falls into a bin, the bin's count (or total weight) is incremented + - Points which do not fall into any bin do not contribute to the output + + :attr:`bins` can be a sequence of N 1D tensors, a sequence of N ints, or a single int. + + If :attr:`bins` is a sequence of N 1D tensors, it explicitly specifies the N sequences + of bin edges. Each 1D tensor should contain a strictly increasing sequence with at + least one element. A sequence of K bin edges defines K-1 bins, explicitly specifying + the left and right edges of all bins. Every bin is exclusive of its left edge. Only + the rightmost bin is inclusive of its right edge. + + If :attr:`bins` is a sequence of N ints, it specifies the number of equal-width bins + in each dimension. By default, the leftmost and rightmost bin edges in each dimension + are determined by the minimum and maximum elements of the input tensor in the + corresponding dimension. The :attr:`range` argument can be provided to manually + specify the leftmost and rightmost bin edges in each dimension. + + If :attr:`bins` is an int, it specifies the number of equal-width bins for all dimensions. + + .. note:: + See also :func:`torch.histogram`, which specifically computes 1D histograms. + While :func:`torch.histogramdd` infers the dimensionality of its bins and + binned values from the shape of :attr:`input`, :func:`torch.histogram` + accepts and flattens :attr:`input` of any shape. + + Args: + input (Tensor): the input tensor. + bins: Tensor[], int[], or int. + If Tensor[], defines the sequences of bin edges. + If int[], defines the number of equal-width bins in each dimension. + If int, defines the number of equal-width bins for all dimensions. + Keyword args: + range (sequence of float): Defines the leftmost and rightmost bin edges + in each dimension. + weight (Tensor): By default, each value in the input has weight 1. If a weight + tensor is passed, each N-dimensional coordinate in input + contributes its associated weight towards its bin's result. + The weight tensor should have the same shape as the :attr:`input` + tensor excluding its innermost dimension N. + density (bool): If False (default), the result will contain the count (or total weight) + in each bin. If True, each count (weight) is divided by the total count + (total weight), then divided by the volume of its associated bin. + Returns: + hist (Tensor): N-dimensional Tensor containing the values of the histogram. + bin_edges(Tensor[]): sequence of N 1D Tensors containing the bin edges. + + Example:: + >>> torch.histogramdd(torch.tensor([[0., 1.], [1., 0.], [2., 0.], [2., 2.]]), bins=[3, 3], + ... weight=torch.tensor([1., 2., 4., 8.])) + torch.return_types.histogramdd( + hist=tensor([[0., 1., 0.], + [2., 0., 0.], + [4., 0., 8.]]), + bin_edges=(tensor([0.0000, 0.6667, 1.3333, 2.0000]), + tensor([0.0000, 0.6667, 1.3333, 2.0000]))) + + >>> torch.histogramdd(torch.tensor([[0., 0.], [1., 1.], [2., 2.]]), bins=[2, 2], + ... range=[0., 1., 0., 1.], density=True) + torch.return_types.histogramdd( + hist=tensor([[2., 0.], + [0., 2.]]), + bin_edges=(tensor([0.0000, 0.5000, 1.0000]), + tensor([0.0000, 0.5000, 1.0000]))) + """ + ... +@overload +def histogramdd(input: Tensor, bins: _size, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> torch.return_types.histogramdd: + r""" + histogramdd(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor[]) + + Computes a multi-dimensional histogram of the values in a tensor. + + Interprets the elements of an input tensor whose innermost dimension has size N + as a collection of N-dimensional points. Maps each of the points into a set of + N-dimensional bins and returns the number of points (or total weight) in each bin. + + :attr:`input` must be a tensor with at least 2 dimensions. + If input has shape (M, N), each of its M rows defines a point in N-dimensional space. + If input has three or more dimensions, all but the last dimension are flattened. + + Each dimension is independently associated with its own strictly increasing sequence + of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D + tensors. Alternatively, bin edges may be constructed automatically by passing a + sequence of integers specifying the number of equal-width bins in each dimension. + + For each N-dimensional point in input: + - Each of its coordinates is binned independently among the bin edges + corresponding to its dimension + - Binning results are combined to identify the N-dimensional bin (if any) + into which the point falls + - If the point falls into a bin, the bin's count (or total weight) is incremented + - Points which do not fall into any bin do not contribute to the output + + :attr:`bins` can be a sequence of N 1D tensors, a sequence of N ints, or a single int. + + If :attr:`bins` is a sequence of N 1D tensors, it explicitly specifies the N sequences + of bin edges. Each 1D tensor should contain a strictly increasing sequence with at + least one element. A sequence of K bin edges defines K-1 bins, explicitly specifying + the left and right edges of all bins. Every bin is exclusive of its left edge. Only + the rightmost bin is inclusive of its right edge. + + If :attr:`bins` is a sequence of N ints, it specifies the number of equal-width bins + in each dimension. By default, the leftmost and rightmost bin edges in each dimension + are determined by the minimum and maximum elements of the input tensor in the + corresponding dimension. The :attr:`range` argument can be provided to manually + specify the leftmost and rightmost bin edges in each dimension. + + If :attr:`bins` is an int, it specifies the number of equal-width bins for all dimensions. + + .. note:: + See also :func:`torch.histogram`, which specifically computes 1D histograms. + While :func:`torch.histogramdd` infers the dimensionality of its bins and + binned values from the shape of :attr:`input`, :func:`torch.histogram` + accepts and flattens :attr:`input` of any shape. + + Args: + input (Tensor): the input tensor. + bins: Tensor[], int[], or int. + If Tensor[], defines the sequences of bin edges. + If int[], defines the number of equal-width bins in each dimension. + If int, defines the number of equal-width bins for all dimensions. + Keyword args: + range (sequence of float): Defines the leftmost and rightmost bin edges + in each dimension. + weight (Tensor): By default, each value in the input has weight 1. If a weight + tensor is passed, each N-dimensional coordinate in input + contributes its associated weight towards its bin's result. + The weight tensor should have the same shape as the :attr:`input` + tensor excluding its innermost dimension N. + density (bool): If False (default), the result will contain the count (or total weight) + in each bin. If True, each count (weight) is divided by the total count + (total weight), then divided by the volume of its associated bin. + Returns: + hist (Tensor): N-dimensional Tensor containing the values of the histogram. + bin_edges(Tensor[]): sequence of N 1D Tensors containing the bin edges. + + Example:: + >>> torch.histogramdd(torch.tensor([[0., 1.], [1., 0.], [2., 0.], [2., 2.]]), bins=[3, 3], + ... weight=torch.tensor([1., 2., 4., 8.])) + torch.return_types.histogramdd( + hist=tensor([[0., 1., 0.], + [2., 0., 0.], + [4., 0., 8.]]), + bin_edges=(tensor([0.0000, 0.6667, 1.3333, 2.0000]), + tensor([0.0000, 0.6667, 1.3333, 2.0000]))) + + >>> torch.histogramdd(torch.tensor([[0., 0.], [1., 1.], [2., 2.]]), bins=[2, 2], + ... range=[0., 1., 0., 1.], density=True) + torch.return_types.histogramdd( + hist=tensor([[2., 0.], + [0., 2.]]), + bin_edges=(tensor([0.0000, 0.5000, 1.0000]), + tensor([0.0000, 0.5000, 1.0000]))) + """ + ... +@overload +def histogramdd(input: Tensor, bins: Optional[Union[tuple[Tensor, ...], list[Tensor]]], range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> torch.return_types.histogramdd: + r""" + histogramdd(input, bins, *, range=None, weight=None, density=False, out=None) -> (Tensor, Tensor[]) + + Computes a multi-dimensional histogram of the values in a tensor. + + Interprets the elements of an input tensor whose innermost dimension has size N + as a collection of N-dimensional points. Maps each of the points into a set of + N-dimensional bins and returns the number of points (or total weight) in each bin. + + :attr:`input` must be a tensor with at least 2 dimensions. + If input has shape (M, N), each of its M rows defines a point in N-dimensional space. + If input has three or more dimensions, all but the last dimension are flattened. + + Each dimension is independently associated with its own strictly increasing sequence + of bin edges. Bin edges may be specified explicitly by passing a sequence of 1D + tensors. Alternatively, bin edges may be constructed automatically by passing a + sequence of integers specifying the number of equal-width bins in each dimension. + + For each N-dimensional point in input: + - Each of its coordinates is binned independently among the bin edges + corresponding to its dimension + - Binning results are combined to identify the N-dimensional bin (if any) + into which the point falls + - If the point falls into a bin, the bin's count (or total weight) is incremented + - Points which do not fall into any bin do not contribute to the output + + :attr:`bins` can be a sequence of N 1D tensors, a sequence of N ints, or a single int. + + If :attr:`bins` is a sequence of N 1D tensors, it explicitly specifies the N sequences + of bin edges. Each 1D tensor should contain a strictly increasing sequence with at + least one element. A sequence of K bin edges defines K-1 bins, explicitly specifying + the left and right edges of all bins. Every bin is exclusive of its left edge. Only + the rightmost bin is inclusive of its right edge. + + If :attr:`bins` is a sequence of N ints, it specifies the number of equal-width bins + in each dimension. By default, the leftmost and rightmost bin edges in each dimension + are determined by the minimum and maximum elements of the input tensor in the + corresponding dimension. The :attr:`range` argument can be provided to manually + specify the leftmost and rightmost bin edges in each dimension. + + If :attr:`bins` is an int, it specifies the number of equal-width bins for all dimensions. + + .. note:: + See also :func:`torch.histogram`, which specifically computes 1D histograms. + While :func:`torch.histogramdd` infers the dimensionality of its bins and + binned values from the shape of :attr:`input`, :func:`torch.histogram` + accepts and flattens :attr:`input` of any shape. + + Args: + input (Tensor): the input tensor. + bins: Tensor[], int[], or int. + If Tensor[], defines the sequences of bin edges. + If int[], defines the number of equal-width bins in each dimension. + If int, defines the number of equal-width bins for all dimensions. + Keyword args: + range (sequence of float): Defines the leftmost and rightmost bin edges + in each dimension. + weight (Tensor): By default, each value in the input has weight 1. If a weight + tensor is passed, each N-dimensional coordinate in input + contributes its associated weight towards its bin's result. + The weight tensor should have the same shape as the :attr:`input` + tensor excluding its innermost dimension N. + density (bool): If False (default), the result will contain the count (or total weight) + in each bin. If True, each count (weight) is divided by the total count + (total weight), then divided by the volume of its associated bin. + Returns: + hist (Tensor): N-dimensional Tensor containing the values of the histogram. + bin_edges(Tensor[]): sequence of N 1D Tensors containing the bin edges. + + Example:: + >>> torch.histogramdd(torch.tensor([[0., 1.], [1., 0.], [2., 0.], [2., 2.]]), bins=[3, 3], + ... weight=torch.tensor([1., 2., 4., 8.])) + torch.return_types.histogramdd( + hist=tensor([[0., 1., 0.], + [2., 0., 0.], + [4., 0., 8.]]), + bin_edges=(tensor([0.0000, 0.6667, 1.3333, 2.0000]), + tensor([0.0000, 0.6667, 1.3333, 2.0000]))) + + >>> torch.histogramdd(torch.tensor([[0., 0.], [1., 1.], [2., 2.]]), bins=[2, 2], + ... range=[0., 1., 0., 1.], density=True) + torch.return_types.histogramdd( + hist=tensor([[2., 0.], + [0., 2.]]), + bin_edges=(tensor([0.0000, 0.5000, 1.0000]), + tensor([0.0000, 0.5000, 1.0000]))) + """ + ... +def hsmm(input: Tensor, mat2: Tensor) -> Tensor: ... +@overload +def hsplit(input: Tensor, sections: _int) -> tuple[Tensor, ...]: + r""" + hsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with one or more dimensions, into multiple tensors + horizontally according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + If :attr:`input` is one dimensional this is equivalent to calling + torch.tensor_split(input, indices_or_sections, dim=0) (the split dimension is + zero), and if :attr:`input` has two or more dimensions it's equivalent to calling + torch.tensor_split(input, indices_or_sections, dim=1) (the split dimension is 1), + except that if :attr:`indices_or_sections` is an integer it must evenly divide + the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.hsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(4,4) + >>> t + tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.], + [12., 13., 14., 15.]]) + >>> torch.hsplit(t, 2) + (tensor([[ 0., 1.], + [ 4., 5.], + [ 8., 9.], + [12., 13.]]), + tensor([[ 2., 3.], + [ 6., 7.], + [10., 11.], + [14., 15.]])) + >>> torch.hsplit(t, [3, 6]) + (tensor([[ 0., 1., 2.], + [ 4., 5., 6.], + [ 8., 9., 10.], + [12., 13., 14.]]), + tensor([[ 3.], + [ 7.], + [11.], + [15.]]), + tensor([], size=(4, 0))) + """ + ... +@overload +def hsplit(input: Tensor, indices: _size) -> tuple[Tensor, ...]: + r""" + hsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with one or more dimensions, into multiple tensors + horizontally according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + If :attr:`input` is one dimensional this is equivalent to calling + torch.tensor_split(input, indices_or_sections, dim=0) (the split dimension is + zero), and if :attr:`input` has two or more dimensions it's equivalent to calling + torch.tensor_split(input, indices_or_sections, dim=1) (the split dimension is 1), + except that if :attr:`indices_or_sections` is an integer it must evenly divide + the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.hsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(4,4) + >>> t + tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.], + [12., 13., 14., 15.]]) + >>> torch.hsplit(t, 2) + (tensor([[ 0., 1.], + [ 4., 5.], + [ 8., 9.], + [12., 13.]]), + tensor([[ 2., 3.], + [ 6., 7.], + [10., 11.], + [14., 15.]])) + >>> torch.hsplit(t, [3, 6]) + (tensor([[ 0., 1., 2.], + [ 4., 5., 6.], + [ 8., 9., 10.], + [12., 13., 14.]]), + tensor([[ 3.], + [ 7.], + [11.], + [15.]]), + tensor([], size=(4, 0))) + """ + ... +def hspmm(mat1: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + hspmm(mat1, mat2, *, out=None) -> Tensor + + Performs a matrix multiplication of a :ref:`sparse COO matrix + ` :attr:`mat1` and a strided matrix :attr:`mat2`. The + result is a (1 + 1)-dimensional :ref:`hybrid COO matrix + `. + + Args: + mat1 (Tensor): the first sparse matrix to be matrix multiplied + mat2 (Tensor): the second strided matrix to be matrix multiplied + + Keyword args: + out (Tensor, optional): the output tensor. + """ + ... +def hstack(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + hstack(tensors, *, out=None) -> Tensor + + Stack tensors in sequence horizontally (column wise). + + This is equivalent to concatenation along the first axis for 1-D tensors, and along the second axis for all other tensors. + + Args: + tensors (sequence of Tensors): sequence of tensors to concatenate + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([1, 2, 3]) + >>> b = torch.tensor([4, 5, 6]) + >>> torch.hstack((a,b)) + tensor([1, 2, 3, 4, 5, 6]) + >>> a = torch.tensor([[1],[2],[3]]) + >>> b = torch.tensor([[4],[5],[6]]) + >>> torch.hstack((a,b)) + tensor([[1, 4], + [2, 5], + [3, 6]]) + """ + ... +def hypot(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + hypot(input, other, *, out=None) -> Tensor + + Given the legs of a right triangle, return its hypotenuse. + + .. math:: + \text{out}_{i} = \sqrt{\text{input}_{i}^{2} + \text{other}_{i}^{2}} + + The shapes of ``input`` and ``other`` must be + :ref:`broadcastable `. + + Args: + input (Tensor): the first input tensor + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.hypot(torch.tensor([4.0]), torch.tensor([3.0, 4.0, 5.0])) + tensor([5.0000, 5.6569, 6.4031]) + """ + ... +def i0(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + i0(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.i0`. + """ + ... +def i0_(input: Tensor) -> Tensor: ... +def igamma(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + igamma(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.special.gammainc`. + """ + ... +def igammac(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + igammac(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.special.gammaincc`. + """ + ... +def imag(input: Tensor) -> Tensor: + r""" + imag(input) -> Tensor + + Returns a new tensor containing imaginary values of the :attr:`self` tensor. + The returned tensor and :attr:`self` share the same underlying storage. + + .. warning:: + :func:`imag` is only supported for tensors with complex dtypes. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x=torch.randn(4, dtype=torch.cfloat) + >>> x + tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)]) + >>> x.imag + tensor([ 0.3553, -0.7896, -0.0633, -0.8119]) + """ + ... +@overload +def index_add(input: Tensor, dim: _int, index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + index_add(input: Tensor, dim: int, index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1, out: Optional[Tensor]) -> Tensor # noqa: B950 + + See :meth:`~Tensor.index_add_` for function description. + """ + ... +@overload +def index_add(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + index_add(input: Tensor, dim: int, index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1, out: Optional[Tensor]) -> Tensor # noqa: B950 + + See :meth:`~Tensor.index_add_` for function description. + """ + ... +@overload +def index_copy(input: Tensor, dim: _int, index: Tensor, source: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + index_copy(input: Tensor, dim: int, index: Tensor, source: Tensor, *, out: Optional[Tensor]) -> Tensor + + See :meth:`~Tensor.index_add_` for function description. + """ + ... +@overload +def index_copy(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor) -> Tensor: + r""" + index_copy(input: Tensor, dim: int, index: Tensor, source: Tensor, *, out: Optional[Tensor]) -> Tensor + + See :meth:`~Tensor.index_add_` for function description. + """ + ... +@overload +def index_fill(input: Tensor, dim: _int, index: Tensor, value: Tensor) -> Tensor: ... +@overload +def index_fill(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, value: Tensor) -> Tensor: ... +@overload +def index_fill(input: Tensor, dim: _int, index: Tensor, value: Union[Number, _complex]) -> Tensor: ... +@overload +def index_fill(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, value: Union[Number, _complex]) -> Tensor: ... +def index_put(input: Tensor, indices: Optional[Union[tuple[Tensor, ...], list[Tensor]]], values: Tensor, accumulate: _bool = False) -> Tensor: ... +def index_put_(input: Tensor, indices: Optional[Union[tuple[Tensor, ...], list[Tensor]]], values: Tensor, accumulate: _bool = False) -> Tensor: ... +def index_reduce(input: Tensor, dim: _int, index: Tensor, source: Tensor, reduce: str, *, include_self: _bool = True, out: Optional[Tensor] = None) -> Tensor: + r""" + index_reduce(input: Tensor, dim: int, index: Tensor, source: Tensor, reduce: str, *, include_self: bool = True, out: Optional[Tensor]) -> Tensor # noqa: B950 + + See :meth:`~Tensor.index_reduce_` for function description. + """ + ... +@overload +def index_select(input: Tensor, dim: _int, index: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + index_select(input, dim, index, *, out=None) -> Tensor + + Returns a new tensor which indexes the :attr:`input` tensor along dimension + :attr:`dim` using the entries in :attr:`index` which is a `LongTensor`. + + The returned tensor has the same number of dimensions as the original tensor + (:attr:`input`). The :attr:`dim`\ th dimension has the same size as the length + of :attr:`index`; other dimensions have the same size as in the original tensor. + + .. note:: The returned tensor does **not** use the same storage as the original + tensor. If :attr:`out` has a different shape than expected, we + silently change it to the correct shape, reallocating the underlying + storage if necessary. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension in which we index + index (IntTensor or LongTensor): the 1-D tensor containing the indices to index + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(3, 4) + >>> x + tensor([[ 0.1427, 0.0231, -0.5414, -1.0009], + [-0.4664, 0.2647, -0.1228, -1.1068], + [-1.1734, -0.6571, 0.7230, -0.6004]]) + >>> indices = torch.tensor([0, 2]) + >>> torch.index_select(x, 0, indices) + tensor([[ 0.1427, 0.0231, -0.5414, -1.0009], + [-1.1734, -0.6571, 0.7230, -0.6004]]) + >>> torch.index_select(x, 1, indices) + tensor([[ 0.1427, -0.5414], + [-0.4664, -0.1228], + [-1.1734, 0.7230]]) + """ + ... +@overload +def index_select(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + index_select(input, dim, index, *, out=None) -> Tensor + + Returns a new tensor which indexes the :attr:`input` tensor along dimension + :attr:`dim` using the entries in :attr:`index` which is a `LongTensor`. + + The returned tensor has the same number of dimensions as the original tensor + (:attr:`input`). The :attr:`dim`\ th dimension has the same size as the length + of :attr:`index`; other dimensions have the same size as in the original tensor. + + .. note:: The returned tensor does **not** use the same storage as the original + tensor. If :attr:`out` has a different shape than expected, we + silently change it to the correct shape, reallocating the underlying + storage if necessary. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension in which we index + index (IntTensor or LongTensor): the 1-D tensor containing the indices to index + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(3, 4) + >>> x + tensor([[ 0.1427, 0.0231, -0.5414, -1.0009], + [-0.4664, 0.2647, -0.1228, -1.1068], + [-1.1734, -0.6571, 0.7230, -0.6004]]) + >>> indices = torch.tensor([0, 2]) + >>> torch.index_select(x, 0, indices) + tensor([[ 0.1427, 0.0231, -0.5414, -1.0009], + [-1.1734, -0.6571, 0.7230, -0.6004]]) + >>> torch.index_select(x, 1, indices) + tensor([[ 0.1427, -0.5414], + [-0.4664, -0.1228], + [-1.1734, 0.7230]]) + """ + ... +def indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.indices`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def init_num_threads() -> None: ... +def inner(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + inner(input, other, *, out=None) -> Tensor + + Computes the dot product for 1D tensors. For higher dimensions, sums the product + of elements from :attr:`input` and :attr:`other` along their last dimension. + + .. note:: + + If either :attr:`input` or :attr:`other` is a scalar, the result is equivalent + to `torch.mul(input, other)`. + + If both :attr:`input` and :attr:`other` are non-scalars, the size of their last + dimension must match and the result is equivalent to `torch.tensordot(input, + other, dims=([-1], [-1]))` + + Args: + input (Tensor): First input tensor + other (Tensor): Second input tensor + + Keyword args: + out (Tensor, optional): Optional output tensor to write result into. The output + shape is `input.shape[:-1] + other.shape[:-1]`. + + Example:: + + # Dot product + >>> torch.inner(torch.tensor([1, 2, 3]), torch.tensor([0, 2, 1])) + tensor(7) + + # Multidimensional input tensors + >>> a = torch.randn(2, 3) + >>> a + tensor([[0.8173, 1.0874, 1.1784], + [0.3279, 0.1234, 2.7894]]) + >>> b = torch.randn(2, 4, 3) + >>> b + tensor([[[-0.4682, -0.7159, 0.1506], + [ 0.4034, -0.3657, 1.0387], + [ 0.9892, -0.6684, 0.1774], + [ 0.9482, 1.3261, 0.3917]], + + [[ 0.4537, 0.7493, 1.1724], + [ 0.2291, 0.5749, -0.2267], + [-0.7920, 0.3607, -0.3701], + [ 1.3666, -0.5850, -1.7242]]]) + >>> torch.inner(a, b) + tensor([[[-0.9837, 1.1560, 0.2907, 2.6785], + [ 2.5671, 0.5452, -0.6912, -1.5509]], + + [[ 0.1782, 2.9843, 0.7366, 1.5672], + [ 3.5115, -0.4864, -1.2476, -4.4337]]]) + + # Scalar input + >>> torch.inner(a, torch.tensor(2)) + tensor([[1.6347, 2.1748, 2.3567], + [0.6558, 0.2469, 5.5787]]) + """ + ... +def instance_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], use_input_stats: _bool, momentum: _float, eps: _float, cudnn_enabled: _bool) -> Tensor: ... +def int_repr(input: Tensor) -> Tensor: ... +def inverse(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + inverse(input, *, out=None) -> Tensor + + Alias for :func:`torch.linalg.inv` + """ + ... +def is_complex(input: Tensor) -> _bool: + r""" + is_complex(input) -> (bool) + + Returns True if the data type of :attr:`input` is a complex data type i.e., + one of ``torch.complex64``, and ``torch.complex128``. + + Args: + input (Tensor): the input tensor. + """ + ... +def is_conj(input: Tensor) -> _bool: + r""" + is_conj(input) -> (bool) + + Returns True if the :attr:`input` is a conjugated tensor, i.e. its conjugate bit is set to `True`. + + Args: + input (Tensor): the input tensor. + """ + ... +def is_distributed(input: Tensor) -> _bool: ... +def is_floating_point(input: Tensor) -> _bool: + r""" + is_floating_point(input) -> (bool) + + Returns True if the data type of :attr:`input` is a floating point data type i.e., + one of ``torch.float64``, ``torch.float32``, ``torch.float16``, and ``torch.bfloat16``. + + Args: + input (Tensor): the input tensor. + """ + ... +def is_grad_enabled() -> _bool: + r""" + is_grad_enabled() -> (bool) + + Returns True if grad mode is currently enabled. + """ + ... +def is_inference(input: Tensor) -> _bool: + r""" + is_inference(input) -> (bool) + + Returns True if :attr:`input` is an inference tensor. + + A non-view tensor is an inference tensor if and only if it was + allocated during inference mode. A view tensor is an inference + tensor if and only if the tensor it is a view of is an inference tensor. + + For details on inference mode please see + `Inference Mode `_. + + Args: + input (Tensor): the input tensor. + """ + ... +def is_inference_mode_enabled() -> _bool: + r""" + is_inference_mode_enabled() -> (bool) + + Returns True if inference mode is currently enabled. + """ + ... +def is_neg(input: Tensor) -> _bool: ... +def is_nonzero(input: Tensor) -> _bool: + r""" + is_nonzero(input) -> (bool) + + Returns True if the :attr:`input` is a single element tensor which is not equal to zero + after type conversions. + i.e. not equal to ``torch.tensor([0.])`` or ``torch.tensor([0])`` or + ``torch.tensor([False])``. + Throws a ``RuntimeError`` if ``torch.numel() != 1`` (even in case + of sparse tensors). + + Args: + input (Tensor): the input tensor. + + Examples:: + + >>> torch.is_nonzero(torch.tensor([0.])) + False + >>> torch.is_nonzero(torch.tensor([1.5])) + True + >>> torch.is_nonzero(torch.tensor([False])) + False + >>> torch.is_nonzero(torch.tensor([3])) + True + >>> torch.is_nonzero(torch.tensor([1, 3, 5])) + Traceback (most recent call last): + ... + RuntimeError: bool value of Tensor with more than one value is ambiguous + >>> torch.is_nonzero(torch.tensor([])) + Traceback (most recent call last): + ... + RuntimeError: bool value of Tensor with no values is ambiguous + """ + ... +def is_same_size(input: Tensor, other: Tensor) -> _bool: ... +def is_signed(input: Tensor) -> _bool: ... +def is_vulkan_available() -> _bool: ... +def isclose(input: Tensor, other: Tensor, rtol: _float = 1e-05, atol: _float = 1e-08, equal_nan: _bool = False) -> Tensor: + r""" + isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> Tensor + + Returns a new tensor with boolean elements representing if each element of + :attr:`input` is "close" to the corresponding element of :attr:`other`. + Closeness is defined as: + + .. math:: + \lvert \text{input}_i - \text{other}_i \rvert \leq \texttt{rtol} \times \lvert \text{other}_i \rvert + \texttt{atol} + + + where :attr:`input` and :attr:`other` are finite. Where :attr:`input` + and/or :attr:`other` are nonfinite they are close if and only if + they are equal, with NaNs being considered equal to each other when + :attr:`equal_nan` is True. + + Args: + input (Tensor): first tensor to compare + other (Tensor): second tensor to compare + rtol (float, optional): relative tolerance. Default: 1e-05 + atol (float, optional): absolute tolerance. Default: 1e-08 + equal_nan (bool, optional): if ``True``, then two ``NaN`` s will be considered equal. Default: ``False`` + + Examples:: + + >>> torch.isclose(torch.tensor((1., 2, 3)), torch.tensor((1 + 1e-10, 3, 4))) + tensor([ True, False, False]) + >>> torch.isclose(torch.tensor((float('inf'), 4)), torch.tensor((float('inf'), 6)), rtol=.5) + tensor([True, True]) + """ + ... +def isfinite(input: Tensor) -> Tensor: + r""" + isfinite(input) -> Tensor + + Returns a new tensor with boolean elements representing if each element is `finite` or not. + + Real values are finite when they are not NaN, negative infinity, or infinity. + Complex values are finite when both their real and imaginary parts are finite. + + Args: + input (Tensor): the input tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is finite and False elsewhere + + Example:: + + >>> torch.isfinite(torch.tensor([1, float('inf'), 2, float('-inf'), float('nan')])) + tensor([True, False, True, False, False]) + """ + ... +@overload +def isin(elements: Tensor, test_elements: Tensor, *, assume_unique: _bool = False, invert: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + isin(elements, test_elements, *, assume_unique=False, invert=False) -> Tensor + + Tests if each element of :attr:`elements` is in :attr:`test_elements`. Returns + a boolean tensor of the same shape as :attr:`elements` that is True for elements + in :attr:`test_elements` and False otherwise. + + .. note:: + One of :attr:`elements` or :attr:`test_elements` can be a scalar, but not both. + + Args: + elements (Tensor or Scalar): Input elements + test_elements (Tensor or Scalar): Values against which to test for each input element + assume_unique (bool, optional): If True, assumes both :attr:`elements` and + :attr:`test_elements` contain unique elements, which can speed up the + calculation. Default: False + invert (bool, optional): If True, inverts the boolean return tensor, resulting in True + values for elements *not* in :attr:`test_elements`. Default: False + + Returns: + A boolean tensor of the same shape as :attr:`elements` that is True for elements in + :attr:`test_elements` and False otherwise + + Example: + >>> torch.isin(torch.tensor([[1, 2], [3, 4]]), torch.tensor([2, 3])) + tensor([[False, True], + [ True, False]]) + """ + ... +@overload +def isin(element: Union[Number, _complex], test_elements: Tensor, *, assume_unique: _bool = False, invert: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + isin(elements, test_elements, *, assume_unique=False, invert=False) -> Tensor + + Tests if each element of :attr:`elements` is in :attr:`test_elements`. Returns + a boolean tensor of the same shape as :attr:`elements` that is True for elements + in :attr:`test_elements` and False otherwise. + + .. note:: + One of :attr:`elements` or :attr:`test_elements` can be a scalar, but not both. + + Args: + elements (Tensor or Scalar): Input elements + test_elements (Tensor or Scalar): Values against which to test for each input element + assume_unique (bool, optional): If True, assumes both :attr:`elements` and + :attr:`test_elements` contain unique elements, which can speed up the + calculation. Default: False + invert (bool, optional): If True, inverts the boolean return tensor, resulting in True + values for elements *not* in :attr:`test_elements`. Default: False + + Returns: + A boolean tensor of the same shape as :attr:`elements` that is True for elements in + :attr:`test_elements` and False otherwise + + Example: + >>> torch.isin(torch.tensor([[1, 2], [3, 4]]), torch.tensor([2, 3])) + tensor([[False, True], + [ True, False]]) + """ + ... +@overload +def isin(elements: Tensor, test_element: Union[Number, _complex], *, assume_unique: _bool = False, invert: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + isin(elements, test_elements, *, assume_unique=False, invert=False) -> Tensor + + Tests if each element of :attr:`elements` is in :attr:`test_elements`. Returns + a boolean tensor of the same shape as :attr:`elements` that is True for elements + in :attr:`test_elements` and False otherwise. + + .. note:: + One of :attr:`elements` or :attr:`test_elements` can be a scalar, but not both. + + Args: + elements (Tensor or Scalar): Input elements + test_elements (Tensor or Scalar): Values against which to test for each input element + assume_unique (bool, optional): If True, assumes both :attr:`elements` and + :attr:`test_elements` contain unique elements, which can speed up the + calculation. Default: False + invert (bool, optional): If True, inverts the boolean return tensor, resulting in True + values for elements *not* in :attr:`test_elements`. Default: False + + Returns: + A boolean tensor of the same shape as :attr:`elements` that is True for elements in + :attr:`test_elements` and False otherwise + + Example: + >>> torch.isin(torch.tensor([[1, 2], [3, 4]]), torch.tensor([2, 3])) + tensor([[False, True], + [ True, False]]) + """ + ... +def isinf(input: Tensor) -> Tensor: + r""" + isinf(input) -> Tensor + + Tests if each element of :attr:`input` is infinite + (positive or negative infinity) or not. + + .. note:: + Complex values are infinite when their real or imaginary part is + infinite. + + Args: + input (Tensor): the input tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is infinite and False elsewhere + + Example:: + + >>> torch.isinf(torch.tensor([1, float('inf'), 2, float('-inf'), float('nan')])) + tensor([False, True, False, True, False]) + """ + ... +def isnan(input: Tensor) -> Tensor: + r""" + isnan(input) -> Tensor + + Returns a new tensor with boolean elements representing if each element of :attr:`input` + is NaN or not. Complex values are considered NaN when either their real + and/or imaginary part is NaN. + + Arguments: + input (Tensor): the input tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is NaN and False elsewhere + + Example:: + + >>> torch.isnan(torch.tensor([1, float('nan'), 2])) + tensor([False, True, False]) + """ + ... +def isneginf(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + isneginf(input, *, out=None) -> Tensor + Tests if each element of :attr:`input` is negative infinity or not. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([-float('inf'), float('inf'), 1.2]) + >>> torch.isneginf(a) + tensor([ True, False, False]) + """ + ... +def isposinf(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + isposinf(input, *, out=None) -> Tensor + Tests if each element of :attr:`input` is positive infinity or not. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([-float('inf'), float('inf'), 1.2]) + >>> torch.isposinf(a) + tensor([False, True, False]) + """ + ... +def isreal(input: Tensor) -> Tensor: + r""" + isreal(input) -> Tensor + + Returns a new tensor with boolean elements representing if each element of :attr:`input` is real-valued or not. + All real-valued types are considered real. Complex values are considered real when their imaginary part is 0. + + Arguments: + input (Tensor): the input tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is real and False elsewhere + + Example:: + + >>> torch.isreal(torch.tensor([1, 1+1j, 2+0j])) + tensor([True, False, True]) + """ + ... +def istft(input: Tensor, n_fft: _int, hop_length: Optional[_int] = None, win_length: Optional[_int] = None, window: Optional[Tensor] = None, center: _bool = True, normalized: _bool = False, onesided: Optional[_bool] = None, length: Optional[_int] = None, return_complex: _bool = False) -> Tensor: ... +@overload +def kaiser_window(window_length: _int, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + kaiser_window(window_length, periodic=True, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Computes the Kaiser window with window length :attr:`window_length` and shape parameter :attr:`beta`. + + Let I_0 be the zeroth order modified Bessel function of the first kind (see :func:`torch.i0`) and + ``N = L - 1`` if :attr:`periodic` is False and ``L`` if :attr:`periodic` is True, + where ``L`` is the :attr:`window_length`. This function computes: + + .. math:: + out_i = I_0 \left( \beta \sqrt{1 - \left( {\frac{i - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta ) + + Calling ``torch.kaiser_window(L, B, periodic=True)`` is equivalent to calling + ``torch.kaiser_window(L + 1, B, periodic=False)[:-1])``. + The :attr:`periodic` argument is intended as a helpful shorthand + to produce a periodic window as input to functions like :func:`torch.stft`. + + .. note:: + If :attr:`window_length` is one, then the returned window is a single element tensor containing a one. + + + Args: + window_length (int): length of the window. + periodic (bool, optional): If True, returns a periodic window suitable for use in spectral analysis. + If False, returns a symmetric window suitable for use in filter design. + beta (float, optional): shape parameter for the window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + """ + ... +@overload +def kaiser_window(window_length: _int, periodic: _bool, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + kaiser_window(window_length, periodic=True, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Computes the Kaiser window with window length :attr:`window_length` and shape parameter :attr:`beta`. + + Let I_0 be the zeroth order modified Bessel function of the first kind (see :func:`torch.i0`) and + ``N = L - 1`` if :attr:`periodic` is False and ``L`` if :attr:`periodic` is True, + where ``L`` is the :attr:`window_length`. This function computes: + + .. math:: + out_i = I_0 \left( \beta \sqrt{1 - \left( {\frac{i - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta ) + + Calling ``torch.kaiser_window(L, B, periodic=True)`` is equivalent to calling + ``torch.kaiser_window(L + 1, B, periodic=False)[:-1])``. + The :attr:`periodic` argument is intended as a helpful shorthand + to produce a periodic window as input to functions like :func:`torch.stft`. + + .. note:: + If :attr:`window_length` is one, then the returned window is a single element tensor containing a one. + + + Args: + window_length (int): length of the window. + periodic (bool, optional): If True, returns a periodic window suitable for use in spectral analysis. + If False, returns a symmetric window suitable for use in filter design. + beta (float, optional): shape parameter for the window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + """ + ... +@overload +def kaiser_window(window_length: _int, periodic: _bool, beta: _float, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + kaiser_window(window_length, periodic=True, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Computes the Kaiser window with window length :attr:`window_length` and shape parameter :attr:`beta`. + + Let I_0 be the zeroth order modified Bessel function of the first kind (see :func:`torch.i0`) and + ``N = L - 1`` if :attr:`periodic` is False and ``L`` if :attr:`periodic` is True, + where ``L`` is the :attr:`window_length`. This function computes: + + .. math:: + out_i = I_0 \left( \beta \sqrt{1 - \left( {\frac{i - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta ) + + Calling ``torch.kaiser_window(L, B, periodic=True)`` is equivalent to calling + ``torch.kaiser_window(L + 1, B, periodic=False)[:-1])``. + The :attr:`periodic` argument is intended as a helpful shorthand + to produce a periodic window as input to functions like :func:`torch.stft`. + + .. note:: + If :attr:`window_length` is one, then the returned window is a single element tensor containing a one. + + + Args: + window_length (int): length of the window. + periodic (bool, optional): If True, returns a periodic window suitable for use in spectral analysis. + If False, returns a symmetric window suitable for use in filter design. + beta (float, optional): shape parameter for the window. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only + ``torch.strided`` (dense layout) is supported. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + """ + ... +def kl_div(input: Tensor, target: Tensor, reduction: _int = 1, *, log_target: _bool = False) -> Tensor: ... +def kron(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + kron(input, other, *, out=None) -> Tensor + + Computes the Kronecker product, denoted by :math:`\otimes`, of :attr:`input` and :attr:`other`. + + If :attr:`input` is a :math:`(a_0 \times a_1 \times \dots \times a_n)` tensor and :attr:`other` is a + :math:`(b_0 \times b_1 \times \dots \times b_n)` tensor, the result will be a + :math:`(a_0*b_0 \times a_1*b_1 \times \dots \times a_n*b_n)` tensor with the following entries: + + .. math:: + (\text{input} \otimes \text{other})_{k_0, k_1, \dots, k_n} = + \text{input}_{i_0, i_1, \dots, i_n} * \text{other}_{j_0, j_1, \dots, j_n}, + + where :math:`k_t = i_t * b_t + j_t` for :math:`0 \leq t \leq n`. + If one tensor has fewer dimensions than the other it is unsqueezed until it has the same number of dimensions. + + Supports real-valued and complex-valued inputs. + + .. note:: + This function generalizes the typical definition of the Kronecker product for two matrices to two tensors, + as described above. When :attr:`input` is a :math:`(m \times n)` matrix and :attr:`other` is a + :math:`(p \times q)` matrix, the result will be a :math:`(p*m \times q*n)` block matrix: + + .. math:: + \mathbf{A} \otimes \mathbf{B}=\begin{bmatrix} + a_{11} \mathbf{B} & \cdots & a_{1 n} \mathbf{B} \\ + \vdots & \ddots & \vdots \\ + a_{m 1} \mathbf{B} & \cdots & a_{m n} \mathbf{B} \end{bmatrix} + + where :attr:`input` is :math:`\mathbf{A}` and :attr:`other` is :math:`\mathbf{B}`. + + Arguments: + input (Tensor) + other (Tensor) + + Keyword args: + out (Tensor, optional): The output tensor. Ignored if ``None``. Default: ``None`` + + Examples:: + + >>> mat1 = torch.eye(2) + >>> mat2 = torch.ones(2, 2) + >>> torch.kron(mat1, mat2) + tensor([[1., 1., 0., 0.], + [1., 1., 0., 0.], + [0., 0., 1., 1.], + [0., 0., 1., 1.]]) + + >>> mat1 = torch.eye(2) + >>> mat2 = torch.arange(1, 5).reshape(2, 2) + >>> torch.kron(mat1, mat2) + tensor([[1., 2., 0., 0.], + [3., 4., 0., 0.], + [0., 0., 1., 2.], + [0., 0., 3., 4.]]) + """ + ... +@overload +def kthvalue(input: Tensor, k: _int, dim: _int = -1, keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.kthvalue: + r""" + kthvalue(input, k, dim=None, keepdim=False, *, out=None) -> (Tensor, LongTensor) + + Returns a namedtuple ``(values, indices)`` where ``values`` is the :attr:`k` th + smallest element of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each element found. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`keepdim` is ``True``, both the :attr:`values` and :attr:`indices` tensors + are the same size as :attr:`input`, except in the dimension :attr:`dim` where + they are of size 1. Otherwise, :attr:`dim` is squeezed + (see :func:`torch.squeeze`), resulting in both the :attr:`values` and + :attr:`indices` tensors having 1 fewer dimension than the :attr:`input` tensor. + + .. note:: + When :attr:`input` is a CUDA tensor and there are multiple valid + :attr:`k` th values, this function may nondeterministically return + :attr:`indices` for any of them. + + Args: + input (Tensor): the input tensor. + k (int): k for the k-th smallest element + dim (int, optional): the dimension to find the kth value along + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the output tuple of (Tensor, LongTensor) + can be optionally given to be used as output buffers + + Example:: + + >>> x = torch.arange(1., 6.) + >>> x + tensor([ 1., 2., 3., 4., 5.]) + >>> torch.kthvalue(x, 4) + torch.return_types.kthvalue(values=tensor(4.), indices=tensor(3)) + + >>> x=torch.arange(1.,7.).resize_(2,3) + >>> x + tensor([[ 1., 2., 3.], + [ 4., 5., 6.]]) + >>> torch.kthvalue(x, 2, 0, True) + torch.return_types.kthvalue(values=tensor([[4., 5., 6.]]), indices=tensor([[1, 1, 1]])) + """ + ... +@overload +def kthvalue(input: Tensor, k: _int, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.kthvalue: + r""" + kthvalue(input, k, dim=None, keepdim=False, *, out=None) -> (Tensor, LongTensor) + + Returns a namedtuple ``(values, indices)`` where ``values`` is the :attr:`k` th + smallest element of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each element found. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`keepdim` is ``True``, both the :attr:`values` and :attr:`indices` tensors + are the same size as :attr:`input`, except in the dimension :attr:`dim` where + they are of size 1. Otherwise, :attr:`dim` is squeezed + (see :func:`torch.squeeze`), resulting in both the :attr:`values` and + :attr:`indices` tensors having 1 fewer dimension than the :attr:`input` tensor. + + .. note:: + When :attr:`input` is a CUDA tensor and there are multiple valid + :attr:`k` th values, this function may nondeterministically return + :attr:`indices` for any of them. + + Args: + input (Tensor): the input tensor. + k (int): k for the k-th smallest element + dim (int, optional): the dimension to find the kth value along + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the output tuple of (Tensor, LongTensor) + can be optionally given to be used as output buffers + + Example:: + + >>> x = torch.arange(1., 6.) + >>> x + tensor([ 1., 2., 3., 4., 5.]) + >>> torch.kthvalue(x, 4) + torch.return_types.kthvalue(values=tensor(4.), indices=tensor(3)) + + >>> x=torch.arange(1.,7.).resize_(2,3) + >>> x + tensor([[ 1., 2., 3.], + [ 4., 5., 6.]]) + >>> torch.kthvalue(x, 2, 0, True) + torch.return_types.kthvalue(values=tensor([[4., 5., 6.]]), indices=tensor([[1, 1, 1]])) + """ + ... +def layer_norm(input: Tensor, normalized_shape: Sequence[Union[_int, SymInt]], weight: Optional[Tensor] = None, bias: Optional[Tensor] = None, eps: _float = 1e-05, cudnn_enable: _bool = True) -> Tensor: ... +def lcm(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + lcm(input, other, *, out=None) -> Tensor + + Computes the element-wise least common multiple (LCM) of :attr:`input` and :attr:`other`. + + Both :attr:`input` and :attr:`other` must have integer types. + + .. note:: + This defines :math:`lcm(0, 0) = 0` and :math:`lcm(0, a) = 0`. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([5, 10, 15]) + >>> b = torch.tensor([3, 4, 5]) + >>> torch.lcm(a, b) + tensor([15, 20, 15]) + >>> c = torch.tensor([3]) + >>> torch.lcm(a, c) + tensor([15, 30, 15]) + """ + ... +def lcm_(input: Tensor, other: Tensor) -> Tensor: ... +def ldexp(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ldexp(input, other, *, out=None) -> Tensor + + Multiplies :attr:`input` by 2 ** :attr:`other`. + + .. math:: + \text{{out}}_i = \text{{input}}_i * 2^\text{{other}}_i + + + Typically this function is used to construct floating point numbers by multiplying + mantissas in :attr:`input` with integral powers of two created from the exponents + in :attr:`other`. + + Args: + input (Tensor): the input tensor. + other (Tensor): a tensor of exponents, typically integers. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.ldexp(torch.tensor([1.]), torch.tensor([1])) + tensor([2.]) + >>> torch.ldexp(torch.tensor([1.0]), torch.tensor([1, 2, 3, 4])) + tensor([ 2., 4., 8., 16.]) + """ + ... +def ldexp_(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def le(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + le(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \leq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or Scalar): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is less than or equal to + :attr:`other` and False elsewhere + + Example:: + + >>> torch.le(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[True, False], [True, True]]) + """ + ... +@overload +def le(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + le(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \leq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or Scalar): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is less than or equal to + :attr:`other` and False elsewhere + + Example:: + + >>> torch.le(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[True, False], [True, True]]) + """ + ... +@overload +def lerp(input: Tensor, end: Tensor, weight: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + lerp(input, end, weight, *, out=None) + + Does a linear interpolation of two tensors :attr:`start` (given by :attr:`input`) and :attr:`end` based + on a scalar or tensor :attr:`weight` and returns the resulting :attr:`out` tensor. + + .. math:: + \text{out}_i = \text{start}_i + \text{weight}_i \times (\text{end}_i - \text{start}_i) + + The shapes of :attr:`start` and :attr:`end` must be + :ref:`broadcastable `. If :attr:`weight` is a tensor, then + the shapes of :attr:`weight`, :attr:`start`, and :attr:`end` must be :ref:`broadcastable `. + + Args: + input (Tensor): the tensor with the starting points + end (Tensor): the tensor with the ending points + weight (float or tensor): the weight for the interpolation formula + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> start = torch.arange(1., 5.) + >>> end = torch.empty(4).fill_(10) + >>> start + tensor([ 1., 2., 3., 4.]) + >>> end + tensor([ 10., 10., 10., 10.]) + >>> torch.lerp(start, end, 0.5) + tensor([ 5.5000, 6.0000, 6.5000, 7.0000]) + >>> torch.lerp(start, end, torch.full_like(start, 0.5)) + tensor([ 5.5000, 6.0000, 6.5000, 7.0000]) + """ + ... +@overload +def lerp(input: Tensor, end: Tensor, weight: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + lerp(input, end, weight, *, out=None) + + Does a linear interpolation of two tensors :attr:`start` (given by :attr:`input`) and :attr:`end` based + on a scalar or tensor :attr:`weight` and returns the resulting :attr:`out` tensor. + + .. math:: + \text{out}_i = \text{start}_i + \text{weight}_i \times (\text{end}_i - \text{start}_i) + + The shapes of :attr:`start` and :attr:`end` must be + :ref:`broadcastable `. If :attr:`weight` is a tensor, then + the shapes of :attr:`weight`, :attr:`start`, and :attr:`end` must be :ref:`broadcastable `. + + Args: + input (Tensor): the tensor with the starting points + end (Tensor): the tensor with the ending points + weight (float or tensor): the weight for the interpolation formula + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> start = torch.arange(1., 5.) + >>> end = torch.empty(4).fill_(10) + >>> start + tensor([ 1., 2., 3., 4.]) + >>> end + tensor([ 10., 10., 10., 10.]) + >>> torch.lerp(start, end, 0.5) + tensor([ 5.5000, 6.0000, 6.5000, 7.0000]) + >>> torch.lerp(start, end, torch.full_like(start, 0.5)) + tensor([ 5.5000, 6.0000, 6.5000, 7.0000]) + """ + ... +@overload +def less(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + less(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.lt`. + """ + ... +@overload +def less(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + less(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.lt`. + """ + ... +@overload +def less_equal(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + less_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.le`. + """ + ... +@overload +def less_equal(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + less_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.le`. + """ + ... +def lgamma(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + lgamma(input, *, out=None) -> Tensor + + Computes the natural logarithm of the absolute value of the gamma function on :attr:`input`. + + .. math:: + \text{out}_{i} = \ln |\Gamma(\text{input}_{i})| + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.arange(0.5, 2, 0.5) + >>> torch.lgamma(a) + tensor([ 0.5724, 0.0000, -0.1208]) + """ + ... +@overload +def linspace(start: Number, end: Number, steps: Optional[_int] = None, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are: + + .. math:: + (\text{start}, + \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \ldots, + \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \text{end}) + + + From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + + Example:: + + >>> torch.linspace(3, 10, steps=5) + tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) + >>> torch.linspace(-10, 10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=1) + tensor([-10.]) + """ + ... +@overload +def linspace(start: Tensor, end: Tensor, steps: _int, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are: + + .. math:: + (\text{start}, + \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \ldots, + \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \text{end}) + + + From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + + Example:: + + >>> torch.linspace(3, 10, steps=5) + tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) + >>> torch.linspace(-10, 10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=1) + tensor([-10.]) + """ + ... +@overload +def linspace(start: Union[Number, _complex], end: Tensor, steps: _int, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are: + + .. math:: + (\text{start}, + \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \ldots, + \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \text{end}) + + + From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + + Example:: + + >>> torch.linspace(3, 10, steps=5) + tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) + >>> torch.linspace(-10, 10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=1) + tensor([-10.]) + """ + ... +@overload +def linspace(start: Tensor, end: Union[Number, _complex], steps: _int, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are: + + .. math:: + (\text{start}, + \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \ldots, + \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \text{end}) + + + From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + + Example:: + + >>> torch.linspace(3, 10, steps=5) + tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) + >>> torch.linspace(-10, 10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=1) + tensor([-10.]) + """ + ... +@overload +def linspace(start: Union[Number, _complex], end: Union[Number, _complex], steps: _int, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :attr:`start` to :attr:`end`, inclusive. That is, the value are: + + .. math:: + (\text{start}, + \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \ldots, + \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, + \text{end}) + + + From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + + Example:: + + >>> torch.linspace(3, 10, steps=5) + tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) + >>> torch.linspace(-10, 10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=5) + tensor([-10., -5., 0., 5., 10.]) + >>> torch.linspace(start=-10, end=10, steps=1) + tensor([-10.]) + """ + ... +def log(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + log(input, *, out=None) -> Tensor + + Returns a new tensor with the natural logarithm of the elements + of :attr:`input`. + + .. math:: + y_{i} = \log_{e} (x_{i}) + + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(5) * 5 + >>> a + tensor([4.7767, 4.3234, 1.2156, 0.2411, 4.5739]) + >>> torch.log(a) + tensor([ 1.5637, 1.4640, 0.1952, -1.4226, 1.5204]) + """ + ... +def log10(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + log10(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Returns a new tensor with the logarithm to the base 10 of the elements + of :attr:`input`. + + .. math:: + y_{i} = \log_{10} (x_{i}) + + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(5) + >>> a + tensor([ 0.5224, 0.9354, 0.7257, 0.1301, 0.2251]) + + + >>> torch.log10(a) + tensor([-0.2820, -0.0290, -0.1392, -0.8857, -0.6476]) + """ + ... +def log10_(input: Tensor) -> Tensor: ... +def log1p(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + log1p(input, *, out=None) -> Tensor + + Returns a new tensor with the natural logarithm of (1 + :attr:`input`). + + .. math:: + y_i = \log_{e} (x_i + 1) + + .. note:: This function is more accurate than :func:`torch.log` for small + values of :attr:`input` + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(5) + >>> a + tensor([-1.0090, -0.9923, 1.0249, -0.5372, 0.2492]) + >>> torch.log1p(a) + tensor([ nan, -4.8653, 0.7055, -0.7705, 0.2225]) + """ + ... +def log1p_(input: Tensor) -> Tensor: ... +def log2(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + log2(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Returns a new tensor with the logarithm to the base 2 of the elements + of :attr:`input`. + + .. math:: + y_{i} = \log_{2} (x_{i}) + + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.rand(5) + >>> a + tensor([ 0.8419, 0.8003, 0.9971, 0.5287, 0.0490]) + + + >>> torch.log2(a) + tensor([-0.2483, -0.3213, -0.0042, -0.9196, -4.3504]) + """ + ... +def log2_(input: Tensor) -> Tensor: ... +def log_(input: Tensor) -> Tensor: ... +@overload +def log_softmax(input: Tensor, dim: _int, dtype: Optional[_dtype] = None, *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def log_softmax(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: ... +def logaddexp(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logaddexp(input, other, *, out=None) -> Tensor + + Logarithm of the sum of exponentiations of the inputs. + + Calculates pointwise :math:`\log\left(e^x + e^y\right)`. This function is useful + in statistics where the calculated probabilities of events may be so small as to + exceed the range of normal floating point numbers. In such cases the logarithm + of the calculated probability is stored. This function allows adding + probabilities stored in such a fashion. + + This op should be disambiguated with :func:`torch.logsumexp` which performs a + reduction on a single tensor. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.logaddexp(torch.tensor([-1.0]), torch.tensor([-1.0, -2, -3])) + tensor([-0.3069, -0.6867, -0.8731]) + >>> torch.logaddexp(torch.tensor([-100.0, -200, -300]), torch.tensor([-1.0, -2, -3])) + tensor([-1., -2., -3.]) + >>> torch.logaddexp(torch.tensor([1.0, 2000, 30000]), torch.tensor([-1.0, -2, -3])) + tensor([1.1269e+00, 2.0000e+03, 3.0000e+04]) + """ + ... +def logaddexp2(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logaddexp2(input, other, *, out=None) -> Tensor + + Logarithm of the sum of exponentiations of the inputs in base-2. + + Calculates pointwise :math:`\log_2\left(2^x + 2^y\right)`. See + :func:`torch.logaddexp` for more details. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword arguments: + out (Tensor, optional): the output tensor. + """ + ... +@overload +def logcumsumexp(input: Tensor, dim: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logcumsumexp(input, dim, *, out=None) -> Tensor + Returns the logarithm of the cumulative summation of the exponentiation of + elements of :attr:`input` in the dimension :attr:`dim`. + + For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is + + .. math:: + \text{logcumsumexp}(x)_{ij} = \log \sum\limits_{k=0}^{j} \exp(x_{ik}) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(10) + >>> torch.logcumsumexp(a, dim=0) + tensor([-0.42296738, -0.04462666, 0.86278635, 0.94622083, 1.05277811, + 1.39202815, 1.83525007, 1.84492621, 2.06084887, 2.06844475])) + """ + ... +@overload +def logcumsumexp(input: Tensor, dim: Union[str, ellipsis, None], *, out: Optional[Tensor] = None) -> Tensor: + r""" + logcumsumexp(input, dim, *, out=None) -> Tensor + Returns the logarithm of the cumulative summation of the exponentiation of + elements of :attr:`input` in the dimension :attr:`dim`. + + For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is + + .. math:: + \text{logcumsumexp}(x)_{ij} = \log \sum\limits_{k=0}^{j} \exp(x_{ik}) + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to do the operation over + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(10) + >>> torch.logcumsumexp(a, dim=0) + tensor([-0.42296738, -0.04462666, 0.86278635, 0.94622083, 1.05277811, + 1.39202815, 1.83525007, 1.84492621, 2.06084887, 2.06844475])) + """ + ... +def logdet(input: Tensor) -> Tensor: + r""" + logdet(input) -> Tensor + + Calculates log determinant of a square matrix or batches of square matrices. + + It returns ``-inf`` if the input has a determinant of zero, and ``NaN`` if it has + a negative determinant. + + .. note:: + Backward through :meth:`logdet` internally uses SVD results when :attr:`input` + is not invertible. In this case, double backward through :meth:`logdet` will + be unstable in when :attr:`input` doesn't have distinct singular values. See + :func:`torch.linalg.svd` for details. + + .. seealso:: + + :func:`torch.linalg.slogdet` computes the sign (resp. angle) and natural logarithm of the + absolute value of the determinant of real-valued (resp. complex) square matrices. + + Arguments: + input (Tensor): the input tensor of size ``(*, n, n)`` where ``*`` is zero or more + batch dimensions. + + Example:: + + >>> A = torch.randn(3, 3) + >>> torch.det(A) + tensor(0.2611) + >>> torch.logdet(A) + tensor(-1.3430) + >>> A + tensor([[[ 0.9254, -0.6213], + [-0.5787, 1.6843]], + + [[ 0.3242, -0.9665], + [ 0.4539, -0.0887]], + + [[ 1.1336, -0.4025], + [-0.7089, 0.9032]]]) + >>> A.det() + tensor([1.1990, 0.4099, 0.7386]) + >>> A.det().log() + tensor([ 0.1815, -0.8917, -0.3031]) + """ + ... +def logical_and(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logical_and(input, other, *, out=None) -> Tensor + + Computes the element-wise logical AND of the given input tensors. Zeros are treated as ``False`` and nonzeros are + treated as ``True``. + + Args: + input (Tensor): the input tensor. + other (Tensor): the tensor to compute AND with + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.logical_and(torch.tensor([True, False, True]), torch.tensor([True, False, False])) + tensor([ True, False, False]) + >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8) + >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8) + >>> torch.logical_and(a, b) + tensor([False, False, True, False]) + >>> torch.logical_and(a.double(), b.double()) + tensor([False, False, True, False]) + >>> torch.logical_and(a.double(), b) + tensor([False, False, True, False]) + >>> torch.logical_and(a, b, out=torch.empty(4, dtype=torch.bool)) + tensor([False, False, True, False]) + """ + ... +def logical_not(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logical_not(input, *, out=None) -> Tensor + + Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool + dtype. If the input tensor is not a bool tensor, zeros are treated as ``False`` and non-zeros are treated as ``True``. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.logical_not(torch.tensor([True, False])) + tensor([False, True]) + >>> torch.logical_not(torch.tensor([0, 1, -10], dtype=torch.int8)) + tensor([ True, False, False]) + >>> torch.logical_not(torch.tensor([0., 1.5, -10.], dtype=torch.double)) + tensor([ True, False, False]) + >>> torch.logical_not(torch.tensor([0., 1., -10.], dtype=torch.double), out=torch.empty(3, dtype=torch.int16)) + tensor([1, 0, 0], dtype=torch.int16) + """ + ... +def logical_or(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logical_or(input, other, *, out=None) -> Tensor + + Computes the element-wise logical OR of the given input tensors. Zeros are treated as ``False`` and nonzeros are + treated as ``True``. + + Args: + input (Tensor): the input tensor. + other (Tensor): the tensor to compute OR with + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.logical_or(torch.tensor([True, False, True]), torch.tensor([True, False, False])) + tensor([ True, False, True]) + >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8) + >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8) + >>> torch.logical_or(a, b) + tensor([ True, True, True, False]) + >>> torch.logical_or(a.double(), b.double()) + tensor([ True, True, True, False]) + >>> torch.logical_or(a.double(), b) + tensor([ True, True, True, False]) + >>> torch.logical_or(a, b, out=torch.empty(4, dtype=torch.bool)) + tensor([ True, True, True, False]) + """ + ... +def logical_xor(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logical_xor(input: Tensor, other: Tensor, *, out: Optional[Tensor]) -> Tensor + + Computes the element-wise logical XOR of the given input tensors. Zeros are treated as ``False`` and nonzeros are + treated as ``True``. + + Args: + input (Tensor): the input tensor. + other (Tensor): the tensor to compute XOR with + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.logical_xor(torch.tensor([True, False, True]), torch.tensor([True, False, False])) + tensor([False, False, True]) + >>> a = torch.tensor([0, 1, 10, 0], dtype=torch.int8) + >>> b = torch.tensor([4, 0, 1, 0], dtype=torch.int8) + >>> torch.logical_xor(a, b) + tensor([ True, True, False, False]) + >>> torch.logical_xor(a.double(), b.double()) + tensor([ True, True, False, False]) + >>> torch.logical_xor(a.double(), b) + tensor([ True, True, False, False]) + >>> torch.logical_xor(a, b, out=torch.empty(4, dtype=torch.bool)) + tensor([ True, True, False, False]) + """ + ... +def logit(input: Tensor, eps: Optional[_float] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logit(input, eps=None, *, out=None) -> Tensor + + Alias for :func:`torch.special.logit`. + """ + ... +def logit_(input: Tensor, eps: Optional[_float] = None) -> Tensor: ... +@overload +def logspace(start: Number, end: Number, steps: Optional[_int] = None, base: _float = 10.0, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + logspace(start, end, steps, base=10.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + + + From PyTorch 1.11 logspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.logspace(start=-10, end=10, steps=5) + tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) + >>> torch.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) + >>> torch.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589]) + >>> torch.logspace(start=2, end=2, steps=1, base=2) + tensor([4.0]) + """ + ... +@overload +def logspace(start: Tensor, end: Tensor, steps: _int, base: _float = 10.0, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + logspace(start, end, steps, base=10.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + + + From PyTorch 1.11 logspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.logspace(start=-10, end=10, steps=5) + tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) + >>> torch.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) + >>> torch.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589]) + >>> torch.logspace(start=2, end=2, steps=1, base=2) + tensor([4.0]) + """ + ... +@overload +def logspace(start: Union[Number, _complex], end: Tensor, steps: _int, base: _float = 10.0, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + logspace(start, end, steps, base=10.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + + + From PyTorch 1.11 logspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.logspace(start=-10, end=10, steps=5) + tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) + >>> torch.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) + >>> torch.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589]) + >>> torch.logspace(start=2, end=2, steps=1, base=2) + tensor([4.0]) + """ + ... +@overload +def logspace(start: Tensor, end: Union[Number, _complex], steps: _int, base: _float = 10.0, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + logspace(start, end, steps, base=10.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + + + From PyTorch 1.11 logspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.logspace(start=-10, end=10, steps=5) + tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) + >>> torch.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) + >>> torch.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589]) + >>> torch.logspace(start=2, end=2, steps=1, base=2) + tensor([4.0]) + """ + ... +@overload +def logspace(start: Union[Number, _complex], end: Union[Number, _complex], steps: _int, base: _float = 10.0, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + logspace(start, end, steps, base=10.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + + + From PyTorch 1.11 logspace requires the steps argument. Use steps=100 to restore the previous behavior. + + Args: + start (float or Tensor): the starting value for the set of points. If `Tensor`, it must be 0-dimensional + end (float or Tensor): the ending value for the set of points. If `Tensor`, it must be 0-dimensional + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (torch.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see torch.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.logspace(start=-10, end=10, steps=5) + tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) + >>> torch.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) + >>> torch.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589]) + >>> torch.logspace(start=2, end=2, steps=1, base=2) + tensor([4.0]) + """ + ... +@overload +def logsumexp(input: Tensor, dim: Union[_int, _size], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logsumexp(input, dim, keepdim=False, *, out=None) + + Returns the log of summed exponentials of each row of the :attr:`input` + tensor in the given dimension :attr:`dim`. The computation is numerically + stabilized. + + For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is + + .. math:: + \text{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij}) + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(3, 3) + >>> torch.logsumexp(a, 1) + tensor([1.4907, 1.0593, 1.5696]) + >>> torch.dist(torch.logsumexp(a, 1), torch.log(torch.sum(torch.exp(a), 1))) + tensor(1.6859e-07) + """ + ... +@overload +def logsumexp(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + logsumexp(input, dim, keepdim=False, *, out=None) + + Returns the log of summed exponentials of each row of the :attr:`input` + tensor in the given dimension :attr:`dim`. The computation is numerically + stabilized. + + For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is + + .. math:: + \text{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij}) + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(3, 3) + >>> torch.logsumexp(a, 1) + tensor([1.4907, 1.0593, 1.5696]) + >>> torch.dist(torch.logsumexp(a, 1), torch.log(torch.sum(torch.exp(a), 1))) + tensor(1.6859e-07) + """ + ... +@overload +def lstm(data: Tensor, batch_sizes: Tensor, hx: Optional[Union[tuple[Tensor, ...], list[Tensor]]], params: Optional[Union[tuple[Tensor, ...], list[Tensor]]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> tuple[Tensor, Tensor, Tensor]: ... +@overload +def lstm(input: Tensor, hx: Optional[Union[tuple[Tensor, ...], list[Tensor]]], params: Optional[Union[tuple[Tensor, ...], list[Tensor]]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> tuple[Tensor, Tensor, Tensor]: ... +def lstm_cell(input: Tensor, hx: Optional[Union[tuple[Tensor, ...], list[Tensor]]], w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None, b_hh: Optional[Tensor] = None) -> tuple[Tensor, Tensor]: ... +@overload +def lt(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + lt(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} < \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is less than :attr:`other` and False elsewhere + + Example:: + + >>> torch.lt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, False], [True, False]]) + """ + ... +@overload +def lt(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + lt(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} < \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is less than :attr:`other` and False elsewhere + + Example:: + + >>> torch.lt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, False], [True, False]]) + """ + ... +def lu_solve(input: Tensor, LU_data: Tensor, LU_pivots: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + lu_solve(b, LU_data, LU_pivots, *, out=None) -> Tensor + + Returns the LU solve of the linear system :math:`Ax = b` using the partially pivoted + LU factorization of A from :func:`~linalg.lu_factor`. + + This function supports ``float``, ``double``, ``cfloat`` and ``cdouble`` dtypes for :attr:`input`. + + .. warning:: + + :func:`torch.lu_solve` is deprecated in favor of :func:`torch.linalg.lu_solve`. + :func:`torch.lu_solve` will be removed in a future PyTorch release. + ``X = torch.lu_solve(B, LU, pivots)`` should be replaced with + + .. code:: python + + X = linalg.lu_solve(LU, pivots, B) + + Arguments: + b (Tensor): the RHS tensor of size :math:`(*, m, k)`, where :math:`*` + is zero or more batch dimensions. + LU_data (Tensor): the pivoted LU factorization of A from :meth:`~linalg.lu_factor` of size :math:`(*, m, m)`, + where :math:`*` is zero or more batch dimensions. + LU_pivots (IntTensor): the pivots of the LU factorization from :meth:`~linalg.lu_factor` of size :math:`(*, m)`, + where :math:`*` is zero or more batch dimensions. + The batch dimensions of :attr:`LU_pivots` must be equal to the batch dimensions of + :attr:`LU_data`. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> A = torch.randn(2, 3, 3) + >>> b = torch.randn(2, 3, 1) + >>> LU, pivots = torch.linalg.lu_factor(A) + >>> x = torch.lu_solve(b, LU, pivots) + >>> torch.dist(A @ x, b) + tensor(1.00000e-07 * + 2.8312) + """ + ... +def lu_unpack(LU_data: Tensor, LU_pivots: Tensor, unpack_data: _bool = True, unpack_pivots: _bool = True, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.lu_unpack: + r""" + lu_unpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True, *, out=None) -> (Tensor, Tensor, Tensor) + + Unpacks the LU decomposition returned by :func:`~linalg.lu_factor` into the `P, L, U` matrices. + + .. seealso:: + + :func:`~linalg.lu` returns the matrices from the LU decomposition. Its gradient formula is more efficient + than that of doing :func:`~linalg.lu_factor` followed by :func:`~linalg.lu_unpack`. + + Args: + LU_data (Tensor): the packed LU factorization data + LU_pivots (Tensor): the packed LU factorization pivots + unpack_data (bool): flag indicating if the data should be unpacked. + If ``False``, then the returned ``L`` and ``U`` are empty tensors. + Default: ``True`` + unpack_pivots (bool): flag indicating if the pivots should be unpacked into a permutation matrix ``P``. + If ``False``, then the returned ``P`` is an empty tensor. + Default: ``True`` + + Keyword args: + out (tuple, optional): output tuple of three tensors. Ignored if `None`. + + Returns: + A namedtuple ``(P, L, U)`` + + Examples:: + + >>> A = torch.randn(2, 3, 3) + >>> LU, pivots = torch.linalg.lu_factor(A) + >>> P, L, U = torch.lu_unpack(LU, pivots) + >>> # We can recover A from the factorization + >>> A_ = P @ L @ U + >>> torch.allclose(A, A_) + True + + >>> # LU factorization of a rectangular matrix: + >>> A = torch.randn(2, 3, 2) + >>> LU, pivots = torch.linalg.lu_factor(A) + >>> P, L, U = torch.lu_unpack(LU, pivots) + >>> # P, L, U are the same as returned by linalg.lu + >>> P_, L_, U_ = torch.linalg.lu(A) + >>> torch.allclose(P, P_) and torch.allclose(L, L_) and torch.allclose(U, U_) + True + """ + ... +def margin_ranking_loss(input1: Tensor, input2: Tensor, target: Tensor, margin: _float = 0.0, reduction: _int = 1) -> Tensor: ... +@overload +def masked_fill(input: Tensor, mask: Tensor, value: Tensor) -> Tensor: ... +@overload +def masked_fill(input: Tensor, mask: Tensor, value: Union[Number, _complex]) -> Tensor: ... +def masked_scatter(input: Tensor, mask: Tensor, source: Tensor) -> Tensor: ... +def masked_select(input: Tensor, mask: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + masked_select(input, mask, *, out=None) -> Tensor + + Returns a new 1-D tensor which indexes the :attr:`input` tensor according to + the boolean mask :attr:`mask` which is a `BoolTensor`. + + The shapes of the :attr:`mask` tensor and the :attr:`input` tensor don't need + to match, but they must be :ref:`broadcastable `. + + .. note:: The returned tensor does **not** use the same storage + as the original tensor + + Args: + input (Tensor): the input tensor. + mask (BoolTensor): the tensor containing the binary mask to index with + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(3, 4) + >>> x + tensor([[ 0.3552, -2.3825, -0.8297, 0.3477], + [-1.2035, 1.2252, 0.5002, 0.6248], + [ 0.1307, -2.0608, 0.1244, 2.0139]]) + >>> mask = x.ge(0.5) + >>> mask + tensor([[False, False, False, False], + [False, True, True, True], + [False, False, False, True]]) + >>> torch.masked_select(x, mask) + tensor([ 1.2252, 0.5002, 0.6248, 2.0139]) + """ + ... +def matmul(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + matmul(input, other, *, out=None) -> Tensor + + Matrix product of two tensors. + + The behavior depends on the dimensionality of the tensors as follows: + + - If both tensors are 1-dimensional, the dot product (scalar) is returned. + - If both arguments are 2-dimensional, the matrix-matrix product is returned. + - If the first argument is 1-dimensional and the second argument is 2-dimensional, + a 1 is prepended to its dimension for the purpose of the matrix multiply. + After the matrix multiply, the prepended dimension is removed. + - If the first argument is 2-dimensional and the second argument is 1-dimensional, + the matrix-vector product is returned. + - If both arguments are at least 1-dimensional and at least one argument is + N-dimensional (where N > 2), then a batched matrix multiply is returned. If the first + argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the + batched matrix multiply and removed after. If the second argument is 1-dimensional, a + 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after. + The non-matrix (i.e. batch) dimensions are :ref:`broadcasted ` (and thus + must be broadcastable). For example, if :attr:`input` is a + :math:`(j \times 1 \times n \times n)` tensor and :attr:`other` is a :math:`(k \times n \times n)` + tensor, :attr:`out` will be a :math:`(j \times k \times n \times n)` tensor. + + Note that the broadcasting logic only looks at the batch dimensions when determining if the inputs + are broadcastable, and not the matrix dimensions. For example, if :attr:`input` is a + :math:`(j \times 1 \times n \times m)` tensor and :attr:`other` is a :math:`(k \times m \times p)` + tensor, these inputs are valid for broadcasting even though the final two dimensions (i.e. the + matrix dimensions) are different. :attr:`out` will be a :math:`(j \times k \times n \times p)` tensor. + + This operation has support for arguments with :ref:`sparse layouts`. In particular the + matrix-matrix (both arguments 2-dimensional) supports sparse arguments with the same restrictions + as :func:`torch.mm` + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + .. note:: + + The 1-dimensional dot product version of this function does not support an :attr:`out` parameter. + + Arguments: + input (Tensor): the first tensor to be multiplied + other (Tensor): the second tensor to be multiplied + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> # vector x vector + >>> tensor1 = torch.randn(3) + >>> tensor2 = torch.randn(3) + >>> torch.matmul(tensor1, tensor2).size() + torch.Size([]) + >>> # matrix x vector + >>> tensor1 = torch.randn(3, 4) + >>> tensor2 = torch.randn(4) + >>> torch.matmul(tensor1, tensor2).size() + torch.Size([3]) + >>> # batched matrix x broadcasted vector + >>> tensor1 = torch.randn(10, 3, 4) + >>> tensor2 = torch.randn(4) + >>> torch.matmul(tensor1, tensor2).size() + torch.Size([10, 3]) + >>> # batched matrix x batched matrix + >>> tensor1 = torch.randn(10, 3, 4) + >>> tensor2 = torch.randn(10, 4, 5) + >>> torch.matmul(tensor1, tensor2).size() + torch.Size([10, 3, 5]) + >>> # batched matrix x broadcasted matrix + >>> tensor1 = torch.randn(10, 3, 4) + >>> tensor2 = torch.randn(4, 5) + >>> torch.matmul(tensor1, tensor2).size() + torch.Size([10, 3, 5]) + """ + ... +def matrix_exp(input: Tensor) -> Tensor: + r""" + matrix_exp(A) -> Tensor + + Alias for :func:`torch.linalg.matrix_exp`. + """ + ... +def matrix_power(input: Tensor, n: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + matrix_power(input, n, *, out=None) -> Tensor + + Alias for :func:`torch.linalg.matrix_power` + """ + ... +@overload +def max(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + max(input) -> Tensor + + Returns the maximum value of all elements in the ``input`` tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6763, 0.7445, -2.2369]]) + >>> torch.max(a) + tensor(0.7445) + + .. function:: max(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each maximum value found + (argmax). + + If ``keepdim`` is ``True``, the output tensors are of the same size + as ``input`` except in the dimension ``dim`` where they are of size 1. + Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than ``input``. + + .. note:: If there are multiple maximal values in a reduced row then + the indices of the first maximal value are returned. + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + keepdim (bool, optional): whether the output tensor has :attr:`dim` retained or not. Default: ``False``. + + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (max, max_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-1.2360, -0.2942, -0.1222, 0.8475], + [ 1.1949, -1.1127, -2.2379, -0.6702], + [ 1.5717, -0.9207, 0.1297, -1.8768], + [-0.6172, 1.0036, -0.6060, -0.2432]]) + >>> torch.max(a, 1) + torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1])) + >>> a = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) + >>> a.max(dim=1, keepdim=True) + torch.return_types.max( + values=tensor([[2.], [4.]]), + indices=tensor([[1], [1]])) + >>> a.max(dim=1, keepdim=False) + torch.return_types.max( + values=tensor([2., 4.]), + indices=tensor([1, 1])) + + .. function:: max(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.maximum`. + """ + ... +@overload +def max(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + max(input) -> Tensor + + Returns the maximum value of all elements in the ``input`` tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6763, 0.7445, -2.2369]]) + >>> torch.max(a) + tensor(0.7445) + + .. function:: max(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each maximum value found + (argmax). + + If ``keepdim`` is ``True``, the output tensors are of the same size + as ``input`` except in the dimension ``dim`` where they are of size 1. + Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than ``input``. + + .. note:: If there are multiple maximal values in a reduced row then + the indices of the first maximal value are returned. + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + keepdim (bool, optional): whether the output tensor has :attr:`dim` retained or not. Default: ``False``. + + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (max, max_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-1.2360, -0.2942, -0.1222, 0.8475], + [ 1.1949, -1.1127, -2.2379, -0.6702], + [ 1.5717, -0.9207, 0.1297, -1.8768], + [-0.6172, 1.0036, -0.6060, -0.2432]]) + >>> torch.max(a, 1) + torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1])) + >>> a = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) + >>> a.max(dim=1, keepdim=True) + torch.return_types.max( + values=tensor([[2.], [4.]]), + indices=tensor([[1], [1]])) + >>> a.max(dim=1, keepdim=False) + torch.return_types.max( + values=tensor([2., 4.]), + indices=tensor([1, 1])) + + .. function:: max(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.maximum`. + """ + ... +@overload +def max(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.max: + r""" + max(input) -> Tensor + + Returns the maximum value of all elements in the ``input`` tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6763, 0.7445, -2.2369]]) + >>> torch.max(a) + tensor(0.7445) + + .. function:: max(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each maximum value found + (argmax). + + If ``keepdim`` is ``True``, the output tensors are of the same size + as ``input`` except in the dimension ``dim`` where they are of size 1. + Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than ``input``. + + .. note:: If there are multiple maximal values in a reduced row then + the indices of the first maximal value are returned. + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + keepdim (bool, optional): whether the output tensor has :attr:`dim` retained or not. Default: ``False``. + + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (max, max_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-1.2360, -0.2942, -0.1222, 0.8475], + [ 1.1949, -1.1127, -2.2379, -0.6702], + [ 1.5717, -0.9207, 0.1297, -1.8768], + [-0.6172, 1.0036, -0.6060, -0.2432]]) + >>> torch.max(a, 1) + torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1])) + >>> a = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) + >>> a.max(dim=1, keepdim=True) + torch.return_types.max( + values=tensor([[2.], [4.]]), + indices=tensor([[1], [1]])) + >>> a.max(dim=1, keepdim=False) + torch.return_types.max( + values=tensor([2., 4.]), + indices=tensor([1, 1])) + + .. function:: max(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.maximum`. + """ + ... +@overload +def max(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.max: + r""" + max(input) -> Tensor + + Returns the maximum value of all elements in the ``input`` tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6763, 0.7445, -2.2369]]) + >>> torch.max(a) + tensor(0.7445) + + .. function:: max(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the maximum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each maximum value found + (argmax). + + If ``keepdim`` is ``True``, the output tensors are of the same size + as ``input`` except in the dimension ``dim`` where they are of size 1. + Otherwise, ``dim`` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than ``input``. + + .. note:: If there are multiple maximal values in a reduced row then + the indices of the first maximal value are returned. + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + keepdim (bool, optional): whether the output tensor has :attr:`dim` retained or not. Default: ``False``. + + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (max, max_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-1.2360, -0.2942, -0.1222, 0.8475], + [ 1.1949, -1.1127, -2.2379, -0.6702], + [ 1.5717, -0.9207, 0.1297, -1.8768], + [-0.6172, 1.0036, -0.6060, -0.2432]]) + >>> torch.max(a, 1) + torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1])) + >>> a = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) + >>> a.max(dim=1, keepdim=True) + torch.return_types.max( + values=tensor([[2.], [4.]]), + indices=tensor([[1], [1]])) + >>> a.max(dim=1, keepdim=False) + torch.return_types.max( + values=tensor([2., 4.]), + indices=tensor([1, 1])) + + .. function:: max(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.maximum`. + """ + ... +def max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def max_pool1d_with_indices(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> tuple[Tensor, Tensor]: ... +def max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def maximum(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + maximum(input, other, *, out=None) -> Tensor + + Computes the element-wise maximum of :attr:`input` and :attr:`other`. + + .. note:: + If one of the elements being compared is a NaN, then that element is returned. + :func:`maximum` is not supported for tensors with complex dtypes. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor((1, 2, -1)) + >>> b = torch.tensor((3, 0, 4)) + >>> torch.maximum(a, b) + tensor([3, 2, 4]) + """ + ... +@overload +def mean(input: Tensor, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + mean(input, *, dtype=None) -> Tensor + + .. note:: + If the `input` tensor is empty, ``torch.mean()`` returns ``nan``. + This behavior is consistent with NumPy and follows the definition + that the mean over an empty set is undefined. + + + Returns the mean value of all elements in the :attr:`input` tensor. Input must be floating point or complex. + + Args: + input (Tensor): + the input tensor, either of floating point or complex dtype + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.2294, -0.5481, 1.3288]]) + >>> torch.mean(a) + tensor(0.3367) + + .. function:: mean(input, dim, keepdim=False, *, dtype=None, out=None) -> Tensor + :noindex: + + Returns the mean value of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + .. seealso:: + + :func:`torch.nanmean` computes the mean value of `non-NaN` elements. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.3841, 0.6320, 0.4254, -0.7384], + [-0.9644, 1.0131, -0.6549, -1.4279], + [-0.2951, -1.3350, -0.7694, 0.5600], + [ 1.0842, -0.9580, 0.3623, 0.2343]]) + >>> torch.mean(a, 1) + tensor([-0.0163, -0.5085, -0.4599, 0.1807]) + >>> torch.mean(a, 1, True) + tensor([[-0.0163], + [-0.5085], + [-0.4599], + [ 0.1807]]) + """ + ... +@overload +def mean(input: Tensor, dim: Optional[Union[_int, _size]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + mean(input, *, dtype=None) -> Tensor + + .. note:: + If the `input` tensor is empty, ``torch.mean()`` returns ``nan``. + This behavior is consistent with NumPy and follows the definition + that the mean over an empty set is undefined. + + + Returns the mean value of all elements in the :attr:`input` tensor. Input must be floating point or complex. + + Args: + input (Tensor): + the input tensor, either of floating point or complex dtype + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.2294, -0.5481, 1.3288]]) + >>> torch.mean(a) + tensor(0.3367) + + .. function:: mean(input, dim, keepdim=False, *, dtype=None, out=None) -> Tensor + :noindex: + + Returns the mean value of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + .. seealso:: + + :func:`torch.nanmean` computes the mean value of `non-NaN` elements. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.3841, 0.6320, 0.4254, -0.7384], + [-0.9644, 1.0131, -0.6549, -1.4279], + [-0.2951, -1.3350, -0.7694, 0.5600], + [ 1.0842, -0.9580, 0.3623, 0.2343]]) + >>> torch.mean(a, 1) + tensor([-0.0163, -0.5085, -0.4599, 0.1807]) + >>> torch.mean(a, 1, True) + tensor([[-0.0163], + [-0.5085], + [-0.4599], + [ 0.1807]]) + """ + ... +@overload +def mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + mean(input, *, dtype=None) -> Tensor + + .. note:: + If the `input` tensor is empty, ``torch.mean()`` returns ``nan``. + This behavior is consistent with NumPy and follows the definition + that the mean over an empty set is undefined. + + + Returns the mean value of all elements in the :attr:`input` tensor. Input must be floating point or complex. + + Args: + input (Tensor): + the input tensor, either of floating point or complex dtype + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.2294, -0.5481, 1.3288]]) + >>> torch.mean(a) + tensor(0.3367) + + .. function:: mean(input, dim, keepdim=False, *, dtype=None, out=None) -> Tensor + :noindex: + + Returns the mean value of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + .. seealso:: + + :func:`torch.nanmean` computes the mean value of `non-NaN` elements. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.3841, 0.6320, 0.4254, -0.7384], + [-0.9644, 1.0131, -0.6549, -1.4279], + [-0.2951, -1.3350, -0.7694, 0.5600], + [ 1.0842, -0.9580, 0.3623, 0.2343]]) + >>> torch.mean(a, 1) + tensor([-0.0163, -0.5085, -0.4599, 0.1807]) + >>> torch.mean(a, 1, True) + tensor([[-0.0163], + [-0.5085], + [-0.4599], + [ 0.1807]]) + """ + ... +@overload +def median(input: Tensor) -> Tensor: + r""" + median(input) -> Tensor + + Returns the median of the values in :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements. In this case the lower of the two medians is returned. To + compute the mean of both medians, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``median(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 1.5219, -1.5212, 0.2202]]) + >>> torch.median(a) + tensor(0.2202) + + .. function:: median(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, and ``indices`` contains the index of the median values found in the dimension :attr:`dim`. + + By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. + + If :attr:`keepdim` is ``True``, the output tensors are of the same size + as :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the outputs tensor having 1 fewer dimension than :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements in the dimension :attr:`dim`. In this case the lower of the + two medians is returned. To compute the mean of both medians in + :attr:`input`, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + ``indices`` does not necessarily contain the first occurrence of each + median value found, unless it is unique. + The exact implementation details are device-specific. + Do not expect the same result when run on CPU and GPU in general. + For the same reason do not expect the gradients to be deterministic. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.randn(4, 5) + >>> a + tensor([[ 0.2505, -0.3982, -0.9948, 0.3518, -1.3131], + [ 0.3180, -0.6993, 1.0436, 0.0438, 0.2270], + [-0.2751, 0.7303, 0.2192, 0.3321, 0.2488], + [ 1.0778, -1.9510, 0.7048, 0.4742, -0.7125]]) + >>> torch.median(a, 1) + torch.return_types.median(values=tensor([-0.3982, 0.2270, 0.2488, 0.4742]), indices=tensor([1, 4, 4, 3])) + """ + ... +@overload +def median(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.median: + r""" + median(input) -> Tensor + + Returns the median of the values in :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements. In this case the lower of the two medians is returned. To + compute the mean of both medians, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``median(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 1.5219, -1.5212, 0.2202]]) + >>> torch.median(a) + tensor(0.2202) + + .. function:: median(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, and ``indices`` contains the index of the median values found in the dimension :attr:`dim`. + + By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. + + If :attr:`keepdim` is ``True``, the output tensors are of the same size + as :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the outputs tensor having 1 fewer dimension than :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements in the dimension :attr:`dim`. In this case the lower of the + two medians is returned. To compute the mean of both medians in + :attr:`input`, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + ``indices`` does not necessarily contain the first occurrence of each + median value found, unless it is unique. + The exact implementation details are device-specific. + Do not expect the same result when run on CPU and GPU in general. + For the same reason do not expect the gradients to be deterministic. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.randn(4, 5) + >>> a + tensor([[ 0.2505, -0.3982, -0.9948, 0.3518, -1.3131], + [ 0.3180, -0.6993, 1.0436, 0.0438, 0.2270], + [-0.2751, 0.7303, 0.2192, 0.3321, 0.2488], + [ 1.0778, -1.9510, 0.7048, 0.4742, -0.7125]]) + >>> torch.median(a, 1) + torch.return_types.median(values=tensor([-0.3982, 0.2270, 0.2488, 0.4742]), indices=tensor([1, 4, 4, 3])) + """ + ... +@overload +def median(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.median: + r""" + median(input) -> Tensor + + Returns the median of the values in :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements. In this case the lower of the two medians is returned. To + compute the mean of both medians, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + This function produces deterministic (sub)gradients unlike ``median(dim=0)`` + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 1.5219, -1.5212, 0.2202]]) + >>> torch.median(a) + tensor(0.2202) + + .. function:: median(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, and ``indices`` contains the index of the median values found in the dimension :attr:`dim`. + + By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. + + If :attr:`keepdim` is ``True``, the output tensors are of the same size + as :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the outputs tensor having 1 fewer dimension than :attr:`input`. + + .. note:: + The median is not unique for :attr:`input` tensors with an even number + of elements in the dimension :attr:`dim`. In this case the lower of the + two medians is returned. To compute the mean of both medians in + :attr:`input`, use :func:`torch.quantile` with ``q=0.5`` instead. + + .. warning:: + ``indices`` does not necessarily contain the first occurrence of each + median value found, unless it is unique. + The exact implementation details are device-specific. + Do not expect the same result when run on CPU and GPU in general. + For the same reason do not expect the gradients to be deterministic. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.randn(4, 5) + >>> a + tensor([[ 0.2505, -0.3982, -0.9948, 0.3518, -1.3131], + [ 0.3180, -0.6993, 1.0436, 0.0438, 0.2270], + [-0.2751, 0.7303, 0.2192, 0.3321, 0.2488], + [ 1.0778, -1.9510, 0.7048, 0.4742, -0.7125]]) + >>> torch.median(a, 1) + torch.return_types.median(values=tensor([-0.3982, 0.2270, 0.2488, 0.4742]), indices=tensor([1, 4, 4, 3])) + """ + ... +@overload +def min(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + min(input) -> Tensor + + Returns the minimum value of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6750, 1.0857, 1.7197]]) + >>> torch.min(a) + tensor(0.6750) + + .. function:: min(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each minimum value found + (argmin). + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: If there are multiple minimal values in a reduced row then + the indices of the first minimal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the tuple of two output tensors (min, min_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.6248, 1.1334, -1.1899, -0.2803], + [-1.4644, -0.2635, -0.3651, 0.6134], + [ 0.2457, 0.0384, 1.0128, 0.7015], + [-0.1153, 2.9849, 2.1458, 0.5788]]) + >>> torch.min(a, 1) + torch.return_types.min(values=tensor([-1.1899, -1.4644, 0.0384, -0.1153]), indices=tensor([2, 0, 1, 0])) + + .. function:: min(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.minimum`. + """ + ... +@overload +def min(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + min(input) -> Tensor + + Returns the minimum value of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6750, 1.0857, 1.7197]]) + >>> torch.min(a) + tensor(0.6750) + + .. function:: min(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each minimum value found + (argmin). + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: If there are multiple minimal values in a reduced row then + the indices of the first minimal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the tuple of two output tensors (min, min_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.6248, 1.1334, -1.1899, -0.2803], + [-1.4644, -0.2635, -0.3651, 0.6134], + [ 0.2457, 0.0384, 1.0128, 0.7015], + [-0.1153, 2.9849, 2.1458, 0.5788]]) + >>> torch.min(a, 1) + torch.return_types.min(values=tensor([-1.1899, -1.4644, 0.0384, -0.1153]), indices=tensor([2, 0, 1, 0])) + + .. function:: min(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.minimum`. + """ + ... +@overload +def min(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.min: + r""" + min(input) -> Tensor + + Returns the minimum value of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6750, 1.0857, 1.7197]]) + >>> torch.min(a) + tensor(0.6750) + + .. function:: min(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each minimum value found + (argmin). + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: If there are multiple minimal values in a reduced row then + the indices of the first minimal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the tuple of two output tensors (min, min_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.6248, 1.1334, -1.1899, -0.2803], + [-1.4644, -0.2635, -0.3651, 0.6134], + [ 0.2457, 0.0384, 1.0128, 0.7015], + [-0.1153, 2.9849, 2.1458, 0.5788]]) + >>> torch.min(a, 1) + torch.return_types.min(values=tensor([-1.1899, -1.4644, 0.0384, -0.1153]), indices=tensor([2, 0, 1, 0])) + + .. function:: min(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.minimum`. + """ + ... +@overload +def min(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.min: + r""" + min(input) -> Tensor + + Returns the minimum value of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.6750, 1.0857, 1.7197]]) + >>> torch.min(a) + tensor(0.6750) + + .. function:: min(input, dim, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` is the minimum + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`. And ``indices`` is the index location of each minimum value found + (argmin). + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: If there are multiple minimal values in a reduced row then + the indices of the first minimal value are returned. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the tuple of two output tensors (min, min_indices) + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[-0.6248, 1.1334, -1.1899, -0.2803], + [-1.4644, -0.2635, -0.3651, 0.6134], + [ 0.2457, 0.0384, 1.0128, 0.7015], + [-0.1153, 2.9849, 2.1458, 0.5788]]) + >>> torch.min(a, 1) + torch.return_types.min(values=tensor([-1.1899, -1.4644, 0.0384, -0.1153]), indices=tensor([2, 0, 1, 0])) + + .. function:: min(input, other, *, out=None) -> Tensor + :noindex: + + See :func:`torch.minimum`. + """ + ... +def minimum(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + minimum(input, other, *, out=None) -> Tensor + + Computes the element-wise minimum of :attr:`input` and :attr:`other`. + + .. note:: + If one of the elements being compared is a NaN, then that element is returned. + :func:`minimum` is not supported for tensors with complex dtypes. + + Args: + input (Tensor): the input tensor. + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor((1, 2, -1)) + >>> b = torch.tensor((3, 0, 4)) + >>> torch.minimum(a, b) + tensor([1, 0, -1]) + """ + ... +def miopen_batch_norm(input: Tensor, weight: Tensor, bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, exponential_average_factor: _float, epsilon: _float) -> tuple[Tensor, Tensor, Tensor]: ... +def miopen_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool) -> Tensor: ... +def miopen_convolution_add_relu(input: Tensor, weight: Tensor, z: Tensor, alpha: Optional[Union[Number, _complex]], bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def miopen_convolution_relu(input: Tensor, weight: Tensor, bias: Optional[Tensor], stride: Sequence[Union[_int, SymInt]], padding: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def miopen_convolution_transpose(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Sequence[Union[_int, SymInt]], output_padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool) -> Tensor: ... +def miopen_depthwise_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt], benchmark: _bool, deterministic: _bool) -> Tensor: ... +def miopen_rnn(input: Tensor, weight: Optional[Union[tuple[Tensor, ...], list[Tensor]]], weight_stride0: _int, hx: Tensor, cx: Optional[Tensor], mode: _int, hidden_size: _int, num_layers: _int, batch_first: _bool, dropout: _float, train: _bool, bidirectional: _bool, batch_sizes: _size, dropout_state: Optional[Tensor]) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: ... +def mkldnn_adaptive_avg_pool2d(input: Tensor, output_size: Union[_int, _size], *, out: Optional[Tensor] = None) -> Tensor: ... +def mkldnn_convolution(input: Tensor, weight: Tensor, bias: Optional[Tensor], padding: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], dilation: Sequence[Union[_int, SymInt]], groups: Union[_int, SymInt]) -> Tensor: ... +def mkldnn_linear_backward_weights(grad_output: Tensor, input: Tensor, weight: Tensor, bias_defined: _bool) -> tuple[Tensor, Tensor]: ... +def mkldnn_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def mkldnn_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def mkldnn_rnn_layer(input: Tensor, weight0: Tensor, weight1: Tensor, weight2: Tensor, weight3: Tensor, hx_: Tensor, cx_: Tensor, reverse: _bool, batch_sizes: _size, mode: _int, hidden_size: _int, num_layers: _int, has_biases: _bool, bidirectional: _bool, batch_first: _bool, train: _bool) -> tuple[Tensor, Tensor, Tensor, Tensor]: ... +def mm(input: Tensor, mat2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + mm(input, mat2, *, out=None) -> Tensor + + Performs a matrix multiplication of the matrices :attr:`input` and :attr:`mat2`. + + If :attr:`input` is a :math:`(n \times m)` tensor, :attr:`mat2` is a + :math:`(m \times p)` tensor, :attr:`out` will be a :math:`(n \times p)` tensor. + + .. note:: This function does not :ref:`broadcast `. + For broadcasting matrix products, see :func:`torch.matmul`. + + Supports strided and sparse 2-D tensors as inputs, autograd with + respect to strided inputs. + + This operation has support for arguments with :ref:`sparse layouts`. + If :attr:`out` is provided its layout will be used. Otherwise, the result + layout will be deduced from that of :attr:`input`. + + + .. warning:: + Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, + or may not have autograd support. If you notice missing functionality please + open a feature request. + + This operator supports :ref:`TensorFloat32`. + + On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. + + Args: + input (Tensor): the first matrix to be matrix multiplied + mat2 (Tensor): the second matrix to be matrix multiplied + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> mat1 = torch.randn(2, 3) + >>> mat2 = torch.randn(3, 3) + >>> torch.mm(mat1, mat2) + tensor([[ 0.4851, 0.5037, -0.3633], + [-0.0760, -3.6705, 2.4784]]) + """ + ... +@overload +def mode(input: Tensor, dim: _int = -1, keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.mode: + r""" + mode(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + + Returns a namedtuple ``(values, indices)`` where ``values`` is the mode + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`, i.e. a value which appears most often + in that row, and ``indices`` is the index location of each mode value found. + + By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: This function is not defined for ``torch.cuda.Tensor`` yet. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> b = torch.tensor([[0, 0, 0, 2, 0, 0, 2], + ... [0, 3, 0, 0, 2, 0, 1], + ... [2, 2, 2, 0, 0, 0, 3], + ... [2, 2, 3, 0, 1, 1, 0], + ... [1, 1, 0, 0, 2, 0, 2]]) + >>> torch.mode(b, 0) + torch.return_types.mode( + values=tensor([0, 2, 0, 0, 0, 0, 2]), + indices=tensor([1, 3, 4, 4, 2, 4, 4])) + """ + ... +@overload +def mode(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.mode: + r""" + mode(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + + Returns a namedtuple ``(values, indices)`` where ``values`` is the mode + value of each row of the :attr:`input` tensor in the given dimension + :attr:`dim`, i.e. a value which appears most often + in that row, and ``indices`` is the index location of each mode value found. + + By default, :attr:`dim` is the last dimension of the :attr:`input` tensor. + + If :attr:`keepdim` is ``True``, the output tensors are of the same size as + :attr:`input` except in the dimension :attr:`dim` where they are of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting + in the output tensors having 1 fewer dimension than :attr:`input`. + + .. note:: This function is not defined for ``torch.cuda.Tensor`` yet. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out (tuple, optional): the result tuple of two output tensors (values, indices) + + Example:: + + >>> b = torch.tensor([[0, 0, 0, 2, 0, 0, 2], + ... [0, 3, 0, 0, 2, 0, 1], + ... [2, 2, 2, 0, 0, 0, 3], + ... [2, 2, 3, 0, 1, 1, 0], + ... [1, 1, 0, 0, 2, 0, 2]]) + >>> torch.mode(b, 0) + torch.return_types.mode( + values=tensor([0, 2, 0, 0, 0, 0, 2]), + indices=tensor([1, 3, 4, 4, 2, 4, 4])) + """ + ... +@overload +def moveaxis(input: Tensor, source: _int, destination: _int) -> Tensor: + r""" + moveaxis(input, source, destination) -> Tensor + + Alias for :func:`torch.movedim`. + + This function is equivalent to NumPy's moveaxis function. + + Examples:: + + >>> t = torch.randn(3,2,1) + >>> t + tensor([[[-0.3362], + [-0.8437]], + + [[-0.9627], + [ 0.1727]], + + [[ 0.5173], + [-0.1398]]]) + >>> torch.moveaxis(t, 1, 0).shape + torch.Size([2, 3, 1]) + >>> torch.moveaxis(t, 1, 0) + tensor([[[-0.3362], + [-0.9627], + [ 0.5173]], + + [[-0.8437], + [ 0.1727], + [-0.1398]]]) + >>> torch.moveaxis(t, (1, 2), (0, 1)).shape + torch.Size([2, 1, 3]) + >>> torch.moveaxis(t, (1, 2), (0, 1)) + tensor([[[-0.3362, -0.9627, 0.5173]], + + [[-0.8437, 0.1727, -0.1398]]]) + """ + ... +@overload +def moveaxis(input: Tensor, source: _size, destination: _size) -> Tensor: + r""" + moveaxis(input, source, destination) -> Tensor + + Alias for :func:`torch.movedim`. + + This function is equivalent to NumPy's moveaxis function. + + Examples:: + + >>> t = torch.randn(3,2,1) + >>> t + tensor([[[-0.3362], + [-0.8437]], + + [[-0.9627], + [ 0.1727]], + + [[ 0.5173], + [-0.1398]]]) + >>> torch.moveaxis(t, 1, 0).shape + torch.Size([2, 3, 1]) + >>> torch.moveaxis(t, 1, 0) + tensor([[[-0.3362], + [-0.9627], + [ 0.5173]], + + [[-0.8437], + [ 0.1727], + [-0.1398]]]) + >>> torch.moveaxis(t, (1, 2), (0, 1)).shape + torch.Size([2, 1, 3]) + >>> torch.moveaxis(t, (1, 2), (0, 1)) + tensor([[[-0.3362, -0.9627, 0.5173]], + + [[-0.8437, 0.1727, -0.1398]]]) + """ + ... +@overload +def movedim(input: Tensor, source: _int, destination: _int) -> Tensor: + r""" + movedim(input, source, destination) -> Tensor + + Moves the dimension(s) of :attr:`input` at the position(s) in :attr:`source` + to the position(s) in :attr:`destination`. + + Other dimensions of :attr:`input` that are not explicitly moved remain in + their original order and appear at the positions not specified in :attr:`destination`. + + Args: + input (Tensor): the input tensor. + source (int or tuple of ints): Original positions of the dims to move. These must be unique. + destination (int or tuple of ints): Destination positions for each of the original dims. These must also be unique. + + Examples:: + + >>> t = torch.randn(3,2,1) + >>> t + tensor([[[-0.3362], + [-0.8437]], + + [[-0.9627], + [ 0.1727]], + + [[ 0.5173], + [-0.1398]]]) + >>> torch.movedim(t, 1, 0).shape + torch.Size([2, 3, 1]) + >>> torch.movedim(t, 1, 0) + tensor([[[-0.3362], + [-0.9627], + [ 0.5173]], + + [[-0.8437], + [ 0.1727], + [-0.1398]]]) + >>> torch.movedim(t, (1, 2), (0, 1)).shape + torch.Size([2, 1, 3]) + >>> torch.movedim(t, (1, 2), (0, 1)) + tensor([[[-0.3362, -0.9627, 0.5173]], + + [[-0.8437, 0.1727, -0.1398]]]) + """ + ... +@overload +def movedim(input: Tensor, source: _size, destination: _size) -> Tensor: + r""" + movedim(input, source, destination) -> Tensor + + Moves the dimension(s) of :attr:`input` at the position(s) in :attr:`source` + to the position(s) in :attr:`destination`. + + Other dimensions of :attr:`input` that are not explicitly moved remain in + their original order and appear at the positions not specified in :attr:`destination`. + + Args: + input (Tensor): the input tensor. + source (int or tuple of ints): Original positions of the dims to move. These must be unique. + destination (int or tuple of ints): Destination positions for each of the original dims. These must also be unique. + + Examples:: + + >>> t = torch.randn(3,2,1) + >>> t + tensor([[[-0.3362], + [-0.8437]], + + [[-0.9627], + [ 0.1727]], + + [[ 0.5173], + [-0.1398]]]) + >>> torch.movedim(t, 1, 0).shape + torch.Size([2, 3, 1]) + >>> torch.movedim(t, 1, 0) + tensor([[[-0.3362], + [-0.9627], + [ 0.5173]], + + [[-0.8437], + [ 0.1727], + [-0.1398]]]) + >>> torch.movedim(t, (1, 2), (0, 1)).shape + torch.Size([2, 1, 3]) + >>> torch.movedim(t, (1, 2), (0, 1)) + tensor([[[-0.3362, -0.9627, 0.5173]], + + [[-0.8437, 0.1727, -0.1398]]]) + """ + ... +def msort(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + msort(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Sorts the elements of the :attr:`input` tensor along its first dimension + in ascending order by value. + + .. note:: `torch.msort(t)` is equivalent to `torch.sort(t, dim=0)[0]`. + See also :func:`torch.sort`. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.randn(3, 4) + >>> t + tensor([[-0.1321, 0.4370, -1.2631, -1.1289], + [-2.0527, -1.1250, 0.2275, 0.3077], + [-0.0881, -0.1259, -0.5495, 1.0284]]) + >>> torch.msort(t) + tensor([[-2.0527, -1.1250, -1.2631, -1.1289], + [-0.1321, -0.1259, -0.5495, 0.3077], + [-0.0881, 0.4370, 0.2275, 1.0284]]) + """ + ... +def mul(input: Union[Tensor, Number, _complex], other: Union[Tensor, Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + mul(input, other, *, out=None) -> Tensor + + Multiplies :attr:`input` by :attr:`other`. + + + .. math:: + \text{out}_i = \text{input}_i \times \text{other}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number) - the tensor or number to multiply input by. + + Keyword args: + out (Tensor, optional): the output tensor. + + Examples:: + + >>> a = torch.randn(3) + >>> a + tensor([ 0.2015, -0.4255, 2.6087]) + >>> torch.mul(a, 100) + tensor([ 20.1494, -42.5491, 260.8663]) + + >>> b = torch.randn(4, 1) + >>> b + tensor([[ 1.1207], + [-0.3137], + [ 0.0700], + [ 0.8378]]) + >>> c = torch.randn(1, 4) + >>> c + tensor([[ 0.5146, 0.1216, -0.5244, 2.2382]]) + >>> torch.mul(b, c) + tensor([[ 0.5767, 0.1363, -0.5877, 2.5083], + [-0.1614, -0.0382, 0.1645, -0.7021], + [ 0.0360, 0.0085, -0.0367, 0.1567], + [ 0.4312, 0.1019, -0.4394, 1.8753]]) + """ + ... +def multinomial(input: Tensor, num_samples: _int, replacement: _bool = False, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor + + Returns a tensor where each row contains :attr:`num_samples` indices sampled + from the multinomial (a stricter definition would be multivariate, + refer to :class:`torch.distributions.multinomial.Multinomial` for more details) + probability distribution located in the corresponding row + of tensor :attr:`input`. + + .. note:: + The rows of :attr:`input` do not need to sum to one (in which case we use + the values as weights), but must be non-negative, finite and have + a non-zero sum. + + Indices are ordered from left to right according to when each was sampled + (first samples are placed in first column). + + If :attr:`input` is a vector, :attr:`out` is a vector of size :attr:`num_samples`. + + If :attr:`input` is a matrix with `m` rows, :attr:`out` is an matrix of shape + :math:`(m \times \text{num\_samples})`. + + If replacement is ``True``, samples are drawn with replacement. + + If not, they are drawn without replacement, which means that when a + sample index is drawn for a row, it cannot be drawn again for that row. + + .. note:: + When drawn without replacement, :attr:`num_samples` must be lower than + number of non-zero elements in :attr:`input` (or the min number of non-zero + elements in each row of :attr:`input` if it is a matrix). + + Args: + input (Tensor): the input tensor containing probabilities + num_samples (int): number of samples to draw + replacement (bool, optional): whether to draw with replacement or not + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> weights = torch.tensor([0, 10, 3, 0], dtype=torch.float) # create a tensor of weights + >>> torch.multinomial(weights, 2) + tensor([1, 2]) + >>> torch.multinomial(weights, 5) # ERROR! + RuntimeError: cannot sample n_sample > prob_dist.size(-1) samples without replacement + >>> torch.multinomial(weights, 4, replacement=True) + tensor([ 2, 1, 1, 1]) + """ + ... +@overload +def multiply(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + multiply(input, other, *, out=None) + + Alias for :func:`torch.mul`. + """ + ... +@overload +def multiply(input: Tensor, other: Union[Number, _complex]) -> Tensor: + r""" + multiply(input, other, *, out=None) + + Alias for :func:`torch.mul`. + """ + ... +def mv(input: Tensor, vec: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + mv(input, vec, *, out=None) -> Tensor + + Performs a matrix-vector product of the matrix :attr:`input` and the vector + :attr:`vec`. + + If :attr:`input` is a :math:`(n \times m)` tensor, :attr:`vec` is a 1-D tensor of + size :math:`m`, :attr:`out` will be 1-D of size :math:`n`. + + .. note:: This function does not :ref:`broadcast `. + + Args: + input (Tensor): matrix to be multiplied + vec (Tensor): vector to be multiplied + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> mat = torch.randn(2, 3) + >>> vec = torch.randn(3) + >>> torch.mv(mat, vec) + tensor([ 1.0404, -0.6361]) + """ + ... +def mvlgamma(input: Tensor, p: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + mvlgamma(input, p, *, out=None) -> Tensor + + Alias for :func:`torch.special.multigammaln`. + """ + ... +def nan_to_num(input: Tensor, nan: Optional[_float] = None, posinf: Optional[_float] = None, neginf: Optional[_float] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None) -> Tensor + + Replaces :literal:`NaN`, positive infinity, and negative infinity values in :attr:`input` + with the values specified by :attr:`nan`, :attr:`posinf`, and :attr:`neginf`, respectively. + By default, :literal:`NaN`\ s are replaced with zero, positive infinity is replaced with the + greatest finite value representable by :attr:`input`'s dtype, and negative infinity + is replaced with the least finite value representable by :attr:`input`'s dtype. + + Args: + input (Tensor): the input tensor. + nan (Number, optional): the value to replace :literal:`NaN`\s with. Default is zero. + posinf (Number, optional): if a Number, the value to replace positive infinity values with. + If None, positive infinity values are replaced with the greatest finite value representable by :attr:`input`'s dtype. + Default is None. + neginf (Number, optional): if a Number, the value to replace negative infinity values with. + If None, negative infinity values are replaced with the lowest finite value representable by :attr:`input`'s dtype. + Default is None. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.tensor([float('nan'), float('inf'), -float('inf'), 3.14]) + >>> torch.nan_to_num(x) + tensor([ 0.0000e+00, 3.4028e+38, -3.4028e+38, 3.1400e+00]) + >>> torch.nan_to_num(x, nan=2.0) + tensor([ 2.0000e+00, 3.4028e+38, -3.4028e+38, 3.1400e+00]) + >>> torch.nan_to_num(x, nan=2.0, posinf=1.0) + tensor([ 2.0000e+00, 1.0000e+00, -3.4028e+38, 3.1400e+00]) + """ + ... +def nan_to_num_(input: Tensor, nan: Optional[_float] = None, posinf: Optional[_float] = None, neginf: Optional[_float] = None) -> Tensor: ... +def nanmean(input: Tensor, dim: Optional[Union[_int, _size]] = None, keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + nanmean(input, dim=None, keepdim=False, *, dtype=None, out=None) -> Tensor + + Computes the mean of all `non-NaN` elements along the specified dimensions. + Input must be floating point or complex. + + This function is identical to :func:`torch.mean` when there are no `NaN` values + in the :attr:`input` tensor. In the presence of `NaN`, :func:`torch.mean` will + propagate the `NaN` to the output whereas :func:`torch.nanmean` will ignore the + `NaN` values (`torch.nanmean(a)` is equivalent to `torch.mean(a[~a.isnan()])`). + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor, either of floating point or complex dtype + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + out (Tensor, optional): the output tensor. + + .. seealso:: + + :func:`torch.mean` computes the mean value, propagating `NaN`. + + Example:: + + >>> x = torch.tensor([[torch.nan, 1, 2], [1, 2, 3]]) + >>> x.mean() + tensor(nan) + >>> x.nanmean() + tensor(1.8000) + >>> x.mean(dim=0) + tensor([ nan, 1.5000, 2.5000]) + >>> x.nanmean(dim=0) + tensor([1.0000, 1.5000, 2.5000]) + + # If all elements in the reduced dimensions are NaN then the result is NaN + >>> torch.tensor([torch.nan]).nanmean() + tensor(nan) + """ + ... +@overload +def nanmedian(input: Tensor) -> Tensor: + r""" + nanmedian(input) -> Tensor + + Returns the median of the values in :attr:`input`, ignoring ``NaN`` values. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in :attr:`input`. + When :attr:`input` has one or more ``NaN`` values, :func:`torch.median` will always return ``NaN``, + while this function will return the median of the non-``NaN`` elements in :attr:`input`. + If all the elements in :attr:`input` are ``NaN`` it will also return ``NaN``. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.tensor([1, float('nan'), 3, 2]) + >>> a.median() + tensor(nan) + >>> a.nanmedian() + tensor(2.) + + .. function:: nanmedian(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, ignoring ``NaN`` values, and ``indices`` contains the index of the median values + found in the dimension :attr:`dim`. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in a reduced row. When a reduced row has + one or more ``NaN`` values, :func:`torch.median` will always reduce it to ``NaN``, while this function will reduce it to the + median of the non-``NaN`` elements. If all the elements in a reduced row are ``NaN`` then it will be reduced to ``NaN``, too. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.tensor([[2, 3, 1], [float('nan'), 1, float('nan')]]) + >>> a + tensor([[2., 3., 1.], + [nan, 1., nan]]) + >>> a.median(0) + torch.return_types.median(values=tensor([nan, 1., nan]), indices=tensor([1, 1, 1])) + >>> a.nanmedian(0) + torch.return_types.nanmedian(values=tensor([2., 1., 1.]), indices=tensor([0, 1, 0])) + """ + ... +@overload +def nanmedian(input: Tensor, dim: _int, keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.nanmedian: + r""" + nanmedian(input) -> Tensor + + Returns the median of the values in :attr:`input`, ignoring ``NaN`` values. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in :attr:`input`. + When :attr:`input` has one or more ``NaN`` values, :func:`torch.median` will always return ``NaN``, + while this function will return the median of the non-``NaN`` elements in :attr:`input`. + If all the elements in :attr:`input` are ``NaN`` it will also return ``NaN``. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.tensor([1, float('nan'), 3, 2]) + >>> a.median() + tensor(nan) + >>> a.nanmedian() + tensor(2.) + + .. function:: nanmedian(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, ignoring ``NaN`` values, and ``indices`` contains the index of the median values + found in the dimension :attr:`dim`. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in a reduced row. When a reduced row has + one or more ``NaN`` values, :func:`torch.median` will always reduce it to ``NaN``, while this function will reduce it to the + median of the non-``NaN`` elements. If all the elements in a reduced row are ``NaN`` then it will be reduced to ``NaN``, too. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.tensor([[2, 3, 1], [float('nan'), 1, float('nan')]]) + >>> a + tensor([[2., 3., 1.], + [nan, 1., nan]]) + >>> a.median(0) + torch.return_types.median(values=tensor([nan, 1., nan]), indices=tensor([1, 1, 1])) + >>> a.nanmedian(0) + torch.return_types.nanmedian(values=tensor([2., 1., 1.]), indices=tensor([0, 1, 0])) + """ + ... +@overload +def nanmedian(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.nanmedian: + r""" + nanmedian(input) -> Tensor + + Returns the median of the values in :attr:`input`, ignoring ``NaN`` values. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in :attr:`input`. + When :attr:`input` has one or more ``NaN`` values, :func:`torch.median` will always return ``NaN``, + while this function will return the median of the non-``NaN`` elements in :attr:`input`. + If all the elements in :attr:`input` are ``NaN`` it will also return ``NaN``. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.tensor([1, float('nan'), 3, 2]) + >>> a.median() + tensor(nan) + >>> a.nanmedian() + tensor(2.) + + .. function:: nanmedian(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor) + :noindex: + + Returns a namedtuple ``(values, indices)`` where ``values`` contains the median of each row of :attr:`input` + in the dimension :attr:`dim`, ignoring ``NaN`` values, and ``indices`` contains the index of the median values + found in the dimension :attr:`dim`. + + This function is identical to :func:`torch.median` when there are no ``NaN`` values in a reduced row. When a reduced row has + one or more ``NaN`` values, :func:`torch.median` will always reduce it to ``NaN``, while this function will reduce it to the + median of the non-``NaN`` elements. If all the elements in a reduced row are ``NaN`` then it will be reduced to ``NaN``, too. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + out ((Tensor, Tensor), optional): The first tensor will be populated with the median values and the second + tensor, which must have dtype long, with their indices in the dimension + :attr:`dim` of :attr:`input`. + + Example:: + + >>> a = torch.tensor([[2, 3, 1], [float('nan'), 1, float('nan')]]) + >>> a + tensor([[2., 3., 1.], + [nan, 1., nan]]) + >>> a.median(0) + torch.return_types.median(values=tensor([nan, 1., nan]), indices=tensor([1, 1, 1])) + >>> a.nanmedian(0) + torch.return_types.nanmedian(values=tensor([2., 1., 1.]), indices=tensor([0, 1, 0])) + """ + ... +@overload +def nanquantile(input: Tensor, q: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear", out: Optional[Tensor] = None) -> Tensor: + r""" + nanquantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) -> Tensor + + This is a variant of :func:`torch.quantile` that "ignores" ``NaN`` values, + computing the quantiles :attr:`q` as if ``NaN`` values in :attr:`input` did + not exist. If all values in a reduced row are ``NaN`` then the quantiles for + that reduction will be ``NaN``. See the documentation for :func:`torch.quantile`. + + Args: + input (Tensor): the input tensor. + q (float or Tensor): a scalar or 1D tensor of quantile values in the range [0, 1] + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword arguments: + interpolation (str): interpolation method to use when the desired quantile lies between two data points. + Can be ``linear``, ``lower``, ``higher``, ``midpoint`` and ``nearest``. + Default is ``linear``. + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.tensor([float('nan'), 1, 2]) + >>> t.quantile(0.5) + tensor(nan) + >>> t.nanquantile(0.5) + tensor(1.5000) + >>> t = torch.tensor([[float('nan'), float('nan')], [1, 2]]) + >>> t + tensor([[nan, nan], + [1., 2.]]) + >>> t.nanquantile(0.5, dim=0) + tensor([1., 2.]) + >>> t.nanquantile(0.5, dim=1) + tensor([ nan, 1.5000]) + """ + ... +@overload +def nanquantile(input: Tensor, q: _float, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear", out: Optional[Tensor] = None) -> Tensor: + r""" + nanquantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) -> Tensor + + This is a variant of :func:`torch.quantile` that "ignores" ``NaN`` values, + computing the quantiles :attr:`q` as if ``NaN`` values in :attr:`input` did + not exist. If all values in a reduced row are ``NaN`` then the quantiles for + that reduction will be ``NaN``. See the documentation for :func:`torch.quantile`. + + Args: + input (Tensor): the input tensor. + q (float or Tensor): a scalar or 1D tensor of quantile values in the range [0, 1] + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword arguments: + interpolation (str): interpolation method to use when the desired quantile lies between two data points. + Can be ``linear``, ``lower``, ``higher``, ``midpoint`` and ``nearest``. + Default is ``linear``. + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.tensor([float('nan'), 1, 2]) + >>> t.quantile(0.5) + tensor(nan) + >>> t.nanquantile(0.5) + tensor(1.5000) + >>> t = torch.tensor([[float('nan'), float('nan')], [1, 2]]) + >>> t + tensor([[nan, nan], + [1., 2.]]) + >>> t.nanquantile(0.5, dim=0) + tensor([1., 2.]) + >>> t.nanquantile(0.5, dim=1) + tensor([ nan, 1.5000]) + """ + ... +def nansum(input: Tensor, dim: Optional[Union[_int, _size]] = None, keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + nansum(input, *, dtype=None) -> Tensor + + Returns the sum of all elements, treating Not a Numbers (NaNs) as zero. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.tensor([1., 2., float('nan'), 4.]) + >>> torch.nansum(a) + tensor(7.) + + .. function:: nansum(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the sum of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`, treating Not a Numbers (NaNs) as zero. + If :attr:`dim` is a list of dimensions, reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> torch.nansum(torch.tensor([1., float("nan")])) + tensor(1.) + >>> a = torch.tensor([[1, 2], [3., float("nan")]]) + >>> torch.nansum(a) + tensor(6.) + >>> torch.nansum(a, dim=0) + tensor([4., 2.]) + >>> torch.nansum(a, dim=1) + tensor([3., 3.]) + """ + ... +@overload +def narrow(input: Tensor, dim: _int, start: Tensor, length: Union[_int, SymInt]) -> Tensor: + r""" + narrow(input, dim, start, length) -> Tensor + + Returns a new tensor that is a narrowed version of :attr:`input` tensor. The + dimension :attr:`dim` is input from :attr:`start` to ``start + length``. The + returned tensor and :attr:`input` tensor share the same underlying storage. + + Args: + input (Tensor): the tensor to narrow + dim (int): the dimension along which to narrow + start (int or Tensor): index of the element to start the narrowed dimension + from. Can be negative, which means indexing from the end of `dim`. If + `Tensor`, it must be an 0-dim integral `Tensor` (bools not allowed) + length (int): length of the narrowed dimension, must be weakly positive + + Example:: + + >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + >>> torch.narrow(x, 0, 0, 2) + tensor([[ 1, 2, 3], + [ 4, 5, 6]]) + >>> torch.narrow(x, 1, 1, 2) + tensor([[ 2, 3], + [ 5, 6], + [ 8, 9]]) + >>> torch.narrow(x, -1, torch.tensor(-1), 1) + tensor([[3], + [6], + [9]]) + """ + ... +@overload +def narrow(input: Tensor, dim: _int, start: Union[_int, SymInt], length: Union[_int, SymInt]) -> Tensor: + r""" + narrow(input, dim, start, length) -> Tensor + + Returns a new tensor that is a narrowed version of :attr:`input` tensor. The + dimension :attr:`dim` is input from :attr:`start` to ``start + length``. The + returned tensor and :attr:`input` tensor share the same underlying storage. + + Args: + input (Tensor): the tensor to narrow + dim (int): the dimension along which to narrow + start (int or Tensor): index of the element to start the narrowed dimension + from. Can be negative, which means indexing from the end of `dim`. If + `Tensor`, it must be an 0-dim integral `Tensor` (bools not allowed) + length (int): length of the narrowed dimension, must be weakly positive + + Example:: + + >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + >>> torch.narrow(x, 0, 0, 2) + tensor([[ 1, 2, 3], + [ 4, 5, 6]]) + >>> torch.narrow(x, 1, 1, 2) + tensor([[ 2, 3], + [ 5, 6], + [ 8, 9]]) + >>> torch.narrow(x, -1, torch.tensor(-1), 1) + tensor([[3], + [6], + [9]]) + """ + ... +def narrow_copy(input: Tensor, dim: _int, start: Union[_int, SymInt], length: Union[_int, SymInt], *, out: Optional[Tensor] = None) -> Tensor: + r""" + narrow_copy(input, dim, start, length, *, out=None) -> Tensor + + Same as :meth:`Tensor.narrow` except this returns a copy rather + than shared storage. This is primarily for sparse tensors, which + do not have a shared-storage narrow method. + + Args: + input (Tensor): the tensor to narrow + dim (int): the dimension along which to narrow + start (int): index of the element to start the narrowed dimension from. Can + be negative, which means indexing from the end of `dim` + length (int): length of the narrowed dimension, must be weakly positive + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + >>> torch.narrow_copy(x, 0, 0, 2) + tensor([[ 1, 2, 3], + [ 4, 5, 6]]) + >>> torch.narrow_copy(x, 1, 1, 2) + tensor([[ 2, 3], + [ 5, 6], + [ 8, 9]]) + >>> s = torch.arange(16).reshape(2, 2, 2, 2).to_sparse(2) + >>> torch.narrow_copy(s, 0, 0, 1) + tensor(indices=tensor([[0, 0], + [0, 1]]), + values=tensor([[[0, 1], + [2, 3]], + + [[4, 5], + [6, 7]]]), + size=(1, 2, 2, 2), nnz=2, layout=torch.sparse_coo) + + .. seealso:: + + :func:`torch.narrow` for a non copy variant + """ + ... +def native_batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, momentum: _float, eps: _float, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> tuple[Tensor, Tensor, Tensor]: ... +def native_channel_shuffle(input: Tensor, groups: Union[_int, SymInt]) -> Tensor: ... +def native_dropout(input: Tensor, p: _float, train: Optional[_bool]) -> tuple[Tensor, Tensor]: ... +def native_group_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], N: Union[_int, SymInt], C: Union[_int, SymInt], HxW: Union[_int, SymInt], group: _int, eps: _float) -> tuple[Tensor, Tensor, Tensor]: ... +def native_layer_norm(input: Tensor, normalized_shape: Sequence[Union[_int, SymInt]], weight: Optional[Tensor], bias: Optional[Tensor], eps: _float) -> tuple[Tensor, Tensor, Tensor]: ... +@overload +def native_norm(input: Tensor, p: Optional[Union[Number, _complex]], dim: Union[_int, _size], keepdim: _bool, dtype: Optional[_dtype]) -> Tensor: ... +@overload +def native_norm(input: Tensor, p: Union[Number, _complex] = 2) -> Tensor: ... +@overload +def ne(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ne(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \neq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is not equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.ne(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, True], [True, False]]) + """ + ... +@overload +def ne(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + ne(input, other, *, out=None) -> Tensor + + Computes :math:`\text{input} \neq \text{other}` element-wise. + + + The second argument can be a number or a tensor whose shape is + :ref:`broadcastable ` with the first argument. + + Args: + input (Tensor): the tensor to compare + other (Tensor or float): the tensor or value to compare + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + A boolean tensor that is True where :attr:`input` is not equal to :attr:`other` and False elsewhere + + Example:: + + >>> torch.ne(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]])) + tensor([[False, True], [True, False]]) + """ + ... +def neg(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + neg(input, *, out=None) -> Tensor + + Returns a new tensor with the negative of the elements of :attr:`input`. + + .. math:: + \text{out} = -1 \times \text{input} + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(5) + >>> a + tensor([ 0.0090, -0.2262, -0.0682, -0.2866, 0.3940]) + >>> torch.neg(a) + tensor([-0.0090, 0.2262, 0.0682, 0.2866, -0.3940]) + """ + ... +def neg_(input: Tensor) -> Tensor: ... +def negative(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + negative(input, *, out=None) -> Tensor + + Alias for :func:`torch.neg` + """ + ... +def negative_(input: Tensor) -> Tensor: ... +def nextafter(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + nextafter(input, other, *, out=None) -> Tensor + + Return the next floating-point value after :attr:`input` towards :attr:`other`, elementwise. + + The shapes of ``input`` and ``other`` must be + :ref:`broadcastable `. + + Args: + input (Tensor): the first input tensor + other (Tensor): the second input tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> eps = torch.finfo(torch.float32).eps + >>> torch.nextafter(torch.tensor([1.0, 2.0]), torch.tensor([2.0, 1.0])) == torch.tensor([eps + 1, 2 - eps]) + tensor([True, True]) + """ + ... +@overload +def nonzero(input: Tensor, *, as_tuple: Literal[False] = False, out: Optional[Tensor] = None) -> Tensor: + r""" + nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors + + .. note:: + :func:`torch.nonzero(..., as_tuple=False) ` (default) returns a + 2-D tensor where each row is the index for a nonzero value. + + :func:`torch.nonzero(..., as_tuple=True) ` returns a tuple of 1-D + index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]`` + gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor + contains nonzero indices for a certain dimension. + + See below for more details on the two behaviors. + + When :attr:`input` is on CUDA, :func:`torch.nonzero() ` causes + host-device synchronization. + + **When** :attr:`as_tuple` **is** ``False`` **(default)**: + + Returns a tensor containing the indices of all non-zero elements of + :attr:`input`. Each row in the result contains the indices of a non-zero + element in :attr:`input`. The result is sorted lexicographically, with + the last index changing the fastest (C-style). + + If :attr:`input` has :math:`n` dimensions, then the resulting indices tensor + :attr:`out` is of size :math:`(z \times n)`, where :math:`z` is the total number of + non-zero elements in the :attr:`input` tensor. + + **When** :attr:`as_tuple` **is** ``True``: + + Returns a tuple of 1-D tensors, one for each dimension in :attr:`input`, + each containing the indices (in that dimension) of all non-zero elements of + :attr:`input` . + + If :attr:`input` has :math:`n` dimensions, then the resulting tuple contains :math:`n` + tensors of size :math:`z`, where :math:`z` is the total number of + non-zero elements in the :attr:`input` tensor. + + As a special case, when :attr:`input` has zero dimensions and a nonzero scalar + value, it is treated as a one-dimensional tensor with one element. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (LongTensor, optional): the output tensor containing indices + + Returns: + LongTensor or tuple of LongTensor: If :attr:`as_tuple` is ``False``, the output + tensor containing indices. If :attr:`as_tuple` is ``True``, one 1-D tensor for + each dimension, containing the indices of each nonzero element along that + dimension. + + Example:: + + >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1])) + tensor([[ 0], + [ 1], + [ 2], + [ 4]]) + >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0], + ... [0.0, 0.4, 0.0, 0.0], + ... [0.0, 0.0, 1.2, 0.0], + ... [0.0, 0.0, 0.0,-0.4]])) + tensor([[ 0, 0], + [ 1, 1], + [ 2, 2], + [ 3, 3]]) + >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1]), as_tuple=True) + (tensor([0, 1, 2, 4]),) + >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0], + ... [0.0, 0.4, 0.0, 0.0], + ... [0.0, 0.0, 1.2, 0.0], + ... [0.0, 0.0, 0.0,-0.4]]), as_tuple=True) + (tensor([0, 1, 2, 3]), tensor([0, 1, 2, 3])) + >>> torch.nonzero(torch.tensor(5), as_tuple=True) + (tensor([0]),) + """ + ... +@overload +def nonzero(input: Tensor, *, as_tuple: Literal[True]) -> tuple[Tensor, ...]: + r""" + nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors + + .. note:: + :func:`torch.nonzero(..., as_tuple=False) ` (default) returns a + 2-D tensor where each row is the index for a nonzero value. + + :func:`torch.nonzero(..., as_tuple=True) ` returns a tuple of 1-D + index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]`` + gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor + contains nonzero indices for a certain dimension. + + See below for more details on the two behaviors. + + When :attr:`input` is on CUDA, :func:`torch.nonzero() ` causes + host-device synchronization. + + **When** :attr:`as_tuple` **is** ``False`` **(default)**: + + Returns a tensor containing the indices of all non-zero elements of + :attr:`input`. Each row in the result contains the indices of a non-zero + element in :attr:`input`. The result is sorted lexicographically, with + the last index changing the fastest (C-style). + + If :attr:`input` has :math:`n` dimensions, then the resulting indices tensor + :attr:`out` is of size :math:`(z \times n)`, where :math:`z` is the total number of + non-zero elements in the :attr:`input` tensor. + + **When** :attr:`as_tuple` **is** ``True``: + + Returns a tuple of 1-D tensors, one for each dimension in :attr:`input`, + each containing the indices (in that dimension) of all non-zero elements of + :attr:`input` . + + If :attr:`input` has :math:`n` dimensions, then the resulting tuple contains :math:`n` + tensors of size :math:`z`, where :math:`z` is the total number of + non-zero elements in the :attr:`input` tensor. + + As a special case, when :attr:`input` has zero dimensions and a nonzero scalar + value, it is treated as a one-dimensional tensor with one element. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (LongTensor, optional): the output tensor containing indices + + Returns: + LongTensor or tuple of LongTensor: If :attr:`as_tuple` is ``False``, the output + tensor containing indices. If :attr:`as_tuple` is ``True``, one 1-D tensor for + each dimension, containing the indices of each nonzero element along that + dimension. + + Example:: + + >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1])) + tensor([[ 0], + [ 1], + [ 2], + [ 4]]) + >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0], + ... [0.0, 0.4, 0.0, 0.0], + ... [0.0, 0.0, 1.2, 0.0], + ... [0.0, 0.0, 0.0,-0.4]])) + tensor([[ 0, 0], + [ 1, 1], + [ 2, 2], + [ 3, 3]]) + >>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1]), as_tuple=True) + (tensor([0, 1, 2, 4]),) + >>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0], + ... [0.0, 0.4, 0.0, 0.0], + ... [0.0, 0.0, 1.2, 0.0], + ... [0.0, 0.0, 0.0,-0.4]]), as_tuple=True) + (tensor([0, 1, 2, 3]), tensor([0, 1, 2, 3])) + >>> torch.nonzero(torch.tensor(5), as_tuple=True) + (tensor([0]),) + """ + ... +def nonzero_static(input: Tensor, *, size: Union[_int, SymInt], fill_value: _int = -1, out: Optional[Tensor] = None) -> Tensor: ... +def norm_except_dim(v: Tensor, pow: _int = 2, dim: _int = 0) -> Tensor: ... +@overload +def normal(mean: Tensor, std: Tensor, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + normal(mean, std, *, generator=None, out=None) -> Tensor + + Returns a tensor of random numbers drawn from separate normal distributions + whose mean and standard deviation are given. + + The :attr:`mean` is a tensor with the mean of + each output element's normal distribution + + The :attr:`std` is a tensor with the standard deviation of + each output element's normal distribution + + The shapes of :attr:`mean` and :attr:`std` don't need to match, but the + total number of elements in each tensor need to be the same. + + .. note:: When the shapes do not match, the shape of :attr:`mean` + is used as the shape for the returned output tensor + + .. note:: When :attr:`std` is a CUDA tensor, this function synchronizes + its device with the CPU. + + Args: + mean (Tensor): the tensor of per-element means + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1)) + tensor([ 1.0425, 3.5672, 2.7969, 4.2925, 4.7229, 6.2134, + 8.0505, 8.1408, 9.0563, 10.0566]) + + .. function:: normal(mean=0.0, std, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means are shared among all drawn + elements. + + Args: + mean (float, optional): the mean for all distributions + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=0.5, std=torch.arange(1., 6.)) + tensor([-1.2793, -1.0732, -2.0687, 5.1177, -1.2303]) + + .. function:: normal(mean, std=1.0, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the standard deviations are shared among + all drawn elements. + + Args: + mean (Tensor): the tensor of per-element means + std (float, optional): the standard deviation for all distributions + + Keyword args: + out (Tensor, optional): the output tensor + + Example:: + + >>> torch.normal(mean=torch.arange(1., 6.)) + tensor([ 1.1552, 2.6148, 2.6535, 5.8318, 4.2361]) + + .. function:: normal(mean, std, size, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means and standard deviations are shared + among all drawn elements. The resulting tensor has size given by :attr:`size`. + + Args: + mean (float): the mean for all distributions + std (float): the standard deviation for all distributions + size (int...): a sequence of integers defining the shape of the output tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(2, 3, size=(1, 4)) + tensor([[-1.3987, -1.9544, 3.6048, 0.7909]]) + """ + ... +@overload +def normal(mean: Tensor, std: _float = 1, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + normal(mean, std, *, generator=None, out=None) -> Tensor + + Returns a tensor of random numbers drawn from separate normal distributions + whose mean and standard deviation are given. + + The :attr:`mean` is a tensor with the mean of + each output element's normal distribution + + The :attr:`std` is a tensor with the standard deviation of + each output element's normal distribution + + The shapes of :attr:`mean` and :attr:`std` don't need to match, but the + total number of elements in each tensor need to be the same. + + .. note:: When the shapes do not match, the shape of :attr:`mean` + is used as the shape for the returned output tensor + + .. note:: When :attr:`std` is a CUDA tensor, this function synchronizes + its device with the CPU. + + Args: + mean (Tensor): the tensor of per-element means + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1)) + tensor([ 1.0425, 3.5672, 2.7969, 4.2925, 4.7229, 6.2134, + 8.0505, 8.1408, 9.0563, 10.0566]) + + .. function:: normal(mean=0.0, std, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means are shared among all drawn + elements. + + Args: + mean (float, optional): the mean for all distributions + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=0.5, std=torch.arange(1., 6.)) + tensor([-1.2793, -1.0732, -2.0687, 5.1177, -1.2303]) + + .. function:: normal(mean, std=1.0, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the standard deviations are shared among + all drawn elements. + + Args: + mean (Tensor): the tensor of per-element means + std (float, optional): the standard deviation for all distributions + + Keyword args: + out (Tensor, optional): the output tensor + + Example:: + + >>> torch.normal(mean=torch.arange(1., 6.)) + tensor([ 1.1552, 2.6148, 2.6535, 5.8318, 4.2361]) + + .. function:: normal(mean, std, size, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means and standard deviations are shared + among all drawn elements. The resulting tensor has size given by :attr:`size`. + + Args: + mean (float): the mean for all distributions + std (float): the standard deviation for all distributions + size (int...): a sequence of integers defining the shape of the output tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(2, 3, size=(1, 4)) + tensor([[-1.3987, -1.9544, 3.6048, 0.7909]]) + """ + ... +@overload +def normal(mean: _float, std: Tensor, *, generator: Optional[Generator] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + normal(mean, std, *, generator=None, out=None) -> Tensor + + Returns a tensor of random numbers drawn from separate normal distributions + whose mean and standard deviation are given. + + The :attr:`mean` is a tensor with the mean of + each output element's normal distribution + + The :attr:`std` is a tensor with the standard deviation of + each output element's normal distribution + + The shapes of :attr:`mean` and :attr:`std` don't need to match, but the + total number of elements in each tensor need to be the same. + + .. note:: When the shapes do not match, the shape of :attr:`mean` + is used as the shape for the returned output tensor + + .. note:: When :attr:`std` is a CUDA tensor, this function synchronizes + its device with the CPU. + + Args: + mean (Tensor): the tensor of per-element means + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1)) + tensor([ 1.0425, 3.5672, 2.7969, 4.2925, 4.7229, 6.2134, + 8.0505, 8.1408, 9.0563, 10.0566]) + + .. function:: normal(mean=0.0, std, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means are shared among all drawn + elements. + + Args: + mean (float, optional): the mean for all distributions + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=0.5, std=torch.arange(1., 6.)) + tensor([-1.2793, -1.0732, -2.0687, 5.1177, -1.2303]) + + .. function:: normal(mean, std=1.0, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the standard deviations are shared among + all drawn elements. + + Args: + mean (Tensor): the tensor of per-element means + std (float, optional): the standard deviation for all distributions + + Keyword args: + out (Tensor, optional): the output tensor + + Example:: + + >>> torch.normal(mean=torch.arange(1., 6.)) + tensor([ 1.1552, 2.6148, 2.6535, 5.8318, 4.2361]) + + .. function:: normal(mean, std, size, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means and standard deviations are shared + among all drawn elements. The resulting tensor has size given by :attr:`size`. + + Args: + mean (float): the mean for all distributions + std (float): the standard deviation for all distributions + size (int...): a sequence of integers defining the shape of the output tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(2, 3, size=(1, 4)) + tensor([[-1.3987, -1.9544, 3.6048, 0.7909]]) + """ + ... +@overload +def normal(mean: _float, std: _float, size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator] = None, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + normal(mean, std, *, generator=None, out=None) -> Tensor + + Returns a tensor of random numbers drawn from separate normal distributions + whose mean and standard deviation are given. + + The :attr:`mean` is a tensor with the mean of + each output element's normal distribution + + The :attr:`std` is a tensor with the standard deviation of + each output element's normal distribution + + The shapes of :attr:`mean` and :attr:`std` don't need to match, but the + total number of elements in each tensor need to be the same. + + .. note:: When the shapes do not match, the shape of :attr:`mean` + is used as the shape for the returned output tensor + + .. note:: When :attr:`std` is a CUDA tensor, this function synchronizes + its device with the CPU. + + Args: + mean (Tensor): the tensor of per-element means + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1)) + tensor([ 1.0425, 3.5672, 2.7969, 4.2925, 4.7229, 6.2134, + 8.0505, 8.1408, 9.0563, 10.0566]) + + .. function:: normal(mean=0.0, std, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means are shared among all drawn + elements. + + Args: + mean (float, optional): the mean for all distributions + std (Tensor): the tensor of per-element standard deviations + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(mean=0.5, std=torch.arange(1., 6.)) + tensor([-1.2793, -1.0732, -2.0687, 5.1177, -1.2303]) + + .. function:: normal(mean, std=1.0, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the standard deviations are shared among + all drawn elements. + + Args: + mean (Tensor): the tensor of per-element means + std (float, optional): the standard deviation for all distributions + + Keyword args: + out (Tensor, optional): the output tensor + + Example:: + + >>> torch.normal(mean=torch.arange(1., 6.)) + tensor([ 1.1552, 2.6148, 2.6535, 5.8318, 4.2361]) + + .. function:: normal(mean, std, size, *, out=None) -> Tensor + :noindex: + + Similar to the function above, but the means and standard deviations are shared + among all drawn elements. The resulting tensor has size given by :attr:`size`. + + Args: + mean (float): the mean for all distributions + std (float): the standard deviation for all distributions + size (int...): a sequence of integers defining the shape of the output tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.normal(2, 3, size=(1, 4)) + tensor([[-1.3987, -1.9544, 3.6048, 0.7909]]) + """ + ... +@overload +def not_equal(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + not_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.ne`. + """ + ... +@overload +def not_equal(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + not_equal(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.ne`. + """ + ... +@overload +def nuclear_norm(input: Tensor, dim: Union[_int, _size], keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: ... +@overload +def nuclear_norm(input: Tensor, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: ... +def numel(self: Tensor) -> _int: + r""" + numel(input: Tensor) -> int + + Returns the total number of elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> a = torch.randn(1, 2, 3, 4, 5) + >>> torch.numel(a) + 120 + >>> a = torch.zeros(4,4) + >>> torch.numel(a) + 16 + """ + ... +@overload +def ones(size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `1`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.ones(2, 3) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.]]) + + >>> torch.ones(5) + tensor([ 1., 1., 1., 1., 1.]) + """ + ... +@overload +def ones(*size: Union[_int, SymInt], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `1`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.ones(2, 3) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.]]) + + >>> torch.ones(5) + tensor([ 1., 1., 1., 1., 1.]) + """ + ... +@overload +def ones(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `1`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.ones(2, 3) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.]]) + + >>> torch.ones(5) + tensor([ 1., 1., 1., 1., 1.]) + """ + ... +@overload +def ones(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `1`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword arguments: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.ones(2, 3) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.]]) + + >>> torch.ones(5) + tensor([ 1., 1., 1., 1., 1.]) + """ + ... +def ones_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + ones_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor filled with the scalar value `1`, with the same size as + :attr:`input`. ``torch.ones_like(input)`` is equivalent to + ``torch.ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. + + .. warning:: + As of 0.4, this function does not support an :attr:`out` keyword. As an alternative, + the old ``torch.ones_like(input, out=output)`` is equivalent to + ``torch.ones(input.size(), out=output)``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + + Keyword arguments: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + Example:: + + >>> input = torch.empty(2, 3) + >>> torch.ones_like(input) + tensor([[ 1., 1., 1.], + [ 1., 1., 1.]]) + """ + ... +def orgqr(input: Tensor, input2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + orgqr(input, tau) -> Tensor + + Alias for :func:`torch.linalg.householder_product`. + """ + ... +def ormqr(input: Tensor, input2: Tensor, input3: Tensor, left: _bool = True, transpose: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + ormqr(input, tau, other, left=True, transpose=False, *, out=None) -> Tensor + + Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix. + + Multiplies a :math:`m \times n` matrix `C` (given by :attr:`other`) with a matrix `Q`, + where `Q` is represented using Householder reflectors `(input, tau)`. + See `Representation of Orthogonal or Unitary Matrices`_ for further details. + + If :attr:`left` is `True` then `op(Q)` times `C` is computed, otherwise the result is `C` times `op(Q)`. + When :attr:`left` is `True`, the implicit matrix `Q` has size :math:`m \times m`. + It has size :math:`n \times n` otherwise. + If :attr:`transpose` is `True` then `op` is the conjugate transpose operation, otherwise it's a no-op. + + Supports inputs of float, double, cfloat and cdouble dtypes. + Also supports batched inputs, and, if the input is batched, the output is batched with the same dimensions. + + .. seealso:: + :func:`torch.geqrf` can be used to form the Householder representation `(input, tau)` of matrix `Q` + from the QR decomposition. + + .. note:: + This function supports backward but it is only fast when ``(input, tau)`` do not require gradients + and/or ``tau.size(-1)`` is very small. + `` + + Args: + input (Tensor): tensor of shape `(*, mn, k)` where `*` is zero or more batch dimensions + and `mn` equals to `m` or `n` depending on the :attr:`left`. + tau (Tensor): tensor of shape `(*, min(mn, k))` where `*` is zero or more batch dimensions. + other (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions. + left (bool): controls the order of multiplication. + transpose (bool): controls whether the matrix `Q` is conjugate transposed or not. + + Keyword args: + out (Tensor, optional): the output Tensor. Ignored if `None`. Default: `None`. + + .. _Representation of Orthogonal or Unitary Matrices: + https://www.netlib.org/lapack/lug/node128.html + """ + ... +def outer(input: Tensor, vec2: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + outer(input, vec2, *, out=None) -> Tensor + + Outer product of :attr:`input` and :attr:`vec2`. + If :attr:`input` is a vector of size :math:`n` and :attr:`vec2` is a vector of + size :math:`m`, then :attr:`out` must be a matrix of size :math:`(n \times m)`. + + .. note:: This function does not :ref:`broadcast `. + + Args: + input (Tensor): 1-D input vector + vec2 (Tensor): 1-D input vector + + Keyword args: + out (Tensor, optional): optional output matrix + + Example:: + + >>> v1 = torch.arange(1., 5.) + >>> v2 = torch.arange(1., 4.) + >>> torch.outer(v1, v2) + tensor([[ 1., 2., 3.], + [ 2., 4., 6.], + [ 3., 6., 9.], + [ 4., 8., 12.]]) + """ + ... +def pairwise_distance(x1: Tensor, x2: Tensor, p: _float = 2, eps: _float = 1e-06, keepdim: _bool = False) -> Tensor: ... +def pdist(input: Tensor, p: _float = 2) -> Tensor: ... +def permute(input: Tensor, dims: _size) -> Tensor: + r""" + permute(input, dims) -> Tensor + + Returns a view of the original tensor :attr:`input` with its dimensions permuted. + + Args: + input (Tensor): the input tensor. + dims (tuple of int): The desired ordering of dimensions + + Example: + >>> x = torch.randn(2, 3, 5) + >>> x.size() + torch.Size([2, 3, 5]) + >>> torch.permute(x, (2, 0, 1)).size() + torch.Size([5, 2, 3]) + """ + ... +def permute_copy(input: Tensor, dims: _size, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.permute`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def pinverse(input: Tensor, rcond: _float = 1e-15) -> Tensor: + r""" + pinverse(input, rcond=1e-15) -> Tensor + + Alias for :func:`torch.linalg.pinv` + """ + ... +def pixel_shuffle(input: Tensor, upscale_factor: _int) -> Tensor: ... +def pixel_unshuffle(input: Tensor, downscale_factor: _int) -> Tensor: ... +def poisson(input: Tensor, generator: Optional[Generator] = None) -> Tensor: + r""" + poisson(input, generator=None) -> Tensor + + Returns a tensor of the same size as :attr:`input` with each element + sampled from a Poisson distribution with rate parameter given by the corresponding + element in :attr:`input` i.e., + + .. math:: + \text{out}_i \sim \text{Poisson}(\text{input}_i) + + :attr:`input` must be non-negative. + + Args: + input (Tensor): the input tensor containing the rates of the Poisson distribution + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + + Example:: + + >>> rates = torch.rand(4, 4) * 5 # rate parameter between 0 and 5 + >>> torch.poisson(rates) + tensor([[9., 1., 3., 5.], + [8., 6., 6., 0.], + [0., 4., 5., 3.], + [2., 1., 4., 2.]]) + """ + ... +def poisson_nll_loss(input: Tensor, target: Tensor, log_input: _bool, full: _bool, eps: _float, reduction: _int) -> Tensor: ... +def polar(abs: Tensor, angle: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + polar(abs, angle, *, out=None) -> Tensor + + Constructs a complex tensor whose elements are Cartesian coordinates + corresponding to the polar coordinates with absolute value :attr:`abs` and angle + :attr:`angle`. + + .. math:: + \text{out} = \text{abs} \cdot \cos(\text{angle}) + \text{abs} \cdot \sin(\text{angle}) \cdot j + + .. note:: + `torch.polar` is similar to + `std::polar `_ + and does not compute the polar decomposition + of a complex tensor like Python's `cmath.polar` and SciPy's `linalg.polar` do. + The behavior of this function is undefined if `abs` is negative or NaN, or if `angle` is + infinite. + + + Args: + abs (Tensor): The absolute value the complex tensor. Must be float or double. + angle (Tensor): The angle of the complex tensor. Must be same dtype as + :attr:`abs`. + + Keyword args: + out (Tensor): If the inputs are ``torch.float32``, must be + ``torch.complex64``. If the inputs are ``torch.float64``, must be + ``torch.complex128``. + + Example:: + + >>> import numpy as np + >>> abs = torch.tensor([1, 2], dtype=torch.float64) + >>> angle = torch.tensor([np.pi / 2, 5 * np.pi / 4], dtype=torch.float64) + >>> z = torch.polar(abs, angle) + >>> z + tensor([(0.0000+1.0000j), (-1.4142-1.4142j)], dtype=torch.complex128) + """ + ... +def polygamma(n: _int, input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + polygamma(n, input, *, out=None) -> Tensor + + Alias for :func:`torch.special.polygamma`. + """ + ... +def positive(input: Tensor) -> Tensor: + r""" + positive(input) -> Tensor + + Returns :attr:`input`. + Throws a runtime error if :attr:`input` is a bool tensor. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> t = torch.randn(5) + >>> t + tensor([ 0.0090, -0.2262, -0.0682, -0.2866, 0.3940]) + >>> torch.positive(t) + tensor([ 0.0090, -0.2262, -0.0682, -0.2866, 0.3940]) + """ + ... +@overload +def pow(input: Tensor, exponent: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + pow(input, exponent, *, out=None) -> Tensor + + Takes the power of each element in :attr:`input` with :attr:`exponent` and + returns a tensor with the result. + + :attr:`exponent` can be either a single ``float`` number or a `Tensor` + with the same number of elements as :attr:`input`. + + When :attr:`exponent` is a scalar value, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ \text{exponent} + + When :attr:`exponent` is a tensor, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ {\text{exponent}_i} + + When :attr:`exponent` is a tensor, the shapes of :attr:`input` + and :attr:`exponent` must be :ref:`broadcastable `. + + Args: + input (Tensor): the input tensor. + exponent (float or tensor): the exponent value + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.4331, 1.2475, 0.6834, -0.2791]) + >>> torch.pow(a, 2) + tensor([ 0.1875, 1.5561, 0.4670, 0.0779]) + >>> exp = torch.arange(1., 5.) + + >>> a = torch.arange(1., 5.) + >>> a + tensor([ 1., 2., 3., 4.]) + >>> exp + tensor([ 1., 2., 3., 4.]) + >>> torch.pow(a, exp) + tensor([ 1., 4., 27., 256.]) + + .. function:: pow(self, exponent, *, out=None) -> Tensor + :noindex: + + :attr:`self` is a scalar ``float`` value, and :attr:`exponent` is a tensor. + The returned tensor :attr:`out` is of the same shape as :attr:`exponent` + + The operation applied is: + + .. math:: + \text{out}_i = \text{self} ^ {\text{exponent}_i} + + Args: + self (float): the scalar base value for the power operation + exponent (Tensor): the exponent tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> exp = torch.arange(1., 5.) + >>> base = 2 + >>> torch.pow(base, exp) + tensor([ 2., 4., 8., 16.]) + """ + ... +@overload +def pow(self: Union[Number, _complex], exponent: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + pow(input, exponent, *, out=None) -> Tensor + + Takes the power of each element in :attr:`input` with :attr:`exponent` and + returns a tensor with the result. + + :attr:`exponent` can be either a single ``float`` number or a `Tensor` + with the same number of elements as :attr:`input`. + + When :attr:`exponent` is a scalar value, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ \text{exponent} + + When :attr:`exponent` is a tensor, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ {\text{exponent}_i} + + When :attr:`exponent` is a tensor, the shapes of :attr:`input` + and :attr:`exponent` must be :ref:`broadcastable `. + + Args: + input (Tensor): the input tensor. + exponent (float or tensor): the exponent value + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.4331, 1.2475, 0.6834, -0.2791]) + >>> torch.pow(a, 2) + tensor([ 0.1875, 1.5561, 0.4670, 0.0779]) + >>> exp = torch.arange(1., 5.) + + >>> a = torch.arange(1., 5.) + >>> a + tensor([ 1., 2., 3., 4.]) + >>> exp + tensor([ 1., 2., 3., 4.]) + >>> torch.pow(a, exp) + tensor([ 1., 4., 27., 256.]) + + .. function:: pow(self, exponent, *, out=None) -> Tensor + :noindex: + + :attr:`self` is a scalar ``float`` value, and :attr:`exponent` is a tensor. + The returned tensor :attr:`out` is of the same shape as :attr:`exponent` + + The operation applied is: + + .. math:: + \text{out}_i = \text{self} ^ {\text{exponent}_i} + + Args: + self (float): the scalar base value for the power operation + exponent (Tensor): the exponent tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> exp = torch.arange(1., 5.) + >>> base = 2 + >>> torch.pow(base, exp) + tensor([ 2., 4., 8., 16.]) + """ + ... +@overload +def pow(input: Tensor, exponent: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + pow(input, exponent, *, out=None) -> Tensor + + Takes the power of each element in :attr:`input` with :attr:`exponent` and + returns a tensor with the result. + + :attr:`exponent` can be either a single ``float`` number or a `Tensor` + with the same number of elements as :attr:`input`. + + When :attr:`exponent` is a scalar value, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ \text{exponent} + + When :attr:`exponent` is a tensor, the operation applied is: + + .. math:: + \text{out}_i = x_i ^ {\text{exponent}_i} + + When :attr:`exponent` is a tensor, the shapes of :attr:`input` + and :attr:`exponent` must be :ref:`broadcastable `. + + Args: + input (Tensor): the input tensor. + exponent (float or tensor): the exponent value + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.4331, 1.2475, 0.6834, -0.2791]) + >>> torch.pow(a, 2) + tensor([ 0.1875, 1.5561, 0.4670, 0.0779]) + >>> exp = torch.arange(1., 5.) + + >>> a = torch.arange(1., 5.) + >>> a + tensor([ 1., 2., 3., 4.]) + >>> exp + tensor([ 1., 2., 3., 4.]) + >>> torch.pow(a, exp) + tensor([ 1., 4., 27., 256.]) + + .. function:: pow(self, exponent, *, out=None) -> Tensor + :noindex: + + :attr:`self` is a scalar ``float`` value, and :attr:`exponent` is a tensor. + The returned tensor :attr:`out` is of the same shape as :attr:`exponent` + + The operation applied is: + + .. math:: + \text{out}_i = \text{self} ^ {\text{exponent}_i} + + Args: + self (float): the scalar base value for the power operation + exponent (Tensor): the exponent tensor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> exp = torch.arange(1., 5.) + >>> base = 2 + >>> torch.pow(base, exp) + tensor([ 2., 4., 8., 16.]) + """ + ... +def prelu(input: Tensor, weight: Tensor) -> Tensor: ... +@overload +def prod(input: Tensor, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + prod(input: Tensor, *, dtype: Optional[_dtype]) -> Tensor + + Returns the product of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[-0.8020, 0.5428, -1.5854]]) + >>> torch.prod(a) + tensor(0.6902) + + .. function:: prod(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the product of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensor having 1 fewer dimension than :attr:`input`. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 2) + >>> a + tensor([[ 0.5261, -0.3837], + [ 1.1857, -0.2498], + [-1.1646, 0.0705], + [ 1.1131, -1.0629]]) + >>> torch.prod(a, 1) + tensor([-0.2018, -0.2962, -0.0821, -1.1831]) + """ + ... +@overload +def prod(input: Tensor, dim: _int, keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + prod(input: Tensor, *, dtype: Optional[_dtype]) -> Tensor + + Returns the product of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[-0.8020, 0.5428, -1.5854]]) + >>> torch.prod(a) + tensor(0.6902) + + .. function:: prod(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the product of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensor having 1 fewer dimension than :attr:`input`. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 2) + >>> a + tensor([[ 0.5261, -0.3837], + [ 1.1857, -0.2498], + [-1.1646, 0.0705], + [ 1.1131, -1.0629]]) + >>> torch.prod(a, 1) + tensor([-0.2018, -0.2962, -0.0821, -1.1831]) + """ + ... +@overload +def prod(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + prod(input: Tensor, *, dtype: Optional[_dtype]) -> Tensor + + Returns the product of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[-0.8020, 0.5428, -1.5854]]) + >>> torch.prod(a) + tensor(0.6902) + + .. function:: prod(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the product of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in + the output tensor having 1 fewer dimension than :attr:`input`. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 2) + >>> a + tensor([[ 0.5261, -0.3837], + [ 1.1857, -0.2498], + [-1.1646, 0.0705], + [ 1.1131, -1.0629]]) + >>> torch.prod(a, 1) + tensor([-0.2018, -0.2962, -0.0821, -1.1831]) + """ + ... +def promote_types(type1: _dtype, type2: _dtype) -> _dtype: + r""" + promote_types(type1, type2) -> dtype + + Returns the :class:`torch.dtype` with the smallest size and scalar kind that is + not smaller nor of lower kind than either `type1` or `type2`. See type promotion + :ref:`documentation ` for more information on the type + promotion logic. + + Args: + type1 (:class:`torch.dtype`) + type2 (:class:`torch.dtype`) + + Example:: + + >>> torch.promote_types(torch.int32, torch.float32) + torch.float32 + >>> torch.promote_types(torch.uint8, torch.long) + torch.long + """ + ... +def put(input: Tensor, index: Tensor, source: Tensor, accumulate: _bool = False) -> Tensor: ... +def q_per_channel_axis(input: Tensor) -> _int: ... +def q_per_channel_scales(input: Tensor) -> Tensor: ... +def q_per_channel_zero_points(input: Tensor) -> Tensor: ... +def q_scale(input: Tensor) -> _float: ... +def q_zero_point(input: Tensor) -> _int: ... +def qr(input: Tensor, some: _bool = True, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.qr: + r""" + qr(input: Tensor, some: bool = True, *, out: Union[Tensor, Tuple[Tensor, ...], List[Tensor], None]) -> (Tensor, Tensor) + + Computes the QR decomposition of a matrix or a batch of matrices :attr:`input`, + and returns a namedtuple (Q, R) of tensors such that :math:`\text{input} = Q R` + with :math:`Q` being an orthogonal matrix or batch of orthogonal matrices and + :math:`R` being an upper triangular matrix or batch of upper triangular matrices. + + If :attr:`some` is ``True``, then this function returns the thin (reduced) QR factorization. + Otherwise, if :attr:`some` is ``False``, this function returns the complete QR factorization. + + .. warning:: + + :func:`torch.qr` is deprecated in favor of :func:`torch.linalg.qr` + and will be removed in a future PyTorch release. The boolean parameter :attr:`some` has been + replaced with a string parameter :attr:`mode`. + + ``Q, R = torch.qr(A)`` should be replaced with + + .. code:: python + + Q, R = torch.linalg.qr(A) + + ``Q, R = torch.qr(A, some=False)`` should be replaced with + + .. code:: python + + Q, R = torch.linalg.qr(A, mode="complete") + + .. warning:: + If you plan to backpropagate through QR, note that the current backward implementation + is only well-defined when the first :math:`\min(input.size(-1), input.size(-2))` + columns of :attr:`input` are linearly independent. + This behavior will probably change once QR supports pivoting. + + .. note:: This function uses LAPACK for CPU inputs and MAGMA for CUDA inputs, + and may produce different (valid) decompositions on different device types + or different platforms. + + Args: + input (Tensor): the input tensor of size :math:`(*, m, n)` where `*` is zero or more + batch dimensions consisting of matrices of dimension :math:`m \times n`. + some (bool, optional): Set to ``True`` for reduced QR decomposition and ``False`` for + complete QR decomposition. If `k = min(m, n)` then: + + * ``some=True`` : returns `(Q, R)` with dimensions (m, k), (k, n) (default) + + * ``'some=False'``: returns `(Q, R)` with dimensions (m, m), (m, n) + + Keyword args: + out (tuple, optional): tuple of `Q` and `R` tensors. + The dimensions of `Q` and `R` are detailed in the description of :attr:`some` above. + + Example:: + + >>> a = torch.tensor([[12., -51, 4], [6, 167, -68], [-4, 24, -41]]) + >>> q, r = torch.qr(a) + >>> q + tensor([[-0.8571, 0.3943, 0.3314], + [-0.4286, -0.9029, -0.0343], + [ 0.2857, -0.1714, 0.9429]]) + >>> r + tensor([[ -14.0000, -21.0000, 14.0000], + [ 0.0000, -175.0000, 70.0000], + [ 0.0000, 0.0000, -35.0000]]) + >>> torch.mm(q, r).round() + tensor([[ 12., -51., 4.], + [ 6., 167., -68.], + [ -4., 24., -41.]]) + >>> torch.mm(q.t(), q).round() + tensor([[ 1., 0., 0.], + [ 0., 1., -0.], + [ 0., -0., 1.]]) + >>> a = torch.randn(3, 4, 5) + >>> q, r = torch.qr(a, some=False) + >>> torch.allclose(torch.matmul(q, r), a) + True + >>> torch.allclose(torch.matmul(q.mT, q), torch.eye(5)) + True + """ + ... +@overload +def quantile(input: Tensor, q: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear", out: Optional[Tensor] = None) -> Tensor: + r""" + quantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) -> Tensor + + Computes the q-th quantiles of each row of the :attr:`input` tensor along the dimension :attr:`dim`. + + To compute the quantile, we map q in [0, 1] to the range of indices [0, n] to find the location + of the quantile in the sorted input. If the quantile lies between two data points ``a < b`` with + indices ``i`` and ``j`` in the sorted order, result is computed according to the given + :attr:`interpolation` method as follows: + + - ``linear``: ``a + (b - a) * fraction``, where ``fraction`` is the fractional part of the computed quantile index. + - ``lower``: ``a``. + - ``higher``: ``b``. + - ``nearest``: ``a`` or ``b``, whichever's index is closer to the computed quantile index (rounding down for .5 fractions). + - ``midpoint``: ``(a + b) / 2``. + + If :attr:`q` is a 1D tensor, the first dimension of the output represents the quantiles and has size + equal to the size of :attr:`q`, the remaining dimensions are what remains from the reduction. + + .. note:: + By default :attr:`dim` is ``None`` resulting in the :attr:`input` tensor being flattened before computation. + + Args: + input (Tensor): the input tensor. + q (float or Tensor): a scalar or 1D tensor of values in the range [0, 1]. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword arguments: + interpolation (str): interpolation method to use when the desired quantile lies between two data points. + Can be ``linear``, ``lower``, ``higher``, ``midpoint`` and ``nearest``. + Default is ``linear``. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(2, 3) + >>> a + tensor([[ 0.0795, -1.2117, 0.9765], + [ 1.1707, 0.6706, 0.4884]]) + >>> q = torch.tensor([0.25, 0.5, 0.75]) + >>> torch.quantile(a, q, dim=1, keepdim=True) + tensor([[[-0.5661], + [ 0.5795]], + + [[ 0.0795], + [ 0.6706]], + + [[ 0.5280], + [ 0.9206]]]) + >>> torch.quantile(a, q, dim=1, keepdim=True).shape + torch.Size([3, 2, 1]) + >>> a = torch.arange(4.) + >>> a + tensor([0., 1., 2., 3.]) + >>> torch.quantile(a, 0.6, interpolation='linear') + tensor(1.8000) + >>> torch.quantile(a, 0.6, interpolation='lower') + tensor(1.) + >>> torch.quantile(a, 0.6, interpolation='higher') + tensor(2.) + >>> torch.quantile(a, 0.6, interpolation='midpoint') + tensor(1.5000) + >>> torch.quantile(a, 0.6, interpolation='nearest') + tensor(2.) + >>> torch.quantile(a, 0.4, interpolation='nearest') + tensor(1.) + """ + ... +@overload +def quantile(input: Tensor, q: _float, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear", out: Optional[Tensor] = None) -> Tensor: + r""" + quantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) -> Tensor + + Computes the q-th quantiles of each row of the :attr:`input` tensor along the dimension :attr:`dim`. + + To compute the quantile, we map q in [0, 1] to the range of indices [0, n] to find the location + of the quantile in the sorted input. If the quantile lies between two data points ``a < b`` with + indices ``i`` and ``j`` in the sorted order, result is computed according to the given + :attr:`interpolation` method as follows: + + - ``linear``: ``a + (b - a) * fraction``, where ``fraction`` is the fractional part of the computed quantile index. + - ``lower``: ``a``. + - ``higher``: ``b``. + - ``nearest``: ``a`` or ``b``, whichever's index is closer to the computed quantile index (rounding down for .5 fractions). + - ``midpoint``: ``(a + b) / 2``. + + If :attr:`q` is a 1D tensor, the first dimension of the output represents the quantiles and has size + equal to the size of :attr:`q`, the remaining dimensions are what remains from the reduction. + + .. note:: + By default :attr:`dim` is ``None`` resulting in the :attr:`input` tensor being flattened before computation. + + Args: + input (Tensor): the input tensor. + q (float or Tensor): a scalar or 1D tensor of values in the range [0, 1]. + dim (int): the dimension to reduce. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword arguments: + interpolation (str): interpolation method to use when the desired quantile lies between two data points. + Can be ``linear``, ``lower``, ``higher``, ``midpoint`` and ``nearest``. + Default is ``linear``. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(2, 3) + >>> a + tensor([[ 0.0795, -1.2117, 0.9765], + [ 1.1707, 0.6706, 0.4884]]) + >>> q = torch.tensor([0.25, 0.5, 0.75]) + >>> torch.quantile(a, q, dim=1, keepdim=True) + tensor([[[-0.5661], + [ 0.5795]], + + [[ 0.0795], + [ 0.6706]], + + [[ 0.5280], + [ 0.9206]]]) + >>> torch.quantile(a, q, dim=1, keepdim=True).shape + torch.Size([3, 2, 1]) + >>> a = torch.arange(4.) + >>> a + tensor([0., 1., 2., 3.]) + >>> torch.quantile(a, 0.6, interpolation='linear') + tensor(1.8000) + >>> torch.quantile(a, 0.6, interpolation='lower') + tensor(1.) + >>> torch.quantile(a, 0.6, interpolation='higher') + tensor(2.) + >>> torch.quantile(a, 0.6, interpolation='midpoint') + tensor(1.5000) + >>> torch.quantile(a, 0.6, interpolation='nearest') + tensor(2.) + >>> torch.quantile(a, 0.4, interpolation='nearest') + tensor(1.) + """ + ... +def quantize_per_channel(input: Tensor, scales: Tensor, zero_points: Tensor, axis: _int, dtype: _dtype) -> Tensor: + r""" + quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor + + Converts a float tensor to a per-channel quantized tensor with given scales and zero points. + + Arguments: + input (Tensor): float tensor to quantize + scales (Tensor): float 1D tensor of scales to use, size should match ``input.size(axis)`` + zero_points (int): integer 1D tensor of offset to use, size should match ``input.size(axis)`` + axis (int): dimension on which apply per-channel quantization + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32`` + + Returns: + Tensor: A newly quantized tensor + + Example:: + + >>> x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]]) + >>> torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8) + tensor([[-1., 0.], + [ 1., 2.]], size=(2, 2), dtype=torch.quint8, + quantization_scheme=torch.per_channel_affine, + scale=tensor([0.1000, 0.0100], dtype=torch.float64), + zero_point=tensor([10, 0]), axis=0) + >>> torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8).int_repr() + tensor([[ 0, 10], + [100, 200]], dtype=torch.uint8) + """ + ... +@overload +def quantize_per_tensor(input: Tensor, scale: Tensor, zero_point: Tensor, dtype: _dtype) -> Tensor: + r""" + quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor + + Converts a float tensor to a quantized tensor with given scale and zero point. + + Arguments: + input (Tensor): float tensor or list of tensors to quantize + scale (float or Tensor): scale to apply in quantization formula + zero_point (int or Tensor): offset in integer value that maps to float zero + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32`` + + Returns: + Tensor: A newly quantized tensor or list of quantized tensors. + + Example:: + + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8).int_repr() + tensor([ 0, 10, 20, 30], dtype=torch.uint8) + >>> torch.quantize_per_tensor([torch.tensor([-1.0, 0.0]), torch.tensor([-2.0, 2.0])], + >>> torch.tensor([0.1, 0.2]), torch.tensor([10, 20]), torch.quint8) + (tensor([-1., 0.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10), + tensor([-2., 2.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=20)) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.tensor(0.1), torch.tensor(10), torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.10, zero_point=10) + """ + ... +@overload +def quantize_per_tensor(input: Tensor, scale: _float, zero_point: _int, dtype: _dtype) -> Tensor: + r""" + quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor + + Converts a float tensor to a quantized tensor with given scale and zero point. + + Arguments: + input (Tensor): float tensor or list of tensors to quantize + scale (float or Tensor): scale to apply in quantization formula + zero_point (int or Tensor): offset in integer value that maps to float zero + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32`` + + Returns: + Tensor: A newly quantized tensor or list of quantized tensors. + + Example:: + + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8).int_repr() + tensor([ 0, 10, 20, 30], dtype=torch.uint8) + >>> torch.quantize_per_tensor([torch.tensor([-1.0, 0.0]), torch.tensor([-2.0, 2.0])], + >>> torch.tensor([0.1, 0.2]), torch.tensor([10, 20]), torch.quint8) + (tensor([-1., 0.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10), + tensor([-2., 2.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=20)) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.tensor(0.1), torch.tensor(10), torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.10, zero_point=10) + """ + ... +@overload +def quantize_per_tensor(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], scales: Tensor, zero_points: Tensor, dtype: _dtype) -> tuple[Tensor, ...]: + r""" + quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor + + Converts a float tensor to a quantized tensor with given scale and zero point. + + Arguments: + input (Tensor): float tensor or list of tensors to quantize + scale (float or Tensor): scale to apply in quantization formula + zero_point (int or Tensor): offset in integer value that maps to float zero + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8``, ``torch.qint32`` + + Returns: + Tensor: A newly quantized tensor or list of quantized tensors. + + Example:: + + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8).int_repr() + tensor([ 0, 10, 20, 30], dtype=torch.uint8) + >>> torch.quantize_per_tensor([torch.tensor([-1.0, 0.0]), torch.tensor([-2.0, 2.0])], + >>> torch.tensor([0.1, 0.2]), torch.tensor([10, 20]), torch.quint8) + (tensor([-1., 0.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.1, zero_point=10), + tensor([-2., 2.], size=(2,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=20)) + >>> torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.tensor(0.1), torch.tensor(10), torch.quint8) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.10, zero_point=10) + """ + ... +def quantize_per_tensor_dynamic(input: Tensor, dtype: _dtype, reduce_range: _bool) -> Tensor: + r""" + quantize_per_tensor_dynamic(input, dtype, reduce_range) -> Tensor + + Converts a float tensor to a quantized tensor with scale and zero_point calculated + dynamically based on the input. + + Arguments: + input (Tensor): float tensor or list of tensors to quantize + dtype (:class:`torch.dtype`): the desired data type of returned tensor. + Has to be one of the quantized dtypes: ``torch.quint8``, ``torch.qint8`` + reduce_range (bool): a flag to indicate whether to reduce the range of quantized + data by 1 bit, it's required to avoid instruction overflow for some hardwares + + Returns: + Tensor: A newly (dynamically) quantized tensor + + Example:: + + >>> t = torch.quantize_per_tensor_dynamic(torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.quint8, False) + >>> print(t) + tensor([-1., 0., 1., 2.], size=(4,), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.011764705882352941, + zero_point=85) + >>> t.int_repr() + tensor([ 0, 85, 170, 255], dtype=torch.uint8) + """ + ... +def quantized_batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], mean: Tensor, var: Tensor, eps: _float, output_scale: _float, output_zero_point: _int) -> Tensor: + r""" + quantized_batch_norm(input, weight=None, bias=None, mean, var, eps, output_scale, output_zero_point) -> Tensor + + Applies batch normalization on a 4D (NCHW) quantized tensor. + + .. math:: + + y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta + + Arguments: + input (Tensor): quantized tensor + weight (Tensor): float tensor that corresponds to the gamma, size C + bias (Tensor): float tensor that corresponds to the beta, size C + mean (Tensor): float mean value in batch normalization, size C + var (Tensor): float tensor for variance, size C + eps (float): a value added to the denominator for numerical stability. + output_scale (float): output quantized tensor scale + output_zero_point (int): output quantized tensor zero_point + + Returns: + Tensor: A quantized tensor with batch normalization applied. + + Example:: + + >>> qx = torch.quantize_per_tensor(torch.rand(2, 2, 2, 2), 1.5, 3, torch.quint8) + >>> torch.quantized_batch_norm(qx, torch.ones(2), torch.zeros(2), torch.rand(2), torch.rand(2), 0.00001, 0.2, 2) + tensor([[[[-0.2000, -0.2000], + [ 1.6000, -0.2000]], + + [[-0.4000, -0.4000], + [-0.4000, 0.6000]]], + + + [[[-0.2000, -0.2000], + [-0.2000, -0.2000]], + + [[ 0.6000, -0.4000], + [ 0.6000, -0.4000]]]], size=(2, 2, 2, 2), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=0.2, zero_point=2) + """ + ... +def quantized_gru_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Union[Number, _complex], scale_hh: Union[Number, _complex], zero_point_ih: Union[Number, _complex], zero_point_hh: Union[Number, _complex]) -> Tensor: ... +def quantized_lstm_cell(input: Tensor, hx: Optional[Union[tuple[Tensor, ...], list[Tensor]]], w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Union[Number, _complex], scale_hh: Union[Number, _complex], zero_point_ih: Union[Number, _complex], zero_point_hh: Union[Number, _complex]) -> tuple[Tensor, Tensor]: ... +def quantized_max_pool1d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: + r""" + quantized_max_pool1d(input, kernel_size, stride=[], padding=0, dilation=1, ceil_mode=False) -> Tensor + + Applies a 1D max pooling over an input quantized tensor composed of several input planes. + + Arguments: + input (Tensor): quantized tensor + kernel_size (list of int): the size of the sliding window + stride (``list of int``, optional): the stride of the sliding window + padding (``list of int``, optional): padding to be added on both sides, must be >= 0 and <= kernel_size / 2 + dilation (``list of int``, optional): The stride between elements within a sliding window, must be > 0. Default 1 + ceil_mode (bool, optional): If True, will use ceil instead of floor to compute the output shape. + Defaults to False. + + + Returns: + Tensor: A quantized tensor with max_pool1d applied. + + Example:: + + >>> qx = torch.quantize_per_tensor(torch.rand(2, 2), 1.5, 3, torch.quint8) + >>> torch.quantized_max_pool1d(qx, [2]) + tensor([[0.0000], + [1.5000]], size=(2, 1), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=1.5, zero_point=3) + """ + ... +def quantized_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: + r""" + quantized_max_pool2d(input, kernel_size, stride=[], padding=0, dilation=1, ceil_mode=False) -> Tensor + + Applies a 2D max pooling over an input quantized tensor composed of several input planes. + + Arguments: + input (Tensor): quantized tensor + kernel_size (``list of int``): the size of the sliding window + stride (``list of int``, optional): the stride of the sliding window + padding (``list of int``, optional): padding to be added on both sides, must be >= 0 and <= kernel_size / 2 + dilation (``list of int``, optional): The stride between elements within a sliding window, must be > 0. Default 1 + ceil_mode (bool, optional): If True, will use ceil instead of floor to compute the output shape. + Defaults to False. + + + Returns: + Tensor: A quantized tensor with max_pool2d applied. + + Example:: + + >>> qx = torch.quantize_per_tensor(torch.rand(2, 2, 2, 2), 1.5, 3, torch.quint8) + >>> torch.quantized_max_pool2d(qx, [2,2]) + tensor([[[[1.5000]], + + [[1.5000]]], + + + [[[0.0000]], + + [[0.0000]]]], size=(2, 2, 1, 1), dtype=torch.quint8, + quantization_scheme=torch.per_tensor_affine, scale=1.5, zero_point=3) + """ + ... +def quantized_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Union[_int, _size] = (), padding: Union[_int, _size] = 0, dilation: Union[_int, _size] = 1, ceil_mode: _bool = False) -> Tensor: ... +def quantized_rnn_relu_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Union[Number, _complex], scale_hh: Union[Number, _complex], zero_point_ih: Union[Number, _complex], zero_point_hh: Union[Number, _complex]) -> Tensor: ... +def quantized_rnn_tanh_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Union[Number, _complex], scale_hh: Union[Number, _complex], zero_point_ih: Union[Number, _complex], zero_point_hh: Union[Number, _complex]) -> Tensor: ... +def rad2deg(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + rad2deg(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Returns a new tensor with each of the elements of :attr:`input` + converted from angles in radians to degrees. + + Args: + input (Tensor): the input tensor. + + Keyword arguments: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([[3.142, -3.142], [6.283, -6.283], [1.570, -1.570]]) + >>> torch.rad2deg(a) + tensor([[ 180.0233, -180.0233], + [ 359.9894, -359.9894], + [ 89.9544, -89.9544]]) + """ + ... +def rad2deg_(input: Tensor) -> Tensor: ... +@overload +def rand(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(*size: Union[_int, SymInt], generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(*size: Union[_int, SymInt], generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(*size: Union[_int, SymInt], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(size: Sequence[Union[_int, SymInt]], *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +@overload +def rand(*size: Union[_int, SymInt], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a tensor filled with random numbers from a uniform distribution + on the interval :math:`[0, 1)` + + The shape of the tensor is defined by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.rand(4) + tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) + >>> torch.rand(2, 3) + tensor([[ 0.8237, 0.5781, 0.6879], + [ 0.3816, 0.7249, 0.0998]]) + """ + ... +def rand_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + rand_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor with the same size as :attr:`input` that is filled with + random numbers from a uniform distribution on the interval :math:`[0, 1)`. + ``torch.rand_like(input)`` is equivalent to + ``torch.rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... +@overload +def randint(low: _int, high: _int, size: _size, *, generator: Optional[Generator] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint(high: _int, size: _size, *, generator: Optional[Generator] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint(high: Union[_int, SymInt], size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint(high: Union[_int, SymInt], size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint(low: Union[_int, SymInt], high: Union[_int, SymInt], size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint(low: Union[_int, SymInt], high: Union[_int, SymInt], size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint(low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly + between :attr:`low` (inclusive) and :attr:`high` (exclusive). + + The shape of the tensor is defined by the variable argument :attr:`size`. + + .. note:: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + size (tuple): a tuple defining the shape of the output tensor. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (`torch.dtype`, optional) - the desired data type of returned tensor. Default: if ``None``, + this function returns a tensor with dtype ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.randint(3, 5, (3,)) + tensor([4, 3, 4]) + + + >>> torch.randint(10, (2, 2)) + tensor([[0, 2], + [5, 5]]) + + + >>> torch.randint(3, 10, (2, 2)) + tensor([[4, 5], + [6, 7]]) + """ + ... +@overload +def randint_like(input: Tensor, high: Union[_int, SymInt], *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint_like(input, low=0, high, \*, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor with the same shape as Tensor :attr:`input` filled with + random integers generated uniformly between :attr:`low` (inclusive) and + :attr:`high` (exclusive). + + .. note: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... +@overload +def randint_like(input: Tensor, low: Union[_int, SymInt], high: Union[_int, SymInt], *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randint_like(input, low=0, high, \*, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor with the same shape as Tensor :attr:`input` filled with + random integers generated uniformly between :attr:`low` (inclusive) and + :attr:`high` (exclusive). + + .. note: + With the global dtype default (``torch.float32``), this function returns + a tensor with dtype ``torch.int64``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... +@overload +def randn(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(*size: Union[_int, SymInt], generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(*size: Union[_int, SymInt], generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(*size: Union[_int, SymInt], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(size: Sequence[Union[_int, SymInt]], *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +@overload +def randn(*size: Union[_int, SymInt], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + + Returns a tensor filled with random numbers from a normal distribution + with mean `0` and variance `1` (also called the standard normal + distribution). + + .. math:: + \text{out}_{i} \sim \mathcal{N}(0, 1) + + For complex dtypes, the tensor is i.i.d. sampled from a `complex normal distribution`_ with zero mean and + unit variance as + + .. math:: + \text{out}_{i} \sim \mathcal{CN}(0, 1) + + This is equivalent to separately sampling the real :math:`(\operatorname{Re})` and imaginary + :math:`(\operatorname{Im})` part of :math:`\text{out}_i` as + + .. math:: + \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad + \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) + + The shape of the tensor is defined by the variable argument :attr:`size`. + + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randn(4) + tensor([-2.1436, 0.9966, 2.3426, -0.6366]) + >>> torch.randn(2, 3) + tensor([[ 1.5954, 2.8929, -1.0923], + [ 1.1719, -0.4709, -0.1996]]) + + .. _complex normal distribution: https://en.wikipedia.org/wiki/Complex_normal_distribution + """ + ... +def randn_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randn_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor with the same size as :attr:`input` that is filled with + random numbers from a normal distribution with mean 0 and variance 1. Please refer to :func:`torch.randn` for the + sampling process of complex dtypes. ``torch.randn_like(input)`` is equivalent to + ``torch.randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + """ + ... +@overload +def randperm(n: Union[_int, SymInt], *, generator: Optional[Generator], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randperm(n, *, generator=None, out=None, dtype=torch.int64,layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a random permutation of integers from ``0`` to ``n - 1``. + + Args: + n (int): the upper bound (exclusive) + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randperm(4) + tensor([2, 1, 0, 3]) + """ + ... +@overload +def randperm(n: Union[_int, SymInt], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + randperm(n, *, generator=None, out=None, dtype=torch.int64,layout=torch.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Returns a random permutation of integers from ``0`` to ``n - 1``. + + Args: + n (int): the upper bound (exclusive) + + Keyword args: + generator (:class:`torch.Generator`, optional): a pseudorandom number generator for sampling + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: ``torch.int64``. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + Example:: + + >>> torch.randperm(4) + tensor([2, 1, 0, 3]) + """ + ... +def range(start: Number, end: Number, step: Number = 1, *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + range(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a 1-D tensor of size :math:`\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1` + with values from :attr:`start` to :attr:`end` with step :attr:`step`. Step is + the gap between two values in the tensor. + + .. math:: + \text{out}_{i+1} = \text{out}_i + \text{step}. + + .. warning:: + This function is deprecated and will be removed in a future release because its behavior is inconsistent with + Python's range builtin. Instead, use :func:`torch.arange`, which produces values in [start, end). + + Args: + start (float, optional): the starting value for the set of points. Default: ``0``. + end (float): the ending value for the set of points + step (float, optional): the gap between each pair of adjacent points. Default: ``1``. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). If `dtype` is not given, infer the data type from the other input + arguments. If any of `start`, `end`, or `step` are floating-point, the + `dtype` is inferred to be the default dtype, see + :meth:`~torch.get_default_dtype`. Otherwise, the `dtype` is inferred to + be `torch.int64`. + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.range(1, 4) + tensor([ 1., 2., 3., 4.]) + >>> torch.range(1, 4, 0.5) + tensor([ 1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000, 4.0000]) + """ + ... +def ravel(input: Tensor) -> Tensor: + r""" + ravel(input) -> Tensor + + Return a contiguous flattened tensor. A copy is made only if needed. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> t = torch.tensor([[[1, 2], + ... [3, 4]], + ... [[5, 6], + ... [7, 8]]]) + >>> torch.ravel(t) + tensor([1, 2, 3, 4, 5, 6, 7, 8]) + """ + ... +def real(input: Tensor) -> Tensor: + r""" + real(input) -> Tensor + + Returns a new tensor containing real values of the :attr:`self` tensor. + The returned tensor and :attr:`self` share the same underlying storage. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x=torch.randn(4, dtype=torch.cfloat) + >>> x + tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)]) + >>> x.real + tensor([ 0.3100, -0.5445, -1.6492, -0.0638]) + """ + ... +def reciprocal(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + reciprocal(input, *, out=None) -> Tensor + + Returns a new tensor with the reciprocal of the elements of :attr:`input` + + .. math:: + \text{out}_{i} = \frac{1}{\text{input}_{i}} + + .. note:: + Unlike NumPy's reciprocal, torch.reciprocal supports integral inputs. Integral + inputs to reciprocal are automatically :ref:`promoted ` to + the default scalar type. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.4595, -2.1219, -1.4314, 0.7298]) + >>> torch.reciprocal(a) + tensor([-2.1763, -0.4713, -0.6986, 1.3702]) + """ + ... +def reciprocal_(input: Tensor) -> Tensor: ... +def relu(input: Tensor) -> Tensor: ... +def relu_(input: Tensor) -> Tensor: ... +@overload +def remainder(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + remainder(input, other, *, out=None) -> Tensor + + Computes + `Python's modulus operation `_ + entrywise. The result has the same sign as the divisor :attr:`other` and its absolute value + is less than that of :attr:`other`. + + It may also be defined in terms of :func:`torch.div` as + + .. code:: python + + torch.remainder(a, b) == a - a.div(b, rounding_mode="floor") * b + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and float inputs. + + .. note:: + Complex inputs are not supported. In some cases, it is not mathematically + possible to satisfy the definition of a modulo operation with complex numbers. + See :func:`torch.fmod` for how division by zero is handled. + + .. seealso:: + + :func:`torch.fmod` which implements C++'s `std::fmod `_. + This one is defined in terms of division rounding towards zero. + + Args: + input (Tensor or Scalar): the dividend + other (Tensor or Scalar): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.remainder(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) + tensor([ 1., 0., 1., 1., 0., 1.]) + >>> torch.remainder(torch.tensor([1, 2, 3, 4, 5]), -1.5) + tensor([ -0.5000, -1.0000, 0.0000, -0.5000, -1.0000 ]) + """ + ... +@overload +def remainder(self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + remainder(input, other, *, out=None) -> Tensor + + Computes + `Python's modulus operation `_ + entrywise. The result has the same sign as the divisor :attr:`other` and its absolute value + is less than that of :attr:`other`. + + It may also be defined in terms of :func:`torch.div` as + + .. code:: python + + torch.remainder(a, b) == a - a.div(b, rounding_mode="floor") * b + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and float inputs. + + .. note:: + Complex inputs are not supported. In some cases, it is not mathematically + possible to satisfy the definition of a modulo operation with complex numbers. + See :func:`torch.fmod` for how division by zero is handled. + + .. seealso:: + + :func:`torch.fmod` which implements C++'s `std::fmod `_. + This one is defined in terms of division rounding towards zero. + + Args: + input (Tensor or Scalar): the dividend + other (Tensor or Scalar): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.remainder(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) + tensor([ 1., 0., 1., 1., 0., 1.]) + >>> torch.remainder(torch.tensor([1, 2, 3, 4, 5]), -1.5) + tensor([ -0.5000, -1.0000, 0.0000, -0.5000, -1.0000 ]) + """ + ... +@overload +def remainder(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + remainder(input, other, *, out=None) -> Tensor + + Computes + `Python's modulus operation `_ + entrywise. The result has the same sign as the divisor :attr:`other` and its absolute value + is less than that of :attr:`other`. + + It may also be defined in terms of :func:`torch.div` as + + .. code:: python + + torch.remainder(a, b) == a - a.div(b, rounding_mode="floor") * b + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer and float inputs. + + .. note:: + Complex inputs are not supported. In some cases, it is not mathematically + possible to satisfy the definition of a modulo operation with complex numbers. + See :func:`torch.fmod` for how division by zero is handled. + + .. seealso:: + + :func:`torch.fmod` which implements C++'s `std::fmod `_. + This one is defined in terms of division rounding towards zero. + + Args: + input (Tensor or Scalar): the dividend + other (Tensor or Scalar): the divisor + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.remainder(torch.tensor([-3., -2, -1, 1, 2, 3]), 2) + tensor([ 1., 0., 1., 1., 0., 1.]) + >>> torch.remainder(torch.tensor([1, 2, 3, 4, 5]), -1.5) + tensor([ -0.5000, -1.0000, 0.0000, -0.5000, -1.0000 ]) + """ + ... +def renorm(input: Tensor, p: Union[Number, _complex], dim: _int, maxnorm: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + renorm(input, p, dim, maxnorm, *, out=None) -> Tensor + + Returns a tensor where each sub-tensor of :attr:`input` along dimension + :attr:`dim` is normalized such that the `p`-norm of the sub-tensor is lower + than the value :attr:`maxnorm` + + .. note:: If the norm of a row is lower than `maxnorm`, the row is unchanged + + Args: + input (Tensor): the input tensor. + p (float): the power for the norm computation + dim (int): the dimension to slice over to get the sub-tensors + maxnorm (float): the maximum norm to keep each sub-tensor under + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.ones(3, 3) + >>> x[1].fill_(2) + tensor([ 2., 2., 2.]) + >>> x[2].fill_(3) + tensor([ 3., 3., 3.]) + >>> x + tensor([[ 1., 1., 1.], + [ 2., 2., 2.], + [ 3., 3., 3.]]) + >>> torch.renorm(x, 1, 0, 5) + tensor([[ 1.0000, 1.0000, 1.0000], + [ 1.6667, 1.6667, 1.6667], + [ 1.6667, 1.6667, 1.6667]]) + """ + ... +@overload +def repeat_interleave(input: Tensor, repeats: Tensor, dim: Optional[_int] = None, *, output_size: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + repeat_interleave(input, repeats, dim=None, *, output_size=None) -> Tensor + + Repeat elements of a tensor. + + .. warning:: + + This is different from :meth:`torch.Tensor.repeat` but similar to ``numpy.repeat``. + + Args: + input (Tensor): the input tensor. + repeats (Tensor or int): The number of repetitions for each element. + repeats is broadcasted to fit the shape of the given axis. + dim (int, optional): The dimension along which to repeat values. + By default, use the flattened input array, and return a flat output + array. + + Keyword args: + output_size (int, optional): Total output size for the given axis + ( e.g. sum of repeats). If given, it will avoid stream synchronization + needed to calculate output shape of the tensor. + + Returns: + Tensor: Repeated tensor which has the same shape as input, except along the given axis. + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> x.repeat_interleave(2) + tensor([1, 1, 2, 2, 3, 3]) + >>> y = torch.tensor([[1, 2], [3, 4]]) + >>> torch.repeat_interleave(y, 2) + tensor([1, 1, 2, 2, 3, 3, 4, 4]) + >>> torch.repeat_interleave(y, 3, dim=1) + tensor([[1, 1, 1, 2, 2, 2], + [3, 3, 3, 4, 4, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0) + tensor([[1, 2], + [3, 4], + [3, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0, output_size=3) + tensor([[1, 2], + [3, 4], + [3, 4]]) + + If the `repeats` is `tensor([n1, n2, n3, ...])`, then the output will be + `tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])` where `0` appears `n1` times, + `1` appears `n2` times, `2` appears `n3` times, etc. + + .. function:: repeat_interleave(repeats, *) -> Tensor + :noindex: + + Repeats 0 repeats[0] times, 1 repeats[1] times, 2 repeats[2] times, etc. + + Args: + repeats (Tensor): The number of repetitions for each element. + + Returns: + Tensor: Repeated tensor of size `sum(repeats)`. + + Example:: + + >>> torch.repeat_interleave(torch.tensor([1, 2, 3])) + tensor([0, 1, 1, 2, 2, 2]) + """ + ... +@overload +def repeat_interleave(repeats: Tensor, *, output_size: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + repeat_interleave(input, repeats, dim=None, *, output_size=None) -> Tensor + + Repeat elements of a tensor. + + .. warning:: + + This is different from :meth:`torch.Tensor.repeat` but similar to ``numpy.repeat``. + + Args: + input (Tensor): the input tensor. + repeats (Tensor or int): The number of repetitions for each element. + repeats is broadcasted to fit the shape of the given axis. + dim (int, optional): The dimension along which to repeat values. + By default, use the flattened input array, and return a flat output + array. + + Keyword args: + output_size (int, optional): Total output size for the given axis + ( e.g. sum of repeats). If given, it will avoid stream synchronization + needed to calculate output shape of the tensor. + + Returns: + Tensor: Repeated tensor which has the same shape as input, except along the given axis. + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> x.repeat_interleave(2) + tensor([1, 1, 2, 2, 3, 3]) + >>> y = torch.tensor([[1, 2], [3, 4]]) + >>> torch.repeat_interleave(y, 2) + tensor([1, 1, 2, 2, 3, 3, 4, 4]) + >>> torch.repeat_interleave(y, 3, dim=1) + tensor([[1, 1, 1, 2, 2, 2], + [3, 3, 3, 4, 4, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0) + tensor([[1, 2], + [3, 4], + [3, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0, output_size=3) + tensor([[1, 2], + [3, 4], + [3, 4]]) + + If the `repeats` is `tensor([n1, n2, n3, ...])`, then the output will be + `tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])` where `0` appears `n1` times, + `1` appears `n2` times, `2` appears `n3` times, etc. + + .. function:: repeat_interleave(repeats, *) -> Tensor + :noindex: + + Repeats 0 repeats[0] times, 1 repeats[1] times, 2 repeats[2] times, etc. + + Args: + repeats (Tensor): The number of repetitions for each element. + + Returns: + Tensor: Repeated tensor of size `sum(repeats)`. + + Example:: + + >>> torch.repeat_interleave(torch.tensor([1, 2, 3])) + tensor([0, 1, 1, 2, 2, 2]) + """ + ... +@overload +def repeat_interleave(input: Tensor, repeats: Union[_int, SymInt], dim: Optional[_int] = None, *, output_size: Optional[Union[_int, SymInt]] = None) -> Tensor: + r""" + repeat_interleave(input, repeats, dim=None, *, output_size=None) -> Tensor + + Repeat elements of a tensor. + + .. warning:: + + This is different from :meth:`torch.Tensor.repeat` but similar to ``numpy.repeat``. + + Args: + input (Tensor): the input tensor. + repeats (Tensor or int): The number of repetitions for each element. + repeats is broadcasted to fit the shape of the given axis. + dim (int, optional): The dimension along which to repeat values. + By default, use the flattened input array, and return a flat output + array. + + Keyword args: + output_size (int, optional): Total output size for the given axis + ( e.g. sum of repeats). If given, it will avoid stream synchronization + needed to calculate output shape of the tensor. + + Returns: + Tensor: Repeated tensor which has the same shape as input, except along the given axis. + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> x.repeat_interleave(2) + tensor([1, 1, 2, 2, 3, 3]) + >>> y = torch.tensor([[1, 2], [3, 4]]) + >>> torch.repeat_interleave(y, 2) + tensor([1, 1, 2, 2, 3, 3, 4, 4]) + >>> torch.repeat_interleave(y, 3, dim=1) + tensor([[1, 1, 1, 2, 2, 2], + [3, 3, 3, 4, 4, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0) + tensor([[1, 2], + [3, 4], + [3, 4]]) + >>> torch.repeat_interleave(y, torch.tensor([1, 2]), dim=0, output_size=3) + tensor([[1, 2], + [3, 4], + [3, 4]]) + + If the `repeats` is `tensor([n1, n2, n3, ...])`, then the output will be + `tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])` where `0` appears `n1` times, + `1` appears `n2` times, `2` appears `n3` times, etc. + + .. function:: repeat_interleave(repeats, *) -> Tensor + :noindex: + + Repeats 0 repeats[0] times, 1 repeats[1] times, 2 repeats[2] times, etc. + + Args: + repeats (Tensor): The number of repetitions for each element. + + Returns: + Tensor: Repeated tensor of size `sum(repeats)`. + + Example:: + + >>> torch.repeat_interleave(torch.tensor([1, 2, 3])) + tensor([0, 1, 1, 2, 2, 2]) + """ + ... +def reshape(input: Tensor, shape: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + reshape(input, shape) -> Tensor + + Returns a tensor with the same data and number of elements as :attr:`input`, + but with the specified shape. When possible, the returned tensor will be a view + of :attr:`input`. Otherwise, it will be a copy. Contiguous inputs and inputs + with compatible strides can be reshaped without copying, but you should not + depend on the copying vs. viewing behavior. + + See :meth:`torch.Tensor.view` on when it is possible to return a view. + + A single dimension may be -1, in which case it's inferred from the remaining + dimensions and the number of elements in :attr:`input`. + + Args: + input (Tensor): the tensor to be reshaped + shape (tuple of int): the new shape + + Example:: + + >>> a = torch.arange(4.) + >>> torch.reshape(a, (2, 2)) + tensor([[ 0., 1.], + [ 2., 3.]]) + >>> b = torch.tensor([[0, 1], [2, 3]]) + >>> torch.reshape(b, (-1,)) + tensor([ 0, 1, 2, 3]) + """ + ... +def resize_as_(input: Tensor, the_template: Tensor, *, memory_format: Optional[memory_format] = None) -> Tensor: ... +def resize_as_sparse_(input: Tensor, the_template: Tensor) -> Tensor: ... +def resolve_conj(input: Tensor) -> Tensor: + r""" + resolve_conj(input) -> Tensor + + Returns a new tensor with materialized conjugation if :attr:`input`'s conjugate bit is set to `True`, + else returns :attr:`input`. The output tensor will always have its conjugate bit set to `False`. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x = torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]) + >>> y = x.conj() + >>> y.is_conj() + True + >>> z = y.resolve_conj() + >>> z + tensor([-1 - 1j, -2 - 2j, 3 + 3j]) + >>> z.is_conj() + False + """ + ... +def resolve_neg(input: Tensor) -> Tensor: + r""" + resolve_neg(input) -> Tensor + + Returns a new tensor with materialized negation if :attr:`input`'s negative bit is set to `True`, + else returns :attr:`input`. The output tensor will always have its negative bit set to `False`. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x = torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]) + >>> y = x.conj() + >>> z = y.imag + >>> z.is_neg() + True + >>> out = z.resolve_neg() + >>> out + tensor([-1., -2., 3.]) + >>> out.is_neg() + False + """ + ... +@overload +def result_type(tensor: Tensor, other: Tensor) -> _dtype: + r""" + result_type(tensor1, tensor2) -> dtype + + Returns the :class:`torch.dtype` that would result from performing an arithmetic + operation on the provided input tensors. See type promotion :ref:`documentation ` + for more information on the type promotion logic. + + Args: + tensor1 (Tensor or Number): an input tensor or number + tensor2 (Tensor or Number): an input tensor or number + + Example:: + + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.int), 1.0) + torch.float32 + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.uint8), torch.tensor(1)) + torch.uint8 + """ + ... +@overload +def result_type(scalar: Union[Number, _complex], tensor: Tensor) -> _dtype: + r""" + result_type(tensor1, tensor2) -> dtype + + Returns the :class:`torch.dtype` that would result from performing an arithmetic + operation on the provided input tensors. See type promotion :ref:`documentation ` + for more information on the type promotion logic. + + Args: + tensor1 (Tensor or Number): an input tensor or number + tensor2 (Tensor or Number): an input tensor or number + + Example:: + + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.int), 1.0) + torch.float32 + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.uint8), torch.tensor(1)) + torch.uint8 + """ + ... +@overload +def result_type(tensor: Tensor, other: Union[Number, _complex]) -> _dtype: + r""" + result_type(tensor1, tensor2) -> dtype + + Returns the :class:`torch.dtype` that would result from performing an arithmetic + operation on the provided input tensors. See type promotion :ref:`documentation ` + for more information on the type promotion logic. + + Args: + tensor1 (Tensor or Number): an input tensor or number + tensor2 (Tensor or Number): an input tensor or number + + Example:: + + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.int), 1.0) + torch.float32 + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.uint8), torch.tensor(1)) + torch.uint8 + """ + ... +@overload +def result_type(scalar1: Union[Number, _complex], scalar2: Union[Number, _complex]) -> _dtype: + r""" + result_type(tensor1, tensor2) -> dtype + + Returns the :class:`torch.dtype` that would result from performing an arithmetic + operation on the provided input tensors. See type promotion :ref:`documentation ` + for more information on the type promotion logic. + + Args: + tensor1 (Tensor or Number): an input tensor or number + tensor2 (Tensor or Number): an input tensor or number + + Example:: + + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.int), 1.0) + torch.float32 + >>> torch.result_type(torch.tensor([1, 2], dtype=torch.uint8), torch.tensor(1)) + torch.uint8 + """ + ... +def rms_norm(input: Tensor, normalized_shape: Sequence[Union[_int, SymInt]], weight: Optional[Tensor] = None, eps: Optional[_float] = None) -> Tensor: ... +@overload +def rnn_relu(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: Optional[Union[tuple[Tensor, ...], list[Tensor]]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> tuple[Tensor, Tensor]: ... +@overload +def rnn_relu(input: Tensor, hx: Tensor, params: Optional[Union[tuple[Tensor, ...], list[Tensor]]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> tuple[Tensor, Tensor]: ... +def rnn_relu_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None, b_hh: Optional[Tensor] = None) -> Tensor: ... +@overload +def rnn_tanh(data: Tensor, batch_sizes: Tensor, hx: Tensor, params: Optional[Union[tuple[Tensor, ...], list[Tensor]]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool) -> tuple[Tensor, Tensor]: ... +@overload +def rnn_tanh(input: Tensor, hx: Tensor, params: Optional[Union[tuple[Tensor, ...], list[Tensor]]], has_biases: _bool, num_layers: _int, dropout: _float, train: _bool, bidirectional: _bool, batch_first: _bool) -> tuple[Tensor, Tensor]: ... +def rnn_tanh_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Optional[Tensor] = None, b_hh: Optional[Tensor] = None) -> Tensor: ... +def roll(input: Tensor, shifts: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]], dims: Union[_int, _size] = ()) -> Tensor: + r""" + roll(input, shifts, dims=None) -> Tensor + + Roll the tensor :attr:`input` along the given dimension(s). Elements that are + shifted beyond the last position are re-introduced at the first position. If + :attr:`dims` is `None`, the tensor will be flattened before rolling and then + restored to the original shape. + + Args: + input (Tensor): the input tensor. + shifts (int or tuple of ints): The number of places by which the elements + of the tensor are shifted. If shifts is a tuple, dims must be a tuple of + the same size, and each dimension will be rolled by the corresponding + value + dims (int or tuple of ints): Axis along which to roll + + Example:: + + >>> x = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]).view(4, 2) + >>> x + tensor([[1, 2], + [3, 4], + [5, 6], + [7, 8]]) + >>> torch.roll(x, 1) + tensor([[8, 1], + [2, 3], + [4, 5], + [6, 7]]) + >>> torch.roll(x, 1, 0) + tensor([[7, 8], + [1, 2], + [3, 4], + [5, 6]]) + >>> torch.roll(x, -1, 0) + tensor([[3, 4], + [5, 6], + [7, 8], + [1, 2]]) + >>> torch.roll(x, shifts=(2, 1), dims=(0, 1)) + tensor([[6, 5], + [8, 7], + [2, 1], + [4, 3]]) + """ + ... +def rot90(input: Tensor, k: _int = 1, dims: _size = (0, 1)) -> Tensor: + r""" + rot90(input, k=1, dims=(0, 1)) -> Tensor + + Rotate an n-D tensor by 90 degrees in the plane specified by dims axis. + Rotation direction is from the first towards the second axis if k > 0, and from the second towards the first for k < 0. + + Args: + input (Tensor): the input tensor. + k (int): number of times to rotate. Default value is 1 + dims (a list or tuple): axis to rotate. Default value is [0, 1] + + Example:: + + >>> x = torch.arange(4).view(2, 2) + >>> x + tensor([[0, 1], + [2, 3]]) + >>> torch.rot90(x, 1, [0, 1]) + tensor([[1, 3], + [0, 2]]) + + >>> x = torch.arange(8).view(2, 2, 2) + >>> x + tensor([[[0, 1], + [2, 3]], + + [[4, 5], + [6, 7]]]) + >>> torch.rot90(x, 1, [1, 2]) + tensor([[[1, 3], + [0, 2]], + + [[5, 7], + [4, 6]]]) + """ + ... +@overload +def round(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + round(input, *, decimals=0, out=None) -> Tensor + + Rounds elements of :attr:`input` to the nearest integer. + + For integer inputs, follows the array-api convention of returning a + copy of the input tensor. + The return type of output is same as that of input's dtype. + + .. note:: + This function implements the "round half to even" to + break ties when a number is equidistant from two + integers (e.g. `round(2.5)` is 2). + + When the :attr:\`decimals\` argument is specified the + algorithm used is similar to NumPy's `around`. This + algorithm is fast but inexact and it can easily + overflow for low precision dtypes. + Eg. `round(tensor([10000], dtype=torch.float16), decimals=3)` is `inf`. + + .. seealso:: + :func:`torch.ceil`, which rounds up. + :func:`torch.floor`, which rounds down. + :func:`torch.trunc`, which rounds towards zero. + + Args: + input (Tensor): the input tensor. + decimals (int): Number of decimal places to round to (default: 0). + If decimals is negative, it specifies the number of positions + to the left of the decimal point. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.round(torch.tensor((4.7, -2.3, 9.1, -7.7))) + tensor([ 5., -2., 9., -8.]) + + >>> # Values equidistant from two integers are rounded towards the + >>> # the nearest even value (zero is treated as even) + >>> torch.round(torch.tensor([-0.5, 0.5, 1.5, 2.5])) + tensor([-0., 0., 2., 2.]) + + >>> # A positive decimals argument rounds to the to that decimal place + >>> torch.round(torch.tensor([0.1234567]), decimals=3) + tensor([0.1230]) + + >>> # A negative decimals argument rounds to the left of the decimal + >>> torch.round(torch.tensor([1200.1234567]), decimals=-3) + tensor([1000.]) + """ + ... +@overload +def round(input: Tensor, *, decimals: _int, out: Optional[Tensor] = None) -> Tensor: + r""" + round(input, *, decimals=0, out=None) -> Tensor + + Rounds elements of :attr:`input` to the nearest integer. + + For integer inputs, follows the array-api convention of returning a + copy of the input tensor. + The return type of output is same as that of input's dtype. + + .. note:: + This function implements the "round half to even" to + break ties when a number is equidistant from two + integers (e.g. `round(2.5)` is 2). + + When the :attr:\`decimals\` argument is specified the + algorithm used is similar to NumPy's `around`. This + algorithm is fast but inexact and it can easily + overflow for low precision dtypes. + Eg. `round(tensor([10000], dtype=torch.float16), decimals=3)` is `inf`. + + .. seealso:: + :func:`torch.ceil`, which rounds up. + :func:`torch.floor`, which rounds down. + :func:`torch.trunc`, which rounds towards zero. + + Args: + input (Tensor): the input tensor. + decimals (int): Number of decimal places to round to (default: 0). + If decimals is negative, it specifies the number of positions + to the left of the decimal point. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> torch.round(torch.tensor((4.7, -2.3, 9.1, -7.7))) + tensor([ 5., -2., 9., -8.]) + + >>> # Values equidistant from two integers are rounded towards the + >>> # the nearest even value (zero is treated as even) + >>> torch.round(torch.tensor([-0.5, 0.5, 1.5, 2.5])) + tensor([-0., 0., 2., 2.]) + + >>> # A positive decimals argument rounds to the to that decimal place + >>> torch.round(torch.tensor([0.1234567]), decimals=3) + tensor([0.1230]) + + >>> # A negative decimals argument rounds to the left of the decimal + >>> torch.round(torch.tensor([1200.1234567]), decimals=-3) + tensor([1000.]) + """ + ... +@overload +def round_(input: Tensor) -> Tensor: ... +@overload +def round_(input: Tensor, *, decimals: _int) -> Tensor: ... +def row_indices_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def row_stack(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + row_stack(tensors, *, out=None) -> Tensor + + Alias of :func:`torch.vstack`. + """ + ... +def rrelu(input: Tensor, lower: Union[Number, _complex] = 0.125, upper: Union[Number, _complex] = 0.3333333333333333, training: _bool = False, generator: Optional[Generator] = None) -> Tensor: ... +def rrelu_(input: Tensor, lower: Union[Number, _complex] = 0.125, upper: Union[Number, _complex] = 0.3333333333333333, training: _bool = False, generator: Optional[Generator] = None) -> Tensor: ... +def rsqrt(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + rsqrt(input, *, out=None) -> Tensor + + Returns a new tensor with the reciprocal of the square-root of each of + the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \frac{1}{\sqrt{\text{input}_{i}}} + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.0370, 0.2970, 1.5420, -0.9105]) + >>> torch.rsqrt(a) + tensor([ nan, 1.8351, 0.8053, nan]) + """ + ... +def rsqrt_(input: Tensor) -> Tensor: ... +@overload +def rsub(input: Tensor, other: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: ... +@overload +def rsub(input: Tensor, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: ... +def saddmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Number = 1, alpha: Number = 1, out: Optional[Tensor] = None) -> Tensor: ... +def scalar_tensor(s: Union[Number, _complex], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... +@overload +def scatter(input: Tensor, dim: _int, index: Tensor, src: Tensor, *, reduce: str, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter(input: Tensor, dim: _int, index: Tensor, src: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter(input: Tensor, dim: _int, index: Tensor, value: Union[Number, _complex], *, reduce: str, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter(input: Tensor, dim: _int, index: Tensor, value: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, value: Union[Number, _complex]) -> Tensor: + r""" + scatter(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_` + """ + ... +@overload +def scatter_add(input: Tensor, dim: _int, index: Tensor, src: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter_add(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_add_` + """ + ... +@overload +def scatter_add(input: Tensor, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: + r""" + scatter_add(input, dim, index, src) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_add_` + """ + ... +def scatter_reduce(input: Tensor, dim: _int, index: Tensor, src: Tensor, reduce: str, *, include_self: _bool = True, out: Optional[Tensor] = None) -> Tensor: + r""" + scatter_reduce(input, dim, index, src, reduce, *, include_self=True) -> Tensor + + Out-of-place version of :meth:`torch.Tensor.scatter_reduce_` + """ + ... +@overload +def searchsorted(sorted_sequence: Tensor, input: Tensor, *, out_int32: _bool = False, right: _bool = False, side: Optional[str] = None, sorter: Optional[Tensor] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + searchsorted(sorted_sequence, values, *, out_int32=False, right=False, side=None, out=None, sorter=None) -> Tensor + + Find the indices from the *innermost* dimension of :attr:`sorted_sequence` such that, if the + corresponding values in :attr:`values` were inserted before the indices, when sorted, the order + of the corresponding *innermost* dimension within :attr:`sorted_sequence` would be preserved. + Return a new tensor with the same size as :attr:`values`. More formally, + the returned index satisfies the following rules: + + .. list-table:: + :widths: 12 10 78 + :header-rows: 1 + + * - :attr:`sorted_sequence` + - :attr:`right` + - *returned index satisfies* + * - 1-D + - False + - ``sorted_sequence[i-1] < values[m][n]...[l][x] <= sorted_sequence[i]`` + * - 1-D + - True + - ``sorted_sequence[i-1] <= values[m][n]...[l][x] < sorted_sequence[i]`` + * - N-D + - False + - ``sorted_sequence[m][n]...[l][i-1] < values[m][n]...[l][x] <= sorted_sequence[m][n]...[l][i]`` + * - N-D + - True + - ``sorted_sequence[m][n]...[l][i-1] <= values[m][n]...[l][x] < sorted_sequence[m][n]...[l][i]`` + + Args: + sorted_sequence (Tensor): N-D or 1-D tensor, containing monotonically increasing sequence on the *innermost* + dimension unless :attr:`sorter` is provided, in which case the sequence does not + need to be sorted + values (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s). + + Keyword args: + out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise. + Default value is False, i.e. default output data type is torch.int64. + right (bool, optional): if False, return the first suitable location that is found. If True, return the + last such index. If no suitable index found, return 0 for non-numerical value + (eg. nan, inf) or the size of *innermost* dimension within :attr:`sorted_sequence` + (one pass the last index of the *innermost* dimension). In other words, if False, + gets the lower bound index for each value in :attr:`values` on the corresponding + *innermost* dimension of the :attr:`sorted_sequence`. If True, gets the upper + bound index instead. Default value is False. :attr:`side` does the same and is + preferred. It will error if :attr:`side` is set to "left" while this is True. + side (str, optional): the same as :attr:`right` but preferred. "left" corresponds to False for :attr:`right` + and "right" corresponds to True for :attr:`right`. It will error if this is set to + "left" while :attr:`right` is True. Default value is None. + out (Tensor, optional): the output tensor, must be the same size as :attr:`values` if provided. + sorter (LongTensor, optional): if provided, a tensor matching the shape of the unsorted + :attr:`sorted_sequence` containing a sequence of indices that sort it in the + ascending order on the innermost dimension + + + Example:: + + >>> sorted_sequence = torch.tensor([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]]) + >>> sorted_sequence + tensor([[ 1, 3, 5, 7, 9], + [ 2, 4, 6, 8, 10]]) + >>> values = torch.tensor([[3, 6, 9], [3, 6, 9]]) + >>> values + tensor([[3, 6, 9], + [3, 6, 9]]) + >>> torch.searchsorted(sorted_sequence, values) + tensor([[1, 3, 4], + [1, 2, 4]]) + >>> torch.searchsorted(sorted_sequence, values, side='right') + tensor([[2, 3, 5], + [1, 3, 4]]) + + >>> sorted_sequence_1d = torch.tensor([1, 3, 5, 7, 9]) + >>> sorted_sequence_1d + tensor([1, 3, 5, 7, 9]) + >>> torch.searchsorted(sorted_sequence_1d, values) + tensor([[1, 3, 4], + [1, 3, 4]]) + """ + ... +@overload +def searchsorted(sorted_sequence: Tensor, self: Union[Number, _complex], *, out_int32: _bool = False, right: _bool = False, side: Optional[str] = None, sorter: Optional[Tensor] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + searchsorted(sorted_sequence, values, *, out_int32=False, right=False, side=None, out=None, sorter=None) -> Tensor + + Find the indices from the *innermost* dimension of :attr:`sorted_sequence` such that, if the + corresponding values in :attr:`values` were inserted before the indices, when sorted, the order + of the corresponding *innermost* dimension within :attr:`sorted_sequence` would be preserved. + Return a new tensor with the same size as :attr:`values`. More formally, + the returned index satisfies the following rules: + + .. list-table:: + :widths: 12 10 78 + :header-rows: 1 + + * - :attr:`sorted_sequence` + - :attr:`right` + - *returned index satisfies* + * - 1-D + - False + - ``sorted_sequence[i-1] < values[m][n]...[l][x] <= sorted_sequence[i]`` + * - 1-D + - True + - ``sorted_sequence[i-1] <= values[m][n]...[l][x] < sorted_sequence[i]`` + * - N-D + - False + - ``sorted_sequence[m][n]...[l][i-1] < values[m][n]...[l][x] <= sorted_sequence[m][n]...[l][i]`` + * - N-D + - True + - ``sorted_sequence[m][n]...[l][i-1] <= values[m][n]...[l][x] < sorted_sequence[m][n]...[l][i]`` + + Args: + sorted_sequence (Tensor): N-D or 1-D tensor, containing monotonically increasing sequence on the *innermost* + dimension unless :attr:`sorter` is provided, in which case the sequence does not + need to be sorted + values (Tensor or Scalar): N-D tensor or a Scalar containing the search value(s). + + Keyword args: + out_int32 (bool, optional): indicate the output data type. torch.int32 if True, torch.int64 otherwise. + Default value is False, i.e. default output data type is torch.int64. + right (bool, optional): if False, return the first suitable location that is found. If True, return the + last such index. If no suitable index found, return 0 for non-numerical value + (eg. nan, inf) or the size of *innermost* dimension within :attr:`sorted_sequence` + (one pass the last index of the *innermost* dimension). In other words, if False, + gets the lower bound index for each value in :attr:`values` on the corresponding + *innermost* dimension of the :attr:`sorted_sequence`. If True, gets the upper + bound index instead. Default value is False. :attr:`side` does the same and is + preferred. It will error if :attr:`side` is set to "left" while this is True. + side (str, optional): the same as :attr:`right` but preferred. "left" corresponds to False for :attr:`right` + and "right" corresponds to True for :attr:`right`. It will error if this is set to + "left" while :attr:`right` is True. Default value is None. + out (Tensor, optional): the output tensor, must be the same size as :attr:`values` if provided. + sorter (LongTensor, optional): if provided, a tensor matching the shape of the unsorted + :attr:`sorted_sequence` containing a sequence of indices that sort it in the + ascending order on the innermost dimension + + + Example:: + + >>> sorted_sequence = torch.tensor([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]]) + >>> sorted_sequence + tensor([[ 1, 3, 5, 7, 9], + [ 2, 4, 6, 8, 10]]) + >>> values = torch.tensor([[3, 6, 9], [3, 6, 9]]) + >>> values + tensor([[3, 6, 9], + [3, 6, 9]]) + >>> torch.searchsorted(sorted_sequence, values) + tensor([[1, 3, 4], + [1, 2, 4]]) + >>> torch.searchsorted(sorted_sequence, values, side='right') + tensor([[2, 3, 5], + [1, 3, 4]]) + + >>> sorted_sequence_1d = torch.tensor([1, 3, 5, 7, 9]) + >>> sorted_sequence_1d + tensor([1, 3, 5, 7, 9]) + >>> torch.searchsorted(sorted_sequence_1d, values) + tensor([[1, 3, 4], + [1, 3, 4]]) + """ + ... +def segment_reduce(data: Tensor, reduce: str, *, lengths: Optional[Tensor] = None, indices: Optional[Tensor] = None, offsets: Optional[Tensor] = None, axis: _int = 0, unsafe: _bool = False, initial: Optional[Union[Number, _complex]] = None) -> Tensor: ... +@overload +def select(input: Tensor, dim: _int, index: Union[_int, SymInt]) -> Tensor: + r""" + select(input, dim, index) -> Tensor + + Slices the :attr:`input` tensor along the selected dimension at the given index. + This function returns a view of the original tensor with the given dimension removed. + + .. note:: If :attr:`input` is a sparse tensor and returning a view of + the tensor is not possible, a RuntimeError exception is + raised. In this is the case, consider using + :func:`torch.select_copy` function. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to slice + index (int): the index to select with + + .. note:: + + :meth:`select` is equivalent to slicing. For example, + ``tensor.select(0, index)`` is equivalent to ``tensor[index]`` and + ``tensor.select(2, index)`` is equivalent to ``tensor[:,:,index]``. + """ + ... +@overload +def select(input: Tensor, dim: Union[str, ellipsis, None], index: _int) -> Tensor: + r""" + select(input, dim, index) -> Tensor + + Slices the :attr:`input` tensor along the selected dimension at the given index. + This function returns a view of the original tensor with the given dimension removed. + + .. note:: If :attr:`input` is a sparse tensor and returning a view of + the tensor is not possible, a RuntimeError exception is + raised. In this is the case, consider using + :func:`torch.select_copy` function. + + Args: + input (Tensor): the input tensor. + dim (int): the dimension to slice + index (int): the index to select with + + .. note:: + + :meth:`select` is equivalent to slicing. For example, + ``tensor.select(0, index)`` is equivalent to ``tensor[index]`` and + ``tensor.select(2, index)`` is equivalent to ``tensor[:,:,index]``. + """ + ... +def select_copy(input: Tensor, dim: _int, index: Union[_int, SymInt], *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.select`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def select_scatter(input: Tensor, src: Tensor, dim: _int, index: Union[_int, SymInt]) -> Tensor: + r""" + select_scatter(input, src, dim, index) -> Tensor + + Embeds the values of the :attr:`src` tensor into :attr:`input` at the given index. + This function returns a tensor with fresh storage; it does not create a view. + + + Args: + input (Tensor): the input tensor. + src (Tensor): The tensor to embed into :attr:`input` + dim (int): the dimension to insert the slice into. + index (int): the index to select with + + .. note:: + + :attr:`src` must be of the proper size in order to be embedded + into :attr:`input`. Specifically, it should have the same shape as + ``torch.select(input, dim, index)`` + + Example:: + + >>> a = torch.zeros(2, 2) + >>> b = torch.ones(2) + >>> a.select_scatter(b, 0, 0) + tensor([[1., 1.], + [0., 0.]]) + """ + ... +def selu(input: Tensor) -> Tensor: ... +def selu_(input: Tensor) -> Tensor: ... +def set_flush_denormal(mode: _bool) -> _bool: + r""" + set_flush_denormal(mode) -> bool + + Disables denormal floating numbers on CPU. + + Returns ``True`` if your system supports flushing denormal numbers and it + successfully configures flush denormal mode. :meth:`~torch.set_flush_denormal` + is supported on x86 architectures supporting SSE3 and AArch64 architecture. + + Args: + mode (bool): Controls whether to enable flush denormal mode or not + + Example:: + + >>> torch.set_flush_denormal(True) + True + >>> torch.tensor([1e-323], dtype=torch.float64) + tensor([ 0.], dtype=torch.float64) + >>> torch.set_flush_denormal(False) + True + >>> torch.tensor([1e-323], dtype=torch.float64) + tensor(9.88131e-324 * + [ 1.0000], dtype=torch.float64) + """ + ... +def set_num_interop_threads(num: _int) -> None: + r""" + set_num_interop_threads(int) + + Sets the number of threads used for interop parallelism + (e.g. in JIT interpreter) on CPU. + + .. warning:: + Can only be called once and before any inter-op parallel work + is started (e.g. JIT execution). + """ + ... +def set_num_threads(num: _int) -> None: + r""" + set_num_threads(int) + + Sets the number of threads used for intraop parallelism on CPU. + + .. warning:: + To ensure that the correct number of threads is used, set_num_threads + must be called before running eager, JIT or autograd code. + """ + ... +def sgn(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sgn(input, *, out=None) -> Tensor + + This function is an extension of torch.sign() to complex tensors. + It computes a new tensor whose elements have + the same angles as the corresponding elements of :attr:`input` and + absolute values (i.e. magnitudes) of one for complex tensors and + is equivalent to torch.sign() for non-complex tensors. + + .. math:: + \text{out}_{i} = \begin{cases} + 0 & |\text{{input}}_i| == 0 \\ + \frac{{\text{{input}}_i}}{|{\text{{input}}_i}|} & \text{otherwise} + \end{cases} + + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.tensor([3+4j, 7-24j, 0, 1+2j]) + >>> t.sgn() + tensor([0.6000+0.8000j, 0.2800-0.9600j, 0.0000+0.0000j, 0.4472+0.8944j]) + """ + ... +def sigmoid(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sigmoid(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.expit`. + """ + ... +def sigmoid_(input: Tensor) -> Tensor: ... +def sign(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sign(input, *, out=None) -> Tensor + + Returns a new tensor with the signs of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \operatorname{sgn}(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([0.7, -1.2, 0., 2.3]) + >>> a + tensor([ 0.7000, -1.2000, 0.0000, 2.3000]) + >>> torch.sign(a) + tensor([ 1., -1., 0., 1.]) + """ + ... +def signbit(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + signbit(input, *, out=None) -> Tensor + + Tests if each element of :attr:`input` has its sign bit set or not. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([0.7, -1.2, 0., 2.3]) + >>> torch.signbit(a) + tensor([ False, True, False, False]) + >>> a = torch.tensor([-0.0, 0.0]) + >>> torch.signbit(a) + tensor([ True, False]) + + .. note:: + signbit handles signed zeros, so negative zero (-0) returns True. + """ + ... +def sin(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sin(input, *, out=None) -> Tensor + + Returns a new tensor with the sine of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \sin(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-0.5461, 0.1347, -2.7266, -0.2746]) + >>> torch.sin(a) + tensor([-0.5194, 0.1343, -0.4032, -0.2711]) + """ + ... +def sin_(input: Tensor) -> Tensor: ... +def sinc(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sinc(input, *, out=None) -> Tensor + + Alias for :func:`torch.special.sinc`. + """ + ... +def sinc_(input: Tensor) -> Tensor: ... +def sinh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sinh(input, *, out=None) -> Tensor + + Returns a new tensor with the hyperbolic sine of the elements of + :attr:`input`. + + .. math:: + \text{out}_{i} = \sinh(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.5380, -0.8632, -0.1265, 0.9399]) + >>> torch.sinh(a) + tensor([ 0.5644, -0.9744, -0.1268, 1.0845]) + + .. note:: + When :attr:`input` is on the CPU, the implementation of torch.sinh may use + the Sleef library, which rounds very large results to infinity or negative + infinity. See `here `_ for details. + """ + ... +def sinh_(input: Tensor) -> Tensor: ... +def slice_copy(input: Tensor, dim: _int = 0, start: Optional[Union[_int, SymInt]] = None, end: Optional[Union[_int, SymInt]] = None, step: Union[_int, SymInt] = 1, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.slice`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def slice_inverse(input: Tensor, src: Tensor, dim: _int = 0, start: Optional[Union[_int, SymInt]] = None, end: Optional[Union[_int, SymInt]] = None, step: Union[_int, SymInt] = 1) -> Tensor: ... +def slice_scatter(input: Tensor, src: Tensor, dim: _int = 0, start: Optional[Union[_int, SymInt]] = None, end: Optional[Union[_int, SymInt]] = None, step: Union[_int, SymInt] = 1, *, out: Optional[Tensor] = None) -> Tensor: + r""" + slice_scatter(input, src, dim=0, start=None, end=None, step=1) -> Tensor + + Embeds the values of the :attr:`src` tensor into :attr:`input` at the given + dimension. + This function returns a tensor with fresh storage; it does not create a view. + + + Args: + input (Tensor): the input tensor. + src (Tensor): The tensor to embed into :attr:`input` + dim (int): the dimension to insert the slice into + start (Optional[int]): the start index of where to insert the slice + end (Optional[int]): the end index of where to insert the slice + step (int): the how many elements to skip in + + Example:: + + >>> a = torch.zeros(8, 8) + >>> b = torch.ones(2, 8) + >>> a.slice_scatter(b, start=6) + tensor([[0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0.], + [1., 1., 1., 1., 1., 1., 1., 1.], + [1., 1., 1., 1., 1., 1., 1., 1.]]) + + >>> b = torch.ones(8, 2) + >>> a.slice_scatter(b, dim=1, start=2, end=6, step=2) + tensor([[0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.], + [0., 0., 1., 0., 1., 0., 0., 0.]]) + """ + ... +def slogdet(input: Tensor, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.slogdet: + r""" + slogdet(input) -> (Tensor, Tensor) + + Alias for :func:`torch.linalg.slogdet` + """ + ... +def smm(input: Tensor, mat2: Tensor) -> Tensor: + r""" + smm(input, mat) -> Tensor + + Performs a matrix multiplication of the sparse matrix :attr:`input` + with the dense matrix :attr:`mat`. + + Args: + input (Tensor): a sparse matrix to be matrix multiplied + mat (Tensor): a dense matrix to be matrix multiplied + """ + ... +@overload +def softmax(input: Tensor, dim: _int, dtype: Optional[_dtype] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + softmax(input, dim, *, dtype=None) -> Tensor + + Alias for :func:`torch.nn.functional.softmax`. + """ + ... +@overload +def softmax(input: Tensor, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + softmax(input, dim, *, dtype=None) -> Tensor + + Alias for :func:`torch.nn.functional.softmax`. + """ + ... +@overload +def sort(input: Tensor, *, stable: Optional[_bool], dim: _int = -1, descending: _bool = False, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.sort: + r""" + sort(input, dim=-1, descending=False, stable=False, *, out=None) -> (Tensor, LongTensor) + + Sorts the elements of the :attr:`input` tensor along a given dimension + in ascending order by value. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`descending` is ``True`` then the elements are sorted in descending + order by value. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. + + A namedtuple of (values, indices) is returned, where the `values` are the + sorted values and `indices` are the indices of the elements in the original + `input` tensor. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): makes the sorting routine stable, which guarantees that the order + of equivalent elements is preserved. + + Keyword args: + out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can + be optionally given to be used as output buffers + + Example:: + + >>> x = torch.randn(3, 4) + >>> sorted, indices = torch.sort(x) + >>> sorted + tensor([[-0.2162, 0.0608, 0.6719, 2.3332], + [-0.5793, 0.0061, 0.6058, 0.9497], + [-0.5071, 0.3343, 0.9553, 1.0960]]) + >>> indices + tensor([[ 1, 0, 2, 3], + [ 3, 1, 0, 2], + [ 0, 3, 1, 2]]) + + >>> sorted, indices = torch.sort(x, 0) + >>> sorted + tensor([[-0.5071, -0.2162, 0.6719, -0.5793], + [ 0.0608, 0.0061, 0.9497, 0.3343], + [ 0.6058, 0.9553, 1.0960, 2.3332]]) + >>> indices + tensor([[ 2, 0, 0, 1], + [ 0, 1, 1, 2], + [ 1, 2, 2, 0]]) + >>> x = torch.tensor([0, 1] * 9) + >>> x.sort() + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 2, 16, 4, 6, 14, 8, 0, 10, 12, 9, 17, 15, 13, 11, 7, 5, 3, 1])) + >>> x.sort(stable=True) + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 1, 3, 5, 7, 9, 11, 13, 15, 17])) + """ + ... +@overload +def sort(input: Tensor, dim: _int = -1, descending: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.sort: + r""" + sort(input, dim=-1, descending=False, stable=False, *, out=None) -> (Tensor, LongTensor) + + Sorts the elements of the :attr:`input` tensor along a given dimension + in ascending order by value. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`descending` is ``True`` then the elements are sorted in descending + order by value. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. + + A namedtuple of (values, indices) is returned, where the `values` are the + sorted values and `indices` are the indices of the elements in the original + `input` tensor. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): makes the sorting routine stable, which guarantees that the order + of equivalent elements is preserved. + + Keyword args: + out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can + be optionally given to be used as output buffers + + Example:: + + >>> x = torch.randn(3, 4) + >>> sorted, indices = torch.sort(x) + >>> sorted + tensor([[-0.2162, 0.0608, 0.6719, 2.3332], + [-0.5793, 0.0061, 0.6058, 0.9497], + [-0.5071, 0.3343, 0.9553, 1.0960]]) + >>> indices + tensor([[ 1, 0, 2, 3], + [ 3, 1, 0, 2], + [ 0, 3, 1, 2]]) + + >>> sorted, indices = torch.sort(x, 0) + >>> sorted + tensor([[-0.5071, -0.2162, 0.6719, -0.5793], + [ 0.0608, 0.0061, 0.9497, 0.3343], + [ 0.6058, 0.9553, 1.0960, 2.3332]]) + >>> indices + tensor([[ 2, 0, 0, 1], + [ 0, 1, 1, 2], + [ 1, 2, 2, 0]]) + >>> x = torch.tensor([0, 1] * 9) + >>> x.sort() + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 2, 16, 4, 6, 14, 8, 0, 10, 12, 9, 17, 15, 13, 11, 7, 5, 3, 1])) + >>> x.sort(stable=True) + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 1, 3, 5, 7, 9, 11, 13, 15, 17])) + """ + ... +@overload +def sort(input: Tensor, *, stable: Optional[_bool], dim: Union[str, ellipsis, None], descending: _bool = False, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.sort: + r""" + sort(input, dim=-1, descending=False, stable=False, *, out=None) -> (Tensor, LongTensor) + + Sorts the elements of the :attr:`input` tensor along a given dimension + in ascending order by value. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`descending` is ``True`` then the elements are sorted in descending + order by value. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. + + A namedtuple of (values, indices) is returned, where the `values` are the + sorted values and `indices` are the indices of the elements in the original + `input` tensor. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): makes the sorting routine stable, which guarantees that the order + of equivalent elements is preserved. + + Keyword args: + out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can + be optionally given to be used as output buffers + + Example:: + + >>> x = torch.randn(3, 4) + >>> sorted, indices = torch.sort(x) + >>> sorted + tensor([[-0.2162, 0.0608, 0.6719, 2.3332], + [-0.5793, 0.0061, 0.6058, 0.9497], + [-0.5071, 0.3343, 0.9553, 1.0960]]) + >>> indices + tensor([[ 1, 0, 2, 3], + [ 3, 1, 0, 2], + [ 0, 3, 1, 2]]) + + >>> sorted, indices = torch.sort(x, 0) + >>> sorted + tensor([[-0.5071, -0.2162, 0.6719, -0.5793], + [ 0.0608, 0.0061, 0.9497, 0.3343], + [ 0.6058, 0.9553, 1.0960, 2.3332]]) + >>> indices + tensor([[ 2, 0, 0, 1], + [ 0, 1, 1, 2], + [ 1, 2, 2, 0]]) + >>> x = torch.tensor([0, 1] * 9) + >>> x.sort() + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 2, 16, 4, 6, 14, 8, 0, 10, 12, 9, 17, 15, 13, 11, 7, 5, 3, 1])) + >>> x.sort(stable=True) + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 1, 3, 5, 7, 9, 11, 13, 15, 17])) + """ + ... +@overload +def sort(input: Tensor, dim: Union[str, ellipsis, None], descending: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.sort: + r""" + sort(input, dim=-1, descending=False, stable=False, *, out=None) -> (Tensor, LongTensor) + + Sorts the elements of the :attr:`input` tensor along a given dimension + in ascending order by value. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`descending` is ``True`` then the elements are sorted in descending + order by value. + + If :attr:`stable` is ``True`` then the sorting routine becomes stable, preserving + the order of equivalent elements. + + A namedtuple of (values, indices) is returned, where the `values` are the + sorted values and `indices` are the indices of the elements in the original + `input` tensor. + + Args: + input (Tensor): the input tensor. + dim (int, optional): the dimension to sort along + descending (bool, optional): controls the sorting order (ascending or descending) + stable (bool, optional): makes the sorting routine stable, which guarantees that the order + of equivalent elements is preserved. + + Keyword args: + out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can + be optionally given to be used as output buffers + + Example:: + + >>> x = torch.randn(3, 4) + >>> sorted, indices = torch.sort(x) + >>> sorted + tensor([[-0.2162, 0.0608, 0.6719, 2.3332], + [-0.5793, 0.0061, 0.6058, 0.9497], + [-0.5071, 0.3343, 0.9553, 1.0960]]) + >>> indices + tensor([[ 1, 0, 2, 3], + [ 3, 1, 0, 2], + [ 0, 3, 1, 2]]) + + >>> sorted, indices = torch.sort(x, 0) + >>> sorted + tensor([[-0.5071, -0.2162, 0.6719, -0.5793], + [ 0.0608, 0.0061, 0.9497, 0.3343], + [ 0.6058, 0.9553, 1.0960, 2.3332]]) + >>> indices + tensor([[ 2, 0, 0, 1], + [ 0, 1, 1, 2], + [ 1, 2, 2, 0]]) + >>> x = torch.tensor([0, 1] * 9) + >>> x.sort() + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 2, 16, 4, 6, 14, 8, 0, 10, 12, 9, 17, 15, 13, 11, 7, 5, 3, 1])) + >>> x.sort(stable=True) + torch.return_types.sort( + values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), + indices=tensor([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 1, 3, 5, 7, 9, 11, 13, 15, 17])) + """ + ... +def sparse_bsc_tensor(ccol_indices: Union[Tensor, list], row_indices: Union[Tensor, list], values: Union[Tensor, list], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None) -> Tensor: + r""" + sparse_bsc_tensor(ccol_indices, row_indices, values, size=None, *, dtype=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None) -> Tensor + + Constructs a :ref:`sparse tensor in BSC (Block Compressed Sparse + Column)) ` with specified 2-dimensional blocks at the + given :attr:`ccol_indices` and :attr:`row_indices`. Sparse matrix + multiplication operations in BSC format are typically faster than that + for sparse tensors in COO format. Make you have a look at :ref:`the + note on the data type of the indices `. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + ccol_indices (array_like): (B+1)-dimensional array of size + ``(*batchsize, ncolblocks + 1)``. The last element of each + batch is the number of non-zeros. This tensor encodes the + index in values and row_indices depending on where the given + column starts. Each successive number in the tensor subtracted + by the number before it denotes the number of elements in a + given column. + row_indices (array_like): Row block co-ordinates of each block in + values. (B+1)-dimensional tensor with the same length + as values. + values (array_list): Initial blocks for the tensor. Can be a list, + tuple, NumPy ``ndarray``, and other types that + represents a (1 + 2 + K)-dimensional tensor where ``K`` is the + number of dense dimensions. + size (list, tuple, :class:`torch.Size`, optional): Size of the + sparse tensor: ``(*batchsize, nrows * blocksize[0], ncols * + blocksize[1], *densesize)`` If not provided, the size will be + inferred as the minimum size big enough to hold all non-zero + blocks. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of + returned tensor. Default: if None, infers data type from + :attr:`values`. + device (:class:`torch.device`, optional): the desired device of + returned tensor. Default: if None, uses the current device + for the default tensor type (see + :func:`torch.set_default_device`). :attr:`device` will be + the CPU for CPU tensor types and the current CUDA device for + CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + + Example:: + >>> ccol_indices = [0, 1, 2] + >>> row_indices = [0, 1] + >>> values = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]] + >>> torch.sparse_bsc_tensor(torch.tensor(ccol_indices, dtype=torch.int64), + ... torch.tensor(row_indices, dtype=torch.int64), + ... torch.tensor(values), dtype=torch.double) + tensor(ccol_indices=tensor([0, 1, 2]), + row_indices=tensor([0, 1]), + values=tensor([[[1., 2.], + [3., 4.]], + [[5., 6.], + [7., 8.]]]), size=(2, 2), nnz=2, dtype=torch.float64, + layout=torch.sparse_bsc) + """ + ... +def sparse_bsr_tensor(crow_indices: Union[Tensor, list], col_indices: Union[Tensor, list], values: Union[Tensor, list], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None) -> Tensor: + r""" + sparse_bsr_tensor(crow_indices, col_indices, values, size=None, *, dtype=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None) -> Tensor + + Constructs a :ref:`sparse tensor in BSR (Block Compressed Sparse Row)) + ` with specified 2-dimensional blocks at the given + :attr:`crow_indices` and :attr:`col_indices`. Sparse matrix + multiplication operations in BSR format are typically faster than that + for sparse tensors in COO format. Make you have a look at :ref:`the + note on the data type of the indices `. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + crow_indices (array_like): (B+1)-dimensional array of size + ``(*batchsize, nrowblocks + 1)``. The last element of each + batch is the number of non-zeros. This tensor encodes the + block index in values and col_indices depending on where the + given row block starts. Each successive number in the tensor + subtracted by the number before it denotes the number of + blocks in a given row. + col_indices (array_like): Column block co-ordinates of each block + in values. (B+1)-dimensional tensor with the same length as + values. + values (array_list): Initial values for the tensor. Can be a list, + tuple, NumPy ``ndarray``, scalar, and other types that + represents a (1 + 2 + K)-dimensional tensor where ``K`` is the + number of dense dimensions. + size (list, tuple, :class:`torch.Size`, optional): Size of the + sparse tensor: ``(*batchsize, nrows * blocksize[0], ncols * + blocksize[1], *densesize)`` where ``blocksize == + values.shape[1:3]``. If not provided, the size will be + inferred as the minimum size big enough to hold all non-zero + blocks. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of + returned tensor. Default: if None, infers data type from + :attr:`values`. + device (:class:`torch.device`, optional): the desired device of + returned tensor. Default: if None, uses the current device + for the default tensor type (see + :func:`torch.set_default_device`). :attr:`device` will be + the CPU for CPU tensor types and the current CUDA device for + CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + + Example:: + >>> crow_indices = [0, 1, 2] + >>> col_indices = [0, 1] + >>> values = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]] + >>> torch.sparse_bsr_tensor(torch.tensor(crow_indices, dtype=torch.int64), + ... torch.tensor(col_indices, dtype=torch.int64), + ... torch.tensor(values), dtype=torch.double) + tensor(crow_indices=tensor([0, 1, 2]), + col_indices=tensor([0, 1]), + values=tensor([[[1., 2.], + [3., 4.]], + [[5., 6.], + [7., 8.]]]), size=(2, 2), nnz=2, dtype=torch.float64, + layout=torch.sparse_bsr) + """ + ... +def sparse_compressed_tensor(compressed_indices: Union[Tensor, list], plain_indices: Union[Tensor, list], values: Union[Tensor, list], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None) -> Tensor: + r""" + sparse_compressed_tensor(compressed_indices, plain_indices, values, size=None, *, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None) -> Tensor + + Constructs a :ref:`sparse tensor in Compressed Sparse format - CSR, + CSC, BSR, or BSC - ` with specified values at + the given :attr:`compressed_indices` and :attr:`plain_indices`. Sparse + matrix multiplication operations in Compressed Sparse format are + typically faster than that for sparse tensors in COO format. Make you + have a look at :ref:`the note on the data type of the indices + `. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + compressed_indices (array_like): (B+1)-dimensional array of size + ``(*batchsize, compressed_dim_size + 1)``. The last element of + each batch is the number of non-zero elements or blocks. This + tensor encodes the index in ``values`` and ``plain_indices`` + depending on where the given compressed dimension (row or + column) starts. Each successive number in the tensor + subtracted by the number before it denotes the number of + elements or blocks in a given compressed dimension. + plain_indices (array_like): Plain dimension (column or row) + co-ordinates of each element or block in values. (B+1)-dimensional + tensor with the same length as values. + + values (array_list): Initial values for the tensor. Can be a list, + tuple, NumPy ``ndarray``, scalar, and other types. that + represents a (1+K)-dimensional (for CSR and CSC layouts) or + (1+2+K)-dimensional tensor (for BSR and BSC layouts) where + ``K`` is the number of dense dimensions. + size (list, tuple, :class:`torch.Size`, optional): Size of the + sparse tensor: ``(*batchsize, nrows * blocksize[0], ncols * + blocksize[1], *densesize)`` where ``blocksize[0] == + blocksize[1] == 1`` for CSR and CSC formats. If not provided, + the size will be inferred as the minimum size big enough to + hold all non-zero elements or blocks. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of + returned tensor. Default: if None, infers data type from + :attr:`values`. + layout (:class:`torch.layout`, required): the desired layout of + returned tensor: :attr:`torch.sparse_csr`, + :attr:`torch.sparse_csc`, :attr:`torch.sparse_bsr`, or + :attr:`torch.sparse_bsc`. + device (:class:`torch.device`, optional): the desired device of + returned tensor. Default: if None, uses the current device + for the default tensor type (see + :func:`torch.set_default_device`). :attr:`device` will be + the CPU for CPU tensor types and the current CUDA device for + CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + + Example:: + >>> compressed_indices = [0, 2, 4] + >>> plain_indices = [0, 1, 0, 1] + >>> values = [1, 2, 3, 4] + >>> torch.sparse_compressed_tensor(torch.tensor(compressed_indices, dtype=torch.int64), + ... torch.tensor(plain_indices, dtype=torch.int64), + ... torch.tensor(values), dtype=torch.double, layout=torch.sparse_csr) + tensor(crow_indices=tensor([0, 2, 4]), + col_indices=tensor([0, 1, 0, 1]), + values=tensor([1., 2., 3., 4.]), size=(2, 2), nnz=4, + dtype=torch.float64, layout=torch.sparse_csr) + """ + ... +def sparse_coo_tensor(indices: Tensor, values: Union[Tensor, list], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None, is_coalesced: Optional[_bool] = None) -> Tensor: + r""" + sparse_coo_tensor(indices, values, size=None, *, dtype=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None, is_coalesced=None) -> Tensor + + Constructs a :ref:`sparse tensor in COO(rdinate) format + ` with specified values at the given + :attr:`indices`. + + .. note:: + + This function returns an :ref:`uncoalesced tensor + ` when :attr:`is_coalesced` is + unspecified or ``None``. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + indices (array_like): Initial data for the tensor. Can be a list, tuple, + NumPy ``ndarray``, scalar, and other types. Will be cast to a :class:`torch.LongTensor` + internally. The indices are the coordinates of the non-zero values in the matrix, and thus + should be two-dimensional where the first dimension is the number of tensor dimensions and + the second dimension is the number of non-zero values. + values (array_like): Initial values for the tensor. Can be a list, tuple, + NumPy ``ndarray``, scalar, and other types. + size (list, tuple, or :class:`torch.Size`, optional): Size of the sparse tensor. If not + provided the size will be inferred as the minimum size big enough to hold all non-zero + elements. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if None, infers data type from :attr:`values`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if None, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + is_coalesced (bool, optional): When``True``, the caller is + responsible for providing tensor indices that correspond to a + coalesced tensor. If the :attr:`check_invariants` flag is + False, no error will be raised if the prerequisites are not + met and this will lead to silently incorrect results. To force + coalescion please use :meth:`coalesce` on the resulting + Tensor. + Default: None: except for trivial cases (e.g. nnz < 2) the + resulting Tensor has is_coalesced set to ``False```. + + Example:: + + >>> i = torch.tensor([[0, 1, 1], + ... [2, 0, 2]]) + >>> v = torch.tensor([3, 4, 5], dtype=torch.float32) + >>> torch.sparse_coo_tensor(i, v, [2, 4]) + tensor(indices=tensor([[0, 1, 1], + [2, 0, 2]]), + values=tensor([3., 4., 5.]), + size=(2, 4), nnz=3, layout=torch.sparse_coo) + + >>> torch.sparse_coo_tensor(i, v) # Shape inference + tensor(indices=tensor([[0, 1, 1], + [2, 0, 2]]), + values=tensor([3., 4., 5.]), + size=(2, 3), nnz=3, layout=torch.sparse_coo) + + >>> torch.sparse_coo_tensor(i, v, [2, 4], + ... dtype=torch.float64, + ... device=torch.device('cuda:0')) + tensor(indices=tensor([[0, 1, 1], + [2, 0, 2]]), + values=tensor([3., 4., 5.]), + device='cuda:0', size=(2, 4), nnz=3, dtype=torch.float64, + layout=torch.sparse_coo) + + # Create an empty sparse tensor with the following invariants: + # 1. sparse_dim + dense_dim = len(SparseTensor.shape) + # 2. SparseTensor._indices().shape = (sparse_dim, nnz) + # 3. SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:]) + # + # For instance, to create an empty sparse tensor with nnz = 0, dense_dim = 0 and + # sparse_dim = 1 (hence indices is a 2D tensor of shape = (1, 0)) + >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1]) + tensor(indices=tensor([], size=(1, 0)), + values=tensor([], size=(0,)), + size=(1,), nnz=0, layout=torch.sparse_coo) + + # and to create an empty sparse tensor with nnz = 0, dense_dim = 1 and + # sparse_dim = 1 + >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), torch.empty([0, 2]), [1, 2]) + tensor(indices=tensor([], size=(1, 0)), + values=tensor([], size=(0, 2)), + size=(1, 2), nnz=0, layout=torch.sparse_coo) + + .. _torch.sparse: https://pytorch.org/docs/stable/sparse.html + """ + ... +def sparse_csc_tensor(ccol_indices: Union[Tensor, list], row_indices: Union[Tensor, list], values: Union[Tensor, list], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None) -> Tensor: + r""" + sparse_csc_tensor(ccol_indices, row_indices, values, size=None, *, dtype=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None) -> Tensor + + Constructs a :ref:`sparse tensor in CSC (Compressed Sparse Column) + ` with specified values at the given + :attr:`ccol_indices` and :attr:`row_indices`. Sparse matrix + multiplication operations in CSC format are typically faster than that + for sparse tensors in COO format. Make you have a look at :ref:`the + note on the data type of the indices `. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + ccol_indices (array_like): (B+1)-dimensional array of size + ``(*batchsize, ncols + 1)``. The last element of each batch + is the number of non-zeros. This tensor encodes the index in + values and row_indices depending on where the given column + starts. Each successive number in the tensor subtracted by the + number before it denotes the number of elements in a given + column. + row_indices (array_like): Row co-ordinates of each element in + values. (B+1)-dimensional tensor with the same length as + values. + values (array_list): Initial values for the tensor. Can be a list, + tuple, NumPy ``ndarray``, scalar, and other types that + represents a (1+K)-dimensional tensor where ``K`` is the number + of dense dimensions. + size (list, tuple, :class:`torch.Size`, optional): Size of the + sparse tensor: ``(*batchsize, nrows, ncols, *densesize)``. If + not provided, the size will be inferred as the minimum size + big enough to hold all non-zero elements. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of + returned tensor. Default: if None, infers data type from + :attr:`values`. + device (:class:`torch.device`, optional): the desired device of + returned tensor. Default: if None, uses the current device + for the default tensor type (see + :func:`torch.set_default_device`). :attr:`device` will be + the CPU for CPU tensor types and the current CUDA device for + CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + + Example:: + >>> ccol_indices = [0, 2, 4] + >>> row_indices = [0, 1, 0, 1] + >>> values = [1, 2, 3, 4] + >>> torch.sparse_csc_tensor(torch.tensor(ccol_indices, dtype=torch.int64), + ... torch.tensor(row_indices, dtype=torch.int64), + ... torch.tensor(values), dtype=torch.double) + tensor(ccol_indices=tensor([0, 2, 4]), + row_indices=tensor([0, 1, 0, 1]), + values=tensor([1., 2., 3., 4.]), size=(2, 2), nnz=4, + dtype=torch.float64, layout=torch.sparse_csc) + """ + ... +def sparse_csr_tensor(crow_indices: Union[Tensor, list], col_indices: Union[Tensor, list], values: Union[Tensor, list], size: Optional[_size] = None, *, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, check_invariants: Optional[_bool] = None) -> Tensor: + r""" + sparse_csr_tensor(crow_indices, col_indices, values, size=None, *, dtype=None, device=None, pin_memory=False, requires_grad=False, check_invariants=None) -> Tensor + + Constructs a :ref:`sparse tensor in CSR (Compressed Sparse Row) ` with specified + values at the given :attr:`crow_indices` and :attr:`col_indices`. Sparse matrix multiplication operations + in CSR format are typically faster than that for sparse tensors in COO format. Make you have a look + at :ref:`the note on the data type of the indices `. + + .. note:: + + If the ``device`` argument is not specified the device of the given + :attr:`values` and indices tensor(s) must match. If, however, the + argument is specified the input Tensors will be converted to the + given device and in turn determine the device of the constructed + sparse tensor. + + Args: + crow_indices (array_like): (B+1)-dimensional array of size + ``(*batchsize, nrows + 1)``. The last element of each batch + is the number of non-zeros. This tensor encodes the index in + values and col_indices depending on where the given row + starts. Each successive number in the tensor subtracted by the + number before it denotes the number of elements in a given + row. + col_indices (array_like): Column co-ordinates of each element in + values. (B+1)-dimensional tensor with the same length + as values. + values (array_list): Initial values for the tensor. Can be a list, + tuple, NumPy ``ndarray``, scalar, and other types that + represents a (1+K)-dimensional tensor where ``K`` is the number + of dense dimensions. + size (list, tuple, :class:`torch.Size`, optional): Size of the + sparse tensor: ``(*batchsize, nrows, ncols, *densesize)``. If + not provided, the size will be inferred as the minimum size + big enough to hold all non-zero elements. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of + returned tensor. Default: if None, infers data type from + :attr:`values`. + device (:class:`torch.device`, optional): the desired device of + returned tensor. Default: if None, uses the current device + for the default tensor type (see + :func:`torch.set_default_device`). :attr:`device` will be + the CPU for CPU tensor types and the current CUDA device for + CUDA tensor types. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + check_invariants (bool, optional): If sparse tensor invariants are checked. + Default: as returned by :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled`, + initially False. + + Example:: + >>> crow_indices = [0, 2, 4] + >>> col_indices = [0, 1, 0, 1] + >>> values = [1, 2, 3, 4] + >>> torch.sparse_csr_tensor(torch.tensor(crow_indices, dtype=torch.int64), + ... torch.tensor(col_indices, dtype=torch.int64), + ... torch.tensor(values), dtype=torch.double) + tensor(crow_indices=tensor([0, 2, 4]), + col_indices=tensor([0, 1, 0, 1]), + values=tensor([1., 2., 3., 4.]), size=(2, 2), nnz=4, + dtype=torch.float64, layout=torch.sparse_csr) + """ + ... +def split_copy(input: Tensor, split_size: Union[_int, SymInt], dim: _int = 0, *, out: Optional[Union[tuple[Tensor, ...], list[Tensor]]] = None) -> None: + r""" + Performs the same operation as :func:`torch.split`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def split_with_sizes(input: Tensor, split_sizes: Sequence[Union[_int, SymInt]], dim: _int = 0) -> tuple[Tensor, ...]: ... +def split_with_sizes_copy(input: Tensor, split_sizes: Sequence[Union[_int, SymInt]], dim: _int = 0, *, out: Optional[Union[tuple[Tensor, ...], list[Tensor]]] = None) -> None: + r""" + Performs the same operation as :func:`torch.split_with_sizes`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def spmm(input: Tensor, mat2: Tensor) -> Tensor: ... +def sqrt(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + sqrt(input, *, out=None) -> Tensor + + Returns a new tensor with the square-root of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \sqrt{\text{input}_{i}} + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-2.0755, 1.0226, 0.0831, 0.4806]) + >>> torch.sqrt(a) + tensor([ nan, 1.0112, 0.2883, 0.6933]) + """ + ... +def sqrt_(input: Tensor) -> Tensor: ... +def square(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + square(input: Tensor, *, out: Optional[Tensor]) -> Tensor + + Returns a new tensor with the square of the elements of :attr:`input`. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-2.0755, 1.0226, 0.0831, 0.4806]) + >>> torch.square(a) + tensor([ 4.3077, 1.0457, 0.0069, 0.2310]) + """ + ... +def square_(input: Tensor) -> Tensor: ... +@overload +def squeeze(input: Tensor) -> Tensor: + r""" + squeeze(input: Tensor, dim: Optional[Union[int, List[int]]]) -> Tensor + + Returns a tensor with all specified dimensions of :attr:`input` of size `1` removed. + + For example, if `input` is of shape: + :math:`(A \times 1 \times B \times C \times 1 \times D)` then the `input.squeeze()` + will be of shape: :math:`(A \times B \times C \times D)`. + + When :attr:`dim` is given, a squeeze operation is done only in the given + dimension(s). If `input` is of shape: :math:`(A \times 1 \times B)`, + ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)`` + will squeeze the tensor to the shape :math:`(A \times B)`. + + .. note:: The returned tensor shares the storage with the input tensor, + so changing the contents of one will change the contents of the other. + + .. warning:: If the tensor has a batch dimension of size 1, then `squeeze(input)` + will also remove the batch dimension, which can lead to unexpected + errors. Consider specifying only the dims you wish to be squeezed. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): if given, the input will be squeezed + only in the specified dimensions. + + .. versionchanged:: 2.0 + :attr:`dim` now accepts tuples of dimensions. + + Example:: + + >>> x = torch.zeros(2, 1, 2, 1, 2) + >>> x.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x) + >>> y.size() + torch.Size([2, 2, 2]) + >>> y = torch.squeeze(x, 0) + >>> y.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x, 1) + >>> y.size() + torch.Size([2, 2, 1, 2]) + >>> y = torch.squeeze(x, (1, 2, 3)) + torch.Size([2, 2, 2]) + """ + ... +@overload +def squeeze(input: Tensor, dim: _int) -> Tensor: + r""" + squeeze(input: Tensor, dim: Optional[Union[int, List[int]]]) -> Tensor + + Returns a tensor with all specified dimensions of :attr:`input` of size `1` removed. + + For example, if `input` is of shape: + :math:`(A \times 1 \times B \times C \times 1 \times D)` then the `input.squeeze()` + will be of shape: :math:`(A \times B \times C \times D)`. + + When :attr:`dim` is given, a squeeze operation is done only in the given + dimension(s). If `input` is of shape: :math:`(A \times 1 \times B)`, + ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)`` + will squeeze the tensor to the shape :math:`(A \times B)`. + + .. note:: The returned tensor shares the storage with the input tensor, + so changing the contents of one will change the contents of the other. + + .. warning:: If the tensor has a batch dimension of size 1, then `squeeze(input)` + will also remove the batch dimension, which can lead to unexpected + errors. Consider specifying only the dims you wish to be squeezed. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): if given, the input will be squeezed + only in the specified dimensions. + + .. versionchanged:: 2.0 + :attr:`dim` now accepts tuples of dimensions. + + Example:: + + >>> x = torch.zeros(2, 1, 2, 1, 2) + >>> x.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x) + >>> y.size() + torch.Size([2, 2, 2]) + >>> y = torch.squeeze(x, 0) + >>> y.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x, 1) + >>> y.size() + torch.Size([2, 2, 1, 2]) + >>> y = torch.squeeze(x, (1, 2, 3)) + torch.Size([2, 2, 2]) + """ + ... +@overload +def squeeze(input: Tensor, dim: _size) -> Tensor: + r""" + squeeze(input: Tensor, dim: Optional[Union[int, List[int]]]) -> Tensor + + Returns a tensor with all specified dimensions of :attr:`input` of size `1` removed. + + For example, if `input` is of shape: + :math:`(A \times 1 \times B \times C \times 1 \times D)` then the `input.squeeze()` + will be of shape: :math:`(A \times B \times C \times D)`. + + When :attr:`dim` is given, a squeeze operation is done only in the given + dimension(s). If `input` is of shape: :math:`(A \times 1 \times B)`, + ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)`` + will squeeze the tensor to the shape :math:`(A \times B)`. + + .. note:: The returned tensor shares the storage with the input tensor, + so changing the contents of one will change the contents of the other. + + .. warning:: If the tensor has a batch dimension of size 1, then `squeeze(input)` + will also remove the batch dimension, which can lead to unexpected + errors. Consider specifying only the dims you wish to be squeezed. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): if given, the input will be squeezed + only in the specified dimensions. + + .. versionchanged:: 2.0 + :attr:`dim` now accepts tuples of dimensions. + + Example:: + + >>> x = torch.zeros(2, 1, 2, 1, 2) + >>> x.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x) + >>> y.size() + torch.Size([2, 2, 2]) + >>> y = torch.squeeze(x, 0) + >>> y.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x, 1) + >>> y.size() + torch.Size([2, 2, 1, 2]) + >>> y = torch.squeeze(x, (1, 2, 3)) + torch.Size([2, 2, 2]) + """ + ... +@overload +def squeeze(input: Tensor, dim: Union[str, ellipsis, None]) -> Tensor: + r""" + squeeze(input: Tensor, dim: Optional[Union[int, List[int]]]) -> Tensor + + Returns a tensor with all specified dimensions of :attr:`input` of size `1` removed. + + For example, if `input` is of shape: + :math:`(A \times 1 \times B \times C \times 1 \times D)` then the `input.squeeze()` + will be of shape: :math:`(A \times B \times C \times D)`. + + When :attr:`dim` is given, a squeeze operation is done only in the given + dimension(s). If `input` is of shape: :math:`(A \times 1 \times B)`, + ``squeeze(input, 0)`` leaves the tensor unchanged, but ``squeeze(input, 1)`` + will squeeze the tensor to the shape :math:`(A \times B)`. + + .. note:: The returned tensor shares the storage with the input tensor, + so changing the contents of one will change the contents of the other. + + .. warning:: If the tensor has a batch dimension of size 1, then `squeeze(input)` + will also remove the batch dimension, which can lead to unexpected + errors. Consider specifying only the dims you wish to be squeezed. + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints, optional): if given, the input will be squeezed + only in the specified dimensions. + + .. versionchanged:: 2.0 + :attr:`dim` now accepts tuples of dimensions. + + Example:: + + >>> x = torch.zeros(2, 1, 2, 1, 2) + >>> x.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x) + >>> y.size() + torch.Size([2, 2, 2]) + >>> y = torch.squeeze(x, 0) + >>> y.size() + torch.Size([2, 1, 2, 1, 2]) + >>> y = torch.squeeze(x, 1) + >>> y.size() + torch.Size([2, 2, 1, 2]) + >>> y = torch.squeeze(x, (1, 2, 3)) + torch.Size([2, 2, 2]) + """ + ... +@overload +def squeeze_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.squeeze`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def squeeze_copy(input: Tensor, dim: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.squeeze`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def squeeze_copy(input: Tensor, dim: _size, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.squeeze`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def sspaddmm(beta: Union[Number, _complex], self: Tensor, alpha: Union[Number, _complex], mat1: Tensor, mat2: Tensor) -> Tensor: + r""" + sspaddmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Matrix multiplies a sparse tensor :attr:`mat1` with a dense tensor + :attr:`mat2`, then adds the sparse tensor :attr:`input` to the result. + + Note: This function is equivalent to :func:`torch.addmm`, except + :attr:`input` and :attr:`mat1` are sparse. + + Args: + input (Tensor): a sparse matrix to be added + mat1 (Tensor): a sparse matrix to be matrix multiplied + mat2 (Tensor): a dense matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + """ + ... +@overload +def sspaddmm(input: Tensor, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + sspaddmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Matrix multiplies a sparse tensor :attr:`mat1` with a dense tensor + :attr:`mat2`, then adds the sparse tensor :attr:`input` to the result. + + Note: This function is equivalent to :func:`torch.addmm`, except + :attr:`input` and :attr:`mat1` are sparse. + + Args: + input (Tensor): a sparse matrix to be added + mat1 (Tensor): a sparse matrix to be matrix multiplied + mat2 (Tensor): a dense matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + """ + ... +@overload +def sspaddmm(beta: Union[Number, _complex], self: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor: + r""" + sspaddmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) -> Tensor + + Matrix multiplies a sparse tensor :attr:`mat1` with a dense tensor + :attr:`mat2`, then adds the sparse tensor :attr:`input` to the result. + + Note: This function is equivalent to :func:`torch.addmm`, except + :attr:`input` and :attr:`mat1` are sparse. + + Args: + input (Tensor): a sparse matrix to be added + mat1 (Tensor): a sparse matrix to be matrix multiplied + mat2 (Tensor): a dense matrix to be matrix multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) + out (Tensor, optional): the output tensor. + """ + ... +def stack(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + stack(tensors, dim=0, *, out=None) -> Tensor + + Concatenates a sequence of tensors along a new dimension. + + All tensors need to be of the same size. + + .. seealso:: + + :func:`torch.cat` concatenates the given sequence along an existing dimension. + + Arguments: + tensors (sequence of Tensors): sequence of tensors to concatenate + dim (int, optional): dimension to insert. Has to be between 0 and the number + of dimensions of concatenated tensors (inclusive). Default: 0 + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 0.3367, 0.1288, 0.2345], + [ 0.2303, -1.1229, -0.1863]]) + >>> torch.stack((x, x)) # same as torch.stack((x, x), dim=0) + tensor([[[ 0.3367, 0.1288, 0.2345], + [ 0.2303, -1.1229, -0.1863]], + + [[ 0.3367, 0.1288, 0.2345], + [ 0.2303, -1.1229, -0.1863]]]) + >>> torch.stack((x, x)).size() + torch.Size([2, 2, 3]) + >>> torch.stack((x, x), dim=1) + tensor([[[ 0.3367, 0.1288, 0.2345], + [ 0.3367, 0.1288, 0.2345]], + + [[ 0.2303, -1.1229, -0.1863], + [ 0.2303, -1.1229, -0.1863]]]) + >>> torch.stack((x, x), dim=2) + tensor([[[ 0.3367, 0.3367], + [ 0.1288, 0.1288], + [ 0.2345, 0.2345]], + + [[ 0.2303, 0.2303], + [-1.1229, -1.1229], + [-0.1863, -0.1863]]]) + >>> torch.stack((x, x), dim=-1) + tensor([[[ 0.3367, 0.3367], + [ 0.1288, 0.1288], + [ 0.2345, 0.2345]], + + [[ 0.2303, 0.2303], + [-1.1229, -1.1229], + [-0.1863, -0.1863]]]) + """ + ... +@overload +def std(input: Tensor, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + std(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the standard deviation over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std(a, dim=1, keepdim=True) + tensor([[1.0311], + [0.7477], + [1.2204], + [0.9087]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std(input: Tensor, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + std(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the standard deviation over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std(a, dim=1, keepdim=True) + tensor([[1.0311], + [0.7477], + [1.2204], + [0.9087]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std(input: Tensor, unbiased: _bool = True) -> Tensor: + r""" + std(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the standard deviation over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std(a, dim=1, keepdim=True) + tensor([[1.0311], + [0.7477], + [1.2204], + [0.9087]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + std(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the standard deviation over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std(a, dim=1, keepdim=True) + tensor([[1.0311], + [0.7477], + [1.2204], + [0.9087]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + std(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the standard deviation over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + dim (int or tuple of ints): the dimension or dimensions to reduce. + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std(a, dim=1, keepdim=True) + tensor([[1.0311], + [0.7477], + [1.2204], + [0.9087]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std_mean(input: Tensor, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False) -> tuple[Tensor, Tensor]: + r""" + std_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the standard deviation and mean over the dimensions specified by + :attr:`dim`. :attr:`dim` can be a single dimension, list of dimensions, or + ``None`` to reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (std, mean) containing the standard deviation and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std_mean(a, dim=0, keepdim=True) + (tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std_mean(input: Tensor, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> tuple[Tensor, Tensor]: + r""" + std_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the standard deviation and mean over the dimensions specified by + :attr:`dim`. :attr:`dim` can be a single dimension, list of dimensions, or + ``None`` to reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (std, mean) containing the standard deviation and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std_mean(a, dim=0, keepdim=True) + (tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std_mean(input: Tensor, unbiased: _bool = True) -> tuple[Tensor, Tensor]: + r""" + std_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the standard deviation and mean over the dimensions specified by + :attr:`dim`. :attr:`dim` can be a single dimension, list of dimensions, or + ``None`` to reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (std, mean) containing the standard deviation and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std_mean(a, dim=0, keepdim=True) + (tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std_mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> tuple[Tensor, Tensor]: + r""" + std_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the standard deviation and mean over the dimensions specified by + :attr:`dim`. :attr:`dim` can be a single dimension, list of dimensions, or + ``None`` to reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (std, mean) containing the standard deviation and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std_mean(a, dim=0, keepdim=True) + (tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def std_mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False) -> tuple[Tensor, Tensor]: + r""" + std_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the standard deviation and mean over the dimensions specified by + :attr:`dim`. :attr:`dim` can be a single dimension, list of dimensions, or + ``None`` to reduce over all dimensions. + + The standard deviation (:math:`\sigma`) is calculated as + + .. math:: \sigma = \sqrt{\frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2} + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (std, mean) containing the standard deviation and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.std_mean(a, dim=0, keepdim=True) + (tensor([[1.2620, 1.0028, 1.0957, 0.6038]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def sub(input: Union[Tensor, Number, _complex], other: Union[Tensor, Number, _complex], *, alpha: Optional[Union[Number, _complex]] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + sub(input, other, *, alpha=1, out=None) -> Tensor + + Subtracts :attr:`other`, scaled by :attr:`alpha`, from :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i - \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to subtract from :attr:`input`. + + Keyword args: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor((1, 2)) + >>> b = torch.tensor((0, 1)) + >>> torch.sub(a, b, alpha=2) + tensor([1, 0]) + """ + ... +@overload +def sub(self: Tensor, alpha: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + sub(input, other, *, alpha=1, out=None) -> Tensor + + Subtracts :attr:`other`, scaled by :attr:`alpha`, from :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i - \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to subtract from :attr:`input`. + + Keyword args: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor((1, 2)) + >>> b = torch.tensor((0, 1)) + >>> torch.sub(a, b, alpha=2) + tensor([1, 0]) + """ + ... +@overload +def sub(self: Tensor, alpha: Union[Number, _complex], other: Tensor, *, out: Tensor) -> Tensor: + r""" + sub(input, other, *, alpha=1, out=None) -> Tensor + + Subtracts :attr:`other`, scaled by :attr:`alpha`, from :attr:`input`. + + .. math:: + \text{{out}}_i = \text{{input}}_i - \text{{alpha}} \times \text{{other}}_i + + + Supports :ref:`broadcasting to a common shape `, + :ref:`type promotion `, and integer, float, and complex inputs. + + Args: + input (Tensor): the input tensor. + other (Tensor or Number): the tensor or number to subtract from :attr:`input`. + + Keyword args: + alpha (Number): the multiplier for :attr:`other`. + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor((1, 2)) + >>> b = torch.tensor((0, 1)) + >>> torch.sub(a, b, alpha=2) + tensor([1, 0]) + """ + ... +@overload +def subtract(input: Tensor, other: Tensor, *, alpha: Union[Number, _complex] = 1, out: Optional[Tensor] = None) -> Tensor: + r""" + subtract(input, other, *, alpha=1, out=None) -> Tensor + + Alias for :func:`torch.sub`. + """ + ... +@overload +def subtract(input: Tensor, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: + r""" + subtract(input, other, *, alpha=1, out=None) -> Tensor + + Alias for :func:`torch.sub`. + """ + ... +@overload +def sum(input: Tensor, *, dtype: Optional[_dtype] = None) -> Tensor: + r""" + sum(input, *, dtype=None) -> Tensor + + Returns the sum of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + .. note:: Use the `dtype` argument if you need the result in a specific tensor type. + Otherwise, the result type may be automatically promoted (e.g., from `torch.int32` to `torch.int64`). + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.1133, -0.9567, 0.2958]]) + >>> torch.sum(a) + tensor(-0.5475) + + .. function:: sum(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the sum of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0569, -0.2475, 0.0737, -0.3429], + [-0.2993, 0.9138, 0.9337, -1.6864], + [ 0.1132, 0.7892, -0.1003, 0.5688], + [ 0.3637, -0.9906, -0.4752, -1.5197]]) + >>> torch.sum(a, 1) + tensor([-0.4598, -0.1381, 1.3708, -2.6217]) + >>> b = torch.arange(4 * 5 * 6).view(4, 5, 6) + >>> torch.sum(b, (2, 1)) + tensor([ 435., 1335., 2235., 3135.]) + """ + ... +@overload +def sum(input: Tensor, dim: Optional[Union[_int, _size]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + sum(input, *, dtype=None) -> Tensor + + Returns the sum of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + .. note:: Use the `dtype` argument if you need the result in a specific tensor type. + Otherwise, the result type may be automatically promoted (e.g., from `torch.int32` to `torch.int64`). + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.1133, -0.9567, 0.2958]]) + >>> torch.sum(a) + tensor(-0.5475) + + .. function:: sum(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the sum of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0569, -0.2475, 0.0737, -0.3429], + [-0.2993, 0.9138, 0.9337, -1.6864], + [ 0.1132, 0.7892, -0.1003, 0.5688], + [ 0.3637, -0.9906, -0.4752, -1.5197]]) + >>> torch.sum(a, 1) + tensor([-0.4598, -0.1381, 1.3708, -2.6217]) + >>> b = torch.arange(4 * 5 * 6).view(4, 5, 6) + >>> torch.sum(b, (2, 1)) + tensor([ 435., 1335., 2235., 3135.]) + """ + ... +@overload +def sum(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None, out: Optional[Tensor] = None) -> Tensor: + r""" + sum(input, *, dtype=None) -> Tensor + + Returns the sum of all elements in the :attr:`input` tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + .. note:: Use the `dtype` argument if you need the result in a specific tensor type. + Otherwise, the result type may be automatically promoted (e.g., from `torch.int32` to `torch.int64`). + + Example:: + + >>> a = torch.randn(1, 3) + >>> a + tensor([[ 0.1133, -0.9567, 0.2958]]) + >>> torch.sum(a) + tensor(-0.5475) + + .. function:: sum(input, dim, keepdim=False, *, dtype=None) -> Tensor + :noindex: + + Returns the sum of each row of the :attr:`input` tensor in the given + dimension :attr:`dim`. If :attr:`dim` is a list of dimensions, + reduce over all of them. + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to :attr:`dtype` before the operation + is performed. This is useful for preventing data type overflows. Default: None. + + Example:: + + >>> a = torch.randn(4, 4) + >>> a + tensor([[ 0.0569, -0.2475, 0.0737, -0.3429], + [-0.2993, 0.9138, 0.9337, -1.6864], + [ 0.1132, 0.7892, -0.1003, 0.5688], + [ 0.3637, -0.9906, -0.4752, -1.5197]]) + >>> torch.sum(a, 1) + tensor([-0.4598, -0.1381, 1.3708, -2.6217]) + >>> b = torch.arange(4 * 5 * 6).view(4, 5, 6) + >>> torch.sum(b, (2, 1)) + tensor([ 435., 1335., 2235., 3135.]) + """ + ... +def svd(input: Tensor, some: _bool = True, compute_uv: _bool = True, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.svd: + r""" + svd(input, some=True, compute_uv=True, *, out=None) -> (Tensor, Tensor, Tensor) + + Computes the singular value decomposition of either a matrix or batch of + matrices :attr:`input`. The singular value decomposition is represented as a + namedtuple `(U, S, V)`, such that :attr:`input` :math:`= U \text{diag}(S) V^{\text{H}}`. + where :math:`V^{\text{H}}` is the transpose of `V` for real inputs, + and the conjugate transpose of `V` for complex inputs. + If :attr:`input` is a batch of matrices, then `U`, `S`, and `V` are also + batched with the same batch dimensions as :attr:`input`. + + If :attr:`some` is `True` (default), the method returns the reduced singular + value decomposition. In this case, if the last two dimensions of :attr:`input` are + `m` and `n`, then the returned `U` and `V` matrices will contain only + `min(n, m)` orthonormal columns. + + If :attr:`compute_uv` is `False`, the returned `U` and `V` will be + zero-filled matrices of shape `(m, m)` and `(n, n)` + respectively, and the same device as :attr:`input`. The argument :attr:`some` + has no effect when :attr:`compute_uv` is `False`. + + Supports :attr:`input` of float, double, cfloat and cdouble data types. + The dtypes of `U` and `V` are the same as :attr:`input`'s. `S` will + always be real-valued, even if :attr:`input` is complex. + + .. warning:: + + :func:`torch.svd` is deprecated in favor of :func:`torch.linalg.svd` + and will be removed in a future PyTorch release. + + ``U, S, V = torch.svd(A, some=some, compute_uv=True)`` (default) should be replaced with + + .. code:: python + + U, S, Vh = torch.linalg.svd(A, full_matrices=not some) + V = Vh.mH + + ``_, S, _ = torch.svd(A, some=some, compute_uv=False)`` should be replaced with + + .. code:: python + + S = torch.linalg.svdvals(A) + + .. note:: Differences with :func:`torch.linalg.svd`: + + * :attr:`some` is the opposite of + :func:`torch.linalg.svd`'s :attr:`full_matrices`. Note that + default value for both is `True`, so the default behavior is + effectively the opposite. + * :func:`torch.svd` returns `V`, whereas :func:`torch.linalg.svd` returns + `Vh`, that is, :math:`V^{\text{H}}`. + * If :attr:`compute_uv` is `False`, :func:`torch.svd` returns zero-filled + tensors for `U` and `Vh`, whereas :func:`torch.linalg.svd` returns + empty tensors. + + .. note:: The singular values are returned in descending order. If :attr:`input` is a batch of matrices, + then the singular values of each matrix in the batch are returned in descending order. + + .. note:: The `S` tensor can only be used to compute gradients if :attr:`compute_uv` is `True`. + + .. note:: When :attr:`some` is `False`, the gradients on `U[..., :, min(m, n):]` + and `V[..., :, min(m, n):]` will be ignored in the backward pass, as those vectors + can be arbitrary bases of the corresponding subspaces. + + .. note:: The implementation of :func:`torch.linalg.svd` on CPU uses LAPACK's routine `?gesdd` + (a divide-and-conquer algorithm) instead of `?gesvd` for speed. Analogously, + on GPU, it uses cuSOLVER's routines `gesvdj` and `gesvdjBatched` on CUDA 10.1.243 + and later, and MAGMA's routine `gesdd` on earlier versions of CUDA. + + .. note:: The returned `U` will not be contiguous. The matrix (or batch of matrices) will + be represented as a column-major matrix (i.e. Fortran-contiguous). + + .. warning:: The gradients with respect to `U` and `V` will only be finite when the input does not + have zero nor repeated singular values. + + .. warning:: If the distance between any two singular values is close to zero, the gradients with respect to + `U` and `V` will be numerically unstable, as they depends on + :math:`\frac{1}{\min_{i \neq j} \sigma_i^2 - \sigma_j^2}`. The same happens when the matrix + has small singular values, as these gradients also depend on `S^{-1}`. + + .. warning:: For complex-valued :attr:`input` the singular value decomposition is not unique, + as `U` and `V` may be multiplied by an arbitrary phase factor :math:`e^{i \phi}` on every column. + The same happens when :attr:`input` has repeated singular values, where one may multiply + the columns of the spanning subspace in `U` and `V` by a rotation matrix + and `the resulting vectors will span the same subspace`_. + Different platforms, like NumPy, or inputs on different device types, + may produce different `U` and `V` tensors. + + Args: + input (Tensor): the input tensor of size `(*, m, n)` where `*` is zero or more + batch dimensions consisting of `(m, n)` matrices. + some (bool, optional): controls whether to compute the reduced or full decomposition, and + consequently, the shape of returned `U` and `V`. Default: `True`. + compute_uv (bool, optional): controls whether to compute `U` and `V`. Default: `True`. + + Keyword args: + out (tuple, optional): the output tuple of tensors + + Example:: + + >>> a = torch.randn(5, 3) + >>> a + tensor([[ 0.2364, -0.7752, 0.6372], + [ 1.7201, 0.7394, -0.0504], + [-0.3371, -1.0584, 0.5296], + [ 0.3550, -0.4022, 1.5569], + [ 0.2445, -0.0158, 1.1414]]) + >>> u, s, v = torch.svd(a) + >>> u + tensor([[ 0.4027, 0.0287, 0.5434], + [-0.1946, 0.8833, 0.3679], + [ 0.4296, -0.2890, 0.5261], + [ 0.6604, 0.2717, -0.2618], + [ 0.4234, 0.2481, -0.4733]]) + >>> s + tensor([2.3289, 2.0315, 0.7806]) + >>> v + tensor([[-0.0199, 0.8766, 0.4809], + [-0.5080, 0.4054, -0.7600], + [ 0.8611, 0.2594, -0.4373]]) + >>> torch.dist(a, torch.mm(torch.mm(u, torch.diag(s)), v.t())) + tensor(8.6531e-07) + >>> a_big = torch.randn(7, 5, 3) + >>> u, s, v = torch.svd(a_big) + >>> torch.dist(a_big, torch.matmul(torch.matmul(u, torch.diag_embed(s)), v.mT)) + tensor(2.6503e-06) + + .. _the resulting vectors will span the same subspace: + (https://en.wikipedia.org/wiki/Singular_value_decomposition#Singular_values,_singular_vectors,_and_their_relation_to_the_SVD) + """ + ... +def swapaxes(input: Tensor, axis0: _int, axis1: _int) -> Tensor: + r""" + swapaxes(input, axis0, axis1) -> Tensor + + Alias for :func:`torch.transpose`. + + This function is equivalent to NumPy's swapaxes function. + + Examples:: + + >>> x = torch.tensor([[[0,1],[2,3]],[[4,5],[6,7]]]) + >>> x + tensor([[[0, 1], + [2, 3]], + + [[4, 5], + [6, 7]]]) + >>> torch.swapaxes(x, 0, 1) + tensor([[[0, 1], + [4, 5]], + + [[2, 3], + [6, 7]]]) + >>> torch.swapaxes(x, 0, 2) + tensor([[[0, 4], + [2, 6]], + + [[1, 5], + [3, 7]]]) + """ + ... +def swapdims(input: Tensor, dim0: _int, dim1: _int) -> Tensor: + r""" + swapdims(input, dim0, dim1) -> Tensor + + Alias for :func:`torch.transpose`. + + This function is equivalent to NumPy's swapaxes function. + + Examples:: + + >>> x = torch.tensor([[[0,1],[2,3]],[[4,5],[6,7]]]) + >>> x + tensor([[[0, 1], + [2, 3]], + + [[4, 5], + [6, 7]]]) + >>> torch.swapdims(x, 0, 1) + tensor([[[0, 1], + [4, 5]], + + [[2, 3], + [6, 7]]]) + >>> torch.swapdims(x, 0, 2) + tensor([[[0, 4], + [2, 6]], + + [[1, 5], + [3, 7]]]) + """ + ... +def sym_constrain_range(size: Union[Number, _complex], *, min: Optional[_int] = None, max: Optional[_int] = None) -> None: ... +def sym_constrain_range_for_size(size: Union[Number, _complex], *, min: Optional[_int] = None, max: Optional[_int] = None) -> None: ... +def t(input: Tensor) -> Tensor: + r""" + t(input) -> Tensor + + Expects :attr:`input` to be <= 2-D tensor and transposes dimensions 0 + and 1. + + 0-D and 1-D tensors are returned as is. When input is a 2-D tensor this + is equivalent to ``transpose(input, 0, 1)``. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x = torch.randn(()) + >>> x + tensor(0.1995) + >>> torch.t(x) + tensor(0.1995) + >>> x = torch.randn(3) + >>> x + tensor([ 2.4320, -0.4608, 0.7702]) + >>> torch.t(x) + tensor([ 2.4320, -0.4608, 0.7702]) + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 0.4875, 0.9158, -0.5872], + [ 0.3938, -0.6929, 0.6932]]) + >>> torch.t(x) + tensor([[ 0.4875, 0.3938], + [ 0.9158, -0.6929], + [-0.5872, 0.6932]]) + + See also :func:`torch.transpose`. + """ + ... +def t_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.t`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def take(input: Tensor, index: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + take(input, index) -> Tensor + + Returns a new tensor with the elements of :attr:`input` at the given indices. + The input tensor is treated as if it were viewed as a 1-D tensor. The result + takes the same shape as the indices. + + Args: + input (Tensor): the input tensor. + index (LongTensor): the indices into tensor + + Example:: + + >>> src = torch.tensor([[4, 3, 5], + ... [6, 7, 8]]) + >>> torch.take(src, torch.tensor([0, 2, 5])) + tensor([ 4, 5, 8]) + """ + ... +def take_along_dim(input: Tensor, indices: Tensor, dim: Optional[_int] = None, *, out: Optional[Tensor] = None) -> Tensor: + r""" + take_along_dim(input, indices, dim=None, *, out=None) -> Tensor + + Selects values from :attr:`input` at the 1-dimensional indices from :attr:`indices` along the given :attr:`dim`. + + If :attr:`dim` is None, the input array is treated as if it has been flattened to 1d. + + Functions that return indices along a dimension, like :func:`torch.argmax` and :func:`torch.argsort`, + are designed to work with this function. See the examples below. + + .. note:: + This function is similar to NumPy's `take_along_axis`. + See also :func:`torch.gather`. + + Args: + input (Tensor): the input tensor. + indices (LongTensor): the indices into :attr:`input`. Must have long dtype. + dim (int, optional): dimension to select along. Default: 0 + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> t = torch.tensor([[10, 30, 20], [60, 40, 50]]) + >>> max_idx = torch.argmax(t) + >>> torch.take_along_dim(t, max_idx) + tensor([60]) + >>> sorted_idx = torch.argsort(t, dim=1) + >>> torch.take_along_dim(t, sorted_idx, dim=1) + tensor([[10, 20, 30], + [40, 50, 60]]) + """ + ... +def tan(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + tan(input, *, out=None) -> Tensor + + Returns a new tensor with the tangent of the elements of :attr:`input`. + + .. math:: + \text{out}_{i} = \tan(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([-1.2027, -1.7687, 0.4412, -1.3856]) + >>> torch.tan(a) + tensor([-2.5930, 4.9859, 0.4722, -5.3366]) + """ + ... +def tan_(input: Tensor) -> Tensor: ... +def tanh(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + tanh(input, *, out=None) -> Tensor + + Returns a new tensor with the hyperbolic tangent of the elements + of :attr:`input`. + + .. math:: + \text{out}_{i} = \tanh(\text{input}_{i}) + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 0.8986, -0.7279, 1.1745, 0.2611]) + >>> torch.tanh(a) + tensor([ 0.7156, -0.6218, 0.8257, 0.2553]) + """ + ... +def tanh_(input: Tensor) -> Tensor: ... +def tensor(data: Any, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: + r""" + tensor(data, *, dtype=None, device=None, requires_grad=False, pin_memory=False) -> Tensor + + Constructs a tensor with no autograd history (also known as a "leaf tensor", see :doc:`/notes/autograd`) by copying :attr:`data`. + + .. warning:: + + When working with tensors prefer using :func:`torch.Tensor.clone`, + :func:`torch.Tensor.detach`, and :func:`torch.Tensor.requires_grad_` for + readability. Letting `t` be a tensor, ``torch.tensor(t)`` is equivalent to + ``t.detach().clone()``, and ``torch.tensor(t, requires_grad=True)`` + is equivalent to ``t.detach().clone().requires_grad_(True)``. + + .. seealso:: + + :func:`torch.as_tensor` preserves autograd history and avoids copies where possible. + :func:`torch.from_numpy` creates a tensor that shares storage with a NumPy array. + + Args: + data (array_like): Initial data for the tensor. Can be a list, tuple, + NumPy ``ndarray``, scalar, and other types. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, infers data type from :attr:`data`. + device (:class:`torch.device`, optional): the device of the constructed tensor. If None and data is a tensor + then the device of data is used. If None and data is not a tensor then + the result tensor is constructed on the current device. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + pin_memory (bool, optional): If set, returned tensor would be allocated in + the pinned memory. Works only for CPU tensors. Default: ``False``. + + + Example:: + + >>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]]) + tensor([[ 0.1000, 1.2000], + [ 2.2000, 3.1000], + [ 4.9000, 5.2000]]) + + >>> torch.tensor([0, 1]) # Type inference on data + tensor([ 0, 1]) + + >>> torch.tensor([[0.11111, 0.222222, 0.3333333]], + ... dtype=torch.float64, + ... device=torch.device('cuda:0')) # creates a double tensor on a CUDA device + tensor([[ 0.1111, 0.2222, 0.3333]], dtype=torch.float64, device='cuda:0') + + >>> torch.tensor(3.14159) # Create a zero-dimensional (scalar) tensor + tensor(3.1416) + + >>> torch.tensor([]) # Create an empty tensor (of size (0,)) + tensor([]) + """ + ... +@overload +def tensor_split(input: Tensor, tensor_indices_or_sections: Tensor, dim: _int = 0) -> tuple[Tensor, ...]: + r""" + tensor_split(input, indices_or_sections, dim=0) -> List of Tensors + + Splits a tensor into multiple sub-tensors, all of which are views of :attr:`input`, + along dimension :attr:`dim` according to the indices or number of sections specified + by :attr:`indices_or_sections`. This function is based on NumPy's + :func:`numpy.array_split`. + + Args: + input (Tensor): the tensor to split + indices_or_sections (Tensor, int or list or tuple of ints): + If :attr:`indices_or_sections` is an integer ``n`` or a zero dimensional long tensor + with value ``n``, :attr:`input` is split into ``n`` sections along dimension :attr:`dim`. + If :attr:`input` is divisible by ``n`` along dimension :attr:`dim`, each + section will be of equal size, :code:`input.size(dim) / n`. If :attr:`input` + is not divisible by ``n``, the sizes of the first :code:`int(input.size(dim) % n)` + sections will have size :code:`int(input.size(dim) / n) + 1`, and the rest will + have size :code:`int(input.size(dim) / n)`. + + If :attr:`indices_or_sections` is a list or tuple of ints, or a one-dimensional long + tensor, then :attr:`input` is split along dimension :attr:`dim` at each of the indices + in the list, tuple or tensor. For instance, :code:`indices_or_sections=[2, 3]` and :code:`dim=0` + would result in the tensors :code:`input[:2]`, :code:`input[2:3]`, and :code:`input[3:]`. + + If :attr:`indices_or_sections` is a tensor, it must be a zero-dimensional or one-dimensional + long tensor on the CPU. + + dim (int, optional): dimension along which to split the tensor. Default: ``0`` + + Example:: + + >>> x = torch.arange(8) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4, 5]), tensor([6, 7])) + + >>> x = torch.arange(7) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4]), tensor([5, 6])) + >>> torch.tensor_split(x, (1, 6)) + (tensor([0]), tensor([1, 2, 3, 4, 5]), tensor([6])) + + >>> x = torch.arange(14).reshape(2, 7) + >>> x + tensor([[ 0, 1, 2, 3, 4, 5, 6], + [ 7, 8, 9, 10, 11, 12, 13]]) + >>> torch.tensor_split(x, 3, dim=1) + (tensor([[0, 1, 2], + [7, 8, 9]]), + tensor([[ 3, 4], + [10, 11]]), + tensor([[ 5, 6], + [12, 13]])) + >>> torch.tensor_split(x, (1, 6), dim=1) + (tensor([[0], + [7]]), + tensor([[ 1, 2, 3, 4, 5], + [ 8, 9, 10, 11, 12]]), + tensor([[ 6], + [13]])) + """ + ... +@overload +def tensor_split(input: Tensor, sections: Union[_int, SymInt], dim: _int = 0) -> tuple[Tensor, ...]: + r""" + tensor_split(input, indices_or_sections, dim=0) -> List of Tensors + + Splits a tensor into multiple sub-tensors, all of which are views of :attr:`input`, + along dimension :attr:`dim` according to the indices or number of sections specified + by :attr:`indices_or_sections`. This function is based on NumPy's + :func:`numpy.array_split`. + + Args: + input (Tensor): the tensor to split + indices_or_sections (Tensor, int or list or tuple of ints): + If :attr:`indices_or_sections` is an integer ``n`` or a zero dimensional long tensor + with value ``n``, :attr:`input` is split into ``n`` sections along dimension :attr:`dim`. + If :attr:`input` is divisible by ``n`` along dimension :attr:`dim`, each + section will be of equal size, :code:`input.size(dim) / n`. If :attr:`input` + is not divisible by ``n``, the sizes of the first :code:`int(input.size(dim) % n)` + sections will have size :code:`int(input.size(dim) / n) + 1`, and the rest will + have size :code:`int(input.size(dim) / n)`. + + If :attr:`indices_or_sections` is a list or tuple of ints, or a one-dimensional long + tensor, then :attr:`input` is split along dimension :attr:`dim` at each of the indices + in the list, tuple or tensor. For instance, :code:`indices_or_sections=[2, 3]` and :code:`dim=0` + would result in the tensors :code:`input[:2]`, :code:`input[2:3]`, and :code:`input[3:]`. + + If :attr:`indices_or_sections` is a tensor, it must be a zero-dimensional or one-dimensional + long tensor on the CPU. + + dim (int, optional): dimension along which to split the tensor. Default: ``0`` + + Example:: + + >>> x = torch.arange(8) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4, 5]), tensor([6, 7])) + + >>> x = torch.arange(7) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4]), tensor([5, 6])) + >>> torch.tensor_split(x, (1, 6)) + (tensor([0]), tensor([1, 2, 3, 4, 5]), tensor([6])) + + >>> x = torch.arange(14).reshape(2, 7) + >>> x + tensor([[ 0, 1, 2, 3, 4, 5, 6], + [ 7, 8, 9, 10, 11, 12, 13]]) + >>> torch.tensor_split(x, 3, dim=1) + (tensor([[0, 1, 2], + [7, 8, 9]]), + tensor([[ 3, 4], + [10, 11]]), + tensor([[ 5, 6], + [12, 13]])) + >>> torch.tensor_split(x, (1, 6), dim=1) + (tensor([[0], + [7]]), + tensor([[ 1, 2, 3, 4, 5], + [ 8, 9, 10, 11, 12]]), + tensor([[ 6], + [13]])) + """ + ... +@overload +def tensor_split(input: Tensor, indices: Sequence[Union[_int, SymInt]], dim: _int = 0) -> tuple[Tensor, ...]: + r""" + tensor_split(input, indices_or_sections, dim=0) -> List of Tensors + + Splits a tensor into multiple sub-tensors, all of which are views of :attr:`input`, + along dimension :attr:`dim` according to the indices or number of sections specified + by :attr:`indices_or_sections`. This function is based on NumPy's + :func:`numpy.array_split`. + + Args: + input (Tensor): the tensor to split + indices_or_sections (Tensor, int or list or tuple of ints): + If :attr:`indices_or_sections` is an integer ``n`` or a zero dimensional long tensor + with value ``n``, :attr:`input` is split into ``n`` sections along dimension :attr:`dim`. + If :attr:`input` is divisible by ``n`` along dimension :attr:`dim`, each + section will be of equal size, :code:`input.size(dim) / n`. If :attr:`input` + is not divisible by ``n``, the sizes of the first :code:`int(input.size(dim) % n)` + sections will have size :code:`int(input.size(dim) / n) + 1`, and the rest will + have size :code:`int(input.size(dim) / n)`. + + If :attr:`indices_or_sections` is a list or tuple of ints, or a one-dimensional long + tensor, then :attr:`input` is split along dimension :attr:`dim` at each of the indices + in the list, tuple or tensor. For instance, :code:`indices_or_sections=[2, 3]` and :code:`dim=0` + would result in the tensors :code:`input[:2]`, :code:`input[2:3]`, and :code:`input[3:]`. + + If :attr:`indices_or_sections` is a tensor, it must be a zero-dimensional or one-dimensional + long tensor on the CPU. + + dim (int, optional): dimension along which to split the tensor. Default: ``0`` + + Example:: + + >>> x = torch.arange(8) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4, 5]), tensor([6, 7])) + + >>> x = torch.arange(7) + >>> torch.tensor_split(x, 3) + (tensor([0, 1, 2]), tensor([3, 4]), tensor([5, 6])) + >>> torch.tensor_split(x, (1, 6)) + (tensor([0]), tensor([1, 2, 3, 4, 5]), tensor([6])) + + >>> x = torch.arange(14).reshape(2, 7) + >>> x + tensor([[ 0, 1, 2, 3, 4, 5, 6], + [ 7, 8, 9, 10, 11, 12, 13]]) + >>> torch.tensor_split(x, 3, dim=1) + (tensor([[0, 1, 2], + [7, 8, 9]]), + tensor([[ 3, 4], + [10, 11]]), + tensor([[ 5, 6], + [12, 13]])) + >>> torch.tensor_split(x, (1, 6), dim=1) + (tensor([[0], + [7]]), + tensor([[ 1, 2, 3, 4, 5], + [ 8, 9, 10, 11, 12]]), + tensor([[ 6], + [13]])) + """ + ... +def threshold(input: Tensor, threshold: Union[Number, _complex], value: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: ... +def threshold_(input: Tensor, threshold: Union[Number, _complex], value: Union[Number, _complex]) -> Tensor: ... +def tile(input: Tensor, dims: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + tile(input, dims) -> Tensor + + Constructs a tensor by repeating the elements of :attr:`input`. + The :attr:`dims` argument specifies the number of repetitions + in each dimension. + + If :attr:`dims` specifies fewer dimensions than :attr:`input` has, then + ones are prepended to :attr:`dims` until all dimensions are specified. + For example, if :attr:`input` has shape (8, 6, 4, 2) and :attr:`dims` + is (2, 2), then :attr:`dims` is treated as (1, 1, 2, 2). + + Analogously, if :attr:`input` has fewer dimensions than :attr:`dims` + specifies, then :attr:`input` is treated as if it were unsqueezed at + dimension zero until it has as many dimensions as :attr:`dims` specifies. + For example, if :attr:`input` has shape (4, 2) and :attr:`dims` + is (3, 3, 2, 2), then :attr:`input` is treated as if it had the + shape (1, 1, 4, 2). + + .. note:: + + This function is similar to NumPy's tile function. + + Args: + input (Tensor): the tensor whose elements to repeat. + dims (tuple): the number of repetitions per dimension. + + Example:: + + >>> x = torch.tensor([1, 2, 3]) + >>> x.tile((2,)) + tensor([1, 2, 3, 1, 2, 3]) + >>> y = torch.tensor([[1, 2], [3, 4]]) + >>> torch.tile(y, (2, 2)) + tensor([[1, 2, 1, 2], + [3, 4, 3, 4], + [1, 2, 1, 2], + [3, 4, 3, 4]]) + """ + ... +def topk(input: Tensor, k: Union[_int, SymInt], dim: _int = -1, largest: _bool = True, sorted: _bool = True, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.topk: + r""" + topk(input, k, dim=None, largest=True, sorted=True, *, out=None) -> (Tensor, LongTensor) + + Returns the :attr:`k` largest elements of the given :attr:`input` tensor along + a given dimension. + + If :attr:`dim` is not given, the last dimension of the `input` is chosen. + + If :attr:`largest` is ``False`` then the `k` smallest elements are returned. + + A namedtuple of `(values, indices)` is returned with the `values` and + `indices` of the largest `k` elements of each row of the `input` tensor in the + given dimension `dim`. + + The boolean option :attr:`sorted` if ``True``, will make sure that the returned + `k` elements are themselves sorted + + .. note:: + When using `torch.topk`, the indices of tied elements are not guaranteed to be stable + and may vary across different invocations. + + Args: + input (Tensor): the input tensor. + k (int): the k in "top-k" + dim (int, optional): the dimension to sort along + largest (bool, optional): controls whether to return largest or + smallest elements + sorted (bool, optional): controls whether to return the elements + in sorted order + + Keyword args: + out (tuple, optional): the output tuple of (Tensor, LongTensor) that can be + optionally given to be used as output buffers + + Example:: + + >>> x = torch.arange(1., 6.) + >>> x + tensor([ 1., 2., 3., 4., 5.]) + >>> torch.topk(x, 3) + torch.return_types.topk(values=tensor([5., 4., 3.]), indices=tensor([4, 3, 2])) + """ + ... +def trace(input: Tensor) -> Tensor: + r""" + trace(input) -> Tensor + + Returns the sum of the elements of the diagonal of the input 2-D matrix. + + Example:: + + >>> x = torch.arange(1., 10.).view(3, 3) + >>> x + tensor([[ 1., 2., 3.], + [ 4., 5., 6.], + [ 7., 8., 9.]]) + >>> torch.trace(x) + tensor(15.) + """ + ... +@overload +def transpose(input: Tensor, dim0: _int, dim1: _int) -> Tensor: + r""" + transpose(input, dim0, dim1) -> Tensor + + Returns a tensor that is a transposed version of :attr:`input`. + The given dimensions :attr:`dim0` and :attr:`dim1` are swapped. + + If :attr:`input` is a strided tensor then the resulting :attr:`out` + tensor shares its underlying storage with the :attr:`input` tensor, so + changing the content of one would change the content of the other. + + If :attr:`input` is a :ref:`sparse tensor ` then the + resulting :attr:`out` tensor *does not* share the underlying storage + with the :attr:`input` tensor. + + If :attr:`input` is a :ref:`sparse tensor ` with compressed + layout (SparseCSR, SparseBSR, SparseCSC or SparseBSC) the arguments + :attr:`dim0` and :attr:`dim1` must be both batch dimensions, or must + both be sparse dimensions. The batch dimensions of a sparse tensor are the + dimensions preceding the sparse dimensions. + + .. note:: + Transpositions which interchange the sparse dimensions of a `SparseCSR` + or `SparseCSC` layout tensor will result in the layout changing between + the two options. Transposition of the sparse dimensions of a ` SparseBSR` + or `SparseBSC` layout tensor will likewise generate a result with the + opposite layout. + + + Args: + input (Tensor): the input tensor. + dim0 (int): the first dimension to be transposed + dim1 (int): the second dimension to be transposed + + Example:: + + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 1.0028, -0.9893, 0.5809], + [-0.1669, 0.7299, 0.4942]]) + >>> torch.transpose(x, 0, 1) + tensor([[ 1.0028, -0.1669], + [-0.9893, 0.7299], + [ 0.5809, 0.4942]]) + + See also :func:`torch.t`. + """ + ... +@overload +def transpose(input: Tensor, dim0: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None]) -> Tensor: + r""" + transpose(input, dim0, dim1) -> Tensor + + Returns a tensor that is a transposed version of :attr:`input`. + The given dimensions :attr:`dim0` and :attr:`dim1` are swapped. + + If :attr:`input` is a strided tensor then the resulting :attr:`out` + tensor shares its underlying storage with the :attr:`input` tensor, so + changing the content of one would change the content of the other. + + If :attr:`input` is a :ref:`sparse tensor ` then the + resulting :attr:`out` tensor *does not* share the underlying storage + with the :attr:`input` tensor. + + If :attr:`input` is a :ref:`sparse tensor ` with compressed + layout (SparseCSR, SparseBSR, SparseCSC or SparseBSC) the arguments + :attr:`dim0` and :attr:`dim1` must be both batch dimensions, or must + both be sparse dimensions. The batch dimensions of a sparse tensor are the + dimensions preceding the sparse dimensions. + + .. note:: + Transpositions which interchange the sparse dimensions of a `SparseCSR` + or `SparseCSC` layout tensor will result in the layout changing between + the two options. Transposition of the sparse dimensions of a ` SparseBSR` + or `SparseBSC` layout tensor will likewise generate a result with the + opposite layout. + + + Args: + input (Tensor): the input tensor. + dim0 (int): the first dimension to be transposed + dim1 (int): the second dimension to be transposed + + Example:: + + >>> x = torch.randn(2, 3) + >>> x + tensor([[ 1.0028, -0.9893, 0.5809], + [-0.1669, 0.7299, 0.4942]]) + >>> torch.transpose(x, 0, 1) + tensor([[ 1.0028, -0.1669], + [-0.9893, 0.7299], + [ 0.5809, 0.4942]]) + + See also :func:`torch.t`. + """ + ... +def transpose_copy(input: Tensor, dim0: _int, dim1: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.transpose`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def trapezoid(y: Tensor, x: Tensor, *, dim: _int = -1) -> Tensor: + r""" + trapezoid(y, x=None, *, dx=None, dim=-1) -> Tensor + + Computes the `trapezoidal rule `_ along + :attr:`dim`. By default the spacing between elements is assumed to be 1, but + :attr:`dx` can be used to specify a different constant spacing, and :attr:`x` can be + used to specify arbitrary spacing along :attr:`dim`. + + + Assuming :attr:`y` is a one-dimensional tensor with elements :math:`{y_0, y_1, ..., y_n}`, + the default computation is + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n} \frac{1}{2} (y_i + y_{i-1}) + \end{aligned} + + When :attr:`dx` is specified the computation becomes + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n} \frac{\Delta x}{2} (y_i + y_{i-1}) + \end{aligned} + + effectively multiplying the result by :attr:`dx`. When :attr:`x` is specified, + assuming :attr:`x` is also a one-dimensional tensor with + elements :math:`{x_0, x_1, ..., x_n}`, the computation becomes + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n} \frac{(x_i - x_{i-1})}{2} (y_i + y_{i-1}) + \end{aligned} + + When :attr:`x` and :attr:`y` have the same size, the computation is as described above and no broadcasting is needed. + The broadcasting behavior of this function is as follows when their sizes are different. For both :attr:`x` + and :attr:`y`, the function computes the difference between consecutive elements along + dimension :attr:`dim`. This effectively creates two tensors, `x_diff` and `y_diff`, that have + the same shape as the original tensors except their lengths along the dimension :attr:`dim` is reduced by 1. + After that, those two tensors are broadcast together to compute final output as part of the trapezoidal rule. + See the examples below for details. + + .. note:: + The trapezoidal rule is a technique for approximating the definite integral of a function + by averaging its left and right Riemann sums. The approximation becomes more accurate as + the resolution of the partition increases. + + Arguments: + y (Tensor): Values to use when computing the trapezoidal rule. + x (Tensor): If specified, defines spacing between values as specified above. + + Keyword arguments: + dx (float): constant spacing between values. If neither :attr:`x` or :attr:`dx` + are specified then this defaults to 1. Effectively multiplies the result by its value. + dim (int): The dimension along which to compute the trapezoidal rule. + The last (inner-most) dimension by default. + + Examples:: + + >>> # Computes the trapezoidal rule in 1D, spacing is implicitly 1 + >>> y = torch.tensor([1, 5, 10]) + >>> torch.trapezoid(y) + tensor(10.5) + + >>> # Computes the same trapezoidal rule directly to verify + >>> (1 + 10 + 10) / 2 + 10.5 + + >>> # Computes the trapezoidal rule in 1D with constant spacing of 2 + >>> # NOTE: the result is the same as before, but multiplied by 2 + >>> torch.trapezoid(y, dx=2) + 21.0 + + >>> # Computes the trapezoidal rule in 1D with arbitrary spacing + >>> x = torch.tensor([1, 3, 6]) + >>> torch.trapezoid(y, x) + 28.5 + + >>> # Computes the same trapezoidal rule directly to verify + >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2 + 28.5 + + >>> # Computes the trapezoidal rule for each row of a 3x3 matrix + >>> y = torch.arange(9).reshape(3, 3) + tensor([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> torch.trapezoid(y) + tensor([ 2., 8., 14.]) + + >>> # Computes the trapezoidal rule for each column of the matrix + >>> torch.trapezoid(y, dim=0) + tensor([ 6., 8., 10.]) + + >>> # Computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with the same arbitrary spacing + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([1, 3, 6]) + >>> torch.trapezoid(y, x) + array([5., 5., 5.]) + + >>> # Computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with different arbitrary spacing per row + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]]) + >>> torch.trapezoid(y, x) + array([2., 4., 6.]) + """ + ... +@overload +def trapezoid(y: Tensor, *, dx: Union[Number, _complex] = 1, dim: _int = -1) -> Tensor: + r""" + trapezoid(y, x=None, *, dx=None, dim=-1) -> Tensor + + Computes the `trapezoidal rule `_ along + :attr:`dim`. By default the spacing between elements is assumed to be 1, but + :attr:`dx` can be used to specify a different constant spacing, and :attr:`x` can be + used to specify arbitrary spacing along :attr:`dim`. + + + Assuming :attr:`y` is a one-dimensional tensor with elements :math:`{y_0, y_1, ..., y_n}`, + the default computation is + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n} \frac{1}{2} (y_i + y_{i-1}) + \end{aligned} + + When :attr:`dx` is specified the computation becomes + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n} \frac{\Delta x}{2} (y_i + y_{i-1}) + \end{aligned} + + effectively multiplying the result by :attr:`dx`. When :attr:`x` is specified, + assuming :attr:`x` is also a one-dimensional tensor with + elements :math:`{x_0, x_1, ..., x_n}`, the computation becomes + + .. math:: + \begin{aligned} + \sum_{i = 1}^{n} \frac{(x_i - x_{i-1})}{2} (y_i + y_{i-1}) + \end{aligned} + + When :attr:`x` and :attr:`y` have the same size, the computation is as described above and no broadcasting is needed. + The broadcasting behavior of this function is as follows when their sizes are different. For both :attr:`x` + and :attr:`y`, the function computes the difference between consecutive elements along + dimension :attr:`dim`. This effectively creates two tensors, `x_diff` and `y_diff`, that have + the same shape as the original tensors except their lengths along the dimension :attr:`dim` is reduced by 1. + After that, those two tensors are broadcast together to compute final output as part of the trapezoidal rule. + See the examples below for details. + + .. note:: + The trapezoidal rule is a technique for approximating the definite integral of a function + by averaging its left and right Riemann sums. The approximation becomes more accurate as + the resolution of the partition increases. + + Arguments: + y (Tensor): Values to use when computing the trapezoidal rule. + x (Tensor): If specified, defines spacing between values as specified above. + + Keyword arguments: + dx (float): constant spacing between values. If neither :attr:`x` or :attr:`dx` + are specified then this defaults to 1. Effectively multiplies the result by its value. + dim (int): The dimension along which to compute the trapezoidal rule. + The last (inner-most) dimension by default. + + Examples:: + + >>> # Computes the trapezoidal rule in 1D, spacing is implicitly 1 + >>> y = torch.tensor([1, 5, 10]) + >>> torch.trapezoid(y) + tensor(10.5) + + >>> # Computes the same trapezoidal rule directly to verify + >>> (1 + 10 + 10) / 2 + 10.5 + + >>> # Computes the trapezoidal rule in 1D with constant spacing of 2 + >>> # NOTE: the result is the same as before, but multiplied by 2 + >>> torch.trapezoid(y, dx=2) + 21.0 + + >>> # Computes the trapezoidal rule in 1D with arbitrary spacing + >>> x = torch.tensor([1, 3, 6]) + >>> torch.trapezoid(y, x) + 28.5 + + >>> # Computes the same trapezoidal rule directly to verify + >>> ((3 - 1) * (1 + 5) + (6 - 3) * (5 + 10)) / 2 + 28.5 + + >>> # Computes the trapezoidal rule for each row of a 3x3 matrix + >>> y = torch.arange(9).reshape(3, 3) + tensor([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> torch.trapezoid(y) + tensor([ 2., 8., 14.]) + + >>> # Computes the trapezoidal rule for each column of the matrix + >>> torch.trapezoid(y, dim=0) + tensor([ 6., 8., 10.]) + + >>> # Computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with the same arbitrary spacing + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([1, 3, 6]) + >>> torch.trapezoid(y, x) + array([5., 5., 5.]) + + >>> # Computes the trapezoidal rule for each row of a 3x3 ones matrix + >>> # with different arbitrary spacing per row + >>> y = torch.ones(3, 3) + >>> x = torch.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]]) + >>> torch.trapezoid(y, x) + array([2., 4., 6.]) + """ + ... +@overload +def trapz(y: Tensor, *, dx: _float = 1, dim: _int = -1) -> Tensor: + r""" + trapz(y, x, *, dim=-1) -> Tensor + + Alias for :func:`torch.trapezoid`. + """ + ... +@overload +def trapz(y: Tensor, x: Tensor, *, dim: _int = -1) -> Tensor: + r""" + trapz(y, x, *, dim=-1) -> Tensor + + Alias for :func:`torch.trapezoid`. + """ + ... +def triangular_solve(input: Tensor, A: Tensor, upper: _bool = True, transpose: _bool = False, unitriangular: _bool = False, *, out: Optional[Union[Tensor, tuple[Tensor, ...], list[Tensor]]] = None) -> torch.return_types.triangular_solve: + r""" + triangular_solve(b, A, upper=True, transpose=False, unitriangular=False, *, out=None) -> (Tensor, Tensor) + + Solves a system of equations with a square upper or lower triangular invertible matrix :math:`A` + and multiple right-hand sides :math:`b`. + + In symbols, it solves :math:`AX = b` and assumes :math:`A` is square upper-triangular + (or lower-triangular if :attr:`upper`\ `= False`) and does not have zeros on the diagonal. + + `torch.triangular_solve(b, A)` can take in 2D inputs `b, A` or inputs that are + batches of 2D matrices. If the inputs are batches, then returns + batched outputs `X` + + If the diagonal of :attr:`A` contains zeros or elements that are very close to zero and + :attr:`unitriangular`\ `= False` (default) or if the input matrix is badly conditioned, + the result may contain `NaN` s. + + Supports input of float, double, cfloat and cdouble data types. + + .. warning:: + + :func:`torch.triangular_solve` is deprecated in favor of :func:`torch.linalg.solve_triangular` + and will be removed in a future PyTorch release. + :func:`torch.linalg.solve_triangular` has its arguments reversed and does not return a + copy of one of the inputs. + + ``X = torch.triangular_solve(B, A).solution`` should be replaced with + + .. code:: python + + X = torch.linalg.solve_triangular(A, B) + + Args: + b (Tensor): multiple right-hand sides of size :math:`(*, m, k)` where + :math:`*` is zero of more batch dimensions + A (Tensor): the input triangular coefficient matrix of size :math:`(*, m, m)` + where :math:`*` is zero or more batch dimensions + upper (bool, optional): whether :math:`A` is upper or lower triangular. Default: ``True``. + transpose (bool, optional): solves `op(A)X = b` where `op(A) = A^T` if this flag is ``True``, + and `op(A) = A` if it is ``False``. Default: ``False``. + unitriangular (bool, optional): whether :math:`A` is unit triangular. + If True, the diagonal elements of :math:`A` are assumed to be + 1 and not referenced from :math:`A`. Default: ``False``. + + Keyword args: + out ((Tensor, Tensor), optional): tuple of two tensors to write + the output to. Ignored if `None`. Default: `None`. + + Returns: + A namedtuple `(solution, cloned_coefficient)` where `cloned_coefficient` + is a clone of :math:`A` and `solution` is the solution :math:`X` to :math:`AX = b` + (or whatever variant of the system of equations, depending on the keyword arguments.) + + Examples:: + + >>> A = torch.randn(2, 2).triu() + >>> A + tensor([[ 1.1527, -1.0753], + [ 0.0000, 0.7986]]) + >>> b = torch.randn(2, 3) + >>> b + tensor([[-0.0210, 2.3513, -1.5492], + [ 1.5429, 0.7403, -1.0243]]) + >>> torch.triangular_solve(b, A) + torch.return_types.triangular_solve( + solution=tensor([[ 1.7841, 2.9046, -2.5405], + [ 1.9320, 0.9270, -1.2826]]), + cloned_coefficient=tensor([[ 1.1527, -1.0753], + [ 0.0000, 0.7986]])) + """ + ... +def tril(input: Tensor, diagonal: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + tril(input, diagonal=0, *, out=None) -> Tensor + + Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices + :attr:`input`, the other elements of the result tensor :attr:`out` are set to 0. + + The lower triangular part of the matrix is defined as the elements on and + below the diagonal. + + The argument :attr:`diagonal` controls which diagonal to consider. If + :attr:`diagonal` = 0, all elements on and below the main diagonal are + retained. A positive value includes just as many diagonals above the main + diagonal, and similarly a negative value excludes just as many diagonals below + the main diagonal. The main diagonal are the set of indices + :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where + :math:`d_{1}, d_{2}` are the dimensions of the matrix. + + Args: + input (Tensor): the input tensor. + diagonal (int, optional): the diagonal to consider + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(3, 3) + >>> a + tensor([[-1.0813, -0.8619, 0.7105], + [ 0.0935, 0.1380, 2.2112], + [-0.3409, -0.9828, 0.0289]]) + >>> torch.tril(a) + tensor([[-1.0813, 0.0000, 0.0000], + [ 0.0935, 0.1380, 0.0000], + [-0.3409, -0.9828, 0.0289]]) + + >>> b = torch.randn(4, 6) + >>> b + tensor([[ 1.2219, 0.5653, -0.2521, -0.2345, 1.2544, 0.3461], + [ 0.4785, -0.4477, 0.6049, 0.6368, 0.8775, 0.7145], + [ 1.1502, 3.2716, -1.1243, -0.5413, 0.3615, 0.6864], + [-0.0614, -0.7344, -1.3164, -0.7648, -1.4024, 0.0978]]) + >>> torch.tril(b, diagonal=1) + tensor([[ 1.2219, 0.5653, 0.0000, 0.0000, 0.0000, 0.0000], + [ 0.4785, -0.4477, 0.6049, 0.0000, 0.0000, 0.0000], + [ 1.1502, 3.2716, -1.1243, -0.5413, 0.0000, 0.0000], + [-0.0614, -0.7344, -1.3164, -0.7648, -1.4024, 0.0000]]) + >>> torch.tril(b, diagonal=-1) + tensor([[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], + [ 0.4785, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], + [ 1.1502, 3.2716, 0.0000, 0.0000, 0.0000, 0.0000], + [-0.0614, -0.7344, -1.3164, 0.0000, 0.0000, 0.0000]]) + """ + ... +def tril_indices(row: _int, col: _int, offset: _int = 0, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + tril_indices(row, col, offset=0, *, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor + + Returns the indices of the lower triangular part of a :attr:`row`-by- + :attr:`col` matrix in a 2-by-N Tensor, where the first row contains row + coordinates of all indices and the second row contains column coordinates. + Indices are ordered based on rows and then columns. + + The lower triangular part of the matrix is defined as the elements on and + below the diagonal. + + The argument :attr:`offset` controls which diagonal to consider. If + :attr:`offset` = 0, all elements on and below the main diagonal are + retained. A positive value includes just as many diagonals above the main + diagonal, and similarly a negative value excludes just as many diagonals below + the main diagonal. The main diagonal are the set of indices + :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` + where :math:`d_{1}, d_{2}` are the dimensions of the matrix. + + .. note:: + When running on CUDA, ``row * col`` must be less than :math:`2^{59}` to + prevent overflow during calculation. + + Args: + row (``int``): number of rows in the 2-D matrix. + col (``int``): number of columns in the 2-D matrix. + offset (``int``): diagonal offset from the main diagonal. + Default: if not provided, 0. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, ``torch.long``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + layout (:class:`torch.layout`, optional): currently only support ``torch.strided``. + + Example:: + + >>> a = torch.tril_indices(3, 3) + >>> a + tensor([[0, 1, 1, 2, 2, 2], + [0, 0, 1, 0, 1, 2]]) + + >>> a = torch.tril_indices(4, 3, -1) + >>> a + tensor([[1, 2, 2, 3, 3, 3], + [0, 0, 1, 0, 1, 2]]) + + >>> a = torch.tril_indices(4, 3, 1) + >>> a + tensor([[0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3], + [0, 1, 0, 1, 2, 0, 1, 2, 0, 1, 2]]) + """ + ... +def triplet_margin_loss(anchor: Tensor, positive: Tensor, negative: Tensor, margin: _float = 1.0, p: _float = 2, eps: _float = 1e-06, swap: _bool = False, reduction: _int = 1) -> Tensor: ... +def triu(input: Tensor, diagonal: _int = 0, *, out: Optional[Tensor] = None) -> Tensor: + r""" + triu(input, diagonal=0, *, out=None) -> Tensor + + Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices + :attr:`input`, the other elements of the result tensor :attr:`out` are set to 0. + + The upper triangular part of the matrix is defined as the elements on and + above the diagonal. + + The argument :attr:`diagonal` controls which diagonal to consider. If + :attr:`diagonal` = 0, all elements on and above the main diagonal are + retained. A positive value excludes just as many diagonals above the main + diagonal, and similarly a negative value includes just as many diagonals below + the main diagonal. The main diagonal are the set of indices + :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where + :math:`d_{1}, d_{2}` are the dimensions of the matrix. + + Args: + input (Tensor): the input tensor. + diagonal (int, optional): the diagonal to consider + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(3, 3) + >>> a + tensor([[ 0.2309, 0.5207, 2.0049], + [ 0.2072, -1.0680, 0.6602], + [ 0.3480, -0.5211, -0.4573]]) + >>> torch.triu(a) + tensor([[ 0.2309, 0.5207, 2.0049], + [ 0.0000, -1.0680, 0.6602], + [ 0.0000, 0.0000, -0.4573]]) + >>> torch.triu(a, diagonal=1) + tensor([[ 0.0000, 0.5207, 2.0049], + [ 0.0000, 0.0000, 0.6602], + [ 0.0000, 0.0000, 0.0000]]) + >>> torch.triu(a, diagonal=-1) + tensor([[ 0.2309, 0.5207, 2.0049], + [ 0.2072, -1.0680, 0.6602], + [ 0.0000, -0.5211, -0.4573]]) + + >>> b = torch.randn(4, 6) + >>> b + tensor([[ 0.5876, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], + [-0.2447, 0.9556, -1.2919, 1.3378, -0.1768, -1.0857], + [ 0.4333, 0.3146, 0.6576, -1.0432, 0.9348, -0.4410], + [-0.9888, 1.0679, -1.3337, -1.6556, 0.4798, 0.2830]]) + >>> torch.triu(b, diagonal=1) + tensor([[ 0.0000, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], + [ 0.0000, 0.0000, -1.2919, 1.3378, -0.1768, -1.0857], + [ 0.0000, 0.0000, 0.0000, -1.0432, 0.9348, -0.4410], + [ 0.0000, 0.0000, 0.0000, 0.0000, 0.4798, 0.2830]]) + >>> torch.triu(b, diagonal=-1) + tensor([[ 0.5876, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], + [-0.2447, 0.9556, -1.2919, 1.3378, -0.1768, -1.0857], + [ 0.0000, 0.3146, 0.6576, -1.0432, 0.9348, -0.4410], + [ 0.0000, 0.0000, -1.3337, -1.6556, 0.4798, 0.2830]]) + """ + ... +def triu_indices(row: _int, col: _int, offset: _int = 0, *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + triu_indices(row, col, offset=0, *, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor + + Returns the indices of the upper triangular part of a :attr:`row` by + :attr:`col` matrix in a 2-by-N Tensor, where the first row contains row + coordinates of all indices and the second row contains column coordinates. + Indices are ordered based on rows and then columns. + + The upper triangular part of the matrix is defined as the elements on and + above the diagonal. + + The argument :attr:`offset` controls which diagonal to consider. If + :attr:`offset` = 0, all elements on and above the main diagonal are + retained. A positive value excludes just as many diagonals above the main + diagonal, and similarly a negative value includes just as many diagonals below + the main diagonal. The main diagonal are the set of indices + :math:`\lbrace (i, i) \rbrace` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` + where :math:`d_{1}, d_{2}` are the dimensions of the matrix. + + .. note:: + When running on CUDA, ``row * col`` must be less than :math:`2^{59}` to + prevent overflow during calculation. + + Args: + row (``int``): number of rows in the 2-D matrix. + col (``int``): number of columns in the 2-D matrix. + offset (``int``): diagonal offset from the main diagonal. + Default: if not provided, 0. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, ``torch.long``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + layout (:class:`torch.layout`, optional): currently only support ``torch.strided``. + + Example:: + + >>> a = torch.triu_indices(3, 3) + >>> a + tensor([[0, 0, 0, 1, 1, 2], + [0, 1, 2, 1, 2, 2]]) + + >>> a = torch.triu_indices(4, 3, -1) + >>> a + tensor([[0, 0, 0, 1, 1, 1, 2, 2, 3], + [0, 1, 2, 0, 1, 2, 1, 2, 2]]) + + >>> a = torch.triu_indices(4, 3, 1) + >>> a + tensor([[0, 0, 1], + [1, 2, 2]]) + """ + ... +def true_divide(input: Union[Tensor, Number], other: Union[Tensor, Number], *, out: Optional[Tensor] = None) -> Tensor: + r""" + true_divide(dividend, divisor, *, out) -> Tensor + + Alias for :func:`torch.div` with ``rounding_mode=None``. + """ + ... +def trunc(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + trunc(input, *, out=None) -> Tensor + + Returns a new tensor with the truncated integer values of + the elements of :attr:`input`. + + For integer inputs, follows the array-api convention of returning a + copy of the input tensor. + + Args: + input (Tensor): the input tensor. + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.randn(4) + >>> a + tensor([ 3.4742, 0.5466, -0.8008, -0.9079]) + >>> torch.trunc(a) + tensor([ 3., 0., -0., -0.]) + """ + ... +def trunc_(input: Tensor) -> Tensor: ... +@overload +def unbind(input: Tensor, dim: _int = 0) -> tuple[Tensor, ...]: + r""" + unbind(input, dim=0) -> seq + + Removes a tensor dimension. + + Returns a tuple of all slices along a given dimension, already without it. + + Arguments: + input (Tensor): the tensor to unbind + dim (int): dimension to remove + + Example:: + + >>> torch.unbind(torch.tensor([[1, 2, 3], + >>> [4, 5, 6], + >>> [7, 8, 9]])) + (tensor([1, 2, 3]), tensor([4, 5, 6]), tensor([7, 8, 9])) + """ + ... +@overload +def unbind(input: Tensor, dim: Union[str, ellipsis, None]) -> tuple[Tensor, ...]: + r""" + unbind(input, dim=0) -> seq + + Removes a tensor dimension. + + Returns a tuple of all slices along a given dimension, already without it. + + Arguments: + input (Tensor): the tensor to unbind + dim (int): dimension to remove + + Example:: + + >>> torch.unbind(torch.tensor([[1, 2, 3], + >>> [4, 5, 6], + >>> [7, 8, 9]])) + (tensor([1, 2, 3]), tensor([4, 5, 6]), tensor([7, 8, 9])) + """ + ... +def unbind_copy(input: Tensor, dim: _int = 0, *, out: Optional[Union[tuple[Tensor, ...], list[Tensor]]] = None) -> None: + r""" + Performs the same operation as :func:`torch.unbind`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def unflatten(input: Tensor, dim: Union[str, ellipsis, None], sizes: Sequence[Union[_int, SymInt]], names: Sequence[Union[str, ellipsis, None]]) -> Tensor: + r""" + unflatten(input, dim, sizes) -> Tensor + + Expands a dimension of the input tensor over multiple dimensions. + + .. seealso:: + + :func:`torch.flatten` the inverse of this function. It coalesces several dimensions into one. + + Args: + input (Tensor): the input tensor. + dim (int): Dimension to be unflattened, specified as an index into + ``input.shape``. + sizes (Tuple[int]): New shape of the unflattened dimension. + One of its elements can be `-1` in which case the corresponding output + dimension is inferred. Otherwise, the product of ``sizes`` *must* + equal ``input.shape[dim]``. + + Returns: + A View of input with the specified dimension unflattened. + + Examples:: + >>> torch.unflatten(torch.randn(3, 4, 1), 1, (2, 2)).shape + torch.Size([3, 2, 2, 1]) + >>> torch.unflatten(torch.randn(3, 4, 1), 1, (-1, 2)).shape + torch.Size([3, 2, 2, 1]) + >>> torch.unflatten(torch.randn(5, 12, 3), -2, (2, 2, 3, 1, 1)).shape + torch.Size([5, 2, 2, 3, 1, 1, 3]) + """ + ... +@overload +def unflatten(input: Tensor, dim: _int, sizes: Sequence[Union[_int, SymInt]]) -> Tensor: + r""" + unflatten(input, dim, sizes) -> Tensor + + Expands a dimension of the input tensor over multiple dimensions. + + .. seealso:: + + :func:`torch.flatten` the inverse of this function. It coalesces several dimensions into one. + + Args: + input (Tensor): the input tensor. + dim (int): Dimension to be unflattened, specified as an index into + ``input.shape``. + sizes (Tuple[int]): New shape of the unflattened dimension. + One of its elements can be `-1` in which case the corresponding output + dimension is inferred. Otherwise, the product of ``sizes`` *must* + equal ``input.shape[dim]``. + + Returns: + A View of input with the specified dimension unflattened. + + Examples:: + >>> torch.unflatten(torch.randn(3, 4, 1), 1, (2, 2)).shape + torch.Size([3, 2, 2, 1]) + >>> torch.unflatten(torch.randn(3, 4, 1), 1, (-1, 2)).shape + torch.Size([3, 2, 2, 1]) + >>> torch.unflatten(torch.randn(5, 12, 3), -2, (2, 2, 3, 1, 1)).shape + torch.Size([5, 2, 2, 3, 1, 1, 3]) + """ + ... +def unfold_copy(input: Tensor, dimension: _int, size: _int, step: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.unfold`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def unique_dim(input: Tensor, dim: _int, sorted: _bool = True, return_inverse: _bool = False, return_counts: _bool = False) -> tuple[Tensor, Tensor, Tensor]: ... +def unsafe_chunk(input: Tensor, chunks: _int, dim: _int = 0) -> tuple[Tensor, ...]: + r""" + unsafe_chunk(input, chunks, dim=0) -> List of Tensors + + Works like :func:`torch.chunk` but without enforcing the autograd restrictions + on inplace modification of the outputs. + + .. warning:: + This function is safe to use as long as only the input, or only the outputs + are modified inplace after calling this function. It is user's + responsibility to ensure that is the case. If both the input and one or more + of the outputs are modified inplace, gradients computed by autograd will be + silently incorrect. + """ + ... +def unsafe_split(input: Tensor, split_size: Union[_int, SymInt], dim: _int = 0) -> tuple[Tensor, ...]: + r""" + unsafe_split(tensor, split_size_or_sections, dim=0) -> List of Tensors + + Works like :func:`torch.split` but without enforcing the autograd restrictions + on inplace modification of the outputs. + + .. warning:: + This function is safe to use as long as only the input, or only the outputs + are modified inplace after calling this function. It is user's + responsibility to ensure that is the case. If both the input and one or more + of the outputs are modified inplace, gradients computed by autograd will be + silently incorrect. + """ + ... +def unsafe_split_with_sizes(input: Tensor, split_sizes: Sequence[Union[_int, SymInt]], dim: _int = 0) -> tuple[Tensor, ...]: ... +def unsqueeze(input: Tensor, dim: _int) -> Tensor: + r""" + unsqueeze(input, dim) -> Tensor + + Returns a new tensor with a dimension of size one inserted at the + specified position. + + The returned tensor shares the same underlying data with this tensor. + + A :attr:`dim` value within the range ``[-input.dim() - 1, input.dim() + 1)`` + can be used. Negative :attr:`dim` will correspond to :meth:`unsqueeze` + applied at :attr:`dim` = ``dim + input.dim() + 1``. + + Args: + input (Tensor): the input tensor. + dim (int): the index at which to insert the singleton dimension + + Example:: + + >>> x = torch.tensor([1, 2, 3, 4]) + >>> torch.unsqueeze(x, 0) + tensor([[ 1, 2, 3, 4]]) + >>> torch.unsqueeze(x, 1) + tensor([[ 1], + [ 2], + [ 3], + [ 4]]) + """ + ... +def unsqueeze_copy(input: Tensor, dim: _int, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.unsqueeze`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def values_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.values`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def vander(x: Tensor, N: Optional[_int] = None, increasing: _bool = False) -> Tensor: + r""" + vander(x, N=None, increasing=False) -> Tensor + + Generates a Vandermonde matrix. + + The columns of the output matrix are elementwise powers of the input vector :math:`x^{(N-1)}, x^{(N-2)}, ..., x^0`. + If increasing is True, the order of the columns is reversed :math:`x^0, x^1, ..., x^{(N-1)}`. Such a + matrix with a geometric progression in each row is named for Alexandre-Theophile Vandermonde. + + Arguments: + x (Tensor): 1-D input tensor. + N (int, optional): Number of columns in the output. If N is not specified, + a square array is returned :math:`(N = len(x))`. + increasing (bool, optional): Order of the powers of the columns. If True, + the powers increase from left to right, if False (the default) they are reversed. + + Returns: + Tensor: Vandermonde matrix. If increasing is False, the first column is :math:`x^{(N-1)}`, + the second :math:`x^{(N-2)}` and so forth. If increasing is True, the columns + are :math:`x^0, x^1, ..., x^{(N-1)}`. + + Example:: + + >>> x = torch.tensor([1, 2, 3, 5]) + >>> torch.vander(x) + tensor([[ 1, 1, 1, 1], + [ 8, 4, 2, 1], + [ 27, 9, 3, 1], + [125, 25, 5, 1]]) + >>> torch.vander(x, N=3) + tensor([[ 1, 1, 1], + [ 4, 2, 1], + [ 9, 3, 1], + [25, 5, 1]]) + >>> torch.vander(x, N=3, increasing=True) + tensor([[ 1, 1, 1], + [ 1, 2, 4], + [ 1, 3, 9], + [ 1, 5, 25]]) + """ + ... +@overload +def var(input: Tensor, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + var(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the variance over the dimensions specified by :attr:`dim`. :attr:`dim` + can be a single dimension, list of dimensions, or ``None`` to reduce over all + dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var(a, dim=1, keepdim=True) + tensor([[1.0631], + [0.5590], + [1.4893], + [0.8258]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var(input: Tensor, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + var(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the variance over the dimensions specified by :attr:`dim`. :attr:`dim` + can be a single dimension, list of dimensions, or ``None`` to reduce over all + dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var(a, dim=1, keepdim=True) + tensor([[1.0631], + [0.5590], + [1.4893], + [0.8258]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var(input: Tensor, unbiased: _bool = True) -> Tensor: + r""" + var(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the variance over the dimensions specified by :attr:`dim`. :attr:`dim` + can be a single dimension, list of dimensions, or ``None`` to reduce over all + dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var(a, dim=1, keepdim=True) + tensor([[1.0631], + [0.5590], + [1.4893], + [0.8258]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False, out: Optional[Tensor] = None) -> Tensor: + r""" + var(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the variance over the dimensions specified by :attr:`dim`. :attr:`dim` + can be a single dimension, list of dimensions, or ``None`` to reduce over all + dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var(a, dim=1, keepdim=True) + tensor([[1.0631], + [0.5590], + [1.4893], + [0.8258]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False, *, out: Optional[Tensor] = None) -> Tensor: + r""" + var(input, dim=None, *, correction=1, keepdim=False, out=None) -> Tensor + + Calculates the variance over the dimensions specified by :attr:`dim`. :attr:`dim` + can be a single dimension, list of dimensions, or ``None`` to reduce over all + dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var(a, dim=1, keepdim=True) + tensor([[1.0631], + [0.5590], + [1.4893], + [0.8258]]) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var_mean(input: Tensor, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False) -> tuple[Tensor, Tensor]: + r""" + var_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the variance and mean over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (var, mean) containing the variance and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var_mean(a, dim=0, keepdim=True) + (tensor([[1.5926, 1.0056, 1.2005, 0.3646]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var_mean(input: Tensor, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> tuple[Tensor, Tensor]: + r""" + var_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the variance and mean over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (var, mean) containing the variance and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var_mean(a, dim=0, keepdim=True) + (tensor([[1.5926, 1.0056, 1.2005, 0.3646]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var_mean(input: Tensor, unbiased: _bool = True) -> tuple[Tensor, Tensor]: + r""" + var_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the variance and mean over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (var, mean) containing the variance and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var_mean(a, dim=0, keepdim=True) + (tensor([[1.5926, 1.0056, 1.2005, 0.3646]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var_mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> tuple[Tensor, Tensor]: + r""" + var_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the variance and mean over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (var, mean) containing the variance and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var_mean(a, dim=0, keepdim=True) + (tensor([[1.5926, 1.0056, 1.2005, 0.3646]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +@overload +def var_mean(input: Tensor, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False) -> tuple[Tensor, Tensor]: + r""" + var_mean(input, dim=None, *, correction=1, keepdim=False, out=None) -> (Tensor, Tensor) + + Calculates the variance and mean over the dimensions specified by :attr:`dim`. + :attr:`dim` can be a single dimension, list of dimensions, or ``None`` to + reduce over all dimensions. + + The variance (:math:`\sigma^2`) is calculated as + + .. math:: \sigma^2 = \frac{1}{\max(0,~N - \delta N)}\sum_{i=0}^{N-1}(x_i-\bar{x})^2 + + where :math:`x` is the sample set of elements, :math:`\bar{x}` is the + sample mean, :math:`N` is the number of samples and :math:`\delta N` is + the :attr:`correction`. + + + + If :attr:`keepdim` is ``True``, the output tensor is of the same size + as :attr:`input` except in the dimension(s) :attr:`dim` where it is of size 1. + Otherwise, :attr:`dim` is squeezed (see :func:`torch.squeeze`), resulting in the + output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + + Args: + input (Tensor): the input tensor. + + dim (int or tuple of ints, optional): the dimension or dimensions to reduce. + If ``None``, all dimensions are reduced. + + + Keyword args: + correction (int): difference between the sample size and sample degrees of freedom. + Defaults to `Bessel's correction`_, ``correction=1``. + + .. versionchanged:: 2.0 + Previously this argument was called ``unbiased`` and was a boolean + with ``True`` corresponding to ``correction=1`` and ``False`` being + ``correction=0``. + keepdim (bool): whether the output tensor has :attr:`dim` retained or not. + out (Tensor, optional): the output tensor. + + Returns: + A tuple (var, mean) containing the variance and mean. + + Example: + + >>> a = torch.tensor( + ... [[ 0.2035, 1.2959, 1.8101, -0.4644], + ... [ 1.5027, -0.3270, 0.5905, 0.6538], + ... [-1.5745, 1.3330, -0.5596, -0.6548], + ... [ 0.1264, -0.5080, 1.6420, 0.1992]]) + >>> torch.var_mean(a, dim=0, keepdim=True) + (tensor([[1.5926, 1.0056, 1.2005, 0.3646]]), + tensor([[ 0.0645, 0.4485, 0.8707, -0.0665]])) + + .. _Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + """ + ... +def vdot(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + vdot(input, other, *, out=None) -> Tensor + + Computes the dot product of two 1D vectors along a dimension. + + In symbols, this function computes + + .. math:: + + \sum_{i=1}^n \overline{x_i}y_i. + + where :math:`\overline{x_i}` denotes the conjugate for complex + vectors, and it is the identity for real vectors. + + .. note:: + + Unlike NumPy's vdot, torch.vdot intentionally only supports computing the dot product + of two 1D tensors with the same number of elements. + + .. seealso:: + + :func:`torch.linalg.vecdot` computes the dot product of two batches of vectors along a dimension. + + Args: + input (Tensor): first tensor in the dot product, must be 1D. Its conjugate is used if it's complex. + other (Tensor): second tensor in the dot product, must be 1D. + + Keyword args: + + .. note:: out (Tensor, optional): the output tensor. + + + Example:: + + >>> torch.vdot(torch.tensor([2, 3]), torch.tensor([2, 1])) + tensor(7) + >>> a = torch.tensor((1 +2j, 3 - 1j)) + >>> b = torch.tensor((2 +1j, 4 - 0j)) + >>> torch.vdot(a, b) + tensor([16.+1.j]) + >>> torch.vdot(b, a) + tensor([16.-1.j]) + """ + ... +def view_as_complex(input: Tensor) -> Tensor: + r""" + view_as_complex(input) -> Tensor + + Returns a view of :attr:`input` as a complex tensor. For an input complex + tensor of :attr:`size` :math:`m1, m2, \dots, mi, 2`, this function returns a + new complex tensor of :attr:`size` :math:`m1, m2, \dots, mi` where the last + dimension of the input tensor is expected to represent the real and imaginary + components of complex numbers. + + .. warning:: + :func:`view_as_complex` is only supported for tensors with + :class:`torch.dtype` ``torch.float64`` and ``torch.float32``. The input is + expected to have the last dimension of :attr:`size` 2. In addition, the + tensor must have a `stride` of 1 for its last dimension. The strides of all + other dimensions must be even numbers. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x=torch.randn(4, 2) + >>> x + tensor([[ 1.6116, -0.5772], + [-1.4606, -0.9120], + [ 0.0786, -1.7497], + [-0.6561, -1.6623]]) + >>> torch.view_as_complex(x) + tensor([(1.6116-0.5772j), (-1.4606-0.9120j), (0.0786-1.7497j), (-0.6561-1.6623j)]) + """ + ... +def view_as_complex_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.view_as_complex`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +def view_as_real(input: Tensor) -> Tensor: + r""" + view_as_real(input) -> Tensor + + Returns a view of :attr:`input` as a real tensor. For an input complex tensor of + :attr:`size` :math:`m1, m2, \dots, mi`, this function returns a new + real tensor of size :math:`m1, m2, \dots, mi, 2`, where the last dimension of size 2 + represents the real and imaginary components of complex numbers. + + .. warning:: + :func:`view_as_real` is only supported for tensors with ``complex dtypes``. + + Args: + input (Tensor): the input tensor. + + Example:: + + >>> x=torch.randn(4, dtype=torch.cfloat) + >>> x + tensor([(0.4737-0.3839j), (-0.2098-0.6699j), (0.3470-0.9451j), (-0.5174-1.3136j)]) + >>> torch.view_as_real(x) + tensor([[ 0.4737, -0.3839], + [-0.2098, -0.6699], + [ 0.3470, -0.9451], + [-0.5174, -1.3136]]) + """ + ... +def view_as_real_copy(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.view_as_real`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def view_copy(input: Tensor, dtype: _dtype, *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.view`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def view_copy(input: Tensor, size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + Performs the same operation as :func:`torch.view`, but all output tensors + are freshly created instead of aliasing the input. + """ + ... +@overload +def vsplit(input: Tensor, sections: _int) -> tuple[Tensor, ...]: + r""" + vsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with two or more dimensions, into multiple tensors + vertically according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=0) + (the split dimension is 0), except that if :attr:`indices_or_sections` is an integer + it must evenly divide the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.vsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(4,4) + >>> t + tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.], + [12., 13., 14., 15.]]) + >>> torch.vsplit(t, 2) + (tensor([[0., 1., 2., 3.], + [4., 5., 6., 7.]]), + tensor([[ 8., 9., 10., 11.], + [12., 13., 14., 15.]])) + >>> torch.vsplit(t, [3, 6]) + (tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.]]), + tensor([[12., 13., 14., 15.]]), + tensor([], size=(0, 4))) + """ + ... +@overload +def vsplit(input: Tensor, indices: _size) -> tuple[Tensor, ...]: + r""" + vsplit(input, indices_or_sections) -> List of Tensors + + Splits :attr:`input`, a tensor with two or more dimensions, into multiple tensors + vertically according to :attr:`indices_or_sections`. Each split is a view of + :attr:`input`. + + This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=0) + (the split dimension is 0), except that if :attr:`indices_or_sections` is an integer + it must evenly divide the split dimension or a runtime error will be thrown. + + This function is based on NumPy's :func:`numpy.vsplit`. + + Args: + input (Tensor): tensor to split. + indices_or_sections (int or list or tuple of ints): See argument in :func:`torch.tensor_split`. + + Example:: + >>> t = torch.arange(16.0).reshape(4,4) + >>> t + tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.], + [12., 13., 14., 15.]]) + >>> torch.vsplit(t, 2) + (tensor([[0., 1., 2., 3.], + [4., 5., 6., 7.]]), + tensor([[ 8., 9., 10., 11.], + [12., 13., 14., 15.]])) + >>> torch.vsplit(t, [3, 6]) + (tensor([[ 0., 1., 2., 3.], + [ 4., 5., 6., 7.], + [ 8., 9., 10., 11.]]), + tensor([[12., 13., 14., 15.]]), + tensor([], size=(0, 4))) + """ + ... +def vstack(tensors: Optional[Union[tuple[Tensor, ...], list[Tensor]]], *, out: Optional[Tensor] = None) -> Tensor: + r""" + vstack(tensors, *, out=None) -> Tensor + + Stack tensors in sequence vertically (row wise). + + This is equivalent to concatenation along the first axis after all 1-D tensors have been reshaped by :func:`torch.atleast_2d`. + + Args: + tensors (sequence of Tensors): sequence of tensors to concatenate + + Keyword args: + out (Tensor, optional): the output tensor. + + Example:: + + >>> a = torch.tensor([1, 2, 3]) + >>> b = torch.tensor([4, 5, 6]) + >>> torch.vstack((a,b)) + tensor([[1, 2, 3], + [4, 5, 6]]) + >>> a = torch.tensor([[1],[2],[3]]) + >>> b = torch.tensor([[4],[5],[6]]) + >>> torch.vstack((a,b)) + tensor([[1], + [2], + [3], + [4], + [5], + [6]]) + """ + ... +@overload +def where(condition: Tensor) -> tuple[Tensor, ...]: + r""" + where(condition, input, other, *, out=None) -> Tensor + + Return a tensor of elements selected from either :attr:`input` or :attr:`other`, depending on :attr:`condition`. + + The operation is defined as: + + .. math:: + \text{out}_i = \begin{cases} + \text{input}_i & \text{if } \text{condition}_i \\ + \text{other}_i & \text{otherwise} \\ + \end{cases} + + .. note:: + The tensors :attr:`condition`, :attr:`input`, :attr:`other` must be :ref:`broadcastable `. + + Arguments: + condition (BoolTensor): When True (nonzero), yield input, otherwise yield other + input (Tensor or Scalar): value (if :attr:`input` is a scalar) or values selected at indices + where :attr:`condition` is ``True`` + other (Tensor or Scalar): value (if :attr:`other` is a scalar) or values selected at indices + where :attr:`condition` is ``False`` + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`input`, :attr:`other` + + Example:: + + >>> x = torch.randn(3, 2) + >>> y = torch.ones(3, 2) + >>> x + tensor([[-0.4620, 0.3139], + [ 0.3898, -0.7197], + [ 0.0478, -0.1657]]) + >>> torch.where(x > 0, 1.0, 0.0) + tensor([[0., 1.], + [1., 0.], + [1., 0.]]) + >>> torch.where(x > 0, x, y) + tensor([[ 1.0000, 0.3139], + [ 0.3898, 1.0000], + [ 0.0478, 1.0000]]) + >>> x = torch.randn(2, 2, dtype=torch.double) + >>> x + tensor([[ 1.0779, 0.0383], + [-0.8785, -1.1089]], dtype=torch.float64) + >>> torch.where(x > 0, x, 0.) + tensor([[1.0779, 0.0383], + [0.0000, 0.0000]], dtype=torch.float64) + + .. function:: where(condition) -> tuple of LongTensor + :noindex: + + ``torch.where(condition)`` is identical to + ``torch.nonzero(condition, as_tuple=True)``. + + .. note:: + See also :func:`torch.nonzero`. + """ + ... +@overload +def where(condition: Tensor, input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + where(condition, input, other, *, out=None) -> Tensor + + Return a tensor of elements selected from either :attr:`input` or :attr:`other`, depending on :attr:`condition`. + + The operation is defined as: + + .. math:: + \text{out}_i = \begin{cases} + \text{input}_i & \text{if } \text{condition}_i \\ + \text{other}_i & \text{otherwise} \\ + \end{cases} + + .. note:: + The tensors :attr:`condition`, :attr:`input`, :attr:`other` must be :ref:`broadcastable `. + + Arguments: + condition (BoolTensor): When True (nonzero), yield input, otherwise yield other + input (Tensor or Scalar): value (if :attr:`input` is a scalar) or values selected at indices + where :attr:`condition` is ``True`` + other (Tensor or Scalar): value (if :attr:`other` is a scalar) or values selected at indices + where :attr:`condition` is ``False`` + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`input`, :attr:`other` + + Example:: + + >>> x = torch.randn(3, 2) + >>> y = torch.ones(3, 2) + >>> x + tensor([[-0.4620, 0.3139], + [ 0.3898, -0.7197], + [ 0.0478, -0.1657]]) + >>> torch.where(x > 0, 1.0, 0.0) + tensor([[0., 1.], + [1., 0.], + [1., 0.]]) + >>> torch.where(x > 0, x, y) + tensor([[ 1.0000, 0.3139], + [ 0.3898, 1.0000], + [ 0.0478, 1.0000]]) + >>> x = torch.randn(2, 2, dtype=torch.double) + >>> x + tensor([[ 1.0779, 0.0383], + [-0.8785, -1.1089]], dtype=torch.float64) + >>> torch.where(x > 0, x, 0.) + tensor([[1.0779, 0.0383], + [0.0000, 0.0000]], dtype=torch.float64) + + .. function:: where(condition) -> tuple of LongTensor + :noindex: + + ``torch.where(condition)`` is identical to + ``torch.nonzero(condition, as_tuple=True)``. + + .. note:: + See also :func:`torch.nonzero`. + """ + ... +@overload +def where(condition: Tensor, self: Union[Number, _complex], other: Tensor) -> Tensor: + r""" + where(condition, input, other, *, out=None) -> Tensor + + Return a tensor of elements selected from either :attr:`input` or :attr:`other`, depending on :attr:`condition`. + + The operation is defined as: + + .. math:: + \text{out}_i = \begin{cases} + \text{input}_i & \text{if } \text{condition}_i \\ + \text{other}_i & \text{otherwise} \\ + \end{cases} + + .. note:: + The tensors :attr:`condition`, :attr:`input`, :attr:`other` must be :ref:`broadcastable `. + + Arguments: + condition (BoolTensor): When True (nonzero), yield input, otherwise yield other + input (Tensor or Scalar): value (if :attr:`input` is a scalar) or values selected at indices + where :attr:`condition` is ``True`` + other (Tensor or Scalar): value (if :attr:`other` is a scalar) or values selected at indices + where :attr:`condition` is ``False`` + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`input`, :attr:`other` + + Example:: + + >>> x = torch.randn(3, 2) + >>> y = torch.ones(3, 2) + >>> x + tensor([[-0.4620, 0.3139], + [ 0.3898, -0.7197], + [ 0.0478, -0.1657]]) + >>> torch.where(x > 0, 1.0, 0.0) + tensor([[0., 1.], + [1., 0.], + [1., 0.]]) + >>> torch.where(x > 0, x, y) + tensor([[ 1.0000, 0.3139], + [ 0.3898, 1.0000], + [ 0.0478, 1.0000]]) + >>> x = torch.randn(2, 2, dtype=torch.double) + >>> x + tensor([[ 1.0779, 0.0383], + [-0.8785, -1.1089]], dtype=torch.float64) + >>> torch.where(x > 0, x, 0.) + tensor([[1.0779, 0.0383], + [0.0000, 0.0000]], dtype=torch.float64) + + .. function:: where(condition) -> tuple of LongTensor + :noindex: + + ``torch.where(condition)`` is identical to + ``torch.nonzero(condition, as_tuple=True)``. + + .. note:: + See also :func:`torch.nonzero`. + """ + ... +@overload +def where(condition: Tensor, input: Tensor, other: Union[Number, _complex]) -> Tensor: + r""" + where(condition, input, other, *, out=None) -> Tensor + + Return a tensor of elements selected from either :attr:`input` or :attr:`other`, depending on :attr:`condition`. + + The operation is defined as: + + .. math:: + \text{out}_i = \begin{cases} + \text{input}_i & \text{if } \text{condition}_i \\ + \text{other}_i & \text{otherwise} \\ + \end{cases} + + .. note:: + The tensors :attr:`condition`, :attr:`input`, :attr:`other` must be :ref:`broadcastable `. + + Arguments: + condition (BoolTensor): When True (nonzero), yield input, otherwise yield other + input (Tensor or Scalar): value (if :attr:`input` is a scalar) or values selected at indices + where :attr:`condition` is ``True`` + other (Tensor or Scalar): value (if :attr:`other` is a scalar) or values selected at indices + where :attr:`condition` is ``False`` + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`input`, :attr:`other` + + Example:: + + >>> x = torch.randn(3, 2) + >>> y = torch.ones(3, 2) + >>> x + tensor([[-0.4620, 0.3139], + [ 0.3898, -0.7197], + [ 0.0478, -0.1657]]) + >>> torch.where(x > 0, 1.0, 0.0) + tensor([[0., 1.], + [1., 0.], + [1., 0.]]) + >>> torch.where(x > 0, x, y) + tensor([[ 1.0000, 0.3139], + [ 0.3898, 1.0000], + [ 0.0478, 1.0000]]) + >>> x = torch.randn(2, 2, dtype=torch.double) + >>> x + tensor([[ 1.0779, 0.0383], + [-0.8785, -1.1089]], dtype=torch.float64) + >>> torch.where(x > 0, x, 0.) + tensor([[1.0779, 0.0383], + [0.0000, 0.0000]], dtype=torch.float64) + + .. function:: where(condition) -> tuple of LongTensor + :noindex: + + ``torch.where(condition)`` is identical to + ``torch.nonzero(condition, as_tuple=True)``. + + .. note:: + See also :func:`torch.nonzero`. + """ + ... +@overload +def where(condition: Tensor, self: Union[Number, _complex], other: Union[Number, _complex]) -> Tensor: + r""" + where(condition, input, other, *, out=None) -> Tensor + + Return a tensor of elements selected from either :attr:`input` or :attr:`other`, depending on :attr:`condition`. + + The operation is defined as: + + .. math:: + \text{out}_i = \begin{cases} + \text{input}_i & \text{if } \text{condition}_i \\ + \text{other}_i & \text{otherwise} \\ + \end{cases} + + .. note:: + The tensors :attr:`condition`, :attr:`input`, :attr:`other` must be :ref:`broadcastable `. + + Arguments: + condition (BoolTensor): When True (nonzero), yield input, otherwise yield other + input (Tensor or Scalar): value (if :attr:`input` is a scalar) or values selected at indices + where :attr:`condition` is ``True`` + other (Tensor or Scalar): value (if :attr:`other` is a scalar) or values selected at indices + where :attr:`condition` is ``False`` + + Keyword args: + out (Tensor, optional): the output tensor. + + Returns: + Tensor: A tensor of shape equal to the broadcasted shape of :attr:`condition`, :attr:`input`, :attr:`other` + + Example:: + + >>> x = torch.randn(3, 2) + >>> y = torch.ones(3, 2) + >>> x + tensor([[-0.4620, 0.3139], + [ 0.3898, -0.7197], + [ 0.0478, -0.1657]]) + >>> torch.where(x > 0, 1.0, 0.0) + tensor([[0., 1.], + [1., 0.], + [1., 0.]]) + >>> torch.where(x > 0, x, y) + tensor([[ 1.0000, 0.3139], + [ 0.3898, 1.0000], + [ 0.0478, 1.0000]]) + >>> x = torch.randn(2, 2, dtype=torch.double) + >>> x + tensor([[ 1.0779, 0.0383], + [-0.8785, -1.1089]], dtype=torch.float64) + >>> torch.where(x > 0, x, 0.) + tensor([[1.0779, 0.0383], + [0.0000, 0.0000]], dtype=torch.float64) + + .. function:: where(condition) -> tuple of LongTensor + :noindex: + + ``torch.where(condition)`` is identical to + ``torch.nonzero(condition, as_tuple=True)``. + + .. note:: + See also :func:`torch.nonzero`. + """ + ... +@overload +def xlogy(input: Tensor, other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + xlogy(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.special.xlogy`. + """ + ... +@overload +def xlogy(self: Union[Number, _complex], other: Tensor, *, out: Optional[Tensor] = None) -> Tensor: + r""" + xlogy(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.special.xlogy`. + """ + ... +@overload +def xlogy(input: Tensor, other: Union[Number, _complex], *, out: Optional[Tensor] = None) -> Tensor: + r""" + xlogy(input, other, *, out=None) -> Tensor + + Alias for :func:`torch.special.xlogy`. + """ + ... +@overload +def xlogy_(input: Tensor, other: Tensor) -> Tensor: ... +@overload +def xlogy_(input: Tensor, other: Union[Number, _complex]) -> Tensor: ... +def zero_(input: Tensor) -> Tensor: ... +@overload +def zeros(size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + zeros(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `0`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.zeros(2, 3) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]]) + + >>> torch.zeros(5) + tensor([ 0., 0., 0., 0., 0.]) + """ + ... +@overload +def zeros(*size: Union[_int, SymInt], out: Optional[Tensor] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + zeros(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `0`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.zeros(2, 3) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]]) + + >>> torch.zeros(5) + tensor([ 0., 0., 0., 0., 0.]) + """ + ... +@overload +def zeros(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + zeros(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `0`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.zeros(2, 3) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]]) + + >>> torch.zeros(5) + tensor([ 0., 0., 0., 0., 0.]) + """ + ... +@overload +def zeros(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + zeros(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + + Returns a tensor filled with the scalar value `0`, with the shape defined + by the variable argument :attr:`size`. + + Args: + size (int...): a sequence of integers defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple. + + Keyword args: + out (Tensor, optional): the output tensor. + dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. + Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). + layout (:class:`torch.layout`, optional): the desired layout of returned Tensor. + Default: ``torch.strided``. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, uses the current device for the default tensor type + (see :func:`torch.set_default_device`). :attr:`device` will be the CPU + for CPU tensor types and the current CUDA device for CUDA tensor types. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + + Example:: + + >>> torch.zeros(2, 3) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]]) + + >>> torch.zeros(5) + tensor([ 0., 0., 0., 0., 0.]) + """ + ... +def zeros_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: + r""" + zeros_like(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor + + Returns a tensor filled with the scalar value `0`, with the same size as + :attr:`input`. ``torch.zeros_like(input)`` is equivalent to + ``torch.zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)``. + + .. warning:: + As of 0.4, this function does not support an :attr:`out` keyword. As an alternative, + the old ``torch.zeros_like(input, out=output)`` is equivalent to + ``torch.zeros(input.size(), out=output)``. + + Args: + input (Tensor): the size of :attr:`input` will determine size of the output tensor. + + Keyword args: + dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. + Default: if ``None``, defaults to the dtype of :attr:`input`. + layout (:class:`torch.layout`, optional): the desired layout of returned tensor. + Default: if ``None``, defaults to the layout of :attr:`input`. + device (:class:`torch.device`, optional): the desired device of returned tensor. + Default: if ``None``, defaults to the device of :attr:`input`. + requires_grad (bool, optional): If autograd should record operations on the + returned tensor. Default: ``False``. + memory_format (:class:`torch.memory_format`, optional): the desired memory format of + returned Tensor. Default: ``torch.preserve_format``. + + Example:: + + >>> input = torch.empty(2, 3) + >>> torch.zeros_like(input) + tensor([[ 0., 0., 0.], + [ 0., 0., 0.]]) + """ + ... + +__all__ = ['__and__', '__lshift__', '__or__', '__rshift__', '__xor__', '_adaptive_avg_pool2d', + '_adaptive_avg_pool3d', '_add_batch_dim', '_add_relu', '_add_relu_', '_addmm_activation', + '_aminmax', '_amp_foreach_non_finite_check_and_unscale_', '_amp_update_scale_', '_assert_async', + '_assert_scalar', '_assert_tensor_metadata', '_batch_norm_impl_index', '_cast_Byte', '_cast_Char', + '_cast_Double', '_cast_Float', '_cast_Half', '_cast_Int', '_cast_Long', '_cast_Short', + '_choose_qparams_per_tensor', '_chunk_cat', '_coalesce', '_compute_linear_combination', '_conj', + '_conj_copy', '_conj_physical', '_convert_indices_from_coo_to_csr', + '_convert_indices_from_csr_to_coo', '_convert_weight_to_int4pack', + '_convert_weight_to_int4pack_for_cpu', '_convolution', '_convolution_mode', '_copy_from', + '_copy_from_and_resize', '_cslt_compress', '_cslt_sparse_mm', '_cslt_sparse_mm_search', + '_ctc_loss', '_cudnn_ctc_loss', '_cudnn_init_dropout_state', '_cudnn_rnn', + '_cudnn_rnn_flatten_weight', '_cufft_clear_plan_cache', '_cufft_get_plan_cache_max_size', + '_cufft_get_plan_cache_size', '_cufft_set_plan_cache_max_size', '_cummax_helper', '_cummin_helper', + '_debug_has_internal_overlap', '_dim_arange', '_dirichlet_grad', '_disable_functionalization', + '_dyn_quant_matmul_4bit', '_dyn_quant_pack_4bit_weight', '_efficientzerotensor', '_embedding_bag', + '_embedding_bag_forward_only', '_empty_affine_quantized', '_empty_per_channel_affine_quantized', + '_enable_functionalization', '_euclidean_dist', '_fake_quantize_learnable_per_channel_affine', + '_fake_quantize_learnable_per_tensor_affine', + '_fake_quantize_per_tensor_affine_cachemask_tensor_qparams', + '_fake_quantize_per_tensor_affine_cachemask_tensor_qparams', '_fft_c2c', '_fft_c2r', '_fft_r2c', + '_fill_mem_eff_dropout_mask_', '_foobar', '_foreach_abs', '_foreach_abs_', '_foreach_acos', + '_foreach_acos_', '_foreach_add', '_foreach_add_', '_foreach_addcdiv', '_foreach_addcdiv_', + '_foreach_addcmul', '_foreach_addcmul_', '_foreach_asin', '_foreach_asin_', '_foreach_atan', + '_foreach_atan_', '_foreach_ceil', '_foreach_ceil_', '_foreach_clamp_max', '_foreach_clamp_max_', + '_foreach_clamp_min', '_foreach_clamp_min_', '_foreach_copy_', '_foreach_cos', '_foreach_cos_', + '_foreach_cosh', '_foreach_cosh_', '_foreach_div', '_foreach_div_', '_foreach_erf', + '_foreach_erf_', '_foreach_erfc', '_foreach_erfc_', '_foreach_exp', '_foreach_exp_', + '_foreach_expm1', '_foreach_expm1_', '_foreach_floor', '_foreach_floor_', '_foreach_frac', + '_foreach_frac_', '_foreach_lerp', '_foreach_lerp_', '_foreach_lgamma', '_foreach_lgamma_', + '_foreach_log', '_foreach_log10', '_foreach_log10_', '_foreach_log1p', '_foreach_log1p_', + '_foreach_log2', '_foreach_log2_', '_foreach_log_', '_foreach_max', '_foreach_maximum', + '_foreach_maximum_', '_foreach_minimum', '_foreach_minimum_', '_foreach_mul', '_foreach_mul_', + '_foreach_neg', '_foreach_neg_', '_foreach_norm', '_foreach_pow', '_foreach_pow_', + '_foreach_reciprocal', '_foreach_reciprocal_', '_foreach_round', '_foreach_round_', + '_foreach_rsqrt', '_foreach_rsqrt_', '_foreach_sigmoid', '_foreach_sigmoid_', '_foreach_sign', + '_foreach_sign_', '_foreach_sin', '_foreach_sin_', '_foreach_sinh', '_foreach_sinh_', + '_foreach_sqrt', '_foreach_sqrt_', '_foreach_sub', '_foreach_sub_', '_foreach_tan', + '_foreach_tan_', '_foreach_tanh', '_foreach_tanh_', '_foreach_trunc', '_foreach_trunc_', + '_foreach_zero_', '_from_functional_tensor', '_functional_assert_async', + '_functional_assert_scalar', '_functional_sym_constrain_range', + '_functional_sym_constrain_range_for_size', '_functionalize_apply_view_metas', + '_functionalize_are_all_mutations_hidden_from_autograd', + '_functionalize_are_all_mutations_under_no_grad_or_inference_mode', '_functionalize_commit_update', + '_functionalize_has_metadata_mutation', '_functionalize_is_symbolic', + '_functionalize_mark_mutation_hidden_from_autograd', '_functionalize_replace', + '_functionalize_set_storage_changed', '_functionalize_sync', '_functionalize_unsafe_set', + '_functionalize_was_inductor_storage_resized', '_functionalize_was_storage_changed', + '_fused_adagrad_', '_fused_adam_', '_fused_adamw_', '_fused_dropout', + '_fused_moving_avg_obs_fq_helper', '_fused_moving_avg_obs_fq_helper', '_fused_sdp_choice', + '_fused_sgd_', '_fw_primal_copy', '_grid_sampler_2d_cpu_fallback', + '_has_compatible_shallow_copy_type', '_histogramdd_bin_edges', '_histogramdd_from_bin_cts', + '_histogramdd_from_bin_tensors', '_index_put_impl_', '_indices_copy', '_int_mm', '_is_all_true', + '_is_any_true', '_is_functional_tensor', '_is_functional_tensor_base', '_is_zerotensor', + '_lazy_clone', '_linalg_check_errors', '_linalg_det', '_linalg_det', '_linalg_eigh', + '_linalg_eigh', '_linalg_slogdet', '_linalg_slogdet', '_linalg_solve_ex', '_linalg_solve_ex', + '_linalg_svd', '_linalg_svd', '_log_softmax', '_log_softmax_backward_data', '_logcumsumexp', + '_lstm_mps', '_lu_with_info', '_lu_with_info', '_make_dep_token', '_make_dual', '_make_dual_copy', + '_make_per_channel_quantized_tensor', '_make_per_tensor_quantized_tensor', '_masked_scale', + '_masked_softmax', '_mixed_dtypes_linear', '_mkldnn_reshape', '_mkldnn_transpose', + '_mkldnn_transpose_', '_mps_convolution', '_mps_convolution_transpose', '_native_batch_norm_legit', + '_native_batch_norm_legit_no_training', '_native_multi_head_attention', '_neg_view', + '_neg_view_copy', '_nested_compute_contiguous_strides_offsets', '_nested_from_padded', + '_nested_from_padded_and_nested_example', '_nested_from_padded_tensor', '_nested_get_jagged_dummy', + '_nested_get_lengths', '_nested_get_max_seqlen', '_nested_get_min_seqlen', '_nested_get_offsets', + '_nested_get_ragged_idx', '_nested_get_values', '_nested_get_values_copy', + '_nested_tensor_from_mask', '_nested_tensor_from_mask_left_aligned', + '_nested_tensor_from_tensor_list', '_nested_tensor_softmax_with_shape', '_nested_view_from_buffer', + '_nested_view_from_buffer_copy', '_nested_view_from_jagged', '_nested_view_from_jagged_copy', + '_nnpack_available', '_nnpack_spatial_convolution', '_pack_padded_sequence', + '_pad_packed_sequence', '_pin_memory', '_prelu_kernel', '_print', '_propagate_xla_data', + '_remove_batch_dim', '_reshape_alias_copy', '_reshape_from_tensor', '_resize_output_', + '_rowwise_prune', '_safe_softmax', '_sample_dirichlet', '_saturate_weight_to_fp16', + '_scaled_dot_product_attention_math', '_scaled_dot_product_attention_math_for_mps', + '_scaled_dot_product_cudnn_attention', '_scaled_dot_product_cudnn_attention', + '_scaled_dot_product_efficient_attention', '_scaled_dot_product_efficient_attention', + '_scaled_dot_product_flash_attention', '_scaled_dot_product_flash_attention', + '_scaled_dot_product_flash_attention_for_cpu', '_scaled_dot_product_flash_attention_for_cpu', + '_scaled_grouped_mm', '_scaled_mm', '_shape_as_tensor', '_sobol_engine_draw', '_sobol_engine_ff_', + '_sobol_engine_initialize_state_', '_sobol_engine_scramble_', '_softmax', '_softmax_backward_data', + '_sparse_broadcast_to', '_sparse_broadcast_to_copy', '_sparse_csr_prod', '_sparse_csr_sum', + '_sparse_log_softmax_backward_data', '_sparse_semi_structured_addmm', + '_sparse_semi_structured_apply', '_sparse_semi_structured_apply_dense', + '_sparse_semi_structured_linear', '_sparse_semi_structured_mm', '_sparse_semi_structured_tile', + '_sparse_softmax_backward_data', '_sparse_sparse_matmul', '_sparse_sum', '_stack', + '_standard_gamma', '_standard_gamma_grad', '_sync', '_test_autograd_multiple_dispatch', + '_test_autograd_multiple_dispatch_view', '_test_autograd_multiple_dispatch_view_copy', + '_test_check_tensor', '_test_functorch_fallback', '_test_parallel_materialize', + '_test_serialization_subcmul', '_to_cpu', '_to_functional_tensor', '_to_sparse_semi_structured', + '_transform_bias_rescale_qkv', '_transformer_encoder_layer_fwd', '_trilinear', + '_triton_multi_head_attention', '_triton_scaled_dot_attention', '_unique', '_unique2', + '_unpack_dual', '_unpack_dual', '_unsafe_index', '_unsafe_index_put', '_unsafe_masked_index', + '_unsafe_masked_index_put_accumulate', '_use_cudnn_ctc_loss', '_use_cudnn_rnn_flatten_weight', + '_validate_compressed_sparse_indices', '_validate_sparse_bsc_tensor_args', + '_validate_sparse_bsr_tensor_args', '_validate_sparse_compressed_tensor_args', + '_validate_sparse_coo_tensor_args', '_validate_sparse_csc_tensor_args', + '_validate_sparse_csr_tensor_args', '_values_copy', '_weight_int4pack_mm', + '_weight_int4pack_mm_for_cpu', '_weight_int8pack_mm', '_weight_norm', '_weight_norm_interface', + '_wrapped_linear_prepack', '_wrapped_quantized_linear_prepacked', 'abs', 'abs_', 'absolute', + 'acos', 'acos_', 'acosh', 'acosh_', 'adaptive_avg_pool1d', 'adaptive_max_pool1d', 'add', 'addbmm', + 'addcdiv', 'addcmul', 'addmm', 'addmv', 'addmv_', 'addr', 'adjoint', 'affine_grid_generator', + 'alias_copy', 'all', 'allclose', 'alpha_dropout', 'alpha_dropout_', 'amax', 'amin', 'aminmax', + 'aminmax', 'angle', 'any', 'arange', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', + 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan_', 'arctanh', 'arctanh_', 'argmax', + 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_copy', + 'as_strided_scatter', 'as_tensor', 'asarray', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', + 'atan_', 'atanh', 'atanh_', 'avg_pool1d', 'baddbmm', 'bartlett_window', 'batch_norm', + 'batch_norm_backward_elemt', 'batch_norm_backward_reduce', 'batch_norm_elemt', + 'batch_norm_gather_stats', 'batch_norm_gather_stats_with_counts', 'batch_norm_stats', + 'batch_norm_update_stats', 'bernoulli', 'bilinear', 'binary_cross_entropy_with_logits', 'bincount', + 'binomial', 'bitwise_and', 'bitwise_left_shift', 'bitwise_not', 'bitwise_or', + 'bitwise_right_shift', 'bitwise_xor', 'blackman_window', 'bmm', 'broadcast_to', 'bucketize', + 'can_cast', 'cat', 'ccol_indices_copy', 'ceil', 'ceil_', 'celu', 'celu_', 'channel_shuffle', + 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'choose_qparams_optimized', 'chunk', 'clamp', + 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', + 'col_indices_copy', 'column_stack', 'combinations', 'complex', 'concat', 'concatenate', 'conj', + 'conj_physical', 'conj_physical_', 'constant_pad_nd', 'conv1d', 'conv2d', 'conv3d', 'conv_tbc', + 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d', 'convolution', 'copysign', 'corrcoef', + 'cos', 'cos_', 'cosh', 'cosh_', 'cosine_embedding_loss', 'cosine_similarity', 'count_nonzero', + 'cov', 'cross', 'crow_indices_copy', 'ctc_loss', 'cudnn_affine_grid_generator', 'cudnn_batch_norm', + 'cudnn_convolution', 'cudnn_convolution_add_relu', 'cudnn_convolution_relu', + 'cudnn_convolution_transpose', 'cudnn_grid_sampler', 'cudnn_is_acceptable', 'cummax', 'cummax', + 'cummin', 'cummin', 'cumprod', 'cumsum', 'cumulative_trapezoid', 'deg2rad', 'deg2rad_', + 'dequantize', 'det', 'detach', 'detach_', 'detach_copy', 'diag', 'diag_embed', 'diagflat', + 'diagonal', 'diagonal_copy', 'diagonal_scatter', 'diff', 'digamma', 'dist', 'div', 'divide', 'dot', + 'dropout', 'dropout_', 'dsmm', 'dsplit', 'dstack', 'embedding', 'embedding_bag', + 'embedding_renorm_', 'empty', 'empty_like', 'empty_permuted', 'empty_quantized', 'empty_strided', + 'eq', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'exp', 'exp2', 'exp2_', 'exp_', + 'expand_copy', 'expm1', 'expm1_', 'eye', 'fake_quantize_per_channel_affine', + 'fake_quantize_per_tensor_affine', 'fbgemm_linear_fp16_weight', + 'fbgemm_linear_fp16_weight_fp32_activation', 'fbgemm_linear_int8_weight', + 'fbgemm_linear_int8_weight_fp32_activation', 'fbgemm_linear_quantize_weight', + 'fbgemm_pack_gemm_matrix_fp16', 'fbgemm_pack_quantized_matrix', 'feature_alpha_dropout', + 'feature_alpha_dropout_', 'feature_dropout', 'feature_dropout_', 'fill', 'fill_', 'fix', 'fix_', + 'flatten', 'flip', 'fliplr', 'flipud', 'float_power', 'floor', 'floor_', 'floor_divide', 'fmax', + 'fmin', 'fmod', 'frac', 'frac_', 'frexp', 'frexp', 'frobenius_norm', 'from_file', 'from_numpy', + 'frombuffer', 'full', 'full_like', 'fused_moving_avg_obs_fake_quant', 'gather', 'gcd', 'gcd_', + 'ge', 'geqrf', 'geqrf', 'ger', 'get_default_dtype', 'get_num_interop_threads', 'get_num_threads', + 'gradient', 'greater', 'greater_equal', 'grid_sampler', 'grid_sampler_2d', 'grid_sampler_3d', + 'group_norm', 'gru', 'gru_cell', 'gt', 'hamming_window', 'hann_window', 'hardshrink', 'heaviside', + 'hinge_embedding_loss', 'histc', 'histogram', 'histogram', 'histogramdd', 'histogramdd', 'hsmm', + 'hsplit', 'hspmm', 'hstack', 'hypot', 'i0', 'i0_', 'igamma', 'igammac', 'imag', 'index_add', + 'index_copy', 'index_fill', 'index_put', 'index_put_', 'index_reduce', 'index_select', + 'indices_copy', 'init_num_threads', 'inner', 'instance_norm', 'int_repr', 'inverse', 'is_complex', + 'is_conj', 'is_distributed', 'is_floating_point', 'is_grad_enabled', 'is_inference', + 'is_inference_mode_enabled', 'is_neg', 'is_nonzero', 'is_same_size', 'is_signed', + 'is_vulkan_available', 'isclose', 'isfinite', 'isin', 'isinf', 'isnan', 'isneginf', 'isposinf', + 'isreal', 'istft', 'kaiser_window', 'kl_div', 'kron', 'kthvalue', 'kthvalue', 'layer_norm', 'lcm', + 'lcm_', 'ldexp', 'ldexp_', 'le', 'lerp', 'less', 'less_equal', 'lgamma', 'linspace', 'log', + 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_softmax', 'logaddexp', + 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', + 'logit', 'logit_', 'logspace', 'logsumexp', 'lstm', 'lstm_cell', 'lt', 'lu_solve', 'lu_unpack', + 'lu_unpack', 'margin_ranking_loss', 'masked_fill', 'masked_scatter', 'masked_select', 'matmul', + 'matrix_exp', 'matrix_power', 'max', 'max', 'max_pool1d', 'max_pool1d_with_indices', 'max_pool2d', + 'max_pool3d', 'maximum', 'mean', 'median', 'median', 'min', 'min', 'minimum', 'miopen_batch_norm', + 'miopen_convolution', 'miopen_convolution_add_relu', 'miopen_convolution_relu', + 'miopen_convolution_transpose', 'miopen_depthwise_convolution', 'miopen_rnn', + 'mkldnn_adaptive_avg_pool2d', 'mkldnn_convolution', 'mkldnn_linear_backward_weights', + 'mkldnn_max_pool2d', 'mkldnn_max_pool3d', 'mkldnn_rnn_layer', 'mm', 'mode', 'mode', 'moveaxis', + 'movedim', 'msort', 'mul', 'multinomial', 'multiply', 'mv', 'mvlgamma', 'nan_to_num', + 'nan_to_num_', 'nanmean', 'nanmedian', 'nanmedian', 'nanquantile', 'nansum', 'narrow', + 'narrow_copy', 'native_batch_norm', 'native_channel_shuffle', 'native_dropout', + 'native_group_norm', 'native_layer_norm', 'native_norm', 'ne', 'neg', 'neg_', 'negative', + 'negative_', 'nextafter', 'nonzero', 'nonzero_static', 'norm_except_dim', 'normal', 'not_equal', + 'nuclear_norm', 'numel', 'ones', 'ones_like', 'orgqr', 'ormqr', 'outer', 'pairwise_distance', + 'pdist', 'permute', 'permute_copy', 'pinverse', 'pixel_shuffle', 'pixel_unshuffle', 'poisson', + 'poisson_nll_loss', 'polar', 'polygamma', 'positive', 'pow', 'prelu', 'prod', 'promote_types', + 'put', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', + 'q_zero_point', 'qr', 'qr', 'quantile', 'quantize_per_channel', 'quantize_per_tensor', + 'quantize_per_tensor_dynamic', 'quantized_batch_norm', 'quantized_gru_cell', 'quantized_lstm_cell', + 'quantized_max_pool1d', 'quantized_max_pool2d', 'quantized_max_pool3d', 'quantized_rnn_relu_cell', + 'quantized_rnn_tanh_cell', 'rad2deg', 'rad2deg_', 'rand', 'rand_like', 'randint', 'randint_like', + 'randn', 'randn_like', 'randperm', 'range', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'relu', + 'relu_', 'remainder', 'renorm', 'repeat_interleave', 'reshape', 'resize_as_', 'resize_as_sparse_', + 'resolve_conj', 'resolve_neg', 'result_type', 'rms_norm', 'rnn_relu', 'rnn_relu_cell', 'rnn_tanh', + 'rnn_tanh_cell', 'roll', 'rot90', 'round', 'round_', 'row_indices_copy', 'row_stack', 'rrelu', + 'rrelu_', 'rsqrt', 'rsqrt_', 'rsub', 'saddmm', 'scalar_tensor', 'scatter', 'scatter_add', + 'scatter_reduce', 'searchsorted', 'segment_reduce', 'select', 'select_copy', 'select_scatter', + 'selu', 'selu_', 'set_flush_denormal', 'set_num_interop_threads', 'set_num_threads', 'sgn', + 'sigmoid', 'sigmoid_', 'sign', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', + 'slice_copy', 'slice_inverse', 'slice_scatter', 'slogdet', 'slogdet', 'smm', 'softmax', 'sort', + 'sort', 'sparse_bsc_tensor', 'sparse_bsr_tensor', 'sparse_compressed_tensor', 'sparse_coo_tensor', + 'sparse_csc_tensor', 'sparse_csr_tensor', 'split_copy', 'split_with_sizes', + 'split_with_sizes_copy', 'spmm', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_copy', + 'sspaddmm', 'stack', 'std', 'std_mean', 'sub', 'subtract', 'sum', 'svd', 'svd', 'swapaxes', + 'swapdims', 'sym_constrain_range', 'sym_constrain_range_for_size', 't', 't_copy', 'take', + 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor', 'tensor_split', 'threshold', + 'threshold_', 'tile', 'topk', 'topk', 'trace', 'transpose', 'transpose_copy', 'trapezoid', 'trapz', + 'triangular_solve', 'triangular_solve', 'tril', 'tril_indices', 'triplet_margin_loss', 'triu', + 'triu_indices', 'true_divide', 'trunc', 'trunc_', 'unbind', 'unbind_copy', 'unflatten', + 'unfold_copy', 'unique_dim', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', + 'unsqueeze', 'unsqueeze_copy', 'values_copy', 'vander', 'var', 'var_mean', 'vdot', + 'view_as_complex', 'view_as_complex_copy', 'view_as_real', 'view_as_real_copy', 'view_copy', + 'vsplit', 'vstack', 'where', 'xlogy', 'xlogy_', 'zero_', 'zeros', 'zeros_like'] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_autograd.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_autograd.pyi new file mode 100644 index 0000000000000000000000000000000000000000..52fea2bdf4418c9171ae057a632da2688b9a152e --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_autograd.pyi @@ -0,0 +1,138 @@ +# mypy: allow-untyped-defs +from enum import Enum +from typing import Any, Callable + +import torch +from torch._C._profiler import ( + _ProfilerEvent, + ActiveProfilerType, + ProfilerActivity, + ProfilerConfig, +) + +# Defined in torch/csrc/autograd/init.cpp + +class DeviceType(Enum): + CPU = ... + CUDA = ... + XPU = ... + MKLDNN = ... + OPENGL = ... + OPENCL = ... + IDEEP = ... + HIP = ... + FPGA = ... + MAIA = ... + XLA = ... + MTIA = ... + MPS = ... + HPU = ... + Meta = ... + Vulkan = ... + Metal = ... + PrivateUse1 = ... + +class ProfilerEvent: + def cpu_elapsed_us(self, other: ProfilerEvent) -> float: ... + def cpu_memory_usage(self) -> int: ... + def cuda_elapsed_us(self, other: ProfilerEvent) -> float: ... + def privateuse1_elapsed_us(self, other: ProfilerEvent) -> float: ... + def cuda_memory_usage(self) -> int: ... + def device(self) -> int: ... + def handle(self) -> int: ... + def has_cuda(self) -> bool: ... + def is_remote(self) -> bool: ... + def kind(self) -> int: ... + def name(self) -> str: ... + def node_id(self) -> int: ... + def sequence_nr(self) -> int: ... + def shapes(self) -> list[list[int]]: ... + def thread_id(self) -> int: ... + def flops(self) -> float: ... + def is_async(self) -> bool: ... + +class _KinetoEvent: + def name(self) -> str: ... + def overload_name(self) -> str: ... + def device_index(self) -> int: ... + def device_resource_id(self) -> int: ... + def start_ns(self) -> int: ... + def end_ns(self) -> int: ... + def duration_ns(self) -> int: ... + def is_async(self) -> bool: ... + def linked_correlation_id(self) -> int: ... + def shapes(self) -> list[list[int]]: ... + def dtypes(self) -> list[str]: ... + def concrete_inputs(self) -> list[Any]: ... + def kwinputs(self) -> dict[str, Any]: ... + def device_type(self) -> DeviceType: ... + def start_thread_id(self) -> int: ... + def end_thread_id(self) -> int: ... + def correlation_id(self) -> int: ... + def fwd_thread_id(self) -> int: ... + def stack(self) -> list[str]: ... + def scope(self) -> int: ... + def sequence_nr(self) -> int: ... + def flops(self) -> int: ... + def cuda_elapsed_us(self) -> int: ... + def privateuse1_elapsed_us(self) -> int: ... + def is_user_annotation(self) -> bool: ... + +class _ProfilerResult: + def events(self) -> list[_KinetoEvent]: ... + def legacy_events(self) -> list[list[ProfilerEvent]]: ... + def save(self, path: str) -> None: ... + def experimental_event_tree(self) -> list[_ProfilerEvent]: ... + def trace_start_ns(self) -> int: ... + +class SavedTensor: ... + +def _enable_profiler( + config: ProfilerConfig, + activities: set[ProfilerActivity], +) -> None: ... +def _prepare_profiler( + config: ProfilerConfig, + activities: set[ProfilerActivity], +) -> None: ... +def _toggle_collection_dynamic( + enable: bool, + activities: set[ProfilerActivity], +) -> None: ... +def _disable_profiler() -> _ProfilerResult: ... +def _profiler_enabled() -> bool: ... +def _add_metadata_json(key: str, value: str) -> None: ... +def _kineto_step() -> None: ... +def _get_current_graph_task_keep_graph() -> bool: ... +def _get_sequence_nr() -> int: ... +def kineto_available() -> bool: ... +def _record_function_with_args_enter(name: str, *args) -> torch.Tensor: ... +def _record_function_with_args_exit(handle: torch.Tensor) -> None: ... +def _supported_activities() -> set[ProfilerActivity]: ... +def _enable_record_function(enable: bool) -> None: ... +def _set_empty_test_observer(is_global: bool, sampling_prob: float) -> None: ... +def _push_saved_tensors_default_hooks( + pack_hook: Callable[[torch.Tensor], Any], + unpack_hook: Callable[[Any], torch.Tensor], +) -> None: ... +def _pop_saved_tensors_default_hooks() -> None: ... +def _unsafe_set_version_counter( + t: tuple[torch.Tensor, ...], prev_version: tuple[int, ...] +) -> None: ... +def _enable_profiler_legacy(config: ProfilerConfig) -> None: ... +def _disable_profiler_legacy() -> list[list[ProfilerEvent]]: ... +def _profiler_type() -> ActiveProfilerType: ... +def _saved_tensors_hooks_enable() -> None: ... +def _saved_tensors_hooks_disable(message: str) -> None: ... +def _saved_tensors_hooks_get_disabled_error_message() -> str | None: ... +def _saved_tensors_hooks_set_tracing(is_tracing: bool) -> bool: ... + +class CreationMeta(Enum): + DEFAULT = ... + IN_CUSTOM_FUNCTION = ... + MULTI_OUTPUT_NODE = ... + NO_GRAD_MODE = ... + INFERENCE_MODE = ... + +def _set_creation_meta(t: torch.Tensor, creation_meta: CreationMeta) -> None: ... +def _get_creation_meta(t: torch.Tensor) -> CreationMeta: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_distributed_c10d.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_distributed_c10d.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e4b5a116fdbdffaeb031304dd7026fc84592c761 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_distributed_c10d.pyi @@ -0,0 +1,757 @@ +# mypy: allow-untyped-defs +# mypy: disable-error-code="type-arg" +from datetime import timedelta +from enum import Enum +from typing import Any, overload + +import torch +from torch import Tensor +from torch._C import ScriptObject +from torch._C._autograd import DeviceType +from torch.futures import Future + +# This module is defined in torch/csrc/distributed/c10d/init.cpp + +_DEFAULT_FIRST_BUCKET_BYTES: int +_DEFAULT_NO_TIMEOUT: timedelta +_DEFAULT_PG_TIMEOUT: timedelta +_DEFAULT_PG_NCCL_TIMEOUT: timedelta + +class BuiltinCommHookType(Enum): + ALLREDUCE = ... + FP16_COMPRESS = ... + +def _register_comm_hook(reducer: Reducer, state: Any, comm_hook: Any): ... +def _register_builtin_comm_hook( + reducer: Reducer, + comm_hook_type: BuiltinCommHookType, +): ... +def _set_global_rank(rank: int) -> None: ... +def _hash_tensors(tensors: list[Tensor]) -> int: ... + +class GradBucket: + def index(self) -> int: ... + def buffer(self) -> Tensor: ... + def gradients(self) -> list[Tensor]: ... + def is_last(self) -> bool: ... + def set_buffer(self, tensor: Tensor) -> None: ... + def parameters(self) -> list[Tensor]: ... + +class Reducer: + def __init__( + self, + params: list[Tensor], + bucket_indices: list[list[int]], + per_bucket_size_limits: list[int], + process_group: ProcessGroup, + expect_sparse_gradients: list[bool] = ..., + bucket_bytes_cap: int = ..., # kDefaultBucketBytesCap in reducer.hpp + find_unused_parameters: bool = ..., + gradient_as_bucket_view: bool = ..., + param_to_name_mapping: dict[int, str] = ..., + first_bucket_types_cap: int = ..., # kDefaultFirstBucketBytes in reducer.hpp + ) -> None: ... + def prepare_for_forward(self) -> None: ... + def prepare_for_backward(self, output: list[Tensor]) -> None: ... + def get_backward_stats(self) -> list[int]: ... + def _install_post_backward_futures(self, futures: list[Future]) -> None: ... + def _rebuild_buckets(self) -> bool: ... + def _get_zeros_like_grad_buckets(self) -> list[GradBucket]: ... + def _push_all_rebuilt_params(self) -> None: ... + def _set_forward_pass_work_handle( + self, + work: Work, + use_static_world_size: bool, + ): ... + def _get_local_used_map(self) -> Tensor: ... + def _set_ddp_runtime_logging_sample_rate(self, sample_rate: int) -> None: ... + def _set_static_graph(self) -> None: ... + def _run_comm_hook(self, bucket: GradBucket) -> Future: ... + def set_logger(self, logger: Logger) -> None: ... + def _remove_autograd_hooks(self) -> None: ... + def _check_reducer_finalized(self) -> None: ... + def _set_sparse_metadata(self, global_unique_ids: dict[str, Tensor]) -> None: ... + def _reset_state(self) -> None: ... + def _update_process_group(self, new_process_group: ProcessGroup) -> None: ... + +class DDPLoggingData: + strs_map: dict[str, str] + ints_map: dict[str, int] + +class Logger: + def __init__(self, reducer: Reducer) -> None: ... + def set_construction_data_and_log( + self, + module_name: str, + device_ids: list[int], + output_device: int, + broadcast_buffers: bool, + has_sync_bn: bool, + static_graph: bool, + ): ... + def set_runtime_stats_and_log(self) -> None: ... + def set_error_and_log(self, error: str) -> None: ... + def _get_ddp_logging_data(self) -> DDPLoggingData: ... + def _set_comm_hook_name(self, comm_hook: str) -> None: ... + def _set_uneven_input_join(self) -> None: ... + def _set_static_graph(self) -> None: ... + +class _WorkerServer: + def __init__(self, socket_path: str) -> None: ... + def shutdown(self) -> None: ... + +def get_debug_level(): ... +def set_debug_level(): ... +def set_debug_level_from_env(): ... + +class DebugLevel(Enum): + OFF = ... + INFO = ... + DETAIL = ... + +class ReduceOp: + def __init__(self, op: RedOpType) -> None: ... + + SUM: RedOpType = ... + AVG: RedOpType = ... + PRODUCT: RedOpType = ... + MIN: RedOpType = ... + MAX: RedOpType = ... + BAND: RedOpType = ... + BOR: RedOpType = ... + BXOR: RedOpType = ... + PREMUL_SUM: RedOpType = ... + UNUSED: RedOpType = ... + + # mypy error being ignored: + # Detected enum "torch._C._distributed_c10d.ReduceOp.RedOpType" in a type + # stub with zero members. There is a chance this is due to a recent change + # in the semantics of enum membership. If so, use `member = value` to mark + # an enum member, instead of `member: type` + class RedOpType(Enum): ... # type: ignore[misc] + +class BroadcastOptions: + rootRank: int + rootTensor: int + timeout: timedelta + asyncOp: bool + +class AllreduceOptions: + reduceOp: ReduceOp + timeout: timedelta + +class AllreduceCoalescedOptions(AllreduceOptions): ... + +class ReduceOptions: + reduceOp: ReduceOp + rootRank: int + rootTensor: int + timeout: timedelta + +class AllgatherOptions: + timeout: timedelta + asyncOp: bool + +class GatherOptions: + rootRank: int + timeout: timedelta + +class ScatterOptions: + rootRank: int + timeout: timedelta + asyncOp: bool + +class ReduceScatterOptions: + reduceOp: ReduceOp + timeout: timedelta + asyncOp: bool + +class BarrierOptions: + device_ids: list[int] + device: torch.device + timeout: timedelta + +class AllToAllOptions: + timeout: timedelta + +class Store: + def set(self, key: str, value: str): ... + def get(self, key: str) -> bytes: ... + def add(self, key: str, value: int) -> int: ... + def compare_set( + self, + key: str, + expected_value: str, + desired_value: str, + ) -> bytes: ... + def delete_key(self, key: str) -> bool: ... + def num_keys(self) -> int: ... + def set_timeout(self, timeout: timedelta): ... + @overload + def wait(self, keys: list[str]): ... + @overload + def wait(self, keys: list[str], timeout: timedelta): ... + +class FileStore(Store): + def __init__(self, path: str, numWorkers: int = ...) -> None: ... + +class HashStore(Store): + def __init__(self) -> None: ... + +class TCPStore(Store): + def __init__( + self, + host_name: str, + port: int, + world_size: int | None = ..., + is_master: bool = ..., + timeout: timedelta = ..., + wait_for_workers: bool = ..., + multi_tenant: bool = ..., + master_listen_fd: int | None = ..., + use_libuv: bool | None = ..., + ) -> None: ... + @property + def host(self) -> str: ... + @property + def port(self) -> int: ... + +class PrefixStore(Store): + def __init__(self, prefix: str, store: Store) -> None: ... + @property + def underlying_store(self) -> Store: ... + +class _ControlCollectives: + def barrier(self, key: str, timeout: timedelta, blocking: bool) -> None: ... + def broadcast_send(self, key: str, data: str, timeout: timedelta) -> None: ... + def broadcast_recv(self, key: str, timeout: timedelta) -> str: ... + def gather_send(self, key: str, data: str, timeout: timedelta) -> None: ... + def gather_recv(self, key: str, timeout: timedelta) -> str: ... + def scatter_send(self, key: str, data: str, timeout: timedelta) -> None: ... + def scatter_recv(self, key: str, timeout: timedelta) -> str: ... + def all_gather(self, key: str, data: str, timeout: timedelta) -> str: ... + def all_sum(self, key: str, data: int, timeout: timedelta) -> int: ... + +class _StoreCollectives(_ControlCollectives): + def __init__(self, store: Store, rank: int, world_size: int) -> None: ... + +class _DistributedBackendOptions: + def __init__(self) -> None: ... + @property + def store(self) -> Store: ... + @store.setter + def store(self, store: Store) -> None: ... + @property + def group_rank(self) -> int: ... + @group_rank.setter + def group_rank(self, rank: int) -> None: ... + @property + def group_size(self) -> int: ... + @group_size.setter + def group_size(self, size: int) -> None: ... + @property + def timeout(self) -> timedelta: ... + @timeout.setter + def timeout(self, timeout: timedelta) -> None: ... + @property + def group_id(self) -> str: ... + @group_id.setter + def group_id(self, group_id: str) -> None: ... + @property + def global_ranks_in_group(self) -> list[int]: ... + @global_ranks_in_group.setter + def global_ranks_in_group(self, ranks: list[int]) -> None: ... + +class Work: + def is_completed(self) -> bool: ... + def is_success(self) -> bool: ... + def exception(self) -> Any: ... + def wait(self, timeout: timedelta = ...) -> bool: ... + def get_future(self) -> Future: ... + def source_rank(self) -> int: ... + def _source_rank(self) -> int: ... + def result(self) -> list[Tensor]: ... + def synchronize(self): ... + def boxed(self) -> ScriptObject: ... + @staticmethod + def unbox(obj: ScriptObject) -> Work: ... + +class Backend: + class Options: + def __init__(self, backend: str, timeout: timedelta = ...) -> None: ... + @property + def backend(self) -> str: ... + @property + def _timeout(self) -> timedelta: ... + @_timeout.setter + def _timeout(self, val: timedelta) -> None: ... + + def __init__( + self, + rank: int, + size: int, + ) -> None: ... + @property + def supports_splitting(self) -> bool: ... + @property + def supports_coalescing(self) -> bool: ... + @property + def options(self) -> Options: ... + def rank(self) -> int: ... + def size(self) -> int: ... + def abort(self) -> None: ... + def shutdown(self) -> None: ... + def eager_connect_single_device(self, device: torch.device | None) -> None: ... + def _set_sequence_number_for_group(self) -> None: ... + def _set_default_timeout(self, timeout: timedelta) -> None: ... + def get_error(self) -> ErrorType: ... + @property + def mem_allocator(self) -> Any: ... + +class ProcessGroup: + class BackendType(Enum): + UNDEFINED = ... + GLOO = ... + NCCL = ... + UCC = ... + MPI = ... + XCCL = ... + CUSTOM = ... + + def __init__( + self, + store: Store, + rank: int, + size: int, + ) -> None: ... + def rank(self) -> int: ... + def size(self) -> int: ... + def abort(self) -> None: ... + def shutdown(self) -> None: ... + @overload + def broadcast( + self, + tensors: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def broadcast( + self, + tensor: Tensor, + root: int, + ) -> Work: ... + @overload + def allreduce( + self, + tensors: list[Tensor], + opts: AllreduceOptions = ..., + ) -> Work: ... + @overload + def allreduce( + self, + tensors: list[Tensor], + op=..., + ) -> Work: ... + @overload + def allreduce( + self, + tensor: Tensor, + op=..., + ) -> Work: ... + def allreduce_coalesced( + self, + tensors: list[Tensor], + opts=..., + ) -> Work: ... + def reduce_scatter_tensor_coalesced( + self, + outputTensors: list[Tensor], + inputTensors: list[Tensor], + opts: ReduceScatterOptions | None = None, + ) -> Work: ... + @overload + def reduce( + self, + tensors: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def reduce( + self, + tensor: Tensor, + root: int, + op=..., + ) -> Work: ... + @overload + def allgather( + self, + output_tensors: list[list[Tensor]], + input_tensors: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def allgather( + self, + output_tensors: list[Tensor], + input_tensor: Tensor, + ) -> Work: ... + def _allgather_base( + self, + output: Tensor, + input: Tensor, + opts=..., + ) -> Work: ... + def allgather_coalesced( + self, + output_lists: list[list[Tensor]], + input_list: list[Tensor], + opts=..., + ) -> Work: ... + def allgather_into_tensor_coalesced( + self, + output_lists: list[Tensor], + input_list: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def gather( + self, + output_tensors: list[list[Tensor]], + input_tensors: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def gather( + self, + output_tensors: list[Tensor], + input_tensor: Tensor, + root: int, + ) -> Work: ... + @overload + def scatter( + self, + output_tensors: list[Tensor], + input_tensors: list[list[Tensor]], + opts=..., + ) -> Work: ... + @overload + def scatter( + self, + output_tensor: Tensor, + input_tensors: list[Tensor], + root: int, + ) -> Work: ... + @overload + def reduce_scatter( + self, + output_tensors: list[Tensor], + input_tensors: list[list[Tensor]], + opts=..., + ) -> Work: ... + @overload + def reduce_scatter( + self, + output_tensors: Tensor, + input_tensor: list[Tensor], + ) -> Work: ... + def _reduce_scatter_base( + self, + outputTensor: Tensor, + inputTensor: Tensor, + opts: ReduceScatterOptions | None, + ) -> Work: ... + @overload + def alltoall_base( + self, + output_tensor: Tensor, + input_tensor: Tensor, + output_split_sizes: list[int], + input_split_sizes: list[int], + opts=..., + ) -> Work: ... + @overload + def alltoall_base( + self, + output: Tensor, + input: Tensor, + output_split_sizes: list[int], + input_split_sizes: list[int], + ) -> Work: ... + @overload + def alltoall( + self, + output_tensor: list[Tensor], + input_tensor: list[Tensor], + opts=..., + ) -> Work: ... + @overload + def alltoall( + self, + output: list[Tensor], + input: list[Tensor], + ) -> Work: ... + def send( + self, + tensors: list[Tensor], + dstRank: int, + tag: int, + ) -> Work: ... + def recv( + self, + tensors: list[Tensor], + srcRank: int, + tag: int, + ) -> Work: ... + def recv_anysource(self, tensors: list[Tensor], tag: int) -> Work: ... + def barrier(self, opts=...) -> Work: ... + def boxed(self) -> ScriptObject: ... + @staticmethod + def unbox(obj: ScriptObject) -> ProcessGroup: ... + def _start_coalescing(self, device: torch.device) -> None: ... + def _end_coalescing(self, device: torch.device) -> Work: ... + def _get_backend_name(self) -> str: ... + def _backend_id(self, backend_type: BackendType) -> int: ... + @property + def _device_types(self) -> list[torch.device]: ... + def _get_backend(self, device: torch.device) -> Backend: ... + def _set_default_backend(self, backend_type: BackendType) -> None: ... + def _register_backend( + self, + device: torch.device, + backend_type: BackendType, + backend: Backend | None, + ) -> None: ... + def _set_group_name(self, name: str) -> None: ... + def _set_group_desc(self, desc: str) -> None: ... + def name(self) -> str: ... + def _has_hooks(self) -> bool: ... + def _wait_for_pending_works(self) -> None: ... + def _set_sequence_number_for_group(self) -> None: ... + @property + def bound_device_id(self) -> torch.device | None: ... + @bound_device_id.setter + def bound_device_id(self, device: torch.device | None) -> None: ... + @property + def group_name(self) -> str: ... + @property + def group_desc(self) -> str: ... + +class FakeProcessGroup(Backend): + def __init__(self, rank: int, world_size: int) -> None: ... + +class FakeWork(Work): + seq_id: int + def __init__(self) -> None: ... + def wait(self, timeout: timedelta = ...) -> bool: ... + def getFuture(self) -> Future: ... + +class ProcessGroupGloo(Backend): + class Device: ... + + class Options(Backend.Options): + devices: list[ProcessGroupGloo.Device] + threads: int + + def __init__(self): ... + + def __init__( + self, + store: Store, + rank: int, + size: int, + timeout: timedelta, + ) -> None: ... + @staticmethod + def create_device(hostname="", interface="") -> Device: ... + @staticmethod + def create_default_device() -> Device: ... + def _set_default_timeout(self, timeout) -> None: ... + +class _ProcessGroupWrapper(Backend): + def __init__(self, pg: Backend, gloo_pg: ProcessGroupGloo) -> None: ... + wrapped_pg: Backend + +class ErrorType(Enum): + SUCCESS = ... + TIMEOUT = ... + COMM_ERROR = ... + REMOTE_ERROR = ... + +class ProcessGroupNCCL(Backend): + class NCCLConfig: + blocking: int + cga_cluster_size: int + min_ctas: int + max_ctas: int + + class Options(Backend.Options): + config: ProcessGroupNCCL.NCCLConfig + is_high_priority_stream: bool + split_from: ProcessGroupNCCL + split_color: int + global_ranks_in_group: list[int] + group_name: str + + def __init__(self, is_high_priority_stream: bool = False): ... + + def __init__( + self, + store: Store, + rank: int, + size: int, + options: Options, + ) -> None: ... + def _group_start(self) -> None: ... + def _group_end(self) -> None: ... + def _set_default_timeout(self, timeout) -> None: ... + def perform_nocolor_split(self, device: torch.device) -> None: ... + def register_mem_pool(self, pool: torch.cuda.MemPool) -> None: ... + def deregister_mem_pool(self, pool: torch.cuda.MemPool) -> None: ... + def comm_split_count(self) -> int: ... + def _add_ephemeral_timeout(self, timeout: timedelta) -> None: ... + def abort(self) -> None: ... + def _is_initialized(self) -> bool: ... + @property + def uid(self) -> int: ... + @property + def options(self) -> Options: ... # type: ignore[override] + +class ProcessGroupUCC(Backend): + def __init__( + self, + store: Store, + rank: int, + size: int, + timeout: timedelta, + ) -> None: ... + +class ProcessGroupMPI(Backend): + def __init__( + self, + rank: int, + size: int, + pgComm: int, + ) -> None: ... + @staticmethod + def create(ranks: list[int]) -> ProcessGroupMPI: ... + +def _compute_bucket_assignment_by_size( + tensors: list[Tensor], + bucket_size_limits: list[int], + expect_sparse_gradient: list[bool] = ..., + tensor_indices: list[int] = ..., +) -> tuple[list[list[int]], list[int]]: ... +def _broadcast_coalesced( + process_group: ProcessGroup, + tensors: list[Tensor], + buffer_size: int, + src: int, +): ... +def _test_python_store(store: Store): ... +def _verify_params_across_processes( + process_group: ProcessGroup, + params: list[Tensor], + logger: Logger | None, +): ... +def _make_nccl_premul_sum(factor: float | list[Tensor]) -> ReduceOp: ... +def _register_process_group( + group_name: str, + process_group: ProcessGroup, +) -> None: ... +def _resolve_process_group(group_name: str) -> ProcessGroup: ... +def _register_work(tensor: torch.Tensor, work: Work) -> ProcessGroup: ... +def _get_work_registry_size() -> int: ... +def _set_allow_inflight_collective_as_graph_input( + value: bool, +) -> None: ... +def _allow_inflight_collective_as_graph_input() -> bool: ... +def _unregister_all_process_groups() -> None: ... +def _unregister_process_group(group_name: str) -> None: ... + +class _SymmetricMemory: + @staticmethod + def set_group_info( + group_name: str, + rank: int, + world_size: int, + store: Store, + ) -> None: ... + @staticmethod + def empty_strided_p2p( + size: torch.types._size, + stride: torch.types._size, + dtype: torch.dtype, + device: torch.device, + group_name: str | None = None, + alloc_id: int | None = None, + ) -> torch.Tensor: ... + @staticmethod + def has_multicast_support( + device_type: DeviceType, + device_idx: int, + ) -> bool: ... + @property + def rank(self) -> int: ... + @property + def world_size(self) -> int: ... + @staticmethod + def rendezvous( + tensor: torch.Tensor, group_name: str | None = None + ) -> _SymmetricMemory: ... + def get_buffer( + self, + rank: int, + sizes: torch.types._size, + dtype: torch.dtype, + storage_offset: int | None = 0, + ) -> torch.Tensor: ... + def get_signal_pad( + self, + rank: int, + sizes: torch.types._size = [], + dtype: torch.dtype | None = None, + storage_offset: int | None = 0, + ) -> torch.Tensor: ... + def barrier(self, channel: int = 0, timeout_ms: int = 0) -> None: ... + def put_signal( + self, + dst_rank: int, + channel: int = 0, + timeout_ms: int = 0, + ) -> None: ... + def wait_signal( + self, + src_rank: int, + channel: int = 0, + timeout_ms: int = 0, + ) -> None: ... + @staticmethod + def memset32( + tensor: torch.Tensor, offset: int, val: int, count: int = 1 + ) -> torch.Tensor: ... + @staticmethod + def stream_write_value32( + tensor: torch.Tensor, offset: int, val: int + ) -> torch.Tensor: ... + @property + def buffer_ptrs(self) -> list[int]: ... + @property + def buffer_ptrs_dev(self) -> int: ... + @property + def signal_pad_ptrs(self) -> list[int]: ... + @property + def signal_pad_ptrs_dev(self) -> int: ... + @property + def multicast_ptr(self) -> int: ... + @property + def buffer_size(self) -> int: ... + @property + def signal_pad_size(self) -> int: ... + +class ProcessGroupXCCL(Backend): + def __init__( + self, + store: Store, + rank: int, + size: int, + ): ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_distributed_rpc.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_distributed_rpc.pyi new file mode 100644 index 0000000000000000000000000000000000000000..48f636d8524637305cd9c758e9f22428d3b055cc --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_distributed_rpc.pyi @@ -0,0 +1,188 @@ +# mypy: allow-untyped-defs +# mypy: disable-error-code="type-arg" +from datetime import timedelta +from typing import Any, Generic, overload, TypeVar + +import torch +from torch._C import Future +from torch._C._autograd import ProfilerEvent +from torch._C._distributed_c10d import Store +from torch._C._profiler import ProfilerConfig + +# This module is defined in torch/csrc/distributed/rpc/init.cpp + +_DEFAULT_INIT_METHOD: str +_DEFAULT_NUM_WORKER_THREADS: int +_UNSET_RPC_TIMEOUT: float +_DEFAULT_RPC_TIMEOUT_SEC: float + +_T = TypeVar("_T") + +class RpcBackendOptions: + rpc_timeout: float + init_method: str + def __init__( + self, + rpc_timeout: float = ..., + init_method: str = ..., + ) -> None: ... + +class WorkerInfo: + def __init__(self, name: str, worker_id: int) -> None: ... + @property + def name(self) -> str: ... + @property + def id(self) -> int: ... + def __eq__(self, other: object) -> bool: ... + +class RpcAgent: + def join(self, shutdown: bool = False, timeout: float = 0): ... + def sync(self): ... + def shutdown(self): ... + @overload + def get_worker_info(self) -> WorkerInfo: ... + @overload + def get_worker_info(self, workerName: str) -> WorkerInfo: ... + def get_worker_infos(self) -> list[WorkerInfo]: ... + def _get_device_map(self, dst: WorkerInfo) -> dict[torch.device, torch.device]: ... + def get_debug_info(self) -> dict[str, str]: ... + def get_metrics(self) -> dict[str, str]: ... + +class PyRRef(Generic[_T]): + def __init__(self, value: _T, type_hint: Any = None) -> None: ... + def is_owner(self) -> bool: ... + def confirmed_by_owner(self) -> bool: ... + def owner(self) -> WorkerInfo: ... + def owner_name(self) -> str: ... + def to_here(self, timeout: float = ...) -> _T: ... + def local_value(self) -> Any: ... + def rpc_sync(self, timeout: float = ...) -> Any: ... + def rpc_async(self, timeout: float = ...) -> Any: ... + def remote(self, timeout: float = ...) -> Any: ... + def _serialize(self) -> tuple: ... + @staticmethod + def _deserialize(tp: tuple) -> PyRRef: ... + def _get_type(self) -> type[_T]: ... + def _get_future(self) -> Future[_T]: ... + def _get_profiling_future(self) -> Future[_T]: ... + def _set_profiling_future(self, profilingFuture: Future[_T]): ... + +class _TensorPipeRpcBackendOptionsBase(RpcBackendOptions): + num_worker_threads: int + device_maps: dict[str, dict[torch.device, torch.device]] + devices: list[torch.device] + def __init__( + self, + num_worker_threads: int, + _transports: list | None, + _channels: list | None, + rpc_timeout: float = ..., + init_method: str = ..., + device_maps: dict[str, dict[torch.device, torch.device]] = {}, # noqa: B006 + devices: list[torch.device] = [], # noqa: B006 + ) -> None: ... + def _set_device_map( + self, + to: str, + device_map: dict[torch.device, torch.device], + ): ... + +class TensorPipeAgent(RpcAgent): + def __init__( + self, + store: Store, + name: str, + worker_id: int, + world_size: int | None, + opts: _TensorPipeRpcBackendOptionsBase, + reverse_device_maps: dict[str, dict[torch.device, torch.device]], + devices: list[torch.device], + ) -> None: ... + def join(self, shutdown: bool = False, timeout: float = 0): ... + def shutdown(self): ... + @overload + def get_worker_info(self) -> WorkerInfo: ... + @overload + def get_worker_info(self, workerName: str) -> WorkerInfo: ... + @overload + def get_worker_info(self, id: int) -> WorkerInfo: ... + def get_worker_infos(self) -> list[WorkerInfo]: ... + def _get_device_map(self, dst: WorkerInfo) -> dict[torch.device, torch.device]: ... + def _update_group_membership( + self, + worker_info: WorkerInfo, + my_devices: list[torch.device], + reverse_device_map: dict[str, dict[torch.device, torch.device]], + is_join: bool, + ): ... + def _get_backend_options(self) -> _TensorPipeRpcBackendOptionsBase: ... + @property + def is_static_group(self) -> bool: ... + @property + def store(self) -> Store: ... + +def _is_current_rpc_agent_set() -> bool: ... +def _get_current_rpc_agent() -> RpcAgent: ... +def _set_and_start_rpc_agent(agent: RpcAgent): ... +def _reset_current_rpc_agent(): ... +def _delete_all_user_and_unforked_owner_rrefs(timeout: timedelta = ...): ... +def _destroy_rref_context(ignoreRRefLeak: bool): ... +def _rref_context_get_debug_info() -> dict[str, str]: ... +def _cleanup_python_rpc_handler(): ... +def _invoke_rpc_builtin( + dst: WorkerInfo, + opName: str, + rpcTimeoutSeconds: float, + *args: Any, + **kwargs: Any, +): ... +def _invoke_rpc_python_udf( + dst: WorkerInfo, + pickledPythonUDF: str, + tensors: list[torch.Tensor], + rpcTimeoutSeconds: float, + isAsyncExecution: bool, +): ... +def _invoke_rpc_torchscript( + dstWorkerName: str, + qualifiedNameStr: str, + argsTuple: tuple, + kwargsDict: dict, + rpcTimeoutSeconds: float, + isAsyncExecution: bool, +): ... +def _invoke_remote_builtin( + dst: WorkerInfo, + opName: str, + rpcTimeoutSeconds: float, + *args: Any, + **kwargs: Any, +): ... +def _invoke_remote_python_udf( + dst: WorkerInfo, + pickledPythonUDF: str, + tensors: list[torch.Tensor], + rpcTimeoutSeconds: float, + isAsyncExecution: bool, +): ... +def _invoke_remote_torchscript( + dstWorkerName: WorkerInfo, + qualifiedNameStr: str, + rpcTimeoutSeconds: float, + isAsyncExecution: bool, + *args: Any, + **kwargs: Any, +): ... +def get_rpc_timeout() -> float: ... +def enable_gil_profiling(flag: bool): ... +def _set_rpc_timeout(rpcTimeoutSeconds: float): ... + +class RemoteProfilerManager: + @staticmethod + def set_current_profiling_key(key: str): ... + +def _enable_server_process_global_profiler(new_config: ProfilerConfig): ... +def _disable_server_process_global_profiler() -> list[list[list[ProfilerEvent]]]: ... +def _set_profiler_node_id(default_node_id: int): ... +def _enable_jit_rref_pickle(): ... +def _disable_jit_rref_pickle(): ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_distributed_rpc_testing.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_distributed_rpc_testing.pyi new file mode 100644 index 0000000000000000000000000000000000000000..9313281027dbd89ae65ebe65c95a1d0c38593b80 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_distributed_rpc_testing.pyi @@ -0,0 +1,32 @@ +import torch +from torch._C._distributed_c10d import Store +from torch._C._distributed_rpc import _TensorPipeRpcBackendOptionsBase, TensorPipeAgent + +# This module is defined in torch/csrc/distributed/rpc/testing/init.cpp + +class FaultyTensorPipeRpcBackendOptions(_TensorPipeRpcBackendOptionsBase): + def __init__( + self, + num_worker_threads: int, + rpc_timeout: float, + init_method: str, + messages_to_fail: list[str], + messages_to_delay: dict[str, float], + num_fail_sends: int, + ) -> None: ... + num_send_recv_threads: int + messages_to_fail: list[str] + messages_to_delay: dict[str, float] + num_fail_sends: int + +class FaultyTensorPipeAgent(TensorPipeAgent): + def __init__( + self, + store: Store, + name: str, + rank: int, + world_size: int, + options: FaultyTensorPipeRpcBackendOptions, + reverse_device_maps: dict[str, dict[torch.device, torch.device]], + devices: list[torch.device], + ) -> None: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_export.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_export.pyi new file mode 100644 index 0000000000000000000000000000000000000000..5351945b9d51637313d4feec88656ff796863a89 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_export.pyi @@ -0,0 +1,10 @@ +# Defined in torch/csrc/export/pybind.cpp + +class CppExportedProgram: ... + +def deserialize_exported_program( + serialized_program: str, +) -> CppExportedProgram: ... +def serialize_exported_program( + cpp_exported_program: CppExportedProgram, +) -> str: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_functions.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_functions.pyi new file mode 100644 index 0000000000000000000000000000000000000000..5b0dee51a71093f1245887aa13c2405d3f2e5720 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_functions.pyi @@ -0,0 +1,19 @@ +from typing import AnyStr, overload + +from torch import Tensor + +class UndefinedGrad: + def __init__(self) -> None: ... + def __call__(self, *inputs: Tensor) -> list[Tensor]: ... + +class DelayedError: + def __init__(self, msg: AnyStr, num_inputs: int) -> None: ... + + # __call__ should really be a higher-kinded type: + # def __call__(self, arg: Tensor) -> Tensor: ... + # def __call__(self, *args: Tensor * num_inputs) -> Tuple[Tensor * num_inputs]: ... + + @overload + def __call__(self, i0: Tensor) -> Tensor: ... + @overload + def __call__(self, *args: Tensor) -> tuple[Tensor, ...]: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_functorch.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_functorch.pyi new file mode 100644 index 0000000000000000000000000000000000000000..4cdfb1346fd17171da6111fcb5e05cd5908f1974 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_functorch.pyi @@ -0,0 +1,83 @@ +# mypy: allow-untyped-defs +from enum import Enum + +from torch import Tensor + +# Defined in torch/csrc/functorch/init.cpp + +def _set_dynamic_layer_keys_included(included: bool) -> None: ... +def get_unwrapped(tensor: Tensor) -> Tensor: ... +def is_batchedtensor(tensor: Tensor) -> bool: ... +def is_functionaltensor(tensor: Tensor) -> bool: ... +def is_functorch_wrapped_tensor(tensor: Tensor) -> bool: ... +def is_gradtrackingtensor(tensor: Tensor) -> bool: ... +def is_legacy_batchedtensor(tensor: Tensor) -> bool: ... +def maybe_get_bdim(tensor: Tensor) -> int: ... +def maybe_get_level(tensor: Tensor) -> int: ... +def maybe_current_level() -> int | None: ... +def unwrap_if_dead(tensor: Tensor) -> Tensor: ... +def _unwrap_for_grad(tensor: Tensor, level: int) -> Tensor: ... +def _wrap_for_grad(tensor: Tensor, level: int) -> Tensor: ... +def _unwrap_batched(tensor: Tensor, level: int) -> tuple[Tensor, int | None]: ... +def current_level() -> int: ... +def count_jvp_interpreters() -> int: ... +def _add_batch_dim(tensor: Tensor, bdim: int, level: int) -> Tensor: ... +def set_single_level_autograd_function_allowed(allowed: bool) -> None: ... +def get_single_level_autograd_function_allowed() -> bool: ... +def _unwrap_functional_tensor(tensor: Tensor, reapply_views: bool) -> Tensor: ... +def _wrap_functional_tensor(tensor: Tensor, level: int) -> Tensor: ... +def _vmap_increment_nesting(batch_size: int, randomness: str) -> int: ... +def _vmap_decrement_nesting() -> int: ... +def _grad_increment_nesting() -> int: ... +def _grad_decrement_nesting() -> int: ... +def _jvp_increment_nesting() -> int: ... +def _jvp_decrement_nesting() -> int: ... + +# Defined in aten/src/ATen/functorch/Interpreter.h +class TransformType(Enum): + Torch = ... + Vmap = ... + Grad = ... + Jvp = ... + Functionalize = ... + +class RandomnessType(Enum): + Error = ... + Same = ... + Different = ... + +class CInterpreter: + def key(self) -> TransformType: ... + def level(self) -> int: ... + +class CGradInterpreterPtr: + def __init__(self, interpreter: CInterpreter) -> None: ... + def lift(self, Tensor) -> Tensor: ... + def prevGradMode(self) -> bool: ... + +class CJvpInterpreterPtr: + def __init__(self, interpreter: CInterpreter) -> None: ... + def lift(self, Tensor) -> Tensor: ... + def prevFwdGradMode(self) -> bool: ... + +class CFunctionalizeInterpreterPtr: + def __init__(self, interpreter: CInterpreter) -> None: ... + def key(self) -> TransformType: ... + def level(self) -> int: ... + def functionalizeAddBackViews(self) -> bool: ... + +class CVmapInterpreterPtr: + def __init__(self, interpreter: CInterpreter) -> None: ... + def key(self) -> TransformType: ... + def level(self) -> int: ... + def batchSize(self) -> int: ... + def randomness(self) -> RandomnessType: ... + +class DynamicLayer: ... + +def get_dynamic_layer_stack_depth() -> int: ... +def get_interpreter_stack() -> list[CInterpreter]: ... +def peek_interpreter_stack() -> CInterpreter: ... +def pop_dynamic_layer_stack() -> DynamicLayer: ... +def pop_dynamic_layer_stack_and_undo_to_depth(int) -> None: ... +def push_dynamic_layer_stack(dl: DynamicLayer) -> int: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_instruction_counter.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_instruction_counter.pyi new file mode 100644 index 0000000000000000000000000000000000000000..4e3c27567eb228b8763a6d2578db827b1ffbde41 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_instruction_counter.pyi @@ -0,0 +1,4 @@ +# Defined in torch/csrc/instruction_counter/Module.cpp + +def start() -> int: ... +def end(id: int) -> int: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_itt.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_itt.pyi new file mode 100644 index 0000000000000000000000000000000000000000..8a54437f527b994821133532f26598c951631b28 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_itt.pyi @@ -0,0 +1,5 @@ +# Defined in torch/csrc/itt.cpp +def is_available() -> None: ... +def rangePush(message: str) -> None: ... +def rangePop() -> None: ... +def mark(message: str) -> None: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_lazy.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_lazy.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c6b2b89fa3a9e89d889d9248097315d7c9d8635c --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_lazy.pyi @@ -0,0 +1,26 @@ +from torch import Tensor + +# defined in torch/csrc/lazy/python/init.cpp +def _mark_step(device: str, devices: list[str], wait: bool) -> None: ... +def _wait_device_ops(devices: list[str]) -> None: ... +def _reset_metrics() -> None: ... +def _counter_names() -> list[str]: ... +def _counter_value(name: str) -> int: ... +def _metrics_report() -> str: ... +def _get_graph_hash(tensors: list[Tensor]) -> str: ... +def _sync_multi( + tensors: list[Tensor], + devices: list[str], + wait: bool = True, + sync_ltc_data: bool = True, +) -> None: ... +def _get_tensor_id(tensor: Tensor) -> int: ... +def _get_tensors_text(tensors: list[Tensor]) -> str: ... +def _get_tensors_dot(tensors: list[Tensor]) -> str: ... +def _get_tensors_backend(tensors: list[Tensor]) -> str: ... +def _get_force_fallback() -> str: ... +def _set_force_fallback(newval: str) -> None: ... +def _clear_ir_cache() -> None: ... +def _dump_ir_cache(filename: str) -> None: ... +def _set_reuse_ir(val: bool) -> None: ... +def _get_default_device_type() -> str: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_lazy_ts_backend.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_lazy_ts_backend.pyi new file mode 100644 index 0000000000000000000000000000000000000000..cd1bef2de069dfc93cd2b6243ab97d550d75f086 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_lazy_ts_backend.pyi @@ -0,0 +1,12 @@ +# mypy: allow-untyped-defs +# defined in torch/csrc/lazy/python/init.cpp + +from typing import Any + +from torch import Tensor + +def _init(): ... +def _get_tensors_ts_device_data_node( + tensors: list[Tensor], +) -> tuple[list[int], list[Any]]: ... +def _run_cached_graph(hash_str: str, graph_inputs: list[Any]) -> list[Tensor]: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_monitor.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_monitor.pyi new file mode 100644 index 0000000000000000000000000000000000000000..d28c373e528bb2a77ebe3329a3d3e0c0a439c8a6 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_monitor.pyi @@ -0,0 +1,58 @@ +# Defined in torch/csrc/monitor/python_init.cpp + +import datetime +from enum import Enum +from types import TracebackType +from typing import Callable, Optional + +class Aggregation(Enum): + VALUE = ... + MEAN = ... + COUNT = ... + SUM = ... + MAX = ... + MIN = ... + +class Stat: + name: str + count: int + def __init__( + self, + name: str, + aggregations: list[Aggregation], + window_size: int, + max_samples: int = -1, + ) -> None: ... + def add(self, v: float) -> None: ... + def get(self) -> dict[Aggregation, float]: ... + +class Event: + name: str + timestamp: datetime.datetime + data: dict[str, int | float | bool | str] + def __init__( + self, + name: str, + timestamp: datetime.datetime, + data: dict[str, int | float | bool | str], + ) -> None: ... + +def log_event(e: Event) -> None: ... + +class EventHandlerHandle: ... + +def register_event_handler(handler: Callable[[Event], None]) -> EventHandlerHandle: ... +def unregister_event_handler(handle: EventHandlerHandle) -> None: ... + +class _WaitCounterTracker: + def __enter__(self) -> None: ... + def __exit__( + self, + exec_type: Optional[type[BaseException]] = None, + exec_value: Optional[BaseException] = None, + traceback: Optional[TracebackType] = None, + ) -> None: ... + +class _WaitCounter: + def __init__(self, key: str) -> None: ... + def guard(self) -> _WaitCounterTracker: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_nn.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_nn.pyi new file mode 100644 index 0000000000000000000000000000000000000000..d99e0c0dff761a499a05516e9c596a1464d9427c --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_nn.pyi @@ -0,0 +1,89 @@ +# @generated by tools/pyi/gen_pyi.py from torch/_C/_nn.pyi.in +# mypy: disable-error-code="type-arg" + +from typing import Literal, Optional, overload, Sequence, Union + +from torch import memory_format, Tensor +from torch.types import _bool, _device, _dtype, _int, _size + +# Defined in tools/autograd/templates/python_nn_functions.cpp + +def adaptive_max_pool2d(input: Tensor, output_size: Union[_int, _size]) -> tuple[Tensor, Tensor]: ... +def adaptive_max_pool3d(input: Tensor, output_size: Union[_int, _size]) -> tuple[Tensor, Tensor]: ... +def avg_pool2d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: Optional[int] = None) -> Tensor: ... +def avg_pool3d(input: Tensor, kernel_size: Union[_int, _size], stride: Optional[Union[_int, _size]] = None, padding: Union[_int, _size] = 0, ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: Optional[int] = None) -> Tensor: ... +def elu_(input: Tensor, alpha: float = ...) -> Tensor: ... +def fractional_max_pool2d(input: Tensor, kernel_size: Union[_int, _size], output_size: Union[_int, _size], _random_samples: Tensor) -> tuple[Tensor, Tensor]: ... +def fractional_max_pool3d(input: Tensor, kernel_size: Union[_int, _size], output_size: Union[_int, _size], _random_samples: Tensor) -> tuple[Tensor, Tensor]: ... +def gelu(input: Tensor, approximate: str = ...) -> Tensor: ... +def hardsigmoid(input: Tensor, *, out: Optional[Tensor] = None) -> Tensor: ... +def hardtanh(input: Tensor, min_val: float = ..., max_val: float = ..., *, out: Optional[Tensor] = None) -> Tensor: ... +def hardtanh_(input: Tensor, min_val: float = ..., max_val: float = ...) -> Tensor: ... +def leaky_relu(input: Tensor, negative_slope: float = ..., *, out: Optional[Tensor] = None) -> Tensor: ... +def leaky_relu_(input: Tensor, negative_slope: float = ...) -> Tensor: ... +def linear(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None) -> Tensor: ... +def log_sigmoid(input: Tensor) -> Tensor: ... +def one_hot(tensor: Tensor, num_classes: int = ...) -> Tensor: ... +def pad(input: Tensor, pad: Sequence[int], mode: str = ..., value: Optional[float] = None) -> Tensor: ... +def scaled_dot_product_attention(query: Tensor, key: Tensor, value: Tensor, attn_mask: Optional[Tensor] = None, dropout_p: float = 0.0, is_causal: bool = False, scale: Optional[float] = None, enable_gqa: bool = False) -> Tensor: ... +def softplus(input: Tensor, beta: float = ..., threshold: float = ...) -> Tensor: ... +def softshrink(input: Tensor, lambd: float = ...) -> Tensor: ... + +# Defined in aten/src/ATen/native/mkldnn/Linear.cpp +def mkldnn_linear(input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: ... + +# Defined at aten/src/ATen/native/mkldnn/MKLDNNConversions.cpp +def mkldnn_reorder_conv2d_weight( + self: Tensor, + padding: list, + stride: list, + dilatation: list, + groups: int, +) -> Tensor: ... +def mkldnn_reorder_conv3d_weight( + self: Tensor, + padding: list, + stride: list, + dilatation: list, + groups: int, +) -> Tensor: ... + +# Defined in aten/src/ATen/native/mkldnn/Prelu.cpp +def mkldnn_prelu(input: Tensor, weight: Tensor) -> Tensor: ... + +# Defined at tools/autograd/templates/python_nn_functions.cpp +@overload +def _parse_to( + device: _device, + dtype: _dtype, + non_blocking: _bool, + copy: _bool, + *, + memory_format: memory_format, +) -> tuple[_device, _dtype, _bool, memory_format]: ... +@overload +def _parse_to( + dtype: _dtype, + non_blocking: _bool, + copy: _bool, + *, + memory_format: memory_format, +) -> tuple[_device, _dtype, _bool, memory_format]: ... +@overload +def _parse_to( + tensor: Tensor, + non_blocking: _bool, + copy: _bool, + *, + memory_format: memory_format, +) -> tuple[_device, _dtype, _bool, memory_format]: ... + +# Defined in aten/src/ATen/native/PackedSequence.cpp +def pad_sequence( + sequences: Union[list[Tensor], tuple[Tensor, ...]], + batch_first: bool = False, + padding_value: float = 0.0, + padding_side: Union[Literal["left", "right"], str] = "right", +) -> Tensor: ... +def flatten_dense_tensors(tensors: list[Tensor]) -> Tensor: ... +def unflatten_dense_tensors(flat: Tensor, tensors: list[Tensor]) -> list[Tensor]: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_nvtx.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_nvtx.pyi new file mode 100644 index 0000000000000000000000000000000000000000..9b96874c36578ebaba065188d726455ff0b771be --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_nvtx.pyi @@ -0,0 +1,9 @@ +# mypy: allow-untyped-defs +# Defined in torch/csrc/cuda/shared/nvtx.cpp +def rangePushA(message: str) -> int: ... +def rangePop() -> int: ... +def rangeStartA(message: str) -> int: ... +def rangeEnd(int) -> None: ... +def markA(message: str) -> None: ... +def deviceRangeStart(message: str, stream: int) -> object: ... +def deviceRangeEnd(range_handle: object, stream: int) -> None: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_onnx.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_onnx.pyi new file mode 100644 index 0000000000000000000000000000000000000000..349e0b9ad12f0dd9306fde89a40718f26b158f0e --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_onnx.pyi @@ -0,0 +1,39 @@ +# Defined in torch/csrc/onnx/init.cpp + +from enum import Enum + +PRODUCER_VERSION: str + +class TensorProtoDataType(Enum): + UNDEFINED = ... + FLOAT = ... + UINT8 = ... + INT8 = ... + UINT16 = ... + INT16 = ... + INT32 = ... + INT64 = ... + STRING = ... + BOOL = ... + FLOAT16 = ... + DOUBLE = ... + UINT32 = ... + UINT64 = ... + COMPLEX64 = ... + COMPLEX128 = ... + BFLOAT16 = ... + FLOAT8E5M2 = ... + FLOAT8E4M3FN = ... + FLOAT8E5M2FNUZ = ... + FLOAT8E4M3FNUZ = ... + +class OperatorExportTypes(Enum): + ONNX = ... + ONNX_ATEN = ... + ONNX_ATEN_FALLBACK = ... + ONNX_FALLTHROUGH = ... + +class TrainingMode(Enum): + EVAL = ... + PRESERVE = ... + TRAINING = ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_profiler.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_profiler.pyi new file mode 100644 index 0000000000000000000000000000000000000000..48b14cc4b4673e166fc1d9b65eeb153700f7b2fc --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_profiler.pyi @@ -0,0 +1,246 @@ +from enum import Enum +from typing import Any, Literal, Optional +from typing_extensions import TypeAlias + +from torch._C import device, dtype, layout + +# defined in torch/csrc/profiler/python/init.cpp + +class RecordScope(Enum): + FUNCTION = ... + BACKWARD_FUNCTION = ... + TORCHSCRIPT_FUNCTION = ... + KERNEL_FUNCTION_DTYPE = ... + CUSTOM_CLASS = ... + BUILD_FEATURE = ... + LITE_INTERPRETER = ... + USER_SCOPE = ... + STATIC_RUNTIME_OP = ... + STATIC_RUNTIME_MODEL = ... + +class ProfilerState(Enum): + Disable = ... + CPU = ... + CUDA = ... + NVTX = ... + ITT = ... + KINETO = ... + KINETO_GPU_FALLBACK = ... + KINETO_PRIVATEUSE1_FALLBACK = ... + KINETO_PRIVATEUSE1 = ... + +class ActiveProfilerType(Enum): + NONE = ... + LEGACY = ... + KINETO = ... + NVTX = ... + ITT = ... + +class ProfilerActivity(Enum): + CPU = ... + CUDA = ... + XPU = ... + MTIA = ... + HPU = ... + PrivateUse1 = ... + +class _EventType(Enum): + TorchOp = ... + Backend = ... + Allocation = ... + OutOfMemory = ... + PyCall = ... + PyCCall = ... + Kineto = ... + +class _ExperimentalConfig: + def __init__( + self, + profiler_metrics: list[str] = ..., + profiler_measure_per_kernel: bool = ..., + verbose: bool = ..., + performance_events: list[str] = ..., + enable_cuda_sync_events: bool = ..., + ) -> None: ... + +class ProfilerConfig: + def __init__( + self, + state: ProfilerState, + report_input_shapes: bool, + profile_memory: bool, + with_stack: bool, + with_flops: bool, + with_modules: bool, + experimental_config: _ExperimentalConfig, + trace_id: Optional[str] = None, + ) -> None: ... + +class _ProfilerEvent: + start_tid: int + start_time_ns: int + children: list[_ProfilerEvent] + + # TODO(robieta): remove in favor of `self.typed` + extra_fields: ( + _ExtraFields_TorchOp + | _ExtraFields_Backend + | _ExtraFields_Allocation + | _ExtraFields_OutOfMemory + | _ExtraFields_PyCall + | _ExtraFields_PyCCall + | _ExtraFields_Kineto + ) + + @property + def typed( + self, + ) -> ( + tuple[Literal[_EventType.TorchOp], _ExtraFields_TorchOp] + | tuple[Literal[_EventType.Backend], _ExtraFields_Backend] + | tuple[Literal[_EventType.Allocation], _ExtraFields_Allocation] + | tuple[Literal[_EventType.OutOfMemory], _ExtraFields_OutOfMemory] + | tuple[Literal[_EventType.PyCall], _ExtraFields_PyCall] + | tuple[Literal[_EventType.PyCCall], _ExtraFields_PyCCall] + | tuple[Literal[_EventType.Kineto], _ExtraFields_Kineto] + ): ... + @property + def name(self) -> str: ... + @property + def tag(self) -> _EventType: ... + @property + def id(self) -> int: ... + @property + def parent(self) -> _ProfilerEvent | None: ... + @property + def correlation_id(self) -> int: ... + @property + def end_time_ns(self) -> int: ... + @property + def duration_time_ns(self) -> int: ... + +class _TensorMetadata: + impl_ptr: int | None + storage_data_ptr: int | None + id: int | None + + @property + def allocation_id(self) -> int | None: ... + @property + def layout(self) -> layout: ... + @property + def device(self) -> device: ... + @property + def dtype(self) -> dtype: ... + @property + def sizes(self) -> list[int]: ... + @property + def strides(self) -> list[int]: ... + +Scalar: TypeAlias = int | float | bool | complex +Input: TypeAlias = _TensorMetadata | list[_TensorMetadata] | Scalar | None + +class _ExtraFields_TorchOp: + name: str + sequence_number: int + allow_tf32_cublas: bool + + @property + def inputs(self) -> list[Input]: ... + @property + def scope(self) -> RecordScope: ... + +class _ExtraFields_Backend: ... + +class _ExtraFields_Allocation: + ptr: int + id: int | None + alloc_size: int + total_allocated: int + total_reserved: int + + @property + def allocation_id(self) -> int | None: ... + @property + def device(self) -> device: ... + +class _ExtraFields_OutOfMemory: ... + +class _PyFrameState: + line_number: int + function_name: str + + @property + def file_name(self) -> str: ... + +class _NNModuleInfo: + @property + def self_ptr(self) -> int: ... + @property + def cls_ptr(self) -> int: ... + @property + def cls_name(self) -> str: ... + @property + def parameters( + self, + ) -> list[tuple[str, _TensorMetadata, _TensorMetadata | None]]: ... + +class _OptimizerInfo: + @property + def parameters( + self, + ) -> list[ + tuple[ + # Parameter + _TensorMetadata, + # + # Gradient (if present during optimizer.step()) + _TensorMetadata | None, + # + # Optimizer state for Parameter as (name, tensor) pairs + list[tuple[str, _TensorMetadata]], + ] + ]: ... + +class _ExtraFields_PyCCall: + @property + def caller(self) -> _PyFrameState: ... + +class _ExtraFields_PyCall: + @property + def callsite(self) -> _PyFrameState: ... + @property + def caller(self) -> _PyFrameState: ... + @property + def module(self) -> _NNModuleInfo | None: ... + @property + def optimizer(self) -> _OptimizerInfo | None: ... + +class _ExtraFields_Kineto: ... + +def _add_execution_trace_observer(output_file_path: str) -> bool: ... +def _remove_execution_trace_observer() -> None: ... +def _enable_execution_trace_observer() -> None: ... +def _disable_execution_trace_observer() -> None: ... +def _set_record_concrete_inputs_enabled_val(val: bool) -> None: ... +def _set_fwd_bwd_enabled_val(val: bool) -> None: ... +def _set_cuda_sync_enabled_val(val: bool) -> None: ... + +class CapturedTraceback: ... + +def gather_traceback(python: bool, script: bool, cpp: bool) -> CapturedTraceback: ... + +# The Dict has name, filename, line +def symbolize_tracebacks( + to_symbolize: list[CapturedTraceback], +) -> list[list[dict[str, str]]]: ... + +class _RecordFunctionFast: + def __init__( + self, + name: str, + input_values: list | tuple | None = None, + keyword_values: dict | None = None, + ) -> None: ... + def __enter__(self) -> None: ... + def __exit__(self, *args: Any) -> None: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_verbose.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_verbose.pyi new file mode 100644 index 0000000000000000000000000000000000000000..2388ce2bb8a5edd4c7640c374537e71607e5b72e --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_C/_verbose.pyi @@ -0,0 +1,3 @@ +# Defined in torch/csrc/utils/verbose.cpp +def mkl_set_verbose(enable: int) -> int: ... +def mkldnn_set_verbose(level: int) -> int: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_awaits/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_awaits/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b08067bdcf45a17dbcf3e032b4156315d9e2981b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_awaits/__init__.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +from typing import Generic, TypeVar + +import torch + +__all__ = ['Await'] + +W = TypeVar("W") + +class _PyAwaitMeta(type(torch._C._Await), type(Generic)): # type: ignore[misc, no-redef] + pass + +class _Await(torch._C._Await, Generic[W], metaclass=_PyAwaitMeta): + r""" + Wrapper around a ``torch._C.Await`` which encapsulates delayed execution + of a callable. All manipulations happen with functions ``torch.jit._awaitable``, + ``torch.jit._awaitable_wait``, ``torch.jit._awaitable_nowait``. + + Torch scriptable manipulations: + ``torch.jit._awaitable(func, *args)`` + Creates ``Await[W]`` object, where W is return type of func. + + Returns: + ``torch.jit._awaitable_wait(Await[W])`` + Returns the result of the function, specified at ``_awaitable``, with specified arguments. + + Returns: + The result of type ``W`` of the function call. The result is owned by ``Await[W]`` + and returned on all following ``_awaitable_wait`` calls. + + + ``torch.jit._awaitable_nowait(W)`` + Returns: + Trivial ``Await[W]`` with specified result. + + + Only in eager mode: + ``fn() -> Callable[Tuple[Any], W]`` + Returns: + Specified at ``_awaitable`` python function ``func``. + + ``args() -> Tuple[Any]`` + Returns: + Specified at ``_awaitable`` python args. + + ``is_nowait() -> _bool`` + Returns: + ``True`` if this object was created via ``_awaitable_nowait`` call (trivial `Await[W]`). + + In eager mode ``Await[W]`` can be used as ``W`` i.e. attributes of W can be called on ``Await[W]``, + ``_awaitable_wait()`` call will be transparently added. + """ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_awaits/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_awaits/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9a7dad04ae69baf42b4cec11d952597d2f01b15f Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_awaits/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..10a55772ab58b21573a6eba0356ddd3080164ac7 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. diff --git 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a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/ac_logging_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/ac_logging_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fe22a3837959711c837123903d01ae0369b51f9b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/ac_logging_utils.py @@ -0,0 +1,145 @@ +import json +import logging +from typing import Any + +from torch._logging import trace_structured +from torch.fx import Graph, Node + + +log: logging.Logger = logging.getLogger(__name__) + + +def create_joint_graph_node_information( + joint_graph: Graph, + recomputable_node_info: dict[str, int], +) -> dict[str, Any]: + joint_graph_node_information: dict[str, Any] = {} + + for i, joint_graph_node in enumerate(joint_graph.nodes): + is_recomputable_candidate: bool = ( + joint_graph_node.name in recomputable_node_info + ) + tensor_meta = joint_graph_node.meta.get("tensor_meta") + shape = getattr(tensor_meta, "shape", []) if tensor_meta else [] + + node_info: dict[str, Any] = { + "index": i, + "name": joint_graph_node.name, + "is_recomputable_candidate": is_recomputable_candidate, + "target": str(joint_graph_node.target), + "shape": str(shape), + "input_arguments": [inp.name for inp in joint_graph_node.all_input_nodes], + "stack_trace": joint_graph_node.meta.get("stack_trace", ""), + } + + if is_recomputable_candidate: + idx: int = recomputable_node_info[joint_graph_node.name] + node_info["recomputable_candidate_info"] = { + "recomputable_node_idx": idx, + } + + joint_graph_node_information[joint_graph_node.name] = node_info + + return joint_graph_node_information + + +def create_joint_graph_edges(joint_graph: Graph) -> list[tuple[str, str]]: + joint_graph_edges: list[tuple[str, str]] = [ + (inp.name, node.name) + for node in joint_graph.nodes + for inp in node.all_input_nodes + ] + return joint_graph_edges + + +def create_activation_checkpointing_logging_structure_payload( + joint_graph: Graph, + joint_graph_node_information: dict[str, Any], + joint_graph_edges: list[tuple[str, str]], + all_recomputable_banned_nodes: list[Node], + expected_runtime: float, + saved_node_idxs: list[int], + recomputable_node_idxs: list[int], + memories_banned_nodes: list[float], + runtimes_banned_nodes: list[float], + min_cut_saved_values: list[Node], +) -> dict[str, Any]: + activation_checkpointing_logging_structure_payload: dict[str, Any] = { + "Joint Graph Size": len(joint_graph.nodes), + "Joint Graph Edges": { + "Total": len(joint_graph_edges), + "Edges": joint_graph_edges, + }, + "Joint Graph Node Information": joint_graph_node_information, + "Recomputable Banned Nodes Order": [ + node.name for node in all_recomputable_banned_nodes + ], + "Expected Runtime": expected_runtime, + "Knapsack Saved Nodes": saved_node_idxs, + "Knapsack Recomputed Nodes": recomputable_node_idxs, + "Knapsack Input Memories": memories_banned_nodes, + "Knapsack Input Runtimes": runtimes_banned_nodes, + "Min Cut Solution Saved Values": [node.name for node in min_cut_saved_values], + } + return activation_checkpointing_logging_structure_payload + + +def create_structured_trace_for_min_cut_info( + joint_graph: Graph, + all_recomputable_banned_nodes: list[Node], + saved_node_idxs: list[int], + recomputable_node_idxs: list[int], + expected_runtime: float, + memories_banned_nodes: list[float], + runtimes_banned_nodes: list[float], + min_cut_saved_values: list[Node], +) -> None: + recomputable_node_info: dict[str, int] = { + node.name: idx for idx, node in enumerate(all_recomputable_banned_nodes) + } + joint_graph_node_information = create_joint_graph_node_information( + joint_graph, recomputable_node_info + ) + + for node_name, node_info in joint_graph_node_information.items(): + if node_info["is_recomputable_candidate"]: + idx = recomputable_node_info[node_name] + node_info["recomputable_candidate_info"]["memory"] = memories_banned_nodes[ + idx + ] + node_info["recomputable_candidate_info"]["runtime"] = runtimes_banned_nodes[ + idx + ] + node_info["recomputable_candidate_info"]["is_saved"] = ( + idx in saved_node_idxs + ) + node_info["recomputable_candidate_info"]["is_recomputed"] = ( + idx in recomputable_node_idxs + ) + + joint_graph_edges = create_joint_graph_edges(joint_graph) + activation_checkpointing_logging_structure_payload = ( + create_activation_checkpointing_logging_structure_payload( + joint_graph, + joint_graph_node_information, + joint_graph_edges, + all_recomputable_banned_nodes, + expected_runtime, + saved_node_idxs, + recomputable_node_idxs, + memories_banned_nodes, + runtimes_banned_nodes, + min_cut_saved_values, + ) + ) + + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "min_cut_information", + "encoding": "json", + }, + payload_fn=lambda: json.dumps( + activation_checkpointing_logging_structure_payload + ), + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/graph_info_provider.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/graph_info_provider.py new file mode 100644 index 0000000000000000000000000000000000000000..d92b3728f543bbd4d6c016a54b2f99ed37cb2a19 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/graph_info_provider.py @@ -0,0 +1,321 @@ +from typing import Any, Optional + +import networkx as nx + +from torch.fx import Graph, Node + + +class GraphInfoProvider: + """ + This class provides information about the graph, such as the nodes, edges, and their runtime and memory requirements. + It also provides methods to create graphs from the information provided. + """ + + __RECOMPUTABLE_NODE_ONLY_GRAPH = "recomputable_node_only_graph" + __RECOMPUTABLE_NODE_ONLY_GRAPH_WITH_LARGER_GRAPH_CONTEXT = ( + "recomputable_node_only_graph_with_larger_graph_context" + ) + __FULL_NX_JOINT_GRAPH = "full_nx_joint_graph" + __SIMPLIFIED_FX_JOINT_GRAPH = "fx_joint_graph" + + def __init__( + self, + graph_nodes_in_order: list[str], + graph_edges: list[tuple[str, str]], + all_recomputable_banned_nodes: list[str], + all_node_runtimes: Optional[dict[str, float]] = None, + all_node_memories: Optional[dict[str, float]] = None, + recorded_knapsack_input_memories: Optional[list[float]] = None, + recorded_knapsack_input_runtimes: Optional[list[float]] = None, + joint_graph: Optional[Graph] = None, + ): + self.graph_nodes_in_order = graph_nodes_in_order + self.graph_edges = graph_edges + self.all_node_runtimes: dict[str, float] = dict() + if all_node_runtimes is None: + if recorded_knapsack_input_runtimes is None: + raise ValueError( + "Either all_node_runtimes or recorded_knapsack_input_runtimes must be provided." + ) + self.all_node_runtimes = { + node: recorded_knapsack_input_runtimes[i] + for i, node in enumerate(all_recomputable_banned_nodes) + } + else: + self.all_node_runtimes.update(all_node_runtimes) + self.all_node_memories: dict[str, float] = dict() + if all_node_memories is None: + if recorded_knapsack_input_memories is None: + raise ValueError( + "Either all_node_memories or recorded_knapsack_input_memories must be provided." + ) + self.all_node_memories = { + node: recorded_knapsack_input_memories[i] + for i, node in enumerate(all_recomputable_banned_nodes) + } + else: + self.all_node_memories.update(all_node_memories) + self.all_recomputable_banned_nodes = all_recomputable_banned_nodes + self.all_recomputable_banned_nodes_set = set(all_recomputable_banned_nodes) + self.recorded_knapsack_input_memories = recorded_knapsack_input_memories + self.recorded_knapsack_input_runtimes = recorded_knapsack_input_runtimes + self._lazily_initialized_graphs: dict[str, Any] = { + self.__RECOMPUTABLE_NODE_ONLY_GRAPH: None, + self.__RECOMPUTABLE_NODE_ONLY_GRAPH_WITH_LARGER_GRAPH_CONTEXT: None, + self.__FULL_NX_JOINT_GRAPH: None, + self.__SIMPLIFIED_FX_JOINT_GRAPH: None, + } + + @classmethod + def inialize_from_graph( + cls, + joint_graph: Graph, + all_recomputable_banned_nodes: list[Node], + recorded_knapsack_input_memories: list[float], + recorded_knapsack_input_runtimes: list[float], + ) -> "GraphInfoProvider": + """ + Enables initialization from a joint graph. + """ + graph_nodes_in_order = [node.name for node in joint_graph.nodes] + graph_edges = [ + (node.name, user.name) for node in joint_graph.nodes for user in node.users + ] + all_recomputable_banned_node_names = [ + node.name for node in all_recomputable_banned_nodes + ] + return cls( + graph_nodes_in_order=graph_nodes_in_order, + graph_edges=graph_edges, + all_recomputable_banned_nodes=all_recomputable_banned_node_names, + recorded_knapsack_input_memories=recorded_knapsack_input_memories, + recorded_knapsack_input_runtimes=recorded_knapsack_input_runtimes, + joint_graph=joint_graph, + ) + + @property + def recomputable_node_only_graph(self) -> nx.DiGraph: + if self._lazily_initialized_graphs[self.__RECOMPUTABLE_NODE_ONLY_GRAPH] is None: + self._lazily_initialized_graphs[ + self.__RECOMPUTABLE_NODE_ONLY_GRAPH + ] = self._create_recomputable_node_only_graph() + return self._lazily_initialized_graphs[self.__RECOMPUTABLE_NODE_ONLY_GRAPH] + + @property + def recomputable_node_only_graph_with_larger_graph_context(self) -> nx.DiGraph: + if ( + self._lazily_initialized_graphs[ + self.__RECOMPUTABLE_NODE_ONLY_GRAPH_WITH_LARGER_GRAPH_CONTEXT + ] + is None + ): + self._lazily_initialized_graphs[ + self.__RECOMPUTABLE_NODE_ONLY_GRAPH_WITH_LARGER_GRAPH_CONTEXT + ] = self._create_recomputable_node_only_graph_with_larger_graph_context() + return self._lazily_initialized_graphs[ + self.__RECOMPUTABLE_NODE_ONLY_GRAPH_WITH_LARGER_GRAPH_CONTEXT + ] + + @property + def full_joint_nx_graph(self) -> nx.DiGraph: + if self._lazily_initialized_graphs[self.__FULL_NX_JOINT_GRAPH] is None: + self._lazily_initialized_graphs[ + self.__FULL_NX_JOINT_GRAPH + ] = self._create_full_joint_graph() + return self._lazily_initialized_graphs[self.__FULL_NX_JOINT_GRAPH] + + @property + def simplified_fx_joint_graph(self) -> Graph: + if self._lazily_initialized_graphs[self.__SIMPLIFIED_FX_JOINT_GRAPH] is None: + self._lazily_initialized_graphs[ + self.__SIMPLIFIED_FX_JOINT_GRAPH + ] = self._recreate_psuedo_joint_graph() + return self._lazily_initialized_graphs[self.__SIMPLIFIED_FX_JOINT_GRAPH] + + def get_non_ac_peak_memory(self) -> float: + return sum( + self.all_node_memories[node_name] + for node_name in self.all_recomputable_banned_nodes_set + ) + + def get_theoretical_max_runtime(self) -> float: + return sum( + self.all_node_runtimes[node_name] + for node_name in self.all_recomputable_banned_nodes_set + ) + + def get_knapsack_memory_input(self) -> list[float]: + return ( + self.recorded_knapsack_input_memories + if self.recorded_knapsack_input_memories + else [ + self.all_node_memories[node_name] + for node_name in self.all_recomputable_banned_nodes + ] + ) + + def get_knapsack_runtime_input(self) -> list[float]: + return ( + self.recorded_knapsack_input_runtimes + if self.recorded_knapsack_input_runtimes + else [ + self.all_node_runtimes[node_name] + for node_name in self.all_recomputable_banned_nodes + ] + ) + + def _create_recomputable_node_only_graph(self) -> nx.DiGraph: + graph = nx.DiGraph() + for recomputable_node in self.all_recomputable_banned_nodes: + graph.add_node(recomputable_node) + + for a, b in self.graph_edges: + if ( + a in self.all_recomputable_banned_nodes_set + and b in self.all_recomputable_banned_nodes_set + ): + graph.add_edge(a, b) + return graph + + def _create_recomputable_node_only_graph_with_larger_graph_context( + self, + ) -> nx.DiGraph: + # Create a dictionary to store the reachable nodes for each node + all_recomputable_banned_nodes_set = set(self.all_recomputable_banned_nodes) + + reachable_nodes = {} + for node in all_recomputable_banned_nodes_set: + # Use BFS to find all reachable nodes + predecessors = dict(nx.bfs_predecessors(self.full_joint_nx_graph, node)) + reachable_recomputable_nodes = set(predecessors.keys()).intersection( + all_recomputable_banned_nodes_set + ) + reachable_nodes[node] = reachable_recomputable_nodes + # Create the candidate graph + candidate_graph = nx.DiGraph() + candidate_graph.add_nodes_from(all_recomputable_banned_nodes_set) + for node1 in all_recomputable_banned_nodes_set: + for node2 in reachable_nodes[node1]: + # Check if there is an overlapping path + overlapping_path = False + for intermediate_node in reachable_nodes[node1]: + if ( + intermediate_node != node2 + and node2 in reachable_nodes[intermediate_node] + ): + overlapping_path = True + break + if not overlapping_path: + candidate_graph.add_edge(node1, node2) + return candidate_graph + + def _create_full_joint_graph(self) -> nx.DiGraph: + graph = nx.DiGraph() + for node in self.graph_nodes_in_order: + if node == "output": + continue + graph.add_node(node) + + for a, b in self.graph_edges: + if a == "output" or b == "output": + continue + graph.add_edge(a, b) + return graph + + def _recreate_psuedo_joint_graph(self) -> Graph: + # Create a dictionary to store the dependencies of each node + node_dependencies: dict[str, list[str]] = { + node: [] for node in self.graph_nodes_in_order + } + for a, b in self.graph_edges: + if a not in node_dependencies or b not in node_dependencies: + raise ValueError(f"Edge ({a}, {b}) references a non-existent node.") + node_dependencies[b].append(a) + + joint_graph = Graph() + # Create nodes in the graph + nodes: dict[str, Node] = {} + for node_name in self.graph_nodes_in_order: + input_nodes = [nodes[dep] for dep in node_dependencies[node_name]] + if input_nodes: + node = joint_graph.call_function(lambda *x: x, tuple(input_nodes)) + node.name = node_name + else: + node = joint_graph.placeholder(node_name) + nodes[node_name] = node + return joint_graph + + def _visualize_recomputable_candidate_graph_with_larger_context( + self, + layout_k: float = 0.5, + layout_iterations: int = 30, + ) -> None: + """ + Visualize the recomputable candidate graph with larger context. + """ + from matplotlib import cm, colors as mcolors, pyplot as plt + + pos = nx.spring_layout( + self.recomputable_node_only_graph_with_larger_graph_context, + k=layout_k, + iterations=layout_iterations, + ) + # pos = nx.spectral_layout(graph_with_indirect_edges) + plt.figure(figsize=(20, 15)) + + # Create a dictionary for node labels using the index + labels = { + node: self.recomputable_node_only_graph_with_larger_graph_context.nodes[ + node + ].get("index", node) + for node in self.recomputable_node_only_graph_with_larger_graph_context.nodes + } + + # Extract memory values and normalize them + norm = mcolors.Normalize( + vmin=min(self.get_knapsack_memory_input()), + vmax=max(self.get_knapsack_memory_input()), + ) + cmap = cm.viridis # type: ignore[attr-defined] + + # Assign colors based on memory + node_colors = [ + cmap( + norm( + float( + self.recomputable_node_only_graph_with_larger_graph_context.nodes[ + node + ][ + "memory" + ] + ) + ) + ) + for node in self.recomputable_node_only_graph_with_larger_graph_context.nodes + ] + + # Draw the graph with parsed nodes only + nx.draw_networkx_nodes( + self.recomputable_node_only_graph_with_larger_graph_context, + pos, + node_color=node_colors, + node_size=300, + label="Parsed Nodes", + ) + nx.draw_networkx_edges( + self.recomputable_node_only_graph_with_larger_graph_context, + pos, + arrows=True, + arrowsize=10, + ) + nx.draw_networkx_labels( + self.recomputable_node_only_graph_with_larger_graph_context, + pos, + labels=labels, + font_size=8, + font_weight="bold", + ) + + plt.title("Memory Colour Coded Dependency Graph for Recomputable Nodes") + plt.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), label="Memory") + plt.show() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/knapsack.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/knapsack.py new file mode 100644 index 0000000000000000000000000000000000000000..67187c92eb7d8d67153353af49fd63fa33e8a9fa --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/knapsack.py @@ -0,0 +1,121 @@ +import torch + + +def greedy_knapsack( + memory: list[float], runtimes: list[float], max_memory: float +) -> tuple[float, list[int], list[int]]: + n = len(runtimes) + items = list(range(n)) + + # Sort items based on the ratio of runtime to memory in descending order + items = sorted(items, key=lambda i: runtimes[i] / memory[i], reverse=True) + + total_memory = 0.0 + total_runtime = 0.0 + items_to_save = [] + items_to_allow_recomputing = [] + + for i in items: + if total_memory + memory[i] <= max_memory: + total_memory += memory[i] + total_runtime += runtimes[i] + items_to_save.append(i) + else: + items_to_allow_recomputing.append(i) + return total_runtime, items_to_save, items_to_allow_recomputing + + +def ilp_knapsack( + memory: list[float], runtimes: list[float], max_memory: float +) -> tuple[float, list[int], list[int]]: + import numpy as np + + try: + from scipy.optimize import Bounds, LinearConstraint, milp + except ImportError: + raise RuntimeError( + "To use the ILP for memory budget checkpointing you need to install scipy" + ) from None + + np_memory = np.array(memory) + np_runtimes = np.array(runtimes) + c = -np_runtimes # type: ignore[operator] + + memory_constraint = LinearConstraint(A=np_memory, ub=np.array(max_memory)) + constraints = [memory_constraint] + + integrality = np.ones_like(c) + res = milp( + c=c, constraints=constraints, integrality=integrality, bounds=Bounds(0, 1) + ) + if not res.success: + raise RuntimeError("Somehow scipy solving failed") + + items_to_save = [] + items_to_allow_recomputing = [] + for idx, i in enumerate(res.x): + if i == 1: + items_to_save.append(idx) + else: + items_to_allow_recomputing.append(idx) + return -res.fun, items_to_save, items_to_allow_recomputing + + +def dp_knapsack( + memory: list[float], runtime: list[float], max_memory: float +) -> tuple[float, list[int], list[int]]: + # Scaling factor to convert floating point weights to integers + S = 10000 + + # Quantize the memory weights + quantized_memory = torch.tensor( + [int(round(m * S)) for m in memory], dtype=torch.long, device="cpu" + ) + runtimes = torch.tensor(runtime, dtype=torch.float32, device="cpu") + + # Quantized pseudopolynomial DP for 0-1 Knapsack + quantized_max_memory = int(round(max_memory * S)) + + n = len(memory) + + # Initialize the DP table + # TODO(chilli): I think if needed, this memory can be optimized with sliding + # window trick + Hirschberg trick: + # https://codeforces.com/blog/entry/47247?#comment-316200 + dp = torch.zeros( + (n + 1, quantized_max_memory + 1), dtype=torch.float32, device="cpu" + ) + + for i in range(1, n + 1): + current_memory = quantized_memory[i - 1] + current_runtime = runtimes[i - 1] + + # Copy the previous row + dp[i, :] = dp[i - 1, :] + + # Update dp[i, j] for all j >= current_memory + if current_memory == 0: + dp[i, :] = dp[i - 1, :] + current_runtime + else: + dp[i, current_memory:] = torch.maximum( + dp[i - 1, current_memory:], + dp[i - 1, :-current_memory] + current_runtime, + ) + + # Backtrack to find the items included in the knapsack + saved_items = [] + recomputable_items = [] + j: int = quantized_max_memory + for i in range(n, 0, -1): + if dp[i][j] != dp[i - 1][j]: + saved_items.append(i - 1) # Include this item (indexing from 0) + j -= int(quantized_memory[i - 1].item()) + else: + recomputable_items.append(i - 1) + + saved_items.reverse() # To get items in the order they were added + + # The maximum runtime that can be achieved within the max_memory constraint + max_runtime = dp[n][quantized_max_memory].item() + + return max_runtime, saved_items, recomputable_items diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/knapsack_evaluator.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/knapsack_evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..b29a05ecdf69ceadca714a1929998b58ba1293bf --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_activation_checkpointing/knapsack_evaluator.py @@ -0,0 +1,261 @@ +from collections import deque +from typing import Callable + +import networkx as nx + +from torch._functorch._activation_checkpointing.graph_info_provider import ( + GraphInfoProvider, +) + + +class KnapsackEvaluator: + """ + This class evaluates the theoretical runtime and peak memory usage of a given checkpointing strategy. + It takes in a graph and a list of nodes that are saved and recomputed, and then simulates the + backward pass to calculate the peak memory usage. + """ + + def __init__( + self, + graph_info_provider: GraphInfoProvider, + ) -> None: + self._graph_info_provider = graph_info_provider + + def _get_backward_memory_from_topologically_sorted_graph( + self, + node_graph: nx.DiGraph, + node_memories: dict[str, float], + saved_nodes_set: set[str], + peak_memory_after_forward_pass: float, + ) -> list[tuple[float, str]]: + """ + Simulates the backward pass and keeps track of the peak memory usage. + + High Level Steps: + 1. Set Initial Peak/Current Memory + Allows you to set the peak memory after the forward pass, but typically this is + the sum of the estimated memory of the saved nodes. + 2. Perform a reverse topological sort of the node_graph. + If full graph is defined then will sort the full graph and only process the subset + of nodes in the node_graph. + 3. Iterate through the sorted graph nodes. + If the node is saved then just drop it's memory from current memory. + If the node is not saved then add it's memory to current memory and then traverse it's + predecessors to simulate recomuptation chain. Will check if new peak memory after all + predecessors are processed. + + Args: + node_graph (nx.DiGraph): A directed graph representing the recomputable forward nodes. + saved_nodes_set (Set[str]): A set of node names that are saved. + peak_memory_after_forward_pass (float): The peak memory usage after the forward pass. + """ + current_memory = [ + (peak_memory_after_forward_pass, "Initial Peak/Current Memory") + ] + already_computed = set() + sorted_nodes = list(reversed(list(nx.topological_sort(node_graph)))) + dependencies_computed = set() + + for node in sorted_nodes: + if node in saved_nodes_set or node in already_computed: + current_memory.append( + ( + current_memory[-1][0] - node_memories[node], + f"Dropping Node(already saved): {node}", + ) + ) + continue + + already_computed.add(node) + current_memory.append( + ( + current_memory[-1][0] + node_memories[node], + f"Recomputing Node: {node}", + ) + ) + # Create a queue of dependencies required for recomputation + predecessor_queue = deque( + [ + dependency + for dependency, v in node_graph.in_edges(node) + if dependency not in already_computed + ] + ) + while predecessor_queue: + dep = predecessor_queue.popleft() + already_computed.add(dep) + dependencies_computed.add(dep) + current_memory.append( + ( + current_memory[-1][0] + node_memories[dep], + f"Recomputing Predecessor of {node}: {dep}", + ) + ) + # Add predecessors of the predecessor to the queue if they haven't been recomputed yet + for dependency_of_dependency, _ in node_graph.in_edges(dep): + if ( + dependency_of_dependency in already_computed + or dependency_of_dependency in saved_nodes_set + or dependency_of_dependency in predecessor_queue + ): + continue + predecessor_queue.append(dependency_of_dependency) + dependencies_computed.clear() + current_memory.append( + (current_memory[-1][0] - node_memories[node], f"Dropping Node: {node}") + ) + return current_memory + + def _validate_all_indexes_accounted_for_in_provided_output( + self, saved_nodes_idxs: list[int], recomputable_node_idxs: list[int] + ) -> None: + """ + Validate that all indexes are accounted for in the provided output. + This function checks that the union of saved nodes and recomputable nodes + covers all candidate nodes without any overlaps. + """ + recomputable_node_idxs_set = set(recomputable_node_idxs) + saved_nodes_idxs_set = set(saved_nodes_idxs) + all_candidate_nodes_idxs = set( + range(len(self._graph_info_provider.all_recomputable_banned_nodes)) + ) + # Check that there are no overlaps between saved nodes and recomputable nodes + assert ( + len(recomputable_node_idxs_set.intersection(saved_nodes_idxs_set)) == 0 + ), "Saved nodes and recomputable nodes cannot have any overlaps" + # Check that all candidate nodes are accounted for + assert ( + recomputable_node_idxs_set.union(saved_nodes_idxs_set) + == all_candidate_nodes_idxs + ), "All candidate nodes must be accounted for in the provided output" + + def evaluate_knapsack_output( + self, + saved_nodes_idxs: list[int], + recomputable_node_idxs: list[int], + account_for_backward_pass: bool = False, + ) -> dict[str, float]: + """ + Evaluate the theoretical runtime and peak memory usage of a given checkpointing strategy. + Args: + - saved_nodes_idxs (List[int]): The indices of nodes that are saved. + - recomputable_node_idxs (List[int]): The indices of nodes that need to be recomputed. + """ + self._validate_all_indexes_accounted_for_in_provided_output( + saved_nodes_idxs, recomputable_node_idxs + ) + recomputation_runtime = sum( + self._graph_info_provider.all_node_runtimes[ + self._graph_info_provider.all_recomputable_banned_nodes[node] + ] + for node in recomputable_node_idxs + ) + if account_for_backward_pass: + memory_list = self._get_backward_memory_from_topologically_sorted_graph( + node_graph=self._graph_info_provider.recomputable_node_only_graph_with_larger_graph_context, + saved_nodes_set={ + self._graph_info_provider.all_recomputable_banned_nodes[i] + for i in saved_nodes_idxs + }, + node_memories=self._graph_info_provider.all_node_memories, + peak_memory_after_forward_pass=sum( + self._graph_info_provider.all_node_memories[ + self._graph_info_provider.all_recomputable_banned_nodes[i] + ] + for i in saved_nodes_idxs + ), + ) + peak_memory = max(memory_list, key=lambda x: x[0])[0] + else: + peak_memory = sum( + self._graph_info_provider.all_node_memories[ + self._graph_info_provider.all_recomputable_banned_nodes[node] + ] + for node in saved_nodes_idxs + ) + return { + "peak_memory": peak_memory, + "recomputation_runtime": recomputation_runtime, + "non_ac_peak_memory": self._graph_info_provider.get_non_ac_peak_memory(), + "theoretical_max_runtime": self._graph_info_provider.get_theoretical_max_runtime(), + "percentage_of_theoretical_peak_memory": peak_memory + / self._graph_info_provider.get_non_ac_peak_memory(), + "percentage_of_theoretical_peak_runtime": recomputation_runtime + / self._graph_info_provider.get_theoretical_max_runtime(), + } + + def evaluate_distribution_of_results_for_knapsack_algo( + self, + knapsack_algo: Callable[ + [list[float], list[float], float], tuple[float, list[int], list[int]] + ], + memory_budget_values: list[float], + ) -> list[dict[str, float]]: + """ + Evaluates the distribution of results for a given knapsack algorithm. + Args: + knapsack_algo (Callable): The knapsack algorithm to use for evaluation. + memory_budget_values (List[float]): A list of memory budgets to evaluate. + """ + results = list() + for memory_budget in memory_budget_values: + _, saved_nodes, recomputed_nodes = knapsack_algo( + self._graph_info_provider.get_knapsack_memory_input(), + self._graph_info_provider.get_knapsack_runtime_input(), + memory_budget, + ) + result = self.evaluate_knapsack_output( + saved_nodes_idxs=saved_nodes, + recomputable_node_idxs=recomputed_nodes, + ) + result["memory_budget"] = memory_budget + results.append(result) + return results + + def get_knee_point_memory_budget( + self, + knapsack_algo: Callable[ + [list[float], list[float], float], tuple[float, list[int], list[int]] + ], + max_mem_budget: float = 0.1, + min_mem_budget: float = 0.001, + iterations: int = 100, + ) -> float: + """ + Finds the memory budget at the knee point in the Pareto frontier. + + The knee point is defined as the point where the trade-off between + runtime and memory usage is optimal. + + Args: + knapsack_algo (callable): Knapsack algorithm to use for evaluation. + max_mem_budget (float, optional): Maximum memory budget. Defaults to 0.1. + min_mem_budget (float, optional): Minimum memory budget. Defaults to 0.001. + iterations (int, optional): Number of memory budgets to evaluate. Defaults to 100. + + Returns: + float: Memory budget at the knee point. + """ + import numpy as np + + results = self.evaluate_distribution_of_results_for_knapsack_algo( + knapsack_algo=knapsack_algo, + memory_budget_values=np.linspace( # type: ignore[arg-type] + min_mem_budget, max_mem_budget, iterations + ).tolist(), + ) + runtime_values = np.array( + [result["percentage_of_theoretical_peak_runtime"] for result in results] + ) + memory_values = np.array( + [result["percentage_of_theoretical_peak_memory"] for result in results] + ) + runtime_range = np.ptp(runtime_values) + memory_range = np.ptp(memory_values) + if runtime_range == 0 or memory_range == 0: + return max_mem_budget + runtime_norm = (runtime_values - runtime_values.min()) / runtime_range + memory_norm = (memory_values - memory_values.min()) / memory_range + distances = np.sqrt(runtime_norm**2 + memory_norm**2) + knee_index = np.argmin(distances) + return results[knee_index]["memory_budget"] diff --git 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allow-untyped-defs +""" +Utils for caching the outputs of AOTAutograd +""" +from __future__ import annotations + +import base64 +import contextlib +import functools +import json +import logging +import os +import pickle +import shutil +import time +from dataclasses import dataclass +from typing import Any, Callable, Optional, TYPE_CHECKING, Union + +import torch +from torch._dynamo.trace_rules import torch_non_c_binding_in_graph_functions +from torch._dynamo.utils import CompileEventLogger, counters +from torch._functorch import config +from torch._inductor.codecache import ( + _ident, + add_ephemeral_timeout_increase_for_distributed, + BypassFxGraphCache, + create_cache, + extract_tensor_metadata_for_cache_key, + FxGraphCache, + FxGraphCachePickler, + FxGraphHashDetails, + write_atomic, +) +from torch._inductor.output_code import CompiledFxGraphConstants +from torch._inductor.runtime.runtime_utils import cache_dir +from torch._inductor.utils import should_use_remote_fx_graph_cache +from torch._logging import LazyString +from torch._utils_internal import log_cache_bypass +from torch.compiler._cache import CacheArtifactManager, CacheArtifactType +from torch.utils._triton import has_triton_package +from torchgen.utils import dataclass_repr + +from .runtime_wrappers import ( + AOTDispatchAutograd, + AOTDispatchSubclassWrapper, + CompilerWrapper, + FunctionalizedRngRuntimeWrapper, + post_compile, + RuntimeWrapper, + SubclassMeta, +) +from .schemas import AOTAutogradCacheInfo, AOTConfig, ViewAndMutationMeta # noqa: F401 + + +if TYPE_CHECKING: + from torch._inductor.compile_fx import _CompileFxKwargs + from torch._inductor.output_code import CompiledFxGraph + from torch._inductor.remote_cache import JsonDataTy, RemoteCache + from torch._inductor.utils import BoxedBool + from torch.fx.node import Node + +log = logging.getLogger(__name__) + + +class BypassAOTAutogradCache(Exception): + pass + + +# Used to signify when FXGraphCache missed when AOTAutogradCache uses it +class FXGraphCacheMiss(BypassAOTAutogradCache): + pass + + +def should_use_remote_autograd_cache(): + if torch._inductor.config.force_disable_caches: + return False + if config.enable_remote_autograd_cache is not None: + return config.enable_remote_autograd_cache + if not config.is_fbcode(): + return False + + if torch._utils_internal.is_fb_unit_test(): + return False + + try: + from torch._inductor.fb.remote_cache import REMOTE_CACHE_VERSION + except ModuleNotFoundError: + return False + + jk_name = "pytorch/remote_cache:aot_autograd_cache_version" + + return REMOTE_CACHE_VERSION >= torch._utils_internal.justknobs_getval_int(jk_name) + + +def should_use_local_autograd_cache(): + if torch._inductor.config.force_disable_caches: + return False + return config.enable_autograd_cache + + +def check_node_safe(node: Node): + """ + Checks that the node only uses supported operators. We are starting with very + conservative cacheability constraints, and incrementally adding more support as we expand. + + [Note: AOTAutograd Cacheability checks] + - Our cache key is computed from the FX graph produced by Dynamo and the input example values + - A node is "safe" if the same cache key results in a compiled artifact that has the same behavior + (i.e, the set of inputs that go into our cache key is sufficient to distinguish its behavior) + + To accomplish this safety check, we consider the following functions to be safe: + - Public functions under modules torch, torch.functional, and torch.nn.functional: these are + allowed in the graph by dynamo, so we can assume they are safe to cache. + - method calls on base tensor types + - Any call_module that dynamo deemed safe to allow AOTAutograd to trace + - Non callable nodes, such as placeholder, output, get_attr + + The test suite test_aot_autograd_cache.py::AOTAutogradCachePicklerTests tries its best to fully cover/specify this behavior. + """ + SAFE_TORCH_MODULES = ("torch.functional", "torch.nn.functional") + SAFE_TORCH_FUNCTIONS = ( + "torch.Size", + "torch.sym_int", + "torch._sym_sqrt", + "torch.sym_float", + "torch.sym_sum", + "einops.einops.rearrange", + ) + + def is_public_torch_api(target): + # Don't blindly allow private functions in the torch namespace + is_private = target.__name__.startswith("_") + + return ( + getattr(target, "__module__", None) in SAFE_TORCH_MODULES and not is_private + ) + + def is_safe_torch_function(target): + """Allowlisted torch functions""" + function_name = f"{target.__module__}.{target.__name__}" + # Functions in torch_non_c_binding_in_graph_functions + # are guaranteed to be cache safe. + # See NOTE: [Cacheability of in-graph torch functions] + return ( + function_name in torch_non_c_binding_in_graph_functions + or function_name in SAFE_TORCH_FUNCTIONS + ) + + def is_torch_function(target): + if isinstance(target, (torch._ops.OpOverload, torch._ops.OpOverloadPacket)): + return True + if is_public_torch_api(target): + return True + is_builtin_fun_or_type = type(target).__name__ == "builtin_function_or_method" + if is_builtin_fun_or_type: + return True + if is_safe_torch_function(target): + return True + return False + + def is_tensor(target): + # Tensors always have example values in meta field + return "example_value" in target.meta + + # I'd love to use a match statement here, but it wasn't introduced until py3.10 + if node.op == "call_function": + # We support only torch.* functions for now + # We can probably add an allowlist of safe non-torch implementations as well + if not is_torch_function(node.target): + module = getattr(node.target, "__module__", None) + name = getattr(node.target, "__name__", None) + raise BypassAOTAutogradCache( + f"Unsupported call_function target {node.target}. \n Function module: {module}, \nFunction name: {name}" + ) + elif node.op == "call_method": + method_name = node.target + method_target = node.args[0] + # Only support method calls on base tensors + if not is_tensor(method_target): + module = getattr(method_target, "__module__", None) + name = getattr(method_target, "__name__", None) + raise BypassAOTAutogradCache( + f"Unsupported call_method target {method_target}. \nMethod module: {module}, \nMethod name: {name}" + ) + if ( + type(method_name) != str + and type(method_name).__name__ != "method_descriptor" + ): + raise BypassAOTAutogradCache( + f"Unsupported call_method method {node.target}: {method_name}" + ) + # Cache safe + elif node.op in ("placeholder", "get_attr", "call_module", "output"): + # Assumption today for call_module being a safe op: + # (1) today the only call_module ops that can show up in a graph come from "built-in-nn-modules" + # that dynamo assumes are safe to trace. If dynamo assumes they are safely to blindly trace, then + # they should be safe to cache as well. + # (2) in the steady-state (some time in H2?) we shouldn't see these anymore, once inline builtin nn modules by default + # (3) We do not allow user made nn modules in the graph today, only function calls. + pass + else: + raise BypassAOTAutogradCache(f"Unsupported node op {node.op}") + + +def check_cacheable(gm: torch.fx.GraphModule): + """ + Checks that the graph module only uses supported operators + """ + nodes = gm.graph.nodes + if torch._inductor.config.freezing: + raise BypassAOTAutogradCache("Cannot cache a graph with freezing enabled") + + if not ( + torch._inductor.config.fx_graph_cache or should_use_remote_fx_graph_cache() + ): + raise BypassAOTAutogradCache("FX graph cache is not enabled") + + tracing_context = torch._guards.TracingContext.try_get() + if tracing_context and tracing_context.fakify_first_call: + raise BypassAOTAutogradCache( + "Won't cache a graph with fakify_first_call enabled" + ) + for node in nodes: + check_node_safe(node) + + +def check_metadata_cacheable(metadata: ViewAndMutationMeta): + """ + When view replay is turned on, we bypass autograd cache if + the output is aliased. + """ + if config.view_replay_for_aliased_outputs: + for info in metadata.output_info: + if info.functional_tensor is not None: + raise BypassAOTAutogradCache( + "Cannot cache a graph with functional tensor" + ) + + +class AOTAutogradCacheDetails(FxGraphHashDetails): + """ + Object to capture all the details for a dynamo graph module relevant to computing + a safe and stable cache key for AOTAutograd. + """ + + def __init__( + self, + gm: torch.fx.GraphModule, + example_inputs, + aot_config: AOTConfig, + fx_config: _CompileFxKwargs, + ): + # FxGraphHashDetails contains all the keys related to inductor. Also includes some system info + self.aot_config = aot_config + self.grad_enabled = torch.is_grad_enabled() + self.disable_amp = torch._C._is_any_autocast_enabled() + self.deterministic_algorithms = torch.are_deterministic_algorithms_enabled() + self.autograd_config = config.save_config() + try: + # FXGraphCache has constraints on what can be pickled in its inductor + # config. Check that the gm is cacheable by inductor first, + # and if it raises an exception, also bypass on our end. + FxGraphCache._check_can_cache(gm) + super().__init__(gm, example_inputs, fx_config, []) + except BypassFxGraphCache as e: + # Sometimes inductor configs are unpickleable and can fail + raise BypassAOTAutogradCache from e + + +class AOTAutogradCachePickler(FxGraphCachePickler): + def __init__(self, gm: torch.fx.GraphModule): + super().__init__(gm) + self.dispatch_table: dict + self.dispatch_table.update( + { + AOTConfig: functools.partial(self._reduce_aot_config), + torch.Tensor: functools.partial(self._reduce_tensor), + } + ) + + def _reduce_aot_config(self, aot_config: AOTConfig): + """ + Reduce the config to a stable key for caching. + """ + return ( + _ident, + ( + aot_config.num_params_buffers, + aot_config.keep_inference_input_mutations, + aot_config.is_export, + aot_config.no_tangents, + aot_config.dynamic_shapes, + aot_config.aot_autograd_arg_pos_to_source, + aot_config.enable_log, + aot_config.pre_dispatch, + ), + ) + + def _reduce_tensor(self, tensor): + """ + Reduce the tensor to a stable key for caching. + """ + metadata = extract_tensor_metadata_for_cache_key(tensor) + return (_ident, (metadata,)) + + +def autograd_cache_key( + gm: torch.fx.GraphModule, + example_inputs, + config: AOTConfig, + fx_config: _CompileFxKwargs, + # TODO: add args and parameters +) -> tuple[str, list[str]]: + """ + Generate a unique hash of the FX graph for caching. + """ + check_cacheable(gm) + if has_triton_package(): + # Due to https://github.com/triton-lang/triton/issues/3729, + # if triton is < 3.2.0, AOTAutogradCache may cause us to + # attempt to load a cache entry without initializing + # the CUDA context on the autograd thread. + + # Without caching, we naturally do this initialization when + # tracing through the graph with the autograd engine. + import triton + + if triton.__version__ < "3.2.0": + raise BypassAOTAutogradCache("AOTAutogradCache requires triton 3.2.0") + + details = AOTAutogradCacheDetails(gm, example_inputs, config, fx_config) + pickler = AOTAutogradCachePickler(gm) + # The prefix distinguishes among the other kinds of objects we cache + key = "a" + pickler.get_hash(details) + debug_lines = pickler.debug_lines(details) + log.debug( + "Autograd graph cache hash details for key %s:\n%s", + key, + LazyString(lambda: "\n".join(debug_lines)), + ) + return key, debug_lines + + +@dataclass +class FXGraphCacheLoadable: + fx_graph_cache_key: str + + def is_backward(self): + return False + + def load(self, example_inputs, fx_config: _CompileFxKwargs) -> CompiledFxGraph: + # [Note: AOTAutogradCache and FXGraphCache Guard interactions] + # As mentioned, AOTAutograd takes in the symint inputs from dynamo's list of arguments. + # FXGraphCache serializes guards that are needed in the shape_env based on these symint inputs to the graph. + # The invariant that AOTAutograd uses here is that the sources for symints given to it by dynamo are exactly + # the same as the ones it passes to inductor, for both the forward and backward passes. + # (This does not mean that the tensor values passed in are the same: only that their symints are). + # That is, AOTAutograd and Inductor never create new guards based on symints with different sources + # than those passed to it by inductor. + + # TODO: We don't cache debug lines for now, but we should for improved debugging + remote_cache = None + constants = CompiledFxGraphConstants() + if should_use_remote_fx_graph_cache(): + remote_cache = FxGraphCache.get_remote_cache() + + result, cache_info = FxGraphCache.load_with_key( + self.fx_graph_cache_key, + [], + example_inputs, + local=True, + remote_cache=remote_cache, + is_backward=self.is_backward(), + constants=constants, + ) + if result is None: + log.info("FXGraphCache cache miss for key %s", self.fx_graph_cache_key) + raise FXGraphCacheMiss + + # No need to log chromium event because AOTAutograd will log that immediately for us + torch._logging.trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "fx_graph_cache_hit", # always a hit + "encoding": "json", + }, + payload_fn=lambda: json.dumps(cache_info), + ) + + # TODO: How come cudagraphs could be None here? + result.post_compile(example_inputs, fx_config["cudagraphs"], constants) # type: ignore[arg-type] + return result + + +@dataclass +class CompiledForward(FXGraphCacheLoadable): + """ + Cacheable entry for a forward function + """ + + def is_backward(self): + return False + + +@dataclass +class CompiledBackward(FXGraphCacheLoadable): + """ + Cacheable entry for a forward function + """ + + # Used by AOTDispatchAutograd.post_compile + backward_state_indices: list[int] + num_symints_saved_for_bw_: int + + def is_backward(self): + return True + + +@dataclass +class AOTAutogradCacheEntry: + """A single entry into the cache.""" + + # Forward and Backward info + compiled_fw: CompiledForward + compiled_bw: Optional[CompiledBackward] + + # Code of the joint graph using print_readable() + # Used for logging purposes + aot_joint_graph_str: Optional[str] + aot_forward_graph_str: Optional[str] + aot_backward_graph_str: Optional[str] + + # Runtime_metadata saved right before compilation + runtime_metadata: ViewAndMutationMeta + + # Wrappers that run after each aot_dispatch_* function + dispatch_wrappers: list[CompilerWrapper] + + # Used by AOTSubclassWrapper + maybe_subclass_meta: Optional[SubclassMeta] + num_fw_outs_saved_for_bw: Optional[int] + + # Used by RuntimeWrapepr + indices_of_inps_to_detach: list[int] + + # Time taken to trace/compile the forward + # forward_time_taken includes AOTAutograd tracing time + inductor compilation time + # backward_time_taken is essentially just the time inductor took to compile + forward_time_taken_ns: int + backward_time_taken_ns: int + + # Turn cache entry into the original callable + def wrap_post_compile( + self, + args: list[torch.Tensor], + aot_config: AOTConfig, + fx_config: _CompileFxKwargs, + ) -> Callable: + """ + This function takes a cache entry and carefully reconstructs the original callable + that AOTAutograd returned the first time it was run. It does this by running the various + post compile steps that AOTAutograd runs on its compiled artifact after running the fw/bw compilers. + + In the inference path, this consists of the Subclass, FunctionalzedRngRuntime, and RuntimeWrappers. + In the autograd path, this consists of AOTAutogradDispatch.post_compile. + + The steps here should match exactly the steps that are run in aot_dispatch_base and aot_dispatch_autograd. + + Notably absent from the cached path are: + - DebugAssertWrapper + - FakifiedOutWrapper + + Which we'll handle separately later on, if necessary. + """ + + # Log the output of AOTAutogradCache + if aot_config.enable_log: + # TODO: maybe also log to aot_graphs_log + # Unfortunately aot_graphs_log uses + # slightly different formatting though + if self.aot_joint_graph_str is not None: + torch._logging.trace_structured( + "aot_joint_graph", payload_fn=lambda: self.aot_joint_graph_str + ) + + if self.aot_forward_graph_str is not None: + torch._logging.trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "aot_forward_graph_fw_metadata", + "encoding": "string", + }, + payload_fn=lambda: dataclass_repr(self.runtime_metadata), + ) + if self.maybe_subclass_meta is not None: + torch._logging.trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "aot_forward_graph_fw_subclass_metadata", + "encoding": "string", + }, + payload_fn=lambda: dataclass_repr(self.maybe_subclass_meta), + ) + + # It's called an inference graph if not running with autograd + name = ( + "aot_forward_graph" + if self.aot_backward_graph_str is not None + else "aot_inference_graph" + ) + torch._logging.trace_structured( + name, payload_fn=lambda: self.aot_forward_graph_str + ) + + if self.aot_backward_graph_str is not None: + torch._logging.trace_structured( + "aot_backward_graph", payload_fn=lambda: self.aot_backward_graph_str + ) + + compiled_fw_func = self.compiled_fw.load(args, fx_config) + compiled_bw_func = None + if self.compiled_bw is not None: + compiled_bw_func = self.compiled_bw.load(args, fx_config) + needs_autograd = True + CompileEventLogger.try_add_pt2_compile( + "backend_compile", dispatch_mode="autograd" + ) + else: + needs_autograd = False + CompileEventLogger.try_add_pt2_compile( + "backend_compile", dispatch_mode="inference" + ) + + # Wrap the forward function in post compile wrappers + compiled_fw_func = AOTDispatchSubclassWrapper( + trace_joint=needs_autograd, + fw_only=None, + maybe_subclass_meta=self.maybe_subclass_meta, + num_fw_outs_saved_for_bw=self.num_fw_outs_saved_for_bw, + ).post_compile( + compiled_fw_func, aot_config, runtime_metadata=self.runtime_metadata + ) + + req_subclass_dispatch = self.maybe_subclass_meta is not None + CompileEventLogger.pt2_compile( + "backend_compile", requires_subclass_dispatch=req_subclass_dispatch + ) + + # In autograd case, functionalizedRngWrapper should not modify outs + return_new_outs = not needs_autograd + compiled_fw_func = FunctionalizedRngRuntimeWrapper( + return_new_outs=return_new_outs + ).post_compile( + compiled_fw_func, aot_config, runtime_metadata=self.runtime_metadata + ) + disable_amp = torch._C._is_any_autocast_enabled() + + if needs_autograd: + assert self.compiled_bw is not None + # This function is run on both cache miss and cache hit, either here + # or in aot_dispatch_autograd. On a cache hit, + # 1. the bw is already compiled + # 2. we don't need to save to the cache again + # so those corresponding arguments are set to None. + compiled_function = AOTDispatchAutograd.post_compile( + compiled_fw_func, + compiled_bw_func, + self.maybe_subclass_meta, + self.compiled_bw.num_symints_saved_for_bw_, + self.compiled_bw.backward_state_indices, + disable_amp, + self.indices_of_inps_to_detach, + None, # lazy_backward_info + aot_config, + fw_metadata=self.runtime_metadata, + try_save_cache_entry=None, + ) + else: + compiled_function = RuntimeWrapper( + indices_of_inps_to_detach=self.indices_of_inps_to_detach, + trace_joint=False, + disable_amp=disable_amp, + ).post_compile( + compiled_fw_func, aot_config, runtime_metadata=self.runtime_metadata + ) + + compiled_function, _ = post_compile( + self.dispatch_wrappers, + compiled_function, + aot_config, + runtime_metadata=self.runtime_metadata, + ) + + return compiled_function + + +@contextlib.contextmanager +def sanitize_gm_for_cache(gm: torch.fx.GraphModule): + """ + Clears a few fields in a dynamo supplied Graph Module that are not stable between graph inputs, but don't + affect inductor or aotdispatch correctness. + + These fields **can** be used by code calling into aotdispatch (namely, dynamo), so we can't null them out completely. + + To ensure that these fields are not accessed by inductor or aotdispatch, we clear them during AOTAutogradCache.load, + and then put them back before returning. This way, we generate a cache key based off of a canonical graph + without these fields, and also guarantee they aren't used to affect the cache's output. + """ + IGNORED_FIELDS = ( + "meta", # metadata used by export + "compile_subgraph_reason", # Used by dynamo only for logging, no change in inductor/autograd behavior + "_param_name_to_source", # Encapsulated by aot_config.aot_autograd_arg_pos_to_source + ) + saved_fields = {} + for field in IGNORED_FIELDS: + saved_fields[field] = getattr(gm, field, None) + # Clear the field + setattr(gm, field, None) + try: + yield + finally: + # Put the fields back after dispatch_and_compile is complete + for field, value in saved_fields.items(): + setattr(gm, field, value) + + +class AOTAutogradCache: + """ + Caches the results of running AOTAutograd. This class mostly handles the save and load logic, whereas + AOTAutogradCacheEntry handles the wrapping/unwrapping logic. + + Cache Inputs (AOTAutogradCacheDetails) + - AOTAutogradCache takes in the following inputs, which are analogous to inputs given + to AOTAutograd by dynamo: + - A fx graph module generated by dynamo + - A list of args, which consists of: + - Symint inputs to the graph, generated by dynamo + - The **real tensor** inputs, which inductor uses for cudagraphs + - Notably, the real tensor inputs don't have symints in their metadata. + AOTAutograd then retraces those real tensor arguments into FakeTensors later during execution. + - A set of global configurations that affect AOTAutograd or Inductor behavior. + + It then generates a cache key given these values. Notably, this means AOTAutogradCache currently + specializes on the sizes and strides of the real tensor inputs when dynamic shapes are turned on. + In a later PR, we'll likely generate the cache key based on the FakeTensors AOTAutograd generates + based on the real tensor inputs, which can contain symints. + + # Cache Outputs (AOTAutogradCacheEntry) + - AOTAutogradCache caches the following values: + - The compiled forward and backward functions from inductor, via keys to the FXGraphCache + - Metadata to reconstruct the AOTModule from the compiled inductor artifacts + - See AOTAutogradCacheEntry for more info + + [Note: Caching guards generated by AOTAutograd and Inductor] + AOTAutograd and inductor both can introduce new guards to the shape environment. FXGraphCache saves guards with each + compiled graph inductor generates. On a cache hit, AOTAutograd reloads the compiled forward and backward functions + from FXGraphCache, giving it new symint arguments from the input args. + FXGraphCache uses those symints and its saved guards to repopulate the ShapeEnv with guards. + **No new guards are generated into the shape env after inductor finishes compiling**, so the guards + saved by inductor are sufficient for correctness for both AOTAutograd and Inductor's caches. + """ + + @staticmethod + def clear(): + """Clear the cache""" + try: + shutil.rmtree(AOTAutogradCache._get_tmp_dir()) + except FileNotFoundError: + pass + + @staticmethod + def load( + dispatch_and_compile: Callable, + mod: Union[torch.fx.GraphModule, torch._dynamo.utils.GmWrapper], + args, + aot_config: AOTConfig, + cudagraphs: BoxedBool, + local: bool, + remote: bool, + ) -> Callable: + """ + Load a result from the cache, and reconstruct a runtime wrapper around the object + """ + + gm = mod.gm if isinstance(mod, torch._dynamo.utils.GmWrapper) else mod + with sanitize_gm_for_cache(gm): + compiled_fn = None + cache_info: dict[str, Any] = {} + cache_key = None + debug_lines: list[str] = [] + cache_event_time = time.time_ns() + cache_state = None + fx_config: _CompileFxKwargs = {"cudagraphs": cudagraphs} + try: + cache_key, debug_lines = autograd_cache_key( + gm, args, aot_config, fx_config + ) + entry: Optional[AOTAutogradCacheEntry] = AOTAutogradCache._lookup( + cache_key, local, remote + ) + if entry is not None: + compiled_fn = entry.wrap_post_compile(args, aot_config, fx_config) + log.info("AOTAutograd cache hit for key %s", cache_key) + + counters["aot_autograd"]["autograd_cache_hit"] += 1 + cache_state = "hit" + cache_event_time = time.time_ns() + forward_time_saved = entry.forward_time_taken_ns // 1e6 + backward_time_saved = entry.backward_time_taken_ns // 1e6 + cache_info.update( + { + "forward_time_saved_ms": forward_time_saved, + "backward_time_saved_ms": backward_time_saved, + "time_saved_ms": forward_time_saved + backward_time_saved, + } + ) + time_saved_ns = ( + entry.forward_time_taken_ns + entry.backward_time_taken_ns + ) + # TODO: should we use the same field for remote cache time saved for both + # FXGraphCache and AOTAutogradCache? + # get_metrics_context().increment(...) + if ( + ephemeral_increase := add_ephemeral_timeout_increase_for_distributed( + time_saved_ns + ) + ) != 0: + cache_info["ephemeral_timeout_increase"] = ephemeral_increase + + if compiled_fn is None: + log.info("AOTAutograd cache miss for key %s", cache_key) + counters["aot_autograd"]["autograd_cache_miss"] += 1 + cache_state = "miss" + cache_event_time = time.time_ns() + # Count missing the FXGraphCache as a miss not a bypass + except FXGraphCacheMiss as e: + counters["aot_autograd"]["autograd_cache_miss"] += 1 + # Special counter when we pass autograd cache but + # fail when on inductor guards + counters["aot_autograd"]["autograd_cache_guard_miss"] += 1 + cache_state = "miss" + if config.strict_autograd_cache: + raise e + # Most often this is BypassAOTAutogradCache, but + # if there's ever different reason we can't cache, + # we still never want to hard throw an exception, since + # we can always fallback to a cache bypass. + # As an example, if the user calls autograd via + # standalone inductor, we will sometimes get a GraphModule + # that doesn't actually have a `.graph` on it. Instead + # of checking every single case, we safely catch the exception + # in those cases. + except Exception as e: + cache_key = None + counters["aot_autograd"]["autograd_cache_bypass"] += 1 + cache_state = "bypass" + cache_event_time = time.time_ns() + cache_info["cache_bypass_reason"] = str(e) + # TODO: this gets logged implicitly by cache_bypass_reason, + # and here we explicitly log it into tlparse. + # We may want to log this as an extra column in Scuba, though. + cache_info["cache_bypass_hard_exception"] = not isinstance( + e, BypassAOTAutogradCache + ) + if remote: + log_cache_bypass("bypass_aot_autograd", str(e)) + if config.strict_autograd_cache: + raise e + if compiled_fn is None: + # Set the cache key so we can save a cache result later + if cache_key is not None: + aot_config.cache_info = AOTAutogradCacheInfo( + cache_key, time.time_ns() + ) + compiled_fn = dispatch_and_compile() + + cache_info.update( + { + "key": cache_key, + "cache_state": cache_state, + "components": debug_lines, + } + ) + CompileEventLogger.instant( + f"autograd_cache_{cache_state}", + metadata=cache_info, + time_ns=cache_event_time, + ) + CompileEventLogger.try_add_pt2_compile( + "backend_compile", + cache_state=cache_state, + cache_event_time=cache_event_time, + key=cache_info.get("key"), + components=cache_info.get("components"), + cache_bypass_reason=cache_info.get("cache_bypass_reason"), + remote_cache_enabled=remote, + local_cache_enabled=local, + ) + + torch._logging.trace_structured( + "artifact", + metadata_fn=lambda: { + "name": f"aotautograd_cache_{cache_state}", + "encoding": "json", + }, + payload_fn=lambda: json.dumps(cache_info), + ) + return compiled_fn + + @staticmethod + def _get_tmp_dir() -> str: + """ + Get the toplevel temporary directory for storing compiled graphs. + """ + return os.path.join(cache_dir(), "aotautograd") + + @staticmethod + def _lookup(key: str, local: bool, remote: bool) -> Optional[AOTAutogradCacheEntry]: + """Given a key generated by AOTAutogradCachePickler, look up its location in the cache.""" + + if local: + subdir = os.path.join(AOTAutogradCache._get_tmp_dir(), key) + # If the directory doesn't exist, we didn't cache this key locally + if os.path.exists(subdir): + path = os.path.join(subdir, "entry") + try: + with open(path, "rb") as f: + pickled_content = f.read() + # NB: We are not sure at this point if this artifact is in fact + # going to be a cache hit: it's possible that we'll cache miss due to a guard failure + # or other reason. But it's safe for CacheArtifactManager to record and save this + # artifact anyway, as it's possible that it will be a hit for a future attempt. + CacheArtifactManager.record_artifact( + CacheArtifactType.AOT_AUTOGRAD, key, pickled_content + ) + entry: AOTAutogradCacheEntry = pickle.loads(pickled_content) + return entry + except Exception as e: + log.info("AOTAutograd cache unable to load compiled graph: %s", e) + if config.strict_autograd_cache: + raise e + + # Prefer local cache to remote, fallback to remote if local missed + if remote: + remote_cache: Optional[ + RemoteCache[JsonDataTy] + ] = AOTAutogradCache.get_remote_cache() + + if remote_cache is not None: + try: + if (cache_data := remote_cache.get(key)) is not None: + assert isinstance(cache_data, dict) + data = cache_data["data"] + assert isinstance(data, (str, bytes)) + content = base64.b64decode(data) + # TODO: we currently don't have a way of logging the AOTAutograd output on a + # cache hit, because we never save it to the cache + # If we need to do that, we should do it here + return pickle.loads(content) + except Exception as e: + log_cache_bypass( + "bypass_aot_autograd", "Unable to deserialize: " + str(e) + ) + log.info( + "remote autograd cache unable to load compiled graph", + exc_info=True, + ) + + # Otherwise both caches missed + return None + + @staticmethod + def _write_to_local_cache(key: str, content: bytes): + """Write an entry to the local cache.""" + subdir = os.path.join(AOTAutogradCache._get_tmp_dir(), key) + if not os.path.exists(subdir): + os.makedirs(subdir, exist_ok=True) + path = os.path.join(subdir, "entry") + log.info("Writing AOTAutograd cache entry to %s", path) + write_atomic(path, content) + + @staticmethod + def save(key: str, entry: AOTAutogradCacheEntry, remote: bool): + """Save a single entry into the cache.""" + try: + check_metadata_cacheable(entry.runtime_metadata) + content = pickle.dumps(entry) + CacheArtifactManager.record_artifact( + CacheArtifactType.AOT_AUTOGRAD, key, content + ) + AOTAutogradCache._write_to_local_cache(key, content) + counters["aot_autograd"]["autograd_cache_saved"] += 1 + except BypassAOTAutogradCache as e: + counters["aot_autograd"]["autograd_cache_bypass"] += 1 + log.info("Bypassing autograd cache due to: %s", e) + if remote: + log_cache_bypass("bypass_aot_autograd", str(e)) + return None + except Exception as e: + log.info("AOTAutograd cache unable to serialize compiled graph: %s", e) + if remote: + log_cache_bypass( + "bypass_aot_autograd", "Unable to serialize: " + str(e) + ) + if config.strict_autograd_cache: + raise e + return None + + if remote: + remote_cache: Optional[ + RemoteCache[JsonDataTy] + ] = AOTAutogradCache.get_remote_cache() + if remote_cache is not None: + time_taken_ms = int( + (entry.forward_time_taken_ns + entry.backward_time_taken_ns) // 1e6 + ) + cache_data: JsonDataTy = { + "data": base64.b64encode(content).decode("ascii"), + "time_taken_ms": time_taken_ms, + } + remote_cache.put(key, cache_data) + + @staticmethod + @functools.lru_cache(None) + def get_remote_cache() -> Optional[RemoteCache[JsonDataTy]]: + """ + Attempts to load the remote cache, returns None on error. + """ + cache_id = "autograd-experimental" + return create_cache( + cache_id, + config.is_fbcode(), + "FbRemoteAOTAutogradCache", + "RemoteAOTAutogradCache", + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/collect_metadata_analysis.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/collect_metadata_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..87d5411c05d6cb8e246429e06572096931311b01 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/collect_metadata_analysis.py @@ -0,0 +1,812 @@ +# mypy: allow-untyped-defs +""" +This module is one of the analysis modules - it takes as input a function or graph +and some preexisting properties, and returns some data that is useful for deciding +how to further proceed with compilation or construct runtime wrappers. + +In particular, the analysis here constructs view and mutation metadata from running +a functionalized version of the graph under compilation. +""" + +import collections +import contextlib +import logging +from functools import wraps +from typing import Callable, Optional + +import torch +import torch.utils._pytree as pytree +from torch import Tensor +from torch._guards import detect_fake_mode +from torch._logging import getArtifactLogger +from torch._subclasses.functional_tensor import FunctionalTensor, FunctionalTensorMode +from torch._subclasses.meta_utils import safe_is_leaf +from torch.fx.experimental.symbolic_shapes import is_concrete_int +from torch.multiprocessing.reductions import StorageWeakRef +from torch.utils._python_dispatch import ( + is_traceable_wrapper_subclass, + transform_subclass, +) + +from .functional_utils import ( + are_all_mutations_hidden_from_autograd, + are_all_mutations_under_no_grad_or_inference_mode, + from_fun, + has_data_mutation, + has_metadata_mutation, + MetadataKey, + to_fun, + was_inductor_storage_resized, +) +from .schemas import ( + FunctionalTensorMetadataEq, + InputAliasInfo, + MutationType, + OutputAliasInfo, + OutputType, + ViewAndMutationMeta, +) +from .subclass_utils import create_subclass_meta +from .utils import _get_autocast_states, KNOWN_TYPES, strict_zip + + +zip = strict_zip + +log = logging.getLogger(__name__) +static_input_logger = getArtifactLogger("torch._dynamo", "cudagraph_static_inputs") + + +# Note [Tangents memory format] +# We assume tangents memory format to be similar to corresponding output's memory_format. +# The idea is that we are technically making a guess about the strides of our tangents, +# while we trace out the joint. +# If runtime specfied tangents will not have the same memory format as predicted traced tangents, +# we coerce them at runtime to traced tangents memory format. + + +# Coercing and collecting traced tangents memory format in one recursive traversal +# mypy: ignore-errors +def coerce_tangent_and_suggest_memory_format(x: Tensor): + updated = False + if not isinstance(x, Tensor): + return x, None, updated + + out = x.detach() + + suggest_memory_format = torch._prims_common.suggest_memory_format + is_subclass = is_traceable_wrapper_subclass(out) + + memory_format = suggest_memory_format(out) + + was = out + out = out.contiguous(memory_format=memory_format) + updated = out is not was + + # For subclass we keep memory format of outer strides at the beggining of the list + out_memory_format = [memory_format] if is_subclass else memory_format + + # Note [Tangents memory format, Part 2] + # In the same way that "what strides do we assigns to our tangents" is a question + # that we can not answer (and therefore have to guess) as we trace the backward ahead-of-time, + # The same applies to any tensor subclass metadata, when we have tangents that are subclasses. + # To handle this situation, we have two new methods that a tensor subclass can implement: + # (1) __coerce_tangent_metadata__(self) + # Given a subclass with "non-standard" metadata, turn it into a new subclass with "normal" metadata. + # The main example here is a DTensor with the "_Partial" placement. + # If we have a forward output with a _Partial placement, and corresponding tangent + # with a Replicate/Shard placement, we have no way to convert the tangent "back" to a _Partial placement. + # This method lets us avoid the problem entirely by allowing subclasses to ensure that we can never + # have a tangent with "problematic" metadata, that we cannot convert to. + # (1) __coerce_same_metadata_as_tangent__(self, metadata) + # Given a subclass, and a target differing metadata, + # convert self to have the same metadata as the target. + # With DTensor being the main example, we can use this to convert a DTensor with a Replicate() + # placement into one with a Shard() placement, in the case that we "guessed wrong", + # and traced tangents with a Shard() placement at compile time. + # + if is_subclass and hasattr(out, "__coerce_tangent_metadata__"): + out = out.__coerce_tangent_metadata__() # type: ignore[attr-defined] + + if is_subclass: + attrs = out.__tensor_flatten__()[0] + + for attr in attrs: + elem = getattr(out, attr) + ( + new_elem, + new_elem_memory_format, + elem_updated, + ) = coerce_tangent_and_suggest_memory_format(elem) + out_memory_format.append(new_elem_memory_format) + if elem_updated: + setattr(out, attr, new_elem) + + return out, out_memory_format, updated + + +# This is a version of functionalization that is specifically designed +# for the AOTAutograd use case. +# +# Unlike functorch's variant, this doesn't use the functorch level system, +# instead it directly uses PyTorch's conventional dispatcher to hit the +# functionalization key. In particular, this means that FunctionalTensorWrapper +# can have autograd data stored directly on it. +# +# In typical AOTAutograd usage, the dispatch key order will look like: +# +# Autograd - Functionalization ~~~~> Proxy Mode - Fake Tensor +# outer tensor inner tensor +# +# Returns: +# - ViewAndMutationMeta, telling us metadata about the inputs and outputs, and +# The list of outputs from the forward, but **only** the outputs that we need +# to pass in as tangents into the backward. +# Specifically, aliased outputs from the forward get regenerated, and don't participate +# in the compiled backward function. +def run_functionalized_fw_and_collect_metadata( + f, + *, + keep_input_mutations: bool, + # TODO: refactor to kill this flag + is_train: bool = False, + # Note: this is guaranteed to be set when running under dynamo + static_input_indices: Optional[list[int]] = None, + pre_dispatch: bool = False, + # is_export is technically only needed to avoid using functionalization V2 + # during analysis + is_export: bool = False, +) -> Callable[..., ViewAndMutationMeta]: + memo: dict[Tensor, Tensor] = {} + + def _to_fun(t): + if isinstance(t, Tensor): + if t in memo: + return memo[t] + r = to_fun(t) + memo[t] = r + return r + else: + return t + + @wraps(f) + def inner(*flat_args): + # This function is meant to be run with the forward, which expects a flat list of tensor/symint/other args. + assert all(isinstance(a, tuple(KNOWN_TYPES)) for a in flat_args) + + input_info: list[InputAliasInfo] = [] + output_info: list[OutputAliasInfo] = [] + + prior_grad_enabled = torch.is_grad_enabled() + prior_autocast_states = _get_autocast_states() + + # See Note [Disabling Functionalize TLS Above Python Functionalization] + disable_above = torch._C._ExcludeDispatchKeyGuard( + torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize) + ) + + # It doesn't matter if we run this under predispatch or not because it is + # only for figuring out metadata + mode = FunctionalTensorMode(_allow_token_discovery=True, export=is_export) + suppress_pending = contextlib.nullcontext() + fake_mode = detect_fake_mode() + if fake_mode and (shape_env := fake_mode.shape_env): + suppress_pending = shape_env.ignore_fresh_unbacked_symbols() + with disable_above, mode, suppress_pending: + # precondition: The passed in function already handles unflattening inputs + flattening outputs + flat_f_args = pytree.tree_map(_to_fun, flat_args) + flat_f_outs = f(*flat_f_args) + # We didn't do any tracing, so we don't need to process the + # unbacked symbols, they will just disappear into the ether. + # Also, prevent memoization from applying. + if fake_mode: + fake_mode.epoch += 1 + fake_mode.reset_nt_tensor_id_counter() + + if prior_autocast_states != _get_autocast_states(): + raise RuntimeError( + "AOTAutograd does not support tracing graphs that mutate the autocast state. " + "Dynamo will only insert autocast context managers (e.g. with torch.autocast(..)) into the graph, " + "which will unwind all of their mutations to autocast state before the graph exits. " + "If you encounter this error while using torch.compile, please file a bug." + ) + + # Inspect the state of the input tensor functional wrapper to detect input mutation info + # If inp[i] has a metadata-only mutation, then maybe_inputs_with_mutated_metadata[i] contains the updated version + for i, (arg, f_arg) in enumerate(zip(flat_args, flat_f_args)): + # NB: Mutation of non-contiguous tensor subclass input can result in a mismatch in + # strides between the functionalized arg inner tensors and non-functionalized arg inner + # tensors. This is a problem as the inner tensor stride change may not be reflected + # correctly in the outer tensor, so disallow this for now. + mutates_data = has_data_mutation(f_arg) + mutates_metadata = has_metadata_mutation( + f_arg, arg, check_only_storage_mutation=False + ) + if mutates_metadata and is_traceable_wrapper_subclass(arg): + raise RuntimeError( + "Metadata mutations are currently not allowed on tensor subclasses" + ) + mutates_storage_metadata = has_metadata_mutation( + f_arg, arg, check_only_storage_mutation=True + ) + mutations_hidden_from_autograd = are_all_mutations_hidden_from_autograd( + f_arg + ) + mutations_under_no_grad_or_inference_mode = ( + mutates_data + and are_all_mutations_under_no_grad_or_inference_mode(f_arg) + ) + mutation_inductor_storage_resize = was_inductor_storage_resized(f_arg) + + if mutates_storage_metadata: + mutates_data = False + + requires_grad = isinstance(f_arg, torch.Tensor) and f_arg.requires_grad + + input_info.append( + InputAliasInfo( + is_leaf=isinstance(arg, Tensor) and safe_is_leaf(arg), + mutates_data=mutates_data, + mutates_metadata=mutates_metadata, + mutations_hidden_from_autograd=mutations_hidden_from_autograd, + mutates_storage_metadata=mutates_storage_metadata, + mutations_under_no_grad_or_inference_mode=mutations_under_no_grad_or_inference_mode, + mutation_inductor_storage_resize=mutation_inductor_storage_resize, + requires_grad=requires_grad, + keep_input_mutations=keep_input_mutations, + ) + ) + + # If a function involves creating a tensor, and returning a view of it, such that its _base is the intermediate, + # We need to make sure our graph returns the _base as a graph output, and we manually recreate the view + # to return to the user. Why? The backend compiler is free to (incorrectly) not set requires_grad + # on the base tensor, but we are obligated to properly set requires-gradness on the real output. + + inp_storage_refs = { + StorageWeakRef(inpt.untyped_storage()): idx + for idx, inpt in enumerate(flat_f_args) + if isinstance(inpt, Tensor) + } + + # We need inp tensor id's to be able to tell if an outputs **are** inputs. + inp_tensor_ids = {id(inpt) for inpt in flat_f_args if isinstance(inpt, Tensor)} + # We need output tensor id's to tell if any output._base` attributes **are** other outputs. + # (This is also a dict because we need to know that output's index, so we can regenerate + # the alias from it). + out_tensor_ids = {id(o): i for i, o in enumerate(flat_f_outs)} + + # Keep track of which outputs alias other outputs + out_tensor_alias_counts: collections.defaultdict = collections.defaultdict(int) + # This tells us, for a given group of outputs that alias each other, + # whether they e.g. all came from an unbind call + num_aliased_tensors_that_are_multi_output_views: collections.defaultdict = ( + collections.defaultdict(int) + ) + + out_storage_to_metadata_key_to_tensors: collections.defaultdict[ + Optional[StorageWeakRef], + collections.defaultdict[MetadataKey, set[torch.Tensor]], + ] = collections.defaultdict(lambda: collections.defaultdict(set)) + + curr_storage = None + for o in flat_f_outs: + if isinstance(o, torch.Tensor): + curr_storage = StorageWeakRef(o.untyped_storage()) + out_tensor_alias_counts[curr_storage] += 1 + # Note: [AOTAutograd: differentiable outputs that alias each other from a multi-output view call] + # This is an optimization on top of the "alias of intermediates" logic, + # which you can read more about under Note [AOT Autograd: outputs aliasing inputs or intermediates!] + # + # Before describing the optimization: this is important for AOTAutograd to have good + # perf around, multi-output views. HOWEVER: + # - There is a more generic change to AOTAutograd that we'd like to make, that subsumes this case, + # around using pre-dispatch tracing to partition out a graph so we can faithfully replay all + # views without having to regenerate them at runtime. + # - It's loosely described in this doc (more details will be added soon): + # https://docs.google.com/document/d/1DlfFq8TKbuAn2zyJxLfoW-X1qkkm5PLdHFtySo03QAk/edit + # - Once that change lands, we should just rip out this "optimization", since: + # (1) It will be fully unnecessary + # (2) Although it is only a few lines of code, it is a bit difficult to reason about + # its correctness with the autograd engine in all cases. + # + # + # What is this optimization? Consider the below case: + # def f(x): + # intermediate = x.mul(2) + # # x and intermediate here require grad + # o1, o2, ... o10 = intermediate.unbind(-1) + # return intermediate, o1, o2, ... o10 + # Now, the "intermediate base" handling in AOTAutograd implies that we must do the following: + # (1) return "intermediate as an extra output of the compiled graph + # (2) regenerate each aliased output off of "intermediate", **outside** of the autograd.Function. + # The reason AOTAutograd ordinarily does this is for safety: the autograd engine needs to know + # that o1 through o10 are all aliased, and if we blindly return o1 through o10 from the autograd.Function, + # this information will be hidden. + # In particular, mutating one alias might require autograd to update autograd metadata on the other aliases + # (like their grad_fn, for example, when the autograd engine needs to do view-replay). + # + # However, intermediate_base logic can be bad for backward performance (we sometimes generate + # as_strided calls during the intermediate base logic, which can have a slow backward formula). + # Is it possible to find a set of conditions where it is **safe** to hide the output aliasing from autograd? + # + # For a set of outputs of the graph that alias each other, o_1...o_k, consider: + # (1) They came from the same multi-output view op, e.g. o_1, ..., o_k = intermediate.unbind(0) + # (2) If there are any other aliases of o_1 through o_k (in the example above, intermediate), + # **at most** 1 can escape from the graph (e.g. there is not some other graph input/output + # o_other, that aliases these outputs) + # (3) o_1...o_k all require_grad, they all share the same ._base, and their ._base requires grad. + # This condition is important because it's what causes slowness in the intermediate_base + # codepath of aot_autograd. Ordinarily, o_1...o_k would all get a grad_fn, and + # aot_autograd's view-replay might give each output an AsStridedBackward as its grad_fn. + # "K" AsStridedBackward calls will be *much* slower than a single UnbindBackward. + # In this setup, is it possible to mutate one of the outputs o_i in a way that would affect the autograd meta + # of the other aliases? + # + # Claim: No! Consider a few example (which I'm pretty sure cover all cases of mutation w.r.t. autograd): + # (a) What happens if we mutate any of o_1 through o_k directly? + # Autograd raises an error: + # "RuntimeError: Output 0 of UnbindBackward0 is a view and is being modified inplace. This view is + # the output of a function that returns multiple views. Such functions do not allow the output + # views to be modified inplace. You should replace the inplace operation by an out-of-place one." + # (b) What if we take a view of o_k and mutate it, o_k.view(o_k.shape).mul_(2)? + # Autograd raises the same error- the "multi-output-view"ness of an alias propagates to future views. + # (c) What if we mutate o_k under no_grad? + # Autograd raises the same error + # (d) What if we detach and mutate, e.g. o_k.detach().mul_(2)? + # Autograd allows this, *but* autograd updates all alias's grad_fn's to be error functions when accessed. + # Autograd raises the same error + # (e) What if we try to mutate another alias of o_1...o_k, that was **not** created from a multi-output view? + # We promised that there is at most **one** such alias, e.g. intermediate in the example above. + # You can mutate intermediate, but in eager mode this will change the grad_fn of o_1...o_k + # to be error fn's. + # Since intermediate was the *only* non-multi-output-alias, there are no other aliases + # of `intermediate` around that were produced by the compiled fn and have a valid grad_fn. + # + # Coming back to this optimization: + # Given that it is not possible for mutating one of these aliases to affect the autograd metadata of another alias + # without causing an error in eager mode, we will simple hide the aliasing from autograd during torch.compile + # if all of the above conditions are met. + # This has the slight downside that it's possible to write some "bad" code that autograd will raise an error on + # in eager but fail to during torch.compile, but it has the benefit that this code has much better performance. + # NOTE: if and when we eventually update AOTAutograd to do the "view graph slicing" defined here: + # https://docs.google.com/document/d/1DlfFq8TKbuAn2zyJxLfoW-X1qkkm5PLdHFtySo03QAk/edit, + # then this optimization will probably matter less and might be ok to remove. + is_cur_tensor_multi_out_view = isinstance( + o, FunctionalTensor + ) and torch._functionalize_is_multi_output_view( # type: ignore[attr-defined] + o.elem + ) + if is_cur_tensor_multi_out_view: + num_aliased_tensors_that_are_multi_output_views[curr_storage] += 1 + if o.requires_grad: + out_storage_to_metadata_key_to_tensors[curr_storage][ + MetadataKey.make(o) + ].add(o) + + # maps the id of an intermediate base to its index in the output of the compiled forward + intermediate_base_tensor_id_to_output_idx: dict[int, int] = {} + intermediate_bases: list[torch.Tensor] = [] + # Why Do We Care If Storage Changed? + # It's important to understand the implications of storage changes in complex scenarios. Take this example: + # + # def f(x): + # x_storage = x.untyped_storage() + # non_leaf_tensor = torch.ones(4, requires_grad=True).clone() + # + # # Using no_grad() and _unsafe_preserve_version_counter to simulate the .data = operation + # with torch.no_grad(), torch.autograd._unsafe_preserve_version_counter(x): + # x.set_(non_leaf_tensor.untyped_storage()) + # + # out = x.view(-1) + # + # # Restoring x to its original storage, again simulating .data = operation + # with torch.no_grad(), torch.autograd._unsafe_preserve_version_counter(x): + # x.set_(x_storage) + # + # return out + # + # In this scenario, 'x' and 'out' have different shapes and are stored at different memory addresses, aka no aliasing. + # However, due to how set_() and more specificlaly, set is functionalized, is defined to preserve eager semantics, + # the autograd engine mistakenly assumes that 'x' and 'out' are aliased, treating 'x' as 'out._base'. + # This misinterpretation leads to an 'alias_of_input' flag, causing an unnecessary as_strided() call to be generated, + # which could lead to issues later in the code. + for o in flat_f_outs: + functional_tensor_storage_changed = isinstance( + o, FunctionalTensor + ) and torch._functionalize_was_storage_changed( # type: ignore[attr-defined] + o.elem + ) + curr_storage = ( + None + if not isinstance(o, torch.Tensor) + else StorageWeakRef(o.untyped_storage()) + ) + outs_with_identical_metadata_that_require_grad = ( + [] + if not isinstance(o, Tensor) + else [ + curr + for curr in out_storage_to_metadata_key_to_tensors[curr_storage][ + MetadataKey.make(o) + ] + if o is not curr + ] + ) + + # See Note [Accessing .grad_fn on FunctionalTensor] + # In-place operations on views will trigger a lazy rebase of the autograd graph; + # this runs during access to the .grad_fn. The rebase logic will invoke view ops + # on FunctionalTensors, so we must enable a FunctionalTensorMode here to ensure + # these op calls succeed. + grad_fn = None + if isinstance(o, Tensor): + with FunctionalTensorMode(): + grad_fn = o.grad_fn + + is_result_of_custom_autograd_fn = False + # Need to check for both custom cpp (CppFunction) and python (BackwardCFunction) + # autograd fns + if type(grad_fn).__name__ == "CppFunction": + is_result_of_custom_autograd_fn = True + if isinstance(grad_fn, torch.autograd.function.BackwardCFunction): + is_result_of_custom_autograd_fn = True + + if not isinstance(o, Tensor): + output_type = OutputType.non_alias + base_idx = None + elif ( + curr_storage in inp_storage_refs + and grad_fn is not None + and is_result_of_custom_autograd_fn + ): + output_type = OutputType.custom_function_view + base_idx = None + elif ( + curr_storage in inp_storage_refs + and not functional_tensor_storage_changed + ): + base_idx = inp_storage_refs[curr_storage] + is_input_tensor = id(o) in inp_tensor_ids + num_aliased_outs = out_tensor_alias_counts[curr_storage] + num_multi_output_view_outs = ( + num_aliased_tensors_that_are_multi_output_views[curr_storage] + ) + num_aliased_outs_that_are_not_multi_output_views = ( + num_aliased_outs - num_multi_output_view_outs + ) + if ( + grad_fn is not None + and num_aliased_outs_that_are_not_multi_output_views == 0 + ): + # See Note: [AOTAutograd: differentiable outputs that alias each other from a multi-output view call] + # In particular, given: + # def f(x): + # return list(x.unbind(0)) + # The main reason we ordinarily try to regenerate these output aliases outside of the + # compiled autograd.Function is because if any of the outputs are later mutated, + # autograd needs to perform view-replay to regenerate them. + # However, autograd does not allow users to mutate multi-output views + # in any way that can change the autograd metadata of other aliases. + # So we hide this aliasing from autograd here. + log.debug( + "Encountered AOTAutograd case: differentiable outputs that \ +alias each other from a multi-output view call" + ) + output_type = OutputType.non_alias + elif is_input_tensor: + output_type = OutputType.is_input + else: + output_type = OutputType.alias_of_input + elif functional_tensor_storage_changed and id(o) in inp_tensor_ids: + # When there is a set_() on an input, we cannot rely on checking storages + # to detect if we are returning an input (since the inputs storage is different) + assert curr_storage is not None + base_idx = inp_storage_refs[curr_storage] + output_type = OutputType.is_input + + # We only need to handle the intermediate base case when both + # the intermediate base and the output require gradients. + # See Note [AOT Autograd: outputs aliasing inputs or intermediates!] + elif o._base is not None and o.requires_grad and o._base.requires_grad: + num_aliased_outs = out_tensor_alias_counts[curr_storage] + num_multi_output_view_outs = ( + num_aliased_tensors_that_are_multi_output_views[curr_storage] + ) + num_aliased_outs_that_are_not_multi_output_views = ( + num_aliased_outs - num_multi_output_view_outs + ) + # Note: [AOTAutograd: differentiable outputs that alias each other from a multi-output view call] + if ( + out_tensor_alias_counts[curr_storage] == 1 + or num_aliased_outs_that_are_not_multi_output_views <= 1 + ): + # Note [Intermediate Bases Optimization] + # Normally if we have an output that aliases an intermediate, + # we need to add the extra "intermediate base" logic further down + # to prevent autograd from yelling at us if the user later tries to + # mutate that output. + # However, the common case here is if we have an output that aliases an intermediate, + # but doesn't alias any other outputs. + # In that case, autograd shouldn't have to worry about the aliasing at all + # (if that output is mutated, there are no other live aliases for autograd to worry about). + # The "intermediate bases" can hurt inductor perf by forcing more variables to become outputs. + # So as an optimization, we won't do intermediate base handling in this case. + # Instead, we'll hide the aliasing from autograd using aten._unsafe_view(). + if ( + out_tensor_alias_counts[curr_storage] != 1 + and num_aliased_outs_that_are_not_multi_output_views <= 1 + ): + log.debug( + "Encountered AOTAutograd case: differentiable outputs that alias each other \ +from a multi-output view call" + ) + output_type = OutputType.unsafe_view_alias + base_idx = None + else: + # First, check if o's ._base is an existing output + maybe_existing_out_idx = out_tensor_ids.get(id(o._base), None) + if maybe_existing_out_idx is not None: + # Special case where the output is an alias of a graph intermediate, but that intermediate + # is itself also a user output. + output_type = ( + OutputType.alias_of_intermediate_base_is_user_output + ) + base_idx = maybe_existing_out_idx + else: + # Next, check if o's ._base is an intermediate base that we already returned + maybe_existing_base_output_idx = ( + intermediate_base_tensor_id_to_output_idx.get( + id(o._base), None + ) + ) + if maybe_existing_base_output_idx is not None: + output_type = OutputType.alias_of_intermediate + base_idx = maybe_existing_base_output_idx + else: + # Otherwise, take o._base and explicitly return it as an output in the compiled graph + new_out_idx = len(intermediate_bases) + base_idx = new_out_idx + # Indicate to the logic later on (when we trace the joint) + # that this particular output should get it's ._base appended to the forward graph outputs + output_type = ( + OutputType.alias_of_intermediate_save_as_output + ) + intermediate_base_tensor_id_to_output_idx[ + id(o._base) + ] = new_out_idx + intermediate_bases.append(o._base) + elif ( + # See https://github.com/pytorch/pytorch/issues/100348 for this case. + # This protects against the specific case where a user fn returns (output, output.detach()) + out_tensor_alias_counts[curr_storage] > 1 + and len(outs_with_identical_metadata_that_require_grad) > 0 + and not o.requires_grad + ): + # In theory we could use any of these tensors to regenerate the aliased outputs from, + # since they all alias each other and have identical metatadata + out_alias = outs_with_identical_metadata_that_require_grad[0] + existing_out_idx = out_tensor_ids[id(out_alias)] + output_type = OutputType.alias_of_intermediate_base_is_user_output + base_idx = existing_out_idx + else: + output_type = OutputType.non_alias + base_idx = None + + if isinstance(o, torch.Tensor): + dynamic_dims = { + i for i, s in enumerate(o.shape) if not is_concrete_int(s) + } + else: + dynamic_dims = None + + # Save the current FunctionalTensor output. + # + # This will be used at runtime for reconstructing output views from + # their respective base tensors. + # + # The FunctionalTensor will be saved if one of the 2 conditions below + # is true: + functional_tensor = None + if ( + # 1. If the output_type is either of: + # (i) alias_of_intermediate; + # (ii) alias_of_intermediate_save_as_output; or + # (iii) alias_of_intermediate_base_is_user_output. + # + # No need to worry about in-place view operations here, since + # this functionalization step elimitates mutations. + # + # i.e. we have access to the actual base tensor, before the + # in-place operation was applied. + output_type + in ( + OutputType.alias_of_intermediate, + OutputType.alias_of_intermediate_save_as_output, + OutputType.alias_of_intermediate_base_is_user_output, + ) + ) or ( + # 2. If the output_type is alias_of_input, and no in-place view + # operationthe was run on the input (base tensor). + # + # In this case, we need to check for metadata mutation because + # the runtime explicitly reconstructs the inputs, before actually + # reconstructing the outputs. Due to in-place view operations, the + # fully reconstructed input may not be this output base tensor + # anymore. + output_type == OutputType.alias_of_input + and base_idx is not None + and not input_info[base_idx].mutates_metadata + ): + if isinstance(o, FunctionalTensor): + functional_tensor = FunctionalTensorMetadataEq(o.elem) + + out_info = OutputAliasInfo( + output_type=output_type, + raw_type=type(o), + base_idx=base_idx, + dynamic_dims=dynamic_dims, + requires_grad=isinstance(o, torch.Tensor) and o.requires_grad, + functional_tensor=functional_tensor, + ) + output_info.append(out_info) + + # See Note [AOT Autograd: Views to avoid tangents aliasing inputs] + def view_avoid_dupes_with_primals(t): + if isinstance(t, Tensor) and is_traceable_wrapper_subclass(t): + return transform_subclass( + t, lambda _, inner_t: view_avoid_dupes_with_primals(inner_t) + ) + if isinstance(t, Tensor): + return t.view(t.shape) + return t + + # This analysis function returns *only* the outputs that are meant to be tangents to the backwards. + # Anything that aliases (inputs returned in the fw due to metadata mutations, or outputs that alias inputs/intermediates) + # are *regenerated* later, and not used directly in the autograd graph + def _plain_fake_tensor_like_subclass(x): + with detect_fake_mode(): + return torch.empty( + x.shape, dtype=x.dtype, device=x.device, layout=x.layout + ) + + def _is_subclass_mutated_input_tangent_always_subclass(inp): + return ( + isinstance(inp, torch.nested._internal.nested_tensor.NestedTensor) + or torch._functorch.config.disable_guess_zero_tangent_for_mutated_input_subclass + ) + + f_input_tangents = [ + # Note: [AOTAutograd Tangent Subclassness for mutated inputs] + # Generally when creating tangents to trace with, we assume that tangents will have + # the same subclass-ness as their forward outs + # however: for tangents that correspond to input mutations, in practice it is more likely + # that these tangents will be plain tensors of zeros at runtime, so we tweak our guess + # to assume that these tangents should always be plaint tensors. + # Example: + # def f(x): + # x.mul_(2) + # return x + 1 + # out = f(x) + # out.sum().backward() + # In the above code, we will have a tangent "x_updated_tangent", + # which will be a plain tensor of zeros, *unless* x is used in some compute after executing f + # + # However, there are exceptions to this logic. If a view is created from mutated input and is used in backward, + # The tangent for this subclass input will be a subclass tensor. + # Example: + # def f(a, b): + # a.mul_(2) + # b.mul_(3) + # return b.view(b.shape), a + b + # a_out, b_out = f(..., Subclass) + # (a * b).sum().backward() + # + # We can not deduce it easily now, so introducing a debug config to be able to turn off this for specific cases. + # NJT gurantees to have its tangent as NJT, because it has dedicated integration in Autograd + # See torch/csrc/autograd/python_function.cpp, use_zeros_like. + ( + _plain_fake_tensor_like_subclass(inp) + if is_traceable_wrapper_subclass(inp) + and not _is_subclass_mutated_input_tangent_always_subclass(inp) + else inp + ) + for inp, info in zip(flat_f_args, input_info) + if info.mutation_type == MutationType.MUTATED_OUT_GRAPH + and info.mutates_data + and info.requires_grad + ] + f_output_tangents = [ + o + for o, info in zip(flat_f_outs, output_info) + if info.output_type + in [ + OutputType.non_alias, + OutputType.unsafe_view_alias, + OutputType.custom_function_view, + ] + and issubclass(info.raw_type, torch.Tensor) + and info.requires_grad + ] + # intermediate bases are also included in the backward graph + f_tangents = f_input_tangents + f_output_tangents + intermediate_bases + traced_tangents = pytree.tree_map(from_fun, f_tangents) + traced_tangents = pytree.tree_map( + view_avoid_dupes_with_primals, traced_tangents + ) + + traced_tangents = [ + coerce_tangent_and_suggest_memory_format(tt)[0] + for i, tt in enumerate(traced_tangents) + ] + nonlocal static_input_indices + static_input_indices = static_input_indices or [] + if torch._dynamo.compiled_autograd.in_compiled_autograd_region: + passed_indices = set(static_input_indices) + static_input_indices = [ + i + for i, arg in enumerate(flat_args) + if (isinstance(arg, torch.nn.Parameter) or i in passed_indices) + ] + + static_input_logger.debug( + "static input indices metadata analysis: %s", static_input_indices + ) + + f_mutated_inputs = [ + inp + for inp, info in zip(flat_f_args, input_info) + if info.mutation_type == MutationType.MUTATED_OUT_GRAPH + ] + f_metadata_mutated_inputs = [ + inp for inp, info in zip(flat_f_args, input_info) if info.mutates_metadata + ] + # This logic (annoyingly) re-figures out exactly what the outputs to the compiled fw graph will be. + # When handling subclasses, we need info about **all** outputs of compiled forward graph, + # so we know precisely which graph outputs to wrap back into tensor subclasses + # Ideally we would refactor this so not have an is_train flag, and have the separate + # inference and training paths decide which inputs/output to ask for subclass info on. + # However, we currently stash indexing information on each SubclassMeta about its order + # in the graph outputs list. + f_fw_graph_outs = list(flat_f_outs) + if is_train or not keep_input_mutations: + f_fw_graph_outs = f_mutated_inputs + f_fw_graph_outs + else: + # even when "keep_input_mutations" is True, + # we never keep metadata-only mutations in the fw graph + f_fw_graph_outs = f_metadata_mutated_inputs + f_fw_graph_outs + if is_train: + f_fw_graph_outs = f_fw_graph_outs + intermediate_bases + fw_graph_outs = pytree.tree_map(from_fun, f_fw_graph_outs) + + grad_enabled_mutation = None + if torch.is_grad_enabled() != prior_grad_enabled: + grad_enabled_mutation = torch.is_grad_enabled() + torch.set_grad_enabled( + prior_grad_enabled + ) # Restore the prior state after tracing it + log.debug( + ( + "grad_mode mutation encountered in graph. " + "Will emit mutation epilogue, to set grad_mode=%s" + ), + grad_enabled_mutation, + ) + + metadata = ViewAndMutationMeta( + input_info=input_info, + output_info=output_info, + num_intermediate_bases=len(intermediate_bases), + keep_input_mutations=keep_input_mutations, + traced_tangents=traced_tangents, + subclass_inp_meta=create_subclass_meta(flat_args), + subclass_fw_graph_out_meta=create_subclass_meta(fw_graph_outs), + subclass_tangent_meta=create_subclass_meta( + traced_tangents, count_symints=False, with_memory_format=True + ), + is_train=is_train, + grad_enabled_mutation=grad_enabled_mutation, + static_input_indices=static_input_indices, + tokens=mode._tokens, + ) + return metadata + + return inner diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..3382cb102dcadb99e0fc9879b7dbef224f9c6c3b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py @@ -0,0 +1,338 @@ +# mypy: allow-untyped-defs +""" +This module dispatches the graphs to either the forward-only or joint compilation +pathways, taking into account the AOTConfig and the collected ViewAndMutationMetadata. +""" + +import dataclasses +from typing import Any, Optional + +import torch +import torch.utils._pytree as pytree +import torch.utils.dlpack +from torch import Tensor +from torch._dispatch.python import enable_python_dispatcher +from torch._dynamo.utils import detect_fake_mode, lazy_format_graph_code +from torch._logging import getArtifactLogger, trace_structured +from torch._subclasses.functional_tensor import FunctionalTensorMode +from torch.fx.experimental.proxy_tensor import make_fx +from torchgen.utils import dataclass_repr + +from .. import config +from .functional_utils import ( + assert_functional_graph, + propagate_input_mutation_stacktraces, +) +from .schemas import AOTConfig, SubclassMeta, ViewAndMutationMeta +from .traced_function_transforms import ( + aot_dispatch_subclass, + create_functionalized_fn, + create_joint, + fn_input_mutations_to_outputs, + fn_prepped_for_autograd, + handle_effect_tokens_fn, +) +from .utils import ( + copy_fwd_metadata_to_bw_nodes, + register_buffer_assignment_hook, + root_module_when_exporting_non_strict, + unlift_tokens, +) + + +aot_graphs_log = getArtifactLogger(__name__, "aot_graphs") + + +def _create_graph(f, args, *, aot_config: AOTConfig) -> torch.fx.GraphModule: + # FunctionalTensorMode must be enabled here. + # See Note [Accessing .grad_fn on FunctionalTensor] + with enable_python_dispatcher(), FunctionalTensorMode( + pre_dispatch=aot_config.pre_dispatch, + export=aot_config.is_export, + # Allow token discovery for joint fn tracing as tokens can be used in backward. + _allow_token_discovery=True, + ): + fx_g = make_fx( + f, + decomposition_table=aot_config.decompositions, + record_module_stack=True, + pre_dispatch=aot_config.pre_dispatch, + )(*args) + + return fx_g + + +# TODO: Refactor the following code so detach() persists item_memo +def _detach_and_copy_item_memo(t): + detached_t = t.detach() + if hasattr(t, "item_memo"): + detached_t.item_memo = t.item_memo + return detached_t + + +def aot_dispatch_base_graph( + flat_fn, + flat_args: list[Tensor], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, +) -> tuple[torch.fx.GraphModule, list[Any], Optional[SubclassMeta]]: + # aot_dispatch_base requires functionalization, but doesn't need to handle as many cases as the autograd case. + # The cases that aot_dispatch_base doesn't need to handle include: + # - outputs that are aliases of graph intermediates + # - outputs that are aliases of graph inputs + # While cases that it does need to handle include: + # - input mutations (including when inputs are aliases of each other) + # - input metadata mutations + fn_to_trace = fn_input_mutations_to_outputs( + flat_fn, + fw_metadata, + keep_data_input_mutations=aot_config.keep_inference_input_mutations, + ) + + fn_to_trace, updated_flat_args = create_functionalized_fn( + fn_to_trace, + flat_args, + meta=fw_metadata, + aot_config=aot_config, + trace_joint=False, + ) + + # TODO: replace with AOTDispatchSubclassWrapper once we refactor + # fn_input_mutations_to_outputs and create_functionalized_fn + # into CompilerWrappers. + ( + fn_to_trace, + updated_flat_args_subclasses_desugared, + maybe_subclass_meta, + ) = aot_dispatch_subclass( + fn_to_trace, + updated_flat_args, + is_joint_structure=False, + meta=fw_metadata, + fw_only=flat_fn, + ) + + (fn_to_trace, updated_flat_args_subclasses_desugared) = handle_effect_tokens_fn( + fn_to_trace, + updated_flat_args_subclasses_desugared, + meta=fw_metadata, + trace_joint=False, + ) + + aot_graphs_log.debug( + "aot_config id: %s, fw_metadata=%s,subclass_metadata=%s", + str(aot_config.aot_id), + str(fw_metadata), + str(maybe_subclass_meta), + ) + + # We track buffer assignments when exporting in non-strict mode. + # (In contrast, strict mode errors on any attribute assignment.) + mod_when_exporting_non_strict = root_module_when_exporting_non_strict(flat_fn) + if aot_config.is_export and mod_when_exporting_non_strict is not None: + # For any buffer that is assigned, we want to associate it to the final proxy node + # that it is assigned to. This node can then be added as a buffer mutation output. + assigned_buffers: dict[str, str] = {} + hook = register_buffer_assignment_hook( + mod_when_exporting_non_strict, assigned_buffers + ) + + fake_mode = detect_fake_mode() + if fake_mode: + saved_updated_flat_args_subclasses_desugared = pytree.tree_map_only( + torch.Tensor, + _detach_and_copy_item_memo, + updated_flat_args_subclasses_desugared, + ) + else: + saved_updated_flat_args_subclasses_desugared = pytree.tree_map_only( + torch.Tensor, lambda t: t.detach(), updated_flat_args_subclasses_desugared + ) + + fw_module = _create_graph( + fn_to_trace, + updated_flat_args_subclasses_desugared, + aot_config=aot_config, + ) + + if aot_config.is_export and mod_when_exporting_non_strict is not None: + # We update metadata to consider any assigned buffers as buffer mutations. + i = len(dict(mod_when_exporting_non_strict.named_parameters())) + for name, _ in mod_when_exporting_non_strict.named_buffers(): + if name in assigned_buffers and not fw_metadata.input_info[i].mutates_data: # type: ignore[possibly-undefined] + fw_metadata.input_info[i] = dataclasses.replace( + fw_metadata.input_info[i], mutates_data=True + ) + fw_metadata.num_mutated_inp_runtime_indices += 1 + i += 1 + + # We add nodes corresponding to buffer assignments as output nodes in the graph. + add_nodes = [] + output_node = list(fw_module.graph.nodes)[-1] + for name in assigned_buffers.values(): # type: ignore[possibly-undefined] + for node in fw_module.graph.nodes: + if node.name == name: + add_nodes.append(node) + node.users[output_node] = None + output_node.args = ((*add_nodes, *output_node.args[0]),) + + hook.remove() # type: ignore[possibly-undefined] + + # As long as we opted to remove input mutations, then + # there should be *NO* mutating ops in the graph at this point. + copy_count = assert_functional_graph(fw_module.graph) + fw_module.graph.eliminate_dead_code() + fw_module.recompile() + + copy_count2 = assert_functional_graph(fw_module.graph) + propagate_input_mutation_stacktraces(fw_module.graph) + + # See Note [Side-Effectful Tokens in AOTAutograd] + num_tokens = len(fw_metadata.tokens) + if num_tokens != 0 and config.unlift_effect_tokens: + unlift_tokens(fw_module, fw_metadata, aot_config) + saved_updated_flat_args_subclasses_desugared = ( + saved_updated_flat_args_subclasses_desugared[num_tokens:] + ) + + assert copy_count == copy_count2 + + if aot_config.enable_log: + aot_graphs_log.info( + "%s", + lazy_format_graph_code( + "Forward graph", + fw_module, + aot_config.aot_id, + include_stride=True, + include_device=True, + colored=True, + ), + ) + + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "aot_forward_graph_fw_metadata", + "encoding": "string", + }, + payload_fn=lambda: dataclass_repr(fw_metadata), + ) + if maybe_subclass_meta is not None: + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "aot_forward_graph_fw_subclass_metadata", + "encoding": "string", + }, + payload_fn=lambda: dataclass_repr(maybe_subclass_meta), + ) + + trace_structured( + "aot_inference_graph", + payload_fn=lambda: fw_module.print_readable( + print_output=False, include_stride=True, include_device=True + ), + ) + + # TODO: should factor this into a separate function for export that always only returns just the graph. + if aot_config.is_export: + assert ( + maybe_subclass_meta is None + ), "aot_export_module does not support tensor subclass inputs for now." + return fw_module, saved_updated_flat_args_subclasses_desugared, maybe_subclass_meta + + +# Has the precondition that there +# are no duplicate arguments in flat_args (e.g., the same Tensor +# object never shows up twice. However, two tensor inputs MAY alias +# the same storage, so long as they have separate TensorImpls.) +def aot_dispatch_autograd_graph( + flat_fn, + flat_args: list[Any], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, +) -> tuple[torch.fx.GraphModule, tuple[list[Any], list[Any]], Optional[SubclassMeta]]: + # traced_tangents corresponds to the set of outputs in the traced forward that should get grad_outputs in the traced backward. + # It includes outputs of the original forward, *and* any updated inputs due to input mutations. + # However, it does *not* include any outputs that are aliases of inputs or intermediates, or any metadata-only input mutations. + joint_inputs = (flat_args, fw_metadata.traced_tangents) + + fn_prepared_for_autograd = fn_prepped_for_autograd( + flat_fn, + fw_metadata, + ) + joint_fn_to_trace = create_joint(fn_prepared_for_autograd, aot_config=aot_config) + + joint_fn_to_trace, updated_joint_inputs = create_functionalized_fn( + joint_fn_to_trace, + joint_inputs, + meta=fw_metadata, + aot_config=aot_config, + trace_joint=True, + ) + + # TODO: replace with AOTDispatchSubclassWrapper once we refactor + # fn_input_mutations_to_outputs and create_functionalized_fn + # into CompilerWrappers. + subclass_tracing_info = aot_dispatch_subclass( + joint_fn_to_trace, + updated_joint_inputs, + is_joint_structure=True, + meta=fw_metadata, + fw_only=flat_fn, + ) + + joint_fn_to_trace = subclass_tracing_info.plain_tensor_trace_fn + updated_joint_inputs = subclass_tracing_info.plain_tensor_args + + (joint_fn_to_trace, updated_joint_inputs) = handle_effect_tokens_fn( + joint_fn_to_trace, + updated_joint_inputs, + meta=fw_metadata, + trace_joint=True, + ) + + # When we call _create_graph, this may mutate the metadata of joint + # inputs. But callers are expecting to get the original joint inputs. So + # we make aliases of all the inputs to make sure we have a copy that + # doesn't get modified. + # + # This destroys requires_grad/grad_fn information. However, backends + # beneath AOTAutograd are indifferent to this information, so it doesn't + # matter. + + fake_mode = detect_fake_mode() + if fake_mode: + saved_updated_joint_inputs = pytree.tree_map_only( + torch.Tensor, _detach_and_copy_item_memo, updated_joint_inputs + ) + else: + saved_updated_joint_inputs = pytree.tree_map_only( + torch.Tensor, lambda t: t.detach(), updated_joint_inputs + ) + maybe_subclass_meta = subclass_tracing_info.maybe_subclass_meta + + fx_g = _create_graph(joint_fn_to_trace, updated_joint_inputs, aot_config=aot_config) + + # There should be *NO* mutating ops in the graph at this point. + assert_functional_graph(fx_g.graph) + + # Redundant with the check above, but worth having in case tracing introduced + # a fake tensor. Unlikely. + # See Note: [Fake Modules and AOTAutograd] + torch._dynamo.utils.assert_no_fake_params_or_buffers(fx_g) + fx_g.graph.eliminate_dead_code() + copy_fwd_metadata_to_bw_nodes(fx_g) + fx_g.recompile() + + # TODO: in AOTAutograd, we create metadata like _indices_of_inps_to_detach to detect + # when we need to manually detach() some inputs in the forward. + # Higher order ops might eventually need to do the same. + if aot_config.is_export: + assert ( + maybe_subclass_meta is None + ), "aot_export_module does not support tensor subclass inputs for now." + return fx_g, saved_updated_joint_inputs, maybe_subclass_meta diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/functional_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/functional_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6d73acc1486222e86ea785fb345e3872943141d9 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/functional_utils.py @@ -0,0 +1,527 @@ +# mypy: allow-untyped-defs +""" +This file contains utilities related to functionalization in AOTAutograd: +1. converting to/from functional tensors +2. detecting Tensor mutations - both metadata and Tensor value +3. regenerating/replaying views from their base +4. checking if a graph is functional i.e. whether it contains any mutation ops +""" +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional + +import torch +from torch import Tensor +from torch._logging import getArtifactLogger +from torch._subclasses.fake_tensor import FakeTensor +from torch._subclasses.functional_tensor import FunctionalTensor +from torch._subclasses.meta_utils import is_sparse_any +from torch.fx.experimental.symbolic_shapes import ( + definitely_true, + sym_eq, + SymIntEqByExpr, +) +from torch.multiprocessing.reductions import StorageWeakRef +from torch.utils._python_dispatch import ( + is_traceable_wrapper_subclass, + transform_subclass, +) + + +aot_joint_log = getArtifactLogger(__name__, "aot_joint_graph") + + +def to_fun(t): + if isinstance(t, Tensor): + if is_traceable_wrapper_subclass(t): + # See Note [Functionalization always runs last] + # This means that if we want to "functionalize" a subclass, we need to ensure that the functional wrapper + # goes at the bottom. + # recurse here, so we can support nested wrapper subclasses + out = transform_subclass(t, lambda _, inner_t: to_fun(inner_t)) + torch._mirror_autograd_meta_to(t, out) # type: ignore[attr-defined] + return out + else: + return FunctionalTensor.to_functional(t) + else: + return t + + +def sync_functional_tensor(t): + if is_traceable_wrapper_subclass(t): + attrs, _ctx = t.__tensor_flatten__() # type: ignore[attr-defined] + for attr in attrs: + sync_functional_tensor(getattr(t, attr)) + else: + torch._sync(t) + + +# When subclasses are involved, t here will usually look something like: +# SubclassA(SubclassB(FunctionalTensor(_to_fun_tensor(FakeTensor)))) +def from_fun(t): + if isinstance(t, Tensor) and is_traceable_wrapper_subclass(t): + # See Note [Functionalization always runs last] + # This means that if we want to "functionalize" a subclass, we need to ensure that the functional wrapper + # goes at the bottom. + # recurse here, so we can support nested wrapper subclasses + out = transform_subclass(t, lambda _, inner_t: from_fun(inner_t)) + torch._mirror_autograd_meta_to(t, out) # type: ignore[attr-defined] + return out + + if not isinstance(t, FunctionalTensor): + # quick sanity assert + if isinstance(t, torch.Tensor): + assert not torch._is_functional_tensor(t) # type: ignore[attr-defined] + return t + sync_functional_tensor(t) + return torch._from_functional_tensor(t.elem) + + +def is_fun(t): + if isinstance(t, Tensor) and is_traceable_wrapper_subclass(t): + # See Note [Functionalization always runs last] + # This means that if we want to "functionalize" a subclass, we need to ensure that the functional wrapper + # goes at the bottom. + # recurse here, so we can support nested wrapper subclasses + t_attrs, _ = t.__tensor_flatten__() # type: ignore[attr-defined] + t_inners = [getattr(t, attr) for attr in t_attrs] + any_fun = any(is_fun(x) for x in t_inners) + all_fun = all(is_fun(x) for x in t_inners) + assert any_fun == all_fun + return any_fun + + return isinstance(t, FunctionalTensor) + + +# t here is either +# (1) A FunctionalTensor(_to_functional_tensor(FakeTensor)) +# (2) A traceable tensor subclass that holds a FunctionalTensor +# (3) Not a tensor +def has_data_mutation(t): + if is_traceable_wrapper_subclass(t): + attrs, _ = t.__tensor_flatten__() + # A tensor subclass was updated if any of its inner elements were updated + return any(has_data_mutation(getattr(t, attr)) for attr in attrs) + else: + if isinstance(t, torch.Tensor): + assert isinstance(t, FunctionalTensor) + return torch._functionalize_has_data_mutation(t.elem) # type: ignore[attr-defined] + return False + + +def are_all_mutations_hidden_from_autograd(t): + if is_traceable_wrapper_subclass(t): + attrs, _ = t.__tensor_flatten__() + # If all inner elements are mutations hidden from autograd, then it is a mutation hidden from autograd. + return all( + are_all_mutations_hidden_from_autograd(getattr(t, attr)) for attr in attrs + ) + elif isinstance(t, torch.Tensor): + assert isinstance(t, FunctionalTensor) + return torch._functionalize_are_all_mutations_hidden_from_autograd(t.elem) + else: + return False + + +def are_all_mutations_under_no_grad_or_inference_mode(t): + if is_traceable_wrapper_subclass(t): + attrs, _ = t.__tensor_flatten__() + return all( + are_all_mutations_under_no_grad_or_inference_mode(getattr(t, attr)) + for attr in attrs + ) + else: + assert isinstance(t, FunctionalTensor) + return torch._functionalize_are_all_mutations_under_no_grad_or_inference_mode( + t.elem + ) + + +def was_inductor_storage_resized(t): + if is_traceable_wrapper_subclass(t): + attrs, _ = t.__tensor_flatten__() + if any(was_inductor_storage_resized(getattr(t, attr)) for attr in attrs): + raise RuntimeError( + f"storage resizing is not supported on tensor subclass: {type(t)}" + ) + elif not isinstance(t, torch.Tensor): + return False + else: + assert isinstance(t, FunctionalTensor) + return torch._functionalize_was_inductor_storage_resized(t.elem) + + +# f_arg here is either +# (1) A FunctionalTensor(_to_functional_tensor(FakeTensor)) +# (2) A traceable tensor subclass that holds a FunctionalTensor +# (3) Not a tensor +# Assumption: arg promises to be the "original" tensor wrapped by f_arg +# Note: "storage mutations" coming from set_() are a type of metadata mutation. So: +# - check_only_storage_mutation=True: only return true if there was a storage mutation +# - check_only_storage_mutation=Flse: return true if there was any metadata mutation (including a storage mutation) +def has_metadata_mutation(f_arg, arg, *, check_only_storage_mutation: bool): + if is_traceable_wrapper_subclass(f_arg): + attrs, _ = f_arg.__tensor_flatten__() + # A tensor subclass was updated if any of its inner elements were updated + f_inner_ts = [getattr(f_arg, attr) for attr in attrs] + inner_ts = [getattr(arg, attr) for attr in attrs] + return any( + has_metadata_mutation( + f_inner_t, + inner_t, + check_only_storage_mutation=check_only_storage_mutation, + ) + for f_inner_t, inner_t in zip(f_inner_ts, inner_ts) + ) + else: + if not isinstance(f_arg, torch.Tensor): + assert not isinstance(arg, torch.Tensor) + return False + assert isinstance(f_arg, FunctionalTensor) + assert isinstance(arg, FakeTensor) + + arg_after = torch._from_functional_tensor(f_arg.elem) + # This is true if the current tensor experienced at least one set_() call + maybe_storage_changed = torch._functionalize_was_storage_changed(f_arg.elem) # type: ignore[attr-defined] + # However, multiple set_() calls can cancel out. So we also check whether the + # storage of the tensor has changed. + # Note: if an input experienced two set_() calls that cancel out, **and** + # it experiences an data mutation, we pessimistically think that the set_() + # call is necessary here. We could in theory fix this, but this will + # hopefully never happen in user code, and is not needed for fsdp. + if is_sparse_any(arg): + # TODO:add sparse tensors support to functionalization + same_storages = False + else: + same_storages = StorageWeakRef(arg.untyped_storage()) == StorageWeakRef( + arg_after.untyped_storage() + ) + has_storage_metadata_mutation = maybe_storage_changed and not same_storages + if check_only_storage_mutation: + return has_storage_metadata_mutation + + # storage metadata mutation is a type of metadata mutation, so return true if we saw one + if has_storage_metadata_mutation: + return True + + maybe_metadata_mutated = torch._functionalize_has_metadata_mutation(f_arg.elem) # type: ignore[attr-defined] + # This is true if the current tensor experienced at least one metadata mutation. + # So if false, we know there was no metadata mutation + if not maybe_metadata_mutated: + return False + + # However, multi metadata mutations can cancel out. + # So we also check if the concrete sizes/strides on the tensor have changed. + same_sizes = arg.shape == arg_after.shape + same_strides = arg.stride() == arg_after.stride() + same_offsets = arg.storage_offset() == arg_after.storage_offset() + has_metadata_mutation_ = maybe_metadata_mutated and not ( + same_sizes and same_strides and same_offsets + ) + # We consider a tensor to have been metadata mutated if its storage was mutated through a set_() call. + return has_metadata_mutation_ + + +def gen_alias_from_base( + aliased_base_tensor, + target_meta_tensor, + target_requires_grad, + target_functional_tensor: Optional[FunctionalTensorMetadataEq] = None, + *, + replay_views, +): + # Patch the correct requires_grad field of the output tensor, depending on whether: + # (i) the reconstructed output (out) was came from a tensor that requires grad or not; + # and (ii) the concrete returned output does require grad or not. + def patch_requires_grad(out): + if aliased_base_tensor.requires_grad and not target_requires_grad: + out = out.detach() + elif not aliased_base_tensor.requires_grad and target_requires_grad: + out.requires_grad_(True) + return out + + # If provided, use the target functional tensor for replaying the views. + # + # In summary, we use the fact that FunctionalTensorWrapper saves the view + # functions applied to itself (collected during functionalization) so as + # to replay them (view functions) on the aliased_base_tensor. + if ( + replay_views + and target_functional_tensor is not None + and not torch._functionalize_is_symbolic(target_functional_tensor.tensor) + ): + functional_tensor = target_functional_tensor.tensor + + out = torch._functionalize_apply_view_metas( + functional_tensor, aliased_base_tensor + ) + # If re-applying the ViewMeta sequence succeeded, there should be no more + # problems going forward. We just check we got to the target shape and + # patch requires_grad flag. + assert out.shape == target_meta_tensor.shape, ( + "incorrect out shape after application of ViewMeta sequence: " + f"{tuple(out.shape)} (actual) vs {tuple(target_meta_tensor.shape)} (expected)" + ) + return patch_requires_grad(out) + + # Try to do view-replay if possible. + # fall back to .as_strided() if we can't. + if target_meta_tensor._base is not None: + # The base that we want to replay our view off of might have a different shape than the view's original base. + b = target_meta_tensor._base + abt = aliased_base_tensor + # Don't unnecessarily call as_strided if nothing changed; as_strided's + # backward is poorly implemented and slow + if abt is not b and ( + abt.size() != b.size() + or abt.stride() != b.stride() + or abt.storage_offset() != b.storage_offset() + ): + reshaped_base_tensor = aliased_base_tensor.as_strided( + b.size(), b.stride(), b.storage_offset() + ) + else: + reshaped_base_tensor = aliased_base_tensor + out = target_meta_tensor._view_func(reshaped_base_tensor) + # This shape mismatch can happen due to a bug in inplace/view handling in autograd. + # Try putting a breakpoint here and running + # `test/functorch/test_aotdispatch TestAOTAutograd.test_output_all_alias_types` + # Also, https://github.com/pytorch/pytorch/issues/49825 + # + # As a stopgap, we'll fall back to as_strided. + if out is not None and out.shape == target_meta_tensor.shape: + return patch_requires_grad(out) + + size = target_meta_tensor.size() + stride = target_meta_tensor.stride() + storage_offset = target_meta_tensor.storage_offset() + if aliased_base_tensor.is_complex() and not target_meta_tensor.is_complex(): + aliased_out = torch.view_as_real(aliased_base_tensor).as_strided( + size, stride, storage_offset + ) + elif not aliased_base_tensor.is_complex() and target_meta_tensor.is_complex(): + aliased_out = torch.view_as_complex(aliased_base_tensor).as_strided( + size, stride, storage_offset + ) + else: + aliased_out = aliased_base_tensor.as_strided(size, stride, storage_offset) + # For outputs aliasing inputs, we need to check if the requires-gradness has changed. + aliased_out = patch_requires_grad(aliased_out) + # For outputs aliasing inputs, we need to check if the dtype has changed. + # as_strided() is the "most generic" view, but it does not cover cross-dtype views + if aliased_out.dtype != target_meta_tensor.dtype: + aliased_out = aliased_out.view(target_meta_tensor.dtype) + return aliased_out + + +def has_same_metadata(t1, t2): + return ( + definitely_true(sym_eq(t1.size(), t2.size())) + and definitely_true(t1.layout == t2.layout) + and ( + is_sparse_any(t1) + or ( + definitely_true(sym_eq(t1.stride(), t2.stride())) + and definitely_true(t1.storage_offset() == t2.storage_offset()) + ) + ) + and t1.is_conj() == t2.is_conj() + and t1.is_neg() == t2.is_neg() + ) + + +@dataclass(frozen=True) +class MetadataKey: + """ + This should be equal whenever has_same_metadata would return True + """ + + size: tuple[SymIntEqByExpr, ...] + layout: torch.layout + is_sparse: bool + # these are empty when is_sparse + stride: Optional[tuple[SymIntEqByExpr, ...]] + storage_offset: Optional[SymIntEqByExpr] + is_conj: bool + is_neg: bool + + @staticmethod + def make(t): + is_sparse = is_sparse_any(t) + return MetadataKey( + size=tuple(SymIntEqByExpr(s) for s in t.size()), + layout=t.layout, + is_sparse=is_sparse, + stride=None if is_sparse else tuple(SymIntEqByExpr(s) for s in t.stride()), + storage_offset=None if is_sparse else SymIntEqByExpr(t.storage_offset()), + is_conj=t.is_conj(), + is_neg=t.is_neg(), + ) + + +# Wrapper around a FunctionalTensorWrapper for comparing only the resulting metadata +# after applying all the ViewMeta operations. +class FunctionalTensorMetadataEq: + def __init__(self, tensor: torch.Tensor) -> None: + assert torch._is_functional_tensor(tensor) + self.tensor = tensor + + def __eq__(self, other: object) -> bool: + # If other is None, then it probably means that we weren't able to recreate + # the FunctionalTensorMetadataEq. One of this cases is when we update the + # view metadata by calling: create_synthetic_base_metadata. + if other is None: + return True + + # Comparison agains any other type is not implemented. + if not isinstance(other, FunctionalTensorMetadataEq): + return NotImplemented + + return has_same_metadata(self.tensor, other.tensor) + + +# new_arg and arg here are either: +# (1) both a FakeTensor +# (2) both a traceable tensor subclass that holds a FakeTensor +# Pre-condition: the two args are the "old" and "new" inputs from running functionalization. +# When we run functionalization and wrap our inputs into FunctionalTensors, +# we can detect whether or not an input was mutated by checking to see if the inner tensor has changed +# +# Normally it would be enough just to check if arg is new_arg, which is normally enough for functionalization +# to confirm that inputs were not mutated when running the user's model with functionalization on. +# But when we have subclass inputs, we can't rely on that: +# `from_fun(to_fun(x)) is x` will return False, because the call to `from_fun` constructs +# a brand new subclass instance: we are calling __tensor_unflatten__, and going +# from Subclass(FakeTensor) to Subclass(FunctionalTensor(FakeTensor)) +def was_tensor_updated(arg, new_arg): + if is_traceable_wrapper_subclass(arg): + assert is_traceable_wrapper_subclass(new_arg) + attrs, _ = arg.__tensor_flatten__() + new_attrs, _ = new_arg.__tensor_flatten__() + assert attrs == new_attrs + # A tensor subclass was updated if any of its inner elements were updated + return any( + was_tensor_updated(getattr(arg, attr), getattr(new_arg, attr)) + for attr in attrs + ) + else: + return arg is not new_arg + + +# new_arg and arg here are either: +# (1) both a FakeTensor +# (2) both a traceable tensor subclass that holds a FakeTensor +# Pre-condition: the two args are the "old" and "new" inputs from running functionalization. +# When we run functionalization and wrap our inputs into FunctionalTensors, +# we can detect whether or not an input was mutated by checking to see if the inner tensor has changed, +# but shares storage with the old input +def was_tensor_metadata_updated(arg, new_arg): + if is_traceable_wrapper_subclass(arg): + assert is_traceable_wrapper_subclass(new_arg) + attrs, _ = arg.__tensor_flatten__() + new_attrs, _ = new_arg.__tensor_flatten__() + assert attrs == new_attrs + # A tensor subclass was updated if any of its inner elements were updated + return any( + was_tensor_metadata_updated(getattr(arg, attr), getattr(new_arg, attr)) + for attr in attrs + ) + else: + return arg is not new_arg and StorageWeakRef( + arg.untyped_storage() + ) == StorageWeakRef(new_arg.untyped_storage()) + + +# Returns the number of detected copy_ +def assert_functional_graph(fx_g: torch.fx.Graph) -> int: + allowed_mutation_ops = [ + torch.ops.aten.copy_.default, + torch.ops.aten.set_.source_Tensor, + ] + if hasattr(torch.ops.fsdp, "copy_"): + allowed_mutation_ops.append(torch.ops.fsdp.copy_.default) + + placeholders = set() + mutation_count = 0 + # NB: It would also be nice to verify that the mutations all happen at the + # end, but we also do some administrative views after mutations so this + # isn't actually true. (TODO: Could this cause problems for Inductor?) + for n in fx_g.nodes: + if n.op == "placeholder": + placeholders.add(n) + if isinstance(n.target, torch._ops.OpOverload): + if n.target in allowed_mutation_ops: + # Can only copy_/set_ into an input + # this is mostly a hack to avoid failing XLA tests. + # See https://github.com/pytorch/pytorch/pull/122434#issuecomment-2101012113 + if "set_buffer_donor_" not in str(n.args[0]): + assert ( + n.args[0] in placeholders + ), f"n={str(n)}, n.args[0]={str(n.args[0])}, placeholders={str(placeholders)}, graph={str(fx_g)}" + mutation_count += 1 + else: + assert ( + not n.target._schema.is_mutable + ), f"aot_autograd expected to have an entirely functional graph, but found {n.format_node()}" + return mutation_count + + +def propagate_input_mutation_stacktraces(fx_g: torch.fx.Graph) -> None: + placeholders = set() + for n in fx_g.nodes: + if n.op == "placeholder": + placeholders.add(n) + if isinstance(n.target, torch._ops.OpOverload): + if n.target is torch.ops.aten.copy_.default: + # Can only copy_ into an input, and can only do so once + if "set_buffer_donor_" not in str(n.args[0]): + assert ( + n.args[0] in placeholders + ), f"n={str(n)}, n.args[0]={str(n.args[0])}, placeholders={str(placeholders)}, graph={str(fx_g)}" + placeholders.remove(n.args[0]) + copy_from_node = n.args[1] + # Pre-condition: every node has a "stack_trace" field in its meta, + # but copy_() nodes do not (since we manually added them during functionalization). + # Instead, we manually propagate here. + if "stack_trace" in copy_from_node.meta: + n.meta["stack_trace"] = copy_from_node.meta["stack_trace"] + + +def _check_if_mutation_can_be_in_graph( + keep_input_mutations: bool, + mutates_data, + mutates_metadata, + mutations_hidden_from_autograd, + mutations_under_no_grad_or_inference_mode, + mutates_storage_metadata, + mutation_inductor_storage_resize, + requires_grad, +): + if keep_input_mutations: + in_graph = ( + mutates_data or mutates_storage_metadata or mutation_inductor_storage_resize + ) and ( + (not mutates_metadata and not requires_grad) + or mutations_hidden_from_autograd + or mutations_under_no_grad_or_inference_mode + ) + else: + in_graph = False + # See Note [set_() Input Mutations in AOTAutograd] + # If there was a `set_()`, we require that all mutations were under no_grad, + # so we can (safely) emit the set_() in the graph at runtime + # resize_() gets the same treatment + if mutation_inductor_storage_resize or mutates_storage_metadata: + op_name = "resize_" if mutation_inductor_storage_resize else "set_" + assert in_graph, f"""\ +Encountered a {op_name} on a graph input, but the input has other mutations that we cannot +keep in the graph. This is not supported today. Current state: + keep_input_mutations={keep_input_mutations} + mutates_data={mutates_data} + mutates_metadata={mutates_metadata} + mutations_hidden_from_autograd={mutations_hidden_from_autograd} + mutations_under_no_grad_or_inference_mode={mutations_under_no_grad_or_inference_mode} + mutation_inductor_storage_resize={mutation_inductor_storage_resize} + requires_grad={requires_grad}""" + return in_graph diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/input_output_analysis.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/input_output_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..7dc4112a101fef2a5f63e2e3a0dadce3ff942a1f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/input_output_analysis.py @@ -0,0 +1,420 @@ +# mypy: allow-untyped-defs +""" +This module is one of the analysis modules - it takes as input a function or graph +and some preexisting properties, and returns some data that is useful for deciding +how to further proceed with compilation or construct runtime wrappers. + +In particular, the following analyses are provided: +1. Refine the view and mutation metadata collected previously - removing duplicate + inputs or mapping views to their bases. +2. We also analyze the function signature for export graphs. +""" + +import contextlib +import itertools +from typing import Any, Optional, Union + +import torch +import torch.utils._pytree as pytree +from torch import Tensor +from torch._C._dynamo.guards import compute_overlapping_tensors +from torch._functorch._aot_autograd.schemas import PlainTensorMeta +from torch._guards import StorageOverlap +from torch._subclasses.functional_tensor import FunctionalTensor +from torch.fx.experimental.symbolic_shapes import is_concrete_int + +from .collect_metadata_analysis import coerce_tangent_and_suggest_memory_format +from .schemas import ( + BackwardSignature, + GraphSignature, + InputAliasInfo, + OutputAliasInfo, + OutputType, + ViewAndMutationMeta, +) +from .utils import strict_zip + + +zip = strict_zip + + +def remove_dupe_metadata( + m: ViewAndMutationMeta, + keep_arg_mask: list[bool], + add_dupe_map: list[int], +) -> ViewAndMutationMeta: + assert len(m.input_info) == len(keep_arg_mask) + # Easy invariant: the first argument should never be a dupe (it will be kept) + assert len(keep_arg_mask) > 0 and keep_arg_mask[0] + + # Filter dupe'd mutated inputs out of traced_tangents + num_data_mutations = len([x for x in m.input_info if x.mutates_data]) + other_traced_tangents = m.traced_tangents[num_data_mutations:] + inp_traced_tangents = m.traced_tangents[:num_data_mutations] + filtered_inp_traced_tangents = [ + # See Note [Tangents memory format] + x + for i, x in enumerate(inp_traced_tangents) + if keep_arg_mask[m.mutated_inp_runtime_indices[i]] + ] + traced_tangents = filtered_inp_traced_tangents + other_traced_tangents + + assert m.subclass_tangent_meta is not None + subclass_tangent_meta = [ + PlainTensorMeta(0, memory_format=torch.contiguous_format) + ] * len(filtered_inp_traced_tangents) + m.subclass_tangent_meta[num_data_mutations:] + + return ViewAndMutationMeta( + input_info=[x for i, x in enumerate(m.input_info) if keep_arg_mask[i]], + # For outputs that are views of inputs, we store the index of the input that the output + # was generated from. Need to update that index to account for removed dupes. + output_info=[ + OutputAliasInfo( + output_type=o.output_type, + raw_type=o.raw_type, + dynamic_dims=o.dynamic_dims, + base_idx=None if o.base_idx is None else add_dupe_map[o.base_idx], + requires_grad=o.requires_grad, + functional_tensor=o.functional_tensor, + ) + for o in m.output_info + ], + num_intermediate_bases=m.num_intermediate_bases, + keep_input_mutations=m.keep_input_mutations, + traced_tangents=traced_tangents, + # We are guaranteed not to get here, since dupes are not supported today with subclass inputs. + subclass_inp_meta=[], + subclass_fw_graph_out_meta=[], + subclass_tangent_meta=subclass_tangent_meta, + is_train=m.is_train, + ) + + +# Given our ViewAndMutation metadata, this fn constructs a new set of metadata, +# after adding synthetic base arguments to the function. +# Most of the work in this fn is slogging through all of the metadata corresponding to inputs, +# and updating it with our synthetic base calling convention. +# +# When config.debug_assert is set, we automatically regenerate the metadata +# and compare it to this output for sanity. +# +# In addition to the updated metadata, also return the list of input indices +# that will need to be updated in the synthetic base epilogue +def create_synthetic_base_metadata( + m: ViewAndMutationMeta, + # Maps each outer argument idx to its inner idx (or, if this outer arg is generated from a + # synthetic base, you get a tuple of (i, TensorMeta), telling you the base tensor idx, and view metadata) + synthetic_base_info: list[Union[int, tuple[int, torch.Tensor]]], + outer_args: list[Any], + inner_args: list[Any], +) -> tuple[ViewAndMutationMeta, list[int]]: + # maps inner arg indices to outer arg indices + synthetic_base_to_indices: dict[int, list[int]] = {} + for inner_idx in range(len(inner_args)): + outer_aliased_indices_of_current_base_arg = [ + outer_idx + for outer_idx, inner_idx_or_tuple in enumerate(synthetic_base_info) + if (isinstance(inner_idx_or_tuple, int) and inner_idx_or_tuple == inner_idx) + or ( + isinstance(inner_idx_or_tuple, tuple) + and inner_idx_or_tuple[0] == inner_idx + ) + ] + synthetic_base_to_indices[inner_idx] = outer_aliased_indices_of_current_base_arg + + # given the requires_grad info on mutated inputs, + # generate the requires_grad info on those same mutated inputs, but after constructing synthetic bases. + input_infos = [] + for outer_indices in synthetic_base_to_indices.values(): + # leaf-ness should be all-or-nothing for aliased tensor. + # (aka if "a" and "b" are views, then a.is_leaf == b.is_leaf) + any_leaf = any(m.input_info[x].is_leaf for x in outer_indices) + all_leaf = all(m.input_info[x].is_leaf for x in outer_indices) + assert any_leaf == all_leaf + + mutates_data = ( + True + if len(outer_indices) > 1 + else m.input_info[outer_indices[0]].mutates_data + ) + mutates_metadata = ( + False + if len(outer_indices) > 1 + else m.input_info[outer_indices[0]].mutates_metadata + ) + requires_grad = any(m.input_info[x].requires_grad for x in outer_indices) + mutations_under_no_grad_or_inference_mode = all( + m.input_info[x].mutations_under_no_grad_or_inference_mode + for x in outer_indices + ) + + mutation_inductor_storage_resize = all( + m.input_info[x].mutation_inductor_storage_resize for x in outer_indices + ) + + inpt_info = InputAliasInfo( + # If len(outer_indices) > 1, then this input is a synthetic base. + # The invariant is that to the rest of aot autograd, synthetic bases only show up if + # one of their aliases gets a data mutation. And if any of their aliases get metadata + # mutations, they will be hidden from the rest of aot autograd. + mutates_data=mutates_data, + mutates_metadata=mutates_metadata, + mutations_hidden_from_autograd=all( + m.input_info[x].mutations_hidden_from_autograd for x in outer_indices + ), + mutates_storage_metadata=( + False + if len(outer_indices) > 1 + else m.input_info[outer_indices[0]].mutates_storage_metadata + ), + mutations_under_no_grad_or_inference_mode=mutations_under_no_grad_or_inference_mode, + mutation_inductor_storage_resize=mutation_inductor_storage_resize, + is_leaf=any_leaf, + requires_grad=requires_grad, + keep_input_mutations=m.keep_input_mutations, + ) + input_infos.append(inpt_info) + + # Find any inputs that fulfill the following criteria: + # (1) They are part of a synthetic base (because they alias another input, + # and at least one input experiences a data mutation) + # (2) They experience a metadata mutation + outer_aliased_arg_idx_with_metadata_mutations = [ + outer_idx + for outer_idx, inpt_info in enumerate(m.input_info) + if inpt_info.mutates_metadata + and not isinstance(synthetic_base_info[outer_idx], int) + ] + + # grab the original requires grad info on the outputs, except the ones from the mutated inputs + input_metadata_output_info = [ + OutputAliasInfo( + output_type=OutputType.alias_of_input, + raw_type=FunctionalTensor, + dynamic_dims={ + i + for i, s in enumerate(outer_args[outer_idx].shape) + if not is_concrete_int(s) + }, + base_idx=synthetic_base_info[outer_idx][0], # type: ignore[index] + requires_grad=outer_args[outer_idx].requires_grad, + ) + for outer_idx in outer_aliased_arg_idx_with_metadata_mutations + ] + existing_output_infos = [] + for o in m.output_info: + new_base_idx = ( + None + if o.base_idx is None + else ( + synthetic_base_info[o.base_idx] + if isinstance(synthetic_base_info[o.base_idx], int) + else synthetic_base_info[o.base_idx][0] # type: ignore[index] + ) + ) + # If base_idx is changed for OutputType.is_input, we need to update the output type to reflect the change + new_output_type = ( + OutputType.alias_of_input + if o.output_type == OutputType.is_input and o.base_idx != new_base_idx + else o.output_type + ) + existing_output_infos.append( + OutputAliasInfo( + output_type=new_output_type, + raw_type=o.raw_type, + dynamic_dims=o.dynamic_dims, + # Map the input idx pre-synthetic-bases to the new idx post-synthetic-bases + base_idx=new_base_idx, # type: ignore[arg-type] + requires_grad=o.requires_grad, + functional_tensor=o.functional_tensor, + ) + ) + + inner_mutated_tangents_and_memory_formats = [ + # See Note [Tangents memory format] + coerce_tangent_and_suggest_memory_format(x) + for inner_idx, x in enumerate(inner_args) + if input_infos[inner_idx].mutates_data and input_infos[inner_idx].requires_grad + ] + inner_mutated_tangents = [x[0] for x in inner_mutated_tangents_and_memory_formats] + inner_mutated_tangents_memory_formats = [ + x[1] for x in inner_mutated_tangents_and_memory_formats + ] + + output_info = existing_output_infos + input_metadata_output_info + # Regenerate traced tangents to include mutated inputs including synthetic bases + traced_tangents = ( + inner_mutated_tangents + m.traced_tangents[len(inner_mutated_tangents) :] + ) + assert m.subclass_tangent_meta is not None + subclass_tangent_meta = [ + PlainTensorMeta(0, memory_format=x) + for x in inner_mutated_tangents_memory_formats + ] + m.subclass_tangent_meta[len(inner_mutated_tangents) :] + + return ( + ViewAndMutationMeta( + input_info=input_infos, + output_info=output_info, + num_intermediate_bases=m.num_intermediate_bases, + keep_input_mutations=m.keep_input_mutations, + traced_tangents=traced_tangents, + # We are guaranteed not to get here, since synthetic_base codepaths are not supported today with subclass inputs. + subclass_inp_meta=[], + subclass_fw_graph_out_meta=[], + subclass_tangent_meta=subclass_tangent_meta, + is_train=m.is_train, + ), + outer_aliased_arg_idx_with_metadata_mutations, + ) + + +def compute_overlapping_inputs(aot_config, fwd_inputs, aliased_input_indices): + num_aliases = len(aliased_input_indices) + + shape_env = None + maybe_suppress_guards = contextlib.nullcontext + tracing_context = torch._guards.TracingContext.try_get() + + if tracing_context is not None: + shape_env = tracing_context.fake_mode.shape_env + + # Check whether we can actually get the dynamo sources from within AOTAutograd. + if aot_config.aot_autograd_arg_pos_to_source and shape_env is not None: + maybe_suppress_guards = shape_env.suppress_guards + + # Check whether there are any symbolic values being used. + # We do this for 2 reasons: + # 1. StorageOverlap guard is only issued whenever dynamic shapes is turned on + # 2. Triggers the fast-path for computing storage overlapping + symbolic = any( + isinstance(x, torch.SymInt) + for i in aliased_input_indices + for x in [ + *fwd_inputs[i].shape, + *fwd_inputs[i].stride(), + fwd_inputs[i].storage_offset(), + ] + ) + + with maybe_suppress_guards(): + aliased_fwd_inputs = [fwd_inputs[i] for i in aliased_input_indices] + actual_aliased_indices = { + aliased_input_indices[i] + for i in compute_overlapping_tensors(aliased_fwd_inputs, symbolic=symbolic) + } + + # Add the StorageOverlap AOTAutograd guard only if we are actually keeping track of + # dynamo sources inside AOTAutograd. + if ( + tracing_context is not None + # Make sure dynamic shapes is currently being used. + and symbolic + # We check that we have more than 1 aliased tensor, which should be true at + # this point, anyway. + and num_aliases > 1 + and aot_config.aot_autograd_arg_pos_to_source + ): + no_overlap_indices = list(set(aliased_input_indices) - actual_aliased_indices) + + overlapping_sources = [ + aot_config.aot_autograd_arg_pos_to_source[i] for i in actual_aliased_indices + ] + non_overlapping_sources = [ + aot_config.aot_autograd_arg_pos_to_source[i] for i in no_overlap_indices + ] + + tracing_context.guards_context.aotautograd_guards.append( + StorageOverlap(overlapping_sources, non_overlapping_sources) + ) + + return actual_aliased_indices + + +def _graph_input_names(gm): + return [node.name for node in gm.graph.find_nodes(op="placeholder")] + + +def _graph_output_names(gm): + output_node = next(iter(reversed(gm.graph.nodes))) + assert output_node.op == "output" and len(output_node.args) == 1 + return_args = output_node.args[0] + return [getattr(return_arg, "name", None) for return_arg in return_args] + + +def create_graph_signature( + fx_g: torch.fx.GraphModule, + fw_metadata: ViewAndMutationMeta, + in_spec: pytree.TreeSpec, + out_spec: pytree.TreeSpec, + *, + user_args_flat: list[Tensor], + params_and_buffers_flat: list[Tensor], + param_names: list[str], + buffer_names: list[str], + trace_joint: bool, + num_user_fw_outs: Optional[int], + loss_index: Optional[int], +) -> GraphSignature: + # Retrieve graph input names + graph_input_names = _graph_input_names(fx_g) + # Retrieve graph output names + graph_output_names = _graph_output_names(fx_g) + + num_params_buffers = len(param_names) + len(buffer_names) + num_tokens = len(fw_metadata.tokens) + # We have enough restrictions on the graph (no de-duping, synthetic bases, etc), + # Such that # graph inps = # user inps + # params + # buffers + num_user_args = len(graph_input_names) - num_params_buffers - num_tokens + + if trace_joint: + assert num_user_fw_outs is not None + num_fw_outs = num_user_fw_outs + fw_metadata.num_mutated_inp_runtime_indices + backward_output_names = graph_output_names[num_fw_outs:] + + grad_index = itertools.count(0) + gradients_to_parameters = { + backward_output_names[next(grad_index)]: param_names[i] + for i, param in enumerate(params_and_buffers_flat) + if param.requires_grad + } + + gradients_to_user_inputs = { + backward_output_names[next(grad_index)]: graph_input_names[ + i + len(params_and_buffers_flat) + ] + for i, user_input in enumerate(user_args_flat) + if user_input.requires_grad + } + + assert len(gradients_to_parameters) + len(gradients_to_user_inputs) == len( + backward_output_names + ) + + # Check that we have fully accounted for all graph outputs + backward_signature = BackwardSignature( + gradients_to_parameters, + gradients_to_user_inputs, + graph_output_names[loss_index], + ) + else: + backward_signature = None + num_user_fw_outs = ( + len(graph_output_names) + - fw_metadata.num_mutated_inp_runtime_indices + - num_tokens + ) + + return GraphSignature.from_tracing_metadata( + in_spec=in_spec, + out_spec=out_spec, + graph_input_names=graph_input_names, + graph_output_names=graph_output_names, + view_mutation_metadata=fw_metadata, + named_parameters=param_names, + named_buffers=buffer_names, + num_user_inputs=num_user_args, + num_user_outputs=num_user_fw_outs, + loss_index=loss_index, + backward_signature=backward_signature, + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..91806726fff7536b0b7dd0ba25ab40fa92951823 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py @@ -0,0 +1,1325 @@ +# mypy: allow-untyped-defs +""" +Functions in this module do most of the "work" of AOTAutograd. +An aot_dispatch_* function: +- Takes in the input flat_fn, flat_args, and some metadata +- Runs a set of pre compile wrappers (e.g. argument deduping) +- Runs the actual compiler +- Wraps the returned callable in a set of post compile wrappers +- Returns the wrapped callable and metadata. +""" + +import copy +import dataclasses +import itertools +import logging +import operator +import time +import traceback +from collections import defaultdict +from contextlib import nullcontext +from typing import Any, Callable, Optional, TYPE_CHECKING + +import torch +import torch.utils.dlpack +from torch import Tensor +from torch._dynamo.utils import detect_fake_mode, lazy_format_graph_code +from torch._guards import CompileContext, TracingContext +from torch._logging import getArtifactLogger, trace_structured +from torch._subclasses import FakeTensor +from torch._subclasses.meta_utils import is_sparse_any +from torch.fx.experimental._backward_state import BackwardState +from torch.fx.experimental.proxy_tensor import is_sym_node +from torch.fx.experimental.symbolic_shapes import fx_placeholder_vals +from torch.fx.graph_module import GraphModule +from torch.fx.passes._tensorify_python_scalars import tensorify_python_scalars +from torch.multiprocessing.reductions import StorageWeakRef +from torchgen.utils import dataclass_repr + +from .. import config +from .autograd_cache import ( + AOTAutogradCache, + AOTAutogradCacheEntry, + CompiledBackward, + CompiledForward, + should_use_remote_autograd_cache, +) +from .dispatch_and_compile_graph import ( + aot_dispatch_autograd_graph, + aot_dispatch_base_graph, +) +from .logging_utils import track_graph_compiling +from .runtime_wrappers import ( + AOTDedupeWrapper, + AOTDispatchAutograd, + AOTDispatchSubclassWrapper, + AOTSyntheticBaseWrapper, + AutogradLazyBackwardCompileInfo, + CompilerWrapper, + DebugAssertWrapper, + EffectTokensWrapper, + FakifiedOutWrapper, + FunctionalizedRngRuntimeWrapper, + make_runtime_safe, + post_compile, + pre_compile, + RuntimeWrapper, +) +from .schemas import AOTConfig, MutationType, ViewAndMutationMeta +from .subclass_utils import compute_inner_mutated_inp_indices_from_subclass_meta +from .utils import ( + _get_symint_hints, + contain_metadata_mutation_ops, + get_cuda_generator_meta_val, + make_boxed_func, + strict_zip, + unlift_tokens, +) + + +if TYPE_CHECKING: + from collections.abc import Sequence + +zip = strict_zip + +log = logging.getLogger(__name__) +aot_joint_log = getArtifactLogger(__name__, "aot_joint_graph") +aot_graphs_log = getArtifactLogger(__name__, "aot_graphs") + +aten = torch.ops.aten + +# Returns a Callable and a ViewAndMutationMeta. +# Currently, only export needs the ViewAndMutationMeta after this function. +DispatchReturn = tuple[Callable, ViewAndMutationMeta] + + +def _create_wrappers_for_dispatch(needs_autograd: bool) -> list[CompilerWrapper]: + """ + Wrappers that run on every dispatch function + """ + return [AOTDedupeWrapper(), AOTSyntheticBaseWrapper(trace_joint=needs_autograd)] + + +# Export's dispatching logic is unique in a few ways: it only needs the "graph" +# bits of aot_autograd, and doesn't need to do any specific wrapping. +def aot_dispatch_export( + flat_fn: Callable, + flat_args: list[Any], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, + needs_autograd: bool, +) -> DispatchReturn: + wrappers = _create_wrappers_for_dispatch(needs_autograd) + flat_fn, flat_args, fw_metadata = pre_compile( + wrappers, + flat_fn, + flat_args, + aot_config, + fw_metadata=fw_metadata, + ) + if needs_autograd and not aot_config.pre_dispatch: + graph, _, _ = aot_dispatch_autograd_graph( + flat_fn, flat_args, aot_config, fw_metadata=fw_metadata + ) + else: + graph, _, _ = aot_dispatch_base_graph( + flat_fn, flat_args, aot_config, fw_metadata=fw_metadata + ) + + # NB: the wrappers that run in pre_compile for export are + # either a no-op, because they're not needed, or will raise a runtime error, + # since they don't support export. + # We still run these wrappers to make sure that they're not needed pre compile, + # but we technically don't need to run them post compile at all here. + compiled_fn, fw_metadata = post_compile( + wrappers, graph, aot_config, runtime_metadata=fw_metadata + ) + + # Therefore, since no wrapperes run, we don't get back a callable - we get back the raw fx graph + # (either a joint or an inference-only graph) + assert isinstance(compiled_fn, torch.fx.GraphModule) + return compiled_fn, fw_metadata + + +def aot_dispatch_base( + flat_fn, + flat_args: list[Any], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, +) -> DispatchReturn: + """ + Handles functions that don't need autograd. Runs wrappers and compiles with fw_compiler. + """ + wrappers = _create_wrappers_for_dispatch(needs_autograd=False) + flat_fn, flat_args, fw_metadata = pre_compile( + wrappers, flat_fn, flat_args, aot_config, fw_metadata=fw_metadata + ) + fw_module, updated_flat_args, maybe_subclass_meta = aot_dispatch_base_graph( # type: ignore[misc] + flat_fn, flat_args, aot_config, fw_metadata=fw_metadata + ) + # Save the forward_graph_str right after aot_dispatch_base_graph, + # to save in the cache + aot_forward_graph_str = None + if aot_config.cache_info is not None: + aot_forward_graph_str = fw_module.print_readable( + print_output=False, include_stride=True, include_device=True + ) + + fakified_out_wrapper = FakifiedOutWrapper() + ( + fw_module, + updated_flat_args, + fw_metadata, + ) = fakified_out_wrapper.pre_compile( + fw_module, updated_flat_args, aot_config, fw_metadata=fw_metadata + ) + functionalized_rng_wrapper = FunctionalizedRngRuntimeWrapper() + ( + fw_module, + updated_flat_args, + fw_metadata, + ) = functionalized_rng_wrapper.pre_compile( + fw_module, updated_flat_args, aot_config, fw_metadata=fw_metadata + ) + + if aot_config.enable_log: + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "torch._functorch.config", + "encoding": "string", + }, + payload_fn=lambda: torch._functorch.config.get_config_copy(), + ) + + disable_amp = torch._C._is_any_autocast_enabled() + context = torch._C._DisableAutocast if disable_amp else nullcontext + + with context(), track_graph_compiling(aot_config, "inference"): + compiler = ( + aot_config.inference_compiler + if aot_config.inference_compiler is not None + else aot_config.fw_compiler + ) + + if tracing_context := torch._guards.TracingContext.try_get(): + tracing_context.fw_metadata = ( + fw_metadata + if maybe_subclass_meta is None + else maybe_subclass_meta.fw_metadata + ) + + with TracingContext.report_output_strides() as fwd_output_strides: + fake_mode = detect_fake_mode() + if fake_mode is not None and fake_mode.shape_env is not None: + assert isinstance(fw_module, GraphModule) + tensorify_python_scalars(fw_module, fake_mode.shape_env, fake_mode) + compiled_fw = compiler(fw_module, updated_flat_args) + + if fakified_out_wrapper.needs_post_compile: + fakified_out_wrapper.set_fwd_output_strides(fwd_output_strides) + + make_runtime_safe(fw_metadata, maybe_subclass_meta) + + # However, RuntimeWrapper does not expect the rng offsets in the + # output. So, we have to create another wrapper and take out the offset. As + # a result, we have to account for not boxed_call compilers as well. + if not hasattr(compiled_fw, "_boxed_call"): + compiled_fw = make_boxed_func(compiled_fw) + + # Create a wrapper to set up the rng functionalize and fakified out bits + compiled_fw = functionalized_rng_wrapper.post_compile( + compiled_fw, aot_config, runtime_metadata=fw_metadata + ) + cache_info = aot_config.cache_info + if cache_info is not None: + if fw_key := getattr(compiled_fw, "_fx_graph_cache_key", None): + time_taken_ns = time.time_ns() - cache_info.start_time_ns + entry = AOTAutogradCacheEntry( + compiled_fw=CompiledForward(fw_key), + compiled_bw=None, + aot_joint_graph_str=None, + aot_forward_graph_str=aot_forward_graph_str, + aot_backward_graph_str=None, + runtime_metadata=fw_metadata, + dispatch_wrappers=wrappers, + maybe_subclass_meta=maybe_subclass_meta, + num_fw_outs_saved_for_bw=None, + indices_of_inps_to_detach=[], + forward_time_taken_ns=time_taken_ns, + backward_time_taken_ns=0, + ) + AOTAutogradCache.save( + cache_info.cache_key, entry, remote=should_use_remote_autograd_cache() + ) + + compiled_fw = fakified_out_wrapper.post_compile( + compiled_fw, + aot_config, + runtime_metadata=fw_metadata, + ) + + compiled_fw = EffectTokensWrapper().post_compile( + compiled_fw, + aot_config, + runtime_metadata=fw_metadata, + ) + + # Why do we need to pass in num_fw_outs_saved_for_bw? + # See Note: [Partitioner handling for Subclasses, Part 2] + compiled_fw = AOTDispatchSubclassWrapper( + trace_joint=False, + # TODO: once we use pre_compile this will be flat_fn at the top of this function + fw_only=None, + maybe_subclass_meta=maybe_subclass_meta, + num_fw_outs_saved_for_bw=None, + ).post_compile( + compiled_fw, + aot_config, # not used + runtime_metadata=fw_metadata, + ) + + if not hasattr(compiled_fw, "_boxed_call"): + compiled_fw = make_boxed_func(compiled_fw) + + compiled_fn = RuntimeWrapper( + indices_of_inps_to_detach=[], + trace_joint=False, + disable_amp=disable_amp, + ).post_compile( + compiled_fw, + aot_config, + runtime_metadata=fw_metadata, + ) + + compiled_fn = post_compile( + wrappers, compiled_fn, aot_config, runtime_metadata=fw_metadata + ) + return compiled_fn + + +def collect_fw_donated_buffer_idxs( + fw_ins: list[Optional[FakeTensor]], + user_fw_outs: list[Optional[FakeTensor]], + bw_outs: list[Optional[FakeTensor]], + saved_tensors: list[FakeTensor], +) -> list[int]: + """ + Checks if the saved tensors are donated buffers, which means a saved tensor is not + an alias of any tensors in fw_ins, user_fw_outs, and bw_outs. + """ + + storage_refs = set() + for t in itertools.chain(fw_ins, user_fw_outs, bw_outs): + # Only access storage if a tensor has storage (not sparse) + if t is not None and isinstance(t, FakeTensor) and not is_sparse_any(t): + storage_refs.add(StorageWeakRef(t.untyped_storage())) + + num_saved_tensor = len(saved_tensors) + donated_buffer_idxs = [] + for i in range(num_saved_tensor): + t = saved_tensors[i] + if ( + t is not None + and not is_sparse_any(t) + and StorageWeakRef(t.untyped_storage()) not in storage_refs + ): + donated_buffer_idxs.append(i) + + return donated_buffer_idxs + + +def collect_bw_donated_buffer_idxs( + fw_module: torch.fx.GraphModule, + bw_module: torch.fx.GraphModule, + fw_metadata: ViewAndMutationMeta, +) -> list[int]: + """ + Collects backward donated buffer indexes from fw_module and bw_module. + """ + + # [Note: Metadata mutation in proxy tracing] + # node.meta["val"] is a snapshot of the tensor value when tracing a graph, + # instead of the final state after the graph has run. node.meta["val"] is + # not updated even if later there is a metadata mutation op. + # See: https://github.com/pytorch/pytorch/pull/141308#issuecomment-2495798947 + # + # Currently, metadata mutation op happens only for sacrificial parameter + # specifically the `set_` op. This motivates banning metadata mutation from + # proxy tracing. + # + # Since node.meta["val"] is used to detect donated buffer, we return an empty + # list if there exists metadata mutation op. + if contain_metadata_mutation_ops(fw_module) or contain_metadata_mutation_ops( + bw_module + ): + return [] + + fw_ins = fw_module.graph.find_nodes(op="placeholder") + bw_outs = next(reversed(bw_module.graph.find_nodes(op="output"))).args[0] + fw_outs = next(reversed(fw_module.graph.find_nodes(op="output"))).args[0] + + fw_ins = [ + n.meta["val"] if (hasattr(n, "meta") and "val" in n.meta) else None + for n in fw_ins + ] + fw_outs = [ + n.meta["val"] if (hasattr(n, "meta") and "val" in n.meta) else None + for n in fw_outs + ] + bw_outs = [ + n.meta["val"] if (hasattr(n, "meta") and "val" in n.meta) else None + for n in bw_outs + ] + + user_fw_outs = fw_outs[: fw_metadata.num_forward] + saved_tensors = fw_outs[fw_metadata.tensors_saved_for_backwards_slice] + + fw_donated_buffer = collect_fw_donated_buffer_idxs( + fw_ins, + user_fw_outs, + bw_outs, + saved_tensors, + ) + + assert fw_metadata.num_symints_saved_for_bw is not None + return [fw_metadata.num_symints_saved_for_bw + i for i in fw_donated_buffer] + + +@dataclasses.dataclass +class InvokeSubgraphHopGraphs: + """ + A data structure to hold all the information needed to partition the + `joint_hop_gm` and joint graph and the restitch the `new_fw_hop_gm` and + `new_bw_hop_gm` into the bigger `joint_gm`. + """ + + # To avoid re-partitioning subgraphs + partitioning_done: bool = False + old_num_fw_outputs: Optional[int] = None + old_num_fw_inputs: Optional[int] = None + + new_fw_hop_gm: Optional[torch.fx.GraphModule] = None + new_bw_hop_gm: Optional[torch.fx.GraphModule] = None + new_num_sym_nodes: Optional[int] = None + new_num_saved_nodes: Optional[int] = None + + +def run_joint_graph_passes_on_hops( + joint_gm: torch.fx.GraphModule, + joint_inputs: Any, + aot_config: AOTConfig, +) -> torch.fx.GraphModule: + """ + This pass runs the joint graph passes on the HOP graph. In torch.compile, we + typically have many passes which work on the joint graph and then end with a + partitioner. + + + The partitioner part is quite mechanical to handle. HOP have their own + forward and backward graph. The process can be broken into following steps + + 1) Get a `joint_hop_gm` from the `fw_hop_gm` and `bw_hop_gm` + 2) Run joint graph passes on the `joint_hop_gm` to get `new_fw_hop_gm` and `new_bw_hop_gm` + 3) Stitch the `new_fw_hop_gm` and `new_bw_hop_gm` back into the `joint_gm`. + + The terminology used in the code is + `joint_graph/joint_gm` : Refers to the main graph. This may contain many HOPs which have their own `hop_graph` + `fw_hop_graph/fw_hop_gm` : Refers to the forward graph associated with a HOP. + `bw_hop_graph/bw_hop_gm` : Refers to the backward graph associated with a HOP. + `joint_hop_graph/joint_hop_gm` : Refers to the subgraph associated with the HOP like invoke_subgraph. + `new_fw_hop_graph/new_fw_hop_gm` : Refers to the forward graph after partitioning is applied to `joint_hop_gm`. + `new_bw_hop_graph/new_bw_hop_gm` : Refers to the backward graph after partitioning is applied to `joint_hop_gm`. + + NB: This pass works for invoke_subgraph today because we took extra care in + the Autograd.Dispatch key of invoke_subgraph to vastly simplify Step 1. + """ + from torch._higher_order_ops import invoke_subgraph + + def num_outputs(mod): + return len(mod.graph.find_nodes(op="output")[0].args[0]) + + def num_inputs(mod): + return len(mod.graph.find_nodes(op="placeholder")) + + def prepare_for_partitioner(mod, num_primals, num_fw_outputs): + # min-cut partitioner requires the placeholders to have primals and + # tangents string in the node.name. The signature of the joint graph is + # (*primals, *tangents) + + # We also have to update the output signature which is right now + # (*grads, *fw_outs) and we have to change to (*fw_outs, *grads) for the + # partitioner to work. + new_graph = torch.fx.Graph() + env = {} + + primals_counter = itertools.count(0) + tangents_counter = itertools.count(0) + + for idx, node in enumerate(mod.graph.nodes): + if node.op == "placeholder": + if idx < num_primals: + env[node] = new_graph.placeholder( + f"primals_{next(primals_counter)}" + ) + else: + env[node] = new_graph.placeholder( + f"tangents_{next(tangents_counter)}" + ) + env[node].meta = copy.copy(node.meta) + elif node.op == "output": + # Reverse the (*grads, *fw_outs) to (*fw_outs, *grads) + # The reason for having the reversed signature in the first + # place is to simplify step 3. + old_outputs = node.args[0] + new_outputs = ( + *old_outputs[-num_fw_outputs:], + *old_outputs[:-num_fw_outputs], + ) + new_outputs = [env[n] if n else None for n in new_outputs] + new_graph.output(tuple(new_outputs)) + else: + env[node] = new_graph.node_copy(node, lambda n: env[n]) + env[node].meta = copy.copy(node.meta) + + new_graph.lint() + + out = torch.fx.GraphModule(joint_gm, new_graph) + return out + + new_hop_graphs: dict[str, InvokeSubgraphHopGraphs] = defaultdict( + lambda: InvokeSubgraphHopGraphs() + ) + + # Step 1 - Get a `joint_hop_gm` from the `fw_hop_gm` and `bw_hop_gm` This is + # easy to do for `invoke_subgraph` HOP. During the Autograd dispatch key + # tracing, we have put the joint_hop_graph in the backward hop graph itself. + # So to recover the joint_hop_gm, we just have to look at the backward + # HOP graphs. + # So we will merge step 1 and step 2 in this next section + + # Save the fw and bwd hop nodes. We will later in-place modify the graph + # using these nodes. + fw_hop_nodes = [] + bw_hop_nodes = [] + + for node in joint_gm.graph.nodes: + if ( + node.op == "call_function" + and node.target is invoke_subgraph + and isinstance(node.args[1], str) + ): + identifier = ( + node.args[1].replace("___forward", "").replace("___backward", "") + ) + + # NB: This is done in a separate if else condition because we early + # return if the partitioning_done is True. + if node.args[1].startswith("___forward"): + fw_hop_nodes.append(node) + elif node.args[1].startswith("___backward"): + bw_hop_nodes.append(node) + + # If partitioning already done for this identifier, skip. This saves + # redundant joint graph passes for same subgraphs. + if new_hop_graphs[identifier].partitioning_done: + continue + + hop_gm = getattr(joint_gm, node.args[0].target) + assert isinstance(hop_gm, torch.fx.GraphModule) + + if node.args[1].startswith("___forward"): + # Collect some information from the forward hop graph + new_hop_graphs[identifier].old_num_fw_inputs = num_inputs(hop_gm) + new_hop_graphs[identifier].old_num_fw_outputs = num_outputs(hop_gm) + elif node.args[1].startswith("___backward"): + num_fw_inputs = new_hop_graphs[identifier].old_num_fw_inputs + assert num_fw_inputs is not None + num_fw_outputs = new_hop_graphs[identifier].old_num_fw_outputs + assert num_fw_outputs is not None + + # Step 1) - Get the `joint_hop_gm`. As mentioned earlier, the + # backward graph is the joint graph. + joint_hop_gm = hop_gm + + # Prepare the graph for the partitioner + joint_hop_gm = prepare_for_partitioner( + joint_hop_gm, num_fw_inputs, num_fw_outputs + ) + + # Step 2) and 3) - Run joint graph passes and partitioner + new_fw_hop_gm, new_bw_hop_gm = aot_config.partition_fn( + joint_hop_gm, [], num_fwd_outputs=num_fw_outputs + ) + + # Save the new forward and backward graph modules + new_hop_graphs[identifier].new_fw_hop_gm = new_fw_hop_gm + new_hop_graphs[identifier].new_bw_hop_gm = new_bw_hop_gm + + # Save the number of symints and saved tensors + new_fw_out_nodes = new_fw_hop_gm.graph.find_nodes(op="output")[0].args[ + 0 + ] + extra_outputs = new_fw_out_nodes[num_fw_outputs:] + symint_outputs = [n for n in extra_outputs if is_sym_node(n)] + + new_hop_graphs[identifier].new_num_sym_nodes = len(symint_outputs) + new_hop_graphs[identifier].new_num_saved_nodes = len( + extra_outputs + ) - len(symint_outputs) + + new_hop_graphs[identifier].partitioning_done = True + + if not new_hop_graphs: + return joint_gm + + # Step 3) Restitch the new fw and bw graphs back into the main graph. + # + # This is a very mechanical process. There are a quite a few pieces that we + # need to connect together to make it work. Lets try to understand the + # problem statement first. + # + # For the forward graph, the signature of the old_fw_hop_gm is + # inputs - (*primals) + # outputs - (*fw_outs) + # Now the signature of the new_fw_hop_gm is + # inputs - (*primals) -- This is same + # outputs - (*fw_outs, *saved_tensors) - This is different + # At a high level, this is an easy transformation, in the new graph we just + # have to replace the old_fw_hop_gm with the new_fw_hop_gm. Everything else + # falls into place, because the input signature (i.e. args) is same. And + # even though output signature is different, fw_outs are still at the same + # indexes as before. So the forward of the `joint_gm` works nicely. + # + # Now, lets look at the backward hop graph. Old signature + # inputs - (*primals, *tangents) + # outputs - (*grad_outs, *fw_outs) + # New signature + # inputs - (*saved_tensors, *tangents) -- Different + # outputs - (*grad_outs) -- Different + # Here both input and output signature change. The output signature handling + # is quite easy because the grads_out are sitting at the right place, so we + # dont have to do anything. + # + # For the input signature, we have to collect the saved tensors from the + # corresponding forward graph output. We collect all saved_tensors when we + # see the forward graph, and save it into a map and then later use it during + # the backward. + + # The stack of fw_nodes for invoke_subgraph HOP. There is an implicit + # assumption about the graph structure, i.e., if we have hop1, hop2, hop3, + # ... in the forward part of the joint graph, we will have .., hop3, hop2, + # hop1 order for the backward. This structure allows us to just use a stack + # to collect all the information that we need to pass from the forward hop + # node to the corresponding backward node. + old_fw_hop_nodes_stack = [] + # Collect the saved tensor nodes that we need to pass as inputs to the + # backward hop. + fw_node_to_saved_tensors_map = {} + + already_added_new_hop_mods = set() + + def add_new_hop_gm(new_subgraph_mod, name): + new_subgraph_attr_name = f"{name}_post_graph" + if new_subgraph_attr_name in already_added_new_hop_mods: + return new_subgraph_attr_name + + joint_gm.register_module(new_subgraph_attr_name, new_subgraph_mod) + already_added_new_hop_mods.add(new_subgraph_attr_name) + return new_subgraph_attr_name + + def propagate_meta_info(new_hop_gm, new_call_function_node, old_call_function_node): + # Copy all the fields from the old call_function node. And then override + # the `val` meta field with the outputs of new_hop_gm. + new_call_function_node.meta = copy.copy(old_call_function_node.meta) + + output = new_hop_gm.graph.find_nodes(op="output")[0] + out_example_vals = [n.meta["val"] if n else None for n in output.args[0]] + new_call_function_node.meta["val"] = tuple(out_example_vals) + + for fw_node in fw_hop_nodes: + # Insert the new_fw_hop_gm. This is straightforward. Get the + # new_fw_hop_gm, insert the hop_gm as a get_attr fw_node, and then + # add a call_function fw_node. Additionally, also use getitem + # call_functions to collect the saved_tensor nodes + + identifier = ( + fw_node.args[1].replace("___forward", "").replace("___backward", "") + ) + new_fw_hop_gm = new_hop_graphs[identifier].new_fw_hop_gm + assert new_fw_hop_gm is not None + + old_num_fw_outputs = new_hop_graphs[identifier].old_num_fw_outputs + new_num_sym_nodes = new_hop_graphs[identifier].new_num_sym_nodes + new_num_saved_nodes = new_hop_graphs[identifier].new_num_saved_nodes + assert old_num_fw_outputs is not None + assert new_num_sym_nodes is not None + assert new_num_saved_nodes is not None + total_outputs = old_num_fw_outputs + new_num_saved_nodes + new_num_sym_nodes + + extra_fw_outputs = [] + + # Insert the new_fw_hop_gm into the joint_gm + with joint_gm.graph.inserting_after(fw_node): + new_fw_mod_attr_name = add_new_hop_gm(new_fw_hop_gm, fw_node.args[1]) + new_fw_mod_attr = joint_gm.graph.get_attr(new_fw_mod_attr_name) + + # new_hop_fw_gm output signature is (*fw_outs, *saved_tensors) + with joint_gm.graph.inserting_after(new_fw_mod_attr): + new_fw_node = joint_gm.graph.call_function( + the_function=invoke_subgraph, + args=( + new_fw_mod_attr, + new_fw_mod_attr_name, + fw_node.args[2], + ), + ) + propagate_meta_info(new_fw_hop_gm, new_fw_node, fw_node) + + # old_num_fw_outputs = (*fw_outs) + # new_num_fw_outputs = (*fw_outs, *saved_tensors, *sym_nodes) + with joint_gm.graph.inserting_after(new_fw_node): + for fw_out_idx in range(old_num_fw_outputs, total_outputs): + saved_tensor_node = joint_gm.graph.call_function( + the_function=operator.getitem, args=(new_fw_node, fw_out_idx) + ) + saved_tensor_node.meta = copy.copy(new_fw_node.meta) + saved_tensor_node.meta["val"] = new_fw_node.meta["val"][fw_out_idx] + extra_fw_outputs.append(saved_tensor_node) + + fw_node.replace_all_uses_with(new_fw_node) + joint_gm.graph.erase_node(fw_node) + + # Save the saved_tensors info for the fw_node. This will be used + # to form the inputs for the backward hop. + old_fw_hop_nodes_stack.append(fw_node) + fw_node_to_saved_tensors_map[fw_node] = (identifier, extra_fw_outputs) + + for bw_node in bw_hop_nodes: + # Get the saved_tensors from the forward graph and find the new + # tangents, and replace the old bw hop with the new bw hop. + identifier = ( + bw_node.args[1].replace("___forward", "").replace("___backward", "") + ) + new_bw_hop_gm = new_hop_graphs[identifier].new_bw_hop_gm + assert new_bw_hop_gm is not None + + # Prepare the operands for the bwd graph + # Old bw graph signature : (*primals, *tangents) + # New signature will be : (*sym_nodes, *saved_tensors, *tangents) + # We have already collected the saved_tensors in the forward hop processing. + + assert len(old_fw_hop_nodes_stack) + fw_hop_node = old_fw_hop_nodes_stack.pop() + ( + fw_hop_node_identifier, + extra_fw_outputs, + ) = fw_node_to_saved_tensors_map[fw_hop_node] + assert fw_hop_node_identifier == identifier + + # extra_fw_outputs are in the order (*saved_nodes, *sym_nodes). + # Partitioner has this quirk where the backward wants sym_nodes + # first. So extract the sym and saved nodes. + num_sym_nodes = new_hop_graphs[fw_hop_node_identifier].new_num_sym_nodes + num_saved_nodes = new_hop_graphs[fw_hop_node_identifier].new_num_saved_nodes + assert num_sym_nodes is not None + assert num_saved_nodes is not None + saved_tensor_nodes = extra_fw_outputs[:num_saved_nodes] + sym_nodes = extra_fw_outputs[num_saved_nodes:] + + num_primals = new_hop_graphs[identifier].old_num_fw_inputs + assert num_primals is not None + tangents = list(bw_node.args[2][num_primals:]) + operands = sym_nodes + saved_tensor_nodes + tangents + + # Insert the new_bw_hop_gm into the joint_gm + with joint_gm.graph.inserting_after(bw_node): + new_bw_mod_attr_name = add_new_hop_gm(new_bw_hop_gm, bw_node.args[1]) + new_bw_mod_attr = joint_gm.graph.get_attr(new_bw_mod_attr_name) + + with joint_gm.graph.inserting_after(new_bw_mod_attr): + new_bw_node = joint_gm.graph.call_function( + the_function=invoke_subgraph, + args=( + new_bw_mod_attr, + new_bw_mod_attr_name, + tuple(operands), + ), + ) + propagate_meta_info(new_bw_hop_gm, new_bw_node, bw_node) + + bw_node.replace_all_uses_with(new_bw_node) + joint_gm.graph.erase_node(bw_node) + + joint_gm.graph.eliminate_dead_code() + joint_gm.graph.lint() + joint_gm.recompile() + return joint_gm + + +def aot_dispatch_autograd( + flat_fn, + flat_args: list[Any], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, +) -> DispatchReturn: + """ + Autograd logic. Generates a joint graph, partitions it, manipulates the input with various wrappers, + and returns a wrapped torch.autograd.Function with a forward and backward. + """ + wrappers = _create_wrappers_for_dispatch(needs_autograd=True) + flat_fn, flat_args, fw_metadata = pre_compile( + wrappers, + flat_fn, + flat_args, + aot_config, + fw_metadata=fw_metadata, + ) + + fw_metadata.deterministic = torch.are_deterministic_algorithms_enabled() + fx_g, joint_inputs, maybe_subclass_meta = aot_dispatch_autograd_graph( + flat_fn, flat_args, aot_config, fw_metadata=fw_metadata + ) + + # Copied from aot_dispatch_autograd_graph. + disable_amp = torch._C._is_any_autocast_enabled() + joint_graph_str = None + if aot_config.enable_log: + aot_joint_log.info( + "%s", + lazy_format_graph_code( + "Joint graph", + fx_g, + aot_config.aot_id, + include_stride=True, + include_device=True, + colored=True, + ), + ) + joint_graph_str = fx_g.print_readable( + print_output=False, include_stride=True, include_device=True + ) + trace_structured( + "aot_joint_graph", + payload_fn=lambda: joint_graph_str, + ) + + with torch.no_grad(): + inner_meta = ( + fw_metadata + if maybe_subclass_meta is None + else maybe_subclass_meta.fw_metadata + ) + with track_graph_compiling(aot_config, "joint"): + # See Note: [Partitioner handling for Subclasses, Part 1] + # See Note: [Recomputing subclass mutation handling] + mutated_inp_runtime_indices = ( + compute_inner_mutated_inp_indices_from_subclass_meta( + fw_metadata, inner_meta + ) + ) + num_tokens = len(fw_metadata.tokens) + num_mutated_inp_runtime_indices = len(mutated_inp_runtime_indices) + num_inner_fwd_outputs = ( + num_mutated_inp_runtime_indices + + inner_meta.num_outputs + + inner_meta.num_intermediate_bases + + inner_meta.num_outputs_rng_offset + + num_tokens # See Note [Side-Effectful Tokens in AOTAutograd] + ) + fake_mode = detect_fake_mode() + fx_g = run_joint_graph_passes_on_hops(fx_g, joint_inputs, aot_config) + + # TODO(anijain2305) - Add tensorify_python_scalars to the HOP graph passes. + if fake_mode is not None and fake_mode.shape_env is not None: + tensorify_python_scalars(fx_g, fake_mode.shape_env, fake_mode) + + fw_module, bw_module = aot_config.partition_fn( + fx_g, joint_inputs, num_fwd_outputs=num_inner_fwd_outputs + ) + rng_states = [ + n + for n in fw_module.graph.find_nodes(op="placeholder") + if "fwd_rng_state" in n.name + ] + fw_metadata.num_graphsafe_rng_states = len(rng_states) + if rng_states: + fw_metadata.graphsafe_rng_state_index = ( + rng_states[0].meta["val"].device.index + ) + + # See Note [Side-Effectful Tokens in AOTAutograd] + if config.unlift_effect_tokens and ( + num_tokens > 0 or fw_metadata.num_backward_tokens > 0 + ): + unlift_tokens(fw_module, fw_metadata, aot_config, bw_module) + + num_inner_fwd_outputs -= num_tokens + joint_inputs = ( + joint_inputs[0][num_tokens:], + joint_inputs[1], + ) + + fw_outs = next(iter(fw_module.graph.find_nodes(op="output"))).args[0] + # we only need to bookkeep the symints that are saved for bw, not any symints + # the user forward might have returned in its own output + fw_outs_saved_for_bw = fw_outs[num_inner_fwd_outputs:] + num_fw_outs_saved_for_bw = len(fw_outs_saved_for_bw) + symint_outs_saved_for_bw = [ + n for n in fw_outs_saved_for_bw if is_sym_node(n) + ] + fw_metadata.num_symints_saved_for_bw = len(symint_outs_saved_for_bw) + inner_meta.num_symints_saved_for_bw = len(symint_outs_saved_for_bw) + num_symints_saved_for_bw = len(symint_outs_saved_for_bw) + + if torch._functorch.config.donated_buffer: + fw_metadata.bw_donated_idxs = collect_bw_donated_buffer_idxs( + fw_module, + bw_module, + inner_meta, + ) + inner_meta.bw_donated_idxs = fw_metadata.bw_donated_idxs + + if aot_config.enable_log: + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "torch._functorch.config", + "encoding": "string", + }, + payload_fn=lambda: torch._functorch.config.get_config_copy(), + ) + aot_graphs_log.info( + "aot_config id: %s, fw_metadata=%s, inner_meta=%s", + str(aot_config.aot_id), + str(fw_metadata), + str(inner_meta), + ) + + # Note [Detaching inputs that never need gradients] + # See https://github.com/pytorch/pytorch/issues/97745 + # Suppose we have a function like this that we want to compile: + # + # def f(x, y): + # return torch.mul(x, y.detach()) + # + # What gradients should we compute for x and y? + # By default, AOTAutograd will compute a gradient for **every** input that requires gradients, + # and so we'll compute: + # x_grad_input = y + # y_grad_input = None + # Does this preserve the semantics of eager mode? + # Unfortunately, no. + # Doing the above will cause autograd to **continue** to backprop the autograd tape + # that was generated from constructing y. + # + # This is **different** from what would have happened in eager mode. + # In eager mode, if we backprop through the output of this function, autograd will only traverse + # the bit of the autograd tape corresponding to "x". + # In particular, if a user had previously backpropped through y's autograd tape, + # And then they try to backprop through the output of the above function, + # then we'll hit the dreaded "Trying to backward through the graph a second time" error. + # + # You might think: If autograd sees that a gradient is None, shouldn't it stop early, + # instead of continuing the backprop through the ancestors of that node in the graph? + # + # Autograd has two passes: + # (1) a first pass that traverses the autograd graph and figures out which nodes need to be executed + # (2) a second pass that actually goes ahead and executes each node when it becomes ready, + # propagating gradients + # By the time we're executing a node and we see that it produces a None, the set of nodes to execute + # is already locked-in. + # + # The fix: instead, we can recognize statically that the graph we're compiling will never contribute + # gradients to y, and prevent autograd from trying to traverse y's autograd tape at all. + # We can do this by manually detach'ing y before sending it through the `CompiledFunction`. + # + # Note that this solution is not bulletproof. + # It's possible to construct a case where eager may or may not have have tried to autograd through y, + # depending on the actual grad_outputs that were passed in during the backward. + # There is no easy fix for this: the simplest fix would be to run with `retain_graph=True`, + # allowing autograd to re-use the graph. + # + # An example of this case is: + # def f(x): + # return x.detach() * 2, x * 3 + # If we were to only backprop through outs[0], in eager, we would stop + # If we backward only on the first output, we shouldn't send a grad through x. + # But the custom autograd function doesn't know that: it will materialize zero grads for x * 3 + # and we will end up with a zero grad at x. + # If we later backprop through the second output, this will also require backprop'ing through x. + # Meaning we'll need to use `retain_graph=True` to be able to backprop through x the second time. + _indices_of_inps_to_detach: list[int] = [] + + # reversed() since we expect output at end of graph + bw_output = next(reversed(bw_module.graph.find_nodes(op="output"))) + bw_outs: Sequence[torch.fx.Node] = bw_output.args[0] # type: ignore[assignment] + + # TODO: we should apply the below "detach inputs if their gradients are statically known to be None" + # optimization even if we have subclass inputs/outputs (we do not handle this today). + # Computing which our our inputs get None gradients is a bit more complicated, + # if any of our inputs are subclasses. Why? + # (a) we need to make sure that we call .detach() on the input subclasses, since autograd sees subclasses. + # (b) The grad_outputs that we AOT computed in our backward graph are the desugared tensor tensors, + # so we need to figure out which subclass fw inputs they map to. + if maybe_subclass_meta is None: + num_backward_tokens: int = inner_meta.num_backward_tokens + assert ( + len(bw_outs) + == len(fw_metadata.input_info) + + inner_meta.num_outputs_rng_offset + + num_backward_tokens + ) + bw_outs_no_rng_no_tokens = bw_outs + if (inner_meta.num_outputs_rng_offset + num_backward_tokens) > 0: + bw_outs_no_rng_no_tokens = bw_outs[ + : -(inner_meta.num_outputs_rng_offset + num_backward_tokens) + ] + assert len(bw_outs_no_rng_no_tokens) == len(fw_metadata.input_info) + + for i, (bw_out) in enumerate(bw_outs_no_rng_no_tokens): + # If our input experiences a metadata mutation inside the graph (e.g. set_()), + # we *must* not detach, otherwise it will be the detach'd input that gets the metadata mutation + metadata_mutation_in_graph = ( + fw_metadata.input_info[i].mutation_type + == MutationType.MUTATED_IN_GRAPH + and fw_metadata.input_info[i].mutates_storage_metadata + ) + is_non_leaf = ( + fw_metadata.input_info[i].requires_grad + and not fw_metadata.input_info[i].is_leaf + ) + if bw_out is None and not metadata_mutation_in_graph and is_non_leaf: + _indices_of_inps_to_detach.append(i) + + fw_module_str = None + bw_module_str = None + if aot_config.enable_log: + aot_graphs_log.info( + "%s", + lazy_format_graph_code( + "Forward graph", + fw_module, + aot_config.aot_id, + include_stride=True, + include_device=True, + colored=True, + ), + ) + aot_graphs_log.info( + "%s", + lazy_format_graph_code( + "Backward graph", + bw_module, + aot_config.aot_id, + include_stride=True, + include_device=True, + colored=True, + ), + ) + fw_module_str = fw_module.print_readable( + print_output=False, include_stride=True, include_device=True + ) + bw_module_str = bw_module.print_readable( + print_output=False, include_stride=True, include_device=True + ) + + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "aot_forward_graph_fw_metadata", + "encoding": "string", + }, + payload_fn=lambda: dataclass_repr(fw_metadata), + ) + if maybe_subclass_meta is not None: + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "aot_forward_graph_fw_subclass_metadata", + "encoding": "string", + }, + payload_fn=lambda: dataclass_repr(maybe_subclass_meta), + ) + + trace_structured( + "aot_forward_graph", + payload_fn=lambda: fw_module_str, + ) + trace_structured( + "aot_backward_graph", + payload_fn=lambda: bw_module_str, + ) + + # AMP is already traced out in joint graph. we do not wish to reapply it accidentally + # in the compiler. + with track_graph_compiling(aot_config, "forward"), torch._C._DisableAutocast(): + # flat_args at this point might still be subclasses- + # make sure to pass the unwrapped fake tensors into the compiler! + adjusted_flat_args = joint_inputs[0] + + fakified_out_wrapper = FakifiedOutWrapper() + ( + fw_module, + adjusted_flat_args, + fw_metadata, + ) = fakified_out_wrapper.pre_compile( + fw_module, adjusted_flat_args, aot_config, fw_metadata=fw_metadata + ) + + functionalized_rng_wrapper = FunctionalizedRngRuntimeWrapper( + return_new_outs=False + ) + + if rng_states: + index = fw_metadata.graphsafe_rng_state_index + assert index is not None + rng_states = [ + get_cuda_generator_meta_val(index) + for _ in range(fw_metadata.num_graphsafe_rng_states) + ] + adjusted_flat_args.extend(rng_states) # type: ignore[arg-type] + + ( + fw_module, + adjusted_flat_args, + fw_metadata, + ) = functionalized_rng_wrapper.pre_compile( + fw_module, adjusted_flat_args, aot_config, fw_metadata=fw_metadata + ) + if tracing_context := torch._guards.TracingContext.try_get(): + tracing_context.fw_metadata = inner_meta + + with TracingContext.report_output_strides() as fwd_output_strides: + compiled_fw_func = aot_config.fw_compiler(fw_module, adjusted_flat_args) + + if not hasattr(compiled_fw_func, "_boxed_call"): + compiled_fw_func = make_boxed_func(compiled_fw_func) + + if fakified_out_wrapper.needs_post_compile: + fakified_out_wrapper.set_fwd_output_strides(fwd_output_strides) + + compiled_fw_func = EffectTokensWrapper().post_compile( + compiled_fw_func, + aot_config, + runtime_metadata=fw_metadata, + ) + + compiled_fw_func = AOTDispatchSubclassWrapper( + fw_only=None, + trace_joint=False, + maybe_subclass_meta=maybe_subclass_meta, + num_fw_outs_saved_for_bw=num_fw_outs_saved_for_bw, + ).post_compile( + compiled_fw_func, + aot_config, # not used + runtime_metadata=fw_metadata, + ) + + compiled_fw_func = functionalized_rng_wrapper.post_compile( + compiled_fw_func, aot_config, runtime_metadata=fw_metadata + ) + compiled_fw_func = fakified_out_wrapper.post_compile( + compiled_fw_func, + aot_config, + runtime_metadata=fw_metadata, + ) + + # NB: It's important to compile backwards ahead of time, as this may + # add extra guards which we need to apply to the Dynamo cache at + # forwards + with track_graph_compiling(aot_config, "backward"), torch._C._DisableAutocast(): + placeholder_list = fx_placeholder_vals(bw_module) + + forward_saved_for_backwards_strides = None + if fwd_output_strides is not None: + forward_saved_for_backwards_strides = fwd_output_strides[ + inner_meta.tensors_saved_for_backwards_slice + ] + + # saved activations can have different stride to eager if + # the compiler does layout optimization. We should restride the + # tensor passed in for compiling the backward graph using the + # saved tensor's stride. + for i in range(len(placeholder_list)): + ph_arg = placeholder_list[i] + if not isinstance(ph_arg, torch.Tensor): + continue + + if forward_saved_for_backwards_strides is None: + continue + + real_stride = None + # Per all_args calling convention + j = i - num_symints_saved_for_bw + if 0 <= j < len(forward_saved_for_backwards_strides): + real_stride = forward_saved_for_backwards_strides[j] + if real_stride is None: + continue + + # Comparing ph_arg.stride() with real_stride directly may + # cause dynamic dimensions in ph_arg being specialized to static + # value. Using the hints to avoid that. + if _get_symint_hints(ph_arg.stride()) != real_stride: + # Note that here we use the stride of the real tensor to + # restride a FakeTensor. This does not cause trouble + # for dynamic shape since this code path only get + # executed if layout optimization is enabled. And we + # disable layout optimization for dynamic shape right + # now. + # + # A solution that decide stride order based on real + # tensor's stride and then apply that stride order to + # the FakeTensor does not work smoothly since some + # tensor's layout is not 'dense'. E.g. mixnet_l has a + # tensor with size [8, 64, 112, 112] and strides + # (2408448, 1, 21504, 192). The solution mentioned will + # decide a stride of (802816, 1, 7168, 64) for this + # tensor which is wrong. + placeholder_list[i] = ph_arg.as_strided(ph_arg.size(), real_stride) + + compiled_bw_func = None + if num_symints_saved_for_bw > 0: + try: + compiled_bw_func = aot_config.bw_compiler( + bw_module, placeholder_list + ) + except Exception as e: + exc = e + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "eager_compile_backwards_failure", + "encoding": "string", + }, + payload_fn=lambda: "\n".join( + traceback.format_exception( + type(exc), exc, exc.__traceback__ + ) + ), + ) + log.warning( + "failed to eagerly compile backwards for dynamic, suppressing in case backwards not needed", + exc_info=True, + ) + # Compiled autograd will run the bw_module in the backward pass, + # so recompilation need happen anyway if the backward pass is ever + # called. + # + # The reason we do the GraphModule recompilation here is because + # the lazy recompilation will cause issue in the backward pass + # with compiled autograd. + # + # Do the _LazyGraphModule.force_recompile here rather than when + # bw_module is first generated by the partitioner because the bw_module.recompile + # may be called in some code path later and cause the _LazyGraphModule.forward + # becomes the lazy version again. One example is when dynamic shape is enabled + # upfront, the bw_compiler will be called above which can cause extra + # graph module recompilation on bw_module. + if torch._dynamo.compiled_autograd.in_compiled_autograd_region: + from torch.fx._lazy_graph_module import _LazyGraphModule + + _LazyGraphModule.force_recompile(bw_module) + + saved_context = TracingContext.try_get() + saved_compile_context = CompileContext.try_get() + + backward_state_indices = [ + idx for idx, x in enumerate(flat_args) if isinstance(x, BackwardState) + ] + assert len(backward_state_indices) <= 1 + + lazy_backward_info = AutogradLazyBackwardCompileInfo( + bw_module, + placeholder_list, + saved_context, + saved_compile_context, + ) + + make_runtime_safe(fw_metadata, maybe_subclass_meta) + + try_save_cache_entry: Optional[Callable] = None + + if aot_config.cache_info is not None: + forward_time_taken_ns = time.time_ns() - aot_config.cache_info.start_time_ns + + # NB: aot_config here is technically not needed as an argument: we could just + # close over aot_config.cache_info, since aot_config never changes. + # But closing over random variables is confusing IMO, so I'm leaving it. + def try_save_cache_entry( # noqa: F811 + compiled_bw_func, _fw_metadata, aot_config + ): + fw_key = getattr(compiled_fw_func, "_fx_graph_cache_key", None) + bw_key = getattr(compiled_bw_func, "_fx_graph_cache_key", None) + cache_info = aot_config.cache_info + if cache_info is not None and fw_key and bw_key: + assert forward_time_taken_ns is not None + # TODO: technically, AOTAutograd does a *little* bit of post processing work + # in the backward that isn't measured here. But it's small enough that it's not worth + # the complexity of threading a bunch of times through the code, so we + # use the compiled_bw_func's inductor compile time instead. + # It's possible this changes in the future, in which case we should + # update backward_time_taken_ns to be more inclusive + backward_time_taken_ns = getattr(compiled_bw_func, "_time_taken_ns", 0) + + aot_forward_graph_str: Optional[str] = fw_module_str + aot_backward_graph_str: Optional[str] = bw_module_str + aot_joint_graph_str: Optional[str] = joint_graph_str + entry = AOTAutogradCacheEntry( + CompiledForward(fw_key), + CompiledBackward( + bw_key, + backward_state_indices, + num_symints_saved_for_bw, + ), + aot_joint_graph_str, + aot_forward_graph_str, + aot_backward_graph_str, + _fw_metadata, + wrappers, + maybe_subclass_meta, + num_fw_outs_saved_for_bw, + _indices_of_inps_to_detach, + forward_time_taken_ns, + backward_time_taken_ns, + ) + remote = should_use_remote_autograd_cache() + AOTAutogradCache.save(cache_info.cache_key, entry, remote) + + if compiled_bw_func is not None: + # If we already compiled it we can just run it right now without waiting + try_save_cache_entry(compiled_bw_func, fw_metadata, aot_config) + try_save_cache_entry = None + + compiled_fn = AOTDispatchAutograd.post_compile( + compiled_fw_func, + compiled_bw_func, + maybe_subclass_meta, + num_symints_saved_for_bw, + backward_state_indices, + disable_amp, + _indices_of_inps_to_detach, + lazy_backward_info, + aot_config, + fw_metadata=fw_metadata, + try_save_cache_entry=try_save_cache_entry, + ) + + if config.debug_assert: + flat_requires_grad: list[Optional[bool]] = [ + a.requires_grad if isinstance(a, Tensor) else None for a in flat_args + ] + compiled_fn = DebugAssertWrapper( + flat_requires_grad=flat_requires_grad + ).post_compile(compiled_fn, aot_config, runtime_metadata=fw_metadata) + + compiled_fn = post_compile( + wrappers, + compiled_fn, + aot_config, + runtime_metadata=fw_metadata, + ) + return compiled_fn diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/logging_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/logging_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b059d6b62b2c5eae86daef3e6512129c65751c53 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/logging_utils.py @@ -0,0 +1,146 @@ +# mypy: allow-untyped-defs +""" +Contains utils for logging in AOTAutograd, including managing the names of the graphs under +compilation, capturing user-friendly tracebacks, and debug messages. +""" + +import collections +from contextlib import contextmanager + +import torch +import torch.fx.traceback as fx_traceback + + +# This is a list since looking forward, we can have this arbitrarily nested. +graph_being_compiled: list[str] = [] +# TODO: It would be nice to reset the numbering every time aot_id goes +# up, but this is annoying to do right now (because we don't know if +# an aot_id will come back from the dead), so right now this also happens +# to be a globally unique number too (at the cost of wobbling if you change +# how the graphs compile) +nth_graph: int = 0 +model_name: str = "model" + + +def set_model_name(name): + global model_name + model_name = name + + +def get_aot_compilation_context() -> tuple[list[str], str, int]: + return list(graph_being_compiled), model_name, nth_graph + + +def get_aot_graph_name() -> str: + """ + Returns the name of the graph being compiled. + """ + global model_name, graph_being_compiled, nth_graph + return f"{model_name}__{'_'.join(graph_being_compiled)}_{nth_graph}" + + +get_graph_being_compiled = get_aot_graph_name + + +@contextmanager +def track_graph_compiling(aot_config, graph_name): + global graph_being_compiled + # TODO: Don't shove the aot_id in here; set it in the context + graph_being_compiled = [f"{aot_config.aot_id}_{graph_name}"] + old_name = None + if tracing_context := torch._guards.TracingContext.try_get(): + old_name = tracing_context.aot_graph_name + tracing_context.aot_graph_name = graph_being_compiled + has_tracing_context = True + else: + has_tracing_context = False + try: + yield + finally: + global nth_graph + nth_graph += 1 + graph_being_compiled = [] + if has_tracing_context: + if tracing_context := torch._guards.TracingContext.try_get(): + tracing_context.aot_graph_name = old_name + + +# Set up hooks so that during backward the fx's stack_trace is properly set +callback_set = False + + +def setup_stacktrace_preservation_hooks(roots: list): + def iter_graph(roots): + if not roots: + return + seen = set() + q = collections.deque() # type: ignore[var-annotated] + for node in roots: + if node is not None and node not in seen: + seen.add(node) + q.append(node) + + while q: + node = q.popleft() + for fn, _idx in node.next_functions: + if fn in seen or fn is None: + continue + seen.add(fn) + q.append(fn) + + yield node + + def get_callback(saved_stack_): + def callback(): + global callback_set + fx_traceback.set_stack_trace(saved_stack_) + callback_set = False + + return callback + + def get_prehook(stack_, seq_nr): + def prehook(grad_output): + global callback_set + + if not callback_set: + torch.autograd.variable.Variable._execution_engine.queue_callback( # type: ignore[attr-defined] + get_callback(fx_traceback.format_stack()) + ) + callback_set = True + + fx_traceback.set_stack_trace(stack_) + fx_traceback.set_grad_fn_seq_nr(seq_nr) + + return prehook + + def get_posthook(special_stack_, seq_nr): + def posthook(grad_input, grad_output): + fx_traceback.set_stack_trace(special_stack_) + fx_traceback.reset_grad_fn_seq_nr() + + return posthook + + for node in iter_graph(roots): + forward_node_stack = node.metadata.get("traceback_", []) + node.register_prehook(get_prehook(forward_node_stack, node._sequence_nr())) + + special_stack = forward_node_stack.copy() + special_stack.append( + "Gradient addition node due to multiple use of tensor around:" + ) + node.register_hook(get_posthook(special_stack, node._sequence_nr())) + + +def describe_input(i, aot_config): + if i < aot_config.num_params_buffers: + return f"parameter/buffer {i}" + else: + return f"input {i - aot_config.num_params_buffers}" + + +def format_guard_bug_msg(aot_config, expected): + return ( + f"At compilation time, graph {aot_config.aot_id} was compiled under the " + f"assumption that {expected}, but at runtime this was not the case. " + "This indicates a guard bug in AOTAutograd or Dynamo, please file a bug to PyTorch." + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..539c1a91052dbcd269ac43465ac4a93dd19b2420 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py @@ -0,0 +1,2326 @@ +# mypy: allow-untyped-defs +""" +This module defines runtime wrappers, which, based on previous analysis attempts to: +1. process the inputs and outputs +2. apply mutations +3. handle functionalized randomness +4. deduplicate inputs and consolidate views into their bases (see input_output_analysis) +""" +import builtins +import collections +import itertools +import pprint +from contextlib import nullcontext +from dataclasses import dataclass, field +from functools import wraps +from typing import Any, Callable, Optional, TYPE_CHECKING, Union + +import torch +import torch.utils.dlpack +from torch import Tensor +from torch._dynamo.utils import CompileEventLogger, dynamo_timed, get_metrics_context +from torch._guards import ( + compile_context, + CompileContext, + detect_fake_mode, + DuplicateInputs, + tracing, + TracingContext, +) +from torch._prims_common import CUDARngStateHelper +from torch._subclasses import FakeTensor +from torch.fx.experimental._backward_state import BackwardState +from torch.multiprocessing.reductions import StorageWeakRef +from torch.utils._python_dispatch import is_traceable_wrapper_subclass + +from .. import config +from .collect_metadata_analysis import run_functionalized_fw_and_collect_metadata +from .functional_utils import gen_alias_from_base +from .input_output_analysis import ( + compute_overlapping_inputs, + create_synthetic_base_metadata, + remove_dupe_metadata, +) +from .logging_utils import describe_input, format_guard_bug_msg, track_graph_compiling +from .schemas import ( + AOTConfig, + InputAliasInfo, + MutationType, + OutputType, + PlainTensorMeta, + SubclassCreationMeta, + SubclassMeta, + TensorAlias, + ViewAndMutationMeta, +) +from .subclass_utils import ( + requires_subclass_dispatch, + runtime_unwrap_tensor_subclasses, + wrap_tensor_subclasses, +) +from .traced_function_transforms import aot_dispatch_subclass +from .utils import ( + call_func_at_runtime_with_args, + make_boxed_func, + partial_flatten_asdict, + strict_zip, +) + + +if TYPE_CHECKING: + from collections.abc import Sequence + + +zip = strict_zip + + +class CompilerWrapper: + """ + A wrapper around the inputs and outputs to the compiler_fn. We separate these into two parts: + + 1. The prologue, which edits the input to the compiler_fn(flat_fn, flat_args, etc) + 2. The epilogue, which edits the outputs of the compiler_fn (compiled_fn, real arguments) + + Each wrapper below should be implemented as a CompilerWrapper, so that we can facilitate + caching on the compiled output, and re-wrapping the output via epilogues. + Extra metadata that is needed to compute pre or post compile can be passed in via attributes. + """ + + def pre_compile( + self, + flat_fn, + flat_args: list[Tensor], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, + ) -> tuple[Callable, list[Tensor], ViewAndMutationMeta]: + """ + Process the inputs to the compiler_fn. You can pass in extra metadata via kwargs. + Args: + flat_fn: The function to compile + flat_args: Metadata from example inputs of the function to compile + aot_config: AOTConfig passed in at compile time + fw_metadata: ViewAndMutationMeta generated from flat_fn and flat_args + """ + return flat_fn, flat_args, fw_metadata + + def post_compile(self, compiled_fn, aot_config, *, runtime_metadata) -> Callable: + """ + Given an output of the compiler, wrap it with information received from prologue. + Args: + compiled_fn: Callable after calling compiler_fn + aot_config: AOTConfig after calling prologue + runtime_metadata: ViewAndMutationMeta after calling all wrappers's pre_compile steps. + Example: + + def wrapped_compiled_fn(args): + # do something with args, aot_config, fw_metadata + return compiled_fn(args) + + return wrapped_compiled_fn + """ + return compiled_fn + + +# The wrapper created by this function handles all of the runtime aliasing and mutation "epilogue" logic +# that needs to run after the compiled function. +# +# This function accepts a trace_joint flag, indicating whether or not we're generating the runtime +# epilogue for a forward-only inference graph, or for an autograd.Function.apply function. +# This is because there are some minor differences in how we treat these cases at runtime: +# - resize_() is currently handled in the inference case, but not fully handled in the autograd case. +# - the autograd cases inserts TensorAlias wrapper objects for outputs that alias inputs +@dataclass +class RuntimeWrapper(CompilerWrapper): + indices_of_inps_to_detach: list[int] + trace_joint: bool + disable_amp: bool + + def post_compile( + self, + compiled_fn, + aot_config: AOTConfig, + *, + runtime_metadata: ViewAndMutationMeta, + ): + return _create_runtime_wrapper( + compiled_fn, + runtime_metadata=runtime_metadata, + indices_of_inps_to_detach=self.indices_of_inps_to_detach, + trace_joint=self.trace_joint, + keep_input_mutations=aot_config.keep_inference_input_mutations, + disable_amp=self.disable_amp, + ) + + +class NoopAliasHandler: + def __init__(self, info, runtime_metadata, trace_joint): + pass + + def __call__(self, orig_inputs, fw_outs, out): + return out + + +def _unwrap_tensoralias(x): + assert isinstance(x, TensorAlias) + return x.alias + + +def _identity(x): + return x + + +class AliasOfInputHandler: + def __init__(self, info, runtime_metadata, trace_joint): + self.base_idx = info.base_idx + self.unwrap_out = _unwrap_tensoralias if trace_joint else _identity + self.requires_grad = info.requires_grad + self.functional_tensor = info.functional_tensor + self.replay_views = config.view_replay_for_aliased_outputs + + def __call__(self, orig_inputs, fw_outs, out): + aliased_base_tensor = orig_inputs[self.base_idx] + return gen_alias_from_base( + aliased_base_tensor, + self.unwrap_out(out), + self.requires_grad, + self.functional_tensor, + replay_views=self.replay_views, + ) + + +class IsInputHandler: + def __init__(self, info, runtime_metadata, trace_joint): + self.base_idx = info.base_idx + self.unwrap_out = _unwrap_tensoralias if trace_joint else _identity + + def __call__(self, orig_inputs, fw_outs, out): + aliased_base_tensor = orig_inputs[self.base_idx] + return aliased_base_tensor + + +class AliasOfIntermediateHandler: + def __init__(self, info, runtime_metadata, trace_joint): + self._unwrap_aliased_base_tensor = _identity + if info.output_type in ( + OutputType.alias_of_intermediate, + OutputType.alias_of_intermediate_save_as_output, + ): + num_user_outputs = len(runtime_metadata.output_info) + self.base_idx = info.base_idx + num_user_outputs + else: + self.base_idx = info.base_idx + if self.base_idx in runtime_metadata.aliased_out_indices: + self._unwrap_aliased_base_tensor = _unwrap_tensoralias + + self.unwrap_out = _unwrap_tensoralias if trace_joint else _identity + self.requires_grad = info.requires_grad + self.functional_tensor = info.functional_tensor + self.replay_views = config.view_replay_for_aliased_outputs + + def __call__(self, orig_inputs, fw_outs, out): + aliased_base_tensor = fw_outs[self.base_idx] + return gen_alias_from_base( + self._unwrap_aliased_base_tensor(aliased_base_tensor), + self.unwrap_out(out), + self.requires_grad, + self.functional_tensor, + replay_views=self.replay_views, + ) + + +_HANDLER_MAP = { + OutputType.non_alias: NoopAliasHandler, + OutputType.unsafe_view_alias: NoopAliasHandler, + OutputType.custom_function_view: NoopAliasHandler, + OutputType.alias_of_input: AliasOfInputHandler, + OutputType.is_input: IsInputHandler, + OutputType.alias_of_intermediate: AliasOfIntermediateHandler, + OutputType.alias_of_intermediate_save_as_output: AliasOfIntermediateHandler, + OutputType.alias_of_intermediate_base_is_user_output: AliasOfIntermediateHandler, +} + + +def make_output_handler(info, runtime_metadata, trace_joint): + handler_type = _HANDLER_MAP[info.output_type] + return handler_type(info, runtime_metadata, trace_joint) + + +def _create_runtime_wrapper( + compiled_fn, + *, + runtime_metadata: ViewAndMutationMeta, + indices_of_inps_to_detach: list[int], + trace_joint: bool, + keep_input_mutations: bool, + disable_amp: bool, +): + if not hasattr(compiled_fn, "_boxed_call"): + compiled_fn = make_boxed_func(compiled_fn) + + # Note [Inputs needed in runtime epilogue after list clearing] + # In Python functions, you can't free the input arguments of a function within the scope of that function. A workaround is to + # wrap the input arguments in a list, and clear the list from within the function. + # Here, this is implemented as `call_func_at_runtime_with_args(..., steal_args=True)`. + # + # This is needed for Compiled Autograd since some of the inputs (activations) should be freed early. + # However, we cannot blindly clear the entire list, because AOTAutograd may need access to some of the graph inputs + # **after** the compiled function has finished running. There are two main cases: + # (1) Input mutations: If there are an input mutations that we must run outside of the graph, we need access to the input. + # (2) Output aliasing: Outputs that aliases graph inputs generally must be regenerated outside of the `autograd.Function`, + # and doing so requires us accessing the corresponding input after the compiled artifact has run. + epilogue_args_idx = [] + epilogue_args_idx.extend(runtime_metadata.mutated_inp_runtime_indices) + for info in runtime_metadata.output_info: + if ( + info.output_type == OutputType.alias_of_input + or info.output_type == OutputType.is_input + ): + assert isinstance(info.base_idx, int) + epilogue_args_idx.append(info.base_idx) + + if config.unlift_effect_tokens: + assert len(runtime_metadata.tokens) == 0 + + if runtime_metadata.num_outputs_aliased > 0: + output_handlers = tuple( + make_output_handler(info, runtime_metadata, trace_joint) + for info in runtime_metadata.output_info + ) + + def runtime_wrapper(args: list[Any]): + # stash a ref to each input tensor we plan to use after the compiled function + orig_inputs = {i: args[i] for i in epilogue_args_idx} + + if keep_input_mutations: + mutated_args = ( + args[i] + for i in runtime_metadata.mutated_graph_handled_indices_seen_by_autograd + ) + torch.autograd.graph.increment_version(mutated_args) + + if trace_joint: + args_ = list(args) + # See Note [Detaching inputs that never need gradients] + for idx in indices_of_inps_to_detach: + if isinstance(args_[idx], torch.Tensor): + args_[idx] = args_[idx].detach() + + # It's possible to have trace_joint inside user specified with no_grad() region, + # if there is a nested with enable_grad(), that forces some outputs to require gradients. + # Therefore, we unconditionally turn on enable_grad() for compiled_fn execution. + with torch.autograd._force_original_view_tracking( + True + ), torch.enable_grad(): + all_outs = call_func_at_runtime_with_args( + compiled_fn, args_, disable_amp=disable_amp, steal_args=True + ) + else: + # When we have an inference graph, we run with grad disabled. + # It's possible to get an inference graph with inputs that require grad, + # in which case we want to make sure autograd is disabled + # (since e.g., inductor will generate aten.addmm.out calls which autograd will complain on) + # NOTE: We use _set_grad_enabled directly to reduce runtime overhead + grad_enabled = torch.is_grad_enabled() + try: + if grad_enabled: + torch._C._set_grad_enabled(False) + all_outs = call_func_at_runtime_with_args( + compiled_fn, args, disable_amp=disable_amp, steal_args=True + ) + finally: + if grad_enabled: + torch._C._set_grad_enabled(True) + del args + + num_mutated_runtime_inps = runtime_metadata.num_mutated_inp_runtime_indices + num_intermediate_bases = runtime_metadata.num_intermediate_bases + + assert ( + len(all_outs) + == num_mutated_runtime_inps + + runtime_metadata.num_outputs + + num_intermediate_bases + ) + + # Step 3: After running the compiled fw, apply updates to mutated inputs + num_mutations_to_apply = runtime_metadata.num_mutated_inp_runtime_indices + if num_mutations_to_apply > 0: + updated_inputs = all_outs[:num_mutations_to_apply] + fw_outs = all_outs[num_mutations_to_apply:] + + for i, inpt_idx in enumerate(runtime_metadata.mutated_inp_runtime_indices): + meta = runtime_metadata.input_info[inpt_idx] + if not meta.mutates_data and not meta.mutates_metadata: + continue + original_inpt = orig_inputs[inpt_idx] + updated_inpt = updated_inputs[i] + if meta.mutates_storage_metadata: + # See Note [set_() Input Mutations in AOTAutograd] + # mutates_storage_metadata means our input saw a x.set_(y) call. + # What if x **also** saw a data and/or a metadata mutation? + # (1) If the [meta]data mutation occurred after the set_(), + # then there is no need to copy_() the data. + # When we perform x.set_(x_updated), we are guaranteed that + # x_updated already has the final version of the data/metadata + # (2) If a data mutation occurred before the set_(). + # This case seems very difficult to support. + # TODO: discuss on the PR and decide if we want to tr to + # either support it, or detect and ban it. + if trace_joint: + assert isinstance(updated_inpt, TensorAlias) + updated_inpt = updated_inpt.alias + with torch.no_grad(): + original_inpt.set_(updated_inpt) + continue + if meta.mutates_metadata and not meta.mutates_data: + if trace_joint: + assert isinstance(updated_inpt, TensorAlias) + updated_inpt = updated_inpt.alias + # We need to grab the size/stride/storage_offset from the compiled forward, + # and use that to mutate the metadata of the input + original_inpt.as_strided_( + updated_inpt.size(), + updated_inpt.stride(), + updated_inpt.storage_offset(), + ) + else: + if meta.mutates_data and meta.mutates_metadata: + original_inpt.as_strided_( + updated_inpt.size(), + updated_inpt.stride(), + updated_inpt.storage_offset(), + ) + else: + assert meta.mutates_data + if meta.is_leaf and original_inpt.requires_grad: + # We can hit this situation in this case: + # def f(x): + # x.detach().mul_(2) + # return x + 1 + # AOTAutograd will see a mutation in the above case, and try to + # apply a copy_() here, in the epilogue. + # But if x required gradients, and is a leaf, then autograd + # will yell at us for trying to mutate it. + # However, it's only possible to end up in this scenario (like the above) + # if all of the mutations to the leaf input were non-autograd-tracking mutations + # (aka mutations under no_grad(), or on detached views). + # In that case, we fully want to hide the mutation from autograd, so detaching is ok. + original_inpt.detach().copy_(updated_inpt) + else: + original_inpt.copy_(updated_inpt) + else: + fw_outs = all_outs + + # Step 4: Manually regenerate any outputs that are aliased to inputs, instead of + # compiling them. + if runtime_metadata.num_outputs_aliased > 0: + # The compiled forward also returned intermediate bases. We don't want to return them to the user. + expect_num_outputs = ( + len(output_handlers) + runtime_metadata.num_intermediate_bases + ) + assert len(fw_outs) == expect_num_outputs + ret_outs = [ + handler(orig_inputs, fw_outs, out) + for out, handler in builtins.zip(fw_outs, output_handlers) + ] + else: + ret_outs = fw_outs + + if runtime_metadata.dynamic_outputs: + for t, o in zip(ret_outs, runtime_metadata.output_info): + if o.dynamic_dims is None: + continue + if hasattr(t, "_dynamo_weak_dynamic_indices"): + t._dynamo_weak_dynamic_indices |= o.dynamic_dims + else: + t._dynamo_weak_dynamic_indices = o.dynamic_dims.copy() + if runtime_metadata.grad_enabled_mutation is not None: + torch._C._set_grad_enabled(runtime_metadata.grad_enabled_mutation) + return ret_outs + + return runtime_wrapper + + +@dataclass +class FunctionalizedRngRuntimeWrapper(CompilerWrapper): + # TODO: I would love to get rid of this argument, but it's + # Wrapped pretty tightly around our aot_dispatch_autograd logic. + # Specifically, tensors_saved_for_backwards_slice's value is both used for calculating indices + # for setting placeholder strides(which is done before runtime, before this wrapper runs) + # and for saving tensors for backward (which is done during runtime, after this wrapper runs) + # So in aot_dispatch_autograd, this wrapper can't edit the set of outs without making one + # of those two indices incorrect. + return_new_outs: bool = True + + def pre_compile( + self, + flat_fn, + flat_args, + aot_config, + *, + fw_metadata, + ) -> tuple[Callable, list[Tensor], ViewAndMutationMeta]: + if config.functionalize_rng_ops: + # Update example inputs for the fw_compiler + fake_mode = detect_fake_mode() + seed, offset = CUDARngStateHelper.get_torch_state_as_tuple(fake_mode) + flat_args.extend([seed, offset]) + # We are not clearing flat_args here because + # 1) There is a check in the debug compiler at the end + # 2) It does not matter as these are fake tensors + return flat_fn, flat_args, fw_metadata + + def post_compile( + self, + compiled_fn, + aot_config: AOTConfig, + *, + runtime_metadata: ViewAndMutationMeta, + ): + @wraps(compiled_fn) + def wrapper(runtime_args: list[Any]): + if runtime_metadata.is_rng_op_functionalized: + # Add the seed and offset to args + seed, offset = CUDARngStateHelper.get_torch_state_as_tuple() + runtime_args.extend([seed, offset]) + out = compiled_fn(runtime_args) + out = self._functionalized_rng_runtime_epilogue( + runtime_metadata, + out, + # TODO: this won't be right for the backward when we convert the call_compiled_backward to use the wrapper + runtime_metadata.num_forward_returns, + ) + return out + return compiled_fn(runtime_args) + + return wrapper + + # Calling convention: If we are running functionalized RNG, then outs consists + # of (user_outs, rng_offset) + def _functionalized_rng_runtime_epilogue( + self, + metadata: ViewAndMutationMeta, + outs, + offset_index, + ): + if metadata.is_rng_op_functionalized: + assert metadata.num_outputs_rng_offset == 1 + new_rng_offset = outs[offset_index] + CUDARngStateHelper.set_new_offset(new_rng_offset) + if self.return_new_outs: + user_outs = outs[:offset_index] + outs[offset_index + 1 :] + return user_outs + else: + return outs + + return outs + + +@dataclass +class FakifiedOutWrapper(CompilerWrapper): + out_metas: list[torch.Tensor] = field(default_factory=list) + # TracingContext.fwd_output_strides + # Generated from actually doing compile + # NB: an entry is None if it's not a Tensor + fwd_output_strides: Optional[list[Optional[list[int]]]] = None + needs_post_compile: bool = True + + def pre_compile( + self, + fw_module, # Must be fw_module from aot_dispatch_*_graph + flat_args, + aot_config, + *, + fw_metadata, + ) -> tuple[Callable, list[Tensor], ViewAndMutationMeta]: + tracing_context = torch._guards.TracingContext.try_get() + if tracing_context and tracing_context.fakify_first_call: + self.out_metas = [ + n.meta["val"] for n in (list(fw_module.graph.nodes)[-1].args[0]) + ] + else: + self.needs_post_compile = False + return fw_module, flat_args, fw_metadata + + def _compute_output_meta_with_inductor_strides(self): + out = self.out_metas + fwd_output_strides = self.fwd_output_strides + if not fwd_output_strides: + return out + + from torch.fx.experimental.symbolic_shapes import statically_known_true + + for i in range(len(out)): + if not isinstance(out[i], Tensor): + continue + strides = fwd_output_strides[i] + # fwd_output_strides is best effort by Inductor. When an output + # Tensor has unbacked SymInts, Inductor may sometimes be unable + # to compute what the output stride would be. If Inductor doesn't + # have any clear direction on the layout, we don't have to run + # as_strided. To repro without this, run: + # + # python test/distributed/test_dynamo_distributed.py + # TestFakeDistributedSingleProc.test_unbacked_symbol_splitting_no_binding + if strides is None: + continue + if all( + statically_known_true(s1 == s2) + for s1, s2 in zip(out[i].stride(), strides) + ): + continue + out[i] = out[i].as_strided(out[i].shape, strides) + return out + + # To be called post compile + def set_fwd_output_strides(self, fwd_output_strides): + self.fwd_output_strides = fwd_output_strides + + def post_compile( + self, + compiled_fn, + aot_config: AOTConfig, + *, + runtime_metadata: ViewAndMutationMeta, + ): + if self.needs_post_compile: + assert self.fwd_output_strides is not None + fakified_out = self._compute_output_meta_with_inductor_strides() + + @wraps(compiled_fn) + def wrapper(runtime_args): + nonlocal fakified_out + if fakified_out is not None: + out = fakified_out + fakified_out = None + return out + return compiled_fn(runtime_args) + + return wrapper + # If we don't need to fakify, we can just return the original compiled function + return compiled_fn + + +# This wrapper handles the AOTDispatch runtime logic for tensor subclasses. +# At runtime, we have a compiled function that knows how to operate on the domain of DenseTensor -> DenseTensor, +# But the user might have passed us some tensor subclass inputs (or expect some subclass tensor outputs). +# This function handles the wrapping and unwrapping of tensor subclasses at runtime. +@dataclass +class AOTDispatchSubclassWrapper(CompilerWrapper): + trace_joint: bool + fw_only: Optional[Callable] # Not cached, only used in pre_compile + maybe_subclass_meta: Optional[SubclassMeta] + num_fw_outs_saved_for_bw: Optional[int] + + def pre_compile( + self, + flat_fn, + flat_args: list[Tensor], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, + ): + (new_flat_fn, new_flat_args, subclass_meta) = aot_dispatch_subclass( + flat_fn, + flat_args, + is_joint_structure=self.trace_joint, + meta=fw_metadata, + fw_only=self.fw_only, # type: ignore[arg-type] + ) + self.maybe_subclass_meta = subclass_meta + return new_flat_fn, new_flat_args, fw_metadata + + def post_compile( + self, + compiled_fn, + _aot_config: AOTConfig, + *, + runtime_metadata: ViewAndMutationMeta, + ): + if self.maybe_subclass_meta is None: + return compiled_fn + + subclass_metas = runtime_metadata.subclass_fw_graph_out_meta + + @wraps(compiled_fn) + def inner_fn(args: list[Any]): + unwrapped_args = runtime_unwrap_tensor_subclasses( + args, + subclass_metas=runtime_metadata.subclass_inp_meta, + append_symints=True, + ) + args.clear() + # expectation: runtime_fn is a boxed fn + unwrapped_outs = compiled_fn(unwrapped_args) + wrapped_outs = wrap_tensor_subclasses( + unwrapped_outs, + subclass_metas=subclass_metas, + num_fw_outs_saved_for_bw=self.num_fw_outs_saved_for_bw, + is_runtime=True, + included_subclass_symints=True, + ) + return wrapped_outs + + # box it + inner_fn._boxed_call = True # type: ignore[attr-defined] + return inner_fn + + +@dataclass +class EffectTokensWrapper(CompilerWrapper): + def post_compile( + self, + compiled_fn, + _aot_config, + *, + runtime_metadata: ViewAndMutationMeta, + ): + num_tokens = len(runtime_metadata.tokens) + + @wraps(compiled_fn) + def inner_fn(args: list[Any]): + if num_tokens > 0: + # Pass in forward effect tokens (See Note [Side-Effectful Tokens in AOTAutograd]) + old_args = args + args = [*([None] * num_tokens), *args] + old_args.clear() + + outs = compiled_fn(args) + + # Inductor cache DummyModule can return None + if outs is None: + return None + # Toss out the effect tokens (See Note [Side-Effectful Tokens in AOTAutograd]) + return outs[num_tokens:] if num_tokens != 0 else outs + + # box it + inner_fn._boxed_call = True # type: ignore[attr-defined] + return inner_fn + + +# MOTIVATION: +# +# When tracing functions for future execution, one must be careful not to pass +# in the same input tensor multiple times (e.g., f(x, x), as this can result +# in graphs that are ONLY valid if you later pass a new tensor in exactly the +# same way (e.g., f(y, y)). (NB: we really mean duplicate; two distinct +# tensors that alias each other is a different situation that is covered by +# aot_dispatch_deduplicated_autograd). Here are two examples: +# +# (1) Suppose you have a function: +# +# def f(x, y): +# return x + y +# +# If you make_fx(f)(x, x), you will trace out: +# +# def f(x, y): +# return y + y +# +# Oops! +# +# (2) For most tensors x and y, you can compute f's gradient with respect to +# these to inputs by saying torch.autograd.grad(f(x, y), (x, y)). However, +# if x is y, you will trace out a program that gets incorrect gradients: +# +# >>> x = torch.randn(1, requires_grad=True) +# >>> torch.autograd.grad(x + x, (x, x)) +# (tensor([2.]), tensor([2.])) +# +# In other words, the gradient is double-counted. Deduplicating the arguments +# gives you an appropriate gradient: +# +# >>> y = torch.randn(1, requires_grad=True) +# >>> torch.autograd.grad(x + y, (x, y)) +# (tensor([1.]), tensor([1.])) +# +# HOW TO DEDUPLICATE: +# +# There are a few strategies, in order of preference: +# +# 1. For every duplicate argument to the function, detach it into +# a separate leaf tensor, so that it is no longer duplicated. +# +# PRO: The resulting compiled graph works for any configuration +# of duplicated arguments. +# +# CON: It does not (naively) work if you mutate the metadata of inputs: +# +# def f(x, y): +# x.transpose_(0, 1) +# y.transpose_(0, 2) +# +# x = torch.randn(2, 3, 4) +# f(x, x) +# +# The ordering of the transposes inside f dictates whether or not +# you get [4, 2, 3] or [3, 4, 2]. This means that you cannot precompute +# what metadata mutations should get applied to each input; you need to +# assume they aren't duplicates (what we do today) or preserve +# the original metadata mutations exactly in order, so that they work +# for any duplicate configuration. +# +# CON: It does not (naively) work if you mutate the data of inputs. +# In particular, leaf tensors that require grad cannot be mutated, +# this makes it impossible to differentiate with respect to the original +# base. +# +# 2. For every duplicate argument to the function, remove it, so it is +# no longer part of the "true" signature: +# +# PRO: Implemented naively, it still works for metadata/data mutation. +# +# CON: The resulting compiled graph is duplicate-specialized: it only +# works if future calls duplicate arguments in exactly the same way. +# Horribly, Dynamo doesn't guard on this at the moment. But even if +# it did, you could still end up recompiling a bunch of each duplicate. +# +# Our strategy is to do (1) if we can, and do (2) otherwise, erroring if +# Dynamo's guards are not enough. In practice, this seems to cover +# everything. +# +@dataclass +class AOTDedupeWrapper(CompilerWrapper): + keep_arg_mask: list[bool] = field(default_factory=list) + add_dupe_map: list[int] = field(default_factory=list) + old_input_metadata: list[InputAliasInfo] = field(default_factory=list) + needs_post_compile: bool = True + + # NB: Hot path, avoid set lookups here + # TODO: Can avoid the zip here too, probably + def remove_dupe_args(self, args): + return [t for t, keep in zip(args, self.keep_arg_mask) if keep] + + def add_dupe_args(self, args): + return [args[i] for i in self.add_dupe_map] + + def pre_compile( + self, + flat_fn, + flat_args: list[Tensor], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, + ) -> tuple[Callable, list[Tensor], ViewAndMutationMeta]: + # Use information about whether or not flat_fn mutates its arguments + # or not to handle dupe args + + # Strategy 1: For any input that is not mutated, we can leafify it if we + # need to remove a duplicate. + leaf_flat_args = [] + args_set = set() + ok = True + + for i, a in enumerate(flat_args): + if not isinstance(a, torch.Tensor): + leaf_flat_args.append(a) + elif a not in args_set: + args_set.add(a) + leaf_flat_args.append(a) + elif ( + not fw_metadata.input_info[i].mutates_data + and not fw_metadata.input_info[i].mutates_metadata + ): + leaf_flat_args.append(a.detach().requires_grad_(a.requires_grad)) + else: + ok = False + break + + if ok: + self.needs_post_compile = False + return flat_fn, leaf_flat_args, fw_metadata + + if requires_subclass_dispatch(leaf_flat_args, fw_metadata): + raise RuntimeError( + """\ + Encountered duplicate inputs that are mutated in the graph, but at least one input/output + to the graph is a tensor subclass. This is not supported today. You can try to + remove the aliasing yourself as a workaround, or otherwise file an issue on github.""" + ) + + # export path: ban duplicate inputs for now, add later if requested. + if aot_config.is_export: + raise RuntimeError( + f"""\ + Encountered duplicated inputs that are mutated in the graph you are trying to export. + This functionality is currently not supported. If needed, please file a github issue. + + fw_metadata={str(fw_metadata)} + """ + ) + + # Strategy 2: Duplicate specialize. + # + # In Haskell types, suppose you have: + # + # add_dupe_args :: DedupedArgs -> Args + # remove_dupe_args :: Args -> DedupedArgs + # + # compiler_fn + # :: (DedupedArgs -> R) -> DedupedArgs -> AOTConfig -> (DedupedArgs -> R) + # deped_compiler_fn + # :: (Args -> R) -> Args -> AOTConfig -> (Args -> R) + # + # Then the code below can be written in point-free style as: + # + # deduped_compiler_fn f a c = + # compiler_fn (f . add_dupe_args) (remove_dupe_args a) c . remove_dupe_args + # + # Suppose you have: + # + # [a, b, a, c] + # + # We want: + # + # remove_dupe_args([a, b, a, c]) == [a, b, c] + # add_dupe_args([a, b, c]) == [a, b, a, c] + # + # This is done via (respectively): + # + # seen_args = {a: 0, b: 1, c: 2} + # enumerate(add_dupe_map) = [ # how to get args from the deduped list + # (0, 0), + # (1, 1), + # (2, 0), + # (3, 2), + # ] + # keep_arg_mask = [True, True, False, True] + + seen_args: dict[Tensor, int] = {} + # Implicitly map duped arg position (list index) to de-duped arg position + keep_arg_mask: list[bool] = [] + add_dupe_map: list[int] = [] + duped_arg_len = len(flat_args) + + j = 0 # index into deduped_flat_args + for t in flat_args: + if isinstance(t, torch.Tensor): + if t in seen_args: + keep_arg_mask.append(False) + add_dupe_map.append(seen_args[t]) + continue + seen_args[t] = j + + keep_arg_mask.append(True) + add_dupe_map.append(j) + j += 1 + assert ( + len(add_dupe_map) == duped_arg_len + ), f"Expects add_dupe_map to have length {duped_arg_len} but got {len(add_dupe_map)}" + + self.keep_arg_mask = keep_arg_mask + self.add_dupe_map = add_dupe_map + + deduped_flat_args = self.remove_dupe_args(flat_args) + + # Update our input metadata to remove duped input metadata. + updated_fw_metadata = remove_dupe_metadata( + fw_metadata, keep_arg_mask, add_dupe_map + ) + + if ( + tracing_context := TracingContext.try_get() + and aot_config.aot_autograd_arg_pos_to_source + ): + # TODO(voz): This structure is 1:1, we could consider an alternate structure like + # kept_pos:[dupe_arg_pos], however, add_dupe_map is 1:1 so we would need a new structure there, + # which feels like needless complexity for a tiny bit of efficiency at this point. + for dupe_arg_pos, (kept_pos, keep_arg) in enumerate( + zip(add_dupe_map, keep_arg_mask) + ): + if not keep_arg: + dupe_arg_source = aot_config.aot_autograd_arg_pos_to_source[ + dupe_arg_pos + ] + kept_arg_source = aot_config.aot_autograd_arg_pos_to_source[ + kept_pos + ] + tracing_context.guards_context.aotautograd_guards.append( # type: ignore[attr-defined] + DuplicateInputs(kept_arg_source, dupe_arg_source) + ) + + @wraps(flat_fn) + def wrapped_flat_fn(*args): + return flat_fn(*self.add_dupe_args(args)) + + if config.debug_assert: + ref_fw_metadata = run_functionalized_fw_and_collect_metadata( + wrapped_flat_fn, + static_input_indices=aot_config.static_input_indices, + keep_input_mutations=fw_metadata.keep_input_mutations, + is_train=fw_metadata.is_train, + )(*deduped_flat_args) + assert ( + ref_fw_metadata == updated_fw_metadata + ), f"ref_metadata={str(ref_fw_metadata)}, actual_metadata={str(updated_fw_metadata)}" + + return wrapped_flat_fn, deduped_flat_args, updated_fw_metadata + + def post_compile( + self, + compiled_fn, + aot_config: AOTConfig, + *, + runtime_metadata: ViewAndMutationMeta, + ): + if not self.needs_post_compile: + return compiled_fn + + @wraps(compiled_fn) + def wrapped_compiled_fn(args: list[Any]): + deduped_args = self.remove_dupe_args(args) + args.clear() + return compiled_fn(deduped_args) + + wrapped_compiled_fn._boxed_call = True # type: ignore[attr-defined] + + # This can be uncommented when we properly guard for duplicates, + # but right now we must not do it. + # if not config.debug_assert: + # return wrapped_compiled_fn + + @wraps(wrapped_compiled_fn) + def debugged_compiled_fn(args): + # Test that the computed remove/add arg functions are an inverse + new_args = self.add_dupe_args(self.remove_dupe_args(args)) + seen: dict[Any, None] = {} + for i, (x, y) in enumerate(zip(new_args, args)): + seen[y] = None + assert x is y, format_guard_bug_msg( + aot_config, + f"{describe_input(i, aot_config)} would be a duplicate of " + f"{describe_input(self.add_dupe_map[i], aot_config)}", + ) + # This is only an error if there is metadata mutation on both of + # the duped arguments; in this case, we need to know what order + # the metadata mutation applies in. You'll get the correct result + # otherwise, because a graph that assumes distinct inputs works if + # you dupe the inputs (the gradient contributions from each input + # will get summed up appropriately.) + # + # TODO: work out how to setup this assert correctly + """ + assert len(seen) == unique_args, format_guard_bug_msg(aot_config, + f"there would be {unique_args} distinct arguments" + ) + """ + return wrapped_compiled_fn(args) + + debugged_compiled_fn._boxed_call = True # type: ignore[attr-defined] + + return debugged_compiled_fn + + +# This layer handles the situation where you have two inputs that alias each other, +# and one of the inputs is mutated. +# We need to take special care to ensure that the mutation is applied to the other aliases in the graph. +# +# pre-condition: AOTDedupWrapper has already run. +# (This function will in theory work if there are duplicate args. +# However, the synthetic base code path is a bit sub-optimal, and running with dupe'd inputs +# would cause us to hit that path more frequently). +@dataclass +class AOTSyntheticBaseWrapper(CompilerWrapper): + # Currently, the only reason we need to plumb this bool is because + # the synthetic base code prohibits more cases in the autograd case than the inference case. + trace_joint: bool # TODO: refactor trace_joint + needs_post_compile: bool = True + aliased_arg_idx_with_metadata_mutations: list[int] = field(default_factory=list) + + def pre_compile( + self, + flat_fn, + flat_args: list[Any], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, + ) -> tuple[Callable, list[Tensor], ViewAndMutationMeta]: + is_inference = not self.trace_joint + flat_args_with_synthetic_bases, synthetic_base_info = merge_view_inputs( + aot_config, + flat_args, + fw_metadata.input_info, + is_inference=is_inference, + ) + + # Happy path: we don't need synthetic bases + if synthetic_base_info is None: + self.needs_post_compile = False + return flat_fn, flat_args, fw_metadata + + # export path: ban synthetic bases for now, add later if requested. + if requires_subclass_dispatch(flat_args, fw_metadata): + raise RuntimeError( + """\ + Encountered aliased inputs that are mutated in the graph, but at least one input/output + to the graph is a tensor subclass. This is not supported today. You can try to + remove the aliasing yourself as a workaround, or otherwise file an issue on github.""" + ) + + if aot_config.is_export: + raise RuntimeError( + f"""\ + Encountered aliased inputs that are mutated in the graph you are trying to export. + This functionality is currently not supported. If needed, please file a github issue. + + synthetic_base_info={str(synthetic_base_info)} + + fw_metadata={str(fw_metadata)} + """ + ) + + assert len(fw_metadata.input_info) == len(synthetic_base_info) + + # Update our forward metadata to take synthetic bases into account + ( + fw_metadata_updated, + aliased_arg_idx_with_metadata_mutations, + ) = create_synthetic_base_metadata( + fw_metadata, synthetic_base_info, flat_args, flat_args_with_synthetic_bases + ) + # Save old input args for post-compile + self.old_input_info = fw_metadata.input_info + + self.aliased_arg_idx_with_metadata_mutations = ( + aliased_arg_idx_with_metadata_mutations + ) + replay_views = config.view_replay_for_aliased_outputs + + def _unpack_synthetic_bases(primals: tuple[Any, ...]) -> list[Any]: + f_args_inner = [] + for inner_idx_or_tuple in synthetic_base_info: + if isinstance(inner_idx_or_tuple, int): + f_args_inner.append(primals[inner_idx_or_tuple]) + else: + inner_base_idx, view_tensor = inner_idx_or_tuple + base = primals[inner_base_idx] + view_arg = gen_alias_from_base( + base, + view_tensor, + view_tensor.requires_grad, + replay_views=replay_views, + ) + f_args_inner.append(view_arg) + return f_args_inner + + @wraps(flat_fn) + def wrapped_flat_fn(*args): + unpacked_args = _unpack_synthetic_bases(args) + # This is a bit subtle. The goal of this entire function (aot_dispatch_synthetic_bases) + # is to relieve the downstream logic from having to reason about mutations on inputs that alias + # each other, by replacing aliased inputs with a synthetic base. + # One area where this breaks down a bit however is if one of those aliased inputs + # experienced a metadata mutation. + # We are now obligated to reapply the metadata mutation directly to the user's input; + # it isn't enough to apply mutations back to the synthetic base in the downstream logic. + # + # The way we handle this is by pretending that those aliased inputs that experience metadata mutations + # are additional outputs in the user's forward function. + # The downstream logic will just treat these as "user outputs that alias inputs". + # However, we will manually grab them at runtime here, use them to reapply the metadata mutation + # to the user inputs, and not return them to the user. + aliased_args_with_metadata_mutations = [ + x + for i, x in enumerate(unpacked_args) + if i in self.aliased_arg_idx_with_metadata_mutations + ] + if len(aliased_args_with_metadata_mutations) > 0: + return *(flat_fn(*unpacked_args)), *aliased_args_with_metadata_mutations + else: + return flat_fn(*unpacked_args) + + if config.debug_assert: + ref_fw_metadata = run_functionalized_fw_and_collect_metadata( + wrapped_flat_fn, + static_input_indices=aot_config.static_input_indices, + keep_input_mutations=fw_metadata.keep_input_mutations, + is_train=fw_metadata.is_train, + )(*flat_args_with_synthetic_bases) + assert ref_fw_metadata == fw_metadata_updated, ( + f"ref_metadata={pprint.pformat(partial_flatten_asdict(ref_fw_metadata))}, " + f"\nactual_metadata={pprint.pformat(partial_flatten_asdict(fw_metadata_updated))}" + ) + return ( + wrapped_flat_fn, + flat_args_with_synthetic_bases, + fw_metadata_updated, + ) + + def post_compile( + self, + compiled_fn, + aot_config: AOTConfig, + *, + runtime_metadata: ViewAndMutationMeta, + ): + if not self.needs_post_compile: + return compiled_fn + + is_inference = not self.trace_joint + + @wraps(compiled_fn) + def wrapped_compiled_fn(args): + args_with_synthetic_bases, synthetic_base_info = merge_view_inputs( + aot_config, args, self.old_input_info, is_inference=is_inference + ) + assert synthetic_base_info is not None + aliased_args_w_metadata_mutations = [ + args[i] for i in self.aliased_arg_idx_with_metadata_mutations + ] + num_aliased_args_with_metadata_mutations = len( + aliased_args_w_metadata_mutations + ) + args.clear() + outs = compiled_fn(args_with_synthetic_bases) + if num_aliased_args_with_metadata_mutations > 0: + # This code does not handle **all** input metadata mutations. + # Instead, it only handles metadata mutations on inputs that were converted into synthetic bases + # (which only happens if at least one aliased input experienced a data mutation). + # e.g: + # def f(a, b): + # a.mul_(2) + # b.t_(1, 0) + # f(x.view(2, 2), x.view(2, 2)) + mutated_metadata_inps = outs[-num_aliased_args_with_metadata_mutations:] + user_outs = outs[:-num_aliased_args_with_metadata_mutations] + for inp, mutated_inp in zip( + aliased_args_w_metadata_mutations, mutated_metadata_inps + ): + inp.as_strided_( + mutated_inp.size(), + mutated_inp.stride(), + mutated_inp.storage_offset(), + ) + return user_outs + return outs + + return wrapped_compiled_fn + + +# Note [Handling mutations on an input that aliases other inputs] +# The easiest example to show-case this edge case is here: +# +# def f(a, b): +# a.mul_(2) +# out = a + b +# return out +# b = torch.ones(...) +# a = b.view(-1) +# f(a, b) +# +# In this situation, if a and b happened to be aliased, we need to trace something different! +# Suppose we had b = a.view(-1) +# (In this case, that means that `a._base is b`) +# +# We need to ensure that the aliasing relationship between a and b is preserved. +# We do that detecting the specific situation above (mutate an input that aliases another input), +# and when we do that, we create a synthetic base argument. Then inside of the traced forward, +# we regenerate a and b off of that base. +# The complete example of the transformed function looks like this: +# +# // The traced forward takes in a synthetic base, and regenerates the aliased inputs as views +# // We could consider getting view-replay support here to minimize as_strided_scatter ops in the graph +# def traced_forward(base): +# a = base.as_strided(...) +# b = base.as_strided(...) +# a_updated = a.mul(2) +# base_updated = torch.as_strided_scatter(base, a_updated, ...) +# b_updated = base_updated.as_strided(...) +# out = a_updated + b_updated +# return a_updated, out +# +# def compiled_fn(a, b): +# // we detect that a is the "differentiable base" here +# base = a +# // In other situations, we might do either: +# // (1) a and b are both views off of some larger differentiable base +# // assert a._base is b._base and a._base is not None +# // base = a._base +# // (2) a and b both don't require gradients. Create a base from the storage +# // assert a._base is None and b._base is None +# // base = torch.Tensor(a.storage()) +# a_updated, out = traced_forward(base) +# a.copy_(a_updated) +# return out +# +# This function: +# (1) Merges input views into a synthetic base argument, when any of those input views are mutated +# (2) Returns metadata telling the autograd.Function how to modify their arguments properly, +# to respect the new calling convention. +# +# The calling convention is as follows. +# Any inputs that were originally views of one another get yanked, and replaced with a synthetic base. +# The argument list ordering goes [base1, ..., baseN], [arg1, ..., argN], +# Where the ordering of the bases is determined from the ordering of the original view args. +# baseA will come before baseB if the earliest original argument coming from baseA +# showed up earlier in the argument list than the earliest original argument coming from baseB. +# +# Example, given some tensors a, b, c, d +# call site: +# f(a, c.view(-1), b.view(-1), b, c, d) +# Modified argument list: +# c_base comes first because the first c view came earlier in arg list than the first b view +# a and d still show up in the modified arg list, but b and c don't- they're regenerated from their bases +# b_base = torch.Tensor(b.storage()) +# c_base = torch.Tensor(c.storage()) +# f(c_base, b_base, a, d) +def merge_view_inputs( + aot_config: AOTConfig, + fwd_inputs: list[Any], + mutated_input_info: list[InputAliasInfo], + *, + # The autograd case currently has more restrictions than the inference case. + is_inference: bool, +) -> tuple[list[Any], Optional[list[Union[int, tuple[int, torch.Tensor]]]]]: + def _are_differentiable_views(view1, view2): + if view1 is view2: + return True + if view1._base is None and view2._base is None: + return False + if view1._base is view2._base or view1._base is view2 or view1 is view2._base: + return True + return False + + def _same_dtype_views(view1, view2): + if view1.dtype != view2.dtype: + return False + if view1._base is not None and view1.dtype != view1._base.dtype: + return False + if view2._base is not None and view2.dtype != view2._base.dtype: + return False + return True + + assert len(fwd_inputs) == len(mutated_input_info) + if not [info for info in mutated_input_info if info.mutates_data]: + # Return early when there are no mutations. + return fwd_inputs, None + + storage_ref_to_idx: dict[StorageWeakRef, list[int]] = collections.defaultdict(list) + base_args = [] + other_args = [] + for i, inpt in enumerate(fwd_inputs): + if isinstance(inpt, Tensor): + storage_ref = StorageWeakRef(inpt.untyped_storage()) + storage_ref_to_idx[storage_ref].append(i) + else: + other_args.append(inpt) + # Note [Synthetic Base Info Metadata] + # This list contains metadata that tells you what the i'th argument in the inner calling convention should be. + # It's either: + # - another int (corresponding to the index in the argument list of the element from the outer calling convention) + # - idx, view_tensor, where we can generate the new output with view_tensor._view_func(old_args[idx]) + # idx corresponds to which synthetic base from the outer calling context to view + inner_calling_convention_meta: dict[int, Union[int, tuple[int, torch.Tensor]]] = {} + for aliased_input_indices in storage_ref_to_idx.values(): + if len(aliased_input_indices) <= 1 or not any( + # We only care about mutations that affect all aliases, + # so metadata mutations on an input doesn't require us to do synthetic base handling. + mutated_input_info[inpt_idx].mutates_data + for inpt_idx in aliased_input_indices + ): + other_args.extend( + fwd_inputs[curr_idx] for curr_idx in aliased_input_indices + ) + continue + + # Here, we attempt to do a more complicated check to detect false aliasing + # (e.g. if all the tensors have the same storage, but don't actually overlap) + # In theory, we could have a large group of tensors that all share storages, where only *some* of them + # have overlapping memory. + # I don't bother with that case for now: here, we only bail out earlier if we detect that **every** pair + # of tensors in the current group that shares a storage is non-overlapping. + aliased_input_indices_no_false_sharing = compute_overlapping_inputs( + aot_config, fwd_inputs, aliased_input_indices + ) + if len(aliased_input_indices_no_false_sharing) <= 1: + other_args.extend( + fwd_inputs[curr_idx] for curr_idx in aliased_input_indices + ) + continue + + # We detected an input that was mutated, AND aliases with another input. + # we need to replace this set of aliased inputs with a single synthetic base. + # For now, I'm banning a bunch of cases. We expect dynamo to properly detect these cases + # and error out. We can fix them later. + # These checks are transitive, so we don't need to check every pair. + for idx1, idx2 in zip( + aliased_input_indices, aliased_input_indices[1:], strict=False + ): + view1 = fwd_inputs[idx1] + view2 = fwd_inputs[idx2] + # The "inputs that are aliased but have different differentiable bases" case + # is more complicated and hopefully pretty rare. Not currently handled. + if not is_inference: + assert _are_differentiable_views( + view1, view2 + ), "aot_autograd() does not yet handle non-differentiable view input mutations." + # Regenerating views when reinterpreting complex / real tensors seems non-trivial, + # not handling for now + assert _same_dtype_views( + view1, view2 + ), "aot_autograd() does not yet handle input mutations on views with different dtypes." + non_none_bases = [ + fwd_inputs[i]._base + for i in aliased_input_indices + if fwd_inputs[i]._base is not None + ] + aliases_with_none_bases = [ + fwd_inputs[i] for i in aliased_input_indices if fwd_inputs[i]._base is None + ] + if len(non_none_bases) == 0: + # Case where none of the aliases have a ._base + # we generate a synthetic base without gradients, and generate views off of it + # We hit this case when we have input tensors to the graph that share a storage, + # but do not have a ._base field. + # Wondering when we hit this case? + # The _base field simply says that autograd knows about the aliasing relationship, + # but sometimes we create tensors which are aliased out of the same storage but guaranteed + # to be disjoint. In these cases, we will skip setting up the _base relationship + # for performance reasons (because the fact that the tensors share the same storage + # is unobservable unless you (1) do naughty things with resize_/as_strided + # or (2) look at the storage--as we are doing here.) + # One particular example of this is optimizer steps on the LSTM module: + # LSTM parameters are packed into a contiguous storage for efficiency reasons when + # calling cuDNN kernels, so when these parameters get passed to the optimizer we will + # find they share the same storage, but do not have _base set since they are all disjoint. + # + # NOTE: There is one case where this is unsafe: + # torch.Tensor(storage) will ALWAYS create a 1D tensor, which is not necessarily + # the same shape as the "actual" base that the tensor came from. + # For the most part this is fine, because we always use as_strided() + # to generate the original aliased inputs again. + # If we were to use view-replay though, this could cause the aliased views + # to have incorrect sizes. + example_idx = aliased_input_indices[0] + example_alias = fwd_inputs[example_idx] + # Note that this function is re-used at both trace time and runtime. + # At trace time, we're under a FakeMode so synthetic_base becomes a FakeTensor. + synthetic_base = torch.empty( + (0,), dtype=example_alias.dtype, device=example_alias.device + ) + # We don't actually have a convenient way of going from storage -> tensor, + # So using set_() here (we suffer some minor overhead, but this case is rare). + synthetic_base.set_(example_alias.untyped_storage()) + else: + # Case where all of the aliases require gradients, and have the same _base. + synthetic_base = non_none_bases[0] + for other_base in non_none_bases[1:]: + assert ( + other_base is synthetic_base + ), "aot_autograd() does not yet handle non-differentiable view input mutations." + for alias in aliases_with_none_bases: + assert ( + alias is synthetic_base + ), "aot_autograd() does not yet handle non-differentiable view input mutations." + base_args.append(synthetic_base) + for curr_view_idx in aliased_input_indices: + curr_view = fwd_inputs[curr_view_idx] + base_idx = len(base_args) - 1 + # We store just enough info here so that we can regenerate the view later. + # Regeneration: curr_view._view_func(args[base_idx]) + inner_calling_convention_meta[curr_view_idx] = (base_idx, curr_view) + if len(base_args) == 0: + assert len(other_args) == len(fwd_inputs) + # If no synthetic bases are necessary, just return the original inputs. + return fwd_inputs, None + else: + from torch.fx.experimental.symbolic_shapes import SymIntEqByExpr + + def make_hashable(arg): + if isinstance(arg, torch.SymInt): + # Since only nested SymInt objects can be hashed, we wrap them with + # SymIntEqByExpr, which is a hashable wrapper of SymInts. + return SymIntEqByExpr(arg) + return arg + + # Otherwise, return: + # (1) The new args according to the updated calling convention: (synthetic_bases, other_args) + # (2) Metadata telling functionalization how to generate the inner argument list given the outer calling convention. + # We post-process it into a list, where meta[i] tells you info about the i'th argument in the inner calling convention. + args_to_functionalization = base_args + other_args + arg_to_old_idx_map = { + make_hashable(arg): i for (i, arg) in enumerate(fwd_inputs) + } + for i, other_arg in enumerate(other_args): + new_idx = len(base_args) + i + old_idx = arg_to_old_idx_map[make_hashable(other_arg)] + inner_calling_convention_meta[old_idx] = new_idx + # post process into a list + post_processed_calling_convention_meta: list[ + Union[int, tuple[int, torch.Tensor]] + ] = [-1 for _ in range(len(inner_calling_convention_meta))] + for k, v in inner_calling_convention_meta.items(): + post_processed_calling_convention_meta[k] = v + # Quick assert: every argument in the inner calling convention should be accounted for. + for x in post_processed_calling_convention_meta: + assert x != -1 + return args_to_functionalization, post_processed_calling_convention_meta + + +@dataclass +class AutogradLazyBackwardCompileInfo: + bw_module: Callable + placeholder_list: list[Any] + saved_context: Optional[TracingContext] + saved_compile_context: Optional[CompileContext] + + +def _raise_if_functorch_active(): + # not ideal but prevent the user from seeing a nasty traceback - See #138422 + stack = torch._C._functorch.peek_interpreter_stack() + torch._check( + stack is None, + lambda: ( + "It looks like you're trying to call a compiled backward function within vmap/grad/vjp, " + "which isn't supported. Try wrapping vmap inside torch.compile, or skip compiling the " + "backward function." + ), + ) + + +# NOTE: this function must be torch._dynamo.allow_in_graph-able. Non tensor/symnode inputs must be constants. +def _backward_prologue_functional( + ctx_saved_tensors, ctx_symints, metadata, maybe_subclass_metadata, *flat_args +): + # Calling convention: we expect a grad_out passed to the backward: + # - for every output of the fw that does *not* alias an input or graph intermediate + # - for every updated_input generated by the fw that does *not* alias an input (aka only data-mutations) + # - for every graph intermediate that we need to use to generate an output later. + # The other outputs in the autograd.Function.forward that do *not* show up in the backward include: + # - outputs that alias inputs or graph intermediates + # - updated inputs due to metadata-only mutations. + # We need to return them in the forward, but ensure that they all do not get gradients in the backward, + # and we filter them out here before passing the remaining grad_outputs into the compiled backward. + _raise_if_functorch_active() + + num_intermediate_bases = metadata.num_intermediate_bases + num_mutated_runtime_inps = metadata.num_mutated_inp_runtime_indices + expected_grad_outs = ( + metadata.num_outputs + num_mutated_runtime_inps + num_intermediate_bases + ) + deterministic = metadata.deterministic + global_deterministic = torch.are_deterministic_algorithms_enabled() + if deterministic is not None: + torch._check( + not (not deterministic and global_deterministic), + lambda: ( + "This compiled backward function is being run with " + "torch.use_deterministic_algorithms(True), " + "but it was previously generated during the forward function while " + "torch.use_deterministic_algorithms(False) was set." + ), + ) + + assert len(flat_args) == expected_grad_outs + out_info = metadata.output_info + + inp_tangents, out_tangents, intermediate_base_tangents = ( + flat_args[:num_mutated_runtime_inps], + flat_args[ + num_mutated_runtime_inps : num_mutated_runtime_inps + metadata.num_outputs + ], + flat_args[num_mutated_runtime_inps + metadata.num_outputs :], + ) + # input_info contains info on *every* input, + # But in the backward(), we are only given grad outputs for every mutated input + # We then need to filter out the grad outputs that correspond to metadata-only mutations or don't require grad + input_info = metadata.input_info + inp_tangents_filtered = [ + x + for x, info_idx in zip( + inp_tangents, + metadata.mutated_inp_runtime_indices, + ) + if input_info[info_idx].mutates_data and input_info[info_idx].requires_grad + ] + # We also need to filter out grad outputs that correspond to outputs aliasing inputs/intermediates + out_tangents_filtered = [ + x + for x, info in zip(out_tangents, out_info) + if info.output_type + in [ + OutputType.non_alias, + OutputType.unsafe_view_alias, + OutputType.custom_function_view, + ] + and issubclass(info.raw_type, torch.Tensor) + and info.requires_grad + ] + # intermediate bases always require gradients, and always participate in the backward graph. + flat_bw_args_with_grads = [ + *inp_tangents_filtered, + *out_tangents_filtered, + *intermediate_base_tangents, + ] + num_flat_bw_args_with_grads = len(flat_bw_args_with_grads) + + # sanity asserts + # metadata_only_inps = [ + # x for x, info_idx in zip(inp_tangents, mutated_inp_indices) + # if not input_info[info_idx].mutates_data + # ] + # aliased_outputs = [ + # x for x, info in zip(out_tangents, out_info) if info.output_type != OutputType.non_alias] + # assert all(x is None for x in metadata_only_inps) + # assert all(x is None for x in aliased_outputs) + # TODO: replace this with FunctionalizedRngRuntimeWrapper + rng_args = [] + if metadata.is_rng_op_functionalized: + # Add the seed and offset to args + rng_args = CUDARngStateHelper.get_torch_state_as_tuple() + + bw_tokens = [None] * metadata.num_backward_tokens + + # - note: donated buffer logic requires (*ctx.symints, *ctx.saved_tensors) showing up first + # in the bw output order. + + # Every dereference of ctx.saved_tensors incurs saved_tensors_hooks calls + # There are tests that count these calls, saving to var. + num_ctx_saved_tensors = len(ctx_saved_tensors) + all_args = [ + *ctx_symints, + *ctx_saved_tensors, + *flat_bw_args_with_grads, + *bw_tokens, + *rng_args, + ] + del ctx_saved_tensors + + # Note: [AOTAutograd Backward Guards] + # During AOTDispatch, we eagerly create and trace out a joint fw-bw graph. + # Doing so requires us to "guess" about some of the metadata of our grad_outputs. + # + # In particular: if an output to the forward is a plain tensor or a subclass, + # its corresponding grad_output in the backward **may or may not** be + # a plain tensor or a subclass. The main cases are: + # (1) If an output is a plain tensor, its grad_out will also be a plain tensor, + # *unless* the output is used in some subclass compute later in the forward graph, + # which will cause its grad_output to become a subclass + # (2) If an output is a subclass, its grad_out will also be a subclass, + # *unless* the output of the forward did not actually participate in the gradient computation, + # in which case autograd will insert a plain tensor of zeros for the grad_output. + # We could avoid this case with `torch.autograd.Function.set_materialize_grads`, + # although this is not turned on today in AOTAutgrad and would require more work. + # + # Today, we make a guess on subclass-ness based on the above examples, + # and hard-error in the backward if we guessed wrong. + # + # In the future, we should add backward guards that would allow us to + # properly handle this case instead of erroring: we would need to retrace the backward graph, + # since we might produce an entirely different trace if our grad_outputs are subclass or not. + del flat_bw_args_with_grads + + tangents_start_idx = ( + len(all_args) - num_flat_bw_args_with_grads - len(rng_args) - len(bw_tokens) + ) + assert tangents_start_idx == len(ctx_symints) + num_ctx_saved_tensors + tangents_end_idx = len(all_args) - len(rng_args) - len(bw_tokens) + + # TODO: figure out how to refactor the backward properly + # so I can use aot_dispatch_subclass_wrapper() here. + if maybe_subclass_metadata is not None: + tangents = all_args[tangents_start_idx:tangents_end_idx] + + if len(tangents) != len(metadata.subclass_tangent_meta): + raise RuntimeError( + "The grad inputs should be same number as forward output tangents" + ) + + flat_processed_tangents = list( + itertools.chain.from_iterable( + ( + AOTDispatchAutograd.process_runtime_tangent( + t, + m, + )[1] + ) + for t, m in zip( + tangents, + metadata.subclass_tangent_meta, + ) + ) + ) + + all_args = ( + runtime_unwrap_tensor_subclasses( + all_args[:tangents_start_idx], + # SymInts that are inputs to the backward graph are + # already included in the "all_args" list. + # Any symints coming from tensor subclasses should always + # come from primals, and so they will show up as extra + # arguments to the forward graph, and they will be saved + # as activation in the backward graph. + append_symints=False, + ) + + flat_processed_tangents + + runtime_unwrap_tensor_subclasses( + all_args[tangents_end_idx:], + append_symints=False, + ) + ) + else: + all_args = [ + ( + AOTDispatchAutograd.process_runtime_tangent( + t, + metadata.subclass_tangent_meta[i - tangents_start_idx], + )[0] + if (tangents_start_idx <= i < tangents_end_idx) + else t + ) + for i, t in enumerate(all_args) + ] + + # Backward with forward inputs mutations is not supported in double backward. + if ( + torch.is_grad_enabled() + and metadata.indices_of_inputs_that_requires_grad_with_mutations_in_bw + ): + raise RuntimeError( + "aot_autograd does not support input mutations with requires_grad in backward for create_graph=True" + ) + + return all_args + + +def initialize_rng_states( + num_rng: int, + graphsafe_idx: int, + fwd_rng_states: list[torch.Generator], + bwd_rng_states: list[torch.Generator], +): + """ + Initialize the cudagraph safe rng states. + + Initialization of rng states should have a few properties: + - the initialization for each rng state should be independent + - the initialization should be deterministic + - the initialization should be based off current rng state, so that independent graphs do not + have equal rng behavior + + We defer initialization of rng states until runtime because compilation is wrapped + with preserve_rng_states. Seed initialization should advance the rng states so consecutive compilations + do not give equal randomness. + """ + with torch.utils._python_dispatch._disable_current_modes(): + seeds = torch.randint(0, torch.iinfo(torch.int64).max, (num_rng,), device="cpu") + fwd_rng_states.extend( + [ + torch.cuda.default_generators[graphsafe_idx] + .clone_state() + .manual_seed(int(seeds[i])) + for i in range(num_rng) + ] + ) + bwd_rng_states.extend( + [ + torch.cuda.default_generators[graphsafe_idx] + .clone_state() + .manual_seed(int(seeds[i])) + for i in range(num_rng) + ] + ) + + +# NOTE: this function must be torch._dynamo.allow_in_graph-able. Non tensor/symnode inputs must be constants. +def _backward_epilogue_functional( + metadata, maybe_subclass_metadata, out, *, make_subclass_override=None +): + # Toss out the backward output tokens + num_bw_tokens = metadata.num_backward_tokens + if num_bw_tokens > 0: + out = out[:-num_bw_tokens] + + # TODO: replace this with FunctionalizedRngRuntimeWrapper.post_compile + out = FunctionalizedRngRuntimeWrapper()._functionalized_rng_runtime_epilogue( + metadata, out, offset_index=len(out) - 1 + ) + out = tuple(out) + + # TODO: figure out how to refactor the backward properly so I can use aot_dispatch_subclass_wrapper() here. + if maybe_subclass_metadata is not None: + assert maybe_subclass_metadata.grad_input_metas is not None + outs_wrapped = wrap_tensor_subclasses( + out, + subclass_metas=maybe_subclass_metadata.grad_input_metas, + included_subclass_symints=True, + is_runtime=True, + make_subclass_override=make_subclass_override, + ) + return outs_wrapped + return out + + +# This is wrapped in a class just for namespacing purposes +# No need to make it into an actual CompilerWrapper because it doesn't fit the abstract as cleanly +class AOTDispatchAutograd: + @staticmethod + def process_runtime_tangent(x, meta: Union[PlainTensorMeta, SubclassCreationMeta]): + if not isinstance(x, torch.Tensor): + return x, [x] + + if isinstance(x, FakeTensor): + if not x.is_contiguous(memory_format=meta.memory_format): + x = x.contiguous(memory_format=meta.memory_format) + return x, [x] + + expected_type: Optional[type] = torch.Tensor + expected_meta = None + if isinstance(meta, SubclassCreationMeta): + expected_type = meta.original_subclass_type + expected_meta = meta.meta + + runtime_type = type(x) + if torch._dynamo.compiled_autograd.in_compiled_autograd_region: + # When we're inside compiled autograd's AOTDispatcher step, + # regular Tensors look like FunctionalTensors. + # Tensor subclasses still look like Tensor subclasses though. + if isinstance(x, torch._subclasses.functional_tensor.FunctionalTensor): + runtime_type = torch.Tensor + + runtime_meta = None + runtime_subclass_keys: Sequence[str] = [] + + if is_traceable_wrapper_subclass(x): + runtime_subclass_keys, runtime_meta = x.__tensor_flatten__() + + def maybe_coerce(x): + same_type: bool = expected_type == runtime_type + same_meta: bool = expected_meta == runtime_meta + + if same_type and same_meta: + return x + + if not hasattr(x, "__coerce_same_metadata_as_tangent__"): + return None + + if same_type: + # Backward Compatibility, as some Subclass impls can have original 1-arg function. + return x.__coerce_same_metadata_as_tangent__(expected_meta) + + return x.__coerce_same_metadata_as_tangent__(expected_meta, expected_type) + + # Coerce to expected type and metadata + orig_x = x + x = maybe_coerce(x) + if x is None: + raise RuntimeError( + f""" +During the backward, we encountered a tensor subclass where we guessed its +metadata incorrectly. + +Expected metadata: {str(expected_meta)}, expected type: {str(expected_type)} + +Runtime metadata: {str(runtime_meta)}, runtime type: {str(runtime_type)} + +shape: {str(orig_x.shape)} +To fix this, your tensor subclass must implement the dunder method __force_to_same_metadata__. +""" + ) + + # Coerce to expected memory format + if not x.is_contiguous(memory_format=meta.memory_format): + x = x.contiguous(memory_format=meta.memory_format) + + if not is_traceable_wrapper_subclass(x): + return x, [x] + + assert isinstance(meta, SubclassCreationMeta) + if orig_x is not x: + runtime_subclass_keys = x.__tensor_flatten__()[0] + + assert len(meta.attrs) == len(runtime_subclass_keys) + leaves = [] + for i, (attr, attr_meta) in enumerate(meta.attrs.items()): + elem = getattr(x, attr) + new_elem, elem_leaves = AOTDispatchAutograd.process_runtime_tangent( + elem, attr_meta + ) + if new_elem is not elem: + setattr(x, attr, new_elem) + leaves.extend(elem_leaves) + + return x, leaves + + @staticmethod + def post_compile( + compiled_fw_func, # fw_module after compilation + wrappers + compiled_bw_func, # bw_module after compilation + wrappers + maybe_subclass_meta: Optional[SubclassMeta], + num_symints_saved_for_bw_: int, + backward_state_indices: list[int], + disable_amp: bool, + indices_of_inps_to_detach: list[int], + lazy_backward_info: Optional[AutogradLazyBackwardCompileInfo], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, # runtime metadata + try_save_cache_entry: Optional[Callable], # Save cache entry after compilation + ): + # For additional context see Note [CUDA Graph Safe RNG Functionalization] + # Each pair forward, backward rng states must be equal prior to its invocation on any + # iteration of forward, backward. Because they are initialized equal, and are computing the same rng op, + # running forward then backward advances them the same amount and keeps them equal. + # However, a user may invoke multiple forwards, then backwards, such that they are not in sync. + # Initially we have: + # fwd_state0 == bwd_state0. + # Lets say we run: + # fwd0: fwd_state0 -> fwd_state1 + # fwd1: fwd_state1 -> fwd_state2 + # fwd2: fwd_state2 -> fwd_state3 + # If we now invoke bwd2, + # we need to update bwd_state equal to the rng that was observed in fwd2. + # we save the rng_state fwd_state2 in forward because we detect that it is not the + # current backward state and therefore would not be accessible if we do not save it. + # Similarly, if we are going to update the backward state to a new value, and there is a pending + # forwards which needs its current state, we will save it. + # Within the autograd context, we keep track of the curr iteration so that on backward + # we know what the generator state must be before the backward is run. + num_rng = fw_metadata.num_graphsafe_rng_states + graphsafe_idx = fw_metadata.graphsafe_rng_state_index + fwd_rng_states: list[torch.Generator] = [] + bwd_rng_states: list[torch.Generator] = [] + curr_fwd_iter = itertools.count(0) + backward_state_position = 0 + pending_forwards: set[int] = set() + saved_backward_tensor_states: dict[int, list[torch.Tensor]] = {} + + class CompiledFunction(torch.autograd.Function): + compiled_fw = compiled_fw_func + compiled_bw = compiled_bw_func + metadata: ViewAndMutationMeta = fw_metadata # type: ignore[assignment] + maybe_subclass_metadata: Optional[SubclassMeta] = maybe_subclass_meta + num_symints_saved_for_bw = num_symints_saved_for_bw_ + _aot_id = aot_config.aot_id + _lazy_backward_info = lazy_backward_info + + @staticmethod + def _compiled_autograd_key(ctx): + return (ctx._autograd_function_id, *ctx.symints) + + @staticmethod + def forward(ctx, *deduped_flat_tensor_args): + args = deduped_flat_tensor_args + if backward_state_indices: + bw_state = args[backward_state_indices[0]] + assert isinstance(bw_state, BackwardState) + ctx._compiled_autograd_backward_state = bw_state + + if num_rng: + if len(fwd_rng_states) == 0: + assert graphsafe_idx is not None + initialize_rng_states( + num_rng, graphsafe_idx, fwd_rng_states, bwd_rng_states + ) + + _curr_iter = next(curr_fwd_iter) + ctx._curr_iter = _curr_iter + + # if this state is not contained in the backward, + # we need to save it for when its backward pass happens + if _curr_iter != backward_state_position: + saved_backward_tensor_states[_curr_iter] = [ + rng_state.get_state() for rng_state in fwd_rng_states + ] + + pending_forwards.add(_curr_iter) + args = (*args, *fwd_rng_states) + + # There is a pretty complicated calling convention around what the compiled fw returns. + # The full list of outputs and their relative order is: + # (*tokens, *mutated_inputs, *fw_outs, *fw_intermediate_bases, *saved_tensors, *saved_symints) + # - Note that in the synthetic bases case, mutated_inputs will correspond to an updated version + # of the original view, and not the synthetic base + # - Note that donated buffer logic requires (*saved_tensors, *saved_symints) showing up last + # in the fw output order. + fw_outs = call_func_at_runtime_with_args( + CompiledFunction.compiled_fw, + args, + disable_amp=disable_amp, + ) + + num_outputs = CompiledFunction.metadata.num_outputs + num_outputs_aliased = CompiledFunction.metadata.num_outputs_aliased + num_mutated_runtime_inps = ( + CompiledFunction.metadata.num_mutated_inp_runtime_indices + ) + num_forward_returns = CompiledFunction.metadata.num_forward_returns + + # Partitioners must put symint arguments at the end separate from tensor arguments + tensors_saved_for_backwards = fw_outs[ + CompiledFunction.metadata.tensors_saved_for_backwards_slice + ] + assert all( + isinstance(x, torch.Tensor) for x in tensors_saved_for_backwards + ) + # See Note [Detaching saved tensors in AOTAutograd] + ctx.save_for_backward( + *( + x.detach() if x._is_view() else x + for x in tensors_saved_for_backwards + ) + ) + symint_outs = fw_outs[ + CompiledFunction.metadata.symints_saved_for_backwards_slice + ] + assert all( + isinstance(x, (int, float, torch.SymInt, torch.SymFloat)) + for x in symint_outs + ), str([type(x) for x in symint_outs]) + ctx.symints = symint_outs + + raw_returns = fw_outs[0:num_forward_returns] + + # Wrap all autograd.Function.forward() outputs that are aliases + # so that autograd.Function doesn't treat them as tensors + if num_mutated_runtime_inps > 0: + for i, idx in enumerate( + CompiledFunction.metadata.mutated_inp_runtime_indices + ): + # We could make this faster by only looping over inputs with metadata-only mutations + # (instead of looping over inputs with either data or metadata mutations), but there shouldn't be many. + info = CompiledFunction.metadata.input_info[idx] + if info.mutates_metadata and not info.mutates_data: + raw_return_idx = i + raw_returns[raw_return_idx] = TensorAlias( + raw_returns[raw_return_idx] + ) + + if config.debug_assert: + user_mutated_inputs_raw = raw_returns[ + 0:num_mutated_runtime_inps + ] + mut_inp_infos = [ + x + for x in CompiledFunction.metadata.input_info + if x.mutates_data or x.mutates_metadata + ] + assert len(user_mutated_inputs_raw) == len(mut_inp_infos) + + if CompiledFunction.metadata.num_unsafe_view_outputs > 0: + for idx in CompiledFunction.metadata.unsafe_view_out_indices: + raw_return_idx = num_mutated_runtime_inps + idx + o = raw_returns[raw_return_idx] + raw_returns[raw_return_idx] = torch.ops.aten._unsafe_view( + o, o.shape + ) + + if num_outputs_aliased > 0: + for idx in CompiledFunction.metadata.aliased_out_indices: + raw_return_idx = num_mutated_runtime_inps + idx + raw_returns[raw_return_idx] = TensorAlias( + raw_returns[raw_return_idx] + ) + + if config.debug_assert: + intermediates_raw = raw_returns[ + num_mutated_runtime_inps + num_outputs : + ] + assert not any( + isinstance(x, TensorAlias) for x in intermediates_raw + ) + + # invariant: intermediate bases always require gradients, so we don't have to + # consider marking them as non-differentiable. + raw_returns_not_including_intermediate_bases = raw_returns[ + : num_mutated_runtime_inps + num_outputs + ] + raw_returns_meta = [ + x + for x in CompiledFunction.metadata.input_info + if x.mutation_type == MutationType.MUTATED_OUT_GRAPH + ] + CompiledFunction.metadata.output_info + + fw_outs_not_requiring_grad = [ + x + for (i, x) in enumerate( + raw_returns_not_including_intermediate_bases + ) + if isinstance(x, torch.Tensor) + and not raw_returns_meta[i].requires_grad + ] + ctx.mark_non_differentiable(*fw_outs_not_requiring_grad) + ctx._materialize_non_diff_grads = False + return tuple(raw_returns) + + @staticmethod + def backward(ctx, *flat_args): + all_args = _backward_prologue_functional( + ctx.saved_tensors, + ctx.symints, + CompiledFunction.metadata, + CompiledFunction.maybe_subclass_metadata, + *flat_args, + ) + + if num_rng: + nonlocal backward_state_position, bwd_rng_states + curr_backward_iter = ctx._curr_iter + retain_graph = ( + torch._C._autograd._get_current_graph_task_keep_graph() + ) + + # Save current state if we have a pending forward that needs this state + # or this state may be needed again because of retain graph + if ( + backward_state_position in pending_forwards + and backward_state_position not in saved_backward_tensor_states + and ( + backward_state_position != curr_backward_iter + or retain_graph + ) + ): + saved_backward_tensor_states[backward_state_position] = [ + rng_state.get_state() for rng_state in bwd_rng_states + ] + + # Restore saved states if needed + if curr_backward_iter in saved_backward_tensor_states: + if backward_state_position != curr_backward_iter: + for bwd_state, saved_state in zip( + bwd_rng_states, + saved_backward_tensor_states[curr_backward_iter], + ): + bwd_state.set_state(saved_state) + if not retain_graph: + del saved_backward_tensor_states[curr_backward_iter] + else: + assert backward_state_position == curr_backward_iter + + backward_state_position = curr_backward_iter + 1 + if not retain_graph: + pending_forwards.remove(curr_backward_iter) + all_args.extend(bwd_rng_states) + + def impl_fn(double_ctx=None): + out = CompiledFunction._backward_impl(ctx, all_args) + return _backward_epilogue_functional( + CompiledFunction.metadata, + CompiledFunction.maybe_subclass_metadata, + out, + ) + + needs_grad = torch.is_grad_enabled() and any( + t.requires_grad for t in all_args if isinstance(t, torch.Tensor) + ) + if needs_grad: + # double backward + return CompiledFunction._double_backward(ctx, impl_fn, all_args) + else: + return impl_fn() + + @staticmethod + def _double_backward(ctx, impl_fn, all_args): + # Ensure that the graph is connected, and error if double backward is performed. + # See comment for why once_differentiable is not sufficient: + # https://github.com/pytorch/pytorch/pull/92348/files#r1072962107 + class CompiledFunctionBackward(torch.autograd.Function): + # CompiledFunctionBackward is not yet supported in dynamo skipfiles + _aot_id = aot_config.aot_id + + @staticmethod + def forward(double_ctx, *unused_args): + return impl_fn(double_ctx) + + @staticmethod + def backward(double_ctx, *args): + raise RuntimeError( + "torch.compile with aot_autograd does not currently support double backward" + ) + + CompiledFunctionBackward._compiled_autograd_key = ( # type: ignore[method-assign] + CompiledFunction._compiled_autograd_key + ) + + return CompiledFunctionBackward.apply(*all_args) + + @staticmethod + def _backward_impl(ctx, all_args): + # compiled autograd reimplements this function at proxy_call_aot_backward + assert ( + not backward_state_indices + ), "BackwardState requires CompiledAutograd" + ctx.maybe_clear_saved_tensors() + + saved_tensors_use_once = ( + not torch._C._autograd._get_current_graph_task_keep_graph() + ) + + if CompiledFunction.compiled_bw is None: + assert lazy_backward_info is not None + + if not saved_tensors_use_once: + fw_metadata.bw_donated_idxs = [] + # Update bw_donated_idxs if using lazy_backward_info from `aot_dispatch_autograd` + if ( + hasattr(lazy_backward_info, "saved_context") + and hasattr(lazy_backward_info.saved_context, "fw_metadata") + and hasattr( + lazy_backward_info.saved_context.fw_metadata, # type: ignore[union-attr] + "bw_donated_idxs", + ) + ): + lazy_backward_info.saved_context.fw_metadata.bw_donated_idxs = ( # type: ignore[union-attr] + [] + ) + + bw_module = lazy_backward_info.bw_module + placeholder_list = lazy_backward_info.placeholder_list + saved_context = lazy_backward_info.saved_context + saved_compile_context = lazy_backward_info.saved_compile_context + + context = torch._C._DisableAutocast if disable_amp else nullcontext + metrics_context = get_metrics_context() + with tracing(saved_context), compile_context( + saved_compile_context + ), context(), track_graph_compiling( + aot_config, "backward" + ), metrics_context, dynamo_timed( + "backward._backward_impl", + phase_name="entire_backward_compile", + log_pt2_compile_event=True, + dynamo_compile_column_us="backward_cumulative_compile_time_us", + ): + CompileEventLogger.compilation_metric(is_forward=False) + CompiledFunction.compiled_bw = aot_config.bw_compiler( + bw_module, placeholder_list + ) + # Maybe save cache entry + if try_save_cache_entry is not None: + # CompiledFunction.metadata + # CompiledFunction.maybe_subclass_metadata + # bw_module + try_save_cache_entry( + CompiledFunction.compiled_bw, + fw_metadata, + aot_config, + ) + + if ( + torch._functorch.config.donated_buffer + and not saved_tensors_use_once + and fw_metadata.bw_donated_idxs != [] + ): + torch._check( + False, + lambda: ( + "This backward function was compiled with non-empty donated " + "buffers which requires create_graph=False and retain_graph=False. " + "Please keep backward(create_graph=False, retain_graph=False) " + "across all backward() function calls, or set " + "torch._functorch.config.donated_buffer=False to disable " + "donated buffer." + ), + ) + + out = call_func_at_runtime_with_args( + CompiledFunction.compiled_bw, + all_args, + steal_args=True, + disable_amp=disable_amp, + ) + return out + + compiled_function = RuntimeWrapper( + indices_of_inps_to_detach=indices_of_inps_to_detach, + trace_joint=True, + disable_amp=disable_amp, + ).post_compile( + CompiledFunction.apply, + aot_config, + runtime_metadata=fw_metadata, + ) + + return compiled_function + + +@dataclass +class DebugAssertWrapper(CompilerWrapper): + flat_requires_grad: list[Optional[bool]] = field(default_factory=list) + + def post_compile( + self, + compiled_fn, + aot_config: AOTConfig, + *, + runtime_metadata: ViewAndMutationMeta, + ): + @wraps(compiled_fn) + def debug_compiled_function(args: list[Any]): + # TODO: Check aliasing relationships + # TODO: Check strides for metadata mutation + # (NB: ideally, this logic is factored out of this function and + # you move these debug checks there) + + # Check requires grad. Bad case is when we compiled with + # requires_grad = False, but input requires_grad = True + # (vice versa is OK; we compute a gradient and then throw + # it away when it hits the input.) + for i, a in enumerate(args): + can_require_grad = self.flat_requires_grad[i] + if can_require_grad is None: + assert not isinstance(a, Tensor) + elif not can_require_grad: + assert not a.requires_grad, format_guard_bug_msg( + aot_config, + f"{describe_input(i, aot_config)} would not require grad", + ) + + return compiled_fn(args) + + return debug_compiled_function + + +def pre_compile( + wrappers: list[CompilerWrapper], + flat_fn: Callable, + flat_args: list[Any], + aot_config: AOTConfig, + *, + fw_metadata: ViewAndMutationMeta, +) -> tuple[Callable, list[Tensor], ViewAndMutationMeta]: + """ + Runs a sequence of wrappers on the given function and arguments. + Mutates wrappers in place. + """ + for wrapper in wrappers: + flat_fn, flat_args, fw_metadata = wrapper.pre_compile( + flat_fn, flat_args, aot_config, fw_metadata=fw_metadata + ) + return flat_fn, flat_args, fw_metadata + + +def post_compile( + wrappers: list[CompilerWrapper], + compiled_fn: Callable, + aot_config: AOTConfig, + *, + runtime_metadata: ViewAndMutationMeta, +) -> tuple[Callable, ViewAndMutationMeta]: + """ + Runs a sequence of wrappers on the given function. Should be called after pre_compile() + """ + for wrapper in reversed(wrappers): + compiled_fn = wrapper.post_compile( + compiled_fn, aot_config, runtime_metadata=runtime_metadata + ) + return compiled_fn, runtime_metadata + + +def make_runtime_safe( + fw_metadata: ViewAndMutationMeta, + maybe_subclass_meta: Optional[SubclassMeta], +): + """ + Calls make_runtime_safe on all ViewAndMutationMetas. + Modifies both arguments. Allows ViewAndMutationMetas to + be safely cached in AOTAutogradCache. + """ + fw_metadata.make_runtime_safe() + if maybe_subclass_meta is not None: + maybe_subclass_meta.fw_metadata.make_runtime_safe() + if maybe_subclass_meta.grad_input_metas: + for meta in maybe_subclass_meta.grad_input_metas: + if isinstance(meta, SubclassCreationMeta): + meta.make_runtime_safe() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/schemas.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/schemas.py new file mode 100644 index 0000000000000000000000000000000000000000..6259d082e2ae8c1b0393dab584a000619db56f54 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/schemas.py @@ -0,0 +1,910 @@ +# mypy: allow-untyped-defs +""" +The various dataclasses, Enums, namedtuples etc used in AOTAutograd. This includes +input/output types, metadata, config, function signatures etc. +""" + +import collections +import dataclasses +import functools +from collections.abc import Iterable +from dataclasses import dataclass, field +from enum import Enum +from typing import Any, Callable, NewType, Optional, Union + +import torch +import torch.utils._pytree as pytree +from torch._guards import Source +from torch._ops import OpOverload +from torch._subclasses import FakeTensor +from torch._subclasses.fake_tensor import is_fake +from torch.utils._python_dispatch import is_traceable_wrapper_subclass + +from .. import config +from .functional_utils import ( + _check_if_mutation_can_be_in_graph, + FunctionalTensorMetadataEq, +) +from .utils import strict_zip + + +zip = strict_zip + + +OutputType = Enum( + "OutputType", + ( + # output is not an alias + "non_alias", + # output aliases an input + "alias_of_input", + # output **is** an input tensor + "is_input", + # output has a ._base tensor, which is a graph intermediate. + # We need to return its ._base as a graph output, + # so its requires_grad info is populated correctly. + # Instructs the runtime code to regenerate the current output + # from a base tensor, graph_intermediates[base_idx] + "alias_of_intermediate_save_as_output", + # Same as above; but we don't need to explicitly add its ._base + # as a graph output, because it already **is** a graph output. + "alias_of_intermediate", + # Same as above; but the output's ._base is **already** a user output. + # Instructs the runtime code to regenerate the current output from + # a base tensor, user_outputs[base_idx] + "alias_of_intermediate_base_is_user_output", + # See Note [Intermediate Bases Optimization] + "unsafe_view_alias", + # output is an alias, but has a custom autograd.Function backward. + # In this case, we don't want to do view-replay, since we won't be able to replay the custom function. + # Instead, we'll treat this output "normally", and trace its backward into the graph. + "custom_function_view", + ), +) + + +# This class stores info about every user output. +@dataclass(frozen=True) +class OutputAliasInfo: + # Tells us if this output is: + # (1) a regular (non-aliased) output + # (2) an alias of a forward input + # (3) **is** a forward input (special case of "alias_of_input") + # (4) an alias of an intermediate (aka an alias of an output of the inner traced forward) + # (5) an alias of an intermediate, that explicitly requires returning the intermediate + # as a graph output + # (6) an alias of an intermediate, where that intermediate is also a user output + output_type: OutputType + # The raw type of the output (torch.Tensor, SymInt, etc) + raw_type: type + # If (1) above, then + # - base_idx is None + # If (2) or (3) above, then + # - Tells us that the base of this alias is user_fwd_input[base_idx] + # (This is an index into the inputs *before* we make synthetic bases) + # If (4) or (5) above, then + # - Tells us that the base of this alias is output_graph_intermediates[base_idx] + # here, this refers to the index of the *direct* traced + # If (6) above, then: + # - Tells us that the base of this alias is output_user_fwds[base_idx] + # here, this refers to the index of the *direct* traced + base_idx: Optional[int] + # If it is a Tensor, what the dynamic dims are (otherwise is None) + dynamic_dims: Optional[set[int]] + # requires_grad + requires_grad: bool + # FunctionalTensorWrapper that represents this output. + # + # Provides us the means to replay views from it. + # + # We need to wrap the actual FunctionalTensorWrapper with this class so that + # we only compare the tensor's metadata. That's because with the transformations + # of the model throughout AOTAutograd, the sequence of ViewMeta and the base + # tensor might change. + functional_tensor: Optional[FunctionalTensorMetadataEq] = None + + +class MutationType(Enum): + NOT_MUTATED = 1 + MUTATED_IN_GRAPH = 2 + MUTATED_OUT_GRAPH = 3 + + +# This class tells us info about user inputs. +@dataclass(frozen=True) +class InputAliasInfo: + is_leaf: bool + mutates_data: bool + mutates_metadata: bool + mutations_hidden_from_autograd: bool + mutations_under_no_grad_or_inference_mode: bool + mutation_inductor_storage_resize: bool + mutates_storage_metadata: bool + requires_grad: bool + keep_input_mutations: bool + + def __post_init__(self): + if self.mutates_storage_metadata: + # For convenience, we guarantee that this is always true. + # In practice, If we call .set_(), then at runtime there is no need + # to additionally fix up the tensor metadata, since our runtime + # call to inp.set_(updated_inp) will already have the right metadata + assert self.mutates_metadata + + @functools.cached_property + def mutation_type(self) -> MutationType: + if ( + (not self.mutates_data) + and (not self.mutates_metadata) + and not (self.mutation_inductor_storage_resize) + ): + return MutationType.NOT_MUTATED + + if _check_if_mutation_can_be_in_graph( + self.keep_input_mutations, + self.mutates_data, + self.mutates_metadata, + self.mutations_hidden_from_autograd, + self.mutations_under_no_grad_or_inference_mode, + self.mutates_storage_metadata, + self.mutation_inductor_storage_resize, + self.requires_grad, + ): + return MutationType.MUTATED_IN_GRAPH + + return MutationType.MUTATED_OUT_GRAPH + + +@dataclass +class PlainTensorMeta: + unwrapped_idx: int + memory_format: Optional[torch.memory_format] = None + + +@dataclass +class SubclassCreationMeta: + """ + Used for AOTDispatch. + This dataclass gives us the information we need to reconstruct a tensor subclass + from our flat inputs. + Why is this important? The graph that we'd like to trace out contains flat tensor inputs, + But the user's original model may have subclass inputs and outputs. + So we need to wrap/unwrap subclasses as necessary to translate between the user's + view (subclass inps/outs), and the backend compiler's view (graph with no subclass args). + + Complications arise mostly from the fact that a subclass can hold more than one inner tensor; + So for a given subclass input/output, we need to carefully track which indices map + to the subclass tensor in the corresponding "dense-tensor-only" graph. + """ + + # In the inner graph that only takes in dense tensor inputs, + # this maps to the first index of "tensors that should go in this subclass wrapper" + flat_tensor_start_idx: int + # arg_count is inclusive of the arg_counts of any + # inner tensor subclasses: If I have a TwoTensor and + # both of its inner elements are TwoTensors, then the + # arg_count of the outer-most sublass will be 4 + arg_count: int + # Mark where or not symints were included. This flag is only used in one assertion + # in "wrap_tensor_subclasses" + included_subclass_symints: bool + # meta and attrs are produced by the subclass's __tensor_flatten__. + # We need to keep them around along with outer_size / outer_stride to plumb them + # into __tensor_unflatten__ + attrs: dict[str, Union["SubclassCreationMeta", PlainTensorMeta]] + outer_size: Iterable[Union[None, int, torch.SymInt]] + outer_stride: Iterable[Union[None, int, torch.SymInt]] + meta: Any + # Stores the original subclass itself. + # This is needed because we need the autograd metadata on the original subclass + # (this is guaranteed to be a wrapper subclass that holds a fake tensor, + # so holding onto this at runtime shouldn't leak memory) + # This field is nulled out after calling make_runtime_safe() + original_subclass: Optional[torch.Tensor] + + # Used at runtime to determine the subclass type, so we don't need to save the original subclass + original_subclass_type: Optional[type] = None + memory_format: Optional[torch.memory_format] = None + + def compute_outer_size_and_stride( + self, + all_args, + *, + curr_start_idx: int, + ): + from .subclass_utils import compute_symint_placeholders + + def compute(outer, start_idx): + placeholders = compute_symint_placeholders(outer) + has_symbolic = any(placeholders) + + if has_symbolic: + start = curr_start_idx + end = start_idx + sum(placeholders) + it_args = iter(all_args[start:end]) + it_placeholders = iter(placeholders) + return pytree.tree_map_only( + lambda _: next(it_placeholders), lambda _: next(it_args), outer + ), start + len(placeholders) + else: + return outer, start_idx + + outer_size, next_idx = compute(self.outer_size, curr_start_idx) + outer_stride, _ = compute(self.outer_stride, next_idx) + return outer_size, outer_stride + + def creation_fn( + self, + all_args, + *, + is_runtime: bool, + ): + inner_tensors = {} + + curr_start_idx = self.flat_tensor_start_idx + for attr, creation_meta in self.attrs.items(): + if isinstance(creation_meta, PlainTensorMeta): + subclass = all_args[curr_start_idx] + curr_start_idx += 1 + else: + subclass = creation_meta.creation_fn( + all_args, + is_runtime=is_runtime, + ) + curr_start_idx += creation_meta.arg_count + inner_tensors[attr] = subclass + + if is_runtime: + assert self.original_subclass_type is not None + original_subclass_type = self.original_subclass_type + else: + original_subclass_type = type(self.original_subclass) + + if is_runtime: + outer_size, outer_stride = self.compute_outer_size_and_stride( + all_args, + curr_start_idx=curr_start_idx, + ) + else: + outer_size, outer_stride = self.outer_size, self.outer_stride + + rebuilt = original_subclass_type.__tensor_unflatten__( # type: ignore[attr-defined] + inner_tensors, self.meta, outer_size, outer_stride + ) + + if not is_runtime: + # After wrapping up the inner dense tensors into a subclass, we need to make sure that our new wrapper + # has correct autograd metadata, since we'll be tracing through the autograd engine with the subclass. + # We don't trace through the autograd engine at runtime though, so no need + # to compute this extra metadata then! + torch._mirror_autograd_meta_to(self.original_subclass, rebuilt) # type: ignore[attr-defined] + + return rebuilt + + def make_runtime_safe(self): + def _make_size_runtime_safe(x: Union[None, int, torch.SymInt]) -> Optional[int]: + dummy = -1 + if isinstance(x, torch.SymInt): + # Replace nested ints by a dummy value (-1) as NJT ignores + # the outer_size/outer_stride at runtime. + return dummy if x.node.is_nested_int() else None + return x + + assert self.original_subclass is not None + self.original_subclass_type = type(self.original_subclass) + self.original_subclass = None + + # Note: NJT outer_size in AOTDispatcher + # `_make_size_runtime_safe` replaces any nested int with a dummy value (-1) + # to prevent serializing a SymInt at runtime. Internally, nested tensor __tensor_unflatten__ + # is designed to safely ignore this dummy value. + # For more details, see: https://github.com/pytorch/pytorch/blob/5141ade8e30c64e873e14dcc8de233da45d15025/torch/nested/_internal/nested_tensor.py#L266-L299 # noqa: B950 + self.outer_size = tuple(map(_make_size_runtime_safe, self.outer_size)) + self.outer_stride = tuple(map(_make_size_runtime_safe, self.outer_stride)) + + # Recurse on nested subclass info + for creation_meta in self.attrs.values(): + if isinstance(creation_meta, SubclassCreationMeta): + creation_meta.make_runtime_safe() + + def __post_init__(self): + # sanity assert to make sure we don't leak memory + assert is_fake(self.original_subclass) + + +# This class encapsulates all aliasing + mutation info we need about the forward graph +# See a more detailed overview of the edge case handling at +# https://docs.google.com/document/d/19UoIh_SVrMy_b2Sx5ZaeOJttm6P0Qmyss2rdBuyfoic/edit +@dataclass(eq=False) +class ViewAndMutationMeta: + # length = # user inputs + # This gives us info about every input, and what sort of mutation happened to it (if any) + input_info: list[InputAliasInfo] + + # length = # user outputs + # This gives us info about every output (mostly around whether it aliases other tensors) + output_info: list[OutputAliasInfo] + + # length = the number of intermediate bases appended as outputs to the end of the forward graph. + # Note: this is not necessarily the same thing as: + # len([x for x in output_info if x.output_type == OutputType.alias_of_intermediate]) + # Because outputs might share a ._base, or an output's ._base might itself be + # another user output (in both cases, we won't redundantly append bases to the end of the graph) + num_intermediate_bases: int + + # For inference only: instructs us to keep data-only input mutations directly in the graph + keep_input_mutations: bool + + # length = (# inputs w data mutations) + (# user outputs that are non_aliasing tensors) + # + (# intermediate bases) + # These are the FakeTensor (or potential SymInt) outputs that we traced from our + # metadata pass of the user's forward function. + # Their only use today is to pass them as a best-guess for tangents when tracing the joint. + # Stashing them as part of our "metadata" makes it simpler if we want to run our analysis + # pass once, and re-use the output throughout AOTAutograd + traced_tangents: list[Any] + + # Each of these is a list telling us about subclasses for the inputs/outputs/grad_outs + # They are used throughout AOTDispatch to tell us how to generate a list of subclass tensors, + # Given a (potentially larger) list of plain torch tensors. + + # Taking subclass_inp_meta as an example: + # subclass_inp_meta[i] = j (an int) tells us: + # "The i'th user input is not a subclass, and corresponds to inputs[j] of the plain-tensor graph." + # subclass_inp_meta[i] = SubclassCreationMeta(flat_tensor_start_idx=3, arg_count=2) + # "The i'th user input is subclass holding two inner tensors, which are + # inputs[3] and inputs[4] of the plain-tensor graph". + + # length = # user inputs + subclass_inp_meta: list[Union[PlainTensorMeta, SubclassCreationMeta]] + # So, the full set of outputs to the forward graph looks something like: + # (*mutated_inps, *user_outs, *intermediate_bases, *saved_for_bw_tensors) + # where the first 3 of those 4 can be subclasses + # (but not saved_for_bw tensors, since these are internal to the compiler + # and not user visible, so there's no point in wrapping/unwrapping them at runtime). + # This list contains subclass information on all of the fw graph outputs + # except for saved_for_bw_tensors. + subclass_fw_graph_out_meta: list[Union[PlainTensorMeta, SubclassCreationMeta]] + # length = # backward graph inputs + subclass_tangent_meta: list[Union[PlainTensorMeta, SubclassCreationMeta]] + # TODO: we should kill this + # (need to default it to not break internal) + is_train: bool = False + + # length = (# inputs w data mutations) + (# user outputs that are non_aliasing tensors) + # + (# intermediate bases) + # At runtime, we don't keep the traced_tangents around since they're not serializable. + # Instead, we keep any necessary subclass metadata necessary about each traced_tangent. + # This list is generated after calling make_runtime_safe(). + traced_tangent_metas: Optional[list[Any]] = None + + num_symints_saved_for_bw: Optional[int] = None + + # The grad_enabled mutation that will be emitted in the runtime_wrapper epilogue + # NOTE: AOTAutograd will assume that the ambient `is_grad_enabled` is the grad mode + # that is intended to be in effect prior to running the graph, in keeping with + # equivalence to eager mode. It is the responsibility of upstream graph acquisition + # to reset the grad mode to its pre-graph value prior to calling aot_autograd. + grad_enabled_mutation: Optional[bool] = None + + # Keeps track of whether `torch.use_deterministic_algorithms` was turned on + # when the forward was run. If deterministic mode was turned off during the + # forward, but is turned on during the backward call, then an error is + # raised + deterministic: Optional[bool] = None + + # Keeps track of which input indices store parameters (which we will treat as static) + static_input_indices: list[int] = field(default_factory=list) + + # Map of effect type (ex. _EffectType.ORDERED) to token. If there are + # side-effectful operators, FunctionalTensorMode will populate this + # dictionary telling us how many tokens we will need during tracing. + tokens: dict[Any, torch.Tensor] = field(default_factory=dict) + + # Only filled in if/when we trace the joint function + # If an input requires grad and is mutated in the backward, it is only safe to keep the mutation + # in the graph if gradients are disabled while the backward runs + # (grad mode is disabled by default when users run the backward, but can be turned on with create_graph=True) + # At runtime during the backward, we use this list of indices to error properly if we find out + # that it was not safe to include a backward mutation in the graph. + indices_of_inputs_that_requires_grad_with_mutations_in_bw: list[int] = field( + default_factory=list + ) + + # Indexes of saved tensors which are donated buffer. + # Donated buffer means the tensor is not alias of any forward user input, forward user output, + # and backward output. + bw_donated_idxs: Optional[list[int]] = None + + # Number of tokens used in backward, appended at the end of backward outputs. + # Filled after tracing joint function. + num_backward_tokens: int = 0 + + # Number of rng states that will get thread into the forward and backward for + # cudagraph compatible run_and_save_rng + num_graphsafe_rng_states: int = 0 + + graphsafe_rng_state_index: Optional[int] = None + + def __post_init__(self): + # pre-compute the indices of the inputs that are mutated. + # When keep_input_mutations is set, we don't need to worry about our epilogue + # handling data-only mutations, because we keep them directly in the graph. + mutated_inp_runtime_indices = [ + i + for i, m in enumerate(self.input_info) + if (m.mutation_type == MutationType.MUTATED_OUT_GRAPH) + ] + + mutated_graph_handled_indices = [ + i + for i, m in enumerate(self.input_info) + if m.mutation_type == MutationType.MUTATED_IN_GRAPH + ] + self.mutated_graph_handled_indices = mutated_graph_handled_indices + self.num_mutated_graph_handled_indices = len(self.mutated_graph_handled_indices) + + mutated_graph_handled_indices_seen_by_autograd = [ + i + for i in mutated_graph_handled_indices + if not self.input_info[i].mutations_hidden_from_autograd + ] + + self.mutated_graph_handled_indices_seen_by_autograd = ( + mutated_graph_handled_indices_seen_by_autograd + ) + self.num_mutated_graph_handled_indices_seen_by_autograd = len( + self.mutated_graph_handled_indices_seen_by_autograd + ) + + aliased_out_indices = [ + i + for i, m in enumerate(self.output_info) + if m.output_type + not in [ + OutputType.non_alias, + OutputType.unsafe_view_alias, + OutputType.custom_function_view, + ] + ] + unsafe_view_out_indices = [ + i + for i, m in enumerate(self.output_info) + if m.output_type is OutputType.unsafe_view_alias + ] + + # This is pre-computed in post_init for perf. + # It contains the index of every element + # of input_info that corresponds to a mutation (data or metadata or both) + self.mutated_inp_runtime_indices = mutated_inp_runtime_indices + self.num_mutated_inp_runtime_indices = len(self.mutated_inp_runtime_indices) + + # This is pre-computed for perf. + # It contains the index of every element + # of output_info that corresponds to an alias (either of an input or intermediate) + self.aliased_out_indices = aliased_out_indices + self.unsafe_view_out_indices = unsafe_view_out_indices + self.num_outputs = len(self.output_info) + self.num_outputs_non_aliased = len( + [ + x + for x in self.output_info + if x.output_type + in [ + OutputType.non_alias, + OutputType.unsafe_view_alias, + OutputType.custom_function_view, + ] + ] + ) + self.num_outputs_aliased_to_inputs = len( + [ + x + for x in self.output_info + if x.output_type + in [ + OutputType.alias_of_input, + OutputType.is_input, + ] + ] + ) + self.num_unsafe_view_outputs = len(self.unsafe_view_out_indices) + self.num_outputs_aliased_to_intermediates = len( + [ + x + for x in self.output_info + if x.output_type + in [ + OutputType.alias_of_intermediate, + OutputType.alias_of_intermediate_save_as_output, + OutputType.alias_of_intermediate_base_is_user_output, + ] + ] + ) + self.num_outputs_aliased = ( + self.num_outputs_aliased_to_inputs + + self.num_outputs_aliased_to_intermediates + ) + + self.dynamic_outputs = any(o.dynamic_dims for o in self.output_info) + # See Note: [AOTAutograd Backward Guards] + # This is pre-computed for fast asserts on the types of our grad_outputs in the backward. + # Eventually, we should kill this and replace with real backward guards. + # (we want to precompute the "runtime" types, so replace FakeTensor with torch.Tensor) + self.output_types = [ + torch.Tensor if isinstance(x, FakeTensor) else type(x) + for x in self.traced_tangents + ] + + self.is_rng_op_functionalized = config.functionalize_rng_ops + # All of the above metadata is collected by tracing the fw function. + # However, extra outputs for rng offsets behave differently. Both fwd + # and bwd graphs have their own outputs for the total consumed offsets. + # Unlike mutated inputs, we don't have to worry about sending the right + # set of tensors between fwd and bwd. Fwd and bwd offsets are + # independent and simpler to handle. Therefore, we track them + # separately. + self.num_outputs_rng_offset = 1 if self.is_rng_op_functionalized else 0 + + # Our forward() returns both (tokens, mutated_inputs, outputs, output_intermediate_bases, saved_tensors, saved_symints) + # Tokens will be split out before mutations/view handling and we do not count them here. + self.num_forward_returns = ( + self.num_mutated_inp_runtime_indices + + self.num_outputs + + self.num_intermediate_bases + ) + # In case of functionalization of rng ops, the fw_module returns one + # additional output for rng offset. This rng offset is used right + # away to advance the rng state, and is not passed on to the raw + # outputs. However, we need to know the exact boundary to identify + # which tensors to be saved for the bwd graph. num_forward captures + # this information. + self.num_forward = self.num_forward_returns + self.num_outputs_rng_offset + + def make_runtime_safe(self): + """ + There are various fields in ViewAndMutationMeta that aren't serializable. This function is called after all tracing + is completed to simplify certain fields in the metadata so that they can be safely cached. + + Doing so may lose information (in the case of traced_tangents), but none of the information is needed at runtime. + """ + # TODO: This function is only a best effort: there are other fields that may not be cache safe + # (i.e., there's no guarantee that tensor_flatten() returns a serializable result), or that + # SubclassCreationMeta is cache safe. + assert self.traced_tangent_metas is None + + def extract_metadata(t): + if isinstance(t, torch.Tensor) and is_traceable_wrapper_subclass(t): + (inner_tensors, flatten_spec) = t.__tensor_flatten__() # type: ignore[attr-defined] + # Technically, we only need the flatten_spec, not the inner tensors. + # However, some Tensor subclasses (like TwoTensor) may have flatten_spec = None. + # And we want to be able to assert that this metadata is non-None, + # to distinguish between "this was a tensor subclass with no metadata" vs. + # "this wasn't a tensor subclass at all". + return (inner_tensors, flatten_spec) + else: + return None + + self.traced_tangent_metas = [extract_metadata(t) for t in self.traced_tangents] + # Clear traced tangents at runtime + self.traced_tangents = [] + new_output_info = [] + for out in self.output_info: + if config.view_replay_for_aliased_outputs: + new_out = out + else: + # If we're not using view_replay, remove the functional tensor. + # Functional tensors are unfortunately not serializable, + # so doing this is required for AOTAutograd caching. + new_out = dataclasses.replace(out, functional_tensor=None) + new_output_info.append(new_out) + self.output_info = new_output_info + for inp_meta in self.subclass_inp_meta: + if isinstance(inp_meta, SubclassCreationMeta): + inp_meta.make_runtime_safe() + for inp_meta in self.subclass_fw_graph_out_meta: + if isinstance(inp_meta, SubclassCreationMeta): + inp_meta.make_runtime_safe() + for inp_meta in self.subclass_tangent_meta: + if isinstance(inp_meta, SubclassCreationMeta): + inp_meta.make_runtime_safe() + + @property + def tensors_saved_for_backwards_slice(self): + assert self.num_symints_saved_for_bw is not None + if self.num_symints_saved_for_bw > 0: + return slice(self.num_forward, -self.num_symints_saved_for_bw) + else: + return slice(self.num_forward, None) + + @property + def symints_saved_for_backwards_slice(self): + assert self.num_symints_saved_for_bw is not None + if self.num_symints_saved_for_bw > 0: + return slice(-self.num_symints_saved_for_bw, None) + else: + return slice(0, 0) # empty slice + + def __eq__(self, other): + if not isinstance(other, ViewAndMutationMeta): + return NotImplemented + return ( + self.input_info == other.input_info + and self.output_info == other.output_info + and self.num_intermediate_bases == other.num_intermediate_bases + and self.keep_input_mutations == other.keep_input_mutations + and self.is_rng_op_functionalized == other.is_rng_op_functionalized + and self.num_outputs_rng_offset == other.num_outputs_rng_offset + and len(self.traced_tangents) == len(other.traced_tangents) + and all( + x.shape == y.shape and x.dtype == y.dtype + for x, y, in zip(self.traced_tangents, other.traced_tangents) + ) + and self.num_backward_tokens == other.num_backward_tokens + ) + + +@dataclass(eq=False) +class SubclassMeta: + # A copy of all forward metadata, but computed on the *dense* tensor forward (after desugaring subclasses) + # So for example, if the user had a model containing two `TwoTensor` inputs, + # Then `SubclassMeta.fw_metadata.input_infos` would have length 4 here. + fw_metadata: ViewAndMutationMeta + + # Note: [Computing Subclass Metadata about grad_inputs] + # Given a list of flattened, plain tensor grad_inputs, this tells us how to reconstruct the grad_input subclasses + # + # You might think: why not just assume that all grad_inputs will have the same subclass-ness as the original inputs? + # (AOTAutograd generally assumes other properties, e.g. that grad_outputs are contiguous) + # + # This doesn't really work though. take this example: + # + # def f(DoubleTensor, DenseTensor): + # return DoubleTensor * DenseTensor + # + # In the above example, the .grad field of *both* DoubleTensor and DenseTensor will be a DoubleTensor. + # When we trace out a joint fw-bw graph, we'll end up returning two subclasses for the two grad_inputs. + # This means that our backward graph will return 4 outputs (two dense tensors for each DoubleTensor grad_input) + # and we need to properly store the metadata that tells us how to turn these 4 outputs back into DoubleTensors. + # + # Note that this info **cannot** easily be figured out from ViewAndMutationMeta. + # We can only compute this info by tracing the entire joint and examining the grad_inputs that we computed. + # + # See Note: [AOTAutograd Backward Guards] + # This will also eventually require us to install backward guards, + # in case we made incorrect assumptions about the subclass-ness of our grad_outputs + # + # Optional field because we don't compute for inference graphs + grad_input_metas: Optional[ + list[Union[PlainTensorMeta, SubclassCreationMeta]] + ] = None + + def __init__(self) -> None: + # The fields in this class get set after its construction. + pass + + +# This class exists because: +# - the autograd.Function.forward() in aot autograd returns outputs that might alias inputs +# - we only care about the metadata on those aliases, so we can regenerate them. +# We do not want them to participate in the autograd.Function. +# We do that by wrapping them in an opaque class, so the autograd.Function +# does not know to treat them as tensors. +@dataclass(frozen=True) +class TensorAlias: + alias: torch.Tensor + + +@dataclass +class BackwardSignature: + """ + Provides information about the backward section of an exported + joint forward-backward graph. + For a particular fx GraphModule, this class contains information on: + (1) A mapping from each gradient (backwards output) to the parameter + it corresponds to (forward input) + (2) A mapping from each gradient (backwards output) to the user input + it corresponds to (forward input) + (3) Which of the forward outputs corresponds to the loss, that we backprop on. + + Each string name is the `node.name` of the corresponding node in the fx graph. + """ + + gradients_to_parameters: dict[str, str] + gradients_to_user_inputs: dict[str, str] + loss_output: str + + +GraphOutputName = NewType("GraphOutputName", str) +GraphInputName = NewType("GraphInputName", str) +FQN = NewType("FQN", str) + + +@dataclass +class GraphSignature: + """ + Provides information about an exported module. + For a particular fx GraphModule, this class contains information on: + (1) Which graph inputs are parameters, buffers, or user inputs + (2) (for params/buffers) a mapping from the name of each graph argument + to its parameter/buffer FQN in the original nn.Module. + (3) If there are input mutations, these are represented as extra outputs + in the fx GraphModule. We provide a mapping from these + extra output names to the names of the actual inputs. + (4) The pytree metadata on how to flatten/unflatten inputs and outputs. + The corresponding FX GraphModule only accepts and returns + pytree-flattened inputs/outputs. + (5) (Optionally) if the FX is a joint forward-backward graph, we provide + a signature on the backward section of the joint graph. + """ + + parameters: list[FQN] + buffers: list[FQN] + + user_inputs: list[GraphInputName] + user_outputs: list[GraphOutputName] + inputs_to_parameters: dict[GraphInputName, FQN] + inputs_to_buffers: dict[GraphInputName, FQN] + + # If the user's module mutates a buffer, + # it's represented in the graph as an extra graph output. + # This dict is a mapping from + # "graph outputs that correspond to updated buffers" + # to the FQN names of those mutated buffers. + buffers_to_mutate: dict[GraphOutputName, FQN] + user_inputs_to_mutate: dict[GraphOutputName, GraphInputName] + + in_spec: pytree.TreeSpec + out_spec: pytree.TreeSpec + + backward_signature: Optional[BackwardSignature] + + input_tokens: list[GraphInputName] + output_tokens: list[GraphOutputName] + + @classmethod + def from_tracing_metadata( + cls, + *, + in_spec: pytree.TreeSpec, + out_spec: pytree.TreeSpec, + graph_input_names: list[str], + graph_output_names: list[str], + view_mutation_metadata: ViewAndMutationMeta, + named_parameters: list[str], + named_buffers: list[str], + num_user_inputs: int, + num_user_outputs: int, + loss_index: Optional[int], + backward_signature: Optional[BackwardSignature], + ) -> "GraphSignature": + graph_inputs = graph_input_names + graph_outputs = graph_output_names + parameters = list(named_parameters) + buffers = list(named_buffers) + num_tokens = len(view_mutation_metadata.tokens) + + # Calling convention assumptions: + # (1) graph inputs = (input_tokens, params, buffers, user_inputs) + # (2) graph outputs = (output_tokens, mutated_inputs, user_outs, param_gradients) + # (If we are capturing an inference graph, this convention is identical + # except that param_gradients is empty) + # See Note [Side-Effectful Tokens in AOTAutograd] for information on tokens + + # Address input calling conventions: + start, stop = 0, num_tokens + input_tokens = graph_inputs[start:stop] + + start, stop = stop, stop + len(parameters) + inputs_to_parameters = dict(zip(graph_inputs[start:stop], parameters)) + + start, stop = stop, stop + len(buffers) + inputs_to_buffers = dict( + zip( + graph_inputs[start:stop], + buffers, + ) + ) + + start, stop = stop, stop + num_user_inputs + user_inputs = graph_inputs[start:stop] + + # We should've gone through all the inputs now + assert len(graph_inputs) - stop == 0 + + # Address output calling conventions: + start, stop = 0, num_tokens + output_tokens = graph_outputs[start:stop] + + names = [*input_tokens, *parameters, *buffers, *user_inputs] + mutations = [] + for idx, input_info in enumerate(view_mutation_metadata.input_info): + if input_info.mutates_data: + # Only buffers can be mutated, not parameters + assert idx >= len(parameters) + mutations.append(names[idx + num_tokens]) + + assert len(mutations) == view_mutation_metadata.num_mutated_inp_runtime_indices + + start, stop = ( + stop, + stop + view_mutation_metadata.num_mutated_inp_runtime_indices, + ) + outputs_to_mutations = dict(zip(graph_outputs[start:stop], mutations)) + + user_inputs_to_mutate = {} + buffers_to_mutate = {} + for output_name, mutation_name in outputs_to_mutations.items(): + if mutation_name in user_inputs: + user_inputs_to_mutate[output_name] = mutation_name + else: + assert mutation_name in buffers + buffers_to_mutate[output_name] = mutation_name + + start, stop = stop, stop + num_user_outputs + user_outputs = graph_outputs[start:stop] + + unused_outputs = len(graph_outputs) - stop + if backward_signature is not None: + unused_outputs -= len(backward_signature.gradients_to_parameters) + len( + backward_signature.gradients_to_user_inputs + ) + assert unused_outputs == 0 + + return GraphSignature( + parameters=parameters, # type: ignore[arg-type] + buffers=buffers, # type: ignore[arg-type] + user_inputs=user_inputs, # type: ignore[arg-type] + user_outputs=user_outputs, # type: ignore[arg-type] + inputs_to_buffers=inputs_to_buffers, # type: ignore[arg-type] + inputs_to_parameters=inputs_to_parameters, # type: ignore[arg-type] + user_inputs_to_mutate=user_inputs_to_mutate, + buffers_to_mutate=buffers_to_mutate, # type: ignore[arg-type] + in_spec=in_spec, + out_spec=out_spec, + backward_signature=backward_signature, + input_tokens=input_tokens, # type: ignore[arg-type] + output_tokens=output_tokens, # type: ignore[arg-type] + ) + + +@dataclass +class AOTAutogradCacheInfo: + cache_key: str + start_time_ns: int + + +@dataclass +class AOTConfig: + """ + Configuration for AOTDispatcher + """ + + fw_compiler: Callable + bw_compiler: Callable + partition_fn: Callable + decompositions: dict[OpOverload, Callable] + num_params_buffers: int + aot_id: int + keep_inference_input_mutations: bool + is_export: bool = False + no_tangents: bool = False + dynamic_shapes: bool = False + aot_autograd_arg_pos_to_source: Optional[list[Source]] = None + static_input_indices: Optional[list[int]] = None + inference_compiler: Optional[Callable] = None + enable_log: bool = True + # this is always false outside of export. + pre_dispatch: bool = False + # Key to use for AOTAutogradCache + cache_info: Optional[AOTAutogradCacheInfo] = None + + def __post_init__(self): + if self.pre_dispatch: + assert self.is_export, "Can only have pre_dispatch IR for export." + + +SubclassTracingInfo = collections.namedtuple( + "SubclassTracingInfo", + ["plain_tensor_trace_fn", "plain_tensor_args", "maybe_subclass_meta"], +) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/subclass_parametrization.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/subclass_parametrization.py new file mode 100644 index 0000000000000000000000000000000000000000..5d6d17ca099c933e23b33cb9bafbb444d98205c4 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/subclass_parametrization.py @@ -0,0 +1,103 @@ +import dataclasses +import itertools +from collections.abc import Iterable +from typing import Any, Union + +import torch +from torch.utils._python_dispatch import is_traceable_wrapper_subclass + + +# This is technically very similar to SubclassCreatingMeta +# in aot_autograd, but we don't need all the stuff in there +# so just recreated a new dataclass. +@dataclasses.dataclass +class SubclassCreationMeta: + start_idx: int + num_tensors: int + class_type: Any + attrs: dict[str, "SubclassCreationMeta"] + metadata: Any + outer_size: Iterable[Union[None, int, torch.SymInt]] + outer_stride: Iterable[Union[None, int, torch.SymInt]] + + +class UnwrapTensorSubclass(torch.nn.Module): + def forward(self, *tensors) -> torch.Tensor: # type: ignore[no-untyped-def] + todo: list[torch.Tensor] = list(tensors) + + def _unwrap_tensor_subclasses(subclass_meta, tensors, offset): # type: ignore[no-untyped-def] + if subclass_meta is None: + return tensors[offset], offset + 1 + inner_tensors = {} + for attr, meta in subclass_meta.attrs.items(): + built_tensor, offset = _unwrap_tensor_subclasses(meta, tensors, offset) + inner_tensors[attr] = built_tensor + rebuilt = subclass_meta.class_type.__tensor_unflatten__( + inner_tensors, + subclass_meta.metadata, + subclass_meta.outer_size, + subclass_meta.outer_stride, + ) + return rebuilt, offset + + return _unwrap_tensor_subclasses(self.subclass_meta, todo, 0)[0] + + def right_inverse(self, tensor: torch.Tensor) -> list[torch.Tensor]: + assert type(tensor) is not torch.Tensor + plain_tensors: list[torch.Tensor] = [] + + def _create_subclass_meta(tensor, idx, plain_tensor_container): # type: ignore[no-untyped-def] + if type(tensor) is torch.Tensor: + plain_tensor_container.append(tensor) + return None, idx + 1 + inner_tensors_attrnames, metadata = tensor.__tensor_flatten__() # type: ignore[attr-defined] + new_idx = idx + attr_to_meta = {} + for attr in inner_tensors_attrnames: + val = getattr(tensor, attr) + subclass_meta, new_idx = _create_subclass_meta( + val, new_idx, plain_tensor_container + ) + attr_to_meta[attr] = subclass_meta + return ( + SubclassCreationMeta( + start_idx=idx, + num_tensors=new_idx - idx, + class_type=type(tensor), + attrs=attr_to_meta, + metadata=metadata, + outer_size=tensor.size(), + outer_stride=tensor.stride(), + ), + new_idx, + ) + + self.subclass_meta = _create_subclass_meta(tensor, 0, plain_tensors)[0] + return plain_tensors + + +def unwrap_tensor_subclass_parameters(module: torch.nn.Module) -> torch.nn.Module: + """ + Model transformation that replaces all the parameters that are subclasses to plain tensors. + This reduces runtime overhead of flattening/unflattening the parameters. + + This transformation adds parametrization with `torch.nn.utils.parametrize`. + The FQNs of the subclass parameters will be changed and state_dict will become incompatible with the original model. + E.g. + Original model state_dict: {"p1": torch.testing._internal.TwoTensor} + becomes: {"parametrizations.p2.original0": torch.Tensor, "parametrizations.p2.original1": torch.Tensor} + + """ + for name, tensor in itertools.chain( + list(module.named_parameters(recurse=False)), + list(module.named_buffers(recurse=False)), + ): + if is_traceable_wrapper_subclass(tensor): + torch.nn.utils.parametrize.register_parametrization( + module, name, UnwrapTensorSubclass() + ) + + for name, child in module.named_children(): + unwrap_tensor_subclass_parameters(child) + + return module diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/subclass_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/subclass_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d352f43da371bc8e0b5b9c8d481bdbf7c8380b5c --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/subclass_utils.py @@ -0,0 +1,475 @@ +# mypy: allow-untyped-defs +""" +This file contains utilities for tracing through __torch_dispatch__ based tensor subclasses and modes. +AOTAutograd's responsibility is to trace through all pytorch capabilities that live in the pytorch dispatcher, +and this includes tensor subclasses that implement __torch_dispatch__. +""" + +import collections +import typing +from collections.abc import Iterable +from typing import Any, Callable, Optional, TypeVar, Union + +import torch +import torch.utils._pytree as pytree +from torch import SymInt, Tensor +from torch._subclasses.fake_tensor import get_plain_tensors +from torch.utils._python_dispatch import is_traceable_wrapper_subclass + +from .schemas import ( + MutationType, + PlainTensorMeta, + SubclassCreationMeta, + ViewAndMutationMeta, +) +from .utils import strict_zip + + +zip = strict_zip + +T = TypeVar("T", bound=torch.Tensor) + + +def requires_subclass_dispatch(args, fw_metadata: ViewAndMutationMeta) -> bool: + args_flattened = pytree.arg_tree_leaves(*args) + any_subclass_args = any( + is_traceable_wrapper_subclass(x) + for x in args_flattened + if isinstance(x, Tensor) + ) + from torch._functorch._aot_autograd.schemas import SubclassCreationMeta + + any_subclass_outputs = any( + type(x) is SubclassCreationMeta for x in fw_metadata.subclass_fw_graph_out_meta + ) + # This tells us whether or not we need to perform any unwrapping/wrapping of tensor subclasses at runtime. + return any_subclass_args or any_subclass_outputs + + +suggest_memory_format = torch._prims_common.suggest_memory_format + + +def maybe_suggest_memory_format( + t, with_memory_format: bool +) -> Optional[torch.memory_format]: + if not with_memory_format: + return None + + return suggest_memory_format(t) + + +def get_subclass_typing_container( + tensor_subclass: torch.Tensor, +) -> dict[type[torch.Tensor], list[type[torch.Tensor]]]: + """ + Given a subclass, returns a recursive dictionary mapping each + inner tensors to its' subclass types. + """ + + def _get_types_for_subclass(tensor_subclass: torch.Tensor) -> None: + if not is_traceable_wrapper_subclass(tensor_subclass): + return + tracker[type(tensor_subclass)].append(tensor_subclass) + inner_keys, _ = tensor_subclass.__tensor_flatten__() + for key in inner_keys: + inner_tensor = getattr(tensor_subclass, key) + _get_types_for_subclass(inner_tensor) + + tracker: dict[Any, list[Any]] = collections.defaultdict(list) + _get_types_for_subclass(tensor_subclass) + return tracker + + +def create_subclass_metadata( + a: Any, start_idx: int, count_symints: bool, with_memory_format: bool = False +): + if not is_traceable_wrapper_subclass(a): + idx = start_idx + 1 + return ( + PlainTensorMeta( + idx, + memory_format=maybe_suggest_memory_format(a, with_memory_format), + ), + idx, + ) + + inner_keys, metadata = a.__tensor_flatten__() + new_start_idx = start_idx + attrs = {} + + for key in inner_keys: + new_subclass_meta, new_start_idx = create_subclass_metadata( + getattr(a, key), + new_start_idx, + count_symints=count_symints, + with_memory_format=with_memory_format, + ) + attrs[key] = new_subclass_meta + + # It *must* be because is_traceable_wrapper_subclass() - but mypy is not smart. + assert isinstance(a, Tensor) + + new_start_idx = ( + new_start_idx + + count_symints * len(filter_symints(a.size())) + + count_symints * len(filter_symints(a.stride())) + ) + + return ( + SubclassCreationMeta( + flat_tensor_start_idx=start_idx, + arg_count=new_start_idx - start_idx, + included_subclass_symints=count_symints, + attrs=attrs, + meta=metadata, + outer_size=a.size(), # type: ignore[attr-defined, arg-type] + outer_stride=a.stride(), # type: ignore[arg-type] + original_subclass=a, + memory_format=maybe_suggest_memory_format(a, with_memory_format), + ), + new_start_idx, + ) + + +# Given a flat list of arguments, some of which may be tensor subclasses, +# computes metadata about "how to reconstruct the current list of subclasses, +# if we were given their flattened dense tensors instead" +def create_subclass_meta( + curr_args: Union[list[Any], tuple[Any, ...]], + *, + count_symints: bool = True, + with_memory_format: bool = False, +) -> list[Union[PlainTensorMeta, SubclassCreationMeta]]: + idx = 0 + infos: list[Union[PlainTensorMeta, SubclassCreationMeta]] = [] + for a in curr_args: + if is_traceable_wrapper_subclass(a): + assert isinstance(a, Tensor) + start_idx = idx + subclass_meta, _ = create_subclass_metadata( + a, + start_idx, + count_symints=count_symints, + with_memory_format=with_memory_format, + ) + infos.append(subclass_meta) + cnt = subclass_meta.arg_count + else: + infos.append( + PlainTensorMeta( + idx, + memory_format=maybe_suggest_memory_format(a, with_memory_format), + ) + ) + cnt = 1 + idx += cnt + return infos + + +def filter_symints(lst: Iterable[Union[int, SymInt]]): + # Capture all SymInts from the iterable. + def symint_check(s: Union[int, SymInt]) -> bool: + return isinstance(s, SymInt) and not s.node.is_nested_int() + + return [s for s in lst if symint_check(s)] + + +def compute_symint_placeholders(lst: Iterable[Union[None, int, SymInt]]) -> list[bool]: + # Non-nested symints are replaced with None in `make_runtime_safe()` + return [s is None for s in lst] + + +# This function takes in a pytree of arguments and unwraps any tensor +# subclasses. +# +# NOTE: The reason for "append_symints": +# +# * At compile time: we append extra symint args when unwrapping primals +# (but not tangents, because they should always share symints with primals). +# We also append extra symints when unwrapping the subclass outputs of the +# traced function, so we can return them as extra outputs +# +# * At runtime: we similarly append subclass sizes when we unwrap subclass +# primals (but not tangents) on entry to the forward. See the runtime version of +# this function below. +def unwrap_tensor_subclasses( + wrapped_args: list[Union[Tensor, int]], + *, + append_symints: bool, +): + def flatten_subclass(t: Union[Tensor, int], *, out=None): + # unwrap a subclass into plain tensors and their size/stride if "append_symint" + # is True + if not is_traceable_wrapper_subclass(t): + out.append(t) + return + + attrs, _ = t.__tensor_flatten__() + + for attr in attrs: + inner_tensor = getattr(t, attr) + flatten_subclass(inner_tensor, out=out) + + if append_symints: + out.extend(filter_symints(t.size())) + out.extend(filter_symints(t.stride())) + + xs_inner: list[Union[int, Tensor, SymInt]] = [] + + for x in wrapped_args: + flatten_subclass(typing.cast(Tensor, x), out=xs_inner) + + return xs_inner + + +# subclass_metas is needed at runtime to compute which indices are symints in +# the outer_size/outer_stride +def runtime_unwrap_tensor_subclasses( + wrapped_args: list[Union[Tensor, int]], + *, + append_symints: bool, + subclass_metas: Optional[list[Union[PlainTensorMeta, SubclassCreationMeta]]] = None, +): + def flatten_subclass(x: Tensor, meta: Optional[SubclassCreationMeta], *, out): + if not is_traceable_wrapper_subclass(x): + out.append(x) + return out + + assert isinstance(x, Tensor) + + attrs, _ = x.__tensor_flatten__() + + for attr in attrs: + inner_tensor = getattr(x, attr) + inner_meta = meta.attrs.get(attr) + flatten_subclass(inner_tensor, inner_meta, out=out) + + if append_symints: + assert isinstance(meta, SubclassCreationMeta) + # outer_size + size = x.size() + symint_placeholders = compute_symint_placeholders(meta.outer_size) + assert len(size) == len(symint_placeholders) + out.extend( + [r for (r, is_symint) in zip(size, symint_placeholders) if is_symint] + ) + + # outer_stride + stride = x.stride() + symint_placeholders = compute_symint_placeholders(meta.outer_stride) + assert len(stride) == len(symint_placeholders) + out.extend( + [r for (r, is_symint) in zip(stride, symint_placeholders) if is_symint] + ) + return out + + xs_inner: list[Union[int, Tensor, SymInt]] = [] + + if append_symints: + assert subclass_metas is not None + + for idx, x in enumerate(wrapped_args): + if not is_traceable_wrapper_subclass(x): + xs_inner.append(x) + continue + + if subclass_metas is None: + get_plain_tensors(typing.cast(Tensor, x), out=xs_inner) + else: + meta = subclass_metas[idx] + assert isinstance(meta, SubclassCreationMeta) + flatten_subclass(typing.cast(Tensor, x), meta, out=xs_inner) + + return xs_inner + + +def unwrap_tensor_subclasses_with_indices_to_original(wrapped_args): + ret_unwrapped = [] + ret_indices_to_original = [] + for i, a in enumerate(wrapped_args): + a_unwrapped = unwrap_tensor_subclasses([a], append_symints=False) + ret_unwrapped.extend(a_unwrapped) + n = len(a_unwrapped) + ret_indices_to_original.extend([i] * n) + + return ret_unwrapped, ret_indices_to_original + + +def remap_unwrapped_subclass_arg_indices(wrapped_args, static_input_indices): + static_input_indices = set(static_input_indices) + new_ind = 0 + remapped_static_indices = [] + for i, arg in enumerate(wrapped_args): + num_indices = 1 + if is_traceable_wrapper_subclass(arg): + num_indices = ( + len(get_plain_tensors(typing.cast(Tensor, arg), out=[])) + + len(filter_symints(arg.size())) + + len(filter_symints(arg.stride())) + ) + + for _ in range(num_indices): + if i in static_input_indices: + remapped_static_indices.append(new_ind) + + new_ind += 1 + + return remapped_static_indices + + +# Turns a flattened list of tensor arguments into (maybe) subclass tensors. +# This function is used both at trace time and runtime, so we have an is_runtime flag telling us which context we're in. +def wrap_tensor_subclasses( + unwrapped_args: Union[tuple[Any, ...], list[Any]], + *, + subclass_metas: list[Union[PlainTensorMeta, SubclassCreationMeta]], + num_fw_outs_saved_for_bw: Optional[int] = None, + included_subclass_symints: bool = False, + is_runtime: bool = False, + make_subclass_override: Optional[Callable] = None, +) -> tuple[Any, ...]: + wrapped_args = [] + num_args_tallied = 0 + for subclass_meta in subclass_metas: + if isinstance(subclass_meta, PlainTensorMeta): + wrapped_args.append(unwrapped_args[subclass_meta.unwrapped_idx]) + num_args_tallied += 1 + else: + assert isinstance(subclass_meta, SubclassCreationMeta) + assert subclass_meta.included_subclass_symints == included_subclass_symints + + if make_subclass_override: + wrapped_args.append( + make_subclass_override(subclass_meta, is_runtime, unwrapped_args) + ) + else: + wrapped_args.append( + subclass_meta.creation_fn(unwrapped_args, is_runtime=is_runtime) + ) + num_args_tallied += subclass_meta.arg_count + + # Note: [Partitioner handling for Subclasses, Part 2] + # At the beginning of AOTAutograd, we collect metadata on the inputs and outputs of the user fw, + # to figure out which inputs/outputs are subclasses, and how to reconstruct the subclasses after flattening them. + # + # When this function is called at runtime in the forward, + # we have been passed a list of (flattened) dense-tensor fw-outs, and need to reconstruct any subclass fw outs. + # + # One reasonable question that you should ask: when should the dense_tensor -> subclass_tensor wrapping happen? + # Answer: we do it **inside of our compiled autograd.Function**. + # This seems like morally the right place: autograd happens above subclass desugaring, + # so autograd should see actual tensor subclasses at runtime, and not flattened dense tensors. + # + # This causes a tricky interaction though: when we run the min-cut partitioner to divvy up the joint graph + # into a forward and backward graph, we end up with some activations that show up as extra outputs + # in the compiled forward graph, that are **not** user outputs. + # These activations are not visible to the user, and so there's no need for us to wrap them back into subclasses. + # + # On top of that, when we first computed subclass metadata (in `run_functionalized_fw_and_collect_metadata`), + # we computed subclass metadata on every forward output, but this did **not** include activations + # created by the partitioner. + # as a result, `unwrapped_args` here will correspond to (*unwrapped_user_fw_outs, *activations), + # but `subclass_metas` will only correspond to subclass metatadata on `user_fw_outs`. + # We then need to make sure that we return (*wrapped_user_fw_outs, *activations). + if num_fw_outs_saved_for_bw is not None: + assert len(unwrapped_args) == num_args_tallied + num_fw_outs_saved_for_bw, ( + f"Expected the number actual unwrapped-subclass outputs {len(unwrapped_args)} to equal " + f"the number of args calculated from subclasses ({num_args_tallied}) plus the number of " + f"additional activations saved for the backward pass ({num_fw_outs_saved_for_bw})" + ) + activations = unwrapped_args[num_args_tallied:] + if isinstance(wrapped_args, tuple) and isinstance(activations, tuple): + return wrapped_args + activations + return tuple(list(wrapped_args) + list(activations)) + else: + assert ( + len(unwrapped_args) == num_args_tallied + ), f"Expected {len(unwrapped_args)} == {num_args_tallied}" + return tuple(wrapped_args) + + +# Given a bunch of "dense" tensor arguments, this function (potentially) wraps them into tensor subclasses. +# This function carefully handles the inference vs. joint cases: +# - when is_joint_structure is True, args is (primals, tangents) +# - when is_joint_structure is False, args is [*primals] +def wrap_tensor_subclasses_maybe_joint( + unwrapped_args, *, is_joint_structure: bool, meta: ViewAndMutationMeta +) -> Union[tuple[Any, ...], list[Any]]: + # Since this function is re-used for both inference and joint graphs, + if is_joint_structure: + assert isinstance(unwrapped_args, tuple) and len(unwrapped_args) == 2 + assert isinstance(unwrapped_args[0], (tuple, list)) and isinstance( + unwrapped_args[1], (tuple, list) + ) + primals, tangents = unwrapped_args[0], unwrapped_args[1] + wrapped_primals = wrap_tensor_subclasses( + primals, + subclass_metas=meta.subclass_inp_meta, + included_subclass_symints=True, + ) + wrapped_tangents = wrap_tensor_subclasses( + tangents, + subclass_metas=meta.subclass_tangent_meta, + included_subclass_symints=False, + ) + return (wrapped_primals, wrapped_tangents) + else: + wrapped_args = wrap_tensor_subclasses( + unwrapped_args, + subclass_metas=meta.subclass_inp_meta, + included_subclass_symints=True, + ) + return wrapped_args + + +def compute_inner_mutated_inp_indices_from_subclass_meta( + fw_metadata: ViewAndMutationMeta, + inner_metadata: ViewAndMutationMeta, +) -> list[int]: + # Note: [Recomputing subclass mutation handling] + # + # Generally, if a subclass requires grad, its components will not require grad. + # But for the purposes of tracking returned tensors, we should treat those component + # tensors as if they require grad. + # + # For example, if the subclass tensor requires grad and will be mutated in a way that + # requires us to handle the mutation outside of the graph, we need to return it + # from the forward graph. The inner_meta data won't consider the component tensors + # as if they need to be returned, because they don't require grad; but really, we + # should handle those tensors the same way we handle the subclass tensor itself; i.e. + # if we'd include the subclass tensor as part of the outputs, then we should also + # include the component tensors. + # + # To do this, we patch num_mutated_inp_runtime_indices below by expanding the inputs + # from the outer subclass tensors and propagating + + updated_input_info = [] + inner_idx = 0 + if not fw_metadata.subclass_inp_meta: + # Sometimes we don't have subclass info, e.g. synthetic_base codepaths + return inner_metadata.mutated_inp_runtime_indices + assert len(fw_metadata.subclass_inp_meta) == len(fw_metadata.input_info) + for outer_idx, inp_meta in enumerate(fw_metadata.subclass_inp_meta): + if isinstance(inp_meta, PlainTensorMeta): + assert outer_idx < len(fw_metadata.input_info) + if inner_metadata is not None: + assert inner_idx < len(inner_metadata.input_info) + assert ( + inner_metadata.input_info[inner_idx] + == fw_metadata.input_info[outer_idx] + ) + updated_input_info.append(fw_metadata.input_info[outer_idx]) + inner_idx += 1 + else: + assert inp_meta.original_subclass is not None + for _ in range(inp_meta.arg_count): + updated_input_info.append(fw_metadata.input_info[outer_idx]) + inner_idx += 1 + if inner_metadata is not None: + assert len(inner_metadata.input_info) == len(updated_input_info) + + return [ + i + for i, inp in enumerate(updated_input_info) + if inp.mutation_type == MutationType.MUTATED_OUT_GRAPH + ] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..433ff7348d900e5e08e00145236bae726fd44d81 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py @@ -0,0 +1,921 @@ +# mypy: allow-untyped-defs +""" +This module is responsible for transforming functions to be traced into a form +that is easier for the downstream infra (e.g. Autograd, FX, AOTAutograd analysis) +to handle. + +It does so by: +1. functionalization (including RNG functionalzation) +2. creating a joint graph when required +3. transforming mutations into extra outputs +4. dispatching subclasses +""" + +import warnings +from contextlib import contextmanager, nullcontext +from functools import wraps +from typing import Any, Callable, Union +from unittest.mock import patch + +import torch +import torch.fx.traceback as fx_traceback +import torch.utils._pytree as pytree +from torch import Tensor +from torch._decomp.decompositions_for_rng import PhiloxStateTracker +from torch._guards import detect_fake_mode +from torch._prims_common import CUDARngStateHelper +from torch.fx.experimental.proxy_tensor import ( + maybe_disable_thunkify, + maybe_enable_thunkify, +) +from torch.fx.experimental.symbolic_shapes import ( + definitely_false, + PropagateUnbackedSymInts, + sym_eq, +) +from torch.nn.utils import stateless + +from .. import config +from .collect_metadata_analysis import run_functionalized_fw_and_collect_metadata +from .functional_utils import ( + _check_if_mutation_can_be_in_graph, + are_all_mutations_hidden_from_autograd, + are_all_mutations_under_no_grad_or_inference_mode, + from_fun, + has_data_mutation, + has_metadata_mutation, + is_fun, + sync_functional_tensor, + to_fun, + was_inductor_storage_resized, +) +from .logging_utils import setup_stacktrace_preservation_hooks +from .schemas import ( + AOTConfig, + MutationType, + OutputType, + SubclassMeta, + SubclassTracingInfo, + ViewAndMutationMeta, +) +from .subclass_utils import ( + create_subclass_meta, + remap_unwrapped_subclass_arg_indices, + requires_subclass_dispatch, + unwrap_tensor_subclasses, + wrap_tensor_subclasses_maybe_joint, +) +from .utils import maybe_to_fresh_input + + +# This function returns a new function that returns mutated inputs as outputs. +# if keep_data_input_mutations is set, then we assume that data-only mutations +# will be left in the graph, and we only return metadata-mutated inputs as outputs. +def fn_input_mutations_to_outputs( + fn: Callable, + meta: ViewAndMutationMeta, + keep_data_input_mutations: bool, +) -> Any: + @wraps(fn) + def inner_fn(*args): + outs = fn(*args) + assert len(meta.output_info) == len(outs) + # The compiled fw will return mutated input tensors, *including* metadata-only mutation. + # However, if keep_data_input_mutations is set, the compiled fw only needs to return metadata-mutated inputs. + # (because data-only input mutations are handled directly in the compiled graph) + mutated_inputs_to_return = [ + x for (i, x) in enumerate(args) if i in meta.mutated_inp_runtime_indices + ] + return *mutated_inputs_to_return, *outs + + return inner_fn + + +# This function takes in a fn with external aliasing and mutation, +# and returns a new fn with no external aliasing and mutation, +# as needed for autograd. +# The main transformations are: +# - Return mutated inputs as extra outputs +# - Clone mutated inputs that require gradients, +# because autograd will require us to pass the pre-mutated inputs into autograd.grad +# - Return intermediate bases of outputs as additional outputs, +# needed to appease autograd.Function +# The new function returns: +# (1) The updated outputs +# (2) A boolean mask of len(new_fn_outputs), +# that can be used to tell autograd.grad which outputs should get tangents +# if we trace the backward. +def fn_prepped_for_autograd( + fn: Callable, + meta: ViewAndMutationMeta, +) -> Any: + @wraps(fn) + def inner_fn(*args): + args_maybe_cloned = [ + maybe_to_fresh_input(i, t, meta) for i, t in enumerate(args) + ] + + outs = fn(*args_maybe_cloned) + assert isinstance(outs, (tuple, list)) + outs = list(outs) + assert len(meta.output_info) == len(outs) + + mutated_inputs_to_return = [ + x + for (i, x) in enumerate(args_maybe_cloned) + if i in meta.mutated_inp_runtime_indices + ] + + intermediate_bases = [] + for i, (o, info) in enumerate(zip(outs, meta.output_info)): + if info.output_type == OutputType.alias_of_intermediate_save_as_output: + intermediate_bases.append(o._base) + + assert meta.num_intermediate_bases == len(intermediate_bases) + + # the compiled forward should return (mutated_inputs, user_outs, intermediate_bases) + fw_outs_to_return = *mutated_inputs_to_return, *outs, *intermediate_bases + + # Also return a boolean mask specifying which outputs to this function will be used as tangents + mutated_inputs_grad_mask = [ + meta.input_info[meta.mutated_inp_runtime_indices[i]].mutates_data + and meta.input_info[meta.mutated_inp_runtime_indices[i]].requires_grad + for (i, x) in enumerate(mutated_inputs_to_return) + ] + + # Pass any (non-aliased) outputs in as tangents, since they'll be returned as outputs in the fw + # For outputs that are aliases of intermediates, we will have returned the output's _base as an output in the graph instead, + # which we *should* send to grad() + output_grad_mask = [ + meta.output_info[i].output_type + in [ + OutputType.non_alias, + OutputType.unsafe_view_alias, + OutputType.custom_function_view, + ] + # Also, only tensor outputs should participate in the backward + # (in particular, Symint outputs in the forward graph shouldn't get tangents) + and issubclass(meta.output_info[i].raw_type, Tensor) + and meta.output_info[i].requires_grad + for (i, x) in enumerate(outs) + ] + + intermediate_base_grad_mask = [True for _ in range(len(intermediate_bases))] + + out_grad_mask = ( + mutated_inputs_grad_mask + output_grad_mask + intermediate_base_grad_mask + ) + assert len(out_grad_mask) == len(fw_outs_to_return) + + # Take care to grab and sync the updated inputs from primals_after_cloning (the inputs we actually mutate!) + # and not primals (the preserved inputs, pre-mutation, that we pass to grad()) + # This is annoying: our joint function needs to be aware of functionalization + # (syncing mutated inputs before calling autograd.grad()) + # In theory, we could make the autograd engine do this automatically, although that probably isn't any cleaner. + for arg in args_maybe_cloned: + if not isinstance(arg, Tensor): + continue + sync_functional_tensor(arg) + + return fw_outs_to_return, out_grad_mask + + return inner_fn + + +# Given a fn, computes the joint. +# NOTE: fn is expects the following behavior: +# (1) fn() needs to return a tuple of (outs, mask), +# where `mask` tells us which outputs are meant to have tangents. +# we don't know this info automatically, because we don't actually want to blindly +# compute tangents for every output that requires grad. +# Specifically, outputs that alias inputs won't participate in the backward and get tangents. +# (2) fn() cannot mutate any inputs that require gradient. +# otherwise, when we compute autograd.grad(), we will not take those input mutations into account +# (the way this is handled is that we ensure any inputs that normally get mutated are cloned first) +def create_joint(fn: Callable, *, aot_config: AOTConfig) -> Any: + def inner_fn(primals: list[Any], tangents: list[Any]): + outs, tangent_mask = fn(*primals) + + assert len(tangent_mask) == len(outs) + outs_to_grad = [ + o for needs_tangent, o in zip(tangent_mask, outs) if needs_tangent + ] + assert len(outs_to_grad) == len(tangents) + + # Get the inputs that need gradients + grad_primals = [] + inputs_needs_grads = [] + # Note that we're not using primals here, + # being carefully not to pass any mutated inputs into autograd.grad() + for p in primals: + is_grad_tensor = isinstance(p, Tensor) and p.requires_grad + inputs_needs_grads.append(is_grad_tensor) + if is_grad_tensor: + grad_primals.append(p) + + # Get the outputs that need gradients + needed_outs = [] + needed_tangents = [] + for out, tangent in zip(outs_to_grad, tangents): + if isinstance(out, Tensor) and out.requires_grad: + # A bit sketchy, but fixes e.g. test_aot_autograd_exhaustive_matmul_cpu_float32 + # The issue is that we are sensitive to decomps that don't accurately maintain + # their output's _base.shape compared to eager mode, and this helps mitigate a bit. + # The not definitely_false is also sketchy; if unbacked + # symints are involved, we're just going to assume that the + # decomps setup the base shape correctly + needed_outs.append( + out + if not definitely_false(sym_eq(out.shape, tangent.shape)) + else out.view(tangent.shape) + ) + needed_tangents.append(tangent) + + setup_stacktrace_preservation_hooks([out.grad_fn for out in needed_outs]) + + if config.functionalize_rng_ops: + PhiloxStateTracker.mark_beginning_of_backward() + backward_out: tuple[Tensor, ...] = () + # Call the backwards pass + if grad_primals: + functional_tensor_mode = torch.utils._python_dispatch._detect_infra_mode( + torch._C._TorchDispatchModeKey.FUNCTIONAL + ) + if functional_tensor_mode is not None: + # Side-Effect Tokens: + # We want to have independent chains of tokens for forward and backward. + # functional_tensor_mode._tokens is used by both. + # We memoize the result tokens of forward in functional_tensor_mode._tokens_forward_output, + # to return them as joint graph outputs. + # We clean functional_tensor_mode._tokens before backward, to prevent reuse of forward tokens in backward. + # Joint graph tracing allows tokens discovery, + # So all the tokens in backward will be created and added as a graph inputs during tracing. + functional_tensor_mode._tokens_forward_output = ( + functional_tensor_mode._tokens + ) + functional_tensor_mode._tokens = {} + + with set_partitioner_tag_is_backward(), fx_traceback.preserve_node_meta(): + # for full graph export, we always export a joint graph where we assume no tangents are needed. + if aot_config.no_tangents: + assert len(needed_tangents) == 1 and needed_tangents[0].numel() == 1 + backward_out = torch.autograd.grad( + needed_outs, + grad_primals, + allow_unused=True, + ) + else: + backward_out = torch.autograd.grad( + needed_outs, + grad_primals, + grad_outputs=needed_tangents, + allow_unused=True, + ) + backward_out_iter = iter(backward_out) + return outs, [ + next(backward_out_iter) if i else None for i in inputs_needs_grads + ] + + def inner_fn_with_anomaly(*args): + with fx_traceback.preserve_node_meta(), warnings.catch_warnings(): + warnings.filterwarnings("ignore", "Anomaly Detection has been enabled.") + with torch.autograd.detect_anomaly(check_nan=False): + return inner_fn(*args) + + return inner_fn_with_anomaly + + +def create_functionalized_rng_ops_wrapper(func, args, trace_joint=True) -> Any: + # Functionalization of rng ops changes the calling convention of the joint graph. + # It goes from (primals, tangents) to (seed, offset, primals, tangents) + # At runtime, we pass on the current seed and offset. This is hidden from + # the user. + fake_mode = detect_fake_mode() + if fake_mode is None: + fake_mode = nullcontext() + + def override_get_rng_state(device: Union[int, str, torch.device] = "cuda"): + out = PhiloxStateTracker.get_state_as_tensor() + return out + + def override_set_rng_state(x, device: Union[int, str, torch.device] = "cuda"): + PhiloxStateTracker.set_state_from_tensor(x) + + def append_rng_offsets(args): + if trace_joint: + # args signature before: Tuple(fwd_outputs), Tuple(bwd_outputs) + # args signature after: Tuple(fwd_outputs, new_fwd_rng_offset), Tuple(bwd_offset, new_bwd_rng_offset) + return ( + (*args[0], PhiloxStateTracker.get_updated_fwd_offset()), + (*args[1], PhiloxStateTracker.get_updated_bwd_offset()), + ) + else: + # args signature before: Tuple(fwd_outputs) + # args signature after: Tuple(fwd_outputs, new_fwd_rng_offset) + return (*args, PhiloxStateTracker.get_updated_fwd_offset()) + + def traced_joint( + primals, tangents, fwd_seed, fwd_base_offset, bwd_seed, bwd_base_offset + ): + with patch("torch.cuda.get_rng_state", override_get_rng_state), patch( + "torch.cuda.set_rng_state", override_set_rng_state + ): + return append_rng_offsets(func(primals, tangents)) + + def traced_forward(*primals_fwd_seed_fwd_base_offset): + # The signature is (*primals, seed, offset) + with patch("torch.cuda.get_rng_state", override_get_rng_state), patch( + "torch.cuda.set_rng_state", override_set_rng_state + ): + return append_rng_offsets(func(*primals_fwd_seed_fwd_base_offset[:-2])) + + if trace_joint: + # Get the current seed and offset to setup tracing. + fwd_seed, fwd_base_offset = CUDARngStateHelper.get_torch_state_as_tuple( + fake_mode + ) + bwd_seed, bwd_base_offset = CUDARngStateHelper.get_torch_state_as_tuple( + fake_mode + ) + PhiloxStateTracker.record_state(fwd_seed, fwd_base_offset, "forward") + PhiloxStateTracker.record_state(bwd_seed, bwd_base_offset, "backward") + return traced_joint, ( + *args, + fwd_seed, + fwd_base_offset, + bwd_seed, + bwd_base_offset, + ) + else: + # Get the current seed and offset to setup tracing. + fwd_seed, fwd_base_offset = CUDARngStateHelper.get_torch_state_as_tuple( + fake_mode + ) + PhiloxStateTracker.record_state(fwd_seed, fwd_base_offset, "forward") + return traced_forward, (*args, fwd_seed, fwd_base_offset) + + +@contextmanager +def set_partitioner_tag(tag: str): + meta_key = "partitioner_tag" + assert fx_traceback.has_preserved_node_meta() + + original_val = fx_traceback.current_meta.get(meta_key, None) + fx_traceback.current_meta[meta_key] = tag + try: + yield + finally: + fx_traceback.current_meta[meta_key] = original_val + + +def set_partitioner_tag_is_backward(): + return set_partitioner_tag("is_backward") + + +def set_partitioner_tag_must_be_in_backward(): + return set_partitioner_tag("must_be_in_backward") + + +# This creates the final function that we want to trace using make_fx(), +# in both aot_dispatch_autograd and aot_dispatch_base. +# Preconditions: +# - fn corresponds to the user's fw function +# - fn arguments have been flattened, duplicate arguments have been handled +# - In the returned function, the "primals" arguments *includes* synthetic bases. +# This function does the work of functionalizing the input function, +# and performing copy_() calls at the end of the function if `keep_input_mutations` is set. +# The function returned has signature that is either: +# (1) "traced_fn(primals: List[Any])" if trace_joint is False +# (2) "traced_fn(primals: List[Any], tangents: List[Any])" if trace_joint is True +# Returns a new (functionalized) function, and updated arguments to call it with. +def create_functionalized_fn( + fn, + args, + *, + meta: ViewAndMutationMeta, + aot_config: AOTConfig, + trace_joint: bool, +) -> Any: + @wraps(fn) + def _functionalized_f_helper(*args): + with maybe_enable_thunkify(): + # See Note [Disabling Functionalize TLS Above Python Functionalization] + disable_above = torch._C._ExcludeDispatchKeyGuard( + torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize) + ) + + with disable_above: + # The functionalization code here can potentially trigger traces + # into the graph, but we'd prefer to NOT do this, because if we + # trace them now, we will end up with FX nodes that don't have + # module stack annotations, which makes unflattener unhappy. + # Wrap inputs into functional wrappers + f_args = pytree.tree_map(to_fun, args) + + # Run the joint + f_outs = fn(*f_args) + + if trace_joint: + # We support a limited amount of mutation of graph inputs during the backward pass. + # (This is used e.g. by Float8, which needs to update buffers during the backward pass) + # Here, we perform extra checks for primals that were mutated in the **backward** + # We're doing the checks here instead of doing them with the rest of the input mutation handling because: + # - We need to detect inputs that were mutated in the backward **separately** from mutations that happened + # during the forward, because the handling is different: some input mutations from the the forward + # can be only handled in a fw-only runtime epilogue, and in theory if we wanted to handle those same + # types of mutations in the backward we would need a bw-only runtime epilogue. + # - We could in theory have our analysis pass differentiate mutations in the fw from mutations in + # the bw by running our analysis first on the fw-only graph, and then on the joint graph. This would + # require an extra round of tracing though, so it's more efficient to do in-line here. + assert ( + isinstance(args, tuple) + and len(args) == 2 + and isinstance(args[0], (list, tuple)) + ) + # Only look at mutations that happened to forward inputs (e.g. fw buffers that were saved for bw) + primals_before = args[0] + primals_after = pytree.tree_map(from_fun, f_args[0]) + for idx, (f_inpt, before, after, inpt_info) in enumerate( + zip(f_args[0], primals_before, primals_after, meta.input_info) + ): + # Store information about mutations in joint(for backward analysis) + joint_mutates_data = has_data_mutation(f_inpt) + + joint_mutates_metadata = has_metadata_mutation( + f_inpt, before, check_only_storage_mutation=False + ) + + # Ban metadata mutations on fw inputs during the bw + if not inpt_info.mutates_metadata: + assert ( + not joint_mutates_metadata + ), "Found a graph input that had its metadata mutated in the backward. This is not supported" + + # Ban storage resizing on fw inputs during the bw + if not inpt_info.mutation_inductor_storage_resize: + assert not was_inductor_storage_resized( + f_inpt + ), "Found a graph input that had storage resizing in the backward. This is not supported" + + # Allow data mutations on fw inputs during the bw, but only if they do not require grad + # So we can guarantee that we can keep the mutations in the graph + if ( + joint_mutates_data + and not inpt_info.mutates_data + and not inpt_info.mutates_storage_metadata + ): + # Not banning here mutations on inpt_info.requires_grad - + # we'll check at runtime and fail only when backward is under torch.is_grad_enabled (create_graph) + # Add node meta for copy_ for partitioner that this node should be in backward graph. + with torch.fx.traceback.preserve_node_meta(), set_partitioner_tag_must_be_in_backward(): + before.copy_(after) + meta.indices_of_inputs_that_requires_grad_with_mutations_in_bw.append( + idx + ) + # Now that we covered mutations to *forward* inputs during the backward, + # we also need to cover mutations to *backward-only* inputs during the backward (e.g. mutation to a grad_out). + # Today, we will just error in all cases of this happening unless someone needs us to support it. + tangents_before = args[1] + tangents_after = pytree.tree_map(from_fun, f_args[1]) + for f_inpt, before, after in zip( + f_args[1], tangents_before, tangents_after + ): + assert not has_metadata_mutation( + f_inpt, before, check_only_storage_mutation=False + ), "Found an input to the backward that had metadata mutated during the backward pass. This is not supported" + if has_data_mutation(f_inpt): + can_be_in_graph = _check_if_mutation_can_be_in_graph( + keep_input_mutations=True, + mutates_data=True, + mutates_metadata=False, + mutations_hidden_from_autograd=are_all_mutations_hidden_from_autograd( + f_inpt + ), + mutations_under_no_grad_or_inference_mode=are_all_mutations_under_no_grad_or_inference_mode( + f_inpt + ), + mutates_storage_metadata=False, + mutation_inductor_storage_resize=was_inductor_storage_resized( + f_inpt + ), + requires_grad=f_inpt.requires_grad, + ) + assert ( + can_be_in_graph + ), "a backward input that had data mutated in an autograd-aware way. This is not supported" + # Perform the input mutation + with torch.fx.traceback.preserve_node_meta(): + before.copy_(after) + + if aot_config.keep_inference_input_mutations: + # Note: This is a bit annoying. There's a layering issue here, where: + # (1) functionalization needs to operate on **synthetic base** inputs, before unpacking them into the "real" inputs. + # (2) For keep_input_mutations, we support tracing a call to copy_() directly on mutated inputs. + # However, we **only** want to support this for inputs that have data-only (and no metadata) mutations, + # because inductor (and backends in generally) would prefer not to see these (e.g. as_strided_(), resize_()). + # This makes it pretty difficult for this logic to operate on synthetic bases. + # (3) In addition, there are cases where it's significantly cheaper to perform the copy on the individual + # (unpacked) input aliases, instead of the synthetic base. + # Example case where (3) could be important: + # + # def f(x, y): + # x.mul_(2) + # y.mul_(3) + # return x, y + # a = torch.ones(1'000'000) + # x, y = out(a[0:9], a[1:10]) + # + # It would be much better to add copy_() calls into the graph for the two tiny slices, instead of materializing + # a giant "updated synthetic base" and copying into a's entire storage. + # + # For now, we are pessimistically not performing the optimization from (3); + # we will materialize an "updated" synthetic base, and copy it back to the synthetic input base. + # This allows us to factor aot autograd much more nicely, since only one area of the code needs to worry + # about synthetic bases. + for i, (inpt_old, inpt_f) in enumerate( + zip(args, f_args) if not trace_joint else zip(args[0], f_args[0]) + ): + if not isinstance(inpt_f, torch.Tensor): + continue + assert is_fun(inpt_f) + inpt_new = from_fun(inpt_f) + if ( + meta.input_info[i].mutation_type + == MutationType.MUTATED_IN_GRAPH + ): + # See Note [set_() Input Mutations in AOTAutograd] + # all mutations on the input must be under no_grad, so it is safe to put in the graph + # Here, we're saying that if an input experienced a set call, inp.set_(other), + # then we can effectively not have to worry about whether its data was mutated. + # There are 3 cases: + # (1) We mutate inp *after* the set_() call. other is a graph intermediate. + # In this case, we're not really mutating the input storage of "inp"; + # we're mutating the storage of an intermdiate value (other), + # and slamming that storage into the input tensor. So no data mutation is necessary. + # (2) We mutate inp *after* the set_() call. other is a graph *input*. + # In this case, the data mutation will be properly handled in the runtime + # epilogue during the processing of "other" + # (3) We mutate inp *before* the set_() call. + # This case is *not* currently handled. + if meta.input_info[i].mutates_storage_metadata: + with torch.no_grad(): + inpt_old.set_(inpt_new) + + # Note [Ordering of resize_() and set_()] + # Importantly: the common usage in FSDP is that we have a dummy parameter + # that sees a set_() and **Then** a resize_(). + # We must put those mutations into the graph in the same order, + # Since running them in the opposite order will have different behavior. + # We fully ban resize_() followed by set_() for now, although in principal + # we could support this + if meta.input_info[i].mutation_inductor_storage_resize: + # resizing is not supported on subclasses (we error earlier if this happens) + from torch._subclasses.functional_tensor import ( + FunctionalTensor, + ) + + assert isinstance(inpt_f, FunctionalTensor) + old_storage_size = torch._functionalize_get_storage_size( # type: ignore[attr-defined] + inpt_f.elem, before=True + ) + new_storage_size = torch._functionalize_get_storage_size( # type: ignore[attr-defined] + inpt_f.elem, before=False + ) + if old_storage_size != new_storage_size: + assert ( + old_storage_size == 0 or new_storage_size == 0 + ), f"""\ + Encountered a storage resize during tracing on input {i}. Old nbytes={old_storage_size}, new nbytes={new_storage_size} + We only support storage resizing on graph inputs as long as the input either starts or ends with a storage size of 0 + (the case for FSDP)""" + torch.ops.inductor.resize_storage_bytes_( + inpt_old, new_storage_size + ) + if new_storage_size == 0: + # Even if we marked the input as having a data mutation (thus needing a copy_()), + # We should **ignore** it if our input has no storage + # (this can happen if, e.g. we temporarily resize our input, copy data into it, + # and resize it back down to zero) + continue + # Optimization: if the copy_() is a no-op then don't include it in the graph. + # In theory inductor could optimize this away, however in fsdp, we end up with + # param.copy_(param), where param is a zero-storage-size tensor, + # and running this op in eager mode (using the aot_eager backend) will result in a segfault. + # So we may as well optimize it away here. + if inpt_old is inpt_new: + # (This check needs to be done after putting resize_() in the graph, + # since a resize_(0) doesn't actually change the FunctionalTensor's inner tensor) + continue + # We found an input that had a (data-only) mutation. + # Since keep_input_mutations is set, we need to faithfully apply a copy_() + # so the compiler will see the input mutation in the graph. + if ( + meta.input_info[i].mutates_data + and meta.input_info[i].mutations_hidden_from_autograd + ): + # Hidden from autograd = run under no_grad, **and** don't bump VC + # (although if the tensor was created in inference mode, it has no VC) + if inpt_old.is_inference(): + maybe_preserve_vc = nullcontext() + else: + maybe_preserve_vc = torch.autograd._unsafe_preserve_version_counter( + inpt_old # type: ignore[assignment] + ) + with torch.no_grad(), maybe_preserve_vc: + inpt_old.copy_(inpt_new) + elif ( + meta.input_info[i].mutates_data + and meta.input_info[ + i + ].mutations_under_no_grad_or_inference_mode + ): + # Under no_grad = run under no_grad (we still bump the VC though) + # (inference_mode will also bump the VC, as long as the tensor in question + # was created outside of inference_mode) + with torch.no_grad(): + inpt_old.copy_(inpt_new) + elif meta.input_info[i].mutates_data: + inpt_old.copy_(inpt_new) + + # When an output tensor is a functionalized mutated input, and we + # were able to move the mutation in to the graph then we can return + # the mutated input directly. This prevents duplicating the + # tensors contents. + flat_outs, outs_spec = pytree.tree_flatten(f_outs) + flat_outs = [from_fun(o) for o in flat_outs] + num_outs = len(meta.output_info) + + for i in range(num_outs): + info = meta.output_info[i] + if info.output_type != OutputType.is_input: + continue + + assert info.base_idx is not None + if ( + meta.input_info[info.base_idx].mutation_type + == MutationType.MUTATED_IN_GRAPH + ): + fw_args = args[0] if trace_joint else args + flat_outs[i] = fw_args[info.base_idx] + return pytree.tree_unflatten(flat_outs, outs_spec) + + return pytree.tree_map(from_fun, f_outs) + + # Kinda annoying, but needed to make sure that the fx graph we trace out has "primals" + # and "tangents" as its input names (which are special-cased by the partitioner) + # TODO (tmanlaibaatar) revisit this if we ever need to turn on non-strict joint graph export + def joint_helper(primals, tangents): + return _functionalized_f_helper(primals, tangents) + + helper = joint_helper if trace_joint else _functionalized_f_helper + if config.functionalize_rng_ops: + # Setup the wrapper for functionalization of rng ops + helper, args = create_functionalized_rng_ops_wrapper(helper, args, trace_joint) + + return helper, args + + +def handle_effect_tokens_fn( + fn, + args, + *, + meta: ViewAndMutationMeta, + trace_joint: bool, +) -> Any: + num_tokens = len(meta.tokens) + + @wraps(fn) + def inner_fn(*args): + # See Note [Disabling Functionalize TLS Above Python Functionalization] + disable_above = torch._C._ExcludeDispatchKeyGuard( + torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize) + ) + + with disable_above: + # See Note [Side-Effectful Tokens in AOTAutograd] + if trace_joint: + assert isinstance(args, tuple) and isinstance(args[0], (list, tuple)) + tokens = args[0][:num_tokens] + assert all(token.numel() == 0 for token in tokens) + args = (args[0][num_tokens:], *args[1:]) + else: + tokens = args[:num_tokens] + assert all(token.numel() == 0 for token in tokens) + args = args[num_tokens:] + + # Populate the current FunctionalTensorMode with the tokens per + # operator. See Note [FunctionalTensorMode is Stateful] + functional_tensor_mode = torch.utils._python_dispatch._detect_infra_mode( + torch._C._TorchDispatchModeKey.FUNCTIONAL + ) + assert functional_tensor_mode is not None + f_tokens = pytree.tree_map(to_fun, tokens) + for i, k in enumerate(meta.tokens.keys()): + functional_tensor_mode._tokens[k] = f_tokens[i] + + # Run the joint + outs = fn(*args) + + # Return both the tokens and the outputs + # See Note [Side-Effectful Tokens in AOTAutograd] + if trace_joint: + assert len(outs) == 2 + assert len(functional_tensor_mode._tokens_forward_output) == num_tokens + fwd_out_tokens = functional_tensor_mode._tokens_forward_output.values() + + bwd_out_tokens = functional_tensor_mode._tokens.values() + + f_fwd_out_tokens = [from_fun(t) for t in fwd_out_tokens] + f_bwd_out_tokens = [from_fun(t) for t in bwd_out_tokens] + + meta.num_backward_tokens = len(bwd_out_tokens) + return ((*f_fwd_out_tokens, *outs[0]), (*outs[1], *f_bwd_out_tokens)) + + out_tokens = [from_fun(t) for t in functional_tensor_mode._tokens.values()] + return (*out_tokens, *outs) + + # Additionally pass in tokens as inputs + # See Note [Side-Effectful Tokens in AOTAutograd] + additional_fwd_token_inputs = [torch.tensor([])] * num_tokens + + if trace_joint: + args = ([*additional_fwd_token_inputs, *args[0]], *args[1:]) + else: + args = [*additional_fwd_token_inputs, *args] + return inner_fn, args + + +# Given a function operating on Subclass -> Subclass, returns an function that operates on Tensor -> Tensor +# Also returns: +# - the new set of arguments to pass into this function (now that tensor subclasses have been eliminated) +# - the updated ViewAndMutationMeta for this dense -> dense function. +# The other important arguments are: +# - flat_fn_maybe_joint: when is_joint_structure=True, this is the joint fw-bw function. +# when is_joint_structure=False, this is just the forward function. +# - fw_only: this is *always* the forward-only function. +# Why do we need this? We need to collect updated ViewAndMutationMeta on our new dense -> dense functions. +# In particular, we need this to tell the partitioner how many dense forward outputs there are. +def aot_dispatch_subclass( + flat_fn_maybe_joint, + args: list[Any], + *, + is_joint_structure: bool, + meta: ViewAndMutationMeta, + fw_only: Callable, +) -> SubclassTracingInfo: + # Skip logic if we don't need to trace through any subclasses + req_subclass_dispatch = requires_subclass_dispatch(args, meta) + if not req_subclass_dispatch: + return SubclassTracingInfo( + plain_tensor_trace_fn=flat_fn_maybe_joint, + plain_tensor_args=args, + maybe_subclass_meta=None, + ) + + # TODO: add subclass guards (later PR). + + # What's going on here? We need to compute subclass metadata about the outputs of the joint (grad_inputs). + # Annoying: we don't know the grad input metas until we're in the middle of tracing the joint, + # so we set it later, while we're tracing the joint (see inner_fn() below). + # Another option would be to run our run_functionalized_fw_and_collect_metadata() function + # directly on the joint, but this would hurt compile time (adding yet another pass through the joint). + subclass_meta = SubclassMeta() + + def inner_fn(fn, args, *, use_trace_joint: bool): + # Step 1: wrap tensor inputs into subclasses if necessary + all_args = wrap_tensor_subclasses_maybe_joint( + args, is_joint_structure=use_trace_joint, meta=meta + ) + + # Step 2: call the inner function, with our (maybe subclass) inputs + wrapped_outs = fn(*all_args) + + if use_trace_joint: + # See Note: [Computing Subclass Metadata about grad_inputs] + # We also stash subclass info on our grad_inputs, if we're tracing the joint. + nonlocal subclass_meta + assert isinstance(wrapped_outs, tuple) and len(wrapped_outs) == 2 + # Don't need fw outs since we already have subclass metadata on them + grad_inputs = wrapped_outs[1] + subclass_meta.grad_input_metas = create_subclass_meta(grad_inputs) + + # Add extra symints as outputs to the forward/backward graphs + # ignore nested ints here + forward_outs = unwrap_tensor_subclasses( + wrapped_outs[0], append_symints=True + ) + # ignore nested ints here + backward_outs = unwrap_tensor_subclasses( + wrapped_outs[1], append_symints=True + ) + return (forward_outs, backward_outs) + + # Step 3: Unwrap any subclass outputs back into dense tensors + unwrapped_outs = unwrap_tensor_subclasses(wrapped_outs, append_symints=True) + return unwrapped_outs + + def joint_fn(primals, tangents): + with maybe_enable_thunkify(): + return inner_fn( + flat_fn_maybe_joint, (primals, tangents), use_trace_joint=True + ) + + def fw_fn(*primals): + with maybe_enable_thunkify(): + return inner_fn(flat_fn_maybe_joint, primals, use_trace_joint=False) + + def metadata_fn(*primals): + return inner_fn(fw_only, primals, use_trace_joint=False) + + if is_joint_structure: + args_unwrapped = ( + # Add extra symints (size/strides) as input to the forward graph + unwrap_tensor_subclasses(args[0], append_symints=True), + # We pass append_symints=False here because the partitioner will + # capture and add any extra argument + unwrap_tensor_subclasses(args[1], append_symints=False), + ) + else: + args_unwrapped = unwrap_tensor_subclasses(args, append_symints=True) + remapped_static_indices = remap_unwrapped_subclass_arg_indices( + args, meta.static_input_indices + ) + + if is_joint_structure: + primals_unwrapped = args_unwrapped[0] + fn_to_trace = joint_fn + else: + primals_unwrapped = args_unwrapped + fn_to_trace = fw_fn + + # Note: [Partitioner handling for Subclasses, Part 1] + # The way the partitioner works is that: + # (1) we pass is a single graph containing the joint fw/bw, + # where the # of graph outputs corresponds to # fw_outputs + # grad_inputs + # (2) The partitioner accepts an arguments, num_fwd_outputs, + # and assumes that the first "num_fwd_outputs" graph outputs correspond + # to outputs of the forward graph. + # How do tensor subclasses enter the picture? + # the num_fwd_outputs in the final graph is actually non-trivial to compute, + # because it can be influenced by input mutations and intermediate bases. + # So we compute it by inspecting the current ViewAndMutationMeta object. + # However, the original ViewAndMutationMeta that we computed was created + # on the subclass -> subclass graph, + # which can have a different number of outputs than the dense -> dense graph. + # That's why we created a fresh metadata object on the dense -> dense function here, + # and plumb it back up to the partitioner. + # See Note: [Partitioner handling for Subclasses, Part 2] for more info. + meta_updated = run_functionalized_fw_and_collect_metadata( + metadata_fn, + static_input_indices=remapped_static_indices, + keep_input_mutations=meta.keep_input_mutations, + is_train=meta.is_train, + )(*primals_unwrapped) + + subclass_meta.fw_metadata = meta_updated + + return SubclassTracingInfo( + plain_tensor_trace_fn=fn_to_trace, + plain_tensor_args=args_unwrapped, + maybe_subclass_meta=subclass_meta, + ) + + +def create_functional_call(mod, params_spec, params_len, store_orig_mod=False): + # Redundant with dynamo, but worth having in case this gets invoked elsewhere. + # https://github.com/pytorch/pytorch/issues/103569 + + def functional_call(*args, **kwargs): + with stateless._reparametrize_module( + mod, pytree.tree_unflatten(args[:params_len], params_spec) + ), maybe_disable_thunkify(): + if isinstance(mod, torch.fx.GraphModule): + with fx_traceback.preserve_node_meta(), warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", "Anomaly Detection has been enabled." + ) + with torch.autograd.detect_anomaly(check_nan=False): + detect_fake_mode().epoch += 1 + out = PropagateUnbackedSymInts(mod).run( + *args[params_len:], **kwargs + ) + else: + out = mod(*args[params_len:], **kwargs) + + if not isinstance(out, (tuple, list)): + raise RuntimeError( + "Graph output must be a (). This is so that we can avoid " + "pytree processing of the outputs. Please change the module to " + "have tuple outputs or use aot_module instead." + ) + return out + + # Note [Preserving the nn module stack metadata during export non-strict mode] + # This path is currently only used by the non-strict export flow, + # where we cannot rely on dynamo to preserve nn stack metadata in our captured graph. + # Instead, we stash the original user nn module here, and rely on `make_fx` to grab + # this stashed module and use it to track nn module stack metadata + if store_orig_mod and not hasattr(functional_call, "_orig_mod"): + functional_call._orig_mod = mod # type: ignore[attr-defined] + + return functional_call diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..cc14a77244f658b9c23b6983be3788635d962c66 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/utils.py @@ -0,0 +1,502 @@ +# mypy: allow-untyped-defs +""" +Contains various utils for AOTAutograd, including those for handling collections. +""" + +import dataclasses +import operator +import warnings +from contextlib import nullcontext +from functools import wraps +from typing import Any, Callable, Optional, Union + +import torch +import torch.utils._pytree as pytree +from torch._library.fake_class_registry import FakeScriptObject +from torch._logging import getArtifactLogger +from torch._subclasses.fake_tensor import FakeTensor +from torch._subclasses.functional_tensor import FunctionalTensor +from torch.fx.experimental._backward_state import BackwardState +from torch.fx.experimental.proxy_tensor import py_sym_types + + +KNOWN_TYPES = [ + torch.Tensor, + BackwardState, + int, + str, + float, + bool, + type(None), + *py_sym_types, + FakeScriptObject, + torch.ScriptObject, +] + +original_zip = zip + +aot_graphs_effects_log = getArtifactLogger(__name__, "aot_graphs_effects") + + +def strict_zip(*iterables, strict=True, **kwargs): + if not strict: + return original_zip(*iterables, **kwargs) + + length = len(iterables[0]) + for iterable in iterables[1:]: + if len(iterable) != length: + raise ValueError( + "The iterables have different lengths and strict mode is enabled." + ) + + return original_zip(*iterables, **kwargs) + + +def _get_symint_hints(exprs): + """ + Get the hints of a list/tuple of int/SymInt. + """ + if isinstance(exprs, (list, tuple)): + return type(exprs)(_get_symint_hints(e) for e in exprs) + elif isinstance(exprs, torch.SymInt): + return exprs.node.shape_env.size_hint(exprs.node.expr) + else: + return exprs + + +def partial_flatten_asdict(obj: Any) -> Any: + if dataclasses.is_dataclass(obj): + return { + field.name: getattr(obj, field.name) for field in dataclasses.fields(obj) + } + elif isinstance(obj, (list, tuple)): + return obj.__class__([partial_flatten_asdict(item) for item in obj]) + elif isinstance(obj, dict): + return {k: partial_flatten_asdict(v) for k, v in obj.items()} + else: + return obj + + +def normalize_as_list(x): + if isinstance(x, tuple): + return list(x) + elif isinstance(x, list): + return x + return [x] + + +def _get_autocast_states(): + return [ + torch.is_autocast_enabled("cuda"), + torch.is_autocast_enabled("cpu"), + torch.get_autocast_dtype("cuda"), + torch.get_autocast_dtype("cpu"), + torch.is_autocast_cache_enabled(), + ] + + +def make_boxed_func(f): + def g(args): + return f(*args) + + g._boxed_call = True # type: ignore[attr-defined] + return g + + +def make_boxed_compiler(compiler): + @wraps(compiler) + def f(fx_g, inps): + out_f = compiler(fx_g, inps) + fx_g = make_boxed_func(out_f) + return fx_g + + return f + + +def call_func_at_runtime_with_args( + f, args: Union[tuple[Any], list[Any]], steal_args=False, disable_amp=False +): + if not steal_args: + args = list(args) + assert isinstance(args, list) + + context = torch._C._DisableAutocast if disable_amp else nullcontext + with context(): + if hasattr(f, "_boxed_call"): + out = normalize_as_list(f(args)) + else: + # TODO: Please remove soon + # https://github.com/pytorch/pytorch/pull/83137#issuecomment-1211320670 + warnings.warn( + "Your compiler for AOTAutograd is returning a function that doesn't take boxed arguments. " + "Please wrap it with functorch.compile.make_boxed_func or handle the boxed arguments yourself. " + "See https://github.com/pytorch/pytorch/pull/83137#issuecomment-1211320670 for rationale." + ) + out = normalize_as_list(f(*args)) + return out + + +# Inspired by autodidax (thanks!) +class PytreeThunk: + spec: Optional[pytree.TreeSpec] = None + # These are some kinda dumb microoptimizations that save about 3-4 us of overhead. + is_simple: Optional[ + bool + ] = None # if the output spec is a tuple/list, we won't bother unflattening it. + is_really_simple: Optional[bool] = None # if the output spec is a LeafSpec + + def set(self, spec: pytree.TreeSpec) -> None: + assert self.spec is None or self.spec == spec + assert spec is not None + self.spec: pytree.TreeSpec = spec + if self.spec.type in {tuple, list} and all( + child.is_leaf() for child in spec.children_specs + ): + self.is_simple = True + if self.spec.is_leaf(): + self.is_really_simple = True + + def unflatten(self, x: list[Any]) -> Any: + if self.is_really_simple: + return x[0] + if self.is_simple: + return x + assert self.spec is not None + return pytree.tree_unflatten(x, self.spec) + + +# Creates a function that returns flattened inputs and outputs +# Also returns the output tree spec, which is needed to recover the "unflattened" +# output tree structure later. +def create_tree_flattened_fn(fn, args, kwargs=None) -> tuple[Callable, PytreeThunk]: + if kwargs is None: + kwargs = {} + # Save the args_spec for flat_tensor_args to unflatten while tracing + _, tensor_args_spec = pytree.tree_flatten((args, kwargs)) + out_spec = PytreeThunk() + + def flat_fn(*flat_args): + # The input are flattened tensor args. Prepare the args in the + # order that original function expects. Add static args as well. + # They will appear as tensor constants in the traced graph. + nonlocal out_spec + args, kwargs = pytree.tree_unflatten(flat_args, tensor_args_spec) + tree_out = fn(*args, **kwargs) + flat_out, spec = pytree.tree_flatten(tree_out) + for i in flat_out: + is_known_type = False + for j in KNOWN_TYPES: + if isinstance(i, j): + is_known_type = True + break + if not is_known_type: + raise RuntimeError( + f"Found {type(i)} in output, which is not a known type. " + "If this type holds tensors, you need to register a pytree for it. " + "See https://github.com/pytorch/functorch/issues/475 for a brief " + "explanation why. If you don't need to register a pytree, please " + "leave a comment explaining your use case and we'll make this more " + "ergonomic to deal with" + ) + out_spec.set(spec) + return flat_out + + # Can't use functools.wraps here because the wrapper has different + # calling convention + if hasattr(fn, "_orig_mod"): + flat_fn._orig_mod = fn._orig_mod # type: ignore[attr-defined] + + return flat_fn, out_spec + + +# This function takes in a tensor t, and returns one of t, t.view(), or t.clone(). +# When tracing the joint forward + backward, for any inputs in the graph that are mutated, +# we need to clone them first (and similarly for metadata-only mutations, we need to view them first). +# The idea is that when we trace the backward, we need to pass in the *original* primals +# to autograd.grad(), before they were mutated. +# Note: when we have synthetic base inputs, we need to clone them *before* creating views off of them. +# This means that "idx" here represents the index of the (potentially) synthetic base. +# What we need to do is: +# (1) map the current (post-synthetic-base calling convention) input argument index +# to int index pre-synthetic-base-calling-convention. +# (2) There could be multiple, if this index corresponds to a synthetic base +# that has multiple input aliases. +# (3) If any of those corresponding inputs get metadata mutations, then we clone the base. +def maybe_to_fresh_input(idx, t, meta): + if not isinstance(t, torch.Tensor): + return t + if idx in meta.mutated_inp_runtime_indices: + # We only need to bother cloning mutated inputs that participate in autograd. + if meta.input_info[idx].requires_grad and meta.input_info[idx].mutates_data: + # Make sure the primal we pass to autograd.grad() + # sees the tensor before the mutation + return t.clone() + if meta.input_info[idx] and meta.input_info[idx].mutates_metadata: + # Make sure the primal we pass to autograd.grad() + # sees the tensor before the metadata mutation + return t.view(t.shape) + return t + + +def is_with_effects(node): + return ( + node.op == "call_function" + and node.target == torch.ops.higher_order.with_effects + ) + + +def is_with_effects_op(node, op): + return is_with_effects(node) and node.args[1] == op + + +def unlift_tokens(fw_module, fw_metadata, aot_config, bw_module=None): + # Remove the tokens from the inputs/outputs of the graph since inductor does + # not want these extra inputs/outputs, and replace them with + # _make_token() to create a token, and _sink_tokens() to collect the + # tokens. See Note [Side-Effectful Tokens in AOTAutograd] + # Logic: + # 1. Inputs identified as input tokens: + # - If used as a first argument in with_effects + # + # 2. Outputs identified as output tokens: + # - If Produced by getitem(with_effects, 0) + # + # 3. Checks invariants of number input output tokens: + # forward: + # expected_num_erased_inputs == len(fw_metadata.tokens) + # expected_num_erased_outputs == len(fw_metadata.tokens) + # backward: + # expected_num_erased_inputs == fw_metadata.num_backward_tokens + # expected_num_erased_outputs == fw_metadata.num_backward_tokens + num_forward_tokens = len(fw_metadata.tokens) + num_backward_tokens = fw_metadata.num_backward_tokens + + def rewrite_with_effects_input_token(module, node): + with module.graph.inserting_before(node): + new_token_node = module.graph.call_function( + torch.ops.prims._make_token.default, () + ) + new_token_node.meta["val"] = torch.tensor([]) + new_token_node.meta["tensor_meta"] = torch.tensor([]) + + args = list(node.args) + args[0] = new_token_node + node.args = tuple(args) + + def rewrite_output(module, node, output_token_nodes, other_output_args): + for output_token_node in output_token_nodes: + assert ( + output_token_node.op == "call_function" + and output_token_node.target == operator.getitem + and output_token_node.args[1] == 0 + ) + with module.graph.inserting_before(node): + module.graph.call_function( + torch.ops.prims._sink_tokens.default, + (output_token_nodes,), + ) + node.args = (other_output_args,) + + def do(module, subgraph, expected_num_erased): + num_erased_inputs = 0 + num_erased_outs = 0 + input_nodes = [] + input_token_nodes = set() + with_effect_nodes = [] + output_token_nodes = [] + other_output_nodes = [] + for node in module.graph.nodes: + if node.op == "placeholder": + input_nodes.append(node) + elif is_with_effects(node): + with_effect_nodes.append(node) + if node.args[0] in input_nodes: + input_token_nodes.add(node.args[0]) + rewrite_with_effects_input_token(module, node) + elif node.op == "output": + outs = node.args[0] + for out in outs: + if ( + isinstance(out, torch.fx.node.Node) + and out.op == "call_function" + and out.target == operator.getitem + and out.args[1] == 0 + and out.args[0] in with_effect_nodes + ): + output_token_nodes.append(out) + else: + other_output_nodes.append(out) + + rewrite_output(module, node, output_token_nodes, other_output_nodes) + num_erased_outs = len(output_token_nodes) + + for input_token_node in input_token_nodes: + module.graph.erase_node(input_token_node) + + num_erased_inputs = len(input_token_nodes) + + assert ( + num_erased_inputs == expected_num_erased + ), f"{subgraph} num_erased_inputs:{num_erased_inputs} {input_token_nodes}!=expected {expected_num_erased}" + assert ( + num_erased_outs == expected_num_erased + ), f"{subgraph} num_erased_outs:{num_erased_outs} {output_token_nodes}!=expected {expected_num_erased}" + + module.recompile() + + if num_forward_tokens > 0: + if aot_config.enable_log: + from torch._dynamo.utils import lazy_format_graph_code + + aot_graphs_effects_log.debug( + "%s", + lazy_format_graph_code( + "Forward graph before unlifting tokens", + fw_module, + aot_config.aot_id, + include_stride=True, + include_device=True, + colored=True, + ), + ) + do( + fw_module, + "forward", + num_forward_tokens, + ) + + if bw_module is not None and num_backward_tokens > 0: + if aot_config.enable_log: + from torch._dynamo.utils import lazy_format_graph_code + + aot_graphs_effects_log.debug( + "%s", + lazy_format_graph_code( + "Backward graph before unlifting tokens", + bw_module, + aot_config.aot_id, + include_stride=True, + include_device=True, + colored=True, + ), + ) + do(bw_module, "backward", num_backward_tokens) + + # This is sad, but we need to update the metadata to get rid of + # the tokens. + fw_metadata.tokens = {} + fw_metadata.num_backward_tokens = 0 + + +def root_module_when_exporting_non_strict(flat_fn): + # When exporting in non-strict mode, we wrap the root module in a specific pattern. + # See `_aot_export_non_strict` in torch.export._trace.py. + # We look for that wrapping pattern here. + if hasattr(flat_fn, "_orig_mod") and hasattr(flat_fn._orig_mod, "_export_root"): + return flat_fn._orig_mod._export_root + else: + return None + + +def copy_fwd_metadata_to_bw_nodes(fx_g): + """ + Input: `fx_g` which contains the joint fwd+bwd FX graph created by + aot_autograd. + + This function walks the graph and copies over metadata from forward nodes + to backward nodes, using the `seq_nr` field as a one-to-many mapping + from forward node to backward node. This metadata is useful for performance + profiling and debugging. + """ + + def _is_forward_node_with_seq_nr(node): + # For now, assume that if nn_module_stack_metadata is populated, this + # node is from the forward. Ignore nodes without `seq_nr`. + # TODO(future): there is likely a less brittle way to do this by walking + # the descendants of graph inputs corresponding to fwd inputs, didn't + # seem obvious at first glance on how to partition graph inputs into + # fwd vs bwd without relying on string names. + return "nn_module_stack" in node.meta and "seq_nr" in node.meta + + def _is_backward_node_with_seq_nr(node): + # For now, assume that if nn_module_stack_metadata is not populated, + # this node is from the backward. Ignore nodes without `seq_nr`. + # TODO(future): there is likely a less brittle way to do this, same + # as with the forward. + return ("nn_module_stack" not in node.meta) and "seq_nr" in node.meta + + fwd_seq_nr_to_node = {} + for node in fx_g.graph.nodes: + if not _is_forward_node_with_seq_nr(node): + continue + seq_nr = node.meta["seq_nr"] + if seq_nr in fwd_seq_nr_to_node: + # If we already saw an op with the current `seq_nr`, that means + # that the current op did not create an autograd node, and there + # is no corresponding backward node, so we skip. + continue + fwd_seq_nr_to_node[node.meta["seq_nr"]] = node + + for node in fx_g.graph.nodes: + if not _is_backward_node_with_seq_nr(node): + continue + # fwd_node should always exist, but handle non-existence just in case + fwd_node = fwd_seq_nr_to_node.get(node.meta["seq_nr"]) + if fwd_node is not None: + node.meta["fwd_nn_module_stack"] = fwd_node.meta["nn_module_stack"] + node.meta["fwd_source_fn_stack"] = fwd_node.meta.get("source_fn_stack") + + +def register_buffer_assignment_hook(mod, assigned_buffers): + """ + Register a hook that intercepts buffer assignments. + This is used to detect when a buffer is assigned to, and then we can + map that buffer to the corresponding proxy node in the graph. + """ + + def _map_assigned_buffer_to_proxy(_mod, name, buffer): + # We intercept buffer assignments on the root module through this hook. + if _mod._buffers is mod._buffers: + # either buffer is a functional tensor, which wraps a fake tensor + if isinstance(buffer, FunctionalTensor): + buffer = buffer.from_functional() + # or buffer is a fake tensor + assert isinstance(buffer, FakeTensor) + # The fake tensor in turn is associated with a proxy node. + proxy_mode = torch.fx.experimental.proxy_tensor.get_proxy_mode() + assert proxy_mode is not None + proxy = torch.fx.experimental.proxy_tensor.get_proxy_slot( + buffer, proxy_mode.tracer + ).proxy.node + # We map the assigned buffer to this proxy node. + assigned_buffers[name] = proxy.name + return buffer + + return torch.nn.modules.module.register_module_buffer_registration_hook( + _map_assigned_buffer_to_proxy + ) + + +def contain_metadata_mutation_ops(module: torch.fx.GraphModule) -> bool: + """ + Checks if the module contains any metadata mutation ops. + """ + for node in module.graph.nodes: + if ( + node.op == "call_function" + and hasattr(node.target, "tags") + and torch.Tag.inplace_view in node.target.tags + ): + return True + return False + + +def get_cuda_generator_meta_val(device_idx: int): + """ + Get a generator value to use as a meta val + + newly cloned generator will not contain tensors. it is only Generators that are + registered to a CUDAGraph that contain tensors. since this does not contain Tensor + it is fine to use in the meta. + """ + return torch.cuda.default_generators[device_idx].clone_state() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py new file mode 100644 index 0000000000000000000000000000000000000000..1ee4ec9a720f0c6a8c44010e65556f988c3ac0bf --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py @@ -0,0 +1,1689 @@ +# mypy: ignore-errors + +import itertools +from collections.abc import KeysView, Sequence +from contextlib import contextmanager, nullcontext +from functools import partial, wraps +from typing import Any, Callable, NewType, Optional, Protocol, TypeVar +from unittest.mock import patch + +import torch +import torch._dynamo.logging +import torch.nn as nn +import torch.utils._pytree as pytree +import torch.utils.dlpack +from torch import Tensor +from torch._decomp.decompositions_for_rng import PhiloxStateTracker, rng_decompositions +from torch._dispatch.python import enable_python_dispatcher +from torch._dynamo import compiled_autograd +from torch._dynamo.utils import ( + CompileEventLogger, + dynamo_timed, + preserve_rng_state, + set_feature_use, +) +from torch._guards import detect_fake_mode +from torch._inductor.output_code import OutputCode +from torch._inductor.utils import BoxedBool, InputType +from torch._subclasses import FakeTensor, FakeTensorMode +from torch.fx.experimental.proxy_tensor import make_fx +from torch.fx.experimental.symbolic_shapes import ShapeEnv +from torch.utils._python_dispatch import is_traceable_wrapper_subclass + + +static_inputs_log = torch._logging.getArtifactLogger( + __name__, "cudagraph_static_inputs" +) +from . import config +from ._aot_autograd.autograd_cache import ( # noqa: F401 + AOTAutogradCache, + autograd_cache_key, + should_use_local_autograd_cache, + should_use_remote_autograd_cache, +) +from ._aot_autograd.collect_metadata_analysis import ( # noqa: F401 + run_functionalized_fw_and_collect_metadata, +) +from ._aot_autograd.functional_utils import ( # noqa: F401 + _check_if_mutation_can_be_in_graph, + are_all_mutations_hidden_from_autograd, + are_all_mutations_under_no_grad_or_inference_mode, + assert_functional_graph, + from_fun, + gen_alias_from_base, + has_data_mutation, + has_metadata_mutation, + is_fun, + sync_functional_tensor, + to_fun, +) +from ._aot_autograd.input_output_analysis import ( # noqa: F401 + compute_overlapping_inputs, + create_graph_signature, + create_synthetic_base_metadata, + remove_dupe_metadata, +) +from ._aot_autograd.jit_compile_runtime_wrappers import ( # noqa: F401 + aot_dispatch_autograd, + aot_dispatch_base, + aot_dispatch_export, +) +from ._aot_autograd.logging_utils import ( # noqa: F401 + callback_set, + describe_input, + format_guard_bug_msg, + get_aot_compilation_context, + get_aot_graph_name, + get_graph_being_compiled, + graph_being_compiled, + model_name, + nth_graph, + set_model_name, + setup_stacktrace_preservation_hooks, + track_graph_compiling, +) +from ._aot_autograd.runtime_wrappers import ( # noqa: F401 + AOTDedupeWrapper, + AOTSyntheticBaseWrapper, +) +from ._aot_autograd.schemas import ( # noqa: F401 + AOTConfig, + BackwardSignature, + FQN, + GraphInputName, + GraphOutputName, + GraphSignature, + InputAliasInfo, + MutationType, + OutputAliasInfo, + OutputType, + SubclassCreationMeta, + SubclassMeta, + TensorAlias, + ViewAndMutationMeta, +) +from ._aot_autograd.subclass_utils import ( # noqa: F401 + requires_subclass_dispatch, + unwrap_tensor_subclasses, + unwrap_tensor_subclasses_with_indices_to_original, + wrap_tensor_subclasses, + wrap_tensor_subclasses_maybe_joint, +) +from ._aot_autograd.traced_function_transforms import ( # noqa: F401 + aot_dispatch_subclass, + create_functional_call, + create_functionalized_fn, + create_functionalized_rng_ops_wrapper, + create_joint, + fn_input_mutations_to_outputs, + fn_prepped_for_autograd, +) +from ._aot_autograd.utils import ( # noqa: F401 + _get_autocast_states, + _get_symint_hints, + call_func_at_runtime_with_args, + create_tree_flattened_fn, + KNOWN_TYPES, + make_boxed_compiler, + make_boxed_func, + maybe_to_fresh_input, + normalize_as_list, + partial_flatten_asdict, + root_module_when_exporting_non_strict, + strict_zip, +) +from .partitioners import default_partition + + +zip = strict_zip + +# This global counter increments every time we compile a graph with +# AOTAutograd. You can use this to correlate runtime error messages +# with compile time (e.g., if you get an error at runtime saying +# compiled graph 3 failed, you can set a breakpoint at compile time +# for this graph number to investigate further at compile time.) +# +# NB: this is different from get_aot_compilation_context, which tracks +# each underlying graph that is compiled. In contrast, AOT_COUNTER +# corresponds to top-level invocations of aot_module/aot_function; +# one counter is allocated per entire compiled block (but this block +# may involve compiling multiple subgraphs; e.g., for forwards/backwards) +AOT_COUNTER = itertools.count() + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# AOT Autograd contains a pretty non-trivial amount of logic to handle edge cases around aliasing and mutation +# that are external to the graph (they show up as side effects in some way when you run the graph). +# +# Take a look at `test_aotdispatch.py TestAOTAutograd.test_input_mutation*` tests for some examples functions +# and what they're compiled graphs looks like. +# Below is a very long comment detailing several edge cases, and showing how AOT Autograd handles them. +# +# Note [AOT Autograd: input data mutations] +# +# If we compile a function that mutates inputs, then those input mutations are real side effects +# that a user expects to see after running the compiled graph. +# However, the graph that we want to send to a backend needs to be *entirely* functional. +# The way we reconcile this difference is that we remove the mutations completely from the graph that we compile +# but we update the graph to return (updated_inputs, user_outputs). +# In the epilogue that runs after the compiled graph is executed, we copy the updated inputs back to the originals. +# +# Example: original user code: +# def f(x): +# x.mul_(2) +# out = x.mul(3) +# return out +# +# After AOT Autograd compiles, we end up with a: +# (a) compiled graph +# (b) autograd.Function.forward() method, that executes the compiled graph +# (c) wrapper function, that calls the autograd.Function.forward() and performs the epilogue +# +# The output of (a, b, c) are all written below. +# +# def compiled_forward_graph(x): +# x_updated = x.mul(2) +# out = x_updated.mul(3) +# return x_updated, out +# +# # x_updated gets a gradient in the compiled backward +# def compiled_backward_graph(grad_x_updated, grad_out): +# grad_x = ... +# return grad_x +# +# def autograd.Function.forward(x): +# x_updated, out = compiled_forward_graph(x) +# return x_updated, out +# +# def compiled_wrapper(x): +# x_updated, out = autograd.Function.apply(x) +# x.copy_(x_updated) +# return out +# +# Another important thing to note is that updated inputs (due to data mutations) *do* participate +# in the compiled backward graph! Since the compiled forward graph gets N extra outputs +# (due to updated inputs showing up as graph outputs), +# The compiled backward gets an additional N inputs. +# That way, during the x.copy_(x_updated) bit in the epilogue, gradients will flow from the updated input +# back to the original input. + + +# Note [AOT Autograd: input metadata mutations] +# +# For the same reason as input mutations, we also don't put input metadata mutations in the graph. +# Instead, we return the updated version of the input (a view), and mutate the input's metadata outside of the graph +# +# Example: original user code: +# def f(x): +# x.t_() +# out = x.mul(3) +# return out +# +# AOT Autograd output (compiled graph, autograd.Function.forward(), wrapper function): +# def compiled_forward_graph(x): +# x_updated = x.t() +# out = x_updated.mul(3) +# return x_updated, out +# +# # x_updated does *not* get a gradient in the compiled backward +# def compiled_backward_graph(grad_out): +# grad_x = ... +# return grad_x +# +# def autograd.Function.forward(x): +# x_updated, out = compiled_forward_graph(x) +# return x_updated, out +# +# def compiled_wrapper(x): +# x_updated, out = autograd.Function.apply(x) +# x.as_strided_(x_updated) +# return out + + +# Note [AOT Autograd: outputs aliasing inputs or intermediates!] +# +# AOT Autograd needs special handling for outputs that alias graph inputs or intermediates! +# Why? +# (1) autograd.Function.forward() has a limitation, where views that returned in the forward cannot later be mutated. +# (2) views don't need to be compiled in the graph anyway - it's cheap to generate them outside of the compiled graph, +# in an epilogue. +# For outputs that alias inputs, we do the following: +# (a) *still* return the aliased output as a graph output +# (b) In the AOT Autograd wrapper/epilogue, we don't return that aliased output. Instead, we use it to regenerate the output. +# +# For outputs that alias *intermediates*, we do the following: +# (a) Return the output in the compiled forward, **and** return it's ._base (a graph intermediates) as an output in the forward +# (b) Use (output, graph_intermediate) to regenerate the alias, and return that to the user (instead of the compiled fw output). +# You might wonder why we return the aliased output directly in the graph (and making the graph compute it), +# only to not return it and instead generate a fresh alias off of the intermediate, +# instead of (say) just storing metadata about the size/stride of the output somewhere to generate the alias. There are two reasons: +# (1) Getting the actual alias tensor allows us to use view-replay to generate the alias, instead of an as_strided() call +# (2) Inductor (and other backends) are free to change the memory format of graph outputs, if it results in better performance. +# This can result in problems if a user later tries to .view() that output expecting it to have one set of strides, +# when it has a different set of strides. +# By including the view op directly in the graph, inductor takes that into account when deciding what memory format +# the graph intermediate should be. +# +# Another important thing to note is how our traced backward() graph handles aliases. +# (this applies to outputs aliasing inputs, outputs aliasing intermediates, +# *and* updated inputs returned in the compiled forward due to metadata-only mutations). +# Any outputs that alias (either inputs or intermediates) do NOT participate in the compiled backward graph +# It would be wasteful to include them in the compiled backward(), because we regenerate them eagerly +# at the end of the forward. +# +# Example: original user code: +# def f(x): +# out1 = x.t() +# intermediate = x.mul(2) +# out2 = intermediate.view(-1) +# return out1, out2 +# +# AOT Autograd output (compiled graph, autograd.Function.forward(), wrapper function): +# def compiled_forward_graph(x): +# out1 = x.t() +# intermediate = x.mul(2) +# out2 = intermediate.view(-1) +# # the compiled graph also returns the intermediate +# return out1, out2, intermediate +# +# # intermediate gets a gradient in the compiled backward. +# # both output aliases (out1 and out2) do not. +# def compiled_backward_graph(grad_intermediate): +# grad_x = ... +# return grad_x +# +# def autograd.Function.forward(x): +# out1, out2, intermediate = compiled_forward_graph(x) +# return out1, out2, intermediate +# +# def compiled_wrapper(x): +# out1, out2, intermediate = autograd.Function.apply(x) +# # regenerate out1 from the input +# out1_regenerated = out1._view_func(x) +# # regenerate out1 from the intermediate +# out2_regenerated = out2._view_func(intermediate) +# return out1_regenerated, out2_regenerated + + +# Note [AOT Autograd: mutations to inputs that alias other inputs] +# +# Another edge case that is (only partially) handled today is when an input is mutated, but itself aliases another input. +# AOT Autograd needs to **ensure** that functionalization knows that the two inputs are aliased to each other. +# That way, when the aliased input is accessed later in the graph, functionalization knows to "update" the alias +# given the mutation that occurred. +# +# This is handled by updating the calling convention: we create a "synthetic base" that becomes a new input +# in the compiled function, and we regenerate the original (aliased) inputs directly off of the base +# inside of the compiled function. +# +# This logic is fully encapsulated in aot_wrapper_synthetic_base() +# +# Example: original user code: +# def f(x, x_view): +# x.mul_(2) +# out = x * x_view +# return out +# f(x, x.view(-1)) +# +# AOT Autograd output (compiled graph, autograd.Function.forward(), wrapper function): +# def compiled_forward_graph(base) +# x = generate_x(base) +# x_view = generate_x_view(base) +# x_updated = x.mul(2) +# x_view_updated = x_updated.view(-1) +# out = x_updated * x_view_updated +# return x_updated, out +# +# # The calling convention change from (aliases) -> (base) happens +# # *outside* of the autograd.Function.forward(). +# # That means the forward() only has 1 input (base), +# # and the backward() only has 1 output (grad_base) +# def compiled_backward_graph(grad_out): +# grad_base = ... +# return grad_base +# +# def autograd.Function.forward(base): +# x_updated, out = compiled_forward_graph(base) +# return x_updated, out +# +# # The compiled wrapper is where we create synthetic bases. +# # The info on which inputs are mutated is also tracked *before* synthetic base creation. +# def compiled_wrapper(x, x_view): +# base = merge_view_inputs(x, x_view) +# x_updated, out = autograd.Function.apply(base) +# # x and x_view are aliased in eager mode, so this mutation to x will automatically affect x_view. +# x.copy_(x_updated) +# return out + + +# Note [AOT Autograd: Views to avoid tangents aliasing inputs] +# +# We view every forward output when creating out tangent tensors to handle the problematic +# case in which a subclass does extra aliasing between graph outputs/inputs in a way that +# is not visible above the sublass. +# +# Ordinarily, when constructing the joint function that we want to trace in AOTAutograd, +# we're guaranteed that the tangent tensors that we pass +# into the joint are distinct tensors from the primals. This is because when +# decide which forward outputs to create tangents for, we only create tangents +# for forward outputs that are not aliases of inputs (See Note +# [AOT Autograd: outputs aliasing inputs or intermediates!]). +# +# However, when wrapper tensor subclasses enter the picture, it is possible +# to have an output of the forward that is a subclass that is not an +# input / alias of an input, but one of its inner tensors is an alias! +# NestedTensor is an example: Performing an out-of-place pointwise op on a +# NestedTensor constructs a fresh NestedTensor that holds onto the input's +# offsets tensor directly. +# +# Having tangent tensors that are the same as the (primal) forward inputs, +# can cause problems during tracing as make_fx() will specialize on our +# duplicate inputs: If we passed in the same tensor for primals_1 and +# tangents_1 during tracing, make_fx() will happily sub out all usages of +# tangents_1 with primals_1 in the graph, which is not what we want. +# +# To work around this, we view every forward output when creating out tangent +# tensors so that tangents can never be the same as forward inputs even if +# forward inputs alias forward outputs. + +# Note [Side-Effectful Tokens in AOTAutograd] +# +# We allow some some side-effectful operators in +# the post-AOTAutograd (functional) graph, such as prints and torchbind operations. +# To ensure that these side-effects are compatible to future graph passes that +# assume that the graph is functional, we will thread "effect tokens" to show +# data dependence between these side-effectful operators. Practically speaking, +# effect tokens are just dummy values (torch.tensor([])). The graph would look +# like the following: +# +# def gm(self, token0, reader): +# token1, frame = with_token(ordered_effect_op, (reader,), token0) +# frame = frame * 2 +# token2, frame2 = with_token(ordered_effect_op, (reader,), token1) +# frame2 = frame2 * 2 +# return token2, frame, frame2 +# +# We will pass the token as an input to the graph, thread it through +# side-effectful operators using the `with_effects` high order operator, and then +# return the updated token as an output. +# So the signature of the graph input would look something like +# (*tokens, *params_buffers, *user_inputs), and the signature of the graph +# output would look something like (*tokens, *outputs). +# +# However, Inductor does not want the concept of tokens in the final generated +# code's input and output. Since changing the graph signature inside of inductor +# is difficult, after generating the forward graph, we will run a pass to +# remove the tokens from the inputgenerate the following graph for Inductor, where +# the tokens are created and sunk within the graph, rather than as inputs and +# outputs: +# +# def gm(self, reader): +# token0 = torch.ops.prims._make_token() +# token1, frame = with_token(ordered_effect_op, (reader,), token0) +# frame = frame * 2 +# token2, frame2 = with_token(ordered_effect_op, (reader,), token1) +# frame2 = frame2 * 2 +# sink_token = torch.ops.prims._sink_tokens([token2]) +# return frame, frame2 + +# +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +aot_autograd_decompositions = {} + +FakifiedFlatArgs = NewType("FakifiedFlatArgs", list[Any]) + + +TOutputCode = TypeVar("TOutputCode", bound=OutputCode) + + +class AOTDispatchCompiler(Protocol): + """ + Represents a fw or bw_compiler passed to AOTAutograd. + """ + + def __call__( + self, + gm: torch.fx.GraphModule, + example_inputs: Sequence[InputType], + ) -> Any: + ... + + +# TODO: bikeshed on this name +class SerializableAOTDispatchCompiler(AOTDispatchCompiler): + """ + Represents an AOTDispatchCompiler that returns an OutputCode, and is + therefore cacheable. SerializableAOTDispatchCompiler always return an OutputCode. + A _CompileFxCallable usually gets converted into an AOTDispatchCompiler after binding all of + the kwargs in _CompileFxKwargs. + """ + + def __init__( + self, + output_code_ty: type[TOutputCode], + compiler_fn: Callable[[torch.fx.GraphModule, Sequence[InputType]], TOutputCode], + ): + self.output_code_ty = output_code_ty + self.compiler_fn = compiler_fn + + def __call__( + self, + gm: torch.fx.GraphModule, + example_inputs: Sequence[InputType], + ) -> OutputCode: + return self.compiler_fn(gm, example_inputs) + + +def process_inputs( + flat_args: list[Any], + aot_config: AOTConfig, + fake_mode: FakeTensorMode, + shape_env: Optional[ShapeEnv], +) -> FakifiedFlatArgs: + with fake_mode: + + def convert(idx, x): + if shape_env is not None: + from torch._dynamo.source import ConstantSource + + if isinstance(x, int): + # We always specialize on scalar values in export. + if aot_config.is_export: + return x + source = ConstantSource(f"sym_{idx}") + return shape_env.create_symintnode( + shape_env.create_symbol(x, source), hint=x, source=source + ) + if isinstance(x, torch.ScriptObject): + return torch._library.fake_class_registry.maybe_to_fake_obj( + fake_mode, x + ) + if not isinstance(x, torch.Tensor): + return x + if isinstance(x, FakeTensor): + assert x.fake_mode is fake_mode + return x + if is_traceable_wrapper_subclass(x): + attrs, _ = x.__tensor_flatten__() + if all(isinstance(getattr(x, attr), FakeTensor) for attr in attrs): + assert all( + getattr(x, attr).fake_mode is fake_mode for attr in attrs + ) + return x + + # see note [Tensor Fakification and Symbol Caching] + symbolic_context = None + source = None + trace = True + if tracing_context := torch._guards.TracingContext.try_get(): + if x in tracing_context.tensor_to_context: + symbolic_context = tracing_context.tensor_to_context[x] + source = symbolic_context.tensor_source + # We already fakeified this tensor in Dynamo, don't + # dump the trace for it again + trace = False + if ( + idx < aot_config.num_params_buffers + and config.static_weight_shapes + and not symbolic_context + ): + # TODO: Ensure that this codepath is never exercised from + # Dynamo + return fake_mode.from_tensor(x, static_shapes=True) + + return fake_mode.from_tensor( + x, + static_shapes=False, + symbolic_context=symbolic_context, + source=source, + trace=trace, + ) + + return FakifiedFlatArgs([convert(idx, x) for idx, x in enumerate(flat_args)]) + + +def construct_fake_mode( + flat_args: list[Any], aot_config: AOTConfig +) -> tuple[FakeTensorMode, Optional[ShapeEnv]]: + fake_mode = detect_fake_mode(flat_args) + if fake_mode is None: + shape_env = ShapeEnv() if aot_config.dynamic_shapes else None + fake_mode = FakeTensorMode(shape_env=shape_env) + else: + shape_env = fake_mode.shape_env + return (fake_mode, shape_env) + + +def create_aot_dispatcher_function( + flat_fn, + fake_flat_args: FakifiedFlatArgs, + aot_config: AOTConfig, + fake_mode: FakeTensorMode, + shape_env: Optional[ShapeEnv], +) -> tuple[Callable, ViewAndMutationMeta]: + with dynamo_timed("create_aot_dispatcher_function", log_pt2_compile_event=True): + return _create_aot_dispatcher_function( + flat_fn, fake_flat_args, aot_config, fake_mode, shape_env + ) + + +def _create_aot_dispatcher_function( + flat_fn, + fake_flat_args: FakifiedFlatArgs, + aot_config: AOTConfig, + fake_mode: FakeTensorMode, + shape_env: Optional[ShapeEnv], +) -> tuple[Callable, ViewAndMutationMeta]: + """ + Traces the forward and backward graphs of the attr:`flat_fn` to generate a + joint graph. The joint graph is an Fx graph with Aten ops. Please refer to + the tracing mechanism to understand the graph capturing details. + + The joint graph is then passed through attr:`partition_fn` to isolate the + forward and backward portions, which are then respectively compiled via the + provided attr:`fw_compiler` and attr:`bw_compiler`. + + The resulting compiled forward and backward graphs are then wrapped up in a + ``torch.autograd.Function`` object. + + The calling convention here is that the first aot_config.num_params_buffers + inputs in flat_args are parameters and buffers, and the rest are inputs. + + We use this to assume that parameters/buffer's shapes don't change. + + Note: this function is used both by aot_function and aot_export (controlled by aot_config.is_export) + When aot_config.is_export is True, we return an FX graph + metadata + When aot_config.is_export is False, we return an ordinary runtime function + """ + + # This is the main entry point. + # TODO: Chillee argues that dynamo itself should pass in fake tensors to + # the list of arguments when compiling; at the moment we do not do this + + if aot_config.decompositions is None: + aot_config.decompositions = {} + + aot_config.decompositions = { + **aot_autograd_decompositions, + **aot_config.decompositions, + } + + if config.functionalize_rng_ops: + # Update the decompositions with functionalized random decompositions + aot_config.decompositions = { + **rng_decompositions, + **aot_config.decompositions, + } + + # Check flat_args to see if they're already fake. If so, use that fake + # mode instead. + + python_dispatcher_mode = ( + enable_python_dispatcher() if shape_env is not None else nullcontext() + ) + + # See NOTE: [Deferring tensor pack/unpack hooks until runtime] + # If any saved tensor hooks are active, we **don't** want to trace them. + # Instead, we'll let them run at runtime, around the custom autograd.Function + # that we generate in torch.compile. + with torch.autograd.set_multithreading_enabled( + False + ), preserve_rng_state(), ( + fake_mode + ), ( + python_dispatcher_mode + ), PhiloxStateTracker(), torch._dynamo.utils._disable_saved_tensors_hooks_during_tracing(): + from torch._library.fake_class_registry import ( + FakeScriptObject, + maybe_to_fake_obj, + ) + + # Tracing may mutate the states the fake script object, + # so we need to duplicate the fake script objects so that subsequent tracing + # won't be affected. + def _dup_fake_script_obj(fake_flat_args): + return [ + maybe_to_fake_obj(detect_fake_mode(fake_flat_args), arg.real_obj) + if isinstance(arg, FakeScriptObject) + else arg + for arg in fake_flat_args + ] + + needs_autograd = any( + x.requires_grad for x in fake_flat_args if isinstance(x, Tensor) + ) + + with enable_python_dispatcher(): + # Patch set_rng_state as set_rng_state with fake tensors is + # nonsensical. This does not affect the collection of metadata. + with patch("torch.cuda.set_rng_state", lambda *args: None): + mod = root_module_when_exporting_non_strict(flat_fn) + if mod is not None: + ctx = _detect_attribute_assignment(mod) + else: + ctx = nullcontext() + with ctx: + fw_metadata = run_functionalized_fw_and_collect_metadata( + flat_fn, + static_input_indices=aot_config.static_input_indices, + keep_input_mutations=aot_config.keep_inference_input_mutations, + is_train=needs_autograd, + pre_dispatch=aot_config.pre_dispatch, + is_export=aot_config.is_export, + )(*_dup_fake_script_obj(fake_flat_args)) + + req_subclass_dispatch = requires_subclass_dispatch( + fake_flat_args, fw_metadata + ) + CompileEventLogger.try_add_pt2_compile( + "backend_compile", requires_subclass_dispatch=req_subclass_dispatch + ) + + output_and_mutation_safe = not any( + x.requires_grad + # view-type operations preserve requires_grad even in no_grad. + # Do not count aliases of inputs with requires_grad as reason to make a training graph, + # as AOTAutograd will perform view-replay to regenerate the view outputs at runtime, + # setting their grad_fn properly. + and not ( + x.output_type + in (OutputType.alias_of_input, OutputType.is_input) + and fw_metadata.input_info[x.base_idx].requires_grad + ) + for x in fw_metadata.output_info + ) and not any( + x.requires_grad + and x.mutates_data + and not x.mutations_under_no_grad_or_inference_mode + and not x.mutations_hidden_from_autograd + for x in fw_metadata.input_info + ) + + if needs_autograd and output_and_mutation_safe: + # We realized that none of the outputs require grad, + # and none of the inputs that require grad are mutated. + # so we actually have an inference graph. + needs_autograd = False + # A bit silly: right now in the subclass codepath, our ViewAndMutationMeta + # changes depending on whether we pass in is_train / keep_input_mutations, + # so we're forced to recompute the metadata. + # TODO: refactor the subclass path of run_functionalized_fw_and_collect_metadata + # so that this is unnecessary. + if req_subclass_dispatch: + fw_metadata = run_functionalized_fw_and_collect_metadata( + flat_fn, + keep_input_mutations=aot_config.keep_inference_input_mutations, + is_train=False, + pre_dispatch=aot_config.pre_dispatch, + static_input_indices=aot_config.static_input_indices, + )(*fake_flat_args) + else: + fw_metadata = ViewAndMutationMeta( + input_info=fw_metadata.input_info, + output_info=fw_metadata.output_info, + num_intermediate_bases=fw_metadata.num_intermediate_bases, + keep_input_mutations=aot_config.keep_inference_input_mutations, + traced_tangents=fw_metadata.traced_tangents, + subclass_inp_meta=fw_metadata.subclass_inp_meta, + subclass_fw_graph_out_meta=fw_metadata.subclass_fw_graph_out_meta, + subclass_tangent_meta=fw_metadata.subclass_tangent_meta, + is_train=False, + tokens=fw_metadata.tokens, + static_input_indices=fw_metadata.static_input_indices, + ) + + if fw_metadata.num_intermediate_bases > 0: + assert not req_subclass_dispatch, f"""\ +torch.compile is currently being used with tensor subclass inputs: +{','.join([str(type(x)) for x in fake_flat_args])}. We are attempting to a compile a graph with two graph outputs +that alias one another, which is currently unsupported in the subclass use case. If you run into this, +please file a github issue""" + + if aot_config.is_export: + # aot_export: ban input metadata mutations for now to keep shared code paths simpler. + # Keeping .resize_() in the graph will require some work + # Allowing it but keeping the graph functional will require some calling convention changes. + if len([x for x in fw_metadata.input_info if x.mutates_metadata]) != 0: + raise RuntimeError( + f"""\ +Found an input that received a metadata mutation, through e.g. a call to `.resize_()` or `.transpose_()`. +This is currently banned in the aot_export workflow. If you need this functionality, please file a github issue. + +fw_metadata={str(fw_metadata)}""" + ) + # In export, banning data mutations on inputs that require grad for now. + # This should be rare, and is tricky to get right. When we trace the backward, + # we currently trace with autograd.grad instead of .backward(), which makes it difficult + # to ensure that we run autograd all the way through the input **before** it saw the mutation. + if ( + len( + [ + x + for x in fw_metadata.input_info + if x.requires_grad and x.mutates_data + ] + ) + != 0 + ): + raise RuntimeError( + f"""\ +Found a graph input that requires gradients, and received a mutation. +This is currently banned in the aot_export workflow. If you need this functionality, please file a github issue. + +fw_metadata={str(fw_metadata)}""" + ) + if req_subclass_dispatch: + raise RuntimeError( + """\ +aot_export is not currently supported with traceable tensor subclass. +If you need this feature, please comment on """ + ) + + # Need to decide on a strategy for functionalized RNG: toggling via global config seems bad, + # and turning it on will require a non-trivial calling convention change for any export runtime. + if config.functionalize_rng_ops: + raise RuntimeError( + """\ +Functionalized RNG is not currently supported in the aot_export workflow. Please file a github issue, +or otherwise set torch._functorch.config.functionalize_rng_ops = False.""" + ) + + def choose_dispatcher(needs_autograd, aot_config): + """ + Pick a dispatcher based on the config rules. + """ + if aot_config.is_export: + # export uses just the "graph bits", whereas the other + # two dispatchers include some extra work around handling a runtime epilogue + CompileEventLogger.try_add_pt2_compile( + "backend_compile", dispatch_mode="export" + ) + return partial(aot_dispatch_export, needs_autograd=needs_autograd) + elif needs_autograd and not aot_config.pre_dispatch: + CompileEventLogger.try_add_pt2_compile( + "backend_compile", dispatch_mode="autograd" + ) + return aot_dispatch_autograd + else: + CompileEventLogger.try_add_pt2_compile( + "backend_compile", dispatch_mode="inference" + ) + return aot_dispatch_base + + compiler_fn = choose_dispatcher(needs_autograd, aot_config) + + compiled_fn, fw_metadata = compiler_fn( + flat_fn, + _dup_fake_script_obj(fake_flat_args), + aot_config, + fw_metadata=fw_metadata, + ) + return compiled_fn, fw_metadata + + +def aot_function( + fn: Callable, + fw_compiler: Callable, + bw_compiler: Optional[Callable] = None, + partition_fn: Callable = default_partition, + decompositions: Optional[dict] = None, + num_params_buffers: int = 0, + keep_inference_input_mutations: bool = False, + inference_compiler: Optional[Callable] = None, + *, + # Whether or not to trace with dynamic shapes + dynamic=False, + enable_log=True, +) -> Callable: + """ + Traces the forward and backward graph of :attr:`fn` using torch dispatch + mechanism, and then compiles the generated forward and backward graphs + through :attr:`fw_compiler` and :attr:`bw_compiler`. + + :func:`aot_function` traces the forward and backward graph ahead of time, + and generates a joint forward and backward graph. :attr:`partition_fn` is + then used to separate out forward and backward graphs. The partitioner + function can be used to perform optimizations such as recomputation. One can + set `decompositions` dictionary to decompose the operators into a sequence + of core or simpler operators supported by the backend compilers. + + .. warning:: + This API is experimental and likely to change. + + Args: + fn (Callable): A Python function that takes one ore more arguments. Must + return one or more Tensors. + fw_compiler (Callable): A Python function that accepts an Fx graph with + Aten ops and input args, and returns a Callable that semantically is + equivalent to the input Fx graph. + bw_compiler (Optional[Callable]): A Python function that accepts an + Fx graph with Aten ops and input args, and returns a Callable that + semantically is equivalent to the input Fx graph. Default: None + (when None, it defaults to the :attr:`fw_compiler`) + partition_fn (Callable): A Python function that takes a joint forward + and backward graph, and partitions it into separate forward and + backward graphs. + decompositions (Dict): A dictionary to define the decomposition of + larger Aten ops into simpler or core Aten ops. + inference_compiler (Optional[Callable]): A Python function that accepts an + Fx graph with Aten ops and input args, and returns a Callable that + semantically is equivalent to the input Fx graph. inference_compiler is invoked + if no autograd is needed. Default: None + (when None, it defaults to the :attr:`fw_compiler`) + Returns: + Returns a ``Callable`` that retains the eager behavior of the original + :attr:`fn`, but with forward and backward graph compiled via + :attr:`fw_compile` and :attr:`bw_compile`. + + A simple example usage of :func:`aot_function` is as follows. This example + will print the forward and backward graphs of the function ``fn`` + + >>> fn = lambda x : x.sin().cos() + >>> def print_compile_fn(fx_module, args): + >>> print(fx_module) + >>> return fx_module + >>> aot_fn = aot_function(fn, print_compile_fn) + >>> x = torch.randn(4, 5, requires_grad=True) + >>> aot_fn(x) + """ + + if bw_compiler is None: + bw_compiler = fw_compiler + if inference_compiler is None: + inference_compiler = fw_compiler + aot_config = AOTConfig( + fw_compiler=fw_compiler, + bw_compiler=bw_compiler, + inference_compiler=inference_compiler, + partition_fn=partition_fn, + decompositions=decompositions, + num_params_buffers=num_params_buffers, + aot_id=next(AOT_COUNTER), + keep_inference_input_mutations=keep_inference_input_mutations, + dynamic_shapes=dynamic, + aot_autograd_arg_pos_to_source=None, + is_export=False, + no_tangents=False, + enable_log=enable_log, + ) + cached_res = None + + @wraps(fn) + def returned_function(*args, **kwargs): + nonlocal cached_res + # Now flatten the tensor args + flat_args = pytree.arg_tree_leaves(*args, **kwargs) + + # Compile the function and save it in the cache + if cached_res is None: + flat_fn, out_spec = create_tree_flattened_fn(fn, args, kwargs) + (fake_mode, shape_env) = construct_fake_mode(flat_args, aot_config) + fake_flat_args: FakifiedFlatArgs = process_inputs( + flat_args, aot_config, fake_mode, shape_env + ) + compiled_fn, _ = create_aot_dispatcher_function( + flat_fn, + fake_flat_args, + aot_config, + fake_mode, + shape_env, + ) + cached_res = (compiled_fn, out_spec) + + cached_fn, out_spec = cached_res + out = cached_fn(flat_args) + return out_spec.unflatten(out) + + return returned_function + + +def aot_module(mod: nn.Module, *args, **kwargs) -> nn.Module: + """ + Traces the forward and backward graph of :attr:`mod` using torch dispatch + tracing mechanism. It is wrapper function, that underneath uses + :func:`aot_function` to perform tracing and compilation. + + :func:`aot_module` lifts the parameters and buffers of ``nn.Module`` as inputs + to a new callable which is then compiled through :func:`aot_function`. + + .. warning:: + This API is experimental and likely to change. + + Args: + mod (Callable): A ``nn.Module`` module. + args : args to be passed to :func:`aot_function` + kwargs : kwargs to be passed to :func:`aot_function` + + Returns: + Returns a ``nn.Module`` that retains the eager behavior of the original + :attr:`mod`, but with forward and backward graph compiled. + + """ + # See Note: [Fake Modules and AOTAutograd] + torch._dynamo.utils.assert_no_fake_params_or_buffers(mod) + + def functional_call(named_params, named_buffers, *args, **kwargs): + params_and_buffers = {**named_params, **named_buffers} + return torch.func.functional_call(mod, params_and_buffers, args, kwargs) + + named_params = dict(mod.named_parameters(remove_duplicate=False)) + named_buffers = dict(mod.named_buffers(remove_duplicate=False)) + num_params_buffers = len(named_params) + len(named_buffers) + compiled_f = aot_function( + functional_call, *args, num_params_buffers=num_params_buffers, **kwargs + ) + + class AOTModule(nn.Module): + def __init__(self) -> None: + super().__init__() + self.orig_module = mod + + def forward(self, *args, **kwargs): + return compiled_f( + named_params, + named_buffers, + *args, + **kwargs, + ) + + return AOTModule() + + +def _try_get_metadata_from_dynamo( + mod: torch.nn.Module, param_keys: KeysView[str], full_args_num: int +) -> tuple[Optional[list[torch._guards.Source]], list[int]]: + """ + Metadata is forwarded from Dynamo to AOTDispatch via special fields on GraphModule. + We first verify that `mod` does come from Dynamo, then we handle cases where + metadata might be missing. + + Returns: + aot_autograd_arg_pos_to_source: used to dedup params and their guards + static_input_indices: used to identify static inputs for cudagraphs + """ + if not (isinstance(mod, torch.fx.GraphModule) and "dynamo_compile_id" in mod.meta): + # graph was not captured by dynamo + return None, [] + + if not hasattr(mod, "_param_name_to_source"): + # is from export + return None, [] + + # We now know this came from dynamo, and (1) we care about guards, + # so setting up aot_autograd_arg_pos_to_source for downstream dedup guards + # can now be done safely. (2) Dynamo logic protects the 1:1 sizing below. + # Additionally, we mark static indices for cudagraphs. + param_name_to_source = mod._param_name_to_source + seen_sources = set() + + aot_autograd_arg_pos_to_source = [] + static_input_indices = [] + # Collect the new inputs lifted by aotdispatch + for i, name in enumerate(param_keys): + assert name in param_name_to_source, f"{name} not found." + source = param_name_to_source[name] + assert source not in seen_sources, source + seen_sources.add(source) + aot_autograd_arg_pos_to_source.append(source) + + static_input_indices.append(i) + + # Collect the dynamo graph inputs + # TODO(mlazos): Revisit if this is still needed. With Dynamo install ID + # matched tensors back into the Fx graph, this might not be necessary. + for pos, node in enumerate(mod.graph.find_nodes(op="placeholder")): + assert hasattr(node, "_dynamo_source") + source = node._dynamo_source + assert source not in seen_sources, source + seen_sources.add(source) + aot_autograd_arg_pos_to_source.append(source) + source_name = source.name() if source else str(source) + + # input[i] in dynamo is now: + # input[i + len(extra_params)] in AOT, + # where extra_params are the params/buffers that dynamo baked into the + # OutputGraph + actual_pos = pos + len(param_keys) + + if "tensor_dict" in node.meta and node.meta["tensor_dict"].get( + "_dynamo_static_input_type", None + ): + static_inputs_log.debug( + "Adding static input pos %s for source %s", actual_pos, source_name + ) + static_input_indices.append(actual_pos) + else: + static_inputs_log.debug( + "Non-static input pos %s for source %s", actual_pos, source_name + ) + + assert full_args_num == len(aot_autograd_arg_pos_to_source) + return aot_autograd_arg_pos_to_source, static_input_indices + + +def aot_module_simplified( + mod: nn.Module, + args, + fw_compiler: AOTDispatchCompiler, + bw_compiler: Optional[AOTDispatchCompiler] = None, + partition_fn: Callable = default_partition, + decompositions: Optional[dict] = None, + keep_inference_input_mutations=False, + inference_compiler: Optional[AOTDispatchCompiler] = None, + cudagraphs: Optional[BoxedBool] = None, +) -> nn.Module: + """ + This is the simplified or low overhead version of aot_module. For frontends + like TorchDynamo, the input functions/modules to AOT are static and have + unpacked inputs/outputs. This gives us an opportunity to remove the + (1) pytree overhead to parse inputs/outputs, + (2) AOT Autograd cache, + (3) Reading of params/buffers in every forward call + + :func:`aot_module_simplified` removes these overheads. + """ + params = { + **dict(mod.named_parameters(remove_duplicate=False)), + **dict(mod.named_buffers(remove_duplicate=False)), + } + params_flat, params_spec = pytree.tree_flatten(params) + params_flat = list(params_flat) + params_len = len(params_flat) + + if cudagraphs is None: + cudagraphs = BoxedBool(torch._inductor.config.triton.cudagraphs) + + if bw_compiler is None: + bw_compiler = fw_compiler + if inference_compiler is None: + inference_compiler = fw_compiler + + full_args = [] + # First, the params + full_args.extend(params_flat) + + if tracing_context := torch._guards.TracingContext.try_get(): + tracing_context.params_flat = params_flat + ( + tracing_context.params_flat_unwrap_subclasses, + tracing_context.params_unwrapped_to_flat_index, + ) = unwrap_tensor_subclasses_with_indices_to_original(params_flat) + + # Next, the input args + full_args.extend(args) + + ( + aot_autograd_arg_pos_to_source, + static_input_indices, + ) = _try_get_metadata_from_dynamo(mod, params.keys(), len(full_args)) + + dynamic_shapes = False + for x in full_args: + if isinstance(x, FakeTensor): + dynamic_shapes = x.fake_mode.shape_env is not None + break + + aot_config = AOTConfig( + fw_compiler=fw_compiler, + bw_compiler=bw_compiler, + inference_compiler=inference_compiler, + partition_fn=partition_fn, + decompositions=decompositions, + num_params_buffers=params_len, + aot_id=next(AOT_COUNTER), + keep_inference_input_mutations=keep_inference_input_mutations, + dynamic_shapes=dynamic_shapes, + aot_autograd_arg_pos_to_source=aot_autograd_arg_pos_to_source, + static_input_indices=static_input_indices, + is_export=False, + no_tangents=False, + cache_info=None, + ) + fake_mode, shape_env = construct_fake_mode(full_args, aot_config) + fake_flat_args = process_inputs(full_args, aot_config, fake_mode, shape_env) + + def dispatch_and_compile(): + functional_call = create_functional_call(mod, params_spec, params_len) + with compiled_autograd._disable(): + compiled_fn, _ = create_aot_dispatcher_function( + functional_call, + fake_flat_args, + aot_config, + fake_mode, + shape_env, + ) + return compiled_fn + + # We only care if the forward will return an OutputCode. + if isinstance(fw_compiler, SerializableAOTDispatchCompiler): + local = should_use_local_autograd_cache() + remote = should_use_remote_autograd_cache() + if local or remote: + set_feature_use("aot_autograd_remote_cache", remote) + compiled_fn = AOTAutogradCache.load( + dispatch_and_compile, + mod, + fake_flat_args, + aot_config, + cudagraphs, + local, + remote, + ) + else: + compiled_fn = dispatch_and_compile() + else: + compiled_fn = dispatch_and_compile() + + if isinstance(mod, torch._dynamo.utils.GmWrapper): + # This function is called by the flatten_graph_inputs wrapper, which boxes + # the inputs so that they can be freed before the end of this scope. + # For overhead reasons, this is not the default wrapper, see comment: + # https://github.com/pytorch/pytorch/pull/122535/files#r1560096481 + def boxed_forward(runtime_args: list[Any]): + flat_args = [] + flat_args.extend(params_flat) + flat_args.extend(runtime_args) + runtime_args.clear() + return compiled_fn(flat_args) + + # Just for convenience + boxed_forward.zero_grad = mod.zero_grad + boxed_forward.named_parameters = mod.named_parameters + boxed_forward.named_buffers = mod.named_buffers + return boxed_forward + + # TODO: There is something deeply wrong here; compiled_fn running with + # the boxed calling convention, but aot_module_simplified somehow + # historically returned a function that was not the boxed calling + # convention. This should get fixed... + # NB: GraphModule/nn.Module rely on the non-boxed calling convention here + def forward(*runtime_args: tuple[Any]): + full_args = [] + full_args.extend(params_flat) + full_args.extend(runtime_args) + return compiled_fn(full_args) + + # Just for convenience + forward.zero_grad = mod.zero_grad + forward.named_parameters = mod.named_parameters + forward.named_buffers = mod.named_buffers + + return forward + + +def aot_export_module( + mod: nn.Module, + args, + *, + decompositions: Optional[dict] = None, + # If true, we'll return a joint forward-backward graph, + # As well as metadata on the loss + gradients in the backward. + trace_joint: bool, + # If trace_joint is True, we expect your module to return a scalar loss. + # Your module can return multiple outputs, so you must specify which output the loss is. + output_loss_index: Optional[int] = None, + pre_dispatch: bool = False, + # If None, will be infered from inputs and mod.graph.nodes if mod is a graph module, but the inferred result might be wrong. + dynamic_shapes: Optional[bool] = None, + kwargs=None, +) -> tuple[torch.fx.GraphModule, GraphSignature]: + """ + This function takes in a module, and returns: + (1) an FX graph that can be exported + (2) some metadata about the graph + + If `trace_joint=True` we will return a joint graph of the forward + backward. + + The traced FX graph will have the following properties compared to the original module: + (1) Inputs and outputs to the module will be pytree-flattened + (2) Parameters and buffers on the module will be lifted into graph inputs, + graph_inputs = (*parameters, *buffers, *user_inputs) + (3) The graph will be fully functionalized + (4) Any input mutations will be converted into additional outputs in the graph, + meaning whoever calls this graph is responsible for applying the mutations + back to the original inputs. + (5) If is_joint is provided the graph will return parameter gradients in addition to user outputs. + The graph output will look like: + graph_outputs = (*updated_inputs, *user_outputs, *param_gradients) + + There are also several restrictions on what modules can use this API. In particular: + (1) If trace_joint is specified, we expect the loss function to be **fused** + into the module forward. One of the outputs to the forward must be a scalar loss, + which is specified with `output_loss_index`. + All other outputs to the forward are presumed to not require gradients. + (2) This API cannot capture optimizers (although in theory we could build an API for this). + (3) Metadata mutations on params/buffers/inputs are banned. + (4) Data mutations on anything that requires gradients are banned (parameters) + (5) If an input is mutated, it is not allowed to alias any other inputs. + (6) Parameters must not be duplicated. + """ + if pre_dispatch and trace_joint: + raise RuntimeError("pre_dispatch is not supported when trace_joint is True.") + named_parameters = dict(mod.named_parameters(remove_duplicate=False)) + named_buffers = dict(mod.named_buffers(remove_duplicate=False)) + + params_and_buffers = { + **dict(named_parameters), + **dict(named_buffers), + } + params_and_buffers_flat, params_spec = pytree.tree_flatten(params_and_buffers) + params_and_buffers_flat = tuple(params_and_buffers_flat) + params_len = len(params_and_buffers_flat) + + kwargs = kwargs or {} + + functional_call = create_functional_call( + mod, params_spec, params_len, store_orig_mod=True + ) + + num_fw_outs = None + + if trace_joint: + # This helper effectively just adds some extra asserts about what the backward will look like: + # Outputs must include a scalar loss, that we compute gradients w.r.t. + # We don't compute gradients w.r.t. anything else: so just in case we detach() + # and other output tensors. + def fn_to_trace(*args): + nonlocal num_fw_outs + out = functional_call(*args) + if output_loss_index is None: + raise RuntimeError( + """\ +If trace_joint=Trueit is required that one of your forward outputs must be a scalar loss. +You must specify the which (index) output is the loss with output_loss_index.""" + ) + if isinstance(out, (torch.Tensor)): + out = (out,) + if not isinstance(out, (tuple, list)): + raise RuntimeError( + f"Expected forward output to be either a tensor or a list/tuple of tensors. found {type(out)}" + ) + + for i, o in enumerate(out): + # We only want to create a backward graph w.r.t. the loss that the user passed in. + # This implies that every other output should not require gradients. + # Instead of making this an error (and forcing the user to detach all other outputs + # of their forward), + # we'll automatically detach them here. + if o.requires_grad and i != output_loss_index: + raise RuntimeError( + f"""\ +Found an output of the forward that requires gradients, that was not the scalar loss. +We require all outputs to the forward that are not the scalar loss to not require gradient, +because we will only compute a backward graph against the scalar loss. +You can fix this by calling .detach() on each of your forward outputs that is not the loss. +You specified that output index {output_loss_index} is the loss, but we found that +the output at index {i} requires gradients.""" + ) + out_loss = out[output_loss_index] + num_fw_outs = len(out) + if not out_loss.requires_grad: + raise RuntimeError( + f"""\ +The output at index {output_loss_index} was marked as the loss, but it does not require gradients""" + ) + if out_loss.numel() != 1: + raise RuntimeError( + f"""\ +We require the output marked as the loss (at index {output_loss_index}) to be a scalar, but it has shape {out_loss.shape}""" + ) + return out + + ctx = nullcontext + else: + # Run under no_grad, so our tracing machinery only traces an inference graph. + # However if pre_dispatch=True, we want to correctly trace set_grad_enabled calls for training. + ctx = nullcontext if pre_dispatch else torch.no_grad + fn_to_trace = functional_call + + full_args = [] + # First, the params + # NB: It is REQUIRED that parameters come first, Inductor infers "fixed" + # parameters by looking at the difference in parameter count outside + # and inside AOTAutograd, and assumes the prefix of arguments are fixed + # arguments + full_args.extend(params_and_buffers_flat) + # Next, the input args + full_args.extend(args) + + with ctx(): + fx_g, metadata, in_spec, out_spec = _aot_export_function( + fn_to_trace, + full_args, + decompositions=decompositions, + num_params_buffers=params_len, + no_tangents=True, + pre_dispatch=pre_dispatch, + dynamic_shapes=dynamic_shapes, + kwargs=kwargs, + ) + if trace_joint: + + @wraps(functional_call) + def flattened_joint(*args): + # The idea here is that the joint graph that AOTAutograd creates has some strict properties: + # (1) It accepts two arguments (primals, tangents), and pytree_flattens them + # (2) It returns a tuple of (fw_outs, gradients) + # This is a very useful convention for anyone who wants to partition the joint graph + # into a separate forward and backward graph. + # However, + # (1) for people exporting a single joint graph, it would be preferable not to have + # any pytrees in the graph. + # (2) We are guaranteed in the aot_export_module case that the forward outputs a loss, + # and there are therefore no tangents that are needed to run the joint graph. + # (3) AOTAutograd creates a grad_input for every input in the forward, + # including None's for inputs that are not grad-requiring tensors. + # we don't want these in our export graph. + # and there are therefore no tangents that are needed to run the joint graph. + # This function "fixes" both of the above by removing any tangent inputs, + # and removing pytrees from the original FX graph. + fake_tangents = [ + None + for _ in range( + metadata.num_outputs + metadata.num_mutated_inp_runtime_indices + ) + ] + fw_outs, gradients = fx_g(args, fake_tangents) + assert len(gradients) == len(args) + output_gradients = [] + for a, grad in zip(args, gradients): + if isinstance(a, torch.Tensor) and a.requires_grad: + assert ( + grad is not None + ), """\ +Found a parameter that did not receive a gradient. +"This is most likely a bug, but if this needs to be supported please comment on this Github issue: +https://github.com/pytorch/pytorch/issues/101192 +""" + output_gradients.append(grad) + else: + assert grad is None + return *fw_outs, *output_gradients + + fx_g = make_fx(flattened_joint, record_module_stack=True)(*full_args) + + user_args_flat = pytree.arg_tree_leaves(*args, **kwargs) + return fx_g, create_graph_signature( + fx_g, + metadata, + in_spec, + out_spec, + user_args_flat=user_args_flat, + params_and_buffers_flat=params_and_buffers_flat, + param_names=list(named_parameters.keys()), + buffer_names=list(named_buffers.keys()), + trace_joint=trace_joint, + num_user_fw_outs=num_fw_outs, + loss_index=output_loss_index, + ) + + +def aot_export_joint_simple( + func: Callable, + args, + *, + trace_joint: bool, + # It looks like the main consequence of this API is that for dynamic shapes, + # it will assume that parms/buffers are static. + # With the new inferred dynamic shapes API, maybe this doesn't matter? + num_params_buffers: int = 0, + decompositions: Optional[dict] = None, +) -> torch.fx.GraphModule: + """ + A simplified version of export. Used by higher order operators. + + This function makes a high-level "no calling convention changes" guarantee: + - If no inputs require grad (so we export an inference graph), + there are *no* calling convention change between the exported graph, and "func". + - If at least one input requires grad (so we trace out and export a joint fw-bw graph), + Then if you were partition the graph into a separate forward and backward graph, + The forward graph will have no calling convention changes compared to "func". + + The above also relies on some strong restrictions around which functions this API accepts: + (1) `args` cannot contain any pytrees (they must have been pytree_flattened already) + (2) `func` cannot mutate any inputs + (3) The outputs of `func` cannot alias any inputs. + + Note: this function is only lightly tested today. It will probably be tested more heavily by higher order ops. + """ + if trace_joint: + ctx = nullcontext + else: + # Run under no_grad, so our tracing machinery only traces an inference graph. + ctx = torch.no_grad + + with ctx(): + fx_g, metadata, in_spec, out_spec = _aot_export_function( + func, + args, + decompositions=decompositions, + ) + in_spec, _kw_in_spec = in_spec.children_specs + # At this point, we can just directly return the (joint or inference graph) that we traced. + # First though: a bunch of assertions to make sure that our graph doesn't require + # any calling convention changes compared to the original function. + # These restrictions are *in addition to* the general restrictions on export. + + # No input mutations + if ( + len([x for x in metadata.input_info if x.mutates_data or x.mutates_metadata]) + != 0 + ): + raise RuntimeError( + f"aot_export_joint_simple does not support input mutations. {str(metadata)}" + ) + # No output aliasing + if ( + len([x for x in metadata.output_info if x.output_type != OutputType.non_alias]) + != 0 + ): + raise RuntimeError( + f"aot_export_joint_simple does not support outputs that alias inputs. {str(metadata)}" + ) + # No pytrees + if in_spec.is_leaf(): + raise RuntimeError( + f"aot_export_joint_simple requires inputs to be a single list/tuple. in_spec={str(in_spec)}" + ) + if not all(child.is_leaf() for child in in_spec.children_specs): + raise RuntimeError( + f"aot_export_joint_simple requires individual inputs not to be pytrees. in_spec={str(in_spec)}" + ) + if out_spec.is_leaf(): + raise RuntimeError( + f"aot_export_joint_simple requires outputs to be a single list/tuple. out_spec={str(out_spec)}" + ) + if not all(child.is_leaf() for child in out_spec.children_specs): + raise RuntimeError( + f"aot_export_joint_simple requires individual outputs not to be pytrees. out_spec={str(out_spec)}" + ) + # TODO: we might have to temporarily patch config.functionalize_rng + # so that it doesn't run when we're exporting a higher order op. + + if config.debug_assert: + # Smoke test that after partitioning, we can run the forward without any calling convention changes. + fw_module, _bw_module = aot_config.default_partition( # noqa: F821 + fx_g, args, num_fwd_outputs=len(fw_metadata.output_infos) # noqa: F821 + ) + # Attempt to run the fw_module with the original user inputs + fake_mode = detect_fake_mode(args) + if fake_mode is None: + fake_mode = FakeTensorMode() + with fake_mode: + fw_module(*args) + return fx_g + + +# Private for now because we aren't providing a contract on what to return +# for joint graphs (we could when there's a clearer use case) +# In the future, we may need to add more export API's that provide their own strong guarantees. +# This is meant as a general helper function for handling various export-y use cases. +def _aot_export_function( + func: Callable, + args, + *, + num_params_buffers: int = 0, + decompositions: Optional[dict] = None, + # If we're exporting a joint graph and we don't want any tangent inputs in the graph + # (because we are backpropping through a scalar 1 loss), + # we need to explicitly specify not to include tangents in the graph. + # It's not enough just to check that our tangent is a scalar, since we also + # need to know if it is a 1 (no need to make it a graph input), or something else + # (requiring it to be a graph input). + # We don't know this info at trace time though, so we need to make it an explicit config. + no_tangents: bool = False, + pre_dispatch: bool = False, + # If None, `dynamic_shapes` will be infered from inputs, but the inferred result might be wrong. + dynamic_shapes: Optional[bool] = None, + kwargs=None, +) -> tuple[torch.fx.GraphModule, ViewAndMutationMeta, pytree.TreeSpec, pytree.TreeSpec]: + kwargs = kwargs or {} + + flat_fn, out_spec = create_tree_flattened_fn(func, args, kwargs) + flat_args, in_spec = pytree.tree_flatten((args, kwargs)) + + fake_mode = None + if dynamic_shapes is None: + # Try to infer `dynamic_shapes from inputs and graph nodes + fake_mode = detect_fake_mode(flat_args) + if ( + fake_mode is None + and hasattr(func, "_orig_mod") + and isinstance(func._orig_mod, torch.fx.GraphModule) + ): + vals = [ + node.meta["val"] + for node in func._orig_mod.graph.nodes + if "val" in node.meta + ] + fake_mode = detect_fake_mode(vals) + dynamic_shapes = fake_mode is not None and fake_mode.shape_env is not None + + # The export use case doesn't care about several bits of AOTConfig + # (1) compilers (we just export the graph) + # (2) partitioners (export is only full graph, user can partition themselves) + aot_config = AOTConfig( + fw_compiler=None, + bw_compiler=None, + inference_compiler=None, + partition_fn=None, + decompositions=decompositions, + num_params_buffers=num_params_buffers, + aot_id=next(AOT_COUNTER), + # For now there's no use case involving keeping input mutations in the graph + # (which we can only do in the inference case anyway). + # We can add this later if we need to. + keep_inference_input_mutations=False, + dynamic_shapes=dynamic_shapes, + aot_autograd_arg_pos_to_source=None, + is_export=True, + no_tangents=no_tangents, + pre_dispatch=pre_dispatch, + ) + if fake_mode is None: + fake_mode, shape_env = construct_fake_mode(flat_args, aot_config) + else: + shape_env = fake_mode.shape_env + fake_flat_args = process_inputs(flat_args, aot_config, fake_mode, shape_env) + + fx_g, meta = create_aot_dispatcher_function( + flat_fn, + fake_flat_args, + aot_config, + fake_mode, + shape_env, + ) + return fx_g, meta, in_spec, out_spec.spec + + +@contextmanager +def _detect_attribute_assignment(mod: torch.nn.Module): + # Do not allow assignment of tensor attributes during export unless + # the attribute is registered as a buffer. + + NN_MODULE_STD_ATTRS = [ + "_backward_hooks", + "_backward_pre_hooks", + "_buffers", + "_forward_hooks", + "_forward_hooks_always_called", + "_forward_hooks_with_kwargs", + "_forward_pre_hooks", + "_forward_pre_hooks_with_kwargs", + "_is_full_backward_hook", + "_load_state_dict_post_hooks", + "_load_state_dict_pre_hooks", + "_modules", + "_non_persistent_buffers_set", + "_parameters", + "_state_dict_hooks", + "_state_dict_pre_hooks", + "training", + ] + NN_MODULE_LAZY_STD_ATTRS = [ + "_initialize_hook", + "_load_hook", + ] + STD_ATTRS = { + *NN_MODULE_STD_ATTRS, + *NN_MODULE_LAZY_STD_ATTRS, + } + + def _get_attributes(mod): + # return any attributes of a module that are not standard attributes + return {k: v for k, v in mod.__dict__.items() if k not in STD_ATTRS} + + def is_leaf(x): + # Ideally is_leaf should not be needed when mapping, but it seems that + # subclasses of a standard container X may sometimes map to X, which + # destroys information and can cause future mapping to fail. + known_subclasses_that_lose_info = ( + torch.Size, + # add more here if needed + ) + return isinstance(x, known_subclasses_that_lose_info) + + # save state of attributes before enter + snapshot = pytree.tree_map(lambda x: x, _get_attributes(mod), is_leaf=is_leaf) + try: + yield + finally: + # after exit, compare state of attributes with snapshot + # to detect which tensor attributes were assigned + assigned_tensor_attributes = [] + + def _collect_assigned_tensor_attributes(kp, v, _v): + if _v is not v: + attr, *rest = kp + if isinstance(v, torch.Tensor): + assigned_tensor_attributes.append( + f"self.{attr.key}{pytree.keystr(rest)}" + ) + # TODO(avik): Assigning all other types are allowed right now. + # Maybe in the future we want to limit this to primitive types? + + pytree.tree_map_with_path( + _collect_assigned_tensor_attributes, snapshot, _get_attributes(mod) + ) + # restore state of all attributes (including, e.g., of primitive types) + mod.__dict__.update(snapshot) + + if assigned_tensor_attributes: + if len(assigned_tensor_attributes) > 1: + noun, verb = "attributes", "were" + else: + noun, verb = "attribute", "was" + raise ValueError( + f"The tensor {noun} {', '.join(assigned_tensor_attributes)} {verb} assigned during export. " + "Such attributes must be registered as buffers using the `register_buffer` API " + "(https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.register_buffer)." + ) + + +compiled_function = aot_function +compiled_module = aot_module diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/apis.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/apis.py new file mode 100644 index 0000000000000000000000000000000000000000..7d7f3e08a5406e8f75ccb76d067b703c62f295bd --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/apis.py @@ -0,0 +1,448 @@ +# mypy: allow-untyped-defs +# NOTE: We allow Dynamo to see this file (via torch/_dynamo/trace_rules.py) so that it can +# trace through functorch transforms. +# Currently, we can't allow Dynamo to see `eager_transforms.py`/`vmap.py` as that break a lot of thing +# and there isn't a mechanism to selectively expose only some functions (eg. grad) from a file +# to Dynamo. +import functools + +from torch._functorch.utils import argnums_t, exposed_in +from torch._functorch.vmap import ( + _check_out_dims_is_int_or_int_pytree, + _check_randomness_arg, + _chunked_vmap, + _process_batched_inputs, + Callable, + in_dims_t, + out_dims_t, + vmap_impl, +) + + +# vmap(func)(inputs) wraps all Tensor inputs to be batched in BatchedTensors, +# sends those into func, and then unwraps the output BatchedTensors. Operations +# on BatchedTensors perform the batched operations that the user is asking for. +# +# vmap's randomness behavior differs from JAX's, which would require a PRNG key +# to be passed everywhere. + + +@exposed_in("torch.func") +def vmap( + func: Callable, + in_dims: in_dims_t = 0, + out_dims: out_dims_t = 0, + randomness: str = "error", + *, + chunk_size=None, +) -> Callable: + """ + vmap is the vectorizing map; ``vmap(func)`` returns a new function that + maps ``func`` over some dimension of the inputs. Semantically, vmap + pushes the map into PyTorch operations called by ``func``, effectively + vectorizing those operations. + + vmap is useful for handling batch dimensions: one can write a function + ``func`` that runs on examples and then lift it to a function that can + take batches of examples with ``vmap(func)``. vmap can also be used to + compute batched gradients when composed with autograd. + + .. note:: + :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for + convenience. Use whichever one you'd like. + + Args: + func (function): A Python function that takes one or more arguments. + Must return one or more Tensors. + in_dims (int or nested structure): Specifies which dimension of the + inputs should be mapped over. ``in_dims`` should have a + structure like the inputs. If the ``in_dim`` for a particular + input is None, then that indicates there is no map dimension. + Default: 0. + out_dims (int or Tuple[int]): Specifies where the mapped dimension + should appear in the outputs. If ``out_dims`` is a Tuple, then + it should have one element per output. Default: 0. + randomness (str): Specifies whether the randomness in this + vmap should be the same or different across batches. If 'different', + the randomness for each batch will be different. If 'same', the + randomness will be the same across batches. If 'error', any calls to + random functions will error. Default: 'error'. WARNING: this flag + only applies to random PyTorch operations and does not apply to + Python's random module or numpy randomness. + chunk_size (None or int): If None (default), apply a single vmap over inputs. + If not None, then compute the vmap :attr:`chunk_size` samples at a time. + Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. + If you run into memory issues computing the vmap, please try a non-None chunk_size. + + Returns: + Returns a new "batched" function. It takes the same inputs as + ``func``, except each input has an extra dimension at the index + specified by ``in_dims``. It takes returns the same outputs as + ``func``, except each output has an extra dimension at the index + specified by ``out_dims``. + + .. warning: + :func:`vmap` works best with functional-style code. Please do not + perform any side-effects in ``func``, with the exception of + in-place PyTorch operations. Examples of side-effects include mutating + Python data structures and assigning values to variables not captured + in ``func``. + + One example of using :func:`vmap` is to compute batched dot products. PyTorch + doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully + rummaging through docs, use :func:`vmap` to construct a new function. + + >>> torch.dot # [D], [D] -> [] + >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] + >>> x, y = torch.randn(2, 5), torch.randn(2, 5) + >>> batched_dot(x, y) + + :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler + model authoring experience. + + >>> batch_size, feature_size = 3, 5 + >>> weights = torch.randn(feature_size, requires_grad=True) + >>> + >>> def model(feature_vec): + >>> # Very simple linear model with activation + >>> return feature_vec.dot(weights).relu() + >>> + >>> examples = torch.randn(batch_size, feature_size) + >>> result = torch.vmap(model)(examples) + + :func:`vmap` can also help vectorize computations that were previously difficult + or impossible to batch. One example is higher-order gradient computation. + The PyTorch autograd engine computes vjps (vector-Jacobian products). + Computing a full Jacobian matrix for some function f: R^N -> R^N usually + requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, + we can vectorize the whole computation, computing the Jacobian in a single + call to ``autograd.grad``. + + >>> # Setup + >>> N = 5 + >>> f = lambda x: x ** 2 + >>> x = torch.randn(N, requires_grad=True) + >>> y = f(x) + >>> I_N = torch.eye(N) + >>> + >>> # Sequential approach + >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] + >>> for v in I_N.unbind()] + >>> jacobian = torch.stack(jacobian_rows) + >>> + >>> # vectorized gradient computation + >>> def get_vjp(v): + >>> return torch.autograd.grad(y, x, v) + >>> jacobian = torch.vmap(get_vjp)(I_N) + + :func:`vmap` can also be nested, producing an output with multiple batched dimensions + + >>> torch.dot # [D], [D] -> [] + >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] + >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) + >>> batched_dot(x, y) # tensor of size [2, 3] + + If the inputs are not batched along the first dimension, ``in_dims`` specifies + the dimension that each inputs are batched along as + + >>> torch.dot # [N], [N] -> [] + >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] + >>> x, y = torch.randn(2, 5), torch.randn(2, 5) + >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension + + If there are multiple inputs each of which is batched along different dimensions, + ``in_dims`` must be a tuple with the batch dimension for each input as + + >>> torch.dot # [D], [D] -> [] + >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] + >>> x, y = torch.randn(2, 5), torch.randn(5) + >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None + + If the input is a Python struct, ``in_dims`` must be a tuple containing a struct + matching the shape of the input: + + >>> f = lambda dict: torch.dot(dict['x'], dict['y']) + >>> x, y = torch.randn(2, 5), torch.randn(5) + >>> input = {'x': x, 'y': y} + >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) + >>> batched_dot(input) + + By default, the output is batched along the first dimension. However, it can be batched + along any dimension by using ``out_dims`` + + >>> f = lambda x: x ** 2 + >>> x = torch.randn(2, 5) + >>> batched_pow = torch.vmap(f, out_dims=1) + >>> batched_pow(x) # [5, 2] + + For any function that uses kwargs, the returned function will not batch the kwargs but will + accept kwargs + + >>> x = torch.randn([2, 5]) + >>> def fn(x, scale=4.): + >>> return x * scale + >>> + >>> batched_pow = torch.vmap(fn) + >>> assert torch.allclose(batched_pow(x), x * 4) + >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] + + .. note:: + vmap does not provide general autobatching or handle variable-length + sequences out of the box. + """ + from torch._dynamo import is_compiling + + _check_randomness_arg(randomness) + if not (chunk_size is None or chunk_size > 0): + raise ValueError( + f"vmap: chunk_size should be None or greater than 0. (got {chunk_size})" + ) + + def wrapped(*args, **kwargs): + return vmap_impl( + func, in_dims, out_dims, randomness, chunk_size, *args, **kwargs + ) + + if not is_compiling(): + wrapped = functools.wraps(func)(wrapped) + + return wrapped + + +def chunk_vmap( + func: Callable, + in_dims: in_dims_t = 0, + out_dims: out_dims_t = 0, + randomness: str = "error", + chunks=2, +) -> Callable: + """ + chunk_vmap is the vectorizing map (vmap) using chunks of input data. It is a mix of vmap (which vectorizes + everything) and map (which executes things sequentially). ``chunk_vmap`` vectorizes the input with number of + chunks at a time. For more details about vectorizing map, see :func:`vmap`. + + .. note:: + Please use :func:`vmap` with ``chunk_size`` argument instead of this API. + + Args: + func (function): A Python function that takes one or more arguments. + Must return one or more Tensors. + in_dims (int or nested structure): Specifies which dimension of the + inputs should be mapped over. ``in_dims`` should have a + structure like the inputs. If the ``in_dim`` for a particular + input is None, then that indicates there is no map dimension. + Default: 0. + out_dims (int or Tuple[int]): Specifies where the mapped dimension + should appear in the outputs. If ``out_dims`` is a Tuple, then + it should have one element per output. Default: 0. + randomness (str): Specifies whether the randomness in this + vmap should be the same or different across batches. If 'different', + the randomness for each batch will be different. If 'same', the + randomness will be the same across batches. If 'error', any calls to + random functions will error. Default: 'error'. WARNING: this flag + only applies to random PyTorch operations and does not apply to + Python's random module or numpy randomness. + chunks (int): Number of chunks to use to split the input data. Default is 2. + If equals to 1 then :func:`vmap` is called. + + Returns: + Returns a new "batched" function. It takes the same inputs as + ``func``, except each input has an extra dimension at the index + specified by ``in_dims``. It takes returns the same outputs as + ``func``, except each output has an extra dimension at the index + specified by ``out_dims``. + """ + _check_randomness_arg(randomness) + + if chunks == 1: + return vmap(func, in_dims=in_dims, out_dims=out_dims, randomness=randomness) + + def _get_chunk_flat_args(flat_args_, flat_in_dims_, chunks_): + flat_args_chunks = tuple( + t.chunk(chunks_, dim=in_dim) + if in_dim is not None + else [ + t, + ] + * chunks_ + for t, in_dim in zip(flat_args_, flat_in_dims_) + ) + # transpose chunk dim and flatten structure + # chunks_flat_args is a list of flatten args + chunks_flat_args = zip(*flat_args_chunks) + return chunks_flat_args + + @functools.wraps(func) + def wrapped_with_chunks(*args, **kwargs): + _check_out_dims_is_int_or_int_pytree(out_dims, func) + _, flat_in_dims, flat_args, args_spec = _process_batched_inputs( + in_dims, args, func + ) + # Chunk flat arguments + chunks_flat_args = _get_chunk_flat_args(flat_args, flat_in_dims, chunks) + + # Apply vmap on chunks + return _chunked_vmap( + func, + flat_in_dims, + chunks_flat_args, + args_spec, + out_dims, + randomness, + **kwargs, + ) + + return wrapped_with_chunks + + +@exposed_in("torch.func") +def grad(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Callable: + """``grad`` operator helps computing gradients of ``func`` with respect to the + input(s) specified by ``argnums``. This operator can be nested to + compute higher-order gradients. + + Args: + func (Callable): A Python function that takes one or more arguments. + Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, + function can return a tuple of single-element Tensor and other auxiliary objects: + ``(output, aux)``. + argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. + ``argnums`` can be single integer or tuple of integers. Default: 0. + has_aux (bool): Flag indicating that ``func`` returns a tensor and other + auxiliary objects: ``(output, aux)``. Default: False. + + Returns: + Function to compute gradients with respect to its inputs. By default, the output of + the function is the gradient tensor(s) with respect to the first argument. + If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects + is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with + respect to each ``argnums`` value is returned. + + Example of using ``grad``: + + >>> # xdoctest: +SKIP + >>> from torch.func import grad + >>> x = torch.randn([]) + >>> cos_x = grad(lambda x: torch.sin(x))(x) + >>> assert torch.allclose(cos_x, x.cos()) + >>> + >>> # Second-order gradients + >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) + >>> assert torch.allclose(neg_sin_x, -x.sin()) + + When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: + + >>> # xdoctest: +SKIP + >>> from torch.func import grad, vmap + >>> batch_size, feature_size = 3, 5 + >>> + >>> def model(weights, feature_vec): + >>> # Very simple linear model with activation + >>> assert feature_vec.dim() == 1 + >>> return feature_vec.dot(weights).relu() + >>> + >>> def compute_loss(weights, example, target): + >>> y = model(weights, example) + >>> return ((y - target) ** 2).mean() # MSELoss + >>> + >>> weights = torch.randn(feature_size, requires_grad=True) + >>> examples = torch.randn(batch_size, feature_size) + >>> targets = torch.randn(batch_size) + >>> inputs = (weights, examples, targets) + >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))(*inputs) + + Example of using ``grad`` with ``has_aux`` and ``argnums``: + + >>> # xdoctest: +SKIP + >>> from torch.func import grad + >>> def my_loss_func(y, y_pred): + >>> loss_per_sample = (0.5 * y_pred - y) ** 2 + >>> loss = loss_per_sample.mean() + >>> return loss, (y_pred, loss_per_sample) + >>> + >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) + >>> y_true = torch.rand(4) + >>> y_preds = torch.rand(4, requires_grad=True) + >>> out = fn(y_true, y_preds) + >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) + + .. note:: + Using PyTorch ``torch.no_grad`` together with ``grad``. + + Case 1: Using ``torch.no_grad`` inside a function: + + >>> # xdoctest: +SKIP + >>> def f(x): + >>> with torch.no_grad(): + >>> c = x ** 2 + >>> return x - c + + In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. + + Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: + + >>> # xdoctest: +SKIP + >>> with torch.no_grad(): + >>> grad(f)(x) + + In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the + outer one. This is because ``grad`` is a "function transform": its result + should not depend on the result of a context manager outside of ``f``. + + """ + # To avoid cyclical dependency. + import torch._functorch.eager_transforms as eager_transforms + from torch._dynamo import is_compiling + + def wrapper(*args, **kwargs): + return eager_transforms.grad_impl(func, argnums, has_aux, args, kwargs) + + if not is_compiling(): + wrapper = functools.wraps(func)(wrapper) + + return wrapper + + +@exposed_in("torch.func") +def grad_and_value( + func: Callable, argnums: argnums_t = 0, has_aux: bool = False +) -> Callable: + """ + Returns a function to compute a tuple of the gradient and primal, or + forward, computation. + + Args: + func (Callable): A Python function that takes one or more arguments. + Must return a single-element Tensor. If specified ``has_aux`` + equals ``True``, function can return a tuple of single-element + Tensor and other auxiliary objects: ``(output, aux)``. + argnums (int or Tuple[int]): Specifies arguments to compute gradients + with respect to. ``argnums`` can be single integer or tuple of + integers. Default: 0. + has_aux (bool): Flag indicating that ``func`` returns a tensor and + other auxiliary objects: ``(output, aux)``. Default: False. + + Returns: + Function to compute a tuple of gradients with respect to its inputs + and the forward computation. By default, the output of the function is + a tuple of the gradient tensor(s) with respect to the first argument + and the primal computation. If specified ``has_aux`` equals + ``True``, tuple of gradients and tuple of the forward computation with + output auxiliary objects is returned. If ``argnums`` is a tuple of + integers, a tuple of a tuple of the output gradients with respect to + each ``argnums`` value and the forward computation is returned. + + See :func:`grad` for examples + """ + from torch._dynamo import is_compiling + from torch._functorch import eager_transforms + + def wrapper(*args, **kwargs): + return eager_transforms.grad_and_value_impl( + func, argnums, has_aux, args, kwargs + ) + + if not is_compiling(): + wrapper = functools.wraps(func)(wrapper) + + return wrapper diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/autograd_function.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/autograd_function.py new file mode 100644 index 0000000000000000000000000000000000000000..bc715c44ed85aafae9f598ef883ebe3b80e53536 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/autograd_function.py @@ -0,0 +1,777 @@ +# mypy: allow-untyped-defs +from typing import NamedTuple + +import torch +import torch.utils._pytree as pytree +from torch._C._functorch import ( + _unwrap_for_grad, + _wrap_for_grad, + current_level, + TransformType, +) +from torch._functorch.apis import vmap +from torch._functorch.utils import enable_single_level_autograd_function +from torch._functorch.vmap import ( + _add_batch_dim, + _broadcast_to_and_flatten, + restore_vmap, + unwrap_batched, + wrap_batched, +) +from torch._ops import HigherOrderOperator +from torch.autograd.forward_ad import _set_fwd_grad_enabled + + +# autograd.Function technically runs before the regular PyTorch dispatcher. +# This is how features like autocast and torch_dispatch (e.g. PythonTLSSnapshot) +# work with it. One day we might decide to change this, but until then, +# we need to give the illusion that autograd.Function runs before those things. +# +# We do this by using creating a custom HigherOrderOperator that only functorch +# dispatches specially. +class CustomFunctionHigherOrderOperator(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("custom_function_call") + + def __call__(self, autograd_function, *args, **kwargs): + # When custom_function_call is done dispatching through functorch, + # it should just invoke the autograd.Function. This is consistent + # with the autograd.Function behavior of being invoked before the + # PyTorch dispatcher. + # + # This will lead us into trouble later down the line, but this is + # pre-existing. There is an invariant that a function traced by + # make_fx should have the same behavior when provided the same + # Tensor. However, make_fx sees autograd.Function as a composite + # (because autograd.Function happens before the Python dispatch key) + # and only traces the forward pass. + if torch._C._are_functorch_transforms_active(): + return super().__call__(autograd_function, *args, **kwargs) + return autograd_function.apply(*args, **kwargs) + + +# "custom_function_call" +# This is the mechanism for an autograd.Function that works with functorch transforms. +# It wraps an autograd.Function; interactions with functorch transforms are defined +# via PyDispatcher and HigherOrderOperator rather than through the traditional PyTorch +# dispatcher. +custom_function_call = CustomFunctionHigherOrderOperator() + + +# The grad rule for custom_function_call is to construct a new _SingleLevelFunction +# (autograd.Function that only works with a single layer (level) of functorch) that: +# - unwraps the inputs +# - redispatches to custom_function_call +# - wraps the outputs +# and whose backward pass calls the original autograd.Function's backward. +# +# Why do we need to redispatch to custom_function_call? +# ----------------------------------------------------- +# This is consistent with how ATen operators work with functorch's grad transform: +# they always redispatch to the original operator. +# Consider torch.sin, and let's say we do grad0(grad1(torch.sin))(x) +# +# grad1 will: +# - set up the autograd graph +# - unwrap the inputs +# - redispatch to at::sin (*) +# - rewrap the outputs on the return +# +# On the redispatch in (*), grad0 will: +# - set up the autograd graph +# - unwrap the inputs +# - redispatch to at::sin +# - rewrap the outputs on the return +# +# To "set up the autograd graph", we generate a _SingleLevelFunction +# and apply it. +@custom_function_call.py_impl(TransformType.Grad) +@custom_function_call.py_impl(TransformType.Jvp) +def custom_function_call_grad(interpreter, autograd_function, *operands): + Generated = generate_single_level_function(interpreter, autograd_function) + with enable_single_level_autograd_function(): + flat_out = Generated.apply(*operands) + return flat_out + + +def generate_single_level_function(interpreter, autograd_function): + level = interpreter.level() + + def forward(*operands): + unwrapped_operands = pytree.tree_map_only( + torch.Tensor, lambda x: _unwrap_for_grad(x, level), operands + ) + # Both enable_grad() and _set_fwd_grad_enabled() are necessary no matter + # the transform. _SingleLevelFunction will turn off both fwd and bwd + # gradient computation and we need to turn it back on here. + with torch.enable_grad(), _set_fwd_grad_enabled(True), interpreter.lower(): + unwrapped_output = custom_function_call( + autograd_function, *unwrapped_operands + ) + + # See NOTE [mark_dirty object identity check] + def wrap_fn(output): + return _wrap_for_grad(output, level) + + return wrap_outputs_maintaining_identity( + unwrapped_output, unwrapped_operands, operands, wrap_fn + ) + + def setup_context(ctx, inputs, output): + return autograd_function.setup_context(ctx, inputs, output) + + # backward is only used if the transform is TransformType.Grad + def backward(ctx, *grads): + result = autograd_function.backward(ctx, *grads) + return result + + # jvp is only used if the transform is TransformType.Jvp + def jvp(ctx, *tangents): + result = autograd_function.jvp(ctx, *tangents) + return result + + # This is the sequence of magic words to dynamically generate a Subclass with + # a given name. A Tensor's .grad_fn field has a class name that is the original + # autograd.Function's name + Backward, so we do this to generate some + # meaningful name. + name = f"{autograd_function.__name__}Generated" + Generated = type( + name, + (torch.autograd.function._SingleLevelFunction,), + { + "forward": staticmethod(forward), + "backward": staticmethod(backward), + "jvp": staticmethod(jvp), + "setup_context": staticmethod(setup_context), + }, + ) + return Generated + + +# wrap_outputs_maintaining_identity handles outputs from the vmap, +# backward (vjp), and jvp staticmethod. The way it distinguishes +# between the vmap case and the {backward, jvp} case is if the out_dims +# are specified or not. +# +# NB: we cannot use out_dims=None as the deciding factor. This because +# out_dims=None can still happen in the vmap staticmethod! What the +# user is saying in that case is that their output does not have a +# dimension that is being vmapped over, which is valid. +NO_OUT_DIMS = "not specified" + + +# NOTE [mark_dirty object identity check] +# autograd.Function's ctx.mark_dirty expect a returned input +# to have the same object identity as the input. +# Mode-only functorch will greatly simplify this logic. +def wrap_outputs_maintaining_identity( + outputs, unwrapped_inputs, orig_inputs, wrap_fn, out_dims=NO_OUT_DIMS +): + flat_unwrapped_inputs = pytree.arg_tree_leaves(*unwrapped_inputs) + flat_orig_inputs = pytree.arg_tree_leaves(*orig_inputs) + + unwrapped_input_to_orig_input = { + id(unwrapped): orig + for unwrapped, orig in zip(flat_unwrapped_inputs, flat_orig_inputs) + } + + flat_outputs, spec = pytree.tree_flatten(outputs) + result = [] + + out_dims_specified = out_dims != NO_OUT_DIMS + + if out_dims_specified: + flat_out_dims = _broadcast_to_and_flatten(out_dims, spec) + # _broadcast_to_and_flatten returns None if it is unable to broadcast. + # TODO: update following link from master to stable once that's out + if flat_out_dims is None: + raise RuntimeError( + f"The autograd.Function's vmap staticmethod returned an " + f"incompatible (output, out_dims) tuple. " + f"Expected out_dims={out_dims} " + f"to be compatible with the structure of `output`. " + f"out_dims has structure {pytree.tree_flatten(out_dims)[1]} " + f"but output has structure {spec}. " + f"For more details, please see " + f"https://pytorch.org/docs/main/notes/extending.func.html" + ) + + for i, output in enumerate(flat_outputs): + if not isinstance(output, torch.Tensor): + result.append(output) + continue + if id(output) in unwrapped_input_to_orig_input: + result.append(unwrapped_input_to_orig_input[id(output)]) + continue + if out_dims_specified: + result.append(wrap_fn(output, flat_out_dims[i])) # type: ignore[possibly-undefined, index] + else: + result.append(wrap_fn(output)) + + return pytree.tree_unflatten(result, spec) + + +# NOTE: [functorch vjp and autograd interaction] +# There's an edge case with the functorch vjp and autograd interaction +# that will eventually be fixed by mode-only functorch. +# The TL;DR is that there's no way to unwrap a dead GradTensorWrapper, +# so we (the framework) need to do it manually. Regular PyTorch operators +# automatically do so this is consistent. +# +# class MyExp(torch.autograd.Function): +# @staticmethod +# def forward(x): +# return x.exp() +# +# @staticmethod +# def setup_context(ctx, inputs, output): +# y = output +# ctx.save_for_backward(y) +# +# @staticmethod +# def backward(gy): +# y, = ctx.saved_tensors() +# return MyMul.apply(gy, y) +# +# x = torch.randn([], requires_grad=True) +# gy = torch.randn([], requires_grad=True) +# _, vjp_fn = vjp(MySin.apply, x) +# result = vjp_fn(gy) +# +# MyMul is an autograd.Function that is not shown here. +# It saves a `y` for backward (since gy requires grad). +# +# in vjp_fn(gy), we get: +# > MyMul.apply(gy, GradTensorWrapper(y, level=dead)) +# Because the y that is saved for backward by MyExp is a GradTensorWrapper +# but is now dead since we are outside the vjp context. +# +# PyTorch dispatcher operations, upon seeing a dead GradTensorWrapper, +# will automatically unwrap the GradTensorWrapper when applied. +# But since autograd.Function technically sits above the regular PyTorch +# dispatcher, it doesn't get this treatment. So we manually do +# the unwrapping to be consistent with regular PyTorch dispatcher operations. + + +class VmapInfo(NamedTuple): + batch_size: int + randomness: str + + +def has_overriden_vmap_rule(autograd_function): + return autograd_function.vmap is not torch.autograd.Function.vmap + + +def validate_vmap_returns_tuple_of_two_elements(result): + base_error_msg = ( + "Expected the vmap staticmethod to have two returns, an output " + "and out_dims with pytree structure compatible with the output. " + ) + if not isinstance(result, tuple): + raise RuntimeError(base_error_msg + f"Got a {type(result)} instead") + if not len(result) == 2: + raise RuntimeError(base_error_msg + f"Got {len(result)} returns instead") + + +@custom_function_call.py_impl(TransformType.Vmap) +def custom_function_call_vmap(interpreter, autograd_function, *operands, **kwargs): + if any( + isinstance(val, torch.Tensor) + for val in torch.utils._pytree.tree_flatten(kwargs)[0] + ): + raise NotImplementedError( + f"Run vmap on autograd.Function with kwarg-only Tensor args. " + f"Please do not pass kwarg-only Tensors to autograd.Function. " + f"Got: {kwargs}" + ) + + if autograd_function.generate_vmap_rule: + if has_overriden_vmap_rule(autograd_function): + # TODO: Update link to stable once that's out + # https://github.com/pytorch/pytorch/issues/92029 + raise RuntimeError( + f"You tried to vmap over {autograd_function.__name__}, but " + f"it has both generate_vmap_rule=True and an overriden vmap " + f"staticmethod. Please set generate_vmap_rule=False or delete " + f"the overriden vmap staticmethod to avoid ambiguity. " + f"For more details, please see " + f"https://pytorch.org/docs/main/notes/extending.func.html" + ) + return custom_function_call_vmap_generate_rule( + interpreter, autograd_function, *operands + ) + + if not has_overriden_vmap_rule(autograd_function): + # TODO: Update link to stable once that's out + # https://github.com/pytorch/pytorch/issues/92029 + raise RuntimeError( + f"You tried to vmap over {autograd_function.__name__}, but " + f"it does not have vmap support. Please override and implement the " + f"vmap staticmethod or set generate_vmap_rule=True. " + f"For more details, please see " + f"https://pytorch.org/docs/main/notes/extending.func.html" + ) + + return custom_function_call_vmap_helper( + interpreter, autograd_function.vmap, autograd_function, *operands, **kwargs + ) + + +def custom_function_call_vmap_helper( + interpreter, vmap_function, op, *operands, **kwargs +): + current_level = interpreter.level() + info = VmapInfo( + batch_size=interpreter.batch_size(), + randomness=interpreter.randomness(), + ) + # We're either in the autograd.Function case (vmap staticmethod) + # or the torch.library.register_vmap case. + autograd_function_case = isinstance(op, torch.autograd.function.FunctionMeta) + + def lower_to_next(): + if autograd_function_case: + return interpreter.lower() + else: + return torch._C._ExcludeDispatchKeyGuard( + torch._C.DispatchKeySet(torch._C.DispatchKey.FuncTorchBatched) + ) + + unwrapped_operands, in_dims = unwrap_batched(operands, current_level) + # If none of the tensors are batched at the current level, then we skip the + # current level. This saves the user from needing to handle this case in + # their vmap staticmethod (and is consistent with our C++ batching rule API) + if pytree.tree_all(lambda dim: dim is None, in_dims): + with lower_to_next(): + if autograd_function_case: + return custom_function_call(op, *operands) + else: + return op(*operands, **kwargs) + + with lower_to_next(): + result = vmap_function(info, in_dims, *unwrapped_operands, **kwargs) + validate_vmap_returns_tuple_of_two_elements(result) + unwrapped_output, out_dims = result + + # See NOTE [mark_dirty object identity check] + def wrap_fn(output, out_dim): + return ( + output + if out_dim is None + else _add_batch_dim(output, out_dim, current_level) + ) + + return wrap_outputs_maintaining_identity( + unwrapped_output, unwrapped_operands, operands, wrap_fn, out_dims=out_dims + ) + + +def unpack_outputs(outputs): + out_dims = outputs[-1] + if isinstance(out_dims, tuple): + outputs = outputs[:-1] + else: + outputs = outputs[0] + return outputs, out_dims + + +def custom_function_call_vmap_generate_rule(interpreter, autograd_function, *operands): + unwrapped_operands, in_dims = unwrap_batched(operands, interpreter.level()) + vmapped_function = vmapify_autograd_function( + autograd_function, in_dims, interpreter.batch_size(), interpreter.randomness() + ) + with interpreter.lower(): + outputs = custom_function_call(vmapped_function, *unwrapped_operands) + + assert isinstance(outputs, tuple) + outputs, out_dims = unpack_outputs(outputs) + return wrap_batched(outputs, out_dims, interpreter.level()) + + +@custom_function_call.py_impl(TransformType.Functionalize) +def custom_function_call_functionalize( + interpreter, autograd_function, generate_vmap_rule, *operands +): + raise RuntimeError("NYI: Functionalize rule for custom_function_call") + + +def vmapify_autograd_function(autograd_function, in_dims, batch_size, randomness): + def forward(*operands): + outputs, out_dims = restore_vmap( + autograd_function.forward, in_dims, batch_size, randomness + )(*operands) + if isinstance(outputs, torch.Tensor): + return outputs, out_dims + else: + return *outputs, out_dims + + def setup_context(ctx, inputs, outputs): + outputs, out_dims = unpack_outputs(outputs) + key = id(Generated) + + def inner(inputs, outputs): + # wrapped_ctx.save_for_backward will: + # - unwrap batchedtensors into (tensor, bdim) + # - save_for_backward(*unwrapped_tensors) + # - assign the bdims to wrapped_ctx._pt_saved_tensors_bdims + wrapped_ctx = CtxCustomSave(ctx, current_level()) + autograd_function.setup_context(wrapped_ctx, inputs, outputs) + + # input_shapes are used for reductify later to reduce expanded gradients + # to the correct shape. + # See NOTE: [Why can't we rely on autograd to reduce expanded gradients?] + # for more details + input_shapes = tuple( + inp.shape if isinstance(inp, torch.Tensor) else None for inp in inputs + ) + if not hasattr(ctx, "_pt_input_shapes"): + ctx._pt_input_shapes = {} + ctx._pt_input_shapes.update({key: input_shapes}) + + if not hasattr(ctx, "_pt_saved_tensors_bdims_stack"): + ctx._pt_saved_tensors_bdims_stack = {} + ctx._pt_saved_tensors_bdims_stack.update( + {key: (wrapped_ctx._pt_saved_tensors_bdims)} + ) + + # See NOTE: [Why do we need to run setup_context under a vmap?] + restore_vmap( + inner, + (in_dims, out_dims), + batch_size, + randomness, + )(inputs, outputs) + + if not hasattr(ctx, "_pt_out_dims"): + ctx._pt_out_dims = {} + ctx._pt_out_dims.update({key: out_dims}) + + def jvp(ctx, *tangents): + key = id(Generated) + + def jvp_no_context(saved_tensors, tangents): + wrapped_ctx = CtxWithSavedTensors(ctx, saved_tensors) + return autograd_function.jvp(wrapped_ctx, *tangents) + + tangent_in_dims = get_tangents_in_dims(in_dims, tangents) + out_tangents, out_tangents_dims = restore_vmap( + jvp_no_context, + (ctx._pt_saved_tensors_bdims_stack[key], tangent_in_dims), + batch_size, + randomness, + )(ctx.saved_tensors, tangents) + + result = reductify( + out_tangents, out_tangents_dims, ctx._pt_out_dims[key], batch_size + ) + if isinstance(result, torch.Tensor): + return result, None + else: + return *result, None + + def backward(ctx, *grad_outputs): + key = id(Generated) + grad_outputs_ = grad_outputs[:-1] + grad_outputs_in_dims = ctx._pt_out_dims[key] + + if not isinstance(grad_outputs_in_dims, tuple): + grad_outputs_in_dims = (grad_outputs_in_dims,) + + grad_outputs_in_dims = tuple( + in_dim if grad_output is not None else None + for grad_output, in_dim in zip(grad_outputs_, grad_outputs_in_dims) + ) + + def backward_no_context(inputs): + saved_tensors, grad_outputs = inputs + wrapped_ctx = CtxWithSavedTensors(ctx, saved_tensors) + return autograd_function.backward(wrapped_ctx, *grad_outputs) + + grad_ins, grad_ins_dims = restore_vmap( + backward_no_context, + ((ctx._pt_saved_tensors_bdims_stack[key], grad_outputs_in_dims),), + batch_size, + randomness, + )((ctx.saved_tensors, grad_outputs_)) + result = reductify( + grad_ins, grad_ins_dims, in_dims, batch_size, ctx._pt_input_shapes[key] + ) + return result + + name = f"Vmapped{autograd_function.__name__}" + Generated = type( + name, + (torch.autograd.Function,), + { + "forward": staticmethod(forward), + "backward": staticmethod(backward), + "jvp": staticmethod(jvp), + "setup_context": staticmethod(setup_context), + "generate_vmap_rule": True, + }, + ) + + return Generated + + +# tangents might be None, so we need to replace +# the corresponding in_dims with None. +def get_tangents_in_dims(input_dims, tangents): + flat_in_dims, spec = pytree.tree_flatten(input_dims) + flat_tangents = pytree.arg_tree_leaves(*tangents) + result = [ + None if tangent is None else in_dim + for in_dim, tangent in zip(flat_in_dims, flat_tangents) + ] + return pytree.tree_unflatten(result, spec) + + +# NOTE: [Why do we need to run setup_context under a vmap?] +# Consider the following autograd.Function +# +# class Sum(torch.autograd.Function): +# @staticmethod +# def forward(x): +# return x.sum() +# @staticmethod +# def setup_context(ctx, inputs, outputs): +# ctx.x_shape = inputs[0] +# @staticmethod +# def backward(ctx, gy): +# return gy.expand(ctx.x_shape) +# +# x = torch.randn(B, 4) +# in_dims = 0 +# vmap(Sum.apply, in_dims)(x) +# +# Let's assume for a moment that we didn't vmap setup_context in VmappedSum: +# +# class VmappedSum(torch.autograd.Function): +# @staticmethod +# def forward(x): +# return vmap(Sum.forward, in_dims)(x) +# +# @staticmethod +# def setup_context(ctx, inputs, outputs): +# Sum.setup_context(ctx, inputs, outputs) +# +# @staticmethod +# def backward(ctx, gy): +# def backward_no_context(gy): +# return gy.expand(ctx.x_shape) +# +# dims = (0,) +# gx = vmap(backward_no_context, dims)(gy) +# return gx +# +# We end up saving [B, 4] as x_shape. In the backward, gy has shape [B], +# and we're doing: +# +# def backward_no_context(gy): +# return gy.expand([B, 4]) +# +# gx = vmap(backward_no_context, dims)(gy: "Tensor[B]") +# +# This gives us the wrong result (gx has shape [B, B, 4], but it should +# have shape [4]). Performing vmap over setup_context means the shape +# saved has shape [4] and leads to a correct result shape for gx. + + +# Wraps a ctx object. Forwards all attr accesses to the underlying object +# except for the attrs in _pt_attrs +class WrappedCtx: + _pt_reserved_attrs: tuple[str, ...] = ("_pt_reserved_attrs", "_pt_inner_ctx") + + def __init__(self, ctx): + if not isinstance(ctx, WrappedCtx): + reserved_attrs = type(self)._pt_reserved_attrs + for name in reserved_attrs: + if not hasattr(ctx, name): + continue + raise RuntimeError( + f"PyTorch reserves the {reserved_attrs} field on ctx. " + "Please name your fields on ctx something else to avoid name " + "collision." + ) + self._pt_inner_ctx = ctx + + def __getattr__(self, name): + return getattr(self._pt_inner_ctx, name) + + def __setattr__(self, name, value): + if name in type(self)._pt_reserved_attrs: + self.__dict__[name] = value + return + return setattr(self._pt_inner_ctx, name, value) + + +# Wraps ctx to create a new ctx object that overrides saved_tensors. +class CtxWithSavedTensors(WrappedCtx): + _pt_reserved_attrs = ("_pt_new_saved_tensors", *WrappedCtx._pt_reserved_attrs) + + def __init__(self, ctx, new_saved_tensors): + super().__init__(ctx) + self._pt_new_saved_tensors = new_saved_tensors + + @property + def saved_tensors(self): + return self._pt_new_saved_tensors + + +class CtxCustomSave(WrappedCtx): + _pt_reserved_attrs = ( + "_pt_saved_tensors_bdims", + "_pt_current_level", + *WrappedCtx._pt_reserved_attrs, + ) + + def __init__(self, ctx, current_level): + super().__init__(ctx) + self._pt_saved_tensors_bdims = () + self._pt_current_level = current_level + + def save_for_backward(self, *tensors): + unwrapped_tensors, bdims = unwrap_batched(tensors, self._pt_current_level) + self._pt_inner_ctx.save_for_backward(*unwrapped_tensors) + self._pt_saved_tensors_bdims = bdims + + def save_for_forward(self, *tensors): + unwrapped_tensors, bdims = unwrap_batched(tensors, self._pt_current_level) + self._pt_inner_ctx.save_for_forward(*unwrapped_tensors) + self._pt_saved_tensors_bdims = bdims + + +def reductify( + grad_input, + grad_input_bdim, + input_bdim, + batch_size, + target_shape_without_bdim_to_reduce_to=None, +): + if not isinstance(grad_input, tuple): + grad_input = (grad_input,) + if not isinstance(grad_input_bdim, tuple): + grad_input_bdim = (grad_input_bdim,) + if not isinstance(input_bdim, tuple): + input_bdim = (input_bdim,) + + if target_shape_without_bdim_to_reduce_to is None: + target_shape_without_bdim_to_reduce_to = len(grad_input) * (None,) + result = tuple( + reductify_leaf(gi, gi_bdim, i_bdim, batch_size, maybe_ishape) + for gi, gi_bdim, i_bdim, maybe_ishape in zip( + grad_input, + grad_input_bdim, + input_bdim, + target_shape_without_bdim_to_reduce_to, + ) + ) + return result + + +def reductify_leaf( + grad_input, + grad_input_bdim, + input_bdim, + batch_size, + target_shape_without_bdim_to_reduce_to=None, +): + if grad_input is None: + return None + + if grad_input_bdim is None and input_bdim is None: + return grad_input + + if grad_input_bdim is not None and input_bdim is None: + return grad_input.sum(grad_input_bdim) + + # NOTE: [Why can't we rely on autograd to reduce expanded gradients?] + # For reverse-mode AD, + # given a grad_input and input, it is valid for the user to return a + # grad_input that has a broadcasted shape when compared to the input. + # In this situation, autograd automatically reduces the grad_input to + # the shape of the input. + # + # However, when input_bdim is not None, we have problems. + # + # [example 1] + # grad_input: Tensor[3, 4], input: Tensor[B, 4] + # We can expand grad_input to Tensor[B, 3, 4], but that isn't broadcastable + # from [B, 4]. + # + # [example 2] + # grad_input: Tensor[3, B, 4], input: Tensor[B, 4] + # We can swizzle grad_input to Tensor[B, 3, 4], but that isn't broadcastable + # from [B, 4]. + # + # This means that we need to also reduce the grad_input to the shape of the + # input. This behavior is controlled by the `target_shape_without_bdim_to_reduce_to` flag; + # if not-None then we do the reducing manually, otherwise, we do not do a reduction. + assert input_bdim is not None + + if grad_input_bdim is None: + grad_input = grad_input.unsqueeze(input_bdim) + new_shape = list(grad_input.shape) + new_shape[input_bdim] = batch_size + grad_input = grad_input.expand(new_shape) + grad_input_bdim = input_bdim + + if target_shape_without_bdim_to_reduce_to is not None: + return vmap( + torch.Tensor.sum_to_size, + in_dims=(grad_input_bdim, None), + out_dims=input_bdim, + )(grad_input, target_shape_without_bdim_to_reduce_to) + + if input_bdim != grad_input_bdim: + grad_input = grad_input.movedim(grad_input_bdim, input_bdim) + return grad_input + + +def autograd_function_forward_rewritten(original_forward, original_setup_context): + def new_forward(ctx, *args, **kwargs): + output = original_forward(*args, **kwargs) + original_setup_context(ctx, args, output) + return output + + return new_forward + + +class AutogradFunctionApply(HigherOrderOperator): + def __init__(self) -> None: + super().__init__("autograd_function_apply") + + def __call__(self, fwd, bwd, *fwd_args, **fwd_kwargs): + saved_values = None + args_tensor_mask = fwd_kwargs["args_tensor_mask"] + non_differentiable_idx = fwd_kwargs["non_differentiable_idx"] + length_of_tensor_args = sum(args_tensor_mask) + # Filter out the original tensor args from fwd_args, + # lifted freevars should not be args of ApplyTemplate.apply + # since we don't need to calculate the gradients of them. + new_fwd_args = fwd_args[:length_of_tensor_args] + + class ApplyTemplate(torch.autograd.Function): + @staticmethod + def forward(ctx, *args): + nonlocal saved_values + output, saved_values = fwd(None, *fwd_args) + + # If users call ctx.mark_non_differentiable() in the original fwd function. + if len(non_differentiable_idx) > 0: + non_differentiable_output = [] + for i, x in enumerate(output): + if i in non_differentiable_idx: + non_differentiable_output.append(x) + ctx.mark_non_differentiable(*non_differentiable_output) + + return output + + @staticmethod + def backward(ctx, *grad): + return bwd(None, *grad, *saved_values) + + return ApplyTemplate.apply(*new_fwd_args) + + +autograd_function_apply = AutogradFunctionApply() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/batch_norm_replacement.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/batch_norm_replacement.py new file mode 100644 index 0000000000000000000000000000000000000000..77aa9b9c2d7c78647d2c850b550754e43c4fe592 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/batch_norm_replacement.py @@ -0,0 +1,27 @@ +import torch.nn as nn +from torch._functorch.utils import exposed_in + + +def batch_norm_without_running_stats(module: nn.Module) -> None: + if ( + isinstance(module, nn.modules.batchnorm._BatchNorm) + and module.track_running_stats + ): + module.running_mean = None + module.running_var = None + module.num_batches_tracked = None + module.track_running_stats = False + + +@exposed_in("torch.func") +def replace_all_batch_norm_modules_(root: nn.Module) -> nn.Module: + """ + In place updates :attr:`root` by setting the ``running_mean`` and ``running_var`` to be None and + setting track_running_stats to be False for any nn.BatchNorm module in :attr:`root` + """ + # base case + batch_norm_without_running_stats(root) + + for obj in root.modules(): + batch_norm_without_running_stats(obj) + return root diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/benchmark_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/benchmark_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ac69e8bd4744c633df7b1a7aa038255e94d48ec4 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/benchmark_utils.py @@ -0,0 +1,231 @@ +# mypy: ignore-errors + +import contextlib +import json +import operator +import os +import time + +import torch +from torch.profiler import profile, ProfilerActivity + + +def synchronize(): + pass + + +def dump_chrome_trace( + f, + input, + trace_filename, + optimize_ctx, + activities, + num_runs=1, + devices=None, + kwargs_for_f=None, + kwargs_for_profiler=None, +): + """ + Output the chrome trace of running f(input, **kwargs_for_f) with [optimize_ctx] + [num_runs] times to [trace_filename]. + + [activities] are the activities that the profiler will record, e.g. ProfilerActivity.CUDA. + Return total runtime without the profiler + + Outputs to trace_filename + """ + + if devices is None: + devices = ["cuda"] + + global synchronize + if devices != ["cpu"] and torch.cuda.is_available(): + synchronize = torch.cuda.synchronize + + if kwargs_for_f is None: + kwargs_for_f = {} + if kwargs_for_profiler is None: + kwargs_for_profiler = {} + + with optimize_ctx: + torch.manual_seed(1337) + for _ in range(5): # warmup runs + f(input, **kwargs_for_f) + synchronize() + torch.manual_seed(1337) + t0 = time.perf_counter() + for _ in range(num_runs): + f(input, **kwargs_for_f) + synchronize() + t1 = time.perf_counter() + timing = t1 - t0 + + with profile(activities=activities, **kwargs_for_profiler) as prof: + with optimize_ctx: + synchronize() + torch.manual_seed(1337) + for _ in range(num_runs): + f(input, **kwargs_for_f) + synchronize() + prof.export_chrome_trace(trace_filename) + + return timing + + +def get_chrome_trace_events(filename): + f = open(filename) + data = json.load(f) + events = data["traceEvents"] + return events + + +def is_gpu_compute_event(event): + global gpu_pids + return ( + "pid" in event + and event["pid"] in gpu_pids + and "ph" in event + and event["ph"] == "X" + ) + + +def get_sorted_gpu_events(events): + sorted_gpu_events = [] + for event in events: + if not is_gpu_compute_event(event): + continue + sorted_gpu_events.append(event) + return sorted(sorted_gpu_events, key=operator.itemgetter("ts")) + + +def get_duration(sorted_gpu_events): + if len(sorted_gpu_events) == 0: + return 0 + event = sorted_gpu_events[0] + current_end_time = event["ts"] + event["dur"] + total_duration = event["dur"] + for event in sorted_gpu_events[1:]: + start_time = max(event["ts"], current_end_time) + end_time = event["ts"] + event["dur"] + total_duration = total_duration + max(end_time - start_time, 0) + current_end_time = max(current_end_time, end_time) + return total_duration + + +def get_sorted_gpu_mm_conv_events(events): + def is_mm_conv_event(event): + return "name" in event and ( + "gemm" in event["name"] + or "conv" in event["name"] + or "cutlass" in event["name"] + or "wgrad" in event["name"] + ) + + gpu_events = get_sorted_gpu_events(events) + sorted_events = [] + for event in gpu_events: + if not is_mm_conv_event(event): + continue + sorted_events.append(event) + return sorted_events + + +gpu_pids = [] + + +def compute_utilization(filename: str, total_length: float): + """ + Process the chrome traces outputs by the pytorch profiler to compute GPU Utilization + and percent of times spent on matmul and convolution + + Args: + filename(str): Name of chrome traces file produced by pytorch profiler + + total_length(float): total length of the process without profiler in second + + Return: + tuple: (GPU Utilization, percent of time spent on matmul and convolution) + """ + events = get_chrome_trace_events(filename) + + # get pids of GPU events + global gpu_pids + gpu_pids = [] + for event in events: + if "name" not in event: + continue + if event["name"] == "process_labels" and "GPU" in event["args"]["labels"]: + gpu_pids.append(event["pid"]) + + total_length = total_length * 1e6 + sorted_gpu_events = get_sorted_gpu_events(events) + utilization = get_duration(sorted_gpu_events) / total_length + + sorted_gpu_mm_conv_events = get_sorted_gpu_mm_conv_events(events) + mm_conv_utilization = get_duration(sorted_gpu_mm_conv_events) / total_length + + return utilization, mm_conv_utilization + + +def benchmark_utilization( + f, + input, + trace_folder, + optimize_ctx=None, + trace_file_name="tmp_chrome_trace", + num_runs=1, +): + """ + Benchmark the GPU Utilization and percent of time spent on matmul and convolution operations of + running f(input, **kwargs_for_f) with [optimize_ctx] [num_runs] times. + It will produce a chrome trace file in trace_folder/trace_file_name.json + + Example: + + ``` + def f(a): + return a.sum() + a = torch.rand(2**20, device="cuda") + utilization, mm_conv_utilization = benchmark_utilization(f, a, "tmp", trace_file_name = "tmp_chrome_trace") + ``` + + Args: + f: function to benchmark + + input: input to :attr:`f` + + trace_folder: name of the folder to store the chrome trace + + optimize_ctx: the context in which f will run + + trace_file_name: name of the dumped chrome trace file, default to "tmp_chrome_trace" + + num_runs: number of times to run f, excluding the warm-up runs, default to 1. + + Return: + tuple: (GPU Utilization, percent of time spent on matmul and convolution) + + """ + isExist = os.path.exists(trace_folder) + if not isExist: + os.makedirs(trace_folder) + print("create folder " + trace_folder) + + if optimize_ctx is None: + optimize_ctx = contextlib.nullcontext() + + chrome_trace_file_name = os.path.join(trace_folder, trace_file_name + ".json") + total_length = dump_chrome_trace( + f, + input, + chrome_trace_file_name, + optimize_ctx, + [ProfilerActivity.CUDA], + num_runs=num_runs, + devices=["cuda"], + ) + utilization, mm_conv_utilization = compute_utilization( + chrome_trace_file_name, total_length + ) + + return utilization, mm_conv_utilization diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/compile_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/compile_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3bf61f1af3bf3c11250911720873197cae78a8c1 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/compile_utils.py @@ -0,0 +1,176 @@ +# mypy: ignore-errors + + +from typing import Callable + +import torch +import torch.fx as fx +from torch.multiprocessing.reductions import StorageWeakRef +from torch.utils import _pytree as pytree +from torch.utils._pytree import tree_flatten + + +aten = torch.ops.aten + + +def get_aten_target(node: fx.Node) -> Callable: + if hasattr(node.target, "overloadpacket"): + return node.target.overloadpacket + return node.target + + +rand_ops = [ + aten.dropout, + aten._fused_dropout, + aten._standard_gamma, + aten.bernoulli, + aten.multinomial, + aten.native_dropout, + aten.normal, + aten.poisson, + aten.binomial, + aten.rrelu, + aten.rand_like, + aten.rand, + aten.randint, + aten.randn, + aten.randperm, +] + + +# return a new copy of torch.fx.graph.Graph with CSE applied to the input graph +def fx_graph_cse(fx_g: torch.fx.graph.Graph): + new_graph = fx.Graph() + env = {} # map from node in the old graph to node in the new graph + hash_env = {} # map from hash to a node in the new graph + token_map = {} # map from hash to token + + from torch._inductor.pattern_matcher import ( + compute_mutation_region_ids, + same_mutation_regions, + ) + + compute_mutation_region_ids(fx_g) # type: ignore[arg-type] + + # Make a set of separate storages returned from the output, which will be preserved + # when pruning. This prevents us from deduplicating returned tensors which have + # experienced identical operations, but are separate data structures in eager mode. + output_node: fx.Node = list(fx_g.nodes)[-1] + assert output_node.op == "output" + + def checkable_node(node: fx.Node) -> bool: + """We can evaluate only nodes that represent tensors with defined storage.""" + if "val" not in node.meta or not isinstance(node.meta["val"], torch.Tensor): + return False + + try: + node.meta["val"].untyped_storage() + except NotImplementedError: + return False + + return True + + output_storages = { + StorageWeakRef(n.meta["val"].untyped_storage()) + for n in output_node.all_input_nodes + if checkable_node(n) + } + nodes_that_alias_outputs = { + n + for n in fx_g.nodes + if checkable_node(n) + and StorageWeakRef(n.meta["val"].untyped_storage()) in output_storages + } + + for n in fx_g.nodes: + # The placeholder, output, and get_attr nodes are copied to the new graph without change + # do not CSE away random operations + if ( + n.op == "placeholder" + or n.op == "output" + or n.op == "get_attr" + or get_aten_target(n) in rand_ops + # aten.empty is non-deterministic, so don't CSE it. + # Also, aten.empty is almost always fusible into its consumer, + # so it's not worth CSEing. + or get_aten_target(n) is aten.empty + or n in nodes_that_alias_outputs + ): + new_node = new_graph.node_copy(n, lambda x: env[x]) + env[n] = new_node + else: # n.op == 'call_function', should never see n.op == 'call_module' or 'call_method' + # substitute args and kwargs members to their mapping in env if exists + # specs can be used to reconstruct nested list/dictionaries + def substitute(arg_list): + arg_list, spec = tree_flatten(arg_list) + for i in range(len(arg_list)): + v = arg_list[i] + if isinstance(v, torch.fx.node.Node) and v in env: + arg_list[i] = env[v] + if isinstance(v, (torch.SymBool, torch.SymInt, torch.SymFloat)): + arg_list[i] = v.node + return tuple(arg_list), spec + + args, args_spec = substitute(n.args) + kwargs, kwargs_spec = substitute(n.kwargs) + + # each token corresponds to a unique node + # nodes with the same token can be substituted + token = { + "target": n.target, + "args": args, + "args_spec": args_spec, + "kwargs": kwargs, + "kwargs_spec": kwargs_spec, + } + + # hash substituted args to a number, do not hash specs because specs are not hashable + # We need to add type into hash to avoid situations like: + # hash((primals_2, 1.0)) == hash((primals_2, 1)) + hash_arg = hash( + (tuple((a, type(a)) for a in args), tuple((a, type(a)) for a in kwargs)) + ) + hash_val = (n.target, hash_arg) + + # check if a node has a substitute and can be eliminated + hash_val_in_hash_env = hash_val in hash_env + overwrite_due_to_mutation = False + if hash_val_in_hash_env and token_map[hash_val] == token: + duplicate_n_prev = hash_env[hash_val] + if same_mutation_regions(n, duplicate_n_prev): + env[n] = duplicate_n_prev + continue + else: + # any futures duplicates should replace with n, not duplicate_n_prev + overwrite_due_to_mutation = True + + new_node = new_graph.node_copy(n, lambda x: env[x]) + env[n] = new_node + if overwrite_due_to_mutation or not hash_val_in_hash_env: + hash_env[hash_val] = new_node + token_map[hash_val] = token + + return new_graph + + +def strip_overloads(gm): + """ + Modifies the target of graph nodes in :attr:`gm` to strip overloads. + + Args: + gm(fx.GraphModule): The input Fx graph module to be modified + """ + for node in gm.graph.nodes: + if isinstance(node.target, torch._ops.OpOverload): + node.target = node.target.overloadpacket + gm.recompile() + + +def get_placeholders(graph): + return graph.find_nodes(op="placeholder") + + +def get_outputs(graph): + for node in graph.find_nodes(op="output"): + return pytree.tree_leaves(node.args[0]) + raise AssertionError("No output node found") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/compilers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/compilers.py new file mode 100644 index 0000000000000000000000000000000000000000..edb17bfedf558796919e89b915cc891bfec515c4 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/compilers.py @@ -0,0 +1,445 @@ +# mypy: ignore-errors + +import copy +import logging +import os +import pickle +import random +from contextlib import contextmanager +from functools import partial +from typing import Callable, Union + +import sympy + +import torch +import torch.fx as fx +import torch.nn as nn +import torch.utils._pytree as pytree +from torch import SymInt +from torch._decomp import get_decompositions +from torch.fx.experimental.symbolic_shapes import bind_symbols + +from .aot_autograd import aot_function, aot_module, make_boxed_compiler +from .compile_utils import strip_overloads +from .partitioners import ( + default_partition, + draw_graph, + min_cut_rematerialization_partition, +) + + +log = logging.getLogger(__name__) + + +# These canonicalizations are needed here (and not decompositions), as the ops +# we're trying to canonicalize to CompositeImplicitAutograd. +def _canonicalize(fx_g): + for node in fx_g.graph.find_nodes( + op="call_function", target=torch.ops.aten._to_copy + ): + node.target = torch.ops.aten.to + fx_g.recompile() + return fx_g + + +@contextmanager +def _disable_jit_autocast(): + old_jit_autocast_flag = torch._C._jit_set_autocast_mode(False) + try: + yield + finally: + torch._C._jit_set_autocast_mode(old_jit_autocast_flag) + + +@make_boxed_compiler +def ts_compile(fx_g: fx.GraphModule, inps) -> Callable: + """ + Compiles the :attr:`fx_g` with Torchscript compiler. + + .. warning:: + This API is experimental and likely to change. + + Args: + fx_g(fx.GraphModule): The input Fx graph module to be compiled. + + Returns: + Torch scripted model. + """ + + with _disable_jit_autocast(): + strip_overloads(fx_g) + + for node in fx_g.graph.find_nodes( + op="call_function", target=torch.ops.aten._to_copy + ): + if len(node.args) == 1 and len(node.kwargs) == 1 and "dtype" in node.kwargs: + node.target = torch.ops.aten.to + + for node in fx_g.graph.nodes: + new_kwargs = {} + for k, v in node.kwargs.items(): + if isinstance(v, torch.device): + v = v.type + new_kwargs[k] = v + node.kwargs = new_kwargs + + fx_g.graph.lint() + + fx_g.recompile() + + f = torch.jit.script(fx_g) + + torch._C._jit_pass_remove_mutation(f.graph) + + f = torch.jit.freeze(f.eval()) + f = torch.jit.optimize_for_inference(f) + if not any(isinstance(t, torch._subclasses.FakeTensor) for t in inps): + f(*inps) + return f + + +def _draw_graph_compile(fx_g, _, name, clear_meta=True): + print(fx_g.code) + draw_graph(fx_g, name, clear_meta=clear_meta) + return fx_g + + +def draw_graph_compile(name): + return make_boxed_compiler(partial(_draw_graph_compile, name=name)) + + +@make_boxed_compiler +def nop(fx_g: fx.GraphModule, _) -> Callable: + """ + Returns the :attr:`fx_g` Fx graph module as it is. This is a no-op compiler + and can be used to check accuracy. + + .. warning:: + This API is experimental and likely to change. + + """ + return fx_g + + +class DebugInterpreter(fx.Interpreter): + def run(self, *args): + self.symbol_mapping = bind_symbols(self.module, *args) + super().run(*args) + + def run_node(self, n): + def subst_symint(ni): + if not isinstance(ni, SymInt): + return ni + r = sympy.expand(ni.node.expr.xreplace(self.symbol_mapping)) + assert r.is_number, r + return int(r) + + def subst_symint_tuple(nis): + return tuple(subst_symint(ni) for ni in nis) + + def check_significant_strides(a, b): + if subst_symint(a.numel()) > 0: + for idx in range(a.ndim): + if ( + subst_symint(a.stride(idx)) != b.stride(idx) + and subst_symint(a.size(idx)) > 1 + ): + return False + return True + + def check(nv, rv, desc): + assert callable(desc) + assert nv.dtype == rv.dtype, f"{desc()}: {nv.dtype} != {rv.dtype}" + assert ( + subst_symint_tuple(nv.size()) == rv.size() + ), f"{desc()}: {nv.size()} aka {subst_symint_tuple(nv.size())} != {rv.size()}" + same_strides = check_significant_strides(nv, rv) + assert ( + same_strides + ), f"{desc()}: {nv.stride()} aka {subst_symint_tuple(nv.stride())} != {rv.stride()}" + + r = super().run_node(n) + if "val" in n.meta: + n_vals, _n_spec = pytree.tree_flatten(n.meta["val"]) + r_vals, _r_spec = pytree.tree_flatten(r) + # TODO: There is some sort of problem where we record that an + # operator returned a tuple/list, and then later it turns out the + # real version of the operator returned a list/tuple. Need to + # figure out what's actually going on here, the error itself is + # harmless enough as we only getitem out the outputs. + # assert n_spec == r_spec, f"{n_spec} != {r_spec}" + assert len(n_vals) == len(r_vals), f"{len(n_vals)} != {len(r_vals)}" + for i, nv, rv in zip(range(len(n_vals)), n_vals, r_vals): + if not isinstance(rv, torch.Tensor): + continue + check(nv, rv, lambda: f"output {i} where {self.symbol_mapping}") + return r + + +@make_boxed_compiler +def debug_nop(fx_g: fx.GraphModule, _) -> Callable: + """ + Returns a (slow) interpreter over the FX graph module that also checks + various debugging properties (e.g., that tracing strides matched real + strides.) + """ + return DebugInterpreter(fx_g).run + + +@make_boxed_compiler +def simple_ts_compile(fx_g, _): + strip_overloads(fx_g) + f = torch.jit.script(fx_g) + f = torch.jit.freeze(f.eval()) + return f + + +def nnc_jit(f): + return aot_function(f, simple_ts_compile) + + +aten = torch.ops.aten +default_decompositions = { + aten.detach, + aten.gelu_backward, + aten.leaky_relu_backward, + aten.sigmoid_backward, + aten.threshold_backward, + aten.hardtanh_backward, + aten.hardsigmoid_backward, + aten.hardswish_backward, + aten.tanh_backward, + aten.silu_backward, + aten.elu_backward, + aten.cudnn_batch_norm, + aten.cudnn_batch_norm_backward, + aten.masked_fill.Scalar, + aten.masked_fill.Tensor, + aten.elu, + aten.leaky_relu, + aten.hardtanh, + aten.hardswish, + aten.hardsigmoid, + aten.conj_physical, + aten.is_same_size, +} + +default_decompositions = get_decompositions(default_decompositions) + + +@make_boxed_compiler +def print_compile(fx_g, _): + print(fx_g.code) + return fx_g + + +def memory_efficient_fusion( + fn: Union[Callable, nn.Module], + **kwargs, +): + """ + Wrapper function over :func:`aot_function` and :func:`aot_module` to perform + memory efficient fusion. It uses the + :func:`min_cut_rematerialization_partition` partitioner to perform efficient + recomputation. It uses NVFuser to compile the generated forward and backward + graphs. + + .. warning:: + This API is experimental and likely to change. + + Args: + fn (Union[Callable, nn.Module]): A Python function or a ``nn.Module`` + that takes one ore more arguments. Must return one or more Tensors. + **kwargs: Any other overrides you want to make to the settings + + Returns: + Returns a ``Callable`` or ``nn.Module`` that retains the eager behavior + of the original :attr:`fn`, but whose forward and backward graphs have + gone through recomputation optimizations, and the graphs have been + compiled with nvfuser. + + """ + config = { + "fw_compiler": ts_compile, + "bw_compiler": ts_compile, + "partition_fn": min_cut_rematerialization_partition, + "decompositions": default_decompositions, + } + config.update(kwargs) + if isinstance(fn, torch.nn.Module): + return aot_module(fn, **config) + else: + return aot_function(fn, **config) + + +def debug_compile(fx_g, inps): + fx_g.to_folder("foo") + print( + f""" +############################################################## +# To minimize FX graph, copy and paste the below and run it # +############################################################## + +import torch +import torch.fx as fx +from functorch.compile import minifier, check_nvfuser_subprocess, check_nvfuser_correctness_subprocess + +inps = {[(i.shape, i.dtype) for i in inps]} +inps = [torch.ones(shape, dtype=dtype, device='cuda') for (shape, dtype) in inps] +from foo import FxModule +mod = FxModule().cuda() + +with torch.jit.fuser("fuser2"): + # check_nvfuser_subprocess can be replaced with check_nvfuser_correctness_subprocess + minifier(fx.symbolic_trace(mod), inps, check_nvfuser_subprocess) +""" + ) + from foo import FxModule + + FxModule().cuda()(*inps) + + return ts_compile(fx_g, inps) + + +graph_index = 0 + + +def get_inputs(input_data_path): + """ + Return a random input for the given inputs meta generated from _save_fx_default. + """ + inputs = [] + with open(input_data_path, "rb") as f: + inputs_meta = pickle.load(f) + inputs = [] + for meta in inputs_meta: + if len(meta) == 1: + type = meta + input = type(random.rand()) + else: + type, shape, _stride, dtype, device = meta + if dtype in { + torch.int, + torch.int32, + torch.int64, + torch.bool, + torch.int, + torch.uint8, + int, + float, + }: + input = torch.randint(0, 1, shape, dtype=dtype, device=device) + else: + input = torch.rand(shape, dtype=dtype, device=device) + inputs.append(input) + return inputs + + +def _save_fx_default(current_name, folder_name, dump_example_input, gm, example_inputs): + """ + The forward, backward, and joint computation graph will be stored in + {folder_name}/{current_name}/{current_name}_forward_{graph_index}, + {folder_name}/{current_name}/{current_name}_backward_{graph_index}, and + {folder_name}/{current_name}/{current_name}_joint_{graph_index} respectively. + The input shape of the graphs will be stored in the .input files. + These files can be loaded with pickle, + and is a list of format (type, shape, stride, dtype, device). + In the case of type = int or float, it is just (type,). + For joint graph input, it is a nested list [[],[]] + where the two inner lists have the same format. + If dump_example_input is True, example_inputs will be stored in .pt file. + Since each function might produce multiple graphs, + the graph_index is used to distinguish difference graphs + """ + from functorch.compile import aot_module_simplified + + def get_input_meta(args): + input_meta = [] + if len(args) > 0 and isinstance(args[0], tuple): # joint input + input_meta += get_input_meta(args[0]) + input_meta += get_input_meta(args[1]) + return input_meta + for arg in args: + if type(arg) == int or type(arg) == float: + input_meta.append((type(arg),)) + else: + input_meta.append( + (type(arg), arg.shape, arg.stride(), arg.dtype, arg.device) + ) + return input_meta + + def graph_saver_helper(gm_to_save, args, type_name): + global graph_index + if len(gm_to_save.graph.nodes) == 0: + log.log( + logging.WARNING, + "No nodes in graph {%s}_{%s}_{%s}.", + current_name, + type_name, + graph_index, + ) + return + + gm = copy.deepcopy(gm_to_save) + gm.graph.set_codegen(torch.fx.graph.CodeGen()) # remove codegen + gm.recompile() + + input_meta = get_input_meta(args) + + os.makedirs(f"{folder_name}/{current_name}", exist_ok=True) + gm.to_folder( + f"{folder_name}/{current_name}/{current_name}_{type_name}_{graph_index}" + ) + pickle.dump( + input_meta, + open( + f"{folder_name}/{current_name}/{current_name}_{type_name}_{graph_index}/{current_name}_{type_name}_{graph_index}.input", # noqa: B950 + "wb", + ), + ) # noqa: E501 + if dump_example_input: + torch.save( + args, + f"{folder_name}/{current_name}/{current_name}_{type_name}_{graph_index}/{current_name}_{type_name}_{graph_index}.pt", # noqa: B950 + ) # noqa: E501 + + def graph_saver_forward(gm, fw_args): + graph_saver_helper(gm, fw_args, "forward") + return gm + + def graph_saver_backward(gm, bw_args): + graph_saver_helper(gm, bw_args, "backward") + global graph_index + graph_index += 1 + return gm + + def graph_saver_joint(gm, joint_args): + graph_saver_helper(gm, joint_args, "joint") + return default_partition(gm, joint_args) + + return aot_module_simplified( + gm, + example_inputs, + fw_compiler=graph_saver_forward, + bw_compiler=graph_saver_backward, + partition_fn=graph_saver_joint, + decompositions=default_decompositions, + ) + + +# WARNING: This isn't tested anywhere!! +def graph_dumper_aot(current_name, folder_name, dump_example_input=False): + """ + Dump the forward, backward, and joint computation graph. + Example Usage: + save_fx_func = graph_dumper_aot(current_name, folder_name, dump_example_input = False) + optimize_ctx = torchdynamo.optimize( + save_fx_func + ) + with torch.enable_grad(): + with optimize_ctx: + result = forward_and_backward_pass(model, example_inputs) + """ + global graph_index + graph_index = 0 + return partial(_save_fx_default, current_name, folder_name, dump_example_input) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/config.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/config.py new file mode 100644 index 0000000000000000000000000000000000000000..844c3d62dfd94189886de6496ddd076bef10a6c1 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/config.py @@ -0,0 +1,235 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Global flags for aot autograd +""" +import os +import sys +from typing import Optional, TYPE_CHECKING + +from torch.utils._config_module import Config, install_config_module + + +# Converts torch rng ops to their functional philox rng equivalents. Note that +# we functionalize only CUDA rng ops today. +functionalize_rng_ops = False + +# can be useful for debugging if we are incorrectly creating meta fake tensors +fake_tensor_allow_meta = os.environ.get("FAKE_ALLOW_META", "1") != "0" + +# Enables optional asserts in hotpath code to check for errors. If +# you are seeing weird accuracy problems, try turning this on. +# This is currently off by default as it will harm tracing time, +# but it is on by default for aot_eager. +debug_assert = False + +debug_partitioner = os.environ.get("AOT_PARTITIONER_DEBUG", "0") != "0" + +# See # NOTE [Export custom triton op] +decompose_custom_triton_ops = True + +static_weight_shapes = True + +# Applies CSE to the graph before partitioning +cse = True + +from torch._inductor.config import is_fbcode + + +enable_autograd_cache: bool = Config( + justknob="pytorch/remote_cache:enable_local_autograd_cache", + env_name_force="TORCHINDUCTOR_AUTOGRAD_CACHE", + default=True, +) + + +def remote_autograd_cache_default() -> Optional[bool]: + if os.environ.get("TORCHINDUCTOR_AUTOGRAD_REMOTE_CACHE") == "1": + return True + if os.environ.get("TORCHINDUCTOR_AUTOGRAD_REMOTE_CACHE") == "0": + return False + return None + + +enable_remote_autograd_cache = remote_autograd_cache_default() + + +# When AOTAutograd regenerates aliased graph outputs, +# attempt to use functionalization's view-replay logic +# before falling back to the autograd engine's view replay or as_strided. +# This can have some perf implications +# (although for many models this will not matter). +# (1) If you have many view ops chained together, replaying all of them +# at runtime can have more overhead compared to a single as_strided call +# (2) If you are doing training, AsStridedBackward is quite slow, +# and the individual view op backward formulas will likely be faster. +# (3) Some backends like XLA do not support as_strided + +# Temporary hack: disable this flag for internal +# (needed to fix an internal issue while avoiding bumping XLA pin) +# eventually: either default this config to false completely +# once XLA pin update works, +# or default config to true and fix relevant bugs + + +# View replay is currently not compatible with AOTAutogradCache, since +# FunctionalTensors are not serializable. We'll need to make them +# serializable before enabling warm cache with this config turned on. +view_replay_for_aliased_outputs = not is_fbcode() + +# Restricts the amount of computation AOTAutograd can do. +# NB: We have essentially disabled this heuristic now. However, this is kept +# here for now in case it's useful. Setting it low can artificially reduce the +# amount of recomputation AOTAutograd performs, although not in any kind of +# principled way. +max_dist_from_bw = 1000 + + +# Bans recomputation of nodes that are reading from nodes that is far before +# the current node +ban_recompute_used_far_apart = True +# Breaks up long chain of fusible ops, as otherwise we can have an arbitrarily +# long chain of recomputation in the backwards pass. +ban_recompute_long_fusible_chains = True +# Bans recomputation of nodes that must be materialized in the backwards pass +# (used by a non-fusible node) +ban_recompute_materialized_backward = True +# Chooses to ban recomputation of nodes based off an allowlist. Setting it to +# False changes it to use a denylist. Main change is on operators like +# sort/pool/stuff that isn't cheap enough to be fusible for free but also isn't +# that expensive +ban_recompute_not_in_allowlist = True +# Chooses to ban recomputation of reductions. This is generally a good idea, as +# the result of reductions is generally very small but recomputing reductions in +# a fusion can be expensive. +ban_recompute_reductions = True +# Prevents the partitioner from ever saving views (i.e. always recompute them). +# Generally a good idea since views are free to recompute. +recompute_views = False + +# By default, the partitioner is purely trying to optimize for runtime (although +# it should always use less memory than eager) +# This knob controls the partitioner to make that tradeoff for you, choosing the +# fastest option that saves less activations than the memory budget. +# Specifically, 0.0 corresponds to the activation memory from applying +# activation checkpointing to the full compiled region, and 1.0 corresponds to +# the activation memory from the default runtime-optimized strategy. So, 0.4 +# would result in a strategy that saves 40% of the activations compared to the +# default strategy. +# It solves a 0-1 knapsack to find the minimum recompute necessary to stay below +# the activation memory budget. +# NOTE: This *cannot* be treated as +activation_memory_budget = 1.0 + +# This controls how we estimate the runtime when deciding what the cheapest +# operators to recompute are. The 3 options are +# "flops": Bases it off of the flop count provided by torch.utils.flop_counter +# "profile": Benchmarks each operator to come up with a runtime +# "testing": Returns 1 for everything +activation_memory_budget_runtime_estimator = "flops" + +# This controls the solver used for the 0-1 knapsack. By default we use a +# quantized DP solution ("dp"). The other approaches are a "greedy" and a "ilp" +# (which has a scipy dependency). +activation_memory_budget_solver = "dp" + +# This dumps out a SVG visualization of the expected runtime vs. activation +# memory tradeoffs for all memory budget values from 0 to 1 in increments of +# 0.5. See an example here: +# https://github.com/pytorch/pytorch/pull/126320#discussion_r1625104015 +visualize_memory_budget_pareto = ( + os.environ.get("PARTITIONER_MEMORY_BUDGET_PARETO", "0") == "1" +) + +# This controls the directory in which to dump the SVG plot with the pareto +# frontier of the activation checkpointing memory-vs-runtime tradeoffs. +memory_budget_pareto_dir = os.environ.get("PARTITIONER_MEMORY_BUDGET_PARETO_DIR") + +# Sets all of the ban_recompute heuristics to False except ban_recompute_reductions +# Generally, this will probably result in some memory improvement, but at the +# cost of some performance +aggressive_recomputation = False + +# If FakeTensor.data_ptr() should error. +# This option is independent of AOTAutograd and torch.compile, but our policy +# is to turn it off during torch.compile. +fake_tensor_allow_unsafe_data_ptr_access = True + +# Unlifts effect tokens from the inputs/outputs in the traced graph and instead +# inserts make_token/sink_token calls in the graph to create tokens and then +# sink them at the end. Note that this means the graph is no longer functional +# which may lead to silent errors unless the backend knows how to handle the +# tokens. +unlift_effect_tokens = False + + +# Run aot eager decomp partition with CrossRefFakeMode +# options = False, "all", "custom_ops" +fake_tensor_crossref = False + +# This mode specifies that we should also keep track of the real +# tensor along with the fake tensor, and do real compute. While +# seemingly this eliminates the whole point of fake tensors, there are +# two obvious use cases for it: +# +# 1. When users call item()/other data dependent operations, +# if we propagate_real_tensors we are able to determine what +# the true value is and keep going. +# +# 2. It can be useful for testing, when you want to see if the fake +# and real tensors agree with each other. (Note that there are +# currently known inaccuracies in how we clone real tensors, that +# would have to be tightened up for this to be useful in this +# case.) +# +# Note that fake tensors are typically understood to be cheap to store +# indefinitely, so we tend to hold on to them longer than we would +# hold onto the real tensors. So we also support you explicitly +# deallocating the real tensor associated with a fake tensor, at which +# point we will stop propagating real tensors. +# +# One more thing: when you provide a real tensor to fakeify, we will +# clone it, so that we can safely perform mutations on it if necessary. +# This will increase live memory usage. This could potentially be +# optimized by using COW. We also currently do not faithfully +# maintain autograd metadata on the real tensor; this is fine because +# AOTAutograd will only use the fake tensor to determine leafness/etc +# of tensors in question. +fake_tensor_propagate_real_tensors = False + +# This controls whether we collect donated buffer. This flag must be set +# False if a user wants to retain_graph=True for backward. +donated_buffer = False if is_fbcode() else True + +# Controls the default graph output format used by draw_graph +# Supported formats are defined here https://graphviz.org/docs/outputs/ +torch_compile_graph_format = os.environ.get("TORCH_COMPILE_GRAPH_FORMAT", "svg") + +# Valid only if fake_tensor_propagate_real_tensors = True; if a fake-real +# kernel mismatch is detected, bypasses by making a fake kernel from the +# real tensor outputs. +generate_fake_kernels_from_real_mismatches = False + +# CUDAGraph save run_with_rng functionalization. +# TODO: turn on by default +graphsafe_rng_functionalization = True + + +# Error on BypassAOTAutogradCache instead of just a warning +# Used for tests +strict_autograd_cache = False + +# See Note [AOTAutograd Tangent Subclassness for mutated inputs] +# TODO(ivankobzarev): Remove this config, being able to deduce it compile time. +disable_guess_zero_tangent_for_mutated_input_subclass = False + +if TYPE_CHECKING: + from torch.utils._config_typing import * # noqa: F401, F403 + + +# adds patch, save_config, invalid config checks, etc +install_config_module(sys.modules[__name__]) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/deprecated.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/deprecated.py new file mode 100644 index 0000000000000000000000000000000000000000..d6e295c65c77cc328ec1a4ac44a0b85480307b5f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/deprecated.py @@ -0,0 +1,172 @@ +# mypy: allow-untyped-defs +""" +The APIs in this file are exposed as `functorch.*`. They are thin wrappers +around the torch.func.* APIs that have deprecation warnings -- we're trying +to move people to the torch.func.* equivalents. + +NB: We don't use *args, **kwargs in the signatures because that changes the +documentation. +""" + +import textwrap +import warnings +from typing import Any, Callable, Optional, Union + +import torch._functorch.apis as apis +import torch._functorch.eager_transforms as _impl +import torch._functorch.make_functional as _nn_impl +import torch.nn as nn +from torch._functorch.eager_transforms import argnums_t +from torch._functorch.vmap import in_dims_t, out_dims_t + + +def get_warning(api, new_api=None, replace_newlines=False): + if new_api is None: + new_api = f"torch.func.{api}" + warning = ( + f"We've integrated functorch into PyTorch. As the final step of the \n" + f"integration, `functorch.{api}` is deprecated as of PyTorch \n" + f"2.0 and will be deleted in a future version of PyTorch >= 2.3. \n" + f"Please use `{new_api}` instead; see the PyTorch 2.0 release notes \n" + f"and/or the `torch.func` migration guide for more details \n" + f"https://pytorch.org/docs/main/func.migrating.html" + ) + if replace_newlines: + warning = warning.replace("\n", "") + return warning + + +def warn_deprecated(api, new_api=None): + warning = get_warning(api, new_api, replace_newlines=True) + warnings.warn(warning, FutureWarning, stacklevel=3) + + +def setup_docs(functorch_api, torch_func_api=None, new_api_name=None): + api_name = functorch_api.__name__ + if torch_func_api is None: + torch_func_api = getattr(_impl, api_name) + # See https://docs.python.org/3/using/cmdline.html#cmdoption-OO + if torch_func_api.__doc__ is None: + return + + warning = get_warning(api_name, new_api_name) + warning_note = "\n.. warning::\n\n" + textwrap.indent(warning, " ") + warning_note = textwrap.indent(warning_note, " ") + functorch_api.__doc__ = torch_func_api.__doc__ + warning_note + + +def vmap( + func: Callable, + in_dims: in_dims_t = 0, + out_dims: out_dims_t = 0, + randomness: str = "error", + *, + chunk_size=None, +) -> Callable: + warn_deprecated("vmap", "torch.vmap") + return apis.vmap(func, in_dims, out_dims, randomness, chunk_size=chunk_size) + + +def grad(func: Callable, argnums: argnums_t = 0, has_aux: bool = False) -> Callable: + warn_deprecated("grad") + return apis.grad(func, argnums, has_aux) + + +def grad_and_value( + func: Callable, argnums: argnums_t = 0, has_aux: bool = False +) -> Callable: + warn_deprecated("grad_and_value") + return apis.grad_and_value(func, argnums, has_aux) + + +def vjp(func: Callable, *primals, has_aux: bool = False): + warn_deprecated("vjp") + return _impl.vjp(func, *primals, has_aux=has_aux) + + +def jvp( + func: Callable, + primals: Any, + tangents: Any, + *, + strict: bool = False, + has_aux: bool = False, +): + warn_deprecated("jvp") + return _impl.jvp(func, primals, tangents, strict=strict, has_aux=has_aux) + + +def jacrev( + func: Callable, + argnums: Union[int, tuple[int]] = 0, + *, + has_aux=False, + chunk_size: Optional[int] = None, + _preallocate_and_copy=False, +): + warn_deprecated("jacrev") + return _impl.jacrev( + func, + argnums, + has_aux=has_aux, + chunk_size=chunk_size, + _preallocate_and_copy=_preallocate_and_copy, + ) + + +def jacfwd( + func: Callable, + argnums: argnums_t = 0, + has_aux: bool = False, + *, + randomness: str = "error", +): + warn_deprecated("jacfwd") + return _impl.jacfwd(func, argnums, has_aux, randomness=randomness) + + +def hessian(func, argnums=0): + warn_deprecated("hessian") + return _impl.hessian(func, argnums=argnums) + + +def functionalize(func: Callable, *, remove: str = "mutations") -> Callable: + warn_deprecated("functionalize") + return _impl.functionalize(func, remove=remove) + + +def make_functional(model: nn.Module, disable_autograd_tracking: bool = False): + warn_deprecated("make_functional", "torch.func.functional_call") + return _nn_impl.make_functional(model, disable_autograd_tracking) + + +def make_functional_with_buffers( + model: nn.Module, disable_autograd_tracking: bool = False +): + warn_deprecated("make_functional_with_buffers", "torch.func.functional_call") + return _nn_impl.make_functional_with_buffers(model, disable_autograd_tracking) + + +def combine_state_for_ensemble(models): + warn_deprecated("combine_state_for_ensemble", "torch.func.stack_module_state") + return _nn_impl.combine_state_for_ensemble(models) + + +setup_docs(vmap, apis.vmap, "torch.vmap") +setup_docs(grad, apis.grad) +setup_docs(grad_and_value, apis.grad_and_value) +setup_docs(vjp) +setup_docs(jvp) +setup_docs(jacrev) +setup_docs(jacfwd) +setup_docs(hessian) +setup_docs(functionalize) +setup_docs(make_functional, _nn_impl.make_functional, "torch.func.functional_call") +setup_docs( + make_functional_with_buffers, _nn_impl.make_functional, "torch.func.functional_call" +) +setup_docs( + combine_state_for_ensemble, + _nn_impl.combine_state_for_ensemble, + "torch.func.stack_module_state", +) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..f058c215c39e5e1c249f10a73cc29bf890c49070 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py @@ -0,0 +1,1815 @@ +# mypy: ignore-errors + +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +from functools import partial, wraps +from typing import Any, Callable, Optional, Union + +import torch +import torch.autograd.forward_ad as fwAD +from torch._C._functorch import ( + _assert_wrapped_functional, + _func_decrement_nesting, + _func_increment_nesting, + _grad_decrement_nesting, + _grad_increment_nesting, + _jvp_decrement_nesting, + _jvp_increment_nesting, + _propagate_functional_input_mutation, + _unwrap_for_grad, + _unwrap_functional_tensor, + _wrap_for_grad, + _wrap_functional_tensor, + get_inplace_requires_grad_allowed, + get_unwrapped, + is_functorch_wrapped_tensor, + set_inplace_requires_grad_allowed, +) +from torch._functorch.utils import argnums_t, exposed_in +from torch._subclasses.functional_tensor import FunctionalTensor +from torch.fx.experimental import const_fold +from torch.fx.experimental.proxy_tensor import make_fx +from torch.utils import _pytree as pytree +from torch.utils._pytree import ( + tree_flatten, + tree_map, + tree_map_, + tree_map_only, + tree_unflatten, + treespec_pprint, +) + +from .apis import vmap +from .vmap import doesnt_support_saved_tensors_hooks, get_chunk_sizes + + +def lazy_dynamo_disallow(func): + import torch._dynamo + + return torch._dynamo.disallow_in_graph(func) + + +@contextlib.contextmanager +def enable_inplace_requires_grad(enabled): + prev_state = get_inplace_requires_grad_allowed() + set_inplace_requires_grad_allowed(enabled) + try: + yield + finally: + set_inplace_requires_grad_allowed(prev_state) + + +def _set_tensor_requires_grad(x): + # avoid graph-break on x.requires_grad_() + # https://github.com/pytorch/pytorch/pull/110053 + return x.requires_grad_() + + +def _create_differentiable(inps, level=None): + def create_differentiable(x): + if isinstance(x, torch.Tensor): + with enable_inplace_requires_grad(True): + return _set_tensor_requires_grad(x) + raise ValueError(f"Thing passed to transform API must be Tensor, got {type(x)}") + + return tree_map(create_differentiable, inps) + + +def _undo_create_differentiable(inps, level=None): + def unwrap_tensors(x): + if isinstance(x, torch.Tensor): + return _unwrap_for_grad(x, level) + # TODO: Remove the following hack for namedtuples + if isinstance(x, tuple): + return tree_map(unwrap_tensors, tuple(x)) + + raise RuntimeError(f"Expected tensors, got unsupported type {type(x)}") + + return tree_map(unwrap_tensors, inps) + + +def _is_differentiable(maybe_tensor): + if not isinstance(maybe_tensor, torch.Tensor): + return False + return maybe_tensor.requires_grad + + +def _any_differentiable(tensor_or_tuple_of_tensors): + flat_args, _ = tree_unflatten(tensor_or_tuple_of_tensors) + return any(tuple(map(_is_differentiable, flat_args))) + + +def _wrap_tensor_for_grad(maybe_tensor, level): + if not isinstance(maybe_tensor, torch.Tensor): + return maybe_tensor + return _wrap_for_grad(maybe_tensor, level) + + +def _wrap_all_tensors(tensor_pytree, level): + return tree_map(partial(_wrap_tensor_for_grad, level=level), tensor_pytree) + + +def _as_tuple(val): + if isinstance(val, tuple): + return val + return (val,) + + +# Version of autograd.grad that handles outputs that don't depend on inputs + + +def _autograd_grad( + outputs, inputs, grad_outputs=None, retain_graph=False, create_graph=True +): + if grad_outputs is None: + diff_outputs = tuple(out for out in outputs if out.requires_grad) + else: + result = tuple( + (out, go) for out, go in zip(outputs, grad_outputs) if out.requires_grad + ) + if len(result) == 0: + diff_outputs, grad_outputs = (), () + else: + diff_outputs, grad_outputs = zip(*result) + if len(diff_outputs) == 0: + return tuple(torch.zeros_like(inp) for inp in inputs) + grad_inputs = torch.autograd.grad( + diff_outputs, + inputs, + grad_outputs, + retain_graph=retain_graph, + create_graph=create_graph, + allow_unused=True, + ) + grad_inputs = tuple( + torch.zeros_like(inp) if gi is None else gi + for gi, inp in zip(grad_inputs, inputs) + ) + return grad_inputs + + +# NOTE [grad and vjp interaction with no_grad] +# +# def f(x): +# with torch.no_grad(): +# c = x ** 2 +# return x - c +# +# The thing to consider is if enable_grad is on/off before grad gets called. +# +# Case 1: enable_grad is on. +# grad(f)(x) +# In this case, `grad` should respect the inner torch.no_grad. +# +# Case 2: enable_grad is off +# with torch.no_grad(): +# grad(f)(x) +# In this case, `grad` should respect the inner torch.no_grad, but not the +# outer one. This is because `grad` is a "function transform": its result +# should not depend on the result of a context manager outside of `f`. +# +# This gives us the following desired behavior: +# - (nested) grad transforms must obey torch.no_grad inside them +# - (nested) grad transforms should not obey torch.no_grad outside them +# +# To achieve this behavior, upon entering grad/vjp: +# - we save the current ("previous") is_grad_enabled (*) +# - we unconditionally enable grad. +# +# Inside DynamicLayerBackFallback, when we're temporarily popping `grad` layer +# off the stack: +# - if grad_mode is disabled, then we do nothing. (there is a torch.no_grad +# active, all subsequent grad transforms must obey it). +# - if grad_mode is enabled, and the previous is_grad_enabled (*) is False, +# then we temporarily restore the previous `is_grad_enabled`. This is +# because we're crossing the boundary from a `grad` outside the +# no_grad to a `grad` inside the no_grad. +# +# NB: vjp has some interesting behavior because the vjp's callable can be called +# under a different grad_mode than the forward computation... +# +# NB: forward-mode AD: forward-mode AD doesn't respect torch.no_grad, but +# it respects c10::AutoFwGradMode. We've implemented the same logic for +# our jvp transform (it will have special handling if FwGradMode is disabled). + + +# How do we increment and decrement the nesting? I don't think we can. +@exposed_in("torch.func") +def vjp(func: Callable, *primals, has_aux: bool = False): + """ + Standing for the vector-Jacobian product, returns a tuple containing the + results of ``func`` applied to ``primals`` and a function that, when + given ``cotangents``, computes the reverse-mode Jacobian of ``func`` with + respect to ``primals`` times ``cotangents``. + + Args: + func (Callable): A Python function that takes one or more arguments. Must + return one or more Tensors. + primals (Tensors): Positional arguments to ``func`` that must all be + Tensors. The returned function will also be computing the + derivative with respect to these arguments + has_aux (bool): Flag indicating that ``func`` returns a + ``(output, aux)`` tuple where the first element is the output of + the function to be differentiated and the second element is + other auxiliary objects that will not be differentiated. + Default: False. + + Returns: + Returns a ``(output, vjp_fn)`` tuple containing the output of ``func`` + applied to ``primals`` and a function that computes the vjp of + ``func`` with respect to all ``primals`` using the cotangents passed + to the returned function. If ``has_aux is True``, then instead returns a + ``(output, vjp_fn, aux)`` tuple. + The returned ``vjp_fn`` function will return a tuple of each VJP. + + When used in simple cases, :func:`vjp` behaves the same as :func:`grad` + + >>> x = torch.randn([5]) + >>> f = lambda x: x.sin().sum() + >>> (_, vjpfunc) = torch.func.vjp(f, x) + >>> grad = vjpfunc(torch.tensor(1.))[0] + >>> assert torch.allclose(grad, torch.func.grad(f)(x)) + + However, :func:`vjp` can support functions with multiple outputs by + passing in the cotangents for each of the outputs + + >>> x = torch.randn([5]) + >>> f = lambda x: (x.sin(), x.cos()) + >>> (_, vjpfunc) = torch.func.vjp(f, x) + >>> vjps = vjpfunc((torch.ones([5]), torch.ones([5]))) + >>> assert torch.allclose(vjps[0], x.cos() + -x.sin()) + + :func:`vjp` can even support outputs being Python structs + + >>> x = torch.randn([5]) + >>> f = lambda x: {'first': x.sin(), 'second': x.cos()} + >>> (_, vjpfunc) = torch.func.vjp(f, x) + >>> cotangents = {'first': torch.ones([5]), 'second': torch.ones([5])} + >>> vjps = vjpfunc(cotangents) + >>> assert torch.allclose(vjps[0], x.cos() + -x.sin()) + + The function returned by :func:`vjp` will compute the partials with + respect to each of the ``primals`` + + >>> x, y = torch.randn([5, 4]), torch.randn([4, 5]) + >>> (_, vjpfunc) = torch.func.vjp(torch.matmul, x, y) + >>> cotangents = torch.randn([5, 5]) + >>> vjps = vjpfunc(cotangents) + >>> assert len(vjps) == 2 + >>> assert torch.allclose(vjps[0], torch.matmul(cotangents, y.transpose(0, 1))) + >>> assert torch.allclose(vjps[1], torch.matmul(x.transpose(0, 1), cotangents)) + + ``primals`` are the positional arguments for ``f``. All kwargs use their + default value + + >>> x = torch.randn([5]) + >>> def f(x, scale=4.): + >>> return x * scale + >>> + >>> (_, vjpfunc) = torch.func.vjp(f, x) + >>> vjps = vjpfunc(torch.ones_like(x)) + >>> assert torch.allclose(vjps[0], torch.full(x.shape, 4.)) + + .. note:: + Using PyTorch ``torch.no_grad`` together with ``vjp``. + Case 1: Using ``torch.no_grad`` inside a function: + + >>> def f(x): + >>> with torch.no_grad(): + >>> c = x ** 2 + >>> return x - c + + In this case, ``vjp(f)(x)`` will respect the inner ``torch.no_grad``. + + Case 2: Using ``vjp`` inside ``torch.no_grad`` context manager: + + >>> # xdoctest: +SKIP(failing) + >>> with torch.no_grad(): + >>> vjp(f)(x) + + In this case, ``vjp`` will respect the inner ``torch.no_grad``, but not the + outer one. This is because ``vjp`` is a "function transform": its result + should not depend on the result of a context manager outside of ``f``. + """ + return _vjp_with_argnums(func, *primals, has_aux=has_aux) + + +@contextlib.contextmanager +def grad_increment_nesting(): + try: + grad_level = _grad_increment_nesting() + yield grad_level + finally: + _grad_decrement_nesting() + + +def enter_jvp_nesting(): + global JVP_NESTING + jvp_level = _jvp_increment_nesting() + JVP_NESTING += 1 + return jvp_level + + +def exit_jvp_nesting(): + global JVP_NESTING + _jvp_decrement_nesting() + JVP_NESTING -= 1 + + +@contextlib.contextmanager +def jvp_increment_nesting(): + try: + yield enter_jvp_nesting() + finally: + exit_jvp_nesting() + + +@doesnt_support_saved_tensors_hooks +def _vjp_with_argnums( + func: Callable, *primals, argnums: Optional[argnums_t] = None, has_aux: bool = False +): + # This is the same function as vjp but also accepts an argnums argument + # All args are the same as vjp except for the added argument + # argnums (Optional[int or tuple[int]]): Optional, specifies the argument(s) to compute gradients with respect to. + # If None, computes the gradients with respect to all inputs (used for vjp). Default: None + # + # WARN: Users should NOT call this function directly and should just be calling vjp. + # It is only separated so that inputs passed to jacrev but not differentiated get the correct wrappers. + # + # NOTE: All error messages are produced as if vjp was being called, even if this was called by jacrev + # + # Returns the same two elements as :func:`vjp` but the function returned, vjp_fn, returns a tuple of VJPs + # for only the primal elements given by argnums. + with grad_increment_nesting() as level: + # See NOTE [grad and vjp interaction with no_grad] + with torch.enable_grad(): + primals = _wrap_all_tensors(primals, level) + if argnums is None: + diff_primals = _create_differentiable(primals, level) + else: + diff_primals = _slice_argnums(primals, argnums, as_tuple=False) + tree_map_(partial(_create_differentiable, level=level), diff_primals) + primals_out = func(*primals) + + if has_aux: + if not (isinstance(primals_out, tuple) and len(primals_out) == 2): + raise RuntimeError( + "vjp(f, *primals): output of function f should be a tuple: (output, aux) " + "if has_aux is True" + ) + primals_out, aux = primals_out + aux = _undo_create_differentiable(aux, level) + + flat_primals_out, primals_out_spec = tree_flatten(primals_out) + assert_non_empty_tensor_output(flat_primals_out, "vjp(f, *primals)") + flat_diff_primals, primals_spec = tree_flatten(diff_primals) + results = _undo_create_differentiable(primals_out, level) + + for primal_out in flat_primals_out: + assert isinstance(primal_out, torch.Tensor) + if primal_out.is_floating_point() or primal_out.is_complex(): + continue + raise RuntimeError( + "vjp(f, ...): All outputs of f must be " + "floating-point or complex Tensors, got Tensor " + f"with dtype {primal_out.dtype}" + ) + + def wrapper(cotangents, retain_graph=True, create_graph=None): + if create_graph is None: + create_graph = torch.is_grad_enabled() + flat_cotangents, cotangents_spec = tree_flatten(cotangents) + if primals_out_spec != cotangents_spec: + raise RuntimeError( + f"Expected pytree structure of cotangents to be the same " + f"as pytree structure of outputs to the function. " + f"cotangents: {treespec_pprint(cotangents_spec)}, " + f"primal output: {treespec_pprint(primals_out_spec)}" + ) + result = _autograd_grad( + flat_primals_out, + flat_diff_primals, + flat_cotangents, + retain_graph=retain_graph, + create_graph=create_graph, + ) + return tree_unflatten(result, primals_spec) + + if has_aux: + return results, wrapper, aux + else: + return results, wrapper + + +def _safe_zero_index(x): + assert len(x) == 1 + return x[0] + + +# jacrev and jacfwd don't support complex functions +# Helper function to throw appropriate error. +def error_if_complex(func_name, args, is_input): + flat_args = pytree.tree_leaves(args) + for idx, arg in enumerate(flat_args): + if isinstance(arg, torch.Tensor) and arg.dtype.is_complex: + input_or_output = "inputs" if is_input else "outputs" + err_msg = ( + f"{func_name}: Expected all {input_or_output} " + f"to be real but received complex tensor at flattened input idx: {idx}" + ) + raise RuntimeError(err_msg) + + +@exposed_in("torch.func") +def jacrev( + func: Callable, + argnums: Union[int, tuple[int]] = 0, + *, + has_aux=False, + chunk_size: Optional[int] = None, + _preallocate_and_copy=False, +): + """ + Computes the Jacobian of ``func`` with respect to the arg(s) at index + ``argnum`` using reverse mode autodiff + + .. note:: + Using :attr:`chunk_size=1` is equivalent to computing the jacobian + row-by-row with a for-loop i.e. the constraints of :func:`vmap` are + not applicable. + + Args: + func (function): A Python function that takes one or more arguments, + one of which must be a Tensor, and returns one or more Tensors + argnums (int or Tuple[int]): Optional, integer or tuple of integers, + saying which arguments to get the Jacobian with respect to. + Default: 0. + has_aux (bool): Flag indicating that ``func`` returns a + ``(output, aux)`` tuple where the first element is the output of + the function to be differentiated and the second element is + auxiliary objects that will not be differentiated. + Default: False. + chunk_size (None or int): If None (default), use the maximum chunk size + (equivalent to doing a single vmap over vjp to compute the jacobian). + If 1, then compute the jacobian row-by-row with a for-loop. + If not None, then compute the jacobian :attr:`chunk_size` rows at a time + (equivalent to doing multiple vmap over vjp). If you run into memory issues computing + the jacobian, please try to specify a non-None chunk_size. + + Returns: + Returns a function that takes in the same inputs as ``func`` and + returns the Jacobian of ``func`` with respect to the arg(s) at + ``argnums``. If ``has_aux is True``, then the returned function + instead returns a ``(jacobian, aux)`` tuple where ``jacobian`` + is the Jacobian and ``aux`` is auxiliary objects returned by ``func``. + + A basic usage with a pointwise, unary operation will give a diagonal array + as the Jacobian + + >>> from torch.func import jacrev + >>> x = torch.randn(5) + >>> jacobian = jacrev(torch.sin)(x) + >>> expected = torch.diag(torch.cos(x)) + >>> assert torch.allclose(jacobian, expected) + + If you would like to compute the output of the function as well as the + jacobian of the function, use the ``has_aux`` flag to return the output + as an auxiliary object: + + >>> from torch.func import jacrev + >>> x = torch.randn(5) + >>> + >>> def f(x): + >>> return x.sin() + >>> + >>> def g(x): + >>> result = f(x) + >>> return result, result + >>> + >>> jacobian_f, f_x = jacrev(g, has_aux=True)(x) + >>> assert torch.allclose(f_x, f(x)) + + :func:`jacrev` can be composed with vmap to produce batched + Jacobians: + + >>> from torch.func import jacrev, vmap + >>> x = torch.randn(64, 5) + >>> jacobian = vmap(jacrev(torch.sin))(x) + >>> assert jacobian.shape == (64, 5, 5) + + Additionally, :func:`jacrev` can be composed with itself to produce + Hessians + + >>> from torch.func import jacrev + >>> def f(x): + >>> return x.sin().sum() + >>> + >>> x = torch.randn(5) + >>> hessian = jacrev(jacrev(f))(x) + >>> assert torch.allclose(hessian, torch.diag(-x.sin())) + + By default, :func:`jacrev` computes the Jacobian with respect to the first + input. However, it can compute the Jacboian with respect to a different + argument by using ``argnums``: + + >>> from torch.func import jacrev + >>> def f(x, y): + >>> return x + y ** 2 + >>> + >>> x, y = torch.randn(5), torch.randn(5) + >>> jacobian = jacrev(f, argnums=1)(x, y) + >>> expected = torch.diag(2 * y) + >>> assert torch.allclose(jacobian, expected) + + Additionally, passing a tuple to ``argnums`` will compute the Jacobian + with respect to multiple arguments + + >>> from torch.func import jacrev + >>> def f(x, y): + >>> return x + y ** 2 + >>> + >>> x, y = torch.randn(5), torch.randn(5) + >>> jacobian = jacrev(f, argnums=(0, 1))(x, y) + >>> expectedX = torch.diag(torch.ones_like(x)) + >>> expectedY = torch.diag(2 * y) + >>> assert torch.allclose(jacobian[0], expectedX) + >>> assert torch.allclose(jacobian[1], expectedY) + + .. note:: + Using PyTorch ``torch.no_grad`` together with ``jacrev``. + Case 1: Using ``torch.no_grad`` inside a function: + + >>> def f(x): + >>> with torch.no_grad(): + >>> c = x ** 2 + >>> return x - c + + In this case, ``jacrev(f)(x)`` will respect the inner ``torch.no_grad``. + + Case 2: Using ``jacrev`` inside ``torch.no_grad`` context manager: + + >>> with torch.no_grad(): + >>> jacrev(f)(x) + + In this case, ``jacrev`` will respect the inner ``torch.no_grad``, but not the + outer one. This is because ``jacrev`` is a "function transform": its result + should not depend on the result of a context manager outside of ``f``. + """ + if not (chunk_size is None or chunk_size > 0): + raise ValueError("jacrev: `chunk_size` should be greater than 0.") + + @wraps(func) + def wrapper_fn(*args): + error_if_complex("jacrev", args, is_input=True) + vjp_out = _vjp_with_argnums(func, *args, argnums=argnums, has_aux=has_aux) + if has_aux: + output, vjp_fn, aux = vjp_out + else: + output, vjp_fn = vjp_out + + # See NOTE: [Computing jacobian with vmap and vjp for multiple outputs] + flat_output, output_spec = tree_flatten(output) + + error_if_complex("jacrev", flat_output, is_input=False) + + # NB: vjp already checks that all outputs are tensors + # Step 1: Construct grad_outputs by splitting the standard basis + flat_output_numels = tuple(out.numel() for out in flat_output) + + primals = _slice_argnums(args, argnums) + flat_primals, primals_spec = tree_flatten(primals) + + def compute_jacobian_stacked(): + # Helper function to compute chunked Jacobian + # The intermediate chunked calculation are only + # scoped at this function level. + chunked_results = [] + for flat_basis_chunk in _chunked_standard_basis_for_( + flat_output, flat_output_numels, chunk_size=chunk_size + ): + if chunk_size == 1: + # sanity check. + for t in flat_basis_chunk: + assert t.size(0) == 1 + + flat_basis_chunk = tree_map( + lambda t: torch.squeeze(t, 0), flat_basis_chunk + ) + + basis = tree_unflatten(flat_basis_chunk, output_spec) + + if chunk_size == 1: + # Behaviour with `chunk_size=1` is same as `for-loop` + # i.e. user shouldn't deal with the limitations of vmap. + chunked_result = vjp_fn(basis) + else: # chunk_size is None or chunk_size != 1 + chunked_result = vmap(vjp_fn)(basis) + + flat_results = pytree.tree_leaves(chunked_result) + + if chunk_size == 1: + flat_results = tree_map( + lambda t: torch.unsqueeze(t, 0), flat_results + ) + + chunked_results.append(flat_results) + + if len(chunked_results) == 1: + # Short-circuit if we used a single chunk + return chunked_results[0] + + # Concatenate chunks. + flat_results = [] + # Iterate and concat the jacobians of different + # inputs. + for idx in range(len(flat_primals)): + r = tuple(r_[idx] for r_ in chunked_results) + flat_results.append(torch.cat(r, 0)) + + return flat_results + + def compute_jacobian_preallocate_and_copy(): + # Helper function to compute chunked Jacobian + # The intermediate chunked calculation are only + # scoped at this function level. + out_vec_size = sum(flat_output_numels) + + # Don't pre-allocate if we have a single chunk. + if not (chunk_size is None or chunk_size >= out_vec_size): + stacked_results = [ + primal.new_zeros(out_vec_size, *primal.shape) + for primal in flat_primals + ] + + for idx, flat_basis_chunk in enumerate( + _chunked_standard_basis_for_( + flat_output, flat_output_numels, chunk_size=chunk_size + ) + ): + if chunk_size == 1: + # sanity check. + for t in flat_basis_chunk: + assert t.size(0) == 1 + + flat_basis_chunk = [torch.squeeze(t, 0) for t in flat_basis_chunk] + + basis = tree_unflatten(flat_basis_chunk, output_spec) + + if chunk_size == 1: + # Behaviour with `chunk_size=1` is same as `for-loop` + # i.e. user shouldn't deal with the limitations of vmap. + chunked_result = vjp_fn(basis) + else: # chunk_size is None or chunk_size != 1 + chunked_result = vmap(vjp_fn)(basis) + + flat_results = pytree.tree_leaves(chunked_result) + + # Short-circuit if we have a single chunk. + if chunk_size is None or chunk_size >= out_vec_size: + if chunk_size == 1: # and out_vec_size == 1 + # Since we squeezed the output dim + flat_results = tree_map( + lambda t: torch.unsqueeze(t, 0), flat_results + ) + return flat_results + + for r, sr in zip(flat_results, stacked_results): + sr[idx * chunk_size : (idx + 1) * chunk_size].copy_(r) + + return stacked_results + + if _preallocate_and_copy: + flat_jacobians_per_input = compute_jacobian_preallocate_and_copy() + else: + flat_jacobians_per_input = compute_jacobian_stacked() + + # Step 2: The returned jacobian is one big tensor per input. In this step, + # we split each Tensor by output. + flat_jacobians_per_input = [ + result.split(flat_output_numels, dim=0) + for result in flat_jacobians_per_input + ] + flat_input_flat_output = [ + tuple( + split.view(out.shape + primal.shape) + for split, out in zip(splits, flat_output) + ) + for splits, primal in zip(flat_jacobians_per_input, flat_primals) + ] + + # Step 3: Right now, `jacobian` is a List[List[Tensor]]. + # The outer List corresponds to the number of primals, + # the inner List corresponds to the number of outputs. + # We need to: + # a. Exchange the order of the outer List and inner List + # b. tree_unflatten the inner Lists (which correspond to the primals) + # c. handle the argnums=int case + # d. tree_unflatten the outer List (which corresponds to the outputs) + flat_output_flat_input = tuple(zip(*flat_input_flat_output)) + + flat_output_input = tuple( + tree_unflatten(flat_input, primals_spec) + for flat_input in flat_output_flat_input + ) + + if isinstance(argnums, int): + flat_output_input = tuple( + _safe_zero_index(flat_input) for flat_input in flat_output_input + ) + output_input = tree_unflatten(flat_output_input, output_spec) + if has_aux: + return output_input, aux + return output_input + + return wrapper_fn + + +# NOTE: [Computing jacobian with vmap and vjp for multiple outputs] +# +# Let's consider f(x) = (x**2, x.sum()) and let x = torch.randn(3). +# It turns out we can compute the jacobian of this function with a single +# call to autograd.grad by using vmap over the correct grad_outputs. +# +# Firstly, one way to compute the jacobian is to stack x**2 and x.sum() +# into a 4D vector. E.g., use g(x) = torch.stack([x**2, x.sum()]) +# +# To get the first row of the jacobian, we call +# >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([1, 0, 0, 0])) +# To get the 2nd row of the jacobian, we call +# >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([0, 1, 0, 0])) +# and so on. +# +# Using vmap, we can vectorize all 4 of these computations into one by +# passing the standard basis for R^4 as the grad_output. +# vmap(partial(autograd.grad, g(x), x))(torch.eye(4)). +# +# Now, how do we compute the jacobian *without stacking the output*? +# We can just split the standard basis across the outputs. So to +# compute the jacobian of f(x), we'd use +# >>> autograd.grad(f(x), x, grad_outputs=_construct_standard_basis_for(...)) +# The grad_outputs looks like the following: +# ( torch.tensor([[1, 0, 0], +# [0, 1, 0], +# [0, 0, 1], +# [0, 0, 0]]), +# torch.tensor([[0], +# [0], +# [0], +# [1]]) ) +# +# But we're not done yet! +# >>> vmap(partial(autograd.grad(f(x), x, grad_outputs=...))) +# returns a Tensor of shape [4, 3]. We have to remember to split the +# jacobian of shape [4, 3] into two: +# - one of shape [3, 3] for the first output +# - one of shape [ 3] for the second output + + +def _chunked_standard_basis_for_(tensors, tensor_numels, chunk_size=None): + # This function: + # - constructs a N=sum(tensor_numels) standard basis. i.e. an NxN identity matrix. + # - Splits the identity matrix into chunks with each chunk size determined by `tensor_numels`. + # - Each chunk corresponds to one tensor. The chunk has the same dtype and + # device as the tensor + # + # For example, with tensor_numels = [1, 2, 1], this function returns: + # ( tensor([[1], tensor([[0, 0], tensor([[0], + # [0], [1, 0], [0], + # [0], [0, 1], [0], + # [0]]) , [0, 0]]) , [1]]) ) + # + # Precondition: tensor_numels == tuple(tensor.numel() for tensor in tensors) + # Precondition: tensors always has at least one element. + # + # See NOTE: [Computing jacobian with vmap and grad for multiple tensors] + # for context behind this function. + # NOTE: Argument `chunk_size` is used to generate chunked basis instead of + # one huge basis matrix. `chunk_size` dictates the maximum size of the + # basis matrix along dim=0. + assert len(tensors) == len(tensor_numels) + assert len(tensors) > 0 + assert chunk_size is None or chunk_size > 0 + total_numel = sum(tensor_numels) + if chunk_size and chunk_size < total_numel: + chunk_numels = get_chunk_sizes(total_numel, chunk_size) + else: # chunk_size is None or chunk_size >= total_numel + chunk_size = total_numel + chunk_numels = [total_numel] + + diag_start_indices = ( + 0, + *torch.tensor(tensor_numels).cumsum(dim=0)[:-1].neg().unbind(), + ) + + for chunk_idx, total_numel in enumerate(chunk_numels): + chunks = tuple( + tensor.new_zeros(total_numel, tensor_numel) + for tensor, tensor_numel in zip(tensors, tensor_numels) + ) + + for chunk, diag_start_idx in zip(chunks, diag_start_indices): + chunk.diagonal(diag_start_idx + chunk_idx * chunk_size).fill_(1) + chunks = tuple( + chunk.view(total_numel, *tensor.shape) + for chunk, tensor in zip(chunks, tensors) + ) + yield chunks + + +def _construct_standard_basis_for(tensors, tensor_numels): + for basis in _chunked_standard_basis_for_(tensors, tensor_numels, chunk_size=None): + return basis + + +def _validate_and_wrap_argnum(argnum, num_args): + if not isinstance(argnum, int): + raise RuntimeError(f"argnum must be int, got: {type(argnum)}") + if argnum >= 0 and argnum < num_args: + return argnum + if argnum < 0 and argnum >= -num_args: + return argnum + num_args + raise RuntimeError(f"Got argnum={argnum}, but only {num_args} positional inputs") + + +def _check_unique_non_empty(argnums): + if isinstance(argnums, tuple): + if len(argnums) == 0: + raise RuntimeError("argnums must be non-empty") + if len(set(argnums)) != len(argnums): + raise RuntimeError(f"argnums elements must be unique, got {argnums}") + + +def _replace_args(old_args, new_args, argnums): + if isinstance(argnums, int): + if len(new_args) != 1: + raise RuntimeError( + f"new_args should be of size 1, was of size {len(new_args)}" + ) + return tuple( + new_args[0] if i == argnums else old_args[i] for i in range(len(old_args)) + ) + if isinstance(argnums, tuple): + if len(new_args) != len(argnums): + raise RuntimeError( + "new_args should have the same size as argnums. " + f"Argnums size {len(argnums)}, new_args size {len(new_args)}" + ) + + def get_right_elem(i): + return new_args[argnums.index(i)] if i in argnums else old_args[i] + + return tuple(get_right_elem(i) for i in range(len(old_args))) + raise RuntimeError(f"argnums must be int or Tuple[int, ...], got: {type(argnums)}") + + +def _validate_and_wrap_argnums(argnums, num_args): + if isinstance(argnums, int): + return _validate_and_wrap_argnum(argnums, num_args) + if isinstance(argnums, tuple): + return tuple(_validate_and_wrap_argnum(argnum, num_args) for argnum in argnums) + raise AssertionError("Should never get here") + + +def _slice_argnums(args, argnums, as_tuple=True): + if not isinstance(argnums, int) and not isinstance(argnums, tuple): + raise RuntimeError( + f"argnums must be int or Tuple[int, ...], got: {type(argnums)}" + ) + argnums = _validate_and_wrap_argnums(argnums, len(args)) + _check_unique_non_empty(argnums) + if isinstance(argnums, int): + if as_tuple: + return (args[argnums],) + else: + return args[argnums] + return tuple(args[i] for i in argnums) + + +JVP_NESTING = 0 + + +def assert_flat_tuple_of_tensors(elts: Any, api: str, argname: str) -> None: + if not isinstance(elts, tuple): + raise RuntimeError( + f"{api}: Expected {argname} to be a tuple of Tensors, got {type(elts)}" + ) + for elt in elts: + if isinstance(elt, torch.Tensor): + continue + raise RuntimeError( + f"{api}: Expected {argname} to be a tuple of Tensors, got " + f"a tuple with an element of type {type(elt)}" + ) + if len(elts) == 0: + raise RuntimeError( + f"{api}: Expected {argname} to be a non-empty tuple of Tensors." + ) + + +def assert_non_empty_tensor_output(output: list[Any], api: str) -> None: + if (len(output) == 1 and output[0] is None) or len(output) < 1: + raise RuntimeError( + f"{api}: Expected f to be a function that has non-empty output (got output = {output})" + ) + for o in output: + if not isinstance(o, torch.Tensor): + raise RuntimeError( + f"{api}: expected f(*primals) to return only tensors" + f", got unsupported type {type(o)}" + ) + + +def assert_output_is_tensor_or_tensors(output: Any, api: str) -> None: + if isinstance(output, torch.Tensor): + return + if not isinstance(output, tuple): + raise RuntimeError( + f"{api}: Expected output of f to be a Tensor or Tensors, got " + f"{type(output)}" + ) + if len(output) == 0: + raise RuntimeError( + f"{api}: Expected output of f to be a non-empty tuple of Tensors." + ) + for out in output: + if isinstance(out, torch.Tensor): + continue + raise RuntimeError( + f"{api}: Expected output of f to be a Tensor or Tensors, got " + f"{type(out)} as an output" + ) + + +def assert_non_empty_list_of_tensors( + output: list[torch.Tensor], api: str, argname: str +) -> None: + if len(output) == 0: + raise RuntimeError(f"{api}: Expected {argname} to contain at least one Tensor.") + for out in output: + if isinstance(out, torch.Tensor): + continue + raise RuntimeError( + f"{api}: Expected {argname} to only contain Tensors, got {type(out)}" + ) + + +jvp_str = "jvp(f, primals, tangents)" + + +def safe_unpack_dual(dual, strict): + if not isinstance(dual, torch.Tensor): + raise RuntimeError( + f"{jvp_str}: expected f(*args) to return only tensors" + f", got unsupported type {type(dual)}" + ) + + primal, tangent = fwAD.unpack_dual(dual) + if tangent is None: + if strict: + raise RuntimeError( + "jvp(f, primals, tangents, strict=True): " + "The output of f is independent of " + "the inputs. This is not allowed with strict=True." + ) + tangent = torch.zeros_like(primal) + return primal, tangent + + +@exposed_in("torch.func") +def jvp( + func: Callable, + primals: Any, + tangents: Any, + *, + strict: bool = False, + has_aux: bool = False, +): + """ + Standing for the Jacobian-vector product, returns a tuple containing + the output of `func(*primals)` and the "Jacobian of ``func`` evaluated at + ``primals``" times ``tangents``. This is also known as forward-mode autodiff. + + Args: + func (function): A Python function that takes one or more arguments, + one of which must be a Tensor, and returns one or more Tensors + primals (Tensors): Positional arguments to ``func`` that must all be + Tensors. The returned function will also be computing the + derivative with respect to these arguments + tangents (Tensors): The "vector" for which Jacobian-vector-product is + computed. Must be the same structure and sizes as the inputs to + ``func``. + has_aux (bool): Flag indicating that ``func`` returns a + ``(output, aux)`` tuple where the first element is the output of + the function to be differentiated and the second element is + other auxiliary objects that will not be differentiated. + Default: False. + + Returns: + Returns a ``(output, jvp_out)`` tuple containing the output of ``func`` + evaluated at ``primals`` and the Jacobian-vector product. + If ``has_aux is True``, then instead returns a ``(output, jvp_out, aux)`` tuple. + + .. note:: + You may see this API error out with "forward-mode AD not implemented + for operator X". If so, please file a bug report and we will prioritize it. + + jvp is useful when you wish to compute gradients of a function R^1 -> R^N + + >>> from torch.func import jvp + >>> x = torch.randn([]) + >>> f = lambda x: x * torch.tensor([1., 2., 3]) + >>> value, grad = jvp(f, (x,), (torch.tensor(1.),)) + >>> assert torch.allclose(value, f(x)) + >>> assert torch.allclose(grad, torch.tensor([1., 2, 3])) + + :func:`jvp` can support functions with multiple inputs by passing in the + tangents for each of the inputs + + >>> from torch.func import jvp + >>> x = torch.randn(5) + >>> y = torch.randn(5) + >>> f = lambda x, y: (x * y) + >>> _, output = jvp(f, (x, y), (torch.ones(5), torch.ones(5))) + >>> assert torch.allclose(output, x + y) + + """ + + return _jvp_with_argnums( + func, primals, tangents, argnums=None, strict=strict, has_aux=has_aux + ) + + +def _jvp_with_argnums( + func: Callable, + primals: Any, + tangents: Any, + argnums: Optional[argnums_t], + *, + strict: bool = False, + has_aux: bool, +): + # This is the same function as jvp but also accepts an argnums argument + # Most args are the same as jvp except for the added argument + # argnums (Optional[int or tuple[int]]): Optional, specifies the argument(s) to compute gradients with respect to. + # If None, computes the gradients with respect to all inputs (used for jvp). Default: None + # Because of this, tangents must be of length argnums and matches up to the corresponding primal whose index is + # given by argnums + # + # WARN: Users should NOT call this function directly and should just be calling jvp. + # It is only separated so that inputs passed to jacfwd but not differentiated get the correct wrappers. + # + # NOTE: All error messages are produced as if jvp was being called, even if this was called by jacfwd + # + # Returns the same two elements as :func:`jvp` but the returned tuple, ``jvp_out``, only has JVPs with respect to + # the primals given by argnums + if not isinstance(primals, tuple): + raise RuntimeError( + f"{jvp_str}: Expected primals to be a tuple. " + f"E.g. it should be valid to call f(*primals)." + ) + diff_args = primals if argnums is None else _slice_argnums(primals, argnums) + flat_primals, primals_spec = tree_flatten(diff_args) + flat_tangents, tangents_spec = tree_flatten(tangents) + if primals_spec != tangents_spec: + raise RuntimeError( + f"{jvp_str}: Expected primals and tangents to have the same python " + f"structure. For example, if primals is a tuple of 3 tensors, " + f"tangents also must be. Got primals with structure {primals_spec} " + f"and tangents with structure {tangents_spec}" + ) + assert_non_empty_list_of_tensors(flat_primals, jvp_str, "primals") + assert_non_empty_list_of_tensors(flat_tangents, jvp_str, "tangents") + + global JVP_NESTING + + with jvp_increment_nesting() as level: + with fwAD._set_fwd_grad_enabled(True): + ctx = fwAD.dual_level if JVP_NESTING == 1 else contextlib.nullcontext + with ctx(): + flat_duals = tuple( + fwAD.make_dual(p, t) for p, t in zip(flat_primals, flat_tangents) + ) + duals = tree_unflatten(flat_duals, primals_spec) + if argnums is not None: + primals = _wrap_all_tensors(primals, level) + duals = _replace_args(primals, duals, argnums) + result_duals = func(*duals) + if has_aux: + if not (isinstance(result_duals, tuple) and len(result_duals) == 2): + raise RuntimeError( + f"{jvp_str}: output of function f should be a tuple: (output, aux) " + "if has_aux is True" + ) + result_duals, aux = result_duals + aux = _undo_create_differentiable(aux, level) + + result_duals, spec = tree_flatten(result_duals) + assert_non_empty_tensor_output(result_duals, jvp_str) + + primals_out, tangents_out = zip( + *[safe_unpack_dual(dual, strict) for dual in result_duals] + ) + primals_out = tree_map( + partial(_undo_create_differentiable, level=level), primals_out + ) + tangents_out = tree_map( + partial(_undo_create_differentiable, level=level), tangents_out + ) + + primals_out_unflatten = tree_unflatten(primals_out, spec) + tangents_out_unflatten = tree_unflatten(tangents_out, spec) + if has_aux: + return primals_out_unflatten, tangents_out_unflatten, aux + + return primals_out_unflatten, tangents_out_unflatten + + +def safe_unflatten(tensor, dim, shape): + if len(shape) == 0: + assert tensor.shape[dim] == 1 + return tensor.squeeze(dim) + return tensor.unflatten(dim, shape) + + +@exposed_in("torch.func") +def jacfwd( + func: Callable, + argnums: argnums_t = 0, + has_aux: bool = False, + *, + randomness: str = "error", +): + """ + Computes the Jacobian of ``func`` with respect to the arg(s) at index + ``argnum`` using forward-mode autodiff + + Args: + func (function): A Python function that takes one or more arguments, + one of which must be a Tensor, and returns one or more Tensors + argnums (int or Tuple[int]): Optional, integer or tuple of integers, + saying which arguments to get the Jacobian with respect to. + Default: 0. + has_aux (bool): Flag indicating that ``func`` returns a + ``(output, aux)`` tuple where the first element is the output of + the function to be differentiated and the second element is + auxiliary objects that will not be differentiated. + Default: False. + randomness(str): Flag indicating what type of randomness to use. + See :func:`vmap` for more detail. Allowed: "different", "same", "error". + Default: "error" + + Returns: + Returns a function that takes in the same inputs as ``func`` and + returns the Jacobian of ``func`` with respect to the arg(s) at + ``argnums``. If ``has_aux is True``, then the returned function + instead returns a ``(jacobian, aux)`` tuple where ``jacobian`` + is the Jacobian and ``aux`` is auxiliary objects returned by ``func``. + + .. note:: + You may see this API error out with "forward-mode AD not implemented + for operator X". If so, please file a bug report and we will prioritize it. + An alternative is to use :func:`jacrev`, which has better operator coverage. + + A basic usage with a pointwise, unary operation will give a diagonal array + as the Jacobian + + >>> from torch.func import jacfwd + >>> x = torch.randn(5) + >>> jacobian = jacfwd(torch.sin)(x) + >>> expected = torch.diag(torch.cos(x)) + >>> assert torch.allclose(jacobian, expected) + + :func:`jacfwd` can be composed with vmap to produce batched + Jacobians: + + >>> from torch.func import jacfwd, vmap + >>> x = torch.randn(64, 5) + >>> jacobian = vmap(jacfwd(torch.sin))(x) + >>> assert jacobian.shape == (64, 5, 5) + + If you would like to compute the output of the function as well as the + jacobian of the function, use the ``has_aux`` flag to return the output + as an auxiliary object: + + >>> from torch.func import jacfwd + >>> x = torch.randn(5) + >>> + >>> def f(x): + >>> return x.sin() + >>> + >>> def g(x): + >>> result = f(x) + >>> return result, result + >>> + >>> jacobian_f, f_x = jacfwd(g, has_aux=True)(x) + >>> assert torch.allclose(f_x, f(x)) + + Additionally, :func:`jacrev` can be composed with itself or :func:`jacrev` + to produce Hessians + + >>> from torch.func import jacfwd, jacrev + >>> def f(x): + >>> return x.sin().sum() + >>> + >>> x = torch.randn(5) + >>> hessian = jacfwd(jacrev(f))(x) + >>> assert torch.allclose(hessian, torch.diag(-x.sin())) + + By default, :func:`jacfwd` computes the Jacobian with respect to the first + input. However, it can compute the Jacboian with respect to a different + argument by using ``argnums``: + + >>> from torch.func import jacfwd + >>> def f(x, y): + >>> return x + y ** 2 + >>> + >>> x, y = torch.randn(5), torch.randn(5) + >>> jacobian = jacfwd(f, argnums=1)(x, y) + >>> expected = torch.diag(2 * y) + >>> assert torch.allclose(jacobian, expected) + + Additionally, passing a tuple to ``argnums`` will compute the Jacobian + with respect to multiple arguments + + >>> from torch.func import jacfwd + >>> def f(x, y): + >>> return x + y ** 2 + >>> + >>> x, y = torch.randn(5), torch.randn(5) + >>> jacobian = jacfwd(f, argnums=(0, 1))(x, y) + >>> expectedX = torch.diag(torch.ones_like(x)) + >>> expectedY = torch.diag(2 * y) + >>> assert torch.allclose(jacobian[0], expectedX) + >>> assert torch.allclose(jacobian[1], expectedY) + + """ + + @wraps(func) + def wrapper_fn(*args): + error_if_complex("jacfwd", args, is_input=True) + primals = args if argnums is None else _slice_argnums(args, argnums) + flat_primals, primals_spec = tree_flatten(primals) + flat_primals_numels = tuple(p.numel() for p in flat_primals) + flat_basis = _construct_standard_basis_for(flat_primals, flat_primals_numels) + basis = tree_unflatten(flat_basis, primals_spec) + + def push_jvp(basis): + output = _jvp_with_argnums( + func, args, basis, argnums=argnums, has_aux=has_aux + ) + # output[0] is the output of `func(*args)` + error_if_complex("jacfwd", output[0], is_input=False) + if has_aux: + _, jvp_out, aux = output + return jvp_out, aux + _, jvp_out = output + return jvp_out + + results = vmap(push_jvp, randomness=randomness)(basis) + if has_aux: + results, aux = results + # aux is in the standard basis format, e.g. NxN matrix + # We need to fetch the first element as original `func` output + flat_aux, aux_spec = tree_flatten(aux) + flat_aux = [value[0] for value in flat_aux] + aux = tree_unflatten(flat_aux, aux_spec) + + jac_outs, spec = tree_flatten(results) + # Most probably below output check can never raise an error + # as jvp should test the output before + # assert_non_empty_output(jac_outs, 'jacfwd(f, ...)(*args)') + + jac_outs_ins = tuple( + tuple( + safe_unflatten(jac_out_in, -1, primal.shape) + for primal, jac_out_in in zip( + flat_primals, + jac_out.movedim(0, -1).split(flat_primals_numels, dim=-1), + ) + ) + for jac_out in jac_outs + ) + jac_outs_ins = tuple( + tree_unflatten(jac_ins, primals_spec) for jac_ins in jac_outs_ins + ) + + if isinstance(argnums, int): + jac_outs_ins = tuple(jac_ins[0] for jac_ins in jac_outs_ins) + if has_aux: + return tree_unflatten(jac_outs_ins, spec), aux + return tree_unflatten(jac_outs_ins, spec) + + return wrapper_fn + + +@exposed_in("torch.func") +def hessian(func, argnums=0): + """ + Computes the Hessian of ``func`` with respect to the arg(s) at index + ``argnum`` via a forward-over-reverse strategy. + + The forward-over-reverse strategy (composing ``jacfwd(jacrev(func))``) is + a good default for good performance. It is possible to compute Hessians + through other compositions of :func:`jacfwd` and :func:`jacrev` like + ``jacfwd(jacfwd(func))`` or ``jacrev(jacrev(func))``. + + Args: + func (function): A Python function that takes one or more arguments, + one of which must be a Tensor, and returns one or more Tensors + argnums (int or Tuple[int]): Optional, integer or tuple of integers, + saying which arguments to get the Hessian with respect to. + Default: 0. + + Returns: + Returns a function that takes in the same inputs as ``func`` and + returns the Hessian of ``func`` with respect to the arg(s) at + ``argnums``. + + .. note:: + You may see this API error out with "forward-mode AD not implemented + for operator X". If so, please file a bug report and we will prioritize it. + An alternative is to use ``jacrev(jacrev(func))``, which has better + operator coverage. + + A basic usage with a R^N -> R^1 function gives a N x N Hessian: + + >>> from torch.func import hessian + >>> def f(x): + >>> return x.sin().sum() + >>> + >>> x = torch.randn(5) + >>> hess = hessian(f)(x) # equivalent to jacfwd(jacrev(f))(x) + >>> assert torch.allclose(hess, torch.diag(-x.sin())) + + """ + return jacfwd(jacrev(func, argnums), argnums) + + +@doesnt_support_saved_tensors_hooks +def grad_and_value_impl(func, argnums, has_aux, args, kwargs) -> Callable: + with grad_increment_nesting() as level: + output, aux, grad_input = None, None, None + # See NOTE [grad and vjp interaction with no_grad] + with torch.enable_grad(): + args = _wrap_all_tensors(args, level) + kwargs = _wrap_all_tensors(kwargs, level) + diff_args = _slice_argnums(args, argnums, as_tuple=False) + tree_map_(partial(_create_differentiable, level=level), diff_args) + + output = func(*args, **kwargs) + if has_aux: + if not (isinstance(output, tuple) and len(output) == 2): + raise RuntimeError( + "grad_and_value(f)(*args): output of function f should be a tuple: (output, aux) " + "if has_aux is True" + ) + output, aux = output + + if not isinstance(output, torch.Tensor): + raise RuntimeError( + "grad_and_value(f)(*args): Expected f(*args) " + f"to return a Tensor, got {type(output)}" + ) + if output.dim() != 0: + raise RuntimeError( + "grad_and_value(f)(*args): Expected f(*args) " + "to return a scalar Tensor, got tensor with " + f"{output.dim()} dims. Maybe you wanted to " + "use the vjp or jacrev APIs instead?" + ) + + flat_diff_args, spec = tree_flatten(diff_args) + + # NB: need create_graph so that backward pass isn't run in no_grad mode + flat_outputs = _as_tuple(output) + flat_grad_input = _autograd_grad( + flat_outputs, flat_diff_args, create_graph=True + ) + grad_input = tree_unflatten(flat_grad_input, spec) + + grad_input = _undo_create_differentiable(grad_input, level) + output = _undo_create_differentiable(output, level) + if has_aux: + aux = _undo_create_differentiable(aux, level) + + if has_aux: + return grad_input, (output, aux) + return grad_input, output + + +def grad_impl(func: Callable, argnums: argnums_t, has_aux: bool, args, kwargs): + results = grad_and_value_impl(func, argnums, has_aux, args, kwargs) + if has_aux: + grad, (_, aux) = results + return grad, aux + grad, _ = results + return grad + + +def _maybe_wrap_functional_tensor( + maybe_tensor, level, *, _python_functionalize: bool = False +): + if not isinstance(maybe_tensor, torch.Tensor): + return maybe_tensor + wrapped = _wrap_functional_tensor(maybe_tensor, level) + _assert_wrapped_functional(maybe_tensor, wrapped) + if _python_functionalize: + out = FunctionalTensor(wrapped) + torch._mirror_autograd_meta_to(maybe_tensor, out) + return out + return wrapped + + +def _wrap_all_tensors_to_functional( + tensor_pytree, level, *, _python_functionalize: bool = False +): + return tree_map( + partial( + lambda x: _maybe_wrap_functional_tensor( + x, level, _python_functionalize=_python_functionalize + ) + ), + tensor_pytree, + ) + + +def _maybe_unwrap_functional_tensor(maybe_tensor, *, reapply_views: bool): + if not isinstance(maybe_tensor, torch.Tensor): + return maybe_tensor + if isinstance(maybe_tensor, FunctionalTensor): + maybe_tensor = maybe_tensor.elem + + if not torch._is_functional_tensor(maybe_tensor): + # If it's not a functional tensor, just return it. + # This can happen if we functionalize a fn that returns a global, + # which was never wrapped properly. + return maybe_tensor + # Sync any pending updates on the output tensor + torch._sync(maybe_tensor) + return _unwrap_functional_tensor(maybe_tensor, reapply_views) + + +def _unwrap_all_tensors_from_functional(tensor_pytree, *, reapply_views: bool): + return tree_map( + lambda t: _maybe_unwrap_functional_tensor(t, reapply_views=reapply_views), + tensor_pytree, + ) + + +@exposed_in("torch.func") +def functionalize(func: Callable, *, remove: str = "mutations") -> Callable: + """ + functionalize is a transform that can be used to remove (intermediate) + mutations and aliasing from a function, while preserving the function's + semantics. + + ``functionalize(func)`` returns a new function with the same semantics + as ``func``, but with all intermediate mutations removed. + Every inplace operation performed on an intermediate tensor: + ``intermediate.foo_()`` + gets replaced by its out-of-place equivalent: + ``intermediate_updated = intermediate.foo()``. + + functionalize is useful for shipping a pytorch program off to + backends or compilers that aren't able to easily represent + mutations or aliasing operators. + + Args: + func (Callable): A Python function that takes one or more arguments. + remove (str): An optional string argument, that takes on either + the value 'mutations' or 'mutations_and_views'. + If 'mutations' is passed in then all mutating operators + will be replaced with their non-mutating equivalents. + If 'mutations_and_views' is passed in, then additionally, all aliasing + operators will be replaced with their non-aliasing equivalents. + Default: 'mutations'. + + Returns: + Returns a new "functionalized" function. It takes the same inputs as + ``func``, and has the same behavior, but any mutations + (and optionally aliasing) performed on intermediate tensors + in the function will be removed. + + functionalize will also remove mutations (and views) that were performed on function inputs. + However to preserve semantics, functionalize will "fix up" the mutations after + the transform has finished running, by detecting if any tensor inputs "should have" + been mutated, and copying the new data back to the inputs if necessary. + + + Example:: + + >>> # xdoctest: +SKIP + >>> import torch + >>> from torch.fx.experimental.proxy_tensor import make_fx + >>> from torch.func import functionalize + >>> + >>> # A function that uses mutations and views, but only on intermediate tensors. + >>> def f(a): + ... b = a + 1 + ... c = b.view(-1) + ... c.add_(1) + ... return b + ... + >>> inpt = torch.randn(2) + >>> + >>> out1 = f(inpt) + >>> out2 = functionalize(f)(inpt) + >>> + >>> # semantics are the same (outputs are equivalent) + >>> print(torch.allclose(out1, out2)) + True + >>> + >>> f_traced = make_fx(f)(inpt) + >>> f_no_mutations_traced = make_fx(functionalize(f))(inpt) + >>> f_no_mutations_and_views_traced = make_fx(functionalize(f, remove='mutations_and_views'))(inpt) + >>> + >>> print(f_traced.code) + + + + def forward(self, a_1): + add = torch.ops.aten.add(a_1, 1); a_1 = None + view = torch.ops.aten.view(add, [-1]) + add_ = torch.ops.aten.add_(view, 1); view = None + return add + + >>> print(f_no_mutations_traced.code) + + + + def forward(self, a_1): + add = torch.ops.aten.add(a_1, 1); a_1 = None + view = torch.ops.aten.view(add, [-1]); add = None + add_1 = torch.ops.aten.add(view, 1); view = None + view_1 = torch.ops.aten.view(add_1, [2]); add_1 = None + return view_1 + + >>> print(f_no_mutations_and_views_traced.code) + + + + def forward(self, a_1): + add = torch.ops.aten.add(a_1, 1); a_1 = None + view_copy = torch.ops.aten.view_copy(add, [-1]); add = None + add_1 = torch.ops.aten.add(view_copy, 1); view_copy = None + view_copy_1 = torch.ops.aten.view_copy(add_1, [2]); add_1 = None + return view_copy_1 + + + >>> # A function that mutates its input tensor + >>> def f(a): + ... b = a.view(-1) + ... b.add_(1) + ... return a + ... + >>> f_no_mutations_and_views_traced = make_fx(functionalize(f, remove='mutations_and_views'))(inpt) + >>> # + >>> # All mutations and views have been removed, + >>> # but there is an extra copy_ in the graph to correctly apply the mutation to the input + >>> # after the function has completed. + >>> print(f_no_mutations_and_views_traced.code) + + + + def forward(self, a_1): + view_copy = torch.ops.aten.view_copy(a_1, [-1]) + add = torch.ops.aten.add(view_copy, 1); view_copy = None + view_copy_1 = torch.ops.aten.view_copy(add, [2]); add = None + copy_ = torch.ops.aten.copy_(a_1, view_copy_1); a_1 = None + return view_copy_1 + + + There are a few "failure modes" for functionalize that are worth calling out: + (1) Like other torch.func transforms, `functionalize()` doesn't work with functions + that directly use `.backward()`. The same is true for torch.autograd.grad. + If you want to use autograd, you can compute gradients directly + with `functionalize(grad(f))`. + (2) Like other torch.func transforms, `functionalize()` doesn't work with global state. + If you call `functionalize(f)` on a function that takes views / mutations of + non-local state, functionalization will simply no-op and pass the view/mutation + calls directly to the backend. + One way to work around this is is to ensure that any non-local state creation + is wrapped into a larger function, which you then call functionalize on. + (3) `resize_()` has some limitations: functionalize will only work on programs + that use resize_()` as long as the tensor being resized is not a view. + (4) `as_strided()` has some limitations: functionalize will not work on + `as_strided()` calls that result in tensors with overlapping memory. + + + Finally, a helpful mental model for understanding functionalization is that + most user pytorch programs are writing with the public torch API. + When executed, torch operators are generally decomposed into + our internal C++ "ATen" API. + The logic for functionalization happens entirely at the level of ATen. + Functionalization knows how to take every aliasing operator in ATen, + and map it to its non-aliasing equivalent + (e.g. ``tensor.view({-1})`` -> ``at::view_copy(tensor, {-1})``), + and how to take every mutating operator in ATen, + and map it to its non-mutating equivalent + (e.g. ``tensor.add_(1)`` -> ``at::add(tensor, -1)``), + while tracking aliases and mutations out-of-line to know when to fix things up. + Information about which ATen operators are aliasing or mutating all comes from + https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml. + """ + if remove == "mutations": + reapply_views = True + elif remove == "mutations_and_views": + reapply_views = False + else: + raise RuntimeError( + f"functionalize(f, remove='mutations'): received invalid argument for remove={remove}." + " Valid options are:\n" + " remove='mutations': all inplace and out= operators will be removed from the program, and replaced" + " with their out-of-place equivalents.\n" + " remove='mutations_and_views': In addition to the above, all aliasing operators {view} will be" + " replaced with their non-aliasing counterparts, {view}_copy.\n" + ) + + @wraps(func) + def wrapped(*args, **kwargs): + try: + func_level = _func_increment_nesting(reapply_views) + func_args = _wrap_all_tensors_to_functional(args, func_level) + func_kwargs = _wrap_all_tensors_to_functional(kwargs, func_level) + + flattened_unwrapped_args = pytree.arg_tree_leaves(*args) + flattened_wrapped_args = pytree.arg_tree_leaves(*func_args) + flattened_unwrapped_kwargs = pytree.arg_tree_leaves(**kwargs) + flattened_wrapped_kwargs = pytree.arg_tree_leaves(**func_kwargs) + + func_outputs = func(*func_args, **func_kwargs) + outputs = _unwrap_all_tensors_from_functional( + func_outputs, reapply_views=reapply_views + ) + + for a in flattened_wrapped_args + flattened_wrapped_kwargs: + if isinstance(a, torch.Tensor): + # Call sync_() on the inputs, to ensure that any pending mutations have been applied. + torch._sync(a) + + # And if any mutations were applied to the inputs, we need to propagate them back to the user. + for unwrapped, wrapped in zip( + flattened_unwrapped_args, flattened_wrapped_args + ): + if isinstance(unwrapped, torch.Tensor) and isinstance( + wrapped, torch.Tensor + ): + _propagate_functional_input_mutation(unwrapped, wrapped) + for unwrapped, wrapped in zip( + flattened_unwrapped_kwargs, flattened_wrapped_kwargs + ): + if isinstance(unwrapped, torch.Tensor) and isinstance( + wrapped, torch.Tensor + ): + _propagate_functional_input_mutation(unwrapped, wrapped) + + return outputs + finally: + _func_decrement_nesting() + + return wrapped + + +@exposed_in("torch.func") +def linearize(func: Callable, *primals) -> tuple[Any, Callable]: + """ + Returns the value of ``func`` at ``primals`` and linear approximation + at ``primals``. + + Args: + func (Callable): A Python function that takes one or more arguments. + primals (Tensors): Positional arguments to ``func`` that must all be + Tensors. These are the values at which the function is linearly approximated. + + Returns: + Returns a ``(output, jvp_fn)`` tuple containing the output of ``func`` + applied to ``primals`` and a function that computes the jvp of + ``func`` evaluated at ``primals``. + + linearize is useful if jvp is to be computed multiple times at ``primals``. However, + to achieve this, linearize saves intermediate computation and has higher memory requirements + than directly applying `jvp`. So, if all the ``tangents`` are known, it maybe more efficient + to compute vmap(jvp) instead of using linearize. + + .. note:: + linearize evaluates ``func`` twice. Please file an issue for an implementation + with a single evaluation. + + Example:: + >>> import torch + >>> from torch.func import linearize + >>> def fn(x): + ... return x.sin() + ... + >>> output, jvp_fn = linearize(fn, torch.zeros(3, 3)) + >>> jvp_fn(torch.ones(3, 3)) + tensor([[1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]]) + >>> + + """ + # Note: We evaluate `fn` twice. + # Once for returning the output and other while + # tracing the graph. + # If this becomes a bottle-neck, we should update + # make_fx such that it also returns the output. + + output = func(*primals) + _, output_spec = tree_flatten(output) + + flat_primals, primals_argspec = tree_flatten(primals) + + # tangents for tracing + flat_tangents = tuple(p.new_empty(()).expand_as(p) for p in flat_primals) + + # function to trace + def trace_fn(flat_tangents): + with fwAD.dual_level(): + flat_duals = tuple( + fwAD.make_dual(p, t) for p, t in zip(flat_primals, flat_tangents) + ) + duals = tree_unflatten(flat_duals, primals_argspec) + output = func(*duals) + tangents = tree_map_only( + torch.Tensor, lambda dual: safe_unpack_dual(dual, False)[1], output + ) + + return tangents + + jvp_graph = lazy_dynamo_disallow(make_fx)(trace_fn)(flat_tangents) + const_folded_jvp_graph = lazy_dynamo_disallow(const_fold.split_const_subgraphs)( + jvp_graph + ) + + # Hold only the meta-data regarding the primals. + flat_primals_shape = tuple(p.shape for p in flat_primals) + flat_primals_device = tuple(p.device for p in flat_primals) + flat_primals_dtype = tuple(p.dtype for p in flat_primals) + + def forward_ad_checks(flat_tangents): + for idx, t in enumerate(flat_tangents): + if t.shape != flat_primals_shape[idx]: + msg = ( + f"tangent:{idx} with shape {t.shape} in flattened " + f"pytree doesn't match the shape {flat_primals_shape[idx]} " + "of the corresponding primal." + ) + raise RuntimeError(msg) + + if t.device != flat_primals_device[idx]: + msg = ( + f"tangent:{idx} with device {t.device} in flattened " + f"pytree doesn't match the device {flat_primals_device[idx]} " + "of the corresponding primal." + ) + raise RuntimeError(msg) + + if t.dtype != flat_primals_dtype[idx]: + msg = ( + f"tangent:{idx} with dtype {t.dtype} in flattened " + f"pytree doesn't match the dtype {flat_primals_dtype[idx]} " + "of the corresponding primal." + ) + raise RuntimeError(msg) + + # jvp_fn : callable to return + # It takes care of checking the argspec of tangents, + # calling the folded fx graph and unflattening fx graph output + def jvp_fn(*tangents): + flat_tangents, tangent_argspec = tree_flatten(tangents) + if tangent_argspec != primals_argspec: + raise RuntimeError( + f"Expected the tangents {tangent_argspec} to have " + f"the same argspec as the primals {primals_argspec}" + ) + + forward_ad_checks(flat_tangents) + + flat_output = const_folded_jvp_graph(*flat_tangents) + # const folded graph can return flat output, + # so transform output. + return tree_unflatten(flat_output, output_spec) + + return output, jvp_fn + + +@exposed_in("torch.func") +def debug_unwrap(tensor: torch.Tensor, *, recurse=True) -> torch.Tensor: + """Unwraps a functorch tensor (e.g. BatchedTensor, GradTrackingTensor) to its underlying tensor. + + This function should only be used in a debug setting (e.g. trying to print the + value of a Tensor in a debugger). Otherwise, using the result of function + inside of a function being transformed will lead to undefined behavior. + """ + if not is_functorch_wrapped_tensor(tensor): + return tensor + result = get_unwrapped(tensor) + if recurse: + return debug_unwrap(result) + return result diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/functional_call.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/functional_call.py new file mode 100644 index 0000000000000000000000000000000000000000..62ca24ab3fdb961c030f9ed721e5dd16ce36772c --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/functional_call.py @@ -0,0 +1,253 @@ +# mypy: allow-untyped-defs +from collections.abc import Sequence +from typing import Any, Optional, Union + +import torch +import torch.nn as nn +from torch import Tensor +from torch._functorch.utils import exposed_in + + +@exposed_in("torch.func") +def functional_call( + module: "torch.nn.Module", + parameter_and_buffer_dicts: Union[dict[str, Tensor], Sequence[dict[str, Tensor]]], + args: Optional[Union[Any, tuple]] = None, + kwargs: Optional[dict[str, Any]] = None, + *, + tie_weights: bool = True, + strict: bool = False, +): + r"""Performs a functional call on the module by replacing the module parameters + and buffers with the provided ones. + + .. note:: If the module has active parametrizations, passing a value in the + :attr:`parameter_and_buffer_dicts` argument with the name set to the regular parameter + name will completely disable the parametrization. + If you want to apply the parametrization function to the value passed + please set the key as ``{submodule_name}.parametrizations.{parameter_name}.original``. + + .. note:: If the module performs in-place operations on parameters/buffers, these will be reflected + in the ``parameter_and_buffer_dicts`` input. + + + Example:: + + >>> a = {'foo': torch.zeros(())} + >>> # xdoctest: +SKIP + >>> mod = Foo() # does self.foo = self.foo + 1 + >>> print(mod.foo) # tensor(0.) + >>> functional_call(mod, a, torch.ones(())) + >>> print(mod.foo) # tensor(0.) + >>> print(a['foo']) # tensor(1.) + + .. note:: If the module has tied weights, whether or not functional_call respects the tying is determined by the + tie_weights flag. + + Example:: + + >>> a = {'foo': torch.zeros(())} + >>> # xdoctest: +SKIP + >>> mod = Foo() # has both self.foo and self.foo_tied which are tied. Returns x + self.foo + self.foo_tied + >>> print(mod.foo) # tensor(1.) + >>> mod(torch.zeros(())) # tensor(2.) + >>> functional_call(mod, a, torch.zeros(())) # tensor(0.) since it will change self.foo_tied too + >>> functional_call(mod, a, torch.zeros(()), tie_weights=False) # tensor(1.)--self.foo_tied is not updated + >>> new_a = {'foo': torch.zeros(()), 'foo_tied': torch.zeros(())} + >>> functional_call(mod, new_a, torch.zeros()) # tensor(0.) + + An example of passing multiple dictionaries + + .. code-block:: python + + a = ({'weight': torch.ones(1, 1)}, {'buffer': torch.zeros(1)}) # two separate dictionaries + mod = nn.Bar(1, 1) # return self.weight @ x + self.buffer + print(mod.weight) # tensor(...) + print(mod.buffer) # tensor(...) + x = torch.randn((1, 1)) + print(x) + functional_call(mod, a, x) # same as x + print(mod.weight) # same as before functional_call + + + And here is an example of applying the grad transform over the parameters + of a model. + + .. code-block:: python + + import torch + import torch.nn as nn + from torch.func import functional_call, grad + + x = torch.randn(4, 3) + t = torch.randn(4, 3) + model = nn.Linear(3, 3) + + def compute_loss(params, x, t): + y = functional_call(model, params, x) + return nn.functional.mse_loss(y, t) + + grad_weights = grad(compute_loss)(dict(model.named_parameters()), x, t) + + .. note:: If the user does not need grad tracking outside of grad transforms, they can detach all of the + parameters for better performance and memory usage + + Example:: + + >>> detached_params = {k: v.detach() for k, v in model.named_parameters()} + >>> grad_weights = grad(compute_loss)(detached_params, x, t) + >>> grad_weights.grad_fn # None--it's not tracking gradients outside of grad + + This means that the user cannot call ``grad_weight.backward()``. However, if they don't need autograd tracking + outside of the transforms, this will result in less memory usage and faster speeds. + + Args: + module (torch.nn.Module): the module to call + parameters_and_buffer_dicts (Dict[str, Tensor] or tuple of Dict[str, Tensor]): the parameters that will be used in + the module call. If given a tuple of dictionaries, they must have distinct keys so that all dictionaries can + be used together + args (Any or tuple): arguments to be passed to the module call. If not a tuple, considered a single argument. + kwargs (dict): keyword arguments to be passed to the module call + tie_weights (bool, optional): If True, then parameters and buffers tied in the original model will be treated as + tied in the reparameterized version. Therefore, if True and different values are passed for the tied + parameters and buffers, it will error. If False, it will not respect the originally tied parameters and + buffers unless the values passed for both weights are the same. Default: True. + strict (bool, optional): If True, then the parameters and buffers passed in must match the parameters and + buffers in the original module. Therefore, if True and there are any missing or unexpected keys, it will + error. Default: False. + + Returns: + Any: the result of calling ``module``. + """ + if isinstance(parameter_and_buffer_dicts, dict): + parameters_and_buffers = parameter_and_buffer_dicts + elif isinstance(parameter_and_buffer_dicts, Sequence): + if not all(isinstance(d, dict) for d in parameter_and_buffer_dicts): + raise ValueError( + "Expected all elements of parameter_and_buffer_dicts to be dictionaries" + ) + all_keys = [k for d in parameter_and_buffer_dicts for k in d.keys()] + all_keys_counter: dict[str, int] = {} + for k in all_keys: + v = all_keys_counter.get(k, 0) + all_keys_counter[k] = v + 1 + repeated_keys = [key for key, n in all_keys_counter.items() if n > 1] + if len(repeated_keys) > 0: + raise ValueError( + f"{repeated_keys} appeared in multiple dictionaries; behavior of functional call is ambiguous" + ) + parameters_and_buffers = { + k: v for d in parameter_and_buffer_dicts for k, v in d.items() + } + else: + raise ValueError( + f"Expected parameter_and_buffer_dicts to be a dict, or a list/tuple of dicts, " + f"but got {type(parameter_and_buffer_dicts)}" + ) + + return nn.utils.stateless._functional_call( + module, + parameters_and_buffers, + args, + kwargs, + tie_weights=tie_weights, + strict=strict, + ) + + +@exposed_in("torch.func") +def stack_module_state( + models: Union[Sequence[nn.Module], nn.ModuleList], +) -> tuple[dict[str, Any], dict[str, Any]]: + """stack_module_state(models) -> params, buffers + + Prepares a list of torch.nn.Modules for ensembling with :func:`vmap`. + + Given a list of ``M`` ``nn.Modules`` of the same class, returns two dictionaries + that stack all of their parameters and buffers together, indexed by name. + The stacked parameters are optimizable (i.e. they are new leaf nodes in the + autograd history that are unrelated to the original parameters and can be + passed directly to an optimizer). + + Here's an example of how to ensemble over a very simple model: + + .. code-block:: python + + num_models = 5 + batch_size = 64 + in_features, out_features = 3, 3 + models = [torch.nn.Linear(in_features, out_features) for i in range(num_models)] + data = torch.randn(batch_size, 3) + + def wrapper(params, buffers, data): + return torch.func.functional_call(models[0], (params, buffers), data) + + params, buffers = stack_module_state(models) + output = vmap(wrapper, (0, 0, None))(params, buffers, data) + + assert output.shape == (num_models, batch_size, out_features) + + When there's submodules, this follows state dict naming conventions + + .. code-block:: python + + import torch.nn as nn + class Foo(nn.Module): + def __init__(self, in_features, out_features): + super().__init__() + hidden = 4 + self.l1 = nn.Linear(in_features, hidden) + self.l2 = nn.Linear(hidden, out_features) + + def forward(self, x): + return self.l2(self.l1(x)) + + num_models = 5 + in_features, out_features = 3, 3 + models = [Foo(in_features, out_features) for i in range(num_models)] + params, buffers = stack_module_state(models) + print(list(params.keys())) # "l1.weight", "l1.bias", "l2.weight", "l2.bias" + + .. warning:: + All of the modules being stacked together must be the same (except for + the values of their parameters/buffers). For example, they should be in the + same mode (training vs eval). + """ + if len(models) == 0: + raise RuntimeError("stack_module_state: Expected at least one model, got 0.") + if not (all(m.training for m in models) or all(not m.training for m in models)): + raise RuntimeError( + "stack_module_state: Expected all models to have the same training/eval mode." + ) + model0_typ = type(models[0]) + if not all(type(m) == model0_typ for m in models): + raise RuntimeError( + "stack_module_state: Expected all models to be of the same class." + ) + all_params = [dict(model.named_parameters()) for model in models] + params = { + k: construct_stacked_leaf(tuple(params[k] for params in all_params), k) + for k in all_params[0] + } + all_buffers = [dict(model.named_buffers()) for model in models] + buffers = { + k: construct_stacked_leaf(tuple(buffers[k] for buffers in all_buffers), k) + for k in all_buffers[0] + } + + return params, buffers + + +def construct_stacked_leaf( + tensors: Union[tuple[Tensor, ...], list[Tensor]], name: str +) -> Tensor: + all_requires_grad = all(t.requires_grad for t in tensors) + none_requires_grad = all(not t.requires_grad for t in tensors) + if not all_requires_grad and not none_requires_grad: + raise RuntimeError( + f"Expected {name} from each model to have the same .requires_grad" + ) + result = torch.stack(tensors) + if all_requires_grad: + result = result.detach().requires_grad_() + return result diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/fx_minifier.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/fx_minifier.py new file mode 100644 index 0000000000000000000000000000000000000000..3cf5fc24f1cbc863235c1c0650ab9971e1cc10c5 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/fx_minifier.py @@ -0,0 +1,501 @@ +# mypy: ignore-errors + +import copy +import math +import os +import sys +from dataclasses import dataclass +from functools import partial, wraps +from typing import Callable + +import torch +import torch.fx as fx +from torch.hub import tqdm +from torch.multiprocessing.reductions import StorageWeakRef +from torch.utils._content_store import ContentStoreWriter + +from .compile_utils import get_outputs, get_placeholders + + +is_tuple = object() + + +@dataclass +class LoadTensorMeta: + size: list[int] + stride: list[int] + dtype: torch.dtype + device: torch.device + + +class ConcreteProp(torch.fx.Interpreter): + def __init__(self, mod, *, writer=None, skip_offload=False): + super().__init__(mod) + self.writer = writer + self.skip_offload = skip_offload + self.seen_storages = set() + + def run_node(self, n): + self.pbar.update(1) + r = super().run_node(n) + name = n.name + + if isinstance(r, torch.Tensor): + if self.writer is None: + n.meta["concrete_value"] = r + else: + if StorageWeakRef(r.untyped_storage()) in self.seen_storages: + # Refuse to offload tensors which alias other live + # tensors, because this will violate operator contracts + n.meta["concrete_value"] = None + else: + if not self.skip_offload: + self.writer.write_tensor(os.path.join("eager", name), r) + n.meta["concrete_value"] = LoadTensorMeta( + r.size(), r.stride(), r.dtype, r.device + ) + self.seen_storages.add(StorageWeakRef(r.untyped_storage())) + else: + n.meta["concrete_value"] = is_tuple + + return r + + def propagate(self, *args): + with tqdm( + desc="Saving intermediates for delta debugging", + total=len(self.module.graph.nodes), + disable=self.writer is None, + ) as pbar: + self.pbar = pbar + r = super().run(*args) + if not self.skip_offload: + pbar.set_description( + "Saved! To skip next time, run with --skip-saving-eager-intermediates" + ) + return r + + +def is_load_tensor_node(node): + return ( + node.op == "call_function" + and node.target is torch.ops.debugprims.load_tensor.default + ) + + +# inplace modifies node/inps +def _convert_node_to_placeholder(graph, node, inps): + if node.op == "output" or node.op == "placeholder": + return False + + if is_load_tensor_node(node): + return False + + concrete_val = node.meta.get("concrete_value", None) + + if isinstance(concrete_val, torch.Tensor): + node.op = "placeholder" + node.target = node.name + node.args = () + node.kwargs = {} + + inps.append(concrete_val) + return True + + elif concrete_val is None: + return False + + elif concrete_val is is_tuple: + r = False + for tuple_user in list(node.users): + r = _convert_node_to_placeholder(graph, tuple_user, inps) or r + # NB: We must not erase the node at this point, because + # we are iterating over the nodes and this would change + # the iteration order + # graph.erase_node(node) + return r + + elif isinstance(concrete_val, LoadTensorMeta): + node.op = "call_function" + node.target = torch.ops.debugprims.load_tensor.default + node.args = ( + os.path.join("eager", node.name), + concrete_val.size, + concrete_val.stride, + ) + node.kwargs = { + "device": concrete_val.device, + "dtype": concrete_val.dtype, + } + return True + + return False + + +def create_minified_hlo_graph(minified_fx_graph, inputs): + """ + Takes minified FX graph as primary input, and ports it to HLO via StableHLO + Provides minified HLO graph as output, and archive them to local directory + """ + hlo_dir = f"{os.getcwd()}/hlo_files" + os.makedirs(hlo_dir, exists_ok=True) + + from torch_xla.stablehlo import save_torch_model_as_stablehlo + + save_torch_model_as_stablehlo(minified_fx_graph, inputs, hlo_dir) + + +def dump_state(fx_g, inps): + print( + f""" +# Working Repro with {len(fx_g.graph.nodes)} nodes +inps = {[(i.shape, i.dtype, i.device.type) for i in inps]} +inps = [torch.zeros(())] + [torch.ones(shape, dtype=dtype, device=device) for (shape, dtype, device) in inps] +{fx_g.code} +""" + ) + + +def is_power_of_two(n): + if n == 0: + return False + return (n & (n - 1)) == 0 + + +@dataclass +class ReproState: + graph: fx.Graph + inps: list[torch.Tensor] + + def __post_init__(self): + ph_nodes = get_placeholders(self.graph) + assert len(ph_nodes) == len(self.inps) + + +def minifier( + fail_f: fx.GraphModule, + inps, + module_fails, + dump_state: Callable = dump_state, + *, + save_dir=None, + offload_to_disk=False, + skip_offload=False, + skip_sanity=False, + max_granularity=None, +): + """ + Minimizes a FX graph with given inputs, such that the resulting FX graph still returns True for module_fails. + + Does 2 main strategies: + 1. Truncates suffix: Removes some suffix from the graph and sets a new output. + 2. Delta Debugging: Tries replacing half of the graph with inputs. If fails, + tries replacing quarter of the graph, etc. + + >>> # xdoctest: +SKIP(failing) + >>> failing_function = fx.symbolic_trace(f) + >>> minimize(failing_function, [torch.randn(5)], lambda fx_g, inps: fx_g(*inps)) + + note: module_fails returns True if it fails. + """ + assert isinstance(inps, (tuple, list)) + + failing_graph = fail_f.graph + cur_size = len(failing_graph.nodes) + + if max_granularity is not None and not is_power_of_two(max_granularity): + raise RuntimeError(f"max_granularity {max_granularity} not power of two") + + num_queries = 0 + + def deepcopy_fx_graph(fx_graph): + return fx.GraphModule(fail_f, copy.deepcopy(fx_graph)).graph + + def graph_fails(graph, inps): + nonlocal num_queries + graph = copy.deepcopy(graph) + num_queries += 1 + mod = fx.GraphModule(fail_f, graph) + mod.graph.lint() + return module_fails(mod, inps) + + writer = None + if offload_to_disk: + writer = ContentStoreWriter(save_dir) + + ConcreteProp(fail_f, writer=writer, skip_offload=skip_offload).propagate(*inps) + if not skip_sanity and not graph_fails(failing_graph, inps): + raise RuntimeError("Input graph did not fail the tester") + print(f"Started off with {cur_size} nodes", file=sys.stderr) + + def _register_strategy(strategy: Callable, name: str): + @wraps(strategy) + def new_func(old_state: ReproState, granularity=1): + print(file=sys.stderr) + print( + f"Strategy: {name} (G: {granularity}) " + f"({len(old_state.graph.nodes)} nodes, {len(old_state.inps)} inputs)", + file=sys.stderr, + ) + new_state = strategy( + deepcopy_fx_graph(old_state.graph), list(old_state.inps), granularity + ) + if new_state is not None: + new_nodes = len(new_state.graph.nodes) + old_nodes = len(old_state.graph.nodes) + new_inps = len(new_state.inps) + old_inps = len(old_state.inps) + new_outs = len(get_outputs(new_state.graph)) + old_outs = len(get_outputs(old_state.graph)) + progress_made = False + if new_nodes < old_nodes: + progress_made = True + print( + f"SUCCESS: Went from {old_nodes} to {new_nodes} nodes", + file=sys.stderr, + ) + if new_inps > old_inps: + progress_made = True + print( + f"SUCCESS: Went from {old_inps} to {new_inps} inputs", + file=sys.stderr, + ) + if new_outs < old_outs: + progress_made = True + print( + f"SUCCESS: Went from {old_outs} to {new_outs} outputs", + file=sys.stderr, + ) + + if not progress_made: + raise RuntimeError("Success raised but no progress made?") + + if not graph_fails(new_state.graph, new_state.inps): + print( + "WARNING: Something went wrong, not applying this minification", + file=sys.stderr, + ) + return None + return new_state + else: + print(f"FAIL: {name}", file=sys.stderr) + return None + + return new_func + + def register_strategy(name: str): + return partial(_register_strategy, name=name) + + @register_strategy("Truncate suffix") + def remove_suffix(cur_graph, cur_inps, granularity): + tested = set() + new_graph = fx.Graph() + env = {} + for idx, node in enumerate(cur_graph.nodes): + new_node = new_graph.node_copy(node, lambda x: env[x]) + if node.op not in ["placeholder", "output"]: + # If idx is divisible by (granularity * 2), it would have been checked already. + if ( + idx % granularity == 0 + and (idx % (granularity * 2) != 0) + and idx not in tested + ): + output_node = new_graph.output((new_node,)) + if len(new_graph.nodes) < len(cur_graph.nodes) and graph_fails( + new_graph, cur_inps + ): + return ReproState(new_graph, cur_inps) + else: + tested.add(idx) + new_graph.erase_node(output_node) + env[node] = new_node + return None + + @register_strategy("Remove outputs") + def remove_outputs(cur_graph, cur_inps, granularity): + granularity = max(1, granularity // 2) + for idx, node in enumerate(cur_graph.nodes): + node.idx = idx + if node.op == "output": + output = node + break + + if isinstance(output.args[0], fx.Node): + return None + + output_args = sorted( + output.args[0], key=lambda x: x.idx if isinstance(x, fx.Node) else int(1e9) + ) + if len(output_args) == 1: + return None + + for idx in range(0, len(output_args), granularity): + output.args = (output_args[:idx] + output_args[idx + granularity :],) + if graph_fails(cur_graph, cur_inps): + return ReproState(cur_graph, cur_inps) + return None + + def remove_unused_inputs_unchecked(cur_state: ReproState): + cur_graph = cur_state.graph + cur_inps = cur_state.inps + ph_nodes = get_placeholders(cur_graph) + assert len(ph_nodes) == len(cur_inps) + + new_inps = [] + for idx in range(len(ph_nodes)): + if len(ph_nodes[idx].users) == 0: + cur_graph.erase_node(ph_nodes[idx]) + else: + new_inps.append(cur_inps[idx]) + if len(new_inps) < len(cur_inps): + return ReproState(cur_graph, new_inps) + return None + + def remove_unused_inputs_checked(cur_state: ReproState): + new_state = remove_unused_inputs_unchecked(cur_state) + if new_state is not None and graph_fails(new_state.graph, new_state.inps): + return new_state + return None + + def _remove_unused_wrapper(cur_graph, cur_inps, granularity): + return remove_unused_inputs_checked(ReproState(cur_graph, cur_inps)) + + remove_unused_inputs = register_strategy("Remove unused inputs")( + _remove_unused_wrapper + ) + + @register_strategy("Eliminate dead code") + def eliminate_dead_code(cur_graph, cur_inps, granularity): + if cur_graph.eliminate_dead_code() and graph_fails(cur_graph, cur_inps): + return ReproState(cur_graph, cur_inps) + return None + + def _consolidate_placeholders(cur_graph, inps): + new_graph = fx.Graph() + env = {} + seen_non_placeholder = False + + # Move all placeholders to the front; also, if any load_tensor + # is at the front, convert it into an input (because it can be live + # all the time) + for node in cur_graph.nodes: + if node.op == "placeholder": + new_node = new_graph.node_copy(node, lambda x: env[x]) + env[node] = new_node + elif not seen_non_placeholder and is_load_tensor_node(node): + new_node = new_graph.placeholder(node.name) + env[node] = new_node + inps.append( + torch.ops.debugprims.load_tensor.default(*node.args, **node.kwargs) + ) + else: + seen_non_placeholder = True + + # Move everyone else + for node in cur_graph.nodes: + if node not in env: + new_node = new_graph.node_copy(node, lambda x: env[x]) + env[node] = new_node + return new_graph + + @register_strategy("Delta Debugging") + def delta_debugging(cur_graph: fx.Graph, cur_inps, granularity): + num_nodes = len(cur_graph.nodes) + for start_range in range(0, num_nodes, granularity): + is_removing = False + new_graph = deepcopy_fx_graph(cur_graph) + new_inps = cur_inps[:] + end_range = min(num_nodes, start_range + granularity) + for idx in range(start_range, end_range): + new_node = list(new_graph.nodes)[idx] + if _convert_node_to_placeholder(new_graph, new_node, new_inps): + is_removing = True + if not is_removing: + continue + new_graph.eliminate_dead_code() + new_graph = _consolidate_placeholders(new_graph, new_inps) + new_state = remove_unused_inputs_unchecked(ReproState(new_graph, new_inps)) + if new_state is None: + new_state = ReproState(new_graph, new_inps) + if graph_fails(new_state.graph, new_state.inps): + return ReproState(new_state.graph, new_state.inps) + + return None + + @register_strategy("Consolidate Inputs") + def consolidate_inputs(cur_graph, cur_inps, granularity): + old_len = len(cur_inps) + cur_graph = _consolidate_placeholders(cur_graph, cur_inps) + if len(cur_inps) > old_len and graph_fails(cur_graph, cur_inps): + return ReproState(cur_graph, cur_inps) + return None + + failing_state = ReproState(failing_graph, inps) + + def try_granularity(failing_state, granularity, use_non_granular): + print(f"Trying granularity {granularity}", file=sys.stderr) + + strategies = [] + num_nodes = len(failing_state.graph.nodes) + num_outputs = len(get_outputs(failing_state.graph)) + if num_outputs > num_nodes // 2: + strategies += [remove_outputs] + + if use_non_granular: + strategies += [ + eliminate_dead_code, + remove_unused_inputs, + consolidate_inputs, + ] + + strategies += [remove_suffix, delta_debugging] + + for strategy in strategies: + new_state = strategy(failing_state, granularity) + if new_state is not None: + return new_state + return None + + while True: + dump_state(fx.GraphModule(fail_f, failing_state.graph), failing_state.inps) + granularity = int(2 ** (math.floor(math.log2(len(failing_state.graph.nodes))))) + if max_granularity is not None: + granularity = min(max_granularity, granularity) + new_state = try_granularity(failing_state, granularity, use_non_granular=True) + if new_state is not None: + failing_state = new_state + continue + + granularity //= 2 + has_progress = False + while granularity >= 1: + new_state = try_granularity( + failing_state, granularity, use_non_granular=False + ) + if new_state is not None: + failing_state = new_state + has_progress = True + break + granularity //= 2 + if has_progress: + continue + + new_state = remove_outputs(failing_state, 1) + if new_state is not None: + failing_state = new_state + continue + + break + + if not graph_fails(failing_state.graph, failing_state.inps): + raise RuntimeError("Uh oh, something went wrong :( Final graph is not failing") + + print(f"Made {num_queries} queries", file=sys.stderr) + failing_fx = fx.GraphModule(fail_f, failing_state.graph) + + # If XLA debugging environment is enabled, create minified HLO graph as well + if "XLA_HLO_DEBUG" in os.environ: + create_minified_hlo_graph(failing_fx, failing_state.inps) + + dump_state(failing_fx, failing_state.inps) + print("Wrote minimal repro out to repro.py", file=sys.stderr) + return failing_fx, failing_state.inps diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/make_functional.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/make_functional.py new file mode 100644 index 0000000000000000000000000000000000000000..576100e2739d7871a47fd05de676713d11174850 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/make_functional.py @@ -0,0 +1,607 @@ +# mypy: allow-untyped-defs +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import copy +from collections.abc import Iterable, Sequence +from typing import Any, Callable, NoReturn, Union + +import torch +import torch.nn as nn +from torch import Tensor +from torch.nn.utils._named_member_accessor import NamedMemberAccessor + + +# Utilities to make nn.Module "functional" +# In particular the goal is to be able to provide a function that takes as input +# the parameters and evaluate the nn.Module using fixed inputs. + + +def raise_parameter_tying_error() -> NoReturn: + raise RuntimeError( + "make_functional(module): we don't yet support models that " + "do parameter tying (also sometimes known as weight sharing). " + "Please try to rewrite your model by replacing all instances of the " + "tied parameter with another and/or comment your support in " + "https://github.com/pytorch/functorch/issues/446" + ) + + +def create_names_map( + named_params: Union[dict[str, Tensor], Iterable[tuple[str, Tensor]]], + tied_named_params: Union[dict[str, Tensor], Iterable[tuple[str, Tensor]]], +) -> dict[str, list[str]]: + """ + named_params is a dictionary of tensors: {'A': A, 'B': B} + tied_named_params is another dictionary of tensors {'A': A, 'B': B, 'B_tied': B} + with potentially tied (or 'duplicated') tensors + + This function creates a mapping from the names in named_params to the + names in tied_named_params: {'A': ['A'], 'B': ['B', 'B_tied']}. + """ + named_params = dict(named_params) + tied_named_params = dict(tied_named_params) + + tensors_dict_keys = set(named_params.keys()) + tied_tensors_dict_keys = set(tied_named_params.keys()) + assert tensors_dict_keys.issubset(tied_tensors_dict_keys) + + tensor_to_mapping: dict[Tensor, tuple[str, list[str]]] = {} + for key, tensor in named_params.items(): + tensor_to_mapping[tensor] = (key, []) + for key, tensor in tied_named_params.items(): + assert tensor in tensor_to_mapping + tensor_to_mapping[tensor][1].append(key) + return dict(tensor_to_mapping.values()) + + +def _extract_members( + mod: nn.Module, + named_members: Callable[..., Iterable[tuple[str, Tensor]]], + subclass: Callable[[Tensor], Tensor], +) -> tuple[tuple[Tensor, ...], tuple[str, ...], dict[str, list[str]]]: + all_named_members = tuple(named_members(remove_duplicate=False)) + unique_named_members = tuple(named_members(remove_duplicate=True)) + names_map = create_names_map(unique_named_members, all_named_members) + + # Remove all the members in the model + memo = {} + accessor = NamedMemberAccessor(mod) + for name, p in all_named_members: + if p not in memo: + memo[p] = subclass(torch.empty_like(p, device="meta")) + replacement = memo[p] + accessor.set_tensor(name, replacement) + + if len(unique_named_members) == 0: + names, params = (), () + else: + names, params = zip(*unique_named_members) # type: ignore[assignment] + return params, names, names_map + + +def extract_weights( + mod: nn.Module, +) -> tuple[tuple[Tensor, ...], tuple[str, ...], dict[str, list[str]]]: + """ + This function removes all the Parameters from the model and + return them as a tuple as well as their original attribute names. + The weights must be re-loaded with `load_weights` before the model + can be used again. + Note that this function modifies the model in place and after this + call, mod.parameters() will be empty. + """ + return _extract_members(mod, mod.named_parameters, nn.Parameter) + + +def extract_buffers( + mod: nn.Module, +) -> tuple[tuple[Tensor, ...], tuple[str, ...], dict[str, list[str]]]: + return _extract_members(mod, mod.named_buffers, lambda x: x) + + +def load_weights( + mod: nn.Module, + names: Sequence[str], + params: Sequence[Tensor], + as_params: bool = False, +) -> None: + """ + Reload a set of weights so that `mod` can be used again to perform a forward pass. + Note that the `params` are regular Tensors (that can have history) and so are left + as Tensors. This means that mod.parameters() will still be empty after this call. + """ + accessor = NamedMemberAccessor(mod) + if as_params: + params = [nn.Parameter(p) for p in params] + accessor.set_tensors(names, params) + + +def _swap_state( + mod: nn.Module, names_map: dict[str, list[str]], elems: Iterable[Tensor] +) -> list[Tensor]: + result: list[Tensor] = [] + accessor = NamedMemberAccessor(mod) + for (_, attr_names), elem in zip(names_map.items(), elems): + for i, attr_name in enumerate(attr_names): + if i == 0: + result.append(accessor.swap_tensor(attr_name, elem)) + else: + accessor.set_tensor(attr_name, elem) + return result + + +def load_buffers( + mod: nn.Module, + names: Sequence[str], + buffers: Sequence[Tensor], + as_params: bool = False, +) -> None: + accessor = NamedMemberAccessor(mod) + accessor.set_tensors(names, buffers) + + +def load_state( + model: nn.Module, + weights: Sequence[Tensor], + weight_names: Sequence[str], + buffers: Sequence[Tensor] = (), + buffer_names: Sequence[str] = (), +) -> nn.Module: + """load_state(model, weights, weight_names, buffers=(), buffer_names=()) -> model + + load_state takes `weights` and `buffers` and assigns them to the model. + This is the inverse operation of `make_functional_deprecated_v1`. + """ + assert len(weight_names) == len(weights) + load_weights(model, weight_names, weights) + if len(buffers) > 0: + assert len(buffer_names) == len(buffers) + load_buffers(model, buffer_names, buffers) + return model + + +def make_functional_deprecated_v1(model: nn.Module): + """make_functional_deprecated_v1(model) -> weights, func, weight_names + + Given an nn.Module, make_functional_deprecated_v1 extracts the state (weights) + and returns a functional version of the model, `func`. This makes + it so that it is possible use transforms over the parameters of + `model`. + + `func` can be invoked as follows: + ``` + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + weights, func, _ = make_functional_deprecated_v1(model) + func(weights, (x,)) + ``` + + And here is an example of applying the grad transform: + ``` + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + weights, _, func = make_functional_deprecated_v1(model) + grad_weights = grad(func)(weights, (x,)) + ``` + + To put the state back into a model, use `load_state`. + """ + buffers = list(model.buffers()) + if len(buffers) > 0: + raise RuntimeError( + "make_functional_deprecated_v1(model): `model` has buffers. Please use " + "make_functional_with_buffers_deprecated_v1(model) instead." + ) + weights, descriptors, _ = extract_weights(model) + + def fun(weights, data): + mutable_model = copy.deepcopy(model) + load_weights(mutable_model, descriptors, weights) + return mutable_model(*data) + + return weights, fun, descriptors + + +def make_functional_with_buffers_deprecated_v1(model: nn.Module): + """make_functional_with_buffers_deprecated_v1(model) -> weights, buffers, func, weight_names, buffer_names + + Given an nn.Module, make_functional_with_buffers_deprecated_v1 extracts the state (weights and buffers) + and returns a functional version of the model, `func`. + + `func` can be invoked as follows: + ``` + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + weights, buffers, func, _, _ = make_functional_with_buffers_deprecated_v1(model) + func(weights, buffers, (x,)) + ``` + + And here is an example of applying the grad transform: + ``` + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + weights, buffers, func, _, _ = make_functional_with_buffers_deprecated_v1(model) + func(weights, buffers, (x,)) + grad_weights = grad(func)(weights, buffers, (x,)) + ``` + + To put the state back into a model, use `load_state`. + """ + weights, weight_descriptors, _ = extract_weights(model) + buffers, buf_descriptors, _ = extract_buffers(model) + + def fun(weights, buffers, data): + mutable_model = copy.deepcopy(model) + load_weights(mutable_model, weight_descriptors, weights) + load_buffers(mutable_model, buf_descriptors, buffers) + return mutable_model(*data) + + return weights, buffers, fun, weight_descriptors, buf_descriptors + + +class FunctionalModuleWithBuffers(nn.Module): + """ + This is the callable object returned by :func:`make_functional_with_buffers`. + """ + + def __init__( + self, + stateless_model: nn.Module, + param_names: tuple[str, ...], + buffer_names: tuple[str, ...], + param_names_map: dict[str, list[str]], + buffer_names_map: dict[str, list[str]], + ) -> None: + super().__init__() + self.stateless_model = stateless_model + self.param_names = param_names + self.buffer_names = buffer_names + + self.all_names_map = dict(param_names_map) + self.all_names_map.update(buffer_names_map) + + @staticmethod + def _create_from( + model: nn.Module, disable_autograd_tracking: bool = False + ) -> tuple["FunctionalModuleWithBuffers", tuple[Tensor, ...], tuple[Tensor, ...]]: + # TODO: We don't need to copy the model to create a stateless copy + model_copy = copy.deepcopy(model) + params, param_names, param_names_map = extract_weights(model_copy) + buffers, buffer_names, buffer_names_map = extract_buffers(model_copy) + if disable_autograd_tracking: + for param in params: + param.requires_grad_(False) + return ( + FunctionalModuleWithBuffers( + model_copy, param_names, buffer_names, param_names_map, buffer_names_map + ), + params, + buffers, + ) + + def forward( + self, params: Iterable[Tensor], buffers: Iterable[Tensor], *args, **kwargs + ) -> Any: + # Temporarily load the state back onto self.stateless_model + old_state = _swap_state( + self.stateless_model, + self.all_names_map, + tuple(params) + tuple(buffers), + ) + try: + return self.stateless_model(*args, **kwargs) + finally: + # Remove the loaded state on self.stateless_model + _swap_state(self.stateless_model, self.all_names_map, old_state) + + +class FunctionalModule(nn.Module): + """ + This is the callable object returned by :func:`make_functional`. + """ + + def __init__( + self, + stateless_model: nn.Module, + param_names: tuple[str, ...], + names_map: dict[str, list[str]], + ) -> None: + super().__init__() + self.stateless_model = stateless_model + self.param_names = param_names + self.names_map = names_map + + @staticmethod + def _create_from( + model: nn.Module, disable_autograd_tracking: bool = False + ) -> tuple["FunctionalModule", tuple[Tensor, ...]]: + # TODO: We don't need to copy the model to create a stateless copy + model_copy = copy.deepcopy(model) + params, param_names, names_map = extract_weights(model_copy) + if disable_autograd_tracking: + for param in params: + param.requires_grad_(False) + return FunctionalModule(model_copy, param_names, names_map), params + + def forward(self, params: Iterable[Tensor], *args, **kwargs) -> Any: + # Temporarily load the state back onto self.stateless_model + old_state = _swap_state(self.stateless_model, self.names_map, params) + try: + return self.stateless_model(*args, **kwargs) + finally: + # Remove the loaded state on self.stateless_model + _swap_state(self.stateless_model, self.names_map, old_state) + + +def make_functional( + model: nn.Module, disable_autograd_tracking: bool = False +) -> tuple[FunctionalModule, tuple[Tensor, ...]]: + """make_functional(model, disable_autograd_tracking=False) -> func, params + + Given a ``torch.nn.Module``, :func:`make_functional` extracts the state + (params) and returns a functional version of the model, ``func``. This + makes it so that it is possible use transforms over the parameters of + ``model``. + + ``func`` can be invoked as follows: + + .. code-block:: python + + import torch + import torch.nn as nn + from functorch import make_functional + + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + func, params = make_functional(model) + func(params, x) + + And here is an example of applying the grad transform over the parameters + of a model. + + .. code-block:: python + + import torch + import torch.nn as nn + from functorch import make_functional, grad + + x = torch.randn(4, 3) + t = torch.randn(4, 3) + model = nn.Linear(3, 3) + func, params = make_functional(model) + + def compute_loss(params, x, t): + y = func(params, x) + return nn.functional.mse_loss(y, t) + + grad_weights = grad(compute_loss)(params, x, t) + + If the model has any buffers, please use :func:`make_functional_with_buffers` instead. + + Args: + model (torch.nn.Module): Input model. + disable_autograd_tracking (bool): Flag to disable gradients tracking for output parameters. + The returned params are unrelated to the set of params from the original model. If False (default), + the params will have ``requires_grad=True`` on them (aka they will be trackable with regular + PyTorch autograd), matching the requires_grad-ness of the params from the original model. + Otherwise, the returned params will have ``requires_grad=False``. Default, False. + If you plan on using regular PyTorch autograd (e.g., if you want to call ``.backward()`` or + ``torch.autograd.grad()``, then set ``disable_autograd_tracking=False``. + Otherwise, if you're only planning on using functorch's gradient transforms, + then please set ``disable_autograd_tracking=True`` to avoid unnecessarily tracking + history with PyTorch autograd. + + """ + buffers = list(model.buffers()) + if len(buffers) > 0: + raise RuntimeError( + "make_functional(model): `model` has buffers. Please use " + "make_functional_with_buffers(model) instead." + ) + return FunctionalModule._create_from( + model, disable_autograd_tracking=disable_autograd_tracking + ) + + +def make_functional_with_buffers( + model: nn.Module, disable_autograd_tracking: bool = False +) -> tuple[FunctionalModuleWithBuffers, tuple[Tensor, ...], tuple[Tensor, ...]]: + """make_functional_with_buffers(model, disable_autograd_tracking=False) -> func, params, buffers + + Given a ``torch.nn.Module``, make_functional_with_buffers extracts the + state (params and buffers) and returns a functional version of the model + ``func`` that can be invoked like a function. + + ``func`` can be invoked as follows: + + .. code-block:: python + + import torch + import torch.nn as nn + from functorch import make_functional_with_buffers + + x = torch.randn(4, 3) + model = nn.Linear(3, 3) + func, params, buffers = make_functional_with_buffers(model) + func(params, buffers, x) + + And here is an example of applying the grad transform over the parameters + of a model: + + .. code-block:: python + + import torch + import torch.nn as nn + from functorch import make_functional_with_buffers, grad + + x = torch.randn(4, 3) + t = torch.randn(4, 3) + model = nn.Linear(3, 3) + func, params, buffers = make_functional_with_buffers(model) + + def compute_loss(params, buffers, x, t): + y = func(params, buffers, x) + return nn.functional.mse_loss(y, t) + + grad_weights = grad(compute_loss)(params, buffers, x, t) + + Args: + model (torch.nn.Module): Input model. + disable_autograd_tracking (bool): Flag to disable gradients tracking for output parameters. + The returned params are unrelated to the set of params from the original model. If False (default), + the params will have ``requires_grad=True`` on them (aka they will be trackable with regular + PyTorch autograd), matching the requires_grad-ness of the params from the original model. + Otherwise, the returned params will have ``requires_grad=False``. Default, False. + If you plan on using regular PyTorch autograd (e.g., if you want to call ``.backward()`` or + ``torch.autograd.grad()``, then set ``disable_autograd_tracking=False``. + Otherwise, if you're only planning on using functorch's gradient transforms, + then please set ``disable_autograd_tracking=True`` to avoid unnecessarily tracking + history with PyTorch autograd. + + """ + return FunctionalModuleWithBuffers._create_from( + model, disable_autograd_tracking=disable_autograd_tracking + ) + + +def transpose_stack( + tuple_of_tuple_of_tensors: tuple[tuple[Tensor, ...], ...] +) -> tuple[Tensor, ...]: + tuple_of_tuple_of_tensors = tuple(zip(*tuple_of_tuple_of_tensors)) + results = tuple( + torch.stack(shards).detach() for shards in tuple_of_tuple_of_tensors + ) + return results + + +def combine_state_for_ensemble( + models: Sequence[nn.Module], +) -> tuple[FunctionalModuleWithBuffers, tuple[Tensor, ...], tuple[Tensor, ...]]: + """combine_state_for_ensemble(models) -> func, params, buffers + + Prepares a list of torch.nn.Modules for ensembling with :func:`vmap`. + + Given a list of ``M`` ``nn.Modules`` of the same class, stacks all of their + parameters and buffers together to make ``params`` and ``buffers``. + Each parameter and buffer in the result will have an additional dimension + of size ``M``. + + :func:`combine_state_for_ensemble` also returns ``func``, a functional + version of one of the models in :attr:`models`. One cannot directly run + ``func(params, buffers, *args, **kwargs)`` directly, you probably want to + use ``vmap(func, ...)(params, buffers, *args, **kwargs)`` + + Here's an example of how to ensemble over a very simple model: + + .. code-block:: python + + num_models = 5 + batch_size = 64 + in_features, out_features = 3, 3 + models = [torch.nn.Linear(in_features, out_features) for i in range(num_models)] + data = torch.randn(batch_size, 3) + + fmodel, params, buffers = combine_state_for_ensemble(models) + output = vmap(fmodel, (0, 0, None))(params, buffers, data) + + assert output.shape == (num_models, batch_size, out_features) + + .. warning:: + All of the modules being stacked together must be the same (except for + the values of their parameters/buffers). For example, they should be in the + same mode (training vs eval). + + This API is subject to change -- we're investigating better ways to + create ensembles and would love your feedback how to improve this. + """ + if len(models) == 0: + raise RuntimeError( + "combine_state_for_ensemble: Expected at least one model, got 0." + ) + if not (all(m.training for m in models) or all(not m.training for m in models)): + raise RuntimeError( + "combine_state_for_ensemble: Expected all models to " + "have the same training/eval mode." + ) + model0_typ = type(models[0]) + if not all(type(m) == model0_typ for m in models): + raise RuntimeError( + "combine_state_for_ensemble: Expected all models to be of the same class." + ) + funcs, params, buffers = zip( + *[make_functional_with_buffers(model) for model in models] + ) + params = transpose_stack(params) + buffers = transpose_stack(buffers) + return funcs[0], params, buffers + + +def functional_init( + model_class: type[nn.Module], + ensemble_shape: Union[tuple[()], tuple[int]] = (), + device: torch.types.Device = "cpu", +): + def wrapped(*args, **kwargs): + if len(ensemble_shape) >= 2: + raise ValueError("NYI: ensemble_shape with more than 1 element") + if len(ensemble_shape) == 0: + model = model_class(*args, **kwargs).to(device) + return make_functional_deprecated_v1(model) + num_models = ensemble_shape[0] # type: ignore[misc] + if num_models <= 0: + raise ValueError(f"num_models {num_models} should be > 0") + # NB: Not very efficient, more of a POC + models = tuple( + model_class(*args, **kwargs).to(device) for _ in range(num_models) + ) + _, fn, names = make_functional_deprecated_v1(model_class(*args, **kwargs)) + weights = tuple(make_functional_deprecated_v1(model)[0] for model in models) + weights = tuple(zip(*weights)) + weights = tuple(torch.stack(shards).detach() for shards in weights) + return weights, fn, names + + return wrapped + + +def functional_init_with_buffers( + model_class: type[nn.Module], + ensemble_shape: Union[tuple[()], tuple[int]] = (), + device: torch.types.Device = "cpu", +): + def wrapped(*args, **kwargs): + if len(ensemble_shape) >= 2: + raise ValueError("NYI: ensemble_shape with more than 1 element") + if len(ensemble_shape) == 0: + model = model_class(*args, **kwargs).to(device) + return make_functional_deprecated_v1(model) + num_models = ensemble_shape[0] # type: ignore[misc] + if num_models <= 0: + raise ValueError(f"num_models {num_models} should be > 0") + # NB: Not very efficient, more of a POC + models = tuple( + model_class(*args, **kwargs).to(device) for _ in range(num_models) + ) + ( + _, + _, + fn, + weight_names, + buffer_names, + ) = make_functional_with_buffers_deprecated_v1(model_class(*args, **kwargs)) + weights, buffers = zip( + *tuple( + make_functional_with_buffers_deprecated_v1(model)[:2] + for model in models + ) + ) + weights = tuple(zip(*weights)) + weights = tuple(torch.stack(shards).detach() for shards in weights) + buffers = tuple(zip(*buffers)) + buffers = tuple(torch.stack(shards).detach() for shards in buffers) + return weights, buffers, fn, weight_names, buffer_names + + return wrapped diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/partitioners.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/partitioners.py new file mode 100644 index 0000000000000000000000000000000000000000..14b2961b5f6b61e61a09375e0bf7e0c546649573 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/partitioners.py @@ -0,0 +1,2087 @@ +# mypy: allow-untyped-defs +import copy +import functools +import heapq +import itertools +import logging +import math +import operator +import os +import os.path +from collections import defaultdict +from dataclasses import dataclass, replace +from typing import Callable, Optional, TYPE_CHECKING, Union + +import torch +import torch._inductor.inductor_prims +import torch.distributed +import torch.fx as fx +import torch.utils._pytree as pytree +from torch._functorch._activation_checkpointing.ac_logging_utils import ( + create_structured_trace_for_min_cut_info, +) +from torch.fx.experimental._backward_state import BackwardState +from torch.fx.experimental.proxy_tensor import is_sym_node, py_sym_types +from torch.fx.experimental.sym_node import magic_methods, method_to_operator +from torch.fx.experimental.symbolic_shapes import ( + find_symbol_binding_fx_nodes, + free_symbols, + hint_int, + is_symbol_binding_fx_node, +) +from torch.fx.passes import graph_drawer +from torch.utils._ordered_set import OrderedSet +from torch.utils.checkpoint import CheckpointPolicy + +from . import config +from ._activation_checkpointing.graph_info_provider import GraphInfoProvider +from ._activation_checkpointing.knapsack import ( + dp_knapsack, + greedy_knapsack, + ilp_knapsack, +) +from ._activation_checkpointing.knapsack_evaluator import KnapsackEvaluator +from ._aot_autograd.logging_utils import get_aot_graph_name +from ._aot_autograd.utils import get_cuda_generator_meta_val, is_with_effects +from .compile_utils import fx_graph_cse, get_aten_target + + +if TYPE_CHECKING: + import sympy + + +AOT_PARTITIONER_DEBUG: bool = config.debug_partitioner +log: logging.Logger = logging.getLogger(__name__) + +aten = torch.ops.aten +prims = torch.ops.prims + + +@dataclass +class OpTypes: + """Class for keeping track of different operator categories""" + + fusible_ops: OrderedSet[Callable] + compute_intensive_ops: OrderedSet[Callable] + random_ops: OrderedSet[Callable] + view_ops: OrderedSet[Callable] + recomputable_ops: OrderedSet[Callable] + + def is_fusible(self, node: fx.Node): + return get_aten_target(node) in self.fusible_ops + + def is_compute_intensive(self, node: fx.Node): + return get_aten_target(node) in self.compute_intensive_ops + + def is_random(self, node: fx.Node): + return get_aten_target(node) in self.random_ops + + def is_view(self, node: fx.Node): + return get_aten_target(node) in self.view_ops + + def is_recomputable(self, node: fx.Node): + return get_aten_target(node) in self.recomputable_ops + + +@dataclass +class NodeInfo: + # Be careful about iterating over these explicitly, as their order may not + # be deterministic + inputs: list[fx.Node] + _required_fw_nodes: OrderedSet[fx.Node] + required_bw_nodes: OrderedSet[fx.Node] + unclaimed_nodes: OrderedSet[fx.Node] + fw_order: dict[fx.Node, int] + + @functools.cached_property + def required_fw_nodes(self) -> list[fx.Node]: + return sorted( + (n for n in self._required_fw_nodes), key=lambda n: self.fw_order[n] + ) + + def is_required_fw(self, n: fx.Node) -> bool: + return n in self._required_fw_nodes + + def is_required_bw(self, n: fx.Node) -> bool: + return n in self.required_bw_nodes + + def is_unclaimed(self, n: fx.Node) -> bool: + return n in self.unclaimed_nodes + + def get_fw_order(self, n: fx.Node) -> int: + assert n in self._required_fw_nodes, f"Node {n} not in fw nodes!" + return self.fw_order[n] + + +@dataclass +class MinCutOptions: + ban_if_used_far_apart: bool + ban_if_long_fusible_chains: bool + ban_if_materialized_backward: bool + ban_if_not_in_allowlist: bool + ban_if_reduction: bool + + +def must_recompute(node: fx.Node) -> bool: + return node.meta.get("recompute", None) in [ + CheckpointPolicy.MUST_RECOMPUTE, + CheckpointPolicy.PREFER_RECOMPUTE, + ] + + +def has_recomputable_ops(fx_g: fx.GraphModule) -> bool: + for node in fx_g.graph.nodes: + if must_recompute(node): + return True + return False + + +def has_recomputable_rng_ops(fx_g: fx.GraphModule) -> bool: + for node in fx_g.graph.nodes: + if ( + must_recompute(node) + and hasattr(node.target, "tags") + and torch.Tag.nondeterministic_seeded in node.target.tags + ): + return True + return False + + +def sym_node_size(node: fx.Node) -> int: + if isinstance(node.meta["val"], (torch.SymInt, torch.SymBool)): + return 1 + assert isinstance(node.meta["val"], torch.SymFloat) + return 4 + + +class InvalidNodeBase: + def __repr__(self): + return "Invalid Node" + + +InvalidNode = InvalidNodeBase() + + +def _extract_graph_with_inputs_outputs( + joint_graph: fx.Graph, + inputs: list[fx.Node], + outputs: list[fx.Node], + subgraph: Optional[str] = None, +) -> fx.Graph: + """ + Given a graph, extracts out a subgraph that takes the specified nodes as + inputs and returns the specified outputs. + + This includes specifying non-placeholder nodes as inputs. + + The general strategy is to initialize all inputs with proxies as we + encounter them, and trace through the graph, only keeping values which take + in valid proxies. Then, all dead code is eliminated. + """ + new_graph = fx.Graph() + env = {} + + # Add new placeholder nodes in the order specified by the inputs + for node in inputs: + new_node = new_graph.placeholder(node.name) + # Can't use node_copy here as we may be turning previous call_function into placeholders + new_node.meta = node.meta + env[node] = new_node + + for node in joint_graph.nodes: + if _must_be_in_backward(node) and subgraph != "backward": + env[node] = InvalidNode # type: ignore[assignment] + continue + + if node in env: + # Node must be one of our inputs. (Any member of env which wasn't an + # input to start must have been created by this loop and won't be in + # joint_graph.nodes). + continue + elif node.op == "placeholder": + env[node] = InvalidNode # type: ignore[assignment] + elif node.op == "call_function": + all_args = pytree.arg_tree_leaves(*node.args, **node.kwargs) + all_args = [ + isinstance(env[x], InvalidNodeBase) + for x in all_args + if isinstance(x, fx.Node) + ] + if any(all_args): + env[node] = InvalidNode # type: ignore[assignment] + continue + env[node] = new_graph.node_copy(node, lambda x: env[x]) + elif node.op == "get_attr": + env[node] = new_graph.node_copy(node, lambda x: env[x]) + elif node.op == "output": + pass + output_values = [] + for x in outputs: + if isinstance(x, fx.Node): + if x not in env: + raise RuntimeError(f"Node {x} couldn't be found in env") + assert not isinstance( + env[x], InvalidNodeBase + ), f"Node {x} was invalid, but is output" + output_values.append(env[x]) + else: + output_values.append(x) + new_graph.output(tuple(output_values)) + + new_graph.eliminate_dead_code() + new_graph.lint() + return new_graph + + +def _is_primal(node: fx.Node) -> bool: + return ( + node.op == "placeholder" + and "tangents" not in str(node.target) + and not _is_bwd_seed_offset(node) + and not _is_fwd_seed_offset(node) + ) + + +def _is_tangent(node: fx.Node) -> bool: + return node.op == "placeholder" and "tangents" in str(node.target) + + +def _is_bwd_seed_offset(node: fx.Node) -> bool: + return node.op == "placeholder" and ( + "bwd_seed" in str(node.target) or "bwd_base_offset" in str(node.target) + ) + + +def _is_fwd_seed_offset(node: fx.Node) -> bool: + return node.op == "placeholder" and ( + "fwd_seed" in str(node.target) or "fwd_base_offset" in str(node.target) + ) + + +def _is_backward_state(node: fx.Node) -> bool: + return node.op == "placeholder" and isinstance(node.meta.get("val"), BackwardState) + + +def _has_tag_is_backward(node: fx.Node) -> bool: + return node.meta.get("partitioner_tag", None) == "is_backward" + + +def _has_tag_must_be_in_backward(node: fx.Node) -> bool: + return node.meta.get("partitioner_tag", None) == "must_be_in_backward" + + +def _must_be_in_backward(node: fx.Node) -> bool: + return _has_tag_must_be_in_backward(node) or ( + _has_tag_is_backward(node) and is_with_effects(node) + ) + + +def _extract_fwd_bwd_outputs( + joint_module: fx.GraphModule, *, num_fwd_outputs +) -> tuple[list[fx.Node], list[fx.Node]]: + outputs = pytree.arg_tree_leaves( + *(node.args for node in joint_module.graph.find_nodes(op="output")) + ) + fwd_outputs = outputs[:num_fwd_outputs] + bwd_outputs = outputs[num_fwd_outputs:] + return fwd_outputs, bwd_outputs + + +def _remove_by_name(saved_values: list[fx.Node], name: str): + for saved_value in saved_values: + if saved_value.name == name: + saved_values.remove(saved_value) + break + + +def _extract_fwd_bwd_modules( + joint_module: fx.GraphModule, + saved_values: list[fx.Node], + saved_sym_nodes: list[fx.Node], + *, + num_fwd_outputs: int, +) -> tuple[fx.GraphModule, fx.GraphModule]: + fwd_outputs, bwd_outputs = _extract_fwd_bwd_outputs( + joint_module, num_fwd_outputs=num_fwd_outputs + ) + placeholders = joint_module.graph.find_nodes(op="placeholder") + primal_inputs = [*filter(_is_primal, placeholders)] + tangent_inputs = [*filter(_is_tangent, placeholders)] + fwd_seed_offset_inputs = [*filter(_is_fwd_seed_offset, placeholders)] + bwd_seed_offset_inputs = [*filter(_is_bwd_seed_offset, placeholders)] + backward_state_inputs = [*filter(_is_backward_state, placeholders)] + + bwd_graph = _extract_graph_with_inputs_outputs( + joint_module.graph, + saved_sym_nodes + saved_values + tangent_inputs + bwd_seed_offset_inputs, + bwd_outputs, + "backward", + ) + + for node in bwd_graph.find_nodes(op="placeholder"): + # This is to filter out saved values that don't actually end up being used by the backwards pass + if not node.users: + _remove_by_name(saved_values, node.name) + _remove_by_name(saved_sym_nodes, node.name) + elif _is_backward_state(node): + # BackwardState is saved directly + _remove_by_name(saved_values, node.name) + assert backward_state_inputs + + # Now that we have the finalized list of saved values, we need to ensure + # we propagate all symbols which are referenced by backwards inputs. + # These are not directly used in the graph but are required for downstream + # sizevar assignment + saved_symbols: OrderedSet[sympy.Symbol] = OrderedSet() + saved_sym_nodes_binding = [] + saved_sym_nodes_derived = [] + + # Some symbols may already be bound in the directly saved_sym_nodes, + # keep track of them so we don't re-bind them + for node in saved_sym_nodes: + symbol = is_symbol_binding_fx_node(node) + if symbol: + saved_symbols.add(symbol) + saved_sym_nodes_binding.append(node) + else: + saved_sym_nodes_derived.append(node) + + # Now go through all of the prospective backward inputs and track any + # other symbols we need to bind + symbol_bindings = find_symbol_binding_fx_nodes(joint_module.graph) + for node in itertools.chain(saved_sym_nodes_derived, saved_values, tangent_inputs): + if "val" not in node.meta: + continue + new_symbols = free_symbols(node.meta["val"]) - saved_symbols + # NB: Deterministic order please! + for s in sorted(new_symbols, key=lambda s: s.name): + # NB: For well formed graphs, the symbol should always be present, + # but we also have ways to produce ill-formed graphs, e.g., direct + # make_fx usages, so don't choke in this case + if s not in symbol_bindings: + continue + saved_sym_nodes_binding.append(symbol_bindings[s]) + saved_symbols |= new_symbols + + # Update saved_sym_nodes that are now reordered to have all bindings at + # front. This can also be used later on to figure out the position of saved + # sym nodes in the output of fwd graph. + saved_sym_nodes.clear() + saved_sym_nodes.extend(saved_sym_nodes_binding + saved_sym_nodes_derived) + + # Now, we re-generate the fwd/bwd graphs. + # NB: This might increase compilation time, but I doubt it matters + fwd_graph = _extract_graph_with_inputs_outputs( + joint_module.graph, + primal_inputs + fwd_seed_offset_inputs, + fwd_outputs + saved_values + saved_sym_nodes, + "forward", + ) + bwd_graph = _extract_graph_with_inputs_outputs( + joint_module.graph, + saved_sym_nodes + + saved_values + + tangent_inputs + + bwd_seed_offset_inputs + + backward_state_inputs, + bwd_outputs, + "backward", + ) + + fwd_module = fx._lazy_graph_module._make_graph_module(joint_module, fwd_graph) + bwd_module = fx._lazy_graph_module._make_graph_module(joint_module, bwd_graph) + return fwd_module, bwd_module + + +def default_partition( + joint_module: fx.GraphModule, _joint_inputs, *, num_fwd_outputs +) -> tuple[fx.GraphModule, fx.GraphModule]: + """ + Partitions the :attr:`joint_module` in a manner that closely resembles the + behavior observed in the original ``.forward()`` and ``.backward()`` of the + callable, i.e., the resulting forward graph contains those operators that + are executed in the original ``.forward()`` callable passed to + :func:`aot_function`. + + The default partitioner collects the operators that are between the forward + inputs and the forward outputs. This helps in finding the tensors which have + to be stashed for the backward pass. These stashed tensors become the output + of the generated forward graph. The remaining operators are then placed in + the backward graph. + + .. warning:: + This API is experimental and likely to change. + + Args: + joint_module(fx.GraphModule): The joint forward and backward graph. This + is the result of AOT Autograd tracing. + + Returns: + Returns the generated forward and backward Fx graph modules. + """ + if has_recomputable_ops(joint_module): + return min_cut_rematerialization_partition( + joint_module, _joint_inputs, num_fwd_outputs=num_fwd_outputs + ) + primal_inputs = list(filter(_is_primal, joint_module.graph.nodes)) + fwd_seed_offset_inputs = list(filter(_is_fwd_seed_offset, joint_module.graph.nodes)) + inputs = primal_inputs + fwd_seed_offset_inputs + fwd_outputs, bwd_outputs = _extract_fwd_bwd_outputs( + joint_module, num_fwd_outputs=num_fwd_outputs + ) + forward_only_graph = _extract_graph_with_inputs_outputs( + joint_module.graph, inputs, fwd_outputs, "forward" + ) + forward_node_names = OrderedSet( + node.name for node in forward_only_graph.nodes if node.op != "output" + ) + saved_values = [] + saved_sym_nodes = [] + + for node in joint_module.graph.nodes: + if node.name not in forward_node_names: + continue + if is_sym_node(node): + # Symints must be kept separate from tensors so that PythonFunction only calls + # save_for_backward on tensors and stashes symints in autograd .ctx + saved_sym_nodes.append(node) + elif "tensor_meta" not in node.meta and node.op == "call_function": + # Since we can't save tuple of tensor values, we need to flatten out what we're saving + users = node.users + assert all(user.target == operator.getitem for user in users) + saved_values.extend(users) + else: + backward_usages = [ + n for n in node.users if n.name not in forward_node_names + ] + if "tensor_meta" in node.meta and all( + is_sym_node(n) for n in backward_usages + ): + # If we have a tensor in the forward, where only its sizes/strides are needed in the backward, + # and not the actual tensor data, + # then it will be a lot cheaper to save only the sizes/strides, and not the actual tensor. + # + # Note that saving the tensor could also cause compilation problems: + # If the user mutated an input in the forward and uses its sizes/strides in the backward, + # then we would be obligated to clone the input before saving it to appease autograd. + # (This is how we originally found this bug). + saved_sym_nodes.extend(backward_usages) + else: + saved_values.append(node) + saved_values = list(dict.fromkeys(saved_values).keys()) + saved_sym_nodes = list(dict.fromkeys(saved_sym_nodes).keys()) + + return _extract_fwd_bwd_modules( + joint_module, + saved_values, + saved_sym_nodes=saved_sym_nodes, + num_fwd_outputs=num_fwd_outputs, + ) + + +INT_INF = int(1e6) + + +def _tensor_nbytes(numel: int, dtype) -> int: + return numel * dtype.itemsize + + +def _size_of(node: fx.Node) -> int: + def object_nbytes(x) -> int: + if not isinstance(x, torch.Tensor): + return 0 + return _tensor_nbytes(hint_int(x.numel(), fallback=4096), x.dtype) + + if "val" in node.meta: + val = node.meta["val"] + if isinstance(val, py_sym_types): + return 1 + # NB: The fallback values here are meaningless, maybe we should respect + # torch._inductor.config.unbacked_symint_fallback (but this is a + # layering violation) + elif isinstance(val, (list, tuple)): + return sum(object_nbytes(n) for n in val) + elif isinstance(val, dict): + return sum(object_nbytes(n) for _, n in val.items()) + elif isinstance(val, torch.Tensor): + return object_nbytes(val) + + raise RuntimeError(f"Unknown metadata type {type(val)} on node {node}") + if node.op == "get_attr" or node.target is torch.ops.aten._assert_scalar.default: + return 0 + raise RuntimeError( + f"Node {node} didn't have `val` metadata; we should always have `val` metadata on the nodes." + ) + + +# Used for some investigative purposes +def _count_ops(graph: fx.Graph): + from collections import defaultdict + + cnt: dict[str, int] = defaultdict(int) + for node in graph.nodes: + if node.op == "call_function": + cnt[node.target.__name__] += 1 + log.info("%s", sorted(cnt.items(), key=lambda x: x[1], reverse=True)) + + +@functools.lru_cache(None) +def pointwise_ops(): + ops = [] + for attr_name in dir(torch.ops.aten): + opoverloadpacket = getattr(torch.ops.aten, attr_name) + if not isinstance(opoverloadpacket, torch._ops.OpOverloadPacket): + continue + + for overload in opoverloadpacket.overloads(): + op_overload = getattr(opoverloadpacket, overload) + if torch.Tag.pointwise in op_overload.tags: + # currently aot autograd uses packet not overload + ops.append(opoverloadpacket) + break + + return ops + + +def sort_depths(args, depth_map: dict[fx.Node, int]) -> list[tuple[fx.Node, int]]: + arg_depths = { + arg: depth_map[arg] for arg in args if isinstance(arg, torch.fx.node.Node) + } + return sorted(arg_depths.items(), key=lambda x: x[1], reverse=True) + + +def reordering_to_mimic_autograd_engine(gm: fx.GraphModule) -> fx.GraphModule: + """ + This pass finds the first bwd node in the graph (by looking at users of + tangents) and then reorders the graph by walking from this node to all the + way to the end of the graph. At each op in this traveral, we insert this op + in a new graph and try to bring only the relevant subgraph from the other + non-bwd edges relevant for this op. This closely mimics the behavior of + autograd engine. + + Why is this pass required in the first place? + + This is an artifact of how partitioners work today. The starting point of + partitioner is a joint graph, which is fwd and then bwd graph. In the case + of checkpointing, we keep portions of fwd graph in their original place in + the joint graph, while obtaining a bwd graph. As a result, the resulting bwd + graph has copies of recomputed fwd subgraphs followed by the original bwd + graph. If we run this naively, this leads to bad memory footprint, because + the fwd subgraphs are live for way longer duration than necessary. This pass + reorders the operations such that we prioritize the ops for the original bwd + graph while only realizing those ops from the fwd graph that are necessary + at any given point in the graph. + """ + + new_graph = fx.Graph() + env: dict[fx.Node, fx.Node] = {} + + # Add new placeholder nodes in the order specified by the inputs + for node in gm.graph.find_nodes(op="placeholder"): + env[node] = new_graph.node_copy(node, lambda x: env[x]) + + order = {} + for idx, node in enumerate(gm.graph.nodes): + order[node] = idx + + def insert_node_in_graph(node): + cur_nodes = [node] + insertable_nodes: OrderedSet[fx.Node] = OrderedSet() + while len(cur_nodes) > 0: + node = cur_nodes.pop() + if node in insertable_nodes or node in env: + continue + insertable_nodes.add(node) + + # Bias traversal towards the nodes that have higher depth - prioritizes + # critical path first. + cur_nodes += node.all_input_nodes + + insertable_nodes = sorted(insertable_nodes, key=lambda n: order[n]) + for node in insertable_nodes: + env[node] = new_graph.node_copy(node, lambda x: env[x]) + + # Find first bwd node in the graph + tangent_inputs = list(filter(_is_tangent, gm.graph.nodes)) + first_node_in_bwd = None + minimum_order = math.inf + for tangent in tangent_inputs: + for user in tangent.users: + if order[user] < minimum_order: + minimum_order = order[user] + first_node_in_bwd = user + + # If gradInp does not depend upon gradOut, we may not find any nodes in the "backwards pass" + if first_node_in_bwd is None: + return gm + + # Build the graph op-by-op by starting from the node all the way to the end + for node in list(gm.graph.nodes)[order[first_node_in_bwd] :]: + insert_node_in_graph(node) + + # The output node is already built by the traversal. + new_gm = torch.fx.GraphModule(gm, new_graph) + return new_gm + + +def apply_graphsafe_rng_functionalization( + fw_module: torch.fx.GraphModule, + bw_module: torch.fx.GraphModule, + fw_node: torch.fx.Node, + bw_node: torch.fx.Node, + device: torch.device, + rng_count: int, + last_fwd_input: torch.fx.Node, + last_bwd_input: torch.fx.Node, +): + """ + Note [CUDA Graph Safe RNG Functionalization] + + CUDA Graph capture doesn't work with get_rng_state and set_rng_state because these functions operate on CPU values, + while CUDA Graph RNG capture uses on-device CUDA tensors. To solve this, we use graphsafe_set_state with a + CUDA Generator registered to the CUDA Graph before capture begins. graphsafe_set_state updates the generator's pointer + to reference a different GeneratorImpl, ensuring subsequent calls are correctly forwarded to the desired generator + (and its cuda-tensor RNG state during graph capture). + + For each RNG operation's forward/backward pair: + + - We create two generators initialized with identical values + - Each forward and backward call advances its respective generator equally + - This keeps generators synchronized so forward and backward operations use matching RNG values + + When forward is called multiple times before backward (causing desynchronization): + + - We save the forward RNG state + - We update the backward Generator's state before executing backward + + Before each CUDA Graph replay, replay_prologue updates captured RNG pointers with current states, ensuring backward Generator + changes are reflected during replay. + + This function modifies both forward and backward computation graphs by: + + Creating RNG state placeholders for both passes + Updating the forward node to use graph-safe RNG state + Updating the backward node to use graph-safe RNG state + + For more details: https://github.com/pytorch/pytorch/issues/113541 + """ + device_idx = device.index + assert device_idx is not None + fw_graph = fw_module.graph + bw_graph = bw_module.graph + graphsafe_run_with_rng_state = torch._prims.rng_prims.graphsafe_run_with_rng_state + + # Handle forward pass + + # Note: [Generator arguments in AOTDispatcher] + # Generator arguments in AOTDispatcher are added to support graphsafe rng + # functionalization. See note above [CUDA Graph Safe RNG Functionalization] + with fw_module.graph.inserting_after(last_fwd_input): + fwd_rng_state = fw_module.graph.placeholder(f"fwd_rng_state_{rng_count}") + fwd_rng_state.meta["val"] = get_cuda_generator_meta_val(device_idx) + last_fwd_input = fwd_rng_state + + # Handle backward pass + with bw_module.graph.inserting_after(last_bwd_input): + bwd_rng_state = bw_module.graph.placeholder(f"bwd_rng_state_{rng_count}") + # as above, clone so that meta val generator will not contain tensors + bwd_rng_state.meta["val"] = get_cuda_generator_meta_val(device_idx) + last_bwd_input = bwd_rng_state + + # Update forward node + fw_kwargs = dict(fw_node.kwargs) + fw_kwargs["rng_state"] = fwd_rng_state + with fw_module.graph.inserting_after(fw_node): + functional_fw_node = fw_graph.create_node( + "call_function", + graphsafe_run_with_rng_state, + args=(fw_node.target, *fw_node.args), # type: ignore[arg-type] + kwargs=fw_kwargs, + ) + fw_node.replace_all_uses_with(functional_fw_node) + fw_graph.erase_node(fw_node) + + # Update backward node + bwd_kwargs = dict(bw_node.kwargs) + bwd_kwargs["rng_state"] = bwd_rng_state + with bw_graph.inserting_before(bw_node): + rng_output = bw_graph.create_node( + "call_function", + graphsafe_run_with_rng_state, + args=(bw_node.target, *bw_node.args), # type: ignore[arg-type] + kwargs=bwd_kwargs, + ) + bw_node.replace_all_uses_with(rng_output) + bw_graph.erase_node(bw_node) + + return last_fwd_input, last_bwd_input + + +def functionalize_rng_ops( + joint_module: fx.GraphModule, + fw_module: fx.GraphModule, + bw_module: fx.GraphModule, + num_sym_nodes: int, +) -> tuple[fx.GraphModule, fx.GraphModule]: + # During user-driven activation checkpointing, we have to ensure that a rng + # op in fwd yields the same output as the recomputed rng op in the bwd. To + # do this, we use functionalize wrappers to wrap the random ops and share + # rng state between the fwd and bwd graphs. + + # There are 3 main steps to do this + # Step 1 - Construct a mapping of rng node between the fwd and its counterpart in bwd. + # Step 2 - Modify the fwd pass such that + # 1) Replace rand with run_and_save_rng_state wrapper + # 2) Replace the users of the original op with the output[1] of this op. + # 3) Collect all the rng_state - output[0] of each op, and make them + # output nodes. Special care needs to be taken here because fwd outputs + # has symints at the very end. + # Step 3 - Modify the bwd pass such that + # 1) Add the input nodes just before the tangents for the stashed rng states + # 2) Replace rand with run_with_save_rng_state wrappers + # 3) Use the stashed states as inputs to these ops + + # Unique id to generate name + uid = itertools.count() + + def get_rng_ops(gmod): + random_nodes = {} + for node in gmod.graph.nodes: + if ( + node.op == "call_function" + and hasattr(node.target, "tags") + and torch.Tag.nondeterministic_seeded in node.target.tags + ): + random_nodes[node.name] = node + return random_nodes + + def get_device(node) -> Optional[torch.device]: + """ + Check the example value of the node outputs to find the device type. + """ + if "val" not in node.meta: + return None + + candidates = node.meta["val"] + if not isinstance(candidates, tuple): + candidates = (candidates,) + + for candidate in candidates: + if isinstance(candidate, torch.Tensor): + if candidate.device.type == "cuda": + return candidate.device + + return torch.device("cpu") + + def get_sample_rng_state(device: Optional[torch.device]): + if device is not None and device.type == "cuda": + return torch.cuda.get_rng_state() + return torch.get_rng_state() + + # Step 1 - Construct a mapping of rng node between the fwd and its counterpart in bwd. + joint_graph_rng_ops = get_rng_ops(joint_module) + fw_graph_rng_ops = get_rng_ops(fw_module) + bw_graph_rng_ops = get_rng_ops(bw_module) + recomputable_rng_ops_map = {} + for node in joint_module.graph.nodes: + if ( + must_recompute(node) + and hasattr(node.target, "tags") + and torch.Tag.nondeterministic_seeded in node.target.tags + ): + base_node = joint_graph_rng_ops[node.name] + fw_node = fw_graph_rng_ops[node.name] + bw_node = bw_graph_rng_ops[node.name] + recomputable_rng_ops_map[base_node] = {"fwd": fw_node, "bwd": bw_node} + + run_and_save_rng = torch._prims.rng_prims.run_and_save_rng_state + run_with_rng_state = torch._prims.rng_prims.run_with_rng_state + + bw_tangent_start_node = None + for node in bw_module.graph.find_nodes(op="placeholder"): + if "tangent" in node.name: + bw_tangent_start_node = node + break + if bw_tangent_start_node is None: + raise RuntimeError( + "Couldn't find tangent node in graph inputs. This is unexpected, please file a bug if you see this" + ) + + fw_rng_state_outputs = [] + + last_fwd_input = next(reversed(fw_module.graph.find_nodes(op="placeholder"))) + last_bwd_input = next(reversed(bw_module.graph.find_nodes(op="placeholder"))) + + devices = OrderedSet( + get_device(node_pair["fwd"]) for node_pair in recomputable_rng_ops_map.values() + ) + devices.discard(torch.device("cpu")) + # multiple cuda devices wont work with cudagraphs anyway, + # fallback to non graphsafe rng checkpointing + multi_cuda_devices = len(devices) > 1 + + # this changes numerics, so if fallback_random is set we will not use it + ind_config = torch._inductor.config + use_rng_graphsafe_rng_functionalization = ( + config.graphsafe_rng_functionalization + and not multi_cuda_devices + and ( + not ind_config.fallback_random + or ind_config.test_configs.graphsafe_rng_func_ignores_fallback_random + ) + ) + + for rng_count, (base_node, node_pair) in enumerate( + recomputable_rng_ops_map.items() + ): + # Step 2 - Modify the fwd pass such that + fw_node = node_pair["fwd"] + bw_node = node_pair["bwd"] + device = get_device(fw_node) + + fw_graph = fw_module.graph + bw_graph = bw_module.graph + + if ( + use_rng_graphsafe_rng_functionalization + and device is not None + and device.type == "cuda" + ): + last_fwd_input, last_bwd_input = apply_graphsafe_rng_functionalization( + fw_module, + bw_module, + fw_node, + bw_node, + device, + rng_count, + last_fwd_input, + last_bwd_input, + ) + else: + with fw_graph.inserting_before(fw_node): + functional_fw_node = fw_graph.create_node( + "call_function", + run_and_save_rng, + args=(fw_node.target, *fw_node.args), + kwargs=fw_node.kwargs, + ) + state = fw_graph.create_node( + "call_function", + operator.getitem, + args=(functional_fw_node, 0), + kwargs={}, + ) + rng_output = fw_graph.create_node( + "call_function", + operator.getitem, + args=( + functional_fw_node, + 1, + ), + kwargs={}, + ) + fw_node.replace_all_uses_with(rng_output) + fw_graph.erase_node(fw_node) + fw_rng_state_outputs.append(state) + + # Step 3 - Modify the bwd pass such that + with bw_graph.inserting_before(bw_tangent_start_node): + state_name = f"rng_state_output_{next(uid)}" + bw_rng_state_node = bw_graph.placeholder(state_name) + bw_rng_state_node.meta["val"] = get_sample_rng_state(device) + + with bw_graph.inserting_before(bw_node): + rng_output = bw_graph.create_node( + "call_function", + run_with_rng_state, + args=(bw_rng_state_node, bw_node.target, *bw_node.args), + kwargs=bw_node.kwargs, + ) + + bw_node.replace_all_uses_with(rng_output) + bw_graph.erase_node(bw_node) + + # Add the rng states in the output of the fwd graph. AOT Autograd assumes + # that symints are at the end of forward graph outputs. So, insert the new + # rng states accordingly. + if fw_rng_state_outputs: + fw_output_node = next(iter(fw_module.graph.find_nodes(op="output"))) + fw_outputs = fw_output_node.args[0] + sym_node_start_idx = len(fw_outputs) - num_sym_nodes + outputs = ( + fw_outputs[:sym_node_start_idx] + + tuple(fw_rng_state_outputs) + + fw_outputs[sym_node_start_idx:] + ) + fw_module.graph.output(outputs) + fw_module.graph.erase_node(fw_output_node) + fw_module.recompile() + bw_module.recompile() + return fw_module, bw_module + + +def cleanup_recompute_tags(joint_module: fx.GraphModule) -> fx.GraphModule: + """ + If there are two consecutive checkpointed blocks with no operator in + between, we would still want to stash the tensor at the boundary of + checkpointed blocks. The following pass makes the last output node + non-recomputable to allow for that. + """ + for node in joint_module.graph.nodes: + if must_recompute(node): + for user in node.users: + if ( + must_recompute(user) + and user.meta["ac_graph_id"] > node.meta["ac_graph_id"] + ): + node.meta["recompute"] = CheckpointPolicy.MUST_SAVE + if node.meta.get("has_backward_hook", False) and not any( + must_recompute(user) for user in node.users + ): + # If node is AC region output and has a backward hook on it, we intentionally choose to save it. + # This is to work around circular dependencies in Traceable FSDP2+AC. + # Example: + # ``` + # out = fully_shard(utils.checkpoint(module))(x) + # norm_out = layer_norm(out) + # ``` + # Here there is a circular dependency: + # 1. In backward, grad_input of layer_norm aka. `out_grad` is actually dependent on `out`. + # 2. `out` depends on `out`'s backward hook created by FSDP2 (which does all-gather for `module` weights) + # in order to be recomputed. + # 3. `out`'s backward hook, as is the case for all eager backward hooks, depends on `out_grad` + # -> circular dependency with (1)! + # + # Solution: check whether `out` has a backward hook, and if so, intentionally save `out` + # in forward graph outputs. With this, we can break the above circular dependency. + node.meta["recompute"] = CheckpointPolicy.MUST_SAVE + return joint_module + + +def solve_min_cut( + joint_graph: fx.Graph, + node_info: NodeInfo, + min_cut_options: MinCutOptions, + dont_ban: Optional[OrderedSet[fx.Node]] = None, +): + if dont_ban is None: + dont_ban = OrderedSet() + op_types = get_default_op_list() + + if AOT_PARTITIONER_DEBUG: + joint_module_ops = OrderedSet( + str(node.target._overloadpacket) + for node in joint_graph.nodes + if node.op == "call_function" and hasattr(node.target, "_overloadpacket") + ) + ops_ignored = joint_module_ops - OrderedSet( + str(i) for i in op_types.recomputable_ops + ) + log.info("Ops banned from re-materialization: %s", ops_ignored) + + def can_fuse_into_auto_functionalized(a, b): + if b.target != torch.ops.higher_order.auto_functionalized: + return False + mutable_op = b.args[0] + ( + mutable_arg_names, + _, + ) = torch._higher_order_ops.auto_functionalize.get_mutable_args(mutable_op) + for name in mutable_arg_names: + arg = b.kwargs[name] + if a is arg: + return True + if isinstance(arg, list): + if a in arg: + return True + return False + + def can_fuse_into_triton_kernel_wrapper_functional(a, b): + if b.target != torch.ops.higher_order.triton_kernel_wrapper_functional: + return False + mutable_arg_names = b.kwargs["tensors_to_clone"] + for name in mutable_arg_names: + arg = b.kwargs["kwargs"][name] + if a is arg: + return True + return False + + def is_fusible(a, b): + # We can perform "memory fusion" into a cat, but cat cannot be a + # producer to a fusion + if get_aten_target(b) == aten.cat: + return True + if can_fuse_into_auto_functionalized(a, b): + return True + if can_fuse_into_triton_kernel_wrapper_functional(a, b): + return True + if ( + a.target is operator.getitem + and a.args[0].target + is torch.ops.higher_order.triton_kernel_wrapper_functional + ): + # if a is the output of a user triton kernel, + # then (by default) we will not be able to fuse b into it + return False + return op_types.is_fusible(a) and op_types.is_fusible(b) + + try: + import networkx as nx + except ImportError as e: + raise RuntimeError( + "Need networkx installed to perform smart recomputation heuristics" + ) from e + + def is_materialized_backwards(node): + if op_types.is_view(node): + return False + cur_nodes = OrderedSet([node]) + while len(cur_nodes) > 0: + cur = cur_nodes.pop() + for user in cur.users: + if not node_info.is_required_fw(user) and not is_fusible(cur, user): + return True + if op_types.is_view(user): + cur_nodes.add(user) + + return False + + def should_ban_recomputation(node): + if node.op != "call_function": + return False + if node.target == operator.getitem: + return False + if node.meta.get("recompute", None) == CheckpointPolicy.MUST_SAVE: + return True + if config.recompute_views and op_types.is_view(node): + return False + if node.target in [aten.lift_fresh_copy.default, aten.lift_fresh.default]: + return False + + if min_cut_options.ban_if_not_in_allowlist: + if not op_types.is_recomputable(node): + return True + else: + if op_types.is_random(node) or op_types.is_compute_intensive(node): + return True + + # If a node *must* be materialized in the backwards pass, then we + # should never recompute it. This is a pretty subtle point. In + # general, the assumption we make is that recomputing a node in the + # backwards pass is "free". However, if a node must be materialized + # in the backwards pass, then recomputing it is never free. + if min_cut_options.ban_if_materialized_backward and is_materialized_backwards( + node + ): + log.debug("materialized backwards: %s %s", node, tuple(node.users)) + return True + + # Arbitrary hack that sometimes seems to help things. The above + # modification appears to have made this heuristic a lot less critical + # for performance. + # NB: As of PR #121692, this hack no longer seems necessary. + if node.dist_from_bw < 1000 and node.dist_from_bw > config.max_dist_from_bw: + return True + + # If the output of an op is 4x smaller (arbitrary choice), + # then we don't allow recomputation. The idea here is that for + # things like reductions, saving the output of the reduction is very + # cheap/small, and it makes sure we don't do things like recompute + # normalizations in the backwards. + if min_cut_options.ban_if_reduction: + input_tensors_size = sum( + _size_of(i) for i in node.args if isinstance(i, fx.Node) + ) + output_size = _size_of(node) + return output_size * 4 < input_tensors_size + return False + + def is_materialized(node): + if node.op == "placeholder": + return True + + return not all(is_fusible(node, user) for user in node.users) + + def get_node_weight(node) -> float: + mem_sz = _size_of(node) + if config.recompute_views and op_types.is_view(node): + # If `config.recompute_views=True`, we don't save views. This is generally + # a good idea since views are free to recompute, and it makes it a bit simpler + # to analyze. + # NB: If they're not free to recompute (e.g. nested tensors)... I + # think we should modify checks for view_ops to `is_view` and check + # that. Basically, with nested tensors, `aten.view` is not a "view + # op". + return math.inf + + if isinstance(node.meta["val"], py_sym_types): + # We never want to save symfloats + if not isinstance(node.meta["val"], torch.SymInt): + return INT_INF + + # Heuristic to bias towards nodes closer to the backwards pass + # Complete guess about current value + mem_sz = int(mem_sz * (1.1 ** max(min(node.dist_from_bw, 100), 1))) + if is_materialized(node): + return mem_sz + else: + return mem_sz * 2 + + nx_graph = nx.DiGraph() + banned_nodes: OrderedSet[fx.Node] = OrderedSet() + + def ban_recomputation_if_allowed(node): + if op_types.is_view(node): + return False + if node in dont_ban: + return False + # This bans recomputation of the node unless we've been forced not to by + # user annotation + if must_recompute(node): + return False + + if "val" in node.meta and isinstance(node.meta["val"], torch.SymFloat): + return False + + banned_nodes.add(node) + # A node will only ever be recomputed if there is a path from an + # ancestor of this node to the backwards path through this node that + # doesn't go through any saved value. If this node is saved, then that + # condition is not possible. + nx_graph.add_edge("source", node.name + "_in", capacity=math.inf) + return True + + for node in joint_graph.nodes: + if node.op == "output": + continue + + if node in node_info.required_bw_nodes: + if node not in node_info.inputs: + nx_graph.add_edge(node.name + "_in", "sink", capacity=math.inf) + continue + # If someone saves a input for backward as-is and backward + # returns that tensor as-is as a grad input, then the node x would + # be both a required_bw_node and an input. In this case we + # (1) connect x_in to to the source, (2) x_out to the sink, and + # (3) assign the proper weight to the x_in-x_out edge, so that + # x would be part of cut nodes. A case where this happens is if + # NestedTensor saves a offset tensor as part of the singleton int + # in sizes. + nx_graph.add_edge(node.name + "_out", "sink", capacity=math.inf) + + if must_recompute(node): + # If user explicitly says they want to recompute a node, we honor it + # by adding an inf-capacity edge from X_in to the sink. + # This way, X_in node is guaranteed to be part of the subgraph that contains "sink" + # after the cut, thus guaranteeing that X op will be recomputed. + nx_graph.add_edge(node.name + "_in", "sink", capacity=math.inf) + continue + + if _is_primal(node) or _is_fwd_seed_offset(node): + ban_recomputation_if_allowed(node) + + # If a node can't be recomputed (too expensive or involves randomness), + # we prevent it from being recomputed by adding an inf edge to the source + # We only need to ban nodes in the fw pass, as those are the only ones that would be recomputed. + if node_info.is_required_fw(node) and should_ban_recomputation(node): + ban_recomputation_if_allowed(node) + + # Checks if a node is actually a tuple. Can be simplified to just an isinstance check if we always use faketensors. + is_non_tensor_node = ( + "val" not in node.meta and "tensor_meta" not in node.meta + ) or ("val" in node.meta and not isinstance(node.meta["val"], torch.Tensor)) + + if is_sym_node(node): + weight = float(sym_node_size(node)) + elif is_non_tensor_node: + weight = ( + 0.0 if isinstance(node.meta.get("val"), BackwardState) else math.inf + ) + else: + weight = get_node_weight(node) + # Creates the weights on the "node" edge + nx_graph.add_edge(node.name + "_in", node.name + "_out", capacity=weight) + for user in node.users: + nx_graph.add_edge(node.name + "_out", user.name + "_in", capacity=math.inf) + + # todo(chilli): This is the most questionable of the 3 heuristics for banning recompute. + # Some example models to look at where this helps perf: poolformer_m36, + # mixer_b16_224, cait_m36_384 + + # The "rough" idea here is that if you have some node that is used by both a + # node nearby downstream as well as a node far downstream, if we recompute + # both of the downstream nodes, we're unlikely to be able to fuse both + # downstream nodes together. + + # Thus, we shouldn't aim to recompute far downstream nodes that depend on + # this node. That intuition of "far downstream" is captured by whether + # there's an unfusible op along the chain somewhere + + # It could probably be improved by properly analyzing what's going on in the + # backwards pass instead of only relying on whether it's unfusible in the + # forwards. + + def find_first_unfusible(start_nodes: list[fx.Node], max_range: int) -> int: + """ + Finds the first unfusible node in the chain of nodes starting from + `start_nodes` and returns its position. + """ + sorted_nodes: list[tuple[int, fx.Node, bool]] = [] + for n in start_nodes: + heapq.heappush(sorted_nodes, (node_info.get_fw_order(n), n, True)) + + while len(sorted_nodes) > 0: + _, node, node_is_fusible = heapq.heappop(sorted_nodes) + if not node_is_fusible: + return node_info.get_fw_order(node) + for user in node.users: + if node_info.is_required_fw(user): + if node_info.get_fw_order(user) > max_range: + continue + val: tuple[int, fx.Node, bool] = ( + node_info.get_fw_order(user), + user, + is_fusible(node, user), + ) + if val not in sorted_nodes: + heapq.heappush(sorted_nodes, val) + return max_range + + if min_cut_options.ban_if_used_far_apart: + for used_node in node_info.required_fw_nodes: + orders = [ + node_info.get_fw_order(user) + for user in used_node.users + if node_info.is_required_fw(user) + ] + fw_users = [ + user for user in used_node.users if node_info.is_required_fw(user) + ] + if len(orders) > 0: + first_unfusible_use = find_first_unfusible(fw_users, max(orders)) + for user in tuple(used_node.users): + if ( + node_info.is_required_fw(user) + and node_info.get_fw_order(user) > first_unfusible_use + and is_fusible(used_node, user) + ): + if user in banned_nodes: + continue + log.info( + "used above/below fusible %s:(%s) -> %s -> %s:(%s)", + used_node, + node_info.get_fw_order(used_node), + first_unfusible_use, + user, + node_info.get_fw_order(user), + ) + ban_recomputation_if_allowed(user) + + # This heuristic is fairly straightforward. The idea is that although it is + # cheap to recompute bandwidth-bound ops, we don't want to end up in a situation + # where we have a long chain of pointwise ops from the beginning to the end + # of the model (like say, residual connections) + + # todo: I'm not totally sure why this heuristic matters. It's possible that this is + # working around Inductor fusion decisions, or that it's a patch over + # suboptimal partitioning decisions + + # Some models it improves perf on are cait_m36_384, mixer_b16_224, poolformer_m36 + + if min_cut_options.ban_if_long_fusible_chains: + visited: OrderedSet[fx.Node] = OrderedSet() + for start_node in joint_graph.nodes: + if not node_info.is_required_fw(start_node): + continue + fusible: list[tuple[int, fx.Node]] = [ + (node_info.get_fw_order(start_node), start_node) + ] + start_order = node_info.get_fw_order(start_node) + while len(fusible) > 0: + _, cur = heapq.heappop(fusible) + if cur in visited: + continue + visited.add(cur) + # 100 is arbitrary choice to try and prevent degenerate cases + if ( + node_info.get_fw_order(cur) > start_order + 100 + and len(fusible) == 0 + ): + log.info( + "too long %s %s %s %s", + cur, + start_node, + node_info.get_fw_order(cur), + node_info.get_fw_order(start_node), + ) + ban_recomputation_if_allowed(cur) + break + + for user in cur.users: + if ( + node_info.is_required_fw(user) + and is_fusible(cur, user) + and user not in banned_nodes + ): + heapq.heappush(fusible, (node_info.get_fw_order(user), user)) + + try: + cut_value, partition = nx.minimum_cut(nx_graph, "source", "sink") + except Exception: + log.info("Failed to compute min-cut on following graph:") + log.info("\n".join(nx.readwrite.edgelist.generate_edgelist(nx_graph))) + visualize_min_cut_graph(nx_graph) + raise + + reachable, non_reachable = partition + cutset: OrderedSet[tuple[str, str]] = OrderedSet() + for u, nbrs in ((n, nx_graph[n]) for n in reachable): + cutset.update((u, v) for v in nbrs if v in non_reachable) + + cut_nodes: OrderedSet[str] = OrderedSet() + for node_in, node_out in cutset: + assert node_in[:-3] == node_out[:-4] + node_name = node_in[:-3] + cut_nodes.add(node_name) + + name_to_node = get_name_to_node(joint_graph) + # To make this stuff deterministic + node_idx = {node: idx for idx, node in enumerate(joint_graph.nodes)} + saved_values = sorted( + (name_to_node[node] for node in cut_nodes), key=lambda x: node_idx[x] + ) + return saved_values, banned_nodes + + +def visualize_min_cut_graph(nx_graph): + import networkx as nx + import pydot + + dot_format = nx.nx_pydot.to_pydot(nx_graph).to_string() + dot_graph = pydot.graph_from_dot_data(dot_format)[0] + for edge in dot_graph.get_edges(): + weight = nx_graph[edge.get_source()][edge.get_destination()]["capacity"] + # Set edge label to weight + edge.set_label(str(weight)) + # Color edges with weight 'inf' as red + if weight == float("inf"): + edge.set_color("red") + log.info("Visualizing the failed graph to min_cut_failed.svg") + dot_graph.write_svg("min_cut_failed.svg") + + +def get_default_op_list() -> OpTypes: + default_recomputable_ops: list[Callable] = [ + aten.add, + aten.sub, + aten.div, + aten.atan2, + aten.mul, + aten.max, + aten.min, + aten.pow, + aten.remainder, + aten.fmod, + aten.__and__, + aten.__or__, + aten.__xor__, + aten.__lshift__, + aten.__rshift__, + aten.eq, + aten.ne, + aten.ge, + aten.gt, + aten.le, + aten.lt, + aten.abs, + aten.bitwise_not, + aten.ceil, + aten.floor, + aten.frac, + aten.neg, + aten.relu, + aten.round, + aten.silu, + aten.trunc, + aten.log, + aten.log10, + aten.log1p, + aten.log2, + aten.lgamma, + aten.exp, + aten.expm1, + aten.erf, + aten.erfc, + aten.cos, + aten.acos, + aten.cosh, + aten.sin, + aten.asin, + aten.sinh, + aten.tan, + aten.atan, + aten.tanh, + aten.atanh, + aten.sqrt, + aten.rsqrt, + aten.reciprocal, + aten.sigmoid, + aten.softplus, + aten.threshold, + aten.threshold_backward, + aten.clamp, + aten.where, + aten.lerp, + aten.addcmul, + aten.gelu, + aten.gelu_backward, + aten.sum, + aten.mean, + aten._grad_sum_to_size, + aten.sum_to_size, + aten.amax, + aten.to, + aten.type_as, + operator.getitem, + aten.squeeze, + aten.unsqueeze, + aten.rsub, + aten._to_copy, + ] # noqa: E501,B950 + recomputable_view_ops = [aten.squeeze, aten.unsqueeze, aten.alias] + recomputable_view_ops += [ + aten.view, + aten.slice, + aten.t, + prims.broadcast_in_dim, + aten.expand, + aten.as_strided, + aten.permute, + aten.select, + ] + view_ops = recomputable_view_ops + default_recomputable_ops += [ + prims.div, + prims.convert_element_type, + aten.clone, + aten._to_copy, + aten.full_like, + prims.var, + prims.sum, + aten.var, + aten.std, + prims.broadcast_in_dim, + aten.select, + aten._unsafe_view, + aten.view, + aten.expand, + aten.slice, + aten.reshape, + aten.broadcast_tensors, + aten.scalar_tensor, + aten.ones, + aten.new_zeros, + aten.lift_fresh_copy, + aten.arange, + aten.triu, + aten.var_mean, + aten.isinf, + aten.any, + aten.full, + aten.as_strided, + aten.zeros, + aten.empty, + aten.empty_like, + aten.argmax, + aten.maximum, + prims.iota, + prims._low_memory_max_pool2d_offsets_to_indices, + ] # noqa: E501,B950 + # Natalia said that we should allow recomputing indexing :) + default_recomputable_ops += [aten.index, aten.gather] + default_recomputable_ops += view_ops + + default_recomputable_ops += pointwise_ops() + + default_recomputable_ops += [ + aten.zeros_like, + ] + + default_recomputable_ops += [method_to_operator(m) for m in magic_methods] + recomputable_ops = OrderedSet(default_recomputable_ops) + + random_ops = OrderedSet([aten.native_dropout, aten.rand_like, aten.randn_like]) + compute_intensive_ops = [ + aten.mm, + aten.convolution, + aten.convolution_backward, + aten.bmm, + aten.addmm, + aten._scaled_dot_product_flash_attention, + aten._scaled_dot_product_efficient_attention, + aten._flash_attention_forward, + aten._efficient_attention_forward, + aten.upsample_bilinear2d, + aten._scaled_mm, + ] # noqa: E501,B950 + + fusible_ops = recomputable_ops | random_ops + return OpTypes( + fusible_ops, + OrderedSet(compute_intensive_ops), + random_ops, + OrderedSet(view_ops), + recomputable_ops, + ) + + +def get_name_to_node(graph: fx.Graph): + name_to_node = {} + for node in graph.nodes: + name_to_node[node.name] = node + return name_to_node + + +def _optimize_runtime_with_given_memory( + joint_graph: fx.Graph, + memory: list[float], + runtimes: list[float], + max_memory: float, + node_info: NodeInfo, + all_recomputable_banned_nodes: list[fx.Node], +) -> tuple[float, list[int], list[int]]: + SOLVER = config.activation_memory_budget_solver + if SOLVER == "greedy": + return greedy_knapsack(memory, runtimes, max_memory) + elif SOLVER == "ilp": + return ilp_knapsack(memory, runtimes, max_memory) + elif SOLVER == "dp": + return dp_knapsack(memory, runtimes, max_memory) + elif SOLVER == "dynamic_memory_budget_dp": + log.warning( + "dynamic_memory_budget_dp is an experimental solver. " + "It does not guarantee performance improvements. " + "Additionally, it is not guaranteed to be stable." + ) + graph_info_provider = GraphInfoProvider.inialize_from_graph( + joint_graph=joint_graph, + all_recomputable_banned_nodes=all_recomputable_banned_nodes, + recorded_knapsack_input_memories=memory, + recorded_knapsack_input_runtimes=runtimes, + ) + return dp_knapsack( + memory, + runtimes, + KnapsackEvaluator( + graph_info_provider=graph_info_provider, + ).get_knee_point_memory_budget( + knapsack_algo=dp_knapsack, + max_mem_budget=max_memory, + ), + ) + elif callable(SOLVER): + saved_node_idx, recomp_node_idx = SOLVER( + memory, joint_graph, max_memory, node_info, all_recomputable_banned_nodes + ) + return (0.0, saved_node_idx, recomp_node_idx) + else: + raise RuntimeError(f"Not aware of memory budget knapsack solver: {SOLVER}") + + +from torch.utils._mode_utils import no_dispatch + + +# replace symbols in size and strides with their hints without guarding. +def _remove_symbols_without_guarding(x: torch.Tensor, fallback: int) -> torch.Tensor: + shape = list(x.shape) + + def realize_symbol(d): + return hint_int(d, fallback=fallback) + + shape = [realize_symbol(s) for s in shape] + stride = [realize_symbol(s) for s in x.stride()] + return x.new_empty_strided(shape, stride=stride) + + +def estimate_runtime(node): + RUNTIME_MODE = config.activation_memory_budget_runtime_estimator + + def materialize_arg(x): + if isinstance(x, fx.Node) and isinstance(x.meta["val"], torch.Tensor): + return _remove_symbols_without_guarding(x.meta["val"], fallback=4096) + elif isinstance(x, fx.Node) and isinstance(x.meta["val"], torch.SymInt): + return hint_int(x.meta["val"], fallback=4096) + elif isinstance(x, fx.Node) and isinstance(x.meta["val"], torch.SymFloat): + return 1.0 + elif isinstance(x, fx.Node) and isinstance(x.meta["val"], torch.SymBool): + return True + else: + return x + + if RUNTIME_MODE == "testing": + return 1 + + elif RUNTIME_MODE == "profile": + with no_dispatch(): + from torch._inductor.runtime.benchmarking import benchmarker + + args, kwargs = pytree.tree_map(materialize_arg, (node.args, node.kwargs)) + ms = benchmarker.benchmark_gpu(lambda: node.target(*args, **kwargs)) + return ms + + elif RUNTIME_MODE == "flops": + # todo(chilli): Normalize this to also return ms + from torch.utils.flop_counter import FlopCounterMode + + args, kwargs = pytree.tree_map(materialize_arg, (node.args, node.kwargs)) + with FlopCounterMode(display=False) as mode: + node.target(*args, **kwargs) + counted_flops = mode.get_total_flops() + return max(counted_flops, 1) + else: + raise RuntimeError(f"Not aware of runtime estimator: {RUNTIME_MODE}") + + +def choose_saved_values_set( + joint_graph: fx.Graph, + node_info: NodeInfo, + memory_budget=1, +) -> list[fx.Node]: + if memory_budget > 1 or memory_budget < 0: + raise RuntimeError( + f"The valid ranges for memory budget are 0 <= m <= 1. The provided value is {memory_budget}" + ) + min_cut_options = MinCutOptions( + ban_if_used_far_apart=config.ban_recompute_used_far_apart, + ban_if_long_fusible_chains=config.ban_recompute_long_fusible_chains, + ban_if_materialized_backward=config.ban_recompute_materialized_backward, + ban_if_not_in_allowlist=config.ban_recompute_not_in_allowlist, + ban_if_reduction=config.ban_recompute_reductions, + ) + + if config.aggressive_recomputation: + min_cut_options = replace( + min_cut_options, + ban_if_used_far_apart=False, + ban_if_long_fusible_chains=False, + ban_if_materialized_backward=False, + ban_if_not_in_allowlist=False, + ) + if memory_budget == 0: + return node_info.inputs + + runtime_optimized_saved_values, _ = solve_min_cut( + joint_graph, + node_info, + min_cut_options, + ) + # return runtime_optimized_saved_values + if memory_budget == 1: + return runtime_optimized_saved_values + + def estimate_activations_size(saved_values: list[fx.Node]) -> float: + return sum(map(_size_of, saved_values)) / 1e9 + + min_act_size = estimate_activations_size(node_info.inputs) + max_act_size = estimate_activations_size(runtime_optimized_saved_values) + # The optimized choice is smaller than the inputs anyways + if max_act_size <= min_act_size: + return runtime_optimized_saved_values + + def get_normalized_size(sz): + return (sz / 1e9) / (max_act_size - min_act_size) + + def get_mem_ratio(activations: list[fx.Node]): + return (estimate_activations_size(activations) - min_act_size) / ( + max_act_size - min_act_size + ) + + more_aggressive_options = replace( + min_cut_options, + ban_if_used_far_apart=False, + ban_if_long_fusible_chains=False, + ban_if_materialized_backward=False, + ) + more_aggressive_saved_values, _ = solve_min_cut( + joint_graph, node_info, more_aggressive_options + ) + if get_mem_ratio(more_aggressive_saved_values) < memory_budget: + return more_aggressive_saved_values + + aggressive_options = replace( + more_aggressive_options, + ban_if_not_in_allowlist=False, + ) + aggressive_recomputation_saved_values, banned_nodes = solve_min_cut( + joint_graph, node_info, aggressive_options + ) + + if get_mem_ratio(aggressive_recomputation_saved_values) < memory_budget: + return aggressive_recomputation_saved_values + + from torch._inductor.fx_utils import get_node_storage + + input_storages = OrderedSet(get_node_storage(node) for node in node_info.inputs) + + def get_recomputable_banned_nodes( + banned_nodes: OrderedSet[fx.Node], + ) -> list[fx.Node]: + return [ + i + for i in banned_nodes + if ( + # Only allow recomputing nodes that are actually required for BW + i.dist_from_bw < int(1e9) # type: ignore[attr-defined] + and get_node_storage(i) not in input_storages + ) + ] + + recomputable_banned_nodes = get_recomputable_banned_nodes(banned_nodes) + # sort first by name, to ensure determinism when multiple nodes have same size + recomputable_banned_nodes = sorted(recomputable_banned_nodes, key=lambda x: x.name) + + # default: runtime_optimized_saved_values + # more aggressive: more_aggressive_saved_values + # full aggressive: aggressive_recomputation_saved_values + + all_recomputable_banned_nodes = sorted( + recomputable_banned_nodes, key=_size_of, reverse=True + ) + if len(all_recomputable_banned_nodes) == 0: + return node_info.inputs + memories_banned_nodes = [ + get_normalized_size(_size_of(i)) for i in all_recomputable_banned_nodes + ] + runtimes_banned_nodes = [ + estimate_runtime(node) for node in all_recomputable_banned_nodes + ] + from torch.utils._mode_utils import no_dispatch + + def get_saved_values_knapsack(memory_budget, node_info, joint_graph): + with no_dispatch(): + ( + expected_runtime, + saved_node_idxs, + recomputable_node_idxs, + ) = _optimize_runtime_with_given_memory( + joint_graph, + memories_banned_nodes, + runtimes_banned_nodes, + max(memory_budget, 0), + node_info, + all_recomputable_banned_nodes, + ) + dont_ban: OrderedSet[fx.Node] = OrderedSet() + for idx in recomputable_node_idxs: + # if idx in all_recomputable_banned_nodes: + try: + dont_ban.add(all_recomputable_banned_nodes[idx]) + except BaseException: + pass + + assert dont_ban.issubset(all_recomputable_banned_nodes) + + saved_values, _ = solve_min_cut( + joint_graph, + node_info, + aggressive_options, + dont_ban, + ) + if AOT_PARTITIONER_DEBUG: + create_structured_trace_for_min_cut_info( + joint_graph=joint_graph, + all_recomputable_banned_nodes=all_recomputable_banned_nodes, + saved_node_idxs=saved_node_idxs, + recomputable_node_idxs=recomputable_node_idxs, + expected_runtime=expected_runtime, + memories_banned_nodes=memories_banned_nodes, + runtimes_banned_nodes=runtimes_banned_nodes, + min_cut_saved_values=saved_values, + ) + return saved_values, expected_runtime + + if config.visualize_memory_budget_pareto: + + def estimate_for_budget(b): + saved_values, expected_runtime = get_saved_values_knapsack( + b, node_info=node_info, joint_graph=joint_graph + ) + return ( + b, + sum(runtimes_banned_nodes) - expected_runtime, + get_mem_ratio(saved_values), + ) + + options = [estimate_for_budget(0.0), estimate_for_budget(1.0)] + + if options[0][1:] != options[1][1:]: + bisects = [(options[0], options[1])] + while bisects: + lhs, rhs = bisects.pop() + if rhs[0] - lhs[0] < 1e-3: + options.append(lhs) + options.append(rhs) + continue + mid = estimate_for_budget((lhs[0] + rhs[0]) / 2) + if mid[1:] != lhs[1:]: + bisects.append((lhs, mid)) + if mid[1:] != rhs[1:]: + bisects.append((mid, rhs)) + options.sort() + + import matplotlib.pyplot as plt + + x_values = [item[2] for item in options] + y_values = [item[1] for item in options] + + # Plotting the values with updated axis labels and chart title + plt.figure(figsize=(10, 6)) + plt.plot(x_values, y_values, marker="o") + + # Adding labels for each point + for i, txt in enumerate(x_values): + plt.annotate( + f"{txt:.4f}", + (txt, y_values[i]), + textcoords="offset points", + xytext=(0, 10), + ha="center", + ) + + plt.xlabel("Memory Budget") + plt.ylabel("Runtime of Recomputed Components") + plt.title("Pareto Frontier of Memory Budget vs. Recomputation Runtime") + plt.grid(True) + fig = plt.gcf() + plt.show() + fig_dir = os.getcwd() + if config.memory_budget_pareto_dir is not None: + fig_dir = config.memory_budget_pareto_dir + os.makedirs(fig_dir, exist_ok=True) + rank_suffix = "" + if torch.distributed.is_available() and torch.distributed.is_initialized(): + rank_suffix = f"_rank_{torch.distributed.get_rank()}" + fig_name = os.path.join( + fig_dir, f"memory_budget_pareto{rank_suffix}_{get_aot_graph_name()}.svg" + ) + fig.savefig(fig_name) + log.warning("Generated Pareto frontier curve at %s", fig_name) + + # todo(chilli): Estimated doesn't align exactly with actual - actual is + # usually less memory than estimated. i'm guessing (actually quite + # unsure about this) that's because estimated is just only including + # tensors we actually banned from recompute, but there may be other + # tensors that we choose to save. + + return get_saved_values_knapsack( + memory_budget=memory_budget, node_info=node_info, joint_graph=joint_graph + )[0] + + +def min_cut_rematerialization_partition( + joint_module: fx.GraphModule, + _joint_inputs, + compiler="inductor", + *, + num_fwd_outputs, +) -> tuple[fx.GraphModule, fx.GraphModule]: + """ + Partitions the joint graph such that the backward recomputes the forward. + Recomputing helps in trading off memory bandwidth with computation. + + To create the fwd and bwd graph, we copy the joint graph, manually set the + outputs to just original forward or backward outputs. And then we run the + resulting graphs through dead code elimination. + + .. warning:: + This API is experimental and likely to change. + + Args: + joint_module(fx.GraphModule): The joint forward and backward graph. This + is the result of AOT Autograd tracing. + _joint_inputs: The inputs to the joint graph. This is unused. + compiler: This option determines the default set of recomputable ops. + Currently, there are two options: ``nvfuser`` and ``inductor``. + recomputable_ops: This is an optional set of recomputable ops. If this + is not None, then this set of ops will be used instead of the + default set of ops. + num_fwd_outputs: The number of outputs from the forward graph. + + Returns: + Returns the generated forward and backward Fx graph modules. + """ + + joint_module.graph.eliminate_dead_code() + joint_module.recompile() + + fx_g = joint_module.graph + + # add the CSE pass + if config.cse: + cse_graph = fx_graph_cse(fx_g) + joint_module.graph = cse_graph + joint_graph = joint_module.graph + + graph_has_recomputable_ops = has_recomputable_ops(joint_module) + graph_has_recomputable_rng_ops = has_recomputable_rng_ops(joint_module) + if graph_has_recomputable_ops: + joint_module = cleanup_recompute_tags(joint_module) + + def classify_nodes(joint_module): + name_to_node = get_name_to_node(joint_module.graph) + required_bw_nodes: OrderedSet[fx.Node] = OrderedSet() + for node in joint_module.graph.nodes: + if node.op == "placeholder" and "tangents" in node.target: + required_bw_nodes.add(node) + elif _must_be_in_backward(node): + required_bw_nodes.add(node) + + if node in required_bw_nodes: + required_bw_nodes.update(node.users) + + primal_inputs = list(filter(_is_primal, joint_module.graph.nodes)) + fwd_seed_offset_inputs = list( + filter(_is_fwd_seed_offset, joint_module.graph.nodes) + ) + inputs = primal_inputs + fwd_seed_offset_inputs + fwd_outputs, bwd_outputs = _extract_fwd_bwd_outputs( + joint_module, num_fwd_outputs=num_fwd_outputs + ) + required_bw_nodes.update( + o for o in bwd_outputs if o is not None and o.op != "output" + ) + forward_only_graph = _extract_graph_with_inputs_outputs( + joint_module.graph, inputs, fwd_outputs, "forward" + ) + required_fw_nodes: OrderedSet[fx.Node] = OrderedSet( + name_to_node[node.name] + for node in forward_only_graph.nodes + if node.op != "output" + ) + unclaimed_nodes: OrderedSet[fx.Node] = OrderedSet( + node + for node in joint_module.graph.nodes + if node not in required_fw_nodes and node not in required_bw_nodes + ) + fw_cnt = 0 + fw_order = {} + for node in joint_module.graph.nodes: + if node in required_fw_nodes: + fw_order[node] = fw_cnt + fw_cnt += 1 + return NodeInfo( + inputs, required_fw_nodes, required_bw_nodes, unclaimed_nodes, fw_order + ) + + node_info = classify_nodes(joint_module) + + # networkx blows up on graphs with no required backward nodes + # Since there's nothing to partition anyway, and the default partitioner can "handle" + # this case, send our graph over to the default partitioner. + if len(node_info.required_bw_nodes) == 0: + return default_partition( + joint_module, _joint_inputs, num_fwd_outputs=num_fwd_outputs + ) + + for node in reversed(joint_module.graph.nodes): + if node.op == "output": + node.dist_from_bw = int(1e9) + elif not node_info.is_required_fw(node): + node.dist_from_bw = 0 + else: + node.dist_from_bw = int(1e9) + for user in node.users: + node.dist_from_bw = min(node.dist_from_bw, user.dist_from_bw + 1) + + memory_budget = config.activation_memory_budget + for node in joint_graph.nodes: + if isinstance(node.meta.get("memory_budget", None), float): + memory_budget = node.meta["memory_budget"] + break + saved_values = choose_saved_values_set( + joint_graph, + node_info, + memory_budget=memory_budget, + ) + # save_for_backward on tensors and stashes symints in autograd .ctx + saved_sym_nodes = list(filter(is_sym_node, saved_values)) + saved_values = list(filter(lambda n: not is_sym_node(n), saved_values)) + + # NB: saved_sym_nodes will be mutated to reflect the actual saved symbols + fw_module, bw_module = _extract_fwd_bwd_modules( + joint_module, + saved_values, + saved_sym_nodes=saved_sym_nodes, + num_fwd_outputs=num_fwd_outputs, + ) + if graph_has_recomputable_ops: + if graph_has_recomputable_rng_ops: + fw_module, bw_module = functionalize_rng_ops( + joint_module, fw_module, bw_module, len(saved_sym_nodes) + ) + bw_module = reordering_to_mimic_autograd_engine(bw_module) + + if AOT_PARTITIONER_DEBUG: + # Calculate sorted sizes of saved values + sorted_sizes = sorted([(_size_of(i), str(i)) for i in saved_values]) + + # Log total theoretical activations stored + total_activations_size_gb = sum(_size_of(i) for i in saved_values) / 1e9 + log.info("Theoretical Activations Stored: %.2f GB", total_activations_size_gb) + + # Log theoretical per activation storage sizes + log.info("Theoretical Per Activation Storage Sizes: %s", sorted_sizes) + fw_module_nodes = OrderedSet( + node.name for node in fw_module.graph.nodes if node.op == "call_function" + ) + bw_module_nodes = OrderedSet( + node.name for node in bw_module.graph.nodes if node.op == "call_function" + ) + remat_nodes = fw_module_nodes & bw_module_nodes + + counts: dict[str, int] = defaultdict(int) + for node in fw_module.graph.nodes: + if node.name in remat_nodes and hasattr(node.target, "_overloadpacket"): + counts[str(node.target._overloadpacket)] += 1 + log.info( + "# remat/fw/bw: %d/%d/%d", + len(remat_nodes), + len(fw_module_nodes), + len(bw_module_nodes), + ) + rematerialized_ops = sorted(counts.items(), key=lambda x: x[1], reverse=True) + log.info("Count of Ops Rematerialized: %s", rematerialized_ops) + return fw_module, bw_module + + +def draw_graph( + traced: torch.fx.GraphModule, + fname: str, + figname: str = "fx_graph", + clear_meta: bool = True, + prog: Optional[Union[str, list[str]]] = None, + parse_stack_trace: bool = False, + dot_graph_shape: Optional[str] = None, +) -> None: + if clear_meta: + new_graph = copy.deepcopy(traced.graph) + traced = fx.GraphModule(traced, new_graph) + for node in traced.graph.nodes: + node.meta = {} + base, ext = os.path.splitext(fname) + if not ext: + ext = "." + config.torch_compile_graph_format + log.info("Writing FX graph to file: %s%s", base, ext) + g = graph_drawer.FxGraphDrawer( + traced, + figname, + parse_stack_trace=parse_stack_trace, + dot_graph_shape=dot_graph_shape, + ) + x = g.get_main_dot_graph() + write_method = getattr(x, "write_" + ext.lstrip(".")) + fname = f"{base}{ext}" + if prog is None: + write_method(fname) + else: + write_method(fname, prog=prog) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/pyfunctorch.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/pyfunctorch.py new file mode 100644 index 0000000000000000000000000000000000000000..28bd74f28d383668dd3c2c384c88e5333bba4bfa --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/pyfunctorch.py @@ -0,0 +1,294 @@ +# mypy: allow-untyped-defs +import contextlib +from abc import ABC, abstractmethod +from typing import Any + +import torch +import torch.utils._pytree as pytree +from torch._C._functorch import ( + CFunctionalizeInterpreterPtr, + CGradInterpreterPtr, + CInterpreter, + CJvpInterpreterPtr, + CVmapInterpreterPtr, + pop_dynamic_layer_stack, + push_dynamic_layer_stack, + RandomnessType, + TransformType, +) +from torch.autograd.forward_ad import _set_fwd_grad_enabled + + +""" +This file contains the functorch integration with PyDispatcher. + +PyDispatcher does not understand functorch's DynamicLayerStack dispatching +logic because it is entirely implemented in C++ in the fallbacks for two +dispatch keys, FuncTorchDynamicLayer{Front, Back}Mode (PyDispatcher is unable +to directly reuse C++ boxed fallbacks). + +Instead of trying to hammer PyDispatcher into understanding those fallbacks, +we re-implement the logic of peeking the top of the stack for an interpreter, +selecting the interpreter to dispatch on, etc, in Python. This leads to a +simpler design. + +The main difference between C++ functorch and PyDispatcher's functorch logic +is that: +- C++ functorch needs to manually tweak dispatch keys to ping-pong between + DynamicLayerFrontMode and DynamicLayerBackMode. +- PyDispatcher's functorch logic pops an Interpreter from the top of the stack + and asks it to execute the rule associated with the Interpreter. + +In C++ we do the ping-pong because e.g. vmap rules are associated with the +batched DispatchKey, but in PyDispatcher we are able to avoid this by asking +the user to register a batching rule directly to a transform that an +interpreter then invokes. +""" + + +# FuncTorchInterpreter is the Python version of Interpreter (recall that +# the DynamicLayerStack is a stack of interpreters). +# It is a wrapper around the actual C++ Interpreter object. +# +# Keep the methods in sync with aten/src/ATen/functorch/Interpreter.h +class FuncTorchInterpreter(ABC): + def __init__(self, cptr: Any): + self._cptr = cptr + + # Process an operation. eg for vmap, this is invoking a batching rule. + # Conceptually this is analogous to Interpreter::process in C++ + @abstractmethod + def process(self, op, args, kwargs): + pass + + # lower an operation from this Interpreter to the next Interpreter on the stack. + # Concretely, this involves temporarily popping the current Interpreter. + # Conceptually this is analogous to Interpreter::sendToNextInterpreter in C++ + def lower(self): + return temporarily_pop_interpreter_stack() + + def level(self): + return self._cptr.level() + + def key(self): + return self._cptr.key() + + def get_state(self): + raise NotImplementedError + + def check_state(self, state): + return state == self.get_state() + + +@contextlib.contextmanager +def temporarily_pop_interpreter_stack(): + try: + saved = pop_dynamic_layer_stack() + yield + finally: + push_dynamic_layer_stack(saved) + + +@contextlib.contextmanager +def temporarily_clear_interpreter_stack(): + stack = [] + try: + while torch._C._functorch.peek_interpreter_stack() is not None: + stack.append(pop_dynamic_layer_stack()) + yield list(stack) + finally: + while stack: + push_dynamic_layer_stack(stack.pop()) + + +@contextlib.contextmanager +def temporarily_restore_interpreter_stack(stack): + pushed = [] + try: + for s in reversed(stack): + push_dynamic_layer_stack(s) + pushed.append(s) + yield + finally: + for s in reversed(pushed): + # TODO: would be nice to assert that the layers are the same, but + # Python object identity is not preserved + pop_dynamic_layer_stack() + + +class VmapInterpreter(FuncTorchInterpreter): + def __init__(self, cdata: CInterpreter): + assert cdata.key() == TransformType.Vmap + # NOTE: [Interpreter cdata vs cptr] + # cdata is a generic CInterpreter. We wrap it in a CVmapInterpreterPtr + # so that we can access methods specific to the vmap interpreter + self._cdata = cdata + self._cptr = CVmapInterpreterPtr(cdata) + + def process(self, op, args, kwargs): + kernel = op.functorch_table[TransformType.Vmap] + return kernel(self, *args, **kwargs) + + def batch_size(self): + return self._cptr.batchSize() + + def randomness(self): + typ = self._cptr.randomness() + if typ == RandomnessType.Error: + return "error" + elif typ == RandomnessType.Same: + return "same" + elif typ == RandomnessType.Different: + return "different" + raise RuntimeError(f"Unknown RandomnessType: {typ}") + + def get_state(self): + return (self.key().name, self.level(), self.randomness()) + + +@contextlib.contextmanager +def nested(*contexts): + with contextlib.ExitStack() as stack: + for ctx in contexts: + stack.enter_context(ctx) + yield contexts + + +class GradInterpreter(FuncTorchInterpreter): + def __init__(self, cdata: CInterpreter): + assert cdata.key() == TransformType.Grad + # See NOTE: [Interpreter cdata vs cptr] + self._cdata = cdata + self._cptr = CGradInterpreterPtr(cdata) + + def lift(self, args, kwargs): + args, kwargs = pytree.tree_map_only( + torch.Tensor, self._cptr.lift, [args, kwargs] + ) + return args, kwargs + + def process(self, op, args, kwargs): + kernel = op.functorch_table[TransformType.Grad] + args, kwargs = self.lift(args, kwargs) + return kernel(self, *args, **kwargs) + + # GradInterpreter has custom lower because of the no_grad interaction + # See NOTE [grad and vjp interaction with no_grad] + # This logic is mirrored from C++ GradInterpreterPtr::sendToNextInterpreter + def lower(self): + prev_grad_mode = self.prev_grad_mode() + if not prev_grad_mode: + return nested(torch.no_grad(), super().lower()) + return super().lower() + + def prev_grad_mode(self): + return self._cptr.prevGradMode() + + def get_state(self): + return (self.key().name, self.level(), self.prev_grad_mode()) + + +class JvpInterpreter(FuncTorchInterpreter): + def __init__(self, cdata: CInterpreter): + assert cdata.key() == TransformType.Jvp + # See NOTE: [Interpreter cdata vs cptr] + self._cdata = cdata + self._cptr = CJvpInterpreterPtr(cdata) + + def lift(self, args, kwargs): + args, kwargs = pytree.tree_map_only( + torch.Tensor, self._cptr.lift, [args, kwargs] + ) + return args, kwargs + + def process(self, op, args, kwargs): + kernel = op.functorch_table[TransformType.Jvp] + args, kwargs = self.lift(args, kwargs) + return kernel(self, *args, **kwargs) + + # Jvp has custom lower because of the no_fwd_grad interaction + # See NOTE [grad and vjp interaction with no_grad] for related info. + # This logic is mirrored from C++ JvpInterpreterPtr::sendToNextInterpreter + def lower(self): + prev_fwd_grad_mode = self.prev_fwd_grad_mode() + if not prev_fwd_grad_mode: + return nested(_set_fwd_grad_enabled(False), super().lower()) + return super().lower() + + def prev_fwd_grad_mode(self): + return self._cptr.prevFwdGradMode() + + def get_state(self): + return (self.key().name, self.level(), self.prev_fwd_grad_mode()) + + +class FunctionalizeInterpreter(FuncTorchInterpreter): + def __init__(self, cdata: CInterpreter): + assert cdata.key() == TransformType.Functionalize + self._cdata = cdata + self._cptr = CFunctionalizeInterpreterPtr(cdata) + + def process(self, op, args, kwargs): + kernel = op.functorch_table[TransformType.Functionalize] + return kernel(self, *args, **kwargs) + + def functionalize_add_back_views(self): + return self._cptr.functionalizeAddBackViews() + + def get_state(self): + return (self.key().name, self.level()) + + +def coerce_cinterpreter(cinterpreter: CInterpreter) -> FuncTorchInterpreter: + key = cinterpreter.key() + if key == TransformType.Grad: + return GradInterpreter(cinterpreter) + if key == TransformType.Vmap: + return VmapInterpreter(cinterpreter) + if key == TransformType.Jvp: + return JvpInterpreter(cinterpreter) + if key == TransformType.Functionalize: + return FunctionalizeInterpreter(cinterpreter) + raise RuntimeError(f"NYI: PyDispatcher has not implemented support for {key}") + + +def retrieve_current_functorch_interpreter() -> FuncTorchInterpreter: + interpreter = torch._C._functorch.peek_interpreter_stack() + assert interpreter is not None + return coerce_cinterpreter(interpreter) + + +def retrieve_all_functorch_interpreters() -> list[FuncTorchInterpreter]: + cis = torch._C._functorch.get_interpreter_stack() + if cis is None: + return [] + return [coerce_cinterpreter(ci) for ci in cis] + + +def compare_functorch_state(states: list[tuple[Any, ...]]) -> bool: + # There are four possible cases covered here: + # 1. Current stack empty AND stack when generated not empty -> Invalidate + # 2. Current stack not empty AND stack when generated empty -> Invalidate + # 3. Current stack and generated stack empty -> Valid FX graph + # 4. Current stack and generated stack not empty -> Valid if both states match + peek = torch._C._functorch.peek_interpreter_stack() + if (peek is None and len(states) != 0) or (peek is not None and len(states) == 0): + return False + + cis = retrieve_all_functorch_interpreters() + return len(cis) == len(states) and all( + ci.check_state(state) for ci, state in zip(cis, states) + ) + + +def dispatch_functorch(op, args, kwargs): + interpreter = retrieve_current_functorch_interpreter() + # In traditional PyTorch operators, DispatchKey::FuncTorchTensorWrapper's + # unwrap_dead_tensors fallback handles unwrapping dead tensor wrappers. + # PyDispatcher sidesteps the PyTorch dispatcher when dealing with functorch + # transforms, so we manually unwrap the dead tensors here. + # This logic won't need to exist when we have mode-only functorch. + args, kwargs = pytree.tree_map_only( + torch.Tensor, torch._C._functorch.unwrap_if_dead, (args, kwargs) + ) + return interpreter.process(op, args, kwargs) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/python_key.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/python_key.py new file mode 100644 index 0000000000000000000000000000000000000000..557334f68928a057a6a9e036c904c8c0bd3231c1 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/python_key.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +__all__ = ["make_fx", "dispatch_trace", "PythonKeyTracer", "pythonkey_decompose"] +from torch.fx.experimental.proxy_tensor import ( + decompose, + dispatch_trace, + make_fx, + PythonKeyTracer, +) + + +pythonkey_decompose = decompose diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/pytree_hacks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/pytree_hacks.py new file mode 100644 index 0000000000000000000000000000000000000000..96dea7ad100705ae53139aa5ae729fd2206182af --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/pytree_hacks.py @@ -0,0 +1,23 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import warnings + +# TODO: remove this file when the migration of the pytree utility is done +from torch.utils._pytree import tree_map_, treespec_pprint + + +__all__ = ["tree_map_", "treespec_pprint"] + + +with warnings.catch_warnings(): + warnings.simplefilter("always") + warnings.warn( + "`torch._functorch.pytree_hacks` is deprecated and will be removed in a future release. " + "Please `use torch.utils._pytree` instead.", + DeprecationWarning, + stacklevel=2, + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/top_operators_github_usage.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/top_operators_github_usage.py new file mode 100644 index 0000000000000000000000000000000000000000..1fcdbe0b41ac00c21198e3a82d58c5a446d19324 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/top_operators_github_usage.py @@ -0,0 +1,632 @@ +# mypy: ignore-errors + +""" +From https://docs.google.com/spreadsheets/d/12R3nCOLskxPYjjiNkdqy4OdQ65eQp_htebXGODsjSeA/edit#gid=0 +Try to keep this list in sync with that. +""" +import operator + + +top_torch = [ + ("t", 6837449), + ("tensor", 585786), + ("mode", 462182), + ("cat", 394818), + ("max", 368038), + ("zeros", 329495), + ("load", 327756), + ("no_grad", 294694), + ("save", 265130), + ("from_numpy", 243063), + ("manual_seed", 165044), + ("ones", 153696), + ("randn", 150796), + ("stack", 133358), + ("sum", 130772), + ("arange", 98087), + ("rand", 94715), + ("mean", 88546), + ("exp", 73883), + ("zeros_like", 72831), + ("min", 72248), + ("sigmoid", 66798), + ("log", 62135), + ("matmul", 47811), + ("clamp", 45304), + ("sqrt", 44911), + ("abs", 43535), + ("tanh", 42793), + ("empty", 40311), + ("argmax", 38435), + ("bmm", 33984), + ("pow", 33571), + ("norm", 31125), + ("mm", 30995), + ("is_tensor", 29546), + ("ones_like", 29512), + ("nonzero", 28681), + ("full", 28373), + ("unsqueeze", 27911), + ("where", 26585), + ("randperm", 26450), + ("eye", 24342), + ("mul", 23236), + ("topk", 22537), + ("as_tensor", 21967), + ("sort", 21412), + ("squeeze", 20863), + ("randint", 20771), + ("linspace", 20041), + ("add", 19201), + ("transpose", 18663), + ("split", 18325), + ("gather", 17904), + ("set_grad_enabled", 16013), + ("sin", 15669), + ("cos", 15562), + ("div", 15513), + ("index_select", 14866), + ("multinomial", 14331), + ("flatten", 14267), + ("isnan", 14170), + ("randn_like", 13096), + ("eq", 12680), + ("einsum", 12480), + ("round", 12367), + ("floor", 11628), + ("allclose", 11000), + ("reshape", 10605), + ("diag", 10167), + ("chunk", 9581), + ("std", 9379), + ("set_default_tensor_type", 9281), + ("triu", 8559), + ("meshgrid", 8292), + ("set_num_threads", 8126), + ("unique", 7964), + ("full_like", 7780), + ("tril", 7538), + ("dot", 7275), + ("sign", 6943), + ("equal", 6916), + ("normal", 6750), + ("cumsum", 6556), + ("dist", 6058), + ("isfinite", 6030), + ("gt", 5935), + ("set_printoptions", 5888), + ("range", 5491), + ("empty_like", 5351), + ("flip", 5342), + ("masked_select", 5341), + ("bernoulli", 5262), + ("atan", 5253), + ("var", 5247), + ("prod", 5200), + ("erf", 5088), + ("inverse", 5072), + ("addmm", 4854), + ("logsumexp", 4582), + ("fft", 4436), + ("lt", 4421), + ("log2", 4316), + ("enable_grad", 4238), + ("rand_like", 4187), + ("argsort", 3972), + ("seed", 3932), + ("mv", 3547), + ("ger", 3309), + ("ge", 3248), + ("atan2", 3210), + ("ceil", 3202), + ("ne", 3075), + ("bincount", 3063), + ("acos", 3055), + ("rsqrt", 3031), + ("svd", 3029), + ("numel", 3003), + ("log1p", 2840), + ("unbind", 2808), + ("le", 2714), + ("isinf", 2707), + ("cross", 2646), + ("set_default_dtype", 2536), + ("argmin", 2535), + ("sparse_coo_tensor", 2489), + ("log10", 2304), + ("kthvalue", 2192), + ("set_rng_state", 2158), + ("get_rng_state", 1996), + ("get_default_dtype", 1879), + ("det", 1868), + ("qr", 1864), + ("histc", 1852), + ("symeig", 1832), + ("trace", 1801), + ("median", 1795), + ("addcmul", 1751), + ("remainder", 1717), + ("baddbmm", 1693), + ("lgamma", 1665), + ("repeat_interleave", 1598), + ("fmod", 1576), + ("reciprocal", 1575), + ("tan", 1560), + ("initial_seed", 1532), + ("take", 1529), + ("stft", 1487), + ("get_num_threads", 1477), + ("real", 1459), + ("cholesky", 1406), + ("quantize_per_tensor", 1392), + ("diag_embed", 1364), + ("lerp", 1363), + ("asin", 1345), + ("eig", 1333), + ("trunc", 1290), + ("diagonal", 1287), + ("cosh", 1279), + ("rfft", 1269), + ("cumprod", 1260), + ("addr", 1211), + ("roll", 1198), + ("narrow", 1188), + ("digamma", 1172), + ("square", 1163), + ("sinh", 1131), + ("logspace", 1084), + ("broadcast_tensors", 1070), + ("irfft", 1013), + ("frac", 997), + ("hann_window", 994), + ("solve", 989), + ("logdet", 977), + ("expm1", 968), + ("cdist", 946), + ("addmv", 903), + ("randint_like", 888), + ("tensordot", 888), + ("ifft", 877), + ("true_divide", 854), + ("erfinv", 830), + ("addcdiv", 819), + ("addbmm", 813), + ("renorm", 781), + ("pinverse", 753), + ("isclose", 740), + ("erfc", 729), + ("is_storage", 725), + ("triangular_solve", 723), + ("rot90", 709), + ("logical_not", 686), + ("geqrf", 681), + ("slogdet", 677), + ("lu", 665), + ("hamming_window", 659), + ("orgqr", 651), + ("ormqr", 622), + ("is_floating_point", 602), + ("diagflat", 562), + ("cholesky_solve", 559), + ("tril_indices", 552), + ("chain_matmul", 551), + ("triu_indices", 548), + ("angle", 522), + ("poisson", 505), + ("matrix_power", 485), + ("unique_consecutive", 471), + ("quantize_per_channel", 465), + ("std_mean", 458), + ("bartlett_window", 447), + ("var_mean", 428), + ("lstsq", 421), + ("logical_and", 419), + ("mvlgamma", 411), + ("blackman_window", 400), + ("bitwise_not", 395), + ("cholesky_inverse", 388), + ("as_strided", 384), + ("floor_divide", 353), + ("cartesian_prod", 321), + ("lu_solve", 317), + ("set_flush_denormal", 310), + ("empty_strided", 283), + ("logical_xor", 282), + ("polygamma", 282), + ("logical_or", 280), + ("set_num_interop_threads", 278), + ("combinations", 274), + ("trapz", 270), + ("matrix_rank", 260), + ("lu_unpack", 255), + ("result_type", 244), + ("conj", 231), + ("cummax", 230), + ("lobpcg", 229), + ("bitwise_xor", 217), + ("promote_types", 213), + ("get_num_interop_threads", 211), + ("cummin", 205), + ("bitwise_and", 198), + ("dequantize", 192), + ("bitwise_or", 191), + ("imag", 191), + ("can_cast", 184), + ("istft", 180), + ("compiled_with_cxx11_abi", 159), + ("is_complex", 151), + ("block_diag", 136), + ("pca_lowrank", 124), + ("absolute", 122), + ("svd_lowrank", 108), + ("neg", 2), +] + +top_nn_functional = [ + ("nn.functional.softmax", 10522), + ("nn.functional.relu", 8572), + ("nn.functional.interpolate", 7277), + ("nn.functional.pad", 5207), + ("nn.functional.log_softmax", 4699), + ("nn.functional.normalize", 2338), + ("nn.functional.cross_entropy", 2083), + ("nn.functional.grid_sample", 1970), + ("nn.functional.one_hot", 1967), + ("nn.functional.mse_loss", 1920), + ("nn.functional.conv2d", 1593), + ("nn.functional.dropout", 1516), + ("nn.functional.softplus", 1385), + ("nn.functional.sigmoid", 1128), + ("nn.functional.linear", 1036), + ("nn.functional.gelu", 930), + ("nn.functional.avg_pool2d", 899), + ("nn.functional.max_pool2d", 876), + ("nn.functional.nll_loss", 863), + ("nn.functional.embedding", 737), + ("nn.functional.tanh", 664), + ("nn.functional.leaky_relu", 640), + ("nn.functional.adaptive_avg_pool2d", 633), + ("nn.functional.cosine_similarity", 627), + ("nn.functional.unfold", 609), + ("nn.functional.conv1d", 596), + ("nn.functional.binary_cross_entropy_with_logits", 591), + ("nn.functional.l1_loss", 571), + ("nn.functional.binary_cross_entropy", 492), + ("nn.functional.elu", 416), + ("nn.functional.batch_norm", 413), + ("nn.functional.upsample", 413), + ("nn.functional.fold", 305), + ("nn.functional.affine_grid", 298), + ("nn.functional.max_pool1d", 297), + ("nn.functional.torch", 294), + ("nn.functional.threshold", 263), + ("nn.functional.smooth_l1_loss", 262), + ("nn.functional.pairwise_distance", 253), + ("nn.functional.logsigmoid", 243), + ("nn.functional.adaptive_max_pool2d", 235), + ("nn.functional.relu6", 213), + ("nn.functional.pixel_shuffle", 209), + ("nn.functional.avg_pool3d", 203), + ("nn.functional.bilinear", 203), + ("nn.functional.conv_transpose2d", 201), + ("nn.functional.gumbel_softmax", 197), + ("nn.functional.max_unpool2d", 196), + ("nn.functional.kl_div", 191), + ("nn.functional.hardtanh", 189), + ("nn.functional.ctc_loss", 185), + ("nn.functional.layer_norm", 178), + ("nn.functional.conv3d", 172), + ("nn.functional.max_unpool3d", 167), + ("nn.functional.hardshrink", 165), + ("nn.functional.hardswish", 156), + ("nn.functional.selu", 156), + ("nn.functional.glu", 155), + ("nn.functional.assert_int_or_pair", 150), + ("nn.functional.hardsigmoid", 146), + ("nn.functional.upsample_bilinear", 146), + ("nn.functional.max_pool3d", 140), + ("nn.functional.adaptive_avg_pool3d", 139), + ("nn.functional.instance_norm", 124), + ("nn.functional.embedding_bag", 122), + ("nn.functional.upsample_nearest", 110), + ("nn.functional.avg_pool1d", 105), + ("nn.functional.prelu", 102), + ("nn.functional.celu", 92), + ("nn.functional.dropout2d", 86), + ("nn.functional.hinge_embedding_loss", 82), + ("nn.functional.softsign", 81), + ("nn.functional.max_unpool1d", 74), + ("nn.functional.silu", 74), + ("nn.functional.softshrink", 70), + ("nn.functional.leaky_relu_", 68), + ("nn.functional.softmin", 67), + ("nn.functional.channel_shuffle", 66), + ("nn.functional.multilabel_margin_loss", 66), + ("nn.functional.dropout3d", 65), + ("nn.functional.multi_margin_loss", 65), + ("nn.functional.lp_pool2d", 64), + ("nn.functional.conv_transpose1d", 62), + ("nn.functional.triplet_margin_loss", 62), + ("nn.functional.tanhshrink", 61), + ("nn.functional.adaptive_max_pool1d", 59), + ("nn.functional.cosine_embedding_loss", 58), + ("nn.functional.multi_head_attention_forward", 58), + ("nn.functional.max_pool1d_with_indices", 53), + ("nn.functional.poisson_nll_loss", 53), + ("nn.functional.margin_ranking_loss", 52), + ("nn.functional.soft_margin_loss", 52), + ("nn.functional.adaptive_max_pool3d", 51), + ("nn.functional.group_norm", 51), + ("nn.functional.local_response_norm", 51), + ("nn.functional.multilabel_soft_margin_loss", 51), + ("nn.functional.relu_", 50), + ("nn.functional.alpha_dropout", 49), + ("nn.functional.feature_alpha_dropout", 49), + ("nn.functional.lp_pool1d", 49), + ("nn.functional.adaptive_max_pool1d_with_indices", 48), + ("nn.functional.adaptive_max_pool2d_with_indices", 48), + ("nn.functional.adaptive_max_pool3d_with_indices", 48), + ("nn.functional.fractional_max_pool2d", 48), + ("nn.functional.fractional_max_pool2d_with_indices", 48), + ("nn.functional.fractional_max_pool3d", 48), + ("nn.functional.fractional_max_pool3d_with_indices", 48), + ("nn.functional.max_pool2d_with_indices", 48), + ("nn.functional.max_pool3d_with_indices", 48), + ("nn.functional.handle_torch_function", 47), + ("nn.functional.has_torch_function", 47), + ("nn.functional.adaptive_avg_pool1d", 43), + ("nn.functional.pdist", 43), + ("nn.functional.rrelu_", 37), + ("nn.functional.elu_", 34), + ("nn.functional.boolean_dispatch", 33), + ("nn.functional.hardtanh_", 26), + ("nn.functional.triplet_margin_with_distance_loss", 23), + ("nn.functional.selu_", 20), + ("nn.functional.pixel_unshuffle", 19), + ("nn.functional.conv_transpose3d", 18), + ("nn.functional.gaussian_nll_loss", 15), + ("nn.functional.has_torch_function_unary", 15), + ("nn.functional.has_torch_function_variadic", 15), + ("nn.functional.celu_", 13), + ("nn.functional.huber_loss", 7), + ("nn.functional.mish", 4), + ("nn.functional.threshold_", 3), + ("nn.functional.grad", 2), + ("nn.functional.conv_tbc", 1), + ("nn.functional.math", 1), +] + +top_nn_module = [ + ("nn.Module", 927129, None), + ("nn.Linear", 530688, "nn.functional.linear"), + ("nn.Sequential", 384968, None), + ("nn.Conv2d", 383320, "nn.functional.conv2d"), + ("nn.ReLU", 318877, "nn.functional.relu"), + ("nn.BatchNorm2d", 233265, "nn.functional.batch_norm"), + ("nn.Dropout", 179268, "nn.functional.dropout"), + ("nn.ModuleList", 171225, None), + ("nn.Parameter", 153291, None), + ("nn.CrossEntropyLoss", 152696, "nn.functional.cross_entropy"), + ("nn.MaxPool2d", 138619, "nn.functional.max_pool2d"), + ("nn.Embedding", 111844, "nn.functional.embedding"), + ("nn.DataParallel", 104238, None), + ("nn.MSELoss", 82954, "nn.functional.mse_loss"), + ("nn.Sigmoid", 75810, "nn.functional.sigmoid"), + ("nn.LeakyReLU", 65632, "nn.functional.leaky_relu"), + ("nn.BatchNorm1d", 65374, "nn.functional.batch_norm"), + ("nn.Softmax", 65114, "nn.functional.softmax"), + ("nn.Tanh", 59445, "nn.functional.tanh"), + ("nn.AdaptiveAvgPool2d", 59071, "nn.functional.adaptive_avg_pool2d"), + ("nn.AvgPool2d", 58377, "nn.functional.avg_pool2d"), + ("nn.ConvTranspose2d", 57524, "nn.functional.conv_transpose2d"), + ("nn.LSTM", 57411, None), + ("nn.Conv1d", 41108, "nn.functional.conv1d"), + ("nn.LayerNorm", 36089, "nn.functional.layer_norm"), + ("nn.BCELoss", 34005, "nn.functional.binary_cross_entropy"), + ("nn.Upsample", 32527, "nn.functional.interpolate"), + ("nn.BCEWithLogitsLoss", 29944, "nn.functional.binary_cross_entropy_with_logits"), + ("nn.GRU", 25421, None), + ("nn.Dropout2d", 23512, "nn.functional.dropout2d"), + ("nn.LogSoftmax", 22897, "nn.functional.log_softmax"), + ("nn.L1Loss", 22778, "nn.functional.l1_loss"), + ("nn.GroupNorm", 22183, "nn.functional.group_norm"), + ("nn.NLLLoss", 21751, "nn.functional.nll_loss"), + ("nn.Conv3d", 20874, "nn.functional.conv3d"), + ("nn.Identity", 17911, None), + ("nn.InstanceNorm2d", 16426, "nn.functional.instance_norm"), + ("nn.BatchNorm3d", 16378, "nn.functional.batch_norm"), + ("nn.PReLU", 13472, "nn.functional.prelu"), + ("nn.ReLU6", 12622, "nn.functional.relu6"), + ("nn.ELU", 12508, "nn.functional.elu"), + ("nn.LSTMCell", 10885, None), + ("nn.Flatten", 10384, "torch.flatten"), + ("nn.ModuleDict", 10255, None), + ("nn.ReflectionPad2d", 9954, "nn.functional.pad"), + ("nn.MaxPool3d", 9526, "nn.functional.max_pool3d"), + ("nn.MaxPool1d", 9154, "nn.functional.max_pool1d"), + ("nn.RNN", 9154, None), + ("nn.ZeroPad2d", 8847, "nn.functional.pad"), + ("nn.ParameterList", 7702, None), + ("nn.SyncBatchNorm", 6814, None), + ("nn.PixelShuffle", 6571, "nn.functional.pixel_shuffle"), + ("nn.SmoothL1Loss", 6517, "nn.functional.smooth_l1_loss"), + ("nn.Hardswish", 6458, "nn.functional.hardswish"), + ("nn.AdaptiveMaxPool2d", 6071, "nn.functional.adaptive_max_pool2d"), + ("nn.SELU", 6043, "nn.functional.selu"), + ("nn.ConvTranspose3d", 6039, "nn.functional.conv_transpose3d"), + ("nn.GRUCell", 5840, None), + ("nn.ReplicationPad2d", 5600, "nn.functional.pad"), + ("nn.KLDivLoss", 5541, "nn.functional.kl_div"), + ("nn.ConvTranspose1d", 5183, "nn.functional.conv_transpose1d"), + ("nn.Softplus", 5120, "nn.functional.softplus"), + ("nn.SiLU", 4895, "nn.functional.silu"), + ("nn.AvgPool3d", 4523, "nn.functional.avg_pool3d"), + ("nn.CosineSimilarity", 4058, "nn.functional.cosine_similarity"), + ("nn.GELU", 3932, "nn.functional.gelu"), + ("nn.UpsamplingBilinear2d", 3673, "nn.functional.interpolate"), + ("nn.InstanceNorm1d", 3658, "nn.functional.instance_norm"), + ("nn.Transformer", 3604, None), + ("nn.MultiheadAttention", 3435, "nn.functional.multi_head_attention_forward"), + ("nn.AvgPool1d", 3195, "nn.functional.avg_pool1d"), + ("nn.Dropout3d", 2964, "nn.functional.dropout3d"), + ("nn.AdaptiveAvgPool3d", 2915, "nn.functional.adaptive_avg_pool3d"), + ("nn.InstanceNorm3d", 2893, "nn.functional.instance_norm"), + ("nn.Hardtanh", 2613, "nn.functional.hardtanh"), + ("nn.MarginRankingLoss", 2568, "nn.functional.margin_ranking_loss"), + ("nn.GLU", 2526, "nn.functional.glu"), + ("nn.AdaptiveAvgPool1d", 2481, "nn.functional.adaptive_avg_pool1d"), + ("nn.EmbeddingBag", 2344, "nn.functional.embedding_bag"), + ("nn.TransformerEncoderLayer", 2292, None), + ("nn.TransformerEncoder", 2091, None), + ("nn.MaxUnpool2d", 2031, "nn.functional.max_unpool2d"), + ("nn.UpsamplingNearest2d", 2004, "nn.functional.interpolate"), + ("nn.ConstantPad1d", 1904, "nn.functional.pad"), + ("nn.ConstantPad2d", 1791, "nn.functional.pad"), + ("nn.CTCLoss", 1789, "nn.functional.ctc_loss"), + ("nn.AdaptiveMaxPool1d", 1713, "nn.functional.adaptive_max_pool1d"), + ("nn.AdaptiveLogSoftmaxWithLoss", 1665, None), + ("nn.Bilinear", 1664, "nn.functional.bilinear"), + ("nn.RNNCell", 1653, None), + ("nn.MultiLabelSoftMarginLoss", 1624, "nn.functional.multilabel_soft_margin_loss"), + ("nn.Unfold", 1452, "nn.functional.unfold"), + ("nn.RReLU", 1431, "nn.functional.rrelu"), + ("nn.CosineEmbeddingLoss", 1357, "nn.functional.cosine_embedding_loss"), + ("nn.LocalResponseNorm", 1331, "nn.functional.local_response_norm"), + ("nn.Softmax2d", 1300, "nn.functional.softmax"), + ("nn.PairwiseDistance", 1241, "nn.functional.pairwise_distance"), + ("nn.LogSigmoid", 1235, "nn.functional.logsigmoid"), + ("nn.TripletMarginLoss", 1230, "nn.functional.triplet_margin_loss"), + ("nn.RNNBase", 1133, None), + ("nn.Threshold", 1043, "nn.functional.threshold"), + ("nn.AdaptiveMaxPool3d", 1025, "nn.functional.adaptive_max_pool3d"), + ("nn.CELU", 1018, "nn.functional.celu"), + ("nn.NLLLoss2d", 966, "nn.functional.nll_loss"), + ("nn.Softsign", 877, "nn.functional.softsign"), + ("nn.ReplicationPad1d", 862, "nn.functional.pad"), + ("nn.SoftMarginLoss", 856, "nn.functional.soft_margin_loss"), + ("nn.ParameterDict", 742, None), + ("nn.ReflectionPad1d", 731, "nn.functional.pad"), + ("nn.Softshrink", 713, "nn.functional.softshrink"), + ("nn.AlphaDropout", 710, "nn.functional.alpha_dropout"), + ("nn.Tanhshrink", 681, "nn.functional.tanhshrink"), + ("nn.PoissonNLLLoss", 676, "nn.functional.poisson_nll_loss"), + ("nn.MaxUnpool3d", 660, "nn.functional.max_unpool3d"), + ("nn.Fold", 630, "nn.functional.fold"), + ("nn.MultiMarginLoss", 622, "nn.functional.multi_margin_loss"), + ("nn.TransformerDecoderLayer", 614, None), + ("nn.TransformerDecoder", 607, None), + ("nn.Hardshrink", 592, "nn.functional.hardshrink"), + ("nn.ConstantPad3d", 582, "nn.functional.pad"), + ("nn.MultiLabelMarginLoss", 580, "nn.functional.multilabel_margin_loss"), + ("nn.LPPool2d", 550, "nn.functional.lp_pool2d"), + ("nn.Softmin", 537, "nn.functional.softmin"), + ("nn.MaxUnpool1d", 518, "nn.functional.max_unpool1d"), + ("nn.FractionalMaxPool2d", 484, "nn.functional.fractional_max_pool2d"), + ("nn.Hardsigmoid", 477, "nn.functional.hardsigmoid"), + ("nn.ReplicationPad3d", 470, "nn.functional.pad"), + ("nn.HingeEmbeddingLoss", 442, "nn.functional.hinge_embedding_loss"), + ("nn.LPPool1d", 386, "nn.functional.lp_pool1d"), + ("nn.FractionalMaxPool3d", 252, "nn.functional.fractional_max_pool3d"), + ("nn.Container", 217, None), + ("nn.Unflatten", 206, "nn.functional.unflatten"), + ("nn.FeatureAlphaDropout", 136, "nn.functional.feature_alpha_dropout"), + ( + "nn.TripletMarginWithDistanceLoss", + 107, + "nn.functional.triplet_margin_with_distance_loss", + ), + ("nn.ChannelShuffle", 90, "nn.functional.channel_shuffle"), + ("nn.RNNCellBase", 88, None), + ("nn.LazyLinear", 81, "nn.functional.linear"), + ("nn.UninitializedParameter", 60, None), + ("nn.CrossMapLRN2d", 59, None), + ("nn.GaussianNLLLoss", 55, "nn.functional.gaussian_nll_loss"), + ("nn.PixelUnshuffle", 45, "nn.functional.pixel_unshuffle"), + ("nn.Mish", 31, "nn.functional.mish"), + ("nn.ReflectionPad3d", 22, "nn.functional.pad"), + ("nn.HuberLoss", 18, "nn.functional.huber_loss"), + ("nn.LazyConv2d", 15, None), + ("nn.LazyConv1d", 9, None), + ("nn.LazyConv3d", 8, None), + ("nn.LazyConvTranspose1d", 8, None), + ("nn.LazyConvTranspose2d", 8, None), + ("nn.LazyConvTranspose3d", 8, None), + ("nn.LazyBatchNorm1d", 3, None), + ("nn.LazyBatchNorm2d", 3, None), + ("nn.LazyBatchNorm3d", 3, None), + ("nn.UninitializedBuffer", 3, None), +] + +# No rankings because these are a little hard to get rankings for +method_only_ops = [ + "bfloat16", + "bool", + "byte", + "char", + "contiguous", + "cpu", + "cuda", + "detach", + "double", + "expand", + "expand_as", + "float", + "get_device", + "half", + "hardshrink", + "index_add", + "index_copy", + "index_fill", + "index_put", + "int", + "is_contiguous", + "is_pinned", + "is_set_to", + "is_shared", + "is_signed", + "item", + "long", + "masked_scatter", + "masked_fill", + "narrow_copy", + "numpy", + "pin_memory", + "repeat", + "reshape_as", + "select", + "short", + "storage_offset", + "sum_to_size", + "to", + "to_mkldnn", + "tolist", + "type", + "type_as", + "unfold", + "view", + "view_as", +] + + +def get_nn_functional_top_list(): + top_nn_functional_ = dict(top_nn_functional) + for _, count, functional_name in top_nn_module: + if functional_name is None: + continue + if functional_name == "torch.flatten": + continue + if functional_name not in top_nn_functional_: + top_nn_functional_[functional_name] = count + else: + top_nn_functional_[functional_name] += count + + top_nn_functional_ = list(top_nn_functional_.items()) + top_nn_functional_.sort(key=operator.itemgetter(1), reverse=True) + return top_nn_functional_ + + +usage_count = {} +for k, v in get_nn_functional_top_list(): + usage_count[k] = v +for k, v in top_torch: + usage_count[k] = v diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a2790a0fdd743171270dfd9e7826bb2f8dac6842 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/utils.py @@ -0,0 +1,40 @@ +import contextlib +from collections.abc import Generator +from typing import Any, Union + +import torch +from torch._C._functorch import ( + get_single_level_autograd_function_allowed, + set_single_level_autograd_function_allowed, + unwrap_if_dead, +) +from torch.utils._exposed_in import exposed_in + + +__all__ = [ + "exposed_in", + "argnums_t", + "enable_single_level_autograd_function", + "unwrap_dead_wrappers", +] + + +@contextlib.contextmanager +def enable_single_level_autograd_function() -> Generator[None, None, None]: + try: + prev_state = get_single_level_autograd_function_allowed() + set_single_level_autograd_function_allowed(True) + yield + finally: + set_single_level_autograd_function_allowed(prev_state) + + +def unwrap_dead_wrappers(args: tuple[Any, ...]) -> tuple[Any, ...]: + # NB: doesn't use tree_map_only for performance reasons + result = tuple( + unwrap_if_dead(arg) if isinstance(arg, torch.Tensor) else arg for arg in args + ) + return result + + +argnums_t = Union[int, tuple[int, ...]] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/vmap.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/vmap.py new file mode 100644 index 0000000000000000000000000000000000000000..f5c6dc1a5db5d35a27c2be856f4ffc9db1a3e286 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_functorch/vmap.py @@ -0,0 +1,539 @@ +# mypy: ignore-errors + +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import functools +import itertools +import os +import threading +from functools import partial +from typing import Any, Callable, Optional, Union + +import torch +from torch import Tensor +from torch._C._functorch import ( + _add_batch_dim, + _remove_batch_dim, + _vmap_decrement_nesting, + _vmap_increment_nesting, + is_batchedtensor, +) +from torch.utils._pytree import ( + _broadcast_to_and_flatten, + tree_flatten, + tree_map_, + tree_unflatten, + TreeSpec, +) + + +in_dims_t = Union[int, tuple] +out_dims_t = Union[int, tuple[int, ...]] + + +def doesnt_support_saved_tensors_hooks(f): + message = ( + "torch.func.{grad, vjp, jacrev, hessian} don't yet support saved tensor hooks. " + "Please open an issue with your use case." + ) + + @functools.wraps(f) + def fn(*args, **kwargs): + with torch.autograd.graph.disable_saved_tensors_hooks(message): + return f(*args, **kwargs) + + return fn + + +# Checks that all args-to-be-batched have the same batch dim size +def _validate_and_get_batch_size( + flat_in_dims: list[Optional[int]], flat_args: list +) -> int: + batch_sizes = [ + arg.size(in_dim) + for in_dim, arg in zip(flat_in_dims, flat_args) + if in_dim is not None + ] + if len(batch_sizes) == 0: + raise ValueError("vmap: Expected at least one Tensor to vmap over") + if batch_sizes and any(size != batch_sizes[0] for size in batch_sizes): + raise ValueError( + f"vmap: Expected all tensors to have the same size in the mapped " + f"dimension, got sizes {batch_sizes} for the mapped dimension" + ) + return batch_sizes[0] + + +def _num_outputs(batched_outputs: Union[Tensor, tuple[Tensor, ...]]) -> int: + if isinstance(batched_outputs, tuple): + return len(batched_outputs) + return 1 + + +# If value is a tuple, check it has length `num_elements`. +# If value is not a tuple, make a tuple with `value` repeated `num_elements` times + + +def _as_tuple( + value: Any, num_elements: int, error_message_lambda: Callable[[], str] +) -> tuple: + if not isinstance(value, tuple): + return (value,) * num_elements + if len(value) != num_elements: + raise ValueError(error_message_lambda()) + return value + + +def _process_batched_inputs( + in_dims: in_dims_t, args: tuple, func: Callable +) -> tuple[int, list[Any], list[Any], TreeSpec]: + if not isinstance(in_dims, int) and not isinstance(in_dims, tuple): + raise ValueError( + f"vmap({_get_name(func)}, in_dims={in_dims}, ...)(): " + f"expected `in_dims` to be int or a (potentially nested) tuple " + f"matching the structure of inputs, got: {type(in_dims)}." + ) + if len(args) == 0: + raise ValueError( + f"vmap({_get_name(func)})(): got no inputs. Maybe you forgot to add " + f"inputs, or you are trying to vmap over a function with no inputs. " + f"The latter is unsupported." + ) + + flat_args, args_spec = tree_flatten(args) + flat_in_dims = _broadcast_to_and_flatten(in_dims, args_spec) + if flat_in_dims is None: + raise ValueError( + f"vmap({_get_name(func)}, in_dims={in_dims}, ...)(): " + f"in_dims is not compatible with the structure of `inputs`. " + f"in_dims has structure {tree_flatten(in_dims)[1]} but inputs " + f"has structure {args_spec}." + ) + + for i, (arg, in_dim) in enumerate(zip(flat_args, flat_in_dims)): + if not isinstance(in_dim, int) and in_dim is not None: + raise ValueError( + f"vmap({_get_name(func)}, in_dims={in_dims}, ...)(): " + f"Got in_dim={in_dim} for an input but in_dim must be either " + f"an integer dimension or None." + ) + if isinstance(in_dim, int) and not isinstance(arg, Tensor): + raise ValueError( + f"vmap({_get_name(func)}, in_dims={in_dims}, ...)(): " + f"Got in_dim={in_dim} for an input but the input is of type " + f"{type(arg)}. We cannot vmap over non-Tensor arguments, " + f"please use None as the respective in_dim" + ) + if in_dim is not None and (in_dim < -arg.dim() or in_dim >= arg.dim()): + raise ValueError( + f"vmap({_get_name(func)}, in_dims={in_dims}, ...)(): " + f"Got in_dim={in_dim} for some input, but that input is a Tensor " + f"of dimensionality {arg.dim()} so expected in_dim to satisfy " + f"-{arg.dim()} <= in_dim < {arg.dim()}." + ) + if in_dim is not None and in_dim < 0: + flat_in_dims[i] = in_dim % arg.dim() + + return ( + _validate_and_get_batch_size(flat_in_dims, flat_args), + flat_in_dims, + flat_args, + args_spec, + ) + + +# Creates BatchedTensors for every Tensor in arg that should be batched. +# Returns the (potentially) batched arguments and the batch_size. + + +def _create_batched_inputs( + flat_in_dims: list[Any], flat_args: list[Any], vmap_level: int, args_spec +) -> tuple: + # See NOTE [Ignored _remove_batch_dim, _add_batch_dim] + batched_inputs = [ + arg if in_dim is None else _add_batch_dim(arg, in_dim, vmap_level) + for in_dim, arg in zip(flat_in_dims, flat_args) + ] + return tree_unflatten(batched_inputs, args_spec) + + +def _maybe_remove_batch_dim(name, batched_output, vmap_level, batch_size, out_dim): + if out_dim is None: + if isinstance(batched_output, torch.Tensor) and is_batchedtensor( + batched_output + ): + raise ValueError( + f"vmap({name}, ...): `{name}` can not return a " + f"BatchedTensor when out_dim is None" + ) + return batched_output + + # out_dim is non None + if not isinstance(batched_output, torch.Tensor): + raise ValueError( + f"vmap({name}, ...): `{name}` must only return " + f"Tensors, got type {type(batched_output)}. " + "Did you mean to set out_dims= to None for output?" + ) + + return _remove_batch_dim(batched_output, vmap_level, batch_size, out_dim) + + +# Undos the batching (and any batch dimensions) associated with the `vmap_level`. +def _unwrap_batched( + batched_outputs: Union[Tensor, tuple[Tensor, ...]], + out_dims: out_dims_t, + vmap_level: int, + batch_size: int, + func: Callable, +) -> tuple: + flat_batched_outputs, output_spec = tree_flatten(batched_outputs) + + def incompatible_error(): + raise ValueError( + f"vmap({_get_name(func)}, ..., out_dims={out_dims})(): " + f"out_dims is not compatible with the structure of `outputs`. " + f"out_dims has structure {tree_flatten(out_dims)[1]} but outputs " + f"has structure {output_spec}." + ) + + if isinstance(batched_outputs, torch.Tensor): + # Some weird edge case requires us to spell out the following + # see test_out_dims_edge_case + if isinstance(out_dims, int): + flat_out_dims = [out_dims] + elif isinstance(out_dims, tuple) and len(out_dims) == 1: + flat_out_dims = out_dims + elif out_dims is None: + flat_out_dims = [out_dims] + else: + incompatible_error() + else: + flat_out_dims = _broadcast_to_and_flatten(out_dims, output_spec) + if flat_out_dims is None: + incompatible_error() + + flat_outputs = [ + _maybe_remove_batch_dim( + _get_name(func), batched_output, vmap_level, batch_size, out_dim + ) + for batched_output, out_dim in zip(flat_batched_outputs, flat_out_dims) + ] + return tree_unflatten(flat_outputs, output_spec) + + +def _check_int_or_none(x, func, out_dims): + if isinstance(x, int): + return + if x is None: + return + raise ValueError( + f"vmap({_get_name(func)}, ..., out_dims={out_dims}): `out_dims` must be " + f"an int, None or a python collection of ints representing where in the outputs the " + f"vmapped dimension should appear." + ) + + +def _check_out_dims_is_int_or_int_pytree(out_dims: out_dims_t, func: Callable) -> None: + if isinstance(out_dims, int): + return + tree_map_(partial(_check_int_or_none, func=func, out_dims=out_dims), out_dims) + + +def _get_name(func: Callable): + if hasattr(func, "__name__"): + return func.__name__ + + if isinstance(func, functools.partial): + return f"functools.partial({_get_name(func.func)}, ...)" + + # Not all callables have __name__, in fact, only static functions/methods + # do. A callable created via nn.Module, to name one example, doesn't have a + # __name__. + return repr(func) + + +DECOMPOSITIONS_LOADED = False +DECOMPOSITIONS_LOCK = threading.Lock() +VMAP_DECOMPOSITIONS_LIB = None + + +# torch.package, Python 3.11, and torch.jit-less environments are unhappy with +# decompositions. Only load them when needed if possible. +def lazy_load_decompositions(): + global DECOMPOSITIONS_LOADED + if DECOMPOSITIONS_LOADED: + return + + with DECOMPOSITIONS_LOCK: + if DECOMPOSITIONS_LOADED: + return + + if not (os.environ.get("PYTORCH_JIT", "1") == "1" and __debug__): + DECOMPOSITIONS_LOADED = True + return + + # use an alternate way to register an operator into the decomposition table + # _register_jit_decomposition doesn't work for some operators, e.g. addr, + # because the Tensor types generated cannot be unioned by torchscript + # decomp should be type OpOverload + global VMAP_DECOMPOSITIONS_LIB + VMAP_DECOMPOSITIONS_LIB = torch.library.Library( + "aten", "IMPL", "FuncTorchBatched" + ) + + from torch._decomp import decomposition_table + + def _register_python_decomposition_vmap(decomp): + if decomp in decomposition_table: + VMAP_DECOMPOSITIONS_LIB.impl(decomp, decomposition_table[decomp]) + else: + raise RuntimeError(f"could not find decomposition for {decomp}") + + _register_python_decomposition_vmap(torch.ops.aten.mse_loss_backward.default) + _register_python_decomposition_vmap( + torch.ops.aten.smooth_l1_loss_backward.default + ) + _register_python_decomposition_vmap(torch.ops.aten.huber_loss_backward.default) + _register_python_decomposition_vmap(torch.ops.aten.nll_loss_forward.default) + _register_python_decomposition_vmap(torch.ops.aten.nll_loss2d_forward.default) + _register_python_decomposition_vmap(torch.ops.aten.nll_loss_backward.default) + _register_python_decomposition_vmap(torch.ops.aten.nll_loss2d_backward.default) + _register_python_decomposition_vmap(torch.ops.aten.addr.default) + + DECOMPOSITIONS_LOADED = True + + +def vmap_impl(func, in_dims, out_dims, randomness, chunk_size, *args, **kwargs): + lazy_load_decompositions() + _check_out_dims_is_int_or_int_pytree(out_dims, func) + batch_size, flat_in_dims, flat_args, args_spec = _process_batched_inputs( + in_dims, args, func + ) + + if chunk_size is not None: + chunks_flat_args = _get_chunked_inputs( + flat_args, flat_in_dims, batch_size, chunk_size + ) + return _chunked_vmap( + func, + flat_in_dims, + chunks_flat_args, + args_spec, + out_dims, + randomness, + **kwargs, + ) + + # If chunk_size is not specified. + return _flat_vmap( + func, + batch_size, + flat_in_dims, + flat_args, + args_spec, + out_dims, + randomness, + **kwargs, + ) + + +def get_chunk_sizes(total_elems, chunk_size): + n_chunks = n_chunks = total_elems // chunk_size + chunk_sizes = [chunk_size] * n_chunks + # remainder chunk + remainder = total_elems % chunk_size + if remainder != 0: + chunk_sizes.append(remainder) + return chunk_sizes + + +def _get_chunked_inputs(flat_args, flat_in_dims, batch_size, chunk_size): + split_idxs = (batch_size,) + if chunk_size is not None: + chunk_sizes = get_chunk_sizes(batch_size, chunk_size) + split_idxs = tuple(itertools.accumulate(chunk_sizes)) + + flat_args_chunks = tuple( + ( + t.tensor_split(split_idxs, dim=in_dim) + if in_dim is not None + else [ + t, + ] + * len(split_idxs) + ) + for t, in_dim in zip(flat_args, flat_in_dims) + ) + + # transpose chunk dim and flatten structure + # chunks_flat_args is a list of flatten args + chunks_flat_args = zip(*flat_args_chunks) + return chunks_flat_args + + +def _flatten_chunks_output(chunks_output_): + # chunks_output is a list of chunked outputs + # flatten chunked outputs: + flat_chunks_output = [] + arg_spec = None + for output in chunks_output_: + flat_output, arg_specs = tree_flatten(output) + flat_chunks_output.append(flat_output) + if arg_spec is None: + arg_spec = arg_specs + + # transpose chunk dim and flatten structure + # flat_output_chunks is flat list of chunks + flat_output_chunks = list(zip(*flat_chunks_output)) + return flat_output_chunks, arg_spec + + +def _concat_chunked_outputs(out_dims, arg_spec, flat_output_chunks): + # concat chunks on out_dim + flat_out_dims = _broadcast_to_and_flatten(out_dims, arg_spec) + assert len(flat_out_dims) == len(flat_output_chunks) + flat_output = [] + for idx, out_dim in enumerate(flat_out_dims): + flat_output.append(torch.cat(flat_output_chunks[idx], dim=out_dim)) + # release tensors + flat_output_chunks[idx] = None + + return flat_output + + +# Applies vmap on chunked_input and returns concatenated output over the chunks. +def _chunked_vmap( + func, flat_in_dims, chunks_flat_args, args_spec, out_dims, randomness, **kwargs +): + chunks_output = [] + rs = torch.get_rng_state() if randomness == "same" else None + for flat_args in chunks_flat_args: + batch_size = _validate_and_get_batch_size(flat_in_dims, flat_args) + + # The way we compute split the input in `_get_chunked_inputs`, + # we may get a tensor with `0` batch-size. We skip any computation + # in that case. + # Eg. + # >>> chunk_size = 1 + # >>> batch_size = 6 + # >>> t = torch.zeros(batch_size, 1) + # >>> t.tensor_split([1, 2, 3, 4, 5, 6]) + # (tensor([[0.]]), tensor([[0.]]), tensor([[0.]]), tensor([[0.]]), + # tensor([[0.]]), tensor([[0.]]), tensor([], size=(0, 1))) + if batch_size == 0: + continue + + if rs is not None: + torch.set_rng_state(rs) + chunks_output.append( + _flat_vmap( + func, + batch_size, + flat_in_dims, + flat_args, + args_spec, + out_dims, + randomness, + **kwargs, + ) + ) + + flat_output_chunks, arg_spec = _flatten_chunks_output(chunks_output) + + # chunked output tensors are held by both `flat_output_chunks` and `chunks_output`. + # eagerly remove the reference from `chunks_output`. + del chunks_output + + # concat chunks on out_dim + flat_output = _concat_chunked_outputs(out_dims, arg_spec, flat_output_chunks) + + # finally unflatten the output + return tree_unflatten(flat_output, arg_spec) + + +# Vmap refactored helper functions: +def _check_randomness_arg(randomness): + if randomness not in ["error", "different", "same"]: + raise RuntimeError( + f"Only allowed values for randomness are 'error', 'different', or 'same'. Got {randomness}" + ) + + +@contextlib.contextmanager +def vmap_increment_nesting(batch_size, randomness): + try: + vmap_level = _vmap_increment_nesting(batch_size, randomness) + yield vmap_level + finally: + _vmap_decrement_nesting() + + +def _flat_vmap( + func, batch_size, flat_in_dims, flat_args, args_spec, out_dims, randomness, **kwargs +): + with vmap_increment_nesting(batch_size, randomness) as vmap_level: + batched_inputs = _create_batched_inputs( + flat_in_dims, flat_args, vmap_level, args_spec + ) + batched_outputs = func(*batched_inputs, **kwargs) + return _unwrap_batched(batched_outputs, out_dims, vmap_level, batch_size, func) + + +# `restore_vmap` is a private helper function. It is vmap but has the following +# differences: +# - instead of returning outputs, it returns an (outputs, out_dims) tuple. +# out_dims is a pytree of same shape as outputs and contains Optional[int] +# specifying where the vmapped dimension, if it exists, is in the corresponding output. +# - does no validation on in_dims or inputs (vmap expects at least one Tensor to be vmapped). +# restore_vmap allows for no inputs to have the vmap dimension +# - does no validation on outputs (vmap expects only Tensor outputs) +# restore_vmap allows for return of arbitrary outputs (not just Tensors) +# +# The TL;DR is that restore_vmap is more general than vmap and has a slightly +# different API. The relaxations are so that we can "pause" vmap in the middle +# of its execution and then "restore" it later (this is what we do in +# the generate_vmap_rule=True implementation of autograd.Function). +# +# restore_vmap can be technically used in the implementation of vmap, but doing +# that refactor is a bit technically challenging because: +# - vmap couples the tensor-wrapping code with error checking +# - vmap's tensor unwrapping code is in C++; we would need to rewrite part of it +# in python because it overlaps with unwrap_batched +def restore_vmap(func, in_dims, batch_size, randomness): + def inner(*args, **kwargs): + with vmap_increment_nesting(batch_size, randomness) as vmap_level: + batched_inputs = wrap_batched(args, in_dims, vmap_level) + batched_outputs = func(*batched_inputs, **kwargs) + return unwrap_batched(batched_outputs, vmap_level) + + return inner + + +def wrap_batched(args, bdims, level): + flat_args, spec = tree_flatten(args) + flat_bdims = _broadcast_to_and_flatten(bdims, spec) + assert flat_bdims is not None + result = _create_batched_inputs(flat_bdims, flat_args, level, spec) + return result + + +def unwrap_batched(args, level): + flat_args, spec = tree_flatten(args) + if len(flat_args) == 0: + return args, () + result = [ + ( + torch._C._functorch._unwrap_batched(arg, level) + if isinstance(arg, torch.Tensor) + else (arg, None) + ) + for arg in flat_args + ] + output, bdims = zip(*result) + return tree_unflatten(output, spec), tree_unflatten(bdims, spec) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6f50d46dde0527d1e31e96d617920e26d9c69acb --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/__init__.py @@ -0,0 +1,6 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Optional, Protocol + +from torch import _C, _ops, autograd, Tensor +from torch.utils import _pytree + +from . import utils + + +class InfoProtocol(Protocol): + _backward_fn: Optional[Callable] + _setup_context_fn: Optional[Callable] + + +@dataclasses.dataclass +class Info: + _backward_fn: Optional[Callable] + _setup_context_fn: Optional[Callable] + + +def make_autograd_impl(op: _ops.OpOverload, info: InfoProtocol) -> Callable: + name: str = f"GeneratedBackwardFor_{op._namespace}_{op._opname}_{op._overloadname}" + + has_kwarg_only_args = utils.has_kwarg_only_args(op._schema) + + @dataclass + class Metadata: + keyset: _C.DispatchKeySet + keyword_only_args: dict[str, Any] + + def forward_no_grad(*args): + metadata = args[-1] + args = args[:-1] + + with _C._AutoDispatchBelowAutograd(): + keyset = metadata.keyset + kwargs = metadata.keyword_only_args + result = op.redispatch(keyset & _C._after_autograd_keyset, *args, **kwargs) + return result + + def forward(ctx, *args): + metadata = args[-1] + args = args[:-1] + + with _C._AutoDispatchBelowAutograd(): + keyset = metadata.keyset + kwargs = metadata.keyword_only_args + result = op.redispatch(keyset & _C._after_autograd_keyset, *args, **kwargs) + if info._setup_context_fn: + # The Dispatcher will remove args that are equal to their default + # values from (args, kwargs). We're going to add it back so that + # the user can access them. + # + # This is OK to do: The Dispatcher removed the args for serialization + # FC/BC reasons (that is, a graph will not store args that are equal + # to their default values), but that doesn't matter here. If the user + # adds a new default arg, then they must update + # their setup_context (along with the rest of their operator + # registrations) + args, kwargs = utils.fill_defaults(op._schema, args, kwargs) + + if has_kwarg_only_args: + info._setup_context_fn( + ctx=ctx, inputs=args, keyword_only_inputs=kwargs, output=result + ) + else: + info._setup_context_fn(ctx=ctx, inputs=args, output=result) + return result + + def backward(ctx, *grads): + if info._backward_fn: + try: + prev_needs_input_grad = ctx.needs_input_grad + ctx.needs_input_grad = ctx.needs_input_grad[:-1] + result = info._backward_fn(ctx, *grads) + finally: + ctx.needs_input_grad = prev_needs_input_grad + if isinstance(result, tuple): + return (*result, None) + return result, None + raise RuntimeError( + f"Trying to backward through {op} but no autograd " + f"formula was registered. " + f"Please use register_autograd to add one." + ) + + Generated = type( + name, + (autograd.Function,), + { + "forward": staticmethod(forward), + "backward": staticmethod(backward), + }, + ) + + schema = op._schema + if any( + utils.is_tensorlist_like_type(a.type) + for a in (*schema.arguments, *schema.returns) + ): + Generated = supports_tensorlist(Generated) + + # The dispatcher passes any keyword-only-args as kwargs and the + # rest of the args (even if specified as kwargs) as args. + def autograd_impl(keyset, *args, **keyword_only_args): + if _C.is_grad_enabled() and _pytree.tree_any_only( + Tensor, lambda x: x.requires_grad, args, not_list_of_tensor + ): + result = Generated.apply(*args, Metadata(keyset, keyword_only_args)) # type: ignore[attr-defined] + else: + result = forward_no_grad(*args, Metadata(keyset, keyword_only_args)) + return result + + return autograd_impl + + +def supports_tensorlist(cls: Any) -> Any: + """Allows a given autograd.Function class to support List[Tensor] inputs/outputs. + + Regular autograd.Function has a constraint that it only directly supports autograd for + Tensors. Applying @supports_tensorlist enables an autograd.Function to support + autograd for List[Tensor] inputs and outputs. + """ + orig_forward = cls.forward + orig_backward = cls.backward + orig_apply = cls.apply + + @dataclass + class Metadata: + input_spec: spec_t + output_spec: Optional[spec_t] = None + result_is_tuple: Optional[bool] = None + + def new_forward(ctx, *args): + metadata = args[-1] + args = args[:-1] + if not isinstance(metadata, Metadata): + raise NotImplementedError( + "NYI: calling supports_tensorlist autograd.Function.forward directly. " + "You should probably be calling .apply instead. " + "Please file an issue if not." + ) + args = unflatten(list(args), metadata.input_spec) + result = orig_forward(ctx, *args) + metadata.result_is_tuple = isinstance(result, tuple) + if not metadata.result_is_tuple: + result = (result,) + flat_result, output_spec = flatten(result, not_list_of_tensor) + metadata.output_spec = output_spec + + if hasattr(ctx, "_pt_metadata"): + raise RuntimeError( + "Please don't set ctx._pt_metadata; PyTorch uses it to store info" + ) + ctx._pt_metadata = metadata + + return tuple(flat_result) + + def new_backward(ctx, *grads): + if not hasattr(ctx, "_pt_metadata"): + raise NotImplementedError( + "NYI: calling supports_tensorlist autograd.Function.backward directly. " + "This will automatically get called by PyTorch autograd. " + "Please file an issue if you need this." + ) + + metadata = ctx._pt_metadata + grads = unflatten(list(grads), metadata.output_spec) + + # If the user's input is ([x, y, z], w), + # then needs_input_grad is (bool, bool, bool, bool, bool). + # We need to + # 1. get rid of the additional bool (which comes from the extra + # `metadata input`) + # 2. unflatten to get the right structure. + prev_needs_input_grad = ctx.needs_input_grad + try: + ctx.needs_input_grad = unflatten( + list(ctx.needs_input_grad[:-1]), metadata.input_spec + ) + grad_inputs = orig_backward(ctx, *grads) + finally: + ctx.needs_input_grad = prev_needs_input_grad + + if not isinstance(grad_inputs, tuple): + grad_inputs = (grad_inputs,) + # Assume that any Nones in the backward are Tensors. + # If the forward has an arg that is [1, 2, 3], the backward should + # return None as the grad. + # If the forward has an arg that is [tensor, tensor], the backward + # may return [None, None], [grad, None], [None, grad], or [grad, grad]. + flat_grad_inputs, grad_inputs_spec = flatten( + grad_inputs, not_list_of_optional_tensor + ) + if grad_inputs_spec != metadata.input_spec: + raise RuntimeError( + f"Expected the return from backward to be of the same structure " + f"as the inputs. Got: {grad_inputs_spec} (return from backward), " + f"{metadata.input_spec} (inputs)" + ) + return tuple(flat_grad_inputs + [None]) + + def new_apply(*args): + flat_args, input_spec = flatten(args, is_leaf=not_list_of_tensor) + metadata = Metadata(input_spec) + result = orig_apply(*flat_args, metadata) # type: ignore[misc] + assert metadata.output_spec is not None + result = unflatten(list(result), metadata.output_spec) + if not metadata.result_is_tuple: + assert isinstance(result, tuple) + assert len(result) == 1 + return result[0] + return result + + cls.forward = new_forward + cls.backward = new_backward + cls.apply = new_apply + return cls + + +def not_list_of_tensor(tree): + if isinstance(tree, tuple): + return False + if isinstance(tree, list): + return any(not isinstance(l, Tensor) for l in tree) + return True + + +def not_list_of_optional_tensor(tree): + if isinstance(tree, tuple): + return False + if isinstance(tree, list): + return any(l is not None and not isinstance(l, Tensor) for l in tree) + return True + + +flatten = _pytree.tree_flatten +unflatten = _pytree.tree_unflatten +spec_t = _pytree.TreeSpec diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/custom_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/custom_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..b693a14ba673838a08e50446970e2645a035d032 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/custom_ops.py @@ -0,0 +1,921 @@ +# mypy: allow-untyped-defs +import collections +import inspect +import logging +import weakref +from collections.abc import Iterable, Sequence +from contextlib import contextmanager +from typing import Any, Callable, Literal, Optional, overload, Union + +import torch +from torch import _C, _ops, Tensor +from torch.types import _dtype +from torch.utils._exposed_in import exposed_in + +from . import autograd, utils + + +device_types_t = Optional[Union[str, Sequence[str]]] +log = logging.getLogger(__name__) + + +@overload +def custom_op( + name: str, + fn: Literal[None] = None, + /, + *, + mutates_args: Union[str, Iterable[str]], + device_types: device_types_t = None, + schema: Optional[str] = None, +) -> Callable[[Callable[..., object]], "CustomOpDef"]: + ... + + +@overload +def custom_op( + name: str, + fn: Callable[..., object], + /, + *, + mutates_args: Union[str, Iterable[str]], + device_types: device_types_t = None, + schema: Optional[str] = None, +) -> "CustomOpDef": + ... + + +@exposed_in("torch.library") +def custom_op( + name: str, + fn: Optional[Callable] = None, + /, + *, + mutates_args: Union[str, Iterable[str]], + device_types: device_types_t = None, + schema: Optional[str] = None, +) -> Union[Callable[[Callable[..., object]], "CustomOpDef"], "CustomOpDef"]: + """Wraps a function into custom operator. + + Reasons why you may want to create a custom op include: + - Wrapping a third-party library or custom kernel to work with PyTorch + subsystems like Autograd. + - Preventing torch.compile/export/FX tracing from peeking inside your function. + + This API is used as a decorator around a function (please see examples). + The provided function must have type hints; these are needed to interface + with PyTorch's various subsystems. + + Args: + name (str): A name for the custom op that looks like "{namespace}::{name}", + e.g. "mylib::my_linear". The name is used as the op's stable identifier + in PyTorch subsystems (e.g. torch.export, FX graphs). + To avoid name collisions, please use your project name as the namespace; + e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. + mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. + This MUST be accurate, otherwise, the behavior is undefined. If "unknown", + it pessimistically assumes that all inputs to the operator are being mutated. + device_types (None | str | Sequence[str]): The device type(s) the function + is valid for. If no device type is provided, then the function + is used as the default implementation for all device types. + Examples: "cpu", "cuda". + When registering a device-specific implementation for an operator that accepts no Tensors, + we require the operator to have a "device: torch.device argument". + schema (None | str): A schema string for the operator. If None + (recommended) we'll infer a schema for the operator from its type + annotations. We recommend letting us infer a schema unless you + have a specific reason not to. + Example: "(Tensor x, int y) -> (Tensor, Tensor)". + + .. note:: + We recommend not passing in a ``schema`` arg and instead letting us infer + it from the type annotations. It is error-prone to write your own schema. + You may wish to provide your own schema if our interpretation of + the type annotation is not what you want. + For more info on how to write a schema string, see + `here `_ + + Examples:: + >>> import torch + >>> from torch import Tensor + >>> from torch.library import custom_op + >>> import numpy as np + >>> + >>> @custom_op("mylib::numpy_sin", mutates_args=()) + >>> def numpy_sin(x: Tensor) -> Tensor: + >>> x_np = x.cpu().numpy() + >>> y_np = np.sin(x_np) + >>> return torch.from_numpy(y_np).to(device=x.device) + >>> + >>> x = torch.randn(3) + >>> y = numpy_sin(x) + >>> assert torch.allclose(y, x.sin()) + >>> + >>> # Example of a custom op that only works for one device type. + >>> @custom_op("mylib::numpy_sin_cpu", mutates_args=(), device_types="cpu") + >>> def numpy_sin_cpu(x: Tensor) -> Tensor: + >>> x_np = x.numpy() + >>> y_np = np.sin(x_np) + >>> return torch.from_numpy(y_np) + >>> + >>> x = torch.randn(3) + >>> y = numpy_sin_cpu(x) + >>> assert torch.allclose(y, x.sin()) + >>> + >>> # Example of a custom op that mutates an input + >>> @custom_op("mylib::numpy_sin_inplace", mutates_args={"x"}, device_types="cpu") + >>> def numpy_sin_inplace(x: Tensor) -> None: + >>> x_np = x.numpy() + >>> np.sin(x_np, out=x_np) + >>> + >>> x = torch.randn(3) + >>> expected = x.sin() + >>> numpy_sin_inplace(x) + >>> assert torch.allclose(x, expected) + >>> + >>> # Example of a factory function + >>> @torch.library.custom_op("mylib::bar", mutates_args={}, device_types="cpu") + >>> def bar(device: torch.device) -> Tensor: + >>> return torch.ones(3) + >>> + >>> bar("cpu") + + """ + + def inner(fn: Callable[..., object]) -> CustomOpDef: + import torch + + if schema is None: + schema_str = torch.library.infer_schema(fn, mutates_args=mutates_args) + else: + schema_str = schema + + namespace, opname = name.split("::") + result = CustomOpDef(namespace, opname, schema_str, fn) + if schema is not None: + # Check that schema's alias annotations match those of `mutates_args`. + expected = set() + for arg in result._opoverload._schema.arguments: + if arg.alias_info is not None and arg.alias_info.is_write: + expected.add(arg.name) + if expected != set(mutates_args): + raise ValueError( + f"Attempted to create a custom op with `mutates_args={mutates_args}` " + f"and `schema={schema}. The schema suggests that the op mutates {expected}" + f"which is different from what was provided to us in `mutates_args`. " + f"Please make these consistent." + ) + result.register_kernel(device_types)(fn) + return result + + if fn is None: + return inner + return inner(fn) + + +class CustomOpDef: + """CustomOpDef is a wrapper around a function that turns it into a custom op. + + It has various methods for registering additional behavior for this + custom op. + + You should not instantiate CustomOpDef directly; instead, use the + :func:`torch.library.custom_op` API. + """ + + def __init__(self, namespace: str, name: str, schema: str, fn: Callable) -> None: + # Fields used to interface with the PyTorch dispatcher + self._namespace = namespace + self._name = name + self._schema = schema + + self._init_fn = fn + + self._backend_fns: dict[Union[str, None], Callable] = {} + self._abstract_fn: Optional[Callable] = None + self._setup_context_fn: Optional[Callable] = None + self._backward_fn: Optional[Callable] = None + self._torch_dispatch_fns: dict[type, Callable] = {} + self._vmap_fn: Optional[Callable] = None + self._autocast_cuda_dtype: Optional[_dtype] = None + self._autocast_cpu_dtype: Optional[_dtype] = None + + self._lib = get_library_allowing_overwrite(self._namespace, self._name) + self._register_to_dispatcher() + self._disabled_kernel: set = set() + OPDEFS[self._qualname] = self + + @property + def _qualname(self) -> str: + return f"{self._namespace}::{self._name}" + + def __repr__(self) -> str: + return f"" + + @contextmanager + def set_kernel_enabled(self, device_type: str, enabled: bool = True): + """ + Disable or re-enable an already registered kernel for this custom operator. + + If the kernel is already disabled/enabled, this is a no-op. + + Note: + If a kernel is first disabled and then registered, it is disabled until enabled again. + + Args: + device_type (str): The device type to disable/enable the kernel for. + disable (bool): Whether to disable or enable the kernel. + + Example: + >>> inp = torch.randn(1) + >>> + >>> # define custom op `f`. + >>> @custom_op("mylib::f", mutates_args=()) + >>> def f(x: Tensor) -> Tensor: + >>> return torch.zeros(1) + >>> + >>> print(f(inp)) # tensor([0.]), default kernel + >>> + >>> @f.register_kernel("cpu") + >>> def _(x): + >>> return torch.ones(1) + >>> + >>> print(f(inp)) # tensor([1.]), CPU kernel + >>> + >>> # temporarily disable the CPU kernel + >>> with f.set_kernel_enabled("cpu", enabled = False): + >>> print(f(inp)) # tensor([0.]) with CPU kernel disabled + + """ + action = "enable" if enabled else "disable" + originally_disabled = device_type in self._disabled_kernel + if device_type not in self._backend_fns: + log.warning( + "Attempted to %s kernel for %s but no kernel was registered for this device type.", + action, + device_type, + ) + + if not enabled: + if originally_disabled: + log.warning( + "Attempted to disable kernel for %s but it was already disabled.", + device_type, + ) + else: + self._disabled_kernel.add(device_type) + else: # enable the kernel + if not originally_disabled: + log.warning( + "Attempted to enable kernel for %s but it was already enabled.", + device_type, + ) + else: + self._disabled_kernel.remove(device_type) + + try: + yield + finally: + # restore original state + if originally_disabled: + self._disabled_kernel.add(device_type) + else: + self._disabled_kernel.discard(device_type) + + def register_kernel( + self, device_types: device_types_t, fn: Optional[Callable] = None, / + ) -> Callable: + """Register an implementation for a device type for this operator. + + Some valid device_types are: "cpu", "cuda", "xla", "mps", "ipu", "xpu". + This API may be used as a decorator. + + Args: + fn (Callable): The function to register as the implementation for + the given device types. + device_types (str | Sequence[str]): The device device_types to register an impl to. + + Examples:: + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) + >>> import torch + >>> from torch import Tensor + >>> from torch.library import custom_op + >>> import numpy as np + >>> + >>> # Create a custom op that works on cpu + >>> @custom_op("mylib::numpy_sin", mutates_args=(), device_types="cpu") + >>> def numpy_sin(x: Tensor) -> Tensor: + >>> x_np = x.numpy() + >>> y_np = np.sin(x_np) + >>> return torch.from_numpy(y_np) + >>> + >>> # Add implementations for the cuda device + >>> @numpy_sin.register_kernel("cuda") + >>> def _(x): + >>> x_np = x.cpu().numpy() + >>> y_np = np.sin(x_np) + >>> return torch.from_numpy(y_np).to(device=x.device) + >>> + >>> x_cpu = torch.randn(3) + >>> x_cuda = x_cpu.cuda() + >>> assert torch.allclose(numpy_sin(x_cpu), x_cpu.sin()) + >>> assert torch.allclose(numpy_sin(x_cuda), x_cuda.sin()) + + """ + + def inner(fn): + if device_types is None or isinstance(device_types, str): + dtypes: list[Union[str, None]] = [device_types] + else: + dtypes = list(device_types) + for device_type in dtypes: + if device_type not in self._backend_fns: + + def backend_impl(*args, **kwargs): + result = self._backend_fns[device_type](*args, **kwargs) + + def get_module(): + fn = self._backend_fns[device_type] + return inspect.getmodule(fn) + + utils.check_aliasing_constraint( + self._name, + utils.iter_tensors(args, kwargs), + result, + get_module, + ) + return result + + if device_type is None: + self._lib.impl( + self._name, backend_impl, "CompositeExplicitAutograd" + ) + else: + self._lib.impl( + self._name, + backend_impl, + _C._dispatch_key_for_device(device_type), + ) + + # Wrap function to choose between the default implementation or the device-specific + # implementation depending on if the kernel is disabled. + @torch._disable_dynamo + def wrapped_fn(*args, **kwargs): + if device_type in self._disabled_kernel: + return self._init_fn(*args, **kwargs) + else: + return fn(*args, **kwargs) + + self._backend_fns[device_type] = wrapped_fn + return fn + + if device_types is not None and not utils.has_tensor_arg( + self._opoverload._schema + ): + device_arg_index = utils.get_device_arg_index(self._opoverload._schema) + if device_arg_index is None: + raise ValueError( + "Functions without tensor inputs are required to have a `device: torch.device` argument" + ) + self._register_backend_select_dispatcher(device_arg_index) + + # See NOTE: [Supporting decorator and non-decorator usage] + if fn is None: + return inner + return inner(fn) + + def register_fake(self, fn: Callable, /) -> Callable: + r"""Register a FakeTensor implementation for this custom op. + + This is necessary to get the operator to work efficiently with torch.compile. + + The Fake impl (sometimes also known as a meta kernel or abstract impl) + specifies the behavior of this operator on Tensors that carry no data. + Given some input Tensors with certain properties + (sizes/strides/storage_offset/device), it specifies what the properties of + the output Tensors are. + + Please see :func:`torch.library.impl_abstract` for more details. + + Args: + fn (Callable): The function to register as the FakeTensor + implementation. + + Examples: + >>> import torch + >>> import numpy as np + >>> from torch import Tensor + >>> + >>> # Example 1: an operator without data-dependent output shape + >>> @torch.library.custom_op("mylib::linear", mutates_args=()) + >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: + >>> return (x @ weight.t()) + bias + >>> + >>> @linear.register_fake + >>> def _(x, weight, bias): + >>> assert x.dim() == 2 + >>> assert weight.dim() == 2 + >>> assert bias.dim() == 1 + >>> assert x.shape[1] == weight.shape[1] + >>> assert weight.shape[0] == bias.shape[0] + >>> assert x.device == weight.device + >>> return x.new_empty(x.size(0), weight.size(0)) + >>> + >>> x = torch.randn(2, 2) + >>> weight = torch.randn(2, 2) + >>> bias = torch.randn(2) + >>> # xdoctest: +SKIP("Requires Python <= 3.11") + >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) + >>> # xdoctest: +SKIP("Requires Python <= 3.11") + >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) + >>> + >>> # Example 2: an operator with data-dependent output shape + >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) + >>> def nonzero(x: Tensor) -> Tensor: + >>> x_np = x.cpu().numpy() + >>> res = np.stack(np.nonzero(x_np), axis=1) + >>> return torch.tensor(res, device=x.device) + >>> + >>> @nonzero.register_fake + >>> def _(x): + >>> # Number of nonzero-elements is data-dependent. + >>> # Since we cannot peek at the data in an abstract impl, + >>> # we use the ctx object to construct a new symint that + >>> # represents the data-dependent size. + >>> ctx = torch.library.get_ctx() + >>> nnz = ctx.new_dynamic_size() + >>> shape = [nnz, x.dim()] + >>> result = x.new_empty(shape, dtype=torch.int64) + >>> return result + >>> + >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) + >>> # xdoctest: +SKIP("Requires Python <= 3.11") + >>> out = torch.compile(nonzero, fullgraph=True)(x) + >>> # xdoctest: +SKIP("Requires Python <= 3.11") + >>> assert torch.allclose(out, x.nonzero()) + + """ + self._abstract_fn = fn + return fn + + def register_torch_dispatch( + self, torch_dispatch_class: Any, fn: Optional[Callable] = None, / + ) -> Callable: + r"""Registers a torch_dispatch rule for the given operator and ``torch_dispatch_class``. + + This allows for open registration to specify the behavior between the operator + and the ``torch_dispatch_class`` without needing to modify the ``torch_dispatch_class`` + or the operator directly. + + Please see :func:`torch.library.register_torch_dispatch` for examples and more details. + """ + + def register(fn): + if torch_dispatch_class not in self._torch_dispatch_fns: + + def inner(*args, **kwargs): + return self._torch_dispatch_fns[torch_dispatch_class]( + *args, **kwargs + ) + + self._lib._register_torch_dispatch_rule( + self._name, torch_dispatch_class, inner + ) + self._torch_dispatch_fns[torch_dispatch_class] = fn + return fn + + if fn is None: + return register + else: + return register(fn) + + def register_autograd( + self, + backward: Callable, + /, + *, + setup_context: Optional[Callable] = None, + ) -> None: + r"""Register a backward formula for this custom op. + + In order for an operator to work with autograd, you need to register + a backward formula: + 1. You must tell us how to compute gradients during the backward pass + by providing us a "backward" function. + 2. If you need any values from the forward to compute gradients, you can + use `setup_context` to save values for backward. + + ``backward_fn`` runs during the backward pass. It accepts ``(ctx, *grads)``: + - ``grads`` is one or more gradients. The number of gradients matches + the number of outputs of the operator. + The ``ctx`` object is `the same ctx object `_ used by + :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the + same as :meth:`torch.autograd.Function.backward`. + + ``setup_context(ctx, inputs, output)`` runs during the forward pass. + Please save quantities needed for backward onto the ``ctx`` object via + either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` + or assigning them as attributes of ``ctx``. If your custom op has + kwarg-only arguments, we expect the signature of ``setup_context`` + to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. + + Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, + they may not directly access :meth:`torch.Tensor.data_ptr` and they must + not depend on or mutate global state. If you need a non-traceable backward, + you can make it a separate custom_op that you call inside ``backward_fn``. + + If you need different autograd behavior on different devices, then we + recommend creating two different custom operators, one for each device + that needs different behavior, and switching between them at runtime. + + Examples: + >>> import torch + >>> import numpy as np + >>> from torch import Tensor + >>> + >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) + >>> def numpy_sin(x: Tensor) -> Tensor: + >>> x_np = x.cpu().numpy() + >>> y_np = np.sin(x_np) + >>> return torch.from_numpy(y_np).to(device=x.device) + >>> + >>> def setup_context(ctx, inputs, output) -> Tensor: + >>> x, = inputs + >>> ctx.save_for_backward(x) + >>> + >>> def backward(ctx, grad): + >>> x, = ctx.saved_tensors + >>> return grad * x.cos() + >>> + >>> numpy_sin.register_autograd(backward, setup_context=setup_context) + >>> + >>> x = torch.randn(3, requires_grad=True) + >>> y = numpy_sin(x) + >>> grad_x, = torch.autograd.grad(y, x, torch.ones_like(y)) + >>> assert torch.allclose(grad_x, x.cos()) + >>> + >>> # Example with a keyword-only arg + >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) + >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: + >>> x_np = x.cpu().numpy() + >>> y_np = x_np * val + >>> return torch.from_numpy(y_np).to(device=x.device) + >>> + >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: + >>> ctx.val = keyword_only_inputs["val"] + >>> + >>> def backward(ctx, grad): + >>> return grad * ctx.val + >>> + >>> numpy_mul.register_autograd(backward, setup_context=setup_context) + >>> + >>> x = torch.randn(3, requires_grad=True) + >>> y = numpy_mul(x, val=3.14) + >>> grad_x, = torch.autograd.grad(y, x, torch.ones_like(y)) + >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) + + """ + schema = self._opoverload._schema + if not utils.is_functional_schema(schema): + raise RuntimeError( + f"Cannot register autograd formula for non-functional operator " + f"{self} with schema {schema}. Please create " + f"a functional operator and register an autograd formula for that." + ) + + self._backward_fn = backward + self._setup_context_fn = setup_context + + def _register_to_dispatcher(self) -> None: + if torch._running_with_deploy(): + utils.warn_deploy(stacklevel=5) + return + + lib = self._lib + schema_str = self._name + self._schema + cpp_schema = _C.parse_schema(schema_str) + if utils.has_kwarg_only_tensors(cpp_schema): + # If you want to support this, the progression is: + # - supporting kwarg-only Tensors that are non-differentiable + # - supporting kwarg-only Tensors (regardless of differentiability) + raise NotImplementedError( + f"custom_op with kwarg-only Tensor args. Please make your " + f"tensors not kwarg-only. Got: {schema_str}" + ) + + lib.define( + schema_str, + tags=[_C.Tag.pt2_compliant_tag, _C.Tag.needs_fixed_stride_order], + ) + self._opoverload = utils.lookup_op(self._qualname) + + def fake_impl(*args, **kwargs): + if self._abstract_fn is None: + if utils.can_generate_trivial_fake_impl(self._opoverload): + return None + raise RuntimeError( + f"There was no fake impl registered for {self}. " + f"This is necessary for torch.compile/export/fx tracing to work. " + f"Please use `{self._init_fn.__name__}.register_fake` to add an " + f"fake impl." + ) + return self._abstract_fn(*args, **kwargs) + + lib._register_fake(self._name, fake_impl, _stacklevel=4) + + autograd_impl = autograd.make_autograd_impl(self._opoverload, self) + lib.impl(self._name, autograd_impl, "Autograd", with_keyset=True) + + schema = self._opoverload._schema + if schema.is_mutable: + mutated_idxs, mutated_keys = utils.mutated_args_kwargs(schema) + + def adinplaceorview_impl(keyset, *args, **kwargs): + for idx in mutated_idxs: + increment_version(args[idx]) + for key in mutated_keys: + increment_version(kwargs[key]) + with _C._AutoDispatchBelowADInplaceOrView(): + return self._opoverload.redispatch( + keyset & _C._after_ADInplaceOrView_keyset, *args, **kwargs + ) + + lib.impl( + self._name, + adinplaceorview_impl, + "ADInplaceOrView", + with_keyset=True, + ) + + def _register_backend_select_dispatcher(self, device_arg_index: int): + """ + Switch on the device argument to select the correct backend to dispatch to. + """ + + def backend_select(keyset, *args, **kwargs): + device = args[device_arg_index].type + if device not in self._backend_fns: + raise RuntimeError( + f"{self._name} does not have a kernel registered for {device}. " + "Please use register_kernel to do so." + ) + dispatch_key = _C._dispatch_key_for_device(device) + dispatch_key = getattr(_C.DispatchKey, dispatch_key) + return self._opoverload.redispatch( + _C.DispatchKeySet(dispatch_key), *args, **kwargs + ) + + self._lib.impl(self._name, backend_select, "BackendSelect", with_keyset=True) + + def __call__(self, *args, **kwargs): + return self._opoverload(*args, **kwargs) + + def register_vmap( + self, + func: Optional[Callable] = None, + ): + r"""Register a vmap implementation to support :func:`torch.vmap` for this custom op. + + This API may be used as a decorator. + + In order for an operator to work with :func:`torch.vmap`, you may need to register a + vmap implementation in the following signature: + + ``vmap_func(info, in_dims: Tuple[Optional[int]], *args, **kwargs)``, + + where ``*args`` and ``**kwargs`` are the arguments and kwargs for ``op``. + + It specifies how do we compute the batched version of ``op`` given inputs with an additional + dimension (specified by ``in_dims``). + + For each arg in ``args``, ``in_dims`` has a corresponding ``Optional[int]``. It is ``None`` + if the arg is not a Tensor or if the arg is not being vmapped over, otherwise, it is an integer + specifying what dimension of the Tensor is being vmapped over. + + ``info`` is a collection of additional metadata that may be helpful: + ``info.batch_size`` specifies the size of the dimension being vmapped over, while + ``info.randomness`` is the ``randomness`` option that was passed to :func:`torch.vmap`. + + The return of the function ``func`` is a tuple of ``(output, out_dims)``. Similar to ``in_dims``, + ``out_dims`` should be of the same structure as ``output`` and contain one ``out_dim`` + per output that specifies if the output has the vmapped dimension and what index it is in. + + Examples: + >>> import torch + >>> import numpy as np + >>> from torch import Tensor + >>> from typing import Tuple + >>> + >>> def to_numpy(tensor): + >>> return tensor.cpu().numpy() + >>> + >>> lib = torch.library.Library("mylib", "FRAGMENT") + >>> @torch.library.custom_op("mylib::numpy_cube", mutates_args=()) + >>> def numpy_cube(x: Tensor) -> Tuple[Tensor, Tensor]: + >>> x_np = to_numpy(x) + >>> dx = torch.tensor(3 * x_np ** 2, device=x.device) + >>> return torch.tensor(x_np ** 3, device=x.device), dx + >>> + >>> def numpy_cube_vmap(info, in_dims, x): + >>> result = numpy_cube(x) + >>> return result, (in_dims[0], in_dims[0]) + >>> + >>> numpy_cube.register_vmap(numpy_cube_vmap) + >>> + >>> x = torch.randn(3) + >>> torch.vmap(numpy_cube)(x) + >>> + >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) + >>> def numpy_mul(x: Tensor, y: Tensor) -> Tensor: + >>> return torch.tensor(to_numpy(x) * to_numpy(y), device=x.device) + >>> + >>> @numpy_mul.register_vmap + >>> def numpy_mul_vmap(info, in_dims, x, y): + >>> x_bdim, y_bdim = in_dims + >>> x = x.movedim(x_bdim, -1) if x_bdim is not None else x.unsqueeze(-1) + >>> y = y.movedim(y_bdim, -1) if y_bdim is not None else y.unsqueeze(-1) + >>> result = x * y + >>> result = result.movedim(-1, 0) + >>> return result, 0 + >>> + >>> + >>> x = torch.randn(3) + >>> y = torch.randn(3) + >>> torch.vmap(numpy_mul)(x, y) + """ + from torch._functorch.autograd_function import custom_function_call_vmap_helper + from torch._functorch.pyfunctorch import retrieve_current_functorch_interpreter + + def register(func): + need_register = self._vmap_fn is None + self._vmap_fn = func + + if need_register: + + def wrapped_func(keyset, *args, **kwargs): + interpreter = retrieve_current_functorch_interpreter() + return custom_function_call_vmap_helper( + interpreter, self._vmap_fn, self._opoverload, *args, **kwargs + ) + + self._lib.impl( + self._name, wrapped_func, "FuncTorchBatched", with_keyset=True + ) + + if func is None: + return register + else: + return register(func) + + def register_autocast( + self, + device_type: str, + cast_inputs: _dtype, + ): + r"""Register an autocast dispatch rule for this custom op. + + Valid `device_type` include: "cpu" and "cuda". + + Args: + op (str | OpOverload): The operator to register an autocast dispatch rule to. + device_type(str): Device type to use. 'cuda' or 'cpu'. + The type is the same as the `type` attribute of a :class:`torch.device`. + Thus, you may obtain the device type of a tensor using `Tensor.device.type`. + cast_inputs (:class:`torch.dtype`): When custom op runs in an autocast-enabled region, + casts incoming floating-point Tensors to the target dtype (non-floating-point Tensors + are not affected), then executes custom op with autocast disabled. + lib (Optional[Library]): If provided, the lifetime of this registration + + Examples:: + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) + >>> import torch + >>> from torch import Tensor + >>> from torch.library import custom_op + >>> + >>> # Create a custom op that works on cuda + >>> @torch.library.custom_op("mylib::my_sin", mutates_args=()) + >>> def my_sin(x: Tensor) -> Tensor: + >>> return torch.sin(x) + >>> + >>> # Register autocast dispatch rule for the cuda device + >>> torch.library.register_autocast("mylib::my_sin", "cuda", torch.float16) + >>> + >>> x = torch.randn(3, dtype=torch.float32, device="cuda") + >>> with torch.autocast("cuda", dtype=torch.float16): + >>> y = torch.ops.mylib.my_sin(x) + >>> assert y.dtype == torch.float16 + + """ + if not isinstance(device_type, str): + raise ValueError( + f"Expected `device_type` of type `str`, got: `{type(device_type)}`" + ) + if device_type not in ["cpu", "cuda"]: + raise ValueError(f"Unknown device type: {device_type}") + + need_register_cuda = self._autocast_cuda_dtype is None + need_register_cpu = self._autocast_cpu_dtype is None + if device_type == "cuda": + self._autocast_cuda_dtype = cast_inputs + else: + self._autocast_cpu_dtype = cast_inputs + + def kernel(_, *args, **kwargs): + assert len(kwargs) == 0, "Custom ops do not support kwargs yet." + autocast_keyset = torch._C.DispatchKeySet( + torch._C.DispatchKey.AutocastCPU + ) | torch._C.DispatchKeySet(torch._C.DispatchKey.AutocastCUDA) + with torch._C._ExcludeDispatchKeyGuard(autocast_keyset): + return self._opoverload(*_cast(args, device_type, cast_inputs)) + + if need_register_cuda and self._autocast_cuda_dtype: + self._lib.impl(self._name, kernel, "AutocastCUDA", with_keyset=True) + elif need_register_cpu and self._autocast_cpu_dtype: + self._lib.impl(self._name, kernel, "AutocastCPU", with_keyset=True) + + return kernel + + +# TODO: Merge this function with torch.amp.autocast_mode._cast, and refactor it +# into a utility function once custom ops support arbitrary input types. +def _cast(value, device_type: str, dtype: _dtype): + if isinstance(value, torch.Tensor): + is_eligible = ( + value.is_floating_point() + and value.device.type == device_type + and (value.dtype is not torch.float64) + ) + return value.to(dtype) if is_eligible else value + elif isinstance(value, (str, bytes)): + return value + elif isinstance(value, collections.abc.Iterable): + iterable = (_cast(v, device_type, dtype) for v in value) + if isinstance(value, (list, tuple)): + return type(value)(iterable) + else: + return iterable + else: + return value + + +def increment_version(val: Any) -> None: + if isinstance(val, Tensor): + torch.autograd.graph.increment_version(val) + elif isinstance(val, (tuple, list)): + for v in val: + if isinstance(v, Tensor): + torch.autograd.graph.increment_version(v) + + +# NOTE: [Supporting decorator and non-decorator usage] +# +# Some APIs may be both used as a decorator and not as a decorator. +# For example: +# +# >>> def fn(x): +# >>> return x.sin() +# >>> +# >>> # Usage 1: not as a decorator +# >>> numpy_sin.register_kernel("cuda", fn) +# >>> +# >>> # Usage 2: as a decorator +# >>> @numpy_sin.register_kernel("cuda") +# >>> def fn2(x): +# >>> return x.sin +# +# The way we support this is that `register_kernel` accepts an optional `fn`. +# If `fn` is provided (Usage 1), then we know that the user is using it not +# as a decorator. +# If `fn` is not provided (Usage 2), then `register_kernel` needs to return a +# decorator. + + +OPDEF_TO_LIB: dict[str, "torch.library.Library"] = {} +OPDEFS: weakref.WeakValueDictionary = weakref.WeakValueDictionary() + + +def get_library_allowing_overwrite( + namespace: str, name: str +) -> "torch.library.Library": + qualname = f"{namespace}::{name}" + + if qualname in OPDEF_TO_LIB: + OPDEF_TO_LIB[qualname]._destroy() + del OPDEF_TO_LIB[qualname] + + lib = torch.library.Library(namespace, "FRAGMENT") # noqa: TOR901 + OPDEF_TO_LIB[qualname] = lib + return lib + + +def _maybe_get_opdef( + op: Union[CustomOpDef, _ops.OpOverload, str] +) -> Optional[CustomOpDef]: + if isinstance(op, CustomOpDef): + return op + if isinstance(op, _ops.OpOverload): + op = op._name + assert isinstance(op, str) + if op in OPDEFS: + return OPDEFS[op] + return None diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/fake_class_registry.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/fake_class_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..e1cea21489663d0448797b2ad40c1b4dbdd5d350 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/fake_class_registry.py @@ -0,0 +1,341 @@ +# mypy: allow-untyped-defs +import copy +import logging +from typing import Any, Optional, Protocol, Union + +import torch +from torch._library.utils import parse_namespace +from torch.utils._python_dispatch import _disable_current_modes + + +log = logging.getLogger(__name__) + + +class FakeScriptObject: + def __init__(self, wrapped_obj: Any, script_class_name: str, x: torch.ScriptObject): + self.wrapped_obj = wrapped_obj + + # The fully qualified name of the class of original script object + self.script_class_name = script_class_name + try: + with _disable_current_modes(): + self.real_obj = copy.deepcopy(x) + except RuntimeError: + log.warning( + "Unable to deepcopy the custom object %s. " + "Defaulting to the user given object. This might be " + "dangerous as side effects may be directly applied " + "to the object.", + script_class_name, + ) + self.real_obj = x + + +class FakeScriptMethod: + def __init__( + self, + self_fake_obj: FakeScriptObject, + method_name: str, + schema: Optional[torch.FunctionSchema], + ): + self.self_fake_obj = self_fake_obj + self.method_name = method_name + self.schema = schema + + def __call__(self, *args, **kwargs): + from torch._higher_order_ops.torchbind import call_torchbind + + return call_torchbind(self.self_fake_obj, self.method_name, *args, **kwargs) + + +class HasStaticMethodFromReal(Protocol): + @classmethod + def from_real(cls, real_obj: torch.ScriptObject): + pass + + +class FakeClassRegistry: + def __init__(self) -> None: + self._registered_class: dict[str, Any] = {} + + def has_impl(self, full_qualname: str) -> bool: + return full_qualname in self._registered_class + + def get_impl(self, full_qualname: str) -> Any: + self._check_registered(full_qualname) + return self._registered_class[full_qualname] + + def register(self, full_qualname: str, fake_class=None) -> None: + if self.has_impl(full_qualname): + log.warning( + "%s is already registered. Previous fake class is overridden with %s.", + full_qualname, + fake_class, + ) + self._registered_class[full_qualname] = fake_class + + def deregister(self, full_qualname: str) -> Any: + if not self.has_impl(full_qualname): + log.warning( + "Cannot deregister %s. Please use register_fake_class to register it first." + " Or do you dereigster it twice?", + full_qualname, + ) + else: + return self._registered_class.pop(full_qualname) + + def clear(self) -> None: + self._registered_class.clear() + + def _check_registered(self, full_qualname: str) -> None: + if full_qualname not in self._registered_class: + raise RuntimeError( + f"{full_qualname} is not registered. Please use register_fake_class to register it first." + ) + + +global_fake_class_registry = FakeClassRegistry() + + +# TODO: add this check at compile time for __obj_flatten__. +def _check_valid_flat_script_obj(flat_x): + if not isinstance(flat_x, tuple): + raise RuntimeError("Expect flat x to be a tuple.") + + for tp in flat_x: + if not isinstance(tp, tuple): + raise RuntimeError("Expect flat x to be a tuple of tuples.") + + if not len(tp) == 2 or not isinstance(tp[0], str): + raise RuntimeError( + "Expect element of flat x to be a tuple of two elements with first element being a string" + ) + + +def tracing_with_real(x: torch.ScriptObject) -> bool: + if not hasattr(x, "tracing_mode"): + return False + + assert x.tracing_mode() in [ + "real", + "fake", + ], f"tracing_mode can be either real or fake but got {x.tracing_mode()}" + return x.tracing_mode() == "real" + + +def maybe_to_fake_obj( + fake_mode, x: torch.ScriptObject +) -> Union[FakeScriptObject, torch.ScriptObject]: + import torch.utils._pytree as pytree + from torch.utils._python_dispatch import _disable_current_modes + + # When tracing with real mode, people should implement meta kernels that can + # handle the case of real script object + fake tensor inputs. + if tracing_with_real(x): + return x + + # x.__obj_flatten__() could be calling some tensor operations inside but we don't + # want to call these ops in surrounding dispatch modes when executing it. + # Otherwise, for example, the fake tensor modes will error out when the tensors inside + # script obeject execute some operations like clone if allow_non_fake_input flag is set. + with _disable_current_modes(): + flat_x = x.__obj_flatten__() # type: ignore[attr-defined] + + _check_valid_flat_script_obj(flat_x) + + fake_flattened = pytree.tree_map_only( + torch.Tensor, + lambda t: fake_mode.from_tensor(t), + flat_x, + ) + + fake_x = _find_fake_class_for_script_object(x).__obj_unflatten__(fake_flattened) + + fake_x_wrapped = FakeScriptObject(fake_x, x._type().qualified_name(), x) # type: ignore[attr-defined] + + for name in x._method_names(): # type: ignore[attr-defined] + attr = getattr(fake_x, name, None) + if attr: + if not callable(attr): + raise RuntimeError(f"Expect {name} to be a callable but got {attr}.") + + real_attr = getattr(x, name) # type: ignore[attr-defined] + + # real attr sometimes is not torch.ScriptMethod thus doesn't have schema e.g. __init___ or __eq__ + method_schema: Optional[torch.FunctionSchema] = None + if isinstance(real_attr, torch.ScriptMethod): + method_schema = real_attr.schema # type: ignore[attr-defined] + + setattr( + fake_x_wrapped, + name, + FakeScriptMethod(fake_x_wrapped, name, method_schema), + ) + else: + override_skip_list = {"__obj_flatten__", "__get_state__", "__set_state__"} + if name not in override_skip_list: + log.warning("fake object of %s doesn't implement method %s.", x, name) + return fake_x_wrapped + + +def register_fake_class(qualname, fake_class: Optional[HasStaticMethodFromReal] = None): + r"""Register a fake implementation for this class. + + It's in the same spirit of registering a fake implementation for + an operator but with the difference that it + associates a fake class with the original torch bind class (registered + with torch::class_). In this way, torch.compile can handle them properly + in components such as Dynamo and AOTAutograd. + + This API may be used as a decorator (see example). For the fake class, users + are required to provide a from_real classmethod that takes a real object and + returns an instance of the fake class. All tensors in the fake object should also + be properly fakified with to_fake_tensor() in from_real. + + + Examples: + # For a custom class Foo defined in test_custom_class_registration.cpp: + + TORCH_LIBRARY(_TorchScriptTesting, m) { + m.class_("_TensorQueue") + .def(torch::init()) + .def("push", &TensorQueue::push) + .def("pop", &TensorQueue::pop) + .def("top", &TensorQueue::top) + .def("size", &TensorQueue::size) + .def("clone_queue", &TensorQueue::clone_queue) + .def("__obj_flatten__", &TensorQueue::__obj_flatten__) + .def_pickle( + // __getstate__ + [](const c10::intrusive_ptr& self) + -> c10::Dict { + return self->serialize(); + }, + // __setstate__ + [](c10::Dict data) + -> c10::intrusive_ptr { + return c10::make_intrusive(std::move(data)); + }); + }; + # We could register a fake class FakeTensorQueue in Python as follows: + import torch + + @torch._library.register_fake_class("_TorchScriptTesting::_TensorQueue") + class FakeTensorQueue: + def __init__(self, queue): + self.queue = queue + + @classmethod + def __obj_unflatten__(cls, flattened_ctx): + return cls(**dict(ctx)) + + def push(self, x): + self.queue.append(x) + + def pop(self): + return self.queue.pop(0) + + def size(self): + return len(self.queue) + + In this example, the original TensorQeue need to addd a __obj_flatten__ method + to the class TensorQueue and the flattend result is passed into FakeTensorQueue's + __obj_unflatten__ as inputs to create a fake class. This protocol allows pytorch to look + at the contents of the script object and properly handle them in the subsystems + like dynamo, aot_aotugrad or more. + """ + + def inner(fake_class: HasStaticMethodFromReal): + ns, name = parse_namespace(qualname) + + # This also checks whether the refered torch::class_ exists. + torch._C._get_custom_class_python_wrapper(ns, name) + + from_method = getattr(fake_class, _CONVERT_FROM_REAL_NAME, None) + if not from_method: + raise RuntimeError( + f"{fake_class} doesn't define a classmethod {_CONVERT_FROM_REAL_NAME}." + ) + + if not isinstance(fake_class.__dict__[_CONVERT_FROM_REAL_NAME], classmethod): + raise RuntimeError( + f"{_CONVERT_FROM_REAL_NAME} method is not a classmethod." + ) + + global_fake_class_registry.register(_full_qual_class_name(qualname), fake_class) + return fake_class + + if fake_class is None: + return inner + return inner(fake_class) + + +def deregister_fake_class(qualname): + return global_fake_class_registry.deregister(_full_qual_class_name(qualname)) + + +def has_fake_class(full_qualname) -> bool: + return global_fake_class_registry.has_impl(full_qualname) + + +def find_fake_class(full_qualname) -> Optional[Any]: + if not has_fake_class(full_qualname): + return None + return global_fake_class_registry.get_impl(full_qualname) + + +def _full_qual_class_name(qualname: str) -> str: + ns, name = parse_namespace(qualname) + return "__torch__.torch.classes." + ns + "." + name + + +def _is_script_object(obj: Any) -> bool: + return isinstance( + obj, torch.ScriptObject + ) and obj._type().qualified_name().startswith( # type: ignore[attr-defined] + "__torch__.torch.classes" + ) + + +# Return the namespace and class name from fully qualified name. +def _ns_and_class_name(full_qualname: str) -> tuple[str, str]: + splits = full_qualname.split(".") + assert len(splits) == 5, f"Could not split {full_qualname=}" + _torch, _torch_ns, _classes, ns, class_name = splits + return ns, class_name + + +def _find_fake_class_for_script_object(x: torch.ScriptObject) -> Any: + full_qualname = x._type().qualified_name() # type: ignore[attr-defined] + ns, class_name = _ns_and_class_name(full_qualname) + fake_class = find_fake_class(full_qualname) + if fake_class is None: + raise RuntimeError( + f" ScriptObject's {full_qualname} haven't registered a fake class." + f" Please use register_fake_class({ns}::{class_name}) to annotate a fake class for the script obj." + f" Specifically, create a python class that implements a fake version for all the methods" + f" that're used in the program and put annotated class in the program e.g. after loading the library." + f" The fake methods can be written in the same way as a meta kernel for an operator but need to additionally" + f" simulate the object's states. Be sure to add a {_CONVERT_FROM_REAL_NAME} classmethod" + f" to enable creating a fake obj from a real one." + ) + return fake_class + + +_CONVERT_FROM_REAL_NAME = "__obj_unflatten__" + + +def _fake_obj_from_real(fake_mode, x) -> Any: + fake_class = _find_fake_class_for_script_object(x) + + from_real_method = getattr(fake_class, _CONVERT_FROM_REAL_NAME, None) + if not from_real_method: + raise RuntimeError( + f"{fake_class} must define a classmethod {_CONVERT_FROM_REAL_NAME}" + f" that converts the real object to the fake object." + ) + + # from_real defined by user need the ctx to fakify the tensor states. + ctx = torch._library.fake_impl.FakeImplCtx(fake_mode, None) + with torch._library.fake_impl.set_ctx_getter(lambda: ctx): + return fake_class.from_real(x) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/fake_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/fake_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..40e3694edf975a7827c36cda18ba19fa22f4d9c6 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/fake_impl.py @@ -0,0 +1,213 @@ +# mypy: allow-untyped-defs +import contextlib +import functools +from typing import Callable, Optional +from typing_extensions import deprecated + +import torch +from torch._library.utils import Kernel, RegistrationHandle + + +class FakeImplHolder: + """A holder where one can register an fake impl to.""" + + def __init__(self, qualname: str): + self.qualname: str = qualname + self.kernel: Optional[Kernel] = None + self.lib: Optional[torch.library.Library] = None + + def register(self, func: Callable, source: str) -> RegistrationHandle: + """Register an fake impl. + + Returns a RegistrationHandle that one can use to de-register this + fake impl. + """ + if self.kernel is not None: + raise RuntimeError( + f"register_fake(...): the operator {self.qualname} " + f"already has an fake impl registered at " + f"{self.kernel.source}." + ) + if torch._C._dispatch_has_kernel_for_dispatch_key(self.qualname, "Meta"): + raise RuntimeError( + f"register_fake(...): the operator {self.qualname} " + f"already has an DispatchKey::Meta implementation via a " + f"pre-existing torch.library or TORCH_LIBRARY registration. " + f"Please either remove that registration or don't call " + f"register_fake." + ) + + if torch._C._dispatch_has_kernel_for_dispatch_key( + self.qualname, "CompositeImplicitAutograd" + ): + raise RuntimeError( + f"register_fake(...): the operator {self.qualname} " + f"already has an implementation for this device type via a " + f"pre-existing registration to " + f"DispatchKey::CompositeImplicitAutograd." + f"CompositeImplicitAutograd operators do not need an fake " + f"impl; " + f"instead, the operator will decompose into its constituents " + f"and those " + f"can have fake impls defined on them." + ) + + # Store the kernel in this holder + self.kernel = Kernel(func, source) + + # Also register the fake impl to Meta key + if self.lib is None: + ns = self.qualname.split("::")[0] + self.lib = torch.library.Library(ns, "FRAGMENT") # noqa: TOR901 + meta_kernel = construct_meta_kernel(self.qualname, self) + self.lib.impl(self.qualname, meta_kernel, "Meta") + + def deregister_fake_class(): + if self.lib: + self.lib._destroy() + self.lib = None + self.kernel = None + + return RegistrationHandle(deregister_fake_class) + + +def construct_meta_kernel(qualname: str, fake_impl_holder: FakeImplHolder) -> Callable: + assert fake_impl_holder.kernel is not None + + @functools.wraps(fake_impl_holder.kernel.func) + def meta_kernel(*args, **kwargs): + assert fake_impl_holder.kernel is not None + source = fake_impl_holder.kernel.source + + def error_on_ctx(): + raise RuntimeError( + f"{qualname} ({source}): You're trying to run this operator " + f"with meta Tensors (as opposed to FakeTensors), but this " + f"operator may return an output Tensor with data-dependent shape. Meta " + f"Tensors don't support operators with outputs that have data-dependent shapes " + f"but FakeTensors do. " + f"If your operator does not return an output with data-dependent shape, " + f"make sure the FakeTensor and/or meta kernel does not call " + f"torch.library.get_ctx(). Otherwise, please use FakeTensors." + ) + + with set_ctx_getter(error_on_ctx): + return fake_impl_holder.kernel(*args, **kwargs) + + return meta_kernel + + +def get_none(): + return None + + +global_ctx_getter: Callable = get_none + + +@contextlib.contextmanager +def set_ctx_getter(ctx_getter): + global global_ctx_getter + prev = global_ctx_getter + try: + global_ctx_getter = ctx_getter + yield + finally: + global_ctx_getter = prev + + +class FakeImplCtx: + """ + Context object for writing fake implementations for custom operators. + """ + + def __init__(self, _fake_mode, _op): + self._fake_mode = _fake_mode + self._shape_env = _fake_mode.shape_env + self._op = _op + + @deprecated( + "`create_unbacked_symint` is deprecated, please use `new_dynamic_size` instead", + category=FutureWarning, + ) + def create_unbacked_symint(self, *, min=2, max=None) -> torch.SymInt: + return self.new_dynamic_size(min=min, max=max) + + def new_dynamic_size(self, *, min=0, max=None) -> torch.SymInt: + """Constructs a new symint (symbolic int) representing a data-dependent value. + + This is useful for writing the fake implementation (which is necessary + for torch.compile) for a CustomOp where an output Tensor has a size + that depends on the data of the input Tensors. + + Args: + min (int): A statically known inclusive lower bound for this symint. Default: 0 + max (Optional[int]): A statically known inclusive upper bound for this + symint. Default: None + + .. warning: + + It is important that the ``min`` and ``max`` (if not None) values are set + correctly, otherwise, there will be undefined behavior under + torch.compile. The default value of ``min`` is 2 due to torch.compile + specializing on 0/1 sizes. + + You must also verify that your implementation on concrete Tensors + (e.g. CPU/CUDA) only returns Tensors where the size that corresponds + to the symint also has respects these constraint. + The easiest way to do this is to add an assertion in the CPU/CUDA/etc + implementation that the size follows these bounds. + + Example:: + + >>> # An operator with data-dependent output shape + >>> lib = torch.library.Library("mymodule", "FRAGMENT") + >>> lib.define("mymodule::custom_nonzero(Tensor x) -> Tensor") + >>> + >>> @torch.library.register_fake("mymodule::custom_nonzero") + >>> def _(x): + >>> # Number of nonzero-elements is data-dependent. + >>> # Since we cannot peek at the data in an fake impl, + >>> # we use the ctx object to construct a new symint that + >>> # represents the data-dependent size. + >>> ctx = torch.library.get_ctx() + >>> nnz = ctx.new_dynamic_size() + >>> shape = [nnz, x.dim()] + >>> result = x.new_empty(shape, dtype=torch.int64) + >>> return result + >>> + >>> @torch.library.impl(lib, "custom_nonzero", "CPU") + >>> def _(x): + >>> x_np = x.numpy() + >>> res = np.stack(np.nonzero(x_np), axis=1) + >>> return torch.tensor(res, device=x.device) + + """ + if ( + self._shape_env is None + or not self._shape_env.allow_dynamic_output_shape_ops + ): + raise torch._subclasses.fake_tensor.DynamicOutputShapeException(self._op) + + if isinstance(min, torch.SymInt) or isinstance(max, torch.SymInt): + raise ValueError( + f"ctx.new_dynamic_size(min={min}, max={max}): expected " + f"min and max to be statically known ints but got SymInt. " + f"This is not supported." + ) + + if min < 0: + raise ValueError( + f"ctx.new_dynamic_size(min={min}, ...): expected min to be " + f"greater than or equal to 0: this API can only create " + f"non-negative sizes." + ) + + return allocate_size(self._shape_env, min, max) + + +def allocate_size(shape_env, min_val=0, max_val=None): + result = shape_env.create_unbacked_symint() + torch.fx.experimental.symbolic_shapes._constrain_range_for_size( + result, min=min_val, max=max_val + ) + return result diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/infer_schema.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/infer_schema.py new file mode 100644 index 0000000000000000000000000000000000000000..b9a0061139d67f2f490f870803a42bc4174d7db1 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/infer_schema.py @@ -0,0 +1,320 @@ +# mypy: allow-untyped-defs +import collections +import inspect +import typing +from types import GenericAlias +from typing import Optional, Union + +import torch +from torch import device, dtype, Tensor, types +from torch.utils._exposed_in import exposed_in + + +# This is used as a negative test for +# test_custom_ops.py::TestTypeConversion::test_type_eval. +_TestTensor = torch.Tensor + + +@exposed_in("torch.library") +def infer_schema( + prototype_function: typing.Callable, + /, + *, + mutates_args, + op_name: Optional[str] = None, +) -> str: + r"""Parses the schema of a given function with type hints. The schema is inferred from the + function's type hints, and can be used to define a new operator. + + We make the following assumptions: + + * None of the outputs alias any of the inputs or each other. + * | String type annotations "device, dtype, Tensor, types" without library specification are + | assumed to be torch.*. Similarly, string type annotations "Optional, List, Sequence, Union" + | without library specification are assumed to be typing.*. + * | Only the args listed in ``mutates_args`` are being mutated. If ``mutates_args`` is "unknown", + | it assumes that all inputs to the operator are being mutates. + + Callers (e.g. the custom ops API) are responsible for checking these assumptions. + + Args: + prototype_function: The function from which to infer a schema for from its type annotations. + op_name (Optional[str]): The name of the operator in the schema. If ``name`` is None, then the + name is not included in the inferred schema. Note that the input schema to + ``torch.library.Library.define`` requires a operator name. + mutates_args ("unknown" | Iterable[str]): The arguments that are mutated in the function. + + Returns: + The inferred schema. + + Example: + >>> def foo_impl(x: torch.Tensor) -> torch.Tensor: + >>> return x.sin() + >>> + >>> infer_schema(foo_impl, op_name="foo", mutates_args={}) + foo(Tensor x) -> Tensor + >>> + >>> infer_schema(foo_impl, mutates_args={}) + (Tensor x) -> Tensor + """ + UNKNOWN_MUTATES = "unknown" + pf_globals = prototype_function.__globals__ + pf_locals = None + # TODO: Once our minimum version is py3.10+ pass `eval_str=True` to + # inspect.signature() and we no longer need to deal with stringified + # annotations below. + sig = inspect.signature(prototype_function) + + def error_fn(what): + raise ValueError(f"infer_schema(func): {what} Got func with signature {sig})") + + def convert_type_string(annotation_type: str): + try: + return eval(annotation_type, pf_globals, pf_locals) + except Exception: + error_fn( + f"Unsupported type annotation {annotation_type}. It is not a type." + ) + + def unstringify_types( + tys: tuple[Union[type[object], str], ...] + ) -> tuple[tuple[typing.Any, ...], bool]: + res = [] + changed = False + for ty in tys: + ty, ty_changed = unstringify_type(ty) + res.append(ty) + changed |= ty_changed + if changed: + return tuple(res), True + else: + return tys, False # type: ignore[return-value] + + def unstringify_type(ty: Union[type[object], str]) -> tuple[typing.Any, bool]: + # Dig through a generic type and if it contains a stringified type + # convert that to a real type. The second return value indicates if the + # type contained a string or not. + if isinstance(ty, str): + return convert_type_string(ty), True + elif origin := typing.get_origin(ty): + args, args_changed = unstringify_types(typing.get_args(ty)) + if args_changed: + return GenericAlias(origin, args), True + + return ty, False + + params = [] + seen_args = set() + saw_kwarg_only_arg = False + for idx, (name, param) in enumerate(sig.parameters.items()): + if not supported_param(param): + error_fn("We do not support positional-only args, varargs, or varkwargs.") + + if param.kind == inspect.Parameter.KEYWORD_ONLY: + # The first time we see a kwarg-only arg, add "*" to the schema. + if not saw_kwarg_only_arg: + params.append("*") + saw_kwarg_only_arg = True + + if param.annotation is inspect.Parameter.empty: + error_fn(f"Parameter {name} must have a type annotation.") + + # The annotation might be converted to a string by annotation, + # we convert it to the actual type. + annotation_type, _ = unstringify_type(param.annotation) + + if annotation_type not in SUPPORTED_PARAM_TYPES: + if annotation_type.__origin__ is tuple: + list_type = tuple_to_list(annotation_type) + example_type_str = "\n\n" + # Only suggest the list type if this type is supported. + if list_type in SUPPORTED_PARAM_TYPES.keys(): + example_type_str = f"For example, {list_type}.\n\n" + error_fn( + f"Parameter {name} has unsupported type {param.annotation}. " + f"We do not support Tuple inputs in schema. As a workaround, please try to use List instead. " + f"{example_type_str}" + f"The valid types are: {SUPPORTED_PARAM_TYPES.keys()}." + ) + else: + error_fn( + f"Parameter {name} has unsupported type {param.annotation}. " + f"The valid types are: {SUPPORTED_PARAM_TYPES.keys()}." + ) + + schema_type = SUPPORTED_PARAM_TYPES[annotation_type] + if type(mutates_args) == str: + if mutates_args != UNKNOWN_MUTATES: + raise ValueError( + "mutates_args must either be a sequence of the names of " + "the arguments that are mutated or the string 'unknown'. " + ) + if schema_type.startswith("Tensor"): + schema_type = f"Tensor(a{idx}!){schema_type[len('Tensor'):]}" + elif name in mutates_args: + if not schema_type.startswith("Tensor"): + error_fn( + f"Parameter {name} is in mutable_args but only Tensors or collections of Tensors can be mutated" + ) + schema_type = f"Tensor(a{idx}!){schema_type[len('Tensor'):]}" + seen_args.add(name) + if param.default is inspect.Parameter.empty: + params.append(f"{schema_type} {name}") + else: + default_repr = None + if param.default is None or isinstance(param.default, (int, float, bool)): + default_repr = str(param.default) + elif isinstance(param.default, (str, torch.device)): + default_repr = f'"{param.default}"' + elif isinstance(param.default, torch.dtype): + dtype_repr = str(param.default) + torch_dot = "torch." + assert dtype_repr.startswith(torch_dot) + default_repr = dtype_repr[len(torch_dot) :] + else: + error_fn( + f"Parameter {name} has an unsupported default value type {type(param.default)}. " + f"Please file an issue on GitHub so we can prioritize this." + ) + params.append(f"{schema_type} {name}={default_repr}") + if mutates_args != UNKNOWN_MUTATES: + mutates_args_not_seen = set(mutates_args) - seen_args + if len(mutates_args_not_seen) > 0: + error_fn( + f"{mutates_args_not_seen} in mutates_args were not found in " + f"the custom op's signature. " + f"mutates_args should contain the names of all args that the " + f"custom op mutates, or just the string 'unknown' if you don't know." + ) + return_annotation, _ = unstringify_type(sig.return_annotation) + ret = parse_return(return_annotation, error_fn) + if op_name is not None: + return f"{op_name}({', '.join(params)}) -> {ret}" + return f"({', '.join(params)}) -> {ret}" + + +def derived_types( + base_type: Union[type, typing._SpecialForm], + cpp_type: str, + list_base: bool, + optional_base_list: bool, + optional_list_base: bool, +): + result: list[tuple[Union[type, typing._SpecialForm, GenericAlias], str]] = [ + (base_type, cpp_type), + (typing.Optional[base_type], f"{cpp_type}?"), + ] + + def derived_seq_types(typ: Union[type, typing._SpecialForm]): + return ( + typing.Sequence[typ], # type: ignore[valid-type] # noqa: UP006 + typing.List[typ], # type: ignore[valid-type] # noqa: UP006 + GenericAlias(collections.abc.Sequence, (typ,)), + GenericAlias(list, (typ,)), + ) + + if list_base: + result.extend( + (seq_typ, f"{cpp_type}[]") for seq_typ in derived_seq_types(base_type) + ) + if optional_base_list: + result.extend( + (seq_typ, f"{cpp_type}?[]") + for seq_typ in derived_seq_types(typing.Optional[base_type]) + ) + if optional_list_base: + result.extend( + (typing.Optional[seq_typ], f"{cpp_type}[]?") + for seq_typ in derived_seq_types(base_type) + ) + return result + + +def get_supported_param_types(): + data: list[tuple[Union[type, typing._SpecialForm], str, bool, bool, bool]] = [ + # (python type, schema type, type[] variant, type?[] variant, type[]? variant + (Tensor, "Tensor", True, True, False), + (int, "SymInt", True, False, True), + (float, "float", True, False, True), + (bool, "bool", True, False, True), + (str, "str", False, False, False), + (types.Number, "Scalar", True, False, False), + (dtype, "ScalarType", False, False, False), + (device, "Device", False, False, False), + ] + result = [] + for line in data: + result.extend(derived_types(*line)) + return dict(result) + + +SUPPORTED_RETURN_TYPES = { + Tensor: "Tensor", + typing.List[Tensor]: "Tensor[]", # noqa: UP006 + list[Tensor]: "Tensor[]", + int: "SymInt", + float: "float", + bool: "bool", + types.Number: "Scalar", +} + + +def parse_return(annotation, error_fn): + if annotation is None: + return "()" + + if annotation is inspect.Parameter.empty: + error_fn("No return type annotation was provided. Please add one.") + + origin = typing.get_origin(annotation) + if origin is not tuple: + if annotation not in SUPPORTED_RETURN_TYPES.keys(): + error_fn( + f"Return has unsupported type {annotation}. " + f"The valid types are: {SUPPORTED_RETURN_TYPES}." + ) + return SUPPORTED_RETURN_TYPES[annotation] + + args = typing.get_args(annotation) + for arg in args: + if arg not in SUPPORTED_RETURN_TYPES: + error_fn( + f"Return has unsupported type {annotation}. " + f"The valid types are: {SUPPORTED_RETURN_TYPES}." + ) + + return "(" + ", ".join([SUPPORTED_RETURN_TYPES[arg] for arg in args]) + ")" + + +SUPPORTED_PARAM_TYPES = get_supported_param_types() + + +def supported_param(param: inspect.Parameter) -> bool: + return param.kind in ( + inspect.Parameter.POSITIONAL_OR_KEYWORD, + inspect.Parameter.KEYWORD_ONLY, + ) + + +def tuple_to_list(tuple_type: type[tuple]) -> type[list]: + """ + Convert `tuple_type` into a list type with the same type arguments. Assumes that `tuple_type` is typing.Tuple type. + """ + type_args = getattr(tuple_type, "__args__", None) + # Account for different python versions, e.g. python 3.8 would give () + # but python 3.12 would give None. + if ( + tuple_type is typing.Tuple # noqa: UP006 + or tuple_type is tuple + or type_args == () + or type_args is None + ): + # Handle the case of an empty tuple type + return list + elif len(type_args) == 1: + # General case: create a List with the same type arguments + return list[type_args[0]] # type: ignore[valid-type] + elif len(type_args) == 2 and type_args[1] is Ellipsis: + return list[type_args[0]] # type: ignore[valid-type] + else: + return list[typing.Union[tuple(type_args)]] # type: ignore[misc, return-value] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/simple_registry.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/simple_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..cfef278679ea56f4d5d15589467ffd47c3edaef4 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/simple_registry.py @@ -0,0 +1,85 @@ +# mypy: allow-untyped-defs +from typing import Callable, Optional + +from .fake_impl import FakeImplHolder +from .utils import RegistrationHandle + + +__all__ = ["SimpleLibraryRegistry", "SimpleOperatorEntry", "singleton"] + + +class SimpleLibraryRegistry: + """Registry for the "simple" torch.library APIs + + The "simple" torch.library APIs are a higher-level API on top of the + raw PyTorch DispatchKey registration APIs that includes: + - fake impl + + Registrations for these APIs do not go into the PyTorch dispatcher's + table because they may not directly involve a DispatchKey. For example, + the fake impl is a Python function that gets invoked by FakeTensor. + Instead, we manage them here. + + SimpleLibraryRegistry is a mapping from a fully qualified operator name + (including the overload) to SimpleOperatorEntry. + """ + + def __init__(self): + self._data = {} + + def find(self, qualname: str) -> "SimpleOperatorEntry": + if qualname not in self._data: + self._data[qualname] = SimpleOperatorEntry(qualname) + return self._data[qualname] + + +singleton: SimpleLibraryRegistry = SimpleLibraryRegistry() + + +class SimpleOperatorEntry: + """This is 1:1 to an operator overload. + + The fields of SimpleOperatorEntry are Holders where kernels can be + registered to. + """ + + def __init__(self, qualname: str): + self.qualname: str = qualname + self.fake_impl: FakeImplHolder = FakeImplHolder(qualname) + self.torch_dispatch_rules: GenericTorchDispatchRuleHolder = ( + GenericTorchDispatchRuleHolder(qualname) + ) + + # For compatibility reasons. We can delete this soon. + @property + def abstract_impl(self): + return self.fake_impl + + +class GenericTorchDispatchRuleHolder: + def __init__(self, qualname): + self._data = {} + self.qualname = qualname + + def register( + self, torch_dispatch_class: type, func: Callable + ) -> RegistrationHandle: + if self.find(torch_dispatch_class): + raise RuntimeError( + f"{torch_dispatch_class} already has a `__torch_dispatch__` rule registered for {self.qualname}" + ) + self._data[torch_dispatch_class] = func + + def deregister(): + del self._data[torch_dispatch_class] + + return RegistrationHandle(deregister) + + def find(self, torch_dispatch_class): + return self._data.get(torch_dispatch_class, None) + + +def find_torch_dispatch_rule(op, torch_dispatch_class: type) -> Optional[Callable]: + return singleton.find(op.__qualname__).torch_dispatch_rules.find( + torch_dispatch_class + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/triton.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/triton.py new file mode 100644 index 0000000000000000000000000000000000000000..72805c765d86da62b2bde847171eb63f86e3bf77 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/triton.py @@ -0,0 +1,274 @@ +import contextlib +import threading +from collections.abc import Generator, Iterable +from typing import Any, Callable, Optional, Union + +from torch.utils._exposed_in import exposed_in + +from .custom_ops import custom_op, CustomOpDef +from .infer_schema import infer_schema + + +@exposed_in("torch.library") +def triton_op( + name: str, + fn: Optional[Callable] = None, + /, + *, + mutates_args: Union[str, Iterable[str]], + schema: Optional[str] = None, +) -> Callable: + """Create a custom operator whose implementation is backed by 1+ triton kernels. + + This is a more structured way of using triton kernels with PyTorch. + Prefer using triton kernels with no ``torch.library`` custom operator wrappers + (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because + that is simpler; + only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you + want to create an operator that behaves like PyTorch built-in operators. + For example, you may use a ``torch.library`` wrapper API to define the + behavior of the triton kernel when passed a tensor subclass or under + a TorchDispatchMode. + + Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` + when the implementation + consists of 1+ triton kernels. :func:`torch.library.custom_op` treats + custom operators as opaque (:func:`torch.compile` and + :func:`torch.export.export` will never trace into them), but ``triton_op`` + makes the implementation visible to these subsystems, allowing them + to optimize the triton kernel(s). + + Note that ``fn`` must only consist of calls to PyTorch-understood + operators and triton kernels. Any triton kernels called inside ``fn`` + must be wrapped in a call to :func:`torch.library.wrap_triton`. + + Args: + name (str): A name for the custom op that looks like "{namespace}::{name}", + e.g. "mylib::my_linear". The name is used as the op's stable identifier + in PyTorch subsystems (e.g. torch.export, FX graphs). + To avoid name collisions, please use your project name as the namespace; + e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. + mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. + This MUST be accurate, otherwise, the behavior is undefined. If "unknown", + it pessimistically assumes that all inputs to the operator are being mutated. + schema (None | str): A schema string for the operator. If None + (recommended) we'll infer a schema for the operator from its type + annotations. We recommend letting us infer a schema unless you + have a specific reason not to. + Example: "(Tensor x, int y) -> (Tensor, Tensor)". + + Example:: + + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) + >>> import torch + >>> from torch.library import triton_op, wrap_triton + >>> + >>> import triton + >>> from triton import language as tl + >>> + >>> @triton.jit + >>> def add_kernel( + >>> in_ptr0, + >>> in_ptr1, + >>> out_ptr, + >>> n_elements, + >>> BLOCK_SIZE: "tl.constexpr", + >>> ): + >>> pid = tl.program_id(axis=0) + >>> block_start = pid * BLOCK_SIZE + >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) + >>> mask = offsets < n_elements + >>> x = tl.load(in_ptr0 + offsets, mask=mask) + >>> y = tl.load(in_ptr1 + offsets, mask=mask) + >>> output = x + y + >>> tl.store(out_ptr + offsets, output, mask=mask) + >>> + >>> @triton_op("mylib::add", mutates_args={}) + >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + >>> output = torch.empty_like(x) + >>> n_elements = output.numel() + >>> + >>> def grid(meta): + >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) + >>> + >>> # NB: we need to wrap the triton kernel in a call to wrap_triton + >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) + >>> return output + >>> + >>> @torch.compile + >>> def f(x, y): + >>> return add(x, y) + >>> + >>> x = torch.randn(3, device="cuda") + >>> y = torch.randn(3, device="cuda") + >>> + >>> z = f(x, y) + >>> assert torch.allclose(z, x + y) + + """ + + def dec(fn: Callable[..., object]) -> CustomOpDef: + def backend_fn(*args, **kwargs): # type: ignore[no-untyped-def] + # Optimization: we're passing regular Tensors into the triton kernel, so + # no need to go through HOP dispatch + with set_wrap_triton_enabled(False): + return fn(*args, **kwargs) + + result = custom_op( + name, + backend_fn, + mutates_args=mutates_args, + schema=infer_schema(fn, mutates_args=mutates_args), + ) + from .._subclasses.functional_tensor import FunctionalTensorMode + + # We require that the user pass us a function that is make_fx traceable, + # so we can just register it as the Fake/meta kernel. + result.register_fake(fn) + + # We decompose the operator when FunctionalTensorMode is active. + # The goal is to decompose the operator in AOTDispatcher. + # - With torch.compile, this means that the backend (usually Inductor) + # can see a call to the triton kernel(s) and so it can directly optimize + # them by inlining them into the lowering process. + def functional_decomp( # type: ignore[no-untyped-def] + mode, op, types, args, kwargs + ): + # NOTE [Export custom triton op] + # For torch.export (strict and non-strict), we don't do functional decomposition. + # Instead, we preserve the custom triton ops as custom ops. This is because we want + # the exported program to be high-level and serializable. If we decompose + # the custom op to a functional hop and make it a node in exported program, + # we need to figure out ways of serializing the hop and its arguments, which can be triton.jited + # functions and triton dtypes. This is undesireble because: + # - it can be tedious to maintain a layer that serializes the jited function (e.g. with a string) and dtypes. + # - exported program will contain the implementation detail (e.g. triton source code) for a specific + # backend (GPU), which is probably at a wrong level of abstraction. + # - changes to triton or the serialization logic for triton arguments can be BC breaking + # + # In the short term, we expect users to have a separate aot_compile stage that compiles the exported program + # into a Cubin file on the same machine that users call export, which does autotuning and removes triton + # dependency and serve the model with Cubin. This guarantees that triton changes won't break BC. + # In the long term, we may export multiple cubins for the triton op directly + from torch.export._trace import custom_triton_ops_decomposition_disabled + + if custom_triton_ops_decomposition_disabled(): + return mode.__torch_dispatch__(op, types, args, kwargs) + else: + with mode: + return fn(*args, **kwargs) + + result.register_torch_dispatch(FunctionalTensorMode, functional_decomp) + return result + + if fn is None: + return dec + else: + return dec(fn) + + +wrap_triton_enabled = threading.local() +wrap_triton_enabled_default = True + + +@contextlib.contextmanager +def set_wrap_triton_enabled(enabled: bool) -> Generator[None, None, None]: + """If triton kernels annotated with @wrap_triton should dispatch via HOP + or go straight to the triton kernel execution. + + We have this switch because eager-mode performance of HOP dispatch is slow + enough to matter (~1ms) and we know that wrap_triton isn't necessary in + some situations (eager-mode with regular Tensors) + """ + try: + prev = is_wrap_triton_enabled() + wrap_triton_enabled.value = enabled + yield + finally: + wrap_triton_enabled.value = prev + + +def is_wrap_triton_enabled() -> bool: + return getattr(wrap_triton_enabled, "value", wrap_triton_enabled_default) + + +def capture_triton(triton_kernel: Callable, /) -> Any: + """This API has been renamed to wrap_triton""" + return wrap_triton(triton_kernel) + + +@exposed_in("torch.library") +def wrap_triton(triton_kernel: Callable, /) -> Any: + """Allows capture of a triton kernel into a graph via make_fx or + non-strict ``torch.export``. + + These technologies perform Dispatcher-based tracing (via + ``__torch_dispatch__``) and cannot see calls to raw triton kernels. + The ``wrap_triton`` API wraps a triton kernel into a callable that + can actually be traced into a graph. + + Please use this API together with :func:`torch.library.triton_op`. + + Examples: + + >>> # xdoctest: +SKIP + >>> import torch + >>> import triton + >>> from triton import language as tl + >>> from torch.fx.experimental.proxy_tensor import make_fx + >>> from torch.library import wrap_triton + >>> + >>> @triton.jit + >>> def add_kernel( + >>> in_ptr0, + >>> in_ptr1, + >>> out_ptr, + >>> n_elements, + >>> BLOCK_SIZE: "tl.constexpr", + >>> ): + >>> pid = tl.program_id(axis=0) + >>> block_start = pid * BLOCK_SIZE + >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) + >>> mask = offsets < n_elements + >>> x = tl.load(in_ptr0 + offsets, mask=mask) + >>> y = tl.load(in_ptr1 + offsets, mask=mask) + >>> output = x + y + >>> tl.store(out_ptr + offsets, output, mask=mask) + >>> + >>> def add(x, y): + >>> output = torch.empty_like(x) + >>> n_elements = output.numel() + >>> + >>> def grid_fn(meta): + >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) + >>> + >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) + >>> return output + >>> + >>> x = torch.randn(3, device="cuda") + >>> y = torch.randn(3, device="cuda") + >>> gm = make_fx(add)(x, y) + >>> print(gm.code) + >>> # def forward(self, x_1, y_1): + >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) + >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( + >>> # kernel_idx = 0, constant_args_idx = 0, + >>> # grid = [(1, 1, 1)], kwargs = { + >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, + >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 + >>> # }) + >>> # return empty_like + + """ + from triton.runtime.autotuner import Autotuner + from triton.runtime.jit import JITFunction + + from torch._higher_order_ops.triton_kernel_wrap import TraceableTritonKernelWrapper + + if not isinstance(triton_kernel, (JITFunction, Autotuner)): + raise RuntimeError( + "wrap_triton only works on functions annotated with triton.jit or triton.autotune" + ) + if not is_wrap_triton_enabled(): + return triton_kernel + return TraceableTritonKernelWrapper(triton_kernel, None, None) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8348883cee30e00c8a888924ccf29c40e484ec82 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_library/utils.py @@ -0,0 +1,478 @@ +# mypy: allow-untyped-defs +import dataclasses +import inspect +import sys +import warnings +from collections.abc import Iterable, Iterator +from typing import Any, Callable, Union + +import torch +import torch.utils._pytree as pytree +from torch import _C, _utils_internal +from torch._ops import OpOverload + + +def warn_deploy(stacklevel=3): + warnings.warn( + "Python torch.library APIs do nothing under torch::deploy (multipy). " + "Please instead use C++ custom operator registration APIs.", + RuntimeWarning, + stacklevel=stacklevel, + ) + + +@dataclasses.dataclass +class Kernel: + """Models a (function, source location)""" + + func: Callable + source: str + + def __call__(self, *args, **kwargs): + return self.func(*args, **kwargs) + + +class RegistrationHandle: + """Does something when someone calls .destroy() on it""" + + def __init__(self, on_destroy: Callable): + self._on_destroy = on_destroy + + def destroy(self) -> None: + self._on_destroy() + + +def get_source(stacklevel: int) -> str: + """Get a string that represents the caller. + + Example: "/path/to/foo.py:42" + + Use stacklevel=1 to get the caller's source + Use stacklevel=2 to get the caller's caller's source + etc. + """ + frame = inspect.getframeinfo(sys._getframe(stacklevel)) + source = f"{frame.filename}:{frame.lineno}" + return source + + +def parse_namespace(qualname: str) -> tuple[str, str]: + splits = qualname.split("::") + if len(splits) != 2: + raise ValueError( + f"Expected `qualname` to be of the form " + f'"namespace::name", but got {qualname}. ' + f"The qualname passed to the torch.library APIs must consist " + f"of a namespace and a name, e.g. aten::sin" + ) + return splits[0], splits[1] + + +def lookup_op(qualname: str) -> OpOverload: + namespace, name = parse_namespace(qualname) + if "." in name: + name, overload = name.split(".") + else: + overload = "default" + ns = getattr(torch.ops, namespace) + packet = getattr(ns, name) + return getattr(packet, overload) + + +def is_builtin(op: OpOverload) -> bool: + assert isinstance(op, OpOverload) + return op.namespace in {"aten", "prim", "prims"} + + +def is_functional_schema(schema: Any) -> bool: + """Check if the schema is functional. + + An operator is functional if: + - it does not mutate any of its inputs + - it does not return a view on any of its inputs + - it has at least one return + """ + + def is_functional(schema): + if schema.is_mutable: + return False + rets = schema.returns + is_non_mutating_view = len(rets) > 0 and any( + r.alias_info is not None and not r.alias_info.is_write for r in rets + ) + if is_non_mutating_view: + return False + if not schema.returns: + return False + return True + + if isinstance(schema, torch._C.FunctionSchema): + return is_functional(schema) + + # Lazy import because not all PyTorch builds have torchgen + from torchgen.model import FunctionSchema + + if isinstance(schema, str): + schema = FunctionSchema.parse(schema) + assert isinstance(schema, FunctionSchema) + return is_functional(schema) + + +# should be torch._C.JitType but that annotation is busted +def is_tensorlist_like_type(typ: Any) -> bool: + return ( + typ == _C.ListType(_C.TensorType.get()) + or typ == _C.ListType(_C.OptionalType(_C.TensorType.get())) + or typ == _C.OptionalType(_C.ListType(_C.TensorType.get())) + or typ == _C.OptionalType(_C.ListType(_C.OptionalType(_C.TensorType.get()))) + ) + + +# should be torch._C.JitType but that annotation is busted +def is_tensor_like_type(typ: Any) -> bool: + return typ == _C.TensorType.get() or typ == _C.OptionalType(_C.TensorType.get()) + + +def mutates_and_returns_first_arg(op: OpOverload): + """Check if an op is an inplace aten op, i.e. it mutates and returns the first arg. + + TODO: torchgen/model.py's FunctionSchema.parse is the source of truth for this, + but not all PyTorch builds have torchgen (due to the yaml dependency being weird). + Figure this out. + + Example: add_(Tensor(a!) x, Tensor y) -> Tensor(a) + """ + if op.namespace != "aten": + return False + schema = op._schema + if not len(schema.returns) == 1: + return False + if schema.returns[0].alias_info is None: + return False + alias_set = schema.returns[0].alias_info.after_set + if len(alias_set) != 1: + return False + loc = next(iter(alias_set)) + if len(schema.arguments) < 1: + return False + first_arg = schema.arguments[0] + if first_arg.alias_info is None: + return False + if not first_arg.alias_info.is_write: + return False + alias_set = first_arg.alias_info.after_set + if len(alias_set) != 1: + return False + if loc != next(iter(alias_set)): + return False + for arg in schema.arguments[1:]: + if arg.alias_info is not None: + return False + return True + + +def fill_defaults(schema, args, kwargs): + new_args = [] + new_kwargs = {} + for i in range(len(schema.arguments)): + info = schema.arguments[i] + if info.kwarg_only: + if info.name in kwargs: + new_kwargs[info.name] = kwargs[info.name] + else: + new_kwargs[info.name] = info.default_value + else: + if i < len(args): + new_args.append(args[i]) + else: + new_args.append(info.default_value) + return tuple(new_args), new_kwargs + + +def zip_schema( + schema: _C.FunctionSchema, args: tuple[Any, ...], kwargs: dict[str, Any] +) -> Iterable[tuple[_C.Argument, Any]]: + """zips schema.arguments and (args, kwargs) together. + + Assumes that (args, kwargs) were the inputs to some torch._ops.OpOverload: + that is, (args, kwargs) must be bindable to the schema (args, kwargs). + """ + assert len(schema.arguments) >= len(args) + len(kwargs) + for i in range(len(schema.arguments)): + info = schema.arguments[i] + if info.kwarg_only: + if info.name in kwargs: + yield info, kwargs[info.name] + continue + if i >= len(args): + if not info.kwarg_only and info.name in kwargs: + yield info, kwargs[info.name] + # args that are equal to their default values are not populated + # if they are followed by args that are equal to their defaults. + # Skip these. + continue + yield info, args[i] + return + + +def hop_schema_from_fx_node(node): + from torchgen.gen_schema_utils import FunctionSchemaGen + + hop = node.target + if not isinstance(hop, torch._ops.HigherOrderOperator): + raise RuntimeError("fx_node's target must be a hop.") + + def _collect_example_val(node): + meta_val = node.meta.get("val", None) + if meta_val is None: + assert node.op == "get_attr" + meta_val = getattr(node.graph.owning_module, node.target) + return meta_val + + example_inputs = [] + for arg in node.args: + if isinstance(arg, (torch.fx.Node, torch.fx.node.Node)): + example_inputs.append(_collect_example_val(arg)) + elif isinstance( + arg, (torch.fx.immutable_collections.immutable_list, list, tuple) + ): + example_inputs.append([_collect_example_val(x) for x in arg]) + else: + raise RuntimeError(f"Unsupported arg type {type(arg)}") + + # Bound the arguments to make sure number of inputs are correct + bound_args: inspect.BoundArguments = inspect.signature(hop.__call__).bind( + *example_inputs + ) + + # We treat example_output as a single value in return. This is to differentiate 1. return a single val + # vs 2. return a tuple with one element. + example_output = _collect_example_val(node) + return FunctionSchemaGen.from_example( + hop._name, tuple(bound_args.arguments.items()), (list(example_output),) + ) + + +def can_generate_trivial_fake_impl(op: OpOverload) -> bool: + assert isinstance(op, OpOverload) + if is_builtin(op): + # We control the built-ins. These may (in rare cases) + # do input metadata mutation (which we have banned on custom ops) + return False + schema = op._schema + # It's suspicious if the op is not mutable but returns nothing, so we return False out of an abundance of caution + if not schema.is_mutable: + return False + if len(schema.returns) > 0: + return False + # If the op returns nothing, then it has a trivial fake impl. + return True + + +def requires_set_python_module() -> bool: + """If an op was defined in C++ and extended from Python using the + torch.library APIs, returns if we require that there have been a + m.set_python_module("mylib.ops") call from C++ that associates + the C++ op with a python module. + """ + return getattr(_utils_internal, "REQUIRES_SET_PYTHON_MODULE", True) + + +def handle_dispatch_mode(curr_mode, op_overload, *args, **kwargs): + assert isinstance(curr_mode, torch.utils._python_dispatch.TorchDispatchMode) + args_flattened, _ = torch.utils._pytree.tree_flatten((args, kwargs.values())) + # TODO: need to double check the semantics of the "types" argument to torch_dispatch. + # It's generated in PyInterpreter.cpp, but seems to be generated in two places, + # where in one case we only include tensors with the python key, and in another + # we include **all** tensors. + overload_types = [ + type(a) + for a in args_flattened + if isinstance(a, torch.Tensor) + and torch._C._dispatch_keys(a).has(torch._C.DispatchKey.Python) + ] + # TODO: check that I got these args correct (in C++, we pass in "0000"??) + + return curr_mode.__torch_dispatch__(op_overload, overload_types, args, kwargs) + + +def has_kwarg_only_args(schema: _C.FunctionSchema): + return any(a.kwarg_only for a in schema.arguments) + + +def has_kwarg_only_tensors(schema: _C.FunctionSchema): + for a in schema.arguments: + if not (is_tensor_like_type(a.type) or is_tensorlist_like_type(a.type)): + continue + if not a.kwarg_only: + continue + return True + return False + + +def has_tensor_arg(schema: _C.FunctionSchema) -> bool: + """ + Given a schema, returns True if the schema has a Tensor arg. + A Tensor arg is any arg with a type annotation that might involve Tensor. + """ + return any( + (is_tensor_like_type(a.type) or is_tensorlist_like_type(a.type)) + for a in schema.arguments + ) + + +def get_device_arg_index(schema: _C.FunctionSchema) -> Union[int, None]: + """ + Given a schema, returns the id of the `device: torch.device` argument. + If it does not exist, returns None. + """ + for index, arg in enumerate(schema.arguments): + if arg.type is _C.DeviceObjType.get() and arg.name == "device": + return index + return None + + +def iter_tensors( + args: tuple[Any], kwargs: dict[str, Any], allowed_nesting: int = 1 +) -> Iterator[torch.Tensor]: + def check(arg): + if isinstance(arg, torch.Tensor): + yield arg + elif allowed_nesting > 0 and isinstance(arg, (tuple, list)): + yield from iter_tensors(tuple(arg), {}, allowed_nesting - 1) + + for arg in args: + yield from check(arg) + for kwarg in kwargs.values(): + yield from check(kwarg) + + +def check_aliasing_constraint(name, prev, result, get_module=lambda: "???"): + """ + custom operators' outputs must not alias any inputs or other outputs. + """ + storages = {id(t.untyped_storage()) for t in prev if isinstance(t, torch.Tensor)} + tuple_result = result + if not isinstance(result, tuple): + tuple_result = (result,) + for tensor in iter_tensors(tuple_result, {}): + key = id(tensor.untyped_storage()) + if id(tensor.untyped_storage()) in storages: + raise RuntimeError( + f"{name} (with implementation in {get_module()}): " + f"The output of this custom operator (1) must not " + f"also be an input to this custom operator and " + f"(2) may not alias any inputs to this custom operator " + f"or other returns. " + f"The most common way to trigger this error is if " + f"we have y = custom_op(x) and y and x are the same Tensor. " + f"Please instead return a clone of the offending output " + f"tensor(s) (e.g. return x.clone()) or refactor the custom " + f"operator to not return y." + ) + storages.add(key) + + +class MutationChecker: + """ + Check if an operator mutated its arguments. + Usage: + + checker = MutationChecker(op, flat_args, args_spec) + op(*args, **kwargs) + checker.check() + """ + + def __init__(self, op, flat_args, args_spec): + self.op = op + self.args_spec = args_spec + self.flat_args = flat_args + self.real_pre_hashes = [ + hash_tensor(a) if isinstance(a, torch.Tensor) else None for a in flat_args + ] + + def check(self): + real_post_hashes = [ + hash_tensor(a) if isinstance(a, torch.Tensor) else None + for a in self.flat_args + ] + was_mutated = [ + not torch.equal(pre, post) + and not (pre.isnan().all() and post.isnan().all()) + if isinstance(pre, torch.Tensor) and isinstance(post, torch.Tensor) + else None + for pre, post in zip(self.real_pre_hashes, real_post_hashes) + ] + was_mutated_args, was_mutated_kwargs = pytree.tree_unflatten( + was_mutated, self.args_spec + ) + for info, was_mutated in zip_schema( + self.op._schema, was_mutated_args, was_mutated_kwargs + ): + + def check_one(info, was_mutated): + if info.is_write == was_mutated: + return + raise RuntimeError( + f"{self.op._name}: for argument '{info.name}': the operator's schema " + f"{self.op._schema} specified that " + f"the operator {'mutates' if info.is_write else 'does not mutate'} " + f"the argument, but this seems to be emperically wrong. " + f"Please make the schema and operator behavior consistent. " + f"You can specify that an operator mutates a Tensor by " + f"e.g. changing its schema type from 'Tensor name' to 'Tensor(a!) name'" + f"(use different identifiers (a, b, c, ...) for different Tensors)" + ) + + if is_tensor_like_type(info.type): + check_one(info, was_mutated) + elif is_tensorlist_like_type(info.type): + was_any_mutated = False if was_mutated is None else any(was_mutated) + check_one(info, was_any_mutated) + + +def hash_tensor(t: torch.Tensor) -> torch.Tensor: + """Some inexpensive hash. Used as a quick and dirty indicator for tensor mutation""" + return t.detach().float().mean() + + +def has_fake_kernel(op: torch._ops.OpOverload) -> bool: + """If an operator (that stays alive until FakeTensorMode) has a Fake kernel. + Don't use this if the operator decomposes before FakeTensorMode. + """ + if can_generate_trivial_fake_impl(op): + return True + name = op._name + if torch._C._dispatch_has_kernel_for_dispatch_key( + name, "CompositeImplicitAutograd" + ): + return True + opdef = torch._library.custom_ops._maybe_get_opdef(name) + if opdef is None: + # the non-torch.library.custom_op path + if torch._C._dispatch_has_kernel_for_dispatch_key( + name, "CompositeExplicitAutograd" + ): + return True + entry = torch._library.simple_registry.singleton.find(name) + if entry.fake_impl.kernel is not None: + return True + if torch._C._dispatch_has_kernel_for_dispatch_key(name, "Meta"): + return True + else: + # the torch.library.custom_op path + if opdef._abstract_fn is not None: + return True + return False + + +def mutated_args_kwargs(schema: _C.FunctionSchema) -> tuple[list[int], list[str]]: + idxs = [] + keys = [] + for i, info in enumerate(schema.arguments): + if info.alias_info is not None and info.alias_info.is_write: + if info.kwarg_only: + keys.append(info.name) + else: + idxs.append(i) + return idxs, keys diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a5b364f437da5c925800c76d4012e98d22008852 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/__init__.py @@ -0,0 +1,2947 @@ +# mypy: allow-untyped-defs +import operator +from collections.abc import Sequence +from enum import Enum +from functools import partial, reduce +from typing import Callable, Optional, Union + +import torch +import torch._prims_common as utils +import torch.library +from torch import sym_float, Tensor +from torch._C import _get_default_device +from torch._higher_order_ops.effects import new_token_tensor +from torch._library.utils import is_functional_schema +from torch._prims.debug_prims import register_debug_prims +from torch._prims.rng_prims import register_rng_prims +from torch._prims_common import ( + Dim, + DimsSequenceType, + DimsType, + IntLike, + Number, + NumberType, + RETURN_TYPE, + ShapeType, + StrideType, + TensorLike, + TensorLikeType, + type_to_dtype, +) +from torch._prims_common.wrappers import backwards_not_supported +from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode +from torch.overrides import handle_torch_function, has_torch_function +from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten + + +prim = torch.library.Library("prims", "DEF") +prim_impl = torch.library.Library("prims", "IMPL", "CompositeExplicitAutograd") +prim_backend_select_impl = torch.library.Library("prims", "IMPL", "BackendSelect") +prim_autograd_impl = torch.library.Library("prims", "IMPL", "Autograd") +prim_meta_impl = torch.library.Library("prims", "IMPL", "Meta") + +# Experimental module containing prototype "primitive" operations. + +__all__ = [ + # + # Common datastructures and helpers + # + "RETURN_TYPE", + # + # Elementwise unary prims + # + "abs", + "acos", + "acosh", + "asin", + "asinh", + "atan", + "atanh", + "cos", + "cosh", + "bessel_i0", + "bessel_i0e", + "bessel_i1", + "bessel_i1e", + "bessel_j0", + "bessel_j1", + "bitwise_not", + "cbrt", + "ceil", + "conj_physical", + "digamma", + "erf", + "erf_inv", + "erfc", + "erfcx", + "exp", + "expm1", + "exp2", + "fill", + "floor", + "imag", + "isfinite", + "lgamma", + "log", + "log1p", + "log2", + "log10", + "ndtri", + "neg", + "real", + "reciprocal", + "round", + "sign", + "signbit", + "sin", + "sinh", + "spherical_bessel_j0", + "sqrt", + "tan", + "tanh", + "trunc", + # + # Elementwise binary prims + # + "add", + "atan2", + "bitwise_and", + "bitwise_or", + "bitwise_xor", + # 'complex', # needs custom meta + "div", + "eq", + "fmax", + "fmin", + "fmod", + "frexp", + "gcd", + "ge", + "gt", + "hypot", + "igamma", + "igammac", + "le", + "lt", + "maximum", + "minimum", + "mul", + "ne", + "nextafter", + "pow", + "remainder", + "rsqrt", + "shift_left", + "shift_right_arithmetic", + "shift_right_logical", # not implemented + "sub", + "zeta", + # + # View prims + # + "as_strided", + "broadcast_in_dim", + "collapse_view", + "conj", + "expand_dims", + "slice", + "slice_in_dim", # implemented using slice -- make this a ref? + "split_dim", + "squeeze", + "transpose", + "view_of", + "view_element_type", + # + # Functionalized view mutations + # + "as_strided_scatter", + # + # Shape prims + # + "collapse", + "cat", + "reshape", + "rev", + # + # Conditional prims + # + "where", + # + # Data conversion and movement prims + # + "clone", + "convert_element_type", + "device_put", + "item", + "maximum_value", + "minimum_value", + "copy_strided", + # + # Inplace prims + # + "copy_to", + "resize", + # "_set", # Commented out, see note below + # + # Reduction prims + # + "amax", + "amin", + "prod", + "sum", + "xor_sum", + "var", + # + # Tensor Creation Prims + # + "empty_strided", + "empty_permuted", + "scalar_tensor", + "iota", + # + # Linear algebra (linalg) Prims + # + "svd", + # + # Randomness Prims + # + "normal", + "_uniform_helper", + # + # FFT prims + # + "fft_r2c", + "fft_c2c", + "fft_c2r", + # + # prims for making/sinking tokens + # + "_make_token", + "_sink_tokens", +] + + +def TensorMeta( + tensorlike: Optional[Union[NumberType, torch.Tensor]] = None, + *, + shape: Optional[ShapeType] = None, + strides: Optional[StrideType] = None, + dtype: Optional[torch.dtype] = None, + device: Optional[Union[torch.device, str]] = None, +): + if isinstance(tensorlike, Number): + assert not shape and (shape is None or isinstance(shape, Sequence)) + assert not strides and (strides is None or isinstance(strides, Sequence)) + inferred_shape: tuple[int, ...] = () + inferred_strides: tuple[int, ...] = () + inferred_dtype = type_to_dtype(type(tensorlike)) + inferred_device = torch.device("cpu") + # TODO: This looks wrong, a number that is wrapped into a tensor + # needs to behave differently than a scalar tensor for type + # promotion purposes + elif tensorlike is not None: + assert isinstance(tensorlike, torch.Tensor) + inferred_shape = tuple(tensorlike.shape) + inferred_strides = tuple(tensorlike.stride()) + inferred_dtype = tensorlike.dtype + inferred_device = tensorlike.device + else: + # If no tensorlike "example" is given then all metadata + # must be provided explicitly + assert shape is not None + assert strides is not None + assert dtype is not None + assert device is not None + + shape = inferred_shape if shape is None else tuple(shape) # type: ignore[possibly-undefined] + strides = inferred_strides if strides is None else tuple(strides) # type: ignore[possibly-undefined] + dtype = inferred_dtype if dtype is None else dtype # type: ignore[possibly-undefined] + device = inferred_device if device is None else device # type: ignore[possibly-undefined] + + if isinstance(device, str): + device = torch.device(device) + + return torch.empty_strided(shape, strides, dtype=dtype, device=device) + + +def _make_prim( + *, + schema: str, + return_type: Union[RETURN_TYPE, tuple[RETURN_TYPE, ...]], + meta: Callable, + impl_aten: Callable, + doc: str, + tags: Optional[Sequence[torch.Tag]] = None, + use_old_custom_ops_api: bool = False, + register_conj_neg_fallthrough: bool = False, +): + """ + Creates a primitive operation. + + """ + + def _prim_impl(*args, **kwargs): + # always run the meta function because aten implementation will + # typically accept more inputs (e.g., it will do promotion and + # broadcasting) which we want to reject + meta(*args, **kwargs) + return impl_aten(*args, **kwargs) + + # Right now prims don't support autograd (we can and should add an + # argument that provides an implementation for backward here.) Because we + # don't have derivative formulas, we must setup a custom autograd function + # that raises an error if backwards is invoked + def _autograd_impl(*args, **kwargs): + return backwards_not_supported(_prim)(*args, **kwargs) + + def _backend_select_impl(*args, **kwargs): + if kwargs.get("device") and kwargs["device"].type == "meta": + return meta(*args, **kwargs) + if any(isinstance(x, torch.device) and x.type == "meta" for x in args): + return meta(*args, **kwargs) + else: + return _prim_impl(*args, **kwargs) + + name = schema.split("(")[0] + schema = schema[len(name) :] + + # register non-functional ops with old custom ops API + cpp_schema = torch._C.parse_schema(name + schema) + if use_old_custom_ops_api or not is_functional_schema(cpp_schema): + prim.define(name + schema, tags=torch.Tag.pt2_compliant_tag) + prim_impl.impl(name, _prim_impl) + prim_autograd_impl.impl(name, _autograd_impl) + prim_meta_impl.impl(name, meta) + else: + mutates_args = [ + arg.name + for arg in cpp_schema.arguments + if arg.alias_info is not None and arg.alias_info.is_write + ] + prim_def = torch.library.custom_op( + "prims::" + name, + _prim_impl, + mutates_args=tuple(mutates_args), + schema=schema, + ) + prim_def.register_fake(meta) + + # all view ops get conj/neg fallthroughs + if return_type == RETURN_TYPE.VIEW or register_conj_neg_fallthrough: + prim_def._lib.impl(name, torch.library.fallthrough_kernel, "Conjugate") + prim_def._lib.impl(name, torch.library.fallthrough_kernel, "Negative") + + _prim_packet = getattr(torch._ops.ops.prims, name) + _prim = _prim_packet.default + if tags: + _prim._tags = tags + elif aten_packet := getattr(torch.ops.aten, name, None): + overload_tags = [ + getattr(aten_packet, overload).tags for overload in aten_packet.overloads() + ] + tags_intersection = set(overload_tags[0]) + tags_intersection.intersection_update(*overload_tags[1:]) + + # dont inadvertently add to prim ops + tags_intersection.discard(torch.Tag.core) + # causes errors with python ref executor tests, none of the + # data dependent pytorch ops actually decompose to prims + tags_intersection.discard(torch.Tag.data_dependent_output) + + # iter over first tags for determinism + _prim._tags = tuple(t for t in overload_tags[0] if t in tags_intersection) + + from torch._subclasses.fake_tensor import contains_tensor_types + + if not any(contains_tensor_types(a.type) for a in _prim._schema.arguments) or str( + _prim + ) in [ + # See https://github.com/pytorch/pytorch/issues/103532 + "prims.device_put.default" + ]: + prim_backend_select_impl.impl(name, _backend_select_impl) + + for p in (_prim_packet, _prim): + p.__doc__ = doc + p.return_type = return_type # type: ignore[attr-defined] + + p.schema = schema + p.prim_impl = _prim_impl + p.prim_meta_impl = meta + p.impl_aten = impl_aten + + return _prim + + +class ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND(Enum): + DEFAULT = (0,) + INT_TO_FLOAT = (2,) + ALWAYS_BOOL = (3,) + COMPLEX_TO_FLOAT = (4,) + + +# TODO: implement dtype validation here, too, or on the corresponding refs +def _prim_elementwise_meta( + *args, + type_promotion: ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND, + args_with_fixed_dtypes: Optional[tuple[TensorLikeType, ...]] = None, +) -> FakeTensor: + """ + Meta function for elementwise operations that produce outputs in the same dtype + as their inputs. + + Stride logic is currently incorrect. + """ + + assert len(args) > 0 + + utils.check_same_dtype(*args) + + args_ = list(args) + if args_with_fixed_dtypes is not None: + args_ = list(args_with_fixed_dtypes) + args_ + + utils.check_same_device(*args_, allow_cpu_scalar_tensors=True) + utils.check_same_shape(*args_, allow_cpu_scalar_tensors=True) + + l2p_perm = utils.compute_elementwise_output_logical_to_physical_perm(*args_) + shape = utils.extract_shape(*args_, allow_cpu_scalar_tensors=True) + + # Acquires the dtype + dtype = None + scalar_type = None + for arg in args: + if isinstance(arg, TensorLike): + if not utils.is_cpu_scalar_tensor(arg): + dtype = arg.dtype + break + else: + dtype = arg.dtype + elif isinstance(arg, Number): + scalar_type = type(arg) + + if dtype is None and scalar_type is not None: + dtype = utils.type_to_dtype(scalar_type) + + # Acquires the device (if it exists) or number + device = None + number = None + for arg in args_: + if isinstance(arg, TensorLike): + if utils.is_cpu_scalar_tensor(arg): + if device is None: + device = arg.device + # keep going, in case there is a cuda tensor later + else: + device = arg.device + break + + elif isinstance(arg, Number): + if number is None: + number = arg + + # NOTE: type promotion behavior here is mostly hidden from tests because + # references will typically handle the type promotion properly even if this doesn't + # (but getting it wrong will cause too many casts to be inserted in traces!) + if device is not None: + assert dtype is not None + if type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT: + dtype = dtype + elif type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL: + dtype = torch.bool + elif type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.INT_TO_FLOAT: + if utils.is_integer_dtype(dtype) or utils.is_boolean_dtype(dtype): + dtype = torch.get_default_dtype() + elif type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT: + if utils.is_complex_dtype(dtype): + dtype = utils.corresponding_real_dtype(dtype) + else: + dtype = dtype + + assert shape is not None + return torch.empty_permuted(shape, l2p_perm, device=device, dtype=dtype) # type: ignore[return-value] + + # Number case + # TODO: fix number type promotion (bool, complex->float) + + # For now for symint/float, just implementing the common / simple cases of (int,float,symint,symfloat) + seen_float = False + if isinstance(number, (torch.SymInt, torch.SymFloat)): + for a in args: + assert isinstance(a, (int, float, torch.SymInt, torch.SymFloat)), "NYI" + seen_float = seen_float or isinstance(a, (float, torch.SymFloat)) + if seen_float: + number = sym_float(number) + + return TensorMeta(number) # type: ignore[arg-type] + + +def _complex_only_elementwise_meta(*args, **kwargs): + torch._check( + utils.is_complex_dtype(args[0].dtype), lambda: "Only complex dtype is supported" + ) + return _prim_elementwise_meta(*args, **kwargs) + + +def _make_elementwise_unary_prim( + name: str, *, type_promotion: ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND, **kwargs +): + """ + Creates an elementwise unary prim. + """ + + return _make_prim( + schema=f"{name}(Tensor self) -> Tensor", + meta=partial(_prim_elementwise_meta, type_promotion=type_promotion), + return_type=RETURN_TYPE.NEW, + **kwargs, + ) + + +def _make_elementwise_binary_prim( + name: str, *, type_promotion: ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND, **kwargs +): + """ + Creates an elementwise binary prim. + """ + + return _make_prim( + schema=f"{name}(Tensor self, Tensor other) -> Tensor", + meta=partial(_prim_elementwise_meta, type_promotion=type_promotion), + return_type=RETURN_TYPE.NEW, + **kwargs, + ) + + +def _not_impl(*args, **kwargs): + raise NotImplementedError + + +# +# Elementwise unary operations +# + + +abs = _make_elementwise_unary_prim( + "abs", + impl_aten=torch.abs, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, +) + +acos = _make_elementwise_unary_prim( + "acos", + impl_aten=torch.acos, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +acosh = _make_elementwise_unary_prim( + "acosh", + impl_aten=torch.acosh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +asin = _make_elementwise_unary_prim( + "asin", + impl_aten=torch.asin, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +asinh = _make_elementwise_unary_prim( + "asinh", + impl_aten=torch.asinh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +atan = _make_elementwise_unary_prim( + "atan", + impl_aten=torch.atan, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +atanh = _make_elementwise_unary_prim( + "atanh", + impl_aten=torch.atanh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +cos = _make_elementwise_unary_prim( + "cos", + impl_aten=torch.cos, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +cosh = _make_elementwise_unary_prim( + "cosh", + impl_aten=torch.cosh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_j0 = _make_elementwise_unary_prim( + "bessel_j0", + impl_aten=torch.special.bessel_j0, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_j1 = _make_elementwise_unary_prim( + "bessel_j1", + impl_aten=torch.special.bessel_j1, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_i0 = _make_elementwise_unary_prim( + "bessel_i0", + impl_aten=torch.i0, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_i0e = _make_elementwise_unary_prim( + "bessel_i0e", + impl_aten=torch.special.i0e, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_i1 = _make_elementwise_unary_prim( + "bessel_i1", + impl_aten=torch.special.i1, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bessel_i1e = _make_elementwise_unary_prim( + "bessel_i1e", + impl_aten=torch.special.i1e, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bitwise_not = _make_elementwise_unary_prim( + "bitwise_not", + impl_aten=torch.bitwise_not, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +def _cbrt_aten(a: torch.Tensor) -> Tensor: + torch._check( + not a.is_complex(), + lambda: "cbrt: Complex inputs not supported. Consider calling torch.pow(a, 1.0/3.0)", + ) + # Returns the real cubic root of the number. + # Note that if a < 0, pow(a, (1. / 3.)) returns th complex number + # exp(1/3 * log(a)) = exp(1/3 * (log(abs(a)) + pi*i)) = cbrt(abs(a)) * e^{pi/3*i} + # which is a complex number. + # For more info see the section Note in + # https://en.cppreference.com/w/cpp/numeric/math/cbrt + return torch.copysign(torch.pow(a.abs(), 1 / 3), a) + + +cbrt = _make_elementwise_unary_prim( + "cbrt", + impl_aten=_cbrt_aten, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +ceil = _make_elementwise_unary_prim( + "ceil", + impl_aten=torch.ceil, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +def _conj_physical_meta(input: TensorLikeType) -> TensorLikeType: + if not input.dtype.is_complex: + raise RuntimeError("prims.conj_physical is only defined for complex dtypes") + + strides = utils.compute_elementwise_output_strides(input) + return TensorMeta(input, strides=strides) + + +conj_physical = _make_prim( + schema="conj_physical(Tensor self) -> Tensor", + meta=_conj_physical_meta, + impl_aten=torch._conj_physical, + doc="Returns the physical conjugation of a complex tensor", + return_type=RETURN_TYPE.NEW, +) + + +def _clone_meta( + input: TensorLikeType, *, memory_format: torch.memory_format = torch.preserve_format +) -> TensorLikeType: + if memory_format != torch.preserve_format: + return torch.empty( + input.shape, + dtype=input.dtype, + layout=input.layout, + device=input.device, + memory_format=memory_format, + ) + + # memory_format == torch.preserve_format + strides = utils.compute_elementwise_output_strides(input) + return torch.empty_strided( + input.shape, + strides, + dtype=input.dtype, + layout=input.layout, + device=input.device, + ) + + +clone = _make_prim( + schema="clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor", + meta=_clone_meta, + impl_aten=torch.clone, + doc="Returns the copy of a tensor", + return_type=RETURN_TYPE.NEW, + register_conj_neg_fallthrough=True, +) + +digamma = _make_elementwise_unary_prim( + "digamma", + impl_aten=torch.digamma, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +erf = _make_elementwise_unary_prim( + "erf", + impl_aten=torch.erf, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +erf_inv = _make_elementwise_unary_prim( + "erf_inv", + impl_aten=torch.special.erfinv, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +erfc = _make_elementwise_unary_prim( + "erfc", + impl_aten=torch.special.erfc, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +erfcx = _make_elementwise_unary_prim( + "erfcx", + impl_aten=torch.special.erfcx, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +exp = _make_elementwise_unary_prim( + "exp", + impl_aten=torch.exp, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +expm1 = _make_elementwise_unary_prim( + "expm1", + impl_aten=torch.special.expm1, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +exp2 = _make_elementwise_unary_prim( + "exp2", + impl_aten=torch.special.exp2, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +def _fill_meta(a: TensorLikeType, value: NumberType) -> TensorLikeType: + return _prim_elementwise_meta( + a, type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT + ) + + +# NOTE: fill uses _make_prim directly because it has a value parameter +fill = _make_prim( + schema="fill(Tensor self, Scalar value) -> Tensor", + return_type=RETURN_TYPE.NEW, + meta=_fill_meta, + impl_aten=torch.fill, + doc="", +) + +floor = _make_elementwise_unary_prim( + "floor", + impl_aten=torch.floor, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +imag = _make_prim( + schema="imag(Tensor(a) self) -> Tensor(a)", + meta=partial( + _complex_only_elementwise_meta, + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, + ), + return_type=RETURN_TYPE.VIEW, + impl_aten=torch.imag, + doc="", +) + +isfinite = _make_elementwise_unary_prim( + "isfinite", + impl_aten=torch.isfinite, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +lgamma = _make_elementwise_unary_prim( + "lgamma", + impl_aten=torch.lgamma, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +log = _make_elementwise_unary_prim( + "log", + impl_aten=torch.log, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +log1p = _make_elementwise_unary_prim( + "log1p", + impl_aten=torch.log1p, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +log2 = _make_elementwise_unary_prim( + "log2", + impl_aten=torch.log2, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +log10 = _make_elementwise_unary_prim( + "log10", + impl_aten=torch.log10, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +real = _make_prim( + schema="real(Tensor(a) self) -> Tensor(a)", + meta=partial( + _complex_only_elementwise_meta, + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, + ), + return_type=RETURN_TYPE.VIEW, + impl_aten=torch.real, + doc="", +) + +reciprocal = _make_elementwise_unary_prim( + "reciprocal", + impl_aten=torch.reciprocal, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +ndtri = _make_elementwise_unary_prim( + "ndtri", + impl_aten=torch.special.ndtri, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +neg = _make_elementwise_unary_prim( + "neg", + impl_aten=torch.neg, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +round = _make_elementwise_unary_prim( + "round", + impl_aten=torch.round, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +rsqrt = _make_elementwise_unary_prim( + "rsqrt", + impl_aten=torch.rsqrt, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +sign = _make_elementwise_unary_prim( + "sign", + impl_aten=torch.sign, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +signbit = _make_elementwise_unary_prim( + "signbit", + impl_aten=torch.signbit, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +sin = _make_elementwise_unary_prim( + "sin", + impl_aten=torch.sin, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +sinh = _make_elementwise_unary_prim( + "sinh", + impl_aten=torch.sinh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +spherical_bessel_j0 = _make_elementwise_unary_prim( + "spherical_bessel_j0", + impl_aten=torch.special.spherical_bessel_j0, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +sqrt = _make_elementwise_unary_prim( + "sqrt", + impl_aten=torch.sqrt, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +tan = _make_elementwise_unary_prim( + "tan", + impl_aten=torch.tan, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +tanh = _make_elementwise_unary_prim( + "tanh", + impl_aten=torch.tanh, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +trunc = _make_elementwise_unary_prim( + "trunc", + impl_aten=torch.trunc, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +# +# Elementwise binary operations +# + +add = _make_elementwise_binary_prim( + name="add", + impl_aten=torch.add, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +atan2 = _make_elementwise_binary_prim( + name="atan2", + impl_aten=torch.atan2, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bitwise_and = _make_elementwise_binary_prim( + "bitwise_and", + impl_aten=torch.bitwise_and, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bitwise_or = _make_elementwise_binary_prim( + "bitwise_or", + impl_aten=torch.bitwise_or, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +bitwise_xor = _make_elementwise_binary_prim( + "bitwise_xor", + impl_aten=torch.bitwise_xor, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +# TODO: complex needs a special meta to account for its float -> complex behavior +# complex = _make_elementwise_binary_prim( +# impl_aten=torch.complex, +# doc="", +# ) + + +# div prim performs truncation division on integer inputs +# and true division for floating and complex inputs +def _div_aten(a, b): + is_integral = isinstance(a, (bool, int, torch.SymInt)) or ( + isinstance(a, torch.Tensor) and utils.is_integer_dtype(a.dtype) + ) + + if is_integral: + return torch.div(a, b, rounding_mode="trunc") + else: + return torch.true_divide(a, b) + + +div = _make_elementwise_binary_prim( + "div", + impl_aten=_div_aten, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +eq = _make_elementwise_binary_prim( + "eq", + impl_aten=torch.eq, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +fmax = _make_elementwise_binary_prim( + "fmax", + impl_aten=torch.fmax, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +fmin = _make_elementwise_binary_prim( + "fmin", + impl_aten=torch.fmin, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +fmod = _make_elementwise_binary_prim( + "fmod", + impl_aten=torch.fmod, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +gcd = _make_elementwise_binary_prim( + "gcd", + impl_aten=torch.gcd, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +ge = _make_elementwise_binary_prim( + "ge", + impl_aten=torch.ge, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +gt = _make_elementwise_binary_prim( + "gt", + impl_aten=torch.gt, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +hypot = _make_elementwise_binary_prim( + "hypot", + impl_aten=torch.hypot, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +igamma = _make_elementwise_binary_prim( + "igamma", + impl_aten=torch.special.gammainc, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +igammac = _make_elementwise_binary_prim( + "igammac", + impl_aten=torch.special.gammaincc, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +le = _make_elementwise_binary_prim( + "le", + impl_aten=torch.le, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +lt = _make_elementwise_binary_prim( + "lt", + impl_aten=torch.lt, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + + +# Note: the following impls are because torch.maximum and torch.minimum do not support scalar inputs +def _maximum_aten( + a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType] +) -> TensorLikeType: + if isinstance(a, TensorLike) and isinstance(b, Number): + b = scalar_tensor(b, dtype=a.dtype, device=a.device) + elif isinstance(b, TensorLike) and isinstance(a, Number): + a = scalar_tensor(a, dtype=b.dtype, device=b.device) + + return torch.maximum(a, b) # type: ignore[arg-type] + + +maximum = _make_elementwise_binary_prim( + "maximum", + impl_aten=_maximum_aten, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +def _minimum_aten( + a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType] +) -> TensorLikeType: + if isinstance(a, TensorLike) and isinstance(b, Number): + b = scalar_tensor(b, dtype=a.dtype, device=a.device) + elif isinstance(b, TensorLike) and isinstance(a, Number): + a = scalar_tensor(a, dtype=b.dtype, device=b.device) + + return torch.minimum(a, b) # type: ignore[arg-type] + + +minimum = _make_elementwise_binary_prim( + "minimum", + impl_aten=_minimum_aten, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +mul = _make_elementwise_binary_prim( + "mul", + impl_aten=torch.mul, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +ne = _make_elementwise_binary_prim( + "ne", + impl_aten=torch.ne, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL, +) + +nextafter = _make_elementwise_binary_prim( + "nextafter", + impl_aten=torch.nextafter, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +pow = _make_elementwise_binary_prim( + "pow", + impl_aten=torch.pow, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +remainder = _make_elementwise_binary_prim( + "remainder", + impl_aten=torch.remainder, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +shift_left = _make_elementwise_binary_prim( + "shift_left", + impl_aten=torch.bitwise_left_shift, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +shift_right_arithmetic = _make_elementwise_binary_prim( + "shift_right_arithmetic", + impl_aten=torch.bitwise_right_shift, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +shift_right_logical = _not_impl + +sub = _make_elementwise_binary_prim( + "sub", + impl_aten=torch.sub, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + +zeta = _make_elementwise_binary_prim( + "zeta", + impl_aten=torch.special.zeta, + doc="", + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, +) + + +# +# View operations +def _as_strided_meta( + a: TensorLikeType, size: ShapeType, stride: StrideType, storage_offset: int +) -> TensorLikeType: + assert len(size) == len(stride) + assert storage_offset >= 0 + utils.validate_strides(stride) + utils.validate_shape(size) + + if reduce(operator.mul, size) == 0: + # NOTE: This special case is to avoid having to acquire the storage below + # as_strided to shapes with no elements are trivially valid, so it's OK + pass + elif isinstance(a, torch.Tensor): + utils.check_in_bounds_for_storage( + a._typed_storage(), size, stride, storage_offset + ) + + return torch.as_strided(a, size, stride, storage_offset) + + +def _as_strided_aten( + a: Tensor, size: ShapeType, stride: StrideType, storage_offset: int +) -> Tensor: + return torch.as_strided(a, size, stride, storage_offset) + + +_as_strided_doc = """ + Creates a view of the tensor with the given shape (size), strides (stride) and + storage offset (storage_offset). +""" + +as_strided = _make_prim( + schema="as_strided(Tensor(a!) a, SymInt[] size, SymInt[] stride, SymInt storage_offset) -> Tensor(a!)", + meta=_as_strided_meta, + impl_aten=_as_strided_aten, + return_type=RETURN_TYPE.VIEW, + doc=_as_strided_doc, +) + + +def _broadcast_in_dim_meta( + a: TensorLikeType, shape: ShapeType, broadcast_dimensions: Sequence[int] +): + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + # Type checks + assert isinstance(a, TensorLike) + assert isinstance(shape, Sequence) + assert isinstance(broadcast_dimensions, Sequence) + + # every dimension must be accounted for + assert a.ndim == len(broadcast_dimensions) + + # broadcast shape must have weakly more dimensions + assert len(shape) >= a.ndim + + # broadcast_dimensions must be an ascending sequence + # (no relative reordering of dims) of integers and + # each dimension must be within the new shape + def _greater_than_reduce(acc, x): + assert isinstance(x, Dim) + assert x > acc + assert x < len(shape) + + return x + + reduce(_greater_than_reduce, broadcast_dimensions, -1) + + # shape must be broadcastable to + for idx, new_idx in enumerate(broadcast_dimensions): + if not guard_size_oblivious(a.shape[idx] == 1): + torch._check( + a.shape[idx] == shape[new_idx], + lambda: f"{a.shape[idx]} must be broadcastable to {shape[new_idx]}", + ) + + new_strides = [] + original_idx = 0 + for idx in range(len(shape)): + if idx in broadcast_dimensions: + # Assigns a stride of zero to dimensions + # which were actually broadcast + if guard_size_oblivious(a.shape[original_idx] != shape[idx]): + new_strides.append(0) + else: + new_strides.append(a.stride()[original_idx]) + original_idx = original_idx + 1 + else: + if guard_size_oblivious(shape[idx] != 1): + new_strides.append(0) + elif original_idx == a.ndim: + new_strides.append(1) + else: + new_strides.append(a.stride()[original_idx] * a.size()[original_idx]) + + return a.as_strided(shape, new_strides, a.storage_offset()) + + +def _broadcast_in_dim_aten(a, shape, broadcast_dimensions): + s = list(shape) + for broadcast_dimension in broadcast_dimensions: + s[broadcast_dimension] = -1 + + v = a + for idx, x in enumerate(s): + if x != -1: + v = v.unsqueeze(idx) + + return v.expand(shape) + + +_broadcast_in_dim_doc = """ + Creates a view of a with the specified shape. + + Allows adding dimensions of any length and broadcasting + dimensions of length one in a to any length. + + The location of the broadcast dimensions must be specified + using the broadcast_dimensions argument. Changing the + relative order of dimensions is not supported. + """ + +broadcast_in_dim = _make_prim( + schema="broadcast_in_dim(Tensor(a) a, SymInt[] shape, int[] broadcast_dimensions) -> Tensor(a)", + meta=_broadcast_in_dim_meta, + impl_aten=_broadcast_in_dim_aten, + return_type=RETURN_TYPE.VIEW, + doc=_broadcast_in_dim_doc, +) + + +def _validate_collapse_args(a: Tensor, start: int, end: int) -> None: + # Special-case for zero dimensional tensors + ndim = max(1, a.dim()) + utils.validate_idx(ndim, start) + utils.validate_idx(ndim, end) + + # Verifies end is strictly greater than start + # (Collapse requires a non-empty interval) + torch._check_value( + end >= start, + lambda: f"Attempting to collapse but end, {end}, is less than start, {start}!", + ) + + +def _collapsed_shape(shape: ShapeType, start: int, end: int) -> tuple[int, ...]: + """ + Returns the shape of a with dims in [start, end) merged into a single dimension. + """ + # Special-case for zero dimensional tensors + shape = (1,) if len(shape) == 0 else tuple(shape) + + dim_length = 1 + for s in shape[start : end + 1]: + dim_length = dim_length * s + + return shape[0:start] + (dim_length,) + shape[end + 1 :] + + +def _collapse_view_helper( + a: TensorLikeType, start: int, end: int +) -> tuple[Optional[ShapeType], Optional[StrideType]]: + assert isinstance(a, TensorLike) + + from torch.fx.experimental.symbolic_shapes import guard_size_oblivious + + _validate_collapse_args(a, start, end) + + # Special-case for zero dimensional tensors + if a.ndim == 0: + shape = (1,) + strides = (1,) + else: + shape = a.shape # type: ignore[assignment] + strides = a.stride() # type: ignore[assignment] + + if a.ndim == 0 or (end == start): + return shape, strides + + length = shape[end] + stride = strides[end] + for idx in range(end - 1, start - 1, -1): + if guard_size_oblivious(shape[idx] == 0) or guard_size_oblivious( + shape[idx + 1] == 0 + ): + length = 0 + stride = 0 + break + + if guard_size_oblivious(shape[idx] == 1): + continue + + length = length * shape[idx] + if guard_size_oblivious(stride < strides[idx]): + stride = stride + else: + stride = strides[idx] + + if ( + guard_size_oblivious(a.numel() > 0) + and guard_size_oblivious(shape[idx + 1] != 1) + and not guard_size_oblivious( + strides[idx] == strides[idx + 1] * shape[idx + 1] + ) + ): + return None, None + + new_shape = shape[:start] + (length,) + shape[end + 1 :] + new_strides = strides[:start] + (stride,) + strides[end + 1 :] + + # NOTE: when the input has no elements it's restrided as if it were contiguous + if guard_size_oblivious(a.numel() == 0): + new_strides = utils.make_contiguous_strides_for(new_shape) + + return new_shape, new_strides + + +def _collapse_view_meta(a: TensorLikeType, start: int, end: int) -> TensorLikeType: + new_shape, new_strides = _collapse_view_helper(a, start, end) + + if new_shape is None: + msg = "Attempting to view a collapsed tensor, but no such view exists!" + raise ValueError(msg) + + assert new_strides is not None + return a.as_strided(new_shape, new_strides, a.storage_offset()) + + +def _collapse_view_aten(a: Tensor, start: int, end: int) -> Tensor: + new_shape = _collapsed_shape(a.shape, start, end) + return a.view(new_shape) + + +_collapse_view_doc = """ + Creates a view of a with the dimensions between + start (inclusive) and end (exclusive) merged into a + single dimension. + + If it's not possible to take such a view then an error + is thrown. See collapse instead. + + The dimensions can be merged if and only if + they are all "nested" with each other. That is, they all + have the property that + + stride[i] = stride[i+1] * shape[i+1] + + for all i in [start, end - 1). + """ + +collapse_view = _make_prim( + schema="collapse_view(Tensor(a) a, int start, int end) -> Tensor(a)", + meta=_collapse_view_meta, + impl_aten=_collapse_view_aten, + return_type=RETURN_TYPE.VIEW, + doc=_collapse_view_doc, +) + + +def _conj_meta(a: TensorLikeType) -> TensorLikeType: + if not a.dtype.is_complex: + raise RuntimeError("Expected complex dtype in prims.conj") + out = a.as_strided(a.shape, a.stride(), a.storage_offset()) + torch._C._set_conj(out, not a.is_conj()) + return out + + +_conj_doc = """ +Returns a conjugated view of the original tensor +""" + +conj = _make_prim( + schema="conj(Tensor(a) a) -> Tensor(a)", + meta=_conj_meta, + impl_aten=torch.conj, + return_type=RETURN_TYPE.VIEW, + doc=_conj_doc, +) + + +def expand_dims( + a: TensorLikeType, dimensions: DimsSequenceType, ndim=None +) -> TensorLikeType: + """ + Creates a view of a with a.ndim + len(dimensions) dimensions, with new + dimensions of length one at the dimensions specified by dimensions. + """ + if ndim is not None: + # TODO: this is only here to support the unsqueeze ref + dims = sorted(utils.canonicalize_dims(ndim, dimensions)) # type: ignore[arg-type] + else: + dims = sorted(utils.canonicalize_dims(a.ndim, dimensions)) # type: ignore[arg-type] + if len(set(dims)) != len(dims): + msg = f"Received duplicate dimensions to expand in {str(dimensions)}" + raise ValueError(msg) + + new_shape = list(a.shape) + for idx in dims: + new_shape.insert(idx, 1) + + broadcast_dimensions = [ + idx for idx in range(len(new_shape)) if idx not in dimensions + ] + return broadcast_in_dim(a, new_shape, broadcast_dimensions) + + +def _split_dim_meta(a: TensorLikeType, dim: int, outer_length: int) -> TensorLikeType: + assert isinstance(a, TensorLike) + utils.validate_idx(a.ndim, dim) + utils.validate_dim_length(outer_length) + + # Verifies the dim can be split with the specified lhs_length + inner_length = a.shape[dim] // outer_length + + if (a.shape[dim] % outer_length) != 0: + msg = ( + f"Attempting to split dimension of length {a.shape[dim]}, " + f"but outer length of {outer_length} divides it with a remainder!" + ) + raise ValueError(msg) + + new_shape: list[int] = [] + new_strides: list[int] = [] + for idx in range(a.ndim): + if idx == dim: + new_shape.extend((outer_length, inner_length)) + new_strides.extend((a.stride()[idx] * inner_length, a.stride()[idx])) + else: + new_shape.append(a.shape[idx]) + new_strides.append(a.stride()[idx]) + + return a.as_strided(new_shape, new_strides, a.storage_offset()) + + +def _split_dim_aten(a: Tensor, dim: int, outer_length: int) -> Tensor: + inner_length = a.shape[dim] // outer_length + new_shape = a.shape[0:dim] + (outer_length, inner_length) + a.shape[dim + 1 :] + + return a.view(new_shape) + + +_split_dim_doc = """ + Creates a view of a with the given dimension (of length l) split + into two dimensions, with the outer of the two having + length outer_length and the inner of the two having computed + length inner_length such outer_length * inner_length = l. + """ + +# TODO: consider renaming split_dim_view +split_dim = _make_prim( + schema="split_dim(Tensor(a) a, int dim, SymInt outer_length) -> Tensor(a)", + meta=_split_dim_meta, + impl_aten=_split_dim_aten, + return_type=RETURN_TYPE.VIEW, + doc=_split_dim_doc, +) + + +# Note: allows dimensions to be specified redundantly +def _squeeze_meta(a: TensorLikeType, dimensions: Sequence) -> TensorLikeType: + assert isinstance(a, TensorLike) + + for idx in dimensions: + utils.validate_idx(a.ndim, idx) + assert a.shape[idx] == 1 + + new_shape = [] + new_strides = [] + for idx in range(len(a.shape)): + if idx in dimensions: + continue + + new_shape.append(a.shape[idx]) + new_strides.append(a.stride()[idx]) + + return a.as_strided(new_shape, new_strides, a.storage_offset()) + + +_squeeze_doc = """ + Creates a view of the tensor with the specified dimensions removed. + + The removed dimensions must each have length one. + """ + +squeeze = _make_prim( + schema="squeeze(Tensor(a) a, int[] dimensions) -> Tensor(a)", + meta=_squeeze_meta, + impl_aten=torch.squeeze, + return_type=RETURN_TYPE.VIEW, + doc=_squeeze_doc, +) + + +def _transpose_meta(a: TensorLikeType, permutation: DimsSequenceType) -> TensorLikeType: + if a.ndim != len(permutation): + msg = f"Attempting to permute a tensor of rank {a.ndim}, but received a permutation of length {len(permutation)}!" + raise ValueError(msg) + + if not utils.is_valid_permutation(a.ndim, permutation): + msg = f"Received an invalid permutation, {permutation}!" + raise ValueError(msg) + + new_shape = [0] * a.ndim + new_strides = [0] * a.ndim + for idx, dim in enumerate(permutation): + new_shape[idx] = a.shape[dim] + new_strides[idx] = a.stride()[dim] + + return a.as_strided(tuple(new_shape), tuple(new_strides), a.storage_offset()) + + +def _transpose_aten(a: Tensor, permutation: DimsSequenceType) -> Tensor: + return torch.permute(a, permutation) + + +_transpose_doc = """ + Creates a view of the tensor with its dimensions permuted. + + The length of the permutation must be the rank of the tensor, + and each element of the permutation specifies the new order + for the corresponding dimension. + """ + +transpose = _make_prim( + schema="transpose(Tensor(a) a, int[] permutation) -> Tensor(a)", + meta=_transpose_meta, + impl_aten=_transpose_aten, + return_type=RETURN_TYPE.VIEW, + doc=_transpose_doc, +) + + +def _view_of_meta(a: TensorLikeType) -> TensorLikeType: + return a.as_strided(a.shape, a.stride(), a.storage_offset()) + + +def _view_of_aten(a: Tensor) -> Tensor: + return a.view(a.shape) + + +_view_of_doc = """ + Creates a view of the tensor. + """ + +view_of = _make_prim( + schema="view_of(Tensor(a) a) -> Tensor(a)", + meta=_view_of_meta, + impl_aten=_view_of_aten, + return_type=RETURN_TYPE.VIEW, + doc=_view_of_doc, +) + + +def _view_element_type_meta(a: TensorLikeType, dtype: torch.dtype) -> TensorLikeType: + return a.view(dtype) + + +def _view_element_type_aten(a: Tensor, dtype: torch.dtype) -> Tensor: + return a.view(dtype) + + +_view_element_type_doc = """ + Creates a view of the tensor with a different dtype. + """ + +view_element_type = _make_prim( + schema="view_of_dtype(Tensor(a) a, ScalarType dtype) -> Tensor(a)", + meta=_view_element_type_meta, + impl_aten=_view_element_type_aten, + return_type=RETURN_TYPE.VIEW, + doc=_view_element_type_doc, +) + +# +# Functionalized view mutations +# + + +def _as_strided_scatter_meta( + input: TensorLikeType, + src: TensorLikeType, + size: ShapeType, + stride: StrideType, + storage_offset: int, +) -> TensorLikeType: + utils.validate_shape(size) + utils.validate_strides(stride) + + required_size = utils.compute_required_storage_length(size, stride, storage_offset) + torch._check( + input.numel() >= required_size, + lambda: ( + f"as_strided_scatter: sizes {size}, strides {stride}, storage offset {storage_offset} " + f" and itemsize {input.element_size()} requiring a storage size of " + f"{required_size * input.element_size()} are out of bounds " + f"for storage of size {input.numel() * input.element_size()}" + ), + ) + torch._check( + utils.is_same_shape(src.shape, size), + lambda: f"expected src to have a size equal to the slice of self. src size = {src.shape}, slice size = {size}", + ) + + return utils.clone_preserve_strides(input) + + +_as_strided_scatter_doc = """ + Creates a new tensor equivalent to ``out = input.clone()`` after mutation by + ``out.as_strided(size, stride, storage_offset).copy_(src)``. +""" + +as_strided_scatter = _make_prim( + schema="as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt storage_offset) -> Tensor", + meta=_as_strided_scatter_meta, + impl_aten=torch.as_strided_scatter, + return_type=RETURN_TYPE.NEW, + doc=_as_strided_scatter_doc, +) + + +# +# Shape operations +# + + +def _collapse_meta(a: Tensor, start: int, end: int) -> Tensor: + # Special-case for zero dimensional tensors + _validate_collapse_args(a, start, end) + new_shape = _collapsed_shape(a.shape, start, end) + return a.new_empty(new_shape) + + +def _collapse_aten(a: Tensor, start: int, end: int) -> Tensor: + new_shape = _collapsed_shape(a.shape, start, end) + out = a.new_empty(new_shape) + with torch.no_grad(): + out.view_as(a).copy_(a) + return out + + +_collapse_doc = """ +Collapse a span of neighboring dimensions into one. + +See collapse_view for the corresponding view operation. +""" +collapse = _make_prim( + schema="collapse(Tensor a, int start, int end) -> Tensor", + meta=_collapse_meta, + impl_aten=_collapse_aten, + return_type=RETURN_TYPE.NEW, + doc=_collapse_doc, +) + + +# TODO: review stride logic +# NB: unlike torch.cat, this is more strict about empty tensors and dim is +# never negative +def _cat_meta(tensors: Sequence[TensorLikeType], dim: int) -> TensorLikeType: + # Verifies same shape (except in the concat dimension) + assert dim >= 0 + shape = tensors[0].shape + sym_sum_args = [] + for tensor_idx, tensor in enumerate(tensors): + assert len(shape) == len(tensor.shape) + for idx, (common_length, length) in enumerate(zip(shape, tensor.shape)): + if idx == dim: + sym_sum_args.append(length) + else: + torch._check( + length == common_length, + lambda: f"Sizes of tensors must match except in dimension {dim}. " + f"Expected {common_length} in dimension {idx} but got {length} for tensor number " + f"{tensor_idx} in the list", + ) + + new_shape = list(tensors[0].shape).copy() + new_shape[dim] = torch.sym_sum(sym_sum_args) + return TensorMeta( + tensors[0], + shape=new_shape, + strides=utils.make_contiguous_strides_for(new_shape), + ) + + +def _cat_aten(tensors: Union[tuple[Tensor, ...], list[Tensor]], dim: int) -> Tensor: + return torch.cat(tensors, dim) + + +_cat_doc = """ + Concatenates tensors along the specified dimension. + + The tensors' shapes must have the same rank and same length for other dimensions. + """ + +cat = _make_prim( + schema="cat(Tensor[] tensors, int dim) -> Tensor", + meta=_cat_meta, + impl_aten=_cat_aten, + return_type=RETURN_TYPE.NEW, + doc=_cat_doc, +) + + +def _reshape_meta(a: TensorLikeType, shape: ShapeType): + assert isinstance(a, TensorLike) + utils.validate_shape(shape) + + # Validates the tensor and the requested shape have the + # same number of elements + numel = reduce(operator.mul, shape) + if numel != a.numel(): + msg = f"Attempting to reshape a tensor with {a.numel()} elements to a shape with {numel} elements!" + raise ValueError(msg) + + return TensorMeta(a, shape=shape, strides=utils.make_contiguous_strides_for(shape)) + + +def _reshape_aten(a: Tensor, shape: ShapeType) -> Tensor: + return a.reshape(shape).clone(memory_format=torch.contiguous_format) + + +_reshape_doc = """ + Creates a contiguous tensor with the specified shape + containing a copy of the data in a. + """ +reshape = _make_prim( + schema="reshape(Tensor a, SymInt[] shape) -> Tensor", + meta=_reshape_meta, + impl_aten=_reshape_aten, + return_type=RETURN_TYPE.NEW, + doc=_reshape_doc, +) + + +def _rev_meta(a: TensorLikeType, dims: DimsSequenceType) -> TensorLikeType: + utils.validate_dimension_indices(a.ndim, dims) + return torch.empty_like(a, memory_format=torch.preserve_format) + + +_rev_doc = """ + Reverses the order of elements along the given dimensions. + """ + +rev = _make_prim( + schema="rev(Tensor a, int[] dims) -> Tensor", + meta=_rev_meta, + impl_aten=torch.flip, + return_type=RETURN_TYPE.NEW, + doc=_rev_doc, +) + +# +# Conditional prims +# + + +def _where_meta( + pred: TensorLikeType, a: TensorLikeType, b: TensorLikeType +) -> TensorLikeType: + return _prim_elementwise_meta( + a, + b, + type_promotion=ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT, + args_with_fixed_dtypes=(pred,), + ) + + +_where_doc = """ + Selects elements from a and b according to pred. + + Where pred is true the result contains the element from a, and + where pred is false the result contains the element from b. + """ + +where = _make_prim( + schema="where(Tensor pred, Tensor a, Tensor b) -> Tensor", + meta=_where_meta, + impl_aten=torch.where, + return_type=RETURN_TYPE.NEW, + doc=_where_doc, +) + + +# +# Type conversions +# +def _convert_element_type_meta(a: TensorLikeType, dtype: torch.dtype) -> TensorLikeType: + # Type checks + assert isinstance(a, TensorLike) + assert isinstance(dtype, torch.dtype) + + # dtype conversion preserves dense strides + if torch._prims_common.is_non_overlapping_and_dense(a): + strides = a.stride() + else: + strides = utils.compute_elementwise_output_strides(a) + + return TensorMeta(a, strides=strides, dtype=dtype) + + +def _convert_element_type_aten(a: Tensor, dtype: torch.dtype) -> Tensor: + # Propagates requires grad when possible + if not utils.is_grad_dtype(dtype): + requires_grad = False + else: + # TODO: update meta objects so this can be acquired directly + try: + requires_grad = a.requires_grad + except Exception: + requires_grad = False + + result = torch.empty_like( + a, device=a.device, dtype=dtype, requires_grad=requires_grad + ) + with torch.no_grad(): + return copy_to(result, a) + + +_convert_element_type_doc = """ + Creates a copy of a tensor with the given dtype. + """ + +convert_element_type = _make_prim( + schema="convert_element_type(Tensor a, ScalarType dtype) -> Tensor", + meta=_convert_element_type_meta, + impl_aten=_convert_element_type_aten, + return_type=RETURN_TYPE.NEW, + doc=_convert_element_type_doc, + tags=(torch.Tag.pointwise,), +) + + +def _device_put_meta( + a: TensorLikeType, device: Union[str, torch.device], non_blocking=False +) -> TensorLikeType: + assert isinstance(a, TensorLike) + assert isinstance(device, (str, torch.device)) + assert isinstance(non_blocking, bool) + + return TensorMeta(a, device=utils.canonicalize_device(device)) + + +def _device_put_aten( + a: Tensor, device: Union[str, torch.device], non_blocking=False +) -> Tensor: + return a.to(device, non_blocking=non_blocking) + + +_device_put_doc = """ + Creates a copy of a tensor on the given device. + """ + +device_put = _make_prim( + schema="device_put(Tensor a, Device device, bool non_blocking=False) -> Tensor", + meta=_device_put_meta, + impl_aten=_device_put_aten, + return_type=RETURN_TYPE.NEW, + doc=_device_put_doc, +) + + +# NOTE: need to model meta scalars +# See https://github.com/pytorch/pytorch/issues/78070 +def _item_meta(a: TensorLikeType) -> FakeTensor: + number_type = utils.dtype_to_type(a.dtype) + return TensorMeta(number_type(-1)) + + +_item_doc = """ + Converts a tensor with one element to a Python number. +""" + + +# We can't call into python dispatcher for item again +# because the current prim decomp calls into python dispatcher +# again. https://github.com/pytorch/pytorch/issues/136050 +def _item_aten_no_python_dispatcher(*args, **kwargs): + with torch._dispatch.python.no_python_dispatcher(): + return torch.Tensor.item(*args, **kwargs) + + +# TODO: create a new return type for scalars? +# FIXME: currently returns integers for boolean tensors +# https://github.com/pytorch/pytorch/issues/78071 +item = _make_prim( + schema="item(Tensor a) -> Scalar", + meta=_item_meta, + impl_aten=_item_aten_no_python_dispatcher, + return_type=RETURN_TYPE.NEW, + doc=_item_doc, +) + + +# NOTE: need to model meta scalars +# See https://github.com/pytorch/pytorch/issues/78070 +def _maximum_value_meta(dtype: torch.dtype) -> FakeTensor: + number_type = utils.dtype_to_type(dtype) + return TensorMeta(number_type(-1)) + + +def _maximum_value_aten(dtype: torch.dtype): + if dtype == torch.bool: + return True + elif dtype.is_complex or dtype.is_floating_point: + return torch.finfo(dtype).max + else: + return torch.iinfo(dtype).max + + +_maximum_value_doc = """ + Return the maximum finite value for a dtype. +""" + +# TODO: create a new return type for scalars? +# FIXME: currently returns integers for boolean tensors +# https://github.com/pytorch/pytorch/issues/78071 +maximum_value = _make_prim( + schema="maximum_value(ScalarType dtype) -> Scalar", + meta=_maximum_value_meta, + impl_aten=_maximum_value_aten, + return_type=RETURN_TYPE.NEW, + doc=_maximum_value_doc, +) + + +# NOTE: need to model meta scalars +# See https://github.com/pytorch/pytorch/issues/78070 +def _minimum_value_meta(dtype: torch.dtype) -> FakeTensor: + number_type = utils.dtype_to_type(dtype) + return TensorMeta(number_type(-1)) + + +def _minimum_value_aten(dtype: torch.dtype): + if dtype == torch.bool: + return False + elif dtype.is_complex or dtype.is_floating_point: + return torch.finfo(dtype).min + else: + return torch.iinfo(dtype).min + + +_minimum_value_doc = """ + Return the minimum finite value for a dtype. +""" + +# TODO: create a new return type for scalars? +# FIXME: currently returns integers for boolean tensors +# https://github.com/pytorch/pytorch/issues/78071 +minimum_value = _make_prim( + schema="minimum_value(ScalarType dtype) -> Scalar", + meta=_minimum_value_meta, + impl_aten=_minimum_value_aten, + return_type=RETURN_TYPE.NEW, + doc=_minimum_value_doc, +) + +# +# Inplace operators +# + + +def _copy_to_meta(a: TensorLikeType, b: TensorLikeType): + assert isinstance(a, TensorLike) + assert isinstance(b, TensorLike) + + # Validates the cast is safe + # TODO: move this as an option on the reference + # a_typ = utils.dtype_to_type(a.dtype) + # b_typ = utils.dtype_to_type(b.dtype) + # if a_typ is not utils.get_higher_type(a_typ, b_typ): + # raise RuntimeError(str(b.dtype), " can't be cast safely to ", str(a.dtype), "!") + + # Validates the tensors have the same number of elements + if a.numel() != b.numel(): + msg = f"Attempting to copy {b.numel()} elements to a tensor with {a.numel()} elements!" + raise RuntimeError(msg) + + return a + + +def _copy_to_aten(a: Tensor, b: Tensor) -> Tensor: + return a.copy_(b) + + +_copy_to_doc = """ + Copies the data in b to a and returns the modified a. + """ + +# TODO: Remove safe casting and implement on reference instead +copy_to = _make_prim( + schema="copy_to(Tensor(a!) a, Tensor b) -> Tensor(a!)", + meta=_copy_to_meta, + impl_aten=_copy_to_aten, + return_type=RETURN_TYPE.INPLACE, + doc=_copy_to_doc, + register_conj_neg_fallthrough=True, +) + + +def _copy_strided_meta(a: TensorLikeType, stride: ShapeType): + assert isinstance(a, TensorLike) + return torch.empty_strided( + a.shape, + stride, + dtype=a.dtype, + layout=a.layout, + device=a.device, + requires_grad=a.requires_grad, + ) + + +def _copy_strided_aten(a: Tensor, stride: ShapeType) -> Tensor: + out = torch.empty_strided( + a.size(), + stride=stride, + dtype=a.dtype, + layout=a.layout, + device=a.device, + requires_grad=a.requires_grad, + ) + out.copy_(a) + return out + + +_copy_strided_doc = """ + Copies the data in a to a new tensor, the new tensor has same shape with a size, but has different stride. + """ + + +copy_strided = _make_prim( + schema="copy_strided(Tensor a, SymInt[] stride) -> Tensor", + meta=_copy_strided_meta, + impl_aten=_copy_strided_aten, + return_type=RETURN_TYPE.NEW, + doc=_copy_strided_doc, +) + + +def _resize_meta(a: TensorLikeType, shape: ShapeType): + return a.resize_(shape) + + +def _resize_aten(a: Tensor, shape: ShapeType) -> Tensor: + return a.resize_(shape) + + +_resize_doc = """ + Gives a tensor with no elements a new shape, returning the modified tensor. + + The tensor's strides are contiguous and its values are unitialized. + """ + +# TODO: review support arbitrary resizes +resize = _make_prim( + schema="resize(Tensor(a!) a, SymInt[] shape) -> Tensor(a!)", + meta=_resize_meta, + impl_aten=_resize_aten, + return_type=RETURN_TYPE.INPLACE, + doc=_resize_doc, +) + + +def _reduction_meta(inp, dims, *, output_dtype=None): + """ + Meta function for single output reduction operations + Stride logic is incorrect + """ + assert isinstance(inp, TensorLike) + if output_dtype is None: + output_dtype = inp.dtype + output_shape = utils.compute_reduction_output_shape(inp.shape, dims) + return TensorMeta( + shape=output_shape, + strides=utils.make_contiguous_strides_for(output_shape), + dtype=output_dtype, + device=inp.device, + ) + + +def _var_reduction_meta(inp, dims, correction): + if utils.is_complex_dtype(inp.dtype): + output_dtype = utils.corresponding_real_dtype(inp.dtype) + else: + output_dtype = inp.dtype + return _reduction_meta(inp, dims, output_dtype=output_dtype) + + +_sum_doc = """ + Computes the sum of elements in the input tensor over the list of dimensions + specified in the dim argument + """ +_xor_sum_doc = """ + Computes the xor sum of elements in the input tensor over the list of dimensions + specified in the dim argument + """ +_prod_doc = """ + Computes the product of elements in the input tensor over the list of dimensions + specified in the dim argument + """ +_amax_doc = """ + Computes the maximum value of elements in the input tensor over the list of dimensions + specified in the dim argument + """ +_amin_doc = """ + Computes the minimum value of elements in the input tensor over the list of dimensions + specified in the dim argument + """ +_var_doc = """ + Computes the biased variance of x over the list of dimensions specified in the dim argument + """ + + +def _make_reduction_prim(name: str, impl_aten, doc): + """Creates a reduction prim.""" + return _make_prim( + schema=f"{name}(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensor", + meta=_reduction_meta, + impl_aten=impl_aten, + return_type=RETURN_TYPE.NEW, + doc=doc, + ) + + +def _make_var_reduction_prim(name: str, impl_aten, doc): + """Creates a reduction prim.""" + return _make_prim( + schema=f"{name}(Tensor inp, int[]? dims, float? correction=1, *, ScalarType? output_dtype=None) -> Tensor", + meta=_var_reduction_meta, + impl_aten=impl_aten, + return_type=RETURN_TYPE.NEW, + doc=doc, + ) + + +sum = _make_reduction_prim( + name="sum", + impl_aten=torch.sum, + doc=_sum_doc, +) + + +def _xor_sum_aten( + inp: TensorLikeType, + dims: Optional[DimsSequenceType], + *, + dtype: Optional[torch.dtype] = None, +) -> Tensor: + raise NotImplementedError("xor_sum only implemented with inductor") + + +xor_sum = _make_reduction_prim( + name="xor_sum", + impl_aten=_xor_sum_aten, + doc=_xor_sum_doc, +) + + +def _prod_aten( + inp: TensorLikeType, + dims: Optional[DimsSequenceType], + *, + dtype: Optional[torch.dtype] = None, +) -> Tensor: + if dims is not None: + if len(dims) == 0: + return inp.clone() + for d in sorted(dims, reverse=True): + assert d >= 0 + inp = torch.prod(inp, d, dtype=dtype) + return inp + else: + return torch.prod(inp, dims, dtype=dtype) + + +prod = _make_reduction_prim( + name="prod", + impl_aten=_prod_aten, + doc=_prod_doc, +) + + +# torch.var, but correction is not kwarg-only +def torch_var(input, dim=None, correction=1, **kwargs): + return torch.var(input, dim=dim, correction=correction, **kwargs) + + +var = _make_var_reduction_prim( + name="var", + impl_aten=torch_var, + doc=_var_doc, +) + +amax = _make_reduction_prim( + name="amax", + impl_aten=torch.amax, + doc=_amax_doc, +) + +amin = _make_reduction_prim( + name="amin", + impl_aten=torch.amin, + doc=_amin_doc, +) + + +_iota_doc = """ + Constructs a 1-D tensor t where ``t[i] == start + i * step``. +""" + + +# TODO: layout, pin_memory, memory_format +# TODO: model requires_grad on TensorMeta +def _iota_meta( + length: int, + *, + start: int, + step: int, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + torch._check( + utils.is_integer_dtype(dtype), + lambda: "prims.iota only supports integer dtypes", + ) + torch._check(step != 0, lambda: "step must be nonzero") + return torch.empty( + length, + dtype=dtype, + device=device, + requires_grad=requires_grad, + ) + + +def _iota_aten( + length: int, + *, + start: int, + step: int, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + end = start + length * step + return torch.arange( + start, end, step, dtype=dtype, device=device, requires_grad=requires_grad + ) + + +iota = _make_prim( + schema="iota(SymInt length, *, SymInt start, SymInt step, ScalarType dtype, Device device, bool requires_grad) -> Tensor", # noqa: B950 + return_type=RETURN_TYPE.NEW, + meta=_iota_meta, + impl_aten=_iota_aten, + doc=_iota_doc, +) + + +# TODO: layout, pin_memory, memory_format +# TODO: model requires_grad on TensorMeta +def _empty_meta( + shape: ShapeType, *, dtype: torch.dtype, device: torch.device, requires_grad: bool +) -> TensorLikeType: + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=device) + + +def _empty_aten( + shape: ShapeType, *, dtype: torch.dtype, device: torch.device, requires_grad: bool +) -> Tensor: + return torch.empty(shape, dtype=dtype, device=device, requires_grad=requires_grad) + + +_empty_doc = """ + Creates a tensor with uninitialized values and the specified shape, dtype, and device. +""" + +empty = _make_prim( + schema="empty(SymInt[] shape, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor", + meta=_empty_meta, + impl_aten=_empty_aten, + return_type=RETURN_TYPE.NEW, + doc=_empty_doc, +) + + +def _empty_strided_meta( + shape: ShapeType, + strides: StrideType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=device) + + +_empty_strided_doc = """ + Creates a tensor with uninitialized values. +""" + +# TODO: add layout, pin_memory +empty_strided = _make_prim( + schema="empty_strided(SymInt[] shape, SymInt[] strides, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor", + return_type=RETURN_TYPE.NEW, + meta=_empty_strided_meta, + impl_aten=torch.empty_strided, + doc=_empty_strided_doc, +) + + +def _empty_permuted_meta( + shape: ShapeType, + physical_layout: DimsSequenceType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + p_strides = utils.make_contiguous_strides_for([shape[l] for l in physical_layout]) + dim = len(shape) + torch._check( + len(physical_layout) == dim, + lambda: ( + "Number of dimensions in the tensor input does not match the " + f"length of the physical layout; i.e. len(size) = {dim} " + f"is not equal to len(physical_layout) = {len(physical_layout)}" + ), + ) + strides = [0] * len(shape) + seen_dims = set() + for p, l in enumerate(physical_layout): + torch._check( + 0 <= l < dim, + lambda: ( + f"Dimension out of range (expected to be between 0 and {dim - 1}, but got " + f"{l} at index {p}). NB: negative dims " + "not currently supported; file an issue if you want it." + ), + ) + torch._check(l not in seen_dims, lambda: "Duplicate dim not allowed") + strides[l] = p_strides[p] + seen_dims.add(l) + return TensorMeta( + shape=shape, + strides=strides, + dtype=dtype, + device=device, + ) + + +_empty_permuted_doc = """ + Creates a tensor with uninitialized values according to some physical layout, + that is guaranteed to be non-overlapping and dense. +""" + +# TODO: add layout, pin_memory +empty_permuted = _make_prim( + schema="empty_permuted(SymInt[] shape, int[] physical_layout, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor", # noqa: B950 + return_type=RETURN_TYPE.NEW, + meta=_empty_permuted_meta, + impl_aten=torch.empty_permuted, + doc=_empty_permuted_doc, +) + + +def _full_meta( + shape: ShapeType, + fill_value: NumberType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=device) + + +def _full_aten( + shape: ShapeType, + fill_value: NumberType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> Tensor: + # Note that Mypy thinks torch.full can't accept a complex fill_value + return torch.full( + shape, fill_value, dtype=dtype, device=device, requires_grad=requires_grad # type: ignore[arg-type] + ) + + +_full_doc = """ + Creates a tensor filled with the given fill value, and with the specified shape, dtype, and device. +""" + +# TODO: add layout +full = _make_prim( + schema="full(SymInt[] shape, Scalar fill_value, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor", + meta=_full_meta, + impl_aten=_full_aten, + return_type=RETURN_TYPE.NEW, + doc=_full_doc, +) + + +def _full_like_meta( + a: TensorLikeType, + fill_value: NumberType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> TensorLikeType: + strides = utils.compute_elementwise_output_strides(a) + if a.numel() == 0: + strides = a.stride() + + return TensorMeta(a, strides=strides, dtype=dtype, device=device) + + +def _full_like_aten( + a: Tensor, + fill_value: NumberType, + *, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, +) -> Tensor: + # Note that Mypy thinks torch.full can't accept a complex fill_value + return torch.full_like( + a, fill_value, dtype=dtype, device=device, requires_grad=requires_grad # type: ignore[arg-type] + ) + + +_full_like_doc = """ + Creates a tensor filled with the given fill value, and the same shape, dtype, and device as the + given tensor by default. The dtype and device settings can be overridden + by specifying them explicitly. +""" + +full_like = _make_prim( + schema="full_like(Tensor a, Scalar fill_value, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor", + meta=_full_like_meta, + impl_aten=_full_like_aten, + return_type=RETURN_TYPE.NEW, + doc=_full_like_doc, +) + + +def _scalar_tensor_meta( + scalar: NumberType, + *, + dtype: torch.dtype, + device: torch.device, +) -> TensorLikeType: + shape: ShapeType = [] + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(scalar, shape=shape, strides=strides, dtype=dtype, device=device) + + +def _scalar_tensor_aten( + scalar: NumberType, + *, + dtype: torch.dtype, + device: torch.device, +) -> Tensor: + if isinstance(scalar, complex) and ( + dtype is None or not utils.is_complex_dtype(dtype) + ): + raise TypeError("Complex scalar requires complex tensor dtype.") + # Note that Mypy thinks torch.scalar can't accept a complex scalar + return torch.scalar_tensor(scalar, dtype=dtype, device=device) # type: ignore[arg-type] + + +_scalar_tensor_doc = """ + Wraps a Number into a Tensor with the specified dtype and device. +""" + +# TODO: add layout and pin_memory support +scalar_tensor = _make_prim( + schema="scalar_tensor(Scalar s, *, ScalarType? dtype=None, Device? device=None) -> Tensor", + meta=_scalar_tensor_meta, + impl_aten=_scalar_tensor_aten, + return_type=RETURN_TYPE.NEW, + doc=_scalar_tensor_doc, +) + + +# +# Linear algebra (linalg) prims +# + + +def _svd_meta( + A: TensorLikeType, *, full_matrices: bool +) -> tuple[TensorLikeType, TensorLikeType, TensorLikeType]: + utils.check_is_matrix(A, "linalg.svd") + utils.check_fp_or_complex(A.dtype, "linalg.svd", allow_low_precision_dtypes=False) + + A_shape = A.shape + batch = A_shape[:-2] + m, n = A_shape[-2:] + k = min(m, n) + + shape_U = batch + (m, m if full_matrices else k) + strides_U = utils.make_contiguous_strides_for(shape_U, row_major=False) + U = TensorMeta(shape=shape_U, strides=strides_U, dtype=A.dtype, device=A.device) + + shape_S = batch + (k,) + strides_S = utils.make_contiguous_strides_for(shape_S) + S = TensorMeta( + shape=shape_S, + strides=strides_S, + dtype=utils.corresponding_real_dtype(A.dtype) if A.is_complex() else A.dtype, + device=A.device, + ) + + shape_Vh = batch + (n if full_matrices else k, n) + # The CPU backend returns V, but the cuSolver backend returns V^H + # TODO The MAGMA backend returns V, so this is wrong if used with the MAGMA backend + is_cuda = A.device.type == "cuda" + strides_Vh = utils.make_contiguous_strides_for(shape_Vh, row_major=is_cuda) + Vh = TensorMeta(shape=shape_Vh, strides=strides_Vh, dtype=A.dtype, device=A.device) + # Also makes sure this is CUDA or HIP: + # https://pytorch.org/docs/stable/notes/hip.html#checking-for-hip + if A.numel() != 0 and Vh.is_complex() and torch.cuda.is_available(): + Vh = Vh.conj() + return U, S, Vh + + +def _svd_aten( + A: TensorLikeType, *, full_matrices: bool +) -> tuple[Tensor, Tensor, Tensor]: + return torch.linalg.svd(A, full_matrices=full_matrices) + + +_svd_doc = """ + Returns the SVD of a matrix or batch of matrices. + + The `full_matrices` flag controls whether the full or reduced SVD decomposition is returned. +""" + +svd = _make_prim( + schema="svd(Tensor A, *, bool full_matrices) -> (Tensor U, Tensor S, Tensor Vh)", + meta=_svd_meta, + impl_aten=_svd_aten, + return_type=(RETURN_TYPE.NEW, RETURN_TYPE.NEW, RETURN_TYPE.NEW), + doc=_svd_doc, +) + + +# +# Randomness Prims +# + + +def _normal_meta( + shape: ShapeType, + *, + mean: Union[float, complex], + std: float, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, + generator: Optional[torch.Generator] = None, +) -> TensorLikeType: + torch._check( + std >= 0.0, + lambda: f"expected non-negative standard deviation, but got std={std}", + ) + + torch._check( + utils.is_float_dtype(dtype) or utils.is_complex_dtype(dtype), + lambda: f"expected a floating-point or complex dtype, but got dtype={dtype}", + ) + + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=device) + + +def _normal_aten( + shape: ShapeType, + *, + mean: Union[float, complex], + std: float, + dtype: torch.dtype, + device: torch.device, + requires_grad: bool, + generator: Optional[torch.Generator] = None, +) -> Tensor: + a = torch.empty(shape, dtype=dtype, device=device, requires_grad=requires_grad) + with torch.no_grad(): + # NOTE: normal_ is incorrectly annotated to expect mean to be a float + a.normal_(mean, std, generator=generator) # type: ignore[arg-type] + return a + + +_normal_doc = """ + Constructs a tensor filled with values drawn from a normal distribution with the specified mean + and standard deviation. + + Only supports floating-point types. +""" + +normal = _make_prim( + schema=( + "normal(SymInt[] shape, *, Scalar mean, Scalar std, ScalarType dtype, Device device, bool requires_grad, Generator? generator=None) -> Tensor" # noqa: B950 + ), + return_type=RETURN_TYPE.NEW, + meta=_normal_meta, + impl_aten=_normal_aten, + doc=_normal_doc, +) + + +def _uniform_meta( + shape: ShapeType, + *, + low: float, + high: float, + dtype: torch.dtype, + device: torch.device, + generator: Optional[torch.Generator] = None, +) -> TensorLikeType: + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=device) + + +def _uniform_aten( + shape: ShapeType, + *, + low: float, + high: float, + dtype: torch.dtype, + device: torch.device, + generator: Optional[torch.Generator] = None, +) -> Tensor: + a = torch.empty(shape, dtype=dtype, device=device) + a.uniform_(low, high, generator=generator) + return a + + +_uniform_doc = """ + Constructs a tensor filled with values drawn uniformly from low to high. +""" + +# TODO: we should more seriously review randomness modeling and prims +_uniform_helper = _make_prim( + schema=( + "uniform(SymInt[] shape, *, Scalar low, Scalar high, ScalarType dtype, Device device, Generator? generator=None) -> Tensor" + ), + return_type=RETURN_TYPE.NEW, + meta=_uniform_meta, + impl_aten=_uniform_aten, + doc=_uniform_doc, +) + +# +# FFT prims +# + + +def _fft_r2c_meta( + input: TensorLike, + *, + dim: DimsSequenceType, + onesided: bool, +) -> TensorLikeType: + dim = utils.canonicalize_dims(input.ndim, dim) + utils.validate_no_repeating_dims(dim) + + shape = list(input.shape) + if onesided: + last_dim = dim[-1] + shape[last_dim] = shape[last_dim] // 2 + 1 + + dtype = utils.corresponding_complex_dtype(input.dtype) + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=input.device) + + +def _fft_r2c_aten( + input: TensorLike, + *, + dim: DimsSequenceType, + onesided: bool, +) -> TensorLikeType: + normalization = 0 # No normalization + return torch._fft_r2c(input, dim, normalization, onesided) + + +_fft_r2c_doc = """ + Performs a real to complex Fast Fourier Transform +""" + + +fft_r2c = _make_prim( + schema="fft_r2c(Tensor self, *, int[] dim, bool onesided) -> Tensor", + meta=_fft_r2c_meta, + impl_aten=_fft_r2c_aten, + return_type=RETURN_TYPE.NEW, + doc=_fft_r2c_doc, +) + + +def _fft_c2c_meta( + input: TensorLike, + *, + dim: DimsSequenceType, + forward: bool, +) -> TensorLikeType: + dim = utils.canonicalize_dims(input.ndim, dim) + utils.validate_no_repeating_dims(dim) + + shape = input.shape + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta( + shape=shape, strides=strides, dtype=input.dtype, device=input.device + ) + + +def _fft_c2c_aten( + input: TensorLike, + *, + dim: DimsSequenceType, + forward: bool, +) -> TensorLikeType: + normalization = 0 # No normalization + return torch._fft_c2c(input, dim, normalization, forward) + + +_fft_c2c_doc = """ + Performs either a Fast Fourier Transform, or its inverse +""" + + +fft_c2c = _make_prim( + schema="fft_c2c(Tensor self, *, int[] dim, bool forward) -> Tensor", + meta=_fft_c2c_meta, + impl_aten=_fft_c2c_aten, + return_type=RETURN_TYPE.NEW, + doc=_fft_c2c_doc, +) + + +def _fft_c2r_meta( + input: TensorLike, + *, + dim: DimsSequenceType, + last_dim_size: int, +) -> TensorLikeType: + dim = utils.canonicalize_dims(input.ndim, dim) + utils.validate_no_repeating_dims(dim) + + shape = list(input.shape) + shape[dim[-1]] = last_dim_size + dtype = utils.corresponding_real_dtype(input.dtype) + strides = utils.make_contiguous_strides_for(shape) + return TensorMeta(shape=shape, strides=strides, dtype=dtype, device=input.device) + + +def _fft_c2r_aten( + input: TensorLike, + *, + dim: DimsSequenceType, + last_dim_size: int, +) -> TensorLikeType: + normalization = 0 # No normalization + return torch._fft_c2r(input, dim, normalization, last_dim_size) + + +_fft_c2r_doc = """ + Performs a complex to real Inverse Fast Fourier Transform +""" + + +fft_c2r = _make_prim( + schema="fft_c2r(Tensor self, *, int[] dim, SymInt last_dim_size) -> Tensor", + meta=_fft_c2r_meta, + impl_aten=_fft_c2r_aten, + return_type=RETURN_TYPE.NEW, + doc=_fft_c2r_doc, +) + + +def _frexp_meta(self: TensorLikeType) -> tuple[TensorLikeType, TensorLikeType]: + torch._check( + self.dtype.is_floating_point, + lambda: "torch.frexp() only supports floating-point dtypes", + ) + return torch.empty_like(self), torch.empty_like(self, dtype=torch.int32) + + +frexp = _make_prim( + schema="frexp(Tensor self) -> (Tensor mantissa, Tensor exponent)", + meta=_frexp_meta, + return_type=(RETURN_TYPE.NEW, RETURN_TYPE.NEW), + impl_aten=torch.frexp, + doc="", +) + + +def _make_token_aten() -> TensorLikeType: + return new_token_tensor() + + +_make_token = _make_prim( + schema="_make_token() -> Tensor", + meta=_make_token_aten, + return_type=RETURN_TYPE.NEW, + impl_aten=_make_token_aten, + doc="Creates a token used for keeping track of side effects.", +) + + +def _sink_tokens_aten(tokens) -> None: + pass + + +_sink_tokens = _make_prim( + schema="_sink_tokens(Tensor[] tokens) -> ()", + meta=_sink_tokens_aten, + return_type=RETURN_TYPE.NONE, + impl_aten=_sink_tokens_aten, + doc="Sink all of the tokens which were previously used for keeping track of side effects.", +) + + +register_rng_prims() +register_debug_prims() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9851b7f982d8317807ec800a57cf5c49dc5dca7e Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/__pycache__/context.cpython-310.pyc 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torch_function_passthrough + + +@functools.lru_cache(None) +def torch_to_refs_map(): + """ + Mapping of torch API functions to torch._refs functions. + E.g. torch_to_refs_map()[torch.add] == torch._refs.add + """ + modules = [ + (torch, torch._refs), + (torch.nn, torch._refs.nn), + (torch.nn.functional, torch._refs.nn.functional), + (torch.special, torch._refs.special), + (torch.fft, torch._refs.fft), + (torch.linalg, torch._refs.linalg), + ] + r: dict[Any, Any] = { + torch.Tensor.__invert__: torch._refs.bitwise_not, + torch.Tensor.__xor__: torch._refs.bitwise_xor, + torch.Tensor.__and__: torch._refs.bitwise_and, + torch.Tensor.__or__: torch._refs.bitwise_or, + torch.Tensor.__eq__: torch._refs.eq, + torch.Tensor.__rsub__: torch._refs.rsub, + torch.Tensor.__rtruediv__: torch._refs.rtruediv, + torch.Tensor.__floordiv__: torch._refs.floor_divide, + torch.Tensor.__rfloordiv__: torch._refs.rfloordiv, + torch.Tensor.__pow__: torch._refs.pow, + torch.Tensor.__rpow__: torch._refs.rpow, + torch.Tensor.new_empty: torch._refs.new_empty, + torch.Tensor.new_full: torch._refs.new_full, + torch.Tensor.new_zeros: torch._refs.new_zeros, + torch.Tensor.new_ones: torch._refs.new_ones, + torch.Tensor.fill_: torch._refs.fill_, + torch.Tensor.zero_: torch._refs.zero_, + torch.Tensor.to: torch._refs.to, + torch.Tensor.sum_to_size: torch._refs.sum_to_size, + # TODO: Should these methods be mapped some other way? + torch.Tensor.copy_: torch._prims.copy_to, + torch.Tensor.resize: torch._prims.resize, + } + for mod_torch, mod_refs in modules: + for s in mod_refs.__all__: # type: ignore[attr-defined] + r[mod_torch.__dict__.get(s)] = mod_refs.__dict__.get(s) + + # Support remapping torch.Tensor.foo to _refs.foo + for s in dir(torch.Tensor): + if s in torch._refs.__all__: + r[getattr(torch.Tensor, s)] = torch._refs.__dict__.get(s) + + # Support conversions + for s in torch._refs._conversions.__all__: + tensor_attr = getattr(torch.Tensor, s, None) or getattr(torch, s) + r[tensor_attr] = torch._refs._conversions.__dict__.get(s) + + return r + + +@functools.lru_cache(None) +def all_prims(): + """ + Set of all prim functions, e.g., torch._prims.add in all_prims() + """ + return {torch._prims.__dict__.get(s) for s in torch._prims.__all__} + + +class TorchRefsMode(torch.overrides.TorchFunctionMode): + """ + Switches the interpretation of torch.* functions and Tensor methods to + use PrimTorch refs in torch._refs. (Direct calls to _refs are unaffected.) + + >>> # xdoctest: +SKIP + >>> with TorchRefsMode(): + ... torch.add(x, y) # calls torch._refs.add(x, y) + + By default, this context manager will fall back on the torch.* if the + ref does not exist; set strict=True to error if this occurs. + If the ref exists we still would like to fall back on the torch.* sometimes, + this behavior can be customized by passing a function to should_fallback_fn. + """ + + def __init__( + self, + strict=False, + should_fallback_fn=lambda *_: False, + prims_mode_cls=nullcontext, + ): + self.strict = strict + self.should_fallback_fn = should_fallback_fn + self.prims_mode_cls = prims_mode_cls + + def __torch_function__( + self, + orig_func: Callable, + types: Sequence, + args: Sequence[Any] = (), + kwargs: Optional[dict] = None, + ): + if kwargs is None: + kwargs = {} + # For primitive operations, run them as is without interception + # Unless we are in prims_mode, in which case we want to use nvprims + if orig_func in torch_function_passthrough or orig_func in all_prims(): + with self.prims_mode_cls(): + return orig_func(*args, **kwargs) + mapping = torch_to_refs_map() + func = mapping.get(orig_func, None) + + # For torch.ops.aten.*, use registered decompositions from torch._decomp + # torch._decomp.decomposition_table provides a mapping from + # torch.ops.aten.* to torch._refs or torch._decomp.decompositions + # implementations. + # There're other ways to implement this functionality, + # see https://github.com/pytorch/pytorch/pull/82657#discussion_r939776417 + if func is None and isinstance(orig_func, torch._ops.OpOverload): + func = torch._decomp.decomposition_table.get(orig_func, None) + elif func is None and isinstance(orig_func, torch._ops.OpOverloadPacket): + default = getattr(orig_func, "default", None) + if default is None and orig_func._dir: + default = getattr(orig_func, orig_func._dir[0], None) + if default is not None: + func = torch._decomp.decomposition_table.get(default, None) + + if func is not None: + # If the ref exists query whether we should use it or not + if self.should_fallback_fn(self, orig_func, func, args, kwargs): + return orig_func(*args, **kwargs) + # torch calls inside func should be interpreted as refs calls + with self: + return func(*args, **kwargs) + if self.strict: + raise RuntimeError( + f"no _refs support for {torch.overrides.resolve_name(orig_func)}" + ) + return orig_func(*args, **kwargs) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/debug_prims.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/debug_prims.py new file mode 100644 index 0000000000000000000000000000000000000000..6958cbcef283dbc7eda544760640126e7524bb2e --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/debug_prims.py @@ -0,0 +1,54 @@ +# mypy: allow-untyped-defs +import contextlib +from typing import Optional + +import torch +from torch.utils._content_store import ContentStoreReader + + +LOAD_TENSOR_READER: Optional[ContentStoreReader] = None + + +@contextlib.contextmanager +def load_tensor_reader(loc): + global LOAD_TENSOR_READER + assert LOAD_TENSOR_READER is None + # load_tensor is an "op", and we will play merry hell on + # Inductor's memory planning if we return a tensor that + # aliases another tensor that we previously returned from + # an operator. So unlike standard ContentStoreReader use, + # we disable the cache so that you always get fresh storages + # (no aliasing for you!) + LOAD_TENSOR_READER = ContentStoreReader(loc, cache=False) + try: + yield + finally: + LOAD_TENSOR_READER = None + + +def register_debug_prims(): + torch.library.define( + "debugprims::load_tensor", + "(str name, int[] size, int[] stride, *, ScalarType dtype, Device device) -> Tensor", + ) + + @torch.library.impl("debugprims::load_tensor", "BackendSelect") + def load_tensor_factory(name, size, stride, dtype, device): + if LOAD_TENSOR_READER is None: + from torch._dynamo.testing import rand_strided + + return rand_strided(size, stride, dtype, device) + else: + from torch._dynamo.utils import clone_input + + # device argument here takes care of coercion + r = LOAD_TENSOR_READER.read_tensor(name, device=device) + assert list(r.size()) == size, f"{r.size()} != {size}" + assert list(r.stride()) == stride, f"{r.stride()} != {stride}" + assert r.device == device, f"{r.device} != {device}" + + # Unlike the other properties, we will do coercions for dtype + # mismatch + if r.dtype != dtype: + r = clone_input(r, dtype=dtype) + return r diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/executor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/executor.py new file mode 100644 index 0000000000000000000000000000000000000000..fdd2e19ab43b98211bdeb04bdcc8c8e528d020bb --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/executor.py @@ -0,0 +1,67 @@ +from typing import Any, Callable, Optional, TypeVar +from typing_extensions import ParamSpec, TypeVarTuple, Unpack + +from torch._prims.context import TorchRefsMode +from torch.fx import GraphModule +from torch.fx.experimental.proxy_tensor import make_fx, wrapper_and_args_for_make_fx + + +T = TypeVar("T") +P = ParamSpec("P") +Ts = TypeVarTuple("Ts") + + +def execute( + gm: GraphModule, + *args: Unpack[Ts], + executor: str = "aten", + executor_parameters: Optional[dict] = None, +) -> Any: + """ + Prototype ATen executor. + + Just executes the context's graph. + """ + + if executor == "aten": + return gm.forward(*args) + + msg = f"Received unexpected value for 'executor': {executor}. Allowed values are: aten." + raise ValueError(msg) + + +def make_traced(fn: Callable[P, T]) -> Callable[P, T]: + """ + Returns a function that, when called, will + trace its torch operations to prims and then + execute those prims on the requested trace executor + (possibly lowering them to that trace executor first). + + Only supports the torch operations defined in _torch_to_reference_map + in context.py and operations with positional args. All args must + be tensors. + In the near future all these restrictions will be lifted. + + Example usage: + + def foo(a, b): + return torch.add(a, b) + + traced_foo = make_traced(foo) + + a = torch.randn((1, 2, 3, 4, 5), device='cuda') + b = torch.randn((1, 2, 3, 4, 5), device='cuda') + result = traced_foo(a, b, executor='aten') + """ + + def _traced(*args: P.args, **kwargs: P.kwargs) -> T: + executor = str(kwargs.pop("executor", "aten")) + + # TODO: caching + wrapped, all_args = wrapper_and_args_for_make_fx(fn, args, kwargs) + + with TorchRefsMode(): + gm = make_fx(wrapped)(all_args) + return execute(gm, all_args, executor=executor) + + return _traced # type: ignore[return-value] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/rng_prims.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/rng_prims.py new file mode 100644 index 0000000000000000000000000000000000000000..70b4bc472358add351c17d88bc2bea83d79f0f26 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_prims/rng_prims.py @@ -0,0 +1,389 @@ +# mypy: allow-untyped-defs +from typing import Optional + +import torch +import torch.utils._pytree as pytree +from torch import _prims +from torch._C import DispatchKey +from torch._higher_order_ops.utils import autograd_not_implemented +from torch._ops import HigherOrderOperator +from torch._prims_common import CUDARngStateHelper, make_contiguous_strides_for +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + ProxyTorchDispatchMode, + track_tensor_tree, +) +from torch.types import _device, _dtype + + +def throw_on_non_cuda(device): + raise RuntimeError( + f"You are trying to functionalize a {device.type} RNG operator but {device.type} does not " + f"use Philox/counter-based RNG. Therefore, functionalizing a {device.type} RNG operator is " + "not supported. We are discussing the possibility of a Philox-based RNG implementation for CPU." + ) + + +def register_rng_prim(name, schema, impl_aten, impl_meta, doc, tags=None): + rngprim_def = torch.library.custom_op( + "rngprims::" + name, impl_aten, mutates_args=(), schema=schema + ) + rngprim_def.register_fake(impl_meta) + + prim_packet = getattr(torch._ops.ops.rngprims, name) + prim = prim_packet.default + if tags: + prim._tags = tags + + for p in (prim_packet, prim): + p.__doc__ = doc + p.return_type = torch._prims_common.RETURN_TYPE.NEW # type: ignore[attr-defined] + + p.schema = name + schema + p.impl_aten = impl_aten + p.prim_meta_impl = impl_meta + + +# Philox rand offsets could be shared in future with other philox ops, so +# keeping these functions in global scope. +def philox_rand_offset_meta( + shape: torch.Size, +): + return _prims.TensorLike(torch.tensor(0, dtype=torch.int64)) + + +def philox_rand_offset( + shape: torch.Size, +): + # For impl, look at the function calc_execution_policy in the file + # aten/src/ATen/native/cuda/DistributionTemplates.h. The impl was copied at + # commit hash 72aa0667bd16707d50eb8fa337092a1f5d11dfb6 + numel_scalar = 1 + for dim_size in shape: + numel_scalar *= dim_size + numel = torch.scalar_tensor(numel_scalar, dtype=torch.int64) + + block_size = 256 + unroll = 4 + curand4_engine_calls = 4 + device_property = torch.cuda.get_device_properties(torch.cuda.current_device()) + blocks_per_sm = device_property.max_threads_per_multi_processor // block_size + grid_size = (numel + block_size - 1) // block_size + grid_size = min(grid_size, device_property.multi_processor_count * blocks_per_sm) + offset = ( + (numel - 1) // (block_size * grid_size * unroll) + 1 + ) * curand4_engine_calls + return offset + + +def register_philox_rand(): + name = "philox_rand" + schema = "(SymInt[] size, Tensor seed, Tensor offset, int[]? stride, Device? device=None, ScalarType? dtype=None) -> (Tensor, Tensor)" # noqa: B950 + + def _philox_rand_meta( + shape: torch.Size, + seed: torch.Tensor, + offset: torch.Tensor, + stride: Optional[tuple[int, ...]], + device: _device, + dtype: _dtype, + ): + # stride arg will be useful for distributed usecase. Currently, its unused. + assert stride is None + stride = make_contiguous_strides_for(shape) + random_values = _prims.TensorMeta( + shape=shape, strides=stride, dtype=dtype, device=device + ) + offset = philox_rand_offset_meta(shape) + return (random_values, offset) + + def _philox_rand( + shape: torch.Size, + seed: torch.Tensor, + offset: torch.Tensor, + stride: Optional[tuple[int, ...]], + device: _device, + dtype: _dtype, + ): + # stride arg will be useful for distributed usecase. Currently, its unused. + assert stride is None + if device.type == "cpu": + devices = [] + else: + devices = [device] + + if device.type != "cuda": + raise throw_on_non_cuda(device) + + with torch.random.fork_rng(devices): + CUDARngStateHelper.set_torch_state_tensor(seed, offset) + random_values = torch.rand(shape, device=device, dtype=dtype) + + return random_values, philox_rand_offset(shape) + + register_rng_prim( + name=name, + schema=schema, + impl_aten=_philox_rand, + impl_meta=_philox_rand_meta, + doc="Philox based stateless rand operator", + tags=(torch.Tag.nondeterministic_seeded,), + ) + + +def get_device(args, kwargs): + if kwargs.get("device"): + device = kwargs.get("device") + if isinstance(device, str): + device = torch.device(device) + return device.type + + devices = {arg.device.type for arg in args if isinstance(arg, torch.Tensor)} + if any(dev == "cuda" for dev in devices): + return "cuda" + elif any(dev == "xpu" for dev in devices): + return "xpu" + elif any(dev == "hpu" for dev in devices): + return "hpu" + elif any(dev == "cpu" for dev in devices): + return "cpu" + return None + + +def register_run_and_save_rng_state_op(): + class RunAndSaveRngState(HigherOrderOperator): + def __init__(self): + super().__init__("run_and_save_rng_state") + + def __call__(self, op, *args, **kwargs): + return super().__call__(op, *args, **kwargs) + + run_and_save_rng_state = RunAndSaveRngState() + + run_and_save_rng_state.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(run_and_save_rng_state, deferred_error=True) + ) + + @run_and_save_rng_state.py_impl(DispatchKey.CUDA) + def impl_cuda(op, *args, **kwargs): + return torch.cuda.get_rng_state(), op(*args, **kwargs) + + @run_and_save_rng_state.py_impl(DispatchKey.CPU) + def impl_cpu(op, *args, **kwargs): + return torch.get_rng_state(), op(*args, **kwargs) + + @run_and_save_rng_state.py_impl(DispatchKey.HPU) + def impl_hpu(op, *args, **kwargs): + if hasattr(torch, "hpu"): + return torch.hpu.get_rng_state(), op(*args, **kwargs) + raise RuntimeError("functionalize a hpu RNG operator is not supported.") + + @run_and_save_rng_state.py_impl(DispatchKey.XPU) + def impl_xpu(op, *args, **kwargs): + return torch.xpu.get_rng_state(), op(*args, **kwargs) + + @run_and_save_rng_state.py_impl(DispatchKey.BackendSelect) + def impl_backend_select(op, *args, **kwargs): + impl_map = { + "cuda": impl_cuda, + "cpu": impl_cpu, + "hpu": impl_hpu, + "xpu": impl_xpu, + } + device = get_device(args, kwargs) + assert device in impl_map, f"Backend not supported for {device}" + impl = impl_map[device] + return impl(op, *args, **kwargs) + + @run_and_save_rng_state.py_impl(FakeTensorMode) + def impl_fake_tensor_mode(mode, op, *args, **kwargs): + # Check device to call the right impl + with mode: + return impl_backend_select(op, *args, **kwargs) + + @run_and_save_rng_state.py_impl(ProxyTorchDispatchMode) + def impl_proxy_dispatch_mode(mode, op, *args, **kwargs): + out = impl_backend_select(op, *args, **kwargs) + proxy_args = pytree.tree_map(mode.tracer.unwrap_proxy, (op, *args)) + proxy_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, kwargs) + out_proxy = mode.tracer.create_proxy( + "call_function", run_and_save_rng_state, proxy_args, proxy_kwargs + ) + return track_tensor_tree(out, out_proxy, constant=None, tracer=mode.tracer) + + return run_and_save_rng_state + + +def register_run_with_rng_state_op(): + class RunWithRngState(HigherOrderOperator): + def __init__(self): + super().__init__("run_with_rng_state") + + def __call__(self, rng_state, op, *args, **kwargs): + return super().__call__(rng_state, op, *args, **kwargs) + + run_with_rng_state = RunWithRngState() + + run_with_rng_state.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(run_with_rng_state, deferred_error=True) + ) + + @run_with_rng_state.py_impl(DispatchKey.CUDA) + def impl_cuda(rng_state, op, *args, **kwargs): + current_state = torch.cuda.get_rng_state() + torch.cuda.set_rng_state(rng_state.cpu()) + out = op(*args, **kwargs) + torch.cuda.set_rng_state(current_state) + return out + + @run_with_rng_state.py_impl(DispatchKey.CPU) + def impl_cpu(rng_state, op, *args, **kwargs): + current_state = torch.get_rng_state() + torch.set_rng_state(rng_state) + out = op(*args, **kwargs) + torch.set_rng_state(current_state) + return out + + @run_with_rng_state.py_impl(DispatchKey.HPU) + def impl_hpu(rng_state, op, *args, **kwargs): + if hasattr(torch, "hpu"): + current_state = torch.hpu.get_rng_state() + torch.hpu.set_rng_state(rng_state) + out = op(*args, **kwargs) + torch.hpu.set_rng_state(current_state) + return out + raise RuntimeError("functionalize a hpu RNG operator is not supported.") + + @run_with_rng_state.py_impl(DispatchKey.XPU) + def impl_xpu(rng_state, op, *args, **kwargs): + current_state = torch.xpu.get_rng_state() + torch.xpu.set_rng_state(rng_state) + out = op(*args, **kwargs) + torch.xpu.set_rng_state(current_state) + return out + + @run_with_rng_state.py_impl(ProxyTorchDispatchMode) + def impl_proxy_dispatch_mode(mode, rng_state, op, *args, **kwargs): + # TODO: you don't need to do this, the dispatch here already disabled + # it + with disable_proxy_modes_tracing(): + out = run_with_rng_state(rng_state, op, *args, **kwargs) + proxy_args = pytree.tree_map(mode.tracer.unwrap_proxy, (rng_state, op, *args)) + proxy_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, kwargs) + out_proxy = mode.tracer.create_proxy( + "call_function", run_with_rng_state, proxy_args, proxy_kwargs + ) + return track_tensor_tree(out, out_proxy, constant=None, tracer=mode.tracer) + + @run_with_rng_state.py_impl(DispatchKey.BackendSelect) + def impl_backend_select(rng_state, op, *args, **kwargs): + impl_map = { + "cuda": impl_cuda, + "cpu": impl_cpu, + "hpu": impl_hpu, + "xpu": impl_xpu, + } + device = get_device(args, kwargs) + assert device in impl_map, f"Backend not supported for {device}" + impl = impl_map[device] + return impl(rng_state, op, *args, **kwargs) + + @run_with_rng_state.py_impl(FakeTensorMode) + def impl_fake_tensor_mode(mode, rng_state, op, *args, **kwargs): + # Skip setting the set_rng_state as it does not work well with fake tensors. + # And it does not matter for the fake tensor mode. + with mode: + return op(*args, **kwargs) + + @run_with_rng_state.py_functionalize_impl + def impl_functional(ctx, rng_state, op, *args, **kwargs): + unwrapped_rng_state = ctx.unwrap_tensors(rng_state) + unwrapped_args = ctx.unwrap_tensors(args) + unwrapped_kwargs = ctx.unwrap_tensors(kwargs) + + with ctx.redispatch_to_next(): + out = run_with_rng_state( + unwrapped_rng_state, op, *unwrapped_args, **unwrapped_kwargs + ) + return ctx.wrap_tensors(out) + + return run_with_rng_state + + +run_and_save_rng_state = register_run_and_save_rng_state_op() +run_with_rng_state = register_run_with_rng_state_op() + + +def register_graphsafe_run_with_rng_state_op(): + class GraphSafeRunWithRngState(HigherOrderOperator): + def __init__(self): + super().__init__("graphsafe_run_with_rng_state") + + def __call__(self, op, *args, rng_state=None, **kwargs): + return super().__call__(op, *args, rng_state=rng_state, **kwargs) + + graphsafe_run_with_rng_state = GraphSafeRunWithRngState() + + graphsafe_run_with_rng_state.py_impl(DispatchKey.Autograd)( + autograd_not_implemented(graphsafe_run_with_rng_state, deferred_error=True) + ) + + @graphsafe_run_with_rng_state.py_impl(DispatchKey.CUDA) + def impl_cuda(op, *args, rng_state=None, **kwargs): + device_idx = rng_state.device.index + generator = torch.cuda.default_generators[device_idx] + current_state = generator.graphsafe_get_state() + generator.graphsafe_set_state(rng_state) + out = op(*args, **kwargs) + generator.graphsafe_set_state(current_state) + return out + + @graphsafe_run_with_rng_state.py_impl(DispatchKey.BackendSelect) + def impl_backend_select(op, *args, rng_state=None, **kwargs): + device = get_device(args, kwargs) + assert ( + device == "cuda" + ), f"GraphSafe RNG operations only supported for CUDA, got {device}" + return impl_cuda(op, *args, rng_state=rng_state, **kwargs) + + @graphsafe_run_with_rng_state.py_impl(FakeTensorMode) + def impl_fake_tensor_mode(mode, op, *args, rng_state=None, **kwargs): + with mode: + return op(*args, **kwargs) + + @graphsafe_run_with_rng_state.py_impl(ProxyTorchDispatchMode) + def impl_proxy_dispatch_mode(mode, op, *args, rng_state=None, **kwargs): + with disable_proxy_modes_tracing(): + out = graphsafe_run_with_rng_state(op, *args, rng_state=rng_state, **kwargs) + proxy_args = pytree.tree_map(mode.tracer.unwrap_proxy, (op, *args)) + proxy_kwargs = pytree.tree_map( + mode.tracer.unwrap_proxy, {"rng_state": rng_state, **kwargs} + ) + out_proxy = mode.tracer.create_proxy( + "call_function", graphsafe_run_with_rng_state, proxy_args, proxy_kwargs + ) + return track_tensor_tree(out, out_proxy, constant=None, tracer=mode.tracer) + + @graphsafe_run_with_rng_state.py_functionalize_impl + def impl_functional(ctx, op, *args, rng_state=None, **kwargs): + unwrapped_rng_state = ( + ctx.unwrap_tensors(rng_state) if rng_state is not None else None + ) + unwrapped_args = ctx.unwrap_tensors(args) + unwrapped_kwargs = ctx.unwrap_tensors(kwargs) + + with ctx.redispatch_to_next(): + out = graphsafe_run_with_rng_state( + op, *unwrapped_args, rng_state=unwrapped_rng_state, **unwrapped_kwargs + ) + return ctx.wrap_tensors(out) + + return graphsafe_run_with_rng_state + + +graphsafe_run_with_rng_state = register_graphsafe_run_with_rng_state_op() + + +def register_rng_prims(): + register_philox_rand() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_strobelight/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_strobelight/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git 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b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_strobelight/cli_function_profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..4fe133cafc03ed64ca6d60305b1fec35856d2fc8 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_strobelight/cli_function_profiler.py @@ -0,0 +1,321 @@ +# mypy: disallow-untyped-defs + +import functools +import logging +import os +import re +import subprocess +import time +from collections.abc import Sequence +from threading import Lock +from timeit import default_timer as timer +from typing import Any, Callable, Optional, TypeVar +from typing_extensions import ParamSpec + + +logger = logging.getLogger("strobelight_function_profiler") + +console_handler = logging.StreamHandler() +formatter = logging.Formatter( + "%(name)s, line %(lineno)d, %(asctime)s, %(levelname)s: %(message)s" +) +console_handler.setFormatter(formatter) + +logger.addHandler(console_handler) +logger.setLevel(logging.INFO) +logger.propagate = False + +_P = ParamSpec("_P") +_R = TypeVar("_R") + + +class StrobelightCLIProfilerError(Exception): + """ + Raised when an error happens during strobelight profiling + """ + + +def _pid_namespace_link(pid: Optional[int] = None) -> str: + """Returns the link to the process's namespace, example: pid:[4026531836]""" + PID_NAMESPACE_PATH = "/proc/{}/ns/pid" + pid = pid or os.getpid() + return os.readlink(PID_NAMESPACE_PATH.format(pid)) + + +def _pid_namespace(pid: Optional[int] = None) -> int: + """Returns the process's namespace id""" + pid = pid or os.getpid() + link = _pid_namespace_link(pid) + return int(link[link.find("[") + 1 : -1]) + + +def _command_to_string(command: Sequence[str]) -> str: + return " ".join(command) + + +class StrobelightCLIFunctionProfiler: + """ + Note: this is a Meta only tool. + + StrobelightCLIFunctionProfiler can be used to profile a python function and + generate a strobelight link with the results. It works on meta servers but + does not requries an fbcode target. + When stop_at_error is false(default), error during profiling does not prevent + the work function from running. + + Check function_profiler_example.py for an example. + """ + + # This lock is used to make sure only one thread is running the profiler at any point. + _lock = Lock() + + def __init__( + self, + *, + stop_at_error: bool = False, + max_profile_duration_sec: int = 60 * 10, + sample_each: float = 1e7, # sample each sample_each cycles. + run_user_name: str = "pytorch-strobelight-ondemand", + timeout_wait_for_running_sec: int = 60, + timeout_wait_for_finished_sec: int = 60, + recorded_env_variables: Optional[list[str]] = None, + sample_tags: Optional[list[str]] = None, + stack_max_len: int = 127, + async_stack_max_len: int = 127, + ): + self.stop_at_error = stop_at_error + self.max_profile_duration_sec = max_profile_duration_sec + self.sample_each = sample_each + self.run_user_name = run_user_name + self.timeout_wait_for_running_sec = timeout_wait_for_running_sec + self.timeout_wait_for_finished_sec = timeout_wait_for_finished_sec + # Results of the most recent run. + # Tracks the strobelight run id of the most recent run + self.current_run_id: Optional[int] = None + self.profile_result: Optional[list[str]] = None + self.sample_tags = sample_tags + + def _run_async(self) -> None: + processId = os.getpid() + namespace = _pid_namespace(processId) + command = [ + "strobeclient", + "run", + "--profiler", + "pyperf", + "--event", + "cycles", + "--async", + "--sample-interval", + f"{int(self.sample_each)}", + "--duration-ms", + f"{int(self.max_profile_duration_sec * 1000)}", + "--pid", + f"{namespace}:{processId}", + ] + + if self.sample_tags: + command.append("--sample-tags") + command.append(",".join(self.sample_tags)) + + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, error in run_async:{output}" + ) + + if match := re.search(r"INFO Run Id: (-?\d+)", output): + self.current_run_id = int(match.group(1)) + return + + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, unexpected result {output}" + ) + + def _wait_for_running(self, counter: int = 0) -> None: + if counter > 20: + raise StrobelightCLIProfilerError( + "wait_for_running called more than 20 times" + ) + + command = ["strobeclient", "getRunStatus", "--run-id", f"{self.current_run_id}"] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, error in wait_for_running:{output}" + ) + + if match := re.search("Profile run status: (.*)", output): + current_status = match.group(1) + if current_status == "RUNNING": + return + elif current_status == "PREPARING": + time.sleep(10) + self._wait_for_running(counter + 1) + return + else: + raise StrobelightCLIProfilerError(f"unexpected {current_status} phase") + + raise StrobelightCLIProfilerError(f"unexpected output\n: {output} ") + + def _stop_run(self) -> None: + command = ["strobeclient", "stopRun", "--run-id", str(self.current_run_id)] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to stop strobelight profiling, return code is not 0 :{output}" + ) + + if match := re.search("INFO ::1:(.*)", output): + current_status = match.group(1) + if current_status.__contains__("Success!"): + return + else: + raise StrobelightCLIProfilerError( + f"failed to stop strobelight profiling, got {current_status} result" + ) + + raise StrobelightCLIProfilerError(f"unexpected output\n: {output} ") + + def _get_results(self) -> None: + command = ["strobeclient", "getRunStatus", "--run-id", str(self.current_run_id)] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to extract profiling results, return code is not 0 : {output}" + ) + + if match := re.search("INFO ::1:(.*)", output): + current_status = match.group(1) + if current_status.__contains__("Profile run status: PROCESSING"): + time.sleep(10) + self._get_results() + return + elif not current_status.__contains__("Profile run finished with SUCCESS"): + raise StrobelightCLIProfilerError( + f"failed to extract profiling results, unexpected response {output}" + ) + + self.profile_result = [] + for item in re.findall( + r"(Total samples(.*)|GraphProfiler(.*)|Icicle view \(python stack\)(.*))", + output, + ): + self.profile_result += item[0] + logger.info(item[0]) + + def _stop_strobelight_no_throw( + self, + collect_results: bool, + ) -> None: + try: + # call stop run + self._stop_run() + logger.info("strobelight profiling stopped") + + logger.debug("collection stopped") + + if not collect_results: + return + + self._get_results() + except Exception: + logger.warning("error during stop_strobelight", exc_info=True) + + # Return true if strobelight started and is running. Never throw. + def _start_strobelight(self) -> bool: + strobelight_started = False + try: + self._run_async() + strobelight_started = True + logger.info("strobelight run id is: %s", self.current_run_id) + self._wait_for_running() + logger.info("strobelight profiling running") + return True + + except Exception: + logger.warning("error during start_strobelight:", exc_info=True) + if strobelight_started: + self._stop_strobelight_no_throw(collect_results=False) + return False + + def profile( + self, work_function: Callable[_P, _R], *args: _P.args, **kwargs: _P.kwargs + ) -> Optional[_R]: + self.current_run_id = None + self.profile_result = None + + if locked := StrobelightCLIFunctionProfiler._lock.acquire(False): + if not locked: + if self.stop_at_error: + raise StrobelightCLIProfilerError("concurrent runs not supported") + + logger.warning("concurrent runs not supported") + return work_function(*args, **kwargs) + + started = self._start_strobelight() + if not started: + if self.stop_at_error: + StrobelightCLIFunctionProfiler._lock.release() + raise StrobelightCLIProfilerError( + "failed to start strobelight profiling" + ) + result = work_function(*args, **kwargs) + StrobelightCLIFunctionProfiler._lock.release() + return result + + try: + logger.debug("collection started") + start = timer() + result = work_function(*args, **kwargs) + end = timer() + total_time = end - start # Time in seconds, e.g. 5.38091952400282 + logger.info("work function took %s seconds", total_time) + self._stop_strobelight_no_throw(collect_results=True) + StrobelightCLIFunctionProfiler._lock.release() + return result + except Exception as error: + logger.warning("work function throw exception", exc_info=True) + self._stop_strobelight_no_throw(collect_results=False) + StrobelightCLIFunctionProfiler._lock.release() + raise error + return None + + +# A function decorator that wraps profile, if no profiler is provided one with +# default args is created. A function can be annotated as: +# @strobelight() +# @strobelight(profiler = StrobelightFunctionProfiler(stop_at_error=True,..)) +# @strobelight(stop_at_error=True,...) +def strobelight( + profiler: Optional[StrobelightCLIFunctionProfiler] = None, **kwargs: Any +) -> Callable[[Callable[_P, _R]], Callable[_P, Optional[_R]]]: + if not profiler: + profiler = StrobelightCLIFunctionProfiler(**kwargs) + + def strobelight_inner( + work_function: Callable[_P, _R] + ) -> Callable[_P, Optional[_R]]: + @functools.wraps(work_function) + def wrapper_function(*args: _P.args, **kwargs: _P.kwargs) -> Optional[_R]: + return profiler.profile(work_function, *args, **kwargs) + + return wrapper_function + + return strobelight_inner diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_strobelight/compile_time_profiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_strobelight/compile_time_profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..2677b75cbbe0591d8b6b12baea4b2e49e1ee9d92 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_strobelight/compile_time_profiler.py @@ -0,0 +1,224 @@ +# mypy: disallow-untyped-defs + +import json +import logging +import os +import re +import subprocess +from datetime import datetime +from socket import gethostname +from typing import Any, Optional + +from torch._strobelight.cli_function_profiler import StrobelightCLIFunctionProfiler + + +logger = logging.getLogger("strobelight_compile_time_profiler") + +console_handler = logging.StreamHandler() +formatter = logging.Formatter( + "%(name)s, line %(lineno)d, %(asctime)s, %(levelname)s: %(message)s" +) +console_handler.setFormatter(formatter) + +logger.addHandler(console_handler) +logger.setLevel(logging.INFO) +logger.propagate = False + + +def get_fburl(url: str) -> str: + short_url = url + # Attempt to shorten the URL + try: + result = subprocess.run( + ["fburl", url], capture_output=True, stdin=subprocess.DEVNULL + ) + if result.returncode == 0: + short_url = result.stdout.decode("utf-8") + except Exception as e: + logger.warning("URL shortening failed: %s, using long URL", repr(e)) + return short_url + + +def get_strobelight_url(identifier: str) -> str: + scuba_json = { + "aggregateList": [], + "aggregation_field": "async_stack_complete", + "b_constraints": [[]], + "c_constraints": [[]], + "cols": ["namespace_id", "namespace_process_id"], + "compare": "none", + "constraints": [ + [{"column": "sample_tags", "op": "all", "value": [f'["{identifier}"]']}] + ], + "derivedCols": [], + "end": "now", + "enumCols": [], + "filterMode": "DEFAULT", + "hideEmptyColumns": "false", + "ignoreGroupByInComparison": "false", + "is_timeseries": "false", + "mappedCols": [], + "metric": "count", + "modifiers": [], + "order": "weight", + "order_desc": "true", + "param_dimensions": [ + {"dim": "py_async_stack", "op": "edge", "param": "0", "anchor": "0"} + ], + "purposes": [], + "return_remainder": "false", + "samplingRatio": "1", + "should_pivot": "false", + "start": "-30 days", + "timezone": "America/Los_Angeles", + "top": 10000, + } + scuba_url_prefix = "https://www.internalfb.com/intern/scuba/query/?dataset=pyperf_experimental/on_demand&drillstate=" + scuba_url_suff = "&view=GraphProfilerView&&normalized=1726332703&pool=uber" + long_url = scuba_url_prefix + json.dumps(scuba_json) + scuba_url_suff + return get_fburl(long_url) + + +class StrobelightCompileTimeProfiler: + success_profile_count: int = 0 + failed_profile_count: int = 0 + ignored_profile_runs: int = 0 + inside_profile_compile_time: bool = False + enabled: bool = False + + # A regex that can be used to filter out what frames to profile. ex: "1/.*" + frame_id_filter: Optional[str] = os.environ.get("COMPILE_STROBELIGHT_FRAME_FILTER") + + # A unique identifier that is used as the run_user_name in the strobelight profile to + # associate all compile time profiles together. + identifier: Optional[str] = None + + current_phase: Optional[str] = None + + profiler: Optional[Any] = None + + max_stack_length: int = int( + os.environ.get("COMPILE_STROBELIGHT_MAX_STACK_LENGTH", 500) + ) + max_profile_time: int = int( + os.environ.get("COMPILE_STROBELIGHT_MAX_PROFILE_TIME", 60 * 30) + ) + # Collect sample each x cycles. + sample_each: int = int( + float(os.environ.get("COMPILE_STROBELIGHT_SAMPLE_RATE", 1e7)) + ) + + @classmethod + def get_frame(cls) -> str: + from torch._guards import CompileContext + + return (str)(CompileContext.current_trace_id()) + + @classmethod + def enable(cls, profiler_class: Any = StrobelightCLIFunctionProfiler) -> None: + if cls.enabled: + logger.info("compile time strobelight profiling already enabled") + return + + logger.info("compile time strobelight profiling enabled") + + if profiler_class is StrobelightCLIFunctionProfiler: + import shutil + + if not shutil.which("strobeclient"): + logger.info( + "strobeclient not found, cant enable compile time strobelight profiling, seems" + "like you are not on a FB machine." + ) + return + + cls.enabled = True + cls._cls_init() + # profiler_class should have public API similar to that of StrobelightCLIFunctionProfiler. + # we have pass different functionProfilerClass for meta-internal fbcode targets. + # NB: the actual implementation in Meta is at + # fbcode/caffe2/fb/strobelight/function_profiler.py + cls.profiler = profiler_class( + sample_each=cls.sample_each, + max_profile_duration_sec=cls.max_profile_time, + stack_max_len=cls.max_stack_length, + async_stack_max_len=cls.max_stack_length, + run_user_name="pt2-profiler/" + + os.environ.get("USER", os.environ.get("USERNAME", "")), + sample_tags={cls.identifier}, + ) + + @classmethod + def _cls_init(cls) -> None: + cls.identifier = "{date}{pid}{hostname}".format( + date=datetime.now().strftime("%Y-%m-%d-%H:%M:%S"), + pid=os.getpid(), + hostname=gethostname(), + ) + + logger.info("Unique sample tag for this run is: %s", cls.identifier) + logger.info( + "URL to access the strobelight profile at the end of the run: %s", + get_strobelight_url(cls.identifier), + ) + + @classmethod + def _log_stats(cls) -> None: + logger.info( + "%s strobelight success runs out of %s non-recursive compilation events.", + cls.success_profile_count, + cls.success_profile_count + cls.failed_profile_count, + ) + + # TODO use threadlevel meta data to tags to record phases. + @classmethod + def profile_compile_time( + cls, func: Any, phase_name: str, *args: Any, **kwargs: Any + ) -> Any: + def skip() -> Any: + return func(*args, **kwargs) + + if not cls.enabled: + return skip() + + if cls.profiler is None: + logger.error("profiler is not set") + return + + frame_id = cls.get_frame() + + if cls.inside_profile_compile_time: + cls.ignored_profile_runs += 1 + logger.info( + "profile_compile_time is requested for phase: %s, frame %s, while already in running phase: %s," + "frame %s, recursive call ignored", + phase_name, + frame_id, + cls.current_phase, + frame_id, + ) + return skip() + + if cls.frame_id_filter is not None: + should_run = re.match(cls.frame_id_filter, frame_id) is not None + if not should_run: + logger.info( + "profiling frame %s is skipped due to frame_id_filter %s", + frame_id, + cls.frame_id_filter, + ) + return skip() + + cls.inside_profile_compile_time = True + cls.current_phase = phase_name + logger.info("profiling frame %s", frame_id) + work_result = cls.profiler.profile(func, *args, **kwargs) + + if cls.profiler.profile_result is not None: + cls.success_profile_count += 1 + else: + cls.failed_profile_count += 1 + + cls._log_stats() + cls.inside_profile_compile_time = False + return work_result diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8f3c167b948303bf4b09a8a0aaa4920b008307ca Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..22809cfd5dc25792d77070c269fc8d111a12eed0 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__init__.py @@ -0,0 +1,15 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +__title__ = "packaging" +__summary__ = "Core utilities for Python packages" +__uri__ = "https://github.com/pypa/packaging" + +__version__ = "23.2" + +__author__ = "Donald Stufft and individual contributors" +__email__ = "donald@stufft.io" + +__license__ = "BSD-2-Clause or Apache-2.0" +__copyright__ = "2014 %s" % __author__ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f7a124a19cd20dbfab2ef020dbd7809e24a0bb45 Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__pycache__/_structures.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__pycache__/_structures.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..33e029e8e728f7df2f28f517a57a43404204912c Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__pycache__/_structures.cpython-310.pyc differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__pycache__/version.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__pycache__/version.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bea734a91890aaf4750ebb06e68ae4efbe72b725 Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/__pycache__/version.cpython-310.pyc differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/_structures.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/_structures.py new file mode 100644 index 0000000000000000000000000000000000000000..90a6465f9682c886363eea5327dac64bf623a6ff --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/_structures.py @@ -0,0 +1,61 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + + +class InfinityType: + def __repr__(self) -> str: + return "Infinity" + + def __hash__(self) -> int: + return hash(repr(self)) + + def __lt__(self, other: object) -> bool: + return False + + def __le__(self, other: object) -> bool: + return False + + def __eq__(self, other: object) -> bool: + return isinstance(other, self.__class__) + + def __gt__(self, other: object) -> bool: + return True + + def __ge__(self, other: object) -> bool: + return True + + def __neg__(self: object) -> "NegativeInfinityType": + return NegativeInfinity + + +Infinity = InfinityType() + + +class NegativeInfinityType: + def __repr__(self) -> str: + return "-Infinity" + + def __hash__(self) -> int: + return hash(repr(self)) + + def __lt__(self, other: object) -> bool: + return True + + def __le__(self, other: object) -> bool: + return True + + def __eq__(self, other: object) -> bool: + return isinstance(other, self.__class__) + + def __gt__(self, other: object) -> bool: + return False + + def __ge__(self, other: object) -> bool: + return False + + def __neg__(self: object) -> InfinityType: + return Infinity + + +NegativeInfinity = NegativeInfinityType() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/version.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/version.py new file mode 100644 index 0000000000000000000000000000000000000000..5faab9bd0dcf28847960162b2b4f13a8a556ef20 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/_vendor/packaging/version.py @@ -0,0 +1,563 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. +""" +.. testsetup:: + + from packaging.version import parse, Version +""" + +import itertools +import re +from typing import Any, Callable, NamedTuple, Optional, SupportsInt, Tuple, Union + +from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType + +__all__ = ["VERSION_PATTERN", "parse", "Version", "InvalidVersion"] + +LocalType = Tuple[Union[int, str], ...] + +CmpPrePostDevType = Union[InfinityType, NegativeInfinityType, Tuple[str, int]] +CmpLocalType = Union[ + NegativeInfinityType, + Tuple[Union[Tuple[int, str], Tuple[NegativeInfinityType, Union[int, str]]], ...], +] +CmpKey = Tuple[ + int, + Tuple[int, ...], + CmpPrePostDevType, + CmpPrePostDevType, + CmpPrePostDevType, + CmpLocalType, +] +VersionComparisonMethod = Callable[[CmpKey, CmpKey], bool] + + +class _Version(NamedTuple): + epoch: int + release: Tuple[int, ...] + dev: Optional[Tuple[str, int]] + pre: Optional[Tuple[str, int]] + post: Optional[Tuple[str, int]] + local: Optional[LocalType] + + +def parse(version: str) -> "Version": + """Parse the given version string. + + >>> parse('1.0.dev1') + + + :param version: The version string to parse. + :raises InvalidVersion: When the version string is not a valid version. + """ + return Version(version) + + +class InvalidVersion(ValueError): + """Raised when a version string is not a valid version. + + >>> Version("invalid") + Traceback (most recent call last): + ... + packaging.version.InvalidVersion: Invalid version: 'invalid' + """ + + +class _BaseVersion: + _key: Tuple[Any, ...] + + def __hash__(self) -> int: + return hash(self._key) + + # Please keep the duplicated `isinstance` check + # in the six comparisons hereunder + # unless you find a way to avoid adding overhead function calls. + def __lt__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key < other._key + + def __le__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key <= other._key + + def __eq__(self, other: object) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key == other._key + + def __ge__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key >= other._key + + def __gt__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key > other._key + + def __ne__(self, other: object) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key != other._key + + +# Deliberately not anchored to the start and end of the string, to make it +# easier for 3rd party code to reuse +_VERSION_PATTERN = r""" + v? + (?: + (?:(?P[0-9]+)!)? # epoch + (?P[0-9]+(?:\.[0-9]+)*) # release segment + (?P
                                          # pre-release
+            [-_\.]?
+            (?Palpha|a|beta|b|preview|pre|c|rc)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+        (?P                                         # post release
+            (?:-(?P[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?Ppost|rev|r)
+                [-_\.]?
+                (?P[0-9]+)?
+            )
+        )?
+        (?P                                          # dev release
+            [-_\.]?
+            (?Pdev)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+    )
+    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+VERSION_PATTERN = _VERSION_PATTERN
+"""
+A string containing the regular expression used to match a valid version.
+
+The pattern is not anchored at either end, and is intended for embedding in larger
+expressions (for example, matching a version number as part of a file name). The
+regular expression should be compiled with the ``re.VERBOSE`` and ``re.IGNORECASE``
+flags set.
+
+:meta hide-value:
+"""
+
+
+class Version(_BaseVersion):
+    """This class abstracts handling of a project's versions.
+
+    A :class:`Version` instance is comparison aware and can be compared and
+    sorted using the standard Python interfaces.
+
+    >>> v1 = Version("1.0a5")
+    >>> v2 = Version("1.0")
+    >>> v1
+    
+    >>> v2
+    
+    >>> v1 < v2
+    True
+    >>> v1 == v2
+    False
+    >>> v1 > v2
+    False
+    >>> v1 >= v2
+    False
+    >>> v1 <= v2
+    True
+    """
+
+    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
+    _key: CmpKey
+
+    def __init__(self, version: str) -> None:
+        """Initialize a Version object.
+
+        :param version:
+            The string representation of a version which will be parsed and normalized
+            before use.
+        :raises InvalidVersion:
+            If the ``version`` does not conform to PEP 440 in any way then this
+            exception will be raised.
+        """
+
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: '{version}'")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
+            post=_parse_letter_version(
+                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+            ),
+            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self) -> str:
+        """A representation of the Version that shows all internal state.
+
+        >>> Version('1.0.0')
+        
+        """
+        return f""
+
+    def __str__(self) -> str:
+        """A string representation of the version that can be rounded-tripped.
+
+        >>> str(Version("1.0a5"))
+        '1.0a5'
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        # Pre-release
+        if self.pre is not None:
+            parts.append("".join(str(x) for x in self.pre))
+
+        # Post-release
+        if self.post is not None:
+            parts.append(f".post{self.post}")
+
+        # Development release
+        if self.dev is not None:
+            parts.append(f".dev{self.dev}")
+
+        # Local version segment
+        if self.local is not None:
+            parts.append(f"+{self.local}")
+
+        return "".join(parts)
+
+    @property
+    def epoch(self) -> int:
+        """The epoch of the version.
+
+        >>> Version("2.0.0").epoch
+        0
+        >>> Version("1!2.0.0").epoch
+        1
+        """
+        return self._version.epoch
+
+    @property
+    def release(self) -> Tuple[int, ...]:
+        """The components of the "release" segment of the version.
+
+        >>> Version("1.2.3").release
+        (1, 2, 3)
+        >>> Version("2.0.0").release
+        (2, 0, 0)
+        >>> Version("1!2.0.0.post0").release
+        (2, 0, 0)
+
+        Includes trailing zeroes but not the epoch or any pre-release / development /
+        post-release suffixes.
+        """
+        return self._version.release
+
+    @property
+    def pre(self) -> Optional[Tuple[str, int]]:
+        """The pre-release segment of the version.
+
+        >>> print(Version("1.2.3").pre)
+        None
+        >>> Version("1.2.3a1").pre
+        ('a', 1)
+        >>> Version("1.2.3b1").pre
+        ('b', 1)
+        >>> Version("1.2.3rc1").pre
+        ('rc', 1)
+        """
+        return self._version.pre
+
+    @property
+    def post(self) -> Optional[int]:
+        """The post-release number of the version.
+
+        >>> print(Version("1.2.3").post)
+        None
+        >>> Version("1.2.3.post1").post
+        1
+        """
+        return self._version.post[1] if self._version.post else None
+
+    @property
+    def dev(self) -> Optional[int]:
+        """The development number of the version.
+
+        >>> print(Version("1.2.3").dev)
+        None
+        >>> Version("1.2.3.dev1").dev
+        1
+        """
+        return self._version.dev[1] if self._version.dev else None
+
+    @property
+    def local(self) -> Optional[str]:
+        """The local version segment of the version.
+
+        >>> print(Version("1.2.3").local)
+        None
+        >>> Version("1.2.3+abc").local
+        'abc'
+        """
+        if self._version.local:
+            return ".".join(str(x) for x in self._version.local)
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        """The public portion of the version.
+
+        >>> Version("1.2.3").public
+        '1.2.3'
+        >>> Version("1.2.3+abc").public
+        '1.2.3'
+        >>> Version("1.2.3+abc.dev1").public
+        '1.2.3'
+        """
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        """The "base version" of the version.
+
+        >>> Version("1.2.3").base_version
+        '1.2.3'
+        >>> Version("1.2.3+abc").base_version
+        '1.2.3'
+        >>> Version("1!1.2.3+abc.dev1").base_version
+        '1!1.2.3'
+
+        The "base version" is the public version of the project without any pre or post
+        release markers.
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        return "".join(parts)
+
+    @property
+    def is_prerelease(self) -> bool:
+        """Whether this version is a pre-release.
+
+        >>> Version("1.2.3").is_prerelease
+        False
+        >>> Version("1.2.3a1").is_prerelease
+        True
+        >>> Version("1.2.3b1").is_prerelease
+        True
+        >>> Version("1.2.3rc1").is_prerelease
+        True
+        >>> Version("1.2.3dev1").is_prerelease
+        True
+        """
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        """Whether this version is a post-release.
+
+        >>> Version("1.2.3").is_postrelease
+        False
+        >>> Version("1.2.3.post1").is_postrelease
+        True
+        """
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        """Whether this version is a development release.
+
+        >>> Version("1.2.3").is_devrelease
+        False
+        >>> Version("1.2.3.dev1").is_devrelease
+        True
+        """
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        """The first item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").major
+        1
+        """
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        """The second item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").minor
+        2
+        >>> Version("1").minor
+        0
+        """
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        """The third item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").micro
+        3
+        >>> Version("1").micro
+        0
+        """
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+def _parse_letter_version(
+    letter: Optional[str], number: Union[str, bytes, SupportsInt, None]
+) -> Optional[Tuple[str, int]]:
+
+    if letter:
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: Optional[str]) -> Optional[LocalType]:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: Tuple[int, ...],
+    pre: Optional[Tuple[str, int]],
+    post: Optional[Tuple[str, int]],
+    dev: Optional[Tuple[str, int]],
+    local: Optional[LocalType],
+) -> CmpKey:
+
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non zero, then take the rest
+    # re-reverse it back into the correct order and make it a tuple and use
+    # that for our sorting key.
+    _release = tuple(
+        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: CmpPrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: CmpPrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: CmpPrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: CmpLocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/accelerator/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/accelerator/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bfc7ca0ce71ad5e4d65e46dad43d22ce32d7961
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/accelerator/__init__.py
@@ -0,0 +1,176 @@
+r"""
+This package introduces support for the current :ref:`accelerator` in python.
+"""
+
+from typing import Optional
+from typing_extensions import deprecated
+
+import torch
+
+from ._utils import _device_t, _get_device_index
+
+
+__all__ = [
+    "current_accelerator",
+    "current_device_idx",  # deprecated
+    "current_device_index",
+    "current_stream",
+    "device_count",
+    "is_available",
+    "set_device_idx",  # deprecated
+    "set_device_index",
+    "set_stream",
+    "synchronize",
+]
+
+
+def device_count() -> int:
+    r"""Return the number of current :ref:`accelerator` available.
+
+    Returns:
+        int: the number of the current :ref:`accelerator` available.
+            If there is no available accelerators, return 0.
+    """
+    return torch._C._accelerator_deviceCount()
+
+
+def is_available() -> bool:
+    r"""Check if the current accelerator is available at runtime: it was build, all the
+    required drivers are available and at least one device is visible.
+    See :ref:`accelerator` for details.
+
+    Returns:
+        bool: A boolean indicating if there is an available :ref:`accelerator`.
+
+    Example::
+
+        >>> assert torch.accelerator.is_available() "No available accelerators detected."
+    """
+    # Why not just check "device_count() > 0" like other is_available call?
+    # Because device like CUDA have a python implementation of is_available that is
+    # non-poisoning and some features like Dataloader rely on it.
+    # So we are careful to delegate to the Python version of the accelerator here
+    acc = current_accelerator()
+    if acc is None:
+        return False
+
+    mod = torch.get_device_module(acc)
+    return mod.is_available()
+
+
+def current_accelerator(check_available: bool = False) -> Optional[torch.device]:
+    r"""Return the device of the accelerator available at compilation time.
+    If no accelerator were available at compilation time, returns None.
+    See :ref:`accelerator` for details.
+
+    Args:
+        check_available (bool, optional): if True, will also do a runtime check to see
+            if the device :func:`torch.accelerator.is_available` on top of the compile-time
+            check.
+            Default: ``False``
+
+    Returns:
+        torch.device: return the current accelerator as :class:`torch.device`.
+
+    .. note:: The index of the returned :class:`torch.device` will be ``None``, please use
+        :func:`torch.accelerator.current_device_index` to know the current index being used.
+
+    Example::
+
+        >>> # xdoctest:
+        >>> # If an accelerator is available, sent the model to it
+        >>> model = torch.nn.Linear(2, 2)
+        >>> if (current_device := current_accelerator(check_available=True)) is not None:
+        >>>     model.to(current_device)
+    """
+    if (acc := torch._C._accelerator_getAccelerator()) is not None:
+        if (not check_available) or (check_available and is_available()):
+            return acc
+    return None
+
+
+def current_device_index() -> int:
+    r"""Return the index of a currently selected device for the current :ref:`accelerator`.
+
+    Returns:
+        int: the index of a currently selected device.
+    """
+    return torch._C._accelerator_getDeviceIndex()
+
+
+current_device_idx = deprecated(
+    "Use `current_device_index` instead.",
+    category=FutureWarning,
+)(current_device_index)
+
+
+def set_device_index(device: _device_t, /) -> None:
+    r"""Set the current device index to a given device.
+
+    Args:
+        device (:class:`torch.device`, str, int): a given device that must match the current
+            :ref:`accelerator` device type.
+
+    .. note:: This function is a no-op if this device index is negative.
+    """
+    device_index = _get_device_index(device)
+    torch._C._accelerator_setDeviceIndex(device_index)
+
+
+set_device_idx = deprecated(
+    "Use `set_device_index` instead.",
+    category=FutureWarning,
+)(set_device_index)
+
+
+def current_stream(device: _device_t = None, /) -> torch.Stream:
+    r"""Return the currently selected stream for a given device.
+
+    Args:
+        device (:class:`torch.device`, str, int, optional): a given device that must match the current
+            :ref:`accelerator` device type. If not given,
+            use :func:`torch.accelerator.current_device_index` by default.
+
+    Returns:
+        torch.Stream: the currently selected stream for a given device.
+    """
+    device_index = _get_device_index(device, True)
+    return torch._C._accelerator_getStream(device_index)
+
+
+def set_stream(stream: torch.Stream) -> None:
+    r"""Set the current stream to a given stream.
+
+    Args:
+        stream (torch.Stream): a given stream that must match the current :ref:`accelerator` device type.
+
+    .. note:: This function will set the current device index to the device index of the given stream.
+    """
+    torch._C._accelerator_setStream(stream)
+
+
+def synchronize(device: _device_t = None, /) -> None:
+    r"""Wait for all kernels in all streams on the given device to complete.
+
+    Args:
+        device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match
+            the current :ref:`accelerator` device type. If not given,
+            use :func:`torch.accelerator.current_device_index` by default.
+
+    .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized.
+
+    Example::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> assert torch.accelerator.is_available() "No available accelerators detected."
+        >>> start_event = torch.Event(enable_timing=True)
+        >>> end_event = torch.Event(enable_timing=True)
+        >>> start_event.record()
+        >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator())
+        >>> sum = torch.sum(tensor)
+        >>> end_event.record()
+        >>> torch.accelerator.synchronize()
+        >>> elapsed_time_ms = start_event.elapsed_time(end_event)
+    """
+    device_index = _get_device_index(device, True)
+    torch._C._accelerator_synchronizeDevice(device_index)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/accelerator/_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/accelerator/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a29acd240cdb3abc748cf09fbec005ea58c84c3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/accelerator/_utils.py
@@ -0,0 +1,28 @@
+from typing import Optional
+
+import torch
+from torch.types import Device as _device_t
+
+
+def _get_device_index(device: _device_t, optional: bool = False) -> int:
+    if isinstance(device, int):
+        return device
+    if isinstance(device, str):
+        device = torch.device(device)
+    device_index: Optional[int] = None
+    if isinstance(device, torch.device):
+        acc = torch.accelerator.current_accelerator()
+        if acc is None:
+            raise RuntimeError("Accelerator expected")
+        if acc.type != device.type:
+            raise ValueError(
+                f"{device.type} doesn't match the current accelerator {torch.accelerator.current_accelerator()}."
+            )
+        device_index = device.index
+    if device_index is None:
+        if not optional:
+            raise ValueError(
+                f"Expected a torch.device with a specified index or an integer, but got:{device}"
+            )
+        return torch.accelerator.current_device_index()
+    return device_index
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/amp/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/amp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..263908eff68bab7c7d9a123bd77a5f5c048b8c5a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/amp/__init__.py
@@ -0,0 +1,9 @@
+from .autocast_mode import (
+    _enter_autocast,
+    _exit_autocast,
+    autocast,
+    custom_bwd,
+    custom_fwd,
+    is_autocast_available,
+)
+from .grad_scaler import GradScaler
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/amp/autocast_mode.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/amp/autocast_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e6c61c2660d3ea54781141057ca815e5c42edcd
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/amp/autocast_mode.py
@@ -0,0 +1,558 @@
+# mypy: allow-untyped-defs
+import collections
+import functools
+import warnings
+from typing import Any, Optional
+
+import torch
+from torch.types import _dtype
+
+
+try:
+    import numpy as np
+
+    HAS_NUMPY = True
+except ModuleNotFoundError:
+    HAS_NUMPY = False
+    np = None  # type: ignore[assignment]
+
+__all__ = [
+    "autocast_decorator",
+    "autocast",
+    "is_autocast_available",
+    "custom_fwd",
+    "custom_bwd",
+]
+
+
+def is_autocast_available(device_type: str) -> bool:
+    r"""
+    Return a bool indicating if autocast is available on :attr:`device_type`.
+
+    Args:
+        device_type(str):  Device type to use. Possible values are: 'cuda', 'cpu', 'mtia', 'xpu' and so on.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+    """
+    return torch._C._is_autocast_available(device_type)
+
+
+def autocast_decorator(autocast_instance, func):
+    @functools.wraps(func)
+    def decorate_autocast(*args, **kwargs):
+        with autocast_instance:
+            return func(*args, **kwargs)
+
+    decorate_autocast.__script_unsupported = "@autocast() decorator is not supported in script mode"  # type: ignore[attr-defined]
+    return decorate_autocast
+
+
+class autocast:
+    r"""
+    Instances of :class:`autocast` serve as context managers or decorators that
+    allow regions of your script to run in mixed precision.
+
+    In these regions, ops run in an op-specific dtype chosen by autocast
+    to improve performance while maintaining accuracy.
+    See the :ref:`Autocast Op Reference` for details.
+
+    When entering an autocast-enabled region, Tensors may be any type.
+    You should not call ``half()`` or ``bfloat16()`` on your model(s) or inputs when using autocasting.
+
+    :class:`autocast` should wrap only the forward pass(es) of your network, including the loss
+    computation(s).  Backward passes under autocast are not recommended.
+    Backward ops run in the same type that autocast used for corresponding forward ops.
+
+    Example for CUDA Devices::
+
+        # Creates model and optimizer in default precision
+        model = Net().cuda()
+        optimizer = optim.SGD(model.parameters(), ...)
+
+        for input, target in data:
+            optimizer.zero_grad()
+
+            # Enables autocasting for the forward pass (model + loss)
+            with torch.autocast(device_type="cuda"):
+                output = model(input)
+                loss = loss_fn(output, target)
+
+            # Exits the context manager before backward()
+            loss.backward()
+            optimizer.step()
+
+    See the :ref:`Automatic Mixed Precision examples` for usage (along with gradient scaling)
+    in more complex scenarios (e.g., gradient penalty, multiple models/losses, custom autograd functions).
+
+    :class:`autocast` can also be used as a decorator, e.g., on the ``forward`` method of your model::
+
+        class AutocastModel(nn.Module):
+            ...
+            @torch.autocast(device_type="cuda")
+            def forward(self, input):
+                ...
+
+    Floating-point Tensors produced in an autocast-enabled region may be ``float16``.
+    After returning to an autocast-disabled region, using them with floating-point
+    Tensors of different dtypes may cause type mismatch errors.  If so, cast the Tensor(s)
+    produced in the autocast region back to ``float32`` (or other dtype if desired).
+    If a Tensor from the autocast region is already ``float32``, the cast is a no-op,
+    and incurs no additional overhead.
+    CUDA Example::
+
+        # Creates some tensors in default dtype (here assumed to be float32)
+        a_float32 = torch.rand((8, 8), device="cuda")
+        b_float32 = torch.rand((8, 8), device="cuda")
+        c_float32 = torch.rand((8, 8), device="cuda")
+        d_float32 = torch.rand((8, 8), device="cuda")
+
+        with torch.autocast(device_type="cuda"):
+            # torch.mm is on autocast's list of ops that should run in float16.
+            # Inputs are float32, but the op runs in float16 and produces float16 output.
+            # No manual casts are required.
+            e_float16 = torch.mm(a_float32, b_float32)
+            # Also handles mixed input types
+            f_float16 = torch.mm(d_float32, e_float16)
+
+        # After exiting autocast, calls f_float16.float() to use with d_float32
+        g_float32 = torch.mm(d_float32, f_float16.float())
+
+    CPU Training Example::
+
+        # Creates model and optimizer in default precision
+        model = Net()
+        optimizer = optim.SGD(model.parameters(), ...)
+
+        for epoch in epochs:
+            for input, target in data:
+                optimizer.zero_grad()
+
+                # Runs the forward pass with autocasting.
+                with torch.autocast(device_type="cpu", dtype=torch.bfloat16):
+                    output = model(input)
+                    loss = loss_fn(output, target)
+
+                loss.backward()
+                optimizer.step()
+
+
+    CPU Inference Example::
+
+        # Creates model in default precision
+        model = Net().eval()
+
+        with torch.autocast(device_type="cpu", dtype=torch.bfloat16):
+            for input in data:
+                # Runs the forward pass with autocasting.
+                output = model(input)
+
+    CPU Inference Example with Jit Trace::
+
+        class TestModel(nn.Module):
+            def __init__(self, input_size, num_classes):
+                super().__init__()
+                self.fc1 = nn.Linear(input_size, num_classes)
+            def forward(self, x):
+                return self.fc1(x)
+
+        input_size = 2
+        num_classes = 2
+        model = TestModel(input_size, num_classes).eval()
+
+        # For now, we suggest to disable the Jit Autocast Pass,
+        # As the issue: https://github.com/pytorch/pytorch/issues/75956
+        torch._C._jit_set_autocast_mode(False)
+
+        with torch.cpu.amp.autocast(cache_enabled=False):
+            model = torch.jit.trace(model, torch.randn(1, input_size))
+        model = torch.jit.freeze(model)
+        # Models Run
+        for _ in range(3):
+            model(torch.randn(1, input_size))
+
+    Type mismatch errors *in* an autocast-enabled region are a bug; if this is what you observe,
+    please file an issue.
+
+    ``autocast(enabled=False)`` subregions can be nested in autocast-enabled regions.
+    Locally disabling autocast can be useful, for example, if you want to force a subregion
+    to run in a particular ``dtype``.  Disabling autocast gives you explicit control over
+    the execution type.  In the subregion, inputs from the surrounding region
+    should be cast to ``dtype`` before use::
+
+        # Creates some tensors in default dtype (here assumed to be float32)
+        a_float32 = torch.rand((8, 8), device="cuda")
+        b_float32 = torch.rand((8, 8), device="cuda")
+        c_float32 = torch.rand((8, 8), device="cuda")
+        d_float32 = torch.rand((8, 8), device="cuda")
+
+        with torch.autocast(device_type="cuda"):
+            e_float16 = torch.mm(a_float32, b_float32)
+            with torch.autocast(device_type="cuda", enabled=False):
+                # Calls e_float16.float() to ensure float32 execution
+                # (necessary because e_float16 was created in an autocasted region)
+                f_float32 = torch.mm(c_float32, e_float16.float())
+
+            # No manual casts are required when re-entering the autocast-enabled region.
+            # torch.mm again runs in float16 and produces float16 output, regardless of input types.
+            g_float16 = torch.mm(d_float32, f_float32)
+
+    The autocast state is thread-local.  If you want it enabled in a new thread, the context manager or decorator
+    must be invoked in that thread.  This affects :class:`torch.nn.DataParallel` and
+    :class:`torch.nn.parallel.DistributedDataParallel` when used with more than one GPU per process
+    (see :ref:`Working with Multiple GPUs`).
+
+    Args:
+        device_type(str, required):  Device type to use. Possible values are: 'cuda', 'cpu', 'mtia', 'xpu', and 'hpu'.
+                                     The type is the same as the `type` attribute of a :class:`torch.device`.
+                                     Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+        enabled(bool, optional):  Whether autocasting should be enabled in the region.
+            Default: ``True``
+        dtype(torch_dtype, optional):  Data type for ops run in autocast. It uses the default value
+            (``torch.float16`` for CUDA and ``torch.bfloat16`` for CPU), given by
+            :func:`~torch.get_autocast_dtype`, if :attr:`dtype` is ``None``.
+            Default: ``None``
+        cache_enabled(bool, optional):  Whether the weight cache inside autocast should be enabled.
+            Default: ``True``
+    """
+
+    def __init__(
+        self,
+        device_type: str,
+        dtype: Optional[_dtype] = None,
+        enabled: bool = True,
+        cache_enabled: Optional[bool] = None,
+    ):
+        if not isinstance(device_type, str):
+            raise ValueError(
+                f"Expected `device_type` of type `str`, got: `{type(device_type)}`"
+            )
+        if dtype is None:
+            dtype = torch.get_autocast_dtype(device_type)
+        if torch._jit_internal.is_scripting():
+            self._enabled = enabled
+            self.device = device_type
+            self.fast_dtype = dtype
+            assert dtype is not None
+            return
+        self.device = device_type
+        if not is_autocast_available(self.device):
+            raise RuntimeError(
+                f"User specified an unsupported autocast device_type '{self.device}'"
+            )
+        self.custom_backend_name = torch._C._get_privateuse1_backend_name()
+        self.fast_dtype = torch.get_autocast_dtype(self.device)
+        if self.device == self.custom_backend_name:
+            necessary_funcs = [
+                "get_amp_supported_dtype",
+            ]
+            message = f"Tried to use AMP with the `{self.custom_backend_name}` backend, but the backend has not "
+            message += "registered a module or  the module miss some necessary funcs. The backend should register "
+            message += "a module by `torch._register_device_module`, and the module must have these funcs: \n"
+            message += "`get_amp_supported_dtype() -> List[torch.dtype]`. \n"
+
+            assert hasattr(torch, self.custom_backend_name), message
+            self.custom_device_mod = getattr(torch, self.custom_backend_name)
+            for func in necessary_funcs:
+                assert hasattr(self.custom_device_mod, func), (
+                    message + f"But the func `{func}` is missing. \n"
+                )
+
+        self._cache_enabled = torch.is_autocast_cache_enabled()
+        if (
+            enabled
+            and torch.cuda.amp.common.amp_definitely_not_available()
+            and self.device == "cuda"
+        ):
+            warnings.warn(
+                "User provided device_type of 'cuda', but CUDA is not available. Disabling"
+            )
+            enabled = False
+        if dtype is not None:
+            self.fast_dtype = dtype
+        if cache_enabled is not None:
+            self._cache_enabled = cache_enabled
+
+        if self.device == "cpu":
+            supported_dtype = [torch.bfloat16, torch.float16]
+            if self.fast_dtype not in supported_dtype and enabled:
+                error_message = "In CPU autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += "CPU Autocast only supports dtype of "
+                error_message += (
+                    ", ".join(str(dtype) for dtype in supported_dtype) + " currently."
+                )
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == "mtia":
+            supported_dtype = [torch.bfloat16, torch.float16]
+            if self.fast_dtype not in supported_dtype:
+                error_message = "In MTIA autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += "MTIA Autocast only supports dtypes of torch.bfloat16 and torch.float16 currently."
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == "xpu":
+            supported_dtype = [torch.bfloat16, torch.float16]
+            if self.fast_dtype not in supported_dtype:
+                error_message = "In XPU autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += "XPU Autocast only supports dtypes of torch.bfloat16 and torch.float16 currently."
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == "ipu":
+            supported_dtypes = [torch.bfloat16, torch.float16]
+            if self.fast_dtype not in supported_dtypes:
+                error_message = "In IPU autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += "IPU Autocast only supports dtypes of torch.bfloat16 and torch.float16 currently."
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == "hpu":
+            supported_dtype = [torch.bfloat16, torch.float16]
+            if self.fast_dtype not in supported_dtype:
+                error_message = "In HPU autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += "HPU Autocast only supports dtypes of torch.bfloat16 and torch.float16 currently."
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == self.custom_backend_name:
+            supported_dtype = self.custom_device_mod.get_amp_supported_dtype()
+            if self.fast_dtype not in supported_dtype:
+                error_message = f"In {self.custom_backend_name} autocast, but the target dtype is not supported. "
+                error_message += f"Disabling autocast.\n {self.custom_backend_name} Autocast only supports dtypes of "
+                error_message += (
+                    ", ".join(str(dtype) for dtype in supported_dtype) + " currently."
+                )
+                warnings.warn(error_message)
+                enabled = False
+        elif self.device == "cuda":
+            if (
+                enabled
+                and self.fast_dtype == torch.bfloat16
+                and not torch.cuda.is_bf16_supported()
+            ):
+                raise RuntimeError(
+                    "Current CUDA Device does not support bfloat16. Please switch dtype to float16."
+                )
+        elif self.device == "mps":
+            supported_dtype = [torch.bfloat16, torch.float16]
+            if self.fast_dtype not in supported_dtype:
+                error_message = (
+                    "In MPS autocast, but the target dtype is not supported. Disabling autocast.\n"
+                    "MPS Autocast only supports dtype of torch.bfloat16 and torch.float16 currently."
+                )
+                warnings.warn(error_message)
+                enabled = False
+            elif self.fast_dtype == torch.bfloat16:
+                if not torch.backends.mps.is_macos_or_newer(14, 0):
+                    error_message = (
+                        "In MPS autocast, but the target dtype torch.bfloat16 is not supported "
+                        "on macOS versions below 14. Disabling autocast."
+                    )
+                    warnings.warn(error_message)
+                    enabled = False
+        elif self.device == "xla":
+            supported_dtype = [torch.float16, torch.bfloat16]
+            if self.fast_dtype not in supported_dtype:
+                error_message = "In XLA autocast, but the target dtype is not supported. Disabling autocast.\n"
+                error_message += (
+                    "XLA Autocast only supports dtype of torch.bfloat16 currently."
+                )
+                warnings.warn(error_message)
+                enabled = False
+        self._enabled = enabled
+
+    def __enter__(self):
+        if torch._jit_internal.is_scripting():
+            assert self.fast_dtype is not None
+            return self
+
+        self.prev_cache_enabled = torch.is_autocast_cache_enabled()
+        self.prev = torch.is_autocast_enabled(self.device)
+        self.prev_fastdtype = torch.get_autocast_dtype(self.device)
+        torch.set_autocast_enabled(self.device, self._enabled)
+        torch.set_autocast_dtype(self.device, self.fast_dtype)  # type: ignore[arg-type]
+        torch.autocast_increment_nesting()
+        torch.set_autocast_cache_enabled(self._cache_enabled)
+
+        # only dispatch to PreDispatchTorchFunctionMode to avoid exposing this
+        # API to other functional modes. We only expose to PreDispatchTorchFunctionMode
+        # for preserving autocast in torch.export.export.
+        if torch._C._is_torch_function_mode_enabled():
+            stacks = torch.overrides._get_current_function_mode_stack()
+            for mode in stacks:
+                if isinstance(
+                    mode,
+                    torch.fx.experimental.proxy_tensor.PreDispatchTorchFunctionMode,
+                ):
+                    args = (
+                        self.device,
+                        self.fast_dtype,
+                        self._enabled,
+                        self._cache_enabled,
+                    )
+                    return mode.__torch_function__(torch.amp._enter_autocast, (), args)
+
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any):  # type: ignore[override]
+        if torch._jit_internal.is_scripting():
+            return
+
+        # Drop the cache when we exit to a nesting level that's outside any instance of autocast.
+        if torch.autocast_decrement_nesting() == 0:
+            torch.clear_autocast_cache()
+        torch.set_autocast_enabled(self.device, self.prev)
+        torch.set_autocast_dtype(self.device, self.prev_fastdtype)
+        torch.set_autocast_cache_enabled(self.prev_cache_enabled)
+
+        # only dispatch to PreDispatchTorchFunctionMode to avoid exposing this
+        # API to other functional modes. We only expose to PreDispatchTorchFunctionMode
+        # for preserving autocast in torch.export.export.
+        if torch._C._is_torch_function_mode_enabled():
+            stacks = torch.overrides._get_current_function_mode_stack()
+            for mode in stacks:
+                if isinstance(
+                    mode,
+                    torch.fx.experimental.proxy_tensor.PreDispatchTorchFunctionMode,
+                ):
+                    return mode.__torch_function__(torch.amp._exit_autocast, (), ())
+        return False
+
+    def __call__(self, func):
+        if torch._jit_internal.is_scripting():
+            return func
+        return autocast_decorator(self, func)
+
+
+# These functions aren't meant for public usage.
+# They are what we trace into a graph during pre_dispatch tracing
+# when we encounter an autocast context manager.
+def _enter_autocast(*vals):
+    # For pre-dispatch tracing, if a TorchFunction mode is active, we'll want to trace this into a graph.
+    if torch._C._is_torch_function_mode_enabled():
+        return torch.overrides.handle_torch_function(
+            torch.amp._enter_autocast, [], *vals
+        )
+    mode = torch.amp.autocast(*vals)
+    mode.__enter__()
+    return mode
+
+
+def _exit_autocast(mode):
+    if torch._C._is_torch_function_mode_enabled():
+        return torch.overrides.handle_torch_function(torch.amp._exit_autocast, [], mode)
+    mode.__exit__(None, None, None)
+
+
+# Casts Tensors and containers of Tensors.  Special-cases passthroughs for strings and np.ndarrays, which
+# may be falsely detected as "Iterables."
+def _cast(value, device_type: str, dtype: _dtype):
+    if isinstance(value, torch.Tensor):
+        is_eligible = (
+            value.is_floating_point()
+            and value.device.type == device_type
+            and (value.dtype is not torch.float64)
+        )
+        return value.to(dtype) if is_eligible else value
+    elif isinstance(value, (str, bytes)):
+        return value
+    elif HAS_NUMPY and isinstance(value, np.ndarray):
+        return value
+    elif isinstance(value, collections.abc.Mapping):
+        return {
+            _cast(k, device_type, dtype): _cast(v, device_type, dtype)
+            for k, v in value.items()
+        }
+    elif isinstance(value, collections.abc.Iterable):
+        iterable = (_cast(v, device_type, dtype) for v in value)
+        if isinstance(value, (list, tuple)):
+            return type(value)(iterable)
+        else:
+            return iterable
+    else:
+        return value
+
+
+def custom_fwd(
+    fwd=None,
+    *,
+    device_type: str,
+    cast_inputs: Optional[_dtype] = None,
+):
+    """
+    Create a helper decorator for ``forward`` methods of custom autograd functions.
+
+    Autograd functions are subclasses of :class:`torch.autograd.Function`.
+    See the :ref:`example page` for more detail.
+
+    Args:
+        device_type(str):  Device type to use. 'cuda', 'cpu', 'mtia', 'xpu' and so on.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+        cast_inputs (:class:`torch.dtype` or None, optional, default=None):  If not ``None``,
+            when ``forward`` runs in an autocast-enabled region, casts incoming
+            floating-point Tensors to the target dtype (non-floating-point Tensors are not affected),
+            then executes ``forward`` with autocast disabled.
+            If ``None``, ``forward``'s internal ops execute with the current autocast state.
+
+    .. note::
+        If the decorated ``forward`` is called outside an autocast-enabled region,
+        :func:`custom_fwd` is a no-op and ``cast_inputs`` has no effect.
+    """
+    if not isinstance(device_type, str):
+        raise ValueError(
+            f"Expected `device_type` of type `str`, got: `{type(device_type)}`"
+        )
+    if fwd is None:
+        return functools.partial(
+            custom_fwd, device_type=device_type, cast_inputs=cast_inputs
+        )
+
+    @functools.wraps(fwd)
+    def decorate_fwd(*args, **kwargs):
+        args[0]._dtype = torch.get_autocast_dtype(device_type)
+        if cast_inputs is None:
+            args[0]._fwd_used_autocast = torch.is_autocast_enabled(device_type)
+            return fwd(*args, **kwargs)
+        else:
+            autocast_context = torch.is_autocast_enabled(device_type)
+            args[0]._fwd_used_autocast = False
+            if autocast_context:
+                with autocast(device_type=device_type, enabled=False):
+                    return fwd(
+                        *_cast(args, device_type, cast_inputs),
+                        **_cast(kwargs, device_type, cast_inputs),
+                    )
+            else:
+                return fwd(*args, **kwargs)
+
+    return decorate_fwd
+
+
+# Autograd ensures incoming gradients are the same type as forward outputs.  Allowing a separate
+# cast_inputs argument on custom_bwd is unnecessary and could cause errors if it doesn't match
+# cast_inputs supplied to custom_fwd.
+def custom_bwd(bwd=None, *, device_type: str):
+    """Create a helper decorator for backward methods of custom autograd functions.
+
+    Autograd functions are subclasses of :class:`torch.autograd.Function`.
+    Ensures that ``backward`` executes with the same autocast state as ``forward``.
+    See the :ref:`example page` for more detail.
+
+    Args:
+        device_type(str):  Device type to use. 'cuda', 'cpu', 'mtia', 'xpu' and so on.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+    """
+
+    if not isinstance(device_type, str):
+        raise ValueError(
+            f"Expected `device_type` of type `str`, got: `{type(device_type)}`"
+        )
+    if bwd is None:
+        return functools.partial(custom_bwd, device_type=device_type)
+
+    @functools.wraps(bwd)
+    def decorate_bwd(*args, **kwargs):
+        with autocast(
+            device_type=device_type,
+            enabled=args[0]._fwd_used_autocast,
+            dtype=args[0]._dtype,
+        ):
+            return bwd(*args, **kwargs)
+
+    return decorate_bwd
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/amp/grad_scaler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/amp/grad_scaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..93b1d667c08ab8d04d2bc882ac2116dd7ce01192
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/amp/grad_scaler.py
@@ -0,0 +1,689 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import inspect
+import warnings
+from collections import abc, defaultdict
+from enum import Enum
+from typing import Any, cast, Optional, overload, TYPE_CHECKING, Union
+
+import torch
+
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+
+__all__ = ["OptState", "GradScaler"]
+
+
+class _MultiDeviceReplicator:
+    """Lazily serves copies of a tensor to requested devices.
+
+    Copies are cached per-device.
+    """
+
+    def __init__(self, master_tensor: torch.Tensor) -> None:
+        self.master = master_tensor
+        self._per_device_tensors: dict[torch.device, torch.Tensor] = {}
+
+    def get(self, device: torch.device) -> torch.Tensor:
+        retval = self._per_device_tensors.get(device, None)
+        if retval is None:
+            retval = self.master.to(device=device, non_blocking=True, copy=True)
+            self._per_device_tensors[device] = retval
+        return retval
+
+
+# Defines default_factory for GradScaler's _per_optimizer_states defaultdict,
+# as well as associated "enum" values.  Prefers defining these at top level because
+# - Lambdas can't be pickled, so we don't want to supply a lambda as the factory.
+# - Defining READY, UNSCALED, STEPPED and _refresh_per_optimizer_state within GradScaler
+#   causes a circular reference, which we'd rather avoid.
+class OptState(Enum):
+    READY = 0
+    UNSCALED = 1
+    STEPPED = 2
+
+
+def _refresh_per_optimizer_state() -> dict[str, Any]:
+    return {"stage": OptState.READY, "found_inf_per_device": {}}
+
+
+class GradScaler:
+    """An instance ``scaler`` of :class:`GradScaler`.
+
+    Helps perform the steps of gradient scaling
+    conveniently.
+
+    * ``scaler.scale(loss)`` multiplies a given loss by ``scaler``'s current scale factor.
+    * ``scaler.step(optimizer)`` safely unscales gradients and calls ``optimizer.step()``.
+    * ``scaler.update()`` updates ``scaler``'s scale factor.
+
+    Example::
+
+        # Creates a GradScaler once at the beginning of training.
+        scaler = GradScaler()
+
+        for epoch in epochs:
+            for input, target in data:
+                optimizer.zero_grad()
+                output = model(input)
+                loss = loss_fn(output, target)
+
+                # Scales loss.  Calls backward() on scaled loss to create scaled gradients.
+                scaler.scale(loss).backward()
+
+                # scaler.step() first unscales gradients of the optimizer's params.
+                # If gradients don't contain infs/NaNs, optimizer.step() is then called,
+                # otherwise, optimizer.step() is skipped.
+                scaler.step(optimizer)
+
+                # Updates the scale for next iteration.
+                scaler.update()
+
+    See the :ref:`Automatic Mixed Precision examples` for usage
+    (along with autocasting) in more complex cases like gradient clipping, gradient accumulation, gradient penalty,
+    and multiple losses/optimizers.
+
+    ``scaler`` dynamically estimates the scale factor each iteration.  To minimize gradient underflow,
+    a large scale factor should be used.  However, ``float16`` values can "overflow" (become inf or NaN) if
+    the scale factor is too large.  Therefore, the optimal scale factor is the largest factor that can be used
+    without incurring inf or NaN gradient values.
+    ``scaler`` approximates the optimal scale factor over time by checking the gradients for infs and NaNs during every
+    ``scaler.step(optimizer)`` (or optional separate ``scaler.unscale_(optimizer)``, see :meth:`unscale_`).
+
+    * If infs/NaNs are found, ``scaler.step(optimizer)`` skips the underlying ``optimizer.step()`` (so the params
+      themselves remain uncorrupted) and ``update()`` multiplies the scale by ``backoff_factor``.
+
+    * If no infs/NaNs are found, ``scaler.step(optimizer)`` runs the underlying ``optimizer.step()`` as usual.
+      If ``growth_interval`` unskipped iterations occur consecutively, ``update()`` multiplies the scale by
+      ``growth_factor``.
+
+    The scale factor often causes infs/NaNs to appear in gradients for the first few iterations as its
+    value calibrates.  ``scaler.step`` will skip the underlying ``optimizer.step()`` for these
+    iterations.  After that, step skipping should occur rarely (once every few hundred or thousand iterations).
+
+    Args:
+        device (str, optional, default="cuda"): Device type to use. Possible values are: 'cuda' and 'cpu'.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+        init_scale (float, optional, default=2.**16):  Initial scale factor.
+        growth_factor (float, optional, default=2.0):  Factor by which the scale is multiplied during
+            :meth:`update` if no inf/NaN gradients occur for ``growth_interval`` consecutive iterations.
+        backoff_factor (float, optional, default=0.5):  Factor by which the scale is multiplied during
+            :meth:`update` if inf/NaN gradients occur in an iteration.
+        growth_interval (int, optional, default=2000):  Number of consecutive iterations without inf/NaN gradients
+            that must occur for the scale to be multiplied by ``growth_factor``.
+        enabled (bool, optional):  If ``False``, disables gradient scaling. :meth:`step` simply
+            invokes the underlying ``optimizer.step()``, and other methods become no-ops.
+            Default: ``True``
+    """
+
+    def __init__(
+        self,
+        device: str = "cuda",
+        init_scale: float = 2.0**16,
+        growth_factor: float = 2.0,
+        backoff_factor: float = 0.5,
+        growth_interval: int = 2000,
+        enabled: bool = True,
+    ) -> None:
+        self._device = device
+        self._enabled = enabled
+        if self._device == "cuda":
+            if enabled and torch.cuda.amp.common.amp_definitely_not_available():
+                warnings.warn(
+                    "torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling."
+                )
+                self._enabled = False
+
+        if self._enabled:
+            assert growth_factor > 1.0, "The growth factor must be > 1.0."
+            assert backoff_factor < 1.0, "The backoff factor must be < 1.0."
+
+            self._init_scale = init_scale
+            # self._scale will be lazily initialized during the first call to scale()
+            self._scale: Optional[torch.Tensor] = None
+            self._growth_factor = growth_factor
+            self._backoff_factor = backoff_factor
+            self._growth_interval = growth_interval
+            self._init_growth_tracker = 0
+            # self._growth_tracker will be lazily initialized during the first call to scale()
+            self._growth_tracker: Optional[torch.Tensor] = None
+            self._per_optimizer_states: dict[int, dict[str, Any]] = defaultdict(
+                _refresh_per_optimizer_state
+            )
+
+    def _check_scale_growth_tracker(
+        self, funcname: str
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        fix = "This may indicate your script did not use scaler.scale(loss or outputs) earlier in the iteration."
+        assert self._scale is not None, (
+            f"Attempted {funcname} but _scale is None.  " + fix
+        )
+        assert self._growth_tracker is not None, (
+            f"Attempted {funcname} but _growth_tracker is None.  " + fix
+        )
+        return (self._scale, self._growth_tracker)
+
+    def _lazy_init_scale_growth_tracker(self, dev: torch.device) -> None:
+        assert self._growth_tracker is None, "_growth_tracker initialized before _scale"
+        self._scale = torch.full((), self._init_scale, dtype=torch.float32, device=dev)
+        self._growth_tracker = torch.full(
+            (), self._init_growth_tracker, dtype=torch.int32, device=dev
+        )
+
+    @overload
+    def scale(self, outputs: torch.Tensor) -> torch.Tensor:
+        ...
+
+    @overload
+    def scale(self, outputs: list[torch.Tensor]) -> list[torch.Tensor]:
+        ...
+
+    @overload
+    def scale(self, outputs: tuple[torch.Tensor, ...]) -> tuple[torch.Tensor, ...]:
+        ...
+
+    @overload
+    def scale(self, outputs: Iterable[torch.Tensor]) -> Iterable[torch.Tensor]:
+        ...
+
+    def scale(
+        self,
+        outputs: Union[torch.Tensor, Iterable[torch.Tensor]],
+    ) -> Union[torch.Tensor, Iterable[torch.Tensor]]:
+        """
+        Multiplies ('scales') a tensor or list of tensors by the scale factor.
+
+        Returns scaled outputs.  If this instance of :class:`GradScaler` is not enabled, outputs are returned
+        unmodified.
+
+        Args:
+            outputs (Tensor or iterable of Tensors):  Outputs to scale.
+        """
+        if not self._enabled:
+            return outputs
+
+        # Short-circuit for the common case.
+        if isinstance(outputs, torch.Tensor):
+            if self._scale is None:
+                self._lazy_init_scale_growth_tracker(outputs.device)
+            assert self._scale is not None
+            return outputs * self._scale.to(device=outputs.device, non_blocking=True)
+
+        # Invoke the more complex machinery only if we're treating multiple outputs.
+        stash: list[
+            _MultiDeviceReplicator
+        ] = []  # holds a reference that can be overwritten by apply_scale
+
+        def apply_scale(val: Union[torch.Tensor, Iterable[torch.Tensor]]):
+            if isinstance(val, torch.Tensor):
+                if len(stash) == 0:
+                    if self._scale is None:
+                        self._lazy_init_scale_growth_tracker(val.device)
+                    assert self._scale is not None
+                    stash.append(_MultiDeviceReplicator(self._scale))
+                return val * stash[0].get(val.device)
+            if isinstance(val, abc.Iterable):
+                iterable = map(apply_scale, val)
+                if isinstance(val, (list, tuple)):
+                    return type(val)(iterable)
+                return iterable
+            raise ValueError("outputs must be a Tensor or an iterable of Tensors")
+
+        return apply_scale(outputs)
+
+    def _unscale_grads_(
+        self,
+        optimizer: torch.optim.Optimizer,
+        inv_scale: torch.Tensor,
+        found_inf: torch.Tensor,
+        allow_fp16: bool,
+    ) -> dict[torch.device, torch.Tensor]:
+        per_device_inv_scale = _MultiDeviceReplicator(inv_scale)
+        per_device_found_inf = _MultiDeviceReplicator(found_inf)
+
+        # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype.
+        # There could be hundreds of grads, so we'd like to iterate through them just once.
+        # However, we don't know their devices or dtypes in advance.
+
+        # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict
+        # Google says mypy struggles with defaultdicts type annotations.
+        per_device_and_dtype_grads: dict[
+            torch.device, dict[torch.dtype, list[torch.Tensor]]
+        ] = defaultdict(lambda: defaultdict(list))
+        with torch.no_grad():
+            for group in optimizer.param_groups:
+                for param in group["params"]:
+                    assert isinstance(param, torch.Tensor)
+                    if param.grad is None:
+                        continue
+                    if (not allow_fp16) and param.grad.dtype == torch.float16:
+                        raise ValueError("Attempting to unscale FP16 gradients.")
+                    if param.grad.is_sparse:
+                        # is_coalesced() == False means the sparse grad has values with duplicate indices.
+                        # coalesce() deduplicates indices and adds all values that have the same index.
+                        # For scaled fp16 values, there's a good chance coalescing will cause overflow,
+                        # so we should check the coalesced _values().
+                        if param.grad.dtype is torch.float16:
+                            param.grad = param.grad.coalesce()
+                        to_unscale = param.grad._values()
+                    else:
+                        to_unscale = param.grad
+
+                    # TODO: is there a way to split by device and dtype without appending in the inner loop?
+                    per_device_and_dtype_grads[to_unscale.device][
+                        to_unscale.dtype
+                    ].append(to_unscale)
+
+            for device, per_dtype_grads in per_device_and_dtype_grads.items():
+                for grads in per_dtype_grads.values():
+                    torch._amp_foreach_non_finite_check_and_unscale_(
+                        grads,
+                        per_device_found_inf.get(device),
+                        per_device_inv_scale.get(device),
+                    )
+
+        return per_device_found_inf._per_device_tensors
+
+    def unscale_(self, optimizer: torch.optim.Optimizer) -> None:
+        """
+        Divides ("unscales") the optimizer's gradient tensors by the scale factor.
+
+        :meth:`unscale_` is optional, serving cases where you need to
+        :ref:`modify or inspect gradients`
+        between the backward pass(es) and :meth:`step`.
+        If :meth:`unscale_` is not called explicitly,  gradients will be unscaled  automatically during :meth:`step`.
+
+        Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients::
+
+            ...
+            scaler.scale(loss).backward()
+            scaler.unscale_(optimizer)
+            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
+            scaler.step(optimizer)
+            scaler.update()
+
+        Args:
+            optimizer (torch.optim.Optimizer):  Optimizer that owns the gradients to be unscaled.
+
+        .. note::
+            :meth:`unscale_` does not incur a CPU-GPU sync.
+
+        .. warning::
+            :meth:`unscale_` should only be called once per optimizer per :meth:`step` call,
+            and only after all gradients for that optimizer's assigned parameters have been accumulated.
+            Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError.
+
+        .. warning::
+            :meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute.
+        """
+        if not self._enabled:
+            return
+
+        self._check_scale_growth_tracker("unscale_")
+
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+        if optimizer_state["stage"] is OptState.UNSCALED:
+            raise RuntimeError(
+                "unscale_() has already been called on this optimizer since the last update()."
+            )
+        elif optimizer_state["stage"] is OptState.STEPPED:
+            raise RuntimeError("unscale_() is being called after step().")
+
+        # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
+        assert self._scale is not None
+        inv_scale = self._scale.double().reciprocal().float()
+        found_inf = torch.full((), 0.0, dtype=torch.float32, device=self._scale.device)
+
+        optimizer_state["found_inf_per_device"] = self._unscale_grads_(
+            optimizer, inv_scale, found_inf, False
+        )
+        optimizer_state["stage"] = OptState.UNSCALED
+
+    def _maybe_opt_step(
+        self,
+        optimizer: torch.optim.Optimizer,
+        optimizer_state: dict[str, Any],
+        *args: Any,
+        **kwargs: Any,
+    ) -> Optional[float]:
+        retval: Optional[float] = None
+        if not sum(v.item() for v in optimizer_state["found_inf_per_device"].values()):
+            retval = optimizer.step(*args, **kwargs)
+        return retval
+
+    def step(
+        self, optimizer: torch.optim.Optimizer, *args: Any, **kwargs: Any
+    ) -> Optional[float]:
+        """Invoke ``unscale_(optimizer)`` followed by parameter update, if gradients are not infs/NaN.
+
+        :meth:`step` carries out the following two operations:
+
+        1.  Internally invokes ``unscale_(optimizer)`` (unless :meth:`unscale_` was explicitly called for ``optimizer``
+            earlier in the iteration).  As part of the :meth:`unscale_`, gradients are checked for infs/NaNs.
+        2.  If no inf/NaN gradients are found, invokes ``optimizer.step()`` using the unscaled
+            gradients.  Otherwise, ``optimizer.step()`` is skipped to avoid corrupting the params.
+
+        ``*args`` and ``**kwargs`` are forwarded to ``optimizer.step()``.
+
+        Returns the return value of ``optimizer.step(*args, **kwargs)``.
+
+        Args:
+            optimizer (torch.optim.Optimizer):  Optimizer that applies the gradients.
+            args:  Any arguments.
+            kwargs:  Any keyword arguments.
+
+        .. warning::
+            Closure use is not currently supported.
+        """
+        if not self._enabled:
+            return optimizer.step(*args, **kwargs)
+
+        if "closure" in kwargs:
+            raise RuntimeError(
+                "Closure use is not currently supported if GradScaler is enabled."
+            )
+
+        self._check_scale_growth_tracker("step")
+
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+        if optimizer_state["stage"] is OptState.STEPPED:
+            raise RuntimeError(
+                "step() has already been called since the last update()."
+            )
+
+        retval: Optional[float] = None
+
+        if getattr(optimizer, "_step_supports_amp_scaling", False):
+            # This optimizer has customized scale-handling logic, so we can call optimizer.step() directly.
+            # The contract with custom optimizers is that their step() should accept an additional,
+            # optional grad_scaler kwarg.  We append self to the kwargs so the custom optimizer has full information:
+            # it can query its own state, invoke unscale_ on itself, etc
+            # The contract above is being deprecated to avoid introducing `grad_scaler: GradScaler` argument
+            # to `Optimizer.step`. The new behavior is going to add two Tensor attributes of `grad_scale`
+            # and `found_inf` to the passed optimizer so that the optimizer can utilize those
+            # to skip the parameter updates or unscale gradients before updating parameters in
+            # the fused kernel, e.g. `FusedAdamMathFunctor`.
+            # In this behavior, `GradScaler._check_inf_per_device` is called if `OptState.READY`,
+            # while the method is expected to be called by users side, i.e. their optimizers.
+            kwargs_ = kwargs
+            has_grad_scaler_kwarg = (
+                "grad_scaler" in inspect.signature(optimizer.step).parameters
+            )
+            if has_grad_scaler_kwarg:
+                warnings.warn(
+                    "GradScaler is going to stop passing itself as a keyword argument to the passed "
+                    "optimizer. In the near future GradScaler registers `grad_scale: Tensor` and "
+                    "`found_inf: Tensor` to the passed optimizer and let the optimizer use them directly.",
+                    FutureWarning,
+                )
+                kwargs_.update({"grad_scaler": self})
+            else:
+                if optimizer_state["stage"] is OptState.READY:
+                    self._check_inf_per_device(optimizer)
+                scaler = self._get_scale_async()
+                assert scaler is not None
+                found_inf = cast(
+                    torch.Tensor,
+                    sum(
+                        [  # noqa: C419
+                            t.to(scaler.device, non_blocking=True)
+                            for t in optimizer_state["found_inf_per_device"].values()
+                        ]
+                    ),
+                )
+                # Take the product of the scales, if the user has already set `optimizer.grad_scale`.
+                optimizer.grad_scale = (  # type: ignore[attr-defined]
+                    getattr(optimizer, "grad_scale", None)
+                    if optimizer_state["stage"] == OptState.UNSCALED
+                    else scaler * getattr(optimizer, "grad_scale", 1)
+                )
+                optimizer.found_inf = found_inf  # type: ignore[attr-defined]
+            retval = optimizer.step(*args, **kwargs_)
+            optimizer_state["stage"] = OptState.STEPPED
+            if not has_grad_scaler_kwarg:
+                del optimizer.grad_scale  # type: ignore[attr-defined]
+                del optimizer.found_inf  # type: ignore[attr-defined]
+            return retval
+
+        if optimizer_state["stage"] is OptState.READY:
+            self.unscale_(optimizer)
+
+        assert (
+            len(optimizer_state["found_inf_per_device"]) > 0
+        ), "No inf checks were recorded for this optimizer."
+
+        retval = self._maybe_opt_step(optimizer, optimizer_state, *args, **kwargs)
+
+        optimizer_state["stage"] = OptState.STEPPED
+
+        return retval
+
+    def update(self, new_scale: Optional[Union[float, torch.Tensor]] = None) -> None:
+        """Update the scale factor.
+
+        If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
+        to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
+        the scale is multiplied by ``growth_factor`` to increase it.
+
+        Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
+        used directly, it's used to fill GradScaler's internal scale tensor. So if
+        ``new_scale`` was a tensor, later in-place changes to that tensor will not further
+        affect the scale GradScaler uses internally.)
+
+        Args:
+            new_scale (float or :class:`torch.Tensor`, optional, default=None):  New scale factor.
+
+        .. warning::
+            :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
+            been invoked for all optimizers used this iteration.
+
+        .. warning::
+            For performance reasons, we do not check the scale factor value to avoid synchronizations,
+            so the scale factor is not guaranteed to be above 1. If the scale falls below 1 and/or
+            you are seeing NaNs in your gradients or loss, something is likely wrong. For example,
+            bf16-pretrained models are often incompatible with AMP/fp16 due to differing dynamic ranges.
+        """
+        if not self._enabled:
+            return
+
+        _scale, _growth_tracker = self._check_scale_growth_tracker("update")
+
+        if new_scale is not None:
+            assert self._scale is not None
+            # Accept a new user-defined scale.
+            if isinstance(new_scale, float):
+                self._scale.fill_(new_scale)
+            else:
+                reason = "new_scale should be a float or a 1-element torch.cuda.FloatTensor or \
+                    torch.FloatTensor with requires_grad=False."
+                assert new_scale.device.type == self._device, reason
+                assert new_scale.numel() == 1, reason
+                assert new_scale.requires_grad is False, reason
+                self._scale.copy_(new_scale)
+        else:
+            # Consume shared inf/nan data collected from optimizers to update the scale.
+            # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous.
+            found_infs = [
+                found_inf.to(device=_scale.device, non_blocking=True)
+                for state in self._per_optimizer_states.values()
+                for found_inf in state["found_inf_per_device"].values()
+            ]
+
+            assert len(found_infs) > 0, "No inf checks were recorded prior to update."
+
+            found_inf_combined = found_infs[0]
+            if len(found_infs) > 1:
+                for i in range(1, len(found_infs)):
+                    found_inf_combined += found_infs[i]
+
+            torch._amp_update_scale_(
+                _scale,
+                _growth_tracker,
+                found_inf_combined,
+                self._growth_factor,
+                self._backoff_factor,
+                self._growth_interval,
+            )
+
+        # To prepare for next iteration, clear the data collected from optimizers this iteration.
+        self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
+
+    def _get_scale_async(self) -> Optional[torch.Tensor]:
+        return self._scale
+
+    def get_scale(self) -> float:
+        """Return a Python float containing the current scale, or 1.0 if scaling is disabled.
+
+        .. warning::
+            :meth:`get_scale` incurs a CPU-GPU sync.
+        """
+        if self._enabled:
+            return (
+                self._init_scale
+                if (scale := self._get_scale_async()) is None
+                else cast(float, scale.item())
+            )
+        return 1.0
+
+    def get_growth_factor(self) -> float:
+        r"""Return a Python float containing the scale growth factor."""
+        return self._growth_factor
+
+    def set_growth_factor(self, new_factor: float) -> None:
+        r"""Set a new scale growth factor.
+
+        Args:
+            new_scale (float):  Value to use as the new scale growth factor.
+        """
+        self._growth_factor = new_factor
+
+    def get_backoff_factor(self) -> float:
+        r"""Return a Python float containing the scale backoff factor."""
+        return self._backoff_factor
+
+    def set_backoff_factor(self, new_factor: float) -> None:
+        r"""Set a new scale backoff factor.
+
+        Args:
+            new_scale (float):  Value to use as the new scale backoff factor.
+        """
+        self._backoff_factor = new_factor
+
+    def get_growth_interval(self) -> int:
+        r"""Return a Python int containing the growth interval."""
+        return self._growth_interval
+
+    def set_growth_interval(self, new_interval: int) -> None:
+        r"""Set a new growth interval.
+
+        Args:
+            new_interval (int):  Value to use as the new growth interval.
+        """
+        self._growth_interval = new_interval
+
+    def _get_growth_tracker(self) -> int:
+        if self._enabled:
+            return (
+                self._init_growth_tracker
+                if self._growth_tracker is None
+                else cast(int, self._growth_tracker.item())
+            )
+        return 0
+
+    def is_enabled(self) -> bool:
+        r"""Return a bool indicating whether this instance is enabled."""
+        return self._enabled
+
+    def state_dict(self) -> dict[str, Any]:
+        r"""Return the state of the scaler as a :class:`dict`.
+
+        It contains five entries:
+
+        * ``"scale"`` - a Python float containing the current scale
+        * ``"growth_factor"`` - a Python float containing the current growth factor
+        * ``"backoff_factor"`` - a Python float containing the current backoff factor
+        * ``"growth_interval"`` - a Python int containing the current growth interval
+        * ``"_growth_tracker"`` - a Python int containing the number of recent consecutive unskipped steps.
+
+        If this instance is not enabled, returns an empty dict.
+
+        .. note::
+           If you wish to checkpoint the scaler's state after a particular iteration, :meth:`state_dict`
+           should be called after :meth:`update`.
+        """
+        if self._enabled:
+            return {
+                "scale": self.get_scale(),
+                "growth_factor": self._growth_factor,
+                "backoff_factor": self._backoff_factor,
+                "growth_interval": self._growth_interval,
+                "_growth_tracker": self._get_growth_tracker(),
+            }
+        return {}
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        r"""Load the scaler state.
+
+        If this instance is disabled, :meth:`load_state_dict` is a no-op.
+
+        Args:
+           state_dict(dict): scaler state.  Should be an object returned from a call to :meth:`state_dict`.
+        """
+        if not self._enabled:
+            return
+
+        if len(state_dict) == 0:
+            raise RuntimeError(
+                "The source state dict is empty, possibly because it was saved "
+                "from a disabled instance of GradScaler."
+            )
+
+        self._init_scale = cast(float, state_dict["scale"])
+        if self._scale is not None:
+            self._scale.fill_(state_dict["scale"])
+        self._growth_factor = cast(float, state_dict["growth_factor"])
+        self._backoff_factor = cast(float, state_dict["backoff_factor"])
+        self._growth_interval = cast(int, state_dict["growth_interval"])
+        self._init_growth_tracker = cast(int, state_dict["_growth_tracker"])
+        if self._growth_tracker is not None:
+            self._growth_tracker.fill_(state_dict["_growth_tracker"])
+
+    def __getstate__(self) -> dict[str, Any]:
+        state = self.__dict__.copy()
+        if self._enabled:
+            assert len(self._per_optimizer_states) == 0, (
+                "A GradScaler instance may only be pickled at the beginning "
+                "of an iteration, or at the end after scaler.update()."
+            )
+            # Pickling _scale and _growth_tracker Tensors directly triggers
+            # "warnings.warn("pickle support for Storage will be removed in 1.5..."
+            # so instead, we set the unpickled instance up to reinitialize them lazily.
+            state["_init_scale"] = self.get_scale()
+            state["_init_growth_tracker"] = self._get_growth_tracker()
+            state["_scale"] = None
+            state["_growth_tracker"] = None
+        return state
+
+    def __setstate__(self, state: dict[str, Any]) -> None:
+        self.__dict__.update(state)
+
+    def _check_inf_per_device(self, optimizer: torch.optim.Optimizer) -> dict[str, Any]:
+        _scale, _ = self._check_scale_growth_tracker("_check_inf_per_device")
+
+        dummy_inv_scale = torch.full((), 1.0, dtype=torch.float32, device=_scale.device)
+        found_inf = torch.full((), 0.0, dtype=torch.float32, device=_scale.device)
+
+        self._per_optimizer_states[id(optimizer)][
+            "found_inf_per_device"
+        ] = self._unscale_grads_(optimizer, dummy_inv_scale, found_inf, True)
+
+        return self._per_optimizer_states[id(optimizer)]["found_inf_per_device"]
+
+    def _found_inf_per_device(self, optimizer: torch.optim.Optimizer) -> dict[str, Any]:
+        return self._per_optimizer_states[id(optimizer)]["found_inf_per_device"]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c370a0368d7101307b45293423dc8d9a84ef1c66
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/__init__.py
@@ -0,0 +1,605 @@
+# mypy: allow-untyped-defs
+"""
+``torch.autograd`` provides classes and functions implementing automatic differentiation of arbitrary scalar valued functions.
+
+It requires minimal changes to the existing code - you only need to declare :class:`Tensor` s
+for which gradients should be computed with the ``requires_grad=True`` keyword.
+As of now, we only support autograd for floating point :class:`Tensor` types (
+half, float, double and bfloat16) and complex :class:`Tensor` types (cfloat, cdouble).
+"""
+
+import warnings
+from collections.abc import Sequence
+from typing import cast, Optional, Union
+
+import torch
+from torch import _vmap_internals
+from torch.overrides import handle_torch_function, has_torch_function, is_tensor_like
+from torch.types import _size, _TensorOrTensors, _TensorOrTensorsOrGradEdge
+
+from . import forward_ad, functional, graph
+from .anomaly_mode import detect_anomaly, set_detect_anomaly
+from .function import Function, NestedIOFunction
+from .grad_mode import (
+    _force_original_view_tracking,
+    _unsafe_preserve_version_counter,
+    enable_grad,
+    inference_mode,
+    no_grad,
+    set_grad_enabled,
+    set_multithreading_enabled,
+)
+from .gradcheck import gradcheck, gradgradcheck
+from .graph import _engine_run_backward
+from .variable import Variable
+
+
+__all__ = [
+    "Variable",
+    "Function",
+    "backward",
+    "grad_mode",
+    "NestedIOFunction",
+    "detect_anomaly",
+    "enable_grad",
+    "grad",
+    "gradcheck",
+    "gradgradcheck",
+    "inference_mode",
+    "no_grad",
+    "set_detect_anomaly",
+    "set_grad_enabled",
+    "set_multithreading_enabled",
+    "variable",
+]
+
+_OptionalTensor = Optional[torch.Tensor]
+_ShapeorNestedShape = Union[_size, Sequence[_size], torch.Tensor]
+
+
+def _calculate_shape(
+    output: Union[torch.Tensor, graph.GradientEdge],
+    grad: torch.Tensor,
+    is_grads_batched: bool,
+) -> tuple[_ShapeorNestedShape, _ShapeorNestedShape]:
+    # is_same_size ensures that both tensors are either nested or non nested
+    # circular import
+    from torch.nested._internal.nested_tensor import NestedTensor
+
+    if isinstance(output, graph.GradientEdge):
+        # We have already checked that we are not a C++ NestedTensor
+        if is_grads_batched:
+            raise RuntimeError("Batched grads are not supported with GradientEdge")
+        out_metadata = output.node._input_metadata[output.output_nr]
+        return torch.Size(out_metadata.shape), grad.shape
+
+    if output.is_nested and not isinstance(output, NestedTensor):
+        if is_grads_batched:
+            raise RuntimeError("Batched grads are not supported with Nested Tensor.")
+        out_shape = output._nested_tensor_size()
+        grad_shape = grad._nested_tensor_size()
+
+        return out_shape, grad_shape
+
+    reg_out_shape = output.shape
+    reg_grad_shape = grad.shape if not is_grads_batched else grad.shape[1:]
+    return reg_out_shape, reg_grad_shape
+
+
+def _make_grads(
+    outputs: Union[Sequence[torch.Tensor], Sequence[graph.GradientEdge]],
+    grads: Sequence[_OptionalTensor],
+    is_grads_batched: bool,
+) -> tuple[_OptionalTensor, ...]:
+    new_grads: list[_OptionalTensor] = []
+    for out, grad in zip(outputs, grads):
+        out = cast(Union[torch.Tensor, graph.GradientEdge], out)
+        out_size = None
+        out_device = None
+
+        if isinstance(out, graph.GradientEdge):
+            out_metadata = out.node._input_metadata[out.output_nr]
+            out_size = torch.Size(out_metadata.shape)
+            out_dtype = out_metadata.dtype
+            out_device = out_metadata.device
+            out_is_nested = out_metadata.is_nested_tensor
+            if out_metadata.is_cpp_nested_tensor:
+                raise RuntimeError(
+                    "C++ NestedTensor are not supported with GradientEdge"
+                )
+            out_is_cpp_nested = False
+        else:
+            # circular import
+            from torch.nested._internal.nested_tensor import NestedTensor
+
+            assert isinstance(out, torch.Tensor)
+            out_dtype = out.dtype
+            out_is_nested = out.is_nested
+            out_is_cpp_nested = out_is_nested and not isinstance(out, NestedTensor)
+            if not out_is_cpp_nested:
+                out_size = out.shape
+
+        if isinstance(grad, torch.Tensor):
+            from torch.fx.experimental.symbolic_shapes import expect_true, sym_eq
+
+            first_grad = grad if not is_grads_batched else grad[0]
+
+            # TODO: We can remove this conditional once we uniformly use
+            # singleton int to represent jagged dimension, so that size() call
+            # on nested tensor works.
+            if out_is_cpp_nested:
+                assert isinstance(out, torch.Tensor)
+                shape_matches = torch.is_same_size(out, first_grad)
+            else:
+                # We need to do a regular size check, without going through
+                # the operator, to be able to handle unbacked symints
+                # (expect_true ensures we can deal with unbacked)
+                assert out_size is not None
+                shape_matches = expect_true(sym_eq(out_size, first_grad.size()))
+
+            if not shape_matches:
+                out = cast(Union[torch.Tensor, graph.GradientEdge], out)
+                out_shape, grad_shape = _calculate_shape(
+                    out, first_grad, is_grads_batched
+                )
+                if is_grads_batched:
+                    raise RuntimeError(
+                        "If `is_grads_batched=True`, we interpret the first "
+                        "dimension of each grad_output as the batch dimension. "
+                        "The sizes of the remaining dimensions are expected to match "
+                        "the shape of corresponding output, but a mismatch "
+                        "was detected: grad_output["
+                        + str(grads.index(grad))
+                        + "] has a shape of "
+                        + str(grad_shape)
+                        + " and output["
+                        + str(outputs.index(out))
+                        + "] has a shape of "
+                        + str(out_shape)
+                        + ". "
+                        "If you only want some tensors in `grad_output` to be considered "
+                        "batched, consider using vmap."
+                    )
+                else:
+                    raise RuntimeError(
+                        "Mismatch in shape: grad_output["
+                        + str(grads.index(grad))
+                        + "] has a shape of "
+                        + str(grad_shape)
+                        + " and output["
+                        + str(outputs.index(out))
+                        + "] has a shape of "
+                        + str(out_shape)
+                        + "."
+                    )
+            if out_dtype.is_complex != grad.dtype.is_complex:
+                raise RuntimeError(
+                    "For complex Tensors, both grad_output and output"
+                    " are required to have the same dtype."
+                    " Mismatch in dtype: grad_output["
+                    + str(grads.index(grad))
+                    + "] has a dtype of "
+                    + str(grad.dtype)
+                    + " and output["
+                    + str(outputs.index(out))
+                    + "] has a dtype of "
+                    + str(out_dtype)
+                    + "."
+                )
+            new_grads.append(grad)
+        elif grad is None:
+            if isinstance(out, graph.GradientEdge) or out.requires_grad:  # type: ignore[attr-defined]
+                if isinstance(out, graph.GradientEdge):
+                    assert out_size is not None
+                    out_numel_is_1 = all(o == 1 for o in out_size)
+                else:
+                    assert isinstance(out, torch.Tensor)
+                    out_numel_is_1 = out.numel() == 1
+                if not out_numel_is_1:
+                    raise RuntimeError(
+                        "grad can be implicitly created only for scalar outputs"
+                    )
+                if not out_dtype.is_floating_point:
+                    msg = (
+                        "grad can be implicitly created only for real scalar outputs"
+                        f" but got {out_dtype}"
+                    )
+                    raise RuntimeError(msg)
+                if isinstance(out, graph.GradientEdge):
+                    assert out_size is not None
+                    assert out_device is not None
+                    new_grads.append(
+                        torch.ones(
+                            out_size,
+                            dtype=out_dtype,
+                            device=out_device,
+                        )
+                    )
+                else:
+                    assert isinstance(out, torch.Tensor)
+                    new_grads.append(
+                        torch.ones_like(out, memory_format=torch.preserve_format)
+                    )
+            else:
+                new_grads.append(None)
+        else:
+            raise TypeError(
+                "gradients can be either Tensors or None, but got "
+                + type(grad).__name__
+            )
+    return tuple(new_grads)
+
+
+def _tensor_or_tensors_to_tuple(
+    tensors: Optional[_TensorOrTensors], length: int
+) -> tuple[_OptionalTensor, ...]:
+    if tensors is None:
+        return (None,) * length
+    if isinstance(tensors, torch.Tensor):
+        return (tensors,)
+    return tuple(tensors)
+
+
+def backward(
+    tensors: _TensorOrTensorsOrGradEdge,
+    grad_tensors: Optional[_TensorOrTensors] = None,
+    retain_graph: Optional[bool] = None,
+    create_graph: bool = False,
+    grad_variables: Optional[_TensorOrTensors] = None,
+    inputs: Optional[_TensorOrTensorsOrGradEdge] = None,
+) -> None:
+    r"""Compute the sum of gradients of given tensors with respect to graph leaves.
+
+    The graph is differentiated using the chain rule. If any of ``tensors``
+    are non-scalar (i.e. their data has more than one element) and require
+    gradient, then the Jacobian-vector product would be computed, in this
+    case the function additionally requires specifying ``grad_tensors``.
+    It should be a sequence of matching length, that contains the "vector"
+    in the Jacobian-vector product, usually the gradient of the differentiated
+    function w.r.t. corresponding tensors (``None`` is an acceptable value for
+    all tensors that don't need gradient tensors).
+
+    This function accumulates gradients in the leaves - you might need to zero
+    ``.grad`` attributes or set them to ``None`` before calling it.
+    See :ref:`Default gradient layouts`
+    for details on the memory layout of accumulated gradients.
+
+    .. note::
+        Using this method with ``create_graph=True`` will create a reference cycle
+        between the parameter and its gradient which can cause a memory leak.
+        We recommend using ``autograd.grad`` when creating the graph to avoid this.
+        If you have to use this function, make sure to reset the ``.grad`` fields of your
+        parameters to ``None`` after use to break the cycle and avoid the leak.
+
+    .. note::
+
+        If you run any forward ops, create ``grad_tensors``, and/or call ``backward``
+        in a user-specified CUDA stream context, see
+        :ref:`Stream semantics of backward passes`.
+
+    .. note::
+
+        When ``inputs`` are provided and a given input is not a leaf,
+        the current implementation will call its grad_fn (even though it is not strictly needed to get this gradients).
+        It is an implementation detail on which the user should not rely.
+        See https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780 for more details.
+
+    Args:
+        tensors (Sequence[Tensor] or Tensor or Sequence[GradientEdge] or GradientEdge): Tensors of which
+            the derivative will be computed.
+        grad_tensors (Sequence[Tensor or None] or Tensor, optional): The "vector" in
+            the Jacobian-vector product, usually gradients w.r.t. each element of
+            corresponding tensors. None values can be specified for scalar Tensors or
+            ones that don't require grad. If a None value would be acceptable for all
+            grad_tensors, then this argument is optional.
+        retain_graph (bool, optional): If ``False``, the graph used to compute the grad
+            will be freed. Note that in nearly all cases setting this option to ``True``
+            is not needed and often can be worked around in a much more efficient
+            way. Defaults to the value of ``create_graph``.
+        create_graph (bool, optional): If ``True``, graph of the derivative will
+            be constructed, allowing to compute higher order derivative products.
+            Defaults to ``False``.
+        inputs (Sequence[Tensor] or Tensor or Sequence[GradientEdge], optional): Inputs w.r.t. which the gradient
+            be will accumulated into ``.grad``. All other Tensors will be ignored. If
+            not provided, the gradient is accumulated into all the leaf Tensors that
+            were used to compute the :attr:`tensors`.
+    """
+    if torch._C._are_functorch_transforms_active():
+        raise RuntimeError(
+            "backward() called inside a functorch transform. This is not "
+            "supported, please use functorch.grad or functorch.vjp instead "
+            "or call backward() outside of functorch transforms."
+        )
+
+    if grad_variables is not None:
+        warnings.warn(
+            "`grad_variables` is deprecated. Use `grad_tensors` instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        if grad_tensors is None:
+            grad_tensors = grad_variables
+        else:
+            raise RuntimeError(
+                "`grad_tensors` and `grad_variables` (deprecated) "
+                "arguments both passed to `backward()`. Please only "
+                "use `grad_tensors`."
+            )
+    if inputs is not None and len(inputs) == 0:
+        raise RuntimeError("`inputs` argument to `backward()` cannot be empty.")
+
+    if is_tensor_like(tensors) or isinstance(tensors, graph.GradientEdge):
+        tensors = cast(
+            Union[tuple[torch.Tensor], tuple[graph.GradientEdge]], (tensors,)
+        )
+    else:
+        tensors = tuple(tensors)
+    inputs = (
+        (inputs,)
+        if isinstance(inputs, (torch.Tensor, graph.GradientEdge))
+        else tuple(inputs)
+        if inputs is not None
+        else ()
+    )
+
+    grad_tensors_ = _tensor_or_tensors_to_tuple(grad_tensors, len(tensors))
+    grad_tensors_ = _make_grads(tensors, grad_tensors_, is_grads_batched=False)
+    if retain_graph is None:
+        retain_graph = create_graph
+
+    # The reason we repeat the same comment below is that
+    # some Python versions print out the first line of a multi-line function
+    # calls in the traceback and some print out the last line
+    _engine_run_backward(
+        tensors,
+        grad_tensors_,
+        retain_graph,
+        create_graph,
+        inputs,
+        allow_unreachable=True,
+        accumulate_grad=True,
+    )
+
+
+def grad(
+    outputs: _TensorOrTensorsOrGradEdge,
+    inputs: _TensorOrTensorsOrGradEdge,
+    grad_outputs: Optional[_TensorOrTensors] = None,
+    retain_graph: Optional[bool] = None,
+    create_graph: bool = False,
+    only_inputs: bool = True,
+    allow_unused: Optional[bool] = None,
+    is_grads_batched: bool = False,
+    materialize_grads: bool = False,
+) -> tuple[torch.Tensor, ...]:
+    r"""Compute and return the sum of gradients of outputs with respect to the inputs.
+
+    ``grad_outputs`` should be a sequence of length matching ``output``
+    containing the "vector" in vector-Jacobian product, usually the pre-computed
+    gradients w.r.t. each of the outputs. If an output doesn't require_grad,
+    then the gradient can be ``None``).
+
+    .. note::
+
+        If you run any forward ops, create ``grad_outputs``, and/or call ``grad``
+        in a user-specified CUDA stream context, see
+        :ref:`Stream semantics of backward passes`.
+
+    .. note::
+
+        ``only_inputs`` argument is deprecated and is ignored now (defaults to ``True``).
+        To accumulate gradient for other parts of the graph, please use
+        ``torch.autograd.backward``.
+
+    Args:
+        outputs (sequence of Tensor or GradientEdge): outputs of the differentiated function.
+        inputs (sequence of Tensor or GradientEdge): Inputs w.r.t. which the gradient will be
+            returned (and not accumulated into ``.grad``).
+        grad_outputs (sequence of Tensor): The "vector" in the vector-Jacobian product.
+            Usually gradients w.r.t. each output. None values can be specified for scalar
+            Tensors or ones that don't require grad. If a None value would be acceptable
+            for all grad_tensors, then this argument is optional. Default: None.
+        retain_graph (bool, optional): If ``False``, the graph used to compute the grad
+            will be freed. Note that in nearly all cases setting this option to ``True``
+            is not needed and often can be worked around in a much more efficient
+            way. Defaults to the value of ``create_graph``.
+        create_graph (bool, optional): If ``True``, graph of the derivative will
+            be constructed, allowing to compute higher order derivative products.
+            Default: ``False``.
+        allow_unused (Optional[bool], optional): If ``False``, specifying inputs
+            that were not used when computing outputs (and therefore their grad is
+            always zero) is an error. Defaults to the value of ``materialize_grads``.
+        is_grads_batched (bool, optional): If ``True``, the first dimension of each
+            tensor in ``grad_outputs`` will be interpreted as the batch dimension.
+            Instead of computing a single vector-Jacobian product, we compute a
+            batch of vector-Jacobian products for each "vector" in the batch.
+            We use the vmap prototype feature as the backend to vectorize calls
+            to the autograd engine so that this computation can be performed in a
+            single call. This should lead to performance improvements when compared
+            to manually looping and performing backward multiple times. Note that
+            due to this feature being experimental, there may be performance
+            cliffs. Please use ``torch._C._debug_only_display_vmap_fallback_warnings(True)``
+            to show any performance warnings and file an issue on github if warnings exist
+            for your use case. Defaults to ``False``.
+        materialize_grads (bool, optional): If ``True``, set the gradient for unused inputs
+            to zero instead of None. This is useful when computing higher-order derivatives.
+            If ``materialize_grads`` is ``True`` and ``allow_unused`` is ``False``, an error
+            will be raised. Defaults to ``False``.
+
+    """
+    if materialize_grads and allow_unused is False:
+        raise ValueError(
+            "Expected allow_unused to be True or not passed when materialize_grads=True, "
+            "but got: allow_unused=False."
+        )
+    if allow_unused is None:
+        allow_unused = materialize_grads
+    if is_tensor_like(outputs) or isinstance(outputs, graph.GradientEdge):
+        outputs = cast(
+            Union[Sequence[torch.Tensor], Sequence[graph.GradientEdge]], (outputs,)
+        )
+    else:
+        outputs = tuple(outputs)
+    if is_tensor_like(inputs) or isinstance(inputs, graph.GradientEdge):
+        inputs = cast(_TensorOrTensorsOrGradEdge, (inputs,))
+    else:
+        inputs = tuple(inputs)
+    t_outputs = tuple(i for i in outputs if is_tensor_like(i))
+    t_inputs = tuple(i for i in inputs if is_tensor_like(i))
+    overridable_args = t_outputs + t_inputs
+    if has_torch_function(overridable_args):
+        return handle_torch_function(
+            grad,
+            overridable_args,
+            outputs,
+            inputs,
+            grad_outputs=grad_outputs,
+            retain_graph=retain_graph,
+            create_graph=create_graph,
+            only_inputs=only_inputs,
+            allow_unused=allow_unused,
+            is_grads_batched=is_grads_batched,
+            materialize_grads=materialize_grads,
+        )
+
+    if not only_inputs:
+        warnings.warn(
+            "only_inputs argument is deprecated and is ignored now "
+            "(defaults to True). To accumulate gradient for other "
+            "parts of the graph, please use torch.autograd.backward.",
+            FutureWarning,
+            stacklevel=2,
+        )
+
+    grad_outputs_ = _tensor_or_tensors_to_tuple(grad_outputs, len(outputs))
+    grad_outputs_ = _make_grads(
+        outputs, grad_outputs_, is_grads_batched=is_grads_batched
+    )
+
+    if retain_graph is None:
+        retain_graph = create_graph
+
+    # The reason we repeat the same comment several times below is because
+    # some Python versions print out the first line of multi-line function
+    # calls in the traceback and some print out the last line
+    if is_grads_batched:
+
+        def vjp(gO):
+            return _engine_run_backward(
+                outputs,
+                gO,
+                retain_graph,
+                create_graph,
+                inputs,
+                allow_unused,
+                accumulate_grad=False,
+            )
+
+        result = _vmap_internals._vmap(vjp, 0, 0, allow_none_pass_through=True)(
+            grad_outputs_
+        )
+    else:
+        result = _engine_run_backward(
+            outputs,
+            grad_outputs_,
+            retain_graph,
+            create_graph,
+            inputs,
+            allow_unused,
+            accumulate_grad=False,
+        )
+    if materialize_grads:
+        if any(
+            result[i] is None and not is_tensor_like(inputs[i])
+            for i in range(len(inputs))
+        ):
+            raise RuntimeError(
+                "materialize_grads cannot be used when the given input is a GradientEdge"
+            )
+        result = tuple(
+            output
+            if output is not None
+            else torch.zeros_like(input, requires_grad=True)
+            for (output, input) in zip(result, inputs)
+        )
+    return result
+
+
+# This function applies in case of gradient checkpointing for memory
+# optimization. Currently, gradient checkpointing is supported only if the
+# execution engine is invoked through torch.autograd.backward() and its
+# inputs argument is not passed. It is not supported for torch.autograd.grad().
+# This is because if inputs are specified, the gradient won't be calculated for
+# anything else e.g. model parameters like weights, bias etc.
+#
+# This function returns whether the checkpointing is valid i.e. torch.autograd.backward
+# or not i.e. torch.autograd.grad. The implementation works by maintaining a thread
+# local variable in torch/csrc/autograd/engine.cpp which looks at the NodeTask
+# in the stack and before a NodeTask is executed in evaluate_function, it
+# checks for whether reentrant backwards is imperative or not.
+# See https://github.com/pytorch/pytorch/pull/4594 for more discussion/context
+def _is_checkpoint_valid():
+    return Variable._execution_engine.is_checkpoint_valid()
+
+
+def variable(*args, **kwargs):  # noqa: D103
+    raise RuntimeError(
+        "torch.autograd.variable(...) is deprecated, use torch.tensor(...) instead"
+    )
+
+
+# Monkey patching variable.Variable to fix FX codegen. FX generates a call by roughly doing
+# f"{fn.__module__}.{fn.__name__}(...). This yields torch.autograd.variable.Variable(...) in the
+# output of an FX graph.  Unfortunately the module name torch.autograd.variable is shadowed by the
+# deprecated function - variable(...).
+variable.Variable = Variable  # type: ignore[attr-defined]
+
+if not torch._C._autograd_init():
+    raise RuntimeError("autograd initialization failed")
+
+# Import all native method/classes
+from torch._C._autograd import (
+    _add_metadata_json,
+    _disable_profiler,
+    _disable_profiler_legacy,
+    _enable_profiler,
+    _enable_profiler_legacy,
+    _enable_record_function,
+    _get_sequence_nr,
+    _kineto_step,
+    _KinetoEvent,
+    _pop_saved_tensors_default_hooks,
+    _prepare_profiler,
+    _profiler_enabled,
+    _ProfilerResult,
+    _push_saved_tensors_default_hooks,
+    _record_function_with_args_enter,
+    _record_function_with_args_exit,
+    _set_empty_test_observer,
+    _supported_activities,
+    _toggle_collection_dynamic,
+    DeviceType,
+    kineto_available,
+    ProfilerEvent,
+    SavedTensor,
+)
+from torch._C._profiler import ProfilerActivity, ProfilerConfig, ProfilerState
+
+from . import profiler
+
+
+def _register_py_tensor_class_for_device(device, cls):
+    if not isinstance(cls, type):
+        raise RuntimeError("cls isn't a typeinfo object")
+    torch._C._register_py_class_for_device(device, cls)
+
+
+is_multithreading_enabled = torch._C._is_multithreading_enabled
+torch._C._add_docstr(
+    is_multithreading_enabled, "Returns True if multithreading is currently enabled."
+)
+
+is_view_replay_enabled = torch._C._is_view_replay_enabled
+torch._C._add_docstr(
+    is_view_replay_enabled, "Returns True if view-replay is currently enabled."
+)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/_functions/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/_functions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4170fad3eeac788dcb36b6ae1ddbee1b44dc25a1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/_functions/__init__.py
@@ -0,0 +1 @@
+from .tensor import *  # noqa: F403
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/_functions/tensor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/_functions/tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..a37c8bf58593bb4f57d307f290070990469a7aba
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/_functions/tensor.py
@@ -0,0 +1,65 @@
+# mypy: allow-untyped-defs
+import operator
+from functools import reduce
+from typing_extensions import deprecated
+
+import torch
+import torch._utils
+from torch.autograd.function import Function
+
+
+class Type(Function):
+    @staticmethod
+    @deprecated(
+        "`torch.autograd._functions.Type` is deprecated as of PyTorch 2.1, "
+        "please use `torch.tensor.to(dtype=dtype)` instead.",
+        category=FutureWarning,
+    )
+    def forward(ctx, i, dest_type):
+        ctx.input_type = type(i)
+        ctx.input_device = -1 if not i.is_cuda else i.get_device()
+        return i.type(dest_type)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.input_device == -1:
+            return grad_output.type(ctx.input_type), None
+        else:
+            with torch.cuda.device(ctx.input_device):
+                return grad_output.type(ctx.input_type), None
+
+
+# TODO: deprecate this
+class Resize(Function):
+    @staticmethod
+    def forward(ctx, tensor, sizes):
+        ctx.sizes = sizes
+        ctx.numel = reduce(operator.mul, sizes, 1)
+        if tensor.numel() != ctx.numel:
+            raise RuntimeError(
+                (
+                    "requested resize to {} ({} elements in total), "
+                    "but the given tensor has a size of {} ({} elements). "
+                    "autograd's resize can only change the shape of a given "
+                    "tensor, while preserving the number of elements. "
+                ).format(
+                    "x".join(map(str, sizes)),
+                    ctx.numel,
+                    "x".join(map(str, tensor.size())),
+                    tensor.numel(),
+                )
+            )
+        ctx.input_sizes = tensor.size()
+        if tensor.is_quantized:
+            tensor.copy_(tensor)
+            return tensor.contiguous().view(*sizes)
+        if tensor.is_contiguous():
+            result = tensor.new(tensor).contiguous().view(*sizes)
+            return result
+        else:
+            return tensor.contiguous().view(*sizes)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        assert grad_output.numel() == ctx.numel
+        return grad_output.contiguous().view(ctx.input_sizes), None
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/_functions/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/_functions/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3f242920c7e163c8b4906e46728c68c29712132
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/_functions/utils.py
@@ -0,0 +1,63 @@
+# mypy: allow-untyped-defs
+import operator
+from functools import reduce
+
+
+def maybe_view(tensor, size, check_same_size=True):
+    if check_same_size and tensor.size() == size:
+        return tensor
+    return tensor.contiguous().view(size)
+
+
+def maybe_unexpand(tensor, old_size, check_same_size=True):
+    if check_same_size and tensor.size() == old_size:
+        return tensor
+    num_unsqueezed = tensor.dim() - len(old_size)
+    expanded_dims = [
+        dim
+        for dim, (expanded, original) in enumerate(
+            zip(tensor.size()[num_unsqueezed:], old_size)
+        )
+        if expanded != original
+    ]
+
+    for _ in range(num_unsqueezed):
+        tensor = tensor.sum(0, keepdim=False)
+    for dim in expanded_dims:
+        tensor = tensor.sum(dim, keepdim=True)
+    return tensor
+
+
+# Check whether the op enable broadcasting, and whether it is supported by ONNX.
+# If dims1 and dims2 are different, then broadcast is True.
+# We always assume the combination of dims1 and dims2 is broadcastable.
+# The following types of broadcasting are supported in ONNX:
+#     1) Only one element in dims2, such as dims2 = [1, 1]
+#     2) dims2 is suffix of dims1, such as dims1 = [2, 3, 4], and dims2 = [3, 4]
+# Details can be found here: https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm
+def check_onnx_broadcast(dims1, dims2):
+    broadcast = False
+    supported = True
+    len1 = len(dims1)
+    len2 = len(dims2)
+
+    numel2 = reduce(operator.mul, dims2)
+    if len1 < len2:
+        broadcast = True
+        if numel2 != 1:
+            supported = False
+    elif len1 > len2:
+        broadcast = True
+        if numel2 != 1 and dims1[len1 - len2 :] != dims2:
+            supported = False
+    else:
+        if dims1 != dims2:
+            broadcast = True
+            if numel2 != 1:
+                supported = False
+
+    if not supported:
+        raise ValueError(
+            f"Numpy style broadcasting is not supported in ONNX. Input dims are: {dims1}, {dims2}"
+        )
+    return broadcast
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/anomaly_mode.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/anomaly_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..511d27df077fab88b68365c1ea9360e0b2755195
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/anomaly_mode.py
@@ -0,0 +1,121 @@
+# mypy: allow-untyped-defs
+r"""Autograd anomaly mode."""
+import warnings
+
+import torch
+
+
+__all__ = ["detect_anomaly", "set_detect_anomaly"]
+
+
+class detect_anomaly:
+    r"""Context-manager that enable anomaly detection for the autograd engine.
+
+    This does two things:
+
+    - Running the forward pass with detection enabled will allow the backward
+      pass to print the traceback of the forward operation that created the failing
+      backward function.
+    - If ``check_nan`` is ``True``, any backward computation that generate "nan"
+      value will raise an error. Default ``True``.
+
+    .. warning::
+        This mode should be enabled only for debugging as the different tests
+        will slow down your program execution.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ANOMALY)
+        >>> import torch
+        >>> from torch import autograd
+        >>> class MyFunc(autograd.Function):
+        ...     @staticmethod
+        ...     def forward(ctx, inp):
+        ...         return inp.clone()
+        ...     @staticmethod
+        ...     def backward(ctx, gO):
+        ...         # Error during the backward pass
+        ...         raise RuntimeError("Some error in backward")
+        ...         return gO.clone()
+        >>> def run_fn(a):
+        ...     out = MyFunc.apply(a)
+        ...     return out.sum()
+        >>> inp = torch.rand(10, 10, requires_grad=True)
+        >>> out = run_fn(inp)
+        >>> out.backward()
+            Traceback (most recent call last):
+              File "", line 1, in 
+              File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
+                torch.autograd.backward(self, gradient, retain_graph, create_graph)
+              File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
+                allow_unreachable=True)  # allow_unreachable flag
+              File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
+                return self._forward_cls.backward(self, *args)
+              File "", line 8, in backward
+            RuntimeError: Some error in backward
+        >>> with autograd.detect_anomaly():
+        ...     inp = torch.rand(10, 10, requires_grad=True)
+        ...     out = run_fn(inp)
+        ...     out.backward()
+            Traceback of forward call that caused the error:
+              File "tmp.py", line 53, in 
+                out = run_fn(inp)
+              File "tmp.py", line 44, in run_fn
+                out = MyFunc.apply(a)
+            Traceback (most recent call last):
+              File "", line 4, in 
+              File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
+                torch.autograd.backward(self, gradient, retain_graph, create_graph)
+              File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
+                allow_unreachable=True)  # allow_unreachable flag
+              File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
+                return self._forward_cls.backward(self, *args)
+              File "", line 8, in backward
+            RuntimeError: Some error in backward
+
+    """
+
+    def __init__(self, check_nan=True) -> None:  # noqa: D107
+        self.prev = torch.is_anomaly_enabled()
+        self.check_nan = check_nan
+        self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
+        warnings.warn(
+            "Anomaly Detection has been enabled. "
+            "This mode will increase the runtime "
+            "and should only be enabled for debugging.",
+            stacklevel=2,
+        )
+
+    def __enter__(self) -> None:  # noqa: D105
+        torch.set_anomaly_enabled(True, self.check_nan)
+
+    def __exit__(self, *args: object) -> None:  # noqa: D105
+        torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
+
+
+class set_detect_anomaly:
+    r"""Context-manager that sets the anomaly detection for the autograd engine on or off.
+
+    ``set_detect_anomaly`` will enable or disable the autograd anomaly detection
+    based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    See ``detect_anomaly`` above for details of the anomaly detection behaviour.
+
+    Args:
+        mode (bool): Flag whether to enable anomaly detection (``True``),
+                     or disable (``False``).
+        check_nan (bool): Flag whether to raise an error when the backward
+                          generate "nan"
+
+    """
+
+    def __init__(self, mode: bool, check_nan: bool = True) -> None:  # noqa: D107
+        self.prev = torch.is_anomaly_enabled()
+        self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
+        torch.set_anomaly_enabled(mode, check_nan)
+
+    def __enter__(self) -> None:  # noqa: D105
+        pass
+
+    def __exit__(self, *args: object) -> None:  # noqa: D105
+        torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/forward_ad.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/forward_ad.py
new file mode 100644
index 0000000000000000000000000000000000000000..42652386529621a0a82b2841b9d3eff25dbbe546
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/forward_ad.py
@@ -0,0 +1,231 @@
+# mypy: allow-untyped-defs
+import os
+from collections import namedtuple
+from typing import Any
+
+import torch
+
+from .grad_mode import _DecoratorContextManager
+
+
+__all__ = [
+    "UnpackedDualTensor",
+    "enter_dual_level",
+    "exit_dual_level",
+    "make_dual",
+    "unpack_dual",
+    "dual_level",
+]
+
+# Global variable used to make the python API simpler to use
+_current_level = -1
+
+
+def enter_dual_level():
+    r"""Enter a new forward grad level.
+
+    This level can be used to make and unpack dual Tensors to compute
+    forward gradients.
+
+    This function also updates the current level that is used by default
+    by the other functions in this API.
+    """
+    global _current_level
+    new_level = torch._C._enter_dual_level()
+    if new_level != _current_level + 1:
+        raise RuntimeError(
+            "Entering a new forward AD level but the current level "
+            "is not valid. Make sure you did not modified it directly."
+        )
+    _current_level = new_level
+    return new_level
+
+
+def exit_dual_level(*, level=None):
+    r"""Exit a forward grad level.
+
+    This function deletes all the gradients associated with this
+    level. Only deleting the latest entered level is allowed.
+
+    This function also updates the current level that is used by default
+    by the other functions in this API.
+    """
+    global _current_level
+    if level is None:
+        level = _current_level
+    if level != _current_level:
+        raise RuntimeError(
+            "Trying to exit a forward AD level that was not the last one "
+            "that was created. This is not supported."
+        )
+    torch._C._exit_dual_level(level=level)
+    _current_level = level - 1
+
+
+def _maybe_load_decompositions():
+    if os.environ.get("PYTORCH_JIT", "1") == "1" and __debug__:
+        from torch._decomp import decompositions_for_jvp  # noqa: F401
+
+
+def make_dual(tensor, tangent, *, level=None):
+    r"""Associate a tensor value with its tangent to create a "dual tensor" for forward AD gradient computation.
+
+    The result is a new tensor aliased to :attr:`tensor` with :attr:`tangent` embedded
+    as an attribute as-is if it has the same storage layout or copied otherwise.
+    The tangent attribute can be recovered with :func:`unpack_dual`.
+
+    This function is backward differentiable.
+
+    Given a function `f` whose jacobian is `J`, it allows one to compute the Jacobian-vector product (`jvp`)
+    between `J` and a given vector `v` as follows.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> with dual_level():
+        ...     inp = make_dual(x, v)
+        ...     out = f(inp)
+        ...     y, jvp = unpack_dual(out)
+
+    Please see the `forward-mode AD tutorial `__
+    for detailed steps on how to use this API.
+
+    """
+    # See NOTE: [forward-mode AD decompositions mechanism]
+    #
+    # Import from torch._decomp import decompositions_for_jvp to register
+    # decompositions for jvp to the jit registry
+    #
+    # FIXME: We specify that __debug__ must be True because
+    # if python is run with -OO or -O flags (i.e., __debug__ is False), we encounter the
+    # following error:
+    #
+    # Return value was annotated as having type Tuple[NoneType, NoneType] but is actually of
+    # type Tuple[Tensor, Tensor]:
+    #   File ".../torch/_decomp/__init__.py", line 1585
+    #     else:
+    #         buffer = z
+    #     return min - torch.log1p(z), buffer
+    #     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
+    _maybe_load_decompositions()
+
+    if level is None:
+        level = _current_level
+
+    if level < 0:
+        raise RuntimeError(
+            "Trying to create a dual Tensor for forward AD but no level "
+            "exists, make sure to enter_dual_level() first."
+        )
+    if not (tensor.is_floating_point() or tensor.is_complex()):
+        raise ValueError(
+            f"Expected primal to be floating point or complex, but got: {tensor.dtype}"
+        )
+    if not (tangent.is_floating_point() or tangent.is_complex()):
+        raise ValueError(
+            f"Expected tangent to be floating point or complex, but got: {tangent.dtype}"
+        )
+
+    return torch._VF._make_dual(tensor, tangent, level=level)
+
+
+_UnpackedDualTensor = namedtuple("_UnpackedDualTensor", ["primal", "tangent"])
+
+
+class UnpackedDualTensor(_UnpackedDualTensor):
+    r"""Namedtuple returned by :func:`unpack_dual` containing the primal and tangent components of the dual tensor.
+
+    See :func:`unpack_dual` for more details.
+
+    """
+
+
+def unpack_dual(tensor, *, level=None):
+    r"""Unpack a "dual tensor" to get both its Tensor value and its forward AD gradient.
+
+    The result is a namedtuple ``(primal, tangent)`` where ``primal`` is a view of
+    :attr:`tensor`'s primal and ``tangent`` is :attr:`tensor`'s tangent as-is.
+    Neither of these tensors can be dual tensor of level :attr:`level`.
+
+    This function is backward differentiable.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> with dual_level():
+        ...     inp = make_dual(x, x_t)
+        ...     out = f(inp)
+        ...     y, jvp = unpack_dual(out)
+        ...     jvp = unpack_dual(out).tangent
+
+    Please see the `forward-mode AD tutorial `__
+    for detailed steps on how to use this API.
+    """
+    if level is None:
+        level = _current_level
+
+    if level < 0:
+        return UnpackedDualTensor(tensor, None)
+
+    primal, dual = torch._VF._unpack_dual(tensor, level=level)
+
+    return UnpackedDualTensor(primal, dual)
+
+
+class dual_level(_DecoratorContextManager):
+    r"""Context-manager for forward AD, where all forward AD computation must occur within the ``dual_level`` context.
+
+    .. Note::
+
+        The ``dual_level`` context appropriately enters and exit the dual level to
+        controls the current forward AD level, which is used by default by the other
+        functions in this API.
+
+        We currently don't plan to support nested ``dual_level`` contexts, however, so
+        only a single forward AD level is supported. To compute higher-order
+        forward grads, one can use :func:`torch.func.jvp`.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> x = torch.tensor([1])
+        >>> x_t = torch.tensor([1])
+        >>> with dual_level():
+        ...     inp = make_dual(x, x_t)
+        ...     # Do computations with inp
+        ...     out = your_fn(inp)
+        ...     _, grad = unpack_dual(out)
+        >>> grad is None
+        False
+        >>> # After exiting the level, the grad is deleted
+        >>> _, grad_after = unpack_dual(out)
+        >>> grad is None
+        True
+
+    Please see the `forward-mode AD tutorial `__
+    for detailed steps on how to use this API.
+    """
+
+    def __enter__(self):
+        return enter_dual_level()
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        exit_dual_level()
+
+
+# Private helper functions
+_is_fwd_grad_enabled = torch._C._is_fwd_grad_enabled
+
+
+# Private helper function to enable or disable fwd grad.
+# If you're a user and want to use this, please file an issue to discuss the use case.
+class _set_fwd_grad_enabled(_DecoratorContextManager):
+    def __init__(self, mode: bool) -> None:
+        self.prev = _is_fwd_grad_enabled()
+        torch._C._set_fwd_grad_enabled(mode)
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_fwd_grad_enabled(self.prev)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/function.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/function.py
new file mode 100644
index 0000000000000000000000000000000000000000..219759ea37b33f808603c4bc5aac2be51b5a7f46
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/function.py
@@ -0,0 +1,843 @@
+# mypy: allow-untyped-defs
+import functools
+import inspect
+import itertools
+import warnings
+from collections import OrderedDict
+from typing import Any, Optional
+from typing_extensions import deprecated
+
+import torch
+import torch._C as _C
+import torch._functorch as _functorch
+import torch.utils.hooks as hooks
+from torch._C import _functions
+from torch._functorch.autograd_function import custom_function_call
+
+
+__all__ = [
+    "FunctionCtx",
+    "BackwardCFunction",
+    "FunctionMeta",
+    "Function",
+    "once_differentiable",
+    "InplaceFunction",
+    "NestedIOFunction",
+]
+
+# Unique id provider for each class inheriting from Function
+# This is incremented in FunctionMeta during class definition
+AUTOGRAD_FUNCTION_COUNTER = itertools.count()
+
+
+# Formerly known as: _ContextMethodMixin
+class FunctionCtx:
+    def save_for_backward(self, *tensors: torch.Tensor):
+        r"""Save given tensors for a future call to :func:`~Function.backward`.
+
+        ``save_for_backward`` should be called at most once, in either the
+        :func:`setup_context` or :func:`forward` methods, and only with tensors.
+
+        All tensors intended to be used in the backward pass should be saved
+        with ``save_for_backward`` (as opposed to directly on ``ctx``) to prevent
+        incorrect gradients and memory leaks, and enable the application of saved
+        tensor hooks. See :class:`torch.autograd.graph.saved_tensors_hooks`.
+
+        Note that if intermediary tensors, tensors that are neither inputs
+        nor outputs of :func:`forward`, are saved for backward, your custom Function
+        may not support double backward.
+        Custom Functions that do not support double backward should decorate their
+        :func:`backward` method with ``@once_differentiable`` so that performing
+        double backward raises an error. If you'd like to support double backward,
+        you can either recompute intermediaries based on the inputs during backward
+        or return the intermediaries as the outputs of the custom Function. See the
+        `double backward tutorial `_
+        for more details.
+
+        In :func:`backward`, saved tensors can be accessed through the :attr:`saved_tensors`
+        attribute. Before returning them to the user, a check is made to ensure
+        they weren't used in any in-place operation that modified their content.
+
+        Arguments can also be ``None``. This is a no-op.
+
+        See :ref:`extending-autograd` for more details on how to use this method.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+            >>> class Func(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int):
+            >>>         w = x * z
+            >>>         out = x * y + y * z + w * y
+            >>>         ctx.save_for_backward(x, y, w, out)
+            >>>         ctx.z = z  # z is not a tensor
+            >>>         return out
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, grad_out):
+            >>>         x, y, w, out = ctx.saved_tensors
+            >>>         z = ctx.z
+            >>>         gx = grad_out * (y + y * z)
+            >>>         gy = grad_out * (x + z + w)
+            >>>         gz = None
+            >>>         return gx, gy, gz
+            >>>
+            >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double)
+            >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double)
+            >>> c = 4
+            >>> d = Func.apply(a, b, c)
+
+        """
+        self.to_save = tensors
+
+    def save_for_forward(self, *tensors: torch.Tensor):
+        r"""Save given tensors for a future call to :func:`~Function.jvp`.
+
+        ``save_for_forward`` should be called at most once, in either the
+        :func:`setup_context` or :func:`forward` methods, and all arguments
+        should be tensors.
+
+        In :func:`jvp`, saved objects can be accessed through the :attr:`saved_tensors`
+        attribute.
+
+        Arguments can also be ``None``. This is a no-op.
+
+        See :ref:`extending-autograd` for more details on how to use this method.
+
+        Example::
+            >>> # xdoctest: +SKIP
+            >>> class Func(torch.autograd.Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int):
+            >>>         ctx.save_for_backward(x, y)
+            >>>         ctx.save_for_forward(x, y)
+            >>>         ctx.z = z
+            >>>         return x * y * z
+            >>>
+            >>>     @staticmethod
+            >>>     def jvp(ctx, x_t, y_t, _):
+            >>>         x, y = ctx.saved_tensors
+            >>>         z = ctx.z
+            >>>         return z * (y * x_t + x * y_t)
+            >>>
+            >>>     @staticmethod
+            >>>     def vjp(ctx, grad_out):
+            >>>         x, y = ctx.saved_tensors
+            >>>         z = ctx.z
+            >>>         return z * grad_out * y, z * grad_out * x, None
+            >>>
+            >>>     a = torch.tensor(1., requires_grad=True, dtype=torch.double)
+            >>>     t = torch.tensor(1., dtype=torch.double)
+            >>>     b = torch.tensor(2., requires_grad=True, dtype=torch.double)
+            >>>     c = 4
+            >>>
+            >>>     with fwAD.dual_level():
+            >>>         a_dual = fwAD.make_dual(a, t)
+            >>>         d = Func.apply(a_dual, b, c)
+
+        """
+        for tensor in tensors:
+            assert isinstance(tensor, torch.Tensor) or tensor is None, (
+                "save_for_forward expects all arguments to be tensors; you should "
+                "save non-tensors as attributes on ctx."
+            )
+
+        self.saved_for_forward = tensors
+
+    def mark_dirty(self, *args: torch.Tensor):
+        r"""Mark given tensors as modified in an in-place operation.
+
+        This should be called at most once, in either the :func:`setup_context`
+        or :func:`forward` methods, and all arguments should be inputs.
+
+        Every tensor that's been modified in-place in a call to :func:`forward`
+        should be given to this function, to ensure correctness of our checks.
+        It doesn't matter whether the function is called before or after
+        modification.
+
+        Examples::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+            >>> class Inplace(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         x_npy = x.numpy() # x_npy shares storage with x
+            >>>         x_npy += 1
+            >>>         ctx.mark_dirty(x)
+            >>>         return x
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, grad_output):
+            >>>         return grad_output
+            >>>
+            >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone()
+            >>> b = a * a
+            >>> Inplace.apply(a)  # This would lead to wrong gradients!
+            >>>                   # but the engine would not know unless we mark_dirty
+            >>> # xdoctest: +SKIP
+            >>> b.backward() # RuntimeError: one of the variables needed for gradient
+            >>>              # computation has been modified by an inplace operation
+
+        """
+        self.dirty_tensors = args
+
+    @deprecated(
+        "`mark_shared_storage` is deprecated. "
+        "Tensors with shared storages are automatically tracked. "
+        "Note that calls to `set_()` are not tracked",
+        category=FutureWarning,
+    )
+    def mark_shared_storage(self, *pairs):
+        pass
+
+    def mark_non_differentiable(self, *args: torch.Tensor):
+        r"""Mark outputs as non-differentiable.
+
+        This should be called at most once, in either the :func:`setup_context`
+        or :func:`forward` methods, and all arguments should be tensor outputs.
+
+        This will mark outputs as not requiring gradients, increasing the
+        efficiency of backward computation. You still need to accept a gradient
+        for each output in :meth:`~Function.backward`, but it's always going to
+        be a zero tensor with the same shape as the shape of a corresponding
+        output.
+
+        This is used e.g. for indices returned from a sort. See example::
+            >>> class Func(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         sorted, idx = x.sort()
+            >>>         ctx.mark_non_differentiable(idx)
+            >>>         ctx.save_for_backward(x, idx)
+            >>>         return sorted, idx
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, g1, g2):  # still need to accept g2
+            >>>         x, idx = ctx.saved_tensors
+            >>>         grad_input = torch.zeros_like(x)
+            >>>         grad_input.index_add_(0, idx, g1)
+            >>>         return grad_input
+
+        """
+        self.non_differentiable = args
+
+    def set_materialize_grads(self, value: bool):
+        r"""Set whether to materialize grad tensors. Default is ``True``.
+
+        This should be called only from either the :func:`setup_context` or
+        :func:`forward` methods.
+
+        If ``True``, undefined grad tensors will be expanded to tensors full of zeros
+        prior to calling the :func:`backward` and :func:`jvp` methods.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+            >>> class SimpleFunc(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         return x.clone(), x.clone()
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, g1, g2):
+            >>>         return g1 + g2  # No check for None necessary
+            >>>
+            >>> # We modify SimpleFunc to handle non-materialized grad outputs
+            >>> class Func(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         ctx.set_materialize_grads(False)
+            >>>         ctx.save_for_backward(x)
+            >>>         return x.clone(), x.clone()
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, g1, g2):
+            >>>         x, = ctx.saved_tensors
+            >>>         grad_input = torch.zeros_like(x)
+            >>>         if g1 is not None:  # We must check for None now
+            >>>             grad_input += g1
+            >>>         if g2 is not None:
+            >>>             grad_input += g2
+            >>>         return grad_input
+            >>>
+            >>> a = torch.tensor(1., requires_grad=True)
+            >>> b, _ = Func.apply(a)  # induces g2 to be undefined
+
+        """
+        self.materialize_grads = value
+
+
+# DO NOT USE: This is only defined to be able to load old serialized models
+_ContextMethodMixin = FunctionCtx
+
+
+class _HookMixin:
+    @staticmethod
+    def _register_hook(backward_hooks, hook):
+        if backward_hooks is None:
+            backward_hooks = OrderedDict()
+        handle = hooks.RemovableHandle(backward_hooks)
+        backward_hooks[handle.id] = hook
+        return backward_hooks, handle
+
+
+class BackwardCFunction(_C._FunctionBase, FunctionCtx, _HookMixin):
+    r"""
+    This class is used for internal autograd work. Do not use.
+    """
+
+    def apply(self, *args):
+        r"""
+        Apply method used when executing this Node during the backward
+        """
+        # _forward_cls is defined by derived class
+        # The user should define either backward or vjp but never both.
+        backward_fn = self._forward_cls.backward  # type: ignore[attr-defined]
+        vjp_fn = self._forward_cls.vjp  # type: ignore[attr-defined]
+        if backward_fn is not Function.backward and vjp_fn is not Function.vjp:
+            raise RuntimeError(
+                "Implementing both 'backward' and 'vjp' for a custom "
+                "Function is not allowed. You should only implement one "
+                "of them."
+            )
+        user_fn = vjp_fn if vjp_fn is not Function.vjp else backward_fn
+        return user_fn(self, *args)
+
+    def apply_jvp(self, *args):
+        r"""
+        Apply method used when executing forward mode AD during the forward
+        """
+        # _forward_cls is defined by derived class
+        return self._forward_cls.jvp(self, *args)  # type: ignore[attr-defined]
+
+    def _compiled_autograd_key(self):
+        return self._forward_cls._compiled_autograd_key(self)  # type: ignore[attr-defined]
+
+
+class FunctionMeta(type):
+    """Function metaclass.
+
+    This metaclass sets up the following properties:
+        _backward_cls: The Function class corresponding to the differentiated
+            version of this function (which is generated on the fly by this
+            metaclass).
+    """
+
+    def __init__(cls, name, bases, attrs):
+        backward_fn = type(
+            name + "Backward", (BackwardCFunction,), {"_forward_cls": cls}
+        )
+        backward_fn._autograd_function_id = next(AUTOGRAD_FUNCTION_COUNTER)  # type: ignore[attr-defined]
+        backward_fn._bw_module = None  # type: ignore[attr-defined]
+        if getattr(cls, "_lazy_backward_info", None):
+            backward_fn._bw_module = cls._lazy_backward_info.bw_module  # type: ignore[attr-defined]
+        cls._backward_cls = backward_fn
+
+        super().__init__(name, bases, attrs)
+
+
+class _SingleLevelFunction(
+    _C._FunctionBase, FunctionCtx, _HookMixin, metaclass=FunctionMeta
+):
+    @staticmethod
+    def forward(*args: Any, **kwargs: Any) -> Any:
+        r"""Define the forward of the custom autograd Function.
+
+        This function is to be overridden by all subclasses.
+        There are two ways to define forward:
+
+        Usage 1 (Combined forward and ctx)::
+
+            @staticmethod
+            def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
+                pass
+
+        - It must accept a context ctx as the first argument, followed by any
+          number of arguments (tensors or other types).
+        - See :ref:`combining-forward-context` for more details
+
+        Usage 2 (Separate forward and ctx)::
+
+            @staticmethod
+            def forward(*args: Any, **kwargs: Any) -> Any:
+                pass
+
+            @staticmethod
+            def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None:
+                pass
+
+        - The forward no longer accepts a ctx argument.
+        - Instead, you must also override the :meth:`torch.autograd.Function.setup_context`
+          staticmethod to handle setting up the ``ctx`` object.
+          ``output`` is the output of the forward, ``inputs`` are a Tuple of inputs
+          to the forward.
+        - See :ref:`extending-autograd` for more details
+
+        The context can be used to store arbitrary data that can be then
+        retrieved during the backward pass. Tensors should not be stored
+        directly on `ctx` (though this is not currently enforced for
+        backward compatibility). Instead, tensors should be saved either with
+        :func:`ctx.save_for_backward` if they are intended to be used in
+        ``backward`` (equivalently, ``vjp``) or :func:`ctx.save_for_forward`
+        if they are intended to be used for in ``jvp``.
+        """
+        raise NotImplementedError(
+            "You must implement the forward function for custom autograd.Function."
+        )
+
+    @staticmethod
+    def setup_context(ctx: Any, inputs: tuple[Any, ...], output: Any) -> Any:
+        r"""There are two ways to define the forward pass of an autograd.Function.
+
+        Either:
+
+        1. Override forward with the signature ``forward(ctx, *args, **kwargs)``.
+           ``setup_context`` is not overridden. Setting up the ctx for backward
+           happens inside the ``forward``.
+        2. Override forward with the signature ``forward(*args, **kwargs)`` and
+           override ``setup_context``. Setting up the ctx for backward happens
+           inside ``setup_context`` (as opposed to inside the ``forward``)
+
+        See :meth:`torch.autograd.Function.forward` and :ref:`extending-autograd` for more details.
+        """
+        raise NotImplementedError("setup_context is not implemented.")
+
+    @staticmethod
+    def backward(ctx: Any, *grad_outputs: Any) -> Any:
+        r"""Define a formula for differentiating the operation with backward mode automatic differentiation.
+
+        This function is to be overridden by all subclasses.
+        (Defining this function is equivalent to defining the ``vjp`` function.)
+
+        It must accept a context :attr:`ctx` as the first argument, followed by
+        as many outputs as the :func:`forward` returned (None will be passed in
+        for non tensor outputs of the forward function),
+        and it should return as many tensors, as there were inputs to
+        :func:`forward`. Each argument is the gradient w.r.t the given output,
+        and each returned value should be the gradient w.r.t. the
+        corresponding input. If an input is not a Tensor or is a Tensor not
+        requiring grads, you can just pass None as a gradient for that input.
+
+        The context can be used to retrieve tensors saved during the forward
+        pass. It also has an attribute :attr:`ctx.needs_input_grad` as a tuple
+        of booleans representing whether each input needs gradient. E.g.,
+        :func:`backward` will have ``ctx.needs_input_grad[0] = True`` if the
+        first input to :func:`forward` needs gradient computed w.r.t. the
+        output.
+        """
+        raise NotImplementedError(
+            "You must implement either the backward or vjp method for "
+            "your custom autograd.Function to use it with backward "
+            "mode AD."
+        )
+
+    # vjp and backward are alias of each other
+    vjp = backward
+
+    @staticmethod
+    def jvp(ctx: Any, *grad_inputs: Any) -> Any:
+        r"""Define a formula for differentiating the operation with forward mode automatic differentiation.
+
+        This function is to be overridden by all subclasses.
+        It must accept a context :attr:`ctx` as the first argument, followed by
+        as many inputs as the :func:`forward` got (None will be passed in
+        for non tensor inputs of the forward function),
+        and it should return as many tensors as there were outputs to
+        :func:`forward`. Each argument is the gradient w.r.t the given input,
+        and each returned value should be the gradient w.r.t. the
+        corresponding output. If an output is not a Tensor or the function is not
+        differentiable with respect to that output, you can just pass None as a
+        gradient for that input.
+
+        You can use the :attr:`ctx` object to pass any value from the forward to this
+        functions.
+        """
+        raise NotImplementedError(
+            "You must implement the jvp function for custom "
+            "autograd.Function to use it with forward mode AD."
+        )
+
+
+class Function(_SingleLevelFunction):
+    r"""Base class to create custom `autograd.Function`.
+
+    To create a custom `autograd.Function`, subclass this class and implement
+    the :meth:`forward` and :meth:`backward` static methods. Then, to use your custom
+    op in the forward pass, call the class method ``apply``. Do not call
+    :meth:`forward` directly.
+
+    To ensure correctness and best performance, make sure you are calling the
+    correct methods on ``ctx`` and validating your backward function using
+    :func:`torch.autograd.gradcheck`.
+
+    See :ref:`extending-autograd` for more details on how to use this class.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> class Exp(Function):
+        >>>     @staticmethod
+        >>>     def forward(ctx, i):
+        >>>         result = i.exp()
+        >>>         ctx.save_for_backward(result)
+        >>>         return result
+        >>>
+        >>>     @staticmethod
+        >>>     def backward(ctx, grad_output):
+        >>>         result, = ctx.saved_tensors
+        >>>         return grad_output * result
+        >>>
+        >>> # Use it by calling the apply method:
+        >>> # xdoctest: +SKIP
+        >>> output = Exp.apply(input)
+    """
+
+    def __init__(self, *args, **kwargs):
+        warnings.warn(
+            f"{self.__class__} should not be instantiated. Methods on autograd functions "
+            "are all static, so you should invoke them on the class itself. "
+            "Instantiating an autograd function will raise an "
+            "error in a future version of PyTorch.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
+    def __call__(self, *args, **kwargs):
+        raise RuntimeError(
+            "Legacy autograd function with non-static forward method is deprecated. "
+            "Please use new-style autograd function with static forward method. "
+            "(Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)"
+        )
+
+    """
+    Bool that specifies if PyTorch should attempt to autogenerate
+    :func:`torch.vmap` support for this autograd.Function. You may set this to
+    True only if this autograd.Function's forward, backward, and jvp (if they
+    exist) are written using PyTorch operations; otherwise, please override
+    :meth:`torch.autograd.Function.vmap` to add support for :func:`torch.vmap`.
+
+    Please see :ref:`func-autograd-function` for more details.
+    """
+    generate_vmap_rule = False
+
+    @staticmethod
+    def vmap(info, in_dims, *args):
+        r"""Define the behavior for this autograd.Function underneath :func:`torch.vmap`.
+
+        For a :func:`torch.autograd.Function` to support
+        :func:`torch.vmap`, you must either override this static method, or set
+        ``generate_vmap_rule`` to ``True`` (you may not do both).
+
+        If you choose to override this staticmethod: it must accept
+
+        - an ``info`` object as the first argument. ``info.batch_size``
+          specifies the size of the dimension being vmapped over,
+          while ``info.randomness`` is the randomness option passed to
+          :func:`torch.vmap`.
+        - an ``in_dims`` tuple as the second argument.
+          For each arg in ``args``, ``in_dims`` has a corresponding
+          ``Optional[int]``. It is ``None`` if the arg is not a Tensor or if
+          the arg is not being vmapped over, otherwise, it is an integer
+          specifying what dimension of the Tensor is being vmapped over.
+        - ``*args``, which is the same as the args to :meth:`~Function.forward`.
+
+        The return of the vmap staticmethod is a tuple of ``(output, out_dims)``.
+        Similar to ``in_dims``, ``out_dims`` should be of the same structure as
+        ``output`` and contain one ``out_dim`` per output that specifies if the
+        output has the vmapped dimension and what index it is in.
+
+        Please see :ref:`func-autograd-function` for more details.
+        """
+        raise NotImplementedError(
+            "To use autograd.Function with vmap, you must either override the "
+            "vmap staticmethod or set generate_vmap_rule=True."
+        )
+
+    @classmethod
+    def apply(cls, *args, **kwargs):
+        def bind_default_args(func, *args, **kwargs):
+            signature = inspect.signature(func)
+            bound_args = signature.bind(*args, **kwargs)
+            bound_args.apply_defaults()
+
+            return bound_args.args
+
+        is_setup_ctx_defined = _is_setup_context_defined(cls.setup_context)
+        if is_setup_ctx_defined:
+            args = bind_default_args(cls.forward, *args, **kwargs)
+
+        if not torch._C._are_functorch_transforms_active():
+            # See NOTE: [functorch vjp and autograd interaction]
+            args = _functorch.utils.unwrap_dead_wrappers(args)
+            return super().apply(*args, **kwargs)  # type: ignore[misc]
+
+        if not is_setup_ctx_defined:
+            raise RuntimeError(
+                "In order to use an autograd.Function with functorch transforms "
+                "(vmap, grad, jvp, jacrev, ...), it must override the setup_context "
+                "staticmethod. For more details, please see "
+                "https://pytorch.org/docs/main/notes/extending.func.html"
+            )
+
+        return custom_function_call(cls, *args, **kwargs)
+
+    @staticmethod
+    def _compiled_autograd_key(ctx):
+        return (ctx._autograd_function_id,)
+
+
+def _is_setup_context_defined(fn):
+    return fn != _SingleLevelFunction.setup_context
+
+
+def once_differentiable(fn):
+    @functools.wraps(fn)
+    def wrapper(ctx, *args):
+        with torch.no_grad():
+            outputs = fn(ctx, *args)
+
+        if not torch.is_grad_enabled():
+            return outputs
+
+        # If any of the inputs have requires_grad=True, we force the outputs
+        # to have requires_grad=True but point to a grad_fn which throws an
+        # error message during (double) back-propagation.
+        # XXX: this is only an approximation of requires_grad - there's no way
+        # to figure out if fn didn't use ctx.saved_tensors and as a result
+        # some Tensors might require grad, even if no args do.
+        # Unfortunately, this leads to unexpected error messages ("no nodes
+        # require computing gradients"), but I don't have a better idea.
+        # These functions would raise an error in backward anyway.
+        requires_grad = any(
+            isinstance(arg, torch.Tensor) and arg.requires_grad for arg in args
+        )
+        if not requires_grad:
+            return outputs
+
+        if not isinstance(outputs, tuple):
+            outputs = (outputs,)
+
+        err_fn = _functions.DelayedError(
+            b"trying to differentiate twice a function that was marked "
+            b"with @once_differentiable",
+            len(outputs),
+        )
+
+        # Create aliases of each output that has requires_grad=True. We need
+        # at least one of the inputs to err_fn to require grad so that the
+        # output will have a grad_fn.
+        def fake_requires_grad(var):
+            if var is not None:
+                var = var.detach()
+                var.requires_grad = True
+            return var
+
+        return err_fn(*[fake_requires_grad(v) for v in outputs])
+
+    return wrapper
+
+
+class InplaceFunction(Function):
+    r"""
+    This class is here only for backward compatibility reasons.
+    Use :class:`Function` instead of this for any new use case.
+    """
+
+    def __init__(self, inplace=False):
+        super().__init__()
+        self.inplace = inplace
+
+
+def _nested_map(condition, fn, condition_msg=None):
+    def _map(obj):
+        if condition(obj):
+            return fn(obj)
+        elif obj is None:
+            return None
+        elif isinstance(obj, (list, tuple)):
+            mapped = (_map(x) for x in obj)
+            if hasattr(obj, "_fields"):
+                # obj is namedtuple
+                return type(obj)(*mapped)
+            return type(obj)(mapped)
+        elif isinstance(obj, dict):
+            return {x: _map(obj[x]) for x in obj}
+        else:
+            raise ValueError(
+                "Auto nesting doesn't know how to process "
+                "an input object of type "
+                + torch.typename(obj)
+                + (
+                    ". Accepted types: " + condition_msg + ", or lists/tuples of them"
+                    if condition_msg
+                    else ""
+                )
+            )
+
+    return _map
+
+
+def _jit_unwrap_structured(obj):
+    if hasattr(obj, "_jit_unwrap"):
+        return obj._jit_unwrap()
+    return obj
+
+
+def _iter_filter(condition, allow_unknown=False, condition_msg=None, conversion=None):
+    def _iter(obj):
+        if conversion is not None:
+            obj = conversion(obj)
+        if condition(obj):
+            yield obj
+        elif obj is None:
+            return
+        elif isinstance(obj, (list, tuple)):
+            for o in obj:
+                yield from _iter(o)
+        elif isinstance(obj, dict):
+            # We only accept primitive key types, so we needn't inspect them
+            for o in obj.values():
+                yield from _iter(o)
+        elif allow_unknown:
+            yield obj
+        else:
+            raise ValueError(
+                "Auto nesting doesn't know how to process "
+                "an input object of type "
+                + torch.typename(obj)
+                + (
+                    ". Accepted types: " + condition_msg + ", or lists/tuples of them"
+                    if condition_msg
+                    else ""
+                )
+            )
+
+    return _iter
+
+
+def _unflatten(input, proto):
+    # unflatten a list or tuple input into a nested list/tuple structure
+    # specified by proto
+    def unflatten_helper(input, proto):
+        res: list[Optional[torch.Tensor]] = []
+        if hasattr(proto, "_jit_wrap"):
+            return proto._jit_wrap(input)
+        if not isinstance(proto, (list, tuple)):
+            return input[0], input[1:]
+        for e in proto:
+            if e is None:
+                res.append(e)
+            else:
+                res_e, input = unflatten_helper(input, e)
+                res.append(res_e)
+        return type(proto)(res), input
+
+    return unflatten_helper(input, proto)[0]
+
+
+_iter_jit_values = _iter_filter(
+    lambda o: o is None or isinstance(o, torch._C.Value),
+    condition_msg="jit's Values or None",
+)
+_iter_tensors = _iter_filter(
+    lambda x: isinstance(x, torch.Tensor),
+    condition_msg="Tensors",
+    conversion=_jit_unwrap_structured,
+)
+_iter_tensors_permissive = _iter_filter(
+    lambda x: isinstance(x, torch.Tensor),
+    allow_unknown=True,
+    condition_msg="Tensors (permissive)",
+)
+_iter_None_tensors = _iter_filter(
+    lambda o: o is None or isinstance(o, torch.Tensor), condition_msg="Tensors or None"
+)
+_map_tensor_data = _nested_map(
+    lambda x: isinstance(x, torch.Tensor), lambda o: o.data, condition_msg="Tensors"
+)
+
+
+class NestedIOFunction(Function):
+    r"""
+    This class is here only for backward compatibility reasons.
+    Use :class:`Function` instead of this for any new use case.
+    """
+    # The 'type: ignore' statements are needed here because these functions are declared as '@staticmethod' in the
+    # superclass (Function) but are instance methods here, which mypy reports as incompatible.
+
+    def _do_forward(self, *input):
+        self._nested_input = input
+        flat_input = tuple(_iter_tensors(input))
+        flat_output = super()._do_forward(*flat_input)  # type: ignore[misc]
+        nested_tensors = _unflatten(flat_output, self._nested_output)
+        return nested_tensors
+
+    def _do_backward(self, gradients, retain_variables):
+        self.retain_variables = retain_variables
+        result = super()._do_backward(gradients, retain_variables)  # type: ignore[misc]
+        if not retain_variables:
+            del self._nested_output
+            del self._to_save_nested
+        return result
+
+    def backward(self, *gradients: Any) -> Any:  # type: ignore[override]
+        r"""
+        Shared backward utility.
+        """
+        nested_gradients = _unflatten(gradients, self._nested_output)
+        result = self.backward_extended(*nested_gradients)  # type: ignore[func-returns-value]
+        return tuple(_iter_None_tensors(result))
+
+    __call__ = _do_forward
+
+    def forward(self, *args: Any) -> Any:  # type: ignore[override]
+        r"""
+        Shared forward utility.
+        """
+        nested_tensors = _map_tensor_data(self._nested_input)
+        result = self.forward_extended(*nested_tensors)  # type: ignore[func-returns-value]
+        del self._nested_input
+        self._nested_output = result
+        return tuple(_iter_tensors(result))
+
+    def save_for_backward(self, *args: Any) -> None:
+        r"""
+        See :meth:`Function.save_for_backward`.
+        """
+        self.to_save = tuple(_iter_tensors(args))
+        self._to_save_nested = args
+
+    @property
+    def saved_tensors(self):
+        r"""
+        See :meth:`Function.saved_tensors`.
+        """
+        flat_tensors = super().saved_tensors  # type: ignore[misc]
+        return _unflatten(flat_tensors, self._to_save_nested)
+
+    def mark_dirty(self, *args: Any, **kwargs: Any) -> None:
+        r"""
+        See :meth:`Function.mark_dirty`.
+        """
+        self.dirty_tensors = tuple(_iter_tensors((args, kwargs)))
+
+    def mark_non_differentiable(self, *args: Any, **kwargs: Any) -> None:
+        r"""
+        See :meth:`Function.mark_non_differentiable`.
+        """
+        self.non_differentiable = tuple(_iter_tensors((args, kwargs)))
+
+    def forward_extended(self, *input: Any) -> None:
+        r"""
+        User defined forward.
+        """
+        raise NotImplementedError
+
+    def backward_extended(self, *grad_output: Any) -> None:
+        r"""
+        User defined backward.
+        """
+        raise NotImplementedError
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/functional.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..de1c13433476152f9aad323e35efb88cb63e38f0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/functional.py
@@ -0,0 +1,1184 @@
+# mypy: allow-untyped-defs
+
+import torch
+from torch._vmap_internals import _vmap
+
+from . import forward_ad as fwAD
+
+
+__all__ = ["vjp", "jvp", "jacobian", "hessian", "hvp", "vhp"]
+
+# Utility functions
+
+
+def _as_tuple_nocheck(x):
+    if isinstance(x, tuple):
+        return x
+    elif isinstance(x, list):
+        return tuple(x)
+    else:
+        return (x,)
+
+
+def _as_tuple(inp, arg_name=None, fn_name=None):
+    # Ensures that inp is a tuple of Tensors
+    # Returns whether or not the original inp was a tuple and the tupled version of the input
+    if arg_name is None and fn_name is None:
+        return _as_tuple_nocheck(inp)
+
+    is_inp_tuple = True
+    if not isinstance(inp, tuple):
+        inp = (inp,)
+        is_inp_tuple = False
+
+    for i, el in enumerate(inp):
+        if not isinstance(el, torch.Tensor):
+            if is_inp_tuple:
+                raise TypeError(
+                    f"The {arg_name} given to {fn_name} must be either a Tensor or a tuple of Tensors but the"
+                    f" value at index {i} has type {type(el)}."
+                )
+            else:
+                raise TypeError(
+                    f"The {arg_name} given to {fn_name} must be either a Tensor or a tuple of Tensors but the"
+                    f" given {arg_name} has type {type(el)}."
+                )
+
+    return is_inp_tuple, inp
+
+
+def _tuple_postprocess(res, to_unpack):
+    # Unpacks a potentially nested tuple of Tensors
+    # to_unpack should be a single boolean or a tuple of two booleans.
+    # It is used to:
+    # - invert _as_tuple when res should match the inp given to _as_tuple
+    # - optionally remove nesting of two tuples created by multiple calls to _as_tuple
+    if isinstance(to_unpack, tuple):
+        assert len(to_unpack) == 2
+        if not to_unpack[1]:
+            res = tuple(el[0] for el in res)
+        if not to_unpack[0]:
+            res = res[0]
+    else:
+        if not to_unpack:
+            res = res[0]
+    return res
+
+
+def _grad_preprocess(inputs, create_graph, need_graph):
+    # Preprocess the inputs to make sure they require gradient
+    # inputs is a tuple of Tensors to preprocess
+    # create_graph specifies if the user wants gradients to flow back to the Tensors in inputs
+    # need_graph specifies if we internally want gradients to flow back to the Tensors in res
+    # Note that we *always* create a new Tensor object to be able to see the difference between
+    # inputs given as arguments and the same Tensors automatically captured by the user function.
+    # Check this issue for more details on how that can happen: https://github.com/pytorch/pytorch/issues/32576
+    res = []
+    for inp in inputs:
+        if create_graph and inp.requires_grad:
+            # Create at least a new Tensor object in a differentiable way
+            if not inp.is_sparse:
+                # Use .view_as() to get a shallow copy
+                res.append(inp.view_as(inp))
+            else:
+                # We cannot use view for sparse Tensors so we clone
+                res.append(inp.clone())
+        else:
+            res.append(inp.detach().requires_grad_(need_graph))
+    return tuple(res)
+
+
+def _grad_postprocess(inputs, create_graph):
+    # Postprocess the generated Tensors to avoid returning Tensors with history when the user did not
+    # request it.
+    if isinstance(inputs[0], torch.Tensor):
+        if not create_graph:
+            return tuple(inp.detach() for inp in inputs)
+        else:
+            return inputs
+    else:
+        return tuple(_grad_postprocess(inp, create_graph) for inp in inputs)
+
+
+def _validate_v(v, other, is_other_tuple):
+    # This assumes that other is the correct shape, and v should match
+    # Both are assumed to be tuples of Tensors
+    if len(other) != len(v):
+        if is_other_tuple:
+            raise RuntimeError(
+                f"v is a tuple of invalid length: should be {len(other)} but got {len(v)}."
+            )
+        else:
+            raise RuntimeError("The given v should contain a single Tensor.")
+
+    for idx, (el_v, el_other) in enumerate(zip(v, other)):
+        if el_v.size() != el_other.size():
+            prepend = ""
+            if is_other_tuple:
+                prepend = f"Entry {idx} in "
+            raise RuntimeError(
+                f"{prepend}v has invalid size: should be {el_other.size()} but got {el_v.size()}."
+            )
+
+
+def _check_requires_grad(inputs, input_type, strict):
+    # Used to make all the necessary checks to raise nice errors in strict mode.
+    if not strict:
+        return
+
+    if input_type not in ["outputs", "grad_inputs", "jacobian", "hessian"]:
+        raise RuntimeError("Invalid input_type to _check_requires_grad")
+    for i, inp in enumerate(inputs):
+        if inp is None:
+            # This can only be reached for grad_inputs.
+            raise RuntimeError(
+                f"The output of the user-provided function is independent of input {i}."
+                " This is not allowed in strict mode."
+            )
+        if not inp.requires_grad:
+            if input_type == "hessian":
+                raise RuntimeError(
+                    f"The hessian of the user-provided function with respect to input {i}"
+                    " is independent of the input. This is not allowed in strict mode."
+                    " You should ensure that your function is thrice differentiable and that"
+                    " the hessian depends on the inputs."
+                )
+            elif input_type == "jacobian":
+                raise RuntimeError(
+                    "While computing the hessian, found that the jacobian of the user-provided"
+                    f" function with respect to input {i} is independent of the input. This is not"
+                    " allowed in strict mode. You should ensure that your function is twice"
+                    " differentiable and that the jacobian depends on the inputs (this would be"
+                    " violated by a linear function for example)."
+                )
+            elif input_type == "grad_inputs":
+                raise RuntimeError(
+                    f"The gradient with respect to input {i} is independent of the inputs of the"
+                    " user-provided function. This is not allowed in strict mode."
+                )
+            else:
+                raise RuntimeError(
+                    f"Output {i} of the user-provided function does not require gradients."
+                    " The outputs must be computed in a differentiable manner from the input"
+                    " when running in strict mode."
+                )
+
+
+def _autograd_grad(
+    outputs,
+    inputs,
+    grad_outputs=None,
+    create_graph=False,
+    retain_graph=None,
+    is_grads_batched=False,
+):
+    # Version of autograd.grad that accepts `None` in outputs and do not compute gradients for them.
+    # This has the extra constraint that inputs has to be a tuple
+    assert isinstance(outputs, tuple)
+    if grad_outputs is None:
+        grad_outputs = (None,) * len(outputs)
+    assert isinstance(grad_outputs, tuple)
+    assert len(outputs) == len(grad_outputs)
+
+    new_outputs: tuple[torch.Tensor, ...] = ()
+    new_grad_outputs: tuple[torch.Tensor, ...] = ()
+    for out, grad_out in zip(outputs, grad_outputs):
+        if out is not None and out.requires_grad:
+            new_outputs += (out,)
+            new_grad_outputs += (grad_out,)
+
+    if len(new_outputs) == 0:
+        # No differentiable output, we don't need to call the autograd engine
+        return (None,) * len(inputs)
+    else:
+        return torch.autograd.grad(
+            new_outputs,
+            inputs,
+            new_grad_outputs,
+            allow_unused=True,
+            create_graph=create_graph,
+            retain_graph=retain_graph,
+            is_grads_batched=is_grads_batched,
+        )
+
+
+def _fill_in_zeros(grads, refs, strict, create_graph, stage):
+    # Used to detect None in the grads and depending on the flags, either replace them
+    # with Tensors full of 0s of the appropriate size based on the refs or raise an error.
+    # strict and create graph allow us to detect when it is appropriate to raise an error
+    # stage gives us information of which backward call we consider to give good error message
+    if stage not in ["back", "back_trick", "double_back", "double_back_trick"]:
+        raise RuntimeError(f"Invalid stage argument '{stage}' to _fill_in_zeros")
+
+    res: tuple[torch.Tensor, ...] = ()
+    for i, grads_i in enumerate(grads):
+        if grads_i is None:
+            if strict:
+                if stage == "back":
+                    raise RuntimeError(
+                        "The output of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode."
+                    )
+                elif stage == "back_trick":
+                    raise RuntimeError(
+                        f"The gradient with respect to the input is independent of entry {i}"
+                        " in the grad_outputs when using the double backward trick to compute"
+                        " forward mode gradients. This is not allowed in strict mode."
+                    )
+                elif stage == "double_back":
+                    raise RuntimeError(
+                        "The jacobian of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode."
+                    )
+                else:
+                    raise RuntimeError(
+                        "The hessian of the user-provided function is independent of "
+                        f"entry {i} in the grad_jacobian. This is not allowed in strict "
+                        "mode as it prevents from using the double backward trick to "
+                        "replace forward mode AD."
+                    )
+
+            grads_i = torch.zeros_like(refs[i])
+        else:
+            if strict and create_graph and not grads_i.requires_grad:
+                if "double" not in stage:
+                    raise RuntimeError(
+                        "The jacobian of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode when create_graph=True."
+                    )
+                else:
+                    raise RuntimeError(
+                        "The hessian of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode when create_graph=True."
+                    )
+
+        res += (grads_i,)
+
+    return res
+
+
+# Public API
+
+
+def vjp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between a vector ``v`` and the Jacobian of the given function at the point given by the inputs.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a tuple of Tensors or a Tensor.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the vector
+            Jacobian product is computed.  Must be the same size as the output
+            of ``func``. This argument is optional when the output of ``func``
+            contains a single element and (if it is not provided) will be set
+            as a Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result
+            will be computed in a differentiable way. Note that when ``strict``
+            is ``False``, the result can not require gradients or be
+            disconnected from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            vjp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            vjp (tuple of Tensors or Tensor): result of the dot product with
+            the same shape as the inputs.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def exp_reducer(x):
+        ...     return x.exp().sum(dim=1)
+        >>> inputs = torch.rand(4, 4)
+        >>> v = torch.ones(4)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> vjp(exp_reducer, inputs, v)
+        (tensor([5.7817, 7.2458, 5.7830, 6.7782]),
+         tensor([[1.4458, 1.3962, 1.3042, 1.6354],
+                [2.1288, 1.0652, 1.5483, 2.5035],
+                [2.2046, 1.1292, 1.1432, 1.3059],
+                [1.3225, 1.6652, 1.7753, 2.0152]]))
+
+        >>> vjp(exp_reducer, inputs, v, create_graph=True)
+        (tensor([5.7817, 7.2458, 5.7830, 6.7782], grad_fn=),
+         tensor([[1.4458, 1.3962, 1.3042, 1.6354],
+                [2.1288, 1.0652, 1.5483, 2.5035],
+                [2.2046, 1.1292, 1.1432, 1.3059],
+                [1.3225, 1.6652, 1.7753, 2.0152]], grad_fn=))
+
+        >>> def adder(x, y):
+        ...     return 2 * x + 3 * y
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = torch.ones(2)
+        >>> vjp(adder, inputs, v)
+        (tensor([2.4225, 2.3340]),
+         (tensor([2., 2.]), tensor([3., 3.])))
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "vjp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "vjp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "vjp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, outputs, is_outputs_tuple)
+        else:
+            if len(outputs) != 1 or outputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the "
+                    "user-provided function returns "
+                    "a single Tensor with a single element."
+                )
+
+    enable_grad = True if create_graph else torch.is_grad_enabled()
+    with torch.set_grad_enabled(enable_grad):
+        grad_res = _autograd_grad(outputs, inputs, v, create_graph=create_graph)
+        vjp = _fill_in_zeros(grad_res, inputs, strict, create_graph, "back")
+
+    # Cleanup objects and return them to the user
+    outputs = _grad_postprocess(outputs, create_graph)
+    vjp = _grad_postprocess(vjp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        vjp, is_inputs_tuple
+    )
+
+
+def jvp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between the Jacobian of the given function at the point given by the inputs and a vector ``v``.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a tuple of Tensors or a Tensor.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the Jacobian
+            vector product is computed. Must be the same size as the input of
+            ``func``. This argument is optional when the input to ``func``
+            contains a single element and (if it is not provided) will be set
+            as a Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result
+            will be computed in a differentiable way. Note that when ``strict``
+            is ``False``, the result can not require gradients or be
+            disconnected from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            jvp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            jvp (tuple of Tensors or Tensor): result of the dot product with
+            the same shape as the output.
+
+    Note:
+        ``autograd.functional.jvp`` computes the jvp by using the backward of
+        the backward (sometimes called the double backwards trick). This is not
+        the most performant way of computing the jvp. Please consider using
+        :func:`torch.func.jvp` or the
+        :ref:`low-level forward-mode AD API ` instead.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def exp_reducer(x):
+        ...     return x.exp().sum(dim=1)
+        >>> inputs = torch.rand(4, 4)
+        >>> v = torch.ones(4, 4)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> jvp(exp_reducer, inputs, v)
+        (tensor([6.3090, 4.6742, 7.9114, 8.2106]),
+         tensor([6.3090, 4.6742, 7.9114, 8.2106]))
+
+        >>> jvp(exp_reducer, inputs, v, create_graph=True)
+        (tensor([6.3090, 4.6742, 7.9114, 8.2106], grad_fn=),
+         tensor([6.3090, 4.6742, 7.9114, 8.2106], grad_fn=))
+
+        >>> def adder(x, y):
+        ...     return 2 * x + 3 * y
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = (torch.ones(2), torch.ones(2))
+        >>> jvp(adder, inputs, v)
+        (tensor([2.2399, 2.5005]),
+         tensor([5., 5.]))
+
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jvp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "jvp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, inputs, is_inputs_tuple)
+        else:
+            if len(inputs) != 1 or inputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the input to "
+                    "the user-provided function is a single Tensor "
+                    "with a single element."
+                )
+
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "jvp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+        # The backward is linear so the value of grad_outputs is not important as
+        # it won't appear in the double backward graph. We only need to ensure that
+        # it does not contain inf or nan.
+        grad_outputs = tuple(
+            torch.zeros_like(out, requires_grad=True) for out in outputs
+        )
+
+        grad_inputs = _autograd_grad(outputs, inputs, grad_outputs, create_graph=True)
+        _check_requires_grad(grad_inputs, "grad_inputs", strict=strict)
+
+    if create_graph:
+        with torch.enable_grad():
+            grad_res = _autograd_grad(
+                grad_inputs, grad_outputs, v, create_graph=create_graph
+            )
+            jvp = _fill_in_zeros(grad_res, outputs, strict, create_graph, "back_trick")
+    else:
+        grad_res = _autograd_grad(
+            grad_inputs, grad_outputs, v, create_graph=create_graph
+        )
+        jvp = _fill_in_zeros(grad_res, outputs, strict, create_graph, "back_trick")
+
+    # Cleanup objects and return them to the user
+    outputs = _grad_postprocess(outputs, create_graph)
+    jvp = _grad_postprocess(jvp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        jvp, is_outputs_tuple
+    )
+
+
+def _construct_standard_basis_for(
+    tensors: tuple[torch.Tensor, ...], tensor_numels: tuple[int, ...]
+) -> tuple[torch.Tensor, ...]:
+    # This function:
+    # - constructs a N=sum(tensor_numels) standard basis. i.e. an NxN identity matrix.
+    # - Splits the identity matrix into chunks with each chunk size determined by `tensor_numels`.
+    # - Each chunk corresponds to one tensor. The chunk has the same dtype and
+    #   device as the tensor
+    #
+    # For example, with tensor_numels = [1, 2, 1], this function returns:
+    # ( tensor([[1],     tensor([[0, 0],      tensor([[0],
+    #           [0],             [1, 0],              [0],
+    #           [0],             [0, 1],              [0],
+    #           [0]])  ,         [0, 0]])  ,          [1]])  )
+    #
+    # Precondition: tensor_numels == tuple(tensor.numel() for tensor in tensors)
+    # Precondition: tensors always has at least one element.
+    #
+    # See NOTE: [Computing jacobian with vmap and grad for multiple tensors]
+    # for context behind this function. All the pre-conditions are guarded for
+    # in torch.autograd.functional.jacobian.
+    assert len(tensors) == len(tensor_numels)
+    assert len(tensors) > 0
+    total_numel = sum(tensor_numels)
+    chunks = tuple(
+        tensor.new_zeros(total_numel, tensor_numel)
+        for tensor, tensor_numel in zip(tensors, tensor_numels)
+    )
+    diag_start_idx = 0
+    for chunk, numel in zip(chunks, tensor_numels):
+        chunk.diagonal(diag_start_idx).fill_(1)
+        diag_start_idx -= numel
+    return chunks
+
+
+def _jacfwd(func, inputs, strict=False, vectorize=False):
+    if strict:
+        raise RuntimeError(
+            "torch.autograd.functional.jacobian: `strict=True` "
+            'and `strategy="forward-mode"` are not supported together (yet). '
+            "Please either set `strict=False` or "
+            '`strategy="reverse-mode"`.'
+        )
+    is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jacobian")
+    output_info = []
+
+    if vectorize:
+        # See NOTE: [Computing jacobian with vmap and grad for multiple outputs]
+        input_numels = tuple(input.numel() for input in inputs)
+
+        # Step 1: Prepare tangents
+        tangents = _construct_standard_basis_for(inputs, input_numels)
+
+        # Step 2: Compute vmap over computation with dual tensors
+        def jvp(tangents):
+            with fwAD.dual_level():
+                dual_inputs = tuple(
+                    fwAD.make_dual(input, tangent.view_as(input))
+                    for input, tangent in zip(inputs, tangents)
+                )
+                _is_outputs_tuple, dual_outputs = _as_tuple(
+                    func(*dual_inputs), "outputs"
+                )
+                output_info.append(_is_outputs_tuple)
+                jv = []
+                primal_outs = []
+                for dual_out in dual_outputs:
+                    primal, tangent = fwAD.unpack_dual(dual_out)
+                    primal_outs.append(primal)
+                    if tangent is not None:
+                        jv.append(tangent)
+                    else:
+                        jv.append(torch.zeros_like(primal))
+                output_info.append(primal_outs)
+                return tuple(jv)
+
+        outputs_before_split = _vmap(jvp)(tangents)
+        is_outputs_tuple, outputs = output_info
+        # Step 3: for each of the output tangents, split along dim 0
+        jacobian_input_output = []
+        for jac_output_i, output_i in zip(outputs_before_split, outputs):
+            jacobian_output_i_output = []
+            for jac, input_j in zip(jac_output_i.split(input_numels, dim=0), inputs):
+                # We need to transpose the Jacobian because in forward AD, the
+                # batch dimension represents that of the inputs
+                jacobian_input_i_output_j = jac.permute(*range(1, jac.ndim), 0).reshape(
+                    (*output_i.shape, *input_j.shape)
+                )  # noqa: C409
+
+                jacobian_output_i_output.append(jacobian_input_i_output_j)
+            jacobian_input_output.append(jacobian_output_i_output)
+
+        # Omit [Step 4] because everything is already transposed w/ forward AD
+        return _tuple_postprocess(
+            jacobian_input_output, (is_outputs_tuple, is_inputs_tuple)
+        )
+    else:
+        raise NotImplementedError(
+            "Computing Jacobian using forward-AD or forward-over-reverse Hessian is"
+            "only implemented for `vectorize=True`."
+        )
+
+
+def jacobian(
+    func,
+    inputs,
+    create_graph=False,
+    strict=False,
+    vectorize=False,
+    strategy="reverse-mode",
+):
+    r"""Compute the Jacobian of a given function.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a tuple of Tensors or a Tensor.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        create_graph (bool, optional): If ``True``, the Jacobian will be
+            computed in a differentiable manner. Note that when ``strict`` is
+            ``False``, the result can not require gradients or be disconnected
+            from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            jacobian for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+        vectorize (bool, optional): This feature is experimental.
+            Please consider using :func:`torch.func.jacrev` or
+            :func:`torch.func.jacfwd` instead if you are looking for something
+            less experimental and more performant.
+            When computing the jacobian, usually we invoke
+            ``autograd.grad`` once per row of the jacobian. If this flag is
+            ``True``, we perform only a single ``autograd.grad`` call with
+            ``batched_grad=True`` which uses the vmap prototype feature.
+            Though this should lead to performance improvements in many cases,
+            because this feature is still experimental, there may be performance
+            cliffs. See :func:`torch.autograd.grad`'s ``batched_grad`` parameter for
+            more information.
+        strategy (str, optional): Set to ``"forward-mode"`` or ``"reverse-mode"`` to
+            determine whether the Jacobian will be computed with forward or reverse
+            mode AD. Currently, ``"forward-mode"`` requires ``vectorized=True``.
+            Defaults to ``"reverse-mode"``. If ``func`` has more outputs than
+            inputs, ``"forward-mode"`` tends to be more performant. Otherwise,
+            prefer to use ``"reverse-mode"``.
+
+    Returns:
+        Jacobian (Tensor or nested tuple of Tensors): if there is a single
+        input and output, this will be a single Tensor containing the
+        Jacobian for the linearized inputs and output. If one of the two is
+        a tuple, then the Jacobian will be a tuple of Tensors. If both of
+        them are tuples, then the Jacobian will be a tuple of tuple of
+        Tensors where ``Jacobian[i][j]`` will contain the Jacobian of the
+        ``i``\th output and ``j``\th input and will have as size the
+        concatenation of the sizes of the corresponding output and the
+        corresponding input and will have same dtype and device as the
+        corresponding input. If strategy is ``forward-mode``, the dtype will be
+        that of the output; otherwise, the input.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def exp_reducer(x):
+        ...     return x.exp().sum(dim=1)
+        >>> inputs = torch.rand(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> jacobian(exp_reducer, inputs)
+        tensor([[[1.4917, 2.4352],
+                 [0.0000, 0.0000]],
+                [[0.0000, 0.0000],
+                 [2.4369, 2.3799]]])
+
+        >>> jacobian(exp_reducer, inputs, create_graph=True)
+        tensor([[[1.4917, 2.4352],
+                 [0.0000, 0.0000]],
+                [[0.0000, 0.0000],
+                 [2.4369, 2.3799]]], grad_fn=)
+
+        >>> def exp_adder(x, y):
+        ...     return 2 * x.exp() + 3 * y
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> jacobian(exp_adder, inputs)
+        (tensor([[2.8052, 0.0000],
+                [0.0000, 3.3963]]),
+         tensor([[3., 0.],
+                 [0., 3.]]))
+    """
+    assert strategy in ("forward-mode", "reverse-mode"), (
+        'Expected strategy to be either "forward-mode" or "reverse-mode". Hint: If your '
+        'function has more outputs than inputs, "forward-mode" tends to be more performant. '
+        'Otherwise, prefer to use "reverse-mode".'
+    )
+    if strategy == "forward-mode":
+        if create_graph:
+            raise NotImplementedError(
+                "torch.autograd.functional.jacobian: `create_graph=True` "
+                'and `strategy="forward-mode"` are not supported together (yet). '
+                "Please either set `create_graph=False` or "
+                '`strategy="reverse-mode"`.'
+            )
+        return _jacfwd(func, inputs, strict, vectorize)
+
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jacobian")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "jacobian"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if vectorize:
+            if strict:
+                raise RuntimeError(
+                    "torch.autograd.functional.jacobian: `strict=True` "
+                    "and `vectorized=True` are not supported together. "
+                    "Please either set `strict=False` or "
+                    "`vectorize=False`."
+                )
+            # NOTE: [Computing jacobian with vmap and grad for multiple outputs]
+            #
+            # Let's consider f(x) = (x**2, x.sum()) and let x = torch.randn(3).
+            # It turns out we can compute the jacobian of this function with a single
+            # call to autograd.grad by using vmap over the correct grad_outputs.
+            #
+            # Firstly, one way to compute the jacobian is to stack x**2 and x.sum()
+            # into a 4D vector. E.g., use g(x) = torch.stack([x**2, x.sum()])
+            #
+            # To get the first row of the jacobian, we call
+            # >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([1, 0, 0, 0]))
+            # To get the 2nd row of the jacobian, we call
+            # >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([0, 1, 0, 0]))
+            # and so on.
+            #
+            # Using vmap, we can vectorize all 4 of these computations into one by
+            # passing the standard basis for R^4 as the grad_output.
+            # vmap(partial(autograd.grad, g(x), x))(torch.eye(4)).
+            #
+            # Now, how do we compute the jacobian *without stacking the output*?
+            # We can just split the standard basis across the outputs. So to
+            # compute the jacobian of f(x), we'd use
+            # >>> autograd.grad(f(x), x, grad_outputs=_construct_standard_basis_for(...))
+            # The grad_outputs looks like the following:
+            # ( torch.tensor([[1, 0, 0],
+            #                 [0, 1, 0],
+            #                 [0, 0, 1],
+            #                 [0, 0, 0]]),
+            #   torch.tensor([[0],
+            #                 [0],
+            #                 [0],
+            #                 [1]]) )
+            #
+            # But we're not done yet!
+            # >>> vmap(partial(autograd.grad(f(x), x, grad_outputs=...)))
+            # returns a Tensor of shape [4, 3]. We have to remember to split the
+            # jacobian of shape [4, 3] into two:
+            # - one of shape [3, 3] for the first output
+            # - one of shape [   3] for the second output
+
+            # Step 1: Construct grad_outputs by splitting the standard basis
+            output_numels = tuple(output.numel() for output in outputs)
+            grad_outputs = _construct_standard_basis_for(outputs, output_numels)
+            flat_outputs = tuple(output.reshape(-1) for output in outputs)
+
+            # Step 2: Call vmap + autograd.grad
+            def vjp(grad_output):
+                vj = list(
+                    _autograd_grad(
+                        flat_outputs,
+                        inputs,
+                        grad_output,
+                        create_graph=create_graph,
+                        is_grads_batched=True,
+                    )
+                )
+                for el_idx, vj_el in enumerate(vj):
+                    if vj_el is not None:
+                        continue
+                    vj[el_idx] = torch.zeros_like(inputs[el_idx]).expand(
+                        (sum(output_numels),) + inputs[el_idx].shape
+                    )
+                return tuple(vj)
+
+            jacobians_of_flat_output = vjp(grad_outputs)
+
+            # Step 3: The returned jacobian is one big tensor per input. In this step,
+            # we split each Tensor by output.
+            jacobian_input_output = []
+            for jac_input_i, input_i in zip(jacobians_of_flat_output, inputs):
+                jacobian_input_i_output = []
+                for jac, output_j in zip(
+                    jac_input_i.split(output_numels, dim=0), outputs
+                ):
+                    jacobian_input_i_output_j = jac.view(output_j.shape + input_i.shape)
+                    jacobian_input_i_output.append(jacobian_input_i_output_j)
+                jacobian_input_output.append(jacobian_input_i_output)
+
+            # Step 4: Right now, `jacobian` is a List[List[Tensor]].
+            # The outer List corresponds to the number of inputs,
+            # the inner List corresponds to the number of outputs.
+            # We need to exchange the order of these and convert to tuples
+            # before returning.
+            jacobian_output_input = tuple(zip(*jacobian_input_output))
+
+            jacobian_output_input = _grad_postprocess(
+                jacobian_output_input, create_graph
+            )
+            return _tuple_postprocess(
+                jacobian_output_input, (is_outputs_tuple, is_inputs_tuple)
+            )
+
+        jacobian: tuple[torch.Tensor, ...] = ()
+
+        for i, out in enumerate(outputs):
+            # mypy complains that expression and variable have different types due to the empty list
+            jac_i: tuple[list[torch.Tensor]] = tuple([] for _ in range(len(inputs)))  # type: ignore[assignment]
+            for j in range(out.nelement()):
+                vj = _autograd_grad(
+                    (out.reshape(-1)[j],),
+                    inputs,
+                    retain_graph=True,
+                    create_graph=create_graph,
+                )
+
+                for el_idx, (jac_i_el, vj_el, inp_el) in enumerate(
+                    zip(jac_i, vj, inputs)
+                ):
+                    if vj_el is not None:
+                        if strict and create_graph and not vj_el.requires_grad:
+                            msg = (
+                                "The jacobian of the user-provided function is "
+                                f"independent of input {i}. This is not allowed in "
+                                "strict mode when create_graph=True."
+                            )
+                            raise RuntimeError(msg)
+                        jac_i_el.append(vj_el)
+                    else:
+                        if strict:
+                            msg = (
+                                f"Output {i} of the user-provided function is "
+                                f"independent of input {el_idx}. This is not allowed in "
+                                "strict mode."
+                            )
+                            raise RuntimeError(msg)
+                        jac_i_el.append(torch.zeros_like(inp_el))
+
+            jacobian += (
+                tuple(
+                    torch.stack(jac_i_el, dim=0).view(
+                        out.size() + inputs[el_idx].size()  # type: ignore[operator]
+                    )
+                    for (el_idx, jac_i_el) in enumerate(jac_i)
+                ),
+            )
+
+        jacobian = _grad_postprocess(jacobian, create_graph)
+
+        return _tuple_postprocess(jacobian, (is_outputs_tuple, is_inputs_tuple))
+
+
+def hessian(
+    func,
+    inputs,
+    create_graph=False,
+    strict=False,
+    vectorize=False,
+    outer_jacobian_strategy="reverse-mode",
+):
+    r"""Compute the Hessian of a given scalar function.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor with a single element.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        create_graph (bool, optional): If ``True``, the Hessian will be computed in
+            a differentiable manner. Note that when ``strict`` is ``False``, the result can not
+            require gradients or be disconnected from the inputs.
+            Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we detect that there exists an input
+            such that all the outputs are independent of it. If ``False``, we return a Tensor of zeros as the
+            hessian for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+        vectorize (bool, optional): This feature is experimental.
+            Please consider using :func:`torch.func.hessian`
+            instead if you are looking for something less experimental and more performant.
+            When computing the hessian, usually we invoke
+            ``autograd.grad`` once per row of the hessian. If this flag is
+            ``True``, we use the vmap prototype feature as the backend to
+            vectorize calls to ``autograd.grad`` so we only invoke it once
+            instead of once per row. This should lead to performance
+            improvements in many use cases, however, due to this feature
+            being incomplete, there may be performance cliffs. Please
+            use `torch._C._debug_only_display_vmap_fallback_warnings(True)`
+            to show any performance warnings and file us issues if
+            warnings exist for your use case. Defaults to ``False``.
+        outer_jacobian_strategy (str, optional): The Hessian is computed by
+            computing the Jacobian of a Jacobian. The inner Jacobian is always
+            computed in reverse-mode AD. Setting strategy to ``"forward-mode"``
+            or ``"reverse-mode"`` determines whether the outer Jacobian will be
+            computed with forward or reverse mode AD. Currently, computing the outer
+            Jacobian in ``"forward-mode"`` requires ``vectorized=True``. Defaults
+            to ``"reverse-mode"``.
+
+    Returns:
+        Hessian (Tensor or a tuple of tuple of Tensors): if there is a single input,
+        this will be a single Tensor containing the Hessian for the input.
+        If it is a tuple, then the Hessian will be a tuple of tuples where
+        ``Hessian[i][j]`` will contain the Hessian of the ``i``\th input
+        and ``j``\th input with size the sum of the size of the ``i``\th input plus
+        the size of the ``j``\th input. ``Hessian[i][j]`` will have the same
+        dtype and device as the corresponding ``i``\th input.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pow_reducer(x):
+        ...     return x.pow(3).sum()
+        >>> inputs = torch.rand(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> hessian(pow_reducer, inputs)
+        tensor([[[[5.2265, 0.0000],
+                  [0.0000, 0.0000]],
+                 [[0.0000, 4.8221],
+                  [0.0000, 0.0000]]],
+                [[[0.0000, 0.0000],
+                  [1.9456, 0.0000]],
+                 [[0.0000, 0.0000],
+                  [0.0000, 3.2550]]]])
+
+        >>> hessian(pow_reducer, inputs, create_graph=True)
+        tensor([[[[5.2265, 0.0000],
+                  [0.0000, 0.0000]],
+                 [[0.0000, 4.8221],
+                  [0.0000, 0.0000]]],
+                [[[0.0000, 0.0000],
+                  [1.9456, 0.0000]],
+                 [[0.0000, 0.0000],
+                  [0.0000, 3.2550]]]], grad_fn=)
+
+
+        >>> def pow_adder_reducer(x, y):
+        ...     return (2 * x.pow(2) + 3 * y.pow(2)).sum()
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> hessian(pow_adder_reducer, inputs)
+        ((tensor([[4., 0.],
+                  [0., 4.]]),
+          tensor([[0., 0.],
+                  [0., 0.]])),
+         (tensor([[0., 0.],
+                  [0., 0.]]),
+          tensor([[6., 0.],
+                  [0., 6.]])))
+    """
+    is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "hessian")
+    assert outer_jacobian_strategy in (
+        "forward-mode",
+        "reverse-mode",
+    ), 'Expected strategy to be either "forward-mode" or "reverse-mode".'
+
+    def ensure_single_output_function(*inp):
+        out = func(*inp)
+        is_out_tuple, t_out = _as_tuple(
+            out, "outputs of the user-provided function", "hessian"
+        )
+        _check_requires_grad(t_out, "outputs", strict=strict)
+
+        if is_out_tuple or not isinstance(out, torch.Tensor):
+            raise RuntimeError(
+                "The function given to hessian should return a single Tensor"
+            )
+
+        if out.nelement() != 1:
+            raise RuntimeError(
+                "The Tensor returned by the function given to hessian should contain a single element"
+            )
+
+        return out.squeeze()
+
+    def jac_func(*inp):
+        if outer_jacobian_strategy == "forward-mode":
+            # _grad_preprocess requires create_graph=True and input to require_grad
+            # or else the input will be detached
+            inp = tuple(t.requires_grad_(True) for t in inp)
+        jac = jacobian(ensure_single_output_function, inp, create_graph=True)
+        _check_requires_grad(jac, "jacobian", strict=strict)
+        return jac
+
+    res = jacobian(
+        jac_func,
+        inputs,
+        create_graph=create_graph,
+        strict=strict,
+        vectorize=vectorize,
+        strategy=outer_jacobian_strategy,
+    )
+    return _tuple_postprocess(res, (is_inputs_tuple, is_inputs_tuple))
+
+
+def vhp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between vector ``v`` and Hessian of a  given scalar function at a specified point.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor with a single element.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the vector Hessian
+            product is computed. Must be the same size as the input of
+            ``func``. This argument is optional when ``func``'s input contains
+            a single element and (if it is not provided) will be set as a
+            Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result
+            will be computed in a differentiable way. Note that when ``strict``
+            is ``False``, the result can not require gradients or be
+            disconnected from the inputs.
+            Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            vhp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            vhp (tuple of Tensors or Tensor): result of the dot product with the
+            same shape as the inputs.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pow_reducer(x):
+        ...     return x.pow(3).sum()
+        >>> inputs = torch.rand(2, 2)
+        >>> v = torch.ones(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> vhp(pow_reducer, inputs, v)
+        (tensor(0.5591),
+         tensor([[1.0689, 1.2431],
+                 [3.0989, 4.4456]]))
+        >>> vhp(pow_reducer, inputs, v, create_graph=True)
+        (tensor(0.5591, grad_fn=),
+         tensor([[1.0689, 1.2431],
+                 [3.0989, 4.4456]], grad_fn=))
+        >>> def pow_adder_reducer(x, y):
+        ...     return (2 * x.pow(2) + 3 * y.pow(2)).sum()
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = (torch.zeros(2), torch.ones(2))
+        >>> vhp(pow_adder_reducer, inputs, v)
+        (tensor(4.8053),
+         (tensor([0., 0.]),
+          tensor([6., 6.])))
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "vhp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "vhp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, inputs, is_inputs_tuple)
+        else:
+            if len(inputs) != 1 or inputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the input to the user-provided function "
+                    "is a single Tensor with a single element."
+                )
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "vhp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if is_outputs_tuple or not isinstance(outputs[0], torch.Tensor):
+            raise RuntimeError(
+                "The function given to vhp should return a single Tensor"
+            )
+
+        if outputs[0].nelement() != 1:
+            raise RuntimeError(
+                "The Tensor returned by the function given to vhp should contain a single element"
+            )
+
+        jac = _autograd_grad(outputs, inputs, create_graph=True)
+        _check_requires_grad(jac, "jacobian", strict=strict)
+
+    enable_grad = True if create_graph else torch.is_grad_enabled()
+    with torch.set_grad_enabled(enable_grad):
+        grad_res = _autograd_grad(jac, inputs, v, create_graph=create_graph)
+        vhp = _fill_in_zeros(grad_res, inputs, strict, create_graph, "double_back")
+
+    outputs = _grad_postprocess(outputs, create_graph)
+    vhp = _grad_postprocess(vhp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        vhp, is_inputs_tuple
+    )
+
+
+def hvp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between the scalar function's Hessian and a vector ``v`` at a specified point.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor with a single element.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the Hessian vector
+            product is computed. Must be the same size as the input of
+            ``func``. This argument is optional when ``func``'s input contains
+            a single element and (if it is not provided) will be set as a
+            Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result will be
+            computed in a differentiable way. Note that when ``strict`` is
+            ``False``, the result can not require gradients or be disconnected
+            from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            hvp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            hvp (tuple of Tensors or Tensor): result of the dot product with
+            the same shape as the inputs.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pow_reducer(x):
+        ...     return x.pow(3).sum()
+        >>> inputs = torch.rand(2, 2)
+        >>> v = torch.ones(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> hvp(pow_reducer, inputs, v)
+        (tensor(0.1448),
+         tensor([[2.0239, 1.6456],
+                 [2.4988, 1.4310]]))
+
+        >>> hvp(pow_reducer, inputs, v, create_graph=True)
+        (tensor(0.1448, grad_fn=),
+         tensor([[2.0239, 1.6456],
+                 [2.4988, 1.4310]], grad_fn=))
+
+
+        >>> def pow_adder_reducer(x, y):
+        ...     return (2 * x.pow(2) + 3 * y.pow(2)).sum()
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = (torch.zeros(2), torch.ones(2))
+        >>> hvp(pow_adder_reducer, inputs, v)
+        (tensor(2.3030),
+         (tensor([0., 0.]),
+          tensor([6., 6.])))
+
+    Note:
+
+        This function is significantly slower than `vhp` due to backward mode AD constraints.
+        If your functions is twice continuously differentiable, then hvp = vhp.t(). So if you
+        know that your function satisfies this condition, you should use vhp instead that is
+        much faster with the current implementation.
+
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "hvp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "hvp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, inputs, is_inputs_tuple)
+        else:
+            if len(inputs) != 1 or inputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the input to the user-provided function "
+                    "is a single Tensor with a single element."
+                )
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "hvp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if is_outputs_tuple or not isinstance(outputs[0], torch.Tensor):
+            raise RuntimeError(
+                "The function given to hvp should return a single Tensor"
+            )
+
+        if outputs[0].nelement() != 1:
+            raise RuntimeError(
+                "The Tensor returned by the function given to hvp should contain a single element"
+            )
+
+        jac = _autograd_grad(outputs, inputs, create_graph=True)
+        _check_requires_grad(jac, "jacobian", strict=strict)
+
+        grad_jac = tuple(torch.zeros_like(inp, requires_grad=True) for inp in inputs)
+
+        double_back = _autograd_grad(jac, inputs, grad_jac, create_graph=True)
+        _check_requires_grad(jac, "hessian", strict=strict)
+
+    enable_grad = True if create_graph else torch.is_grad_enabled()
+    with torch.set_grad_enabled(enable_grad):
+        grad_res = _autograd_grad(double_back, grad_jac, v, create_graph=create_graph)
+        hvp = _fill_in_zeros(
+            grad_res, inputs, strict, create_graph, "double_back_trick"
+        )
+
+    outputs = _grad_postprocess(outputs, create_graph)
+    hvp = _grad_postprocess(hvp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        hvp, is_inputs_tuple
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/grad_mode.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/grad_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..73c07294819876b913b11d4920349d5d8f9075e1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/grad_mode.py
@@ -0,0 +1,398 @@
+# mypy: allow-untyped-defs
+from typing import Any, Union
+
+import torch
+from torch.utils._contextlib import (
+    _DecoratorContextManager,
+    _NoParamDecoratorContextManager,
+    F,
+)
+
+
+__all__ = [
+    "no_grad",
+    "enable_grad",
+    "set_grad_enabled",
+    "inference_mode",
+    "set_multithreading_enabled",
+]
+
+
+class no_grad(_NoParamDecoratorContextManager):
+    r"""Context-manager that disables gradient calculation.
+
+    Disabling gradient calculation is useful for inference, when you are sure
+    that you will not call :meth:`Tensor.backward()`. It will reduce memory
+    consumption for computations that would otherwise have `requires_grad=True`.
+
+    In this mode, the result of every computation will have
+    `requires_grad=False`, even when the inputs have `requires_grad=True`.
+    There is an exception! All factory functions, or functions that create
+    a new Tensor and take a requires_grad kwarg, will NOT be affected by
+    this mode.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Also functions as a decorator.
+
+    .. note::
+        No-grad is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+        If you want to disable forward AD for a computation, you can unpack
+        your dual tensors.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> x = torch.tensor([1.], requires_grad=True)
+        >>> with torch.no_grad():
+        ...     y = x * 2
+        >>> y.requires_grad
+        False
+        >>> @torch.no_grad()
+        ... def doubler(x):
+        ...     return x * 2
+        >>> z = doubler(x)
+        >>> z.requires_grad
+        False
+        >>> @torch.no_grad()
+        ... def tripler(x):
+        ...     return x * 3
+        >>> z = tripler(x)
+        >>> z.requires_grad
+        False
+        >>> # factory function exception
+        >>> with torch.no_grad():
+        ...     a = torch.nn.Parameter(torch.rand(10))
+        >>> a.requires_grad
+        True
+    """
+
+    def __init__(self) -> None:
+        if not torch._jit_internal.is_scripting():
+            super().__init__()
+        self.prev = False
+
+    def __enter__(self) -> None:
+        self.prev = torch.is_grad_enabled()
+        torch.set_grad_enabled(False)
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch.set_grad_enabled(self.prev)
+
+
+class enable_grad(_NoParamDecoratorContextManager):
+    r"""Context-manager that enables gradient calculation.
+
+    Enables gradient calculation, if it has been disabled via :class:`~no_grad`
+    or :class:`~set_grad_enabled`.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Also functions as a decorator.
+
+    .. note::
+        enable_grad is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> x = torch.tensor([1.], requires_grad=True)
+        >>> with torch.no_grad():
+        ...     with torch.enable_grad():
+        ...         y = x * 2
+        >>> y.requires_grad
+        True
+        >>> y.backward()
+        >>> x.grad
+        tensor([2.])
+        >>> @torch.enable_grad()
+        ... def doubler(x):
+        ...     return x * 2
+        >>> with torch.no_grad():
+        ...     z = doubler(x)
+        >>> z.requires_grad
+        True
+        >>> @torch.enable_grad()
+        ... def tripler(x):
+        ...     return x * 3
+        >>> with torch.no_grad():
+        ...     z = tripler(x)
+        >>> z.requires_grad
+        True
+
+    """
+
+    def __enter__(self) -> None:
+        self.prev = torch.is_grad_enabled()
+        torch._C._set_grad_enabled(True)
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_grad_enabled(self.prev)
+
+
+class set_grad_enabled(_DecoratorContextManager):
+    r"""Context-manager that sets gradient calculation on or off.
+
+    ``set_grad_enabled`` will enable or disable grads based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Args:
+        mode (bool): Flag whether to enable grad (``True``), or disable
+                     (``False``). This can be used to conditionally enable
+                     gradients.
+
+    .. note::
+        set_grad_enabled is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> x = torch.tensor([1.], requires_grad=True)
+        >>> is_train = False
+        >>> with torch.set_grad_enabled(is_train):
+        ...     y = x * 2
+        >>> y.requires_grad
+        False
+        >>> _ = torch.set_grad_enabled(True)
+        >>> y = x * 2
+        >>> y.requires_grad
+        True
+        >>> _ = torch.set_grad_enabled(False)
+        >>> y = x * 2
+        >>> y.requires_grad
+        False
+
+    """
+
+    def __init__(self, mode: bool) -> None:
+        self.prev = torch.is_grad_enabled()
+        self.mode = mode
+        torch._C._set_grad_enabled(mode)
+
+    def __call__(self, orig_func: F) -> F:
+        torch._C._set_grad_enabled(self.prev)
+        return super().__call__(orig_func)
+
+    def __enter__(self) -> None:
+        torch._C._set_grad_enabled(self.mode)
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_grad_enabled(self.prev)
+
+    def clone(self) -> "set_grad_enabled":
+        r"""
+        Create a copy of this class
+        """
+        return self.__class__(self.mode)
+
+
+class inference_mode(_DecoratorContextManager):
+    r"""Context-manager that enables or disables inference mode.
+
+    InferenceMode is a context manager analogous to :class:`~no_grad`
+    to be used when you are certain your operations will have no interactions
+    with autograd (e.g., model training). Code run under this mode gets better
+    performance by disabling view tracking and version counter bumps. Note that
+    unlike some other mechanisms that locally enable or disable grad,
+    entering inference_mode also disables to :ref:`forward-mode AD `.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Also functions as a decorator.
+
+    .. note::
+        Inference mode is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    Args:
+        mode (bool or function): Either a boolean flag whether to enable or
+            disable inference mode or a Python function to decorate with
+            inference mode enabled
+
+    Example::
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> import torch
+        >>> x = torch.ones(1, 2, 3, requires_grad=True)
+        >>> with torch.inference_mode():
+        ...     y = x * x
+        >>> y.requires_grad
+        False
+        >>> # xdoctest: +SKIP("want string isnt quite right")
+        >>> y._version
+        Traceback (most recent call last):
+        File "", line 1, in 
+        RuntimeError: Inference tensors do not track version counter.
+        >>> @torch.inference_mode()
+        ... def func(x):
+        ...     return x * x
+        >>> out = func(x)
+        >>> out.requires_grad
+        False
+        >>> @torch.inference_mode()
+        ... def doubler(x):
+        ...     return x * 2
+        >>> out = doubler(x)
+        >>> out.requires_grad
+        False
+
+    """
+
+    def __init__(self, mode: bool = True) -> None:
+        if not torch._jit_internal.is_scripting():
+            super().__init__()
+        self.mode = mode
+
+    def __new__(cls, mode=True):
+        if isinstance(mode, bool):
+            return super().__new__(cls)
+        return cls()(mode)
+
+    def __enter__(self) -> None:
+        self._inference_mode_context = torch._C._InferenceMode(self.mode)
+        self._inference_mode_context.__enter__()
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        self._inference_mode_context.__exit__(exc_type, exc_value, traceback)
+
+    def clone(self) -> "inference_mode":
+        r"""
+        Create a copy of this class
+        """
+        return self.__class__(self.mode)
+
+
+def _enter_inference_mode(mode):
+    mode_context = torch._C._InferenceMode(mode)
+    mode_context.__enter__()
+    return mode_context
+
+
+def _exit_inference_mode(mode):
+    mode.__exit__(None, None, None)
+
+
+class set_multithreading_enabled(_DecoratorContextManager):
+    r"""Context-manager that sets multithreaded backwards on or off.
+
+    ``set_multithreading_enabled`` will enable or disable multithreaded backwards based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Args:
+        mode (bool): Flag whether to enable multithreaded backwards (``True``), or disable
+                     (``False``).
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    """
+
+    def __init__(self, mode: bool) -> None:
+        self.prev = torch._C._is_multithreading_enabled()
+        torch._C._set_multithreading_enabled(mode)
+        self.mode = mode
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_multithreading_enabled(self.prev)
+
+    def clone(self) -> "set_multithreading_enabled":
+        r"""
+        Create a copy of this class
+        """
+        return self.__class__(self.mode)
+
+
+class _force_original_view_tracking(_DecoratorContextManager):
+    r"""Context-manager that sets whether or not to always enable view-replay in autograd.
+
+    ``set_view_replay_enabled`` will enable or disable view-replay based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    When a tensor view is mutated, the autograd engine needs to decide whether or not
+    to regenerate the "updated view" by either replaying the chain of views from the updated base,
+    or with a single call to as_strided.
+
+    If set_view_replay_enabled is set to True, then autograd will always use view replay.
+    Otherwise, it will fall back to its existing logic.
+
+    Args:
+        mode (bool): Flag whether to enable view-replay (``True``), or disable
+                     (``False``).
+
+    """
+
+    def __init__(self, mode: bool) -> None:
+        self.prev = torch._C._is_view_replay_enabled()
+        torch._C._set_view_replay_enabled(mode)
+        self.mode = mode
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_view_replay_enabled(self.prev)
+
+    def clone(self):
+        return self.__class__(self.mode)
+
+
+class _unsafe_preserve_version_counter(_DecoratorContextManager):
+    r"""DO NOT USE THIS UNLESS YOU KNOW EXACTLY WHAT YOU'RE DOING.
+
+    This context manager can lead to arbitrary silent-correctness issues in any other part of your code
+    (even the ones not touched directly by the context manager)!
+
+    Ordinarily, autograd will track mutations to tensors by incrementing it's `._version` attribute.
+    This is generally important for correctness, as for example, mutating a tensor that autograd has saved
+    for the backwards pass can result in incorrect gradients, and autograd uses the version counter to detect
+    and error out in this situation.
+
+    However, there are rare instances where it might be useful to hide mutations from autograd. For example:
+    if a tensor is very large, and you'd like to free its memory by storing it elsewhere, and re-populate
+    the tensor right before it is needed by autograd.
+
+    Args:
+        tensor (torch.Tensor): the tensor in question, that you would like to preserve the version counter of.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    """
+
+    def __init__(self, tensors: Union[torch.Tensor, tuple[torch.Tensor, ...]]) -> None:
+        self.tensors = (tensors,) if isinstance(tensors, torch.Tensor) else tensors
+        assert isinstance(self.tensors, tuple)
+        self.prev_versions = tuple(t._version for t in self.tensors)
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, *args) -> None:
+        torch._C._autograd._unsafe_set_version_counter(self.tensors, self.prev_versions)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/gradcheck.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/gradcheck.py
new file mode 100644
index 0000000000000000000000000000000000000000..20f4d9704f50ae3c6eb72ba652dccf409c44f8cf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/gradcheck.py
@@ -0,0 +1,2273 @@
+# mypy: allow-untyped-defs
+import collections
+import functools
+import warnings
+from collections.abc import Iterable
+from itertools import product
+from typing import Callable, Optional, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.testing
+from torch._vmap_internals import _vmap, vmap
+from torch.overrides import is_tensor_like
+from torch.types import _TensorOrTensors
+
+
+# Note: `get_*_jacobian` functions are added here even though we didn't intend to make them public
+# since they have been exposed from before we added `__all__`  and we already maintain BC for them
+# We should eventually deprecate them and remove them from `__all__`
+__all__ = [
+    "gradcheck",
+    "gradgradcheck",
+    "GradcheckError",
+    "get_numerical_jacobian",
+    "get_analytical_jacobian",
+    "get_numerical_jacobian_wrt_specific_input",
+]
+
+
+class GradcheckError(RuntimeError):
+    r"""Error raised by :func:`gradcheck` and :func:`gradgradcheck`."""
+
+
+def _is_sparse_compressed_tensor(obj: torch.Tensor):
+    return obj.layout in {
+        torch.sparse_csr,
+        torch.sparse_csc,
+        torch.sparse_bsr,
+        torch.sparse_bsc,
+    }
+
+
+def _is_sparse_any_tensor(obj: torch.Tensor):
+    return _is_sparse_compressed_tensor(obj) or obj.layout is torch.sparse_coo
+
+
+def _is_float_or_complex_tensor(obj):
+    return is_tensor_like(obj) and (obj.is_floating_point() or obj.is_complex())
+
+
+def _allocate_jacobians_with_inputs(
+    input_tensors: tuple, numel_output
+) -> tuple[torch.Tensor, ...]:
+    # Makes zero-filled tensors from inputs. If `numel_output` is not None, for
+    # each tensor in `input_tensors`, returns a new zero-filled tensor with height
+    # of `t.numel` and width of `numel_output`. Otherwise, for each tensor, returns
+    # a 1-d tensor with size `(t.numel,)`. Each new tensor will be strided and have
+    # the same dtype and device as those of the corresponding input.
+    out: list[torch.Tensor] = [
+        t.new_zeros((t.numel(), numel_output), layout=torch.strided)
+        for t in input_tensors
+        if _is_float_or_complex_tensor(t) and t.requires_grad
+    ]
+    return tuple(out)
+
+
+def _allocate_jacobians_with_outputs(
+    output_tensors: tuple, numel_input, dtype=None, device=None
+) -> tuple[torch.Tensor, ...]:
+    # Makes zero-filled tensors from outputs. If `dim` is not None, for each tensor
+    # in `output_tensors`, returns a new zero-filled tensor with height of `dim` and
+    # width of `t.numel`. Otherwise, for each tensor, returns a 1-d tensor with size
+    # (t.numel,).
+    options = {"dtype": dtype, "device": device, "layout": torch.strided}
+    out: list[torch.Tensor] = [
+        t.new_zeros((numel_input, t.numel()), **options)
+        for t in output_tensors
+        if _is_float_or_complex_tensor(t)
+    ]
+    return tuple(out)
+
+
+def _iter_tensors(
+    x: Union[torch.Tensor, Iterable[torch.Tensor]], only_requiring_grad: bool = False
+) -> Iterable[torch.Tensor]:
+    if is_tensor_like(x):
+        # mypy doesn't narrow type of `x` to torch.Tensor
+        if x.requires_grad or not only_requiring_grad:  # type: ignore[union-attr]
+            yield x  # type: ignore[misc]
+    elif isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+        for elem in x:
+            yield from _iter_tensors(elem, only_requiring_grad)
+
+
+def _densify(x):
+    # return a copy of sparse x with all unspecified elements
+    # "replaced" with zero-valued elements
+    if isinstance(x, (list, tuple)):
+        return type(x)(map(_densify, x))
+    elif not is_tensor_like(x) or x.layout in {torch.strided, torch._mkldnn}:  # type: ignore[attr-defined] # no attr _mkldnn
+        return x
+    elif x.layout is torch.sparse_coo:
+        device = x.device
+        indices_dtype = x._indices().dtype
+        tmp = torch.ones(x.shape[: x.sparse_dim()], dtype=torch.int8, device=device)
+        indices = tmp.nonzero().t().to(dtype=indices_dtype)
+        values = torch.zeros(
+            (tmp.numel(), *x.shape[x.sparse_dim() :]), dtype=x.dtype, device=device
+        )
+        x_coalesced = x.detach().coalesce()
+        if x_coalesced.numel() > 0:
+            stride = tmp.stride()
+            flat_indices = (
+                x_coalesced.indices()
+                .mul(
+                    torch.tensor(stride, dtype=indices_dtype, device=device).unsqueeze(
+                        1
+                    )
+                )
+                .sum(0)
+            )
+            values[flat_indices] = x_coalesced.values()
+        return (
+            torch.sparse_coo_tensor(indices, values, x.shape)
+            ._coalesced_(True)
+            .requires_grad_(x.requires_grad)
+        )
+    elif _is_sparse_compressed_tensor(x):
+        blocksize = (
+            x.values().shape[1:3]
+            if x.layout in {torch.sparse_bsr, torch.sparse_bsc}
+            else None
+        )
+        compressed_indices = (
+            x.crow_indices()
+            if x.layout in {torch.sparse_csr, torch.sparse_bsr}
+            else x.ccol_indices()
+        )
+        # We'll use intermediate sparse COO for simplicity
+        r = _densify(x.detach().to_sparse(layout=torch.sparse_coo)).to_sparse(
+            layout=x.layout, blocksize=blocksize
+        )
+        # Check that all elements are specified also after `to_sparse` op:
+        dense_numel = r.values().numel() // max(1, r.values().shape[0])
+        batch_numel = compressed_indices.numel() // compressed_indices.shape[-1]
+        sparse_numel = r.numel() // max(1, dense_numel * batch_numel)
+        if sparse_numel != r._nnz():
+            raise AssertionError(
+                f"{x.layout} densify failed: expected nnz={sparse_numel} but got {r._nnz()}"
+            )
+        return r.requires_grad_(x.requires_grad)
+    elif _is_sparse_any_tensor(x):
+        raise NotImplementedError(x.layout)
+    return x
+
+
+def _iter_tensor(x_tensor):
+    # (Only used for slow gradcheck) Returns a generator that yields the following
+    # elements at each iteration:
+    #  1) a tensor: the same tensor is returned across all iterations. The tensor
+    #     is not the same as the original x_tensor as given as input - it is
+    #     prepared so that it can be modified in-place. Depending on whether the
+    #     input tensor is strided, sparse, or dense, the returned tensor may or may
+    #     not share storage with x_tensor.
+    #  2) a tuple of indices that can be used with advanced indexing (yielded in
+    #     dictionary order)
+    #  3) flattened index that will be used to index into the Jacobian tensor
+    #
+    # For a tensor t with size (2, 2), _iter_tensor yields:
+    #     `x, (0, 0), 0`, `x, (0, 1), 1`, `x, (1, 0), 2`, `x, (1, 1), 3`
+    #
+    # where x is the t.data of the original tensor. Perturbing the entry of x
+    # at index (1, 1) yields the 3rd column of the overall Jacobian matrix.
+    if _is_sparse_any_tensor(x_tensor):
+
+        def get_stride(size):
+            dim = len(size)
+            tmp = 1
+            stride = [0] * dim
+            for i in reversed(range(dim)):
+                stride[i] = tmp
+                tmp *= size[i]
+            return stride
+
+        x_nnz = x_tensor._nnz()
+        x_size = list(x_tensor.size())
+        if x_tensor.layout is torch.sparse_coo:
+            x_indices = x_tensor._indices().t()
+            x_values = x_tensor._values()
+        elif x_tensor.layout is torch.sparse_csr:
+            x_indices = torch._convert_indices_from_csr_to_coo(
+                x_tensor.crow_indices(), x_tensor.col_indices()
+            ).t()
+            x_values = x_tensor.values()
+        elif x_tensor.layout is torch.sparse_csc:
+            x_indices = torch._convert_indices_from_csr_to_coo(
+                x_tensor.ccol_indices(), x_tensor.row_indices(), transpose=True
+            ).t()
+            x_values = x_tensor.values()
+        elif x_tensor.layout is torch.sparse_bsr:
+            x_block_values = x_tensor.values()
+            x_blocksize = x_block_values.size()[1:3]
+            x_indices = (
+                torch._convert_indices_from_csr_to_coo(
+                    x_tensor.crow_indices(), x_tensor.col_indices()
+                )
+                .repeat_interleave(x_blocksize[0] * x_blocksize[1], 1)
+                .mul_(torch.tensor(x_blocksize, device=x_tensor.device).reshape(2, 1))
+                .add_(
+                    torch.stack(
+                        torch.where(torch.ones(x_blocksize, device=x_tensor.device))
+                    ).repeat(1, x_nnz)
+                )
+                .t()
+            )
+            x_values = x_block_values.flatten(0, 2)
+            x_nnz = x_values.size(0)
+        elif x_tensor.layout is torch.sparse_bsc:
+            x_block_values = x_tensor.values()
+            x_blocksize = x_block_values.size()[1:3]
+            x_indices = (
+                torch._convert_indices_from_csr_to_coo(
+                    x_tensor.ccol_indices(), x_tensor.row_indices(), transpose=True
+                )
+                .repeat_interleave(x_blocksize[0] * x_blocksize[1], 1)
+                .mul_(torch.tensor(x_blocksize, device=x_tensor.device).reshape(2, 1))
+                .add_(
+                    torch.stack(
+                        torch.where(torch.ones(x_blocksize, device=x_tensor.device))
+                    ).repeat(1, x_nnz)
+                )
+                .t()
+            )
+            x_values = x_block_values.flatten(0, 2)
+            x_nnz = x_values.size(0)
+        else:
+            raise NotImplementedError(f"_iter_tensor for {x_tensor.layout} input")
+        x_stride = get_stride(x_size)
+        # Use .data here to get around the version check
+        x_values = x_values.data
+        for i in range(x_nnz):
+            x_value = x_values[i]
+            for x_idx in product(*[range(m) for m in x_values.size()[1:]]):
+                indices = x_indices[i].tolist() + list(x_idx)
+                d_idx = sum(indices[k] * x_stride[k] for k in range(len(x_size)))
+                yield x_value, x_idx, d_idx
+    elif x_tensor.layout == torch._mkldnn:  # type: ignore[attr-defined]
+        for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])):
+            # this is really inefficient, but without indexing implemented, there's
+            # not really a better way than converting back and forth
+            x_tensor_dense = x_tensor.to_dense()
+            yield x_tensor_dense, x_idx, d_idx
+    else:
+        # Use .data here to get around the version check
+        x_tensor = x_tensor.data
+        for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])):
+            yield x_tensor, x_idx, d_idx
+
+
+def _get_numerical_jacobian(
+    fn, inputs, outputs=None, target=None, eps=1e-3, is_forward_ad=False
+) -> list[tuple[torch.Tensor, ...]]:
+    """Compute the numerical Jacobian of `fn(inputs)` with respect to `target`.
+
+    If not specified, targets are the input. Returns M * N Jacobians where N is the
+    number of tensors in target that require grad and M is the number of non-integral
+    outputs.
+
+    Args:
+        fn: the function to compute the jacobian for
+        inputs: inputs to `fn`
+        outputs: provide precomputed outputs to avoid one extra invocation of fn
+        target: the Tensors wrt whom Jacobians are calculated (default=`inputs`)
+        eps: the magnitude of the perturbation during finite differencing
+             (default=`1e-3`)
+        is_forward_ad: if this numerical jacobian is computed to be checked wrt
+                       forward AD gradients (this is used for error checking only)
+
+    Returns:
+        A list of M N-tuples of tensors
+
+    Note that `target` may not even be part of `input` to `fn`, so please be
+    **very careful** in this to not clone `target`.
+    """
+    jacobians: list[tuple[torch.Tensor, ...]] = []
+    if outputs is None:
+        outputs = _as_tuple(fn(*_as_tuple(inputs)))
+    if not is_forward_ad and any(o.is_complex() for o in outputs):
+        raise ValueError(
+            "Expected output to be non-complex. get_numerical_jacobian no "
+            "longer supports functions that return complex outputs."
+        )
+    if target is None:
+        target = inputs
+    inp_indices = [
+        i for i, a in enumerate(target) if is_tensor_like(a) and a.requires_grad
+    ]
+    for i, (inp, inp_idx) in enumerate(zip(_iter_tensors(target, True), inp_indices)):
+        jacobians += [
+            get_numerical_jacobian_wrt_specific_input(
+                fn,
+                inp_idx,
+                inputs,
+                outputs,
+                eps,
+                input=inp,
+                is_forward_ad=is_forward_ad,
+            )
+        ]
+    return jacobians
+
+
+@deprecated(
+    "`get_numerical_jacobian` was part of PyTorch's private API and not "
+    "meant to be exposed. We are deprecating it and it will be removed "
+    "in a future version of PyTorch. If you have a specific use for "
+    "this or feature request for this to be a stable API, please file "
+    "us an issue at https://github.com/pytorch/pytorch/issues/new",
+    category=FutureWarning,
+)
+def get_numerical_jacobian(fn, inputs, target=None, eps=1e-3, grad_out=1.0):
+    """Compute the numerical Jacobian for a given fn and its inputs.
+
+    This is a Deprecated API.
+
+    Args:
+        fn: the function to compute the Jacobian for (must take inputs as a tuple)
+        inputs: input to `fn`
+        target: the Tensors wrt whom Jacobians are calculated (default=`input`)
+        eps: the magnitude of the perturbation during finite differencing
+             (default=`1e-3`)
+        grad_out: defaults to 1.0.
+
+    Returns:
+        A list of Jacobians of `fn` (restricted to its first output) with respect to
+        each input or target, if provided.
+
+    Note that `target` may not even be part of `input` to `fn`, so please be
+    **very careful** in this to not clone `target`.
+    """
+    if (
+        grad_out != 1.0
+    ):  # grad_out param is only kept for backward compatibility reasons
+        raise ValueError(
+            "Expected grad_out to be 1.0. get_numerical_jacobian no longer "
+            "supports values of grad_out != 1.0."
+        )
+
+    def fn_pack_inps(*inps):
+        return fn(inps)
+
+    jacobians = _get_numerical_jacobian(fn_pack_inps, inputs, None, target, eps)
+
+    return tuple(jacobian_for_each_output[0] for jacobian_for_each_output in jacobians)
+
+
+def _compute_numerical_gradient(fn, entry, v, norm_v, nbhd_checks_fn):
+    # Computes numerical directional derivative as finite difference
+    # of function `fn` at input `entry`, perturbed by vector `v`.
+    if _is_sparse_compressed_tensor(entry):
+        # sparse compressed tensors don't implement sub/add/copy_
+        # yet. However, in non-masked semantics context entry and v
+        # have the same sparse indices ...
+        assert entry.layout == v.layout, (entry.layout, v.layout)
+        assert entry._nnz() == v._nnz(), (entry._nnz(), v._nnz(), entry.shape)
+        # ... the finite differencing can be performed on values only:
+        entry = entry.values()
+        v = v.values()
+        # we'll detach to avoid backward computations that sparse
+        # tensors have limited support for.
+        entry = entry.detach()
+
+    orig = entry.clone()
+    entry.copy_(orig - v)
+    outa = fn()
+    entry.copy_(orig + v)
+    outb = fn()
+    entry.copy_(orig)
+
+    def compute(a, b):
+        nbhd_checks_fn(a, b)
+        ret = (b - a) / (2 * norm_v)  # use central difference approx
+        return ret.detach().reshape(-1)
+
+    return tuple(compute(a, b) for (a, b) in zip(outa, outb))
+
+
+def _compute_numerical_jvps_wrt_specific_input(
+    jvp_fn, delta, input_is_complex, is_forward_ad=False
+) -> list[torch.Tensor]:
+    # Computing the jacobian only works for real delta
+    # For details on the algorithm used here, refer:
+    # Section 3.5.3 https://arxiv.org/pdf/1701.00392.pdf
+    # s = fn(z) where z = x for real valued input
+    # and z = x + yj for complex valued input
+    jvps: list[torch.Tensor] = []
+    ds_dx_tup = jvp_fn(delta[0] if isinstance(delta, tuple) else delta)
+
+    if input_is_complex:  # C -> R
+        ds_dy_tup = (
+            jvp_fn(delta[1] * 1j) if isinstance(delta, tuple) else jvp_fn(delta * 1j)
+        )
+        for ds_dx, ds_dy in zip(ds_dx_tup, ds_dy_tup):
+            assert not ds_dx.is_complex()
+            # conjugate wirtinger derivative
+            conj_w_d = ds_dx + ds_dy * 1j
+            jvps.append(conj_w_d)
+    else:
+        for ds_dx in ds_dx_tup:  # R -> R or (R -> C for the forward AD case)
+            assert is_forward_ad or not ds_dx.is_complex()
+            jvps.append(ds_dx)
+    return jvps
+
+
+def _combine_jacobian_cols(
+    jacobians_cols: dict[int, list[torch.Tensor]], outputs, input, numel
+) -> tuple[torch.Tensor, ...]:
+    # jacobian_cols maps column_idx -> output_idx -> single column of jacobian Tensor
+    # we return a list that maps output_idx -> full jacobian Tensor
+    jacobians = _allocate_jacobians_with_outputs(
+        outputs, numel, dtype=input.dtype if input.dtype.is_complex else None
+    )
+    for i, jacobian in enumerate(jacobians):
+        for k, v in jacobians_cols.items():
+            jacobian[k] = v[i]
+    return jacobians
+
+
+def _prepare_input(
+    input: torch.Tensor, maybe_perturbed_input: Optional[torch.Tensor], fast_mode=False
+) -> torch.Tensor:
+    # Prepares the inputs to be passed into the function while including the new
+    # modified input.
+    if input.layout == torch._mkldnn:  # type: ignore[attr-defined] # no attr _mkldnn
+        # Convert back to mkldnn
+        if maybe_perturbed_input is not None:
+            return maybe_perturbed_input.to_mkldnn()
+        else:
+            return input
+    elif _is_sparse_any_tensor(input):
+        if fast_mode and maybe_perturbed_input is not None:
+            # entry is already a "cloned" version of the original tensor
+            # thus changes to entry are not reflected in the input
+            return maybe_perturbed_input
+        else:
+            return input
+    else:
+        # We cannot use entry (input.data) if we want gradgrad to work because
+        # fn (in the gradgrad case) needs to compute grad wrt input
+        return input
+
+
+def _check_outputs_same_dtype_and_shape(output1, output2, eps, idx=None) -> None:
+    # Check that the returned outputs don't have different dtype or shape when you
+    # perturb the input
+    on_index = "on index {idx} " if idx is not None else ""
+    assert output1.shape == output2.shape, (
+        f"Expected `func` to return outputs with the same shape"
+        f" when inputs are perturbed {on_index}by {eps}, but got:"
+        f" shapes {output1.shape} and {output2.shape}."
+    )
+    assert output1.dtype == output2.dtype, (
+        f"Expected `func` to return outputs with the same dtype"
+        f" when inputs are perturbed {on_index}by {eps}, but got:"
+        f" dtypes {output1.dtype} and {output2.dtype}."
+    )
+
+
+def get_numerical_jacobian_wrt_specific_input(
+    fn, input_idx, inputs, outputs, eps, input=None, is_forward_ad=False
+) -> tuple[torch.Tensor, ...]:
+    # Computes the numerical jacobians wrt to a single input. Returns N jacobian
+    # tensors, where N is the number of outputs. We use a dictionary for
+    # jacobian_cols because indices aren't necessarily consecutive for sparse inputs
+    # When we perturb only a single element of the input tensor at a time, the jvp
+    # is equivalent to a single col of the Jacobian matrix of fn.
+    jacobian_cols: dict[int, list[torch.Tensor]] = {}
+    input = inputs[input_idx] if input is None else input
+    assert input.requires_grad
+    for x, idx, d_idx in _iter_tensor(input):
+        wrapped_fn = _with_prepare_inputs(fn, inputs, input_idx, x)
+        input_to_perturb = x[idx]
+        nbhd_checks_fn = functools.partial(
+            _check_outputs_same_dtype_and_shape, idx=idx, eps=eps
+        )
+        jvp_fn = _get_numerical_jvp_fn(
+            wrapped_fn, input_to_perturb, eps, nbhd_checks_fn
+        )
+        jacobian_cols[d_idx] = _compute_numerical_jvps_wrt_specific_input(
+            jvp_fn, eps, x.is_complex(), is_forward_ad
+        )
+    return _combine_jacobian_cols(jacobian_cols, outputs, input, input.numel())
+
+
+def _get_analytical_jacobian_forward_ad(
+    fn, inputs, outputs, *, check_grad_dtypes=False, all_u=None
+) -> tuple[tuple[torch.Tensor, ...], ...]:
+    """Compute the analytical Jacobian using forward mode AD of `fn(inputs)` using forward mode AD with respect to `target`.
+
+    Return N * M Jacobians where N is the number of tensors in target that require grad and
+    M is the number of non-integral outputs.
+    Contrary to other functions here, this function requires "inputs" to actually be used by the function.
+    The computed value is expected to be wrong if the function captures the inputs by side effect instead of
+    using the passed ones (many torch.nn tests do this).
+
+    Args:
+        fn: the function to compute the jacobian for
+        inputs: inputs to `fn`
+        outputs: provide precomputed outputs to avoid one extra invocation of fn
+        check_grad_dtypes: if True, will check that the gradient dtype are valid
+        all_u (optional): if provided, the Jacobian will be right multiplied with this vector
+
+    Returns:
+        A tuple of M N-tuples of tensors
+    """
+    # To avoid early import issues
+    fwAD = torch.autograd.forward_ad
+
+    tensor_inputs = tuple(i for i in inputs if is_tensor_like(i) and i.requires_grad)
+
+    if any(i.is_complex() for i in tensor_inputs):
+        raise ValueError(
+            "Expected inputs to be non-complex for _get_analytical_jacobian_forward_ad."
+        )
+
+    if all_u:
+        jacobians = tuple(
+            _allocate_jacobians_with_outputs(outputs, 1) for i in tensor_inputs
+        )
+    else:
+        jacobians = tuple(
+            _allocate_jacobians_with_outputs(outputs, i.numel()) for i in tensor_inputs
+        )
+
+    with fwAD.dual_level():
+        fw_grads = []
+        dual_inputs = []
+        for i, inp in enumerate(inputs):
+            if is_tensor_like(inp) and inp.requires_grad:
+                if inp.layout == torch._mkldnn:  # type: ignore[attr-defined]
+                    raise ValueError(
+                        "MKLDNN inputs are not support for forward AD gradcheck."
+                    )
+
+                inp = fwAD.make_dual(inp.detach(), torch.zeros_like(inp))
+                # If inp is a differentiable view, the dual might not be the tangent given to
+                # make_dual, so read it explicitly from the dual tensor
+                fw_grads.append(fwAD.unpack_dual(inp)[1])
+            dual_inputs.append(inp)
+
+        if all_u:
+            # Do the full reduction in one pass
+            # To be consistent with numerical evaluation, we actually compute one reduction per input
+            for i, (fw_grad, u) in enumerate(zip(fw_grads, all_u)):
+                fw_grad.copy_(u.view_as(fw_grad))
+                raw_outputs = _as_tuple(fn(*dual_inputs))
+                dual_outputs = filter(_is_float_or_complex_tensor, raw_outputs)
+                for index_o, d_o in enumerate(dual_outputs):
+                    val, res = fwAD.unpack_dual(d_o)
+                    if (
+                        check_grad_dtypes
+                        and res is not None
+                        and val.is_complex() != res.is_complex()
+                    ):
+                        raise GradcheckError("Forward AD gradient has dtype mismatch.")
+
+                    # Remove extra dimension of size 1 corresponding to the reduced input
+                    jacobians[i][index_o].squeeze_(0)
+                    if res is None:
+                        jacobians[i][index_o].zero_()
+                    else:
+                        jacobians[i][index_o].copy_(res.reshape(-1))
+                fw_grad.zero_()
+        else:
+            # Reconstruct the full Jacobian column by column
+            for i, fw_grad in enumerate(fw_grads):
+                for lin_idx, grad_idx in enumerate(
+                    product(*[range(m) for m in fw_grad.size()])
+                ):
+                    fw_grad[grad_idx] = 1.0
+                    raw_outputs = _as_tuple(fn(*dual_inputs))
+                    dual_outputs = filter(_is_float_or_complex_tensor, raw_outputs)
+                    for index_o, d_o in enumerate(dual_outputs):
+                        val, res = fwAD.unpack_dual(d_o)
+                        if (
+                            check_grad_dtypes
+                            and res is not None
+                            and val.is_complex() != res.is_complex()
+                        ):
+                            raise GradcheckError(
+                                "Forward AD gradient has dtype mismatch."
+                            )
+
+                        if res is None:
+                            jacobians[i][index_o][lin_idx].zero_()
+                        else:
+                            jacobians[i][index_o][lin_idx].copy_(res.reshape(-1))
+                    fw_grad[grad_idx] = 0.0
+
+    return jacobians
+
+
+def _get_input_to_perturb(input):
+    # Prepare the input so that it can be modified in-place and do certain
+    # operations that require the tensor to have strides. If fast_mode=False,
+    # _iter_tensor would handle the below cases:
+    if input.layout == torch._mkldnn:  # type: ignore[attr-defined] # no attr _mkldnn
+        # Convert to dense so we can perform operations that require strided tensors
+        input_to_perturb = input.to_dense()
+    elif _is_sparse_any_tensor(input):
+        # Clone because input may require grad, and copy_ calls resize_,
+        # which is not allowed for .data
+        input_to_perturb = input.clone()
+    else:
+        input_to_perturb = input.data
+    return input_to_perturb
+
+
+def _with_prepare_inputs(fn, inputs, input_idx, input_to_perturb, fast_mode=False):
+    # Wraps `fn` so that its inputs are already supplied
+    def wrapped_fn():
+        inp = tuple(
+            _prepare_input(a, input_to_perturb if i == input_idx else None, fast_mode)
+            if is_tensor_like(a)
+            else a
+            for i, a in enumerate(_as_tuple(inputs))
+        )
+        return tuple(a.clone() for a in _as_tuple(fn(*inp)))
+
+    return wrapped_fn
+
+
+def _get_numerical_jvp_fn(wrapped_fn, input_to_perturb, eps, nbhd_checks_fn):
+    # Wraps jvp_fn so that certain arguments are already supplied
+    def jvp_fn(delta):
+        return _compute_numerical_gradient(
+            wrapped_fn, input_to_perturb, delta, eps, nbhd_checks_fn
+        )
+
+    return jvp_fn
+
+
+def _reshape_tensor_or_tuple(u, shape):
+    # We don't need to reshape when input corresponding to u is sparse
+    if isinstance(u, tuple):
+        if not _is_sparse_any_tensor(u[0]):
+            return (u[0].reshape(shape), u[1].reshape(shape))
+    else:
+        if not _is_sparse_any_tensor(u):
+            return u.reshape(shape)
+    return u
+
+
+def _mul_tensor_or_tuple(u, k):
+    if isinstance(u, tuple):
+        return (k * u[0], k * u[1])
+    else:
+        return k * u
+
+
+def _get_numerical_jvp_wrt_specific_input(
+    fn, input_idx, inputs, u, eps, is_forward_ad=False
+) -> list[torch.Tensor]:
+    input = inputs[input_idx]
+    input_to_perturb = _get_input_to_perturb(input)
+    wrapped_fn = _with_prepare_inputs(fn, inputs, input_idx, input_to_perturb, True)
+    nbhd_checks_fn = functools.partial(_check_outputs_same_dtype_and_shape, eps=eps)
+    jvp_fn = _get_numerical_jvp_fn(wrapped_fn, input_to_perturb, eps, nbhd_checks_fn)
+    u = _reshape_tensor_or_tuple(u, input_to_perturb.shape)
+    u = _mul_tensor_or_tuple(u, eps)
+    return _compute_numerical_jvps_wrt_specific_input(
+        jvp_fn, u, input.is_complex(), is_forward_ad
+    )
+
+
+def _get_numerical_vJu(
+    fn, inputs, inp_indices, func_out, all_u, all_v, eps, is_forward_ad
+):
+    # Note that all_v can also be None, in that case, this function only computes Ju.
+    reduced_jacobians: list[list[torch.Tensor]] = []
+    for inp_idx, u in zip(inp_indices, all_u):
+        all_Ju = _get_numerical_jvp_wrt_specific_input(
+            fn, inp_idx, inputs, u, eps, is_forward_ad
+        )
+        # Filter out the Ju for non floating point outputs
+        filtered_Ju = []
+        func_out = _as_tuple(func_out)
+        assert len(all_Ju) == len(func_out)
+        for Ju, output in zip(all_Ju, func_out):
+            if _is_float_or_complex_tensor(output):
+                filtered_Ju.append(Ju)
+            else:
+                # TODO: handle the other Ju
+                pass
+        if all_v is not None:
+            jacobian_scalars: list[torch.Tensor] = []
+            for v, Ju in zip(all_v, filtered_Ju):
+                jacobian_scalars.append(_dot_with_type_promotion(v, Ju))
+            reduced_jacobians.append(jacobian_scalars)
+        else:
+            reduced_jacobians.append(filtered_Ju)
+    return reduced_jacobians
+
+
+def _check_jacobians_equal(j1, j2, atol):
+    # Check whether the max difference between two Jacobian tensors are within some
+    # tolerance `atol`.
+    for j1_x, j2_x in zip(j1, j2):
+        if j1_x.numel() != 0 and (j1_x - j2_x).abs().max() > atol:
+            return False
+    return True
+
+
+def _stack_and_check_tensors(
+    list_of_list_of_tensors, inputs, numel_outputs
+) -> tuple[tuple[torch.Tensor, ...], bool, bool]:
+    # For the ith tensor in the inner list checks whether it has the same size and
+    # dtype as the ith differentiable input.
+    out_jacobians = _allocate_jacobians_with_inputs(inputs, numel_outputs)
+    diff_input_list = list(_iter_tensors(inputs, True))
+    correct_grad_sizes = True
+    correct_grad_types = True
+    for i, tensor_list in enumerate(list_of_list_of_tensors):
+        inp = diff_input_list[i]
+        out_jacobian = out_jacobians[i]
+        for j, tensor in enumerate(tensor_list):
+            if tensor is not None and tensor.size() != inp.size():
+                correct_grad_sizes = False
+            elif tensor is not None and tensor.dtype != inp.dtype:
+                correct_grad_types = False
+            if tensor is None:
+                out_jacobian[:, j].zero_()
+            else:
+                dense = (
+                    tensor.to_dense() if not tensor.layout == torch.strided else tensor
+                )
+                assert out_jacobian[:, j].numel() == dense.numel()
+                out_jacobian[:, j] = dense.reshape(-1)
+    return out_jacobians, correct_grad_sizes, correct_grad_types
+
+
+FAILED_NONDET_MSG = """\n
+NOTE: If your op relies on non-deterministic operations i.e., it is listed here:
+https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html
+this failure might be expected.
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `nondet_tol=` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `gradcheck_nondet_tol=`.
+- is a Module test (e.g., in common_nn.py), then modify the corresponding
+  module_test entry to have `gradcheck_nondet_tol=`
+"""
+
+
+def _check_analytical_jacobian_attributes(
+    inputs, output, nondet_tol, check_grad_dtypes, fast_mode=False, v=None
+) -> tuple[torch.Tensor, ...]:
+    # This is used by both fast and slow mode:
+    #  - For slow mode, vjps[i][j] is the jth row of the Jacobian wrt the ith
+    #    input.
+    #  - For fast mode, vjps[i][0] is a linear combination of the rows
+    #    of the Jacobian wrt the ith input
+    diff_input_list = list(_iter_tensors(inputs, True))
+
+    def vjp_fn(grad_output):
+        return torch.autograd.grad(
+            output, diff_input_list, grad_output, retain_graph=True, allow_unused=True
+        )
+
+    # Compute everything twice to check for nondeterminism (which we call reentrancy)
+    if fast_mode:
+        vjps1 = _get_analytical_vjps_wrt_specific_output(vjp_fn, output.clone(), v)
+        vjps2 = _get_analytical_vjps_wrt_specific_output(vjp_fn, output.clone(), v)
+    else:
+        vjps1 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+        vjps2 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+
+    output_numel = output.numel() if not fast_mode else 1
+    jacobians1, types_ok, sizes_ok = _stack_and_check_tensors(
+        vjps1, inputs, output_numel
+    )
+    jacobians2, _, _ = _stack_and_check_tensors(vjps2, inputs, output_numel)
+    reentrant = _check_jacobians_equal(jacobians1, jacobians2, nondet_tol)
+
+    if not types_ok and check_grad_dtypes:
+        raise GradcheckError("Gradient has dtype mismatch")
+    if not sizes_ok:
+        raise GradcheckError("Analytical gradient has incorrect size")
+    if not reentrant:
+        raise GradcheckError(
+            "Backward is not reentrant, i.e., running backward with "
+            "same input and grad_output multiple times gives different values, "
+            "although analytical gradient matches numerical gradient."
+            f"The tolerance for nondeterminism was {nondet_tol}." + FAILED_NONDET_MSG
+        )
+    return jacobians1
+
+
+def _get_analytical_vJu_backward_mode(
+    inputs, outputs, nondet_tol, check_grad_dtypes, all_v, all_u
+):
+    reduced_jacobians: list[list[torch.Tensor]] = []
+    for output, v in zip(outputs, all_v):
+        all_vJ = _check_analytical_jacobian_attributes(
+            inputs, output, nondet_tol, check_grad_dtypes, fast_mode=True, v=v
+        )
+        jacobian_scalars: list[torch.Tensor] = []
+        for vJ, u in zip(all_vJ, all_u):
+            # Why do we need squeeze here? vJ is a 2-d tensor so that we can reuse
+            # the error checking logic from slow mode
+            vJ = vJ.T.squeeze(0)
+            if vJ.is_complex():  # C -> R
+                tv = torch.view_as_real(vJ.resolve_conj())
+                tr = tv.select(-1, 0)
+                ti = tv.select(-1, 1)
+                jacobian_scalars.append(tr.dot(u[0]) + 1j * ti.dot(u[1]))
+            else:  # R -> R
+                jacobian_scalars.append(vJ.dot(u))
+        reduced_jacobians.append(jacobian_scalars)
+    return reduced_jacobians
+
+
+@deprecated(
+    "`get_analytical_jacobian` was part of PyTorch's private API and not "
+    "meant to be exposed. We are deprecating it and it will be removed "
+    "in a future version of PyTorch. If you have a specific use for "
+    "this or feature request for this to be a stable API, please file "
+    "us an issue at https://github.com/pytorch/pytorch/issues/new",
+    category=FutureWarning,
+)
+def get_analytical_jacobian(inputs, output, nondet_tol=0.0, grad_out=1.0):
+    # Replicates the behavior of the old get_analytical_jacobian before the refactor
+    # This shares much of its code with _check_analytical_jacobian_attributes
+    if (
+        grad_out != 1.0
+    ):  # grad_out param is only kept for backward compatibility reasons
+        raise ValueError(
+            "Expected grad_out to be 1.0. get_analytical_jacobian no longer "
+            "supports values of grad_out != 1.0."
+        )
+    if output.is_complex():
+        raise ValueError(
+            "Expected output to be non-complex. get_analytical_jacobian no "
+            "longer supports functions that return complex outputs."
+        )
+    diff_input_list = list(_iter_tensors(inputs, True))
+
+    def vjp_fn(grad_output):
+        return torch.autograd.grad(
+            output, diff_input_list, grad_output, retain_graph=True, allow_unused=True
+        )
+
+    # Compute everything twice to check for nondeterminism (which we call reentrancy)
+    vjps1 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+    vjps2 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+
+    output_numel = output.numel()
+    jacobians1, types_ok, sizes_ok = _stack_and_check_tensors(
+        vjps1, inputs, output_numel
+    )
+    jacobians2, _, _ = _stack_and_check_tensors(vjps2, inputs, output_numel)
+    reentrant = _check_jacobians_equal(jacobians1, jacobians2, nondet_tol)
+
+    return jacobians1, reentrant, sizes_ok, types_ok
+
+
+def _get_analytical_jacobian(inputs, outputs, input_idx, output_idx):
+    # Computes the analytical Jacobian in slow mode for a single input-output pair.
+    # Forgoes performing checks on dtype, shape, and reentrancy.
+    jacobians = _check_analytical_jacobian_attributes(
+        inputs, outputs[output_idx], nondet_tol=float("inf"), check_grad_dtypes=False
+    )
+    return jacobians[input_idx]
+
+
+def _compute_analytical_jacobian_rows(
+    vjp_fn, sample_output
+) -> list[list[Optional[torch.Tensor]]]:
+    # Computes Jacobian row-by-row by projecting `vjp_fn` = v^T J on standard basis
+    # vectors: vjp_fn(e) = e^T J is a corresponding row of the Jacobian.
+    # NB: this function does not assume vjp_fn(v) to return tensors with the same
+    # number of elements for different v. This is checked when we later combine the
+    # rows into a single tensor.
+    grad_out_base = torch.zeros_like(
+        sample_output, memory_format=torch.legacy_contiguous_format
+    )
+    flat_grad_out = grad_out_base.view(-1)
+    # jacobians_rows[i][j] is the Jacobian jth row for the ith input
+    jacobians_rows: list[list[Optional[torch.Tensor]]] = []
+    for j in range(flat_grad_out.numel()):
+        flat_grad_out.zero_()
+        flat_grad_out[j] = 1.0  # projection for jth row of Jacobian
+        grad_inputs = vjp_fn(grad_out_base)
+        for i, d_x in enumerate(grad_inputs):
+            if j == 0:
+                jacobians_rows.append([])
+            jacobians_rows[i] += [
+                d_x.clone() if isinstance(d_x, torch.Tensor) else None
+            ]
+    return jacobians_rows
+
+
+def _get_analytical_vjps_wrt_specific_output(
+    vjp_fn, sample_output, v
+) -> list[list[Optional[torch.Tensor]]]:
+    grad_inputs = vjp_fn(v.reshape(sample_output.shape))
+    vjps: list[list[Optional[torch.Tensor]]] = [
+        [vjp.clone() if isinstance(vjp, torch.Tensor) else None] for vjp in grad_inputs
+    ]
+    return vjps
+
+
+def _check_inputs(tupled_inputs) -> bool:
+    # Make sure that gradients are saved for at least one input
+    any_input_requiring_grad = False
+    for idx, inp in enumerate(tupled_inputs):
+        if is_tensor_like(inp) and inp.requires_grad:
+            if not (inp.dtype == torch.float64 or inp.dtype == torch.complex128):
+                warnings.warn(
+                    f"Input #{idx} requires gradient and "
+                    "is not a double precision floating point or complex. "
+                    "This check will likely fail if all the inputs are "
+                    "not of double precision floating point or complex. "
+                )
+            if inp.is_sparse:
+                content = inp._values()
+            elif _is_sparse_compressed_tensor(inp):
+                content = inp.values()
+            else:
+                content = inp
+            # TODO: To cover more problematic cases, replace stride = 0 check with
+            # "any overlap in memory" once we have a proper function to check it.
+            if content.layout is not torch._mkldnn:  # type: ignore[attr-defined]
+                if not all(
+                    st > 0 or sz <= 1
+                    for st, sz in zip(content.stride(), content.size())
+                ):
+                    raise RuntimeError(
+                        f"The {idx}th input has a dimension with stride 0. gradcheck only "
+                        "supports inputs that are non-overlapping to be able to "
+                        "compute the numerical gradients correctly. You should call "
+                        ".contiguous on the input before passing it to gradcheck."
+                    )
+            any_input_requiring_grad = True
+
+    if not any_input_requiring_grad:
+        raise ValueError(
+            "gradcheck expects at least one input tensor to require gradient, "
+            "but none of the them have requires_grad=True."
+        )
+    return True
+
+
+def _check_outputs(outputs) -> None:
+    if any(_is_sparse_any_tensor(t) for t in outputs if isinstance(t, torch.Tensor)):
+        # it is easier to call to_dense() on the sparse output than
+        # to modify analytical jacobian
+        raise ValueError(
+            "Sparse output is not supported at gradcheck yet. "
+            "Please call to_dense(masked_grad=...) on the output of fn for gradcheck."
+        )
+    if any(t.layout == torch._mkldnn for t in outputs if isinstance(t, torch.Tensor)):  # type: ignore[attr-defined]
+        raise ValueError(
+            "MKLDNN output is not supported at gradcheck yet. "
+            "Please call to_dense(masked_grad=...) on the output of fn for gradcheck."
+        )
+
+
+def _check_no_differentiable_outputs(
+    func, inputs, func_out, eps, *, is_forward_ad
+) -> bool:
+    # When there are no differentiable outputs, numerical gradient for a function is
+    # expected to be zero.
+    jacobians_all_inputs_outputs = _get_numerical_jacobian(
+        func, inputs, func_out, eps=eps, is_forward_ad=is_forward_ad
+    )
+    for jacobians_all_outputs_and_fixed_input in jacobians_all_inputs_outputs:
+        for jacobian in jacobians_all_outputs_and_fixed_input:
+            if torch.ne(jacobian, 0).sum() > 0:
+                raise GradcheckError(
+                    "Numerical gradient for function expected to be zero"
+                )
+    return True
+
+
+def _check_no_differentiable_outputs_fast(
+    func, func_out, all_inputs, inputs_indices, all_u, eps, nondet_tol
+):
+    for inp_idx, u in zip(inputs_indices, all_u):
+        jvps = _get_numerical_jvp_wrt_specific_input(func, inp_idx, all_inputs, u, eps)
+        for jvp in jvps:
+            if jvp.numel() == 0:
+                continue
+            if (jvp - torch.zeros_like(jvp)).abs().max() > nondet_tol:
+                raise GradcheckError(
+                    "Numerical gradient for function expected to be zero"
+                )
+    return True
+
+
+FAILED_BATCHED_GRAD_MSG = """
+gradcheck or gradgradcheck failed while testing batched gradient computation.
+This could have been invoked in a number of ways (via a test that calls
+gradcheck/gradgradcheck directly or via an autogenerated test).
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `check_batched_grad=False` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `check_batched_grad=False` and/or `check_batched_gradgrad=False`.
+
+If you're modifying an existing operator that supports batched grad computation,
+or wish to make a new operator work with batched grad computation, please read
+the following.
+
+To compute batched grads (e.g., jacobians, hessians), we vmap over the backward
+computation. The most common failure case is if there is a 'vmap-incompatible
+operation' in the backward pass. Please see
+NOTE: [How to write vmap-compatible backward formulas]
+in the codebase for an explanation of how to fix this.
+""".strip()
+
+FAILED_BATCHED_GRAD_MSG_FWD_AD = """
+gradcheck failed while testing batched gradient computation with forward-mode AD.
+This test is enabled automatically when both `check_batched_grad=True`
+and `check_forward_ad=True`, but can be disabled in the following ways
+dependong on how the test was invoked (via a test that calls gradcheck
+directly or via an autogenerated test).
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `check_batched_forward_grad=False` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `check_batched_forward_grad=False`
+"""
+
+
+def _get_failed_batched_grad_test_msg(
+    output_idx, input_idx, res, exp, is_forward_ad=False
+):
+    return f"""
+For output {output_idx} and input {input_idx}:
+
+{FAILED_BATCHED_GRAD_MSG_FWD_AD if is_forward_ad else FAILED_BATCHED_GRAD_MSG}
+
+Got:
+{res}
+
+Expected:
+{exp}
+""".strip()
+
+
+def _test_batched_grad_forward_ad(func, inputs) -> bool:
+    fwAD = torch.autograd.forward_ad  # To avoid early import issues (do we need this?)
+    assert isinstance(inputs, tuple)
+
+    for input_idx, current_input in enumerate(inputs):
+        if not (is_tensor_like(current_input) and current_input.requires_grad):
+            continue
+
+        def jvp(tangent: torch.Tensor):
+            with fwAD.dual_level():
+                dual = fwAD.make_dual(current_input.detach(), tangent)
+                inputs_with_dual = tuple(
+                    dual
+                    if idx == input_idx
+                    else (inp.detach() if is_tensor_like(inp) else inp)
+                    for idx, inp in enumerate(inputs)
+                )
+                dual_outputs = _as_tuple(func(*inputs_with_dual))
+                ret = []
+                for dual_output in dual_outputs:
+                    if dual_output is None:
+                        continue
+                    primal_out, tangent_out = fwAD.unpack_dual(dual_output)
+                    if tangent_out is not None:
+                        ret.append(tangent_out)
+                    else:
+                        ret.append(
+                            torch.zeros(
+                                [], dtype=primal_out.dtype, device=primal_out.device
+                            ).expand(primal_out.shape)
+                        )
+                return tuple(ret)
+
+        if not _is_float_or_complex_tensor(current_input):
+            continue
+
+        tangents = [torch.randn_like(current_input) for _ in range(2)]
+        expected = [jvp(t) for t in tangents]
+        expected = [torch.stack(shards) for shards in zip(*expected)]
+
+        try:
+            result = _vmap(jvp)(torch.stack(tangents))
+        except RuntimeError as ex:
+            # Rethrow to provide a better error message
+            raise GradcheckError(
+                f"While computing batched gradients, got: {ex}\n\n{FAILED_BATCHED_GRAD_MSG_FWD_AD}"
+            ) from ex
+
+        for input_idx, (res, exp) in enumerate(zip(result, expected)):
+            if torch.allclose(res, exp):
+                continue
+            raise GradcheckError(
+                _get_failed_batched_grad_test_msg(
+                    input_idx, input_idx, res, exp, is_forward_ad=True
+                )
+            )
+    return True
+
+
+def _test_batched_grad(input, output, output_idx) -> bool:
+    # NB: _test_batched_grad compares two autograd.grad invocations with a single
+    # vmap(autograd.grad) invocation. It's not exactly a "gradcheck" in the
+    # sense that we're not comparing an analytical jacobian with a numeric one,
+    # but it is morally similar (we could have computed a full analytic jac
+    # via vmap, but that is potentially slow)
+    diff_input_list = list(_iter_tensors(input, True))
+    grad = functools.partial(
+        torch.autograd.grad,
+        output,
+        diff_input_list,
+        retain_graph=True,
+        allow_unused=True,
+    )
+
+    def vjp(v):
+        results = grad(v)
+        results = tuple(
+            grad
+            if grad is not None
+            else torch.zeros([], dtype=inp.dtype, device=inp.device).expand(inp.shape)
+            for grad, inp in zip(results, diff_input_list)
+        )
+        return results
+
+    grad_outputs = [torch.randn_like(output) for _ in range(2)]
+
+    expected = [vjp(gO) for gO in grad_outputs]
+    expected = [torch.stack(shards) for shards in zip(*expected)]
+
+    # Squash warnings since these are expected to happen in most cases
+    # NB: this doesn't work for CUDA tests: https://github.com/pytorch/pytorch/issues/50209
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", message="There is a performance drop")
+        warnings.filterwarnings("ignore", message="Please use `torch.vmap`")
+        try:
+            result = vmap(vjp)(torch.stack(grad_outputs))
+        except RuntimeError as ex:
+            # It's OK that we're not raising the error at the correct callsite.
+            # That's because the callsite is always going to inside the Python
+            # autograd.grad instead of the C++ traceback of what line in the
+            # backward formula
+            raise GradcheckError(
+                f"While computing batched gradients, got: {ex}\n\n{FAILED_BATCHED_GRAD_MSG}"
+            ) from ex
+
+    for input_idx, (res, exp) in enumerate(zip(result, expected)):
+        if torch.allclose(res, exp):
+            continue
+        raise GradcheckError(
+            _get_failed_batched_grad_test_msg(output_idx, input_idx, res, exp)
+        )
+    return True
+
+
+def _test_backward_mul_by_grad_output(outputs, inputs, masked) -> bool:
+    # Tests that backward is multiplied by grad_output
+    diff_input_list: list[torch.Tensor] = list(_iter_tensors(inputs, True))
+    if not diff_input_list:
+        raise GradcheckError("no Tensors requiring grad found in input")
+    grads_input = torch.autograd.grad(
+        outputs,
+        diff_input_list,
+        [
+            torch.zeros_like(o, memory_format=torch.legacy_contiguous_format)
+            for o in outputs
+        ],
+        allow_unused=True,
+    )
+    for gi, di in zip(grads_input, diff_input_list):
+        if gi is None:
+            continue
+        if isinstance(gi, torch.Tensor) and gi.layout != torch.strided:
+            if gi.layout != di.layout:
+                raise GradcheckError(
+                    "grad is incorrect layout ("
+                    + str(gi.layout)
+                    + " is not "
+                    + str(di.layout)
+                    + ")"
+                )
+            if _is_sparse_any_tensor(gi):
+                sparse_kind = str(gi.layout).replace("torch.", "").replace("_coo", "")
+                if gi.sparse_dim() != di.sparse_dim():
+                    raise GradcheckError(
+                        f"grad is {sparse_kind} tensor, but has incorrect sparse_dim"
+                        f" {gi.sparse_dim()}, expected {di.sparse_dim()}"
+                    )
+                if gi.dense_dim() != di.dense_dim():
+                    raise GradcheckError(
+                        f"grad is {sparse_kind} tensor, but has incorrect dense_dim"
+                        f" {gi.dense_dim()}, expected {di.dense_dim()}"
+                    )
+            gi = gi.to_dense()
+            di = di.to_dense()
+        if masked:
+            if not torch.allclose(gi, torch.zeros_like(gi)):
+                raise GradcheckError("backward not multiplied by grad_output")
+        elif not gi.eq(0).all():
+            raise GradcheckError("backward not multiplied by grad_output")
+        if gi.dtype != di.dtype:
+            raise GradcheckError("grad is incorrect type")
+        if gi.device != di.device:
+            raise GradcheckError("grad is incorrect device")
+        if gi.size() != di.size():
+            raise GradcheckError("grad is incorrect size")
+    return True
+
+
+def _test_undefined_forward_mode(func, outputs, inputs):
+    fwAD = torch.autograd.forward_ad
+
+    _inp_tensors_idx, inp_tensors = _get_inp_tensors(inputs)
+    _all_v, all_u, _all_u_dense = _make_vectors(
+        inp_tensors, outputs, use_forward_ad=True
+    )
+
+    with fwAD.dual_level():
+        fw_grads = []
+        dual_inputs = []
+        tensor_indices = set()
+        for i, inp in enumerate(inputs):
+            if is_tensor_like(inp) and inp.requires_grad:
+                if inp.layout == torch._mkldnn:  # type: ignore[attr-defined]
+                    raise ValueError(
+                        "MKLDNN inputs are not support for forward AD gradcheck."
+                    )
+
+                inp = fwAD.make_dual(inp.detach(), torch.zeros_like(inp))
+                # If inp is a differentiable view, the dual might not be the tangent given to
+                # make_dual, so read it explicitly from the dual tensor
+                fw_grads.append(fwAD.unpack_dual(inp)[1])
+                tensor_indices.add(i)
+            dual_inputs.append(inp)
+
+        for i, (fw_grad, u) in enumerate(zip(fw_grads, all_u)):
+            fw_grad.copy_(u.view_as(fw_grad))
+
+        for idx, inp in enumerate(inputs):
+            if idx not in tensor_indices:
+                continue
+            dual_inp_obj = dual_inputs[idx]
+
+            # case 1 (Materialized Zero Tensor Tangent)
+            dual_inputs[idx] = fwAD.make_dual(inp.detach(), torch.zeros_like(inp))
+            raw_outputs = _as_tuple(func(*dual_inputs))
+            dual_outputs1 = filter(_is_float_or_complex_tensor, raw_outputs)
+
+            # case 2 (Efficient Zero Tensor Tangent since we don't make a dual object and pass a regular tensor)
+            dual_inputs[idx] = inp.detach()
+            raw_outputs = _as_tuple(func(*dual_inputs))
+            dual_outputs2 = filter(_is_float_or_complex_tensor, raw_outputs)
+
+            # reset
+            dual_inputs[idx] = dual_inp_obj
+
+            for index_o, (d_o1, d_o2) in enumerate(zip(dual_outputs1, dual_outputs2)):
+                _val1, res1 = fwAD.unpack_dual(d_o1)
+                _val2, res2 = fwAD.unpack_dual(d_o2)
+
+                if not (res1 is None or res2 is None):
+                    if not torch.allclose(res1, res2):
+                        raise GradcheckError(
+                            "Mismatch in tangent values for output with index: ",
+                            index_o,
+                            " when input: ",
+                            inp,
+                            " has an undefined tangent value. ",
+                            " Got: ",
+                            res1,
+                            " but expected: ",
+                            res2,
+                        )
+    return True
+
+
+def _test_undefined_backward_mode(func, outputs, inputs) -> bool:
+    diff_input_list: list[torch.Tensor] = list(_iter_tensors(inputs, True))
+    if not diff_input_list:
+        raise GradcheckError("no Tensors requiring grad found in input")
+
+    def warn_bc_breaking():
+        warnings.warn(
+            "Backwards compatibility: New undefined gradient support checking "
+            "feature is enabled by default, but it may break existing callers "
+            "of this function. If this is true for you, you can call this "
+            'function with "check_undefined_grad=False" to disable the feature'
+        )
+
+    def check_undefined_grad_support(output_to_check):
+        grads_output = [
+            torch.zeros_like(o, memory_format=torch.legacy_contiguous_format)
+            for o in output_to_check
+        ]
+        try:
+            grads_input = torch.autograd.grad(
+                output_to_check, diff_input_list, grads_output, allow_unused=True
+            )
+        except RuntimeError as e:
+            warn_bc_breaking()
+            raise GradcheckError(
+                "Expected backward function to handle undefined output grads. "
+                'Please look at "Notes about undefined output gradients" in '
+                '"tools/autograd/derivatives.yaml"'
+            ) from e
+
+        for gi in grads_input:
+            if (gi is not None) and (not gi.eq(0).all()):
+                warn_bc_breaking()
+                raise GradcheckError(
+                    "Expected all input grads to be undefined or zero when all output grads are undefined "
+                    'or zero. Please look at "Notes about undefined output gradients" in '
+                    '"tools/autograd/derivatives.yaml"'
+                )
+        return True
+
+    # All backward functions must work properly if all output grads are undefined
+    outputs_to_check = [
+        [
+            torch._C._functions.UndefinedGrad()(o)
+            for o in _differentiable_outputs(func(*inputs))
+            # This check filters out Tensor-likes that aren't instances of Tensor.
+            if isinstance(o, torch.Tensor)
+        ]
+    ]
+
+    # If there are multiple output grads, we should be able to undef one at a time without error
+    if len(outputs_to_check[0]) > 1:
+        for undef_grad_idx in range(len(outputs)):
+            output_to_check = _differentiable_outputs(func(*inputs))
+            outputs_to_check.append(
+                [
+                    torch._C._functions.UndefinedGrad()(o)
+                    if idx == undef_grad_idx
+                    else o
+                    for idx, o in enumerate(output_to_check)
+                ]
+            )
+
+    return all(check_undefined_grad_support(output) for output in outputs_to_check)
+
+
+def _as_tuple(x):
+    if isinstance(x, tuple):
+        return x
+    elif isinstance(x, list):
+        return tuple(x)
+    else:
+        return (x,)
+
+
+def _differentiable_outputs(x):
+    return tuple(o for o in _as_tuple(x) if o.requires_grad)
+
+
+def _get_notallclose_msg(
+    analytical,
+    numerical,
+    output_idx,
+    input_idx,
+    complex_indices,
+    test_imag=False,
+    is_forward_ad=False,
+) -> str:
+    out_is_complex = (
+        (not is_forward_ad) and complex_indices and output_idx in complex_indices
+    )
+    inp_is_complex = is_forward_ad and complex_indices and input_idx in complex_indices
+    part = "imaginary" if test_imag else "real"
+    element = "inputs" if is_forward_ad else "outputs"
+    prefix = (
+        ""
+        if not (out_is_complex or inp_is_complex)
+        else f"While considering the {part} part of complex {element} only, "
+    )
+    mode = "computed with forward mode " if is_forward_ad else ""
+    return (
+        prefix
+        + f"Jacobian {mode}mismatch for output {output_idx:d} with respect to input {input_idx:d},\n"
+        f"numerical:{numerical}\nanalytical:{analytical}\n"
+    )
+
+
+def _transpose(matrix_of_tensors):
+    # returns list of tuples
+    return list(zip(*matrix_of_tensors))
+
+
+def _real_and_imag_output(fn):
+    # returns new functions real(fn), and imag(fn) where real(fn) and imag(fn) behave the same as
+    # the original fn, except torch.real or torch.imag are applied to the complex outputs
+    def apply_to_c_outs(fn, fn_to_apply):
+        def wrapped_fn(*inputs):
+            outs = _as_tuple(fn(*inputs))
+            return tuple(fn_to_apply(o) if o.is_complex() else o for o in outs)
+
+        return wrapped_fn
+
+    return apply_to_c_outs(fn, torch.real), apply_to_c_outs(fn, torch.imag)
+
+
+def _real_and_imag_input(fn, complex_inp_indices, tupled_inputs):
+    # returns new functions that take real inputs instead of complex inputs as
+    # (x, y) -> fn(x + y * 1j). And it computes: inp -> fn(inp + y * 1j) and inp -> fn(x + inp * 1j).
+    # In each case, the other part is considered constant.
+    # We do not use 0 for the constant here to make sure we always call the user function with a valid input.
+    def apply_to_c_inps(fn, fn_to_apply):
+        def wrapped_fn(*inputs):
+            new_inputs = list(inputs)
+            for should_be_complex in complex_inp_indices:
+                new_inputs[should_be_complex] = fn_to_apply(
+                    new_inputs[should_be_complex], tupled_inputs[should_be_complex]
+                )
+            return _as_tuple(fn(*new_inputs))
+
+        return wrapped_fn
+
+    real_fn = apply_to_c_inps(fn, lambda inp, orig: inp + orig.imag * 1j)
+    imag_fn = apply_to_c_inps(fn, lambda inp, orig: orig.real + inp * 1j)
+    return real_fn, imag_fn
+
+
+def _gradcheck_real_imag(
+    gradcheck_fn,
+    func,
+    func_out,
+    tupled_inputs,
+    outputs,
+    eps,
+    rtol,
+    atol,
+    check_grad_dtypes,
+    check_forward_ad,
+    check_backward_ad,
+    nondet_tol,
+    check_undefined_grad,
+):
+    complex_out_indices = [i for i, o in enumerate(outputs) if o.is_complex()]
+    has_any_complex_output = any(o.is_complex() for o in _as_tuple(func_out))
+    if check_backward_ad:
+        if has_any_complex_output:
+            real_fn, imag_fn = _real_and_imag_output(func)
+
+            imag_func_out = imag_fn(*tupled_inputs)
+            imag_outputs = _differentiable_outputs(imag_func_out)
+            gradcheck_fn(
+                imag_fn,
+                imag_func_out,
+                tupled_inputs,
+                imag_outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_out_indices,
+                test_imag=True,
+            )
+
+            real_func_out = real_fn(*tupled_inputs)
+            real_outputs = _differentiable_outputs(real_func_out)
+            gradcheck_fn(
+                real_fn,
+                real_func_out,
+                tupled_inputs,
+                real_outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_out_indices,
+            )
+        else:
+            gradcheck_fn(
+                func,
+                func_out,
+                tupled_inputs,
+                outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+            )
+
+    if check_forward_ad:
+        complex_inp_indices = [
+            i
+            for i, inp in enumerate(tupled_inputs)
+            if is_tensor_like(inp) and inp.is_complex()
+        ]
+        if complex_inp_indices:
+            real_fn, imag_fn = _real_and_imag_input(
+                func, complex_inp_indices, tupled_inputs
+            )
+
+            imag_inputs = [
+                inp.imag if is_tensor_like(inp) and inp.is_complex() else inp
+                for inp in tupled_inputs
+            ]
+            imag_func_out = imag_fn(*imag_inputs)
+            diff_imag_func_out = _differentiable_outputs(imag_func_out)
+            gradcheck_fn(
+                imag_fn,
+                imag_func_out,
+                imag_inputs,
+                diff_imag_func_out,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_inp_indices,
+                test_imag=True,
+                use_forward_ad=True,
+            )
+
+            real_inputs = [
+                inp.real if is_tensor_like(inp) and inp.is_complex() else inp
+                for inp in tupled_inputs
+            ]
+            real_func_out = real_fn(*real_inputs)
+            diff_real_func_out = _differentiable_outputs(real_func_out)
+            gradcheck_fn(
+                real_fn,
+                real_func_out,
+                real_inputs,
+                diff_real_func_out,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_inp_indices,
+                use_forward_ad=True,
+            )
+            if check_undefined_grad:
+                _test_undefined_forward_mode(imag_fn, imag_func_out, imag_inputs)
+                _test_undefined_forward_mode(real_fn, real_func_out, real_inputs)
+        else:
+            gradcheck_fn(
+                func,
+                func_out,
+                tupled_inputs,
+                outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                use_forward_ad=True,
+            )
+            if check_undefined_grad:
+                _test_undefined_forward_mode(func, outputs, tupled_inputs)
+
+
+def _slow_gradcheck(
+    func,
+    func_out,
+    tupled_inputs,
+    outputs,
+    eps,
+    rtol,
+    atol,
+    check_grad_dtypes,
+    nondet_tol,
+    *,
+    use_forward_ad=False,
+    complex_indices=None,
+    test_imag=False,
+    masked=False,
+):
+    func_out = _as_tuple(func_out)
+    if not outputs:
+        return _check_no_differentiable_outputs(
+            func, tupled_inputs, func_out, eps=eps, is_forward_ad=use_forward_ad
+        )
+    tupled_inputs_numerical = tupled_inputs if masked else _densify(tupled_inputs)
+
+    numerical = _transpose(
+        _get_numerical_jacobian(
+            func,
+            tupled_inputs_numerical,
+            func_out,
+            eps=eps,
+            is_forward_ad=use_forward_ad,
+        )
+    )
+    # Note: [numerical vs analytical output length]
+    # The numerical path returns jacobian quantity for all outputs, even if requires_grad of that
+    # output is False. This behavior is necessary for _check_no_differentiable_outputs to work.
+    numerical = [nj for o, nj in zip(func_out, numerical) if o.requires_grad]
+    if use_forward_ad:
+        analytical_forward = _get_analytical_jacobian_forward_ad(
+            func, tupled_inputs, func_out, check_grad_dtypes=check_grad_dtypes
+        )
+
+        for i, n_per_out in enumerate(numerical):
+            for j, n in enumerate(n_per_out):
+                a = analytical_forward[j][i]
+                if not _allclose_with_type_promotion(a, n.to(a.device), rtol, atol):
+                    raise GradcheckError(
+                        _get_notallclose_msg(
+                            a, n, i, j, complex_indices, test_imag, is_forward_ad=True
+                        )
+                    )
+    else:
+        for i, o in enumerate(outputs):
+            analytical = _check_analytical_jacobian_attributes(
+                tupled_inputs, o, nondet_tol, check_grad_dtypes
+            )
+
+            for j, (a, n) in enumerate(zip(analytical, numerical[i])):
+                if not _allclose_with_type_promotion(a, n.to(a.device), rtol, atol):
+                    raise GradcheckError(
+                        _get_notallclose_msg(a, n, i, j, complex_indices, test_imag)
+                    )
+
+    return True
+
+
+def _dot_with_type_promotion(u, v):
+    assert u.dim() == 1 and v.dim() == 1
+    return (u * v).sum()
+
+
+def _allclose_with_type_promotion(a, b, rtol, atol):
+    promoted_type = torch.promote_types(a.dtype, b.dtype)
+    a = a.to(dtype=promoted_type)
+    b = b.to(dtype=promoted_type)
+    return torch.allclose(a, b, rtol, atol)
+
+
+def _to_real_dtype(dtype):
+    if dtype == torch.complex128:
+        return torch.float64
+    elif dtype == torch.complex64:
+        return torch.float32
+    else:
+        return dtype
+
+
+def _vec_from_tensor(x, generator, downcast_complex=False):
+    # Create a random vector with the same number of elements as x and the same
+    # dtype/device. If x is complex and downcast_complex is False, we create a
+    # complex tensor with only real component.
+    if x.layout == torch.sparse_coo:
+        # For sparse, create a random sparse vec with random values in the same
+        # indices. Make sure size is set so that it isn't inferred to be smaller.
+        x_values = x._values()
+        dtype = _to_real_dtype(x.dtype) if downcast_complex else x.dtype
+        values = (
+            torch.rand(x_values.numel(), generator=generator)
+            .to(dtype=dtype, device=x.device)
+            .view(x_values.shape)
+        )
+        values /= values.norm()
+        vec = torch.sparse_coo_tensor(x._indices(), values, x.size(), device=x.device)
+    elif _is_sparse_compressed_tensor(x):
+        if x.layout in {torch.sparse_csr, torch.sparse_bsr}:
+            compressed_indices, plain_indices = x.crow_indices(), x.col_indices()
+        else:
+            compressed_indices, plain_indices = x.ccol_indices(), x.row_indices()
+        x_values = x.values()
+        dtype = _to_real_dtype(x.dtype) if downcast_complex else x.dtype
+        values = (
+            torch.rand(x_values.numel(), generator=generator)
+            .to(dtype=dtype, device=x.device)
+            .view(x_values.shape)
+        )
+        values /= values.norm()
+        vec = torch.sparse_compressed_tensor(
+            compressed_indices,
+            plain_indices,
+            values,
+            x.size(),
+            layout=x.layout,
+            device=x.device,
+        )
+    else:
+        dtype = _to_real_dtype(x.dtype) if downcast_complex else x.dtype
+        vec = torch.rand(x.numel(), generator=generator).to(
+            dtype=dtype, device=x.device
+        )
+        vec /= vec.norm()
+    return vec
+
+
+def _get_inp_tensors(tupled_inputs):
+    inp_idx_tup = [
+        (i, t)
+        for i, t in enumerate(tupled_inputs)
+        if is_tensor_like(t) and t.requires_grad
+    ]
+    return [tup[0] for tup in inp_idx_tup], [tup[1] for tup in inp_idx_tup]
+
+
+def _adjusted_atol(atol, u, v):
+    # In slow gradcheck, we compare A and B element-wise, i.e., for some a, b we
+    # allow: |a - b| < atol + rtol * b. But since we now compare q1 = v^T A u and
+    # q2 = v^T B u, we must allow |q1 - q2| < v^T E u + rtol * v^T B u, where E is
+    # the correctly sized matrix in which each entry is atol.
+    #
+    # We see that atol needs to be scaled by v^T M u (where M is an all-ones M x N
+    # matrix): v^T M u = \sum_{i} \sum_{j} u_i * v_j = (\sum_{i} u_i)(\sum_{i} v_i)
+    # TODO: properly handle case when u is tuple instead of only taking first element
+    u = u[0] if isinstance(u, tuple) else u
+    sum_u = u.sum()
+    sum_v = 1.0 if v is None else v.sum()
+    return atol * float(sum_u) * float(sum_v)
+
+
+FAST_FAIL_SLOW_OK_MSG = """
+Fast gradcheck failed but element-wise differences are small. This means that the
+test might've passed in slow_mode!
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck:
+
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `fast_mode=False` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `gradcheck_fast_mode=False`
+- is a Module test (e.g., in common_nn.py), then modify the corresponding
+  module_test entry to have `gradcheck_fast_mode=False`
+""".strip()
+
+
+def _run_slow_mode_and_get_error(
+    func, tupled_inputs, outputs, input_idx, output_idx, rtol, atol, eps, is_forward_ad
+):
+    # Compute jacobians in slow mode for better error message
+    slow_numerical = _get_numerical_jacobian(
+        func, tupled_inputs, outputs, eps=eps, is_forward_ad=is_forward_ad
+    )[input_idx][output_idx]
+    if is_forward_ad:
+
+        def new_fn(inp):
+            new_inputs = list(tupled_inputs)
+            new_inputs[input_idx] = inp
+            return _as_tuple(func(*new_inputs))[output_idx]
+
+        slow_analytical = _get_analytical_jacobian_forward_ad(
+            new_fn, (tupled_inputs[input_idx],), (outputs[output_idx],)
+        )[0][0]
+    else:
+        slow_analytical = _get_analytical_jacobian(
+            tupled_inputs, outputs, input_idx, output_idx
+        )
+
+    # Assume jacobians are non-empty and have the same shape
+    slow_max_diff = (slow_numerical - slow_analytical).abs().max()
+
+    slow_allclose = torch.allclose(slow_analytical, slow_numerical, rtol, atol)
+    msg = (
+        "\nThe above quantities relating the numerical and analytical jacobians are computed \n"
+        "in fast mode. See: https://github.com/pytorch/pytorch/issues/53876 for more background \n"
+        "about fast mode. Below, we recompute numerical and analytical jacobians in slow mode:\n\n"
+        f"Numerical:\n {slow_numerical}\n"
+        f"Analytical:\n{slow_analytical}\n\n"
+        f"The max per-element difference (slow mode) is: {slow_max_diff}.\n"
+    )
+    if slow_allclose:
+        # Slow gradcheck would've passed!
+        msg += FAST_FAIL_SLOW_OK_MSG
+    return msg
+
+
+def _to_flat_dense_if_sparse(tensor):
+    if _is_sparse_any_tensor(tensor):
+        return tensor.to_dense().reshape(-1)
+    else:
+        return tensor
+
+
+def _make_vectors(inp_tensors, outputs, *, use_forward_ad):
+    # Use our own generator to avoid messing with the user's RNG state
+    g_cpu = torch.Generator()
+
+    def _vec_from_tensor_cpu(*args):
+        # Default allocate all tensors on CPU, so they are on the same device as the generator
+        # even if the user specified a default device
+        with torch.device("cpu"):
+            return _vec_from_tensor(*args)
+
+    all_u = []
+    all_u_dense = []
+    for inp in inp_tensors:
+        ur = _vec_from_tensor_cpu(inp, g_cpu, True)
+        ur_dense = _to_flat_dense_if_sparse(ur)
+        if inp.is_complex():
+            ui = _vec_from_tensor_cpu(inp, g_cpu, True)
+            all_u.append((ur, ui))
+            ui_dense = _to_flat_dense_if_sparse(ui)
+            all_u_dense.append((ur_dense, ui_dense))
+        else:
+            all_u.append(ur)
+            all_u_dense.append(ur_dense)
+    all_v = (
+        None
+        if use_forward_ad
+        else [_vec_from_tensor_cpu(out, g_cpu) for out in outputs]
+    )
+    return all_v, all_u, all_u_dense
+
+
+def _check_analytical_numerical_equal(
+    all_analytical,
+    all_numerical,
+    complex_indices,
+    tupled_inputs,
+    outputs,
+    func,
+    all_v,
+    all_u,
+    rtol,
+    atol,
+    eps,
+    test_imag,
+    *,
+    is_forward_ad=False,
+):
+    for i, all_numerical_for_input_i in enumerate(all_numerical):
+        for j, n in enumerate(all_numerical_for_input_i):
+            # Forward AD generates the transpose of what this function expects
+            if is_forward_ad:
+                a = all_analytical[i][j]
+            else:
+                a = all_analytical[j][i]
+            n = n.to(device=a.device)
+            updated_atol = _adjusted_atol(atol, all_u[i], all_v[j] if all_v else None)
+            if not _allclose_with_type_promotion(a, n.to(a.device), rtol, updated_atol):
+                jacobians_str = _run_slow_mode_and_get_error(
+                    func, tupled_inputs, outputs, i, j, rtol, atol, eps, is_forward_ad
+                )
+                raise GradcheckError(
+                    _get_notallclose_msg(
+                        a, n, j, i, complex_indices, test_imag, is_forward_ad
+                    )
+                    + jacobians_str
+                )
+
+
+def _fast_gradcheck(
+    func,
+    func_out,
+    inputs,
+    outputs,
+    eps,
+    rtol,
+    atol,
+    check_grad_dtypes,
+    nondet_tol,
+    *,
+    use_forward_ad=False,
+    complex_indices=None,
+    test_imag=False,
+    masked=False,
+):
+    # See https://github.com/pytorch/pytorch/issues/53876 for details
+    inp_tensors_idx, inp_tensors = _get_inp_tensors(inputs)
+    # Backward mode computes v^T * J (VJP)
+    # Since we computed J * u (JVP) through finite difference method, we perform an equality check
+    # between VJP * u, v * JVP
+    # ----
+    # Forward mode computes J * u (JVP)
+    # Since we already compute JVP through finite difference method,
+    # we don't need v for correctness check here as asserted below
+    all_v, all_u, all_u_dense = _make_vectors(
+        inp_tensors, outputs, use_forward_ad=use_forward_ad
+    )
+
+    inputs_numerical, all_u_numerical, all_v_numerical = (
+        (inputs, all_u, all_v) if masked else _densify((inputs, all_u, all_v))
+    )
+
+    numerical_vJu = _get_numerical_vJu(
+        func,
+        inputs_numerical,
+        inp_tensors_idx,
+        func_out,
+        all_u_numerical,
+        all_v_numerical,
+        eps,
+        is_forward_ad=use_forward_ad,
+    )
+    # TODO: replicate https://github.com/pytorch/pytorch/pull/77743 for fast gradcheck as well
+    if use_forward_ad:
+        assert all_v is None
+        analytical_vJu = _get_analytical_jacobian_forward_ad(
+            func,
+            inputs,
+            _as_tuple(func_out),
+            all_u=all_u,
+            check_grad_dtypes=check_grad_dtypes,
+        )
+    else:
+        if not outputs:
+            _check_no_differentiable_outputs_fast(
+                func, func_out, inputs, inp_tensors_idx, all_u, eps, nondet_tol
+            )
+
+        analytical_vJu = _get_analytical_vJu_backward_mode(
+            inputs, outputs, nondet_tol, check_grad_dtypes, all_v, all_u_dense
+        )
+
+    _check_analytical_numerical_equal(
+        analytical_vJu,
+        numerical_vJu,
+        complex_indices,
+        inputs,
+        outputs,
+        func,
+        all_v,
+        all_u,
+        rtol,
+        atol,
+        eps,
+        test_imag,
+        is_forward_ad=use_forward_ad,
+    )
+
+    return True
+
+
+# Note [VarArg of Tensors]
+# ~~~~~~~~~~~~~~~~~~~~~~~~
+# 'func' accepts a vararg of tensors, which isn't expressable in the type system at the moment.
+# If https://mypy.readthedocs.io/en/latest/additional_features.html?highlight=callable#extended-callable-types is accepted,
+# the '...' first argument of Callable can be replaced with VarArg(Tensor).
+# For now, we permit any input.
+def gradcheck(
+    func: Callable[..., Union[_TensorOrTensors]],  # See Note [VarArg of Tensors]
+    inputs: _TensorOrTensors,
+    *,
+    eps: float = 1e-6,
+    atol: float = 1e-5,
+    rtol: float = 1e-3,
+    raise_exception: bool = True,
+    nondet_tol: float = 0.0,
+    check_undefined_grad: bool = True,
+    check_grad_dtypes: bool = False,
+    check_batched_grad: bool = False,
+    check_batched_forward_grad: bool = False,
+    check_forward_ad: bool = False,
+    check_backward_ad: bool = True,
+    fast_mode: bool = False,
+    masked: Optional[bool] = None,
+) -> bool:  # noqa: D400,D205
+    r"""Check gradients computed via small finite differences against analytical
+    gradients wrt tensors in :attr:`inputs` that are of floating point or complex type
+    and with ``requires_grad=True``.
+
+    The check between numerical and analytical gradients uses :func:`~torch.allclose`.
+
+    For most of the complex functions we consider for optimization purposes, no notion of
+    Jacobian exists. Instead, gradcheck verifies if the numerical and analytical values of
+    the Wirtinger and Conjugate Wirtinger derivatives are consistent. Because the gradient
+    computation is done under the assumption that the overall function has a real-valued
+    output, we treat functions with complex output in a special way. For these functions,
+    gradcheck is applied to two real-valued functions corresponding to taking the real
+    components of the complex outputs for the first, and taking the imaginary components
+    of the complex outputs for the second. For more details, check out
+    :ref:`complex_autograd-doc`.
+
+    .. note::
+        The default values are designed for :attr:`input` of double precision.
+        This check will likely fail if :attr:`input` is of less precision, e.g.,
+        ``FloatTensor``.
+
+    .. note::
+        Gradcheck may fail when evaluated on non-differentiable points
+        because the numerically computed gradients via finite differencing may differ
+        those computed analytically (not necessarily because either is incorrect).
+        For more context, see :ref:`non-differentiable-func-grad`.
+
+    .. warning::
+       If any checked tensor in :attr:`input` has overlapping memory, i.e.,
+       different indices pointing to the same memory address (e.g., from
+       :func:`torch.Tensor.expand`), this check will likely fail because the numerical
+       gradients computed by point perturbation at such indices will change
+       values at all other indices that share the same memory address.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor or a tuple of Tensors
+        inputs (tuple of Tensor or Tensor): inputs to the function
+        eps (float, optional): perturbation for finite differences
+        atol (float, optional): absolute tolerance
+        rtol (float, optional): relative tolerance
+        raise_exception (bool, optional): indicating whether to raise an exception if
+            the check fails. The exception gives more information about the
+            exact nature of the failure. This is helpful when debugging gradchecks.
+        nondet_tol (float, optional): tolerance for non-determinism. When running
+            identical inputs through the differentiation, the results must either match
+            exactly (default, 0.0) or be within this tolerance.
+        check_undefined_grad (bool, optional): if ``True``, check if undefined output grads
+            are supported and treated as zeros, for ``Tensor`` outputs.
+        check_batched_grad (bool, optional): if ``True``, check if we can compute
+            batched gradients using prototype vmap support. Defaults to False.
+        check_batched_forward_grad (bool, optional): if ``True``, checks if we can compute
+            batched forward gradients using forward ad and prototype vmap support. Defaults to ``False``.
+        check_forward_ad (bool, optional): if ``True``, check that the gradients computed with forward
+            mode AD match the numerical ones. Defaults to ``False``.
+        check_backward_ad (bool, optional): if ``False``, do not perform any checks that rely on
+            backward mode AD to be implemented. Defaults to ``True``.
+        fast_mode (bool, optional): Fast mode for gradcheck and gradgradcheck is currently only
+            implemented for R to R functions. If none of the inputs and outputs are complex
+            a faster implementation of gradcheck that no longer computes the entire jacobian
+            is run; otherwise, we fall back to the slow implementation.
+        masked (bool, optional): if ``True``, the gradients of unspecified elements of
+            sparse tensors are ignored. Defaults to ``False``.
+    Returns:
+        ``True`` if all differences satisfy allclose condition
+
+    """
+    assert (
+        check_forward_ad or check_backward_ad
+    ), "Expected at least one of check_forward_ad or check_backward_ad to be True"
+    assert not (
+        check_batched_grad and not check_backward_ad
+    ), "Setting check_batched_grad=True requires check_backward_ad to be True"
+    assert not (
+        check_batched_forward_grad and not check_forward_ad
+    ), "Setting check_batched_forward_grad=True requires check_forward_ad to be True"
+    args = locals().copy()
+    args.pop("raise_exception")
+    if not raise_exception:
+        try:
+            return _gradcheck_helper(**args)
+        except GradcheckError:
+            return False
+    else:
+        return _gradcheck_helper(**args)
+
+
+def _gradcheck_helper(
+    func,
+    inputs,
+    eps,
+    atol,
+    rtol,
+    nondet_tol,
+    check_undefined_grad,
+    check_grad_dtypes,
+    check_batched_grad,
+    check_batched_forward_grad,
+    check_forward_ad,
+    check_backward_ad,
+    fast_mode,
+    masked,
+):
+    tupled_inputs = _as_tuple(inputs)
+    _check_inputs(tupled_inputs)
+
+    func_out = func(*tupled_inputs)
+    outputs = _differentiable_outputs(func_out)
+    _check_outputs(outputs)
+
+    gradcheck_fn = functools.partial(
+        _fast_gradcheck if fast_mode else _slow_gradcheck, masked=masked
+    )
+    _gradcheck_real_imag(
+        gradcheck_fn,
+        func,
+        func_out,
+        tupled_inputs,
+        outputs,
+        eps,
+        rtol,
+        atol,
+        check_grad_dtypes,
+        check_forward_ad=check_forward_ad,
+        check_backward_ad=check_backward_ad,
+        nondet_tol=nondet_tol,
+        check_undefined_grad=check_undefined_grad,
+    )
+
+    if check_batched_forward_grad:
+        _test_batched_grad_forward_ad(func, tupled_inputs)
+
+    # Short circuit because remaining tests rely on backward AD to be implemented
+    if not check_backward_ad:
+        return True
+
+    for i, o in enumerate(outputs):
+        if check_batched_grad:
+            _test_batched_grad(tupled_inputs, o, i)
+
+    _test_backward_mul_by_grad_output(outputs, tupled_inputs, masked)
+
+    if check_undefined_grad and check_backward_ad:
+        _test_undefined_backward_mode(func, outputs, tupled_inputs)
+    return True
+
+
+def gradgradcheck(
+    func: Callable[..., _TensorOrTensors],  # See Note [VarArg of Tensors]
+    inputs: _TensorOrTensors,
+    grad_outputs: Optional[_TensorOrTensors] = None,
+    *,
+    eps: float = 1e-6,
+    atol: float = 1e-5,
+    rtol: float = 1e-3,
+    gen_non_contig_grad_outputs: bool = False,
+    raise_exception: bool = True,
+    nondet_tol: float = 0.0,
+    check_undefined_grad: bool = True,
+    check_grad_dtypes: bool = False,
+    check_batched_grad: bool = False,
+    check_fwd_over_rev: bool = False,
+    check_rev_over_rev: bool = True,
+    fast_mode: bool = False,
+    masked: bool = False,
+) -> bool:  # noqa: D400,D205
+    r"""Check gradients of gradients computed via small finite differences
+    against analytical gradients wrt tensors in :attr:`inputs` and
+    :attr:`grad_outputs` that are of floating point or complex type and with
+    ``requires_grad=True``.
+
+    This function checks that backpropagating through the gradients computed
+    to the given :attr:`grad_outputs` are correct.
+
+    The check between numerical and analytical gradients uses :func:`~torch.allclose`.
+
+    .. note::
+        The default values are designed for :attr:`input` and
+        :attr:`grad_outputs` of double precision. This check will likely fail if
+        they are of less precision, e.g., ``FloatTensor``.
+
+    .. warning::
+       If any checked tensor in :attr:`input` and :attr:`grad_outputs` has
+       overlapping memory, i.e., different indices pointing to the same memory
+       address (e.g., from :func:`torch.Tensor.expand`), this check will likely fail
+       because the numerical gradients computed by point perturbation at such
+       indices will change values at all other indices that share the same
+       memory address.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor or a tuple of Tensors
+        inputs (tuple of Tensor or Tensor): inputs to the function
+        grad_outputs (tuple of Tensor or Tensor, optional): The gradients with
+            respect to the function's outputs.
+        eps (float, optional): perturbation for finite differences
+        atol (float, optional): absolute tolerance
+        rtol (float, optional): relative tolerance
+        gen_non_contig_grad_outputs (bool, optional): if :attr:`grad_outputs` is
+            ``None`` and :attr:`gen_non_contig_grad_outputs` is ``True``, the
+            randomly generated gradient outputs are made to be noncontiguous
+        raise_exception (bool, optional): indicating whether to raise an exception if
+            the check fails. The exception gives more information about the
+            exact nature of the failure. This is helpful when debugging gradchecks.
+        nondet_tol (float, optional): tolerance for non-determinism. When running
+            identical inputs through the differentiation, the results must either match
+            exactly (default, 0.0) or be within this tolerance. Note that a small amount
+            of nondeterminism in the gradient will lead to larger inaccuracies in
+            the second derivative.
+        check_undefined_grad (bool, optional): if True, check if undefined output grads
+            are supported and treated as zeros
+        check_batched_grad (bool, optional): if True, check if we can compute
+            batched gradients using prototype vmap support. Defaults to False.
+        fast_mode (bool, optional): if True, run a faster implementation of gradgradcheck that
+            no longer computes the entire jacobian.
+        masked (bool, optional): if True, the gradients of unspecified elements of
+            sparse tensors are ignored (default, False).
+    Returns:
+        True if all differences satisfy allclose condition
+    """
+    assert (
+        check_fwd_over_rev or check_rev_over_rev
+    ), "Expected at least one of check_fwd_over_rev or check_rev_over_rev to be True"
+    assert not (
+        check_undefined_grad and not check_rev_over_rev
+    ), "Setting check_undefined_grad=True requires check_rev_over_rev to be True"
+    assert not (
+        check_batched_grad and not check_rev_over_rev
+    ), "Setting check_batched_grad=True requires check_rev_over_rev to be True"
+    # TODO: do we want to test this too?
+    # assert not (check_batched_forward_grad and not check_fwd_over_rev), (
+    #     "Setting check_batched_forward_grad=True requires check_fwd_over_rev to be True")
+    tupled_inputs = _as_tuple(inputs)
+
+    if grad_outputs is None:
+        # If grad_outputs is not specified, create random Tensors of the same shape, type, and device as the outputs
+
+        outputs = _differentiable_outputs(func(*tupled_inputs))
+        tupled_grad_outputs = tuple(
+            torch.testing.make_tensor(
+                x.shape,
+                dtype=x.dtype
+                if x.is_floating_point() or x.is_complex()
+                else torch.double,
+                device=x.device,
+                low=-1,
+                high=1,
+                requires_grad=True,
+                noncontiguous=gen_non_contig_grad_outputs,
+            )
+            for x in outputs
+        )
+    else:
+        tupled_grad_outputs = _as_tuple(grad_outputs)
+
+    num_outputs = len(tupled_grad_outputs)
+
+    # NB: We need to save the requires_grad information about the inputs here because gradcheck detaches inputs
+    #     before running forward mode AD
+    diff_input_args_indices = {
+        i for i, x in enumerate(tupled_inputs) if is_tensor_like(x) and x.requires_grad
+    }
+    diff_grad_output_indices = {
+        i for i, x in enumerate(tupled_grad_outputs) if x.requires_grad
+    }
+
+    def new_func(*args):
+        # Restore the requires_grad information
+        input_args = tuple(
+            x.requires_grad_() if i in diff_input_args_indices else x
+            for i, x in enumerate(args[:-num_outputs])
+        )
+        outputs = _differentiable_outputs(func(*input_args))
+        grad_outputs = tuple(
+            x.requires_grad_() if i in diff_grad_output_indices else x
+            for i, x in enumerate(args[-num_outputs:])
+        )
+        diff_input_args = tuple(
+            x for i, x in enumerate(input_args) if i in diff_input_args_indices
+        )
+        grad_inputs = torch.autograd.grad(
+            outputs, diff_input_args, grad_outputs, create_graph=True, allow_unused=True
+        )
+        grad_inputs = tuple(g for g in grad_inputs if g is not None)
+        return grad_inputs
+
+    return gradcheck(
+        new_func,
+        tupled_inputs + tupled_grad_outputs,
+        eps=eps,
+        atol=atol,
+        rtol=rtol,
+        raise_exception=raise_exception,
+        nondet_tol=nondet_tol,
+        check_undefined_grad=check_undefined_grad,
+        check_grad_dtypes=check_grad_dtypes,
+        check_batched_grad=check_batched_grad,
+        fast_mode=fast_mode,
+        check_forward_ad=check_fwd_over_rev,
+        check_backward_ad=check_rev_over_rev,
+        masked=masked,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/graph.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..549f31d349e08f7c8b6d3b73431e35ab6c2165dd
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/graph.py
@@ -0,0 +1,829 @@
+import abc
+import contextlib
+import functools
+import logging
+import threading
+from collections import defaultdict, deque
+from collections.abc import Generator, Iterable, Iterator, MutableMapping, Sequence
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Literal,
+    NamedTuple,
+    Optional,
+    TYPE_CHECKING,
+    Union,
+)
+from typing_extensions import TypeAlias
+from weakref import WeakKeyDictionary, WeakValueDictionary
+
+import torch
+from torch.autograd.variable import Variable
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils.hooks import RemovableHandle
+
+
+if TYPE_CHECKING:
+    from torch._ops import OpOverload
+
+
+__all__ = [
+    "saved_tensors_hooks",
+    "save_on_cpu",
+    "disable_saved_tensors_hooks",
+    "register_multi_grad_hook",
+    "allow_mutation_on_saved_tensors",
+    "Node",
+    "GradientEdge",
+    "get_gradient_edge",
+    "increment_version",
+]
+
+
+log = logging.getLogger(__name__)
+
+
+class Node(abc.ABC):
+    @abc.abstractmethod
+    def name(self) -> str:
+        r"""Return the name.
+
+        Example::
+
+            >>> import torch
+            >>> a = torch.tensor([0., 0., 0.], requires_grad=True)
+            >>> b = a.clone()
+            >>> assert isinstance(b.grad_fn, torch.autograd.graph.Node)
+            >>> print(b.grad_fn.name())
+            CloneBackward0
+        """
+        raise NotImplementedError
+
+    @property
+    @abc.abstractmethod
+    def next_functions(self) -> tuple[tuple[Optional["Node"], int], ...]:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def metadata(self) -> dict:
+        r"""Return the metadata."""
+        raise NotImplementedError
+
+    @property
+    @abc.abstractmethod
+    def _input_metadata(self) -> list[Any]:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _register_hook_dict(self, tensor: torch.Tensor) -> None:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def register_hook(self, fn: Callable[..., Any]) -> RemovableHandle:
+        r"""Register a backward hook.
+
+        The hook will be called every time a gradient with respect to the
+        Node is computed. The hook should have the following signature::
+
+            hook(grad_inputs: Tuple[Tensor], grad_outputs: Tuple[Tensor]) -> Tuple[Tensor] or None
+
+
+        The hook should not modify its argument, but it can optionally return
+        a new gradient which will be used in place of :attr:`grad_inputs`.
+
+        This function returns a handle with a method ``handle.remove()``
+        that removes the hook from the module.
+
+        .. note::
+            See :ref:`backward-hooks-execution` for more information on how when this hook
+            is executed, and how its execution is ordered relative to other hooks.
+
+        .. note::
+            In the rare case where the hook is registered while the Node has already
+            begun execution, there is no longer any guarantee on :attr:`grad_outputs`
+            content (it might be as usual or empty depending on other factors). The
+            hook can still optionally return a new gradient to be used in place of
+            :attr:`grad_inputs` independent of :attr:`grad_outputs`.
+
+        Example::
+
+            >>> import torch
+            >>> a = torch.tensor([0., 0., 0.], requires_grad=True)
+            >>> b = a.clone()
+            >>> assert isinstance(b.grad_fn, torch.autograd.graph.Node)
+            >>> handle = b.grad_fn.register_hook(lambda gI, gO: (gO[0] * 2,))
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([2., 2., 2.])
+            >>> handle.remove() # Removes the hook
+            >>> a.grad = None
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([1., 1., 1.])
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def register_prehook(self, fn: Callable[..., Any]) -> RemovableHandle:
+        r"""Register a backward pre-hook.
+
+        The hook will be called every time a gradient with respect to the
+        Node is computed. The hook should have the following signature::
+
+            hook(grad_outputs: Tuple[Tensor]) -> Tuple[Tensor] or None
+
+        The hook should not modify its argument, but it can optionally return
+        a new gradient which will be used in place of :attr:`grad_outputs`.
+
+        This function returns a handle with a method ``handle.remove()``
+        that removes the hook from the module.
+
+        .. note::
+            See :ref:`backward-hooks-execution` for more information on how when this hook
+            is executed, and how its execution is ordered relative to other hooks.
+
+        Example::
+
+            >>> a = torch.tensor([0., 0., 0.], requires_grad=True)
+            >>> b = a.clone()
+            >>> assert isinstance(b.grad_fn, torch.autograd.graph.Node)
+            >>> handle = b.grad_fn.register_prehook(lambda gI: (gI[0] * 2,))
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([2., 2., 2.])
+            >>> handle.remove()
+            >>> a.grad = None
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([1., 1., 1.])
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def __subclasshook__(cls, subclass: type) -> bool:
+        if cls is Node and (
+            (
+                subclass is not None
+                and subclass is getattr(torch._C._functions, subclass.__name__, None)
+            )
+            or issubclass(subclass, torch.autograd.function.BackwardCFunction)
+        ):
+            return True
+        return NotImplemented
+
+
+def _get_grad_fn_or_grad_acc(t: Union[torch.Tensor, "GradientEdge"]) -> Node:
+    if isinstance(t, GradientEdge):
+        return t.node
+    if t.requires_grad and t.grad_fn is None:
+        with torch.enable_grad():
+            node = t.view_as(t).grad_fn.next_functions[0][0]  # type: ignore[union-attr]
+    else:
+        node = t.grad_fn
+    assert node is not None
+    return node
+
+
+class GradientEdge(NamedTuple):
+    """Object representing a given gradient edge within the autograd graph.
+
+    To get the gradient edge where a given Tensor gradient will be computed,
+    you can do ``edge = autograd.graph.get_gradient_edge(tensor)``.
+    """
+
+    node: Node
+    output_nr: int
+
+
+def get_gradient_edge(tensor: torch.Tensor) -> GradientEdge:
+    """Get the gradient edge for computing the gradient of the given Tensor.
+
+    In particular, it is equivalent to call
+    ``g = autograd.grad(loss, input)`` and ``g = autograd.grad(loss, get_gradient_edge(input))``.
+    """
+    if not tensor.requires_grad:
+        raise RuntimeError(
+            "It is not possible to get the gradient edge for a Tensor "
+            "that does not require gradients",
+        )
+    grad_fn = _get_grad_fn_or_grad_acc(tensor)
+
+    # Note that output_nr default to 0 which is the right value
+    # for the AccumulateGrad node.
+    return GradientEdge(grad_fn, tensor.output_nr)
+
+
+def increment_version(tensor: Union[torch.Tensor, Iterable[torch.Tensor]]) -> None:
+    """Update autograd metadata tracking whether the given Tensor was modified in place.
+
+    This is to enable more accurate error checking within the autograd engine.
+    It is already done automatically by PyTorch functions and within custom Function
+    when mark_dirty() is called appropriately so you only need to call this explicitly
+    if you are doing inplace operation on the Tensor data in a way that Pytorch doesn't
+    know about. For example a custom kernel that reads the Tensor data_ptr and modifies
+    the memory inplace based on this pointer. Can accept either a tensor, or a list of tensors.
+
+    Note that incrementing the version counter multiple times for a single inplace operation
+    is not problematic.
+
+    Note that if you pass in tensor constructed under torch.inference_mode(),
+    we will not bump its version counter (because your tensor does not have one).
+    """
+    if isinstance(tensor, torch.Tensor):
+        tensor = (tensor,)
+    torch._C._increment_version(tensor)
+
+
+class saved_tensors_hooks:
+    """Context-manager that sets a pair of pack / unpack hooks for saved tensors.
+
+    Use this context-manager to define how intermediary results of an operation
+    should be packed before saving, and unpacked on retrieval.
+
+    In that context, the ``pack_hook`` function will be called everytime an
+    operation saves a tensor for backward (this includes intermediary results
+    saved using
+    :func:`~torch.autograd.function._ContextMethodMixin.save_for_backward` but
+    also those recorded by a PyTorch-defined operation). The output of
+    ``pack_hook`` is then stored in the computation graph instead of the
+    original tensor.
+
+    The ``unpack_hook`` is called when the saved tensor needs to be accessed,
+    namely when executing :func:`torch.Tensor.backward()` or
+    :func:`torch.autograd.grad()`. It takes as argument the *packed* object
+    returned by ``pack_hook`` and should return a tensor which has the same
+    content as the original tensor (passed as input to the corresponding
+    ``pack_hook``).
+
+    The hooks should have the following signatures:
+
+        pack_hook(tensor: Tensor) -> Any
+
+        unpack_hook(Any) -> Tensor
+
+    where the return value of ``pack_hook`` is a valid input to ``unpack_hook``.
+
+    In general, you want ``unpack_hook(pack_hook(t))`` to be equal to ``t`` in terms
+    of value, size, dtype and device.
+
+    Example::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pack_hook(x):
+        ...     print("Packing", x)
+        ...     return x
+        >>>
+        >>> def unpack_hook(x):
+        ...     print("Unpacking", x)
+        ...     return x
+        >>>
+        >>> a = torch.ones(5, requires_grad=True)
+        >>> b = torch.ones(5, requires_grad=True) * 2
+        >>> with torch.autograd.graph.saved_tensors_hooks(pack_hook, unpack_hook):
+        ...     y = a * b
+        Packing tensor([1., 1., 1., 1., 1.], requires_grad=True)
+        Packing tensor([2., 2., 2., 2., 2.], grad_fn=)
+        >>> y.sum().backward()
+        Unpacking tensor([1., 1., 1., 1., 1.], requires_grad=True)
+        Unpacking tensor([2., 2., 2., 2., 2.], grad_fn=)
+
+    .. warning ::
+        Performing an inplace operation on the input to either hooks may lead
+        to undefined behavior.
+
+    .. warning ::
+        Only one pair of hooks is allowed at a time. When recursively nesting this
+        context-manager, only the inner-most pair of hooks will be applied.
+    """
+
+    def __init__(
+        self,
+        pack_hook: Callable[[torch.Tensor], Any],
+        unpack_hook: Callable[[Any], torch.Tensor],
+    ) -> None:
+        self.pack_hook = pack_hook
+        self.unpack_hook = unpack_hook
+
+    def __enter__(self) -> None:
+        torch._C._autograd._push_saved_tensors_default_hooks(
+            self.pack_hook, self.unpack_hook
+        )
+
+    def __exit__(self, *args: object) -> None:
+        torch._C._autograd._pop_saved_tensors_default_hooks()
+
+
+class save_on_cpu(saved_tensors_hooks):
+    """Context manager under which tensors saved by the forward pass will be stored on cpu, then retrieved for backward.
+
+    When performing operations within this context manager, intermediary
+    results saved in the graph during the forward pass will be moved to CPU,
+    then copied back to the original device when needed for the backward pass.
+    If the graph was already on CPU, no tensor copy is performed.
+
+    Use this context-manager to trade compute for GPU memory usage (e.g.
+    when your model doesn't fit in GPU memory during training).
+
+    Args:
+        pin_memory (bool): If ``True`` tensors will be saved to CPU pinned memory
+                           during packing and copied to GPU asynchronously during unpacking.
+                           Defaults to ``False``.
+                           Also see :ref:`cuda-memory-pinning`.
+
+
+    Example::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> a = torch.randn(5, requires_grad=True, device="cuda")
+        >>> b = torch.randn(5, requires_grad=True, device="cuda")
+        >>> c = torch.randn(5, requires_grad=True, device="cuda")
+        >>>
+        >>> def f(a, b, c):
+        ...     prod_1 = a * b           # a and b are saved on GPU
+        ...     with torch.autograd.graph.save_on_cpu():
+        ...         prod_2 = prod_1 * c  # prod_1 and c are saved on CPU
+        ...     y = prod_2 * a           # prod_2 and a are saved on GPU
+        ...     return y
+        >>>
+        >>> y = f(a, b, c)
+        >>> del a, b, c  # for illustration only
+        >>> # the content of a, b, and prod_2 are still alive on GPU
+        >>> # the content of prod_1 and c only live on CPU
+        >>> y.sum().backward()  # all CPU tensors are moved back to GPU, for backward
+        >>> # all intermediary tensors are released (deleted) after the call to backward
+    """
+
+    def __init__(self, pin_memory: bool = False, device_type: str = "cuda") -> None:
+        device_module = getattr(torch, device_type, torch.cuda)
+
+        def pack_to_cpu(tensor: torch.Tensor) -> tuple[torch.device, torch.Tensor]:
+            if not pin_memory:
+                return (tensor.device, tensor.cpu())
+            packed = torch.empty(
+                tensor.size(),
+                dtype=tensor.dtype,
+                layout=tensor.layout,
+                pin_memory=(device_module.is_available() and not tensor.is_sparse),
+            )
+            packed.copy_(tensor)
+            return (tensor.device, packed)
+
+        def unpack_from_cpu(packed: tuple[torch.device, torch.Tensor]) -> torch.Tensor:
+            device, tensor = packed
+            return tensor.to(device, non_blocking=pin_memory)
+
+        super().__init__(pack_to_cpu, unpack_from_cpu)
+
+
+@contextlib.contextmanager
+def disable_saved_tensors_hooks(error_message: str) -> Generator[None, None, None]:
+    """Context-manager that disables the saved tensors default hooks feature.
+
+    Useful for if you are creating a feature that does not work with saved
+    tensors default hooks.
+
+    Args:
+        error_message (str): When saved tensors default hooks are used when they
+                             have been are disabled, a RuntimeError with this
+                             error message gets raised.
+
+    Example::
+
+        >>> # xdoctest: +SKIP(failing)
+        >>> message = "saved tensors default hooks are disabled"
+        >>> with torch.autograd.graph.disable_saved_tensors_hooks(message):
+        ...     # Raises RuntimeError: saved tensors default hooks are disabled
+        ...     with torch.autograd.graph.save_on_cpu():
+        ...         pass
+    """
+    maybe_prev_message = None
+    try:
+        maybe_prev_message = (
+            torch._C._autograd._saved_tensors_hooks_get_disabled_error_message()
+        )
+        torch._C._autograd._saved_tensors_hooks_disable(error_message)
+        yield
+    finally:
+        # See NOTE: [disabled_error_message invariant]
+        if maybe_prev_message is None:
+            torch._C._autograd._saved_tensors_hooks_enable()
+        else:
+            torch._C._autograd._saved_tensors_hooks_disable(maybe_prev_message)
+
+
+class _MultiHandle(RemovableHandle):
+    handles: tuple[RemovableHandle, ...]
+
+    def __init__(self, handles: tuple[RemovableHandle, ...]) -> None:
+        self.handles = handles
+
+    def remove(self) -> None:
+        for handle in self.handles:
+            handle.remove()
+
+    def __getstate__(self) -> tuple[RemovableHandle, ...]:
+        return self.handles
+
+    def __setstate__(self, state: tuple[RemovableHandle, ...]) -> None:
+        self.handles = state
+
+
+def register_multi_grad_hook(
+    tensors: Sequence[torch.Tensor],
+    fn: Union[
+        Callable[[Sequence[Optional[torch.Tensor]]], None],
+        Callable[[torch.Tensor], None],
+    ],
+    *,
+    mode: Literal["all", "any"] = "all",
+) -> RemovableHandle:
+    r"""Register a multi-grad backward hook.
+
+    There are two supported modes: ``"all"`` and ``"any"``.
+
+    Under the ``"all"`` mode, the hook will be called after gradients with respect to every tensor in
+    :attr:`tensors` have been computed. If a tensor is in :attr:`tensors` but
+    is not part of the graph, or if a tensor is not needed to compute the gradients
+    for any ``inputs`` specified for the current ``.backward()`` or ``.grad()`` call,
+    this tensor will be ignored and the hook will not wait for its gradient to be
+    computed.
+
+    After every non-ignored tensor's gradient has been computed, :attr:`fn` will be
+    called with those gradients. ``None`` will be passed for tensors that did not
+    have their gradients computed.
+
+    Under the ``"any"`` mode, the hook will be called after the first gradient
+    with respect to a tensor in :attr:`tensors` has been computed. The hook
+    will be called with that gradient as its argument.
+
+    The hook should not modify its arguments.
+
+    This function returns a handle with a method ``handle.remove()`` that removes the hook.
+
+    .. note::
+        See :ref:`backward-hooks-execution` for more information on how when this hook
+        is executed, and how its execution is ordered relative to other hooks.
+
+    Example::
+
+        >>> import torch
+        >>>
+        >>> a = torch.rand(2, 3, requires_grad=True)
+        >>> b = torch.rand(2, 3, requires_grad=True)
+        >>> c = a * b
+        >>> d = a * b
+        >>>
+        >>> def fn(grads):
+        ...     print([g is not None for g in grads])
+        ...
+        >>> torch.autograd.graph.register_multi_grad_hook((a, b, c, d), fn)
+        >>>
+        >>> c.sum().backward(retain_graph=True)
+        [True, True, True, False]
+        >>> c.sum().backward(inputs=(a,), retain_graph=True)
+        [True, False, True, False]
+        >>>
+    """
+    supported_modes = ("all", "any")
+    lock = threading.Lock()
+
+    if mode not in supported_modes:
+        raise ValueError(f"Expects mode to be one of {supported_modes} but got {mode}")
+
+    if mode == "all":
+        count: dict[int, int] = {}
+        nb_calls = None
+        buffer: dict[int, list[Optional[torch.Tensor]]] = {}
+
+        grad_fns = list(map(_get_grad_fn_or_grad_acc, tensors))
+        len_tensors = len(tensors)
+
+        def get_inner_hook(idx: int) -> Callable[[torch.Tensor], None]:
+            def inner_hook(grad: torch.Tensor) -> None:
+                nonlocal count, nb_calls, buffer, fn
+                id = torch._C._current_graph_task_id()
+                assert (
+                    id != -1
+                ), "expected this hook to be called inside a backward call"
+                count[id] = count.get(id, 0)
+                buffer[id] = buffer.get(id, [None] * len_tensors)
+
+                with lock:
+                    curr_count, count[id] = count[id], count[id] + 1
+
+                    if curr_count == 0:
+                        # On the first call, compute the actual nb_calls and buffer
+                        nb_calls = sum(
+                            map(torch._C._will_engine_execute_node, grad_fns)
+                        )
+
+                buffer[id][idx] = grad
+
+                assert nb_calls is not None
+                if curr_count == nb_calls - 1:
+                    fn = cast(Callable[[Sequence[Optional[torch.Tensor]]], None], fn)
+                    fn(buffer[id])
+                    del count[id]
+                    del buffer[id]
+
+            return inner_hook
+
+        handles = tuple(
+            t.register_hook(get_inner_hook(i)) for i, t in enumerate(tensors)
+        )
+    elif mode == "any":
+        fn = cast(Callable[[torch.Tensor], None], fn)
+        ran_hook: dict[int, bool] = defaultdict(bool)
+
+        @functools.wraps(fn)
+        def wrapped_fn(grad: torch.Tensor) -> None:
+            nonlocal ran_hook
+            id = torch._C._current_graph_task_id()
+            assert id != -1, "expected this hook to be called inside a backward call"
+            with lock:
+                prev, ran_hook[id] = ran_hook[id], True
+            if prev:
+                return
+            fn(grad)
+
+        handles = tuple(
+            tensor.register_hook(wrapped_fn)
+            for tensor in tensors
+            if tensor.requires_grad
+        )
+
+    return _MultiHandle(handles)  # type: ignore[possibly-undefined]
+
+
+# NOTE [Allow mutation on tensors saved for backward]
+#
+# 1. Tensor gets saved for backward
+#    - remember the python object id and the version of the tensor
+#    - remember aliasing information (data_ptr of base + version)
+#    - save the original so we control its lifetime
+# 2. Any time a tensor gets in-placed
+#    - for each tensor aliased to it:
+#      - check using its object id and version to see if it has been saved
+#      - if it has been saved, clone it
+#      - delete the reference to the original
+# 3. during backward
+#    - if the clone exists, the tensor must've been modified in-place
+_allow_mutation_on_saved_tensors_enabled: bool = False
+
+
+_TID: TypeAlias = tuple[int, int, int]
+_SID: TypeAlias = tuple[int, int]
+
+
+def _get_tid(tensor: torch.Tensor) -> _TID:
+    # FIXME: This is almost definitely a bug.
+    if isinstance(
+        tensor,
+        (
+            torch._subclasses.fake_tensor.FakeTensor,
+            torch._subclasses.functional_tensor.FunctionalTensor,
+        ),
+    ):
+        data_ptr = 0
+    else:
+        data_ptr = tensor.data_ptr()
+    return (id(tensor), data_ptr, tensor._version)
+
+
+def _get_sid(tensor: torch.Tensor) -> _SID:
+    # FIXME: This is almost definitely a bug.
+    if isinstance(
+        tensor,
+        (
+            torch._subclasses.fake_tensor.FakeTensor,
+            torch._subclasses.functional_tensor.FunctionalTensor,
+        ),
+    ):
+        data_ptr = 0
+    else:
+        data_ptr = tensor.data_ptr()
+    return (data_ptr, tensor._version)
+
+
+class _Handle:
+    pass
+
+
+class _swap_with_cloned(saved_tensors_hooks):
+    def __init__(self, ctx: "_AllowMutationOnSavedContext") -> None:
+        def pack_hook(tensor: torch.Tensor) -> _Handle:
+            tid = _get_tid(tensor)
+            sid = _get_sid(tensor)
+            # Tensors saved for backward have an entry in _tid_to_weakhandle
+            handle: Optional[_Handle] = None
+
+            # Save aliasing information
+            ctx.sid_to_tid[sid].add(tid)
+
+            # NB: The same tensor (of the same version) can be saved multiple times
+            if tid not in ctx.tid_to_weakhandle:
+                handle = _Handle()
+                ctx.tid_to_weakhandle[tid] = handle
+                ctx.original[handle] = tensor
+            else:
+                # Store an additional strong reference to the handle
+                handle = ctx.tid_to_weakhandle[tid]
+            return handle
+
+        def unpack_hook(handle: _Handle) -> torch.Tensor:
+            error_msg = (
+                "Trying to backward outside of the 'allow_mutation_on_saved_tensors' context"
+                "in which the graph was originally recorded."
+            )
+            assert _allow_mutation_on_saved_tensors_enabled, error_msg
+            if handle in ctx.cloned:
+                res = ctx.cloned[handle]
+            else:
+                assert handle in ctx.original, error_msg
+                res = ctx.original[handle]
+            return res
+
+        super().__init__(pack_hook, unpack_hook)
+
+
+class _CloneArgBeforeMutateMode(TorchDispatchMode):
+    def __init__(self, ctx: "_AllowMutationOnSavedContext") -> None:
+        self.ctx = ctx
+
+    def __torch_dispatch__(
+        self,
+        func: "OpOverload",
+        types: Iterable[type],
+        args: tuple[Any, ...] = (),
+        kwargs: Optional[dict[Any, Any]] = None,
+    ) -> Any:
+        kwargs = kwargs or {}
+
+        def maybe_clone(t: torch.Tensor) -> None:
+            tid = _get_tid(t)
+            sid = _get_sid(t)
+            ctx = self.ctx
+            if sid in ctx.sid_to_tid:
+                for tid in ctx.sid_to_tid[sid]:
+                    if tid not in ctx.tid_to_weakhandle:
+                        # We know that if tid is in sid_to_tid, then it must also be in
+                        # tid_to_weakhandle. However, it is possible for the tensor to be
+                        # saved at one point, but cleared by backward before it is modified
+                        # in-place. Consider the following example:
+                        #
+                        # >>> a = torch.randn(2, 3, requires_grad=True).clone()
+                        # >>> out = (a**2).sum()
+                        # >>> out.backward()
+                        # >>> a.sin_()
+                        continue
+                    handle = ctx.tid_to_weakhandle[tid]
+                    if handle in ctx.cloned:
+                        # The same exact tensor has been cloned already
+                        continue
+                    ctx.cloned[handle] = ctx.original[handle].clone()
+                    del ctx.original[handle]
+
+        for idx, arg in enumerate(func._schema.arguments):
+            if arg.alias_info is not None and arg.alias_info.is_write:
+                if arg.is_out:
+                    maybe_clone(kwargs["out"])
+                elif isinstance(args[idx], list):
+                    # Foreach case. (Possible optimization: if most of the
+                    # tensors need to be cloned, use a for each clone?)
+                    for t in args[idx]:
+                        maybe_clone(t)
+                else:
+                    maybe_clone(args[idx])
+
+        return func(*args, **kwargs)
+
+
+class _AllowMutationOnSavedContext:
+    def __init__(self) -> None:
+        self.cloned: MutableMapping[_Handle, torch.Tensor] = WeakKeyDictionary()
+        self.original: MutableMapping[_Handle, torch.Tensor] = WeakKeyDictionary()
+        self.tid_to_weakhandle: MutableMapping[_TID, _Handle] = WeakValueDictionary()
+        self.sid_to_tid: dict[_SID, set[_TID]] = defaultdict(set)
+
+    def clear(self) -> None:
+        self.cloned.clear()
+        self.original.clear()
+        self.tid_to_weakhandle.clear()
+        self.sid_to_tid.clear()
+
+
+@contextlib.contextmanager
+def allow_mutation_on_saved_tensors() -> (
+    Generator[_AllowMutationOnSavedContext, None, None]
+):
+    """Context manager under which mutating tensors saved for backward is allowed.
+
+    Under this context manager, tensors saved for backward are cloned on mutation,
+    so the original version can still be used during backward. Normally, mutating a tensor
+    saved for backward will result in an error raised when it's used during backward.
+
+    To ensure the correct behavior, both the forward and backward should be run under
+    the same context manager.
+
+    Returns:
+        An _AllowMutationOnSavedContext object storing the state managed by this
+        context manager. This object can be useful for debugging purposes. The state
+        managed by the context manager is automatically cleared upon exiting.
+
+    Example::
+
+        >>> import torch
+        >>> with torch.autograd.graph.allow_mutation_on_saved_tensors():
+        ...     # forward
+        ...     a = torch.ones(2, 3, requires_grad=True)
+        ...     b = a.clone()
+        ...     out = (b**2).sum()
+        ...     b.sin_()
+        ...     # backward
+        ...     out.sum().backward()
+        ...
+        tensor([[0.8415, 0.8415, 0.8415],
+                [0.8415, 0.8415, 0.8415]], grad_fn=)
+    """
+    global _allow_mutation_on_saved_tensors_enabled
+
+    ctx = _AllowMutationOnSavedContext()
+
+    with _swap_with_cloned(ctx), _CloneArgBeforeMutateMode(ctx):
+        try:
+            if _allow_mutation_on_saved_tensors_enabled:
+                raise RuntimeError(
+                    "allow_mutation_on_saved_tensors contexts cannot be nested"
+                )
+            _allow_mutation_on_saved_tensors_enabled = True
+            yield ctx
+        finally:
+            ctx.clear()
+            _allow_mutation_on_saved_tensors_enabled = False
+
+
+def _register_logging_hooks_on_whole_graph(
+    t_outputs: Sequence[Union[torch.Tensor, GradientEdge]],
+) -> Callable[[], None]:
+    grad_fns = list(map(_get_grad_fn_or_grad_acc, t_outputs))
+
+    def iter_graph(roots: list[Node]) -> Iterator[Node]:
+        if not roots:
+            return
+        seen: set[Node] = set()
+        q: deque[Node] = deque()
+        for node in roots:
+            if node is not None:
+                seen.add(node)
+                q.append(node)
+
+        while q:
+            node = q.popleft()
+            for fn, _ in node.next_functions:
+                if fn in seen or fn is None:
+                    continue
+                seen.add(fn)
+                q.append(fn)
+
+            yield node
+
+    def fmt(t: Optional[torch.Tensor]) -> str:
+        # Avoid circular import
+        from torch.testing._internal.common_utils import dtype_abbrs
+
+        if t is None:
+            return "None"
+        return f"{dtype_abbrs[t.dtype]}[{', '.join(map(str, t.shape))}]"
+
+    def prehook(grad_outputs: Sequence[Optional[torch.Tensor]]) -> None:
+        node = torch._C._current_autograd_node()
+        grad_outputs_str = f"[{','.join(fmt(t) for t in grad_outputs)}]"
+        log_str = f"Executing: {node} with grad_outputs: {grad_outputs_str}"
+        log.debug(log_str)
+
+    handles = [node.register_prehook(prehook) for node in iter_graph(grad_fns)]
+
+    def unregister_hooks() -> None:
+        for handle in handles:
+            handle.remove()
+
+    return unregister_hooks
+
+
+def _engine_run_backward(
+    t_outputs: Sequence[Union[torch.Tensor, GradientEdge]],
+    *args: Any,
+    **kwargs: Any,
+) -> tuple[torch.Tensor, ...]:
+    attach_logging_hooks = log.getEffectiveLevel() <= logging.DEBUG
+    if attach_logging_hooks:
+        unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)
+    try:
+        return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+            t_outputs, *args, **kwargs
+        )  # Calls into the C++ engine to run the backward pass
+    finally:
+        if attach_logging_hooks:
+            unregister_hooks()  # type: ignore[possibly-undefined]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/profiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..50745586ca63e549663ea28fb43e151cd0f3e8ca
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/profiler.py
@@ -0,0 +1,1208 @@
+# mypy: allow-untyped-defs
+import uuid
+from collections import defaultdict
+from collections.abc import Iterable
+from dataclasses import dataclass
+from time import perf_counter_ns
+from typing import Any, Optional
+from warnings import warn
+
+import torch
+import torch.cuda
+from torch._C import _get_privateuse1_backend_name
+from torch._C._profiler import _ExperimentalConfig
+from torch.autograd import (
+    _disable_profiler,
+    _enable_profiler,
+    _kineto_step,
+    _prepare_profiler,
+    _ProfilerResult,
+    _supported_activities,
+    _toggle_collection_dynamic,
+    DeviceType,
+    kineto_available,
+    ProfilerActivity,
+    ProfilerConfig,
+    ProfilerState,
+)
+from torch.autograd.profiler_util import (
+    _filter_name,
+    _filter_stack_entry,
+    _rewrite_name,
+    EventList,
+    FunctionEvent,
+    MEMORY_EVENT_NAME,
+    MemRecordsAcc,
+    OUT_OF_MEMORY_EVENT_NAME,
+)
+from torch.futures import Future
+
+
+__all__ = [
+    "profile",
+    "record_function",
+    "emit_itt",
+    "emit_nvtx",
+    "load_nvprof",
+    "EnforceUnique",
+    "parse_nvprof_trace",
+    "KinetoStepTracker",
+    "EventList",
+    "FunctionEvent",
+    "MemRecordsAcc",
+]
+
+try:
+    # Available in Python >= 3.2
+    from contextlib import ContextDecorator as _ContextDecorator
+except ImportError:
+    import functools
+
+    class _ContextDecorator:  # type: ignore[no-redef]
+        def __enter__(self):
+            raise NotImplementedError
+
+        def __exit__(self, exc_type, exc_val, exc_tb):
+            raise NotImplementedError
+
+        def __call__(self, func):
+            @functools.wraps(func)
+            def wrapped(*args, **kwargs):
+                with self:
+                    return func(*args, **kwargs)
+
+            return wrapped
+
+
+# global python state - whether profiler is currently enabled
+# useful for fast python checks to reduce latency
+_is_profiler_enabled: bool = False
+
+
+def _set_is_profiler_enabled(enable: bool):
+    global _is_profiler_enabled
+    _is_profiler_enabled = enable
+
+
+def _run_on_profiler_start():
+    _set_is_profiler_enabled(True)
+
+
+def _run_on_profiler_stop():
+    _set_is_profiler_enabled(False)
+
+
+@dataclass
+class _ProfilerStats:
+    "Profiler timing and stats used by developers to catch issues/regressions"
+    profiling_window_duration_sec: float = 0
+    number_of_events: int = 0
+    profiler_prepare_call_duration_us: int = 0
+    profiler_enable_call_duration_us: int = 0
+    profiler_disable_call_duration_us: int = 0
+    parse_kineto_call_duration_us: int = 0
+    function_events_build_tree_call_duration_us: int = 0
+
+
+class profile:
+    """Context manager that manages autograd profiler state and holds a summary of results.
+
+    Under the hood it just records events of functions being executed in C++ and
+    exposes those events to Python. You can wrap any code into it and it will
+    only report runtime of PyTorch functions.
+    Note: profiler is thread local and is automatically propagated into the async tasks
+
+    Args:
+        enabled (bool, optional): Setting this to False makes this context manager a no-op.
+
+        use_cuda (bool, optional): Enables timing of CUDA events as well
+            using the cudaEvent API. (will be deprecated)
+
+        use_device (str, optional): Enables timing of device events.
+            Adds approximately 4us of overhead to each tensor operation when use cuda.
+            The valid devices options are 'cuda', 'xpu', 'mtia' and 'privateuseone'.
+
+        record_shapes (bool, optional): If shapes recording is set, information
+            about input dimensions will be collected. This allows one to see which
+            dimensions have been used under the hood and further group by them
+            using prof.key_averages(group_by_input_shape=True). Please note that
+            shape recording might skew your profiling data. It is recommended to
+            use separate runs with and without shape recording to validate the timing.
+            Most likely the skew will be negligible for bottom most events (in a case
+            of nested function calls). But for higher level functions the total
+            self cpu time might be artificially increased because of the shape
+            collection.
+
+        with_flops (bool, optional): If with_flops is set, the profiler will estimate
+            the FLOPs (floating point operations) value using the operator's input shape.
+            This allows one to estimate the hardware performance. Currently,
+            this option only works for the matrix multiplication and 2D convolution operators.
+
+        profile_memory (bool, optional): track tensor memory allocation/deallocation.
+
+        with_stack (bool, optional): record source information (file and line number) for the ops.
+
+        with_modules (bool): record module hierarchy (including function names)
+            corresponding to the callstack of the op. e.g. If module A's forward call's
+            module B's forward which contains an aten::add op,
+            then aten::add's module hierarchy is A.B
+            Note that this support exist, at the moment, only for TorchScript models
+            and not eager mode models.
+
+        use_kineto (bool, optional): experimental, enable profiling with Kineto profiler.
+
+        use_cpu (bool, optional): profile CPU events; setting to ``False`` requires
+            ``use_kineto=True`` and can be used to lower the overhead for GPU-only profiling.
+
+        experimental_config (_ExperimentalConfig) : A set of experimental options
+            used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.
+
+        acc_events (bool): Enable the accumulation of FunctionEvents across multiple profiling cycles
+
+
+    .. warning:
+        Enabling memory profiling or source attribution incurs additional profiler
+        overhead
+
+    .. warning:
+        This context managers should not be called recursively, i.e. no nested
+        instances are allowed
+
+    .. warning:
+        Due to some CUDA multiprocessing limitations (multiprocessing-cuda-note_),
+        one cannot use the profiler with ``use_device = 'cuda'`` to benchmark
+        DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
+        please use ``use_device = None`` or ``num_workers = 0``.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> x = torch.randn((1, 1), requires_grad=True)
+        >>> with torch.autograd.profiler.profile() as prof:
+        >>>     for _ in range(100):  # any normal python code, really!
+        >>>         y = x ** 2
+        >>>         y.backward()
+        >>> # NOTE: some columns were removed for brevity
+        >>> print(prof.key_averages().table(sort_by="self_cpu_time_total"))
+        -----------------------------------  ---------------  ---------------  ---------------
+        Name                                 Self CPU total   CPU time avg     Number of Calls
+        -----------------------------------  ---------------  ---------------  ---------------
+        mul                                  32.048ms         32.048ms         200
+        pow                                  27.041ms         27.041ms         200
+        PowBackward0                         9.727ms          55.483ms         100
+        torch::autograd::AccumulateGrad      9.148ms          9.148ms          100
+        torch::autograd::GraphRoot           691.816us        691.816us        100
+        -----------------------------------  ---------------  ---------------  ---------------
+
+    """
+
+    def __init__(
+        self,
+        enabled=True,
+        *,
+        use_cuda=False,  # Deprecated
+        use_device=None,
+        record_shapes=False,
+        with_flops=False,
+        profile_memory=False,
+        with_stack=False,
+        with_modules=False,
+        use_kineto=False,
+        use_cpu=True,
+        experimental_config=None,
+        acc_events=False,
+        custom_trace_id_callback=None,
+    ):
+        self.enabled: bool = enabled
+        if not self.enabled:
+            return
+        self.use_cuda = use_cuda
+        if self.use_cuda:
+            warn(
+                "The attribute `use_cuda` will be deprecated soon, "
+                "please use ``use_device = 'cuda'`` instead.",
+                FutureWarning,
+                stacklevel=2,
+            )
+            self.use_device: Optional[str] = "cuda"
+        else:
+            self.use_device = use_device
+        # TODO Consider changing _function_events into data structure with size cap
+        self._function_events: Optional[EventList] = None
+        self._old_function_events: Optional[EventList] = None
+        # Function event processing is done lazily
+        self._needs_processing = False
+        self.entered = False
+        self.record_shapes = record_shapes
+        self.with_flops = with_flops
+        self.record_shapes |= self.with_flops
+        self.profile_memory = profile_memory
+        self.with_stack = with_stack
+        self.with_modules = with_modules
+        self.use_cpu = use_cpu
+        self.acc_events = acc_events
+        if experimental_config is None:
+            experimental_config = _ExperimentalConfig()
+        self.experimental_config = experimental_config
+        self.kineto_results: Optional[_ProfilerResult] = None
+        self.profiling_start_time_ns = 0
+        self.profiling_end_time_ns = 0
+        self._stats = _ProfilerStats()
+        self.custom_trace_id_callback = custom_trace_id_callback
+        self.trace_id = ""
+        if not self.use_cpu:
+            assert (
+                use_kineto
+            ), "Device-only events supported only with Kineto (use_kineto=True)"
+
+        if self.use_device is not None:
+            VALID_DEVICE_OPTIONS = ["cuda", "xpu", "mtia", "hpu"]
+            if _get_privateuse1_backend_name() != "privateuseone":
+                VALID_DEVICE_OPTIONS.append(_get_privateuse1_backend_name())
+            if self.use_device not in VALID_DEVICE_OPTIONS:
+                warn(f"The {self.use_device} is not a valid device option.")
+                self.use_device = None
+
+            if self.use_device == "cuda" and not torch.cuda.is_available():
+                warn("CUDA is not available, disabling CUDA profiling")
+                self.use_cuda = False
+                self.use_device = None
+
+            if self.use_device == "xpu" and not torch.xpu.is_available():
+                warn("XPU is not available, disabling XPU profiling")
+                self.use_device = None
+
+            if self.use_device == "hpu" and not (
+                hasattr(torch, "hpu") and torch.hpu.is_available()
+            ):
+                warn("HPU is not available, disabling HPU profiling")
+                self.use_device = None
+
+        self.kineto_activities = set()
+        if self.use_cpu:
+            self.kineto_activities.add(ProfilerActivity.CPU)
+
+        self.profiler_kind = ProfilerState.KINETO
+        if self.use_device == "cuda":
+            if not use_kineto or ProfilerActivity.CUDA not in _supported_activities():
+                assert self.use_cpu, "Legacy CUDA profiling requires use_cpu=True"
+                self.profiler_kind = ProfilerState.KINETO_GPU_FALLBACK
+            else:
+                self.kineto_activities.add(ProfilerActivity.CUDA)
+        elif self.use_device == "xpu":
+            assert (
+                use_kineto and ProfilerActivity.XPU in _supported_activities()
+            ), "Legacy XPU profiling is not supported. Requires use_kineto=True on XPU devices."
+            self.kineto_activities.add(ProfilerActivity.XPU)
+        elif self.use_device == "mtia":
+            assert (
+                use_kineto and ProfilerActivity.MTIA in _supported_activities()
+            ), "Legacy MTIA profiling is not supported. Requires use_kineto=True on MTIA devices."
+            self.kineto_activities.add(ProfilerActivity.MTIA)
+        elif self.use_device == "hpu":
+            assert (
+                use_kineto and ProfilerActivity.HPU in _supported_activities()
+            ), "Legacy HPU profiling is not supported. Requires use_kineto=True on HPU devices."
+            self.kineto_activities.add(ProfilerActivity.HPU)
+        elif self.use_device is not None and self.use_device != "privateuseone":
+            if (
+                not use_kineto
+                or ProfilerActivity.PrivateUse1 not in _supported_activities()
+            ):
+                assert (
+                    self.use_cpu
+                ), "Legacy custombackend profiling requires use_cpu=True"
+                self.profiler_kind = ProfilerState.KINETO_PRIVATEUSE1_FALLBACK
+            else:
+                self.kineto_activities.add(ProfilerActivity.PrivateUse1)
+
+        assert (
+            len(self.kineto_activities) > 0
+        ), "No activities specified for the profiler"
+
+    def default_trace_id(self):
+        # Generate a UUID
+        uuid_raw = uuid.uuid4()
+
+        return f"{uuid_raw.int:032X}"
+
+    def create_trace_id(self):
+        if self.custom_trace_id_callback:
+            return self.custom_trace_id_callback()
+        return self.default_trace_id()
+
+    def config(self, create_trace_id=False):
+        # only need to generate new trace id upon prepare trace not start trace
+        if create_trace_id:
+            trace_id = self.create_trace_id()
+            self.trace_id = trace_id
+        return ProfilerConfig(
+            self.profiler_kind,
+            self.record_shapes,
+            self.profile_memory,
+            self.with_stack,
+            self.with_flops,
+            self.with_modules,
+            self.experimental_config,
+            self.trace_id,
+        )
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("Profiler context manager is not reentrant")
+        self._prepare_trace()
+        self._start_trace()
+        return self
+
+    def _prepare_trace(self):
+        self.entered = True
+        t0 = perf_counter_ns()
+        _prepare_profiler(self.config(create_trace_id=True), self.kineto_activities)
+        t1 = perf_counter_ns()
+        self._stats.profiler_prepare_call_duration_us = int((t1 - t0) / 1000)
+
+    def _start_trace(self):
+        self.entered = True
+        _run_on_profiler_start()
+        t0 = perf_counter_ns()
+        _enable_profiler(self.config(create_trace_id=False), self.kineto_activities)
+        t1 = perf_counter_ns()
+        self._stats.profiler_enable_call_duration_us = int((t1 - t0) / 1000)
+        self.profiling_start_time_ns = t1
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        if self.use_device and hasattr(torch, self.use_device):
+            device_module = getattr(torch, self.use_device)
+            if hasattr(device_module, "synchronize"):
+                device_module.synchronize()
+
+        if self._function_events and self.acc_events:
+            self._old_function_events = self._function_events
+        self._function_events = None
+        self._needs_processing = True
+
+        t0 = perf_counter_ns()
+
+        self.kineto_results = _disable_profiler()
+        t1 = perf_counter_ns()
+        self._stats.profiler_disable_call_duration_us = int((t1 - t0) / 1000)
+        self.profiling_end_time_ns = t0
+
+        _run_on_profiler_stop()
+
+        self._stats.profiling_window_duration_sec = (
+            (self.profiling_end_time_ns - self.profiling_start_time_ns) * 1.0 / 1e9
+        )
+
+        # If we plan to accumulate events we should post process the function events
+        # right away to retain the state across mulitple start/stop calls
+        if self.acc_events:
+            self._ensure_function_events()
+        return False
+
+    def __repr__(self):
+        if self._needs_processing:
+            self._ensure_function_events()
+        if self._function_events is None:
+            return ""
+        return repr(self._function_events)
+
+    def __str__(self):
+        if self._needs_processing:
+            self._ensure_function_events()
+        if self._function_events is None:
+            return ""
+        return str(self._function_events)
+
+    def _ensure_function_events(self):
+        """Process function events lazily if required"""
+        if self._function_events is not None:
+            return
+        self._needs_processing = False
+
+        t0 = perf_counter_ns()
+        parsed_results = []
+        if self.kineto_results:
+            parsed_results = self._parse_kineto_results(self.kineto_results)
+        t1 = perf_counter_ns()
+        self._stats.parse_kineto_call_duration_us = int((t1 - t0) / 1000)
+
+        self._function_events = EventList(
+            parsed_results,
+            use_device=self.use_device,
+            profile_memory=self.profile_memory,
+            with_flops=self.with_flops,
+        )
+        t0 = perf_counter_ns()
+        self._function_events._build_tree()
+        t1 = perf_counter_ns()
+        self._stats.function_events_build_tree_call_duration_us = int((t1 - t0) / 1000)
+        self._stats.number_of_events = len(self._function_events)
+
+        if self._old_function_events and self.acc_events:
+            for evt in self._old_function_events:
+                self._function_events.append(evt)
+            self._old_function_events = None
+
+        if self._function_events is None:
+            raise RuntimeError("Profiler didn't finish running")
+
+    @property
+    def function_events(self):
+        if self._function_events is None or self._needs_processing:
+            self._ensure_function_events()
+        return self._function_events
+
+    def table(
+        self,
+        sort_by=None,
+        row_limit=100,
+        max_src_column_width=75,
+        max_name_column_width=55,
+        max_shapes_column_width=80,
+        header=None,
+        top_level_events_only=False,
+    ):
+        self._ensure_function_events()
+        assert self._function_events is not None
+        return self._function_events.table(
+            sort_by=sort_by,
+            row_limit=row_limit,
+            max_src_column_width=max_src_column_width,
+            max_name_column_width=max_name_column_width,
+            max_shapes_column_width=max_shapes_column_width,
+            header=header,
+            top_level_events_only=top_level_events_only,
+        )
+
+    table.__doc__ = EventList.table.__doc__
+
+    def export_chrome_trace(self, path):
+        """
+        Exports the collected trace in Chrome JSON format. If kineto is enabled, only
+        last cycle in schedule is exported.
+        """
+        if kineto_available():
+            self.kineto_results.save(path)  # type: ignore[union-attr]
+        else:
+            self._ensure_function_events()
+            return self._function_events.export_chrome_trace(path)  # type: ignore[union-attr]
+
+    export_chrome_trace.__doc__ = EventList.export_chrome_trace.__doc__
+
+    def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
+        self._ensure_function_events()
+        assert self._function_events is not None, "Expected profiling results"
+        assert self.with_stack, "export_stacks() requires with_stack=True"
+        return self._function_events.export_stacks(path, metric)
+
+    def toggle_collection_dynamic(
+        self, enabled: bool, activities: Iterable[ProfilerActivity]
+    ):
+        """
+        Toggles the collection of activities for the current profiler instance.
+        """
+        return _toggle_collection_dynamic(enabled, set(activities))
+
+    def key_averages(
+        self,
+        group_by_input_shape=False,
+        group_by_stack_n=0,
+        group_by_overload_name=False,
+    ):
+        self._ensure_function_events()
+        assert self._function_events is not None, "Expected profiling results"
+        return self._function_events.key_averages(
+            group_by_input_shape, group_by_stack_n, group_by_overload_name
+        )
+
+    key_averages.__doc__ = EventList.key_averages.__doc__
+
+    def total_average(self):
+        self._ensure_function_events()
+        assert self._function_events is not None, "Expected profiling results"
+        return self._function_events.total_average()
+
+    total_average.__doc__ = EventList.total_average.__doc__
+
+    @property
+    def self_cpu_time_total(self):
+        """Returns total time spent on CPU.
+
+        The total time is a sum of all self times across all the events.
+        """
+        self._ensure_function_events()
+        assert self._function_events is not None
+        return self._function_events.self_cpu_time_total
+
+    def _parse_kineto_results(self, result: _ProfilerResult):
+        # result.events() has most of the events - PyTorch op-level and device-level events
+
+        trace_start_ns = result.trace_start_ns()
+        mem_records = [
+            [evt, False] for evt in result.events() if evt.name() == MEMORY_EVENT_NAME
+        ]
+        oom_records = [
+            evt for evt in result.events() if evt.name() == OUT_OF_MEMORY_EVENT_NAME
+        ]
+        mem_records_acc = MemRecordsAcc(mem_records)
+
+        def _cpu_memory_usage(mem_record):
+            return (
+                mem_record.nbytes()
+                if mem_record.device_type()
+                in [DeviceType.CPU, DeviceType.MKLDNN, DeviceType.IDEEP]
+                else 0
+            )
+
+        def _device_memory_usage(mem_record):
+            return (
+                mem_record.nbytes()
+                if mem_record.device_type()
+                in [DeviceType.CUDA, DeviceType.PrivateUse1, DeviceType.HIP]
+                else 0
+            )
+
+        # Create and return FunctionEvent list, which contains all function events
+        # Here 2 function events are created:
+        # all_function_events contains all events associated with each kineto event from result
+        all_function_events = []
+        # frontend_function_events contains the events in aten or torch frontend level,
+        # whose correlation id is 0
+        frontend_function_events = []
+        device_corr_map: dict[int, list[FunctionEvent]] = {}
+        max_evt_id = 0
+        for kineto_event in result.events():
+            if _filter_name(kineto_event.name()):
+                continue
+            rel_start_ns = kineto_event.start_ns() - trace_start_ns
+            rel_end_ns = kineto_event.end_ns() - trace_start_ns
+            abs_end_ns = kineto_event.end_ns()
+
+            cpu_memory_usage = 0
+            device_memory_usage = 0
+            if kineto_event.device_type() == DeviceType.CPU:
+                # find the corresponding memory allocation events
+                for mem_record in mem_records_acc.in_interval(
+                    kineto_event.start_ns() / 1000, abs_end_ns / 1000
+                ):
+                    cpu_memory_usage += _cpu_memory_usage(mem_record[0])
+                    device_memory_usage += _device_memory_usage(mem_record[0])
+                    mem_record[1] = True
+
+            is_async = kineto_event.is_async() or (
+                kineto_event.start_thread_id() != kineto_event.end_thread_id()
+            )
+
+            fe = FunctionEvent(
+                id=kineto_event.correlation_id(),
+                name=_rewrite_name(name=kineto_event.name(), with_wildcard=True),
+                overload_name=kineto_event.overload_name(),
+                trace_name=_rewrite_name(name=kineto_event.name(), with_wildcard=False),
+                thread=kineto_event.start_thread_id(),
+                start_us=rel_start_ns / 1000,
+                end_us=rel_end_ns / 1000,
+                fwd_thread=kineto_event.fwd_thread_id(),
+                input_shapes=kineto_event.shapes(),
+                concrete_inputs=kineto_event.concrete_inputs(),
+                kwinputs=kineto_event.kwinputs(),
+                stack=[
+                    entry
+                    for entry in kineto_event.stack()
+                    if _filter_stack_entry(entry)
+                ],
+                scope=kineto_event.scope(),
+                use_device=self.use_device,
+                cpu_memory_usage=cpu_memory_usage,
+                device_memory_usage=device_memory_usage,
+                is_async=is_async,
+                sequence_nr=kineto_event.sequence_nr(),
+                device_type=kineto_event.device_type(),
+                device_index=kineto_event.device_index(),
+                device_resource_id=kineto_event.device_resource_id(),
+                flops=kineto_event.flops(),
+                is_user_annotation=kineto_event.is_user_annotation(),
+            )
+            max_evt_id = max(max_evt_id, fe.id)
+            if fe.device_type == DeviceType.CPU and not fe.is_async:
+                if self.use_device == "privateuseone":
+                    privateuse1_time = kineto_event.privateuse1_elapsed_us()
+                    if privateuse1_time > 0:
+                        fe.append_kernel(fe.name, fe.device_index, privateuse1_time)
+                        fe.is_legacy = True
+                elif self.use_device == "cuda":
+                    # Check if we have CUDA time as a fallback
+                    cuda_time = kineto_event.cuda_elapsed_us()
+                    if cuda_time > 0:
+                        fe.append_kernel(fe.name, fe.device_index, cuda_time)
+                        fe.is_legacy = True
+            all_function_events.append(fe)
+            corr_id = kineto_event.linked_correlation_id()
+            if corr_id > 0:
+                if corr_id not in device_corr_map:
+                    device_corr_map[corr_id] = []
+                device_corr_map[corr_id].append(fe)
+            elif corr_id == 0:
+                frontend_function_events.append(fe)
+            else:
+                raise RuntimeError(
+                    f"Got negative correlation id {corr_id} in profiler post processing"
+                )
+
+        # associate device kernels and device runtime (CPU) with CPU events
+        for fe in frontend_function_events:
+            if (
+                fe.device_type == DeviceType.CPU
+                and not fe.is_async
+                and fe.id in device_corr_map
+            ):
+                for f_evt in device_corr_map[fe.id]:
+                    if (
+                        f_evt.device_type == DeviceType.CUDA
+                        or f_evt.device_type == DeviceType.PrivateUse1
+                    ):
+                        fe.append_kernel(
+                            f_evt.name,
+                            f_evt.device_index,
+                            f_evt.time_range.end - f_evt.time_range.start,
+                        )
+                    elif f_evt.device_type == DeviceType.CPU:
+                        # make sure that 'thread' of a CPU Kineto (e.g. Device Runtime) event is associated
+                        # with the 'thread' of the corresponding linked PyTorch event to properly track
+                        # parents and children
+                        f_evt.thread = fe.thread
+
+        def createFunctionEventForMemoryEvents(evt):
+            rel_start_ns = evt.start_ns() - trace_start_ns
+            fe = FunctionEvent(
+                id=max_evt_id,
+                name=evt.name(),
+                overload_name="",
+                trace_name=None,  # not outputting in the trace
+                thread=evt.start_thread_id(),
+                start_us=rel_start_ns / 1000,
+                end_us=rel_start_ns / 1000,  # no duration
+                fwd_thread=evt.start_thread_id(),
+                input_shapes=[],
+                stack=[],
+                scope=0,  # RecordScope::FUNCTION
+                use_device=self.use_device,
+                cpu_memory_usage=_cpu_memory_usage(evt),
+                device_memory_usage=_device_memory_usage(evt),
+                is_async=False,
+                sequence_nr=-1,
+                device_type=DeviceType.CPU,
+                device_index=0,
+            )
+            return fe
+
+        # output top-level memory events
+        for mem_record in mem_records:
+            if not mem_record[1]:
+                max_evt_id += 1
+                fe = createFunctionEventForMemoryEvents(mem_record[0])
+                all_function_events.append(fe)
+
+        for oom_record in oom_records:
+            max_evt_id += 1
+            fe = createFunctionEventForMemoryEvents(oom_record)
+            all_function_events.append(fe)
+
+        all_function_events.sort(
+            key=lambda evt: [evt.time_range.start, -evt.time_range.end]
+        )
+        return all_function_events
+
+
+class record_function(_ContextDecorator):
+    """Context manager/function decorator that adds a label to a code block/function when running autograd profiler.
+    Label will only appear if CPU activity tracing is enabled.
+
+    It is useful when tracing the code profile.
+
+    Args:
+        name (str): Label assigned to the block of code.
+        node_id (int): ID of node, for distributed profiling. Unset in
+        non-distributed cases.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> x = torch.randn((1, 1), requires_grad=True)
+        >>> with torch.autograd.profiler.profile() as prof:
+        ...     y = x ** 2
+        ...     with torch.autograd.profiler.record_function("label-z"): # label the block
+        ...         z = y ** 3
+        ...     y.backward()
+        ...
+        >>> # xdoctest: +IGNORE_WANT
+        >>> # NOTE: some columns were removed for brevity
+        >>> print(prof.key_averages().table(sort_by="self_cpu_time_total"))
+        -----------------------------------  ---------------  ---------------  ---------------
+        Name                                 Self CPU total %  CPU time avg     Number of Calls
+        -----------------------------------  ---------------  ---------------  ---------------
+        pow                                  60.77%           47.470us         3
+        mul                                  21.73%           25.465us         2
+        PowBackward0                         12.03%           121.891us        1
+        torch::autograd::AccumulateGrad      2.70%            6.324us          1
+        label-z                              2.13%            12.421us         1
+        torch::autograd::GraphRoot           0.64%            1.503us          1
+        -----------------------------------  ---------------  ---------------  ---------------
+        Self CPU time total: 234.344us
+        CUDA time total: 0.000us
+
+    """
+
+    def __init__(self, name: str, args: Optional[str] = None):
+        self.name: str = name
+        self.args: Optional[str] = args
+        # Whether or not we should run record function's end callbacks when exiting.
+        self.run_callbacks_on_exit: bool = True
+        # TODO: TorchScript ignores standard type annotation here
+        # self.record: Optional["torch.classes.profiler._RecordFunction"] = None
+        self.record = torch.jit.annotate(
+            Optional["torch.classes.profiler._RecordFunction"], None
+        )
+
+    def __enter__(self):
+        self.record = torch.ops.profiler._record_function_enter_new(
+            self.name, self.args
+        )
+        return self
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any):
+        if not self.run_callbacks_on_exit:
+            return
+
+        # Local variable is needed by TorchScript to refine Optional[T] to T
+        record = self.record
+        assert record is not None
+
+        # TODO: Too slow with __torch_function__ handling enabled
+        # See https://github.com/pytorch/pytorch/issues/76410
+        if not torch.jit.is_scripting():
+            with torch._C.DisableTorchFunctionSubclass():
+                torch.ops.profiler._record_function_exit._RecordFunction(record)
+        else:
+            torch.ops.profiler._record_function_exit(record)
+
+    def _call_end_callbacks_on_future(self, fut: Future[Any]) -> Future[Any]:
+        """Use for profiling async calls that return a future.
+
+        Calling this function will extend recording beyond this scope, until the future is
+        satisfied. It is useful for profiling the end to end time of asynchronous calls.
+        This function should only be called once to attach the callback onto the future, and
+        will throw if called multiple times.
+
+        Args:
+            fut: (torch._C.Future): future for which to schedule
+            callback for.
+
+        Returns:
+            A future that completes with the value of the passed in future when
+            the profiling callbacks have ran.
+
+        """
+        # Throw if we have already attached a callback onto the future.
+        if not self.run_callbacks_on_exit:
+            raise RuntimeError("_call_end_callbacks_on_future can only be called once.")
+
+        # We are scheduling to run this RecordFunction's end callbacks when the
+        # passed in future completes, so don't run end callbacks on exit.
+        self.run_callbacks_on_exit = False
+
+        # Local variable is needed by TorchScript to refine Optional[T] to T
+        record = self.record
+        assert record is not None
+
+        # TODO: Too slow with __torch_function__ handling enabled
+        # See https://github.com/pytorch/pytorch/issues/76410
+        if not torch.jit.is_scripting():
+            with torch._C.DisableTorchFunctionSubclass():
+                profiled_future = (
+                    torch.ops.profiler._call_end_callbacks_on_jit_fut._RecordFunction(
+                        record, fut
+                    )
+                )
+        else:
+            profiled_future = torch.ops.profiler._call_end_callbacks_on_jit_fut(
+                record, fut
+            )
+        return profiled_future
+
+
+class emit_itt:
+    """Context manager that makes every autograd operation emit an ITT range.
+
+    It is useful when running the program under Intel(R) VTune Profiler::
+
+        vtune <--vtune-flags> 
+
+    The Instrumentation and Tracing Technology (ITT) API enables your application to generate and
+    control the collection of trace data during its execution across different Intel tools.
+    This context manager is to annotate Intel(R) VTune Profiling trace. With help of this context manager,
+    you will be able to see labled ranges in Intel(R) VTune Profiler GUI.
+
+    .. warning:
+        This context manager should not be called recursively, i.e. at most one
+        instance should be enabled at any given time.
+
+    Args:
+        enabled (bool, optional): Setting ``enabled=False`` makes this context manager a no-op.
+            Default: ``True``.
+        record_shapes (bool, optional): If ``record_shapes=True``, the itt range wrapping
+            each autograd op will append information about the sizes of Tensor arguments received
+            by that op, in the following format:
+            ``[[arg0.size(0), arg0.size(1), ...], [arg1.size(0), arg1.size(1), ...], ...]``
+            Non-tensor arguments will be represented by ``[]``.
+            Arguments will be listed in the order they are received by the backend op.
+            Please note that this order may not match the order in which those arguments were passed
+            on the Python side.  Also note that shape recording may increase the overhead of itt range creation.
+            Default: ``False``
+
+    Example:
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> with torch.autograd.profiler.emit_itt():
+        ...     model(x)
+
+    """
+
+    def __init__(self, enabled=True, record_shapes=False):
+        self.enabled = enabled
+        self.entered = False
+        self.record_shapes = record_shapes
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("ITT annotation context manager is not reentrant")
+        self.entered = True
+        _run_on_profiler_start()
+        _enable_profiler(
+            ProfilerConfig(
+                ProfilerState.ITT,
+                self.record_shapes,
+                False,
+                False,
+                False,
+                False,
+                _ExperimentalConfig(),
+            ),
+            set(),
+        )
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        _disable_profiler()
+        _run_on_profiler_stop()
+        return False
+
+
+class emit_nvtx:
+    """Context manager that makes every autograd operation emit an NVTX range.
+
+    It is useful when running the program under nvprof::
+
+        nvprof --profile-from-start off -o trace_name.prof -- 
+
+    Unfortunately, there's no way to force nvprof to flush the data it collected
+    to disk, so for CUDA profiling one has to use this context manager to annotate
+    nvprof traces and wait for the process to exit before inspecting them.
+    Then, either NVIDIA Visual Profiler (nvvp) can be used to visualize the timeline, or
+    :func:`torch.autograd.profiler.load_nvprof` can load the results for inspection
+    e.g. in Python REPL.
+
+    .. warning:
+        This context manager should not be called recursively, i.e. at most one
+        instance should be enabled at any given time.
+
+    Args:
+        enabled (bool, optional): Setting ``enabled=False`` makes this context manager a no-op.
+            Default: ``True``.
+        record_shapes (bool, optional): If ``record_shapes=True``, the nvtx range wrapping
+            each autograd op will append information about the sizes of Tensor arguments received
+            by that op, in the following format:
+            ``[[arg0.size(0), arg0.size(1), ...], [arg1.size(0), arg1.size(1), ...], ...]``
+            Non-tensor arguments will be represented by ``[]``.
+            Arguments will be listed in the order they are received by the backend op.
+            Please note that this order may not match the order in which those arguments were passed
+            on the Python side.  Also note that shape recording may increase the overhead of nvtx range creation.
+            Default: ``False``
+
+    Example:
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> with torch.cuda.profiler.profile():
+        ...     model(x)  # Warmup CUDA memory allocator and profiler
+        ...     with torch.autograd.profiler.emit_nvtx():
+        ...         model(x)
+
+    **Forward-backward correlation**
+
+    When viewing a profile created using :class:`emit_nvtx` in the Nvidia Visual Profiler,
+    correlating each backward-pass op with the corresponding forward-pass op can be difficult.
+    To ease this task, :class:`emit_nvtx` appends sequence number information to the ranges it
+    generates.
+
+    During the forward pass, each function range is decorated with ``seq=``.  ``seq`` is a running
+    counter, incremented each time a new backward Function object is created and stashed for backward.
+    Thus, the ``seq=`` annotation associated with each forward function range tells you that
+    if a backward Function object is created by this forward function,
+    the backward object will receive sequence number N.
+    During the backward pass, the top-level range wrapping each C++ backward Function's
+    ``apply()`` call is decorated with ``stashed seq=``.  ``M`` is the sequence number that
+    the backward object was created with.  By comparing ``stashed seq`` numbers in backward with ``seq``
+    numbers in forward, you can track down which forward op created each backward Function.
+
+    Any functions executed during the backward pass are also decorated with ``seq=``.  During
+    default backward (with ``create_graph=False``) this information is irrelevant, and in fact,
+    ``N`` may simply be 0 for all such functions.  Only the top-level ranges associated with
+    backward Function objects' ``apply()`` methods are useful, as a way to correlate these Function
+    objects with the earlier forward pass.
+
+    **Double-backward**
+
+    If, on the other hand, a backward pass with ``create_graph=True`` is underway (in other words,
+    if you are setting up for a double-backward), each function's execution during backward
+    is given a nonzero, useful ``seq=``.  Those functions may themselves create Function objects
+    to be executed later during double-backward, just as the original functions in the forward pass did.
+    The relationship between backward and double-backward is conceptually the same as the relationship
+    between forward and backward: The functions still emit current-sequence-number-tagged ranges,
+    the Function objects they create still stash those sequence numbers, and during the eventual
+    double-backward, the Function objects' ``apply()`` ranges are still tagged with ``stashed seq``
+    numbers, which can be compared to `seq` numbers from the backward pass.
+
+    .. warning:
+        The sequence number is thread-local, and some forward functions don't create an associated
+        backward Function object (instead delegating that to sub-functions further down the call chain).
+        For these reasons, the correspondence of stashed sequence numbers in
+        backward Function ``apply()`` ranges with `seq` numbers in forward-pass ranges is
+        not guaranteed to be 1 to 1.  The sequence numbers alone may not be enough to fully
+        disambiguate which forward function created which
+        backward Function object.  You may need to make a judgment based on analytic knowledge of what
+        the expected correspondence should be.
+    """
+
+    def __init__(self, enabled=True, record_shapes=False):
+        self.enabled = enabled
+        self.entered = False
+        self.record_shapes = record_shapes
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("NVTX annotation context manager is not reentrant")
+        self.entered = True
+        torch.cuda.synchronize()
+        _run_on_profiler_start()
+        _enable_profiler(
+            ProfilerConfig(
+                ProfilerState.NVTX,
+                self.record_shapes,
+                False,
+                False,
+                False,
+                False,
+                _ExperimentalConfig(),
+            ),
+            set(),
+        )
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        torch.cuda.synchronize()
+        _disable_profiler()
+        _run_on_profiler_stop()
+        return False
+
+
+def load_nvprof(path):
+    """Open an nvprof trace file and parses autograd annotations.
+
+    Args:
+        path (str): path to nvprof trace
+    """
+    return EventList(parse_nvprof_trace(path))
+
+
+class EnforceUnique:
+    """Raises an error if a key is seen more than once."""
+
+    def __init__(self):
+        self.seen = set()
+
+    def see(self, *key):
+        r"""
+        Observe a key and raise an error if it is seen multiple times.
+        """
+        if key in self.seen:
+            raise RuntimeError("duplicate key: " + str(key))
+        self.seen.add(key)
+
+
+def parse_nvprof_trace(path):
+    import sqlite3
+
+    conn = sqlite3.connect(path)
+    conn.row_factory = sqlite3.Row
+
+    # Parse strings table
+    strings = {}
+    for r in conn.execute("SELECT _id_ as id, value FROM StringTable"):
+        strings[r["id"]] = torch._C._demangle(r["value"])
+
+    # First, find all functions and create FunctionEvents for them
+    marker_query = """
+    SELECT
+        start.id AS marker_id, start.name, start.timestamp AS start_time, end.timestamp AS end_time
+    FROM
+        CUPTI_ACTIVITY_KIND_MARKER AS start INNER JOIN CUPTI_ACTIVITY_KIND_MARKER AS end
+        ON start.id = end.id
+    WHERE
+        start.name != 0 AND end.name = 0
+    """
+    functions = []
+    functions_map = {}
+    unique = EnforceUnique()
+    for row in conn.execute(marker_query):
+        unique.see(row["marker_id"])
+        evt = FunctionEvent(
+            id=row["marker_id"],
+            node_id=0,  # missing a node_id when calling FunctionEvent. This is just to ensure
+            # that pytorch doesn't crash when creating a FunctionEvent() object
+            name=strings[row["name"]],
+            start_us=row["start_time"],
+            end_us=row["end_time"],
+            thread=0,
+        )  # TODO: find in sqlite database
+        functions.append(evt)
+        functions_map[evt.id] = evt
+
+    # Now, correlate all kernels with FunctionEvents
+    kernel_query = """
+    SELECT
+        start.id AS marker_id, start.name, start.timestamp, end.timestamp,
+        runtime._id_ AS runtime_id, runtime.cbid, runtime.start AS runtime_start, runtime.end AS runtime_end,
+        kernel.start AS kernel_start, kernel.end AS kernel_end, kernel.name AS kernel_name
+    FROM
+        CUPTI_ACTIVITY_KIND_MARKER AS start
+        INNER JOIN CUPTI_ACTIVITY_KIND_MARKER AS end
+            ON start.id = end.id
+        INNER JOIN CUPTI_ACTIVITY_KIND_RUNTIME as runtime
+            ON (start.timestamp < runtime.start AND runtime.end < end.timestamp)
+        INNER JOIN CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL AS kernel
+            ON kernel.correlationId = runtime.correlationId
+    """
+    unique = EnforceUnique()
+    for row in conn.execute(kernel_query):
+        unique.see(row["marker_id"], row["runtime_id"])
+        # 211 is cudaKernelLaunch for cuda >= 9.2
+        assert row["cbid"] == 211
+        evt = functions_map[row["marker_id"]]
+        evt.append_kernel(
+            row["kernel_name"], 0, row["kernel_end"] - row["kernel_start"]
+        )
+
+    functions.sort(key=lambda evt: evt.time_range.start)
+    return functions
+
+
+class KinetoStepTracker:
+    """Provides an abstraction for incrementing the step count globally.
+
+    Previously, we only had one place to mark that a step() has occurred
+    in the program via pytorch profiler step(). We will now add step hooks
+    in the Optimizer class https://github.com/pytorch/pytorch/issues/88446
+
+    - This could mean programs that already call profiler.step() every
+      iteration can end up double incrementing step count.
+    - If a model uses multiple optimizers we can also have double or more
+      counting of the step.
+
+    We fix this by adding a layer of abstraction before calling step()
+    to the kineto library. The idea is to maintain steps per requester in a dict:
+
+    .. code-block::
+
+        {
+           "ProfilerStep": 100,  # triggered by profiler step() call
+           "Optimizer1Step": 100,   # Optimizer 1 or 2 are just examples, could be SGD, Adam etc
+           "Optimizer2Step": 100,
+        }
+
+    To figure out the global step count just take the max of dict values (100).
+
+    If one of the count increments the max will go up.
+
+    .. code-block::
+
+        {
+           "ProfilerStep": 100,
+           "Optimizer1Step": 101,   # Optimizer1 got incremented first say
+           "Optimizer2Step": 100,
+        }
+
+    Then global step count is 101
+    We only call the kineto step() function when global count increments.
+
+    NOTE: Please do not use the KinetoStepTracker in modules beside the Optimizer
+    for now. The result could be incorrect increments of the step count.
+    """
+
+    _current_step = 0
+    _step_dict: dict[str, int] = defaultdict(int)
+
+    @classmethod
+    def init_step_count(cls, requester: str):
+        r"""
+        Initialize for a given requester.
+        """
+        cls._step_dict[requester] = cls._current_step
+
+    @classmethod
+    def erase_step_count(cls, requester: str) -> bool:
+        r"""
+        Remove a given requester.
+        """
+        return cls._step_dict.pop(requester, None) is not None
+
+    @classmethod
+    def increment_step(cls, requester: str) -> int:
+        """Increments the step count for the requester.
+
+        Additionally if the max over all step counts has incremented then
+        trigger the _kineto_step() returns global step count
+        """
+        if requester not in cls._step_dict:
+            cls.init_step_count(requester)
+        cls._step_dict[requester] += 1
+
+        new_step = max(cls._step_dict.values())
+        if new_step > cls._current_step:
+            delta = new_step - cls._current_step
+            if delta > 1:
+                warn(
+                    "Profiler step count has increased more than 1 - "
+                    f"current_step = {cls._current_step} step dict =  {cls._step_dict}"
+                )
+            for _ in range(0, delta):
+                _kineto_step()
+            cls._current_step = new_step
+        return cls._current_step
+
+    @classmethod
+    def current_step(cls) -> int:
+        r"""
+        Get the latest step for any requester
+        """
+        return cls._current_step
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/profiler_legacy.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/profiler_legacy.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e5c9264ee5858a43e720204483ab89f7466a7a7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/profiler_legacy.py
@@ -0,0 +1,312 @@
+# mypy: allow-untyped-defs
+import itertools
+import warnings
+from typing_extensions import deprecated
+
+import torch
+import torch.cuda
+from torch.autograd import (
+    _disable_profiler_legacy,
+    _enable_profiler_legacy,
+    DeviceType,
+    ProfilerConfig,
+    ProfilerState,
+)
+from torch.autograd.profiler_util import (
+    _filter_name,
+    _filter_stack_entry,
+    _rewrite_name,
+    EventList,
+    FunctionEvent,
+    MEMORY_EVENT_NAME,
+)
+
+
+__all__ = ["profile"]
+
+
+@deprecated(
+    "`torch.autograd.profiler_legacy.profile` is deprecated and will be removed in a future release. "
+    "Please use `torch.profiler` instead.",
+    category=None,  # TODO: change to `FutureWarning`
+)
+class profile:
+    """DEPRECATED: use torch.profiler instead."""
+
+    def __init__(
+        self,
+        enabled=True,
+        *,
+        use_cuda=False,
+        record_shapes=False,
+        with_flops=False,
+        profile_memory=False,
+        with_stack=False,
+        with_modules=False,
+    ):
+        self.enabled: bool = enabled
+        if not self.enabled:
+            return
+        self.use_cuda = use_cuda
+        self.function_events = None
+        self.entered = False
+        self.record_shapes = record_shapes
+        self.with_flops = with_flops
+        self.record_shapes |= self.with_flops
+        self.profile_memory = profile_memory
+        self.with_stack = with_stack
+        self.with_modules = with_modules
+
+        if self.use_cuda and not torch.cuda.is_available():
+            warnings.warn(
+                "CUDA is not available, disabling CUDA profiling",
+                stacklevel=2,
+            )
+            self.use_cuda = False
+
+        if self.use_cuda:
+            self.profiler_kind = ProfilerState.CUDA
+        else:
+            self.profiler_kind = ProfilerState.CPU
+
+    def config(self):
+        return ProfilerConfig(
+            self.profiler_kind,
+            self.record_shapes,
+            self.profile_memory,
+            self.with_stack,
+            self.with_flops,
+            self.with_modules,
+            # avoid exposing _ExperimentalConfig this in legacy public API
+            torch._C._profiler._ExperimentalConfig(),
+        )
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("Profiler context manager is not reentrant")
+        self.entered = True
+        self._start_trace()
+        return self
+
+    def _start_trace(self):
+        _enable_profiler_legacy(self.config())
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        if self.use_cuda:
+            torch.cuda.synchronize()
+
+        records = _disable_profiler_legacy()
+        parsed_results = _parse_legacy_records(records)
+        self.function_events = EventList(
+            parsed_results,
+            use_device="cuda" if self.use_cuda else None,
+            profile_memory=self.profile_memory,
+            with_flops=self.with_flops,
+        )
+        self.function_events._build_tree()
+        return False
+
+    def __repr__(self):
+        if self.function_events is None:
+            return ""
+        return repr(self.function_events)
+
+    def __str__(self):
+        if self.function_events is None:
+            return ""
+        return str(self.function_events)
+
+    def _check_finish(self):
+        if self.function_events is None:
+            raise RuntimeError("Profiler didn't finish running")
+
+    def table(
+        self,
+        sort_by=None,
+        row_limit=100,
+        max_src_column_width=75,
+        max_name_column_width=55,
+        max_shapes_column_width=80,
+        header=None,
+        top_level_events_only=False,
+    ):
+        self._check_finish()
+        assert self.function_events is not None
+        return self.function_events.table(
+            sort_by=sort_by,
+            row_limit=row_limit,
+            max_src_column_width=max_src_column_width,
+            max_name_column_width=max_name_column_width,
+            max_shapes_column_width=max_shapes_column_width,
+            header=header,
+            top_level_events_only=top_level_events_only,
+        )
+
+    table.__doc__ = EventList.table.__doc__
+
+    def export_chrome_trace(self, path):
+        self._check_finish()
+        assert self.function_events is not None
+        return self.function_events.export_chrome_trace(path)
+
+    export_chrome_trace.__doc__ = EventList.export_chrome_trace.__doc__
+
+    def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
+        self._check_finish()
+        assert self.function_events is not None, "Expected profiling results"
+        assert self.with_stack, "export_stacks() requires with_stack=True"
+        return self.function_events.export_stacks(path, metric)
+
+    def key_averages(self, group_by_input_shape=False, group_by_stack_n=0):
+        self._check_finish()
+        assert self.function_events is not None, "Expected profiling results"
+        return self.function_events.key_averages(group_by_input_shape, group_by_stack_n)
+
+    key_averages.__doc__ = EventList.key_averages.__doc__
+
+    def total_average(self):
+        self._check_finish()
+        assert self.function_events is not None, "Expected profiling results"
+        return self.function_events.total_average()
+
+    total_average.__doc__ = EventList.total_average.__doc__
+
+    @property
+    def self_cpu_time_total(self):
+        """Return CPU time as the sum of self times across all events."""
+        self._check_finish()
+        assert self.function_events is not None
+        return self.function_events.self_cpu_time_total
+
+
+def _parse_legacy_records(thread_records):
+    def _get_record_key(record):
+        """Return a tuple for correlating start and end records in `_parse_legacy_records`."""
+        return (record.handle(), record.node_id())
+
+    start_record = None
+    functions = []
+
+    # '__start_profile' is not guaranteed to be first, so we must find it here
+    for record in itertools.chain.from_iterable(thread_records):
+        name = record.name()
+        if start_record is None and name == "__start_profile":
+            start_record = record
+
+    assert start_record is not None and not start_record.is_remote()
+
+    for thread_record_list in thread_records:
+        # accumulated memory allocations per handle
+        cpu_memory_allocs = {}
+        cuda_memory_allocs = {}
+        # ranges per handle
+        range_starts = {}
+
+        filtered_handles = set()
+        prev_record = None
+        for record in thread_record_list:
+            record_key = _get_record_key(record)
+            if _filter_name(record.name()) or record_key in filtered_handles:
+                filtered_handles.add(record_key)
+                continue
+
+            if record.kind() == "push":
+                # workaround to reduce double logging from operator
+                # wrappers and redispatch
+                if prev_record is not None:
+                    duplicate = (
+                        prev_record.name() == record.name()
+                        and prev_record.kind() == record.kind()
+                        and prev_record.node_id() == record.node_id()
+                    )
+                    if duplicate:
+                        filtered_handles.add(record_key)
+                        continue
+
+                range_starts[record_key] = record
+                cpu_memory_allocs[record_key] = 0
+                cuda_memory_allocs[record_key] = 0
+            elif record.kind() == "pop":
+                assert (
+                    record_key in range_starts
+                ), f"""Expected record with key {record_key} to exist in range_starts.
+                    This means that the pop event did not have a corresponding push."""
+
+                start = range_starts[record_key]
+
+                cpu_memory_usage = cpu_memory_allocs[record_key]
+                cuda_memory_usage = cuda_memory_allocs[record_key]
+                is_async = start.is_async() or (start.thread_id() != record.thread_id())
+                is_remote_event = record.is_remote()
+                start_flops = start.flops()
+
+                fe = FunctionEvent(
+                    id=record.handle(),
+                    node_id=record.node_id(),
+                    name=_rewrite_name(name=start.name(), with_wildcard=True),
+                    trace_name=_rewrite_name(name=start.name(), with_wildcard=False),
+                    thread=start.thread_id(),
+                    start_us=start_record.cpu_elapsed_us(start),
+                    end_us=start_record.cpu_elapsed_us(record),
+                    fwd_thread=start.fwd_thread_id(),
+                    input_shapes=start.shapes(),
+                    stack=[
+                        entry for entry in start.stack() if _filter_stack_entry(entry)
+                    ],
+                    scope=start.scope(),
+                    use_device="cuda" if start.has_cuda() else None,
+                    cpu_memory_usage=cpu_memory_usage,
+                    device_memory_usage=cuda_memory_usage,
+                    is_async=is_async,
+                    is_remote=is_remote_event,
+                    sequence_nr=start.sequence_nr(),
+                    device_type=DeviceType.CPU,
+                    is_legacy=True,
+                    flops=start_flops,
+                )
+                # note: async events have only cpu total time
+                if not is_async and start.has_cuda():
+                    duration = start.cuda_elapsed_us(record)
+                    if duration > 0:
+                        fe.append_kernel(start.name(), start.device(), duration)
+                functions.append(fe)
+                del range_starts[record_key]
+                del cpu_memory_allocs[record_key]
+                del cuda_memory_allocs[record_key]
+            elif record.kind() == "memory_alloc":
+                num_open_handles_cpu = len(cpu_memory_allocs)
+                num_open_handles_cuda = len(cuda_memory_allocs)
+                assert num_open_handles_cpu == num_open_handles_cuda
+                for handle in cpu_memory_allocs.keys():
+                    cpu_memory_allocs[handle] += record.cpu_memory_usage()
+                for handle in cuda_memory_allocs.keys():
+                    cuda_memory_allocs[handle] += record.cuda_memory_usage()
+                if num_open_handles_cpu == 0:
+                    # output event as a top-level memory event
+                    fe = FunctionEvent(
+                        id=0,
+                        name=MEMORY_EVENT_NAME,
+                        trace_name=None,
+                        thread=0,
+                        start_us=0,
+                        end_us=0,
+                        stack=[],
+                        cpu_memory_usage=record.cpu_memory_usage(),
+                        device_memory_usage=record.cuda_memory_usage(),
+                        is_legacy=True,
+                    )
+                    functions.append(fe)
+            prev_record = record
+
+    # Sort functions by start time then by end time ascending.
+    # This ensures that--in the case of nested events which
+    # have the same start time (which may happen due to the
+    # granularity of the given clock tick)--we always show
+    # the outermost nested call first. This adds stability
+    # in how FunctionEvents appear
+    functions.sort(key=lambda evt: [evt.time_range.start, -evt.time_range.end])
+    return functions
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/profiler_util.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/profiler_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cf36a8df7efe0b739bc759185f17393b912a8f7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/profiler_util.py
@@ -0,0 +1,1148 @@
+# mypy: allow-untyped-defs
+import bisect
+import itertools
+import math
+from collections import defaultdict, namedtuple
+from operator import attrgetter
+from typing import Any, Optional
+from typing_extensions import deprecated
+
+import torch
+from torch.autograd import DeviceType
+
+
+__all__ = [
+    "EventList",
+    "FormattedTimesMixin",
+    "Interval",
+    "Kernel",
+    "FunctionEvent",
+    "FunctionEventAvg",
+    "StringTable",
+    "MemRecordsAcc",
+]
+
+
+class EventList(list):
+    """A list of Events (for pretty printing)."""
+
+    def __init__(self, *args, **kwargs):
+        use_device = kwargs.pop("use_device", None)
+        profile_memory = kwargs.pop("profile_memory", False)
+        with_flops = kwargs.pop("with_flops", False)
+        super().__init__(*args, **kwargs)
+        self._use_device = use_device
+        self._profile_memory = profile_memory
+        self._tree_built = False
+        self._with_flops = with_flops
+
+    def _build_tree(self):
+        self._populate_cpu_children()
+        self._remove_dup_nodes()
+        self._set_backward_stacktraces()
+        self._tree_built = True
+
+    def __str__(self):
+        return self.table()
+
+    def _remove_dup_nodes(self):
+        while True:
+            to_delete = set()
+            for idx in range(len(self)):
+                if (
+                    self[idx].cpu_parent is not None
+                    and self[idx].cpu_parent.name == self[idx].name
+                    and len(self[idx].cpu_parent.cpu_children) == 1
+                ):
+                    self[idx].cpu_parent.cpu_children = self[idx].cpu_children
+                    self[idx].cpu_parent.kernels = self[idx].kernels  # lift kernels up
+                    for ch in self[idx].cpu_children:
+                        ch.cpu_parent = self[idx].cpu_parent
+                    to_delete.add(idx)
+            if len(to_delete) == 0:
+                break
+            new_evts = [ev for ind, ev in enumerate(self) if ind not in to_delete]
+            self.clear()
+            self.extend(new_evts)
+
+    def _populate_cpu_children(self):
+        """Populate child events into each underlying FunctionEvent object.
+
+        One event is a child of another if [s1, e1) is inside [s2, e2). Where
+        s1 and e1 would be start and end of the child event's interval. And
+        s2 and e2 start and end of the parent event's interval
+
+        Example: In event list [[0, 10], [1, 3], [3, 4]] would have make [0, 10]
+        be a parent of two other intervals.
+
+        If for any reason two intervals intersect only partially, this function
+        will not record a parent child relationship between then.
+        """
+        # Some events can be async (i.e. start and end on different threads),
+        # since it's generally undefined how to attribute children ranges to
+        # async ranges, we do not use them when calculating nested ranges and stats
+        sync_events = [
+            evt
+            for evt in self
+            if not evt.is_async and evt.device_type == DeviceType.CPU
+        ]
+        events = sorted(
+            sync_events,
+            key=attrgetter("thread"),
+        )
+        # Group by both thread and node_id, so that events that happen to have
+        # the same thread_id but are from different nodes aren't incorrectly
+        # grouped together.
+        threads = itertools.groupby(
+            events, key=lambda event: (event.thread, event.node_id)
+        )
+
+        # For each thread we keep a stack of current nested parents.
+        # We maintain the invariant that each interval is a subset of all other
+        # intervals lower in the stack.
+        #
+        # First we sort the intervals by their start time. Then we iterate over them.
+        # Every time we see a new interval we remove several parents from
+        # the top until we restore the invariant. Then parent child relationship
+        # if recorded if the stack is not empty.
+        # Finally we add new interval to the list
+        #
+        # Algorithm has O(N * log(N)) complexity where N is number of
+        # intervals
+        for _thread_id, thread_events in threads:
+            thread_events_ = sorted(
+                thread_events,
+                key=lambda event: [event.time_range.start, -event.time_range.end],
+            )
+            current_events: list[FunctionEvent] = []
+            for event in thread_events_:
+                while len(current_events) > 0:
+                    parent = current_events[-1]
+                    if (
+                        event.time_range.start >= parent.time_range.end
+                        or event.time_range.end > parent.time_range.end
+                    ):
+                        # this can't be a parent
+                        current_events.pop()
+                    else:
+                        parent.append_cpu_child(event)
+                        assert (
+                            event.cpu_parent is None
+                        ), f"There is already a CPU parent event for {event.key}"
+                        event.set_cpu_parent(parent)
+                        break
+
+                current_events.append(event)
+
+    def _set_backward_stacktraces(self):
+        def bw_parent(evt):
+            if evt is None:
+                return None
+            elif evt.scope == 1:  # BACKWARD_FUNCTION
+                return evt
+            else:
+                return bw_parent(evt.cpu_parent)
+
+        fwd_stacks = {}
+        for evt in self:
+            if bw_parent(evt) is None and evt.stack is not None:
+                t = (evt.sequence_nr, evt.thread)
+                if t not in fwd_stacks:
+                    fwd_stacks[t] = evt.stack
+
+        for evt in self:
+            p = bw_parent(evt)
+            if p is not None:
+                assert p.fwd_thread is not None
+                t = (p.sequence_nr, p.fwd_thread)
+                if t in fwd_stacks:
+                    evt.stack = fwd_stacks[t]
+                else:
+                    evt.stack = []
+
+    @property
+    def self_cpu_time_total(self):
+        return sum(event.self_cpu_time_total for event in self)
+
+    def table(
+        self,
+        sort_by=None,
+        row_limit=100,
+        max_src_column_width=75,
+        max_name_column_width=55,
+        max_shapes_column_width=80,
+        header=None,
+        top_level_events_only=False,
+    ):
+        """Print an EventList as a nicely formatted table.
+
+        Args:
+            sort_by (str, optional): Attribute used to sort entries. By default
+                they are printed in the same order as they were registered.
+                Valid keys include: ``cpu_time``, ``cuda_time``, ``xpu_time``,
+                ``cpu_time_total``, ``cuda_time_total``, ``xpu_time_total``,
+                ``cpu_memory_usage``, ``cuda_memory_usage``, ``xpu_memory_usage``,
+                ``self_cpu_memory_usage``, ``self_cuda_memory_usage``,
+                ``self_xpu_memory_usage``, ``count``.
+            top_level_events_only(bool, optional): Boolean flag to determine the
+                selection of events to display. If true, the profiler will only
+                display events at top level like top-level invocation of python
+                `lstm`, python `add` or other functions, nested events like low-level
+                cpu/cuda/xpu ops events are omitted for profiler result readability.
+
+        Returns:
+            A string containing the table.
+        """
+        return _build_table(
+            self,
+            sort_by=sort_by,
+            row_limit=row_limit,
+            max_src_column_width=max_src_column_width,
+            max_name_column_width=max_name_column_width,
+            max_shapes_column_width=max_shapes_column_width,
+            header=header,
+            profile_memory=self._profile_memory,
+            with_flops=self._with_flops,
+            top_level_events_only=top_level_events_only,
+        )
+
+    def export_chrome_trace(self, path):
+        """Export an EventList as a Chrome tracing tools file.
+
+        The checkpoint can be later loaded and inspected under ``chrome://tracing`` URL.
+
+        Args:
+            path (str): Path where the trace will be written.
+        """
+        import os
+
+        device_name = "cuda" if not self._use_device else self._use_device
+        with open(path, "w") as f:
+            next_id = 0
+            # Use file IO over using json.dump since JSON dumping is very slow and
+            # this technique is proven to give a 4x speedup.
+            f.write("[")
+            for evt in self:
+                if evt.trace_name is None:
+                    continue
+                f.write(
+                    '{{"name": "{}", '
+                    '"ph": "X", '
+                    '"ts": {}, '
+                    '"dur": {}, '
+                    '"tid": {}, '
+                    '"pid": "CPU functions", '
+                    '"args": {{}}}}, '.format(
+                        evt.trace_name,
+                        evt.time_range.start,
+                        evt.time_range.elapsed_us(),
+                        evt.thread
+                        if not evt.is_remote
+                        else f'" node_id:{evt.node_id}, thread_id:{evt.thread} "',
+                    )
+                )
+                for _ in evt.kernels:
+                    # 's' and 'f' draw Flow arrows from
+                    # the CPU launch to the GPU kernel
+                    f.write(
+                        f'{{"name": "{evt.trace_name}", '
+                        '"ph": "s", '
+                        f'"ts": {evt.time_range.start}, '
+                        f'"tid": {evt.thread}, '
+                        '"pid": "CPU functions", '
+                        f'"id": {next_id}, '
+                        f'"cat": "cpu_to_{device_name}", '
+                        '"args": {}}, '
+                    )
+                    # Note: use torch.profiler to get device kernel trace
+                    next_id += 1
+            if len(self) > 0:
+                # remove trailing whitespace and comma
+                f.seek(f.tell() - 2, os.SEEK_SET)
+                f.truncate()
+            f.write("]")
+
+    def supported_export_stacks_metrics(self):
+        return [
+            "self_cpu_time_total",
+            "self_cuda_time_total",
+            "self_xpu_time_total",
+            "self_privateuse1_time_total",
+        ]
+
+    def export_stacks(self, path: str, metric: str):
+        if metric not in self.supported_export_stacks_metrics():
+            raise ValueError(
+                "metric should be one of: "
+                + str(self.supported_export_stacks_metrics())
+            )
+        translate_table = str.maketrans(" ;\t\n", "____")
+        with open(path, "w") as f:
+            for evt in self:
+                if evt.stack and len(evt.stack) > 0:
+                    metric_value = getattr(
+                        evt,
+                        metric.replace("cuda", "device")
+                        .replace("xpu", "device")
+                        .replace("privateuse1", "device"),
+                    )
+                    if int(metric_value) > 0:
+                        stack_str = ""
+                        for entry in reversed(evt.stack):
+                            stack_str += entry.translate(translate_table)
+                            stack_str += ";"
+                        stack_str = stack_str[:-1] + " " + str(int(metric_value))
+                        f.write(stack_str + "\n")
+
+    def key_averages(
+        self,
+        group_by_input_shapes=False,
+        group_by_stack_n=0,
+        group_by_overload_name=False,
+    ):
+        """Averages all function events over their keys.
+
+        Args:
+            group_by_input_shapes: group entries by
+                (event name, input shapes) rather than just event name.
+                This is useful to see which input shapes contribute to the runtime
+                the most and may help with size-specific optimizations or
+                choosing the best candidates for quantization (aka fitting a roof line)
+
+            group_by_stack_n: group by top n stack trace entries
+
+            group_by_overload_name: Differentiate operators by their overload name e.g. aten::add.Tensor
+            and aten::add.out will be aggregated separately
+
+        Returns:
+            An EventList containing FunctionEventAvg objects.
+        """
+        assert self._tree_built
+        stats: dict[tuple[str, ...], FunctionEventAvg] = defaultdict(FunctionEventAvg)
+
+        def get_key(
+            event, group_by_input_shapes, group_by_stack_n, group_by_overload_name
+        ) -> tuple[str, ...]:
+            key = [
+                str(event.key),
+                str(event.node_id),
+                str(event.device_type),
+                str(event.is_legacy),
+                str(event.is_user_annotation),
+            ]
+            if group_by_overload_name:
+                key.append(evt.overload_name)
+            if group_by_input_shapes:
+                key.append(str(event.input_shapes))
+            if group_by_stack_n > 0:
+                key += event.stack[:group_by_stack_n]
+            return tuple(key)
+
+        for evt in self:
+            stats[
+                get_key(
+                    evt, group_by_input_shapes, group_by_stack_n, group_by_overload_name
+                )
+            ].add(evt)
+
+        avg_list = EventList(
+            stats.values(),
+            use_device=self._use_device,
+            profile_memory=self._profile_memory,
+            with_flops=self._with_flops,
+        )
+        for evt in avg_list:
+            evt.stack = evt.stack[:group_by_stack_n]
+            if not group_by_input_shapes:
+                evt.input_shapes = ""
+            if not group_by_overload_name:
+                evt.overload_name = ""
+        return avg_list
+
+    def total_average(self):
+        """Averages all events.
+
+        Returns:
+            A FunctionEventAvg object.
+        """
+        total_stat = FunctionEventAvg()
+        for evt in self:
+            total_stat += evt
+            total_stat.key = None
+        total_stat.key = "Total"
+        return total_stat
+
+
+def _format_time(time_us):
+    """Define how to format time in FunctionEvent."""
+    US_IN_SECOND = 1000.0 * 1000.0
+    US_IN_MS = 1000.0
+    if time_us >= US_IN_SECOND:
+        return f"{time_us / US_IN_SECOND:.3f}s"
+    if time_us >= US_IN_MS:
+        return f"{time_us / US_IN_MS:.3f}ms"
+    return f"{time_us:.3f}us"
+
+
+def _format_time_share(time_us, total_time_us):
+    """Define how to format time in FunctionEvent."""
+    if total_time_us == 0:
+        assert time_us == 0, f"Expected time_us == 0 but got {time_us}"
+        return "NaN"
+    return f"{time_us * 100.0 / total_time_us:.2f}%"
+
+
+def _format_memory(nbytes):
+    """Return a formatted memory size string."""
+    KB = 1024
+    MB = 1024 * KB
+    GB = 1024 * MB
+    if abs(nbytes) >= GB:
+        return f"{nbytes * 1.0 / GB:.2f} Gb"
+    elif abs(nbytes) >= MB:
+        return f"{nbytes * 1.0 / MB:.2f} Mb"
+    elif abs(nbytes) >= KB:
+        return f"{nbytes * 1.0 / KB:.2f} Kb"
+    else:
+        return str(nbytes) + " b"
+
+
+def _attr_formatter(name):
+    return property(lambda self: _format_time(getattr(self, name)))
+
+
+class FormattedTimesMixin:
+    """Helpers for FunctionEvent and FunctionEventAvg.
+
+    The subclass should define `*_time_total` and `count` attributes.
+    """
+
+    cpu_time_str = _attr_formatter("cpu_time")
+    device_time_str = _attr_formatter("device_time")
+    cpu_time_total_str = _attr_formatter("cpu_time_total")
+    device_time_total_str = _attr_formatter("device_time_total")
+    self_cpu_time_total_str = _attr_formatter("self_cpu_time_total")
+    self_device_time_total_str = _attr_formatter("self_device_time_total")
+
+    @property
+    def cpu_time(self):
+        return 0.0 if self.count == 0 else 1.0 * self.cpu_time_total / self.count  # type: ignore[attr-defined]
+
+    @property
+    def device_time(self):
+        return 0.0 if self.count == 0 else 1.0 * self.device_time_total / self.count  # type: ignore[attr-defined]
+
+    @property
+    @deprecated(
+        "`cuda_time` is deprecated, please use `device_time` instead.",
+        category=FutureWarning,
+    )
+    def cuda_time(self):  # To be deprecated
+        return self.device_time
+
+
+class Interval:
+    def __init__(self, start, end):
+        self.start = start
+        self.end = end
+
+    def elapsed_us(self):
+        r"""
+        Returns the length of the interval
+        """
+        return self.end - self.start
+
+
+Kernel = namedtuple("Kernel", ["name", "device", "duration"])
+
+
+class FunctionEvent(FormattedTimesMixin):
+    """Profiling information about a single function."""
+
+    def __init__(
+        self,
+        id,
+        name,
+        thread,
+        start_us,
+        end_us,
+        overload_name=None,
+        fwd_thread=None,
+        input_shapes=None,
+        stack=None,
+        scope=0,
+        use_device=None,
+        cpu_memory_usage=0,
+        device_memory_usage=0,
+        is_async=False,
+        is_remote=False,
+        sequence_nr=-1,
+        node_id=-1,
+        device_type=DeviceType.CPU,
+        device_index=0,
+        device_resource_id=None,
+        is_legacy=False,
+        flops=None,
+        trace_name=None,
+        concrete_inputs=None,
+        kwinputs=None,
+        is_user_annotation=False,
+    ):
+        self.id: int = id
+        self.node_id: int = node_id
+        self.name: str = name
+        self.overload_name: str = overload_name
+        self.trace_name: str = trace_name
+        self.time_range: Interval = Interval(start_us, end_us)
+        self.thread: int = thread
+        self.fwd_thread: Optional[int] = fwd_thread
+        self.kernels: list[Kernel] = []
+        self.count: int = 1
+        self.cpu_children: list[FunctionEvent] = []
+        self.cpu_parent: Optional[FunctionEvent] = None
+        self.input_shapes: tuple[int, ...] = input_shapes
+        self.concrete_inputs: list[Any] = concrete_inputs
+        self.kwinputs: dict[str, Any] = kwinputs
+        self.stack: list = stack
+        self.scope: int = scope
+        self.use_device: Optional[str] = use_device
+        self.cpu_memory_usage: int = cpu_memory_usage
+        self.device_memory_usage: int = device_memory_usage
+        self.is_async: bool = is_async
+        self.is_remote: bool = is_remote
+        self.sequence_nr: int = sequence_nr
+        self.device_type: DeviceType = device_type
+        self.device_index: int = device_index
+        self.device_resource_id: int = (
+            thread if device_resource_id is None else device_resource_id
+        )
+        self.is_legacy: bool = is_legacy
+        self.flops: Optional[int] = flops
+        self.is_user_annotation: Optional[bool] = is_user_annotation
+        self.self_cpu_percent = -1
+        self.total_cpu_percent = -1
+        self.total_device_percent = -1
+
+    def append_kernel(self, name, device, duration):
+        assert self.device_type == DeviceType.CPU
+        self.kernels.append(Kernel(name, device, duration))
+
+    def append_cpu_child(self, child):
+        """Append a CPU child of type FunctionEvent.
+
+        One is supposed to append only direct children to the event to have
+        correct self cpu time being reported.
+        """
+        assert self.device_type == DeviceType.CPU
+        assert isinstance(child, FunctionEvent)
+        assert child.device_type == DeviceType.CPU
+        self.cpu_children.append(child)
+
+    def set_cpu_parent(self, parent):
+        """Set the immediate CPU parent of type FunctionEvent.
+
+        One profiling FunctionEvent should have only one CPU parent such that
+        the child's range interval is completely inside the parent's. We use
+        this connection to determine the event is from top-level op or not.
+        """
+        assert self.device_type == DeviceType.CPU
+        assert isinstance(parent, FunctionEvent)
+        assert parent.device_type == DeviceType.CPU
+        self.cpu_parent = parent
+
+    # Note: async events don't have children, are not used when computing 'self'
+    # metrics of other events, have only total cpu time
+    @property
+    def self_cpu_memory_usage(self):
+        if self.is_async or self.device_type != DeviceType.CPU:
+            return 0
+        return self.cpu_memory_usage - sum(
+            child.cpu_memory_usage for child in self.cpu_children
+        )
+
+    @property
+    def self_device_memory_usage(self):
+        if self.is_async or self.device_type != DeviceType.CPU:
+            return 0
+        return self.device_memory_usage - sum(
+            child.device_memory_usage for child in self.cpu_children
+        )
+
+    @property
+    @deprecated(
+        "`self_cuda_memory_usage` is deprecated. Use `self_device_memory_usage` instead.",
+        category=FutureWarning,
+    )
+    def self_cuda_memory_usage(self):  # To be deprecated
+        return self.self_device_memory_usage
+
+    @property
+    def cpu_time_total(self):
+        if self.device_type == DeviceType.CPU:
+            return self.time_range.elapsed_us()
+        else:
+            return 0
+
+    @property
+    def self_cpu_time_total(self):
+        if self.is_async or self.device_type != DeviceType.CPU:
+            return 0
+        return self.cpu_time_total - sum(
+            child.cpu_time_total for child in self.cpu_children
+        )
+
+    @property
+    def device_time_total(self):
+        if self.is_async or not self.use_device:
+            return 0
+        if self.device_type == DeviceType.CPU:
+            if not self.is_legacy:
+                # account for the kernels in the children ops
+                return sum(kinfo.duration for kinfo in self.kernels) + sum(
+                    ch.device_time_total for ch in self.cpu_children
+                )
+            else:
+                # each legacy cpu events has a single (fake) kernel
+                return sum(kinfo.duration for kinfo in self.kernels)
+        else:
+            assert self.device_type in [
+                DeviceType.CUDA,
+                DeviceType.PrivateUse1,
+                DeviceType.MTIA,
+            ]
+            return self.time_range.elapsed_us()
+
+    @property
+    @deprecated(
+        "`cuda_time_total` is deprecated. Use `device_time_total` instead.",
+        category=FutureWarning,
+    )
+    def cuda_time_total(self):  # To be deprecated
+        return self.device_time_total
+
+    @property
+    def self_device_time_total(self):
+        if self.is_async or not self.use_device:
+            return 0
+        if self.device_type == DeviceType.CPU:
+            return self.device_time_total - sum(
+                child.device_time_total for child in self.cpu_children
+            )
+        else:
+            assert self.device_type in [
+                DeviceType.CUDA,
+                DeviceType.PrivateUse1,
+                DeviceType.MTIA,
+            ]
+            return self.device_time_total
+
+    @property
+    @deprecated(
+        "`self_cuda_time_total` is deprecated. Use `self_device_time_total` instead.",
+        category=FutureWarning,
+    )
+    def self_cuda_time_total(self):  # To be deprecated
+        return self.self_device_time_total
+
+    @property
+    def key(self):
+        return self.name
+
+    def __repr__(self):
+        device_name = self.use_device
+        device_time = self.device_time_str
+        device_memory_usage = self.device_memory_usage
+        return (
+            f""
+        )
+
+
+class FunctionEventAvg(FormattedTimesMixin):
+    """Used to average stats over multiple FunctionEvent objects."""
+
+    def __init__(self) -> None:
+        self.key: Optional[str] = None
+        self.count: int = 0
+        self.node_id: int = 0
+        self.is_async: bool = False
+        self.is_remote: bool = False
+        self.use_device: Optional[str] = None
+        self.cpu_time_total: int = 0
+        self.device_time_total: int = 0
+        self.self_cpu_time_total: int = 0
+        self.self_device_time_total: int = 0
+        self.input_shapes: Optional[list[list[int]]] = None
+        self.overload_name: Optional[str] = None
+        self.stack: Optional[list] = None
+        self.scope: Optional[int] = None
+        self.cpu_memory_usage: int = 0
+        self.device_memory_usage: int = 0
+        self.self_cpu_memory_usage: int = 0
+        self.self_device_memory_usage: int = 0
+        self.cpu_children: Optional[list[FunctionEvent]] = None
+        self.cpu_parent: Optional[FunctionEvent] = None
+        self.device_type: DeviceType = DeviceType.CPU
+        self.is_legacy: bool = False
+        self.flops: int = 0
+
+    def add(self, other):
+        if self.key is None:
+            # First function being recorded as part of FunctionEventAvg, propagate
+            # fields.
+            self.key = other.key
+            self.node_id = other.node_id
+            self.is_async = other.is_async
+            self.is_remote = other.is_remote
+            self.cpu_parent = other.cpu_parent
+            self.cpu_children = other.cpu_children
+
+            self.overload_name = other.overload_name
+            self.input_shapes = other.input_shapes
+            self.stack = other.stack
+            self.scope = other.scope
+            self.device_type = other.device_type
+            self.is_legacy = other.is_legacy
+            self.use_device = other.use_device
+            self.is_user_annotation = other.is_user_annotation
+
+        assert isinstance(other, (FunctionEvent, FunctionEventAvg))
+        assert other.key == self.key
+
+        self.cpu_time_total += other.cpu_time_total
+        self.device_time_total += other.device_time_total
+        self.self_cpu_time_total += other.self_cpu_time_total
+        self.self_device_time_total += other.self_device_time_total
+        self.cpu_memory_usage += other.cpu_memory_usage
+        self.device_memory_usage += other.device_memory_usage
+        self.self_cpu_memory_usage += other.self_cpu_memory_usage
+        self.self_device_memory_usage += other.self_device_memory_usage
+        self.count += other.count
+        if self.flops is None:
+            self.flops = other.flops
+        elif other.flops is not None:
+            self.flops += other.flops
+        return self
+
+    def __iadd__(self, other):
+        return self.add(other)
+
+    def __repr__(self):
+        device_name = "cuda" if not self.use_device else self.use_device
+        self_device_time = self.self_device_time_total_str
+        device_time = self.device_time_str
+        device_memory = self.device_memory_usage
+        return (
+            f""
+        )
+
+
+class StringTable(defaultdict):
+    def __missing__(self, key):
+        # manage cases like 't' (demangled to 'unsigned short') separately,
+        # for now simply check the length to avoid unexpected results for
+        # the short sequences
+        self[key] = torch._C._demangle(key) if len(key) > 1 else key
+        return self[key]
+
+
+class MemRecordsAcc:
+    """Acceleration structure for accessing mem_records in interval."""
+
+    def __init__(self, mem_records):
+        self._mem_records = mem_records
+        self._start_nses: list[int] = []
+        self._indices: list[int] = []
+        if len(mem_records) > 0:
+            tmp = sorted([(r[0].start_ns(), i) for i, r in enumerate(mem_records)])
+            self._start_nses, self._indices = zip(*tmp)  # type: ignore[assignment]
+
+    def in_interval(self, start_us, end_us):
+        r"""
+        Return all records in the given interval
+        To maintain backward compatibility, convert us to ns in function
+        """
+        start_idx = bisect.bisect_left(self._start_nses, start_us * 1000)
+        end_idx = bisect.bisect_right(self._start_nses, end_us * 1000)
+        for i in range(start_idx, end_idx):
+            yield self._mem_records[self._indices[i]]
+
+
+def _filter_stack_entry(entry):
+    filtered_entries = [
+        ("autograd/__init__", "_make_grads"),
+        ("autograd/__init__", "backward"),
+        ("torch/tensor", "backward"),
+        ("_internal/common_utils", "prof_callable"),
+        ("_internal/common_utils", "prof_func_call"),
+        ("_internal/common_utils", "prof_meth_call"),
+    ]
+    return all(not (f[0] in entry and f[1] in entry) for f in filtered_entries)
+
+
+MEMORY_EVENT_NAME = "[memory]"
+OUT_OF_MEMORY_EVENT_NAME = "[OutOfMemory]"
+
+
+def _filter_name(name):
+    # ignoring the following utility ops
+    filtered_out_names = [
+        MEMORY_EVENT_NAME,  # used only for the top-level memory events
+        OUT_OF_MEMORY_EVENT_NAME,
+        "profiler::_record_function_enter",
+        "profiler::_record_function_enter_new",
+        "profiler::_record_function_exit",
+        "aten::is_leaf",
+        "aten::output_nr",
+        "aten::_version",
+    ]
+    return name in filtered_out_names
+
+
+# Demangles and optionally rewrites the provided event name,
+# with_wildcard - whether to replace certain numbered event names
+# with a wildcard name to aggregate them together in the profiler table
+# output
+def _rewrite_name(name, with_wildcard=False):
+    string_table = StringTable()
+    name = string_table[name]
+    if with_wildcard:
+        if name.startswith("ProfilerStep#"):
+            name = "ProfilerStep*"
+    return name
+
+
+def _build_table(
+    events,
+    sort_by=None,
+    header=None,
+    row_limit=100,
+    max_src_column_width=75,
+    max_name_column_width=55,
+    max_shapes_column_width=80,
+    with_flops=False,
+    profile_memory=False,
+    top_level_events_only=False,
+):
+    """Print a summary of events (which can be a list of FunctionEvent or FunctionEventAvg)."""
+    if len(events) == 0:
+        return ""
+
+    has_device_time = any(event.self_device_time_total > 0 for event in events)
+    has_device_mem = any(event.self_device_memory_usage > 0 for event in events)
+    use_device = events[0].use_device
+    # Running on PrivateUse1 device with profiler but not enable
+    # ProfilerActivity.PrivateUse1 can also catch privateuse1 memory usage.
+    # Here only need to check has_privateuse1_time if not use_device.
+    if not use_device and has_device_time:
+        raise RuntimeError("use_device is None, but there is device performance data.")
+
+    has_input_shapes = any(
+        (event.input_shapes is not None and len(event.input_shapes) > 0)
+        for event in events
+    )
+
+    has_overload_names = any(
+        (event.overload_name is not None and len(event.overload_name) > 0)
+        for event in events
+    )
+
+    if sort_by is not None:
+        events = EventList(
+            sorted(
+                events,
+                key=lambda evt: getattr(
+                    evt,
+                    sort_by.replace("cuda", "device")
+                    .replace("xpu", "device")
+                    .replace("privateuse1", "device"),
+                ),
+                reverse=True,
+            ),
+            use_device=use_device,
+            profile_memory=profile_memory,
+            with_flops=with_flops,
+        )
+
+    name_column_width = max(len(evt.key) for evt in events) + 4
+    if max_name_column_width is not None:
+        name_column_width = min(name_column_width, max_name_column_width)
+
+    shapes_column_width = max(len(str(evt.input_shapes)) for evt in events) + 4
+    if max_shapes_column_width is not None:
+        shapes_column_width = min(shapes_column_width, max_shapes_column_width)
+
+    DEFAULT_COLUMN_WIDTH = 12
+    flops_column_width = DEFAULT_COLUMN_WIDTH
+
+    src_column_width = None
+    stacks = [
+        evt.stack for evt in events if evt.stack is not None and len(evt.stack) > 0
+    ]
+    has_stack = len(stacks) > 0
+    if has_stack:
+        src_column_width = (
+            max(max(len(entry) for entry in stack) for stack in stacks) + 4
+        )
+        if max_src_column_width is not None:
+            src_column_width = min(src_column_width, max_src_column_width)
+
+    headers = ["Name"]
+    if has_overload_names:
+        headers.append("Overload Name")
+    headers += [
+        "Self CPU %",
+        "Self CPU",
+        "CPU total %",
+        "CPU total",
+        "CPU time avg",
+    ]
+
+    device_name = use_device.upper() if use_device is not None else "None"
+    if has_device_time:
+        headers.extend(
+            [
+                f"Self {device_name}",
+                f"Self {device_name} %",
+                f"{device_name} total",
+                f"{device_name} time avg",
+            ]
+        )
+    if profile_memory:
+        headers.extend(
+            [
+                "CPU Mem",
+                "Self CPU Mem",
+            ]
+        )
+        if use_device and has_device_mem:
+            headers.extend(
+                [
+                    f"{device_name} Mem",
+                    f"Self {device_name} Mem",
+                ]
+            )
+    headers.append("# of Calls")
+    # Only append Node ID if any event has a valid (>= 0) Node ID
+    append_node_id = any(evt.node_id != -1 for evt in events)
+    if append_node_id:
+        headers.append("Node ID")
+
+    # Have to use a list because nonlocal is Py3 only...
+    SPACING_SIZE = 2
+    row_format_lst = [""]
+    header_sep_lst = [""]
+    line_length_lst = [-SPACING_SIZE]
+
+    def add_column(padding, text_dir=">"):
+        row_format_lst[0] += (
+            "{: " + text_dir + str(padding) + "}" + (" " * SPACING_SIZE)
+        )
+        header_sep_lst[0] += "-" * padding + (" " * SPACING_SIZE)
+        line_length_lst[0] += padding + SPACING_SIZE
+
+    def auto_scale_flops(flops):
+        flop_headers = [
+            "FLOPs",
+            "KFLOPs",
+            "MFLOPs",
+            "GFLOPs",
+            "TFLOPs",
+            "PFLOPs",
+        ]
+        assert flops > 0
+        log_flops = max(0, min(math.log10(flops) / 3, float(len(flop_headers) - 1)))
+        assert log_flops >= 0 and log_flops < len(flop_headers)
+        return (pow(10, (math.floor(log_flops) * -3.0)), flop_headers[int(log_flops)])
+
+    add_column(name_column_width)
+    if has_overload_names:
+        add_column(name_column_width)
+    for _ in headers[1 + has_overload_names :]:
+        add_column(DEFAULT_COLUMN_WIDTH)
+
+    if has_input_shapes:
+        headers.append("Input Shapes")
+        add_column(shapes_column_width)
+
+    if has_stack:
+        headers.append("Source Location")
+        add_column(src_column_width, text_dir="<")
+
+    if with_flops:
+        # Auto-scaling of flops header
+        raw_flops = [evt.flops for evt in events if evt.flops > 0]
+        if len(raw_flops) != 0:
+            (flops_scale, flops_header) = auto_scale_flops(min(raw_flops))
+            headers.append(f"Total {flops_header}")
+            add_column(flops_column_width)
+        else:
+            with_flops = False  # can't find any valid flops
+
+    row_format = row_format_lst[0]
+    header_sep = header_sep_lst[0]
+    line_length = line_length_lst[0]
+    add_column = None  # type: ignore[assignment]
+
+    # Have to use a list because nonlocal is Py3 only...
+    result = []
+
+    def append(s):
+        result.append(s)
+        result.append("\n")  # Yes, newline after the end as well
+
+    sum_self_cpu_time_total = 0
+    sum_self_device_time_total = 0
+    for evt in events:
+        sum_self_cpu_time_total += evt.self_cpu_time_total
+        if evt.device_type == DeviceType.CPU and evt.is_legacy:
+            # in legacy profiler, kernel info is stored in cpu events
+            sum_self_device_time_total += evt.self_device_time_total
+        elif (
+            evt.device_type
+            in [
+                DeviceType.CUDA,
+                DeviceType.PrivateUse1,
+                DeviceType.MTIA,
+            ]
+            and not evt.is_user_annotation
+        ):
+            # in kineto profiler, there're events with the correct device type (e.g. CUDA)
+            sum_self_device_time_total += evt.self_device_time_total
+
+    # Actual printing
+    if header is not None:
+        append("=" * line_length)
+        append(header)
+    if top_level_events_only:
+        append("=" * line_length)
+        append("This report only display top-level ops statistics")
+    append(header_sep)
+    append(row_format.format(*headers))
+
+    append(header_sep)
+
+    def trim_path(path, src_column_width):
+        if len(path) > src_column_width:
+            offset = len(path) - src_column_width
+            path = path[offset:]
+            if len(path) > 3:
+                path = "..." + path[3:]
+        return path
+
+    event_limit = 0
+    for evt in events:
+        if event_limit == row_limit:
+            break
+        if top_level_events_only and evt.cpu_parent is not None:
+            continue
+        else:
+            event_limit += 1
+        name = evt.key
+        if max_name_column_width is not None and len(name) >= max_name_column_width - 3:
+            name = name[: (max_name_column_width - 3)] + "..."
+
+        evt.self_cpu_percent = _format_time_share(
+            evt.self_cpu_time_total, sum_self_cpu_time_total
+        )
+        evt.total_cpu_percent = (
+            _format_time_share(evt.cpu_time_total, sum_self_cpu_time_total)
+            if not evt.is_async
+            else 0
+        )
+
+        row_values = [name]
+        if has_overload_names:
+            overload_name = evt.overload_name
+            if (
+                max_name_column_width is not None
+                and len(overload_name) >= max_name_column_width - 3
+            ):
+                overload_name = overload_name[: (max_name_column_width - 3)] + "..."
+            row_values += [overload_name]
+        row_values += [
+            # Self CPU total %, 0 for async events.
+            evt.self_cpu_percent,
+            evt.self_cpu_time_total_str,  # Self CPU total
+            # CPU total %, 0 for async events.
+            evt.total_cpu_percent,
+            evt.cpu_time_total_str,  # CPU total
+            evt.cpu_time_str,  # CPU time avg
+        ]
+        if has_device_time:
+            evt.total_device_percent = _format_time_share(
+                evt.self_device_time_total, sum_self_device_time_total
+            )
+            row_values.extend(
+                [
+                    evt.self_device_time_total_str,
+                    # device time total %
+                    evt.total_device_percent,
+                    evt.device_time_total_str,
+                    evt.device_time_str,  # device time avg
+                ]
+            )
+        if profile_memory:
+            row_values.extend(
+                [
+                    # CPU Mem Total
+                    _format_memory(evt.cpu_memory_usage),
+                    # Self CPU Mem Total
+                    _format_memory(evt.self_cpu_memory_usage),
+                ]
+            )
+            if use_device and has_device_mem:
+                row_values.extend(
+                    [
+                        # Device Mem Total
+                        _format_memory(evt.device_memory_usage),
+                        # Self Device Mem Total
+                        _format_memory(evt.self_device_memory_usage),
+                    ]
+                )
+        row_values.append(
+            evt.count,  # Number of calls
+        )
+
+        if append_node_id:
+            row_values.append(evt.node_id)
+        if has_input_shapes:
+            row_values.append(str(evt.input_shapes)[:shapes_column_width])
+        if with_flops:
+            if evt.flops <= 0:
+                row_values.append("--")
+            else:
+                row_values.append(f"{evt.flops * flops_scale:8.3f}")  # type: ignore[possibly-undefined]
+        if has_stack:
+            src_field = ""
+            if len(evt.stack) > 0:
+                src_field = trim_path(evt.stack[0], src_column_width)
+            row_values.append(src_field)
+        append(row_format.format(*row_values))
+
+        if has_stack:
+            empty_headers = [""] * (len(headers) - 1)
+            for entry in evt.stack[1:]:
+                append(
+                    row_format.format(
+                        *(empty_headers + [trim_path(entry, src_column_width)])
+                    )
+                )
+            empty_headers.append("")
+            append(row_format.format(*empty_headers))
+
+    append(header_sep)
+    append(f"Self CPU time total: {_format_time(sum_self_cpu_time_total)}")
+    if has_device_time:
+        append(
+            f"Self {use_device.upper() if use_device is not None else 'None'} "
+            f"time total: {_format_time(sum_self_device_time_total)}"
+        )
+    return "".join(result)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/variable.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/variable.py
new file mode 100644
index 0000000000000000000000000000000000000000..84b504a9c82c7ab855df9ba58d934fa92d936253
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/autograd/variable.py
@@ -0,0 +1,15 @@
+# mypy: allow-untyped-defs
+import torch
+from torch._C import _ImperativeEngine as ImperativeEngine
+
+
+__all__ = ["VariableMeta", "Variable"]
+
+
+class VariableMeta(type):
+    def __instancecheck__(cls, other):
+        return isinstance(other, torch.Tensor)
+
+
+class Variable(torch._C._LegacyVariableBase, metaclass=VariableMeta):  # type: ignore[misc]
+    _execution_engine = ImperativeEngine()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4dd3459783643007796da877520155995f6d9bb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/__init__.py
@@ -0,0 +1,157 @@
+# mypy: allow-untyped-defs
+import logging
+import pdb
+import sys
+import traceback
+import typing
+
+import torch
+
+
+log = logging.getLogger(__name__)
+
+
+def is_available() -> bool:
+    """
+    Return ``True`` if the distributed package is available.
+
+    Otherwise,
+    ``torch.distributed`` does not expose any other APIs. Currently,
+    ``torch.distributed`` is available on Linux, MacOS and Windows. Set
+    ``USE_DISTRIBUTED=1`` to enable it when building PyTorch from source.
+    Currently, the default value is ``USE_DISTRIBUTED=1`` for Linux and Windows,
+    ``USE_DISTRIBUTED=0`` for MacOS.
+    """
+    return hasattr(torch._C, "_c10d_init")
+
+
+if is_available() and not torch._C._c10d_init():
+    raise RuntimeError("Failed to initialize torch.distributed")
+
+# Custom Runtime Errors thrown from the distributed package
+DistError = torch._C._DistError
+DistBackendError = torch._C._DistBackendError
+DistNetworkError = torch._C._DistNetworkError
+DistStoreError = torch._C._DistStoreError
+
+if is_available():
+    from torch._C._distributed_c10d import (
+        _broadcast_coalesced,
+        _compute_bucket_assignment_by_size,
+        _ControlCollectives,
+        _DEFAULT_FIRST_BUCKET_BYTES,
+        _make_nccl_premul_sum,
+        _register_builtin_comm_hook,
+        _register_comm_hook,
+        _StoreCollectives,
+        _test_python_store,
+        _verify_params_across_processes,
+        Backend as _Backend,
+        BuiltinCommHookType,
+        DebugLevel,
+        FileStore,
+        get_debug_level,
+        GradBucket,
+        Logger,
+        PrefixStore,
+        ProcessGroup as ProcessGroup,
+        Reducer,
+        set_debug_level,
+        set_debug_level_from_env,
+        Store,
+        TCPStore,
+        Work as _Work,
+    )
+
+    class _DistributedPdb(pdb.Pdb):
+        """
+        Supports using PDB from inside a multiprocessing child process.
+
+        Usage:
+        _DistributedPdb().set_trace()
+        """
+
+        def interaction(self, *args, **kwargs):
+            _stdin = sys.stdin
+            try:
+                sys.stdin = open("/dev/stdin")
+                pdb.Pdb.interaction(self, *args, **kwargs)
+            finally:
+                sys.stdin = _stdin
+
+    _breakpoint_cache: dict[int, typing.Any] = {}
+
+    def breakpoint(rank: int = 0, skip: int = 0):
+        """
+        Set a breakpoint, but only on a single rank.  All other ranks will wait for you to be
+        done with the breakpoint before continuing.
+
+        Args:
+            rank (int): Which rank to break on.  Default: ``0``
+            skip (int): Skip the first ``skip`` calls to this breakpoint. Default: ``0``.
+        """
+        if skip > 0:
+            key = hash(str(traceback.format_exc()))
+            counter = _breakpoint_cache.get(key, 0) + 1
+            _breakpoint_cache[key] = counter
+            if counter <= skip:
+                log.warning("Skip the breakpoint, counter=%d", counter)
+                return
+
+        if get_rank() == rank:
+            pdb = _DistributedPdb()
+            pdb.message(
+                "\n!!! ATTENTION !!!\n\n"
+                f"Type 'up' to get to the frame that called dist.breakpoint(rank={rank})\n"
+            )
+            pdb.set_trace()
+        # If Meta/Python keys are in the TLS, we want to make sure that we ignore them
+        # and hit the (default) CPU/CUDA implementation of barrier.
+        meta_in_tls = torch._C._meta_in_tls_dispatch_include()
+        guard = torch._C._DisableTorchDispatch()  # type: ignore[attr-defined]
+        torch._C._set_meta_in_tls_dispatch_include(False)
+        try:
+            barrier()
+        finally:
+            torch._C._set_meta_in_tls_dispatch_include(meta_in_tls)
+            del guard
+
+    if sys.platform != "win32":
+        from torch._C._distributed_c10d import HashStore
+
+    from .device_mesh import DeviceMesh, init_device_mesh
+
+    # Variables prefixed with underscore are not auto imported
+    # See the comment in `distributed_c10d.py` above `_backend` on why we expose
+    # this.
+    from .distributed_c10d import *  # noqa: F403
+    from .distributed_c10d import (
+        _all_gather_base,
+        _coalescing_manager,
+        _CoalescingManager,
+        _create_process_group_wrapper,
+        _get_process_group_name,
+        _rank_not_in_group,
+        _reduce_scatter_base,
+        get_node_local_rank,
+    )
+    from .remote_device import _remote_device
+    from .rendezvous import (
+        _create_store_from_options,
+        register_rendezvous_handler,
+        rendezvous,
+    )
+
+    set_debug_level_from_env()
+
+else:
+    # This stub is sufficient to get
+    #   python test/test_public_bindings.py -k test_correct_module_names
+    # working even when USE_DISTRIBUTED=0.  Feel free to add more
+    # stubs as necessary.
+    # We cannot define stubs directly because they confuse pyre
+
+    class _ProcessGroupStub:
+        pass
+
+    sys.modules["torch.distributed"].ProcessGroup = _ProcessGroupStub  # type: ignore[attr-defined]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_checkpointable.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_checkpointable.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc0a288f1291f37b3642d6b0e0830fc13b13292a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_checkpointable.py
@@ -0,0 +1,37 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from typing import Protocol, runtime_checkable
+
+import torch
+
+
+@runtime_checkable
+class _Checkpointable(Protocol):  # noqa: PYI046
+    """
+    Interface for checkpointable objects.
+    Implemented as a protocol, implicit subtyping is supported so subclasses do not need to inherit this explicitly.
+    This is to allow arbitrary objects/tensor subclasses to hook into DCP seamlessly through implementing the interface.
+    """
+
+    def __create_write_items__(self, fqn: str, object: object) -> list[object]:
+        """
+        Return a list of WriteItems based on object's contents.
+        """
+        raise NotImplementedError(
+            "_Checkpointable._create_write_items is not implemented"
+        )
+
+    def __create_chunk_list__(self) -> list[object]:
+        """
+        Return a list of `ChunkStorageMetadata` based on object's contents.
+        """
+        raise NotImplementedError(
+            "_Checkpointable._create_chunk_list is not implemented"
+        )
+
+    def __get_tensor_shard__(self, index: int) -> torch.Tensor:
+        """
+        Return a 'torch.Tensor' shard based on 'MetadataIndex'.
+        """
+        raise NotImplementedError(
+            "_Checkpointable._get_tensor_shard is not implemented"
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_composable_state.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_composable_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d2b8baed766ffaf7d2b1fccaaebe608616f66c1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_composable_state.py
@@ -0,0 +1,44 @@
+import weakref
+from typing import cast, Optional
+
+import torch.nn as nn
+
+
+class _State:
+    pass
+
+
+_module_state_mapping: weakref.WeakKeyDictionary[
+    nn.Module, weakref.ReferenceType[_State]
+] = weakref.WeakKeyDictionary()
+
+
+def _insert_module_state(module: nn.Module, state: _State) -> None:
+    global _module_state_mapping
+    assert module not in _module_state_mapping, f"Inserting {module} more than once."
+    _module_state_mapping[module] = weakref.ref(state)
+
+
+def _get_module_state(module: nn.Module) -> Optional[_State]:
+    """
+    Return the ``_State`` in ``model``.
+
+    Given a ``module``, this API finds out if the module is also a ``_State``
+    instance or if the module is managed by a composable API. If the module
+    is also a ``_State``, ``module`` will be casted to ``_State` and returned.
+    If it is managed by a composable API, the corresponding ``_State`` will
+    be returned.
+    """
+    global _module_state_mapping
+    if isinstance(module, _State):
+        return cast(_State, module)
+    else:
+        # https://github.com/pytorch/pytorch/issues/107054
+        if module in _module_state_mapping:
+            state_ref = _module_state_mapping[module]
+            state = state_ref()
+            if state is None:
+                raise AssertionError("State has already been garbage collected")
+            return state
+        else:
+            return None
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_functional_collectives.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_functional_collectives.py
new file mode 100644
index 0000000000000000000000000000000000000000..921d875455f7f8f73f75f099fbc95f849a2d5e39
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_functional_collectives.py
@@ -0,0 +1,1200 @@
+# mypy: allow-untyped-defs
+import contextlib
+import sys
+import warnings
+from typing import Any, cast, Optional, TYPE_CHECKING, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.distributed_c10d as c10d
+from torch.distributed.device_mesh import DeviceMesh
+from torch.fx.experimental.proxy_tensor import get_proxy_mode
+
+from . import _functional_collectives_impl as fun_col_impl
+
+
+try:
+    from torch.utils._cxx_pytree import tree_map_only
+except ImportError:
+    from torch.utils._pytree import tree_map_only  # type: ignore[no-redef]
+
+
+if torch._running_with_deploy():
+
+    def is_torchdynamo_compiling():
+        """Can't import torchdynamo in torchdeploy builds currently."""
+        return False
+
+else:
+    try:
+        from torch.compiler import is_dynamo_compiling as is_torchdynamo_compiling
+    except Exception:
+        warnings.warn(
+            "Unable to import torchdynamo util `is_torchdynamo_compiling`, so won't support torchdynamo correctly"
+        )
+
+        def is_torchdynamo_compiling():
+            return False
+
+
+"""
+New traceable, functional collectives.
+RFC: https://github.com/pytorch/pytorch/issues/93173
+
+  compiler: trace these ops with plain-old-data schemas, then choose how to lower them.
+  eager: execute these 'functional' ops which in eager return AsyncCollectiveTensor subclasses,
+         automatically calling .wait() on underlying/hidden async 'work' obj only when fed to
+         a downstream op.
+
+Issues:
+* Where should these ops live? Couldn't `import torch` if putting these ops in existing torch.distributed files
+* Proper support for eager requires inplace ops. We should explore having it as an option for the API.
+"""
+
+"""
+Functional collectives are asynchronous only and we perform implicit stream synchronization
+on behalf of the user.
+
+We use AsyncCollectiveTensor to wrap the result tensor of a collective and it lets us witness
+first usage of the tensor and insert cross stream sync at the right place.
+
+The above are the easy bits, the hard one is how we match the Work object returned by
+c10d and the tensor AsyncCollectiveTensor wraps. We alloc the tensor inside the collective
+op implementation (see ``clone()`` call in ``_all_reduce``) and then it's handled by the
+dispatcher which might call other implementations that are allowed to change the returned
+tensor - even return a tensor with a different shape (see ``torch.vmap``).
+
+This means the caller of our ops receives a Tensor that is not guaranteed to be the same
+allocated by our implementations and that makes pairing The AsyncTensor to the original
+tensor a lot harder. This pairing is needed so we can lookup the Work object to use.
+
+Originally, we tried WeakKeyDictionary to map from Tensor to Work, but because Tensor's
+identity is not stable across dispatch, the op caller would end up with a different Tensor
+instance that would not match any in the dictionary.
+
+With Tensor identity out of the question, we decided use the tensor data pointer, which
+should be stable across all the Tensor changes done during dispatch.
+
+We have a dictionary of tensor::data_ptr -> Work that we insert right after we call into c10d.
+
+We use this dictionary when AsyncCollectiveTensor is used to invoke Work::wait()
+
+Finally, we setup a finalizer against the tensor wrapper to observe it getting collected so we
+can clean up stale entries in the dictionary.
+
+To eliminate the possibility of races we have a global version counter that is used by the finalizer.
+
+As a wise man said once: Don't cross the streams (https://www.youtube.com/watch?v=wyKQe_i9yyo)
+
+"""
+
+"""
+Functional collectives can accept any of these types to describe the ranks participating in collectives.
+
+The different types will be desugared to a canonical format
+"""
+RANK_TYPES = Union[
+    list[int],
+    list[list[int]],
+    dist.ProcessGroup,
+    DeviceMesh,
+    tuple["dist.tensor.DeviceMesh", int],
+    str,
+]
+
+
+"""
+User facing APIs for functional collectives
+-------------------------------------------
+
+These apis are called by user code and expected to work both in eager execution and compilation,
+but there are significant differences to how the two modes are implemented underneath.
+
+Eager execution is 'optimized' using a tensor subclass that schedules the synchronization (via wait_tensor() op)
+just before the tensor is first used.  Compiled tracing currently relies on the compiler to perform this optimization,
+and cannot yet correctly trace the AsyncTensor wrapper class.  In the future, these paths may be unified
+if sufficient subclass support is added in dynamo.
+
+Example: all_reduce is an entrypoint API, and other collectives follow a similar pattern.
+
+Here's how it works under torch.compile/dynamo:
+all_reduce(...)
+  |--> _expand_group(...)               - desugars processgroup into canonical/traceable format
+  |--> c10d_functional.all_reduce(...)  - dynamo captures this op call, doesn't trace deeper
+  |--> _maybe_wrap_tensor(...)          - wait_tensor() op is immediately called, no AsyncTensor subclass needed
+
+And under eager execution:
+all_reduce(...)
+  |--> _expand_group(...)               - same as above, but less critical for eager
+  |--> c10d_functional.all_reduce(...)  - dispatches to real kernel OR records op in trace
+  |--> _maybe_wrap_tensor(...)          - AsyncTensor wrapper applied to returned tensor,
+                                          which issues wait_tensor() at the time of first use
+"""
+
+
+def wait_tensor(tensor):
+    """
+    Wait on a tensor returned by the collectives ops.
+
+    Waiting follows device semantics, which means blocking on CPU and synchronizing streams on CUDA.
+    """
+    return torch.ops._c10d_functional.wait_tensor(tensor)  # type: ignore[attr-defined]
+
+
+def broadcast(self: torch.Tensor, src: int, group: RANK_TYPES, tag: str = ""):
+    """
+    Broadcasts the tensor to all processes in the given process group.
+
+    Args:
+        src (int): Source rank
+        group (ProcessGroup or List[int]): The process group to work on.
+        tag (str, optional): A unique identifier for the collective. Default: empty string
+    """
+    group_name = _resolve_group_name(group, tag)
+    tensor = torch.ops._c10d_functional.broadcast(self, src, group_name)
+    return _maybe_wrap_tensor(tensor)
+
+
+def all_reduce(self: torch.Tensor, reduceOp: str, group: RANK_TYPES, tag: str = ""):
+    """
+    Reduces the tensor data across all machines in such a way that all get
+    the final result.
+
+    The input tensor is left unmodified.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    tensor = torch.ops._c10d_functional.all_reduce(self, reduceOp.lower(), group_name)
+    return _maybe_wrap_tensor(tensor)
+
+
+def all_gather_tensor(
+    self: torch.Tensor,
+    gather_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Gather tensor data across from all machines and concatenate over ``gather_dim``.
+
+    Note that it currently only supports gather_dim = 0.
+
+    The input tensor is left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    assert self.is_contiguous()
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    tensor = torch.ops._c10d_functional.all_gather_into_tensor(
+        self, group_size, group_name
+    )
+    res = _maybe_wrap_tensor(tensor)
+    # TODO this should be done inside AsyncCollectiveTensor to delay the wait() call
+    if gather_dim != 0:
+        # torch.cat access the data so we already need to wait here, first do wait
+        # and then chunk + cat avoid us going through ACT dispatching logic again
+        if isinstance(res, AsyncCollectiveTensor):
+            res = res.wait()  # type: ignore[attr-defined]
+        res = torch.cat(torch.chunk(res, group_size, dim=0), dim=gather_dim)
+    return res
+
+
+def all_gather_tensor_autograd(
+    self: torch.Tensor,
+    gather_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+):
+    """
+    Gather tensor data across from all machines and concatenate over ``gather_dim``.
+
+    Note that it currently only supports gather_dim = 0.
+
+    This function is the same as all_gather_tensor but will propagate the
+    backwards gradient across workers.
+
+    See all_gather_tensor for more details on usage.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    tensor = torch.ops._c10d_functional_autograd.all_gather_into_tensor(
+        self, group_size, group_name
+    )
+    res = _FromTorchTensor.apply(tensor)
+    # TODO this should be done inside AsyncCollectiveTensor to delay the wait() call
+    if gather_dim != 0:
+        # torch.cat access the data so we already need to wait here, first do wait
+        # and then chunk + cat avoid us going through ACT dispatching logic again
+        if isinstance(res, AsyncCollectiveTensor):
+            res = res.wait()  # type: ignore[attr-defined]
+        res = torch.cat(torch.chunk(res, group_size, dim=0), dim=gather_dim)
+    return res
+
+
+def reduce_scatter_tensor(
+    self: torch.Tensor,
+    reduceOp: str,
+    scatter_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+):
+    """
+    Reduces the tensor data across all machines in such a way that all get
+    the final result, then scatter the results to corresponding ranks.
+
+
+    The input tensor is left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    assert self.size(scatter_dim) % group_size == 0, (
+        f"input dimension 0 ({self.size(0)} must be a multiple of group_size {group_size}"
+    )
+    if scatter_dim != 0:
+        tensor_list = torch.chunk(self, group_size, dim=scatter_dim)
+        self = torch.cat(tensor_list)
+
+    tensor = torch.ops._c10d_functional.reduce_scatter_tensor(
+        self,
+        reduceOp.lower(),
+        group_size,
+        group_name,  # type: ignore[possibly-undefined]
+    )
+    res = _maybe_wrap_tensor(tensor)
+    return res
+
+
+def reduce_scatter_tensor_autograd(
+    self: torch.Tensor,
+    reduceOp: str,
+    scatter_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+):
+    """
+    Reduces the tensor data across all machines in such a way that all get
+    the final result, then scatter the results to corresponding ranks.
+
+    This function is the same as reduce_scatter_tensor but will propagate the
+    backwards gradient across workers.
+
+    Currently only the "sum" reduceOp is supported.
+
+    See reduce_scatter_tensor for more details on usage.
+    """
+
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    assert self.size(scatter_dim) % group_size == 0, (
+        f"input dimension 0 ({self.size(0)} must be a multiple of group_size {group_size}"
+    )
+    if scatter_dim != 0:
+        tensor_list = torch.chunk(self, group_size, dim=scatter_dim)
+        self = torch.cat(tensor_list)
+
+    tensor = torch.ops._c10d_functional_autograd.reduce_scatter_tensor(
+        self,
+        reduceOp.lower(),
+        group_size,
+        group_name,  # type: ignore[possibly-undefined]
+    )
+    res = _FromTorchTensor.apply(tensor)
+    return res
+
+
+def all_reduce_coalesced(
+    self: list[torch.Tensor], reduceOp: str, group: RANK_TYPES, tag: str = ""
+) -> list[torch.Tensor]:
+    """
+    Reduces a list of tensors across all machines in such a way that all get
+    the final result.
+
+    The all tensors in the input list are left unmodified.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    tensor_list = torch.ops._c10d_functional.all_reduce_coalesced(  # type: ignore[attr-defined]
+        self,
+        reduceOp.lower(),
+        group_name,
+    )
+    return list(map(_maybe_wrap_tensor, tensor_list))
+
+
+def all_gather_into_tensor_coalesced(
+    self: list[torch.Tensor], group: RANK_TYPES, tag: str = ""
+) -> list[torch.Tensor]:
+    """
+    Gather a list of tensors across from all machines.
+
+    Note that it currently only supports gather_dim = 0.
+
+    The input tensor is left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    tensor_list = torch.ops._c10d_functional.all_gather_into_tensor_coalesced(  # type: ignore[attr-defined]
+        self,
+        group_size,
+        group_name,
+    )
+    return list(map(_maybe_wrap_tensor, tensor_list))
+
+
+def reduce_scatter_tensor_coalesced(
+    inputs: list[torch.Tensor],
+    reduceOp: str,
+    scatter_dim: list[int],
+    group: RANK_TYPES,
+    tag: str = "",
+) -> list[torch.Tensor]:
+    """
+    Reduces a list of tensors across all machines in such a way that all get
+    the final result, then scatter the results to corresponding ranks.
+
+    The input tensors are left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    assert len(scatter_dim) == len(inputs)
+    for idx, (dim, tensor) in enumerate(zip(scatter_dim, inputs)):
+        assert tensor.size(dim) % group_size == 0, (
+            f"input dimension {dim} ({tensor.size(dim)} must be a multiple of group_size {group_size} for tensor at index {idx}"
+        )
+        if dim != 0:
+            tensor_list = torch.chunk(tensor, group_size, dim=dim)
+            inputs[idx] = torch.cat(tensor_list)
+
+    tensor_list = torch.ops._c10d_functional.reduce_scatter_tensor_coalesced(  # type: ignore[attr-defined]
+        inputs,
+        reduceOp.lower(),
+        group_size,
+        group_name,  # type: ignore[possibly-undefined]
+    )
+
+    return list(map(_maybe_wrap_tensor, tensor_list))
+
+
+# This is a bit unsafe: it checks if the first argument in the schema reports as a non-mutable alias.
+# Today, this maps 1:1 with "aten ops that are views".
+def _is_view_op(tgt):
+    assert isinstance(tgt, torch._ops.OpOverload)
+    # Don't apply the view optimization to any `CompositeImplicitAutograd` ops.
+    # See issue: https://github.com/pytorch/pytorch/issues/133421
+    if torch._C._dispatch_has_kernel_for_dispatch_key(
+        tgt.name(), torch.DispatchKey.CompositeImplicitAutograd
+    ):
+        return False
+    schema = tgt._schema
+    if len(schema.arguments) > 0:
+        first_arg = schema.arguments[0]
+        # check if op is a view
+        return first_arg.alias_info is not None and not first_arg.alias_info.is_write
+
+
+def all_to_all_single(
+    self: torch.Tensor,
+    output_split_sizes: Optional[list[int]],
+    input_split_sizes: Optional[list[int]],
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Each process splits input tensor and then scatters the split list
+    to all processes in a group. Then concatenate the received tensors from all
+    the processes in the group and return single output tensor.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    if output_split_sizes is not None:
+        assert all(
+            isinstance(size, (int, torch.SymInt)) for size in output_split_sizes
+        ), output_split_sizes
+    if input_split_sizes is not None:
+        assert all(
+            isinstance(size, (int, torch.SymInt)) for size in input_split_sizes
+        ), input_split_sizes
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    if output_split_sizes is None or input_split_sizes is None:
+        assert output_split_sizes is None and input_split_sizes is None, (
+            "output_split_sizes and input_split_sizes must either be "
+            "specified together or both set to None"
+        )
+        output_split_sizes = [self.shape[0] // group_size] * group_size
+        input_split_sizes = output_split_sizes
+    tensor = torch.ops._c10d_functional.all_to_all_single(  # type: ignore[attr-defined]
+        self,
+        output_split_sizes,
+        input_split_sizes,
+        group_name,
+    )
+    return _maybe_wrap_tensor(tensor)
+
+
+def all_to_all_single_autograd(
+    self: torch.Tensor,
+    output_split_sizes: Optional[list[int]],
+    input_split_sizes: Optional[list[int]],
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Same as all_to_all_single but supports autograd.
+    """
+    if output_split_sizes is not None:
+        assert all(
+            isinstance(size, (int, torch.SymInt)) for size in output_split_sizes
+        ), output_split_sizes
+    if input_split_sizes is not None:
+        assert all(
+            isinstance(size, (int, torch.SymInt)) for size in input_split_sizes
+        ), input_split_sizes
+
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    if output_split_sizes is None or input_split_sizes is None:
+        assert output_split_sizes is None and input_split_sizes is None, (
+            "output_split_sizes and input_split_sizes must either be "
+            "specified together or both set to None"
+        )
+        output_split_sizes = [self.shape[0] // group_size] * group_size
+        input_split_sizes = output_split_sizes
+    tensor = torch.ops._c10d_functional_autograd.all_to_all_single(  # type: ignore[attr-defined]
+        self,
+        output_split_sizes,
+        input_split_sizes,
+        group_name,
+    )
+    return _FromTorchTensor.apply(tensor)
+
+
+def permute_tensor(
+    self: torch.Tensor,
+    src_dst: list[int],
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Permutes the elements of the tensor according to the given source/destination pairs. `src_dst` should
+    be defined such that src_dst[m] == n means m sends to n.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one
+    """
+    t, rankset, group_size = _expand_group(group, tag)
+    local_pg = c10d._find_or_create_pg_by_ranks_and_tag(t, rankset, group_size)
+
+    output_split_sizes = [0] * group_size
+    input_split_sizes = [0] * group_size
+    for src, dst in enumerate(src_dst):
+        if src == dist.get_rank(local_pg):
+            input_split_sizes[dst] = self.numel()
+        if dst == dist.get_rank(local_pg):
+            output_split_sizes[src] = self.numel()
+
+    return all_to_all_single(self, output_split_sizes, input_split_sizes, group, tag)
+
+
+class AsyncCollectiveTensor(torch.Tensor):
+    r"""
+    A Tensor wrapper subclass that is used to trigger a call to wait
+    prior to first use of the underlying tensor.
+    Use it inside functional collective pytorch wrappers like the following:
+    def functional_collective(self, group, tag):
+        tag, rankset, group_size = _expand_group(group, tag)
+        tensor = torch.ops.c10d_functional.{collective}(self, tag, rankset, group_size)
+        return _maybe_wrap_tensor(tensor)
+    """
+
+    elem: torch.Tensor
+    completed: bool
+
+    __slots__ = ["elem", "completed"]
+
+    @staticmethod
+    def __new__(cls, elem: torch.Tensor):
+        r = torch.Tensor._make_wrapper_subclass(  # type: ignore[attr-defined]
+            cls,
+            elem.size(),
+            strides=elem.stride(),
+            storage_offset=elem.storage_offset(),
+            dtype=elem.dtype,
+            layout=elem.layout,
+            device=elem.device,
+            requires_grad=elem.requires_grad,
+        )
+        r.elem = elem
+        r.completed = False
+        return r
+
+    def __tensor_flatten__(self):
+        return ["elem"], None
+
+    def tolist(self):
+        return self.trigger_wait().tolist()
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors, meta, outer_size, outer_stride):
+        assert meta is None
+        elem = inner_tensors["elem"]
+        return AsyncCollectiveTensor(elem)
+
+    def __coerce_same_metadata_as_tangent__(
+        self, expected_metadata: Any, expected_type: Optional[type] = None
+    ):
+        if expected_type is not torch.Tensor:
+            return None
+
+        return self.trigger_wait()
+
+    def __repr__(self) -> str:  # type: ignore[override]
+        return f"AsyncCollectiveTensor({self.trigger_wait()})"
+
+    def trigger_wait(self):
+        if not self.completed:
+            out = wait_tensor(self.elem)
+            self.completed = True
+            return out
+        else:
+            return self.elem
+
+    def wait(self) -> torch.Tensor:
+        return wait_tensor(self.elem)
+
+    def _get_acs_underlying_tensor(self):
+        """This method enables  _functional_collectives_impl to test if a tensor is an ACS"""
+        return self.elem
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+        if func == torch.ops.aten.view.default:
+            # Fast handle aten.view as a lot of view related op goes to aten.view
+            # eventually, this avoids pytree slowdown
+            res = func(args[0].elem, args[1])
+            wrapper_res = AsyncCollectiveTensor(res)
+            return wrapper_res
+
+        is_view_op = _is_view_op(func)
+
+        def unwrap(e: AsyncCollectiveTensor):
+            # wait_tensor is idepotent and will do stream sync only once
+            if not is_view_op:
+                return e.trigger_wait()
+            return e.elem
+
+        def wrap(e: torch.Tensor):
+            # wait_tensor is idepotent and will do stream sync only once
+            assert not isinstance(e, AsyncCollectiveTensor)
+            res = AsyncCollectiveTensor(e)
+            return res
+
+        unwrapped_args = tree_map_only(AsyncCollectiveTensor, unwrap, args)
+        unwrapped_kwargs = tree_map_only(AsyncCollectiveTensor, unwrap, kwargs)
+
+        # we don't wrap the result as it doesn't need to be waited on.
+        out = func(*unwrapped_args, **unwrapped_kwargs)
+
+        # View ops dont require a sync, so we should re-wrap the outputs.
+        if is_view_op:
+            out = tree_map_only(torch.Tensor, wrap, out)
+
+        return out
+
+    def numpy(self):  # type: ignore[override]
+        return self.wait().numpy()
+
+
+"""
+Utils and infrastructure for tracing support
+"""
+
+
+def _expand_group(group: RANK_TYPES, tag: str = "") -> tuple[str, list[int], int]:
+    """
+    _expand_group desugars the different RANK_TYPES types into a canonical format that is traceable.
+
+    By having this be part of the explicit eager codepath, we avoid having to specialize behavior inside
+    torchdynamo and can still interoperate with processgroup objects or other untraceable forms.
+    """
+    # had to define this hack _inside_ expand_group to avoid
+    # graph_break [('torch.* op returned non-Tensor int
+    # caused by 'cast_*` functions being treated as 'torch.*' ops (iiuc)
+    if TYPE_CHECKING:
+
+        def cast_listlistint(x):
+            return cast(list[list[int]], x)
+
+        def cast_listint(x):
+            return cast(list[int], x)
+
+    else:
+        # fake cast op for use at runtime since dynamo doesn't support real cast
+        # also, dynamo didn't like encountering 'typing' objects ()
+        # NotImplementedError: argument of type: 
+        def cast_listlistint(x):
+            return x
+
+        def cast_listint(x):
+            return x
+
+    rankset: list[int]
+    if isinstance(group, list):
+        if isinstance(group[0], list):
+            nested_list = cast_listlistint(group)
+            rankset = []
+            group_size = -1
+            for rs in nested_list:
+                rankset.extend(rs)
+                if group_size != -1 and group_size != len(rs):
+                    raise ValueError(
+                        f"group sizes must be identical found {group_size} and {len(rs)}"
+                    )
+                group_size = len(rs)
+        else:
+            rankset = cast_listint(group)
+            group_size = len(rankset)
+    elif isinstance(group, dist.ProcessGroup):
+        rankset = dist.get_process_group_ranks(group)
+        group_size = len(rankset)
+        tag = tag or c10d._get_group_tag(group)
+    elif isinstance(group, DeviceMesh):
+        assert group.ndim == 1, (
+            "Only 1D mesh is supported, pass in (DeviceMesh, int) together if mesh > 1D"
+        )
+        # TODO: it should run collective in the whole mesh instead of dim 0
+        tag, rankset, _ = group._dim_group_infos[0]
+        group_size = len(rankset)
+    elif isinstance(group, tuple):
+        if (
+            len(group) == 2
+            and isinstance(group[0], DeviceMesh)
+            and isinstance(group[1], int)
+        ):
+            dmesh = group[0]
+            dim = group[1]
+            tag, rankset, _ = dmesh._dim_group_infos[dim]
+            group_size = len(rankset)
+        else:
+            raise ValueError("Invalid tuple for group must be (DeviceMesh, int)")
+    else:
+        raise ValueError(
+            "Invalid type for group, must be one of List, Processgroup, DeviceMesh or (DeviceMesh, int)."
+        )
+
+    return (tag, rankset, group_size)
+
+
+def _resolve_group_name(group: RANK_TYPES, tag: str = "") -> str:
+    """
+    Given group in RANK_TYPES, return the group name.
+    """
+    # `tag` will be deprecated. See details in:
+    # https://github.com/pytorch/pytorch/issues/93173#issuecomment-1907095208
+    if isinstance(group, dist.ProcessGroup):
+        return group.group_name
+    elif isinstance(group, str):
+        return group
+    elif isinstance(group, DeviceMesh):
+        assert group.ndim == 1, (
+            "Only 1D mesh is supported, pass in (DeviceMesh, int) together if mesh > 1D"
+        )
+        return group._dim_group_infos[0][2]
+    elif isinstance(group, tuple):
+        if (
+            len(group) == 2
+            and isinstance(group[0], DeviceMesh)
+            and isinstance(group[1], int)
+        ):
+            dmesh = group[0]
+            dim = group[1]
+            return dmesh._dim_group_infos[dim][2]
+        else:
+            raise ValueError("Invalid tuple for group must be (DeviceMesh, int)")
+    elif isinstance(group, list):
+        if not is_torchdynamo_compiling():
+            warnings.warn(
+                "The combination of ranks + tag as process group "
+                "identifier has been deprecated. Please switch to "
+                "using ProcessGroup, DeviceMesh, or group name instead.",
+                FutureWarning,
+                stacklevel=3,
+            )
+        return c10d._resolve_group_name_by_ranks_and_tag(cast(list[int], group), tag)
+    else:
+        raise ValueError(f"Unsupported group type: {type(group)}, {group}")
+
+
+class _FromTorchTensor(torch.autograd.Function):
+    """
+    _FromTorchTensor allows autograd to propagate from a normal Tensor to an
+    AsyncCollectiveTensor.
+    """
+
+    @staticmethod
+    def forward(  # type: ignore[override]
+        ctx,  # pyre-ignore[2]: Parameter must be annotated.
+        input: torch.Tensor,
+    ) -> torch.Tensor:
+        return _maybe_wrap_tensor(input)
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:  # type: ignore[override]
+        return grad_output
+
+
+def _are_we_tracing() -> bool:
+    if is_torchdynamo_compiling():
+        return True
+    # If fake mode is turned on, we are almost definitely compiling/tracing.
+    if torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE) is not None:
+        return True
+    return get_proxy_mode() is not None
+
+
+def _maybe_wrap_tensor(self) -> torch.Tensor:
+    if _are_we_tracing():
+        return wait_tensor(self)
+    res = AsyncCollectiveTensor(self)
+    return cast(torch.Tensor, res)
+
+
+@contextlib.contextmanager
+def allow_inflight_collective_as_graph_input_ctx(value: bool = True):
+    """
+    Context manager to temporarily set whether inflight collectives are allowed as torch.compile graph inputs.
+    Common use case is when the collective is issued in eager (with `async_op=True`) but waited in compiled region:
+    ```
+    def all_reduce_eager(x):
+        y = x * x
+        req = dist.all_reduce(y, op=dist.ReduceOp.SUM, async_op=True)
+        return y
+
+
+    @torch.compile(fullgraph=True)
+    def all_reduce_wait_compiled(y):
+        torch.ops.c10d_functional.wait_tensor(y)
+        return y * y
+
+
+    x = torch.ones(1280, 1280, device="cuda") + self.rank
+    # the context manager ensures that `wait_tensor(y)` will wait on the correct work object
+    with allow_inflight_collective_as_graph_input_ctx():
+        y = all_reduce_eager(x)
+        z = all_reduce_wait_compiled(y)
+    ```
+    With this context manager, when a collective is called, under the hood the work object of the collective
+    will be registered in the work registry, and the wait_tensor() in compiled region called on
+    the output tensor of the collective will wait on the correct work object.
+    """
+    previous = torch._C._distributed_c10d._allow_inflight_collective_as_graph_input()
+
+    try:
+        torch._C._distributed_c10d._set_allow_inflight_collective_as_graph_input(value)
+        yield
+    finally:
+        torch._C._distributed_c10d._set_allow_inflight_collective_as_graph_input(
+            previous
+        )
+
+
+def _all_gather_into_tensor_coalesced_meta(self, tag, rankset, group_size):
+    def mk_out_tensor(shard):
+        out_size = list(shard.size())
+        out_size[0] *= group_size
+        out_tensor = shard.new_empty(out_size)
+        return out_tensor
+
+    return [mk_out_tensor(t) for t in self]
+
+
+# We now register meta kernels to deal with tracing
+def _broadcast_meta(self, *args):
+    return torch.empty_like(self)
+
+
+def _all_reduce_meta(self, *args):
+    return torch.empty_like(self)
+
+
+def _wait_tensor_meta(self, *args):
+    return torch.empty_like(self)
+
+
+def _all_gather_into_tensor_meta(shard, tag, rankset, group_size):
+    out_size = list(shard.size())
+    out_size[0] *= group_size
+    return shard.new_empty(out_size)
+
+
+def _reduce_scatter_tensor_meta(input, reduce_op, tag, rankset, group_size):
+    out_size = list(input.size())
+    out_size[0] //= group_size
+    return input.new_empty(out_size)
+
+
+def _all_reduce_coalesced_meta(self, *args):
+    return [torch.empty_like(t) for t in self]
+
+
+def _all_reduce__meta(inp, *args):
+    return inp
+
+
+def _broadcast__meta(inp, *args):
+    return inp
+
+
+def _all_reduce_coalesced__meta(inputs, *args):
+    return inputs
+
+
+def _reduce_scatter_tensor_coalesced_meta(inputs, reduceOp, tag, rankset, group_size):
+    def mk_out_tensor(input):
+        out_size = list(input.size())
+        out_size[0] //= group_size
+        out_tensor = input.new_empty(out_size)
+        return out_tensor
+
+    return [mk_out_tensor(t) for t in inputs]
+
+
+# NB: We often say all_to_all has dynamic output size, but this is not
+# technically true: instead, what typically happens is you manually
+# communicate the output_split_sizes ahead of time (which is dynamic),
+# but then you pass those sizes explicitly, and the all to all itself
+# isn't dynamic, it just follows the specified output splits
+def _all_to_all_single_meta(
+    input, output_split_sizes, input_split_sizes, *args, **kwargs
+):
+    if output_split_sizes is None:
+        return input.new_empty(input.size())
+    else:
+        for s in output_split_sizes:
+            torch._check_is_size(s)
+        out_size = list(input.size())
+        out_size[0] = sum(output_split_sizes)
+        return input.new_empty(out_size)
+
+
+def _all_gather_into_tensor_out_native_meta(input, group_size, group_name, *, out):
+    shape = list(input.size())
+    shape[0] *= group_size
+    return input.new_empty(shape)
+
+
+def _all_gather_into_tensor_native_meta(input, group_size, group_name):
+    shape = list(input.size())
+    shape[0] *= group_size
+    return input.new_empty(shape)
+
+
+def _all_gather_into_tensor_coalesced_native_meta(inputs, group_size, group_name):
+    return [
+        _all_gather_into_tensor_native_meta(input, group_size, group_name)
+        for input in inputs
+    ]
+
+
+def _reduce_scatter_tensor_native_meta(inp, reduce_op, group_size, group_name):
+    shape = list(inp.size())
+    shape[0] //= group_size
+    return inp.new_empty(shape)
+
+
+def _reduce_scatter_tensor_coalesced_native_meta(
+    inputs, reduce_op, group_size, group_name
+):
+    return [
+        _reduce_scatter_tensor_native_meta(inp, reduce_op, group_size, group_name)
+        for inp in inputs
+    ]
+
+
+if not torch._running_with_deploy():
+    # Library MUST be defined at module scope or it doesn't work
+    # Creating a "DEF" Library always crashes torch::deploy so we create our
+    # Library instances here guarded against running inside it
+    lib_impl = torch.library.Library("_c10d_functional", "IMPL")
+    lib_impl.impl("all_reduce", _all_reduce_meta, "Meta")
+    lib_impl.impl("all_reduce_", _all_reduce__meta, "Meta")
+    lib_impl.impl("all_reduce_coalesced", _all_reduce_coalesced_meta, "Meta")
+    lib_impl.impl("all_reduce_coalesced_", _all_reduce_coalesced__meta, "Meta")
+    lib_impl.impl("wait_tensor", _wait_tensor_meta, "Meta")
+    lib_impl.impl(
+        "all_gather_into_tensor_out", _all_gather_into_tensor_out_native_meta, "Meta"
+    )
+    lib_impl.impl("all_gather_into_tensor", _all_gather_into_tensor_native_meta, "Meta")
+    lib_impl.impl(
+        "all_gather_into_tensor_coalesced",
+        _all_gather_into_tensor_coalesced_native_meta,
+        "Meta",
+    )
+    lib_impl.impl("reduce_scatter_tensor", _reduce_scatter_tensor_native_meta, "Meta")
+    lib_impl.impl(
+        "reduce_scatter_tensor_coalesced",
+        _reduce_scatter_tensor_coalesced_native_meta,
+        "Meta",
+    )
+    lib_impl.impl("all_to_all_single", _all_to_all_single_meta, "Meta")
+    lib_impl.impl("broadcast", _broadcast_meta, "Meta")
+    lib_impl.impl("broadcast_", _broadcast__meta, "Meta")
+
+    # mark these ops has side effect so that they won't be removed by DCE
+    torch.fx.node.has_side_effect(torch.ops._c10d_functional.wait_tensor.default)
+    torch.fx.node.has_side_effect(torch.ops._c10d_functional.wait_tensor)
+
+    # Register legacy ops for backward compatibility
+    # TODO(yifu): remove these in functional collective beta release
+    legacy_lib = torch.library.Library("c10d_functional", "DEF")
+    legacy_lib_impl = torch.library.Library("c10d_functional", "IMPL")
+    ops_defs = [
+        "broadcast(Tensor self, int src, str tag, int[] ranks, int group_size) -> Tensor",
+        "all_reduce(Tensor self, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor",
+        "all_reduce_coalesced(Tensor[] self, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor[]",
+        "wait_tensor(Tensor self) -> Tensor",
+        "all_gather_into_tensor(Tensor shard, str tag, int[] ranks, int group_size) -> Tensor",
+        "all_gather_into_tensor_coalesced(Tensor[] input, str tag, int[] ranks, int group_size) -> Tensor[]",
+        "reduce_scatter_tensor(Tensor input, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor",
+        "reduce_scatter_tensor_coalesced(Tensor[] inputs, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor[]",
+        "all_to_all_single(Tensor input, SymInt[]? output_split_sizes, SymInt[]? input_split_sizes, str tag, int[] ranks, int group_size) -> Tensor",  # noqa: B950
+    ]
+
+    my_module = sys.modules[__name__]
+    for op_def in ops_defs:
+        op_name = op_def[0 : op_def.index("(")]
+        backend_impl = getattr(fun_col_impl, f"_{op_name}")
+        legacy_lib.define(op_def, tags=torch.Tag.pt2_compliant_tag)
+        legacy_lib_impl.impl(op_name, backend_impl, "CompositeImplicitAutograd")
+
+else:
+    warnings.warn(
+        "PyTorch Distributed functional collectives do not work with torch::deploy."
+    )
+
+
+"""
+Dynamo Remappings allow seamless translation from non-functional collectives of supportable form into
+functional collective calls followed by inplace copy ops, allowing them to be traced into a functional graph.
+
+We implement this by writing a decomposition and teaching dynamo how to associate it to a corresponding op via
+the mapping dict below.
+
+These schemas intentionally match torch.distributed.distributed_c10d.* ops that we are trying to remap from
+"""
+
+
+def all_gather_tensor_inplace(
+    output_tensor: torch.Tensor,
+    input_tensor: torch.Tensor,
+    group=None,  # TODO add a type,
+    async_op: bool = False,
+    tag: str = "",
+    gather_dim: int = 0,
+):
+    assert not async_op, (
+        "Can't remap async version of inplace op to functional collective"
+    )
+
+    group = group or dist.group.WORLD
+    assert group is not None
+
+    return output_tensor.copy_(all_gather_tensor(input_tensor, gather_dim, group, tag))
+
+
+def reduce_scatter_tensor_inplace(
+    output: torch.Tensor,
+    input: torch.Tensor,
+    op: str = "sum",  # TODO type is actually c10d ReduceOp. is this ok?
+    group=None,  # TODO add a type
+    async_op: bool = False,
+    scatter_dim: int = 0,
+    tag: str = "",
+):
+    assert not async_op, (
+        "Can't remap async version of inplace op to functional collective"
+    )
+
+    group = group or dist.group.WORLD
+    assert group is not None
+
+    return output.copy_(reduce_scatter_tensor(input, op, scatter_dim, group, tag))
+
+
+REDUCE_OP_TO_STR = {
+    dist.ReduceOp.SUM: "sum",
+    dist.ReduceOp.AVG: "avg",
+    dist.ReduceOp.PRODUCT: "product",
+    dist.ReduceOp.MIN: "min",
+    dist.ReduceOp.MAX: "max",
+    dist.ReduceOp.BAND: "band",
+    dist.ReduceOp.BOR: "bor",
+    dist.ReduceOp.BXOR: "bxor",
+}
+
+
+def all_reduce_inplace(
+    tensor: torch.Tensor,
+    op: str = "sum",
+    group=None,
+    async_op: bool = False,
+    tag: str = "",
+):
+    assert not async_op, (
+        "Can't remap async version of inplace op to functional collective"
+    )
+
+    group = group or dist.group.WORLD
+    assert group is not None
+
+    return tensor.copy_(all_reduce(tensor, op, group, tag))
+
+
+def all_to_all_inplace(
+    output: torch.Tensor,
+    input: torch.Tensor,
+    output_split_sizes=None,
+    input_split_sizes=None,
+    group=None,
+    async_op=False,
+    tag: str = "",
+):
+    assert not async_op, (
+        "Can't remap async version of inplace op to functional collective"
+    )
+
+    group = group or dist.group.WORLD
+    assert group is not None
+
+    return output.copy_(
+        all_to_all_single(
+            input,
+            output_split_sizes,
+            input_split_sizes,
+            group,
+            tag,
+        )
+    )
+
+
+def all_gather_inplace(
+    tensor_list: list[torch.Tensor],
+    tensor: torch.Tensor,
+    group=None,
+    async_op=False,
+    tag: str = "",
+):
+    assert not async_op, (
+        "Can't remap async version of inplace op to functional collective"
+    )
+    assert all(t.size(0) == tensor.size(0) for t in tensor_list), (
+        "Remapping variable size all_gather is not yet supported"
+    )
+
+    group = group or dist.group.WORLD
+    assert group is not None
+
+    output = all_gather_tensor(tensor, 0, group, tag)
+
+    # Use aten.slice instead of aten.split because the latter causes
+    # tensor.shape(0) to be unnecessarily baked in when it's a SymInt.
+    output_splits = []
+    offset = 0
+    for t in tensor_list:
+        output_splits.append(output[offset : offset + t.size(0)])
+        offset += t.size(0)
+    for dst, src in zip(tensor_list, output_splits):
+        dst.copy_(src)
+    return tensor_list
+
+
+from torch.distributed.distributed_c10d import (
+    _all_gather_base as legacy_all_gather_base,
+    _reduce_scatter_base as legacy_reduce_scatter_base,
+    all_gather as legacy_all_gather,
+    all_gather_into_tensor as legacy_allgather,
+    all_reduce as legacy_allreduce,
+    all_to_all_single as legacy_all_to_all_single,
+    reduce_scatter_tensor as legacy_reducescatter,
+)
+
+
+# This dict should contain sets of functions that dynamo is allowed to remap.
+# Functions in this set should accept the same args/kwargs 1:1 as their mapping.
+traceable_collective_remaps = {
+    legacy_allgather: all_gather_tensor_inplace,
+    legacy_reducescatter: reduce_scatter_tensor_inplace,
+    legacy_allreduce: all_reduce_inplace,
+    legacy_all_to_all_single: all_to_all_inplace,
+    legacy_all_gather: all_gather_inplace,
+    legacy_reduce_scatter_base: reduce_scatter_tensor_inplace,
+    legacy_all_gather_base: all_gather_tensor_inplace,
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_functional_collectives_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_functional_collectives_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c1ac0a079dec96ed0b1b4536c770a3465264df6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_functional_collectives_impl.py
@@ -0,0 +1,117 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.distributed.distributed_c10d as c10d
+
+
+"""
+This file contains the op impls for the legacy (c10d_functional) functional collectives.
+These impls simply call into the native (_c10d_functional) functional collectives.
+"""
+
+
+def _broadcast(input, src, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.broadcast(
+        input,
+        src,
+        group_name,
+    )
+
+
+def _all_reduce(input, reduce_op, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_reduce(
+        input,
+        reduce_op,
+        group_name,
+    )
+
+
+def _all_reduce_coalesced(inputs, reduce_op, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_reduce_coalesced(
+        inputs,
+        reduce_op,
+        group_name,
+    )
+
+
+def _all_gather_into_tensor(input, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_gather_into_tensor(
+        input,
+        group_size,
+        group_name,
+    )
+
+
+def _all_gather_into_tensor_coalesced(input, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_gather_into_tensor_coalesced(
+        input,
+        group_size,
+        group_name,
+    )
+
+
+def _reduce_scatter_tensor(
+    input: torch.Tensor,
+    reduce_op: str,
+    tag: str,
+    ranks: list[int],
+    group_size: int,
+):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.reduce_scatter_tensor(
+        input,
+        reduce_op,
+        group_size,
+        group_name,
+    )
+
+
+def _reduce_scatter_tensor_coalesced(
+    inputs: list[torch.Tensor],
+    reduce_op: str,
+    tag: str,
+    ranks: list[int],
+    group_size: int,
+):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.reduce_scatter_tensor_coalesced(
+        inputs,
+        reduce_op,
+        group_size,
+        group_name,
+    )
+
+
+def _all_to_all_single(
+    input: torch.Tensor,
+    output_split_sizes: Optional[list[int]],
+    input_split_sizes: Optional[list[int]],
+    tag: str,
+    ranks: list[int],
+    group_size: int,
+):
+    if output_split_sizes is None or input_split_sizes is None:
+        assert output_split_sizes is None and input_split_sizes is None, (
+            "output_split_sizes and input_split_sizes must either be "
+            "specified together or both set to None"
+        )
+        output_split_sizes = [input.shape[0] // group_size] * group_size
+        input_split_sizes = output_split_sizes
+
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_to_all_single(
+        input,
+        output_split_sizes,
+        input_split_sizes,
+        group_name,
+    )
+
+
+def _wait_tensor(tensor: torch.Tensor) -> torch.Tensor:
+    return torch.ops._c10d_functional.wait_tensor(tensor)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_serialization.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_serialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c49f2585bcb6d1cd421a19f15a6984fdd0e6759
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_serialization.py
@@ -0,0 +1,155 @@
+import pickle
+from dataclasses import dataclass
+from io import BufferedIOBase
+from typing import Any
+
+import torch
+import torch._weights_only_unpickler as _weights_only_unpickler
+from torch.serialization import _load, _save, DEFAULT_PROTOCOL, MAP_LOCATION
+
+
+__all__: list[str] = []
+
+
+@dataclass
+class _Entry:
+    key: str
+    is_storage: bool
+    length: int
+
+
+_weights_only_unpickler._add_safe_globals([_Entry])
+
+
+class _PseudoZipFile:
+    def __init__(self) -> None:
+        self.records: dict[str, tuple[object, int]] = {}
+
+    def write_record(self, key: str, data: object, length: int) -> None:
+        self.records[key] = (data, length)
+
+    def write_to(self, f: BufferedIOBase) -> None:
+        entries = []
+        for key, (data, length) in self.records.items():
+            entries.append(
+                _Entry(
+                    key=key,
+                    is_storage=isinstance(data, torch.UntypedStorage),
+                    length=length,
+                )
+            )
+
+        pickle.dump(entries, f, protocol=DEFAULT_PROTOCOL)
+
+        for key, (data, length) in self.records.items():
+            if isinstance(data, bytes):
+                f.write(data)
+            elif isinstance(data, str):
+                f.write(data.encode("utf-8"))
+            elif isinstance(data, torch.UntypedStorage):
+                data._write_file(f, False, False, 1)
+            else:
+                raise TypeError(f"unknown type: {type(data)}")
+
+    def read_from(self, f: BufferedIOBase) -> None:
+        entries = _weights_only_unpickler.load(f)
+
+        for entry in entries:
+            data = f.read(entry.length)
+            if entry.is_storage:
+                storage = torch.frombuffer(
+                    data,
+                    dtype=torch.uint8,
+                ).untyped_storage()
+
+                self.records[entry.key] = (
+                    storage,
+                    entry.length,
+                )
+            else:
+                self.records[entry.key] = (data, entry.length)
+
+    def has_record(self, key: str) -> bool:
+        return key in self.records
+
+    def get_record(self, key: str) -> object:
+        return self.records[key][0]
+
+    def get_storage_from_record(
+        self, key: str, _length: int, _type: int
+    ) -> torch.Tensor:
+        return torch.tensor(self.records[key][0], dtype=torch.uint8)
+
+    def serialization_id(self) -> str:
+        return "torchft"
+
+
+def _streaming_save(
+    obj: object,
+    f: BufferedIOBase,
+    pickle_module: Any = pickle,
+    pickle_protocol: int = DEFAULT_PROTOCOL,
+) -> None:
+    """
+    Save the object to a file-like object in a streaming fashion compatible with
+    network sockets.
+
+    This behaves similarly to :func:`torch.save` with a few notable differences:
+
+    * A non-seekable file like object can be used when loading.
+    * No forwards/backwards compatiblity is provided for the serialization
+      format. This is only intended to be used with a single version of PyTorch
+      with transient storage (i.e. sockets or temp files).
+    * mmap is not supported
+
+    See :func:`torch.save` for more details on specific arguments.
+    """
+
+    zip_file = _PseudoZipFile()
+    _save(
+        obj,
+        zip_file=zip_file,
+        pickle_module=pickle_module,
+        pickle_protocol=pickle_protocol,
+        _disable_byteorder_record=False,
+    )
+    zip_file.write_to(f)
+
+
+def _streaming_load(
+    f: BufferedIOBase,
+    map_location: MAP_LOCATION = None,
+    pickle_module: Any = None,
+    *,
+    weights_only: bool = True,
+    **pickle_load_args: Any,
+) -> object:
+    """
+    Load the object from a file-like object in a streaming fashion compatible with
+    network sockets.
+
+    See :func:`_streaming_save` for more details about the streaming behavior.
+
+    See :func:`torch.load` for more details on specific arguments.
+    """
+    if weights_only:
+        if pickle_module is not None:
+            raise RuntimeError(
+                "Can not safely load weights when explicit pickle_module is specified"
+            )
+        pickle_module = _weights_only_unpickler
+    else:
+        if pickle_module is None:
+            pickle_module = pickle
+
+    if "encoding" not in pickle_load_args.keys():
+        pickle_load_args["encoding"] = "utf-8"
+
+    zip_file = _PseudoZipFile()
+    zip_file.read_from(f)
+    return _load(
+        zip_file=zip_file,
+        map_location=map_location,
+        pickle_module=pickle_module,
+        **pickle_load_args,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_state_dict_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_state_dict_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..640922762386fe68c825710993042c5df9ec1a3d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_state_dict_utils.py
@@ -0,0 +1,779 @@
+# mypy: allow-untyped-defs
+import copy
+import io
+import math
+import weakref
+from collections.abc import Mapping, MutableMapping
+from typing import Any, Callable, cast, NamedTuple, Optional, TYPE_CHECKING, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch.distributed._functional_collectives import AsyncCollectiveTensor
+
+
+if dist.is_available() or TYPE_CHECKING:
+    from torch.distributed import distributed_c10d
+    from torch.distributed._shard.sharded_tensor import ShardedTensor
+    from torch.distributed.tensor import distribute_tensor, DTensor, Replicate
+    from torch.distributed.tensor._utils import compute_local_shape_and_global_offset
+
+
+def _identity_func(
+    obj: torch.Tensor,
+    pg: Optional[dist.ProcessGroup],
+    device: Optional[torch.device],
+    companion_obj: Any,
+) -> torch.Tensor:
+    return obj
+
+
+def _all_gather_sharded_tensor(
+    sharded_tensor: "ShardedTensor",
+    pg: Optional[dist.ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+) -> torch.Tensor:
+    if pg is None:
+        pg = distributed_c10d._get_default_group()
+    world_size = dist.get_world_size(pg)
+    shards = sharded_tensor.local_shards()
+    dim_0_size = sharded_tensor.size()[0]  # type: ignore[index]
+    tensor_numel = sharded_tensor.size().numel()  # type: ignore[union-attr]
+    chunk_size = math.ceil(dim_0_size / world_size) * tensor_numel // dim_0_size
+    pg_device = (
+        distributed_c10d._get_pg_default_device(pg) if device is None else device
+    )
+    if shards:
+        local_tensor = shards[0].tensor.flatten()
+        if local_tensor.device.type != pg_device.type:
+            local_tensor = local_tensor.to(pg_device)
+        num_padding = chunk_size - local_tensor.numel()
+        if num_padding > 0:
+            local_tensor = F.pad(local_tensor, [0, num_padding])
+    else:
+        local_tensor = torch.zeros(
+            chunk_size, dtype=sharded_tensor.dtype, device=pg_device
+        )
+
+    tensor = torch.empty(
+        chunk_size * world_size,
+        dtype=local_tensor.dtype,
+        device=pg_device,
+    )
+    dist.all_gather_into_tensor(tensor, local_tensor, group=pg)
+
+    tensor = tensor.narrow(0, 0, tensor_numel).reshape(sharded_tensor.size())
+    return tensor
+
+
+class CompanionMismatch(Exception):
+    pass
+
+
+def _iterate_state_dict(
+    iter_object: Any,
+    sharded_tensor_func: Callable,
+    dtensor_func: Callable,
+    tensor_func: Callable,
+    *,
+    pg: Optional[dist.ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+    cpu_offload: bool = False,
+    companion_obj: Any = None,
+    ranks_only: tuple[int, ...] = (),
+    type_check: bool = True,
+    non_blocking: bool = True,
+) -> dict[str, Any]:
+    """Iterate through the state dict, applying the given functions to each tensor type.
+
+    Args:
+        iter_object (Any): the target state_dict.
+        sharded_tensor_func (Callable): the function to apply to ShardedTensor
+        dtensor_func (Callable): the function to apply to DTensor
+        tensor_func (Callable): the function to apply to Tensor
+        pg (Optional[dist.ProcessGroup]): process group passed to tensor functions
+        device (Optional[torch.device]): device passed to tensor functions
+        cpu_offload (bool): whether to offload the tensors to CPU memory. This option is ignored
+            if a companion_obj is supplied.
+        companion_obj (Any): A companion object to the state dict. If this object
+            is supplied, we attempt to copy the tensor to the companion object.
+        ranks_only (Tuple[int, ...]): if this tuple is empty, all ranks will
+            have the same state_dicts. Otherwise only ranks that in ``ranks_only``
+            have the same state_dicts. Other ranks will get empty state_dicts.
+        type_check (bool): check if the instance data type is a supported type
+            that can be saved by DCP.  The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+        non_blocking (bool): whether to use non-blocking copy when copying to the companion object.
+    """
+    # TODO: should we use pytree?
+    cpu_device = torch.device("cpu")
+    if isinstance(iter_object, ShardedTensor):
+        ret = sharded_tensor_func(iter_object, pg, device, companion_obj)
+    elif isinstance(iter_object, DTensor):
+        ret = dtensor_func(iter_object, pg, device, companion_obj)
+    elif isinstance(iter_object, torch.Tensor):
+        ret = tensor_func(iter_object, pg, device, companion_obj)
+    elif (
+        isinstance(iter_object, (int, float, str, bytes, io.BytesIO))
+        or iter_object is None
+    ):
+        ret = iter_object
+    elif isinstance(iter_object, dict):
+        if companion_obj is not None and (
+            not isinstance(companion_obj, dict)
+            or set(companion_obj.keys()) != set(iter_object.keys())
+        ):
+            msg = (
+                ""
+                if isinstance(companion_obj, dict)
+                else f"{set(companion_obj.keys())=} {set(iter_object.keys())=}"
+            )
+            raise CompanionMismatch(msg)
+
+        ret = {
+            key: _iterate_state_dict(
+                value,
+                sharded_tensor_func,
+                dtensor_func,
+                tensor_func,
+                pg=pg,
+                device=device,
+                cpu_offload=cpu_offload,
+                companion_obj=companion_obj[key] if companion_obj is not None else None,
+                ranks_only=ranks_only,
+                type_check=type_check,
+                non_blocking=non_blocking,
+            )
+            for key, value in iter_object.items()
+        }
+    elif isinstance(iter_object, (list, tuple)):
+        if companion_obj is not None and (
+            not isinstance(companion_obj, (list, tuple))
+            or len(companion_obj) != len(iter_object)
+        ):
+            raise CompanionMismatch
+
+        ret = [
+            _iterate_state_dict(
+                v,
+                sharded_tensor_func,
+                dtensor_func,
+                tensor_func,
+                pg=pg,
+                device=device,
+                cpu_offload=cpu_offload,
+                companion_obj=companion_obj[idx] if companion_obj is not None else None,
+                ranks_only=ranks_only,
+                type_check=type_check,
+                non_blocking=non_blocking,
+            )
+            for idx, v in enumerate(iter_object)
+        ]
+        if isinstance(iter_object, tuple):
+            ret = tuple(ret)
+    elif not type_check:
+        ret = copy.deepcopy(iter_object)
+    else:
+        raise ValueError(f"Unexpected value type {type(iter_object)}")
+
+    if not ranks_only or dist.get_rank(pg) in ranks_only:
+        if isinstance(ret, torch.Tensor):
+            if cpu_offload and companion_obj is None:
+                ret = ret.to(cpu_device)
+
+            if companion_obj is not None:
+                if isinstance(companion_obj, DTensor):
+                    assert isinstance(ret, DTensor)
+                    companion_obj._local_tensor.copy_(
+                        ret._local_tensor, non_blocking=non_blocking
+                    )
+                else:
+                    companion_obj.copy_(ret, non_blocking=non_blocking)
+                ret = companion_obj
+    else:
+        ret = {} if isinstance(ret, dict) else None
+
+    return ret
+
+
+def _gather_state_dict(
+    state_dict: dict[str, Any],
+    *,
+    pg: Optional[dist.ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+    cpu_offload: bool = False,
+    ranks_only: tuple[int, ...] = (),
+    type_check: bool = True,
+) -> dict[str, Any]:
+    """
+    Given a state_dict, this API gathers all the ShardedTensors or DTensors in
+    the state_dict.
+
+
+    Args:
+        state_dict (Dict[str, Any]): the target sharded state_dict.
+        pg (Optional[dist.ProcessGroup]): the process group that is used to
+            gather ShardedTensor. Note that gathering a DTensor will use
+            the DeviceMesh. So this argument will be ignored when gathering a
+            DTensor.
+        device: (Optional[torch.device]): the device that is used to
+            perform allgather for ShardedTensor. Note that gathering a DTensor
+            will use the DeviceMesh. So this argument will be ignored when
+            gathering a DTensor.
+        cpu_offload (bool): whether to offload the tensors to CPU memory. The
+            default value is False.
+        ranks_only: (Tuple[int, ...]): if this tuple is empty, all ranks will
+            have the same state_dicts. Otherwise only ranks that in ``ranks_only``
+            have the same state_dicts. Other ranks will get empty state_dicts.
+        type_check: (bool): check if the instance data type is a supported type
+            that can be saved by DCP.  The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Returns:
+        The gathered state dictionary.
+    """
+
+    def sharded_tensor_func(value, pg, device, companion_obj):
+        # ShardedTensor does not seem to record the original device type.
+        # So if the tensor is moved to CPU, we won't know the original type.
+        # As a result, we have to rely on the user to tell us the correct one.
+        cpu_device = torch.device("cpu")
+        output_tensor = _all_gather_sharded_tensor(value, pg, device)
+        local_shard_device = (
+            value.local_shards()[0].tensor.device
+            if value.local_shards()
+            else cpu_device
+        )
+        if output_tensor.device != local_shard_device:
+            value = output_tensor.to(local_shard_device)
+        else:
+            value = output_tensor
+        return value
+
+    def dtensor_func(value, pg, device, companion_obj):
+        if value.device != value.device_mesh.device_type:
+            value = value.to(value.device_mesh.device_type)
+        # FSDP all_gather: [Shard(0)] -> [Replicate()]
+        # HSDP all_gather: [Replicate(), Shard(0)] -> [Replicate(), Replicate()]
+        # 2D FSDP + TP all_gather:
+        # - [Shard(0), Shard(n)] -> [Replicate(), Replicate()]
+        # - [Shard(0), Replicate()] -> [Replicate(), Replicate()]
+        placements = [Replicate() for _ in value.placements]
+        value = value.redistribute(
+            device_mesh=value.device_mesh,
+            placements=placements,
+        )
+        # Call `wait()` to force the tensor to be synchronous with respect
+        # to the main stream.
+        # See the discussion in https://github.com/pytorch/pytorch/pull/117799.
+        value = value.to_local()
+        if isinstance(value, AsyncCollectiveTensor):
+            value = value.wait()
+        return value
+
+    return _iterate_state_dict(
+        state_dict,
+        sharded_tensor_func,
+        dtensor_func,
+        _identity_func,
+        pg=pg,
+        device=device,
+        cpu_offload=cpu_offload,
+        ranks_only=ranks_only,
+        type_check=type_check,
+    )
+
+
+def _offload_state_dict_to_cpu(
+    state_dict: dict[str, Any],
+    *,
+    ranks_only: tuple[int, ...] = (),
+    type_check: bool = True,
+) -> dict[str, Any]:
+    """
+    Given a state_dict, this API offload all the tensors to CPU memory.
+
+    Args:
+        state_dict (Dict[str, Any]): the target state_dict.
+        pg (Optional[dist.ProcessGroup]): the process group that is used to
+            gather ShardedTensor. Note that gathering a DTensor will use
+            the DeviceMesh. So this argument will be ignored when gathering a
+            DTensor.
+        ranks_only: (Tuple[int, ...]): if this tuple is empty, all ranks will
+            have the same state_dicts. Otherwise only ranks that in ``ranks_only``
+            have the same state_dicts. Other ranks will get empty state_dicts.
+        type_check: (bool): check if the instance data type is a supported type
+            that can be saved by DCP.  The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Returns:
+        The gathered state dictionary.
+    """
+
+    ret = _iterate_state_dict(
+        state_dict,
+        _identity_func,
+        _identity_func,
+        _identity_func,
+        pg=None,
+        device=None,
+        cpu_offload=True,
+        ranks_only=ranks_only,
+        type_check=type_check,
+    )
+    return ret
+
+
+@torch.no_grad()
+def _copy_state_dict(
+    state_dict: dict[str, Any],
+    copy_state_dict: dict[str, Any],
+    non_blocking: bool = False,
+    type_check: bool = True,
+) -> dict[str, Any]:
+    """
+    Copies all tensors in a given state dict into a different state_dict with the
+    same structure. Additionally, a copied state dict with the same value references
+    is returned. Editing the keys on this state dict will not affect the
+    passed in copy_state_dict (but the value references are the same).
+
+    .. warning::
+        It is expected by this function that state_dict and copy_state_dict share
+        the same structure and data types.
+
+    .. warning::
+        The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Args:
+        state_dict (Dict[str, Any]): the target state_dict.
+        copy_state_dict (Dict[str, Any]):
+            The state dict we are copying into. This state_dict must have exactly
+             the same structure as the source `state_dict`.
+        non_blocking: (bool): Whether copy ops should be performed asynchronously
+        type_check (bool): check if the instance data type is a supported type
+            that can be saved by DCP. The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Returns:
+        State Dict copy
+    """
+
+    return _iterate_state_dict(
+        state_dict,
+        _identity_func,
+        _identity_func,
+        _identity_func,
+        pg=None,
+        device=None,
+        cpu_offload=False,
+        ranks_only=(),
+        companion_obj=copy_state_dict,
+        type_check=type_check,
+        non_blocking=non_blocking,
+    )
+
+
+@torch.no_grad()
+def _create_cpu_state_dict(
+    state_dict: dict[str, Any], pin_memory: bool = False, share_memory: bool = False
+) -> dict[str, Any]:
+    """
+    Given a state_dict, create another state_dict with the same structure and elements.
+    However, all tensors in the returned state_dict are new tensors on CPU. These
+    tensors can be placed on pin_memory or share_memory based on the provided arguments.
+
+    .. warning::
+        Setting both `pin_memory` and `share_memory` to True significantly increases the
+        latency of this method because of the nuances which require us to register memory
+        as pinned directly as opposed to relying on the pin_memory cache allocator. This
+        option should only be used for long lived tensors which are required to be shared.
+        This is not the case as long as at least one of `pin_memory` or `share_memory` is
+         set to False.
+
+    """
+
+    def tensor_func(
+        obj: torch.Tensor,
+        pg: Optional[dist.ProcessGroup],
+        device: Optional[torch.device],
+        _: Any,
+    ) -> torch.Tensor:
+        if len(obj.size()) == 0:
+            return torch.tensor(0, dtype=obj.dtype)
+
+        if share_memory:
+            t = torch.empty(*tuple(obj.size()), dtype=obj.dtype)
+            t = t.share_memory_()
+            if pin_memory:
+
+                def unpin_memory(t):
+                    succ = int(torch.cuda.cudart().cudaHostUnregister(t.data_ptr()))
+                    assert succ == 0, (
+                        f"Unpinning shared memory failed with error-code: {succ}"
+                    )
+
+                weakref.finalize(t, unpin_memory, t)
+                succ = int(
+                    torch.cuda.cudart().cudaHostRegister(
+                        t.data_ptr(),
+                        t.numel() * t.element_size(),
+                        1,  # lines up with 'cudaHostRegisterPortable'
+                    )
+                )
+                assert succ == 0, (
+                    f"Pinning shared memory failed with error-code: {succ}"
+                )
+            return t
+        elif pin_memory:
+            return torch.empty(*tuple(obj.size()), dtype=obj.dtype).pin_memory()
+        else:
+            return torch.empty(*tuple(obj.size()), dtype=obj.dtype)
+
+    def dtensor_func(
+        obj: DTensor,
+        pg: Optional[dist.ProcessGroup],
+        device: Optional[torch.device],
+        _: Any,
+    ) -> DTensor:
+        if len(obj.size()) == 0:
+            return obj
+
+        if obj.device != torch.device("cpu"):
+            ret = cast(DTensor, obj.to(device="cpu"))
+        else:
+            ret = copy.deepcopy(obj)
+        ret._local_tensor = tensor_func(ret._local_tensor, pg, device, None)
+        return ret
+
+    ret = _iterate_state_dict(
+        state_dict,
+        _identity_func,
+        dtensor_func,
+        tensor_func,
+        pg=None,
+        device=None,
+        cpu_offload=False,
+        ranks_only=(),
+        type_check=False,
+    )
+    return ret
+
+
+def _check_state_dict_similarity(
+    state_dict: dict[str, Any],
+    compared_state_dict: dict[str, Any],
+) -> bool:
+    """
+    Given two state_dicts, check if the structures are the same. And
+    if a [key, tensor] pair exist in one state_dict there must be
+    the a corresponding pait, [key, other_tensor], in the other state_dict,
+    where tensor and other_tensor have the same size and dtype.
+
+    Return the check result.
+    """
+
+    def tensor_func(
+        obj: torch.Tensor,
+        pg: Optional[dist.ProcessGroup],
+        device: Optional[torch.device],
+        companion_obj: Any,
+    ) -> torch.Tensor:
+        if companion_obj.dtype != obj.dtype or companion_obj.size() != obj.size():
+            raise CompanionMismatch
+        return obj
+
+    try:
+        _iterate_state_dict(
+            state_dict,
+            _identity_func,
+            _identity_func,
+            tensor_func,
+            pg=None,
+            device=None,
+            cpu_offload=False,
+            ranks_only=(),
+            companion_obj=compared_state_dict,
+            type_check=False,
+        )
+    except CompanionMismatch:
+        return False
+
+    return True
+
+
+class _TensorInfo(NamedTuple):
+    size: torch.Size
+    dtype: torch.dtype
+
+
+def _broadcast_tensors(
+    full_state_dict: dict[str, Any],
+    local_state_dict: dict[str, Any],
+    keys: list[str],
+    device: torch.device,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> None:
+    tensors = []
+    for key in keys:
+        if dist.get_rank() == 0:
+            full_state = full_state_dict[key]
+            assert isinstance(full_state, torch.Tensor)
+            full_tensor = full_state.detach().to(device)
+        else:
+            tensor_info = full_state_dict[key]
+            full_tensor = torch.empty(
+                size=tensor_info.size,
+                device=device,
+                dtype=tensor_info.dtype,
+            )
+        tensors.append(full_tensor)
+        local_state = local_state_dict.get(key, None)
+        if local_state is None:
+            continue
+        elif isinstance(local_state, DTensor):
+            local_state_dict[key] = (local_state, full_tensor)
+        else:
+            local_state_dict[key] = full_tensor
+
+    if pg is None:
+        pg = dist.distributed_c10d._get_default_group()
+
+    if len(tensors) > 1:
+        dist._broadcast_coalesced(pg, tensors, 500, 0)
+    else:
+        dist.broadcast(tensors[0], src=0, group=pg)
+
+    _distribute_tensors(local_state_dict, keys, device, pg)
+
+
+def _distribute_tensors(
+    local_state_dict: dict[str, Any],
+    keys: list[str],
+    device: torch.device,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> None:
+    if pg is None:
+        pg = dist.distributed_c10d._get_default_group()
+    for key in keys:
+        _local_state = local_state_dict.get(key, None)
+        if _local_state is None or torch.is_tensor(_local_state):
+            continue
+
+        local_state = _local_state[0]
+        full_tensor = _local_state[1]
+
+        shape, offset = compute_local_shape_and_global_offset(
+            full_tensor.shape, local_state.device_mesh, local_state.placements
+        )
+        slices = [
+            slice(cur_offset, cur_offset + cur_shape)
+            for cur_shape, cur_offset in zip(shape, offset)
+        ]
+        if local_state.is_meta:
+            # Use .clone() here rather than view to clone and return only the sliced portion, minimizing memory access and cost.
+            local_tensor = full_tensor[slices].detach().clone()
+            # TODO: currently, we cannot handle strided sharding if the dp dimension is not even. For example,
+            # one of the case that is not yet supported is when placements = (Shard(0), _StridedShard(0, sf=2)).
+            ret = DTensor.from_local(
+                local_tensor,
+                local_state.device_mesh,
+                local_state.placements,
+                shape=local_state.shape,
+                stride=local_state.stride(),
+            )
+        else:
+            ret = local_state
+            # Copy full_tensor[slices] into local_state.to_local() to reduce memory footprint.
+            ret.to_local().copy_(full_tensor[slices])
+        local_state_dict[key] = ret
+
+
+def _broadcast_state_dict(
+    full_state_dict: dict[str, Any],
+    local_state_dict: dict[str, Any],
+    device: torch.device,
+    pg: Optional[dist.ProcessGroup] = None,
+    strict: bool = False,
+    cpu_offload: bool = False,
+) -> None:
+    # Broadcast from rank0's `full_state_dict` to all ranks' `local_state_dict`.
+    # If strict is True, any keys in `local_state_dict` but not in `full_state_dict`
+    # will be removed from `local_state_dict`.
+    ret = {}
+    if dist.get_rank() == 0:
+        for key, value in full_state_dict.items():
+            if not torch.is_tensor(value):
+                ret[key] = value
+            elif value.dim() == 0:
+                ret[key] = value.cpu()
+            else:
+                ret[key] = _TensorInfo(value.size(), value.dtype)
+
+    broadcast_list = [ret]
+    dist.broadcast_object_list(broadcast_list, src=0, group=pg)
+    ret = broadcast_list[0]
+    # Gather values
+    keys = []
+    local_state_dict_keys = set(local_state_dict.keys())
+    global_keys = set()
+    for key, value in ret.items():
+        global_keys.add(key)
+        if not isinstance(value, _TensorInfo):
+            if key in local_state_dict:
+                local_state_dict[key] = value
+            continue
+
+        if dist.get_rank() == 0:
+            ret[key] = full_state_dict[key]
+
+        keys.append(key)
+        # Broadcast every tensor to avoid OOM for now.
+        if len(keys) >= 1:
+            _broadcast_tensors(ret, local_state_dict, keys, device, pg)
+            if cpu_offload:
+                for key in keys:
+                    local_state_dict[key] = local_state_dict[key].cpu()
+            keys.clear()
+
+    if strict:
+        if missing_keys := (local_state_dict_keys - global_keys):
+            for key in missing_keys:
+                local_state_dict.pop(key)
+
+    if keys:
+        _broadcast_tensors(ret, local_state_dict, keys, device, pg)
+        if cpu_offload:
+            for key in keys:
+                local_state_dict[key] = local_state_dict[key].cpu()
+
+
+def _distribute_state_dict(
+    full_state_dict: dict[str, Any],
+    local_state_dict: dict[str, Any],
+    device: torch.device,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> None:
+    # Full_state_dict = True, broadcast_from_rank0 = False here. Each rank has
+    # full_state_dict. Skip the broadcast in ``_broadcast_state_dict`` and
+    # distribute tensors in each rank
+    for key, value in full_state_dict.items():
+        if key not in full_state_dict:
+            continue
+        if not torch.is_tensor(value):
+            local_state_dict[key] = value
+        elif value.dim() == 0:
+            local_state_dict[key] = value.cpu()
+        else:
+            assert isinstance(value, torch.Tensor)
+            local_state = local_state_dict.get(key, None)
+            if local_state is None:
+                continue
+            elif isinstance(local_state, DTensor):
+                local_state_dict[key] = distribute_tensor(
+                    value.detach().to(device),
+                    local_state.device_mesh,
+                    local_state.placements,
+                )
+            else:
+                local_state_dict[key] = value.detach().to(device)
+
+
+# These APIs are from torch.distributed.checkpoint.
+# TODO: We should consolidate the code here as some not all modules can depend on
+# DCP.
+PATH_ITEM = Union[str, int]
+OBJ_PATH = tuple[PATH_ITEM, ...]
+FLATTEN_MAPPING = dict[str, OBJ_PATH]
+STATE_DICT_TYPE = dict[str, Any]
+CONTAINER_TYPE = MutableMapping[PATH_ITEM, Any]
+
+
+def _traverse_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    visitor: Callable[[OBJ_PATH, Any], None],
+) -> None:
+    """
+    Invoke ``visitor`` for each value recursively in ``state_dict``.
+    Mapping, list, and tuple will be flattened and other value types are treated
+    as the terminal values and will invoke ``visitor``.
+    """
+
+    def _traverse_obj(path: OBJ_PATH, value: Any) -> None:
+        if isinstance(value, Mapping):
+            for k, v in value.items():
+                _traverse_obj(path + (str(k),), v)
+        elif isinstance(value, (list, tuple)):
+            for i, v in enumerate(value):
+                _traverse_obj(path + (i,), v)
+        else:
+            visitor(path, value)
+
+    for key, value in state_dict.items():
+        _traverse_obj((str(key),), value)
+
+
+def _flatten_state_dict(
+    state_dict: STATE_DICT_TYPE,
+) -> tuple[STATE_DICT_TYPE, FLATTEN_MAPPING]:
+    """
+    Flatten ``state_dict`` made of nested dicts and lists into a top level dictionary.
+
+    Use ``unflatten_state_dict`` to revert this process.
+    Returns:
+        A tuple with the flatten state_dict and a mapping from original to new state_dict.
+    N.B. The new keys are derived from the object paths, joined by dot.
+        For example: ``{ 'a': {'b':...}}`` results in the key `a.b`.
+    """
+    flattened: STATE_DICT_TYPE = {}
+    mappings: FLATTEN_MAPPING = {}
+
+    def flat_copy(path: OBJ_PATH, value: Any) -> None:
+        new_fqn = ".".join(map(str, path))
+        if new_fqn in flattened:
+            raise ValueError(f"duplicated flatten key {new_fqn}")
+        flattened[new_fqn] = value
+        mappings[new_fqn] = path
+
+    _traverse_state_dict(state_dict, flat_copy)
+    return flattened, mappings
+
+
+def _set_element(root_dict: STATE_DICT_TYPE, path: OBJ_PATH, value: Any) -> None:
+    """Set ``value`` in ``root_dict`` along the ``path`` object path."""
+    cur_container = cast(CONTAINER_TYPE, root_dict)
+
+    def extend_list(lst: list[Any], idx: int) -> None:
+        while len(lst) <= idx:
+            lst.append(None)
+
+    for i in range(1, len(path)):
+        prev_key = path[i - 1]
+        key = path[i]
+        def_val: Union[CONTAINER_TYPE, list[Any]] = {} if type(key) == str else []
+
+        if isinstance(cur_container, Mapping):
+            cur_container = cast(
+                CONTAINER_TYPE, cur_container.setdefault(prev_key, def_val)
+            )
+        else:
+            extend_list(cur_container, prev_key)
+            if cur_container[prev_key] is None:
+                cur_container[prev_key] = def_val
+            cur_container = cur_container[prev_key]
+
+    key = path[-1]
+    if type(key) == int:
+        extend_list(cast(list[Any], cur_container), key)
+
+    cur_container[key] = value
+
+
+def _unflatten_state_dict(
+    state_dict: STATE_DICT_TYPE, mapping: FLATTEN_MAPPING
+) -> STATE_DICT_TYPE:
+    """Restore the original nested state_dict according to ``mapping`` and the flattened ``state_dict``."""
+    nested: STATE_DICT_TYPE = {}
+    for key, value in state_dict.items():
+        _set_element(nested, mapping[key], value)
+    return nested
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_tools/fake_collectives.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_tools/fake_collectives.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6cb23a06b671846a20e41e82ff41f7e7178d661
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/_tools/fake_collectives.py
@@ -0,0 +1,307 @@
+import random
+from typing import Any
+
+import torch
+from torch._C._distributed_c10d import (
+    _resolve_process_group,
+    FakeWork,
+    ProcessGroup,
+    Work,
+)
+from torch.utils._pytree import tree_map_only
+
+
+torch.distributed.batch_isend_irecv
+
+c10d = torch.ops.c10d
+_c10d_functional = torch.ops._c10d_functional
+_c10d_functional_autograd = torch.ops._c10d_functional_autograd
+_dtensor = torch.ops._dtensor
+used_ids: set[int] = set()
+
+
+def generate_unique_id() -> int:
+    while True:
+        new_id = random.randint(1, 10**9)
+        if new_id not in used_ids:
+            used_ids.add(new_id)
+            return new_id
+
+
+# Function to create and return FakeWork object
+def create_fakework(args, return_first_arg=True):  # type: ignore[no-untyped-def]
+    work = FakeWork()
+    work.seq_id = generate_unique_id()
+    fakework_script_obj = work.boxed()
+    return (args[0], fakework_script_obj) if return_first_arg else fakework_script_obj
+
+
+# Dictionary mapping collective operations to their meta functions
+# All 20 ops from torch.csrc.distributed.c10d.Ops.cpp are included
+# _DEPRECATED_META_FUNCTIONS = {
+#     "allreduce_coalesced_": lambda *args: create_fakework(args, return_first_arg=False),
+#     "allgather_coalesced_": lambda *args: create_fakework(args, return_first_arg=False),
+#     "allgather_into_tensor_coalesced_": lambda *args: create_fakework(args, return_first_arg=False),
+#     "reduce_scatter_tensor_coalesced_": lambda *args: create_fakework(args, return_first_arg=False),
+# }
+_META_FUNCTIONS = {
+    "broadcast_": lambda *args: create_fakework(args),
+    "allreduce_": lambda *args: create_fakework(args),
+    "allgather_": lambda *args: create_fakework(args),
+    "_allgather_base_": lambda *args: create_fakework(args),
+    "reduce_scatter_": lambda *args: create_fakework(args),
+    "_reduce_scatter_base_": lambda *args: create_fakework(args),
+    "reduce_": lambda *args: create_fakework(args, return_first_arg=False),
+    "gather_": lambda *args: create_fakework(args, return_first_arg=False),
+    "scatter_": lambda *args: create_fakework(args),
+    "alltoall_": lambda *args: create_fakework(args),
+    "alltoall_base_": lambda *args: create_fakework(args, return_first_arg=False),
+    "barrier": lambda *args: create_fakework(args, return_first_arg=False),
+    "monitored_barrier_": lambda *args: None,
+    "send": lambda *args: create_fakework(args, return_first_arg=False),
+    "recv_": lambda *args: create_fakework(args, return_first_arg=False),
+    "recv_any_source_": lambda *args: create_fakework(args, return_first_arg=False),
+}
+
+if not torch._running_with_deploy():
+    lib_impl = torch.library.Library("c10d", "IMPL")  # noqa: TOR901
+    for op, meta_func in _META_FUNCTIONS.items():
+        lib_impl.impl(op, meta_func, "Meta")
+
+# List of collective operation functions including functional collectives
+# Note: The following collectives might be deprecated soon hence not adding them
+# depcreated_non_functional_collectives = [
+#     c10d.allreduce_coalesced_.default,
+#     c10d.reduce_scatter_tensor_coalesced_.default,
+#     c10d.allgather_into_tensor_coalesced_.default,
+#     c10d.allgather_coalesced_.default,
+# ]
+non_functional_collectives: set[torch._ops.OpOverload] = {
+    c10d.broadcast_.default,
+    c10d.allreduce_.default,
+    c10d.reduce_.default,
+    c10d.send.default,
+    c10d.recv_.default,
+    c10d.recv_any_source_.default,
+    c10d.allgather_.default,
+    c10d.reduce_scatter_.default,
+    c10d._reduce_scatter_base_.default,
+    c10d._allgather_base_.default,
+    c10d.gather_.default,
+    c10d.scatter_.default,
+    c10d.alltoall_.default,
+    c10d.alltoall_base_.default,
+    c10d.barrier.default,
+    c10d.monitored_barrier_.default,
+}
+functional_collectives: set[torch._ops.OpOverload] = {
+    _c10d_functional.broadcast.default,
+    _c10d_functional.all_reduce.default,
+    _c10d_functional.all_gather_into_tensor.default,
+    _c10d_functional.reduce_scatter_tensor.default,
+    _c10d_functional.all_to_all_single.default,
+    _c10d_functional_autograd.all_to_all_single.default,
+    _c10d_functional.wait_tensor.default,
+    _c10d_functional.all_reduce_.default,
+    _c10d_functional.all_reduce_coalesced.default,
+    _c10d_functional.all_reduce_coalesced_.default,
+    _c10d_functional.all_gather_into_tensor_out.default,
+    _c10d_functional.all_gather_into_tensor_coalesced.default,
+    _c10d_functional_autograd.all_gather_into_tensor.default,
+    _c10d_functional.reduce_scatter_tensor_coalesced.default,
+    _c10d_functional_autograd.reduce_scatter_tensor.default,
+    _c10d_functional.broadcast_.default,
+    _dtensor.shard_dim_alltoall.default,
+}
+
+sync_ops: set[torch._ops.OpOverload] = {
+    c10d.barrier.default,
+    c10d.monitored_barrier_.default,
+    _c10d_functional.wait_tensor.default,
+}
+
+collective_ops = set.union(functional_collectives, non_functional_collectives)
+
+
+class CollectiveOp:
+    # Static sets for performance optimization
+    PG_ARG_1 = {
+        c10d.broadcast_.default,
+        c10d.allreduce_.default,
+        c10d.reduce_.default,
+        c10d.send.default,
+        c10d.recv_.default,
+        c10d.recv_any_source_.default,
+        c10d.barrier.default,
+        # c10d.allreduce_coalesced_.default
+    }
+
+    PG_ARG_2 = {
+        c10d.allgather_.default,
+        c10d._allgather_base_.default,
+        c10d.reduce_scatter_.default,
+        c10d._reduce_scatter_base_.default,
+        c10d.gather_.default,
+        c10d.scatter_.default,
+        c10d.alltoall_.default,
+        c10d.alltoall_base_.default,
+        # c10d.allgather_coalesced_.default,
+        # c10d.allgather_into_tensor_coalesced_.default
+        # c10d.reduce_scatter_tensor_coalesced_.default
+    }
+
+    PG_ARG_3 = {
+        _c10d_functional.broadcast.default,
+        _c10d_functional.broadcast_.default,
+        _c10d_functional.all_reduce.default,
+        _c10d_functional.all_reduce_.default,
+        _c10d_functional.all_reduce_coalesced.default,
+        _c10d_functional.all_reduce_coalesced_.default,
+        _c10d_functional.all_gather_into_tensor.default,
+        _c10d_functional.all_gather_into_tensor_out.default,
+        _c10d_functional_autograd.all_gather_into_tensor.default,
+        _c10d_functional.all_gather_into_tensor_coalesced.default,
+    }
+
+    PG_ARG_4 = {
+        _c10d_functional.reduce_scatter_tensor.default,
+        _c10d_functional.reduce_scatter_tensor_coalesced.default,
+        _c10d_functional_autograd.reduce_scatter_tensor.default,
+        _c10d_functional.all_to_all_single.default,
+        _c10d_functional_autograd.all_to_all_single.default,
+        _dtensor.shard_dim_alltoall.default,
+    }
+
+    WK_ARG_1 = {
+        c10d.broadcast_.default,
+        c10d.allreduce_.default,
+        c10d.allgather_.default,
+        c10d.reduce_scatter_.default,
+        c10d._reduce_scatter_base_.default,
+        c10d._allgather_base_.default,
+        c10d.scatter_.default,
+        c10d.alltoall_.default,
+    }
+
+    WK = {
+        c10d.send.default,
+        c10d.recv_.default,
+        c10d.recv_any_source_.default,
+        c10d.reduce_.default,
+        c10d.gather_.default,
+        c10d.alltoall_base_.default,
+        c10d.barrier.default,
+    }
+
+    COMM_TENSOR_ARG_0 = {
+        c10d.allreduce_.default,
+        c10d.send.default,
+        c10d.recv_.default,
+        c10d.recv_any_source_.default,
+        c10d.allgather_.default,
+        c10d.gather_.default,
+        c10d.reduce_.default,
+        c10d.broadcast_.default,
+        _c10d_functional.all_reduce_coalesced.default,
+        _c10d_functional.all_reduce_coalesced_.default,
+        # c10d.allreduce_coalesced_.default
+        # c10d.allgather_coalesced_.default
+        # c10d.allgather_into_tensor_coalesced_.default,
+    }
+
+    COMM_TENSOR_ARG_1 = {
+        c10d.reduce_scatter_.default,
+        c10d.scatter_.default,
+        # c10d.reduce_scatter_tensor_coalesced_.default,
+    }
+
+    COMM_TENSOR_ARG_RES = {
+        _c10d_functional.all_gather_into_tensor.default,
+        _c10d_functional_autograd.all_gather_into_tensor.default,
+    }
+
+    COMM_TENSOR_SINGLE_UNTYPED_STORAGE = {
+        c10d._allgather_base_.default,
+        _c10d_functional.broadcast.default,
+        _c10d_functional.broadcast_.default,
+        _c10d_functional.all_reduce.default,
+        _c10d_functional.all_reduce_.default,
+        _c10d_functional.reduce_scatter_tensor.default,
+        _c10d_functional_autograd.reduce_scatter_tensor.default,
+    }
+
+    COMM_TENSOR_ARG_0_AND_RES = {
+        _c10d_functional.all_to_all_single.default,
+        _c10d_functional_autograd.all_to_all_single.default,
+        _dtensor.shard_dim_alltoall.default,
+    }
+
+    COMM_TENSOR_RES_SUM = {
+        _c10d_functional.all_gather_into_tensor_coalesced.default,
+        _c10d_functional.reduce_scatter_tensor_coalesced.default,
+    }
+
+    @staticmethod
+    def sum_tensors(arg: Any) -> int:
+        """Calculate total memory consumed by the tensors in the argument."""
+        total_memory = 0
+
+        def sum_bytes(t: torch.Tensor) -> None:
+            nonlocal total_memory
+            total_memory += t.untyped_storage().nbytes()
+
+        tree_map_only(torch.Tensor, sum_bytes, arg)
+        return total_memory
+
+    @staticmethod
+    def get_process_group(func, args) -> ProcessGroup:  # type: ignore[no-untyped-def]
+        """Retrieve the process group for collective operations, except `wait_tensor`."""
+        if func in CollectiveOp.PG_ARG_1:
+            return ProcessGroup.unbox(args[1])
+        if func in CollectiveOp.PG_ARG_2:
+            return ProcessGroup.unbox(args[2])
+        if func in CollectiveOp.PG_ARG_3:
+            return _resolve_process_group(args[2])
+        if func in CollectiveOp.PG_ARG_4:
+            return _resolve_process_group(args[3])
+        raise TypeError(f"Func {func} not found in {collective_ops}")
+
+    @staticmethod
+    def get_comm_tensor_size(func, res, args, kwargs) -> int:  # type: ignore[no-untyped-def]
+        """Compute the communication tensor size, except for `wait_tensor`, `barrier`, and `monitored_barrier`."""
+        if func in CollectiveOp.COMM_TENSOR_ARG_0:
+            return CollectiveOp.sum_tensors(args[0])
+        if func in CollectiveOp.COMM_TENSOR_ARG_1:
+            return CollectiveOp.sum_tensors(args[1])
+        if func in CollectiveOp.COMM_TENSOR_ARG_RES:
+            return res.untyped_storage().nbytes()
+        if func in CollectiveOp.COMM_TENSOR_SINGLE_UNTYPED_STORAGE:
+            return args[0].untyped_storage().nbytes()
+        if func == c10d._reduce_scatter_base_.default:
+            return args[1].untyped_storage().nbytes()
+        if func == c10d.alltoall_.default:
+            # TODO(@sanketpurandare) - Confirm size computation
+            return max(
+                CollectiveOp.sum_tensors(args[0]), CollectiveOp.sum_tensors(args[1])
+            )
+        if func == c10d.alltoall_base_.default:
+            # TODO(@sanketpurandare) - Confirm size computation
+            return max(
+                args[0].untyped_storage().nbytes(), args[1].untyped_storage().nbytes()
+            )
+        if func == _c10d_functional.all_gather_into_tensor_out.default:
+            return args[-1].untyped_storage().nbytes()
+        if func in CollectiveOp.COMM_TENSOR_RES_SUM:
+            return CollectiveOp.sum_tensors(res)
+        if func in CollectiveOp.COMM_TENSOR_ARG_0_AND_RES:
+            # TODO(@sanketpurandare) - Confirm size computation
+            return args[0].untyped_storage().nbytes() + res.untyped_storage().nbytes()
+        raise TypeError(f"Unknown function: {func} in {collective_ops}")
+
+    @staticmethod
+    def get_work(func, res) -> Work:  # type: ignore[no-untyped-def]
+        if func in CollectiveOp.WK:
+            return FakeWork.unbox(res)
+        elif func in CollectiveOp.WK_ARG_1:
+            return FakeWork.unbox(res[1])
+        raise TypeError(f"Func {func} not found in {collective_ops}")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..06c814295699405de9a8f8cf7f6a861b07b63a05
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/__init__.py
@@ -0,0 +1 @@
+from .join import Join, Joinable, JoinHook
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/__pycache__/__init__.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_checkpoint/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_checkpoint/__init__.py
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_checkpoint/checkpoint_wrapper.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_checkpoint/checkpoint_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..98e213792b73e957c305fd99f201a3afdd22551d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_checkpoint/checkpoint_wrapper.py
@@ -0,0 +1,324 @@
+# mypy: allow-untyped-defs
+import warnings
+from abc import ABC, abstractmethod
+from collections.abc import Iterator
+from enum import auto, Enum
+from functools import partial
+from typing import Any, Callable, Optional
+
+import torch
+import torch.nn as nn
+from torch.autograd.graph import save_on_cpu
+from torch.distributed.utils import _pack_kwargs, _replace_by_prefix, _unpack_kwargs
+from torch.utils.checkpoint import checkpoint as torch_utils_checkpoint
+
+
+_CHECKPOINT_WRAPPED_MODULE = "_checkpoint_wrapped_module"
+_CHECKPOINT_PREFIX = _CHECKPOINT_WRAPPED_MODULE + "."
+
+
+class CheckpointImpl(Enum):
+    REENTRANT = auto()
+    NO_REENTRANT = auto()
+
+
+class ActivationWrapper(torch.nn.Module, ABC):
+    """
+    Base class for Activation Checkpoint and Activation Offload.
+
+    Not meant to be instantiated directly.
+    """
+
+    def __init__(self, mod):
+        super().__init__()
+        self._checkpoint_wrapped_module = mod
+        # state_dict post hook to remove prefix to allow loading into a
+        # non-checkpoint wrapped module.
+        self._register_state_dict_hook(self._post_state_dict_hook)
+        # load_state_dict pre-hook to allow loading back into
+        # checkpoint-wrapped module.
+        self.register_load_state_dict_pre_hook(self._pre_load_state_dict_hook)
+
+    @abstractmethod
+    def forward(self, *args, **kwargs):
+        raise ValueError("Subclasses should implement forward().")
+
+    def __getattr__(self, name: str) -> Any:
+        """Forward missing attributes to wrapped module."""
+        try:
+            return super().__getattr__(name)  # defer to nn.Module's logic
+        except AttributeError:
+            return getattr(self._checkpoint_wrapped_module, name)
+
+    def __getitem__(self, key: int) -> Any:
+        """Forward indexing calls in case the module is a nn.Sequential."""
+        return self._checkpoint_wrapped_module.__getitem__(key)  # type: ignore[operator]
+
+    def named_parameters(
+        self,
+        *args,
+        **kwargs,
+    ) -> Iterator[tuple[str, torch.nn.Parameter]]:
+        """
+        Override :meth:`named_parameters()` to intercept parameter names.
+
+        remove all occurrences of ``_CHECKPOINT_PREFIX``.
+        """
+        for param_name, param in super().named_parameters(*args, **kwargs):
+            yield param_name.replace(_CHECKPOINT_PREFIX, ""), param
+
+    @staticmethod
+    def _post_state_dict_hook(
+        module: nn.Module,
+        state_dict: dict[str, Any],
+        prefix: str,
+        *args: Any,
+    ) -> dict[str, Any]:
+        """
+        _post_state_dict_hook() is called after the state_dict() of this FSDP module is executed.
+
+        For ``checkpoint_wrapper``, it will strip checkpoint-wrapped module prefix,
+        so that this module can be loaded into non-checkpointed modules.
+        It would still be able to be loaded into checkpoint-wrapped modules as this class,
+        adds the prefix back before loading the state_dict.
+        """
+        _replace_by_prefix(state_dict, f"{prefix}{_CHECKPOINT_PREFIX}", prefix)
+        return state_dict
+
+    @staticmethod
+    def _pre_load_state_dict_hook(
+        module: nn.Module,
+        state_dict: dict[str, Any],
+        prefix: str,
+        *args: Any,
+    ) -> None:
+        """
+        ``_pre_state_dict_hook` is called before ``self._load_from_state_dict()`` is called.
+
+        For ``checkpoint_wrapper``, it will add back the module
+        prefix so that non-checkpointed modules can be loaded into
+        checkpoint_wrapper modules properly.
+        """
+        _replace_by_prefix(state_dict, prefix, prefix + f"{_CHECKPOINT_PREFIX}")
+
+
+class OffloadWrapper(ActivationWrapper):
+    def __init__(self, mod):
+        super().__init__(mod)
+
+    def forward(self, *args, **kwargs):
+        with save_on_cpu(pin_memory=True):
+            return self._checkpoint_wrapped_module(*args, **kwargs)
+
+
+class CheckpointWrapper(ActivationWrapper):
+    """
+    An ``nn.Module`` that wraps another ``nn.Module`` with checkpointing.
+
+    Note that this module is not meant to be used directly but instead,
+    it is to be used through the ``checkpoint_wrapper`` function.
+    """
+
+    def __init__(
+        self,
+        mod: torch.nn.Module,
+        checkpoint_impl: CheckpointImpl = CheckpointImpl.NO_REENTRANT,
+        checkpoint_fn=None,
+        **checkpoint_fn_kwargs,
+    ):
+        super().__init__(mod)
+        self.checkpoint_impl = checkpoint_impl
+        if checkpoint_fn is None:
+            # use torch.utils.checkpoint
+            self.checkpoint_fn = partial(
+                torch_utils_checkpoint,
+                use_reentrant=(self.checkpoint_impl == CheckpointImpl.REENTRANT),
+                **checkpoint_fn_kwargs,
+            )
+        else:
+            # Construct user-specified checkpoint function.
+            self.checkpoint_fn = partial(
+                checkpoint_fn,
+                **checkpoint_fn_kwargs,
+            )
+
+    def forward(self, *args, **kwargs):
+        # Support keyword arguments for reentrant checkpoint. Note that this
+        # only works if user has specified self.checkpoint_impl and is not
+        # using their own custom checkpoint_fn.
+        if self.checkpoint_impl == CheckpointImpl.REENTRANT and kwargs != {}:
+            # Pack the args and kwargs
+            flat_args, kwarg_keys = _pack_kwargs(*args, **kwargs)
+
+            # Function that only takes (packed) args, but can unpack them
+            # into the original args and kwargs for the checkpointed
+            # function, and runs that function.
+            def my_function(*inputs):
+                # unpack back into args and kwargs
+                unpacked_args, unpacked_kwargs = _unpack_kwargs(inputs, kwarg_keys)
+                # run original module
+                return self._checkpoint_wrapped_module(
+                    *unpacked_args, **unpacked_kwargs
+                )
+
+            # Pass the function that only takes packed args into reentrant
+            # checkpoint API.
+            return self.checkpoint_fn(  # type: ignore[misc]
+                my_function,
+                *flat_args,
+            )
+        else:
+            return self.checkpoint_fn(  # type: ignore[misc]
+                self._checkpoint_wrapped_module, *args, **kwargs
+            )
+
+
+def offload_wrapper(module: torch.nn.Module) -> torch.nn.Module:
+    """
+    Wrap a module for activation offloading to CPU.
+
+    Offloads intermediate activations to the CPU for modules wrapped with this function.
+    Wrappers with activation offload can be composed with ones that do recomputation-based
+    checkpoint to trade off increased compute versus increased CPU
+    memory usage and additional H2D transfers.
+
+    Usage::
+        offloaded_module = offload_wrapper(module)
+        outputs = checkpointed_module(inputs)
+    Args:
+        module (nn.Module):
+            The module to be wrapped
+    Returns:
+        (nn.Module):
+            Wrapped module
+    """
+    return OffloadWrapper(module)
+
+
+def checkpoint_wrapper(
+    module: torch.nn.Module,
+    checkpoint_impl: CheckpointImpl = CheckpointImpl.NO_REENTRANT,
+    checkpoint_fn=None,
+    **checkpoint_fn_kwargs,
+) -> torch.nn.Module:
+    """
+    Wrap a module for activation checkpointing.
+
+    If the module is wrapped with this function, all subsequent calls to the module will,
+    automatically perform checkpointing without the user having to explicitly call ``checkpoint`` function.
+
+    Usage::
+        checkpointed_module = checkpoint_wrapper(module)
+        outputs = checkpointed_module(inputs)
+    Args:
+        module (nn.Module):
+            The module to be wrapped
+        checkpoint_impl (Optional[CheckpointImpl]):
+            The checkpointing implementation to use. Note that this will only
+            be passed into the ``torch.utils.checkpoint.checkpoint``
+            implementation, and is ignored if a custom ``checkpoint_fn`` is
+            specified. Note that for implementations using reentrant checkpoint
+            from ``torch.utils.checkpoint``, keyword arguments will only be
+            supported if ``checkpoint_impl`` is passed as ``CheckpointImpl.REENTRANT`.
+        checkpoint_fn (Optional[Callable]):
+            Functional checkpoint implementation to use. If this is specified,
+            it will be used over the default ``torch.utils.checkpoint.checkpoint``
+            implementation and the `checkpoint_impl` argument will be ignored.
+        **checkpoint_fn_kwargs: (Dict[str, Any]): Keyword arguments to pass into `checkpoint_fn`.
+
+    Returns:
+        (nn.Module):
+            Wrapped module
+    """
+
+    if checkpoint_impl == CheckpointImpl.REENTRANT:
+        warnings.warn(
+            f"Please specify {CheckpointImpl.NO_REENTRANT} as "
+            f"{CheckpointImpl.REENTRANT} will soon be removed as "
+            "the default and eventually deprecated.",
+            FutureWarning,
+            stacklevel=2,
+        )
+    return CheckpointWrapper(
+        module,
+        checkpoint_impl,
+        checkpoint_fn,
+        **checkpoint_fn_kwargs,
+    )
+
+
+def apply_activation_checkpointing(
+    model,
+    checkpoint_wrapper_fn=checkpoint_wrapper,
+    check_fn=lambda _: True,
+    auto_wrap_policy: Optional[Callable[[nn.Module, bool, int], bool]] = None,
+):
+    """
+    Apply :func:`checkpoint_wrapper` to modules within `model` based on a user-defined configuration.
+
+    For each module within `model`, the `check_fn` is used to decide
+    whether `module` should be wrapped with :func:`checkpoint_wrapper` or not.
+
+    Note::
+        This function modifies `model` in place and replaces appropriate layers with
+        their checkpoint-wrapped modules.
+    Note::
+        This function will not wrap the overall root module. If this is needed, please directly use
+        :func:`checkpoint_wrapper` or :func:`offload_wrapper`.
+    Usage::
+        model = nn.Sequential(
+            nn.Linear(10, 10), nn.Linear(10, 10), nn.Linear(10, 10)
+        )
+        check_fn = lambda l: isinstance(l, nn.Linear)
+        # checkpoint activations
+        apply_activation_checkpointing(model, checkpoint_wrapper_fn=checkpoint_wrapper, check_fn=check_fn)
+        # Or offload activations to CPU
+        apply_activation_checkpointing(model, checkpoint_wrapper_fn=offload_wrapper, check_fn=check_fn)
+    Args:
+        model (nn.Module):
+            The model whose submodules should be wrapped with activation checkpointing.
+        checkpoint_wrapper_fn (Optional[Callable[nn.Module]])
+            A ``Callable`` which will wrap modules
+        check_fn (Optional[Callable[nn.Module, nn.Module]])
+            A lambda function which will be passed each child submodule of ``model`` and returns
+            ``True`` or ``False`` depending on whether the submodule should be wrapped.
+        auto_wrap_policy (Optional[Callable[[nn.Module, bool, int], bool]]): A policy to wrap model's
+            submodules with AC. Note that if this is specified, it takes precedence over ``check_fn``.
+    Returns: None (`model` is modified inplace)
+    """
+    # TODO: Importing inside function to avoid circular import issue between FSDP and
+    # checkpoint_wrapper. This can be resolved once wrap() APIs are decoupled from FSDP code.
+    from torch.distributed.fsdp._wrap_utils import _construct_wrap_fn, _post_order_apply
+    from torch.distributed.fsdp.wrap import (
+        _Policy,
+        _recursive_wrap,
+        lambda_auto_wrap_policy,
+    )
+
+    policy = (
+        auto_wrap_policy
+        if auto_wrap_policy is not None
+        else partial(lambda_auto_wrap_policy, lambda_fn=check_fn)
+    )
+    if not callable(policy):
+        if not isinstance(policy, _Policy):
+            raise ValueError(
+                f"Expected {policy} to be callable or be a pre-defined wrap policy"
+            )
+        target_module_to_kwargs = policy._run_policy(
+            model, ignored_modules=set(), root_kwargs={}
+        )
+        wrap_fn = _construct_wrap_fn(
+            model, target_module_to_kwargs, checkpoint_wrapper_fn
+        )
+        _post_order_apply(model, wrap_fn)
+        return
+
+    _recursive_wrap(
+        module=model,
+        auto_wrap_policy=policy,  # type: ignore[arg-type]
+        wrapper_cls=checkpoint_wrapper_fn,
+        ignored_modules=set(),
+        ignored_params=set(),
+        only_wrap_children=True,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b57a075ad729d0ae3004dc15585250b04810f43
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/__init__.py
@@ -0,0 +1,7 @@
+from . import default_hooks as default
+
+
+LOW_PRECISION_HOOKS = [
+    default.fp16_compress_hook,
+    default.bf16_compress_hook,
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/__pycache__/__init__.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/__pycache__/default_hooks.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/__pycache__/default_hooks.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/default_hooks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/default_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..872ad0e2a7673107c0b96f7f90abafa3f89a3e3c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/default_hooks.py
@@ -0,0 +1,192 @@
+# mypy: allow-untyped-defs
+import functools
+from typing import Optional
+
+import torch
+import torch.distributed as dist
+
+
+class DefaultState:
+    r"""
+    Stores state needed to perform the default communication algorithm within a communication hook.
+
+    Args:
+        process_group (ProcessGroup): The process group to be used.
+    """
+
+    __slots__ = [
+        "process_group",
+        "world_size",
+        "gradient_predivide_factor",
+        "gradient_postdivide_factor",
+    ]
+
+    def __init__(self, process_group: dist.ProcessGroup):
+        if process_group is None:
+            raise ValueError(f"Expected to pass in an explicit ProcessGroup to {self}.")
+        self.process_group = process_group
+        self.world_size = dist.get_world_size(process_group)
+        # Setting two factors `self.gradient_predivide_factor`
+        # and `self.gradient_postdivide_factor` to avoid underflow and overflow
+        self.gradient_predivide_factor = self._get_gradient_predivide_factor(
+            self.world_size
+        )
+        self.gradient_postdivide_factor = (
+            self.world_size / self.gradient_predivide_factor
+        )
+
+    @staticmethod
+    def _get_gradient_predivide_factor(world_size: int) -> float:
+        factor: int = 1
+        while world_size % factor == 0 and world_size / factor > factor:
+            factor *= 2
+        return float(factor)
+
+
+class LowPrecisionState(DefaultState):
+    r"""
+    Stores state needed to perform gradient communication in a lower precision within a communication hook.
+
+    Communication hook will cast gradients back to the original
+    parameter precision specified by ``parameter_type`` (default: torch.float32).
+    Builds on top of the :class:`DefaultState`.
+
+    Args:
+        parameter_type (torch.dtype): The precision of model's parameters.
+        Required for a hook to cast gradients back to a parameter's precision.
+    """
+
+    __slots__ = [
+        "parameter_type",
+    ]
+
+    def __init__(
+        self,
+        process_group,
+        parameter_type=torch.float32,
+    ):
+        super().__init__(process_group)
+        self.parameter_type = parameter_type
+
+
+def _decompress(state: LowPrecisionState, grad: torch.Tensor):
+    """
+    Casts gradients back to full parameter precision so that further computation happens in full precision.
+    """
+    orig_grad_data = grad.data
+    grad.data = grad.data.to(state.parameter_type)
+    device_type = ""
+    try:
+        if grad.device.type == "privateuse1":
+            device_type = torch._C._get_privateuse1_backend_name()
+        else:
+            device_type = grad.device.type
+        backend = getattr(torch, device_type)
+    except AttributeError as e:
+        raise AttributeError(
+            f"Device {grad.device}  does not have a \
+                corresponding backend registered as 'torch.device_type'."
+        ) from e
+
+    # Don't let this memory get reused until after the transfer.
+    orig_grad_data.record_stream(backend.current_stream())  # type: ignore[arg-type]
+
+
+def allreduce_hook(state: DefaultState, grad: torch.Tensor):
+    r"""
+    Implement the  FSDP communication hook for ``all_reduce`` algorithm and a necessary pre- and post-division of gradients.
+
+    Args:
+        state (DefaultState): State information, configures pre- and post-division factors.
+        grad (torch.Tensor): A gradient for the local batch that needs to be communicated across ranks.
+    """
+    # Average grad by pre-division factor. Together pre- and post-division factors
+    # lead to an overall averaging by world_size, required for consistency with PyTorch DDP.
+    # This is a two-step process to avoid potential underflow and overflow.
+    if state.gradient_predivide_factor > 1:
+        grad.div_(state.gradient_predivide_factor)
+    dist.all_reduce(grad, group=state.process_group)
+    # Average grad by post-division factor.
+    if state.gradient_postdivide_factor > 1:
+        grad.div_(state.gradient_postdivide_factor)
+
+
+def reduce_scatter_hook(state: DefaultState, grad: torch.Tensor, output: torch.Tensor):
+    r"""
+    Implement the  FSDP communication hook for ``reduce_scatter`` algorithm.
+
+    For sharded FSDP strategies and a necessary pre- and post-division of gradients.
+
+    Args:
+        state (DefaultState): State information, configures pre- and post-division factors.
+        grad (torch.Tensor): An unsharded gradient for the local batch that needs to be
+        communicated across ranks.
+        output (torch.Tensor): Stores a single shard of the gradient after ``reduce_scatter``.
+    """
+    # Average grad by pre-division factor.
+    if state.gradient_predivide_factor > 1:
+        grad.div_(state.gradient_predivide_factor)
+    dist.reduce_scatter_tensor(output, grad, group=state.process_group)
+    # Average grad's shard by post-division factor.
+    if state.gradient_postdivide_factor > 1:
+        output.div_(state.gradient_postdivide_factor)
+
+
+def _low_precision_hook(
+    prec: torch.dtype,
+    state: LowPrecisionState,
+    grad: torch.Tensor,
+    output: Optional[torch.Tensor],
+):
+    if grad.dtype != prec:
+        grad.data = grad.data.to(prec)
+    if output is not None:
+        if output.dtype != prec:
+            output.data = output.data.to(prec)
+        reduce_scatter_hook(state, grad, output)
+        _decompress(state, output)
+    else:
+        allreduce_hook(state, grad)
+        _decompress(state, grad)
+
+
+def fp16_compress_hook(
+    state: LowPrecisionState, grad: torch.Tensor, output: Optional[torch.Tensor] = None
+):
+    r"""
+    Implement FSDP communication hook for a simple gradient compression approach.
+    Casts ``grad`` to half-precision floating-point format (``torch.float16``).
+
+    It also averages gradients by ``world_size`` in two steps: first it pre-divides gradients by a
+    ``state.gradient_predivide_factor``, and after a communication step (``all_reduce`` or ``reduce_scatter``)
+    gradients are averaged by a ``state.gradient_postdivide_factor``.
+    Once post-division is done, compressed gradients are casted back to parameters' precision.
+
+    Args:
+        state (LowPrecisionState): State information, configures pre- and post-division factors, parameters' precision.
+        grad (torch.Tensor): A gradient for the local batch that needs to be communicated across ranks in a lower precision.
+        output (torch.Tensor): Stores a single shard of the gradient after ``reduce_scatter``.
+    """
+    fp16_hook = functools.partial(_low_precision_hook, torch.float16)
+    return fp16_hook(state, grad, output)
+
+
+def bf16_compress_hook(
+    state: LowPrecisionState, grad: torch.Tensor, output: Optional[torch.Tensor] = None
+):
+    r"""
+    Implement FSDP communication hook for a simple gradient compression approach .
+    Casts ``grad`` to half-precision floating-point format.
+
+    It also averages gradients by ``world_size`` in two steps: first it pre-divides gradients by a
+    ``state.gradient_predivide_factor``, and after a communication step (``all_reduce`` or ``reduce_scatter``)
+    gradients are averaged by a ``state.gradient_postdivide_factor``.
+    Once post-division is done, compressed gradients are casted back to parameters' precision.
+
+    Args:
+        state (LowPrecisionState): State information, configures pre- and post-division factors, parameters' precision.
+        grad (torch.Tensor): A gradient for the local batch that needs to be communicated across ranks in a lower precision.
+        output (torch.Tensor): Stores a single shard of the gradient after ``reduce_scatter``.
+    """
+    bf16_hook = functools.partial(_low_precision_hook, torch.bfloat16)
+    return bf16_hook(state, grad, output)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_quantization/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_quantization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_quantization/quantization.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_quantization/quantization.py
new file mode 100644
index 0000000000000000000000000000000000000000..a579a0a02feae930c3e0528dee1f951fb63b1d21
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/_quantization/quantization.py
@@ -0,0 +1,150 @@
+# mypy: allow-untyped-defs
+import functools
+from enum import Enum
+
+import torch
+import torch.distributed as dist
+
+
+TORCH_HALF_MIN = torch.finfo(torch.float16).min
+TORCH_HALF_MAX = torch.finfo(torch.float16).max
+
+
+class DQuantType(Enum):
+    """
+    Different quantization methods for auto_quantize API are identified here.
+
+    auto_quantize API currently supports fp16 and bfp16 methods.
+    """
+
+    FP16 = ("fp16",)
+    BFP16 = "bfp16"
+
+    def __str__(self) -> str:
+        return self.value
+
+
+def _fp32_to_fp16_with_clamp(tensor: torch.Tensor) -> torch.Tensor:
+    return torch.clamp(tensor, TORCH_HALF_MIN, TORCH_HALF_MAX).half()
+
+
+def _quantize_tensor(tensor, qtype):
+    if not isinstance(tensor, torch.Tensor):
+        raise RuntimeError(
+            f"_quantize_tensor expecting torch.Tensor as input but found {type(tensor)}"
+        )
+    if qtype == DQuantType.FP16:
+        return _fp32_to_fp16_with_clamp(tensor)
+    elif qtype == DQuantType.BFP16:
+        return torch.ops.quantization._FloatToBfloat16Quantized(tensor)
+    else:
+        raise RuntimeError(f"Quantization type {qtype} is not supported")
+
+
+def _quantize_tensor_list(tensor_list, qtype):
+    if not isinstance(tensor_list, list) or not all(
+        isinstance(p, torch.Tensor) for p in tensor_list
+    ):
+        raise RuntimeError(
+            f"_quantize_tensor_list expecting list of torch.Tensor as input but found {type(tensor_list)}"
+        )
+    quantized_tensor_list = [_quantize_tensor(t, qtype) for t in tensor_list]
+    return quantized_tensor_list
+
+
+def _dequantize_tensor(tensor, qtype, quant_loss=None):
+    if not isinstance(tensor, torch.Tensor):
+        raise RuntimeError(
+            f"_dequantize_tensor expecting torch.Tensor as input but found {type(tensor)}"
+        )
+    if qtype == DQuantType.FP16:
+        if tensor.dtype != torch.float16:
+            raise RuntimeError(
+                f"tensor dtype is {tensor.dtype} while expected to be FP16."
+            )
+        elif tensor.dtype == torch.float16 and quant_loss is None:
+            return tensor.float()
+        else:
+            return tensor.float() / quant_loss
+    elif qtype == DQuantType.BFP16:
+        if tensor.dtype != torch.float16:
+            raise RuntimeError(
+                f"tensor dtype is {tensor.dtype} while expected to be FP16."
+            )
+        else:
+            return torch.ops.quantization._Bfloat16QuantizedToFloat(tensor)
+    else:
+        raise RuntimeError(f"Quantization type {qtype} is not supported")
+
+
+def _dequantize_tensor_list(tensor_list, qtype, quant_loss=None):
+    if not isinstance(tensor_list, list) or not all(
+        isinstance(p, torch.Tensor) for p in tensor_list
+    ):
+        raise RuntimeError(
+            f"_dequantize_tensor_list expecting list of torch.Tensor as input but found {type(tensor_list)}"
+        )
+    dequantized_tensor_list = [_dequantize_tensor(t, qtype) for t in tensor_list]
+    return dequantized_tensor_list
+
+
+def auto_quantize(func, qtype, quant_loss=None):
+    """
+    Quantize the input tensors, choose the precision types, and pass other necessary arguments and then dequantizes the output.
+
+    Currently it only supports:
+        . FP16 and BFP16 quantization method supported for gloo and nccl backends
+        . all_gather, all_to_all collective ops
+    Note: BFP16 only supports 2D tensors.
+    Args:
+        func (Callable): A function representing collective operations.
+        qtype (QuantType): Quantization method
+        quant_loss (float, optional): This can be used to improve accuracy in the dequantization.
+    Returns:
+        (Callable): the same collective as func but enables automatic quantization/dequantization.
+    """
+
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        group = kwargs.get("group", None)
+        async_op = kwargs.get("async_op", False)
+        if async_op is True:
+            raise RuntimeError("The async_op=True mode is not supported yet.")
+        if func == dist.all_gather:
+            tensors = args[0]
+            input_tensors = _quantize_tensor(args[1], qtype)
+            out_tensors = _quantize_tensor_list(tensors, qtype)
+            dist.all_gather(out_tensors, input_tensors, group=group, async_op=async_op)
+            for i, t in enumerate(
+                _dequantize_tensor_list(out_tensors, qtype, quant_loss=quant_loss)
+            ):
+                tensors[i] = t
+
+        elif func == dist.all_to_all:
+            tensors = args[0]
+            input_tensors = _quantize_tensor_list(args[1], qtype)
+            out_tensors = _quantize_tensor_list(tensors, qtype)
+            dist.all_to_all(out_tensors, input_tensors, group=group, async_op=async_op)
+            for i, t in enumerate(
+                _dequantize_tensor_list(out_tensors, qtype, quant_loss=quant_loss)
+            ):
+                tensors[i] = t
+
+        elif func == dist.all_to_all_single:
+            tensors = args[0]
+            out_splits = kwargs.get("out_splits", None)
+            in_splits = kwargs.get("in_splits", None)
+            # Quantizing the input/output tensor
+            input_tensors = _quantize_tensor(args[1], qtype)
+            out_tensors = _quantize_tensor(tensors, qtype)
+            dist.all_to_all_single(
+                out_tensors, input_tensors, out_splits, in_splits, group=group
+            )
+            for i, t in enumerate(
+                _dequantize_tensor(out_tensors, qtype, quant_loss=quant_loss)
+            ):
+                tensors[i] = t
+        else:
+            raise RuntimeError(f"The collective op {func} is not supported yet")
+
+    return wrapper
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1d1ffd2fc8771ce346556f988dfd764683ce94a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py
@@ -0,0 +1,110 @@
+# mypy: allow-untyped-defs
+from enum import Enum
+from functools import partial
+
+import torch.distributed as dist
+
+from . import (
+    debugging_hooks as debugging,
+    default_hooks as default,
+    optimizer_overlap_hooks as optimizer_overlap,
+    powerSGD_hook as powerSGD,
+    quantization_hooks as quantization,
+)
+
+
+__all__ = ["DDPCommHookType", "register_ddp_comm_hook"]
+
+
+def _ddp_comm_hook_wrapper(comm_hook, model, state):
+    model.register_comm_hook(state, comm_hook)
+
+
+def _powerSGD_comm_hook_wrapper(
+    comm_hook,
+    model,
+    state,
+    matrix_approximation_rank,
+    start_powerSGD_iter=1_000,
+):
+    """
+    Wrap PowerSGD communication hook.
+
+    To be consistent with the wrappers of other DDP comm hooks, the input state only needs to be a process group,
+    which will be wrapped up with other state info.
+    """
+    powerSGD_state = powerSGD.PowerSGDState(
+        process_group=state,
+        matrix_approximation_rank=matrix_approximation_rank,
+        start_powerSGD_iter=start_powerSGD_iter,
+    )
+    model.register_comm_hook(powerSGD_state, comm_hook)
+
+
+class DDPCommHookType(Enum):
+    """
+    Enumerate ``ddp_comm_hooks`` and ``ddp_comm_hook_wrapper`` communucation hook types.
+
+    DDPCommHookType enumerates the hooks of ``torch.distributed.algorithms.ddp_comm_hooks``
+    as names and ``ddp_comm_hook_wrapper`` partials with hook specified. As an example,
+    you can register allreduce hook by
+    ``DDPCommHookType.ALLREDUCE.value(model=model, state=process_group)``.
+    """
+
+    ALLREDUCE = partial(_ddp_comm_hook_wrapper, comm_hook=default.allreduce_hook)
+    FP16_COMPRESS = partial(
+        _ddp_comm_hook_wrapper, comm_hook=default.fp16_compress_hook
+    )
+    BF16_COMPRESS = partial(
+        _ddp_comm_hook_wrapper, comm_hook=default.bf16_compress_hook
+    )
+    QUANTIZE_PER_TENSOR = partial(
+        _ddp_comm_hook_wrapper, comm_hook=quantization.quantization_pertensor_hook
+    )
+    QUANTIZE_PER_CHANNEL = partial(
+        _ddp_comm_hook_wrapper, comm_hook=quantization.quantization_perchannel_hook
+    )
+    POWER_SGD = partial(
+        _powerSGD_comm_hook_wrapper,
+        comm_hook=powerSGD.powerSGD_hook,
+        matrix_approximation_rank=1,
+    )
+    # Rank-2 PowerSGD can give a higher accuracy than the default rank-1 version,
+    # but it runs slower and consumes more memory.
+    POWER_SGD_RANK2 = partial(
+        _powerSGD_comm_hook_wrapper,
+        comm_hook=powerSGD.powerSGD_hook,
+        matrix_approximation_rank=2,
+    )
+    # Batching can lead to a faster training at the cost of accuracy.
+    BATCHED_POWER_SGD = partial(
+        _powerSGD_comm_hook_wrapper,
+        comm_hook=powerSGD.batched_powerSGD_hook,
+        matrix_approximation_rank=1,
+    )
+    BATCHED_POWER_SGD_RANK2 = partial(
+        _powerSGD_comm_hook_wrapper,
+        comm_hook=powerSGD.batched_powerSGD_hook,
+        matrix_approximation_rank=2,
+    )
+    NOOP = partial(
+        _ddp_comm_hook_wrapper,
+        comm_hook=debugging.noop_hook,
+    )
+
+
+def register_ddp_comm_hook(comm_hook_type: DDPCommHookType, model, state=None):
+    """
+    Register ``ddp_comm_hooks`` to DDP model.
+
+    Registers the hooks of ``torch.distributed.algorithms.ddp_comm_hooks``
+    to the DDP model. User can specify the type of hook as an enum
+    ``DDPCommHookType`` type using ``comm_hook_type`` input. State input will
+    be passed to the model.
+    Uses Python comm hook implementations.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> register_ddp_comm_hook(DDPCommHookType.FP16_COMPRESS, model, state)
+    """
+    comm_hook_type.value(model=model, state=state)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/ddp_zero_hook.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/ddp_zero_hook.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a08212dfa9cc8aee8ab340b047889245136d089
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/ddp_zero_hook.py
@@ -0,0 +1,460 @@
+# mypy: allow-untyped-defs
+import weakref
+from typing import Any, Callable, Optional
+
+import torch
+import torch.distributed as dist
+from torch.distributed.optim import ZeroRedundancyOptimizer
+from torch.distributed.optim.zero_redundancy_optimizer import _OverlapStatus
+from torch.nn.parallel.distributed import DistributedDataParallel
+
+
+__all__ = ["hook_with_zero_step", "hook_with_zero_step_interleaved"]
+
+# Functional optimizers require passing a list of gradients to their `step()`
+# method, and ZeRO requires a functional optimizer to overlap with DDP
+# Passing a `None` instead of an actual gradient indicates to the optimizer
+# to not update the corresponding parameter
+_NO_PARAM_UPDATE: None = None
+
+
+def _perform_local_step(
+    bucket: dist.GradBucket,
+    zero: ZeroRedundancyOptimizer,
+    rank: int,
+):
+    r"""
+    Perform a local optimizer step using the gradients provided by ``bucket``.
+
+    Arguments:
+        bucket (dist.GradBucket): the bucket providing the gradients.
+        zero (ZeroRedundancyOptimizer): the :class:`ZeroRedundancyOptimizer`
+            instance to perform the :meth:`_local_step`.
+        rank (int): the calling process's rank.
+
+    .. warning::
+        This function assumes that appropriate synchronization has taken place
+        so that the bucket's gradients can be used.
+    """
+    overlap_info = zero._overlap_info
+    bucket_index = bucket.index()
+    assert len(zero.optim.param_groups) == 1, (
+        "Overlapping DDP with ZeRO only supports a single parameter group"
+    )
+
+    # Construct the `gradients` input for the local optimizer step, which
+    # expects `None` in a list position to indicate that the corresponding
+    # parameter should not be updated
+    num_local_optim_params = len(zero.optim.param_groups[0]["params"])
+    gradients: list[Optional[torch.Tensor]] = [
+        _NO_PARAM_UPDATE for _ in range(num_local_optim_params)
+    ]
+    assert bucket_index in overlap_info.offsets, (
+        f"Bucket index {bucket_index} was not assigned to rank {rank}"
+    )
+    gradients_offset = overlap_info.offsets[bucket_index]
+    bucket_assignment = zero._bucket_assignments_per_rank[rank][bucket_index]
+    bucket_offset = bucket_assignment.offset
+    length = len(bucket_assignment.parameters)
+    bucket_gradients = bucket.gradients()[bucket_offset : bucket_offset + length]
+    for i, grad in enumerate(bucket_gradients):
+        gradients[gradients_offset + i] = grad
+
+    zero._local_step(gradients)
+
+
+def _broadcast_bucket(
+    bucket_index: int,
+    zero: ZeroRedundancyOptimizer,
+):
+    r"""
+    Broadcasts a bucket's parameters.
+
+    Arguments:
+        bucket_index (int): the index of the bucket corresponding to the
+            parameters to broadcast.
+        zero (ZeroRedundancyOptimizer): the calling process's
+            :class:`ZeroRedundancyOptimizer` instance.
+    """
+    overlap_info = zero._overlap_info
+    assert len(overlap_info.assigned_ranks_per_bucket) > bucket_index, (
+        "`assigned_ranks_per_bucket` is not fully constructed"
+    )
+    # Sort to ensure the same ordering across ranks
+    assigned_ranks = sorted(overlap_info.assigned_ranks_per_bucket[bucket_index])
+    assert len(assigned_ranks) > 0, (
+        f"Bucket {bucket_index} should be assigned to at least one rank"
+    )
+    for assigned_rank in assigned_ranks:
+        bucket_assignments = zero._bucket_assignments_per_rank[assigned_rank]
+        if bucket_index in bucket_assignments:
+            send_tensor = bucket_assignments[bucket_index].tensor
+            assert send_tensor is not None
+            overlap_info.broadcast_handles.append(
+                dist.broadcast(
+                    send_tensor,
+                    src=dist.get_global_rank(zero.process_group, assigned_rank),
+                    group=zero.process_group,
+                    async_op=True,
+                )
+            )
+
+
+def _save_ddp_bucket_info(
+    bucket: dist.GradBucket,
+    zero: ZeroRedundancyOptimizer,
+):
+    r"""
+    Save :class:`DistributedDataParallel` gradient bucket information for :class:`ZeroRedundancyOptimizer` instance ``zero``.
+
+    In particular, this function is meant to be called upon seeing each
+    gradient bucket to use when overlapping, meaning it does not save or compute any global
+    information.
+
+    Arguments:
+        bucket (dist.GradBucket): the current gradient bucket.
+        zero (ZeroRedundancyOptimizer): the calling process's
+            :class:`ZeroRedundancyOptimizer` instance.
+    """
+    overlap_info = zero._overlap_info
+    bucket_params = bucket.parameters()
+    assert len(bucket_params) > 0, "Empty bucket"
+
+    # Save the parameters in the bucket
+    overlap_info.params_per_bucket.append(bucket_params)
+    if overlap_info.shard_buckets:
+        # Additionally save the bucket size for the assignment heuristic to use
+        bucket_size = 0
+        for param in bucket_params:
+            bucket_size += param.numel()
+        assert overlap_info.total_size is not None
+        overlap_info.total_size += bucket_size
+
+
+def _hook_with_zero_step_setup(
+    ddp_ref: weakref.ReferenceType,
+    zero: ZeroRedundancyOptimizer,
+    bucket: dist.GradBucket,
+):
+    r"""
+    Encapsulate the setup logic for :func:`hook_with_zero_step` and :func:`hook_with_zero_step_interleaved`.
+
+    This means the logic to run in the
+    hook before the backward pass and optimizer step can actually be
+    overlapped. This is factored out since it is common to both
+    :func:`hook_with_zero_step` and :func:`hook_with_zero_step_interleaved`.
+
+    Arguments:
+        ddp_ref (weakref.ReferenceType): weak reference to the process's
+            :class:`DistributedDataParallel` instance.
+        zero (ZeroRedundancyOptimizer): the calling process's
+            :class:`ZeroRedundancyOptimizer` instance.
+        bucket (dist.GradBucket): the current gradient bucket.
+    """
+    # Proceed as normal until the DDP buckets have been rebuilt
+    if not ddp_ref()._has_rebuilt_buckets:  # type: ignore[union-attr]
+        assert zero._overlap_info.status == _OverlapStatus.UNINITIALIZED
+        return
+
+    bucket_index = bucket.index()
+    overlap_info = zero._overlap_info
+    if overlap_info.status == _OverlapStatus.UNINITIALIZED:
+        overlap_info.status = _OverlapStatus.DDP_HAS_REBUILT_BUCKETS
+
+    if overlap_info.status == _OverlapStatus.DDP_HAS_REBUILT_BUCKETS:
+        if bucket_index == 0 and len(overlap_info.params_per_bucket) > 0:
+            # This corresponds to the first bucket of the backward pass
+            # immediately after all information has been saved, so we
+            # can perform the delayed ZeRO initialization
+            zero._init_zero_for_overlap()
+        else:
+            # Once DDP buckets have been rebuilt but ZeRO has not been
+            # properly initialized yet, save the information needed
+            _save_ddp_bucket_info(bucket, zero)
+
+
+def hook_with_zero_step(
+    hook: Callable[[Any, dist.GradBucket], torch.futures.Future],
+    ddp: DistributedDataParallel,
+    zero: ZeroRedundancyOptimizer,
+    shard_buckets: bool = False,
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    r"""
+    Modify ``hook`` to overlap :class:`ZeroRedundancyOptimizer` optimizer step with :class:`DistributedDataParallel` backward pass.
+
+    This approach overlaps the optimizer computation and communication with the
+    backward communication. In particular, the backward computation proceeds
+    contiguously, and the optimizer computation follows, overlapping with
+    outstanding backward communication (i.e. all-reduces) and possibly other
+    optimizer communication (i.e. broadcasts).
+    The optimizer step computation begins after the last gradient bucket computation has finished.
+
+    This approach may be preferred over :meth:`hook_with_zero_step_interleaved`
+    if communication is relatively slow compared to computation.
+
+    Arguments:
+        hook (Callable[[Any, dist.GradBucket], torch.futures.Future]): the hook
+            to modify.
+        ddp (DistributedDataParallel): the :class:`DistributedDataParallel`
+            instance to use.
+        zero (ZeroRedundancyOptimizer): the :class:`ZeroRedundancyOptimizer`
+            instance to use.
+        shard_buckets (bool): if ``True``, then the assignment of each
+            :class:`DistributedDataParallel` bucket is partitioned across
+            possibly multiple :class:`ZeroRedundancyOptimizer` instances (i.e.
+            across possibly multiple ranks) to approximate uniformity; if
+            ``False``, then each bucket is wholly assigned to a single
+            :class:`ZeroRedundancyOptimizer` instance (i.e. to a single rank).
+
+    Returns:
+        The modified hook.
+
+    Raises:
+        ValueError: if ``zero`` was constructed with ``overlap_with_ddp=False``.
+        RuntimeError: if using any backend other than NCCL/HCCL since currently
+            Gloo may hang.
+
+    .. warning::
+        Given the way that overlapping :class:`DistributedDataParallel` with
+        :class:`ZeroRedundancyOptimizer` is currently implemented, the first
+        two or three training iterations do not perform parameter updates in
+        the optimizer step, depending on if ``static_graph=False`` or
+        ``static_graph=True``, respectively. This is because it needs
+        information about the gradient bucketing strategy used by
+        :class:`DistributedDataParallel`, which is not finalized until the
+        second forward pass if ``static_graph=False`` or until the third
+        forward pass if ``static_graph=True``.
+    """
+    if not zero._overlap_with_ddp:
+        raise ValueError(
+            "ZeroRedundancyOptimizer must be constructed with "
+            "`overlap_with_ddp=True` to use this hook properly"
+        )
+    ddp_ref = weakref.ref(ddp)
+
+    # NOTE: Gloo may hang with this overlapping approach, so we require
+    # NCCL/HCCL backend for now; see https://github.com/pytorch/pytorch/issues/62300
+    pg = dist.get_backend(ddp_ref().process_group)  # type: ignore[union-attr]
+    if (pg != dist.Backend.NCCL) and (pg != "hccl"):
+        raise RuntimeError(
+            "Overlapping DDP with ZeRO using this approach currently requires "
+            "NCCL/HCCL backend to avoid hangs"
+        )
+
+    if shard_buckets:
+        zero._overlap_info.shard_buckets = True
+        zero._overlap_info.total_size = 0
+
+    def hook_with_zero_fn(
+        state: Any,
+        bucket: dist.GradBucket,
+    ) -> torch.futures.Future[torch.Tensor]:
+        r"""
+        Return :class:`Future` that runs the optimizer step if this corresponds to the last gradient bucket.
+
+        Perform equivalent of :class:`ZeroRedundancyOptimizer` :meth:`step` if ``bucket`` is last gradient bucket.
+        The function gives a gradient bucket tensor and
+        performs additional computation on the iteration that
+        the :class:`DistributedDataParallel` buckets are rebuilt to collect
+        information used to implement the modified hook.
+
+        Arguments:
+            state (Any): any state for the hook.
+            bucket (dist.GradBucket): the :class:`DistributedDataParallel`
+                gradient bucket.
+        """
+        fut = hook(state, bucket)
+        _hook_with_zero_step_setup(ddp_ref, zero, bucket)
+        if zero._overlap_info.status != _OverlapStatus.INITIALIZED:
+            return fut
+
+        overlap_info = zero._overlap_info
+        bucket_index = bucket.index()
+        rank = zero.global_rank
+
+        assert overlap_info.status == _OverlapStatus.INITIALIZED
+        assert len(overlap_info.assigned_ranks_per_bucket) > bucket_index, (
+            "`assigned_ranks_per_bucket` is not fully constructed"
+        )
+        assigned_to_bucket = (
+            rank in overlap_info.assigned_ranks_per_bucket[bucket_index]
+        )
+
+        # Save the bucket reference and all-reduce future for the final bucket
+        if assigned_to_bucket:
+            overlap_info.bucket_index_to_bucket[bucket_index] = bucket
+            overlap_info.bucket_index_to_future[bucket_index] = fut
+
+        # Check that buckets are indexed incrementally starting from 0 in the
+        # order of their autograd hooks firing
+        if len(overlap_info.bucket_indices_seen) > 0:
+            assert overlap_info.bucket_indices_seen[-1] == bucket_index - 1, (
+                "Bucket indices are not in incremental order"
+            )
+        else:
+            assert bucket_index == 0, "Bucket indices do not start from 0"
+        overlap_info.bucket_indices_seen.append(bucket_index)
+
+        # Directly return the future without any optimizer computation if this
+        # is not the last bucket
+        num_buckets = len(overlap_info.params_per_bucket)
+        is_last_bucket = bucket_index == num_buckets - 1
+        if not is_last_bucket:
+            return fut
+
+        # Perform partial optimizer step on all buckets after the final
+        # bucket has been computed
+        # NOTE: This should not be chained as a callback to the last bucket's
+        # all-reduce future since that would add synchronization that delays
+        # all optimizer computation to wait for that last all-reduce
+        for bucket_index in range(num_buckets):
+            assigned_ranks = overlap_info.assigned_ranks_per_bucket[bucket_index]
+            if rank in assigned_ranks:
+                # Wait on the bucket's all-reduce future to ensure correct
+                # gradients
+                assert bucket_index in overlap_info.bucket_index_to_future, (
+                    f"All-reduce future for bucket {bucket_index} not saved "
+                    f"on rank {rank}"
+                )
+                allreduce_future = overlap_info.bucket_index_to_future[bucket_index]
+                allreduce_future.wait()
+
+                # Perform the partial optimizer step
+                curr_bucket = overlap_info.bucket_index_to_bucket[bucket_index]
+                _perform_local_step(curr_bucket, zero, rank)
+
+            _broadcast_bucket(bucket_index, zero)
+
+        # Ensure that all parameter updates are finished before the
+        # next forward pass
+        overlap_info.wait_for_broadcasts()
+        overlap_info.clear_per_iter_info()
+
+        return fut
+
+    return hook_with_zero_fn
+
+
+def hook_with_zero_step_interleaved(
+    hook: Callable[[Any, dist.GradBucket], torch.futures.Future],
+    ddp: DistributedDataParallel,
+    zero: ZeroRedundancyOptimizer,
+    shard_buckets: bool = False,
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    r"""
+    Modify ``hook`` to overlap :class:`ZeroRedundancyOptimizer` optimizer step with :class:`DistributedDataParallel` backward pass
+
+    This approach overlaps the optimizer computation and communication with the
+    backward computation and communication. In particular, once a bucket's
+    gradients have been computed, the optimizer computation using those
+    gradients is launched (though the actual computation must wait for the
+    bucket's all-reduce to complete). This yields an interleaving of all-
+    reduces and broadcasts in the communication stream.
+
+    This approach may be preferred over :meth:`hook_with_zero_step` if
+    communication is relatively fast compared to computation.
+
+    Arguments:
+        hook (Any * dist.GradBucket -> torch.futures.Future): the hook to
+            modify.
+        ddp (DistributedDataParallel): the :class:`DistributedDataParallel`
+            instance to use.
+        zero (ZeroRedundancyOptimizer): the :class:`ZeroRedundancyOptimizer`
+            instance to use.
+        shard_buckets (bool): if ``True``, then the assignment of each
+            :class:`DistributedDataParallel` bucket is partitioned across
+            possibly multiple :class:`ZeroRedundancyOptimizer` instances (i.e.
+            across possibly multiple ranks) to approximate uniformity; if
+            ``False``, then each bucket is wholly assigned to a single
+            :class:`ZeroRedundancyOptimizer` instance (i.e. to a single rank).
+
+    Returns:
+        The modified hook.
+
+    Raises:
+        ValueError: if ``zero`` was constructed with ``overlap_with_ddp=False``.
+        RuntimeError: if using any backend other than NCCL since currently
+            Gloo may hang.
+
+    .. warning::
+        Given the way that overlapping :class:`DistributedDataParallel` with
+        :class:`ZeroRedundancyOptimizer` is currently implemented, the first
+        two or three training iterations do not perform parameter updates in
+        the optimizer step, depending on if ``static_graph=False`` or
+        ``static_graph=True``, respectively. This is because it needs
+        information about the gradient bucketing strategy used by
+        :class:`DistributedDataParallel`, which is not finalized until the
+        second forward pass if ``static_graph=False`` or until the third
+        forward pass if ``static_graph=True``.
+    """
+    if not zero._overlap_with_ddp:
+        raise ValueError(
+            "ZeroRedundancyOptimizer must be constructed with "
+            "`overlap_with_ddp=True` to use this hook properly"
+        )
+    ddp_ref = weakref.ref(ddp)
+
+    # NOTE: Gloo may hang with this overlapping approach, so we require
+    # NCCL/HCCL backend for now; see https://github.com/pytorch/pytorch/issues/62300
+    pg = dist.get_backend(ddp_ref().process_group)  # type: ignore[union-attr]
+    if (pg != dist.Backend.NCCL) and (pg != "hccl"):
+        raise RuntimeError(
+            "Overlapping DDP with ZeRO using this approach currently requires "
+            "NCCL/HCCL backend to avoid hangs"
+        )
+
+    if shard_buckets:
+        zero._overlap_info.shard_buckets = True
+        zero._overlap_info.total_size = 0
+
+    def hook_with_zero_interleaved_fn(
+        state,
+        bucket: dist.GradBucket,
+    ) -> torch.futures.Future[torch.Tensor]:
+        r"""
+        Return :class:`Future` that gives gradient bucket tensor and performs partial :class:`ZeroRedundancyOptimizer` :meth:`step`.
+
+        This function uses the gradients in gradient in given bucket to perform a partial
+        :class:`ZeroRedundancyOptimizer` :meth:`step`
+
+        Arguments:
+            state: any state for the hook.
+            bucket (dist.GradBucket): the :class:`DistributedDataParallel`
+                gradient bucket.
+        """
+        fut = hook(state, bucket)
+        _hook_with_zero_step_setup(ddp_ref, zero, bucket)
+        if zero._overlap_info.status != _OverlapStatus.INITIALIZED:
+            return fut
+
+        def zero_step(fut: torch.futures.Future) -> torch.Tensor:
+            r"""
+            Perform partial :class:`ZeroRedundancyOptimizer` :meth:`step` using gradients in the :class:`DistributedDataParallel`.
+
+            Returns:
+                A :class:`torch.Tensor` representing the contents of the
+                gradient bucket.
+            """
+            overlap_info = zero._overlap_info
+            bucket_index = bucket.index()
+            rank = zero.global_rank
+
+            assigned_ranks = overlap_info.assigned_ranks_per_bucket[bucket_index]
+            overlap_info.bucket_indices_seen.append(bucket_index)
+            if rank in assigned_ranks:
+                _perform_local_step(bucket, zero, rank)
+
+            _broadcast_bucket(bucket_index, zero)
+
+            num_buckets = len(overlap_info.params_per_bucket)
+            if len(overlap_info.bucket_indices_seen) == num_buckets:
+                # Ensure that all parameter updates are finished before the
+                # next forward pass
+                overlap_info.wait_for_broadcasts()
+                overlap_info.clear_per_iter_info()
+
+            return bucket.buffer()
+
+        return fut.then(zero_step)
+
+    return hook_with_zero_interleaved_fn
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..53a184839a06f4787471f14f48137f4aa344fd91
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py
@@ -0,0 +1,29 @@
+from typing import Any
+
+import torch
+from torch.distributed import GradBucket
+
+
+__all__ = ["noop_hook"]
+
+
+def noop_hook(_: Any, bucket: GradBucket) -> torch.futures.Future[torch.Tensor]:
+    """
+    Return a future that wraps the input, so it is a no-op that does not incur any communication overheads.
+
+    This hook should **only** be used for headroom analysis of allreduce optimization,
+    instead of the normal gradient synchronization.
+    For example, if only less than 10% speedup of training time can be observed after this hook is registered,
+    it usually implies that allreduce is not a performance bottleneck for this case.
+    Such instrumentation can be particularly useful
+    if GPU traces cannot be easily retrieved or the trace analysis is complicated
+    some factors such as the overlap between allreduce and computation or the desynchronization across ranks.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(None, noop_hook)
+    """
+    fut: torch.futures.Future[torch.Tensor] = torch.futures.Future()
+    fut.set_result(bucket.buffer())
+
+    return fut
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..a64b502255f657ae395bfd14be098ce6505bb8b7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py
@@ -0,0 +1,205 @@
+# mypy: allow-untyped-defs
+from typing import Any, Callable, cast
+
+import torch
+import torch.distributed as dist
+
+
+__all__ = [
+    "allreduce_hook",
+    "fp16_compress_hook",
+    "bf16_compress_hook",
+    "fp16_compress_wrapper",
+    "bf16_compress_wrapper",
+]
+
+
+def _allreduce_fut(
+    process_group: dist.ProcessGroup, tensor: torch.Tensor
+) -> torch.futures.Future[torch.Tensor]:
+    """Average the input gradient tensor by allreduce and returns a future."""
+    group_to_use = process_group if process_group is not None else dist.group.WORLD
+
+    # Apply the division first to avoid overflow, especially for FP16.
+    tensor.div_(group_to_use.size())
+
+    return (
+        dist.all_reduce(tensor, group=group_to_use, async_op=True)
+        .get_future()
+        .then(lambda fut: fut.value()[0])
+    )
+
+
+def allreduce_hook(
+    process_group: dist.ProcessGroup, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Call ``allreduce`` using ``GradBucket`` tensors.
+
+    Once gradient tensors are aggregated across all workers, its ``then``
+    callback takes the mean and returns the result.
+
+    If user registers this DDP communication hook,
+    DDP results is expected to be same as the case where no hook was registered.
+    Hence, this won't change behavior of DDP and user can use this as a reference
+    or modify this hook to log useful information or any other purposes while
+    unaffecting DDP behavior.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(process_group, allreduce_hook)
+    """
+    return _allreduce_fut(process_group, bucket.buffer())
+
+
+def _compress_hook(
+    dtype: torch.dtype,
+    process_group: dist.ProcessGroup,
+    bucket: dist.GradBucket,
+) -> torch.futures.Future[torch.Tensor]:
+    group_to_use = process_group if process_group is not None else dist.group.WORLD
+    world_size = group_to_use.size()
+
+    buffer = (
+        cast(tuple[torch.Tensor, ...], bucket)[0]
+        if isinstance(bucket, tuple)
+        else bucket.buffer()
+    )
+    compressed_tensor = buffer.to(dtype).div_(world_size)
+
+    def decompress(fut):
+        decompressed_tensor = buffer
+        # Decompress in place to reduce the peak memory.
+        # See: https://github.com/pytorch/pytorch/issues/45968
+        value = fut if isinstance(fut, torch.Tensor) else fut.value()[0]
+        decompressed_tensor.copy_(value)
+        return decompressed_tensor
+
+    if torch.compiler.is_compiling():
+        grad = dist._functional_collectives.all_reduce(
+            compressed_tensor, "sum", group_to_use
+        )
+        return decompress(grad)
+    else:
+        fut = dist.all_reduce(
+            compressed_tensor, group=group_to_use, async_op=True
+        ).get_future()
+        return fut.then(decompress)
+
+
+def fp16_compress_hook(
+    process_group: dist.ProcessGroup,
+    bucket: dist.GradBucket,
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Compress by casting ``GradBucket`` to ``torch.float16`` divided by process group size.
+
+    This DDP communication hook implements a simple gradient compression
+    approach that casts ``GradBucket`` tensor to half-precision floating-point format (``torch.float16``)
+    and then divides it by the process group size.
+    It allreduces those ``float16`` gradient tensors. Once compressed gradient
+    tensors are allreduced, the chained callback ``decompress`` casts it back to the input data type (such as ``float32``).
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(process_group, fp16_compress_hook)
+    """
+    return _compress_hook(torch.float16, process_group, bucket)
+
+
+def bf16_compress_hook(
+    process_group: dist.ProcessGroup,
+    bucket: dist.GradBucket,
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Warning: This API is experimental, and it requires NCCL version later than 2.9.6.
+
+    This DDP communication hook implements a simple gradient compression
+    approach that casts ``GradBucket`` tensor to half-precision
+    `Brain floating point format `_ (``torch.bfloat16``)
+    and then divides it by the process group size.
+    It allreduces those ``bfloat16`` gradient tensors. Once compressed gradient
+    tensors are allreduced, the chained callback ``decompress`` casts it back to the input data type (such as ``float32``).
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(process_group, bf16_compress_hook)
+    """
+    return _compress_hook(torch.bfloat16, process_group, bucket)
+
+
+def fp16_compress_wrapper(
+    hook: Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]],
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    """
+    Cast input tensor to ``torch.float16``, cast result of hook back to input dtype.
+
+    This wrapper casts the input gradient tensor of a given DDP communication hook to half-precision
+    floating point format (``torch.float16``), and casts the resulting tensor of the given hook back to
+    the input data type, such as ``float32``.
+    Therefore, ``fp16_compress_hook`` is equivalent to ``fp16_compress_wrapper(allreduce_hook)``.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, start_powerSGD_iter=10)
+        >>> ddp_model.register_comm_hook(state, fp16_compress_wrapper(powerSGD_hook))
+    """
+
+    def fp16_compress_wrapper_hook(
+        hook_state, bucket: dist.GradBucket
+    ) -> torch.futures.Future[torch.Tensor]:
+        # Cast bucket tensor to FP16.
+        bucket.set_buffer(bucket.buffer().to(torch.float16))
+
+        fut = hook(hook_state, bucket)
+
+        def decompress(fut):
+            decompressed_tensor = bucket.buffer()
+            # Decompress in place to reduce the peak memory.
+            # See: https://github.com/pytorch/pytorch/issues/45968
+            decompressed_tensor.copy_(fut.value())
+            return decompressed_tensor
+
+        # Decompress after hook has run.
+        return fut.then(decompress)
+
+    return fp16_compress_wrapper_hook
+
+
+def bf16_compress_wrapper(
+    hook: Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]],
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    """
+    Warning: This API is experimental, and it requires NCCL version later than 2.9.6.
+
+    This wrapper casts the input gradient tensor of a given DDP communication hook to half-precision
+    `Brain floating point format  `_  (``torch.bfloat16``),
+    and casts the resulting tensor of the given hook back to the input data type, such as ``float32``.
+
+    Therefore, ``bf16_compress_hook`` is equivalent to ``bf16_compress_wrapper(allreduce_hook)``.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, start_powerSGD_iter=10)
+        >>> ddp_model.register_comm_hook(state, bf16_compress_wrapper(powerSGD_hook))
+    """
+
+    def bf16_compress_wrapper_hook(
+        hook_state, bucket: dist.GradBucket
+    ) -> torch.futures.Future[torch.Tensor]:
+        # Cast bucket tensor to BF16.
+        bucket.set_buffer(bucket.buffer().to(torch.bfloat16))
+
+        fut = hook(hook_state, bucket)
+
+        def decompress(fut):
+            decompressed_tensor = bucket.buffer()
+            # Decompress in place to reduce the peak memory.
+            # See: https://github.com/pytorch/pytorch/issues/45968
+            decompressed_tensor.copy_(fut.value())
+            return decompressed_tensor
+
+        # Decompress after hook has run.
+        return fut.then(decompress)
+
+    return bf16_compress_wrapper_hook
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/mixed_precision_hooks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/mixed_precision_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1968042e5e21aa1b6714f78356b43896cccdf60
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/mixed_precision_hooks.py
@@ -0,0 +1,86 @@
+from dataclasses import dataclass
+from typing import Any, no_type_check
+
+import torch
+import torch.distributed as dist
+from torch.autograd import Variable
+from torch.distributed.utils import _free_storage
+
+
+@dataclass
+class _AllreduceUpcastHookState:
+    """
+    State to manage DDP mixed precision in backward / gradient communication.
+
+    This contains a weakref to the DDP module for access to reducer and process
+    group, and a stream to run parameter and gradient upcasts.
+    """
+
+    ddp_weakref: Any
+    upcast_stream: torch.Stream
+    wait_for_stream_enqueued: bool = False
+
+
+@no_type_check
+def _reducer_allreduce_and_upcast_hook(
+    hook_state: _AllreduceUpcastHookState, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Perform allreduce in precision ``reduce_dtype``, upcast to prepare for optimizer.
+
+    Performs allreduce in the reduced precision given by DDP's mixed precision
+    reduce_dtype, and upcasts parameters and gradients to fp32 in preparation
+    to run the optimizer.
+    """
+    ddp_weakref = hook_state.ddp_weakref
+    reducer, process_group = ddp_weakref().reducer, ddp_weakref().process_group
+    # Cast bucket if different than param_dtype.
+    if (
+        ddp_weakref().mixed_precision.param_dtype
+        != ddp_weakref().mixed_precision.reduce_dtype
+    ):
+        # Cast bucket tensor to reduce_dtype
+        bucket.set_buffer(
+            bucket.buffer().to(ddp_weakref().mixed_precision.reduce_dtype)
+        )
+    fut = reducer._run_allreduce_hook(bucket)
+    ret_fut = torch.futures.Future()
+    stream = hook_state.upcast_stream
+    with stream:
+        fut.wait()
+        bucket.buffer().div_(process_group.size())
+        ret_fut.set_result(bucket.buffer())
+
+        # Upcast parameters and gradients so optimizer step can run in fp32.
+        for p in bucket.parameters():
+            p.data = p._fp_param
+            # free storage for mp param as it will be allocated again in next
+            # forward pass.
+            _free_storage(p._mp_param)
+            p.grad.data = p.grad.to(p.data.dtype)
+
+    # enqueue a callback to wait for this stream at end of backward
+    def wait_for_stream_cb():
+        torch.accelerator.current_stream().wait_stream(stream)
+        # Remove post-backward hooks since they are re-installed in next
+        # iteration, similar to FSDP.
+        # Parameters that don't require grad still needed to be casted since
+        # they may participate in computation. However, they would not be recast
+        # by hook above as they don't have a grad hook installed, so cast them
+        # back here.
+        for _, p in ddp_weakref().module.named_parameters():
+            if hasattr(p, "_ddp_mp_hook_state"):
+                p._ddp_mp_hook_state[1].remove()
+                delattr(p, "_ddp_mp_hook_state")
+            if not p.requires_grad and not hasattr(p, "_ddp_ignored"):
+                p.data = p._fp_param
+
+        # reset for next backward pass
+        hook_state.wait_for_stream_enqueued = False
+
+    if not hook_state.wait_for_stream_enqueued:
+        Variable._execution_engine.queue_callback(wait_for_stream_cb)
+        # mark that the callback is enqueued
+        hook_state.wait_for_stream_enqueued = True
+
+    return ret_fut
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/optimizer_overlap_hooks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/optimizer_overlap_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae8136a135934fe46aac5177f0d7dc0e838794c9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/optimizer_overlap_hooks.py
@@ -0,0 +1,162 @@
+# mypy: allow-untyped-defs
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Callable, no_type_check
+
+import torch
+import torch.distributed as dist
+from torch.autograd import Variable
+
+
+__all__: list[str] = []
+
+_FUNCTIONAL_OPTIM_STEP_METHOD_NAME = "step_param"
+
+
+class _OptimizerHookState:
+    """
+    Holds state for running optimizer in-line after DDP communication hook.
+
+    Currently contains only optimizer class which must have a method `step_param`.
+    """
+
+    __slots__ = ["functional_optimizer", "params_to_optimize"]
+
+    def __init__(self, functional_optim, params=None):
+        self.functional_optimizer = functional_optim
+        self._check_valid_functional_optim()
+        self._set_params_to_optimize(params)
+
+    def _set_params_to_optimize(self, params):
+        if params is not None:
+            self.params_to_optimize = set(params)
+
+    def _check_valid_functional_optim(self):
+        if not hasattr(self.functional_optimizer, _FUNCTIONAL_OPTIM_STEP_METHOD_NAME):
+            raise ValueError(
+                f"Class {type(self.functional_optimizer)} must implement method "
+                f"{_FUNCTIONAL_OPTIM_STEP_METHOD_NAME}."
+            )
+
+
+@dataclass
+class _OptimInBackwardHookState:
+    optim_stream: torch.Stream
+    wait_for_optim_stream_enqueued: bool
+
+
+@no_type_check
+def _apply_optim_in_backward_hook(
+    gradient_is_bucket_view: bool,
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    r"""
+    Register hook to apply the optimizer in backward.
+
+    If torch.distributed.optim._apply_optimizer_in_backward is used to overlap
+    optimizer with backward pass, DDP will run the below hook to run optimizer
+    step for parameters after gradient communication has taken place.
+    """
+    optim_in_bwd_state = _OptimInBackwardHookState(
+        optim_stream=torch.Stream(),
+        wait_for_optim_stream_enqueued=False,
+    )
+
+    def apply_optim_in_backward_hook(
+        hook_state: Any,
+        bucket: dist.GradBucket,
+        optim_stream_state,
+    ) -> torch.futures.Future[torch.Tensor]:
+        # Run original hook
+        ddp_weakref = hook_state
+        ddp_inst = ddp_weakref()
+        reducer, process_group = ddp_inst.reducer, ddp_inst.process_group
+        fut = reducer._run_allreduce_hook(bucket)
+        optimizer_stream = optim_stream_state.optim_stream
+        with optimizer_stream:
+            fut.wait()
+            # Apply gradient division since C++ side only allreduces and does
+            # not average. TODO: (rohan-varma) the div factor may be different
+            # when running with join hook
+            bucket.buffer().div_(process_group.size())
+            model_params = bucket.parameters()
+            grads = bucket.gradients()
+            # TODO (rohan-varma): upcast as needed for DDP mixed precision,
+            # once optimizer in backward + DDP mixed precision is supported.
+            for p, g in zip(model_params, grads):
+                if hasattr(p, "_in_backward_optimizers"):
+                    # Note: need to set grad to the bucket's grad, because
+                    # running allreduce results in the bucket's grad being
+                    # reduced, but not grad field.
+                    if not gradient_is_bucket_view:
+                        p.grad = g
+                    for optim in p._in_backward_optimizers:
+                        optim.step()
+
+        # Need to return a Future[Tensor] to obey comm hook API contract.
+        ret_fut = torch.futures.Future()
+        ret_fut.set_result(bucket.buffer())
+
+        # enqueue a callback to wait for this optimizer stream at the end of
+        # backward and set all DDP managed grads to None.
+        def wait_for_optim_stream_callback():
+            torch.accelerator.current_stream().wait_stream(
+                optim_stream_state.optim_stream
+            )
+            # Set DDP managed grads to None
+            for param in ddp_inst._get_data_parallel_params(ddp_inst.module):
+                if hasattr(param, "_in_backward_optimizers"):
+                    param.grad = None
+
+            # reset for the next backwards pass
+            optim_stream_state.wait_for_optim_stream_enqueued = False
+
+        if not optim_stream_state.wait_for_optim_stream_enqueued:
+            Variable._execution_engine.queue_callback(wait_for_optim_stream_callback)
+            # mark that the callback is enqueued
+            optim_stream_state.wait_for_optim_stream_enqueued = True
+
+        return ret_fut
+
+    comm_hook = partial(
+        apply_optim_in_backward_hook, optim_stream_state=optim_in_bwd_state
+    )
+    # These are needed for DDP's logging of comm hooks
+    comm_hook.__name__ = apply_optim_in_backward_hook.__name__
+    comm_hook.__qualname__ = apply_optim_in_backward_hook.__qualname__
+
+    return comm_hook
+
+
+def _hook_then_optimizer(
+    hook: Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]],
+    optimizer_state: _OptimizerHookState,
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    r"""Run optimizer in a functional fashion after DDP communication hook."""
+    has_set_params = (
+        hasattr(optimizer_state, "params_to_optimize")
+        and optimizer_state.params_to_optimize is not None
+    )
+
+    def hook_then_optimizer_wrapper(
+        hook_state, bucket: dist.GradBucket
+    ) -> torch.futures.Future[torch.Tensor]:
+        # Run original hook
+        fut = hook(hook_state, bucket)
+
+        def optimizer_step(fut):
+            gradient_tensors = bucket.gradients()
+            model_params = bucket.parameters()
+            for grad_tensor, model_param in zip(gradient_tensors, model_params):
+                if (
+                    not has_set_params
+                    or model_param in optimizer_state.params_to_optimize
+                ):
+                    optimizer_state.functional_optimizer.step_param(
+                        model_param,
+                        grad_tensor,
+                    )
+            return bucket.buffer()
+
+        return fut.then(optimizer_step)
+
+    return hook_then_optimizer_wrapper
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff513f62183c516b96c62ca89eee51d2b1793e85
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py
@@ -0,0 +1,124 @@
+# mypy: allow-untyped-defs
+import logging
+
+import torch
+import torch.distributed as dist
+
+from . import default_hooks as default
+
+
+logger = logging.getLogger(__name__)
+
+
+class PostLocalSGDState:
+    r"""
+    Store state for all-reducing gradients globally until given step, then locally after.
+
+    Stores the state for all-reducing gradients globally using ``process_group`` until step ``start_localSGD_iter``,
+    and all-reducing gradients locally using ``subgroup`` afterwards.
+
+    If ``process_group`` is ``None``, the global process group will be used.
+    If ``subgroup`` is ``None``, the intra-node process group on each machine will be used.
+
+    Additionally, ``post_local_gradient_allreduce`` may be worth tuning,
+    because both true and false may give a faster convergence.
+    """
+
+    __slots__ = [
+        "process_group",
+        "subgroup",
+        "start_localSGD_iter",
+        "post_local_gradient_allreduce",
+        "iter",
+    ]
+
+    def __init__(
+        self,
+        process_group,
+        subgroup,
+        start_localSGD_iter,
+        post_local_gradient_allreduce=True,
+    ):
+        """Initialize state object with given parameters and log when localSGD start."""
+        logger.info(
+            "Local SGD will be started after %s iterations", start_localSGD_iter
+        )
+
+        # The group used for all-reducing gradients globally.
+        self.process_group = process_group
+        # The group used for all-reducing gradients locally.
+        self.subgroup = subgroup
+        self.start_localSGD_iter = start_localSGD_iter
+        # Allreduce gradients locally since iteration `start_localSGD_iter`.
+        # This may help with the convergence efficiency at the cost of relatively cheap intra-subgroup communication.
+        self.post_local_gradient_allreduce = post_local_gradient_allreduce
+        # Iteration/step in the training loop.
+        self.iter = 0
+
+    def maybe_increase_iter(self, bucket):
+        """Track iterations and trigger log message at start of local SGD."""
+        # Since bucket 0 is the last bucket to allreduce in an iteration.
+        # Only increase `iter` when bucket 0 is processed.
+        if bucket.is_last():
+            self.iter += 1
+
+        if self.iter == self.start_localSGD_iter:
+            logger.info("Start to apply local SGD after %s iterations.", self.iter)
+
+
+def post_localSGD_hook(
+    state: PostLocalSGDState, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Run post-localSGD algorithm.
+
+    This DDP communication hook is used for running post-localSGD algorithm,
+    by combining with a model averaging component (e.g.,
+    :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`)
+    that runs after the optimizer step.
+
+    Args:
+        state (PostLocalSGDState): State information to run post-localSGD.
+            Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD.
+        bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
+            Note that since DDP comm hook only supports single process single device mode,
+            only exactly one tensor is stored in this bucket.
+
+    Returns:
+        Future handler of the communication, which updates the gradients in place.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup,
+                                  start_localSGD_iter=10)
+        >>> ddp_model.register_comm_hook(state, post_localSGD_hook)
+        >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``.
+        >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module.
+    """
+    global_group_to_use = (
+        state.process_group if state.process_group is not None else dist.group.WORLD
+    )
+
+    # The input tensor is a flattened 1D tensor.
+    input_tensor = bucket.buffer()
+
+    # Run allreduce using `global_group_to_use` in the first `start_localSGD_iter` iterations.
+    if state.iter < state.start_localSGD_iter:
+        state.maybe_increase_iter(bucket)
+        return default._allreduce_fut(global_group_to_use, input_tensor)  # type: ignore[arg-type]
+
+    # If `post_local_gradient_allreduce` is not set,
+    # then no gradient synchronization after the first `start_localSGD_iter` iterations.
+    if not state.post_local_gradient_allreduce:
+        fut: torch.futures.Future[torch.Tensor] = torch.futures.Future()
+        fut.set_result(input_tensor)
+        return fut
+
+    # Run allreduce using `subgroup` after the first `start_localSGD_iter` iterations.
+    # Note that by default, a separate subgroup for each node is created which
+    # causes an intra-node allreduce to be done at each training step.
+    # From this moment, model averaging should run after the optimizer step,
+    # to globally allreduce all the parameters.
+    if state.subgroup is None:
+        state.subgroup, _ = dist.new_subgroups()
+    return default._allreduce_fut(state.subgroup, input_tensor)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py
new file mode 100644
index 0000000000000000000000000000000000000000..00b84d6c28eeccf01b268b39d66f73d76f3d59d8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py
@@ -0,0 +1,860 @@
+# mypy: allow-untyped-defs
+import logging
+import math
+from collections import defaultdict
+
+import torch
+import torch.distributed as dist
+from torch.distributed import distributed_c10d
+from torch.utils._typing_utils import not_none
+
+from . import default_hooks as default
+
+
+__all__ = ["PowerSGDState", "powerSGD_hook", "batched_powerSGD_hook"]
+
+logger = logging.getLogger(__name__)
+
+
+def _orthogonalize(matrices, epsilon=0):
+    """
+    Decide between Gram-Schmidt or QR factorization to orthogonalize a batch of matrices.
+
+    QR factorization doesn't work with half-precision, but it is usually faster with a rank > 2.
+    """
+    assert len(matrices.shape) == 3 and matrices.shape[2] <= matrices.shape[1]
+
+    num_matrices = matrices.shape[0]
+    rank = matrices.shape[2]
+    dtype = matrices.dtype
+    if rank <= 2 or dtype in [torch.float16, torch.bfloat16]:
+        _orthogonalize_gram_schmidt(matrices, epsilon=epsilon)
+    else:
+        torch.linalg.qr(
+            matrices,
+            out=(
+                matrices,
+                torch.empty(
+                    num_matrices, rank, rank, device=matrices.device, dtype=dtype
+                ),
+            ),
+        )
+
+
+def _orthogonalize_gram_schmidt(matrices, epsilon=0):
+    """
+    Apply Gram-Schmidt procedure to orthogonalize a batch of matrices.
+
+    If epsilon is 0, this is equivalent to `torch.qr(matrices, out=(matrices, _))`,
+    """
+    num_cols = matrices.shape[2]
+    for i in range(num_cols):
+        # Normalize the i'th column.
+        col = matrices[:, :, i : i + 1]
+        # If no epsilon is added here, division by zero may be caused by vanishing gradients.
+        # This epsilon is not needed if the input batch of matrices covers the gradients of at least one entire layer
+        # in the neural network.
+        if epsilon == 0:
+            # Note that col ** 2 can underflow/overflow if we use FP16.
+            # May need to consider multiplying a scaling factor and dividing it later, or using bfloat16 instead.
+            try:
+                col /= torch.norm(col, dim=1, keepdim=True)
+            except ZeroDivisionError:
+                logger.error(
+                    "The matrices to be orthogonalized has at least a column of all 0s. Please set a small value such as 1e-8 "
+                    "as `orthogonalization_epsilon` in PowerSGD state."
+                )
+                # Recover the values from NaNs to 0s.
+                col.fill_(0.0)
+        else:
+            col /= torch.norm(col, dim=1, keepdim=True) + epsilon
+        # Project it on the rest and remove it.
+        if i + 1 < num_cols:
+            rest = matrices[:, :, i + 1 :]
+            rest -= torch.sum(col * rest, dim=1, keepdim=True) * col
+
+
+def _should_compress(
+    num_rows, num_cols, matrix_approximation_rank, min_compression_rate
+):
+    """
+    Recommend if tensor given is worth compressing.
+
+    Returns a recommendation as to whether the 2D tensor described by the arguments is worth compressing,
+    including statistics describing the expected savings from compression.  We consider a tensor worth
+    compressing when ``min_compression_rate`` < uncompressed size / compressed size, where
+    uncompressed size = ``num_rows`` * ``num_cols``,
+    and compressed size = (``num_rows`` + ``num_cols``) * ``matrix_approximation_rank``.
+
+    The result of this function is a tuple of the form (compression_recommendation, uncompressed_el_count, compressed_el_count), where:
+
+    compression_recommendation is true if the tensor is worth compressing, and false otherwise (see above);
+
+    uncompressed_el_count is the uncompressed element count, i.e. ``num_rows`` * ``num_cols``; and,
+
+    compress_el_count is the element count after compression, i.e. (``num_rows`` + ``num_cols``) * ``matrix_approximation_rank``.
+    """  # noqa: B950
+    uncompressed_size = num_rows * num_cols
+    compressed_size = (num_rows + num_cols) * matrix_approximation_rank
+    return (
+        compressed_size * min_compression_rate < uncompressed_size,
+        uncompressed_size,
+        compressed_size,
+    )
+
+
+def _report_compression_stats(bucket, state):
+    """Report compression stats at frequency of ``compression_stats_logging_frequency`` specified in PowerSGD state."""
+    if bucket.is_last() and state.iter >= state.next_stats_report:
+        stats = state.compression_stats()
+        logger.info(
+            "Compression stats: iter %s, total before compression %s, total after compression %s, "
+            "rate %s",
+            state.iter,
+            stats[1],
+            stats[2],
+            stats[0],
+        )
+        state.next_stats_report = state.iter + state.compression_stats_logging_frequency
+
+
+class PowerSGDState:
+    r"""
+    Store both the algorithm's hyperparameters and internal state for all gradients during training.
+
+    Particularly, ``matrix_approximation_rank`` and ``start_powerSGD_iter`` are the main hyperparameters that should be tuned by the user.
+    For performance, we suggest to keep binary hyperparameters ``use_error_feedback`` and ``warm_start`` on.
+
+    1. ``matrix_approximation_rank`` controls the size of compressed low-rank tensors, which determines the compression rate. The lower the rank, the stronger the compression.
+
+        1.1. If ``matrix_approximation_rank`` is too low, the full model quality will need more training steps to reach or will never reach and yield loss in accuracy.
+
+        1.2. The increase of ``matrix_approximation_rank`` can substantially increase the computation costs of the compression, and the accuracy may not be further improved beyond a certain ``matrix_approximation_rank`` threshold.
+
+    To tune ``matrix_approximation_rank``, we suggest to start from 1 and increase by factors of 2 (like an exponential grid search, 1, 2, 4, ...), until a satisfactory accuracy is reached. Typically only a small value 1-4 is used. For some NLP tasks (as shown in Appendix D of the original paper), this value has been increased to 32.
+
+    2. ``start_powerSGD_iter`` defers PowerSGD compression until step ``start_powerSGD_iter``, and vanilla allreduce runs prior to step ``start_powerSGD_iter``. This hybrid scheme of **vanilla allreduce + PowerSGD** can effectively improve the accuracy, even a relatively small ``matrix_approximation_rank`` is used. This is because that, the beginning of training phase is usually very sensitive to inaccurate gradients, and compressing gradients too early may make the training quickly take a suboptimal trajectory, which can result in an irrecoverable impact on the accuracy.
+
+    To tune ``start_powerSGD_iter``, we suggest to start with 10% of total training steps, and increase it until a satisfactory accuracy is reached. If there is a warm-up stage in the training, ``start_powerSGD_iter`` typically should be no less than the number of warm-up steps.
+
+    3. ``min_compression_rate`` is the minimum compression rate required when a layer is compressed. Due to the computation overheads incurred by the compression, a tensor is worth compressing only if there can be sufficient saving in bandwidth, where ``(num_rows + num_cols) * matrix_approximation_rank * min_compression_rate < num_rows * num_cols``. If the specified compression rate threshold cannot be satisfied, the tensor will be directly allreduced without compression.
+
+    Compression statistics are logged every ``compression_stats_logging_frequency`` iterations once PowerSGD compression starts.
+
+    4. ``orthogonalization_epsilon`` can be a very small value (e.g., 1e-8) added to every normalized matrix column in orthogonalization step, to prevent div-by-zero error if any column has all 0s. If this can already be prevented (e.g., by batch normalization), an epsilon of 0 is recommended for accuracy.
+
+    5. ``batch_tensors_with_same_shape`` controls whether to compress and decompress tensors with same shape in a batched operation to achieve higher parallelism. Note that you should also increase the bucket size (i.e., ``bucket_cap_mb`` arg in DDP constructor) to make more same-shaped tensors appear in the same bucket, however this may reduce the overlap between computation and communication, and increase the memory footprint due to stacking the tensors of the same shape. Set to ``True`` if the compression / decompression computation is a bottleneck.
+
+    .. warning ::
+        If error feedback or warm-up is enabled, the minimum value of ``start_powerSGD_iter`` allowed in DDP is 2.
+        This is because there is another internal optimization that rebuilds buckets at iteration 1 in DDP,
+        and this can conflict with any tensor memorized before the rebuild process.
+    """  # noqa: B950
+
+    __slots__ = [
+        "process_group",
+        # The fields below are the hyperparameters that often need to be tuned by the user.
+        "matrix_approximation_rank",
+        "start_powerSGD_iter",
+        # The fields below are the hyperparameters that seldom need be tuned by the user.
+        "min_compression_rate",
+        "orthogonalization_epsilon",
+        # The fields below are the binary hyperparameters recommended to be turned on for performance and accuracy.
+        "use_error_feedback",
+        "warm_start",
+        "batch_tensors_with_same_shape",
+        # The fields below are internal state.
+        "rng",
+        "error_dict",
+        "p_memory_dict",
+        "q_memory_dict",
+        "iter",
+        # The fields below are for recording compression stats.
+        "total_numel_before_compression",
+        "total_numel_after_compression",
+        "compression_stats_logging_frequency",
+        "next_stats_report",
+    ]
+
+    def __init__(
+        self,
+        process_group,
+        matrix_approximation_rank=1,
+        start_powerSGD_iter=1_000,
+        min_compression_rate=2,
+        use_error_feedback=True,
+        warm_start=True,
+        orthogonalization_epsilon=0,
+        random_seed=0,
+        compression_stats_logging_frequency=10_000,
+        batch_tensors_with_same_shape: bool = False,
+    ):
+        logger.info(
+            "PowerSGD config: matrix_approximation_rank = %s; start_powerSGD_iter = %s; "
+            "min_compression_rate = %s; orthogonalization_epsilon = %s; use_error_feedback = %s; warm_start = %s; "
+            "random_seed = %s; compression_stats_logging_frequency = %s; batch_tensors_with_same_shape = %s",
+            matrix_approximation_rank,
+            start_powerSGD_iter,
+            min_compression_rate,
+            orthogonalization_epsilon,
+            use_error_feedback,
+            warm_start,
+            random_seed,
+            compression_stats_logging_frequency,
+            batch_tensors_with_same_shape,
+        )
+
+        self.process_group = process_group
+        self.matrix_approximation_rank = matrix_approximation_rank
+        # Deferring PowerSGD compression util step 'start_powerSGD_iter' can have two advantages:
+        # 1) It turns out that PowerSGD may lead to a non-trivial accuracy loss,
+        # even if the matrix approximation rank is increased to a large value.
+        # To mitigate the accuracy loss, a simple yet effective way is mixing vanilla allreduce
+        # (or a more conservative compression such as FP16 compression) with PowerSGD.
+        # 2) There is an internal optimization of rebuilding buckets process in DDP,
+        # in order to save the memory space.
+        # This step takes place after the first iteration.
+        # However, this means that the shape of input bucketized tensors is subject to change,
+        # which will complicate the implementations of error feedback and warm-up.
+        # Running vanilla allreduce in the first few iterations can avoid this complexity.
+        if (use_error_feedback or warm_start) and start_powerSGD_iter <= 1:
+            raise ValueError(
+                "Expect `start_powerSGD_iter` > 1 if `use_error_feedback` or `warm_start` is enabled, "
+                "because PowerSGD can only be applied after the first two iterations in DDP."
+            )
+        self.start_powerSGD_iter = start_powerSGD_iter
+        self.min_compression_rate = min_compression_rate
+        # Error feedback is usually crucial for both for convergence and generalization,
+        # because PowerSGD is a biased compressor,
+        # i.e., compressing and decompressing a random gradient does not yield the original in expectation.
+        # This mechanism requires a temporary copy of the input gradients,
+        # so it increases the peak memory consumption by the size of the gradient tensor.
+        # However, if the target matrices are known to be exactly low-ranked (instead of just low stable rank),
+        # sometimes it is possible to converge to the optima without error feedback.
+        # See: http://proceedings.mlr.press/v54/yurtsever17a/yurtsever17a.pdf
+        self.use_error_feedback = use_error_feedback
+        # Warm-start reuses P(s) and Q(s) from the previous iteration.
+        # This can improve the approximation quality and hence improve the accuracy.
+        # Additionally, by avoiding the initialization of these low-rank tensors at every step,
+        # this can also accelerate training.
+        # However, this is at the cost of extra memory.
+        self.warm_start = warm_start
+        # Can use a very small value to prevent div-by-zero error caused by orthogonalization of vanishing gradients.
+        self.orthogonalization_epsilon = orthogonalization_epsilon
+        # The purpose of this RNG is to generate different random seeds for initializing Q across iterations,
+        # but in the same order for all the DDP replicas.
+        # Different random seeds across iterations indicate different 'projections' of the gradients at different SGD steps.
+        # If the same random projection is used,
+        # there will be differences between the gradients that are never synchronized.
+        import numpy as np
+
+        self.rng = np.random.RandomState(random_seed)
+        # Since there is only a single state instance for all the input buckets,
+        # need to maintain a dictionary that maps each bucket index to the local error.
+        self.error_dict: dict[int, torch.Tensor] = {}
+        self.p_memory_dict: dict[int, torch.Tensor] = {}
+        self.q_memory_dict: dict[int, torch.Tensor] = {}
+        # Iteration/step in the training loop.
+        self.iter = 0
+        # Compression stats accumulators
+        self.total_numel_before_compression = 0
+        self.total_numel_after_compression = 0
+        # We'll report compression stats every 'compression_stats_logging_frequency' iterations
+        # Note that we always report compression stats at least once.
+        self.compression_stats_logging_frequency = max(
+            1, compression_stats_logging_frequency
+        )
+        self.next_stats_report = 0
+        # Batching tensors with same shape can increase parallelism in compression / decompression computation.
+        # This requires a larger bucket size to make more same-shaped tensor to appear in one bucket, however
+        # this may reduce the overlap between computation and communication, and increase the memory footprint
+        # due to stacking tensors.
+        # Turn on if compression / decompression computation is a bottleneck.
+        self.batch_tensors_with_same_shape = batch_tensors_with_same_shape
+
+    def __getstate__(self):
+        r"""
+        Return a ``Dict[str, Any]`` which will be pickled and saved.
+
+        ``process_group`` is not serializable and excluded from
+        a returned state.
+        """
+        logger.warning(
+            "NOTE: Process group is not serializable and excluded from a saved state."
+        )
+        return {
+            slot: getattr(self, slot)
+            for slot in self.__slots__
+            if slot != "process_group"
+        }
+
+    def __setstate__(self, state):
+        r"""
+        Take a provided ``state`` and set to this ``PowerSGDState`` instance.
+
+        ``process_group`` is set to default.
+        """
+        self.process_group = distributed_c10d._get_default_group()
+        logger.warning(
+            "NOTE: Process group will be set to a default group (i.e. the world size).\
+                If a different group is desired, please set `self.process_group` after PowerSGD state is loaded."
+        )
+        for slot, value in state.items():
+            setattr(self, slot, value)
+
+    def maybe_increase_iter(self, bucket):
+        """Track iterations and trigger log message at start of local SGD."""
+        # Since bucket 0 is the last bucket to allreduce in an iteration.
+        # Only increase `iter` when bucket 0 is processed.
+        if bucket.is_last():
+            self.iter += 1
+
+        if self.iter == self.start_powerSGD_iter:
+            logger.info("Start to apply PowerSGD after %s iterations.", self.iter)
+
+    def compression_stats(self):
+        r"""
+        Return latest compression statistics as tuple.
+
+        Returns tuple of form (compress_rate, numel_before_compression, numel_after_compression) where:
+
+        compress_rate is the effective compression rate i.e. (number of elements before compression) / (number of elements after compression);
+
+        numel_before_compression is the total number of elements before compression was applied; and,
+
+        numel_after_compression is the total number of elements after compression was applied.
+        """  # noqa: B950
+        compress_rate = (
+            self.total_numel_before_compression / self.total_numel_after_compression
+            if self.total_numel_after_compression > 0
+            else 0
+        )
+        return (
+            compress_rate,
+            self.total_numel_before_compression,
+            self.total_numel_after_compression,
+        )
+
+
+def powerSGD_hook(
+    state: PowerSGDState, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    r"""
+    Implement PowerSGD algorithm.
+
+    This DDP communication hook implements PowerSGD gradient compression
+    algorithm described in the `paper `_.
+    Once gradient tensors are aggregated across all workers, this hook applies
+    compression as follows:
+
+    1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups:
+
+        1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth.
+
+        1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases).
+
+    2. Handles uncompressed tensors:
+
+        2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression;
+
+        2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor.
+
+    3. Handles the tensors that should be compressed by PowerSGD compression:
+
+        3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M,
+        such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
+
+        3.2. Computes each P in Ps, which is equal to MQ;
+
+        3.3. Allreduces Ps as a batch;
+
+        3.4. Orthogonalizes each P in Ps;
+
+        3.5. Computes each Q in Qs, which is approximately equal to M^TP;
+
+        3.6. Allreduces Qs as a batch;
+
+        3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T.
+
+    Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
+    This not only gives the user more control over the tradeoff between speedup and accuracy,
+    but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
+
+    Args:
+        state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
+            To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter``
+            and ``min_compression_rate``.
+        bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
+            Note that since DDP comm hook only supports single process single device mode,
+            only exactly one tensor is stored in this bucket.
+
+    Returns:
+        Future handler of the communication, which updates the gradients in place.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1,
+                                  start_powerSGD_iter=10, min_compression_rate=0.5)
+        >>> ddp_model.register_comm_hook(state, powerSGD_hook)
+    """  # noqa: B950
+    process_group = state.process_group
+    group_to_use = (
+        process_group if process_group is not None else not_none(dist.group.WORLD)
+    )
+    world_size = group_to_use.size()
+
+    # The input tensor is a flattened 1D tensor.
+    input_tensor = bucket.buffer()
+
+    # Run vanilla allreduce in the first `start_powerSGD_iter` iterations.
+    if state.iter < state.start_powerSGD_iter:
+        state.maybe_increase_iter(bucket)
+        return default._allreduce_fut(group_to_use, input_tensor)
+
+    # Apply PowerSGD after `start_powerSGD_iter` iterations.
+    device = input_tensor.device
+    dtype = input_tensor.dtype
+
+    # Incorporate the error from the previous state into the gradients.
+    bucket_index = bucket.index()
+    input_tensor_cp = None
+    total_length = input_tensor.shape[0]
+    if state.use_error_feedback:
+        if bucket_index in state.error_dict:
+            input_tensor.add_(state.error_dict[bucket_index])
+        else:
+            logger.info(
+                "A zero tensor of length %s that represents local error is created.",
+                total_length,
+            )
+            state.error_dict[bucket_index] = torch.zeros(
+                total_length, device=device, dtype=dtype
+            )
+
+        # Keep a copy of the input tensor,
+        # so that we can compute the local error caused by compression later,
+        # by comparing this copy and the input tensor updated after decompression.
+        input_tensor_cp = torch.clone(input_tensor).detach()
+
+    # Unflatten the input tensor into per-parameter tensors, for layer-wise compression.
+    tensors = bucket.gradients()
+
+    # Step I: Divide all the tensors into two groups,
+    # one will be compressed before allreduce and the other will be directly allreduced without compression.
+    tensors_to_compress, uncompressed_tensors = [], []
+    total_Ps_size = 0
+    total_Qs_size = 0
+    for tensor in tensors:
+        matrix = tensor.view(tensor.shape[0], -1)
+        n, m = matrix.shape
+        matrix_approximation_rank = min(n, m, state.matrix_approximation_rank)
+        compress_test = _should_compress(
+            n, m, matrix_approximation_rank, state.min_compression_rate
+        )
+        state.total_numel_before_compression += compress_test[1]
+        if compress_test[0]:
+            tensors_to_compress.append(matrix)
+            total_Ps_size += n * matrix_approximation_rank
+            total_Qs_size += m * matrix_approximation_rank
+            state.total_numel_after_compression += compress_test[2]
+        else:
+            uncompressed_tensors.append(tensor)
+            state.total_numel_after_compression += compress_test[1]
+
+    _report_compression_stats(bucket, state)
+
+    # Step II: Handle uncompressed tensors.
+    # Allocate contiguous memory for these tensors to allreduce efficiently.
+    uncompressed_tensors_memory = (
+        torch.cat([tensor.view(-1) for tensor in uncompressed_tensors])
+        if uncompressed_tensors
+        else torch.tensor([], device=device, dtype=dtype)
+    )
+
+    # Step III: Handle the tensors that should be compressed.
+    # Allocate contiguous memory for Ps and Qs to allreduce efficiently.
+    # If warm-start is enabled, reuse Ps and Qs from the previous iteration if possible.
+    # The memory spaces of Ps and Qs need to be allocated in the first iteration when PowerSGD is applied.
+    need_randomize_qs = False
+    if not state.warm_start or bucket_index not in state.p_memory_dict:
+        need_randomize_qs = True
+        # If warm-start is disabled, low-rank tensors will be initialized at every step.
+        # Only log this if warm-start to avoid spamming.
+        if state.warm_start:
+            logger.info(
+                "Allocating contiguous memory of length %s for Ps, and of length %s for Qs, respectively.",
+                total_Ps_size,
+                total_Qs_size,
+            )
+        state.p_memory_dict[bucket_index] = torch.empty(
+            total_Ps_size, device=device, dtype=dtype
+        )
+        state.q_memory_dict[bucket_index] = torch.empty(
+            total_Qs_size, device=device, dtype=dtype
+        )
+
+    # Batch tensors to compress by shape.
+    shape_to_tensors = defaultdict(list)
+    for tensor in tensors_to_compress:
+        shape_to_tensors[tensor.shape].append(tensor)
+
+    # This function decides whether to batch tensors with same shape or not according to the argument,
+    # so the following process could share the same code.
+    def maybe_batched_tensors_to_compress():
+        for tensors in shape_to_tensors.values():
+            if state.batch_tensors_with_same_shape:
+                batch_size = len(tensors)
+                if batch_size == 1:
+                    # Use the original tensor to avoid copy.
+                    yield tensors[0].unsqueeze(0)
+                else:
+                    yield torch.stack(tensors)
+            else:
+                for tensor in tensors:
+                    yield tensor.unsqueeze(0)
+
+    # Create Ps and Qs that point to the allocated memory.
+    tensors_to_compress = []
+    ps = []
+    qs = []
+    p_idx = 0
+    q_idx = 0
+    for tensor in maybe_batched_tensors_to_compress():
+        batch_size, n, m = tensor.shape
+        matrix_approximation_rank = min(n, m, state.matrix_approximation_rank)
+        tensors_to_compress.append(tensor)
+        ps.append(
+            state.p_memory_dict[bucket_index][
+                p_idx : p_idx + batch_size * n * matrix_approximation_rank
+            ].view(batch_size, n, matrix_approximation_rank)
+        )
+        qs.append(
+            state.q_memory_dict[bucket_index][
+                q_idx : q_idx + batch_size * m * matrix_approximation_rank
+            ].view(batch_size, m, matrix_approximation_rank)
+        )
+        p_idx += batch_size * n * matrix_approximation_rank
+        q_idx += batch_size * m * matrix_approximation_rank
+
+    # If warm-start is enabled, reuse Qs from the previous iteration if possible and skip filling random values.
+    # The exception is the first iteration when PowerSGD is applied.
+    if not need_randomize_qs:
+        for q in qs:
+            _orthogonalize(q, state.orthogonalization_epsilon)
+    else:
+        with torch.random.fork_rng(devices=[]):
+            # Fork this RNG to avoid changing the seed globally and affecting the random sampling anywhere else in the training.
+            # The seed makes sure that the initial random values are the same across all the DDP replicas.
+            # This seed should differ at every step.
+            # Since it is very slow to fork RNG state across all the CUDA devices,
+            # only fork on CPU and then move the generated tensor to the CUDA device (by overwriting q).
+            torch.manual_seed(state.rng.randint(1_000_000_000))
+            for q in qs:
+                q.copy_(
+                    torch.randn(
+                        *q.shape,
+                        device="cpu",
+                        dtype=dtype,
+                    )
+                )
+                _orthogonalize(q, state.orthogonalization_epsilon)
+
+    # Compute Ps.
+    for tensor, q, p in zip(tensors_to_compress, qs, ps):
+        torch.bmm(tensor, q, out=p)
+
+    # This allreduce is only applied to uncompressed tensors,
+    # so it should have been kicked off before the above computation on the compressed tensors to hide more communication costs.
+    # However, this somehow requires a separate future chain at this time.
+    allreduce_contiguous_uncompressed_tensors_fut = dist.all_reduce(
+        uncompressed_tensors_memory, group=group_to_use, async_op=True
+    ).get_future()
+
+    def unpack_uncompressed_tensors_and_allreduce_ps(fut):
+        uncompressed_tensors_memory = fut.value()[0].div_(world_size)
+        idx = 0
+        for tensor in uncompressed_tensors:
+            tensor.copy_(
+                uncompressed_tensors_memory[idx : idx + tensor.numel()].view_as(tensor)
+            )
+            idx += tensor.numel()
+
+        # Since these Ps will be orthogonalized later, no need to divide them by world size.
+        return (
+            dist.all_reduce(
+                state.p_memory_dict[bucket_index], group=group_to_use, async_op=True
+            )
+            .get_future()
+            .wait()[0]
+        )
+
+    def compute_qs(fut):
+        state.p_memory_dict[bucket_index] = fut.value()
+        for p in ps:
+            _orthogonalize(p, state.orthogonalization_epsilon)
+
+        # Compute Qs.
+        for tensor, p, q in zip(tensors_to_compress, ps, qs):
+            torch.bmm(tensor.transpose(1, 2), p, out=q)
+
+        # TODO: The above procedure does two matmul+allreduce steps per iteration --
+        # one left multiplication and one right multiplication.
+        # For warm-start, can take one such step at a time, and alternate between them.
+
+        # Allreduce Qs.
+        return (
+            dist.all_reduce(
+                state.q_memory_dict[bucket_index], group=group_to_use, async_op=True
+            )
+            .get_future()
+            .wait()[0]
+        )
+
+    def decompress(fut):
+        state.q_memory_dict[bucket_index] = fut.value().div_(world_size)
+
+        for p, q, tensor in zip(ps, qs, tensors_to_compress):
+            torch.bmm(p, q.transpose(1, 2), out=tensor)
+
+        # Copy batched tensors back to original buffer.
+        if state.batch_tensors_with_same_shape:
+            for tensor in tensors_to_compress:
+                if tensor.shape[0] == 1:
+                    # Skip tensor with batch_size == 1 since itself is the original tensor.
+                    continue
+                original_tensors = shape_to_tensors[tensor.shape[1:]]
+                for i, original_tensor in enumerate(original_tensors):
+                    original_tensor.copy_(tensor[i])
+
+        if torch.cuda.is_available():
+            torch.cuda.synchronize(device)
+
+        if state.use_error_feedback:
+            # Memorize the local errors.
+            state.error_dict[bucket_index] = input_tensor_cp - input_tensor
+        if not state.warm_start:
+            state.p_memory_dict.clear()
+            state.q_memory_dict.clear()
+
+        state.maybe_increase_iter(bucket)
+
+        return input_tensor
+
+    return (
+        allreduce_contiguous_uncompressed_tensors_fut.then(
+            unpack_uncompressed_tensors_and_allreduce_ps
+        )
+        .then(compute_qs)
+        .then(decompress)
+    )
+
+
+def batched_powerSGD_hook(
+    state: PowerSGDState, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    r"""
+    Implement simplified PowerSGD algorithm.
+
+    This DDP communication hook implements a simplified PowerSGD gradient compression
+    algorithm described in the `paper `_.
+    This variant does not compress the gradients layer by layer,
+    but instead compresses the flattened input tensor that batches all the gradients.
+    Therefore, it is **faster** than :meth:`powerSGD_hook`,
+    but usually results in a **much lower accuracy**, unless ``matrix_approximation_rank`` is 1.
+
+    .. warning ::
+        Increasing ``matrix_approximation_rank`` here may not necessarily increase the accuracy,
+        because batching per-parameter tensors without column/row alignment can destroy low-rank structure.
+        Therefore, the user should always consider :meth:`powerSGD_hook` first,
+        and only consider this variant when a satisfactory accuracy can be achieved when ``matrix_approximation_rank`` is 1.
+
+    Once gradient tensors are aggregated across all workers, this hook applies
+    compression as follows:
+
+    1. Views the input flattened 1D gradient tensor as a square-shaped tensor M with 0 paddings;
+
+    2. Creates two low-rank tensors P and Q for decomposing M, such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
+
+    3. Computes P, which is equal to MQ;
+
+    4. Allreduces P;
+
+    5. Orthogonalizes P;
+
+    6. Computes Q, which is approximately equal to M^TP;
+
+    7. Allreduces Q;
+
+    8. Computes M, which is approximately equal to PQ^T.
+
+    9. Truncates the input tensor to the original length.
+
+    Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
+    This not only gives the user more control over the tradeoff between speedup and accuracy,
+    but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
+
+    Args:
+        state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
+            To tune the compression configs, mainly need to tune ``matrix_approximation_rank`` and ``start_powerSGD_iter``.
+        bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
+            Note that since DDP comm hook only supports single process single device mode,
+            only exactly one tensor is stored in this bucket.
+
+    Returns:
+        Future handler of the communication, which updates the gradients in place.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1)
+        >>> ddp_model.register_comm_hook(state, batched_powerSGD_hook)
+    """  # noqa: B950
+    process_group = state.process_group
+    group_to_use = (
+        process_group if process_group is not None else not_none(dist.group.WORLD)
+    )
+    world_size = group_to_use.size()
+
+    # The input tensor is a flattened 1D tensor.
+    input_tensor = bucket.buffer()
+
+    # Run vanilla allreduce in the first `start_powerSGD_iter` iterations.
+    if state.iter < state.start_powerSGD_iter:
+        state.maybe_increase_iter(bucket)
+        return default._allreduce_fut(group_to_use, input_tensor)
+
+    # Apply PowerSGD after `start_powerSGD_iter` iterations.
+    device = input_tensor.device
+    total_length = input_tensor.shape[0]
+    state.total_numel_before_compression += total_length
+
+    # View the input tensor as a 2D square-shape tensor, and pad 0s if necessary.
+    square_side_length = math.ceil(math.sqrt(total_length))
+    state.total_numel_after_compression += (
+        square_side_length * state.matrix_approximation_rank * 2
+    )
+    padded_total_length = square_side_length**2
+    input_tensor.resize_(padded_total_length)
+    input_tensor[total_length:padded_total_length].fill_(0)
+
+    _report_compression_stats(bucket, state)
+
+    # Incorporate the error from the previous state into the gradients.
+    bucket_index = bucket.index()
+    input_tensor_cp = None
+    if state.use_error_feedback:
+        if bucket_index in state.error_dict:
+            input_tensor.add_(state.error_dict[bucket_index])
+        else:
+            logger.info(
+                "A zero tensor of length %s that represents local error is created.",
+                padded_total_length,
+            )
+            state.error_dict[bucket_index] = torch.zeros(
+                padded_total_length, device=device, dtype=input_tensor.dtype
+            )
+
+        # Keep a copy of the input tensor,
+        # so that we can compute the local error caused by compression later,
+        # by comparing this copy and the input tensor updated after decompression.
+        input_tensor_cp = torch.clone(input_tensor).detach()
+    matrix = input_tensor.view(square_side_length, square_side_length)
+
+    # Reuse P and Q from the previous iteration if possible.
+    # The memory spaces of P and Q need to be allocated in the first iteration when PowerSGD is applied.
+    if not state.warm_start or bucket_index not in state.p_memory_dict:
+        # If warm-start is disabled, low-rank tensors will be initialized at every step.
+        # Only log this if warm-start to avoid spamming.
+        if state.warm_start:
+            logger.info(
+                "Initializing low-rank tensors P and Q, each of which has a shape of %s x %s.",
+                square_side_length,
+                state.matrix_approximation_rank,
+            )
+
+        def create_low_rank_tensor(fill_random_values, rng):
+            """Return a low-rank 2D tensor of square_side_length * matrix_approximation_rank."""
+            if fill_random_values:
+                with torch.random.fork_rng(devices=[]):
+                    # Fork this RNG to avoid changing the seed globally and affecting the random sampling
+                    # anywhere else in the training.
+                    # The seed makes sure that the initial random values are the same across all the DDP replicas.
+                    # This seed should differ at every step.
+                    # Since it is very slow to fork RNG state across all the CUDA devices,
+                    # only fork on CPU and then move the generated tensor to the CUDA device.
+                    torch.manual_seed(rng.randint(1_000_000_000))
+                    return torch.randn(
+                        square_side_length,
+                        state.matrix_approximation_rank,
+                        device="cpu",
+                        dtype=input_tensor.dtype,
+                    ).to(device)
+            else:
+                return torch.empty(
+                    square_side_length,
+                    state.matrix_approximation_rank,
+                    device=device,
+                    dtype=input_tensor.dtype,
+                )
+
+        state.p_memory_dict[bucket_index] = create_low_rank_tensor(
+            fill_random_values=False, rng=state.rng
+        )
+        state.q_memory_dict[bucket_index] = create_low_rank_tensor(
+            fill_random_values=True, rng=state.rng
+        )
+    _orthogonalize(state.q_memory_dict[bucket_index])
+
+    torch.matmul(
+        matrix, state.q_memory_dict[bucket_index], out=state.p_memory_dict[bucket_index]
+    )
+    allreduce_p_fut = dist.all_reduce(
+        state.p_memory_dict[bucket_index], group=group_to_use, async_op=True
+    ).get_future()
+
+    def compute_q(fut):
+        state.p_memory_dict[bucket_index] = fut.value()[0]
+        _orthogonalize(state.p_memory_dict[bucket_index])
+
+        torch.matmul(
+            matrix.t(),
+            state.p_memory_dict[bucket_index],
+            out=state.q_memory_dict[bucket_index],
+        )
+
+        # TODO: The above procedure does two matmul+allreduce steps per iteration --
+        # one left multiplication and one right multiplication.
+        # For warm-start, can take one such step at a time, and alternate between them.
+
+        return (
+            dist.all_reduce(
+                state.q_memory_dict[bucket_index], group=group_to_use, async_op=True
+            )
+            .get_future()
+            .wait()[0]
+        )
+
+    def decompress(fut):
+        state.q_memory_dict[bucket_index] = fut.value().div_(world_size)
+        torch.matmul(
+            state.p_memory_dict[bucket_index],
+            state.q_memory_dict[bucket_index].t(),
+            out=matrix,
+        )
+
+        if state.use_error_feedback:
+            # Memorize the local errors.
+            state.error_dict[bucket_index] = input_tensor_cp - input_tensor
+        # Removing this seemingly unnecessary sync somehow may cause failures.
+        # See: https://github.com/pytorch/pytorch/pull/54838
+        if torch.cuda.is_available():
+            torch.cuda.synchronize(device)
+        if not state.warm_start:
+            state.p_memory_dict.clear()
+            state.q_memory_dict.clear()
+        ret = input_tensor.resize_(total_length)
+
+        state.maybe_increase_iter(bucket)
+
+        return ret
+
+    return allreduce_p_fut.then(compute_q).then(decompress)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..838d5f3b926612c9eaa2b7bb7dcde02f69c00766
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py
@@ -0,0 +1,218 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.distributed as dist
+from torch import nn
+
+
+def _quantize_per_tensor_backend(x, scale, zero_point):
+    y = torch.round(x / scale) + zero_point
+    y = torch.clamp(y, 0, 255).to(torch.uint8)
+    return y
+
+
+def _dequantize_per_tensor_backend(y, scale, zero_point):
+    x = scale * (y.to(torch.float32) - zero_point)
+    return x
+
+
+def _quantize_per_channel_backend(x, scale, zero_point):
+    y = torch.zeros(x.size(), device=x.device)
+    for i in range(x.size()[0]):
+        y[i, :] = torch.round(x[i, :] / scale[i]) + zero_point[i]
+    y = torch.clamp(y, 0, 255).to(torch.uint8)
+    return y
+
+
+def _dequantize_per_channel_backend(y, scale, zero_point):
+    y = y.to(torch.float32).to(y.device)
+    x = torch.zeros_like(y, device=y.device)
+    for i in range(x.size()[0]):
+        x[i, :] = scale[i] * (y[i, :] - zero_point[i])
+    return x
+
+
+def _get_allgather_out_list(all_gather_in_list, world_size):
+    out_list = [
+        torch.zeros_like(
+            all_gather_in_list,
+            device=all_gather_in_list.device,
+            dtype=all_gather_in_list.dtype,
+        )
+        for _ in range(world_size)
+    ]
+    return out_list
+
+
+def quantization_pertensor_hook(
+    process_group: dist.ProcessGroup, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Apply ``torch.quantize_per_tensor`` logic to DDP using ``allgather`` protocol.
+
+    Workers first allgather the scale and zero point of their own
+    ``GradBucket`` prior to the quantization. After all workers have that information,
+    the first ``then`` callback called ``quantize_and_allgather`` quantizes worker's
+    own gradient tensor, and uses ``allgather`` to communicate these across all workers.
+    The final ``then`` callback called ``dequantize_and_aggregate``, dequantizes and
+    aggregates each quantized gradient tensor locally and returns the mean.
+
+    .. warning ::
+        This is experimental, and uses ``allgather`` protocol which is considerably slower than
+        ``allreduce`` protocol. It works only with flattened grads.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(process_group, quantization_pertensor_hook)
+    """
+    group_to_use = process_group if process_group is not None else dist.group.WORLD
+    rank = process_group.rank() if process_group is not None else dist.get_rank()
+    world_size = group_to_use.size()
+
+    tensor = bucket.buffer()
+
+    myObserver = torch.ao.quantization.MinMaxObserver().to(tensor.device)
+    myObserver(tensor)
+
+    s, z = myObserver.calculate_qparams()
+    s_and_z = torch.FloatTensor([s, z]).to(tensor.device)
+
+    all_ranks_s_and_z = _get_allgather_out_list(s_and_z, world_size)
+
+    # First, allgather scale and zeros.
+    fut = dist.all_gather(
+        all_ranks_s_and_z, s_and_z, group=group_to_use, async_op=True
+    ).get_future()
+
+    def quantize_and_allgather(fut):
+        # Store scale and zeros across all workers.
+        all_ranks_s_and_z = fut.wait()[0]
+        # All workers quantize their own ``GradBucket`` tensors.
+        quantized_tensor = _quantize_per_tensor_backend(
+            tensor, all_ranks_s_and_z[rank][0], all_ranks_s_and_z[rank][1]
+        )
+        # Allgather quantized tensors.
+        fut = dist.all_gather(
+            _get_allgather_out_list(quantized_tensor, world_size),
+            quantized_tensor,
+            group=group_to_use,
+            async_op=True,
+        ).get_future()
+
+        return fut.wait()
+
+    def dequantize_and_aggregate(fut):
+        all_ranks_quantized_tensor = fut.wait()[0]
+
+        aggregated_dequantized_tensor = torch.zeros_like(
+            all_ranks_quantized_tensor[0], device=tensor.device, dtype=torch.float32
+        )
+        # Using previously allgathered scales and zeros, dequantize gradient tensors
+        # locally and then aggregate them.
+        for r, quantized_tensor in enumerate(all_ranks_quantized_tensor):
+            aggregated_dequantized_tensor += _dequantize_per_tensor_backend(
+                quantized_tensor, all_ranks_s_and_z[r][0], all_ranks_s_and_z[r][1]
+            )
+
+        return aggregated_dequantized_tensor / world_size
+
+    return fut.then(quantize_and_allgather).then(dequantize_and_aggregate)
+
+
+def quantization_perchannel_hook(
+    process_group: dist.ProcessGroup, bucket: dist.GradBucket, bucket_size=512
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Apply``torch.quantize_per_channel`` logic to DDP using ``allgather`` protocol.
+
+    Compared to per-tensor, the main motivation of per-channel is
+    for considerably large tensors such as a tensor that contains 6 million
+    elements quantizing per a bucket size of 512 (or 128) elements may significantly
+    increase the resolution.
+
+    It first splits ``GradBucket`` tensor into multiple chunks (channels) of ``bucket_size``
+    elements. Then, workers allgather the scales and zero points of their own
+    ``GradBucket`` prior to the quantization. After all workers have that information,
+    the first ``then`` callback called ``quantize_and_allgather`` quantizes worker's
+    own gradient tensor, and uses ``allgather`` to communicate these across all workers.
+    The final ``then`` callback called ``dequantize_and_aggregate``, dequantizes, flattens, and
+    aggregates each quantized gradient tensor locally and returns the mean.
+
+    .. warning ::
+        This is experimental, and uses ``allgather`` protocol which is considerably slower than
+        ``allreduce`` protocol. It works only with flattened grads.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(process_group, quantization_perchannel_hook)
+    """
+    group_to_use = process_group if process_group is not None else dist.group.WORLD
+    rank = process_group.rank() if process_group is not None else dist.get_rank()
+    world_size = group_to_use.size()
+
+    tensor = bucket.buffer()
+
+    tensor_in_channels = (
+        nn.functional.pad(
+            input=tensor,
+            pad=(0, bucket_size - len(tensor) % bucket_size),
+            mode="constant",
+            value=0,
+        )
+        .view(-1, bucket_size)
+        .to(tensor.device)
+    )
+
+    myPerChannelObserver = torch.ao.quantization.PerChannelMinMaxObserver().to(
+        tensor.device
+    )
+    myPerChannelObserver(tensor_in_channels)
+
+    s_ch, z_ch = myPerChannelObserver.calculate_qparams()
+    s_and_z = torch.stack((s_ch, z_ch)).to(tensor.device)
+
+    all_ranks_s_and_z = _get_allgather_out_list(s_and_z, world_size)
+    # First, allgather scale and zeros.
+    fut = dist.all_gather(
+        all_ranks_s_and_z, s_and_z, group=group_to_use, async_op=True
+    ).get_future()
+
+    def quantize_and_allgather(fut):
+        # Store scale and zeros across all workers.
+        all_ranks_s_and_z = fut.wait()[0]
+        # All workers quantize their corresponding ``GradBucket`` tensors.
+        quantized_tensor = _quantize_per_channel_backend(
+            tensor_in_channels,
+            all_ranks_s_and_z[rank, 0, :],
+            all_ranks_s_and_z[rank, 1, :],
+        )
+        # Allgather quantized tensors.
+        fut = dist.all_gather(
+            _get_allgather_out_list(quantized_tensor, world_size),
+            quantized_tensor,
+            group=group_to_use,
+            async_op=True,
+        ).get_future()
+
+        return fut.wait()
+
+    def dequantize_and_aggregate(fut):
+        all_ranks_quantized_tensor = fut.wait()[0]
+
+        aggregated_dequantized_tensor = torch.zeros_like(
+            all_ranks_quantized_tensor[0], device=tensor.device, dtype=torch.float32
+        )
+        # Using previously allgathered scales and zeros, dequantize gradient tensors
+        # locally and then aggregate them.
+        for r, quantized_tensor in enumerate(all_ranks_quantized_tensor):
+            aggregated_dequantized_tensor += _dequantize_per_channel_backend(
+                quantized_tensor, all_ranks_s_and_z[r][0], all_ranks_s_and_z[r][1]
+            )
+
+        return (
+            torch.flatten(aggregated_dequantized_tensor).to(tensor.device)[
+                : tensor.size()[0]
+            ]
+            / world_size
+        )
+
+    return fut.then(quantize_and_allgather).then(dequantize_and_aggregate)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/join.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/join.py
new file mode 100644
index 0000000000000000000000000000000000000000..70d74af7ead04c420b0523dcc5e974581efe7171
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/join.py
@@ -0,0 +1,348 @@
+# mypy: allow-untyped-defs
+import warnings
+from abc import ABC, abstractmethod
+from types import TracebackType
+from typing import Any, NamedTuple, Optional
+
+import torch
+import torch.distributed as dist
+
+
+__all__ = ["JoinHook", "Joinable", "Join"]
+
+
+class JoinHook:
+    r"""
+    This defines a join hook, which provides two entry points in the join context manager.
+
+    Entry points : a main hook, which is called repeatedly while there exists a non-joined
+    process, and a post-hook, which is called once all processes have joined.
+
+    To implement a join hook for the generic join context manager, define a
+    class that inherits from :class:`JoinHook` and override ``main_hook()`` and
+    ``post_hook()`` as appropriate.
+    """
+
+    def main_hook(self) -> None:
+        r"""Call this hook while there exists a non-joined process to shadow collective communications in a training iteration.
+
+        Training iteration i.e., in one forward pass, backward pass, and optimizer step.
+        """
+
+    def post_hook(self, is_last_joiner: bool) -> None:
+        r"""
+        Call hook after all processes have joined.
+
+        It is passed an additional ``bool`` argument ``is_last_joiner``, which indicates if the rank is one of the last to join.
+
+        Arguments:
+            is_last_joiner (bool): ``True`` if the rank is one of the last to
+                join; ``False`` otherwise.
+        """
+
+
+class Joinable(ABC):
+    r"""
+    This defines an abstract base class for joinable classes.
+
+    A joinable class
+    (inheriting from :class:`Joinable`) should implement :meth:`join_hook`,
+    which returns a :class:`JoinHook` instance, in addition to
+    :meth:`join_device` and :meth:`join_process_group` that return device and
+    process group information, respectively.
+    """
+
+    @abstractmethod
+    def __init__(self) -> None:
+        super().__init__()
+        self._join_config = _JoinConfig.construct_disabled_join_config()
+
+    @abstractmethod
+    def join_hook(self, **kwargs) -> JoinHook:
+        r"""
+        Return a :class:`JoinHook` instance for the given :class:`Joinable`.
+
+        Arguments:
+            kwargs (dict): a :class:`dict` containing any keyword arguments
+                to modify the behavior of the join hook at run time; all
+                :class:`Joinable` instances sharing the same join context
+                manager are forwarded the same value for ``kwargs``.
+        """
+        ...
+
+    @property
+    @abstractmethod
+    def join_device(self) -> torch.device:
+        r"""Return the device from which to perform collective communications needed by the join context manager."""
+        ...
+
+    @property
+    @abstractmethod
+    def join_process_group(self) -> Any:
+        r"""Returns the process group for the collective communications needed by the join context manager itself."""
+        ...
+
+
+class _JoinConfig(NamedTuple):
+    r"""This includes all fields needed from a :class:`Joinable` instance for the join context manager side."""
+
+    enable: bool
+    throw_on_early_termination: bool
+    is_first_joinable: bool
+
+    @staticmethod
+    def construct_disabled_join_config():
+        r"""Return a :class:`_JoinConfig` instance indicating that join-related logic should be disabled.
+
+        e.g. if the caller is not in a join context manager.
+        """
+        return _JoinConfig(
+            enable=False, throw_on_early_termination=False, is_first_joinable=False
+        )
+
+
+class Join:
+    r"""
+    This class defines the generic join context manager, which allows custom hooks to be called after a process joins.
+
+    These hooks should shadow the
+    collective communications of non-joined processes to prevent hanging and
+    erroring and to ensure algorithmic correctness. Refer to :class:`JoinHook`
+    for details about the hook definition.
+
+    .. warning::
+        The context manager requires each participating :class:`Joinable` to
+        call the method :meth:`notify_join_context()` before its own per-
+        iteration collective communications to ensure correctness.
+
+    .. warning::
+        The context manager requires that all ``process_group`` attributes in
+        the :class:`JoinHook` objects are the same. If there are multiple
+        :class:`JoinHook` objects, then the ``device`` of the first is used.
+        The process group and device information is used for checking for non-
+        joined processes and for notifying processes to throw an exception if
+        ``throw_on_early_termination`` is enabled, both of which using an all-
+        reduce.
+
+    Arguments:
+        joinables (List[Joinable]): a list of the participating
+            :class:`Joinable` s; their hooks are iterated over in the given
+            order.
+
+        enable (bool): a flag enabling uneven input detection; setting to
+            ``False`` disables the context manager's functionality and should
+            only be set when the user knows the inputs will not be uneven
+            (default: ``True``).
+
+        throw_on_early_termination (bool): a flag controlling whether to throw an
+            exception upon detecting uneven inputs (default: ``False``).
+
+    Example::
+
+        >>> import os
+        >>> import torch
+        >>> import torch.distributed as dist
+        >>> import torch.multiprocessing as mp
+        >>> # xdoctest: +SKIP
+        >>> import torch.nn.parallel.DistributedDataParallel as DDP
+        >>> import torch.distributed.optim.ZeroRedundancyOptimizer as ZeRO
+        >>> from torch.distributed.algorithms.join import Join
+        >>>
+        >>> # On each spawned worker
+        >>> def worker(rank):
+        >>>     dist.init_process_group("nccl", rank=rank, world_size=2)
+        >>>     model = DDP(torch.nn.Linear(1, 1).to(rank), device_ids=[rank])
+        >>>     optim = ZeRO(model.parameters(), torch.optim.Adam, lr=0.01)
+        >>>     # Rank 1 gets one more input than rank 0
+        >>>     inputs = [torch.tensor([1.]).to(rank) for _ in range(10 + rank)]
+        >>>     with Join([model, optim]):
+        >>>         for input in inputs:
+        >>>             loss = model(input).sum()
+        >>>             loss.backward()
+        >>>             optim.step()
+        >>>     # All ranks reach here without hanging/erroring
+    """
+
+    def __init__(
+        self,
+        joinables: list[Joinable],
+        enable: bool = True,
+        throw_on_early_termination: bool = False,
+        **kwargs,
+    ):
+        if len(joinables) == 0:
+            raise ValueError("The join context manager requires at least one joinable")
+        self._joinables = joinables
+        self._join_hooks = [
+            joinable.join_hook(**kwargs) for joinable in self._joinables
+        ]
+        self._enable = enable
+        self._throw_on_early_termination = throw_on_early_termination
+        self._set_joinable_configs()
+        self._extract_dist_info()
+
+    def _set_joinable_configs(self) -> None:
+        r"""Set the :class:`_JoinConfig` of each participating :class:`Joinable`."""
+        assert len(self._joinables) > 0
+        is_first_joinable = True
+        for joinable in self._joinables:
+            joinable._join_config = _JoinConfig(
+                enable=self._enable,
+                throw_on_early_termination=self._throw_on_early_termination,
+                is_first_joinable=is_first_joinable,
+            )
+            is_first_joinable = False
+
+    def _extract_dist_info(self) -> None:
+        r"""
+        Extract the process group and device information from the joinables.
+
+        If there are multiple joinables, then the context manager uses the
+        first specified device.
+
+        Preconditions:
+            ``self._joinables`` is not ``None`` and is non-empty.
+
+        Raises:
+            ValueError
+                If there are multiple conflicting ``process_group`` attributes
+                among the ``Joinable`` objects.
+        """
+        process_group = None
+        device = None
+        for joinable in self._joinables:
+            if process_group is None:
+                process_group = joinable.join_process_group
+            elif process_group != joinable.join_process_group:
+                raise ValueError(
+                    "Using join context manager with multiple process groups"
+                )
+            if device is None:
+                device = joinable.join_device
+        self._process_group = process_group
+        self._rank = dist.get_rank(self._process_group)
+        self._device = device
+
+    def __enter__(self): ...
+
+    def __exit__(
+        self,
+        type: Optional[type[BaseException]],
+        value: Optional[BaseException],
+        traceback: Optional[TracebackType],
+    ):
+        r"""
+        Repeatedly runs the main hooks until all processes join; then, runs the post-hooks.
+
+        Raises:
+            RuntimeError
+                If ``throw_on_early_termination=True``.
+        """
+        if not self._enable or type:
+            return  # propagate the exception directly if one was raised
+
+        all_procs_joined = False
+        is_last_joiner = True
+
+        i = 0
+        WARN_THRESHOLD = 1000
+        warnings.simplefilter("once")
+
+        while not all_procs_joined:
+            if i > WARN_THRESHOLD:
+                warnings.warn(
+                    "Detected uneven input skew of greater than "
+                    f"{WARN_THRESHOLD}. This means that rank "
+                    f"{self._rank} has at least {WARN_THRESHOLD} "
+                    f"fewer inputs than other currently-active ranks. "
+                    "This level of skew could lead to performance "
+                    "degradation during training."
+                )
+            # Shadow the all-reduce in non-joined processes
+            num_nonjoined_procs = self._get_num_nonjoined_procs()
+            if num_nonjoined_procs == 0:
+                all_procs_joined = True
+            else:
+                if self._throw_on_early_termination:
+                    self._notify_procs_to_terminate()
+
+                # Run main hooks
+                for join_hook in self._join_hooks:
+                    join_hook.main_hook()
+
+                is_last_joiner = False
+                i += 1
+
+        # Run post-hooks
+        for join_hook in self._join_hooks:
+            join_hook.post_hook(is_last_joiner)
+
+    def _get_num_nonjoined_procs(self):
+        r"""Return the number of non-joined processes by shadowing an all-reduce in the non-joined processes."""
+        num_nonjoined_procs = torch.zeros(1, device=self._device)
+        dist.all_reduce(num_nonjoined_procs, group=self._process_group)
+        return num_nonjoined_procs.item()
+
+    def _notify_procs_to_terminate(self):
+        r"""Schedule an all-reduce to notify non-joined processes to terminate.
+
+        Also raise a ``RuntimeError`` indicating that the current process has exhausted its inputs.
+        """
+        ones = torch.ones(1, device=self._device)
+        dist.all_reduce(ones, group=self._process_group)
+        raise RuntimeError(f"Rank {self._rank} exhausted all inputs.")
+
+    @staticmethod
+    def notify_join_context(joinable: Joinable):
+        r"""
+        Notifies the join context manager that the calling process has not yet joined.
+
+        Then, if ``throw_on_early_termination=True``, checks if uneven inputs have been detected
+        (i.e. if one process has already joined) and throws an exception if so.
+
+        This method should be called from a :class:`Joinable` object before
+        its per-iteration collective communications. For example, this should
+        be called at the beginning of the forward pass in
+        :class:`DistributedDataParallel`.
+
+        Only the first :class:`Joinable` object passed into the context
+        manager performs the collective communications in this method, and
+        for the others, this method is vacuous.
+
+        Arguments:
+            joinable (Joinable): the :class:`Joinable` object calling this
+                method.
+
+        Returns:
+            An async work handle for the all-reduce meant to notify the context
+            manager that the process has not yet joined if ``joinable`` is the
+            first one passed into the context manager; ``None`` otherwise.
+        """
+        assert hasattr(joinable, "_join_config"), (
+            f"Check that the {type(joinable)} constructor calls the "
+            "``Joinable`` constructor"
+        )
+
+        join_config = joinable._join_config
+        # First joinable is responsible for the collective communications
+        if not join_config.is_first_joinable or not join_config.enable:
+            return None
+
+        device = joinable.join_device
+        process_group = joinable.join_process_group
+
+        # Schedule an all-reduce to indicate that the caller has not yet joined
+        ones = torch.ones(1, device=device)
+        work = dist.all_reduce(ones, group=process_group, async_op=True)
+
+        if join_config.throw_on_early_termination:
+            # Check if uneven inputs have been detected
+            zeros = torch.zeros(1, device=device)
+            dist.all_reduce(zeros, group=process_group)
+            should_throw = zeros.item()
+            if should_throw:
+                raise RuntimeError(
+                    "Detected at least one rank that exhausted inputs. "
+                    "Throwing across all ranks."
+                )
+        return work
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/averagers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/averagers.py
new file mode 100644
index 0000000000000000000000000000000000000000..eec0846416700d5c8666194f98edb16f80be1415
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/averagers.py
@@ -0,0 +1,130 @@
+# mypy: allow-untyped-defs
+import warnings
+from abc import ABC, abstractmethod
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.algorithms.model_averaging.utils as utils
+from torch.utils._typing_utils import not_none as _not_none
+
+
+__all__ = ["ModelAverager", "PeriodicModelAverager"]
+
+
+class ModelAverager(ABC):
+    r"""Base class for all model averagers.
+
+    Args:
+        process_group: The process group to be used for all-reduce.
+                       If ``None``, the default process group, which
+                       is created by :func:`torch.distributed.init_process_group`,
+                       will be used. (default: ``None``)
+    """
+
+    def __init__(self, process_group: Optional[dist.ProcessGroup] = None):
+        self.process_group = (
+            process_group if process_group is not None else _not_none(dist.group.WORLD)
+        )
+        self.step = 0
+
+    @abstractmethod
+    def average_parameters(self, params):
+        raise NotImplementedError
+
+
+class PeriodicModelAverager(ModelAverager):
+    r"""
+    Averages parameters periodically after the warm-up stage.
+
+    This can be used for running `post-local SGD `_,
+    by running :class:`~torch.nn.DistributedDataParallel` (DDP)
+    using the subgroups created by :meth:`~torch.distributed.new_subgroups`.
+
+    Args:
+        period (int): The number of steps per model averaging.
+                      Usually the period should be greater than ``1`` to reduce the communication cost.
+                      Otherwise, only DDP needs to be used.
+        warmup_steps (int): The number of warm-up steps. During this stage,
+                            model averaging is skipped.
+        process_group: The process group to be used for all-reduce.
+                       If ``None``, the default process group, which
+                       is created by :func:`torch.distributed.init_process_group`,
+                       will be used. (default: ``None``)
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> import torch
+        >>> import torch.distributed as dist
+        >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
+        >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
+        >>> import torch.nn as nn
+        >>>
+        >>> dist.init_process_group("nccl", rank=rank, world_size=16)
+        >>> torch.cuda.set_device(rank)
+        >>> module = nn.Linear(1, 1, bias=False).cuda()
+        >>> model = nn.parallel.DistributedDataParallel(
+        >>>    module, device_ids=[rank], output_device=rank
+        >>> )
+        >>> # Register a post-localSGD communication hook.
+        >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
+        >>> model.register_comm_hook(state, post_localSGD_hook)
+        >>>
+        >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
+        >>> # After 100 steps, run model averaging every 4 steps.
+        >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
+        >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100)
+        >>> for step in range(0, 200):
+        >>>    optimizer.zero_grad()
+        >>>    loss = loss_fn(output, labels)
+        >>>    loss.backward()
+        >>>    optimizer.step()
+        >>>    # Will average model parameters globally every 4 steps. Thus,
+        >>>    # inter-node communication only occurs every 4 iterations after
+        >>>    # the initial ``warmup_steps`` period.
+        >>>    averager.average_parameters(model.parameters())
+    """
+
+    def __init__(
+        self, period, warmup_steps=0, process_group: Optional[dist.ProcessGroup] = None
+    ):
+        super().__init__(process_group)
+        if warmup_steps < 0:
+            raise ValueError("Arg ``warmup_steps`` must be a non-negative number.")
+        self.warmup_steps = warmup_steps
+        if period < 1:
+            raise ValueError("Arg ``period`` must be a positive value.")
+        elif period == 1:
+            warnings.warn(
+                "When period is 1, no need to use model averaging because the communication cost "
+                "of all-reducing parameters will be no less than the cost of all-reducing gradients "
+                "by DistributedDataParallel in the backward pass. Therefore, only "
+                "DistributedDataParallel should be used for this case."
+            )
+        self.period = period
+
+    def average_parameters(
+        self,
+        params: Union[
+            Iterable[torch.nn.Parameter], Iterable[dict[str, torch.nn.Parameter]]
+        ],
+    ):
+        """
+        Averages parameters or parameter groups of an optimizer if ``step`` is no less than ``warmup_steps``.
+
+        Can be divided by ``period``, where ``step`` is increased by 1
+        at each iteration in the training loop.
+        Args:
+            params: The parameters of a model or parameter groups of an optimizer.
+
+        """
+        if (
+            self.step >= self.warmup_steps
+            and (self.step - self.warmup_steps) % self.period == 0
+        ):
+            utils.average_parameters_or_parameter_groups(
+                params, _not_none(self.process_group)
+            )
+        self.step += 1
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py
new file mode 100644
index 0000000000000000000000000000000000000000..a52fc2babed1945b88739f5837df14d0f078be43
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py
@@ -0,0 +1,181 @@
+# mypy: allow-untyped-defs
+# Copyright 2022 Cruise LLC
+import logging
+import warnings
+from collections import OrderedDict
+from collections.abc import Iterable
+from typing import Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.algorithms.model_averaging.averagers as averagers
+import torch.distributed.algorithms.model_averaging.utils as utils
+
+
+logger = logging.getLogger(__name__)
+
+
+class HierarchicalModelAverager(averagers.ModelAverager):
+    r"""
+    Runs hierarchical model averaging (`hierarchical SGD `_).
+
+    Process groups of different sizes are organized in a hierarchy, and they average parameters
+    by using different periods concurrently after the warm-up stage.
+    This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`
+    that supports `post-local SGD `_, which essentially only supports
+    a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
+    level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`.
+    Similarly, the process groups within this class do not have such an intra-machine process
+    subgroup, which should be embedded by the post-local SGD communication hook instead.
+
+    Args:
+        period_group_size_dict: An ordered dict mapping keys of model averaging period to
+                                process group size, used for initializing process groups of
+                                different sizes in a hierarchy to average parameters concurrently.
+                                Particularly, at each iteration, there will be at most a single
+                                process group that runs averaging -- the period of such group should
+                                have the largest period which the current step can be divided by.
+                                For example, if the dict has three keys: 2, 4, and 8,
+                                then this means totally three process groups will be created to
+                                average parameters every 2, 4, and 8 iterations, respectively.
+                                At the 4th iteration, only the second process group will run
+                                averaging, because the first process group should be a
+                                subset of the second process group, and no need to execute the first
+                                process group redundantly.
+                                On the other hand, the third process group can only be triggered
+                                every 8 iterations, so it will not be triggered at the 4th iteration.
+        warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped.
+        process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging.
+                                                If ``None``, the default process group, which is created
+                                                by :func:`torch.distributed.init_process_group`, will be used.
+                                                (default: ``None``)
+
+    Example::
+        >>> # xdoctest: +SKIP('undefined rank')
+        >>> from collections import OrderedDict
+        >>> import torch
+        >>> import torch.distributed as dist
+        >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
+        >>>     PostLocalSGDState,
+        >>>     post_localSGD_hook,
+        >>> )
+        >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD
+        >>> import torch.nn as nn
+        >>>
+        >>> dist.init_process_group("nccl", rank=rank, world_size=16)
+        >>> torch.cuda.set_device(rank)
+        >>> module = nn.Linear(1, 1, bias=False).to(rank)
+        >>> model = nn.parallel.DistributedDataParallel(
+        >>>    module, device_ids=[rank], output_device=rank
+        >>> )
+        >>> # Register a post-localSGD communication hook.
+        >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4.
+        >>> subgroup, _ = dist.new_subgroups()
+        >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100)
+        >>> model.register_comm_hook(state, post_localSGD_hook)
+        >>>
+        >>> # Average parameters among each group of 8 processes every 4 iterations, and among all
+        >>> # the 16 processes every 16 iterations.
+        >>> averager = hierarchicalSGD.HierarchicalModelAverager(
+        >>>     period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100)
+        >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
+        >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
+        >>> # After 100 steps, run model averaging at two levels.
+        >>> for step in range(0, 200):
+        >>>    optimizer.zero_grad()
+        >>>    loss = loss_fn(output, labels)
+        >>>    loss.backward()
+        >>>    optimizer.step()
+        >>>    # Average parameters after ``optimizer.step()``.
+        >>>    # Thus, the inter-node communication only occurs periodically after ``warmup_steps``.
+        >>>    averager.average_parameters(model.parameters())
+
+    .. warning ::
+        The last group size in the dict must be the size of the provided ``process_group``,
+        which indicates model averaging at the highest level of the hierarchy.
+        If ``process_group`` is not provided, then the last group size should be equal to the world size.
+
+    .. warning ::
+        `HierarchicalModelAverager` is experimental and subject to change.
+    """
+
+    def __init__(self, period_group_size_dict=None, warmup_steps=0, process_group=None):
+        super().__init__(process_group)
+        if not period_group_size_dict:
+            raise ValueError("Arg ``period_group_size_dict`` must not be empty.")
+        self._periods = list(period_group_size_dict.keys())
+        if self._periods[0] <= 0:
+            raise ValueError(
+                "The minimum period in arg ``period_group_size_dict`` must be a positive value."
+            )
+        elif self._periods[-1] == 1:
+            warnings.warn(
+                "When the maximum period in arg ``period_group_size_dict`` is 1, "
+                "no need to use model averaging because the communication cost "
+                "of all-reducing parameters will be no less than the cost of all-reducing gradients "
+                "by DistributedDataParallel in the backward pass. Therefore, only "
+                "DistributedDataParallel should be used for this case."
+            )
+        overall_group_size = dist.get_world_size(group=self.process_group)
+        if list(period_group_size_dict.values())[-1] != overall_group_size:
+            raise ValueError(
+                f"The last value in arg ``period_process_group_dict`` {list(period_group_size_dict.values())[-1]} "
+                f"must be equal to the size of arg ``process_group`` {overall_group_size}."
+            )
+
+        self.period_process_group_dict = OrderedDict()
+        logger.info("Model averaging hierarchy:")
+        for period, group_size in period_group_size_dict.items():
+            logger.info(
+                "\tEach group that has %s processes average parameters every %s iterations, "
+                "if no higher-level averaging.",
+                group_size,
+                period,
+            )
+            if group_size != overall_group_size:
+                self.period_process_group_dict[period], _ = dist.new_subgroups(
+                    group_size=group_size, group=self.process_group
+                )
+            else:
+                self.period_process_group_dict[period] = self.process_group
+
+        if warmup_steps < 0:
+            raise ValueError("Arg ``warmup_steps`` must be a non-negative number.")
+        self.warmup_steps = warmup_steps
+
+    def _find_process_group(self):
+        """
+        Return a process group as the value of an ``period_process_group_dict`` entry.
+
+        If ``step`` can be divided by multiple periods in the keys of ``period_process_group_dict``,
+        then the returned process group is the one corresponding to the largest period,
+        since this process group will be used for averaging parameters at this ``step``.
+        Returns ``None`` if not found.
+        """
+        for period in reversed(self._periods):
+            if self.step % period == 0:
+                return self.period_process_group_dict[period]
+        return None
+
+    def average_parameters(
+        self,
+        params: Union[
+            Iterable[torch.nn.Parameter], Iterable[dict[str, torch.nn.Parameter]]
+        ],
+    ):
+        """
+        Averages parameters or parameter groups of an optimizer.
+
+        Averaging only occurs if ``step`` is no less than ``warmup_steps``
+        and it can be divided by a period in the keys of ``period_process_group_dict``,
+        where ``step`` is increased by 1 at each iteration in the training loop.
+        If ``step`` can be divided by multiple periods in the keys of ``period_process_group_dict``,
+        only the largest period is used, and the corresponding process group is used for averaging parameters.
+        Args:
+            params: The parameters of a model or parameter groups of an optimizer.
+        """
+        if self.step >= self.warmup_steps:
+            group = self._find_process_group()
+            if group is not None:
+                utils.average_parameters_or_parameter_groups(params, group)
+        self.step += 1
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..407014418ecce36bf8876cd572c08007e8e4250d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/utils.py
@@ -0,0 +1,93 @@
+# mypy: allow-untyped-defs
+# flake8: noqa C101
+import itertools
+from collections.abc import Iterable, Iterator
+from typing import Union
+
+import torch
+import torch.distributed as dist
+
+# The two imports below are not always available depending on the
+# USE_DISTRIBUTED compile flag. Make sure they raise import error
+# if we're trying to use them.
+from torch.distributed import group, ProcessGroup
+
+
+__all__ = [
+    "average_parameters",
+    "get_params_to_average",
+    "average_parameters_or_parameter_groups",
+]
+
+
+def average_parameters(
+    params: Iterator[torch.nn.Parameter], process_group: ProcessGroup
+):
+    """
+    Averages all the given parameters.
+
+    For allreduce efficiency, all the parameters are flattened into a contiguous buffer.
+    Thus, it requires extra memory of the same size as the given parameters.
+    """
+    group_to_use = process_group if process_group is not None else group.WORLD
+    # Do not update any parameter if not in the process group.
+    if dist._rank_not_in_group(group_to_use):
+        return
+
+    params_it1, params_it2 = itertools.tee(params)
+    # If the input parameters have different data types,
+    # packing these parameters will trigger an implicit type up-casting.
+    # The original parameter data types will be restored during the subsequent unpacking.
+    flat_params = torch.cat([p.data.reshape(-1) for p in params_it1])
+    flat_params /= dist.get_world_size(group_to_use)
+    # Make sure the allreduce will not conflict with any other ongoing process group.
+    if torch.accelerator.is_available():
+        torch.accelerator.synchronize()
+    dist.all_reduce(flat_params, group=group_to_use)
+
+    offset = 0
+    for p in params_it2:
+        p.data = flat_params[offset : offset + p.numel()].view_as(p).type_as(p)
+        offset += p.numel()
+
+
+def get_params_to_average(
+    params: Union[
+        Iterable[torch.nn.Parameter],
+        Iterable[dict[str, torch.nn.Parameter]],
+    ],
+):
+    """
+    Return a list of parameters that need to average.
+
+    This filters out the parameters that do not contain any gradients.
+    Args:
+        params: The parameters of a model or parameter groups of an optimizer.
+    """
+    filtered_params = []
+    for param in params:
+        if isinstance(param, torch.nn.Parameter):
+            # model.parameters() input
+            param_data = param
+            if param_data.grad is not None:
+                filtered_params.append(param_data)
+        elif isinstance(param, dict):
+            # optimizer.param_groups input
+            for param_data in param["params"]:
+                if param_data.grad is not None:
+                    filtered_params.append(param_data)
+        else:
+            raise NotImplementedError(
+                f"Parameter input of type {type(param)} is not supported"
+            )
+    return filtered_params
+
+
+def average_parameters_or_parameter_groups(
+    params: Union[
+        Iterable[torch.nn.Parameter], Iterable[dict[str, torch.nn.Parameter]]
+    ],
+    process_group: ProcessGroup,
+):
+    """Averages parameters of a model or parameter groups of an optimizer."""
+    average_parameters(iter(get_params_to_average(params)), process_group)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/argparse_util.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/argparse_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..c475eebf21273abb53ab99e3edcbdef18e9f0c8f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/argparse_util.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+from argparse import Action
+
+
+class env(Action):
+    """
+    Get argument values from ``PET_{dest}`` before defaulting to the given ``default`` value.
+
+    For flags (e.g. ``--standalone``)
+    use ``check_env`` instead.
+
+    .. note:: when multiple option strings are specified, ``dest`` is
+              the longest option string (e.g. for ``"-f", "--foo"``
+              the env var to set is ``PET_FOO`` not ``PET_F``)
+
+    Example:
+    ::
+
+     parser.add_argument("-f", "--foo", action=env, default="bar")
+
+     ./program                                      -> args.foo="bar"
+     ./program -f baz                               -> args.foo="baz"
+     ./program --foo baz                            -> args.foo="baz"
+     PET_FOO="env_bar" ./program -f baz    -> args.foo="baz"
+     PET_FOO="env_bar" ./program --foo baz -> args.foo="baz"
+     PET_FOO="env_bar" ./program           -> args.foo="env_bar"
+
+     parser.add_argument("-f", "--foo", action=env, required=True)
+
+     ./program                                      -> fails
+     ./program -f baz                               -> args.foo="baz"
+     PET_FOO="env_bar" ./program           -> args.foo="env_bar"
+     PET_FOO="env_bar" ./program -f baz    -> args.foo="baz"
+    """
+
+    def __init__(self, dest, default=None, required=False, **kwargs) -> None:
+        env_name = f"PET_{dest.upper()}"
+        default = os.environ.get(env_name, default)
+
+        # ``required`` means that it NEEDS to be present  in the command-line args
+        # rather than "this option requires a value (either set explicitly or default"
+        # so if we found default then we don't "require" it to be in the command-line
+        # so set it to False
+        if default:
+            required = False
+
+        super().__init__(dest=dest, default=default, required=required, **kwargs)
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, values)
+
+
+class check_env(Action):
+    """
+    Check whether the env var ``PET_{dest}`` exists before defaulting to the given ``default`` value.
+
+    Equivalent to
+    ``store_true`` argparse built-in action except that the argument can
+    be omitted from the commandline if the env var is present and has a
+    non-zero value.
+
+    .. note:: it is redundant to pass ``default=True`` for arguments
+              that use this action because a flag should be ``True``
+              when present and ``False`` otherwise.
+
+    Example:
+    ::
+
+     parser.add_argument("--verbose", action=check_env)
+
+     ./program                                  -> args.verbose=False
+     ./program --verbose                        -> args.verbose=True
+     PET_VERBOSE=1 ./program           -> args.verbose=True
+     PET_VERBOSE=0 ./program           -> args.verbose=False
+     PET_VERBOSE=0 ./program --verbose -> args.verbose=True
+
+    Anti-pattern (don't do this):
+
+    ::
+
+     parser.add_argument("--verbose", action=check_env, default=True)
+
+     ./program                                  -> args.verbose=True
+     ./program --verbose                        -> args.verbose=True
+     PET_VERBOSE=1 ./program           -> args.verbose=True
+     PET_VERBOSE=0 ./program           -> args.verbose=False
+
+    """
+
+    def __init__(self, dest, default=False, **kwargs) -> None:
+        env_name = f"PET_{dest.upper()}"
+        default = bool(int(os.environ.get(env_name, "1" if default else "0")))
+        super().__init__(dest=dest, const=True, default=default, nargs=0, **kwargs)
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, self.const)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/autograd/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/autograd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1cf0aec6140f8c61e36ca90de5165399447b290
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/autograd/__init__.py
@@ -0,0 +1,53 @@
+# mypy: allow-untyped-defs
+
+import torch
+
+
+def is_available():
+    return hasattr(torch._C, "_dist_autograd_init")
+
+
+if is_available() and not torch._C._dist_autograd_init():
+    raise RuntimeError("Failed to initialize torch.distributed.autograd")
+
+if is_available():
+    from torch._C._distributed_autograd import (
+        _current_context,
+        _get_debug_info,
+        _get_max_id,
+        _init,
+        _is_valid_context,
+        _new_context,
+        _release_context,
+        _retrieve_context,
+        backward,
+        DistAutogradContext,
+        get_gradients,
+    )
+
+
+class context:
+    """
+    Context object to wrap forward and backward passes when using
+    distributed autograd. The ``context_id`` generated in the ``with``
+    statement  is required to uniquely identify a distributed backward pass
+    on all workers. Each worker stores metadata associated with this
+    ``context_id``, which is required to correctly execute a distributed
+    autograd pass.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> import torch.distributed.autograd as dist_autograd
+        >>> with dist_autograd.context() as context_id:
+        >>>     t1 = torch.rand((3, 3), requires_grad=True)
+        >>>     t2 = torch.rand((3, 3), requires_grad=True)
+        >>>     loss = rpc.rpc_sync("worker1", torch.add, args=(t1, t2)).sum()
+        >>>     dist_autograd.backward(context_id, [loss])
+    """
+
+    def __enter__(self):
+        self.autograd_context = _new_context()
+        return self.autograd_context._context_id()
+
+    def __exit__(self, type, value, traceback):
+        _release_context(self.autograd_context._context_id())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/autograd/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/autograd/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..59b2ec86bf264edae6663572a13b486d7d9be80b
Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/autograd/__pycache__/__init__.cpython-310.pyc differ
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/c10d_logger.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/c10d_logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4dfb2b99e8243f948422720f1e7a843ed99fea8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/c10d_logger.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import functools
+import logging
+from typing import Any, Callable, TypeVar
+from typing_extensions import ParamSpec
+
+import torch
+import torch.distributed as dist
+from torch.distributed.logging_handlers import _log_handlers
+from torch.monitor import _WaitCounter
+
+
+__all__: list[str] = []
+
+_DEFAULT_DESTINATION = "default"
+
+
+def _get_or_create_logger(destination: str = _DEFAULT_DESTINATION) -> logging.Logger:
+    logging_handler, log_handler_name = _get_logging_handler(destination)
+    logger = logging.getLogger(f"c10d-{log_handler_name}")
+    logger.setLevel(logging.DEBUG)
+    formatter = logging.Formatter(
+        "%(asctime)s %(filename)s:%(lineno)s %(levelname)s p:%(processName)s t:%(threadName)s: %(message)s"
+    )
+    logging_handler.setFormatter(formatter)
+    logger.propagate = False
+    logger.addHandler(logging_handler)
+    return logger
+
+
+def _get_logging_handler(
+    destination: str = _DEFAULT_DESTINATION,
+) -> tuple[logging.Handler, str]:
+    log_handler = _log_handlers[destination]
+    log_handler_name = f"{type(log_handler).__name__}-{destination}"
+    return (log_handler, log_handler_name)
+
+
+global _c10d_logger
+_c10d_logger = _get_or_create_logger()
+
+
+def _get_msg_dict(func_name, *args, **kwargs) -> dict[str, Any]:
+    if dist.is_initialized():
+        group = kwargs.get("group") or kwargs.get("process_group")
+        msg_dict = {
+            "func_name": f"{func_name}",
+            "pg_name": f"{dist._get_process_group_name(kwargs.get('pg'))}",  # type: ignore[arg-type]
+            "backend": f"{dist.get_backend(group)}",
+            "world_size": f"{dist.get_world_size()}",
+            "group_size": f"{dist.get_world_size(group)}",
+            "global_rank": f"{dist.get_rank()}",
+            "local_rank": f"{dist.get_rank(group)}",
+        }
+        if msg_dict["backend"] == "nccl":
+            nccl_version = torch.cuda.nccl.version()
+            msg_dict["nccl_version"] = ".".join(str(v) for v in nccl_version)
+    else:
+        msg_dict = {
+            "func_name": f"{func_name}",
+        }
+    return msg_dict
+
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+
+def _exception_logger(func: Callable[_P, _T]) -> Callable[_P, _T]:
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T:
+        try:
+            return func(*args, **kwargs)
+        except Exception as error:
+            msg_dict = _get_msg_dict(func.__name__, *args, **kwargs)
+            msg_dict["error"] = f"{error}"
+            _c10d_logger.debug(msg_dict)
+            raise
+
+    return wrapper
+
+
+def _time_logger(func: Callable[_P, _T]) -> Callable[_P, _T]:
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T:
+        with _WaitCounter(f"pytorch.wait_counter.c10d.{func.__name__}").guard():
+            func_return = func(*args, **kwargs)
+        return func_return
+
+    return wrapper
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecd8b0723490c299e645bf3f349092dd8e6156c3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/__init__.py
@@ -0,0 +1,16 @@
+from . import _extension
+from ._hf_storage import _HuggingFaceStorageReader, _HuggingFaceStorageWriter
+from .api import CheckpointException
+from .default_planner import DefaultLoadPlanner, DefaultSavePlanner
+from .filesystem import FileSystemReader, FileSystemWriter
+from .metadata import (
+    BytesStorageMetadata,
+    ChunkStorageMetadata,
+    Metadata,
+    TensorStorageMetadata,
+)
+from .optimizer import load_sharded_optimizer_state_dict
+from .planner import LoadPlan, LoadPlanner, ReadItem, SavePlan, SavePlanner, WriteItem
+from .state_dict_loader import load, load_state_dict
+from .state_dict_saver import async_save, save, save_state_dict
+from .storage import StorageReader, StorageWriter
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_executor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..7da04c12b4b8aae14bcdd7eaf4c279115d8501b6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_executor.py
@@ -0,0 +1,32 @@
+# pyre-strict
+# mypy: allow-untyped-defs
+import abc
+import os
+from concurrent.futures import Future
+from typing import Optional, Union
+
+import torch.distributed as dist
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+from torch.distributed.checkpoint.planner import SavePlanner
+from torch.distributed.checkpoint.storage import StorageWriter
+
+
+class _AsyncCheckpointExecutor(abc.ABC):
+    @abc.abstractmethod
+    def execute_save(
+        self,
+        staged_state_dict: STATE_DICT_TYPE,
+        *,
+        checkpoint_id: Union[str, os.PathLike, None] = None,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+        process_group: Optional[dist.ProcessGroup] = None,
+    ) -> Future:
+        """
+        Execute the checkpoint save request asynchronously.
+
+        This method is intended to be used as an abstraction for
+        implementing async checkpointing. The actual checkpoint save
+        operation is executed in a separate thread or process depending
+        on the implementation of this interface.
+        """
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_process_executor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_process_executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..801c8d79e8d6e56834f0befa06a271ebfaf65c0d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_process_executor.py
@@ -0,0 +1,307 @@
+# pyre-strict
+# mypy: allow-untyped-defs
+import logging
+import os
+from concurrent.futures import Future, ThreadPoolExecutor
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Optional, Union
+from uuid import uuid4
+
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from torch.distributed.checkpoint._async_executor import _AsyncCheckpointExecutor
+from torch.distributed.checkpoint.logger import _dcp_method_logger, _init_logger
+from torch.distributed.checkpoint.metadata import Metadata, STATE_DICT_TYPE
+from torch.distributed.checkpoint.planner import SavePlanner
+from torch.distributed.checkpoint.storage import StorageWriter
+from torch.distributed.checkpoint.utils import _DistWrapper
+from torch.distributed.elastic.agent.server.api import _get_fq_hostname
+from torch.distributed.elastic.utils.distributed import get_free_port
+
+
+logger = logging.getLogger()
+
+
+class _CheckpointSaveProcessControlOpts(Enum):
+    INIT_COMPLETE = "init_complete"
+    TERMINATE = "terminate"
+
+
+@dataclass(init=False, unsafe_hash=True)
+class _CheckpointRequestIdentifier:
+    checkpoint_id: Union[str, os.PathLike, None]
+    uuid: str
+
+    def __init__(self, checkpoint_id: Union[str, os.PathLike, None]):
+        self.checkpoint_id = checkpoint_id
+        self.uuid = str(uuid4())
+
+
+@dataclass
+class _AsyncCheckpointRequest:
+    staged_state_dict: STATE_DICT_TYPE
+    checkpoint_request_id: _CheckpointRequestIdentifier
+    storage_writer: Optional[StorageWriter] = None
+    planner: Optional[SavePlanner] = None
+
+
+@dataclass(init=False)
+class _ProcessGroupInitInfo:
+    local_rank: int
+    global_rank: int
+    world_size: int
+    tcp_store_master_addr: str
+    tcp_store_master_port: int
+
+    def __init__(self, process_group: Optional[dist.ProcessGroup] = None):
+        self.local_rank = dist.get_node_local_rank(fallback_rank=0)
+        self.global_rank = dist.get_rank(process_group)
+        self.world_size = dist.get_world_size(process_group)
+
+        # Let coordinator rank find a free port on the localhost.
+        # Broadcast the (master_addr, free_port) to all ranks; each rank in the
+        # checkpoint daemon process will use TCPStore (master_addr, master_port)
+        # for collective communication.
+        dist_wrapper: _DistWrapper = _DistWrapper(
+            group=process_group,
+            use_dist=True,
+            coordinator_rank=0,
+        )
+
+        def get_master_addr_and_port() -> tuple[str, int]:
+            master_addr = os.environ.get("MASTER_ADDR")
+            if master_addr is None:
+                master_addr = _get_fq_hostname()
+            return master_addr, get_free_port()
+
+        self.tcp_store_master_addr, self.tcp_store_master_port = dist_wrapper.broadcast(
+            step="get_master_addr_and_port",
+            map_fun=get_master_addr_and_port,
+        )
+
+
+class _AsyncCheckpointProcess:
+    def __init__(
+        self,
+        pg_init_info: _ProcessGroupInitInfo,
+    ):
+        self.ctx = mp.get_context("spawn")
+        self._mp_queue_send: mp.Queue = self.ctx.Queue()
+        self._mp_queue_recv: mp.Queue = self.ctx.Queue()
+
+        self._save_process = self.ctx.Process(
+            target=self._checkpointing_subprocess,
+            args=(
+                pg_init_info,
+                self._mp_queue_send,
+                self._mp_queue_recv,
+            ),
+            daemon=True,
+        )
+
+        self._save_process.start()
+        response = self._wait_for_response()
+        assert response == _CheckpointSaveProcessControlOpts.INIT_COMPLETE
+
+    def __del__(self) -> None:
+        if self._save_process.is_alive():
+            logger.info("Terminating the checkpoint background process...")
+            self._mp_queue_send.put(_CheckpointSaveProcessControlOpts.TERMINATE)
+            self._save_process.join()
+
+    def save(
+        self,
+        staged_state_dict: STATE_DICT_TYPE,
+        *,
+        checkpoint_id: Union[str, os.PathLike, None] = None,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+    ) -> Metadata:
+        # Create a unique identifier to locate requests/responses
+        # from the checkpoint daemon process.
+        checkpoint_request_id = _CheckpointRequestIdentifier(checkpoint_id)
+        async_cp_request = _AsyncCheckpointRequest(
+            staged_state_dict=staged_state_dict,
+            checkpoint_request_id=checkpoint_request_id,
+            storage_writer=storage_writer,
+            planner=planner,
+        )
+        self._mp_queue_send.put(async_cp_request)
+        result = self._wait_for_response()
+        assert isinstance(result, Metadata)
+        return result
+
+    def _wait_for_response(self) -> Any:
+        if not self._save_process.is_alive():
+            logger.info("Checkpoint background process is dead calling join()...")
+            self._save_process.join()
+            raise RuntimeError("Checkpoint background process is dead.")
+        response = self._mp_queue_recv.get()
+        if isinstance(response, BaseException):
+            raise response
+        return response
+
+    @staticmethod
+    def _execute_save(
+        state_dict: STATE_DICT_TYPE,
+        *,
+        checkpoint_request_id: _CheckpointRequestIdentifier,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+    ) -> Metadata:
+        from torch.distributed.checkpoint.state_dict_saver import save
+
+        metadata = save(
+            state_dict,
+            checkpoint_id=checkpoint_request_id.checkpoint_id,
+            storage_writer=storage_writer,
+            planner=planner,
+        )
+        return metadata
+
+    @staticmethod
+    def _checkpointing_subprocess(
+        pg_init_info: _ProcessGroupInitInfo,
+        recv: mp.Queue,
+        send: mp.Queue,
+    ) -> None:
+        try:
+            _init_logger(pg_init_info.global_rank)
+
+            # Setup environment variables for process group initialization.
+            os.environ["TORCHELASTIC_USE_AGENT_STORE"] = "False"
+            os.environ["MASTER_ADDR"] = pg_init_info.tcp_store_master_addr
+            os.environ["MASTER_PORT"] = str(pg_init_info.tcp_store_master_port)
+            os.environ["LOCAL_RANK"] = str(pg_init_info.local_rank)
+            os.environ["RANK"] = str(pg_init_info.global_rank)
+            os.environ["WORLD_SIZE"] = str(pg_init_info.world_size)
+
+            logger.info(
+                "Initializing dist.ProcessGroup in checkpoint background process"
+            )
+            # NOTE: GLOO backend is enforced here.
+            dist.init_process_group(backend=dist.Backend.GLOO)
+            dist.barrier()
+
+            logger.info("Checkpoint background process is running...")
+            send.put(_CheckpointSaveProcessControlOpts.INIT_COMPLETE)
+
+            # Serving loop.
+            while True:
+                logger.info("Waiting for checkpoint save request...")
+                obj = recv.get()
+                if (
+                    isinstance(obj, _CheckpointSaveProcessControlOpts)
+                    and obj == _CheckpointSaveProcessControlOpts.TERMINATE
+                ):
+                    logger.info("Terminating the checkpoint background process.")
+                    return
+                assert isinstance(obj, _AsyncCheckpointRequest)
+                logger.info(
+                    f"Received async checkpoint request with id={obj.checkpoint_request_id.checkpoint_id}"  # noqa: G004
+                )
+
+                response = _AsyncCheckpointProcess._execute_save(
+                    obj.staged_state_dict,
+                    checkpoint_request_id=obj.checkpoint_request_id,
+                    storage_writer=obj.storage_writer,
+                    planner=obj.planner,
+                )
+                send.put(response)
+                logger.info(
+                    f"Submitted checkpoint save request for checkpoint_id={obj.checkpoint_request_id}"  # noqa: G004
+                )
+        except BaseException as e:
+            logger.error(
+                f"Checkpoint background process encountered an exception: {e}"  # noqa: G004
+            )
+            send.put(e)
+            raise
+        finally:
+            logger.info("Checkpoint background process is shutting down...")
+            dist.destroy_process_group()
+
+
+_CHECKPOINT_PROCESS: Optional[_AsyncCheckpointProcess] = None
+
+
+class _ProcessBasedAsyncCheckpointExecutor(_AsyncCheckpointExecutor):
+    def __init__(self) -> None:
+        self._executor = ThreadPoolExecutor(max_workers=1)
+
+    @staticmethod
+    def _execute_save_impl(
+        *,
+        pg_init_info: Optional[_ProcessGroupInitInfo],
+        staged_state_dict: STATE_DICT_TYPE,
+        checkpoint_id: Union[str, os.PathLike, None] = None,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+        process_group: Optional[dist.ProcessGroup] = None,
+    ) -> Metadata:
+        global _CHECKPOINT_PROCESS
+        if _CHECKPOINT_PROCESS is None:
+            assert pg_init_info is not None
+            ckpt_kwargs = {}
+            if (ckpt_id := getattr(storage_writer, "checkpoint_id", None)) is not None:
+                ckpt_kwargs["checkpoint_id"] = ckpt_id
+                ckpt_kwargs["process_group"] = process_group
+
+            @_dcp_method_logger(**ckpt_kwargs)
+            def create_checkpoint_daemon_process() -> None:
+                global _CHECKPOINT_PROCESS
+                _CHECKPOINT_PROCESS = _AsyncCheckpointProcess(pg_init_info=pg_init_info)
+
+            create_checkpoint_daemon_process()
+
+        assert _CHECKPOINT_PROCESS is not None
+        return _CHECKPOINT_PROCESS.save(
+            staged_state_dict=staged_state_dict,
+            checkpoint_id=checkpoint_id,
+            storage_writer=storage_writer,
+            planner=planner,
+        )
+
+    def execute_save(
+        self,
+        staged_state_dict: STATE_DICT_TYPE,
+        *,
+        checkpoint_id: Union[str, os.PathLike, None] = None,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+        process_group: Optional[dist.ProcessGroup] = None,
+    ) -> Future:
+        """
+        NOTE:
+
+        - Checkpoint process is implemented as a daemon process.
+        The AsyncCheckpointProcess' lifetime is tied to the lifetime of the
+        main process (e.g. trainer process).
+
+        - The first call to execute_save_in_process() will initialize the checkpoint
+        daemon process. Subsequent async checkpoint requests will not need process
+        initialization. Therefore, the first async checkpoint request will take longer to complete.
+
+        - Process initialization can have significant overhead, dominated by latency for all ranks to spawn
+        a background process + process group initialization in the background process.
+        """
+
+        global _CHECKPOINT_PROCESS
+        pg_init_info: Optional[_ProcessGroupInitInfo] = None
+        if _CHECKPOINT_PROCESS is None:
+            # Find a free port on coordinator rank and broadcast
+            # to all ranks.
+            pg_init_info = _ProcessGroupInitInfo(process_group)
+
+        f: Future = self._executor.submit(
+            self._execute_save_impl,
+            pg_init_info=pg_init_info,
+            staged_state_dict=staged_state_dict,
+            checkpoint_id=checkpoint_id,
+            storage_writer=storage_writer,
+            planner=planner,
+        )
+        f.add_done_callback(lambda f: self._executor.shutdown(wait=False))
+
+        return f
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_thread_executor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_thread_executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..541ad1d8c8eb3cadb0a4e81ce4bdc64b01b354be
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_thread_executor.py
@@ -0,0 +1,39 @@
+# pyre-strict
+# mypy: allow-untyped-defs
+import os
+from concurrent.futures import Future, ThreadPoolExecutor
+from typing import Optional, Union
+
+import torch.distributed as dist
+from torch.distributed.checkpoint._async_executor import _AsyncCheckpointExecutor
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+from torch.distributed.checkpoint.planner import SavePlanner
+from torch.distributed.checkpoint.storage import StorageWriter
+
+
+class _ThreadBasedAsyncCheckpointExecutor(_AsyncCheckpointExecutor):
+    def __init__(self) -> None:
+        self._executor = ThreadPoolExecutor(max_workers=1)
+
+    def execute_save(
+        self,
+        staged_state_dict: STATE_DICT_TYPE,
+        *,
+        checkpoint_id: Union[str, os.PathLike, None] = None,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+        process_group: Optional[dist.ProcessGroup] = None,
+    ) -> Future:
+        from torch.distributed.checkpoint.state_dict_saver import save
+
+        f: Future = self._executor.submit(
+            save,
+            staged_state_dict,
+            checkpoint_id=checkpoint_id,
+            storage_writer=storage_writer,
+            planner=planner,
+            process_group=process_group,
+        )
+        f.add_done_callback(lambda f: self._executor.shutdown(wait=False))
+
+        return f
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_checkpointer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_checkpointer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d35c8b59ca36091d65f470fd983b692b252f525b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_checkpointer.py
@@ -0,0 +1,100 @@
+from concurrent.futures import Future
+from typing import Any, Optional
+
+import torch.distributed as dist
+import torch.distributed.checkpoint.state_dict_loader as loader
+import torch.distributed.checkpoint.state_dict_saver as saver
+from torch.distributed.checkpoint.metadata import Metadata, STATE_DICT_TYPE
+from torch.distributed.checkpoint.storage import (
+    LoadPlanner,
+    SavePlanner,
+    StorageReader,
+    StorageWriter,
+)
+
+
+__all__: list[str] = []
+
+
+class _Checkpointer:
+    """This base class specefies a high level API for saving and loading
+    distributed `state_dict` 's. It provides an abstraction over the low-level APIs
+    provided by :py:mod:`torch.distributed.checkpoint.storage`, essentially calling
+    :py:meth: `torch.distributed.state_dict_saver.save` and
+    :py:meth: `torch.distributed.state_dict_loader.load` with the provided storage
+    readers and writers.
+
+    .. warning::
+        This feature is experimental and subject to removal/change.
+
+    """
+
+    def __init__(
+        self,
+        storage_writer: StorageWriter,
+        storage_reader: StorageReader,
+        *,
+        process_group: Optional[dist.ProcessGroup] = None,
+        coordinator_rank: int = 0,
+        no_dist: bool = False,
+        load_planner: Optional[LoadPlanner] = None,
+        save_planner: Optional[SavePlanner] = None,
+    ):
+        """Initializes the Checkpointer instance.
+
+        Args:
+            storage_writer: Instance of StorageWrite use to perform writes.
+            storage_reader: StorageReader used to load data from.
+            process_group: ProcessGroup to be used for cross-rank synchronization.
+            coordinator_rank: Rank to use to coordinate the checkpoint. rank0 is used by default.
+            no_dist: If ``True``, distributed checkpoint will not load in SPMD style. (Default: ``False``)
+            loader_planner: Instance of LoadPlanner to use when loading.
+            save_planner: Instance of SavePlanner to use when saving.
+        """
+        self.storage_writer = storage_writer
+        self.storage_reader = storage_reader
+        self.process_group = process_group
+        self.coordinator_rank = coordinator_rank
+        self.no_dist = no_dist
+        self.load_planner = load_planner
+        self.save_planner = save_planner
+
+    def save(
+        self,
+        state_dict: STATE_DICT_TYPE,
+    ) -> Metadata:
+        """Calls :py:meth: `torch.distributed.state_dict_saver.save`. Utilizing values passed during initialization."""
+        return saver.save(
+            state_dict,
+            self.storage_writer,
+            process_group=self.process_group,
+            coordinator_rank=self.coordinator_rank,
+            no_dist=self.no_dist,
+            planner=self.save_planner,
+        )
+
+    def async_save(
+        self,
+        state_dict: STATE_DICT_TYPE,
+    ) -> Future:
+        """
+        Calls :py:meth: `torch.distributed.state_dict_saver._async_save`. Utilizing values passed during initialization.
+
+        Returns:
+            Future: A future holding the resultant Metadata object from `save`.
+        """
+        return saver.async_save(
+            state_dict,
+            storage_writer=self.storage_writer,
+            process_group=self.process_group,
+            planner=self.save_planner,
+        )
+
+    def load(self, state_dict: dict[str, Any]) -> None:
+        """Calls :py:meth: `torch.distributed.state_dict_loader.load`. Utilizing values passed during initialization."""
+        loader.load(
+            state_dict,
+            storage_reader=self.storage_reader,
+            process_group=self.process_group,
+            planner=self.load_planner,
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_save_plans.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_save_plans.py
new file mode 100644
index 0000000000000000000000000000000000000000..e416030a816a899c758b0a16ff125929448de9cc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_save_plans.py
@@ -0,0 +1,60 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import dataclasses
+from collections import defaultdict
+from typing import TYPE_CHECKING
+
+from torch.distributed.checkpoint.planner import SavePlan, WriteItem
+
+
+if TYPE_CHECKING:
+    from torch.distributed.checkpoint.metadata import MetadataIndex
+
+__all__ = ["dedup_save_plans"]
+
+
+def dedup_save_plans(
+    all_plans: list[SavePlan],
+    save_to_lowest_rank: bool = False,
+) -> list[SavePlan]:
+    """
+    Removes duplicate entries from appearing on multiple SavePlans. For each duplicate across
+    a set of SavePlans, only the smallest SavePlan in terms of planned storage keeps the entry.
+    """
+
+    write_item_to_plan_indices: dict[MetadataIndex, set[int]] = defaultdict(set)
+    write_item_idx_to_write_item: dict[MetadataIndex, WriteItem] = {}
+    for plan_idx, plan in enumerate(all_plans):
+        for write_item in plan.items:
+            # map each write item to its plan
+            write_item_to_plan_indices[write_item.index].add(plan_idx)
+            write_item_idx_to_write_item[write_item.index] = write_item
+
+    # put item in the plan with the smallest size and remove it from the other plan_indices
+    to_remove: list[set] = [set() for _ in range(len(all_plans))]
+    plan_to_size = [0] * len(all_plans)
+    for write_item_idx, plan_indices in write_item_to_plan_indices.items():
+        if save_to_lowest_rank:
+            select_plan_idx = min(plan_indices)
+        else:
+            select_plan_idx = min(
+                plan_indices, key=lambda plan_idx: plan_to_size[plan_idx]
+            )
+
+        write_item = write_item_idx_to_write_item[write_item_idx]
+        # essentially ignores the storage size of anything that is not a tensor, since
+        # we don't know how much storage they represent
+        plan_to_size[select_plan_idx] += write_item.tensor_storage_size() or 1
+
+        plan_indices.remove(select_plan_idx)
+        for plan_idx in plan_indices:
+            to_remove[plan_idx].add(write_item_idx)
+
+    for plan_idx, remove_set in enumerate(to_remove):
+        new_items = [
+            write_item
+            for write_item in all_plans[plan_idx].items
+            if write_item.index not in remove_set
+        ]
+        all_plans[plan_idx] = dataclasses.replace(all_plans[plan_idx], items=new_items)
+
+    return all_plans
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_tensors.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_tensors.py
new file mode 100644
index 0000000000000000000000000000000000000000..c57b2e149106abbac66522aa571d1a462db4157d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_tensors.py
@@ -0,0 +1,62 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import dataclasses
+import logging
+from typing import TYPE_CHECKING
+
+from torch.distributed.checkpoint.planner import SavePlan
+
+
+if TYPE_CHECKING:
+    from torch.distributed.checkpoint.metadata import MetadataIndex
+
+__all__ = ["dedup_tensors"]
+
+
+def init_logger() -> logging.Logger:
+    logger = logging.getLogger(__name__)
+    level = logging.INFO
+    logger.setLevel(level)
+    console = logging.StreamHandler()
+    formatter = logging.Formatter(
+        "%(asctime)s %(filename)s:%(lineno)s %(levelname)s p:%(processName)s t:%(threadName)s: %(message)s"
+    )
+    console.setFormatter(formatter)
+    console.setLevel(level)
+    logger.addHandler(console)
+    logger.propagate = False
+    return logger
+
+
+logger = init_logger()
+
+
+# TODO add docstring for dedup_tensors
+def dedup_tensors(all_plans: list[SavePlan]) -> list[SavePlan]:
+    all_plans = list(all_plans)
+    key_to_plan: dict[MetadataIndex, list[int]] = {}
+    for plan_idx, plan in enumerate(all_plans):
+        for write_item in plan.items:
+            key_to_plan.setdefault(write_item.index, []).append(plan_idx)
+
+    replicated_items = {k: v for k, v in key_to_plan.items() if len(v) > 1}
+
+    # Remove duplicates by always keeping the first entry.
+    # Compute the per-rank remove set.
+    plan_to_keys: dict[int, list[MetadataIndex]] = {}
+    for key, plans in replicated_items.items():
+        for plan_idx in plans[1:]:
+            plan_to_keys.setdefault(plan_idx, []).append(key)
+    if len(plan_to_keys) > 0:
+        logger.info("Duplicate keys to remove: %s", plan_to_keys)
+
+    for plan_idx, keys in plan_to_keys.items():
+        key_set = set(keys)
+        # rewrite items and remove elements
+        new_items = [
+            write_item
+            for write_item in all_plans[plan_idx].items
+            if write_item.index not in key_set
+        ]
+        all_plans[plan_idx] = dataclasses.replace(all_plans[plan_idx], items=new_items)
+
+    return all_plans
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_extension.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c56dd0b36e1e9567306cc7185338899205e8e76
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_extension.py
@@ -0,0 +1,221 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import abc
+import io
+from collections.abc import Sequence
+from typing import cast, IO, Optional
+
+# introduced as collections.abc.Buffer in Python 3.12
+from typing_extensions import Buffer
+
+from torch._utils import try_import
+
+
+# NOTE: everything in this file is experimental, and subject to
+# change.  Feedback and bug fixes are always welcome.
+
+pyzstd_module_name = "pyzstd"
+pyzstd = try_import(pyzstd_module_name)
+zstandard_module_name = "zstandard"
+zstandard = try_import(zstandard_module_name)
+
+
+__all__ = [
+    "Extension",
+    "StreamTransformExtension",
+    "ZStandard",
+    "ExtensionRegistry",
+]
+
+
+class Extension(abc.ABC):
+    """
+    Extensions provide modular additions to functionality within distributed checkpointing,
+    which affect the layout or format of the written artifacts.  Extensions may be
+    built into pytorch, or provided externally.
+
+    When writing, the caller provides a list of extension instances of the appropriate
+    type.  Each extension can output a descriptor which is used to reconstitute the
+    extension at read-time.
+    """
+
+    @staticmethod
+    @abc.abstractmethod
+    def registry_name() -> str:
+        """
+        See ExtensionRegistry.from_descriptor_list
+        """
+
+    @staticmethod
+    @abc.abstractmethod
+    def from_descriptor(version: str) -> "Extension":
+        """
+        See ExtensionRegistry.from_descriptor_list
+        """
+
+    @abc.abstractmethod
+    def get_descriptor(self) -> str:
+        """
+        Return descriptor name to be included in metadata.  The form should be
+        "extension_name[@local-domain][/version]".
+        """
+
+
+class StreamTransformExtension(Extension):
+    """
+    An extension which performs transformation on a byte stream, such as compression
+    or encryption.
+
+    Implementations should try to be memory friendly and performant.  For example, don't
+    read the whole input, then transform it, and write it back.  If at all possible, do it in
+    chunks.  But, don't read/transform/write one byte at a time, either.
+    """
+
+    @abc.abstractmethod
+    def transform_to(self, output: IO[bytes]) -> IO[bytes]:
+        """
+        Takes a writeable output stream, and generates a new stream which implements the
+        output transform.  Input data written to the returned stream will be transformed
+        and written to the `output` argument stream.
+        """
+
+    @abc.abstractmethod
+    def transform_from(self, input: IO[bytes]) -> IO[bytes]:
+        """
+        Takes a readable input stream, and generates a new stream which implements the
+        input transform.  When the returned stream is read, data will be read from the
+        'input' stream, transformed, and returned.
+        """
+
+
+class ZStandard(StreamTransformExtension):
+    @staticmethod
+    def is_available() -> bool:
+        return zstandard is not None or pyzstd is not None
+
+    @staticmethod
+    def from_descriptor(version: str) -> "ZStandard":
+        if version.partition(".")[0] != "1":
+            raise ValueError(f"Unknown extension {version=}")
+        if not ZStandard.is_available():
+            raise ValueError(
+                f"Stream with ZStandard compression cannot be processed because "
+                f"no module named '{zstandard_module_name}' or '{pyzstd_module_name}'"
+            )
+        return ZStandard()
+
+    @staticmethod
+    def registry_name() -> str:
+        return "stream.zstd"
+
+    def __init__(self) -> None:
+        super().__init__()
+        if not ZStandard.is_available():
+            raise ValueError(
+                f"ZStandard extension is unavailable because no module named '{zstandard_module_name}' or '{pyzstd_module_name}'"
+            )
+
+    def get_descriptor(self) -> str:
+        return f"{self.registry_name()}/1"
+
+    def transform_to(self, output: IO[bytes]) -> IO[bytes]:
+        if zstandard is not None:
+            compressor = zstandard.ZstdCompressor()  # type: ignore[union-attr]
+            return compressor.stream_writer(output)
+
+        class Writer(io.RawIOBase):
+            def __init__(self, output: IO[bytes]) -> None:
+                self.output = output
+                self.compressor = pyzstd.ZstdCompressor()  # type: ignore[union-attr]
+
+            def writeable(self) -> bool:
+                return True
+
+            def write(self, b: Buffer) -> Optional[int]:
+                outdata = self.compressor.compress(b)
+                if outdata:
+                    self.output.write(outdata)
+                return len(memoryview(b))
+
+            def flush(self) -> None:
+                outdata = self.compressor.flush()
+                if outdata:
+                    self.output.write(outdata)
+                self.output.flush()
+
+        return cast(IO[bytes], Writer(output))
+
+    def transform_from(self, input: IO[bytes]) -> IO[bytes]:
+        if zstandard is not None:
+            decompressor = zstandard.ZstdDecompressor()  # type: ignore[union-attr]
+            return decompressor.stream_reader(input)
+
+        class Reader(io.RawIOBase):
+            def __init__(self, input: IO[bytes]) -> None:
+                self.input = input
+                self.decompressor = pyzstd.EndlessZstdDecompressor()  # type: ignore[union-attr]
+
+            def readable(self) -> bool:
+                return True
+
+            def readinto(self, b: Buffer) -> Optional[int]:
+                # This needs to read enough so it can decompress
+                # something so the output doesn't look like EOF.  This
+                # means reading at least one block.  The max block
+                # size is 128KB, so we read that plus some
+                # overhead to be sure.
+
+                if self.decompressor.needs_input:
+                    indata = self.input.read((128 + 6) * 1024)
+                else:
+                    indata = b""
+
+                bview = memoryview(b)
+                blen = len(bview)
+                outdata = self.decompressor.decompress(indata, blen)
+                if outdata is None:
+                    return None
+
+                count = len(outdata)
+                bview[:count] = outdata
+                return count
+
+            def seekable(self) -> bool:
+                return False
+
+        return cast(IO[bytes], Reader(input))
+
+
+class ExtensionRegistry:
+    def __init__(self) -> None:
+        # Populate default registry contents
+        self.extensions: dict[str, type[Extension]] = {
+            cls.registry_name(): cls for cls in (ZStandard,)
+        }
+
+    def register(self, cls: type[Extension]) -> None:
+        self.extensions[cls.registry_name()] = cls
+
+    def from_descriptor_list(self, descriptors: Sequence[str]) -> Sequence[Extension]:
+        """
+        Given a seuquence of descriptor strings as returned by
+        Extension.get_descriptor at save time, creates a sequence of
+        Extension instances.  The name[@local-domain] preceding the
+        version number is used to look up an implementation class in
+        the registry, and the version is passed to the class's
+        from_descriptor static method.  If the registry contains no
+        match, this will throw ValueError.  If the from_descriptor
+        method raises an exception, that will pass through to the
+        caller.
+        """
+
+        def from_descriptor(desc: str) -> Extension:
+            name, _, version = desc.partition("/")
+            if version is None:
+                version = 0
+            ext = self.extensions.get(name)
+            if not ext:
+                raise ValueError(f"Unknown extension {name=}")
+            return ext.from_descriptor(version)
+
+        return [from_descriptor(desc) for desc in descriptors]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_fsspec_filesystem.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_fsspec_filesystem.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7b71bdf4b2bff955fd749c99014f5a67d639e8d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_fsspec_filesystem.py
@@ -0,0 +1,159 @@
+# Mypy will not try inferring the types of any 3rd party libraries installed.
+# mypy: ignore-errors
+
+import io
+import os
+from collections.abc import Generator, Sequence
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Optional, TYPE_CHECKING, Union
+
+from fsspec.core import url_to_fs
+
+from torch.distributed.checkpoint._extension import StreamTransformExtension
+from torch.distributed.checkpoint.filesystem import (
+    FileSystemBase,
+    FileSystemReader,
+    FileSystemWriter,
+)
+
+
+if TYPE_CHECKING:
+    from fsspec import AbstractFileSystem
+
+
+__all__ = [
+    "FsspecWriter",
+    "FsspecReader",
+]
+
+
+class FileSystem(FileSystemBase):
+    def __init__(self) -> None:
+        self.fs: Optional[AbstractFileSystem] = None
+
+    @contextmanager
+    def create_stream(
+        self, path: Union[str, os.PathLike], mode: str
+    ) -> Generator[io.IOBase, None, None]:
+        assert self.fs is not None
+        path = os.fspath(path)
+
+        # fsspec does not support concurrent transactions, and not all
+        # AbstractFileSystem have working rollback implementations, so
+        # just manually delete the file if necessary on errors.
+        with self.fs.open(path, mode) as stream:
+            try:
+                yield stream
+            except:  # noqa: B001,E722
+                if any(ch in mode for ch in "w+a"):  # cleanup file if not read-only
+                    try:
+                        self.rm_file(path)
+                    except:  # noqa: B001,E722
+                        pass
+                raise
+
+    def concat_path(
+        self, path: Union[str, os.PathLike], suffix: str
+    ) -> Union[str, os.PathLike]:
+        return os.path.join(path, suffix)
+
+    def init_path(
+        self, path: Union[str, os.PathLike], **kwargs
+    ) -> Union[str, os.PathLike]:
+        self.fs, _ = url_to_fs(path, **kwargs)
+        return path
+
+    def rename(
+        self, path: Union[str, os.PathLike], new_path: Union[str, os.PathLike]
+    ) -> None:
+        self.fs.rename(path, new_path)
+
+    def mkdir(self, path: Union[str, os.PathLike]) -> None:
+        self.fs.makedirs(path, exist_ok=True)
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        if isinstance(checkpoint_id, Path):
+            return False
+
+        try:
+            url_to_fs(checkpoint_id)
+        except ValueError:
+            return False
+
+        return True
+
+    def exists(self, path: Union[str, os.PathLike]) -> bool:
+        return self.fs.exists(path)
+
+    def rm_file(self, path: Union[str, os.PathLike]) -> None:
+        self.fs.rm(path)
+
+
+# TODO: add the dcp.async_save mixin
+class FsspecWriter(FileSystemWriter):
+    """
+    Basic implementation of StorageWriter using FFspec.
+
+    This implementation makes the following assumptions and simplifications:
+
+    * The checkpoint path is an empty or non-existing directory.
+    * File creation is atomic
+
+    The checkpoint consist of one file per write request plus
+    a `.metadata` file with the serialized metadata.
+
+    """
+
+    def __init__(
+        self,
+        path: Union[str, os.PathLike],
+        single_file_per_rank: bool = True,
+        sync_files: bool = True,
+        thread_count: int = 1,
+        per_thread_copy_ahead: int = 10_000_000,
+        overwrite: bool = True,
+        _extensions: Optional[Sequence[StreamTransformExtension]] = None,
+        **kwargs,
+    ) -> None:
+        """
+        Initialize the writer pointing to `path`.
+
+        Args:
+            path: directory where the checkpoint will be written to.
+            single_file_per_rank: Produce one file per rank instead of one file per tensor/blob. Default to True.
+            sync_files : force files to be synced to permanent storage. Default to True.
+            thread_count: Number of IO threads to use to write. Default to 1.
+            per_thread_copy_ahead: How many bytes to copy from the GPU ahead of saving then. Default 10Mb.
+            overwrite: Whether to allow overwriting existing checkpoints. Defaults to True.
+            _extensions: Extensions to apply to output streams (EXPERIMENTAL)
+
+        N. B. If sync_files is disabled, there's no guarantee that the checkpoint will be consistent in the case of a failure.
+        """
+        super().__init__(
+            path,
+            single_file_per_rank,
+            sync_files,
+            thread_count,
+            per_thread_copy_ahead,
+            overwrite=overwrite,
+            _extensions=_extensions,
+        )
+        self.fs = FileSystem()
+        self.path = self.fs.init_path(path, **kwargs)
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        return FileSystem.validate_checkpoint_id(checkpoint_id)
+
+
+class FsspecReader(FileSystemReader):
+    def __init__(self, path: Union[str, os.PathLike], **kwargs) -> None:
+        super().__init__(path)
+        self.fs = FileSystem()
+        self.path = self.fs.init_path(path, **kwargs)
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        return FileSystem.validate_checkpoint_id(checkpoint_id)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_hf_storage.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_hf_storage.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b81d5be751050922da15d3a8447015921fd1883
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_hf_storage.py
@@ -0,0 +1,200 @@
+# mypy: allow-untyped-defs
+import dataclasses
+import json
+import queue
+from typing import Optional
+
+import fsspec  # type: ignore[import-untyped]
+
+from torch.distributed.checkpoint._fsspec_filesystem import FsspecReader, FsspecWriter
+from torch.distributed.checkpoint.metadata import (
+    BytesStorageMetadata,
+    Metadata,
+    STORAGE_TYPES,
+    StorageMeta,
+)
+from torch.distributed.checkpoint.planner import (
+    LoadPlan,
+    LoadPlanner,
+    ReadItem,
+    SavePlan,
+    SavePlanner,
+    WriteItem,
+)
+from torch.distributed.checkpoint.storage import WriteResult
+from torch.futures import Future
+
+
+__all__ = ["_HuggingFaceStorageWriter", "_HuggingFaceStorageReader"]
+
+_metadata_fn: str = "model.safetensors.index.json"
+
+FILE_NAME = "model-{cpt_idx}-of-{num_shards}"
+SUFFIX = ".safetensors"
+
+
+class _HuggingFaceStorageWriter(FsspecWriter):
+    """
+    A writer that writes to a huggingface repository in the huggingface format.
+    Uses in Fsspec back-end to communicate with the huggingface hub.
+    """
+
+    def __init__(
+        self,
+        path: str,
+        fqn_to_index_mapping: dict[str, int],
+        token: Optional[str] = None,
+    ) -> None:
+        """
+        Initialize the huggingface writer pointing to path.
+
+        Args:
+            path: hf directory where the checkpoint will be written to. Should begin with hf://.
+            token: The token to use to authenticate with huggingface hub.
+            fqn_to_index_mapping: A mapping from tensor FQN to the index of the file that the tensor should be written to.
+                              Indices are from 1 to N, where N is the number of files.
+
+        """
+        from huggingface_hub import HfFileSystem  # type: ignore[import-not-found]
+
+        if HfFileSystem.protocol not in fsspec.available_protocols():
+            fsspec.register_implementation(HfFileSystem.protocol, HfFileSystem)
+
+        super().__init__(path=path, token=token)
+        self._fqn_to_index_mapping: dict[str, int] = fqn_to_index_mapping
+
+    def prepare_local_plan(self, plan: SavePlan) -> SavePlan:
+        super().prepare_local_plan(plan)
+        return dataclasses.replace(plan, storage_data=self._fqn_to_index_mapping)
+
+    def prepare_global_plan(self, plans: list[SavePlan]) -> list[SavePlan]:
+        assert len(plans) == 1, "distributed checkpointing is not yet supported"
+        return plans
+
+    def write_data(
+        self,
+        plan: SavePlan,
+        planner: SavePlanner,
+    ) -> Future[list[WriteResult]]:
+        # storage_plan is a map from key to file index
+        storage_plan: dict[str, int] = plan.storage_data
+
+        buckets = self._split_by_storage_plan(storage_plan, plan.items)
+        highest_index = max(buckets.keys())
+
+        file_queue: queue.Queue = queue.Queue()
+        for file_index, write_items in buckets.items():
+            file_name = self._gen_file_name(file_index, highest_index)
+            file_queue.put(
+                (self.fs.concat_path(self.path, file_name), file_name, write_items)
+            )
+
+        return super()._write_data(planner, file_queue, safe_tensors=True)
+
+    def finish(self, metadata: Metadata, results: list[list[WriteResult]]) -> None:
+        metadata_to_write = {}
+        storage_md = {}
+        total_size = 0
+        for wr_list in results:
+            storage_md.update(
+                {wr.index.fqn: wr.storage_data.relative_path for wr in wr_list}
+            )
+            total_size += sum([wr.storage_data.length for wr in wr_list])
+        metadata_to_write["metadata"] = {"total_size": total_size}
+        metadata_to_write["weight_map"] = storage_md
+
+        metadata_path = self.fs.concat_path(self.path, f"{_metadata_fn}")
+        with self.fs.create_stream(metadata_path, "w") as metadata_file:
+            json.dump(metadata_to_write, metadata_file, indent=2)
+
+    def _split_by_storage_plan(
+        self, storage_plan: dict[str, int], items: list[WriteItem]
+    ) -> dict[int, list[WriteItem]]:
+        # storage_plan is a map from key to index
+        buckets = {}
+        for item in items:
+            key = item.index.fqn
+            idx = storage_plan[key]
+            if idx not in buckets:
+                buckets[idx] = [item]
+            else:
+                buckets[idx].append(item)
+
+        return buckets
+
+    def _gen_file_name(self, index: int, largest_index: int) -> str:
+        return (
+            FILE_NAME.format(
+                cpt_idx=f"{index}".zfill(5), num_shards=f"{largest_index}".zfill(5)
+            )
+            + SUFFIX
+        )
+
+    @property
+    def metadata_path(self) -> str:
+        return _metadata_fn
+
+
+class _HuggingFaceStorageReader(FsspecReader):
+    """
+    A reader that reads from a huggingface repository in the huggingface format.
+    Uses in Fsspec back-end to communicate with the huggingface hub.
+    """
+
+    def __init__(self, path: str, token: Optional[str] = None) -> None:
+        """
+        Initialize the huggingface reader pointing to path.
+
+        Args:
+            path: hf directory where the checkpoint will be read from. Should begin with hf://.
+            token: The token to use to authenticate with huggingface hub.
+        """
+        from huggingface_hub import HfFileSystem  # type: ignore[import-not-found]
+
+        if HfFileSystem.protocol not in fsspec.available_protocols():
+            fsspec.register_implementation(HfFileSystem.protocol, HfFileSystem)
+        super().__init__(path=path, token=token)
+        self.storage_data: dict[str, str] = {}
+
+    def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
+        from safetensors.torch import load  # type: ignore[import-not-found]
+
+        per_file: dict[str, list[ReadItem]] = {}
+
+        for read_item in plan.items:
+            file_name = self.storage_data[read_item.storage_index.fqn]
+            per_file.setdefault(file_name, []).append(read_item)
+
+        for file_name, reqs in per_file.items():
+            new_path = self.fs.concat_path(self.path, file_name)
+            with self.fs.create_stream(new_path, "rb") as stream:
+                loaded_tensors = load(stream.read())
+                for req in reqs:
+                    tensor = loaded_tensors[req.dest_index.fqn]
+
+                    target_tensor = planner.resolve_tensor(req).detach()
+                    target_tensor.resize_(tensor.size())
+                    target_tensor.copy_(tensor)
+                    planner.commit_tensor(req, target_tensor)
+
+        fut: Future = Future()
+        fut.set_result(None)
+        return fut
+
+    def read_metadata(self) -> Metadata:
+        path = self.fs.concat_path(self.path, _metadata_fn)
+        with self.fs.create_stream(path, "r") as metadata_file:
+            metadata = json.load(metadata_file)
+
+        state_dict_metadata: dict[str, STORAGE_TYPES] = {}
+        for key in metadata["weight_map"].keys():
+            state_dict_metadata[key] = BytesStorageMetadata()
+        metadata = Metadata(
+            state_dict_metadata=state_dict_metadata, storage_data=metadata["weight_map"]
+        )
+
+        if getattr(metadata, "storage_meta", None) is None:
+            metadata.storage_meta = StorageMeta()
+        metadata.storage_meta.load_id = self.load_id
+
+        return metadata
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_nested_dict.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_nested_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb26058370f766fbb96e4a5f1530577234eed62a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_nested_dict.py
@@ -0,0 +1,69 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+
+from . import _version
+from ._traverse import (
+    OBJ_PATH,
+    set_element,
+    STATE_DICT_ITEM,
+    traverse_state_dict,
+    traverse_state_dict_v_2_3,
+)
+
+
+"""
+TODO:
+Need to add ability to handle tuple, OrderedDict, NamedTuple.
+Update mappings from dict to a class.
+Change set_element to recreate the right type for tuple, OrderedDict, and NamedTuple.
+"""
+
+
+FLATTEN_MAPPING = dict[str, OBJ_PATH]
+
+
+# TODO: Update Docstring for nested_dict.py
+def flatten_state_dict(
+    state_dict: STATE_DICT_TYPE,
+) -> tuple[STATE_DICT_TYPE, FLATTEN_MAPPING]:
+    """
+    Flatten ``state_dict`` made of nested dicts and lists into a top level dictionary.
+
+    Use ``unflatten_state_dict`` to revert this process.
+    Returns:
+        A tuple with the flatten state_dict and a mapping from original to new state_dict.
+    N.B. The new keys are derived from the object paths, joined by dot.
+        For example: ``{ 'a': {'b':...}}`` results in the key `a.b`.
+    """
+    flattened: STATE_DICT_TYPE = {}
+    mappings: FLATTEN_MAPPING = {}
+
+    def flat_copy(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        new_fqn = ".".join(map(str, path))
+        if new_fqn in flattened:
+            raise ValueError(f"duplicated flatten key {new_fqn}")
+        flattened[new_fqn] = value
+        mappings[new_fqn] = path
+
+    # We started to flatten dictionary since v2.4. But in order to not break
+    # the checkpoints that were saved before v2.4, we need to keep the old
+    # traversal so that we can reconstruct those checkpoints.
+    use_v_2_3 = (
+        _version._derived_version is not None and _version._derived_version == "2_3"
+    )
+    if use_v_2_3:
+        traverse_state_dict_v_2_3(state_dict, flat_copy)
+    else:
+        traverse_state_dict(state_dict, flat_copy)
+    return flattened, mappings
+
+
+def unflatten_state_dict(
+    state_dict: STATE_DICT_TYPE, mapping: FLATTEN_MAPPING
+) -> STATE_DICT_TYPE:
+    """Restore the original nested state_dict according to ``mapping`` and the flattened ``state_dict``."""
+    nested: STATE_DICT_TYPE = {}
+    for key, value in state_dict.items():
+        set_element(nested, mapping[key], value)
+    return nested
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_sharded_tensor_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_sharded_tensor_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a68bcddeb7f9d9ffe6f89056dfe1ccc30cc12eb5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_sharded_tensor_utils.py
@@ -0,0 +1,107 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import copy
+from typing import TYPE_CHECKING
+
+import torch.distributed as dist
+from torch.distributed._shard.sharded_tensor import Shard, ShardedTensor, ShardMetadata
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+from torch.distributed.remote_device import _remote_device
+
+from ._traverse import OBJ_PATH, set_element, STATE_DICT_ITEM, traverse_state_dict
+from .utils import _element_wise_add, _normalize_device_info
+
+
+if TYPE_CHECKING:
+    from torch.distributed._shard.sharded_tensor.metadata import ShardedTensorMetadata
+
+
+# TODO: We need to refactor this code.
+def _flatten_sharded_tensors(state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE:
+    r"""
+    Transform ``state_dict`` by flattening all nested ShardedTensor instances found.
+
+    The resulting ShardedTensor instances are only correct regarding the local shard and
+    MUST not be used for any other purpose but checkpointing, as no operator will work with them.
+
+    This function should be used in conjunction with a state_dict produced by FSDP's
+    StateDictType.SHARDED_STATE_DICT methods.
+    """
+    new_state_dict: STATE_DICT_TYPE = {}
+
+    def rewrite_dict(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        if not isinstance(value, ShardedTensor):
+            set_element(new_state_dict, path, value)
+            return
+        shards = value.local_shards()
+
+        if len(shards) == 0:
+            return
+        if len(shards) != 1:
+            set_element(new_state_dict, path, value)
+            return
+
+        outer_shard = shards[0]
+
+        inner_st = outer_shard.tensor
+        if not isinstance(inner_st, ShardedTensor):
+            set_element(new_state_dict, path, value)
+            return
+
+        if len(inner_st.local_shards()) != 1:
+            raise ValueError("Cannot handle inner tensor with more than 1 shard")
+        inner_shard = inner_st.local_shards()[0]
+
+        local_shards = [
+            Shard(
+                tensor=inner_shard.tensor,
+                metadata=ShardMetadata(
+                    shard_offsets=_element_wise_add(
+                        outer_shard.metadata.shard_offsets,
+                        inner_shard.metadata.shard_offsets,
+                    ),
+                    shard_sizes=inner_shard.metadata.shard_sizes,
+                    placement=f"rank:{dist.get_rank()}/{inner_shard.tensor.device}",
+                ),
+            )
+        ]
+
+        st_meta: ShardedTensorMetadata = copy.deepcopy(value.metadata())
+        other_rank = 0 if dist.get_rank() > 0 else 1
+        device_info = _normalize_device_info(inner_shard.tensor.device.type, 0)
+
+        # Remove the outer ST shard the inner ST covers
+        for i, shard_md in enumerate(st_meta.shards_metadata):
+            if shard_md.shard_offsets == outer_shard.metadata.shard_offsets:
+                st_meta.shards_metadata.pop(i)
+                break
+
+        # Attribute other rank for the other shards
+        for shard_md in st_meta.shards_metadata:
+            shard_md.placement = _remote_device(f"rank:{other_rank}/{device_info}")
+
+        # Add other inner shards from the inner tensor
+        for inner_md in inner_st.metadata().shards_metadata:
+            if inner_md.shard_offsets != inner_shard.metadata.shard_offsets:
+                st_meta.shards_metadata.append(
+                    ShardMetadata(
+                        shard_offsets=_element_wise_add(
+                            outer_shard.metadata.shard_offsets,
+                            inner_md.shard_offsets,
+                        ),
+                        shard_sizes=inner_md.shard_sizes,
+                        placement=f"rank:{other_rank}/{device_info}",
+                    )
+                )
+
+        # Finally add this shard
+        st_meta.shards_metadata.append(local_shards[0].metadata)
+
+        st = ShardedTensor._init_from_local_shards_and_global_metadata(
+            local_shards=local_shards,
+            sharded_tensor_metadata=st_meta,
+        )
+        set_element(new_state_dict, path, st)
+
+    traverse_state_dict(state_dict, rewrite_dict)
+    return new_state_dict
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_storage_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_storage_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d8d9a0806ae0f41540d3719089d6a131c3d28a9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_storage_utils.py
@@ -0,0 +1,49 @@
+import os
+from typing import Union
+
+from .filesystem import FileSystemReader, FileSystemWriter
+from .storage import StorageReader, StorageWriter
+
+
+def _storage_setup(
+    storage: Union[StorageReader, StorageWriter, None],
+    checkpoint_id: Union[str, os.PathLike, None],
+    reader: bool = False,
+) -> Union[None, StorageReader, StorageWriter]:
+    if storage:
+        if checkpoint_id is not None:
+            storage.reset(checkpoint_id)
+        return storage
+
+    if not checkpoint_id:
+        raise RuntimeError(
+            "`checkpoint_id` must be specificed if "
+            "storage_reader/storage_writer is None."
+        )
+
+    targets: list[type[Union[StorageReader, StorageWriter]]] = []
+    if reader:
+        targets = [
+            FileSystemReader,
+        ]
+    else:
+        targets = [
+            FileSystemWriter,
+        ]
+    try:
+        from ._fsspec_filesystem import FsspecReader, FsspecWriter
+
+        targets.append(FsspecReader if reader else FsspecWriter)
+    except Exception:
+        pass
+
+    for target in targets:
+        if target.validate_checkpoint_id(checkpoint_id):
+            storage = target(checkpoint_id)  # type: ignore[call-arg]
+            storage.reset(checkpoint_id)
+            return storage
+
+    raise RuntimeError(
+        "Cannot detect which StorageReader or StorageWriter to use. "
+        "Please specify the storage_reader/storage_writer."
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_traverse.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_traverse.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc29207093db60a98cb5c1f8416ecc21b1cbe105
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_traverse.py
@@ -0,0 +1,198 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from collections.abc import Collection, Mapping, MutableMapping
+from typing import Callable, cast, Optional, TypeVar, Union
+
+import torch
+from torch.distributed._shard.sharded_tensor.api import ShardedTensor
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+from torch.distributed.tensor import DTensor
+
+
+PATH_ITEM = Union[str, int]
+OBJ_PATH = tuple[PATH_ITEM, ...]
+T = TypeVar("T")
+
+STATE_DICT_ITEM = object
+CONTAINER_TYPE = MutableMapping[PATH_ITEM, STATE_DICT_ITEM]
+
+__all__ = ["traverse_state_dict", "set_element", "get_element", "print_tensor"]
+
+
+def _keep_visiting_tensors(value: STATE_DICT_ITEM) -> bool:
+    return isinstance(value, torch.Tensor)
+
+
+# TODO: update docstring for traverse.py
+def traverse_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    visitor: Callable[[OBJ_PATH, STATE_DICT_ITEM], None],
+    keep_traversing: Callable[[STATE_DICT_ITEM], bool] = _keep_visiting_tensors,
+) -> None:
+    """
+    Invoke ``visitor`` for each value recursively in ``state_dict``.
+    Mapping will be traversed and ``visitor`` will be applied to the leaf elements.
+    ``visitor`` will only be applied to elements in a list or a tuple, if the
+    container contains tensors or mappings.
+    """
+
+    def _is_terminal(value: STATE_DICT_ITEM) -> bool:
+        values: Collection[STATE_DICT_ITEM]
+        if isinstance(value, Mapping):
+            return False
+        elif isinstance(value, list):
+            values = value
+        else:
+            return True
+
+        for entry in values:
+            if isinstance(entry, (Mapping, list)) and not _is_terminal(entry):
+                return False
+            if keep_traversing is not None and keep_traversing(entry):
+                return False
+        return True
+
+    def _traverse_obj(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        if isinstance(value, Mapping):
+            for k, v in value.items():
+                _traverse_obj(path + (str(k),), v)
+        elif _is_terminal(value):
+            visitor(path, value)
+        elif isinstance(value, (list, tuple)):
+            for i, v in enumerate(value):
+                _traverse_obj(path + (i,), v)
+
+    for key, value in state_dict.items():
+        _traverse_obj((str(key),), value)
+
+
+def traverse_state_dict_v_2_3(
+    state_dict: STATE_DICT_TYPE,
+    visitor: Callable[[OBJ_PATH, STATE_DICT_ITEM], None],
+    keep_traversing: Callable[[STATE_DICT_ITEM], bool] = _keep_visiting_tensors,
+) -> None:
+    """
+    Traversal is short-circuited when if finds a collection for which ``keep_visiting_tensors`` evaluates
+    to false for all elements.
+    By default, all collections with at least one ``torch.Tensor`` element are traversed.
+    Visitor takes a path argument that is a tuple of the keys used to reach it.
+    """
+
+    # a value is terminal if it has no other containers values inside it
+    def _is_terminal(value: STATE_DICT_ITEM) -> bool:
+        values: Collection[STATE_DICT_ITEM]
+        if isinstance(value, Mapping):
+            values = value.values()
+        elif isinstance(value, list):
+            values = value
+        else:
+            return True
+
+        for entry in values:
+            if isinstance(entry, (Mapping, list)) and not _is_terminal(entry):
+                return False
+            if keep_traversing is not None and keep_traversing(entry):
+                return False
+        return True
+
+    def _traverse_obj(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        if _is_terminal(value):
+            visitor(path, value)
+        elif isinstance(value, Mapping):
+            for k, v in value.items():
+                _traverse_obj(path + (str(k),), v)
+        elif isinstance(value, list):
+            for i, v in enumerate(value):
+                _traverse_obj(path + (i,), v)
+
+    for key, value in state_dict.items():
+        _traverse_obj((str(key),), value)
+
+
+def set_element(
+    root_dict: STATE_DICT_TYPE, path: OBJ_PATH, value: STATE_DICT_ITEM
+) -> None:
+    """Set ``value`` in ``root_dict`` along the ``path`` object path."""
+    cur_container = cast(CONTAINER_TYPE, root_dict)
+
+    def extend_list(lst: list[STATE_DICT_ITEM], idx: int) -> None:
+        while len(lst) <= idx:
+            lst.append(None)
+
+    for i in range(1, len(path)):
+        prev_key = path[i - 1]
+        key = path[i]
+        def_val = cast(STATE_DICT_ITEM, {} if type(key) == str else [])
+
+        if isinstance(cur_container, Mapping):
+            cur_container = cast(
+                CONTAINER_TYPE, cur_container.setdefault(prev_key, def_val)
+            )
+        else:
+            extend_list(cur_container, prev_key)
+            if cur_container[prev_key] is None:
+                cur_container[prev_key] = def_val
+            cur_container = cur_container[prev_key]
+
+    key = path[-1]
+    if type(key) == int:
+        extend_list(cast(list[STATE_DICT_ITEM], cur_container), key)
+
+    cur_container[key] = value
+
+
+def get_element(
+    root_dict: STATE_DICT_TYPE,
+    path: OBJ_PATH,
+    default_value: Optional[T] = None,
+) -> Optional[T]:
+    """Retrieve the value at ``path``from ``root_dict``, returning ``default_value`` if not found."""
+    cur_value = cast(CONTAINER_TYPE, root_dict)
+    for part in path:
+        if type(part) is int:
+            if not isinstance(cur_value, list) or len(cur_value) < part:
+                return default_value
+        elif not isinstance(cur_value, Mapping) or part not in cur_value:
+            return default_value
+
+        cur_value = cast(CONTAINER_TYPE, cur_value[part])
+    return cast(Optional[T], cur_value)
+
+
+def _print_nested(
+    value: STATE_DICT_ITEM,
+    prefix: str = "",
+    print_fun: Callable[[str], None] = print,
+) -> None:
+    if type(value) is ShardedTensor:
+        print_fun(f"{prefix} ShardedTensor size: {value.size()}")
+        for shard in value.local_shards():
+            _print_nested(
+                shard.tensor,
+                f"{shard.metadata.shard_offsets} ",
+                print_fun=print_fun,
+            )
+    elif type(value) is (DTensor):
+        print_fun(f"{prefix} DistributedTensor size: {value.size()}")
+        # TODO: add local offset for _local_tensor in print_nested.
+        _print_nested(
+            value._local_tensor,
+            print_fun=print_fun,
+        )
+    elif isinstance(value, torch.Tensor):
+        print_fun(f"{prefix} Tensor size: {value.size()}")
+    else:
+        print_fun(f"{prefix} Type: {type(value)}")
+
+
+def print_tensor(
+    path: OBJ_PATH,
+    value: STATE_DICT_ITEM,
+    print_fun: Callable[[str], None] = print,
+) -> None:
+    """
+    Use this callback with traverse_state_dict to print its content.
+
+    By default the content is printed using the builtin ``print`` but this can
+    be change by passing a different ``print_fun` callable.
+    """
+    _print_nested(value, prefix=str(path), print_fun=print_fun)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_version.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3065bdfd6a2c141a959ef0ffe30aeafdc2dc54f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_version.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+from typing import Optional
+
+
+_derived_version: Optional[str] = None
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..4aa4854db2358ae4361403d37d59563ab8963fbd
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/api.py
@@ -0,0 +1,42 @@
+import traceback as tb
+from typing import Any
+
+
+WRAPPED_EXCEPTION = tuple[BaseException, tb.StackSummary]
+
+__all__ = ["CheckpointException"]
+
+
+def _wrap_exception(exc: BaseException) -> WRAPPED_EXCEPTION:
+    return (exc, tb.extract_tb(exc.__traceback__))
+
+
+def _is_wrapped_exception(obj: Any) -> bool:
+    if not isinstance(obj, tuple):
+        return False
+    if len(obj) != 2:
+        return False
+    return isinstance(obj[0], BaseException) and isinstance(obj[1], tb.StackSummary)
+
+
+class CheckpointException(BaseException):
+    """Exception raised if failure was detected as part of a checkpoint load or save."""
+
+    def __init__(self, msg: str, failures: dict[int, WRAPPED_EXCEPTION]):
+        super().__init__(msg, failures)
+        self._failures = failures
+
+    @property
+    def failures(self) -> dict[int, WRAPPED_EXCEPTION]:
+        """Return a dictionary mapping node ranks to their associated exceptions in case of failure."""
+        return self._failures
+
+    def __str__(self) -> str:
+        str = f"CheckpointException ranks:{self._failures.keys()}\n"
+        for rank, exc_pair in self._failures.items():
+            exc, trace = exc_pair
+            str += f"Traceback (most recent call last): (RANK {rank})\n"
+            if trace is not None:
+                str += "".join(tb.format_list(trace))
+            str += "".join(tb.format_exception_only(type(exc), value=exc))
+        return str
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/default_planner.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/default_planner.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffeb5a01ec725156eb508bb96a7f00051d4f9ad1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/default_planner.py
@@ -0,0 +1,653 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import copy
+import dataclasses
+import io
+import logging
+import operator
+from collections import ChainMap
+from functools import reduce
+from typing import Any, cast, Optional, Union
+
+import torch
+from torch.distributed._shard._utils import narrow_tensor_by_index
+from torch.distributed.checkpoint._dedup_save_plans import dedup_save_plans
+from torch.distributed.checkpoint._nested_dict import (
+    FLATTEN_MAPPING,
+    flatten_state_dict,
+)
+from torch.distributed.checkpoint._sharded_tensor_utils import _flatten_sharded_tensors
+from torch.distributed.checkpoint._traverse import set_element
+from torch.distributed.checkpoint.metadata import (
+    BytesStorageMetadata,
+    ChunkStorageMetadata,
+    Metadata,
+    MetadataIndex,
+    STATE_DICT_TYPE,
+    STORAGE_TYPES,
+    StorageMeta,
+    TensorStorageMetadata,
+)
+from torch.distributed.checkpoint.planner import (
+    LoadPlan,
+    LoadPlanner,
+    ReadItem,
+    SavePlan,
+    SavePlanner,
+    WriteItem,
+    WriteItemType,
+)
+from torch.distributed.checkpoint.planner_helpers import (
+    _compare_save_plans,
+    _create_default_metadata_only_plan,
+    _create_read_items,
+    _create_write_items,
+    _init_state_dict,
+    _merge_delta_local_plans,
+)
+from torch.distributed.checkpoint.utils import find_state_dict_object
+from torch.distributed.tensor import DTensor
+
+from . import _version
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+__all__ = [
+    "DefaultSavePlanner",
+    "DefaultLoadPlanner",
+    "create_default_local_load_plan",
+    "create_default_global_load_plan",
+    "create_default_local_save_plan",
+    "create_default_global_save_plan",
+]
+
+
+# TODO: Update docstrings for default_planner.py
+class DefaultSavePlanner(SavePlanner):
+    mappings: FLATTEN_MAPPING
+
+    def __init__(
+        self,
+        flatten_state_dict: bool = True,
+        flatten_sharded_tensors: bool = True,
+        dedup_replicated_tensors: Optional[bool] = None,
+        dedup_save_to_lowest_rank: bool = False,
+        enable_plan_caching: bool = False,
+    ) -> None:
+        self.flatten_state_dict = flatten_state_dict
+        self.flatten_sharded_tensors = flatten_sharded_tensors
+        self.mappings = {}
+        self.dedup_save_to_lowest_rank = dedup_save_to_lowest_rank
+        if dedup_replicated_tensors is not None:
+            logger.warning(
+                "DefaultSavePlanner's `dedup_replicated_tensors` argument is being "
+                "deprecated, and no longer has any effect. Please remove this argument "
+                "from your call."
+            )
+        self._cached_plans_key: str = self.__class__.__name__
+        self._enable_plan_caching = enable_plan_caching
+
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        storage_meta: Optional[StorageMeta] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        if self.flatten_state_dict:
+            state_dict, self.mappings = flatten_state_dict(state_dict)
+        if self.flatten_sharded_tensors:
+            state_dict = _flatten_sharded_tensors(state_dict)
+        self.state_dict = state_dict
+        self.is_coordinator = is_coordinator
+
+    def create_local_plan(self) -> SavePlan:
+        plan = create_default_local_save_plan(self.state_dict, self.is_coordinator)
+        if self.flatten_state_dict:
+            plan = dataclasses.replace(plan, planner_data=self.mappings)
+        self.plan = plan
+
+        if self._enable_plan_caching:
+            # If plans are equal, we can skip sending the plan to the coordinator.
+            if (
+                self._cached_plans_key in SavePlanner._cached_save_plan
+                and _compare_save_plans(
+                    plan, SavePlanner._cached_save_plan[self._cached_plans_key]
+                )
+            ):
+                logger.info(
+                    "No change in the local plan. Skipping sending the plan to the coordinator"
+                )
+                return SavePlan([], usable=False)
+            else:
+                SavePlanner._cached_save_plan[self._cached_plans_key] = plan
+
+        return self.plan
+
+    def _create_global_plan(
+        self, all_plans: list[SavePlan]
+    ) -> tuple[list[SavePlan], Metadata]:
+        all_plans = dedup_save_plans(all_plans, self.dedup_save_to_lowest_rank)
+
+        global_plan, metadata = create_default_global_save_plan(all_plans)
+
+        if self.flatten_state_dict:
+            # | does not work for Python 3.8 or older version.
+            # merged_mappings = reduce(
+            #     lambda x, y: x | y, (p.planner_data for p in global_plan)
+            # )
+            planner_data_dict = [p.planner_data for p in global_plan]
+            merged_mappings = dict(ChainMap(*planner_data_dict))
+            metadata = dataclasses.replace(metadata, planner_data=merged_mappings)
+
+        if not _validate_global_plan(global_plan, metadata):
+            raise ValueError("Failed to validate global plan")
+
+        return global_plan, metadata
+
+    def _create_global_plan_with_caching(
+        self, all_plans: list[SavePlan]
+    ) -> tuple[list[SavePlan], list[SavePlan], Metadata]:
+        """
+        Create global plan with caching.
+        Returns a tuple of global_plan_delta, global_plan, metadata.
+        """
+        global_plan_delta: list[SavePlan] = []
+
+        if self._cached_plans_key not in SavePlanner._cached_all_plans:
+            # Make a deepcopy of all_plans to avoid caching the modified plans post de-dupe
+            SavePlanner._cached_all_plans[self._cached_plans_key] = copy.deepcopy(
+                all_plans
+            )
+            global_plan, metadata = self._create_global_plan(all_plans)
+            SavePlanner._cached_global_plan[self._cached_plans_key] = global_plan
+            # If plans are not cached, global_plan delta will be the same as global plan.
+            return global_plan, global_plan, metadata
+
+        # We get global plan for the new delta plans.
+        # Ranks have already cached the plans which have not changed.
+        merged_plans = _merge_delta_local_plans(
+            SavePlanner._cached_all_plans[self._cached_plans_key], all_plans
+        )
+        # Make a deepcopy of merged_plans to avoid caching the modified plans post de-dupe
+        SavePlanner._cached_all_plans[self._cached_plans_key] = copy.deepcopy(
+            merged_plans
+        )
+
+        global_plan, metadata = self._create_global_plan(merged_plans)
+
+        if self._cached_plans_key in self._cached_global_plan:
+            for cached_plan, new_plan in zip(
+                SavePlanner._cached_global_plan[self._cached_plans_key], global_plan
+            ):
+                if _compare_save_plans(cached_plan, new_plan):
+                    global_plan_delta.append(SavePlan([], usable=False))
+                else:
+                    global_plan_delta.append(new_plan)
+
+        SavePlanner._cached_global_plan[self._cached_plans_key] = global_plan
+
+        # If the plans are cached, global_plan delta will be the delta
+        # of new global plan and cached global plan.
+        return global_plan_delta, global_plan, metadata
+
+    def create_global_plan(
+        self, all_plans: list[SavePlan]
+    ) -> tuple[list[SavePlan], Metadata]:
+        global_plan_delta: list[SavePlan] = []
+        if self._enable_plan_caching:
+            # If the plans are cached, we only need to send the global plan delta to be scattered
+            # across ranks. Ranks will use the cached final plans instead.
+            (
+                global_plan_delta,
+                global_plan,
+                metadata,
+            ) = self._create_global_plan_with_caching(all_plans)
+        else:
+            global_plan, metadata = self._create_global_plan(all_plans)
+            # If the caching is not enabled, global delta plan will always be same as the new global plan.
+            global_plan_delta = global_plan
+
+        self.global_plan = global_plan
+        self.metadata = metadata
+
+        return global_plan_delta, self.metadata
+
+    def _finish_plan_with_caching(self, new_plan: SavePlan) -> SavePlan:
+        finished_plan: SavePlan = new_plan
+
+        if not new_plan.usable:
+            finished_plan = SavePlanner._cached_final_save_plan[self._cached_plans_key]
+        else:
+            finished_plan = new_plan
+            SavePlanner._cached_final_save_plan[self._cached_plans_key] = new_plan
+        return finished_plan
+
+    def finish_plan(self, new_plan: SavePlan) -> SavePlan:
+        finished_plan: SavePlan = new_plan
+
+        if self._enable_plan_caching:
+            finished_plan = self._finish_plan_with_caching(new_plan)
+
+        self.plan = finished_plan
+        return self.plan
+
+    def resolve_data(self, write_item: WriteItem) -> Union[torch.Tensor, io.BytesIO]:
+        object = self.lookup_object(write_item.index)
+        return self.transform_object(write_item, object)
+
+    def lookup_object(self, index: MetadataIndex) -> Any:
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        return find_state_dict_object(self.state_dict, index)
+
+    def transform_object(self, write_item: WriteItem, object: Any):
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        if write_item.type == WriteItemType.BYTE_IO:
+            bytes = io.BytesIO()
+            torch.save(object, bytes)
+            object = bytes
+        return object
+
+
+class DefaultLoadPlanner(LoadPlanner):
+    """
+    DefaultLoadPlanner that adds multiple features on top of LoadPlanner.
+
+    In particular it adds the following:
+
+    flatten_state_dict: Handle state_dict with nested dicts
+    flatten_sharded_tensors: For FSDP in 2D parallel mode
+    allow_partial_load: If False, will raise a runtime error if a key is present in state_dict, but not in the checkpoint.
+    """
+
+    original_state_dict: STATE_DICT_TYPE
+    mappings: FLATTEN_MAPPING
+
+    def __init__(
+        self,
+        flatten_state_dict: bool = True,
+        flatten_sharded_tensors: bool = True,
+        allow_partial_load: bool = False,
+    ) -> None:
+        self.flatten_state_dict = flatten_state_dict
+        self.flatten_sharded_tensors = flatten_sharded_tensors
+        self.original_state_dict = {}
+        self.mappings = {}
+        self.allow_partial_load = allow_partial_load
+
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        metadata: Optional[Metadata] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        _init_state_dict(state_dict)
+        self.original_state_dict = state_dict
+
+        if self.flatten_sharded_tensors:
+            state_dict = _flatten_sharded_tensors(state_dict)
+
+        if self.flatten_state_dict:
+            state_dict, self.mappings = flatten_state_dict(state_dict)
+
+        self.state_dict = state_dict
+        self.metadata = metadata
+        self.is_coordinator = is_coordinator
+
+    def create_local_plan(self) -> LoadPlan:
+        assert self.metadata is not None
+        if self.flatten_state_dict:
+            # To support checkpoints that are saved before v2.4, we have to
+            # differentiate if the missing keys are due to old checkpoints.
+            # The contracts are:
+            # 1. There are 3 cases when we found a missing key.
+            #    1.1 Actual missing key, but allow_partial_load is False
+            #    1.2 Actual missing key, but allow_partial load is True
+            #    1.3 Old checkpoint, but allow_partial_load is False
+            #    1.4 Old checkpoint, but allow_partial_load is True
+            # 2. If we found a missing key, we first convert the keys back to
+            #    the key format of v2.3
+            # 3. If the previous missing keys are in the v2.3 keys, we assume
+            #    this is a old checkpoint.
+            # 4. Pass the state_dict to `create_default_local_load_plan()`,
+            #    which has the logic to check missing for allow_partial_load.
+            # So for 1.2 and 1.4 cases, we delegate allow_partial_load check to
+            # `create_default_local_load_plan()`. The logic here is to determine
+            # whether the checkpoint belong to 2.3 (or before) or 2.4 (or after).
+            current_keys = set(self.state_dict.keys())
+            load_keys = set(self.metadata.state_dict_metadata.keys())
+            missing_keys = load_keys - current_keys
+            if missing_keys:
+                _version._derived_version = "2_3"
+                old_state_dict, old_mappings = flatten_state_dict(
+                    self.original_state_dict
+                )
+                old_keys = set(old_state_dict.keys())
+                if old_keys & missing_keys:
+                    self.state_dict, self.mappings = old_state_dict, old_mappings
+                # _derived_version is only used by flatten_state_dict now.
+                # Set it back to None so that later we can save to a new version.
+                _version._derived_version = None
+
+        return create_default_local_load_plan(
+            self.state_dict, self.metadata, not self.allow_partial_load
+        )
+
+    def create_global_plan(self, global_plan: list[LoadPlan]) -> list[LoadPlan]:
+        return create_default_global_load_plan(global_plan)
+
+    def finish_plan(self, new_plan: LoadPlan) -> LoadPlan:
+        return new_plan
+
+    def load_bytes(self, read_item: ReadItem, value: io.BytesIO) -> None:
+        if self.flatten_state_dict:
+            set_element(
+                self.original_state_dict,
+                self.mappings[read_item.dest_index.fqn],
+                torch.load(value, weights_only=False),
+            )
+        else:
+            self.state_dict[read_item.dest_index.fqn] = torch.load(
+                value, weights_only=False
+            )
+
+    def resolve_tensor(self, read_item: ReadItem):
+        tensor = self.lookup_tensor(read_item.dest_index)
+        return self.transform_tensor(read_item, tensor)
+
+    def commit_tensor(self, read_item: ReadItem, tensor: torch.Tensor) -> None:
+        pass
+
+    def lookup_tensor(self, index: MetadataIndex) -> torch.Tensor:
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        return find_state_dict_object(self.state_dict, index)
+
+    def transform_tensor(self, read_item: ReadItem, tensor: torch.Tensor):
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        return narrow_tensor_by_index(tensor, read_item.dest_offsets, read_item.lengths)
+
+
+class _EmptyStateDictLoadPlanner(DefaultLoadPlanner):
+    """
+    Extension of DefaultLoadPlanner, which rebuilds state_dict from the saved metadata.
+    Useful for loading in state_dict without first initializing a model, such as
+    when converting a DCP checkpoint into a Torch save file.
+
+    . N.B. `state_dict` must be an empty dictionary when used with this LoadPlanner
+
+    .. warning::
+        Because the entire state dict is initialized, It's recommended to only utilize
+        this LoadPlanner on a single rank or process to avoid OOM.
+
+    """
+
+    def __init__(self, keys=None, *args, **kwargs):
+        self.keys = keys
+        super().__init__(*args, **kwargs)
+
+    def _should_include_key(self, key: str, metadata: Metadata) -> bool:
+        if self.keys is None:
+            return True
+
+        if key in self.keys:
+            True
+
+        unflattened_keys: list[str] = []
+        planner_data = metadata.planner_data.get(key)
+        for unflattened_key in planner_data:
+            if unflattened_keys:
+                unflattened_keys.append(
+                    ".".join([unflattened_keys[-1], str(unflattened_key)])
+                )
+
+            else:
+                unflattened_keys.append(unflattened_key)
+
+        if any(unflattened_key in self.keys for unflattened_key in unflattened_keys):
+            return True
+
+        return False
+
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        metadata: Optional[Metadata] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        assert not state_dict
+        assert metadata is not None
+
+        # rebuild the state dict from the metadata
+        for k, v in metadata.state_dict_metadata.items():
+            if not self._should_include_key(k, metadata):
+                continue
+
+            if isinstance(v, TensorStorageMetadata):
+                v = torch.empty(v.size, dtype=v.properties.dtype)  # type: ignore[assignment]
+            if k in metadata.planner_data:
+                set_element(state_dict, metadata.planner_data[k], v)
+            else:
+                state_dict[k] = v
+
+        super().set_up_planner(state_dict, metadata, is_coordinator)
+
+
+def create_default_local_load_plan(
+    state_dict: dict[str, Any], metadata: Metadata, strict: bool = True
+) -> LoadPlan:
+    requests = []
+    """
+    Create the ``LoadPlan`` used by DefaultLoadPlanner.
+
+    It produces one read item per value in ``state_dict`` using the metadata in ``metadata``.
+
+    The default behavior is to match key exactly between state_dict and metadata.
+    It handles resharding by issuing multiple read requests against storage in order to match
+    load requirements.
+    """
+
+    for fqn, obj in state_dict.items():
+        # ignore state_dict keys which do not exist in `state_dict` if strict=False
+        if fqn not in metadata.state_dict_metadata:
+            if strict:
+                raise RuntimeError(f"Missing key in checkpoint state_dict: {fqn}.")
+            else:
+                continue
+
+        md = metadata.state_dict_metadata[fqn]
+        if (
+            isinstance(md, TensorStorageMetadata)
+            and getattr(obj, "size", None) is not None
+            and md.size != obj.size()
+        ):
+            raise ValueError(
+                f"Size mismatch between saved {md.size} and current: {obj.size()} for {fqn}",
+            )
+        # Since DTensor supports submesh, adding extra check to ensure _create_read_items()
+        # gets called only when the current rank is part of the mesh for the corresponding DTensor.
+        if isinstance(obj, DTensor):
+            if obj.device_mesh.get_coordinate() is not None:
+                requests += _create_read_items(fqn, md, obj)
+        else:
+            requests += _create_read_items(fqn, md, obj)
+
+    return LoadPlan(requests)
+
+
+def create_default_global_load_plan(
+    all_plans: list[LoadPlan],
+) -> list[LoadPlan]:
+    """
+    Create global load plan used by DefaultLoadPlanner.
+
+    The default load behavior involved no global coordination and this function
+    currently doesn't change the local plans.
+    """
+    return all_plans
+
+
+def create_default_local_save_plan(
+    state_dict: dict[str, Any], is_coordinator: bool
+) -> SavePlan:
+    """
+    Create the ``SavePlan`` used by DefaultSavePlanner.
+
+    On non-coordinator ranks, this function ignores tensors and non-tensor objects,
+    only producing writes for ShardedTensor objects.
+
+    On the coordinator rank, produce writes for all values.
+    """
+    requests = []
+    for fqn, obj in state_dict.items():
+        # Since DTensor supports submesh, adding extra check to ensure _create_write_items()
+        # gets called only when the current rank is part of the mesh for the corresponding DTensor.
+        if isinstance(obj, DTensor):
+            if obj.device_mesh.get_coordinate() is not None:
+                requests += _create_write_items(fqn, obj)
+        else:
+            # For the plain tensor and non-tensor values, add the request for all
+            # the ranks. Coordinator will decides whether to deduplicate the
+            # values based on the keys.
+            requests += _create_write_items(fqn, obj)
+
+    return SavePlan(requests)
+
+
+def create_default_global_save_plan(
+    all_plans: list[SavePlan],
+    rewrite_index_hints: bool = True,
+) -> tuple[list[SavePlan], Metadata]:
+    """
+    Create the global plan and metadata used by DefaultSavePlanner.
+
+    Metadata is produced by concatenating the metadata of all ``WriteItem`` from the supplied plans.
+
+    The only global planning change is to update index hints in all ``MetadataIndex`` objects if
+    ``rewrite_index_hints`` is True.
+    """
+    md: dict[str, STORAGE_TYPES] = {}
+    new_plans = []
+    for plan in all_plans:
+        new_items = []
+        for item in plan.items:
+            if not item.type == WriteItemType.SHARD:
+                assert item.index.fqn not in md
+
+            if item.type == WriteItemType.BYTE_IO:
+                md[item.index.fqn] = BytesStorageMetadata()
+                new_items.append(item)
+            else:
+                assert item.tensor_data is not None
+                tensor_md = cast(
+                    TensorStorageMetadata,
+                    md.setdefault(
+                        item.index.fqn,
+                        TensorStorageMetadata(
+                            properties=item.tensor_data.properties,
+                            size=item.tensor_data.size,
+                            chunks=[],
+                        ),
+                    ),
+                )
+                new_item = item
+                if rewrite_index_hints:
+                    new_index = dataclasses.replace(
+                        item.index, index=len(tensor_md.chunks)
+                    )
+                    new_item = dataclasses.replace(item, index=new_index)
+                new_items.append(new_item)
+
+                assert item.tensor_data.chunk is not None, f"""
+                    Cannot create MD for tensor without bounds.
+                    FQN: {item.index.fqn}
+                """
+                tensor_md.chunks.append(item.tensor_data.chunk)
+        new_plans.append(dataclasses.replace(plan, items=new_items))
+    return (new_plans, Metadata(md))
+
+
+def _create_default_local_metadata(state_dict: STATE_DICT_TYPE) -> Metadata:
+    """Return the ``Metadata`` if DefaultSavePlanner was used to checkpoint ``state_dict``."""
+    plan = _create_default_metadata_only_plan(state_dict)
+    _, md = create_default_global_save_plan([plan])
+    return md
+
+
+def _check_box_overlap(box0: ChunkStorageMetadata, box1: ChunkStorageMetadata) -> bool:
+    """Check if two boxes overlap. Tuples are (offset, lengths)."""
+    # For each dim of each shard, check if one shard resides on the other
+    # end of second shard with respect to that dim. As an example for a 2D
+    # shard, we would check if one shard is above or on the left of the
+    # other shard.
+    ndims = len(box0.offsets)
+    for i in range(ndims):
+        if box0.offsets[i] >= box1.offsets[i] + box1.sizes[i]:
+            return False
+        if box1.offsets[i] >= box0.offsets[i] + box0.sizes[i]:
+            return False
+
+    return True
+
+
+def _check_box_bounds(
+    outer_box_size: torch.Size, inner_box: ChunkStorageMetadata
+) -> bool:
+    for i in range(len(outer_box_size)):
+        if inner_box.offsets[i] < 0:
+            return False
+        if inner_box.sizes[i] < 0:
+            return False
+        if inner_box.offsets[i] + inner_box.sizes[i] > outer_box_size[i]:
+            return False
+
+    return True
+
+
+def _validate_global_plan(global_plan: list[SavePlan], metadata: Metadata) -> bool:
+    all_good = True
+    for key, value in metadata.state_dict_metadata.items():
+        if isinstance(value, BytesStorageMetadata):
+            continue
+        if len(value.size) == 0:
+            continue
+        chunks_volume = 0
+        for chunk_idx, chunk0 in enumerate(value.chunks):
+            # Compute the volume
+            if not _check_box_bounds(value.size, chunk0):
+                logger.warning(
+                    """
+                        key:%s has out of bounds chunk:
+                        tensor-size:%s chunk: %s
+                    """,
+                    key,
+                    value.size,
+                    chunk0,
+                )
+                all_good = False
+            chunks_volume += reduce(operator.mul, chunk0.sizes, 1)
+
+            # Check for overlap
+            for chunk1 in value.chunks[chunk_idx + 1 :]:
+                if _check_box_overlap(chunk0, chunk1):
+                    logger.warning(
+                        "key:%s has overlapping chunks: %s %s", key, chunk0, chunk1
+                    )
+                    all_good = False
+
+        # Check whether combined chunk cover the whole tensor
+        tensor_volume = reduce(operator.mul, value.size, 1)
+        if chunks_volume != tensor_volume:
+            logger.warning(
+                """
+                    key:%s invalid fill tensor-volume:
+                    %s chunks-volume: %s
+                """,
+                key,
+                tensor_volume,
+                chunks_volume,
+            )
+            all_good = False
+
+    return all_good
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/filesystem.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/filesystem.py
new file mode 100644
index 0000000000000000000000000000000000000000..89b82e7bc127325d3f725b8f489658f477fc4532
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/filesystem.py
@@ -0,0 +1,933 @@
+# mypy: allow-untyped-defs
+import collections
+import dataclasses
+import io
+import operator
+import os
+import pickle
+import queue
+import threading
+import uuid
+import warnings
+from abc import ABC, abstractmethod
+from collections.abc import Generator, Iterable, Iterator, Sequence
+from contextlib import contextmanager
+from dataclasses import dataclass
+from io import UnsupportedOperation
+from pathlib import Path
+from typing import Any, Callable, cast, IO, Optional, Union
+
+# introduced as collections.abc.Buffer in Python 3.12
+from typing_extensions import Buffer
+
+import torch
+from torch import Tensor
+from torch._utils import _get_available_device_type, _get_device_module
+from torch.distributed._shard._utils import narrow_tensor_by_index
+from torch.distributed.checkpoint._extension import (
+    ExtensionRegistry,
+    StreamTransformExtension,
+)
+from torch.distributed.checkpoint.metadata import Metadata, STATE_DICT_TYPE, StorageMeta
+from torch.distributed.checkpoint.planner import (
+    LoadItemType,
+    LoadPlan,
+    LoadPlanner,
+    ReadItem,
+    SavePlan,
+    SavePlanner,
+    WriteItem,
+    WriteItemType,
+)
+from torch.distributed.checkpoint.staging import BlockingAsyncStager
+from torch.distributed.checkpoint.storage import (
+    StorageReader,
+    StorageWriter,
+    WriteResult,
+)
+from torch.distributed.checkpoint.utils import _create_file_view
+from torch.futures import Future
+
+
+__all__ = ["FileSystemWriter", "FileSystemReader", "FileSystem", "FileSystemBase"]
+
+_metadata_fn: str = ".metadata"
+
+
+@dataclass
+class _StorageInfo:
+    """This is the per entry storage info."""
+
+    relative_path: str
+    offset: int
+    length: int
+    transform_descriptors: Optional[Sequence[str]] = None
+
+    def __getstate__(self):
+        return {k: v for k, v in self.__dict__.items() if v is not None}
+
+
+@dataclass
+class _StoragePrefix:
+    prefix: str
+
+
+DEFAULT_SUFFIX = ".distcp"
+
+
+def _generate_uuid() -> str:
+    return str(uuid.uuid4())
+
+
+class _TensorLoader(ABC):
+    @abstractmethod
+    def add(self, size: int, obj: object) -> None:
+        pass
+
+    @abstractmethod
+    def start_loading(self) -> None:
+        pass
+
+    @abstractmethod
+    def values(self) -> Iterator[tuple[torch.Tensor, object]]:
+        pass
+
+
+class _SerialCpuLoader(_TensorLoader):
+    def __init__(self, resolve_fun: Callable) -> None:
+        self.resolve_fun = resolve_fun
+        self.items: list[tuple[int, object]] = []
+
+    def add(self, size: int, obj: object) -> None:
+        self.items.append((size, obj))
+
+    def start_loading(self) -> None:
+        pass
+
+    def values(self) -> Iterator[tuple[torch.Tensor, object]]:
+        for _, obj in self.items:
+            tensor = self.resolve_fun(obj).detach()
+            tensor = tensor.cpu()
+            if tensor.storage().size() != tensor.numel():
+                tensor = tensor.clone()
+            yield (
+                tensor,
+                obj,
+            )
+
+
+class _OverlappingCpuLoader(_TensorLoader):
+    def __init__(
+        self,
+        resolve_fun: Callable,
+        stream: Optional[torch.Stream] = None,
+        inflight_threshhold: int = 1_000_000,
+    ) -> None:
+        self.resolve_fun = resolve_fun
+        self.items: list[tuple[int, object]] = []
+        self.inflight_threshhold = inflight_threshhold
+        self.in_flight_data = 0
+        self.current_items: collections.deque = collections.deque()
+        self.idx = 0
+        self.started = False
+        self.device_type = (
+            stream.device_type if stream else _get_available_device_type()
+        )
+        self.device_module = _get_device_module(self.device_type)
+        self.stream = cast(
+            torch.cuda.Stream, stream or self.device_module.current_stream()
+        )
+        if self.stream != self.device_module.current_stream():
+            self.stream.wait_stream(self.device_module.current_stream())
+
+    @property
+    def _done(self) -> bool:
+        return self.idx >= len(self.items)
+
+    def _drain(self) -> list[tuple[torch.Tensor, object]]:
+        drained = []
+        if self.in_flight_data >= self.inflight_threshhold:
+            self.stream.synchronize()
+        while self.in_flight_data >= self.inflight_threshhold:
+            val = self.current_items.popleft()
+            self.in_flight_data -= val[0].numel() * val[0].element_size()
+            drained.append(val)
+        return drained
+
+    def _refill(self) -> None:
+        with self.device_module.stream(self.stream):
+            while not self._done and self.in_flight_data < self.inflight_threshhold:
+                _, obj = self.items[self.idx]
+                self.idx += 1
+                tensor = self.resolve_fun(obj).detach()
+                if tensor.device.type == self.device_type:
+                    tensor = tensor.to(device="cpu", non_blocking=True)
+                elif tensor.device == torch.device("cpu"):
+                    if (
+                        tensor.untyped_storage().size()
+                        != tensor.numel() * tensor.itemsize
+                    ):
+                        # this forces the tensor to be both contiguous and with minimal storage
+                        tensor = tensor.clone()
+
+                self.current_items.append(
+                    (
+                        tensor,
+                        obj,
+                    )
+                )
+                self.in_flight_data += tensor.numel() * tensor.element_size()
+
+    def _finish(self) -> Iterable[tuple[torch.Tensor, object]]:
+        assert self._done
+        if len(self.current_items) > 0:
+            self.stream.synchronize()
+        return self.current_items
+
+    def add(self, size: int, obj: object) -> None:
+        if self.started:
+            raise RuntimeError("cannot add items after loading started")
+        self.items.append((size, obj))
+
+    def start_loading(self) -> None:
+        if self.started:
+            return
+        self.started = True
+        self.items.sort(key=operator.itemgetter(0))
+        self._refill()
+
+    def values(self) -> Iterator[tuple[torch.Tensor, object]]:
+        self.start_loading()
+        while not self._done:
+            drained = self._drain()
+            self._refill()
+            yield from drained
+
+        yield from self._finish()
+
+
+class _StorageWriterTransforms:
+    """
+    This is experimental, and will likely move elsewhere in the
+    future.  It lives here to minimize changes while we are still
+    learning and gathering feedback.
+    """
+
+    def __init__(
+        self, extensions: Optional[Sequence[StreamTransformExtension]] = None
+    ) -> None:
+        """
+        If the extensions arg is None, this means the implementation
+        should provide whatever defaults it chooses.  An empty
+        sequence indicates no extensions should be used.  At this
+        time, the default extensions sequence is empty.
+        """
+        self.extensions = () if extensions is None else extensions
+
+    def transform_save_stream(
+        self, write_item: WriteItem, raw_stream: io.IOBase
+    ) -> tuple[IO[bytes], list[str]]:
+        # In order to avoid leaking fds, transformers' close must
+        # cascade to wrapped streams, but since this function can
+        # append to the raw stream, we can't close the actual stream.
+        # So, we use this to put a wrapper around the raw stream's
+        # close() to make it a noop, and it gets closed once all files
+        # are appended.
+
+        class NoCloseWriter(io.IOBase):
+            def __init__(self, raw: io.IOBase):
+                self.raw = raw
+
+            def writeable(self) -> bool:
+                return True
+
+            def write(self, b: Buffer) -> int:
+                return self.raw.write(b)
+
+            def close(self):
+                self.flush()
+                self.raw.flush()
+                # but not close.
+
+        transform_to = cast(IO[bytes], NoCloseWriter(raw_stream))
+
+        for ex in self.extensions:
+            transform_to = ex.transform_to(transform_to)
+
+        return (transform_to, [ex.get_descriptor() for ex in reversed(self.extensions)])
+
+
+def _item_size(item: WriteItem) -> int:
+    size = 1
+    assert item.tensor_data is not None
+    # can't use math.prod as PT needs to support older python
+    for s in item.tensor_data.size:
+        size *= s
+
+    dtype = item.tensor_data.properties.dtype
+    return size * torch._utils._element_size(dtype)
+
+
+def _split_by_size_and_type(bins: int, items: list[WriteItem]) -> list[list[WriteItem]]:
+    if bins == 1:
+        return [items]
+
+    bytes_w = [wi for wi in items if wi.type == WriteItemType.BYTE_IO]
+    tensor_w = [wi for wi in items if wi.type != WriteItemType.BYTE_IO]
+
+    buckets: list[list[WriteItem]] = [[] for _ in range(bins)]
+    bucket_sizes = [0 for _ in range(bins)]
+
+    tensor_w.sort(key=_item_size, reverse=True)
+
+    for i, wi in enumerate(bytes_w):
+        buckets[i % bins].append(wi)
+
+    for wi in tensor_w:
+        # TODO replace with headq
+        idx = min(enumerate(bucket_sizes), key=operator.itemgetter(1))[0]
+        buckets[idx].append(wi)
+        bucket_sizes[idx] += _item_size(wi)
+
+    return buckets
+
+
+def _write_item(
+    transforms: _StorageWriterTransforms,
+    stream: io.IOBase,
+    data: Union[io.BytesIO, torch.Tensor],
+    write_item: WriteItem,
+    storage_key: str,
+    safe_tensors: bool = False,
+) -> WriteResult:
+    offset = stream.tell()
+
+    (transform_to, transform_descriptors) = transforms.transform_save_stream(
+        write_item, stream
+    )
+
+    if write_item.type == WriteItemType.BYTE_IO:
+        assert isinstance(data, io.BytesIO)
+        transform_to.write(data.getbuffer())
+    else:
+        assert isinstance(data, torch.Tensor)
+        assert data.device == torch.device("cpu")
+        if not safe_tensors:
+            torch.save(data, transform_to)
+
+    transform_to.close()
+
+    if not safe_tensors or isinstance(data, io.BytesIO):
+        length = stream.tell() - offset
+    else:
+        length = data.numel() * data.element_size()
+
+    # For consistency with earlier versions, leave this field out of the
+    # metadata if there are no extensions.
+    info_transform_descriptors = (
+        None if len(transform_descriptors) == 0 else transform_descriptors
+    )
+
+    return WriteResult(
+        index=write_item.index,
+        size_in_bytes=length,
+        storage_data=_StorageInfo(
+            storage_key,
+            offset,
+            length,
+            transform_descriptors=info_transform_descriptors,
+        ),
+    )
+
+
+def _write_files_from_queue(
+    create_stream: Callable,
+    file_queue: queue.Queue,
+    result_queue: queue.Queue,
+    planner: SavePlanner,
+    transforms: _StorageWriterTransforms,
+    inflight_threshhold: int,
+    use_fsync: bool,
+    thread_count: int,
+    safe_tensors: bool,
+) -> None:
+    try:
+        while True:
+            file_name, storage_key, write_items = file_queue.get_nowait()
+            loader: _TensorLoader
+
+            custom_backend_name = torch._C._get_privateuse1_backend_name()
+            custom_device_mod = getattr(torch, custom_backend_name, None)
+
+            # TODO: Using the OverlappingCpuLoader with multiple threads creates significant
+            # performance degredation, observed as being related to cuda stream syncs. We
+            # should try to fix this and use _OverlappingCpuLoader for all threaded cases
+            if (
+                thread_count == 1
+                and (
+                    torch.cuda.is_available()
+                    or (custom_device_mod and custom_device_mod.is_available())
+                )
+                and inflight_threshhold > 0
+            ):
+                loader = _OverlappingCpuLoader(
+                    planner.resolve_data,
+                    inflight_threshhold=inflight_threshhold,
+                )
+            else:
+                loader = _SerialCpuLoader(
+                    planner.resolve_data,
+                )
+
+            tensor_w = [wi for wi in write_items if wi.type != WriteItemType.BYTE_IO]
+            for write_item in tensor_w:
+                loader.add(_item_size(write_item), write_item)
+            loader.start_loading()
+
+            bytes_w = [wi for wi in write_items if wi.type == WriteItemType.BYTE_IO]
+            write_results = []
+
+            with create_stream(file_name, "wb") as stream:
+                for write_item in bytes_w:
+                    data = planner.resolve_data(write_item)
+                    write_results.append(
+                        _write_item(
+                            transforms,
+                            stream,
+                            data,
+                            write_item,
+                            storage_key,
+                            safe_tensors,
+                        )
+                    )
+
+                tensor_dict = {}
+                for tensor, write_item in loader.values():
+                    assert tensor.is_cpu
+                    write_results.append(
+                        _write_item(
+                            transforms,
+                            stream,
+                            tensor,
+                            write_item,
+                            storage_key,
+                            safe_tensors,
+                        )
+                    )
+                    tensor_dict[write_item.index.fqn] = tensor
+
+                if safe_tensors:
+                    from safetensors.torch import save  # type: ignore[import-not-found]
+
+                    stream.write(save(tensor_dict))
+
+                if use_fsync:
+                    try:
+                        os.fsync(stream.fileno())
+                    except (AttributeError, UnsupportedOperation):
+                        os.sync()
+                stream.close()
+            result_queue.put(write_results)
+    except queue.Empty:
+        pass
+
+
+class FileSystemBase(ABC):
+    @contextmanager
+    @abstractmethod
+    def create_stream(
+        self, path: Union[str, os.PathLike], mode: str
+    ) -> Generator[io.IOBase, None, None]: ...
+
+    @abstractmethod
+    def concat_path(
+        self, path: Union[str, os.PathLike], suffix: str
+    ) -> Union[str, os.PathLike]: ...
+
+    @abstractmethod
+    def rename(
+        self, path: Union[str, os.PathLike], new_path: Union[str, os.PathLike]
+    ) -> None: ...
+
+    @abstractmethod
+    def init_path(self, path: Union[str, os.PathLike]) -> Union[str, os.PathLike]: ...
+
+    @abstractmethod
+    def mkdir(self, path: Union[str, os.PathLike]) -> None: ...
+
+    @classmethod
+    @abstractmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool: ...
+
+    @abstractmethod
+    def exists(self, path: Union[str, os.PathLike]) -> bool: ...
+
+    @abstractmethod
+    def rm_file(self, path: Union[str, os.PathLike]) -> None: ...
+
+
+class FileSystem(FileSystemBase):
+    @contextmanager
+    def create_stream(
+        self, path: Union[str, os.PathLike], mode: str
+    ) -> Generator[io.IOBase, None, None]:
+        if not isinstance(path, Path):
+            path = Path(path)
+        with path.open(mode) as stream:
+            yield cast(io.IOBase, stream)
+
+    def concat_path(
+        self, path: Union[str, os.PathLike], suffix: str
+    ) -> Union[str, os.PathLike]:
+        if not isinstance(path, Path):
+            path = Path(path)
+        return path / suffix
+
+    def init_path(self, path: Union[str, os.PathLike]) -> Union[str, os.PathLike]:
+        if not isinstance(path, Path):
+            path = Path(path)
+        return path
+
+    def rename(
+        self, path: Union[str, os.PathLike], new_path: Union[str, os.PathLike]
+    ) -> None:
+        if not isinstance(path, Path):
+            path = Path(path)
+
+        path.rename(cast(Path, new_path))
+
+    def mkdir(self, path: Union[str, os.PathLike]) -> None:
+        if not isinstance(path, Path):
+            path = Path(path)
+        path.mkdir(parents=True, exist_ok=True)
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        if isinstance(checkpoint_id, Path):
+            return True
+
+        if "://" in str(checkpoint_id):
+            return False
+
+        for p in Path(checkpoint_id).parents:
+            if p.exists() and os.access(str(p), os.W_OK):
+                return True
+
+        return False
+
+    def exists(self, path: Union[str, os.PathLike]) -> bool:
+        if not isinstance(path, Path):
+            path = Path(path)
+        return path.exists()
+
+    def rm_file(self, path: Union[str, os.PathLike]) -> None:
+        if not isinstance(path, Path):
+            path = Path(path)
+        path.unlink()
+
+
+class _FileSystemWriter(StorageWriter):
+    """
+    Basic implementation of StorageWriter using file IO.
+
+    This implementation makes the following assumptions and simplifications:
+
+    * The checkpoint path is an empty or non-existing directory.
+    * File creation is atomic
+
+    The checkpoint consist of one file per write request plus
+    a `.metadata` file with the serialized metadata.
+
+    """
+
+    def __init__(
+        self,
+        path: Union[str, os.PathLike],
+        single_file_per_rank: bool = True,
+        sync_files: bool = True,
+        thread_count: int = 1,
+        per_thread_copy_ahead: int = 10_000_000,
+        overwrite: bool = True,
+        _extensions: Optional[Sequence[StreamTransformExtension]] = None,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        """
+        Initialize the writer pointing to `path`.
+
+        Args:
+            path: directory where the checkpoint will be written to.
+            single_file_per_rank: Produce one file per rank instead of one file per tensor/blob. Default to True.
+            sync_files : force files to be synced to permanent storage. Default to True.
+            thread_count: Number of IO threads to use to write. Default to 1.
+            per_thread_copy_ahead: How many bytes to copy from the GPU ahead of saving then. Default 10Mb.
+            overwrite: Whether to allow overwriting existing checkpoints. Defaults to True.
+            _extensions: Extensions to apply to output streams (EXPERIMENTAL)
+
+        N. B. If sync_files is disabled, there's no guarantee that the checkpoint will be consistent in the case of a failure.
+        """
+        super().__init__()
+        self.fs = FileSystem()
+        self.path = self.fs.init_path(path)
+        self.single_file_per_rank = single_file_per_rank
+        self.sync_files = sync_files
+        self.thread_count = thread_count
+        self.per_thread_copy_ahead = per_thread_copy_ahead
+        self.save_id = _generate_uuid()
+        self.overwrite = overwrite
+        self.transforms = _StorageWriterTransforms(_extensions)
+
+    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
+        if checkpoint_id:
+            self.path = self.fs.init_path(checkpoint_id)
+        self.save_id = _generate_uuid()
+
+    def set_up_storage_writer(self, is_coordinator: bool) -> None:
+        pass
+
+    def prepare_local_plan(self, plan: SavePlan) -> SavePlan:
+        self.fs.mkdir(self.path)
+        if self.fs.exists(self.metadata_path):
+            if self.overwrite:
+                warnings.warn(
+                    f"Detected an existing checkpoint in {self.metadata_path}, overwriting since {self.overwrite=}."
+                    " Past version 2.5 of PyTorch, `overwrite` will default to False. Set this variable to True to"
+                    " maintain this functionality or False to raise when an existing checkpoint is found."
+                )
+            else:
+                raise RuntimeError(f"Checkpoint already exists and {self.overwrite=}.")
+
+        return plan
+
+    def prepare_global_plan(self, plans: list[SavePlan]) -> list[SavePlan]:
+        new_plans = [
+            dataclasses.replace(plan, storage_data=_StoragePrefix(f"__{i}_"))
+            for i, plan in enumerate(plans)
+        ]
+        return new_plans
+
+    def write_data(
+        self,
+        plan: SavePlan,
+        planner: SavePlanner,
+    ) -> Future[list[WriteResult]]:
+        storage_plan: _StoragePrefix = plan.storage_data
+        file_count = 0
+
+        def gen_file():
+            nonlocal file_count
+            file_name = f"{storage_plan.prefix}{file_count}{DEFAULT_SUFFIX}"
+            file_count += 1
+            return file_name
+
+        file_queue: queue.Queue = queue.Queue()
+        if self.single_file_per_rank:
+            for bucket in _split_by_size_and_type(self.thread_count, plan.items):
+                file_name = gen_file()
+                path = self.fs.concat_path(self.path, file_name)
+                file_queue.put((path, file_name, bucket))
+        else:
+            for item in plan.items:
+                file_name = gen_file()
+                path = self.fs.concat_path(self.path, file_name)
+                file_queue.put((path, file_name, [item]))
+
+        return self._write_data(planner, file_queue)
+
+    def _write_data(
+        self,
+        planner: SavePlanner,
+        file_queue: queue.Queue,
+        safe_tensors: bool = False,
+    ) -> Future[list[WriteResult]]:
+        result_queue: queue.Queue = queue.Queue()
+
+        threads = []
+        for _ in range(1, self.thread_count):
+            t = threading.Thread(
+                target=_write_files_from_queue,
+                args=(
+                    self.fs.create_stream,
+                    file_queue,
+                    result_queue,
+                    planner,
+                    self.transforms,
+                    self.per_thread_copy_ahead,
+                    self.sync_files,
+                    self.thread_count,
+                    safe_tensors,
+                ),
+            )
+            t.start()
+            threads.append(t)
+
+        _write_files_from_queue(
+            create_stream=self.fs.create_stream,
+            file_queue=file_queue,
+            result_queue=result_queue,
+            planner=planner,
+            transforms=self.transforms,
+            inflight_threshhold=self.per_thread_copy_ahead,
+            use_fsync=self.sync_files,
+            thread_count=self.thread_count,
+            safe_tensors=safe_tensors,
+        )
+
+        for t in threads:
+            t.join()
+
+        res = []
+        try:
+            while True:
+                res += result_queue.get_nowait()
+        except queue.Empty:
+            fut: Future[list[WriteResult]] = Future()
+            fut.set_result(res)
+            return fut
+
+    def finish(self, metadata: Metadata, results: list[list[WriteResult]]) -> None:
+        storage_md = {}
+        for wr_list in results:
+            storage_md.update({wr.index: wr.storage_data for wr in wr_list})
+        metadata.storage_data = storage_md
+
+        metadata.storage_meta = self.storage_meta()
+
+        tmp_path = cast(Path, self.fs.concat_path(self.path, f"{_metadata_fn}.tmp"))
+        with self.fs.create_stream(tmp_path, "wb") as metadata_file:
+            pickle.dump(metadata, metadata_file)
+            if self.sync_files:
+                try:
+                    os.fsync(metadata_file.fileno())
+                except (AttributeError, UnsupportedOperation):
+                    os.sync()
+
+        # delete in-case other checkpoints were present.
+        if self.fs.exists(self.metadata_path):
+            self.fs.rm_file(self.metadata_path)
+
+        self.fs.rename(tmp_path, self.metadata_path)
+
+    def storage_meta(self) -> Optional[StorageMeta]:
+        return StorageMeta(checkpoint_id=self.checkpoint_id, save_id=self.save_id)
+
+    @property
+    def metadata_path(self) -> Union[str, os.PathLike]:
+        return cast(Path, self.fs.concat_path(self.path, _metadata_fn))
+
+    @property
+    def checkpoint_id(self) -> Union[str, os.PathLike]:
+        """
+        return the checkpoint_id that will be used to save the checkpoint.
+        """
+        return self.path
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        return FileSystem.validate_checkpoint_id(checkpoint_id)
+
+
+class _StorageReaderTransforms:
+    """
+    This is experimental, and will likely move elsewhere in the
+    future.  It lives here to minimize changes while we are still
+    learning and gathering feedback.
+    """
+
+    def __init__(self, extension_registry: Optional[ExtensionRegistry] = None) -> None:
+        self.extension_registry = (
+            ExtensionRegistry() if extension_registry is None else extension_registry
+        )
+
+    def transform_load_stream(
+        self,
+        read_item: ReadItem,
+        transform_descriptors: Sequence[str],
+        raw_stream: IO[bytes],
+    ) -> IO[bytes]:
+        extensions = self.extension_registry.from_descriptor_list(transform_descriptors)
+        transform_from = raw_stream
+        for ex in extensions:
+            if isinstance(ex, StreamTransformExtension):
+                transform_from = ex.transform_from(transform_from)
+        return transform_from
+
+
+class FileSystemReader(StorageReader):
+    def __init__(
+        self,
+        path: Union[str, os.PathLike],
+        _extension_registry: Optional[ExtensionRegistry] = None,  # EXPERIMENTAL
+    ) -> None:
+        super().__init__()
+        self.fs = FileSystem()
+        self.path = self.fs.init_path(path)
+        self.storage_data: dict[Any, Any] = {}
+        self.load_id = _generate_uuid()
+        self.transforms = _StorageReaderTransforms(_extension_registry)
+
+    def _slice_file(self, file, sinfo: _StorageInfo) -> IO[bytes]:
+        return cast(IO[bytes], _create_file_view(file, sinfo.offset, sinfo.length))
+
+    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
+        self.storage_data = {}
+        if checkpoint_id:
+            self.path = self.fs.init_path(checkpoint_id)
+        self.load_id = _generate_uuid()
+
+    def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
+        # group requests by file
+        per_file: dict[str, list[ReadItem]] = {}
+        for read_item in plan.items:
+            item_md: _StorageInfo = self.storage_data[read_item.storage_index]
+            path = item_md.relative_path
+            per_file.setdefault(path, []).append(read_item)
+
+        for relative_path, reqs in per_file.items():
+            new_path = self.fs.concat_path(self.path, relative_path)
+            with self.fs.create_stream(new_path, "rb") as stream:
+                # TODO sort by offset and cache the reading
+                for req in reqs:
+                    item_md = self.storage_data[req.storage_index]
+                    file_slice = self._slice_file(stream, item_md)
+                    transform_from = self.transforms.transform_load_stream(
+                        req,
+                        # This field wasn't present in older
+                        # implementations so provide a fallback.
+                        item_md.transform_descriptors or (),
+                        file_slice,
+                    )
+
+                    if req.type == LoadItemType.BYTE_IO:
+                        read_bytes = io.BytesIO(transform_from.read(-1))
+                        read_bytes.seek(0)
+                        planner.load_bytes(req, read_bytes)
+                    else:
+                        if transform_from.seekable():
+                            seekable = transform_from
+                        else:
+                            # torch.load requires a seekable input, so read the transform
+                            # stream now and store the output if needed
+                            seekable = io.BytesIO(transform_from.read(-1))
+                            seekable.seek(0)
+
+                        tensor = cast(
+                            Tensor,
+                            torch.load(
+                                seekable,
+                                map_location="cpu",
+                                weights_only=True,
+                            ),
+                        )
+                        tensor = narrow_tensor_by_index(
+                            tensor, req.storage_offsets, req.lengths
+                        )
+                        target_tensor = planner.resolve_tensor(req).detach()
+
+                        assert target_tensor.size() == tensor.size(), (
+                            f"req {req.storage_index} mismatch sizes {target_tensor.size()} vs {tensor.size()}"
+                        )
+                        target_tensor.copy_(tensor)
+                        planner.commit_tensor(req, target_tensor)
+
+        fut: Future = Future()
+        fut.set_result(None)
+        return fut
+
+    # Implementing the abstract function in StorageReader
+    def read_metadata(self) -> Metadata:
+        path = self.fs.concat_path(self.path, ".metadata")
+        with self.fs.create_stream(path, "rb") as metadata_file:
+            metadata = pickle.load(metadata_file)
+
+        if getattr(metadata, "storage_meta", None) is None:
+            metadata.storage_meta = StorageMeta()
+        metadata.storage_meta.load_id = self.load_id
+
+        return metadata
+
+    def set_up_storage_reader(self, metadata: Metadata, is_coordinator: bool) -> None:
+        self.storage_data = metadata.storage_data
+        assert self.storage_data is not None
+
+    def prepare_local_plan(self, plan: LoadPlan) -> LoadPlan:
+        return plan
+
+    def prepare_global_plan(self, plans: list[LoadPlan]) -> list[LoadPlan]:
+        return plans
+
+    @property
+    def checkpoint_id(self) -> Union[str, os.PathLike]:
+        """
+        return the checkpoint_id that will be used to load the checkpoint.
+        """
+        return self.path
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        return FileSystem.validate_checkpoint_id(checkpoint_id)
+
+
+class FileSystemWriter(_FileSystemWriter, BlockingAsyncStager):
+    """
+    Basic implementation of StorageWriter using file IO.
+
+    This implementation makes the following assumptions and simplifications:
+
+    * The checkpoint path is an empty or non-existing directory.
+    * File creation is atomic
+
+    The checkpoint consist of one file per write request plus
+    a `.metadata` file with the serialized metadata.
+
+    """
+
+    def __init__(
+        self,
+        path: Union[str, os.PathLike],
+        single_file_per_rank: bool = True,
+        sync_files: bool = True,
+        thread_count: int = 1,
+        per_thread_copy_ahead: int = 10_000_000,
+        cache_staged_state_dict: bool = False,
+        overwrite: bool = True,
+        _extensions: Optional[Sequence[StreamTransformExtension]] = None,
+    ) -> None:
+        """
+        Initialize the writer pointing to `path`.
+
+        Args:
+            path: directory where the checkpoint will be written to.
+            single_file_per_rank: Produce one file per rank instead of one file per tensor/blob. Default to True.
+            sync_files : force files to be synced to permanent storage. Default to True.
+            thread_count: Number of IO threads to use to write. Default to 1.
+            per_thread_copy_ahead: How many bytes to copy from the GPU ahead of saving then. Default 10Mb.
+            cache_staged_state_dict: Whether to cache the staged state_dict. This option decreases staging latency
+                at the cost of increases memory usage. Additionally, if this parameter is set to True, it's the expectation
+                that the stager is maintained and re-used for multiple dcp.async_save calls. Default to False.
+            overwrite: Whether to allow overwriting existing checkpoints. Defaults to True.
+            _extensions: Extensions to apply to output streams (EXPERIMENTAL)
+
+        N. B. If sync_files is disabled, there's no guarantee that the checkpoint will be consistent in the case of a failure.
+        """
+        _FileSystemWriter.__init__(
+            self,
+            path=path,
+            single_file_per_rank=single_file_per_rank,
+            sync_files=sync_files,
+            thread_count=thread_count,
+            per_thread_copy_ahead=per_thread_copy_ahead,
+            overwrite=overwrite,
+            _extensions=_extensions,
+        )
+        BlockingAsyncStager.__init__(
+            self,
+            cache_staged_state_dict=cache_staged_state_dict,
+        )
+
+    def stage(self, state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE:
+        """Override of AsyncStager.stage"""
+        # in the async case, the state dict is already on CPU, so maintaining this
+        # buffer makes no sense
+        self.per_thread_copy_ahead = 0
+        return super().stage(state_dict)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/format_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/format_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc695c495cb59f959c43cfe3276942fcaadc66a8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/format_utils.py
@@ -0,0 +1,280 @@
+# mypy: allow-untyped-defs
+import argparse
+import os
+from enum import Enum
+from typing import cast, Optional, Union
+
+import torch
+import torch.distributed as dist
+from torch.distributed._shard._utils import narrow_tensor_by_index
+from torch.distributed.checkpoint import FileSystemReader, FileSystemWriter
+from torch.distributed.checkpoint._nested_dict import flatten_state_dict
+from torch.distributed.checkpoint.default_planner import (
+    _EmptyStateDictLoadPlanner,
+    DefaultLoadPlanner,
+)
+from torch.distributed.checkpoint.metadata import (
+    Metadata,
+    STATE_DICT_TYPE,
+    STORAGE_TYPES,
+    TensorProperties,
+    TensorStorageMetadata,
+)
+from torch.distributed.checkpoint.planner import LoadItemType, LoadPlan, LoadPlanner
+from torch.distributed.checkpoint.planner_helpers import _create_chunk_list
+from torch.distributed.checkpoint.state_dict_loader import _load_state_dict
+from torch.distributed.checkpoint.state_dict_saver import _save_state_dict
+from torch.distributed.checkpoint.storage import StorageReader
+from torch.futures import Future
+
+
+__all__ = [
+    "dcp_to_torch_save",
+    "torch_save_to_dcp",
+    "BroadcastingTorchSaveReader",
+    "DynamicMetaLoadPlanner",
+]
+
+
+class BroadcastingTorchSaveReader(StorageReader):
+    """
+    StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
+    on the coordinator rank, and then broadcast and shard each tensor to all ranks.
+
+    . N.B. Intended to be used with DynamicMetaLoadPlanner
+
+    .. warning::
+        Current implementation only supports loading Tensors.
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> sd = {"mode": model}
+    >>> dcp.load(
+    >>>    sd,
+    >>>    storage_reader=BroadcastingTorchSaveReader(),
+    >>>    planner=DynamicMetaLoadPlanner(),
+    >>>    checkpoint_id="path_to_model.pt"
+    >>> )
+    """
+
+    def __init__(
+        self,
+        checkpoint_id: Optional[Union[str, os.PathLike]] = None,
+        coordinator_rank: int = 0,
+    ) -> None:
+        self.checkpoint_id = checkpoint_id
+        self.coordinator_rank = coordinator_rank
+
+    def read_metadata(self) -> Metadata:
+        """Extends the default StorageReader to support building the metadata file"""
+        # Metadata is built in planner.set_up_planner, since we are not actually reading metadata from
+        # the disk
+        return Metadata(state_dict_metadata={})
+
+    def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
+        """
+        Reads torch save data on the coordinator rank, and broadcast afterwards
+        this incurrs a communication cost, but avoids having to load
+        the entire checkpoint on each rank, hopefully preventing OOM issues
+        """
+        planner = cast(DefaultLoadPlanner, planner)
+
+        # data is read in on the coordinator rank, and broadcast afterwards
+        # this incurrs a communication cost, but it avoids having to load
+        # the entire checkpoint on each rank, hopefully preventing OOM issues
+        # TODO: read on each host, instead of only the coordinator
+        if self.is_coordinator:
+            assert self.checkpoint_id is not None
+            torch_state_dict = torch.load(
+                self.checkpoint_id, map_location="cpu", weights_only=False
+            )
+            if planner.flatten_state_dict:
+                torch_state_dict, _ = flatten_state_dict(torch_state_dict)
+        else:
+            torch_state_dict = None
+
+        for req in plan.items:
+            if req.type == LoadItemType.BYTE_IO:
+                raise RuntimeError(
+                    f"Non-tensor value identified at {req.storage_index.fqn}. "
+                    f"At this time {type(self).__name__} only supports loading Tensors."
+                )
+
+            #  Broadcast the tensor from the coordinator rank
+            if self.is_coordinator:
+                pg_device = dist.distributed_c10d._get_pg_default_device()
+                tensor = torch_state_dict[req.storage_index.fqn].to(pg_device)
+            else:
+                tensor = torch.empty_like(planner.state_dict[req.storage_index.fqn])
+
+            dist.broadcast(tensor, src=self.coordinator_rank, async_op=False)
+
+            tensor = narrow_tensor_by_index(tensor, req.storage_offsets, req.lengths)
+            target_tensor = planner.resolve_tensor(req).detach()
+            assert target_tensor.size() == tensor.size(), (
+                f"req {req.storage_index} mismatch sizes, "
+                f"{target_tensor.size()} vs {tensor.size()}"
+            )
+            target_tensor.copy_(tensor)
+            planner.commit_tensor(req, target_tensor)
+
+        fut: Future = Future()
+        fut.set_result(None)
+        return fut
+
+    def set_up_storage_reader(self, metadata: Metadata, is_coordinator: bool) -> None:
+        """Implementation of the StorageReader method"""
+        self.is_coordinator = is_coordinator
+        if self.is_coordinator:
+            assert dist.get_rank() == self.coordinator_rank
+
+        assert self.checkpoint_id is not None
+
+    def prepare_local_plan(self, plan: LoadPlan) -> LoadPlan:
+        """Implementation of the StorageReader method"""
+        return plan
+
+    def prepare_global_plan(self, global_plan: list[LoadPlan]) -> list[LoadPlan]:
+        """Implementation of the StorageReader method"""
+        return global_plan
+
+    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
+        """Implementation of the StorageReader method"""
+        self.checkpoint_id = checkpoint_id
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        """Implementation of the StorageReader method"""
+        return os.path.isfile(checkpoint_id)
+
+
+class DynamicMetaLoadPlanner(DefaultLoadPlanner):
+    """
+    Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
+    avoiding the need to read metadata from disk. This is useful when reading formats which don't have a
+    metadata file, like Torch Save files.
+
+    . N.B. Intended to be used with BroadcastingTorchSaveReader
+
+    .. warning::
+        Current implementation only supports loading Tensors.
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> sd = {"mode": model}
+    >>> dcp.load(
+    >>>    sd,
+    >>>    storage_reader=BroadcastingTorchSaveReader(),
+    >>>    planner=DynamicMetaLoadPlanner(),
+    >>>    checkpoint_id="path_to_model.pt"
+    >>> )
+    """
+
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        metadata: Optional[Metadata] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        """Setups of the planner, extnding default behavior by creating the Metadata object from the state dict"""
+        super().set_up_planner(state_dict, metadata, is_coordinator)
+
+        state_dict_metadata: dict[str, STORAGE_TYPES] = {}
+        for key, tensor in self.state_dict.items():
+            if not torch.is_tensor(tensor):
+                raise RuntimeError(
+                    f"Non-tensor value identified at {key}. "
+                    f"At this time {type(self).__name__} only supports loading Tensors."
+                )
+
+            state_dict_metadata[key] = TensorStorageMetadata(
+                TensorProperties(dtype=tensor.dtype),
+                tensor.size(),
+                _create_chunk_list(tensor),
+            )
+        self.metadata = Metadata(state_dict_metadata=state_dict_metadata)
+
+
+def dcp_to_torch_save(
+    dcp_checkpoint_dir: Union[str, os.PathLike],
+    torch_save_path: Union[str, os.PathLike],
+):
+    """
+    Given a directory containing a DCP checkpoint, this function will convert it into a
+    Torch save file.
+
+    Args:
+        dcp_checkpoint_dir: Directory containing the DCP checkpoint.
+        torch_save_path: Filename to store the converted Torch save file.
+
+    .. warning::
+        To avoid OOM, it's recommended to only run this function on a single rank.
+    """
+    sd: STATE_DICT_TYPE = {}
+    _load_state_dict(
+        sd,
+        storage_reader=FileSystemReader(dcp_checkpoint_dir),
+        planner=_EmptyStateDictLoadPlanner(),
+        no_dist=True,
+    )
+    torch.save(sd, torch_save_path)
+
+
+def torch_save_to_dcp(
+    torch_save_path: Union[str, os.PathLike],
+    dcp_checkpoint_dir: Union[str, os.PathLike],
+):
+    """
+    Given the location of a torch save file, converts it into a DCP checkpoint.
+
+    Args:
+        torch_save_path: Filename of the Torch save file.
+        dcp_checkpoint_dir: Directory to store the DCP checkpoint.
+
+    .. warning::
+        To avoid OOM, it's recommended to only run this function on a single rank.
+    """
+
+    state_dict = torch.load(torch_save_path, weights_only=False)
+    # we don't need stateful behavior here because the expectation is anything loaded by
+    # torch.load would not contain stateful objects.
+    _save_state_dict(
+        state_dict, storage_writer=FileSystemWriter(dcp_checkpoint_dir), no_dist=True
+    )
+
+
+if __name__ == "__main__":
+
+    class FormatMode(Enum):
+        TORCH_TO_DCP = "torch_to_dcp"
+        DCP_TO_TORCH = "dcp_to_torch"
+
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "mode",
+        type=str,
+        help="Conversion mode",
+        choices=[m.value for m in FormatMode],
+        default=FormatMode.TORCH_TO_DCP,
+    )
+    parser.add_argument("src", type=str, help="Path to the source model")
+    parser.add_argument("dst", type=str, help="Path to the destination model")
+    args = parser.parse_args()
+
+    print(
+        f"Converting checkpoint from {args.src} to {args.dst} using method: '{args.mode}'"
+    )
+    checkpoint_missing_warning = (
+        f"No checkpoint found at {args.src}. Skipping conversion."
+    )
+    if args.mode == FormatMode.TORCH_TO_DCP.value:
+        if os.path.isfile(args.src):
+            torch_save_to_dcp(args.src, args.dst)
+        else:
+            print(checkpoint_missing_warning)
+    elif args.mode == FormatMode.DCP_TO_TORCH.value:
+        if os.path.isdir(args.src):
+            dcp_to_torch_save(args.src, args.dst)
+        else:
+            print(checkpoint_missing_warning)
+    else:
+        raise ValueError(f"Unknown conversion mode: {args.mode}")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/logger.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8961493cbee43a201c091f4bde23f3fd7a9b869
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/logger.py
@@ -0,0 +1,118 @@
+# mypy: allow-untyped-defs
+import functools
+import logging
+import time
+from typing import Any, Callable, TypeVar
+from typing_extensions import ParamSpec
+from uuid import uuid4
+
+import torch.distributed.c10d_logger as c10d_logger
+from torch.distributed.checkpoint.logging_handlers import DCP_LOGGER_NAME
+
+
+logger = logging.getLogger()
+
+
+__all__: list[str] = []
+
+global _dcp_logger
+_dcp_logger = c10d_logger._get_or_create_logger(DCP_LOGGER_NAME)
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+
+def _msg_dict_from_dcp_method_args(*args, **kwargs) -> dict[str, Any]:
+    """
+    Extracts log data from dcp method args
+    """
+    msg_dict = {}
+
+    # checkpoint ID can be passed in through the serializer or through the checkpoint id directly
+    storage_writer = kwargs.get("storage_writer", None)
+    storage_reader = kwargs.get("storage_reader", None)
+    planner = kwargs.get("planner", None)
+
+    checkpoint_id = kwargs.get("checkpoint_id", None)
+    if not checkpoint_id and (serializer := storage_writer or storage_reader):
+        checkpoint_id = getattr(serializer, "checkpoint_id", None)
+
+    msg_dict["checkpoint_id"] = (
+        str(checkpoint_id) if checkpoint_id is not None else checkpoint_id
+    )
+
+    # Uniquely identify a _dcp_method_logger wrapped function call.
+    msg_dict["uuid"] = str(uuid4().int)
+
+    if storage_writer:
+        msg_dict["storage_writer"] = storage_writer.__class__.__name__
+
+    if storage_reader:
+        msg_dict["storage_reader"] = storage_reader.__class__.__name__
+
+    if planner:
+        msg_dict["planner"] = planner.__class__.__name__
+
+    return msg_dict
+
+
+def _get_msg_dict(func_name, *args, **kwargs) -> dict[str, Any]:
+    msg_dict = _msg_dict_from_dcp_method_args(*args, **kwargs)
+    msg_dict.update(c10d_logger._get_msg_dict(func_name, *args, **kwargs))
+
+    return msg_dict
+
+
+def _dcp_method_logger(
+    log_exceptions: bool = False, **wrapper_kwargs: Any
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:  # pyre-ignore
+    """This method decorator logs the start, end, and exception of wrapped events."""
+
+    def decorator(func: Callable[_P, _T]):
+        @functools.wraps(func)
+        def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T:
+            msg_dict = _get_msg_dict(
+                func.__name__, *args, **{**wrapper_kwargs, **kwargs}
+            )
+
+            # log start event
+            msg_dict["event"] = "start"
+            t0 = time.time_ns()
+            msg_dict["time"] = t0
+            msg_dict["log_exceptions"] = log_exceptions
+            _dcp_logger.debug(msg_dict)
+
+            # exceptions
+            try:
+                result = func(*args, **kwargs)
+            except BaseException as error:
+                if log_exceptions:
+                    msg_dict["event"] = "exception"
+                    msg_dict["error"] = f"{error}"
+                    msg_dict["time"] = time.time_ns()
+                    _dcp_logger.error(msg_dict)
+                raise
+
+            # end event
+            msg_dict["event"] = "end"
+            t1 = time.time_ns()
+            msg_dict["time"] = time.time_ns()
+            msg_dict["times_spent"] = t1 - t0
+            _dcp_logger.debug(msg_dict)
+
+            return result
+
+        return wrapper
+
+    return decorator
+
+
+def _init_logger(rank: int):
+    logger.setLevel(logging.INFO)
+    ch = logging.StreamHandler()
+    ch.setLevel(logging.INFO)
+    formatter = logging.Formatter(
+        f"[{rank}] %(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    )
+    ch.setFormatter(formatter)
+    logger.addHandler(ch)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/logging_handlers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/logging_handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..99c3ee4156ce340e37a2723106df5ea64b19170d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/logging_handlers.py
@@ -0,0 +1,14 @@
+import logging
+
+from torch.distributed.logging_handlers import _log_handlers
+
+
+__all__: list[str] = []
+
+DCP_LOGGER_NAME = "dcp_logger"
+
+_log_handlers.update(
+    {
+        DCP_LOGGER_NAME: logging.NullHandler(),
+    }
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/metadata.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..3587943b30109b1a6d4632f23aeee16adc4f3a28
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/metadata.py
@@ -0,0 +1,184 @@
+# mypy: allow-untyped-defs
+import os
+from collections.abc import Sequence
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Optional, Union
+
+import torch
+from torch.distributed.checkpoint.stateful import StatefulT
+
+
+__all__ = [
+    "ChunkStorageMetadata",
+    "TensorStorageMetadata",
+    "BytesStorageMetadata",
+    "Metadata",
+    "MetadataIndex",
+    "TensorProperties",
+    "StorageMeta",
+]
+
+
+@dataclass
+class ChunkStorageMetadata:
+    """
+    Each chunk is expected to have the same properties of the TensorStorageMetadata
+    that includes it.
+    """
+
+    offsets: torch.Size
+    sizes: torch.Size
+
+
+class _MEM_FORMAT_ENCODING(Enum):
+    """Describe the memory format of a tensor."""
+
+    TORCH_CONTIGUOUS_FORMAT = 0
+    TORCH_CHANNELS_LAST = 1
+    TORCH_PRESERVE_FORMAT = 2
+
+
+@dataclass
+class TensorProperties:
+    """Properties used to create :class:`Tensor`"""
+
+    # Regular tensor fields
+    dtype: torch.dtype = field(default_factory=torch.get_default_dtype)
+    # This field is deprecated.
+    layout: torch.layout = field(default=torch.strided)
+    # This field is deprecated.
+    requires_grad: bool = False
+    # This field is deprecated.
+    memory_format: torch.memory_format = field(default=torch.contiguous_format)
+    # This field is deprecated.
+    pin_memory: bool = False
+
+    def __getstate__(self):
+        # Since torch.memory_format cannot be pickled!
+        memory_format = self.memory_format
+        if memory_format == torch.contiguous_format:
+            mem_format_encoding = _MEM_FORMAT_ENCODING.TORCH_CONTIGUOUS_FORMAT
+        elif memory_format == torch.channels_last:
+            mem_format_encoding = _MEM_FORMAT_ENCODING.TORCH_CHANNELS_LAST
+        elif memory_format == torch.preserve_format:
+            mem_format_encoding = _MEM_FORMAT_ENCODING.TORCH_PRESERVE_FORMAT
+        else:
+            raise RuntimeError(f"Invalid torch.memory_format: {memory_format}")
+
+        return (
+            self.dtype,
+            self.layout,
+            self.requires_grad,
+            mem_format_encoding,
+            self.pin_memory,
+        )
+
+    def __setstate__(
+        self,
+        state,
+    ):
+        (
+            self.dtype,
+            self.layout,
+            self.requires_grad,
+            mem_format_encoding,
+            self.pin_memory,
+        ) = state
+
+        if mem_format_encoding == _MEM_FORMAT_ENCODING.TORCH_CONTIGUOUS_FORMAT:
+            memory_format = torch.contiguous_format
+        elif mem_format_encoding == _MEM_FORMAT_ENCODING.TORCH_CHANNELS_LAST:
+            memory_format = torch.channels_last
+        elif mem_format_encoding == _MEM_FORMAT_ENCODING.TORCH_PRESERVE_FORMAT:
+            memory_format = torch.preserve_format
+        else:
+            raise RuntimeError(
+                f"Invalid torch.memory_format encoding: {mem_format_encoding}"
+            )
+
+        self.memory_format = memory_format
+
+    @staticmethod
+    def create_from_tensor(tensor: torch.Tensor) -> "TensorProperties":
+        return TensorProperties(
+            dtype=tensor.dtype,
+            layout=tensor.layout,
+            requires_grad=tensor.requires_grad,
+            memory_format=torch.contiguous_format,
+            pin_memory=tensor.is_pinned(),
+        )
+
+
+@dataclass
+class TensorStorageMetadata:
+    properties: TensorProperties
+    size: torch.Size
+    chunks: list[ChunkStorageMetadata]
+
+
+@dataclass
+class BytesStorageMetadata:
+    pass
+
+
+STORAGE_TYPES = Union[TensorStorageMetadata, BytesStorageMetadata]
+STATE_DICT_TYPE = dict[str, Union[StatefulT, Any]]
+
+
+@dataclass
+class StorageMeta:
+    checkpoint_id: Union[str, os.PathLike, None] = None
+    save_id: Optional[str] = None
+    load_id: Optional[str] = None
+    modules: list[str] = field(default_factory=list)
+
+
+@dataclass
+class Metadata:
+    """This class represents the metadata of the checkpoint."""
+
+    # Keys are the same from the `state_dict` used.
+    state_dict_metadata: dict[str, STORAGE_TYPES]
+    # It is the responsibility of the planner and storage plugins to ensure
+    # backward compatibility of the planner_data and storage_data. DCP will
+    # also ensure the backward compatibility of the metadata in this file and
+    # the metadata of the built-in planner and storage plugins.
+    planner_data: Any = None
+    storage_data: Any = None
+    storage_meta: Optional[StorageMeta] = None
+
+
+@dataclass(frozen=True)
+class MetadataIndex:
+    """This class represents a lookup key for items in a state dict or Metadata."""
+
+    fqn: str
+    """Fully Qualified Name of the object"""
+
+    offset: Optional[torch.Size] = None
+    """If the object is a tensor, offset into the tensor we're looking for"""
+
+    index: Optional[int] = field(hash=False, compare=False, default=None)
+    """
+    Index hint when searching for tensor chunk to speedup lookups (optional)
+
+    A common representation of a sharded tensor is as a list of chunks so to
+    find the index in such a list you need to linear search it.
+
+    When constructing an instance of MetadataIndex that points to that list,
+    one can provide the index as a hint and it will be probed first before
+    the linear search and thus making it significantly faster.
+    """
+
+    def __init__(
+        self,
+        fqn: str,
+        offset: Optional[Sequence[int]] = None,
+        index: Optional[int] = None,
+    ):
+        # We must use object.__setattr__ due to frozen=True
+        object.__setattr__(self, "fqn", fqn)
+        object.__setattr__(self, "index", index)
+        if offset is not None:
+            object.__setattr__(self, "offset", torch.Size(offset))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/optimizer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..43193afe6e67c42f19f554fb57fbc1b1f1b2c7ac
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/optimizer.py
@@ -0,0 +1,357 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import dataclasses
+from collections.abc import Sequence
+from typing import cast, Optional, Union
+
+import torch
+import torch.distributed as dist
+from torch._utils import _get_device_module
+from torch.distributed._shard.sharded_tensor.api import ShardedTensor
+from torch.distributed._shard.sharded_tensor.metadata import (
+    TensorProperties as ShardTensorProperties,
+)
+from torch.distributed._shard.sharded_tensor.shard import Shard
+from torch.distributed._shard.sharding_spec.chunk_sharding_spec import ChunkShardingSpec
+from torch.distributed.checkpoint._nested_dict import unflatten_state_dict
+from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner
+from torch.distributed.checkpoint.metadata import (
+    BytesStorageMetadata,
+    ChunkStorageMetadata,
+    Metadata,
+    MetadataIndex,
+    STATE_DICT_TYPE,
+    TensorProperties,
+    TensorStorageMetadata,
+)
+from torch.distributed.checkpoint.planner import LoadPlan, LoadPlanner
+from torch.distributed.checkpoint.planner_helpers import (
+    _create_read_items,
+    create_read_items_for_chunk_list,
+)
+from torch.distributed.checkpoint.state_dict_loader import load_state_dict
+from torch.distributed.checkpoint.storage import StorageReader
+from torch.distributed.checkpoint.utils import (
+    _element_wise_add,
+    _element_wise_sub,
+    _normalize_device_info,
+)
+from torch.distributed.distributed_c10d import _get_default_group
+from torch.distributed.fsdp._shard_utils import _create_chunk_sharded_tensor
+from torch.distributed.remote_device import _remote_device
+from torch.distributed.tensor import DTensor
+
+
+STATE_DICT_2D_LAYOUT = dict[str, tuple[Optional[Sequence[int]], Sequence[int]]]
+
+
+# TODO: Update docstrings for optimizer.py
+__all__ = [
+    "load_sharded_optimizer_state_dict",
+]
+
+
+def _gen_rank_device(global_rank: int, device_type: str = "cuda") -> str:
+    if device_type == "cpu":
+        return "cpu"
+    device_module = _get_device_module(device_type)
+    if device_module.is_available():
+        return _normalize_device_info(
+            device_type, global_rank % device_module.device_count()
+        )
+    return "cpu"
+
+
+def _create_colwise_spec(
+    pg: Optional[dist.ProcessGroup] = None,
+) -> ChunkShardingSpec:
+    pg_device_type = dist.distributed_c10d._get_pg_default_device(pg).type
+    if pg is None:
+        placements = [
+            f"rank:{idx}/{_gen_rank_device(idx, pg_device_type)}"
+            for idx in range(dist.get_world_size())
+        ]
+    else:
+        placements = [
+            f"rank:{idx}/{_gen_rank_device(dist.get_global_rank(pg, idx), pg_device_type)}"
+            for idx in range(pg.size())
+        ]
+    return ChunkShardingSpec(
+        dim=0,
+        placements=cast(list[Union[_remote_device, str]], placements),
+    )
+
+
+def _is_nested_tensor(val: torch.Tensor) -> bool:
+    if type(val) is ShardedTensor:
+        if len(val.local_shards()) == 0:
+            return False
+        if type(val.local_shards()[0].tensor) is ShardedTensor:
+            return True
+        if type(val.local_shards()[0].tensor) is DTensor:
+            raise ValueError("Cannot handle DTensor nested insided ShardedTensor")
+    elif type(val) is DTensor and (
+        type(val._local_tensor) is DTensor or type(val._local_tensor) is ShardedTensor
+    ):
+        raise ValueError("Cannot handle nested DTensor")
+    return False
+
+
+def _alloc_tensor(
+    props: TensorProperties, size: Sequence[int], device_type: str = "cuda"
+) -> torch.Tensor:
+    if device_type == "cpu":
+        device = cast(torch.device, _get_device_module(device_type).current_device())
+    else:
+        device = torch.device(
+            device_type, _get_device_module(device_type).current_device()
+        )
+
+    return torch.empty(
+        size=size,
+        dtype=props.dtype,
+        layout=props.layout,
+        requires_grad=props.requires_grad,
+        pin_memory=props.pin_memory,
+        device=device,
+    )
+
+
+def _get_state_dict_2d_layout(
+    state_dict: STATE_DICT_TYPE,
+) -> tuple[STATE_DICT_2D_LAYOUT, Optional[dist.ProcessGroup]]:
+    """
+    Load the right TP slice of the optimizer state.
+
+    This is not easy since the per-tensor slicing can't be inferred from checkpoint metadata.
+    We take advantage of the model state_dict producing a sliced ST to figure out what we need to load.
+    This is pretty fragile and it might be easier for FSDP to compute this info for us.
+    Returns a dictionary where keys are the same of the state_dict and the value is a tuple of
+    (offset, size) for the current rank TP slice.
+    N.B. The state_dict *MUST* come from FSDP.sharded_state_dict.
+    """
+    specs: STATE_DICT_2D_LAYOUT = {}
+    dp_pg: Optional[dist.ProcessGroup] = None
+    for key, value in state_dict.items():
+        specs[key] = (None, value.size())
+        if _is_nested_tensor(value):
+            assert len(value.local_shards()) == 1, (
+                "Cannot handle ST with multiple shards"
+            )
+            assert isinstance(value, ShardedTensor), (
+                "Can only handle nested ShardedTensor"
+            )
+            shard = value.local_shards()[0]
+            specs[key] = (
+                shard.metadata.shard_offsets,
+                shard.metadata.shard_sizes,
+            )
+            dp_pg = shard.tensor._process_group  # type: ignore[attr-defined]
+
+    return (
+        specs,
+        dp_pg,
+    )
+
+
+class _ReaderWithOffset(DefaultLoadPlanner):
+    translation: dict[MetadataIndex, MetadataIndex]
+    state_dict: STATE_DICT_TYPE
+    metadata: Metadata
+
+    def __init__(self, fqn_to_offset: dict[str, Sequence[int]]) -> None:
+        super().__init__()
+        self.fqn_to_offset = fqn_to_offset
+        self.metadata = Metadata({})
+        self.state_dict = {}
+        self.translation = {}
+
+    def create_local_plan(self) -> LoadPlan:
+        requests = []
+        self.translation = {}
+        for fqn, obj in self.state_dict.items():
+            md = self.metadata.state_dict_metadata[fqn]
+            if not isinstance(obj, ShardedTensor):
+                requests += _create_read_items(fqn, md, obj)
+                continue
+
+            if fqn not in self.fqn_to_offset:
+                requests += _create_read_items(fqn, md, obj)
+                continue
+
+            offset = self.fqn_to_offset[fqn]
+
+            assert len(obj.local_shards()) == 1
+            original_shard = obj.local_shards()[0]
+            local_chunks = [
+                ChunkStorageMetadata(
+                    offsets=torch.Size(
+                        _element_wise_add(original_shard.metadata.shard_offsets, offset)
+                    ),
+                    sizes=torch.Size(original_shard.metadata.shard_sizes),
+                )
+            ]
+
+            reqs = create_read_items_for_chunk_list(
+                fqn, cast(TensorStorageMetadata, md), local_chunks
+            )
+            # TODO: The ReadItems will have a displaced MetadataIndex, fix it.
+            # TODO: we should change _create_sharded_read_items to have more ergonomic API
+            for ri in reqs:
+                assert ri.dest_index.offset is not None
+                original_offset = _element_wise_sub(ri.dest_index.offset, offset)
+                original_index = dataclasses.replace(
+                    ri.dest_index, offset=torch.Size(original_offset)
+                )
+                self.translation[ri.dest_index] = original_index
+
+            requests += reqs
+        return LoadPlan(requests)
+
+    def lookup_tensor(self, index: MetadataIndex) -> torch.Tensor:
+        return super().lookup_tensor(self.translation.get(index, index))
+
+
+def load_sharded_optimizer_state_dict(
+    model_state_dict: STATE_DICT_TYPE,
+    optimizer_key: str,
+    storage_reader: StorageReader,
+    planner: Optional[LoadPlanner] = None,
+) -> STATE_DICT_TYPE:
+    """
+    Load a state_dict in conjunction with FSDP sharded optimizer state.
+
+    This is the current recommended way to checkpoint FSDP.
+    >>> # xdoctest: +SKIP
+    >>> import torch.distributed.checkpoint as dist_cp
+    >>> # Save
+    >>> model: torch.nn.Model
+    >>> optim_params = model.parameters()
+    >>> optim = torch.optim.SGD(optim_params, lr=0.01)
+    >>> # Save
+    >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
+    >>>     state_dict = {
+    >>>         "optimizer": FSDP.optim_state_dict(model, optim),
+    >>>         "model": model.state_dict()
+    >>>     }
+    >>>     dist_cp.save_state_dict(
+    >>>         state_dict=optim_state,
+    >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
+    >>>         planner=dist_cp.DefaultSavePlanner(),
+    >>>     )
+    >>>
+    >>> # Load
+    >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
+    >>>     model_state_dict = model_tp.state_dict()
+    >>>     checkpoint = {
+    >>>         "model": model_state_dict
+    >>>     }
+    >>>     dist_cp.load_state_dict(
+    >>>         state_dict=checkpoint,
+    >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
+    >>>         planner=dist_cp.DefaultLoadPlanner(),
+    >>>     )
+    >>>     model.load_state_dict(checkpoint["model_state"])
+    >>>
+    >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
+    >>>         model_state_dict,
+    >>>         optimizer_key="optimizer",
+    >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
+    >>>     )
+    >>>
+    >>>     flattened_osd = FSDP.optim_state_dict_to_load(
+    >>>        model, optim, optim_state["optimizer"]
+    >>>     )
+    >>>
+    >>>     optim.load_state_dict(flattened_osd)
+    """
+    metadata = storage_reader.read_metadata()
+
+    layout_specs, dp_pg = _get_state_dict_2d_layout(model_state_dict)
+    dp_pg_device_type = dist.distributed_c10d._get_pg_default_device(dp_pg).type
+    device_module = _get_device_module(dp_pg_device_type)
+
+    if dp_pg is None:
+        placements = []
+        for i in range(dist.get_world_size()):
+            device_info = _normalize_device_info(
+                dp_pg_device_type, i % device_module.device_count()
+            )
+            placements.append(f"rank:{i}/{device_info}")
+        sharding_spec = ChunkShardingSpec(dim=0, placements=placements)  # type: ignore[arg-type]
+    else:
+        sharding_spec = _create_colwise_spec(dp_pg)
+
+    # Create a state_dict for optimizer state
+    state_dict: STATE_DICT_TYPE = {}
+
+    fqn_to_offset: dict[str, Sequence[int]] = {}
+    for key, value in metadata.state_dict_metadata.items():
+        key_path = metadata.planner_data[key]
+        if key_path[0] != optimizer_key:
+            continue
+
+        if isinstance(value, BytesStorageMetadata):
+            state_dict[key] = ""
+            continue
+
+        # value: TensorStorageMetadata
+        if value.size.numel() == 1:
+            state_dict[key] = _alloc_tensor(
+                value.properties, value.size, dp_pg_device_type
+            )
+        elif dp_pg is None:
+            state_dict[key] = _create_chunk_sharded_tensor(
+                _alloc_tensor(value.properties, value.size, dp_pg_device_type),
+                rank=dist.get_rank(),
+                world_size=dist.get_world_size(),
+                num_devices_per_node=device_module.device_count(),
+                pg=_get_default_group(),
+            )
+        else:
+            spec_key = key_path[2]
+            alloc_size = layout_specs.get(spec_key, (None, value.size))[1]
+
+            properties = ShardTensorProperties(
+                dtype=value.properties.dtype,
+                layout=value.properties.layout,
+                requires_grad=value.properties.requires_grad,
+                memory_format=value.properties.memory_format,
+                pin_memory=value.properties.pin_memory,
+            )
+
+            st_md = sharding_spec.build_metadata(torch.Size(alloc_size), properties)
+            local_shards = []
+            current_rank = dist.get_rank(dp_pg)
+            for shard_md in st_md.shards_metadata:
+                if cast(_remote_device, shard_md.placement).rank() != current_rank:
+                    continue
+                local_shards.append(
+                    Shard(
+                        tensor=_alloc_tensor(
+                            value.properties, shard_md.shard_sizes, dp_pg_device_type
+                        ),
+                        metadata=shard_md,
+                    )
+                )
+
+            st = ShardedTensor._init_from_local_shards_and_global_metadata(
+                local_shards, st_md, process_group=dp_pg
+            )
+
+            if spec_key in layout_specs and layout_specs[spec_key][0] is not None:
+                fqn_to_offset[key] = cast(Sequence[int], layout_specs[spec_key][0])
+
+            state_dict[key] = st
+
+    # Whether we unflatten before or after doesn't matter
+    load_state_dict(
+        state_dict=state_dict,
+        storage_reader=storage_reader,
+        # FIXME the type of planner is wrong in load_state_dict
+        planner=_ReaderWithOffset(fqn_to_offset) if dp_pg is not None else planner,
+    )
+
+    state_dict = unflatten_state_dict(state_dict, metadata.planner_data)
+
+    return state_dict
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/planner.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/planner.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc0b26dfe4d0aee850bbdecd5871c11e39d1e8c7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/planner.py
@@ -0,0 +1,438 @@
+import abc
+import io
+import operator
+from dataclasses import dataclass
+from enum import auto, Enum
+from functools import reduce
+from typing import Any, Optional, Union
+
+import torch
+from torch.distributed.checkpoint.metadata import (
+    ChunkStorageMetadata,
+    Metadata,
+    MetadataIndex,
+    STATE_DICT_TYPE,
+    StorageMeta,
+    TensorProperties,
+)
+
+
+__all__ = [
+    "WriteItemType",
+    "LoadItemType",
+    "TensorWriteData",
+    "WriteItem",
+    "ReadItem",
+    "SavePlan",
+    "LoadPlan",
+    "SavePlanner",
+    "LoadPlanner",
+]
+
+
+class WriteItemType(Enum):
+    TENSOR = auto()
+    SHARD = auto()
+    BYTE_IO = auto()
+
+
+class LoadItemType(Enum):
+    TENSOR = auto()
+    BYTE_IO = auto()
+
+
+@dataclass(frozen=True)
+class TensorWriteData:
+    chunk: ChunkStorageMetadata
+    properties: TensorProperties
+    size: torch.Size
+
+
+@dataclass(frozen=True)
+class WriteItem:
+    """Dataclass which holds information about what needs to be written to storage."""
+
+    index: MetadataIndex
+    type: WriteItemType
+
+    # Value present if it's a tensor write
+    tensor_data: Optional[TensorWriteData] = None
+
+    def tensor_storage_size(self) -> Optional[int]:
+        """
+        Calculates the storage size of the underlying tensor, or None if this is not a tensor write.
+
+        Returns:
+            Optional[int] storage size, in bytes of underlying tensor if any.
+        """
+        if self.tensor_data is None:
+            return None
+
+        numels = reduce(operator.mul, self.tensor_data.size, 1)
+        dtype_size = torch._utils._element_size(self.tensor_data.properties.dtype)
+        return numels * dtype_size
+
+
+@dataclass(frozen=True)
+class ReadItem:
+    # Read Item
+    type: LoadItemType
+
+    # Index into the state_dict
+    dest_index: MetadataIndex
+    # Offsets into destination tensor
+    dest_offsets: torch.Size
+
+    # Index into the checkpoint
+    storage_index: MetadataIndex
+    # Offset into the checkpoint data
+    storage_offsets: torch.Size
+
+    # Size of the hypercube to copy
+    lengths: torch.Size
+
+
+@dataclass(frozen=True)
+class SavePlan:
+    items: list[WriteItem]
+    storage_data: Any = None
+    planner_data: Any = None
+    # This is used to indicate that the ranks should
+    # use the cached plans to write data instead.
+    usable: bool = True
+
+
+@dataclass
+class LoadPlan:
+    items: list[ReadItem]
+    storage_data: Any = None
+    planner_data: Any = None
+
+
+class SavePlanner(abc.ABC):
+    """
+    Abstract class defining the protocol used by save_state_dict to plan the save process.
+
+    SavePlanners are stateful objects that can be used to customize the whole save process.
+
+    SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
+    will be visible to the whole process.
+
+    A planner subclass can expect the following sequence of calls during save_state_dict:
+
+    1) set_up_planner - called on all ranks.
+        Signals the start of a checkpoint save.
+
+    2) create_local_plan - called on all ranks.
+        Process the state_dict and produces a `SavePlan` that will be sent for global planning.
+
+    3) create_global_plan - called on the coordinator rank only.
+        Takes the SavePlan from all ranks and make any global decision.
+
+    4) finish_plan - called on all ranks.
+        This gives each rank a chance to adjust to global planning decisions.
+
+    5) resolve_data - called multiple times on each rank
+        Lookups a value on the `state_dict` for the storage layer to write.
+
+    Users are recommended to extend DefaultSavePlanner instead of this interface directly as
+    most changes can be expressed by changes in a single method.
+
+    There are 3 usual patterns of extension:
+
+    Rewriting state_dict. This is the simplest way to extend the save process as it
+    doesn't requite understanding the intrincacies of how SavePlan works:
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> class RenamePlanner(DefaultSavePlanner):
+    >>>     def set_up_planner(
+    >>>         self,
+    >>>         state_dict: STATE_DICT_TYPE,
+    >>>         storage_meta: Optional[StorageMeta],
+    >>>         is_coordinator: bool,
+    >>>     ) -> None:
+    >>> # prefix all keys with `foo_``
+    >>>         super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator)
+
+    Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> class FP16Planner(DefaultSavePlanner):
+    >>>     def create_local_plan(self):
+    >>>         plan = super().create_local_plan()
+    >>>         for p in plan:
+    >>>             if p.tensor_data is not None:
+    >>>                 p.tensor_data.properties.dtype = torch.float16
+    >>>         return plan
+    >>>
+    >>>     def resolve_data(self, write_item):
+    >>>         item = super().resolve_data(write_item)
+    >>>         return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16)
+
+    Using the global planning step to make central decisions that can't be made individually by each rank
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> from itertools import zip_longest
+    >>> from dataclasses import replace
+    >>> class DDPLoadBalancingPlanner(DefaultSavePlanner):
+    >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0
+    >>> # This sample doesn't handle ShardedTensors
+    >>>     def create_global_plan(self, all_plans):
+    >>>         iters = [iter(all_plans[0].items)] * len(all_plans)
+    >>>         items_per_rank = [
+    >>>             [item for item in items if item is not None]
+    >>>             for items in zip(*zip_longest(*iters), strict=True)
+    >>>         ]
+    >>>         all_plans = [
+    >>>             replace(plan, items=items)
+    >>>             for plan, items in zip(all_plans, items_per_rank, strict=True)
+    >>>         ]
+    >>>         return super().create_global_plan(all_plans)
+
+    Finally, some planners need to save additional metadata in the checkpoint, this is
+    accomplished by having each rank contribute their data items in the local plan and
+    the global planner aggregate them:
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> class SaveExtraDataPlanner(DefaultSavePlanner):
+    >>>     def create_local_plan(self) -> SavePlan:
+    >>>         plan = super().create_local_plan()
+    >>>         return replace(plan, planner_data="per-rank-data")
+    >>>
+    >>>     def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]:
+    >>>         global_plan, metadata = super().create_global_plan(all_plans)
+    >>>         merged_data = [p.planner_data for p in global_plan]
+    >>>         metadata = replace(metadata, planner_data=merged_data)
+    >>>         return global_plan, metadata
+    """
+
+    # Save plan for the current rank as computed by `create_local_plan` API
+    # Cached on the local rank.
+    _cached_save_plan: dict[str, SavePlan] = {}
+    # Final save plan for the current rank.
+    # This is created by merging the plan created by `create_local_plan` API
+    # and the result of `create_global_plan` for the given rank.
+    # This is the final plan computed by the `finish_plan` API that gets
+    # sent to the `write_data`.
+    # Cached on the local rank.
+    _cached_final_save_plan: dict[str, SavePlan] = {}
+    # Collection of all the local plans from all the ranks.
+    # This is the input to the `create_global_plan` API.
+    # Cached on the coordinator rank.
+    _cached_all_plans: dict[str, list[SavePlan]] = {}
+    # Global checkpoint plan as computed by `create_global_plan` API.
+    # Cached on the coordinator rank.
+    _cached_global_plan: dict[str, list[SavePlan]] = {}
+
+    @abc.abstractmethod
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        storage_meta: Optional[StorageMeta] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        """
+        Initialize this planner to save ``state_dict``.
+
+        Implementations should save those values as they won't be provided lated in the save process.
+
+        This is called on all ranks.
+        """
+
+    @abc.abstractmethod
+    def create_local_plan(self) -> SavePlan:
+        """
+        Compute the save plan for the current rank.
+
+        This will be aggregated and passed to create_global_plan.
+        Planner specific data can be passed through SavePlan::planner_data.
+
+        This is called on all ranks.
+        """
+
+    @abc.abstractmethod
+    def create_global_plan(
+        self, all_plans: list[SavePlan]
+    ) -> tuple[list[SavePlan], Metadata]:
+        """
+        Compute the global checkpoint plan and return the local plan of each rank.
+
+        This is called on the coordinator rank only.
+        """
+
+    @abc.abstractmethod
+    def finish_plan(self, new_plan: SavePlan) -> SavePlan:
+        """
+        Merge the plan created by `create_local_plan` and the result of `create_global_plan`.
+
+        This is called on all ranks.
+        """
+
+    @abc.abstractmethod
+    def resolve_data(self, write_item: WriteItem) -> Union[torch.Tensor, io.BytesIO]:
+        """
+        Transform and prepare ``write_item`` from ``state_dict`` for storage, ensuring idempotency and thread-safety.
+
+        Lookup the object associated with ``write_item`` in ``state_dict`` and apply any
+        transformation (such as serialization) prior to the storage layer consuming it.
+
+        Called on each rank multiple times, at least once per WriteItem in the final SavePlan.
+
+        This method should be idempotent and thread-save. StorageWriter implementations
+        are free to call it as frequently as they need.
+
+        Any transformation that allocates memory should be lazily done when his method
+        is called in order to reduce peak memory required by checkpointing.
+
+        When returning tensors, they can be on any device or format, they can be views too.
+        It's the storage layer responsibility to figure out how to save them.
+        """
+
+
+class LoadPlanner:
+    """
+    Abstract class defining the protocol used by load_state_dict to plan the load process.
+
+    LoadPlanner are stateful objects that can be used to customize the whole load process.
+
+    LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
+    will be visible to the whole process.
+
+    A planner subclass can expect the following sequence of calls during load_state_dict:
+
+    1) set_up_planner - called on all ranks.
+        Signals the start of loading a checkpoint.
+
+    2) create_local_plan - called on all ranks.
+        Process the state_dict and produces a `LoadPlan` that will be sent for global planning.
+
+    3) create_global_plan - called on the coordinator rank only.
+        Takes the LoadPlan from all ranks and make any global decision.
+
+    4) load_bytes - called multiple times on each rank
+        This is called once per non-tensor value in state_dict.
+
+    5) resolve_tensor and commit_tensor - called multiple times on each rank
+        They are called in pair for each Tensor value in state_dict.
+
+    Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
+    most changes can be expressed by changes in a single method.
+
+    There are two usual patterns of extension:
+
+    Rewriting state_dict. This is the simplest way to extend the load process as it
+    doesn't requite understanding the intrincacies of how LoadPlan works. We need
+    to keep a reference to the original state_dict as load happens in place so
+    we need to be able to perform it in place
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> class RenamePlanner(DefaultLoadPlanner):
+    >>>     def set_up_planner(
+    >>>         self,
+    >>>         state_dict: STATE_DICT_TYPE,
+    >>>         metadata: Metadata,
+    >>>         is_coordinator: bool,
+    >>>     ) -> None:
+    >>>         self.original_state_dict = state_dict
+    >>>         state_dict = {"foo_" + k: v for k, v in state_dict.items()}
+    >>>
+    >>>         if self.flatten_sharded_tensors:
+    >>>             state_dict = _flatten_sharded_tensors(state_dict)
+    >>>
+    >>>         if self.flatten_state_dict:
+    >>>             state_dict, self.mappings = flatten_state_dict(state_dict)
+    >>>
+    >>>         self.state_dict = state_dict
+    >>>         self.metadata = metadata
+    >>>         self.is_coordinator = is_coordinator
+    >>>
+    >>>     def load_bytes(self, read_item, value):
+    >>> # Remove the "foo_" prefix
+    >>>         self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False)
+
+
+    Modifying resolve_tensor and commit_tensor to handle load time transformation.
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> class MetaModelMaterialize(DefaultSavePlanner):
+    >>>     def resolve_tensor(self, read_item):
+    >>>         tensor = super().resolve_tensor(read_item)
+    >>>         return torch.empty_like(tensor, device="cpu")
+    >>>
+    >>>     def commit_tensor(self, read_item, tensor):
+    >>>         self.state_dict[read_item.dest_index.fqn] = tensor
+    """
+
+    @abc.abstractmethod
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        metadata: Optional[Metadata] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        """
+        Initialize this instance to load data into ``state_dict``.
+
+        . N.B. This is called on every rank.
+        """
+
+    @abc.abstractmethod
+    def create_local_plan(self) -> LoadPlan:
+        """
+        Create a LoadPlan based on state_dict and metadata provided by set_up_planner.
+
+        . N.B. This is called on every rank.
+        """
+
+    @abc.abstractmethod
+    def create_global_plan(self, global_plan: list[LoadPlan]) -> list[LoadPlan]:
+        """
+        Compute the global load plan and return plans for each rank.
+
+        . N.B. This is called on the coordinator rank only
+        """
+
+    @abc.abstractmethod
+    def finish_plan(self, central_plan: LoadPlan) -> LoadPlan:
+        """Accept the plan from coordinator and return final LoadPlan."""
+
+    @abc.abstractmethod
+    def load_bytes(self, read_item: ReadItem, value: io.BytesIO) -> None:
+        """
+        Load the item described by ``read_item``and ``value``.
+
+        This method is expected to modify in-place the underlying state_dict.
+
+        The contents of ``value`` are defined by the SavePlanner used to produce
+        the checkpoint being loaded.
+        """
+
+    def resolve_bytes(self, read_item: ReadItem) -> io.BytesIO:
+        """
+        Return the BytesIO to be used by the StorageReader to load `read_item`.
+
+        The BytesIO should alias with one on the underlying state_dict as StorageReader will replace its contents.
+        """
+        raise NotImplementedError("LoadPlanner.resolve_bytes is not implemented")
+
+    @abc.abstractmethod
+    def resolve_tensor(self, read_item: ReadItem) -> torch.Tensor:
+        """
+        Return the tensor described by ``read_item`` to be used by the StorageReader to load `read_item`.
+
+        The tensor should alias with one on the underlying state_dict as StorageReader will replace its contents.
+        If, for any reason, that's not possible, the planner can use the ``commit_tensor`` method to copy the data
+        back to the one in state_dict.
+        """
+
+    @abc.abstractmethod
+    def commit_tensor(self, read_item: ReadItem, tensor: torch.Tensor) -> None:
+        """
+        Call once the StorageReader finished loading data into ``tensor``.
+
+        The provided tensor is the same one returned by the call to ``resolve_tensor``.
+        This method is only needed if this LoadPlanner needs to post process ``tensor`` prior to
+        copying it back to the one in the state_dict.
+
+        The contents of tensor will follow its device synchronization model.
+        """
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/planner_helpers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/planner_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..66b2174b73f7e92679f7f6d2dbbaa1fa793c2b8a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/planner_helpers.py
@@ -0,0 +1,478 @@
+# mypy: allow-untyped-defs
+import io
+from typing import Any, Callable, cast
+
+import torch
+import torch.distributed as dist
+from torch._utils import _get_device_module
+from torch.distributed._shard.metadata import ShardMetadata
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed.tensor import DTensor
+from torch.distributed.tensor._utils import compute_local_shape_and_global_offset
+
+from .metadata import (
+    BytesStorageMetadata,
+    ChunkStorageMetadata,
+    MetadataIndex,
+    STATE_DICT_TYPE,
+    STORAGE_TYPES,
+    TensorProperties,
+    TensorStorageMetadata,
+)
+from .planner import (
+    LoadItemType,
+    ReadItem,
+    SavePlan,
+    TensorWriteData,
+    WriteItem,
+    WriteItemType,
+)
+from .resharding import (
+    _check_shard_metadata_pair_overlap,
+    _shards_get_overlap_region_wrt_saved_tensor,
+)
+
+
+__all__: list[str] = ["create_read_items_for_chunk_list"]
+
+
+def _compare_save_plans(plan: SavePlan, other_plan: SavePlan) -> bool:
+    """
+    Compare the two Save plans and return True if they are equal.
+
+    Args:
+        plan (SavePlan): First SavePlan to compare.
+        other_plan (SavePlan): Second SavePlan to compare.
+
+    Returns:
+       True if the two plans are equal, False otherwise.
+    """
+    if plan.usable != other_plan.usable:
+        return False
+
+    # Both the plans should have the same number of items
+    if len(plan.items) != len(other_plan.items):
+        return False
+
+    # Both the plans should have the same write items.
+    for plan_item, other_plan_item in zip(plan.items, other_plan.items):
+        # Write item type should be same
+        if plan_item.type != other_plan_item.type:
+            return False
+
+        plan_metadata_index = plan_item.index
+        other_plan_metadata_index = other_plan_item.index
+
+        # Write item metadata_index should be same
+        if (
+            plan_metadata_index.fqn != other_plan_metadata_index.fqn
+            or plan_metadata_index.offset != other_plan_metadata_index.offset
+            or plan_metadata_index.index != other_plan_metadata_index.index
+        ):
+            return False
+
+        # Write item tensor_data should be present in both the write items plans, if it exists in either of them.
+        tensor_data = plan_item.tensor_data
+        other_tensor_data = other_plan_item.tensor_data
+        if (tensor_data and not other_tensor_data) or (
+            not tensor_data and other_tensor_data
+        ):
+            return False
+
+        if tensor_data and other_tensor_data:
+            # Write item tensor_data size should be same
+            if tensor_data.size != other_tensor_data.size:
+                return False
+
+            # Write item tensor_data chunk should be present in both the write items, if it exists in either of them.
+            chunk = tensor_data.chunk
+            other_chunk = other_tensor_data.chunk
+            if (chunk and not other_chunk) or (not chunk and other_chunk):
+                return False
+
+            # Write item tensor_data chunk offsets and sizes should be same
+            if chunk and other_chunk:
+                if (
+                    chunk.offsets != other_chunk.offsets
+                    or chunk.sizes != other_chunk.sizes
+                ):
+                    return False
+
+    return True
+
+
+def _merge_delta_local_plans(
+    cached_plans: list[SavePlan],
+    delta_plans: list[SavePlan],
+) -> list[SavePlan]:
+    """
+    Merge a list of delta plans into a single plan.
+
+    Args:
+        cached_plans (List[SavePlan]): A list of cached plans.
+        delta_plans (List[SavePlan]): A list of delta plans to merge. It can contain empty plans
+
+    Returns:
+        A single merged plan. If a delta plan is not usable, use the cached plan. Otherwise, use the delta plan.
+    """
+    merged_plans = []
+
+    for cached_plan, delta_plan in zip(cached_plans, delta_plans):
+        if delta_plan and not delta_plan.usable:
+            merged_plans.append(cached_plan)
+        else:
+            merged_plans.append(delta_plan)
+
+    return merged_plans
+
+
+def _create_chunk_from_tensor(tensor: torch.Tensor) -> ChunkStorageMetadata:
+    return ChunkStorageMetadata(
+        offsets=torch.Size([0] * len(tensor.size())), sizes=tensor.size()
+    )
+
+
+def _chunk_for_shard(shard_md: ShardMetadata) -> ChunkStorageMetadata:
+    return ChunkStorageMetadata(
+        offsets=torch.Size(shard_md.shard_offsets),
+        sizes=torch.Size(shard_md.shard_sizes),
+    )
+
+
+def _sharded_tensor_metadata(
+    sharded_tensor: ShardedTensor, shard_md: ShardMetadata
+) -> TensorWriteData:
+    shard_properties = sharded_tensor.metadata().tensor_properties
+
+    properties = TensorProperties(
+        dtype=shard_properties.dtype,
+        layout=shard_properties.layout,
+        requires_grad=shard_properties.requires_grad,
+        memory_format=shard_properties.memory_format,
+        pin_memory=shard_properties.pin_memory,
+    )
+
+    return TensorWriteData(
+        chunk=_chunk_for_shard(shard_md),
+        properties=properties,
+        size=sharded_tensor.metadata().size,
+    )
+
+
+def _create_write_items_for_dtensor(fqn: str, tensor: DTensor) -> WriteItem:
+    sizes, offsets = compute_local_shape_and_global_offset(
+        tensor.shape, tensor.device_mesh, tensor.placements
+    )
+    sizes, offsets = torch.Size(sizes), torch.Size(offsets)
+
+    return WriteItem(
+        index=MetadataIndex(fqn, offsets),
+        type=WriteItemType.SHARD,
+        tensor_data=TensorWriteData(
+            chunk=ChunkStorageMetadata(
+                offsets=offsets,
+                sizes=sizes,
+            ),
+            properties=TensorProperties.create_from_tensor(tensor.to_local()),
+            size=tensor.size(),
+        ),
+    )
+
+
+def _create_write_item_for_shard(
+    fqn: str, sharded_tensor: ShardedTensor, shard_md: ShardMetadata
+) -> WriteItem:
+    offsets = torch.Size(shard_md.shard_offsets)
+    return WriteItem(
+        index=MetadataIndex(fqn, offsets),
+        type=WriteItemType.SHARD,
+        tensor_data=_sharded_tensor_metadata(sharded_tensor, shard_md),
+    )
+
+
+def _create_write_item_for_tensor(fqn: str, tensor: torch.Tensor) -> WriteItem:
+    offsets = torch.Size([0] * len(tensor.size()))
+    return WriteItem(
+        index=MetadataIndex(fqn, offsets),
+        type=WriteItemType.TENSOR,
+        tensor_data=TensorWriteData(
+            chunk=ChunkStorageMetadata(offsets=offsets, sizes=tensor.size()),
+            properties=TensorProperties.create_from_tensor(tensor),
+            size=tensor.size(),
+        ),
+    )
+
+
+def _create_write_item_for_bytesio(fqn: str, bytes: Any):
+    return WriteItem(
+        index=MetadataIndex(fqn),
+        type=WriteItemType.BYTE_IO,
+    )
+
+
+def _create_read_item_for_byteio(
+    dest_index, dest_offset, storage_index, storage_offset, length
+):
+    return ReadItem(
+        type=LoadItemType.BYTE_IO,
+        dest_index=dest_index,
+        dest_offsets=torch.Size((dest_offset,)),
+        storage_index=storage_index,
+        storage_offsets=torch.Size((storage_offset,)),
+        lengths=torch.Size((length,)),
+    )
+
+
+def _create_read_item_for_tensor(
+    dest_index, dest_offsets, storage_index, storage_offsets, lengths
+):
+    return ReadItem(
+        type=LoadItemType.TENSOR,
+        dest_index=dest_index,
+        dest_offsets=torch.Size(dest_offsets),
+        storage_index=storage_index,
+        storage_offsets=torch.Size(storage_offsets),
+        lengths=torch.Size(lengths),
+    )
+
+
+def create_read_items_for_chunk_list(
+    fqn: str,
+    checkpoint_md: TensorStorageMetadata,
+    local_chunks: list[ChunkStorageMetadata],
+) -> list[ReadItem]:
+    """
+    Create a list of ``ReadItem`` based on the checkpoint and local chunks.
+
+    This applies the resharding algorithm and computes the reads needed
+    to satisfy ``local_chunks`` with a checkpoint described by ``checkpoint_md``.
+
+    Args:
+        fqn (str) : The state_dict FQN to pass to ``ReadItem``.
+        checkpoint_md (TensorStorageMetadata): metadata for a given tensor
+            from a checkpoint.
+        local_chunks (List[ChunkStorageMetadata]): Local chunks that needs to be
+            loaded.
+
+    Returns:
+        A list of ``ReadItem`` that will satisfy all input chunks.
+    """
+    read_items = []
+    # this is a naive quadratic algo that can be optimized later
+    for idx, shard in enumerate(local_chunks):
+        for storage_idx, storage_md in enumerate(checkpoint_md.chunks):
+            if not _check_shard_metadata_pair_overlap(shard, storage_md):
+                continue
+
+            storage_offsets = []
+            dest_offsets = []
+            lengths = []
+            for (
+                _dim,
+                offset_for_saved_tensor,
+                offset_for_current_tensor,
+                length,
+            ) in _shards_get_overlap_region_wrt_saved_tensor(
+                saved_shard=storage_md, current_shard=shard
+            ):
+                storage_offsets.append(offset_for_saved_tensor)
+                dest_offsets.append(offset_for_current_tensor)
+                lengths.append(length)
+
+            read_items.append(
+                _create_read_item_for_tensor(
+                    dest_index=MetadataIndex(fqn, shard.offsets, idx),
+                    dest_offsets=dest_offsets,
+                    storage_index=MetadataIndex(fqn, storage_md.offsets, storage_idx),
+                    storage_offsets=storage_offsets,
+                    lengths=lengths,
+                )
+            )
+    return read_items
+
+
+def _create_default_metadata_only_plan(state_dict: STATE_DICT_TYPE) -> SavePlan:
+    requests = []
+    for fqn, obj in state_dict.items():
+        if isinstance(obj, DTensor):
+            requests.append(_create_write_items_for_dtensor(fqn, obj))
+        elif isinstance(obj, ShardedTensor):
+            requests.extend(
+                _create_write_item_for_shard(fqn, obj, shard_md)
+                for shard_md in obj.metadata().shards_metadata
+            )
+        elif isinstance(obj, torch.Tensor):
+            requests.append(_create_write_item_for_tensor(fqn, obj))
+        else:
+            requests.append(_create_write_item_for_bytesio(fqn, obj))
+    return SavePlan(requests)
+
+
+def _create_write_items(fqn: str, object: Any) -> list[WriteItem]:
+    if hasattr(object, "__create_write_items__"):
+        # DTensor implements _Checkpointable
+        return object.__create_write_items__(fqn, object)
+    elif isinstance(object, ShardedTensor):
+        return [
+            _create_write_item_for_shard(fqn, object, shard.metadata)
+            for shard in object.local_shards()
+        ]
+    elif isinstance(object, torch.Tensor):
+        return [_create_write_item_for_tensor(fqn, object)]
+    else:
+        return [_create_write_item_for_bytesio(fqn, object)]
+
+
+def _create_chunk_from_dtensor(tensor: DTensor) -> ChunkStorageMetadata:
+    sizes, offsets = compute_local_shape_and_global_offset(
+        tensor.shape, tensor.device_mesh, tensor.placements
+    )
+    sizes, offsets = torch.Size(sizes), torch.Size(offsets)
+    return ChunkStorageMetadata(
+        offsets=offsets,
+        sizes=sizes,
+    )
+
+
+def _create_chunk_list(tensor: torch.Tensor) -> list[ChunkStorageMetadata]:
+    if hasattr(tensor, "__create_chunk_list__"):
+        # DTensor implements _Checkpointable
+        local_chunks = tensor.__create_chunk_list__()  # type: ignore[attr-defined]
+    elif isinstance(tensor, ShardedTensor):
+        local_chunks = [
+            _chunk_for_shard(shard.metadata) for shard in tensor.local_shards()
+        ]
+    elif isinstance(tensor, torch.Tensor):
+        local_chunks = [_create_chunk_from_tensor(tensor)]
+    else:
+        raise ValueError(
+            "Unsupported Type, expecting one of [Tensor, DTensor, ShardedTensor] "
+            f",but got {type(tensor)}"
+        )
+
+    return local_chunks
+
+
+def _create_read_items(fqn: str, md: STORAGE_TYPES, obj: Any) -> list[ReadItem]:
+    if not isinstance(md, BytesStorageMetadata):
+        try:
+            local_chunks = _create_chunk_list(obj)
+        except ValueError as ex:
+            raise ValueError(
+                f"Invalid checkpoint metadata for {fqn}, "
+                + f"expected BytesStorageMetadata but found {type(md)}",
+            ) from ex
+
+        return create_read_items_for_chunk_list(fqn, md, local_chunks)
+    else:
+        return [
+            _create_read_item_for_byteio(
+                dest_index=MetadataIndex(fqn),
+                dest_offset=0,
+                storage_index=MetadataIndex(fqn),
+                storage_offset=0,
+                length=0,
+            )
+        ]
+
+
+def _init_state_dict(state_dict: dict[str, Any]) -> Any:
+    """
+    Initializes meta tensor if the meta tensor is DTensor or torch.Tensor.
+    """
+
+    def dtensor_func(value: DTensor):
+        device = getattr(value, "device", None)
+        if device == torch.device("meta"):
+            device_type = dist.distributed_c10d._get_pg_default_device().type
+            device = cast(
+                torch.device, _get_device_module(device_type).current_device()
+            )
+            new_local_tensor = torch.empty_like(value.to_local(), device=device)
+            # We need to pass shape and stride explicitly, since DTensor might be
+            # sharded unevenly.
+            dtensor = DTensor.from_local(
+                new_local_tensor,
+                device_mesh=value.device_mesh,
+                placements=value.placements,
+                shape=value.size(),
+                stride=value.stride(),
+            )
+            return dtensor
+        else:
+            return value
+
+    def sharded_tensor_func(value: Any):
+        device = getattr(value, "device", None)
+        if device == torch.device("meta"):
+            raise RuntimeError(
+                f"Found unsupported type {type(value)} for meta device loading."
+            )
+        else:
+            return value
+
+    def tensor_func(value: torch.Tensor):
+        device = getattr(value, "device", None)
+        if device == torch.device("meta"):
+            device_type = dist.distributed_c10d._get_pg_default_device().type
+            device = cast(
+                torch.device, _get_device_module(device_type).current_device()
+            )
+            tensor = torch.empty_like(value, device=device)
+            return tensor
+        else:
+            return value
+
+    _iterate_state_dict(
+        state_dict,
+        dtensor_func,
+        sharded_tensor_func,
+        tensor_func,
+    )
+
+
+def _iterate_state_dict(
+    iter_object: Any,
+    dtensor_func: Callable,
+    sharded_tensor_func: Callable,
+    tensor_func: Callable,
+):
+    """
+    Iterate through the state dict, applying the given functions to each tensor type
+    and update the state dict in place.
+
+    Args:
+        iter_object (Any): the target state_dict.
+        sharded_tensor_func (Callable): the function to apply to ShardedTensor
+        dtensor_func (Callable): the function to apply to DTensor
+        tensor_func (Callable): the function to apply to Tensor
+
+    # TODO: let state_dict_util._iterate_state_dict() to support in place option
+    so we don't need to have two versions of _iterate_state_dict.
+    """
+
+    if isinstance(iter_object, DTensor):
+        return dtensor_func(iter_object)
+    elif isinstance(iter_object, ShardedTensor):
+        return sharded_tensor_func(iter_object)
+    elif isinstance(iter_object, torch.Tensor):
+        return tensor_func(iter_object)
+    elif (
+        isinstance(iter_object, (int, float, str, bytes, io.BytesIO))
+        or iter_object is None
+    ):
+        return iter_object
+    elif isinstance(iter_object, dict):
+        for key, value in iter_object.items():
+            iter_object[key] = _iterate_state_dict(
+                value, dtensor_func, sharded_tensor_func, tensor_func
+            )
+        return iter_object
+    elif isinstance(iter_object, (list, tuple)):
+        ret = [
+            _iterate_state_dict(v, dtensor_func, sharded_tensor_func, tensor_func)
+            for v in iter_object
+        ]
+        if isinstance(iter_object, tuple):
+            ret = tuple(ret)  # type: ignore[assignment]
+        return ret
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/resharding.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/resharding.py
new file mode 100644
index 0000000000000000000000000000000000000000..a911bda05485eb5f9e29d4537ffd60dc51c19557
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/resharding.py
@@ -0,0 +1,71 @@
+# mypy: allow-untyped-defs
+
+from torch.distributed.checkpoint.metadata import ChunkStorageMetadata
+
+
+__all__: list[str] = []
+
+
+def _check_shard_metadata_pair_overlap(
+    shard1: ChunkStorageMetadata, shard2: ChunkStorageMetadata
+):
+    """Check if two shards overlap."""
+    # For each dim of each shard, check if one shard resides on the other
+    # end of second shard with respect to that dim. As an example for a 2D
+    # shard, we would check if one shard is above or on the left of the
+    # other shard.
+    ndims = len(shard1.offsets)
+    for i in range(ndims):
+        if shard1.offsets[i] >= shard2.offsets[i] + shard2.sizes[i]:
+            return False
+        if shard2.offsets[i] >= shard1.offsets[i] + shard1.sizes[i]:
+            return False
+
+    return True
+
+
+def _shards_get_overlap_region_wrt_saved_tensor(
+    saved_shard: ChunkStorageMetadata, current_shard: ChunkStorageMetadata
+) -> list[tuple[int, int, int, int]]:
+    """
+    Return the overlapping region between saved_shard and current_shard.
+
+    There returned list has the same number of elements as the tensor's dimension.
+    For each element, we produce a tuple with the following contents:
+        (dimension, `saved_shard` offset, `current_shard` offset, length)
+
+    Offsets are relative to each shard.
+    """
+    narrows = []
+    for dim, (
+        saved_shard_offset,
+        current_shard_offset,
+        saved_shard_size,
+        current_shard_size,
+    ) in enumerate(
+        zip(
+            saved_shard.offsets,
+            current_shard.offsets,
+            saved_shard.sizes,
+            current_shard.sizes,
+        )
+    ):
+        min_range_end = min(
+            saved_shard_offset + saved_shard_size,
+            current_shard_offset + current_shard_size,
+        )
+
+        length = min_range_end - max(current_shard_offset, saved_shard_offset)
+
+        if saved_shard_offset > current_shard_offset:
+            offset_for_saved_tensor = 0
+            offset_for_current_tensor = saved_shard_offset - current_shard_offset
+        else:
+            offset_for_saved_tensor = current_shard_offset - saved_shard_offset
+            offset_for_current_tensor = 0
+
+        narrows.append(
+            (dim, offset_for_saved_tensor, offset_for_current_tensor, length)
+        )
+
+    return narrows
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/staging.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/staging.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f3233ad06d5f85f128fbe8e4248ce0245283fd0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/staging.py
@@ -0,0 +1,115 @@
+from typing import Optional, runtime_checkable
+from typing_extensions import Protocol
+
+from torch.distributed._state_dict_utils import _copy_state_dict, _create_cpu_state_dict
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+
+
+__all__ = ["AsyncStager", "BlockingAsyncStager"]
+
+
+@runtime_checkable
+class AsyncStager(Protocol):
+    """
+    This protocol is meant to provide customization and extensibility for dcp.async_save, allowing users
+    to customize how data is staged previous to executing the usual dcp.save path in parallel.
+    The expected order of operations (concretely defined in `torch.distributed.state_dict_saver.async_save`)
+    is the following:
+
+    1. AsyncStager.stage_data(state_dict):
+        This call gives the AsyncStager the opportunity to 'stage'
+        the state_dict. The expectation and purpose of staging in this context is to create a "training-safe"
+        representation of the state dict, meaning that any updates to module data after staging is complete
+        should not be reflected in the state dict returned from this method. For example, in the default
+        case a copy of the entire state dict is created on CPU RAM and returned here, allowing users
+        to continue training without risking changes to data which is being serialized.
+
+    2. dcp.save is called on the state_dict returned from stage in parallel. This call is responsible
+        for serializing the state_dict and writing it to storage.
+
+    3. If AsyncStager.should_synchronize_after_execute is True, this method will be called immediately after
+        the serialization thread starts and before returning from dcp.async_save. If this is set to False,
+        the assumption is the user has defined a custom synchronization point for the the purpose of further
+        optimizing save latency in the training loop (for example, by overlapping staging with the
+        forward/backward pass), and it is the respondsibility of the user to call `AsyncStager.synchronize_staging`
+        at the appropriate time.
+
+    """
+
+    # default to True since the common case is to stage synchronously
+    _synchronize_after_execute: bool = True
+
+    @property
+    def should_synchronize_after_execute(self) -> bool:
+        """
+        Whether to synchronize after executing the stage.
+        """
+
+        return self._synchronize_after_execute
+
+    def stage(self, state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE:
+        """
+        Returns a "staged" copy of `state_dict`. The expectation of the staged copy is that it is
+        innoculated from any updates incurred after the stage call is complete.
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} must implement stage method"
+        )
+
+    def synchronize_staging(self) -> None:
+        """
+        In the case `stage` is async in some way, this method should be called to ensure staging
+        is complete and it is safe to begin modifying the original `state_dict`
+        """
+
+
+class BlockingAsyncStager(AsyncStager):
+    """
+    An implementation of AsyncStager which stages the state_dict on CPU RAM and blocks until the copy is complete.
+    This implementation also provides an option to optimize stage latency using pinned memory.
+
+    N.B. synchronize_staging is a no-op in this case.
+
+
+    """
+
+    # default to True since the common case is to stage synchronously
+    _synchronize_after_execute: bool = False
+
+    def __init__(
+        self,
+        cache_staged_state_dict: bool = False,
+        type_check: bool = False,
+    ):
+        """
+        Initializes the BlockingAsyncStager.
+
+        Args:
+            cache_staged_state_dict: Whether to cache the staged state_dict. This option decreases staging latency
+                at the cost of increases memory usage. Additionally, if this parameter is set to True, it's the expectation
+                that the stager is maintained and re-used for multiple dcp.async_save calls. Default to False.
+            type_check: Whether to perform a type check during cpu_offload. Defaults to False.
+
+        """
+        self.cache_staged_state_dict = cache_staged_state_dict
+        self.type_check = type_check
+        self.state_dict_cache: Optional[STATE_DICT_TYPE] = None
+
+    def stage(self, state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE:
+        """
+        Returns a copy of `state_dict` on the CPU.
+        """
+
+        if not self.cache_staged_state_dict:
+            staged_state_dict = _create_cpu_state_dict(state_dict)
+            _copy_state_dict(state_dict, staged_state_dict, type_check=self.type_check)
+            return staged_state_dict
+
+        if self.state_dict_cache is None:
+            self.state_dict_cache = _create_cpu_state_dict(state_dict, pin_memory=True)
+        return _copy_state_dict(state_dict, self.state_dict_cache)
+
+    def synchronize_staging(self) -> None:
+        """
+        No-op function, since staging is blocking.
+        """
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..033528093c8c58747964ad36891383ae234a5cf6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict.py
@@ -0,0 +1,1504 @@
+# mypy: allow-untyped-defs
+import contextlib
+import functools
+import gc
+import warnings
+from collections.abc import Generator, Iterable
+from dataclasses import asdict, dataclass, field
+from itertools import chain
+from typing import Any, Callable, cast, no_type_check, Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed._state_dict_utils import (
+    _broadcast_state_dict,
+    _distribute_state_dict,
+    _flatten_state_dict,
+    _gather_state_dict,
+    _offload_state_dict_to_cpu,
+    _unflatten_state_dict,
+)
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    _CHECKPOINT_PREFIX,
+)
+from torch.distributed.fsdp import (
+    FullOptimStateDictConfig,
+    FullStateDictConfig,
+    FullyShardedDataParallel as FSDP,
+    OptimStateDictConfig,
+    ShardedOptimStateDictConfig,
+    ShardedStateDictConfig,
+    StateDictConfig,
+    StateDictType,
+)
+from torch.distributed.fsdp._common_utils import (
+    _get_module_fsdp_state_if_fully_sharded_module,
+    FSDP_WRAPPED_MODULE,
+)
+from torch.distributed.tensor import DTensor
+from torch.nn.modules.module import _IncompatibleKeys
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils._pytree import tree_map_only
+
+
+__all__ = [
+    "FQNS_T",
+    "PrimitiveType",
+    "ValueType",
+    "DictValueType",
+    "ListDictValueType",
+    "OptimizerStateType",
+    "StateDictOptions",
+    "get_model_state_dict",
+    "get_optimizer_state_dict",
+    "get_state_dict",
+    "set_model_state_dict",
+    "set_optimizer_state_dict",
+    "set_state_dict",
+]
+
+
+_FLAT_PARAM = "_flat_param"
+_PG = "param_groups"
+_PARAMS = "params"
+_STATE = "state"
+
+FQNS_T = set[str]
+PrimitiveType = Union[DTensor, ShardedTensor, torch.Tensor, int, float, str]
+ValueType = Union[
+    PrimitiveType, list[PrimitiveType], tuple[PrimitiveType], dict[str, "ValueType"]
+]
+DictValueType = dict[str, ValueType]
+ListDictValueType = list[DictValueType]
+OptimizerStateType = dict[str, Union[DictValueType, ListDictValueType]]
+
+
+_patched_state_dict: set[Callable] = set()
+
+
+@contextlib.contextmanager
+def _gc_context():
+    is_enabled = gc.isenabled()
+    gc.disable()
+    try:
+        yield
+    finally:
+        if is_enabled:
+            gc.enable()
+
+
+@dataclass
+class StateDictOptions:
+    """
+    This dataclass specifies how get_state_dict/set_state_dict will work.
+
+    - ``full_state_dict``: if this is set to True, all the tensors in the
+      returned state_dict will be gathered. No ShardedTensor and DTensor
+      will be in the returned state_dict.
+
+    - ``cpu_offload``: offload all the tensors to cpu. To prevent CPU OOM, if
+      ``full_state_dict`` is also true, then only the rank0 will get the
+      state_dict and all other ranks will get empty state_dict.
+
+    - ``ignore_frozen_params``: if the value is True, the returned state_dict
+      won't contain any frozen parameters -- the ``requires_grad`` is False.
+      The default value is False.
+
+    - ``keep_submodule_prefixes`` (deprecated): when ``submodules`` is not None, this option
+      indicates whether to keep the submodule prefixes from the state_dict keys.
+      or example, if the submodule is ``module.pretrain`` and the full FQN of
+      the parameter is ``pretrain.layer1.weight`` of the param. When this option
+      is True, the parameter's key in the returned state_dict will be
+      ``pretrain.layer1.weight``. If the options is False, the key will be
+      ``layer1.weight``.
+      Note that if ``keep_submodule_prefixes`` is False, there may be conflicted
+      FQNs, hence there should be only one submodule in ``submodules``.
+
+    - ``strict``: the ``strict`` option when ``set_state_dict`` calls
+      model.load_state_dict().
+
+    - ``broadcast_from_rank0``: when the option is True, rank0 should receive a
+       full state_dict and will broadcast the tensors in the state_dict/
+       optim_state_dict one by one to other ranks. Other ranks will receive
+       the tensors and shard according to the local shards in the model and
+       optimizer. ``full_state_dict`` must be set to True when using this option.
+       This option currently only supports DTensor, not the legacy ShardedTensor.
+    """
+
+    full_state_dict: bool = False
+    cpu_offload: bool = False
+    ignore_frozen_params: bool = False
+    keep_submodule_prefixes: bool = True
+    strict: bool = True
+    broadcast_from_rank0: bool = False
+    flatten_optimizer_state_dict: bool = False
+    dsd_fqn_modifiers: str = "_fqn_modifiers"
+
+
+@dataclass
+class _StateDictInfo(StateDictOptions):
+    fqn_param_mapping: dict[
+        Union[str, torch.Tensor],
+        Union[FQNS_T, torch.Tensor],
+    ] = field(default_factory=dict)
+    shared_params_mapping: dict[
+        Union[str, torch.Tensor],
+        Union[FQNS_T, torch.Tensor],
+    ] = field(default_factory=dict)
+    submodule_prefixes: set[str] = field(default_factory=set)
+    handle_model: bool = True
+    handle_optim: bool = True
+    fsdp_context: Callable = contextlib.nullcontext
+    fsdp_modules: list[nn.Module] = field(default_factory=list)
+
+
+@functools.cache
+def _get_fqns(
+    model: nn.Module,
+    name: str,
+    dsd_fqn_modifiers: str = "_fqn_modifiers",
+    skip_ddp_prefix: bool = True,
+    skip_compiler_prefix: bool = True,
+) -> FQNS_T:
+    """
+    This API is used to convert the name of a parameter to the FQNs. For FSDP
+    without `use_orig_params`, the name of FlatParameter can be mapped to
+    multiple original parameters. As a result, the return type of this function
+    is `set[str]`.
+
+    Args:
+        module (nn.Module): the root model.
+        name (str): the name
+        skip_ddp_prefix (bool): whether to skip DDP's `module` prefix
+
+    Returns:
+        The canonical FQNs based on the model traversal.
+    """
+
+    # Remove the checkpoint prefix, if it exists.
+    name = name.replace(_CHECKPOINT_PREFIX, "")
+    if "." not in name:
+        return {name}
+
+    obj_names = name.split(".")
+    fqn_obj_names = []
+    curr_obj = model
+    for i, curr_obj_name in enumerate(obj_names):
+        if isinstance(curr_obj, DDP):
+            assert curr_obj_name == "module"
+            curr_obj = curr_obj.module
+            if not skip_ddp_prefix:
+                fqn_obj_names.append(curr_obj_name)
+        elif isinstance(curr_obj, FSDP):
+            if i < len(obj_names) - 1 and obj_names[i + 1] == _FLAT_PARAM:
+                prefix = ".".join(fqn_obj_names)
+                flat_param = getattr(curr_obj, _FLAT_PARAM)
+                if prefix:
+                    prefix = f"{prefix}."
+                return {f"{prefix}{fqn}" for fqn in flat_param._fqns}
+            curr_obj = getattr(curr_obj, FSDP_WRAPPED_MODULE)
+            if curr_obj_name != FSDP_WRAPPED_MODULE:
+                fqn_obj_names.append(curr_obj_name)
+                curr_obj = getattr(curr_obj, curr_obj_name)
+        elif isinstance(curr_obj, torch._dynamo.eval_frame.OptimizedModule):
+            assert curr_obj_name == "_orig_mod"
+            curr_obj = curr_obj._orig_mod
+            if not skip_compiler_prefix:
+                fqn_obj_names.append(curr_obj_name)
+        else:
+            # In some modeuls, _fqn_modifiers would not shown in the state_dict keys,
+            # skip them in the fqn to ensure load stat dict successfully for them.
+            if hasattr(curr_obj, dsd_fqn_modifiers):
+                if removed_fqn := getattr(curr_obj, dsd_fqn_modifiers)().get(
+                    curr_obj_name
+                ):
+                    if hasattr(curr_obj, removed_fqn):
+                        curr_obj = getattr(curr_obj, removed_fqn)
+            fqn_obj_names.append(curr_obj_name)
+            if curr_obj_name == nn.modules.module._EXTRA_STATE_KEY_SUFFIX:
+                if i != len(obj_names) - 1:
+                    raise RuntimeError("Expect `_extra_state` to be the last obj name")
+            else:
+                curr_obj = getattr(curr_obj, curr_obj_name)
+
+    return {".".join(fqn_obj_names).replace(_CHECKPOINT_PREFIX, "")}
+
+
+class _EXTRA_STATE:
+    pass
+
+
+def _iterate_valid_model_state(model, dsd_fqn_modifiers="_fqn_modifiers"):
+    visited_modules: set[nn.Module] = set()
+
+    def recurse(module: nn.Module, curr_fqn: str) -> Generator:
+        visited_modules.add(module)
+
+        curr_fqn = f"{curr_fqn}." if curr_fqn else ""
+        for name, submodule in module.named_children():
+            if submodule in visited_modules:
+                continue
+            # if user have state_dict_hooks in their model, they can add the state_dict key changes
+            # at dsd_fqn_modifiers in input to align with the function of state_dict_hook
+            if (
+                hasattr(module, dsd_fqn_modifiers)
+                and name in getattr(module, dsd_fqn_modifiers)().values()
+            ):
+                # skip _fqn_modifiers here thus remove the last `.` added
+                new_fqn = curr_fqn[:-1]
+            else:
+                new_fqn = f"{curr_fqn}{name}"
+            yield from recurse(submodule, new_fqn)
+
+        for name, obj in chain(
+            module.named_buffers(recurse=False), module.named_parameters(recurse=False)
+        ):
+            if name in module._non_persistent_buffers_set:
+                continue
+            new_fqn = f"{curr_fqn}{name}"
+            yield new_fqn, obj
+
+        if (
+            getattr(module.__class__, "get_extra_state", nn.Module.get_extra_state)
+            != nn.Module.get_extra_state
+        ):
+            new_fqn = f"{curr_fqn}{nn.modules.module._EXTRA_STATE_KEY_SUFFIX}"
+            yield new_fqn, _EXTRA_STATE()
+
+    yield from recurse(model, "")
+
+
+def _verify_options(
+    model: nn.Module,
+    optims: tuple[torch.optim.Optimizer, ...],
+    optim_only: bool,
+    *,
+    submodules: Optional[set[nn.Module]] = None,
+    options: Optional[StateDictOptions] = None,
+) -> _StateDictInfo:
+    """
+    Verify the model and options passed by the user and generates _StateDictInfo.
+    """
+    if submodules:
+        warnings.warn(
+            "Getting submodules only model/optim state_dict is deprecated and "
+            "will be removed in 2.5. This feature can be achieved by manually "
+            "filtering out the state_dict returned from get_state_dict.",
+            FutureWarning,
+        )
+    if optim_only and not optims:
+        raise RuntimeError(
+            "Optimizers are not passed in but optim_only is set to True."
+        )
+
+    options = options or StateDictOptions()
+
+    fqn_param_mapping: dict[
+        Union[str, torch.Tensor], Union[set[str], torch.Tensor]
+    ] = {}
+    shared_params_mapping: dict[
+        Union[str, torch.Tensor], Union[set[str], torch.Tensor]
+    ] = {}
+    for name, param in _iterate_valid_model_state(model):
+        if isinstance(param, _EXTRA_STATE):
+            continue
+
+        fqns = _get_fqns(model, name)
+        fqn = fqn_param_mapping.get(param, None)
+        if fqn is not None:
+            cast(set[str], fqn_param_mapping[param]).update(fqns)
+            shared_params_mapping[param] = fqn_param_mapping[param]
+        else:
+            # We need to do copy as _get_fqns is lru_cached
+            fqn_param_mapping[param] = fqns.copy()
+        for fqn in fqns:
+            if not isinstance(param, _EXTRA_STATE):
+                fqn_param_mapping[fqn] = param
+
+    for param_, fqns_ in list(shared_params_mapping.items()):
+        for fqn in fqns_:
+            shared_params_mapping[fqn] = cast(torch.Tensor, param_)
+
+    submodule_prefixes: set[str] = set()
+    if submodules:
+        submodules = set(submodules)
+        for name, module in model.named_modules():
+            if module not in submodules:
+                continue
+            fqns = _get_fqns(model, name)
+            assert len(fqns) == 1, "Submodule FQN should only have 1 instance"
+            submodule_prefixes.update(f"{fqn}." for fqn in fqns)
+
+    if options.broadcast_from_rank0 and not options.full_state_dict:
+        raise ValueError(
+            "full_state_dict must be True when broadcast_from_rank0 is True."
+        )
+    fsdp_modules = FSDP.fsdp_modules(model)
+    state_dict_config: StateDictConfig
+    optim_state_dict_config: OptimStateDictConfig
+    fsdp_context: Callable
+    if fsdp_modules:
+        # FSDP API only work if at least one FSDP instance exists.
+        if options.full_state_dict:
+            state_dict_config = FullStateDictConfig(
+                offload_to_cpu=options.cpu_offload, rank0_only=options.cpu_offload
+            )
+            optim_state_dict_config = FullOptimStateDictConfig(
+                offload_to_cpu=options.cpu_offload,
+                rank0_only=(options.cpu_offload or options.broadcast_from_rank0),
+            )
+            state_dict_type = StateDictType.FULL_STATE_DICT
+        else:
+            state_dict_config = ShardedStateDictConfig(
+                offload_to_cpu=options.cpu_offload,
+            )
+            optim_state_dict_config = ShardedOptimStateDictConfig(
+                offload_to_cpu=options.cpu_offload,
+            )
+            state_dict_type = StateDictType.SHARDED_STATE_DICT
+
+        @contextlib.contextmanager
+        def fsdp_state_dict_type_without_warning(
+            module,
+            state_dict_type,
+            state_dict_config,
+            optim_state_dict_config,
+        ):
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    "ignore", message="FSDP.state_dict_type", category=FutureWarning
+                )
+                with FSDP.state_dict_type(
+                    module=module,
+                    state_dict_type=state_dict_type,
+                    state_dict_config=state_dict_config,
+                    optim_state_dict_config=optim_state_dict_config,
+                ):
+                    yield
+
+        fsdp_context = functools.partial(
+            fsdp_state_dict_type_without_warning,
+            module=model,
+            state_dict_type=state_dict_type,
+            state_dict_config=state_dict_config,
+            optim_state_dict_config=optim_state_dict_config,
+        )
+    else:
+        fsdp_context = contextlib.nullcontext
+
+    return _StateDictInfo(
+        **asdict(options),
+        fqn_param_mapping=fqn_param_mapping,
+        shared_params_mapping=shared_params_mapping,
+        submodule_prefixes=submodule_prefixes,
+        fsdp_context=fsdp_context,
+        fsdp_modules=cast(list[nn.Module], fsdp_modules),
+        handle_model=not optim_only,
+        handle_optim=(len(optims) > 0),
+    )
+
+
+def _verify_state_dict(
+    model_state_dict: dict[str, ValueType],
+    optim_state_dict: OptimizerStateType,
+    info: _StateDictInfo,
+) -> None:
+    for module in info.fsdp_modules:
+        fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+        assert fsdp_state is not None, "Expected a fsdp_state with a fsdp module."
+
+    # Verify if the model_state_dict and optim_state_dict are valid. This API
+    # should give the users an explicit error message to debug or report.
+    if (
+        info.handle_model
+        and not model_state_dict
+        and not info.submodule_prefixes
+        and not info.ignore_frozen_params
+        and not (info.cpu_offload and info.full_state_dict)
+        and info.strict
+        and not info.broadcast_from_rank0
+    ):
+        raise RuntimeError(
+            "The option indicates that model state_dict is required to save "
+            "or load, but model state_dict is empty."
+            f"rank = {dist.get_rank()=}."
+        )
+
+    if info.handle_optim:
+        if (
+            not optim_state_dict
+            and not (info.cpu_offload and info.full_state_dict)
+            and (not info.broadcast_from_rank0)
+        ):
+            raise RuntimeError(
+                "The option indicates that model state_dict is required to save, "
+                f"or load but optim state_dict is empty. {optim_state_dict}"
+            )
+
+    for key in model_state_dict.keys():
+        if _FLAT_PARAM in key:
+            raise RuntimeError(
+                f"{key} contains {_FLAT_PARAM}. This can happen if the model "
+                "is not the root module."
+            )
+
+
+def _state_dict_fn(obj: Union[nn.Module, torch.optim.Optimizer], api: str) -> Callable:
+    call = getattr(obj, api)
+    if call in _patched_state_dict:
+        call = functools.partial(getattr(obj.__class__, api), self=obj)
+    return call
+
+
+def _maybe_full_or_cpu_state_dict(
+    state_dict: dict[str, Any], info: _StateDictInfo
+) -> dict[str, Any]:
+    if info.full_state_dict:
+        ranks_only = (
+            ()
+            if (not info.cpu_offload or not torch.distributed.is_initialized())
+            else (0,)
+        )
+        return _gather_state_dict(
+            state_dict, cpu_offload=info.cpu_offload, ranks_only=ranks_only
+        )
+    elif info.cpu_offload:
+        return _offload_state_dict_to_cpu(state_dict)
+    else:
+        return state_dict
+
+
+@torch.no_grad()
+def _get_model_state_dict(
+    model: nn.Module, info: _StateDictInfo
+) -> dict[str, ValueType]:
+    if not info.handle_model:
+        return {}
+
+    with info.fsdp_context():
+        state_dict = _state_dict_fn(model, "state_dict")()
+
+    for key in list(state_dict.keys()):
+        fqns = _get_fqns(model, key)
+        assert len(fqns) == 1, (key, fqns)
+        fqn = next(iter(fqns))
+        if fqn != key:
+            # As we only support FSDP, DDP, and TP, the only cases are
+            # wrapper-based DDP and compiler. Verify if the assumption
+            # is correct.
+            def verify(key, fqn) -> bool:
+                if len(fqn) >= len(key):
+                    return False
+                fqn_split = fqn.split(".")
+                key_split = key.split(".")
+                fqn_idx = 0
+                for key_idx, key_name in enumerate(key_split):
+                    if key_name == fqn_split[fqn_idx]:
+                        fqn_idx += 1
+                        if fqn_idx == len(fqn_split):
+                            return key_idx == len(key_split) - 1
+                    elif key_name in ("module", "_orig_mod"):
+                        continue
+                    else:
+                        return False
+                return True
+
+            if not verify(key, fqn):
+                raise RuntimeError(f"An unexpected key, {key}, exists. FQN is {fqn}")
+            state_dict[fqn] = state_dict.pop(key)
+
+    if info.submodule_prefixes:
+        new_state_dict: dict[str, ValueType] = {}
+        # TODO: make this faster.
+        for fqn in state_dict.keys():
+            for prefix in info.submodule_prefixes:
+                if not fqn.startswith(prefix):
+                    continue
+                if info.keep_submodule_prefixes:
+                    new_state_dict[fqn] = state_dict[fqn]
+                else:
+                    new_fqn = fqn[len(prefix) :]
+                    new_state_dict[new_fqn] = state_dict[fqn]
+        state_dict = new_state_dict
+
+    if info.ignore_frozen_params:
+        for key, param in model.named_parameters():
+            if param.requires_grad:
+                continue
+            fqns = _get_fqns(model, key)
+            for fqn in fqns:
+                state_dict.pop(fqn)
+
+    for key, p in list(state_dict.items()):
+        if torch.is_tensor(p) and p.is_meta:
+            state_dict.pop(key)
+
+    return _maybe_full_or_cpu_state_dict(state_dict, info)
+
+
+@torch.no_grad()
+def _load_model_state_dict(
+    model: nn.Module,
+    state_dict: dict[str, ValueType],
+    info: _StateDictInfo,
+) -> _IncompatibleKeys:
+    if not info.handle_model or (not state_dict and not info.broadcast_from_rank0):
+        return _IncompatibleKeys({}, {})
+
+    local_state_dict = {}
+    for key, value in _iterate_valid_model_state(model, info.dsd_fqn_modifiers):
+        fqns = _get_fqns(model, key, info.dsd_fqn_modifiers)
+        fqns_with_prefix = _get_fqns(
+            model,
+            key,
+            info.dsd_fqn_modifiers,
+            skip_ddp_prefix=False,
+            skip_compiler_prefix=False,
+        )
+
+        for fqn, fqn_with_prefix in zip(fqns, fqns_with_prefix):
+            if (
+                not info.broadcast_from_rank0 or dist.get_rank() == 0
+            ) and fqn != fqn_with_prefix:
+                load_value = state_dict.pop(fqn, None)
+                if load_value is None:
+                    if info.strict:
+                        raise RuntimeError(f"Missing key: {fqn}.")
+                else:
+                    state_dict[fqn_with_prefix] = load_value
+            local_state_dict[fqn_with_prefix] = value
+
+    assign = False
+    if info.broadcast_from_rank0 or info.full_state_dict:
+        devices = set()
+        for key, value in local_state_dict.items():
+            if torch.is_tensor(value) and value.dim() > 0:
+                devices.add(value.device)
+        # In lora state_dict, there could be multiple devices, with meta device inside.
+        # Take the other device in the broadcast/distribtue, and set assign to True
+        if torch.device("meta") in devices:
+            devices.remove(torch.device("meta"))
+            assign = True
+        if len(devices) == 0:
+            devices.add(dist.distributed_c10d._get_pg_default_device())
+        elif len(devices) > 1:
+            raise ValueError("Multiple devices found")
+
+        if info.broadcast_from_rank0:
+            _broadcast_state_dict(
+                state_dict,
+                local_state_dict,
+                device=devices.pop(),
+                strict=info.strict,
+                cpu_offload=info.cpu_offload,
+            )
+        elif info.full_state_dict:
+            _distribute_state_dict(state_dict, local_state_dict, device=devices.pop())
+        for fqn, local_state in local_state_dict.items():
+            state_dict[fqn] = local_state
+
+    with info.fsdp_context():
+        return cast(
+            _IncompatibleKeys,
+            _state_dict_fn(model, "load_state_dict")(
+                state_dict=state_dict, strict=info.strict, assign=assign
+            ),
+        )
+
+
+def _init_optim_state(optim: torch.optim.Optimizer) -> None:
+    """
+    Initialize optim states by calling the step() with zero grads.
+    """
+    if optim.state:
+        # The optimizer state is initialized.
+        return
+
+    # There are some stateless optimizers like SGD. These optimizer will
+    # not return in the above condition. So if gradients exist, we should also
+    # return. If gradients do not exist, the following initialization should
+    # not disturb SGD because the gradients and lr are both zero.
+    for param_group in optim.param_groups:
+        for param in param_group[_PARAMS]:
+            if param.grad is not None:
+                return
+
+    for param_group in optim.param_groups:
+        for param in param_group[_PARAMS]:
+            if param.requires_grad:
+                param.grad = torch.zeros_like(param)
+
+    # Some optimizers will update parameters regardless of grads due to lr, so
+    # make lr to zero when calling `step()`.
+    lrs = []
+    for param_group in optim.param_groups:
+        if "lr" in param_group:
+            lrs.append(param_group["lr"])
+            param_group["lr"] = (
+                torch.tensor(0.0)
+                if isinstance(param_group["lr"], torch.Tensor)
+                else 0.0
+            )
+    optim.step(closure=None)
+    # Whether to recover the "lr" should not matter too much as we will
+    # restore checkpointing later.
+    for param_group in optim.param_groups:
+        if "lr" in param_group:
+            param_group["lr"] = lrs.pop(0)
+    optim.zero_grad(set_to_none=True)
+
+
+def _flatten_optim_state_dict(state_dict: OptimizerStateType) -> dict[str, ValueType]:
+    """
+    This API flattens the optimizer state_dict to support optimizer resharding for
+    MPMD, e.g., pipeline parallelism.
+
+    Without the API, the original optimizer state_dict looks like:
+    {
+        "state": {
+            "layer1.weight": {
+                "step": 10, "exp_avg": SomeTensor, "exp_avg_sq": SomeTensor
+            },
+            "layer2.weight": {
+                "step": 10, "exp_avg": SomeTensor, "exp_avg_sq": SomeTensor
+            },
+        },
+        "param_group": [
+            {
+                "lr": 0.0,
+                "betas": (0.9, 0.95), ...,
+                "params": ["layer1.weight", "layer2.weight"]
+            }
+        ]
+    }
+
+    With this API, the optimizer state_dict looks like:
+    {
+        "state.layer1.weight.step": 10,
+        "state.layer2.weight.step": 10,
+        "state.layer1.weight.exp_avg": SomeTensor,
+        "state.layer2.weight.exp_avg": SomeTensor,
+        "state.layer1.weight.exp_avg_sq": SomeTensor,
+        "state.layer2.weight.exp_avg_sq": SomeTensor,
+        "param_group.layer1.weight.lr" : 0.1,
+        "param_group.layer2.weight.lr" : 0.1,
+        "param_group.layer1.weight.betas" : (0.9, 0.95),
+        "param_group.layer2.weight.betas" : (0.9, 0.95),
+    }
+
+    Note that if any of the value is a container, like the betas in the example,
+    this API won't flattent it.
+    """
+
+    def _raise_if_type_not_supported(v):
+        if not isinstance(v, (torch.Tensor, int, float)):
+            raise NotImplementedError(
+                "Flattening optimizer state_dict only supports "
+                "tensor, int, float states now. "
+                f"Type is {type(v)}."
+            )
+
+    ret: dict[str, ValueType] = {}
+    for fqn, state in cast(DictValueType, state_dict[_STATE]).items():
+        for k, v in cast(DictValueType, state).items():
+            _raise_if_type_not_supported(v)
+            ret[f"{_STATE}.{fqn}.{k}"] = v
+
+    for param_group in cast(ListDictValueType, state_dict[_PG]):
+        fqns = param_group.pop(_PARAMS)
+        for fqn in cast(list[str], fqns):
+            for k, v in param_group.items():
+                ret[f"{_PG}.{fqn}.{k}"] = v
+    return ret
+
+
+def _unflatten_optim_state_dict(
+    optim: torch.optim.Optimizer,
+    state_dict: dict[str, ValueType],
+    info: _StateDictInfo,
+) -> OptimizerStateType:
+    """
+    This API unflattens the state_dict generated by _flatten_optim_state_dict().
+    See the docstring of _flatten_optim_state_dict() for more detail.
+    """
+    state: DictValueType = {}
+    pg_state: ListDictValueType = []
+    return_osd: OptimizerStateType = {_STATE: state, _PG: pg_state}
+
+    for param_group in optim.param_groups:
+        pg_state.append({_PARAMS: []})
+        for param in param_group[_PARAMS]:
+            for fqn in info.fqn_param_mapping[param]:
+                # If a parameter is shared, only one of the FQN will be used.
+                # So we need to verify which if this fqn is actually used in
+                # the state_dict.
+                if fqn in info.shared_params_mapping:
+                    in_params = False
+                    for k in param_group.keys():
+                        if k == _PARAMS:
+                            continue
+                        flatten_key = f"{_PG}.{fqn}.{k}"
+                        if flatten_key in state_dict:
+                            in_params = True
+                        break
+                else:
+                    in_params = True
+
+                if not in_params:
+                    continue
+
+                params = pg_state[-1][_PARAMS]
+                assert isinstance(params, list)  # typing
+                params.append(fqn)
+                if not param.requires_grad:
+                    continue
+                state[fqn] = {}
+                for state_name in optim.state[param].keys():
+                    cast(DictValueType, state[fqn])[state_name] = state_dict[
+                        f"{_STATE}.{fqn}.{state_name}"
+                    ]
+
+        first_param_fqn = cast(list[str], pg_state[-1][_PARAMS])[0]
+        for k in param_group.keys():
+            if k == _PARAMS:
+                continue
+            value = state_dict[f"{_PG}.{first_param_fqn}.{k}"]
+            if k not in pg_state[-1]:
+                pg_state[-1][k] = value
+            elif pg_state[-1][k] != value:
+                raise RuntimeError(
+                    "All the parameters in the same parameter group should have "
+                    f"the same saved param_group value. But {first_param_fqn}.{k} "
+                    f"is {value} while other(s) is {pg_state[-1][k]}."
+                )
+
+    return return_osd
+
+
+@torch.no_grad()
+def _get_optim_state_dict(
+    model: nn.Module,
+    optimizers: tuple[torch.optim.Optimizer, ...],
+    info: _StateDictInfo,
+) -> OptimizerStateType:
+    if not info.handle_optim:
+        return {}
+
+    optim_state_dict: OptimizerStateType = {_STATE: {}, _PG: []}
+    for optim in optimizers:
+        _init_optim_state(optim)
+        osd = _state_dict_fn(optim, "state_dict")()
+        if info.fsdp_modules:
+            with info.fsdp_context():
+                osd = FSDP.optim_state_dict(model, optim, osd)
+
+            # We need to specially handle FlatParameter FSDP as
+            # FlatParameter FSDP converts the FQNs.
+            # There are no easy ways to do this conversion systematically.
+            # We can only use a string replacment without correctness check.
+            if not osd:
+                continue
+            for k in list(osd[_STATE].keys()):
+                if "_orig_mod" in k:
+                    osd[_STATE][k.replace("_orig_mod.", "")] = osd[_STATE].pop(k)
+            for g in osd[_PG]:
+                params = [k.replace("_orig_mod.", "") for k in g[_PARAMS]]
+                g[_PARAMS] = params
+        else:
+            params = list(chain.from_iterable(g[_PARAMS] for g in optim.param_groups))
+            param_pid_mapping = dict(zip(params, range(len(params))))
+            fqn_pid_mapping = {}
+            for key, param in model.named_parameters():
+                fqns = _get_fqns(model, key)
+                assert len(fqns) == 1
+                fqn = next(iter(fqns))
+                if param not in param_pid_mapping:
+                    continue
+                pid = param_pid_mapping[param]
+                fqn_pid_mapping[fqn] = pid
+                fqn_pid_mapping[pid] = fqn
+
+            for key in list(osd[_STATE].keys()):
+                fqn = fqn_pid_mapping[key]
+                osd[_STATE][fqn] = osd[_STATE].pop(key)
+
+            for group in osd[_PG]:
+                group[_PARAMS] = [fqn_pid_mapping[pid] for pid in group[_PARAMS]]
+
+        if not osd:
+            continue
+
+        cast(DictValueType, optim_state_dict[_STATE]).update(osd[_STATE])
+        cast(ListDictValueType, optim_state_dict[_PG]).extend(osd[_PG])
+
+    if info.flatten_optimizer_state_dict:
+        optim_state_dict = cast(
+            OptimizerStateType, _flatten_optim_state_dict(optim_state_dict)
+        )
+
+    return _maybe_full_or_cpu_state_dict(optim_state_dict, info)
+
+
+def _split_optim_state_dict(
+    model: nn.Module,
+    optim: torch.optim.Optimizer,
+    optim_state_dict: OptimizerStateType,
+    info: _StateDictInfo,
+) -> OptimizerStateType:
+    """
+    Extract the corresponding optim state_dict from ``optim_state_dict`` for
+    ``optim`` and return the result optim state_dict.
+
+    Args:
+        model (nn.Module): the root model.
+        optim (torch.optim.Optimizer): the optimizer.
+        optim_state_dict (Dict[str, ValueType]): the superset optim state_dict that
+            contains the optim state_dict of ``optim``.
+        info (_StateDictInfo): state dict information.
+
+    Returns:
+        The optim state_dict of ``optim``.
+    """
+
+    state: DictValueType = {}
+    pg_state: ListDictValueType = []
+    return_osd: OptimizerStateType = {_STATE: state, _PG: pg_state}
+    pg_mapping: dict[int, int] = {}
+
+    if all(
+        isinstance(k, int) for k in cast(DictValueType, optim_state_dict[_STATE]).keys()
+    ):
+        return optim_state_dict
+
+    for param_group in optim.param_groups:
+        pg_state.append({_PARAMS: []})
+        for param in param_group[_PARAMS]:
+            for fqn in info.fqn_param_mapping[param]:
+                if fqn in info.shared_params_mapping:
+                    in_params = False
+                    for loaded_param_group in cast(
+                        ListDictValueType, optim_state_dict[_PG]
+                    ):
+                        if fqn in cast(list[str], loaded_param_group[_PARAMS]):
+                            in_params = True
+                            break
+                else:
+                    in_params = True
+                if not in_params:
+                    continue
+
+                params = pg_state[-1][_PARAMS]
+                assert isinstance(params, list)
+                params.append(fqn)
+                if param.requires_grad:
+                    state[fqn] = cast(DictValueType, optim_state_dict[_STATE])[fqn]
+                for loaded_param_group in cast(
+                    ListDictValueType, optim_state_dict[_PG]
+                ):
+                    if fqn in cast(list[str], loaded_param_group[_PARAMS]):
+                        pg_mapping[id(loaded_param_group)] = len(return_osd[_PG]) - 1
+
+        if len(param_group[_PARAMS]) == 0:
+            # Param_group with empty params.
+            ret = []
+            for loaded_param_group in cast(ListDictValueType, optim_state_dict[_PG]):
+                if len(cast(list[str], loaded_param_group[_PARAMS])) == 0:
+                    ret.append(loaded_param_group)
+            if len(ret) != 1:
+                raise ValueError(
+                    "There are param groups that have zero parameters. "
+                    "In such a case, DSD only support exactly one param group "
+                    "with zero parameters."
+                    "But the loaded state_dict has zero or more than one param groups "
+                    "that have zero parameters."
+                )
+            if len(optim_state_dict[_PG]) != len(optim.param_groups):
+                raise ValueError(
+                    "When there is a parameter group that has zero parameters, "
+                    "multiple optimizers are not supported."
+                )
+            pg_mapping[id(loaded_param_group)] = len(return_osd[_PG]) - 1
+
+    for param_group in cast(ListDictValueType, optim_state_dict[_PG]):
+        pg_idx = pg_mapping.get(id(param_group), -1)
+        if pg_idx == -1:
+            continue
+
+        for key, value in param_group.items():
+            if key == _PARAMS:
+                continue
+            # TODO: check if value is the same if exists.
+            pg_state[pg_idx][key] = value
+
+    return return_osd
+
+
+@torch.no_grad()
+def _load_optim_state_dict(
+    model: nn.Module,
+    optimizers: tuple[torch.optim.Optimizer, ...],
+    state_dict: OptimizerStateType,
+    info: _StateDictInfo,
+) -> None:
+    if not info.handle_optim:
+        return
+
+    for optim in optimizers:
+        _init_optim_state(optim)
+        if state_dict:
+            if _STATE in state_dict:
+                optim_state_dict = _split_optim_state_dict(
+                    model, optim, state_dict, info
+                )
+            else:
+                optim_state_dict = _unflatten_optim_state_dict(
+                    optim, cast(dict[str, ValueType], state_dict), info
+                )
+        else:
+            optim_state_dict = {}
+        if info.fsdp_modules:
+            # We need to specially handle FlatParameter FSDP as
+            # FlatParameter FSDP converts the FQNs.
+            for original_fqn, _ in model.named_parameters():
+                fqns = _get_fqns(model, original_fqn)
+                fqns_with_compiler = _get_fqns(
+                    model, original_fqn, skip_compiler_prefix=False
+                )
+                if fqns == fqns_with_compiler:
+                    continue
+
+                assert len(fqns) == 1
+                fqn = fqns.pop()
+                fqn_with_compiler = fqns_with_compiler.pop()
+                for g in optim_state_dict[_PG]:
+                    val = cast(dict[str, Any], g)
+                    params = [
+                        key.replace(fqn, fqn_with_compiler) for key in val[_PARAMS]
+                    ]
+                    val[_PARAMS] = params
+                osd_state = cast(DictValueType, optim_state_dict[_STATE])
+                for k in list(osd_state.keys()):
+                    if fqn in k:
+                        osd_state[k.replace(fqn, fqn_with_compiler)] = osd_state.pop(k)
+
+            with info.fsdp_context():
+                optim_state_dict = FSDP.optim_state_dict_to_load(
+                    model, optim, optim_state_dict
+                )
+        elif info.full_state_dict:
+            info.full_state_dict = False
+            local_state_dict = _get_optim_state_dict(model, (optim,), info)
+            info.full_state_dict = True
+            device = None
+
+            def _device(t):
+                if t.dim() > 0:
+                    nonlocal device
+                    if device is None:
+                        device = t.device
+                    elif device != t.device:
+                        raise ValueError("Device mismatch")
+                return t
+
+            _ = tree_map_only(torch.Tensor, _device, local_state_dict)
+            assert device is not None
+            flatten_osd, osd_mapping = _flatten_state_dict(optim_state_dict)
+            flatten_local_osd, local_osd_mapping = _flatten_state_dict(local_state_dict)
+            if info.broadcast_from_rank0:
+                _broadcast_state_dict(flatten_osd, flatten_local_osd, device=device)
+            else:
+                _distribute_state_dict(flatten_osd, flatten_local_osd, device=device)
+            # The modifications listed seek to address the problem where optim might possess
+            # dissimilar parameters in comparison to optim_state_dict. This is achieved by
+            # incorporating differential parameters within local, which may result in optim
+            # having additional parameters ultimately.
+            for optim_key in flatten_osd.keys():
+                if optim_key not in flatten_local_osd:
+                    assert optim_key in osd_mapping
+                    flatten_local_osd[optim_key] = flatten_osd[optim_key]
+                    local_osd_mapping[optim_key] = osd_mapping[optim_key]
+            optim_state_dict = _unflatten_state_dict(
+                flatten_local_osd, local_osd_mapping
+            )
+            for pg in optim_state_dict[_PG]:
+                if _PARAMS not in pg:
+                    cast(dict[str, ValueType], pg)[_PARAMS] = []
+
+        # Note that we do not have to convert the FQN back to param id here if
+        # order in optim.param_groups[idx][_PARAMS] is the same as the one in
+        # optim_state_dict[_PG][idx][_PARAMS].
+        _state_dict_fn(optim, "load_state_dict")(state_dict=optim_state_dict)
+
+
+def get_model_state_dict(
+    model: nn.Module,
+    *,
+    submodules: Optional[set[nn.Module]] = None,
+    options: Optional[StateDictOptions] = None,
+) -> dict[str, ValueType]:
+    """
+    Return the model state_dict of ``model``.
+
+    See ``get_state_dict`` for the detail usage.
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters
+            that belong to the submodules.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be returned. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        The state_dict for ``model``.
+
+    :rtype: typing.Dict[str, ValueType]
+    """
+    with _gc_context():
+        info = _verify_options(
+            model,
+            (),
+            optim_only=False,
+            submodules=submodules,
+            options=options,
+        )
+        model_state_dict = _get_model_state_dict(model, info)
+        _verify_state_dict(model_state_dict, {}, info)
+        return model_state_dict
+
+
+def get_optimizer_state_dict(
+    model: nn.Module,
+    optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
+    *,
+    submodules: Optional[set[nn.Module]] = None,
+    options: Optional[StateDictOptions] = None,
+) -> OptimizerStateType:
+    """
+    Return the combined state_dict for optimizers.
+
+    See ``get_state_dict`` for the detail usage.
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
+            The optimizers that are used to optimize ``model``.
+        submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters
+            that belong to the submodules.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be returned. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        The state_dict for ``optimizers``.
+
+    :rtype: OptimizerStateType
+    """
+    with _gc_context():
+        optimizers = (
+            (optimizers,)
+            if isinstance(optimizers, torch.optim.Optimizer)
+            else tuple(optimizers)
+        )
+        info = _verify_options(
+            model,
+            optimizers,
+            optim_only=True,
+            submodules=submodules,
+            options=options,
+        )
+        optim_state_dict = _get_optim_state_dict(model, optimizers, info)
+        _verify_state_dict({}, optim_state_dict, info)
+        return optim_state_dict
+
+
+def get_state_dict(
+    model: nn.Module,
+    optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
+    *,
+    submodules: Optional[set[nn.Module]] = None,
+    options: Optional[StateDictOptions] = None,
+) -> tuple[dict[str, ValueType], OptimizerStateType]:
+    """
+    Return the model state_dict and optimizers state_dict.
+
+    ``get_state_dict`` can process any module that is parallelized by PyTorch
+    FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
+    combination of these parallelisms. The main functions of ``get_state_dict``
+    are: 1.) returning a model and optimizer state_dict that can be resharded
+    with a different number of trainers and/or different parallelisms.
+    2.) hiding the parallelism-specific state_dict APIs. Users don't have to call
+    these APIs.
+    3.) sanity checking the result state_dict.
+
+    The keys of the result state dictionary are the canonical FQNs (Fully
+    Qualified Names).  A canonical FQN refers to the FQN based on a parameter's
+    position in an nn.Module hierarchy. More specifically, a canonical FQN to a
+    parameter is the FQN returned by ``module.named_parameters()`` or
+    ``module.named_buffers()`` when the module is not distributed by any
+    parallelisms. Since the optimizer internally uses parameter IDs to represent
+    a parameter, there will be a conversion from the parameter IDs to the
+    canonical FQNs when calling this API.
+
+    ``get_state_dict`` can also process a module that is not parallelized. In
+    such a case, ``get_state_dict`` only performs one function -- converting the
+    optimizer parameter IDs to the canonical FQNs.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> import torch
+        >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        >>> from torch.nn.parallel import DistributedDataParallel as DDP
+        >>> from torch.distributed.checkpoint.state_dict import get_state_dict
+
+        >>> fsdp_model = FSDP(copy.deepcopy(model))
+        >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
+        >>> ddp_model = DDP(copy.deepcopy(model))
+        >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
+
+
+        >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim)
+        >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict(
+        ...     fsdp_model, fsdp_optim
+        ... )
+
+        >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(),
+        >>> # the asserts will fail.
+        >>> assert ddp_state_dict == fsdp_state_dict
+        >>> assert ddp_optim_state == fsdp_optim_state_dict
+
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
+            The optimizers that are used to optimize ``model``.
+        submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters
+            that belong to the submodules.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be returned. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        ``Tuple`` that contain model state_dict and optimizer state_dict.
+
+    :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType]
+    """
+
+    with _gc_context():
+        optimizers = (
+            (optimizers,)
+            if isinstance(optimizers, torch.optim.Optimizer)
+            else tuple(optimizers)
+        )
+        info = _verify_options(
+            model,
+            optimizers,
+            optim_only=False,
+            submodules=submodules,
+            options=options,
+        )
+        model_state_dict = _get_model_state_dict(model, info)
+        optim_state_dict = _get_optim_state_dict(model, optimizers, info)
+        _verify_state_dict(model_state_dict, optim_state_dict, info)
+        return model_state_dict, optim_state_dict
+
+
+def _unflatten_model_state_dict(
+    model: nn.Module,
+    state_dict: Union[dict[nn.Module, dict[str, ValueType]], dict[str, ValueType]],
+) -> dict[str, ValueType]:
+    if not state_dict:
+        return {}
+
+    if isinstance(next(iter(state_dict.keys())), nn.Module):
+        warnings.warn(
+            "Passing model_state_dict as a ``Dict[nn.Module, Dict[str, Any]]``"
+            "is deprecated and will be removed in 2.5. If you need this "
+            "feature, please preprocessing the model_state_dict to achieve the "
+            "same functionality.",
+            FutureWarning,
+        )
+        cast_state_dict = cast(dict[nn.Module, dict[str, ValueType]], state_dict)
+        new_state_dict: dict[str, ValueType] = {}
+        for submodule, sub_state_dict in cast_state_dict.items():
+            for name, m in model.named_modules():
+                if m != submodule:
+                    continue
+
+                fqns = _get_fqns(model, name)
+                assert len(fqns) == 1, "FQNs for a submodule should only have 1 element"
+                prefix = f"{next(iter(fqns))}."
+                new_state_dict.update(
+                    {prefix + subfqn: value for subfqn, value in sub_state_dict.items()}
+                )
+        return new_state_dict
+    else:
+        return cast(dict[str, ValueType], state_dict)
+
+
+def set_model_state_dict(
+    model: nn.Module,
+    model_state_dict: dict[str, ValueType],
+    *,
+    options: Optional[StateDictOptions] = None,
+) -> _IncompatibleKeys:
+    """Load the model state_dict.
+
+    The counterpart of ``get_model_state_dict`` to set the state_dict to the
+    model. See ``set_state_dict`` for the detail usage.
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        model_state_dict: (Dict[str, ValueType]):
+           the model state_dict to load. If the key of the ``model_state_dict``
+           is nn.Module, the key is a submodule of ``model`` and the value should
+           be the state_dict of the submodule. When loading the state_dict,
+           the prefix of the submodule will be append to the state_dict.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be loaded. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
+            * **missing_keys** is a list of str containing the missing keys
+            * **unexpected_keys** is a list of str containing the unexpected keys
+
+    :type model_state_dict: typing.Dict[str, ValueType]
+    """
+    model_state_dict: dict[str, ValueType] = _unflatten_model_state_dict(
+        model, model_state_dict
+    )
+    with _gc_context():
+        info = _verify_options(model, (), optim_only=False, options=options)
+
+        _verify_state_dict(model_state_dict, {}, info)
+        return _load_model_state_dict(model, model_state_dict, info)
+
+
+def set_optimizer_state_dict(
+    model: nn.Module,
+    optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
+    optim_state_dict: OptimizerStateType,
+    *,
+    options: Optional[StateDictOptions] = None,
+) -> None:
+    """Load the optimizers state_dict.
+
+    The counterpart of ``get_optimizer_state_dict`` to set the state_dict to the
+    optimizers. See ``set_state_dict`` for the detail usage.
+
+    WARN: ``set_optimizer_state_dict`` can only be called before ``backward()`` or after
+        ``step()`` is called on the optimizers. Otherwise, the optimizer states won't be
+        initialized correctly.
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        optimizers (Union[Optimizer, Iterable[Optimizer]]):
+            The optimizers that are used to optimize ``model``.
+        optim_state_dict: OptimizerStateType:
+            the optimizer state_dict to load.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be loaded. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        None
+
+    :type optim_state_dict: typing.OptimizerStateType
+    """
+    with _gc_context():
+        optimizers = (
+            (optimizers,)
+            if isinstance(optimizers, torch.optim.Optimizer)
+            else tuple(optimizers)
+        )
+        info = _verify_options(model, optimizers, optim_only=True, options=options)
+
+        _verify_state_dict({}, optim_state_dict, info)
+        _load_optim_state_dict(model, optimizers, optim_state_dict, info)
+
+
+def set_state_dict(
+    model: nn.Module,
+    optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
+    *,
+    model_state_dict: dict[str, ValueType],
+    optim_state_dict: OptimizerStateType,
+    options: Optional[StateDictOptions] = None,
+) -> _IncompatibleKeys:
+    """Load the model state_dict and optimizers state_dict.
+
+    The counterpart of ``get_state_dict`` to set the state_dict to the model and
+    optimizers.  The given ``model_state_dict`` and ``optim_state_dict`` do not
+    have to be returned by ``get_state_dict`` but must meet the following
+    requirements: 1) all FQNs are canonical FQNs as defined in ``get_state_dict``,
+    2) if a tensor is sharded, it must be either a ShardedTensor or DTensor,
+    3) optimizer state_dict cannot contain the parameter IDs; the keys should be
+    the canonical FQNs.
+
+    WARN: ``set_state_dict`` can only be called before ``backward()`` or after ``step()``
+        is called on the optimizers. Otherwise, the optimizer states won't be initialized
+        correctly.
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        optimizers (Union[Optimizer, Iterable[Optimizer]]):
+            The optimizers that are used to optimize ``model``.
+        model_state_dict: (Union[Dict[nn.Module, Dict[str, ValueType]], Dict[str, ValueType]]):
+           the model state_dict to load. If the key of the ``model_state_dict``
+           is nn.Module, the key is a submodule of ``model`` and the value should
+           be the state_dict of the submodule. When loading the state_dict,
+           the prefix of the submodule will be append to the state_dict.
+        optim_state_dict: OptimizerStateType:
+            the optimizer state_dict to load.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be loaded. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
+            * **missing_keys** is a list of str containing the missing keys of the model state_dict.
+            * **unexpected_keys** is a list of str containing the unexpected keys of the model state_dict.
+
+    :type model_state_dict: typing.Dict[str, ValueType]
+    :type optim_state_dict: typing.OptimizerStateType
+    """
+
+    model_state_dict: dict[str, ValueType] = _unflatten_model_state_dict(
+        model, model_state_dict
+    )
+    with _gc_context():
+        optimizers = (
+            (optimizers,)
+            if isinstance(optimizers, torch.optim.Optimizer)
+            else tuple(optimizers)
+        )
+        info = _verify_options(
+            model, optimizers, optim_only=not model_state_dict, options=options
+        )
+
+        _verify_state_dict(model_state_dict, optim_state_dict, info)
+        _load_optim_state_dict(model, optimizers, optim_state_dict, info)
+        return _load_model_state_dict(model, model_state_dict, info)
+
+
+# TODO: correct the state_dict function signature.
+# TODO: this API is not yet fully tested. Make it private
+@no_type_check
+def _patch_model_state_dict(
+    model: nn.Module,
+    *,
+    options: Optional[StateDictOptions] = None,
+) -> None:
+    """Patch the ``state_dict`` and ``load_state_dict`` attributes of ``model``.
+
+    Patch the ``state_dict`` and ``load_state_dict`` attributes of ``model`` to
+    be a partial function to call ``get_state_dict`` and ``set_state_dict``.
+
+    Example:
+        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        from torch.distributed.checkpoint.state_dict import patch_model_state_dict
+
+        model = fsdp(model)
+        patch_model_state_dict(model)
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be loaded. See
+            `StateDictOptions` for the details.
+    Returns:
+        None
+    """
+
+    _state_dict_call = functools.partial(
+        get_model_state_dict,
+        model=model,
+        options=options,
+    )
+
+    def state_dict_call():
+        return _state_dict_call()
+
+    model.state_dict = state_dict_call
+
+    _load_state_dict_call = functools.partial(
+        set_model_state_dict,
+        model=model,
+        options=options,
+    )
+
+    def load_state_dict_call(state_dict: dict[str, Any]):
+        _load_state_dict_call(model_state_dict=state_dict)
+
+    model.load_state_dict = load_state_dict_call
+
+    _patched_state_dict.add(state_dict_call)
+    _patched_state_dict.add(load_state_dict_call)
+
+
+# TODO: correct the load_state_dict function signature.
+# TODO: this API is not yet fully tested. Make it private
+@no_type_check
+def _patch_optimizer_state_dict(
+    model: nn.Module,
+    *,
+    optimizers: tuple[torch.optim.Optimizer, ...],
+    options: Optional[StateDictOptions] = None,
+) -> None:
+    """Patch the ``state_dict`` and ``load_state_dict`` attributes of ``optimizers``.
+
+    Patch the ``state_dict`` and ``load_state_dict`` attributes of ``optimizers`` to
+    be a partial function to call ``get_state_dict`` and ``set_state_dict``.
+
+    Note that if there are multiple optimizers, all of the optimizers will be patched.
+    So users only need to call one of the state_dict() to get the full result.
+
+    Example:
+        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        from torch.distributed.checkpoint.state_dict import patch_model_state_dict
+
+        model = fsdp(model)
+        patch_model_state_dict(model)
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be loaded. See
+            `StateDictOptions` for the details.
+    Returns:
+        None
+    """
+
+    _state_dict_call = functools.partial(
+        get_optimizer_state_dict,
+        model=model,
+        optimizers=optimizers,
+        options=options,
+    )
+
+    def state_dict_call():
+        return _state_dict_call()
+
+    _load_state_dict_call = functools.partial(
+        set_optimizer_state_dict,
+        model=model,
+        optimizers=optimizers,
+        options=options,
+    )
+
+    def load_state_dict_call(state_dict: dict[str, Any]):
+        _load_state_dict_call(optim_state_dict=state_dict)
+
+    _patched_state_dict.add(state_dict_call)
+    _patched_state_dict.add(load_state_dict_call)
+    optimizers = (
+        (optimizers,)
+        if isinstance(optimizers, torch.optim.Optimizer)
+        else tuple(optimizers)
+    )
+    for optim in optimizers:
+        optim.state_dict = state_dict_call
+        optim.load_state_dict = load_state_dict_call
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_loader.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..b199d42a42c6c3b8331d0e38cb729766a6b554a3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_loader.py
@@ -0,0 +1,328 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import os
+import warnings
+from typing import Any, cast, Optional, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.distributed as dist
+from torch.distributed.checkpoint.default_planner import _EmptyStateDictLoadPlanner
+from torch.distributed.checkpoint.logger import _dcp_method_logger
+from torch.distributed.checkpoint.stateful import Stateful
+
+from ._storage_utils import _storage_setup
+from .default_planner import DefaultLoadPlanner
+from .planner import LoadPlan, LoadPlanner
+from .storage import StorageReader
+from .utils import _api_bc_check, _DistWrapper, _profile
+
+
+__all__ = ["load_state_dict", "load"]
+
+
+@deprecated(
+    "`load_state_dict` is deprecated and will be removed in future versions. "
+    "Please use `load` instead.",
+    category=FutureWarning,
+)
+def load_state_dict(
+    state_dict: dict[str, Any],
+    storage_reader: StorageReader,
+    process_group: Optional[dist.ProcessGroup] = None,
+    coordinator_rank: int = 0,
+    no_dist: bool = False,
+    planner: Optional[LoadPlanner] = None,
+) -> None:
+    """This method is deprecated. Please switch to 'load'."""
+    storage_reader.reset()
+    with _profile():
+        # TODO: test returning `load` here instead.
+        return _load_state_dict(
+            state_dict,
+            storage_reader,
+            process_group,
+            coordinator_rank,
+            no_dist,
+            planner,
+        )
+
+
+@_dcp_method_logger(log_exceptions=True)
+@_api_bc_check
+def load(
+    state_dict: dict[str, Any],
+    *,
+    checkpoint_id: Union[str, os.PathLike, None] = None,
+    storage_reader: Optional[StorageReader] = None,
+    planner: Optional[LoadPlanner] = None,
+    process_group: Optional[dist.ProcessGroup] = None,
+    no_dist: bool = False,
+) -> None:
+    """
+    Load a checkpoint into a distributed state dict in SPMD style.
+
+    Each rank must have the same keys in their ``state_dict`` provided to this
+    API. Mismatched keys may result in hangs or errors. If unsure, you can use
+    the ``utils._assert_same_keys`` API to check (but may incur communication
+    costs).
+
+    Each rank will try to read the least amount of data necessary
+    to fulfill the requested `state_dict`. When loading :class:`ShardedTensor`
+    or :class:`DTensor` instances, each rank only reads data for their local shards.
+
+    For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
+    load will first call ``state_dict`` before attempting deserialization, followed by
+    ``load_state_dict`` once the deserialization is complete.
+    For each non-``Stateful`` object, load will deserailize the object, and then replace
+    it in the ``state_dict`` with the deserialized object.
+
+    .. warning::
+        All tensors in ``state_dict`` must be allocated on their
+        destination device *prior to* calling this function.
+
+        All non-tensor data is loaded using `torch.load()` and modified in place
+        on state_dict.
+
+    .. warning::
+        Users must call `load_state_dict` on the root module to ensure load
+        pos-processing and non-tensor data properly propagates.
+
+    .. note:
+        If no process group is initialized, this function will assume the intent
+        is to load a checkpoint into the local process. This can be useful in the
+        case of local inference, and when using regular Tensors (as opposed to DTensor
+         or ShardedTensor)
+
+    .. note:
+        Rank 0 is assumed to be the coordinator rank.
+
+    Args:
+        state_dict (Dict[str, Any]): The state_dict to load the checkpoint into.
+        checkpoint_id (Union[str, os.PathLike, None]):
+            The ID of this checkpoint instance. The meaning of the checkpoint_id
+            depends on the storage. It can be a path to a folder or to a file.
+            It can also be a key if the storage is a key-value store.
+            (Default: ``None``)
+        storage_reader (Optional[StorageReader]):
+            Instance of StorageWriter used to perform reads. If this is not
+            specified, DCP will automatically infer the reader based on the
+            checkpoint_id. If checkpoint_id is also None, an exception will
+            be raised. (Default: ``None``)
+        planner (Optional[LoadPlanner]):
+            Instance of LoadPlanner. If this is not specificed, the default
+            planner will be used. (Default: ``None``)
+        process_group (Optional[ProcessGroup]):
+            ProcessGroup to be used for cross-rank synchronization.
+            (Default: ``None``)
+        no_dist (bool): If ``True``, this function will assume the intent is to load
+            a checkpoint without using cross-rank synchronization. (Default: ``False``)
+    Returns:
+        None.
+
+    Examples
+        >>> # xdoctest: +SKIP
+        >>> my_model = MyModule()
+        >>> optimizer = Adagrad(my_model.parameters())
+        >>> model_state_dict = my_model.state_dict()
+        >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader(
+        ...     "/checkpoint/1"
+        ... )
+
+        >>> torch.distributed.checkpoint.load_state_dict(
+        >>>     state_dict=model_state_dict,
+        >>>     storage_reader=fs_storage_reader,
+        >>> )
+
+        >>> # module.load_state_dict() function might have customized steps
+        >>> # to flush the state_dict, must call it to
+        >>> # ensure correct behavior.
+        >>> my_model.load_state_dict(model_state_dict)
+
+    .. note::
+        load_state_dict uses collectives to coordinate reads across ranks.
+        For NCCL-based process groups, internal tensor representations of
+        objects must be moved to the GPU device before communication takes place.
+        In this case, the device used is given by ``torch.cuda.current_device()``
+        and it is the user's responsibility to ensure that this is set so that each
+        rank has an individual GPU, via ``torch.cuda.set_device()``.
+    """
+
+    no_dist = no_dist or (not dist.is_available()) or (not dist.is_initialized())
+    if no_dist:
+        warnings.warn(
+            "torch.distributed is disabled, unavailable or uninitialized, assuming the intent is to load in a single process."
+        )
+
+    with _profile():
+        storage_reader = cast(
+            StorageReader, _storage_setup(storage_reader, checkpoint_id, reader=True)
+        )
+
+        # All ranks must have the same keys in their `state_dict` provided to
+        # this API.  See documentation for more details.
+        # Here we simply sort the keys to ensure that all ranks load values in
+        # the same order.
+        keys = sorted(state_dict.keys())
+
+        statetful_sd = {}
+        for key in keys:
+            if key not in state_dict:
+                continue
+            elem = state_dict[key]
+            statetful_sd[key] = (
+                elem.state_dict() if isinstance(elem, Stateful) else elem
+            )
+
+        _load_state_dict(
+            state_dict=statetful_sd,
+            storage_reader=storage_reader,
+            process_group=process_group,
+            no_dist=no_dist,
+            planner=planner,
+        )
+        for key in keys:
+            if key not in state_dict:
+                continue
+            elem = state_dict[key]
+            if isinstance(elem, Stateful):
+                # If the state_dict is a Stateful object,
+                # DCP does an in-place load in the original state dict.
+                elem.load_state_dict(statetful_sd[key])
+            else:
+                # Otherwise, replace the state_dict with the loaded state_dict.
+                state_dict[key] = statetful_sd[key]
+
+
+def _load_state_dict(
+    state_dict: dict[str, Any],
+    storage_reader: StorageReader,
+    process_group: Optional[dist.ProcessGroup] = None,
+    coordinator_rank: int = 0,
+    no_dist: bool = False,
+    planner: Optional[LoadPlanner] = None,
+) -> None:
+    torch._C._log_api_usage_once("torch.distributed.checkpoint.load_state_dict")
+
+    distW = _DistWrapper(process_group, not no_dist, coordinator_rank)
+    if planner is None:
+        planner = DefaultLoadPlanner()
+
+    ckpt_kwargs = {}
+    if (ckpt_id := getattr(storage_reader, "checkpoint_id", None)) is not None:
+        ckpt_kwargs["checkpoint_id"] = ckpt_id
+        ckpt_kwargs["process_group"] = distW.group
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def local_step():
+        assert planner is not None
+        metadata = storage_reader.read_metadata()
+        planner.set_up_planner(state_dict, metadata, distW.is_coordinator)
+        storage_reader.set_up_storage_reader(metadata, distW.is_coordinator)
+
+        local_plan = planner.create_local_plan()
+        local_plan = storage_reader.prepare_local_plan(local_plan)
+        return local_plan
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def global_step(all_local_plans):
+        assert planner is not None
+        all_local_plans = planner.create_global_plan(all_local_plans)
+        all_local_plans = storage_reader.prepare_global_plan(all_local_plans)
+        return all_local_plans
+
+    central_plan: LoadPlan = distW.reduce_scatter("plan", local_step, global_step)
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def read_data():
+        assert planner is not None
+        final_local_plan = planner.finish_plan(central_plan)
+        all_reads = storage_reader.read_data(final_local_plan, planner)
+
+        all_reads.wait()
+        return None
+
+    _ = distW.all_gather("read", read_data)
+
+
+def _load_state_dict_from_keys(
+    keys: Optional[Union[set[str], str]] = None,
+    *,
+    checkpoint_id: Union[str, os.PathLike, None] = None,
+    storage_reader: Optional[StorageReader] = None,
+    process_group: Optional[dist.ProcessGroup] = None,
+) -> dict[str, Any]:
+    """
+    Load only the specified keys from the checkpoint, if no keys are specified, the entire
+    checkpoint will be loaded. Note, this method completely loads the checkpoint into the
+    current process and is not distributed.
+
+    .. warning::
+
+
+    .. warning::
+
+        All non-tensor data is loaded using `torch.load()`
+
+    .. note:
+        As opposed to the usual pattern, this function does not take a state dict as input
+        and does not load inplace. Instead, a new state dict is directly initialized and read
+        from file.
+
+    .. note:
+        If no process group is initialized, this function will assume the intent
+        is to load a checkpoint into the local process. This can be useful in the
+        case of local inference, and when using regular Tensors (as opposed to DTensor
+         or ShardedTensor)
+
+    .. note:
+        Rank 0 is assumed to be the coordinator rank.
+
+    Args:
+        keys (Optional[Union[set[str], str]]):
+            Loads any key specified in this set. If no keys are specified, the entire checkpoint
+            is loaded.
+        checkpoint_id (Union[str, os.PathLike, None]):
+            The ID of this checkpoint instance. The meaning of the checkpoint_id
+            depends on the storage. It can be a path to a folder or to a file.
+            It can also be a key if the storage is a key-value store.
+            (Default: ``None``)
+        storage_reader (Optional[StorageReader]):
+            Instance of StorageWriter used to perform reads. If this is not
+            specified, DCP will automatically infer the reader based on the
+            checkpoint_id. If checkpoint_id is also None, an exception will
+            be raised. (Default: ``None``)
+        process_group (Optional[ProcessGroup]):
+            ProcessGroup to be used for cross-rank synchronization.
+            (Default: ``None``)
+
+    Returns:
+        State dict from specified keys
+    """
+    torch._C._log_api_usage_once(
+        "torch.distributed.checkpoint._load_state_dict_from_keys"
+    )
+
+    no_dist = not (dist.is_available() and dist.is_initialized())
+    if no_dist:
+        warnings.warn(
+            "torch.distributed is unavailable or uninitialized, assuming the intent is to load in a single process."
+        )
+
+    storage_reader = cast(
+        StorageReader, _storage_setup(storage_reader, checkpoint_id, reader=True)
+    )
+
+    if isinstance(keys, str):
+        keys = {keys}
+
+    sd: dict[str, Any] = {}
+    _load_state_dict(
+        state_dict=sd,
+        storage_reader=storage_reader,
+        process_group=process_group,
+        no_dist=no_dist,
+        planner=_EmptyStateDictLoadPlanner(keys=keys or set()),
+    )
+
+    return sd
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_saver.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_saver.py
new file mode 100644
index 0000000000000000000000000000000000000000..d16c10783c95cc144147094302282c8dc50c0f1a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_saver.py
@@ -0,0 +1,367 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import inspect
+import os
+import warnings
+from concurrent.futures import Future
+from enum import Enum
+from typing import cast, Optional, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.distributed as dist
+from torch.distributed._state_dict_utils import _copy_state_dict, _create_cpu_state_dict
+from torch.distributed.checkpoint._async_executor import (  # noqa: TC001
+    _AsyncCheckpointExecutor,
+)
+from torch.distributed.checkpoint._async_process_executor import (
+    _ProcessBasedAsyncCheckpointExecutor,
+)
+from torch.distributed.checkpoint._async_thread_executor import (
+    _ThreadBasedAsyncCheckpointExecutor,
+)
+from torch.distributed.checkpoint._storage_utils import _storage_setup
+from torch.distributed.checkpoint.default_planner import DefaultSavePlanner
+from torch.distributed.checkpoint.logger import _dcp_method_logger
+from torch.distributed.checkpoint.metadata import Metadata, STATE_DICT_TYPE
+from torch.distributed.checkpoint.planner import SavePlan, SavePlanner
+from torch.distributed.checkpoint.staging import AsyncStager
+from torch.distributed.checkpoint.stateful import Stateful
+from torch.distributed.checkpoint.storage import StorageWriter
+from torch.distributed.distributed_c10d import _get_default_group
+
+from .utils import _api_bc_check, _DistWrapper, _profile
+
+
+__all__ = ["save_state_dict", "save", "async_save", "AsyncCheckpointerType"]
+
+
+class AsyncCheckpointerType(Enum):
+    """Enum for async checkpointer type."""
+
+    THREAD = "thread"
+    PROCESS = "process"
+
+
+@deprecated(
+    "`save_state_dict` is deprecated and will be removed in future versions."
+    "Please use `save` instead.",
+    category=FutureWarning,
+)
+def save_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    storage_writer: StorageWriter,
+    process_group: Optional[dist.ProcessGroup] = None,
+    coordinator_rank: int = 0,
+    no_dist: bool = False,
+    planner: Optional[SavePlanner] = None,
+) -> Metadata:
+    """This method is deprecated. Please switch to 'save'."""
+    storage_writer.reset()
+
+    # TODO: test returning `save` here instead.
+    with _profile():
+        return _save_state_dict(
+            state_dict,
+            storage_writer,
+            process_group,
+            coordinator_rank,
+            no_dist,
+            planner,
+        )
+
+
+@_dcp_method_logger(log_exceptions=True)  # type: ignore[arg-type]
+@_api_bc_check
+def save(
+    state_dict: STATE_DICT_TYPE,
+    *,
+    checkpoint_id: Union[str, os.PathLike, None] = None,
+    storage_writer: Optional[StorageWriter] = None,
+    planner: Optional[SavePlanner] = None,
+    process_group: Optional[dist.ProcessGroup] = None,
+    no_dist: bool = False,
+) -> Metadata:
+    """
+    Save a distributed model in SPMD style.
+
+    This function is different from ``torch.save()`` as it handles
+    ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards.
+
+    For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
+    save will call ``state_dict`` before serialization.
+
+    .. warning::
+        There is no guarantees of Backwards Compatibility across PyTorch versions
+        for saved state_dicts.
+
+    .. warning::
+        If using the `process_group` argument, make sure that only its ranks
+        call `save_state_dict` and that all data in state_dict belong to it.
+
+    .. note::
+        When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of
+        the shard_group should be calling `save_state_dict` and the corresponding process
+        group needs to be passed in.
+
+    .. note::
+        If no process group is available, this function assumes the intention is to save the
+         state_dict in the local process.
+
+    .. note:
+        Rank 0 is assumed to be the coordinator rank.
+
+
+    Args:
+        state_dict (Dict[str, Any]): The state_dict to save.
+        checkpoint_id (Union[str, os.PathLike, None]):
+            The ID of this checkpoint instance. The meaning of the checkpoint_id
+            depends on the storage. It can be a path to a folder or to a file.
+            It can also be a key if the storage is a key-value store.
+            (Default: ``None``)
+        storage_writer (Optional[StorageWriter]):
+            Instance of StorageWriter used to perform writes. If this is not
+            specified, DCP will automatically infer the writer based on the
+            checkpoint_id. If checkpoint_id is also None, an exception will
+            be raised. (Default: ``None``)
+        planner (Optional[SavePlanner]):
+            Instance of SavePlanner. If this is not specificed, the default
+            planner will be used. (Default: ``None``)
+        process_group (Optional[ProcessGroup]):
+            ProcessGroup to be used for cross-rank synchronization.
+            (Default: ``None``)
+        no_dist (bool):
+            If ``True``, this function will assume the intent is to load
+            a checkpoint without using cross-rank synchronization.
+            (Default: ``False``)
+
+    Returns:
+        Metadata: Metadata object for the saved checkpoint.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> my_model = MyModule()
+
+        >>> state_dict = {"model": my_model}
+
+        >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
+        ...     "/checkpoint/1"
+        ... )
+        >>> torch.distributed.checkpoint.save(
+        >>>     state_dict=state_dict,
+        >>>     storage_writer=fs_storage_writer,
+        >>> )
+
+    .. note::
+        save_state_dict uses collectives to coordinate writes across ranks.
+        For NCCL-based process groups, internal tensor representations of
+        objects must be moved to the GPU device before communication takes place.
+        In this case, the device used is given by ``torch.cuda.current_device()``
+        and it is the user's responsibility to ensure that this is set so that
+        each rank has an individual GPU, via ``torch.cuda.set_device()``.
+    """
+    torch._C._log_api_usage_once("torch.distributed.checkpoint.save")
+
+    no_dist = no_dist or (not dist.is_available()) or (not dist.is_initialized())
+    if no_dist:
+        warnings.warn(
+            "torch.distributed is disabled, unavailable or uninitialized, assuming the intent is to save in a single process."
+        )
+
+    with _profile():
+        storage_writer = cast(
+            StorageWriter, _storage_setup(storage_writer, checkpoint_id, reader=False)
+        )
+
+        return _save_state_dict(
+            state_dict=_stateful_to_state_dict(state_dict),
+            storage_writer=storage_writer,
+            process_group=process_group,
+            no_dist=no_dist,
+            planner=planner,
+        )
+
+
+@_dcp_method_logger(log_exceptions=True)
+def async_save(
+    state_dict: STATE_DICT_TYPE,
+    *,
+    checkpoint_id: Union[str, os.PathLike, None] = None,
+    storage_writer: Optional[StorageWriter] = None,
+    planner: Optional[SavePlanner] = None,
+    process_group: Optional[dist.ProcessGroup] = None,
+    async_checkpointer_type: AsyncCheckpointerType = AsyncCheckpointerType.THREAD,
+) -> Future:
+    """Asynchronous version of ``save``. This code first de-stages the state_dict on to the
+    staging storage (defaults to CPU memory), and then calls the `save` in a separate thread.
+
+    .. warning::
+        This feature is experimental and subject to change.
+
+    Args:
+        state_dict (Dict[str, Any]): The state_dict to save.
+        checkpoint_id (Union[str, os.PathLike, None]):
+            The ID of this checkpoint instance. The meaning of the checkpoint_id
+            depends on the storage. It can be a path to a folder or to a file.
+            It can also be a key if the storage is a key-value store.
+            (Default: ``None``)
+        storage_writer (Optional[StorageWriter]):
+            Instance of StorageWriter used to perform 'stage' and  'save'. If
+            this is not specified, DCP will automatically infer the writer based on the
+            checkpoint_id. If checkpoint_id is also None, an exception will
+            be raised. (Default: ``None``)
+        planner (Optional[SavePlanner]):
+            Instance of SavePlanner. If this is not specificed, the default
+            planner will be used. (Default: ``None``)
+        process_group (Optional[ProcessGroup]):
+            ProcessGroup to be used for cross-rank synchronization.
+            (Default: ``None``)
+
+    Returns:
+        Future: A future holding the resultant Metadata object from `save`.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> my_model = MyModule()
+
+        >>> state_dict = {"model": my_model}
+
+        >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
+        ...     "/checkpoint/1"
+        ... )
+        >>> checkpoint_future = torch.distributed.checkpoint.async_save(
+        >>>     state_dict=state_dict,
+        >>>     storage_writer=fs_storage_writer,
+        >>> )
+        >>>
+        >>> # ... do some work ...
+        >>>
+        >>> checkpoint_future.result()
+
+    """
+    torch._C._log_api_usage_once("torch.distributed.checkpoint.async_save")
+
+    if dist.is_available() and dist.is_initialized():
+        pg = process_group or _get_default_group()
+        assert (
+            torch.device("cpu") in pg._device_types  # type: ignore[attr-defined]
+        ), (
+            "A CPU backend must be enabled for async save; try initializing process group with 'cpu:gloo,cuda:nccl'"
+        )
+
+    storage_writer = cast(
+        StorageWriter, _storage_setup(storage_writer, checkpoint_id, reader=False)
+    )
+
+    state_dict = _stateful_to_state_dict(state_dict)
+    if isinstance(storage_writer, AsyncStager):
+        staged_state_dict = storage_writer.stage(state_dict)
+    else:  # provides bwc for storage_writers not implementing AsyncStager
+        staged_state_dict = _create_cpu_state_dict(state_dict)
+        _copy_state_dict(state_dict, staged_state_dict, type_check=False)
+
+    executor: _AsyncCheckpointExecutor = (
+        _ProcessBasedAsyncCheckpointExecutor()
+        if async_checkpointer_type == AsyncCheckpointerType.PROCESS
+        else _ThreadBasedAsyncCheckpointExecutor()
+    )
+
+    f: Future = executor.execute_save(
+        staged_state_dict,
+        checkpoint_id=checkpoint_id,
+        storage_writer=storage_writer,
+        planner=planner,
+        process_group=process_group,
+    )
+
+    if (
+        isinstance(storage_writer, AsyncStager)
+        and storage_writer.should_synchronize_after_execute
+    ):
+        storage_writer.synchronize_staging()
+
+    return f
+
+
+def _stateful_to_state_dict(state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE:
+    """Creates a shallow copy of `state_dict` where `state_dict` is called for each Stateful object."""
+    stateful_state_dict = {}
+    for key, elem in state_dict.items():
+        stateful_state_dict[key] = (
+            elem.state_dict() if isinstance(elem, Stateful) else elem
+        )
+    return stateful_state_dict
+
+
+def _save_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    storage_writer: StorageWriter,
+    process_group: Optional[dist.ProcessGroup] = None,
+    coordinator_rank: int = 0,
+    no_dist: bool = False,
+    planner: Optional[SavePlanner] = None,
+) -> Metadata:
+    torch._C._log_api_usage_once("torch.distributed.checkpoint.save_state_dict")
+
+    distW = _DistWrapper(process_group, not no_dist, coordinator_rank)
+    if planner is None:
+        planner = DefaultSavePlanner()
+    assert planner is not None
+
+    global_metadata = None
+
+    ckpt_kwargs = {}
+    if (ckpt_id := getattr(storage_writer, "checkpoint_id", None)) is not None:
+        ckpt_kwargs["checkpoint_id"] = ckpt_id
+        ckpt_kwargs["process_group"] = distW.group
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def local_step():
+        assert planner is not None
+        storage_meta = storage_writer.storage_meta()
+        if "storage_meta" not in inspect.signature(planner.set_up_planner).parameters:
+            warnings.warn(
+                "The function definition for SavePlanner.set_up_planner has been updated"
+                " to include the storage_meta argument. Please update your implementation"
+                " to include this parameter."
+            )
+            planner.set_up_planner(state_dict, distW.is_coordinator)  # type: ignore[call-arg, arg-type]
+        else:
+            planner.set_up_planner(
+                state_dict=state_dict,
+                storage_meta=storage_meta,
+                is_coordinator=distW.is_coordinator,
+            )
+        storage_writer.set_up_storage_writer(distW.is_coordinator)
+
+        local_plan = planner.create_local_plan()
+        local_plan = storage_writer.prepare_local_plan(local_plan)
+        return local_plan
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def global_step(all_local_plans):
+        nonlocal global_metadata
+
+        assert planner is not None
+        all_local_plans, global_metadata = planner.create_global_plan(all_local_plans)
+        all_local_plans = storage_writer.prepare_global_plan(all_local_plans)
+        return all_local_plans
+
+    central_plan: SavePlan = distW.reduce_scatter("plan", local_step, global_step)
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def write_data():
+        assert planner is not None
+        final_local_plan = planner.finish_plan(central_plan)
+        all_writes = storage_writer.write_data(final_local_plan, planner)
+
+        all_writes.wait()
+        return all_writes.value()
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def finish_checkpoint(all_results):
+        assert global_metadata is not None
+        storage_writer.finish(metadata=global_metadata, results=all_results)
+        return global_metadata
+
+    return distW.all_reduce("write", write_data, finish_checkpoint)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/stateful.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/stateful.py
new file mode 100644
index 0000000000000000000000000000000000000000..95cbb1873d6490bac9655404627cdd0362046795
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/stateful.py
@@ -0,0 +1,42 @@
+from typing import Any, runtime_checkable, TypeVar
+from typing_extensions import Protocol
+
+
+__all__ = ["Stateful", "StatefulT"]
+
+
+@runtime_checkable
+class Stateful(Protocol):
+    """
+    Stateful protocol for objects that can be checkpointed and restored.
+    """
+
+    def state_dict(self) -> dict[str, Any]:
+        """
+        Objects should return their state_dict representation as a dictionary.
+        The output of this function will be checkpointed, and later restored in
+        `load_state_dict()`.
+
+        .. warning::
+            Because of the inplace nature of restoring a checkpoint, this function
+            is also called during `torch.distributed.checkpoint.load`.
+
+
+        Returns:
+            Dict: The objects state dict
+        """
+
+        ...
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        """
+        Restore the object's state from the provided state_dict.
+
+        Args:
+            state_dict: The state dict to restore from
+        """
+
+        ...
+
+
+StatefulT = TypeVar("StatefulT", bound=Stateful)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/storage.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/storage.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c682bc1aff4fde29b958f27c073e2bcd975968a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/storage.py
@@ -0,0 +1,284 @@
+import abc
+import os
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+from torch.distributed.checkpoint.metadata import Metadata, MetadataIndex, StorageMeta
+from torch.distributed.checkpoint.planner import (
+    LoadPlan,
+    LoadPlanner,
+    SavePlan,
+    SavePlanner,
+)
+from torch.futures import Future
+
+
+__all__ = ["WriteResult", "StorageWriter", "StorageReader"]
+
+
+@dataclass(frozen=True)
+class WriteResult:
+    index: MetadataIndex
+
+    size_in_bytes: int
+    storage_data: Any
+
+
+class StorageWriter(abc.ABC):
+    """
+    Interface used by ``save_state_dict`` to write to storage.
+
+    One StorageWriter instance acts as both the coordinator and the follower
+    in a distributed checkpoint. As part of initialization, each instance
+    is told its role.
+
+    A subclass should expect the following sequence of calls.
+
+    0) (all ranks) set checkpoint_id if users pass a valid checkpoint_id.
+    1) (all ranks) set_up_storage_writer()
+    2) (all ranks) prepare_local_plan()
+    3) (coordinator) prepare_global_plan()
+    4) (all ranks) write_data()
+    5) (coordinator) finish()
+    """
+
+    @abc.abstractmethod
+    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
+        """
+        Calls to indicates a brand new checkpoint write is going to happen.
+        A checkpoint_id may be present if users set the checkpoint_id for
+        this checkpoint write. The meaning of the checkpiont_id is
+        storage-dependent. It can be a path to a folder/file or a key for
+        a key-value storage.
+
+        Args:
+            checkpoint_id (Union[str, os.PathLike, None]):
+                The ID of this checkpoint instance. The meaning of the checkpoint_id
+                depends on the storage. It can be a path to a folder or to a file.
+                It can also be a key if the storage is a key-value store.
+                (Default: ``None``)
+        """
+        ...
+
+    @abc.abstractmethod
+    def set_up_storage_writer(self, is_coordinator: bool) -> None:
+        """
+        Initialize this instance.
+
+        Args:
+            is_coordinator (bool): Whether this instance is responsible for coordinating
+              the checkpoint.
+        """
+
+    @abc.abstractmethod
+    def prepare_local_plan(self, plan: SavePlan) -> SavePlan:
+        """
+        Perform storage-specific local planning.
+
+        While this method can produce a completely different plan, the recommended
+        way is to store storage specific data in SavePlan::storage_data.
+
+        Args:
+            plan (SavePlan): The local plan from the ``SavePlanner`` in use.
+
+        Returns:
+            A transformed ``SavePlan`` after storage local planning
+        """
+
+    @abc.abstractmethod
+    def prepare_global_plan(self, plans: list[SavePlan]) -> list[SavePlan]:
+        """
+        Perform centralized planning of storage.
+
+        This method is only called on the coordinator instance.
+
+        While this method can produce a completely different plan, the preferred
+        way is to store storage specific data in SavePlan::storage_data.
+
+        Args:
+            plans: A list of ``SavePlan`` instances, one for each rank.
+
+        Returns:
+            A list of transformed ``SavePlan`` after storage global planning
+        """
+
+    @abc.abstractmethod
+    def write_data(
+        self, plan: SavePlan, planner: SavePlanner
+    ) -> Future[list[WriteResult]]:
+        """
+        Write all items from ``plan`` using ``planner`` to resolve the data.
+
+        A subclass should call ``SavePlanner::resolve_data`` on each item
+        from the plan to get access to the underlying object to write.
+
+        Subclasses should lazily call `resolve_data` as it can allocate memory.
+        In case of tensors, make following assumptions:
+
+        - They might be on any device, including not matching the one on ``WriteItem::tensor_data``
+        - They might be views or not contiguous. Only the projection needs to be saved.
+
+        Args:
+            plan (SavePlan): The save plan to execute.
+            planner (SavePlanner): Planner object to be used to resolve items to data.
+
+        Returns:
+            A future that completes to a list of WriteResult
+        """
+
+    @abc.abstractmethod
+    def finish(self, metadata: Metadata, results: list[list[WriteResult]]) -> None:
+        """
+        Write the metadata and marks the current checkpoint as successful.
+
+        The actual format/schema used for serializing `metadata` is an
+        implementation detail. The only requirement is that it's recoverable
+        in to the same object graph.
+
+        Args:
+            metadata (Metadata): metadata for the new checkpoint
+            results: A list of WriteResults from all ranks.
+
+        Returns:
+            None
+        """
+
+    @classmethod
+    @abc.abstractmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        """
+        Check if the given checkpoint_id is supported by the stroage. This allow
+        us to enable automatic storage selection.
+        """
+        ...
+
+    def storage_meta(self) -> Optional[StorageMeta]:
+        """
+        Return the storage-specific metadata. This is used to store additional information
+        in a checkpoint that can be useful for providing request-level observability. StorageMeta
+        is passed to the ``SavePlanner`` during save calls. Returns None by default.
+
+        TODO: provide an example
+        """
+        return None
+
+
+class StorageReader(abc.ABC):
+    """
+    Interface used by ``load_state_dict`` to read from storage.
+
+    One StorageReader instance acts as both the coordinator and the follower
+    in a distributed checkpoint. As part of initialization, each instance
+    is told its role.
+
+    A subclass should expected the following sequence of calls by ``load_state_dict``:
+
+    0) (all ranks) set checkpoint_id if users pass a valid checkpoint_id.
+    1) (all ranks) read_metadata()
+    2) (all ranks) set_up_storage_reader()
+    3) (all ranks) prepare_local_plan()
+    4) (coordinator) prepare_global_plan()
+    5) (all ranks) read_data()
+    """
+
+    @abc.abstractmethod
+    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
+        """
+        Calls to indicates a brand new checkpoint read is going to happen.
+        A checkpoint_id may be present if users set the checkpoint_id for
+        this checkpoint read. The meaning of the checkpiont_id is
+        storage-dependent. It can be a path to a folder/file or a key for
+        a key-value storage.
+
+        Args:
+            checkpoint_id (Union[str, os.PathLike, None]):
+                The ID of this checkpoint instance. The meaning of the checkpoint_id
+                depends on the storage. It can be a path to a folder or to a file.
+                It can also be a key if the storage is more like a key-value store.
+                (Default: ``None``)
+        """
+        ...
+
+    @abc.abstractmethod
+    def read_metadata(self) -> Metadata:
+        """
+        Read the checkpoint metadata.
+
+        Returns:
+            The metadata object associated with the checkpoint being loaded.
+
+        """
+
+    @abc.abstractmethod
+    def set_up_storage_reader(self, metadata: Metadata, is_coordinator: bool) -> None:
+        """
+        Initialize this instance.
+
+        Args:
+            metadata (Metadata): The metadata schema to use.
+            is_coordinator (bool): Whether this instance is responsible for coordinating
+              the checkpoint.
+        """
+
+    @abc.abstractmethod
+    def prepare_local_plan(self, plan: LoadPlan) -> LoadPlan:
+        """
+        Perform storage-specific local planning.
+
+        While this method can produce a completely different plan, the recommended
+        way is to store storage specific data in LoadPlan::storage_data.
+
+        Args:
+            plan (LoadPlan): The local plan from the ``LoadPlan`` in use.
+
+        Returns:
+            A transformed ``LoadPlan`` after storage local planning
+        """
+
+    @abc.abstractmethod
+    def prepare_global_plan(self, plans: list[LoadPlan]) -> list[LoadPlan]:
+        """
+        Perform centralized planning of storage loading.
+
+        This method is only called on the coordinator instance.
+
+        While this method can produce a completely different plan, the preferred
+        way is to store storage specific data in LoadPlan::storage_data.
+
+        Args:
+            plans: A list of ``LoadPlan`` instances, one for each rank.
+
+        Returns:
+            A list of transformed ``LoadPlan`` after storage global planning
+        """
+
+    @abc.abstractmethod
+    def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
+        """
+        Read all items from ``plan`` using ``planner`` to resolve the data.
+
+        A subclass should call ``LoadPlanner::load_bytes`` to deserialize a BytesIO
+        object into the right place.
+
+        A subclass should call ``LoadPlanner::resolve_tensor`` to get access to the
+        tensors that in should load data into.
+
+        It's the StorageLayer responsibility to properly schedule any cross device copies
+        required.
+
+        Args:
+            plan (LoadPlan): The local plan to execute on
+            planner (LoadPlanner): The planner object to use to resolve items.
+
+        Returns:
+            A future that completes once all reads are finished.
+        """
+
+    @classmethod
+    @abc.abstractmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        """
+        Check if the given checkpoint_id is supported by the stroage. This allow
+        us to enable automatic storage selection.
+        """
+        ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0615721228b09a621bc76ce8794ff4e50778465c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/checkpoint/utils.py
@@ -0,0 +1,467 @@
+# mypy: allow-untyped-defs
+import cProfile
+import inspect
+import io
+import itertools
+import os
+import warnings
+from collections.abc import Sequence
+from contextlib import contextmanager
+from functools import wraps
+from pstats import Stats
+from typing import Any, Callable, cast, Optional, TypeVar, Union
+
+import torch
+import torch.distributed as dist
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed._shard.sharded_tensor.shard import Shard
+
+from .api import (
+    _is_wrapped_exception,
+    _wrap_exception,
+    CheckpointException,
+    WRAPPED_EXCEPTION,
+)
+from .metadata import MetadataIndex, STATE_DICT_TYPE
+
+
+__all__ = ["find_tensor_shard", "find_state_dict_object"]
+
+T = TypeVar("T")
+R = TypeVar("R")
+
+
+def _get_failure_dict(
+    results: list[Union[T, WRAPPED_EXCEPTION]],
+) -> dict[int, WRAPPED_EXCEPTION]:
+    return cast(
+        dict[int, WRAPPED_EXCEPTION],
+        {i: err for i, err in enumerate(results) if _is_wrapped_exception(err)},
+    )
+
+
+def _all_gather_keys(
+    local_dict: dict[str, Any], group: Optional[dist.ProcessGroup] = None
+) -> set[str]:
+    """Gathers all keys, and returns them sorted."""
+    keys = list(local_dict.keys())
+    gathered_keys: list[list[str]] = [None] * dist.get_world_size(group)  # type: ignore[list-item]
+
+    dist.all_gather_object(gathered_keys, keys, group=group)
+    return set(itertools.chain.from_iterable(gathered_keys))
+
+
+def _assert_same_keys(
+    state_dict: dict[str, Any], process_group: Optional[dist.ProcessGroup] = None
+) -> None:
+    """
+    Asserts that all ranks have the same keys in their state dict.
+    This is a collective call which requires all ranks in ``process_group`` to
+    join. It will also induce cross-rank communication and block CPU.
+    """
+
+    if dist.get_world_size(process_group) == 1:
+        return
+
+    all_keys = _all_gather_keys(state_dict, process_group)
+    my_keys = set(state_dict.keys())
+    diff = all_keys - my_keys
+    if len(diff) > 0:
+        raise AssertionError(
+            f"Key(s) present in other ranks but not this one, difference: {diff}"
+        )
+
+
+class _DistWrapper:
+    """
+    This is a wrapper around PG that provides a series of features around object collectives.
+
+    It works without distributed initialized, where most collectives turns into nops.
+
+    All variants that take functions are exception robust, meaning that if one or more
+    ranks raise errors, all ranks will observe those.
+    """
+
+    def __init__(
+        self,
+        group: Optional[dist.ProcessGroup],
+        use_dist: bool,
+        coordinator_rank: int,
+    ):
+        self.group = group
+        self.use_dist = use_dist
+        self.coordinator_rank = coordinator_rank
+        if self.use_dist:
+            self.global_coordinator_rank = (
+                dist.get_global_rank(group, coordinator_rank)
+                if group is not None
+                else coordinator_rank
+            )
+            self.rank = dist.get_rank(group)
+            self.is_coordinator = self.rank == coordinator_rank
+        else:
+            self.global_coordinator_rank = 0
+            self.rank = 0
+            self.is_coordinator = True
+
+    def get_rank(self) -> int:
+        return self.rank
+
+    def get_world_size(self) -> int:
+        if self.use_dist:
+            return dist.get_world_size(self.group)
+        return 1
+
+    def broadcast_object(self, object: Optional[T]) -> T:
+        """Implement functionality similar to c10d::broadcast_object_list but without distributed enabled."""
+        object_list = [object]
+        if self.use_dist:
+            dist.broadcast_object_list(
+                object_list=object_list,
+                group=self.group,
+                src=self.coordinator_rank,
+            )
+        return cast(T, object_list[0])
+
+    def gather_object(self, object: T) -> Optional[list[T]]:
+        """Implement functionality similar to c10d::gather_object but without distributed enabled."""
+        if self.use_dist:
+            gather_objs = (
+                cast(list[T], [None] * dist.get_world_size(self.group))
+                if self.is_coordinator
+                else None
+            )
+
+            dist.gather_object(
+                obj=object,
+                object_gather_list=gather_objs if self.is_coordinator else None,
+                dst=self.global_coordinator_rank,
+                group=self.group,
+            )
+            result = gather_objs
+        else:
+            result = [object]
+        return result
+
+    def all_gather_object(self, object: T) -> list[T]:
+        """Implement functionality similar to c10d::all_gather_object but without distributed enabled."""
+        if self.use_dist:
+            gather_objs = cast(list[T], [None] * dist.get_world_size(self.group))
+
+            dist.all_gather_object(
+                object_list=gather_objs, obj=object, group=self.group
+            )
+        else:
+            gather_objs = [object]
+        return gather_objs
+
+    def scatter_object(self, object_list: Optional[list[T]]) -> T:
+        """Implement functionality similar to c10d::scatter_object but without distributed enabled."""
+        if self.use_dist:
+            gather_result = cast(list[T], [None])
+            dist.scatter_object_list(
+                scatter_object_output_list=gather_result,
+                scatter_object_input_list=object_list if self.is_coordinator else None,
+                src=self.global_coordinator_rank,
+                group=self.group,
+            )
+
+            local_reply = gather_result[0]
+        else:
+            assert object_list is not None
+            local_reply = object_list[0]
+        return local_reply
+
+    def reduce_scatter(
+        self,
+        step: str,
+        map_fun: Callable[[], T],
+        reduce_fun: Callable[[list[T]], list[R]],
+    ) -> R:
+        """
+        Compute a value on each rank, then do centralized reduce on a single rank, followed by a scatter.
+
+        This method operates in the following way:
+            Run ``map_fun`` on all ranks
+            Gather results on rank 0
+            Call ``reduce_fun`` on all those values
+            Scatter to each rank part of the result.
+        """
+        local_data: Union[WRAPPED_EXCEPTION, T]
+        try:
+            local_data = map_fun()
+        except BaseException as e:
+            local_data = _wrap_exception(e)
+
+        all_data = self.gather_object(local_data)
+        all_results: Optional[list[Union[R, CheckpointException]]] = None
+        if self.is_coordinator:
+            assert all_data is not None
+            node_failures = _get_failure_dict(all_data)
+
+            if len(node_failures) == 0:
+                try:
+                    # N.B. why can't mypy cast List[R] to List[Union[R, WRAPPED_EXCEPTION]]?
+                    all_results = cast(
+                        list[Union[R, CheckpointException]],
+                        reduce_fun(cast(list[T], all_data)),
+                    )
+                except BaseException as e:
+                    node_failures[self.rank] = _wrap_exception(e)
+
+            if len(node_failures) > 0:
+                all_results = [
+                    CheckpointException(step, node_failures)
+                ] * self.get_world_size()
+
+        result = self.scatter_object(all_results)
+        if isinstance(result, CheckpointException):
+            raise result
+        return result
+
+    def all_reduce(
+        self,
+        step: str,
+        map_fun: Callable[[], T],
+        reduce_fun: Callable[[list[T]], R],
+    ) -> R:
+        """
+        Compute a value on each rank, then do centralized reduce on a single rank, followed by a broadcast.
+
+        This method operates in the following way:
+            Run ``map_fun`` on all ranks
+            Gather results on rank 0
+            Call ``reduce_fun`` on all those values
+            Broadcast the reduced value to all ranks.
+        """
+        local_data: Union[T, WRAPPED_EXCEPTION]
+        try:
+            local_data = map_fun()
+        except BaseException as e:
+            local_data = _wrap_exception(e)
+
+        all_data = self.gather_object(local_data)
+        result: Optional[Union[R, CheckpointException]] = None
+        if self.is_coordinator:
+            assert all_data is not None
+            node_failures = _get_failure_dict(all_data)
+            if len(node_failures) == 0:
+                try:
+                    result = reduce_fun(cast(list[T], all_data))
+                except BaseException as e:
+                    node_failures[self.rank] = _wrap_exception(e)
+
+            if len(node_failures) > 0:
+                result = CheckpointException(step, node_failures)
+
+        final_result = self.broadcast_object(result)
+        if isinstance(final_result, CheckpointException):
+            raise final_result
+        return cast(R, final_result)
+
+    def all_gather(
+        self,
+        step: str,
+        map_fun: Callable[[], T],
+    ) -> list[T]:
+        """
+        Compute a value on each rank, then all_gather them.
+
+        This method operates in the following way:
+            Run ``map_cp`` on all ranks
+            all_gather the values to all ranks
+        """
+        result: Union[T, WRAPPED_EXCEPTION]
+        try:
+            result = map_fun()
+        except BaseException as e:
+            result = _wrap_exception(e)
+
+        all_results = self.all_gather_object(result)
+
+        node_failures = _get_failure_dict(all_results)
+        if len(node_failures) > 0:
+            raise CheckpointException(step, node_failures)
+        return cast(list[T], all_results)
+
+    def broadcast(
+        self,
+        step: str,
+        map_fun: Callable[[], T],
+    ) -> T:
+        """
+        Compute a value on rank 0 and broadcast it.
+
+        This method operates in the following way:
+            Run ``map_cp`` on rank 0
+            broadcast the value
+        """
+        result: Optional[Union[T, CheckpointException]] = None
+        if self.is_coordinator:
+            try:
+                result = map_fun()
+            except BaseException as e:
+                result = CheckpointException(step, {self.rank: _wrap_exception(e)})
+        final_result = self.broadcast_object(result)
+        if isinstance(final_result, CheckpointException):
+            raise final_result
+        return cast(T, final_result)
+
+
+def _find_shard(tensor: ShardedTensor, index: MetadataIndex) -> Shard:
+    if index.offset is None:
+        raise ValueError(
+            f"Cannot lookup {index.fqn} since its a ShardedTensor and no offset was provided"
+        )
+
+    shards = tensor.local_shards()
+    # index fast path
+    if index.index is not None:
+        if (
+            len(shards) > index.index
+            and torch.Size(shards[index.index].metadata.shard_offsets) == index.offset
+        ):
+            return shards[index.index]
+
+    for shard in shards:
+        if torch.Size(shard.metadata.shard_offsets) == index.offset:
+            return shard
+    raise ValueError(f"Could not find shard at '{index.offset}' for FQN: '{index.fqn}'")
+
+
+def find_tensor_shard(tensor: torch.Tensor, index: MetadataIndex) -> torch.Tensor:
+    if hasattr(tensor, "__get_tensor_shard__"):
+        # DTensor implements _Checkpointable
+        return tensor.__get_tensor_shard__(index)  # type: ignore[attr-defined]
+    if isinstance(tensor, ShardedTensor):
+        return _find_shard(tensor, index).tensor
+    if index.offset is not None:
+        # special case looking up a tensor by origin
+        if index.offset == torch.Size([0] * len(tensor.size())):
+            return tensor
+        raise ValueError(
+            f"FQN: '{index.fqn}' is not a ShardedTensor, can't find by offset: '{index.offset}'"
+        )
+    return tensor
+
+
+def find_state_dict_object(state_dict: STATE_DICT_TYPE, index: MetadataIndex) -> Any:
+    if index.fqn not in state_dict:
+        raise ValueError(f"Could not find FQN: '{index.fqn}'")
+    obj = state_dict[index.fqn]
+
+    if isinstance(obj, torch.Tensor):
+        return find_tensor_shard(obj, index)
+    elif index.offset is not None:
+        raise ValueError(
+            f"FQN: '{index.fqn}' is not a ShardedTensor, can't find by offset: '{index.offset}'"
+        )
+    return obj
+
+
+def _element_wise_add(a: Sequence[int], b: Sequence[int]) -> list[int]:
+    return [i_a + i_b for i_a, i_b in zip(a, b)]
+
+
+def _element_wise_sub(a: Sequence[int], b: Sequence[int]) -> list[int]:
+    return [i_a - i_b for i_a, i_b in zip(a, b)]
+
+
+class _ReaderView(io.IOBase):
+    def __init__(self, base_stream: io.IOBase, offset: int, len: int):
+        super().__init__()
+        self.offset = offset
+        self.len = len
+        self.base_stream = base_stream
+        self.seek(0)
+
+    def seek(self, offset: int, whence: int = os.SEEK_SET, /) -> int:
+        if whence == os.SEEK_SET:
+            offset = self.offset + offset
+        elif whence == os.SEEK_END:
+            whence = os.SEEK_SET
+            offset = (self.offset + self.len) - offset
+        return self.base_stream.seek(offset, whence)
+
+    def tell(self) -> int:
+        return self.base_stream.tell() - self.offset
+
+    def readable(self) -> bool:
+        return self.base_stream.readable()
+
+    def seekable(self) -> bool:
+        return self.base_stream.seekable()
+
+    def readinto(self, b):
+        max_size = self.len - self.tell()
+        if max_size == 0:
+            return 0
+        if len(b) > max_size:
+            b = memoryview(b)[:max_size]
+        return self.base_stream.readinto(b)  # type: ignore[attr-defined]
+
+    def read(self, size=-1):
+        max_size = self.len - self.tell()
+        if size == -1 or size > max_size:
+            size = max_size
+        return self.base_stream.read(size)
+
+
+def _create_file_view(file: io.IOBase, offset: int, length: int) -> io.IOBase:
+    # FIXME (kumpera) torch.load fails if we wrap with io.BufferedReader
+    return _ReaderView(file, offset, length)
+
+
+def _normalize_device_info(device_type: str, device_id: int) -> str:
+    """Device info normalization."""
+    if device_type == "cpu":
+        return "cpu"
+    return f"{device_type}:{device_id}"
+
+
+# TODO: integrate with distributed logging flag
+ENABLE_PROFILE = False
+
+
+@contextmanager
+def _profile():
+    # Only log the profiling when it is enable and is on rank0  or dist is not
+    # avaiable.
+    if ENABLE_PROFILE and (not dist.is_available() or dist.get_rank() == 0):
+        profiler = cProfile.Profile()
+        profiler.enable()
+        try:
+            yield
+        finally:
+            profiler.disable()
+            stats = Stats(profiler)
+            stats.sort_stats("time").print_stats(10)
+    else:
+        yield
+
+
+def _api_bc_check(func):
+    @wraps(func)
+    def inner_func(*args, **kwargs) -> Any:
+        if len(args) == 2:
+            warnings.warn(
+                f"The argument order of {func.__name__} has been changed. "
+                "Please check the document to avoid future breakages."
+            )
+            sig = inspect.signature(func)
+            kwonlyargs = [
+                p.name for p in sig.parameters.values() if p.kind == p.KEYWORD_ONLY
+            ]
+            if "storage_writer" in kwonlyargs:
+                assert "storage_writer" not in kwargs, (args, kwargs)
+                kwargs["storage_writer"] = args[1]
+            elif "storage_reader" in kwonlyargs:
+                assert "storage_reader" not in kwargs, (args, kwargs)
+                kwargs["storage_reader"] = args[1]
+            else:
+                raise RuntimeError(f"Unexpected kwonlyargs = {kwonlyargs}")
+            return func(args[0], **kwargs)
+        else:
+            return func(*args, **kwargs)
+
+    return inner_func
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/collective_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/collective_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b77e1ba8956e3d37dfab20a555da7b908aea9ab4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/collective_utils.py
@@ -0,0 +1,217 @@
+#!/usr/bin/env python3
+
+
+"""
+A set of primitive functions for performing collective ops.
+
+Each should also handle single rank scenario.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Callable, cast, Generic, Optional, TypeVar, Union
+
+import torch.distributed as dist
+
+
+T = TypeVar("T")
+
+
+@dataclass
+class SyncPayload(Generic[T]):
+    stage_name: Optional[str]
+    success: bool
+    payload: T
+    exception: Optional[Exception] = None
+
+
+def broadcast(
+    data_or_fn: Union[T, Callable[[], T]],
+    *,
+    success: bool = True,
+    stage_name: Optional[str] = None,
+    rank: int = 0,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> T:
+    """
+    Broadcasts the data payload from rank 0 to all other ranks.
+    Or if a function is passed, execute it in rank 0 and broadcast result to all other ranks.
+
+    Can be used to broadcast a failure signal to stop all ranks.
+
+    If the function raises an exception, all ranks will raise.
+
+    Args:
+        data_or_fn: the data to broadcast or function to execute and broadcast result.
+        success: False to stop all ranks.
+        stage_name: the name of the logical stage for synchronization and debugging
+        rank: rank to broadcast data or execute function and broadcast resutls.
+        pg: the process group for sync
+    Throws:
+        RuntimeError from original exception trace
+    Returns:
+        the value after synchronization
+
+    Example usage:
+    >> id = broadcast(data_or_fn=allocate_id, rank=0, pg=ext_pg.my_pg)
+    """
+
+    if not success and data_or_fn is not None:
+        raise AssertionError(
+            "Data or Function is expected to be None if not successful"
+        )
+
+    payload: Optional[T] = None
+    exception: Optional[Exception] = None
+    # if no pg is passed then execute if rank is 0
+    if (pg is None and rank == 0) or (pg is not None and pg.rank() == rank):
+        # determine if it is an executable function or data payload only
+        if callable(data_or_fn):
+            try:
+                payload = data_or_fn()
+            except Exception as e:
+                success = False
+                exception = e
+        else:
+            payload = data_or_fn
+
+    # broadcast the exception type if any to all ranks for failure categorization
+    sync_obj = SyncPayload(
+        stage_name=stage_name,
+        success=success,
+        payload=payload,
+        exception=exception,
+    )
+
+    if pg is not None:
+        broadcast_list = [sync_obj]
+        dist.broadcast_object_list(broadcast_list, src=rank, group=pg)
+        assert len(broadcast_list) == 1
+        sync_obj = broadcast_list[0]
+
+    # failure in any rank will trigger a throw in every rank.
+    if not sync_obj.success:
+        error_msg = f"Rank {rank} failed"
+        if stage_name is not None:
+            error_msg += f": stage {sync_obj.stage_name}"
+        if sync_obj.exception is not None:
+            error_msg += f": exception {sync_obj.exception}"
+        raise RuntimeError(error_msg) from sync_obj.exception
+
+    return cast(T, sync_obj.payload)
+
+
+def all_gather(
+    data_or_fn: Union[T, Callable[[], T]],
+    stage_name: Optional[str] = None,
+    pg: Optional[dist.ProcessGroup] = None,
+) -> list[T]:
+    """
+    A simple all_gather primitive with basic synchronization guard logic,
+    by checking payload from all ranks has the same stage name.
+
+    Args:
+        data_or_fn: the data to be all gathered across ranks or function to be executed
+        stage_name: the sync stage name for out-of-sync protection
+        pg: the process group for sync
+    Throws:
+        RuntimeError from original exception trace
+    Returns:
+        a list of synced data from all ranks
+
+    Example usage:
+    >> all_ids = all_gather(data_or_fn=allocate_id, pg=ext_pg.my_pg)
+    """
+    payload: Optional[T] = None
+    exception: Optional[Exception] = None
+    success = True
+    # determine if it is an executable function or data payload only
+    if callable(data_or_fn):
+        try:
+            payload = data_or_fn()
+        except Exception as e:
+            success = False
+            exception = e
+    else:
+        payload = data_or_fn
+
+    sync_obj = SyncPayload(
+        stage_name=stage_name,
+        success=success,
+        payload=payload,
+        exception=exception,
+    )
+
+    if pg is not None:
+        # List of success/failure across all ranks.
+        total_list = [None] * dist.get_world_size(pg)
+        all_gather_object_enforce_type(pg, total_list, sync_obj)
+        # Each rank will throw RuntimeError in case of failure on any rank.
+        stage_name = cast(SyncPayload[T], total_list[0]).stage_name
+        exception_list: list[tuple[int, Exception]] = []
+        ret_list: list[T] = []
+        error_msg: str = ""
+
+        for i, sp in enumerate(cast(list[SyncPayload[T]], total_list)):
+            if sp.stage_name != stage_name:
+                error_msg += (
+                    f"Unexpected stage name received from rank {i}: {sp.stage_name} "
+                )
+                continue
+            if not sp.success and sp.exception is not None:
+                exception_list.append((i, sp.exception))
+                continue
+            ret_list.append(sp.payload)
+
+        if len(exception_list) > 0:
+            raise RuntimeError(  # type: ignore[misc]
+                error_msg, exception_list
+            ) from exception_list[0]
+        return ret_list
+    else:
+        if not sync_obj.success:
+            raise RuntimeError(
+                f"all_gather failed with exception {sync_obj.exception}",
+            ) from sync_obj.exception
+        return [sync_obj.payload]  # type: ignore[list-item]
+
+
+# Note: use Any for typing for now so users can pass in
+# either a list of None or target type placeholders
+# otherwise pyre would complain
+def all_gather_object_enforce_type(
+    pg: dist.ProcessGroup,
+    # pyre-fixme[2]: Parameter must have a type that does not contain `Any`
+    object_list: list[Any],
+    # pyre-fixme[2]: Parameter must have a type other than `Any`
+    obj: Any,
+    # pyre-fixme[2]: Parameter must have a type that does not contain `Any`
+    type_checker: Callable[[Any, Any], bool] = lambda x, y: type(x) == type(y),
+) -> None:
+    """
+    Similar to plain all_gather_object but with additional type checking
+    AFTER gather is done to ensure basic consistency.
+    If check does not pass, all ranks will fail with exception.
+
+    This is generally to prevent conditional logic leading to
+    unexpected messages being received. This is considered fatal code error,
+    but due to logic stacks this might happen implicitly in practice.
+
+    The default check does not check sub type (considered different)
+    or covariance (considered same) but users can pass in custom checker
+    if more complicated check is needed.
+    """
+    dist.all_gather_object(object_list, obj, group=pg)
+
+    # conservative check
+    list_len = len(object_list)
+    if list_len == 0:
+        return
+    first_obj = object_list[0]
+    for i in range(1, list_len):
+        if not type_checker(first_obj, object_list[i]):
+            raise TypeError(
+                f"Object type at index {i} is {type(object_list[i])}, "
+                f"while first object type is {type(first_obj)}"
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/constants.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3754043644b8cc96feee06a4fc1cc9fa824b648
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/constants.py
@@ -0,0 +1,26 @@
+from datetime import timedelta
+from typing import Optional
+
+from torch._C._distributed_c10d import _DEFAULT_PG_TIMEOUT
+
+
+__all__ = ["default_pg_timeout", "default_pg_nccl_timeout"]
+
+# Default process group wide timeout, if applicable.
+# This only applies to the non-nccl backends
+# To make an attempt at backwards compatibility with THD, we use an
+# extraordinarily high default timeout, given that THD did not have timeouts.
+default_pg_timeout: timedelta = _DEFAULT_PG_TIMEOUT
+# Separate timeout for PGNCCL mainly becuase it's always been that way in the C++ layer, but until recently
+# there was one default that applied across all backends in the python layer.
+# Later, we could consider merging them back together at the c++ layer if we can align on a same value.
+# (only if TORCH_NCCL_BLOCKING_WAIT or TORCH_NCCL_ASYNC_ERROR_HANDLING is set to 1).
+
+try:
+    from torch._C._distributed_c10d import _DEFAULT_PG_NCCL_TIMEOUT
+
+    default_pg_nccl_timeout: Optional[timedelta] = _DEFAULT_PG_NCCL_TIMEOUT
+except ImportError:
+    # if C++ NCCL support is not compiled, we don't have access to the default nccl value.
+    # if anyone is actually trying to use nccl in this state, it should error.
+    default_pg_nccl_timeout = None
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/device_mesh.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/device_mesh.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cd80ecae43248e8e591a27f2bde445a657e430f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/device_mesh.py
@@ -0,0 +1,1050 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import logging
+import math
+import threading
+from functools import reduce
+from itertools import chain
+from typing import Optional, TYPE_CHECKING, Union
+
+import torch
+from torch.distributed import is_available
+from torch.utils._typing_utils import not_none
+
+
+__all__ = ["init_device_mesh", "DeviceMesh"]
+
+
+if not is_available():
+    import sys
+
+    # We need to create the stubs when distributed is not available.
+    # Otherwise, we would fail the doc tests (```./.ci/pytorch/docs-test.sh```),
+    # since it would try to import ``torch.distributed.device_mesh`` or
+    # ``torch.distributed.init_device_mesh`` but cannot find them.
+
+    class _DeviceMeshStub:
+        pass
+
+    def _init_device_mesh_stub():
+        pass
+
+    sys.modules["torch.distributed.device_mesh"].DeviceMesh = _DeviceMeshStub  # type: ignore[attr-defined]
+    sys.modules[
+        "torch.distributed.device_mesh"
+    ].init_device_mesh = _init_device_mesh_stub  # type: ignore[attr-defined]
+
+
+else:
+    from torch._C._distributed_c10d import Backend as C10dBackend
+    from torch.distributed.distributed_c10d import (
+        _find_pg_by_ranks_and_tag,
+        _get_default_group,
+        _get_group_tag,
+        get_backend,
+        get_process_group_ranks,
+        get_rank,
+        get_world_size,
+        init_process_group,
+        is_initialized,
+        new_group,
+        ProcessGroup,
+        split_group,
+    )
+
+    logger = logging.getLogger(__name__)
+
+    # only import numpy typing when type checking
+    if TYPE_CHECKING:
+        try:
+            from numpy.typing import ArrayLike
+        except ImportError:
+            logger.warning(
+                "DeviceMesh requires numpy >= 1.21 to be installed for type checking"
+            )
+
+    class _MeshEnv(threading.local):
+        def __init__(self) -> None:
+            self.mesh_stack: list[DeviceMesh] = []
+            self.child_to_root_mapping: dict[DeviceMesh, DeviceMesh] = {}
+            self.mesh_dim_group_options: dict[
+                int, tuple[str, Optional[C10dBackend.Options]]
+            ] = {}
+            self.root_to_flatten_mapping: dict[DeviceMesh, dict[str, DeviceMesh]] = {}
+            # Record flatten mesh name to its mesh dim index in root mesh.
+            self.flatten_name_to_root_dims: dict[
+                DeviceMesh, dict[str, tuple[int, ...]]
+            ] = {}
+
+        def get_current_mesh(self) -> "DeviceMesh":
+            if len(self.mesh_stack) == 0:
+                raise RuntimeError("No device mesh is currently active!")
+            return self.mesh_stack[-1]
+
+        def create_sub_mesh(
+            self,
+            device_mesh: "DeviceMesh",
+            submesh_dim_names: tuple[str, ...],
+            submesh_dims: list[tuple[int, ...]],
+        ) -> "DeviceMesh":
+            # Get the submesh dim size from the submesh_dims.
+            # For example, if we have a 3D mesh with mesh_shape (2, 2, 2) mesh_dim_names ("dp", "cp", "tp") and we want
+            # to slice out mesh["dp_cp"], then submesh_dims = [(0, 1), (2,)] and submesh_dim_size = [2 * 2, 2] = [4, 2].
+            # If we want to slice out mesh["dp", "cp"], then submesh_dims = [(0,), (1,)] and submesh_dim_size = [2, 2].
+            slice_dim_size = [
+                reduce(
+                    lambda x, y: x * device_mesh.mesh.size(y),
+                    mesh_dim,
+                    1,
+                )
+                for mesh_dim in submesh_dims
+            ]
+
+            mesh_tensor = device_mesh.mesh
+            # slice_dim_idx could be differnt from submesh_dims, as we may need to flatten out some dims.
+            slice_dim_idx = []
+            slice_dim_group_info = []
+            # keep track of the number of dims that have been flattened so we can get the correct slice_dim_idx in the
+            # flattened mesh tensor.
+            num_dims_flatten = 0
+            for mesh_dim_indices, mesh_dim_name in zip(submesh_dims, submesh_dim_names):
+                # Currently, this only allows slicing out a contiguous flattened dim.
+                # TODO: we need to handle reconstructing a non-contiguous flattened dim.
+                if len(mesh_dim_indices) > 1:
+                    # We need to move the start_dim and end_dim to the left if some dims are already flattened.
+                    mesh_tensor = mesh_tensor.flatten(
+                        start_dim=mesh_dim_indices[0] - num_dims_flatten,
+                        end_dim=mesh_dim_indices[-1] - num_dims_flatten,
+                    )
+                    # If some dims are already flattened, we need to adjust the slice_dim_idx accordingly.
+                    # For example, if the submesh_dims = [(0, 1), (2,), (3, 4)] with 0-1 flattened and 3-4 flattened,
+                    # then the final slice_dim_idx should be [0, 1, 2].
+                    slice_dim_idx.append(mesh_dim_indices[0] - num_dims_flatten)
+                    num_dims_flatten += len(mesh_dim_indices) - 1
+                    slice_dim_group_info.append(
+                        self.root_to_flatten_mapping[device_mesh][
+                            mesh_dim_name
+                        ]._dim_group_infos[0]
+                    )
+                else:
+                    slice_dim_idx.append(mesh_dim_indices[0] - num_dims_flatten)
+                    slice_dim_group_info.append(
+                        device_mesh._dim_group_infos[mesh_dim_indices[0]]
+                    )
+
+            # mesh_tensor has already been flattened if needed. So mesh_tensor.ndim <= device_mesh.mesh.ndim now.
+            mesh_dims_remained_idx = list(range(mesh_tensor.ndim))
+            for idx in slice_dim_idx:
+                mesh_dims_remained_idx.remove(idx)
+
+            # pg_ranks_by_dim is the size of [number of local ranks of the outermost submesh dimension, *slice_dim_idx]
+            # This means on each local rank of the outermost slice mesh dim, we have a tensor of submesh size with
+            # the pg ranks of the submesh. From this, we can extract the submesh mesh tensor contains the current rank.
+            pg_ranks_by_dim = mesh_tensor.permute(
+                *mesh_dims_remained_idx, *slice_dim_idx
+            ).reshape(-1, *slice_dim_size)
+
+            cur_rank = device_mesh.get_rank()
+            for mesh_nd in pg_ranks_by_dim:
+                submesh = DeviceMesh(
+                    device_mesh.device_type,
+                    mesh_nd,
+                    mesh_dim_names=submesh_dim_names,
+                    _init_backend=False,
+                )
+                if cur_rank in mesh_nd:
+                    res_submesh = submesh
+
+            res_submesh._dim_group_infos = slice_dim_group_info  # type: ignore[possibly-undefined]
+            self.child_to_root_mapping[res_submesh] = device_mesh
+
+            return res_submesh
+
+        def create_flatten_mesh(
+            self, device_mesh: "DeviceMesh", mesh_dim_name: Optional[str] = None
+        ) -> "DeviceMesh":
+            root_mesh = _mesh_resources.get_root_mesh(device_mesh)
+
+            flatten_dims_in_root = [
+                not_none(root_mesh.mesh_dim_names).index(flattened_mesh_dim_name)
+                for flattened_mesh_dim_name in not_none(device_mesh.mesh_dim_names)
+            ]
+
+            if not mesh_dim_name:
+                mesh_dim_name = "_".join(
+                    [
+                        not_none(root_mesh.mesh_dim_names)[dim]
+                        for dim in flatten_dims_in_root
+                    ]
+                )
+
+            # Check whether the mesh_dim_name for flattened mesh is valid.
+            self.flatten_name_to_root_dims.setdefault(root_mesh, {})
+            invalid_dim_names = chain(
+                *list(not_none(root_mesh.mesh_dim_names)),
+                *self.flatten_name_to_root_dims[root_mesh].keys(),
+            )
+            if mesh_dim_name in invalid_dim_names:
+                raise RuntimeError(
+                    f"{mesh_dim_name} already exists for submesh of the {root_mesh}. ",
+                    f"The mesh_dim_names of submesh and flattened mesh are {invalid_dim_names}. "
+                    f"Please specify another valid mesh_dim_name.",
+                )
+
+            # Quick return if the flatten mesh has been created before.
+            # TODO: If we decide to restrict flatten initialization once, we should remove
+            # this check and throw an error if the flatten mesh is already created before.
+            if (
+                root_mesh in self.root_to_flatten_mapping
+                and mesh_dim_name in self.root_to_flatten_mapping[root_mesh]
+            ):
+                return self.root_to_flatten_mapping[root_mesh][mesh_dim_name]
+
+            flattened_mesh_dim_size = math.prod(device_mesh.mesh.size())
+
+            remained_dims_in_root = list(range(root_mesh.mesh.ndim))
+            for flatten_dim_in_root in flatten_dims_in_root:
+                remained_dims_in_root.remove(flatten_dim_in_root)
+
+            pg_ranks_by_dim = root_mesh.mesh.permute(
+                *remained_dims_in_root, *flatten_dims_in_root
+            ).reshape(-1, flattened_mesh_dim_size)
+
+            cur_rank = root_mesh.get_rank()
+            for mesh_nd in pg_ranks_by_dim:
+                # need to init backend here since the flattened pg doesn't exist in root mesh.
+                flattened_mesh = DeviceMesh(
+                    root_mesh.device_type,
+                    mesh_nd,
+                    mesh_dim_names=(mesh_dim_name,),
+                )
+                if cur_rank in mesh_nd:
+                    res_flattened_mesh = flattened_mesh
+            self.child_to_root_mapping[res_flattened_mesh] = root_mesh  # type: ignore[possibly-undefined]
+            self.root_to_flatten_mapping.setdefault(root_mesh, {})[mesh_dim_name] = (
+                res_flattened_mesh  # type: ignore[possibly-undefined]
+            )
+            self.flatten_name_to_root_dims[root_mesh][mesh_dim_name] = tuple(
+                flatten_dims_in_root
+            )  # type: ignore[possibly-undefined]
+
+            return res_flattened_mesh
+
+        def get_root_mesh(self, device_mesh: "DeviceMesh") -> "DeviceMesh":
+            # If a mesh could not be found in the child_to_root_mapping, it is a root mesh itself.
+            # A root mesh is not created through slicing.
+            # We considers the root mesh of a root mesh is itself.
+            root_mesh = self.child_to_root_mapping.get(device_mesh, None)
+            return device_mesh if not root_mesh else root_mesh
+
+        def get_root_mesh_dim(self, device_mesh: "DeviceMesh") -> Optional[int]:
+            """
+            Returns the index of the mesh dim in the root mesh.
+            The device_mesh passed in needs to be sliced out from the root mesh
+            or submesh of the root mesh.
+            """
+            root_mesh = self.get_root_mesh(device_mesh)
+            child_mesh_dim_names = device_mesh.mesh_dim_names
+            if root_mesh and child_mesh_dim_names:
+                assert len(child_mesh_dim_names) == 1, (
+                    "The submesh can only be a 1D mesh."
+                )
+                child_mesh_dim_name = child_mesh_dim_names[0]
+                return self.get_mesh_dim_by_name(root_mesh, child_mesh_dim_name)
+            return None
+
+        @staticmethod
+        def num_devices_per_host(device_type: str) -> int:
+            return _get_device_handle(device_type).device_count()
+
+        @staticmethod
+        def num_hosts(device_type: str) -> int:
+            # ProcessGroup can't tell us this info so we have to infer it, assume
+            # homogeneous hardware for now
+            return get_world_size() // _MeshEnv.num_devices_per_host(device_type)
+
+        def get_mesh_dim_by_name(
+            self, device_mesh: "DeviceMesh", mesh_dim_name: str
+        ) -> int:
+            if (
+                device_mesh.mesh_dim_names is None
+                or len(device_mesh.mesh_dim_names) == 0
+            ):
+                raise KeyError(
+                    "No `mesh_dim_names` found.",
+                )
+            if mesh_dim_name not in device_mesh.mesh_dim_names:
+                raise KeyError(
+                    f"Mesh dimension '{mesh_dim_name}' does not exist.",
+                    f"Available mesh dimensions are: mesh_dim_names={device_mesh.mesh_dim_names}",
+                )
+            return not_none(device_mesh.mesh_dim_names.index(mesh_dim_name))
+
+        def _set_mesh_dim_group_options(
+            self,
+            dim: int,
+            backend: str,
+            pg_options: Optional[C10dBackend.Options] = None,
+        ) -> None:
+            self.mesh_dim_group_options[dim] = (backend, pg_options)
+
+        def _get_slice_mesh_dims(
+            self, device_mesh, mesh_dim_names
+        ) -> list[tuple[int, ...]]:
+            """
+            Validate whether the mesh_dim_names is valid for slicing the given device_mesh.
+            If valid, return dim indexes of the slice mesh in the device mesh.
+            """
+            if device_mesh != self.get_root_mesh(device_mesh):
+                raise RuntimeError("Cannot create a submesh from a submesh.")
+
+            # The slice mesh_dim_names should consist either the device_mesh's mesh_dim_names
+            # or its flattened mesh's mesh_dim_names.
+            self.flatten_name_to_root_dims.setdefault(device_mesh, {})
+            flatten_name_to_root_dims = self.flatten_name_to_root_dims[device_mesh]
+            valid_mesh_dim_names = [
+                *device_mesh.mesh_dim_names,
+                *flatten_name_to_root_dims,
+            ]
+
+            if not all(
+                mesh_dim_name in valid_mesh_dim_names
+                for mesh_dim_name in mesh_dim_names
+            ):
+                raise KeyError(
+                    f"Invalid mesh_dim_names {mesh_dim_names} specified. "
+                    f"Valid mesh_dim_names are {valid_mesh_dim_names}."
+                )
+
+            # Validate the order of the slice mesh dim indices.
+            # This needs to be in ascending order.
+            curr_idx = -1
+            slice_mesh_dims = []
+            for mesh_dim_name in mesh_dim_names:
+                if mesh_dim_name in flatten_name_to_root_dims:
+                    mesh_indices = flatten_name_to_root_dims[mesh_dim_name]
+                    # TODO: this doesn't allow non-contiguous slicing with flatten dim yet. next_idx
+                    # should be mesh_indices[0] once we support non-contiguous slicing with flatten dim.
+                    next_idx = mesh_indices[-1]
+                    slice_mesh_dims.append(mesh_indices)
+                else:
+                    next_idx = device_mesh.mesh_dim_names.index(mesh_dim_name)
+                    slice_mesh_dims.append((next_idx,))
+                if next_idx <= curr_idx:
+                    raise KeyError(
+                        f"Invalid mesh_dim_names {mesh_dim_names} specified. ",
+                        f"Found mesh dim indices to slice: {slice_mesh_dims}. ",
+                        "Mesh dim indices should be in ascending order.",
+                    )
+                curr_idx = next_idx
+
+            return slice_mesh_dims
+
+        def _get_all_submeshes(
+            self, device_mesh: "DeviceMesh", mesh_dim_name: str
+        ) -> list["DeviceMesh"]:
+            """
+            Return all the submeshes of a given mesh dimension of the device mesh.
+            """
+            mesh_dim = self.get_mesh_dim_by_name(device_mesh, mesh_dim_name)
+            pg_ranks_by_dim = device_mesh.mesh.swapdims(-1, mesh_dim).reshape(
+                -1, device_mesh.mesh.size(mesh_dim)
+            )
+
+            cur_rank = device_mesh.get_rank()
+            res_submeshes = []
+            for mesh_1d in pg_ranks_by_dim:
+                submesh = DeviceMesh(
+                    device_mesh.device_type,
+                    mesh_1d,
+                    mesh_dim_names=(mesh_dim_name,),
+                    _init_backend=False,
+                )
+                submesh._dim_group_infos = (
+                    [device_mesh._dim_group_infos[mesh_dim]]
+                    if cur_rank in mesh_1d
+                    else []
+                )
+                res_submeshes.append(submesh)
+
+            return res_submeshes
+
+    _mesh_resources: _MeshEnv = _MeshEnv()
+
+    def _get_device_handle(device_type: str = "cuda"):
+        """
+        Get the module corresponding to the device_type which is cuda or cuda-like device.
+        For example, when the device_type is cuda, the module `torch.cuda` is returned.
+        Return None when there is no corresponding module for device_type, otherwise
+        return the corresponding module.
+        """
+        return getattr(torch, device_type, None)
+
+    class DeviceMesh:
+        """
+        DeviceMesh represents a mesh of devices, where layout of devices could be
+        represented as a n-d dimension array, and each value of the n-d dimensional
+        array is the global id of the default process group ranks.
+
+        DeviceMesh could be used to describe the layout of devices across the cluster,
+        and serves as a proxy for communication among the device lists within the cluster.
+
+        DeviceMesh can be used as a context manager.
+
+        .. note::
+            DeviceMesh follows SPMD programming model, which means the same PyTorch Python program
+            is running on all processes/ranks in the cluster. Therefore, users need to make sure the
+            `mesh` array (which describes the layout of devices) should be identical across all ranks.
+            Inconsistent `mesh` will lead to silent hang.
+
+        Args:
+            device_type (str): The device type of the mesh. Currently supports: "cpu", "cuda/cuda-like".
+            mesh (ndarray): A multi-dimensional array or an integer tensor describing the layout
+                of devices, where the IDs are global IDs of the default process group.
+
+        Returns:
+            DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
+
+        The following program runs on each process/rank in an SPMD manner. In this example, we have 2
+        hosts with 4 GPUs each.
+        A reduction over the first dimension of mesh will reduce across
+        columns (0, 4), .. and (3, 7), a reduction over the second dimension
+        of mesh reduces across rows (0, 1, 2, 3) and (4, 5, 6, 7).
+
+        Example::
+            >>> # xdoctest: +SKIP("no rank")
+            >>> from torch.distributed.device_mesh import DeviceMesh
+            >>>
+            >>> # Initialize device mesh as (2, 4) to represent the topology
+            >>> # of cross-host(dim 0), and within-host (dim 1).
+            >>> mesh = DeviceMesh(device_type="cuda", mesh=[[0, 1, 2, 3],[4, 5, 6, 7]])
+        """
+
+        device_type: str
+        mesh: torch.Tensor
+        mesh_dim_names: Optional[tuple[str, ...]]
+
+        def __init__(
+            self,
+            device_type: str,
+            mesh: Union[torch.Tensor, "ArrayLike"],
+            *,
+            mesh_dim_names: Optional[tuple[str, ...]] = None,
+            _init_backend: bool = True,
+        ) -> None:
+            self.device_type = device_type
+            if isinstance(mesh, torch.Tensor) and mesh.device.type != "cpu":
+                raise ValueError(f"`mesh` must be a CPU tensor, got {mesh}")
+            self.mesh = (
+                mesh.detach().to(dtype=torch.int)
+                if isinstance(mesh, torch.Tensor)
+                else torch.tensor(mesh, device="cpu", dtype=torch.int)
+            )
+            self.mesh_dim_names = tuple(mesh_dim_names) if mesh_dim_names else None
+
+            # private field to pre-generate DeviceMesh's hash
+            self._flatten_mesh_list = tuple(self.mesh.flatten().tolist())
+            self._thread_id = None
+
+            # Skip process group initialization if xla device or init backend is False
+            # TODO(yeounoh) implement DeviceMesh backend and register XLA backend.
+            if device_type != "xla":
+                # always try to create default (world) pg, even if it is not initialized
+                # already. The world pg is used for device mesh identity (rank) on each
+                # process (we need to know if the current global rank is in the mesh or not).
+                if _init_backend:
+                    self._get_or_create_default_group()
+                    self._init_process_groups()
+
+                if is_initialized() and get_backend() == "threaded":
+                    self._thread_id = threading.get_ident()
+
+                # calculate the coordinates of the current global rank on the mesh
+                rank_coords = (self.mesh == get_rank()).nonzero()
+                assert rank_coords.size(0) in (0, 1)
+                self._coordinate_on_dim: Optional[list[int]] = (
+                    rank_coords[0].tolist() if rank_coords.size(0) > 0 else None
+                )
+
+        def _get_or_create_default_group(self):
+            default_initialized = is_initialized()
+            if not default_initialized:
+                init_process_group()
+
+            world_size = get_world_size()
+            if self.mesh.numel() > world_size:
+                raise RuntimeError(
+                    f"Mesh should not be bigger than default world size {world_size}, but found {self.mesh.numel()} ranks!"
+                )
+
+            device_handle = _get_device_handle(self.device_type)
+            # TODO: if user want to pass pg_options, offer a way to do it
+            if not default_initialized and device_handle:
+                # automatically set the current cuda/cuda-like device base on num of gpu devices available in each host
+                # NOTE: This device selection would only work for homogeneous hardware.
+                num_devices_per_host = device_handle.device_count()
+                if (
+                    world_size > num_devices_per_host
+                    and world_size % num_devices_per_host != 0
+                ):
+                    raise RuntimeError(
+                        f"DeviceMesh only support homogeneous hardware, but found "
+                        f"{world_size} ranks and {num_devices_per_host} {self.device_type} devices!"
+                    )
+                device_handle.set_device(get_rank() % num_devices_per_host)
+
+            return _get_default_group()
+
+        def _init_process_groups(self):
+            # tag/ranks/group_name associated with each mesh dimension, each
+            # mesh dimension should have one sub-group per rank
+            #
+            # TODO(yifu): remove tag and ranks once we fully migrate to native
+            # functional collectives. See details in:
+            # https://github.com/pytorch/pytorch/issues/93173#issuecomment-1907095208
+            dim_group_infos: list[tuple[str, list[int], str]] = []
+            default_group = _get_default_group()
+
+            if self.mesh.ndim == 1 and self.mesh.numel() == get_world_size():
+                # Append the default pg to the first dim groups only if the default pg is compatible with `self.device_type`.
+                # Otherwise, create new pg.
+                ranks = list(range(get_world_size()))
+                dim_group = (
+                    new_group(
+                        backend="cpu:gloo,cuda:nccl",
+                        ranks=ranks,
+                        group_desc="mesh_default",
+                    )
+                    if torch.cuda.is_available()
+                    and get_backend(default_group) == "gloo"
+                    else default_group
+                )
+                dim_group_infos.append(
+                    (
+                        _get_group_tag(dim_group),
+                        ranks,
+                        dim_group.group_name,
+                    )
+                )
+            else:
+                # create sub pgs base on the mesh argument specified
+                for dim in range(self.mesh.ndim):
+                    # swap the current dim to the last dim
+                    # then reshape to flatten out other dims
+                    pg_ranks_by_dim = self.mesh.swapdims(-1, dim).reshape(
+                        -1, self.mesh.size(dim)
+                    )
+
+                    # Respect dim group options specified via _MeshEnv.set_dim_group_options().
+                    # Inherit from the parent group if no options are specified for the group.
+                    if dim in _mesh_resources.mesh_dim_group_options:
+                        (
+                            backend,
+                            pg_options,
+                        ) = _mesh_resources.mesh_dim_group_options[dim]
+                    else:
+                        backend, pg_options = None, None
+
+                    # If we have a 2D mesh with mesh_dim_names ("dp", "tp"), the group description
+                    # of the subgroups would be `mesh_dim_dp` and `mesh_name_tp`.
+                    # If the mesh doesn't not have a mesh_dim_names, then the group description of the
+                    # subgroup would be `mesh_dim_0` and `mesh_dim_1`.
+                    group_desc = (
+                        f"mesh_{self.mesh_dim_names[dim]}"
+                        if self.mesh_dim_names
+                        else f"mesh_dim_{dim}"
+                    )
+
+                    # If bound_device_id exists, it means the nccl communicator has been eagerly initialized
+                    # so that we can use `split_group` to create subgroups through `ncclCommSplit`.
+                    # In this case, we only need to make one API call (`split_group``) for the subgroup creation
+                    # for each mesh dimension. In a 2 * 4 mesh, we only need to make 2 API calls per ranks to create
+                    # all the subgroups.
+                    # Otherwise, we need to make more than one API call (`new_group`) for subgroup creations. The
+                    # numbers of API calls are equal to the number of subgroups for each mesh dimension. In a 2 * 4
+                    # mesh, we need to make 2 + 4 = 6 API calls per ranks to create all the subgroups.
+                    dim_group = None
+                    has_split_group = False
+                    if (
+                        bound_device_id := getattr(
+                            default_group, "bound_device_id", None
+                        )
+                    ) is not None and torch.cuda.is_available():
+                        dim_group = split_group(
+                            parent_pg=default_group,
+                            pg_options=pg_options,
+                            split_ranks=pg_ranks_by_dim.tolist(),
+                            group_desc=group_desc,
+                        )
+                        has_split_group = True
+
+                    # If the subgroup has been already created through `split_group`, we simply loop over `pg_ranks_by_dim`
+                    # and append the `(group_tag, subgroup_ranks, and group_name)` tuple to the `dim_group_infos` list when
+                    # the current rank is in the subgroup.
+                    # Otherwise, we use `new_group` instead of `split_group` to create subgroups by looping over `pg_ranks_by_dim`
+                    # along with appending information to the `dim_group_infos` list whenever necessary.
+                    for dim_mesh in pg_ranks_by_dim:
+                        subgroup_ranks = dim_mesh.tolist()
+
+                        # We temporarily revert the re-use subgroup, since it breaks two internal tests.
+                        # Temporarily reverting to resolve test timeout while root-causing.
+                        # TODO: Add two tests to cover internal tests scenarios and re-enable reuse subgroup if exists.
+                        if bound_device_id is None or not has_split_group:
+                            dim_group = new_group(
+                                ranks=subgroup_ranks,
+                                backend=backend,
+                                pg_options=pg_options,
+                                group_desc=group_desc,
+                            )
+
+                        # only add to dim_groups if the current rank in the subgroup
+                        if self.get_rank() in subgroup_ranks:
+                            if len(dim_group_infos) > dim:
+                                raise RuntimeError(
+                                    f"Each device mesh dimension should get only one process group, but got {self.get_rank()} "
+                                    f"in {subgroup_ranks}!"
+                                )
+                            dim_group_infos.append(
+                                (
+                                    _get_group_tag(not_none(dim_group)),
+                                    subgroup_ranks,
+                                    dim_group.group_name,
+                                )
+                            )
+            self._dim_group_infos = dim_group_infos
+
+        def __enter__(self) -> "DeviceMesh":
+            # set this mesh as the current mesh in mesh env
+            _mesh_resources.mesh_stack.append(self)
+            return self
+
+        # pyre-fixme[2]: Parameter must be annotated.
+        def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
+            # pop this mesh from mesh env
+            _mesh_resources.mesh_stack.pop()
+
+        def __repr__(self) -> str:
+            device_mesh_repr = (
+                f"DeviceMesh('{self.device_type}', {self.mesh.tolist()})"
+                if not self.mesh_dim_names
+                else f"DeviceMesh('{self.device_type}', {self.mesh.tolist()}, mesh_dim_names={self.mesh_dim_names})"
+            )
+            return device_mesh_repr
+
+        def __hash__(self):
+            # lazily compute hash
+            self._hash = getattr(self, "_hash", None)
+            if not self._hash:
+                self._hash = hash(
+                    (
+                        self._flatten_mesh_list,
+                        self.mesh.shape,
+                        self.device_type,
+                        self.mesh_dim_names,
+                        self._thread_id,
+                    )
+                )
+            return self._hash
+
+        def __eq__(self, other: object) -> bool:
+            if not isinstance(other, DeviceMesh):
+                return False
+            if id(self) == id(other):
+                return True
+            else:
+                return (
+                    self._flatten_mesh_list == other._flatten_mesh_list
+                    and self.mesh.shape == other.mesh.shape
+                    and self.device_type == other.device_type
+                    and self.mesh_dim_names == other.mesh_dim_names
+                    and self._thread_id == other._thread_id
+                )
+
+        def __getitem__(
+            self, mesh_dim_names: Union[str, tuple[str, ...]]
+        ) -> "DeviceMesh":
+            """
+            Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
+            The submesh created consists of the dimensions and the communicators indicated by
+            ``mesh_dim_names``
+
+            Args:
+                mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
+                mesh dimension of the DeviceMesh to create the submesh for.
+            Returns:
+                A :class:`DeviceMesh` object
+
+            The following program runs on each process/rank in an SPMD manner in a world size of 8.
+            In the first example:
+                Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
+                Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
+                Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
+                Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
+                Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
+                Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
+
+            In the second example:
+                Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
+                Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
+                Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
+                Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
+
+            Example::
+                >>> # xdoctest: +SKIP("no rank")
+                >>> from torch.distributed.device_mesh import DeviceMesh
+                >>>
+                >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
+                >>> # of cross-host(dim 0), and within-host (dim 1).
+                >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
+                >>> tp_mesh = mesh_2d["tp"]
+                >>> dp_mesh = mesh_2d["dp"]
+                >>>
+                >>> # Initialize a 3D mesh.
+                >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
+                >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
+                >>> dp_cp_mesh = mesh_3d["dp", "cp"]
+                >>> cp_dp_mesh = mesh_3d["cp", "dp"]
+            """
+            if not self.mesh_dim_names:
+                raise RuntimeError("Cannot slice a DeviceMesh without mesh_dim_names!")
+
+            mesh_dim_names = (
+                (mesh_dim_names,) if isinstance(mesh_dim_names, str) else mesh_dim_names
+            )
+
+            if mesh_dim_names == self.mesh_dim_names:
+                return self
+            else:
+                slice_mesh_dims = _mesh_resources._get_slice_mesh_dims(
+                    self, mesh_dim_names
+                )
+                # When using FakeTensorMode to trace the model, `create_sub_mesh()` will
+                # fail as it will require a real tensor to manipulate.
+                # `unset_fake_temporarily()` will allow us to materialize the tensors
+                # within `_mesh_resources`, which should not affect modling.
+                #
+                # Note that this should be orthogonal to torch.compile(). But whether
+                # we can compile device_mesh `slicing` (no graph break) is not verified
+                # yet and need a follow-up,
+                # TODO: compiler + device_mesh slicing.
+                with torch._subclasses.fake_tensor.unset_fake_temporarily():
+                    submesh = _mesh_resources.create_sub_mesh(
+                        self, mesh_dim_names, slice_mesh_dims
+                    )
+                return submesh
+
+        def get_group(self, mesh_dim: Optional[Union[int, str]] = None) -> ProcessGroup:
+            """
+            Returns the single ProcessGroup specified by mesh_dim, or, if mesh_dim is not specified and the
+            DeviceMesh is 1-dimensional, returns the only ProcessGroup in the mesh.
+
+            Args:
+                mesh_dim (str/int, optional): it can be the name of the mesh dimension or the index
+                of the mesh dimension. Default is None.
+
+            Returns:
+                A :class:`ProcessGroup` object.
+            """
+            if not hasattr(self, "_dim_group_infos"):
+                raise RuntimeError("DeviceMesh process groups not initialized!")
+
+            if self.mesh.ndim > 1 and mesh_dim is None:
+                raise RuntimeError(
+                    f"Found the DeviceMesh have {self.mesh.ndim} dimensions",
+                    "Optional kwarg `mesh_dim` needs to be specified when device_mesh.ndim > 1.",
+                    "If you want to get the list of all the ProcessGroups in the DeviceMesh,"
+                    "please use `get_all_groups()` instead.",
+                )
+
+            # Quick return if the current device_mesh is a 1D mesh.
+            if self.mesh.ndim == 1 and mesh_dim is None:
+                return not_none(
+                    _find_pg_by_ranks_and_tag(*self._dim_group_infos[0][:2])  # type: ignore[index]
+                )
+
+            root_mesh = _mesh_resources.get_root_mesh(self)
+            root_to_flatten_mapping = _mesh_resources.root_to_flatten_mapping.get(
+                root_mesh, None
+            )
+            if root_to_flatten_mapping and mesh_dim in root_to_flatten_mapping.keys():
+                dim_group_infos = root_to_flatten_mapping[
+                    mesh_dim  # type: ignore[index]
+                ]._dim_group_infos[0][:2]
+                return not_none(_find_pg_by_ranks_and_tag(*dim_group_infos))
+            else:
+                mesh_dim = (
+                    _mesh_resources.get_mesh_dim_by_name(self, mesh_dim)
+                    if isinstance(mesh_dim, str)
+                    else mesh_dim
+                )
+                return not_none(
+                    _find_pg_by_ranks_and_tag(*self._dim_group_infos[mesh_dim][:2])  # type: ignore[index]
+                )
+
+        def get_all_groups(self) -> list[ProcessGroup]:
+            """
+            Returns a list of ProcessGroups for all mesh dimensions.
+
+            Returns:
+                A list of :class:`ProcessGroup` object.
+            """
+            return [self.get_group(i) for i in range(self.mesh.ndim)]
+
+        @staticmethod
+        def from_group(
+            group: Union[ProcessGroup, list[ProcessGroup]],
+            device_type: str,
+            mesh: Optional[Union[torch.Tensor, "ArrayLike"]] = None,
+            *,
+            mesh_dim_names: Optional[tuple[str, ...]] = None,
+        ) -> "DeviceMesh":
+            """
+            Constructs a :class:`DeviceMesh` with ``device_type`` from an
+            existing :class:`ProcessGroup` or a list of existing :class:`ProcessGroup`.
+
+            The constructed device mesh has number of dimensions equal to the
+            number of groups passed. For example, if a single process group is passed in,
+            the resulted DeviceMesh is a 1D mesh. If a list of 2 process groups is passed in,
+            the resulted DeviceMesh is a 2D mesh.
+
+            If more than one group is passed, then the ``mesh`` and ``mesh_dim_names`` arguments
+            are required. The order of the process groups passed in determines the topology of
+            the mesh. For example, the first process group will be the 0th dimension of the DeviceMesh.
+            The `mesh` tensor passed in must have the same number of dimensions as the number of process
+            groups passed in, and the order of the dimensions in the `mesh` tensor must match the order
+            in the process groups passed in.
+
+            Args:
+                group (ProcessGroup or list[ProcessGroup]): the existing ProcessGroup
+                    or a list of existing ProcessGroups.
+                device_type (str): The device type of the mesh. Currently supports: "cpu",
+                    "cuda/cuda-like". Passing in a device type with a GPU index, such as "cuda:0",
+                    is not allowed.
+                mesh (torch.Tensor or ArrayLike, optional): A multi-dimensional array or an
+                    integer tensor describing the layout of devices, where the IDs are global IDs
+                    of the default process group. Default is None.
+                mesh_dim_names (tuple[str], optional): A tuple of mesh dimension names to assign
+                    to each dimension of the multi-dimensional array describing the layout of devices.
+                    Its length must match the length of `mesh_shape`. Each string in `mesh_dim_names`
+                    must be unique. Default is None.
+
+            Returns:
+                DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
+            """
+
+            # 1D scenario
+            if isinstance(group, ProcessGroup):
+                group_ranks = get_process_group_ranks(group)
+                if (
+                    isinstance(mesh, torch.Tensor) and mesh.tolist() != group_ranks
+                ) or (
+                    mesh is not None
+                    and not isinstance(mesh, torch.Tensor)
+                    and mesh != group_ranks
+                ):
+                    raise ValueError(
+                        f"Invalid mesh {str(mesh)} for ProcessGroup with ranks {group_ranks}"
+                    )
+                mesh = torch.tensor(group_ranks, device="cpu", dtype=torch.int)
+                device_mesh = DeviceMesh(
+                    device_type,
+                    mesh,
+                    mesh_dim_names=mesh_dim_names,
+                    _init_backend=False,
+                )
+                device_mesh._dim_group_infos = [
+                    (_get_group_tag(group), group_ranks, group.group_name)
+                ]
+                return device_mesh
+
+            # nD scenario
+            groups = list(group)
+            if len(groups) == 0:
+                raise ValueError("Expects at least one ProcessGroup to be passed")
+            if mesh is None:
+                raise ValueError("Must pass mesh if passing multiple ProcessGroups")
+            if mesh_dim_names is None:
+                raise ValueError(
+                    "Must pass mesh_dim_names if passing multiple ProcessGroups"
+                )
+            mesh = (
+                mesh.detach().to(dtype=torch.int, device="cpu")
+                if isinstance(mesh, torch.Tensor)
+                else torch.tensor(mesh, device="cpu", dtype=torch.int)
+            )
+            if mesh.ndim != len(groups):
+                raise ValueError(
+                    "Expects mesh with ndim equal to number of ProcessGroups but got "
+                    f"mesh {mesh.tolist()} and {len(groups)} ProcessGroups"
+                )
+            device_mesh = DeviceMesh(
+                device_type, mesh, mesh_dim_names=mesh_dim_names, _init_backend=False
+            )
+            device_mesh._dim_group_infos = [
+                (
+                    _get_group_tag(group),
+                    get_process_group_ranks(group),
+                    group.group_name,
+                )
+                for group in groups
+            ]
+            return device_mesh
+
+        def size(self, mesh_dim: Optional[int] = None) -> int:
+            return self.mesh.numel() if mesh_dim is None else self.mesh.size(mesh_dim)
+
+        @property
+        def ndim(self) -> int:
+            return self.mesh.ndim
+
+        @property
+        def shape(self) -> tuple[int, ...]:
+            return tuple(self.mesh.shape)
+
+        def get_rank(self) -> int:
+            """
+            Returns the current global rank.
+            """
+            return get_rank()
+
+        def get_local_rank(self, mesh_dim: Optional[Union[int, str]] = None) -> int:
+            """
+            Returns the local rank of the given mesh_dim of the DeviceMesh.
+
+            Args:
+                mesh_dim (str/int, optional): it can be the name of the mesh dimension or the index
+                of the mesh dimension. Default is None.
+
+            Returns:
+                An integer denotes the local rank.
+
+            The following program runs on each process/rank in an SPMD manner. In this example, we have 2
+            hosts with 4 GPUs each.
+            Calling mesh_2d.get_local_rank(mesh_dim=0) on rank 0, 1, 2, 3 would return 0.
+            Calling mesh_2d.get_local_rank(mesh_dim=0) on rank 4, 5, 6, 7 would return 1.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 0, 4 would return 0.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 1, 5 would return 1.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 2, 6 would return 2.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 3, 7 would return 3.
+
+            Example::
+                >>> # xdoctest: +SKIP("no rank")
+                >>> from torch.distributed.device_mesh import DeviceMesh
+                >>>
+                >>> # Initialize device mesh as (2, 4) to represent the topology
+                >>> # of cross-host(dim 0), and within-host (dim 1).
+                >>> mesh = DeviceMesh(device_type="cuda", mesh=[[0, 1, 2, 3],[4, 5, 6, 7]])
+            """
+            if self.ndim > 1 and mesh_dim is None:
+                raise RuntimeError(
+                    f"Found the DeviceMesh have {self.mesh.ndim} dimensions",
+                    "Optional kwarg `mesh_dim` needs to be specified when device_mesh.ndim > 1.",
+                )
+            elif mesh_dim is None:
+                mesh_dim = 0
+
+            mesh_dim_group = not_none(self.get_group(mesh_dim))
+            assert isinstance(mesh_dim_group, ProcessGroup), (
+                "We expect ProcessGroup before calling `get_rank`!"
+            )
+            return not_none(get_rank(mesh_dim_group))
+
+        def get_coordinate(self) -> Optional[list[int]]:
+            """
+            Return the relative indices of this rank relative to all
+            dimensions of the mesh. If this rank is not part of the mesh, return None.
+            """
+            return self._coordinate_on_dim if self._coordinate_on_dim else None
+
+        def _flatten(self, mesh_dim_name: Optional[str] = None) -> "DeviceMesh":
+            """
+            Returns a 1D DeviceMesh by flattening the current DeviceMesh.
+
+            If no mesh_dim_name is provided, the default is a string concatentaing the mesh_dim_names of the
+            given submesh with each mesh_dim_name separated by "_". For example, if we have a 3D mesh
+            DeviceMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], mesh_dim_names=("dp", "cp", "tp")), calling
+            mesh_3d["dp", "cp"]._flatten() will create a 1D submesh DeviceMesh([0, 1, 2, 3], mesh_dim_names=("dp_cp",))
+            on rank 0, 1, 2, 3 and a 1D submesh DeviceMesh([4, 5, 6, 7], mesh_dim_names=("dp_cp",)) on rank 4, 5, 6, 7.
+
+            After the flattened dimension is created, to access the flattened dimesnion in mesh_3d, one can use the
+            existing slicing method to obtain the flattened mesh through calling mesh_3d["dp_cp"].
+            """
+            if not self.mesh_dim_names:
+                raise RuntimeError(
+                    "Cannot flatten a DeviceMesh without mesh_dim_names!"
+                )
+
+            return _mesh_resources.create_flatten_mesh(self, mesh_dim_name)
+
+    def init_device_mesh(
+        device_type: str,
+        mesh_shape: tuple[int, ...],
+        *,
+        mesh_dim_names: Optional[tuple[str, ...]] = None,
+    ) -> DeviceMesh:
+        """
+        Initializes a `DeviceMesh` based on `device_type`, `mesh_shape`, and `mesh_dim_names` parameters.
+
+        This creates a DeviceMesh with an n-dimensional array layout, where `n` is the length of `mesh_shape`.
+        If `mesh_dim_names` is provided, each dimension is labeled as `mesh_dim_names[i]`.
+
+        .. note::
+            `init_device_mesh` follows SPMD programming model, meaning the same PyTorch Python program
+            runs on all processes/ranks in the cluster. Ensure `mesh_shape` (the dimensions of the nD array
+            describing device layout) is identical across all ranks. Inconsistent `mesh_shape` may lead to hanging.
+
+        .. note::
+            If no process group is found, init_device_mesh will initialize distributed process group/groups
+            required for distributed communications behind the scene.
+
+        Args:
+            device_type (str): The device type of the mesh. Currently supports: "cpu", "cuda/cuda-like".
+                Passing in a device type with a GPU index, such as "cuda:0", is not allowed.
+            mesh_shape (Tuple[int]): A tuple defining the dimensions of the multi-dimensional array
+                describing the layout of devices.
+            mesh_dim_names (Tuple[str], optional): A tuple of mesh dimension names to assign to each dimension
+                of the multi-dimensional array describing the layout of devices. Its length must match the length
+                of `mesh_shape`. Each string in `mesh_dim_names` must be unique.
+
+        Returns:
+            DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
+
+        Example::
+            >>> # xdoctest: +SKIP("no rank")
+            >>> from torch.distributed.device_mesh import init_device_mesh
+            >>>
+            >>> mesh_1d = init_device_mesh("cuda", mesh_shape=(8,))
+            >>> mesh_2d = init_device_mesh("cuda", mesh_shape=(2, 8), mesh_dim_names=("dp", "tp"))
+
+        """
+        if mesh_dim_names is not None:
+            if len(set(mesh_dim_names)) != len(mesh_dim_names):
+                raise RuntimeError(
+                    "Each mesh_dim_name must be unique.",
+                    f"Found repeated mesh_dim_name in mesh_dim_names {mesh_dim_names}",
+                )
+
+            if len(mesh_shape) != len(mesh_dim_names):
+                raise RuntimeError(
+                    "mesh_shape and mesh_dim_names should have same length!",
+                    f"Found len(mesh_dim_names): {len(mesh_dim_names)} and len(mesh_shape):{len(mesh_shape)}.",
+                )
+
+        # assume valid device types are all letters
+        if device_type and not device_type.isalpha():
+            raise RuntimeError(
+                f"Device type with index is not supported but got {device_type}. ",
+                "If you maintained a 'torch.device' object, it's recommended to pass in 'device.type'.",
+            )
+
+        # Always initialize the mesh's tensor on CPU, regardless of what the
+        # external device type has been set to be (e.g. meta)
+        with torch.device("cpu"):
+            mesh = torch.arange(math.prod(mesh_shape), dtype=torch.int).view(mesh_shape)
+        device_mesh = DeviceMesh(
+            device_type=device_type,
+            mesh=mesh,
+            mesh_dim_names=mesh_dim_names,
+        )
+
+        return device_mesh
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py
new file mode 100644
index 0000000000000000000000000000000000000000..547030cbd353fcc3c9cd2806760c2914e53418f3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py
@@ -0,0 +1,5461 @@
+# mypy: allow-untyped-defs
+"""Distributed Collective Communication (c10d)."""
+
+import collections.abc
+import contextlib
+import ctypes
+import hashlib
+import io
+import itertools
+import logging
+import os
+import pickle
+import sys
+import time
+import warnings
+from collections import namedtuple
+from datetime import timedelta
+from typing import Any, Callable, Optional, TYPE_CHECKING, Union
+from typing_extensions import deprecated
+
+import torch
+from torch._C import _DistStoreError as DistStoreError
+from torch._C._distributed_c10d import (
+    _DistributedBackendOptions,
+    _register_process_group,
+    _resolve_process_group,
+    _unregister_all_process_groups,
+    _unregister_process_group,
+    AllgatherOptions,
+    AllreduceCoalescedOptions,
+    AllreduceOptions,
+    AllToAllOptions,
+    BarrierOptions,
+    BroadcastOptions,
+    DebugLevel,
+    GatherOptions,
+    get_debug_level,
+    PrefixStore,
+    ProcessGroup,
+    ReduceOp,
+    ReduceOptions,
+    ReduceScatterOptions,
+    ScatterOptions,
+    Store,
+    Work,
+)
+from torch._utils_internal import set_pytorch_distributed_envs_from_justknobs
+from torch.monitor import _WaitCounter
+from torch.overrides import handle_torch_function, has_torch_function
+from torch.utils._typing_utils import not_none
+
+from .c10d_logger import _exception_logger, _time_logger
+from .constants import default_pg_nccl_timeout, default_pg_timeout
+from .rendezvous import register_rendezvous_handler, rendezvous  # noqa: F401
+
+
+__all__ = [
+    "Backend",
+    "BackendConfig",
+    "GroupMember",
+    "P2POp",
+    "all_gather",
+    "all_gather_coalesced",
+    "all_gather_object",
+    "all_reduce",
+    "all_reduce_coalesced",
+    "all_to_all",
+    "all_to_all_single",
+    "barrier",
+    "batch_isend_irecv",
+    "broadcast",
+    "send_object_list",
+    "recv_object_list",
+    "broadcast_object_list",
+    "destroy_process_group",
+    "gather",
+    "gather_object",
+    "get_backend_config",
+    "get_backend",
+    "get_default_backend_for_device",
+    "get_rank",
+    "get_world_size",
+    "get_pg_count",
+    "group",
+    "init_process_group",
+    "irecv",
+    "is_gloo_available",
+    "is_initialized",
+    "is_mpi_available",
+    "is_backend_available",
+    "is_nccl_available",
+    "is_torchelastic_launched",
+    "is_ucc_available",
+    "is_xccl_available",
+    "isend",
+    "monitored_barrier",
+    "new_group",
+    "new_subgroups",
+    "new_subgroups_by_enumeration",
+    "recv",
+    "reduce",
+    "reduce_scatter",
+    "scatter",
+    "scatter_object_list",
+    "send",
+    "supports_complex",
+    "AllreduceCoalescedOptions",
+    "AllreduceOptions",
+    "AllToAllOptions",
+    "BarrierOptions",
+    "BroadcastOptions",
+    "GatherOptions",
+    "PrefixStore",
+    "ProcessGroup",
+    "ReduceOp",
+    "ReduceOptions",
+    "ReduceScatterOptions",
+    "ScatterOptions",
+    "Store",
+    "DebugLevel",
+    "get_debug_level",
+    "Work",
+    "default_pg_timeout",
+    "get_group_rank",
+    "get_global_rank",
+    "get_process_group_ranks",
+    "reduce_op",
+    "all_gather_into_tensor",
+    "reduce_scatter_tensor",
+    "get_node_local_rank",
+    "split_group",
+]
+
+_MPI_AVAILABLE = True
+_NCCL_AVAILABLE = True
+_GLOO_AVAILABLE = True
+_UCC_AVAILABLE = True
+_XCCL_AVAILABLE = True
+
+_pickler = pickle.Pickler
+_unpickler = pickle.Unpickler
+
+
+# Change __module__ of all imported types from torch._C._distributed_c10d that are public
+def _export_c_types() -> None:
+    _public_types_to_change_module = [
+        AllreduceCoalescedOptions,
+        AllreduceOptions,
+        AllToAllOptions,
+        BarrierOptions,
+        BroadcastOptions,
+        GatherOptions,
+        PrefixStore,
+        ProcessGroup,
+        ReduceOp,
+        ReduceOptions,
+        ReduceScatterOptions,
+        ScatterOptions,
+        Store,
+        DebugLevel,
+        get_debug_level,
+        Work,
+    ]
+    for type in _public_types_to_change_module:
+        type.__module__ = "torch.distributed.distributed_c10d"
+
+
+_export_c_types()
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupMPI
+
+    ProcessGroupMPI.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupMPI"]
+except ImportError:
+    _MPI_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupNCCL
+
+    ProcessGroupNCCL.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupNCCL"]
+except ImportError:
+    _NCCL_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import _ProcessGroupWrapper, ProcessGroupGloo
+
+    ProcessGroupGloo.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupGloo"]
+except ImportError:
+    _GLOO_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupUCC
+
+    ProcessGroupUCC.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupUCC"]
+except ImportError:
+    _UCC_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupXCCL
+
+    ProcessGroupXCCL.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupXCCL"]
+except ImportError:
+    _XCCL_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+PG_WRAPPER_STORE_PREFIX = "pg_wrapper"
+
+
+# Some reduce ops are not supported by complex numbers and will result in an error.
+# We currently provide complex support to the distributed API by viewing
+# complex tensors as real (torch.view_as_real), meaning that calling
+# these unsupported ops will return garbage values rather than error out.
+# (e.g. max(2+3i, 3+2i) = 3+3i)
+# We'd like calls to unsupported ops to error out accordingly,
+# rather than returning garbage values.
+def supports_complex(reduceOp: ReduceOp) -> bool:
+    """Return true if reduce ops is supported. False otherwise."""
+    denyList = [
+        ReduceOp.MAX,
+        ReduceOp.MIN,
+        ReduceOp.PRODUCT,
+        ReduceOp.BAND,
+        ReduceOp.BOR,
+        ReduceOp.BXOR,
+    ]
+    return reduceOp not in denyList
+
+
+# TODO refactor into enum/strenum
+class Backend(str):  # noqa: SLOT000
+    """
+    An enum-like class for backends.
+
+    Available backends: GLOO, NCCL, UCC, MPI, XCCL, and other registered backends.
+
+    The values of this class are lowercase strings, e.g., ``"gloo"``. They can
+    be accessed as attributes, e.g., ``Backend.NCCL``.
+
+    This class can be directly called to parse the string, e.g.,
+    ``Backend(backend_str)`` will check if ``backend_str`` is valid, and
+    return the parsed lowercase string if so. It also accepts uppercase strings,
+    e.g., ``Backend("GLOO")`` returns ``"gloo"``.
+
+    .. note:: The entry ``Backend.UNDEFINED`` is present but only used as
+              initial value of some fields. Users should neither use it directly
+              nor assume its existence.
+    """
+
+    UNDEFINED = "undefined"
+    GLOO = "gloo"
+    NCCL = "nccl"
+    UCC = "ucc"
+    MPI = "mpi"
+    XCCL = "xccl"
+
+    _BackendPlugin = namedtuple("_BackendPlugin", ["creator_fn", "extended_api"])
+
+    _plugins: dict[str, _BackendPlugin] = {}
+
+    backend_list = [UNDEFINED, GLOO, NCCL, XCCL, UCC, MPI]
+
+    # 3rd-party devices can register the default backend support here
+    default_device_backend_map: dict[str, str] = {
+        "cpu": GLOO,
+        "cuda": NCCL,
+        "xpu": XCCL,
+    }
+
+    backend_capability: dict[str, list[str]] = {
+        GLOO: ["cpu", "cuda"],
+        NCCL: ["cuda"],
+        XCCL: ["xpu"],
+        UCC: ["cpu", "cuda"],
+        MPI: ["cpu", "cuda"],
+    }
+
+    backend_type_map: dict[str, ProcessGroup.BackendType] = {
+        UNDEFINED: ProcessGroup.BackendType.UNDEFINED,
+        GLOO: ProcessGroup.BackendType.GLOO,
+        NCCL: ProcessGroup.BackendType.NCCL,
+        XCCL: ProcessGroup.BackendType.XCCL,
+        UCC: ProcessGroup.BackendType.UCC,
+        MPI: ProcessGroup.BackendType.MPI,
+    }
+
+    def __new__(cls, name: str):
+        """Create and return a new instance of the class."""
+        if not isinstance(name, str):
+            raise ValueError("Backend constructor parameter must be string-ish")
+        value = getattr(Backend, name.upper(), Backend.UNDEFINED)
+
+        if value == Backend.UNDEFINED:
+            value = name.lower()
+        return value
+
+    @classmethod
+    def register_backend(
+        cls,
+        name,
+        func,
+        extended_api=False,
+        devices: Optional[Union[str, list[str]]] = None,
+    ) -> None:
+        """
+        Register a new backend with the given name and instantiating function.
+
+        This class method is used by 3rd party ``ProcessGroup`` extension to
+        register new backends.
+
+        Args:
+            name (str): Backend name of the ``ProcessGroup`` extension. It
+                        should match the one in ``init_process_group()``.
+            func (function): Function handler that instantiates the backend.
+                             The function should be implemented in the backend
+                             extension and takes four arguments, including
+                             ``store``, ``rank``, ``world_size``, and ``timeout``.
+            extended_api (bool, optional): Whether the backend supports extended argument structure.
+                                           Default: ``False``. If set to ``True``, the backend
+                                           will get an instance of ``c10d::DistributedBackendOptions``, and
+                                           a process group options object as defined by the backend implementation.
+            device (str or list of str, optional): device type this backend
+                            supports, e.g. "cpu", "cuda", etc. If `None`,
+                            assuming both "cpu" and "cuda"
+
+        .. note:: This support of 3rd party backend is experimental and subject to change.
+
+        """
+        # This takes care of CUSTOM Out-of-tree backend types, update in backend_list indicates availability
+        if not hasattr(Backend, name.upper()):
+            setattr(Backend, name.upper(), name.lower())
+        if name.lower() not in Backend.backend_list:
+            Backend.backend_list.append(name.lower())
+
+        if devices is not None:
+            for device in devices:
+                if device != "cpu" and device != "cuda":
+                    Backend.default_device_backend_map[device] = name.lower()
+        Backend.backend_type_map[name.lower()] = ProcessGroup.BackendType.CUSTOM
+
+        # Update device capability matrix in Backend class
+        if devices is None:
+            # This is more of a backward support for groups like `threaded`:
+            # assume default devices "cpu" and "cuda", but warn
+            warnings.warn(
+                f"Device capability of {name} unspecified, assuming `cpu` and "
+                "`cuda`. Please specify it via the `devices` argument of "
+                "`register_backend`."
+            )
+            Backend.backend_capability[name.lower()] = ["cpu", "cuda"]
+        elif isinstance(devices, str):
+            # Single device string specified. Simply convert to list.
+            Backend.backend_capability[name.lower()] = [devices]
+        else:
+            Backend.backend_capability[name.lower()] = devices
+
+        Backend._plugins[name.upper()] = Backend._BackendPlugin(func, extended_api)
+
+
+class BackendConfig:
+    """Backend configuration class."""
+
+    def __init__(self, backend: Backend):
+        """Init."""
+        self.device_backend_map: dict[str, Backend] = {}
+        backend = str(backend)
+
+        if backend == Backend.UNDEFINED:
+            # Detect the accelerator on the machine. If no accelerator is
+            # available, it returns CPU.
+            device_type = torch._C._get_accelerator().type
+            try:
+                backend_str = Backend.default_device_backend_map[device_type]
+                self.device_backend_map[device_type] = Backend(backend_str)
+            except KeyError:
+                raise ValueError(
+                    f"We detected accelerator {device_type} on your machine. "
+                    f"But we don't know which communication backend to use for this accelerator. "
+                    f"Please specify the `backend` argument in the `init_process_group` call."
+                ) from None
+        elif backend.lower() in Backend.backend_list:
+            # Cases for when backend is a single string (without device types)
+            # e.g. "nccl", "gloo", "ucc", "mpi"
+            supported_devices = Backend.backend_capability[backend.lower()]
+            backend_val = Backend(backend)
+            self.device_backend_map = dict.fromkeys(supported_devices, backend_val)
+        elif ":" in backend.lower():
+            # Backend specified in "device:backend" format
+            # make sure the backend string is in the correct format
+            # "{device_type1}:{backend1},{device_type2}:{backend2}"
+            # e.g. "cpu:gloo,cuda:nccl"
+            backend_str_error_message = f"""The custom backend string argument is invalid: {backend}.
+                Custom backend string is an experimental feature where the backend string must be in the format:
+                ":,:...". e.g. 'cpu:gloo,cuda:nccl'"""
+
+            # parse the backend string and populate the device_backend_map
+            for device_backend_pair_str in backend.lower().split(","):
+                device_backend_pair = device_backend_pair_str.split(":")
+                if len(device_backend_pair) != 2:
+                    raise ValueError(
+                        f"Invalid device:backend pairing: \
+                                     {device_backend_pair_str}. {backend_str_error_message}"
+                    )
+                device, backend = device_backend_pair
+                if device in self.device_backend_map:
+                    raise ValueError(
+                        f"Duplicate device type {device} \
+                                     in backend string: {backend}. {backend_str_error_message}"
+                    )
+                self.device_backend_map[device] = Backend(backend)
+        else:
+            # User specified a single backend name whose device capability is
+            # unknown, assuming it can support the default devices of PyTorch
+            # (cpu and cuda)
+            warnings.warn(
+                f"Device capability of {backend} unknown, assuming `cpu` and "
+                "`cuda`. You can specify it in `device:backend` format in "
+                "`init_process_group` call."
+            )
+            backend_val = Backend(backend)
+            self.device_backend_map = {
+                "cpu": backend_val,
+                "cuda": backend_val,
+                "xpu": backend_val,
+            }
+
+        logger.info("Using backend config: %s", self.device_backend_map)
+
+    def __repr__(self):
+        """Return all the device:backend pairs separated by commas."""
+        return ",".join(
+            f"{device}:{backend}" for device, backend in self.device_backend_map.items()
+        )
+
+    def get_device_backend_map(self) -> dict[str, Backend]:
+        """Return backend map of the device."""
+        return self.device_backend_map
+
+
+class _reduce_op:
+    r"""
+    Deprecated enum-like class.
+
+    For reduction operations: ``SUM``, ``PRODUCT``, ``MIN``, and ``MAX``.
+
+    :class:`~torch.distributed.ReduceOp` is recommended to use instead.
+    """
+
+    def __init__(self) -> None:
+        # __members__ is a dict storing key-value pairs for enum classes
+        for k, v in ReduceOp.RedOpType.__members__.items():
+            setattr(self, k, v)
+        self.__members__ = ReduceOp.RedOpType.__members__
+
+    @deprecated(
+        "`torch.distributed.reduce_op` is deprecated, "
+        "please use `torch.distributed.ReduceOp` instead",
+        category=FutureWarning,
+    )
+    def __getattribute__(self, key):
+        return object.__getattribute__(self, key)
+
+
+reduce_op = _reduce_op()
+
+
+class P2POp:
+    """
+    A class to build point-to-point operations for ``batch_isend_irecv``.
+
+    This class builds the type of P2P operation, communication buffer, peer rank,
+    Process Group, and tag. Instances of this class will be passed to
+    ``batch_isend_irecv`` for point-to-point communications.
+
+    Args:
+        op (Callable): A function to send data to or receive data from a peer process.
+            The type of ``op`` is either ``torch.distributed.isend`` or
+            ``torch.distributed.irecv``.
+        tensor (Tensor): Tensor to send or receive.
+        peer (int, optional): Destination or source rank.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match send with recv.
+        group_peer (int, optional): Destination or source rank.
+    """
+
+    def __init__(
+        self,
+        op: Callable,
+        tensor: torch.Tensor,
+        peer: Optional[int] = None,
+        group: Optional[ProcessGroup] = None,
+        tag: int = 0,
+        group_peer: Optional[int] = None,
+    ):
+        """Init."""
+        self.op = op
+        self.tensor = tensor
+        self.group = _group_or_default_group(group)
+        self.peer = _canonicalize_group_rank(
+            self.group, peer, group_peer, return_global=True
+        )
+        self.tag = tag
+        self.group_peer = _canonicalize_group_rank(self.group, peer, group_peer)
+
+    def __new__(
+        cls,
+        op: Callable,
+        tensor: torch.Tensor,
+        peer: Optional[int] = None,
+        group: Optional[ProcessGroup] = None,
+        tag: int = 0,
+        group_peer: Optional[int] = None,
+    ):
+        """Create and return a new instance of the class."""
+        _check_op(op)
+        _check_single_tensor(tensor, "tensor")
+
+        return object.__new__(cls)
+
+    def __repr__(self):
+        my_group_rank = get_rank(self.group)
+        op_name = self.op.__name__
+        group_name = self.group.group_name if self.group else "default_pg"
+        if "send" in op_name:
+            s = my_group_rank
+            d = self.group_peer
+        elif "recv" in op_name:
+            s = self.group_peer
+            d = my_group_rank
+        else:
+            return super().__repr__()
+
+        return f"P2POp({op_name} pg={group_name}, group_src={s}, group_dst={d},  {self.tensor.shape}, {self.tensor.dtype})"
+
+
+class _CollOp:
+    """
+    A class to capture collective operations.
+
+    Args:
+        op (Callable): A collective function, e.g. ``torch.distributed.all_reduce``.
+        tensor (Tensor): Tensor to operate on.
+        dst_tensor (Tensor, optional): Provided when source and destinaton tensors are not the same.
+        redop (ReduceOp, optional): reduce operation.
+        root (int, optional): root of broadcast or reduce.
+    """
+
+    def __init__(
+        self,
+        op: Callable,
+        tensor: torch.Tensor,
+        dst_tensor: Optional[torch.Tensor] = None,
+        redop: Optional[ReduceOp] = None,
+        root: Optional[int] = None,
+    ):
+        self.op = op
+        self.tensor = tensor
+        self.dst_tensor = dst_tensor
+        self.redop = redop
+        self.root = root
+
+
+# DO NOT USE THESE FIELDS DIRECTLY.
+# Use them through the _world object to make sure the _world override mechanism
+_pg_map: dict[ProcessGroup, tuple[str, Store]] = {}
+_pg_names: dict[ProcessGroup, str] = {}
+_pg_group_ranks: dict[ProcessGroup, dict[int, int]] = {}
+# For a pg, it is a map from ProcessGroup to BackendConfig
+_pg_backend_config: dict[ProcessGroup, str] = {}
+_group_count = 0
+_tags_to_pg: dict[str, list[ProcessGroup]] = {}
+_pg_to_tag: dict[ProcessGroup, str] = {}
+_backend: Optional[str] = None
+
+
+class _World:
+    """
+    Container class for c10d process group state.
+
+    This is used during registration and lookup of PG state.
+
+    .. warning:: This is an experimental API intended to expose the inner workings
+       of c10d and is subject to change..
+    """
+
+    def __init__(self) -> None:
+        self._default_pg = None
+        self._pg_coalesce_state: dict[ProcessGroup, list[_CollOp]] = {}
+
+    @property
+    def default_pg(self) -> Optional[ProcessGroup]:
+        """
+        Process group that includes all ranks of the cluster.
+
+        This default ProcessGroup is used by c10d APIs when a ProcessGroup is needed
+        but None is provided.
+        """
+        return self._default_pg
+
+    @default_pg.setter
+    def default_pg(self, value) -> None:
+        self._default_pg = value
+
+    @property
+    def pg_map(self) -> dict[ProcessGroup, tuple[str, Store]]:
+        """
+        Provide Mapping from ProcessGroup to backend name and store.
+
+        For NCCL and GLOO pg, it is a map from ProcessGroup to (Backend, Store)
+        For MPI pg, it is a map from ProcessGroup to (Backend, None)
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_map
+        return _pg_map
+
+    @property
+    def pg_names(self) -> dict[ProcessGroup, str]:
+        """
+        Process group's names, map from ProcessGroup to str.
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_names
+        return _pg_names
+
+    @property
+    def pg_group_ranks(self) -> dict[ProcessGroup, dict[int, int]]:
+        """
+        Process group's global rank to local rank mapping.
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_group_ranks
+        return _pg_group_ranks
+
+    @property
+    def pg_backend_config(self) -> dict[ProcessGroup, str]:
+        """
+        Process group's backend config.
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_backend_config
+        return _pg_backend_config
+
+    @property
+    def group_count(self) -> int:
+        """
+        Process group count for default naming.
+
+        TODO don't expose group_count, use something else instead
+        """
+        global _group_count
+        return _group_count
+
+    @group_count.setter
+    def group_count(self, value: int) -> None:
+        """Use to compute the name of ProcessGroups when using global synchronization."""
+        global _group_count
+        _group_count = value
+
+    @property
+    def tags_to_pg(self) -> dict[str, list[ProcessGroup]]:
+        global _tags_to_pg
+        return _tags_to_pg
+
+    @property
+    def pg_to_tag(self) -> dict[ProcessGroup, str]:
+        global _pg_to_tag
+        return _pg_to_tag
+
+    @property
+    def pg_coalesce_state(self) -> dict[ProcessGroup, list[_CollOp]]:
+        return self._pg_coalesce_state
+
+    @property
+    def pg_config_info(self) -> list[dict[str, Any]]:
+        """
+        Return a list of dict with process groups and backends.
+
+        Along with their unique IDs and configurations (types and ranks).
+        """
+        config_info: list[dict[str, Any]] = []
+        default_pg_size = _get_group_size(None)
+        for pg in self.pg_map.keys():
+            ranks = self.pg_group_ranks[pg]
+            config_info.append(
+                {
+                    "pg_name": self.pg_names[pg],
+                    "pg_desc": pg.group_desc,
+                    "backend_config": self.pg_backend_config[pg],
+                    "ranks": (
+                        list(ranks.keys()) if len(ranks) != default_pg_size else []
+                    ),  # 'ranks' is an empty list when all ranks are involved in a pg
+                    "group_size": len(ranks),
+                    "group_count": self.group_count,
+                }
+            )
+        return config_info
+
+
+_world = _World()
+"""Holds the singleton instance of ``_World`` used by c10. Experimental extension point to override it"""
+
+
+class _WorldMeta(type):
+    """
+    Meta class of ``group`` and ``GroupMember``.
+
+    Allows them to have the class property ``WORLD``.
+    """
+
+    # Points to the default PG once initialized.
+    @property
+    def WORLD(cls) -> Optional[ProcessGroup]:
+        return _world.default_pg
+
+    @WORLD.setter
+    def WORLD(cls, pg: Optional[ProcessGroup]):
+        _world.default_pg = pg
+
+
+class group(metaclass=_WorldMeta):
+    """Group class. Placeholder."""
+
+
+class GroupMember(metaclass=_WorldMeta):
+    """Group member class."""
+
+    NON_GROUP_MEMBER = -100
+
+
+def _get_default_timeout(backend: Backend) -> timedelta:
+    # see note on nccl vs other backend timeout (constants.py)
+    if backend == Backend.NCCL:
+        if not isinstance(default_pg_nccl_timeout, timedelta):
+            # TODO moco benchmark on CPU initializes pgnccl backend today, triggered this assert in CI before it was
+            # changed to be a warning.  We should fix the moco model.
+            warnings.warn(
+                "Attempted to get default timeout for nccl backend, but NCCL support is not compiled"
+            )
+            return default_pg_timeout
+        return default_pg_nccl_timeout
+    else:
+        return default_pg_timeout
+
+
+def _check_valid_timeout(timeout: Any) -> None:
+    if not isinstance(timeout, timedelta):
+        raise TypeError(
+            f"Expected timeout argument to be of type datetime.timedelta, got {timeout}"
+        )
+
+
+# Default process group state
+_default_pg_init_method: Optional[str] = None
+
+STORE_BASED_BARRIER_PREFIX = "store_based_barrier_key"
+
+
+def _get_object_coll_device(group: Optional[ProcessGroup] = None) -> str:
+    """
+    .. note:: This is an internal helper and does not have backward
+        compatibility, please use with caution.
+
+    Return the device type to use with ``group`` for object collectives or
+    barrier.
+
+    There are selection rules:
+        1. If user specifies exactly one backend in ``init_process_group`` call:
+            use that backend
+        2. Else if user specifies multiple "device:backend" pairs in init_process_group:
+            If "cpu" is among those pairs, use "cpu" (because the object is in cpu memory);
+            Otherwise, use the first backend (sort of a random pick).
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        str: The device type to use for object collective with ``group``.
+
+    """
+    group = group or _get_default_group()
+
+    if not isinstance(group, ProcessGroup):
+        warnings.warn(
+            f"You are using a Backend {type(group)} as a ProcessGroup. "
+            "This usage is deprecated since PyTorch 2.0. Please use a public API "
+            "of PyTorch Distributed instead.",
+        )
+        # Provide backward compatibility to cases where `group` passed in is
+        # actually a Backend (like `ProcessGroupGloo`) rather than a
+        # `ProcessGroup` in PT 2.0 sense
+        if isinstance(group, ProcessGroupGloo):
+            # RPC uses Gloo for object collectives
+            return "cpu"
+        else:
+            raise ValueError(f"Expecting a ProcessGroup, but got a {type(group)}.")
+
+    """
+    ``group._device_types`` is a property pybind that returns the devices
+    ("cpu", "cuda", etc) supported by ``group``. Can be multiple if the
+    ``group`` supports multiple devices.
+    """
+    devices = group._device_types
+
+    if len(devices) == 1:
+        # User fixed exactly one backend in `init_process_group`
+        return devices[0].type
+    elif len(devices) == 0:
+        # No backend has been registered with this PG (maybe because no
+        # collective has been run?) We pick cpu as the default and hopefully
+        # this would lazily init Gloo or other available cpu backend.
+        return "cpu"
+    elif torch.device("cpu") in devices:
+        # There are multiple backends in this PG and cpu is among them.
+        # cpu is preferred as the object is in cpu memory. No need for device
+        # copy.
+        return "cpu"
+    else:
+        # No cpu in the backend list. Randomly pick the first backend
+        return devices[0].type
+
+
+def _get_pg_default_device(group: Optional[ProcessGroup] = None) -> torch.device:
+    """
+    .. note:: This method will be deprecated, it only stays for
+        backward-compatiblity reason. Alternatives:
+
+        - If you need to find a device for object collectives, please use
+        `_get_object_coll_device(group)`.
+
+        - If you need to query the device types supported by group, please use
+        `_device_capability(group)`.
+
+    Return the device type registered with ``group``.
+
+    For example, if `init_process_group("nccl", ...)` was called, the returned
+    value would be `torch.device("cuda")`.
+
+    Errors out if no device has been registered.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        torch.device: The device type registered with ``group``.
+    """
+
+    warnings.warn(
+        "`_get_pg_default_device` will be deprecated, it only stays for "
+        "backward-compatiblity reason. If you need to find a device for object "
+        "collectives, please use `_get_object_coll_device`. If you need to query "
+        "the device types supported by group, please use "
+        "`_device_capability(group)`. "
+    )
+    group = group or _get_default_group()
+
+    if not isinstance(group, ProcessGroup):
+        # Provide backward compatibility to cases where `group` passed in is
+        # actually a Backend (like `ProcessGroupGloo`) rather than a
+        # `ProcessGroup` in PT 2.0 sense
+        warnings.warn(
+            f"You are using a Backend {type(group)} as a ProcessGroup. "
+            "This usage is deprecated since PyTorch 2.0. Please use a public API "
+            "of PyTorch Distributed instead.",
+            FutureWarning,
+            stacklevel=3,
+        )
+        # Most users create Gloo with private API for object collectives
+        return torch.device("cpu")
+
+    """
+    ``group._device_types`` is a property pybind that returns the devices
+    ("cpu", "cuda", etc) supported by ``group``. Can be multiple if the
+    ``group`` supports multiple devices.
+    """
+    devices = group._device_types
+
+    if len(devices) == 1:
+        # User fixed exactly one backend in `init_process_group`
+        return devices[0]
+    elif len(devices) == 0:
+        raise RuntimeError(
+            "Default device not found, because no backend has been registered "
+            "with this ProcessGroup."
+        )
+    else:
+        # There are multiple backends in this PG.
+        if torch.device("cpu") in devices:
+            rv = torch.device("cpu")
+        else:
+            rv = devices[0]
+        warnings.warn(
+            "Multiple backends are registered with this ProcessGroup. We cannot "
+            f"determine which one is the default. Returning {rv}. "
+            "Please consider using other APIs."
+        )
+        return rv
+
+
+def _device_capability(group: Optional[ProcessGroup] = None) -> list[str]:
+    """
+    Return the device type(s) supported by ``group``.
+
+    Args:
+        group (ProcessGroup, optional): The process group to query. If None,
+            the default process group will be used.
+
+    Returns:
+        List[str]: A list of device types supported by ``group``.
+    """
+    group = group or _get_default_group()
+    return [device.type for device in group._device_types]
+
+
+@_time_logger
+def _store_based_barrier(
+    rank,
+    store,
+    group_name,
+    rendezvous_count,
+    timeout,
+    logging_interval=timedelta(seconds=10),
+) -> None:
+    """
+    Store based barrier for synchronizing processes.
+
+    Barrier based on store which is used for synchronizing processes after
+    ``init_process_group`` or ``new_group``. Intended to be used only with
+    those two methods and is not a generic alternative to ``barrier()``.
+    """
+    store_key = f"{STORE_BASED_BARRIER_PREFIX}:{group_name}"
+    store.add(store_key, 1)
+    logger.debug("Added key: %s to store for rank: %s", store_key, rank)
+
+    # Now wait for all workers to check in with the store.
+    world_size = rendezvous_count
+    worker_count = store.add(store_key, 0)
+
+    last_worker_key = f"{store_key}:last_worker"
+    if worker_count == world_size:
+        store.set(last_worker_key, "1")
+
+    # adjust the timeout to be at least 10secs + 1sec per thousand ranks to reduce the odds of timeout
+    # this value was empirically found while scale testing.
+    logging_interval = max(logging_interval, timedelta(seconds=10 + world_size / 1000))
+
+    start = time.time()
+    while True:
+        try:
+            # This will throw an exception after the logging_interval in which we print out
+            # the status of the group or time out officially, throwing runtime error
+            store.wait([last_worker_key], logging_interval)
+            break
+        except RuntimeError as e:
+            worker_count = store.add(store_key, 0)
+            # Print status periodically to keep track.
+            logger.debug(
+                "Waiting in store based barrier to initialize process group for %s seconds"
+                "rank: %s, key: %s (world_size=%s, num_workers_joined=%s, timeout=%s error=%s)",
+                time.time() - start,
+                rank,
+                store_key,
+                world_size,
+                worker_count,
+                timeout,
+                e,
+            )
+
+            if timedelta(seconds=(time.time() - start)) > timeout:
+                raise DistStoreError(  # noqa: B904
+                    "Timed out initializing process group in store based barrier on "
+                    f"rank {rank}, for key: {store_key} (world_size={world_size}, "
+                    f"num_workers_joined={worker_count}, timeout={timeout} error={e})"
+                )
+
+    logger.info(
+        "Rank %s: Completed store-based barrier for key:%s with %s nodes.",
+        rank,
+        store_key,
+        world_size,
+    )
+
+
+def _rank_not_in_group(group: Optional[ProcessGroup]) -> bool:
+    """Check if the current process's rank is not in a given group."""
+    if group is None:
+        return False
+    return group == GroupMember.NON_GROUP_MEMBER
+
+
+def _warn_not_in_group(op_name) -> None:
+    global_rank = -1 if GroupMember.WORLD is None else GroupMember.WORLD.rank()
+    warnings.warn(
+        f"Running {op_name} on global rank {global_rank} which does not "
+        "belong to the given group."
+    )
+
+
+def get_group_rank(group: ProcessGroup, global_rank: int) -> int:
+    """
+    Translate a global rank into a group rank.
+
+    ``global_rank`` must be part of ``group`` otherwise this raises RuntimeError.
+
+    Args:
+        group (ProcessGroup): ProcessGroup to find the relative rank.
+        global_rank (int): Global rank to query.
+
+    Returns:
+        Group rank of ``global_rank`` relative to ``group``
+
+    N.B. calling this function on the default process group returns identity
+    """
+    if group is GroupMember.WORLD:
+        return global_rank
+    if group not in _world.pg_group_ranks:
+        raise ValueError(
+            f"Group {group} is not registered, please create group with torch.distributed.new_group API"
+        )
+    group_ranks = _world.pg_group_ranks[group]
+    if global_rank not in group_ranks:
+        raise ValueError(f"Global rank {global_rank} is not part of group {group}")
+
+    return group_ranks[global_rank]
+
+
+def get_global_rank(group: ProcessGroup, group_rank: int) -> int:
+    """
+    Translate a group rank into a global rank.
+
+    ``group_rank`` must be part of `group` otherwise this raises RuntimeError.
+
+    Args:
+        group (ProcessGroup): ProcessGroup to find the global rank from.
+        group_rank (int): Group rank to query.
+
+    Returns:
+        Global rank of ``group_rank`` relative to ``group``
+
+    N.B. calling this function on the default process group returns identity
+    """
+    if group is GroupMember.WORLD:
+        return group_rank
+    if group not in _world.pg_group_ranks:
+        raise ValueError(
+            f"Group {group} is not registered, please create group with torch.distributed.new_group API"
+        )
+    for rank, grp_rank in _world.pg_group_ranks[group].items():
+        if grp_rank == group_rank:
+            return rank
+    raise ValueError(f"Group rank {group_rank} is not part of group {group}")
+
+
+# TODO: remove this once the ecosystem moves away from it.
+@deprecated(
+    "`torch.distributed.distributed_c10d._get_global_rank` is deprecated, "
+    "please use `torch.distributed.distributed_c10d.get_global_rank` instead",
+    category=FutureWarning,
+)
+def _get_global_rank(group, rank) -> int:
+    """Use get_global_rank as this method is deprecated."""
+    return get_global_rank(group, rank)
+
+
+def get_process_group_ranks(group: ProcessGroup) -> list[int]:
+    """
+    Get all ranks associated with ``group``.
+
+    Args:
+        group (ProcessGroup): ProcessGroup to get all ranks from.
+
+    Returns:
+        List of global ranks ordered by group rank.
+    """
+    return list(_world.pg_group_ranks[group].keys())
+
+
+def _get_group_size(group) -> int:
+    """Get a given group's world size."""
+    if group is GroupMember.WORLD or group is None:
+        default_pg = _get_default_group()
+        return default_pg.size()
+    return group.size()
+
+
+def _get_group_size_by_name(group_name: str) -> int:
+    group = _resolve_process_group(group_name)
+    return group.size()
+
+
+def _resolve_group_name_by_ranks_and_tag(ranks: list[int], tag: str) -> str:
+    # TODO(yifu): remove this function once ranks + tag is not a supported
+    # identifier for process group for functional collectives.
+    group = _find_pg_by_ranks_and_tag(tag, ranks)
+    if group is None:
+        raise ValueError("")
+    return group.group_name
+
+
+def _check_single_tensor(param, param_name) -> None:
+    """Check that the parameter ``param_name`` is a single tensor."""
+    if not isinstance(param, torch.Tensor):
+        raise TypeError(
+            f"""Invalid function argument. Expected parameter `{param_name}` of type torch.Tensor
+             but got {type(param)} instead."""
+        )
+
+
+def _check_tensor_list(param, param_name) -> None:
+    """Check that the parameter ``param_name`` is a list of tensors."""
+    if not isinstance(param, list):
+        raise TypeError(
+            f"""Invalid function argument. Expected parameter `{param_name}` of type List[torch.Tensor]
+             but got {type(param)} instead."""
+        )
+    elif not all(isinstance(p, torch.Tensor) for p in param):
+        raise TypeError(
+            f"""Invalid function argument. Expected parameter `{param_name}` of type List[torch.Tensor]
+             but got {type(param)} with elements of type {[type(p) for p in param]}."""
+        )
+
+
+def _group_or_default_group(group: Optional[ProcessGroup] = None) -> ProcessGroup:
+    if group is None or group is GroupMember.WORLD:
+        group = _get_default_group()
+    return group
+
+
+def _canonicalize_group_rank(
+    group: ProcessGroup,
+    global_rank: Optional[int] = None,
+    group_rank: Optional[int] = None,
+    return_global: bool = False,
+) -> int:
+    """
+    Helper method to take _either_ a global rank or a group rank and produce a group rank.
+
+    If 'return_global' is true, produce a global rank instead of a group rank.
+    """
+
+    if group_rank is not None:
+        if global_rank is not None:
+            raise ValueError("Can't specify both group_rank and global_rank")
+        global_rank = get_global_rank(group, group_rank)
+    else:
+        if global_rank is None:
+            raise ValueError("Must specify global_rank or group_rank")
+        group_rank = get_group_rank(group, global_rank)
+    return global_rank if return_global else group_rank
+
+
+def _check_not_self_rank(group: ProcessGroup, rank: int, rank_type: str):
+    if group.rank() == rank:
+        raise ValueError(
+            f"Invalid {rank_type} rank: {rank_type} rank should not be the same as "
+            "the rank of the current process."
+        )
+
+
+def _as_iterable(obj) -> collections.abc.Iterable:
+    return obj if isinstance(obj, list) else (obj,)
+
+
+def _ensure_all_tensors_same_dtype(*tensors) -> None:
+    last_dtype = None
+    for tensor in itertools.chain.from_iterable(map(_as_iterable, tensors)):
+        tensor_dtype = tensor.dtype
+        # Mixing complex and its element type is allowed
+        if tensor_dtype.is_complex:
+            tensor_dtype = (
+                torch.float32 if tensor_dtype == torch.complex64 else torch.complex128
+            )
+
+        if last_dtype is None:
+            last_dtype = tensor_dtype
+        else:
+            if last_dtype != tensor_dtype:
+                raise ValueError(
+                    "Invalid usage of tensors with different dtypes"
+                    f"Found {last_dtype} and  {tensor.dtype}"
+                )
+
+
+def _check_op(op) -> None:
+    """Check that the ``op`` is either isend or irecv."""
+    if op not in [isend, irecv]:
+        raise ValueError(
+            "Invalid ``op``. Expected ``op`` "
+            "to be of type ``torch.distributed.isend`` or "
+            "``torch.distributed.irecv``."
+        )
+
+
+def _check_p2p_op_list(p2p_op_list) -> None:
+    """
+    Check that the ``p2p_op_list`` is a list of P2POp instances.
+
+    Also, check that all ops use the same group.
+    """
+    if not isinstance(p2p_op_list, list) or not all(
+        isinstance(p2p_op, P2POp) for p2p_op in p2p_op_list
+    ):
+        raise ValueError(
+            "Invalid ``p2p_op_list``. Each op is expected to "
+            "to be of type ``torch.distributed.P2POp``."
+        )
+
+    group = p2p_op_list[0].group
+    if not all(group == p2p_op.group for p2p_op in p2p_op_list):
+        raise ValueError("All ops need to use the same group.")
+
+
+def is_mpi_available() -> bool:
+    """Check if the MPI backend is available."""
+    return _MPI_AVAILABLE
+
+
+def is_nccl_available() -> bool:
+    """Check if the NCCL backend is available."""
+    return _NCCL_AVAILABLE
+
+
+def is_gloo_available() -> bool:
+    """Check if the Gloo backend is available."""
+    return _GLOO_AVAILABLE
+
+
+def is_ucc_available() -> bool:
+    """Check if the UCC backend is available."""
+    return _UCC_AVAILABLE
+
+
+def is_xccl_available() -> bool:
+    """Check if the XCCL backend is available."""
+    return _XCCL_AVAILABLE
+
+
+def is_backend_available(backend: str) -> bool:
+    """
+    Check backend availability.
+
+    Checks if the given backend is available and supports the built-in backends or
+    third-party backends through function ``Backend.register_backend``.
+
+    Args:
+        backend (str): Backend name.
+    Returns:
+        bool: Returns true if the backend is available otherwise false.
+    """
+    # If the backend has an ``is_backend_available`` function, return the result of that function directly
+    available_func = getattr(torch.distributed, f"is_{backend.lower()}_available", None)
+    if available_func:
+        return available_func()
+
+    return backend.lower() in Backend.backend_list
+
+
+def is_initialized() -> bool:
+    """Check if the default process group has been initialized."""
+    return GroupMember.WORLD is not None
+
+
+def is_torchelastic_launched() -> bool:
+    """
+    Check whether this process was launched with ``torch.distributed.elastic`` (aka torchelastic).
+
+    The existence of ``TORCHELASTIC_RUN_ID`` environment
+    variable is used as a proxy to determine whether the current process
+    was launched with torchelastic. This is a reasonable proxy since
+    ``TORCHELASTIC_RUN_ID`` maps to the rendezvous id which is always a
+    non-null value indicating the job id for peer discovery purposes..
+    """
+    return os.getenv("TORCHELASTIC_RUN_ID") is not None
+
+
+def _is_barrier_after_init() -> int:
+    # Environment variable to control whether process group should perform a
+    # barrier after its init. Default value is 0, i.e. no barrier. If you
+    # experience issue with this setting, you may set
+    # `TORCH_DIST_INIT_BARRIER=1` to add the barrier.
+    return int(os.getenv("TORCH_DIST_INIT_BARRIER", "0"))
+
+
+def _get_default_group() -> ProcessGroup:
+    """Get the default process group created by init_process_group."""
+    if not is_initialized():
+        raise ValueError(
+            "Default process group has not been initialized, "
+            "please make sure to call init_process_group."
+        )
+    if TYPE_CHECKING:
+        return not_none(GroupMember.WORLD)
+    else:
+        return GroupMember.WORLD
+
+
+def _get_default_store() -> Store:
+    """Get the default store created by init_process_group."""
+    if not is_initialized():
+        raise ValueError(
+            "Default process group has not been initialized, "
+            "please make sure to call init_process_group."
+        )
+    default_pg = _get_default_group()
+    _, default_store = _world.pg_map[default_pg]
+    return default_store
+
+
+def _update_default_pg(pg) -> None:
+    _world.default_pg = pg
+    rank = pg.rank() if pg is not None and pg != GroupMember.NON_GROUP_MEMBER else -1
+    torch._C._distributed_c10d._set_global_rank(rank)
+
+
+def get_backend_config(group: Optional[ProcessGroup] = None) -> str:
+    """
+    Return the backend configuration of the given process group.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. The
+            default is the general main process group. If another specific group
+            is specified, the calling process must be part of :attr:`group`.
+
+    Returns:
+        The backend configuration of the given process group as a lower case string.
+
+    """
+    pg = group or _get_default_group()
+    if _rank_not_in_group(pg):
+        raise ValueError("Invalid process group specified")
+    backend_config = _world.pg_backend_config.get(pg)
+    return str(not_none(backend_config))
+
+
+def get_backend(group: Optional[ProcessGroup] = None) -> Backend:
+    """
+    Return the backend of the given process group.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. The
+            default is the general main process group. If another specific group
+            is specified, the calling process must be part of :attr:`group`.
+
+    Returns:
+        The backend of the given process group as a lower case string.
+
+    """
+    pg = group or _get_default_group()
+    if _rank_not_in_group(pg):
+        raise ValueError("Invalid process group specified")
+    pg_store = _world.pg_map[pg] if pg in _world.pg_map else None
+    return Backend(not_none(pg_store)[0])
+
+
+def get_default_backend_for_device(device: Union[str, torch.device]) -> str:
+    """
+    Return the default backend for the given device.
+
+    Args:
+        Union[str, torch.device]: The device to get the default backend for.
+
+    Returns:
+        The default backend for the given device as a lower case string.
+
+    """
+    if isinstance(device, torch.device):
+        device_str = device.type
+    else:
+        device_str = torch.device(device).type
+
+    backend = Backend.default_device_backend_map.get(device_str)
+    if backend is None:
+        raise ValueError(f"Default backend not registered for device : {device}")
+
+    return backend
+
+
+def _get_process_group_uid(pg: ProcessGroup) -> int:
+    backend = None
+    try:
+        backend = pg._get_backend(torch.device("cuda"))
+    except RuntimeError:
+        pass
+    if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+        return backend.uid
+    return -1
+
+
+def _get_pg_config(group: Optional[ProcessGroup] = None) -> dict[str, Any]:
+    """
+    Return the pg configuration of the given process group.
+
+    """
+    pg = group or _get_default_group()
+    return {
+        "pg_name": _get_process_group_name(pg),
+        "pg_desc": pg.group_desc,
+        "backend_config": get_backend_config(pg),
+        "pg_size": _get_group_size(pg),
+        "ranks": get_process_group_ranks(pg),
+    }
+
+
+def _get_all_pg_configs() -> list[dict[str, Any]]:
+    """
+    Return the pg configuration of all the process groups.
+
+    """
+    config_info: list[dict[str, Any]] = [
+        _get_pg_config(pg) for pg in _world.pg_map.keys()
+    ]
+    return config_info
+
+
+def get_pg_count() -> int:
+    """
+    Return the number of process groups.
+
+    """
+    return _world.group_count
+
+
+def get_node_local_rank(fallback_rank: Optional[int] = None) -> int:
+    """
+    Return the local rank of the current process relative to the node.
+
+    Semantically, this is a useful concept for mapping processes to devices.
+    For example, on a node with 8 accelerator you could use the node local rank to decide
+    which accelerator device to bind the process to.
+
+    In practice, the actual assignment of node local ranks is handled by the process launcher outside of pytorch,
+    and communicated via the `LOCAL_RANK` environment variable.
+
+    Torchrun will automatically populate `LOCAL_RANK`, but other launchers may not.  If `LOCAL_RANK` is unspecified,
+    this API will fall back to the provided kwarg 'fallback_rank' if specified, otherwise it will raise an error. The
+    intent is to allow writing an application that runs either in single or multi device contexts without error.
+
+    """
+    if "LOCAL_RANK" in os.environ:
+        return int(os.environ["LOCAL_RANK"])
+    elif fallback_rank is not None:
+        return int(fallback_rank)
+    raise RuntimeError(
+        "LOCAL_RANK is not in the environment. Consider passing fallback_rank to allow `get_node_local_rank` to work, "
+        "assuming you are not running in a multi-device context and want the code to run locally instead."
+    )
+
+
+def _add_ephemeral_timeout_for_all_pgs(timeout: timedelta) -> None:
+    """
+    This API adds an ephemeral timeout extension for all PGs locally
+    on one rank. The timeout gets reset when the first collective issued
+    after API called finished.
+    NOTE: We only support to set timeout for cuda backends for now.
+    NOTE: While this feature
+    provides flexibility in specific scenarios, it introduces statefulness
+    to timeout setting. Therefore, it is advisable to use this API sparingly
+    and consider alternative approaches, such as directly setting the timeout
+    or utilizing a barrier collective (one can set any timeout to the barrier),
+    whenever feasible.
+
+    Args:
+        timeout (timedelta): The delta of timeout to extend.
+
+    Returns:
+        None.
+    """
+    for pg in _world.pg_map.keys():
+        devices = pg._device_types
+        if torch.device("cuda") in devices:
+            backend = pg._get_backend(torch.device("cuda"))
+            if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+                backend._add_ephemeral_timeout(timeout)
+
+
+def _set_pg_timeout(timeout: timedelta, group: Optional[ProcessGroup] = None) -> None:
+    """
+    Set the timeout for the given process group when users want to use a different timeout instead of
+    default values.
+
+    Args:
+        timeout (timedelta): Timeout for operations executed against the process group which
+            users want to set. Default value is 10 minutes for NCCL and 30 minutes for other backends.
+            This is the duration after which collectives will be aborted asynchronously and the process will crash.
+            This is done since CUDA execution is async and it is no longer safe to continue executing user code since
+            failed async NCCL operations might result in subsequent CUDA operations running on corrupted data.
+            When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout.
+
+        group (ProcessGroup, optional): The process group to work on. The
+            default is the general main process group. If another specific group
+            is specified, the calling process must be part of :attr:`group`.
+
+    Returns:
+        None
+    """
+    if group is None:
+        group = _get_default_group()
+    if _rank_not_in_group(group):
+        raise ValueError("Invalid process group specified")
+    assert isinstance(group, ProcessGroup)
+    devices = group._device_types
+    backends = set()
+    if torch.device("cpu") in devices and is_gloo_available():
+        backend = group._get_backend(torch.device("cpu"))
+        if isinstance(backend, ProcessGroupGloo):
+            backends.add(backend)
+    if torch.device("cuda") in devices:
+        backend = group._get_backend(torch.device("cuda"))
+        if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+            backends.add(backend)  # type: ignore[arg-type]
+        elif is_gloo_available() and isinstance(backend, ProcessGroupGloo):
+            backends.add(backend)  # type: ignore[arg-type]
+    if len(backends) == 0:
+        warnings.warn("Set timeout is now only supported for either nccl or gloo.")
+    for backend in backends:
+        backend._set_default_timeout(timeout)
+
+
+@_exception_logger
+@_time_logger
+def init_process_group(
+    backend: Optional[str] = None,
+    init_method: Optional[str] = None,
+    timeout: Optional[timedelta] = None,
+    world_size: int = -1,
+    rank: int = -1,
+    store: Optional[Store] = None,
+    group_name: str = "",
+    pg_options: Optional[Any] = None,
+    device_id: Optional[torch.device] = None,
+) -> None:
+    """
+    Initialize the default distributed process group.
+
+    This will also initialize the distributed package.
+
+    There are 2 main ways to initialize a process group:
+        1. Specify ``store``, ``rank``, and ``world_size`` explicitly.
+        2. Specify ``init_method`` (a URL string) which indicates where/how
+           to discover peers. Optionally specify ``rank`` and ``world_size``,
+           or encode all required parameters in the URL and omit them.
+
+    If neither is specified, ``init_method`` is assumed to be "env://".
+
+
+    Args:
+        backend (str or Backend, optional): The backend to use. Depending on
+            build-time configurations, valid values include ``mpi``, ``gloo``,
+            ``nccl``, ``ucc``, or one that is registered by a third-party
+            plugin.
+            Since 2.6, if ``backend`` is not provided, c10d will use a backend
+            registered for the device type indicated by the `device_id` kwarg
+            (if provided). The known default registrations today are: ``nccl``
+            for ``cuda``, ``gloo`` for ``cpu``.
+            If neither ``backend`` nor ``device_id`` is provided, c10d will
+            detect the accelerator on the run-time machine and use a backend
+            registered for that detected accelerator (or ``cpu``).
+            This field can be given as a lowercase string (e.g., ``"gloo"``),
+            which can also be accessed via :class:`Backend` attributes (e.g.,
+            ``Backend.GLOO``).
+            If using multiple processes per machine with ``nccl`` backend, each
+            process must have exclusive access to every GPU it uses, as sharing
+            GPUs between processes can result in deadlock or NCCL invalid usage.
+            ``ucc`` backend is experimental.
+        init_method (str, optional): URL specifying how to initialize the
+                                     process group. Default is "env://" if no
+                                     ``init_method`` or ``store`` is specified.
+                                     Mutually exclusive with ``store``.
+        world_size (int, optional): Number of processes participating in
+                                    the job. Required if ``store`` is specified.
+        rank (int, optional): Rank of the current process (it should be a
+                              number between 0 and ``world_size``-1).
+                              Required if ``store`` is specified.
+        store(Store, optional): Key/value store accessible to all workers, used
+                                to exchange connection/address information.
+                                Mutually exclusive with ``init_method``.
+        timeout (timedelta, optional): Timeout for operations executed against
+            the process group. Default value is 10 minutes for NCCL and 30 minutes for other backends.
+            This is the duration after which collectives will be aborted asynchronously and the process will crash.
+            This is done since CUDA execution is async and it is no longer safe to continue executing user code since
+            failed async NCCL operations might result in subsequent CUDA operations running on corrupted data.
+            When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout.
+
+        group_name (str, optional, deprecated): Group name. This argument is ignored
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. As of now, the only
+            options we support is ``ProcessGroupNCCL.Options`` for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            the nccl backend can pick up high priority cuda streams when
+            there're compute kernels waiting. For other availble options to config nccl,
+            See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-t
+        device_id (torch.device, optional): a single, specific device
+            to "bind" this process to, allowing for backend-specific
+            optimizations.  Currently this has two effects, only under
+            NCCL: the communicator is immediately formed (calling
+            ``ncclCommInit*`` immediately rather than the normal lazy
+            call) and sub-groups will use ``ncclCommSplit`` when
+            possible to avoid unnecessary overhead of group creation. If you
+            want to know NCCL initialization error early, you can also use this
+            field.
+
+    .. note:: To enable ``backend == Backend.MPI``, PyTorch needs to be built from source
+        on a system that supports MPI.
+
+    .. note:: Support for multiple backends is experimental. Currently when no backend is
+        specified, both ``gloo`` and ``nccl`` backends will be created. The ``gloo`` backend
+        will be used for collectives with CPU tensors and the ``nccl`` backend will be used
+        for collectives with CUDA tensors. A custom backend can be specified by passing in
+        a string with format ":,:", e.g.
+        "cpu:gloo,cuda:custom_backend".
+
+    """
+
+    global _world
+
+    global _backend
+    global _default_pg_init_method
+
+    if GroupMember.WORLD is not None:
+        raise ValueError("trying to initialize the default process group twice!")
+
+    set_pytorch_distributed_envs_from_justknobs()
+
+    # Depending on the import order, some trace_rules functions may be evaluated
+    # during the import phase. In such a case, these functions may not correctly
+    # add the distributed related rules due to import circular dependency.
+    # We need to clear the lru_cache during the runtime to ensure the correctness
+    # of these trace_rules.
+    #
+    # Since this API must be called before all distributed code being compiled,
+    # clearing the cache here should be safe.
+    if "torch._dynamo" in sys.modules:
+        torch._dynamo.trace_rules.clear_lru_cache()
+
+    assert (store is None) or (init_method is None), (
+        "Cannot specify both init_method and store."
+    )
+
+    if store is not None:
+        assert world_size > 0, "world_size must be positive if using store"
+        assert rank >= 0, "rank must be non-negative if using store"
+    elif init_method is None:
+        init_method = "env://"
+
+    # If user did not provide a backend string but provided a device id, e.g.
+    # >>> init_process_group(device_id=device)
+    # we try to figure out the backend name based on the device type.
+    if backend is None and device_id is not None:
+        # Note: 3rd-party devices can register default backend through the
+        # default map below.
+        backend = Backend.default_device_backend_map.get(device_id.type)
+
+    # If we still cannot figure it out, e.g.
+    # >>> init_process_group()
+    # we set it to `undefined` and rely on lazy init.
+    if backend is None:
+        backend = "undefined"
+
+    # Convert string into `Backend` type
+    backend = Backend(backend)
+
+    if timeout is None:
+        timeout = _get_default_timeout(backend)
+
+    _check_valid_timeout(timeout)
+
+    """
+    Group name is not visible to users unless they access
+    internals of c10d. This means we can ignore the value
+    they provide as it not exposed in a public way.
+    """
+    group_name = _process_group_name([], use_hashed_name=False)
+    if backend == Backend.MPI:
+        if world_size != -1 or rank != -1:
+            warnings.warn(
+                f"For MPI backend, world_size ({world_size}) and rank ({rank}) "
+                "are ignored since they are assigned by the "
+                "MPI runtime."
+            )
+
+        default_pg, _ = _new_process_group_helper(
+            -1,
+            -1,
+            [],
+            backend,
+            Store(),  # Placeholder value since store cannot be None
+            group_name,
+            timeout=timeout,
+            group_desc="default_pg",
+        )
+        _update_default_pg(default_pg)
+    else:
+        # backward compatible API
+        if store is None:
+            rendezvous_iterator = rendezvous(
+                not_none(init_method), rank, world_size, timeout=timeout
+            )
+            store, rank, world_size = next(rendezvous_iterator)
+            store.set_timeout(timeout)
+
+            # Use a PrefixStore to avoid accidental overrides of keys used by
+            # different systems (e.g. RPC) in case the store is multi-tenant.
+            store = PrefixStore("default_pg", store)
+
+        default_pg, _ = _new_process_group_helper(
+            world_size,
+            rank,
+            [],
+            backend,
+            store,
+            group_name,
+            backend_options=pg_options,
+            timeout=timeout,
+            device_id=device_id,
+            group_desc="default_pg",
+        )
+        _update_default_pg(default_pg)
+
+    _world.pg_group_ranks[GroupMember.WORLD] = {  # type: ignore[index]
+        i: i
+        for i in range(GroupMember.WORLD.size())  # type: ignore[attr-defined]
+    }
+    _backend = _world.pg_map[not_none(GroupMember.WORLD)][0]
+    _default_pg_init_method = init_method
+
+    old_hook = sys.excepthook
+    excepthook_prefix = f"[rank{get_rank()}]"
+
+    def _distributed_excepthook(*args):
+        old_stderr = sys.stderr
+        sys.stderr = buf = io.StringIO()
+        try:
+            old_hook(*args)
+        finally:
+            sys.stderr = old_stderr
+        msg = buf.getvalue()
+        msg = "\n".join(
+            f"{excepthook_prefix}: {s}" if s != "" else "" for s in msg.split("\n")
+        )
+        sys.stderr.write(msg)
+        sys.stderr.flush()
+
+    sys.excepthook = _distributed_excepthook
+
+    if _is_barrier_after_init() == 1:
+        # barrier at the end to ensure that once we return from this method, all
+        # process groups including global variables (if any) are updated
+        # correctly on all ranks.
+        # Update 04/2023: for large-scale runs, this barrier (esp. store-based
+        # barrier) may be costly and/or unscalable. Also, in a lot of cases,
+        # these barriers may be unnecessary, as proven by a green CI after
+        # removal. An environment variable `TORCH_DIST_INIT_BARRIER` has been
+        # added which enables this barrier only when set to 1.
+        logger.debug(
+            "Performing barrier after ProcessGroup initialization since "
+            "TORCH_DIST_INIT_BARRIER = 1"
+        )
+        if backend == Backend.MPI:
+            # MPI backend doesn't use store.
+            barrier()
+        else:
+            # Use store based barrier here since barrier() used a bunch of
+            # default devices and messes up NCCL internal state.
+            _store_based_barrier(rank, store, group_name, world_size, timeout)
+
+
+def _get_split_source(pg):
+    split_from = None
+    if pg.bound_device_id:
+        split_from = pg._get_backend(pg.bound_device_id)
+    elif pg is _world.default_pg:
+        try:
+            split_from = pg._get_backend(torch.device("cuda"))
+        except RuntimeError:
+            # no cuda device associated with this backend
+            pass
+
+    if not split_from or not split_from.supports_splitting:
+        return None
+
+    # If necessary, find a backend to split from by peeling process
+    # group wrappers from our potentially wrapped process group.
+    while _GLOO_AVAILABLE and isinstance(split_from, _ProcessGroupWrapper):
+        split_from = split_from.wrapped_pg
+
+    return split_from
+
+
+def _new_process_group_helper(
+    group_size,
+    group_rank,
+    global_ranks_in_group,
+    backend,
+    store,
+    group_name,
+    backend_options=None,
+    timeout=None,
+    pg_tag=None,
+    device_id=None,
+    group_desc=None,
+):
+    """
+    Create a new distributed process group.
+
+    This function must be called by ALL processes in the global group, even if
+    the calling process is not part of the newly created group. In that case,
+    this function returns GroupMember.NON_GROUP_MEMBER.
+
+    This function is called with ``global_ranks_in_group == []`` for the default group.
+    """
+    global _world
+
+    if group_name in _world.pg_names.values():
+        raise ValueError(
+            "The specified group name has already been "
+            "created, please use a different group name"
+        )
+
+    if device_id is not None and (device_id.index is None or device_id.type == "cpu"):
+        raise ValueError(
+            "init_process_group device_id parameter must be an accelerator with an index"
+        )
+
+    # Note: _new_process_group_helper is only called from init_process_group, which always provides a timeout value
+    _check_valid_timeout(timeout)
+
+    if pg_tag not in [None, ""]:
+        # creating with the same tag and rank set results in the same underlying PG
+        existing_group = _find_pg_by_ranks_and_tag(pg_tag, global_ranks_in_group)
+        if existing_group:
+            _, prefix_store = _world.pg_map[existing_group]
+            return existing_group, prefix_store
+
+    group_desc = "undefined" if group_desc is None else group_desc
+
+    # The list of group ranks is empty if we're creating the default group.
+    is_default_group = len(global_ranks_in_group) == 0
+
+    # nccl and potentially other backends allow creation of
+    # communicators based on pre-existing ones, which can save
+    # initialization time.  Due to lazy initialization of
+    # communicators in some backends, we have to be careful and only
+    # split when we *know* the default PG has already started communicator initialization.
+    # We know this if we have bound a device id to the default pg (eager initialized).
+    if is_initialized() and _get_default_group().bound_device_id:
+        split_from = _get_split_source(_get_default_group())
+    else:
+        split_from = None
+
+    # If this is a subgroup (which means group_ranks is specified),
+    # we check if the current process is a member of the new group.
+    if not is_default_group:
+        global_rank = _get_default_group().rank()
+        if global_rank not in global_ranks_in_group:
+            # If we are using `ncclCommSplit` (or similar split from
+            # other APIs) to create the communicator, we will need to
+            # call `ncclCommSplit` on *all* ranks in this new group's
+            # parent group, even those not in the new group.  This is
+            # a requirement of the NCCL API as otherwise we would get
+            # out of sync.
+            if split_from:
+                split_from.perform_nocolor_split(_get_default_group().bound_device_id)
+            return GroupMember.NON_GROUP_MEMBER, None
+
+    prefix_store = PrefixStore(f"{group_name}/", store)
+    # The backend for PG will be set later based on what's inside BackendConfig
+    # and timeout are set in each backend's option.
+    pg: ProcessGroup = ProcessGroup(
+        prefix_store,
+        group_rank,
+        group_size,
+    )
+    backend_config = BackendConfig(backend)
+    # Set the default backend when single backend is passed in.
+    if "," not in str(backend) and ":" not in str(backend):
+        assert backend in Backend.backend_type_map, f"Unknown backend type {backend}"
+        if backend == Backend.UNDEFINED:
+            # Currently when backend is UNDEFINED, both ``gloo`` and ``nccl`` backends
+            # will be created, we use nccl(if cuda is available) or gloo as default
+            # backend so we can correctly call getDefaultBackend which in ProcessGroup.
+            if Backend.NCCL in backend_config.get_device_backend_map().values():
+                pg._set_default_backend(ProcessGroup.BackendType.NCCL)
+            else:
+                pg._set_default_backend(ProcessGroup.BackendType.GLOO)
+        else:
+            pg._set_default_backend(Backend.backend_type_map[backend])
+    # In order to correctly call pg._has_hooks(), we should set the default backend
+    # when multi backend is passed in
+    else:
+        if Backend.NCCL in backend_config.device_backend_map.values():
+            pg._set_default_backend(ProcessGroup.BackendType.NCCL)
+        elif Backend._plugins.keys():
+            custom_backend = next(iter(Backend._plugins.keys()))
+            if custom_backend in backend_config.device_backend_map.values():
+                pg._set_default_backend(ProcessGroup.BackendType.CUSTOM)
+        else:
+            pg._set_default_backend(ProcessGroup.BackendType.GLOO)
+
+    if device_id:
+        pg.bound_device_id = device_id
+    backend_class: torch._C._distributed_c10d.Backend
+    for device, backend_str in backend_config.get_device_backend_map().items():
+        # Use the group name as prefix in the default store, such that
+        # a single store can be reused by multiple groups.
+        backend_prefix_store = PrefixStore(f"{device}/", prefix_store)
+
+        if backend_str == Backend.MPI:
+            if not is_mpi_available():
+                raise RuntimeError(
+                    "Distributed package doesn't have MPI built in."
+                    " MPI is only included if you build PyTorch from"
+                    " source on a host that has MPI installed."
+                )
+            backend_class = ProcessGroupMPI.create(global_ranks_in_group)
+            backend_type = ProcessGroup.BackendType.MPI
+            if not backend_class:
+                return GroupMember.NON_GROUP_MEMBER, None
+            # create new process group with accurate rank and size
+            if pg.rank() == -1 and pg.size() == -1:
+                pg = ProcessGroup(
+                    backend_prefix_store,
+                    backend_class.rank(),
+                    backend_class.size(),
+                )
+                pg._set_default_backend(backend_type)
+        elif backend_str == Backend.GLOO:
+            # TODO: remove this check after lazy initialization is supported
+            # if pg_options is not None:
+            #     raise RuntimeError("GLOO options not supported")
+            backend_class = ProcessGroupGloo(
+                backend_prefix_store, group_rank, group_size, timeout=timeout
+            )
+            backend_type = ProcessGroup.BackendType.GLOO
+        elif backend_str == Backend.NCCL:
+            if not is_nccl_available():
+                raise RuntimeError("Distributed package doesn't have NCCL built in")
+            if backend_options is not None:
+                assert isinstance(backend_options, ProcessGroupNCCL.Options), (
+                    "Expected backend_options argument to be of type ProcessGroupNCCL.Options"
+                )
+                if backend_options._timeout != timeout:
+                    warnings.warn(
+                        "backend_options._timeout was specified, "
+                        "but timeout kwarg has a default value that will always override it. "
+                    )
+            else:
+                # default backend_options for NCCL
+                backend_options = ProcessGroupNCCL.Options()
+                backend_options.is_high_priority_stream = False
+            backend_options._timeout = timeout
+
+            if split_from:
+                backend_options.split_from = split_from
+                backend_options.split_color = _process_group_color(
+                    global_ranks_in_group
+                )
+            backend_options.global_ranks_in_group = global_ranks_in_group
+            backend_options.group_name = group_name
+            backend_class = ProcessGroupNCCL(
+                backend_prefix_store, group_rank, group_size, backend_options
+            )
+            backend_type = ProcessGroup.BackendType.NCCL
+        elif backend_str == Backend.UCC and is_ucc_available():
+            # TODO: once UCC plugin is fully deprecated, remove
+            # is_ucc_available() from above elif-condition and raise
+            # RuntimeError if is_ucc_available() returns false.
+
+            backend_class = ProcessGroupUCC(
+                backend_prefix_store, group_rank, group_size, timeout=timeout
+            )
+            backend_type = ProcessGroup.BackendType.UCC
+        elif backend_str == Backend.XCCL:
+            if not is_xccl_available():
+                raise RuntimeError("Distributed package doesn't have XCCL built in")
+            backend_class = ProcessGroupXCCL(
+                backend_prefix_store, group_rank, group_size
+            )
+            backend_type = ProcessGroup.BackendType.XCCL
+        else:
+            assert backend_str.upper() in Backend._plugins, (
+                f"Unknown c10d backend type {backend_str.upper()}"
+            )
+
+            backend_plugin = Backend._plugins[backend_str.upper()]
+            creator_fn = backend_plugin.creator_fn
+            extended_api = backend_plugin.extended_api
+            backend_type = ProcessGroup.BackendType.CUSTOM
+
+            if not extended_api:
+                backend_class = creator_fn(
+                    backend_prefix_store, group_rank, group_size, timeout
+                )
+            else:
+                dist_backend_opts = _DistributedBackendOptions()
+                dist_backend_opts.store = backend_prefix_store
+                dist_backend_opts.group_rank = group_rank
+                dist_backend_opts.group_size = group_size
+                dist_backend_opts.timeout = timeout
+                dist_backend_opts.group_id = group_name
+                dist_backend_opts.global_ranks_in_group = global_ranks_in_group
+
+                backend_class = creator_fn(dist_backend_opts, backend_options)
+
+        # Set sequence numbers for gloo and nccl backends.
+        if backend_str == Backend.GLOO:
+            assert isinstance(backend_class, ProcessGroupGloo)
+            backend_class._set_sequence_number_for_group()
+        elif backend_str == Backend.NCCL:
+            assert isinstance(backend_class, ProcessGroupNCCL)
+            backend_class._set_sequence_number_for_group()
+
+        # If the type is a subclass of ProcessGroup then return this process group immediately
+        # TODO: This defaults to the old behavior for PythonProcessGroups which overwrites the
+        # ProcessGroup instance
+        if issubclass(type(backend_class), ProcessGroup):
+            pg = backend_class  # type: ignore[assignment]
+            break
+
+        # Process group wrapper initialization for supported PGs when TORCH_DISTRIBUTED_DEBUG is set
+        if (
+            backend_str in [Backend.GLOO, Backend.NCCL, Backend.UCC]
+            or backend_str.upper() in Backend._plugins
+        ):
+            # In debug mode and if GLOO is available, wrap in a wrapper PG that
+            # enables enhanced collective checking for debuggability.
+            if get_debug_level() == DebugLevel.DETAIL:
+                if not _GLOO_AVAILABLE:
+                    logger.info(
+                        """TORCH_DISTRIBUTED_DEBUG was set to DETAIL, but
+                                GLOO is not available. Build with Gloo to
+                                create a wrapper process group in debug mode
+                                to aid collective desynchronization debugging."""
+                    )
+                else:
+                    backend_class = _create_process_group_wrapper(
+                        wrapped_pg=backend_class,
+                        store_prefix=group_name,
+                        store=backend_prefix_store,
+                        rank=group_rank,
+                        world_size=group_size,
+                        timeout=timeout,
+                    )
+
+        # register only a single backend when all get_device_backend_map values are the same
+        if len(set(backend_config.get_device_backend_map().values())) == 1:
+            for device in backend_config.get_device_backend_map().keys():
+                pg._register_backend(torch.device(device), backend_type, backend_class)
+
+            # break out of outer loop to not create any more backends
+            break
+
+        pg._register_backend(torch.device(device), backend_type, backend_class)
+
+    # set group_name and group_dsec to backend
+    assert group_name is not None
+    assert group_desc is not None
+    pg._set_group_name(group_name)
+    pg._set_group_desc(group_desc)
+
+    if device_id and pg._get_backend(device_id).supports_splitting:
+        eager_backend = pg._get_backend(device_id)
+        eager_backend.eager_connect_single_device(device_id)
+
+    # update global state
+    _world.pg_map[pg] = (backend, prefix_store)
+    _world.pg_names[pg] = group_name
+    _register_process_group(group_name, pg)
+
+    _world.pg_backend_config[pg] = str(backend_config)
+    # "" is the default tag for user PGs
+    if pg_tag in [None, ""]:
+        pg_tag = f"ptd:{group_name}"
+        _world.tags_to_pg.setdefault("", []).append(pg)
+    else:
+        pg_tag = f"user:{pg_tag}"
+
+    _world.tags_to_pg.setdefault(pg_tag, []).append(pg)
+    _world.pg_to_tag[pg] = pg_tag
+    return pg, prefix_store
+
+
+def destroy_process_group(group: Optional[ProcessGroup] = None):
+    """
+    Destroy a given process group, and deinitialize the distributed package.
+
+    Args:
+        group (ProcessGroup, optional): The process group to be destroyed, if
+                                        group.WORLD is given, all process
+                                        groups including the default one will
+                                        be destroyed.
+    """
+    global _world
+
+    if group == GroupMember.NON_GROUP_MEMBER:
+        return
+
+    if group is None:
+        pg = GroupMember.WORLD
+    else:
+        pg = group
+
+    assert pg is not None
+    if _world.pg_map.get(pg, None) is None:
+        raise ValueError("Invalid process group specified")
+
+    # When users register Python onCompletion hooks, those hooks will run on a
+    # different thread than the main thread. Today, the ProcessGroup dtor does
+    # wait for that thread. However, the dtor might finish after the Python
+    # Interpreter exits. After that grabbing the GIL for the Python hook will crash.
+    # We can either revive the interpreter when running hooks or keep the main one
+    # alive until all works and hooks are done. The current implementation does the
+    # latter. Therefore, we explicitly call _wait_for_pending_works() here to wait
+    # for the pending hooks to finish.
+    if type(pg) == ProcessGroup and pg._has_hooks():
+        pg._wait_for_pending_works()
+
+    if group is None or group == GroupMember.WORLD:
+        # shutdown all backends in the order of pg names. shutting down in order because
+        # ncclCommAbort() was a 'collective' call in some versions of NCCL.
+        for pg_to_shutdown in sorted(
+            _world.pg_names, key=lambda x: _world.pg_names[x], reverse=True
+        ):
+            pg_to_shutdown.shutdown()
+
+        _update_default_pg(None)
+        _world.pg_map.clear()
+        _world.pg_names.clear()
+        _world.pg_group_ranks.clear()
+        _world.pg_backend_config.clear()
+        _world.pg_to_tag.clear()
+        _world.tags_to_pg.clear()
+        _world.pg_coalesce_state.clear()
+        _unregister_all_process_groups()
+
+        # when process group doesn't have an explicit name (only WORLD (default)
+        # process group can have an explicit name), we use global _world.group_count
+        # to generate the name. We need to reset the counter on destruction to
+        # allow consistent value to be generated when we re-create process
+        # groups after some trainers recover from failure
+        #
+        # We only reset this when WORLD is being destroyed because if this
+        # process group is in good state, we aren't dealing with failures.
+        _world.group_count = 0
+    else:
+        pg.shutdown()
+        del _world.pg_map[pg]
+        del _world.pg_names[pg]
+        del _world.pg_group_ranks[pg]
+        del _world.pg_backend_config[pg]
+        if pg in _world.pg_coalesce_state.keys():
+            warnings.warn(
+                "Some coalesced collectives haven't been launched when "
+                "ProcessGroup is destroyed. They will be cleaned."
+            )
+            del _world.pg_coalesce_state[pg]
+
+        tag = _world.pg_to_tag.get(pg)
+        del _world.pg_to_tag[pg]
+        if tag is not None:
+            try:
+                _world.tags_to_pg[tag].remove(pg)
+                if tag.startswith("ptd:"):
+                    _world.tags_to_pg[""].remove(pg)
+            except Exception:
+                pass
+        _unregister_process_group(pg.group_name)
+
+
+def _abort_process_group(group: Optional[ProcessGroup] = None):
+    """
+    Abort a given process group. If group.WORLD (i.e. `None`) is given, all
+    process groups including the default one will be aborted.
+
+    Args:
+        group (ProcessGroup, optional): The process group to be aborted.
+
+    .. note:: this API is experimental and currently only works with the NCCL
+        backend.
+
+    .. note:: this API should be used with `TORCH_NCCL_ASYNC_ERROR_HANDLING`
+        turned off (i.e. set to 0). Otherwise, ProcessGroupNCCL's watchdog may
+        automatically handle errors or timeouts for you including aborting the
+        ProcessGroup.
+    """
+    global _world
+
+    if group == GroupMember.NON_GROUP_MEMBER:
+        return
+
+    pg = group or GroupMember.WORLD
+
+    assert pg is not None
+    if _world.pg_map.get(pg, None) is None:
+        raise ValueError("Invalid process group specified or has been destroyed.")
+
+    try:
+        backend = pg._get_backend(torch.device("cuda"))
+    except RuntimeError:
+        backend = None
+
+    if group is None or group == GroupMember.WORLD:
+        # Abort all backends within a ncclGroupStart|End semantic.
+        # This ensures that different NCCL communicators' abort calls won't
+        # deadlock each other.
+        # For details, please see: https://github.com/pytorch/pytorch/issues/119797
+        if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+            backend._group_start()
+        for pg_to_abort in sorted(
+            _world.pg_names, key=lambda x: _world.pg_names[x], reverse=True
+        ):
+            pg_to_abort.abort()
+        if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+            backend._group_end()
+
+        _update_default_pg(None)
+        _world.pg_map.clear()
+        _world.pg_names.clear()
+        _world.pg_group_ranks.clear()
+        _world.pg_backend_config.clear()
+        _world.pg_to_tag.clear()
+        _world.tags_to_pg.clear()
+        _world.pg_coalesce_state.clear()
+        _unregister_all_process_groups()
+
+        # when process group doesn't have an explicit name (only WORLD (default)
+        # process group can have an explicit name), we use global _world.group_count
+        # to generate the name. We need to reset the counter on destruction to
+        # allow consistent value to be generated when we re-create process
+        # groups after some trainers recover from failure
+        #
+        # We only reset this when WORLD is being destroyed because if this
+        # process group is in good state, we aren't dealing with failures.
+        _world.group_count = 0
+    else:
+        pg.abort()
+        del _world.pg_map[pg]
+        del _world.pg_names[pg]
+        del _world.pg_group_ranks[pg]
+        del _world.pg_backend_config[pg]
+        if pg in _world.pg_coalesce_state.keys():
+            warnings.warn(
+                "Some coalesced collectives haven't been launched when "
+                "ProcessGroup is aborted. They will be cleaned."
+            )
+            del _world.pg_coalesce_state[pg]
+
+        tag = _world.pg_to_tag.get(pg)
+        del _world.pg_to_tag[pg]
+        if tag is not None:
+            try:
+                _world.tags_to_pg[tag].remove(pg)
+                if tag.startswith("ptd:"):
+                    _world.tags_to_pg[""].remove(pg)
+            except Exception:
+                pass
+        _unregister_process_group(pg.group_name)
+
+
+def get_rank(group: Optional[ProcessGroup] = None) -> int:
+    """
+    Return the rank of the current process in the provided ``group``, default otherwise.
+
+    Rank is a unique identifier assigned to each process within a distributed
+    process group. They are always consecutive integers ranging from 0 to
+    ``world_size``.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        The rank of the process group
+        -1, if not part of the group
+
+    """
+    if _rank_not_in_group(group):
+        return -1
+
+    default_pg = _get_default_group()
+    if group is None or group is GroupMember.WORLD:
+        return default_pg.rank()
+
+    return get_group_rank(group, default_pg.rank())
+
+
+def get_world_size(group: Optional[ProcessGroup] = None) -> int:
+    """
+    Return the number of processes in the current process group.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        The world size of the process group
+        -1, if not part of the group
+
+    """
+    if _rank_not_in_group(group):
+        return -1
+
+    return _get_group_size(group)
+
+
+def isend(
+    tensor: torch.Tensor,
+    dst: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    tag: int = 0,
+    group_dst: Optional[int] = None,
+) -> Optional[Work]:
+    """
+    Send a tensor asynchronously.
+
+    .. warning::
+        Modifying ``tensor`` before the request completes causes undefined
+        behavior.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Unlike send, which is blocking, isend allows src == dst rank, i.e. send to self.
+
+    Args:
+        tensor (Tensor): Tensor to send.
+        dst (int): Destination rank on global process group (regardless of ``group`` argument)
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match send with remote recv
+        group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
+
+    Returns:
+        A distributed request object.
+        None, if not part of the group
+
+    """
+    group = _group_or_default_group(group)
+    group_dst = _canonicalize_group_rank(group, dst, group_dst)
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("isend")
+        return None
+
+    if tensor.is_complex():
+        tensor = torch.view_as_real(tensor)
+
+    return group.send([tensor], group_dst, tag)
+
+
+def irecv(
+    tensor: torch.Tensor,
+    src: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    tag: int = 0,
+    group_src: Optional[int] = None,
+) -> Optional[Work]:
+    """
+    Receives a tensor asynchronously.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Unlike recv, which is blocking, irecv allows src == dst rank, i.e. recv from self.
+
+    Args:
+        tensor (Tensor): Tensor to fill with received data.
+        src (int, optional): Source rank on global process group (regardless of ``group`` argument).
+            Will receive from any process if unspecified.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match recv with remote send
+        group_src (int, optional): Destination rank on ``group``.  Invalid to specify both ``src`` and ``group_src``.
+
+    Returns:
+        A distributed request object.
+        None, if not part of the group
+
+    """
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("irecv")
+        return None
+
+    if tensor.is_complex():
+        tensor = torch.view_as_real(tensor)
+
+    group = _group_or_default_group(group)
+    if src is None and group_src is None:
+        return group.recv_anysource([tensor], tag)
+    else:
+        group_src = _canonicalize_group_rank(group, src, group_src)
+        return group.recv([tensor], group_src, tag)
+
+
+@_exception_logger
+def send(
+    tensor: torch.Tensor,
+    dst: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    tag: int = 0,
+    group_dst: Optional[int] = None,
+) -> None:
+    """
+    Send a tensor synchronously.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Args:
+        tensor (Tensor): Tensor to send.
+        dst (int): Destination rank on global process group (regardless of ``group`` argument).
+            Destination rank should not be the same as the rank of the current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match send with remote recv
+        group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``.
+
+    """
+    group = _group_or_default_group(group)
+    group_dst = _canonicalize_group_rank(group, dst, group_dst)
+    _check_not_self_rank(group, group_dst, "destination")
+    work = isend(tensor, group=group, tag=tag, group_dst=group_dst)
+    if work is not None:
+        work.wait()
+
+
+@_exception_logger
+def recv(
+    tensor: torch.Tensor,
+    src: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    tag: int = 0,
+    group_src: Optional[int] = None,
+) -> int:
+    """
+    Receives a tensor synchronously.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Args:
+        tensor (Tensor): Tensor to fill with received data.
+        src (int, optional): Source rank on global process group (regardless of ``group`` argument).
+            Will receive from any process if unspecified.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match recv with remote send
+        group_src (int, optional): Destination rank on ``group``.  Invalid to specify both ``src`` and ``group_src``.
+    Returns:
+        Sender rank
+        -1, if not part of the group
+
+    """
+    work = irecv(tensor, src=src, group=group, tag=tag, group_src=group_src)
+    if work is None:
+        return -1
+    work.wait()
+    if src is None:
+        if group_src is None:
+            group_src = work._source_rank()
+        group = _group_or_default_group(group)
+        _check_not_self_rank(group, group_src, "source")
+        src = get_global_rank(group, group_src)
+    return src
+
+
+class _IllegalWork(Work):
+    def __getattribute__(self, name):
+        if name in [
+            "is_success",
+            "exception",
+            "wait",
+            "source_rank",
+            "_source_rank",
+            "result",
+            "synchronize",
+        ]:
+            raise ValueError(f"Illegal to call {name} on IllegalWork object")
+
+
+class _CoalescingManager:
+    def __init__(self) -> None:
+        self.works: list[Work] = []
+
+    def append(self, work: Work):
+        if work:
+            self.works.append(work)
+
+    def wait(self):
+        for work in self.works:
+            work.wait()
+
+
+@contextlib.contextmanager
+def _coalescing_manager(
+    group: Optional[ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+    async_ops: Optional[bool] = False,
+):
+    """
+    Context manager used to coalesce collectives or P2P operations when possible.
+
+    Args:
+        group (`ProcessGroup`, optional): The process group to work on. If None,
+            the default process group will be used.
+        device (`torch.device`, optional): Default is None, set to a device if
+            there isn't a `**_coalesced` implementation by the backend.
+        async_ops (`bool`, optional): whether the coalesced ops are async ops.
+
+    Examples:
+        >>> # xdoctest: +SKIP("no rank")
+        >>> # Synchronous ops
+        >>> with _coalescing_manager():
+        >>>     for i in range(num_colls):
+        >>>         dist.all_reduce(tensors[i])
+        >>> # Asynchronous ops
+        >>> with _coalescing_manager(async_ops=True) as cm:
+        >>>     for i in range(num_colls):
+        >>>         dist.all_reduce(tensors[i])
+        >>> cm.wait()
+
+    .. warning::
+       :func:`_coalescing_manager` currently do not support coalescing
+       all-reduces with different reduce operators, e.g.  `ReduceOp.SUM` mixed
+       with `ReduceOp.PRODUCT`.
+    """
+    group = group or _get_default_group()
+    op_list = _world.pg_coalesce_state.setdefault(group, [])
+    if op_list:
+        raise ValueError(
+            "ProcessGroup has non-empty op list at the start of coalescing"
+        )
+    if device:
+        group._start_coalescing(device)
+    cm = _CoalescingManager()
+    yield cm
+    op_list = _world.pg_coalesce_state.pop(group)
+    if op_list:
+        # Collectives supporting "Fast Path" coalescing are captured.
+        # See implementation in corresponding collective APIs.
+        # Currently supported:
+        # - coalesced `all_reduce`
+        # - coalesced `all_gather_into_tensor`
+        # - coalesced `reduce_scatter_tensor`
+        op0 = op_list[0].op
+        if op0 == all_reduce:
+            tensors = [op.tensor for op in op_list]
+            all_reduce_opts = AllreduceCoalescedOptions()
+            all_reduce_opts.reduceOp = not_none(op_list[0].redop)
+            work = group.allreduce_coalesced(tensors, all_reduce_opts)
+        elif op0 == all_gather_into_tensor:
+            inputs = []
+            outputs = []
+            for op in op_list:
+                inputs.append(op.tensor)
+                outputs.append(not_none(op.dst_tensor))
+            work = group.allgather_into_tensor_coalesced(outputs, inputs)
+        elif op0 == reduce_scatter_tensor:
+            inputs = []
+            outputs = []
+            for op in op_list:
+                inputs.append(op.tensor)
+                outputs.append(not_none(op.dst_tensor))
+            reduce_opts = ReduceScatterOptions()
+            reduce_opts.reduceOp = not_none(op_list[0].redop)
+            work = group.reduce_scatter_tensor_coalesced(outputs, inputs, reduce_opts)
+        else:
+            raise AssertionError(
+                f"Coalescing manager does not support fast-path coalescing of {op0}, "
+                f"yet {op0} is still recorded in op list. This is an internal error of c10d."
+            )
+
+    if device:
+        # Old style of letting each coll inside the context manager to call into C++ counterpart via python binding
+        work = group._end_coalescing(device)
+
+    if async_ops:
+        cm.append(work)  # type: ignore[possibly-undefined]
+    else:
+        work.wait()  # type: ignore[possibly-undefined]
+
+
+def batch_isend_irecv(p2p_op_list: list[P2POp]) -> list[Work]:
+    """
+    Send or Receive a batch of tensors asynchronously and return a list of requests.
+
+    Process each of the operations in ``p2p_op_list`` and return the corresponding
+    requests. NCCL, Gloo, and UCC backend are currently supported.
+
+    Args:
+        p2p_op_list: A list of point-to-point operations(type of each operator is
+            ``torch.distributed.P2POp``). The order of the isend/irecv in the list
+            matters and it needs to match with corresponding isend/irecv on the
+            remote end.
+
+    Returns:
+        A list of distributed request objects returned by calling the corresponding
+        op in the op_list.
+
+    Examples:
+        >>> # xdoctest: +SKIP("no rank")
+        >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank
+        >>> recv_tensor = torch.randn(2, dtype=torch.float32)
+        >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size)
+        >>> recv_op = dist.P2POp(
+        ...     dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size
+        ... )
+        >>> reqs = batch_isend_irecv([send_op, recv_op])
+        >>> for req in reqs:
+        >>>     req.wait()
+        >>> recv_tensor
+        tensor([2, 3])     # Rank 0
+        tensor([0, 1])     # Rank 1
+
+    .. note:: Note that when this API is used with the NCCL PG backend, users must set
+        the current GPU device with `torch.cuda.set_device`, otherwise it will
+        lead to unexpected hang issues.
+
+        In addition, if this API is the first collective call in the ``group``
+        passed to ``dist.P2POp``, all ranks of the ``group`` must participate in
+        this API call; otherwise, the behavior is undefined. If this API call is
+        not the first collective call in the ``group``, batched P2P operations
+        involving only a subset of ranks of the ``group`` are allowed.
+    """
+    _check_p2p_op_list(p2p_op_list)
+    group = p2p_op_list[0].group
+    if group is None:
+        group = _get_default_group()
+    device = p2p_op_list[0].tensor.device
+
+    def peer_kwarg(op: P2POp) -> dict[str, int]:
+        key = "group_dst" if op.op == isend else "group_src"
+        return {key: op.group_peer}
+
+    if type(group) == ProcessGroup and group._get_backend(device).supports_coalescing:
+        # NCCL style coalescing
+        with _coalescing_manager(group, device, async_ops=True) as cm:
+            for p2p_op in p2p_op_list:
+                p2p_op.op(
+                    p2p_op.tensor,
+                    group=p2p_op.group,
+                    tag=p2p_op.tag,
+                    **peer_kwarg(p2p_op),
+                )
+
+        return cm.works
+    else:
+        # backend not support coalescing
+        reqs = []
+        for p2p_op in p2p_op_list:
+            work = p2p_op.op(
+                p2p_op.tensor,
+                group=p2p_op.group,
+                tag=p2p_op.tag,
+                **peer_kwarg(p2p_op),
+            )
+            if work:
+                reqs.append(work)
+        return reqs
+
+
+@_exception_logger
+def broadcast(
+    tensor: torch.Tensor,
+    src: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    async_op: bool = False,
+    group_src: Optional[int] = None,
+):
+    """
+    Broadcasts the tensor to the whole group.
+
+    ``tensor`` must have the same number of elements in all processes
+    participating in the collective.
+
+    Args:
+        tensor (Tensor): Data to be sent if ``src`` is the rank of current
+            process, and tensor to be used to save received data otherwise.
+        src (int): Source rank on global process group (regardless of ``group`` argument).
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        group_src (int): Source rank on ``group``.  Must specify one of ``group_src``
+            and ``src`` but not both.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    """
+    group = _group_or_default_group(group)
+    group_src = _canonicalize_group_rank(group, src, group_src, return_global=False)
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("broadcast")
+        return
+
+    opts = BroadcastOptions()
+    opts.rootRank = group_src
+    opts.rootTensor = 0
+    opts.asyncOp = async_op
+    work = group.broadcast([tensor], opts)
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def all_reduce(tensor, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    Reduces the tensor data across all machines in a way that all get the final result.
+
+    After the call ``tensor`` is going to be bitwise identical in all processes.
+
+    Complex tensors are supported.
+
+    Args:
+        tensor (Tensor): Input and output of the collective. The function
+            operates in-place.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Examples:
+        >>> # xdoctest: +SKIP("no rank")
+        >>> # All tensors below are of torch.int64 type.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> device = torch.device(f"cuda:{rank}")
+        >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
+        >>> tensor
+        tensor([1, 2], device='cuda:0') # Rank 0
+        tensor([3, 4], device='cuda:1') # Rank 1
+        >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
+        >>> tensor
+        tensor([4, 6], device='cuda:0') # Rank 0
+        tensor([4, 6], device='cuda:1') # Rank 1
+
+        >>> # All tensors below are of torch.cfloat type.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> tensor = torch.tensor(
+        ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
+        ... ) + 2 * rank * (1 + 1j)
+        >>> tensor
+        tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
+        tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
+        >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
+        >>> tensor
+        tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0
+        tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1
+
+    """
+    # Dynamo has built-in logic to map legacy distributed ops to functional collectives.
+    # Let's redirect to a torch function mode that can mimic this logic outside Dynamo
+    # (e.g., non-strict export implements such a torch function mode).
+    relevant_args = (tensor,)
+    if has_torch_function(relevant_args):
+        return handle_torch_function(
+            all_reduce,
+            relevant_args,
+            tensor,
+            op=op,
+            group=group,
+            async_op=async_op,
+        )
+
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_reduce")
+        return
+
+    if tensor.is_complex():
+        if not supports_complex(op):
+            raise ValueError(f"all_reduce does not support {op} on complex tensors")
+        tensor = torch.view_as_real(tensor)
+
+    opts = AllreduceOptions()
+    opts.reduceOp = op
+    if group is None:
+        group = _get_default_group()
+
+    if group in _world.pg_coalesce_state.keys():
+        # We are in coalescing context, do not issue single operation, just append a collective representation
+        coll = _CollOp(all_reduce, tensor, None, op, None)
+        _world.pg_coalesce_state[group].append(coll)
+        if async_op:
+            return _IllegalWork()
+        else:
+            return None
+
+    work = group.allreduce([tensor], opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+@deprecated(
+    "`torch.distributed.all_reduce_coalesced` will be deprecated. If you must "
+    "use it, please revisit our documentation later at "
+    "https://pytorch.org/docs/main/distributed.html#collective-functions",
+    category=FutureWarning,
+)
+def all_reduce_coalesced(tensors, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    WARNING: at this time individual shape checking is not implemented across nodes.
+
+    For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the
+    rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the allreduce
+    operation will proceed without complaint and return erroneous outputs. This lack
+    of shape checking results in significant performance improvements but users of this
+    function should take extra care to ensure that each node passes in tensors whose
+    shapes match across nodes.
+
+    Reduces each tensor in tensors (residing on the same device) across all machines
+    in such a way that all get the final result.
+
+    After the call each tensor in tensors is going to bitwise identical
+    in all processes.
+
+    Complex tensors are supported.
+
+    Args:
+        tensors (Union[List[Tensor], Tensor]): Input and output of the collective.
+            The function operates in-place.
+        op (Optional[ReduceOp]): One of the values from
+            ``torch.distributed.ReduceOp`` enum. Specifies an operation used for
+            element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (Optional[bool]): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    """
+    if isinstance(tensors, torch.Tensor):
+        tensors = [tensors]
+    _check_tensor_list(tensors, "tensor")
+    _ensure_all_tensors_same_dtype(tensors)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_reduce_coalesced")
+        return
+
+    if any(t.is_complex() for t in tensors) and not supports_complex(op):
+        raise ValueError(f"all_reduce does not support {op} on complex tensors")
+
+    tensors = [t if not t.is_complex() else torch.view_as_real(t) for t in tensors]
+
+    opts = AllreduceCoalescedOptions()
+    opts.reduceOp = op
+    group = group or _get_default_group()
+    work = group.allreduce_coalesced(tensors, opts)
+
+    if async_op:
+        return work.get_future()
+    else:
+        work.wait()
+
+
+@_exception_logger
+def reduce(
+    tensor: torch.Tensor,
+    dst: Optional[int] = None,
+    op=ReduceOp.SUM,
+    group: Optional[ProcessGroup] = None,
+    async_op: bool = False,
+    group_dst: Optional[int] = None,
+):
+    """
+    Reduces the tensor data across all machines.
+
+    Only the process with rank ``dst`` is going to receive the final result.
+
+    Args:
+        tensor (Tensor): Input and output of the collective. The function
+            operates in-place.
+        dst (int): Destination rank on global process group (regardless of ``group`` argument)
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        group_dst (int): Destination rank on ``group``.  Must specify one of ``group_dst``
+            and ``dst`` but not both.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    """
+    group = _group_or_default_group(group)
+    group_dst = _canonicalize_group_rank(group, dst, group_dst, return_global=False)
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("reduce")
+        return
+
+    opts = ReduceOptions()
+    opts.reduceOp = op
+    opts.rootRank = group_dst
+    work = group.reduce([tensor], opts)
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+def _object_to_tensor(obj, device, group):
+    with _WaitCounter("pytorch.wait_counter.c10d._object_to_tensor").guard():
+        f = io.BytesIO()
+        _pickler(f).dump(obj)
+        byte_storage = torch.ByteStorage._from_buffer(f.getvalue())  # type: ignore[attr-defined]
+        # Do not replace `torch.ByteTensor` or `torch.LongTensor` with torch.tensor and specifying dtype.
+        # Otherwise, it will casue 100X slowdown.
+        # See: https://github.com/pytorch/pytorch/issues/65696
+        byte_tensor = torch.ByteTensor(byte_storage).to(device)
+        if get_debug_level() == DebugLevel.DETAIL and is_nccl_available():
+            backend = get_backend(group)
+            if backend == Backend.NCCL:
+                hash = torch._C._distributed_c10d._hash_tensors([byte_tensor])
+                logger.warning(
+                    "_object_to_tensor size: %s hash value: %s",
+                    byte_tensor.numel(),
+                    hash,
+                )
+        local_size = torch.LongTensor([byte_tensor.numel()]).to(device)
+        return byte_tensor, local_size
+
+
+def _tensor_to_object(tensor, tensor_size, group):
+    with _WaitCounter("pytorch.wait_counter.c10d._tensor_to_object").guard():
+        if get_debug_level() == DebugLevel.DETAIL and is_nccl_available():
+            backend = get_backend(group)
+            if backend == Backend.NCCL:
+                hash = torch._C._distributed_c10d._hash_tensors([tensor])
+                logger.warning(
+                    "_tensor_to_object size: %s hash value: %s", tensor.numel(), hash
+                )
+        tensor = tensor.cpu()
+        buf = tensor.numpy().tobytes()[:tensor_size]
+        return _unpickler(io.BytesIO(buf)).load()
+
+
+@_exception_logger
+def all_gather_object(object_list, obj, group=None):
+    """
+    Gathers picklable objects from the whole group into a list.
+
+    Similar to :func:`all_gather`, but Python objects can be passed in.
+    Note that the object must be picklable in order to be gathered.
+
+    Args:
+        object_list (list[Any]): Output list. It should be correctly sized as the
+            size of the group for this collective and will contain the output.
+        obj (Any): Pickable Python object to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+
+    Returns:
+        None. If the calling rank is part of this group, the output of the
+        collective will be populated into the input ``object_list``. If the
+        calling rank is not part of the group, the passed in ``object_list`` will
+        be unmodified.
+
+    .. note:: Note that this API differs slightly from the :func:`all_gather`
+        collective since it does not provide an ``async_op`` handle and thus
+        will be a blocking call.
+
+    .. note:: For NCCL-based processed groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsiblity to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        :func:`all_gather_object` uses ``pickle`` module implicitly, which is
+        known to be insecure. It is possible to construct malicious pickle data
+        which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`all_gather_object` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`all_gather` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes world_size of 3.
+        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> output = [None for _ in gather_objects]
+        >>> dist.all_gather_object(output, gather_objects[dist.get_rank()])
+        >>> output
+        ['foo', 12, {1: 2}]
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather_object")
+        return
+
+    current_device = _get_object_coll_device(group)
+    input_tensor, local_size = _object_to_tensor(obj, current_device, group)
+
+    # Gather all local sizes. This is so that we can find the max size, and index
+    # until the correct size when deserializing the tensors.
+    group_size = get_world_size(group=group)
+    object_sizes_tensor = torch.zeros(
+        group_size, dtype=torch.long, device=current_device
+    )
+    object_size_list = [
+        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
+    ]
+    # Allgather tensor sizes
+    all_gather(object_size_list, local_size, group=group)
+    max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
+    # Resize tensor to max size across all ranks.
+    input_tensor.resize_(max_object_size)
+    coalesced_output_tensor = torch.empty(
+        max_object_size * group_size, dtype=torch.uint8, device=current_device
+    )
+    # Output tensors are nonoverlapping views of coalesced_output_tensor
+    output_tensors = [
+        coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
+        for i in range(group_size)
+    ]
+    all_gather(output_tensors, input_tensor, group=group)
+    # Deserialize outputs back to object.
+    for i, tensor in enumerate(output_tensors):
+        tensor = tensor.type(torch.uint8)
+        tensor_size = object_size_list[i]
+        object_list[i] = _tensor_to_object(tensor, tensor_size, group)
+
+
+@_exception_logger
+def gather_object(
+    obj: Any,
+    object_gather_list: Optional[list[Any]] = None,
+    dst: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    group_dst: Optional[int] = None,
+):
+    """
+    Gathers picklable objects from the whole group in a single process.
+
+    Similar to :func:`gather`, but Python objects can be passed in. Note that the
+    object must be picklable in order to be gathered.
+
+    Args:
+        obj (Any): Input object. Must be picklable.
+        object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
+            should be correctly sized as the size of the group for this
+            collective and will contain the output. Must be ``None`` on non-dst
+            ranks. (default is ``None``)
+        dst (int, optional): Destination rank on global process group (regardless of ``group`` argument).
+            (If both ``dst`` and ``group_dst`` are None, default is global rank 0)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
+
+    Returns:
+        None. On the ``dst`` rank, ``object_gather_list`` will contain the
+        output of the collective.
+
+    .. note:: Note that this API differs slightly from the gather collective
+        since it does not provide an async_op handle and thus will be a blocking
+        call.
+
+    .. note:: For NCCL-based processed groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsiblity to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        :func:`gather_object` uses ``pickle`` module implicitly, which is
+        known to be insecure. It is possible to construct malicious pickle data
+        which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`gather_object` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`gather` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes world_size of 3.
+        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> output = [None for _ in gather_objects]
+        >>> dist.gather_object(
+        ...     gather_objects[dist.get_rank()],
+        ...     output if dist.get_rank() == 0 else None,
+        ...     dst=0
+        ... )
+        >>> # On rank 0
+        >>> output
+        ['foo', 12, {1: 2}]
+    """
+    group = _group_or_default_group(group)
+    if dst is None and group_dst is None:
+        dst = 0
+    global_dst = _canonicalize_group_rank(group, dst, group_dst, return_global=True)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("gather_object")
+        return
+
+    # Ensure object_gather_list is specified appropriately.
+    my_global_rank = get_rank()
+    _validate_output_list_for_rank(my_global_rank, global_dst, object_gather_list)
+    current_device = _get_object_coll_device(group)
+    input_tensor, local_size = _object_to_tensor(obj, current_device, group)
+
+    # Gather all local sizes. This is so that we can find the max size, and index
+    # until the correct size when deserializing the tensors.
+    group_size = get_world_size(group=group)
+    object_sizes_tensor = torch.zeros(
+        group_size, dtype=torch.long, device=current_device
+    )
+    object_size_list = [
+        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
+    ]
+    # Allgather tensor sizes. An all-gather is needed here despite this being a
+    # gather, since each rank needs to broadcast a tensor of the same (maximal)
+    # size.
+    all_gather(object_size_list, local_size, group=group)
+    max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
+    # Resize tensor to max size across all ranks.
+    input_tensor.resize_(max_object_size)
+    # Avoid populating output tensors if the result won't be gathered on this rank.
+    if my_global_rank == global_dst:
+        coalesced_output_tensor = torch.empty(
+            max_object_size * group_size, dtype=torch.uint8, device=current_device
+        )
+        # Output tensors are nonoverlapping views of coalesced_output_tensor
+        output_tensors = [
+            coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
+            for i in range(group_size)
+        ]
+    # All ranks call gather with equal-sized tensors.
+    gather(
+        input_tensor,
+        gather_list=output_tensors if my_global_rank == global_dst else None,  # type: ignore[possibly-undefined]
+        dst=global_dst,
+        group=group,
+    )
+    if my_global_rank != global_dst:
+        return
+
+    assert object_gather_list is not None, "Must provide object_gather_list on dst rank"
+    for i, tensor in enumerate(output_tensors):
+        tensor = tensor.type(torch.uint8)
+        tensor_size = object_size_list[i]
+        object_gather_list[i] = _tensor_to_object(tensor, tensor_size, group)
+
+
+@_exception_logger
+def send_object_list(
+    object_list: list[Any],
+    dst: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+    group_dst: Optional[int] = None,
+):
+    """
+    Sends picklable objects in ``object_list`` synchronously.
+
+    Similar to :func:`send`, but Python objects can be passed in.
+    Note that all objects in ``object_list`` must be picklable in order to be
+    sent.
+
+    Args:
+        object_list (List[Any]): List of input objects to sent.
+            Each object must be picklable. Receiver must provide lists of equal sizes.
+        dst (int): Destination rank to send ``object_list`` to.
+            Destination rank is based on global process group (regardless of ``group`` argument)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        device (``torch.device``, optional): If not None, the objects are
+            serialized and converted to tensors which are moved to the
+            ``device`` before sending. Default is ``None``.
+        group_dst (int, optional): Destination rank on ``group``.
+            Must specify one of ``dst`` and ``group_dst`` but not both
+    Returns:
+        ``None``.
+
+    .. note:: For NCCL-based process groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        :func:`send_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`send_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`send` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes backend is not NCCL
+        >>> device = torch.device("cpu")
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 2.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>>     dist.send_object_list(objects, dst=1, device=device)
+        >>> else:
+        >>>     objects = [None, None, None]
+        >>>     dist.recv_object_list(objects, src=0, device=device)
+        >>> objects
+        ['foo', 12, {1: 2}]
+    """
+    group = _group_or_default_group(group)
+    group_dst = _canonicalize_group_rank(group, dst, group_dst)
+    _check_not_self_rank(group, group_dst, "destination")
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("send_object_list")
+        return
+
+    # Current device selection.
+    # To preserve backwards compatibility, ``device`` is default to ``None``
+    # in which case we run current logic of device selection, i.e.
+    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
+    # case it is not ``None`` we move the size and object tensors to be
+    # sent to this device.
+    current_device = device or _get_object_coll_device(group)
+    # Serialize object_list elements to tensors on src rank.
+    tensor_list, size_list = zip(
+        *[_object_to_tensor(obj, current_device, group) for obj in object_list]
+    )
+    object_sizes_tensor = torch.cat(size_list)
+
+    # Send object sizes
+    send(object_sizes_tensor, group_dst=group_dst, group=group)
+
+    # Concatenate and send serialized object tensors
+    # Note: torch.cat will do an extra memory copy to the current device, if the tensor_list
+    # has only one element, we can skip the copy.
+    if len(tensor_list) == 1:  # type: ignore[possibly-undefined]
+        object_tensor = tensor_list[0]
+    else:
+        object_tensor = torch.cat(tensor_list)
+
+    send(object_tensor, group_dst=group_dst, group=group)
+
+
+@_exception_logger
+def recv_object_list(
+    object_list: list[Any],
+    src: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+    group_src: Optional[int] = None,
+):
+    """
+    Receives picklable objects in ``object_list`` synchronously.
+
+    Similar to :func:`recv`, but can receive Python objects.
+
+    Args:
+        object_list (List[Any]): List of objects to receive into.
+            Must provide a list of sizes equal to the size of the list being sent.
+        src (int, optional): Source rank from which to recv ``object_list``.
+            Source rank is based on global process group (regardless of ``group`` argument)
+            Will receive from any rank if set to None. Default is ``None``.
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        device (``torch.device``, optional): If not None, receives on this device.
+            Default is ``None``.
+        group_src (int, optional): Destination rank on ``group``.  Invalid to specify both ``src`` and ``group_src``.
+
+    Returns:
+        Sender rank. -1 if rank is not part of the group. If rank is part of the group,
+        ``object_list`` will contain the sent objects from ``src`` rank.
+
+    .. note:: For NCCL-based process groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        :func:`recv_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`recv_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`recv` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes backend is not NCCL
+        >>> device = torch.device("cpu")
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 2.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>>     dist.send_object_list(objects, dst=1, device=device)
+        >>> else:
+        >>>     objects = [None, None, None]
+        >>>     dist.recv_object_list(objects, src=0, device=device)
+        >>> objects
+        ['foo', 12, {1: 2}]
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("recv_object_list")
+        return -1
+
+    # Current device selection.
+    # To preserve backwards compatibility, ``device`` is default to ``None``
+    # in which case we run current logic of device selection, i.e.
+    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
+    # case it is not ``None`` we move the size and object tensors to be
+    # received to this device.
+    current_device = device or _get_object_coll_device(group)
+    object_sizes_tensor = torch.empty(
+        len(object_list), dtype=torch.long, device=current_device
+    )
+
+    # Receive object sizes
+    rank_sizes = recv(object_sizes_tensor, src=src, group=group, group_src=group_src)
+
+    # Tensor to receive serialized objects into.
+    object_tensor = torch.empty(  # type: ignore[call-overload]
+        torch.sum(object_sizes_tensor).item(),  # type: ignore[arg-type]
+        dtype=torch.uint8,
+        device=current_device,
+    )
+
+    rank_objects = recv(object_tensor, src=src, group=group, group_src=group_src)
+    assert rank_sizes == rank_objects, (
+        "Mismatch in return ranks for object sizes and objects."
+    )
+    # Deserialize objects using their stored sizes.
+    offset = 0
+    for i, obj_size in enumerate(object_sizes_tensor):
+        obj_view = object_tensor[offset : offset + obj_size]
+        obj_view = obj_view.type(torch.uint8)
+        offset += obj_size
+        object_list[i] = _tensor_to_object(obj_view, obj_size, group)
+    return rank_objects
+
+
+@_exception_logger
+def broadcast_object_list(
+    object_list: list[Any],
+    src: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    device: Optional[torch.device] = None,
+    group_src: Optional[int] = None,
+):
+    """
+    Broadcasts picklable objects in ``object_list`` to the whole group.
+
+    Similar to :func:`broadcast`, but Python objects can be passed in.
+    Note that all objects in ``object_list`` must be picklable in order to be
+    broadcasted.
+
+    Args:
+        object_list (List[Any]): List of input objects to broadcast.
+            Each object must be picklable. Only objects on the ``src`` rank will
+            be broadcast, but each rank must provide lists of equal sizes.
+        src (int): Source rank from which to broadcast ``object_list``.
+            Source rank is based on global process group (regardless of ``group`` argument)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        device (``torch.device``, optional): If not None, the objects are
+            serialized and converted to tensors which are moved to the
+            ``device`` before broadcasting. Default is ``None``.
+        group_src (int): Source rank on ``group``.  Must not specify one of ``group_src``
+            and ``src`` but not both.
+
+    Returns:
+        ``None``. If rank is part of the group, ``object_list`` will contain the
+        broadcasted objects from ``src`` rank.
+
+    .. note:: For NCCL-based process groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. note:: Note that this API differs slightly from the :func:`broadcast`
+        collective since it does not provide an ``async_op`` handle and thus
+        will be a blocking call.
+
+    .. warning::
+        :func:`broadcast_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`broadcast_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`broadcast` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 3.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> else:
+        >>>     objects = [None, None, None]
+        >>> # Assumes backend is not NCCL
+        >>> device = torch.device("cpu")
+        >>> dist.broadcast_object_list(objects, src=0, device=device)
+        >>> objects
+        ['foo', 12, {1: 2}]
+    """
+    group = _group_or_default_group(group)
+    if src is None and group_src is None:
+        src = 0
+    global_src = _canonicalize_group_rank(group, src, group_src, return_global=True)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("broadcast_object_list")
+        return
+
+    # Current device selection.
+    # To preserve backwards compatibility, ``device`` is default to ``None``
+    # in which case we run current logic of device selection, i.e.
+    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
+    # case it is not ``None`` we move the size and object tensors to be
+    # broadcasted to this device.
+    current_device = device or _get_object_coll_device(group)
+    my_global_rank = get_rank()
+    # Serialize object_list elements to tensors on src rank.
+    if my_global_rank == global_src:
+        tensor_list, size_list = zip(
+            *[_object_to_tensor(obj, current_device, group) for obj in object_list]
+        )
+        object_sizes_tensor = torch.cat(size_list)
+    else:
+        object_sizes_tensor = torch.empty(
+            len(object_list), dtype=torch.long, device=current_device
+        )
+
+    # Broadcast object sizes
+    broadcast(object_sizes_tensor, src=global_src, group=group)
+
+    # Concatenate and broadcast serialized object tensors
+    # Note: torch.cat will do an extra memory copy to the current device, if the tensor_list
+    # has only one element, we can skip the copy.
+    if my_global_rank == global_src:
+        if len(tensor_list) == 1:  # type: ignore[possibly-undefined]
+            object_tensor = tensor_list[0]
+        else:
+            object_tensor = torch.cat(tensor_list)
+    else:
+        object_tensor = torch.empty(  # type: ignore[call-overload]
+            torch.sum(object_sizes_tensor).item(),  # type: ignore[arg-type]
+            dtype=torch.uint8,
+            device=current_device,
+        )
+
+    broadcast(object_tensor, src=global_src, group=group)
+    # Deserialize objects using their stored sizes.
+    offset = 0
+    if my_global_rank != global_src:
+        for i, obj_size in enumerate(object_sizes_tensor):
+            obj_view = object_tensor[offset : offset + obj_size]
+            obj_view = obj_view.type(torch.uint8)
+            offset += obj_size
+            object_list[i] = _tensor_to_object(obj_view, obj_size, group)
+
+
+@_exception_logger
+def scatter_object_list(
+    scatter_object_output_list: list[Any],
+    scatter_object_input_list: Optional[list[Any]] = None,
+    src: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    group_src: Optional[int] = None,
+):
+    """
+    Scatters picklable objects in ``scatter_object_input_list`` to the whole group.
+
+    Similar to :func:`scatter`, but Python objects can be passed in. On
+    each rank, the scattered object will be stored as the first element of
+    ``scatter_object_output_list``. Note that all objects in
+    ``scatter_object_input_list`` must be picklable in order to be scattered.
+
+    Args:
+        scatter_object_output_list (List[Any]): Non-empty list whose first
+            element will store the object scattered to this rank.
+        scatter_object_input_list (List[Any], optional): List of input objects to scatter.
+            Each object must be picklable. Only objects on the ``src`` rank will
+            be scattered, and the argument can be ``None`` for non-src ranks.
+        src (int): Source rank from which to scatter ``scatter_object_input_list``.
+            Source rank is based on global process group (regardless of ``group`` argument).
+            (If both ``src`` and ``group_src`` are None, default is global rank 0)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        group_src (int, optional): Source rank on ``group``.  Invalid to specify both ``src`` and ``group_src``
+
+    Returns:
+        ``None``. If rank is part of the group, ``scatter_object_output_list``
+        will have its first element set to the scattered object for this rank.
+
+    .. note:: Note that this API differs slightly from the scatter collective
+        since it does not provide an ``async_op`` handle and thus will be a
+        blocking call.
+
+    .. warning::
+        :func:`scatter_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`scatter_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`scatter` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 3.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> else:
+        >>>     # Can be any list on non-src ranks, elements are not used.
+        >>>     objects = [None, None, None]
+        >>> output_list = [None]
+        >>> dist.scatter_object_list(output_list, objects, src=0)
+        >>> # Rank i gets objects[i]. For example, on rank 2:
+        >>> output_list
+        [{1: 2}]
+    """
+    group = _group_or_default_group(group)
+    if src is None and group_src is None:
+        src = 0
+    global_src = _canonicalize_group_rank(group, src, group_src, return_global=True)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("scatter_object_list")
+        return
+
+    if (
+        not isinstance(scatter_object_output_list, list)
+        or len(scatter_object_output_list) < 1
+    ):
+        raise ValueError(
+            "Expected argument scatter_object_output_list to be a list of size at least 1."
+        )
+
+    my_global_rank = get_rank()
+    pg_device = _get_object_coll_device(group)
+    if my_global_rank == global_src:
+        if scatter_object_input_list is None:
+            raise ValueError(
+                "source rank must provide non-None scatter_object_input_list"
+            )
+        tensor_list, tensor_sizes = zip(
+            *[
+                _object_to_tensor(obj, pg_device, group)
+                for obj in scatter_object_input_list
+            ]
+        )
+        tensor_list, tensor_sizes = list(tensor_list), list(tensor_sizes)
+
+        # Src rank broadcasts the maximum tensor size. This is because all ranks are
+        # expected to call into scatter() with equal-sized tensors.
+        max_tensor_size = max(tensor_sizes)  # type: ignore[possibly-undefined]
+        for tensor in tensor_list:  # type: ignore[possibly-undefined]
+            tensor.resize_(max_tensor_size)
+    else:
+        max_tensor_size = torch.tensor([0], dtype=torch.long, device=pg_device)
+    broadcast(max_tensor_size, src=global_src, group=group)
+
+    # Scatter actual serialized objects
+    output_tensor = torch.empty(
+        max_tensor_size.item(), dtype=torch.uint8, device=pg_device
+    )
+    scatter(
+        output_tensor,
+        scatter_list=None if my_global_rank != global_src else tensor_list,  # type: ignore[possibly-undefined]
+        src=global_src,
+        group=group,
+    )
+
+    # Scatter per-object sizes to trim tensors when deserializing back to object
+    obj_tensor_size = torch.tensor([0], dtype=torch.long, device=pg_device)
+    scatter(
+        obj_tensor_size,
+        scatter_list=None if my_global_rank != global_src else tensor_sizes,  # type: ignore[possibly-undefined]
+        src=global_src,
+        group=group,
+    )
+
+    # Deserialize back to object
+    scatter_object_output_list[0] = _tensor_to_object(
+        output_tensor, obj_tensor_size, group
+    )
+
+
+@_exception_logger
+def all_gather(tensor_list, tensor, group=None, async_op=False):
+    """
+    Gathers tensors from the whole group in a list.
+
+    Complex and uneven sized tensors are supported.
+
+    Args:
+        tensor_list (list[Tensor]): Output list. It should contain
+            correctly-sized tensors to be used for output of the collective.
+            Uneven sized tensors are supported.
+        tensor (Tensor): Tensor to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Examples:
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # All tensors below are of torch.int64 dtype.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> device = torch.device(f"cuda:{rank}")
+        >>> tensor_list = [
+        ...     torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2)
+        ... ]
+        >>> tensor_list
+        [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0
+        [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1
+        >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
+        >>> tensor
+        tensor([1, 2], device='cuda:0') # Rank 0
+        tensor([3, 4], device='cuda:1') # Rank 1
+        >>> dist.all_gather(tensor_list, tensor)
+        >>> tensor_list
+        [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0
+        [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1
+
+        >>> # All tensors below are of torch.cfloat dtype.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> tensor_list = [
+        ...     torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2)
+        ... ]
+        >>> tensor_list
+        [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0
+        [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1
+        >>> tensor = torch.tensor(
+        ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
+        ... ) + 2 * rank * (1 + 1j)
+        >>> tensor
+        tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
+        tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
+        >>> dist.all_gather(tensor_list, tensor)
+        >>> tensor_list
+        [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0
+        [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1
+
+    """
+    # Dynamo has built-in logic to map legacy distributed ops to functional collectives.
+    # Let's redirect to a torch function mode that can mimic this logic outside Dynamo
+    # (e.g., non-strict export implements such a torch function mode).
+    relevant_args = (tensor,)
+    if has_torch_function(relevant_args):
+        return handle_torch_function(
+            all_gather,
+            relevant_args,
+            tensor_list,
+            tensor,
+            group=group,
+            async_op=async_op,
+        )
+
+    _check_tensor_list(tensor_list, "tensor_list")
+    _check_single_tensor(tensor, "tensor")
+    _ensure_all_tensors_same_dtype(tensor_list, tensor)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather")
+        return
+
+    tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in tensor_list
+    ]
+    tensor = tensor if not tensor.is_complex() else torch.view_as_real(tensor)
+
+    group = group or _get_default_group()
+    work = group.allgather([tensor_list], [tensor])
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def all_gather_into_tensor(output_tensor, input_tensor, group=None, async_op=False):
+    """
+    Gather tensors from all ranks and put them in a single output tensor.
+
+    This function requires all tensors to be the same size on each process.
+
+    Args:
+        output_tensor (Tensor): Output tensor to accommodate tensor elements
+            from all ranks. It must be correctly sized to have one of the
+            following forms:
+            (i) a concatenation of all the input tensors along the primary
+            dimension; for definition of "concatenation", see ``torch.cat()``;
+            (ii) a stack of all the input tensors along the primary dimension;
+            for definition of "stack", see ``torch.stack()``.
+            Examples below may better explain the supported output forms.
+        input_tensor (Tensor): Tensor to be gathered from current rank.
+            Different from the ``all_gather`` API, the input tensors in this
+            API must have the same size across all ranks.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Examples:
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # All tensors below are of torch.int64 dtype and on CUDA devices.
+        >>> # We have two ranks.
+        >>> device = torch.device(f"cuda:{rank}")
+        >>> tensor_in = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
+        >>> tensor_in
+        tensor([1, 2], device='cuda:0') # Rank 0
+        tensor([3, 4], device='cuda:1') # Rank 1
+        >>> # Output in concatenation form
+        >>> tensor_out = torch.zeros(world_size * 2, dtype=torch.int64, device=device)
+        >>> dist.all_gather_into_tensor(tensor_out, tensor_in)
+        >>> tensor_out
+        tensor([1, 2, 3, 4], device='cuda:0') # Rank 0
+        tensor([1, 2, 3, 4], device='cuda:1') # Rank 1
+        >>> # Output in stack form
+        >>> tensor_out2 = torch.zeros(world_size, 2, dtype=torch.int64, device=device)
+        >>> dist.all_gather_into_tensor(tensor_out2, tensor_in)
+        >>> tensor_out2
+        tensor([[1, 2],
+                [3, 4]], device='cuda:0') # Rank 0
+        tensor([[1, 2],
+                [3, 4]], device='cuda:1') # Rank 1
+
+    .. warning::
+        The Gloo backend does not support this API.
+
+    """
+    # Dynamo has built-in logic to map legacy distributed ops to functional collectives.
+    # Let's redirect to a torch function mode that can mimic this logic outside Dynamo
+    # (e.g., non-strict export implements such a torch function mode).
+    relevant_args = (input_tensor,)
+    if has_torch_function(relevant_args):
+        return handle_torch_function(
+            all_gather_into_tensor,
+            relevant_args,
+            output_tensor,
+            input_tensor,
+            group=group,
+            async_op=async_op,
+        )
+
+    _check_single_tensor(input_tensor, "input_tensor")
+    _check_single_tensor(output_tensor, "output_tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather_into_tensor")
+        return
+
+    output_tensor = (
+        output_tensor
+        if not output_tensor.is_complex()
+        else torch.view_as_real(output_tensor)
+    )
+    input_tensor = (
+        input_tensor
+        if not input_tensor.is_complex()
+        else torch.view_as_real(input_tensor)
+    )
+
+    opts = AllgatherOptions()
+    opts.asyncOp = async_op
+
+    group = group or _get_default_group()
+
+    if group in _world.pg_coalesce_state.keys():
+        # We are in coalescing context, do not issue single operation, just append a collective representation
+        coll = _CollOp(all_gather_into_tensor, input_tensor, output_tensor)
+        _world.pg_coalesce_state[group].append(coll)
+        if async_op:
+            return _IllegalWork()
+        else:
+            return None
+
+    work = group._allgather_base(output_tensor, input_tensor, opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+@deprecated(
+    "`torch.distributed._all_gather_base` is a private function and will be deprecated. "
+    "Please use `torch.distributed.all_gather_into_tensor` instead.",
+    category=FutureWarning,
+)
+def _all_gather_base(output_tensor, input_tensor, group=None, async_op=False):
+    """
+    Single tensor all gather. Gathers a single tensor from all ranks, and puts them in a single output tensor.
+
+    Args:
+        output_tensor (Tensor): Output tensor. It should contain
+            correctly-sized tensors to be used for output of the collective.
+        input_tensor (Tensor): Tensor to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. warning::
+        `_all_gather_base` is a private function. Users should use
+        `all_gather_into_tensor` instead.
+
+    """
+    return all_gather_into_tensor(output_tensor, input_tensor, group, async_op)
+
+
+@_exception_logger
+@deprecated(
+    "`torch.distributed.all_gather_coalesced` will be deprecated. If you must use it, "
+    "please revisit our documentation later at "
+    "https://pytorch.org/docs/main/distributed.html#collective-functions",
+    category=FutureWarning,
+)
+def all_gather_coalesced(
+    output_tensor_lists, input_tensor_list, group=None, async_op=False
+):
+    """
+    Gathers input tensors from the whole group in a list in a coalesced manner.
+
+    Complex tensors are supported.
+
+    Args:
+        output_tensor_lists (list[list[Tensor]]): Output list. It should contain
+            correctly-sized tensors to be used for output of the collective.
+        input_tensor_list (list[Tensor]): Tensors to be broadcast from
+            current process. At least one tensor has to be non empty.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Example:
+        we have 2 process groups, 2 ranks.
+        rank 0 passes:
+            input_tensor_list = [[[1, 1], [1, 1]], [2], [3, 3]]
+            output_tensor_lists =
+               [[[[-1, -1], [-1, -1]], [-1], [-1, -1]],
+                [[[-1, -1], [-1, -1]], [-1], [-1, -1]]]
+        rank 1 passes:
+            input_tensor_list = [[[3, 3], [3, 3]], [5], [1, 1]]
+            output_tensor_lists =
+               [[[[-1, -1], [-1, -1]], [-1], [-1, -1]],
+                [[[-1, -1], [-1, -1]], [-1], [-1, -1]]]
+        both rank 0 and 1 get:
+            output_tensor_lists =
+               [[[1, 1], [1, 1]], [2], [3, 3]],
+                [[3, 3], [3, 3]], [5], [1, 1]]].
+
+    WARNING: at this time individual shape checking is not implemented across nodes.
+    For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the
+    rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the
+    all_gather_coalesced operation will proceed without complaint and return
+    erroneous outputs. This lack of shape checking results in significant
+    performance improvements but users of this function should take extra care
+    to ensure that each node passes in tensors whose shapes match across nodes.
+    """
+    # We only check basic compatibility with C++ params here, C++ code will
+    # do shape and type checking.
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather_coalesced")
+        return
+    _check_tensor_list(input_tensor_list, "input_tensor_list")
+    _ensure_all_tensors_same_dtype(input_tensor_list)
+    if not isinstance(output_tensor_lists, list):
+        raise TypeError(
+            "Invalid function argument: output_tensor_lists should be a list"
+        )
+    for output_tensor_list in output_tensor_lists:
+        _check_tensor_list(output_tensor_list, "output_tensor_lists")
+        _ensure_all_tensors_same_dtype(output_tensor_list)
+
+    output_tensor_lists = [
+        [t if not t.is_complex() else torch.view_as_real(t) for t in l]
+        for l in output_tensor_lists
+    ]
+    input_tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in input_tensor_list
+    ]
+
+    group = group or _get_default_group()
+    work = group.allgather_coalesced(output_tensor_lists, input_tensor_list)
+
+    if async_op:
+        return work.get_future()
+    else:
+        work.wait()
+
+
+def _validate_output_list_for_rank(my_rank, dst, gather_list):
+    if dst == my_rank:
+        if not gather_list:
+            raise ValueError(
+                "Argument ``gather_list`` must be specified on destination rank."
+            )
+    elif gather_list:
+        raise ValueError(
+            "Argument ``gather_list`` must NOT be specified on non-destination ranks."
+        )
+
+
+@_exception_logger
+def gather(
+    tensor: torch.Tensor,
+    gather_list: Optional[list[torch.Tensor]] = None,
+    dst: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    async_op: bool = False,
+    group_dst: Optional[int] = None,
+):
+    """
+    Gathers a list of tensors in a single process.
+
+    This function requires all tensors to be the same size on each process.
+
+    Args:
+        tensor (Tensor): Input tensor.
+        gather_list (list[Tensor], optional): List of appropriately,
+            same-sized tensors to use for gathered data
+            (default is None, must be specified on the destination rank)
+        dst (int, optional): Destination rank on global process group (regardless of ``group`` argument).
+            (If both ``dst`` and ``group_dst`` are None, default is global rank 0)
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. note:: Note that all Tensors in gather_list must have the same size.
+
+    Example::
+        >>> # xdoctest: +SKIP("no rank")
+        >>> # We have 2 process groups, 2 ranks.
+        >>> tensor_size = 2
+        >>> device = torch.device(f'cuda:{rank}')
+        >>> tensor = torch.ones(tensor_size, device=device) + rank
+        >>> if dist.get_rank() == 0:
+        >>>     gather_list = [torch.zeros_like(tensor, device=device) for i in range(2)]
+        >>> else:
+        >>>     gather_list = None
+        >>> dist.gather(tensor, gather_list, dst=0)
+        >>> # Rank 0 gets gathered data.
+        >>> gather_list
+        [tensor([1., 1.], device='cuda:0'), tensor([2., 2.], device='cuda:0')] # Rank 0
+        None                                                                   # Rank 1
+
+    """
+    _check_single_tensor(tensor, "tensor")
+
+    # Parameter ``gather_list`` may be left unspecified on non-dst ranks.
+    if gather_list:
+        _check_tensor_list(gather_list, "gather_list")
+    else:
+        gather_list = []
+    _ensure_all_tensors_same_dtype(tensor, gather_list)
+    group = _group_or_default_group(group)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("gather")
+        return
+    if dst is None and group_dst is None:
+        dst = 0
+    global_dst = _canonicalize_group_rank(group, dst, group_dst, return_global=True)
+    group_dst = _canonicalize_group_rank(group, dst, group_dst, return_global=False)
+    my_global_rank = get_rank()
+    _validate_output_list_for_rank(my_global_rank, global_dst, gather_list)
+    output_tensors = [gather_list] if global_dst == my_global_rank else []
+    input_tensors = [tensor]
+
+    opts = GatherOptions()
+    opts.rootRank = group_dst
+    work = group.gather(output_tensors, input_tensors, opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def scatter(
+    tensor: torch.Tensor,
+    scatter_list: Optional[list[torch.Tensor]] = None,
+    src: Optional[int] = None,
+    group: Optional[ProcessGroup] = None,
+    async_op: bool = False,
+    group_src: Optional[int] = None,
+):
+    """
+    Scatters a list of tensors to all processes in a group.
+
+    Each process will receive exactly one tensor and store its data in the
+    ``tensor`` argument.
+
+    Complex tensors are supported.
+
+    Args:
+        tensor (Tensor): Output tensor.
+        scatter_list (list[Tensor]): List of tensors to scatter (default is
+            None, must be specified on the source rank)
+        src (int): Source rank on global process group (regardless of ``group`` argument).
+            (If both ``src`` and ``group_src`` are None, default is global rank 0)
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        group_src (int, optional): Source rank on ``group``.  Invalid to specify both ``src`` and ``group_src``
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. note:: Note that all Tensors in scatter_list must have the same size.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> tensor_size = 2
+        >>> device = torch.device(f'cuda:{rank}')
+        >>> output_tensor = torch.zeros(tensor_size, device=device)
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 2.
+        >>>     # Only tensors, all of which must be the same size.
+        >>>     t_ones = torch.ones(tensor_size, device=device)
+        >>>     t_fives = torch.ones(tensor_size, device=device) * 5
+        >>>     scatter_list = [t_ones, t_fives]
+        >>> else:
+        >>>     scatter_list = None
+        >>> dist.scatter(output_tensor, scatter_list, src=0)
+        >>> # Rank i gets scatter_list[i].
+        >>> output_tensor
+        tensor([1., 1.], device='cuda:0') # Rank 0
+        tensor([5., 5.], device='cuda:1') # Rank 1
+
+    """
+    _check_single_tensor(tensor, "tensor")
+    # Parameter ``scatter_list`` may be left unspecified on non-src ranks.
+    if scatter_list:
+        _check_tensor_list(scatter_list, "scatter_list")
+    else:
+        scatter_list = []
+    _ensure_all_tensors_same_dtype(tensor, scatter_list)
+    group = _group_or_default_group(group)
+    if src is None and group_src is None:
+        src = 0
+    global_src = _canonicalize_group_rank(group, src, group_src, return_global=True)
+    group_src = _canonicalize_group_rank(group, src, group_src, return_global=False)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("scatter")
+        return
+    scatter_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in scatter_list
+    ]
+    tensor = tensor if not tensor.is_complex() else torch.view_as_real(tensor)
+
+    my_global_rank = get_rank()
+    if global_src == my_global_rank:
+        if not scatter_list:
+            raise ValueError(
+                "Argument ``scatter_list`` must be specified on source rank."
+            )
+        input_tensors = [scatter_list]
+        output_tensors = [tensor]
+    else:
+        if scatter_list:
+            raise ValueError(
+                "Argument ``scatter_list`` must NOT be specified on non-source ranks."
+            )
+        input_tensors = []
+        output_tensors = [tensor]
+
+    opts = ScatterOptions()
+    opts.rootRank = group_src
+    opts.asyncOp = async_op
+    work = group.scatter(output_tensors, input_tensors, opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def reduce_scatter(output, input_list, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    Reduces, then scatters a list of tensors to all processes in a group.
+
+    Args:
+        output (Tensor): Output tensor.
+        input_list (list[Tensor]): List of tensors to reduce and scatter.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    """
+    _check_single_tensor(output, "output")
+    _check_tensor_list(input_list, "input_list")
+    _ensure_all_tensors_same_dtype(output, input_list)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("reduce_scatter")
+        return
+
+    opts = ReduceScatterOptions()
+    opts.reduceOp = op
+
+    group = group or _get_default_group()
+    work = group.reduce_scatter([output], [input_list], opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def reduce_scatter_tensor(output, input, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    Reduces, then scatters a tensor to all ranks in a group.
+
+    Args:
+        output (Tensor): Output tensor. It should have the same size across all
+            ranks.
+        input (Tensor): Input tensor to be reduced and scattered. Its size
+            should be output tensor size times the world size. The input tensor
+            can have one of the following shapes:
+            (i) a concatenation of the output tensors along the primary
+            dimension, or
+            (ii) a stack of the output tensors along the primary dimension.
+            For definition of "concatenation", see ``torch.cat()``.
+            For definition of "stack", see ``torch.stack()``.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    Examples:
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # All tensors below are of torch.int64 dtype and on CUDA devices.
+        >>> # We have two ranks.
+        >>> device = torch.device(f"cuda:{rank}")
+        >>> tensor_out = torch.zeros(2, dtype=torch.int64, device=device)
+        >>> # Input in concatenation form
+        >>> tensor_in = torch.arange(world_size * 2, dtype=torch.int64, device=device)
+        >>> tensor_in
+        tensor([0, 1, 2, 3], device='cuda:0') # Rank 0
+        tensor([0, 1, 2, 3], device='cuda:1') # Rank 1
+        >>> dist.reduce_scatter_tensor(tensor_out, tensor_in)
+        >>> tensor_out
+        tensor([0, 2], device='cuda:0') # Rank 0
+        tensor([4, 6], device='cuda:1') # Rank 1
+        >>> # Input in stack form
+        >>> tensor_in = torch.reshape(tensor_in, (world_size, 2))
+        >>> tensor_in
+        tensor([[0, 1],
+                [2, 3]], device='cuda:0') # Rank 0
+        tensor([[0, 1],
+                [2, 3]], device='cuda:1') # Rank 1
+        >>> dist.reduce_scatter_tensor(tensor_out, tensor_in)
+        >>> tensor_out
+        tensor([0, 2], device='cuda:0') # Rank 0
+        tensor([4, 6], device='cuda:1') # Rank 1
+
+    .. warning::
+        The Gloo backend does not support this API.
+
+    """
+    # Dynamo has built-in logic to map legacy distributed ops to functional collectives.
+    # Let's redirect to a torch function mode that can mimic this logic outside Dynamo
+    # (e.g., non-strict export implements such a torch function mode).
+    relevant_args = (input,)
+    if has_torch_function(relevant_args):
+        return handle_torch_function(
+            reduce_scatter_tensor,
+            relevant_args,
+            output,
+            input,
+            op=op,
+            group=group,
+            async_op=async_op,
+        )
+
+    _check_single_tensor(output, "output")
+    _check_single_tensor(input, "input")
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("reduce_scatter_tensor")
+        return
+
+    opts = ReduceScatterOptions()
+    opts.reduceOp = op
+    opts.asyncOp = async_op
+
+    group = group or _get_default_group()
+
+    # Check if we are in coalescing context
+    # If we are, do not issue single operation, just append a collective representation
+    if group in _world.pg_coalesce_state.keys():
+        coll = _CollOp(reduce_scatter_tensor, input, output, op, None)
+        _world.pg_coalesce_state[group].append(coll)
+        if async_op:
+            return _IllegalWork()
+        else:
+            return None
+
+    work = group._reduce_scatter_base(output, input, opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@deprecated(
+    "`torch.distributed._reduce_scatter_base` is a private function and will be deprecated. "
+    "Please use `torch.distributed.reduce_scatter_tensor` instead.",
+    category=FutureWarning,
+)
+def _reduce_scatter_base(output, input, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    Reduces, then scatters a flattened tensor to all processes in a group.
+
+    Args:
+        output (Tensor): Output tensor.
+        input (Tensor): Input tensor that is of size output tensor size times world size
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    .. warning::
+        `_reduce_scatter_base` is a private function. Users should use
+        `reduce_scatter_tensor` instead.
+
+    """
+    return reduce_scatter_tensor(output, input, op, group, async_op)
+
+
+@_exception_logger
+def all_to_all_single(
+    output,
+    input,
+    output_split_sizes=None,
+    input_split_sizes=None,
+    group=None,
+    async_op=False,
+):
+    """
+    Split input tensor and then scatter the split list to all processes in a group.
+
+    Later the received tensors are concatenated from all the processes in the group
+    and returned as a single output tensor.
+
+    Complex tensors are supported.
+
+    Args:
+        output (Tensor): Gathered concatenated output tensor.
+        input (Tensor): Input tensor to scatter.
+        output_split_sizes: (list[Int], optional): Output split sizes for dim 0
+            if specified None or empty, dim 0 of ``output`` tensor must divide
+            equally by ``world_size``.
+        input_split_sizes: (list[Int], optional): Input split sizes for dim 0
+            if specified None or empty, dim 0 of ``input`` tensor must divide
+            equally by ``world_size``.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    .. warning::
+        `all_to_all_single` is experimental and subject to change.
+
+    Examples:
+        >>> # xdoctest: +SKIP("Undefined rank")
+        >>> input = torch.arange(4) + rank * 4
+        >>> input
+        tensor([0, 1, 2, 3])     # Rank 0
+        tensor([4, 5, 6, 7])     # Rank 1
+        tensor([8, 9, 10, 11])   # Rank 2
+        tensor([12, 13, 14, 15]) # Rank 3
+        >>> output = torch.empty([4], dtype=torch.int64)
+        >>> dist.all_to_all_single(output, input)
+        >>> output
+        tensor([0, 4, 8, 12])    # Rank 0
+        tensor([1, 5, 9, 13])    # Rank 1
+        tensor([2, 6, 10, 14])   # Rank 2
+        tensor([3, 7, 11, 15])   # Rank 3
+
+        >>> # Essentially, it is similar to following operation:
+        >>> scatter_list = list(input.chunk(world_size))
+        >>> gather_list = list(output.chunk(world_size))
+        >>> for i in range(world_size):
+        >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
+
+        >>> # Another example with uneven split
+        >>> input
+        tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
+        tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
+        tensor([20, 21, 22, 23, 24])                                     # Rank 2
+        tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
+        >>> input_splits
+        [2, 2, 1, 1]                                                     # Rank 0
+        [3, 2, 2, 2]                                                     # Rank 1
+        [2, 1, 1, 1]                                                     # Rank 2
+        [2, 2, 2, 1]                                                     # Rank 3
+        >>> output_splits
+        [2, 3, 2, 2]                                                     # Rank 0
+        [2, 2, 1, 2]                                                     # Rank 1
+        [1, 2, 1, 2]                                                     # Rank 2
+        [1, 2, 1, 1]                                                     # Rank 3
+        >>> output = ...
+        >>> dist.all_to_all_single(output, input, output_splits, input_splits)
+        >>> output
+        tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0
+        tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1
+        tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2
+        tensor([ 5, 17, 18, 24, 36])                                     # Rank 3
+
+
+        >>> # Another example with tensors of torch.cfloat type.
+        >>> input = torch.tensor(
+        ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
+        ... ) + 4 * rank * (1 + 1j)
+        >>> input
+        tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0
+        tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1
+        tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2
+        tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3
+        >>> output = torch.empty([4], dtype=torch.int64)
+        >>> dist.all_to_all_single(output, input)
+        >>> output
+        tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0
+        tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1
+        tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2
+        tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3
+    """
+    # Dynamo has built-in logic to map legacy distributed ops to functional collectives.
+    # Let's redirect to a torch function mode that can mimic this logic outside Dynamo
+    # (e.g., non-strict export implements such a torch function mode).
+    relevant_args = (input,)
+    if has_torch_function(relevant_args):
+        return handle_torch_function(
+            all_to_all_single,
+            relevant_args,
+            output,
+            input,
+            output_split_sizes=output_split_sizes,
+            input_split_sizes=input_split_sizes,
+            group=group,
+            async_op=async_op,
+        )
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_to_all_single")
+        return
+
+    opts = AllToAllOptions()
+    _check_single_tensor(output, "output")
+    _check_single_tensor(input, "input")
+    _ensure_all_tensors_same_dtype(output, input)
+
+    if input.is_complex():
+        input = torch.view_as_real(input)
+    if output.is_complex():
+        output = torch.view_as_real(output)
+
+    output_split_sizes = [] if output_split_sizes is None else output_split_sizes
+    input_split_sizes = [] if input_split_sizes is None else input_split_sizes
+
+    group = group or _get_default_group()
+    work = group.alltoall_base(
+        output, input, output_split_sizes, input_split_sizes, opts
+    )
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def all_to_all(output_tensor_list, input_tensor_list, group=None, async_op=False):
+    """
+    Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
+
+    Complex tensors are supported.
+
+    Args:
+        output_tensor_list (list[Tensor]): List of tensors to be gathered one
+            per rank.
+        input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    .. warning::
+        `all_to_all` is experimental and subject to change.
+
+    Examples:
+        >>> # xdoctest: +SKIP("Undefined rank")
+        >>> input = torch.arange(4) + rank * 4
+        >>> input = list(input.chunk(4))
+        >>> input
+        [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0
+        [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1
+        [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2
+        [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
+        >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
+        >>> dist.all_to_all(output, input)
+        >>> output
+        [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0
+        [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1
+        [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2
+        [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3
+
+        >>> # Essentially, it is similar to following operation:
+        >>> scatter_list = input
+        >>> gather_list = output
+        >>> for i in range(world_size):
+        >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)
+
+        >>> input
+        tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
+        tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
+        tensor([20, 21, 22, 23, 24])                                     # Rank 2
+        tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
+        >>> input_splits
+        [2, 2, 1, 1]                                                     # Rank 0
+        [3, 2, 2, 2]                                                     # Rank 1
+        [2, 1, 1, 1]                                                     # Rank 2
+        [2, 2, 2, 1]                                                     # Rank 3
+        >>> output_splits
+        [2, 3, 2, 2]                                                     # Rank 0
+        [2, 2, 1, 2]                                                     # Rank 1
+        [1, 2, 1, 2]                                                     # Rank 2
+        [1, 2, 1, 1]                                                     # Rank 3
+        >>> input = list(input.split(input_splits))
+        >>> input
+        [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0
+        [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
+        [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2
+        [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3
+        >>> output = ...
+        >>> dist.all_to_all(output, input)
+        >>> output
+        [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0
+        [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1
+        [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2
+        [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3
+
+        >>> # Another example with tensors of torch.cfloat type.
+        >>> input = torch.tensor(
+        ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
+        ... ) + 4 * rank * (1 + 1j)
+        >>> input = list(input.chunk(4))
+        >>> input
+        [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0
+        [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1
+        [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2
+        [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3
+        >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
+        >>> dist.all_to_all(output, input)
+        >>> output
+        [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0
+        [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1
+        [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2
+        [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3
+
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_to_all")
+        return
+
+    opts = AllToAllOptions()
+    _check_tensor_list(output_tensor_list, "output_tensor_list")
+    _check_tensor_list(input_tensor_list, "input_tensor_list")
+    _ensure_all_tensors_same_dtype(output_tensor_list, input_tensor_list)
+
+    input_tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in input_tensor_list
+    ]
+    output_tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in output_tensor_list
+    ]
+
+    group = group or _get_default_group()
+    work = group.alltoall(output_tensor_list, input_tensor_list, opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+@_exception_logger
+def barrier(
+    group: Optional[ProcessGroup] = GroupMember.WORLD, async_op=False, device_ids=None
+):
+    """
+    Synchronize all processes.
+
+    This collective blocks processes until the whole group enters this function,
+    if async_op is False, or if async work handle is called on wait().
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        device_ids ([int], optional): List of device/GPU ids. Only one id is expected.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. note:: `ProcessGroupNCCL` now blocks the cpu thread till the completion of the barrier collective.
+    """
+    group = group or _get_default_group()
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("barrier")
+        return
+
+    opts = BarrierOptions()
+    # Detect the accelerator on the machine. If no accelerator is available, it
+    # returns CPU.
+    device = torch._C._get_accelerator()
+    if isinstance(device_ids, list):
+        opts.device_ids = device_ids
+        # use only the first device id
+        opts.device = torch.device(device.type, device_ids[0])
+    elif getattr(group, "bound_device_id", None) is not None:
+        # Use device id from `init_process_group(device_id=...)`
+        opts.device = group.bound_device_id  # type: ignore[assignment]
+    elif device.type == "cpu" or _get_object_coll_device(group) == "cpu":
+        opts.device = torch.device("cpu")
+    else:
+        # Use the current device set by the user. If user did not set any, this
+        # may use default device 0, causing issues like hang or all processes
+        # creating context on device 0.
+        opts.device = device
+        warnings.warn(  # warn only once
+            "No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. "
+        )
+
+    work = group.barrier(opts=opts)
+
+    if async_op:
+        return work
+    else:
+        work.wait()
+
+
+def monitored_barrier(
+    group: Optional[ProcessGroup] = GroupMember.WORLD,
+    timeout=None,
+    wait_all_ranks=False,
+):
+    """
+    Synchronize processes similar to ``torch.distributed.barrier``, but consider a configurable timeout.
+
+    It is able to report ranks that did not pass this barrier within the provided timeout.
+    Specifically, for non-zero ranks, will block until a send/recv is processed from rank 0.
+    Rank 0 will block until all send /recv from other ranks are processed, and will report
+    failures for ranks that failed to respond in time. Note that if one rank does not reach the
+    monitored_barrier (for example due to a hang), all other ranks would fail in monitored_barrier.
+
+    This collective will block all processes/ranks in the group, until the
+    whole group exits the function successfully, making it useful for debugging
+    and synchronizing. However, it can have a performance impact and should only
+    be used for debugging or scenarios that require full synchronization points
+    on the host-side. For debugging purposes, this barrier can be inserted
+    before the application's collective calls to check if any ranks are
+    desynchronized.
+
+    .. note:: Note that this collective is only supported with the GLOO backend.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If
+            ``None``, the default process group will be used.
+        timeout (datetime.timedelta, optional): Timeout for monitored_barrier.
+            If ``None``, the default process group timeout will be used.
+        wait_all_ranks (bool, optional): Whether to collect all failed ranks or
+            not. By default, this is ``False`` and ``monitored_barrier`` on rank 0
+            will throw on the first failed rank it encounters in order to fail
+            fast. By setting ``wait_all_ranks=True`` ``monitored_barrier`` will
+            collect all failed ranks and throw an error containing information
+            about all failed ranks.
+
+    Returns:
+        ``None``.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> if dist.get_rank() != 1:
+        >>>     dist.monitored_barrier() # Raises exception indicating that
+        >>> # rank 1 did not call into monitored_barrier.
+        >>> # Example with wait_all_ranks=True
+        >>> if dist.get_rank() == 0:
+        >>>     dist.monitored_barrier(wait_all_ranks=True) # Raises exception
+        >>> # indicating that ranks 1, 2, ... world_size - 1 did not call into
+        >>> # monitored_barrier.
+    """
+    # Need to call rank not in group before using the group, otherwise
+    # "Invalid process group" error is raised.
+    if _rank_not_in_group(group):
+        _warn_not_in_group("monitored_barrier")
+        return
+
+    if get_backend(group) != Backend.GLOO:
+        raise ValueError("monitored_barrier is only implemented for GLOO backend.")
+
+    if timeout is None:
+        timeout = _get_default_timeout(get_backend(group))
+    elif isinstance(timeout, float):
+        # TODO(whc) aparently some existing test case for monitored_barrier passes in a timeout in float format?
+        warnings.warn(
+            "Please specify timeout arg as a timedelta. "
+            f"Converting current value of {timeout} assuming it represents seconds",
+        )
+        timeout = timedelta(seconds=timeout)
+
+    _check_valid_timeout(timeout)
+
+    group_to_use = _get_default_group() if group is None else group
+    return group_to_use.monitored_barrier(  # type:ignore[attr-defined]
+        timeout, wait_all_ranks=wait_all_ranks
+    )
+
+
+def _create_process_group_wrapper(
+    wrapped_pg: torch._C._distributed_c10d.Backend,
+    store_prefix: str,
+    store: Store,
+    rank: int,
+    world_size: int,
+    timeout: timedelta = default_pg_timeout,
+):
+    assert _GLOO_AVAILABLE, "ProcessGroupWrapper unsupported without GLOO backend."
+
+    # (whc) this appears to be just for the gloo backend? if so, `default_pg_timeout` is appropriate...
+
+    # Create a separate prefix store for the helper process group.
+    prefix = f"{PG_WRAPPER_STORE_PREFIX}:{store_prefix}"
+    store = PrefixStore(prefix, store)
+    helper_pg = ProcessGroupGloo(store, rank, world_size, timeout=timeout)
+    # Wrap the underlying pg with ProcessGroupWrapper.
+    wrapped_pg = _ProcessGroupWrapper(wrapped_pg, helper_pg)
+    return wrapped_pg
+
+
+# helper function for deterministically hashing a list of ranks to a unique
+# string
+def _hash_ranks_to_str(ranks: list[int]) -> str:
+    rank_join: str = "_".join(map(str, ranks))
+    # In case there is already a PG with the same rank composition
+    unique_str = "_".join([rank_join, str(len(_world.pg_names))])
+    return hashlib.sha1(bytes(unique_str, "utf-8"), usedforsecurity=False).hexdigest()
+
+
+# Takes a list of ranks and computes an integer color
+def _process_group_color(ranks: list[int]) -> int:
+    # Convert list to tuple to make it hashable
+    ranks = tuple(ranks)
+    hash_value = hash(ranks)
+    # Split color must be:
+    # - a non-negative integer;
+    # - a type compatible with C's int because we are pybinding to the latter.
+    # Thus, we limit the hash value within c_int's max value.
+    max_c_int = 2 ** (ctypes.sizeof(ctypes.c_int) * 8 - 1)
+    color = abs(hash_value) % max_c_int
+    return color
+
+
+def _process_group_name(ranks, use_hashed_name):
+    # Create name for a process group.
+    global _world
+    if use_hashed_name:
+        pg_name = _hash_ranks_to_str(ranks)
+    else:
+        pg_name = str(_world.group_count)
+        _world.group_count += 1
+    # TODO: why is group count incremented only in the else path?
+    return pg_name
+
+
+def _get_backend_from_str(backend: Optional[str] = None) -> Backend:
+    # Default to the same backend as the global process group
+    #  if backend is not specified.
+    if not backend:
+        backend = get_backend(_get_default_group())
+    return Backend(backend)
+
+
+def _is_safe_to_split() -> bool:
+    """
+    Checks if it is safe to split the any process group in the world.
+    This is only safe if the default pg has a bound device id, otherwise
+    users must be aware that a pg is only splittable after the first collective is
+    issued.
+    """
+    return False if _get_default_group().bound_device_id is None else True
+
+
+@_time_logger
+def split_group(
+    parent_pg: Optional[ProcessGroup] = None,
+    split_ranks: Optional[list] = None,
+    timeout: Optional[timedelta] = None,
+    pg_options: Optional[Any] = None,
+    group_desc: Optional[str] = None,
+) -> Optional[ProcessGroup]:
+    """
+    Create a new process group splitted from the given parent process group.
+
+    warning:: This is an experimental API and only the ``NCCL`` backend supports this API.
+    Other backends will raise an error.
+    Users of this API must gurantee that all ranks in the parent group enter this API call,
+    and the split of the sub groups is the same across all ranks in the parent group.
+
+    Args:
+        parent_pg (ProcessGroup, optional): The parent process group. If None,
+            the default process group will be used. Users need to gurantee that
+            the parent group is fully initialized (e.g, communicators are initialized)
+        split_ranks (list[list[int]]): the split ranks, which is a list of list of ranks.
+            Users need to make sure the validity of the split ranks such that one
+            split (represented by one inner list of ints) does not overlap with any other split.
+            Note that the ranks in each split is the group rank (instead of global rank)
+            in the parent pg. For example, if the parent group has 4 ranks, and split_ranks can be
+            [[0, 1], [2, 3]]. Note [[0,1]] is also a valid split, in which case ranks 2, 3 would
+            return a non-group member.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        pg_options (ProcessGroupOptions, optional): only ProcessGroupNCCLOptions is supported now.
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e.``is_high_priority_stream``
+            can be specified so that process group can pick up high priority cuda streams.
+            For other availble options to config nccl,
+            See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-t
+        group_desc (str, optional): a string to describe the process group.
+
+    Returns:
+        ProcessGroup if the current rank is within one split/subgroup given by split_ranks,
+        or None if the current rank is not part of any split_ranks`.
+
+    """
+    # check inputs
+    if split_ranks is None:
+        raise ValueError("split_ranks cannot be None")
+
+    global _world
+    default_pg = _get_default_group()
+    device_id = default_pg.bound_device_id
+    if not device_id:
+        raise RuntimeError(
+            "No device associated with the default pg, not safe to split any process groups"
+        )
+    _default_backend, default_store = _world.pg_map[default_pg]
+    global_rank = default_pg.rank()
+    global_world_size = default_pg.size()
+
+    if not parent_pg:
+        parent_pg = default_pg
+    if parent_pg not in _world.pg_group_ranks:
+        raise ValueError(f"Group {parent_pg} is not registered")
+
+    parent_global_to_group_ranks = _world.pg_group_ranks[parent_pg]
+    parent_group_to_global_ranks = {
+        group_rank: global_rank
+        for global_rank, group_rank in parent_global_to_group_ranks.items()
+    }
+
+    if global_rank not in parent_global_to_group_ranks:
+        raise ValueError(
+            f"Global rank {global_rank} is not part of the parent group {parent_pg}"
+        )
+
+    parent_group_rank = parent_global_to_group_ranks[global_rank]
+    parent_backend = parent_pg._get_backend(torch.device("cuda"))
+
+    # if the parent backend does not support splitting, raise error
+    # currently this API only support NCCL backend
+    if (
+        not parent_backend
+        or not parent_backend.supports_splitting
+        or not isinstance(parent_backend, ProcessGroupNCCL)
+    ):
+        raise RuntimeError(
+            "No backend for the parent process group or its backend does not support splitting"
+        )
+
+    # set the group_desc before the color or no_cloor split
+    group_desc = (
+        f"{parent_pg.group_desc}:split:{parent_backend.comm_split_count()}"
+        if group_desc is None
+        else group_desc
+    )
+
+    parent_backend_str, _ = _world.pg_map[parent_pg]
+    # same type of backend as the parent process group
+    backend = Backend(parent_backend_str)
+    backend_config = BackendConfig(backend)
+
+    if pg_options is not None:
+        assert isinstance(pg_options, ProcessGroupNCCL.Options), (
+            "Expected pg_options argument to be of type ProcessGroupNCCL.Options"
+        )
+    else:
+        # default pg_options same as the parent process group
+        pg_options = parent_backend.options
+
+    # this timeout defaulting/validation is used for all the new_groups/new_subgroups variants,
+    # which may just pass their timeout value (or None)
+    if timeout is None:
+        timeout = _get_default_timeout(backend)
+    _check_valid_timeout(timeout)
+
+    # find my group of ranks and my group local rank in split_ranks
+    my_group = None
+    group_rank = -1
+
+    for split_group in split_ranks:
+        if len(split_group) == 0:
+            raise ValueError("the split group cannot be empty")
+        if len(split_group) > global_world_size:
+            raise ValueError(
+                "the split group's size should be less or equal to the world_size set by init_process_group"
+            )
+        if len(split_group) != len(set(split_group)):
+            raise ValueError("the split group cannot have duplicate ranks")
+        split_group = sorted(split_group)
+        if parent_group_rank in split_group:
+            my_group = split_group
+            group_rank = split_group.index(parent_group_rank)
+            break
+    # if my rank does not belong to any sub group,
+    # no_color split should be called
+    if my_group is None or group_rank == -1:
+        parent_backend.perform_nocolor_split(device_id)
+        return None
+
+    group_name = _process_group_name(my_group, use_hashed_name=False)
+    global_ranks_in_my_group = [parent_group_to_global_ranks[rank] for rank in my_group]
+
+    prefix_store = PrefixStore(f"{group_name}/", default_store)
+    # We register the backend after initializing and timeout is set in pg_options.
+    pg: ProcessGroup = ProcessGroup(
+        prefix_store,
+        group_rank,
+        len(my_group),
+    )
+    backend_type = ProcessGroup.BackendType.NCCL
+    pg.bound_device_id = device_id
+    pg._set_default_backend(backend_type)
+
+    pg_options._timeout = timeout
+    pg_options.split_from = parent_backend
+    pg_options.split_color = _process_group_color(my_group)
+    pg_options.global_ranks_in_group = global_ranks_in_my_group
+    pg_options.group_name = group_name
+    backend_class = ProcessGroupNCCL(
+        prefix_store, group_rank, len(my_group), pg_options
+    )
+    backend_class._set_sequence_number_for_group()
+
+    pg._register_backend(torch.device("cuda"), backend_type, backend_class)
+
+    # set group_name and group_desc to backend
+    assert group_name is not None
+    assert group_desc is not None
+    pg._set_group_name(group_name)
+    pg._set_group_desc(group_desc)
+
+    # always eagerly initialize the backend in split_group
+    eager_backend = pg._get_backend(device_id)
+    eager_backend.eager_connect_single_device(device_id)
+
+    # update global state
+    _world.pg_map[pg] = (backend, prefix_store)
+    _world.pg_names[pg] = group_name
+    _register_process_group(group_name, pg)
+    _world.pg_backend_config[pg] = str(backend_config)
+    pg_tag = f"ptd:{group_name}"
+    _world.tags_to_pg.setdefault(pg_tag, []).append(pg)
+    _world.pg_to_tag[pg] = pg_tag
+
+    # Create the global rank to group rank mapping
+    _world.pg_group_ranks[pg] = {
+        global_rank: group_rank
+        for group_rank, global_rank in enumerate(global_ranks_in_my_group)
+    }
+
+    return pg
+
+
+@_time_logger
+def new_group(
+    ranks=None,
+    timeout=None,
+    backend=None,
+    pg_options=None,
+    use_local_synchronization=False,
+    group_desc=None,
+    device_id: Optional[torch.device] = None,
+):
+    """
+    Create a new distributed group.
+
+    This function requires that all processes in the main group (i.e. all
+    processes that are part of the distributed job) enter this function, even
+    if they are not going to be members of the group. Additionally, groups
+    should be created in the same order in all processes.
+
+    .. warning::
+        Safe concurrent usage:
+        When using multiple process groups with the ``NCCL`` backend, the user
+        must ensure a globally consistent execution order of collectives across
+        ranks.
+
+        If multiple threads within a process issue collectives, explicit
+        synchronization is necessary to ensure consistent ordering.
+
+        When using async variants of torch.distributed communication APIs,
+        a work object is returned and the communication kernel is
+        enqueued on a separate CUDA stream, allowing overlap of communication
+        and computation. Once one or more async ops have been issued on one process
+        group, they must be synchronized with other cuda streams by calling `work.wait()`
+        before using another process group.
+
+        See `Using multiple NCCL communicators concurrently `_ for more details.
+
+    Args:
+        ranks (list[int]): List of ranks of group members. If ``None``, will be
+            set to all ranks. Default is ``None``.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        backend (str or Backend, optional): The backend to use. Depending on
+            build-time configurations, valid values are ``gloo`` and ``nccl``.
+            By default uses the same backend as the global group. This field
+            should be given as a lowercase string (e.g., ``"gloo"``), which can
+            also be accessed via :class:`Backend` attributes (e.g.,
+            ``Backend.GLOO``). If ``None`` is passed in, the backend
+            corresponding to the default process group will be used. Default is
+            ``None``.
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e. for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            process group can pick up high priority cuda streams. For other availble options to config nccl,
+            See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-t
+        use_local_synchronization (bool, optional): perform a group-local
+            barrier at the end of the process group creation. This is different
+            in that non-member ranks don't need to call into API and don't
+            join the barrier.
+        group_desc (str, optional): a string to describe the process group.
+        device_id (torch.device, optional): a single, specific device
+            to "bind" this process to,  The `new_group` call will try to initialize
+            a communication backend immediately for the device if this field is given.
+
+    Returns:
+        A handle of distributed group that can be given to collective calls or
+        GroupMember.NON_GROUP_MEMBER if the rank is not part of ``ranks``.
+
+    N.B. use_local_synchronization doesn't work with MPI.
+
+    N.B. While use_local_synchronization=True can be significantly faster with larger
+    clusters and small process groups, care must be taken since it changes cluster behavior
+    as non-member ranks don't join the group barrier().
+
+    N.B. use_local_synchronization=True can lead to deadlocks when each rank creates
+    multiple overlaping process groups. To avoid that, make sure all ranks follow the
+    same global creation order.
+    """
+    return _new_group_with_tag(
+        ranks,
+        timeout,
+        backend,
+        pg_options,
+        None,
+        use_local_synchronization=use_local_synchronization,
+        group_desc=group_desc,
+        device_id=device_id,
+    )
+
+
+def _new_group_with_tag(
+    ranks=None,
+    timeout=None,
+    backend=None,
+    backend_options=None,
+    pg_tag=None,
+    use_local_synchronization=False,
+    group_desc=None,
+    device_id: Optional[torch.device] = None,
+):
+    """
+    Variant of ``new_group`` that exposes tag creation.
+
+    :: N.B. The mechanism is experimental and tied to the functional collectives effort, see
+    ``torch.distributed._functional_collectives`` for reference on how to use it.
+    """
+    global _world
+
+    default_pg = _get_default_group()
+    if device_id is None:
+        device_id = default_pg.bound_device_id
+    elif default_pg.bound_device_id is not None:
+        assert device_id == default_pg.bound_device_id, (
+            "Mismatched bound device between new pg and the default pg."
+        )
+    default_backend, default_store = _world.pg_map[default_pg]
+    global_rank = default_pg.rank()
+    global_world_size = default_pg.size()
+
+    # Default to the same backend as the global process group
+    # if the backend is not specified.
+    if not backend:
+        backend = default_backend
+    backend = Backend(backend)
+
+    # this timeout defaulting/validation is used for all the new_groups/new_subgroups variants,
+    # which may just pass their timeout value (or None)
+    if timeout is None:
+        timeout = _get_default_timeout(backend)
+    _check_valid_timeout(timeout)
+
+    if use_local_synchronization:
+        # MPI backend doesn't have have a way for us to perform a partial sync
+        if backend == Backend.MPI:
+            raise ValueError(
+                "MPI backend doesn't support use_local_synchronization=True"
+            )
+        if ranks is not None and get_rank() not in ranks:
+            return None
+
+    # checks the input ranks
+    if ranks is not None:
+        ranks = sorted(ranks)
+        group_world_size = len(ranks)
+        if group_world_size > global_world_size:
+            raise ValueError(
+                "the new group's world size should be less or "
+                "equal to the world size set by "
+                "init_process_group"
+            )
+        # check ranks' sanity
+        for rank in ranks:
+            if rank < 0 or rank >= global_world_size:
+                raise ValueError(
+                    "The new group's rank should be within "
+                    "the world_size set by init_process_group"
+                )
+        if global_rank in ranks:
+            group_rank = ranks.index(global_rank)
+        else:
+            group_rank = None
+    else:
+        ranks = list(range(global_world_size))
+        group_world_size = global_world_size
+        group_rank = global_rank
+
+    group_name = _process_group_name(ranks, use_hashed_name=use_local_synchronization)
+
+    pg, pg_store = _new_process_group_helper(
+        group_world_size,
+        group_rank,
+        ranks,
+        backend,
+        default_store,
+        group_name,
+        backend_options=backend_options,
+        timeout=timeout,
+        pg_tag=pg_tag,
+        device_id=device_id,
+        group_desc=group_desc,
+    )
+
+    # Create the global rank to group rank mapping
+    _world.pg_group_ranks[pg] = {
+        global_rank: group_rank for group_rank, global_rank in enumerate(ranks)
+    }
+
+    if _is_barrier_after_init() == 1:
+        # barrier at the end to ensure that once we return from this method, all
+        # process groups including global variables (if any) are updated
+        # correctly on all ranks.
+        # Update 04/2023: for large-scale runs, this barrier (esp. store-based
+        # barrier) may be costly and/or unscalable. Also, in a lot of cases,
+        # these barriers may be unnecessary, as proven by a green CI after
+        # removal. An environment variable `TORCH_DIST_INIT_BARRIER` has been
+        # added which enables this barrier only when set to 1.
+        logger.info(
+            "Performing barrier after ProcessGroup initialization since "
+            "TORCH_DIST_INIT_BARRIER = 1"
+        )
+        if backend == Backend.MPI:
+            # MPI doesn't have store.
+            barrier()
+        else:
+            barrier_store = pg_store if use_local_synchronization else default_store
+            world_size = len(ranks) if use_local_synchronization else get_world_size()
+            # Use store based barrier here since barrier() used a bunch of
+            # default devices and messes up NCCL internal state.
+            _store_based_barrier(
+                global_rank, barrier_store, group_name, world_size, timeout
+            )
+
+    return pg
+
+
+def new_subgroups(
+    group_size=None,
+    group=None,
+    timeout=None,
+    backend=None,
+    pg_options=None,
+    group_desc=None,
+):
+    """
+    Create subgroups of equal size.
+
+    By default, it creates intra-machine subgroups,
+    where each of which contains all the ranks of a machine, based on the assumption
+    that each machine has the same number of devices.
+
+    This is a convenience API that calls ``new_group`` to generate multiple subgroups.
+    It requires that all processes in the main group (i.e. all
+    processes that are part of the distributed job) enter this function, even
+    if they are not going to be members of the group.
+
+    .. warning::
+        If ``group_size`` is passed in, the world size must be divisible by ``group_size``.
+        If no ``group_size`` is passed in, it believe that you are creating a group based
+        on CUDA and determining the group size by number of CUDA devices, and if not all
+        the machines have the same number of devices, the subgroup division will be
+        different across nodes and can cause unexpected behaviors. Therefore, if you are
+        creating a subgroup that does not depend on CUDA (such as Gloo on CPU), please
+        pass in ``group_size`` correctly.
+
+    .. warning::
+        See warning `Safe concurrent usage` for `new_group` API for important details about
+        using multiple process groups concurrently in a safe manner.
+
+    Args:
+        group_size (int, optional): The size of each subgroup. If ``None``,
+            the default subgroup size is equal to the number of devices on each machine,
+            based on the assumption that each machine has exactly the same
+            number of devices. Default is ``None``.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        backend (str or Backend, optional): The backend to use. Depending on
+            build-time configurations, valid values are ``gloo`` and ``nccl``.
+            By default uses the same backend as the global group. This field
+            should be given as a lowercase string (e.g., ``"gloo"``), which can
+            also be accessed via :class:`Backend` attributes (e.g.,
+            ``Backend.GLOO``). If ``None`` is passed in, the backend
+            corresponding to the default process group will be used. Default is
+            ``None``.
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e. for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            process group can pick up high priority cuda streams.
+        group_desc (str, optional): A string describing the group. Each subgroup will
+            inherit its group_desc
+
+    Returns:
+        The subgroup containing the current rank, and all the subgroups used for cleanup.
+
+    Examples:
+        >>> # Create intra-machine subgroups.
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> cur_subgroup, subgroups = dist.new_subgroups()
+        >>> # Allreduce within the machine.
+        >>> rank = dist.get_rank()
+        >>> tensor = torch.ones(1, device=rank) * rank
+        >>> dist.all_reduce(tensor, group=cur_subgroup)
+        >>> tensor
+        tensor([28])  # Assume 8 CUDA devices per machine.  28 is sum(range(8)).
+        >>> # Cleanup.
+        >>> for subgroup in subgroups:
+        >>>     dist.destroy_process_group(subgroup)
+    """
+    if group_size is None:
+        if not torch.cuda.is_available():
+            raise ValueError(
+                "Default group size only takes effect when CUDA is available."
+                "If your subgroup using a backend that does not depend on CUDA,"
+                "please pass in 'group_size' correctly."
+            )
+        group_size = torch.cuda.device_count()
+    if group_size <= 0:
+        raise ValueError(f"The arg 'group_size' ({group_size}) must be positive")
+
+    world_size = get_world_size()
+    if world_size < group_size:
+        raise ValueError(
+            f"The arg 'group_size' ({group_size}) must not exceed the world size ({world_size})"
+        )
+    if world_size % group_size != 0:
+        raise ValueError("The world size must be divisible by 'group_size'")
+
+    subgroups = []
+    cur_subgroup = None
+
+    for subgroup_id in range(world_size // group_size):
+        start_rank = subgroup_id * group_size
+        end_rank = start_rank + group_size
+        ranks_in_subgroup = list(range(start_rank, end_rank))
+        subgroup = new_group(
+            ranks=ranks_in_subgroup,
+            timeout=timeout,
+            backend=backend,
+            pg_options=pg_options,
+            group_desc=group_desc,
+        )
+        subgroups.append(subgroup)
+
+        rank = get_rank()
+        if rank in ranks_in_subgroup:
+            cur_subgroup = subgroup
+            logger.info("Rank %s is assigned to subgroup %s", rank, ranks_in_subgroup)
+
+    return cur_subgroup, subgroups
+
+
+def new_subgroups_by_enumeration(
+    ranks_per_subgroup_list,
+    timeout=None,
+    backend=None,
+    pg_options=None,
+    group_desc=None,
+):
+    """
+    Create subgroups by dividing the global world.
+
+    The division is specified by a nested list of ranks. The subgroups cannot have
+    overlap, and some ranks may not have to be in any subgroup.
+
+    This is a convenience API that calls ``new_group`` to generate multiple subgroups.
+    It requires that all processes in the main group (i.e. all
+    processes that are part of the distributed job) enter this function, even
+    if they are not going to be members of the group.
+
+    .. warning::
+        See warning `Safe concurrent usage` for `new_group` API for important details about
+        using multiple process groups concurrently in a safe manner.
+
+    Args:
+        ranks_per_subgroup_list (list[list[int]]): A nested list of ranks of
+            group members.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        backend (str or Backend, optional): The backend to use. Depending on
+             build-time configurations, valid values are ``gloo`` and ``nccl``.
+             By default uses the same backend as the global group. This field
+             should be given as a lowercase string (e.g., ``"gloo"``), which can
+             also be accessed via :class:`Backend` attributes (e.g.,
+             ``Backend.GLOO``). If ``None`` is passed in, the backend
+             corresponding to the default process group will be used. Default is
+             ``None``.
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e. for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            process group can pick up high priority cuda streams.
+        group_desc (str, optional): A string describing the group. Each subgroup will
+            inherit its group_desc.
+
+    Returns:
+        The subgroup containing the current rank, and all the subgroups used for cleanup.
+
+    Examples:
+        >>> # Create two subgroups, where each has 2 processes.
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> cur_subgroup, subgroups = dist.new_subgroups(ranks=[[0, 2], [1, 3]])
+        >>> rank = dist.get_rank()
+        >>> tensor = torch.ones(1, device=rank) * rank
+        >>> dist.all_reduce(tensor, group=cur_subgroup)
+        >>> tensor
+        tensor([2])     # Subgroup 0: ranks 0 and 2
+        tensor([4])     # Subgroup 1: ranks 1 and 3
+    """
+    if ranks_per_subgroup_list is None or len(ranks_per_subgroup_list) == 0:
+        raise ValueError("The arg 'ranks_per_subgroup_list' cannot be empty")
+
+    subgroups = []
+    cur_subgroup = None
+    # Create a mapping from rank to subgroup to check if there is any subgroup overlap.
+    rank_to_ranks_dict = {}  # type: ignore[var-annotated]
+    for ranks in ranks_per_subgroup_list:
+        subgroup = new_group(
+            ranks=ranks,
+            timeout=timeout,
+            backend=backend,
+            pg_options=pg_options,
+            group_desc=group_desc,
+        )
+        subgroups.append(subgroup)
+        my_rank = get_rank()
+        for rank in ranks:
+            if rank in rank_to_ranks_dict:
+                raise ValueError(
+                    f"Rank {rank} has appeared in both subgroup {rank_to_ranks_dict[rank]} and {ranks}"
+                )
+            rank_to_ranks_dict[rank] = ranks
+            if my_rank == rank:
+                cur_subgroup = subgroup
+                logger.info("Rank %s is assigned to subgroup %s", rank, ranks)
+
+    return cur_subgroup, subgroups
+
+
+def _find_pg_by_ranks_and_tag(tag: str, ranks: list[int]) -> Optional[ProcessGroup]:
+    if len(tag) > 0 and not tag.startswith("ptd:") and not tag.startswith("user:"):
+        tag = f"user:{tag}"
+
+    for group in _world.tags_to_pg.get(tag, []):
+        if group.size() != len(ranks):
+            continue
+
+        group_ranks = get_process_group_ranks(group)
+        good = all(r in group_ranks for r in ranks)
+        if good:
+            return group
+    return None
+
+
+def _find_or_create_pg_by_ranks_and_tag(
+    tag: str, ranks: list[int], stride: int
+) -> ProcessGroup:
+    assert len(ranks) % stride == 0, (
+        f"Ranks length ({len(ranks)}) must be divisible by stride ({stride})"
+    )
+
+    my_rank = get_rank()
+    my_ranks = None
+
+    if stride == len(ranks):
+        my_ranks = ranks.copy()
+        assert my_rank in my_ranks, "rankset doesn't include the current node"
+    else:
+        for i in range(0, len(ranks), stride):
+            rank_set = ranks[i : i + stride]
+            if my_rank in rank_set:
+                my_ranks = rank_set
+        assert my_ranks is not None, "rankset doesn't include the current node"
+
+    my_ranks = sorted(my_ranks)
+
+    pg = _find_pg_by_ranks_and_tag(tag, my_ranks)
+    if pg is not None:
+        return pg
+    if tag == "":
+        raise ValueError("Cannot automatically create PG with empty tag")
+    # TODO copy settings and timeout from default PG
+    return _new_group_with_tag(my_ranks, pg_tag=tag)
+
+
+def _get_group_tag(pg: ProcessGroup) -> str:
+    """Return the tag associated with ``pg``."""
+    tag = _world.pg_to_tag[pg]
+    tag = tag.removeprefix("user:")
+    return tag
+
+
+def _get_process_group_name(pg: ProcessGroup) -> str:
+    return _world.pg_names.get(pg, "None")
+
+
+def _get_process_group_store(pg: ProcessGroup) -> Store:
+    return _world.pg_map[pg][1]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7c9b29a750593a812907ce2cf4c800d7d1435bb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/__init__.py
@@ -0,0 +1,77 @@
+#!/usr/bin/env/python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+
+Torchelastic agent and user worker failover contract:
+
+**TL;DR;**:
+
+* TE(torchelastic) expects user workers to finish with the 5 minutes drift
+* It is better to design DDP app to fail for all workers, rather than a single one.
+* TE does not synchronize number of restarts between agents
+* TE re-rendezvous does not trigger restart decrease
+* When a single agent finishes its job(successfully or not), it will close rendezvous.
+  If other agents still have workers in progress, they will be terminated.
+* Based on above, scale down does not work if at least single agent finishes the job.
+* When Scale up is detected by agents, it will not decrease ``max_restarts``
+
+
+In general TE(torchelastic) can launch arbitrary user code, but there is some
+clarifications need to be done around what failover mechanism torchelastic
+provides and what failover mechanism it expects from user workers.
+
+Torchelastic currently supports DDP style applications.  That means that
+TE expects *ALL* workers finish approximately at the same time. In practice,
+it is nearly to impossible to guarantee that all workers in arbitrary
+DDP application finish at the time, so TE provides a finalization barrier
+that waits for TIMEOUT(5 minutes) for worker finalization.
+
+**Worker Failure**
+
+When worker fails, TE will check the number of restarts
+available, if there is more than 0 restarts, TE will start a new rendezvous
+round and restart the worker process. New rendezvous round will other
+TE agents to terminate their workers.
+
+.. note:: The TE agent does not synchronize restarts between themselves.
+          When a single agent performs restart, it will trigger a local ``max_restarts``
+          decrease, other agent will not decrease their ``max_restarts``.
+          the user to run the distributed application locally on a dev host.
+
+A single worker failure can cause the whole cluster to fail:
+If a single worker is constantly failing, it will cause the TE agent
+``max_restarts``  to go to zero. This will cause an agent to finish its
+work and close rendezvous. If there are any other workers on different
+agents, they will be terminated.
+
+
+**Re-Rendezvous**
+
+Re-rendezvous occurs when TE agents detect a new node
+trying to joint a cluster. TE will not decrease ``max_restarts``. TE agents
+will terminate its workers and start a new rendezvous round.
+
+Note about DynamicRendezvous(etcd-v2, c10d-experimental): If the rendezvous
+has already max_nodes, the new node won't be added to the wait list right
+away since there is no need to tear down a rendezvous that is already fully
+utilized. The new node will wait until its timeout (600 secs by default)
+and periodically check the number of participants. If the number becomes
+less than max_nodes, it will be added to the wait list; otherwise, it will time out after 600 secs.
+
+*Scale up event*. When scale up event happens, torchelastic rendezvous
+will detect that there are new nodes trying to join. Torchelastic agent
+will stop all workers and perform re-rendezvous. Note: when scale up event
+happens, *``max_restarts``* will *not* decrease.
+
+*Scale down event*. When scale down event happens, rendezvous will not
+notify the torchelastic agent about it. If TE agent launched with ``max_restarts=0`` ,
+it relies on the underlying scheduler to handle job restart. If the ``max_restarts>0`` ,
+TE agent will terminate workers and start a new rdzv round, which is a *Scale up event*.
+
+"""
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c0d76131fe40d70945ffa8ff97431954151d50e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/__init__.py
@@ -0,0 +1,41 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+The elastic agent is the control plane of torchelastic.
+
+It is a process that launches and manages underlying worker processes.
+The agent is responsible for:
+
+1. Working with distributed torch: the workers are started with all the
+   necessary information to successfully and trivially call
+   ``torch.distributed.init_process_group()``.
+
+2. Fault tolerance: monitors workers and upon detecting worker failures
+   or unhealthiness, tears down all workers and restarts everyone.
+
+3. Elasticity: Reacts to membership changes and restarts workers with the new
+   members.
+
+The simplest agents are deployed per node and works with local processes.
+A more advanced agent can launch and manage workers remotely. Agents can
+be completely decentralized, making decisions based on the workers it manages.
+Or can be coordinated, communicating to other agents (that manage workers
+in the same job) to make a collective decision.
+"""
+
+from .api import (  # noqa: F401
+    ElasticAgent,
+    RunResult,
+    SimpleElasticAgent,
+    Worker,
+    WorkerGroup,
+    WorkerSpec,
+    WorkerState,
+)
+from .local_elastic_agent import TORCHELASTIC_ENABLE_FILE_TIMER, TORCHELASTIC_TIMER_FILE
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..b468e1dd1b616d453741dbf020658e97aa5d6134
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py
@@ -0,0 +1,957 @@
+# mypy: ignore-errors
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import abc
+import json
+import os
+import signal
+import socket
+import time
+import traceback
+import warnings
+from collections import defaultdict
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Callable, Optional, Union
+
+import torch.distributed.elastic.rendezvous as rdzv
+import torch.distributed.elastic.utils.store as store_util
+from torch.distributed.elastic.events import Event, EventSource, record
+from torch.distributed.elastic.metrics import prof, put_metric
+from torch.distributed.elastic.multiprocessing import ProcessFailure, SignalException
+from torch.distributed.elastic.rendezvous import RendezvousGracefulExitError
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+__all__ = [
+    "WorkerSpec",
+    "Worker",
+    "WorkerState",
+    "WorkerGroup",
+    "RunResult",
+    "ElasticAgent",
+    "SimpleElasticAgent",
+]
+_TERMINAL_STATE_SYNC_ID = "torchelastic/agent/terminal_state"
+
+DEFAULT_ROLE = "default"
+logger = get_logger(__name__)
+
+
+@dataclass
+class WorkerSpec:
+    """Blueprint information about a particular type of worker.
+
+    For a given role, there must only exist a single worker spec.
+    Worker spec is expected to be homogeneous across all nodes (machine),
+    that is each node runs the same number of workers for a particular spec.
+
+    Args:
+        role: user-defined role for the workers with this spec
+        local_world_size: number local workers to run
+        fn: (deprecated use entrypoint instead)
+        entrypoint: worker function or command
+        args: arguments to pass to ``entrypoint``
+        rdzv_handler: handles rdzv for this set of workers
+        max_restarts: number of max retries for the workers
+        monitor_interval: monitor status of workers every ``n`` seconds
+        master_port: fixed port to run the c10d store on rank 0
+                     if not specified then will chose a random free port
+        master_addr: fixed master_addr to run the c10d store on rank 0
+                     if not specified then will chose hostname on agent rank 0
+        redirects: redirect std streams to a file,
+                   selectively redirect for a particular
+                   local rank by passing a map
+        tee: tees the specified std stream(s) to console + file,
+             selectively tee for a particular local rank by passing a map,
+             takes precedence over ``redirects`` settings.
+
+    """
+
+    role: str
+    local_world_size: int
+    rdzv_handler: rdzv.RendezvousHandler
+    fn: Optional[Callable] = None
+    # TODO @kiuk - make entrypoint a required field
+    entrypoint: Union[Callable, str, None] = None
+    args: tuple = ()
+    max_restarts: int = 3
+    monitor_interval: float = 0.1
+    master_port: Optional[int] = None
+    master_addr: Optional[str] = None
+    local_addr: Optional[str] = None
+
+    def __post_init__(self):
+        assert self.local_world_size > 0
+        assert self.monitor_interval > 0
+
+        if self.fn:
+            warnings.warn(
+                "WorkerSpec.fn will be deprecated,"
+                " please use WorkerSpec.entrypoint instead",
+                category=DeprecationWarning,
+            )
+            self.entrypoint = self.fn
+        assert self.entrypoint
+
+    def get_entrypoint_name(self):
+        """Get the entry point name.
+
+        If the entrypoint is a function (e.g. ``Callable``) returns its ``__qualname__``
+        else if the entrypoint is a binary (e.g. ``str``), returns the binary name.
+        """
+        if isinstance(self.entrypoint, str):
+            return os.path.basename(self.entrypoint)
+        else:
+            assert self.entrypoint is not None
+            return self.entrypoint.__qualname__
+
+
+class Worker:
+    """A worker instance.
+
+    Contrast this with ``WorkerSpec`` that represents the specifications of a
+    worker. A ``Worker`` is created from a ``WorkerSpec``. A ``Worker`` is to
+    a ``WorkerSpec`` as an object is to a class.
+
+    The ``id`` of the worker is interpreted
+    by the specific implementation of ``ElasticAgent``. For a local
+    agent, it could be the ``pid (int)`` of the worker, for a remote
+    agent it could be encoded as ``host:port (string)``.
+
+    Args:
+        id (Any): uniquely identifies a worker (interpreted by the agent)
+        local_rank (int): local rank of the worker
+        global_rank (int): global rank of the worker
+        role_rank (int): rank of the worker across all workers that have the same role
+        world_size (int): number of workers (globally)
+        role_world_size (int): number of workers that have the same role
+    """
+
+    __slots__ = [
+        "id",
+        "local_rank",
+        "global_rank",
+        "role_rank",
+        "world_size",
+        "role_world_size",
+    ]
+
+    def __init__(
+        self,
+        local_rank: int,
+        global_rank: int = -1,
+        role_rank: int = -1,
+        world_size: int = -1,
+        role_world_size: int = -1,
+    ):
+        # unique identifier for this worker
+        self.id: Any = None
+
+        # rank of the worker among workers with the same role being monitored
+        # by the same ``agent`` instance.
+        self.local_rank: int = local_rank
+
+        #  rank of the worker among all the workers across all roles
+        #  across all ``agent`` instances.
+        #  Global rank is not stable between re-rendezvous.
+        self.global_rank: int = global_rank
+
+        #  rank of the worker among all the workers with the same role
+        #  across all ``agent`` instances.
+        #  Role rank is not stable between re-rendezvous.
+        self.role_rank: int = role_rank
+
+        # total number of workers (globally). Due to elasticity
+        # the world size may change between re-rendezvous.
+        self.world_size: int = world_size
+
+        # total number of workers that share the same role. Due to elasticity
+        # the role world size may change between re-rendezvous.
+        self.role_world_size: int = role_world_size
+
+    def __str__(self):
+        return (
+            f"local_rank={self.local_rank},global_rank={self.global_rank}"
+            f",role_rank={self.role_rank},world_size={self.world_size}"
+            f",role_world_size={self.role_world_size}"
+        )
+
+    def __repr__(self):
+        return str(self)
+
+
+class WorkerState(str, Enum):
+    """A state of the ``WorkerGroup``.
+
+    Workers in a worker group change state as a unit. If a single worker
+    in a worker group fails the entire set is considered failed::
+
+      UNKNOWN - agent lost track of worker group state, unrecoverable
+      INIT - worker group object created not yet started
+      HEALTHY - workers running and healthy
+      UNHEALTHY - workers running and unhealthy
+      STOPPED - workers stopped (interrupted) by the agent
+      SUCCEEDED - workers finished running (exit 0)
+      FAILED - workers failed to successfully finish (exit !0)
+
+
+    A worker group starts from an initial ``INIT`` state,
+    then progresses to ``HEALTHY`` or ``UNHEALTHY`` states,
+    and finally reaches a terminal ``SUCCEEDED`` or ``FAILED`` state.
+
+    Worker groups can be interrupted and temporarily put into ``STOPPED`` state
+    by the agent. Workers in ``STOPPED`` state are scheduled to be restarted
+    in the near future by the agent. Some examples of workers being put into
+    ``STOPPED`` state are:
+
+    1. Worker group failure|unhealthy observed
+    2. Membership change detected
+
+    When actions (start, stop, rdzv, retry, etc) on worker group fails
+    and results in the action being partially applied to the worker group
+    the state will be ``UNKNOWN``. Typically this happens on uncaught/unhandled
+    exceptions during state change events on the agent. The agent is not
+    expected to recover worker groups in ``UNKNOWN`` state and is better off
+    self terminating and allowing the job manager to retry the node.
+    """
+
+    UNKNOWN = "UNKNOWN"
+    INIT = "INIT"
+    HEALTHY = "HEALTHY"
+    UNHEALTHY = "UNHEALTHY"
+    STOPPED = "STOPPED"
+    SUCCEEDED = "SUCCEEDED"
+    FAILED = "FAILED"
+
+    @staticmethod
+    def is_running(state: "WorkerState") -> bool:
+        """Return the state of the Worker.
+
+        Returns:
+             True if the worker state represents workers still running
+             (e.g. that the process exists but not necessarily healthy).
+        """
+        return state in {WorkerState.HEALTHY, WorkerState.UNHEALTHY}
+
+
+class WorkerGroup:
+    """A set of ``Worker`` instances.
+
+    The class defines a set of ``Worker`` instances for the given ``WorkerSpec`` managed by ``ElasticAgent``. Whether the worker
+    group contains cross instance workers or not depends on the implementation of the agent.
+    """
+
+    __slots__ = [
+        "spec",
+        "workers",
+        "store",
+        "group_rank",
+        "group_world_size",
+        "state",
+        "master_addr",
+        "master_port",
+    ]
+
+    def __init__(self, spec: WorkerSpec):
+        self.spec = spec
+        self.workers = [Worker(local_rank=i) for i in range(self.spec.local_world_size)]
+
+        # assigned after rdzv
+        self.store = None
+        self.group_rank = None
+        self.group_world_size = None
+        self.master_addr = None
+        self.master_port = None
+
+        self.state = WorkerState.INIT
+
+
+class _RoleInstanceInfo:
+    """The class is used by the agent to exchange the information with other agents.
+
+    The information is used to determine the rank of the workers that agent
+    manages in heterogeneous environments, where different agents can have
+    different number of workers.
+    """
+
+    __slots__ = ["role", "rank", "local_world_size"]
+
+    def __init__(self, role: str, rank: int, local_world_size: int):
+        r"""Initialize the agent class instance.
+
+        Args:
+            role (str): user-defined role for the workers with this spec
+            rank (int): the rank of the agent
+            local_world_size (int): number of local workers to run
+        """
+        self.role = role
+        self.rank = rank
+        self.local_world_size = local_world_size
+
+    def serialize(self) -> bytes:
+        dict_data = {
+            "role": self.role,
+            "rank": self.rank,
+            "local_world_size": self.local_world_size,
+        }
+        return json.dumps(dict_data).encode(encoding="UTF-8")
+
+    @staticmethod
+    def deserialize(data: bytes):
+        dict_data = json.loads(data.decode(encoding="UTF-8"))
+        return _RoleInstanceInfo(
+            dict_data["role"], dict_data["rank"], dict_data["local_world_size"]
+        )
+
+    @staticmethod
+    def compare(obj1, obj2) -> int:
+        if obj1.role == obj2.role:
+            return obj1.rank - obj2.rank
+        elif obj1.role > obj2.role:
+            return 1
+        else:
+            return -1
+
+    @staticmethod
+    def find_role_boundaries(roles_infos: list, role: str) -> tuple[int, int]:
+        start_idx, end_idx = -1, -1
+        for idx, role_info in enumerate(roles_infos):
+            if role_info.role == role:
+                if start_idx == -1:
+                    start_idx = idx
+                end_idx = idx
+        return (start_idx, end_idx)
+
+
+@dataclass
+class RunResult:
+    """Return results of the worker executions.
+
+    Run results follow an "all-or-nothing" policy where the run is successful if and
+    only if ALL local workers managed by this agent complete successfully.
+
+    If the result is successful (e.g. ``is_failed() = False``) then the ``return_values``
+    field contains the outputs (return values) of the workers managed by THIS agent mapped
+    by their GLOBAL ranks. That is ``result.return_values[0]`` is the return value of
+    global rank 0.
+
+    .. note:: ``return_values`` are only meaningful for when the worker entrypoint
+              is a function. Workers specified as a binary entrypoint do not canonically
+              have a return value and the ``return_values`` field is meaningless and
+              may be empty.
+
+    If ``is_failed()`` returns ``True`` then the ``failures`` field contains the
+    failure information, again, mapped by the GLOBAL rank of the worker that failed.
+
+    The keys in ``return_values`` and ``failures`` are mutually exclusive, that is,
+    a worker's final state can only be one of: succeeded, failed. Workers intentionally
+    terminated by the agent according to the agent's restart policy, are not represented
+    in either ``return_values`` nor ``failures``.
+    """
+
+    state: WorkerState
+    return_values: dict[int, Any] = field(default_factory=dict)
+    failures: dict[int, ProcessFailure] = field(default_factory=dict)
+
+    def is_failed(self) -> bool:
+        return self.state == WorkerState.FAILED
+
+
+def _get_fq_hostname() -> str:
+    return socket.getfqdn(socket.gethostname())
+
+
+class ElasticAgent(abc.ABC):
+    """An agent process responsible for managing one or more worker processes.
+
+    The worker processes are assumed to be regular distributed PyTorch scripts.
+    When the worker process is created by the agent, the agent provides the
+    necessary information for the worker processes to properly initialize
+    a torch process group.
+
+    The exact deployment topology and ratio of agent-to-worker is dependent
+    on the specific implementation of the agent and the user's job placement
+    preferences. For instance, to run a distributed training job on GPU with
+    8 trainers (one per GPU) one can:
+
+    1. Use 8 x single GPU instances, place an agent per instance, managing
+       1 worker per agent.
+    2. Use 4 x double GPU instances, place an agent per instance, managing
+       2 workers per agent.
+    3. Use 2 x quad GPU instances, place an agent per instance, managing
+       4 workers per agent.
+    4. Use 1 x 8 GPU instance, place an agent per instance, managing
+       8 workers per agent.
+
+    Usage
+    ::
+
+     group_result = agent.run()
+      if group_result.is_failed():
+        # workers failed
+        failure = group_result.failures[0]
+        logger.exception("worker 0 failed with exit code : %s", failure.exit_code)
+      else:
+        return group_result.return_values[0] # return rank 0's results
+
+    """
+
+    @abc.abstractmethod
+    def run(self, role: str = DEFAULT_ROLE) -> RunResult:
+        """Run the agent.
+
+        Supports retrying the worker group on failures up to ``max_restarts``.
+
+        Returns:
+            The result of the execution, containing the return values or
+            failure details for each worker mapped by the worker's global rank.
+
+        Raises:
+            Exception - any other failures NOT related to worker process
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def get_worker_group(self, role: str = DEFAULT_ROLE) -> WorkerGroup:
+        """Return the ``WorkerGroup`` for the given ``role``.
+
+        Note that the worker group is a mutable object and hence in a
+        multi-threaded/process environment it may change state.
+        Implementors are encouraged (but not required) to return
+        a defensive read-only copy.
+        """
+        raise NotImplementedError
+
+
+class SimpleElasticAgent(ElasticAgent):
+    """An ``ElasticAgent`` that manages one particular type of worker role.
+
+    An ``ElasticAgent`` that manages workers (``WorkerGroup``) for a single ``WorkerSpec``
+    such as one particular type of worker role.
+    """
+
+    def __init__(self, spec: WorkerSpec, exit_barrier_timeout: float = 300):
+        self._worker_group = WorkerGroup(spec)
+        self._remaining_restarts = self._worker_group.spec.max_restarts
+        self._store = None
+        self._exit_barrier_timeout = exit_barrier_timeout
+        self._total_execution_time = 0
+
+    def get_worker_group(self, role: str = DEFAULT_ROLE) -> WorkerGroup:
+        return self._worker_group
+
+    @abc.abstractmethod
+    def _start_workers(self, worker_group: WorkerGroup) -> dict[int, Any]:
+        r"""Start ``worker_group.spec.local_world_size`` number of workers.
+
+        This is according to worker spec for the worker group .
+        Returns a map of ``local_rank`` to worker ``id``.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _stop_workers(
+        self, worker_group: WorkerGroup, is_restart: bool = False
+    ) -> None:
+        r"""Stop all workers in the given worker group.
+
+        Implementors must deal with workers in all states defined by
+        ``WorkerState``. That is, it must gracefully handle stopping
+        non-existent workers, unhealthy (stuck) workers, etc.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _monitor_workers(self, worker_group: WorkerGroup) -> RunResult:
+        r"""Check on the workers for the ``worker_group``.
+
+        This function also returns the new state of the worker group.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _shutdown(
+        self, death_sig: signal.Signals = signal.SIGTERM, is_restart: bool = False
+    ) -> None:
+        """Clean up any resources that were allocated during the agent's work.
+
+        Args:
+            death_sig: Signal to send to the child process, SIGTERM is default
+        """
+        raise NotImplementedError
+
+    @prof
+    def _rendezvous(self, worker_group: WorkerGroup) -> None:
+        r"""Run rendezvous for the workers specified by the worker spec.
+
+        Assigns workers a new global rank and world size.
+        Updates the rendezvous store for the worker group.
+        """
+        spec = worker_group.spec
+
+        with self.record_duration("RENDEZVOUS"):
+            rdzv_info = spec.rdzv_handler.next_rendezvous()
+        store = rdzv_info.store
+        group_rank = rdzv_info.rank
+        group_world_size = rdzv_info.world_size
+
+        # master_addr/master_port could be explicitly overriden
+        # TODO: BC - specific to static rdzv and can be simplifed further
+        master_addr = spec.master_addr or rdzv_info.bootstrap_store_info.master_addr
+        master_port = spec.master_port or rdzv_info.bootstrap_store_info.master_port
+
+        self._store = store
+
+        with self.record_duration("ASSIGN_WORKER_RANKS"):
+            workers = self._assign_worker_ranks(
+                store, group_rank, group_world_size, spec
+            )
+        worker_group.workers = workers
+        worker_group.store = store
+        worker_group.group_rank = group_rank
+        worker_group.group_world_size = group_world_size
+        worker_group.master_addr = master_addr
+        worker_group.master_port = master_port
+
+        restart_count = spec.max_restarts - self._remaining_restarts
+
+        logger.info(
+            "[%(role)s] Rendezvous complete for workers. Result:\n"
+            "  restart_count=%(restart_count)s\n"
+            "  master_addr=%(master_addr)s\n"
+            "  master_port=%(master_port)s\n"
+            "  group_rank=%(group_rank)s\n"
+            "  group_world_size=%(group_world_size)s\n"
+            "  local_ranks=%(local_ranks)s\n"
+            "  role_ranks=%(role_ranks)s\n"
+            "  global_ranks=%(global_ranks)s\n"
+            "  role_world_sizes=%(role_world_sizes)s\n"
+            "  global_world_sizes=%(global_world_sizes)s\n",
+            {
+                "role": spec.role,
+                "restart_count": restart_count,
+                "master_addr": master_addr,
+                "master_port": master_port,
+                "group_rank": group_rank,
+                "group_world_size": group_world_size,
+                "local_ranks": [worker.local_rank for worker in workers],
+                "role_ranks": [worker.role_rank for worker in workers],
+                "global_ranks": [worker.global_rank for worker in workers],
+                "role_world_sizes": [worker.role_world_size for worker in workers],
+                "global_world_sizes": [worker.world_size for worker in workers],
+            },
+        )
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _assign_worker_ranks(
+        self, store, group_rank: int, group_world_size: int, spec: WorkerSpec
+    ) -> list[Worker]:
+        """Determine proper ranks for worker processes.
+
+        Fast Path: when all workers have the same role and world size. We calculate
+        the global rank to be group_rank * group_world_size + local_rank. And the
+        `role_world_size` is the same as `global_world_size`. No TCP store is used in
+        this case. This is only enabled when users set the environment variable
+        `TORCH_ELASTIC_WORKER_IDENTICAL` to 1.
+
+        Time complexity: each worker O(1), overall O(1)
+
+        Slow Path: when workers have different roles and world sizes. We use the
+        the following algorithm:
+
+        1. Each agent writes its configuration(group_rank, group_world_size
+           , num_workers) to the common store.
+        2. The rank 0 agent reads all the role_info from the store and
+           determines each agents worker ranks.
+        3. Determine the global rank: the global rank of the workers is computed
+           by cumulative sum of the local_world_size for all workers in front of it.
+           For efficiency reasons each worker is assigned a base global rank
+           such that it's workers are in the range [base_global_rank,
+           base_global_rank + local_world_size).
+        4. Determine the role rank: The role rank is determined using the algorithms
+           in the point 3 with the exception that the ranks are calculated with
+           respect to the role name.
+        5. The rank 0 agent writes the assigned ranks to the store.
+        6. Each agent reads the assigned ranks from the store.
+
+        Time complexity: each worker O(1), rank0 O(n), overall O(n)
+        """
+
+        if os.environ.get("TORCH_ELASTIC_WORKER_IDENTICAL", "0") == "1":
+            global_world_size = group_world_size * spec.local_world_size
+            base_global_rank = group_rank * spec.local_world_size
+            base_role_rank = base_global_rank
+            role_world_size = global_world_size
+        else:
+            ROLE_INFO_PREFIX = "torchelastic/role_info/"
+            ASSIGNED_RANKS_PREFIX = "torchelastic/assigned_ranks/"
+
+            agent_role_info = _RoleInstanceInfo(
+                spec.role, group_rank, spec.local_world_size
+            )
+            store.set(f"{ROLE_INFO_PREFIX}{group_rank}", agent_role_info.serialize())
+
+            # tcp store is collocated with rank 0 so we can use it to do extra compute to reduce overall # of operations.
+            if group_rank == 0:
+                role_infos_bytes = store.multi_get(
+                    [f"torchelastic/role_info/{i}" for i in range(group_world_size)]
+                )
+                role_infos = [
+                    _RoleInstanceInfo.deserialize(info_bytes)
+                    for info_bytes in role_infos_bytes
+                ]
+
+                role_sizes = defaultdict(lambda: 0)
+                global_size = 0
+                for role_info in role_infos:
+                    role_sizes[role_info.role] += role_info.local_world_size
+                    global_size += role_info.local_world_size
+
+                base_global_rank = 0
+                role_ranks = defaultdict(lambda: 0)
+
+                keys = []
+                values = []
+                for i, role_info in enumerate(role_infos):
+                    keys.append(f"{ASSIGNED_RANKS_PREFIX}{i}")
+                    values.append(
+                        json.dumps(
+                            [
+                                base_global_rank,
+                                global_size,
+                                role_ranks[role_info.role],
+                                role_sizes[role_info.role],
+                            ]
+                        )
+                    )
+
+                    base_global_rank += role_info.local_world_size
+                    role_ranks[role_info.role] += role_info.local_world_size
+
+                store.multi_set(keys, values)
+
+            # get will block until the data is available in the store.
+            (
+                base_global_rank,
+                global_world_size,
+                base_role_rank,
+                role_world_size,
+            ) = json.loads(store.get(f"{ASSIGNED_RANKS_PREFIX}{group_rank}"))
+
+        workers = []
+        for local_rank in range(spec.local_world_size):
+            worker = Worker(
+                local_rank=local_rank,
+                global_rank=base_global_rank + local_rank,
+                role_rank=base_role_rank + local_rank,
+                world_size=global_world_size,
+                role_world_size=role_world_size,
+            )
+            workers.append(worker)
+        return workers
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _initialize_workers(self, worker_group: WorkerGroup) -> None:
+        r"""Start a fresh set of workers for the worker_group.
+
+        Essentially, a rendezvous followed by a ``start_workers``.
+        The caller should first call ``_stop_workers()`` to stop running workers
+        prior to calling this method.
+
+        Optimistically sets the state of the worker group that
+        just started as ``HEALTHY`` and delegates the actual monitoring
+        of state to ``_monitor_workers()`` method
+        """
+        role = worker_group.spec.role
+        logger.info("[%s] Rendezvous'ing worker group", role)
+
+        # TODO after stopping workers, wait at least monitor_interval*2 for
+        # workers on different nodes to fail on a collective op before waiting
+        # on the rdzv barrier, this way we ensure that nodes enter rdzv
+        # at around the same time and reduce false positive rdzv timeout errors
+        self._rendezvous(worker_group)
+
+        logger.info("[%s] Starting worker group", role)
+        worker_ids = self._start_workers(worker_group)
+        for local_rank, w_id in worker_ids.items():
+            worker = worker_group.workers[local_rank]
+            worker.id = w_id
+
+        worker_group.state = WorkerState.HEALTHY
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _restart_workers(self, worker_group: WorkerGroup) -> None:
+        """Restart (stops, rendezvous, starts) all local workers in the group."""
+        role = worker_group.spec.role
+        logger.info("[%s] Stopping worker group", role)
+        self._stop_workers(worker_group, is_restart=True)
+        worker_group.state = WorkerState.STOPPED
+        self._initialize_workers(worker_group)
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def run(self, role: str = DEFAULT_ROLE) -> RunResult:
+        start_time = time.monotonic()
+        shutdown_called: bool = False
+        try:
+            result = self._invoke_run(role)
+            self._total_execution_time = int(time.monotonic() - start_time)
+            self._record_metrics(result)
+            self._record_worker_events(result)
+            return result
+        except RendezvousGracefulExitError as e:
+            logger.info("Rendezvous gracefully exited: %s", e)
+        except SignalException as e:
+            logger.warning("Received %s death signal, shutting down workers", e.sigval)
+            self._shutdown(e.sigval)
+            shutdown_called = True
+            raise
+        finally:
+            if not shutdown_called:
+                self._shutdown()
+            # record the execution time in case there were any exceptions during run.
+            self._total_execution_time = int(time.monotonic() - start_time)
+
+    def get_event_failed(self) -> Event:
+        return self._construct_event(
+            state="FAILED",
+            source=EventSource.AGENT,
+            raw_error=traceback.format_exc(),
+        )
+
+    def get_event_succeeded(self) -> Event:
+        return self._construct_event(
+            state="SUCCEEDED",
+            source=EventSource.AGENT,
+        )
+
+    def _record_worker_events(self, result: RunResult) -> None:
+        for worker in self._worker_group.workers:
+            failure = result.failures.get(worker.global_rank)
+            state: str = self._get_worker_state(worker, result)
+            raw_error = json.dumps(failure.error_file_data) if failure else None
+            record(self._construct_event(state, EventSource.WORKER, worker, raw_error))
+
+    def _get_worker_state(self, worker: Worker, result: RunResult) -> str:
+        failure = result.failures.get(worker.global_rank)
+        if result.state in {WorkerState.UNHEALTHY, WorkerState.FAILED} and not failure:
+            # The worker got terminated by the torchelastic agent via SIGTERM signal
+            return "TERMINATED"
+        elif failure or worker.global_rank in result.return_values:
+            return result.state.value
+        else:
+            raise ValueError(f"Unknown worker: {worker.global_rank}")
+
+    @contextmanager
+    def record_duration(self, state: str):
+        start_time = time.perf_counter()
+        try:
+            yield
+        finally:
+            end_time = time.perf_counter()
+            duration_ms = (end_time - start_time) * 1000
+            record(
+                self._construct_event(
+                    state=state, source=EventSource.AGENT, duration_ms=duration_ms
+                )
+            )
+
+    def _construct_event(
+        self,
+        state: str,
+        source: EventSource,
+        worker: Optional[Worker] = None,
+        raw_error: Optional[str] = None,
+        duration_ms: Optional[float] = None,
+    ) -> Event:
+        wg = self._worker_group
+        spec = wg.spec
+        md = {
+            "group_world_size": wg.group_world_size,
+            "entry_point": spec.get_entrypoint_name(),
+        }
+        if worker:
+            md["local_rank"] = (worker.local_rank,)
+            md["role_rank"] = (worker.role_rank,)
+            md["role_world_size"] = (worker.role_world_size,)
+            global_rank = worker.global_rank
+            worker_id = str(worker.id)
+        else:
+            global_rank = None
+            worker_id = None
+        md_str = json.dumps(md)
+        metadata = {
+            "run_id": spec.rdzv_handler.get_run_id(),
+            "global_rank": global_rank,
+            "group_rank": wg.group_rank,
+            "worker_id": worker_id,
+            "role": spec.role,
+            "hostname": _get_fq_hostname(),
+            "state": state,
+            "total_run_time": self._total_execution_time,
+            "rdzv_backend": spec.rdzv_handler.get_backend(),
+            "raw_error": raw_error,
+            "metadata": md_str,
+            "agent_restarts": spec.max_restarts - self._remaining_restarts,
+            "duration_ms": duration_ms,
+        }
+        return Event(
+            f"torchelastic.worker.status.{state}", source=source, metadata=metadata
+        )
+
+    def _record_metrics(self, group_results: RunResult):
+        is_failed = group_results.is_failed()
+        self._record_flakiness_metric(is_failed)
+        spec = self._worker_group.spec
+        restarts_happened = self._remaining_restarts != spec.max_restarts
+        put_metric(f"workers.{spec.role}.run_total", 1)
+        self._record_metric_with_condition(
+            "run_success_with_retries", not is_failed and restarts_happened
+        )
+        self._record_metric_with_condition(
+            "run_success_no_retries", not is_failed and not restarts_happened
+        )
+        self._record_metric_with_condition(
+            "run_failed_with_retries", is_failed and restarts_happened
+        )
+        self._record_metric_with_condition(
+            "run_failed_no_retries", is_failed and not restarts_happened
+        )
+
+    def _record_metric_with_condition(self, metric_name, condition):
+        spec = self._worker_group.spec
+        if condition:
+            put_metric(f"workers.{spec.role}.{metric_name}", 1)
+        else:
+            put_metric(f"workers.{spec.role}.{metric_name}", 0)
+
+    def _record_flakiness_metric(self, is_failed: bool = False):
+        if is_failed:
+            flakiness = 100.0
+        else:
+            spec = self._worker_group.spec
+            flakiness = 100.0 - 100.0 * (self._remaining_restarts + 1) / (
+                spec.max_restarts + 1
+            )
+        spec = self._worker_group.spec
+
+        put_metric(f"workers.{spec.role}.flakiness", int(flakiness))
+
+    def _invoke_run(self, role: str = DEFAULT_ROLE) -> RunResult:
+        # NOTE: currently only works for a single role
+
+        spec = self._worker_group.spec
+        role = spec.role
+
+        logger.info(
+            "[%s] starting workers for entrypoint: %s", role, spec.get_entrypoint_name()
+        )
+
+        self._initialize_workers(self._worker_group)
+        monitor_interval = spec.monitor_interval
+        rdzv_handler = spec.rdzv_handler
+
+        while True:
+            assert self._worker_group.state != WorkerState.INIT
+            time.sleep(monitor_interval)
+            run_result = self._monitor_workers(self._worker_group)
+            state = run_result.state
+            self._worker_group.state = state
+
+            put_metric(f"workers.{role}.remaining_restarts", self._remaining_restarts)
+            put_metric(f"workers.{role}.{state.name.lower()}", 1)
+
+            if state == WorkerState.SUCCEEDED:
+                logger.info(
+                    "[%s] worker group successfully finished."
+                    " Waiting %s seconds for other agents to finish.",
+                    role,
+                    self._exit_barrier_timeout,
+                )
+                self._exit_barrier()
+                return run_result
+            elif state in {WorkerState.UNHEALTHY, WorkerState.FAILED}:
+                if self._remaining_restarts > 0:
+                    logger.info(
+                        "[%s] Worker group %s. "
+                        "%s/%s attempts left;"
+                        " will restart worker group",
+                        role,
+                        state.name,
+                        self._remaining_restarts,
+                        spec.max_restarts,
+                    )
+                    self._remaining_restarts -= 1
+                    self._restart_workers(self._worker_group)
+                else:
+                    self._stop_workers(self._worker_group)
+                    self._worker_group.state = WorkerState.FAILED
+                    return run_result
+            elif state == WorkerState.HEALTHY:
+                # membership changes do not count as retries
+                num_nodes_waiting = rdzv_handler.num_nodes_waiting()
+                group_rank = self._worker_group.group_rank
+                if num_nodes_waiting > 0:
+                    logger.info(
+                        "[%s] Detected %s "
+                        "new nodes from group_rank=%s; "
+                        "will restart worker group",
+                        role,
+                        num_nodes_waiting,
+                        group_rank,
+                    )
+                    self._restart_workers(self._worker_group)
+            else:
+                raise Exception(  # noqa: TRY002
+                    f"[{role}] Worker group in {state.name} state"
+                )
+
+    def _exit_barrier(self):
+        """
+        Define a barrier that keeps the agent process alive until all workers finish.
+
+        Wait for ``exit_barrier_timeout`` seconds for all agents to finish
+        executing their local workers (either successfully or not). This
+        acts as a safety guard against user scripts that terminate at different
+        times.
+        """
+        logger.info(
+            "Local worker group finished (%s). "
+            "Waiting %s seconds for other agents to finish",
+            self._worker_group.state,
+            self._exit_barrier_timeout,
+        )
+        start = time.time()
+        try:
+            store_util.barrier(
+                store=self._store,
+                world_size=self._worker_group.group_world_size,
+                key_prefix=_TERMINAL_STATE_SYNC_ID,
+                barrier_timeout=self._exit_barrier_timeout,
+            )
+            logger.info(
+                "Done waiting for other agents. Elapsed: %s seconds",
+                time.time() - start,
+            )
+        except SignalException as e:
+            logger.warning("Got termination signal: %s", e.sigval)
+            raise
+        except Exception:
+            logger.exception(
+                "Error waiting on exit barrier. Elapsed: %s seconds",
+                time.time() - start,
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/health_check_server.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/health_check_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..d54915f7461685b9a49f87ea6dfa69a0c7d5e4c9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/health_check_server.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Callable
+
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+log = get_logger(__name__)
+
+__all__ = ["HealthCheckServer", "create_healthcheck_server"]
+
+
+class HealthCheckServer:
+    """
+    Interface for health check monitoring server, which can be extended
+    by starting tcp/http server on the specified port.
+
+    Args:
+
+        alive_callback: Callable[[], int], callback to last progress time of agent
+
+        port: int, port number to start tcp/http server
+
+        timeout: int, timeout seconds to decide agent is alive/dead
+    """
+
+    _alive_callback: Callable[[], int]
+    _port: int
+    _timeout: int
+
+    def __init__(
+        self, alive_callback: Callable[[], int], port: int, timeout: int
+    ) -> None:
+        self._alive_callback = alive_callback
+        self._port = port
+        self._timeout = timeout
+
+    def start(self) -> None:
+        """
+        Unsupported functionality for Pytorch, doesn't start any health check server
+        """
+        log.warning("No health check server started")
+
+    def stop(self) -> None:
+        """
+        Function to stop health check server
+        """
+        log.info("Stopping noop health check server.")
+
+
+def create_healthcheck_server(
+    alive_callback: Callable[[], int],
+    port: int,
+    timeout: int,
+) -> HealthCheckServer:
+    """
+    creates health check server object
+    """
+    return HealthCheckServer(alive_callback, port, timeout)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8906bbc9b5bce925b2e771328d304cdaade0538
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py
@@ -0,0 +1,417 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import json
+import os
+import signal
+import socket
+import time
+import uuid
+from string import Template
+from typing import Any, Optional, TYPE_CHECKING
+
+import torch.distributed.elastic.timer as timer
+from torch.distributed.elastic import events
+from torch.distributed.elastic.agent.server.api import (
+    RunResult,
+    SimpleElasticAgent,
+    WorkerGroup,
+    WorkerSpec,
+    WorkerState,
+)
+from torch.distributed.elastic.agent.server.health_check_server import (
+    create_healthcheck_server,
+    HealthCheckServer,
+)
+from torch.distributed.elastic.metrics.api import prof
+from torch.distributed.elastic.multiprocessing import (
+    LogsSpecs,
+    PContext,
+    start_processes,
+)
+from torch.distributed.elastic.utils import macros
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+if TYPE_CHECKING:
+    from torch.distributed.elastic.events.api import EventMetadataValue
+
+logger = get_logger(__name__)
+
+__all__ = [
+    "LocalElasticAgent",
+    "TORCHELASTIC_ENABLE_FILE_TIMER",
+    "TORCHELASTIC_TIMER_FILE",
+    "TORCHELASTIC_HEALTH_CHECK_PORT",
+]
+
+TORCHELASTIC_ENABLE_FILE_TIMER = "TORCHELASTIC_ENABLE_FILE_TIMER"
+TORCHELASTIC_HEALTH_CHECK_PORT = "TORCHELASTIC_HEALTH_CHECK_PORT"
+TORCHELASTIC_TIMER_FILE = "TORCHELASTIC_TIMER_FILE"
+
+
+class LocalElasticAgent(SimpleElasticAgent):
+    """An implementation of :py:class:`torchelastic.agent.server.ElasticAgent` that handles host-local workers.
+
+    This agent is deployed per host and is configured to spawn ``n`` workers.
+    When using GPUs, ``n`` maps to the number of GPUs available on the host.
+
+    The local agent does not communicate to other local agents deployed on
+    other hosts, even if the workers may communicate inter-host. The worker id
+    is interpreted to be a local process. The agent starts and stops all worker
+    processes as a single unit.
+
+
+    The worker function and argument passed to the worker function must be
+    python multiprocessing compatible. To pass multiprocessing data structures
+    to the workers you may create the data structure in the same multiprocessing
+    context as the specified ``start_method`` and pass it as a function argument.
+
+    The ``exit_barrier_timeout`` specifies the amount of time (in seconds) to wait
+    for other agents to finish. This acts as a safety net to handle cases where
+    workers finish at different times, to prevent agents from viewing workers
+    that finished early as a scale-down event. It is strongly advised that the
+    user code deal with ensuring that workers are terminated in a synchronous
+    manner rather than relying on the exit_barrier_timeout.
+
+    A named pipe based watchdog can be enabled in ```LocalElasticAgent``` if an
+    environment variable ``TORCHELASTIC_ENABLE_FILE_TIMER`` with value 1 has
+    been defined in the ```LocalElasticAgent``` process.
+    Optionally, another environment variable ```TORCHELASTIC_TIMER_FILE```
+    can be set with a unique file name for the named pipe. If the environment
+    variable ```TORCHELASTIC_TIMER_FILE``` is not set, ```LocalElasticAgent```
+    will internally create a unique file name and set it to the environment
+    variable ```TORCHELASTIC_TIMER_FILE```, and this environment variable will
+    be propagated to the worker processes to allow them to connect to the same
+    named pipe that ```LocalElasticAgent``` uses.
+
+    Logs are written to the specified log directory. Each log line will be by default
+    prefixed by ``[${role_name}${local_rank}]:`` (e.g. ``[trainer0]: foobar``).
+    Log prefixes can be customized by passing a `template string
+    `_ as the
+    ``log_line_prefix_template`` argument.
+    The following macros (identifiers) are substituted at runtime:
+    ``${role_name}, ${local_rank}, ${rank}``. For example, to prefix each log line with
+    global rank instead of the local rank, set ``log_line_prefix_template = "[${rank}]:``.
+
+
+    Example launching function
+
+    ::
+
+        def trainer(args) -> str:
+            return "do train"
+
+        def main():
+            start_method="spawn"
+            shared_queue= multiprocessing.get_context(start_method).Queue()
+            spec = WorkerSpec(
+                        role="trainer",
+                        local_world_size=nproc_per_process,
+                        entrypoint=trainer,
+                        args=("foobar",),
+                        ...)
+            agent = LocalElasticAgent(spec, start_method)
+            results = agent.run()
+
+            if results.is_failed():
+                print("trainer failed")
+            else:
+                print(f"rank 0 return value: {results.return_values[0]}")
+                # prints -> rank 0 return value: do train
+
+    Example launching binary
+
+    ::
+
+        def main():
+            spec = WorkerSpec(
+                        role="trainer",
+                        local_world_size=nproc_per_process,
+                        entrypoint="/usr/local/bin/trainer",
+                        args=("--trainer-args", "foobar"),
+                        ...)
+            agent = LocalElasticAgent(spec)
+            results = agent.run()
+
+            if not results.is_failed():
+                print("binary launches do not have return values")
+
+    """
+
+    def __init__(
+        self,
+        spec: WorkerSpec,
+        logs_specs: LogsSpecs,
+        start_method="spawn",
+        exit_barrier_timeout: float = 300,
+        log_line_prefix_template: Optional[str] = None,
+    ):
+        super().__init__(spec, exit_barrier_timeout)
+        self._start_method = start_method
+        self._pcontext: Optional[PContext] = None
+        self._rdzv_handler = spec.rdzv_handler
+        self._log_line_prefix_template = log_line_prefix_template
+        self._worker_watchdog: Optional[timer.FileTimerServer] = None
+        self._logs_specs = logs_specs
+        self._health_check_server: Optional[HealthCheckServer] = None
+
+    def _setup_local_watchdog(self, envs: dict[int, dict[str, str]]) -> None:
+        enable_watchdog_env_name = TORCHELASTIC_ENABLE_FILE_TIMER
+        watchdog_enabled = os.getenv(enable_watchdog_env_name)
+        watchdog_file_env_name = TORCHELASTIC_TIMER_FILE
+        watchdog_file_path = os.getenv(watchdog_file_env_name)
+        if watchdog_enabled is not None and str(watchdog_enabled) == "1":
+            if watchdog_file_path is None:
+                watchdog_file_path = "/tmp/watchdog_timer_" + str(uuid.uuid4())
+            logger.info("Starting a FileTimerServer with %s ...", watchdog_file_path)
+            if not envs:
+                logger.warning(
+                    "Empty envs variables, using empty run_id for FileTimerServer"
+                )
+                run_id = ""
+            else:
+                run_id = envs[0]["TORCHELASTIC_RUN_ID"]
+            self._worker_watchdog = timer.FileTimerServer(
+                file_path=watchdog_file_path,
+                run_id=run_id,
+                max_interval=0.1,
+                daemon=True,
+                log_event=self._log_watchdog_event,
+            )
+            self._worker_watchdog.start()
+            logger.info("FileTimerServer started")
+        else:
+            logger.info(
+                "Environment variable '%s' not found. Do not start FileTimerServer.",
+                enable_watchdog_env_name,
+            )
+        # Propagate the watchdog file env to worker processes
+        if watchdog_file_path is not None:
+            for worker_env in envs.values():
+                worker_env[watchdog_file_env_name] = watchdog_file_path
+
+    @staticmethod
+    def _get_current_time_secs() -> int:
+        return int(time.time())
+
+    def _setup_healthcheck(self) -> None:
+        healthcheck_port_env_name = TORCHELASTIC_HEALTH_CHECK_PORT
+        healthcheck_port = os.getenv(healthcheck_port_env_name)
+        if healthcheck_port is not None:
+            logger.info(
+                "Found healthcheck port %s: %s",
+                healthcheck_port_env_name,
+                healthcheck_port,
+            )
+            if self._worker_watchdog is None:
+                logger.info(
+                    "FileTimerServer doesn't exist, using current time as dummy callback"
+                )
+                alive_callback = LocalElasticAgent._get_current_time_secs
+            else:
+                alive_callback = self._worker_watchdog.get_last_progress_time
+
+            try:
+                healthcheck_port_as_int = int(healthcheck_port)
+                self._health_check_server = create_healthcheck_server(
+                    alive_callback=alive_callback,
+                    port=healthcheck_port_as_int,
+                    timeout=60,
+                )
+                self._health_check_server.start()
+            except ValueError:
+                logger.info(
+                    "Invalid healthcheck port value: '%s', expecting integer. Not starting healthcheck server.",
+                    healthcheck_port,
+                )
+        else:
+            logger.info(
+                "Environment variable '%s' not found. Do not start health check.",
+                healthcheck_port_env_name,
+            )
+
+    def _get_fq_hostname(self) -> str:
+        return socket.getfqdn(socket.gethostname())
+
+    def _log_watchdog_event(
+        self,
+        name: str,
+        request: Optional[timer.FileTimerRequest],
+    ) -> None:
+        wg = self._worker_group
+        spec = wg.spec
+        md = {"watchdog_event": name}
+        if request is not None:
+            md["worker_pid"] = str(request.worker_pid)
+            md["scope_id"] = request.scope_id
+            md["expiration_time"] = str(request.expiration_time)
+            md["signal"] = str(request.signal)
+        md_str = json.dumps(md)
+        state = "RUNNING"
+        metadata: dict[str, EventMetadataValue] = {
+            "run_id": spec.rdzv_handler.get_run_id(),
+            "global_rank": None,
+            "group_rank": wg.group_rank,
+            "worker_id": None,
+            "role": spec.role,
+            "hostname": self._get_fq_hostname(),
+            "state": state,
+            "total_run_time": self._total_execution_time,
+            "rdzv_backend": spec.rdzv_handler.get_backend(),
+            "raw_error": None,
+            "metadata": md_str,
+            "agent_restarts": spec.max_restarts - self._remaining_restarts,
+        }
+        # Note: The 'metadata' field of the Event is converted to a TorchelasticStatusLogEntry later.
+        #       The 'name' field of the Event is NOT used in the TorchelasticStatusLogEntry.
+        event = events.Event(
+            name=name, source=events.EventSource.AGENT, metadata=metadata
+        )
+        events.record(event)
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _stop_workers(
+        self, worker_group: WorkerGroup, is_restart: bool = False
+    ) -> None:
+        self._shutdown(is_restart=is_restart)
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _start_workers(self, worker_group: WorkerGroup) -> dict[int, Any]:
+        spec = worker_group.spec
+        store = worker_group.store
+        assert store is not None
+        restart_count = spec.max_restarts - self._remaining_restarts
+
+        use_agent_store: bool = spec.rdzv_handler.use_agent_store
+        logger.info("use_agent_store: %s", use_agent_store)
+
+        args: dict[int, tuple] = {}
+        envs: dict[int, dict[str, str]] = {}
+        log_line_prefixes: Optional[dict[int, str]] = (
+            {} if self._log_line_prefix_template else None
+        )
+        for worker in worker_group.workers:
+            local_rank = worker.local_rank
+            worker_env = {
+                "LOCAL_RANK": str(local_rank),
+                "RANK": str(worker.global_rank),
+                "GROUP_RANK": str(worker_group.group_rank),
+                "ROLE_RANK": str(worker.role_rank),
+                "ROLE_NAME": spec.role,
+                "LOCAL_WORLD_SIZE": str(spec.local_world_size),
+                "WORLD_SIZE": str(worker.world_size),
+                "GROUP_WORLD_SIZE": str(worker_group.group_world_size),
+                "ROLE_WORLD_SIZE": str(worker.role_world_size),
+                "MASTER_ADDR": worker_group.master_addr,
+                "MASTER_PORT": str(worker_group.master_port),
+                "TORCHELASTIC_RESTART_COUNT": str(restart_count),
+                "TORCHELASTIC_MAX_RESTARTS": str(spec.max_restarts),
+                "TORCHELASTIC_RUN_ID": spec.rdzv_handler.get_run_id(),
+                "TORCHELASTIC_USE_AGENT_STORE": str(use_agent_store),
+                "TORCH_NCCL_ASYNC_ERROR_HANDLING": os.getenv(
+                    "TORCH_NCCL_ASYNC_ERROR_HANDLING", str(1)
+                ),
+            }
+            if "OMP_NUM_THREADS" in os.environ:
+                worker_env["OMP_NUM_THREADS"] = os.environ["OMP_NUM_THREADS"]
+
+            if self._log_line_prefix_template:
+                log_line_prefix = Template(
+                    self._log_line_prefix_template
+                ).safe_substitute(
+                    role_name=spec.role,
+                    rank=worker.global_rank,
+                    local_rank=local_rank,
+                )
+                log_line_prefixes[local_rank] = log_line_prefix
+
+            envs[local_rank] = worker_env
+            worker_args = list(spec.args)
+            worker_args = macros.substitute(worker_args, str(local_rank))
+            args[local_rank] = tuple(worker_args)
+
+        self._setup_local_watchdog(envs=envs)
+        self._setup_healthcheck()
+
+        assert spec.entrypoint is not None
+        assert self._logs_specs is not None
+        self._pcontext = start_processes(
+            name=spec.role,
+            entrypoint=spec.entrypoint,
+            args=args,
+            envs=envs,
+            logs_specs=self._logs_specs,
+            log_line_prefixes=log_line_prefixes,
+            start_method=self._start_method,
+        )
+
+        return self._pcontext.pids()
+
+    def _shutdown(
+        self, death_sig: signal.Signals = signal.SIGTERM, is_restart: bool = False
+    ) -> None:
+        if self._worker_watchdog is not None:
+            self._worker_watchdog.stop()
+            self._worker_watchdog = None
+        if self._health_check_server is not None:
+            self._health_check_server.stop()
+            self._health_check_server = None
+        if self._pcontext:
+            self._pcontext.close(death_sig)
+        if not is_restart and self._rdzv_handler:
+            self._rdzv_handler.shutdown()
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _monitor_workers(self, worker_group: WorkerGroup) -> RunResult:
+        role = worker_group.spec.role
+        worker_pids = {w.id for w in worker_group.workers}
+        assert self._pcontext is not None
+        pc_pids = set(self._pcontext.pids().values())
+        if worker_pids != pc_pids:
+            logger.error(
+                "[%s] worker pids do not match process_context pids."
+                " Expected: %s, actual: %s",
+                role,
+                worker_pids,
+                pc_pids,
+            )
+            return RunResult(state=WorkerState.UNKNOWN)
+
+        result = self._pcontext.wait(0)
+        if result:
+            if result.is_failed():
+                # map local rank failure to global rank
+                worker_failures = {}
+                for local_rank, failure in result.failures.items():
+                    worker = worker_group.workers[local_rank]
+                    worker_failures[worker.global_rank] = failure
+                return RunResult(
+                    state=WorkerState.FAILED,
+                    failures=worker_failures,
+                )
+            else:
+                # copy ret_val_queue into a map with a global ranks
+                workers_ret_vals = {}
+                for local_rank, ret_val in result.return_values.items():
+                    worker = worker_group.workers[local_rank]
+                    workers_ret_vals[worker.global_rank] = ret_val
+                return RunResult(
+                    state=WorkerState.SUCCEEDED,
+                    return_values=workers_ret_vals,
+                )
+        else:
+            return RunResult(state=WorkerState.HEALTHY)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/control_plane.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/control_plane.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e47868e297731a7fb99ce68b7a5398052c18fcf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/control_plane.py
@@ -0,0 +1,53 @@
+import os
+from collections.abc import Generator
+from contextlib import contextmanager, ExitStack
+
+from torch.distributed.elastic.multiprocessing.errors import record
+
+
+__all__ = [
+    "worker_main",
+]
+
+TORCH_WORKER_SERVER_SOCKET = "TORCH_WORKER_SERVER_SOCKET"
+
+
+@contextmanager
+def _worker_server(socket_path: str) -> Generator[None, None, None]:
+    from torch._C._distributed_c10d import _WorkerServer
+
+    server = _WorkerServer(socket_path)
+    try:
+        yield
+    finally:
+        server.shutdown()
+
+
+@contextmanager
+@record
+def worker_main() -> Generator[None, None, None]:
+    """
+    This is a context manager that wraps your main entry function. This combines
+    the existing ``errors.record`` logic as well as a new ``_WorkerServer`` that
+    exposes handlers via a unix socket specified by
+    ``Torch_WORKER_SERVER_SOCKET``.
+
+    Example
+
+    ::
+
+     @worker_main()
+     def main():
+         pass
+
+
+     if __name__ == "__main__":
+         main()
+
+    """
+    with ExitStack() as stack:
+        socket_path = os.environ.get(TORCH_WORKER_SERVER_SOCKET)
+        if socket_path is not None:
+            stack.enter_context(_worker_server(socket_path))
+
+        yield
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/events/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/events/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..02e158b021a0e32e084933b7a8fa7f9e20d8087a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/events/__init__.py
@@ -0,0 +1,173 @@
+#!/usr/bin/env/python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Module contains events processing mechanisms that are integrated with the standard python logging.
+
+Example of usage:
+
+::
+
+  from torch.distributed.elastic import events
+
+  event = events.Event(
+      name="test_event", source=events.EventSource.WORKER, metadata={...}
+  )
+  events.get_logging_handler(destination="console").info(event)
+
+"""
+
+import inspect
+import logging
+import os
+import socket
+import traceback
+from typing import Optional
+
+from torch.distributed.elastic.events.handlers import get_logging_handler
+
+from .api import (  # noqa: F401
+    Event,
+    EventMetadataValue,
+    EventSource,
+    NodeState,
+    RdzvEvent,
+)
+
+
+_events_loggers: dict[str, logging.Logger] = {}
+
+
+def _get_or_create_logger(destination: str = "null") -> logging.Logger:
+    """
+    Construct python logger based on the destination type or extends if provided.
+
+    Available destination could be found in ``handlers.py`` file.
+    The constructed logger does not propagate messages to the upper level loggers,
+    e.g. root logger. This makes sure that a single event can be processed once.
+
+    Args:
+        destination: The string representation of the event handler.
+            Available handlers found in ``handlers`` module
+    """
+    global _events_loggers
+
+    if destination not in _events_loggers:
+        _events_logger = logging.getLogger(f"torchelastic-events-{destination}")
+        _events_logger.setLevel(os.environ.get("LOGLEVEL", "INFO"))
+        # Do not propagate message to the root logger
+        _events_logger.propagate = False
+
+        logging_handler = get_logging_handler(destination)
+        _events_logger.addHandler(logging_handler)
+
+        # Add the logger to the global dictionary
+        _events_loggers[destination] = _events_logger
+
+    return _events_loggers[destination]
+
+
+def record(event: Event, destination: str = "null") -> None:
+    _get_or_create_logger(destination).info(event.serialize())
+
+
+def record_rdzv_event(event: RdzvEvent) -> None:
+    _get_or_create_logger("dynamic_rendezvous").info(event.serialize())
+
+
+def construct_and_record_rdzv_event(
+    run_id: str,
+    message: str,
+    node_state: NodeState,
+    name: str = "",
+    hostname: str = "",
+    pid: Optional[int] = None,
+    master_endpoint: str = "",
+    local_id: Optional[int] = None,
+    rank: Optional[int] = None,
+) -> None:
+    """
+    Initialize rendezvous event object and record its operations.
+
+    Args:
+        run_id (str): The run id of the rendezvous.
+        message (str): The message describing the event.
+        node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED).
+        name (str): Event name. (E.g. Current action being performed).
+        hostname (str): Hostname of the node.
+        pid (Optional[int]): The process id of the node.
+        master_endpoint (str): The master endpoint for the rendezvous store, if known.
+        local_id (Optional[int]):  The local_id of the node, if defined in dynamic_rendezvous.py
+        rank (Optional[int]): The rank of the node, if known.
+    Returns:
+        None
+    Example:
+        >>> # See DynamicRendezvousHandler class
+        >>> def _record(
+        ...     self,
+        ...     message: str,
+        ...     node_state: NodeState = NodeState.RUNNING,
+        ...     rank: Optional[int] = None,
+        ... ) -> None:
+        ...     construct_and_record_rdzv_event(
+        ...         name=f"{self.__class__.__name__}.{get_method_name()}",
+        ...         run_id=self._settings.run_id,
+        ...         message=message,
+        ...         node_state=node_state,
+        ...         hostname=self._this_node.addr,
+        ...         pid=self._this_node.pid,
+        ...         local_id=self._this_node.local_id,
+        ...         rank=rank,
+        ...     )
+    """
+    # We don't want to perform an extra computation if not needed.
+    if isinstance(get_logging_handler("dynamic_rendezvous"), logging.NullHandler):
+        return
+
+    # Set up parameters.
+    if not hostname:
+        hostname = socket.getfqdn()
+    if not pid:
+        pid = os.getpid()
+
+    # Determines which file called this function.
+    callstack = inspect.stack()
+    filename = "no_file"
+    if len(callstack) > 1:
+        stack_depth_1 = callstack[1]
+        filename = os.path.basename(stack_depth_1.filename)
+        if not name:
+            name = stack_depth_1.function
+
+    # Delete the callstack variable. If kept, this can mess with python's
+    # garbage collector as we are holding on to stack frame information in
+    # the inspect module.
+    del callstack
+
+    # Set up error trace if this is an exception
+    if node_state == NodeState.FAILED:
+        error_trace = traceback.format_exc()
+    else:
+        error_trace = ""
+
+    # Initialize event object
+    event = RdzvEvent(
+        name=f"{filename}:{name}",
+        run_id=run_id,
+        message=message,
+        hostname=hostname,
+        pid=pid,
+        node_state=node_state,
+        master_endpoint=master_endpoint,
+        rank=rank,
+        local_id=local_id,
+        error_trace=error_trace,
+    )
+
+    # Finally, record the event.
+    record_rdzv_event(event)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/events/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/events/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0c350d7bcaa14768f43fbda8a6b930b8a12a222
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/events/api.py
@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+from dataclasses import asdict, dataclass, field
+from enum import Enum
+from typing import Optional, Union
+
+
+__all__ = ["EventSource", "Event", "NodeState", "RdzvEvent"]
+
+EventMetadataValue = Union[str, int, float, bool, None]
+
+
+class EventSource(str, Enum):
+    """Known identifiers of the event producers."""
+
+    AGENT = "AGENT"
+    WORKER = "WORKER"
+
+
+@dataclass
+class Event:
+    """
+    The class represents the generic event that occurs during the torchelastic job execution.
+
+    The event can be any kind of meaningful action.
+
+    Args:
+        name: event name.
+        source: the event producer, e.g. agent or worker
+        timestamp: timestamp in milliseconds when event occurred.
+        metadata: additional data that is associated with the event.
+    """
+
+    name: str
+    source: EventSource
+    timestamp: int = 0
+    metadata: dict[str, EventMetadataValue] = field(default_factory=dict)
+
+    def __str__(self):
+        return self.serialize()
+
+    @staticmethod
+    def deserialize(data: Union[str, "Event"]) -> "Event":
+        if isinstance(data, Event):
+            return data
+        if isinstance(data, str):
+            data_dict = json.loads(data)
+        data_dict["source"] = EventSource[data_dict["source"]]  # type: ignore[possibly-undefined]
+        return Event(**data_dict)
+
+    def serialize(self) -> str:
+        return json.dumps(asdict(self))
+
+
+class NodeState(str, Enum):
+    """The states that a node can be in rendezvous."""
+
+    INIT = "INIT"
+    RUNNING = "RUNNING"
+    SUCCEEDED = "SUCCEEDED"
+    FAILED = "FAILED"
+
+
+@dataclass
+class RdzvEvent:
+    """
+    Dataclass to represent any rendezvous event.
+
+    Args:
+        name: Event name. (E.g. Current action being performed)
+        run_id: The run id of the rendezvous
+        message: The message describing the event
+        hostname: Hostname of the node
+        pid: The process id of the node
+        node_state: The state of the node (INIT, RUNNING, SUCCEEDED, FAILED)
+        master_endpoint: The master endpoint for the rendezvous store, if known
+        rank: The rank of the node, if known
+        local_id: The local_id of the node, if defined in dynamic_rendezvous.py
+        error_trace: Error stack trace, if this is an error event.
+    """
+
+    name: str
+    run_id: str
+    message: str
+    hostname: str
+    pid: int
+    node_state: NodeState
+    master_endpoint: str = ""
+    rank: Optional[int] = None
+    local_id: Optional[int] = None
+    error_trace: str = ""
+
+    def __str__(self):
+        return self.serialize()
+
+    @staticmethod
+    def deserialize(data: Union[str, "RdzvEvent"]) -> "RdzvEvent":
+        if isinstance(data, RdzvEvent):
+            return data
+        if isinstance(data, str):
+            data_dict = json.loads(data)
+        data_dict["node_state"] = NodeState[data_dict["node_state"]]  # type: ignore[possibly-undefined]
+        return RdzvEvent(**data_dict)
+
+    def serialize(self) -> str:
+        return json.dumps(asdict(self))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/events/handlers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/events/handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..30d925353253d5bab4c4780f298e7fa68a4409e5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/events/handlers.py
@@ -0,0 +1,21 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+
+_log_handlers: dict[str, logging.Handler] = {
+    "console": logging.StreamHandler(),
+    "dynamic_rendezvous": logging.NullHandler(),
+    "null": logging.NullHandler(),
+}
+
+
+def get_logging_handler(destination: str = "null") -> logging.Handler:
+    global _log_handlers
+    return _log_handlers[destination]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/metrics/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/metrics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b07671fbac9d34b218256e57558098364358959d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/metrics/__init__.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env/python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Metrics API.
+
+**Overview**:
+
+The metrics API in torchelastic is used to publish telemetry metrics.
+It is designed to be used by torchelastic's internal modules to
+publish metrics for the end user with the goal of increasing visibility
+and helping with debugging. However you may use the same API in your
+jobs to publish metrics to the same metrics ``sink``.
+
+A ``metric`` can be thought of as timeseries data
+and is uniquely identified by the string-valued tuple
+``(metric_group, metric_name)``.
+
+torchelastic makes no assumptions about what a ``metric_group`` is
+and what relationship it has with ``metric_name``. It is totally up
+to the user to use these two fields to uniquely identify a metric.
+
+.. note:: The metric group ``torchelastic`` is reserved by torchelastic for
+          platform level metrics that it produces.
+          For instance torchelastic may output the latency (in milliseconds)
+          of a re-rendezvous operation from the agent as
+          ``(torchelastic, agent.rendezvous.duration.ms)``
+
+A sensible way to use metric groups is to map them to a stage or module
+in your job. You may also encode certain high level properties
+the job such as the region or stage (dev vs prod).
+
+**Publish Metrics**:
+
+Using torchelastic's metrics API is similar to using python's logging
+framework. You first have to configure a metrics handler before
+trying to add metric data.
+
+The example below measures the latency for the ``calculate()`` function.
+
+::
+
+  import time
+  import torch.distributed.elastic.metrics as metrics
+
+  # makes all metrics other than the one from "my_module" to go /dev/null
+  metrics.configure(metrics.NullMetricsHandler())
+  metrics.configure(metrics.ConsoleMetricsHandler(), "my_module")
+
+
+  def my_method():
+      start = time.time()
+      calculate()
+      end = time.time()
+      metrics.put_metric("calculate_latency", int(end - start), "my_module")
+
+You may also use the torch.distributed.elastic.metrics.prof` decorator
+to conveniently and succinctly profile functions
+
+::
+
+  # -- in module examples.foobar --
+
+  import torch.distributed.elastic.metrics as metrics
+
+  metrics.configure(metrics.ConsoleMetricsHandler(), "foobar")
+  metrics.configure(metrics.ConsoleMetricsHandler(), "Bar")
+
+
+  @metrics.prof
+  def foo():
+      pass
+
+
+  class Bar:
+      @metrics.prof
+      def baz():
+          pass
+
+``@metrics.prof`` will publish the following metrics
+::
+
+  .success - 1 if the function finished successfully
+  .failure - 1 if the function threw an exception
+  .duration.ms - function duration in milliseconds
+
+**Configuring Metrics Handler**:
+
+`torch.distributed.elastic.metrics.MetricHandler` is responsible for emitting
+the added metric values to a particular destination. Metric groups can be
+configured with different metric handlers.
+
+By default torchelastic emits all metrics to ``/dev/null``.
+By adding the following configuration metrics,
+``torchelastic`` and ``my_app`` metric groups will be printed out to
+console.
+
+::
+
+  import torch.distributed.elastic.metrics as metrics
+
+  metrics.configure(metrics.ConsoleMetricHandler(), group="torchelastic")
+  metrics.configure(metrics.ConsoleMetricHandler(), group="my_app")
+
+**Writing a Custom Metric Handler**:
+
+If you want your metrics to be emitted to a custom location, implement
+the `torch.distributed.elastic.metrics.MetricHandler` interface
+and configure your job to use your custom metric handler.
+
+Below is a toy example that prints the metrics to ``stdout``
+
+::
+
+  import torch.distributed.elastic.metrics as metrics
+
+
+  class StdoutMetricHandler(metrics.MetricHandler):
+      def emit(self, metric_data):
+          ts = metric_data.timestamp
+          group = metric_data.group_name
+          name = metric_data.name
+          value = metric_data.value
+          print(f"[{ts}][{group}]: {name}={value}")
+
+
+  metrics.configure(StdoutMetricHandler(), group="my_app")
+
+Now all metrics in the group ``my_app`` will be printed to stdout as:
+
+::
+
+  [1574213883.4182858][my_app]: my_metric=
+  [1574213940.5237644][my_app]: my_metric=
+
+"""
+
+from typing import Optional
+
+from .api import (  # noqa: F401
+    configure,
+    ConsoleMetricHandler,
+    get_elapsed_time_ms,
+    getStream,
+    MetricData,
+    MetricHandler,
+    MetricsConfig,
+    NullMetricHandler,
+    prof,
+    profile,
+    publish_metric,
+    put_metric,
+)
+
+
+def initialize_metrics(cfg: Optional[MetricsConfig] = None):
+    pass
+
+
+try:
+    from torch.distributed.elastic.metrics.static_init import *  # type: ignore[import] # noqa: F401 F403
+except ModuleNotFoundError:
+    pass
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f4100a461adc80bca0edd07ec8afce6c1991e8f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py
@@ -0,0 +1,217 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import abc
+import time
+from collections import namedtuple
+from functools import wraps
+from typing import Optional
+from typing_extensions import deprecated
+
+
+__all__ = [
+    "MetricsConfig",
+    "MetricHandler",
+    "ConsoleMetricHandler",
+    "NullMetricHandler",
+    "MetricStream",
+    "configure",
+    "getStream",
+    "prof",
+    "profile",
+    "put_metric",
+    "publish_metric",
+    "get_elapsed_time_ms",
+    "MetricData",
+]
+
+MetricData = namedtuple("MetricData", ["timestamp", "group_name", "name", "value"])
+
+
+class MetricsConfig:
+    __slots__ = ["params"]
+
+    def __init__(self, params: Optional[dict[str, str]] = None):
+        self.params = params
+        if self.params is None:
+            self.params = {}
+
+
+class MetricHandler(abc.ABC):
+    @abc.abstractmethod
+    def emit(self, metric_data: MetricData):
+        pass
+
+
+class ConsoleMetricHandler(MetricHandler):
+    def emit(self, metric_data: MetricData):
+        print(
+            f"[{metric_data.timestamp}][{metric_data.group_name}]: {metric_data.name}={metric_data.value}"
+        )
+
+
+class NullMetricHandler(MetricHandler):
+    def emit(self, metric_data: MetricData):
+        pass
+
+
+class MetricStream:
+    def __init__(self, group_name: str, handler: MetricHandler):
+        self.group_name = group_name
+        self.handler = handler
+
+    def add_value(self, metric_name: str, metric_value: int):
+        self.handler.emit(
+            MetricData(time.time(), self.group_name, metric_name, metric_value)
+        )
+
+
+_metrics_map: dict[str, MetricHandler] = {}
+_default_metrics_handler: MetricHandler = NullMetricHandler()
+
+
+# pyre-fixme[9]: group has type `str`; used as `None`.
+def configure(handler: MetricHandler, group: Optional[str] = None):
+    if group is None:
+        global _default_metrics_handler
+        # pyre-fixme[9]: _default_metrics_handler has type `NullMetricHandler`; used
+        #  as `MetricHandler`.
+        _default_metrics_handler = handler
+    else:
+        _metrics_map[group] = handler
+
+
+def getStream(group: str):
+    if group in _metrics_map:
+        handler = _metrics_map[group]
+    else:
+        handler = _default_metrics_handler
+    return MetricStream(group, handler)
+
+
+def _get_metric_name(fn):
+    qualname = fn.__qualname__
+    split = qualname.split(".")
+    if len(split) == 1:
+        module = fn.__module__
+        if module:
+            return module.split(".")[-1] + "." + split[0]
+        else:
+            return split[0]
+    else:
+        return qualname
+
+
+def prof(fn=None, group: str = "torchelastic"):
+    r"""
+    @profile decorator publishes duration.ms, count, success, failure metrics for the function that it decorates.
+
+    The metric name defaults to the qualified name (``class_name.def_name``) of the function.
+    If the function does not belong to a class, it uses the leaf module name instead.
+
+    Usage
+
+    ::
+
+     @metrics.prof
+     def x():
+         pass
+
+
+     @metrics.prof(group="agent")
+     def y():
+         pass
+    """
+
+    def wrap(f):
+        @wraps(f)
+        def wrapper(*args, **kwargs):
+            key = _get_metric_name(f)
+            try:
+                start = time.time()
+                result = f(*args, **kwargs)
+                put_metric(f"{key}.success", 1, group)
+            except Exception:
+                put_metric(f"{key}.failure", 1, group)
+                raise
+            finally:
+                put_metric(f"{key}.duration.ms", get_elapsed_time_ms(start), group)  # type: ignore[possibly-undefined]
+            return result
+
+        return wrapper
+
+    if fn:
+        return wrap(fn)
+    else:
+        return wrap
+
+
+@deprecated("Deprecated, use `@prof` instead", category=FutureWarning)
+def profile(group=None):
+    """
+    @profile decorator adds latency and success/failure metrics to any given function.
+
+    Usage
+
+    ::
+
+     @metrics.profile("my_metric_group")
+     def some_function():
+    """
+
+    def wrap(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            try:
+                start_time = time.time()
+                result = func(*args, **kwargs)
+                publish_metric(group, f"{func.__name__}.success", 1)
+            except Exception:
+                publish_metric(group, f"{func.__name__}.failure", 1)
+                raise
+            finally:
+                publish_metric(
+                    group,
+                    f"{func.__name__}.duration.ms",
+                    get_elapsed_time_ms(start_time),  # type: ignore[possibly-undefined]
+                )
+            return result
+
+        return wrapper
+
+    return wrap
+
+
+def put_metric(metric_name: str, metric_value: int, metric_group: str = "torchelastic"):
+    """
+    Publish a metric data point.
+
+    Usage
+
+    ::
+
+     put_metric("metric_name", 1)
+     put_metric("metric_name", 1, "metric_group_name")
+    """
+    getStream(metric_group).add_value(metric_name, metric_value)
+
+
+@deprecated(
+    "Deprecated, use `put_metric(metric_group)(metric_name, metric_value)` instead",
+    category=FutureWarning,
+)
+def publish_metric(metric_group: str, metric_name: str, metric_value: int):
+    metric_stream = getStream(metric_group)
+    metric_stream.add_value(metric_name, metric_value)
+
+
+def get_elapsed_time_ms(start_time_in_seconds: float):
+    """Return the elapsed time in millis from the given start time."""
+    end_time = time.time()
+    return int((end_time - start_time_in_seconds) * 1000)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe829a26ce8462ef8321cde28579abc77a38098b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/__init__.py
@@ -0,0 +1,234 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Library that launches and manages ``n`` copies of worker subprocesses either specified by a function or a binary.
+
+For functions, it uses ``torch.multiprocessing`` (and therefore python
+``multiprocessing``) to spawn/fork worker processes. For binaries it uses python
+``subprocessing.Popen`` to create worker processes.
+
+
+Usage 1: Launching two trainers as a function
+
+::
+
+ from torch.distributed.elastic.multiprocessing import Std, start_processes
+
+
+ def trainer(a, b, c):
+     pass  # train
+
+
+ # runs two trainers
+ # LOCAL_RANK=0 trainer(1,2,3)
+ # LOCAL_RANK=1 trainer(4,5,6)
+ ctx = start_processes(
+     name="trainer",
+     entrypoint=trainer,
+     args={0: (1, 2, 3), 1: (4, 5, 6)},
+     envs={0: {"LOCAL_RANK": 0}, 1: {"LOCAL_RANK": 1}},
+     log_dir="/tmp/foobar",
+     redirects=Std.ALL,  # write all worker stdout/stderr to a log file
+     tee={0: Std.ERR},  # tee only local rank 0's stderr to console
+ )
+
+ # waits for all copies of trainer to finish
+ ctx.wait()
+
+Usage 2: Launching 2 echo workers as a binary
+
+::
+
+ # same as invoking
+ # echo hello
+ # echo world > stdout.log
+ ctx = start_processes(
+         name="echo"
+         entrypoint="echo",
+         log_dir="/tmp/foobar",
+         args={0: "hello", 1: "world"},
+         redirects={1: Std.OUT},
+        )
+
+Just like ``torch.multiprocessing``, the return value of the function
+:func:`start_processes` is a process context (:class:`api.PContext`). If a function
+was launched, a :class:`api.MultiprocessContext` is returned and if a binary
+was launched a :class:`api.SubprocessContext` is returned. Both are specific
+implementations of the parent :class:`api.PContext` class.
+"""
+
+from typing import Callable, Optional, Union
+
+from torch.distributed.elastic.multiprocessing.api import (  # noqa: F401
+    _validate_full_rank,
+    DefaultLogsSpecs,
+    LogsDest,
+    LogsSpecs,
+    MultiprocessContext,
+    PContext,
+    ProcessFailure,
+    RunProcsResult,
+    SignalException,
+    Std,
+    SubprocessContext,
+    to_map,
+)
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+__all__ = [
+    "start_processes",
+    "MultiprocessContext",
+    "PContext",
+    "ProcessFailure",
+    "RunProcsResult",
+    "SignalException",
+    "Std",
+    "LogsDest",
+    "LogsSpecs",
+    "DefaultLogsSpecs",
+    "SubprocessContext",
+    "to_map",
+]
+
+
+def start_processes(
+    name: str,
+    entrypoint: Union[Callable, str],
+    args: dict[int, tuple],
+    envs: dict[int, dict[str, str]],
+    logs_specs: LogsSpecs,
+    log_line_prefixes: Optional[dict[int, str]] = None,
+    start_method: str = "spawn",
+) -> PContext:
+    """
+    Start ``n`` copies of ``entrypoint`` processes with the provided options.
+
+    ``entrypoint`` is either a ``Callable`` (function) or a ``str`` (binary).
+    The number of copies is determined by the number of entries for ``args`` and
+    ``envs`` arguments, which need to have the same key set.
+
+    ``args`` and ``env`` parameters are the arguments and environment variables
+    to pass down to the entrypoint mapped by the replica index (local rank).
+    All local ranks must be accounted for.
+    That is, the keyset should be ``{0,1,...,(nprocs-1)}``.
+
+    .. note:: When the ``entrypoint`` is a binary (``str``), ``args`` can only be strings.
+              If any other type is given, then it is casted to a string representation
+              (e.g. ``str(arg1)``). Furthermore, a binary failure will only write
+              an ``error.json`` error file if the main function is annotated with
+              ``torch.distributed.elastic.multiprocessing.errors.record``. For function launches,
+              this is done by default and there is no need to manually annotate
+              with the ``@record`` annotation.
+
+    ``redirects`` and ``tee`` are bitmasks specifying which std stream(s) to redirect
+    to a log file in the ``log_dir``. Valid mask values are defined in ``Std``.
+    To redirect/tee only certain local ranks, pass ``redirects`` as a map with the key as
+    the local rank to specify the redirect behavior for.
+    Any missing local ranks will default to ``Std.NONE``.
+
+    ``tee`` acts like the unix "tee" command in that it redirects + prints to console.
+    To avoid worker stdout/stderr from printing to console, use the ``redirects`` parameter.
+
+    For each process, the ``log_dir`` will contain:
+
+    #. ``{local_rank}/error.json``: if the process failed, a file with the error info
+    #. ``{local_rank}/stdout.json``: if ``redirect & STDOUT == STDOUT``
+    #. ``{local_rank}/stderr.json``: if ``redirect & STDERR == STDERR``
+
+    .. note:: It is expected that the ``log_dir`` exists, is empty, and is a directory.
+
+    Example:
+    ::
+
+     log_dir = "/tmp/test"
+
+     # ok; two copies of foo: foo("bar0"), foo("bar1")
+     start_processes(
+        name="trainer",
+        entrypoint=foo,
+        args:{0:("bar0",), 1:("bar1",),
+        envs:{0:{}, 1:{}},
+        log_dir=log_dir
+     )
+
+     # invalid; envs missing for local rank 1
+     start_processes(
+        name="trainer",
+        entrypoint=foo,
+        args:{0:("bar0",), 1:("bar1",),
+        envs:{0:{}},
+        log_dir=log_dir
+     )
+
+     # ok; two copies of /usr/bin/touch: touch file1, touch file2
+     start_processes(
+        name="trainer",
+        entrypoint="/usr/bin/touch",
+        args:{0:("file1",), 1:("file2",),
+        envs:{0:{}, 1:{}},
+        log_dir=log_dir
+      )
+
+     # caution; arguments casted to string, runs:
+     # echo "1" "2" "3" and echo "[1, 2, 3]"
+     start_processes(
+        name="trainer",
+        entrypoint="/usr/bin/echo",
+        args:{0:(1,2,3), 1:([1,2,3],),
+        envs:{0:{}, 1:{}},
+        log_dir=log_dir
+      )
+
+    Args:
+        name: a human readable short name that describes what the processes are
+              (used as header when tee'ing stdout/stderr outputs)
+        entrypoint: either a ``Callable`` (function) or ``cmd`` (binary)
+        args: arguments to each replica
+        envs: env vars to each replica
+        log_dir: directory used to write log files
+        start_method: multiprocessing start method (spawn, fork, forkserver)
+                      ignored for binaries
+        redirects: which std streams to redirect to a log file
+        tee: which std streams to redirect + print to console
+        local_ranks_filter: which ranks' logs to print to console
+
+    """
+
+    nprocs = len(args)
+    _validate_full_rank(args, nprocs, "args")
+    _validate_full_rank(envs, nprocs, "envs")
+
+    context: PContext
+    if isinstance(entrypoint, str):
+        context = SubprocessContext(
+            name=name,
+            entrypoint=entrypoint,
+            args=args,
+            envs=envs,
+            logs_specs=logs_specs,
+            log_line_prefixes=log_line_prefixes,
+        )
+    else:
+        context = MultiprocessContext(
+            name=name,
+            entrypoint=entrypoint,
+            args=args,
+            envs=envs,
+            log_line_prefixes=log_line_prefixes,
+            start_method=start_method,
+            logs_specs=logs_specs,
+        )
+
+    try:
+        context.start()
+        return context
+    except Exception:
+        context.close()
+        raise
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d899a95d6a7d84b81aefab44dff8a8e1f844b7a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py
@@ -0,0 +1,926 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import abc
+import logging
+import os
+import re
+import shutil
+import signal
+import subprocess
+import sys
+import tempfile
+import threading
+import time
+from abc import ABC, abstractmethod
+from contextlib import nullcontext
+from dataclasses import dataclass, field
+from enum import IntFlag
+from multiprocessing import synchronize
+from types import FrameType
+from typing import Any, Callable, Optional, Union
+
+import torch.multiprocessing as mp
+from torch.distributed.elastic.multiprocessing.errors import ProcessFailure, record
+from torch.distributed.elastic.multiprocessing.redirects import (
+    redirect_stderr,
+    redirect_stdout,
+)
+from torch.distributed.elastic.multiprocessing.subprocess_handler import (
+    get_subprocess_handler,
+    SubprocessHandler,
+)
+from torch.distributed.elastic.multiprocessing.tail_log import TailLog
+
+
+IS_WINDOWS = sys.platform == "win32"
+IS_MACOS = sys.platform == "darwin"
+
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "DefaultLogsSpecs",
+    "SignalException",
+    "Std",
+    "to_map",
+    "RunProcsResult",
+    "PContext",
+    "get_std_cm",
+    "MultiprocessContext",
+    "SubprocessContext",
+    "LogsDest",
+    "LogsSpecs",
+]
+
+
+class SignalException(Exception):
+    """
+    Exception is raised inside the torchelastic agent process by the termination handler
+    if the death signal got received by the process.
+    """
+
+    def __init__(self, msg: str, sigval: signal.Signals) -> None:
+        super().__init__(msg)
+        self.sigval = sigval
+
+
+def _terminate_process_handler(signum: int, frame: Optional[FrameType]) -> None:
+    """Termination handler that raises exceptions on the main process.
+
+    When the process receives death signal(SIGTERM, SIGINT), this termination handler will
+    be invoked. It raises the ``SignalException`` exception that should be processed by the
+    user code. Python does not terminate process after the termination handler is finished,
+    so the exception should not be silently ignored, otherwise the process will never
+    be terminated.
+    """
+    sigval = signal.Signals(signum)
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+
+
+def _get_kill_signal() -> signal.Signals:
+    """Get the kill signal. SIGKILL for unix, CTRL_C_EVENT for windows."""
+    if IS_WINDOWS:
+        return signal.CTRL_C_EVENT  # type: ignore[attr-defined] # noqa: F821
+    else:
+        return signal.SIGKILL
+
+
+def _get_default_signal() -> signal.Signals:
+    """Get the default termination signal. SIGTERM for unix, CTRL_C_EVENT for windows."""
+    if IS_WINDOWS:
+        return signal.CTRL_C_EVENT  # type: ignore[attr-defined] # noqa: F821
+    else:
+        return signal.SIGTERM
+
+
+def _validate_full_rank(d: dict[int, Any], nprocs: int, what: str):
+    actual_keys = set(d.keys())
+    expected_keys = set(range(nprocs))
+
+    if actual_keys != expected_keys:
+        raise RuntimeError(
+            f"{what}, local rank mapping mismatch,"
+            f" expected: {expected_keys}, actual: {actual_keys}"
+        )
+
+
+_MAPPING_REGEX = r"^(\d:[0123],)*(\d:[0123])$"
+_VALUE_REGEX = r"^[0123]$"
+
+
+class Std(IntFlag):
+    NONE = 0
+    OUT = 1
+    ERR = 2
+    ALL = OUT | ERR
+
+    @classmethod
+    def from_str(cls, vm: str) -> Union["Std", dict[int, "Std"]]:
+        """
+        Example:
+        ::
+
+         from_str("0") -> Std.NONE
+         from_str("1") -> Std.OUT
+         from_str("0:3,1:0,2:1,3:2") -> {0: Std.ALL, 1: Std.NONE, 2: Std.OUT, 3: Std.ERR}
+
+        Any other input raises an exception
+        """
+
+        def to_std(v: str) -> Std:  # type: ignore[return]
+            s = Std(int(v))
+            if s in Std:
+                return s
+            # return None -> should NEVER reach here since we regex check input
+
+        if re.match(_VALUE_REGEX, vm):  # vm is a number (e.g. 0)
+            return to_std(vm)
+        elif re.match(_MAPPING_REGEX, vm):  # vm is a mapping (e.g. 0:1,1:2)
+            d: dict[int, Std] = {}
+            for m in vm.split(","):
+                i, v = m.split(":")
+                d[int(i)] = to_std(v)
+            return d
+        else:
+            raise ValueError(
+                f"{vm} does not match: <{_VALUE_REGEX}> or <{_MAPPING_REGEX}>"
+            )
+
+
+def to_map(
+    val_or_map: Union[Std, dict[int, Std]], local_world_size: int
+) -> dict[int, Std]:
+    """
+    Certain APIs take redirect settings either as a single value (e.g. apply to all
+    local ranks) or as an explicit user-provided mapping. This method is a convenience
+    method that converts a value or mapping into a mapping.
+
+    Example:
+    ::
+
+     to_map(Std.OUT, local_world_size=2)  # returns: {0: Std.OUT, 1: Std.OUT}
+     to_map({1: Std.OUT}, local_world_size=2)  # returns: {0: Std.NONE, 1: Std.OUT}
+     to_map(
+         {0: Std.OUT, 1: Std.OUT}, local_world_size=2
+     )  # returns: {0: Std.OUT, 1: Std.OUT}
+    """
+    if isinstance(val_or_map, Std):
+        return dict.fromkeys(range(local_world_size), val_or_map)
+    else:
+        map = {}
+        for i in range(local_world_size):
+            map[i] = val_or_map.get(i, Std.NONE)
+        return map
+
+
+@dataclass
+class LogsDest:
+    """
+    For each log type, holds mapping of local rank ids to file paths.
+    """
+
+    stdouts: dict[int, str] = field(default_factory=dict)
+    stderrs: dict[int, str] = field(default_factory=dict)
+    tee_stdouts: dict[int, str] = field(default_factory=dict)
+    tee_stderrs: dict[int, str] = field(default_factory=dict)
+    error_files: dict[int, str] = field(default_factory=dict)
+
+
+class LogsSpecs(ABC):
+    """
+    Defines logs processing and redirection for each worker process.
+
+    Args:
+        log_dir:
+            Base directory where logs will be written.
+        redirects:
+            Streams to redirect to files. Pass a single ``Std``
+            enum to redirect for all workers, or a mapping keyed
+            by local_rank to selectively redirect.
+        tee:
+            Streams to duplicate to stdout/stderr.
+            Pass a single ``Std`` enum to duplicate streams for all workers,
+            or a mapping keyed by local_rank to selectively duplicate.
+    """
+
+    def __init__(
+        self,
+        log_dir: Optional[str] = None,
+        redirects: Union[Std, dict[int, Std]] = Std.NONE,
+        tee: Union[Std, dict[int, Std]] = Std.NONE,
+        local_ranks_filter: Optional[set[int]] = None,
+    ) -> None:
+        self._root_log_dir = log_dir
+        self._redirects = redirects
+        self._tee = tee
+        self._local_ranks_filter = local_ranks_filter
+
+    @abstractmethod
+    def reify(
+        self,
+        envs: dict[int, dict[str, str]],
+    ) -> LogsDest:
+        """
+        Given the environment variables, builds destination of log files for each of the local ranks.
+
+        Envs parameter contains env variables dict for each of the local ranks, where entries are defined in:
+        :func:`~torchelastic.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent._start_workers`.
+        """
+
+    @property
+    @abstractmethod
+    def root_log_dir(self) -> str:
+        pass
+
+
+class DefaultLogsSpecs(LogsSpecs):
+    """
+    Default LogsSpecs implementation:
+
+    - `log_dir` will be created if it doesn't exist
+    - Generates nested folders for each attempt and rank.
+    """
+
+    def __init__(
+        self,
+        log_dir: Optional[str] = None,
+        redirects: Union[Std, dict[int, Std]] = Std.NONE,
+        tee: Union[Std, dict[int, Std]] = Std.NONE,
+        local_ranks_filter: Optional[set[int]] = None,
+    ) -> None:
+        if log_dir != os.devnull:
+            if not log_dir:
+                log_dir = tempfile.mkdtemp(prefix="torchelastic_")
+            elif not os.path.exists(log_dir):
+                os.makedirs(log_dir, exist_ok=True)
+            else:
+                if os.path.isfile(log_dir):
+                    raise NotADirectoryError(f"log_dir: {log_dir} is a file")
+        super().__init__(log_dir, redirects, tee, local_ranks_filter)
+        # initialized only once
+        self._run_log_dir = None
+
+    @property
+    def root_log_dir(self) -> str:
+        return str(self._root_log_dir)
+
+    def _make_log_dir(self, log_dir: Optional[str], rdzv_run_id: str):
+        base_log_dir = log_dir or tempfile.mkdtemp(prefix="torchelastic_")
+        os.makedirs(base_log_dir, exist_ok=True)
+        dir = tempfile.mkdtemp(prefix=f"{rdzv_run_id}_", dir=base_log_dir)
+        logger.info("log directory set to: %s", dir)
+        return dir
+
+    def reify(
+        self,
+        envs: dict[int, dict[str, str]],
+    ) -> LogsDest:
+        """
+        Uses following scheme to build log destination paths:
+
+        - `//attempt_//stdout.log`
+        - `//attempt_//stderr.log`
+        - `//attempt_//error.json`
+        """
+        nprocs = len(envs)
+        global_env = {}  # use only to query properies that are not dependent on a rank
+        if nprocs > 0:
+            global_env = envs[0]
+        else:
+            logger.warning(
+                "Empty envs map provided when defining logging destinations."
+            )
+        # Keys are always defined, but values can be missing in unit tests
+        run_id = global_env.get("TORCHELASTIC_RUN_ID", "test_run_id")
+        restart_count = global_env.get("TORCHELASTIC_RESTART_COUNT", "0")
+
+        attempt_log_dir: str = ""
+        if self._root_log_dir != os.devnull:
+            if not self._run_log_dir:
+                self._run_log_dir = self._make_log_dir(self._root_log_dir, run_id)
+
+            attempt_log_dir = os.path.join(
+                self._run_log_dir, f"attempt_{restart_count}"
+            )  # type: ignore[call-overload]
+            shutil.rmtree(attempt_log_dir, ignore_errors=True)
+            os.makedirs(attempt_log_dir)
+
+        if self._root_log_dir == os.devnull:
+            attempt_log_dir = os.devnull
+
+        # create subdirs for each local rank in the logs_dir
+        # logs_dir
+        #       |- 0
+        #          |- error.json
+        #          |- stdout.log
+        #          |- stderr.log
+        #       |- ...
+        #       |- (nprocs-1)
+        redirs = to_map(self._redirects, nprocs)
+        ts = to_map(self._tee, nprocs)
+
+        # to tee stdout/stderr we first redirect into a file
+        # then tail -f stdout.log/stderr.log so add tee settings to redirects
+        for local_rank, tee_std in ts.items():
+            redirect_std = redirs[local_rank]
+            redirs[local_rank] = redirect_std | tee_std
+
+        SYS_STREAM = ""  # special case to indicate to output to console
+        stdouts = dict.fromkeys(range(nprocs), SYS_STREAM)
+        stderrs = dict.fromkeys(range(nprocs), SYS_STREAM)
+        tee_stdouts: dict[int, str] = {}
+        tee_stderrs: dict[int, str] = {}
+        error_files = {}
+
+        for local_rank in range(nprocs):
+            if attempt_log_dir == os.devnull:
+                tee_stdouts[local_rank] = os.devnull
+                tee_stderrs[local_rank] = os.devnull
+                error_files[local_rank] = os.devnull
+                envs[local_rank]["TORCHELASTIC_ERROR_FILE"] = ""
+            else:
+                clogdir = os.path.join(attempt_log_dir, str(local_rank))
+                os.mkdir(clogdir)
+
+                rd = redirs[local_rank]
+                if (rd & Std.OUT) == Std.OUT:
+                    stdouts[local_rank] = os.path.join(clogdir, "stdout.log")
+                if (rd & Std.ERR) == Std.ERR:
+                    stderrs[local_rank] = os.path.join(clogdir, "stderr.log")
+
+                t = ts[local_rank]
+                if t & Std.OUT == Std.OUT:
+                    tee_stdouts[local_rank] = stdouts[local_rank]
+                if t & Std.ERR == Std.ERR:
+                    tee_stderrs[local_rank] = stderrs[local_rank]
+
+                if (
+                    self._local_ranks_filter
+                    and local_rank not in self._local_ranks_filter
+                ):
+                    # If stream is tee'd, only write to file, but don't tail
+                    if local_rank in tee_stdouts:
+                        tee_stdouts.pop(local_rank, None)
+                    if local_rank in tee_stderrs:
+                        tee_stderrs.pop(local_rank, None)
+
+                    # If stream is not redirected, don't print
+                    if stdouts[local_rank] == SYS_STREAM:
+                        stdouts[local_rank] = os.devnull
+                    if stderrs[local_rank] == SYS_STREAM:
+                        stderrs[local_rank] = os.devnull
+
+                error_file = os.path.join(clogdir, "error.json")
+                error_files[local_rank] = error_file
+                logger.info(
+                    "Setting worker%s reply file to: %s", local_rank, error_file
+                )
+                envs[local_rank]["TORCHELASTIC_ERROR_FILE"] = error_file
+
+        return LogsDest(stdouts, stderrs, tee_stdouts, tee_stderrs, error_files)
+
+    def __repr__(self) -> str:
+        return (
+            f"DefaultLogsSpecs(root_log_dir={self._root_log_dir}, redirects={self._redirects}, "
+            f"tee={self._tee}, local_ranks_filter={self._local_ranks_filter})"
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, DefaultLogsSpecs):
+            return False
+
+        return (
+            self._root_log_dir == other._root_log_dir
+            and self._redirects == other._redirects
+            and self._tee == other._tee
+            and self._local_ranks_filter == other._local_ranks_filter
+        )
+
+
+@dataclass
+class RunProcsResult:
+    """
+    Results of a completed run of processes started with ``start_processes()``. Returned by ``PContext``.
+
+    Note the following:
+
+    1. All fields are mapped by local rank
+    2. ``return_values`` - only populated for functions (not the binaries).
+    3. ``stdouts`` - path to stdout.log (empty string if no redirect)
+    4. ``stderrs`` - path to stderr.log (empty string if no redirect)
+
+    """
+
+    return_values: dict[int, Any] = field(default_factory=dict)
+    failures: dict[int, ProcessFailure] = field(default_factory=dict)
+    stdouts: dict[int, str] = field(default_factory=dict)
+    stderrs: dict[int, str] = field(default_factory=dict)
+
+    def is_failed(self) -> bool:
+        return len(self.failures) > 0
+
+
+class PContext(abc.ABC):
+    """
+    The base class that standardizes operations over a set of processes that are launched via different mechanisms.
+
+    The name ``PContext`` is intentional to disambiguate with ``torch.multiprocessing.ProcessContext``.
+
+    .. warning:: stdouts and stderrs should ALWAYS be a superset of
+                 tee_stdouts and tee_stderrs (respectively) this is b/c
+                 tee is implemented as a redirect + tail -f 
+    """
+
+    def __init__(
+        self,
+        name: str,
+        entrypoint: Union[Callable, str],
+        args: dict[int, tuple],
+        envs: dict[int, dict[str, str]],
+        logs_specs: LogsSpecs,
+        log_line_prefixes: Optional[dict[int, str]] = None,
+    ):
+        self.name = name
+        # validate that all mappings have the same number of keys and
+        # all local ranks are accounted for
+        nprocs = len(args)
+
+        # TODO log_line_prefixes can be exanded too
+        logs_dest = logs_specs.reify(envs)
+
+        _validate_full_rank(logs_dest.stdouts, nprocs, "stdouts")
+        _validate_full_rank(logs_dest.stderrs, nprocs, "stderrs")
+
+        self.entrypoint = entrypoint
+        self.args = args
+        self.envs = envs
+        self.stdouts = logs_dest.stdouts
+        self.stderrs = logs_dest.stderrs
+        self.error_files = logs_dest.error_files
+        self.nprocs = nprocs
+
+        self._stdout_tail = TailLog(
+            name, logs_dest.tee_stdouts, sys.stdout, log_line_prefixes
+        )
+        self._stderr_tail = TailLog(
+            name, logs_dest.tee_stderrs, sys.stderr, log_line_prefixes
+        )
+
+    def start(self) -> None:
+        """Start processes using parameters defined in the constructor."""
+        if threading.current_thread() is threading.main_thread():
+            signal.signal(signal.SIGTERM, _terminate_process_handler)
+            signal.signal(signal.SIGINT, _terminate_process_handler)
+            if not IS_WINDOWS:
+                signal.signal(signal.SIGHUP, _terminate_process_handler)
+                signal.signal(signal.SIGQUIT, _terminate_process_handler)
+        else:
+            logger.warning(
+                "Failed to register signal handlers since torchelastic is running on a child thread. "
+                "This could lead to orphaned worker processes if the torchrun is terminated."
+            )
+        self._start()
+        self._stdout_tail.start()
+        self._stderr_tail.start()
+
+    @abc.abstractmethod
+    def _start(self) -> None:
+        """Start processes using strategy defined in a particular context."""
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _poll(self) -> Optional[RunProcsResult]:
+        """
+        Poll the run status of the processes running under this context.
+        This method follows an "all-or-nothing" policy and returns
+        a ``RunProcessResults`` object if either all processes complete
+        successfully or any process fails. Returns ``None`` if
+        all processes are still running.
+        """
+        raise NotImplementedError
+
+    def wait(self, timeout: float = -1, period: float = 1) -> Optional[RunProcsResult]:
+        """
+        Wait for the specified ``timeout`` seconds, polling every ``period`` seconds
+        for the processes to be done. Returns ``None`` if the processes are still running
+        on timeout expiry. Negative timeout values are interpreted as "wait-forever".
+        A timeout value of zero simply queries the status of the processes (e.g. equivalent
+        to a poll).
+
+        .. note::
+            Multiprocessing library registers SIGTERM and SIGINT signal handlers that raise
+            ``SignalException`` when the signals received. It is up to the consumer of the code
+            to properly handle the exception. It is important not to swallow the exception otherwise
+            the process would not terminate. Example of the typical workflow can be:
+
+        .. code-block:: python
+            pc = start_processes(...)
+            try:
+                pc.wait(1)
+                .. do some other work
+            except SignalException as e:
+                pc.shutdown(e.sigval, timeout=30)
+
+        If SIGTERM or SIGINT occurs, the code above will try to shutdown child processes by propagating
+        received signal. If child processes will not terminate in the timeout time, the process will send
+        the SIGKILL.
+        """
+        if timeout == 0:
+            return self._poll()
+
+        if timeout < 0:
+            timeout = sys.maxsize
+
+        expiry = time.time() + timeout
+        while time.time() < expiry:
+            pr = self._poll()
+            if pr:
+                return pr
+            time.sleep(period)
+
+        return None
+
+    @abc.abstractmethod
+    def pids(self) -> dict[int, int]:
+        """Return pids of processes mapped by their respective local_ranks."""
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
+        r"""
+        Terminates all processes managed by this context and cleans up any
+        meta resources (e.g. redirect, error_file files).
+        """
+        raise NotImplementedError
+
+    def close(
+        self, death_sig: Optional[signal.Signals] = None, timeout: int = 30
+    ) -> None:
+        r"""
+        Terminates all processes managed by this context and cleans up any
+        meta resources (e.g. redirect, error_file files).
+
+        Args:
+            death_sig: Death signal to terminate processes.
+            timeout: Time to wait for processes to finish, if process is
+                still alive after this time, it will be terminated via SIGKILL.
+        """
+        if not death_sig:
+            death_sig = _get_default_signal()
+        self._close(death_sig=death_sig, timeout=timeout)
+        if self._stdout_tail:
+            self._stdout_tail.stop()
+        if self._stderr_tail:
+            self._stderr_tail.stop()
+
+
+def get_std_cm(std_rd: str, redirect_fn):
+    if IS_WINDOWS or IS_MACOS or not std_rd:
+        return nullcontext()
+    else:
+        return redirect_fn(std_rd)
+
+
+def _wrap(
+    local_rank: int,
+    fn: Callable,
+    args: dict[int, tuple],
+    envs: dict[int, dict[str, str]],
+    stdout_redirects: dict[int, str],  # redirect file for stdout (to console if None)
+    stderr_redirects: dict[int, str],  # redirect file for stderr (to console if None)
+    ret_vals: dict[int, mp.SimpleQueue],
+    queue_finished_reading_event: synchronize.Event,
+) -> None:
+    # get the per-rank params up front so we fail fast if no mapping is found
+    args_ = args[local_rank]
+    env_ = envs[local_rank]
+    ret_val_ = ret_vals[local_rank]
+
+    stdout_rd = stdout_redirects[local_rank]
+    stderr_rd = stderr_redirects[local_rank]
+
+    stdout_cm = get_std_cm(stdout_rd, redirect_stdout)
+    stderr_cm = get_std_cm(stderr_rd, redirect_stderr)
+
+    for k, v in env_.items():
+        os.environ[k] = v
+
+    with stdout_cm, stderr_cm:
+        ret = record(fn)(*args_)
+    ret_val_.put(ret)
+    queue_finished_reading_event.wait()
+
+
+class MultiprocessContext(PContext):
+    """``PContext`` holding worker processes invoked as a function."""
+
+    def __init__(
+        self,
+        name: str,
+        entrypoint: Callable,
+        args: dict[int, tuple],
+        envs: dict[int, dict[str, str]],
+        start_method: str,
+        logs_specs: LogsSpecs,
+        log_line_prefixes: Optional[dict[int, str]] = None,
+    ):
+        super().__init__(
+            name,
+            entrypoint,
+            args,
+            envs,
+            logs_specs,
+            log_line_prefixes,
+        )
+
+        self.start_method = start_method
+        # each ret_val queue will always contain a single element.
+        self._ret_vals = {
+            local_rank: mp.get_context(self.start_method).SimpleQueue()
+            for local_rank in range(self.nprocs)
+        }
+
+        # see comments in ``join()`` for what this is
+        self._return_values: dict[int, Any] = {}
+        self._pc: Optional[mp.ProcessContext] = None
+        # Note: set method should ONLY be invoked for the use case when all processes finished
+        # successfully. If any process died on event.wait() calling set() method will deadlock.
+        self._worker_finished_event = mp.get_context(self.start_method).Event()
+
+    def _start(self):
+        if self._pc:
+            raise ValueError(
+                "The process context already initialized."
+                " Most likely the start method got called twice."
+            )
+        self._pc = mp.start_processes(
+            fn=_wrap,
+            args=(
+                self.entrypoint,
+                self.args,
+                self.envs,
+                self.stdouts,
+                self.stderrs,
+                self._ret_vals,
+                self._worker_finished_event,
+            ),
+            nprocs=self.nprocs,
+            join=False,
+            daemon=False,
+            start_method=self.start_method,
+        )
+
+    def _is_done(self) -> bool:
+        return len(self._return_values) == self.nprocs
+
+    def _poll(self) -> Optional[RunProcsResult]:
+        assert self._pc is not None  # assertion for mypy type checker
+
+        try:
+            # torch.mp.ProcessContext Throws an Exception if some/all of
+            # worker processes failed
+            # timeout < 0 checks worker status and return immediately
+            # Join will never return success since we use synchronize.Event to wait
+            # for all processes to finish.
+            self._pc.join(-1)
+
+            # IMPORTANT: we use multiprocessing.Queue to carry worker return values
+            # back to the parent, the worker process will wait before terminating
+            # until all the buffered items are fed by the feeder thread to the underlying
+            # pipe. Hence to prevent deadlocks on large return values,
+            # we opportunistically try queue.get on each join call
+            # See: https://docs.python.org/2/library/multiprocessing.html#all-platforms
+            for local_rank in range(0, self.nprocs):
+                return_queue = self._ret_vals[local_rank]
+                if not return_queue.empty():
+                    # save the return values temporarily into a member var
+                    self._return_values[local_rank] = return_queue.get()
+
+            if self._is_done():
+                # we should ALWAYS have ALL the return values when all the processes are done
+                self._worker_finished_event.set()
+
+                # At this point workers finished running the user function
+                # But the child process might still have not exited. Wait for them.
+                # pc.join() blocks [forever] until "a" proc exits. Loop until all of them exits.
+                while not self._pc.join():
+                    logger.debug(
+                        "entrypoint fn finished, waiting for all child procs to exit..."
+                    )
+
+                _validate_full_rank(
+                    self._return_values, self.nprocs, "return_value queue"
+                )
+                self.close()
+                return RunProcsResult(
+                    return_values=self._return_values,
+                    stdouts=self.stdouts,
+                    stderrs=self.stderrs,
+                )
+            else:
+                return None
+        except (mp.ProcessRaisedException, mp.ProcessExitedException) as e:
+            failed_local_rank = e.error_index
+
+            # entrypoint for MultiprocessContext will always be a Callable
+            fn_name = self.entrypoint.__qualname__  # type: ignore[union-attr]
+            failed_proc = self._pc.processes[failed_local_rank]
+            error_filepath = self.error_files[failed_local_rank]
+
+            logger.exception(
+                "failed (exitcode: %s)"
+                " local_rank: %s (pid: %s)"
+                " of fn: %s (start_method: %s)",
+                failed_proc.exitcode,
+                failed_local_rank,
+                e.pid,
+                fn_name,
+                self.start_method,
+            )
+
+            self.close()
+            return RunProcsResult(
+                failures={
+                    failed_local_rank: ProcessFailure(
+                        local_rank=failed_local_rank,
+                        pid=e.pid,
+                        exitcode=failed_proc.exitcode,
+                        error_file=error_filepath,
+                    )
+                },
+                stdouts=self.stdouts,
+                stderrs=self.stderrs,
+            )
+
+    def pids(self) -> dict[int, int]:
+        assert self._pc is not None  # assertion for mypy type checking
+        return dict(enumerate(self._pc.pids()))
+
+    def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
+        if not self._pc:
+            return
+        for proc in self._pc.processes:
+            if proc.is_alive():
+                logger.warning(
+                    "Closing process %s via signal %s", proc.pid, death_sig.name
+                )
+                try:
+                    os.kill(proc.pid, death_sig)
+                except ProcessLookupError:
+                    # If the process exited because of some reason,
+                    # `ProcessLookupError` will be raised, it is safe to ignore it.
+                    pass
+        end = time.monotonic() + timeout
+        for proc in self._pc.processes:
+            time_to_wait = end - time.monotonic()
+            if time_to_wait <= 0:
+                break
+            proc.join(time_to_wait)
+        for proc in self._pc.processes:
+            if proc.is_alive():
+                logger.warning(
+                    "Unable to shutdown process %s via %s, forcefully exiting via %s",
+                    proc.pid,
+                    death_sig,
+                    _get_kill_signal(),
+                )
+                try:
+                    os.kill(proc.pid, _get_kill_signal())
+                except ProcessLookupError:
+                    # If the process exited because of some reason,
+                    # `ProcessLookupError` will be raised, it is safe to ignore it.
+                    pass
+            proc.join()
+
+
+class SubprocessContext(PContext):
+    """``PContext`` holding worker processes invoked as a binary."""
+
+    def __init__(
+        self,
+        name: str,
+        entrypoint: str,
+        args: dict[int, tuple],
+        envs: dict[int, dict[str, str]],
+        logs_specs: LogsSpecs,
+        log_line_prefixes: Optional[dict[int, str]] = None,
+    ):
+        super().__init__(
+            name,
+            entrypoint,
+            args,
+            envs,
+            logs_specs,
+            log_line_prefixes,
+        )
+
+        # state vector; _vdone[local_rank] -> is local_rank finished or not
+        self._running_local_ranks: set[int] = set(range(self.nprocs))
+        self._failures: dict[int, ProcessFailure] = {}
+        self.subprocess_handlers: dict[int, SubprocessHandler] = {}
+
+    def _start(self):
+        if self.subprocess_handlers:
+            raise ValueError(
+                "The subprocess handlers already initialized. Most likely the start method got called twice."
+            )
+        self.subprocess_handlers = {
+            local_rank: get_subprocess_handler(
+                entrypoint=self.entrypoint,  # type: ignore[arg-type] # entrypoint is always a str
+                args=self.args[local_rank],
+                env=self.envs[local_rank],
+                stdout=self.stdouts[local_rank],
+                stderr=self.stderrs[local_rank],
+                local_rank_id=local_rank,
+            )
+            for local_rank in range(self.nprocs)
+        }
+
+    def _poll(self) -> Optional[RunProcsResult]:
+        done_local_ranks = set()
+        for local_rank in self._running_local_ranks:
+            handler = self.subprocess_handlers[local_rank]
+            exitcode = handler.proc.poll()
+            if exitcode is not None:
+                done_local_ranks.add(local_rank)
+                if exitcode != 0:  # failed or signaled
+                    self._failures[local_rank] = ProcessFailure(
+                        local_rank=local_rank,
+                        pid=handler.proc.pid,
+                        exitcode=exitcode,
+                        error_file=self.error_files[local_rank],
+                    )
+                # else: --> succeeded; nothing to do
+
+        self._running_local_ranks.difference_update(done_local_ranks)
+
+        # if ALL procs are finished or ANY have failed
+        if not self._running_local_ranks or self._failures:
+            self.close()  # terminate all running procs
+            result = RunProcsResult(
+                failures=self._failures,
+                stdouts=self.stdouts,
+                stderrs=self.stderrs,
+            )
+            if result.is_failed():
+                first_failure = min(result.failures.values(), key=lambda f: f.timestamp)
+                logger.error(
+                    "failed (exitcode: %s) local_rank: %s (pid: %s) of binary: %s",
+                    first_failure.exitcode,
+                    first_failure.local_rank,
+                    first_failure.pid,
+                    self.entrypoint,
+                )
+            else:
+                # Populate return with dummy values. This provides consistency with MultiprocessingHandler
+                result.return_values = dict.fromkeys(range(self.nprocs))
+
+            return result
+        else:  # there are no failures and procs still running
+            return None
+
+    def pids(self) -> dict[int, int]:
+        return {
+            local_rank: sh.proc.pid
+            for local_rank, sh in self.subprocess_handlers.items()
+        }
+
+    def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
+        if not self.subprocess_handlers:
+            return
+        for handler in self.subprocess_handlers.values():
+            if handler.proc.poll() is None:
+                logger.warning(
+                    "Sending process %s closing signal %s",
+                    handler.proc.pid,
+                    death_sig.name,
+                )
+                handler.close(death_sig=death_sig)
+        end = time.monotonic() + timeout
+        for handler in self.subprocess_handlers.values():
+            time_to_wait = end - time.monotonic()
+            if time_to_wait <= 0:
+                break
+            try:
+                handler.proc.wait(time_to_wait)
+            except subprocess.TimeoutExpired:
+                # Ignore the timeout expired exception, since
+                # the child process will be forcefully terminated via SIGKILL
+                pass
+        for handler in self.subprocess_handlers.values():
+            if handler.proc.poll() is None:
+                logger.warning(
+                    "Unable to shutdown process %s via %s, forcefully exiting via %s",
+                    handler.proc.pid,
+                    death_sig,
+                    _get_kill_signal(),
+                )
+                handler.close(death_sig=_get_kill_signal())
+                handler.proc.wait()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..57e445a3d02a4f84971dde853d7f777a54347335
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py
@@ -0,0 +1,383 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Each host in a distributed PyTorch job runs with a single TorchElastic agent,
+and multiple workers (as children processes of the TorchElastic agent).
+Since the workers are user-provided (your PyTorch script/job), TorchElastic
+has a way to propagate errors on the trainers through the agent and up to the
+scheduler, which ultimately informs the end-user about the state of the job
+and applies any retry policies.
+
+TorchElastic categorizes errors into 3 categories:
+
++----------------+----------------+--------------------------------------------------------------+
+| Category       | Sub-Category   |  Description                                                 |
++================+================+==============================================================+
+| User Error     | Input Error    | invalid inputs to TorchElastic APIs (e.g. min > max nodes)   |
+|                +----------------+--------------------------------------------------------------+
+|                | Worker Failure | any failures on the worker child process                     |
++----------------+----------------+--------------------------------------------------------------+
+| Platform Error |      n/a       | failures caused by the agent                                 |
++----------------+----------------+--------------------------------------------------------------+
+| Infra Error    |      n/a       | failures outside the domain of the agent and workers         |
+|                |                | (e.g. host failures)                                         |
++----------------+----------------+--------------------------------------------------------------+
+
+All errors other than "Worker Failure" are either raised canonically from the
+agent process or implicitly or explicitly crash the agent process. So the
+standard language (python) provided exception handling strategies apply.
+
+Worker Failures are special because the exception/failure originates on a different
+process from the agent so the error needs to be propagated inter-process
+(e.g. the agent cannot simply ``try-catch`` an exception raised on the worker process).
+
+TorchElastic agents use :func:`torch.distributed.elastic.multiprocessing.start_processes`
+to launch the workers which has a simple file based inter-process error propagation
+built-in.
+
+Any function or binary entrypoint decorated with :func:`record`
+will write uncaught exceptions (with the trace information) to a file specified by the
+environment variable ``TORCHELASTIC_ERROR_FILE``. The parent process (e.g. agent)
+sets this env var on each child it launches, then aggregates the error files for all
+children, and propagates the one with the **smallest** timestamp (e.g. the **first** error).
+"""
+
+import json
+import os
+import signal
+import socket
+import time
+from dataclasses import dataclass, field
+from datetime import datetime
+from functools import wraps
+from string import Template
+from typing import Any, Callable, Optional, TypeVar
+
+from torch.distributed.elastic.utils.logging import get_logger
+
+from .error_handler import ErrorHandler  # noqa: F401
+from .handlers import get_error_handler  # noqa: F401
+
+
+__all__ = [
+    "ProcessFailure",
+    "ChildFailedError",
+    "record",
+    "ErrorHandler",
+    "get_error_handler",
+]
+
+logger = get_logger(__name__)
+
+
+JSON = dict
+
+_EMPTY_ERROR_DATA = {"message": ""}
+_NOT_AVAILABLE = ""
+
+T = TypeVar("T")
+
+
+@dataclass
+class ProcessFailure:
+    """
+    Represent the failed process result. When the worker process fails, it may record failure root cause into the file.
+
+    Tries to read the failure timestamp from the provided ``error_file``,
+    if the ``error_file`` does not exist, the timestamp is the current
+    timestamp (seconds since epoch).
+
+    The ``message`` field is a concise explanation of the failure. If
+    the error file exists then the message is obtained from the error file.
+    Otherwise one is generated based on the failure signature.
+
+    .. note:: It is assumed that the ``error_file`` is written by
+              ``torch.distributed.elastic.multiprocessing.errors.error_handler.ErrorHandler``.
+              Otherwise the behavior is undefined.
+
+    """
+
+    local_rank: int
+    pid: int
+    exitcode: int
+    error_file: str
+    error_file_data: JSON = field(init=False)
+    message: str = field(init=False)
+    timestamp: int = field(init=False)
+
+    def __post_init__(self):
+        self.error_file_data = _EMPTY_ERROR_DATA
+        if os.path.isfile(self.error_file):
+            try:
+                with open(self.error_file) as fp:
+                    self.error_file_data = json.load(fp)
+                    logger.debug(
+                        "User process failed with error data: %s",
+                        json.dumps(self.error_file_data, indent=2),
+                    )
+                    self.message, self.timestamp = self._get_error_data(
+                        self.error_file_data
+                    )
+            except Exception:
+                logger.exception("Failed to parse reply file: %s", self.error_file)
+                raise
+        else:
+            self._set_no_reply_file()
+
+        # make up an informative message if not already present
+        if not self.message:
+            # signals typically do not generate an error file message
+            if self.exitcode < 0:
+                self.message = (
+                    f"Signal {-self.exitcode} ({self.signal_name()})"
+                    f" received by PID {self.pid}"
+                )
+            else:
+                self.message = "To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html"
+
+    def _get_error_data(self, error_file_data: dict[str, Any]) -> tuple[str, int]:
+        message = error_file_data["message"]
+        if isinstance(message, str):
+            timestamp = int(error_file_data.get("timestamp", 0))
+        else:
+            timestamp = int(message["extraInfo"]["timestamp"])
+        return (message, timestamp)
+
+    def _set_no_reply_file(self):
+        self.error_file = _NOT_AVAILABLE
+        self.error_file_data = _EMPTY_ERROR_DATA
+        self.message = ""
+        self.timestamp = int(time.time())
+
+    def signal_name(self) -> str:
+        if self.exitcode < 0:
+            # We don't want to kill the parent process trying to find the signal name.
+            # if the signal doesn't map to a known name, use not available.
+            try:
+                return signal.Signals(-self.exitcode).name
+            except Exception:
+                return _NOT_AVAILABLE
+        else:
+            return _NOT_AVAILABLE
+
+    def timestamp_isoformat(self):
+        """Return timestamp in ISO format (YYYY-MM-DD_HH:MM:SS)."""
+        return datetime.fromtimestamp(self.timestamp).isoformat(sep="_")
+
+
+GlobalRank = int
+
+_FAILURE_FORMAT_TEMPLATE = """[${idx}]:
+  time      : ${time}
+  host      : ${hostname}
+  rank      : ${rank} (local_rank: ${local_rank})
+  exitcode  : ${exitcode} (pid: ${pid})
+  error_file: ${error_file}
+  traceback : ${message}"""
+
+# extra new lines before and after are intentional
+_MSG_FORMAT_TEMPLATE = """
+${boarder}
+${title}
+${section}
+Failures:
+${other_failures}
+${section}
+Root Cause (first observed failure):
+${root_failure}
+${boarder}"""
+
+
+class ChildFailedError(Exception):
+    """
+    Special exception type that can be raised from a function annotated with the
+    ``@record`` decorator to have the child process' (root exception) propagate
+    up the stack as-is (e.g. without being wrapped in the parent's traceback).
+
+    Useful in cases where the parent is a simple nanny process
+    and the child (worker) processes are actually doing meaningful compute.
+    In this case, errors typically occur on the child process as the parent
+    is not doing anything non-trivial, and child errors should be propagated
+    to the scheduler for accurate root cause diagnostics.
+
+    .. note:: The propagation relies on error files rather than exception handling to
+              support both function and binary launches.
+
+    Example:
+    ::
+
+     # process tree on a host (container)
+     0: scheduler-init-process:
+                |- 1: torchelastic_agent:
+                         |- 2: trainer_0 (ok)
+                         |- 3: trainer_1 (fail) -> error.json
+                         |- ...
+                         |- n+2: trainer_n (ok)
+                |- n+3: other processes
+                |- ...
+
+    In the example above, trainer 1's failure (written into error.json) is
+    the root cause and should be reported to the scheduler's init process.
+    The torchelastic agent raises a ``ChildFailedError("trainer", {1: "trainer_1/error.json"})``
+    upon detecting trainer 1's failure which would propagate the contents
+    of trainer 1's error file to the scheduler's init process.
+    """
+
+    def __init__(self, name: str, failures: dict[GlobalRank, ProcessFailure]):
+        self.name = name
+        self.failures = failures
+        assert (
+            self.failures
+        )  # does not make sense to create a ChildFaileError with no failures
+        super().__init__(self.format_msg())
+
+    def get_first_failure(self) -> tuple[GlobalRank, ProcessFailure]:
+        rank = min(self.failures.keys(), key=lambda r: self.failures[r].timestamp)
+        return rank, self.failures[rank]
+
+    def format_msg(self, boarder_delim="=", section_delim="-"):
+        title = f"{self.name} FAILED"
+        root_rank, _root_failure = self.get_first_failure()
+
+        root_failure_fmt: str = ""
+        other_failures_fmt: list[str] = []
+        width = len(title)
+        for idx, (rank, failure) in enumerate(self.failures.items()):
+            fmt, w = self._format_failure(idx, rank, failure)
+            width = max(width, w)
+            if rank == root_rank:
+                root_failure_fmt = fmt
+            else:
+                other_failures_fmt.append(fmt)
+
+        # upper boundary on width
+        width = min(width, 60)
+
+        return Template(_MSG_FORMAT_TEMPLATE).substitute(
+            boarder=boarder_delim * width,
+            title=title,
+            section=section_delim * width,
+            root_failure=root_failure_fmt,
+            other_failures="\n".join(other_failures_fmt or ["  "]),
+        )
+
+    def _format_failure(
+        self, idx: int, rank: int, failure: ProcessFailure
+    ) -> tuple[str, int]:
+        # failure.message is either a str (when the failure does not generate a traceback - e.g. signals)
+        # or a dict (json) of the form
+        # {"message": $ERROR_MSG, "extraInfo": {"py_callstack": $TRACEBACK, timestamp: $TS}}
+        # so the display logic is:
+        # 1. if failure.message is not a dict (it is a str) just show it as is
+        # 2. else try to get the traceback (py_callstack)
+        # 3.      if the traceback is not there, use the message
+        # 4.      if the message  is not there show 
+        msg = failure.message
+        if isinstance(failure.message, dict):
+            msg = (
+                failure.message.get("extraInfo", {})
+                .get("py_callstack", failure.message.get("message", ""))
+                .replace("\n", "\n  ")  # to properly indent the traceback
+            )
+
+        fmt = Template(_FAILURE_FORMAT_TEMPLATE).substitute(
+            idx=idx,
+            time=failure.timestamp_isoformat(),
+            hostname=socket.getfqdn(),
+            rank=rank,
+            local_rank=failure.local_rank,
+            exitcode=failure.exitcode,
+            pid=failure.pid,
+            error_file=failure.error_file,
+            message=msg,
+        )
+        width = 0
+        for line in fmt.split("\n"):
+            width = max(width, len(line))
+        return fmt, width
+
+
+def record(
+    fn: Callable[..., T], error_handler: Optional[ErrorHandler] = None
+) -> Callable[..., T]:
+    """
+    Syntactic sugar to record errors/exceptions that happened in the decorated
+    function using the provided ``error_handler``.
+
+    Using this decorator is equivalent to:
+
+    ::
+
+     error_handler = get_error_handler()
+     error_handler.initialize()
+     try:
+         foobar()
+     except ChildFailedError as e:
+         _, failure = e.get_first_failure()
+         error_handler.dump_error_file(failure.error_file, failure.exitcode)
+         raise
+     except Exception as e:
+         error_handler.record_exception(e)
+         raise
+
+    .. important:: use this decorator once per process at the top level method,
+                   typically this is the main method.
+
+    Example
+
+    ::
+
+     @record
+     def main():
+         pass
+
+
+     if __name__ == "__main__":
+         main()
+
+    """
+    if not error_handler:
+        error_handler = get_error_handler()
+
+    def wrap(f):
+        @wraps(f)
+        def wrapper(*args, **kwargs):
+            assert error_handler is not None  # assertion for mypy type checker
+            error_handler.initialize()
+            try:
+                return f(*args, **kwargs)
+            except SystemExit as se:
+                # For run_path based entrypoints, SystemExit with code = 0 will never exit.
+                # Handling it here by returning a value:
+                if se.code == 0:
+                    return None
+                else:
+                    raise
+            except ChildFailedError as e:
+                rank, failure = e.get_first_failure()
+                if failure.error_file != _NOT_AVAILABLE:
+                    error_handler.dump_error_file(failure.error_file, failure.exitcode)
+                else:
+                    logger.info(
+                        (
+                            "local_rank %s FAILED with no error file."
+                            " Decorate your entrypoint fn with @record for traceback info."
+                            " See: https://pytorch.org/docs/stable/elastic/errors.html",
+                            rank,
+                        )
+                    )
+                raise
+            except Exception as e:
+                error_handler.record_exception(e)
+                raise
+
+        return wrapper
+
+    return wrap(fn)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/error_handler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/error_handler.py
new file mode 100644
index 0000000000000000000000000000000000000000..f15ce4f241d6f324135c80ca068d77da17a33ac1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/error_handler.py
@@ -0,0 +1,166 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import faulthandler
+import json
+import logging
+import os
+import time
+import traceback
+import warnings
+from typing import Any, Optional
+
+
+__all__ = ["ErrorHandler"]
+
+logger = logging.getLogger(__name__)
+
+
+class ErrorHandler:
+    """
+    Write the provided exception object along with some other metadata about
+    the error in a structured way in JSON format to an error file specified by the
+    environment variable: ``TORCHELASTIC_ERROR_FILE``. If this environment
+    variable is not set, then simply logs the contents of what would have been
+    written to the error file.
+
+    This handler may be subclassed to customize the handling of the error.
+    Subclasses should override ``initialize()`` and ``record_exception()``.
+    """
+
+    def _get_error_file_path(self) -> Optional[str]:
+        """
+        Return the error file path.
+
+        May return ``None`` to have the structured error be logged only.
+        """
+        return os.environ.get("TORCHELASTIC_ERROR_FILE", None)
+
+    def initialize(self) -> None:
+        """
+        Call prior to running code that we wish to capture errors/exceptions.
+
+        Typically registers signal/fault handlers. Users can override this
+        function to add custom initialization/registrations that aid in
+        propagation/information of errors/signals/exceptions/faults.
+        """
+        try:
+            faulthandler.enable(all_threads=True)
+        except Exception as e:
+            warnings.warn(f"Unable to enable fault handler. {type(e).__name__}: {e}")
+
+    def _write_error_file(self, file_path: str, error_msg: str) -> None:
+        """Write error message to the file."""
+        try:
+            with open(file_path, "w") as fp:
+                fp.write(error_msg)
+        except Exception as e:
+            warnings.warn(f"Unable to write error to file. {type(e).__name__}: {e}")
+
+    def record_exception(self, e: BaseException) -> None:
+        """
+        Write a structured information about the exception into an error file in JSON format.
+
+        If the error file cannot be determined, then logs the content
+        that would have been written to the error file.
+        """
+        file = self._get_error_file_path()
+        if file:
+            data = {
+                "message": {
+                    "message": f"{type(e).__name__}: {e}",
+                    "extraInfo": {
+                        "py_callstack": traceback.format_exc(),
+                        "timestamp": str(int(time.time())),
+                    },
+                }
+            }
+            with open(file, "w") as fp:
+                json.dump(data, fp)
+
+    def override_error_code_in_rootcause_data(
+        self,
+        rootcause_error_file: str,
+        rootcause_error: dict[str, Any],
+        error_code: int = 0,
+    ):
+        """Modify the rootcause_error read from the file, to correctly set the exit code."""
+        if "message" not in rootcause_error:
+            logger.warning(
+                "child error file (%s) does not have field `message`. \n"
+                "cannot override error code: %s",
+                rootcause_error_file,
+                error_code,
+            )
+        elif isinstance(rootcause_error["message"], str):
+            logger.warning(
+                "child error file (%s) has a new message format. \n"
+                "skipping error code override",
+                rootcause_error_file,
+            )
+        else:
+            rootcause_error["message"]["errorCode"] = error_code
+
+    def dump_error_file(self, rootcause_error_file: str, error_code: int = 0):
+        """Dump parent error file from child process's root cause error and error code."""
+        with open(rootcause_error_file) as fp:
+            rootcause_error = json.load(fp)
+            # Override error code since the child process cannot capture the error code if it
+            # is terminated by signals like SIGSEGV.
+            if error_code:
+                self.override_error_code_in_rootcause_data(
+                    rootcause_error_file, rootcause_error, error_code
+                )
+            logger.debug(
+                "child error file (%s) contents:\n%s",
+                rootcause_error_file,
+                json.dumps(rootcause_error, indent=2),
+            )
+
+        my_error_file = self._get_error_file_path()
+        if my_error_file:
+            # Guard against existing error files
+            # This can happen when the child is created using multiprocessing
+            # and the same env var (TORCHELASTIC_ERROR_FILE) is used on the
+            # parent and child to specify the error files (respectively)
+            # because the env vars on the child is set in the wrapper function
+            # and by default the child inherits the parent's env vars, if the child
+            # process receives a signal before the wrapper function kicks in
+            # and the signal handler writes to the error file, then the child
+            # will write to the parent's error file. In this case just log the
+            # original error file contents and overwrite the error file.
+            self._rm(my_error_file)
+            self._write_error_file(my_error_file, json.dumps(rootcause_error))
+            logger.info("dumped error file to parent's %s", my_error_file)
+        else:
+            logger.error(
+                "no error file defined for parent, to copy child error file (%s)",
+                rootcause_error_file,
+            )
+
+    def _rm(self, my_error_file):
+        if os.path.isfile(my_error_file):
+            # Log the contents of the original file.
+            with open(my_error_file) as fp:
+                try:
+                    original = json.dumps(json.load(fp), indent=2)
+                    logger.warning(
+                        "%s already exists"
+                        " and will be overwritten."
+                        " Original contents:\n%s",
+                        my_error_file,
+                        original,
+                    )
+                except json.decoder.JSONDecodeError:
+                    logger.warning(
+                        "%s already exists"
+                        " and will be overwritten."
+                        " Unable to load original contents:\n",
+                        my_error_file,
+                    )
+            os.remove(my_error_file)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/handlers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..6721217a41190c2bdd6bf2293540a33c893c145d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/handlers.py
@@ -0,0 +1,18 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+# Multiprocessing error-reporting module
+
+
+from torch.distributed.elastic.multiprocessing.errors.error_handler import ErrorHandler
+
+
+__all__ = ["get_error_handler"]
+
+
+def get_error_handler() -> ErrorHandler:
+    return ErrorHandler()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/redirects.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/redirects.py
new file mode 100644
index 0000000000000000000000000000000000000000..057013fbb9e5b8a2aeca69b41d7679cbe75c0e28
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/redirects.py
@@ -0,0 +1,104 @@
+# mypy: allow-untyped-defs
+# !/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Taken and modified from original source:
+# https://eli.thegreenplace.net/2015/redirecting-all-kinds-of-stdout-in-python/
+import ctypes
+import logging
+import os
+import sys
+from contextlib import contextmanager
+from functools import partial
+
+
+IS_WINDOWS = sys.platform == "win32"
+IS_MACOS = sys.platform == "darwin"
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_libc():
+    if IS_WINDOWS or IS_MACOS:
+        logger.warning(
+            "NOTE: Redirects are currently not supported in Windows or MacOs."
+        )
+        return None
+    else:
+        return ctypes.CDLL("libc.so.6")
+
+
+libc = get_libc()
+
+
+def _c_std(stream: str):
+    return ctypes.c_void_p.in_dll(libc, stream)
+
+
+def _python_std(stream: str):
+    return {"stdout": sys.stdout, "stderr": sys.stderr}[stream]
+
+
+_VALID_STD = {"stdout", "stderr"}
+
+
+@contextmanager
+def redirect(std: str, to_file: str):
+    """
+    Redirect ``std`` (one of ``"stdout"`` or ``"stderr"``) to a file in the path specified by ``to_file``.
+
+    This method redirects the underlying std file descriptor (not just python's ``sys.stdout|stderr``).
+    See usage for details.
+
+    Directory of ``dst_filename`` is assumed to exist and the destination file
+    is overwritten if it already exists.
+
+    .. note:: Due to buffering cross source writes are not guaranteed to
+              appear in wall-clock order. For instance in the example below
+              it is possible for the C-outputs to appear before the python
+              outputs in the log file.
+
+    Usage:
+
+    ::
+
+     # syntactic-sugar for redirect("stdout", "tmp/stdout.log")
+     with redirect_stdout("/tmp/stdout.log"):
+        print("python stdouts are redirected")
+        libc = ctypes.CDLL("libc.so.6")
+        libc.printf(b"c stdouts are also redirected"
+        os.system("echo system stdouts are also redirected")
+
+     print("stdout restored")
+
+    """
+    if std not in _VALID_STD:
+        raise ValueError(
+            f"unknown standard stream <{std}>, must be one of {_VALID_STD}"
+        )
+
+    c_std = _c_std(std)
+    python_std = _python_std(std)
+    std_fd = python_std.fileno()
+
+    def _redirect(dst):
+        libc.fflush(c_std)
+        python_std.flush()
+        os.dup2(dst.fileno(), std_fd)
+
+    with os.fdopen(os.dup(std_fd)) as orig_std, open(to_file, mode="w+b") as dst:
+        _redirect(dst)
+        try:
+            yield
+        finally:
+            _redirect(orig_std)
+
+
+redirect_stdout = partial(redirect, "stdout")
+redirect_stderr = partial(redirect, "stderr")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f56d423ce080fd7c331dc9b43eda58e5370678fc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/__init__.py
@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+from torch.distributed.elastic.multiprocessing.subprocess_handler.handlers import (
+    get_subprocess_handler,
+)
+from torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler import (
+    SubprocessHandler,
+)
+
+
+__all__ = ["SubprocessHandler", "get_subprocess_handler"]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..fea707a3c3ab2f82fa1ad010e6b68b53d3d3096b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py
@@ -0,0 +1,30 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler import (
+    SubprocessHandler,
+)
+
+
+__all__ = ["get_subprocess_handler"]
+
+
+def get_subprocess_handler(
+    entrypoint: str,
+    args: tuple,
+    env: dict[str, str],
+    stdout: str,
+    stderr: str,
+    local_rank_id: int,
+) -> SubprocessHandler:
+    return SubprocessHandler(
+        entrypoint=entrypoint,
+        args=args,
+        env=env,
+        stdout=stdout,
+        stderr=stderr,
+        local_rank_id=local_rank_id,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb8d6d0196e25905c6d6c2bb3cff41e34a9648e1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py
@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import signal
+import subprocess
+import sys
+from typing import Any, Optional
+
+
+__all__ = ["SubprocessHandler"]
+
+IS_WINDOWS = sys.platform == "win32"
+
+
+def _get_default_signal() -> signal.Signals:
+    """Get the default termination signal. SIGTERM for unix, CTRL_C_EVENT for windows."""
+    if IS_WINDOWS:
+        return signal.CTRL_C_EVENT  # type: ignore[attr-defined] # noqa: F821
+    else:
+        return signal.SIGTERM
+
+
+class SubprocessHandler:
+    """
+    Convenience wrapper around python's ``subprocess.Popen``. Keeps track of
+    meta-objects associated to the process (e.g. stdout and stderr redirect fds).
+    """
+
+    def __init__(
+        self,
+        entrypoint: str,
+        args: tuple,
+        env: dict[str, str],
+        stdout: Optional[str],
+        stderr: Optional[str],
+        local_rank_id: int,
+    ):
+        self._stdout = open(stdout, "w") if stdout else None
+        self._stderr = open(stderr, "w") if stderr else None
+        # inherit parent environment vars
+        env_vars = os.environ.copy()
+        env_vars.update(env)
+
+        args_str = (entrypoint, *[str(e) for e in args])
+        self.local_rank_id = local_rank_id
+        self.proc: subprocess.Popen = self._popen(args_str, env_vars)
+
+    def _popen(self, args: tuple, env: dict[str, str]) -> subprocess.Popen:
+        kwargs: dict[str, Any] = {}
+        if not IS_WINDOWS:
+            kwargs["start_new_session"] = True
+        return subprocess.Popen(
+            # pyre-fixme[6]: Expected `Union[typing.Sequence[Union[_PathLike[bytes],
+            #  _PathLike[str], bytes, str]], bytes, str]` for 1st param but got
+            #  `Tuple[str, *Tuple[Any, ...]]`.
+            args=args,
+            env=env,
+            stdout=self._stdout,
+            stderr=self._stderr,
+            **kwargs,
+        )
+
+    def close(self, death_sig: Optional[signal.Signals] = None) -> None:
+        if not death_sig:
+            death_sig = _get_default_signal()
+        if IS_WINDOWS:
+            self.proc.send_signal(death_sig)
+        else:
+            os.killpg(self.proc.pid, death_sig)
+        if self._stdout:
+            self._stdout.close()
+        if self._stderr:
+            self._stderr.close()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/tail_log.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/tail_log.py
new file mode 100644
index 0000000000000000000000000000000000000000..034072109b7f09f46dc693744b0753f76d06d86a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/tail_log.py
@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import time
+from concurrent.futures.thread import ThreadPoolExecutor
+from threading import Event
+from typing import Optional, TextIO, TYPE_CHECKING
+
+
+if TYPE_CHECKING:
+    from concurrent.futures._base import Future
+
+__all__ = ["tail_logfile", "TailLog"]
+
+logger = logging.getLogger(__name__)
+
+
+def tail_logfile(
+    header: str, file: str, dst: TextIO, finished: Event, interval_sec: float
+):
+    while not os.path.exists(file):
+        if finished.is_set():
+            return
+        time.sleep(interval_sec)
+
+    with open(file, errors="replace") as fp:
+        while True:
+            line = fp.readline()
+
+            if line:
+                dst.write(f"{header}{line}")
+            else:  # reached EOF
+                if finished.is_set():
+                    # log line producer is finished
+                    break
+                else:
+                    # log line producer is still going
+                    # wait for a bit before looping again
+                    time.sleep(interval_sec)
+
+
+class TailLog:
+    """
+    Tail the given log files.
+
+    The log files do not have to exist when the ``start()`` method is called. The tail-er will gracefully wait until
+    the log files are created by the producer and will tail the contents of the
+    log files until the ``stop()`` method is called.
+
+    .. warning:: ``TailLog`` will wait indefinitely for the log file to be created!
+
+    Each log file's line will be suffixed with a header of the form: ``[{name}{idx}]:``,
+    where the ``name`` is user-provided and ``idx`` is the index of the log file
+    in the ``log_files`` mapping. ``log_line_prefixes`` can be used to override the
+    header for each log file.
+
+    Usage:
+
+    ::
+
+     log_files = {0: "/tmp/0_stdout.log", 1: "/tmp/1_stdout.log"}
+     tailer = TailLog("trainer", log_files, sys.stdout).start()
+     # actually run the trainers to produce 0_stdout.log and 1_stdout.log
+     run_trainers()
+     tailer.stop()
+
+     # once run_trainers() start writing the ##_stdout.log files
+     # the tailer will print to sys.stdout:
+     # >>> [trainer0]:log_line1
+     # >>> [trainer1]:log_line1
+     # >>> [trainer0]:log_line2
+     # >>> [trainer0]:log_line3
+     # >>> [trainer1]:log_line2
+
+    .. note:: Due to buffering log lines between files may not necessarily
+              be printed out in order. You should configure your application's
+              logger to suffix each log line with a proper timestamp.
+
+    """
+
+    def __init__(
+        self,
+        name: str,
+        log_files: dict[int, str],
+        dst: TextIO,
+        log_line_prefixes: Optional[dict[int, str]] = None,
+        interval_sec: float = 0.1,
+    ):
+        n = len(log_files)
+        self._threadpool = None
+        if n > 0:
+            self._threadpool = ThreadPoolExecutor(
+                max_workers=n,
+                thread_name_prefix=f"{self.__class__.__qualname__}_{name}",
+            )
+
+        self._name = name
+        self._dst = dst
+        self._log_files = log_files
+        self._log_line_prefixes = log_line_prefixes
+        self._finished_events: dict[int, Event] = {
+            local_rank: Event() for local_rank in log_files.keys()
+        }
+        self._futs: list[Future] = []
+        self._interval_sec = interval_sec
+        self._stopped = False
+
+    def start(self) -> "TailLog":
+        if not self._threadpool:
+            return self
+
+        for local_rank, file in self._log_files.items():
+            header = f"[{self._name}{local_rank}]:"
+            if self._log_line_prefixes and local_rank in self._log_line_prefixes:
+                header = self._log_line_prefixes[local_rank]
+            self._futs.append(
+                self._threadpool.submit(
+                    tail_logfile,
+                    header=header,
+                    file=file,
+                    dst=self._dst,
+                    finished=self._finished_events[local_rank],
+                    interval_sec=self._interval_sec,
+                )
+            )
+        return self
+
+    def stop(self) -> None:
+        for finished in self._finished_events.values():
+            finished.set()
+
+        for local_rank, f in enumerate(self._futs):
+            try:
+                f.result()
+            except Exception as e:
+                logger.error(
+                    "error in log tailor for %s%s. %s: %s",
+                    self._name,
+                    local_rank,
+                    e.__class__.__qualname__,
+                    e,
+                )
+
+        if self._threadpool:
+            self._threadpool.shutdown(wait=True)
+
+        self._stopped = True
+
+    def stopped(self) -> bool:
+        return self._stopped
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0766df8e5f3a77962f5c07e97d445b843d1a4b43
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/__init__.py
@@ -0,0 +1,163 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+In the context of Torch Distributed Elastic we use the term *rendezvous* to
+refer to a particular functionality that combines a **distributed
+synchronization** primitive with **peer discovery**.
+
+It is used by Torch Distributed Elastic to gather participants of a training
+job (i.e. nodes) such that they all agree on the same list of participants and
+everyone's roles, as well as make a consistent collective decision on when
+training can begin/resume.
+
+Torch Distributed Elastic rendezvous provides the following critical
+functionalities:
+
+**Barrier**:
+
+Nodes performing rendezvous will all block until the rendezvous is considered
+complete - this happens when at least ``min`` total number of nodes have joined
+the rendezvous barrier (for the same job). This also implies the barrier is not
+necessarily of fixed size.
+
+There's an additional small waiting time after reaching ``min`` number of
+nodes - this is used to ensure the rendezvous is not completed "too quickly"
+(which could potentially exclude additional nodes attempting to join at
+approximately the same time).
+
+If ``max`` number of nodes is gathered at the barrier, the rendezvous is
+completed immediately.
+
+There's also an overall timeout which causes the rendezvous to fail if ``min``
+number of nodes is never reached - this is meant to be a simple fail-safe to
+help release partially allocated job resources, in case there's a problem with
+the resource manager, and is meant to be interpreted as non-retryable.
+
+**Exclusivity**:
+
+A simple distributed barrier would not be sufficient, as we also need to ensure
+that only one group of nodes exists at any given time (for a given job). In
+other words, new nodes (i.e. joining late) should not be able to form a parallel
+independent group of workers for the same job.
+
+Torch Distributed Elastic rendezvous ensures that if a group of nodes has
+already completed a rendezvous (and hence might already be training), then
+additional "late" nodes attempting to rendezvous will only announce themselves
+as waiting, and will have to wait until the (previously completed) existing
+rendezvous is destroyed first.
+
+**Consistency**:
+
+When a rendezvous is completed, all its members will agree on the job membership
+and everyone's role in it. This role is represented using an integer, called
+rank, that is between between 0 and world size.
+
+Note that ranks are *not stable*, in the sense that the same node can be
+assigned a different rank in the next (re-)rendezvous.
+
+**Fault-tolerance**:
+
+Torch Distributed Elastic rendezvous is designed to tolerate node failures
+during the rendezvous process. Should a process crash (or lose network
+connectivity, etc), between joining the rendezvous and it being completed, then
+a re-rendezvous with remaining healthy nodes will happen automatically.
+
+A node can also fail *after* it has completed (or *has been observered* by other
+nodes to have completed) the rendezvous - this scenario will be handled by the
+Torch Distributed Elastic ``train_loop`` instead (where it will also trigger a
+re-rendezvous).
+
+**Shared key-value store**:
+
+When the rendezvous is completed, a shared key-value store is created and
+returned. This store implements a ``torch.distributed.Store`` API (see
+`distributed communication docs
+`__).
+
+This store is only shared by the members of the completed rendezvous. It
+is intended to be used by Torch Distributed Elastic to exchange information
+necessary to initialize job control and data-planes.
+
+**Waiting workers and rendezvous closing**:
+
+Torch Distributed Elastic rendezvous handler object provides additional
+functionalities, which are technically not part of the rendezvous process:
+
+1. Querying how many workers arrived late at the barrier, who can participate in
+   *next* rendezvous.
+
+2. Setting the rendezvous *closed* to signal all nodes not to participate in
+   next rendezvous.
+
+**DynamicRendezvousHandler**:
+
+Torch Distributed Elastic comes with the :py:class:`.DynamicRendezvousHandler`
+class that implements the rendezvous mechanism described above. It is a backend-
+agnostic type that expects a particular :py:class:`.RendezvousBackend` instance
+to be specified during construction.
+
+Torch distributed users can either implement their own backend type or use one
+of the following implementations that come with PyTorch:
+
+- :py:class:`.C10dRendezvousBackend`: Uses a C10d store (by default
+  ``TCPStore``) as the rendezvous backend. The main advantage of using a C10d
+  store is that it requires no 3rd-party dependency (such as etcd) to establish
+  a rendezvous.
+- :py:class:`.EtcdRendezvousBackend`: Supersedes the legacy
+  :py:class:`.EtcdRendezvousHandler` class. Passing an
+  :py:class:`.EtcdRendezvousBackend` instance to
+  :py:class:`.DynamicRendezvousHandler` is functionally equivalent to
+  instantiating an :py:class:`.EtcdRendezvousHandler`.
+
+  ::
+
+     store = TCPStore("localhost")
+
+     backend = C10dRendezvousBackend(store, "my_run_id")
+
+     rdzv_handler = DynamicRendezvousHandler.from_backend(
+         run_id="my_run_id", store=store, backend=backend, min_nodes=2, max_nodes=4
+     )
+"""
+
+from .api import (
+    rendezvous_handler_registry,
+    RendezvousClosedError,
+    RendezvousConnectionError,
+    RendezvousError,
+    RendezvousGracefulExitError,
+    RendezvousHandler,
+    RendezvousHandlerCreator,
+    RendezvousHandlerRegistry,
+    RendezvousInfo,
+    RendezvousParameters,
+    RendezvousStateError,
+    RendezvousStoreInfo,
+    RendezvousTimeoutError,
+)
+from .registry import _register_default_handlers, _register_out_of_tree_handlers
+
+
+_register_default_handlers()
+_register_out_of_tree_handlers()
+
+
+__all__ = [
+    "RendezvousClosedError",
+    "RendezvousConnectionError",
+    "RendezvousError",
+    "RendezvousGracefulExitError",
+    "RendezvousHandler",
+    "RendezvousHandlerCreator",
+    "RendezvousHandlerRegistry",
+    "RendezvousInfo",
+    "RendezvousParameters",
+    "RendezvousStateError",
+    "RendezvousStoreInfo",
+    "RendezvousTimeoutError",
+    "rendezvous_handler_registry",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/_etcd_stub.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/_etcd_stub.py
new file mode 100644
index 0000000000000000000000000000000000000000..066a1c973e4d969e67648c8e1cddf1693a0289e2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/_etcd_stub.py
@@ -0,0 +1,75 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Any, Optional
+
+
+"""
+This file is not meant to be used directly. It serves as a stub to allow
+other files to be safely imported without requiring the installation of
+the 'etcd' library. The classes and methods here raise exceptions to
+indicate that the real 'etcd' module is needed.
+"""
+
+
+class EtcdStubError(ImportError):
+    """Custom exception to indicate that the real etcd module is required."""
+
+    def __init__(self) -> None:
+        super().__init__("The 'etcd' module is required but not installed.")
+
+
+class EtcdAlreadyExist(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdCompareFailed(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdKeyNotFound(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdWatchTimedOut(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdEventIndexCleared(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdException(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdResult:
+    def __init__(self) -> None:
+        raise EtcdStubError
+
+
+class Client:
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+    def read(self, key: str) -> None:
+        raise EtcdStubError
+
+    def write(
+        self, key: str, value: Any, ttl: Optional[int] = None, **kwargs: Any
+    ) -> None:
+        raise EtcdStubError
+
+    def test_and_set(
+        self, key: str, value: Any, prev_value: Any, ttl: Optional[int] = None
+    ) -> None:
+        raise EtcdStubError
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..be0d6e28536f6efb0e4c24c71a739b1dffb007b4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/api.py
@@ -0,0 +1,390 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import socket
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Callable, ClassVar, Optional
+
+from torch.distributed import Store
+from torch.distributed.elastic.utils.distributed import get_free_port
+
+
+__all__ = [
+    "RendezvousClosedError",
+    "RendezvousConnectionError",
+    "RendezvousError",
+    "RendezvousGracefulExitError",
+    "RendezvousHandler",
+    "RendezvousHandlerCreator",
+    "RendezvousHandlerRegistry",
+    "RendezvousInfo",
+    "RendezvousParameters",
+    "RendezvousStateError",
+    "RendezvousStoreInfo",
+    "RendezvousTimeoutError",
+    "rendezvous_handler_registry",
+]
+
+
+class RendezvousError(Exception):
+    """Represents the base type for rendezvous errors."""
+
+
+class RendezvousClosedError(RendezvousError):
+    """Raised when a rendezvous is closed."""
+
+
+class RendezvousTimeoutError(RendezvousError):
+    """Raised when a rendezvous did not complete on time."""
+
+
+class RendezvousConnectionError(RendezvousError):
+    """Raised when the connection to a rendezvous backend has failed."""
+
+
+class RendezvousStateError(RendezvousError):
+    """Raised when the state of a rendezvous is corrupt."""
+
+
+class RendezvousGracefulExitError(RendezvousError):
+    """Raised when node wasn't not included in rendezvous and gracefully exits.
+
+    Exception is a mechanism to exit the stack, however does not mean a failure.
+    """
+
+
+@dataclass
+class RendezvousStoreInfo:
+    """Store address and port that can be used to bootstrap trainer distributed comms"""
+
+    MASTER_ADDR_KEY: ClassVar[str] = "MASTER_ADDR"
+    MASTER_PORT_KEY: ClassVar[str] = "MASTER_PORT"
+    master_addr: str
+    master_port: int
+
+    @staticmethod
+    def build(
+        rank: int,
+        store: Store,
+        local_addr: Optional[str],
+        server_port: Optional[int] = None,
+    ) -> "RendezvousStoreInfo":
+        """Factory method, finds unused new port on rank0 host and addr/port info with all ranks.
+
+        If master_addr/master_port is knowns (useful when sharing existing tcp store server) use the constructor.
+
+        Args:
+            rank: rank of the current node
+            store: store to use for rendezvous
+            local_addr: address of the current node, if not provided will be resolved from hostname
+            server_port: port of the TCPStore server, when the TCPStore is shared.
+        """
+        # TODO swap to collectives comms API
+        if rank == 0:
+            addr = local_addr or socket.getfqdn()
+            # When TCPStore is not shared, we fallback to get_free_port.
+            port = server_port or get_free_port()
+            store.set(
+                RendezvousStoreInfo.MASTER_ADDR_KEY,
+                addr.encode(encoding="UTF-8"),  # type: ignore[arg-type]
+            )
+            store.set(
+                RendezvousStoreInfo.MASTER_PORT_KEY,
+                str(port).encode(encoding="UTF-8"),  # type: ignore[arg-type]
+            )
+
+        addr = store.get(RendezvousStoreInfo.MASTER_ADDR_KEY).decode(encoding="UTF-8")
+        port = int(
+            store.get(RendezvousStoreInfo.MASTER_PORT_KEY).decode(encoding="UTF-8")
+        )
+        return RendezvousStoreInfo(master_addr=addr, master_port=port)
+
+
+class RendezvousInfo:
+    """Holds the information about the rendezvous."""
+
+    def __init__(
+        self,
+        store: Store,
+        rank: int,
+        world_size: int,
+        bootstrap_store_info: RendezvousStoreInfo,
+    ):
+        self._store = store
+        self._rank = rank
+        self._world_size = world_size
+        self._bootstrap_store_info = bootstrap_store_info
+
+    @property
+    def store(self) -> Store:
+        """Store used by torchelastic control plane"""
+        return self._store
+
+    @property
+    def rank(self) -> int:
+        """Rank within a group"""
+        return self._rank
+
+    @property
+    def world_size(self) -> int:
+        """Global group size"""
+        return self._world_size
+
+    @property
+    def bootstrap_store_info(self) -> Optional[RendezvousStoreInfo]:
+        """Store information that can used by trainer code to bootstrap distributed comms."""
+        return self._bootstrap_store_info
+
+
+class RendezvousHandler(ABC):
+    """Main rendezvous interface.
+
+    Note:
+        Distributed Torch users normally **do not** need to implement their own
+        ``RendezvousHandler``. An implementation based on C10d Store is already
+        provided, and is recommended for most users.
+    """
+
+    @abstractmethod
+    def get_backend(self) -> str:
+        """Return the name of the rendezvous backend."""
+
+    @property
+    def use_agent_store(self) -> bool:
+        """Indicates that store reference returned by :py:meth:`next_rendezvous` can be shared with user
+        applications and will be available during application lifecyle.
+
+        Rendezous handler impl will share store details as instance of :py:class:`RendezvousStoreInfo`.
+        Applications as a convention use `MASTER_ADDR`/`MASTER_PORT` env variables to lookup the store.
+        """
+        return False
+
+    @abstractmethod
+    def next_rendezvous(self) -> RendezvousInfo:
+        """Main entry-point into the rendezvous barrier.
+
+        Blocks until the rendezvous is complete and the current process is
+        included in the formed worker group, or a timeout occurs, or the
+        rendezvous was marked closed.
+
+        Returns:
+            Instance of :py:class:`RendezvousInfo`.
+
+        Raises:
+            RendezvousClosedError:
+                The rendezvous is closed.
+            RendezvousConnectionError:
+                The connection to the rendezvous backend has failed.
+            RendezvousStateError:
+                The rendezvous state is corrupt.
+            RendezvousTimeoutError:
+                The rendezvous did not complete on time.
+        """
+
+    @abstractmethod
+    def is_closed(self) -> bool:
+        """Check whether the rendezvous has been closed.
+
+        A closed rendezvous means all future attempts to re-rendezvous within
+        same job will fail.
+
+        ``is_closed()`` and :py:meth:`set_closed` have semantics of eventual
+        propagation and should not be used for synchronization. The intention is
+        that if at least one node decides the job is finished, it will close the
+        rendezvous, and other nodes will soon observe this and stop running as
+        well.
+        """
+
+    @abstractmethod
+    def set_closed(self):
+        """Mark the rendezvous as closed."""
+
+    @abstractmethod
+    def num_nodes_waiting(self) -> int:
+        """Return the number of nodes who arrived late at the rendezvous
+        barrier, hence were not included in the current worker group.
+
+        Callers should periodically call this method to check whether new
+        nodes are waiting to join the job and if so admit them by calling
+        :py:meth:`next_rendezvous()` (re-rendezvous).
+        """
+
+    @abstractmethod
+    def get_run_id(self) -> str:
+        """Return the run id of the rendezvous.
+
+        The run id is a user-defined id that uniquely identifies an instance of
+        a distributed application. It typically maps to a job id and is used to
+        allow nodes to join the correct distributed application.
+        """
+
+    @abstractmethod
+    def shutdown(self) -> bool:
+        """Close all resources that were open for the rendezvous.
+
+        Example::
+
+            rdzv_handler = ...
+            try:
+                store, rank, world_size = rdzv_handler.next_rendezvous()
+            finally:
+                rdzv_handler.shutdown()
+        """
+
+
+class RendezvousParameters:
+    """Hold the parameters to construct a :py:class:`RendezvousHandler`.
+
+    Args:
+        backend:
+            The name of the backend to use to handle the rendezvous.
+        endpoint:
+            The endpoint of the rendezvous, usually in form [:].
+        run_id:
+            The id of the rendezvous.
+        min_nodes:
+            The minimum number of nodes to admit to the rendezvous.
+        max_nodes:
+            The maximum number of nodes to admit to the rendezvous.
+        local_addr:
+            The address of the local node.
+        **kwargs:
+            Additional parameters for the specified backend.
+    """
+
+    def __init__(
+        self,
+        backend: str,
+        endpoint: str,
+        run_id: str,
+        min_nodes: int,
+        max_nodes: int,
+        local_addr: Optional[str] = None,
+        **kwargs,
+    ):
+        if not backend:
+            raise ValueError("The rendezvous backend name must be a non-empty string.")
+
+        if min_nodes < 1:
+            raise ValueError(
+                f"The minimum number of rendezvous nodes ({min_nodes}) must be greater than zero."
+            )
+        if max_nodes < min_nodes:
+            raise ValueError(
+                f"The maximum number of rendezvous nodes ({max_nodes}) must be greater than or "
+                f"equal to the minimum number of rendezvous nodes ({min_nodes})."
+            )
+
+        self.backend = backend
+        self.endpoint = endpoint
+        self.run_id = run_id
+        self.min_nodes = min_nodes
+        self.max_nodes = max_nodes
+        self.config = kwargs
+        self.local_addr = local_addr
+
+    def get(self, key: str, default: Any = None) -> Any:
+        """Return the value for ``key`` if ``key`` exists, else ``default``."""
+        return self.config.get(key, default)
+
+    def get_as_bool(self, key: str, default: Optional[bool] = None) -> Optional[bool]:
+        """Return the value for ``key`` as a ``bool``."""
+        value = self.get(key, default)
+        if value is None or isinstance(value, bool):
+            return value
+        if isinstance(value, int):
+            if value == 1:
+                return True
+            if value == 0:
+                return False
+        elif isinstance(value, str):
+            if value.lower() in ["1", "true", "t", "yes", "y"]:
+                return True
+            if value.lower() in ["0", "false", "f", "no", "n"]:
+                return False
+        raise ValueError(
+            f"The rendezvous configuration option '{key}' does not represent a valid boolean value."
+        )
+
+    def get_as_int(self, key: str, default: Optional[int] = None) -> Optional[int]:
+        """Return the value for ``key`` as an ``int``."""
+        value = self.get(key, default)
+        if value is None:
+            return value
+        try:
+            return int(value)
+        except ValueError as e:
+            raise ValueError(
+                f"The rendezvous configuration option '{key}' does not represent a valid integer "
+                "value."
+            ) from e
+
+
+RendezvousHandlerCreator = Callable[[RendezvousParameters], RendezvousHandler]
+
+
+class RendezvousHandlerRegistry:
+    """Represent a registry of :py:class:`RendezvousHandler` backends."""
+
+    _registry: dict[str, RendezvousHandlerCreator]
+
+    def __init__(self) -> None:
+        self._registry = {}
+
+    def register(self, backend: str, creator: RendezvousHandlerCreator) -> None:
+        """Register a new rendezvous backend.
+
+        Args:
+            backend:
+                The name of the backend.
+            creator:
+                The callback to invoke to construct the
+                :py:class:`RendezvousHandler`.
+        """
+        if not backend:
+            raise ValueError("The rendezvous backend name must be a non-empty string.")
+
+        current_creator: Optional[RendezvousHandlerCreator]
+        try:
+            current_creator = self._registry[backend]
+        except KeyError:
+            current_creator = None
+
+        if current_creator is not None and current_creator != creator:
+            raise ValueError(
+                f"The rendezvous backend '{backend}' cannot be registered with '{creator}' as it "
+                f"is already registered with '{current_creator}'."
+            )
+
+        self._registry[backend] = creator
+
+    def create_handler(self, params: RendezvousParameters) -> RendezvousHandler:
+        """Create a new :py:class:`RendezvousHandler`."""
+        try:
+            creator = self._registry[params.backend]
+        except KeyError as e:
+            raise ValueError(
+                f"The rendezvous backend '{params.backend}' is not registered. Did you forget "
+                f"to call `{self.register.__name__}`?"
+            ) from e
+
+        handler = creator(params)
+
+        # Do some sanity check.
+        if handler.get_backend() != params.backend:
+            raise RuntimeError(
+                f"The rendezvous backend '{handler.get_backend()}' does not match the requested "
+                f"backend '{params.backend}'."
+            )
+
+        return handler
+
+
+# The default global registry instance used by launcher scripts to instantiate
+# rendezvous handlers.
+rendezvous_handler_registry = RendezvousHandlerRegistry()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..7183085b870429c590e4c02a947c26b2bddd52f4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py
@@ -0,0 +1,273 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import binascii
+import logging
+import os
+import tempfile
+from base64 import b64decode, b64encode
+from datetime import timedelta
+from typing import Any, cast, Optional
+
+from torch.distributed import FileStore, Store, TCPStore
+from torch.distributed.elastic.events import construct_and_record_rdzv_event, NodeState
+
+from .api import (
+    RendezvousConnectionError,
+    RendezvousError,
+    RendezvousParameters,
+    RendezvousStateError,
+)
+from .dynamic_rendezvous import RendezvousBackend, Token
+from .utils import _matches_machine_hostname, parse_rendezvous_endpoint
+
+
+logger = logging.getLogger(__name__)
+
+# default port for the TCP store
+DEFAULT_PORT = 29400
+
+
+class C10dRendezvousBackend(RendezvousBackend):
+    """Represents a C10d-backed rendezvous backend.
+
+    Args:
+        store:
+            The :py:class:`torch.distributed.Store` instance to use to
+            communicate with the C10d store.
+        run_id:
+            The run id of the rendezvous.
+    """
+
+    # See the explanation in the __init__ method.
+    _NULL_SENTINEL = "Y2FuaW1hZGFt"
+
+    _store: Store
+    _key: str
+
+    def __init__(self, store: Store, run_id: str) -> None:
+        if not run_id:
+            raise ValueError("The run id must be a non-empty string.")
+
+        self._store = store
+
+        self._key = "torch.rendezvous." + run_id
+
+        # The read operation of a store blocks the caller until the specified
+        # key becomes available. This behavior makes it tricky to use a store
+        # as a regular key-value dictionary.
+        #
+        # As a workaround we initially set a sentinel value as the rendezvous
+        # state. Whenever this value gets returned we treat it as a None.
+        self._call_store("compare_set", self._key, "", self._NULL_SENTINEL)
+
+    @property
+    def name(self) -> str:
+        """See base class."""
+        return "c10d"
+
+    def get_state(self) -> Optional[tuple[bytes, Token]]:
+        """See base class."""
+        base64_state: bytes = self._call_store("get", self._key)
+
+        return self._decode_state(base64_state)
+
+    def set_state(
+        self, state: bytes, token: Optional[Token] = None
+    ) -> Optional[tuple[bytes, Token, bool]]:
+        """See base class."""
+        base64_state_str: str = b64encode(state).decode()
+
+        if token:
+            # Shortcut if we know for sure that the token is not valid.
+            if not isinstance(token, bytes):
+                result = self.get_state()
+                if result is not None:
+                    tmp = *result, False
+                    # Python 3.6 does not support tuple unpacking in return
+                    # statements.
+                    return tmp
+                return None
+
+            token = token.decode()
+        else:
+            token = self._NULL_SENTINEL
+
+        base64_state: bytes = self._call_store(
+            "compare_set", self._key, token, base64_state_str
+        )
+
+        state_token_pair = self._decode_state(base64_state)
+        if state_token_pair is None:
+            return None
+
+        new_state, new_token = state_token_pair
+
+        # C10d Store's compare_set method does not offer an easy way to find out
+        # whether our write attempt was successful. As a brute-force solution we
+        # perform a bitwise comparison of our local state and the remote state.
+        return new_state, new_token, new_state == state
+
+    def _call_store(self, store_op: str, *args, **kwargs) -> Any:
+        try:
+            return getattr(self._store, store_op)(*args, **kwargs)
+        except (ValueError, RuntimeError, TimeoutError) as exc:
+            raise RendezvousConnectionError(
+                "The connection to the C10d store has failed. See inner exception for details."
+            ) from exc
+
+    def _decode_state(self, base64_state: bytes) -> Optional[tuple[bytes, Token]]:
+        if base64_state == self._NULL_SENTINEL.encode():
+            return None
+
+        try:
+            state = b64decode(base64_state)
+        except binascii.Error as exc:
+            raise RendezvousStateError(
+                "The state object is corrupt. See inner exception for details."
+            ) from exc
+
+        return state, base64_state
+
+
+def _create_tcp_store(params: RendezvousParameters) -> TCPStore:
+    host, port = parse_rendezvous_endpoint(params.endpoint, default_port=DEFAULT_PORT)
+
+    cfg_is_host = params.get_as_bool("is_host")
+    # If the user has explicitly specified whether our process should host the
+    # the store, respect it.
+    if cfg_is_host is not None:
+        is_host = cfg_is_host
+    # Otherwise try to determine whether we are the host based on our hostname
+    # and IP address.
+    else:
+        is_host = _matches_machine_hostname(host)
+
+    # The timeout
+    read_timeout = cast(int, params.get_as_int("read_timeout", 60))
+    if read_timeout <= 0:
+        raise ValueError("The read timeout must be a positive integer.")
+
+    # In specific cases we attempt to instantiate the store twice. For details
+    # see the explanation in the except clause below.
+    for is_server in [is_host, False]:
+        try:
+            store = TCPStore(
+                host,
+                port,
+                is_master=is_server,
+                multi_tenant=True,
+                timeout=timedelta(seconds=read_timeout),
+            )
+
+            if is_server:
+                msg = f"Process {os.getpid()} hosts the TCP store for the C10d rendezvous backend."
+                construct_and_record_rdzv_event(
+                    run_id=params.run_id, message=msg, node_state=NodeState.INIT
+                )
+                logger.info(msg)
+
+            break
+        except (ValueError, RuntimeError, TimeoutError) as exc:
+            # If we heuristically inferred the value of is_host as True and our
+            # first attempt to instantiate the TCP store has failed, try it one
+            # more time with is_host set to False. As an edge case there can be
+            # more than one process that is part of the same rendezvous on this
+            # machine and only one of them will eventually host the store.
+
+            if not is_server or cfg_is_host is not None:
+                raise RendezvousConnectionError(
+                    "The connection to the C10d store has failed. See inner exception for details."
+                ) from exc
+
+    return store  # type: ignore[possibly-undefined]
+
+
+def _create_file_store(params: RendezvousParameters) -> FileStore:
+    # If a user specifies an endpoint, we treat it as a path to a file.
+    if params.endpoint:
+        path = params.endpoint
+    else:
+        try:
+            # The temporary file is readable and writable only by the user of
+            # this process.
+            _, path = tempfile.mkstemp()
+        except OSError as exc:
+            raise RendezvousError(
+                "The file creation for C10d store has failed. See inner exception for details."
+            ) from exc
+
+    try:
+        store = FileStore(path)
+    except (ValueError, RuntimeError) as exc:
+        raise RendezvousConnectionError(
+            "The connection to the C10d store has failed. See inner exception for details."
+        ) from exc
+
+    return store
+
+
+def create_backend(params: RendezvousParameters) -> tuple[C10dRendezvousBackend, Store]:
+    """Create a new :py:class:`C10dRendezvousBackend` from the specified parameters.
+
+    +--------------+-----------------------------------------------------------+
+    | Parameter    | Description                                               |
+    +==============+===========================================================+
+    | store_type   | The type of the C10d store. The currently supported types |
+    |              | are "tcp" and "file" which correspond to                  |
+    |              | :py:class:`torch.distributed.TCPStore` and                |
+    |              | :py:class:`torch.distributed.FileStore`, respectively.    |
+    |              | Defaults to "tcp".                                        |
+    +--------------+-----------------------------------------------------------+
+    | read_timeout | The read timeout, in seconds, for store operations.       |
+    |              | Defaults to 60 seconds.                                   |
+    |              |                                                           |
+    |              | Note this only applies to                                 |
+    |              | :py:class:`torch.distributed.TCPStore`. It is not relevant|
+    |              | to :py:class:`torch.distributed.FileStore` which does not |
+    |              | take in timeout as a parameter.                           |
+    +--------------+-----------------------------------------------------------+
+    | is_host      | A boolean value indicating whether this backend instance  |
+    |              | will host the C10d store. If not specified it will be     |
+    |              | inferred heuristically by matching the hostname or the IP |
+    |              | address of this machine against the specified rendezvous  |
+    |              | endpoint. Defaults to ``None``.                           |
+    |              |                                                           |
+    |              | Note that this configuration option only applies to       |
+    |              | :py:class:`torch.distributed.TCPStore`. In normal         |
+    |              | circumstances you can safely skip it; the only time when  |
+    |              | it is needed is if its value cannot be correctly          |
+    |              | determined (e.g. the rendezvous endpoint has a CNAME as   |
+    |              | the hostname or does not match the FQDN of the machine).  |
+    +--------------+-----------------------------------------------------------+
+    """
+    # As of today we only support TCPStore and FileStore. Other store types do
+    # not have the required functionality (e.g. compare_set) yet.
+    store_type = params.get("store_type", "tcp").strip().lower()
+    store: Store
+
+    try:
+        if store_type == "file":
+            store = _create_file_store(params)
+        elif store_type == "tcp":
+            store = _create_tcp_store(params)
+        else:
+            raise ValueError(
+                "Invalid store type given. Currently only supports file and tcp."
+            )
+
+        backend = C10dRendezvousBackend(store, params.run_id)
+
+    except Exception as e:
+        construct_and_record_rdzv_event(
+            message=f"{type(e).__name__}: {str(e)}",
+            run_id=params.run_id,
+            node_state=NodeState.FAILED,
+        )
+        raise
+
+    return backend, store
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cbb37a1b510f3a76c236a06c15d26cea8a2533c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py
@@ -0,0 +1,1455 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import inspect
+import logging
+import os
+import pickle
+import socket
+import threading
+import time
+import weakref
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from datetime import datetime, timedelta, timezone
+from enum import Enum
+from typing import Any, Callable, Optional
+
+import torch.distributed as dist
+from torch.distributed import Store
+from torch.distributed.elastic.events import construct_and_record_rdzv_event, NodeState
+
+from .api import (
+    RendezvousClosedError,
+    RendezvousError,
+    RendezvousGracefulExitError,
+    RendezvousHandler,
+    RendezvousInfo,
+    RendezvousParameters,
+    RendezvousStateError,
+    RendezvousStoreInfo,
+    RendezvousTimeoutError,
+)
+from .utils import _delay, _PeriodicTimer
+
+
+__all__ = [
+    "RendezvousBackend",
+    "RendezvousTimeout",
+    "RendezvousSettings",
+    "DynamicRendezvousHandler",
+    "create_handler",
+]
+
+logger = logging.getLogger(__name__)
+
+
+def get_method_name(depth=2):
+    if len(inspect.stack()) > depth:
+        return inspect.stack()[depth].function
+    return "no_method_name"
+
+
+Token = Any
+"""Represent an opaque fencing token used by the rendezvous backend."""
+
+
+class RendezvousBackend(ABC):
+    """Represent a backend that holds the rendezvous state."""
+
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """Get the name of the backend."""
+
+    @abstractmethod
+    def get_state(self) -> Optional[tuple[bytes, Token]]:
+        """Get the rendezvous state.
+
+        Returns:
+            A tuple of the encoded rendezvous state and its fencing token or
+            ``None`` if no state is found in the backend.
+
+        Raises:
+            RendezvousConnectionError:
+                The connection to the backend has failed.
+            RendezvousStateError:
+                The rendezvous state is corrupt.
+        """
+
+    @abstractmethod
+    def set_state(
+        self, state: bytes, token: Optional[Token] = None
+    ) -> Optional[tuple[bytes, Token, bool]]:
+        """Set the rendezvous state.
+
+        The new rendezvous state is set conditionally:
+
+          - If the specified ``token`` matches the fencing token stored in the
+            backend, the state will be updated. The new state will be returned
+            to the caller along with its fencing token.
+          - If the specified ``token`` does not match the fencing token stored
+            in the backend, the state won't be updated; instead the existing
+            state along with its fencing token will be returned to the caller.
+          - If the specified ``token`` is ``None``, the new state will be set
+            only if there is no existing state in the backend. Either the new
+            state or the existing state along with its fencing token will be
+            returned to the caller.
+
+        Args:
+            state:
+                The encoded rendezvous state.
+            token:
+                An optional fencing token that was retrieved by a previous call
+                to :py:meth:`get_state` or ``set_state()``.
+
+        Returns:
+            A tuple of the serialized rendezvous state, its fencing token, and
+            a boolean value indicating whether our set attempt succeeded.
+
+        Raises:
+            RendezvousConnectionError:
+                The connection to the backend has failed.
+            RendezvousStateError:
+                The rendezvous state is corrupt.
+        """
+
+
+class RendezvousTimeout:
+    """Hold the timeout configuration of a rendezvous.
+
+    Args:
+        join:
+            The time within which the rendezvous is expected to complete.
+        last_call:
+            An additional wait amount before completing the rendezvous once the
+            rendezvous has the minimum number of required participants.
+        close:
+            The time within which the rendezvous is expected to close after a
+            call to :py:meth:`RendezvousHandler.set_closed` or
+            :py:meth:`RendezvousHandler.shutdown`.
+        heartbeat:
+            The time within which a keep-alive heartbeat is expected to
+            complete.
+    """
+
+    _ZERO = timedelta(0)
+
+    _DEFAULT_TIMEOUTS = {
+        "join": timedelta(seconds=600),
+        "last_call": timedelta(seconds=30),
+        "close": timedelta(seconds=30),
+        "heartbeat": timedelta(seconds=5),
+    }
+
+    _join: timedelta
+    _last_call: timedelta
+    _close: timedelta
+    _heartbeat: timedelta
+
+    def __init__(
+        self,
+        join: Optional[timedelta] = None,
+        last_call: Optional[timedelta] = None,
+        close: Optional[timedelta] = None,
+        heartbeat: Optional[timedelta] = None,
+    ) -> None:
+        self._set_timeouts(
+            join=join, last_call=last_call, close=close, heartbeat=heartbeat
+        )
+
+    @property
+    def join(self) -> timedelta:
+        """Get the join timeout."""
+        return self._join
+
+    @property
+    def last_call(self) -> timedelta:
+        """Get the last call timeout."""
+        return self._last_call
+
+    @property
+    def close(self) -> timedelta:
+        """Get the close timeout."""
+        return self._close
+
+    @property
+    def heartbeat(self) -> timedelta:
+        """Get the keep-alive heartbeat timeout."""
+        return self._heartbeat
+
+    def _set_timeouts(self, **timeouts: Optional[timedelta]):
+        for name, timeout in timeouts.items():
+            if timeout is None:
+                timeout = self._DEFAULT_TIMEOUTS[name]
+            if timeout <= self._ZERO:
+                raise ValueError(f"The {name} timeout ({timeout}) must be positive.")
+            setattr(self, "_" + name, timeout)
+
+
+@dataclass(repr=False, eq=False, frozen=True)
+class RendezvousSettings:
+    """Hold the settings of the rendezvous.
+
+    Attributes:
+        run_id:
+            The run id of the rendezvous.
+        min_nodes:
+            The minimum number of nodes to admit to the rendezvous.
+        max_nodes:
+            The maximum number of nodes to admit to the rendezvous.
+        timeout:
+            The timeout configuration of the rendezvous.
+        keep_alive_interval:
+            The amount of time a node waits before sending a heartbeat to keep
+            it alive in the rendezvous.
+        keep_alive_max_attempt:
+            The maximum number of failed heartbeat attempts after which a node
+            is considered dead.
+    """
+
+    run_id: str
+    min_nodes: int
+    max_nodes: int
+    timeout: RendezvousTimeout
+    keep_alive_interval: timedelta
+    keep_alive_max_attempt: int
+
+
+@dataclass(eq=True, order=True, frozen=True)
+class _NodeDesc:
+    """Describe a node in the rendezvous.
+
+    Attributes:
+        addr:
+            The FQDN of the node or user specified local node address.
+        pid:
+            The id of the process in which the rendezvous handler runs.
+        local_id:
+            A process-wide unique id.
+    """
+
+    addr: str
+    pid: int
+    local_id: int
+
+    def __repr__(self) -> str:
+        return f"{self.addr}_{self.pid}_{self.local_id}"
+
+
+class _NodeDescGenerator:
+    """Generate node descriptors.
+
+    A node descriptor is a combination of an FQDN, a process id, and an auto-
+    incremented integer that uniquely identifies a node in the rendezvous.
+    """
+
+    _lock: threading.Lock
+    _local_id: int
+
+    def __init__(self) -> None:
+        self._lock = threading.Lock()
+
+        # An integer that is incremented with each call to generate().
+        self._local_id = 0
+
+    def generate(self, local_addr: Optional[str] = None) -> _NodeDesc:
+        # This method can be called by multiple threads concurrently; therefore,
+        # we must increment the integer atomically.
+        with self._lock:
+            local_id = self._local_id
+
+            self._local_id += 1
+
+        return _NodeDesc(local_addr or socket.getfqdn(), os.getpid(), local_id)
+
+
+class _RendezvousState:
+    """Hold the state of a rendezvous.
+
+    Attributes:
+        round:
+            The current round of the rendezvous.
+        complete:
+            A boolean value indicating whether the current round of the
+            rendezvous is complete.
+        deadline:
+            The time at which the current round of the rendezvous will be
+            considered complete if it is still waiting for nodes to join.
+        closed:
+            A boolean value indicating whether the rendezvous is closed.
+        participants:
+            A dictionary of the participants and their corresponding ranks.
+        wait_list:
+            A set of nodes that are waiting to participate in the next round of
+            the rendezvous.
+        redundancy_list:
+            A set of nodes that are redundant in the current round and can join
+            the next rendezvous without triggering re-rendezvous.
+        last_heartbeats:
+            A dictionary containing each node's last heartbeat time.
+    """
+
+    round: int
+    complete: bool
+    deadline: Optional[datetime]
+    closed: bool
+    participants: dict[_NodeDesc, int]
+    wait_list: set[_NodeDesc]
+    redundancy_list: set[_NodeDesc]
+    last_heartbeats: dict[_NodeDesc, datetime]
+
+    def __init__(self) -> None:
+        self.round = 0
+        self.complete = False
+        self.deadline = None
+        self.closed = False
+        self.participants = {}
+        self.wait_list = set()
+        self.redundancy_list = set()
+        self.last_heartbeats = {}
+
+
+def _remove_participant_epilogue(
+    state: _RendezvousState, settings: RendezvousSettings
+) -> None:
+    if state.complete:
+        # If we do not have any participants left, move to the next round.
+        if not state.participants:
+            msg = "No participants left in the rendezvous, marking rendezvous as incomplete"
+            logger.debug(msg)
+            state.complete = False
+
+            state.round += 1
+    else:
+        if len(state.participants) < settings.min_nodes:
+            msg = (
+                f"Number of participants {len(state.participants)}) less than"
+                f"min_nodes {settings.min_nodes}, clearning deadline in state"
+            )
+            logger.debug(msg)
+            state.deadline = None
+
+
+class _RendezvousStateHolder(ABC):
+    """Hold the shared rendezvous state synced with other nodes."""
+
+    @property
+    @abstractmethod
+    def state(self) -> _RendezvousState:
+        """Get the local state."""
+
+    @abstractmethod
+    def sync(self) -> Optional[bool]:
+        """Read or writes the latest state.
+
+        Returns:
+            A boolean value indicating whether the local state, in case marked
+            as dirty, was successfully synced with other nodes.
+        """
+
+    @abstractmethod
+    def mark_dirty(self) -> None:
+        """Mark the local state as dirty."""
+
+
+class _BackendRendezvousStateHolder(_RendezvousStateHolder):
+    """Hold the rendezvous state synced with other nodes via a backend.
+
+    Args:
+        backend:
+            The rendezvous backend to use.
+        settings:
+            The rendezvous settings.
+        cache_duration:
+            The amount of time, in seconds, to cache the last rendezvous state
+            before requesting it from the backend again.
+    """
+
+    _backend: RendezvousBackend
+    _state: _RendezvousState
+    _settings: RendezvousSettings
+    _cache_duration: int
+    _token: Token
+    _dirty: bool
+    _last_sync_time: float
+    _dead_nodes: list[_NodeDesc]
+
+    def __init__(
+        self,
+        backend: RendezvousBackend,
+        settings: RendezvousSettings,
+        cache_duration: int = 1,
+    ) -> None:
+        self._backend = backend
+        self._state = _RendezvousState()
+        self._settings = settings
+        self._cache_duration = cache_duration
+        self._token = None
+        self._dirty = False
+        self._last_sync_time = -1
+        self._dead_nodes = []
+
+    def _record(self, message: str, node_state: NodeState = NodeState.RUNNING):
+        construct_and_record_rdzv_event(
+            name=f"{self.__class__.__name__}.{get_method_name()}",
+            run_id=self._settings.run_id,
+            message=message,
+            node_state=node_state,
+        )
+
+    @property
+    def state(self) -> _RendezvousState:
+        """See base class."""
+        return self._state
+
+    def sync(self) -> Optional[bool]:
+        """See base class."""
+        state_bits: Optional[bytes] = None
+
+        token = None
+
+        has_set: Optional[bool]
+
+        if self._dirty:
+            has_set = False
+
+            state_bits = pickle.dumps(self._state)
+
+            set_response = self._backend.set_state(state_bits, self._token)
+            if set_response is not None:
+                state_bits, token, has_set = set_response
+        else:
+            has_set = None
+
+            if self._cache_duration > 0:
+                # Avoid overloading the backend if we are asked to retrieve the
+                # state repeatedly. Try to serve the cached state.
+                if self._last_sync_time >= max(
+                    time.monotonic() - self._cache_duration, 0
+                ):
+                    return None
+
+            get_response = self._backend.get_state()
+            if get_response is not None:
+                state_bits, token = get_response
+
+        if state_bits is not None:
+            try:
+                self._state = pickle.loads(state_bits)
+            except pickle.PickleError as exc:
+                raise RendezvousStateError(
+                    "The rendezvous state is corrupt. See inner exception for details."
+                ) from exc
+        else:
+            self._state = _RendezvousState()
+
+        if has_set and self._dead_nodes and logger.isEnabledFor(logging.DEBUG):
+            node_list = ", ".join(f"'{dead_node}'" for dead_node in self._dead_nodes)
+
+            msg = (
+                f"As part of the sync operation the node(s) {node_list} have been removed from the "
+                f"rendezvous '{self._settings.run_id}' since they had no heartbeat."
+            )
+            self._record(message=msg)
+            logger.debug(msg)
+
+        self._token = token
+
+        self._dirty = False
+
+        self._last_sync_time = time.monotonic()
+
+        self._sanitize()
+
+        return has_set
+
+    def _sanitize(self) -> None:
+        state = self._state
+
+        expire_time = datetime.now(timezone.utc) - (
+            self._settings.keep_alive_interval * self._settings.keep_alive_max_attempt
+        )
+
+        # Filter out the dead nodes.
+        self._dead_nodes = [
+            node
+            for node, last_heartbeat in state.last_heartbeats.items()
+            if last_heartbeat < expire_time
+        ]
+
+        participant_removed = False
+
+        for dead_node in self._dead_nodes:
+            msg = f"Detected dead node '{dead_node}', removing it from the rendezvous"
+            logger.debug(msg)
+            del state.last_heartbeats[dead_node]
+
+            try:
+                del state.participants[dead_node]
+
+                participant_removed = True
+            except KeyError:
+                pass
+
+            try:
+                state.wait_list.remove(dead_node)
+            except KeyError:
+                pass
+
+            try:
+                state.redundancy_list.remove(dead_node)
+            except KeyError:
+                pass
+
+        if participant_removed:
+            # Common epilogue shared with the _remove_from_participants()
+            # function of _DistributedRendezvousOpExecutor.
+            _remove_participant_epilogue(state, self._settings)
+
+    def mark_dirty(self) -> None:
+        """See base class.
+
+        If the local rendezvous state is dirty, the next sync call will try to
+        write the changes back to the backend. However this attempt might fail
+        if another node, which had the same state, also made changes and wrote
+        them before us.
+        """
+        self._dirty = True
+
+
+class _Action(Enum):
+    """Specifies the possible actions based on the state of the rendezvous."""
+
+    KEEP_ALIVE = 1
+    ADD_TO_PARTICIPANTS = 2
+    ADD_TO_WAIT_LIST = 3
+    ADD_TO_REDUNDANCY_LIST = 4
+    REMOVE_FROM_PARTICIPANTS = 5
+    REMOVE_FROM_WAIT_LIST = 6
+    REMOVE_FROM_REDUNDANCY_LIST = 7
+    MARK_RENDEZVOUS_COMPLETE = 8
+    MARK_RENDEZVOUS_CLOSED = 9
+    SYNC = 10
+    ERROR_CLOSED = 11
+    ERROR_TIMEOUT = 12
+    FINISH = 13
+
+
+class _RendezvousContext:
+    """Holds the context of the rendezvous.
+
+    Attributes:
+        node:
+            The node descriptor associated with the current rendezvous handler
+            instance.
+        state:
+            The current state of the rendezvous.
+        settings:
+            The rendezvous settings.
+    """
+
+    node: _NodeDesc
+    state: _RendezvousState
+    settings: RendezvousSettings
+
+    def __init__(
+        self, node: _NodeDesc, state: _RendezvousState, settings: RendezvousSettings
+    ) -> None:
+        self.node = node
+        self.state = state
+        self.settings = settings
+
+
+class _RendezvousOpExecutor(ABC):
+    """Execute rendezvous operations."""
+
+    @abstractmethod
+    def run(
+        self,
+        state_handler: Callable[[_RendezvousContext, float], _Action],
+        deadline: float,
+        update_deadline: Optional[Callable[[timedelta], float]] = None,
+    ) -> None:
+        """Execute a rendezvous operation.
+
+        An operation is run inside a state machine and is expected to transition
+        the rendezvous from one state to another.
+
+        Args:
+            state_handler:
+                A callable that is expected to return the next state transition
+                action based on the current state of the rendezvous.
+            deadline:
+                The time, in seconds, at which the operation will be considered
+                timed-out.
+            update_deadline:
+                Function to generate a new operation deadline if the current
+                node may participate in the next rendezvous.
+        """
+
+
+class _DistributedRendezvousOpExecutor(_RendezvousOpExecutor):
+    """Execute rendezvous operations using a shared state.
+
+    Args:
+        node:
+            The node descriptor associated with the current rendezvous handler
+            instance.
+        state_holder:
+            The ``RendezvousStateHolder`` to use to sync the rendezvous state
+            with other nodes.
+        settings:
+            The rendezvous settings.
+    """
+
+    _node: _NodeDesc
+    _state: _RendezvousState
+    _state_holder: _RendezvousStateHolder
+    _settings: RendezvousSettings
+
+    def __init__(
+        self,
+        node: _NodeDesc,
+        state_holder: _RendezvousStateHolder,
+        settings: RendezvousSettings,
+    ) -> None:
+        self._node = node
+        self._state_holder = state_holder
+        self._settings = settings
+
+    def _record(self, message: str, node_state: NodeState = NodeState.RUNNING) -> None:
+        construct_and_record_rdzv_event(
+            name=f"{self.__class__.__name__}.{get_method_name()}",
+            run_id=self._settings.run_id,
+            message=message,
+            node_state=node_state,
+            hostname=self._node.addr,
+            pid=self._node.pid,
+            local_id=self._node.local_id,
+        )
+
+    def run(
+        self,
+        state_handler: Callable[[_RendezvousContext, float], _Action],
+        deadline: float,
+        update_deadline: Optional[Callable[[timedelta], float]] = None,
+    ) -> None:
+        """See base class."""
+        action = None
+        while action != _Action.FINISH:
+            # Reads or writes the latest rendezvous state shared by all nodes in
+            # the rendezvous. Note that our local changes might get overridden
+            # by another node if that node synced its changes before us.
+            has_set = self._state_holder.sync()
+            if has_set is not None:
+                if has_set:
+                    msg = (
+                        f"The node '{self._node}' has successfully synced its local changes with "
+                        f"other nodes in the rendezvous '{self._settings.run_id}'."
+                    )
+                else:
+                    msg = (
+                        f"The node '{self._node}' has a stale state and failed to sync its local "
+                        f"changes with other nodes in the rendezvous '{self._settings.run_id}'."
+                    )
+
+                self._record(message=msg)
+                logger.debug(msg)
+
+            self._state = self._state_holder.state
+
+            ctx = _RendezvousContext(self._node, self._state, self._settings)
+
+            # Determine the next action to take based on the current state of
+            # the rendezvous.
+            action = state_handler(ctx, deadline)
+
+            if action == _Action.FINISH:
+                continue
+
+            if action == _Action.ERROR_CLOSED:
+                raise RendezvousClosedError
+
+            if action == _Action.ERROR_TIMEOUT:
+                raise RendezvousTimeoutError
+
+            if action == _Action.SYNC:
+                # Delay the execution by one second to avoid overloading the
+                # backend if we are asked to poll for state changes.
+                _delay(seconds=1)
+            else:
+                if action == _Action.KEEP_ALIVE:
+                    self._keep_alive()
+                elif action == _Action.ADD_TO_PARTICIPANTS:
+                    self._add_to_participants()
+                elif action == _Action.ADD_TO_WAIT_LIST:
+                    self._add_to_wait_list()
+                elif action == _Action.ADD_TO_REDUNDANCY_LIST:
+                    self._add_to_redundancy_list()
+                elif action == _Action.REMOVE_FROM_PARTICIPANTS:
+                    self._remove_from_participants()
+                elif action == _Action.REMOVE_FROM_WAIT_LIST:
+                    self._remove_from_wait_list()
+                elif action == _Action.REMOVE_FROM_REDUNDANCY_LIST:
+                    self._remove_from_redundancy_list()
+                    # update deadline since the node may participate in rendezvous process
+                    if update_deadline:
+                        deadline = update_deadline(self._settings.timeout.join)
+                elif action == _Action.MARK_RENDEZVOUS_COMPLETE:
+                    self._mark_rendezvous_complete()
+                elif action == _Action.MARK_RENDEZVOUS_CLOSED:
+                    self._mark_rendezvous_closed()
+
+                # Attempt to sync our changes back to other nodes.
+                self._state_holder.mark_dirty()
+
+    def _keep_alive(self) -> None:
+        msg = (
+            f"The node '{self._node}' updated its keep-alive heartbeat time for the rendezvous "
+            f"'{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        self._state.last_heartbeats[self._node] = datetime.now(timezone.utc)
+
+    def _add_to_participants(self) -> None:
+        msg = (
+            f"The node '{self._node}' added itself to the participants of round "
+            f"{self._state.round} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        state = self._state
+
+        try:
+            state.wait_list.remove(self._node)
+        except KeyError:
+            pass
+
+        # The ranks of the participants will be set once the rendezvous is
+        # complete.
+        state.participants[self._node] = 0
+
+        self._keep_alive()
+
+        if len(state.participants) == self._settings.min_nodes:
+            state.deadline = (
+                datetime.now(timezone.utc) + self._settings.timeout.last_call
+            )
+
+        if len(state.participants) == self._settings.max_nodes:
+            self._mark_rendezvous_complete()
+
+    def _add_to_wait_list(self) -> None:
+        msg = (
+            f"The node '{self._node}' added itself to the wait list of round "
+            f"{self._state.round + 1} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        if self._node in self._state.redundancy_list:
+            self._state.redundancy_list.remove(self._node)
+        self._state.wait_list.add(self._node)
+
+        self._keep_alive()
+
+    def _add_to_redundancy_list(self) -> None:
+        msg = (
+            f"The node '{self._node}' added itself to the redundancy list of round "
+            f"{self._state.round + 1} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        self._state.redundancy_list.add(self._node)
+
+        self._keep_alive()
+
+    def _remove_from_participants(self) -> None:
+        msg = (
+            f"The node '{self._node}' removed itself from the participants of round "
+            f"{self._state.round} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        state = self._state
+
+        del state.participants[self._node]
+
+        del state.last_heartbeats[self._node]
+
+        # Common epilogue shared with the sanitizer() function of
+        # _BackendRendezvousStateHolder.
+        _remove_participant_epilogue(state, self._settings)
+
+    def _remove_from_wait_list(self) -> None:
+        msg = (
+            f"The node '{self._node}' removed itself from the wait list of round "
+            f"{self._state.round + 1} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        self._state.wait_list.remove(self._node)
+
+        del self._state.last_heartbeats[self._node]
+
+    def _remove_from_redundancy_list(self) -> None:
+        msg = (
+            f"The node '{self._node}' removed itself from the redunant list of round "
+            f"{self._state.round + 1} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        self._state.redundancy_list.remove(self._node)
+
+        del self._state.last_heartbeats[self._node]
+
+    def _mark_rendezvous_complete(self) -> None:
+        msg = (
+            f"The node '{self._node}' marked round {self._state.round} of the rendezvous "
+            f"'{self._settings.run_id}' as complete. Pending sync."
+        )
+        self._record(message=msg, node_state=NodeState.SUCCEEDED)
+        logger.debug(msg)
+
+        state = self._state
+
+        state.complete = True
+        state.deadline = None
+
+        # Assign the ranks.
+        for rank, node in enumerate(sorted(state.participants)):
+            state.participants[node] = rank
+
+    def _mark_rendezvous_closed(self) -> None:
+        msg = (
+            f"The node '{self._node}' marked the rendezvous '{self._settings.run_id}' as closed. "
+            "Pending sync."
+        )
+        self._record(message=msg, node_state=NodeState.SUCCEEDED)
+        logger.debug(msg)
+
+        self._state.closed = True
+
+
+def _should_keep_alive(ctx: _RendezvousContext) -> bool:
+    """Determine whether a keep-alive heartbeat should be sent."""
+    try:
+        last_heartbeat = ctx.state.last_heartbeats[ctx.node]
+    except KeyError:
+        return False
+
+    return (
+        last_heartbeat <= datetime.now(timezone.utc) - ctx.settings.keep_alive_interval
+    )
+
+
+class _RendezvousExitOp:
+    """Represent a rendezvous exit operation."""
+
+    def __call__(self, ctx: _RendezvousContext, deadline: float) -> _Action:
+        if ctx.node in ctx.state.participants:
+            if time.monotonic() > deadline:
+                return _Action.ERROR_TIMEOUT
+            return _Action.REMOVE_FROM_PARTICIPANTS
+        return _Action.FINISH
+
+
+class _RendezvousJoinOp:
+    """Represent a rendezvous join operation."""
+
+    def __call__(self, ctx: _RendezvousContext, deadline: float) -> _Action:
+        state = ctx.state
+
+        # A closed rendezvous means that it no longer accepts new nodes.
+        if state.closed:
+            if ctx.node in state.redundancy_list:
+                msg = f"The rendezvous '{ctx.settings.run_id}' is closed, terminating pending rendezvous."
+                raise RendezvousGracefulExitError(msg)
+            return _Action.ERROR_CLOSED
+
+        if ctx.node in state.redundancy_list:
+            msg = f"The node {ctx.node} is in redunancy list"
+            logger.debug(msg)
+            # don't apply the timeout logic here, since we want to allow the node to rejoin
+            if len(state.participants) == ctx.settings.max_nodes:
+                if _should_keep_alive(ctx):
+                    return _Action.KEEP_ALIVE
+                else:
+                    return _Action.SYNC
+            else:
+                # transition to waiting state that will respect timeouts.
+                msg = f"The node {ctx.node} is removed from redunancy list"
+                logger.debug(msg)
+                return _Action.REMOVE_FROM_REDUNDANCY_LIST
+
+        is_participant = ctx.node in state.participants
+
+        # If we are part of the rendezvous and it is already complete there is
+        # no further action to take.
+        if state.complete and is_participant:
+            return _Action.FINISH
+
+        now = time.monotonic()
+        if now > deadline:
+            rollback_period = 5  # 5 seconds
+
+            # If we still have time to rollback (a short period on top of the
+            # operation deadline), try to remove ourself from the rendezvous.
+            # It is okay if we can't though as our keep-alive will eventually
+            # expire.
+            if now <= deadline + rollback_period:
+                # If we are part of the rendezvous, it means we couldn't find
+                # enough participants to complete it on time.
+                if is_participant:
+                    return _Action.REMOVE_FROM_PARTICIPANTS
+                # If we are in the wait list, it means we couldn't wait till the
+                # next round of the rendezvous.
+                if ctx.node in state.wait_list:
+                    return _Action.REMOVE_FROM_WAIT_LIST
+            return _Action.ERROR_TIMEOUT
+
+        if state.complete:
+            # If we are here, it means we are not part of the rendezvous. In
+            # case the rendezvous has capacity for additional participants add
+            # ourself to the wait list for the next round.
+            if len(state.participants) < ctx.settings.max_nodes:
+                if ctx.node not in state.wait_list:
+                    return _Action.ADD_TO_WAIT_LIST
+            elif len(state.participants) >= ctx.settings.max_nodes:
+                if (
+                    ctx.node not in state.redundancy_list
+                    and ctx.node not in state.wait_list
+                ):
+                    return _Action.ADD_TO_REDUNDANCY_LIST
+        elif is_participant:
+            # If the rendezvous has enough number of participants including us,
+            # check whether we have passed the rendezvous deadline. If yes,
+            # complete it.
+            if (
+                len(state.participants) >= ctx.settings.min_nodes
+                and len(state.participants) <= ctx.settings.max_nodes
+                and state.deadline is not None
+            ):
+                if state.deadline < datetime.now(timezone.utc):
+                    msg = (
+                        f"The node '{ctx.node}' marking the rendezvous complete, "
+                        f"quorum established within deadline"
+                    )
+                    logger.debug(msg)
+                    return _Action.MARK_RENDEZVOUS_COMPLETE
+                else:
+                    msg = f"The node '{ctx.node}' can't complete rendezvous: deadline reached"
+                    logger.debug(msg)
+            else:
+                msg = f"The node '{ctx.node}' can't complete rendezvous: not enough participants"
+                logger.debug(msg)
+        else:
+            # The rendezvous is not complete yet and we are not part of it. Try
+            # to join.
+            return _Action.ADD_TO_PARTICIPANTS
+
+        if _should_keep_alive(ctx):
+            return _Action.KEEP_ALIVE
+
+        # At this point either the rendezvous is not complete, but we are part
+        # of it, which means we have to wait for other participants to join; or
+        # the rendezvous is complete, but we are not part of it, which means we
+        # have to wait for the next round.
+        return _Action.SYNC
+
+
+class _RendezvousCloseOp:
+    """Represent a rendezvous close operation."""
+
+    def __call__(self, ctx: _RendezvousContext, deadline: float) -> _Action:
+        if ctx.state.closed:
+            return _Action.FINISH
+        if time.monotonic() > deadline:
+            return _Action.ERROR_TIMEOUT
+        return _Action.MARK_RENDEZVOUS_CLOSED
+
+
+class _RendezvousKeepAliveOp:
+    """Represent a rendezvous keep-alive update operation."""
+
+    def __call__(self, ctx: _RendezvousContext, deadline: float) -> _Action:
+        if _should_keep_alive(ctx):
+            if time.monotonic() > deadline:
+                return _Action.ERROR_TIMEOUT
+            return _Action.KEEP_ALIVE
+        return _Action.FINISH
+
+
+class DynamicRendezvousHandler(RendezvousHandler):
+    """Represent a handler that sets up a rendezvous among a set of nodes."""
+
+    # Static
+    _node_desc_generator = _NodeDescGenerator()
+
+    _this_node: _NodeDesc
+    _settings: RendezvousSettings
+    _backend_name: str
+    _store: Store
+    _state_holder: _RendezvousStateHolder
+    _op_executor: _RendezvousOpExecutor
+    _heartbeat_lock: threading.Lock
+    _keep_alive_timer: Optional[_PeriodicTimer]
+
+    @classmethod
+    def from_backend(
+        cls,
+        run_id: str,
+        store: Store,
+        backend: RendezvousBackend,
+        min_nodes: int,
+        max_nodes: int,
+        local_addr: Optional[str] = None,
+        timeout: Optional[RendezvousTimeout] = None,
+        keep_alive_interval: int = 5,
+        keep_alive_max_attempt: int = 3,
+    ):
+        """Create a new :py:class:`DynamicRendezvousHandler`.
+
+        Args:
+            run_id:
+                The run id of the rendezvous.
+            store:
+                The C10d store to return as part of the rendezvous.
+            backend:
+                The backend to use to hold the rendezvous state.
+            min_nodes:
+                The minimum number of nodes to admit to the rendezvous.
+            max_nodes:
+                The maximum number of nodes to admit to the rendezvous.
+            local_addr:
+                The local node address.
+            timeout:
+                The timeout configuration of the rendezvous.
+            keep_alive_interval:
+                The amount of time a node waits before sending a heartbeat to keep
+                it alive in the rendezvous.
+            keep_alive_max_attempt:
+                The maximum number of failed heartbeat attempts after which a node
+                is considered dead.
+        """
+        # We associate each handler instance with a unique node descriptor.
+        node = cls._node_desc_generator.generate(local_addr)
+
+        settings = RendezvousSettings(
+            run_id,
+            min_nodes,
+            max_nodes,
+            timeout or RendezvousTimeout(),
+            keep_alive_interval=timedelta(seconds=keep_alive_interval),
+            keep_alive_max_attempt=keep_alive_max_attempt,
+        )
+
+        state_holder = _BackendRendezvousStateHolder(backend, settings)
+
+        return cls(node, settings, backend.name, store, state_holder)
+
+    def __init__(
+        self,
+        node: _NodeDesc,
+        settings: RendezvousSettings,
+        backend_name: str,
+        store: Store,
+        state_holder: _RendezvousStateHolder,
+    ) -> None:
+        if not settings.run_id:
+            raise ValueError("The run id must be a non-empty string.")
+
+        if settings.min_nodes < 1:
+            raise ValueError(
+                f"The minimum number of nodes ({settings.min_nodes}) must be greater than zero."
+            )
+
+        if settings.max_nodes < settings.min_nodes:
+            raise ValueError(
+                f"The maximum number of nodes ({settings.max_nodes}) must be greater than or equal "
+                f"to the minimum number of nodes ({settings.min_nodes})."
+            )
+
+        self._this_node = node
+
+        self._settings = settings
+
+        self._backend_name = backend_name
+
+        self._store = store
+
+        self._state_holder = state_holder
+
+        self._op_executor = _DistributedRendezvousOpExecutor(
+            self._this_node, self._state_holder, self._settings
+        )
+
+        self._heartbeat_lock = threading.Lock()
+
+        self._keep_alive_timer = None
+
+        # Cached shared store server reference
+        self._shared_tcp_store_server: Optional[dist.Store] = None
+
+        self._bootstrap_store_info: Optional[RendezvousStoreInfo] = None
+
+    def _record(
+        self,
+        message: str,
+        node_state: NodeState = NodeState.RUNNING,
+        rank: Optional[int] = None,
+    ) -> None:
+        construct_and_record_rdzv_event(
+            name=f"{self.__class__.__name__}.{get_method_name()}",
+            run_id=self._settings.run_id,
+            message=message,
+            node_state=node_state,
+            hostname=self._this_node.addr,
+            pid=self._this_node.pid,
+            local_id=self._this_node.local_id,
+            rank=rank,
+        )
+
+    def _create_tcp_store_server(self, master_addr, master_port) -> dist.TCPStore:
+        return dist.TCPStore(
+            host_name=master_addr,
+            port=master_port,
+            is_master=True,
+            multi_tenant=True,
+        )
+
+    @property
+    def settings(self) -> RendezvousSettings:
+        """Get the settings of the rendezvous."""
+        return self._settings
+
+    def get_backend(self) -> str:
+        """See base class."""
+        return self._backend_name
+
+    @property
+    def use_agent_store(self) -> bool:
+        """See base class."""
+        return os.getenv("TORCH_DISABLE_SHARE_RDZV_TCP_STORE", "0") != "1"
+
+    def next_rendezvous(self) -> RendezvousInfo:
+        """See base class."""
+        msg = (
+            f"The node '{self._this_node}' attempts to join the next round of the rendezvous "
+            f"'{self._settings.run_id}'."
+        )
+        self._record(message=msg)
+        logger.info(msg)
+
+        try:
+            self._stop_heartbeats()
+
+            # Delay the execution for a small random amount of time if this is our
+            # first run. This will slightly skew the rendezvous attempts across the
+            # nodes and reduce the load on the backend.
+            if self._state_holder.state.round == 0:
+                _delay(seconds=(0, 0.3))
+
+            exit_op = _RendezvousExitOp()
+            join_op = _RendezvousJoinOp()
+
+            deadline = self._get_deadline(self._settings.timeout.join)
+            self._op_executor.run(exit_op, deadline)
+            self._op_executor.run(join_op, deadline, self._get_deadline)
+
+            self._start_heartbeats()
+
+            rank, world_size = self._get_world()
+            store = self._get_store()
+
+        except Exception as e:
+            self._record(
+                message=f"{type(e).__name__}: {str(e)}",
+                node_state=NodeState.FAILED,
+            )
+            raise
+
+        msg = (
+            f"The node '{self._this_node}' has joined round {self._state_holder.state.round} of "
+            f"the rendezvous '{self._settings.run_id}' as rank {rank} in a world of size "
+            f"{world_size}."
+        )
+        self._record(message=msg, rank=rank)
+        logger.info(msg)
+
+        # opt-out option of TCPStore sharing
+        if os.getenv("TORCH_DISABLE_SHARE_RDZV_TCP_STORE", "0") == "1":
+            bootstrap_store_info = RendezvousStoreInfo.build(
+                rank, store, local_addr=self._this_node.addr
+            )
+            return RendezvousInfo(
+                store,
+                rank,
+                world_size,
+                bootstrap_store_info,
+            )
+
+        # This will only be hit when TCPStore sharing is enabled.
+        if self._bootstrap_store_info is None:
+            # To avoid race in get_free_port because we release the port after the call,
+            # we want to create a TCPStore server soon afterwards.
+            server_port = 0
+            if rank == 0:
+                self._shared_tcp_store_server = self._create_tcp_store_server(
+                    self._this_node.addr, server_port
+                )
+                server_port = self._shared_tcp_store_server.port
+            self._bootstrap_store_info = RendezvousStoreInfo.build(
+                rank,
+                store,
+                local_addr=self._this_node.addr,
+                server_port=server_port,  # For non-0 rank, this is a no-op
+            )
+
+        assert self._bootstrap_store_info is not None
+        if rank == 0:
+            assert self._shared_tcp_store_server is not None
+
+        return RendezvousInfo(
+            store,
+            rank,
+            world_size,
+            self._bootstrap_store_info,  # type: ignore[assignment]
+        )
+
+    def is_closed(self) -> bool:
+        """See base class."""
+        try:
+            with self._heartbeat_lock:
+                self._state_holder.sync()
+
+                return self._state_holder.state.closed
+
+        except Exception as e:
+            self._record(
+                message=f"{type(e).__name__}: {str(e)}",
+                node_state=NodeState.FAILED,
+            )
+            raise
+
+    def set_closed(self) -> None:
+        """See base class."""
+        try:
+            with self._heartbeat_lock:
+                self._close()
+        except Exception as e:
+            self._record(
+                message=f"{type(e).__name__}: {str(e)}",
+                node_state=NodeState.FAILED,
+            )
+            raise
+
+    def num_nodes_waiting(self) -> int:
+        """See base class."""
+        try:
+            with self._heartbeat_lock:
+                self._state_holder.sync()
+
+                return len(self._state_holder.state.wait_list)
+
+        except Exception as e:
+            self._record(
+                message=f"{type(e).__name__}: {str(e)}",
+                node_state=NodeState.FAILED,
+            )
+            raise
+
+    def get_run_id(self) -> str:
+        """See base class."""
+        return self._settings.run_id
+
+    def shutdown(self) -> bool:
+        """See base class."""
+        self._stop_heartbeats()
+
+        try:
+            self._close()
+
+            return True
+        except RendezvousError as ex:
+            msg = (
+                f"The node '{self._this_node}' has failed to shutdown the rendezvous "
+                f"'{self._settings.run_id}' due to an error of type {type(ex).__name__}."
+            )
+            self._record(message=msg, node_state=NodeState.FAILED)
+            logger.warning(msg)
+
+            return False
+        except Exception as e:
+            self._record(
+                message=f"{type(e).__name__}: {str(e)}",
+                node_state=NodeState.FAILED,
+            )
+            raise
+
+    def _close(self) -> None:
+        op = _RendezvousCloseOp()
+
+        deadline = self._get_deadline(self._settings.timeout.close)
+
+        self._op_executor.run(op, deadline)
+
+        msg = f"The node '{self._this_node}' has closed the rendezvous '{self._settings.run_id}'."
+        self._record(message=msg, node_state=NodeState.SUCCEEDED)
+        logger.info(msg)
+
+    @staticmethod
+    def _keep_alive_weak(weak_self) -> None:
+        self = weak_self()
+        if self is not None:
+            self._keep_alive()
+
+    def _keep_alive(self) -> None:
+        self._heartbeat_lock.acquire()
+
+        op = _RendezvousKeepAliveOp()
+
+        deadline = self._get_deadline(self._settings.timeout.heartbeat)
+
+        try:
+            self._op_executor.run(op, deadline)
+
+            msg = (
+                f"The node '{self._this_node}' has sent a keep-alive heartbeat to the rendezvous "
+                f"'{self._settings.run_id}'."
+            )
+            self._record(message=msg)
+            logger.debug(msg)
+        except RendezvousError as ex:
+            msg = (
+                f"The node '{self._this_node}' has failed to send a keep-alive heartbeat to the "
+                f"rendezvous '{self._settings.run_id}' due to an error of type {type(ex).__name__}."
+            )
+            self._record(message=msg, node_state=NodeState.FAILED)
+            logger.warning(msg)
+        finally:
+            self._heartbeat_lock.release()
+
+    def _start_heartbeats(self) -> None:
+        self._keep_alive_timer = _PeriodicTimer(
+            self._settings.keep_alive_interval, self._keep_alive_weak, weakref.ref(self)
+        )
+
+        self._keep_alive_timer.set_name(
+            f"RendezvousKeepAliveTimer_{self._this_node.local_id}"
+        )
+
+        self._keep_alive_timer.start()
+
+    def _stop_heartbeats(self) -> None:
+        if self._keep_alive_timer is None:
+            return
+
+        self._keep_alive_timer.cancel()
+
+    def _get_world(self) -> tuple[int, int]:
+        state = self._state_holder.state
+
+        return state.participants[self._this_node], len(state.participants)
+
+    def _wrap_store(self, store: Store) -> Store:
+        key_prefix = (
+            f"torch.rendezvous.{self._settings.run_id}.{self._state_holder.state.round}"
+        )
+
+        return dist.PrefixStore(key_prefix, store)
+
+    def _get_store(self) -> Store:
+        return self._wrap_store(self._store)
+
+    def _get_deadline(self, timeout: timedelta) -> float:
+        return time.monotonic() + timeout.total_seconds()
+
+
+def _get_timeout(params: RendezvousParameters, key: str) -> Optional[timedelta]:
+    timeout = params.get_as_int(key + "_timeout")
+    if timeout is None:
+        return None
+    return timedelta(seconds=timeout)
+
+
+def create_handler(
+    store: Store, backend: RendezvousBackend, params: RendezvousParameters
+) -> DynamicRendezvousHandler:
+    """Create a new :py:class:`DynamicRendezvousHandler` from the specified parameters.
+
+    Args:
+        store:
+            The C10d store to return as part of the rendezvous.
+        backend:
+            The backend to use to hold the rendezvous state.
+
+    +-------------------+------------------------------------------------------+
+    | Parameter         | Description                                          |
+    +===================+======================================================+
+    | join_timeout      | The total time, in seconds, within which the         |
+    |                   | rendezvous is expected to complete. Defaults to 600  |
+    |                   | seconds.                                             |
+    +-------------------+------------------------------------------------------+
+    | last_call_timeout | An additional wait amount, in seconds, before        |
+    |                   | completing the rendezvous once the minimum number of |
+    |                   | nodes has been reached. Defaults to 30 seconds.      |
+    +-------------------+------------------------------------------------------+
+    | close_timeout     | The time, in seconds, within which the rendezvous is |
+    |                   | expected to close after a call to                    |
+    |                   | :py:meth:`RendezvousHandler.set_closed` or           |
+    |                   | :py:meth:`RendezvousHandler.shutdown`. Defaults to   |
+    |                   | 30 seconds.                                          |
+    +-------------------+------------------------------------------------------+
+    | heartbeat         | The time, in seconds, within which a keep-alive      |
+    |                   | heartbeat is expected to complete                    |
+    +-------------------+------------------------------------------------------+
+    """
+    try:
+        timeout = RendezvousTimeout(
+            _get_timeout(params, "join"),
+            _get_timeout(params, "last_call"),
+            _get_timeout(params, "close"),
+            _get_timeout(params, "heartbeat"),
+        )
+        keep_alive_interval = params.get_as_int("keep_alive_interval", 5)
+        if keep_alive_interval is None:
+            raise TypeError(
+                "You passed 'keep_alive_interval=None' as a rendezvous configuration option"
+            )
+        keep_alive_max_attempt = params.get_as_int("keep_alive_max_attempt", 3)
+        if keep_alive_max_attempt is None:
+            raise TypeError(
+                "You passed 'keep_alive_max_attempt=None' as a rendezvous configuration option"
+            )
+
+        return DynamicRendezvousHandler.from_backend(
+            params.run_id,
+            store,
+            backend,
+            params.min_nodes,
+            params.max_nodes,
+            params.local_addr,
+            timeout,
+            keep_alive_interval=keep_alive_interval,
+            keep_alive_max_attempt=keep_alive_max_attempt,
+        )
+    except Exception as e:
+        construct_and_record_rdzv_event(
+            message=f"{type(e).__name__}: {str(e)}",
+            run_id=params.run_id,
+            node_state=NodeState.FAILED,
+        )
+        raise
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b049423ffc6d86ad5d6a1833c3e5c4a52a1ff51
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous.py
@@ -0,0 +1,1081 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+import logging
+import sys
+import threading
+import time
+from typing import Optional
+
+
+try:
+    import etcd  # type: ignore[import]
+except ModuleNotFoundError:
+    from . import _etcd_stub as etcd
+
+from torch.distributed.elastic.rendezvous import (
+    RendezvousClosedError,
+    RendezvousError,
+    RendezvousHandler,
+    RendezvousInfo,
+    RendezvousParameters,
+    RendezvousStoreInfo,
+    RendezvousTimeoutError,
+)
+
+from .etcd_store import cas_delay, EtcdStore
+from .utils import parse_rendezvous_endpoint
+
+
+__all__ = [
+    "EtcdRendezvousRetryableFailure",
+    "EtcdRendezvousRetryImmediately",
+    "EtcdRendezvousHandler",
+    "EtcdRendezvous",
+    "create_rdzv_handler",
+]
+
+_log_fmt = logging.Formatter("%(levelname)s %(asctime)s %(message)s")
+_log_handler = logging.StreamHandler(sys.stderr)
+_log_handler.setFormatter(_log_fmt)
+
+logger = logging.getLogger(__name__)
+logger.propagate = False
+logger.setLevel(logging.INFO)
+logger.addHandler(_log_handler)
+
+
+# Retryable failure exception means the we were too late to make
+# a desired state transition (e.g. because of a race condition),
+# and should now restart from the beginning.
+# A small delay is recommended to avoid spamming Etcd.
+class EtcdRendezvousRetryableFailure(Exception):
+    pass
+
+
+# Similar to retryable failure, but the new state we observed suggests we
+# can re-try immediately, i.e. without a need for "safety delay".
+class EtcdRendezvousRetryImmediately(Exception):
+    pass
+
+
+# Default timeout for the rendezvous.
+_DEFAULT_TIMEOUT: int = 600  # 10 minutes
+
+# Additional waiting time after reaching the minimum number of nodes
+# in case the rendezvous is elastic (min != max).
+_DEFAULT_LAST_CALL_TIMEOUT: int = 30  # 30 seconds
+
+# Various constants used internally in EtcdRendezvous
+CONST_ETCD_SETUP_TTL = 5
+CONST_ETCD_FROZEN_TTL = 10
+CONST_ETCD_JOINABLE_EPHEMERAL_TTL = 10
+
+# Ephemeral node TTL for worker's keep-alive key:
+CONST_WORKER_KEEPALIVE_TTL = 10
+
+# TTL for the ephemeral run_id-specific directory. All rendezvous state data
+# for a specific run_id (job instance) is contained within directory.
+# Its only role is to clean-up rendezvous data from old runs (for the case when
+# etcd server is persistent), and has no affect on correctness, but should be
+# larger than any timeouts that a worker process is expected to survive:
+CONST_RUNID_SUBROOT_TTL = 7200  # 2 hours
+
+
+class EtcdRendezvousHandler(RendezvousHandler):
+    """
+    Implements a
+    :py:class:`torch.distributed.elastic.rendezvous.RendezvousHandler` interface
+    backed by
+    :py:class:`torch.distributed.elastic.rendezvous.etcd_rendezvous.EtcdRendezvous`.
+    ``EtcdRendezvousHandler`` uses a URL to configure the type of rendezvous to
+    use and to pass implementation specific configurations to the rendezvous
+    module. The basic etcd rendezvous configuration URL looks like the following
+    ::
+
+     etcd://:/?min_workers=&max_workers=  # noqa: W605
+
+     -- example --
+
+     etcd://localhost:2379/1234?min_workers=1&max_workers=3
+
+    The URL above is interpreted as follows:
+
+    1. Use the rendezvous handler that is registered with the ``etcd``
+       scheme
+    2. The ``etcd`` endpoint to use is ``localhost:2379``
+    3. ``job_id == 1234`` is used as the prefix in etcd (this allows one to
+       share a common etcd server for multiple jobs so long as the
+       ``job_ids`` are guaranteed to be unique). Note that the job id can be
+       any string (e.g. does not need to be a number) as long as it is
+       unique.
+    4. ``min_workers=1`` and ``max_workers=3`` specifies a range for
+       membership size - Torch Distributed Elastic starts running the job as
+       long as the cluster size is greater than or equal to ``min_workers``
+       and admits up to ``max_workers`` into the cluster.
+
+    Below are a full list of the parameters that can be passed to etcd
+    rendezvous:
+
+    +--------------------------------------------+--------------------------+
+    | Parameter                                  | Description              |
+    +============================================+==========================+
+    | min_workers                                | minimum number of        |
+    |                                            | workers for the          |
+    |                                            | rendezvous to be valid   |
+    +--------------------------------------------+--------------------------+
+    | max_workers                                | maximum number of        |
+    |                                            | workers to admit         |
+    +--------------------------------------------+--------------------------+
+    | timeout                                    | total timeout within     |
+    |                                            | which next_rendezvous is |
+    |                                            | expected to succeed      |
+    |                                            | (default 600s)           |
+    +--------------------------------------------+--------------------------+
+    | last_call_timeout                          | additional wait amount   |
+    |                                            | ("last call") after min  |
+    |                                            | number of workers has    |
+    |                                            | been reached (defaults   |
+    |                                            | to 30s)                  |
+    +--------------------------------------------+--------------------------+
+    | etcd_prefix                                | path prefix (from etcd   |
+    |                                            | root), inside which all  |
+    |                                            | etcd nodes will be       |
+    |                                            | created (defaults to     |
+    |                                            | ``/torchelastic/p2p``)   |
+    +--------------------------------------------+--------------------------+
+    """
+
+    def __init__(self, rdzv_impl: "EtcdRendezvous", local_addr: Optional[str]):
+        """
+        Args:
+            rdzv_impl: the implementation of the rendezvous
+            local_addr: the local address of the current node
+        """
+
+        self._rdzv_impl = rdzv_impl
+        self._local_addr = local_addr
+
+    def __del__(self):
+        # TODO: look into using weakref here instead.
+        del self._rdzv_impl
+
+    def get_backend(self) -> str:
+        return "etcd"
+
+    def next_rendezvous(self):
+        rdzv_version, rank, world_size = self._rdzv_impl.rendezvous_barrier()
+
+        logger.info("Creating EtcdStore as the c10d::Store implementation")
+        store = self._rdzv_impl.setup_kv_store(rdzv_version)
+
+        bootstrap_store_info = RendezvousStoreInfo.build(
+            rank, store, local_addr=self._local_addr
+        )
+        return RendezvousInfo(store, rank, world_size, bootstrap_store_info)
+
+    def is_closed(self):
+        try:
+            _, state = self._rdzv_impl.get_rdzv_state()
+            return state["status"] == "closed"
+        except etcd.EtcdKeyNotFound:
+            # No rendezvous state, so it cannot be closed.
+            return False
+
+    def set_closed(self):
+        self._rdzv_impl.set_closed()
+
+    def num_nodes_waiting(self):
+        try:
+            _, state = self._rdzv_impl.get_rdzv_state()
+            if state["status"] == "final":
+                return state["num_workers_waiting"]
+        except etcd.EtcdKeyNotFound:
+            pass
+        return 0
+
+    def get_run_id(self) -> str:
+        return self._rdzv_impl._run_id
+
+    def shutdown(self) -> bool:
+        try:
+            self.set_closed()
+            return True
+        except BaseException as e:
+            logger.warning("Shutdown failed. Error occurred: %s", str(e))
+            return False
+
+
+# TODO: we should probably handle a few additional errors,
+# like EtcdLeaderElectionInProgress and EtcdWatcherCleared. These are
+# only relevant for multi-node Etcd ensemble. A simple retry would work,
+# but is verbose to add everywhere. Consider wrapping the client calls
+# into auto-retry for these errors?
+#
+class EtcdRendezvous:
+    """A rendezvous implementation that uses `etcd `__ as the backend store."""
+
+    def __init__(
+        self,
+        client,
+        prefix,
+        run_id,
+        num_min_workers,
+        num_max_workers,
+        timeout,
+        last_call_timeout,
+    ):
+        self.client = client
+        logger.info("Etcd machines: %s", self.client.machines)
+
+        self._prefix = prefix
+        self._run_id = run_id
+        self._num_min_workers = num_min_workers
+        self._num_max_workers = num_max_workers
+        self._timeout = timeout
+        self._last_call_timeout = last_call_timeout
+
+        # For cleaning up TTL refresher threads (for ephemeral keys)
+        self._lease_run_id_stop = None
+        self._lease_this_rank_stop = None
+
+        if not self._prefix.endswith("/"):
+            self._prefix += "/"
+
+        # Setup a permanent prefix dir, if didn't exist
+        if self._prefix != "/":
+            self.create_path_if_not_exists(self._prefix)
+
+        # Lease a "sub-root" node specific to this job instance (run_id)
+        self.create_path_if_not_exists(self.get_path(""), ttl=CONST_RUNID_SUBROOT_TTL)
+        self._lease_run_id_stop = self.setup_lease_renewal(
+            self.get_path(""), ttl=CONST_RUNID_SUBROOT_TTL
+        )
+
+        # Subdir for all rendezvous work
+        self.create_path_if_not_exists(self.get_path("/rdzv"))
+
+        # Create a rendezvous version counter, if doesn't exist
+        try:
+            self.client.write(
+                key=self.get_path("/rdzv/version_counter"), value="0", prevExist=False
+            )
+        except etcd.EtcdAlreadyExist:
+            pass
+
+    def __del__(self):
+        # TODO: look into using weakref here instead.
+        if self._lease_run_id_stop is not None:
+            self._lease_run_id_stop.set()
+
+        if self._lease_this_rank_stop is not None:
+            self._lease_this_rank_stop.set()
+
+    def rendezvous_barrier(self):
+        """
+        Main entry point for next rendezvous.
+
+        This method is blocking until rendezvous succeeds or a timeout occurs.
+
+        Returns:
+             ``(rdzv_version, rank, world_size)``
+
+        Raises:
+            RendezvousTimeoutError - timeout waiting for rendezvous
+            RendezvousClosedError - rendezvous is or was closed while waiting
+            RendezvousError - other persistent errors that
+             render the rendezvous non-retryable
+        """
+        self._rendezvous_deadline = time.time() + self._timeout
+        while True:
+            if time.time() > self._rendezvous_deadline:
+                raise RendezvousTimeoutError
+
+            logger.info("Attempting to join next rendezvous")
+            try:
+                # Dis-own our lease in the previous rendezvous, if exists
+                if self._lease_this_rank_stop is not None:
+                    self._lease_this_rank_stop.set()
+
+                return self.init_phase()
+
+            except EtcdRendezvousRetryImmediately:
+                # The type of failure suggests we can retry without delay
+                pass
+
+            except EtcdRendezvousRetryableFailure:
+                # In case of retryable failure, wait a small delay
+                # to avoid spamming etcd
+                time.sleep(1)
+
+            except RendezvousTimeoutError:
+                logger.info("Rendezvous timeout occurred in EtcdRendezvousHandler")
+                raise
+
+            except RendezvousClosedError:
+                logger.info(
+                    "Rendezvous for run_id=%s was observed to be closed", self._run_id
+                )
+                raise
+
+            except RendezvousError:
+                raise
+
+            except Exception as e:
+                # In case of a general exception, wait a small delay
+                # to avoid spamming etcd
+                # FIXME: there are a few things that fall under this like
+                # etcd.EtcdKeyNotFound, etc, which could be handled more explicitly.
+                logger.info("Rendezvous attempt failed, will retry. Reason: %s", e)
+                time.sleep(1)
+
+    def init_phase(self):
+        """
+        Initially, the rendezvous state is expected to be one of:
+
+        1. empty (non-existent) - in this case we try to create a new one.
+        2. joinable - we try to join it.
+        3. final - we announce ourselves as waiting, and go into monitoring mode
+
+        Any other state is considered transitional, and will be retried after
+        a short delay.
+
+        Returns:
+            ``(rdzv_version, rank, world_size)``
+
+        Raises:
+            RendezvousClosedError - current rendezvous was/is closed
+            EtcdRendezvousRetryableFailure - observed some intermediate
+             state, which is best handled by retrying later
+        """
+        try:
+            active_version = self.try_create_rendezvous()
+            state = json.loads(active_version.value)
+            logger.info("New rendezvous state created: %s", state)
+        except etcd.EtcdAlreadyExist:
+            active_version, state = self.get_rdzv_state()
+            # Note: it is possible for above query to fail (etcd.EtcdKeyNotFound),
+            # but this is ok for us - just means we'll restart from beginning.
+            logger.info("Observed existing rendezvous state: %s", state)
+
+        if state["status"] == "closed":
+            raise RendezvousClosedError
+
+        if state["status"] == "joinable":
+            return self.join_phase(state["version"])
+
+        if state["status"] == "final":
+            self.handle_existing_rendezvous(state["version"])
+            raise EtcdRendezvousRetryImmediately
+
+        self.try_wait_for_state_change(etcd_index=active_version.etcd_index + 1)
+        raise EtcdRendezvousRetryableFailure
+
+    def join_phase(self, expected_version):
+        """
+        We observed a rendezvous state in 'joinable' state, and attempt to join this
+        particular version, and then wait for all other peers to join.
+        """
+        # Failure to join will propagate an exception, causing a re-entry.
+        active_version, this_rank = self.join_rendezvous(expected_version)
+        state = json.loads(active_version.value)
+        logger.info(
+            "Joined rendezvous version %s as rank %s. Full state: %s",
+            state["version"],
+            this_rank,
+            state,
+        )
+
+        # If this worker was first to reach num_min_workers requirement,
+        # and rendezvous is still joinable (therefore it is elastic),
+        # then this worker will be responsible for waiting out the "last call"
+        # timeout and closing (i.e. transitioning to 'frozen') the rendezvous
+        # afterwards.
+        # As a safety against a potential failure of this worker (during the
+        # last call timeout), the rendezvous state is made ephemeral
+        # when min_num_workers is reached.
+
+        if this_rank == self._num_min_workers - 1 and state["status"] == "joinable":
+            logger.info("Rank %s is responsible for join last call.", this_rank)
+            last_call_deadline = time.time() + self._last_call_timeout
+            self.handle_join_last_call(expected_version, last_call_deadline)
+            logger.info("Rank %s finished join last call.", this_rank)
+
+        # Wait for rendezvous state to be frozen, which means a fixed set of peers
+        logger.info("Waiting for remaining peers.")
+        active_version = self.wait_for_peers(expected_version)
+        state = json.loads(active_version.value)
+
+        assert state["version"] == expected_version, (
+            "Logic error: failed to observe version mismatch"
+        )
+
+        return self.confirm_phase(expected_version, this_rank)
+
+    def confirm_phase(self, expected_version, this_rank):
+        """
+        Once the rendezvous state transitions from 'joinable' to 'frozen',
+        we have every participant confirm their membership and setup per-member
+        keep-alive TTL keys, and then wait for all other participants to confirm,
+        which would then successfully conclude this rendezvous.
+        """
+        logger.info("All peers arrived. Confirming membership.")
+        self.confirm_membership(expected_version, this_rank)
+
+        logger.info("Waiting for confirmations from all peers.")
+        active_version = self.wait_for_final(expected_version)
+        state = json.loads(active_version.value)
+
+        logger.info(
+            "Rendezvous version %s is complete. Final state: %s",
+            state["version"],
+            state,
+        )
+
+        # Rendezvous version number; our rank in it; world size
+        return state["version"], this_rank, len(state["participants"])
+
+    def handle_existing_rendezvous(self, expected_version):
+        """
+        Handle the case when there's an existing (state 'final) rendezvous already
+        in place, and we have to announce ourselves waiting, and wait until
+        the next rendezvous opportunity.
+        """
+        # If state is 'final' -> increment num_workers_waiting
+        # Then, observe state changes:
+        #   1. if it's no longer final -> bail out and re-try
+        #   2. if keep alives are missing, destroy it and bail out.
+        active_state = self.announce_self_waiting(expected_version)
+        logger.info(
+            "Added self to waiting list. Rendezvous full state: %s", active_state.value
+        )
+
+        self.wait_for_rendezvous_to_free(expected_version)
+        logger.info(
+            "Previously existing rendezvous state changed. Will re-try joining."
+        )
+
+    def try_create_rendezvous(self):
+        """
+        Create new rendezvous state or raise an exception that indicates an unexpected state (e.g. already exists).
+
+        Raises:
+             RendezvousError - on unexpected state
+        """
+        # Initially active_version is ephemeral - this is to handle the
+        # possibility that might fail to complete the setup transaction,
+        # i.e. the transition "setup" -> "joinable".
+        active_version = self.client.write(
+            key=self.get_path("/rdzv/active_version"),
+            value=json.dumps({"status": "setup"}),
+            prevExist=False,
+            ttl=CONST_ETCD_SETUP_TTL,
+        )
+
+        try:
+            version_counter = self.client.get(self.get_path("/rdzv/version_counter"))
+            version_counter.value = str(int(version_counter.value) + 1)
+            self.client.update(version_counter)
+        except (etcd.EtcdKeyNotFound, etcd.EtcdCompareFailed) as e:
+            raise RendezvousError(
+                "Unexpected state of EtcdRendezvousHandler, worker needs to die."
+            ) from e
+
+        # Any failure below results in declaring a retryable rendezvous failure.
+        # The ephemeral /rdzv/active_version will expire and someone can then
+        # re-try the setup process.
+
+        # Create directory node for participant data
+        self.client.write(
+            key=self.get_path(f"/rdzv/v_{version_counter.value}"),
+            value=None,
+            dir=True,
+            prevExist=False,
+        )
+
+        # Publish rendezvous version and signal it is ready-to-be-joined.
+        # If rendezvous was set closed just before this, a retry will happen,
+        # where the closed condition will be handled.
+        return self.client.test_and_set(
+            key=self.get_path("/rdzv/active_version"),
+            value=json.dumps(
+                {
+                    "status": "joinable",
+                    "version": version_counter.value,
+                    "participants": [],
+                }
+            ),
+            prev_value=active_version.value,
+        )
+
+    def join_rendezvous(self, expected_version):
+        """Helper method for the join phase."""
+        # Use compare-and-swap to add self to rendezvous state:
+        while True:
+            cas_delay()
+            active_version, state = self.get_rdzv_state()
+
+            if state["status"] != "joinable":
+                raise EtcdRendezvousRetryableFailure(
+                    "Rendezvous state became non-joinable before we could join. "
+                    "Must join next one."
+                )
+
+            if state["version"] != expected_version:
+                raise EtcdRendezvousRetryImmediately(
+                    "Rendezvous version changed. Must try join the new one."
+                )
+
+            assert len(state["participants"]) < self._num_max_workers, (
+                "Logic error: joinable rendezvous should always have space left"
+            )
+
+            this_rank = len(state["participants"])
+            state["participants"].append(this_rank)
+
+            # When reaching min workers, or changing state to frozen, we'll set
+            # the active_version node to be ephemeral.
+            set_ttl: Optional[int] = None
+            if len(state["participants"]) == self._num_max_workers:
+                state["status"] = "frozen"
+                state["keep_alives"] = []
+                set_ttl = CONST_ETCD_FROZEN_TTL
+            elif len(state["participants"]) >= self._num_min_workers:
+                set_ttl = CONST_ETCD_JOINABLE_EPHEMERAL_TTL
+
+            try:
+                # Compare-and-swap.
+                active_version = self.client.test_and_set(
+                    key=self.get_path("/rdzv/active_version"),
+                    value=json.dumps(state),
+                    prev_value=active_version.value,
+                    ttl=set_ttl,
+                )
+                # We succeeded joining.
+                return active_version, this_rank
+
+            except etcd.EtcdCompareFailed:
+                logger.info("Join rendezvous CAS unsuccessful, retrying")
+
+    def wait_for_peers(self, expected_version):
+        """Helper method for the join phase."""
+        active_version, state = self.get_rdzv_state()
+        while True:
+            if state["status"] == "frozen" and state["version"] == expected_version:
+                # Success, all peers arrived.
+                return active_version
+
+            elif state["status"] == "joinable" and state["version"] == expected_version:
+                # Continue waiting for any interesting events.
+                active_version, state = self.try_wait_for_state_change(
+                    etcd_index=active_version.etcd_index + 1
+                )
+
+            else:
+                # No valid transition possible at this point
+                raise EtcdRendezvousRetryableFailure(
+                    "Rendezvous state transition no longer possible. Must re-enter."
+                )
+
+    def confirm_membership(self, expected_version, this_rank):
+        """Helper method for the confirm phase."""
+        # Compare-and-swap loop
+        while True:
+            cas_delay()
+            active_version, state = self.get_rdzv_state()
+
+            if state["status"] != "frozen":
+                raise EtcdRendezvousRetryImmediately(
+                    "Rendezvous no longer frozen, before we confirmed. "
+                    "Must join next one"
+                )
+            if state["version"] != expected_version:
+                raise EtcdRendezvousRetryImmediately(
+                    "Rendezvous version changed. Must try join the new one."
+                )
+
+            this_lease_key = self.get_path(
+                f"/rdzv/v_{expected_version}/rank_{this_rank}"
+            )
+            self.client.set(this_lease_key, value=None, ttl=CONST_WORKER_KEEPALIVE_TTL)
+
+            state["keep_alives"].append(this_lease_key)
+            if len(state["keep_alives"]) == len(state["participants"]):
+                # Everyone confirmed (this rank is last to do so)
+                state["status"] = "final"
+                state["num_workers_waiting"] = 0
+                finalize = True
+            else:
+                finalize = False
+
+            try:
+                # Compare-and-swap. If new state is still frozen, keep it ephemeral.
+                active_version = self.client.test_and_set(
+                    key=self.get_path("/rdzv/active_version"),
+                    value=json.dumps(state),
+                    prev_value=active_version.value,
+                    ttl=None if finalize else CONST_ETCD_FROZEN_TTL,
+                )
+
+                self._lease_this_rank_stop = self.setup_lease_renewal(
+                    this_lease_key, ttl=CONST_WORKER_KEEPALIVE_TTL
+                )
+                return active_version
+
+            except etcd.EtcdCompareFailed:
+                logger.info("Confirm membership CAS unsuccessful, retrying")
+
+    def wait_for_final(self, expected_version):
+        """Helper method for the confirm phase."""
+        active_version, state = self.get_rdzv_state()
+        while True:
+            if state["status"] == "final" and state["version"] == expected_version:
+                # Success. This rendezvous is final, and we accept it.
+                return active_version
+
+            elif state["status"] == "frozen" and state["version"] == expected_version:
+                # Continue waiting for any interesting events.
+                active_version, state = self.try_wait_for_state_change(
+                    etcd_index=active_version.etcd_index + 1
+                )
+
+            else:
+                # No valid transition possible at this point
+                raise EtcdRendezvousRetryableFailure(
+                    "Rendezvous state transition no longer possible. Must re-enter."
+                )
+
+    def announce_self_waiting(self, expected_version):
+        """
+        Announce this worker is waiting (via num_workers_waiting counter) to join next
+        rendezvous, but only if state and version match.
+        """
+        while True:
+            cas_delay()
+            active_version, state = self.get_rdzv_state()
+
+            if state["status"] != "final" or state["version"] != expected_version:
+                raise EtcdRendezvousRetryImmediately
+
+            # Increment counter to signal an additional waiting worker.
+            state["num_workers_waiting"] += 1
+
+            try:
+                active_version = self.client.test_and_set(
+                    key=self.get_path("/rdzv/active_version"),
+                    value=json.dumps(state),
+                    prev_value=active_version.value,
+                )
+                return active_version
+
+            except etcd.EtcdCompareFailed:
+                logger.info("Announce self as waiting CAS unsuccessful, retrying")
+
+    def wait_for_rendezvous_to_free(self, expected_version):
+        """
+        When there's an existing valid rendezvous in state 'final', we have to wait until the next opportunity to join.
+
+        Such opportunity may come from:
+
+        1. rendezvous state changed by someone else, in which case we unblock and retry.
+        2. rendezvous becomes invalid because at least one member failed to renew their
+           leased keep_alive node. We detect this, and destroy the rendezvous.
+        """
+        active_version, state = self.get_rdzv_state()
+        while True:
+            if state["status"] != "final" or state["version"] != expected_version:
+                return
+
+            # Check if current rendezvous state is valid, in the sense that all
+            # its members are alive (renewing their lease).
+            # If not, try destroy this rendezvous, so a new one can be created.
+            alive_members = self.client.get(
+                self.get_path(f"/rdzv/v_{expected_version}")
+            )
+            keep_alive_keys = [ch.key for ch in alive_members.children]
+
+            for key in state["keep_alives"]:
+                if key not in keep_alive_keys:
+                    # This participant didn't renew their lease. We'll declare this
+                    # rendezvous version as dead (but only if it hadn't changed)
+                    logger.info("Keep-alive key %s is not renewed.", key)
+                    logger.info(
+                        "Rendezvous version %s is incomplete. ", expected_version
+                    )
+                    logger.info("Attempting to destroy it.")
+
+                    # Compare-and-delete operation. Throws if compare failed,
+                    # which means rendezvous was already destroyed/re-created/closed,
+                    # and we can try to re-enter the barrier.
+                    self.client.delete(
+                        key=self.get_path("/rdzv/active_version"),
+                        prevValue=active_version.value,
+                    )
+
+                    logger.info(
+                        "Destroyed rendezvous version %s successfully.",
+                        expected_version,
+                    )
+
+                    # We can return (and retry) immediately
+                    return
+
+            # Existing rendezvous seems valid, no reason to destroy it.
+            # We just have to wait until something changes and re-check.
+            try:
+                overall_timeout = (
+                    max(self._rendezvous_deadline - time.time(), 0.0) + 1.0
+                )
+                self.client.watch(
+                    key=self.get_path("/rdzv"),
+                    index=active_version.etcd_index + 1,
+                    recursive=True,
+                    timeout=overall_timeout,
+                )
+            except (etcd.EtcdEventIndexCleared, etcd.EtcdWatchTimedOut):
+                pass
+
+            if time.time() > self._rendezvous_deadline:
+                raise RendezvousTimeoutError
+            active_version, state = self.get_rdzv_state()
+
+    def handle_join_last_call(self, expected_version, deadline):
+        """
+        After we reach min number of workers, one particular worker takes on the
+        responsibility of waiting an additional timeout before closing the join window.
+        If the worker responsible for this fails, the rendezvous will be destroyed due
+        to expiring TTL, and the other participants will re-rendezvous.
+
+        Here we expect to see state 
+        Exit gracefully if either:
+
+        1. state becomes 
+        2. timeout happens (reaching deadline), in which case
+           we try the transition to 
+
+        Exit with exception otherwise.
+        """
+        active_version, state = self.get_rdzv_state()
+        while True:
+            if state["status"] == "frozen" and state["version"] == expected_version:
+                # Worker set became frozen before last-call timeout. This is possible
+                # when num_max_workers is reached before the timeout.
+                return
+
+            if state["status"] != "joinable" or state["version"] != expected_version:
+                raise EtcdRendezvousRetryableFailure(
+                    "Rendezvous state transition no longer possible. Must re-enter."
+                )
+
+            # If timeout occurred, attempt a state transition (joinable -> frozen)
+            if time.time() >= deadline:
+                state["status"] = "frozen"
+                state["keep_alives"] = []
+                try:
+                    active_version = self.client.test_and_set(
+                        key=self.get_path("/rdzv/active_version"),
+                        value=json.dumps(state),
+                        prev_value=active_version.value,
+                        ttl=CONST_ETCD_FROZEN_TTL,
+                    )
+                    # We successfully made this rendezvous frozen.
+                    return
+                except etcd.EtcdCompareFailed:
+                    logger.info(
+                        "Join last-call transition CAS unsuccessful. Will retry"
+                    )
+                    cas_delay()
+                    active_version, state = self.get_rdzv_state()
+                    continue
+
+            # Timeout did not occur, so we must refresh TTL, and wait for
+            # further changes. Note: we only want TTL to be refreshed if
+            # state is still joinable, hence we use CAS for that here,
+            # even though we don't change any of the data.
+            try:
+                active_version = self.client.test_and_set(
+                    key=self.get_path("/rdzv/active_version"),
+                    value=active_version.value,
+                    prev_value=active_version.value,
+                    ttl=CONST_ETCD_JOINABLE_EPHEMERAL_TTL,
+                )
+
+                # Minimize "oversleeping":
+                timeout = min(
+                    CONST_ETCD_JOINABLE_EPHEMERAL_TTL / 2,
+                    deadline - time.time() + 1.0,  # Oversleeping by 1s is ok.
+                )
+                active_version, state = self.try_wait_for_state_change(
+                    etcd_index=active_version.etcd_index + 1, timeout=timeout
+                )
+            except etcd.EtcdCompareFailed:
+                logger.info("Join last-call TTL refresh CAS unsuccessful, will retry")
+                cas_delay()
+                active_version, state = self.get_rdzv_state()
+
+    def set_closed(self):
+        """
+        Mark rendezvous 'closed' for current run_id, which is used to signal other
+        participants to not attempt to perform (re-)rendezvous. This is useful
+        when one of the workers decides the job is complete.
+        """
+        while True:
+            active_version, state = self.get_rdzv_state()
+
+            if state["status"] == "closed":
+                # Already closed by someone else.
+                return
+
+            state["status"] = "closed"
+            try:
+                self.client.test_and_set(
+                    key=self.get_path("/rdzv/active_version"),
+                    value=json.dumps(state),
+                    prev_value=active_version.value,
+                )
+                return
+
+            except etcd.EtcdCompareFailed:
+                logger.info("Set closed CAS unsuccessful, retrying")
+                cas_delay()
+
+    def get_rdzv_state(self):
+        active_version = self.client.get(key=self.get_path("/rdzv/active_version"))
+        return active_version, json.loads(active_version.value)
+
+    def try_wait_for_state_change(self, etcd_index, timeout=None):
+        # Don't sleep past the overall deadline (at least more than by 1s)
+        overall_timeout = max(self._rendezvous_deadline - time.time(), 0.0) + 1.0
+        timeout = overall_timeout if timeout is None else min(timeout, overall_timeout)
+
+        try:
+            self.client.watch(
+                self.get_path("/rdzv/active_version"), index=etcd_index, timeout=timeout
+            )
+        except (etcd.EtcdEventIndexCleared, etcd.EtcdWatchTimedOut):
+            pass
+
+        if time.time() > self._rendezvous_deadline:
+            raise RendezvousTimeoutError
+
+        # Unfortunately, we have to do another fetch in order to get last etcd_index.
+        return self.get_rdzv_state()
+
+    def get_path(self, path):
+        if not path.startswith("/"):
+            path = "/" + path
+
+        return f"{self._prefix}run_{self._run_id}{path}"
+
+    def create_path_if_not_exists(self, full_path, ttl=None):
+        try:
+            self.client.write(
+                key=full_path, value=None, dir=True, prevExist=False, ttl=ttl
+            )
+        except etcd.EtcdAlreadyExist:
+            pass
+
+    def setup_lease_renewal(self, full_path, ttl):
+        # NOTE: For ephemeral key TTL renewal (~lease) to work correctly,
+        # make sure you don't call any long-blocking methods that do not
+        # release the Python's GIL! An example of this is calling a pybind11
+        # extension function that is blocking / long-running, but is not
+        # doing a scoped release of the GIL.
+        def lease_worker(client, path, ttl, stop_event):
+            while True:
+                try:
+                    client.refresh(path, ttl=ttl)
+                except etcd.EtcdKeyNotFound:
+                    break
+                except ConnectionRefusedError:
+                    # This error usually occurs during test when the server already got terminated but the
+                    # python garbage collector have not yet invoked the __del__ method.
+                    break
+
+                if stop_event.wait(timeout=ttl / 2):
+                    break
+
+        lease_stop_event = threading.Event()
+        lease_thread = threading.Thread(
+            target=lease_worker, args=(self.client, full_path, ttl, lease_stop_event)
+        )
+
+        lease_thread.daemon = True
+        lease_thread.start()
+
+        return lease_stop_event
+
+    def store_extra_data(self, rdzv_version, key, value):
+        node = self.get_path(f"/rdzv/v_{rdzv_version}/extra_data")
+        try:
+            # If first time we are storing anything:
+            extra_data = self.client.write(
+                key=node, value=json.dumps({key: value}), prevExist=False
+            )
+            return
+        except etcd.EtcdAlreadyExist:
+            pass
+
+        # CAS loop, to make sure we don't lose concurrent stores.
+        while True:
+            # We never delete extra_data. Failure here should be fatal, no special handling.
+            extra_data = self.client.get(node)
+
+            new_extra_data_value = json.loads(extra_data.value)
+            new_extra_data_value[key] = value
+
+            try:
+                extra_data = self.client.test_and_set(
+                    key=node,
+                    value=json.dumps(new_extra_data_value),
+                    prev_value=extra_data.value,
+                )
+                return
+            except etcd.EtcdCompareFailed:
+                logger.info("Store extra_data CAS unsuccessful, retrying")
+                time.sleep(0.1)
+
+    def load_extra_data(self, rdzv_version, key, timeout=None):
+        # 'extra_data' node itself, and the directory it is located in:
+        node = self.get_path(f"/rdzv/v_{rdzv_version}/extra_data")
+        node_dir = self.get_path(f"/rdzv/v_{rdzv_version}")
+
+        # TODO: implement timeout
+        # https://github.com/pytorch/elastic/issues/12
+        while True:
+            # Combined wait for the node itself, and the key inside it.
+            root = self.client.get(node_dir)
+
+            # Find the extra_data node, if it exists
+            extra_data = [n for n in root.children if n.key == node]
+            assert len(extra_data) <= 1
+
+            # Node for extra_data exists, check the desired key inside it.
+            if len(extra_data) == 1:
+                extra_data_dict = json.loads(extra_data[0].value)
+                if key in extra_data_dict:
+                    return extra_data_dict[key]
+
+            # The 'extra_data' node doesn't exist, or they key isn't published yet.
+            # Wait for interesting events on the extra_data node and retry.
+            try:
+                self.client.watch(node, index=root.etcd_index + 1)
+            except (etcd.EtcdEventIndexCleared, etcd.EtcdWatchTimedOut):
+                pass
+
+    def setup_kv_store(self, rdzv_version):
+        store_path = self.get_path(f"/rdzv/v_{rdzv_version}/kv")
+        self.create_path_if_not_exists(store_path)
+        return EtcdStore(etcd_client=self.client, etcd_store_prefix=store_path)
+
+
+def _create_etcd_client(params: RendezvousParameters) -> etcd.Client:
+    """Create a new ``etcd.Client`` from the specified ``RendezvousParameters``."""
+    hostname, port = parse_rendezvous_endpoint(params.endpoint, 2379)
+
+    # The communication protocol
+    protocol = params.config.get("protocol")
+    if protocol is None:
+        protocol = "http"
+    else:
+        if protocol != "http" and protocol != "https":
+            raise ValueError("The etcd protocol must be HTTP or HTTPS.")
+
+    # The SSL client certificate
+    ssl_cert = params.config.get("cert")
+    if ssl_cert is not None:
+        cert_key = params.config.get("key")
+        if cert_key is not None:
+            # The etcd client expects the certificate key as the second element
+            # of the `cert` tuple.
+            ssl_cert = (ssl_cert, cert_key)
+
+    # The root certificate
+    ca_cert = params.config.get("cacert")
+
+    return etcd.Client(
+        hostname,
+        port,
+        protocol=protocol,
+        cert=ssl_cert,
+        ca_cert=ca_cert,
+        allow_reconnect=True,
+    )
+
+
+# Handler for torch.distributed "static" registration
+def create_rdzv_handler(params: RendezvousParameters) -> RendezvousHandler:
+    """
+    Usage:
+
+    ::
+
+    rdzv_params = RendezvousParameters(
+                        backend="etcd",
+                        endpoint="192.168.0.42:2379",
+                        run_id="123",
+                        min_nodes=4,
+                        max_nodes=8,
+                        timeout=300,
+                        last_call_timeout=30,
+                        etcd_prefix="custom_prefix",
+                        protocol="https",
+                        cacert="/etc/kubernetes/certs/ca.crt",
+                        cert="/etc/kubernetes/certs/client.crt",
+                        key="/etc/kubernetes/certs/client.key")
+    # -- or --
+    rdzv_params = RendezvousParameters(
+                        backend="etcd",
+                        endpoint="192.168.0.42:2379",
+                        run_id="123",
+                        min_nodes=4,
+                        max_nodes=8)
+
+    etcd_rdzv_handler = create_etcd_rendezvous_handler(rdzv_params)
+
+
+    Where:
+        run_id - unique id for this training job instance,
+        min_nodes - min number of workers expected to join the rendezvous,
+        max_nodes - max number of workers allowed to join the rendezvous,
+                        defaults to min_workers is not specified.
+        timeout - total timeout within which next_rendezvous is expected to
+                      succeed; a RendezvousTimeoutError is raised otherwise;
+                      Defaults is 600 (10 minutes).
+        last_call_timeout - additional wait amount ("last call") after
+                            min number of workers has been reached.
+                            Defaults to 30 seconds.
+        etcd_prefix - path prefix (from etcd root), inside which all
+                      etcd nodes will be created.
+                      Default is "/torchelastic/p2p".
+        protocol - http (default) or https to access etcd.
+        cacert - CA cert to access etcd, only makes sense with https.
+        cert - client cert to access etcd, only makes sense with https.
+        key - client key to access etcd, only makes sense with https.
+    """
+    client = _create_etcd_client(params)
+
+    etcd_prefix = params.get("etcd_prefix", "/torchelastic/p2p")
+
+    rdzv = EtcdRendezvous(
+        client=client,
+        prefix=etcd_prefix,
+        run_id=params.run_id,
+        num_min_workers=params.min_nodes,
+        num_max_workers=params.max_nodes,
+        timeout=params.get_as_int("timeout", _DEFAULT_TIMEOUT),
+        last_call_timeout=params.get_as_int(
+            "last_call_timeout", _DEFAULT_LAST_CALL_TIMEOUT
+        ),
+    )
+    return EtcdRendezvousHandler(
+        rdzv_impl=rdzv,
+        local_addr=params.local_addr,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ebb680bef17a1394d0d98fcb976c308ced88ad5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py
@@ -0,0 +1,215 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import binascii
+from base64 import b64decode, b64encode
+from typing import cast, Optional
+
+import urllib3.exceptions  # type: ignore[import]
+
+
+try:
+    import etcd  # type: ignore[import]
+except ModuleNotFoundError:
+    from . import _etcd_stub as etcd
+
+from torch.distributed import Store
+
+from .api import RendezvousConnectionError, RendezvousParameters, RendezvousStateError
+from .dynamic_rendezvous import RendezvousBackend, Token
+from .etcd_store import EtcdStore
+from .utils import parse_rendezvous_endpoint
+
+
+class EtcdRendezvousBackend(RendezvousBackend):
+    """Represents an etcd-based rendezvous backend.
+
+    Args:
+        client:
+            The ``etcd.Client`` instance to use to communicate with etcd.
+        run_id:
+            The run id of the rendezvous.
+        key_prefix:
+            The path under which to store the rendezvous state in etcd.
+        ttl:
+            The TTL of the rendezvous state. If not specified, defaults to two hours.
+    """
+
+    _DEFAULT_TTL = 7200  # 2 hours
+
+    _client: etcd.Client
+    _key: str
+    _ttl: int
+
+    def __init__(
+        self,
+        client: etcd.Client,
+        run_id: str,
+        key_prefix: Optional[str] = None,
+        ttl: Optional[int] = None,
+    ) -> None:
+        if not run_id:
+            raise ValueError("The run id must be a non-empty string.")
+
+        self._client = client
+
+        if key_prefix:
+            self._key = key_prefix + "/" + run_id
+        else:
+            self._key = run_id
+
+        if ttl and ttl > 0:
+            self._ttl = ttl
+        else:
+            self._ttl = self._DEFAULT_TTL
+
+    @property
+    def name(self) -> str:
+        """See base class."""
+        return "etcd-v2"
+
+    def get_state(self) -> Optional[tuple[bytes, Token]]:
+        """See base class."""
+        try:
+            result = self._client.read(self._key)
+        except etcd.EtcdKeyNotFound:
+            return None
+        except (etcd.EtcdException, urllib3.exceptions.TimeoutError) as exc:
+            raise RendezvousConnectionError(
+                "The connection to etcd has failed. See inner exception for details."
+            ) from exc
+
+        return self._decode_state(result)
+
+    def set_state(
+        self, state: bytes, token: Optional[Token] = None
+    ) -> Optional[tuple[bytes, Token, bool]]:
+        """See base class."""
+        base64_state = b64encode(state).decode()
+
+        kwargs = {}
+
+        def get_state():
+            result = self.get_state()
+            if result is not None:
+                tmp = *result, False
+                # Python 3.6 does not support tuple unpacking in return
+                # statements.
+                return tmp
+            return None
+
+        if token:
+            try:
+                token = int(token)
+            except ValueError:
+                return get_state()
+
+        if token:
+            kwargs["prevIndex"] = token
+        else:
+            kwargs["prevExist"] = False
+
+        try:
+            result = self._client.write(self._key, base64_state, self._ttl, **kwargs)
+        except (etcd.EtcdAlreadyExist, etcd.EtcdCompareFailed):
+            result = None
+        except (etcd.EtcdException, urllib3.exceptions.TimeoutError) as exc:
+            raise RendezvousConnectionError(
+                "The connection to etcd has failed. See inner exception for details."
+            ) from exc
+
+        if result is None:
+            return get_state()
+
+        tmp = *self._decode_state(result), True
+        return tmp
+
+    def _decode_state(self, result: etcd.EtcdResult) -> tuple[bytes, Token]:
+        base64_state = result.value.encode()
+
+        try:
+            state = b64decode(base64_state)
+        except binascii.Error as exc:
+            raise RendezvousStateError(
+                "The state object is corrupt. See inner exception for details."
+            ) from exc
+
+        return state, result.modifiedIndex
+
+
+def _create_etcd_client(params: RendezvousParameters) -> etcd.Client:
+    host, port = parse_rendezvous_endpoint(params.endpoint, default_port=2379)
+
+    # The timeout
+    read_timeout = cast(int, params.get_as_int("read_timeout", 60))
+    if read_timeout <= 0:
+        raise ValueError("The read timeout must be a positive integer.")
+
+    # The communication protocol
+    protocol = params.get("protocol", "http").strip().lower()
+    if protocol != "http" and protocol != "https":
+        raise ValueError("The protocol must be HTTP or HTTPS.")
+
+    # The SSL client certificate
+    ssl_cert = params.get("ssl_cert")
+    if ssl_cert:
+        ssl_cert_key = params.get("ssl_cert_key")
+        if ssl_cert_key:
+            # The etcd client expects the certificate key as the second element
+            # of the `cert` tuple.
+            ssl_cert = (ssl_cert, ssl_cert_key)
+
+    # The root certificate
+    ca_cert = params.get("ca_cert")
+
+    try:
+        return etcd.Client(
+            host,
+            port,
+            read_timeout=read_timeout,
+            protocol=protocol,
+            cert=ssl_cert,
+            ca_cert=ca_cert,
+            allow_reconnect=True,
+        )
+    except (etcd.EtcdException, urllib3.exceptions.TimeoutError) as exc:
+        raise RendezvousConnectionError(
+            "The connection to etcd has failed. See inner exception for details."
+        ) from exc
+
+
+def create_backend(params: RendezvousParameters) -> tuple[EtcdRendezvousBackend, Store]:
+    """Create a new :py:class:`EtcdRendezvousBackend` from the specified parameters.
+
+    +--------------+-----------------------------------------------------------+
+    | Parameter    | Description                                               |
+    +==============+===========================================================+
+    | read_timeout | The read timeout, in seconds, for etcd operations.        |
+    |              | Defaults to 60 seconds.                                   |
+    +--------------+-----------------------------------------------------------+
+    | protocol     | The protocol to use to communicate with etcd. Valid       |
+    |              | values are "http" and "https". Defaults to "http".        |
+    +--------------+-----------------------------------------------------------+
+    | ssl_cert     | The path to the SSL client certificate to use along with  |
+    |              | HTTPS. Defaults to ``None``.                              |
+    +--------------+-----------------------------------------------------------+
+    | ssl_cert_key | The path to the private key of the SSL client certificate |
+    |              | to use along with HTTPS. Defaults to ``None``.            |
+    +--------------+-----------------------------------------------------------+
+    | ca_cert      | The path to the rool SSL authority certificate. Defaults  |
+    |              | to ``None``.                                              |
+    +--------------+-----------------------------------------------------------+
+    """
+    client = _create_etcd_client(params)
+
+    backend = EtcdRendezvousBackend(
+        client, params.run_id, key_prefix="/torch/elastic/rendezvous"
+    )
+
+    store = EtcdStore(client, "/torch/elastic/store")
+
+    return backend, store
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_server.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..8af8c01c028ae04801b41b5dd6b883ea34159fb8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_server.py
@@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import atexit
+import logging
+import os
+import shlex
+import shutil
+import socket
+import subprocess
+import tempfile
+import time
+from typing import Optional, TextIO, Union
+
+
+try:
+    import etcd  # type: ignore[import]
+except ModuleNotFoundError:
+    pass
+
+
+logger = logging.getLogger(__name__)
+
+
+def find_free_port():
+    """
+    Find a free port and binds a temporary socket to it so that the port can be "reserved" until used.
+
+    .. note:: the returned socket must be closed before using the port,
+              otherwise a ``address already in use`` error will happen.
+              The socket should be held and closed as close to the
+              consumer of the port as possible since otherwise, there
+              is a greater chance of race-condition where a different
+              process may see the port as being free and take it.
+
+    Returns: a socket binded to the reserved free port
+
+    Usage::
+
+    sock = find_free_port()
+    port = sock.getsockname()[1]
+    sock.close()
+    use_port(port)
+    """
+    addrs = socket.getaddrinfo(
+        host="localhost", port=None, family=socket.AF_UNSPEC, type=socket.SOCK_STREAM
+    )
+
+    for addr in addrs:
+        family, type, proto, _, _ = addr
+        try:
+            s = socket.socket(family, type, proto)
+            s.bind(("localhost", 0))
+            s.listen(0)
+            return s
+        except OSError as e:
+            s.close()  # type: ignore[possibly-undefined]
+            print(f"Socket creation attempt failed: {e}")
+    raise RuntimeError("Failed to create a socket")
+
+
+def stop_etcd(subprocess, data_dir: Optional[str] = None):
+    if subprocess and subprocess.poll() is None:
+        logger.info("stopping etcd server")
+        subprocess.terminate()
+        subprocess.wait()
+
+    if data_dir:
+        logger.info("deleting etcd data dir: %s", data_dir)
+        shutil.rmtree(data_dir, ignore_errors=True)
+
+
+class EtcdServer:
+    """
+    .. note:: tested on etcd server v3.4.3.
+
+    Starts and stops a local standalone etcd server on a random free
+    port. Useful for single node, multi-worker launches or testing,
+    where a sidecar etcd server is more convenient than having to
+    separately setup an etcd server.
+
+    This class registers a termination handler to shutdown the etcd
+    subprocess on exit. This termination handler is NOT a substitute for
+    calling the ``stop()`` method.
+
+    The following fallback mechanism is used to find the etcd binary:
+
+    1. Uses env var TORCHELASTIC_ETCD_BINARY_PATH
+    2. Uses ``/bin/etcd`` if one exists
+    3. Uses ``etcd`` from ``PATH``
+
+    Usage
+    ::
+
+     server = EtcdServer("/usr/bin/etcd", 2379, "/tmp/default.etcd")
+     server.start()
+     client = server.get_client()
+     # use client
+     server.stop()
+
+    Args:
+        etcd_binary_path: path of etcd server binary (see above for fallback path)
+    """
+
+    def __init__(self, data_dir: Optional[str] = None):
+        self._port = -1
+        self._host = "localhost"
+
+        root = os.path.dirname(__file__)
+        default_etcd_bin = os.path.join(root, "bin/etcd")
+        self._etcd_binary_path = os.environ.get(
+            "TORCHELASTIC_ETCD_BINARY_PATH", default_etcd_bin
+        )
+        if not os.path.isfile(self._etcd_binary_path):
+            self._etcd_binary_path = "etcd"
+
+        self._base_data_dir = (
+            data_dir if data_dir else tempfile.mkdtemp(prefix="torchelastic_etcd_data")
+        )
+        self._etcd_cmd = None
+        self._etcd_proc: Optional[subprocess.Popen] = None
+
+    def _get_etcd_server_process(self) -> subprocess.Popen:
+        if not self._etcd_proc:
+            raise RuntimeError(
+                "No etcd server process started. Call etcd_server.start() first"
+            )
+        else:
+            return self._etcd_proc
+
+    def get_port(self) -> int:
+        """Return the port the server is running on."""
+        return self._port
+
+    def get_host(self) -> str:
+        """Return the host the server is running on."""
+        return self._host
+
+    def get_endpoint(self) -> str:
+        """Return the etcd server endpoint (host:port)."""
+        return f"{self._host}:{self._port}"
+
+    def start(
+        self,
+        timeout: int = 60,
+        num_retries: int = 3,
+        stderr: Union[int, TextIO, None] = None,
+    ) -> None:
+        """
+        Start the server, and waits for it to be ready. When this function returns the sever is ready to take requests.
+
+        Args:
+            timeout: time (in seconds) to wait for the server to be ready
+                before giving up.
+            num_retries: number of retries to start the server. Each retry
+                will wait for max ``timeout`` before considering it as failed.
+            stderr: the standard error file handle. Valid values are
+                `subprocess.PIPE`, `subprocess.DEVNULL`, an existing file
+                descriptor (a positive integer), an existing file object, and
+                `None`.
+
+        Raises:
+            TimeoutError: if the server is not ready within the specified timeout
+        """
+        curr_retries = 0
+        while True:
+            try:
+                data_dir = os.path.join(self._base_data_dir, str(curr_retries))
+                os.makedirs(data_dir, exist_ok=True)
+                return self._start(data_dir, timeout, stderr)
+            except Exception as e:
+                curr_retries += 1
+                stop_etcd(self._etcd_proc)
+                logger.warning(
+                    "Failed to start etcd server, got error: %s, retrying", str(e)
+                )
+                if curr_retries >= num_retries:
+                    shutil.rmtree(self._base_data_dir, ignore_errors=True)
+                    raise
+        atexit.register(stop_etcd, self._etcd_proc, self._base_data_dir)
+
+    def _start(
+        self, data_dir: str, timeout: int = 60, stderr: Union[int, TextIO, None] = None
+    ) -> None:
+        sock = find_free_port()
+        sock_peer = find_free_port()
+        self._port = sock.getsockname()[1]
+        peer_port = sock_peer.getsockname()[1]
+
+        etcd_cmd = shlex.split(
+            " ".join(
+                [
+                    self._etcd_binary_path,
+                    "--enable-v2",
+                    "--data-dir",
+                    data_dir,
+                    "--listen-client-urls",
+                    f"http://{self._host}:{self._port}",
+                    "--advertise-client-urls",
+                    f"http://{self._host}:{self._port}",
+                    "--listen-peer-urls",
+                    f"http://{self._host}:{peer_port}",
+                ]
+            )
+        )
+
+        logger.info("Starting etcd server: [%s]", etcd_cmd)
+
+        sock.close()
+        sock_peer.close()
+        self._etcd_proc = subprocess.Popen(etcd_cmd, close_fds=True, stderr=stderr)
+        self._wait_for_ready(timeout)
+
+    def get_client(self):
+        """Return an etcd client object that can be used to make requests to this server."""
+        return etcd.Client(
+            host=self._host, port=self._port, version_prefix="/v2", read_timeout=10
+        )
+
+    def _wait_for_ready(self, timeout: int = 60) -> None:
+        client = etcd.Client(
+            host=f"{self._host}", port=self._port, version_prefix="/v2", read_timeout=5
+        )
+        max_time = time.time() + timeout
+
+        while time.time() < max_time:
+            if self._get_etcd_server_process().poll() is not None:
+                # etcd server process finished
+                exitcode = self._get_etcd_server_process().returncode
+                raise RuntimeError(
+                    f"Etcd server process exited with the code: {exitcode}"
+                )
+            try:
+                logger.info("etcd server ready. version: %s", client.version)
+                return
+            except Exception:
+                time.sleep(1)
+        raise TimeoutError("Timed out waiting for etcd server to be ready!")
+
+    def stop(self) -> None:
+        """Stop the server and cleans up auto generated resources (e.g. data dir)."""
+        logger.info("EtcdServer stop method called")
+        stop_etcd(self._etcd_proc, self._base_data_dir)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_store.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..676303216f1113b480ed8fe69e73ea58c4ae802b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_store.py
@@ -0,0 +1,216 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import datetime
+import random
+import time
+from base64 import b64decode, b64encode
+from typing import Optional
+
+# pyre-ignore[21]: Could not find name `Store` in `torch.distributed`.
+from torch.distributed import Store
+
+
+try:
+    import etcd  # type: ignore[import]
+except ModuleNotFoundError:
+    from . import _etcd_stub as etcd
+
+
+# Delay (sleep) for a small random amount to reduce CAS failures.
+# This does not affect correctness, but will reduce requests to etcd server.
+def cas_delay():
+    time.sleep(random.uniform(0, 0.1))
+
+
+# pyre-fixme[11]: Annotation `Store` is not defined as a type.
+class EtcdStore(Store):
+    """
+    Implement a c10 Store interface by piggybacking on the rendezvous etcd instance.
+
+    This is the store object returned by ``EtcdRendezvous``.
+    """
+
+    def __init__(
+        self,
+        etcd_client,
+        etcd_store_prefix,
+        # Default timeout same as in c10d/Store.hpp
+        timeout: Optional[datetime.timedelta] = None,
+    ):
+        super().__init__()  # required for pybind trampoline.
+
+        self.client = etcd_client
+        self.prefix = etcd_store_prefix
+
+        if timeout is not None:
+            self.set_timeout(timeout)
+
+        if not self.prefix.endswith("/"):
+            self.prefix += "/"
+
+    def set(self, key, value):
+        """
+        Write a key/value pair into ``EtcdStore``.
+
+        Both key and value may be either Python ``str`` or ``bytes``.
+        """
+        self.client.set(key=self.prefix + self._encode(key), value=self._encode(value))
+
+    def get(self, key) -> bytes:
+        """
+        Get a value by key, possibly doing a blocking wait.
+
+        If key is not immediately present, will do a blocking wait
+        for at most ``timeout`` duration or until the key is published.
+
+
+        Returns:
+            value ``(bytes)``
+
+        Raises:
+            LookupError - If key still not published after timeout
+        """
+        b64_key = self.prefix + self._encode(key)
+        kvs = self._try_wait_get([b64_key])
+
+        if kvs is None:
+            raise LookupError(f"Key {key} not found in EtcdStore")
+
+        return self._decode(kvs[b64_key])
+
+    def add(self, key, num: int) -> int:
+        """
+        Atomically increment a value by an integer amount.
+
+        The integer is represented as a string using base 10. If key is not present,
+        a default value of ``0`` will be assumed.
+
+        Returns:
+             the new (incremented) value
+
+
+        """
+        b64_key = self._encode(key)
+        # c10d Store assumes value is an integer represented as a decimal string
+        try:
+            # Assume default value "0", if this key didn't yet:
+            node = self.client.write(
+                key=self.prefix + b64_key,
+                value=self._encode(str(num)),  # i.e. 0 + num
+                prevExist=False,
+            )
+            return int(self._decode(node.value))
+        except etcd.EtcdAlreadyExist:
+            pass
+
+        while True:
+            # Note: c10d Store does not have a method to delete keys, so we
+            # can be sure it's still there.
+            node = self.client.get(key=self.prefix + b64_key)
+            new_value = self._encode(str(int(self._decode(node.value)) + num))
+            try:
+                node = self.client.test_and_set(
+                    key=node.key, value=new_value, prev_value=node.value
+                )
+                return int(self._decode(node.value))
+            except etcd.EtcdCompareFailed:
+                cas_delay()
+
+    def wait(self, keys, override_timeout: Optional[datetime.timedelta] = None):
+        """
+        Wait until all of the keys are published, or until timeout.
+
+        Raises:
+            LookupError - if timeout occurs
+        """
+        b64_keys = [self.prefix + self._encode(key) for key in keys]
+        kvs = self._try_wait_get(b64_keys, override_timeout)
+        if kvs is None:
+            raise LookupError("Timeout while waiting for keys in EtcdStore")
+        # No return value on success
+
+    def check(self, keys) -> bool:
+        """Check if all of the keys are immediately present (without waiting)."""
+        b64_keys = [self.prefix + self._encode(key) for key in keys]
+        kvs = self._try_wait_get(
+            b64_keys,
+            override_timeout=datetime.timedelta(microseconds=1),  # as if no wait
+        )
+        return kvs is not None
+
+    #
+    # Encode key/value data in base64, so we can store arbitrary binary data
+    # in EtcdStore. Input can be `str` or `bytes`.
+    # In case of `str`, utf-8 encoding is assumed.
+    #
+    def _encode(self, value) -> str:
+        if type(value) == bytes:
+            return b64encode(value).decode()
+        elif type(value) == str:
+            return b64encode(value.encode()).decode()
+        raise ValueError("Value must be of type str or bytes")
+
+    #
+    # Decode a base64 string (of type `str` or `bytes`).
+    # Return type is `bytes`, which is more convenient with the Store interface.
+    #
+    def _decode(self, value) -> bytes:
+        if type(value) == bytes:
+            return b64decode(value)
+        elif type(value) == str:
+            return b64decode(value.encode())
+        raise ValueError("Value must be of type str or bytes")
+
+    #
+    # Get all of the (base64-encoded) etcd keys at once, or wait until all the keys
+    # are published or timeout occurs.
+    # This is a helper method for the public interface methods.
+    #
+    # On success, a dictionary of {etcd key -> etcd value} is returned.
+    # On timeout, None is returned.
+    #
+    def _try_wait_get(self, b64_keys, override_timeout=None):
+        timeout = self.timeout if override_timeout is None else override_timeout  # type: ignore[attr-defined]
+        deadline = time.time() + timeout.total_seconds()
+
+        while True:
+            # Read whole directory (of keys), filter only the ones waited for
+            all_nodes = None
+            try:
+                all_nodes = self.client.get(key=self.prefix)
+                req_nodes = {
+                    node.key: node.value
+                    for node in all_nodes.children
+                    if node.key in b64_keys
+                }
+
+                if len(req_nodes) == len(b64_keys):
+                    # All keys are available
+                    return req_nodes
+            except etcd.EtcdKeyNotFound:
+                pass
+
+            watch_timeout = deadline - time.time()
+            if watch_timeout <= 0:
+                return None
+
+            try:
+                index = all_nodes.etcd_index + 1 if all_nodes else 0
+                self.client.watch(
+                    key=self.prefix,
+                    recursive=True,
+                    timeout=watch_timeout,
+                    index=index,
+                )
+            except etcd.EtcdWatchTimedOut:
+                if time.time() >= deadline:
+                    return None
+                else:
+                    continue
+            except etcd.EtcdEventIndexCleared:
+                continue
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/registry.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..75f0d16f7d1954c29d3a69ce7564f96734f88c97
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/registry.py
@@ -0,0 +1,100 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import sys
+
+from .api import (
+    rendezvous_handler_registry as handler_registry,
+    RendezvousHandler,
+    RendezvousParameters,
+)
+from .dynamic_rendezvous import create_handler
+
+
+if sys.version_info < (3, 10):
+    from importlib_metadata import entry_points
+else:
+    from importlib.metadata import entry_points
+
+log = logging.getLogger(__name__)
+
+__all__ = ["get_rendezvous_handler"]
+
+
+def _create_static_handler(params: RendezvousParameters) -> RendezvousHandler:
+    from . import static_tcp_rendezvous
+
+    return static_tcp_rendezvous.create_rdzv_handler(params)
+
+
+def _create_etcd_handler(params: RendezvousParameters) -> RendezvousHandler:
+    from . import etcd_rendezvous
+
+    return etcd_rendezvous.create_rdzv_handler(params)
+
+
+def _create_etcd_v2_handler(params: RendezvousParameters) -> RendezvousHandler:
+    from .etcd_rendezvous_backend import create_backend
+
+    backend, store = create_backend(params)
+
+    return create_handler(store, backend, params)
+
+
+def _create_c10d_handler(params: RendezvousParameters) -> RendezvousHandler:
+    from .c10d_rendezvous_backend import create_backend
+
+    backend, store = create_backend(params)
+
+    return create_handler(store, backend, params)
+
+
+def _register_default_handlers() -> None:
+    handler_registry.register("etcd", _create_etcd_handler)
+    handler_registry.register("etcd-v2", _create_etcd_v2_handler)
+    handler_registry.register("c10d", _create_c10d_handler)
+    handler_registry.register("static", _create_static_handler)
+
+
+def _register_out_of_tree_handlers() -> None:
+    discovered_handler_generators = entry_points(group="torchrun.handlers")
+
+    for handler_generator in discovered_handler_generators:
+        try:
+            get_handler = discovered_handler_generators[handler_generator.name].load()
+            handler_registry.register(handler_generator.name, get_handler())
+        except Exception:
+            log.warning(
+                "Exception while registering out of tree plugin %s: ",
+                handler_generator.name,
+                exc_info=True,
+            )
+
+
+def get_rendezvous_handler(params: RendezvousParameters) -> RendezvousHandler:
+    """
+    Obtain a reference to a :py:class`RendezvousHandler`.
+
+    Custom rendezvous handlers can be registered by
+
+    ::
+
+      from torch.distributed.elastic.rendezvous import rendezvous_handler_registry
+      from torch.distributed.elastic.rendezvous.registry import get_rendezvous_handler
+
+
+      def create_my_rdzv(params: RendezvousParameters):
+          return MyCustomRdzv(params)
+
+
+      rendezvous_handler_registry.register("my_rdzv_backend_name", create_my_rdzv)
+
+      my_rdzv_handler = get_rendezvous_handler(
+          "my_rdzv_backend_name", RendezvousParameters
+      )
+    """
+    return handler_registry.create_handler(params)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/static_tcp_rendezvous.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/static_tcp_rendezvous.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6395b70be2b432130e35f4b0fe702bfb5301542
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/static_tcp_rendezvous.py
@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import datetime
+import logging
+from typing import cast, Optional
+
+from torch.distributed import PrefixStore, Store, TCPStore
+from torch.distributed.elastic.rendezvous import (
+    RendezvousHandler,
+    RendezvousInfo,
+    RendezvousParameters,
+    RendezvousStoreInfo,
+)
+from torch.distributed.elastic.rendezvous.utils import parse_rendezvous_endpoint
+
+
+__all__ = ["StaticTCPRendezvous", "create_rdzv_handler"]
+
+logger = logging.getLogger(__name__)
+
+_default_timeout_seconds = 600
+
+
+class StaticTCPRendezvous(RendezvousHandler):
+    """
+    Static rendezvous that is a wrapper around the TCPStore.
+
+    Creates TCPStore based on the input parameters with the
+    listener on the agent with group_rank=0
+    """
+
+    def __init__(
+        self,
+        master_addr: str,
+        master_port: int,
+        rank: int,
+        world_size: int,
+        run_id: str,
+        timeout: int,
+    ):
+        self.master_addr = master_addr
+        self.master_port = master_port
+        self.rank = rank
+        self.world_size = world_size
+        self.run_id = run_id
+        self.timeout = datetime.timedelta(seconds=timeout)
+        self._store: Optional[Store] = None
+
+    def get_backend(self) -> str:
+        return "static"
+
+    @property
+    def use_agent_store(self) -> bool:
+        return True
+
+    def next_rendezvous(self) -> RendezvousInfo:
+        logger.info("Creating TCPStore as the c10d::Store implementation")
+        is_master = self.rank == 0
+        if not self._store:
+            self._store = TCPStore(  # type: ignore[call-arg]
+                self.master_addr,
+                self.master_port,
+                self.world_size,
+                is_master,
+                self.timeout,
+                multi_tenant=True,
+            )
+        store = PrefixStore(self.run_id, self._store)
+        # TCPStore server instance is used by trainer code
+        bootstrap_store_info = RendezvousStoreInfo(self.master_addr, self.master_port)
+        return RendezvousInfo(
+            store,
+            self.rank,
+            self.world_size,
+            bootstrap_store_info,
+        )
+
+    def is_closed(self):
+        return False
+
+    def set_closed(self):
+        pass
+
+    def num_nodes_waiting(self):
+        return 0
+
+    def get_run_id(self) -> str:
+        return self.run_id
+
+    def shutdown(self) -> bool:
+        return True
+
+
+def create_rdzv_handler(params: RendezvousParameters) -> RendezvousHandler:
+    if "rank" not in params.config:
+        raise ValueError(
+            "rank is absent in RendezvousParameters."
+            "Try add --node-rank to the cmd request"
+        )
+    endpoint = params.endpoint.strip()
+    if not endpoint:
+        raise ValueError(
+            "endpoint is absent in RendezvousParameters"
+            "Try add --master-port and --master-addr to the cmd request"
+        )
+    master_addr, master_port = parse_rendezvous_endpoint(endpoint, -1)
+    if master_port == -1:
+        raise ValueError(
+            f"Port is absent in endpoint: {endpoint}. Try launching with --master-port"
+        )
+    world_size = params.max_nodes
+    rank = cast(int, params.config.get("rank"))
+    run_id = params.run_id
+    if "timeout" in params.config:
+        timeout = int(params.config["timeout"])
+    else:
+        timeout = _default_timeout_seconds
+
+    return StaticTCPRendezvous(
+        master_addr, master_port, rank, world_size, run_id, timeout
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a292c8c6184a4c6fbc18ee418922e2b1fd5c7f54
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/utils.py
@@ -0,0 +1,284 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import ipaddress
+import random
+import re
+import socket
+import time
+import weakref
+from datetime import timedelta
+from threading import Event, Thread
+from typing import Any, Callable, Optional, Union
+
+
+__all__ = ["parse_rendezvous_endpoint"]
+
+
+def _parse_rendezvous_config(config_str: str) -> dict[str, str]:
+    """Extract key-value pairs from a rendezvous configuration string.
+
+    Args:
+        config_str:
+            A string in format =,...,=.
+    """
+    config: dict[str, str] = {}
+
+    config_str = config_str.strip()
+    if not config_str:
+        return config
+
+    key_values = config_str.split(",")
+    for kv in key_values:
+        key, *values = kv.split("=", 1)
+
+        key = key.strip()
+        if not key:
+            raise ValueError(
+                "The rendezvous configuration string must be in format "
+                "=,...,=."
+            )
+
+        value: Optional[str]
+        if values:
+            value = values[0].strip()
+        else:
+            value = None
+        if not value:
+            raise ValueError(
+                f"The rendezvous configuration option '{key}' must have a value specified."
+            )
+
+        config[key] = value
+    return config
+
+
+def _try_parse_port(port_str: str) -> Optional[int]:
+    """Try to extract the port number from ``port_str``."""
+    if port_str and re.match(r"^[0-9]{1,5}$", port_str):
+        return int(port_str)
+    return None
+
+
+def parse_rendezvous_endpoint(
+    endpoint: Optional[str], default_port: int
+) -> tuple[str, int]:
+    """Extract the hostname and the port number from a rendezvous endpoint.
+
+    Args:
+        endpoint:
+            A string in format [:].
+        default_port:
+            The port number to use if the endpoint does not include one.
+
+    Returns:
+        A tuple of hostname and port number.
+    """
+    if endpoint is not None:
+        endpoint = endpoint.strip()
+
+    if not endpoint:
+        return ("localhost", default_port)
+
+    # An endpoint that starts and ends with brackets represents an IPv6 address.
+    if endpoint[0] == "[" and endpoint[-1] == "]":
+        host, *rest = endpoint, *[]
+    else:
+        host, *rest = endpoint.rsplit(":", 1)
+
+    # Sanitize the IPv6 address.
+    if len(host) > 1 and host[0] == "[" and host[-1] == "]":
+        host = host[1:-1]
+
+    if len(rest) == 1:
+        port = _try_parse_port(rest[0])
+        if port is None or port >= 2**16:
+            raise ValueError(
+                f"The port number of the rendezvous endpoint '{endpoint}' must be an integer "
+                "between 0 and 65536."
+            )
+    else:
+        port = default_port
+
+    if not re.match(r"^[\w\.:-]+$", host):
+        raise ValueError(
+            f"The hostname of the rendezvous endpoint '{endpoint}' must be a dot-separated list of "
+            "labels, an IPv4 address, or an IPv6 address."
+        )
+
+    return host, port
+
+
+def _matches_machine_hostname(host: str) -> bool:
+    """Indicate whether ``host`` matches the hostname of this machine.
+
+    This function compares ``host`` to the hostname as well as to the IP
+    addresses of this machine. Note that it may return a false negative if this
+    machine has CNAME records beyond its FQDN or IP addresses assigned to
+    secondary NICs.
+    """
+    if host == "localhost":
+        return True
+
+    try:
+        addr = ipaddress.ip_address(host)
+    except ValueError:
+        addr = None
+
+    if addr and addr.is_loopback:
+        return True
+
+    try:
+        host_addr_list = socket.getaddrinfo(
+            host, None, proto=socket.IPPROTO_TCP, flags=socket.AI_CANONNAME
+        )
+    except (ValueError, socket.gaierror) as _:
+        host_addr_list = []
+
+    host_ip_list = [host_addr_info[4][0] for host_addr_info in host_addr_list]
+
+    this_host = socket.gethostname()
+    if host == this_host:
+        return True
+
+    addr_list = socket.getaddrinfo(
+        this_host, None, proto=socket.IPPROTO_TCP, flags=socket.AI_CANONNAME
+    )
+    for addr_info in addr_list:
+        # If we have an FQDN in the addr_info, compare it to `host`.
+        if addr_info[3] and addr_info[3] == host:
+            return True
+
+        # Otherwise if `host` represents an IP address, compare it to our IP
+        # address.
+        if addr and addr_info[4][0] == str(addr):
+            return True
+
+        # If the IP address matches one of the provided host's IP addresses
+        if addr_info[4][0] in host_ip_list:
+            return True
+
+    return False
+
+
+def _delay(seconds: Union[float, tuple[float, float]]) -> None:
+    """Suspend the current thread for ``seconds``.
+
+    Args:
+        seconds:
+            Either the delay, in seconds, or a tuple of a lower and an upper
+            bound within which a random delay will be picked.
+    """
+    if isinstance(seconds, tuple):
+        seconds = random.uniform(*seconds)
+    # Ignore delay requests that are less than 10 milliseconds.
+    if seconds >= 0.01:
+        time.sleep(seconds)
+
+
+class _PeriodicTimer:
+    """Represent a timer that periodically runs a specified function.
+
+    Args:
+        interval:
+            The interval, in seconds, between each run.
+        function:
+            The function to run.
+    """
+
+    # The state of the timer is hold in a separate context object to avoid a
+    # reference cycle between the timer and the background thread.
+    class _Context:
+        interval: float
+        function: Callable[..., None]
+        args: tuple[Any, ...]
+        kwargs: dict[str, Any]
+        stop_event: Event
+
+    _name: Optional[str]
+    _thread: Optional[Thread]
+    _finalizer: Optional[weakref.finalize]
+
+    # The context that is shared between the timer and the background thread.
+    _ctx: _Context
+
+    def __init__(
+        self,
+        interval: timedelta,
+        function: Callable[..., None],
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        self._name = None
+
+        self._ctx = self._Context()
+        self._ctx.interval = interval.total_seconds()
+        self._ctx.function = function  # type: ignore[assignment]
+        self._ctx.args = args or ()
+        self._ctx.kwargs = kwargs or {}
+        self._ctx.stop_event = Event()
+
+        self._thread = None
+        self._finalizer = None
+
+    @property
+    def name(self) -> Optional[str]:
+        """Get the name of the timer."""
+        return self._name
+
+    def set_name(self, name: str) -> None:
+        """Set the name of the timer.
+
+        The specified name will be assigned to the background thread and serves
+        for debugging and troubleshooting purposes.
+        """
+        if self._thread:
+            raise RuntimeError("The timer has already started.")
+
+        self._name = name
+
+    def start(self) -> None:
+        """Start the timer."""
+        if self._thread:
+            raise RuntimeError("The timer has already started.")
+
+        self._thread = Thread(
+            target=self._run,
+            name=self._name or "PeriodicTimer",
+            args=(self._ctx,),
+            daemon=True,
+        )
+
+        # We avoid using a regular finalizer (a.k.a. __del__) for stopping the
+        # timer as joining a daemon thread during the interpreter shutdown can
+        # cause deadlocks. The weakref.finalize is a superior alternative that
+        # provides a consistent behavior regardless of the GC implementation.
+        self._finalizer = weakref.finalize(
+            self, self._stop_thread, self._thread, self._ctx.stop_event
+        )
+
+        # We do not attempt to stop our background thread during the interpreter
+        # shutdown. At that point we do not even know whether it still exists.
+        self._finalizer.atexit = False
+
+        self._thread.start()
+
+    def cancel(self) -> None:
+        """Stop the timer at the next opportunity."""
+        if self._finalizer:
+            self._finalizer()
+
+    @staticmethod
+    def _run(ctx) -> None:
+        while not ctx.stop_event.wait(ctx.interval):
+            ctx.function(*ctx.args, **ctx.kwargs)
+
+    @staticmethod
+    def _stop_thread(thread, stop_event):
+        stop_event.set()
+
+        thread.join()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9c2ea349cc67ff7175d5ef17ec63aecddbf52a7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/__init__.py
@@ -0,0 +1,54 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Expiration timers are set up on the same process as the agent and
+used from your script to deal with stuck workers. When you go into
+a code-block that has the potential to get stuck you can acquire
+an expiration timer, which instructs the timer server to kill the
+process if it does not release the timer by the self-imposed expiration
+deadline.
+
+Usage::
+
+    import torchelastic.timer as timer
+    import torchelastic.agent.server as agent
+
+    def main():
+        start_method = "spawn"
+        message_queue = mp.get_context(start_method).Queue()
+        server = timer.LocalTimerServer(message, max_interval=0.01)
+        server.start() # non-blocking
+
+        spec = WorkerSpec(
+                    fn=trainer_func,
+                    args=(message_queue,),
+                    ...)
+        agent = agent.LocalElasticAgent(spec, start_method)
+        agent.run()
+
+    def trainer_func(message_queue):
+        timer.configure(timer.LocalTimerClient(message_queue))
+        with timer.expires(after=60): # 60 second expiry
+            # do some work
+
+In the example above if ``trainer_func`` takes more than 60 seconds to
+complete, then the worker process is killed and the agent retries the worker group.
+"""
+
+from .api import (  # noqa: F401
+    configure,
+    expires,
+    TimerClient,
+    TimerRequest,
+    TimerServer,
+)
+from .file_based_local_timer import (  # noqa: F401
+    FileTimerClient,
+    FileTimerRequest,
+    FileTimerServer,
+)
+from .local_timer import LocalTimerClient, LocalTimerServer  # noqa: F401
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c856f078d89a1fbbd924aa9d388f4c93ad66ddb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/api.py
@@ -0,0 +1,283 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import abc
+import logging
+import threading
+import time
+from contextlib import contextmanager
+from inspect import getframeinfo, stack
+from typing import Any, Optional
+
+
+__all__ = [
+    "TimerRequest",
+    "TimerClient",
+    "RequestQueue",
+    "TimerServer",
+    "configure",
+    "expires",
+]
+
+logger = logging.getLogger(__name__)
+
+
+class TimerRequest:
+    """
+    Data object representing a countdown timer acquisition and release
+    that is used between the ``TimerClient`` and ``TimerServer``.
+    A negative ``expiration_time`` should be interpreted as a "release"
+    request.
+
+    .. note:: the type of ``worker_id`` is implementation specific.
+              It is whatever the TimerServer and TimerClient implementations
+              have on to uniquely identify a worker.
+    """
+
+    __slots__ = ["worker_id", "scope_id", "expiration_time"]
+
+    def __init__(self, worker_id: Any, scope_id: str, expiration_time: float):
+        self.worker_id = worker_id
+        self.scope_id = scope_id
+        self.expiration_time = expiration_time
+
+    def __eq__(self, other):
+        if isinstance(other, TimerRequest):
+            return (
+                self.worker_id == other.worker_id
+                and self.scope_id == other.scope_id
+                and self.expiration_time == other.expiration_time
+            )
+        return False
+
+
+class TimerClient(abc.ABC):
+    """
+    Client library to acquire and release countdown timers by communicating
+    with the TimerServer.
+    """
+
+    @abc.abstractmethod
+    def acquire(self, scope_id: str, expiration_time: float) -> None:
+        """
+        Acquires a timer for the worker that holds this client object
+        given the scope_id and expiration_time. Typically registers
+        the timer with the TimerServer.
+        """
+
+    @abc.abstractmethod
+    def release(self, scope_id: str):
+        """
+        Releases the timer for the ``scope_id`` on the worker this
+        client represents. After this method is
+        called, the countdown timer on the scope is no longer in effect.
+        """
+
+
+class RequestQueue(abc.ABC):
+    """
+    Consumer queue holding timer acquisition/release requests
+    """
+
+    @abc.abstractmethod
+    def size(self) -> int:
+        """
+        Returns the size of the queue at the time this method is called.
+        Note that by the time ``get`` is called the size of the queue
+        may have increased. The size of the queue should not decrease
+        until the ``get`` method is called. That is, the following assertion
+        should hold:
+
+        size = q.size()
+        res = q.get(size, timeout=0)
+        assert size == len(res)
+
+        -- or --
+
+        size = q.size()
+        res = q.get(size * 2, timeout=1)
+        assert size <= len(res) <= size * 2
+        """
+
+    @abc.abstractmethod
+    def get(self, size: int, timeout: float) -> list[TimerRequest]:
+        """
+        Gets up to ``size`` number of timer requests in a blocking fashion
+        (no more than ``timeout`` seconds).
+        """
+
+
+class TimerServer(abc.ABC):
+    """
+    Entity that monitors active timers and expires them
+    in a timely fashion. This server is responsible for
+    reaping workers that have expired timers.
+    """
+
+    def __init__(
+        self, request_queue: RequestQueue, max_interval: float, daemon: bool = True
+    ):
+        """
+        :param request_queue: Consumer ``RequestQueue``
+        :param max_interval: max time (in seconds) to wait
+                             for an item in the request_queue
+        :param daemon: whether to run the watchdog thread as a daemon
+        """
+        super().__init__()
+        self._request_queue = request_queue
+        self._max_interval = max_interval
+        self._daemon = daemon
+        self._watchdog_thread: Optional[threading.Thread] = None
+        self._stop_signaled = False
+
+    @abc.abstractmethod
+    def register_timers(self, timer_requests: list[TimerRequest]) -> None:
+        """
+        Processes the incoming timer requests and registers them with the server.
+        The timer request can either be a acquire-timer or release-timer request.
+        Timer requests with a negative expiration_time should be interpreted
+        as a release-timer request.
+        """
+
+    @abc.abstractmethod
+    def clear_timers(self, worker_ids: set[Any]) -> None:
+        """
+        Clears all timers for the given ``worker_ids``.
+        """
+
+    @abc.abstractmethod
+    def get_expired_timers(self, deadline: float) -> dict[str, list[TimerRequest]]:
+        """
+        Returns all expired timers for each worker_id. An expired timer
+        is a timer for which the expiration_time is less than or equal to
+        the provided deadline.
+        """
+
+    @abc.abstractmethod
+    def _reap_worker(self, worker_id: Any) -> bool:
+        """
+        Reaps the given worker. Returns True if the worker has been
+        successfully reaped, False otherwise. If any uncaught exception
+        is thrown from this method, the worker is considered reaped
+        and all associated timers will be removed.
+        """
+
+    def _reap_worker_no_throw(self, worker_id: Any) -> bool:
+        """
+        Wraps ``_reap_worker(worker_id)``, if an uncaught exception is
+        thrown, then it considers the worker as reaped.
+        """
+        try:
+            return self._reap_worker(worker_id)
+        except Exception:
+            logger.exception(
+                "Uncaught exception thrown from _reap_worker(), "
+                "check that the implementation correctly catches exceptions",
+            )
+            return True
+
+    def _watchdog_loop(self):
+        while not self._stop_signaled:
+            try:
+                self._run_watchdog()
+            except Exception:
+                logger.exception("Error running watchdog")
+
+    def _run_watchdog(self):
+        batch_size = max(1, self._request_queue.size())
+        timer_requests = self._request_queue.get(batch_size, self._max_interval)
+        self.register_timers(timer_requests)
+        now = time.time()
+        reaped_worker_ids = set()
+        for worker_id, expired_timers in self.get_expired_timers(now).items():
+            logger.info(
+                "Reaping worker_id=[%s]. Expired timers: %s",
+                worker_id,
+                self._get_scopes(expired_timers),
+            )
+            if self._reap_worker_no_throw(worker_id):
+                logger.info("Successfully reaped worker=[%s]", worker_id)
+                reaped_worker_ids.add(worker_id)
+            else:
+                logger.error(
+                    "Error reaping worker=[%s]. Will retry on next watchdog.", worker_id
+                )
+        self.clear_timers(reaped_worker_ids)
+
+    def _get_scopes(self, timer_requests):
+        return [r.scope_id for r in timer_requests]
+
+    def start(self) -> None:
+        logger.info(
+            "Starting %s... max_interval=%s, daemon=%s",
+            type(self).__name__,
+            self._max_interval,
+            self._daemon,
+        )
+        self._watchdog_thread = threading.Thread(
+            target=self._watchdog_loop, daemon=self._daemon
+        )
+        logger.info("Starting watchdog thread...")
+        self._watchdog_thread.start()
+
+    def stop(self) -> None:
+        logger.info("Stopping %s", type(self).__name__)
+        self._stop_signaled = True
+        if self._watchdog_thread:
+            logger.info("Stopping watchdog thread...")
+            self._watchdog_thread.join(self._max_interval)
+            self._watchdog_thread = None
+        else:
+            logger.info("No watchdog thread running, doing nothing")
+
+
+_timer_client: Optional[TimerClient] = None
+
+
+def configure(timer_client: TimerClient):
+    """
+    Configures a timer client. Must be called before using ``expires``.
+    """
+    global _timer_client
+    _timer_client = timer_client
+    logger.info("Timer client configured to: %s", type(_timer_client).__name__)
+
+
+@contextmanager
+def expires(
+    after: float, scope: Optional[str] = None, client: Optional[TimerClient] = None
+):
+    """
+    Acquires a countdown timer that expires in ``after`` seconds from now,
+    unless the code-block that it wraps is finished within the timeframe.
+    When the timer expires, this worker is eligible to be reaped. The
+    exact meaning of "reaped" depends on the client implementation. In
+    most cases, reaping means to terminate the worker process.
+    Note that the worker is NOT guaranteed to be reaped at exactly
+    ``time.now() + after``, but rather the worker is "eligible" for being
+    reaped and the ``TimerServer`` that the client talks to will ultimately
+    make the decision when and how to reap the workers with expired timers.
+
+    Usage::
+
+        torch.distributed.elastic.timer.configure(LocalTimerClient())
+        with expires(after=10):
+            torch.distributed.all_reduce(...)
+    """
+    if client is None:
+        if _timer_client is None:
+            raise RuntimeError("Configure timer client before using countdown timers.")
+        client = _timer_client
+    if scope is None:
+        # grab the caller file + lineno
+        caller = getframeinfo(stack()[1][0])
+        scope = f"{caller.filename}#{caller.lineno}"
+    expiration = time.time() + after
+    client.acquire(scope, expiration)
+    try:
+        yield
+    finally:
+        client.release(scope)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/debug_info_logging.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/debug_info_logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..e385d91283a7b610f00397bfa4bc4800a89761ca
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/debug_info_logging.py
@@ -0,0 +1,24 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+logger = get_logger(__name__)
+
+__all__ = ["log_debug_info_for_expired_timers"]
+
+
+def log_debug_info_for_expired_timers(
+    run_id: str,
+    expired_timers: dict[int, list[str]],
+):
+    if expired_timers:
+        logger.info("Timers expired for run:[%s] [%s].", run_id, expired_timers)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/file_based_local_timer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/file_based_local_timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1c91b63d998e921681779d4e5dcd3cc2050764f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/file_based_local_timer.py
@@ -0,0 +1,438 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import io
+import json
+import os
+import select
+import signal
+import sys
+import threading
+import time
+from typing import Callable, Optional
+
+from torch.distributed.elastic.timer.api import TimerClient, TimerRequest
+from torch.distributed.elastic.timer.debug_info_logging import (
+    log_debug_info_for_expired_timers,
+)
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+__all__ = ["FileTimerClient", "FileTimerRequest", "FileTimerServer"]
+
+logger = get_logger(__name__)
+
+
+def _retry(max_retries: int, sleep_time: float) -> Callable:
+    """
+    A simple retry wrapper.
+
+    Args:
+        max_retries: int, the maximum number of retries.
+        sleep_time: float, the time to sleep between retries.
+    """
+
+    def wrapper(func: Callable) -> Callable:
+        def wrapper(*args, **kwargs):
+            for i in range(max_retries):
+                try:
+                    return func(*args, **kwargs)
+                except Exception:
+                    logger.exception("Error running %s. Retrying...", func.__name__)
+                    if i < max_retries - 1:
+                        time.sleep(sleep_time)
+                    else:
+                        raise
+
+        return wrapper
+
+    return wrapper
+
+
+class FileTimerRequest(TimerRequest):
+    """
+    Data object representing a countdown timer acquisition and release
+    that is used between the ``FileTimerClient`` and ``FileTimerServer``.
+    A negative ``expiration_time`` should be interpreted as a "release"
+    request.
+    ``signal`` is the signal to reap the worker process from the server
+    process.
+    """
+
+    __slots__ = ["version", "worker_pid", "scope_id", "expiration_time", "signal"]
+
+    def __init__(
+        self, worker_pid: int, scope_id: str, expiration_time: float, signal: int = 0
+    ) -> None:
+        self.version = 1
+        self.worker_pid = worker_pid
+        self.scope_id = scope_id
+        self.expiration_time = expiration_time
+        self.signal = signal
+
+    def __eq__(self, other) -> bool:
+        if isinstance(other, FileTimerRequest):
+            return (
+                self.version == other.version
+                and self.worker_pid == other.worker_pid
+                and self.scope_id == other.scope_id
+                and self.expiration_time == other.expiration_time
+                and self.signal == other.signal
+            )
+        return False
+
+    def to_json(self) -> str:
+        return json.dumps(
+            {
+                "version": self.version,
+                "pid": self.worker_pid,
+                "scope_id": self.scope_id,
+                "expiration_time": self.expiration_time,
+                "signal": self.signal,
+            },
+        )
+
+
+class FileTimerClient(TimerClient):
+    """
+    Client side of ``FileTimerServer``. This client is meant to be used
+    on the same host that the ``FileTimerServer`` is running on and uses
+    pid to uniquely identify a worker.
+    This client uses a named_pipe to send timer requests to the
+    ``FileTimerServer``. This client is a producer while the
+    ``FileTimerServer`` is a consumer. Multiple clients can work with
+    the same ``FileTimerServer``.
+
+    Args:
+
+        file_path: str, the path of a FIFO special file. ``FileTimerServer``
+                        must have created it by calling os.mkfifo().
+
+        signal: signal, the signal to use to kill the process. Using a
+                        negative or zero signal will not kill the process.
+    """
+
+    def __init__(
+        self,
+        file_path: str,
+        signal=(signal.SIGKILL if sys.platform != "win32" else signal.CTRL_C_EVENT),  # type: ignore[attr-defined]
+    ) -> None:
+        super().__init__()
+        self._file_path = file_path
+        self.signal = signal
+
+    @_retry(max_retries=10, sleep_time=0.1)
+    def _open_non_blocking(self) -> Optional[io.TextIOWrapper]:
+        # The server may have crashed or may haven't started yet.
+        # In such case, calling open() in blocking model blocks the client.
+        # To avoid such issue, open it in non-blocking mode, and an OSError will
+        # be raised if the server is not there.
+        fd = os.open(self._file_path, os.O_WRONLY | os.O_NONBLOCK)
+        return os.fdopen(fd, "wt")
+
+    def _send_request(self, request: FileTimerRequest) -> None:
+        try:
+            file = self._open_non_blocking()
+        except Exception as e:
+            raise BrokenPipeError(
+                "Could not send the FileTimerRequest because FileTimerServer is not available."
+            ) from e
+        with file:
+            json_request = request.to_json()
+            # Write request with no greater than select.PIPE_BUF is guarantee to be atomic.
+            if len(json_request) > select.PIPE_BUF:
+                raise RuntimeError(
+                    f"FileTimerRequest larger than {select.PIPE_BUF} bytes "
+                    f"is not supported: {json_request}"
+                )
+            file.write(json_request + "\n")
+
+    def acquire(self, scope_id: str, expiration_time: float) -> None:
+        self._send_request(
+            request=FileTimerRequest(
+                worker_pid=os.getpid(),
+                scope_id=scope_id,
+                expiration_time=expiration_time,
+                signal=self.signal,
+            ),
+        )
+
+    def release(self, scope_id: str) -> None:
+        self._send_request(
+            request=FileTimerRequest(
+                worker_pid=os.getpid(), scope_id=scope_id, expiration_time=-1, signal=0
+            ),
+        )
+
+
+class FileTimerServer:
+    """
+    Server that works with ``FileTimerClient``. Clients are expected to be
+    running on the same host as the process that is running this server.
+    Each host in the job is expected to start its own timer server locally
+    and each server instance manages timers for local workers (running on
+    processes on the same host).
+
+    Args:
+
+        file_path: str, the path of a FIFO special file to be created.
+
+        max_interval: float, max interval in seconds for each watchdog loop.
+
+        daemon: bool, running the watchdog thread in daemon mode or not.
+                      A daemon thread will not block a process to stop.
+        log_event: Callable[[Dict[str, str]], None], an optional callback for
+                logging the events in JSON format.
+    """
+
+    def __init__(
+        self,
+        file_path: str,
+        run_id: str,
+        max_interval: float = 10,
+        daemon: bool = True,
+        log_event: Optional[Callable[[str, Optional[FileTimerRequest]], None]] = None,
+    ) -> None:
+        self._file_path = file_path
+        self._run_id = run_id
+        self._max_interval = max_interval
+        self._daemon = daemon
+        self._timers: dict[tuple[int, str], FileTimerRequest] = {}
+        self._stop_signaled = False
+        self._watchdog_thread: Optional[threading.Thread] = None
+
+        self._is_client_started = False
+        if os.path.exists(self._file_path):
+            os.remove(self._file_path)
+        os.mkfifo(self._file_path)
+        # For test only. Count the number of requests received.
+        self._request_count = 0
+        # For test only. Process all requests and stop the server.
+        self._run_once = False
+        self._log_event = (
+            log_event if log_event is not None else lambda name, request: None
+        )
+        self._last_progress_time = int(time.time())
+
+    def start(self) -> None:
+        logger.info(
+            "Starting %s... max_interval=%s, daemon=%s, file_path=%s",
+            type(self).__name__,
+            self._max_interval,
+            self._daemon,
+            self._file_path,
+        )
+        self._watchdog_thread = threading.Thread(
+            target=self._watchdog_loop, daemon=self._daemon
+        )
+        logger.info("Starting watchdog thread...")
+        self._watchdog_thread.start()
+        self._log_event("watchdog started", None)
+
+    def stop(self) -> None:
+        logger.info("Stopping %s", type(self).__name__)
+        self._stop_signaled = True
+        if self._watchdog_thread:
+            logger.info("Stopping watchdog thread...")
+            self._watchdog_thread.join(self._max_interval)
+            self._watchdog_thread = None
+        else:
+            logger.info("No watchdog thread running, doing nothing")
+        if os.path.exists(self._file_path):
+            os.remove(self._file_path)
+        self._log_event("watchdog stopped", None)
+
+    def run_once(self) -> None:
+        self._run_once = True
+        if self._watchdog_thread:
+            logger.info("Stopping watchdog thread...")
+            self._watchdog_thread.join()
+            self._watchdog_thread = None
+        else:
+            logger.info("No watchdog thread running, doing nothing")
+        if os.path.exists(self._file_path):
+            os.remove(self._file_path)
+
+    @staticmethod
+    def is_process_running(pid: int):
+        """
+        function to check process is running or not
+        """
+        try:
+            # Check if the process exists and we can send signals to it
+            os.kill(pid, 0)
+            return True
+        except OSError:
+            return False
+
+    def _watchdog_loop(self) -> None:
+        # Open the pipe in blocking mode blocks the server thread.
+        # This is fine for the following reasons:
+        #  1. No client case usually does not happen.
+        #  2. We are running the watchdog loop in a separate daemon
+        #     thread, which will not block the process to stop.
+        try:
+            fd = open(self._file_path)
+        except Exception:
+            logger.exception("Could not open the FileTimerServer pipe")
+            raise
+
+        with fd:
+            self._is_client_started = True
+            while not self._stop_signaled:
+                try:
+                    run_once = self._run_once
+                    self._run_watchdog(fd)
+                    if run_once:
+                        break
+                    self._last_progress_time = int(time.time())
+                except Exception:
+                    logger.exception("Error running watchdog")
+
+    def _run_watchdog(self, fd: io.TextIOWrapper) -> None:
+        timer_requests = self._get_requests(fd, self._max_interval)
+        self.register_timers(timer_requests)
+        now = time.time()
+        reaped_worker_pids = set()
+        kill_process = False
+        reap_signal = 0
+
+        all_expired_timers = self.get_expired_timers(now)
+        log_debug_info_for_expired_timers(
+            self._run_id,
+            {
+                pid: [expired_timer.to_json() for expired_timer in expired_timers]
+                for pid, expired_timers in all_expired_timers.items()
+            },
+        )
+
+        for worker_pid, expired_timers in all_expired_timers.items():
+            logger.info(
+                "Reaping worker_pid=[%s]. Expired timers: %s",
+                worker_pid,
+                self._get_scopes(expired_timers),
+            )
+            reaped_worker_pids.add(worker_pid)
+            # In case we have multiple expired timers, we find the first timer
+            # with a valid signal (>0) in the expiration time order.
+            expired_timers.sort(key=lambda timer: timer.expiration_time)
+            signal = 0
+            expired_timer = None
+            for timer in expired_timers:
+                self._log_event("timer expired", timer)
+                if timer.signal > 0:
+                    signal = timer.signal
+                    expired_timer = timer
+                    break
+            if signal <= 0:
+                logger.info(
+                    "No signal specified with worker=[%s]. Do not reap it.", worker_pid
+                )
+                continue
+            if self._reap_worker(worker_pid, signal):
+                logger.info(
+                    "Successfully reaped worker=[%s] with signal=%s", worker_pid, signal
+                )
+                self._log_event("kill worker process", expired_timer)
+                kill_process = True
+                reap_signal = signal
+            else:
+                logger.error(
+                    "Error reaping worker=[%s]. Will retry on next watchdog.",
+                    worker_pid,
+                )
+        if kill_process and reap_signal > 0:
+            logger.info(
+                "Terminating the server process=[%s] because of expired timers",
+                os.getpid(),
+            )
+            self._reap_worker(os.getpid(), reap_signal)
+
+        self.clear_timers(reaped_worker_pids)
+
+    def _get_scopes(self, timer_requests: list[FileTimerRequest]) -> list[str]:
+        return [r.scope_id for r in timer_requests]
+
+    def _get_requests(
+        self, fd: io.TextIOWrapper, max_interval: float
+    ) -> list[FileTimerRequest]:
+        start = time.time()
+        requests = []
+        while not self._stop_signaled or self._run_once:
+            # For named pipe, readline() is blocking when at least one writer opens.
+            # It returns only when flush() is called at the writer side.
+            # Note that flush() is automatically called inside close().
+            # After the last writer closes, readline() is not blocking.
+            # It will return an empty string when it's at end-of-file.
+            # Since the client side always opens the pipe, writes a message and closes
+            # the pipe immediately, the readline() call below is not blocking for long.
+            json_request = fd.readline()
+            if len(json_request) == 0:
+                if self._run_once:
+                    break
+                time.sleep(min(max_interval, 1))
+            else:
+                request = json.loads(json_request)
+                pid = request["pid"]
+                scope_id = request["scope_id"]
+                expiration_time = request["expiration_time"]
+                signal = request["signal"]
+                requests.append(
+                    FileTimerRequest(
+                        worker_pid=pid,
+                        scope_id=scope_id,
+                        expiration_time=expiration_time,
+                        signal=signal,
+                    )
+                )
+            now = time.time()
+            if now - start > max_interval:
+                break
+        return requests
+
+    def register_timers(self, timer_requests: list[FileTimerRequest]) -> None:
+        for request in timer_requests:
+            pid = request.worker_pid
+            scope_id = request.scope_id
+            expiration_time = request.expiration_time
+            self._request_count += 1
+
+            key = (pid, scope_id)
+            # negative expiration is a proxy for a release call
+            if expiration_time < 0:
+                if key in self._timers:
+                    del self._timers[key]
+            else:
+                self._timers[key] = request
+
+    def clear_timers(self, worker_pids: set[int]) -> None:
+        for pid, scope_id in list(self._timers.keys()):
+            if pid in worker_pids or not FileTimerServer.is_process_running(pid):
+                del self._timers[(pid, scope_id)]
+
+    def get_expired_timers(self, deadline: float) -> dict[int, list[FileTimerRequest]]:
+        # pid -> [timer_requests...]
+        expired_timers: dict[int, list[FileTimerRequest]] = {}
+        for request in self._timers.values():
+            if request.expiration_time <= deadline:
+                expired_scopes = expired_timers.setdefault(request.worker_pid, [])
+                expired_scopes.append(request)
+        return expired_timers
+
+    def _reap_worker(self, worker_pid: int, signal: int) -> bool:
+        try:
+            os.kill(worker_pid, signal)
+            return True
+        except ProcessLookupError:
+            logger.info("Process with pid=%s does not exist. Skipping", worker_pid)
+            return True
+        except Exception:
+            logger.exception("Error terminating pid=%s", worker_pid)
+        return False
+
+    def get_last_progress_time(self) -> int:
+        return self._last_progress_time if self._is_client_started else int(time.time())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/local_timer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/local_timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d55cc6ac6e370650a885f3aea020468519566f77
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/timer/local_timer.py
@@ -0,0 +1,128 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import logging
+import multiprocessing as mp
+import os
+import signal
+import time
+from queue import Empty
+from typing import Any
+
+from .api import RequestQueue, TimerClient, TimerRequest, TimerServer
+
+
+__all__ = ["LocalTimerClient", "MultiprocessingRequestQueue", "LocalTimerServer"]
+
+logger = logging.getLogger(__name__)
+
+
+class LocalTimerClient(TimerClient):
+    """
+    Client side of ``LocalTimerServer``. This client is meant to be used
+    on the same host that the ``LocalTimerServer`` is running on and uses
+    pid to uniquely identify a worker. This is particularly useful in situations
+    where one spawns a subprocess (trainer) per GPU on a host with multiple
+    GPU devices.
+    """
+
+    def __init__(self, mp_queue):
+        super().__init__()
+        self._mp_queue = mp_queue
+
+    def acquire(self, scope_id, expiration_time):
+        pid = os.getpid()
+        acquire_request = TimerRequest(pid, scope_id, expiration_time)
+        self._mp_queue.put(acquire_request)
+
+    def release(self, scope_id):
+        pid = os.getpid()
+        release_request = TimerRequest(pid, scope_id, -1)
+        self._mp_queue.put(release_request)
+
+
+class MultiprocessingRequestQueue(RequestQueue):
+    """
+    A ``RequestQueue`` backed by python ``multiprocessing.Queue``
+    """
+
+    def __init__(self, mp_queue: mp.Queue):
+        super().__init__()
+        self._mp_queue = mp_queue
+
+    def size(self) -> int:
+        return self._mp_queue.qsize()
+
+    def get(self, size, timeout: float) -> list[TimerRequest]:
+        requests = []
+        wait = timeout
+        for _ in range(0, size):
+            start = time.time()
+
+            try:
+                r = self._mp_queue.get(block=True, timeout=wait)
+            except Empty:
+                break
+
+            requests.append(r)
+            wait = wait - (time.time() - start)
+            if wait <= 0:
+                break
+
+        return requests
+
+
+class LocalTimerServer(TimerServer):
+    """
+    Server that works with ``LocalTimerClient``. Clients are expected to be
+    subprocesses to the parent process that is running this server. Each host
+    in the job is expected to start its own timer server locally and each
+    server instance manages timers for local workers (running on processes
+    on the same host).
+    """
+
+    def __init__(
+        self, mp_queue: mp.Queue, max_interval: float = 60, daemon: bool = True
+    ):
+        super().__init__(MultiprocessingRequestQueue(mp_queue), max_interval, daemon)
+        self._timers: dict[tuple[Any, str], TimerRequest] = {}
+
+    def register_timers(self, timer_requests: list[TimerRequest]) -> None:
+        for request in timer_requests:
+            pid = request.worker_id
+            scope_id = request.scope_id
+            expiration_time = request.expiration_time
+
+            # negative expiration is a proxy for a release call
+            if expiration_time < 0:
+                self._timers.pop((pid, scope_id), None)
+            else:
+                self._timers[(pid, scope_id)] = request
+
+    def clear_timers(self, worker_ids: set[int]) -> None:
+        for pid, scope_id in list(self._timers.keys()):
+            if pid in worker_ids:
+                self._timers.pop((pid, scope_id))
+
+    def get_expired_timers(self, deadline: float) -> dict[Any, list[TimerRequest]]:
+        # pid -> [timer_requests...]
+        expired_timers: dict[Any, list[TimerRequest]] = {}
+        for request in self._timers.values():
+            if request.expiration_time <= deadline:
+                expired_scopes = expired_timers.setdefault(request.worker_id, [])
+                expired_scopes.append(request)
+        return expired_timers
+
+    def _reap_worker(self, worker_id: int) -> bool:
+        try:
+            os.kill(worker_id, signal.SIGKILL)
+            return True
+        except ProcessLookupError:
+            logger.info("Process with pid=%s does not exist. Skipping", worker_id)
+            return True
+        except Exception:
+            logger.exception("Error terminating pid=%s", worker_id)
+        return False
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce2bbf5bbe2348bb0eaa411a034710dd14f7648e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/__init__.py
@@ -0,0 +1,9 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .api import get_env_variable_or_raise, get_socket_with_port, macros  # noqa: F401
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b881137047c23789a061a719437a43b1743959f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/api.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import socket
+from string import Template
+from typing import Any
+
+
+def get_env_variable_or_raise(env_name: str) -> str:
+    r"""
+    Tries to retrieve environment variable. Raises ``ValueError``
+    if no environment variable found.
+
+    Args:
+        env_name (str): Name of the env variable
+    """
+    value = os.environ.get(env_name, None)
+    if value is None:
+        msg = f"Environment variable {env_name} expected, but not set"
+        raise ValueError(msg)
+    return value
+
+
+def get_socket_with_port() -> socket.socket:
+    addrs = socket.getaddrinfo(
+        host="localhost", port=None, family=socket.AF_UNSPEC, type=socket.SOCK_STREAM
+    )
+    for addr in addrs:
+        family, type, proto, _, _ = addr
+        s = socket.socket(family, type, proto)
+        try:
+            s.bind(("localhost", 0))
+            s.listen(0)
+            return s
+        except OSError:
+            s.close()
+    raise RuntimeError("Failed to create a socket")
+
+
+class macros:
+    """
+    Defines simple macros for caffe2.distributed.launch cmd args substitution
+    """
+
+    local_rank = "${local_rank}"
+
+    @staticmethod
+    def substitute(args: list[Any], local_rank: str) -> list[str]:
+        args_sub = []
+        for arg in args:
+            if isinstance(arg, str):
+                sub = Template(arg).safe_substitute(local_rank=local_rank)
+                args_sub.append(sub)
+            else:
+                args_sub.append(arg)
+        return args_sub
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c39bca6f3c8a31f5f2d7115ad12c1fc4925fe1d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/__init__.py
@@ -0,0 +1,10 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .cycling_iterator import CyclingIterator  # noqa: F401
+from .elastic_distributed_sampler import ElasticDistributedSampler  # noqa: F401
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/cycling_iterator.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/cycling_iterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d3b79f18dfe4ac614b4ac764562be7c34b93f6b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/cycling_iterator.py
@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+
+from collections.abc import Iterator
+from typing import Callable, TypeVar
+from typing_extensions import Self
+
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+_T = TypeVar("_T")
+
+__all__ = ["CyclingIterator"]
+
+
+class CyclingIterator(Iterator[_T]):
+    """
+    An iterator decorator that cycles through the
+    underlying iterator "n" times. Useful to "unroll"
+    the dataset across multiple training epochs.
+
+    The generator function is called as ``generator_fn(epoch)``
+    to obtain the underlying iterator, where ``epoch`` is a
+    number less than or equal to ``n`` representing the ``k``th cycle
+
+    For example if ``generator_fn`` always returns ``[1,2,3]``
+    then ``CyclingIterator(n=2, generator_fn)`` will iterate through
+    ``[1,2,3,1,2,3]``
+    """
+
+    def __init__(
+        self,
+        n: int,
+        generator_fn: Callable[[int], Iterator[_T]],
+        start_epoch: int = 0,
+    ):
+        self._n = n
+        self._epoch = start_epoch
+        self._generator_fn = generator_fn
+        self._iter = generator_fn(self._epoch)
+
+    def __iter__(self) -> Self:
+        return self
+
+    def __next__(self) -> _T:
+        try:
+            return next(self._iter)
+        except StopIteration as eod:  # eod == end of data
+            if self._epoch < self._n - 1:
+                self._epoch += 1
+                self._iter = self._generator_fn(self._epoch)
+                return self.__next__()
+            else:
+                raise eod
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/elastic_distributed_sampler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/elastic_distributed_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e378c6a1be1a435f6fa8afacf554f50a926d089
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/elastic_distributed_sampler.py
@@ -0,0 +1,71 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch
+from torch.utils.data.distributed import DistributedSampler
+
+
+class ElasticDistributedSampler(DistributedSampler):
+    """
+    Sampler that restricts data loading to a subset of
+    the dataset for elastic training.
+
+    It is especially useful in conjunction with
+    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
+    process can pass a DistributedSampler instance as a DataLoader sampler,
+    and load a subset of the original dataset that is exclusive to it.
+
+    .. note::
+        Dataset is assumed to be of constant size.
+
+    Args:
+        dataset: Dataset used for sampling.
+        num_replicas (optional): Number of processes participating in
+            distributed training.
+        rank (optional): Rank of the current process within num_replicas.
+        start_index (optional):  Which index of the dataset to start sampling from
+    """
+
+    def __init__(self, dataset, num_replicas=None, rank=None, start_index=0):
+        super().__init__(dataset=dataset, num_replicas=num_replicas, rank=rank)
+        if start_index >= len(dataset):
+            raise ValueError(
+                f"Start index {start_index} should be less than dataset size {len(dataset)}"
+            )
+
+        self.start_index = start_index
+        self.num_samples = int(
+            math.ceil(float(len(self.dataset) - self.start_index) / self.num_replicas)  # type: ignore[arg-type]
+        )
+        self.total_size = self.num_samples * self.num_replicas
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+        indices = (
+            torch.randperm(len(self.dataset) - self.start_index, generator=g)  # type: ignore[arg-type]
+            .add(self.start_index)
+            .tolist()
+        )
+
+        # add extra samples to make it evenly divisible
+        indices += indices[: (self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.rank : self.total_size : self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/distributed.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..34a8cd8a22bb5d76b86450267dcf4eeb6d7b861a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/distributed.py
@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import datetime
+import os
+import socket
+from contextlib import closing
+from typing import Optional
+
+import torch.distributed as dist
+from torch.distributed.elastic.utils.logging import get_logger
+from torch.distributed.elastic.utils.store import barrier
+
+
+__all__ = ["create_c10d_store", "get_free_port", "get_socket_with_port"]
+
+logger = get_logger(__name__)
+
+_ADDRESS_IN_USE = "Address already in use"
+_SOCKET_TIMEOUT = "Socket Timeout"
+
+_TCP_STORE_INIT = "_tcp_store/num_members"
+
+
+def create_c10d_store(
+    is_server: bool,
+    server_addr: str,
+    server_port: int = -1,
+    world_size: int = 1,
+    timeout: float = (60 * 10),  # 10 min
+    wait_for_workers: bool = True,
+    retries=3,
+    use_libuv: Optional[bool] = None,
+):
+    if use_libuv is not None:
+        logger.warning(
+            "argument use_libuv is deprecated and ignored. Set USE_LIBUV environment "
+            'variable to "0" to disable libuv, or "1" to enable it. If the env var '
+            "is not set, libuv will be used by default."
+        )
+
+    # check os.environ for use_libuv
+    use_libuv = os.environ.get("USE_LIBUV", "1") == "1"  # libuv is the default option
+
+    if server_port == -1 and world_size > 1:
+        raise ValueError(
+            f"server_port must be specified when world_size > 1, got server_port={server_port}, world_size={world_size}"
+        )
+
+    if server_port != -1:
+        logger.info("sever_port: %s, specified, ignoring retries", server_port)
+
+    # only retry when server_port is NOT static
+    attempt = retries if server_port == -1 else 1
+    while True:
+        if server_port != -1:
+            port = server_port
+        else:
+            port = get_free_port()
+
+        logger.info(
+            "Creating c10d store on %s:%s\n"
+            "  world_size  : %s\n"
+            "  is_server   : %s\n"
+            "  timeout(sec): %s\n"
+            "  use_libuv   : %s\n",
+            server_addr,
+            port,
+            world_size,
+            is_server,
+            timeout,
+            use_libuv,
+        )
+
+        try:
+            store = dist.TCPStore(
+                host_name=server_addr,
+                port=port,
+                world_size=world_size,
+                is_master=is_server,
+                timeout=datetime.timedelta(seconds=timeout),
+                wait_for_workers=wait_for_workers,
+                use_libuv=use_libuv,
+            )
+            # skips full rank check when we don't have to wait for all workers
+            if wait_for_workers:
+                _check_full_rank(store, world_size, timeout=timeout)
+            logger.info("Successfully created c10d store")
+            return store
+        except RuntimeError as e:
+            # this is brittle, but the underlying exception type is not properly pybinded
+            # so we parse the error msg for now, interestingly this is how torch itself
+            # detects timeouts and port conflicts in their own unittests
+            # see - caffe2/torch/testing/_internal/common_utils.py
+            # TODO properly map the exceptions in pybind (c10d/init.cpp)
+            if str(e) == _ADDRESS_IN_USE:  # this will only happen on the server
+                if attempt < retries:
+                    logger.warning(
+                        "port: %s already in use, attempt: [%s/%s]",
+                        port,
+                        attempt,
+                        retries,
+                    )
+                    attempt += 1
+                else:
+                    raise RuntimeError(
+                        f"on {server_addr}, port: {port} already in use"
+                    ) from e
+            else:
+                raise
+
+
+def _check_full_rank(store, world_size, timeout):
+    try:
+        barrier(store, world_size, key_prefix=_TCP_STORE_INIT, barrier_timeout=timeout)
+    except RuntimeError as e:
+        if str(e) == _SOCKET_TIMEOUT:
+            raise TimeoutError(
+                f"timed out waiting for all {world_size} members to join"
+            ) from e
+        else:
+            raise
+
+
+def get_free_port():
+    """
+    Returns an unused port on localhost.
+
+    This function finds an unused port on localhost by opening to socket to bind
+    to a port and then closing it.
+
+    Returns:
+        int: an unused port on localhost
+
+    Example:
+        >>> # xdoctest: +SKIP("Nondeterministic")
+        >>> get_free_port()
+        63976
+
+    .. note::
+        The port returned by :func:`get_free_port` is not reserved and may be
+        taken by another process after this function returns.
+    """
+    sock = get_socket_with_port()
+    with closing(sock):
+        return sock.getsockname()[1]
+
+
+def get_socket_with_port() -> socket.socket:
+    """
+    Returns a free port on localhost that is "reserved" by binding a temporary
+    socket on it. Close the socket before passing the port to the entity
+    that requires it. Usage example
+
+    ::
+
+    sock = _get_socket_with_port()
+    with closing(sock):
+        port = sock.getsockname()[1]
+        sock.close()
+        # there is still a race-condition that some other process
+        # may grab this port before func() runs
+        func(port)
+    """
+
+    addrs = socket.getaddrinfo(
+        host="localhost", port=None, family=socket.AF_UNSPEC, type=socket.SOCK_STREAM
+    )
+    for addr in addrs:
+        family, type, proto, _, _ = addr
+        s = socket.socket(family, type, proto)
+        try:
+            s.bind(("localhost", 0))
+            s.listen(0)
+            return s
+        except OSError as e:
+            s.close()
+            logger.warning("Socket creation attempt failed.", exc_info=e)
+    raise RuntimeError("Failed to create a socket")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/log_level.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/log_level.py
new file mode 100644
index 0000000000000000000000000000000000000000..87ea0f7d64182488b40fd7fed6965ce57ec475a0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/log_level.py
@@ -0,0 +1,14 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+def get_log_level() -> str:
+    """
+    Return default log level for pytorch.
+    """
+    return "WARNING"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/logging.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..d87504d255d6f79aaf05606776e20db690cf23da
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/logging.py
@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import inspect
+import logging
+import os
+import warnings
+from typing import Optional
+
+from torch.distributed.elastic.utils.log_level import get_log_level
+
+
+def get_logger(name: Optional[str] = None):
+    """
+    Util function to set up a simple logger that writes
+    into stderr. The loglevel is fetched from the LOGLEVEL
+    env. variable or WARNING as default. The function will use the
+    module name of the caller if no name is provided.
+
+    Args:
+        name: Name of the logger. If no name provided, the name will
+              be derived from the call stack.
+    """
+
+    # Derive the name of the caller, if none provided
+    # Use depth=2 since this function takes up one level in the call stack
+    return _setup_logger(name or _derive_module_name(depth=2))
+
+
+def _setup_logger(name: Optional[str] = None):
+    logger = logging.getLogger(name)
+    logger.setLevel(os.environ.get("LOGLEVEL", get_log_level()))
+    return logger
+
+
+def _derive_module_name(depth: int = 1) -> Optional[str]:
+    """
+    Derives the name of the caller module from the stack frames.
+
+    Args:
+        depth: The position of the frame in the stack.
+    """
+    try:
+        stack = inspect.stack()
+        assert depth < len(stack)
+        # FrameInfo is just a named tuple: (frame, filename, lineno, function, code_context, index)
+        frame_info = stack[depth]
+
+        module = inspect.getmodule(frame_info[0])
+        if module:
+            module_name = module.__name__
+        else:
+            # inspect.getmodule(frame_info[0]) does NOT work (returns None) in
+            # binaries built with @mode/opt
+            # return the filename (minus the .py extension) as modulename
+            filename = frame_info[1]
+            module_name = os.path.splitext(os.path.basename(filename))[0]
+        return module_name
+    except Exception as e:
+        warnings.warn(
+            f"Error deriving logger module name, using . Exception: {e}",
+            RuntimeWarning,
+        )
+        return None
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/store.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/store.py
new file mode 100644
index 0000000000000000000000000000000000000000..0afe82c46d89633707af852c8e118f30ea538f82
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/elastic/utils/store.py
@@ -0,0 +1,226 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from collections.abc import Iterable
+from contextlib import contextmanager
+from datetime import timedelta
+from typing import Callable, Optional
+
+import torch
+
+
+DistStoreError = torch._C._DistStoreError
+
+_NUM_MEMBERS = "/num_members"
+_LAST_MEMBER_CHECKIN = "/last_member"
+_TRACE = "/TRACE"
+_TRACING_GATE = "/TRACING_GATE"
+_MAX_TRACE_MISSING_RANKS = 16
+
+
+__all__ = ["store_timeout", "get_all", "synchronize", "barrier"]
+
+
+@contextmanager
+def store_timeout(store, timeout: float):
+    """
+    This sets the timeout and then restores the old timeout when the context
+    manager exits.
+
+    Args:
+        store: the store to set the timeout on
+        timeout: the timeout to set
+    """
+
+    old_timeout = store.timeout
+    store.set_timeout(timedelta(seconds=timeout))
+    yield
+    store.set_timeout(old_timeout)
+
+
+def get_all(store, rank: int, prefix: str, world_size: int):
+    r"""
+    Given a store and a prefix, the method goes through the array of keys
+    of the following format: ``{prefix}{idx}``, where idx is in a range
+    from 0 to size, and tries to retrieve the data.
+
+    The Rank0 process waits at the end to make sure all other processes
+    finished the procedure before exiting.
+
+    Usage
+
+    ::
+
+     values = get_all(store, "torchelastic/data", 3)
+     value1 = values[0]  # retrieves the data for key torchelastic/data0
+     value2 = values[1]  # retrieves the data for key torchelastic/data1
+     value3 = values[2]  # retrieves the data for key torchelastic/data2
+
+    """
+    data_arr = store.multi_get([f"{prefix}{idx}" for idx in range(world_size)])
+
+    barrier_key = _barrier_nonblocking(
+        store=store,
+        world_size=world_size,
+        key_prefix=f"{prefix}/finished",
+    )
+    if rank == 0:
+        # Rank0 runs the TCPStore daemon, as a result it needs to exit last.
+        # Otherwise, the barrier may timeout if rank0 process finished the work
+        # before other processes finished `get_all` method
+        store.wait([barrier_key])
+
+    return data_arr
+
+
+def synchronize(
+    store,
+    data: bytes,
+    rank: int,
+    world_size: int,
+    key_prefix: str,
+    timeout: float = 300,
+) -> list[bytes]:
+    """
+    Synchronizes ``world_size`` agents between each other using the underlying c10d store.
+    The ``data`` will be available on each of the agents.
+
+    Note: The data on the path is not deleted, as a result there can be stale data if
+        you use the same key_prefix twice.
+
+    Time complexity: O(N) per worker, O(N^2) globally.
+    """
+    with store_timeout(store, timeout):
+        store.set(f"{key_prefix}{rank}", data)
+        agent_data = get_all(store, rank, key_prefix, world_size)
+        return agent_data
+
+
+def _try_detecting_missing_ranks(
+    store,
+    world_size: int,
+    key_prefix: str,
+    rank: int,
+    rank_decoder: Callable[[int], str],
+    trace_timeout: float,
+) -> Optional[Iterable[str]]:
+    store.set(f"{key_prefix}{rank}{_TRACE}", "")
+
+    def _find_missing_ranks():
+        missing_rank_info = set()
+        ranks_missing = 0
+        for i in range(1, world_size):
+            # reduce noise, assuming in general 8 ranks per node
+            # It is valuable to know that 1 or >1 nodes have timed-out.
+            if ranks_missing >= _MAX_TRACE_MISSING_RANKS:
+                break
+            try:
+                if ranks_missing == 0:
+                    store.wait(
+                        [f"{key_prefix}{i}{_TRACE}"], timedelta(seconds=trace_timeout)
+                    )
+                else:
+                    # use a shortest timeout, some ranks have failed to check-in
+                    store.wait([f"{key_prefix}{i}{_TRACE}"], timedelta(milliseconds=1))
+            except DistStoreError:
+                ranks_missing += 1
+                missing_rank_info.add(rank_decoder(i))
+        return missing_rank_info
+
+    def _checkin():
+        try:
+            store.wait([f"{key_prefix}{_TRACING_GATE}"])
+            return [f"[]"]
+        except DistStoreError:
+            # in case rank0 is the source of the timeout, original exception will be raised
+            return None
+
+    if rank == 0:
+        missing_rank_info = _find_missing_ranks()
+        store.set(f"{key_prefix}{_TRACING_GATE}", "")
+        return missing_rank_info
+    else:
+        return _checkin()
+
+
+def _barrier_nonblocking(store, world_size: int, key_prefix: str) -> str:
+    """
+    Does all the non-blocking operations for a barrier and returns the final key
+    that can be waited on.
+    """
+    num_members_key = key_prefix + _NUM_MEMBERS
+    last_member_key = key_prefix + _LAST_MEMBER_CHECKIN
+
+    idx = store.add(num_members_key, 1)
+    if idx == world_size:
+        store.set(last_member_key, "")
+
+    return last_member_key
+
+
+def barrier(
+    store,
+    world_size: int,
+    key_prefix: str,
+    barrier_timeout: float = 300,
+    rank: Optional[int] = None,
+    rank_tracing_decoder: Optional[Callable[[int], str]] = None,
+    trace_timeout: float = 10,
+) -> None:
+    """
+    A global lock between agents. This will pause all workers until at least
+    ``world_size`` workers respond.
+
+    This uses a fast incrementing index to assign waiting ranks and a success
+    flag set by the last worker.
+
+    Time complexity: O(1) per worker, O(N) globally.
+
+    Optionally, passing rank will enable tracing of missing ranks on timeouts.
+    `rank_tracing_decoder` lambda arg can be used to convert rank data
+    into a more meaninful information at an app level (e.g. hostname).
+
+    Note: Since the data is not removed from the store, the barrier can be used
+        once per unique ``key_prefix``.
+    """
+
+    if rank is None:
+        assert rank_tracing_decoder is None, "Tracing requires rank information"
+
+    with store_timeout(store, barrier_timeout):
+        last_member_key = _barrier_nonblocking(
+            store=store, world_size=world_size, key_prefix=key_prefix
+        )
+        try:
+            store.wait([last_member_key])
+        except DistStoreError as e:
+            if rank is None:
+                raise e
+            else:
+                missing_ranks = _try_detecting_missing_ranks(
+                    store,
+                    world_size,
+                    key_prefix,
+                    rank,
+                    rank_tracing_decoder or (lambda x: str(x)),
+                    trace_timeout,
+                )
+                if missing_ranks is not None:
+                    raise DistStoreError(
+                        "Timed out waiting on barrier on "
+                        "rank {}, for key prefix: {} (world_size={}, missing_ranks={}, timeout={})".format(
+                            rank,
+                            key_prefix,
+                            world_size,
+                            f"[{', '.join(missing_ranks)}]",
+                            barrier_timeout,
+                        )
+                    ) from None
+                else:
+                    raise e
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9db45a71932815d2bebac0d0010d794b6c721ceb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/__init__.py
@@ -0,0 +1,66 @@
+from ._flat_param import FlatParameter as FlatParameter
+from ._fully_shard import (
+    CPUOffloadPolicy,
+    FSDPModule,
+    fully_shard,
+    MixedPrecisionPolicy,
+    OffloadPolicy,
+    register_fsdp_forward_method,
+    UnshardHandle,
+)
+from .fully_sharded_data_parallel import (
+    BackwardPrefetch,
+    CPUOffload,
+    FullOptimStateDictConfig,
+    FullStateDictConfig,
+    FullyShardedDataParallel,
+    LocalOptimStateDictConfig,
+    LocalStateDictConfig,
+    MixedPrecision,
+    OptimStateDictConfig,
+    OptimStateKeyType,
+    ShardedOptimStateDictConfig,
+    ShardedStateDictConfig,
+    ShardingStrategy,
+    StateDictConfig,
+    StateDictSettings,
+    StateDictType,
+)
+
+
+__all__ = [
+    # FSDP1
+    "BackwardPrefetch",
+    "CPUOffload",
+    "FullOptimStateDictConfig",
+    "FullStateDictConfig",
+    "FullyShardedDataParallel",
+    "LocalOptimStateDictConfig",
+    "LocalStateDictConfig",
+    "MixedPrecision",
+    "OptimStateDictConfig",
+    "OptimStateKeyType",
+    "ShardedOptimStateDictConfig",
+    "ShardedStateDictConfig",
+    "ShardingStrategy",
+    "StateDictConfig",
+    "StateDictSettings",
+    "StateDictType",
+    # FSDP2
+    "CPUOffloadPolicy",
+    "FSDPModule",
+    "fully_shard",
+    "MixedPrecisionPolicy",
+    "OffloadPolicy",
+    "register_fsdp_forward_method",
+    "UnshardHandle",
+]
+
+# Set namespace for exposed private names
+CPUOffloadPolicy.__module__ = "torch.distributed.fsdp"
+FSDPModule.__module__ = "torch.distributed.fsdp"
+fully_shard.__module__ = "torch.distributed.fsdp"
+MixedPrecisionPolicy.__module__ = "torch.distributed.fsdp"
+OffloadPolicy.__module__ = "torch.distributed.fsdp"
+register_fsdp_forward_method.__module__ = "torch.distributed.fsdp"
+UnshardHandle.__module__ = "torch.distributed.fsdp"
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_common_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_common_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d4fb2a88c344168c4214ace5dc6b645dc94367d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_common_utils.py
@@ -0,0 +1,546 @@
+# mypy: allow-untyped-defs
+"""
+This file includes private common utilities for FSDP.
+"""
+
+import logging
+import traceback
+import warnings
+import weakref
+from collections.abc import Generator, Iterable
+from enum import auto, Enum
+from functools import partial
+from itertools import chain
+from typing import Any, Callable, cast, no_type_check, Optional, TYPE_CHECKING
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._flat_param as flat_param_file
+import torch.nn as nn
+from torch.distributed._composable_state import _get_module_state, _State
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    _CHECKPOINT_PREFIX,
+)
+from torch.distributed.utils import _apply_to_tensors
+from torch.utils._mode_utils import no_dispatch
+
+from .api import (
+    FullOptimStateDictConfig,
+    FullStateDictConfig,
+    OptimStateDictConfig,
+    ShardingStrategy,
+    StateDictConfig,
+    StateDictType,
+)
+
+
+if TYPE_CHECKING:
+    from torch.distributed.device_mesh import DeviceMesh
+    from torch.distributed.fsdp._fsdp_extensions import FSDPExtensions
+
+    from ._flat_param import FlatParamHandle
+
+FSDP_WRAPPED_MODULE = "_fsdp_wrapped_module"
+FSDP_PREFIX = FSDP_WRAPPED_MODULE + "."
+FSDP_FLATTENED = "_fsdp_flattened"
+
+# Save a global mapping from module to its input tensor dtype to be populated
+# during the forward pre-hook and consumed in the forward post-hook when
+# overriding a module's mixed precision
+# NOTE: We currently take the last input tensor's dtype in the case of multiple
+# floating-point input tensors, which may be incorrect. However, since there is
+# not a 1:1 correspondence between input and output tensors, we must use *some*
+# heuristic like this to predict the desired output dtype.
+_MODULE_TO_INP_DTYPE: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
+
+
+class _FSDPDeviceHandle:
+    """
+    This is a simple abstraction for FSDP computing devices,
+    which enables custom backends that implement CUDA-like
+    semantics to be integrated with FSDP.
+    """
+
+    def __init__(self, device: torch.device, backend: Any = None):
+        if backend is None:
+            try:
+                self.__backend = getattr(torch, device.type)
+                self.__device = device
+            except AttributeError as exc:
+                raise AttributeError(
+                    f"Device '{device}' does not have a corresponding backend registered as 'torch.{device.type}'."
+                ) from exc
+        else:
+            self.__backend = backend
+
+    @classmethod
+    def from_device(cls, device: torch.device) -> "_FSDPDeviceHandle":
+        """
+        Return a device handle corresponding to the device, and through this handle,
+        operations with the same semantics as CUDA can be performed on the device.
+        Just return torch.cuda if the device is cuda to make attribute-access faster.
+        Custom backend must first register a module with the same name with {device.type} on torch.
+        """
+        if device.type == "cuda":
+            return cast(_FSDPDeviceHandle, torch.cuda)
+        elif device.type == "mtia":
+            return cast(_FSDPDeviceHandle, torch.mtia)
+        return cls(device)
+
+    def __getattr__(self, name: str, /) -> Any:
+        try:
+            return getattr(self.__backend, name)
+        except AttributeError as exc:
+            raise AttributeError(
+                f"Custom backend '{self.__device.type}' not implement 'torch.{self.__device.type}.{name}'"
+            ) from exc
+
+
+class _UninitializedDeviceHandle(_FSDPDeviceHandle):
+    def __init__(self) -> None:
+        pass
+
+    def __getattribute__(self, name: str, /) -> Any:
+        raise RuntimeError("Trying to use an uninitialized device handle.")
+
+
+class _FSDPState(_State):
+    def __init__(self) -> None:
+        # TODO: Move all the attributes to this class to enable typing for
+        # FSDP/fully_shard.
+        self._ignored_modules: set[nn.Module] = set()
+        self._ignored_params: set[nn.Parameter] = set()
+        # Buffer names are cleaned (without wrapper prefixes)
+        self._ignored_buffer_names: set[str] = set()
+        self.process_group: Optional[dist.ProcessGroup] = None
+        self.rank: int = -1
+        self.world_size: int = -1
+        self._device_mesh: Optional[DeviceMesh] = None
+        self.sharding_strategy = ShardingStrategy.FULL_SHARD
+        self._use_orig_params: bool = False
+        self.training_state = TrainingState.IDLE
+        self._unshard_params_ctx: dict[nn.Module, Generator] = {}
+        self._state_dict_type: StateDictType = StateDictType.FULL_STATE_DICT
+        self._state_dict_config: StateDictConfig = FullStateDictConfig()
+        self._optim_state_dict_config: OptimStateDictConfig = FullOptimStateDictConfig()
+        self._is_root: Optional[bool] = None
+        self._handle: Optional[flat_param_file.FlatParamHandle] = None
+        self._fully_sharded_module_to_handle: dict[
+            nn.Module, Optional[flat_param_file.FlatParamHandle]
+        ] = {}
+        self.compute_device: Optional[torch.device] = None
+        self._gradient_predivide_factor: int = 0
+        self._gradient_postdivide_factor: int = 0
+        self._comm_hook: Optional[Callable] = None
+        self._comm_hook_state: Optional[Any] = None
+        self._unshard_event: Optional[torch.Event] = None
+        # Abstract device handle for fsdp compute device. For now,
+        # the compute device must implement cuda semantics used by fsdp
+        self._device_handle: _FSDPDeviceHandle = _UninitializedDeviceHandle()
+        # All following attributes should only be used for root states:
+        # Save these static lists to avoid the repeated tree traversals
+        self._all_fsdp_states: list[_FSDPState] = []
+        self._all_handles: list[flat_param_file.FlatParamHandle] = []
+        self._fsdp_extension: Optional[FSDPExtensions] = None
+
+
+def _get_module_fsdp_state(module: nn.Module) -> Optional[_FSDPState]:
+    state = _get_module_state(module)
+    if state is None or not isinstance(state, _FSDPState):
+        return None
+    return state
+
+
+def _get_module_fsdp_state_if_fully_sharded_module(
+    module: nn.Module,
+) -> Optional[_FSDPState]:
+    state = _get_module_fsdp_state(module)
+    if state is None:
+        return None
+    if state == module:  # FullyShardedDataParallel module case.
+        return state
+    if module in state._fully_sharded_module_to_handle:  # fully_shard case.
+        return state
+    return None
+
+
+class TrainingState(Enum):
+    """
+    An enum that indicates the state of a ``FullyShardedDataParallel` instance.
+    """
+
+    IDLE = auto()
+    FORWARD_BACKWARD = auto()
+    SUMMON_FULL_PARAMS = auto()
+
+
+class HandleTrainingState(Enum):
+    """
+    An enum that indicates the state of a ``FlatParamHandle`.
+    """
+
+    IDLE = auto()
+    FORWARD = auto()
+    BACKWARD_PRE = auto()
+    BACKWARD_POST = auto()
+    SUMMON_FULL_PARAMS = auto()
+
+
+def _is_composable(state: _FSDPState):
+    # TODO: This is a temporary hack for differentiate between code paths.
+    return not isinstance(state, nn.Module)
+
+
+@no_type_check
+def _module_handle(state: _FSDPState, module: nn.Module) -> Optional["FlatParamHandle"]:
+    """
+    Returns the ``FlatParamHandle`` s corresponding to ``module``. This is
+    the handle that contains some parameter in ``module``.
+    """
+    if _is_composable(state):
+        # A valid FSDP state may have no managed parameters and hence no
+        # handles, meaning no entry in `_fully_sharded_module_to_handles`
+        if state._handle is None:
+            return None
+        assert module in state._fully_sharded_module_to_handle, (
+            f"Expects a fully sharded module but got {module} on rank {state.rank}"
+        )
+        return state._fully_sharded_module_to_handle[module]
+    else:
+        # NOTE: This assumes `module` is a `FullyShardedDataParallel` instance.
+        return module._handle
+
+
+@no_type_check
+def _has_fsdp_params(state: _FSDPState, module: nn.Module) -> bool:
+    """Returns if ``module`` has parameters managed by FSDP."""
+    return _module_handle(state, module) is not None
+
+
+def _get_sharding_strategy(handle):
+    """
+    Returns the sharding strategy of the handle.
+    """
+    return handle._sharding_strategy if handle else None
+
+
+def clean_tensor_name(tensor_name: str) -> str:
+    """
+    Cleans the parameter or buffer name by removing any module wrapper
+    prefixes.
+    """
+    tensor_name = tensor_name.replace(FSDP_PREFIX, "")
+    # TODO: Explicitly replacing the checkpoint wrapper prefix is not ideal as
+    # it couples `CheckpointWrapper` and FSDP and also does not scale for more
+    # module wrappers.
+    tensor_name = tensor_name.replace(_CHECKPOINT_PREFIX, "")
+    return tensor_name
+
+
+def _set_fsdp_flattened(tensor: torch.Tensor) -> None:
+    """
+    Sets an attribute on ``tensor`` to mark it as flattened by FSDP. This is to
+    avoid re-flattening it during nested construction.
+    """
+    setattr(tensor, FSDP_FLATTENED, True)
+
+
+def _is_fsdp_flattened(tensor: torch.Tensor) -> bool:
+    """Returns if ``tensor`` has been marked as flattened by FSDP."""
+    return getattr(tensor, FSDP_FLATTENED, False)
+
+
+def _named_parameters_with_duplicates(
+    module: nn.Module, **kwargs: Any
+) -> list[tuple[str, nn.Parameter]]:
+    """
+    This API is required as some modules overwrite `named_parameters()` but do not support
+    `remove_duplicate`.
+    """
+    assert "remove_duplicate" not in kwargs, (
+        "_named_parameters_with_duplicates cannot be used with `remove_duplicate` argument."
+    )
+    kwargs["remove_duplicate"] = False
+    try:
+        ret = list(module.named_parameters(**kwargs))
+    except AssertionError:
+        kwargs.pop("remove_duplicate")
+        ret = list(module.named_parameters(**kwargs))
+    return ret
+
+
+def _get_param_to_fqns(
+    model: torch.nn.Module,
+    dedup_shared_params: bool = True,
+) -> dict[nn.Parameter, list[str]]:
+    """
+    Constructs a mapping from parameter to a list of its \"canonical\" FQNs. Here,
+    we use canonical to mean the fully-qualified name assigned to the parameter
+    based on its position in the original nn.Module hierarchy before any wrapper
+    or parallelism has been applied to it. This is in contrast to FQNs that may be
+    generated after parallelisms or wrappers have been applied to the model.
+
+    Each normal parameter maps to a singleton list containing its FQN, while each
+    ``FlatParameter`` maps to a list of its original parameter FQNs, which may
+    have length greater than one.  All FQNs are prefixed starting from ``model``.
+
+    In the case where FSDP was applied with ``use_orig_params=True``, there should be no
+    ``FlatParameter`` s registered to the model's modules and this mapping will only
+    contain mappings from ``nn.Parameter`` s to singleton FQN lists.
+
+    It is only in the case where FSDP was applied with ``use_orig_params=False`` where
+    a ``FlatParameter`` will be registered in place of the original parameters and there
+    will be mappings from each ``FlatParameter`` to lists of FQNs corresponding to the
+    original parameters.
+
+    Args:
+        model (torch.nn.Module): Root module (which may or may not be a
+            :class:`FullyShardedDataParallel` instance).
+        dedup_shared_params (bool): For shared parameters, if ``True``, only
+            includes the FQNs corresponding to the first encounter of the
+            shared parameter in the module traversal; if ``False``, then
+            includes the FQNs across all encounters. (Default: ``True``)
+    """
+
+    def module_fn(module, prefix, tree_level, param_to_fqns):
+        for param_name, param in _named_parameters_with_duplicates(
+            module, recurse=False
+        ):
+            local_fqns = (
+                param._fqns
+                if isinstance(param, flat_param_file.FlatParameter)
+                else [param_name]
+            )  # prefixed from `module`
+            global_fqns = [
+                clean_tensor_name(prefix + name) for name in local_fqns
+            ]  # prefixed from the top level `model` (i.e. including `prefix`)
+            is_shared_param = param in param_to_fqns
+            if not is_shared_param:
+                param_to_fqns[param] = global_fqns
+            else:
+                if isinstance(param, flat_param_file.FlatParameter):
+                    # DMP overwrites `named_parameters` and skip (advance to
+                    # the next child module) the wrapped_module (e.g.,
+                    # _dmp_wrapped_module and _fsdp_wrapped_module). When a user
+                    # calls `named_child` to traverse the module recursively and
+                    # calls `named_parameters` with `recurse=False`, parameters
+                    # will be traversed more than once.
+                    # This hack is specified designed for DMP + FSDP. We
+                    # overwrite the flat_parameters traversal result to only obtain
+                    # the last one, which happens to be the correct one.
+                    #
+                    # TODO: Remove this hack once DMP + FSDP is not supported.
+                    warnings.warn(
+                        "FlatParameter is being traversed more than once. "
+                        "This case should only happen when using "
+                        "DistributedModelParallel with FullyShardedDataParallel."
+                    )
+                    param_to_fqns[param] = global_fqns
+                elif not dedup_shared_params:
+                    param_to_fqns[param].extend(global_fqns)
+
+    def return_fn(param_to_fqns):
+        return param_to_fqns
+
+    param_to_unflat_param_names: dict[torch.nn.Parameter, list[str]] = {}
+    return _apply_to_modules(
+        model,
+        module_fn,
+        return_fn,
+        [key for key, _ in _named_parameters_with_duplicates(model)],
+        param_to_unflat_param_names,
+    )
+
+
+@no_type_check
+def _log_post_backward_hook(
+    state: _FSDPState, handle: "FlatParamHandle", logger: logging.Logger
+) -> None:
+    # Under TORCH_DISTRIBUTED_DEBUG=INFO, log the module names this hook fires for.
+    # Below logging of module names this post-bwd hook fires for can help debug certain
+    # cases where hooks don't fire, such as under certain activation checkpoint configs.
+    if state._use_orig_params and handle._debug_level == dist.DebugLevel.INFO:
+        param_fqns = _get_handle_fqns_from_root(state, handle)
+        logger.warning("FSDP firing post-backward hooks for parameters %s", param_fqns)
+
+
+@no_type_check
+def _get_handle_fqns_from_root(
+    state: _FSDPState, handle: "FlatParamHandle"
+) -> Optional[list[str]]:
+    if handle is None:
+        return None
+    param_to_fqn = state._exec_order_data.param_to_fqn
+    handle_params = handle.flat_param._params  # only populated for use_orig_params
+    param_fqns = [*chain.from_iterable(param_to_fqn[p] for p in handle_params)]
+    return param_fqns
+
+
+def _apply_to_modules(
+    root_module: torch.nn.Module,
+    module_fn: Callable,
+    return_fn: Callable,
+    filter_fqns: Optional[list[str]] = None,
+    *args,
+    **kwargs,
+):
+    """
+    Performs a pre-order traversal of the modules in the hierarchy rooted at
+    ``root_module``, applying ``module_fn`` at each module and finally
+    returning a value using ``return_fn``. The traversal constructs the full
+    module prefix name (e.g. "module.submodule." just like in model state dict)
+    and makes that available to ``module_fn``.
+
+    ``filter_fqns`` is used because some module may have its own prefix similar
+    to ``FullyShardedDataParallel`` and the ``named_parameters()`` is overwritten
+    to remove the prefix.
+    """
+
+    def f(module: torch.nn.Module, prefix: str, tree_level: int, *args, **kwargs):
+        # Call the module function before recursing over children (pre-order)
+        module_fn(module, prefix, tree_level, *args, **kwargs)
+        for submodule_name, submodule in module.named_children():
+            if submodule is None:
+                continue
+            new_prefix = prefix + submodule_name + "."
+            new_tree_level = tree_level + 1
+            if filter_fqns is not None:
+                for fqn in filter_fqns:
+                    if fqn.startswith(new_prefix):
+                        break
+                else:
+                    # DMP's named_parameter() will mess up the traversal with
+                    # ``named_children`` + `named_parameter(recurse=False)``.
+                    # This hack is a must to make the traversal work.
+                    # TODO: Remove this hack once DMP + FSDP is not supported.
+                    # It turns out that recursive wrapping may trigger this as
+                    # well.
+                    if (
+                        submodule_name == "_fsdp_wrapped_module"
+                        or submodule_name == "_dmp_wrapped_module"
+                    ):
+                        new_prefix = prefix
+                    elif submodule_name == "module":
+                        new_prefix = prefix
+            f(submodule, new_prefix, new_tree_level, *args, **kwargs)
+
+    f(root_module, "", 0, *args, **kwargs)
+    return return_fn(*args, **kwargs)
+
+
+@no_type_check
+def _assert_in_training_states(
+    state: _FSDPState,
+    training_states: list[TrainingState],
+) -> None:
+    """Asserts that FSDP is in the states ``_training_states``."""
+    # Raise a `ValueError` instead of using `assert` to ensure that these
+    # logical assertions run even if `assert`s are disabled
+    if state.training_state not in training_states:
+        msg = (
+            f"expected to be in states {training_states} but current state is "
+            f"{state.training_state}"
+        )
+        # Print the error on rank 0 in case this is called in the backward pass
+        if state.rank == 0:
+            if isinstance(state, nn.Module):
+                print(f"Asserting FSDP instance is: {state}")
+            print(f"ERROR: {msg}")
+            traceback.print_stack()
+        raise ValueError(msg)
+
+
+def _get_root_modules(modules: set[nn.Module]) -> set[nn.Module]:
+    """
+    Returns:
+        Set[nn.Module]: The subset of ``modules`` that are root modules (i.e.
+        parent-less) with respect to the modules in the set itself. In other
+        words, these are the modules in ``modules`` that are not the child of
+        any other module in ``modules``.
+    """
+    root_modules: set[nn.Module] = set()
+    module_to_submodules = {module: set(module.modules()) for module in modules}
+    for candidate_module in modules:
+        is_root_module = True
+        for module, submodules in module_to_submodules.items():
+            is_child_module = (
+                candidate_module is not module and candidate_module in submodules
+            )
+            if is_child_module:
+                is_root_module = False
+                break
+        if is_root_module:
+            root_modules.add(candidate_module)
+    return root_modules
+
+
+def _override_module_mixed_precision(
+    root: torch.nn.Module,
+    module_classes_to_override: Iterable[type[nn.Module]],
+    wrap_override_dict: dict[str, Any] = {"mixed_precision": None},  # noqa: B006
+) -> set[type[nn.Module]]:
+    module_classes_to_override = tuple(set(module_classes_to_override))
+    # Return a set of the actually overridden module classes
+    overridden_module_classes: set[type[nn.Module]] = set()
+    for mod in root.modules():
+        if isinstance(mod, module_classes_to_override):
+            overridden_module_classes.add(type(mod))
+            mod._wrap_overrides = wrap_override_dict  # type: ignore[assignment]
+            # TODO: We need to run this mixed precision ignored module in fp32,
+            # but ensure subsequent modules, that may possibly be running with
+            # mixed precision, still receive the appropriate precision inputs
+            # without user having to adjust mixed precision config too much.
+            # As a result, we attach pre and post forward hooks to up / down
+            # cast. We should revisit this design.
+
+            def cast_fn(
+                dtype: torch.dtype, module: nn.Module, x: torch.Tensor
+            ) -> torch.Tensor:
+                if not torch.is_floating_point(x) or x.dtype == dtype:
+                    return x
+                _MODULE_TO_INP_DTYPE[module] = x.dtype
+                return x.to(dtype)
+
+            def forward_pre_hook(module, args):
+                return _apply_to_tensors(partial(cast_fn, torch.float32, module), args)
+
+            def forward_post_hook(module, args, output):
+                # NOTE: If the forward did not have any floating-point tensors,
+                # then the dtype will not be set for this module, and we do not
+                # upcast the dtype.
+                if module in _MODULE_TO_INP_DTYPE:
+                    old_dtype = _MODULE_TO_INP_DTYPE[module]
+                    return _apply_to_tensors(
+                        partial(cast_fn, old_dtype, module), output
+                    )
+
+            # We intentionally append both of these hooks so that they run after
+            # all other hooks.
+            mod.register_forward_pre_hook(forward_pre_hook, prepend=False)
+            mod.register_forward_hook(forward_post_hook, prepend=False)
+    return overridden_module_classes
+
+
+def _no_dispatch_record_stream(tensor: torch.Tensor, stream: torch.Stream) -> None:
+    # FIXME record_stream doesn't work with non-cuda/mtia/xpu tensors
+    if tensor.device.type not in [
+        "cuda",
+        "mtia",
+        "xpu",
+        torch._C._get_privateuse1_backend_name(),
+    ]:
+        return
+
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        return
+        # from @ezyang:
+        # The no_dispatch was added in https://github.com/pytorch/pytorch/pull/88014 cc @fegin
+        # Looking over the PR, it looks like this is because we don't actually support Stream arguments
+        # in torch dispatch, so it just chokes.
+        # If Dynamo is able to answer "are there any torch dispatch modes" active (it should answer False),
+        # a better version of this would just be to check if there are any modes before disabling dispatch.
+        # TODO(voz): Extend a dynamo util to answer the above, unify the codepaths here.
+        tensor.record_stream(stream)
+    else:
+        with no_dispatch():
+            tensor.record_stream(stream)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_debug_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_debug_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2103da08a976b8f2d34aa54d06482bd1b45eca3a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_debug_utils.py
@@ -0,0 +1,157 @@
+# mypy: allow-untyped-defs
+import logging
+import time
+from collections import defaultdict
+from collections.abc import Iterator
+from contextlib import contextmanager
+from enum import Enum
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._flat_param as flat_param_file
+from torch.distributed.fsdp._common_utils import (
+    _apply_to_modules,
+    _get_module_fsdp_state,
+    clean_tensor_name,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+class SimpleProfiler:
+    class Type(str, Enum):
+        ALL = "all"
+        ALLGATHER = "all_gather"
+        ALLGATHER_OBJ = "all_gather_object"
+        RESHARDING = "resharding"
+        H2D = "H2D"
+        D2H = "D2H"
+
+    results: dict[str, float] = defaultdict(float)
+    profiling: set[str] = set()
+
+    @classmethod
+    def reset(cls) -> None:
+        cls.results.clear()
+        cls.profiling.clear()
+
+    @classmethod
+    @contextmanager
+    def profile(cls, profile_type: str) -> Iterator[None]:
+        assert profile_type not in cls.profiling, (
+            f"{profile_type} is already being profiled. "
+            "SimpleProfiler does not support profiling multiple instances at "
+            "the same time. "
+        )
+
+        cls.profiling.add(profile_type)
+        begin = time.monotonic()
+        try:
+            yield
+        finally:
+            end = time.monotonic()
+            cls.results[profile_type] += end - begin
+            cls.profiling.remove(profile_type)
+
+    @classmethod
+    def dump_and_reset(cls, msg: str) -> None:
+        # This cannot be combined with DETAIL distributed log
+        # as the profiling will be very incorrect.
+        if dist.get_rank() == 0 and dist.get_debug_level() == dist.DebugLevel.INFO:
+            logger.info("%s %s", msg, cls.results)
+        cls.reset()
+
+
+def _get_sharded_module_tree_with_module_name_to_fqns(
+    model: torch.nn.Module,
+) -> tuple[str, dict[str, list[str]]]:
+    """
+    It is used for composable fully_shard() code path, it returns
+      1. sharded module tree info: each line reprents a submodule name that contats the
+    submodule's FQN and its submodule class name, if the submodule is sharded by `fully_shard`,
+    the submodule name will add a postfix with ' FULLY SHARDED'. Each increased tree
+    level adds 4 spaces before the printed name. A printed sharded module tree info for a toy model
+    is like this:
+        [CompositeModel] FULLY SHARDED
+            l1[Linear]
+            u1[UnitModule] FULLY SHARDED
+                u1.l1[Linear]
+                u1.seq[Sequential]
+                    u1.seq.0[ReLU]
+                    u1.seq.1[Linear]
+                    u1.seq.2[ReLU]
+                u1.l2[Linear]
+            u2[UnitModule] FULLY SHARDED
+                u2.l1[Linear]
+                u2.seq[Sequential]
+                    u2.seq.0[ReLU]
+                    u2.seq.1[Linear]
+                    u2.seq.2[ReLU]
+                u2.l2[Linear]
+            l2[Linear]
+      2. a dict mapping from the concated module FQN and class name to a list of its managed
+    original parameters' FQNs. An example of the dict for the above toy sharded model is like this:
+            {'[CompositeModel]': ['l1.weight', 'l1.bias', 'l2.weight', 'l2.bias'],
+             'u1[UnitModule]': ['u1.l1.weight', 'u1.l1.bias', 'u1.seq.1.weight', 'u1.seq.1.bias', 'u1.l2.weight', 'u1.l2.bias'],
+             'u2[UnitModule]': ['u2.l1.weight', 'u2.l1.bias', 'u2.seq.1.weight', 'u2.seq.1.bias', 'u2.l2.weight', 'u2.l2.bias']
+            }
+    All FQNs are prefixed starting from ``model``.
+
+    Args:
+        model (torch.nn.Module): Root module (which may or may not be passed to
+                                 composable `fully_shard()`).
+    """
+
+    def module_fn(
+        module, prefix, tree_level, sharded_tree_info, sharded_module_name_to_fqns
+    ):
+        num_spaces = tree_level * 4
+        trimed_prefix = (
+            prefix[:-1] if (len(prefix) > 0 and prefix[-1] == ".") else prefix
+        )
+        prefixed_module_name = trimed_prefix + "[" + module.__class__.__name__ + "]"
+        printed_prefixed_module_name = " " * num_spaces + prefixed_module_name
+
+        state = _get_module_fsdp_state(module)
+        if state is None:
+            sharded_tree_info[0] += printed_prefixed_module_name + "\n"
+            return
+
+        handle = state._fully_sharded_module_to_handle.get(module, None)
+
+        if handle:
+            sharded_tree_info[0] += (
+                printed_prefixed_module_name + " FULLY SHARDED" + "\n"
+            )
+        else:
+            sharded_tree_info[0] += printed_prefixed_module_name + "\n"
+
+        if handle:
+            param = handle.flat_param
+            assert isinstance(param, flat_param_file.FlatParameter)
+            global_fqns = [
+                clean_tensor_name(prefix + name) for name in param._fqns
+            ]  # prefixed from the top level `model` (i.e. including `prefix`)
+
+            if prefixed_module_name in sharded_module_name_to_fqns:
+                sharded_module_name_to_fqns[prefixed_module_name].extend(global_fqns)
+            else:
+                sharded_module_name_to_fqns[prefixed_module_name] = global_fqns
+
+    def return_fn(sharded_tree_info, sharded_module_name_to_fqns):
+        return sharded_tree_info[0], sharded_module_name_to_fqns
+
+    # Use List to mutate its value in place while running the recursive functions
+    sharded_tree_info: list[str] = [
+        "",
+    ]
+    sharded_module_name_to_fqns: dict[str, list[str]] = {}
+    return _apply_to_modules(
+        model,
+        module_fn,
+        return_fn,
+        [key for key, _ in model.named_parameters()],
+        sharded_tree_info,
+        sharded_module_name_to_fqns,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_dynamo_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_dynamo_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..77bcd43b63be27da8e8b79f877ce7cb9d67c74b8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_dynamo_utils.py
@@ -0,0 +1,43 @@
+import torch.nn as nn
+
+
+def _annotate_modules_for_dynamo(
+    module: nn.Module,
+    ignored_modules: set[nn.Module],
+    use_orig_params: bool,
+) -> None:
+    """
+    Annotates the submodules in ``module`` 's tree, except those in
+    ``ignored_modules``, indicating that the submodules are FSDP-managed and
+    saving the ``use_orig_params`` setting passed to the FSDP constructor.
+    """
+    for submodule in module.modules():
+        if submodule not in ignored_modules:
+            """[note: Dynamo treats FSDP wrapped modules as UnspecializedNNModule]
+
+            Dynamo doesn't get to see this instance (FullyShardedDataParallel) during tracing, since
+            it skips tracing all the torch.distributed.fsdp code.
+                - Why? Running the FSDP code eagerly avoids lots of issues trying to trace complex hooks, and also
+                gets us graph-breaks on FSDP module boundaries which we want anyway for comm ops.
+                - However, we _also_ want dynamo to treat the wrapped module inside FSDP 'unspecially' (*),
+                and we need a way to indicate to dynamo which modules are wrapped by FSDP.
+
+            (*) UnspecializedNNModules in dynamo are traced-through without any assumptions, and with thorough
+            guards.  NNModules otherwise are 'specialized', meaning there is less overhead due to assuming
+            their code is well-behaved.
+
+            One particular issue with specialized NNModules for FSDP is that the
+            views created for orig_params are captured into the compiled graph on the first iteration, and while
+            they are always going to point to the correct flatparameter and give correct results, their order
+            of creation influences the order of backward execution, preventing overlap of comm and computation
+            during backward.  We need to _use_ the new parameter views created on each forward iteration, in
+            order for backward to interleave hooks with compute per layer.  UnspecializedNNModule lets us achieve
+            this by capturing the module code more 'functionally' and passing parameters in as inputs each time.
+            """
+            submodule._is_fsdp_managed_module = True  # type: ignore[assignment]
+
+            # Dynamo only supports FSDP with use_orig_params=True.
+            # This is hacky, but I could not think of another way to add an assertion to dynamo
+            # for this, since Dynamo skips all the FSDP code frames and thus can't inspect the
+            # FSDP module directly
+            submodule._fsdp_use_orig_params = use_orig_params  # type: ignore[assignment]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_exec_order_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_exec_order_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..519ce39b1678cd8512b44c51e02d05ad473c29bf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_exec_order_utils.py
@@ -0,0 +1,364 @@
+# mypy: allow-untyped-defs
+import itertools
+import warnings
+from enum import auto, Enum
+from typing import Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+from torch.distributed.fsdp._common_utils import _FSDPState, _get_param_to_fqns
+from torch.distributed.fsdp._flat_param import FlatParamHandle
+
+
+class _ExecOrderWarnStatus(Enum):
+    """Used internally for execution order validation."""
+
+    NONE = auto()  # no deviation yet
+    WARNING = auto()  # deviated this iteration; currently issuing warnings
+    WARNED = auto()  # deviated in a previous iteration
+
+
+class _ExecOrderData:
+    """
+    This contains the data structures to track the execution order. We track
+    the pre-forward order on the *first* iteration for forward prefetching
+    (which thus assumes static graph) and the post-forward order on *every*
+    iteration for backward prefetching (which thus does not assume static
+    graph but may be provide an incorrect order).
+    """
+
+    def __init__(
+        self,
+        debug_level: dist.DebugLevel,
+        backward_prefetch_limit: int,
+        forward_prefetch_limit: int,
+    ) -> None:
+        # Tracks the (static) pre-forward order for execution order validation
+        # and forward prefetching
+        self.handles_pre_forward_order: list[FlatParamHandle] = []
+        # Tracks the post-forward order for pre-backward prefetching
+        self.handles_post_forward_order: list[Optional[FlatParamHandle]] = []
+        self._iter = 0
+
+        # Gives the max number of backward/forward prefetched all-gathers by a
+        # single module
+        self._backward_prefetch_limit = backward_prefetch_limit
+        self._forward_prefetch_limit = forward_prefetch_limit
+
+        # Data structures for execution order validation
+        self._checking_order: bool = debug_level == dist.DebugLevel.DETAIL
+        self.process_group: Optional[dist.ProcessGroup] = None
+        self.world_size: Optional[int] = None
+        self.all_handles: list[FlatParamHandle] = []
+        # Names are prefixed from the root module
+        self.param_to_fqn: dict[nn.Parameter, list[str]] = {}
+        # Current index in the pre-forward execution order
+        self.current_order_index = 0
+        self.warn_status = _ExecOrderWarnStatus.NONE
+
+    def init(
+        self,
+        state: _FSDPState,
+        root_module: nn.Module,
+        process_group: dist.ProcessGroup,
+    ) -> None:
+        """
+        Initializes the data structures needed for checking the forward order.
+        This should be called after a root FSDP instance has been set during
+        lazy initialization.
+        """
+        self.process_group = process_group
+        self.rank = process_group.rank()
+        self.world_size = process_group.size()
+        # Fix an order over the handles, which should be the same across ranks
+        for handle in traversal_utils._get_fsdp_handles(root_module):
+            index = len(self.all_handles)
+            self.all_handles.append(handle)
+            handle._handle_index = index
+        self.param_to_fqn = _get_param_to_fqns(root_module)
+        # TODO (awgu): We can broadcast the metadata of rank 0's `all_handles`
+        # to check that all ranks have the same handles in the same order.
+        # https://github.com/pytorch/pytorch/issues/79620
+
+    @property
+    def is_first_iter(self) -> bool:
+        return self._iter == 0
+
+    def get_handle_to_backward_prefetch(
+        self,
+        current_handle: FlatParamHandle,
+    ) -> Optional[FlatParamHandle]:
+        """
+        Returns a :class:`list` of the handles keys of the handles to backward
+        prefetch given the current handles key. If there are no valid handles
+        keys to prefetch, then this returns an empty :class:`list`.
+        """
+        current_index = current_handle._post_forward_index
+        if current_index is None:
+            return None
+        target_index = current_index - 1
+        target_handle: Optional[FlatParamHandle] = None
+        for _ in range(self._backward_prefetch_limit):
+            if target_index < 0:
+                break
+            target_handle = self.handles_post_forward_order[target_index]
+            target_index -= 1
+        return target_handle
+
+    def get_handle_to_forward_prefetch(
+        self,
+        current_handle: FlatParamHandle,
+    ) -> Optional[FlatParamHandle]:
+        """
+        Returns a :class:`list` of the handles keys of the handles to forward
+        prefetch given the current handles key. If there are no valid handles
+        keys to prefetch, then this returns an empty :class:`list`.
+        """
+        current_index = current_handle._pre_forward_order_index
+        if current_index is None:
+            return None
+        target_index = current_index + 1
+        target_handle: Optional[FlatParamHandle] = None
+        for _ in range(self._forward_prefetch_limit):
+            if target_index >= len(self.handles_pre_forward_order):
+                break
+            target_handle = self.handles_pre_forward_order[target_index]
+            target_index += 1
+        return target_handle
+
+    def record_post_forward(self, handle: Optional[FlatParamHandle]) -> None:
+        """
+        Records ``handles`` in the post-forward order, where ``handles`` should
+        be a group of handles used in the same module's forward. If ``handles``
+        is empty, then it is omitted.
+
+        Unlike :meth:`record_pre_forward`, this records the order *every*
+        iteration with the expectation that the recorded order is reset in
+        :meth:`next_iter`.
+        """
+        if not handle:
+            return
+        # Only record the first usage of a handles key
+        if handle._post_forward_index:
+            self.handles_post_forward_order.append(handle)
+            return
+        index = len(self.handles_post_forward_order)
+        handle._post_forward_index = index
+        self.handles_post_forward_order.append(handle)
+
+    def record_pre_forward(
+        self, handle: Optional[FlatParamHandle], is_training: bool
+    ) -> None:
+        """
+        Records ``handles`` in the pre-forward order, where ``handles`` should
+        be a group of handles used in the same module's forward. If ``handles``
+        is empty, then it is omitted.
+
+        On the first iteration, this checks the execution order across ranks.
+        See :meth:`_check_order` for details.
+        """
+        if not handle:
+            return
+        self._check_order(handle, is_training)
+        # Fix the order after the first iteration and only record the first
+        # usage of a handles key
+        if not self.is_first_iter or handle._pre_forward_order_index is not None:
+            return
+        index = len(self.handles_pre_forward_order)
+        handle._pre_forward_order_index = index
+        self.handles_pre_forward_order.append(handle)
+
+    def _check_order(self, handle: FlatParamHandle, is_training: bool) -> None:
+        """
+        Checks the forward execution order as long as ``is_training`` is
+        ``True`` since checking in eval mode is not supported. This only checks
+        if the distributed debug level is DETAIL.
+
+        - On the first iteration, this uses all-gathers to check that all ranks
+        are all-gathering the same handles and hence ``FlatParameter`` s,
+        raising an error if not.
+        - On subsequent iterations, this checks that each rank is locally
+        consistent with its own forward order from the first iteration, issuing
+        a warning if not. This issues a warning on the first deviating
+        iteration and stops warning thereafter.
+        """
+        # Do not check order in eval mode since the post-backward callback does
+        # not run so it cannot be used to mark the end of an iteration
+        if not is_training or not self._checking_order:
+            return
+        if self.is_first_iter:
+            msg_prefix = "Forward order differs across ranks:"
+            optional_local_indices: tuple[Optional[int], ...] = (
+                self._get_handle_indices(handle)
+            )
+            device = handle.device  # guaranteed to be non-CPU
+            num_valid_indices = sum(
+                (index is not None) for index in optional_local_indices
+            )
+            tensor_kwargs: dict[str, Union[torch.dtype, torch.device]] = {
+                "dtype": torch.int32,
+                "device": device,
+            }
+            world_num_valid_indices = torch.zeros(self.world_size, **tensor_kwargs)  # type: ignore[arg-type, call-overload]
+            local_num_valid_indices = torch.tensor([num_valid_indices], **tensor_kwargs)  # type: ignore[arg-type, call-overload]
+            dist.all_gather_into_tensor(
+                world_num_valid_indices,
+                local_num_valid_indices,
+                group=self.process_group,
+            )
+            # Copy entire tensor from D2H once to avoid per element D2H copies
+            world_num_valid_indices = world_num_valid_indices.cpu()
+            # Check that all ranks plan to all-gather the same number of
+            # parameters
+            # TODO (awgu): Since every module has at most one handle in the
+            # current implementation, this should never raise the error.
+            assert self.world_size is not None  # mypy
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                # TODO(voz): Don't graph break on this - dynamo hates the n1 != n2
+                # tensor comparison control flow.
+                # https://github.com/pytorch/pytorch/issues/107055
+                for (r1, n1), (r2, n2) in itertools.combinations(
+                    (
+                        (rank, world_num_valid_indices[rank])
+                        for rank in range(self.world_size)
+                    ),
+                    2,
+                ):
+                    if n1 != n2:
+                        raise RuntimeError(
+                            f"{msg_prefix} rank {r1} is all-gathering {n1} parameters "
+                            f"while rank {r2} is all-gathering {n2} parameters"
+                        )
+            world_indices = torch.zeros(  # type: ignore[call-overload]
+                self.world_size * num_valid_indices, **tensor_kwargs
+            )
+            local_indices = torch.tensor(optional_local_indices, **tensor_kwargs)  # type: ignore[arg-type]
+            dist.all_gather_into_tensor(
+                world_indices, local_indices, group=self.process_group
+            )
+            # Copy entire tensor from D2H once to avoid per element D2H copies
+            world_indices = world_indices.cpu()
+            # Check that all ranks plan to all-gather the same index parameters
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                # TODO(voz): Don't graph break on this - dynamo hates the i1 != i2
+                # tensor comparison control flow.
+                # https://github.com/pytorch/pytorch/issues/107055
+                for (r1, i1), (r2, i2) in itertools.combinations(
+                    (
+                        (
+                            rank,
+                            world_indices[
+                                rank * num_valid_indices : (rank + 1)
+                                * num_valid_indices
+                            ],
+                        )
+                        for rank in range(self.world_size)
+                    ),
+                    2,
+                ):
+                    if i1 != i2:
+                        r1_param_names = self._get_names_from_handle_indices(i1)
+                        r2_param_names = self._get_names_from_handle_indices(i2)
+                        raise RuntimeError(
+                            f"{msg_prefix} rank {r1} is all-gathering parameters "
+                            f"for {r1_param_names} while rank {r2} is all-gathering "
+                            f"parameters for {r2_param_names}"
+                        )
+        else:
+            # Only issue warnings on the first deviating iteration and stop
+            # checking thereafter to avoid flooding the console
+            if self.warn_status == _ExecOrderWarnStatus.WARNED:
+                return
+            msg_prefix = None  # non-`None` means we should warn
+            if self.current_order_index >= len(self.handles_pre_forward_order):
+                # This iteration sees extra all-gather(s) compared to the first
+                msg_prefix = (
+                    "Expected to not all-gather any more parameters in the "
+                    "forward but trying to all-gather parameters for "
+                )
+            else:
+                expected_handle = self.handles_pre_forward_order[
+                    self.current_order_index
+                ]
+                if expected_handle != handle:
+                    expected_param_names = self._get_names_from_handles(expected_handle)
+                    msg_prefix = (
+                        f"Expected to all-gather for {expected_param_names} "
+                        "but trying to all-gather parameters for "
+                    )
+            if msg_prefix is not None:
+                param_names = self._get_names_from_handles(handle)
+                msg_suffix = (
+                    f"{param_names}"
+                    if param_names
+                    else "a newly-added parameter since construction time"
+                )
+                warnings.warn(
+                    "Forward order differs from that of the first iteration "
+                    f"on rank {self.rank}. Collectives are unchecked and may "
+                    f"give incorrect results or hang.\n{msg_prefix}{msg_suffix}"
+                )
+                self.warn_status = _ExecOrderWarnStatus.WARNING
+            self.current_order_index += 1
+
+    def _get_handle_indices(
+        self,
+        handle: FlatParamHandle,
+    ) -> tuple[Optional[int], ...]:
+        """
+        Returns the handle indices (i.e. indices into ``self.all_handles``)
+        corresponding to the handles in ``handle``. An entry in the
+        returned tuple is ``None`` if the handle is invalid.
+        """
+        indices: list[Optional[int]] = []
+        if handle:
+            indices.append(handle._handle_index)
+        return tuple(indices)
+
+    def _get_names_from_handle_indices(
+        self,
+        handle_indices: tuple[int, ...],
+    ) -> list[list[str]]:
+        """
+        Returns a list of FQNs for each handle in ``handle_indices``. If a
+        handle index is invalid, then its FQNs are omitted from the returned
+        list.
+        """
+        fqns: list[list[str]] = []
+        for index in handle_indices:
+            if index is None or index < 0 or index >= len(self.all_handles):
+                continue
+            handle = self.all_handles[index]
+            flat_param = handle.flat_param
+            fqns.append(self.param_to_fqn[flat_param])
+        return fqns
+
+    def _get_names_from_handles(
+        self,
+        handle: FlatParamHandle,
+    ) -> list[list[str]]:
+        """
+        Returns a list of FQNs for each handle in ``handles_key``. If a handle
+        is invalid, then its FQNs are omitted from the returned list.
+        """
+        fqns: list[list[str]] = []
+        if handle:
+            flat_param = handle.flat_param
+            if flat_param in self.param_to_fqn:
+                fqns.append(self.param_to_fqn[flat_param])
+        return fqns
+
+    def next_iter(self):
+        """
+        Advances the internal data structures per iteration. This should be
+        called in the post-backward callback since that marks the true end of
+        an iteration.
+        """
+        self._iter += 1
+        self.handles_post_forward_order.clear()
+        if self._checking_order:
+            self.current_order_index = 0
+            if self.warn_status == _ExecOrderWarnStatus.WARNING:
+                self.warn_status = _ExecOrderWarnStatus.WARNED
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_flat_param.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_flat_param.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d508758d3fb19c17e0894b5d8727d6534d1a257
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_flat_param.py
@@ -0,0 +1,2785 @@
+# mypy: allow-untyped-defs
+import contextlib
+import functools
+import logging
+import os
+import warnings
+from collections.abc import Generator, Iterator, Sequence
+from enum import auto, Enum
+from itertools import accumulate, chain
+from typing import Any, Callable, cast, NamedTuple, no_type_check, Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+from torch.distributed.fsdp._common_utils import (
+    _FSDPDeviceHandle,
+    _named_parameters_with_duplicates,
+    _no_dispatch_record_stream,
+    _set_fsdp_flattened,
+    HandleTrainingState,
+)
+from torch.distributed.utils import (
+    _alloc_storage,
+    _data_ptr_allocated,
+    _free_storage,
+    _p_assert,
+)
+from torch.nn.parameter import _ParameterMeta  # type: ignore[attr-defined]
+from torch.testing._internal.distributed.fake_pg import FakeProcessGroup
+
+from ._fsdp_extensions import (
+    _ext_post_unflatten_transform,
+    _ext_pre_flatten_transform,
+    FSDPExtensions,
+)
+
+
+__all__ = [
+    "FlatParameter",
+    "FlatParamHandle",
+    "FlatParamShardMetadata",
+    "ParamInfo",
+    "SharedParamInfo",
+    "HandleShardingStrategy",
+]
+
+logger = logging.getLogger(__name__)
+
+
+"""
+[Note: Fully Sharded Module]
+We define the "fully sharded module" to be the original ``nn.Module`` that owns
+a ``FlatParamHandle``. It is the *single* module logically responsible for the
+*single* unshard/reshard pair for the handle's ``FlatParameter`` for a given
+forward or backward pass. The fully sharded module should be passed to the
+``FlatParamHandle`` constructor.
+
+For the wrapper code path:
+- The ``FullyShardedDataParallel`` module wrapping the fully sharded module
+runs the unshard/reshard on behalf of the fully sharded module by overriding
+``nn.Module.forward``.
+- The fully sharded module is exactly the module passed to the
+``FullyShardedDataParallel`` constructor's ``module`` argument.
+
+For the non-wrapper code path:
+- Hooks registered on the fully sharded module run the unshard/reshard.
+- The fully sharded module may either be the direct argument to ``fully_shard``
+or a submodule chosen by the provided wrapping policy.
+"""
+
+# Environment variable toggling whether to use unsafe `setattr()` for view
+# setting in `_use_sharded_views()` and `_use_unsharded_views()`
+# We should use 'safe' by default since it respects method overrides, but for
+# special cases such as for high CPU overhead or for intentionally bypassing
+# checks in the overrides, we may use 'unsafe'.
+_FSDP_USE_UNSAFE_SETATTR = "FSDP_USE_UNSAFE_SETATTR"
+
+# Environment variable toggling whether to check for parameter/gradient
+# writeback in case their storages change after FSDP initialization
+# We should check by default since it prevents silent correctness errors, but
+# since such changes are atypical, we may want to skip the check to save CPU
+# overhead, especially since the check happens in the pre-forward and
+# pre-backward each iteration.
+_FSDP_SKIP_WRITEBACK_CHECK = "FSDP_SKIP_WRITEBACK_CHECK"
+
+# Env var toggling whether when model is in .eval() mode, should we run in fp32
+# or the reduced precision.
+_FSDP_USE_FULL_PREC_IN_EVAL = "FSDP_USE_FULL_PREC_IN_EVAL"
+
+# Some value to set padding in tensors to for debuggability
+_FLAT_PARAM_PADDING_VALUE = 42
+
+# Environment variables for disabling the all-gather and reduce-scatter
+# communication ops for ablation studies. Note that without these communication
+# ops the training won't converge, and you probably need to disable correctness
+# checks in your model.
+_FSDP_USE_FAKE_ALL_GATHER = "FSDP_USE_FAKE_ALL_GATHER"
+_FSDP_USE_FAKE_REDUCE = "FSDP_USE_FAKE_REDUCE"
+
+
+# TODO: Define this for now to avoid circular imports. See if we can remove.
+class HandleShardingStrategy(Enum):
+    FULL_SHARD = auto()
+    SHARD_GRAD_OP = auto()
+    NO_SHARD = auto()
+    HYBRID_SHARD = auto()
+    _HYBRID_SHARD_ZERO2 = auto()
+
+
+RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES = (
+    HandleShardingStrategy.FULL_SHARD,
+    HandleShardingStrategy.HYBRID_SHARD,
+)
+NO_RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES = (
+    HandleShardingStrategy.SHARD_GRAD_OP,
+    HandleShardingStrategy._HYBRID_SHARD_ZERO2,
+)
+
+
+class ParamInfo(NamedTuple):
+    """Information for an original parameter."""
+
+    param_name: str  # unprefixed
+    module: nn.Module
+    module_name: str
+
+
+class SharedParamInfo(NamedTuple):
+    """
+    Additional information for a shared parameter.
+
+    For each shared parameter, we designate one module and its parameter
+    variable to be the primary owner, determined as the first one encountered
+    in the parameter walk. These are prefixed with "prim". The primary module
+    and parameter do not have their own :class:`SharedParamInfo` instance.
+    """
+
+    param_name: str  # unprefixed
+    module: nn.Module
+    module_name: str
+    prim_param_name: str  # unprefixed
+    prim_module: nn.Module
+    prim_module_name: str
+
+
+class _ShardParamInfo(NamedTuple):
+    """Shard-related information for an original parameter."""
+
+    in_shard: bool
+    # Use to index into the sharded flat parameter, e.g.
+    # `flat_param[offset_in_shard : offset_in_shard + numel_in_shard]`
+    offset_in_shard: Optional[int]
+    numel_in_shard: Optional[int]
+    # Use to get part of the parameter in the local shard from a flattened
+    # version of the unsharded parameter, e.g. either
+    # `param.flatten()[intra_param_start_idx : intra_param_end_idx + 1]` or
+    # `param.as_strided((param.numel(),), (1,))[intra_param_start_idx : intra_param_end_idx + 1]`
+    intra_param_start_idx: Optional[int]
+    intra_param_end_idx: Optional[int]  # inclusive
+
+
+class FlatParamShardMetadata(NamedTuple):
+    """
+    This holds metadata specific to this rank's shard of the flat parameter.
+
+    Attributes:
+        param_names (Tuple[str, ...]): Prefixed parameter names of this rank's
+            shard of the parameters; see :class:`FlatParameter`.
+        param_shapes (Tuple[torch.Size, ...]): Parameter shapes of this rank's
+            shard of the parameters; see :class:`FlatParameter`.
+        param_strides (Tuple[torch.Size, ...]): Parameter strides of this rank's
+            shard of the parameters; see :class:`FlatParameter`.
+        param_contiguities (Tuple[bool, ...]): Parameter `.contiguous` call results
+            of this rank's shard of the parameters; see :class:`FlatParameter`.
+        param_numels (Tuple[int, ...]): Parameter numels of this rank's shard
+            of the parameters; see :class:`FlatParameter`.
+        param_offsets (Tuple[Tuple[int, int], ...]): [start, end] offsets (in
+            units of numels) giving this rank's part of each flattened
+            original parameter.
+    """
+
+    param_names: tuple[str, ...]
+    param_shapes: tuple[torch.Size, ...]
+    param_strides: tuple[tuple[int, ...], ...]
+    param_contiguities: tuple[bool, ...]
+    param_numels: tuple[int, ...]
+    param_offsets: tuple[tuple[int, int], ...]
+
+
+class _FlatParameterMeta(_ParameterMeta):
+    # Make `isinstance(t, FlatParameter)` return True for custom tensor
+    # instances that have the _is_flat_param flag for BC
+    def __instancecheck__(self, instance):
+        # NB: do NOT test the super implementation
+        return isinstance(instance, torch.Tensor) and getattr(
+            instance, "_is_flat_param", False
+        )
+
+
+class FlatParameter(nn.Parameter, metaclass=_FlatParameterMeta):
+    """
+    This is the flat parameter used by :class:`FullyShardedDataParallel`.
+
+    It is comprised of one or more original parameters, which are flattened and
+    concatenated to construct the flat parameter.
+
+    Under the current design, this parameter logically represents both the
+    unsharded and sharded flat parameter, and its data changes storages
+    dynamically.
+        - In the :class:`FullyShardedDataParallel` constructor, the parameter
+        is initialized as unsharded and then sharded in-place.
+        - At runtime, the parameter is lazily (re)-initialized. The sharded
+        parameter data is saved in ``self._local_shard``, and a new ``Tensor``
+        ``self._full_param_padded`` is created, which is the all-gather
+        destination and owns the unsharded parameter storage thereafter. (See
+        :meth:`FlatParamHandle.init_flat_param_attributes`.)
+        - Throughout runtime, the parameter data changes storages as needed,
+        e.g. to the sharded flat parameter, low precision sharded flat
+        parameter, or the unsharded flat parameter.
+
+    NOTE: Since ``use_orig_params=True`` supports intra-``FlatParameter``
+    padding, we have two versions of the per-parameter numels, one that
+    includes the padding (``_numels_with_padding``) and one that does not
+    (``_numels``). The former may have length longer than the other data
+    structures, while the latter has the same length as the number of actual
+    original parameters like the other per-parameter data structures.
+
+    NOTE: This is not a real class; instead, you will always get a Parameter
+    back out if you try to create one of these.  This is similar to the trick
+    we implemented for Parameter to get it to work with subclasses; this
+    is primarily so that FlatParameter supports combination with FakeTensor.
+
+    Attributes:
+        _unpadded_unsharded_size (torch.Size): Unsharded flat parameter's size
+            without right-hand-side padding for divisibility by the world size.
+            For ``use_orig_params=True``, this includes alignment padding.
+        _padded_unsharded_size (torch.Size): Unsharded flat parameter's size
+            with right-hand-side padding for divisibility by the world size.
+            For ``use_orig_params=True``, this includes alignment padding. This
+            is only set for sharded strategies since they require padding for
+            the all-gather.
+        _sharded_size (torch.Size): Sharded flat parameter's size with padding.
+            This is also set for ``NO_SHARD``, in which case it is the same as
+            the unsharded sizes. (We omit "padded" because there is no
+            analogous unpadded one.)
+
+        _num_params (int): Number of original parameters flattened into this
+            flat parameter. This is the length of the per-parameter data
+            structures.
+        _param_infos (Tuple[ParamInfo, ...]): Each parameter's parameter info
+            entry; see :class:`ParamInfo` for details.
+        _shapes (Tuple[torch.Size, ...]): Each parameter's original shape.
+        _strides (Tuple[torch.Size, ...]): Each parameter's original stride.
+        _contiguities (Tuple[bool, ...]): Each parameter's ``contiguous()``
+            call result.
+        _fqns (Tuple[str, ...]): Each parameter's fully-qualified name (FQN)
+            prefixed from the ``_fully_sharded_module``. The names are
+            guaranteed to be unique in the subtree rooted at that module.
+        _param_extensions (Tuple[Optional[Any], ...]): Each parameter's
+            extension (i.e. some per-parameter state) used to customize
+            pre-flatten and post-unflatten behavior or ``None``. This is
+            experimental, and users should not depend on its existence in the
+            future.
+        _numels_with_padding (Tuple[int, ...]): Each parameter's numel
+            including entries for the padding. This is used to construct views
+            into the flat parameter via ``torch.split()``. This may have length
+            longer than ``_num_params``.
+        _numels (Tuple[int, ...]): Each parameter's numel excluding entries for
+            padding. This has length equal to ``_num_params``.
+        _shard_param_infos (Tuple[_ShardParamInfo, ...]): Each parameter's
+            shard parameter info; see :class:`_ShardParamInfo` for details.
+        _shared_param_infos (Tuple[SharedParamInfo, ...]): Shared parameter
+            info entries; see :class:`SharedParamInfo` for details.
+        _modules (set[nn.Module]): Modules that contain some original parameter
+            that is flattened into the flat parameter.
+
+        _shard_numel_padded (int): Numel padded for this rank's sharded flat
+            parameter.
+        _local_shard (Tensor): Sharded flat parameter with padding if using a
+            sharded strategy. If using ``NO_SHARD``, then this is the unpadded
+            unsharded flat parameter, and there is no notion of a sharded flat
+            parameter or padded unsharded flat parameter.
+        _full_param_padded (Tensor): Unsharded flat parameter with padding.
+            This is not defined for ``NO_SHARD``. When using mixed precision
+            for parameters, this has the low precision.
+        _full_prec_full_param_padded (Tensor): Full precision unsharded flat
+            parameter with padding. This is used for unsharding outside of
+            computation when using mixed precision for parameters. This is
+            never defined for ``NO_SHARD``.
+        _post_backward_hook_handle (RemovableHandle):
+            Flat parameter's post-backward hook handle. (Compile only)
+        _post_backward_hook_state (Tuple[AccumulateGrad, RemovableHandle]):
+            Flat parameter's :class:`AccumulateGrad` object and post-backward
+            hook handle. (Eager only)
+        _mp_shard (Tensor): Low precision sharded flat parameter with padding.
+            This is only defined when parameter mixed precision is enabled. For
+            ``NO_SHARD``, this is used for computation.
+        _cpu_grad (Tensor): Sharded gradient with padding stored on CPU.
+            This is only defined when offloading parameters is enabled.
+        _saved_grad_shard (Tensor): Sharded gradient with padding from previous
+            iterations for gradient accumulation without :meth:`no_sync`.
+
+        _params (Optional[List[nn.Parameter]]): If ``use_orig_params=True``,
+            then each original parameter variable; otherwise, ``None``. This
+            does not include any padding tensors.
+        _shared_params (Optional[List[nn.Parameter]]): The original shared
+            parameter variables if ``use_orig_params=True`` and ``None``
+            otherwise.
+        _tensors (Optional[List[Optional[Tensor]]]): This saves the ``Tensor``
+            views created in the forward and tracked by autograd when
+            ``use_orig_params=True`` and is ``None`` otherwise. This is to
+            preserve those ``Tensor`` variables for the backward to ensure that
+            the ``FlatParameter`` 's ``AccumulateGrad`` object does not change
+            in which case the post-backward hook does not run. This is relevant
+            for cases like reentrant activation checkpointing.
+        _is_grad_none_mask (Optional[List[bool]]): If ``use_orig_params=True``,
+            a mask over the original parameters' gradients indicating if it is
+            logically ``None`` or not; otherwise, ``None``. This does not
+            include entries for padding. This mask is needed because only some
+            of the parameters may have ``None`` gradient, in which case the
+            flat gradient must be non-``None`` and must use zeros to
+            approximate those original ``None`` gradients. This mask informs
+            FSDP to set the original parameter gradients to ``None`` (instead
+            of zeros) as needed.
+    """
+
+    _unpadded_unsharded_size: torch.Size
+    _padded_unsharded_size: torch.Size
+    _sharded_size: torch.Size
+    _num_params: int
+    _param_infos: tuple[ParamInfo, ...]
+    _shapes: tuple[torch.Size, ...]
+    _strides: tuple[tuple[int, ...], ...]
+    _contiguities: tuple[bool, ...]
+    _fqns: tuple[str, ...]
+    _param_extensions: tuple[Optional[Any], ...]
+    _numels_with_padding: tuple[int, ...]
+    _numels: tuple[int, ...]
+    _shard_param_infos: tuple[_ShardParamInfo, ...]
+    _shared_param_infos: tuple[SharedParamInfo, ...]
+    _modules: set[nn.Module]
+    _shard_numel_padded: int
+    _local_shard: Tensor
+    _full_param_padded: Tensor
+    _full_prec_full_param_padded: Tensor
+    # Eager only
+    _post_backward_hook_state: tuple[Any, Any]
+    # Compile only
+    _post_backward_hook_handle: Any
+    _mp_shard: Tensor
+    _cpu_grad: Tensor
+    _saved_grad_shard: Tensor
+    _params: Optional[list[nn.Parameter]]
+    _shared_params: Optional[list[nn.Parameter]]
+    _tensors: Optional[list[Optional[Tensor]]]
+    _is_grad_none_mask: Optional[list[bool]]
+
+    _is_padding_mask: list[bool]
+
+    def __new__(cls, data=None, requires_grad=True):
+        assert cls is FlatParameter, "subclasses FlatParameter not supported"
+        r = nn.Parameter.__new__(nn.Parameter, data, requires_grad)  # type: ignore[call-arg]
+        r._is_flat_param = True  # type: ignore[attr-defined]
+        return r
+
+    # NB: This is not a regular method, because FlatParameters are not actually
+    # instances of this class (see __new__ above).  So you must indirectly
+    # call this directly through the classmethod.
+    @classmethod
+    def _init_metadata(
+        cls,
+        self,
+        param_infos: list[ParamInfo],
+        numels: list[int],
+        shapes: list[torch.Size],
+        strides: list[tuple[int, ...]],
+        contiguities: list[bool],
+        fqns: list[str],
+        shared_param_infos: list[SharedParamInfo],
+        param_extensions: list[Optional[Any]],
+        params: Optional[list[nn.Parameter]],
+        shared_params: Optional[list[nn.Parameter]],
+        is_padding_mask: list[bool],
+    ) -> None:
+        """
+        Initialize attributes holding metadata about the original parameters comprising the flat parameter.
+
+        We expose this method separate from the constructor to keep the
+        constructor only responsible for the flat parameter's tensor data. This
+        method should only be called once per model, while the constructor may
+        be called multiple times, e.g. when reloading from a checkpoint, in
+        which case only the tensor data needs to be passed to the constructor.
+        Since :meth:`load_state_dict` is implemented via :meth:`copy_`, the
+        metadata is correctly assumed to be unchanged.
+
+        Args:
+            See the Attributes in the class docstring.
+        """
+        assert len(param_infos) == len(shapes)
+        assert len(param_infos) == len(strides)
+        assert len(param_infos) == len(contiguities)
+        assert len(param_infos) == len(fqns)
+        assert len(param_infos) == len(param_extensions)
+        self._num_params = len(param_infos)
+        self._param_infos = param_infos
+        self._shapes = shapes
+        self._strides = strides
+        self._contiguities = contiguities
+        self._fqns = fqns
+        self._param_extensions = param_extensions
+        self._is_padding_mask = is_padding_mask
+
+        numels_without_padding: list[int] = []
+        for numel, is_padding in zip(numels, is_padding_mask):
+            if not is_padding:
+                numels_without_padding.append(numel)
+        self._numels = tuple(numels_without_padding)
+        self._numels_with_padding = tuple(numels)
+        assert len(self._numels) == self._num_params
+
+        self._shared_param_infos = tuple(shared_param_infos)
+        self._modules = {pi.module for pi in self._param_infos}.union(
+            {spi.module for spi in self._shared_param_infos}
+        )
+        assert (params is None) == (shared_params is None)
+        if params is not None:
+            assert shared_params is not None and len(shared_params) == len(
+                shared_param_infos
+            )
+            self._params = []
+            for param, is_padding in zip(params, is_padding_mask):
+                if not is_padding:
+                    self._params.append(param)
+            self._shared_params = shared_params
+            # Mark the original parameters to avoid flattening them into
+            # another `FlatParameter` during recursive construction
+            for param in chain(self._params, self._shared_params):
+                _set_fsdp_flattened(param)
+            self._is_grad_none_mask = [False for _ in range(self._num_params)]
+            self._tensors = [None for _ in range(self._num_params)]
+        else:
+            self._params = None
+            self._shared_params = None
+            self._is_grad_none_mask = None
+            self._tensors = None
+        self._unpadded_unsharded_size = self.size()
+        _set_fsdp_flattened(self)
+        # Tracks whether the `FlatParameter`'s post-backward hook has been
+        # called to modify the behavior of the post-backward callback
+        self._post_backward_called = False
+
+
+class FlatParamHandle:
+    """
+    A handle that manages a flat parameter (:class:`FlatParameter`).
+
+    This includes sharding and view management.
+
+    Args:
+        params (Sequence[nn.Parameter]): The parameters to flatten into the
+            flat parameter.
+        fully_sharded_module (nn.Module): See [Note: Fully Sharded Module].
+        device (torch.device): The compute and communication device, which
+            should be a non-CPU device. We refer to it as the compute device.
+        sharding_strategy (ShardingStrategy): Sharding strategy to apply to
+            this handle's ``FlatParameter``.
+        offload_params (bool): Whether to offload the handle's
+            ``FlatParameter`` to CPU.
+        mp_param_dtype (Optional[torch.dtype]): Parameter mixed precision
+            setting passed to the FSDP constructor.
+        mp_reduce_dtype (Optional[torch.dtype]): Gradient reduction mixed
+            precision setting passed to the FSDP constructor.
+        keep_low_precision_grads (bool): Whether to keep gradients in low
+            precision.
+        use_orig_params (bool): If ``True``, then FSDP preserves the original
+            parameter variables and returns them from ``named_parameters()``
+            (e.g. to support different optimizer hyperparameters within one
+            :class:`FlatParameter`). If ``False``, then FSDP reconstructs the
+            parameters every iteration and returns the :class:`FlatParameter` s
+            from ``named_parameters()``.
+    """
+
+    ##################
+    # INITIALIZATION #
+    ##################
+    def __init__(
+        self,
+        params: Sequence[Union[nn.Parameter, Tensor]],
+        fully_sharded_module: nn.Module,
+        device: torch.device,
+        sharding_strategy: HandleShardingStrategy,
+        offload_params: bool,
+        mp_param_dtype: Optional[torch.dtype],
+        mp_reduce_dtype: Optional[torch.dtype],
+        keep_low_precision_grads: bool,
+        process_group: dist.ProcessGroup,
+        use_orig_params: bool,
+        *,
+        fsdp_extension: Optional[FSDPExtensions] = None,
+    ):
+        super().__init__()
+        params = list(params)
+        if len(params) == 0:
+            raise ValueError(
+                f"Cannot construct a {self.__class__.__name__} with an empty parameter list"
+            )
+        self._init_setattr_fns()
+        self._skip_writeback_check = (
+            os.environ.get(_FSDP_SKIP_WRITEBACK_CHECK, "") == "1"
+        )
+        self._use_full_prec_in_eval = (
+            os.environ.get(_FSDP_USE_FULL_PREC_IN_EVAL, "") == "1"
+        )
+        self._use_fake_all_gather = os.environ.get(_FSDP_USE_FAKE_ALL_GATHER, "") == "1"
+        self._use_fake_reduce = os.environ.get(_FSDP_USE_FAKE_REDUCE, "") == "1"
+        if self._skip_writeback_check:
+            _warn_skip_writeback_check(
+                logger,
+                f"Since {_FSDP_SKIP_WRITEBACK_CHECK}=1, FSDP will not check "
+                "for parameter or gradient writeback. Changing parameter or "
+                "gradient storages may lead to silent correctness errors.",
+            )
+        if self._use_fake_all_gather:
+            _warn_use_fake_all_gather(
+                logger,
+                f"Since {_FSDP_USE_FAKE_ALL_GATHER}=1, FSDP will not execute "
+                "all-gather ops. Your training will be incorrect, but "
+                "can reveal how much time spent on all-gather ops.",
+            )
+        if self._use_fake_reduce:
+            _warn_use_fake_reduce(
+                logger,
+                f"Since {_FSDP_USE_FAKE_REDUCE}=1, FSDP will not execute "
+                "reduce-scatter ops. Your training will be incorrect, but "
+                "can reveal how much time spent on reduce-scatter ops.",
+            )
+        # Only align addresses for `use_orig_params=True` (for now)
+        align_addresses = use_orig_params
+        self._init_get_unflat_views_fn(align_addresses)
+        self.device = device
+        self._device_handle = _FSDPDeviceHandle.from_device(self.device)
+        self.process_group = process_group
+        if self._use_fake_all_gather or self._use_fake_reduce:
+            self._fake_process_group = FakeProcessGroup(
+                rank=process_group.rank(), world_size=process_group.size()
+            )
+        self.rank = process_group.rank()
+        self.world_size = process_group.size()
+        self._sharding_strategy = sharding_strategy
+        self._offload_params = offload_params
+        self._use_orig_params = use_orig_params
+        self._keep_low_precision_grads = keep_low_precision_grads
+        self._training_state = HandleTrainingState.IDLE
+        self._debug_level = dist.get_debug_level()
+        self._fully_sharded_module = fully_sharded_module
+        # For strategies that do not free after forward, we skip using sharded
+        # views after forward since the unsharded data exists. We still switch
+        # `self.flat_param` to point to the sharded flat parameter since what
+        # it points to parameterizes behavior. We use the following attribute
+        # to track which tensor data the parameters are unsharded views into.
+        self._unsharded_flat_param_for_skipped_views: Optional[Tensor] = None
+        # The index in the state's `all_handles`, which must be the
+        # same across ranks for the execution order validation to work
+        self._handle_index: Optional[int] = None
+        # Index in handles_to_pre_forward_order
+        self._pre_forward_order_index: Optional[int] = None
+        # Index in `handles_post_forward_order`
+        self._post_forward_index: Optional[int] = None
+        # Used for guarding against mistargeted forward prefetches
+        self._needs_pre_forward_unshard = False
+        # Used for guarding against mistargeted backward prefetches
+        self._needs_pre_backward_unshard = False
+        # Was the handle prefetched? Set on successful _prefetch_handle and unshard
+        self._prefetched = False
+        # Optimistically assume a valid input `params` and set dtype attributes
+        # before `_init_flat_param()`, which performs the actual validation
+        self._orig_param_dtype = params[0].dtype
+        self._init_param_reduce_dtypes(mp_param_dtype, mp_reduce_dtype)
+        assert self._fwd_bwd_param_dtype is not None  # mypy
+        self._aligned_numel = (
+            _get_aligned_numel(unsharded_dtype=self._fwd_bwd_param_dtype)
+            if align_addresses
+            else 0
+        )
+        self._fsdp_extension = fsdp_extension
+        self._init_flat_param_and_metadata(
+            params,
+            fully_sharded_module,
+            self._aligned_numel,
+            use_orig_params,  # type: ignore[arg-type]
+        )
+        self._use_unsharded_views(as_params=False)
+
+    def _init_setattr_fns(self):
+        use_unsafe_setattr = os.environ.get(_FSDP_USE_UNSAFE_SETATTR, "") == "1"
+        self._setattr_tensor: Callable[[nn.Module, str, Tensor], None]
+        self._setattr_param: Callable[[nn.Module, str, nn.Parameter], None]
+        if use_unsafe_setattr:
+            self._setattr_tensor = _unsafe_setattr_tensor
+            self._setattr_param = _unsafe_setattr_param
+        else:
+            self._setattr_tensor = _safe_setattr_tensor_or_param
+            self._setattr_param = _safe_setattr_tensor_or_param
+
+    def _init_get_unflat_views_fn(self, align_addresses: bool):
+        self._get_unflat_views = (
+            self._get_unflat_views_aligned
+            if align_addresses
+            else self._get_unflat_views_unaligned
+        )
+
+    def _init_flat_param_and_metadata(
+        self,
+        params: list[Union[Tensor, nn.Parameter]],
+        module: nn.Module,
+        aligned_numel: int,
+        use_orig_params: bool,
+    ) -> None:
+        """
+        Initialize the ``FlatParameter`` and its metadata.
+
+        NOTE: This should only be called once at construction time, after which
+        the ``FlatParameter`` metadata is assumed to be static.
+
+        NOTE: The elements of ``params`` should only be ``Tensor`` s when
+        composing with ``DTensor`` -based tensor parallelism, in which case the
+        elements may be ``DTensor`` local shards.
+        """
+        if len(params) == 0:
+            raise ValueError("Expects non-empty `params`")
+        if aligned_numel < 0:
+            raise ValueError(
+                f"Expects non-negative `aligned_numel` but got {aligned_numel}"
+            )
+        (
+            dtype,
+            flat_param_requires_grad,
+            device,
+        ) = self._validate_tensors_to_flatten(params)
+        params_set = set(params)
+        # For alignment padding, only `numels` gets strictly non-`None`
+        # elements, and all other lists get `None` elements for padding.
+        param_infos: list[ParamInfo] = []
+        numels: list[int] = []
+        shapes: list[torch.Size] = []
+        strides: list[tuple[int, ...]] = []
+        contiguities: list[bool] = []
+        fqns: list[str] = []
+        shared_param_infos: list[SharedParamInfo] = []
+        shared_param_memo: dict[
+            Union[Tensor, nn.Parameter], tuple[nn.Module, str, str]
+        ] = {}
+        params_to_flatten: list[Union[Tensor, nn.Parameter]] = []
+        shared_params: list[Union[Tensor, nn.Parameter]] = []
+        param_extensions: list[Any] = []
+        is_padding_mask: list[bool] = []
+        total_numel = total_numel_without_padding = 0
+        for submodule_name, submodule in module.named_modules(remove_duplicate=False):
+            for param_name, param in _named_parameters_with_duplicates(
+                submodule, recurse=False
+            ):
+                if param not in params_set:
+                    continue
+                if param in shared_param_memo:  # shared reference
+                    prim_module, prim_module_name, prim_param_name = shared_param_memo[
+                        param
+                    ]
+                    shared_params.append(param)
+                    shared_param_infos.append(
+                        SharedParamInfo(
+                            param_name,
+                            submodule,
+                            submodule_name,
+                            prim_param_name,
+                            prim_module,
+                            prim_module_name,
+                        )
+                    )
+                else:
+                    if aligned_numel > 0:
+                        numel_to_pad = aligned_numel - (total_numel % aligned_numel)
+                        if numel_to_pad > 0 and numel_to_pad < aligned_numel:
+                            padding_tensor = _construct_padding_tensor(
+                                numel_to_pad, dtype, False, device
+                            )
+                            params_to_flatten.append(padding_tensor)
+                            is_padding_mask.append(True)
+                            numels.append(numel_to_pad)
+                            total_numel += numel_to_pad
+                    transform_t, extension = _ext_pre_flatten_transform(
+                        param,
+                        self._fsdp_extension,
+                    )
+                    param = cast(nn.Parameter, transform_t)
+                    param_extensions.append(extension)
+                    shared_param_memo[param] = (submodule, submodule_name, param_name)
+                    params_to_flatten.append(param)
+                    is_padding_mask.append(False)
+                    param_infos.append(ParamInfo(param_name, submodule, submodule_name))
+                    numels.append(param.numel())
+                    shapes.append(param.shape)
+                    strides.append(param.stride())
+                    contiguities.append(_is_truly_contiguous(param))
+                    fqn = (
+                        submodule_name + "." + param_name
+                        if submodule_name
+                        else param_name
+                    )
+                    fqns.append(fqn)
+                    total_numel += param.numel()
+                    total_numel_without_padding += param.numel()
+        if len(params_to_flatten) == 0:
+            raise ValueError(
+                f"`params` were not found in `module`'s tree"
+                f"params: {params}\nmodule: {module}"
+            )
+        if (
+            self.rank == 0
+            and aligned_numel > 0
+            and total_numel != total_numel_without_padding
+        ):
+            logger.debug(
+                "FSDP FlatParameter address alignment created "
+                "%s numel of padding (%s vs. %s)",
+                total_numel - total_numel_without_padding,
+                total_numel,
+                total_numel_without_padding,
+            )
+        if aligned_numel > 0:
+            # Pad to be divisible by world size to avoid a copy for the
+            # post-backward reduce-scatter
+            numel_to_pad = self.world_size - (total_numel % self.world_size)
+            if numel_to_pad > 0 and numel_to_pad < self.world_size:
+                if self.rank == 0:
+                    logger.info(
+                        "FSDP FlatParameter world size divisibility created "
+                        "%s numel of padding",
+                        numel_to_pad,
+                    )
+                padding_tensor = _construct_padding_tensor(
+                    numel_to_pad, dtype, False, device
+                )
+                params_to_flatten.append(padding_tensor)
+                is_padding_mask.append(True)
+                numels.append(numel_to_pad)
+                total_numel += numel_to_pad
+        # Pass `aligned_numel=0` since we already included padding tensors
+        self.flat_param: FlatParameter = self.flatten_tensors_into_flat_param(
+            params_to_flatten,
+            aligned_numel=0,
+            requires_grad=flat_param_requires_grad,
+        )
+        FlatParameter._init_metadata(
+            self.flat_param,
+            param_infos,
+            numels,
+            shapes,
+            strides,
+            contiguities,
+            fqns,
+            shared_param_infos,
+            param_extensions,
+            _convert_to_params(params_to_flatten) if use_orig_params else None,
+            _convert_to_params(shared_params) if use_orig_params else None,
+            is_padding_mask,
+        )
+
+    def _validate_tensors_to_flatten(
+        self, tensors: list[Union[Tensor, nn.Parameter]]
+    ) -> tuple:
+        """Validate the tensors to flatten and returns any necessary metadata."""
+        dtype: Optional[torch.dtype] = None
+        # Return as the logical OR over each tensor's value
+        flat_param_requires_grad: Optional[bool] = None
+        device: Optional[torch.device] = None
+        # For `use_orig_params=True`, permit non-uniform `requires_grad`
+        for tensor in tensors:
+            if isinstance(tensor, FlatParameter):
+                raise ValueError("Cannot flatten a `FlatParameter`")
+            if dtype is None and not tensor.is_floating_point():
+                raise ValueError("Cannot flatten integer dtype tensors")
+            if dtype is not None and tensor.dtype != dtype:
+                raise ValueError(
+                    f"Must flatten tensors with uniform dtype but got {dtype} "
+                    f"and {tensor.dtype}"
+                )
+            if (
+                not self._use_orig_params
+                and flat_param_requires_grad is not None
+                and tensor.requires_grad != flat_param_requires_grad
+            ):
+                raise ValueError(
+                    "Must flatten tensors with uniform `requires_grad` when "
+                    "`use_orig_params=False`"
+                )
+            if device is not None and tensor.device != device:
+                raise ValueError(
+                    "Must flatten tensors on the same device but got both "
+                    f"{device} and {tensor.device}"
+                )
+            dtype = tensor.dtype
+            flat_param_requires_grad = flat_param_requires_grad or tensor.requires_grad
+            device = tensor.device
+        assert flat_param_requires_grad is not None, "Requires non-empty `tensors` list"
+        return dtype, flat_param_requires_grad, device
+
+    def flatten_tensors(
+        self,
+        tensors: list[Tensor],
+        aligned_numel: int,
+    ) -> Tensor:
+        """
+        Flatten ``tensors`` into a single flat tensor.
+
+        The flattening optionally includes
+        padding if ``aligned_numel`` is greater than 0, where ``aligned_numel``
+        gives the numel required to have address alignment.
+
+        NOTE: The padding alignment algorithm must be kept in sync with
+        :meth:`_init_flat_param_metadata`. We separate the two methods because
+        the initialization happens once, whereas this method may be called
+        multiple times throughout training (e.g. for checkpointing).
+        """
+        if len(tensors) == 0:
+            raise ValueError("Expects non-empty `tensors`")
+        if aligned_numel < 0:
+            raise ValueError(
+                f"Expects non-negative `aligned_numel` but got {aligned_numel}"
+            )
+        dtype, _, device = self._validate_tensors_to_flatten(tensors)
+        flat_tensors: list[Tensor] = []
+        if aligned_numel > 0:
+            total_numel = 0
+            for tensor in tensors:
+                numel_to_pad = aligned_numel - (total_numel % aligned_numel)
+                if numel_to_pad > 0 and numel_to_pad < aligned_numel:
+                    padding_tensor = _construct_padding_tensor(
+                        numel_to_pad, dtype, False, device
+                    )
+                    flat_tensors.append(padding_tensor)
+                    total_numel += numel_to_pad
+                flat_tensors.append(
+                    torch.flatten(_detach_if_needed(tensor))
+                    if _is_truly_contiguous(tensor)
+                    else _detach_if_needed(tensor).as_strided((tensor.numel(),), (1,))
+                )
+                total_numel += tensor.numel()
+            numel_to_pad = self.world_size - (total_numel % self.world_size)
+            if numel_to_pad > 0 and numel_to_pad < self.world_size:
+                padding_tensor = _construct_padding_tensor(
+                    numel_to_pad, dtype, False, device
+                )
+                flat_tensors.append(padding_tensor)
+                total_numel += numel_to_pad
+        else:
+            flat_tensors = [
+                torch.flatten(_detach_if_needed(tensor))
+                if _is_truly_contiguous(tensor)
+                else _detach_if_needed(tensor).as_strided((tensor.numel(),), (1,))
+                for tensor in tensors
+            ]
+        return torch.cat(flat_tensors, dim=0)
+
+    def flatten_tensors_into_flat_param(
+        self,
+        tensors: list[Tensor],
+        aligned_numel: int,
+        requires_grad: bool,
+    ) -> FlatParameter:
+        flat_param_data = self.flatten_tensors(tensors, aligned_numel)
+        return FlatParameter(flat_param_data, requires_grad=requires_grad)
+
+    def _init_param_reduce_dtypes(
+        self,
+        mp_param_dtype: Optional[torch.dtype],
+        mp_reduce_dtype: Optional[torch.dtype],
+    ) -> None:
+        """
+        Initialize param and reduce dtypes.
+
+        Precondition: ``self.flat_param`` is set. This ensures that this
+        handle's parameters have a single dtype.
+
+        Postcondition: This sets ``self._fwd_bwd_param_dtype`` and
+        ``self._reduce_dtype``. If ``mp_param_dtype`` or ``mp_reduce_dtype``
+        is ``None``, then we assume the original parameter dtype. One special
+        case is if ``mp_param_dtype`` is not ``None`` and ``mp_reduce_dtype``
+        is ``None``, in which case we assume the gradient reduction dtype
+        matches the forward/backward parameter dtype.
+        """
+        # Save whether these dtypes were specified so that we permit the
+        # parameter dtype to change up until the lazy initialization
+        self._low_prec_param_dtype_specified = mp_param_dtype is not None
+        self._low_prec_reduce_dtype_specified = mp_reduce_dtype is not None
+        if (
+            self._low_prec_param_dtype_specified
+            and not self._low_prec_reduce_dtype_specified
+        ):
+            # Special case: infer gradient reduction mixed precision
+            self._fwd_bwd_param_dtype = mp_param_dtype
+            self._reduce_dtype = self._fwd_bwd_param_dtype
+        else:
+            self._fwd_bwd_param_dtype = mp_param_dtype or self._orig_param_dtype
+            self._reduce_dtype = mp_reduce_dtype or self._orig_param_dtype
+        assert self._fwd_bwd_param_dtype is not None
+        assert self._reduce_dtype is not None
+
+    ###################################
+    # SHARD INITIALIZATION & METADATA #
+    ###################################
+    @torch.no_grad()
+    def shard(self):
+        """
+        Shard the handle's ``FlatParameter``.
+
+        This allocates new memory for
+        the sharded flat parameter and frees the unsharded flat parameter's
+        storage.
+
+        Postcondition: ``self.flat_param`` is the sharded flat parameter. Shard
+        metadata attributes are set for all sharding strategies.
+        """
+        flat_param = self.flat_param
+        if not self.uses_sharded_strategy:
+            self._init_shard_metadata(0, 0, flat_param.numel() - 1)
+        else:
+            _p_assert(
+                flat_param.storage_offset() == 0,
+                "The `FlatParameter` is not the sole occupant of its storage",
+            )
+            sharded_flat_param, numel_padded = FlatParamHandle._get_shard(
+                flat_param, self.rank, self.world_size
+            )
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                allocated = flat_param._typed_storage()._size() > 0
+                if allocated:
+                    flat_param._typed_storage()._resize_(0)
+            flat_param.set_(sharded_flat_param)  # type: ignore[call-overload]
+            start_idx = sharded_flat_param.numel() * self.rank
+            end_idx = sharded_flat_param.numel() * (self.rank + 1) - 1  # inclusive
+            self._init_shard_metadata(numel_padded, start_idx, end_idx)
+        if self._use_orig_params:
+            self._use_sharded_views()
+
+    def _init_shard_metadata(
+        self,
+        numel_padded: int,
+        unsharded_start_idx: int,
+        unsharded_end_idx: int,
+    ) -> None:
+        """
+        Initialize shard-related metadata for this rank's shard of the flat parameter.
+
+        This includes ``_sharded_size``, ``_shard_param_infos``, and ``_shard_numel_padded``.
+
+        Args:
+            numel_padded (int): Numel padded for this rank's sharded flat
+                parameter.
+            unsharded_start_idx (int): Start index in the unsharded flat
+            parameter assigned to this rank.
+            unsharded_end_idx (int): End index (inclusive) in the unsharded
+                flat parameter assigned to this rank.
+
+        Precondition: ``self.flat_param`` 's data is the sharded flat
+        parameter.
+        """
+        flat_param = self.flat_param
+        flat_param._sharded_size = flat_param.size()  # type: ignore[attr-defined]
+        sharded_flat_param_numel = flat_param.numel()  # includes `numel_padded`
+        _p_assert(
+            unsharded_start_idx >= 0 and unsharded_start_idx <= unsharded_end_idx,
+            f"unsharded_start_idx: {unsharded_start_idx} unsharded_end_idx: {unsharded_end_idx}",
+        )
+        _p_assert(
+            numel_padded <= sharded_flat_param_numel,
+            f"numel_padded: {numel_padded} "
+            f"sharded_flat_param_numel: {sharded_flat_param_numel}",
+        )
+        shard_param_infos = self._get_shard_metadata(
+            unsharded_start_idx, unsharded_end_idx
+        )
+        assert len(shard_param_infos) == flat_param._num_params, (
+            f"Expects length {flat_param._num_params} but got {len(shard_param_infos)}"
+        )
+        flat_param._shard_param_infos = shard_param_infos  # type: ignore[attr-defined]
+        flat_param._shard_numel_padded = numel_padded  # type: ignore[attr-defined]
+
+    def _get_shard_metadata(
+        self,
+        unsharded_start_idx: int,
+        unsharded_end_idx: int,
+    ) -> tuple[_ShardParamInfo, ...]:
+        """
+        Compute the shard metadata based on ``unsharded_start_idx`` and ``unsharded_end_idx`` (inclusive).
+
+        ``unsharded_start_idx`` and ``unsharded_end_idx`` give the interval of the
+        unsharded flat parameter specifying the shard.
+        """
+        flat_param_offsets = self._get_flat_param_offsets()
+        assert len(flat_param_offsets) == len(self.flat_param._numels_with_padding), (
+            f"Expected {len(self.flat_param._numels_with_padding)} but got {len(flat_param_offsets)}"
+        )
+        shard_param_infos: list[_ShardParamInfo] = []
+        sharded_flat_param_numel = unsharded_end_idx - unsharded_start_idx + 1
+        # `unsharded_param_start_idx` and `unsharded_param_end_idx` are indices
+        # into the unsharded flat parameter (inclusive) of the given parameter
+        for (
+            (unsharded_param_start_idx, unsharded_param_end_idx),
+            is_padding,
+        ) in zip(flat_param_offsets, self.flat_param._is_padding_mask):
+            if is_padding:
+                continue
+            in_sharded_flat_param = (
+                unsharded_start_idx <= unsharded_param_end_idx
+                and unsharded_end_idx >= unsharded_param_start_idx
+            )
+            if not in_sharded_flat_param:
+                shard_param_info = _ShardParamInfo(False, None, None, None, None)
+            else:
+                if unsharded_start_idx <= unsharded_param_start_idx:
+                    # This branch can only happen once since the rank's
+                    # unsharded start index can only intersect one parameter
+                    intra_param_start_idx = 0
+                    offset_in_shard = unsharded_param_start_idx - unsharded_start_idx
+                else:
+                    intra_param_start_idx = (
+                        unsharded_start_idx - unsharded_param_start_idx
+                    )
+                    offset_in_shard = 0
+                assert (
+                    offset_in_shard >= 0 and offset_in_shard < sharded_flat_param_numel
+                ), (
+                    f"Invalid `offset_in_shard` of {offset_in_shard} for "
+                    f"sharded flat parameter with {sharded_flat_param_numel} numel"
+                )
+                intra_param_end_idx = (
+                    min(unsharded_param_end_idx, unsharded_end_idx)
+                    - unsharded_param_start_idx
+                )
+                numel_in_shard = intra_param_end_idx - intra_param_start_idx + 1
+                shard_param_info = _ShardParamInfo(
+                    True,
+                    offset_in_shard,
+                    numel_in_shard,
+                    intra_param_start_idx,
+                    intra_param_end_idx,
+                )
+            shard_param_infos.append(shard_param_info)
+        return tuple(shard_param_infos)
+
+    @staticmethod
+    def _get_unpadded_shard(
+        tensor: Tensor,
+        rank: int,
+        world_size: int,
+    ) -> tuple[Tensor, int]:
+        """
+        Return the unpadded shard of ``tensor`` for the given ``rank`` and ``world_size``.
+
+        The returned value is a tuple of the shard of ``tensor`` without any
+        padding and the numel to pad for that shard.
+
+        If ``tensor`` is already flattened or may be viewed in the flattened
+        shape (which is true in the expected usage), then this method does not
+        allocate any new tensor memory.
+        """
+        chunks = (
+            torch.flatten(tensor).chunk(world_size)
+            if _is_truly_contiguous(tensor)
+            else tensor.as_strided((tensor.numel(),), (1,)).chunk(world_size)
+        )
+        if len(chunks) < (rank + 1):
+            # This rank gets an empty chunk fully padded with zeros since there
+            # are not enough chunks across ranks
+            chunk = chunks[0].new_empty(0)
+        else:
+            chunk = chunks[rank]
+        numel_to_pad = chunks[0].numel() - chunk.numel()
+        assert numel_to_pad >= 0, (
+            "Chunk's size should be at most the first chunk's size"
+        )
+        return chunk, numel_to_pad
+
+    @staticmethod
+    def _get_shard(
+        tensor: Tensor,
+        rank: int,
+        world_size: int,
+    ) -> tuple[Tensor, int]:
+        """
+        Return the shard of ``tensor`` with padding for the given ``rank`` and ``world_size`` and the numel padded for that shard.
+
+        This method allocates new memory (via :meth:`clone`) since the
+        unsharded ``tensor`` may be deallocated after this method returns.
+        """
+        chunk, numel_to_pad = FlatParamHandle._get_unpadded_shard(
+            tensor, rank, world_size
+        )
+        shard = chunk.clone()
+        if numel_to_pad > 0:
+            shard = F.pad(shard, [0, numel_to_pad])
+        return shard, numel_to_pad
+
+    @staticmethod
+    def _get_sharded_size(tensor: Tensor, rank: int, world_size: int) -> torch.Size:
+        """
+        Return the shape of ``tensor`` after sharding including padding.
+
+        This requires ``tensor`` to have 1D shape and ensures that the returned
+        shape is 1D.
+        """
+        assert len(tensor.shape) == 1, f"{tensor.shape}"
+        unpadded_sharded_tensor, numel_to_pad = FlatParamHandle._get_unpadded_shard(
+            tensor, rank, world_size
+        )
+        unpadded_sharded_size = unpadded_sharded_tensor.size()
+        assert len(unpadded_sharded_size) == 1, f"{unpadded_sharded_size}"
+        return torch.Size([unpadded_sharded_size[0] + numel_to_pad])
+
+    def _get_flat_param_offsets(self) -> list[tuple[int, int]]:
+        """
+        Return [start, end] offsets of each original parameter's flattened data in the unsharded flat parameter (without padding).
+
+        NOTE: The returned list includes elements for alignment padding.
+        """
+        cumulative_sum = list(accumulate(self.flat_param._numels_with_padding))
+        starts = [0] + cumulative_sum[:-1]
+        ends = [end - 1 for end in cumulative_sum]  # inclusive
+        param_offsets = list(zip(starts, ends))
+        return param_offsets
+
+    @no_type_check
+    def shard_metadata(
+        self,
+    ) -> FlatParamShardMetadata:
+        """
+        Return the shard-related metadata specific to this rank's shard of the flat parameter.
+
+        NOTE: The returned tuple does not include elements for alignment
+        padding but does account for the padding.
+        """
+        fqns_list = []
+        shapes_list = []
+        strides_list = []
+        contiguities_list = []
+        numels_list = []
+        shard_param_offsets = []
+        for fqn, shape, stride, contiguous, numel, shard_param_info in zip(
+            self.flat_param._fqns,
+            self.flat_param._shapes,
+            self.flat_param._strides,
+            self.flat_param._contiguities,
+            self.flat_param._numels,
+            self.flat_param._shard_param_infos,
+        ):
+            if not shard_param_info.in_shard:
+                continue
+            fqns_list.append(fqn)
+            shapes_list.append(shape)
+            strides_list.append(stride)
+            contiguities_list.append(contiguous)
+            numels_list.append(numel)
+            shard_param_offsets.append(
+                (
+                    shard_param_info.intra_param_start_idx,
+                    shard_param_info.intra_param_end_idx,
+                )
+            )
+        return FlatParamShardMetadata(
+            tuple(fqns_list),
+            tuple(shapes_list),
+            tuple(strides_list),
+            tuple(contiguities_list),
+            tuple(numels_list),
+            tuple(shard_param_offsets),
+        )
+
+    @no_type_check
+    @torch.no_grad()
+    def init_flat_param_attributes(self) -> None:
+        """
+        This initializes some attributes on the handle's ``FlatParameter``.
+        This should be called during lazy initialization since it requires the
+        parameter to be on the compute device if not offloading to CPU and we
+        want to give users the chance to move the parameter appropriately after
+        the FSDP constructor.
+
+        For each tensor attribute on the ``FlatParameter``, see the unshard and
+        reshard methods in this class for the allocation and free pattern.
+        """
+        flat_param = self.flat_param
+        if flat_param.dtype != self._orig_param_dtype:
+            # Entering this branch means that the user changed the parameter
+            # dtype after FSDP initialization, in which case we may need to
+            # refresh some saved dtype attributes (dtypes specified as a part
+            # of mixed precision take precedence).
+            if not self._low_prec_param_dtype_specified:
+                self._fwd_bwd_param_dtype = flat_param.dtype
+            # For `reduce_dtype`, require `param_dtype` was not specified since
+            # then we infer the `reduce_dtype` from the specified `param_dtype`
+            if (
+                not self._low_prec_reduce_dtype_specified
+                and not self._low_prec_param_dtype_specified
+            ):
+                self._reduce_dtype = flat_param.dtype
+            self._orig_param_dtype = flat_param.dtype
+        cpu_device = torch.device("cpu")
+        if self._offload_params:
+            _p_assert(
+                flat_param.device == cpu_device,
+                f"Expects the `FlatParameter` to be on CPU when parameter CPU "
+                f"offloading is enabled, not {flat_param.device}",
+            )
+        else:
+            self._check_on_compute_device(self.flat_param)
+        flat_param._local_shard = flat_param.data
+        if self._offload_params:
+            # Pin the memory for faster H2D transfer
+            flat_param._local_shard = flat_param._local_shard.pin_memory()
+            # Pre-allocate the sharded gradient on CPU to enable non-blocking
+            # D2H transfer during the backward pass
+            flat_param._cpu_grad = torch.zeros_like(
+                flat_param._local_shard, device=cpu_device
+            ).pin_memory()
+        if self._uses_param_mixed_precision:
+            # For parameter mixed precision, we maintain a low precision
+            # sharded tensor on the compute device to be all-gathered (for
+            # sharded strategies) or directly used (for `NO_SHARD`) for
+            # computation.
+            flat_param._mp_shard = torch.empty_like(
+                flat_param._local_shard,
+                device=self.device,
+                dtype=self._fwd_bwd_param_dtype,
+            )
+            _free_storage(flat_param._mp_shard)
+        if self.uses_sharded_strategy:
+            # We maintain a padded unsharded tensor that serves as the
+            # all-gather destination and owns the original parameter storages.
+            unsharded_param_dtype = (
+                self._fwd_bwd_param_dtype
+                if self._uses_param_mixed_precision
+                else flat_param.dtype
+            )  # use low precision if parameter mixed precision is enabled
+            padded_unsharded_numel = flat_param.numel() * self.world_size
+            flat_param._full_param_padded = torch.empty(
+                padded_unsharded_numel,
+                device=self.device,
+                dtype=unsharded_param_dtype,
+            )
+            flat_param._padded_unsharded_size = flat_param._full_param_padded.size()
+            _free_storage(flat_param._full_param_padded)
+
+            if self._uses_param_mixed_precision:
+                # For parameter mixed precision, we maintain a full precision
+                # padded unsharded tensor for when we force full precision.
+                flat_param._full_prec_full_param_padded = torch.empty(
+                    padded_unsharded_numel,
+                    device=self.device,
+                    dtype=flat_param.dtype,  # full precision
+                )
+                _free_storage(flat_param._full_prec_full_param_padded)
+
+    ###################
+    # UNSHARD/RESHARD #
+    ###################
+    def pre_unshard(self) -> bool:
+        """
+        Return ``False`` if this is a no-op and ``True`` otherwise.
+
+        Postcondition: ``self.flat_param`` 's data is on the device for
+        communication and is what should be all-gathered. This means that it
+        matches the dtype of the expected unsharded parameter.
+        """
+        if (
+            self._training_state == HandleTrainingState.SUMMON_FULL_PARAMS
+            and self._skipped_use_sharded_views
+        ):
+            # Since this path imposes special semantics for the unsharded flat
+            # parameter (e.g. forcing full precision), use sharded views to
+            # reuse the existing logic for that special handling
+            self._use_sharded_views()
+        ret = False
+        if self._use_orig_params and not self._skip_writeback_check:
+            ret = self._writeback_orig_params()
+        if (
+            self.uses_sharded_strategy
+            and not self._offload_params
+            and not self.needs_unshard()
+        ):
+            pass  # no-op
+        elif self._uses_param_mixed_precision and not self._force_full_precision:
+            self._use_low_precision_shard()
+            ret = True
+        elif self._offload_params and self.flat_param.device != self.device:
+            # NOTE: This creates a new tensor distinct from any attributes.
+            self.flat_param_to(self.device, non_blocking=True)
+            ret = True
+        self._check_on_compute_device(self.flat_param)
+        return ret
+
+    def _use_low_precision_shard(self):
+        """Allocate on the compute device and switch to using the low precision sharded flat parameter."""
+        self._check_low_precision_shard()
+        flat_param = self.flat_param
+        _alloc_storage(
+            flat_param._mp_shard,
+            flat_param._local_shard.size(),  # type: ignore[attr-defined]
+        )
+        # `copy_()` implicitly casts to the low precision
+        flat_param._mp_shard.copy_(  # type: ignore[attr-defined]
+            flat_param._local_shard.to(  # type: ignore[attr-defined]
+                self.device, non_blocking=True
+            )
+        )
+        # Invariant: `_mp_shard` is always on the compute device.
+        flat_param.data = flat_param._mp_shard  # type: ignore[attr-defined]
+
+    def unshard(self):
+        """
+        Run the unshard logic.
+
+        This includes all-gathering the flat parameter
+        and switching to using the unsharded flat parameter. If the handle does
+        not need unsharding, then this only switches to using the unsharded
+        flat parameter. For ``NO_SHARD``, this is a no-op.
+
+        If FSDP is in :meth:`summon_full_params` and the handle uses parameter
+        mixed precision, then the parameter is forced to full precision.
+        """
+        if not self.needs_unshard():
+            # Even when not needing an unshard, we should switch to using
+            # the unsharded flat parameter
+            unsharded_flat_param = (
+                self._get_padded_unsharded_flat_param()
+                if self.uses_sharded_strategy
+                else self.flat_param
+            )
+            self._use_unsharded_flat_param(unsharded_flat_param)
+            return
+        unsharded_flat_param = self._alloc_padded_unsharded_flat_param()
+        padded_unsharded_flat_param = self._all_gather_flat_param(unsharded_flat_param)
+        self._use_unsharded_flat_param(padded_unsharded_flat_param)
+
+    def needs_unshard(self) -> bool:
+        """Return if the handle's flat parameter needs to be unsharded."""
+        if not self.uses_sharded_strategy:
+            return False
+        unsharded_flat_param = self._get_padded_unsharded_flat_param()
+        already_unsharded = _same_storage_size(
+            unsharded_flat_param, unsharded_flat_param.numel()
+        )
+        return not already_unsharded
+
+    def _alloc_padded_unsharded_flat_param(self):
+        """
+        Allocate the *padded* unsharded flat parameter.
+
+        The unpadded unsharded
+        flat parameter is always a view into the padded one. This padded
+        parameter is saved to a different attribute on the ``FlatParameter``
+        depending on if we force full precision.
+        """
+        self._check_sharded_strategy()
+        flat_param = self.flat_param
+        unsharded_flat_param = self._get_padded_unsharded_flat_param()
+        self._check_storage_freed(unsharded_flat_param)
+        _alloc_storage(unsharded_flat_param, flat_param._padded_unsharded_size)  # type: ignore[attr-defined]
+        return unsharded_flat_param
+
+    def _get_padded_unsharded_flat_param(self) -> torch.Tensor:
+        """
+        Return a reference to the padded unsharded flat parameter depending on the calling context.
+
+        This should only be called if using a sharded strategy.
+        """
+        self._check_sharded_strategy()
+        flat_param = self.flat_param
+        if self._force_full_precision and self._uses_param_mixed_precision:
+            # When parameter mixed precision is enabled, we use a different
+            # tensor as the all-gather destination to preserve the invariant
+            # that  `_full_param_padded` is in the low precision
+            unsharded_flat_param = flat_param._full_prec_full_param_padded  # type: ignore[attr-defined]
+            _p_assert(
+                unsharded_flat_param.dtype != self._fwd_bwd_param_dtype,
+                f"Expects full precision but got {self._fwd_bwd_param_dtype}",
+            )
+            # For no-reshard-after-forward strategies, `_full_param_padded` may
+            # still be allocated from a previous forward. As we are forcing
+            # full precision here, the full-precision unsharded copy may be
+            # modified, invalidating the existing low-precision unsharded copy,
+            # so we should free it here to ensure a new all-gather for the next
+            # forward/backward computation to persist the modifications.
+            if flat_param._full_param_padded.untyped_storage().size() > 0:
+                _free_storage(flat_param._full_param_padded)
+        else:
+            unsharded_flat_param = flat_param._full_param_padded  # type: ignore[attr-defined]
+        return unsharded_flat_param
+
+    def _all_gather_flat_param(
+        self,
+        padded_unsharded_flat_param: Tensor,
+    ) -> Tensor:
+        """
+        All-gather the handle's flat parameter to the destination ``padded_unsharded_flat_param``.
+
+        Then switch to use the all-gathered tensor.
+        """
+        _p_assert(
+            hasattr(self, "process_group") and hasattr(self, "world_size"),
+            "Expects a process group and world size to have been set via `shard()`",
+        )
+        sharded_flat_param = self.flat_param.data
+        expected_numel = sharded_flat_param.numel() * self.world_size
+        _p_assert(
+            padded_unsharded_flat_param.numel() == expected_numel,
+            f"Expects {expected_numel} numel but got {padded_unsharded_flat_param.numel()}",
+        )
+
+        pg = (
+            self._fake_process_group
+            if self._use_fake_all_gather
+            else self.process_group
+        )
+
+        # HACK this should be handled by C10D
+        if sharded_flat_param.is_cpu:  # type: ignore[attr-defined]
+            tensor_list = list(
+                torch.chunk(
+                    padded_unsharded_flat_param,
+                    dist.get_world_size(pg),  # type: ignore[arg-type]
+                )
+            )
+            dist.all_gather(tensor_list, sharded_flat_param, group=pg)
+        else:
+            dist.all_gather_into_tensor(
+                padded_unsharded_flat_param,
+                sharded_flat_param,
+                pg,
+            )
+
+        if self._offload_params:
+            # In case of offloading, `flat_param.data` (i.e. sharded param) is
+            # created on the pre-unshard stream. We need to hand it over to the
+            # unshard stream for all-gather
+            _no_dispatch_record_stream(
+                sharded_flat_param,
+                self._device_handle.current_stream(),  # unshard_stream
+            )
+        return padded_unsharded_flat_param
+
+    def _use_unsharded_flat_param(
+        self,
+        padded_unsharded_flat_param: torch.Tensor,
+    ) -> None:
+        """
+        Switch to use the *unpadded* unsharded flat parameter.
+
+        This is a view into the *padded* unsharded flat parameter.
+        """
+        unsharded_size = self.flat_param._unpadded_unsharded_size
+        flat_param_part = padded_unsharded_flat_param[: unsharded_size.numel()]
+        # slicing [:] is not visible to autograd because of .data
+        self.flat_param.data = flat_param_part
+        in_forward = self._training_state == HandleTrainingState.FORWARD
+        in_pre_backward = self._training_state == HandleTrainingState.BACKWARD_PRE
+        if self._use_orig_params:
+            if self._skipped_use_sharded_views and in_pre_backward:
+                # This call corresponds to the complementary pre-backward
+                # `_use_unsharded_views()` to the skipped pre-forward
+                # `_use_sharded_views()`, so we should skip this one too.
+                return
+            # We use `Tensor` views in the forward so that they are tracked by
+            # autograd. We use them in the pre-backward as well to support
+            # reentrant activation checkpointing, which needs the views to be
+            # tracked by autograd in the backward pass's recomputed forward.
+            self._use_unsharded_views(
+                as_params=(not in_forward and not in_pre_backward)
+            )
+        elif in_forward:
+            self._use_unsharded_views(as_params=False)
+
+    def post_unshard(self):
+        """
+        Run the post-unshard logic.
+
+        This includes freeing the low precision shard if needed.
+        """
+        if self._uses_param_mixed_precision and self.uses_sharded_strategy:
+            self._free_low_precision_sharded_param()
+        self._check_on_compute_device(self.flat_param)
+
+    def _free_low_precision_sharded_param(self):
+        """Frees the low precision sharded flat parameter."""
+        self._check_low_precision_shard()
+        # `_mp_shard` is allocated in the pre-unshard stream, consumed in the
+        # unshard stream for sharded strategies, and consumed in both the
+        # unshard and default streams for `NO_SHARD`. For sharded strategies,
+        # the current stream here is the unshard stream, and for `NO_SHARD`,
+        # it is the default stream. For `NO_SHARD`, only recording for the
+        # default stream suffices since the default stream waits for the
+        # unshard stream.
+        _no_dispatch_record_stream(
+            self.flat_param._mp_shard,
+            self._device_handle.current_stream(),  # type: ignore[attr-defined]
+        )
+        _free_storage(self.flat_param._mp_shard)  # type: ignore[attr-defined]
+
+    @torch.no_grad()
+    def unshard_grad(self):
+        """
+        Unshard the handle's ``FlatParameter``'s gradient.
+
+        If all ranks have
+        ``None`` gradient, then all original parameters will as well. This
+        method performs an all-reduce and an all-gather. The additional
+        all-reduce is tolerable since this method is not meant to be used on
+        the computation critical path.
+
+        Postcondition: ``_saved_grad_shard`` is defined and contains the value
+        to set ``flat_param.grad`` after gradients are resharded.
+        """
+        if not self.uses_sharded_strategy:
+            self._use_unsharded_grad_views()
+            return
+        flat_param = self.flat_param
+        self._check_unsharded(flat_param)
+
+        # Check if all ranks have a `None` gradient
+        num_grad_none = torch.zeros(1, dtype=torch.int32, device=self.device)
+        num_grad_none[0] = flat_param.grad is None
+        dist.all_reduce(num_grad_none, group=self.process_group)
+        if num_grad_none[0] == self.world_size:
+            flat_param._saved_grad_shard = None  # type: ignore[assignment]
+            self._use_unsharded_grad_views()
+            return
+
+        if flat_param.grad is None:
+            # In the case that only some ranks have `None` gradient, we use
+            # zeros to approximate as a best effort attempt
+            if self._debug_level == dist.DebugLevel.INFO:
+                warnings.warn(
+                    f"[Rank {self.rank}] Only some but not all ranks have a "
+                    "`None` `FlatParameter` gradient, so FSDP is using zeros to "
+                    "approximate those ranks' sharded gradients being `None`"
+                )
+            flat_param._saved_grad_shard = None  # type: ignore[assignment]
+            sharded_grad = torch.zeros(flat_param._sharded_size, device=self.device)  # type: ignore[attr-defined]
+        else:
+            self._check_sharded(flat_param.grad)
+            flat_param._saved_grad_shard = flat_param.grad  # type: ignore[attr-defined]
+            sharded_grad = flat_param._saved_grad_shard  # type: ignore[attr-defined]
+        padded_unsharded_grad = torch.empty(
+            flat_param._padded_unsharded_size,  # type: ignore[attr-defined]
+            device=self.device,
+            dtype=sharded_grad.dtype,
+        )
+        dist.all_gather_into_tensor(
+            padded_unsharded_grad, sharded_grad, self.process_group
+        )
+        unsharded_size = self.flat_param._unpadded_unsharded_size
+        flat_param.grad = padded_unsharded_grad[: unsharded_size.numel()].view(
+            unsharded_size
+        )
+        self._use_unsharded_grad_views()
+
+    def reshard_grad(self):
+        if self._use_orig_params:
+            self._use_sharded_grad_views()
+        if not self.uses_sharded_strategy:
+            return
+        self.flat_param.grad = self.flat_param._saved_grad_shard  # type: ignore[attr-defined]
+        delattr(self.flat_param, "_saved_grad_shard")
+
+    def prepare_gradient_for_backward(self):
+        """
+        Prepare the gradient for the backward computation.
+
+        This is done by saving and clearing any existing sharded gradient
+        in ``.grad`` to enable computing a new unsharded gradient.
+        """
+        _p_assert(
+            self._training_state
+            in (HandleTrainingState.BACKWARD_PRE, HandleTrainingState.IDLE),
+            "Expects to be in `BACKWARD_PRE` or `IDLE` (if prefetching)",
+        )
+        flat_param = self.flat_param
+        if flat_param.grad is not None and (
+            flat_param.grad.size() != flat_param._unpadded_unsharded_size
+            or flat_param.grad.device != flat_param.device  # grad on CPU
+        ):
+            self._check_on_compute_device(self.flat_param)
+            grad_offloaded = flat_param.grad.device != self.device
+            _p_assert(
+                not grad_offloaded or self._offload_params,
+                f"Expects the sharded gradient to be on {self.device} "
+                f"but got {flat_param.grad.device}",
+            )
+            prev_iter_synced_gradients = (
+                flat_param.grad.size() == flat_param._local_shard.size()  # type: ignore[attr-defined]
+            )
+            if prev_iter_synced_gradients:
+                # TODO (awgu): Gradient accumulation outside `no_sync()`
+                # does not work with CPU offloading. The issue should be
+                # that, in the post-backward hook, we cannot do an addition
+                # between a CPU tensor (the existing sharded gradient) and
+                # a GPU tensor (the new sharded gradient).
+                if not grad_offloaded:
+                    flat_param._saved_grad_shard = flat_param.grad.data  # type: ignore[attr-defined]
+                    sharded_grad = flat_param._saved_grad_shard  # type: ignore[attr-defined]
+                else:
+                    _p_assert(
+                        hasattr(flat_param, "_cpu_grad"),
+                        "`_cpu_grad` should be defined if the gradient is on CPU",
+                    )
+                    sharded_grad = flat_param._cpu_grad  # type: ignore[attr-defined]
+                # If user specified to keep the gradient in low precision, then
+                # the gradient may still be of the low precision dtype if the
+                # user did not set the gradient to `None` after the previous
+                # backward, in which case FSDP should cast back to the full
+                # precision dtype so that FSDP can accumulate in that dtype in
+                # the post-backward hook and assign to `.grad` in that dtype in
+                # the post-backward callback.
+                local_shard_dtype = flat_param._local_shard.dtype  # type: ignore[attr-defined]
+                if (
+                    self._keep_low_precision_grads
+                    and sharded_grad.dtype != local_shard_dtype
+                ):
+                    sharded_grad.data = sharded_grad.to(local_shard_dtype)
+            else:
+                padded_unsharded_size = flat_param._padded_unsharded_size  # type: ignore[attr-defined]
+                _p_assert(
+                    flat_param.grad.size() == padded_unsharded_size,
+                    "Expects `.grad` to be the unsharded gradient in "
+                    f"`no_sync()` with size {padded_unsharded_size} "
+                    f"but got size {flat_param.grad.size()}",
+                )
+            flat_param.grad = None
+
+    def prepare_gradient_for_optim(self):
+        """Prepare the gradient for optimizer computation by moving the sharded gradient to the ``.grad`` attribute."""
+
+        def cast_grad_to_param_dtype_if_needed(flat_param):
+            # TODO (rohan-varma): test for full precision with keep_low_precision_grads
+            if not self._force_full_precision and self._keep_low_precision_grads:
+                _p_assert(flat_param.grad is not None, "Unexpected None grad!")
+                if flat_param.grad.dtype != self._fwd_bwd_param_dtype:
+                    flat_param.grad.data = flat_param.grad.to(self._fwd_bwd_param_dtype)
+                    if self._use_orig_params:
+                        self._use_sharded_grad_views()
+
+        flat_param = self.flat_param
+        # TODO (awgu): We should replace these conditional checks to encode
+        # the logical intention more directly.
+        if hasattr(flat_param, "_cpu_grad"):
+            # NOTE: This branch includes `NO_SHARD`.
+            self._check_sharded(flat_param)
+            self._check_on_cpu(flat_param)
+            flat_param.grad = flat_param._cpu_grad  # type: ignore[attr-defined]
+            cast_grad_to_param_dtype_if_needed(flat_param)
+        elif hasattr(flat_param, "_saved_grad_shard"):
+            self._check_sharded(flat_param)
+            self._check_on_compute_device(flat_param)
+            if flat_param._saved_grad_shard is not None:
+                self._check_on_compute_device(flat_param._saved_grad_shard)  # type: ignore[attr-defined]
+            # If no sharded gradient was computed this iteration, then there is
+            # no need to forward `_saved_grad_shard` to `grad`
+            if flat_param._post_backward_called:  # type: ignore[attr-defined]
+                flat_param.grad = flat_param._saved_grad_shard  # type: ignore[attr-defined]
+                if flat_param.grad is not None:
+                    cast_grad_to_param_dtype_if_needed(flat_param)
+        else:
+            _p_assert(
+                not self.uses_sharded_strategy or not flat_param._post_backward_called,  # type: ignore[attr-defined]
+                "All sharded parameters that received a gradient in the "
+                "post-backward should use `_saved_grad_shard`",
+            )
+        # Delete `_saved_grad_shard` since its existence indicates a previous
+        # gradient to accumulate with in the post-backward hook
+        if hasattr(flat_param, "_saved_grad_shard"):
+            delattr(flat_param, "_saved_grad_shard")
+
+    @contextlib.contextmanager
+    def to_cpu(self):
+        """
+        Move the unpadded unsharded flat parameter to CPU while in the context and moves it back to the previous device upon exit.
+
+        For now, this assumes the ``FlatParameter`` is the unpadded unsharded flat parameter
+        since (1) there is no reason to include the padding in the copy and (2)
+        there is no use case for the sharded flat parameter.
+
+        Precondition: ``self.flat_param`` 's data is the unpadded unsharded
+        flat parameter on the compute device, and the handle uses a sharded
+        strategy.
+        Postcondition: Same as the precondition.
+        """
+        self._check_sharded_strategy()
+        _p_assert(
+            self.flat_param.size() == self.flat_param._unpadded_unsharded_size,
+            f"Expects size {self.flat_param._unpadded_unsharded_size} but got {self.flat_param.size()}",
+        )
+        self._check_on_compute_device(self.flat_param)
+        # Check that the unpadded unsharded flat parameter is a view into the
+        # padded unsharded flat parameter as expected
+        # NOTE: This check is not strictly needed for correctness but is a
+        # useful sanity check since the tensor should only be used internally.
+        _p_assert(
+            _same_storage(self.flat_param, self._get_padded_unsharded_flat_param()),
+            "Expects the unpadded parameter to be a view into the padded parameter",
+        )
+        self.flat_param_to(torch.device("cpu"))
+        self._free_unsharded_flat_param()
+        try:
+            yield
+        finally:
+            _p_assert(
+                self.flat_param.size() == self.flat_param._unpadded_unsharded_size,
+                f"Expects size {self.flat_param._unpadded_unsharded_size} but got {self.flat_param.size()}",
+            )
+            padded_unsharded_flat_param = self._alloc_padded_unsharded_flat_param()
+            # Copy from CPU to the compute device
+            padded_unsharded_flat_param[: self.flat_param.numel()].copy_(
+                self.flat_param
+            )
+            self._use_unsharded_flat_param(padded_unsharded_flat_param)
+
+    def reshard(self, free_unsharded_flat_param: bool):
+        """
+        Run the reshard logic.
+
+        This includes freeing the unsharded flat
+        parameter if ``free_unsharded_flat_param`` and switching to using the
+        sharded flat parameter. Note that this also implicitly offloads
+        the sharded flat parameter (if CPU offload is enabled) by pointing
+        it to the ``_local_shard`` attribute which resides on CPU.
+        """
+        # Switch to the sharded `FlatParameter` before freeing to prevent
+        # "use-after-free"-type bugs with external profiling tools, where for
+        # `use_orig_params=True`, the `param` does not point to valid memory
+        # when setting `param.data = ...` in `_use_sharded_views()`.
+        self._use_sharded_flat_param()
+        if free_unsharded_flat_param:
+            self._free_unsharded_flat_param()
+
+    def post_reshard(self):
+        """
+        Run the post-reshard logic.
+
+        This includes freeing any memory that
+        can now be freed given that the ``FlatParameter`` points to the full
+        precision sharded flat parameter.
+
+        Precondition: ``self.flat_param`` 's data points to the full precision
+        sharded flat parameter.
+        """
+        # For `NO_SHARD`, `_mp_shard` is not freed in the post-unshard since it
+        # is also the low precision *unsharded* flat parameter. Hence, we delay
+        # the free until the reshard.
+        if (
+            self._uses_param_mixed_precision
+            and not self.uses_sharded_strategy
+            and not self._force_full_precision  # did not use the low precision shard
+        ):
+            self._free_low_precision_sharded_param()
+
+    def _free_unsharded_flat_param(self):
+        """
+        Free the padded unsharded flat parameter. We allow this
+        function to be called even when storage is not allocated
+
+        The tensor to free depends
+        on the calling context since the unshard may have forced full
+        precision, in which case a different tensor is used.
+        """
+        self._check_sharded_strategy()
+        unsharded_flat_param = self._get_padded_unsharded_flat_param()
+        self._check_on_compute_device(unsharded_flat_param)
+        # Do not free the memory until all ops in the current stream finish
+        _no_dispatch_record_stream(
+            unsharded_flat_param, self._device_handle.current_stream()
+        )
+        _free_storage(unsharded_flat_param)
+
+    def _use_sharded_flat_param(self) -> None:
+        """Switches to using the sharded flat parameter."""
+        flat_param = self.flat_param
+        if self._use_orig_params:
+            in_forward = self._training_state == HandleTrainingState.FORWARD
+            skip_use_sharded_views = (
+                torch.is_grad_enabled()
+                and in_forward
+                and self._sharding_strategy
+                in NO_RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES
+            )
+            # Only incur the extra `.data` call if needed
+            if skip_use_sharded_views:
+                unsharded_flat_param = flat_param.data
+        if self._offload_params:
+            device = flat_param._local_shard.device  # type: ignore[attr-defined]
+            _p_assert(
+                device == torch.device("cpu"),
+                f"Expects the local shard to be on CPU but got {device}",
+            )
+        flat_param.data = flat_param._local_shard  # type: ignore[attr-defined]
+        if self._use_orig_params:
+            if skip_use_sharded_views:  # type: ignore[possibly-undefined]
+                self._unsharded_flat_param_for_skipped_views = unsharded_flat_param  # type: ignore[possibly-undefined]
+            else:
+                self._use_sharded_views()
+            # For the post-forward reshard, we may try to use sharded gradient
+            # views (or unsharded gradient views if a gradient was accumulated
+            # in `no_sync()`), but for the post-backward reshard, we delay the
+            # call to after the reduce-scatter.
+            if (
+                in_forward  # type: ignore[possibly-undefined]
+                # Skip using gradient views if skipped using sharded views
+                # since exposing unsharded parameters with sharded gradients
+                # may be confusing to the user
+                and not self._skipped_use_sharded_views
+            ):
+                # TODO: Change `_unpadded_unsharded_size` if we change the
+                # gradient to be computed directly with padding.
+                accumulated_grad_in_no_sync = (
+                    flat_param.grad is not None
+                    and self.uses_sharded_strategy
+                    and flat_param.grad.shape == flat_param._unpadded_unsharded_size
+                )
+                if accumulated_grad_in_no_sync:
+                    self._use_unsharded_grad_views()
+                else:
+                    self._use_sharded_grad_views()
+
+    #########
+    # VIEWS #
+    #########
+    @no_type_check
+    def _get_unflat_views_unaligned(
+        self,
+        tensor: Optional[torch.Tensor] = None,
+    ) -> Iterator[Tensor]:
+        """
+        Return unflattened ``Tensor`` views into ``tensor``.
+
+        If `tensor`` is ``None``,  ``flat_param`` is used. The unflattening is based
+        on ``flat_param`` 's metadata.
+
+        Examples for ``tensor`` include ``flat_param.grad`` or unsharded
+        tensor optimizer state.
+        """
+        flat_param = self.flat_param
+        if tensor is None:
+            tensor = flat_param
+        views = (
+            _ext_post_unflatten_transform(
+                subtensor.view(shape)
+                if contiguous
+                else subtensor.as_strided(shape, stride),
+                param_extension,
+                self._fsdp_extension,
+            )
+            for (subtensor, shape, stride, contiguous, param_extension) in zip(
+                torch.split(tensor, flat_param._numels, dim=0),
+                flat_param._shapes,
+                flat_param._strides,
+                flat_param._contiguities,
+                flat_param._param_extensions,
+            )
+        )
+        return views
+
+    @no_type_check
+    def _get_unflat_views_aligned(
+        self,
+        tensor: Optional[Tensor] = None,
+    ) -> list[Tensor]:
+        """
+        Return unflattened ``Tensor`` views into ``tensor`` with handling for padding.
+
+        This method has the same contract as :meth:`_get_unflat_views_unaligned`
+        except it checks for ``None`` placeholders representing padding for
+        alignment, which may incur slightly more CPU overhead.
+        """
+        flat_param = self.flat_param
+        if tensor is None:
+            tensor = flat_param
+        splits: list[Tensor] = torch.split(
+            tensor, flat_param._numels_with_padding, dim=0
+        )
+        idx = 0
+        views: list[Tensor] = []
+        for split, is_padding in zip(splits, flat_param._is_padding_mask):
+            if is_padding:
+                continue
+            views.append(
+                _ext_post_unflatten_transform(
+                    split.view(flat_param._shapes[idx])
+                    if flat_param._contiguities[idx]
+                    else split.as_strided(
+                        flat_param._shapes[idx], flat_param._strides[idx]
+                    ),
+                    flat_param._param_extensions[idx],
+                    self._fsdp_extension,
+                )
+            )
+            idx += 1
+        return views
+
+    @no_type_check
+    @torch.enable_grad()
+    def _use_unsharded_views(self, as_params: bool) -> None:
+        """
+        Unflatten the unsharded flat parameter by setting the original parameter variables to be views into it.
+
+        Args:
+            as_params (bool): If ``True``, then registers the original
+                parameters as ``nn.Parameter`` s; if ``False``, then registers
+                the original parameters only as ``Tensor`` s. ``False`` should
+                be used during forward/backward computation and when hiding the
+                original parameters from :meth:`nn.Module.named_parameters`.
+
+        Note:
+            when prefetching for next forward, current forward may be
+            annotated with `@torch.no_grad()`
+            `@torch.enable_grad()` ensures non-empty `view.grad_fn`
+            otherwise `_post_backward_hook` will not get called
+        """
+        flat_param = self.flat_param
+        self._check_unsharded(flat_param)
+        views = self._get_unflat_views()
+        from torch.distributed.tensor import DTensor
+
+        for i, (view, (param_name, module, _)) in enumerate(
+            zip(views, flat_param._param_infos)
+        ):
+            if self._use_orig_params and as_params:
+                if type(view) is DTensor:
+                    # A `DTensor` `view` is not compatible with assigning
+                    # `param.data = view`, so we cannot preserve the parameter
+                    # variable.
+                    self._setattr_param(
+                        module,
+                        param_name,
+                        nn.Parameter(view, requires_grad=flat_param.requires_grad),
+                    )
+                    continue
+                param = self.flat_param._params[i]
+                self._setattr_param(module, param_name, param)
+                param.data = view
+            elif as_params:
+                self._setattr_param(
+                    module,
+                    param_name,
+                    nn.Parameter(view, requires_grad=flat_param.requires_grad),
+                )
+            else:  # `as_params=False`
+                param_var: Tensor = view
+                if self._use_orig_params:
+                    if self._training_state == HandleTrainingState.FORWARD:
+                        # Save the `Tensor` for the pre-backward
+                        self.flat_param._tensors[i] = view  # save for pre-backward
+                    elif self._training_state == HandleTrainingState.BACKWARD_PRE:
+                        # Use the saved `Tensor` variable from the forward to
+                        # preserve the autograd graph so that the post-backward
+                        # hook fires (e.g. for reentrant AC)
+                        tensor = self.flat_param._tensors[i]
+                        tensor.data = view
+                        param_var = tensor
+                self._setattr_tensor(module, param_name, param_var)
+                if (
+                    self._use_orig_params
+                    and self._training_state == HandleTrainingState.FORWARD
+                ):
+                    module._parameters[param_name] = param_var
+        for i, (
+            param_name,
+            module,
+            _,
+            prim_param_name,
+            prim_module,
+            _,
+        ) in enumerate(self.flat_param._shared_param_infos):
+            prim_param: Union[Tensor, nn.Parameter] = getattr(
+                prim_module, prim_param_name
+            )
+            _p_assert(
+                not as_params or isinstance(prim_param, nn.Parameter),
+                f"as_params={as_params} type(prim_param)={type(prim_param)}",
+            )
+            if self._use_orig_params and as_params:
+                shared_param = self.flat_param._shared_params[i]
+                self._setattr_param(module, param_name, shared_param)
+                shared_param.data = prim_param
+            elif as_params:
+                self._setattr_param(module, param_name, prim_param)
+            else:
+                self._setattr_tensor(module, param_name, prim_param)
+                if (
+                    self._use_orig_params
+                    and self._training_state == HandleTrainingState.FORWARD
+                ):
+                    module._parameters[param_name] = prim_param
+
+    @no_type_check
+    def _use_unsharded_grad_views(self) -> None:
+        """
+        Unflatten the unsharded flat parameter's gradient.
+
+        The original parameter variables' gradients are set to be views into
+        the unsharded flat parameter's gradient.
+        """
+        # Expects the gradient to be in `flat_param.grad`
+        if self.flat_param.grad is None:
+            for param in chain(self.flat_param._params, self.flat_param._shared_params):
+                param.grad = None
+            return
+        self._check_unsharded(self.flat_param.grad)
+        views = self._get_unflat_views(self.flat_param.grad)
+        for i, (view, (param_name, module, _)) in enumerate(
+            zip(views, self.flat_param._param_infos)
+        ):
+            _p_assert(
+                hasattr(module, param_name),
+                f"{self.flat_param._fqns[i]} is missing",
+            )
+            param = getattr(module, param_name)
+            if (
+                param.shape != view.shape
+                or param.dtype != view.dtype
+                or param.device != view.device
+            ):
+                # NOTE: This is a hack using `.data` to side step the check
+                # that parameter/gradient sizes/dtypes/devices match. From
+                # calling `reshard()`, `param` has the sharded size, has the
+                # full precision dtype, and if CPU offloading is enabled, is on
+                # CPU. Thus, one or more of the following cases can hold when
+                # in `no_sync()`, where `view` is the original parameter's
+                # gradient:
+                # 1. `view` can have the unsharded size.
+                # 2. `view` can have the parameter low precision dtype.
+                # 3. `view` can be on GPU.
+                if param.grad is None:
+                    param.grad = torch.empty_like(param)
+                param.grad.data = view
+            else:
+                param.grad = view
+        for i, (
+            param_name,
+            module,
+            module_name,
+            prim_param_name,
+            prim_module,
+            _,
+        ) in enumerate(self.flat_param._shared_param_infos):
+            _p_assert(
+                hasattr(module, param_name),
+                f"{module_name + '.' + param_name if module_name else param_name} is missing",
+            )  # did not save FQN info in `_shared_param_infos`
+            param = getattr(module, param_name)
+            prim_param = getattr(prim_module, prim_param_name)
+            if (
+                param.shape != prim_param.grad.shape
+                or param.dtype != prim_param.grad.dtype
+                or param.device != prim_param.grad.device
+            ):
+                # NOTE: This is the same hack to use `.data` to side step the
+                # size check.
+                if param.grad is None:
+                    param.grad = torch.empty_like(param)
+                param.grad.data = prim_param.grad
+            else:
+                param.grad = prim_param.grad
+
+    @contextlib.contextmanager
+    def unflatten_as_params(self) -> Generator:
+        """
+        Unflatten the original parameters.
+
+        The function assumes that the flat parameter is unsharded. When in the context,
+        unflattens the original parameters as ``nn.Parameter`` views into the
+        flat parameter, and after the context, restores the original parameters
+        as ``Tensor`` views into the flat parameter.
+        """
+        self._use_unsharded_views(as_params=True)
+        try:
+            yield
+        finally:
+            self._use_unsharded_views(as_params=False)
+
+    @no_type_check
+    @torch.no_grad()
+    def _use_sharded_views(self) -> None:
+        """
+        Set the original parameter variables' data to be flattened views into the sharded flat parameter.
+
+        The views are kept as flattened to simplify the case where a parameter
+        is sharded across ranks. Parameters whose data is not present in the
+        sharded flat parameter have their data set to a size-0 empty tensor. We
+        do not delete them to ensure to preserve expected behaviors like model
+        printability. Parameters whose data is present must preserve their
+        variables to be passable to an optimizer.
+        """
+        self._unsharded_flat_param_for_skipped_views = None
+        if not self.uses_sharded_strategy:
+            # For `NO_SHARD`, use the *unflattened* unsharded views since we
+            # have the unsharded parameter
+            self._use_unsharded_views(as_params=True)
+            return
+        flat_param = self.flat_param
+        self._check_sharded(flat_param)
+        # Construct once and reuse for all parameters not in the local shard
+        size_0_empty_tensor = torch.empty(
+            0,
+            dtype=self.flat_param.dtype,  # in case `flat_param` changed dtype
+            device=self.flat_param.device,
+            requires_grad=False,
+        )
+        for param, shard_param_info, (param_name, module, _) in zip(
+            flat_param._params, flat_param._shard_param_infos, flat_param._param_infos
+        ):
+            self._setattr_param(module, param_name, param)
+            if not shard_param_info.in_shard:
+                # Allow the original data to be freed via garbage collection
+                param.data = size_0_empty_tensor
+            else:
+                offset = shard_param_info.offset_in_shard
+                numel_in_shard = shard_param_info.numel_in_shard
+                param.data = flat_param[offset : offset + numel_in_shard]
+        assert self.flat_param._shared_params is not None
+        for i, (
+            param,
+            (param_name, module, _, prim_param_name, prim_module, _),
+        ) in enumerate(
+            zip(self.flat_param._shared_params, self.flat_param._shared_param_infos)
+        ):
+            self._setattr_param(module, param_name, param)
+            prim_param = getattr(prim_module, prim_param_name)
+            param.data = prim_param  # could be both empty and non-empty
+        if self._training_state == HandleTrainingState.BACKWARD_POST:
+            # Clear the saved `Tensor`s since they are unneeded now
+            for i in range(len(self.flat_param._tensors)):
+                self.flat_param._tensors[i] = None
+
+    @no_type_check
+    @torch.no_grad()
+    def _use_sharded_grad_views(self) -> None:
+        """
+        Set the original parameter variables' gradients to be flattened views into the sharded flat parameter's gradient.
+
+        This is a no-op if there is no gradient.
+
+        Parameters whose data is not present in the sharded flat parameter and
+        parameters with ``requires_grad=False`` have their gradients set to
+        ``None``. Since the gradient variables do not need to be preserved,
+        this method does not manipulate existing ``Tensor`` data directly and
+        creates new ``Tensor`` variables instead.
+        """
+        flat_param = self.flat_param
+        self._check_sharded(flat_param)
+        grad = self.sharded_grad
+        if grad is None:
+            for param in chain(flat_param._params, flat_param._shared_params):
+                param.grad = None
+            return
+        self._check_sharded(grad)
+        for param, shard_param_info, is_grad_none in zip(
+            flat_param._params,
+            flat_param._shard_param_infos,
+            flat_param._is_grad_none_mask,
+        ):
+            if not shard_param_info.in_shard:
+                param.grad = None
+            else:
+                numel_in_shard = shard_param_info.numel_in_shard
+                if param.requires_grad and not is_grad_none:
+                    offset = shard_param_info.offset_in_shard
+                    if self._keep_low_precision_grads or param.dtype != grad.dtype:
+                        # NOTE: This is a hack using `.data` to side step the
+                        # check that parameter/gradient dtypes match. Here,
+                        # `param` has full precision; `grad` has low precision.
+                        if param.grad is None:
+                            # `.grad` must have the same shape as `param`
+                            param.grad = torch.empty_like(param)
+                        param.grad.data = grad[
+                            offset : offset + numel_in_shard
+                        ].reshape(param.shape)
+                    else:
+                        param.grad = grad[offset : offset + numel_in_shard].reshape(
+                            param.shape
+                        )
+                else:
+                    param.grad = None
+        assert flat_param._shared_params is not None
+        for param, (_, _, _, prim_param_name, prim_module, _) in zip(
+            flat_param._shared_params, flat_param._shared_param_infos
+        ):
+            in_sharded_flat_param = hasattr(prim_module, prim_param_name)
+            if in_sharded_flat_param and param.requires_grad:
+                prim_param = getattr(prim_module, prim_param_name)
+                param.grad = prim_param.grad  # share the same reference
+            else:
+                param.grad = None
+
+    @no_type_check
+    @torch.no_grad()
+    def _writeback_orig_params(self) -> bool:
+        """
+        Write back any parameters that changed storage to the handle's ``FlatParameter``.
+
+        Iterates over the original parameters and writes back any parameters
+        that changed storages (due to a non-inplace operator) to the handle's
+        ``FlatParameter``. This method preserves the ``FlatParameter` 's
+        device even if an original parameter's device changes.
+
+        Raises:
+            RuntimeError: If an original parameter or gradient changes storages
+            but no longer has the expected flattened shape.
+        Returns: ``True`` if some writeback happened, and ``False`` otherwise.
+        """
+        if (
+            self.uses_sharded_strategy
+            and not self.is_sharded(self.flat_param)
+            and not self._skipped_use_sharded_views
+        ):
+            # For `NO_SHARD`, we may still need to writeback
+            return False
+        flat_param = self.flat_param
+        wroteback = False
+        if self._skipped_use_sharded_views and self.uses_sharded_strategy:
+            # NOTE: We must use the unsharded flat parameter from which the
+            # unsharded views were computed, not the one from the current
+            # calling context (`_get_padded_unsharded_flat_param()`) since that
+            # may be different (e.g. the model changed from train to eval).
+            flat_param_tensor = self._unsharded_flat_param_for_skipped_views
+            _p_assert(
+                _data_ptr_allocated(flat_param_tensor),
+                "If skipped using sharded views, the unsharded flat parameter "
+                "should be allocated",
+            )
+        else:
+            flat_param_tensor = flat_param
+        # NOTE: Since this method is called in the pre-unshard, which is only
+        # called during computation in the pre-forward or pre-backward, the
+        # sharded gradient should be guaranteed to be in `.grad`, not in
+        # `._saved_grad_shard`.
+        flat_param_grad = (
+            flat_param.grad
+            if self.uses_sharded_strategy or not self._offload_params
+            else flat_param._cpu_grad
+        )
+        for i, (
+            param,
+            (in_shard, offset_in_shard, numel_in_shard, _, _),
+            (param_name, module, _),
+        ) in enumerate(
+            zip(
+                flat_param._params,
+                flat_param._shard_param_infos,
+                flat_param._param_infos,
+            )
+        ):
+            if not in_shard:
+                continue
+            if not hasattr(module, param_name):
+                # Do not writeback if original parameters are deregistered
+                # (e.g. during model checkpointing)
+                continue
+
+            # Check for parameter writeback
+            if self._skipped_use_sharded_views:
+                param = flat_param._tensors[i]
+                _p_assert(
+                    param is not None,
+                    f"Expects to have saved tensor for {flat_param._fqns[i]}",
+                )
+            param_changed = getattr(module, param_name) is not param
+            needs_param_writeback = (
+                param_changed  # changed parameter variable itself
+                or not _same_storage(param, flat_param_tensor)
+            )
+            if self._skipped_use_sharded_views and (
+                param_changed or needs_param_writeback
+            ):
+                raise AssertionError(
+                    "FSDP does not support changing the parameters between "
+                    f"forward and backward for {self._sharding_strategy}"
+                )
+            if param_changed:
+                # NOTE: The gradient is not preserved after a parameter change.
+                param = getattr(module, param_name)
+                flat_param._params[i] = param
+            if needs_param_writeback:
+                expected_shape = torch.Size([numel_in_shard])
+                self._writeback_tensor(
+                    param, flat_param, i, expected_shape, offset_in_shard, True
+                )
+                wroteback = True
+
+            # Check for gradient writeback
+            if self._skipped_use_sharded_views:
+                # Skip the writeback check because we do not expose gradients
+                # when we skipped using sharded views
+                continue
+            if param.grad is None and flat_param.grad is not None:
+                expected_shape = torch.Size([numel_in_shard])
+                self._writeback_tensor(
+                    None, flat_param.grad, i, expected_shape, offset_in_shard, False
+                )
+            elif param.grad is not None:
+                # For `NO_SHARD` + CPU offloading, `_cpu_grad` is always in
+                # memory and owns the gradient storage, so it will never
+                # require gradient writeback.
+                if not self.uses_sharded_strategy and self._offload_params:
+                    # Explicitly continue to handle the case of `no_sync()`,
+                    # where `param.grad` is a view into the GPU gradient
+                    # referenced by `flat_param.grad`, while `flat_param_grad`
+                    # is `flat_param._cpu_grad`, which is on CPU
+                    continue
+
+                needs_grad_writeback = flat_param_grad is None or not _same_storage(
+                    param.grad, flat_param_grad
+                )
+                if needs_grad_writeback:
+                    if flat_param_grad is None:
+                        flat_param_grad = torch.zeros_like(flat_param)
+                    expected_shape = torch.Size([numel_in_shard])
+                    self._writeback_tensor(
+                        param.grad,
+                        flat_param_grad,
+                        i,
+                        expected_shape,
+                        offset_in_shard,
+                        False,
+                    )
+                    flat_param.grad = flat_param_grad
+                    flat_param_grad = flat_param.grad
+
+        # TODO: If we want to handle shared parameters, we need to re-generate
+        # the shared parameter data structures in case sharedness changed.
+        for i, (
+            param_name,
+            module,
+            _,
+            prim_param_name,
+            prim_module,
+            _,
+        ) in enumerate(flat_param._shared_param_infos):
+            if getattr(module, param_name) is not getattr(prim_module, prim_param_name):
+                raise NotImplementedError(
+                    "Changing shared parameters is not supported yet"
+                )
+        return wroteback
+
+    def _writeback_tensor(
+        self,
+        src_tensor: Optional[Tensor],
+        dst_tensor: Tensor,
+        tensor_index: int,
+        expected_shape: torch.Size,
+        offset: int,
+        is_param: bool,  # else gradient
+    ) -> None:
+        """
+        Write back ``src_tensor`` to ``dst_tensor`` at offset ``offset``, where ``src_tensor`` should have shape ``expected_shape``.
+
+        ``is_param`` indicates if the tensor is the parameter (if ``True``) or gradient (if
+        ``False``). If ``src_tensor`` is ``None``, then the effect is zeroing
+        instead of copying. ``tensor_index`` gives the index of ``src_tensor``
+        in the metadata structures.
+
+        Raises:
+            RuntimeError: If the ``src_tensor`` does not have the expected
+            shape.
+        """
+        _p_assert(
+            len(expected_shape) == 1,
+            f"Expects a 1D expected shape but got {expected_shape}",
+        )
+        if self._debug_level == dist.DebugLevel.INFO:
+            rank = self.rank if hasattr(self, "rank") else dist.get_rank()
+            src_shape = src_tensor.shape if src_tensor is not None else None
+            src_device = src_tensor.device if src_tensor is not None else None
+            warnings.warn(
+                f"[Rank {rank}] {'Parameter' if is_param else 'Gradient'} needs "
+                f"writeback in {self._training_state}\n"
+                f"expected shape={expected_shape} shape={src_shape} "
+                f"expected device={dst_tensor.device} device={src_device}"
+            )
+        if src_tensor is not None and src_tensor.shape != expected_shape:
+            # NOTE: Gradient shape mismatch is not possible in practice since
+            # the gradient shape is enforced to match that of the parameter and
+            # we already check for parameter shape mismatch.
+            raise RuntimeError(
+                f"Cannot writeback when the {'parameter' if is_param else 'gradient'} "
+                f"shape changes\nExpects {expected_shape} but got {src_tensor.shape}"
+            )
+        if src_tensor is not None:
+            dst_tensor[offset : offset + expected_shape.numel()].copy_(src_tensor)
+        else:
+            dst_tensor[offset : offset + expected_shape.numel()].zero_()
+            assert self.flat_param._is_grad_none_mask is not None
+            self.flat_param._is_grad_none_mask[tensor_index] = True
+
+    def _reset_flat_param_grad_info_if_needed(self):
+        """
+        Reset ``flat_param.grad`` if needed.
+
+        When ``use_orig_params=True``:
+        (1) sets the underlying ``flat_param.grad`` to ``None`` if *all* of the
+        original parameters' ``.grad`` are ``None``, and
+        (2) sets ``flat_param.requires_grad=False`` if *none* of the original
+        parameters require gradient.
+        For (1), this is targeting ``optim.zero_grad(set_to_none=True)``, in
+        which case we want to free the gradients as soon after the
+        ``zero_grad()`` call as possible.
+        """
+        if not self._use_orig_params:
+            return
+        flat_param = self.flat_param
+        assert flat_param._params is not None  # mypy
+        all_grad_none = True
+        requires_grad = False
+        for param in flat_param._params:
+            all_grad_none &= param.grad is None
+            requires_grad |= param.requires_grad
+        if all_grad_none:
+            flat_param.grad = None
+        # As long as one parameter requires gradient, then the flat parameter
+        # must require gradient
+        flat_param.requires_grad = requires_grad
+
+    def _deregister_orig_params(self):
+        for param_info in self.flat_param._param_infos:
+            param_name, module, _ = param_info
+            if hasattr(module, param_name):
+                delattr(module, param_name)
+        for param_name, module, _, _, _, _ in self.flat_param._shared_param_infos:
+            if hasattr(module, param_name):
+                delattr(module, param_name)
+
+    ###########
+    # HELPERS #
+    ###########
+    def flat_param_to(self, *args, **kwargs):
+        """Wrap an in-place call to ``.to()`` for ``self.flat_param``."""
+        self.flat_param.data = self.flat_param.to(*args, **kwargs)
+        if self._use_orig_params:
+            # Refresh the views because their storage may have changed
+            if self.is_sharded(self.flat_param):
+                self._use_sharded_views()
+            else:
+                self._use_unsharded_views(as_params=True)
+
+    def _get_modules(self) -> set[nn.Module]:
+        """Return a :class:`set` of the modules whose parameters are included in this handle's flat parameter."""
+        return {pi.module for pi in self.flat_param._param_infos}.union(
+            {spi.module for spi in self.flat_param._shared_param_infos}
+        )
+
+    def is_sharded(self, tensor: Tensor) -> bool:
+        """
+        Return whether ``tensor`` is *currently* sharded.
+
+        For ``NO_SHARD``, we choose to have this always return ``False`` for clarity.
+        """
+        if (
+            not hasattr(self.flat_param, "_sharded_size")
+            or not self.uses_sharded_strategy
+        ):
+            # `_sharded_size` is defined iff `handle.shard()` has been called
+            return False
+        sharded_size = self.flat_param._sharded_size  # type: ignore[attr-defined]
+        return tensor.size() == sharded_size
+
+    def param_module_names(self) -> Iterator[tuple[str, str]]:
+        shared_param_infos = [
+            ParamInfo(param_name, module, module_name)
+            for (
+                param_name,
+                module,
+                module_name,
+                _,
+                _,
+                _,
+            ) in self.flat_param._shared_param_infos
+        ]
+        for param_info in chain(self.flat_param._param_infos, shared_param_infos):
+            param_name, _, module_name = param_info  # type: ignore[misc]
+            yield (param_name, module_name)
+
+    def shared_param_module_names(self) -> Iterator[tuple[str, str]]:
+        for param_name, _, module_name in [
+            ParamInfo(param_name, module, module_name)
+            for (
+                param_name,
+                module,
+                module_name,
+                _,
+                _,
+                _,
+            ) in self.flat_param._shared_param_infos
+        ]:
+            yield (param_name, module_name)
+
+    @property
+    def _fqns_in_shard(self) -> list[str]:
+        """Return the FQNs of the parameters present in this rank's shard."""
+        fqns_in_shard: list[str] = []
+        for fqn, shard_param_info in zip(
+            self.flat_param._fqns,
+            self.flat_param._shard_param_infos,  # type: ignore[attr-defined]
+        ):
+            if shard_param_info.in_shard:
+                fqns_in_shard.append(fqn)
+        return fqns_in_shard
+
+    @property
+    def sharded_grad(self) -> Optional[Tensor]:
+        """Return the handle's sharded gradient."""
+        flat_param = self.flat_param
+        # Priority for non-`None`: `_cpu_grad` > `_saved_grad_shard` > `grad`
+        # - CPU offloading: `_cpu_grad`
+        # - No CPU offloading + sharded strategies: `_saved_grad_shard`
+        # - No CPU offloading + `NO_SHARD`: `grad`
+        grad: Optional[Tensor]
+        if hasattr(flat_param, "_cpu_grad"):
+            grad = flat_param._cpu_grad  # type: ignore[attr-defined]
+        elif hasattr(flat_param, "_saved_grad_shard"):
+            # In the post-backward hook, the sharded gradient is still in
+            # `_saved_grad_shard`.
+            grad = flat_param._saved_grad_shard  # type: ignore[attr-defined]
+        else:
+            # If in IDLE or in FORWARD states, then there may be an
+            # (accumulated) gradient. If accessed in IDLE, then this should
+            # be due to re-registering the original parameters (e.g. in state
+            # dict load).
+            _p_assert(
+                flat_param.grad is None
+                or not self.uses_sharded_strategy
+                or self._training_state
+                in (HandleTrainingState.FORWARD, HandleTrainingState.IDLE),
+                "Sharded strategies should use `_cpu_grad` or `_saved_grad_shard` "
+                "unless in IDLE or FORWARD",
+            )
+            grad = flat_param.grad
+        return grad
+
+    def _reset_is_grad_none(self) -> None:
+        """
+        Reset ``_is_grad_none_mask`` as needed.
+
+        This method should only be
+        called in the post-backward after gradient computation, in which case
+        if a parameter requires gradient, then it will surely receive a
+        gradient and we may reset its mask entry to ``False``.
+        """
+        if not self._use_orig_params:
+            return
+        _p_assert(
+            self._training_state == HandleTrainingState.BACKWARD_POST,
+            "Expects to only be called in the post-backward after gradient computation",
+        )
+        flat_param = self.flat_param
+        assert flat_param._params is not None  # mypy
+        for i, param in enumerate(flat_param._params):  # type: ignore[arg-type]
+            # As long as the parameter requires gradient, it should receive a
+            # meaningful gradient (even if the gradient happens to be zeros)
+            if param.requires_grad:
+                assert flat_param._is_grad_none_mask is not None  # mypy
+                flat_param._is_grad_none_mask[i] = False
+
+    #######################
+    # CHECKS & INVARIANTS #
+    #######################
+    def _check_sharded_strategy(self):
+        _p_assert(self.uses_sharded_strategy, "Expects sharded strategy")
+
+    def _check_on_compute_device(self, tensor: Tensor):
+        _p_assert(
+            tensor.device == self.device,
+            f"Expects tensor to be on the compute device {self.device}, was on {tensor.device}",
+        )
+
+    def _check_on_cpu(self, tensor: Tensor):
+        _p_assert(
+            tensor.device == torch.device("cpu"),
+            f"Expects tensor to be on CPU but got {tensor.device}",
+        )
+
+    @staticmethod
+    def _check_storage_freed(tensor: Tensor):
+        # Compile does not resize during trace
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            _p_assert(
+                _same_storage_size(tensor, 0),
+                "Expects storage to be freed but got storage with size > 0",
+            )
+
+    @staticmethod
+    def _check_storage_allocated(tensor: Tensor):
+        _p_assert(_storage_size_allocated(tensor), "Expects storage to be allocated")
+
+    def _check_low_precision_shard(self):
+        _p_assert(
+            self._uses_param_mixed_precision,
+            "Not using low precision for parameters",
+        )
+        _p_assert(
+            getattr(self.flat_param, "_mp_shard", None) is not None,
+            "Expects `_mp_shard` to exist",
+        )
+        device = self.flat_param._mp_shard.device  # type: ignore[attr-defined]
+        _p_assert(
+            device == self.device,
+            f"Expects the low precision shard to be on {self.device} but got {device}",
+        )
+
+    def _check_unsharded(self, tensor: Tensor):
+        msg_prefix = "Expects tensor to be unsharded "
+        _p_assert(tensor is not None, msg_prefix + "but got `None`")
+        unsharded_size = self.flat_param._unpadded_unsharded_size
+        _p_assert(
+            tensor.size() == unsharded_size,
+            msg_prefix + f"with size {unsharded_size} but got {tensor.size()}",
+        )
+
+    def _check_sharded(self, tensor: Tensor):
+        msg_prefix = "Expects tensor to be sharded "
+        _p_assert(tensor is not None, msg_prefix + "but got `None`")
+        sharded_size = self.flat_param._sharded_size  # type: ignore[attr-defined]
+        _p_assert(
+            tensor.size() == sharded_size,
+            msg_prefix + f"with size {sharded_size} but got {tensor.size()}",
+        )
+
+    ##############
+    # PROPERTIES #
+    ##############
+    @property
+    def uses_sharded_strategy(self) -> bool:
+        return self._sharding_strategy != HandleShardingStrategy.NO_SHARD
+
+    @property
+    def _uses_param_mixed_precision(self) -> bool:
+        return self._fwd_bwd_param_dtype != self._orig_param_dtype
+
+    @property
+    def _uses_reduce_mixed_precision(self) -> bool:
+        return self._reduce_dtype != self._orig_param_dtype
+
+    @property
+    def _force_full_precision(self) -> bool:
+        return (
+            self._uses_param_mixed_precision or self._uses_reduce_mixed_precision
+        ) and (
+            self._training_state == HandleTrainingState.SUMMON_FULL_PARAMS
+            or
+            # Also disable mixed precision in model eval mode, if configured
+            (not self._fully_sharded_module.training and self._use_full_prec_in_eval)
+        )
+
+    @property
+    def _skipped_use_sharded_views(self) -> bool:
+        """
+        This property is used for sharding strategies that do not free after forward with ``use_orig_params=True``.
+
+        This returns if this handle is
+        currently in a state where it has skipped using sharded views, in which
+        case it can restore view invariants via ``_use_sharded_views()``.
+        """
+        return self._unsharded_flat_param_for_skipped_views is not None
+
+
+# NOTE: These are hacks to bypass `nn.Module.__setattr__` checks.
+def _unsafe_setattr_param(
+    module: nn.Module, param_name: str, param: nn.Parameter
+) -> None:
+    module._parameters[param_name] = param
+    # This bypasses any overrides in case `module` is an instance of an
+    # `nn.Module` subclass
+    super(nn.Module, module).__setattr__(param_name, param)
+
+
+def _unsafe_setattr_tensor(module: nn.Module, param_name: str, tensor: Tensor) -> None:
+    module._parameters.pop(param_name, None)
+    # This bypasses any overrides in case `module` is an instance of an
+    # `nn.Module` subclass
+    super(nn.Module, module).__setattr__(param_name, tensor)
+
+
+def _safe_setattr_tensor_or_param(
+    module: nn.Module, param_name: str, tensor_or_param: Union[Tensor, nn.Parameter]
+):
+    # Call `delattr()` and `setattr()` to go through `nn.Module` checks
+    if hasattr(module, param_name):
+        delattr(module, param_name)
+    setattr(module, param_name, tensor_or_param)
+
+
+def _convert_to_params(
+    tensors: list[Union[torch.Tensor, nn.Parameter]],
+) -> list[nn.Parameter]:
+    return [t if isinstance(t, nn.Parameter) else nn.Parameter(t) for t in tensors]
+
+
+def _is_truly_contiguous(x: Tensor) -> bool:
+    # Special case: Pytorch thinks that 1x1 channels_last convolution weights are
+    # both contiguous and channels_last contiguous at the same time.
+    # CuDNN does not agree though and refuses to select faster kernels.
+    # It is the reason of having the extra check here.
+    return x.stride(-1) == 1 and x.is_contiguous()
+
+
+def _detach_if_needed(param_or_tensor: Union[nn.Parameter, Tensor]) -> Tensor:
+    return (
+        param_or_tensor.detach()
+        if isinstance(param_or_tensor, nn.Parameter)
+        else param_or_tensor
+    )
+
+
+def _get_aligned_numel(unsharded_dtype: torch.dtype):
+    # NOTE: This alignment constraint comes from TorchInductor.
+    ALIGNMENT = 16  # bytes
+    unsharded_dtype_size = _get_dtype_size(unsharded_dtype)
+    aligned_numel = ALIGNMENT // unsharded_dtype_size
+    return aligned_numel
+
+
+@functools.lru_cache(8)
+def _get_dtype_size(dtype):
+    return torch.empty((), dtype=dtype).element_size()
+
+
+def _construct_padding_tensor(
+    padding_numel: int, dtype: torch.dtype, requires_grad: bool, device: torch.device
+):
+    # NOTE: Set the padding value as a magic number for debuggability. The
+    # value itself should never be used in any user-facing computation.
+    return (
+        torch.ones(
+            (padding_numel,), dtype=dtype, requires_grad=requires_grad, device=device
+        )
+        * _FLAT_PARAM_PADDING_VALUE
+    )
+
+
+# Use `lru_cache(1)` to only log the warning once (assuming the fixed warning
+# messasge is passed in)
+@functools.lru_cache(1)
+def _warn_skip_writeback_check(log: logging.Logger, warning: str):
+    logger.warning(warning)
+
+
+# Use `lru_cache(1)` to only log the warning once
+@functools.lru_cache(1)
+def _warn_use_fake_all_gather(log: logging.Logger, warning: str):
+    logger.warning(warning)
+
+
+# Use `lru_cache(1)` to only log the warning once
+@functools.lru_cache(1)
+def _warn_use_fake_reduce(log: logging.Logger, warning: str):
+    logger.warning(warning)
+
+
+def _same_storage(a, b):
+    # Params are DTensors in backward
+    # with SHARD_GRAD_OP + TP
+    from torch.distributed.tensor import DTensor
+
+    if isinstance(a, DTensor):
+        a = a._local_tensor
+    if isinstance(b, DTensor):
+        b = b._local_tensor
+    return a.untyped_storage().data_ptr() == b.untyped_storage().data_ptr()
+
+
+def _same_storage_size(a: torch.Tensor, b: int):
+    return a.untyped_storage().size() // a.element_size() == b
+
+
+def _storage_size_allocated(tensor: Tensor):
+    storage_size: int = tensor.untyped_storage().size()
+    return storage_size > 0
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fsdp_extensions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fsdp_extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f861a90ce58a0328c55fbc825ffc959c02c0b5c3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fsdp_extensions.py
@@ -0,0 +1,179 @@
+from abc import ABC, abstractmethod
+from typing import Any, Optional
+
+import torch
+import torch.distributed as dist
+from torch.distributed._shard.sharded_tensor.api import ShardedTensor
+from torch.distributed._shard.sharded_tensor.shard import Shard
+from torch.distributed.fsdp._shard_utils import (
+    _all_gather_dtensor,
+    _create_chunk_dtensor,
+    _create_chunk_sharded_tensor,
+)
+from torch.distributed.tensor import DeviceMesh, DTensor
+
+
+class FSDPExtensions(ABC):
+    """
+    This enables some customizable hooks to enable composability with tensor
+    parallelism. To activate these hooks, use :func:`_set_fsdp_extensions` to
+    set a custom :class:`FSDPExtensions` that implements the hooks.
+    """
+
+    @abstractmethod
+    def pre_flatten_transform(
+        self,
+        tensor: torch.Tensor,
+    ) -> tuple[torch.Tensor, Optional[Any]]:
+        """E.g. converting ``DistributedTensor`` to local tensor."""
+        ...
+
+    @abstractmethod
+    def post_unflatten_transform(
+        self,
+        tensor: torch.Tensor,
+        param_extension: Any,
+    ) -> torch.Tensor:
+        """E.g. converting local tensor to ``DistributedTensor``."""
+        ...
+
+    @abstractmethod
+    def chunk_tensor(
+        self,
+        tensor: torch.Tensor,
+        rank: int,
+        world_size: int,
+        num_devices_per_node: int,
+        pg: dist.ProcessGroup,
+        device: Optional[torch.device] = None,
+    ) -> torch.Tensor:
+        """Shards a tensor to chunks and returns the local chunk."""
+        ...
+
+    @abstractmethod
+    def chunk_dtensor(
+        self,
+        tensor: torch.Tensor,
+        rank: int,
+        device_mesh: DeviceMesh,
+    ) -> torch.Tensor:
+        """Shards a tensor/DTensor to DTensor and returns the local DTensor."""
+        ...
+
+    @abstractmethod
+    def pre_load_state_dict_transform(
+        self,
+        tensor: torch.Tensor,
+    ) -> tuple[torch.Tensor, list[Shard]]:
+        """
+        This is to be called before loading a *sharded* model state dict and
+        should return the tensor and list of shards from which to load data.
+        """
+        ...
+
+    @abstractmethod
+    def all_gather_dtensor(
+        self,
+        tensor: DTensor,
+        parent_mesh: Optional[DeviceMesh],
+    ) -> torch.Tensor:
+        """
+        This is to be called before loading a *sharded* DTensor state dict.
+        This gathers tensor in FSDP dimension and returns local tensor of
+        TP DTensor.
+        """
+        ...
+
+
+_extensions: Optional[FSDPExtensions] = None
+
+
+def _set_fsdp_extensions(flattener: FSDPExtensions) -> None:
+    global _extensions
+    _extensions = flattener
+
+
+def _ext_pre_flatten_transform(
+    tensor: torch.Tensor,
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> tuple[torch.Tensor, Optional[Any]]:
+    if fsdp_extension is not None:
+        new_tensor, param_extension = fsdp_extension.pre_flatten_transform(tensor)
+        if param_extension is not None:
+            return new_tensor, param_extension
+    return tensor, None
+
+
+def _ext_post_unflatten_transform(
+    tensor: torch.Tensor,
+    param_extension: Any,
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> torch.Tensor:
+    if fsdp_extension is not None and param_extension is not None:
+        return fsdp_extension.post_unflatten_transform(tensor, param_extension)
+    return tensor
+
+
+def _ext_chunk_tensor(
+    tensor: torch.Tensor,
+    rank: int,
+    world_size: int,
+    num_devices_per_node: int,
+    pg: dist.ProcessGroup,
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> torch.Tensor:
+    chunk_tensor_fn = (
+        fsdp_extension.chunk_tensor
+        if fsdp_extension is not None
+        else _create_chunk_sharded_tensor
+    )
+    return chunk_tensor_fn(
+        tensor,
+        rank,
+        world_size,
+        num_devices_per_node,
+        pg,
+    )
+
+
+def _ext_chunk_dtensor(
+    tensor: torch.Tensor,
+    rank: int,
+    device_mesh: DeviceMesh,
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> torch.Tensor:
+    chunk_dtensor_fn = (
+        fsdp_extension.chunk_dtensor
+        if fsdp_extension is not None
+        else _create_chunk_dtensor
+    )
+    return chunk_dtensor_fn(
+        tensor,
+        rank,
+        device_mesh,
+    )
+
+
+def _ext_pre_load_state_dict_transform(
+    tensor: torch.Tensor,
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> tuple[torch.Tensor, list[Shard]]:
+    if fsdp_extension is not None:
+        return fsdp_extension.pre_load_state_dict_transform(tensor)
+
+    assert type(tensor) is ShardedTensor
+    shards = tensor.local_shards()
+    return (tensor, shards)
+
+
+def _ext_all_gather_dtensor(
+    tensor: DTensor,
+    parent_mesh: Optional[DeviceMesh],
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> torch.Tensor:
+    all_gather_dtensor_fn = (
+        fsdp_extension.all_gather_dtensor
+        if fsdp_extension is not None
+        else _all_gather_dtensor
+    )
+    return all_gather_dtensor_fn(tensor, parent_mesh)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7592385955a9f8660189a30d249fe795030e774a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/__init__.py
@@ -0,0 +1,18 @@
+from ._fsdp_api import CPUOffloadPolicy, MixedPrecisionPolicy, OffloadPolicy
+from ._fully_shard import (
+    FSDPModule,
+    fully_shard,
+    register_fsdp_forward_method,
+    UnshardHandle,
+)
+
+
+__all__ = [
+    "CPUOffloadPolicy",
+    "FSDPModule",
+    "fully_shard",
+    "MixedPrecisionPolicy",
+    "OffloadPolicy",
+    "register_fsdp_forward_method",
+    "UnshardHandle",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/__pycache__/__init__.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e04396f07fe450c70382307fa32e5d3edc42c82
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_api.py
@@ -0,0 +1,75 @@
+# mypy: allow-untyped-defs
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+
+
+@dataclass(frozen=True)
+class MixedPrecisionPolicy:
+    """
+    This configures FSDP's mixed precision. Unlike autocast, this applies mixed
+    precision at the module level, not op level, which means low-precision
+    activations are saved for backward and high-to-low-precision casts are
+    incurred only at module boundaries.
+
+    FSDP works well with module-level mixed precision since it keeps the
+    high-precision sharded parameters in memory anyway. In other words, FSDP
+    does not require any extra memory to keep a high-precision copy of the
+    parameters for the optimizer step.
+
+    Attributes:
+        param_dtype (Optional[torch.dtype]): This specifies the dtype for
+            the unsharded parameter and hence the dtype for forward/backward
+            computation and the parameter all-gather. If this is ``None``, then
+            the unsharded parameter uses the original dtype. The optimizer step
+            uses the sharded parameter in the original dtype. (Default:
+            ``None``)
+        reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
+            gradient reduction (i.e. reduce-scatter or all-reduce). If this is
+            ``None`` but ``param_dtype`` is not ``None``, then the reduction
+            uses the compute dtype. This can be used to run gradient reduction
+            in full precision while using low precision for compute. If also
+            gradient reduction is disabled via :meth:`set_requires_gradient_sync`,
+            then FSDP will accumulate gradients using ``reduce_dtype``.
+            (Default: ``None``)
+        output_dtype (Optional[torch.dtype]): This specifies the dtype for
+            casting floating-point forward outputs. This can be used to
+            help implement cases where different modules have different mixed
+            precision policies. (Default: ``None``)
+        cast_forward_inputs (bool): This specifies whether FSDP should cast the
+            forward's floating-point input tensors to ``param_dtype`` or not.
+    """
+
+    param_dtype: Optional[torch.dtype] = None
+    reduce_dtype: Optional[torch.dtype] = None
+    output_dtype: Optional[torch.dtype] = None
+    cast_forward_inputs: bool = True
+
+
+@dataclass
+class OffloadPolicy:
+    """
+    This base class represents the policy of no offloading and is only used as
+    the default value for the ``offload_policy`` arg.
+    """
+
+
+@dataclass
+class CPUOffloadPolicy(OffloadPolicy):
+    """
+    This offload policy offloads parameters, gradients, and optimizer states to
+    CPU. Sharded parameters are copied host-to-device before all-gather. The
+    all-gathered parameters are freed according to ``reshard_after_forward``.
+    Sharded gradients are copied device-to-host in backward, and the optimizer
+    step runs on CPU with CPU optimizer states.
+
+    Attributes:
+        pin_memory (bool): Whether to pin sharded parameter and gradient
+            memory. Pinning memory allows both more efficient H2D/D2H copies
+            and for the copies to overlap with compute. However, the pinned
+            memory cannot be used by other processes. Set this to ``False`` if
+            you have insufficient CPU memory. (Default: ``True``)
+    """
+
+    pin_memory: bool = True
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_collectives.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_collectives.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8d635cc8691e494a5beb3812ee9344eb37d6ef5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_collectives.py
@@ -0,0 +1,580 @@
+from itertools import chain
+from typing import Callable, cast, NamedTuple, Optional, Union
+
+import torch
+import torch.distributed as dist
+from torch.distributed.device_mesh import _get_device_handle
+from torch.distributed.distributed_c10d import ReduceOp
+from torch.distributed.tensor import DTensor
+
+from ._fsdp_common import (
+    _get_dim0_padded_size,
+    _raise_assert_with_print,
+    _to_dtype_if_needed,
+    compiled_autograd_enabled,
+)
+from ._fsdp_param import FSDPParam, ShardedState
+
+
+class AllGatherResult(NamedTuple):
+    all_gather_output: torch.Tensor
+    all_gather_event: Optional[torch.Event]
+    all_gather_work: Optional[dist.distributed_c10d.Work]
+    # For each parameter, the all-gather input dtype for each input
+    param_all_gather_input_dtypes: list[list[torch.dtype]]
+    # For each parameter, the all-gather input numel for each input
+    param_all_gather_input_numels: list[list[int]]
+    # 1D flattened version of `param_all_gather_input_numels` saved to avoid
+    # CPU overhead from recomputing
+    all_gather_input_split_sizes: list[int]
+
+
+lib = torch.library.Library("fsdp", "FRAGMENT")  # noqa: TOR901
+
+lib.define(
+    """
+    all_gather_copy_in(
+        Tensor[] all_gather_inputs,
+        SymInt[] inp_split_sizes,
+        SymInt all_gather_input_numel,
+        SymInt world_size,
+        SymInt rank,
+        ScalarType dtype,
+        Device device
+    ) -> (Tensor, Tensor)
+    """
+)
+
+
+@torch.library.impl(lib, "all_gather_copy_in", "Meta")
+def all_gather_copy_in_meta(
+    all_gather_inputs: list[torch.Tensor],
+    inp_split_sizes: list[int],
+    all_gather_input_numel: int,
+    world_size: int,
+    rank: int,
+    dtype: torch.dtype,
+    device: torch.device,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    all_gather_output = torch.empty(
+        (all_gather_input_numel * world_size,), dtype=dtype, device="meta"
+    )
+    all_gather_input = all_gather_output.narrow(
+        0, all_gather_input_numel * rank, all_gather_input_numel
+    )
+    return all_gather_input, all_gather_output
+
+
+@torch.library.impl(lib, "all_gather_copy_in", "CUDA")
+@torch.library.impl(lib, "all_gather_copy_in", "XPU")
+@torch.library.impl(lib, "all_gather_copy_in", "HPU")
+@torch.library.impl(lib, "all_gather_copy_in", "CPU")
+@torch.library.impl(lib, "all_gather_copy_in", "MTIA")
+def all_gather_copy_in_cuda(
+    all_gather_inputs: list[torch.Tensor],
+    inp_split_sizes: list[int],
+    all_gather_input_numel: int,
+    world_size: int,
+    rank: int,
+    dtype: torch.dtype,
+    device: torch.device,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    all_gather_output = torch.empty(
+        (all_gather_input_numel * world_size,), dtype=dtype, device=device
+    )
+    all_gather_input = all_gather_output.narrow(
+        0, all_gather_input_numel * rank, all_gather_input_numel
+    )
+    foreach_copy_dsts = torch.split(all_gather_input, inp_split_sizes)
+    with torch.no_grad():
+        torch._foreach_copy_(foreach_copy_dsts, all_gather_inputs)
+    return all_gather_input, all_gather_output
+
+
+lib.define(
+    "split_with_sizes_copy(Tensor all_gather_output, SymInt[] all_gather_input_split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()"
+)
+
+
+@torch.library.impl(lib, "split_with_sizes_copy", "Meta")
+@torch.library.impl(lib, "split_with_sizes_copy", "CUDA")
+@torch.library.impl(lib, "split_with_sizes_copy", "XPU")
+@torch.library.impl(lib, "split_with_sizes_copy", "HPU")
+@torch.library.impl(lib, "split_with_sizes_copy", "CPU")
+@torch.library.impl(lib, "split_with_sizes_copy", "MTIA")
+def split_with_sizes_copy(
+    all_gather_output: torch.Tensor,
+    all_gather_input_split_sizes: list[int],
+    dim: int,
+    out: list[torch.Tensor],
+) -> None:
+    torch.split_with_sizes_copy(
+        all_gather_output, all_gather_input_split_sizes, dim=dim, out=out
+    )
+
+
+lib.define(
+    "chunk_cat(Tensor[] tensors, int dim, int num_chunks, *, Tensor(a!) out) -> ()"
+)
+
+
+@torch.library.impl(lib, "chunk_cat", "Meta")
+@torch.library.impl(lib, "chunk_cat", "CUDA")
+@torch.library.impl(lib, "chunk_cat", "XPU")
+@torch.library.impl(lib, "chunk_cat", "HPU")
+@torch.library.impl(lib, "chunk_cat", "CPU")
+@torch.library.impl(lib, "chunk_cat", "MTIA")
+def chunk_cat(
+    tensors: list[torch.Tensor],
+    dim: int,
+    num_chunks: int,
+    out: torch.Tensor,
+) -> None:
+    torch._chunk_cat(tensors, dim, num_chunks, out=out)
+
+
+@torch.no_grad()
+def foreach_all_gather(
+    fsdp_params: list[FSDPParam],
+    group: dist.ProcessGroup,
+    async_op: bool,
+    all_gather_copy_in_stream: torch.Stream,
+    all_gather_stream: torch.Stream,
+    device: torch.device,
+) -> Optional[AllGatherResult]:
+    world_size, rank = group.size(), group.rank()
+    device_handle = _get_device_handle(device.type)
+    with device_handle.stream(all_gather_copy_in_stream):
+        param_all_gather_inputs = _get_param_all_gather_inputs(fsdp_params)
+        (
+            param_all_gather_input_dtypes,
+            param_all_gather_input_numels,
+            dtype,
+        ) = _get_all_gather_input_metadatas(param_all_gather_inputs)
+        if dtype == torch.uint8:
+            all_gather_inputs = [
+                t.view(torch.uint8) for ts in param_all_gather_inputs for t in ts
+            ]
+        else:
+            all_gather_inputs = [*chain.from_iterable(param_all_gather_inputs)]
+        inp_split_sizes = [t.numel() for t in all_gather_inputs]
+        all_gather_input_numel = sum(inp_split_sizes)
+        all_gather_input, all_gather_output = torch.ops.fsdp.all_gather_copy_in(
+            all_gather_inputs,
+            inp_split_sizes,
+            all_gather_input_numel,
+            world_size,
+            rank,
+            dtype,
+            device,
+        )
+        del param_all_gather_inputs
+    all_gather_stream.wait_stream(all_gather_copy_in_stream)
+    with device_handle.stream(all_gather_stream):
+        all_gather_work = dist.all_gather_into_tensor(
+            output_tensor=all_gather_output,
+            input_tensor=all_gather_input,
+            group=group,
+            async_op=async_op,
+        )
+        all_gather_event = all_gather_stream.record_event()
+        return AllGatherResult(
+            all_gather_output,
+            all_gather_event,
+            all_gather_work,
+            param_all_gather_input_dtypes,
+            param_all_gather_input_numels,
+            inp_split_sizes,
+        )
+
+
+@torch.no_grad()
+def _get_param_all_gather_inputs(
+    fsdp_params: list[FSDPParam],
+) -> list[list[torch.Tensor]]:
+    if compiled_autograd_enabled():
+        return [fsdp_param.all_gather_inputs for fsdp_param in fsdp_params]
+
+    # Intentionally try to run a fast-path that bypasses abstractions for the
+    # common FSDP case of bf16/fp32 mixed precision in order to use foreach
+    # copy for lower CPU overhead and more efficient copying in eager
+    def use_foreach_copy(fsdp_param: FSDPParam) -> bool:
+        return (
+            fsdp_param.param_dtype is not None
+            and not fsdp_param.offload_to_cpu
+            and not hasattr(fsdp_param._sharded_local_tensor, "fsdp_pre_all_gather")
+        )
+
+    param_all_gather_inputs: list[list[torch.Tensor]] = [[] for _ in fsdp_params]
+    foreach_copy_indices: list[int] = []
+    foreach_copy_inputs: list[torch.Tensor] = []
+    foreach_copy_input_numels: list[int] = []
+
+    # 1st pass: for foreach-copy parameters, get inputs and metadata for the
+    # foreach copy, and for the others, actually get their all-gather inputs
+    for i, fsdp_param in enumerate(fsdp_params):
+        if use_foreach_copy(fsdp_param):
+            foreach_copy_indices.append(i)
+            all_gather_input = (
+                fsdp_param._sharded_param_data
+                if fsdp_param.sharded_state == ShardedState.SHARDED
+                else cast(torch.Tensor, fsdp_param._sharded_post_forward_param_data)
+            )
+            foreach_copy_inputs.append(all_gather_input)
+            foreach_copy_input_numels.append(all_gather_input.numel())
+        else:
+            param_all_gather_inputs[i] = fsdp_param.all_gather_inputs
+
+    # 2nd pass: use foreach copy to compute the remaining all-gather inputs
+    if foreach_copy_inputs:
+        fsdp_param_0 = fsdp_params[foreach_copy_indices[0]]
+        param_dtype, device = fsdp_param_0.param_dtype, fsdp_param_0.device
+        flat_foreach_copy_input = torch.empty(
+            (sum(foreach_copy_input_numels),), device=device, dtype=param_dtype
+        )
+        splits = torch.split(flat_foreach_copy_input, foreach_copy_input_numels)
+        torch._foreach_copy_(splits, foreach_copy_inputs)
+        for i, split in zip(foreach_copy_indices, splits):
+            param_all_gather_inputs[i] = [split]
+
+    return param_all_gather_inputs
+
+
+@torch.no_grad()
+def foreach_all_gather_copy_out(
+    all_gather_result: AllGatherResult,
+    fsdp_params: list[FSDPParam],
+    group: dist.ProcessGroup,
+) -> None:
+    (
+        all_gather_output,
+        all_gather_event,
+        all_gather_work,
+        param_all_gather_input_dtypes,
+        param_all_gather_input_numels,
+        all_gather_input_split_sizes,
+    ) = all_gather_result
+    _dtype, device = all_gather_output.dtype, all_gather_output.device
+    device_handle = _get_device_handle(device.type)
+    if all_gather_event is not None:  # sync op
+        device_handle.current_stream().wait_event(all_gather_event)
+    if isinstance(all_gather_work, dist.distributed_c10d.Work):  # async op
+        all_gather_work.wait()
+    world_size, device = group.size(), all_gather_output.device
+
+    split_with_sizes_out: list[torch.Tensor] = []
+    shard_i_copy_infos: list[tuple[FSDPParam, list[torch.Tensor]]] = []
+    for all_gather_input_numels, all_gather_input_dtypes, fsdp_param in zip(
+        param_all_gather_input_numels, param_all_gather_input_dtypes, fsdp_params
+    ):
+        # NOTE: Under compile, make sure we always recreate all_gather_outputs
+        # per AllGather. See [Note: Invariants for torch.compile Traceable FSDP2].
+        force_recreate = compiled_autograd_enabled()
+        fsdp_param.init_all_gather_outputs(
+            all_gather_input_numels,
+            all_gather_input_dtypes,
+            world_size,
+            device,
+            force_recreate=force_recreate,
+        )
+        if not force_recreate:
+            fsdp_param.alloc_all_gather_outputs()
+        param_all_gather_outputs = fsdp_param.all_gather_outputs
+        if fsdp_param.fsdp_placement.dim != 0:
+            # Copy to a temporary and then chunk-cat into the final all-gather
+            # output tensors
+            param_all_gather_outputs = [
+                torch.empty_like(t) for t in param_all_gather_outputs
+            ]
+            shard_i_copy_infos.append((fsdp_param, param_all_gather_outputs))
+        split_with_sizes_out.extend(param_all_gather_outputs)
+
+    all_gather_output = all_gather_output.view(world_size, -1)
+    if all_gather_output.dtype == torch.uint8:
+        out = [t.view(world_size, -1).view(torch.uint8) for t in split_with_sizes_out]
+    else:
+        out = [t.view(world_size, -1) for t in split_with_sizes_out]
+
+    # only avoid VC bump if we are not in inference mode
+    non_inference_outs = [o for o in out if not o.is_inference()]
+    if len(non_inference_outs) > 0:
+        with torch.autograd._unsafe_preserve_version_counter(tuple(non_inference_outs)):
+            torch.ops.fsdp.split_with_sizes_copy(
+                all_gather_output, all_gather_input_split_sizes, dim=1, out=out
+            )
+    else:
+        torch.ops.fsdp.split_with_sizes_copy(
+            all_gather_output, all_gather_input_split_sizes, dim=1, out=out
+        )
+
+    for fsdp_param, param_all_gather_outputs in shard_i_copy_infos:
+        # Chunk-cat from the temporary to the final all-gather output tensors
+        shard_dim = fsdp_param.fsdp_placement.dim
+
+        with torch.autograd._unsafe_preserve_version_counter(
+            tuple(fsdp_param.all_gather_outputs)
+        ):
+            for param_all_gather_output, target_all_gather_output in zip(
+                param_all_gather_outputs, fsdp_param.all_gather_outputs
+            ):
+                padded_sharded_size = (
+                    fsdp_param.padded_sharded_param_size
+                    if fsdp_param.sharded_state == ShardedState.SHARDED
+                    else cast(
+                        torch.Tensor, fsdp_param._sharded_post_forward_param_data
+                    ).size()
+                )
+                pre_param_size = list(padded_sharded_size)
+                pre_param_size[0] *= world_size
+                chunks = torch.chunk(
+                    param_all_gather_output.view(pre_param_size), world_size, dim=0
+                )
+                post_param_size = list(padded_sharded_size)
+                post_param_size[shard_dim] *= world_size
+                cat_out = target_all_gather_output.view(post_param_size)
+                torch.cat(chunks, dim=shard_dim, out=cat_out)
+
+
+@torch.no_grad()
+def foreach_reduce(
+    fsdp_params: list[FSDPParam],
+    unsharded_grads: list[torch.Tensor],
+    reduce_scatter_group: dist.ProcessGroup,
+    reduce_scatter_stream: torch.Stream,
+    orig_dtype: torch.dtype,
+    reduce_dtype: Optional[torch.dtype],
+    device: torch.device,
+    reduce_scatter_reduce_op: Optional[Union[dist.ReduceOp, dist.ReduceOp.RedOpType]],
+    all_reduce_group: Optional[dist.ProcessGroup],  # not `None` iff HSDP
+    all_reduce_stream: torch.Stream,
+    all_reduce_grads: bool,
+    partial_reduce_output: Optional[torch.Tensor],  # only used for HSDP
+    all_reduce_hook: Optional[Callable[[torch.Tensor], None]],
+) -> tuple[
+    torch.Tensor,
+    torch.Event,
+    torch.Event,
+    Optional[torch.Tensor],
+    Optional[torch.Event],
+    Optional[torch.Tensor],
+]:
+    """
+    ``unsharded_grads`` owns the references to the gradients computed by
+    autograd, so clearing the list frees the gradients.
+    """
+    grad_dtypes = {grad.dtype for grad in unsharded_grads}
+    if len(grad_dtypes) != 1:
+        # Check this at runtime since it could be a real runtime error if e.g.
+        # fp8 weights do not produce the correct higher precision gradients
+        _raise_assert_with_print(
+            f"FSDP reduce-scatter expects uniform gradient dtype but got {grad_dtypes}"
+        )
+    grad_dtype = unsharded_grads[0].dtype
+    reduce_dtype = reduce_dtype or grad_dtype
+    predivide_factor, postdivide_factor = _get_gradient_divide_factors(
+        reduce_scatter_group, all_reduce_group, reduce_dtype, device.type
+    )
+    world_size = reduce_scatter_group.size()
+    for i, (fsdp_param, unsharded_grad) in enumerate(zip(fsdp_params, unsharded_grads)):
+        if (shard_dim := fsdp_param.fsdp_placement.dim) == 0:
+            continue
+        assert unsharded_grad.size(shard_dim) % world_size == 0, (
+            f"Shard({shard_dim}) requires even sharding: {unsharded_grad.size()=} {world_size=}"
+        )
+        chunks = torch.chunk(unsharded_grad, world_size, dim=shard_dim)
+        unsharded_grads[i] = torch.cat(chunks, dim=0)
+    padded_unsharded_sizes = tuple(
+        _get_dim0_padded_size(grad.size(), world_size) for grad in unsharded_grads
+    )
+    reduce_scatter_input_numel = sum(s.numel() for s in padded_unsharded_sizes)
+    reduce_scatter_output_numel = reduce_scatter_input_numel // world_size
+    reduce_scatter_input = torch.empty(
+        (reduce_scatter_input_numel,), dtype=reduce_dtype, device=device
+    )
+    device_handle = _get_device_handle(device.type)
+    foreach_reduce_scatter_copy_in(unsharded_grads, reduce_scatter_input, world_size)
+    current_stream = device_handle.current_stream()
+    # Only after the copy-in finishes can we free the gradients
+    unsharded_grads.clear()
+    reduce_scatter_stream.wait_stream(current_stream)
+    all_reduce_input = None
+    all_reduce_event = None
+    with device_handle.stream(reduce_scatter_stream):
+        reduce_output = reduce_scatter_input.new_empty((reduce_scatter_output_numel,))
+        _div_if_needed(reduce_scatter_input, predivide_factor)
+        if reduce_scatter_reduce_op is None:
+            if predivide_factor is None:
+                reduce_scatter_reduce_op = ReduceOp.AVG
+            else:
+                reduce_scatter_reduce_op = ReduceOp.SUM
+        dist.reduce_scatter_tensor(
+            output=reduce_output,
+            input=reduce_scatter_input,
+            group=reduce_scatter_group,
+            op=reduce_scatter_reduce_op,
+        )
+        reduce_scatter_event = reduce_scatter_stream.record_event()
+        post_reduce_stream = reduce_scatter_stream
+        if all_reduce_group is not None:  # HSDP
+            # Accumulations must run in the reduce-scatter stream
+            if not all_reduce_grads:
+                if partial_reduce_output is not None:
+                    partial_reduce_output += reduce_output
+                else:
+                    partial_reduce_output = reduce_output
+                return (
+                    reduce_scatter_input,
+                    reduce_scatter_event,
+                    post_reduce_stream.record_event(),
+                    all_reduce_input,
+                    all_reduce_event,
+                    partial_reduce_output,
+                )
+            if partial_reduce_output is not None:
+                reduce_output += partial_reduce_output
+            post_reduce_stream = all_reduce_stream
+            all_reduce_stream.wait_stream(reduce_scatter_stream)
+            with device_handle.stream(all_reduce_stream):
+                dist.all_reduce(
+                    reduce_output,
+                    group=all_reduce_group,
+                    op=ReduceOp.AVG if predivide_factor is None else ReduceOp.SUM,
+                )
+                all_reduce_input = reduce_output
+                all_reduce_event = all_reduce_stream.record_event()
+    # -- END: ops in reduce_scatter stream
+
+    if all_reduce_hook is not None:
+        # Execute user-specified all reduce hook.
+        # If native HSDP is used, this is executed after the HSDP all reduce.
+        # If 1-d FSDP is used, this is executed post reduce-scatter.
+        post_reduce_stream = all_reduce_stream
+        all_reduce_stream.wait_stream(reduce_scatter_stream)
+        with device_handle.stream(all_reduce_stream):
+            all_reduce_hook(reduce_output)
+    # -- END: ops post reduce_scatter
+
+    with device_handle.stream(post_reduce_stream):
+        _div_if_needed(reduce_output, postdivide_factor)
+        reduce_output = _to_dtype_if_needed(reduce_output, orig_dtype)
+        # View out and accumulate sharded gradients
+        flat_grad_offset = 0  # [0, reduce_scatter_output_numel - 1]
+        for padded_unsharded_size, fsdp_param in zip(
+            padded_unsharded_sizes, fsdp_params
+        ):
+            # Assume even sharding for Shard(i), i > 0; otherwise would require
+            # copy-out for contiguous strides
+            new_sharded_grad = torch.as_strided(
+                reduce_output,
+                size=fsdp_param.sharded_size,
+                stride=fsdp_param.contiguous_sharded_stride,
+                storage_offset=flat_grad_offset,
+            )
+            to_accumulate_grad = fsdp_param.sharded_param.grad is not None
+            if fsdp_param.offload_to_cpu:
+                # Only overlap the D2H copy (copying to pinned memory) if not
+                # accumulating gradients since the CPU add kernel depends on
+                # the copy result and we cannot run the add as a callback
+                non_blocking = fsdp_param.pin_memory and not to_accumulate_grad
+                # Since the GPU sharded gradient is allocated in the RS stream,
+                # we can free it here by not keeping a ref without waiting for
+                # the D2H copy since future RS-stream ops run after the copy
+                new_sharded_grad = new_sharded_grad.to(
+                    torch.device("cpu"), non_blocking=non_blocking
+                )
+                if non_blocking:
+                    # Record an event on which to block the CPU thread to
+                    # ensure that the D2H copy finishes before the optimizer
+                    fsdp_param.grad_offload_event = reduce_scatter_stream.record_event()
+            if to_accumulate_grad:
+                assert isinstance(fsdp_param.sharded_param.grad, DTensor)
+                fsdp_param.sharded_param.grad._local_tensor += new_sharded_grad
+            else:
+                new_sharded_dtensor_grad = fsdp_param.to_sharded_dtensor(
+                    new_sharded_grad
+                )
+                fsdp_param.sharded_param.grad = new_sharded_dtensor_grad
+            if not compiled_autograd_enabled():
+                for hook in (
+                    getattr(fsdp_param.sharded_param, "_post_accumulate_grad_hooks", {})
+                    or {}
+                ).values():
+                    hook(fsdp_param.sharded_param)
+            padded_sharded_numel = padded_unsharded_size.numel() // world_size
+            flat_grad_offset += padded_sharded_numel
+        post_reduce_event = post_reduce_stream.record_event()
+    # The RS output is allocated in the RS stream and used in the default
+    # stream (for optimizer). To ensure its memory is not reused for later
+    # RSs, we do not need extra synchronization since the sharded parameters
+    # hold refs through the end of backward.
+    return (
+        reduce_scatter_input,
+        reduce_scatter_event,
+        post_reduce_event,
+        all_reduce_input,
+        all_reduce_event,
+        None,
+    )
+
+
+def foreach_reduce_scatter_copy_in(
+    unsharded_grads: list[torch.Tensor],
+    reduce_scatter_input: torch.Tensor,
+    world_size: int,
+) -> None:
+    reduce_scatter_input = reduce_scatter_input.view(world_size, -1)
+    torch.ops.fsdp.chunk_cat(
+        unsharded_grads, dim=0, num_chunks=world_size, out=reduce_scatter_input
+    )
+
+
+def _get_all_gather_input_metadatas(
+    param_all_gather_inputs: list[list[torch.Tensor]],
+) -> tuple[list[list[torch.dtype]], list[list[int]], torch.dtype]:
+    param_all_gather_input_dtypes: list[list[torch.dtype]] = []
+    param_all_gather_input_numels: list[list[int]] = []
+    all_gather_dtype = param_all_gather_inputs[0][0].dtype
+    for all_gather_inputs in param_all_gather_inputs:
+        input_dtypes: list[torch.dtype] = []
+        input_numels: list[int] = []
+        for all_gather_input in all_gather_inputs:
+            if all_gather_input.dtype != all_gather_dtype:
+                all_gather_dtype = torch.uint8
+            input_dtypes.append(all_gather_input.dtype)
+            input_numels.append(all_gather_input.numel())
+        param_all_gather_input_dtypes.append(input_dtypes)
+        param_all_gather_input_numels.append(input_numels)
+    return (
+        param_all_gather_input_dtypes,
+        param_all_gather_input_numels,
+        all_gather_dtype,
+    )
+
+
+def _get_gradient_divide_factors(
+    reduce_scatter_group: dist.ProcessGroup,
+    all_reduce_group: Optional[dist.ProcessGroup],
+    reduce_dtype: torch.dtype,
+    device_type: str = "",
+) -> Union[tuple[None, None], tuple[float, float]]:
+    # For fp32/bf16, we do not need to worry about overflow/underflow, so we
+    # use NCCL's built-in division to avoid separate div kernels
+    if reduce_dtype in (torch.float32, torch.bfloat16) and device_type != "mtia":
+        return None, None
+    data_parallel_size = reduce_scatter_group.size()
+    if all_reduce_group is not None:
+        data_parallel_size *= all_reduce_group.size()
+    # Since fp16 has smaller dynamic range than fp32/bf16, we want to avoid
+    # overflow/underflow. For N data parallel workers, each worker computes
+    # g_i, and they collectively reduce (g_1 + ... + g_N) / N. To avoid
+    # overflow/underflow, we divide by ~sqrt(N) before/after the reduction.
+    factor: int = 1
+    while data_parallel_size % factor == 0 and data_parallel_size / factor > factor:
+        factor *= 2
+    factor = float(factor)
+    return (factor, data_parallel_size / factor)
+
+
+def _div_if_needed(tensor: torch.Tensor, div_factor: Optional[float]) -> None:
+    if div_factor is not None and div_factor > 1:
+        tensor.div_(div_factor)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_common.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdcf32e22a338dcc1a47b8358cadfe2bc148e1cd
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_common.py
@@ -0,0 +1,181 @@
+# mypy: allow-untyped-defs
+import math
+import traceback
+from dataclasses import dataclass
+from enum import auto, Enum
+from typing import Any, Optional
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed._composable.contract import _get_registry
+from torch.distributed.tensor import DeviceMesh, DTensor
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+
+
+_compiled_autograd_enabled: bool = False
+
+if torch._running_with_deploy():
+
+    def detect_compiled_autograd():
+        pass
+
+    def compiled_autograd_enabled():
+        return False
+
+else:
+
+    def detect_compiled_autograd():
+        assert not torch.compiler.is_compiling(), (
+            "`detect_compiled_autograd()` is designed to be called in eager mode"
+        )
+        global _compiled_autograd_enabled
+        import torch._dynamo.compiled_autograd as ca
+
+        _compiled_autograd_enabled = (
+            ca.compiled_autograd_enabled
+            or ca.compiled_autograd_enabled_force_eager
+            or ca.in_compiled_autograd_region
+        )
+
+    def compiled_autograd_enabled():
+        global _compiled_autograd_enabled
+        return _compiled_autograd_enabled
+
+
+@dataclass
+class DataParallelMeshInfo:
+    mesh: DeviceMesh
+    shard_mesh_dim: Optional[int] = None
+    replicate_mesh_dim: Optional[int] = None
+
+    def __post_init__(self):
+        if self.shard_mesh_dim is None and self.replicate_mesh_dim is None:
+            raise AssertionError(
+                "At least one of shard_mesh_dim and replicate_mesh_dim must not be None"
+            )
+
+
+@dataclass
+class FSDPMeshInfo(DataParallelMeshInfo):
+    def __post_init__(self):
+        super().__post_init__()
+        if self.shard_mesh_dim is None:
+            raise AssertionError("Expects non-None shard_mesh_dim")
+        self.shard_mesh_size: int = self.mesh.size(self.shard_mesh_dim)
+        self.shard_process_group = self.mesh.get_group(self.shard_mesh_dim)
+        self.shard_mesh_rank: int = self.shard_process_group.rank()
+
+
+@dataclass
+class DDPMeshInfo(DataParallelMeshInfo):
+    def __post_init__(self):
+        super().__post_init__()
+        if self.replicate_mesh_dim is None:
+            raise AssertionError("Expects non-None replicate_mesh_dim")
+        self.replicate_mesh_size: int = self.mesh.size(self.replicate_mesh_dim)
+        self.replicate_process_group = self.mesh.get_group(self.replicate_mesh_dim)
+        self.replicate_mesh_rank: int = self.replicate_process_group.rank()
+
+
+@dataclass
+class HSDPMeshInfo(FSDPMeshInfo, DDPMeshInfo):
+    def __post_init__(self):
+        # Calls `FSDPMeshInfo` -> `DDPMeshInfo` -> `DataParallelMeshInfo`
+        super().__post_init__()
+
+
+class TrainingState(Enum):
+    """Describes the training state of one FSDP state / parameter group."""
+
+    # Transition to forward starting pre-forward until post-forward
+    FORWARD = auto()
+    # Transition to pre-backward when unsharding in backward
+    PRE_BACKWARD = auto()
+    # Transition to post-backward when resharding and reducing gradients
+    POST_BACKWARD = auto()
+    # Idle before/after forward or before pre-backward/after post-backward
+    IDLE = auto()
+
+
+def _raise_assert_with_print(*args: Any, **kwargs: Any):
+    print(f"[Rank {dist.get_rank()}] ", end="")
+    print(*args, **kwargs)
+    traceback.print_stack()
+    raise AssertionError(*args, **kwargs)
+
+
+def _is_composable_with_fsdp(module: nn.Module) -> bool:
+    registry = _get_registry(module)
+    if registry is None:
+        return True
+    # Registry keys by function name
+    return "replicate" not in registry
+
+
+def _get_dim0_padded_size(tensor_size: torch.Size, dim0_factor: int) -> torch.Size:
+    padded_dim0 = math.ceil(tensor_size[0] / dim0_factor) * dim0_factor
+    return torch.Size([padded_dim0]) + tensor_size[1:]
+
+
+def _chunk_with_empty(
+    tensor: torch.Tensor, num_chunks: int, dim: int
+) -> list[torch.Tensor]:
+    chunks = list(torch.chunk(tensor, num_chunks, dim=dim))
+    while len(chunks) < num_chunks:
+        chunks.append(chunks[0].new_empty(0))
+    return chunks
+
+
+def _get_dim_chunked_size(
+    chunk: torch.Tensor, unchunked_size: torch.Size, dim: int
+) -> torch.Size:
+    if chunk.numel() > 0:
+        return chunk.size()
+    # For 0 numel, we need to preserve nonzero-sized dims for DTensor APIs
+    return unchunked_size[:dim] + torch.Size([0]) + unchunked_size[dim + 1 :]
+
+
+def _from_local_no_grad(
+    local_tensor: torch.Tensor,
+    sharding_spec: DTensorSpec,
+) -> DTensor:
+    """
+    This method is similar to ``DTensor.from_local()`` except that in eager mode
+    it avoids some CPU overhead by avoiding default args and not being differentiable.
+    """
+
+    if not compiled_autograd_enabled():
+        return DTensor(
+            # Use the local tensor directly instead of constructing a new tensor
+            # variable, e.g. with `view_as()`, since this is not differentiable
+            local_tensor,
+            sharding_spec,
+            requires_grad=local_tensor.requires_grad,
+        )
+    else:
+        return DTensor.from_local(
+            local_tensor,
+            sharding_spec.mesh,
+            sharding_spec.placements,
+            shape=sharding_spec.shape,
+            stride=sharding_spec.stride,
+        )
+
+
+def _to_dtype_if_needed(
+    tensor: torch.Tensor, dtype: Optional[torch.dtype]
+) -> torch.Tensor:
+    if dtype is not None and tensor.dtype != dtype:
+        return tensor.to(dtype)
+    return tensor
+
+
+def _cast_fp_tensor(dtype: torch.dtype, x: torch.Tensor) -> torch.Tensor:
+    if (
+        not isinstance(x, torch.Tensor)
+        or not torch.is_floating_point(x)
+        or x.dtype == dtype
+    ):
+        return x
+    return x.to(dtype)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_init.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..444fa8d36ef87509c1ece30667dfd4e4aed7acf1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_init.py
@@ -0,0 +1,232 @@
+import itertools
+from typing import Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed.device_mesh import _get_device_handle
+from torch.distributed.tensor import DeviceMesh, DTensor, init_device_mesh
+from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+
+from ._fsdp_common import _is_composable_with_fsdp, FSDPMeshInfo, HSDPMeshInfo
+from ._fsdp_state import _get_module_fsdp_state
+
+
+def _get_post_forward_mesh_info(
+    reshard_after_forward: Union[bool, int], mesh_info: FSDPMeshInfo
+) -> Optional[FSDPMeshInfo]:
+    shard_mesh_size = mesh_info.shard_mesh_size
+    if not isinstance(reshard_after_forward, (bool, int)):
+        raise ValueError(
+            "reshard_after_forward should be a bool or an int representing the "
+            f"group size to reshard to, not {reshard_after_forward}"
+        )
+    # NOTE: `isinstance(False, int)` returns `True`.
+    if not isinstance(reshard_after_forward, bool) and isinstance(
+        reshard_after_forward, int
+    ):
+        if (
+            reshard_after_forward < 1
+            or reshard_after_forward > shard_mesh_size
+            or shard_mesh_size % reshard_after_forward != 0
+        ):
+            raise ValueError(
+                "If passing reshard_after_forward as an int, it should be a "
+                f"factor of {shard_mesh_size}, not {reshard_after_forward}"
+            )
+        elif reshard_after_forward == 1:
+            reshard_after_forward = False
+        elif reshard_after_forward == shard_mesh_size:
+            reshard_after_forward = True
+    post_forward_mesh_info = None
+    if reshard_after_forward is True:
+        post_forward_mesh_info = mesh_info
+    elif reshard_after_forward is not False:  # int case
+        # For HSDP, we can flatten the two replicate dims into the 0th dim
+        post_forward_mesh_tensor = mesh_info.mesh.mesh.view(-1, reshard_after_forward)
+        post_forward_mesh = DeviceMesh(
+            mesh_info.mesh.device_type, post_forward_mesh_tensor
+        )
+        post_forward_mesh_info = HSDPMeshInfo(
+            post_forward_mesh, shard_mesh_dim=1, replicate_mesh_dim=0
+        )
+    return post_forward_mesh_info
+
+
+def _init_default_fully_shard_mesh() -> DeviceMesh:
+    """Default to global CUDA mesh if possible else global CPU mesh."""
+    if not dist.distributed_c10d.is_initialized():
+        dist.distributed_c10d.init_process_group()
+    default_pg = dist.distributed_c10d._get_default_group()
+    device = torch._C._get_accelerator()
+    mesh = init_device_mesh(device.type, mesh_shape=(default_pg.size(),))
+    return mesh
+
+
+def _get_device_from_mesh(mesh: DeviceMesh) -> torch.device:
+    if mesh.device_type == "cpu":
+        return torch.device("cpu")
+    device_handle = _get_device_handle(mesh.device_type)
+    return torch.device(mesh.device_type, device_handle.current_device())
+
+
+def _ignore_module(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    ignore_decision: dict[nn.Module, bool],
+) -> bool:
+    """
+    Decide if it is safe to ignore a module for applying fully_shard.
+    """
+    if module in ignore_decision:
+        return ignore_decision[module]
+
+    if len(list(module.buffers(recurse=False))) > 0:
+        # Cannot ignore a module with any buffer
+        ignore_decision[module] = False
+        return False
+
+    for _, param in module.named_parameters(recurse=False):
+        if param not in ignored_params:
+            # at least one param is not ignored. So this module shouldn't be.
+            ignore_decision[module] = False
+            return False
+
+    # Need to consider descendants of module
+    for child in list(module.children()):
+        ignore_child = _ignore_module(child, ignored_params, ignore_decision)
+        if not ignore_child:
+            # Cannot ignore module if one of its children is not ignored
+            ignore_decision[module] = False
+            return False
+
+    # Safe to ignore module
+    ignore_decision[module] = True
+    return True
+
+
+def _adjust_managed_modules(
+    modules: list[nn.Module], ignored_params: set[nn.Parameter]
+) -> list[nn.Module]:
+    """
+    Adjust the given list of managed modules by removing those with all parameters ignored.
+    """
+    ignore_decision: dict[nn.Module, bool] = {}
+    new_modules = []
+    for module in modules:
+        ignored = _ignore_module(module, ignored_params, ignore_decision)
+        if not ignored:
+            new_modules.append(module)
+    return new_modules
+
+
+def _get_managed_modules(
+    root_modules: tuple[nn.Module, ...],
+    ignored_params: Optional[set[nn.Parameter]] = None,
+) -> list[nn.Module]:
+    modules: list[nn.Module] = []
+    root_modules_set = set(root_modules)
+    # Track visisted modules to avoid visiting shared modules multiple times
+    visited_modules: set[nn.Module] = set()
+
+    def dfs(module: nn.Module) -> None:
+        """
+        Runs a DFS to collect managed modules, not recursing into modules with
+        a non-composable API or ``fully_shard`` already applied.
+        """
+        if not _is_composable_with_fsdp(module):
+            return
+        elif (
+            module not in root_modules_set
+            and _get_module_fsdp_state(module) is not None
+        ):
+            return  # nested `fully_shard` module
+        visited_modules.add(module)
+        for submodule in module.children():
+            if submodule not in visited_modules:
+                dfs(submodule)
+        modules.append(module)
+
+    for root_module in root_modules:
+        dfs(root_module)
+
+    if ignored_params is None:
+        return modules
+
+    adjusted_modules = _adjust_managed_modules(modules, ignored_params)
+    return adjusted_modules
+
+
+def _verify_managed_param(name: str, param: nn.Parameter) -> None:
+    """
+    Verify if the parameter is accepted by fully_shard. The only restriction now
+    is that the parameter cannot be a scalar tensor (param.numel == 0) since we
+    need at least one dim to shard.
+    """
+    if len(param.shape) == 0:
+        raise ValueError(
+            "fully_shard doesn't support scalar parameters. "
+            f"Change {name} to a 1D tensor with numel equal to 1."
+        )
+
+
+def _get_managed_states(
+    modules: list[nn.Module], ignored_params: Optional[set[nn.Parameter]] = None
+) -> tuple[list[nn.Parameter], list[torch.Tensor]]:
+    params: list[nn.Parameter] = []
+    buffers: list[torch.Tensor] = []
+    # Track visited parameters/buffers to avoid visiting shared parameters and
+    # buffers multiple times
+    visited_params: set[nn.Parameter] = set()
+    visited_buffers: set[torch.Tensor] = set()
+    if ignored_params is None:
+        ignored_params = set()
+
+    for module in modules:
+        for name, param in module.named_parameters(recurse=False):
+            if param in ignored_params:
+                # do not include an ignored parameters
+                continue
+            if param not in visited_params:
+                _verify_managed_param(name, param)
+                params.append(param)
+                visited_params.add(param)
+        for buffer in module.buffers(recurse=False):
+            if buffer not in visited_buffers:
+                buffers.append(buffer)
+                visited_buffers.add(buffer)
+    return params, buffers
+
+
+def _move_states_to_device(
+    params: list[nn.Parameter],
+    buffers: list[torch.Tensor],
+    device: torch.device,
+) -> None:
+    """
+    We have FSDP move states to device for simpler and faster initialization
+    since FSDP almost always uses CUDA for training. We move parameters/buffers
+    rather than modules since modules to support ignoring parameters/buffers in
+    the future.
+    """
+    # Follow the logic in `nn.Module._apply`
+    for tensor in itertools.chain(params, buffers):
+        if tensor.device == device or tensor.device.type == "meta":
+            # Keep meta-device tensors on meta device for deferred init
+            continue
+        if isinstance(tensor, DTensor):
+            if (dtensor_mesh_type := tensor.device_mesh.device_type) != device.type:
+                raise ValueError(
+                    "Requires DTensor to have mesh of the same type as the FSDP mesh "
+                    f"but got {dtensor_mesh_type} for DTensor and {device.type} for FSDP"
+                )
+            raise AssertionError(
+                f"Expects DTensor to be moved to {dtensor_mesh_type} but got {tensor.device}"
+            )
+        tensor_ = tensor
+        if is_traceable_wrapper_subclass(tensor_):
+            with torch.no_grad():  # avoid autograd increasing C++ refcount by 1
+                tensor_on_device = nn.Parameter(tensor.to(device))
+            torch.utils.swap_tensors(tensor, tensor_on_device)
+        else:
+            tensor.data = tensor.to(device)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param.py
new file mode 100644
index 0000000000000000000000000000000000000000..274bb9d1cc10e30de1e049385c9d69c39729b789
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param.py
@@ -0,0 +1,906 @@
+# mypy: allow-untyped-defs
+import inspect
+import itertools
+from collections.abc import Sequence
+from dataclasses import dataclass, field
+from enum import auto, Enum
+from typing import Any, Callable, cast, Optional
+
+import torch
+import torch.nn as nn
+from torch._prims_common import make_contiguous_strides_for
+from torch.distributed._functional_collectives import AsyncCollectiveTensor
+from torch.distributed.tensor import DTensor, Replicate, Shard
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor.device_mesh import _mesh_resources
+from torch.distributed.tensor.placement_types import _StridedShard, Placement
+
+from ._fsdp_api import CPUOffloadPolicy, MixedPrecisionPolicy, OffloadPolicy
+from ._fsdp_common import (
+    _chunk_with_empty,
+    _from_local_no_grad,
+    _get_dim_chunked_size,
+    _raise_assert_with_print,
+    _to_dtype_if_needed,
+    compiled_autograd_enabled,
+    FSDPMeshInfo,
+    HSDPMeshInfo,
+)
+
+
+"""
+[Note: FSDP tensors]
+FSDP considers the following tensors:
+- Original parameter: parameter passed to :class:`FSDPParam`, i.e. the one
+  on the module when applying FSDP
+- Sharded parameter: sharding the original parameter on dim-0 (or a
+  user-specified dim) as a DTensor over the main mesh
+- All-gather inputs: the ``torch.Tensor`` or ``Tensor`` s passed to all-gather,
+  derived from the sharded parameter
+- All-gather output: the ``torch.Tensor`` or ``Tensor`` s resulting from
+  all-gathering the all-gather inputs
+- Unsharded parameter: parameter used for forward/backward computation, derived
+  from the all-gather output; autograd leaf
+
+We define these tensors to describe the general framework that can accomodate
+extensions, where:
+- all-gather-inputs = pre-all-gather-transform(sharded-parameter)
+- unsharded-parameter = post-all-gather-transform(all-gather-outputs)
+
+For the default ``torch.Tensor`` case, there is only one all-gather input, and
+it shares the same underlying tensor data as the sharded parameter, meaning
+that they can be thought of as the same tensors. The same applies for the
+all-gather output and unsharded parameter. For non-``torch.Tensor`` extensions,
+these equivalences may no longer hold due to the pre/post-all-gather
+transforms, and some may have multiple all-gather inputs/outputs (e.g.
+quantized data and scales).
+
+[Note: FSDP and autograd]
+FSDP dynamically frees and allocates the unsharded parameter. Since autograd
+can pack a reference to it or a view to save for backward, we use storage
+resizing to implement the freeing/allocation since that preserves the aliasing.
+This implies that we construct the unsharded parameter object once and write to
+it in-place thereafter. For the default ``torch.Tensor` original parameter
+case, the all-gather output and unsharded parameter share the same
+data, so we use storage resizing on the all-gather output.
+"""
+
+lib = torch.library.Library("fsdp", "FRAGMENT")  # noqa: TOR901
+
+lib.define("copy_(Tensor(a!) tensor, Tensor data) -> ()")
+
+
+@torch.library.impl(lib, "copy_", "Meta")
+@torch.library.impl(lib, "copy_", "CUDA")
+@torch.library.impl(lib, "copy_", "XPU")
+@torch.library.impl(lib, "copy_", "HPU")
+@torch.library.impl(lib, "copy_", "CPU")
+@torch.library.impl(lib, "copy_", "MTIA")
+def copy_(tensor, data):
+    tensor.copy_(data)
+
+
+"""
+[Note: Avoiding functionalization for fsdp.copy_ and inductor.resize_storage_bytes_]
+
+Currently we don't functionalize `fsdp.copy_` op or `inductor.resize_storage_bytes_` op
+(i.e. they show up as a mutation op in the middle of the AOT joint graph).
+
+Reason:
+Traceable FSDP2 compiled autograd BWD graph have the following traits:
+(1) Two inputs of the graph were aliased to each other (one from hook closed-over tensors, one from FWD saved tensors).
+(2) One of them is mutated (copy_ and resize_ to handle the all-gathered param).
+(3) They are both subclasses.
+The combination of these traits is not supported by AOTAutograd (it's difficult to reason about subclass aliasing).
+So this doesn't work at all for Traceable FSDP2.
+
+The compromise we use is to avoid functionalization for the FSDP2 copy_ and resize_ ops.
+This avoids the problem above, because from AOTAutograd point-of-view there are no mutations
+that functionalization needs to handle. (Although we need to be careful not to DCE those mutable ops.)
+
+We can avoid this functionalization because:
+(1) The nn.Parameter is never used before its .copy_() is called in eager code (i.e. no alias of it is created),
+so it's safe to call .copy_() in the middle of the graph to update its content and start using the nn.Parameter downstream.
+(2) We always re-allocate the buffer for nn.Parameter to store the AllGather output and to be used in downstream user ops.
+So calling resize-to-0 in the middle of the graph to free nn.Parameter memory after use should always be okay
+(since we always allocate anew next time we need it, we strictly don't need to keep the old tensor storage around anymore).
+
+Q: Wouldn't the extra resize_ and copy_ ops hurt both memory usage and performance?
+A: Yes it would. As an optimization, we have an Inductor post-grad FX pass to remove those resize_ and copy_ ops
+for unsharded params that have this pattern: resize_(full) -> copy_ -> resize_(0).
+
+TODO:
+Now that we are maintaining the invariant of "no aliased + mutated graph inputs" in both the forward and backward,
+it is now more feasible to functionalize all of the mutable FSDP ops. Some of the pros and cons are:
+
+Cons (of functionalizing those ops):
+(1) By not functionalizing them as we are today, we are making it more likely that they will run at the "correct" time
+in the generated code. If we start to functionalize them, we will need to make sure that Inductor reinplaces them
+in a way where it properly moves the mutations back to exactly where they should have run, or we risk suffering worse
+peak memory than eager. (We probably already need to do something similar in Inductor's reinplacing for copy_:
+https://github.com/pytorch/pytorch/issues/135305#issuecomment-2334888089)
+
+Pros (of functionalizing):
+(1) Better safety, we don't need to worry about the graph passes in inductor/partitioning handling input mutations
+mid-graph quite as much (to be fair we've already done some amount of auditing, but we might have to do some more).
+(2) Better perf: each mutation midway through the graph prevents Inductor from pattern matching across it.
+But maybe there are few enough mutations induced by FSDP for this to matter.
+"""
+
+
+@torch.library.impl(lib, "copy_", "Functionalize")
+def copy__functionalize(tensor, data):
+    torch._sync(tensor)
+    torch._sync(data)
+    tensor_inner = torch._from_functional_tensor(tensor)
+    data_inner = torch._from_functional_tensor(data)
+    with torch._C._ExcludeDispatchKeyGuard(
+        torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize)
+    ):
+        torch.ops.fsdp.copy_.default(tensor_inner, data_inner)
+
+
+if not torch._running_with_deploy():
+    torch.fx.node.has_side_effect(torch.ops.fsdp.copy_.default)
+
+
+class ShardedState(Enum):
+    """
+    - ``SHARDED``: The sharded parameter is registered to the module. It is the
+      only contributor to parameter memory.
+    - ``SHARDED_POST_FORWARD``: The unsharded parameter is resharded to a
+      smaller world size. Since this data should not be used for computation,
+      we do not register it to the module. Users should reshard the module
+      before any in-place modifications. Both it and the sharded parameter
+      contribute to parameter memory.
+    - ``UNSHARDED``: The unsharded parameter is registered to the module. Both
+      it and the sharded parameter contribute to parameter memory.
+    """
+
+    SHARDED = auto()
+    SHARDED_POST_FORWARD = auto()
+    UNSHARDED = auto()
+
+
+@dataclass
+class ParamModuleInfo:
+    """
+    For a parameter, this stores the module and the parameter name to be able
+    to do a parameter swap via ``setattr(module, param_name, ...)`` or to get
+    the parameter via ``getattr(module, param_name)``. We additionally save
+    shared modules and shared parameter names to update them accordingly.
+    """
+
+    # Parameter names are unprefixed, e.g. "weight", not "lin.weight"
+    module: nn.Module
+    param_name: str
+    shared_modules: list[nn.Module] = field(default_factory=list)
+    shared_param_names: list[str] = field(default_factory=list)
+
+
+@dataclass
+class ExtensionsData:
+    # User-defined metadata passed from pre to post-all-gather
+    all_gather_metadata: Optional[Any] = None
+    # Save the all-gather input sizes to unflatten the all-gather outputs to ND
+    all_gather_input_sizes: Sequence[torch.Size] = ()  # ND
+
+    def clear(self):
+        self.all_gather_metadata = None
+        self.all_gather_input_sizes = ()
+
+
+class FSDPParam:
+    """
+    This class manages a parameter with FSDP or FSDP variants applied,
+    implementing dim-0 per-parameter sharding.
+    """
+
+    orig_dtype: torch.dtype
+    param_dtype: Optional[torch.dtype]
+    reduce_dtype: Optional[torch.dtype]
+    _orig_size: torch.Size  # ND
+    sharded_size: torch.Size  # ND
+    contiguous_sharded_stride: tuple[int, ...]
+    padded_sharded_param_size: torch.Size  # ND
+    sharded_post_forward_size: torch.Size  # ND
+    contiguous_sharded_post_forward_stride: tuple[int, ...]
+    _sharded_param_data: torch.Tensor  # 1D
+    sharded_param: nn.Parameter  # ND
+    _sharded_post_forward_param_data: Optional[torch.Tensor]  # 1D
+    _sharded_post_forward_param: Optional[nn.Parameter]  # ND
+    _unsharded_param: nn.Parameter  # ND
+    unsharded_accumulated_grad: Optional[torch.Tensor]  # ND
+    _sharding_spec: DTensorSpec
+    # DTensor attributes (only defined for DTensor `param`):
+    _tp_spec: DTensorSpec
+    all_gather_outputs: list[torch.Tensor]  # 1D
+    # All-gather extension attributes
+    _extensions_data: ExtensionsData
+    _unsharded_inner_tensors: list[torch.Tensor]
+
+    def __init__(
+        self,
+        param: nn.Parameter,
+        module_info: ParamModuleInfo,
+        mesh_info: FSDPMeshInfo,
+        post_forward_mesh_info: Optional[FSDPMeshInfo],
+        device: torch.device,
+        shard_placement_fn: Optional[Callable[[nn.Parameter], Optional[Shard]]],
+        mp_policy: MixedPrecisionPolicy,
+        offload_policy: OffloadPolicy,
+    ):
+        self._module_info: ParamModuleInfo = module_info
+        self.mesh_info = mesh_info
+        self.post_forward_mesh_info = post_forward_mesh_info
+        self.device = device
+        self.mp_policy = mp_policy
+        self.offload_to_cpu: bool = isinstance(offload_policy, CPUOffloadPolicy)
+        self.pin_memory = (
+            self.offload_to_cpu and cast(CPUOffloadPolicy, offload_policy).pin_memory
+        )
+        self.grad_offload_event: Optional[torch.Event] = None
+        self._init_sharded_param(param, device, shard_placement_fn)
+        if self.post_forward_mesh_info:
+            self._init_sharded_post_forward_param_metadata(param)
+        self._init_extensions()
+        self.all_gather_outputs: list[torch.Tensor] = []
+        self.unsharded_accumulated_grad = None
+        self._param_fqn: Optional[str] = None  # prefixed from root module
+        # TODO: Remove this padding logic once DTensor pads the local tensor:
+        # https://github.com/pytorch/pytorch/issues/113045
+        self._post_load_hook_handle = (
+            module_info.module.register_load_state_dict_post_hook(
+                lambda *args, **kwargs: self.reset_sharded_param()
+            )
+        )
+
+    @torch.no_grad()
+    def _init_sharded_param(
+        self,
+        param: nn.Parameter,
+        device: torch.device,
+        shard_placement_fn: Optional[Callable],
+    ):
+        if param.device != device and param.device.type != "meta":
+            raise AssertionError(
+                f"Expects the parameter to already be moved to device {device} but got {param.device}"
+            )
+        if not param.is_contiguous():
+            raise NotImplementedError(
+                f"FSDP does not support non-contiguous parameters yet: {param.shape=} {param.stride()=}"
+            )
+        fsdp_placement = shard_placement_fn(param) if shard_placement_fn else None
+        if fsdp_placement is None:
+            fsdp_placement = Shard(0)
+        elif fsdp_placement.dim < 0:
+            fsdp_placement = Shard(fsdp_placement.dim + param.ndim)
+        assert isinstance(fsdp_placement, Shard), f"{fsdp_placement}"
+        self.fsdp_placement = fsdp_placement
+        shard_dim = fsdp_placement.dim
+        # TODO: Replace the sharded DTensor parameter construction logic with
+        # `distribute_tensor` after https://github.com/pytorch/pytorch/issues/116101
+        # TODO: Simplify the following sharded parameter padding logic after
+        # https://github.com/pytorch/pytorch/issues/113045
+        self.is_dtensor = isinstance(param, DTensor)
+        if self.is_dtensor:
+            self._tp_spec = cast(DTensor, param)._spec
+            dp_mesh, tp_mesh = (self.mesh_info.mesh, self._tp_spec.mesh)
+            dp_global_mesh = _mesh_resources.get_root_mesh(dp_mesh)
+            tp_global_mesh = _mesh_resources.get_root_mesh(tp_mesh)
+            if dp_global_mesh != tp_global_mesh or (
+                dp_global_mesh is None or tp_global_mesh is None
+            ):
+                raise AssertionError(
+                    "FSDP requires the DP and TP mesh to have the same parent mesh but got: \n"
+                    f"DP's global mesh: {dp_global_mesh}\nTP's global mesh: {tp_global_mesh}"
+                )
+            name_dims_error = "FSDP requires named DeviceMesh dims for ND parallelism"
+            assert dp_mesh.mesh_dim_names is not None, name_dims_error
+            assert tp_mesh.mesh_dim_names is not None, name_dims_error
+            submesh_names = dp_mesh.mesh_dim_names + tp_mesh.mesh_dim_names
+            self._spmd_mesh = dp_global_mesh[submesh_names]
+            if len(self._tp_spec.placements) != 1:
+                raise NotImplementedError(
+                    f"FSDP only supports 1D TP, not {self._tp_spec.placements}"
+                )
+            split_factor = self._tp_spec.num_shards_map[shard_dim]
+            assert 2 <= self._spmd_mesh.ndim <= 3, (
+                f"_spmd_mesh.ndim can only be 2 or 3 but got {self._spmd_mesh.ndim}."
+            )
+            self._spmd_placements: tuple[Placement, ...]
+            dp_shard_tp_placement = (
+                (
+                    _StridedShard(shard_dim, split_factor=split_factor)
+                    if split_factor > 1
+                    else fsdp_placement
+                ),
+                self._tp_spec.placements[0],
+            )
+            if self._spmd_mesh.ndim == 2:
+                self._spmd_placements = dp_shard_tp_placement
+            else:
+                assert self.mesh_info.replicate_mesh_dim == 0
+                self._spmd_placements = (Replicate(),) + dp_shard_tp_placement
+            self._sharding_spec = DTensorSpec(
+                self._spmd_mesh,
+                self._spmd_placements,
+                tensor_meta=self._tp_spec.tensor_meta,
+            )
+            # TODO: Enable uneven sharding for FSDP+TP.
+            if split_factor > 1:  # FSDP has strided sharding on tensor dim 0
+                num_shards = self._sharding_spec.num_shards_map[0]
+                tensor_size_dim_0 = self._sharding_spec.shape[0]
+                if tensor_size_dim_0 % num_shards != 0:
+                    raise NotImplementedError(
+                        "FSDP+TP sharding does not support uneven sharding for now: "
+                        f"tensor dim 0 has size {tensor_size_dim_0} which cannot be "
+                        f"evenly sharded into {num_shards} shards."
+                    )
+            param_data = cast(DTensor, param)._local_tensor
+        else:
+            self._spmd_mesh = self.mesh_info.mesh
+            if isinstance(self.mesh_info, HSDPMeshInfo):
+                self._spmd_placements = (Replicate(), fsdp_placement)
+            else:
+                self._spmd_placements = (fsdp_placement,)
+            self._sharding_spec = DTensorSpec(
+                self._spmd_mesh,
+                self._spmd_placements,
+                tensor_meta=TensorMeta(param.size(), param.stride(), param.dtype),
+            )
+            param_data = param
+        assert param_data.is_contiguous(), f"{param_data.shape=} {param_data.stride()=}"
+        shard_dim = fsdp_placement.dim
+        if shard_dim >= param_data.ndim:
+            raise AssertionError(
+                f"Shard dim {shard_dim} is invalid for {param_data.ndim}D tensor: {param.shape}"
+            )
+        self._orig_size = param_data.size()
+        self._contiguous_orig_stride = make_contiguous_strides_for(self._orig_size)
+        shard_rank = self.mesh_info.shard_mesh_rank
+        shard_world_size = self.mesh_info.shard_mesh_size
+        if shard_dim > 0 and param_data.size(shard_dim) % shard_world_size != 0:
+            # If sharding on nonzero dim, require even sharding for now because
+            # the uneven sharding (1) requires extra copies before/after FSDP
+            # collectives and (2) introduces extra complexity to handle padding
+            # and unpadding
+            raise NotImplementedError(
+                f"FSDP does not support uneven sharding on dim {shard_dim}: "
+                f"{param_data.size()} (world size: {shard_world_size})"
+            )
+        chunks = _chunk_with_empty(param_data, shard_world_size, dim=shard_dim)
+        sharded_param = chunks[shard_rank]
+        self.sharded_size = _get_dim_chunked_size(
+            sharded_param, param_data.size(), dim=shard_dim
+        )
+        self.contiguous_sharded_stride = make_contiguous_strides_for(self.sharded_size)
+        padded_sharded_size = chunks[0].size()  # 0th always padded
+        self.padded_sharded_param_size = padded_sharded_size
+        # Pre-pad the sharded parameter to avoid padding before all-gather
+        padded_sharded_param = param_data.new_zeros(padded_sharded_size)
+        if sharded_param.numel() > 0:
+            padded_sharded_param.narrow(
+                dim=shard_dim, start=0, length=sharded_param.size(shard_dim)
+            ).copy_(sharded_param)
+        if self.offload_to_cpu and not padded_sharded_param.is_meta:
+            padded_sharded_param = padded_sharded_param.cpu()
+            if self.pin_memory:
+                padded_sharded_param = padded_sharded_param.pin_memory(
+                    device=self.device
+                )
+        self._sharded_param_data = padded_sharded_param.view(-1)
+        length = sharded_param.size(shard_dim) if sharded_param.numel() > 0 else 0
+        sharded_param = padded_sharded_param.narrow(
+            dim=shard_dim, start=0, length=length
+        )
+        assert sharded_param.is_contiguous(), f"{self.fsdp_placement=}"
+        self.sharded_param = nn.Parameter(self.to_sharded_dtensor(sharded_param))
+        self.sharded_param.requires_grad_(param.requires_grad)
+        # Let `param_data` be freed normally when its ref count reaches 0 when
+        # the `fully_shard` call returns to allow provided parameters to alias
+        self._setattr_on_modules(self.sharded_param)
+        self.sharded_state = ShardedState.SHARDED
+
+    def _init_sharded_post_forward_param_metadata(self, param: torch.Tensor) -> None:
+        mesh_info = self.post_forward_mesh_info
+        assert mesh_info is not None  # mypy
+        param_data = param._local_tensor if isinstance(param, DTensor) else param
+        chunks = _chunk_with_empty(param_data, mesh_info.shard_mesh_size, dim=0)
+        self.sharded_post_forward_size = _get_dim_chunked_size(
+            chunks[mesh_info.shard_mesh_rank],
+            param_data.size(),
+            dim=self.fsdp_placement.dim,
+        )
+        self.contiguous_sharded_post_forward_stride = make_contiguous_strides_for(
+            self.sharded_post_forward_size
+        )
+
+    def init_dtype_attrs(self, mp_policy: MixedPrecisionPolicy):
+        param_dtype, reduce_dtype = (mp_policy.param_dtype, mp_policy.reduce_dtype)
+        self.orig_dtype = self.sharded_param.dtype
+        # Clamp `reduce_dtype` to `None` if no casting is required: since
+        # gradients are computed in `param_dtype`, if `reduce_dtype` matches,
+        # then we do not need extra casting
+        if reduce_dtype == param_dtype:
+            reduce_dtype = None
+        # Clamp `param_dtype` to `None` if no casting is required
+        if param_dtype == self.orig_dtype:
+            param_dtype = None
+        self.param_dtype = param_dtype
+        self.reduce_dtype = reduce_dtype
+        # None indicates that the mixed precision is not enabled
+
+    def _init_extensions(self) -> None:
+        inner_tensor = self._sharded_local_tensor
+        has_fsdp_pre_all_gather = hasattr(inner_tensor, "fsdp_pre_all_gather")
+        has_fsdp_post_all_gather = hasattr(inner_tensor, "fsdp_post_all_gather")
+        if has_fsdp_pre_all_gather != has_fsdp_post_all_gather:
+            raise AssertionError(
+                "Both fsdp_pre_all_gather and fsdp_post_all_gather should be defined "
+                f"if using all-gather extensions: {inner_tensor}"
+            )
+        if has_fsdp_pre_all_gather:
+            self._extensions_data = ExtensionsData()
+        self._unsharded_inner_tensors: list[torch.Tensor] = []
+
+    def init_all_gather_outputs(
+        self,
+        all_gather_input_numels: list[int],
+        all_gather_input_dtypes: list[torch.dtype],
+        world_size: int,
+        device: torch.device,
+        force_recreate: bool = False,
+    ):
+        if not force_recreate and len(self.all_gather_outputs) > 0:
+            return  # already initialized
+        self.all_gather_outputs = [
+            torch.empty(torch.Size([numel * world_size]), dtype=dtype, device=device)
+            for numel, dtype in zip(all_gather_input_numels, all_gather_input_dtypes)
+        ]
+
+    def init_unsharded_param(self):
+        """
+        [Note: Invariants for torch.compile Traceable FSDP2]
+        1. Under compile, we always re-populate the content of `self._unsharded_param`
+           per AllGather using the slow path.
+        2. Under compile, we always recreate `self.all_gather_outputs` per AllGather.
+           This is to ensure the buffer creation is internal to the graph and
+           avoid `self.all_gather_outputs` being captured as a graph input.
+        3. Under compile, at the end of `free_unsharded_param()`, we always clean up
+           `self.all_gather_outputs` and `self._unsharded_inner_tensors`,
+           to avoid them being captured as graph output.
+
+        With these invariants, only these tensors will be inputs to the graph:
+        - Sharded parameters
+        - Placeholders for the `self._unsharded_param` nn.Parameter
+        """
+        if not compiled_autograd_enabled() and hasattr(
+            self, "_unsharded_param"
+        ):  # after the 1st all-gather
+            inner_tensor = self._sharded_local_tensor
+            if not hasattr(inner_tensor, "fsdp_post_all_gather"):
+                return  # already initialized
+            for tensor in self._unsharded_inner_tensors:
+                alloc_storage(tensor)
+            all_gather_outputs = self._unflatten_all_gather_outputs()
+            inner_tensor.fsdp_post_all_gather(
+                all_gather_outputs,
+                self._extensions_data.all_gather_metadata,
+                self.param_dtype or self.orig_dtype,
+                out=self._unsharded_param,
+            )
+            self._extensions_data.clear()
+            return
+        inner_tensor = self._sharded_local_tensor
+        if not compiled_autograd_enabled() and hasattr(
+            inner_tensor, "fsdp_post_all_gather"
+        ):
+            all_gather_outputs = self._unflatten_all_gather_outputs()
+            (
+                unsharded_tensor,
+                self._unsharded_inner_tensors,
+            ) = inner_tensor.fsdp_post_all_gather(
+                all_gather_outputs,
+                self._extensions_data.all_gather_metadata,
+                self.param_dtype or self.orig_dtype,
+            )
+            self._extensions_data.clear()
+        else:
+            # For the default path (no post-all-gather), the all-gather output
+            # gives the unsharded parameter data directly
+            assert len(self.all_gather_outputs) == 1, f"{len(self.all_gather_outputs)}"
+            unsharded_tensor = self.all_gather_outputs[0]
+        unsharded_param = torch.as_strided(
+            unsharded_tensor,
+            self._orig_size,
+            self._contiguous_orig_stride,
+            storage_offset=0,
+        )
+        if self.is_dtensor:
+            unsharded_param = _from_local_no_grad(unsharded_param, self._tp_spec)
+        if hasattr(self, "_unsharded_param"):
+            assert compiled_autograd_enabled()
+            with (
+                torch.no_grad(),
+                torch.autograd._unsafe_preserve_version_counter(self._unsharded_param),
+            ):
+                # NOTE: Under compile, if an unsharded param goes through
+                # resize_(full) -> copy_ -> resize_(0) pattern, we will remove those
+                # resize_ and copy_ ops in a compiler graph pass
+                # `remove_fsdp2_unsharded_param_graph_input_usage` to recover performance.
+                self._unsharded_param.untyped_storage().resize_(
+                    self._unsharded_param.numel() * self._unsharded_param.itemsize
+                )
+                torch.ops.fsdp.copy_(self._unsharded_param, unsharded_param)
+        else:
+            self._unsharded_param = nn.Parameter(
+                unsharded_param, requires_grad=self.sharded_param.requires_grad
+            )
+
+    def _unflatten_all_gather_outputs(self) -> tuple[torch.Tensor, ...]:
+        return tuple(
+            t.view(-1, *s[1:])
+            for t, s in zip(
+                self.all_gather_outputs, self._extensions_data.all_gather_input_sizes
+            )
+        )
+
+    def to_sharded(self) -> None:
+        self._setattr_on_modules(self.sharded_param)
+        self.free_unsharded_param()
+        self.sharded_state = ShardedState.SHARDED
+
+    def to_sharded_post_forward(self) -> None:
+        if self.is_dtensor:
+            raise NotImplementedError(
+                "Resharding to smaller mesh with TP is not supported yet"
+            )
+        self._assert_in_states(ShardedState.UNSHARDED)
+        assert self.post_forward_mesh_info is not None  # mypy
+        assert len(self.all_gather_outputs) == 1
+        shard_world_size = self.post_forward_mesh_info.shard_mesh_size
+        if (numel := self.all_gather_outputs[0].numel()) % shard_world_size != 0:
+            _raise_assert_with_print(
+                f"All-gather output size ({numel}) must be divisible by the shard "
+                f"world size ({shard_world_size})"
+            )
+        shard_rank = self.post_forward_mesh_info.shard_mesh_rank
+        sharded_numel = numel // shard_world_size
+        self._sharded_post_forward_param_data = (
+            self.all_gather_outputs[0].narrow(
+                0, sharded_numel * shard_rank, sharded_numel
+            )
+        ).clone()  # clone to be able to free all-gather output
+        sharded_post_forward_tensor = torch.as_strided(
+            self._sharded_post_forward_param_data,
+            size=self.sharded_post_forward_size,
+            stride=self.contiguous_sharded_post_forward_stride,
+            storage_offset=0,
+        )
+        self._sharded_post_forward_param = nn.Parameter(
+            self.to_sharded_post_forward_dtensor(sharded_post_forward_tensor)
+        )
+        self._setattr_on_modules(self._sharded_post_forward_param)
+        self.free_unsharded_param()
+        self.sharded_state = ShardedState.SHARDED_POST_FORWARD
+
+    def to_unsharded(self) -> None:
+        # Assume that the data has been allocated and all-gathered
+        set_requires_grad_if_needed(self.sharded_param, self._unsharded_param)
+        self._setattr_on_modules(self._unsharded_param)
+        if self.sharded_state == ShardedState.SHARDED_POST_FORWARD:
+            # The data is allocated in the default stream via the post-forward
+            # reshard and must be kept alive for the next all-gather copy-in.
+            # Since we call this method after the copy-out, the data's lifetime
+            # is ensured without further synchronization.
+            self._sharded_post_forward_param = None
+            self._sharded_post_forward_param_data = None  # free
+        self.sharded_state = ShardedState.UNSHARDED
+
+    def _setattr_on_modules(self, param: nn.Parameter) -> None:
+        unsafe_setattr_param(
+            self._module_info.module, self._module_info.param_name, param
+        )
+        for shared_module, shared_param_name in zip(
+            self._module_info.shared_modules, self._module_info.shared_param_names
+        ):
+            unsafe_setattr_param(shared_module, shared_param_name, param)
+
+    def to_sharded_dtensor(self, tensor: torch.Tensor) -> DTensor:
+        """
+        Converts a local tensor representing either the sharded parameter or
+        sharded gradient to DTensor.
+        """
+        if tensor.shape != self.sharded_size:
+            _raise_assert_with_print(
+                f"Expects size {self.sharded_size} but got {tensor.shape}"
+            )
+        return _from_local_no_grad(
+            tensor,
+            self._sharding_spec,
+        )
+
+    def to_sharded_post_forward_dtensor(self, tensor: torch.Tensor) -> DTensor:
+        if tensor.shape != self.sharded_post_forward_size:
+            _raise_assert_with_print(
+                f"Expects size {self.sharded_post_forward_size} but got {tensor.shape}"
+            )
+        assert isinstance(self.post_forward_mesh_info, HSDPMeshInfo)
+        # TODO: Prefer this DTensor to be read-only and generalize the
+        # placement once we support TP.
+        post_forward_sharding_spec = DTensorSpec(
+            self.post_forward_mesh_info.mesh,
+            (Replicate(), Shard(0)),
+            tensor_meta=self._sharding_spec.tensor_meta,
+        )
+        return _from_local_no_grad(tensor, post_forward_sharding_spec)
+
+    def to_accumulated_grad_if_needed(self) -> None:
+        # Access `_unsharded_param` to bypass the sharded state check since we
+        # prefer to reshard before upcasting the gradient to save memory
+        if (
+            self.reduce_dtype is None
+            or self._unsharded_param.grad is None
+            or self._unsharded_param.grad.dtype == self.reduce_dtype
+        ):
+            return
+        unsharded_grad = self._unsharded_param.grad
+        self._unsharded_param.grad = None
+        self.unsharded_accumulated_grad = unsharded_grad.to(self.reduce_dtype)
+
+    def accumulate_unsharded_grad_if_needed(self) -> None:
+        if (
+            self.unsharded_accumulated_grad is not None
+            and self.unsharded_param.grad is not None
+        ):
+            self.unsharded_accumulated_grad += self.unsharded_param.grad
+            self.unsharded_param.grad = None
+
+    def alloc_all_gather_outputs(self) -> None:
+        for tensor in self.all_gather_outputs:
+            alloc_storage(tensor)
+
+    def free_unsharded_param(self) -> None:
+        if compiled_autograd_enabled():
+            """
+            Assumptions under compile:
+            - `self._unsharded_param` is NOT an alias of `self.all_gather_outputs`.
+            Instead, we resize `self._unsharded_param` storage size to full and then
+            explicitly *copy* the data from `self.all_gather_outputs` to `self._unsharded_param`
+            in `init_unsharded_param()`. (For full-graph FSDP2 case, we will then remove
+            the resize_ and copy_ ops in a compiler graph pass to recover performance.)
+            - `self.all_gather_outputs` and `self._unsharded_inner_tensors` are NOT
+            graph inputs. They are created within the graph and is guaranteed to be freed
+            by the end of the graph. They don't leak outside of the graph.
+            """
+            self._unsharded_param.untyped_storage().resize_(0)
+            self.all_gather_outputs = []
+            self._unsharded_inner_tensors = []
+        else:
+            for tensor in itertools.chain(
+                self.all_gather_outputs, self._unsharded_inner_tensors
+            ):
+                free_storage(tensor)
+
+    @property
+    def all_gather_inputs(self) -> list[torch.Tensor]:  # 1D
+        self._assert_in_states(ShardedState.SHARDED, ShardedState.SHARDED_POST_FORWARD)
+        if self.sharded_state == ShardedState.SHARDED:
+            if not compiled_autograd_enabled() and hasattr(
+                self._sharded_local_tensor, "fsdp_pre_all_gather"
+            ):
+                sharded_local_tensor = self._sharded_local_tensor
+                if self.offload_to_cpu:
+                    sharded_local_tensor = sharded_local_tensor.to(
+                        self.device, non_blocking=True
+                    )
+                pre_all_gather_signature = inspect.signature(
+                    sharded_local_tensor.fsdp_pre_all_gather
+                )
+                num_fn_params = len(pre_all_gather_signature.parameters)
+                # Old signature only passes mesh; keep for BC for now
+                assert num_fn_params in (
+                    1,
+                    5,
+                ), (
+                    f"Invalid fsdp_pre_all_gather: {pre_all_gather_signature}\n"
+                    "Expects fsdp_pre_all_gather(self, mesh: DeviceMesh, "
+                    "module: nn.Module, mp_policy: MixedPrecisionPolicy)"
+                )
+                if num_fn_params == 1:
+                    (
+                        all_gather_inputs,
+                        self._extensions_data.all_gather_metadata,
+                    ) = sharded_local_tensor.fsdp_pre_all_gather(
+                        self.shard_mesh_from_root
+                    )
+                else:
+                    (
+                        all_gather_inputs,
+                        self._extensions_data.all_gather_metadata,
+                    ) = sharded_local_tensor.fsdp_pre_all_gather(
+                        self.shard_mesh_from_root,
+                        self._orig_size,
+                        self._contiguous_orig_stride,
+                        self._module_info.module,
+                        self.mp_policy,
+                    )
+                    if (
+                        sharded_local_tensor.size() != self.padded_sharded_param_size
+                        and any(
+                            all_gather_input.size() != self.padded_sharded_param_size
+                            for all_gather_input in all_gather_inputs
+                        )
+                    ):
+                        # NOTE: Since this error can only be raised on the
+                        # ranks that have padding, this can manifest as a NCCL
+                        # watchdog timeout, as the other ranks will not error.
+                        raise AssertionError(
+                            "When a parameter is unevenly sharded by FSDP "
+                            f"(orig size={self._orig_size}, FSDP world size={self.mesh_info.mesh.size()}), "
+                            "fsdp_pre_all_gather must return all-gather inputs with the padded sharded size "
+                            f"{self.padded_sharded_param_size} but got {[t.size() for t in all_gather_inputs]}"
+                        )
+                self._extensions_data.all_gather_input_sizes = [
+                    t.size() for t in all_gather_inputs
+                ]
+                return [t.view(-1) for t in all_gather_inputs]
+            sharded_param_data = self._sharded_param_data
+            if self.offload_to_cpu:
+                sharded_param_data = sharded_param_data.to(
+                    self.device, non_blocking=True
+                )
+            return [_to_dtype_if_needed(sharded_param_data, self.param_dtype)]
+        elif self.sharded_state == ShardedState.SHARDED_POST_FORWARD:
+            if not compiled_autograd_enabled() and hasattr(
+                self._sharded_local_tensor, "fsdp_pre_all_gather"
+            ):
+                raise NotImplementedError
+            all_gather_input = _to_dtype_if_needed(
+                cast(torch.Tensor, self._sharded_post_forward_param_data),
+                self.param_dtype,
+            )
+            return [all_gather_input]
+        return [torch.empty(0)]  # mypy
+
+    @property
+    def unsharded_param(self) -> nn.Parameter:  # ND
+        return self._unsharded_param
+
+    @property
+    def unsharded_grad_data(self) -> torch.Tensor:
+        grad = self.unsharded_param.grad
+        assert grad is not None, "Expects unsharded_param.grad to not be None"
+        return self._get_grad_inner_tensor(grad)
+
+    @property
+    def unsharded_accumulated_grad_data(self) -> torch.Tensor:
+        grad = self.unsharded_accumulated_grad
+        assert grad is not None, "Expects unsharded_accumulated_grad to not be None"
+        return self._get_grad_inner_tensor(grad)
+
+    def _get_grad_inner_tensor(self, grad: torch.Tensor) -> torch.Tensor:
+        if self.is_dtensor:
+            if isinstance(grad, AsyncCollectiveTensor):
+                grad = grad.wait()
+            assert isinstance(grad, DTensor), f"{type(grad)}"
+            placements = self._tp_spec.placements
+            if placements != grad.placements:
+                assert len(self._tp_spec.placements) == len(grad.placements), (
+                    f"{self._tp_spec=} {grad.placements=}"
+                )
+                grad = grad.redistribute(placements=placements)
+            grad = grad._local_tensor
+        return grad
+
+    @property
+    def _sharded_local_tensor(self) -> torch.Tensor:
+        return cast(DTensor, self.sharded_param)._local_tensor
+
+    @property
+    def shard_mesh(self):
+        mesh = self.mesh_info.mesh
+        if mesh.ndim == 1:
+            return mesh
+        elif mesh.ndim == 2:
+            assert mesh.mesh_dim_names is not None
+            return mesh[mesh.mesh_dim_names[-1]]
+        raise ValueError(f"Invalid mesh: {mesh}")
+
+    @property
+    def shard_mesh_from_root(self):
+        mesh = self.mesh_info.mesh
+
+        if mesh.ndim == 1:
+            return mesh
+        else:
+            assert mesh.mesh_dim_names is not None
+            shard_dim_name = mesh.mesh_dim_names[-1]
+
+            root_mesh = _mesh_resources.get_root_mesh(mesh)
+            return root_mesh[shard_dim_name]
+
+    def _assert_in_states(self, *states: ShardedState) -> None:
+        if self.sharded_state not in states:
+            _raise_assert_with_print(
+                f"Expects to be in one of {states}, not {self.sharded_state}"
+            )
+
+    def reset_sharded_param(self):
+        # For ops like `nn.Module._apply` or `load_state_dict(assign=True)`
+        # that change the sharded parameter tensor, we may need to re-pad the
+        # sharded local tensor and re-save the reference.
+        module_info = self._module_info
+        new_param = getattr(module_info.module, module_info.param_name)
+        if new_param is not self.sharded_param:
+            if torch.__future__.get_swap_module_params_on_conversion():
+                raise AssertionError(
+                    f"Expects swap_tensors to preserve object but got {new_param} "
+                    f"instead of {self.sharded_param}"
+                )
+            self.sharded_param = new_param
+        local_tensor = new_param._local_tensor
+        if local_tensor.is_meta:
+            return
+        updated_local_tensor = False
+        padded_sharded_size = self.padded_sharded_param_size
+        shard_dim = self.fsdp_placement.dim
+        length = local_tensor.size(shard_dim) if local_tensor.numel() > 0 else 0
+        if local_tensor.size() != padded_sharded_size:
+            assert shard_dim == 0, (
+                f"Shard({shard_dim}) requires even sharding: {local_tensor.size()=}"
+            )
+            padded_local_tensor = local_tensor.new_zeros(padded_sharded_size)
+            padded_local_tensor.narrow(dim=shard_dim, start=0, length=length).copy_(
+                local_tensor
+            )
+            local_tensor = padded_local_tensor
+            updated_local_tensor = True
+        if self.pin_memory and not local_tensor.is_pinned():
+            local_tensor = local_tensor.cpu().pin_memory(device=self.device)
+            updated_local_tensor = True
+        self._sharded_param_data = local_tensor.view(-1)
+        assert isinstance(self.sharded_param, DTensor)  # mypy
+        if updated_local_tensor:
+            # Only change the local tensor object if needed
+            self.sharded_param._local_tensor = local_tensor.narrow(
+                dim=shard_dim, start=0, length=length
+            )
+            assert self.sharded_param._local_tensor.is_contiguous()
+        self._sharding_spec = self.sharded_param._spec
+
+    def __repr__(self):
+        return f"FSDPParam(fqn={self._param_fqn}, orig_size={self._orig_size})"
+
+
+def alloc_storage(tensor: torch.Tensor) -> None:
+    size = tensor.numel() * tensor.itemsize
+    if (storage := tensor.untyped_storage()).size() != size:
+        storage.resize_(size)
+
+
+def free_storage(tensor: torch.Tensor) -> None:
+    if (storage := tensor.untyped_storage()).size() != 0:
+        storage.resize_(0)
+
+
+# NOTE: These bypass `nn.Module.__setattr__` checks, which incur non-trivial
+# CPU overhead, if the module did not override it. For FSDP, we know we do not
+# need those checks when transitioning between sharded/unsharded parameters.
+def unsafe_setattr_param(
+    module: nn.Module, param_name: str, param: nn.Parameter
+) -> None:
+    if getattr(module.__setattr__, "__func__", None) is nn.Module.__setattr__:
+        module._parameters[param_name] = param
+    else:  # slow path
+        setattr(module, param_name, param)
+
+
+def set_requires_grad_if_needed(
+    src_tensor: torch.Tensor, dst_tensor: torch.Tensor
+) -> None:
+    # Only call `requires_grad_` if needed to avoid the Python <> C++ context
+    # switch overhead
+    if src_tensor.requires_grad != dst_tensor.requires_grad:
+        dst_tensor.requires_grad_(src_tensor.requires_grad)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param_group.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param_group.py
new file mode 100644
index 0000000000000000000000000000000000000000..e149005ffc2c466829a39ca3c020da8d5a4b9b6f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param_group.py
@@ -0,0 +1,748 @@
+# mypy: allow-untyped-defs
+import contextlib
+import logging
+from typing import Any, Callable, cast, NamedTuple, Optional
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed.device_mesh import _get_device_handle
+from torch.distributed.fsdp._common_utils import _named_parameters_with_duplicates
+from torch.distributed.tensor import Shard
+from torch.profiler import record_function
+from torch.utils._pytree import tree_flatten, tree_unflatten
+from torch.utils.hooks import RemovableHandle
+
+from ._fsdp_api import CPUOffloadPolicy, MixedPrecisionPolicy, OffloadPolicy
+from ._fsdp_collectives import (
+    AllGatherResult,
+    foreach_all_gather,
+    foreach_all_gather_copy_out,
+    foreach_reduce,
+)
+from ._fsdp_common import (
+    compiled_autograd_enabled,
+    FSDPMeshInfo,
+    HSDPMeshInfo,
+    TrainingState,
+)
+from ._fsdp_param import FSDPParam, ParamModuleInfo, ShardedState
+
+
+logger = logging.getLogger("torch.distributed.fsdp.fully_shard")
+
+_ModuleToHandleDict = dict[nn.Module, RemovableHandle]  # for state dict
+
+
+"""
+[Note: Overlapping all-gather copy-in and all-gather]
+For implicit forward prefetching, we want to overlap the next copy-in with the
+current all-gather. We do so using a separate copy-in stream. However, since
+we have the all-gather input as a view into the output, we must make sure to
+copy into different memory from the current all-gather's output. Thus, we keep
+a reference to the current all-gather's output and have the next FSDP parameter
+group free it after its copy-in. Finally, we have the last FSDP state flush the
+reference to avoid holding onto memory after forward.
+"""
+
+
+class FSDPCommContext:
+    """This has the communication state shared across FSDP states/parameter groups."""
+
+    def lazy_init(self, device: torch.device):
+        self.device_handle = _get_device_handle(device.type)
+        # Setting the all-gather/reduce-scatter streams to be higher priority
+        # can help avoid some issues where their copies in/out are delayed and
+        # block computation (this is different from high-pri NCCL streams)
+        high_priority = -1
+        # All-gather state and copy-in stream allow overlapping the next
+        # copy-in with the current all-gather in forward; copy-in overlaps with
+        # reduce-scatter in backward without the separate copy-in stream
+        self.all_gather_copy_in_stream = self.device_handle.Stream(
+            priority=high_priority
+        )
+        # All-gather stream allows overlapping next all-gather with current
+        # forward compute
+        self.all_gather_stream = self.device_handle.Stream(priority=high_priority)
+        # Reduce-scatter stream gives separate execution "thread" for post-
+        # backward logic like pre/post-gradient division and reduce-scatter
+        self.reduce_scatter_stream = self.device_handle.Stream(priority=high_priority)
+        # Run the HSDP all-reduces concurrently with all-gather/reduce-scatter
+        # since collectives use different network resources and can overlap
+        # in the typical intra-node sharding / inter-node replication case
+        self.all_reduce_stream = self.device_handle.Stream()
+        # All-gather/reduce-scatter states keep references to collective
+        # tensors produced in one stream and used in another and accompanying
+        # CUDA events for synchronization
+        self.all_gather_state: Optional[AllGatherState] = None
+        self.reduce_scatter_state: Optional[ReduceScatterState] = None
+        # Post-forward order for explicit backward prefetching
+        self.post_forward_order: list[FSDPParamGroup] = []  # will cause ref cycles
+
+    def get_all_gather_streams(
+        self, async_op: bool, training_state: TrainingState
+    ) -> tuple[torch.Stream, torch.Stream]:
+        if not async_op and training_state in (
+            TrainingState.FORWARD,
+            TrainingState.PRE_BACKWARD,
+        ):
+            # Use separate streams for implicit prefetching
+            return self.all_gather_copy_in_stream, self.all_gather_stream
+        current_stream = self.device_handle.current_stream()
+        return current_stream, current_stream
+
+
+# See [Note: Overlapping all-gather copy-in and all-gather]
+class AllGatherState(NamedTuple):
+    all_gather_result: AllGatherResult
+    event: torch.Event  # all-gather copy-out
+
+
+class ReduceScatterState(NamedTuple):
+    reduce_scatter_input: torch.Tensor
+    event: torch.Event  # reduce-scatter event
+
+
+class AllReduceState(NamedTuple):
+    all_reduce_input: torch.Tensor
+    event: torch.Event  # all-reduce event
+
+
+class FSDPParamGroup:
+    """This class represents a parameter group to communicate together."""
+
+    _orig_dtype: torch.dtype
+    _reduce_dtype: Optional[torch.dtype]
+
+    def __init__(
+        self,
+        params: list[nn.Parameter],
+        modules: tuple[nn.Module, ...],
+        mesh_info: FSDPMeshInfo,
+        post_forward_mesh_info: Optional[FSDPMeshInfo],
+        device: torch.device,
+        shard_placement_fn: Optional[Callable[[nn.Parameter], Optional[Shard]]],
+        mp_policy: MixedPrecisionPolicy,
+        offload_policy: OffloadPolicy,
+    ):
+        self.modules = modules  # permit ref cycle because 1:1 lifetime
+        param_module_infos = _get_param_module_infos(params, modules)
+
+        self.fsdp_params = [
+            FSDPParam(
+                param,
+                module_info,
+                mesh_info,
+                post_forward_mesh_info,
+                device,
+                shard_placement_fn,
+                mp_policy,
+                offload_policy,
+            )
+            for param, module_info in zip(params, param_module_infos)
+        ]
+        self.mesh_info = mesh_info
+        self.post_forward_mesh_info = post_forward_mesh_info
+        self.device = device
+        self.device_handle = _get_device_handle(device.type)
+        self.mp_policy = mp_policy
+        self.offload_policy = offload_policy
+        self._training_state = TrainingState.IDLE
+        # Group's sharded state always matches its parameters' sharded states
+        self._sharded_state = ShardedState.SHARDED
+        self._module_fqn: Optional[str] = None  # prefixed from root module
+        # Only consider resetting sharded parameters once in lazy init since it
+        # can incur nontrivial overhead to reset them
+        self._reset_sharded_params: bool = False
+
+        # - Hook state
+        self._module_to_pre_save_state_dict_hook_handle: _ModuleToHandleDict = {}
+        self._module_to_pre_load_state_dict_hook_handle: _ModuleToHandleDict = {}
+        self._all_reduce_hook: Optional[Callable[[torch.Tensor], None]] = None
+        # Optional stream to run the user-defined all-reduce hook in
+        # Saved here and not in the comm. context because we allow the user to
+        # specify it, possibly at construction time before lazy init
+        self._all_reduce_hook_stream: Optional[torch.cuda.Stream] = None
+
+        # - Communication and communication/computation overlap
+        self.comm_ctx = FSDPCommContext()
+        # Group's indices in the shared post-forward order
+        self._post_forward_indices: list[int] = []
+        # Whether to reduce gradients at all (whether for FSDP or HSDP)
+        self.reduce_grads: bool = True
+        # Whether to all-reduce gradients for HSDP; only used if
+        # `self.reduce_grads` is true, in which case setting this to false
+        # means reduce-scatter but no all-reduce
+        self.all_reduce_grads: bool = True
+        # Whether to reshard parameters after backward (only useful for
+        # gradient accumulation)
+        self.reshard_after_backward: bool = True
+        # Optional custom reduce-scatter reduce op (e.g. to divide by a
+        # factor other than the shard world size)
+        self.reduce_scatter_reduce_op: Optional[dist.ReduceOp] = None
+        # `async_op` arg used for pre-forward/pre-backward unshard; can be
+        # overridden to only do explicit prefetching and avoid inter-stream
+        # fragmentation from using separate unshard streams
+        self.unshard_async_op: bool = False
+        # Whether to unshard in backward: can be overridden by the user if the
+        # parameters in this group are not needed for backward (e.g. embedding)
+        self.unshard_in_backward: bool = True
+
+        # - CUDA events for stream synchronization
+        # Holds the all-gather output buffer, sync objects, and metadata
+        self._all_gather_result: Optional[AllGatherResult] = None
+        # Holds the reduce-scatter/all-reduce view-out CUDA event that marks the end of
+        # the group's post-backward (e.g. reduce-scatter, all-reduce and div), which
+        # should be waited on at the end of backward
+        self._post_reduce_event: Optional[torch.Event] = None
+        # Holds the reshard-after-forward CUDA event when resharding to a
+        # different world size, which should be waited on in the next unshard
+        self._reshard_after_forward_event: Optional[torch.Event] = None
+
+        # Only for HSDP, if accumulating gradients without all-reduce, save the
+        # partial reduce output (only reduce-scattered but not all-reduced)
+        self._partial_reduce_output: Optional[torch.Tensor] = None
+        # Holds the all-reduce input and all-reduce event to keep it alive
+        # until the end of backward (critical when doing bf16 reduction with
+        # fp32 parameters since the all-reduce input is allocated in the RS
+        # stream and will have no refs to it after being upcast to fp32)
+        self._all_reduce_state: Optional[AllReduceState] = None
+
+    # Initialization #
+    def _init_mp_dtypes(self) -> None:
+        for fsdp_param in self.fsdp_params:
+            fsdp_param.init_dtype_attrs(self.mp_policy)
+        orig_dtypes = {fsdp_param.orig_dtype for fsdp_param in self.fsdp_params}
+        if len(orig_dtypes) != 1:
+            # This can be relaxed if we copy-out for the reduce-scatter
+            raise AssertionError(
+                f"FSDP expects uniform original parameter dtype but got {orig_dtypes}"
+            )
+        self._orig_dtype = next(iter(orig_dtypes))
+        reduce_dtypes = {fsdp_param.reduce_dtype for fsdp_param in self.fsdp_params}
+        if len(reduce_dtypes) != 1:
+            # This can be relaxed if we issue one reduce-scatter per reduce
+            # dtype (but we would need a way for users to specify multiple
+            # reduce dtypes)
+            raise AssertionError(
+                f"FSDP expects uniform reduce dtype but got {reduce_dtypes}"
+            )
+        self._reduce_dtype = next(iter(reduce_dtypes))
+
+    def lazy_init(self):
+        # Lazy init should be idempotent
+        # Users may change or register parameters after construction time.
+        # For example, DoRA (https://arxiv.org/abs/2402.09353) initializes linear magnitudes based on
+        # other parameters (e.g. loaded from the state dict).
+        if not hasattr(self.comm_ctx, "device_handle"):
+            self.comm_ctx.device_handle = _get_device_handle(self.device.type)
+        if self.is_sharded and not self._reset_sharded_params:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.reset_sharded_param()
+                fsdp_param._init_extensions()  # allow monkey patch after init
+            self._reset_sharded_params = True
+        self._validate_no_meta_params()
+        self._validate_cpu_offload_params()
+        # Initialize mixed precision attributes lazily in case the user changes
+        # the parameter dtypes after construction time but before forward
+        self._init_mp_dtypes()
+        self._register_state_dict_hooks()
+
+    # Runtime #
+    def unshard(self, async_op: bool = False):
+        if self._all_gather_result is not None:  # already called, pending wait
+            return
+        if self.is_unsharded:
+            return  # no-op
+        if (
+            not self.unshard_in_backward
+            and self._training_state == TrainingState.PRE_BACKWARD
+        ):
+            return
+        if self._reshard_after_forward_event is not None:
+            # Resharded parameter data is allocated in the default stream and
+            # used in the all-gather streams
+            self._wait_all_gather_streams_on_event(self._reshard_after_forward_event)
+            self._reshard_after_forward_event = None
+        with record_function(self._with_fqn("FSDP::all_gather")):
+            self._all_gather_result = foreach_all_gather(
+                self.fsdp_params,
+                self._all_gather_process_group,
+                async_op,
+                *self.comm_ctx.get_all_gather_streams(async_op, self._training_state),
+                self.device,
+            )
+
+    def wait_for_unshard(self):
+        """
+        1. In forward with implict prefetching, to overlap the current copy-out
+        with the next all-gather, we save a reference to the current all-gather
+        result to free after the next copy-out.
+        2. Otherwise (explicit prefetching or in backward), we free the
+        all-gather result immediately after the current copy-out since we can
+        already overlap the current copy-out with the previous reduce-scatter.
+        """
+        if not self._all_gather_result:
+            return  # no preceding unshard
+        async_op = self._all_gather_result.all_gather_work is not None
+        if self._training_state == TrainingState.FORWARD:  # implicit prefetch
+            if prev_all_gather_state := self.comm_ctx.all_gather_state:
+                self._wait_all_gather_streams_on_event(prev_all_gather_state.event)
+                self.comm_ctx.all_gather_state = None  # free the all-gather result
+        with record_function(self._with_fqn("FSDP::all_gather_copy_out")):
+            foreach_all_gather_copy_out(
+                self._all_gather_result,
+                self.fsdp_params,
+                self._all_gather_process_group,
+            )
+        for fsdp_param in self.fsdp_params:
+            fsdp_param.init_unsharded_param()
+        self._to_unsharded()
+        all_gather_copy_out_event = self.device_handle.Event()
+        all_gather_copy_out_event.record()
+        if not async_op and self._training_state == TrainingState.FORWARD:
+            # Defer free to allow for overlap of this copy-out with next
+            # all-gather collective
+            self.comm_ctx.all_gather_state = AllGatherState(
+                self._all_gather_result, all_gather_copy_out_event
+            )
+        else:
+            self._wait_all_gather_streams_on_event(all_gather_copy_out_event)
+        self._all_gather_result = None  # free unless saved in `all_gather_state`
+
+    def _wait_all_gather_streams_on_event(self, event: torch.Event):
+        # Calling `unshard` before lazy init means streams are not initialized
+        if hasattr(self.comm_ctx, "all_gather_copy_in_stream"):
+            self.comm_ctx.all_gather_copy_in_stream.wait_event(event)
+        if hasattr(self.comm_ctx, "all_gather_stream"):
+            self.comm_ctx.all_gather_stream.wait_event(event)
+
+    def reshard(self):
+        if self._training_state == TrainingState.FORWARD:
+            if not self._reshard_after_forward:
+                return
+            if self._use_post_forward_mesh:
+                self._to_sharded_post_forward()
+                self._reshard_after_forward_event = self.device_handle.Event()
+                if self._reshard_after_forward_event is not None:
+                    self._reshard_after_forward_event.record()
+                return
+        self._to_sharded()
+
+    def pre_forward(
+        self, module: nn.Module, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        if not compiled_autograd_enabled():
+            logger.debug("%s", self._with_fqn("FSDP::pre_forward"))
+        with record_function(self._with_fqn("FSDP::pre_forward")):
+            self._training_state = TrainingState.FORWARD
+            self.unshard(self.unshard_async_op)
+            self.wait_for_unshard()
+            args, kwargs = self._register_post_backward_hook(args, kwargs)
+            return args, kwargs
+
+    def post_forward(self, module: nn.Module, input: Any, output: Any):
+        if not compiled_autograd_enabled():
+            logger.debug("%s", self._with_fqn("FSDP::post_forward"))
+        with record_function(self._with_fqn("FSDP::post_forward")):
+            self.reshard()
+            self._record_post_forward()
+            self._training_state = TrainingState.IDLE
+            return output
+
+    def _record_post_forward(self) -> None:
+        # Since a group has one pre-backward unshard for each forward call
+        # before the backward, we record each usage (with multiplicity)
+        post_forward_index = len(self.comm_ctx.post_forward_order)
+        self.comm_ctx.post_forward_order.append(self)
+        self._post_forward_indices.append(post_forward_index)
+
+    def pre_backward(self, default_prefetch: bool, *unused: Any):
+        if (
+            compiled_autograd_enabled()
+            and self._training_state == TrainingState.PRE_BACKWARD
+        ):
+            # Traceable FSDP2 cannot trigger the param group's `post_backward` immediately after param usage;
+            # instead it relies on this to trigger the previously unexecuted `post_backward`.
+            self.post_backward()
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            return
+        if not compiled_autograd_enabled():
+            logger.debug("%s", self._with_fqn("FSDP::pre_backward"))
+        with record_function(self._with_fqn("FSDP::pre_backward")):
+            self._training_state = TrainingState.PRE_BACKWARD
+            self.unshard(self.unshard_async_op)  # no-op if prefetched
+            self.wait_for_unshard()
+            if default_prefetch and not compiled_autograd_enabled():
+                self._backward_prefetch()
+
+    def post_backward(self, *unused: Any):
+        # This method should be idempotent and safe to call even when this
+        # FSDP parameter group was not used in backward (should be a no-op)
+        if not compiled_autograd_enabled():
+            logger.debug("%s", self._with_fqn("FSDP::post_backward"))
+        self._training_state = TrainingState.POST_BACKWARD
+        with record_function(self._with_fqn("FSDP::post_backward_accumulate")):
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.accumulate_unsharded_grad_if_needed()
+        with record_function(self._with_fqn("FSDP::post_backward_reshard")):
+            if not self.reduce_grads:
+                if self.reshard_after_backward:
+                    self.reshard()
+                for fsdp_param in self.fsdp_params:
+                    fsdp_param.to_accumulated_grad_if_needed()
+                return
+            # Save the autograd-computed gradients before resharding to only
+            # access the unsharded parameters when their data is present
+            fsdp_params_with_grad: list[FSDPParam] = []
+            unsharded_grads: list[torch.Tensor] = []
+            for fsdp_param in self.fsdp_params:
+                if not hasattr(fsdp_param, "_unsharded_param"):
+                    continue
+                # May have an accumulated gradient of the reduce dtype if the
+                # previous backward did not reduce-scatter
+                if fsdp_param.unsharded_accumulated_grad is not None:
+                    fsdp_params_with_grad.append(fsdp_param)
+                    unsharded_grads.append(fsdp_param.unsharded_accumulated_grad_data)
+                    fsdp_param.unsharded_accumulated_grad = None
+                elif fsdp_param.unsharded_param.grad is not None:
+                    fsdp_params_with_grad.append(fsdp_param)
+                    unsharded_grads.append(fsdp_param.unsharded_grad_data)
+                    fsdp_param.unsharded_param.grad = None
+            if self.reshard_after_backward:
+                self.reshard()
+        if len(fsdp_params_with_grad) == 0:
+            return
+        with record_function(self._with_fqn("FSDP::post_backward_reduce")):
+            if self.comm_ctx.reduce_scatter_state is not None:
+                self.device_handle.current_stream().wait_event(
+                    self.comm_ctx.reduce_scatter_state.event
+                )
+                self.comm_ctx.reduce_scatter_state = None
+            all_reduce_pg = self._all_reduce_process_group if self._is_hsdp else None
+            all_reduce_stream: torch.cuda.Stream
+            if all_reduce_pg is None and self._all_reduce_hook_stream is not None:
+                # this means the native HSDP is not enabled,
+                # but user may want to have a custom HSDP setup
+                assert self._all_reduce_hook is not None, (
+                    "all reduce hook stream is specified but hook itself is missing."
+                )
+                all_reduce_stream = self._all_reduce_hook_stream
+            else:
+                all_reduce_stream = self.comm_ctx.all_reduce_stream
+
+            self._wait_for_post_backward()
+            (
+                reduce_scatter_input,
+                reduce_scatter_event,
+                self._post_reduce_event,
+                all_reduce_input,
+                all_reduce_event,
+                self._partial_reduce_output,
+            ) = foreach_reduce(
+                fsdp_params_with_grad,
+                unsharded_grads,
+                self._reduce_scatter_process_group,
+                self.comm_ctx.reduce_scatter_stream,
+                self._orig_dtype,
+                self._reduce_dtype,
+                self.device,
+                self.reduce_scatter_reduce_op,
+                self._all_reduce_process_group if self._is_hsdp else None,
+                all_reduce_stream,
+                self.all_reduce_grads,
+                self._partial_reduce_output,
+                self._all_reduce_hook,
+            )
+            self.comm_ctx.reduce_scatter_state = ReduceScatterState(
+                reduce_scatter_input, reduce_scatter_event
+            )
+            if all_reduce_input is not None:
+                assert all_reduce_event is not None
+                self._all_reduce_state = AllReduceState(
+                    all_reduce_input, all_reduce_event
+                )
+
+    def finalize_backward(self):
+        self._wait_for_post_backward()
+        for fsdp_param in self.fsdp_params:
+            if fsdp_param.grad_offload_event is not None:
+                fsdp_param.grad_offload_event.synchronize()
+                fsdp_param.grad_offload_event = None
+        if self._all_gather_result is not None:
+            # If there was a mistargeted unshard without a corresponding wait,
+            # then we wait here and clear the unshard
+            if (event := self._all_gather_result.all_gather_event) is not None:
+                torch.accelerator.current_stream().wait_event(event)
+            work = self._all_gather_result.all_gather_work
+            if isinstance(work, dist.distributed_c10d.Work):
+                work.wait()
+            self._all_gather_result = None
+        self._post_forward_indices.clear()
+
+    def _wait_for_post_backward(self):
+        if self._post_reduce_event is not None:
+            self.device_handle.current_stream().wait_event(self._post_reduce_event)
+            self._post_reduce_event = None
+        if self._all_reduce_state is not None:
+            self.device_handle.current_stream().wait_event(self._all_reduce_state.event)
+            self._all_reduce_state = None
+
+    def _backward_prefetch(self) -> None:
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            if not self._post_forward_indices:
+                # Can be cleared if running multiple `backward`s
+                return
+            curr_index = self._post_forward_indices.pop()
+            if (target_index := curr_index - 1) < 0:
+                return
+            # Prefetch naively using the reverse post-forward order, which may
+            # have mistargeted prefetches if not all modules used in forward
+            # are used in this backward
+            target_fsdp_param_group = self.comm_ctx.post_forward_order[target_index]
+            self._prefetch_unshard(target_fsdp_param_group, "backward")
+
+    @staticmethod
+    def _prefetch_unshard(
+        target_fsdp_param_group: "FSDPParamGroup", pass_type: str
+    ) -> None:
+        if pass_type == "backward":
+            training_state = TrainingState.PRE_BACKWARD
+        elif pass_type == "forward":
+            training_state = TrainingState.FORWARD
+        else:
+            raise ValueError(f"Unknown pass type: {pass_type}")
+        target_fqn = target_fsdp_param_group._module_fqn
+        with (
+            record_function(f"FSDP::{pass_type}_prefetch for {target_fqn}"),
+            target_fsdp_param_group.use_training_state(training_state),
+        ):
+            async_op = target_fsdp_param_group.unshard_async_op
+            target_fsdp_param_group.unshard(async_op)
+
+    # Utilities #
+    def _to_sharded(self):
+        if not self.is_sharded:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.to_sharded()
+            self._sharded_state = ShardedState.SHARDED
+
+    def _to_sharded_post_forward(self):
+        if not self.is_sharded_post_forward:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.to_sharded_post_forward()
+            self._sharded_state = ShardedState.SHARDED_POST_FORWARD
+
+    def _to_unsharded(self):
+        if not self.is_unsharded:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.to_unsharded()
+            self._sharded_state = ShardedState.UNSHARDED
+
+    @property
+    def is_sharded(self) -> bool:
+        return self._sharded_state == ShardedState.SHARDED
+
+    @property
+    def is_sharded_post_forward(self) -> bool:
+        return self._sharded_state == ShardedState.SHARDED_POST_FORWARD
+
+    @property
+    def is_unsharded(self) -> bool:
+        return self._sharded_state == ShardedState.UNSHARDED
+
+    @contextlib.contextmanager
+    def use_training_state(self, training_state: TrainingState):
+        old_training_state = self._training_state
+        self._training_state = training_state
+        try:
+            yield
+        finally:
+            self._training_state = old_training_state
+
+    # Hook Registration #
+    def _register_post_backward_hook(
+        self, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        # Traceable FSDP2 relies on `root_post_backward_callback` to call each
+        # `FSDPParamGroup.post_backward`
+        if (not torch._dynamo.config.skip_fsdp_hooks) or compiled_autograd_enabled():
+            return args, kwargs
+        if not torch.is_grad_enabled():
+            return args, kwargs
+        args_list, args_spec = tree_flatten(args)
+        kwargs_list, kwargs_spec = tree_flatten(kwargs)
+        args_kwargs_list = list(args_list) + list(kwargs_list)
+        inp_tensor_indices: list[int] = []
+        inp_tensors: list[torch.Tensor] = []
+        for i, obj in enumerate(args_kwargs_list):
+            if torch.is_tensor(obj) and obj.requires_grad:
+                inp_tensor_indices.append(i)
+                inp_tensors.append(obj)
+        if len(inp_tensors) == 0:
+            return args, kwargs  # no tensors that require gradients
+        inp_tensors = RegisterPostBackwardFunction.apply(self, *inp_tensors)
+        for inp_tensor_idx, inp_tensor in zip(inp_tensor_indices, inp_tensors):
+            args_kwargs_list[inp_tensor_idx] = inp_tensor
+        args_list = args_kwargs_list[: len(args_list)]
+        kwargs_list = args_kwargs_list[len(args_list) :]
+        args = tree_unflatten(args_list, args_spec)
+        kwargs = tree_unflatten(kwargs_list, kwargs_spec)
+        return args, kwargs
+
+    def _register_state_dict_hooks(self) -> None:
+        num_pre_save_hooks = len(self._module_to_pre_save_state_dict_hook_handle)
+        num_pre_load_hooks = len(self._module_to_pre_load_state_dict_hook_handle)
+        assert num_pre_save_hooks == num_pre_load_hooks, (
+            f"Pre-save: {num_pre_save_hooks} pre-load: {num_pre_load_hooks}"
+        )
+        if num_pre_save_hooks > 0:
+            return  # already registered
+        modules_with_fsdp_params: set[nn.Module] = {
+            fsdp_param._module_info.module for fsdp_param in self.fsdp_params
+        }
+
+        def to_sharded_hook(*args: Any, **kwargs: Any) -> None:
+            self._to_sharded()
+
+        for module in modules_with_fsdp_params:
+            self._module_to_pre_save_state_dict_hook_handle[module] = (
+                module.register_state_dict_pre_hook(to_sharded_hook)
+            )
+            self._module_to_pre_load_state_dict_hook_handle[module] = (
+                module._register_load_state_dict_pre_hook(to_sharded_hook)
+            )
+
+    # Properties #
+    @property
+    def _reshard_after_forward(self) -> bool:
+        return self.post_forward_mesh_info is not None
+
+    @property
+    def _use_post_forward_mesh(self) -> bool:
+        return (
+            self._reshard_after_forward
+            and self.mesh_info != self.post_forward_mesh_info
+        )
+
+    @property
+    def _is_hsdp(self) -> bool:
+        return isinstance(self.mesh_info, HSDPMeshInfo)
+
+    @property
+    def _all_gather_process_group(self) -> dist.ProcessGroup:
+        mesh_info = (
+            cast(FSDPMeshInfo, self.post_forward_mesh_info)
+            if self.is_sharded_post_forward
+            else self.mesh_info
+        )
+        assert isinstance(mesh_info, FSDPMeshInfo)
+        return mesh_info.shard_process_group
+
+    @property
+    def _reduce_scatter_process_group(self) -> dist.ProcessGroup:
+        assert isinstance(self.mesh_info, FSDPMeshInfo)
+        return self.mesh_info.shard_process_group
+
+    @property
+    def _all_reduce_process_group(self) -> dist.ProcessGroup:
+        assert isinstance(self.mesh_info, HSDPMeshInfo)
+        return self.mesh_info.replicate_process_group
+
+    def _with_fqn(self, label: str) -> str:
+        if self._module_fqn:
+            return f"{label} ({self._module_fqn})"
+        return label
+
+    def __repr__(self):
+        return f"FSDPParamGroup(fqn={self._module_fqn})"
+
+    def _validate_no_meta_params(self):
+        param_names_on_meta = [
+            fsdp_param._param_fqn
+            for fsdp_param in self.fsdp_params
+            if fsdp_param.sharded_param.device.type == "meta"
+        ]
+        if param_names_on_meta:
+            raise RuntimeError(
+                "FSDP parameters should be materialized from meta device before training, "
+                f"but the following were still on meta device: {param_names_on_meta}\n"
+                "For example, call module.to_empty(device) to materialize to device and "
+                "call module.reset_parameters() on each module to initialize values."
+            )
+
+    def _validate_cpu_offload_params(self):
+        if not isinstance(self.offload_policy, CPUOffloadPolicy):
+            return
+        fsdp_params_not_on_cpu = [
+            fsdp_param
+            for fsdp_param in self.fsdp_params
+            if fsdp_param.sharded_param.device.type != "cpu"
+        ]
+        if fsdp_params_not_on_cpu:
+            raise RuntimeError(
+                "FSDP parameters should be materialized on CPU when enabling CPU offloading. "
+                'For example, load a CPU state dict or call module.to_empty(device="cpu"). '
+                "Found following parameters on non-CPU device: "
+                f"{[(fsdp_param._param_fqn, fsdp_param.sharded_param.device) for fsdp_param in fsdp_params_not_on_cpu]}\n"
+            )
+
+
+def _get_param_module_infos(
+    params: list[nn.Parameter], modules: tuple[nn.Module, ...]
+) -> list[ParamModuleInfo]:
+    """
+    Shared parameter: lin1.weight = lin2.weight
+    Shared module: mlp.lin1 = mlp.lin2
+    We do not remove duplicates when traversing both modules and parameters to
+    find shared modules' parameters and shared parameters within a module.
+    """
+    params_set = set(params)
+    param_to_module_info: dict[nn.Parameter, ParamModuleInfo] = {}
+    for module in modules:
+        for _, submodule in module.named_modules(remove_duplicate=False):
+            for param_name, param in _named_parameters_with_duplicates(
+                submodule, recurse=False
+            ):
+                if param in params_set:
+                    if param not in param_to_module_info:
+                        param_to_module_info[param] = ParamModuleInfo(
+                            submodule, param_name
+                        )
+                    else:
+                        param_to_module_info[param].shared_modules.append(submodule)
+                        param_to_module_info[param].shared_param_names.append(
+                            param_name
+                        )
+    if len(param_to_module_info) != len(params):
+        raise AssertionError(f"Some parameters are not in the module tree of {module}")
+    return [param_to_module_info[param] for param in params]
+
+
+class RegisterPostBackwardFunction(torch.autograd.Function):
+    @staticmethod
+    def _assert_not_tracing_fsdp():
+        if compiled_autograd_enabled():
+            # TODO: Find a way to print the offending FSDP2 module.
+            msg = """\
+When Traceable FSDP2 is enabled, we should not be calling into `RegisterPostBackwardFunction`.
+Instead, we rely on the param group's next `pre_backward` hook to trigger its previously unexecuted
+`post_backward`, and we rely on FSDPState's `root_post_backward_callback` to trigger the resharding
+of any leftover unsharded param groups.
+If you are here, it means the forward part of this FSDP2 instance is not compiled, and you must also
+compile the forward part if you want to use Traceable FSDP2."""
+            torch._dynamo.comptime.comptime.print(msg)
+            raise RuntimeError(msg)
+
+    @staticmethod
+    def forward(ctx, param_group: FSDPParamGroup, *inputs: torch.Tensor):
+        # All tensors in `inputs` should require gradient
+        RegisterPostBackwardFunction._assert_not_tracing_fsdp()
+        ctx.param_group = param_group
+        return inputs
+
+    @staticmethod
+    def backward(ctx, *grads: torch.Tensor):
+        RegisterPostBackwardFunction._assert_not_tracing_fsdp()
+        ctx.param_group.post_backward()
+        return (None,) + grads
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_state.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d11f0359f1f074924d1720aebbe9a1357b510fe
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_state.py
@@ -0,0 +1,385 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import functools
+import logging
+from collections.abc import Sequence
+from typing import Any, Callable, Optional, TYPE_CHECKING
+
+import torch
+import torch.nn as nn
+from torch._logging import warning_once
+from torch.autograd import Variable
+from torch.autograd.graph import _MultiHandle
+from torch.distributed._composable_state import (
+    _get_module_state,
+    _insert_module_state,
+    _State,
+)
+from torch.distributed.device_mesh import _get_device_handle
+from torch.distributed.utils import _to_kwargs
+from torch.utils._pytree import tree_flatten, tree_map
+
+from ._fsdp_api import MixedPrecisionPolicy
+from ._fsdp_common import (
+    _cast_fp_tensor,
+    compiled_autograd_enabled,
+    detect_compiled_autograd,
+    TrainingState,
+)
+from ._fsdp_param_group import FSDPCommContext, FSDPParamGroup
+
+
+if TYPE_CHECKING:
+    from ._fsdp_param import FSDPParam
+
+
+logger = logging.getLogger("torch.distributed.fsdp.fully_shard")
+
+
+class FSDPStateContext:
+    """This has state shared across FSDP states."""
+
+    def __init__(self) -> None:
+        # All FSDP states in the root state's module tree
+        self.all_states: list[FSDPState] = []
+        # Iteration's forward root runs the once-per-forward logic; this root
+        # may not be the overall root set by lazy initialization in cases where
+        # only a submodule runs forward (e.g. encoder-only for eval)
+        self.iter_forward_root: Optional[FSDPState] = None
+        # Final callback should only be queued once per backward
+        self.post_backward_final_callback_queued: bool = False
+        # Whether to finalize backward in this backward's final callback
+        self.is_last_backward: bool = True
+        # Optional user-provided event recorded after optimizer for the
+        # all-gather streams to wait on in the root pre-forward
+        self.post_optim_event: Optional[torch.Event] = None
+
+
+def disable_if_config_true(func):
+    @functools.wraps(func)
+    def fsdp_hook_wrapper(*args, **kwargs):
+        if torch._dynamo.config.skip_fsdp_hooks:
+            return torch._dynamo.disable(func, recursive=True)(*args, **kwargs)
+        else:
+            return func(*args, **kwargs)
+
+    return fsdp_hook_wrapper
+
+
+class FSDPState(_State):
+    def __init__(self) -> None:
+        super().__init__()
+        self._fsdp_param_group: Optional[FSDPParamGroup] = None
+        self._is_root: Optional[bool] = None  # root set during lazy init
+        self._state_ctx = FSDPStateContext()
+        self._comm_ctx = FSDPCommContext()
+        self._training_state: TrainingState = TrainingState.IDLE
+        self._states_to_forward_prefetch: list[FSDPState] = []
+        self._states_to_backward_prefetch: list[FSDPState] = []
+        self._modules_to_run_forward: set[nn.Module] = set()
+
+    # Define a separate init since `__init__` is called in the contract
+    def init(
+        self,
+        modules: tuple[nn.Module, ...],
+        device: torch.device,
+        mp_policy: MixedPrecisionPolicy,
+    ) -> None:
+        for module in modules:
+            _insert_module_state(module, self)
+        self._modules = modules
+        self._device = device
+        self._device_handle = _get_device_handle(device.type)
+        self._mp_policy = mp_policy
+        if len(modules) == 1:
+            self._pre_forward_hook_handle = modules[0].register_forward_pre_hook(
+                self._pre_forward, prepend=True, with_kwargs=True
+            )
+            self._post_forward_hook_handle = modules[0].register_forward_hook(
+                self._post_forward, prepend=False
+            )
+        else:
+            hook_handle = _register_group_forward_hooks(
+                modules,
+                self._pre_forward,
+                self._post_forward,
+                self._modules_to_run_forward,
+            )
+            self._pre_forward_hook_handle = hook_handle
+            self._post_forward_hook_handle = hook_handle
+
+    def _root_pre_forward(
+        self, module: nn.Module, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        self._lazy_init()
+        if self._state_ctx.iter_forward_root is not None:
+            return args, kwargs
+        if not compiled_autograd_enabled():
+            logger.debug("FSDP::root_pre_forward")
+        self._state_ctx.iter_forward_root = self
+        with torch.profiler.record_function("FSDP::root_pre_forward"):
+            # Wait for optimizer before implicitly prefetched all-gathers
+            if (event := self._state_ctx.post_optim_event) is not None:
+                self._comm_ctx.all_gather_copy_in_stream.wait_event(event)
+                self._comm_ctx.all_gather_stream.wait_event(event)
+                self._state_ctx.post_optim_event = None
+            else:
+                current_stream = self._device_handle.current_stream()
+                self._comm_ctx.all_gather_copy_in_stream.wait_stream(current_stream)
+                self._comm_ctx.all_gather_stream.wait_stream(current_stream)
+            if self._device.type in ["cuda", "hpu", "xpu", "mtia"]:
+                with torch.profiler.record_function("FSDP::inputs_to_device"):
+                    args_tuple, kwargs_tuple = _to_kwargs(
+                        args, kwargs, self._device, False
+                    )  # same as DDP
+                args, kwargs = args_tuple[0], kwargs_tuple[0]
+        return args, kwargs
+
+    def _lazy_init(self) -> None:
+        """
+        Lazy initialization represents when all modules' parallelisms have
+        finalized (e.g. FSDP has been applied to all desired modules). This
+        means that we can determine which state is the root, and we do so by
+        the 1st state to run forward.
+        """
+        if self._is_root is not None:
+            return  # no-op: already initialized
+        self._is_root = True
+        if len(self._modules) > 1:
+            raise RuntimeError(
+                f"FSDP requires a single root module but got {self._modules}"
+            )
+        detect_compiled_autograd()
+        root_module = self._modules[0]
+        visited_states: set[FSDPState] = set()
+        for module_name, module in root_module.named_modules():
+            if (state := _get_module_fsdp_state(module)) is None:
+                continue
+            if module is not root_module:
+                if state not in visited_states and state._is_root is not None:
+                    raise RuntimeError(
+                        "FSDP state has already been lazily initialized for "
+                        f"{module_name}\nFSDP requires running forward through "
+                        "the root module first"
+                    )
+                state._is_root = False
+            self._state_ctx.all_states.append(state)
+            visited_states.add(state)
+        if self._fsdp_param_group:
+            # For the root, do not reshard after forward since for training,
+            # the parameters would be freed and all-gathered immediately
+            self._fsdp_param_group.post_forward_mesh_info = None
+        self._init_fqns()
+        self._init_shared_state()
+        # Run parameter group lazy inits after initializing FQNs for improved
+        # error messages
+        for state in self._state_ctx.all_states:
+            if state._fsdp_param_group:
+                state._fsdp_param_group.lazy_init()
+
+    def _init_shared_state(self) -> None:
+        self._comm_ctx.lazy_init(self._device)
+        for state in self._state_ctx.all_states:
+            state._state_ctx = self._state_ctx
+            state._comm_ctx = self._comm_ctx
+            if fsdp_param_group := state._fsdp_param_group:
+                fsdp_param_group.comm_ctx = self._comm_ctx
+
+    def _init_fqns(self) -> None:
+        """Sets module and parameter FQN attributes for debugging."""
+        assert self._is_root
+        root_module = self._modules[0]
+        param_to_fsdp_param: dict[nn.Parameter, FSDPParam] = {}
+        module_to_fsdp_param_group: dict[nn.Module, FSDPParamGroup] = {}
+        for state in self._state_ctx.all_states:
+            if fsdp_param_group := state._fsdp_param_group:
+                for fsdp_param in fsdp_param_group.fsdp_params:
+                    param_to_fsdp_param[fsdp_param.sharded_param] = fsdp_param
+                for module in fsdp_param_group.modules:
+                    module_to_fsdp_param_group[module] = fsdp_param_group
+        for param_name, param in root_module.named_parameters():
+            if param in param_to_fsdp_param:
+                param_to_fsdp_param[param]._param_fqn = param_name
+        for module_name, module in root_module.named_modules():
+            if module in module_to_fsdp_param_group:
+                module_fqn = module_to_fsdp_param_group[module]._module_fqn
+                if module_fqn is None:
+                    module_to_fsdp_param_group[module]._module_fqn = module_name
+                else:
+                    assert isinstance(module_fqn, str), f"{module_fqn}"
+                    module_fqn += f", {module_name}"
+                    module_to_fsdp_param_group[module]._module_fqn = module_fqn
+
+    @disable_if_config_true
+    def _pre_forward(
+        self, module: nn.Module, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        # When composing with module-hook-based activation checkpointing, the
+        # the pre-backward hook is responsible for the unshard
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            return args, kwargs
+        self._training_state = TrainingState.FORWARD
+        args, kwargs = self._root_pre_forward(module, args, kwargs)
+        if self._mp_policy.cast_forward_inputs and self._mp_policy.param_dtype:
+            with torch.profiler.record_function("FSDP::cast_forward_inputs"):
+                cast_fn = functools.partial(
+                    _cast_fp_tensor, self._mp_policy.param_dtype
+                )
+                args, kwargs = tree_map(cast_fn, args), tree_map(cast_fn, kwargs)
+        if self._fsdp_param_group:
+            args, kwargs = self._fsdp_param_group.pre_forward(module, args, kwargs)
+        for fsdp_state in self._states_to_forward_prefetch:
+            if (target_param_group := fsdp_state._fsdp_param_group) is not None:
+                FSDPParamGroup._prefetch_unshard(target_param_group, "forward")
+        return args, kwargs
+
+    @disable_if_config_true
+    def _post_forward(self, module: nn.Module, input: Any, output: Any) -> Any:
+        # When composing with module-hook-based activation checkpointing, the
+        # post-backward hook is responsible for the reshard
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            return output
+        if self._fsdp_param_group:
+            output = self._fsdp_param_group.post_forward(module, input, output)
+        output = self._register_pre_backward_hook(output)
+        self._training_state = TrainingState.IDLE
+        if self._state_ctx.iter_forward_root is self:
+            if all_gather_state := self._comm_ctx.all_gather_state:
+                # Free the last all-gather result if needed; refer to
+                # [Note: Overlapping all-gather copy-in and all-gather]
+                self._comm_ctx.all_gather_copy_in_stream.wait_event(
+                    all_gather_state.event
+                )
+                self._comm_ctx.all_gather_stream.wait_event(all_gather_state.event)
+                self._comm_ctx.all_gather_state = None  # free the all-gather result
+            self._state_ctx.iter_forward_root = None
+        if self._mp_policy.output_dtype is not None:
+            with torch.profiler.record_function("FSDP::cast_forward_outputs"):
+                output = tree_map(
+                    functools.partial(_cast_fp_tensor, self._mp_policy.output_dtype),
+                    output,
+                )
+        return output
+
+    def _pre_backward(self, grad: torch.Tensor) -> torch.Tensor:
+        self._training_state = TrainingState.PRE_BACKWARD
+        self._register_root_post_backward_final_callback()
+        if self._fsdp_param_group:
+            default_prefetch = len(self._states_to_backward_prefetch) == 0
+            self._fsdp_param_group.pre_backward(default_prefetch)
+        for fsdp_state in self._states_to_backward_prefetch:
+            if (target_param_group := fsdp_state._fsdp_param_group) is not None:
+                FSDPParamGroup._prefetch_unshard(target_param_group, "backward")
+        return grad
+
+    def _root_post_backward_final_callback(self) -> None:
+        if not compiled_autograd_enabled():
+            logger.debug("FSDP::root_post_backward")
+        with torch.profiler.record_function("FSDP::root_post_backward_callback"):
+            for state in self._state_ctx.all_states:
+                fsdp_param_group = state._fsdp_param_group
+                if (
+                    fsdp_param_group
+                    and fsdp_param_group._training_state != TrainingState.POST_BACKWARD
+                ):
+                    # Run post-backward in case forward inputs did not require
+                    # gradient so the autograd backward did not run
+                    fsdp_param_group.post_backward()
+                state._training_state = TrainingState.IDLE
+                if fsdp_param_group:
+                    fsdp_param_group._training_state = TrainingState.IDLE
+                if self._state_ctx.is_last_backward:
+                    state._finalize_backward()
+            if self._state_ctx.is_last_backward:
+                self._comm_ctx.post_forward_order.clear()
+                if self._comm_ctx.reduce_scatter_state is not None:
+                    self._device_handle.current_stream().wait_event(
+                        self._comm_ctx.reduce_scatter_state.event
+                    )
+                    self._comm_ctx.reduce_scatter_state = None
+            self._state_ctx.post_backward_final_callback_queued = False
+
+    def _finalize_backward(self) -> None:
+        if self._modules_to_run_forward:
+            msg = (
+                f"{len(self._modules_to_run_forward)} of the {len(self._modules)} "
+                f"modules passed to fully_shard did not run forward before backward, "
+                "which is error-prone since FSDP post-forward/pre-backward logic "
+                "will not run for these modules. We recommend passing only modules "
+                "that run forward together. Modules that did not run forward: "
+                f"{list(self._modules_to_run_forward)}"
+            )
+            warning_once(logger, msg, stacklevel=2)
+            # Clear since we want the next forward to run
+            self._modules_to_run_forward.clear()
+        if self._fsdp_param_group:
+            self._fsdp_param_group.finalize_backward()
+
+    def _register_pre_backward_hook(self, output: Any) -> Any:
+        if not torch.is_grad_enabled():
+            return output
+        flat_outputs, _ = tree_flatten(output)
+        for t in flat_outputs:
+            if torch.is_tensor(t) and t.requires_grad:
+                t.register_hook(self._pre_backward)
+        return output
+
+    def _register_root_post_backward_final_callback(self):
+        if self._state_ctx.post_backward_final_callback_queued:
+            return
+        self._state_ctx.post_backward_final_callback_queued = True
+        Variable._execution_engine.queue_callback(
+            self._root_post_backward_final_callback
+        )
+
+
+def _get_module_fsdp_state(module: nn.Module) -> Optional[FSDPState]:
+    state = _get_module_state(module)
+    if isinstance(state, FSDPState):
+        return state
+    return None
+
+
+def _register_group_forward_hooks(
+    modules: Sequence[nn.Module],
+    pre_hook: Callable,
+    post_hook: Callable,
+    modules_to_run: set[nn.Module],
+):
+    """
+    Registers group forward pre and post-hooks. The pre-hook runs upon the
+    first module pre-forward, and the post-hook runs upon the last. If at least
+    one module does not run forward, then the post-hook does not run.
+    """
+    modules_set = set(modules)
+
+    @disable_if_config_true
+    @functools.wraps(pre_hook)
+    def wrapped_pre_hook(*args: Any, **kwargs: Any):
+        if len(modules_to_run) == 0:  # first to run
+            modules_to_run.update(modules_set)
+            return pre_hook(*args, **kwargs)
+
+    @disable_if_config_true
+    def get_wrapped_post_hook(module: nn.Module):
+        @functools.wraps(post_hook)
+        def wrapped_post_hook(*args: Any, **kwargs: Any):
+            modules_to_run.discard(module)
+            if len(modules_to_run) == 0:
+                return post_hook(*args, **kwargs)
+
+        return wrapped_post_hook
+
+    pre_handles = [
+        module.register_forward_pre_hook(
+            wrapped_pre_hook, prepend=True, with_kwargs=True
+        )
+        for module in modules
+    ]
+    post_handles = [
+        module.register_forward_hook(
+            get_wrapped_post_hook(module), prepend=False, always_call=True
+        )
+        for module in modules
+    ]
+    return _MultiHandle(tuple(pre_handles + post_handles))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fully_shard.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fully_shard.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cc147171b30adb68d25c1cf8fc40a49e4008f22
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fully_shard.py
@@ -0,0 +1,590 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+
+from __future__ import annotations
+
+import functools
+from typing import (
+    Any,
+    Callable,
+    cast,
+    NoReturn,
+    Optional,
+    overload,
+    TYPE_CHECKING,
+    Union,
+)
+
+import torch
+import torch.nn as nn
+from torch.distributed._composable import contract
+from torch.distributed.utils import _get_root_modules
+
+from ._fsdp_api import MixedPrecisionPolicy, OffloadPolicy
+from ._fsdp_common import FSDPMeshInfo, HSDPMeshInfo
+from ._fsdp_init import (
+    _get_device_from_mesh,
+    _get_managed_modules,
+    _get_managed_states,
+    _get_post_forward_mesh_info,
+    _init_default_fully_shard_mesh,
+    _move_states_to_device,
+)
+from ._fsdp_param_group import FSDPParamGroup
+from ._fsdp_state import _get_module_fsdp_state, FSDPState
+
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+    from torch.distributed.tensor import DeviceMesh, Shard
+
+__all__ = [
+    "fully_shard",
+    "FSDPModule",
+    "UnshardHandle",
+    "register_fsdp_forward_method",
+]
+
+
+cls_to_fsdp_cls: dict[type, type] = {}
+
+
+@overload
+def fully_shard(
+    module: nn.Module,
+    *,
+    mesh: Optional[DeviceMesh] = ...,
+    reshard_after_forward: Union[bool, int] = ...,
+    shard_placement_fn: Optional[Callable[[nn.Parameter], Optional[Shard]]] = ...,
+    mp_policy: MixedPrecisionPolicy = ...,
+    offload_policy: OffloadPolicy = ...,
+    ignored_params: Optional[set[nn.Parameter]] = ...,
+) -> FSDPModule: ...
+
+
+@overload
+def fully_shard(
+    module: list[nn.Module],
+    *,
+    mesh: Optional[DeviceMesh] = ...,
+    reshard_after_forward: Union[bool, int] = ...,
+    shard_placement_fn: Optional[Callable[[nn.Parameter], Optional[Shard]]] = ...,
+    mp_policy: MixedPrecisionPolicy = ...,
+    offload_policy: OffloadPolicy = ...,
+    ignored_params: Optional[set[nn.Parameter]] = ...,
+) -> list[FSDPModule]: ...
+
+
+# The decorator adds a state object to `module` that can be accessed via
+# `fully_shard.state(module)`. The state object and module are 1:1.
+# [1] Python runtime decorator does not play well with static type checking
+# so suppressing some type checks to support type overloads
+# such that caller can still get correct return types based on input type
+@contract(state_cls=FSDPState)  # type: ignore[misc] # see [1]
+def fully_shard(
+    module,
+    *,
+    mesh: Optional[DeviceMesh] = None,
+    reshard_after_forward: Union[bool, int] = True,
+    shard_placement_fn: Optional[Callable[[nn.Parameter], Optional[Shard]]] = None,
+    mp_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
+    offload_policy: OffloadPolicy = OffloadPolicy(),
+    ignored_params: Optional[set[nn.Parameter]] = None,
+):
+    """
+    Apply fully sharded data parallelism (FSDP) to ``module``, where FSDP
+    shards module parameters, gradients, and optimizer states across data
+    parallel workers to save memory at the cost of communication.
+
+    At initialization, FSDP shards the module's parameters across the data
+    parallel workers given by ``mesh``. Before forward, FSDP all-gathers the
+    sharded parameters across the data-parallel workers to get the unsharded
+    parameters for forward computation. If ``reshard_after_forward`` is
+    ``True``, then FSDP frees the unsharded parameters after forward and
+    re-all-gathers them in backward before gradient computation. After gradient
+    computation, FSDP frees the unsharded parameters and reduce-scatters the
+    unsharded gradients across data-parallel workers.
+
+    This implementation represents the sharded parameters as :class:`DTensor` s
+    sharded on dim-0, while the unsharded parameters will be like the original
+    parameters on ``module`` (e.g. :class:`torch.Tensor` if originally
+    :class:`torch.Tensor`). A module
+    `forward pre-hook `_
+    on ``module`` all-gathers the parameters, and a module
+    `forward hook `_
+    on ``module`` frees them (if needed). Similar backward hooks all-gather
+    parameters and later free parameters and reduce-scatter gradients.
+
+    Since grouping multiple tensors together for one collective is critical for
+    communication efficiency, this implementation makes this grouping first
+    class. Calling :meth:`fully_shard` on ``module`` constructs one group that
+    includes the parameters in ``module.parameters()`` except those already
+    assigned to a group from an earlier call on a submodule. This means that
+    :meth:`fully_shard` should be called bottom-up on your model. Each group's
+    parameters are all-gathered in one collective, and its gradients are
+    reduce-scattered in one collective. Partitioning the model into multiple
+    groups ("layer by layer") allows for peak memory savings and communication/computation
+    overlap. Users generally should *not* call :meth:`fully_shard` only on the
+    topmost root module.
+
+    Args:
+        module (Union[nn.Module, List[nn.Module]): The module or modules to
+            shard with FSDP and group together for communication.
+        mesh (Optional[DeviceMesh]): This data parallel mesh defines the
+            sharding and device. If 1D, then parameters are fully sharded
+            across the 1D mesh (FSDP) with ``(Shard(0),)`` placement. If 2D,
+            then parameters are sharded across the 1st dim and replicated
+            across the 0th dim (HSDP) with ``(Replicate(), Shard(0))``
+            placement. The mesh's device type gives the device type used for
+            communication; if a CUDA or CUDA-like device type, then we use the
+            current device.
+        reshard_after_forward (Union[bool, int]): This controls the parameter
+            behavior after forward and can trade off memory and communication:
+
+            - If ``True``, then this reshards parameters after forward and
+              re-all-gathers in backward.
+            - If ``False``, then this keeps the unsharded parameters in memory
+              after forward and avoids the all-gather in backward.
+            - If an ``int``, then this represents the world size to reshard to
+              after forward. It should be a non-trivial divisor of the ``mesh``
+              shard dim size (i.e. excluding 1 and the dim size itself). A
+              choice may be the intra-node size (e.g. ``torch.cuda.device_count()``).
+              This allows the all-gather in backward to be over a smaller world
+              size at the cost of higher memory usage than setting to ``True``.
+            - The root FSDP state has its value specially set to ``False`` as a
+              heuristic since its parameters would typically be immediately
+              all-gathered for backward.
+            - After forward, the parameters registered to the module depend on
+              to this: The registered parameters are the sharded parameters if
+              ``True``; unsharded parameters if ``False``; and the paramters
+              resharded to the smaller mesh otherwise. To modify the parameters
+              between forward and backward, the registered parameters must be
+              the sharded parameters. For ``False`` or an ``int``, this can be
+              done by manually resharding via :meth:`reshard`.
+        shard_placement_fn (Optional[Callable[[nn.Parameter], Optional[Shard]]]):
+            This callable can be used to override the sharding placement for a
+            parameter to shard a parameter on a dimension other than dim-0. If
+            this callable returns a :class:`Shard` placement (not ``None``),
+            then FSDP will shard according to that placement (e.g. ``Shard(1)``).
+            If sharding on a nonzero dim, we currently require even sharding,
+            i.e. the tensor dim size on that dim must be divisible by the FSDP
+            shard mesh size.
+        mp_policy (MixedPrecisionPolicy): This controls the mixed precision
+            policy, which offers parameter/reduction mixed precision for this
+            module. See :class:`MixedPrecisionPolicy` for details.
+        offload_policy (OffloadPolicy): This controls the offloading policy,
+            which offers parameter/gradient/optimizer state offloading. See
+            :class:`OffloadPolicy` and its subclasses for details.
+        ignored_params: Optional(Set[nn.Parameter]): The set of parameters that we
+            don't want to shard with FSDP.
+
+    Returns:
+        FSDPModule: The module with FSDP applied (in-place).
+    """
+    if isinstance(module, (nn.ModuleList, nn.ModuleDict)):
+        raise ValueError(
+            f"fully_shard does not support containers that do not implement forward: {module}"
+        )
+    mesh = mesh or _init_default_fully_shard_mesh()
+    if mesh.ndim not in (1, 2):
+        raise ValueError(f"fully_shard expects a 1D or 2D DeviceMesh but got {mesh}")
+    elif mesh.ndim == 1:
+        mesh_info = FSDPMeshInfo(mesh, shard_mesh_dim=0)
+    else:
+        if mesh.mesh_dim_names is None:
+            raise AssertionError(
+                "Please init the 2D mesh for HSDP with mesh_dim_names specified"
+            )
+        mesh_info = HSDPMeshInfo(mesh, shard_mesh_dim=1, replicate_mesh_dim=0)
+    device = _get_device_from_mesh(mesh)
+    post_forward_mesh_info = _get_post_forward_mesh_info(
+        reshard_after_forward, mesh_info
+    )
+
+    arg_module = module
+    modules = (
+        (module,) if isinstance(module, nn.Module) else tuple(_get_root_modules(module))
+    )
+    state = fully_shard.state(modules[0])  # type: ignore[attr-defined] # see [1]
+    state.init(modules, device, mp_policy)
+
+    managed_modules = _get_managed_modules(modules, ignored_params)
+    params, buffers = _get_managed_states(managed_modules, ignored_params)
+
+    _move_states_to_device(params, buffers, device)
+    if params:
+        state._fsdp_param_group = FSDPParamGroup(
+            params,
+            modules,
+            mesh_info,
+            post_forward_mesh_info,
+            device,
+            shard_placement_fn,
+            mp_policy,
+            offload_policy,
+        )
+
+    # For Dynamo
+    for managed_module in managed_modules:
+        managed_module._is_fsdp_managed_module = True  # type: ignore[assignment]
+        managed_module._fsdp_use_orig_params = True  # type: ignore[assignment]
+
+    # Place FSDP leftmost for highest priority in the method resolution order
+    for module in modules:
+        cls = module.__class__
+        new_cls = cls_to_fsdp_cls.get(cls, None)
+        if not new_cls:
+            dct = {"__deepcopy__": _unimplemented_deepcopy}
+            new_cls = type(f"FSDP{cls.__name__}", (FSDPModule, cls), dct)
+            cls_to_fsdp_cls[cls] = new_cls
+        module.__class__ = new_cls
+    return arg_module
+
+
+def _unimplemented_deepcopy(*args: Any, **kwargs: Any) -> NoReturn:
+    raise AssertionError(
+        "FSDP does not support deepcopy. Please use state dict for serialization."
+    )
+
+
+class FSDPModule:
+    def __new__(cls, *args, **kwargs):
+        """
+        Override ``__new__`` to remove the FSDP class and directly construct
+        the original class for cases like indexing into a container module.
+        """
+        # Use index 2 since 0 is the dynamically constructed `FSDP<...>` class
+        # and index 1 is the `FSDPModule` class itself
+        orig_cls = cls.__mro__[2]
+        self = orig_cls.__new__(orig_cls, *args, **kwargs)
+        self.__init__(*args, **kwargs)
+        return self
+
+    def reshard(self) -> None:
+        """
+        Reshards the module's parameters, freeing the unsharded parameters if
+        they are allocated and registering the sharded parameters to the
+        module. This method is *not* recursive.
+        """
+        state = self._get_fsdp_state()
+        if fsdp_param_group := state._fsdp_param_group:
+            fsdp_param_group.reshard()
+
+    def unshard(self, async_op: bool = False) -> Optional[UnshardHandle]:
+        """
+        Unshards the module's parameters by allocating memory and all-gathering
+        the parameters. This method is *not* recursive. The unshard follows the
+        :class:`MixedPrecisionPolicy`, so it will all-gather following
+        ``param_dtype`` if set.
+
+        Args:
+            async_op (bool): If ``True``, then returns a :class:`UnshardHandle`
+                that has a :meth:`wait` method to wait on the unshard op. If
+                ``False``, then returns ``None`` and waits on the handle inside
+                this function.
+
+        .. note:: If ``async_op=True``, then FSDP will wait on the pending
+            unshard in the module's pre-forward for the user. The user only
+            needs to call :meth:`wait` explicitly if the wait should happen
+            before pre-forward.
+        """
+        state = self._get_fsdp_state()
+        fsdp_param_group = state._fsdp_param_group
+        if fsdp_param_group is not None:
+            fsdp_param_group.lazy_init()
+            fsdp_param_group.unshard(async_op=async_op)
+        handle = _UnshardHandleImpl(fsdp_param_group)
+        if async_op:
+            return handle
+        handle.wait()
+        return None
+
+    def set_is_last_backward(self, is_last_backward: bool) -> None:
+        """
+        Sets whether the next backward is the last one. On the last backward,
+        FSDP waits on pending gradient reduction and clears internal data
+        data structures for backward prefetching. This can be useful for
+        microbatching.
+        """
+        state = self._get_fsdp_state()
+        state._state_ctx.is_last_backward = is_last_backward
+
+    def set_requires_gradient_sync(
+        self, requires_gradient_sync: bool, *, recurse: bool = True
+    ) -> None:
+        """
+        Sets if the module should sync gradients. This can be used to implement
+        gradient accumulation *without communication*. For HSDP, this controls
+        both reduce-scatter and all-reduce together. This is the equivalence of
+        `no_sync` in FSDP1.
+
+        Args:
+            requires_gradient_sync (bool): Whether to reduce gradients for the
+                module's parameters.
+            recurse (bool): Whether to set for all FSDP submodules or just the
+                passed-in module.
+        """
+        self_module = cast(nn.Module, self)
+        modules = list(self_module.modules()) if recurse else [self_module]
+        for module in modules:
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.reduce_grads = requires_gradient_sync
+                    fsdp_param_group.all_reduce_grads = requires_gradient_sync
+
+    def set_requires_all_reduce(
+        self, requires_all_reduce: bool, *, recurse: bool = True
+    ) -> None:
+        """
+        Sets if the module should all-reduce gradients. This can be used to
+        implement gradient accumulation with only reduce-scatter but not
+        all-reduce for HSDP.
+        """
+        self_module = cast(nn.Module, self)
+        modules = list(self_module.modules()) if recurse else [self_module]
+        for module in modules:
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.all_reduce_grads = requires_all_reduce
+
+    def set_reshard_after_backward(
+        self, reshard_after_backward: bool, *, recurse: bool = True
+    ) -> None:
+        """
+        Sets if the module should reshard parameters after backward. This can
+        be used during gradient accumulation to trade off higher memory for
+        reduced communication since the unsharded parameters do not need to be
+        re-all-gathered before the next forward.
+
+        Args:
+            reshard_after_backward (bool): Whether to reshard parameters after
+                backward.
+            recurse (bool): Whether to set for all FSDP submodules or just the
+                passed-in module.
+        """
+        self_module = cast(nn.Module, self)
+        modules = list(self_module.modules()) if recurse else [self_module]
+        for module in modules:
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.reshard_after_backward = reshard_after_backward
+
+    def set_modules_to_forward_prefetch(self, modules: list[FSDPModule]) -> None:
+        """
+        Sets the FSDP modules for which this FSDP module should explicitly
+        prefetch all-gathers in forward. The prefetching runs after this
+        module's all-gather copy-out.
+
+        Passing a singleton list containing the next FSDP module gives the same
+        all-gather overlap behavior as the default overlap behavior, except the
+        prefetched all-gather is issued earlier from the CPU. Passing a list
+        with at least length two is required for more aggressive overlap and
+        will use more reserved memory.
+
+        Args:
+            modules (List[FSDPModule]): FSDP modules to prefetch.
+        """
+        _assert_all_fsdp_modules(modules)
+        self._get_fsdp_state()._states_to_forward_prefetch = [
+            module._get_fsdp_state() for module in modules
+        ]
+
+    def set_modules_to_backward_prefetch(self, modules: list[FSDPModule]) -> None:
+        """
+        Sets the FSDP modules for which this FSDP module should explicitly
+        prefetch all-gathers in backward. This overrides the default backward
+        pretching implementation that prefetches the next FSDP module based on
+        the reverse post-forward order.
+
+        Passing a singleton list containing the previous FSDP module gives the
+        same all-gather overlap behavior as the default overlap behavior.
+        Passing a list with at least length two is required for more aggressive
+        overlap and will use more reserved memory.
+
+        Args:
+            modules (List[FSDPModule]): FSDP modules to prefetch.
+        """
+        _assert_all_fsdp_modules(modules)
+        self._get_fsdp_state()._states_to_backward_prefetch = [
+            module._get_fsdp_state() for module in modules
+        ]
+
+    def set_all_reduce_hook(
+        self,
+        hook: Callable[[torch.Tensor], None],
+        *,
+        stream: Optional[torch.cuda.Stream] = None,
+    ):
+        """
+        Args:
+            hook (Callable[[torch.Tensor], None]): User-defined all-reduce hook
+                with expected signature ``hook(reduce_output: torch.Tensor) -> None``
+                where ``reduce_output`` is the reduce-scatter output if only
+                using FSDP or the all-reduce output if using native HSDP.
+            stream (Optional[torch.cuda.Stream]): Stream to run the all-reduce
+                hook in. This should only be set if not using native HSDP. If
+                using native HSDP, the hook will run in the internally defined
+                all-reduce stream used by the native HSDP all-reduce.
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            fsdp_param_group._all_reduce_hook = hook
+            if stream is not None:
+                if fsdp_param_group._is_hsdp:
+                    raise ValueError("stream cannot be set when using native HSDP")
+                fsdp_param_group._all_reduce_hook_stream = stream
+
+    def set_post_optim_event(self, event: torch.Event) -> None:
+        """
+        Sets a post-optimizer-step event for the root FSDP module to wait the
+        all-gather streams on.
+
+        By default, the root FSDP module waits the all-gather streams on the
+        current stream to ensure that the optimizer step has finished before
+        all-gathering. However, this may introduce false dependencies if
+        there is unrelated computation after the optimizer step. This API
+        allows the user to provide their own event to wait on. After the root
+        waits on the event, the event is discarded, so this API should be
+        called with a new event each iteration.
+
+        Args:
+            event (torch.Event): Event recorded after the optimizer step
+                to wait all-gather streams on.
+        """
+        self._get_fsdp_state()._state_ctx.post_optim_event = event
+
+    def set_reduce_scatter_divide_factor(self, factor: float) -> None:
+        """
+        Sets a custom divide factor for the reduce-scatter. This becomes a
+        custom reduce op using NCCL's PreMulSum, which allows multiplying by
+        the factor before reduction.
+
+        Args:
+            factor (float): Custom divide factor.
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            mul_factor = 1.0 / float(factor)
+            reduce_op = torch.distributed._make_nccl_premul_sum(mul_factor)
+            fsdp_param_group.reduce_scatter_reduce_op = reduce_op
+
+    def set_unshard_in_backward(self, unshard_in_backward: bool) -> None:
+        """
+        Sets whether the FSDP module's parameters need to be unsharded in
+        backward. This can be used in expert cases when the user knows that all
+        parameters in this FSDP module's parameter group are not needed for
+        backward computation (e.g. embedding).
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            fsdp_param_group.unshard_in_backward = unshard_in_backward
+
+    def _set_unshard_async_op(self, async_op: bool):
+        """
+        Sets whether to use ``async_op=True`` or ``False`` for the pre-forward
+        and pre-backward unshard op. This defaults to ``False`` but can be set
+        to ``True`` with this method.
+
+        Setting this to ``True`` allows the all-gather allocations to happen in
+        the default stream, avoiding inter-stream memory fragmentation.
+        However, you must use explicit prefetching (e.g. via :meth:`unshard`)
+        in forward to still get overlap, and the pre-all-gather ops like dtype
+        casting and copy-in will not overlap with compute.
+        """
+        self_module = cast(nn.Module, self)
+        for module in self_module.modules():
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.unshard_async_op = async_op
+
+    def _get_fsdp_state(self) -> FSDPState:
+        if (state := _get_module_fsdp_state(cast(nn.Module, self))) is None:
+            raise AssertionError(f"No FSDP state found on {self}")
+        return state
+
+    def _apply(self, *args: Any, **kwargs: Any) -> Any:
+        # Reshard to ensure that sharded parameters are registered
+        self.reshard()
+        ret = super()._apply(*args, **kwargs)  # type: ignore[misc]
+        state = self._get_fsdp_state()
+        if not (fsdp_param_group := state._fsdp_param_group):
+            return ret
+        # TODO: Remove this padding logic once DTensor pads the local tensor:
+        # https://github.com/pytorch/pytorch/issues/113045
+        with torch.no_grad():
+            for fsdp_param in fsdp_param_group.fsdp_params:
+                fsdp_param.reset_sharded_param()
+        return ret
+
+
+class UnshardHandle:
+    """
+    A handle to wait on a :meth:`FSDPModule.unshard` op.
+    """
+
+    def wait(self) -> None:
+        """
+        Waits on the unshard op. This ensures that the current stream can use
+        the unsharded parameters, which are now registered to the module.
+        """
+        return
+
+
+class _UnshardHandleImpl(UnshardHandle):
+    def __init__(self, fsdp_param_group: Optional[FSDPParamGroup]):
+        self._fsdp_param_group = fsdp_param_group
+
+    def wait(self):
+        if self._fsdp_param_group is not None:
+            self._fsdp_param_group.wait_for_unshard()
+            # Avoid keeping a reference
+            self._fsdp_param_group = None
+
+
+def register_fsdp_forward_method(module: nn.Module, method_name: str) -> None:
+    """
+    Registers a method on ``module`` to be considered a forward method for
+    FSDP.
+
+    FSDP all-gathers parameters pre-forward and optionally frees parameters
+    post-forward (depending on ``reshard_after_forward``). FSDP only knows to
+    do this for :meth:`nn.Module.forward` by default. This function patches a
+    user-specified method to run the pre/post-forward hooks before/after the
+    method, respectively. If ``module`` is not an :class:`FSDPModule`, then
+    this is a no-op.
+
+    Args:
+        module (nn.Module): Module to register the forward method on.
+        method_name (str): Name of the forward method.
+    """
+    if not isinstance(module, FSDPModule):
+        # Make no-op to allow including both when using/not using FSDP
+        return
+    if not hasattr(module, method_name):
+        raise ValueError(f"{type(module)} does not have a method {method_name}")
+    orig_method = getattr(module, method_name)
+
+    @functools.wraps(orig_method)
+    def wrapped_method(self, *args, **kwargs):
+        fsdp_state = self._get_fsdp_state()
+        args, kwargs = fsdp_state._pre_forward(self, args, kwargs)
+        out = orig_method(*args, **kwargs)
+        return fsdp_state._post_forward(self, args, out)
+
+    # Use `__get__` to make `wrapped_method` an instance method
+    setattr(
+        module,
+        method_name,
+        wrapped_method.__get__(module, type(module)),  # type:ignore[attr-defined]
+    )
+
+
+def _assert_all_fsdp_modules(modules: Iterable[Any]) -> None:
+    for module in modules:
+        if not isinstance(module, FSDPModule):
+            raise ValueError(f"Expects FSDPModule but got {type(module)}: {module}")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_init_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_init_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..feaf8b8829630d7126aa0c58f2b5fdaeedaf0df2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_init_utils.py
@@ -0,0 +1,1186 @@
+# mypy: allow-untyped-defs
+import collections
+import itertools
+import os
+import warnings
+from collections.abc import Generator, Iterable, Iterator
+from typing import Any, Callable, no_type_check, Optional, TYPE_CHECKING, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._exec_order_utils as exec_order_utils
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.distributed.fsdp.fully_sharded_data_parallel as fsdp_file
+import torch.nn as nn
+from torch.distributed.algorithms._comm_hooks import default_hooks
+from torch.distributed.device_mesh import _mesh_resources, DeviceMesh
+from torch.distributed.distributed_c10d import _get_default_group
+from torch.distributed.fsdp._common_utils import (
+    _FSDPDeviceHandle,
+    _FSDPState,
+    _get_module_fsdp_state,
+    _is_fsdp_flattened,
+    _named_parameters_with_duplicates,
+    clean_tensor_name,
+    TrainingState,
+)
+from torch.distributed.fsdp._flat_param import (
+    _FSDP_USE_FULL_PREC_IN_EVAL,
+    FlatParameter,
+    FlatParamHandle,
+    HandleShardingStrategy,
+)
+from torch.distributed.fsdp._limiter_utils import _FreeEventQueue
+from torch.distributed.fsdp.api import (
+    BackwardPrefetch,
+    CPUOffload,
+    FullOptimStateDictConfig,
+    FullStateDictConfig,
+    MixedPrecision,
+    ShardingStrategy,
+    StateDictConfig,
+    StateDictType,
+)
+from torch.distributed.fsdp.wrap import _Policy
+from torch.distributed.tensor.parallel.fsdp import DTensorExtensions
+from torch.distributed.utils import _sync_params_and_buffers
+from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+
+
+if TYPE_CHECKING:
+    from torch.utils.hooks import RemovableHandle
+
+_TORCHDISTX_AVAIL = True
+try:
+    from torchdistx import deferred_init, fake  # type: ignore[import]
+except ImportError:
+    _TORCHDISTX_AVAIL = False
+
+PARAM_BROADCAST_BUCKET_SIZE = int(250 * 1024 * 1024)
+FSDP_SYNCED = "_fsdp_synced"
+# Specification of process groups for hybrid sharding strategies.
+HybridShardProcessGroupType = tuple[dist.ProcessGroup, dist.ProcessGroup]
+# Overall specification of process group.
+ProcessGroupType = Optional[Union[dist.ProcessGroup, HybridShardProcessGroupType]]
+
+
+# TODO (awgu): Refactor this later
+SHARDING_STRATEGY_MAP = {
+    ShardingStrategy.NO_SHARD: HandleShardingStrategy.NO_SHARD,
+    ShardingStrategy.FULL_SHARD: HandleShardingStrategy.FULL_SHARD,
+    ShardingStrategy.SHARD_GRAD_OP: HandleShardingStrategy.SHARD_GRAD_OP,
+    ShardingStrategy.HYBRID_SHARD: HandleShardingStrategy.HYBRID_SHARD,
+    ShardingStrategy._HYBRID_SHARD_ZERO2: HandleShardingStrategy._HYBRID_SHARD_ZERO2,
+}
+HYBRID_SHARDING_STRATEGIES = [
+    ShardingStrategy.HYBRID_SHARD,
+    ShardingStrategy._HYBRID_SHARD_ZERO2,
+]
+NO_RESHARD_AFTER_FORWARD_STRATEGIES = (
+    ShardingStrategy.SHARD_GRAD_OP,
+    ShardingStrategy._HYBRID_SHARD_ZERO2,
+)
+
+
+# NOTE: Since non-self attributes cannot be type annotated, several attributes
+# on `state` are defined first as local variables before being assigned.
+
+
+@no_type_check
+def _init_process_group_state(
+    state: _FSDPState,
+    process_group: ProcessGroupType,
+    sharding_strategy: ShardingStrategy,
+    policy: Optional[_Policy],
+    device_mesh: Optional[DeviceMesh] = None,
+) -> _FSDPState:
+    if process_group is not None and device_mesh is not None:
+        raise ValueError(
+            "Cannot pass both process_group and device_mesh at the "
+            "same time. Please just pass only one of them."
+        )
+    is_hybrid_strategy = sharding_strategy in HYBRID_SHARDING_STRATEGIES
+    if is_hybrid_strategy:
+        if process_group is None and policy is None and device_mesh is None:
+            # Raise an error here, since this is manual wrapping with no process group
+            # passed in, there is no way to ensure all wrapped FSDP instances use the same
+            # process groups.
+            raise ValueError(
+                f"Manual wrapping with {sharding_strategy} "
+                "requires explicit specification of process group or device_mesh."
+            )
+        else:
+            state = _init_process_group_state_for_hybrid_shard(
+                state, process_group, device_mesh
+            )
+    else:
+        if device_mesh:
+            state._device_mesh = device_mesh
+            state.process_group = device_mesh.get_group(mesh_dim=0)
+        else:
+            state.process_group = (
+                process_group if process_group is not None else _get_default_group()
+            )
+
+    state.rank = state.process_group.rank()
+    state.world_size = state.process_group.size()
+    data_parallel_world_size = state.world_size
+    if is_hybrid_strategy:
+        data_parallel_world_size *= state._inter_node_pg.size()
+    state._gradient_predivide_factor = (
+        default_hooks.DefaultState._get_gradient_predivide_factor(
+            data_parallel_world_size
+        )
+    )
+    state._gradient_postdivide_factor = (
+        data_parallel_world_size / state._gradient_predivide_factor
+    )
+    return state
+
+
+@no_type_check
+def _init_process_group_state_for_hybrid_shard(
+    state: _FSDPState,
+    process_group: ProcessGroupType,
+    device_mesh: DeviceMesh,
+) -> _FSDPState:
+    if device_mesh:
+        if _is_valid_hybrid_shard_device_mesh(device_mesh):
+            state._device_mesh = device_mesh
+            # We currently only allow _inter_node_pg to be the outermost dimension, and the
+            # process_group(intra_node) to be the innermost dimension.
+            state._inter_node_pg = device_mesh.get_group(mesh_dim=0)
+            state.process_group = device_mesh.get_group(mesh_dim=1)
+        else:
+            raise ValueError(
+                f"Expected device_mesh to have ndim=2 but got {device_mesh.ndim}"
+            )
+    elif process_group is None:
+        default_group = _get_default_group()
+        intra_node_group, inter_node_group = _init_intra_and_inter_node_groups(
+            default_group, state._device_handle.device_count()
+        )
+        # we shard across intra-node
+        state.process_group = intra_node_group
+        # save _inter_node_pg to allreduce across.
+        state._inter_node_pg = inter_node_group
+    else:
+        # Check type and assign state.process_group and state._inter_node_pg.
+        if _is_valid_hybrid_shard_pg_type(process_group):
+            # Assuming that user passed in as intra node group and inter node group
+            # as documented.
+            state.process_group, state._inter_node_pg = process_group
+        else:
+            raise ValueError(
+                "Expected process_group to be passed in as either None or "
+                f"Tuple[dist.ProcessGroup, dist.ProcessGroup] but got {type(process_group)}"
+            )
+    # Create state for allreduce
+    state._inter_node_state = _get_default_comm_hook_state(
+        process_group=state._inter_node_pg,
+    )
+    return state
+
+
+@no_type_check
+def _is_valid_hybrid_shard_pg_type(process_group: Any) -> bool:
+    return (
+        isinstance(process_group, tuple)
+        and len(process_group) == 2
+        and all(isinstance(pg, dist.ProcessGroup) for pg in process_group)
+    )
+
+
+@no_type_check
+def _is_valid_hybrid_shard_device_mesh(device_mesh: DeviceMesh) -> bool:
+    return isinstance(device_mesh, DeviceMesh) and device_mesh.ndim == 2
+
+
+@no_type_check
+def _init_intra_node_process_group(num_devices_per_node: int) -> dist.ProcessGroup:
+    """
+    Return a process group across the current node.
+
+    For example, given each row is a distinct node:
+    0  1  2  3  4  5  6  7
+    8  9 10 11 12 13 14 15
+    This API would return an intra-node subgroup across
+    [0, 1, ..., 7] or [8, 9, ..., 15] depending on the process's rank.
+    For example, rank 3 would get [0, 1, ..., 7].
+    """
+    intra_node_subgroup, _ = dist.new_subgroups(num_devices_per_node)
+    return intra_node_subgroup
+
+
+@no_type_check
+def _init_inter_node_process_group(
+    global_process_group: dist.ProcessGroup,
+    num_devices_per_node: int,
+) -> dist.ProcessGroup:
+    """
+    Return an inter-node process group where each contained rank has the same local rank.
+
+    For example, given each row is a distinct node:
+    0  1  2  3  4  5  6  7
+    8  9 10 11 12 13 14 15
+    This API would return inter-node process group [0, 8], [1, 9], [2, 10], and so forth
+    depending on the process's rank. For example, rank 1 would get [1, 9], rank 5
+    would get [5, 13].
+    """
+    # the inter-node pg that is returned
+    inter_node_pg = None
+    sharding_backend = dist.get_backend(global_process_group)
+    world_size = dist.get_world_size(global_process_group)
+    # Assuming fully homogeneous setup
+    num_nodes = world_size // num_devices_per_node
+    my_local_rank = dist.get_rank(global_process_group) % num_devices_per_node
+    for local_rank in range(num_devices_per_node):
+        ranks_for_inter_group = [
+            local_rank + (i * num_devices_per_node) for i in range(num_nodes)
+        ]
+        # every rank always needs to call dist.new_group
+        grp = dist.new_group(ranks=ranks_for_inter_group, backend=sharding_backend)
+        if local_rank == my_local_rank:
+            inter_node_pg = grp
+
+    assert inter_node_pg is not None, (
+        f"{my_local_rank} expected to assign inter-node pg, but did not"
+    )
+    return inter_node_pg
+
+
+def _init_intra_and_inter_node_groups(
+    global_process_group: dist.ProcessGroup,
+    num_devices_per_node: int,
+) -> tuple[dist.ProcessGroup, dist.ProcessGroup]:
+    """
+    Initialize intra and inter-node process groups and return the ones corresponding to this process's rank.
+
+    This function can be used to initialize process groups for ``HYBRID_SHARD`` or
+    ``_HYBRID_SHARD_ZERO2`` in FSDP.
+    This function assumes each node has an equal number of CUDA-enabled devices.
+    Returns:
+        Tuple[dist.ProcessGroup, dist.ProcessGroup]: Intra and inter-node process group.
+    """
+    return (
+        _init_intra_node_process_group(num_devices_per_node),
+        _init_inter_node_process_group(global_process_group, num_devices_per_node),
+    )
+
+
+@no_type_check
+def _init_ignored_module_states(
+    state: _FSDPState,
+    module: nn.Module,
+    ignored_modules: Optional[Iterable[torch.nn.Module]],
+    ignored_states: Union[
+        Optional[Iterable[torch.nn.Parameter]], Optional[Iterable[torch.nn.Module]]
+    ] = None,
+) -> _FSDPState:
+    if ignored_modules is not None and ignored_states is not None:
+        raise ValueError(
+            "Cannot pass both ignored_modules and ignored_states at the "
+            "same time. Please just pass ignored_states."
+        )
+    ignored_parameters = None
+    passed_as_ignored_states = ignored_states is not None
+    if passed_as_ignored_states:
+        ignored_states_list = list(ignored_states)
+        _check_ignored_states(ignored_states_list, True)
+    else:
+        ignored_states_list = []
+        _check_ignored_states(
+            list(ignored_modules) if ignored_modules is not None else [], False
+        )
+    if len(ignored_states_list) > 0:
+        if isinstance(ignored_states_list[0], nn.Parameter):
+            ignored_parameters = ignored_states_list
+        else:
+            ignored_modules = ignored_states_list
+    state._ignored_modules = _get_ignored_modules(module, ignored_modules)
+    state._ignored_params = _get_ignored_params(
+        module,
+        state._ignored_modules,
+        ignored_parameters,
+    )
+    state._ignored_buffer_names = _get_ignored_buffer_names(
+        module,
+        state._ignored_modules,
+    )
+    # TODO: FSDP's contract for buffers is not well-defined. They are
+    # implicitly ignored for most functionality since they are not sharded;
+    # however, FSDP still imposes some semantics on buffers (e.g. buffer mixed
+    # precision). We should formalize this contract and decide if we need to
+    # compute and store `_ignored_buffers`.
+    return state
+
+
+def _check_ignored_states(
+    ignored_states: list[Any], passed_as_ignored_states: bool
+) -> None:
+    """
+    Check that the ignored states are uniformly parameters or uniformly modules.
+
+    We may remove this check in the future if we permit mixing.
+    """
+    if len(ignored_states) == 0:
+        return
+    if passed_as_ignored_states:
+        all_params = all(isinstance(state, nn.Parameter) for state in ignored_states)
+        all_modules = all(isinstance(state, nn.Module) for state in ignored_states)
+        if not all_params and not all_modules:
+            # Sort for consistent ordering for unit test regex matching
+            sorted_types = sorted({type(state) for state in ignored_states}, key=repr)
+            raise ValueError(
+                "ignored_states expects all nn.Parameter or all nn.Module list "
+                f"elements but got types {sorted_types}"
+            )
+    else:
+        if not all(isinstance(state, nn.Module) for state in ignored_states):
+            sorted_types = sorted({type(state) for state in ignored_states}, key=repr)
+            raise ValueError(
+                "ignored_modules expects nn.Module list elements but got "
+                f"types {sorted_types}"
+            )
+
+
+@no_type_check
+def _init_device_handle(
+    state: _FSDPState,
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    device_id: Optional[Union[int, torch.device]],
+) -> _FSDPState:
+    """
+    Determine device handle used for initializing FSDP.
+
+    If a device is specified by ``device_id``,
+    then returns device handle corresponds to that device type. Otherwise, If the
+    module is already on a non-CPU device, then the device type is that non-CPU device type.
+    If the module is on CPU or meta, then the device type is the current accelerator device.
+    See the :ref:`Accelerators` for details.
+
+
+    This method will be called once ignored paramters was determined, as the device handle maybe needed
+    for other initialization.
+    """
+    determined_device = None
+    if device_id is not None:
+        determined_device = (
+            device_id
+            if isinstance(device_id, torch.device)
+            else torch.device(device_id)
+        )
+    if determined_device is None:
+        for param in _get_orig_params(module, ignored_params):
+            if param.device.type in {"cpu", "meta"}:
+                continue
+            if determined_device is None:
+                determined_device = param.device
+            else:
+                if param.device.type != determined_device.type:
+                    raise RuntimeError(
+                        f"FSDP does not support modules with different device types "
+                        f"but got params on {determined_device.type} and {param.device.type}"
+                    )
+        determined_device = determined_device or torch._C._get_accelerator()
+        if determined_device.type == "cpu":
+            raise RuntimeError(
+                "FSDP needs a non-CPU accelerator device, but no accelerator device is detected."
+            )
+
+    state._device_handle = _FSDPDeviceHandle.from_device(determined_device)
+    return state
+
+
+@no_type_check
+def _init_buffer_state(
+    state: _FSDPState,
+    module: nn.Module,
+) -> _FSDPState:
+    state._buffer_names = _get_buffer_names(module)
+    # Save a mapping from clean fully-qualified buffer name (starting from
+    # `module`) to its original dtype for restoring that dtype during model
+    # checkpointing when buffer mixed precision is enabled. The names should
+    # be clean since the casting happens in a `summon_full_params()` context.
+    _buffer_name_to_orig_dtype: dict[str, torch.dtype] = {}
+    for buffer_name, buffer in module.named_buffers():
+        buffer_name = clean_tensor_name(buffer_name)
+        _buffer_name_to_orig_dtype[buffer_name] = buffer.dtype
+    state._buffer_name_to_orig_dtype = _buffer_name_to_orig_dtype
+    return state
+
+
+@no_type_check
+def _init_core_state(
+    state: _FSDPState,
+    sharding_strategy: Optional[ShardingStrategy],
+    mixed_precision: Optional[MixedPrecision],
+    cpu_offload: Optional[CPUOffload],
+    limit_all_gathers: bool,
+    use_orig_params: bool,
+    backward_prefetch_limit: int,
+    forward_prefetch_limit: int,
+) -> _FSDPState:
+    # We clamp the strategy to `NO_SHARD` for world size of 1 since they are
+    # currently functionally equivalent. This may change if/when we integrate
+    # FSDP with MoE.
+    if state.world_size == 1:
+        if sharding_strategy != ShardingStrategy.NO_SHARD:
+            warnings.warn(
+                "FSDP is switching to use `NO_SHARD` instead of "
+                f"{sharding_strategy or ShardingStrategy.FULL_SHARD} since "
+                "the world size is 1."
+            )
+        sharding_strategy = ShardingStrategy.NO_SHARD
+    elif sharding_strategy == ShardingStrategy.NO_SHARD:
+        warnings.warn(
+            "The `NO_SHARD` sharding strategy is deprecated. If having issues, "
+            "please use `DistributedDataParallel` instead.",
+            FutureWarning,
+            # Level 1 is here, level 2 is from `FullyShardedDataParallel`, and
+            # level 3 is from the true caller
+            stacklevel=3,
+        )
+    state.sharding_strategy = sharding_strategy or ShardingStrategy.FULL_SHARD
+    state.mixed_precision = mixed_precision or MixedPrecision()
+    if mixed_precision is not None:
+        torch._C._log_api_usage_once(
+            f"torch.distributed.fsdp.mixed_precision.{str(state.mixed_precision)}"
+        )
+    state._use_full_prec_in_eval = (
+        os.environ.get(_FSDP_USE_FULL_PREC_IN_EVAL, "") == "1"
+    )
+    state.cpu_offload = cpu_offload or CPUOffload()
+    state.limit_all_gathers = limit_all_gathers
+    state._use_orig_params = use_orig_params
+    state.training_state = TrainingState.IDLE
+    state._is_root = None
+    state._free_event_queue = _FreeEventQueue()
+    state._debug_level = dist.get_debug_level()
+    state._exec_order_data = exec_order_utils._ExecOrderData(
+        state._debug_level,
+        backward_prefetch_limit,
+        forward_prefetch_limit,
+    )
+    state._unshard_event = None
+    # Mapping from fully sharded module to the handles it is responsible to
+    # unshard and reshard (see [Note: Fully Sharded Module])
+    _fully_sharded_module_to_handle: dict[nn.Module, FlatParamHandle] = {}
+    state._fully_sharded_module_to_handle = _fully_sharded_module_to_handle
+    # Invariant: `state.params` contains exactly the `FlatParameter`s of the
+    # handles in `state._handle`
+    _handle: Optional[FlatParamHandle] = None
+    state._handle = _handle
+    params: list[FlatParameter] = []
+    state.params = params
+    return state
+
+
+@no_type_check
+def _init_runtime_state(
+    state: _FSDPState,
+) -> _FSDPState:
+    _root_pre_forward_handles: list[RemovableHandle] = []
+    state._root_pre_forward_handles = _root_pre_forward_handles
+    _pre_forward_handles: list[RemovableHandle] = []
+    state._pre_forward_handles = _pre_forward_handles
+    _post_forward_handles: list[RemovableHandle] = []
+    state._post_forward_handles = _post_forward_handles
+    state._sync_gradients = True
+    state._comm_hook = None
+    state._comm_hook_state = None
+    # Used to prevent running the pre-backward hook multiple times
+    return state
+
+
+@no_type_check
+def _init_prefetching_state(
+    state: _FSDPState,
+    backward_prefetch: BackwardPrefetch,
+    forward_prefetch: bool,
+) -> _FSDPState:
+    state.backward_prefetch = backward_prefetch
+    state.forward_prefetch = forward_prefetch
+    # The data structures use tuples of handles to generalize over the case
+    # where a module's forward involves multiple handles.
+    return state
+
+
+@no_type_check
+def _init_extension(state: _FSDPState, device_mesh: DeviceMesh = None) -> _FSDPState:
+    # TODO: we need to add additional check once we support FSDP + PiPPy.
+    # This check is currently sufficient, since we only support FSDP + TP.
+    root_mesh = _mesh_resources.get_root_mesh(device_mesh)
+    # if a root mesh is not the same as device_mesh,
+    # meaning the device_mesh is sliced out from the root mesh.
+    if device_mesh and root_mesh != state._device_mesh:
+        state._fsdp_extension = DTensorExtensions(state._device_handle)
+    else:
+        # We need to explicilty set _fsdp_extension to None.
+        # Otherwise, we will run into an infinite recursion when getting the attribute.
+        state._fsdp_extension = None
+    return state
+
+
+@no_type_check
+def _init_state_dict_state(state: _FSDPState) -> _FSDPState:
+    state._state_dict_type = StateDictType.FULL_STATE_DICT
+    state_dict_config: StateDictConfig = FullStateDictConfig()
+    state._optim_state_dict_config = FullOptimStateDictConfig()
+    state._state_dict_config = state_dict_config
+    unshard_params_ctx: dict[nn.Module, Generator] = {}
+    state._unshard_params_ctx = unshard_params_ctx
+
+    return state
+
+
+def _verify_managed_params(module: nn.Module, params: list[nn.Parameter]) -> None:
+    """
+    Verify if the parameters are accepted by FSDP. The only restriction now
+    is that the parameter cannot be a scalar tensor (param.shape == []).
+    """
+    for param in params:
+        if len(param.shape) == 0:
+            param_name = ""
+            for name, param_ in module.named_parameters():
+                if param is param_:
+                    param_name = name
+                    break
+            assert param_name
+            raise ValueError(
+                "FSDP doesn't support scalar parameters. "
+                f"Change {param_name} to a 1D tensor with numel equal to 1."
+            )
+
+
+@no_type_check
+def _init_param_handle_from_module(
+    state: _FSDPState,
+    fully_sharded_module: nn.Module,
+    device_id: Optional[Union[int, torch.device]],
+    param_init_fn: Optional[Callable[[nn.Module], None]],
+    sync_module_states: bool,
+) -> _FSDPState:
+    """Initialize a ``FlatParamHandle`` from a module ``fully_sharded_module``."""
+    _check_single_device_module(fully_sharded_module, state._ignored_params, device_id)
+    device_from_device_id = _get_device_from_device_id(
+        device_id, state.rank, state._device_handle
+    )
+    is_meta_module, is_torchdistX_deferred_init = _need_to_materialize_module(
+        fully_sharded_module, state._ignored_params, state._ignored_modules
+    )
+    # Materialize the module if needed
+    if (is_meta_module or is_torchdistX_deferred_init) and param_init_fn is not None:
+        _materialize_with_param_init_fn(
+            fully_sharded_module, param_init_fn, state._ignored_modules
+        )
+    elif is_meta_module:
+        _materialize_meta_module(
+            fully_sharded_module,
+            device_id,
+            state._ignored_modules,
+            state._device_handle,
+        )
+    elif is_torchdistX_deferred_init:
+        deferred_init.materialize_module(
+            fully_sharded_module,
+            check_fn=lambda submodule: _get_module_fsdp_state(submodule) is None
+            and submodule not in state._ignored_modules,
+        )
+
+    ignored_buffers = {
+        buffer
+        for ignored_module in state._ignored_modules
+        for buffer in ignored_module.buffers()
+    }
+
+    _move_module_to_device(
+        fully_sharded_module,
+        state._ignored_params,
+        ignored_buffers,
+        device_from_device_id,
+    )
+    state.compute_device = _get_compute_device(
+        fully_sharded_module,
+        state._ignored_params,
+        device_from_device_id,
+        state.rank,
+        state._device_handle,
+    )
+
+    managed_params = list(_get_orig_params(fully_sharded_module, state._ignored_params))
+    _verify_managed_params(fully_sharded_module, managed_params)
+    if sync_module_states:
+        _sync_module_params_and_buffers(
+            fully_sharded_module, managed_params, state.process_group
+        )
+        if state.sharding_strategy in HYBRID_SHARDING_STRATEGIES:
+            _sync_module_params_and_buffers(
+                fully_sharded_module, managed_params, state._inter_node_pg
+            )
+    _init_param_handle_from_params(state, managed_params, fully_sharded_module)
+    return state
+
+
+@no_type_check
+def _init_param_handle_from_params(
+    state: _FSDPState,
+    params: list[nn.Parameter],
+    fully_sharded_module: nn.Module,
+):
+    if len(params) == 0:
+        return
+    handle = FlatParamHandle(
+        params,
+        fully_sharded_module,
+        state.compute_device,
+        SHARDING_STRATEGY_MAP[state.sharding_strategy],
+        state.cpu_offload.offload_params,
+        state.mixed_precision.param_dtype,
+        state.mixed_precision.reduce_dtype,
+        state.mixed_precision.keep_low_precision_grads,
+        state.process_group,
+        state._use_orig_params,
+        fsdp_extension=state._fsdp_extension,
+    )
+    handle.shard()
+    assert not state._handle
+    state.params.append(handle.flat_param)
+    state._handle = handle
+    state._fully_sharded_module_to_handle[handle._fully_sharded_module] = handle
+    cpu_device = torch.device("cpu")
+    if state.cpu_offload.offload_params and handle.flat_param.device != cpu_device:
+        handle.flat_param_to(cpu_device)
+
+
+def _get_ignored_modules(
+    root_module: nn.Module,
+    _ignored_modules: Optional[Iterable[torch.nn.Module]],
+) -> set[nn.Module]:
+    """
+    Check that ``_ignored_modules`` is an iterable of ``nn.Module`` s without any FSDP instances.
+
+    Return the modules contained in their module
+    subtrees as a :class:`set`. Nested FSDP instances are excluded, but their
+    already-computed ignored modules are included.
+
+    ``_ignored_modules`` represents the argument passed by the user to FSDP.
+    """
+    msg_prefix = "`ignored_modules` should be an iterable of `torch.nn.Module`s "
+    try:
+        ignored_root_modules = (
+            set(_ignored_modules) if _ignored_modules is not None else set()
+        )
+    except TypeError as e:
+        raise TypeError(msg_prefix + f"but got {type(_ignored_modules)}") from e
+    for module in ignored_root_modules:
+        if not isinstance(module, torch.nn.Module):
+            raise TypeError(msg_prefix + f"but got an iterable with {type(module)}")
+        if _get_module_fsdp_state(module):
+            # TODO: We may relax this by taking the FSDP instance's wrapped
+            # module to provide more flexibility to the user.
+            raise ValueError("`ignored_modules` should not include FSDP modules")
+    # Treat modules that cannot compose with `fully_shard` as ignored modules,
+    # meaning that their subtrees are ignored
+    for module in root_module.modules():
+        if not traversal_utils._composable(module):
+            ignored_root_modules.add(module)
+    # NOTE: Even if `ignored_root_modules` is empty, do not return early so
+    # that this FSDP instance can get any ignored modules from its children.
+
+    # Include child modules and exclude nested FSDP modules themselves
+    ignored_modules = {
+        child
+        for module in ignored_root_modules
+        for child in module.modules()
+        if not isinstance(child, fsdp_file.FullyShardedDataParallel)
+    }
+    if root_module in ignored_modules:
+        warnings.warn(
+            "Trying to ignore the top-level module passed into the FSDP "
+            "constructor itself will result in all parameters being "
+            f"ignored and is not well-supported: {module}"
+        )
+    # Include nested FSDP modules' ignored modules
+    for submodule in root_module.modules():
+        optional_fsdp_state = _get_module_fsdp_state(submodule)
+        if optional_fsdp_state is not None:
+            assert hasattr(optional_fsdp_state, "_ignored_modules")
+            ignored_modules.update(optional_fsdp_state._ignored_modules)
+    return ignored_modules
+
+
+def _get_ignored_params(
+    root_module: torch.nn.Module,
+    ignored_modules: set[torch.nn.Module],
+    ignored_parameters: Optional[Iterable[torch.nn.Parameter]] = None,
+) -> set[torch.nn.Parameter]:
+    """
+    Return the parameters of the modules in ``ignored_modules`` and the parameters in ``ignored_parameters``.
+
+    :class:`FlatParameter` s are excluded from the result.
+    """
+    all_ignored_params: set[torch.nn.Parameter] = set()
+
+    params_in_ignored_modules = {
+        p for m in ignored_modules for p in m.parameters() if not _is_fsdp_flattened(p)
+    }
+
+    all_ignored_params.update(params_in_ignored_modules)
+
+    if ignored_parameters is not None:
+        params_in_ignored_parameters = {
+            p for p in ignored_parameters if not _is_fsdp_flattened(p)
+        }
+        all_ignored_params.update(params_in_ignored_parameters)
+
+    # Always include nested FSDP modules' ignored parameters
+    for submodule in root_module.modules():
+        optional_fsdp_state = _get_module_fsdp_state(submodule)
+        if optional_fsdp_state is not None:
+            assert hasattr(optional_fsdp_state, "_ignored_params")
+            all_ignored_params.update(optional_fsdp_state._ignored_params)
+
+    return all_ignored_params
+
+
+def _get_ignored_buffer_names(
+    root_module: torch.nn.Module,
+    ignored_modules: set[torch.nn.Module],
+) -> set[str]:
+    """Return the cleaned buffer FQNs in ``ignored_modules``."""
+    all_ignored_buffer_names: set[str] = set()
+
+    buffers_in_ignored_modules = {
+        buffer for m in ignored_modules for buffer in m.buffers()
+    }
+
+    all_ignored_buffer_names.update(
+        {
+            clean_tensor_name(buffer_name)
+            for buffer_name, buffer in root_module.named_buffers()
+            if buffer in buffers_in_ignored_modules
+        }
+    )
+
+    # Always include nested FSDP modules' ignored buffer names
+    for submodule in root_module.modules():
+        optional_fsdp_state = _get_module_fsdp_state(submodule)
+        if optional_fsdp_state is not None:
+            assert hasattr(optional_fsdp_state, "_ignored_buffer_names")
+            all_ignored_buffer_names.update(optional_fsdp_state._ignored_buffer_names)
+
+    return all_ignored_buffer_names
+
+
+def _get_buffer_names(root_module: nn.Module) -> set[str]:
+    """Return the fully prefixed names of all buffers in the module hierarchy rooted at ``root_module`` as a class:`set`."""
+    return {
+        clean_tensor_name(buffer_name) for buffer_name, _ in root_module.named_buffers()
+    }
+
+
+def _check_single_device_module(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    device_id: Optional[Union[int, torch.device]],
+) -> None:
+    """
+    Raise an error if ``module`` has original parameters on multiple devices, ignoring the parameters in ``ignored_params``.
+
+    Thus, after this method, the
+    module must be either fully on the CPU or fully on a non-CPU device.
+    """
+    devices = {param.device for param in _get_orig_params(module, ignored_params)}
+    # We allow module to be partially on CPU and partially on GPU if device_id is not
+    # None, since the device_id arg will result in the CPU portion being moved to
+    # GPU. This is useful in cases where part of the module may be parallelized
+    # by another algorithm and may already be on GPU. We'd like to enforce device_id
+    # to not be None, otherwise we'd flatten parameters in a mixed module which is
+    # not supported.
+    if len(devices) == 2 and torch.device("cpu") in devices:
+        if device_id is None:
+            raise RuntimeError(
+                "To support a module with both CPU and GPU params, "
+                "please pass in device_id argument."
+            )
+    elif len(devices) > 1:
+        raise RuntimeError(
+            f"FSDP only supports single device modules but got params on {devices}"
+        )
+
+
+def _get_device_from_device_id(
+    device_id: Optional[Union[int, torch.device]],
+    rank: int,
+    device_handle: _FSDPDeviceHandle,
+) -> Optional[torch.device]:
+    """
+    Return a ``torch.device`` for the specified ``device_id``.
+
+    Processes ``device_id`` and returns either the corresponding device or
+    ``None`` if ``device_id`` is ``None``.
+    """
+    if device_id is None:
+        return None
+    device = (
+        device_id if isinstance(device_id, torch.device) else torch.device(device_id)
+    )
+    if device.type != "cpu" and device.index is None:
+        warnings.warn(
+            f"FSDP got the argument `device_id` {device_id} on rank "
+            f"{rank}, which does not have an explicit index. "
+            f"FSDP will use the current device {device_handle.current_device()}. "
+            f"If this is incorrect, please explicitly call `torch.{device.type}.set_device()` "
+            "before FSDP initialization or pass in the explicit device "
+            "index as the `device_id` argument."
+        )
+        device = torch.device(device_handle.current_device())
+    return device
+
+
+def _need_to_materialize_module(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    ignored_modules: set[nn.Module],
+) -> tuple[bool, bool]:
+    """
+    Return if ``module`` has parameters on meta device and if ``module`` is using torchdistX deferred initialization.
+
+    At most of the returned bools can
+    be ``True``. If either is ``True``, then ``module`` needs to be
+    materialized.
+    """
+    managed_params = list(_get_orig_params(module, ignored_params))
+    is_meta_module = any(param.is_meta for param in managed_params)
+    # TODO: We need to establish a contract for FSDP and buffers. For now, we
+    # skip checking for meta buffers from ignored modules. We should consider
+    # refactoring the initialization holistically to avoid so many traversals.
+    for submodule in module.modules():
+        if submodule in ignored_modules:
+            continue
+        for buf in submodule.buffers(recurse=False):
+            is_meta_module |= buf.is_meta
+    is_torchdistX_deferred_init = (
+        not is_meta_module
+        and _TORCHDISTX_AVAIL
+        and any(fake.is_fake(param) for param in managed_params)
+    )
+    return is_meta_module, is_torchdistX_deferred_init
+
+
+def _materialize_with_param_init_fn(
+    root_module: nn.Module,
+    param_init_fn: Callable[[nn.Module], None],
+    ignored_modules: set[nn.Module],
+) -> None:
+    if not callable(param_init_fn):
+        raise ValueError(
+            f"Expected {param_init_fn} to be callable but got {type(param_init_fn)}"
+        )
+    modules_to_materialize = _get_modules_to_materialize(root_module, ignored_modules)
+    for module in modules_to_materialize:
+        param_init_fn(module)
+
+
+def _materialize_meta_module(
+    root_module: nn.Module,
+    device_from_device_id: Optional[torch.device],
+    ignored_modules: set[nn.Module],
+    device_handle: _FSDPDeviceHandle,
+):
+    # Run default meta device initialization
+    materialization_device = device_from_device_id or torch.device(
+        device_handle.current_device()
+    )
+    modules_to_materialize = _get_modules_to_materialize(root_module, ignored_modules)
+    module = None
+    try:
+        # Assume that each module's `reset_parameters()` only initializes its
+        # own parameters and not those of its children
+        with torch.no_grad():
+            for module in modules_to_materialize:
+                # As a contract to the user, only call `reset_parameters()` if
+                # the module has directly managed parameters/buffers
+                module_state_iter = itertools.chain(
+                    module.parameters(recurse=False), module.buffers(recurse=False)
+                )
+                has_module_states = len(list(module_state_iter)) > 0
+                if has_module_states:
+                    module.to_empty(device=materialization_device, recurse=False)
+                    module.reset_parameters()  # type: ignore[operator]
+    except BaseException as e:
+        warnings.warn(
+            "Unable to call `reset_parameters()` for module on meta "
+            f"device with error {str(e)}. Please ensure that your module of"
+            f"type {type(module)} implements a `reset_parameters()` method."  # type: ignore[possibly-undefined]
+        )
+        raise e
+
+
+def _get_modules_to_materialize(
+    root_module: nn.Module, ignored_modules: set[nn.Module]
+) -> list[nn.Module]:
+    # Run BFS to collect the modules to materialize via `reset_parameters()`,
+    # stopping at any module with FSDP already applied or at ignored modules.
+    modules_to_materialize: list[nn.Module] = []
+    queue = collections.deque([root_module])
+    visited_modules: set[nn.Module] = {root_module}
+    while queue:
+        module = queue.popleft()
+        modules_to_materialize.append(module)
+        for child_module in module.children():
+            if (
+                child_module not in visited_modules
+                and _get_module_fsdp_state(child_module) is None
+                and child_module not in ignored_modules
+            ):
+                visited_modules.add(child_module)
+                queue.append(child_module)
+    return modules_to_materialize
+
+
+def _move_module_to_device(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    ignored_buffers: set[torch.Tensor],
+    device_from_device_id: Optional[torch.device],
+) -> None:
+    """
+    Move ``module`` depending on ``device_from_device_id`` and its current device.
+
+    This includes moving ignored modules' parameters.
+
+    - If ``device_from_device_id`` is not ``None``, then this moves
+    ``module`` to the device.
+    - If ``device_from_device_id`` is ``None``, then this does not move
+    ``module`` but warns the user if it is on CPU.
+
+    Precondition: ``_check_single_device_module()``.
+    """
+    cpu_device = torch.device("cpu")
+    if device_from_device_id is not None:
+        # BFS from `module` without traversing any nested FSDP instances to
+        # collect the parameters/buffers that have not yet been managed
+        queue: collections.deque[nn.Module] = collections.deque()
+        queue.append(module)
+        params: list[nn.Parameter] = []
+        buffers: list[torch.Tensor] = []
+        while queue:
+            curr_module = queue.popleft()
+            # NOTE: We include a check to only move parameters/buffers that are
+            # on CPU device. If they are on a CUDA device different from the
+            # one specified by `device_id`, then this does NOT move them. This
+            # is so that we can raise an error in `_get_compute_device()`.
+            params.extend(
+                param
+                for param in curr_module.parameters(recurse=False)
+                if param.device == cpu_device
+            )
+            buffers.extend(
+                buffer
+                for buffer in curr_module.buffers(recurse=False)
+                if buffer.device == cpu_device
+            )
+            for submodule in curr_module.children():
+                if not isinstance(submodule, fsdp_file.FullyShardedDataParallel):
+                    queue.append(submodule)
+        params_to_move = [p for p in params if p not in ignored_params]
+        bufs_to_move = [p for p in buffers if p not in ignored_buffers]
+        _move_states_to_device(params_to_move, bufs_to_move, device_from_device_id)
+        return
+    param = next(_get_orig_params(module, ignored_params), None)
+    if param is not None and param.device == cpu_device:
+        _warn_cpu_init()
+
+
+def _move_states_to_device(
+    params: list[nn.Parameter],
+    buffers: list[torch.Tensor],
+    device_from_device_id: Optional[torch.device],
+) -> None:
+    """
+    Move states to the specified device.
+
+    Precondition: ``_check_single_device_module()`` and module's parameters and
+    buffers have been materialized if needed.
+    """
+    if len(params) == 0 and len(buffers) == 0:
+        return
+    if len(params) > 0:
+        current_device = params[0].device
+    elif len(buffers) > 0:
+        current_device = buffers[0].device
+    cpu_device = torch.device("cpu")
+    if device_from_device_id is not None:
+        # Move the parameters and buffers like the `.data` code path in
+        # `nn.Module._apply()`, which underlies `nn.Module.to()`
+        for param in params:
+            with torch.no_grad():
+                param.data = param.to(device_from_device_id)
+                if param.grad is not None:
+                    param.grad.data = param.grad.to(device_from_device_id)
+        for buffer in buffers:
+            buffer.data = buffer.to(device_from_device_id)
+    elif current_device == cpu_device:  # type: ignore[possibly-undefined]
+        _warn_cpu_init()
+
+
+def _warn_cpu_init():
+    warnings.warn(
+        "The passed-in `module` is on CPU and will thus have FSDP's sharding "
+        "initialization run on CPU, which may be slower than on GPU. We "
+        "recommend passing in the `device_id` argument for FSDP to move "
+        "`module` to GPU for the sharding initialization. `module` must also "
+        "be on GPU device to work with the `sync_module_states=True` flag "
+        "since that requires GPU communication."
+    )
+
+
+def _get_compute_device(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    device_from_device_id: Optional[torch.device],
+    rank: int,
+    device_handle: _FSDPDeviceHandle,
+) -> torch.device:
+    """
+    Determine and return this FSDP instance's compute device.
+
+    If the module is already on a non-CPU device, then the compute device is that non-CPU
+    device. If the module is on CPU, then the compute device is the current
+    device.
+
+    Since this method should be called after materializing the module, any
+    non-CPU device should not be meta device. For now, the compute device is
+    always a CUDA or CUDA-like device with its explicit index.
+
+    Precondition: ``_check_single_device_module()`` and
+    ``_move_module_to_device()``.
+    """
+    param = next(_get_orig_params(module, ignored_params), None)
+    if param is not None and param.device.type != "cpu":
+        compute_device = param.device  # Determined by model param placement
+    else:
+        compute_device = torch.device(device_handle.current_device())
+    if device_from_device_id is not None and compute_device != device_from_device_id:
+        raise ValueError(
+            f"Inconsistent compute device and `device_id` on rank {rank}: "
+            f"{compute_device} vs {device_from_device_id}"
+        )
+    return compute_device
+
+
+# TODO: See how to deprecate!
+def _sync_module_params_and_buffers(
+    module: nn.Module,
+    params: list[nn.Parameter],
+    process_group: dist.ProcessGroup,
+) -> None:
+    """
+    Synchronize module states (i.e. parameters ``params`` and all not-yet-synced buffers) by broadcasting from rank 0 to all ranks.
+
+    Precondition: ``sync_module_states == True`` and ``self.process_group`` has
+    been set.
+    """
+    module_states: list[torch.Tensor] = []
+    for buffer in module.buffers():
+        # Avoid re-synchronizing buffers in case of nested wrapping
+        if not getattr(buffer, FSDP_SYNCED, False):
+            setattr(buffer, FSDP_SYNCED, True)
+            detached_buffer = buffer.detach()
+            if is_traceable_wrapper_subclass(detached_buffer):
+                # NOTE: Here we assume no nested subclasses, at most one level of subclass
+                # in both model's buffers and params
+                attrs, _ = detached_buffer.__tensor_flatten__()  # type: ignore[attr-defined]
+                inner_buffers = [getattr(detached_buffer, attr) for attr in attrs]
+                module_states.extend(inner_buffers)
+            else:
+                module_states.append(detached_buffer)
+
+    for param in params:
+        detached_param = param.detach()
+        if is_traceable_wrapper_subclass(detached_param):
+            attrs, _ = detached_param.__tensor_flatten__()  # type: ignore[attr-defined]
+            inner_params = [getattr(detached_param, attr) for attr in attrs]
+            module_states.extend(inner_params)
+        else:
+            module_states.append(detached_param)
+
+    _check_module_states_for_sync_module_states(module_states)
+    _sync_params_and_buffers(
+        process_group,
+        module_states,
+        PARAM_BROADCAST_BUCKET_SIZE,
+        src=0,
+    )
+
+
+def _check_module_states_for_sync_module_states(
+    module_states: list[torch.Tensor],
+) -> None:
+    if module_states and any(
+        tensor.device == torch.device("cpu") for tensor in module_states
+    ):
+        raise ValueError(
+            "The module has CPU parameters or buffers when `sync_module_states=True`, "
+            "which requires them to be on GPU. Please specify the `device_id` argument "
+            "or move the module to GPU before passing it to FSDP."
+        )
+
+
+def _get_orig_params(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+) -> Iterator[nn.Parameter]:
+    """
+    Return an iterator over the original parameters in ``module``.
+
+    The iterator does not return
+    the parameters in ``ignored_params``, any ``FlatParameter`` s (which may be
+    present due to nested FSDP wrapping), or any original parameters already
+    flattened (only relevant when ``use_orig_params=True``).
+    """
+    param_gen = module.parameters()
+    try:
+        while True:
+            param = next(param_gen)
+            if param not in ignored_params and not _is_fsdp_flattened(param):
+                yield param
+    except StopIteration:
+        pass
+
+
+def _check_orig_params_flattened(
+    fsdp_module,
+    ignored_params: set[nn.Parameter],
+) -> None:
+    """
+    Check that original parameters in ``fsdp_module`` have been flattened.
+
+    The flattened parameters are made
+    invisible to ``named_parameters()`` for the module hierarchy rooted at
+    ``fsdp_module``. This should be called as a sanity check after flattening
+    the wrapped module's parameters.
+    """
+    for param_name, param in _named_parameters_with_duplicates(fsdp_module):
+        if param not in ignored_params and not _is_fsdp_flattened(param):
+            raise RuntimeError(
+                f"Found an unflattened parameter: {param_name}; "
+                f"{param.size()} {param.__class__}"
+            )
+
+
+def _get_default_comm_hook(sharding_strategy: ShardingStrategy):
+    return (
+        default_hooks.allreduce_hook
+        if sharding_strategy == ShardingStrategy.NO_SHARD
+        else default_hooks.reduce_scatter_hook
+    )
+
+
+def _get_default_comm_hook_state(
+    process_group: dist.ProcessGroup,
+) -> default_hooks.DefaultState:
+    return default_hooks.DefaultState(process_group=process_group)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_limiter_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_limiter_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9b190585342ee267716abace19add022b4d6b3e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_limiter_utils.py
@@ -0,0 +1,33 @@
+import collections
+from typing import Optional
+
+import torch
+
+
+class _FreeEventQueue:
+    """
+    This tracks all pending frees corresponding to inflight all-gathers. The
+    queueing pattern is iterative enqueues with a single dequeue per iteration
+    once the limit ``_max_num_inflight_all_gathers`` is reached.
+    """
+
+    def __init__(self) -> None:
+        self._queue: collections.deque[torch.Event] = collections.deque()
+        self._max_num_inflight_all_gathers = 2  # empirically chosen
+
+    def enqueue(self, free_event: torch.Event) -> None:
+        """Enqueues a free event."""
+        self._queue.append(free_event)
+
+    def dequeue_if_needed(self) -> Optional[torch.Event]:
+        """Dequeues a single event if the limit is reached."""
+        if len(self._queue) >= self._max_num_inflight_all_gathers:
+            return self._dequeue()
+        return None
+
+    def _dequeue(self) -> Optional[torch.Event]:
+        """Dequeues a free event if possible."""
+        if self._queue:
+            event = self._queue.popleft()
+            return event
+        return None
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..de33ed8ef3f2699f05e9df736d4367eb17724359
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py
@@ -0,0 +1,2073 @@
+# mypy: allow-untyped-defs
+import copy
+import functools
+import logging
+import warnings
+from collections.abc import Iterable, Iterator, Sequence
+from contextlib import ExitStack
+from dataclasses import dataclass, field
+from itertools import chain
+from typing import Any, cast, NamedTuple, no_type_check, Optional, TYPE_CHECKING, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+from torch.distributed._state_dict_utils import _gather_state_dict
+from torch.distributed.distributed_c10d import _get_pg_default_device
+from torch.distributed.fsdp._common_utils import (
+    _apply_to_modules,
+    _FSDPState,
+    _get_module_fsdp_state_if_fully_sharded_module,
+    _get_param_to_fqns,
+    _module_handle,
+    _named_parameters_with_duplicates,
+    clean_tensor_name,
+)
+from torch.distributed.fsdp._debug_utils import SimpleProfiler
+from torch.distributed.fsdp._flat_param import FlatParameter, FlatParamHandle
+from torch.distributed.fsdp._fsdp_extensions import (
+    _ext_chunk_dtensor,
+    _ext_chunk_tensor,
+)
+from torch.distributed.fsdp._runtime_utils import (
+    _lazy_init,
+    _reset_flat_param_grad_info_if_needed,
+)
+from torch.distributed.fsdp.api import (
+    ShardingStrategy,
+    StateDictSettings,
+    StateDictType,
+)
+from torch.distributed.tensor import DTensor, Replicate
+from torch.utils._pytree import tree_map_only
+
+
+if TYPE_CHECKING:
+    from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class FSDPParamInfo:
+    state: _FSDPState
+    handle: FlatParamHandle
+    param_indices: dict[str, int]
+    param_requires_grad: list[bool]
+
+
+def sorted_items(dictionary: dict[str, Any]) -> Iterator[tuple[str, Any]]:
+    keys = sorted(dictionary.keys())
+    for k in keys:
+        yield k, dictionary[k]
+
+
+@dataclass
+class _ConsolidatedOptimState:
+    """
+    This holds the consolidated optimizer state on the target rank. Positive-
+    dimension tensor state is communicated across ranks, while zero-dimension
+    tensor state and non-tensor state is taken directly from the target rank.
+
+    PyTorch version 1.12 moved to using zero-dimension tensors for scalar
+    values, but user implemented optimizers may still use float (i.e. a
+    non-tensor). Thus, we support both and handle them identically.
+
+    Attributes:
+        tensor_state (Dict[str, torch.Tensor]): Mapping from positive-dimension
+            tensor state name to the unsharded flat tensor representing the
+            state.
+        zero_dim_tensor_state (Dict[str, torch.Tensor]): Mapping from zero-
+            dimension tensor state name to its value.
+        non_tensor_state (Dict[str, Any]): Mapping from non-tensor state
+            name to its value.
+    """
+
+    tensor_state: dict[str, torch.Tensor] = field(default_factory=dict)
+    zero_dim_tensor_state: dict[str, torch.Tensor] = field(default_factory=dict)
+    non_tensor_state: dict[str, Any] = field(default_factory=dict)
+
+
+class _PosDimTensorInfo(NamedTuple):
+    """
+    Metadata for positive-dimension tensors used internally for
+    :meth:`scatter_full_optim_state_dict`.
+
+    Attributes:
+        shape (torch.Size): Sharded tensor shape (which is equal to the
+            unsharded tensor shape if the tensor is optimizer state for a
+            non-FSDP parameter and is hence not sharded).
+        dtype (torch.dtype): Data type of the tensor.
+    """
+
+    shape: torch.Size
+    dtype: torch.dtype
+
+
+class _OptimStateKey(NamedTuple):
+    """
+    This represents an optimizer state key that may be used commonly across
+    ranks. It is based on the unflattened parameter names rather than parameter
+    IDs to make it independent of each rank's own optimizer construction.
+    """
+
+    unflat_param_names: tuple[str, ...]
+    is_fsdp_managed: bool
+
+
+def _unflatten_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    flat_param_state: dict[str, Any],
+    to_save: bool,
+    shard_state: bool,
+    cpu_offload: bool,
+) -> list[dict[str, Any]]:
+    """
+    Unflattens the optimizer state, consisting of the "state" part and the
+    "param_groups" part. Unflattening the "state" part involves consolidating
+    the state on the target rank and remapping from flattened to unflattened
+    parameter IDs, and the "param_groups" part only involves remapping from
+    flattened to unflattened parameter IDs.
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        flat_param_state (Dict[str, Any]): Entry for the flat parameter in the
+            "state" part of the optimizer state dict.
+        to_save (bool): Whether to save the state on this rank.
+
+    Returns:
+        List[Dict[str, Any]]: A :class:`list` holding the entries in the
+        "state" part of the optimizer state dict corresponding to the
+        unflattened parameters comprising the flat parameter if on the target
+        rank or an empty :class:`list` otherwise. The final optimizer state
+        dict will need to map these entries using the proper unflattened
+        parameter IDs.
+    """
+    assert not shard_state or to_save, (
+        "If ``shard_state`` is True, ``to_save`` has to be True."
+    )
+    consolidated_state = _communicate_optim_state(
+        fsdp_param_info,
+        flat_param_state,
+    )
+    if to_save:
+        unflat_param_state = _unflatten_communicated_optim_state(
+            fsdp_param_info,
+            consolidated_state,
+            shard_state,
+        )
+        for optim_state in unflat_param_state:
+            # We can't use .items() below cuz we'd run into a concurrent modification error
+            if cpu_offload:
+                for key in list(optim_state.keys()):
+                    state = optim_state[key]
+                    if not isinstance(state, torch.Tensor):
+                        continue
+                    optim_state[key] = state.cpu()
+        return unflat_param_state
+    else:
+        return []
+
+
+def _is_zero_dim_tensor(x: Any) -> bool:
+    return torch.is_tensor(x) and x.dim() == 0
+
+
+def _communicate_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    flat_param_state: dict[str, Any],
+) -> _ConsolidatedOptimState:
+    """
+    Communicates the optimizer state for a flat parameter across ranks. All
+    ranks will hold the entire non-sharded optimizer state on GPU.
+
+    If ``N`` is the number of tensor optimizer states in the optimizer state
+    dict, then the communication complexity is 0 if ``N = 0`` and ``N + 1``
+    otherwise (where the plus 1 comes from all-gathering the padding per rank).
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        flat_param_state (Dict[str, Any]): The entry in the "state" part of the
+            optimizer state dict corresponding to the flat parameter.
+
+    Returns:
+        ConsolidatedOptimState: Consolidated optimizer state for the target
+        flat parameter.
+    """
+    fsdp_state = fsdp_param_info.state
+    flat_param = fsdp_param_info.handle.flat_param
+    state = _ConsolidatedOptimState()
+    tensor_state, zero_dim_tensor_state, non_tensor_state = (
+        state.tensor_state,
+        state.zero_dim_tensor_state,
+        state.non_tensor_state,
+    )
+
+    for state_name, value in sorted_items(flat_param_state):
+        # Positive-dimension tensor state: communicate across ranks
+        if torch.is_tensor(value) and value.dim() > 0:
+            # If the parameter is not sharded, then neither is the
+            # positive-dimension tensor state, so no need to communicate it --
+            # we take the target rank's value
+            if (
+                fsdp_state.world_size == 1
+                or fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD
+            ):
+                tensor_state[state_name] = value
+                continue
+            assert fsdp_state.compute_device is not None, (
+                "compute_device has not been initialized"
+            )
+            if value.device.type != fsdp_state.compute_device.type:
+                value = value.to(fsdp_state.compute_device)
+            # Assume that positive-dimension tensor optimizer state
+            # has the same shape as the sharded flat parameter
+            buffer_size = flat_param._full_param_padded.size()  # type: ignore[attr-defined]
+            tensor_buffer = value.new_zeros(*buffer_size)
+            dist.all_gather_into_tensor(
+                tensor_buffer, value, group=fsdp_state.process_group
+            )
+            fsdp_state._device_handle.synchronize()
+            unpadded_numel = cast(
+                nn.Parameter, flat_param._unpadded_unsharded_size
+            ).numel()
+            tensor_state[state_name] = tensor_buffer[:unpadded_numel]
+        # Zero-dimension tensor state and non-tensor state: take this rank's
+        # value directly
+        else:
+            if _is_zero_dim_tensor(value):
+                zero_dim_tensor_state[state_name] = value.detach().clone()
+            else:
+                non_tensor_state[state_name] = value
+    return state
+
+
+def _unflatten_communicated_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    state: _ConsolidatedOptimState,
+    shard_state: bool,
+) -> list[dict[str, Any]]:
+    """
+    Unflattens the communicated optimizer state (given by ``tensor_state``,
+    ``non_tensor_state``, and ``zero_dim_tensor_state``) for a single flat
+    parameter. This should only be called on the target rank.
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        state (_ConsolidatedOptimState): Consolidated optimizer state.
+
+    Returns:
+        List[Dict[str, Any]]: A :class:`list` holding the entries in the
+        "state" part of the optimizer state dict corresponding to the
+        unflattened parameters comprising the flat parameter. The final
+        optimizer state dict will need to map these entries using the proper
+        unflattened parameter IDs.
+    """
+    fsdp_state = fsdp_param_info.state
+    handle = fsdp_param_info.handle
+    flat_param = handle.flat_param
+    unflat_param_state: list[dict[str, Any]] = []
+    flat_param_views: dict[str, Iterator] = {}
+    num_unflat_params = flat_param._num_params
+    tensor_state, zero_dim_tensor_state, non_tensor_state = (
+        state.tensor_state,
+        state.zero_dim_tensor_state,
+        state.non_tensor_state,
+    )
+
+    for _ in range(num_unflat_params):
+        unflat_state_param = {}
+        # Add positive-dimension tensor state: unflatten with views
+        for state_name, flat_tensor in sorted_items(tensor_state):
+            views_generated = state_name in flat_param_views
+            if not views_generated:
+                views = handle._get_unflat_views(flat_tensor)
+                flat_param_views[state_name] = views
+            else:
+                views = flat_param_views[state_name]
+            optim_state: Union[torch.Tensor, ShardedTensor, DTensor] = next(views)
+            if shard_state:
+                osd_config = fsdp_state._optim_state_dict_config
+                if getattr(osd_config, "_use_dtensor", False):
+                    assert fsdp_state._device_mesh is not None
+                    optim_state = _ext_chunk_dtensor(
+                        optim_state,
+                        fsdp_state.rank,
+                        fsdp_state._device_mesh,
+                        fsdp_state._fsdp_extension,
+                    )
+                else:
+                    assert fsdp_state.process_group is not None
+                    optim_state = _ext_chunk_tensor(
+                        optim_state,
+                        fsdp_state.rank,
+                        fsdp_state.world_size,
+                        fsdp_state._device_handle.device_count(),
+                        fsdp_state.process_group,
+                        fsdp_state._fsdp_extension,
+                    )
+            unflat_state_param[state_name] = optim_state
+
+        # Add zero-dimension tensor state: take the target rank's value
+        for state_name, zero_dim_tensor in sorted_items(zero_dim_tensor_state):
+            unflat_state_param[state_name] = zero_dim_tensor
+        # Add non-tensor state: take the target rank's value
+        for state_name, non_tensor in sorted_items(non_tensor_state):
+            unflat_state_param[state_name] = non_tensor
+        unflat_param_state.append(unflat_state_param)
+    return unflat_param_state
+
+
+def _broadcast_processed_state(
+    fsdp_state: _FSDPState,
+    optim_state: dict[str, Any],
+    group: Optional[dist.ProcessGroup],
+) -> dict[str, Any]:
+    objects: list[Any] = [None]
+    if dist.get_rank(group) == 0:
+        objects[0] = tree_map_only(
+            torch.Tensor,
+            lambda v: v.cpu() if v.dim() == 0 else _PosDimTensorInfo(v.shape, v.dtype),  # type: ignore[union-attr]
+            optim_state,
+        )
+    dist.broadcast_object_list(objects, src=0, group=group)
+    if dist.get_rank(group) == 0:
+        return optim_state
+    else:
+        return objects[0]
+
+
+def _broadcast_state(
+    fsdp_state: _FSDPState, state: Any, group: Optional[dist.ProcessGroup]
+) -> Any:
+    if dist.get_rank(group) == 0:
+        if not isinstance(state, torch.Tensor) or state.dim() == 0:
+            return state
+        tensor = state.to(fsdp_state.compute_device)
+    else:
+        if isinstance(state, torch.Tensor):
+            assert state.dim() == 0, (
+                "For non-zero ranks, a tensor state should have zero dimension, "
+                "but got the state with shape {state.shape()}."
+            )
+            return state
+        elif not isinstance(state, _PosDimTensorInfo):
+            return state
+        tensor = torch.zeros(
+            state.shape, dtype=state.dtype, device=fsdp_state.compute_device
+        )
+    dist.broadcast(tensor, src=0, group=group)
+    return tensor
+
+
+def _shard_orig_param_state(
+    fsdp_param_info: FSDPParamInfo,
+    fqn: str,
+    optim_state: dict[str, Any],
+) -> dict[str, Any]:
+    """
+    Shard the optimizer state for the original parameter with the name ``fqn``.
+    This API should only be used when ``use_orig_params`` is True.
+    """
+    if not optim_state:
+        return {}
+    fsdp_state = fsdp_param_info.state
+    flat_param = fsdp_param_info.handle.flat_param
+    param_idx = fsdp_param_info.param_indices[fqn]
+    shard_param_info = flat_param._shard_param_infos[param_idx]  # type: ignore[attr-defined]
+    optim_state = _gather_state_dict(
+        optim_state, pg=fsdp_state.process_group, device=fsdp_state.compute_device
+    )
+    if not shard_param_info.in_shard:
+        return {}
+    # Flatten and shard the state.
+    new_optim_state: dict[str, Any] = {}
+    intra_param_start_idx = shard_param_info.intra_param_start_idx
+    intra_param_end_idx = shard_param_info.intra_param_end_idx
+    for state_name, value in optim_state.items():
+        if (
+            torch.is_tensor(value)
+            and value.dim() > 0
+            and fsdp_state.sharding_strategy != ShardingStrategy.NO_SHARD
+        ):
+            value = value.flatten()[
+                intra_param_start_idx : intra_param_end_idx  # type: ignore[operator]
+                + 1
+            ].clone()
+        new_optim_state[state_name] = value
+    return new_optim_state
+
+
+def _flatten_optim_state_dict(
+    optim_state_dict: dict[str, Any],
+    model: nn.Module,
+    use_orig_params: bool = False,
+    optim: Optional[torch.optim.Optimizer] = None,
+    rank0_only: bool = False,
+    group: Optional[dist.ProcessGroup] = None,
+) -> dict[str, Any]:
+    """
+    Flattens the full optimizer state dict, still keying by unflattened parameter
+    names.
+
+    If ``use_orig_params`` is True, each rank will have all FSDP-managed
+    parameters but some of these parameters may be empty due to the sharding.
+    For a regular optim.Optimizer, states for those empty parameters will
+    not be initialized. So, when aggregating the FQNs across ranks, no assert
+    will be raised on a rank even if it does not have all the states -- it is
+    valid and FSDP know how to aggregate them. However, FSDP has to ignore
+    handling those parameters that are not managed by FSDP and do not exist on
+    the local rank -- it is managed by other parallelism and FSDP does not
+    know ho to handle/aggregate them.
+
+    Note that ``_flatten_tensor_optim_state`` does not need ``optim`` to
+    flatten/shard the state. However, NamedOptimizer and KeyedOptimizer require
+    all the states even if the corresponding parameters are empty. To this end,
+    ``optim`` will be used to to get the initial state of the empty parameters.
+    ``optim`` should only be non-None if the ``optim` is KeyedOptimizer or
+    NamedOptimizer.
+
+    Returns:
+        Dict[str, Any]: The flattened optimizer state dict.
+    """
+    SimpleProfiler.reset()
+
+    unflat_osd = optim_state_dict
+    if "state" not in unflat_osd and not rank0_only:
+        raise ValueError(
+            '`optim_state_dict` must have the keys "state"'
+            "to be a valid optimizer state dict"
+        )
+    param_to_fqns = _get_param_to_fqns(model)
+    fqn_to_fsdp_param_info = _get_fqn_to_fsdp_param_info(model)
+    fsdp_state = next(iter(fqn_to_fsdp_param_info.values())).state
+
+    # Broadcast unflat_osd without non-scalar tensor if rank0_only is True.
+    if rank0_only:
+        unflat_osd = _broadcast_processed_state(fsdp_state, unflat_osd, group=group)
+
+    # Construct the "state" part
+    flat_osd_state: dict[Union[_OptimStateKey, str], Any] = {}
+    unflat_osd_state = unflat_osd["state"]
+    all_state_keys = set(unflat_osd_state.keys())
+
+    for param, fqns in param_to_fqns.items():
+        fqn = fqns[0]
+        if fqn not in unflat_osd_state:
+            continue
+        all_state_keys.difference_update(fqns)
+
+        if rank0_only:
+            for fqn in fqns:
+                if not unflat_osd_state[fqn]:
+                    continue
+                for state_name in unflat_osd_state[fqn].keys():
+                    unflat_osd_state[fqn][state_name] = _broadcast_state(
+                        fsdp_state, unflat_osd_state[fqn][state_name], group=group
+                    )
+            fqn = fqns[0]
+        if fqn in fqn_to_fsdp_param_info:
+            fsdp_param_info = fqn_to_fsdp_param_info[fqn]
+            if use_orig_params:
+                with SimpleProfiler.profile(SimpleProfiler.Type.RESHARDING):
+                    flat_state = _shard_orig_param_state(
+                        fsdp_param_info,
+                        fqn,
+                        unflat_osd_state[fqn],
+                    )
+            else:
+                flat_state = _flatten_optim_state(
+                    fsdp_param_info,
+                    unflat_osd_state,
+                    fqns,
+                )
+            key = _OptimStateKey(tuple(fqns), True)
+            # Only include non-empty states since as expected by
+            # `torch.optim.Optimizer` s unless the optimizer is KeyedOptimizer
+            # or NamedOptimizer.
+            if flat_state:
+                flat_osd_state[key] = flat_state
+            elif use_orig_params:
+                assert len(fqns) == 1, (
+                    f"use_orig_params is True but there are multiple FQNs, {fqns}."
+                )
+                if optim is not None:  # NamedOptimizer or KeyedOptimizer case.
+                    state = optim.state.get(param, None)  # type: ignore[call-overload]
+                    if state is not None:
+                        flat_osd_state[key] = copy.deepcopy(state)
+                    else:
+                        warnings.warn(
+                            f"optim_state[{key}] is not on rank{fsdp_state.rank}."
+                        )
+
+            else:
+                raise RuntimeError(
+                    f"The state of {key} is empty. This should happen when "
+                    "use_orig_params=True."
+                )
+        else:  # do not flatten non-FSDP parameters' states
+            assert len(fqns) == 1
+            key = _OptimStateKey(tuple(fqns), False)
+            flat_osd_state[key] = copy.copy(unflat_osd_state[fqn])
+
+        if rank0_only:
+            for fqn in fqns:
+                if not unflat_osd_state[fqn]:
+                    continue
+                for state_name, param_state in list(unflat_osd_state[fqn].items()):
+                    if fsdp_state.rank > 0:
+                        # Deference the tensor so that PyTorch can collect the memory.
+                        del unflat_osd_state[fqn][state_name]
+                    else:
+                        # Move the tensor in the original osd back to CPU to make the
+                        # original osd unaffected.
+                        unflat_osd_state[fqn][state_name] = param_state.cpu()
+
+    # Handle user-defined state, states that are not associated with parameters.
+    for key in all_state_keys:
+        user_state = unflat_osd_state[key]
+        if isinstance(user_state, torch.Tensor) and rank0_only and use_orig_params:
+            user_state = _broadcast_state(fsdp_state, user_state, group=group)
+        flat_osd_state[key] = copy.copy(user_state)
+
+    SimpleProfiler.dump_and_reset("FSDP _flatten_optim_state_dict() profiling: ")
+    # Construct the "param_groups" part -- copy as is since it will be
+    # rekeyed later according to the target rank's optimizer
+    # Only copy param_groups if it exists in unflat_osd
+    if "param_groups" in unflat_osd:
+        flat_osd_param_groups = copy.deepcopy(unflat_osd["param_groups"])
+        return {"state": flat_osd_state, "param_groups": flat_osd_param_groups}
+    else:
+        return {"state": flat_osd_state}
+
+
+def _flatten_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    unflat_osd_state: dict[str, dict[str, Any]],
+    unflat_param_names: list[str],
+) -> dict[str, Any]:
+    """
+    Flattens the optimizer state in ``full_optim_state_dict`` for a single
+    flat parameter in ``fsdp_param_info`` corresponding to the unflattened
+    parameter names in ``unflat_param_names``.
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        unflat_osd_state (Dict[str, Dict[str, Any]]): The "state" part of the
+            optimizer state dict corresponding to the unflattened parameters.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the flat parameter ``flat_param``.
+
+    Returns:
+        Dict[str, Any]: A :class:`dict` mapping state names to their values for
+        a particular flat parameter. The sharded optimizer state dict's "state"
+        part will map a key to this returned value.
+    """
+    fsdp_state = fsdp_param_info.state
+    handle = fsdp_param_info.handle
+    flat_param = handle.flat_param
+    num_unflat_params = len(unflat_param_names)
+    assert num_unflat_params > 0, (
+        "Expects at least one unflattened parameter corresponding to the flat parameter"
+    )
+    unflat_param_shapes = flat_param._shapes
+    num_unflat_param_shapes = len(unflat_param_shapes)
+    assert num_unflat_params == num_unflat_param_shapes, (
+        f"Expects {num_unflat_params} shapes but got {num_unflat_param_shapes}"
+    )
+
+    # Check if these unflattened parameters have any optimizer state
+    has_state = [
+        bool(unflat_param_name in unflat_osd_state)
+        for unflat_param_name in unflat_param_names
+    ]
+    # If none of the unflattened parameters comprising this flat parameter have
+    # any state, then we do not want an entry in the optimizer state dict
+    if not any(has_state):
+        return {}  # no need to flatten any state
+    # There may still be some unflattened parameters with state and some
+    # without
+    unflat_param_states = [
+        _gather_state_dict(
+            unflat_osd_state[unflat_param_name],
+            pg=fsdp_state.process_group,
+            device=fsdp_state.compute_device,
+        )
+        if unflat_param_name in unflat_osd_state
+        else None
+        for unflat_param_name in unflat_param_names
+    ]
+    # Check that the unflattened parameters have the same state names
+    state_names = None
+    for unflat_param_state in unflat_param_states:
+        if unflat_param_state is None:
+            continue
+        if state_names is None:
+            state_names = set(unflat_param_state.keys())
+        else:
+            if state_names != set(unflat_param_state.keys()):
+                raise ValueError(
+                    "Differing optimizer state names for the unflattened "
+                    f"parameters: {unflat_param_names}"
+                )
+    assert state_names is not None
+
+    # Flatten the state
+    flat_state: dict[str, Optional[torch.Tensor]] = {}
+    for state_name in state_names:
+        state_values = [
+            unflat_param_state[state_name] if unflat_param_state is not None else None
+            for unflat_param_state in unflat_param_states
+        ]
+        non_none_state_values = [v for v in state_values if v is not None]
+        # If all ranks have None, this is a None value
+        if not non_none_state_values:
+            flat_state[state_name] = None
+            continue
+        are_pos_dim_tensors = are_zero_dim_tensors = are_non_tensors = True
+        for v in non_none_state_values:
+            are_pos_dim_tensors &= torch.is_tensor(v) and v.dim() > 0
+            are_zero_dim_tensors &= _is_zero_dim_tensor(v)
+            are_non_tensors &= not torch.is_tensor(v)
+        types = {type(v) for v in non_none_state_values}
+        if len(types) != 1 or not (
+            are_pos_dim_tensors or are_zero_dim_tensors or are_non_tensors
+        ):
+            raise ValueError(
+                f"Differing optimizer state types for state {state_name}, "
+                f"values {non_none_state_values}, and unflattened parameter "
+                f"names {unflat_param_names}"
+            )
+        if are_pos_dim_tensors:
+            flat_tensor = _flatten_tensor_optim_state(
+                state_name,
+                state_values,  # type: ignore[arg-type]
+                unflat_param_names,
+                unflat_param_shapes,
+                handle,
+            )
+            # Shard the flattened tensor immediately to minimize max memory
+            # usage
+            if (
+                fsdp_state.world_size != 1
+                and fsdp_state.sharding_strategy != ShardingStrategy.NO_SHARD
+            ):
+                sharded_flat_tensor, _ = FlatParamHandle._get_shard(
+                    flat_tensor,
+                    fsdp_state.rank,
+                    fsdp_state.world_size,
+                )
+            else:
+                sharded_flat_tensor = flat_tensor
+            flat_state[state_name] = sharded_flat_tensor
+        elif are_zero_dim_tensors:
+            flat_state[state_name] = _flatten_zero_dim_tensor_optim_state(
+                state_name,
+                state_values,  # type: ignore[arg-type]
+                unflat_param_names,
+            )
+        else:
+            assert are_non_tensors
+            flat_state[state_name] = _flatten_non_tensor_optim_state(
+                state_name,
+                state_values,
+                unflat_param_names,
+            )
+
+    return flat_state
+
+
+def _flatten_tensor_optim_state(
+    state_name: str,
+    pos_dim_tensors: list[torch.Tensor],
+    unflat_param_names: list[str],
+    unflat_param_shapes: Sequence[torch.Size],
+    handle: FlatParamHandle,
+) -> torch.Tensor:
+    """
+    Flattens the positive-dimension tensor optimizer state given by the values
+    ``tensors`` for the state ``state_name`` for a single flat parameter
+    from ``handle`` corresponding to the unflattened parameter names
+    ``unflat_param_names`` and unflatted parameter shapes
+    ``unflat_param_shapes``. This flattens each unflattened parameter's tensor
+    state into one tensor.
+
+    NOTE: We use zero tensors for any unflattened parameters without state
+    since some value is required to fill those entries. This assumes that the
+    zero tensor is mathematically equivalent to having no state, which is true
+    for Adam's "exp_avg" and "exp_avg_sq" but may not be true for all
+    optimizers.
+
+    Args:
+        state_name (str): Optimizer state name.
+        pos_dim_tensors (List[torch.Tensor]): Positive-dimension tensor
+            optimizer state values for the unflattened parameters corresponding
+            to the single flat parameter.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the single flat parameter.
+        unflat_param_shapes (List[torch.Size]): Unflattened parameter shapes
+            corresponding to the single flat parameter.
+        handle (FlatParamHandle): The flat parameter's handle.
+
+    Returns:
+        torch.Tensor: A flat tensor containing the optimizer state
+        corresponding to ``state_name`` constructed by concatenating the
+        unflattened parameter tensor states in ``pos_dim_tensors`` (using zero
+        tensors for any unflattened parameters without the state).
+    """
+    flat_param = handle.flat_param
+    non_none_tensors = [t for t in pos_dim_tensors if t is not None]
+    # Check that all are tensors with the same dtype
+    dtypes = {t.dtype for t in non_none_tensors}
+    if len(dtypes) != 1:
+        raise ValueError(
+            "All unflattened parameters comprising a single flat "
+            "parameter must have positive-dimension tensor state with the "
+            f"same dtype but got dtypes {dtypes} for state {state_name} and "
+            f"unflattened parameter names {unflat_param_names}"
+        )
+    dtype = next(iter(dtypes))
+    # Check that each tensor state matches its parameter's shape
+    for tensor, shape in zip(pos_dim_tensors, unflat_param_shapes):
+        if tensor is None and len(shape) == 0:
+            raise ValueError("Flattening a zero-dimension parameter is not supported")
+        elif tensor is not None and tensor.shape != shape:
+            raise ValueError(
+                "Tensor optimizer state does not have same shape as its "
+                f"parameter: {tensor.shape} {shape}"
+            )
+    # Flatten the tensor states: we do not need to add any right-hand-side
+    # padding since the flat optimizer state tensor is sharded via
+    # `_get_shard()`, which pads the shard as needed (just like for the flat
+    # parameter)
+    cpu_device = torch.device("cpu")
+    tensors_to_flatten = [
+        torch.flatten(state_value.to(cpu_device))
+        if state_value is not None
+        else torch.flatten(
+            torch.zeros(
+                size=shape,
+                dtype=dtype,
+                device=cpu_device,
+            )
+        )
+        for state_value, shape in zip(pos_dim_tensors, unflat_param_shapes)
+    ]
+    flat_tensor = handle.flatten_tensors(tensors_to_flatten, handle._aligned_numel)
+    flat_param_shape = flat_param._unpadded_unsharded_size  # type: ignore[attr-defined]
+    assert flat_tensor.shape == flat_param_shape, (
+        f"tensor optim state: {flat_tensor.shape} flat parameter: {flat_param_shape}"
+    )
+    return flat_tensor
+
+
+def _flatten_zero_dim_tensor_optim_state(
+    state_name: str,
+    zero_dim_tensors: list[torch.Tensor],
+    unflat_param_names: list[str],
+) -> torch.Tensor:
+    """
+    Flattens the zero-dimension tensor optimizer state given by the values
+    ``zero_dim_tensors`` for the state ``state_name`` for a single flat
+    parameter corresponding to the unflattened parameter names
+    ``unflat_param_names`` by enforcing that all tensors are the same and using
+    that common value.
+
+    NOTE: The requirement that the tensors are the same across all unflattened
+    parameters comprising the flat parameter is needed to maintain the
+    invariant that FSDP performs the same computation as its non-sharded
+    equivalent. This means that none of the unflattened parameters can be
+    missing this state since imposing a value may differ from having no value.
+    For example, for Adam's "step", no value means maximum bias correction,
+    while having some positive value means less bias correction.
+
+    Args:
+        state_name (str): Optimizer state name.
+        zero_dim_tensors (List[torch.Tensor]): Zero-dimension optimizer state
+            for the unflattened parameters corresponding to the single
+            flat parameter.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the single flat parameter.
+
+    Returns:
+        torch.Tensor: A zero-dimensional tensor giving the value of the state
+        ``state_name`` for all unflattened parameters corresponding to the
+        names ``unflat_param_names``.
+    """
+    non_none_tensors = [t for t in zero_dim_tensors if t is not None]
+    # Enforce that all have the same value and dtype
+    values_set = {t.item() if t is not None else None for t in zero_dim_tensors}
+    dtypes = {t.dtype if t is not None else None for t in zero_dim_tensors}
+    if (
+        len(non_none_tensors) != len(zero_dim_tensors)
+        or len(values_set) != 1
+        or len(dtypes) != 1
+    ):
+        raise ValueError(
+            "All unflattened parameters comprising a single flat "
+            "parameter must have scalar state with the same value and dtype "
+            f"but got values {values_set} and dtypes {dtypes} for state "
+            f"{state_name} and unflattened parameter names "
+            f"{unflat_param_names}"
+        )
+    value = next(iter(values_set))
+    dtype = next(iter(dtypes))
+    return torch.tensor(value, dtype=dtype, device=torch.device("cpu"))
+
+
+def _flatten_non_tensor_optim_state(
+    state_name: str,
+    non_tensors: list[Any],
+    unflat_param_names: list[str],
+) -> Any:
+    """
+    Flattens the non-tensor optimizer state given by the values ``non_tensors``
+    for the state ``state_name`` for a single flat parameter corresponding
+    to the unflattened parameter names ``unflat_param_names`` by enforcing that
+    all values are the same and using that common value.
+
+    See the note in :func:`_flatten_zero_dim_tensor_optim_state`.
+
+    Args:
+        state_name (str): Optimizer state name.
+        non_tensors (List[Any]): Non-tensor optimizer state for the unflattened
+            parameters corresponding to the single flat parameter.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the single flat parameter.
+
+    Returns:
+        Any: A non-tensor giving the value of the state ``state_name`` for all
+        unflattened parameters corresponding to the names
+        ``unflat_param_names``.
+    """
+    non_none_non_tensors = [nt for nt in non_tensors if nt is not None]
+    # Enforce that all have the same value (same type already checked)
+    non_tensor_set = set(non_tensors)
+    if len(non_none_non_tensors) != len(non_tensors) or len(non_tensor_set) != 1:
+        raise ValueError(
+            "All unflattened parameters comprising a single flat "
+            "parameter must have scalar state with the same value and dtype "
+            f"but got values {non_tensor_set} for state {state_name} and  "
+            f"unflattened parameter names {unflat_param_names}"
+        )
+    non_tensor = next(iter(non_tensor_set))
+    return non_tensor
+
+
+def _rekey_sharded_optim_state_dict(
+    sharded_osd: dict[str, Any],
+    model: nn.Module,
+    optim: torch.optim.Optimizer,
+    optim_input: Optional[
+        Union[
+            list[dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ],
+    using_optim_input: bool,
+    is_named_optimizer: bool = False,
+) -> dict[str, Any]:
+    """
+    Rekeys the optimizer state dict from unflattened parameter names to flat
+    parameter IDs according to the calling rank's ``optim``, which may be
+    different across ranks. In particular, the unflattened parameter names are
+    represented as :class:`_OptimStateKey` s.
+    """
+    param_to_fqns = _get_param_to_fqns(model)
+    flat_param_to_fqn = _get_flat_param_to_fqn(model)
+    param_to_param_key: dict[nn.Parameter, Union[int, str]] = cast(
+        dict[nn.Parameter, Union[int, str]],
+        (
+            _get_param_to_param_id_from_optim_input(model, optim_input)
+            if using_optim_input
+            else _get_param_to_param_key(
+                optim, model, is_named_optimizer, param_to_fqns, flat_param_to_fqn
+            )
+        ),
+    )
+    # All parameter keys in `param_to_param_key` should be in
+    # `param_to_fqns` -- strict inequality follows when not all parameters are
+    # passed to the optimizer
+    assert len(param_to_param_key) <= len(param_to_fqns)
+
+    unflat_param_names_to_flat_param_key: dict[
+        tuple[str, ...], Union[int, str]
+    ] = {}  # for "state"
+    unflat_param_name_to_flat_param_key: dict[
+        str, Union[int, str]
+    ] = {}  # for "param_groups"
+    for param, unflat_param_names in param_to_fqns.items():
+        if param not in param_to_param_key:
+            # This parameter was not passed to the optimizer
+            continue
+        flat_param_key = param_to_param_key[param]
+        unflat_param_names_to_flat_param_key[tuple(unflat_param_names)] = flat_param_key
+        for unflat_param_name in unflat_param_names:
+            unflat_param_name_to_flat_param_key[unflat_param_name] = flat_param_key
+
+    sharded_osd_state = sharded_osd["state"]
+    rekeyed_osd_state: dict[Union[str, int], Any] = {}
+    for key, param_state in sharded_osd_state.items():
+        if isinstance(key, str):
+            rekeyed_osd_state[key] = param_state
+            continue
+        flat_param_key = unflat_param_names_to_flat_param_key.get(
+            key.unflat_param_names, key.unflat_param_names
+        )
+        rekeyed_osd_state[flat_param_key] = param_state
+
+    # Only process param_groups if it exists in sharded_osd
+    if "param_groups" in sharded_osd:
+        rekeyed_osd_param_groups: list[dict[str, Any]] = []
+        for unflat_param_group in sharded_osd["param_groups"]:
+            flat_param_group = copy.deepcopy(unflat_param_group)
+            flat_param_keys = sorted(
+                {
+                    unflat_param_name_to_flat_param_key[unflat_param_name]
+                    for unflat_param_name in unflat_param_group["params"]
+                }
+            )
+            flat_param_group["params"] = flat_param_keys
+            rekeyed_osd_param_groups.append(flat_param_group)
+        return {"state": rekeyed_osd_state, "param_groups": rekeyed_osd_param_groups}
+    else:
+        return {"state": rekeyed_osd_state}
+
+
+def _get_param_id_to_param_from_optim_input(
+    model: nn.Module,
+    optim_input: Optional[
+        Union[
+            list[dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ] = None,
+) -> dict[int, nn.Parameter]:
+    """
+    Constructs a mapping from parameter IDs to parameters. This may be used
+    both for models with ``FlatParameter`` s and without.
+
+    NOTE: This method is only preserved for backward compatibility. The method
+    :meth:`_get_param_key_to_param` is the preferred code path that does not
+    rely on ``optim_input``.
+
+    NOTE: We critically assume that, whether the optimizer input is a list of
+    parameters or a list of parameter groups, :class:`torch.optim.Optimizer`
+    enumerates the parameter IDs in order. In other words, for a parameter list
+    input, the parameter IDs should be in that list order, and for a parameter
+    groups input, the parameter IDs should be in order within each parameter
+    group and in order across parameter groups.
+
+    Args:
+        model (nn.Module): Model whose parameters are passed into the
+            optimizer.
+        optim_input (Optional[Union[List[Dict[str, Any]],
+        Iterable[nn.Parameter]]]): Input passed into the optimizer
+            representing either a :class:`list` of parameter groups or an
+            iterable of parameters; if ``None``, then this method assumes the
+            input was ``model.parameters()``. (Default: ``None``)
+
+    Returns:
+        List[nn.Parameter]: Mapping from parameter IDs to parameters,
+        where the parameter ID is implicitly the index in the :class:`list`.
+    """
+    # Assume the standard case of passing `model.parameters()` to the optimizer
+    # if `optim_input` is not specified
+    if optim_input is None:
+        return dict(enumerate(model.parameters()))
+    try:
+        params = cast(list[nn.Parameter], list(optim_input))
+    except TypeError as e:
+        raise TypeError(
+            "Optimizer input should be an iterable of Tensors or dicts, "
+            f"but got {optim_input}"
+        ) from e
+    if len(params) == 0:
+        raise ValueError("Optimizer input should not be empty")
+
+    # Check if the optimizer input represents tensors or parameter groups
+    all_tensors = True
+    all_dicts = True
+    for param in params:
+        all_tensors &= isinstance(param, torch.Tensor)
+        all_dicts &= isinstance(param, dict)
+    if not all_tensors and not all_dicts:
+        raise TypeError("Optimizer input should be an iterable of Tensors or dicts")
+    if all_tensors:
+        return dict(enumerate(params))
+    assert all_dicts
+    param_id_to_param: list[nn.Parameter] = []
+    for param_group in params:
+        has_params_key = "params" in param_group  # type: ignore[operator]
+        assert has_params_key, (
+            'A parameter group should map "params" to a list of the '
+            "parameters in the group"
+        )
+        # Implicitly map `flat_param_id` (current length of the list) to
+        # `param`
+        param_id_to_param.extend(param_group["params"])  # type: ignore[index]
+    return dict(enumerate(param_id_to_param))
+
+
+def _get_flat_param_to_fqn(model: torch.nn.Module) -> dict[FlatParameter, str]:
+    """
+    Constructs a mapping from ``FlatParameter`` to a cleaned (devoid of prefixes
+    from wrappers) fully qualified name (FQN). Note that this FQN is "non-canonical"
+    because ``FlatParameter``  s do not come from the original module but are
+    registered only after FSDP has been applied. This function returns the FSDP-given
+    name for the ``FlatParameter`` (usually module._flat_param) as opposed to the
+    canonical FQNs returned for ``FlatParameter`` s in ``_common_utils._get_param_to_fqns(...)``).
+
+    Consequently, this function will only return a non-empty mapping if FSDP was
+    applied with ``use_orig_params=False`` as, otherwise, the original parameters
+    are used within the module and there would be no ``FlatParameter`` s in the module.
+
+    """
+
+    def module_fn(module, prefix, tree_level, flat_param_to_fqn):
+        for param_name, param in _named_parameters_with_duplicates(
+            module, recurse=False
+        ):
+            if not isinstance(param, FlatParameter):
+                continue
+            fqn = clean_tensor_name(prefix + param_name)
+            flat_param_to_fqn[param] = fqn
+
+    def return_fn(flat_param_to_fqn):
+        return flat_param_to_fqn
+
+    flat_param_to_fqn_ret: dict[FlatParameter, str] = {}
+    return _apply_to_modules(
+        model,
+        module_fn,
+        return_fn,
+        [fqn for fqn, _ in _named_parameters_with_duplicates(model)],
+        flat_param_to_fqn_ret,
+    )
+
+
+def _get_param_key_to_param(
+    optim: torch.optim.Optimizer,
+    model: Optional[nn.Module] = None,
+    is_named_optimizer: bool = False,
+    param_to_fqns: Optional[dict[nn.Parameter, list[str]]] = None,
+    flat_param_to_fqn: Optional[dict[FlatParameter, str]] = None,
+) -> dict[Union[int, str], nn.Parameter]:
+    """
+    Constructs a mapping from parameter keys to parameters. For the regular
+    optimizers, the keys are parameter IDs. For NamedOptimizer, the keys
+    are FQNs. This API may be used both for models with ``FlatParameter`` s and
+    without.
+    """
+    clean_fqn_to_curr_fqn: dict[str, str] = {}
+    if is_named_optimizer:
+        assert param_to_fqns is not None and flat_param_to_fqn is not None, (
+            "The optimizer is a NamedOptimizer, `param_to_fqns` must not be None."
+        )
+        assert model is not None
+        for key, _ in _named_parameters_with_duplicates(model):
+            clean_fqn_to_curr_fqn[clean_tensor_name(key)] = key
+
+    param_key_to_param: dict[Union[str, int], nn.Parameter] = {}
+    pid = 0
+    for param_group in optim.param_groups:
+        if is_named_optimizer:
+            for param in param_group["params"]:
+                assert flat_param_to_fqn is not None
+                if param in flat_param_to_fqn:
+                    # FlatParameter case
+                    key = flat_param_to_fqn[param]
+                else:
+                    assert param_to_fqns is not None
+                    # use_orig_params case
+                    assert len(param_to_fqns[param]) == 1
+                    key = param_to_fqns[param][0]
+                try:
+                    key = clean_fqn_to_curr_fqn[key]
+                except KeyError as e:
+                    raise KeyError(
+                        f"Can't find {key} from {list(clean_fqn_to_curr_fqn.keys())}."
+                    ) from e
+                param_key_to_param[key] = param
+        else:
+            for param in param_group["params"]:
+                param_key_to_param[pid] = param
+                pid += 1
+
+    return param_key_to_param
+
+
+def _get_param_to_param_key(
+    optim: torch.optim.Optimizer,
+    model: Optional[nn.Module] = None,
+    is_named_optimizer: bool = False,
+    param_to_fqns: Optional[dict[nn.Parameter, list[str]]] = None,
+    flat_param_to_fqn: Optional[dict[FlatParameter, str]] = None,
+) -> dict[nn.Parameter, Union[int, str]]:
+    """
+    Constructs the inverse mapping of :func:`_get_param_key_to_param`. This API
+    only supports the case where `optim` is a regular optimizer, not NamedOptimizer.
+    So the parameter keys will be parameter ids.
+    """
+    param_id_to_param = _get_param_key_to_param(
+        optim, model, is_named_optimizer, param_to_fqns, flat_param_to_fqn
+    )
+    return {param: param_id for param_id, param in param_id_to_param.items()}
+
+
+def _get_param_to_param_id_from_optim_input(
+    model: nn.Module,
+    optim_input: Optional[
+        Union[
+            list[dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ] = None,
+) -> dict[nn.Parameter, int]:
+    """Constructs the inverse mapping of :func:`_get_param_id_to_param_from_optim_input`."""
+    param_id_to_param = _get_param_id_to_param_from_optim_input(model, optim_input)
+    return {param: param_id for param_id, param in param_id_to_param.items()}
+
+
+def _check_missing_keys_on_rank(
+    r0_optim_state_keys: list[_OptimStateKey],
+    optim_state_key_to_param_key: dict[_OptimStateKey, Union[str, int]],
+    param_key_to_param: dict[Union[str, int], nn.Parameter],
+    group: Optional[dist.ProcessGroup],
+) -> None:
+    # Ensure that all ranks have at least the optimizer states needed by
+    # rank 0's optimizer
+    missing_keys: list[_OptimStateKey] = []
+    for r0_optim_state_key in r0_optim_state_keys:
+        if r0_optim_state_key not in optim_state_key_to_param_key:
+            # A parameter from rank 0's optimizer does not exist for this
+            # rank's optimizer
+            missing_keys.append(r0_optim_state_key)
+            continue
+        param_key = optim_state_key_to_param_key[r0_optim_state_key]
+        if isinstance(param_key, int):
+            assert param_key >= 0 and param_key < len(param_key_to_param), (
+                "Check the `param_key_to_param` construction"
+            )
+    # We cannot use FSDPState.compute_device as this API is a global view.
+    device = _get_pg_default_device(group)
+    num_missing = torch.tensor([len(missing_keys)], dtype=torch.int32, device=device)
+    dist.all_reduce(num_missing, group=group)
+    if num_missing.item() > 0:
+        obj_list = [None for _ in range(dist.get_world_size(group))]
+        dist.all_gather_object(obj_list, missing_keys, group=group)
+        error_msg = (
+            "FSDP currently requires each rank to have at least the "
+            "optimizer states needed by rank 0's optimizer but some ranks "
+            "are missing some of those states"
+        )
+        for rank, keys in enumerate(obj_list):
+            keys = cast(list[_OptimStateKey], keys)
+            if len(keys) > 0:
+                error_msg += (
+                    f"\nRank {rank} is missing states for the parameters: "
+                    f"{[key.unflat_param_names for key in keys]}"
+                )
+        raise RuntimeError(error_msg)
+
+
+def _map_param_key_to_optim_keys(
+    optim_state_dict: dict[str, Any],
+    group: Optional[dist.ProcessGroup],
+    param_key_to_param: dict[Union[int, str], nn.Parameter],
+    param_to_fqns: dict[nn.Parameter, list[str]],
+    fqn_to_fsdp_param_info: dict[str, FSDPParamInfo],
+    merge_keys: bool = False,
+) -> tuple[list[_OptimStateKey], dict[_OptimStateKey, Union[int, str]]]:
+    """
+    Construct the local mapping between the ``_OptimStateKey`` and parameter keys
+    and all the ``_OptimStateKey`` across ranks. If ``merge_keys`` is False, rank0
+    must contain all the ``_OptimStateKey``, an exception will be raised otherwise.
+    Note that ``merge_keys`` should equal to ``use_orig_params``.
+    """
+    rank = dist.get_rank(group)
+    optim_state_key_to_param_key: dict[_OptimStateKey, Union[int, str]] = {}  # local
+    all_optim_state_keys: list[_OptimStateKey] = []
+
+    for param_key, param in param_key_to_param.items():
+        # Do not include parameters without state to avoid empty mappings
+        # just like in normal `torch.optim.Optimizer.state_dict()`
+        if param_key not in optim_state_dict["state"]:
+            continue
+        fqns = param_to_fqns[param]
+        is_fsdp_managed = isinstance(param, FlatParameter)
+        if is_fsdp_managed:
+            assert fqns[0] in fqn_to_fsdp_param_info, (
+                fqns[0],
+                list(fqn_to_fsdp_param_info.keys()),
+            )
+        is_fsdp_managed = fqns[0] in fqn_to_fsdp_param_info
+        optim_state_key = _OptimStateKey(
+            unflat_param_names=tuple(fqns),
+            is_fsdp_managed=is_fsdp_managed,
+        )
+        if rank == 0 or merge_keys:
+            all_optim_state_keys.append(optim_state_key)
+        optim_state_key_to_param_key[optim_state_key] = param_key
+
+    if merge_keys:
+        all_keys: list[list[_OptimStateKey]] = [
+            [] for _ in range(dist.get_world_size(group))
+        ]
+        dist.all_gather_object(all_keys, all_optim_state_keys, group=group)
+        merge_all_optim_state_keys = [*chain.from_iterable(all_keys)]
+        all_optim_state_keys = sorted(set(merge_all_optim_state_keys))
+    else:
+        key_obj_list: list[Optional[list[_OptimStateKey]]] = (
+            [all_optim_state_keys] if rank == 0 else [None]
+        )
+        dist.broadcast_object_list(key_obj_list, src=0, group=group)
+        assert key_obj_list[0] is not None
+        all_optim_state_keys = key_obj_list[0]
+        _check_missing_keys_on_rank(
+            all_optim_state_keys,
+            optim_state_key_to_param_key,
+            param_key_to_param,
+            group,
+        )
+
+    return all_optim_state_keys, optim_state_key_to_param_key
+
+
+def _unflatten_param_groups(
+    state_dict: dict[str, Any],
+    param_key_to_param: dict[Union[int, str], nn.Parameter],
+    param_to_fqns: dict[nn.Parameter, list[str]],
+) -> list[dict[str, Any]]:
+    param_groups: list[dict[str, Any]] = []
+    for flat_param_group in state_dict["param_groups"]:
+        unflat_param_group = copy.deepcopy(flat_param_group)
+        param_group_params = [
+            param_key_to_param[flat_param_key]
+            for flat_param_key in flat_param_group["params"]
+        ]
+        nested_unflat_param_names = [
+            param_to_fqns[param] for param in param_group_params
+        ]
+        unflat_param_group["params"] = [
+            *chain.from_iterable(nested_unflat_param_names)
+        ]  # flatten the list of lists
+        param_groups.append(unflat_param_group)
+    return param_groups
+
+
+def _is_named_optimizer(optim_state_dict: dict[str, Any]) -> bool:
+    """
+    Returns whether the state_dict is from a NamedOptimizer.
+    This function checks that the keys in the state_dict['state'] are strings
+    (which usually are FQNs) versus integers (which usually refer to param_ids
+    from a vanilla torch.optim.Optimizer).
+    """
+    state = optim_state_dict.get("state", None)
+    if not state:
+        # If we cannot find a state, assume it is not NamedOptimizer as
+        # NamedOptimizer has eager initialization.
+        return False
+    try:
+        key = next(iter(state.keys()))
+    except Exception as e:
+        raise Exception(optim_state_dict) from e  # noqa: TRY002
+    return isinstance(key, str)
+
+
+@dataclass
+class StateInfo:
+    # The key of these dictionaries are the state name, e.g., `exp_avg`.
+    tensors: dict[str, _PosDimTensorInfo]
+    scalar_tensors: dict[str, torch.Tensor]
+    non_tensors: dict[str, Any]
+
+
+def _allgather_state_info(
+    fsdp_state: _FSDPState,
+    input_states: dict[str, Any],
+) -> list[dict[str, StateInfo]]:
+    """
+    Given the ``input_states``, allgather StateInfo for each state. The function
+    uses all_gather_object to gather StateInfo so no GPU tensors are sent.
+    """
+
+    processed_state_dict: dict[str, StateInfo] = {}
+    gathered_state_info: list[dict[str, StateInfo]] = [
+        {} for _ in range(fsdp_state.world_size)
+    ]
+
+    for fqn, optim_state in input_states.items():
+        # Allgather the scalar tensor state, non-tensor states and tensors metadata.
+        processed_state = StateInfo({}, {}, {})
+        for state_name, value in sorted_items(optim_state):
+            if torch.is_tensor(value):
+                if value.dim() == 0:
+                    # Ensure that `step` is on CPU.
+                    processed_state.scalar_tensors[state_name] = value.cpu()
+                else:
+                    processed_state.tensors[state_name] = _PosDimTensorInfo(
+                        value.shape, value.dtype
+                    )
+            else:
+                processed_state.non_tensors[state_name] = value
+        processed_state_dict[fqn] = processed_state
+    dist.all_gather_object(
+        gathered_state_info,
+        processed_state_dict,
+        group=fsdp_state.process_group,
+    )
+    return gathered_state_info
+
+
+def _convert_all_state_info(
+    fsdp_param_info: FSDPParamInfo,
+    gathered_state_info: list[dict[str, StateInfo]],
+    input_states: dict[str, Any],
+    output_states: dict[str, dict[str, Any]],
+) -> tuple[Optional[torch.dtype], dict[str, list[Optional[torch.Tensor]]]]:
+    """
+    Given the ``gathered_state_info`` and ``input_states``, the API converted
+    the StateInfo into the original state if the state is not a non-scalar
+    tensor. For a multi-dimensional tensor, the local state will be stored in
+    ``state_buffer`` in a correct order for later allgather purpose.
+    """
+
+    state_buffers: dict[str, list[Optional[torch.Tensor]]] = {}
+
+    for fqn, gathered_state in output_states.items():
+        state_info = [s[fqn] for s in gathered_state_info]
+        all_tensor_states = sorted(
+            {n for state in state_info for n in state.tensors.keys()}
+        )
+        empty_ranks: set[int] = set()
+        dtype: Optional[torch.dtype] = None
+        # First check all the non-scalar states and get the information of
+        # states on each rank.
+        for state_name in all_tensor_states:
+            numels = []
+            _empty_ranks: set[int] = set()
+            for rank, object_state in enumerate(state_info):
+                numels.append(0)
+                info = object_state.tensors.get(state_name, None)
+                if info is not None:
+                    numels[-1] = info.shape.numel()
+                    if not dtype:
+                        dtype = info.dtype
+                    else:
+                        assert dtype == info.dtype
+                if numels[-1] == 0:
+                    _empty_ranks.add(rank)
+
+            assert not empty_ranks or empty_ranks == _empty_ranks
+            empty_ranks = _empty_ranks
+            if state_name not in state_buffers:
+                state_buffers[state_name] = [
+                    None for _ in fsdp_param_info.param_indices
+                ]
+            local_state = input_states[fqn].get(state_name, None)
+            # N.B. We need to move the state to compute_device. The reason is
+            # not yet clear and we need to figure out why the state may be on a
+            # different device.
+            if local_state is not None:
+                local_state = local_state.to(fsdp_param_info.state.compute_device)
+            state_buffers[state_name][fsdp_param_info.param_indices[fqn]] = local_state
+
+        # Restoring the scalar and non-tensor states. If the corresponding
+        # non-scalar states do not exist on the rank, we also skip the scalar
+        # non-tensor states on that rank.
+        for rank, object_state in enumerate(state_info):
+            if rank in empty_ranks:
+                continue
+            for name, non_tensor_value in object_state.non_tensors.items():
+                curr_non_tensor_value = gathered_state.get(name, None)
+                assert (
+                    curr_non_tensor_value is None
+                    or curr_non_tensor_value == non_tensor_value
+                ), (
+                    f"Rank {rank} has different values for {name}: {non_tensor_value}."
+                    + f" Other ranks: {curr_non_tensor_value}"
+                )
+                gathered_state[name] = non_tensor_value
+
+            for name, scalar_tensor_value in object_state.scalar_tensors.items():
+                curr_scalar_tensor_value = gathered_state.get(name, None)
+                assert curr_scalar_tensor_value is None or torch.equal(
+                    scalar_tensor_value, curr_scalar_tensor_value
+                ), (
+                    f"Rank {rank} has different values for {name}: {scalar_tensor_value}."
+                    + f" Other ranks: {curr_scalar_tensor_value}"
+                )
+                gathered_state[name] = scalar_tensor_value
+
+    return dtype, state_buffers  # type: ignore[possibly-undefined]
+
+
+def _unflatten_orig_param_states(
+    fsdp_param_info: FSDPParamInfo,
+    output_states: dict[str, dict[str, Any]],
+    state_name: str,
+    shard_state: bool,
+    to_save: bool,
+    cpu_offload: bool,
+) -> None:
+    """
+    Given a output state dict, ``output_states``, which the keys are FQNs to the
+    original parameters (not FlatParameters nor parmeter ID), and the values
+    are gathered states, unflatten the states to the original dimensions.
+
+    This function performs the unflattening process in-place.
+    """
+    if not to_save:
+        return
+    flat_param = fsdp_param_info.handle.flat_param
+    fsdp_state = fsdp_param_info.state
+    for fqn, gathered_state in output_states.items():
+        value = gathered_state[state_name]
+        param_idx = fsdp_param_info.param_indices[fqn]
+
+        # TODO: This solution is not general and only apply to PTD TP solution.
+        if isinstance(value, DTensor):
+            placement = value.placements[0]
+            # If gathered state is a DTensor and its TP placement is not Replicate(), we need to
+            # gather the tensor on its TP dimension before chunking them into DTensor again.
+            if placement != Replicate():
+                placement_dim = placement.dim  # type: ignore[attr-defined]
+                value.redistribute(placements=(Replicate(),))
+                reshape_size = list(flat_param._shapes[param_idx])
+                reshape_size[placement_dim] *= value.device_mesh.size(0)
+                reshape_size = torch.Size(reshape_size)
+                value = value.reshape(reshape_size)
+            # If gathered state is a replicate DTensor, we directly reshape it.
+            else:
+                value = value.reshape(flat_param._shapes[param_idx])
+        else:
+            # If gathered state is a tensor, we directly reshape it into unflatten state.
+            value = value.reshape(flat_param._shapes[param_idx])
+
+        if shard_state:
+            osd_config = fsdp_state._optim_state_dict_config
+            if getattr(osd_config, "_use_dtensor", False):
+                assert fsdp_state._device_mesh is not None
+                value = _ext_chunk_dtensor(
+                    value,
+                    fsdp_state.rank,
+                    fsdp_state._device_mesh,
+                    fsdp_state._fsdp_extension,
+                )
+            else:
+                assert fsdp_state.process_group is not None
+                value = _ext_chunk_tensor(
+                    value,
+                    fsdp_state.rank,
+                    fsdp_state.world_size,
+                    fsdp_state._device_handle.device_count(),
+                    fsdp_state.process_group,
+                    fsdp_state._fsdp_extension,
+                )
+        elif not cpu_offload:
+            with SimpleProfiler.profile("clone"):
+                value = value.detach().clone()
+
+        if cpu_offload:
+            with SimpleProfiler.profile(SimpleProfiler.Type.D2H):
+                value = value.cpu()
+        gathered_state[state_name] = value
+
+
+def _allgather_orig_param_states(
+    fsdp_param_info: FSDPParamInfo,
+    gathered_state_info: list[dict[str, StateInfo]],
+    input_states: dict[str, Any],
+    shard_state: bool,
+    to_save: bool,
+    cpu_offload: bool,
+) -> dict[str, dict[str, Any]]:
+    """
+    Given the ``gathered_state_info`` and ``input_states``, the API allgathers
+    all tensor states and restore non-tensor states from ``gathered_state_info``.
+    """
+    fsdp_state = fsdp_param_info.state
+    if fsdp_state.rank == 0 and dist.get_debug_level() == dist.DebugLevel.DETAIL:
+        logger.info(
+            "Memory Summary before calling to _allgather_orig_param_states %s",
+            fsdp_state._device_handle.memory_summary(),
+        )
+
+    output_states: dict[str, dict[str, Any]] = {fqn: {} for fqn in input_states.keys()}
+
+    dtype, state_buffers = _convert_all_state_info(
+        fsdp_param_info, gathered_state_info, input_states, output_states
+    )
+
+    if len(state_buffers) == 0:
+        return output_states
+
+    has_state_params: list[bool] = [
+        True if fqn in output_states else False
+        for fqn, idx in fsdp_param_info.param_indices.items()
+    ]
+
+    # Loop through the ``state_buffers`` and construct the flattened, concatenated,
+    # sharded states. The size of the constructed state will be the same size as
+    # flat_param (also sharded).
+    # Then we perform an allgather_into_tensor to get the full flat_param state.
+    # The full flat_param state is the result of concatenation of multiple states
+    # the order of of flat_param._fqns.
+    # The final step is to split the flat_param state into original param states
+    # and return the result.
+    flat_param = fsdp_param_info.handle.flat_param
+    empty_func = functools.partial(
+        torch.empty, dtype=dtype, device=fsdp_state.compute_device
+    )
+    gathered_tensor = empty_func(flat_param._padded_unsharded_size)
+    # Synchronize can be slow but this will be easier for us to debug.
+    fsdp_state._device_handle.synchronize()
+    for state_name, buffers in state_buffers.items():
+        local_buffers: list[torch.Tensor] = []
+        begin = fsdp_state.rank * flat_param._sharded_size.numel()
+        # End is inclusive.
+        end = begin + flat_param._sharded_size.numel() - 1
+        # param_idx corresponds to the parameter index in the FlatParameter.
+        mem_offset, param_idx = 0, 0
+        for numel, is_padding in zip(
+            flat_param._numels_with_padding, flat_param._is_padding_mask
+        ):
+            frozen_and_no_state = not is_padding and (
+                not fsdp_param_info.param_requires_grad[param_idx]
+                and not has_state_params[param_idx]
+            )
+
+            if is_padding or frozen_and_no_state:
+                # This memory range is a padding or the param is frozen and does
+                # not require gradient. For the later case, we treat it as a
+                # padding and add empty values to the local_buffers.
+
+                padding_begin, padding_end = mem_offset, mem_offset + numel - 1
+                if padding_begin <= begin <= padding_end:
+                    # The range is an align padding before the first parameter in
+                    # the shard. The shard includes parts of this align padding.
+                    padding_len = (
+                        padding_end - begin + 1
+                        if end >= padding_end
+                        else end - begin + 1
+                    )
+                elif padding_begin <= end <= padding_end:
+                    # The range is an align padding after the last parameter in
+                    # the shard. The shard includes parts of this align padding.
+                    padding_len = (
+                        end - padding_begin + 1
+                        if begin <= padding_begin
+                        else end - begin + 1
+                    )
+                elif begin < padding_begin <= padding_end < end:
+                    # The range is an align padding that is completely in the
+                    # shard.
+                    padding_len = numel
+                else:
+                    padding_len = 0
+                if padding_len:
+                    local_buffers.append(empty_func(padding_len))
+
+            if not is_padding:
+                # This memory range is a parameter in FlatParameter. So there
+                # should be an corresponding state in the optimizer unless the
+                # parameter is frozen, which we treat it as a padding above.
+
+                # We need to check if this rank owns the buffer. If this is None:
+                # 1.) the rank does not own any part of the original parameter.
+                #     As a result, there is no corresponding optimizer state on
+                #     the rank as well.
+                # 2.) the parameter is frozen AND no optimizer state for the
+                #     parameter. If a parameter is frozen, there can still be
+                #     optimizer state if the parameter is not frozen in the
+                #     previous steps.
+                if buffers[param_idx] is not None:
+                    local_buffers.append(cast(torch.Tensor, buffers[param_idx]))
+                param_idx += 1
+
+            mem_offset += numel
+
+        shard_numel_padded = flat_param._sharded_size.numel() - (
+            sum(t.numel() for t in local_buffers)
+        )
+
+        assert flat_param._shard_numel_padded == shard_numel_padded, (
+            "Manually calculated _sharded_numel_padded is incorrect. "
+            f"_shard_numel_padded={flat_param._shard_numel_padded}, "
+            f"shard_numel_padded={shard_numel_padded}, "
+            f"_sharded_size.numel={flat_param._sharded_size.numel()}, "
+            f"_numels_with_padding={flat_param._numels_with_padding}, "
+            f"begin={begin}, end={end},"
+        )
+        if shard_numel_padded > 0:
+            # Add right-handed padding.
+            local_buffers.append(empty_func(shard_numel_padded))
+        local_shard = torch.cat(local_buffers)
+        assert local_shard.numel() * fsdp_state.world_size == gathered_tensor.numel(), (
+            "The size of local shard times the world size should equal to the "
+            "gathered tensor size. The inconsistency may be from a bug of "
+            "FlatParameter's metadata or the reconstruction logic in optimizer "
+            "state dict."
+        )
+        fsdp_state._device_handle.synchronize()
+        with SimpleProfiler.profile(SimpleProfiler.Type.ALLGATHER):
+            dist.all_gather_into_tensor(
+                gathered_tensor, local_shard, group=fsdp_state.process_group
+            )
+            # Synchronize can be slow but this will be easier for us to debug.
+            fsdp_state._device_handle.synchronize()
+
+        unpadded_tensor = gathered_tensor[: flat_param._unpadded_unsharded_size.numel()]
+        flat_param_handle = fsdp_param_info.handle
+        orig_states = flat_param_handle._get_unflat_views_aligned(unpadded_tensor)
+        assert len(orig_states) == len(fsdp_param_info.param_indices), (
+            "The number of parameters from FlatParameter is not consistent to "
+            "the number of states used by optimizer state dict reconstruction "
+            "logic."
+        )
+        for fqn, idx in fsdp_param_info.param_indices.items():
+            if fsdp_param_info.param_requires_grad[idx] or fqn in output_states:
+                output_states[fqn][state_name] = orig_states[idx]
+
+        _unflatten_orig_param_states(
+            fsdp_param_info,
+            output_states,
+            state_name,
+            shard_state,
+            to_save,
+            cpu_offload,
+        )
+
+    del gathered_tensor
+    return output_states
+
+
+def _gather_all_orig_param_state(
+    fsdp_param_info: FSDPParamInfo,
+    input_states: dict[str, Any],
+    shard_state: bool,
+    to_save: bool,
+    cpu_offload: bool,
+) -> dict[str, Any]:
+    """
+    Given a optimizer state dict, ``input_states``, which the keys are FQNs to the
+    original parameters (not FlatParameters nor parmeter ID), gather all the
+    states and unflatten them to the original dimensions. Note that all the
+    params referred by the ``input_states`` must be managed by FSDP.
+    """
+    fsdp_state = fsdp_param_info.state
+    if (
+        fsdp_state.world_size == 1
+        or fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD
+    ):
+        return input_states if to_save else {}
+
+    with SimpleProfiler.profile(SimpleProfiler.Type.RESHARDING):
+        with SimpleProfiler.profile(SimpleProfiler.Type.ALLGATHER_OBJ):
+            gathered_state_info = _allgather_state_info(fsdp_state, input_states)
+        output_states = _allgather_orig_param_states(
+            fsdp_param_info,
+            gathered_state_info,
+            input_states,
+            shard_state,
+            to_save,
+            cpu_offload,
+        )
+    if to_save:
+        for key, idx in fsdp_param_info.param_indices.items():
+            if key in output_states:
+                continue
+            if not fsdp_param_info.param_requires_grad[idx]:
+                continue
+
+            raise RuntimeError(
+                f"{key} is not in the output state. "
+                "The FSDPParamInfo has the param keys "
+                f"{sorted(fsdp_param_info.param_indices.keys())} while "
+                "the output_states has the param keys "
+                f"{sorted(output_states.keys())}."
+            )
+        return output_states
+    else:
+        return {}
+
+
+def _convert_state_with_orig_params(
+    all_optim_state_keys: list[_OptimStateKey],
+    optim_state_key_to_param_key: dict[_OptimStateKey, Union[int, str]],
+    fqn_to_fsdp_param_info: dict[str, FSDPParamInfo],
+    optim_state_dict: dict[Union[str, int], Any],
+    to_save: bool,
+    shard_state: bool,
+    cpu_offload: bool = True,
+) -> dict[str, Any]:
+    fsdp_osd_state: dict[str, Any] = {}
+    # This variable is used to deduplicate the FSDPParamInfo as one FSDPParamInfo
+    # usually corresponds to multiple parameters. We could not use FSDPParamInfo
+    # as the key because FSDPParamInfo is not hashable. As a result, we fall back
+    # to `id(FSDPParamInfo)`, which the type is an integer.
+    all_states: dict[int, dict[str, Any]] = {}
+    # Iterate in rank 0's flat parameter ID order to ensure aligned all-gathers
+    # across ranks
+    for optim_state_key in all_optim_state_keys:
+        param_key: Union[str, int, None] = optim_state_key_to_param_key.get(
+            optim_state_key, None
+        )
+
+        if param_key is None and not optim_state_key.is_fsdp_managed:
+            continue
+
+        if optim_state_key.is_fsdp_managed:
+            fqn = optim_state_key.unflat_param_names[0]
+            fsdp_param_info = fqn_to_fsdp_param_info.get(fqn, None)
+            if fsdp_param_info is None:
+                # This can happen if the not all FSDP instances have all the
+                # parameters. This can happen with FSDP + some MPMD style
+                # parallelism.
+
+                # TODO: it is unclear if we need to do the same check with
+                # non-FSDP managed keys.
+                continue
+            state = {} if param_key is None else optim_state_dict[param_key]
+            if id(fsdp_param_info) not in all_states:
+                all_states[id(fsdp_param_info)] = {}
+            all_states[id(fsdp_param_info)][fqn] = state
+
+        elif to_save:
+            assert len(optim_state_key.unflat_param_names) == 1
+            unflat_param_name = optim_state_key.unflat_param_names[0]
+            with SimpleProfiler.profile("none_fsdp_managed_copy"):
+                param_key = cast(Union[str, int], param_key)
+                fsdp_osd_state[unflat_param_name] = copy.copy(
+                    optim_state_dict[param_key]
+                )
+                if cpu_offload:
+                    for state_name, value in sorted_items(
+                        fsdp_osd_state[unflat_param_name]
+                    ):
+                        if not torch.is_tensor(value):
+                            continue
+                        fsdp_osd_state[unflat_param_name][state_name] = value.cpu()
+
+    # Instead of gathering the state of each parameter individually, we perform
+    # the gathering  all at once to speed up the process.
+    for _all_states in all_states.values():
+        fqn = next(iter(_all_states.keys()))
+        fsdp_param_info = fqn_to_fsdp_param_info[fqn]
+        assert len(fsdp_param_info.param_requires_grad) > 0, (
+            "With use_orig_params, FSDPParamInfo should have requires_grad "
+            "information. However, the length is zero."
+        )
+        for key, idx in fsdp_param_info.param_indices.items():
+            if key in _all_states:
+                continue
+            if not fsdp_param_info.param_requires_grad[idx]:
+                continue
+            raise RuntimeError(
+                f"{key} is not in the optimizer state. "
+                "The FSDPParamInfo has the param keys "
+                f"{sorted(fsdp_param_info.param_indices.keys())} while "
+                "the optimizer has the param keys "
+                f"{sorted(_all_states.keys())}."
+            )
+        fsdp_osd_state.update(
+            _gather_all_orig_param_state(
+                fsdp_param_info,
+                _all_states,
+                shard_state,
+                to_save,
+                cpu_offload,
+            )
+        )
+
+    return fsdp_osd_state
+
+
+def _convert_state_with_flat_params(
+    all_optim_state_keys: list[_OptimStateKey],
+    optim_state_key_to_param_key: dict[_OptimStateKey, Union[int, str]],
+    fqn_to_fsdp_param_info: dict[str, FSDPParamInfo],
+    optim_state_dict: dict[Union[str, int], Any],
+    to_save: bool,
+    shard_state: bool,
+    cpu_offload: bool = True,
+) -> dict[str, Any]:
+    fsdp_osd_state: dict[str, Any] = {}
+    # Iterate in rank 0's flat parameter ID order to ensure aligned all-gathers
+    # across ranks
+    for optim_state_key in all_optim_state_keys:
+        param_key: Union[str, int, None] = optim_state_key_to_param_key.get(
+            optim_state_key, None
+        )
+
+        assert param_key is not None, (
+            "If use_orig_params is False, we must be able to find the "
+            f"corresponding param id. {optim_state_key} {param_key}"
+        )
+
+        if optim_state_key.is_fsdp_managed:
+            # If there are multiple unflat_param_names (not use_orig_params),
+            # they share the same FSDPParamInfo. So the first unflat_param_name
+            # is sufficient to fetch the FSDPParamInfo.
+            fqn = optim_state_key.unflat_param_names[0]
+            fsdp_param_info = fqn_to_fsdp_param_info[fqn]
+            unflat_state = _unflatten_optim_state(
+                fsdp_param_info,
+                optim_state_dict[param_key],
+                to_save,
+                shard_state,
+                cpu_offload,
+            )
+            if to_save:
+                assert len(unflat_state) == len(optim_state_key.unflat_param_names)
+                for unflat_param_name, unflat_param_state in zip(
+                    optim_state_key.unflat_param_names,
+                    unflat_state,
+                ):
+                    fsdp_osd_state[unflat_param_name] = unflat_param_state
+        elif to_save:
+            assert len(optim_state_key.unflat_param_names) == 1
+            unflat_param_name = optim_state_key.unflat_param_names[0]
+            fsdp_osd_state[unflat_param_name] = copy.copy(optim_state_dict[param_key])
+            if cpu_offload:
+                for state_name, value in sorted_items(
+                    fsdp_osd_state[unflat_param_name]
+                ):
+                    if not torch.is_tensor(value):
+                        continue
+                    fsdp_osd_state[unflat_param_name][state_name] = value.cpu()
+
+    return fsdp_osd_state
+
+
+@torch.no_grad()
+def _optim_state_dict(
+    model: nn.Module,
+    optim: torch.optim.Optimizer,
+    optim_state_dict: dict[str, Any],
+    optim_input: Optional[
+        Union[
+            list[dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ],
+    rank0_only: bool,
+    shard_state: bool,
+    group: Optional[dist.ProcessGroup],
+    using_optim_input: bool,
+    use_orig_params: bool = False,
+    cpu_offload: bool = True,
+) -> dict[str, Any]:
+    """
+    Consolidates the optimizer state and returns it as a :class:`dict`
+    following the convention of :meth:`torch.optim.Optimizer.state_dict`,
+    i.e. with keys ``"state"`` and ``"param_groups"``.
+    The flat parameters in ``FSDP`` modules contained in ``model`` are mapped
+    back to their unflattened parameters.
+
+    Parameter keys are not well-defined. For a regular optimizer, the optimizer
+    state_dict contains a mapping from parameter IDs to parameter states.
+    Parameter IDs are the order of parameters in ``optim.param_groups()`` across
+    all the groups. This API also allows user to pass ``optim_input`` for the
+    mapping between parameters and parameter IDs. Using ``optim_input`` is being
+    deprecated.
+
+    If the optimizer is a ``NamedOptimizer``, the optimizer state_dict does not
+    contain parameter IDs mapping but a mapping from parameter FQNs to parameter
+    states. This API finds the mapping from FQNs to parameters if the optimizer
+    is a ``NamedOptimizer``.
+
+    If ``use_orig_params`` is True, each rank will have all FSDP-managed
+    parameters but some of these parameters may be empty due to the sharding.
+    For a regular optim.Optimizer, states for those empty parameters will
+    not be initialized. So, when aggregating the FQNs across ranks, no assert
+    will be raised on a rank even if it does not have all the states -- it is
+    valid and FSDP knows how to aggregate them. However, FSDP has to ignore
+    handling those parameters that are not managed by FSDP and do not exist on
+    the local rank -- those are managed by other parallelisms and FSDP does not
+    know how to handle/aggregate them.
+
+    Args:
+        model (nn.Module): Root module (which may or may not be a
+            :class:`FullyShardedDataParallel` instance) whose parameters
+            were passed into the optimizer ``optim``.
+        optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+            parameters.
+        rank0_only (bool): If ``True``, saves the populated :class:`dict`
+            only on rank 0; if ``False``, saves it on all ranks. (Default:
+            ``True``)
+        shard_state (bool): If ``True``, shard and distribute all
+            non-zero-dimension states.
+
+    Returns:
+        Dict[str, Any]: A :class:`dict` containing the optimizer state for
+        ``model`` 's original unflattened parameters and including keys
+        "state" and "param_groups" following the convention of
+        :meth:`torch.optim.Optimizer.state_dict`. If ``rank0_only=False``,
+        then nonzero ranks return an empty :class:`dict`.
+    """
+    SimpleProfiler.reset()
+    cm = ExitStack()
+    cm.enter_context(SimpleProfiler.profile(SimpleProfiler.Type.ALL))
+    _reset_flat_param_grad_info_if_needed(traversal_utils._get_fsdp_handles(model))
+    to_save = not rank0_only or dist.get_rank(group) == 0 or shard_state
+
+    with SimpleProfiler.profile("preprocessing"):
+        param_to_fqns = _get_param_to_fqns(model)
+        flat_param_to_fqn = _get_flat_param_to_fqn(model)
+        is_named_optimizer = _is_named_optimizer(optim_state_dict)
+
+        param_key_to_param = cast(
+            dict[Union[int, str], nn.Parameter],
+            (
+                _get_param_id_to_param_from_optim_input(model, optim_input)
+                if using_optim_input
+                else _get_param_key_to_param(
+                    optim, model, is_named_optimizer, param_to_fqns, flat_param_to_fqn
+                )
+            ),
+        )
+        fqn_to_fsdp_param_info = _get_fqn_to_fsdp_param_info(model)
+
+    with SimpleProfiler.profile("preprocessing_with_comm"):
+        (
+            all_optim_state_keys,
+            optim_state_key_to_param_key,
+        ) = _map_param_key_to_optim_keys(
+            optim_state_dict,
+            group,
+            param_key_to_param,
+            param_to_fqns,
+            fqn_to_fsdp_param_info,
+            merge_keys=use_orig_params,
+        )
+
+    with SimpleProfiler.profile("state_converting"):
+        convert_fn = (
+            _convert_state_with_orig_params
+            if use_orig_params
+            else _convert_state_with_flat_params
+        )
+        fsdp_osd_state = convert_fn(
+            all_optim_state_keys,
+            optim_state_key_to_param_key,
+            fqn_to_fsdp_param_info,
+            optim_state_dict["state"],
+            to_save,
+            shard_state,
+            cpu_offload,
+        )
+
+    # At this point, communication is complete and ranks can return early if nothing
+    # will be saved on that rank.
+    if not to_save:
+        return {}
+
+    fsdp_osd: dict[str, Any] = {"state": fsdp_osd_state}
+
+    flat_param_fqns = set(flat_param_to_fqn.values())
+    for key, value in optim_state_dict["state"].items():
+        if key in fsdp_osd_state:
+            continue
+        if key in flat_param_fqns:
+            continue
+        if key in param_key_to_param:
+            continue
+        # This key is not recognized by FSDP. It may be a user-defined state
+        # or some parameters state that FSDP is unable to map from
+        # ``optim.param_groups``.
+        warnings.warn(
+            f"Found a optim state, {key}, that FSDP cannot process. FSDP "
+            "will directly copy everything to the returned state_dict. In "
+            "most cases, this is a user-defined state that is not "
+            "associated with any particular parameter. Another possible "
+            "case is this state is managed by TorchRec. Otherwise, there may "
+            " be a mismatched assumption of optim_state_dict of this mode."
+        )
+        fsdp_osd_state[key] = value
+
+    if "param_groups" in optim_state_dict:
+        fsdp_osd["param_groups"] = _unflatten_param_groups(
+            optim_state_dict, param_key_to_param, param_to_fqns
+        )
+
+    cm.close()
+    SimpleProfiler.dump_and_reset("FSDP _optim_state_dict() profiling: ")
+
+    return fsdp_osd
+
+
+def _get_fqn_to_fsdp_param_info(model: nn.Module) -> dict[str, FSDPParamInfo]:
+    """
+    Construct the mapping from a param's fqn to its corresponding ``FSDPParamInfo``
+    if the param is managed by FSDP. Shared parameters, or original parameters that
+    are shared across multiple nn.Modules, are required to belong to one and only
+    one FSDP instance and thus correspond to one ``FlatParameter``. Within the one
+    ``FlatParameter``, ``FlatParameter._fqns`` only stores the first FQN of a shared
+    parameter. Thus, the keys in the mapping are guaranteed to map to unique parameters.
+    """
+
+    def module_fn(module, prefix, tree_level, fqn_to_param_info):
+        fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+        if fsdp_state is None:
+            return
+        _lazy_init(fsdp_state, module)
+        handle = _module_handle(fsdp_state, module)
+        if not handle:
+            return
+        flat_param = handle.flat_param
+        fsdp_param_info = FSDPParamInfo(fsdp_state, handle, {}, [])
+        # NOTE: `idx` indexes into the data structures *without* padding
+        # elements
+        for idx, local_fqn in enumerate(flat_param._fqns):
+            fqn = clean_tensor_name(prefix + local_fqn)
+            if fqn in fqn_to_param_info:
+                assert fqn_to_param_info[fqn].handle.flat_param is flat_param, fqn
+            fqn_to_param_info[fqn] = fsdp_param_info
+            fsdp_param_info.param_indices[fqn] = idx
+            if flat_param._params is not None:
+                fsdp_param_info.param_requires_grad.append(
+                    flat_param._params[idx].requires_grad
+                )
+
+    def return_fn(fqn_to_param_info):
+        return fqn_to_param_info
+
+    fqn_to_param_info: dict[str, FSDPParamInfo] = {}
+    # FlatParameter._fqns stores the local fqn, starting from the root of the
+    # FSDP. Using _apply_to_modules() with model (may not be the FSDP root
+    # module) allows us to construct the global fqn.
+    return _apply_to_modules(
+        model,
+        module_fn,
+        return_fn,
+        [fqn for fqn, _ in _named_parameters_with_duplicates(model)],
+        fqn_to_param_info,
+    )
+
+
+@no_type_check
+def _set_optim_use_dtensor(
+    fsdp_state: _FSDPState,
+    state_dict_settings: StateDictSettings,
+) -> None:
+    # If device_mesh is passed in when initalizing FSDP, we automatically turn the
+    # _use_dtensor flag to be true for ShardedOptimStateDictConfig() if state_dict_type
+    # has to be set to SHARDED_STATE_DICT.
+    if getattr(fsdp_state, "_device_mesh", None):
+        state_dict_type = state_dict_settings.state_dict_type
+        if state_dict_type == StateDictType.LOCAL_STATE_DICT:
+            raise RuntimeError(
+                "Found state_dict_type LOCAL_STATE_DICT.",
+                "DeviceMesh is not compatible with LOCAL_STATE_DICT.",
+                "Please set state_dict_type to SHARDED_STATE_DICT to get DTensor state_dict.",
+            )
+        else:
+            state_dict_settings.optim_state_dict_config._use_dtensor = True
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_runtime_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_runtime_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f723e8e0464edd89605ba79b844739fe6f9ca413
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_runtime_utils.py
@@ -0,0 +1,1645 @@
+# mypy: allow-untyped-defs
+import functools
+import logging
+from enum import auto, Enum
+from typing import Any, Callable, no_type_check, Optional
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from torch.autograd.graph import register_multi_grad_hook
+from torch.distributed.algorithms._comm_hooks import LOW_PRECISION_HOOKS
+from torch.distributed.fsdp._common_utils import (
+    _assert_in_training_states,
+    _FSDPState,
+    _get_module_fsdp_state,
+    _is_composable,
+    _log_post_backward_hook,
+    _no_dispatch_record_stream,
+    clean_tensor_name,
+    TrainingState,
+)
+from torch.distributed.fsdp._flat_param import (
+    FlatParameter,
+    FlatParamHandle,
+    HandleShardingStrategy,
+    HandleTrainingState,
+    RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES,
+)
+from torch.distributed.fsdp._init_utils import HYBRID_SHARDING_STRATEGIES
+from torch.distributed.fsdp.api import BackwardPrefetch
+from torch.distributed.utils import (
+    _apply_to_tensors,
+    _cast_forward_inputs,
+    _p_assert,
+    _to_kwargs,
+)
+from torch.utils import _pytree as pytree
+
+
+logger = logging.getLogger(__name__)
+
+# Do not include "process_group" to enable hybrid shard and MoE cases
+HOMOGENEOUS_ATTR_NAMES = (
+    "_use_orig_params",
+    "limit_all_gathers",
+    "_use_full_prec_in_eval",
+)
+
+
+class _PrefetchMode(Enum):
+    BACKWARD = auto()
+    FORWARD = auto()
+
+
+def _get_fsdp_root_states_with_modules(
+    module: nn.Module,
+) -> tuple[list[_FSDPState], list[nn.Module]]:
+    """
+    Returns a tuple containing:
+    1. A list of the root ``_FSDPState`` instances in the module tree rooted at
+    ``module`` without any duplicates and following the ``module.modules()``
+    traversal order (which is assumed to be depth-first).
+    2. A corresponding list of the root modules owning the states in the first
+    list.
+
+    This is similar to :func:`_get_fsdp_states_with_modules` except that we
+    must call :func:`_is_fsdp_root` to force a lazy initialization to determine
+    the FSDP root in case lazy initialization has not yet happened.
+    """
+    fsdp_root_states: list[_FSDPState] = []
+    fsdp_root_modules: list[nn.Module] = []
+    visited_fsdp_states: set[_FSDPState] = set()
+    # NOTE: This function assumes that `module.modules()` proceeds top-down.
+    for submodule in module.modules():
+        optional_state = _get_module_fsdp_state(submodule)
+        if (
+            optional_state is not None
+            and optional_state not in visited_fsdp_states
+            and _is_fsdp_root(optional_state, submodule)
+        ):
+            visited_fsdp_states.add(optional_state)
+            fsdp_root_states.append(optional_state)
+            fsdp_root_modules.append(submodule)
+    return fsdp_root_states, fsdp_root_modules
+
+
+def _get_fsdp_root_states(module: nn.Module) -> list[_FSDPState]:
+    """See :func:`_get_fsdp_root_states_with_modules`."""
+    fsdp_root_states, _ = _get_fsdp_root_states_with_modules(module)
+    return fsdp_root_states
+
+
+def _is_fsdp_root(state: _FSDPState, module: nn.Module) -> bool:
+    """
+    Returns if ``state`` corresponds to that of an FSDP root.
+
+    For the wrapper code path, ``state`` and ``module`` should be the same. For
+    the non-wrapper code path, ``state`` should be ``module`` 's state.
+    """
+    # Force a lazy initialization to determine the FSDP root
+    _lazy_init(state, module)
+    assert state._is_root is not None  # mypy
+    return state._is_root
+
+
+@no_type_check
+def _lazy_init(
+    state: _FSDPState,
+    root_module: nn.Module,
+) -> _FSDPState:
+    """
+    Performs initialization lazily, typically right before the first forward
+    pass. The laziness is needed to ensure that the parameter device/dtype and
+    the FSDP hierarchy have finalized. This method's actual logic only runs on
+    the root FSDP instance, which performs initialization for all non-root FSDP
+    instances to avoid partial initialization.
+
+    For the non-composable code path, ``state`` and ``root_module`` should be
+    the same, namely the FSDP instance itself.
+    """
+    if state._is_root is not None:
+        return  # no-op: already lazily initialized
+    if not state._device_handle.is_available():
+        # Allow the FSDP constructor to run even without CUDA but check this
+        # once we start real execution
+        raise RuntimeError("FSDP does not support CPU only execution")
+    # The following logic is only run on the root FSDP instance since it will
+    # set `_is_root=False` for the non-root instances
+    state._is_root = True
+    _assert_in_training_states(state, [TrainingState.IDLE])
+    _check_flat_params_on_expected_device(state, root_module)
+    state._all_fsdp_states = traversal_utils._get_fsdp_states(root_module)
+    _init_streams(state)
+    buffers, buffer_dtypes = _get_buffers_and_dtypes_for_computation(state, root_module)
+    _cast_buffers_to_dtype_and_device(buffers, buffer_dtypes, state.compute_device)
+    state._exec_order_data.init(state, root_module, state.process_group)
+    _share_state_and_init_handle_attrs(state, root_module)
+    return state
+
+
+def _check_flat_params_on_expected_device(state: _FSDPState, module: nn.Module):
+    """
+    Checks that all ``FlatParameter``s in ``module`` 's tree managed by
+    ``state`` are on the expected device for *lazy initialization*.
+    """
+    cpu_device = torch.device("cpu")
+    for handle in traversal_utils._get_fsdp_handles(module):
+        if (
+            not handle._offload_params
+            and handle.flat_param.device != state.compute_device
+        ):
+            raise RuntimeError(
+                "An FSDP-managed module unexpectedly has parameters on "
+                f"{handle.flat_param.device}. Make sure to move the module to "
+                f"{state.compute_device} before training."
+            )
+        elif handle._offload_params and handle.flat_param.device != cpu_device:
+            raise RuntimeError(
+                "An FSDP-managed module with parameter CPU offloading enabled "
+                f"has parameters on {handle.flat_param.device}. Make sure to "
+                f"not move the module from CPU when offloading parameters."
+            )
+
+
+@no_type_check
+def _share_state_and_init_handle_attrs(
+    root_state: _FSDPState,
+    root_module: nn.Module,
+) -> None:
+    """
+    Shares data structure state from the ``root_state`` to all FSDP states in
+    ``root_module`` 's module tree, and initializes handle attributes. These
+    are done together to require a single loop over the states.
+    """
+    handle = root_state._handle
+    if handle:
+        handle.init_flat_param_attributes()
+    attr_name_to_values: dict[str, set[Any]] = {}
+    for attr_name in HOMOGENEOUS_ATTR_NAMES:
+        attr_name_to_values[attr_name] = set()
+    root_state._all_handles = root_state._exec_order_data.all_handles  # share reference
+    # Update _has_optim_in_backward for each handle.
+    for handle in root_state._all_handles:
+        flat_param = handle.flat_param
+        if hasattr(flat_param, "_in_backward_optimizers"):
+            raise RuntimeError(
+                "FSDP optimizer in backward only supported with use_orig_params=True!"
+            )
+        handle._has_optim_in_backward = flat_param._params is not None and any(
+            hasattr(param, "_in_backward_optimizers") for param in flat_param._params
+        )
+        if handle._has_optim_in_backward:
+            torch._C._log_api_usage_once("fsdp.optimizer_in_backward")
+    for fsdp_state in root_state._all_fsdp_states:
+        for attr_name in HOMOGENEOUS_ATTR_NAMES:
+            _p_assert(
+                hasattr(fsdp_state, attr_name),
+                f"FSDP state missing attribute {attr_name}",
+            )
+            attr_name_to_values[attr_name].add(getattr(fsdp_state, attr_name))
+        if fsdp_state is root_state:
+            continue
+        # Relax the assert for non-root FSDP instances in case the nested
+        # initialized module is wrapped again in FSDP later (e.g. after
+        # training to run inference)
+        _p_assert(
+            fsdp_state._is_root is None or not fsdp_state._is_root,
+            "Non-root FSDP instance's `_is_root` should not have been "
+            "set yet or should have been set to `False`",
+        )
+        fsdp_state._is_root = False
+        fsdp_state._unshard_stream = root_state._unshard_stream
+        fsdp_state._post_backward_stream = root_state._post_backward_stream
+        fsdp_state._pre_unshard_stream = root_state._pre_unshard_stream
+        fsdp_state._all_reduce_stream = root_state._all_reduce_stream
+        fsdp_state._default_stream = root_state._default_stream
+        fsdp_state._exec_order_data = root_state._exec_order_data
+        fsdp_state._free_event_queue = root_state._free_event_queue
+        if fsdp_state._fsdp_extension is not None:
+            fsdp_state._fsdp_extension.compute_stream = root_state._default_stream
+        handle = fsdp_state._handle
+        if handle:
+            handle.init_flat_param_attributes()
+    for attr_name, attr_values in attr_name_to_values.items():
+        if len(attr_values) != 1:
+            raise ValueError(
+                f"Expects one homogeneous value for {attr_name} but got {attr_values}"
+            )
+
+
+@no_type_check
+def _init_streams(
+    state: _FSDPState,
+) -> None:
+    """
+    Initializes CUDA streams for overlapping communication, computation, and
+    data transfers. The streams should be shared across FSDP instances.
+    """
+    assert state._is_root
+    assert state._device_handle.is_available()
+    uses_hybrid_sharding = any(
+        fsdp_state.sharding_strategy in HYBRID_SHARDING_STRATEGIES
+        for fsdp_state in state._all_fsdp_states
+    )
+    # Prioritize all-gathers/reduce-scatters over async all-reduce for HSDP and
+    # preserve the default priority of 0 otherwise
+    high_priority = -1 if state.limit_all_gathers and uses_hybrid_sharding else 0
+    # Default stream for computation
+    state._default_stream = state._device_handle.current_stream()
+    if state._fsdp_extension is not None:
+        # set the compute stream to the FSDP extension
+        state._fsdp_extension.compute_stream = state._default_stream
+
+    # Stream for unshard logic, including allocating the all-gather destination
+    # tensors and the all-gathers themselves
+    state._unshard_stream = state._device_handle.Stream(priority=high_priority)
+    # Stream for overlapping gradient reduction with the backward pass gradient
+    # computation
+    state._post_backward_stream = state._device_handle.Stream(priority=high_priority)
+    # Stream for pre-unshard logic, namely allocations and writes for CPU
+    # offloading (H2D copy) and mixed precision (low precision cast)
+    state._pre_unshard_stream = state._device_handle.Stream(priority=high_priority)
+    # Stream to run HSDP's all-reduce as async (if using HSDP)
+    state._all_reduce_stream = (
+        state._device_handle.Stream() if uses_hybrid_sharding else state._default_stream
+    )
+
+
+@no_type_check
+def _unshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    unshard_stream: torch.Stream,
+    pre_unshard_stream: torch.Stream,
+) -> None:
+    """
+    Unshards the handles in ``handles``. If the handles are in
+    :meth:`summon_full_params` and are using mixed precision, then they are
+    forced to full precision.
+
+    Postcondition: handle's ``FlatParameter`` 's data is the padded
+    unsharded flat parameter on the compute device.
+    """
+    if not handle:
+        return
+    with state._device_handle.stream(pre_unshard_stream):
+        ran_pre_unshard = handle.pre_unshard()
+    if ran_pre_unshard:
+        unshard_stream.wait_stream(pre_unshard_stream)
+    if state.limit_all_gathers:
+        event = state._free_event_queue.dequeue_if_needed()
+        if event:
+            with torch.profiler.record_function(
+                "FullyShardedDataParallel.rate_limiter"
+            ):
+                event.synchronize()
+    with state._device_handle.stream(unshard_stream):
+        handle.unshard()
+        handle.post_unshard()
+
+
+@no_type_check
+def _reshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    free_unsharded_flat_param: bool,
+):
+    """
+    Reshards the handle. ``free_unsharded_flat_param`` indicates whether to
+    free the handle's padded unsharded flat parameter.
+    """
+    handle.reshard(free_unsharded_flat_param)
+    if state.limit_all_gathers and free_unsharded_flat_param:
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            # We don't run a even queue for freeing under torch compile atm
+            # But maybe we need to? TODO(voz): Look into this
+            free_event = state._device_handle.Event()
+            free_event.record()
+            state._free_event_queue.enqueue(free_event)
+    handle.post_reshard()
+    # Flat parameter freed or not, we always have to "unshard" the parameter
+    # upon next access to get its shape correct.
+    handle._prefetched = False
+
+
+def _unshard_grads(
+    handle: Optional[FlatParamHandle],
+) -> None:
+    if handle:
+        handle.unshard_grad()
+
+
+def _reshard_grads(
+    handle: Optional[FlatParamHandle],
+) -> None:
+    if handle:
+        handle.reshard_grad()
+
+
+@no_type_check
+def _pre_forward(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+    unshard_fn: Callable,
+    module: nn.Module,
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any],
+) -> tuple[tuple[Any, ...], dict[str, Any]]:
+    """
+    Runs the pre-forward logic. This includes an opportunity to unshard
+    currently sharded parameters such as those for the current forward and
+    registering post-backward hooks for these current parameters. This function
+    also converts forward ``args`` and ``kwargs`` to the given precision.
+
+    Args:
+        handles (List[FlatParamHandle]): Handles giving the parameters used in
+            the current forward.
+        unshard_fn (Optional[Callable]): A callable to unshard any currently
+            sharded parameters or ``None`` to not do any unsharding.
+        module (nn.Module): Module whose forward this method runs right before;
+            expected by the hook signature.
+        args (Tuple[Any, ...]): Module forward ``args``.
+        kwargs (Dict[str, Any]): Module forward ``kwargs``.
+    """
+    with torch.profiler.record_function("FullyShardedDataParallel._pre_forward"):
+        # For `fully_shard` + `checkpoint`, skip pre-forward logic in the
+        # recomputed forward
+        if handle and handle._training_state == HandleTrainingState.BACKWARD_PRE:
+            # For both checkpoint implementations, we do not need to re-cast
+            # inputs here since they will be checkpointed in the low precision
+            # either by AC or normally by autograd as long as the AC region is
+            # nested within FSDP
+            return args, kwargs
+        state.training_state = TrainingState.FORWARD_BACKWARD
+        state._exec_order_data.record_pre_forward(handle, module.training)
+        if handle:
+            handle._training_state = HandleTrainingState.FORWARD
+        if unshard_fn is not None:
+            unshard_fn(state, handle)
+        # Register post-backward hooks to reshard the parameters and reduce-scatter
+        # their gradients. They must be re-registered every forward pass in case
+        # the `grad_fn` is mutated.
+        _register_post_backward_hook(state, handle)
+        # We have to reallocate the _cpu_grad if optimizer overlap
+        # set the grad to None in the backward pass.
+        if handle and handle._offload_params and handle.flat_param._cpu_grad is None:
+            handle.flat_param._cpu_grad = torch.zeros_like(
+                handle.flat_param._local_shard, device=torch.device("cpu")
+            ).pin_memory()
+
+        should_cast_forward_inputs = (
+            state._handle and not state._handle._force_full_precision
+        )
+
+        if should_cast_forward_inputs and state.mixed_precision.cast_forward_inputs:
+            # Recursively convert args and kwargs to specified precision.
+            input_dtype: Optional[torch.dtype] = state.mixed_precision.param_dtype
+            args, kwargs = _cast_forward_inputs(input_dtype, *args, **kwargs)
+        _register_post_backward_reshard_only_hook(state, handle, args, kwargs)
+        return args, kwargs
+
+
+@no_type_check
+def _pre_forward_unshard(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+) -> None:
+    """Unshards parameters in the pre-forward."""
+    if not handle:
+        return
+    # If the handles have been prefetched, then there is no need to call
+    # `_unshard()` again
+    if not handle._prefetched:
+        _unshard(state, handle, state._unshard_stream, state._pre_unshard_stream)
+    handle._needs_pre_forward_unshard = False
+    # Don't wait during trace
+    if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        current_stream = state._device_handle.current_stream()
+        if state._unshard_event is not None:
+            current_stream.wait_event(state._unshard_event)
+            state._unshard_event = None
+        else:
+            current_stream.wait_stream(state._unshard_stream)
+    with torch.profiler.record_function(
+        "FullyShardedDataParallel._pre_forward_prefetch"
+    ):
+        _prefetch_handle(state, handle, _PrefetchMode.FORWARD)
+
+
+@no_type_check
+def _post_forward(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+    reshard_fn: Callable,
+    module: nn.Module,
+    input: Any,
+    output: Any,
+) -> Any:
+    """
+    Runs the post-forward logic. This includes an opportunity to reshard
+    currently unsharded parameters such as those used in the current forward
+    and registering pre-backward hooks on the forward outputs.
+
+    Args:
+        handles (List[FlatParamHandle]): Handles giving the parameters used in
+            the current forward.
+        reshard_fn (Optional[Callable]): A callable to reshard any currently
+            unsharded parameters (e.g. from the current forward) or ``None`` to
+            not do any resharding.
+        module (nn.Module): Module whose forward just ran, which should be a
+            fully sharded module (see [Note: Fully Sharded Module]); expected
+            by the hook signature.
+        input (Any): Unused; expected by the hook signature.
+        output (Any): Forward pass output; pre-backward hooks are registered on
+            the tensors that require gradients in this output.
+
+    Postcondition: Each ``FlatParameter`` 's data points to the sharded flat
+    parameter.
+    """
+    with torch.profiler.record_function("FullyShardedDataParallel._post_forward"):
+        # For `fully_shard` + `checkpoint`, skip post-forward logic in the
+        # recomputed forward
+        if handle and handle._training_state == HandleTrainingState.BACKWARD_PRE:
+            return output
+
+        state._exec_order_data.record_post_forward(handle)
+        if reshard_fn is not None:
+            reshard_fn(state, handle)
+        # Register pre-backward hooks to unshard the flat parameters for the
+        # gradient computation (if needed)
+        output = _register_pre_backward_hooks(state, module, output, handle)
+        state.training_state = TrainingState.IDLE
+        if handle:
+            handle._training_state = HandleTrainingState.IDLE
+        return output
+
+
+@no_type_check
+def _post_forward_reshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+) -> None:
+    """Reshards parameters in the post-forward."""
+    if not handle:
+        return
+    # Do not free the root's parameters in the post-forward for `FULL_SHARD`
+    # with the intention that they are immediately used for backward
+    # computation (though this may not be true)
+    free_unsharded_flat_param = (
+        not state._is_root
+        and handle._sharding_strategy in RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES
+    )
+    _reshard(state, handle, free_unsharded_flat_param)
+
+
+@no_type_check
+def _root_pre_forward(
+    state: _FSDPState,
+    module: nn.Module,
+    args,
+    kwargs,
+) -> None:
+    """
+    Runs pre-forward logic specific to the root FSDP instance, which should run
+    before any individual module's pre-forward. This starts with an attempt at
+    lazy initialization (which only runs non-vacuously once). Otherwise, if
+    this is called on a non-root FSDP instance, then it returns directly.
+
+    Args:
+        module (nn.Module): Module for which this logic tries to run. It may or
+            may not be the root. If not, then this method does not do anything.
+    """
+    with torch.profiler.record_function("FullyShardedDataParallel._root_pre_forward"):
+        _lazy_init(state, module)
+        _p_assert(state._is_root is not None, "Expects a root FSDP to have been set")
+        if not state._is_root:
+            # Always cast forward inputs in the root of this local FSDP unit for mixed
+            # precision, as this is where mixed precision could be configed.
+            # This is more useful for auto wrapping that is recommended in composable path.
+            # For manual wrapping, cast forward inputs on each local FSDP unit root will
+            # increase some overhead, so not turned on for model wrapper path right now where
+            # manual wrapping is more broadly used.
+            if _is_composable(state):
+                return _root_cast_forward_input(state, module, args, kwargs)
+            return args, kwargs
+
+        # We cast buffers back to full precision if we're forcing full precision. Disjointly, we check if buffers
+        # are in full precision and if we should cast them back to lower precision, which happens when
+        # exiting eval() mode.
+        handle = state._handle
+        if handle:
+            should_cast_buffers_to_full_prec = handle._force_full_precision
+        else:
+            # If the root has no handle (no managed parameters), then we fall
+            # back to checking if any child wants to force full precision as a
+            # workaround
+            handles = traversal_utils._get_fsdp_handles(module)
+            should_cast_buffers_to_full_prec = any(
+                handle._force_full_precision for handle in handles
+            )
+
+        if should_cast_buffers_to_full_prec:
+            _cast_buffers_to_dtype_and_device(
+                buffers=dict(module.named_buffers()).values(),
+                buffer_dtypes=list(state._buffer_name_to_orig_dtype.values()),
+                device=state.compute_device,
+            )
+            # This flag is only set when we cast buffers to full precision, to avoid the
+            # CPU overhead that can stem from retrieving all buffers and their types in the
+            # following else branch.
+            state._needs_buffer_dtype_restore_check = True
+        elif getattr(state, "_needs_buffer_dtype_restore_check", False):
+            # Check if buffers are in full precision and we need to cast them
+            # back down.
+            (
+                buffers,
+                buffer_dtypes_for_computation,
+            ) = _get_buffers_and_dtypes_for_computation(state, module)
+            if len(buffers) > 0 and len(buffer_dtypes_for_computation) > 0:
+                if any(
+                    buffer.dtype != buffer_dtype_for_computation
+                    for buffer, buffer_dtype_for_computation in zip(
+                        buffers, buffer_dtypes_for_computation
+                    )
+                ):
+                    # Assume we have to cast everything if there is one mismatch
+                    _cast_buffers_to_dtype_and_device(
+                        buffers, buffer_dtypes_for_computation, state.compute_device
+                    )
+            # We don't have to check this again until we cast buffers to full precision again.
+            state._needs_buffer_dtype_restore_check = False
+
+        if state.forward_prefetch:
+            handles = [
+                fsdp_state._handle
+                for fsdp_state in state._all_fsdp_states
+                if fsdp_state._handle
+            ]
+            for handle in handles:
+                handle._needs_pre_forward_unshard = True
+                handle._prefetched = False
+        _wait_for_computation_stream(
+            state._device_handle.current_stream(),
+            state._unshard_stream,
+            state._pre_unshard_stream,
+        )
+        _reset_flat_param_grad_info_if_needed(state._all_handles)
+
+        # Prepares the forward inputs by moving them to ``compute_device``
+        # TODO: Do not use the side stream for tensor copies for now; investigate
+        # the perf with/without it.
+        with torch.profiler.record_function("FullyShardedDataParallel._to_kwargs"):
+            args_tuple, kwargs_tuple = _to_kwargs(
+                args, kwargs, state.compute_device, False
+            )
+        args = args_tuple[0] if args_tuple else tuple()
+        kwargs = kwargs_tuple[0] if kwargs_tuple else {}
+
+        return _root_cast_forward_input(state, module, args, kwargs)
+
+
+@no_type_check
+def _root_cast_forward_input(
+    state: _FSDPState, module: torch.nn.Module, args, kwargs
+) -> tuple[Any, Any]:
+    if state._handle:
+        force_full_precision = not state._handle._force_full_precision
+    else:
+        force_full_precision = True
+
+    should_cast_forward_inputs = (
+        (module.training or not state._use_full_prec_in_eval) and force_full_precision
+    ) and state.mixed_precision.cast_root_forward_inputs
+
+    if should_cast_forward_inputs:
+        input_dtype: Optional[torch.dtype] = state.mixed_precision.param_dtype
+        args, kwargs = _cast_forward_inputs(input_dtype, *args, **kwargs)
+
+    return args, kwargs
+
+
+@no_type_check
+def _pre_backward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+    handle: FlatParamHandle,
+    grad,
+    *unused: Any,
+) -> Any:
+    """
+    Prepares ``_handle`` 's ``FlatParameter`` s for gradient computation.
+
+    Args:
+        module (nn.Module): Fully sharded module (see [Note: Fully Sharded
+            Module]).
+    """
+    # Only run the pre-backward hook once per group of handles involved in the
+    # same module forward computation
+    if (
+        handle
+        and hasattr(handle, "_ran_pre_backward_hook")
+        and handle._ran_pre_backward_hook
+    ):
+        return grad
+
+    with torch.profiler.record_function("FullyShardedDataParallel._pre_backward_hook"):
+        # Queue the post-backward callback once for the root FSDP instance to
+        # attach it to the outermost backward graph task so that it is called
+        # after all backward calls complete
+        if state._is_root and not state._post_backward_callback_queued:
+            _register_post_backward_final_callback(state, module)
+            _reset_flat_param_grad_info_if_needed(state._all_handles)
+        elif handle:
+            allowed_states = [TrainingState.IDLE]
+            if _is_composable(state):
+                allowed_states.append(TrainingState.FORWARD_BACKWARD)
+            _assert_in_training_states(state, allowed_states)
+        state.training_state = TrainingState.FORWARD_BACKWARD
+        # Queueing the post-backward callback is the only logic that is not
+        # per-handle in the pre-backward hook, so we can return early here if
+        # there are no handles.
+        if not handle:
+            return grad
+        handle._training_state = HandleTrainingState.BACKWARD_PRE
+
+        if handle._needs_pre_backward_unshard:
+            # If the handles have been prefetched, then there is no need to
+            # call `_unshard()` again
+            if not handle._prefetched:
+                _unshard(
+                    state,
+                    handle,
+                    state._unshard_stream,
+                    state._pre_unshard_stream,
+                )
+            # Don't wait during trace
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                state._device_handle.current_stream().wait_stream(state._unshard_stream)
+
+        # Set this to `False` to ensure that a mistargeted prefetch does not
+        # actually unshard these handles
+        handle._needs_pre_backward_unshard = False
+        with torch.profiler.record_function(
+            "FullyShardedDataParallel._pre_backward_prefetch"
+        ):
+            _prefetch_handle(state, handle, _PrefetchMode.BACKWARD)
+        handle.prepare_gradient_for_backward()
+        handle._ran_pre_backward_hook = True
+        return grad
+
+
+@no_type_check
+@torch.no_grad()
+def _post_backward_hook(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    flat_param,
+    *unused: Any,
+):
+    """
+    Reduce-scatters the gradient of ``handle`` 's ``FlatParameter``.
+
+    Precondition: The ``FlatParameter`` 's ``.grad`` attribute contains the
+    unsharded gradient for the local batch.
+
+    Postcondition:
+    - If using ``NO_SHARD``, then the ``.grad`` attribute is the reduced
+    unsharded gradient.
+    - Otherwise, the ``_saved_grad_shard`` attribute is the reduced sharded
+    gradient (accumulating with any existing gradient).
+    """
+    _log_post_backward_hook(state, handle, logger)
+    flat_param = handle.flat_param
+    flat_param._post_backward_called = True
+    with torch.autograd.profiler.record_function(
+        "FullyShardedDataParallel._post_backward_hook"
+    ):
+        _assert_in_training_states(state, [TrainingState.FORWARD_BACKWARD])
+        # For multiple applications of reentrant AC across submodules sharing
+        # the same `FlatParameter`, the post-backward hook may run multiple
+        # times in one backward, in which case we permit the state to already
+        # be in `BACKWARD_POST`.
+        _p_assert(
+            handle._training_state
+            in (HandleTrainingState.BACKWARD_PRE, HandleTrainingState.BACKWARD_POST),
+            f"Expects `BACKWARD_PRE` or `BACKWARD_POST` state but got {handle._training_state}",
+        )
+        handle._training_state = HandleTrainingState.BACKWARD_POST
+
+        if flat_param.grad is None:
+            return
+        if flat_param.grad.requires_grad:
+            raise RuntimeError("FSDP does not support gradients of gradients")
+
+        _post_backward_reshard(state, handle)
+        if not state._sync_gradients:
+            if handle._use_orig_params:
+                handle._use_unsharded_grad_views()
+            return
+
+        # Wait for all ops in the current stream (e.g. gradient computation) to
+        # finish before reduce-scattering the gradient
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            state._post_backward_stream.wait_stream(
+                state._device_handle.current_stream()
+            )
+
+        with state._device_handle.stream(state._post_backward_stream):
+            autograd_computed_grad = flat_param.grad.data
+            if (
+                not _low_precision_hook_enabled(state)
+                and flat_param.grad.dtype != handle._reduce_dtype
+                # If we are forcing full precision but communicating grads
+                # (i.e. model.eval() + full precision in eval was configured), don't downcast gradient.
+                and not handle._force_full_precision
+            ):
+                flat_param.grad.data = flat_param.grad.to(handle._reduce_dtype)
+            if handle.uses_sharded_strategy:
+                _reduce_grad(state, handle)
+            else:
+                _reduce_grad_no_shard(state, handle)
+            # Since the unsharded gradient is produced in the computation
+            # stream and consumed in the post-backward stream, inform the
+            # caching allocator (before it goes out of scope)
+            _no_dispatch_record_stream(
+                autograd_computed_grad, state._post_backward_stream
+            )
+
+
+def _post_backward_reshard_only_hook(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    *unused: Any,
+) -> None:
+    with torch.profiler.record_function(
+        "FullyShardedDataParallel._post_backward_hook_reshard_only"
+    ):
+        # `_pre_backward_hook` may not get executed
+        # if forward output does not require grad
+        # overwrite IDLE state for post-backward prefetching
+        state.training_state = TrainingState.FORWARD_BACKWARD
+        handle._training_state = HandleTrainingState.BACKWARD_POST
+        _post_backward_reshard(state, handle)
+
+
+def _post_backward_reshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    *unused: Any,
+) -> None:
+    free_unsharded_flat_param = _should_free_in_backward(state, handle)
+    _reshard(state, handle, free_unsharded_flat_param)
+
+    # TODO: Post-backward prefetching does not support the multiple handles
+    # per module case since the post-backward hook runs per handle, not per
+    # group of handles.
+    with torch.profiler.record_function(
+        "FullyShardedDataParallel._post_backward_prefetch"
+    ):
+        _prefetch_handle(state, handle, _PrefetchMode.BACKWARD)
+
+
+@no_type_check
+def _should_free_in_backward(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+) -> bool:
+    """
+    Returns whether FSDP should free the unsharded flat parameter in the
+    post-backward or not.
+    """
+    if not handle.uses_sharded_strategy:
+        return False
+    # If not syncing gradients, then we do not free for strategies that do not
+    # reshard after forward as a *heuristic* to tradeoff higher memory for
+    # higher throughput.
+    return (
+        state._sync_gradients
+        or handle._sharding_strategy in RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES
+    )
+
+
+@no_type_check
+def _reduce_grad(state: _FSDPState, handle: FlatParamHandle) -> None:
+    """
+    For sharded strategies, this runs gradient reduction, sharded gradient
+    accumulation if needed, and the post-reduction callback.
+    """
+    flat_param = handle.flat_param
+    uses_hybrid_sharded_strategy = handle._sharding_strategy in (
+        HandleShardingStrategy.HYBRID_SHARD,
+        HandleShardingStrategy._HYBRID_SHARD_ZERO2,
+    )
+    # We clear `.grad` to permit multiple backwards. This avoids a race where
+    # the second backward pass computation precedes ahead of the first backward
+    # pass reduction, which is possible since the reduction is issued in a
+    # separate stream and is async and would result in reducing the wrong
+    # gradient.
+    unsharded_grad = flat_param.grad.data
+    flat_param.grad = None
+    padded_unsharded_grad, new_sharded_grad = _get_reduce_scatter_tensors(
+        state, unsharded_grad
+    )
+    if state._comm_hook is None:  # default path
+        _div_if_needed(padded_unsharded_grad, state._gradient_predivide_factor)
+        pg = (
+            handle._fake_process_group
+            if handle._use_fake_reduce
+            else state.process_group
+        )
+        dist.reduce_scatter_tensor(
+            new_sharded_grad,
+            padded_unsharded_grad,
+            group=pg,
+        )
+        if uses_hybrid_sharded_strategy:
+            # Don't wait during trace
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                state._all_reduce_stream.wait_stream(state._post_backward_stream)
+            with state._device_handle.stream(state._all_reduce_stream):
+                # Since the new sharded gradient is produced in the post-
+                # backward stream and consumed in the all-reduce stream,
+                # inform the caching allocator
+                _no_dispatch_record_stream(new_sharded_grad, state._all_reduce_stream)
+                dist.all_reduce(new_sharded_grad, group=state._inter_node_pg)
+                _div_if_needed(new_sharded_grad, state._gradient_postdivide_factor)
+                grad_to_offload = _accumulate_sharded_grad(
+                    state, handle, new_sharded_grad
+                )
+                _post_reduce_grad_callback(state, handle, grad_to_offload)
+                return
+        _div_if_needed(new_sharded_grad, state._gradient_postdivide_factor)
+    else:
+        state._comm_hook(
+            state._comm_hook_state, padded_unsharded_grad, new_sharded_grad
+        )
+        # NOTE: HSDP variants do not support communication hook.
+    grad_to_offload = _accumulate_sharded_grad(state, handle, new_sharded_grad)
+    _post_reduce_grad_callback(state, handle, grad_to_offload)
+
+
+@no_type_check
+def _get_reduce_scatter_tensors(
+    state: _FSDPState, unsharded_grad: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Returns the input and output tensors to reduce-scatter, respectively.
+    """
+    chunks = list(unsharded_grad.chunk(state.world_size))
+    numel_to_pad = state.world_size * chunks[0].numel() - unsharded_grad.numel()
+    padded_unsharded_grad = (
+        F.pad(unsharded_grad, [0, numel_to_pad]) if numel_to_pad > 0 else unsharded_grad
+    )
+    new_sharded_grad = torch.empty_like(chunks[0])  # padded
+    return padded_unsharded_grad, new_sharded_grad
+
+
+@no_type_check
+def _accumulate_sharded_grad(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    sharded_grad: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Accumulates the reduce-scattered sharded gradient with any existing sharded
+    gradient if needed, returning the gradient to offload (if CPU offloading is
+    enabled).
+    """
+    flat_param = handle.flat_param
+    _cast_grad_to_param_dtype(state, sharded_grad, flat_param)
+    # Save the sharded gradient in `_saved_grad_shard` to support gradient
+    # accumulation -- for multiple backwards, the gradient reductions may
+    # happen in arbitrary order
+    accumulate_grad = hasattr(flat_param, "_saved_grad_shard")
+    if accumulate_grad:
+        _check_grad_to_accumulate(sharded_grad, flat_param._saved_grad_shard)
+        flat_param._saved_grad_shard += sharded_grad
+    else:
+        flat_param._saved_grad_shard = sharded_grad
+    grad_to_offload = flat_param._saved_grad_shard
+    return grad_to_offload
+
+
+@no_type_check
+def _reduce_grad_no_shard(state: _FSDPState, handle: FlatParamHandle) -> None:
+    """
+    For no-shard, this runs gradient reduction (which directly covers any
+    gradient accumulation implicitly) and the post-reduction callback.
+    """
+    flat_param = handle.flat_param
+    if state._comm_hook is None:  # default path
+        _div_if_needed(flat_param.grad, state._gradient_predivide_factor)
+        dist.all_reduce(flat_param.grad, group=state.process_group)
+        _div_if_needed(flat_param.grad, state._gradient_postdivide_factor)
+    else:
+        state._comm_hook(state._comm_hook_state, flat_param.grad)
+    # For `NO_SHARD`, we can keep the low precision gradients by simply
+    # omitting the cast altogether
+    if not handle._keep_low_precision_grads:
+        _cast_grad_to_param_dtype(state, flat_param.grad, flat_param)
+    grad_to_offload = flat_param.grad.data
+    _post_reduce_grad_callback(state, handle, grad_to_offload)
+
+
+@no_type_check
+def _post_reduce_grad_callback(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    # Additional arguments needed for the callback logic
+    grad_to_offload: torch.Tensor,
+):
+    """
+    This callback captures any logic to run after the gradient reduction
+    finishes. Currently, this offloads the gradient to CPU if CPU offloading is
+    enabled and uses sharded gradient views if ``use_orig_params=True``.
+    """
+    _offload_grad(state, handle, grad_to_offload)
+    _post_backward_use_sharded_grad_views(handle)
+
+
+@no_type_check
+def _offload_grad(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    grad_to_offload: torch.Tensor,
+):
+    if not handle._offload_params:
+        return
+    # Offload the gradient to CPU to ensure parameters and gradients are on the
+    # same device as required by the optimizer
+    # TODO: Investigate why `NO_SHARD` breaks correctness when using
+    # `non_blocking=True` here.
+    # TODO (rohan-varma): When CPU offload and optimizer overlap,
+    # non_blocking=True won't work since the copy may have not finished before
+    # the optimizer step executes on CPU. If we want to use non-blocking=True
+    # here, we'll have to synchronize before using result on CPU.
+    non_blocking = handle.uses_sharded_strategy and not handle._has_optim_in_backward
+    handle.flat_param._cpu_grad.copy_(
+        grad_to_offload.detach(), non_blocking=non_blocking
+    )  # synchronized in the post-backward callback
+    # Since the gradient being offloaded may have been produced in the
+    # computation stream and is being consumed here in the post-backward
+    # stream, inform the caching allocator
+    _no_dispatch_record_stream(grad_to_offload.data, state._post_backward_stream)
+
+
+@no_type_check
+def _post_backward_use_sharded_grad_views(handle: FlatParamHandle):
+    if not handle._use_orig_params:
+        return
+    # Since the handle's `FlatParameter` completed its gradient computation, we
+    # should reset the gradient noneness mask
+    handle._reset_is_grad_none()
+    # Delay using sharded gradient views until after the reduce-scatter instead
+    # of immediately after resharding
+    handle._use_sharded_grad_views()
+    if handle._has_optim_in_backward:
+        handle.prepare_gradient_for_optim()
+        for orig_param in handle.flat_param._params:
+            # Check for `None` gradient to filter parameters not in the rank
+            if orig_param.grad is not None and hasattr(
+                orig_param, "_in_backward_optimizers"
+            ):
+                # TODO (rohan-varma): For CPU offload, this unfortunately
+                # operates on CPU because the parameters and gradients have
+                # already been offloaded. We should run this on GPU after
+                # refactoring.
+                for optim in orig_param._in_backward_optimizers:
+                    optim.step()
+
+                optim.zero_grad(set_to_none=True)
+        handle._reset_flat_param_grad_info_if_needed()
+        if handle._offload_params:
+            handle.flat_param._cpu_grad = None
+
+
+def _div_if_needed(tensor: torch.Tensor, div_factor: float) -> None:
+    if div_factor > 1:
+        tensor.div_(div_factor)
+
+
+@no_type_check
+def _cast_grad_to_param_dtype(
+    state: _FSDPState,
+    sharded_grad: torch.Tensor,
+    param: FlatParameter,
+):
+    """
+    Casts ``sharded_grad`` back to the full parameter dtype so that the
+    optimizer step runs with that dtype. This performs an actual cast if
+    1. parameters were in reduced precision during the forward since then
+    gradients would be in that reduced precision, or
+    2. parameters were not in reduced precision but gradients were in
+    reduced precision for communication.
+    However, if a low precision communication hook is registered, then this
+    dtype cast happens in the hook instead.
+    """
+    _assert_in_training_states(state, [TrainingState.FORWARD_BACKWARD])
+    if not _low_precision_hook_enabled(state) and sharded_grad.dtype != param.dtype:
+        low_prec_grad_data = sharded_grad.data
+        sharded_grad.data = sharded_grad.data.to(dtype=param.dtype)
+        # Since for `NO_SHARD`, the gradient is produced in the computation
+        # stream and consumed here in the post-backward stream, inform the
+        # caching allocator; for the sharded strategies, the gradient is
+        # produced in the post-backward stream, so this `record_stream()`
+        # should be a no-op
+        _no_dispatch_record_stream(
+            low_prec_grad_data, state._device_handle.current_stream()
+        )
+
+
+def _check_grad_to_accumulate(
+    new_sharded_grad: torch.Tensor,
+    accumulated_grad: torch.Tensor,
+) -> None:
+    _p_assert(
+        accumulated_grad.shape == new_sharded_grad.shape,
+        "Shape mismatch when accumulating gradients: "
+        f"existing gradient shape={accumulated_grad.shape} "
+        f"new gradient shape={new_sharded_grad.shape}",
+    )
+    _p_assert(
+        accumulated_grad.device == new_sharded_grad.device,
+        "Device mismatch when accumulating gradients: "
+        f"existing gradient device={accumulated_grad.device} "
+        f"new gradient device={new_sharded_grad.device}",
+    )
+
+
+@no_type_check
+def _low_precision_hook_enabled(state: _FSDPState) -> bool:
+    return state._comm_hook in LOW_PRECISION_HOOKS
+
+
+@no_type_check
+@torch.no_grad()
+def _post_backward_final_callback(
+    state: _FSDPState,
+    module: nn.Module,
+):
+    """
+    This waits for the post-backward to finish and performs some final cleanup.
+    This runs at the end of the entire backward pass and should only be called
+    on the root FSDP instance.
+    """
+    _p_assert(
+        state._is_root,
+        "The post-backward callback should only be called on the root FSDP instance",
+    )
+    root_state = state
+
+    if root_state._sync_gradients:
+        current_stream = state._device_handle.current_stream()
+        # TODO (rohan-varma): this also waits for the overlapped optimizer step to finish
+        # since it currently runs in the post-backward stream. That can be
+        # pushed to the next forward if run in a different stream
+        current_stream.wait_stream(root_state._post_backward_stream)
+        if root_state._all_reduce_stream is not current_stream:  # uses HSDP
+            current_stream.wait_stream(root_state._all_reduce_stream)
+        if root_state.cpu_offload.offload_params:
+            # Wait for non-blocking GPU -> CPU sharded gradient copies from the
+            # post-backward hooks to finish explicitly since CPU gradients do
+            # not automatically synchronize with the GPU
+            state._device_handle.current_stream().synchronize()
+    root_state._exec_order_data.next_iter()
+
+    for fsdp_state in state._all_fsdp_states:
+        _catch_all_reshard(fsdp_state)
+        _finalize_params(fsdp_state)
+        fsdp_state.training_state = TrainingState.IDLE
+        handle = fsdp_state._handle
+        if handle:
+            handle._ran_pre_backward_hook = False
+            handle._needs_pre_backward_unshard = False
+            handle._post_forward_index = None
+            handle._training_state = HandleTrainingState.IDLE
+            handle._prefetched = False
+    # Reset for cases like one forward and multiple backwards
+    root_state._post_backward_callback_queued = False
+
+
+@no_type_check
+def _catch_all_reshard(
+    state: _FSDPState,
+) -> None:
+    """
+    Reshards the parameters that may not have been resharded in the
+    post-backward hook. This can happen when a module's output is used in the
+    forward pass, meaning that its pre-backward hook runs (unsharding the
+    parameter), but the post-backward hook does not run because the output was
+    not jused in the loss computation corresponding to this backward pass.
+    """
+    # Wrap with a try-except to provide a more informative traceback if an
+    # error is raised
+    try:
+        if state._handle:
+            # TODO: This already-resharded check is brittle:
+            # https://github.com/pytorch/pytorch/issues/83956
+            already_resharded = (
+                state._handle.flat_param.data_ptr()
+                == state._handle.flat_param._local_shard.data_ptr()
+                # If FSDP skipped using sharded views, then the flat parameter
+                # still points to the sharded data, so we need to reshard to
+                # use sharded views
+                and not state._handle._skipped_use_sharded_views
+            )
+            if already_resharded:
+                return
+            free_unsharded_flat_param = _should_free_in_backward(state, state._handle)
+            _reshard(state, state._handle, free_unsharded_flat_param)
+    except Exception as e:
+        _p_assert(
+            False,
+            f"Got exception in the catch-all reshard for {state}: {str(e)}",
+            raise_assertion_error=False,
+        )
+        raise e
+
+
+@no_type_check
+def _finalize_params(
+    state: _FSDPState,
+) -> None:
+    """Finalizes the parameters before the next iteration."""
+    handle = state._handle
+    if not handle:
+        return
+    flat_param = handle.flat_param
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        if hasattr(flat_param, "_post_backward_hook_handle"):
+            pbhs_handle = flat_param._post_backward_hook_handle
+            pbhs_handle.remove()
+            del flat_param._post_backward_hook_handle
+    else:
+        if hasattr(flat_param, "_post_backward_hook_state"):
+            post_backward_hook_state_len = len(flat_param._post_backward_hook_state)
+            expected_post_backward_hook_state_len = int(flat_param.requires_grad) + 1
+            _p_assert(
+                post_backward_hook_state_len == expected_post_backward_hook_state_len,
+                f"Invalid: ``_post_backward_hook_state``: {flat_param._post_backward_hook_state}",
+            )
+            flat_param._post_backward_hook_state[-1].remove()
+            delattr(flat_param, "_post_backward_hook_state")
+    if flat_param.requires_grad:
+        if not state._sync_gradients:
+            # Preserve the gradient accumulation state if not synchronizing
+            # gradients: `.grad` remains the unsharded gradient  from prior
+            # `no_sync()` iterations, and `_saved_grad_shard` remains the
+            # sharded gradient from the last synchronized iteration
+            return
+        if not handle._has_optim_in_backward:
+            handle.prepare_gradient_for_optim()
+        _p_assert(
+            hasattr(flat_param, "_post_backward_called"),
+            "Expects `_post_backward_called` to be set on the `FlatParameter`",
+        )
+        flat_param._post_backward_called = False
+
+
+@no_type_check
+def _prefetch_handle(
+    state: _FSDPState,
+    current_handle: Optional[FlatParamHandle],
+    prefetch_mode: _PrefetchMode,
+) -> None:
+    """
+    Prefetches the next handles if needed (without synchronization). An empty
+    handles key cannot prefetch.
+    """
+    if not current_handle:
+        return
+    handle = _get_handle_to_prefetch(state, current_handle)
+    if not handle:
+        return
+    # Temporarily emulate the training state while calling `_unshard` to
+    # ensure the correct `as_params` for `_use_unsharded_views()`
+    prev_training_state = handle._training_state
+    if prefetch_mode == _PrefetchMode.BACKWARD:
+        handle._training_state = HandleTrainingState.BACKWARD_PRE
+    elif prefetch_mode == _PrefetchMode.FORWARD:
+        handle._training_state = HandleTrainingState.FORWARD
+    else:
+        raise ValueError(f"Invalid prefetch mode on rank {state.rank}: {prefetch_mode}")
+    # Prefetch the next set of handles without synchronizing to allow
+    # the sync to happen as late as possible to maximize overlap
+    _unshard(state, handle, state._unshard_stream, state._pre_unshard_stream)
+    handle._training_state = prev_training_state
+    handle._prefetched = True
+
+
+@no_type_check
+def _get_handle_to_prefetch(
+    state: _FSDPState,
+    current_handle: FlatParamHandle,
+) -> FlatParamHandle:
+    """
+    Returns a :class:`list` of the handles keys to prefetch for the next
+    module(s), where ``current_handle`` represents the current module.
+
+    "Prefetching" refers to running the unshard logic early (without
+    synchronization), and the "next" modules depend on the recorded execution
+    order and the current training state.
+    """
+    training_state = _get_training_state(current_handle)
+    valid_training_states = (
+        HandleTrainingState.BACKWARD_PRE,
+        HandleTrainingState.BACKWARD_POST,
+        HandleTrainingState.FORWARD,
+    )
+    _p_assert(
+        training_state in valid_training_states,
+        f"Prefetching is only supported in {valid_training_states} but "
+        f"currently in {training_state}",
+    )
+    eod = state._exec_order_data
+    target_handle: Optional[FlatParamHandle] = None
+    if (
+        training_state == HandleTrainingState.BACKWARD_PRE
+        and state.backward_prefetch == BackwardPrefetch.BACKWARD_PRE
+    ) or (
+        training_state == HandleTrainingState.BACKWARD_POST
+        and state.backward_prefetch == BackwardPrefetch.BACKWARD_POST
+    ):
+        target_handle_candidate = eod.get_handle_to_backward_prefetch(current_handle)
+        if (
+            target_handle_candidate
+            and target_handle_candidate._needs_pre_backward_unshard
+            and not target_handle_candidate._prefetched
+        ):
+            target_handle = target_handle_candidate
+        else:
+            target_handle = None
+    elif training_state == HandleTrainingState.FORWARD and state.forward_prefetch:
+        target_handle_candidate = eod.get_handle_to_forward_prefetch(current_handle)
+        if (
+            target_handle_candidate
+            and target_handle_candidate._needs_pre_forward_unshard
+            and not target_handle_candidate._prefetched
+        ):
+            target_handle = target_handle_candidate
+        else:
+            target_handle = None
+
+    return target_handle
+
+
+def _get_training_state(
+    handle: FlatParamHandle,
+) -> HandleTrainingState:
+    """Returns the training state of the handles in ``handle``."""
+    _p_assert(handle, "Expects a non-empty handle")
+    return handle._training_state
+
+
+@no_type_check
+def _register_pre_forward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+) -> None:
+    """
+    Registers a pre-forward hook on ``module``.
+    """
+    for forward_handle in state._pre_forward_handles:
+        forward_handle.remove()
+    state._pre_forward_handles.clear()
+    module_param_handle = state._fully_sharded_module_to_handle.get(module, None)
+    hook = functools.partial(
+        _pre_forward, state, module_param_handle, _pre_forward_unshard
+    )
+    state._pre_forward_handles.append(
+        module.register_forward_pre_hook(hook, prepend=True, with_kwargs=True)
+    )
+
+
+@no_type_check
+def _register_post_forward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+) -> None:
+    """
+    Registers a post-forward hook on ``module``. Even if the module has no
+    handles, we should register the hook since it will register the module's
+    pre-backward hook.
+    """
+    for forward_handle in state._post_forward_handles:
+        forward_handle.remove()
+    state._post_forward_handles.clear()
+    module_param_handle = state._fully_sharded_module_to_handle.get(module, None)
+    hook = functools.partial(
+        _post_forward,
+        state,
+        module_param_handle,
+        _post_forward_reshard,
+    )
+    state._post_forward_handles.append(module.register_forward_hook(hook))
+
+
+@no_type_check
+def _register_root_pre_forward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+):
+    """
+    Registers root pre-forward hook on ``module``, which should be the local
+    FSDP root.
+
+    NOTE: For the current composable FSDP design, we have each application of
+    ``fully_shard()`` to a module to indicate that that module is the local
+    FSDP root. We may remove this assumption in the future, in which case we
+    will need to register this root pre-forward hook on any candidate module
+    that may be the local FSDP root.
+    """
+    for forward_handle in state._root_pre_forward_handles:
+        forward_handle.remove()
+    state._root_pre_forward_handles.clear()
+    hook = functools.partial(_root_pre_forward, state)
+    state._root_pre_forward_handles.append(
+        module.register_forward_pre_hook(hook, prepend=True, with_kwargs=True)
+    )
+
+
+@no_type_check
+def _register_pre_backward_hooks(
+    state: _FSDPState,
+    module: nn.Module,
+    outputs: Any,
+    handle: FlatParamHandle,
+) -> None:
+    """
+    Registers pre-backward hooks on the tensors that require gradients in the
+    forward pass outputs ``outputs``, which were computed using the
+    ``FlatParameter`` s of ``handles``.
+
+    Args:
+        module (nn.Module): Fully sharded module (see [Note: Fully Sharded
+            Module]).
+
+    Returns:
+        Forward pass outputs with pre-backward hooks registered to tensors that
+        require gradients.
+    """
+    # If there is no gradient computation, then there is no need for
+    # pre-backward logic
+    if not torch.is_grad_enabled():
+        return outputs
+    if state._is_root:
+        state._post_backward_callback_queued = False  # only defined on the root
+
+    if handle:
+        handle._needs_pre_backward_unshard = False
+        # Since these handles' `FlatParameter`s participated in a forward, we
+        # conservatively assume that they will be used in the backward
+        handle._ran_pre_backward_hook = False
+
+    def _register_hook(t: torch.Tensor) -> torch.Tensor:
+        if t.requires_grad:
+            t.register_hook(
+                torch.utils.hooks.unserializable_hook(
+                    functools.partial(_pre_backward_hook, state, module, handle)
+                )
+            )
+            if handle:
+                handle._needs_pre_backward_unshard = True
+        return t
+
+    return _apply_to_tensors(_register_hook, outputs)
+
+
+def _register_post_backward_hook(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+) -> None:
+    """
+    Registers post-backward hooks on the ``FlatParameter`` s'
+    ``AccumulateGrad`` objects to reshard and to reduce-scatter gradients.
+
+    The ``AccumulateGrad`` object represents the last function that finalizes
+    the ``FlatParameter`` 's gradient, so it only runs after its entire
+    gradient computation has finished.
+
+    We register the post-backward hook only once in the *first* forward that a
+    ``FlatParameter`` participates in. This relies on the ``AccumulateGrad``
+    object being preserved through multiple forwards.
+
+    NOTE: We follow this heuristic to prefer the *first* forward to target the
+    parameter mixed precision case, where there are *separate*
+    ``AccumulateGrad`` objects across the different forwards. (Without
+    parameter mixed precision, the ``AccumulateGrad`` objects are the same.) If
+    we instead prefer the *last* forward, then the hook runs early.
+    """
+    # If there is no gradient computation, then there is no need for
+    # post-backward logic
+    if not torch.is_grad_enabled():
+        return
+    if not handle:
+        return
+    flat_param = handle.flat_param
+
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        already_registered = hasattr(flat_param, "_post_backward_hook_handle")
+        if already_registered or not flat_param.requires_grad:
+            return
+        hook = functools.partial(_post_backward_hook, state, handle)
+        hook_handle = flat_param.register_post_accumulate_grad_hook(hook)
+        flat_param._post_backward_hook_handle = hook_handle  # type: ignore[attr-defined]
+    else:
+        already_registered = hasattr(flat_param, "_post_backward_hook_state")
+        if already_registered or not flat_param.requires_grad:
+            return
+        # Get the `AccumulateGrad` object
+        temp_flat_param = flat_param.expand_as(flat_param)
+        _p_assert(
+            temp_flat_param.grad_fn is not None,
+            "The `grad_fn` is needed to access the `AccumulateGrad` and "
+            "register the post-backward hook",
+        )
+        acc_grad = temp_flat_param.grad_fn.next_functions[0][0]  # type: ignore[union-attr]
+        assert acc_grad is not None
+        hook_handle = acc_grad.register_hook(
+            functools.partial(_post_backward_hook, state, handle)
+        )
+        flat_param._post_backward_hook_state = (acc_grad, hook_handle)  # type: ignore[attr-defined]
+
+
+def _register_post_backward_reshard_only_hook(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any],
+) -> None:
+    """
+    Registers post-backward hooks to reshard flat parameters that do not
+    require gradient. We register these using multi-post-grad hooks on the
+    input activations to ensure that all gradients that may depend on the
+    parameters have been computed before resharding.
+    """
+    # If there is no gradient computation, then there is no need for
+    # post-backward logic
+    if not torch.is_grad_enabled():
+        return
+    # Construct `inp_tensors` lazily to avoid CPU overhead in typical case
+    # where each flat parameter requires gradient
+    inp_tensors: Optional[list[torch.Tensor]] = None
+    if not handle:
+        return
+    flat_param = handle.flat_param
+
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        already_registered = hasattr(flat_param, "_post_backward_hook_handle")
+    else:
+        already_registered = hasattr(flat_param, "_post_backward_hook_state")
+
+    if already_registered or flat_param.requires_grad:
+        return
+    if inp_tensors is None:
+        args_flat = pytree.arg_tree_leaves(*args, **kwargs)
+        inp_tensors = [
+            obj for obj in args_flat if torch.is_tensor(obj) and obj.requires_grad
+        ]
+    assert inp_tensors is not None  # mypy
+    hook_handle = register_multi_grad_hook(
+        inp_tensors, functools.partial(_post_backward_reshard_only_hook, state, handle)
+    )
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        flat_param._post_backward_hook_handle = hook_handle  # type: ignore[attr-defined, assignment]
+    else:
+        flat_param._post_backward_hook_state = (hook_handle,)  # type: ignore[attr-defined, assignment]
+
+
+@no_type_check
+def _register_post_backward_final_callback(
+    state: _FSDPState, module: nn.Module
+) -> None:
+    """
+    Registers the post-backward final callback that runs at the end of the
+    backward pass. This should be called from the root FSDP instance at the
+    beginning of the pre-backward.
+    """
+    _p_assert(
+        state._is_root,
+        "Only the root FSDP instance should register the post-backward callback",
+    )
+    if state._post_backward_callback_queued:
+        return
+    _assert_in_training_states(state, [TrainingState.IDLE])
+    # Trace does not need this callback
+    if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        state._post_backward_callback_queued = True
+        Variable._execution_engine.queue_callback(
+            functools.partial(_post_backward_final_callback, state, module)
+        )
+
+
+def _wait_for_computation_stream(
+    computation_stream: torch.Stream,
+    unshard_stream: torch.Stream,
+    pre_unshard_stream: torch.Stream,
+):
+    """
+    Has the unshard and pre-unshard streams wait for the computation stream.
+    For example, this should be called in the FSDP root's pre-forward to
+    respect optimizer step computation.
+    """
+    # Tracing does not need to wait
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        return
+    unshard_stream.wait_stream(computation_stream)  # type: ignore[attr-defined]
+    # Having the pre-all-gather stream wait for the current stream even if we
+    # do not leverage the pre-all-gather stream is tolerable since this only
+    # runs once per iteration
+    pre_unshard_stream.wait_stream(computation_stream)  # type: ignore[attr-defined]
+
+
+def _reset_flat_param_grad_info_if_needed(
+    handles: list[FlatParamHandle],
+):
+    """
+    Clears the original parameters' gradients if needed. This method's CPU
+    overhead is minimal, so we may call it throughout FSDP methods, which serve
+    as callsites to free the gradient memory earlier.
+    """
+    if not isinstance(handles, list):
+        handles = [handles]
+    for handle in handles:
+        if handle._use_orig_params:
+            handle._reset_flat_param_grad_info_if_needed()
+
+
+@no_type_check
+def _get_buffers_and_dtypes_for_computation(
+    state: _FSDPState,
+    root_module: nn.Module,
+) -> tuple[list[torch.Tensor], list[Optional[torch.dtype]]]:
+    """
+    Returns all buffers in the module tree rooted at ``root_module`` and a
+    corresponding list of the buffer dtypes for computation. Each buffer dtype
+    is either ``None`` if buffer mixed precision is not enabled or the buffer
+    low precision dtype otherwise.
+    """
+    _p_assert(state._is_root, "Expects the root to cast buffers")
+    buffers: list[torch.Tensor] = []
+    buffer_dtypes: list[Optional[torch.dtype]] = []
+    visited_buffers: set[torch.Tensor] = set()
+    # Traverse the FSDP states bottom-up so that we prefer the owning FSDP
+    # instance's mixed precision setting for each buffer
+    fsdp_states, fsdp_modules = traversal_utils._get_fsdp_states_with_modules(
+        root_module
+    )
+    for fsdp_state, fsdp_module in zip(reversed(fsdp_states), reversed(fsdp_modules)):
+        for buffer_name, buffer in fsdp_module.named_buffers():
+            if buffer in visited_buffers:
+                continue
+            visited_buffers.add(buffer)
+            if clean_tensor_name(buffer_name) in fsdp_state._ignored_buffer_names:
+                continue
+            buffers.append(buffer)
+            buffer_dtypes.append(fsdp_state.mixed_precision.buffer_dtype)
+    assert len(buffers) == len(buffer_dtypes), f"{len(buffers)} {len(buffer_dtypes)}"
+    return buffers, buffer_dtypes
+
+
+@no_type_check
+def _get_orig_buffer_dtypes(
+    state: _FSDPState,
+    buffer_names: list[str],
+) -> list[torch.dtype]:
+    """
+    Returns the original buffer types of the given buffer names.
+    """
+    buffer_dtypes: list[torch.dtype] = []
+    for buffer_name in buffer_names:
+        _p_assert(
+            buffer_name in state._buffer_name_to_orig_dtype,
+            f"{buffer_name} is missing from pre-computed dict on rank "
+            f"{state.rank}, which only has keys "
+            f"{state._buffer_name_to_orig_dtype.keys()}",
+        )
+        buffer_dtypes.append(state._buffer_name_to_orig_dtype[buffer_name])
+    return buffer_dtypes
+
+
+def _cast_buffers_to_dtype_and_device(
+    buffers: list[torch.Tensor],
+    buffer_dtypes: list[Optional[torch.dtype]],
+    device: torch.device,
+) -> None:
+    """
+    Casts ``buffers`` to the dtypes given by ``buffer_dtypes`` and moves them
+    to ``device``. If an element in ``buffer_dtypes`` is ``None``, then the
+    corresponding buffer is only moved to ``device``.
+    """
+    _p_assert(
+        buffer_dtypes is None or len(buffers) == len(buffer_dtypes),
+        f"Expects `buffers` and `buffer_dtypes` to have the same length if "
+        f"`buffer_dtypes` is specified but got {len(buffers)} and "
+        f"{len(buffer_dtypes)}",
+    )
+    for buffer, buffer_dtype in zip(buffers, buffer_dtypes):
+        if not torch.is_floating_point(buffer) or buffer_dtype is None:
+            buffer.data = buffer.to(device=device)
+        else:
+            buffer.data = buffer.to(device=device, dtype=buffer_dtype)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_shard_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_shard_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..037bef9be3b363c66b8eece907b4e4f38dd07e26
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_shard_utils.py
@@ -0,0 +1,137 @@
+# mypy: allow-untyped-defs
+import copy
+import itertools
+import math
+from typing import Optional
+
+import torch
+import torch.distributed as dist
+from torch._utils import _get_device_module
+from torch.distributed import distributed_c10d
+from torch.distributed._shard.sharded_tensor import (
+    Shard,
+    ShardedTensor,
+    ShardedTensorMetadata,
+    TensorProperties,
+)
+from torch.distributed._shard.sharding_spec import ShardMetadata
+from torch.distributed.tensor import DeviceMesh, DTensor, Replicate, Shard as DShard
+
+
+def _get_remote_device_str(rank, device_type, num_devices_per_node):
+    if device_type.lower() == "cpu":
+        return f"rank:{rank}/{device_type}"
+    elif device_type.lower() == "hpu":
+        return f"rank:{rank}/{device_type}:{_get_device_module(device_type).current_device()}"
+    else:
+        return f"rank:{rank}/{device_type}:{rank % num_devices_per_node}"
+
+
+def _create_chunk_sharded_tensor(
+    tensor: torch.Tensor,
+    rank: int,
+    world_size: int,
+    num_devices_per_node: int,
+    pg: dist.ProcessGroup,
+    device: Optional[torch.device] = None,
+) -> ShardedTensor:
+    """
+    Shard a tensor to chunks along the first dimension. The local rank will gets its
+    corresponding chunk as the local shard to create a ShardedTensor.
+    """
+    chunks = tensor.chunk(world_size, dim=0)
+    if len(chunks) > rank:
+        local_shard = chunks[rank].clone()
+        offsets = [0 for _ in tensor.size()]
+        offsets[0] = math.ceil(tensor.size()[0] / world_size) * rank
+        local_shards = [Shard.from_tensor_and_offsets(local_shard, offsets, rank)]
+    else:
+        local_shards = []
+
+    # Create a ShardedTensor without invoking communication.
+    chunk_sizes = [list(chunk.size()) for chunk in chunks]
+    dim0_offsets = [0] + list(
+        itertools.accumulate([chunk_size[0] for chunk_size in chunk_sizes])
+    )[:-1]
+    offsets = [0] * (len(chunk_sizes[0]) - 1)
+    chunk_offsets = [[d0] + offsets for d0 in dim0_offsets]
+    device_type = (
+        distributed_c10d._get_pg_default_device(pg).type
+        if device is None
+        else device.type
+    )
+    placements = [
+        _get_remote_device_str(
+            dist.get_global_rank(pg, r),
+            device_type,
+            num_devices_per_node,
+        )
+        for r in range(len(chunk_sizes))
+    ]
+    assert len(chunk_sizes) == len(chunk_offsets) == len(placements)
+    shard_metadata = [
+        ShardMetadata(offset, size, placement)
+        for offset, size, placement in zip(chunk_offsets, chunk_sizes, placements)
+    ]
+    sharded_tensor_metadata = ShardedTensorMetadata(
+        shards_metadata=shard_metadata,
+        size=tensor.size(),
+        tensor_properties=TensorProperties(
+            dtype=tensor.dtype,
+            layout=tensor.layout,
+            requires_grad=False,
+            memory_format=torch.contiguous_format,
+            pin_memory=tensor.is_pinned(),
+        ),
+    )
+    return ShardedTensor._init_from_local_shards_and_global_metadata(
+        local_shards, sharded_tensor_metadata=sharded_tensor_metadata, process_group=pg
+    )
+
+
+def _create_chunk_dtensor(
+    tensor: torch.Tensor,
+    rank: int,
+    device_mesh: DeviceMesh,
+) -> DTensor:
+    """
+    Shard a tensor to chunks along the first dimension. The local rank will gets its
+    corresponding chunk as the local tensor to create a DTensor.
+    """
+    # We need to explicitly call .detach() to return a new tensor detached from the current graph.
+    tensor = tensor.detach().clone()
+
+    # FSDP placements: [Shard(0)]
+    # HSDP placements: [Replicate(), Shard(0)]
+    replicate_placements = [Replicate() for _ in range(device_mesh.ndim)]
+    shard_placements = [Replicate() for _ in range(device_mesh.ndim)]
+    shard_placements[-1] = DShard(0)  # type: ignore[call-overload]
+
+    return DTensor.from_local(
+        tensor, device_mesh, replicate_placements, run_check=False
+    ).redistribute(
+        placements=shard_placements,
+    )
+
+
+def _all_gather_dtensor(
+    tensor: DTensor,
+    root_mesh: Optional[DeviceMesh],
+) -> torch.Tensor:
+    """
+    All gather a DTensor in its sharded dimension and return the local tensor.
+    """
+    assert root_mesh == tensor.device_mesh, (
+        "The device mesh of a tensor should be a root mesh."
+    )
+
+    placements = list(copy.deepcopy(tensor.placements))
+    # FSDP placements: [Shard(0)] -> [Replicate()]
+    # HSDP placements: [Replicate(), Shard(0)] -> [Replicate(), Replicate()]
+    placements[-1] = Replicate()
+    tensor = tensor.redistribute(
+        device_mesh=tensor.device_mesh,
+        placements=placements,
+    )
+
+    return tensor.to_local()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_state_dict_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_state_dict_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d3285255df2c9564e7ac4f21af465a4f0fe9286
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_state_dict_utils.py
@@ -0,0 +1,919 @@
+# mypy: allow-untyped-defs
+import contextlib
+import logging
+import math
+import warnings
+from collections.abc import Generator, Iterator
+from typing import Any, Callable, cast, no_type_check
+
+import torch
+import torch.distributed as dist
+import torch.distributed.algorithms._checkpoint.checkpoint_wrapper as checkpoint_wrapper
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributed._shard.sharded_tensor import (
+    init_from_local_shards,
+    Shard,
+    ShardedTensor,
+)
+from torch.distributed.device_mesh import _mesh_resources
+from torch.distributed.fsdp._common_utils import (
+    _FSDPState,
+    _get_module_fsdp_state_if_fully_sharded_module,
+    _has_fsdp_params,
+    _is_composable,
+    _module_handle,
+    clean_tensor_name,
+    FSDP_PREFIX,
+    FSDP_WRAPPED_MODULE,
+)
+from torch.distributed.fsdp._debug_utils import SimpleProfiler
+from torch.distributed.fsdp._runtime_utils import (
+    _cast_buffers_to_dtype_and_device,
+    _get_orig_buffer_dtypes,
+    _lazy_init,
+    _reset_flat_param_grad_info_if_needed,
+)
+from torch.distributed.fsdp.api import (
+    FullStateDictConfig,
+    ShardingStrategy,
+    StateDictType,
+)
+from torch.distributed.tensor import DTensor
+from torch.distributed.utils import _replace_by_prefix
+
+from ._fsdp_extensions import (
+    _ext_all_gather_dtensor,
+    _ext_chunk_dtensor,
+    _ext_chunk_tensor,
+    _ext_post_unflatten_transform,
+    _ext_pre_load_state_dict_transform,
+)
+from ._unshard_param_utils import _unshard_fsdp_state_params, FLAT_PARAM
+
+
+logger = logging.getLogger(__name__)
+
+
+def _should_unshard_params(fsdp_state: _FSDPState) -> bool:
+    return not (
+        fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD
+        and (_is_composable(fsdp_state) or fsdp_state._use_orig_params)
+    )
+
+
+def _convert_to_wrapped_module_name(module_name: str) -> str:
+    module_name = module_name.replace(f"{FSDP_PREFIX}", "")
+    module_name = module_name.replace(f"{FSDP_WRAPPED_MODULE}", "")
+    if module_name:
+        module_name = f"{module_name}."
+    # `CheckpointWrapper` adds a prefix that has to be removed as well.
+    module_name = module_name.replace(checkpoint_wrapper._CHECKPOINT_PREFIX, "")
+    return module_name
+
+
+def _param_name_infos(
+    module: nn.Module, fsdp_state: _FSDPState
+) -> Iterator[tuple[str, str, str]]:
+    if not _has_fsdp_params(fsdp_state, module):
+        return
+    for param_name, module_name in _module_handle(
+        fsdp_state, module
+    ).param_module_names():
+        module_name = _convert_to_wrapped_module_name(module_name)
+        fqn = f"{module_name}{param_name}"
+        yield fqn, param_name, module_name
+
+
+def _shared_param_name_infos(
+    module: nn.Module, fsdp_state
+) -> Iterator[tuple[str, str, str]]:
+    for param_name, module_name in _module_handle(
+        fsdp_state, module
+    ).shared_param_module_names():
+        module_name = _convert_to_wrapped_module_name(module_name)
+        fqn = f"{module_name}{param_name}"
+        yield fqn, param_name, module_name
+
+
+@no_type_check
+def _enter_unshard_params_ctx(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    writeback: bool = False,
+    rank0_only: bool = False,
+    offload_to_cpu: bool = False,
+    with_grads: bool = False,
+) -> None:
+    """
+    state_dict hooks cannot use the pure context call as the checkpoint flow
+    requires to enter the context in the pre-hook but leave the context in the
+    post-hook. This API enters the context of ``_unshard_fsdp_state_params``.
+    """
+    assert module not in fsdp_state._unshard_params_ctx, (
+        "Entering the ``_unshard_fsdp_state_params`` context but _unshard_params_ctx[module] "
+        "is not None."
+    )
+    fsdp_state._unshard_params_ctx[module] = _unshard_fsdp_state_params(
+        module,
+        fsdp_state,
+        writeback=writeback,
+        rank0_only=rank0_only,
+        offload_to_cpu=offload_to_cpu,
+        with_grads=with_grads,
+    )
+    fsdp_state._unshard_params_ctx[module].__enter__()
+
+
+@no_type_check
+def _exit_unshard_params_ctx(module: nn.Module, fsdp_state: _FSDPState) -> None:
+    """A helper function to exit ``_unshard_fsdp_state_params`` context."""
+    fsdp_state._unshard_params_ctx[module].__exit__(None, None, None)
+    fsdp_state._unshard_params_ctx.pop(module)
+
+
+def _common_pre_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+) -> None:
+    """Performs the pre-state_dict tasks shared by all state_dict types."""
+    if fsdp_state._device_handle.is_available():
+        fsdp_state._device_handle.synchronize()
+    # TODO: need to check if this is always correct for composable FSDP.
+    _lazy_init(fsdp_state, module)
+    if fsdp_state._is_root:
+        _reset_flat_param_grad_info_if_needed(fsdp_state._all_handles)
+
+
+def _common_unshard_pre_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    offload_to_cpu: bool,
+    rank0_only: bool,
+) -> None:
+    """
+    Performs the pre-state_dict tasks shared by all state_dict types that require
+    ``_unshard_fsdp_state_params()``. FULL_STATE_DICT and SHARDED_STATE_DICT use this hook.
+    """
+    # For composable `fully_shard`, it does not need to unshard parameters for `NO_SHARD` cases.
+    if not _should_unshard_params(fsdp_state):
+        return
+    _enter_unshard_params_ctx(
+        module,
+        fsdp_state,
+        writeback=False,
+        offload_to_cpu=offload_to_cpu,
+        rank0_only=rank0_only,
+    )
+
+
+@no_type_check
+def _common_unshard_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+    param_hook: Callable,
+) -> dict[str, Any]:
+    """
+    The post-state_dict flow that shared by all state_dict types that require
+    ``_unshard_fsdp_state_params()``. FULL_STATE_DICT and SHARDED_STATE_DICT use this
+    hook.
+    """
+    _replace_by_prefix(state_dict, prefix + f"{FSDP_PREFIX}", prefix)
+    # Return early for trivial cases
+    if not state_dict or not _has_fsdp_params(fsdp_state, module):
+        if _should_unshard_params(fsdp_state):
+            _exit_unshard_params_ctx(module, fsdp_state)
+        return state_dict
+
+    # If a rank does not have unsharded parameters(when `rank0_only=True`
+    # and `rank != 0`), then the rank only needed to participate in the
+    # all-gather and does not need to save the # state dict. We simply check
+    # rank0_only to ensure this issue.
+    rank0_only = (
+        fsdp_state._state_dict_type == StateDictType.FULL_STATE_DICT
+        and cast(FullStateDictConfig, fsdp_state._state_dict_config).rank0_only
+    )
+    # no_fsdp_return means the state_dict returned by this rank should contain
+    # only non-FSDP controlled parameters and buffers.
+    no_fsdp_return = rank0_only and fsdp_state.rank != 0
+    if no_fsdp_return and not fsdp_state._use_orig_params:
+        for clean_key in fsdp_state._buffer_names:
+            # This is a hack to support activation checkpoint.
+            clean_key = clean_key.replace(
+                f"{checkpoint_wrapper._CHECKPOINT_PREFIX}.", ""
+            )
+            state_dict.pop(f"{prefix}{clean_key}", None)
+        # Non-zero ranks have flat_param key when rank0_only=True, because rank0_only=True is
+        # passed in to unshard context, but nonzero ranks reshard early, causing this flat_param
+        # to appear in state_dict.
+        state_dict.pop(f"{prefix}{FLAT_PARAM}")
+        _exit_unshard_params_ctx(module, fsdp_state)
+        return state_dict
+
+    # Loop only the parameters saved in this instance's wrapped module to
+    # avoid processing buffers.
+    for fqn, param_name, module_name in _param_name_infos(module, fsdp_state):
+        fqn = f"{prefix}{fqn}"
+        if no_fsdp_return:
+            state_dict.pop(fqn)
+            continue
+        assert fqn in state_dict, (
+            f"FSDP assumes {fqn} is in the state_dict but the state_dict only "
+            f"has {state_dict.keys()}. "
+            f"prefix={prefix}, module_name={module_name}, "
+            f"param_name={param_name} rank={fsdp_state.rank}."
+        )
+
+        param_hook(state_dict, prefix, fqn)
+
+    if _should_unshard_params(fsdp_state):
+        _exit_unshard_params_ctx(module, fsdp_state)
+
+    cpu_device = torch.device("cpu")
+    buffer_clean_fqns = []
+    buffers = []
+    for clean_key in fsdp_state._buffer_names:
+        # This is a hack to support activation checkpoint.
+        clean_key = clean_tensor_name(clean_key)
+        fqn = f"{prefix}{clean_key}"
+        if fqn not in state_dict:
+            # A buffer can be registered as non-persistent.
+            continue
+        if no_fsdp_return:
+            state_dict.pop(fqn)
+        else:
+            buffer = state_dict[fqn]
+            if (
+                fsdp_state._state_dict_config.offload_to_cpu
+                and buffer.device != cpu_device
+            ):
+                state_dict[fqn] = buffer.to(cpu_device)
+            # skip upcasting for ignored buffers
+            if clean_key not in fsdp_state._ignored_buffer_names:
+                buffer_clean_fqns.append(clean_key)
+                buffers.append(state_dict[fqn])
+
+    if buffers:
+        mixed_precision_enabled_for_buffers = (
+            fsdp_state._mixed_precision_enabled_for_buffers()
+            if not _is_composable(fsdp_state)
+            else (fsdp_state.mixed_precision.buffer_dtype is not None)
+        )
+        if mixed_precision_enabled_for_buffers:
+            buffer_dtypes = _get_orig_buffer_dtypes(fsdp_state, buffer_clean_fqns)
+            _cast_buffers_to_dtype_and_device(
+                buffers, buffer_dtypes, fsdp_state.compute_device
+            )
+            for buffer, clean_fqn in zip(buffers, buffer_clean_fqns):
+                fqn = f"{prefix}{clean_fqn}"
+                logger.info("FSDP is casting the dtype of %s to %s", fqn, buffer.dtype)
+                state_dict[fqn] = buffer.clone()
+    return state_dict
+
+
+@no_type_check
+def _full_pre_state_dict_hook(
+    fsdp_state: _FSDPState,
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    Hook that runs before model.state_dict() is called. pre-state_dict hook is
+    not actually supported by ``nn.Module``. As a result, this API is called
+    from ``_full_post_state_dict_hook()`` to simulate the case. Once pre-state_dict
+    is supported in ``nn.Module``, this hook will be registered as a hook in
+    ``nn.Module``.
+    """
+    if getattr(fsdp_state, "_device_mesh", False):
+        _mesh_resources.get_root_mesh(fsdp_state._device_mesh)
+
+    _common_pre_state_dict_hook(module, fsdp_state)
+    _common_unshard_pre_state_dict_hook(
+        module,
+        fsdp_state,
+        offload_to_cpu=fsdp_state._state_dict_config.offload_to_cpu,
+        rank0_only=cast(FullStateDictConfig, fsdp_state._state_dict_config).rank0_only,
+    )
+
+
+@no_type_check
+def _full_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> dict[str, Any]:
+    """
+    Hook that runs after model.state_dict() is called before returning result to
+    user. For FSDP, we may have to clone the tensors in state_dict as params go
+    back to sharded version after _unshard_fsdp_state_params ends, and also remove
+    the ``FSDP_WRAPPED_MODULE`` prefix.
+    """
+
+    def param_hook(
+        state_dict: dict[str, Any],
+        prefix: str,
+        fqn: str,
+    ) -> None:
+        clean_key = fqn
+        clean_prefix = clean_tensor_name(prefix)
+        # Strip prefix out of key if needed as buffer names and param names
+        # do not have prefix considered as they are not computed in `state_dict`
+        # call.
+        clean_key = clean_key.removeprefix(clean_prefix)
+
+        # Clone parameters before exiting the `_unshard_fsdp_state_params()` context.
+        if not getattr(state_dict[fqn], "_has_been_cloned", False):
+            try:
+                state_dict[fqn] = state_dict[fqn].detach().clone()
+                state_dict[fqn]._has_been_cloned = True  # type: ignore[attr-defined]
+            except BaseException as e:
+                warnings.warn(
+                    f"Failed to clone() tensor with name {fqn} on rank {fsdp_state.rank}. "
+                    "This may mean that this state_dict entry could point to invalid "
+                    "memory regions after returning from state_dict() call if this "
+                    "parameter is managed by FSDP. Please check clone "
+                    f"implementation of {fqn}. Error: {str(e)}"
+                )
+
+    return _common_unshard_post_state_dict_hook(
+        module, fsdp_state, state_dict, prefix, param_hook
+    )
+
+
+def _full_pre_load_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> None:
+    _lazy_init(fsdp_state, module)
+    if _should_unshard_params(fsdp_state):
+        with SimpleProfiler.profile("_enter_unshard_params_ctx"):
+            _enter_unshard_params_ctx(module, fsdp_state, writeback=True)
+    # Add FSDP_PREFIX only for wrapper-based FSDP.
+    if not _is_composable(fsdp_state):
+        _replace_by_prefix(state_dict, prefix, prefix + f"{FSDP_PREFIX}")
+
+
+def _full_post_load_state_dict_hook(
+    module: nn.Module, fsdp_state: _FSDPState, *args, **kwargs
+) -> None:
+    if _should_unshard_params(fsdp_state):
+        with SimpleProfiler.profile("_exit_unshard_params_ctx"):
+            _exit_unshard_params_ctx(module, fsdp_state)
+
+
+def _local_pre_state_dict_hook(
+    fsdp_state: _FSDPState,
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    Hook that runs before model.state_dict() is called. Right now, pre-state_dict
+    hook is not supported by the PyTorch core. So this API is called from
+    `_local_post_state_dict_hook()` to simulate the case.
+    """
+    if (
+        _has_fsdp_params(fsdp_state, module)
+        and not _module_handle(fsdp_state, module).uses_sharded_strategy
+    ):
+        raise RuntimeError(
+            "``local_state_dict`` can only be used when parameters are flatten "
+            "and sharded."
+        )
+    _common_pre_state_dict_hook(module, fsdp_state)
+
+
+@no_type_check
+def _local_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> dict[str, Any]:
+    """
+    This hook create a ShardedTensor from the local flat_param and replace
+    the state_dict[f"{prefix}{FLAT_PARAM}] with the ShardedTensor. No copy
+    will happen. The underlying storage is the same.
+    """
+
+    _replace_by_prefix(state_dict, f"{prefix}{FSDP_PREFIX}", prefix)
+    if not _has_fsdp_params(fsdp_state, module):
+        return state_dict
+
+    # state_dict[f"{prefix}{FLAT_PARAM}"] exists and has the same tensor
+    # value as the flat_param but it is a pure Tensor because
+    # nn.Module.state_dict() will detach the parameter. Therefore, we need
+    # to get flat_param to get the metadata.
+    assert _module_handle(fsdp_state, module), "Should have returned early"
+    flat_param = _module_handle(fsdp_state, module).flat_param
+    # Constructs a ShardedTensor from the flat_param "without" padding.
+    # Removing the padding allows users to change the number of ranks
+    # when loading the local_state_dict.
+    full_numel = flat_param._unpadded_unsharded_size.numel()  # type: ignore[attr-defined]
+    shard_offset = flat_param.numel() * fsdp_state.rank
+    valid_data_size = flat_param.numel() - flat_param._shard_numel_padded
+    if valid_data_size > 0:
+        # If FlatParameter is returned, FlatParameter._local_shard cause a
+        # pickling issue (can be torch.save but not torch.load). Since there
+        # is no benefit for state_dict to return the actual FlatParameter class,
+        # a view (which is a tensor) of the FlatParameter will be returned.
+        flat_param = flat_param[:valid_data_size].view(valid_data_size)
+        local_shards = [
+            Shard.from_tensor_and_offsets(flat_param, [shard_offset], fsdp_state.rank)
+        ]
+    else:
+        local_shards = []
+    sharded_tensor = init_from_local_shards(
+        local_shards, full_numel, process_group=fsdp_state.process_group
+    )  # type: ignore[assignment]
+    # TODO: Add DTensor state_dict support for LOCAL_STATE_DICT.
+    if fsdp_state._state_dict_config.offload_to_cpu:
+        sharded_tensor = sharded_tensor.cpu()
+    state_dict[f"{prefix}{FLAT_PARAM}"] = sharded_tensor
+    return state_dict
+
+
+def _local_post_load_state_dict_hook(
+    module: nn.Module, fsdp_state: _FSDPState, *args, **kwargs
+) -> None:
+    pass
+
+
+def _local_pre_load_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> None:
+    """
+    This hook finds the local flat_param for this FSDP module from the
+    state_dict. The flat_param should be a ShardedTensor. This hook converts
+    the ShardedTensor to a tensor. No copy happen unless padding is required.
+    """
+    _lazy_init(fsdp_state, module)
+    _replace_by_prefix(state_dict, prefix, f"{prefix}{FSDP_PREFIX}")
+    fqn = f"{prefix}{FSDP_PREFIX}{FLAT_PARAM}"
+    if fqn not in state_dict:
+        assert not _has_fsdp_params(fsdp_state, module), (
+            "No `FlatParameter` in `state_dict` for this FSDP instance "
+            "but it has parameters"
+        )
+        return
+    load_tensor = state_dict[fqn]
+    assert isinstance(load_tensor, ShardedTensor), (
+        "Tensors in local_state_dict should be ShardedTensor."
+    )
+
+    # Convert the ShardedTensor to a Tensor.
+    flat_param = _module_handle(fsdp_state, module).flat_param
+    assert flat_param is not None
+    valid_data_size = flat_param.numel() - flat_param._shard_numel_padded
+    shards = load_tensor.local_shards()
+    if valid_data_size > 0:
+        assert len(shards), "load_local_state_dict assume one shard per ShardedTensor."
+        load_tensor = shards[0].tensor
+
+        # Get the metadata of the flat_param to decide whether to pad the loaded
+        # tensor.
+        if flat_param._shard_numel_padded > 0:
+            assert load_tensor.numel() < flat_param.numel(), (
+                f"Local shard size = {flat_param.numel()} and the tensor in "
+                f"the state_dict is {load_tensor.numel()}."
+            )
+            load_tensor = F.pad(load_tensor, [0, flat_param._shard_numel_padded])
+    else:
+        load_tensor = flat_param
+    # TODO: Add DTensor state_dict support for LOCAL_STATE_DICT.
+    state_dict[fqn] = load_tensor
+
+
+def _sharded_pre_state_dict_hook(
+    fsdp_state: _FSDPState,
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    Hook that runs before model.state_dict() is called. Check
+    ``_full_pre_load_state_dict_hook`` for the detail.
+    """
+    if (
+        _has_fsdp_params(fsdp_state, module)
+        and not _module_handle(fsdp_state, module).uses_sharded_strategy
+    ):
+        raise RuntimeError(
+            "``sharded_state_dict`` can only be used when parameters are flatten "
+            "and sharded."
+        )
+    _common_pre_state_dict_hook(module, fsdp_state)
+    # Setting offload_to_cpu here does not work even if offload_to_cpu is True.
+    # We have to create ShardedTensor first then move it to CPU.
+    _common_unshard_pre_state_dict_hook(
+        module,
+        fsdp_state,
+        offload_to_cpu=False,
+        rank0_only=False,
+    )
+
+
+@no_type_check
+def _sharded_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> dict[str, Any]:
+    """
+    The hook replaces the unflattened, unsharded parameter in the state_dict
+    with a unflattened, sharded parameter (a ShardedTensor).
+    """
+
+    def param_hook(state_dict: dict[str, Any], prefix: str, fqn: str):
+        param = state_dict[fqn]
+        if not fsdp_state._state_dict_config._use_dtensor:
+            sharded_tensor = _ext_chunk_tensor(
+                tensor=param,
+                rank=fsdp_state.rank,
+                world_size=fsdp_state.world_size,
+                num_devices_per_node=fsdp_state._device_handle.device_count(),
+                pg=fsdp_state.process_group,
+                fsdp_extension=fsdp_state._fsdp_extension,
+            )
+        else:
+            sharded_tensor = _ext_chunk_dtensor(
+                tensor=param,
+                rank=fsdp_state.rank,
+                device_mesh=fsdp_state._device_mesh,
+                fsdp_extension=fsdp_state._fsdp_extension,
+            )
+        if fsdp_state._state_dict_config.offload_to_cpu:
+            sharded_tensor = sharded_tensor.cpu()
+        state_dict[fqn] = sharded_tensor
+
+    return _common_unshard_post_state_dict_hook(
+        module, fsdp_state, state_dict, prefix, param_hook
+    )
+
+
+@no_type_check
+def _sharded_post_load_state_dict_hook(
+    module: nn.Module, fsdp_state: _FSDPState, *args, **kwargs
+) -> None:
+    if _has_fsdp_params(fsdp_state, module):
+        with SimpleProfiler.profile("_exit_unshard_params_ctx"):
+            _exit_unshard_params_ctx(module, fsdp_state)
+
+
+@no_type_check
+def _sharded_pre_load_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> None:
+    """
+    The hook combines the unflattened, sharded parameters (ShardedTensor) to
+    a new FlatParameter and shards the new FlatParameter to the local chunk.
+    """
+    _lazy_init(fsdp_state, module)
+    if not _is_composable(fsdp_state):
+        _replace_by_prefix(state_dict, prefix, prefix + f"{FSDP_PREFIX}")
+    if not _has_fsdp_params(fsdp_state, module):
+        return
+
+    handle = _module_handle(fsdp_state, module)
+    if not handle.uses_sharded_strategy:
+        raise RuntimeError(
+            "load_sharded_state_dict can only be called when parameters "
+            "are flattened and sharded."
+        )
+    fqn_to_param_ext = dict(
+        zip(handle.flat_param._fqns, handle.flat_param._param_extensions)
+    )
+
+    for fqn, _, _ in _param_name_infos(module, fsdp_state):
+        if not _is_composable(fsdp_state):
+            fqn_from_global_root = f"{prefix}{FSDP_PREFIX}{fqn}"
+        else:
+            fqn_from_global_root = f"{prefix}{fqn}"
+        try:
+            param = state_dict.pop(fqn_from_global_root)
+        except KeyError:
+            logger.warning(
+                f"Did not find param with FQN {fqn_from_global_root}, skipping it. "  # noqa: G004
+                "The weight will not be filled if you expect it to be."
+            )
+            continue  # TODO: Improve unittesting for state_dict finetuning
+            # cases: https://github.com/pytorch/pytorch/issues/109134
+
+        if not fsdp_state._state_dict_config._use_dtensor:
+            # All-gather the param (ShardedTensor)
+            param, shards = _ext_pre_load_state_dict_transform(
+                param, fsdp_state._fsdp_extension
+            )
+
+            assert len(shards) < 2, (
+                "Expects 0 or 1 shard per rank "
+                f"but got {len(shards)} shards on rank {fsdp_state.rank}."
+            )
+            param_numel = param.size().numel()
+            dim_0_size = param.size()[0]
+            chunk_size = (
+                math.ceil(dim_0_size / fsdp_state.world_size)
+                * param_numel
+                // dim_0_size
+            )
+            if len(shards) == 1:
+                local_tensor = shards[0].tensor.flatten()
+                with SimpleProfiler.profile(SimpleProfiler.Type.H2D):
+                    local_tensor = local_tensor.to(fsdp_state.compute_device)
+                num_padding = chunk_size - local_tensor.numel()
+                if num_padding > 0:
+                    local_tensor = F.pad(local_tensor, [0, num_padding])
+            else:
+                local_tensor = torch.zeros(
+                    chunk_size, dtype=param.dtype, device=fsdp_state.compute_device
+                )
+            tensor = torch.empty(
+                chunk_size * fsdp_state.world_size,
+                dtype=local_tensor.dtype,
+                device=fsdp_state.compute_device,
+            )
+            with SimpleProfiler.profile(SimpleProfiler.Type.ALLGATHER):
+                dist.all_gather_into_tensor(
+                    tensor, local_tensor, group=fsdp_state.process_group
+                )
+            tensor = tensor.narrow(0, 0, param_numel).reshape(param.size())
+            state_dict[fqn_from_global_root] = tensor
+        else:
+            if param.device != fsdp_state._device_mesh.device_type:
+                param = param.to(fsdp_state._device_mesh.device_type)
+
+            root_mesh = _mesh_resources.get_root_mesh(fsdp_state._device_mesh)
+            local_tensor = _ext_all_gather_dtensor(
+                param, root_mesh, fsdp_state._fsdp_extension
+            )
+
+            if fqn_to_param_ext.get(fqn) is not None:
+                ext = fqn_to_param_ext[fqn]
+                local_tensor = _ext_post_unflatten_transform(
+                    local_tensor, ext, fsdp_state._fsdp_extension
+                )
+            state_dict[fqn_from_global_root] = local_tensor
+
+    with SimpleProfiler.profile("_enter_unshard_params_ctx"):
+        _enter_unshard_params_ctx(module, fsdp_state, writeback=True)
+
+
+@contextlib.contextmanager
+def _replace_with_full_state_dict_type(fsdp_state: _FSDPState) -> Generator:
+    old_state_dict_config = fsdp_state._state_dict_config
+    old_state_dict_type = fsdp_state._state_dict_type
+    fsdp_state._state_dict_config = FullStateDictConfig()
+    fsdp_state._state_dict_type = StateDictType.FULL_STATE_DICT
+    yield
+    fsdp_state._state_dict_config = old_state_dict_config
+    fsdp_state._state_dict_type = old_state_dict_type
+
+
+@no_type_check
+@torch.no_grad()
+def _post_state_dict_hook(
+    module: nn.Module,
+    state_dict: dict[str, Any],
+    prefix: str,
+    *args: Any,
+) -> dict[str, Any]:
+    """
+    _post_state_dict_hook() is called after the state_dict() of this
+    FSDP module is executed. ``fsdp_state._state_dict_type`` is used to decide
+    what postprocessing will be done.
+    """
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
+            "be returned."
+        )
+    else:
+        context = contextlib.nullcontext()
+
+    with context:
+        _post_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_post_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_post_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_post_state_dict_hook,
+        }
+        processed_state_dict = _post_state_dict_hook_fn[fsdp_state._state_dict_type](
+            module, fsdp_state, state_dict, prefix
+        )
+
+    if fsdp_state._is_root:
+        logger.info("FSDP finished processing state_dict(), prefix=%s", prefix)
+        for key, tensor in sorted(processed_state_dict.items()):
+            if key.startswith(prefix) and isinstance(tensor, torch.Tensor):
+                local_shape = tensor.shape
+                device = None
+                if isinstance(tensor, ShardedTensor):
+                    local_shape = None
+                    shards = tensor.local_shards()
+                    if shards:
+                        local_shape = shards[0].tensor.shape
+                        device = shards[0].tensor.device
+                elif isinstance(tensor, DTensor):
+                    local_shape = tensor.to_local().shape
+                    device = tensor.device
+                else:
+                    device = tensor.device
+                logger.info(
+                    "FQN=%s: type=%s, shape=%s, local_shape=%s, dtype=%s, device=%s",
+                    key,
+                    type(tensor),
+                    tensor.shape,
+                    local_shape,
+                    tensor.dtype,
+                    device,
+                )
+
+    return processed_state_dict
+
+
+@no_type_check
+@torch.no_grad()
+def _pre_state_dict_hook(
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    This is called before the core state dict saving logic of ``module``.
+    ``fsdp_state._state_dict_type`` is used to decide what postprocessing will
+    be done.
+    """
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
+            "be returned."
+        )
+    else:
+        _set_use_dtensor(fsdp_state)
+        context = contextlib.nullcontext()
+
+    with context:
+        _pre_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_pre_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_pre_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_pre_state_dict_hook,
+        }
+        _pre_state_dict_hook_fn[fsdp_state._state_dict_type](
+            fsdp_state,
+            module,
+            *args,
+            **kwargs,
+        )
+
+
+@no_type_check
+def _set_use_dtensor(fsdp_state: _FSDPState) -> None:
+    # If device_mesh is passed in when initalizing FSDP, we automatically turn the
+    # _use_dtensor flag to be true for ShardedStateDictConfig().
+    if getattr(fsdp_state, "_device_mesh", None):
+        state_dict_type = fsdp_state._state_dict_type
+        if state_dict_type == StateDictType.LOCAL_STATE_DICT:
+            raise RuntimeError(
+                "Found state_dict_type LOCAL_STATE_DICT",
+                "DeviceMesh is not compatible with LOCAL_STATE_DICT.",
+                "Please set state_dict_type to SHARDED_STATE_DICT to get DTensor state_dict.",
+            )
+        else:
+            fsdp_state._state_dict_config._use_dtensor = True
+
+
+@no_type_check
+@torch.no_grad()
+def _pre_load_state_dict_hook(
+    module: nn.Module,
+    state_dict: dict[str, Any],
+    prefix: str,
+    *args: Any,
+) -> None:
+    """
+    This is called before ``module._load_from_state_dict()``.
+    ``fsdp_state._state_dict_type`` is used to decide what preprocessing will
+    be done.
+    """
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
+            "be returned."
+        )
+    else:
+        _set_use_dtensor(fsdp_state)
+        context = contextlib.nullcontext()
+
+    _lazy_init(fsdp_state, module)
+    if fsdp_state._is_root:
+        SimpleProfiler.reset()
+
+    with context:
+        _pre_load_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_pre_load_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_pre_load_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_pre_load_state_dict_hook,
+        }
+        # Code that is common for all state_dict impls
+        if fsdp_state._device_handle.is_available():
+            fsdp_state._device_handle.synchronize()
+        # Dispatch into state_dict specific implementation of pre-hook.
+        _pre_load_state_dict_hook_fn[fsdp_state._state_dict_type](
+            module, fsdp_state, state_dict, prefix
+        )
+
+
+@no_type_check
+@torch.no_grad()
+def _post_load_state_dict_hook(
+    module: nn.Module,
+    incompatible_keys: tuple[list[str], list[str]],
+    *args: Any,
+) -> None:
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
+            "be returned."
+        )
+    else:
+        context = contextlib.nullcontext()
+
+    with context:
+        _post_load_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_post_load_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_post_load_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_post_load_state_dict_hook,
+        }
+        # Code that is common for all state_dict impls
+        # Dispatch into state_dict type specific implementation of post-hook for
+        # loading state_dict.
+        _post_load_state_dict_hook_fn[fsdp_state._state_dict_type](module, fsdp_state)
+
+    # When reporting incompatible keys, trim FSDP prefixes.
+    missing_keys = incompatible_keys[0]
+    unexpected_keys = incompatible_keys[1]
+    for i in range(len(missing_keys)):
+        missing_keys[i] = clean_tensor_name(missing_keys[i])
+
+    for i in range(len(unexpected_keys)):
+        unexpected_keys[i] = clean_tensor_name(unexpected_keys[i])
+
+    if fsdp_state._is_root:
+        SimpleProfiler.dump_and_reset("FSDP model load_state_dict profiling: ")
+
+
+def _register_all_state_dict_hooks(state: _FSDPState):
+    """
+    Registers pre-save, post-save, pre-load, and post-load state dict hooks.
+    """
+    for hook_registration_fn_str, hook, hook_registration_fn_kwargs in (
+        ("register_state_dict_pre_hook", _pre_state_dict_hook, {}),
+        ("_register_state_dict_hook", _post_state_dict_hook, {}),
+        (
+            "_register_load_state_dict_pre_hook",
+            _pre_load_state_dict_hook,
+            {"with_module": True},
+        ),
+        ("register_load_state_dict_post_hook", _post_load_state_dict_hook, {}),
+    ):
+        _register_state_dict_hooks_base(
+            state, hook_registration_fn_str, hook, hook_registration_fn_kwargs
+        )
+
+
+@no_type_check
+def _register_state_dict_hooks_base(
+    state: _FSDPState,
+    hook_registration_fn_name: str,
+    hook: Callable,
+    hook_registration_fn_kwargs: dict[str, Any],
+) -> None:
+    """Registers ``hook`` using ``hook_registration_fn``."""
+    if not _is_composable(state):
+        getattr(state, hook_registration_fn_name)(hook, **hook_registration_fn_kwargs)
+    else:
+        handle = state._handle
+        if handle:
+            getattr(handle._fully_sharded_module, hook_registration_fn_name)(
+                hook, **hook_registration_fn_kwargs
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_trace_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_trace_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..22cde2abc966aba960753663a4944513e3d5087f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_trace_utils.py
@@ -0,0 +1,238 @@
+# mypy: allow-untyped-defs
+import functools
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from typing import Any, Callable, NamedTuple, Optional
+
+import torch
+import torch.nn as nn
+
+
+@dataclass
+class TracingConfig:
+    """
+    This represents a symbolic tracing configuration.
+
+    Args:
+        tracer (torch.fx.Tracer): An instance of :class:`torch.fx.Tracer` to
+            use for symbolic tracing. The default value is the native
+            :class:`torch.fx.Tracer` constructed with default arguments.
+            However, the user may want to pass a different value such as the
+            ``HFTracer`` for models in the HuggingFace Transformers_ library.
+            .. _Transformers: https://huggingface.co/docs/transformers/index
+        concrete_args (Optional[Dict[str, Any]]): Concrete arguments that
+            should not be treated as ``torch.fx.Proxy`` when tracing the
+            module ``forward()``. Passing ``concrete_args`` allows partially
+            specializing the forward, e.g. to remove control flow or data
+            structures. This ``concrete_args`` here is the same argument used
+            in :meth:`~torch.fx.Tracer.trace`.
+    """
+
+    tracer: torch.fx.Tracer = field(default_factory=torch.fx.Tracer)
+    concrete_args: Optional[dict[str, Any]] = None
+
+
+class _ParamUsageInfo(NamedTuple):
+    """
+    This is used for ``_ExecutionInfo.module_to_param_usage_infos`` to record
+    execution information. The ``dict`` maps modules to a list of these
+    ``_ParamUsageInfo`` instances, where each instance represents a group of
+    parameters used together.
+
+    Specifically, for each module key in the ``dict``, each instance of this
+    class represents either:
+    (1) the module and some sublist of its ``named_parameters()`` used
+    together in execution (see ``_patched_create_proxy()``), or
+    (2) a submodule and all of ``submodule.named_parameters()`` (see
+    ``_patched_call_module()``).
+
+    Type (1) corresponds to directly using parameters in ops without calling
+    ``forward()``, and type (2) corresponds to calling ``forward()``. The
+    mapped-to lists in the ``dict`` follow the execution order.
+    """
+
+    module: nn.Module
+    named_params: list[tuple[str, nn.Parameter]]
+
+
+class _ExecutionInfo:
+    """
+    This represents the execution order information from the forward pass.
+
+    Attributes:
+        curr_module (nn.Module): Current module being traced.
+        module_forward_order (List[nn.Module]): The modules in (pre-)forward
+            order, i.e. the order in which their ``forward()`` methods are
+            called. Each call to a module's ``forward()`` corresponds to one
+            element in the list.
+        module_to_param_usage_infos (Dict[nn.Module, List[_ParamUsageInfo]]):
+            Maps a module to a list of module execution infos. See
+            :class:`_ParamUsageInfo` for details.
+        param_forward_order (List[nn.Parameter]): The parameters in forward
+            execution order, where only a parameter's first participation is
+            included.
+        visited_params (Set[nn.Parameter]): The parameters visited so far
+            during the trace. This is only used during tracing for fast
+            membership check. Invariant: The parameters in
+            ``param_forward_order`` are exactly those in ``visited_params``.
+    """
+
+    def __init__(self, root_module: nn.Module) -> None:
+        self.curr_module: nn.Module = root_module
+        self.module_forward_order: list[nn.Module] = [root_module]
+        self.module_to_param_usage_infos: dict[nn.Module, list[_ParamUsageInfo]] = {
+            root_module: []
+        }
+        self.param_forward_order: list[nn.Parameter] = []
+        self.visited_params: set[nn.Parameter] = set()
+
+
+class _ExecOrderTracer:
+    def __init__(self) -> None:
+        self.exec_info: Optional[_ExecutionInfo] = None
+
+    @contextmanager
+    def patch_tracer(self, tracer: torch.fx.Tracer, root_module: nn.Module):
+        self.exec_info = _ExecutionInfo(root_module)
+        orig_call_module = tracer.call_module
+        orig_create_proxy = tracer.create_proxy
+        tracer.call_module = functools.partial(  # type: ignore[method-assign]
+            self._patched_call_module, orig_call_module, self.exec_info
+        )
+        fqn_to_param = dict(root_module.named_parameters())
+        tracer.create_proxy = functools.partial(  # type: ignore[method-assign]
+            self._patched_create_proxy,
+            orig_create_proxy,
+            self.exec_info,
+            fqn_to_param,
+        )
+        try:
+            yield
+        finally:
+            tracer.call_module = orig_call_module  # type: ignore[method-assign]
+            tracer.create_proxy = orig_create_proxy  # type: ignore[method-assign]
+
+    def _patched_call_module(
+        self,
+        call_module: Callable,
+        exec_info: _ExecutionInfo,
+        # Below are the expected arguments to `call_module()`
+        module: nn.Module,
+        forward: Callable,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any],
+    ) -> Any:
+        """
+        Overrides ``call_module`` to save execution information to
+        ``exec_info``. Note that ``call_module`` is called during symbolic
+        tracing for each non-root module.
+
+        Args:
+            call_module (Callable): Original ``call_module`` to override.
+            exec_info (_ExecutionInfo): Used to record execution information.
+            module (nn.Module): Module corresponding to this ``call_module``.
+            forward (Callable): ``forward()`` method of ``module`` to be called
+                for this ``call_module``.
+            args (Tuple[Any, ...]): Positional arguments for ``forward``.
+            kwargs (Dict[str, Any]): Keyword arguments for ``forward``.
+
+        Returns:
+            Same return value as ``call_module``.
+        """
+        exec_info.module_forward_order.append(module)
+        named_params = list(module.named_parameters())
+        curr_module = exec_info.curr_module
+        if named_params:
+            assert curr_module in exec_info.module_to_param_usage_infos, (
+                "The current module should have already been processed by a patched `call_module`"
+            )
+            exec_info.module_to_param_usage_infos[exec_info.curr_module].append(
+                _ParamUsageInfo(module, named_params)
+            )
+        prev_curr_module = curr_module
+        exec_info.curr_module = module
+        exec_info.module_to_param_usage_infos[module] = []
+        output = call_module(module, forward, args, kwargs)
+        exec_info.curr_module = prev_curr_module
+        return output
+
+    def _patched_create_proxy(
+        self,
+        create_proxy: Callable,
+        exec_info: _ExecutionInfo,
+        fqn_to_param: dict[str, nn.Parameter],
+        # Below are the expected arguments to `create_proxy()`
+        kind: str,
+        target: torch.fx.node.Target,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any],
+        name: Optional[str] = None,
+        type_expr: Optional[Any] = None,
+        proxy_factory_fn: Optional[Callable[[torch.fx.Node], torch.fx.Proxy]] = None,
+    ) -> torch.fx.Proxy:
+        """
+        Overrides ``create_proxy`` to save execution information to
+        ``exec_info``. Note that ``create_proxy`` is called during symbolic
+        tracing for each leaf function/method/module.
+
+        Args:
+            create_proxy (Callable): Original ``create_proxy`` to override.
+            exec_info (_ExecutionInfo): Used to record execution information.
+            fqn_to_param (Dict[str, nn.Parameter]): ``dict`` version of the
+                root module's ``named_parameters()`` with FQN as key and
+                parameter as value.
+            kind (str): Kind of the target method ('call_function',
+                'call_method', 'get_attr', 'call_module', 'placeholder', or
+                'output'). See :class:`torch.fx.Graph` for details. This is
+                passed to ``create_proxy``.
+            target (torch.fx.node.Target): Contains the string name of the
+                function/method/module. This is passed to ``create_proxy``.
+            args (Tuple[Any, ...]): Positional arguments for the function/
+                method/module. This is passed to ``create_proxy``.
+            kwargs (Dict[str, Any]): Keyword arguments for the function/method/
+                module. This is passed to ``create_proxy``
+            name (Optional[str]): An optional string name for the ``Node``
+                created in ``create_proxy``. This is passed to
+                ``create_proxy``.
+            type_expr (Optional[Any]): An optional type annotation representing
+                the Python type that the output of the node has. This is passed
+                to ``create_proxy``.
+            proxy_factory_fn (Callable[[torch.fx.Node], torch.fx.Proxy]):
+                An alternative proxy constructor used in ``create_proxy``. This
+                is passed to ``create_proxy``.
+
+        Returns:
+            torch.fx.Proxy: Created ``Node`` wrapped in a ``Proxy`` object.
+        """
+        proxy = create_proxy(
+            kind, target, args, kwargs, name, type_expr, proxy_factory_fn
+        )
+        curr_module = exec_info.curr_module
+        if kind in ("call_function", "call_method"):
+            if args is not None:
+                named_params: list[tuple[str, nn.Parameter]] = []
+                for arg in args:
+                    if (
+                        isinstance(arg, torch.fx.Proxy)
+                        and arg.node.target in fqn_to_param
+                    ):
+                        param = fqn_to_param[arg.node.target]  # type: ignore[index]
+                        named_params.append((arg.node.target, param))  # type: ignore[arg-type]
+                        if param not in exec_info.visited_params:
+                            exec_info.visited_params.add(param)
+                            exec_info.param_forward_order.append(param)
+                if named_params:
+                    exec_info.module_to_param_usage_infos[curr_module].append(
+                        _ParamUsageInfo(curr_module, named_params)
+                    )
+        elif kind == "call_module":
+            named_params = list(curr_module.named_parameters())
+            if named_params:
+                exec_info.module_to_param_usage_infos[curr_module].append(
+                    _ParamUsageInfo(curr_module, named_params)
+                )
+            for _, param in named_params:
+                if param not in exec_info.visited_params:
+                    exec_info.visited_params.add(param)
+                    exec_info.param_forward_order.append(param)
+        return proxy
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_traversal_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_traversal_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ca758c83a9729f1d2d7ea74a1720e489ee3298b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_traversal_utils.py
@@ -0,0 +1,112 @@
+"""
+NOTE: This file must be imported like
+``import torch.distributed.fsdp._traversal_utils`` and not like
+``from torch.distirbuted.fsdp._traversal_utils import ...`` to avoid circular
+imports. For brevity, we may import the file as ``traversal_utils``.
+"""
+
+import collections
+
+import torch.nn as nn
+from torch.distributed._composable.contract import _get_registry
+from torch.distributed.fsdp._common_utils import _FSDPState, _get_module_fsdp_state
+
+
+"""
+[Note: FSDP State Traversal]
+For the wrapper code path, ``_FSDPState`` is the ``FullyShardedDataParallel``
+module wrapping a fully sharded module, and for the non-wrapper code path,
+``_FSDPState`` is an object that gets embedded on a fully sharded module.
+See [Note: Fully Sharded Module] for the definition.
+
+There are three common traversal idioms: Given a root module,
+- ``_get_fsdp_states()`` returns all ``_FSDPState`` s in the tree.
+- ``get_fsdp_root_states()`` returns all local root ``_FSDPState`` s in the
+tree (i.e. those with ``_is_root == True``).
+- ``_get_fsdp_handles()``returns all ``FlatParamHandle`` s in the tree.
+
+All of these methods must take in the root module (i.e. an ``nn.Module``) and
+not a general ``_FSDPState`` because ``_FSDPState`` does not support a graph
+traversal, whereas ``nn.Module`` has ``nn.Module.modules()`` for traversal.
+"""
+
+
+def _composable(module: nn.Module) -> bool:
+    """
+    Returns if ``module`` can compose with ``fully_shard``.
+    """
+    # TODO: Add any other composable APIs that are mutually exclusive.
+    registry = _get_registry(module)
+    if registry is None:
+        return True
+    return "replicate" not in registry
+
+
+# TODO (awgu): We may be able to remove this function if we retired the
+# `use_orig_params=False` code path since so far we only need the module for
+# `FlatParameter` registration, which is not needed for `use_orig_params=True`.
+def _get_fsdp_states_with_modules(
+    module: nn.Module,
+) -> tuple[list[_FSDPState], list[nn.Module]]:
+    """
+    Returns a tuple containing:
+    1. A list of the ``_FSDPState`` instances in the module tree rooted at
+    ``module`` without any duplicates and following the ``module.modules()``
+    traversal order (which is assumed to be depth-first).
+    2. A corresponding list of the modules owning the states in the first list.
+
+    For the wrapper code path, both returned lists are the same, each
+    containing all ``FullyShardedDataParallel`` instances. For the composable
+    code path, this returns a list of all composable state instances and a list
+    of the corresponding fully sharded modules. See [Note: Fully Sharded
+    Module].
+
+    NOTE: The traversal does not proceed into any module annotated by an
+    incompatible API (e.g. ``replicate``).
+    """
+    fsdp_states: list[_FSDPState] = []
+    fsdp_modules: list[nn.Module] = []
+    # Track the visited FSDP states since multiple modules may share the same
+    # one and we want to return a de-duplicated list
+    visited_fsdp_states: set[_FSDPState] = set()
+    # Track the visited modules in case of shared modules, which implies the
+    # module graph is no longer a tree
+    visited_modules: set[nn.Module] = set()
+
+    # Perform depth-first search from `module` to ensure that we do not
+    # traverse into an incompatible API's subtree (use DFS instead of BFS to
+    # match `.modules()` order)
+    deque: collections.deque[nn.Module] = collections.deque([module])
+    while deque:
+        submodule = deque.popleft()
+        visited_modules.add(submodule)
+        if not _composable(submodule):
+            continue
+        for child_module in reversed(list(submodule.children())):
+            if child_module not in visited_modules:
+                deque.appendleft(child_module)
+        optional_state = _get_module_fsdp_state(submodule)
+        if optional_state is not None and optional_state not in visited_fsdp_states:
+            visited_fsdp_states.add(optional_state)
+            fsdp_states.append(optional_state)
+            fsdp_modules.append(submodule)
+    return fsdp_states, fsdp_modules
+
+
+def _get_fsdp_states(module: nn.Module) -> list[_FSDPState]:
+    """See :func:`_get_fsdp_states_with_modules`."""
+    fsdp_states, _ = _get_fsdp_states_with_modules(module)
+    return fsdp_states
+
+
+def _get_fsdp_handles(module: nn.Module) -> list:
+    """
+    Returns all ``FlatParamHandle`` s in the module tree rooted at ``module``
+    following the rules in :func:`_get_fsdp_state`.
+    """
+    handles = [
+        fsdp_state._handle
+        for fsdp_state in _get_fsdp_states(module)
+        if fsdp_state._handle is not None
+    ]
+    return handles
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_unshard_param_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_unshard_param_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1876c4a44431077f7a4482847c58bd79be2b9a32
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_unshard_param_utils.py
@@ -0,0 +1,337 @@
+# mypy: allow-untyped-defs
+import contextlib
+import warnings
+from collections.abc import Generator
+from typing import cast
+
+import torch
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+from torch.distributed.fsdp._common_utils import (
+    _FSDPState,
+    _get_module_fsdp_state,
+    _has_fsdp_params,
+    _module_handle,
+    HandleTrainingState,
+    TrainingState,
+)
+from torch.distributed.fsdp._runtime_utils import (
+    _lazy_init,
+    _reset_flat_param_grad_info_if_needed,
+    _reshard,
+    _reshard_grads,
+    _unshard,
+    _unshard_grads,
+)
+from torch.distributed.utils import _p_assert
+
+from ._flat_param import FlatParamHandle
+
+
+FLAT_PARAM = "_flat_param"
+
+
+@torch.no_grad()
+def _writeback_to_local_shard(
+    handle: FlatParamHandle,
+    writeback_grad: bool,
+):
+    """
+    For the handle, writes back the this rank's shard of the unsharded
+    flattened parameter to the sharded flattened parameter. If
+    ``writeback_grad=True``, then writes back to the sharded gradient as
+    well.
+
+    Precondition: The handle's ``FlatParameter`` 's data points to the
+    padded unsharded flattened parameter.
+    """
+
+    def _get_shard(flat_param_or_grad: torch.Tensor) -> torch.Tensor:
+        if handle.uses_sharded_strategy:
+            # For sharded strategies, get the *unpadded* shard instead of
+            # the *padded* shard to persist user changes to the padding
+            # (though FSDP does not explicitly support this)
+            shard, _ = FlatParamHandle._get_unpadded_shard(
+                flat_param_or_grad,
+                handle.rank,
+                handle.world_size,
+            )
+            return shard
+        # For `NO_SHARD`, the `flat_param` or its gradient may be modified,
+        # so we write it back directly
+        return flat_param_or_grad
+
+    param_shard = _get_shard(handle.flat_param)
+    handle.flat_param._local_shard[: param_shard.numel()].copy_(param_shard)  # type: ignore[attr-defined]
+    if writeback_grad:
+        existing_grad = handle.sharded_grad
+        if existing_grad is not None:
+            assert handle.flat_param.grad is not None
+            grad_shard = _get_shard(handle.flat_param.grad)
+            existing_grad[: grad_shard.numel()].copy_(grad_shard)
+
+
+def _deregister_flat_param(state: _FSDPState, module: nn.Module) -> None:
+    """
+    De-registers the flattened parameter from the wrapped module, hiding it
+    from ``nn.Module`` methods.
+
+    We do not use ``del`` because we want ``FLAT_PARAM`` to always be an
+    attribute but dynamically change whether it is visible to ``nn.Module``
+    methods.
+    """
+    if _has_fsdp_params(state, module):
+        # TODO: figure out the case for the composable APIs.
+        cast(nn.Module, module.module)._parameters.pop(FLAT_PARAM, None)
+
+
+def _register_flat_param(state: _FSDPState, module: nn.Module) -> None:
+    """
+    Registers the flattened parameter to the wrapped module, making it
+    visible to ``nn.Module`` methods.
+
+    We do not use :meth:`nn.Module.register_parameter` because we want
+    ``FLAT_PARAM`` to always be an attribute but dynamically change whether
+    it is visible to ``nn.Module`` methods.
+    """
+    handle = _module_handle(state, module)
+    if _has_fsdp_params(state, module):
+        # TODO: figure out the case for the composable APIs.
+        cast(nn.Module, module.module)._parameters[FLAT_PARAM] = handle.flat_param
+
+
+@contextlib.contextmanager
+def _unflatten_as_params(state: _FSDPState, module: nn.Module) -> Generator:
+    """
+    Assumes that the flattened parameter is unsharded. When in the context,
+    de-registers the flattened parameter and unflattens the original
+    parameters as ``nn.Parameter`` views into the flattened parameter.
+    After the context, re-registers the flattened parameter and restores
+    the original parameters as ``Tensor`` views into the flattened
+    parameter.
+    """
+    handle = _module_handle(state, module)
+    if not handle:
+        yield
+    else:
+        _deregister_flat_param(state, module)
+        try:
+            with handle.unflatten_as_params():
+                yield
+        finally:
+            if not handle._use_orig_params:
+                _register_flat_param(state, module)
+
+
+def _validate_unshard_params_args(
+    state: _FSDPState,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+) -> None:
+    if with_grads and (offload_to_cpu or not state._use_orig_params):
+        raise NotImplementedError(
+            f"with_grads={with_grads}, "
+            f"use_orig_params={state._use_orig_params}, "
+            f"offload_to_cpu={offload_to_cpu} "
+            f"is not supported yet"
+        )
+    if offload_to_cpu and state._handle and (not state._handle.uses_sharded_strategy):
+        raise NotImplementedError(
+            "offload_to_cpu=True and NO_SHARD is not supported yet"
+        )
+    if writeback and rank0_only:
+        # TODO: Rank 0 can broadcast the `FlatParameter` to allow all ranks to
+        # persist the changes.
+        raise NotImplementedError(
+            "writeback=True and rank0_only=True is not supported yet"
+        )
+    if offload_to_cpu and not rank0_only:
+        warnings.warn(
+            "offload_to_cpu=True and rank0_only=False may result in the"
+            "unsharded parameters being redundantly copied to CPU memory for "
+            "GPUs sharing the same CPU memory, which risks CPU OOM. We "
+            "recommend using offload_to_cpu=True with rank0_only=True."
+        )
+
+
+@contextlib.contextmanager
+def _unshard_fsdp_state_params(
+    module: nn.Module,
+    state: _FSDPState,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+):
+    """
+    This unshards the parameters for a single FSDP state ``state`` that
+    corresponds to ``module``.
+    """
+    _validate_unshard_params_args(
+        state, writeback, rank0_only, offload_to_cpu, with_grads
+    )
+    state._device_handle.synchronize()
+    # If handles are shared by other module(s), the handle may be already unsharded.
+    maybe_handle = _module_handle(state, module)
+    handle = None
+    if (
+        maybe_handle
+        and maybe_handle._training_state != HandleTrainingState.SUMMON_FULL_PARAMS
+    ):
+        handle = maybe_handle
+    if not handle:
+        yield
+        return
+
+    assert handle._training_state == HandleTrainingState.IDLE, (
+        f"Expects the handle training to be IDLE but got {handle._training_state}"
+    )
+
+    handle._training_state = HandleTrainingState.SUMMON_FULL_PARAMS
+
+    _reset_flat_param_grad_info_if_needed(handle)
+    free_unsharded_flat_param = handle.needs_unshard()
+    # No need to call `wait_stream()` since we unshard in the computation
+    # stream directly
+    computation_stream = state._device_handle.current_stream()
+    _unshard(state, handle, computation_stream, computation_stream)
+    if with_grads:
+        _unshard_grads(handle)
+
+    if rank0_only and state.rank != 0:
+        # Free the unsharded flattened parameter early
+        _reshard(state, handle, free_unsharded_flat_param)
+        if with_grads:
+            _reshard_grads(handle)
+        try:
+            yield
+        finally:
+            handle._training_state = HandleTrainingState.IDLE
+    else:
+        # Unflatten the unsharded flattened parameters
+        with contextlib.ExitStack() as stack:
+            # Invariant: rank == 0 or !rank0_only
+            if offload_to_cpu and handle.uses_sharded_strategy:
+                stack.enter_context(handle.to_cpu())
+                # NOTE: Since PyTorch enforces that a parameter and its
+                # gradients need to match metadata (e.g. device), we must
+                # move gradients to CPU *after* we move parameters.
+            # NOTE: This assumes 1 `FlatParameter`
+            if not state._use_orig_params:
+                stack.enter_context(_unflatten_as_params(state, module))
+            try:
+                yield
+            finally:
+                stack.close()
+                if writeback:
+                    _writeback_to_local_shard(handle, with_grads)
+                _reshard(state, handle, free_unsharded_flat_param)
+                if with_grads:
+                    _reshard_grads(handle)
+                handle._training_state = HandleTrainingState.IDLE
+
+
+@contextlib.contextmanager
+def _unshard_params_for_summon(
+    module: nn.Module,
+    state: _FSDPState,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+):
+    _validate_unshard_params_args(
+        state, writeback, rank0_only, offload_to_cpu, with_grads
+    )
+    _lazy_init(state, module)
+    if state.training_state == TrainingState.FORWARD_BACKWARD:
+        raise AssertionError(
+            "Cannot manually unshard parameters during forward/backward"
+        )
+    elif state.training_state == TrainingState.SUMMON_FULL_PARAMS:
+        raise AssertionError(
+            "Cannot manually unshard parameters when already unsharding parameters"
+        )
+    with _unshard_fsdp_state_params(
+        module=module,
+        state=state,
+        writeback=writeback,
+        rank0_only=rank0_only,
+        offload_to_cpu=offload_to_cpu,
+        with_grads=with_grads,
+    ):
+        try:
+            state.training_state = TrainingState.SUMMON_FULL_PARAMS
+            yield
+        finally:
+            state.training_state = TrainingState.IDLE
+
+
+@contextlib.contextmanager
+def _unshard_params(
+    module: nn.Module,
+    recurse: bool,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+):
+    """
+    This unshards FSDP-managed parameters for all modules with FSDP applied in
+    the module tree rooted at ``module``.
+    """
+    if not recurse:
+        optional_state = _get_module_fsdp_state(module)
+        if optional_state is None:
+            with contextlib.nullcontext():
+                yield
+            return
+        states_and_modules = ([optional_state], [module])
+    else:
+        states_and_modules = traversal_utils._get_fsdp_states_with_modules(module)
+    with contextlib.ExitStack() as stack:
+        for state, module in zip(*states_and_modules):
+            stack.enter_context(
+                _unshard_params_for_summon(
+                    module=module,
+                    state=state,
+                    writeback=writeback,
+                    rank0_only=rank0_only,
+                    offload_to_cpu=offload_to_cpu,
+                    with_grads=with_grads,
+                )
+            )
+        yield
+
+
+def _deregister_orig_params(state: _FSDPState, module: nn.Module) -> None:
+    """
+    Deregisters the original parameters; registers the ``FlatParameter``.
+    """
+    handle = _module_handle(state, module)
+    if not handle:
+        return
+    _p_assert(
+        handle._use_orig_params,
+        f"Inconsistent `_use_orig_params` -- FSDP: {state._use_orig_params} "
+        f"handle: {handle._use_orig_params}",
+    )
+    handle._deregister_orig_params()
+    _register_flat_param(state, module)
+
+
+def _register_orig_params(state: _FSDPState, module: nn.Module) -> None:
+    """
+    Deregisters the ``FlatParameter``; registers the original parameters.
+    """
+    handle = _module_handle(state, module)
+    if not handle:
+        return
+    _deregister_flat_param(state, module)
+    if handle.is_sharded(handle.flat_param):
+        handle._use_sharded_views()
+        handle._use_sharded_grad_views()
+    else:
+        handle._use_unsharded_views(as_params=True)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_wrap_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_wrap_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ceecabcacf74c070248257dc2061700d0db3f00b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/_wrap_utils.py
@@ -0,0 +1,262 @@
+# mypy: allow-untyped-defs
+import collections
+import functools
+import inspect
+import warnings
+from functools import partial
+from typing import Any, Callable, Union
+
+import torch.nn as nn
+from torch.distributed.fsdp._common_utils import (
+    _get_module_fsdp_state,
+    _override_module_mixed_precision,
+)
+from torch.distributed.fsdp.wrap import (
+    _construct_wrap_fn,
+    _or_policy,
+    _Policy,
+    _post_order_apply,
+    _recursive_wrap,
+    _run_mixed_precision_override_policy,
+    _wrap_module_cls_individually,
+)
+
+
+def _auto_wrap(
+    root_module: nn.Module,
+    policy: Union[Callable, _Policy],
+    ignored_modules: set[nn.Module],
+    ignored_params: set[nn.Parameter],
+    root_kwargs: dict[str, Any],
+    fsdp_fn: Callable,  # e.g. `FullyShardedDataParallel` or `fully_shard`
+):
+    """
+    Auto wraps modules in ``root_module`` 's tree according to ``policy``
+    following a post-order traversal.
+
+    Precondition: ``root_kwargs`` should contain all arguments except
+    ``module``. This function accepts the kwargs dict directly since it gets
+    forwarded into the post-order traversal function.
+    """
+    mixed_precision = root_kwargs["mixed_precision"]
+    is_wrapper = inspect.isclass(fsdp_fn)
+    # TODO: We may relax this no-nested-wrapping constraint to support manual
+    # wrapping followed by auto wrapping.
+    _check_nested_wrapping(root_module)
+
+    if isinstance(policy, _Policy):
+        root_kwargs["auto_wrap_policy" if is_wrapper else "policy"] = None
+        target_module_to_kwargs = policy._run_policy(
+            root_module, ignored_modules, root_kwargs
+        )
+        if mixed_precision is not None:
+            target_module_to_kwargs = _run_mixed_precision_override_policy(
+                root_module,
+                mixed_precision._module_classes_to_ignore,
+                ignored_modules,
+                root_kwargs,
+                target_module_to_kwargs,
+            )
+            overridden_module_classes = _override_module_mixed_precision(
+                root_module, mixed_precision._module_classes_to_ignore
+            )
+            _warn_on_overridden_mixed_precision(overridden_module_classes)
+        use_orig_params = root_kwargs.get("use_orig_params", False)
+        _validate_frozen_params(
+            root_module,
+            set(target_module_to_kwargs.keys()),
+            ignored_params,
+            use_orig_params,
+        )
+        wrap_fn = _construct_wrap_fn(root_module, target_module_to_kwargs, fsdp_fn)
+        _post_order_apply(root_module, wrap_fn)
+        return
+
+    recursive_wrap_kwargs = {
+        "module": root_module,
+        "auto_wrap_policy": policy,
+        "wrapper_cls": fsdp_fn,
+        "ignored_modules": ignored_modules,
+        "ignored_params": ignored_params,
+        "only_wrap_children": True,
+    }
+    if mixed_precision is not None:
+        # Wrap modules of the ignored types separately and register forward
+        # hooks to cast to fp32 and back to the original dtype, respectively
+        overridden_module_classes = _override_module_mixed_precision(
+            root_module, mixed_precision._module_classes_to_ignore
+        )
+        policy = functools.partial(
+            _or_policy,
+            policies=[
+                policy,
+                partial(
+                    _wrap_module_cls_individually,
+                    module_classes=mixed_precision._module_classes_to_ignore,
+                ),
+            ],
+        )
+        recursive_wrap_kwargs["auto_wrap_policy"] = policy
+        _warn_on_overridden_mixed_precision(overridden_module_classes)
+    _recursive_wrap(**recursive_wrap_kwargs, **root_kwargs)  # type: ignore[arg-type]
+
+
+def _check_nested_wrapping(root_module: nn.Module):
+    for module_name, module in root_module.named_modules():
+        if _get_module_fsdp_state(module) is not None:
+            raise ValueError(
+                "FSDP auto wrapping requires modules to not already have "
+                f"FSDP applied but found {module_name} in\n{root_module}"
+            )
+
+
+def _warn_on_overridden_mixed_precision(
+    overridden_module_classes: set[type[nn.Module]],
+):
+    if len(overridden_module_classes) == 0:
+        return
+    warnings.warn(
+        "Both mixed precision and an auto_wrap_policy were specified to FSDP, "
+        f"where the wrapped module has submodules of type:\n{overridden_module_classes}\n"
+        "These modules will be wrapped as separate FSDP instacnes with mixed "
+        "precision disabled."
+    )
+
+
+def _validate_frozen_params(
+    root_module: nn.Module,
+    modules_to_wrap: set[nn.Module],
+    ignored_params: set[nn.Parameter],
+    use_orig_params: bool,
+):
+    """
+    This checks that, given ``modules_to_wrap``, each module would manage
+    parameters that are uniformly frozen or non-frozen. This uniformity
+    requirement is strict for ``use_orig_params=False`` (hard error) and highly
+    recommended for ``use_orig_params=True`` (user warning).
+    """
+    post_order_named_modules = _get_post_order_named_modules(root_module)
+    visited_modules: set[nn.Module] = set()
+    for module_name, module in post_order_named_modules:
+        if module in modules_to_wrap:
+            param_to_fqn = _get_managed_param_to_fqn(
+                module, ignored_params, visited_modules, module_name
+            )
+            frozen_param_fqns: list[str] = []
+            frozen_param_numel = 0
+            nonfrozen_param_fqns: list[str] = []
+            nonfrozen_param_numel = 0
+            for param, fqn in param_to_fqn.items():
+                if param.requires_grad:
+                    nonfrozen_param_fqns.append(fqn)
+                    nonfrozen_param_numel += param.numel()
+                else:
+                    frozen_param_fqns.append(fqn)
+                    frozen_param_numel += param.numel()
+            if len(frozen_param_fqns) > 0 and len(nonfrozen_param_fqns) > 0:
+                msg = f"{module_name} has both parameters with requires_grad=True and False."
+                if use_orig_params:
+                    total_param_numel = frozen_param_numel + nonfrozen_param_numel
+                    msg += (
+                        " We do not recommend wrapping such modules since "
+                        "the gradient memory usage will be higher than expected "
+                        f"({total_param_numel} numel instead of {nonfrozen_param_numel} numel "
+                        "before sharding via reduce-scatter). "
+                    )
+                else:
+                    msg += " FSDP does not support wrapping such modules when use_orig_params=False. "
+                msg += "If possible, wrap the frozen parameters with FSDP separately.\n"
+                msg += (
+                    f"The following parameters have requires_grad=True:\n{nonfrozen_param_fqns}\n"
+                    f"The following parameters have requires_grad=False:\n{frozen_param_fqns}"
+                )
+                if use_orig_params:
+                    warnings.warn(msg)
+                else:
+                    raise ValueError(msg)
+
+
+def _get_post_order_named_modules(
+    root_module: nn.Module,
+) -> list[tuple[str, nn.Module]]:
+    """
+    This returns the named modules following a post-order traversal, which is a
+    valid reverse topological sort. We achieve this using the reverse of a
+    stack-based DFS order instead of reversing ``root_module.named_modules()``
+    since the former gives the modules in registration order at each level in
+    the module tree (as opposed to the reverse), which allows us to error/warn
+    on the first registered module that violates the condition.
+
+    For example, consider the following module structure:
+        M(
+          S1(),
+          S2(
+            SS1(),
+            SS2(),
+          ),
+          S3(),
+        )
+    The reverse DFS order is [S1, SS1, SS2, S2, S3, M], while the reverse
+    ``named_modules()`` order is [S3, SS2, SS1, S2, S1, M].
+    """
+    visited_modules = {root_module}
+    stack = [("", root_module)]
+    # Append and reverse at the end for linear-time algorithm
+    reverse_post_order_named_modules: list[tuple[str, nn.Module]] = []
+    while stack:
+        module_name, module = stack.pop()
+        reverse_post_order_named_modules.append((module_name, module))
+        for child_module_name, child_module in module.named_children():
+            if child_module is None:  # only for overrides of `named_children()`
+                continue
+            if child_module not in visited_modules:
+                visited_modules.add(child_module)
+                if module_name != "":
+                    child_module_name = module_name + "." + child_module_name
+                stack.append((child_module_name, child_module))
+    post_order_named_modules = list(reversed(reverse_post_order_named_modules))
+    return post_order_named_modules
+
+
+def _get_managed_param_to_fqn(
+    module_to_wrap: nn.Module,
+    ignored_params: set[nn.Parameter],
+    visited_modules: set[nn.Module],
+    root_prefix: str,
+) -> dict[nn.Parameter, str]:
+    """
+    This returns a dict that maps managed parameter to its FQN for the given
+    ``module_to_wrap``. The dict's keys are exactly the parameters that would
+    be managed by the module, where this is achieved by calling this function
+    on the modules to wrap in reverse topological order, destructively updating
+    ``visited_modules``, and not traversing into those modules. The FQNs are
+    prefixed from the root (via ``root_prefix``) to be more informative.
+
+    NOTE: This function is meant to be called pre-wrapping and iteratively in
+    reverse topological order to cover the full module tree. This differs from
+    the ``_get_param_to_fqn()`` function meant to be called post-wrapping and
+    on the full module tree in one shot. Given those differences, we do not try
+    to unify the two.
+    """
+    param_to_fqn: dict[nn.Parameter, str] = {}
+    # Run BFS (or any tree traversal works)
+    queue = collections.deque([(module_to_wrap, root_prefix)])
+    visited_modules.add(module_to_wrap)
+    while queue:
+        module, prefix = queue.popleft()
+        for param_name, param in module.named_parameters(recurse=False):
+            if param not in ignored_params:
+                fqn = param_name if prefix == "" else prefix + "." + param_name
+                param_to_fqn[param] = fqn
+        for child_module_name, child_module in module.named_children():
+            if child_module is None:  # only for overrides of `named_children()`
+                continue
+            if child_module not in visited_modules:
+                visited_modules.add(child_module)
+                child_prefix = (
+                    child_module_name
+                    if prefix == ""
+                    else prefix + "." + child_module_name
+                )
+                queue.append((child_module, child_prefix))
+    return param_to_fqn
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..17ed0483f1c26248673fe888bc5489e099b1313b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/api.py
@@ -0,0 +1,417 @@
+"""
+This file includes public APIs for FSDP such as the classes used for the
+constructor arguments.
+"""
+
+from collections.abc import Sequence
+from dataclasses import dataclass
+from enum import auto, Enum
+from typing import Optional
+
+import torch
+from torch.nn.modules.batchnorm import _BatchNorm
+
+
+__all__ = [
+    "ShardingStrategy",
+    "BackwardPrefetch",
+    "MixedPrecision",
+    "CPUOffload",
+    "StateDictType",
+    "StateDictConfig",
+    "FullStateDictConfig",
+    "LocalStateDictConfig",
+    "ShardedStateDictConfig",
+    "OptimStateDictConfig",
+    "FullOptimStateDictConfig",
+    "LocalOptimStateDictConfig",
+    "ShardedOptimStateDictConfig",
+    "StateDictSettings",
+]
+
+
+class ShardingStrategy(Enum):
+    """
+    This specifies the sharding strategy to be used for distributed training by
+    :class:`FullyShardedDataParallel`.
+
+    - ``FULL_SHARD``: Parameters, gradients, and optimizer states are sharded.
+      For the parameters, this strategy unshards (via all-gather) before the
+      forward, reshards after the forward, unshards before the backward
+      computation, and reshards after the backward computation. For gradients,
+      it synchronizes and shards them (via reduce-scatter) after the backward
+      computation. The sharded optimizer states are updated locally per rank.
+    - ``SHARD_GRAD_OP``: Gradients and optimizer states are sharded during
+      computation, and additionally, parameters are sharded outside
+      computation. For the parameters, this strategy unshards before the
+      forward, does not reshard them after the forward, and only reshards them
+      after the backward computation. The sharded optimizer states are updated
+      locally per rank. Inside ``no_sync()``, the parameters are not resharded
+      after the backward computation.
+    - ``NO_SHARD``: Parameters, gradients, and optimizer states are not sharded
+      but instead replicated across ranks similar to PyTorch's
+      :class:`DistributedDataParallel` API. For gradients, this strategy
+      synchronizes them (via all-reduce) after the backward computation. The
+      unsharded optimizer states are updated locally per rank.
+    - ``HYBRID_SHARD``: Apply ``FULL_SHARD`` within a node, and replicate parameters across
+      nodes. This results in reduced communication volume as expensive all-gathers and
+      reduce-scatters are only done within a node, which can be more performant for medium
+      -sized models.
+    - ``_HYBRID_SHARD_ZERO2``: Apply ``SHARD_GRAD_OP`` within a node, and replicate parameters across
+      nodes. This is like ``HYBRID_SHARD``, except this may provide even higher throughput
+      since the unsharded parameters are not freed after the forward pass, saving the
+      all-gathers in the pre-backward.
+    """
+
+    FULL_SHARD = auto()
+    SHARD_GRAD_OP = auto()
+    NO_SHARD = auto()
+    HYBRID_SHARD = auto()
+    _HYBRID_SHARD_ZERO2 = auto()
+
+
+class BackwardPrefetch(Enum):
+    """
+    This configures explicit backward prefetching, which improves throughput by
+    enabling communication and computation overlap in the backward pass at the
+    cost of slightly increased memory usage.
+
+    - ``BACKWARD_PRE``: This enables the most overlap but increases memory
+      usage the most. This prefetches the next set of parameters *before* the
+      current set of parameters' gradient computation. This overlaps the *next
+      all-gather* and the *current gradient computation*, and at the peak, it
+      holds the current set of parameters, next set of parameters, and current
+      set of gradients in memory.
+    - ``BACKWARD_POST``: This enables less overlap but requires less memory
+      usage. This prefetches the next set of parameters *after* the current
+      set of parameters' gradient computation. This overlaps the *current
+      reduce-scatter* and the *next gradient computation*, and it frees the
+      current set of parameters before allocating memory for the next set of
+      parameters, only holding the next set of parameters and current set of
+      gradients in memory at the peak.
+    - FSDP's ``backward_prefetch`` argument accepts ``None``, which disables
+      the backward prefetching altogether. This has no overlap and does not
+      increase memory usage. In general, we do not recommend this setting since
+      it may degrade throughput significantly.
+
+    For more technical context: For a single process group using NCCL backend,
+    any collectives, even if issued from different streams, contend for the
+    same per-device NCCL stream, which implies that the relative order in which
+    the collectives are issued matters for overlapping. The two backward
+    prefetching values correspond to different issue orders.
+    """
+
+    # NOTE: For both modes, the ordering that defines "current" and "next" is
+    # not always exact in the current implementation. A mistargeted prefetch
+    # simply means that the parameter memory is allocated earlier than needed,
+    # possibly increasing peak memory usage, but does not affect correctness.
+    BACKWARD_PRE = auto()
+    BACKWARD_POST = auto()
+
+
+@dataclass
+class MixedPrecision:
+    """
+    This configures FSDP-native mixed precision training.
+
+    Attributes:
+        param_dtype (Optional[torch.dtype]): This specifies the dtype for model
+            parameters during forward and backward and thus the dtype for
+            forward and backward computation. Outside forward and backward, the
+            *sharded* parameters are kept in full precision (e.g. for the
+            optimizer step), and for model checkpointing, the parameters are
+            always saved in full precision. (Default: ``None``)
+        reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
+            gradient reduction (i.e. reduce-scatter or all-reduce). If this is
+            ``None`` but ``param_dtype`` is not ``None``, then this takes on
+            the ``param_dtype`` value, still running gradient reduction in low
+            precision. This is permitted to differ from ``param_dtype``, e.g.
+            to force gradient reduction to run in full precision. (Default:
+            ``None``)
+        buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
+            buffers. FSDP does not shard buffers. Rather, FSDP casts them to
+            ``buffer_dtype`` in the first forward pass and keeps them in that
+            dtype thereafter. For model checkpointing, the buffers are saved
+            in full precision except for ``LOCAL_STATE_DICT``. (Default:
+            ``None``)
+        keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
+            gradients to full precision after the backward pass in preparation
+            for the optimizer step. If ``True``, then FSDP keeps the gradients
+            in the dtype used for gradient reduction, which can save memory if
+            using a custom optimizer that supports running in low precision.
+            (Default: ``False``)
+        cast_forward_inputs (bool): If ``True``, then this FSDP module casts
+            its forward args and kwargs to ``param_dtype``. This is to ensure
+            that parameter and input dtypes match for forward computation, as
+            required by many ops. This may need to be set to ``True`` when only
+            applying mixed precision to some but not all FSDP modules, in which
+            case a mixed-precision FSDP submodule needs to recast its inputs.
+            (Default: ``False``)
+        cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
+            casts its forward args and kwargs to ``param_dtype``, overriding
+            the value of ``cast_forward_inputs``. For non-root FSDP modules,
+            this does not do anything. (Default: ``True``)
+        _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
+            module classes to ignore for mixed precision when using an
+            ``auto_wrap_policy``: Modules of these classes will have FSDP
+            applied to them separately with mixed precision disabled (meaning
+            that the final FSDP construction would deviate from the specified
+            policy). If ``auto_wrap_policy`` is not specified, then this does
+            not do anything. This API is experimental and subject to change.
+            (Default: ``(_BatchNorm,)``)
+
+    .. note:: This API is experimental and subject to change.
+
+    .. note:: Only floating point tensors are cast to their specified dtypes.
+
+    .. note:: In ``summon_full_params``, parameters are forced to full
+        precision, but buffers are not.
+
+    .. note:: Layer norm and batch norm accumulate in ``float32`` even when
+        their inputs are in a low precision like ``float16`` or ``bfloat16``.
+        Disabling FSDP's mixed precision for those norm modules only means that
+        the affine parameters are kept in ``float32``. However, this incurs
+        separate all-gathers and reduce-scatters for those norm modules, which
+        may be inefficient, so if the workload permits, the user should prefer
+        to still apply mixed precision to those modules.
+
+    .. note:: By default, if the user passes a model with any ``_BatchNorm``
+        modules and specifies an ``auto_wrap_policy``, then the batch norm
+        modules will have FSDP applied to them separately with mixed precision
+        disabled. See the ``_module_classes_to_ignore`` argument.
+
+    .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
+        ``cast_forward_inputs=False`` by default. For the root FSDP instance,
+        its ``cast_root_forward_inputs`` takes precedence over its
+        ``cast_forward_inputs``. For non-root FSDP instances, their
+        ``cast_root_forward_inputs`` values are ignored. The default setting is
+        sufficient for the typical case where each FSDP instance has the same
+        ``MixedPrecision`` configuration and only needs to cast inputs to the
+        ``param_dtype`` at the beginning of the model's forward pass.
+
+    .. note:: For nested FSDP instances with different ``MixedPrecision``
+        configurations, we recommend setting individual ``cast_forward_inputs``
+        values to configure casting inputs or not before each instance's
+        forward. In such a case, since the casts happen before each FSDP
+        instance's forward, a parent FSDP instance should have its non-FSDP
+        submodules run before its FSDP submodules to avoid the activation dtype
+        being changed due to a different ``MixedPrecision`` configuration.
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
+            >>> model[1] = FSDP(
+            >>>     model[1],
+            >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
+            >>> )
+            >>> model = FSDP(
+            >>>     model,
+            >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
+            >>> )
+
+        The above shows a working example. On the other hand, if ``model[1]``
+        were replaced with ``model[0]``, meaning that the submodule using
+        different ``MixedPrecision`` ran its forward first, then ``model[1]``
+        would incorrectly see ``float16`` activations instead of ``bfloat16``
+        ones.
+
+    """
+
+    param_dtype: Optional[torch.dtype] = None
+    reduce_dtype: Optional[torch.dtype] = None
+    buffer_dtype: Optional[torch.dtype] = None
+    keep_low_precision_grads: bool = False
+    cast_forward_inputs: bool = False
+    cast_root_forward_inputs: bool = True
+    _module_classes_to_ignore: Sequence[type[torch.nn.Module]] = (_BatchNorm,)
+
+
+@dataclass
+class CPUOffload:
+    """
+    This configures CPU offloading.
+
+    Attributes:
+        offload_params (bool): This specifies whether to offload parameters to
+            CPU when not involved in computation. If ``True``, then this
+            offloads gradients to CPU as well, meaning that the optimizer step
+            runs on CPU.
+    """
+
+    offload_params: bool = False
+
+
+class StateDictType(Enum):
+    """
+    This enum indicates that which type of ``state_dict`` the FSDP module is
+    currently processing (returning or loading).
+    The default value is FULL_STATE_DICT to comply the PyTorch convention.
+
+    .. note::
+        FSDP currently supports three types of ``state_dict``:
+            1. ``state_dict/load_state_dict`: this pair of APIs return and load
+               the non-sharded, unflattened parameters. The semantics is the
+               same as using DDP.
+            2. ``_local_state_dict/_load_local_state_dict``: this pair of APIs return
+               and load local sharded, flattened parameters. The values returned
+               by ``_local_state_dict`` can be directly used by FSDP and is only
+               meaningful to FSDP (because parameters are flattened). Note that
+               these APIs are meant for use via the :func:`state_dict_type`
+               context manager as follows:
+                   >>> # xdoctest: +SKIP("undefined variables")
+                   >>> with fsdp.state_dict_type(StateDictType.LOCAL_STATE_DICT):
+                   ...     state = fsdp.state_dict()  # loads local state dict
+            3. ``_sharded_state_dict/_load_sharded_state_dict``: this pair of APIs
+               return and load sharded, unflattened parameters. The ``state_dict``
+               return by ``sharded_state_dict`` can be used by all other parallel
+               schemes (resharding may be required).
+    """
+
+    FULL_STATE_DICT = auto()
+    LOCAL_STATE_DICT = auto()
+    SHARDED_STATE_DICT = auto()
+
+
+@dataclass
+class StateDictConfig:
+    """
+    ``StateDictConfig`` is the base class for all ``state_dict`` configuration
+    classes. Users should instantiate a child class (e.g.
+    ``FullStateDictConfig``) in order to configure settings for the
+    corresponding ``state_dict`` type supported by FSDP.
+
+    Attributes:
+        offload_to_cpu (bool): If ``True``, then FSDP offloads the state dict
+            values to CPU, and if ``False``, then FSDP keeps them on GPU.
+            (Default: ``False``)
+    """
+
+    offload_to_cpu: bool = False
+
+
+@dataclass
+class FullStateDictConfig(StateDictConfig):
+    """
+    ``FullStateDictConfig`` is a config class meant to be used with
+    ``StateDictType.FULL_STATE_DICT``. We recommend enabling both
+    ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state
+    dicts to save GPU memory and CPU memory, respectively. This config class
+    is meant to be used via the :func:`state_dict_type` context manager as
+    follows:
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        >>> fsdp = FSDP(model, auto_wrap_policy=...)
+        >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+        >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg):
+        >>>     state = fsdp.state_dict()
+        >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0.
+        >>> # To reload checkpoint for inference, finetuning, transfer learning, etc:
+        >>> model = model_fn()  # Initialize model in preparation for wrapping with FSDP
+        >>> if dist.get_rank() == 0:
+        >>> # Load checkpoint only on rank 0 to avoid memory redundancy
+        >>>     state_dict = torch.load("my_checkpoint.pt")
+        >>>     model.load_state_dict(state_dict)
+        >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument
+        >>> # communicates loaded checkpoint states from rank 0 to rest of the world.
+        >>> fsdp = FSDP(
+        ...     model,
+        ...     device_id=torch.cuda.current_device(),
+        ...     auto_wrap_policy=...,
+        ...     sync_module_states=True,
+        ... )
+        >>> # After this point, all ranks have FSDP model with loaded checkpoint.
+
+    Attributes:
+        rank0_only (bool): If ``True``, then only rank 0 saves the full state
+            dict, and nonzero ranks save an empty dict. If ``False``, then all
+            ranks save the full state dict. (Default: ``False``)
+    """
+
+    rank0_only: bool = False
+
+
+@dataclass
+class LocalStateDictConfig(StateDictConfig):
+    pass
+
+
+@dataclass
+class ShardedStateDictConfig(StateDictConfig):
+    """
+    ``ShardedStateDictConfig`` is a config class meant to be used with
+    ``StateDictType.SHARDED_STATE_DICT``.
+
+    Attributes:
+        _use_dtensor (bool): If ``True``, then FSDP saves the state dict values
+            as ``DTensor``, and if ``False``, then FSDP saves them as
+            ``ShardedTensor``. (Default: ``False``)
+
+    .. warning:: ``_use_dtensor`` is a private field of :class:`ShardedStateDictConfig`
+      and it is used by FSDP to determine the type of state dict values. Users should not
+      manually modify ``_use_dtensor``.
+    """
+
+    _use_dtensor: bool = False
+
+
+@dataclass
+class OptimStateDictConfig:
+    """
+    ``OptimStateDictConfig`` is the base class for all ``optim_state_dict``
+    configuration classes.  Users should instantiate a child class (e.g.
+    ``FullOptimStateDictConfig``) in order to configure settings for the
+    corresponding ``optim_state_dict`` type supported by FSDP.
+
+    Attributes:
+        offload_to_cpu (bool): If ``True``, then FSDP offloads the state dict's
+            tensor values to CPU, and if ``False``, then FSDP keeps them on the
+            original device (which is GPU unless parameter CPU offloading is
+            enabled). (Default: ``True``)
+    """
+
+    offload_to_cpu: bool = True
+
+
+@dataclass
+class FullOptimStateDictConfig(OptimStateDictConfig):
+    """
+    Attributes:
+        rank0_only (bool): If ``True``, then only rank 0 saves the full state
+            dict, and nonzero ranks save an empty dict. If ``False``, then all
+            ranks save the full state dict. (Default: ``False``)
+    """
+
+    rank0_only: bool = False
+
+
+@dataclass
+class LocalOptimStateDictConfig(OptimStateDictConfig):
+    offload_to_cpu: bool = False
+
+
+@dataclass
+class ShardedOptimStateDictConfig(OptimStateDictConfig):
+    """
+    ``ShardedOptimStateDictConfig`` is a config class meant to be used with
+    ``StateDictType.SHARDED_STATE_DICT``.
+
+    Attributes:
+        _use_dtensor (bool): If ``True``, then FSDP saves the state dict values
+            as ``DTensor``, and if ``False``, then FSDP saves them as
+            ``ShardedTensor``. (Default: ``False``)
+
+    .. warning:: ``_use_dtensor`` is a private field of :class:`ShardedOptimStateDictConfig`
+      and it is used by FSDP to determine the type of state dict values. Users should not
+      manually modify ``_use_dtensor``.
+    """
+
+    _use_dtensor: bool = False
+
+
+@dataclass
+class StateDictSettings:
+    state_dict_type: StateDictType
+    state_dict_config: StateDictConfig
+    optim_state_dict_config: OptimStateDictConfig
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py
new file mode 100644
index 0000000000000000000000000000000000000000..0eafd26e31f9610b0ead91bd2a61d1003d6b62d9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py
@@ -0,0 +1,2177 @@
+# mypy: ignore-errors
+
+import contextlib
+import copy
+import functools
+import math
+import traceback
+import warnings
+from collections.abc import Generator, Iterable, Iterator
+from contextlib import contextmanager
+from enum import auto, Enum
+from typing import Any, Callable, Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    _CHECKPOINT_WRAPPED_MODULE,
+    ActivationWrapper,
+)
+from torch.distributed.algorithms._comm_hooks import LOW_PRECISION_HOOKS
+from torch.distributed.fsdp._common_utils import (
+    _FSDPState,
+    _get_param_to_fqns,
+    FSDP_PREFIX,
+    FSDP_WRAPPED_MODULE,
+    HandleTrainingState,
+    TrainingState,
+)
+from torch.distributed.fsdp._dynamo_utils import _annotate_modules_for_dynamo
+from torch.distributed.fsdp._init_utils import (
+    _check_orig_params_flattened,
+    _init_buffer_state,
+    _init_core_state,
+    _init_device_handle,
+    _init_extension,
+    _init_ignored_module_states,
+    _init_param_handle_from_module,
+    _init_prefetching_state,
+    _init_process_group_state,
+    _init_runtime_state,
+    _init_state_dict_state,
+    HYBRID_SHARDING_STRATEGIES,
+    ProcessGroupType,
+)
+from torch.distributed.fsdp._runtime_utils import (
+    _get_fsdp_root_states,
+    _is_fsdp_root,
+    _lazy_init,
+    _post_forward,
+    _post_forward_reshard,
+    _pre_forward,
+    _pre_forward_unshard,
+    _root_pre_forward,
+    _unshard,
+    _wait_for_computation_stream,
+)
+from torch.distributed.fsdp._wrap_utils import _auto_wrap
+from torch.distributed.fsdp.api import (
+    BackwardPrefetch,
+    CPUOffload,
+    FullOptimStateDictConfig,
+    FullStateDictConfig,
+    LocalOptimStateDictConfig,
+    LocalStateDictConfig,
+    MixedPrecision,
+    OptimStateDictConfig,
+    ShardedOptimStateDictConfig,
+    ShardedStateDictConfig,
+    ShardingStrategy,
+    StateDictConfig,
+    StateDictSettings,
+    StateDictType,
+)
+from torch.distributed.tensor import DeviceMesh
+from torch.distributed.utils import _p_assert
+
+from ._flat_param import FlatParameter, FlatParamHandle
+from ._optim_utils import (
+    _flatten_optim_state_dict,
+    _get_param_id_to_param_from_optim_input,
+    _get_param_key_to_param,
+    _get_param_to_param_id_from_optim_input,
+    _get_param_to_param_key,
+    _optim_state_dict,
+    _rekey_sharded_optim_state_dict,
+    _set_optim_use_dtensor,
+)
+from ._state_dict_utils import _register_all_state_dict_hooks
+from ._unshard_param_utils import (
+    _deregister_orig_params,
+    _register_flat_param,
+    _register_orig_params,
+    _unshard_params,
+    _unshard_params_for_summon,
+)
+from .wrap import CustomPolicy, ModuleWrapPolicy
+
+
+__all__ = [
+    "FullyShardedDataParallel",
+    "OptimStateKeyType",
+]
+
+
+FLAT_PARAM = "_flat_param"
+
+
+class OptimStateKeyType(Enum):
+    """Represents the type of key in an optimizer state-dict."""
+
+    PARAM_NAME = auto()
+    PARAM_ID = auto()
+
+
+class FullyShardedDataParallel(nn.Module, _FSDPState):
+    """A wrapper for sharding module parameters across data parallel workers.
+
+    This is inspired by `Xu et al.`_ as well as the ZeRO Stage 3 from DeepSpeed_.
+    FullyShardedDataParallel is commonly shortened to FSDP.
+
+    .. _`Xu et al.`: https://arxiv.org/abs/2004.13336
+    .. _DeepSpeed: https://www.deepspeed.ai/
+
+    To understand FSDP internals, refer to the
+    :ref:`fsdp_notes`.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> import torch
+        >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        >>> torch.cuda.set_device(device_id)
+        >>> sharded_module = FSDP(my_module)
+        >>> optim = torch.optim.Adam(sharded_module.parameters(), lr=0.0001)
+        >>> x = sharded_module(x, y=3, z=torch.Tensor([1]))
+        >>> loss = x.sum()
+        >>> loss.backward()
+        >>> optim.step()
+
+    Using FSDP involves wrapping your module and then initializing your
+    optimizer after. This is required since FSDP changes the parameter
+    variables.
+
+    When setting up FSDP, you need to consider the destination CUDA
+    device. If the device has an ID (``dev_id``), you have three options:
+
+    * Place the module on that device
+    * Set the device using ``torch.cuda.set_device(dev_id)``
+    * Pass ``dev_id`` into the ``device_id`` constructor argument.
+
+    This ensures that the FSDP instance's compute device is the
+    destination device. For option 1 and 3, the FSDP initialization
+    always occurs on GPU. For option 2, the FSDP initialization
+    happens on module's current device, which may be a CPU.
+
+    If you're using the ``sync_module_states=True`` flag, you need to
+    ensure that the module is on a GPU or use the ``device_id``
+    argument to specify a CUDA device that FSDP will move the module
+    to in the FSDP constructor. This is necessary because
+    ``sync_module_states=True`` requires GPU communication.
+
+    FSDP also takes care of moving input tensors to the forward method
+    to the GPU compute device, so you don't need to manually move them
+    from CPU.
+
+    For ``use_orig_params=True``,
+    ``ShardingStrategy.SHARD_GRAD_OP`` exposes the unsharded
+    parameters, not the sharded parameters after forward, unlike
+    ``ShardingStrategy.FULL_SHARD``. If you want
+    to inspect the gradients, you can use the ``summon_full_params``
+    method with ``with_grads=True``.
+
+    With ``limit_all_gathers=True``, you may see a gap in the FSDP
+    pre-forward where the CPU thread is not issuing any kernels. This is
+    intentional and shows the rate limiter in effect. Synchronizing the CPU
+    thread in that way prevents over-allocating memory for subsequent
+    all-gathers, and it should not actually delay GPU kernel execution.
+
+    FSDP replaces managed modules' parameters with ``torch.Tensor``
+    views during forward and backward computation for autograd-related
+    reasons. If your module's forward relies on saved references to
+    the parameters instead of reacquiring the references each
+    iteration, then it will not see FSDP's newly created views,
+    and autograd will not work correctly.
+
+    Finally, when using ``sharding_strategy=ShardingStrategy.HYBRID_SHARD``
+    with the sharding process group being intra-node and the
+    replication process group being inter-node, setting
+    ``NCCL_CROSS_NIC=1`` can help improve the all-reduce times over
+    the replication process group for some cluster setups.
+
+    **Limitations**
+
+    There are several limitations to be aware of when using FSDP:
+
+    * FSDP currently does not support gradient accumulation outside
+      ``no_sync()`` when using CPU offloading. This is because FSDP
+      uses the newly-reduced gradient instead of accumulating with any
+      existing gradient, which can lead to incorrect results.
+
+    * FSDP does not support running the forward pass of a submodule
+      that is contained in an FSDP instance. This is because the
+      submodule's parameters will be sharded, but the submodule itself
+      is not an FSDP instance, so its forward pass will not all-gather
+      the full parameters appropriately.
+
+    * FSDP does not work with double backwards due to the way it
+      registers backward hooks.
+
+    * FSDP has some constraints when freezing parameters.
+      For ``use_orig_params=False``, each FSDP instance must manage
+      parameters that are all frozen or all non-frozen. For
+      ``use_orig_params=True``, FSDP supports mixing frozen and
+      non-frozen parameters, but it's recommended to avoid doing so to
+      prevent higher than expected gradient memory usage.
+
+    * As of PyTorch 1.12, FSDP offers limited support for shared
+      parameters. If enhanced shared parameter support is needed for
+      your use case, please post in
+      `this issue `__.
+
+    * You should avoid modifying the parameters between forward and
+      backward without using the ``summon_full_params`` context, as
+      the modifications may not persist.
+
+    Args:
+        module (nn.Module):
+            This is the module to be wrapped with FSDP.
+        process_group (Optional[Union[ProcessGroup, Tuple[ProcessGroup, ProcessGroup]]]):
+            This is the process group over which the model is sharded and thus
+            the one used for FSDP's all-gather and reduce-scatter collective
+            communications. If ``None``, then FSDP uses the default process
+            group. For hybrid sharding strategies such as
+            ``ShardingStrategy.HYBRID_SHARD``, users can pass in a tuple of
+            process groups, representing the groups over which to shard and
+            replicate, respectively. If ``None``, then FSDP constructs process
+            groups for the user to shard intra-node and replicate inter-node.
+            (Default: ``None``)
+        sharding_strategy (Optional[ShardingStrategy]):
+            This configures the sharding strategy, which may trade off memory
+            saving and communication overhead. See :class:`ShardingStrategy`
+            for details. (Default: ``FULL_SHARD``)
+        cpu_offload (Optional[CPUOffload]):
+            This configures CPU offloading. If this is set to ``None``, then
+            no CPU offloading happens. See :class:`CPUOffload` for details.
+            (Default: ``None``)
+        auto_wrap_policy (Optional[Union[Callable[[nn.Module, bool, int], bool], ModuleWrapPolicy, CustomPolicy]]):
+            This specifies a policy to apply FSDP to submodules of ``module``,
+            which is needed for communication and computation overlap and thus
+            affects performance. If ``None``, then FSDP only applies to
+            ``module``, and users should manually apply FSDP to parent modules
+            themselves (proceeding bottom-up). For convenience, this accepts
+            ``ModuleWrapPolicy`` directly, which allows users to specify the
+            module classes to wrap (e.g. the transformer block). Otherwise,
+            this should be a callable that takes in three arguments
+            ``module: nn.Module``, ``recurse: bool``, and
+            ``nonwrapped_numel: int`` and should return a ``bool`` specifying
+            whether the passed-in ``module`` should have FSDP applied if
+            ``recurse=False`` or if the traversal should continue into the
+            module's subtree if ``recurse=True``. Users may add additional
+            arguments to the callable. The ``size_based_auto_wrap_policy`` in
+            ``torch.distributed.fsdp.wrap.py`` gives an example callable that
+            applies FSDP to a module if the parameters in its subtree exceed
+            100M numel. We recommend printing the model after applying FSDP
+            and adjusting as needed.
+
+            Example::
+
+                >>> def custom_auto_wrap_policy(
+                >>>     module: nn.Module,
+                >>>     recurse: bool,
+                >>>     nonwrapped_numel: int,
+                >>>     # Additional custom arguments
+                >>>     min_num_params: int = int(1e8),
+                >>> ) -> bool:
+                >>>     return nonwrapped_numel >= min_num_params
+                >>> # Configure a custom `min_num_params`
+                >>> my_auto_wrap_policy = functools.partial(custom_auto_wrap_policy, min_num_params=int(1e5))
+
+        backward_prefetch (Optional[BackwardPrefetch]):
+            This configures explicit backward prefetching of all-gathers. If
+            ``None``, then FSDP does not backward prefetch, and there is no
+            communication and computation overlap in the backward pass. See
+            :class:`BackwardPrefetch` for details. (Default: ``BACKWARD_PRE``)
+        mixed_precision (Optional[MixedPrecision]):
+            This configures native mixed precision for FSDP. If this is set to
+            ``None``, then no mixed precision is used. Otherwise, parameter,
+            buffer, and gradient reduction dtypes can be set. See
+            :class:`MixedPrecision` for details. (Default: ``None``)
+        ignored_modules (Optional[Iterable[torch.nn.Module]]): Modules whose
+            own parameters and child modules' parameters and buffers are
+            ignored by this instance. None of the modules directly in
+            ``ignored_modules`` should be :class:`FullyShardedDataParallel`
+            instances, and any child modules that are already-constructed
+            :class:`FullyShardedDataParallel` instances will not be ignored if
+            they are nested under this instance. This argument may be used to
+            avoid sharding specific parameters at module granularity when using an
+            ``auto_wrap_policy`` or if parameters' sharding is not managed by
+            FSDP. (Default: ``None``)
+        param_init_fn (Optional[Callable[[nn.Module], None]]):
+            A ``Callable[torch.nn.Module] -> None`` that
+            specifies how modules that are currently on the meta device should
+            be initialized onto an actual device. As of v1.12, FSDP detects
+            modules with parameters or buffers on meta device via ``is_meta``
+            and either applies ``param_init_fn`` if specified or calls
+            ``nn.Module.reset_parameters()`` otherwise. For both cases, the
+            implementation should *only* initialize the parameters/buffers of
+            the module, not those of its submodules. This is to avoid
+            re-initialization. In addition, FSDP also supports deferred
+            initialization via torchdistX's (https://github.com/pytorch/torchdistX)
+            ``deferred_init()`` API, where the deferred modules are initialized
+            by calling ``param_init_fn`` if specified or torchdistX's default
+            ``materialize_module()`` otherwise. If ``param_init_fn`` is
+            specified, then it is applied to all meta-device modules, meaning
+            that it should probably case on the module type. FSDP calls the
+            initialization function before parameter flattening and sharding.
+
+            Example::
+
+                >>> # xdoctest: +SKIP("undefined variables")
+                >>> module = MyModule(device="meta")
+                >>> def my_init_fn(module: nn.Module):
+                >>>     # E.g. initialize depending on the module type
+                >>>     ...
+                >>> fsdp_model = FSDP(module, param_init_fn=my_init_fn, auto_wrap_policy=size_based_auto_wrap_policy)
+                >>> print(next(fsdp_model.parameters()).device) # current CUDA device
+                >>> # With torchdistX
+                >>> module = deferred_init.deferred_init(MyModule, device="cuda")
+                >>> # Will initialize via deferred_init.materialize_module().
+                >>> fsdp_model = FSDP(module, auto_wrap_policy=size_based_auto_wrap_policy)
+
+        device_id (Optional[Union[int, torch.device]]): An ``int`` or
+            ``torch.device`` giving the CUDA device on which FSDP
+            initialization takes place, including the module initialization
+            if needed and the parameter sharding. This should be specified to
+            improve initialization speed if ``module`` is on CPU. If the
+            default CUDA device was set (e.g. via ``torch.cuda.set_device``),
+            then the user may pass ``torch.cuda.current_device`` to this.
+            (Default: ``None``)
+        sync_module_states (bool): If ``True``, then each FSDP module will
+            broadcast module parameters and buffers from rank 0 to ensure that
+            they are replicated across ranks (adding communication overhead to
+            this constructor). This can help load ``state_dict`` checkpoints
+            via ``load_state_dict`` in a memory efficient way. See
+            :class:`FullStateDictConfig` for an example of this. (Default:
+            ``False``)
+        forward_prefetch (bool): If ``True``, then FSDP *explicitly* prefetches
+            the next forward-pass all-gather before the current forward
+            computation. This is only useful for CPU-bound workloads, in which
+            case issuing the next all-gather earlier may improve overlap. This
+            should only be used for static-graph models since the prefetching
+            follows the first iteration's execution order. (Default: ``False``)
+        limit_all_gathers (bool): If ``True``, then FSDP explicitly
+            synchronizes the CPU thread to ensure GPU memory usage from only
+            *two* consecutive FSDP instances (the current instance running
+            computation and the next instance whose all-gather is prefetched).
+            If ``False``, then FSDP allows the CPU thread to issue all-gathers
+            without any extra synchronization. (Default: ``True``) We often
+            refer to this feature as the "rate limiter". This flag should only
+            be set to ``False`` for specific CPU-bound workloads with low
+            memory pressure in which case the CPU thread can aggressively issue
+            all kernels without concern for the GPU memory usage.
+        use_orig_params (bool): Setting this to ``True`` has FSDP use
+            ``module`` 's original parameters. FSDP exposes those original
+            parameters to the user via :meth:`nn.Module.named_parameters`
+            instead of FSDP's internal :class:`FlatParameter` s. This means
+            that the optimizer step runs on the original parameters, enabling
+            per-original-parameter hyperparameters. FSDP preserves the original
+            parameter variables and manipulates their data between unsharded
+            and sharded forms, where they are always views into the underlying
+            unsharded or sharded :class:`FlatParameter`, respectively. With the
+            current algorithm, the sharded form is always 1D, losing the
+            original tensor structure. An original parameter may have all,
+            some, or none of its data present for a given rank. In the none
+            case, its data will be like a size-0 empty tensor. Users should not
+            author programs relying on what data is present for a given
+            original parameter in its sharded form. ``True`` is required to
+            use ``torch.compile()``. Setting this to ``False`` exposes FSDP's
+            internal :class:`FlatParameter` s to the user via
+            :meth:`nn.Module.named_parameters`. (Default: ``False``)
+        ignored_states (Optional[Iterable[torch.nn.Parameter]], Optional[Iterable[torch.nn.Module]]):
+            Ignored parameters or modules that will not be managed by this FSDP
+            instance, meaning that the parameters are not sharded and their
+            gradients are not reduced across ranks. This argument unifies with
+            the existing ``ignored_modules`` argument, and we may deprecate
+            ``ignored_modules`` soon. For backward compatibility, we keep both
+            ``ignored_states`` and `ignored_modules``, but FSDP only allows one
+            of them to be specified as not ``None``.
+        device_mesh (Optional[DeviceMesh]): DeviceMesh can be used as an altenative to
+            process_group. When device_mesh is passed, FSDP will use the underlying process
+            groups for all-gather and reduce-scatter collective communications. Therefore,
+            these two args need to be mutually exclusive. For hybrid sharding strategies such as
+            ``ShardingStrategy.HYBRID_SHARD``, users can pass in a 2D DeviceMesh instead
+            of a tuple of process groups. For 2D FSDP + TP, users are required to pass in
+            device_mesh instead of process_group. For more DeviceMesh info, please visit:
+            https://pytorch.org/tutorials/recipes/distributed_device_mesh.html
+    """
+
+    def __init__(
+        self,
+        module: nn.Module,
+        process_group: ProcessGroupType = None,
+        sharding_strategy: Optional[ShardingStrategy] = None,
+        cpu_offload: Optional[CPUOffload] = None,
+        auto_wrap_policy: Optional[
+            Union[Callable, ModuleWrapPolicy, CustomPolicy]
+        ] = None,
+        backward_prefetch: Optional[BackwardPrefetch] = BackwardPrefetch.BACKWARD_PRE,
+        mixed_precision: Optional[MixedPrecision] = None,
+        ignored_modules: Optional[Iterable[torch.nn.Module]] = None,
+        param_init_fn: Optional[Callable[[nn.Module], None]] = None,
+        device_id: Optional[Union[int, torch.device]] = None,
+        sync_module_states: bool = False,
+        forward_prefetch: bool = False,
+        limit_all_gathers: bool = True,
+        use_orig_params: bool = False,
+        ignored_states: Union[
+            Optional[Iterable[torch.nn.Parameter]], Optional[Iterable[torch.nn.Module]]
+        ] = None,
+        device_mesh: Optional[DeviceMesh] = None,
+    ):
+        torch._C._log_api_usage_once("torch.distributed.fsdp")
+        super().__init__()
+        if isinstance(module, (nn.ModuleList, nn.ModuleDict)):
+            warnings.warn(
+                "FSDP will not all-gather parameters for containers that do "
+                f"not implement forward: {module}",
+                stacklevel=2,
+            )
+        _init_ignored_module_states(self, module, ignored_modules, ignored_states)
+        _init_device_handle(self, module, self._ignored_params, device_id)
+
+        # Add module annotations for Dynamo support (see function for details)
+        _annotate_modules_for_dynamo(module, self._ignored_modules, use_orig_params)
+
+        # Initializes self.process_group, along with rank and world size. This will
+        # also set another attribute, _inter_node_pg, to control the process group
+        # over which sharding occurs, if sharding_strategy is {HYBRID_SHARD, _HYBRID_SHARD_ZERO2}.
+        # Note that this is done before auto_wrapping, so that child FSDP modules simply pick up
+        # the same process group state as the root FSDP module.
+        self._device_mesh = device_mesh
+        _init_process_group_state(
+            self,
+            process_group,
+            sharding_strategy,
+            auto_wrap_policy,
+            device_mesh,
+        )
+        if auto_wrap_policy is not None:
+            root_kwargs = {
+                "process_group": process_group,
+                "sharding_strategy": sharding_strategy,
+                "cpu_offload": cpu_offload,
+                "backward_prefetch": backward_prefetch,
+                "mixed_precision": mixed_precision,
+                "param_init_fn": param_init_fn,
+                "device_id": device_id,
+                "sync_module_states": sync_module_states,
+                "forward_prefetch": forward_prefetch,
+                "limit_all_gathers": limit_all_gathers,
+                "use_orig_params": use_orig_params,
+                "ignored_states": self._ignored_params,
+                "device_mesh": device_mesh,
+            }
+            if sharding_strategy in HYBRID_SHARDING_STRATEGIES and device_mesh is None:
+                # Share root process groups with children to maintain
+                # the invariant that all FSDP modules will have the same
+                # process groups.
+                root_kwargs["process_group"] = (self.process_group, self._inter_node_pg)
+
+            _auto_wrap(
+                module,
+                auto_wrap_policy,
+                self._ignored_modules,
+                self._ignored_params,
+                root_kwargs,
+                FullyShardedDataParallel,
+            )
+
+        backward_prefetch_limit = 1
+        forward_prefetch_limit = 1
+        _init_core_state(
+            self,
+            sharding_strategy,
+            mixed_precision,
+            cpu_offload,
+            limit_all_gathers,
+            use_orig_params,
+            backward_prefetch_limit,
+            forward_prefetch_limit,
+        )
+        _init_runtime_state(self)
+        _init_prefetching_state(self, backward_prefetch, forward_prefetch)
+        _init_buffer_state(self, module)
+        # extension needs to be set before `_init_param_handle_from_module()`
+        _init_extension(self, device_mesh)
+        _init_param_handle_from_module(
+            self,
+            module,
+            device_id,
+            param_init_fn,
+            sync_module_states,
+        )
+        self._fsdp_wrapped_module = module
+        if not use_orig_params:
+            _check_orig_params_flattened(self, self._ignored_params)
+            _register_flat_param(self, self)
+
+        # `_state_dict_type` controls the `state_dict()` behavior, which is
+        # implemented using post-save and pre-load hooks
+        _init_state_dict_state(self)
+        _register_all_state_dict_hooks(self)
+        self._zero_scalar = None
+
+    @property
+    def module(self) -> nn.Module:
+        """Return the wrapped module."""
+        # FSDP's `.module` must refer to the innermost wrapped module when
+        # composing with other module wrappers in order for state dict to work
+        if isinstance(self._fsdp_wrapped_module, ActivationWrapper):
+            return getattr(self._fsdp_wrapped_module, _CHECKPOINT_WRAPPED_MODULE)
+        return self._fsdp_wrapped_module
+
+    @property
+    def _has_params(self) -> bool:
+        """Returns whether this FSDP instance manages any parameters."""
+        return hasattr(self, "_handle") and self._handle is not None
+
+    @property
+    def _flat_param(self) -> Optional[FlatParameter]:
+        return self._handle.flat_param if self._handle else None
+
+    def __getattr__(self, name: str) -> Any:
+        """Forward missing attributes to the wrapped module."""
+        try:
+            return super().__getattr__(name)  # defer to nn.Module's logic
+        except AttributeError:
+            return getattr(self._fsdp_wrapped_module, name)
+
+    def __getitem__(self, key: int) -> Any:
+        """Forward indexing calls in case the module is an ``nn.Sequential``."""
+        if hasattr(self, FSDP_WRAPPED_MODULE):
+            return self._fsdp_wrapped_module.__getitem__(key)  # type: ignore[operator]
+        return super().__getitem__(key)
+
+    def check_is_root(self) -> bool:
+        """Check if this instance is a root FSDP module."""
+        return _is_fsdp_root(self, self)
+
+    @staticmethod
+    def fsdp_modules(
+        module: nn.Module,
+        root_only: bool = False,
+    ) -> list["FullyShardedDataParallel"]:
+        """Return all nested FSDP instances.
+
+        This possibly includes ``module`` itself and only includes FSDP root modules if ``root_only=True``.
+
+        Args:
+            module (torch.nn.Module): Root module, which may or may not be an
+                ``FSDP`` module.
+            root_only (bool): Whether to return only FSDP root modules.
+                (Default: ``False``)
+
+        Returns:
+            List[FullyShardedDataParallel]: FSDP modules that are nested in
+            the input ``module``.
+        """
+        if root_only:
+            return _get_fsdp_root_states(module)
+        return traversal_utils._get_fsdp_states(module)
+
+    def apply(self, fn: Callable[[nn.Module], None]) -> "FullyShardedDataParallel":
+        r"""Apply ``fn`` recursively to every submodule (as returned by ``.children()``) as well as self.
+
+        Typical use includes initializing the parameters of a model (see also :ref:`nn-init-doc`).
+
+        Compared to ``torch.nn.Module.apply``, this version additionally gathers
+        the full parameters before applying ``fn``. It should not be called from
+        within another ``summon_full_params`` context.
+
+        Args:
+            fn (:class:`Module` -> None): function to be applied to each submodule
+
+        Returns:
+            Module: self
+        """
+        uninitialized = self._is_root is None
+        self._assert_state(TrainingState.IDLE)
+        # Use `_unshard_params_for_summon()` with `recurse=False` instead of
+        # `_unshard_fsdp_state_params()` directly to perform lazy
+        # initialization, which is needed to initialize `FlatParameter`
+        # parameter attributes as required by the unshard logic
+        with _unshard_params_for_summon(
+            self,
+            self,
+            writeback=True,
+            rank0_only=False,
+            offload_to_cpu=False,
+            with_grads=False,
+        ):
+            ret = super().apply(fn)
+
+        # Reset lazy init called in `_unshard_params_for_summon()` since
+        # `apply()` may have been called on FSDP instance that is not truly a
+        # root, in which case it will be incorrectly marked as one.
+        if uninitialized and self._is_root:
+            for module in traversal_utils._get_fsdp_states(self):
+                module._reset_lazy_init()
+
+        return ret
+
+    def _mixed_precision_enabled_for_buffers(self) -> bool:
+        """Return whether the user explicitly enabled buffer mixed precision.
+
+        NOTE: Unlike parameters and gradient reduction, buffer mixed precision
+        is applied at the FSDP instance level, not the ``FlatParameter`` level,
+        which may be different for the composable code path.
+        """
+        return self.mixed_precision.buffer_dtype is not None
+
+    def _low_precision_hook_enabled(self) -> bool:
+        """Whether a low precision hook is registered or not."""
+        return self._comm_hook is not None and self._comm_hook in LOW_PRECISION_HOOKS
+
+    def _reset_lazy_init(self) -> None:
+        """Reset instance so :func:`_lazy_init` will run on the next forward."""
+        self._is_root: Optional[bool] = None
+
+    @staticmethod
+    def set_state_dict_type(
+        module: nn.Module,
+        state_dict_type: StateDictType,
+        state_dict_config: Optional[StateDictConfig] = None,
+        optim_state_dict_config: Optional[OptimStateDictConfig] = None,
+    ) -> StateDictSettings:
+        """Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
+
+        Also takes (optional) configuration for the model's and optimizer's state dict.
+        The target module does not have to be a FSDP module. If the target
+        module is a FSDP module, its ``state_dict_type`` will also be changed.
+
+        .. note:: This API should be called for only the top-level (root)
+            module.
+
+        .. note:: This API enables users to transparently use the conventional
+            ``state_dict`` API to take model checkpoints in cases where the
+            root FSDP module is wrapped by another ``nn.Module``. For example,
+            the following will ensure ``state_dict`` is called on all non-FSDP
+            instances, while dispatching into `sharded_state_dict` implementation
+            for FSDP:
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> model = DDP(FSDP(...))
+            >>> FSDP.set_state_dict_type(
+            >>>     model,
+            >>>     StateDictType.SHARDED_STATE_DICT,
+            >>>     state_dict_config = ShardedStateDictConfig(offload_to_cpu=True),
+            >>>     optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True),
+            >>> )
+            >>> param_state_dict = model.state_dict()
+            >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
+
+        Args:
+            module (torch.nn.Module): Root module.
+            state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
+            state_dict_config (Optional[StateDictConfig]): the configuration for the
+                target ``state_dict_type``.
+            optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration
+                for the optimizer state dict.
+
+        Returns:
+            A StateDictSettings that include the previous state_dict type and
+            configuration for the module.
+        """
+        warnings.warn(
+            "FSDP.state_dict_type() and FSDP.set_state_dict_type() are being "
+            "deprecated. Please use APIs, get_state_dict() and set_state_dict(), "
+            "which can support different parallelisms, FSDP1, FSDP2, DDP. "
+            "API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html"
+            "#torch.distributed.checkpoint.state_dict.get_state_dict ."
+            "Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .",
+            FutureWarning,
+        )
+        _state_dict_type_to_config = {
+            StateDictType.FULL_STATE_DICT: FullStateDictConfig,
+            StateDictType.LOCAL_STATE_DICT: LocalStateDictConfig,
+            StateDictType.SHARDED_STATE_DICT: ShardedStateDictConfig,
+        }
+        _optim_state_dict_type_to_config = {
+            StateDictType.FULL_STATE_DICT: FullOptimStateDictConfig,
+            StateDictType.LOCAL_STATE_DICT: LocalOptimStateDictConfig,
+            StateDictType.SHARDED_STATE_DICT: ShardedOptimStateDictConfig,
+        }
+
+        # Use the default config if a state_dict config is not set.
+        state_dict_config_type = _state_dict_type_to_config[state_dict_type]
+        optim_state_dict_config_type = _optim_state_dict_type_to_config[state_dict_type]
+        if state_dict_config is None:
+            state_dict_config = state_dict_config_type()
+        if optim_state_dict_config is None:
+            optim_state_dict_config = optim_state_dict_config_type()
+        if state_dict_config_type != type(state_dict_config):
+            raise RuntimeError(
+                f"Expected state_dict_config of type {state_dict_config_type} "
+                f"but got {type(state_dict_config)}"
+            )
+        if optim_state_dict_config_type != type(optim_state_dict_config):
+            raise RuntimeError(
+                f"Expected optim_state_dict_config of type {optim_state_dict_config_type} "
+                f"but got {type(optim_state_dict_config)}"
+            )
+
+        # Set the state_dict type and configurations.
+        prev_state_dict_type = None
+        prev_state_dict_config = None
+        prev_optim_state_dict_config = None
+        for submodule in traversal_utils._get_fsdp_states(module):
+            if prev_state_dict_type is None:
+                prev_state_dict_type = submodule._state_dict_type
+            else:
+                assert prev_state_dict_type == submodule._state_dict_type, (
+                    "All FSDP modules should have the same state_dict_type."
+                )
+            if prev_state_dict_config is None:
+                prev_state_dict_config = submodule._state_dict_config
+            else:
+                assert isinstance(
+                    submodule._state_dict_config, type(prev_state_dict_config)
+                ), "All FSDP modules must have the same type of state_dict_config."
+            if prev_optim_state_dict_config is None:
+                prev_optim_state_dict_config = submodule._optim_state_dict_config
+            else:
+                assert isinstance(
+                    submodule._optim_state_dict_config,
+                    type(prev_optim_state_dict_config),
+                ), (
+                    "All FSDP modules must have the same type of optim_state_dict_config."
+                )
+
+            submodule._state_dict_type = state_dict_type
+            submodule._state_dict_config = state_dict_config
+            submodule._optim_state_dict_config = optim_state_dict_config
+
+        return StateDictSettings(
+            prev_state_dict_type, prev_state_dict_config, prev_optim_state_dict_config
+        )
+
+    @staticmethod
+    def get_state_dict_type(module: nn.Module) -> StateDictSettings:
+        """Get the state_dict_type and the corresponding configurations for the FSDP modules rooted at ``module``.
+
+        The target module does not have to be an FSDP module.
+
+        Returns:
+            A ``StateDictSettings`` containing the state_dict_type and
+            state_dict / optim_state_dict configs that are currently set.
+
+        Raises:
+            ``AssertionError`` if the ``StateDictSettings`` for different
+            FSDP submodules differ.
+        """
+        state_dict_settings: Optional[StateDictSettings] = None
+        for submodule in FullyShardedDataParallel.fsdp_modules(module):
+            if state_dict_settings is None:
+                state_dict_settings = StateDictSettings(
+                    state_dict_type=submodule._state_dict_type,
+                    state_dict_config=submodule._state_dict_config,
+                    optim_state_dict_config=submodule._optim_state_dict_config,
+                )
+                _set_optim_use_dtensor(submodule, state_dict_settings)
+            else:
+                submodule_settings = StateDictSettings(
+                    submodule._state_dict_type,
+                    submodule._state_dict_config,
+                    submodule._optim_state_dict_config,
+                )
+                assert state_dict_settings == submodule_settings, (
+                    "All FSDP modules must have the same state dict settings."
+                    f"Got {submodule_settings} and {state_dict_settings}."
+                )
+                _set_optim_use_dtensor(submodule, submodule_settings)
+        return state_dict_settings
+
+    @staticmethod
+    @contextlib.contextmanager
+    def state_dict_type(
+        module: nn.Module,
+        state_dict_type: StateDictType,
+        state_dict_config: Optional[StateDictConfig] = None,
+        optim_state_dict_config: Optional[OptimStateDictConfig] = None,
+    ) -> Generator:
+        """Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
+
+        This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of
+        :meth:`set_state_dict_type` for the detail.
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> model = DDP(FSDP(...))
+            >>> with FSDP.state_dict_type(
+            >>>     model,
+            >>>     StateDictType.SHARDED_STATE_DICT,
+            >>> ):
+            >>>     checkpoint = model.state_dict()
+
+        Args:
+            module (torch.nn.Module): Root module.
+            state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
+            state_dict_config (Optional[StateDictConfig]): the model ``state_dict``
+                configuration for the target ``state_dict_type``.
+            optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer
+               ``state_dict`` configuration for the target ``state_dict_type``.
+        """
+        prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
+            module,
+            state_dict_type,
+            state_dict_config,
+            optim_state_dict_config,
+        )
+        yield
+        FullyShardedDataParallel.set_state_dict_type(
+            module,
+            prev_state_dict_settings.state_dict_type,
+            prev_state_dict_settings.state_dict_config,
+            prev_state_dict_settings.optim_state_dict_config,
+        )
+
+    def forward(self, *args: Any, **kwargs: Any) -> Any:
+        """Run the forward pass for the wrapped module, inserting FSDP-specific pre- and post-forward sharding logic."""
+        handle = self._handle
+        with torch.autograd.profiler.record_function(
+            "FullyShardedDataParallel.forward"
+        ):
+            args, kwargs = _root_pre_forward(self, self, args, kwargs)
+            unused = None
+            args, kwargs = _pre_forward(
+                self,
+                handle,
+                _pre_forward_unshard,
+                self._fsdp_wrapped_module,
+                args,
+                kwargs,
+            )
+            if handle:
+                _p_assert(
+                    handle.flat_param.device == self.compute_device,
+                    "Expected `FlatParameter` to be on the compute device "
+                    f"{self.compute_device} but got {handle.flat_param.device}",
+                )
+            output = self._fsdp_wrapped_module(*args, **kwargs)
+            return _post_forward(
+                self, handle, _post_forward_reshard, self, unused, output
+            )
+
+    @staticmethod
+    @contextlib.contextmanager
+    def summon_full_params(
+        module: nn.Module,
+        recurse: bool = True,
+        writeback: bool = True,
+        rank0_only: bool = False,
+        offload_to_cpu: bool = False,
+        with_grads: bool = False,
+    ) -> Generator:
+        r"""Expose full params for FSDP instances with this context manager.
+
+        Can be useful *after* forward/backward for a model to get
+        the params for additional processing or checking. It can take a non-FSDP
+        module and will summon full params for all contained FSDP modules as
+        well as their children, depending on the ``recurse`` argument.
+
+        .. note:: This can be used on inner FSDPs.
+        .. note:: This can *not* be used within a forward or backward pass. Nor
+            can forward and backward be started from within this context.
+        .. note:: Parameters will revert to their local shards after the context
+            manager exits, storage behavior is the same as forward.
+        .. note:: The full parameters can be modified, but only the portion
+            corresponding to the local param shard will persist after the
+            context manager exits (unless ``writeback=False``, in which case
+            changes will be discarded). In the case where FSDP does not shard
+            the parameters, currently only when ``world_size == 1``, or ``NO_SHARD``
+            config, the modification is persisted regardless of ``writeback``.
+        .. note:: This method works on modules which are not FSDP themselves but
+            may contain multiple independent FSDP units. In that case, the given
+            arguments will apply to all contained FSDP units.
+
+        .. warning:: Note that ``rank0_only=True`` in conjunction with
+            ``writeback=True`` is not currently supported and will raise an
+            error. This is because model parameter shapes would be different
+            across ranks within the context, and writing to them can lead to
+            inconsistency across ranks when the context is exited.
+
+        .. warning:: Note that ``offload_to_cpu`` and ``rank0_only=False`` will
+            result in full parameters being redundantly copied to CPU memory for
+            GPUs that reside on the same machine, which may incur the risk of
+            CPU OOM. It is recommended to use ``offload_to_cpu`` with
+            ``rank0_only=True``.
+
+        Args:
+            recurse (bool, Optional): recursively summon all params for nested
+                FSDP instances (default: True).
+            writeback (bool, Optional): if ``False``, modifications to params are
+                discarded after the context manager exits;
+                disabling this can be slightly more efficient (default: True)
+            rank0_only (bool, Optional): if ``True``, full parameters are
+                materialized on only global rank 0. This means that within the
+                context, only rank 0 will have full parameters and the other
+                ranks will have sharded parameters. Note that setting
+                ``rank0_only=True`` with ``writeback=True`` is not supported,
+                as model parameter shapes will be different across ranks
+                within the context, and writing to them can lead to
+                inconsistency across ranks when the context is exited.
+            offload_to_cpu (bool, Optional): If ``True``, full parameters are
+                offloaded to CPU. Note that this offloading currently only
+                occurs if the parameter is sharded (which is only not the case
+                for world_size = 1 or ``NO_SHARD`` config). It is recommended
+                to use ``offload_to_cpu`` with ``rank0_only=True`` to avoid
+                redundant copies of model parameters being offloaded to the same CPU memory.
+            with_grads (bool, Optional): If ``True``, gradients are also
+                unsharded with the parameters. Currently, this is only
+                supported when passing ``use_orig_params=True`` to the FSDP
+                constructor and ``offload_to_cpu=False`` to this method.
+                (Default: ``False``)
+        """
+        with _unshard_params(
+            module, recurse, writeback, rank0_only, offload_to_cpu, with_grads
+        ):
+            yield
+
+    @contextlib.contextmanager
+    def _deregister_orig_params_ctx(self):
+        """Deregister the original parameters and expose the :class:`FlatParameter`.
+
+        If a :class:`FlatParameter` is sharded, then
+        this refreshes the sharded views before exiting. This method should
+        only be called when using the original parameters.
+        """
+        _p_assert(
+            self._use_orig_params,
+            "`_deregister_orig_params_ctx()` should only be called when "
+            "`_use_orig_params=True`",
+        )
+        for fsdp_module in traversal_utils._get_fsdp_states(self):
+            _deregister_orig_params(fsdp_module, fsdp_module)
+        try:
+            yield
+        finally:
+            for fsdp_module in traversal_utils._get_fsdp_states(self):
+                _register_orig_params(fsdp_module, fsdp_module)
+
+    def _apply(self, *args, **kwargs):
+        """Deregister the original parameters and expose the :class:`FlatParameter` s before calling ``_apply()``."""
+        # When using the original parameters: Since (1) the `FlatParameter`s
+        # own the storage and (2) `_apply()` is the subroutine underlying the
+        # most common storage-changing ops like `to()` and `cuda()`, we
+        # override `_apply()` to have the storage change directly performed on
+        # the `FlatParameter`s instead of applying to the original parameters
+        # and then writing back to the `FlatParameter`s.
+        context = (
+            self._deregister_orig_params_ctx()
+            if self._use_orig_params
+            else contextlib.nullcontext()
+        )
+        with context:
+            return super()._apply(*args, **kwargs)
+
+    def named_buffers(
+        self,
+        *args,
+        **kwargs,
+    ) -> Iterator[tuple[str, torch.Tensor]]:
+        """Return an iterator over module buffers, yielding both the name of the buffer and the buffer itself.
+
+        Intercepts buffer names and removes all occurrences of the FSDP-specific flattened buffer prefix
+        when inside the :meth:`summon_full_params` context manager.
+        """
+        should_clean_name = self.training_state == TrainingState.SUMMON_FULL_PARAMS
+        for buffer_name, buffer in super().named_buffers(*args, **kwargs):
+            if should_clean_name:
+                # Remove any instances of the FSDP-specific prefix; there can
+                # be multiple in the case of nested FSDP modules
+                buffer_name = buffer_name.replace(FSDP_PREFIX, "")
+            yield (buffer_name, buffer)
+
+    def named_parameters(
+        self,
+        *args,
+        **kwargs,
+    ) -> Iterator[tuple[str, torch.nn.Parameter]]:
+        """Return an iterator over module parameters, yielding both the name of the parameter and the parameter itself.
+
+        Intercepts parameter names and removes all occurrences of the FSDP-specific flattened parameter prefix
+        when inside the :meth:`summon_full_params` context manager.
+        """
+        should_clean_name = self.training_state == TrainingState.SUMMON_FULL_PARAMS
+        for param_name, param in super().named_parameters(*args, **kwargs):
+            if should_clean_name:
+                # Remove any instances of the FSDP-specific prefix; there can
+                # be multiple in the case of nested FSDP modules
+                param_name = param_name.replace(FSDP_PREFIX, "")
+            yield (param_name, param)
+
+    def _assert_state(self, state: Union[TrainingState, list[TrainingState]]) -> None:
+        """Assert we are in the given state."""
+        # Since assert can be turned off and this error checking
+        # is really important, we use explicit error checking
+        # and raise a ValueError if needed.
+        if isinstance(state, TrainingState):
+            state = [state]
+        if self.training_state not in state:
+            msg = (
+                f"expected to be in states {state} but current state "
+                f"is {self.training_state}"
+            )
+            # In case we are failing in the context of autograd hook, asserting
+            # may not generate useful msg. So, let's print it to be sure.
+            if self.rank == 0:
+                print(f"Asserting FSDP instance is: {self}")
+                print(f"ERROR: {msg}")
+                traceback.print_stack()
+            raise ValueError(msg)
+
+    @contextmanager
+    def no_sync(self) -> Generator:
+        """Disable gradient synchronizations across FSDP instances.
+
+        Within this context, gradients will be accumulated in module
+        variables, which will later be synchronized in the first
+        forward-backward pass after exiting the context. This should only be
+        used on the root FSDP instance and will recursively apply to all
+        children FSDP instances.
+
+        .. note:: This likely results in higher memory usage because FSDP will
+            accumulate the full model gradients (instead of gradient shards)
+            until the eventual sync.
+
+        .. note:: When used with CPU offloading, the gradients will not be
+            offloaded to CPU when inside the context manager. Instead, they
+            will only be offloaded right after the eventual sync.
+        """
+        _lazy_init(self, self)
+        if not self._is_root:
+            raise RuntimeError(
+                "`no_sync()` on inner FSDP instances is not supported. Please call `no_sync()` on root FSDP module."
+            )
+        self._assert_state(TrainingState.IDLE)
+        old_flags = []
+        for m in self.modules():
+            if isinstance(m, FullyShardedDataParallel):
+                old_flags.append((m, m._sync_gradients))
+                m._sync_gradients = False
+        try:
+            yield
+        finally:
+            for m, old_flag in old_flags:
+                assert not m._sync_gradients, (
+                    "`_sync_gradients` was incorrectly set to "
+                    "`True` while in the `no_sync()` context manager"
+                )
+                m._sync_gradients = old_flag
+
+    @torch.no_grad()
+    def clip_grad_norm_(
+        self, max_norm: Union[float, int], norm_type: Union[float, int] = 2.0
+    ) -> torch.Tensor:
+        """Clip the gradient norm of all parameters.
+
+        The norm is computed over all parameters' gradients as viewed as a single vector, and the
+        gradients are modified in-place.
+
+        Args:
+            max_norm (float or int): max norm of the gradients
+            norm_type (float or int): type of the used p-norm. Can be ``'inf'``
+                for infinity norm.
+
+        Returns:
+            Total norm of the parameters (viewed as a single vector).
+
+        If every FSDP instance uses ``NO_SHARD``, meaning that no
+        gradients are sharded across ranks, then you may directly use
+        :func:`torch.nn.utils.clip_grad_norm_`.
+
+        If at least some FSDP instance uses a sharded strategy (i.e.
+        one other than ``NO_SHARD``), then you should use this method
+        instead of :func:`torch.nn.utils.clip_grad_norm_` since this method
+        handles the fact that gradients are sharded across ranks.
+
+        The total norm returned will have the "largest" dtype across
+        all parameters/gradients as defined by PyTorch's type promotion
+        semantics. For example, if *all* parameters/gradients use a low
+        precision dtype, then the returned norm's dtype will be that low
+        precision dtype, but if there exists at least one parameter/
+        gradient using FP32, then the returned norm's dtype will be FP32.
+
+        .. warning:: This needs to be called on all ranks since it uses
+            collective communications.
+        """
+        _lazy_init(self, self)
+        if not self._is_root:
+            raise RuntimeError(
+                "`clip_grad_norm_()` should only be called on the root FSDP instance"
+            )
+        if self._zero_scalar is None:
+            self._zero_scalar = torch.tensor(0.0, device=self.compute_device)
+        self._assert_state(TrainingState.IDLE)
+        # If every FSDP instance uses `NO_SHARD`, then we can directly use
+        # the normal `nn.utils` one targeting local gradients
+        all_no_shard = all(
+            not handle.uses_sharded_strategy for handle in self._all_handles
+        )
+        if all_no_shard:
+            return torch.nn.utils.clip_grad_norm_(
+                self.parameters(), max_norm, norm_type
+            )
+        # Otherwise, there exists some FSDP instance using a sharded strategy,
+        # where sharded and non-sharded parameters must be handled separately
+        max_norm = float(max_norm)
+        norm_type = float(norm_type)
+        sharded_params_set = set()
+        nonsharded_params_set = set()  # `NO_SHARD` or not FSDP-managed
+        # Make sure to compute the local norm using lists for deterministic
+        # iteration order and hence deterministic total norm computation
+        sharded_params = []
+        nonsharded_params = []
+        grads: list[torch.Tensor] = []
+        for handle in self._all_handles:
+            if handle.uses_sharded_strategy:
+                target_set = sharded_params_set
+                target_list = sharded_params
+            else:
+                target_set = nonsharded_params_set
+                target_list = nonsharded_params
+            if handle._use_orig_params:
+                for param in handle.flat_param._params:
+                    if param not in target_set:
+                        target_set.add(param)
+                        target_list.append(param)
+                        if param.grad is not None:
+                            grads.append(param.grad)
+            else:
+                if handle.flat_param not in target_set:
+                    target_set.add(handle.flat_param)
+                    target_list.append(handle.flat_param)
+                    if handle.flat_param.grad is not None:
+                        grads.append(handle.flat_param.grad)
+        for param in self.parameters():
+            not_fsdp_managed = (
+                param not in sharded_params_set and param not in nonsharded_params_set
+            )
+            if not_fsdp_managed:
+                nonsharded_params_set.add(param)
+                nonsharded_params.append(param)
+                if param.grad is not None:
+                    grads.append(param.grad)
+        # Compute local norms (forced to be in FP32)
+        local_sharded_norm = _get_grad_norm(
+            sharded_params, norm_type, self._zero_scalar, self.compute_device
+        )
+        local_nonsharded_norm = (
+            _get_grad_norm(
+                nonsharded_params, norm_type, self._zero_scalar, self.compute_device
+            )
+            if nonsharded_params
+            else None
+        )
+        # Reconstruct the total gradient norm depending on the norm type
+        if norm_type == math.inf:
+            total_norm = (
+                torch.maximum(local_sharded_norm, local_nonsharded_norm)
+                if local_nonsharded_norm is not None
+                else local_sharded_norm
+            )
+            dist.all_reduce(
+                total_norm, op=torch.distributed.ReduceOp.MAX, group=self.process_group
+            )
+        else:
+            total_norm = local_sharded_norm**norm_type
+            dist.all_reduce(total_norm, group=self.process_group)
+            # All-reducing the local non-sharded norm would count it an extra
+            # world-size-many times
+            if local_nonsharded_norm is not None:
+                total_norm += local_nonsharded_norm**norm_type
+            total_norm = total_norm ** (1.0 / norm_type)
+        if self.cpu_offload.offload_params:
+            total_norm = total_norm.cpu()
+
+        clip_coef = max_norm / (total_norm + 1e-6)
+        # Multiplying by the clamped coefficient is meaningless when it is
+        # equal to 1, but it avoids the host-device sync that would result from
+        # `if clip_coef < 1`
+        clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
+        for grad in grads:
+            grad.mul_(clip_coef_clamped.to(grad.device, grad.dtype))
+        # Use the "largest" dtype by type promotion semantics to use the same
+        # dtype as if we did not force local norm computation to be in FP32
+        if len(grads) == 0:
+            # If this rank has no gradients, then we must default to FP32
+            # unless we use additional communication, which we prefer to avoid
+            # since `clip_grad_norm_()` is called in the training loop
+            warnings.warn(
+                f"Called FSDP.clip_grad_norm_() on rank {self.rank} with no "
+                "gradients -- returning the total norm in the default dtype "
+                f"{total_norm.dtype}"
+            )  # warn since this is generally unexpected
+            return total_norm
+        total_norm_dtype = functools.reduce(
+            torch.promote_types,
+            [grad.dtype for grad in grads],
+        )
+        return total_norm.to(total_norm_dtype)
+
+    @staticmethod
+    def _warn_optim_input(optim_input, *, stacklevel: int = 1):
+        if optim_input is not None:
+            warnings.warn(
+                "The `optim_input` argument is deprecated and will be removed after PyTorch 1.13. "
+                "You may remove it from your code without changing its functionality.",
+                FutureWarning,
+                stacklevel=stacklevel + 1,
+            )
+
+    @staticmethod
+    def _is_using_optim_input(optim_input, optim) -> bool:
+        if optim_input is None and optim is None:
+            # Use the default behavior of `optim_input``
+            return True
+        if optim_input is not None:
+            # Use the `optim_input` code path
+            return True
+        # Use the `optim` code path
+        return False
+
+    @staticmethod
+    def _warn_legacy_optim_state_dict(curr: str, new: str, *, stacklevel: int = 1):
+        warnings.warn(
+            f"``FullyShardedDataParallel.{curr}``is being deprecated and is "
+            f"replaced by ``FullyShardedDataParallel.{new}``. "
+            f"``FullyShardedDataParallel.{curr}`` may be removed after PyTorch 2.2.",
+            FutureWarning,
+            stacklevel=stacklevel + 1,
+        )
+
+    @staticmethod
+    def _optim_state_dict_impl(
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        optim_state_dict: dict[str, Any],
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        rank0_only: bool = True,
+        full_state_dict: bool = True,
+        group: Optional[dist.ProcessGroup] = None,
+        cpu_offload: bool = True,
+        *,
+        _stacklevel: int = 1,
+    ) -> dict[str, Any]:
+        """Transform the state-dict of an optimizer corresponding to a sharded model.
+
+        This is the internal API that is used by all the optim_state_dict implementations.
+        Given model, optim, the original optim_state_dict, this API removes the
+        FSDP internal information and internal sharding from the optim_state_dict.
+        """
+        if full_state_dict:
+            FullyShardedDataParallel._warn_optim_input(
+                optim_input, stacklevel=_stacklevel + 1
+            )
+            using_optim_input = FullyShardedDataParallel._is_using_optim_input(
+                optim_input,
+                optim,
+            )
+        else:
+            using_optim_input = False
+            assert optim_input is None and not rank0_only
+
+        use_orig_params = FullyShardedDataParallel.fsdp_modules(model)[
+            0
+        ]._use_orig_params
+        assert all(
+            use_orig_params == m._use_orig_params
+            for m in FullyShardedDataParallel.fsdp_modules(model)
+        ), "Not all FSDP modules have the same _use_orig_params value"
+
+        return _optim_state_dict(
+            model=model,
+            optim=optim,
+            optim_state_dict=optim_state_dict,
+            optim_input=optim_input,
+            rank0_only=rank0_only,
+            shard_state=not full_state_dict,
+            group=group,
+            using_optim_input=using_optim_input,
+            use_orig_params=use_orig_params,
+            cpu_offload=cpu_offload,
+        )
+
+    @staticmethod
+    def _optim_state_dict_to_load_impl(
+        optim_state_dict: dict[str, Any],
+        model: torch.nn.Module,
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        optim: Optional[torch.optim.Optimizer] = None,
+        full_state_dict: bool = True,
+        rank0_only: bool = False,
+        is_named_optimizer: bool = False,
+        group: Optional[dist.ProcessGroup] = None,
+    ) -> dict[str, Any]:
+        """
+        Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
+
+        This is the internal API that is used by all the load optim_state_dict implementations.
+        Given model, optim, and the saved optim_state_dict, this API adds the FSDP
+        internal information and internal sharding to the optim_state_dict.
+        """
+        if full_state_dict:
+            FullyShardedDataParallel._warn_optim_input(optim_input)
+            using_optim_input = FullyShardedDataParallel._is_using_optim_input(
+                optim_input,
+                optim,
+            )
+        else:
+            using_optim_input = False
+            assert optim_input is None and not rank0_only
+
+        use_orig_params = FullyShardedDataParallel.fsdp_modules(model)[
+            0
+        ]._use_orig_params
+        assert all(
+            use_orig_params == m._use_orig_params
+            for m in FullyShardedDataParallel.fsdp_modules(model)
+        ), "Not all FSDP modules have the same _use_orig_params value"
+
+        if rank0_only and dist.get_rank(group) > 0:
+            optim_state_dict = {}
+        sharded_osd = _flatten_optim_state_dict(
+            optim_state_dict,
+            model=model,
+            use_orig_params=use_orig_params,
+            optim=(optim if is_named_optimizer else None),
+            rank0_only=rank0_only,
+            group=group,
+        )
+        return _rekey_sharded_optim_state_dict(
+            sharded_osd,
+            model=model,
+            optim=optim,
+            optim_input=optim_input,
+            using_optim_input=using_optim_input,
+            is_named_optimizer=is_named_optimizer,
+        )
+
+    @staticmethod
+    def full_optim_state_dict(
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        rank0_only: bool = True,
+        group: Optional[dist.ProcessGroup] = None,
+    ) -> dict[str, Any]:
+        """Return the full optimizer state-dict.
+
+        Consolidates the full optimizer state on rank 0 and returns it
+        as a :class:`dict` following the convention of
+        :meth:`torch.optim.Optimizer.state_dict`, i.e. with keys ``"state"``
+        and ``"param_groups"``. The flattened parameters in ``FSDP`` modules
+        contained in ``model`` are mapped back to their unflattened parameters.
+
+        This needs to be called on all ranks since it uses
+        collective communications. However, if ``rank0_only=True``, then
+        the state dict is only populated on rank 0, and all other ranks
+        return an empty :class:`dict`.
+
+        Unlike ``torch.optim.Optimizer.state_dict()``, this method
+        uses full parameter names as keys instead of parameter IDs.
+
+        Like in :meth:`torch.optim.Optimizer.state_dict`, the tensors
+        contained in the optimizer state dict are not cloned, so there may
+        be aliasing surprises. For best practices, consider saving the
+        returned optimizer state dict immediately, e.g. using
+        ``torch.save()``.
+
+        Args:
+            model (torch.nn.Module): Root module (which may or may not be a
+                :class:`FullyShardedDataParallel` instance) whose parameters
+                were passed into the optimizer ``optim``.
+            optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+                parameters.
+            optim_input (Optional[Union[List[Dict[str, Any]], Iterable[torch.nn.Parameter]]]):
+                Input passed into the optimizer ``optim`` representing either a
+                :class:`list` of parameter groups or an iterable of parameters;
+                if ``None``, then this method assumes the input was
+                ``model.parameters()``. This argument is deprecated, and there
+                is no need to pass it in anymore. (Default: ``None``)
+            rank0_only (bool): If ``True``, saves the populated :class:`dict`
+                only on rank 0; if ``False``, saves it on all ranks. (Default:
+                ``True``)
+            group (dist.ProcessGroup): Model's process group or ``None`` if using
+                the default process group. (Default: ``None``)
+
+        Returns:
+            Dict[str, Any]: A :class:`dict` containing the optimizer state for
+            ``model`` 's original unflattened parameters and including keys
+            "state" and "param_groups" following the convention of
+            :meth:`torch.optim.Optimizer.state_dict`. If ``rank0_only=True``,
+            then nonzero ranks return an empty :class:`dict`.
+        """
+        FullyShardedDataParallel._warn_legacy_optim_state_dict(
+            "full_optim_state_dict",
+            "optim_state_dict",
+            stacklevel=2,
+        )
+        return FullyShardedDataParallel._optim_state_dict_impl(
+            model=model,
+            optim=optim,
+            optim_state_dict=optim.state_dict(),
+            optim_input=optim_input,
+            rank0_only=rank0_only,
+            group=group,
+            full_state_dict=True,
+            _stacklevel=2,
+        )
+
+    @staticmethod
+    def sharded_optim_state_dict(
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        group: Optional[dist.ProcessGroup] = None,
+    ) -> dict[str, Any]:
+        """Return the optimizer state-dict in its sharded form.
+
+        The API is similar to :meth:`full_optim_state_dict` but this API chunks
+        all non-zero-dimension states to :class:`ShardedTensor` to save memory.
+        This API should only be used when the model ``state_dict`` is derived
+        with the context manager ``with state_dict_type(SHARDED_STATE_DICT):``.
+
+        For the detailed usage, refer to :meth:`full_optim_state_dict`.
+
+        .. warning:: The returned state dict contains ``ShardedTensor`` and
+            cannot be directly used by the regular ``optim.load_state_dict``.
+        """
+        FullyShardedDataParallel._warn_legacy_optim_state_dict(
+            "sharded_optim_state_dict",
+            "optim_state_dict",
+            stacklevel=2,
+        )
+        return FullyShardedDataParallel._optim_state_dict_impl(
+            model=model,
+            optim=optim,
+            optim_state_dict=optim.state_dict(),
+            optim_input=None,
+            rank0_only=False,
+            full_state_dict=False,
+            group=group,
+            _stacklevel=2,
+        )
+
+    @staticmethod
+    def shard_full_optim_state_dict(
+        full_optim_state_dict: dict[str, Any],
+        model: torch.nn.Module,
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        optim: Optional[torch.optim.Optimizer] = None,
+    ) -> dict[str, Any]:
+        """Shard a full optimizer state-dict.
+
+        Remaps the state in ``full_optim_state_dict`` to flattened parameters instead of unflattened
+        parameters and restricts to only this rank's part of the optimizer state.
+        The first argument should be the return value of :meth:`full_optim_state_dict`.
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+            >>> model, optim = ...
+            >>> full_osd = FSDP.full_optim_state_dict(model, optim)
+            >>> torch.save(full_osd, PATH)
+            >>> # Define new model with possibly different world size
+            >>> new_model, new_optim = ...
+            >>> full_osd = torch.load(PATH)
+            >>> sharded_osd = FSDP.shard_full_optim_state_dict(full_osd, new_model)
+            >>> new_optim.load_state_dict(sharded_osd)
+
+        .. note:: Both :meth:`shard_full_optim_state_dict` and
+            :meth:`scatter_full_optim_state_dict` may be used to get the
+            sharded optimizer state dict to load. Assuming that the full
+            optimizer state dict resides in CPU memory, the former requires
+            each rank to have the full dict in CPU memory, where each rank
+            individually shards the dict without any communication, while the
+            latter requires only rank 0 to have the full dict in CPU memory,
+            where rank 0 moves each shard to GPU memory (for NCCL) and
+            communicates it to ranks appropriately. Hence, the former has
+            higher aggregate CPU memory cost, while the latter has higher
+            communication cost.
+
+        Args:
+            full_optim_state_dict (Dict[str, Any]): Optimizer state dict
+                corresponding to the unflattened parameters and holding the
+                full non-sharded optimizer state.
+            model (torch.nn.Module): Root module (which may or may not be a
+                :class:`FullyShardedDataParallel` instance) whose parameters
+                correspond to the optimizer state in ``full_optim_state_dict``.
+            optim_input (Optional[Union[List[Dict[str, Any]], Iterable[torch.nn.Parameter]]]):
+                Input passed into the optimizer representing either a
+                :class:`list` of parameter groups or an iterable of parameters;
+                if ``None``, then this method assumes the input was
+                ``model.parameters()``. This argument is deprecated, and there
+                is no need to pass it in anymore. (Default: ``None``)
+            optim (Optional[torch.optim.Optimizer]): Optimizer that will load
+                the state dict returned by this method. This is the preferred
+                argument to use over ``optim_input``. (Default: ``None``)
+
+        Returns:
+            Dict[str, Any]: The full optimizer state dict now remapped to
+            flattened parameters instead of unflattened parameters and
+            restricted to only include this rank's part of the optimizer state.
+        """
+        FullyShardedDataParallel._warn_legacy_optim_state_dict(
+            "shard_full_optim_state_dict",
+            "optim_state_dict_to_load",
+            stacklevel=2,
+        )
+        return FullyShardedDataParallel._optim_state_dict_to_load_impl(
+            optim_state_dict=full_optim_state_dict,
+            model=model,
+            optim_input=optim_input,
+            optim=optim,
+            full_state_dict=True,
+            is_named_optimizer=False,
+        )
+
+    @staticmethod
+    def flatten_sharded_optim_state_dict(
+        sharded_optim_state_dict: dict[str, Any],
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+    ) -> dict[str, Any]:
+        """Flatten a sharded optimizer state-dict.
+
+        The API is similar to :meth:`shard_full_optim_state_dict`. The only
+        difference is that the input ``sharded_optim_state_dict`` should be
+        returned from :meth:`sharded_optim_state_dict`. Therefore, there will
+        be all-gather calls on each rank to gather ``ShardedTensor`` s.
+
+        Args:
+            sharded_optim_state_dict (Dict[str, Any]): Optimizer state dict
+                corresponding to the unflattened parameters and holding the
+                sharded optimizer state.
+            model (torch.nn.Module):
+                Refer to :meth:`shard_full_optim_state_dict`.
+            optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+                parameters.
+
+        Returns:
+            Refer to :meth:`shard_full_optim_state_dict`.
+        """
+        FullyShardedDataParallel._warn_legacy_optim_state_dict(
+            "flatten_sharded_optim_state_dict",
+            "optim_state_dict_to_load",
+            stacklevel=2,
+        )
+        return FullyShardedDataParallel._optim_state_dict_to_load_impl(
+            optim_state_dict=sharded_optim_state_dict,
+            model=model,
+            optim_input=None,
+            optim=optim,
+            full_state_dict=False,
+            is_named_optimizer=False,
+        )
+
+    @staticmethod
+    def scatter_full_optim_state_dict(
+        full_optim_state_dict: Optional[dict[str, Any]],
+        model: torch.nn.Module,
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        optim: Optional[torch.optim.Optimizer] = None,
+        group: Optional[Any] = None,
+    ) -> dict[str, Any]:
+        """Scatter the full optimizer state dict from rank 0 to all other ranks.
+
+        Returns the sharded optimizer state dict on each rank.
+        The return value is the same as :meth:`shard_full_optim_state_dict`, and on rank
+        0, the first argument should be the return value of
+        :meth:`full_optim_state_dict`.
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+            >>> model, optim = ...
+            >>> full_osd = FSDP.full_optim_state_dict(model, optim)  # only non-empty on rank 0
+            >>> # Define new model with possibly different world size
+            >>> new_model, new_optim, new_group = ...
+            >>> sharded_osd = FSDP.scatter_full_optim_state_dict(full_osd, new_model, group=new_group)
+            >>> new_optim.load_state_dict(sharded_osd)
+
+        .. note:: Both :meth:`shard_full_optim_state_dict` and
+            :meth:`scatter_full_optim_state_dict` may be used to get the
+            sharded optimizer state dict to load. Assuming that the full
+            optimizer state dict resides in CPU memory, the former requires
+            each rank to have the full dict in CPU memory, where each rank
+            individually shards the dict without any communication, while the
+            latter requires only rank 0 to have the full dict in CPU memory,
+            where rank 0 moves each shard to GPU memory (for NCCL) and
+            communicates it to ranks appropriately. Hence, the former has
+            higher aggregate CPU memory cost, while the latter has higher
+            communication cost.
+
+        Args:
+            full_optim_state_dict (Optional[Dict[str, Any]]): Optimizer state
+                dict corresponding to the unflattened parameters and holding
+                the full non-sharded optimizer state if on rank 0; the argument
+                is ignored on nonzero ranks.
+            model (torch.nn.Module): Root module (which may or may not be a
+                :class:`FullyShardedDataParallel` instance) whose parameters
+                correspond to the optimizer state in ``full_optim_state_dict``.
+            optim_input (Optional[Union[List[Dict[str, Any]], Iterable[torch.nn.Parameter]]]):
+                Input passed into the optimizer representing either a
+                :class:`list` of parameter groups or an iterable of parameters;
+                if ``None``, then this method assumes the input was
+                ``model.parameters()``. This argument is deprecated, and there
+                is no need to pass it in anymore. (Default: ``None``)
+            optim (Optional[torch.optim.Optimizer]): Optimizer that will load
+                the state dict returned by this method. This is the preferred
+                argument to use over ``optim_input``. (Default: ``None``)
+            group (dist.ProcessGroup): Model's process group or ``None`` if
+                using the default process group. (Default: ``None``)
+
+        Returns:
+            Dict[str, Any]: The full optimizer state dict now remapped to
+            flattened parameters instead of unflattened parameters and
+            restricted to only include this rank's part of the optimizer state.
+        """
+        FullyShardedDataParallel._warn_legacy_optim_state_dict(
+            "scatter_full_optim_state_dict",
+            "optim_state_dict_to_load",
+            stacklevel=2,
+        )
+        return FullyShardedDataParallel._optim_state_dict_to_load_impl(
+            optim_state_dict=full_optim_state_dict,
+            model=model,
+            optim_input=optim_input,
+            optim=optim,
+            full_state_dict=True,
+            rank0_only=True,
+            is_named_optimizer=False,
+            group=group,
+        )
+
+    @staticmethod
+    def rekey_optim_state_dict(
+        optim_state_dict: dict[str, Any],
+        optim_state_key_type: OptimStateKeyType,
+        model: torch.nn.Module,
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        optim: Optional[torch.optim.Optimizer] = None,
+    ) -> dict[str, Any]:
+        """Re-keys the optimizer state dict ``optim_state_dict`` to use the key type ``optim_state_key_type``.
+
+        This can be used to achieve compatibility between optimizer state dicts from models with FSDP
+        instances and ones without.
+
+        To re-key an FSDP full optimizer state dict (i.e. from
+        :meth:`full_optim_state_dict`) to use parameter IDs and be loadable to
+        a non-wrapped model::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> wrapped_model, wrapped_optim = ...
+            >>> full_osd = FSDP.full_optim_state_dict(wrapped_model, wrapped_optim)
+            >>> nonwrapped_model, nonwrapped_optim = ...
+            >>> rekeyed_osd = FSDP.rekey_optim_state_dict(full_osd, OptimStateKeyType.PARAM_ID, nonwrapped_model)
+            >>> nonwrapped_optim.load_state_dict(rekeyed_osd)
+
+        To re-key a normal optimizer state dict from a non-wrapped model to be
+        loadable to a wrapped model::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> nonwrapped_model, nonwrapped_optim = ...
+            >>> osd = nonwrapped_optim.state_dict()
+            >>> rekeyed_osd = FSDP.rekey_optim_state_dict(osd, OptimStateKeyType.PARAM_NAME, nonwrapped_model)
+            >>> wrapped_model, wrapped_optim = ...
+            >>> sharded_osd = FSDP.shard_full_optim_state_dict(rekeyed_osd, wrapped_model)
+            >>> wrapped_optim.load_state_dict(sharded_osd)
+
+        Returns:
+            Dict[str, Any]: The optimizer state dict re-keyed using the
+            parameter keys specified by ``optim_state_key_type``.
+        """
+        FullyShardedDataParallel._warn_optim_input(optim_input)
+        using_optim_input = FullyShardedDataParallel._is_using_optim_input(
+            optim_input,
+            optim,
+        )
+        assert optim_state_key_type in (
+            OptimStateKeyType.PARAM_NAME,
+            OptimStateKeyType.PARAM_ID,
+        )
+        osd = optim_state_dict  # alias
+        # Validate that the existing parameter keys are uniformly typed
+        uses_param_name_mask = [type(param_key) is str for param_key in osd["state"]]
+        uses_param_id_mask = [type(param_key) is int for param_key in osd["state"]]
+        if (any(uses_param_name_mask) and not all(uses_param_name_mask)) or (
+            any(uses_param_id_mask) and not all(uses_param_id_mask)
+        ):
+            error_msg = f"Invalid parameter keys: {osd['state'].keys()}"
+            raise ValueError(error_msg)
+        # Return directly if the existing key type matches the target key type
+        if (
+            optim_state_key_type == OptimStateKeyType.PARAM_NAME
+            and all(uses_param_name_mask)
+        ) or (
+            optim_state_key_type == OptimStateKeyType.PARAM_ID
+            and all(uses_param_id_mask)
+        ):
+            return osd
+        # Otherwise, actually perform the re-keying
+        new_osd = {}
+        if optim_state_key_type == OptimStateKeyType.PARAM_NAME:  # ID -> name
+            param_id_to_param = (
+                _get_param_id_to_param_from_optim_input(model, optim_input)
+                if using_optim_input
+                else _get_param_key_to_param(optim)
+            )
+            param_to_param_name = _get_param_to_fqn(model)
+            param_id_to_param_name: list[str] = [
+                param_to_param_name[param] for param in param_id_to_param.values()
+            ]
+            new_osd["state"] = {
+                param_id_to_param_name[param_id]: param_state
+                for param_id, param_state in osd["state"].items()
+            }
+            new_osd["param_groups"] = copy.deepcopy(osd["param_groups"])
+            for param_group in new_osd["param_groups"]:
+                param_group["params"] = sorted(
+                    [
+                        param_id_to_param_name[param_id]
+                        for param_id in param_group["params"]
+                    ]
+                )
+            return new_osd
+        elif optim_state_key_type == OptimStateKeyType.PARAM_ID:  # name -> ID
+            param_name_to_param = _get_fqn_to_param(model)
+            param_to_param_id = (
+                _get_param_to_param_id_from_optim_input(model, optim_input)
+                if using_optim_input
+                else _get_param_to_param_key(optim)
+            )
+            # Because not all model parameters may be passed as the optimizer
+            # input, we may need to drop some parameters from this mapping
+            param_name_to_param_id = {
+                param_name: param_to_param_id[param]
+                for param_name, param in param_name_to_param.items()
+                if param in param_to_param_id
+            }
+            new_osd["state"] = {
+                param_name_to_param_id[param_name]: param_state
+                for param_name, param_state in osd["state"].items()
+            }
+            new_osd["param_groups"] = copy.deepcopy(osd["param_groups"])
+            for param_group in new_osd["param_groups"]:
+                param_group["params"] = sorted(
+                    [
+                        param_name_to_param_id[param_name]
+                        for param_name in param_group["params"]
+                    ]
+                )
+            return new_osd
+        return new_osd  # should never reach here
+
+    @staticmethod
+    def optim_state_dict(
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        optim_state_dict: Optional[dict[str, Any]] = None,
+        group: Optional[dist.ProcessGroup] = None,
+    ) -> dict[str, Any]:
+        """
+        Transform the state-dict of an optimizer corresponding to a sharded model.
+
+        The given state-dict can be transformed to one of three types:
+        1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.
+
+        For full optimizer state_dict, all states are unflattened and not sharded.
+        Rank0 only and CPU only can be specified via :meth:`state_dict_type` to
+        avoid OOM.
+
+        For sharded optimizer state_dict, all states are unflattened but sharded.
+        CPU only can be specified via :meth:`state_dict_type` to further save
+        memory.
+
+        For local state_dict, no transformation will be performed. But a state
+        will be converted from nn.Tensor to ShardedTensor to represent its sharding
+        nature (this is not supported yet).
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+            >>> from torch.distributed.fsdp import StateDictType
+            >>> from torch.distributed.fsdp import FullStateDictConfig
+            >>> from torch.distributed.fsdp import FullOptimStateDictConfig
+            >>> # Save a checkpoint
+            >>> model, optim = ...
+            >>> FSDP.set_state_dict_type(
+            >>>     model,
+            >>>     StateDictType.FULL_STATE_DICT,
+            >>>     FullStateDictConfig(rank0_only=False),
+            >>>     FullOptimStateDictConfig(rank0_only=False),
+            >>> )
+            >>> state_dict = model.state_dict()
+            >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
+            >>> save_a_checkpoint(state_dict, optim_state_dict)
+            >>> # Load a checkpoint
+            >>> model, optim = ...
+            >>> state_dict, optim_state_dict = load_a_checkpoint()
+            >>> FSDP.set_state_dict_type(
+            >>>     model,
+            >>>     StateDictType.FULL_STATE_DICT,
+            >>>     FullStateDictConfig(rank0_only=False),
+            >>>     FullOptimStateDictConfig(rank0_only=False),
+            >>> )
+            >>> model.load_state_dict(state_dict)
+            >>> optim_state_dict = FSDP.optim_state_dict_to_load(
+            >>>     model, optim, optim_state_dict
+            >>> )
+            >>> optim.load_state_dict(optim_state_dict)
+
+        Args:
+            model (torch.nn.Module): Root module (which may or may not be a
+                :class:`FullyShardedDataParallel` instance) whose parameters
+                were passed into the optimizer ``optim``.
+            optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+                parameters.
+            optim_state_dict (Dict[str, Any]): the target optimizer state_dict to
+                transform. If the value is None, optim.state_dict() will be used. (
+                Default: ``None``)
+            group (dist.ProcessGroup): Model's process group across which parameters
+                are sharded or ``None`` if using the default process group. (
+                Default: ``None``)
+
+        Returns:
+            Dict[str, Any]: A :class:`dict` containing the optimizer state for
+            ``model``. The sharding of the optimizer state is based on
+            ``state_dict_type``.
+        """
+        state_dict_settings = FullyShardedDataParallel.get_state_dict_type(model)
+        if optim_state_dict is None:
+            optim_state_dict = optim.state_dict()
+        return FullyShardedDataParallel._optim_state_dict_impl(
+            model=model,
+            optim=optim,
+            optim_state_dict=optim_state_dict,
+            optim_input=None,
+            rank0_only=getattr(
+                state_dict_settings.optim_state_dict_config, "rank0_only", False
+            ),
+            full_state_dict=state_dict_settings.state_dict_type
+            == StateDictType.FULL_STATE_DICT,
+            group=group,
+            cpu_offload=getattr(
+                state_dict_settings.optim_state_dict_config, "offload_to_cpu", True
+            ),
+            _stacklevel=2,
+        )
+
+    @staticmethod
+    def optim_state_dict_to_load(
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        optim_state_dict: dict[str, Any],
+        is_named_optimizer: bool = False,
+        load_directly: bool = False,
+        group: Optional[dist.ProcessGroup] = None,
+    ) -> dict[str, Any]:
+        """
+        Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
+
+        Given a ``optim_state_dict`` that is transformed through
+        :meth:`optim_state_dict`, it gets converted to the flattened optimizer
+        state_dict that can be loaded to ``optim`` which is the optimizer for
+        ``model``. ``model`` must be sharded by FullyShardedDataParallel.
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+            >>> from torch.distributed.fsdp import StateDictType
+            >>> from torch.distributed.fsdp import FullStateDictConfig
+            >>> from torch.distributed.fsdp import FullOptimStateDictConfig
+            >>> # Save a checkpoint
+            >>> model, optim = ...
+            >>> FSDP.set_state_dict_type(
+            >>>     model,
+            >>>     StateDictType.FULL_STATE_DICT,
+            >>>     FullStateDictConfig(rank0_only=False),
+            >>>     FullOptimStateDictConfig(rank0_only=False),
+            >>> )
+            >>> state_dict = model.state_dict()
+            >>> original_osd = optim.state_dict()
+            >>> optim_state_dict = FSDP.optim_state_dict(
+            >>>     model,
+            >>>     optim,
+            >>>     optim_state_dict=original_osd
+            >>> )
+            >>> save_a_checkpoint(state_dict, optim_state_dict)
+            >>> # Load a checkpoint
+            >>> model, optim = ...
+            >>> state_dict, optim_state_dict = load_a_checkpoint()
+            >>> FSDP.set_state_dict_type(
+            >>>     model,
+            >>>     StateDictType.FULL_STATE_DICT,
+            >>>     FullStateDictConfig(rank0_only=False),
+            >>>     FullOptimStateDictConfig(rank0_only=False),
+            >>> )
+            >>> model.load_state_dict(state_dict)
+            >>> optim_state_dict = FSDP.optim_state_dict_to_load(
+            >>>     model, optim, optim_state_dict
+            >>> )
+            >>> optim.load_state_dict(optim_state_dict)
+
+        Args:
+            model (torch.nn.Module): Root module (which may or may not be a
+                :class:`FullyShardedDataParallel` instance) whose parameters
+                were passed into the optimizer ``optim``.
+            optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+                parameters.
+            optim_state_dict (Dict[str, Any]): The optimizer states to be loaded.
+            is_named_optimizer (bool): Is this optimizer a NamedOptimizer or
+                KeyedOptimizer. Only set to True if ``optim`` is TorchRec's
+                KeyedOptimizer or torch.distributed's NamedOptimizer.
+            load_directly (bool): If this is set to True, this API will also
+                call optim.load_state_dict(result) before returning the result.
+                Otherwise, users are responsible to call ``optim.load_state_dict()``
+                (Default: ``False``)
+            group (dist.ProcessGroup): Model's process group across which parameters
+                are sharded or ``None`` if using the default process group. (
+                Default: ``None``)
+        """
+        state_dict_settings = FullyShardedDataParallel.get_state_dict_type(model)
+        result = FullyShardedDataParallel._optim_state_dict_to_load_impl(
+            optim_state_dict=optim_state_dict,
+            model=model,
+            optim_input=None,
+            optim=optim,
+            full_state_dict=(
+                state_dict_settings.state_dict_type == StateDictType.FULL_STATE_DICT
+            ),
+            rank0_only=getattr(
+                state_dict_settings.optim_state_dict_config, "rank0_only", False
+            ),
+            is_named_optimizer=is_named_optimizer,
+            group=group,
+        )
+        if load_directly:
+            optim.load_state_dict(result)
+        return result
+
+    def register_comm_hook(self, state: object, hook: callable):
+        """Register a communication hook.
+
+        This is an enhancement that provides a flexible hook to users where they can specify how FSDP aggregates
+        gradients across multiple workers.
+        This hook can be used to implement several algorithms like
+        `GossipGrad `_ and gradient compression
+        which involve different communication strategies for
+        parameter syncs while training with :class:`FullyShardedDataParallel`.
+
+        .. warning ::
+            FSDP communication hook should be registered before running an initial forward pass
+            and only once.
+
+        Args:
+            state (object): Passed to the hook to maintain any state information during the training process.
+                            Examples include error feedback in gradient compression,
+                            peers to communicate with next in `GossipGrad `_, etc.
+                            It is locally stored by each worker
+                            and shared by all the gradient tensors on the worker.
+            hook (Callable): Callable, which has one of the following signatures:
+                            1) ``hook: Callable[torch.Tensor] -> None``:
+                            This function takes in a Python tensor, which represents
+                            the full, flattened, unsharded gradient with respect to all variables
+                            corresponding to the model this FSDP unit is wrapping
+                            (that are not wrapped by other FSDP sub-units).
+                            It then performs all necessary processing and returns ``None``;
+                            2) ``hook: Callable[torch.Tensor, torch.Tensor] -> None``:
+                            This function takes in two Python tensors, the first one represents
+                            the full, flattened, unsharded gradient with respect to all variables
+                            corresponding to the model this FSDP unit is wrapping
+                            (that are not wrapped by other FSDP sub-units). The latter
+                            represents a pre-sized tensor to store a chunk of a sharded gradient after
+                            reduction.
+                            In both cases, callable performs all necessary processing and returns ``None``.
+                            Callables with signature 1 are expected to handle gradient communication for a `NO_SHARD` case.
+                            Callables with signature 2 are expected to handle gradient communication for sharded cases.
+
+        """
+        if not self.check_is_root():
+            raise AssertionError(
+                "register_comm_hook can only be called on a root instance."
+            )
+        for fsdp_state in traversal_utils._get_fsdp_states(self):
+            if fsdp_state.sharding_strategy in HYBRID_SHARDING_STRATEGIES:
+                raise AssertionError(
+                    f"Communication hook is not supported for hybrid strategies: {fsdp_state.sharding_strategy}"
+                )
+            if fsdp_state._comm_hook is not None:
+                raise AssertionError("A communication hook is already registered")
+            if not callable(hook):
+                raise ValueError(
+                    f"The communication hook must be callable but got {hook}"
+                )
+            fsdp_state._comm_hook = hook
+            fsdp_state._comm_hook_state = state
+
+    def _unshard(self, async_op: bool = False):
+        class UnshardHandle:
+            def __init__(
+                self,
+                flat_param_handle: Optional[FlatParamHandle],
+                unshard_event: torch.Event,
+            ):
+                self._flat_param_handle = flat_param_handle
+                self._unshard_event = unshard_event
+
+            def wait(self):
+                if self._flat_param_handle is not None:
+                    current_stream = (
+                        self._flat_param_handle._device_handle.current_stream()
+                    )
+                    current_stream.wait_event(self._unshard_event)
+                    self._flat_param_handle = None
+
+        if self._handle:
+            with self._use_training_state(
+                TrainingState.FORWARD_BACKWARD, HandleTrainingState.FORWARD
+            ):
+                _unshard(
+                    self, self._handle, self._unshard_stream, self._pre_unshard_stream
+                )
+                self._unshard_event = self._unshard_stream.record_event()
+            self._handle._prefetched = True
+        unshard_handle = UnshardHandle(self._handle, self._unshard_stream)
+        if async_op:
+            return unshard_handle
+        unshard_handle.wait()
+        return None
+
+    def _wait_unshard_streams_on_current_stream(self):
+        _wait_for_computation_stream(
+            self._device_handle.current_stream(),
+            self._unshard_stream,
+            self._pre_unshard_stream,
+        )
+
+    @contextlib.contextmanager
+    def _use_training_state(
+        self, training_state: TrainingState, handle_training_state: HandleTrainingState
+    ):
+        prev_training_state = self.training_state
+        self.training_state = training_state
+        if self._handle:
+            prev_handle_training_state = self._handle._training_state
+            self._handle._training_state = handle_training_state
+        try:
+            yield
+        finally:
+            self.training_state = prev_training_state
+            if self._handle:
+                self._handle._training_state = prev_handle_training_state
+
+
+def _get_grad_norm(
+    params: Iterable[nn.Parameter],
+    norm_type: float,
+    zero: torch.Tensor,
+    device: torch.device,
+) -> torch.Tensor:
+    """
+    Return the gradient norm of parameters ``param`` s, where the gradients are viewed as a single vector.
+
+    The returned norm is in FP32 even if parameters/gradients are in a low precision. This is because the downstream
+    use of this return value is a reduction across ranks.
+    """
+    params_with_grad = [param for param in params if param.grad is not None]
+    if len(params_with_grad) == 0:
+        # Reuse a tensor for zero to avoid a GPU sync
+        return zero
+    grads = [param.grad for param in params_with_grad]
+    grad_dtypes = {grad.dtype for grad in grads}
+    if len(grad_dtypes) != 1:
+        raise ValueError(
+            f"Requires uniform dtype across all gradients but got {grad_dtypes}"
+        )
+    # Compute the gradient norm in FP32, where we treat the gradients as a
+    # single vector
+    grad_norm = torch.linalg.vector_norm(
+        torch.stack(
+            [
+                torch.linalg.vector_norm(grad.detach(), norm_type, dtype=torch.float32)
+                for grad in grads
+            ],
+        ),
+        norm_type,
+        dtype=torch.float32,
+    )
+    return grad_norm.to(device=device)
+
+
+def _get_param_to_fqn(
+    model: torch.nn.Module,
+) -> dict[torch.nn.Parameter, str]:
+    """
+    Construct a mapping from parameters to their parameter names.
+
+    The ``model`` should not contain any :class:`FullyShardedDataParallel` instances, which
+    means that none of the parameters should be ``FlatParameter`` s. As a
+    result, compared to :meth:`_get_param_to_fqns`, the mapped
+    values may be flattened from singleton :class:`list` s to the contained
+    names themselves.
+
+    Args:
+        model (torch.nn.Module): Root module, which should not contain any
+            :class:`FullyShardedDataParallel` instances.
+    """
+    param_to_param_names = _get_param_to_fqns(model)
+    for param_names in param_to_param_names.values():
+        assert len(param_names) > 0, (
+            "`_get_param_to_fqns()` should not construct empty lists"
+        )
+        if len(param_names) > 1:
+            raise RuntimeError(
+                "Each parameter should only map to one parameter name but got "
+                f"{len(param_names)}: {param_names}"
+            )
+    param_to_param_name = {
+        param: param_names[0] for param, param_names in param_to_param_names.items()
+    }
+    return param_to_param_name
+
+
+def _get_fqn_to_param(
+    model: torch.nn.Module,
+) -> dict[str, torch.nn.Parameter]:
+    """Construct the inverse mapping of :meth:`_get_param_to_fqn`."""
+    param_to_param_name = _get_param_to_fqn(model)
+    return dict(zip(param_to_param_name.values(), param_to_param_name.keys()))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1611130c9e03aa520617a36f4b78cc3d249bea7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py
@@ -0,0 +1,359 @@
+# mypy: allow-untyped-defs
+import logging
+from collections import abc, defaultdict
+from collections.abc import Iterable
+from typing import Any, Optional, overload, Union
+
+import torch
+import torch.distributed as dist
+from torch.amp.grad_scaler import _MultiDeviceReplicator, GradScaler, OptState
+from torch.distributed.distributed_c10d import ProcessGroup
+
+
+logger = logging.getLogger(__name__)
+
+
+def _refresh_per_optimizer_state() -> dict[str, Any]:
+    return {"stage": OptState.READY, "found_inf_per_device": {}}
+
+
+def _is_supported_device(tensor: torch.Tensor) -> bool:
+    return tensor.is_cuda or tensor.device.type in (
+        "xla",
+        "cpu",
+        "hpu",
+        "mtia",
+        "xpu",
+        torch._C._get_privateuse1_backend_name(),
+    )
+
+
+class _GeneralMultiDeviceReplicator(_MultiDeviceReplicator):
+    """
+    Lazily serves tensor to request device. This class extends
+    _MultiDeviceReplicator to allow support for "cpu" as a device.
+    """
+
+    def __init__(self, master_tensor: torch.Tensor) -> None:
+        assert _is_supported_device(master_tensor)
+        self.master = master_tensor
+        self._per_device_tensors: dict[torch.device, torch.Tensor] = {}
+
+
+class ShardedGradScaler(GradScaler):
+    """
+    ShardedGradScaler helps perform gradient scaling in a shard aware manner. It extends
+    functionality from GradScaler:
+    * Supports Pytorch DDP and FSDP implementations
+    * Support CPU offloaded tensors (as used in fully sharded data parallel[FSDP])
+    * Supports the custom Mixed Precision loss dtype (fp16, bf16) that FSDP returns
+    * Sync inf/nan for scaled gradient tensors on any torch.device (where tensors are placed) across
+    nodes
+
+    Example::
+
+        # Creates a ShardedGradScaler once at the beginning of training.
+        scaler = ShardedGradScaler()
+
+        for epoch in epochs:
+            for input, target in data:
+                optimizer.zero_grad()
+                output = model(input)
+                loss = loss_fn(output, target)
+
+                # Scales loss.  Calls backward() on scaled loss to create scaled gradients.
+                scaler.scale(loss).backward()
+
+                # scaler.step() first unscales gradients of the optimizer's params.
+                # If gradients don't contain infs/NaNs, optimizer.step() is then called,
+                # otherwise, optimizer.step() is skipped.
+                scaler.step(optimizer)
+
+                # Updates the scale for next iteration.
+                scaler.update()
+
+    See :class:`GradScaler` for explanation of scaling/unscaling and more use cases.
+
+    Args:
+        init_scale (float, optional, default=2.**16):  Initial scale factor.
+        growth_factor (float, optional, default=2.0):  Factor by which the scale is multiplied during
+            :meth:`update` if no inf/NaN gradients occur for ``growth_interval`` consecutive iterations.
+        backoff_factor (float, optional, default=0.5):  Factor by which the scale is multiplied during
+            :meth:`update` if inf/NaN gradients occur in an iteration.
+        growth_interval (int, optional, default=2000):  Number of consecutive iterations without inf/NaN gradients
+            that must occur for the scale to be multiplied by ``growth_factor``.
+        enabled (bool, optional):  If ``False``, disables gradient scaling. :meth:`step` simply
+            invokes the underlying ``optimizer.step()``, and other methods become no-ops.
+            Default: ``True``
+        process_group (ProcessGroup, optional, default=torch.distributed.group.WORLD):
+            process group for sharding
+    """
+
+    def __init__(
+        self,
+        device: str = "cuda",
+        init_scale: float = 2.0**16,
+        backoff_factor: float = 0.5,
+        growth_factor: float = 2.0,
+        growth_interval: int = 2000,
+        enabled: bool = True,
+        process_group: Optional[ProcessGroup] = dist.group.WORLD,
+    ) -> None:
+        super().__init__(
+            device,
+            init_scale=init_scale,
+            backoff_factor=backoff_factor,
+            growth_factor=growth_factor,
+            growth_interval=growth_interval,
+            enabled=enabled,
+        )
+        if self._enabled:
+            self.process_group = process_group
+            self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
+
+    @overload
+    def scale(self, outputs: torch.Tensor) -> torch.Tensor: ...
+
+    @overload
+    def scale(self, outputs: list[torch.Tensor]) -> list[torch.Tensor]: ...
+
+    @overload
+    def scale(self, outputs: tuple[torch.Tensor, ...]) -> tuple[torch.Tensor, ...]: ...
+
+    @overload
+    def scale(self, outputs: Iterable[torch.Tensor]) -> Iterable[torch.Tensor]: ...
+
+    def scale(
+        self, outputs: Union[torch.Tensor, Iterable[torch.Tensor]]
+    ) -> Union[torch.Tensor, Iterable[torch.Tensor]]:
+        if not self._enabled:
+            return outputs
+
+        if isinstance(outputs, torch.Tensor):
+            assert _is_supported_device(outputs)
+            if self._scale is None:
+                self._lazy_init_scale_growth_tracker(outputs.device)
+            assert self._scale is not None
+            scaled_output = outputs * self._scale.to(
+                device=outputs.device, non_blocking=True
+            )
+            # Here we ensure the return dtype is the same as the outputs dtype.
+            # For the FSDP + Mixed Precision use case, the loss output is in the Mixed Precision
+            # format (fp16, bf16) and so the scaled loss should be of the same dtype.
+            return scaled_output.type(outputs.dtype)
+
+        stash: list[_GeneralMultiDeviceReplicator] = []
+
+        def apply_scale(val: Union[torch.Tensor, Iterable[torch.Tensor]]):
+            if isinstance(val, torch.Tensor):
+                assert _is_supported_device(val)
+                if len(stash) == 0:
+                    if self._scale is None:
+                        self._lazy_init_scale_growth_tracker(val.device)
+                    assert self._scale is not None
+                    stash.append(_GeneralMultiDeviceReplicator(self._scale))
+                scaled_val = val * stash[0].get(val.device)
+                # Here we ensure the return dtype is the same as the outputs dtype.
+                # For the FSDP + Mixed Precision use case, the loss output is in the Mixed Precision
+                # format (fp16, bf16) and so the scaled loss should be of the same dtype.
+                return scaled_val.type(val.dtype)
+            if isinstance(val, abc.Iterable):
+                iterator = map(apply_scale, val)
+                if isinstance(val, (list, tuple)):
+                    return type(val)(iterator)
+                return iterator
+            raise ValueError("outputs must be a Tensor or an iterable of Tensors")
+
+        return apply_scale(outputs)
+
+    def _unscale_grads_(
+        self,
+        optimizer: torch.optim.Optimizer,
+        inv_scale: torch.Tensor,
+        found_inf: torch.Tensor,
+        allow_fp16: bool = True,
+    ) -> dict[torch.device, torch.Tensor]:
+        per_device_inv_scale = _GeneralMultiDeviceReplicator(inv_scale)
+        per_device_found_inf = _GeneralMultiDeviceReplicator(found_inf)
+
+        # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype.
+        # There could be thousands of grads, so we'd like to iterate through them just once.
+        # However, we don't know their devices or dtypes in advance.
+
+        # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict
+        # Google says mypy struggles with defaultdicts type annotations.
+        per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))  # type: ignore[var-annotated]
+        with torch.no_grad():
+            for group in optimizer.param_groups:
+                for param in group["params"]:
+                    if param.grad is None:
+                        continue
+                    if (not allow_fp16) and param.grad.dtype == torch.float16:
+                        raise ValueError("Attempting to unscale FP16 gradients.")
+                    if param.grad.is_sparse:
+                        # is_coalesced() == False means the sparse grad has values with duplicate indices.
+                        # coalesce() deduplicates indices and adds all values that have the same index.
+                        # For scaled fp16 values, there's a good chance coalescing will cause overflow,
+                        # so we should check the coalesced _values().
+                        if param.grad.dtype is torch.float16:
+                            # coalesce is not supported in torch.float16
+                            param_grad_fp32 = param.grad.type(torch.float32).coalesce()
+                            param.grad = param_grad_fp32.type(torch.float16)
+                        to_unscale = param.grad._values()
+                    else:
+                        to_unscale = param.grad
+
+                    per_device_and_dtype_grads[to_unscale.device][
+                        to_unscale.dtype
+                    ].append(to_unscale)
+
+            for device, per_dtype_grads in per_device_and_dtype_grads.items():
+                for grads in per_dtype_grads.values():
+                    torch._amp_foreach_non_finite_check_and_unscale_(
+                        grads,
+                        per_device_found_inf.get(device),
+                        per_device_inv_scale.get(device),
+                    )
+        # There exist contexts (e.g. w/ `use_orig_params=True`) wherein some
+        # ranks may have no (non-zero sized) parameter shards, necessitating the
+        # initialization of `per_device_found_inf._per_device_tensors` here
+        if not per_device_found_inf._per_device_tensors:
+            assert self._scale is not None
+            per_device_found_inf.get(self._scale.device)
+        return per_device_found_inf._per_device_tensors
+
+    def unscale_(self, optimizer: torch.optim.Optimizer) -> None:
+        if not self._enabled:
+            return
+
+        self._check_scale_growth_tracker("unscale_")
+
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+        if optimizer_state["stage"] is OptState.UNSCALED:
+            raise RuntimeError(
+                "unscale_() has already been called on this optimizer since the last update()."
+            )
+        elif optimizer_state["stage"] is OptState.STEPPED:
+            raise RuntimeError("unscale_() is being called after step().")
+
+        # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
+        assert self._scale is not None
+        inv_scale = self._scale.double().reciprocal().float()
+        found_inf = torch.full(
+            (1,), 0.0, dtype=torch.float32, device=self._scale.device
+        )
+
+        optimizer_state["found_inf_per_device"] = self._unscale_grads_(
+            optimizer, inv_scale, found_inf, True
+        )
+        optimizer_state["stage"] = OptState.UNSCALED
+
+        # Synchronize the detected inf across the ranks
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+        works = []
+        found_inf_on_cpus = []
+        found_inf_on_devices = []
+
+        for found_inf in optimizer_state["found_inf_per_device"].values():
+            if self._device != "cpu" and found_inf.device.type == "cpu":
+                found_inf_on_cpus.append(found_inf)
+                found_inf_on_device = found_inf.to(self._device)
+                found_inf_on_devices.append(found_inf_on_device)
+                works.append(
+                    dist.all_reduce(
+                        found_inf_on_device, async_op=True, group=self.process_group
+                    )
+                )
+            else:
+                works.append(
+                    dist.all_reduce(found_inf, async_op=True, group=self.process_group)
+                )
+        for work in works:
+            work.wait()
+        if found_inf_on_cpus:
+            torch._foreach_copy_(found_inf_on_cpus, found_inf_on_devices)
+
+    def _amp_update_scale_cpu_(self, found_inf: torch.Tensor) -> None:
+        """
+        If found_inf is 1.0 (True), then scale is multiplied by backoff_factor and growth_tracker is set to zero.
+        Otherwise, scale is multiplied by the growth factor when the growth interval is reached.
+        """
+        assert self._scale is not None and self._growth_tracker is not None
+
+        if found_inf.item() >= 1.0:
+            self._scale *= self._backoff_factor
+            self._growth_tracker.fill_(0)
+        else:
+            successful = self._growth_tracker + 1
+            if successful == self._growth_interval:
+                self._scale *= self._growth_factor
+                self._growth_tracker.fill_(0)
+            else:
+                self._growth_tracker = successful
+
+    def update(self, new_scale: Optional[Union[float, torch.Tensor]] = None) -> None:
+        """
+        Updates the scale factor.
+        If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
+        to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
+        the scale is multiplied by ``growth_factor`` to increase it.
+        Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
+        used directly, it's used to fill GradScaler's internal scale tensor. So if
+        ``new_scale`` was a tensor, later in-place changes to that tensor will not further
+        affect the scale GradScaler uses internally.)
+        Args:
+            new_scale (float or :class:`torch.Tensor`, optional, default=None):  New scale factor.
+        .. warning::
+            :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
+            been invoked for all optimizers used this iteration.
+        """
+
+        if not self._enabled:
+            return
+
+        _scale, _growth_tracker = self._check_scale_growth_tracker("update")  # type: ignore[var-annotated]
+
+        if new_scale is not None:
+            # Accept a new user-defined scale.
+            if isinstance(new_scale, float):
+                self._scale.fill_(new_scale)  # type: ignore[union-attr]
+            else:
+                reason = (
+                    "new_scale should be a float or a 1-element torch.cuda.FloatTensor or \
+                    torch.FloatTensor with requires_grad=False."
+                )
+                assert new_scale.device.type == self._device, reason
+                assert new_scale.numel() == 1, reason
+                assert new_scale.requires_grad is False, reason
+                self._scale.copy_(new_scale)  # type: ignore[union-attr]
+        else:
+            # Consume shared inf/nan data collected from optimizers to update the scale.
+            # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous.
+            found_infs = [
+                found_inf.to(device=_scale.device, non_blocking=True)
+                for state in self._per_optimizer_states.values()
+                for found_inf in state["found_inf_per_device"].values()
+            ]
+
+            assert len(found_infs) > 0, "No inf checks were recorded prior to update."
+
+            found_inf_combined = found_infs[0]
+            if len(found_infs) > 1:
+                for i in range(1, len(found_infs)):
+                    found_inf_combined += found_infs[i]
+
+            if _scale.device.type == "cpu":
+                self._amp_update_scale_cpu_(found_inf_combined)
+            else:
+                torch._amp_update_scale_(
+                    self._scale,  # type: ignore[arg-type]
+                    self._growth_tracker,  # type: ignore[arg-type]
+                    found_inf_combined,
+                    self._growth_factor,  # type: ignore[arg-type]
+                    self._backoff_factor,  # type: ignore[arg-type]
+                    self._growth_interval,  # type: ignore[arg-type]
+                )
+
+        # To prepare for next iteration, clear the data collected from optimizers this iteration.
+        self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/wrap.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/wrap.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad1bfef5a4ff798985d30658006e0c386319a3e0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/fsdp/wrap.py
@@ -0,0 +1,596 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+import contextlib
+import copy
+from abc import ABC, abstractmethod
+from collections.abc import Generator, Iterable, Sequence
+from typing import Any, Callable, cast, Optional, Union
+
+import torch.nn as nn
+
+
+__all__ = [
+    "always_wrap_policy",
+    "lambda_auto_wrap_policy",
+    "transformer_auto_wrap_policy",
+    "size_based_auto_wrap_policy",
+    "enable_wrap",
+    "wrap",
+    "CustomPolicy",
+    "ModuleWrapPolicy",
+]
+
+
+# NOTE: We intentionally keep this function simple and isolate the complexity
+# to `fn` to enable using this function generically. We may move this to a
+# non-FSDP-specific folder and/or make it public in the future.
+def _post_order_apply(
+    root_module: nn.Module,
+    fn: Callable[[nn.Module], Optional[nn.Module]],
+):
+    """
+    This applies ``fn`` to every module in the module tree of ``root_module``
+    following a post-order traversal. If ``fn`` returns an :class:`nn.Module`,
+    then this replaces the original module with the newly returned one in the
+    tree. Otherwise, ``fn`` should return ``None``, in which case the module is
+    not changed.
+    """
+    # Track visited modules to avoid visiting shared modules multiple times
+    visited_modules: set[nn.Module] = {root_module}
+
+    def _post_order_apply_inner(
+        module: nn.Module,
+        module_name: str,
+        parent_module: Optional[nn.Module],
+    ):
+        for child_module_name, child_module in module.named_children():
+            if child_module not in visited_modules:
+                visited_modules.add(child_module)
+                _post_order_apply_inner(child_module, child_module_name, module)
+        optional_module = fn(module)
+        if optional_module is not None:
+            assert isinstance(parent_module, nn.Module), (
+                "Non-root modules should have their parent module set but got "
+                f"{parent_module} for {module}"
+            )
+            assert module_name, (
+                "Non-root modules should have their module name set but got "
+                f"an empty module name for {module}"
+            )
+            assert isinstance(optional_module, nn.Module), (
+                f"fn should return None or an nn.Module but got {optional_module}"
+            )
+            setattr(parent_module, module_name, optional_module)
+
+    _post_order_apply_inner(root_module, "", None)
+
+
+def _construct_wrap_fn(
+    root_module: nn.Module,
+    target_module_to_kwargs: dict[nn.Module, dict[str, Any]],
+    fsdp_fn: Callable,
+) -> Callable[[nn.Module], Optional[nn.Module]]:
+    """
+    This constructs the "wrap" function to pass to :func:`_post_order_apply`
+    based on ``target_module_to_kwargs``, which should be constructed from the
+    wrapping policy.
+    """
+
+    def fn(module: nn.Module) -> Optional[nn.Module]:
+        # Explicitly avoid wrapping the root module since for FSDP, it is
+        # handled by the caller
+        if module in target_module_to_kwargs and module is not root_module:
+            kwargs = target_module_to_kwargs[module]
+            return fsdp_fn(module, **kwargs)
+        return None
+
+    return fn
+
+
+def _run_mixed_precision_override_policy(
+    root_module: nn.Module,
+    module_classes: Iterable[type[nn.Module]],
+    ignored_modules: set[nn.Module],
+    root_kwargs: dict[str, Any],
+    target_module_to_kwargs: dict[nn.Module, dict[str, Any]],
+):
+    module_classes_tuple = tuple(set(module_classes))
+    for module in root_module.modules():
+        if module in ignored_modules:
+            continue
+        elif isinstance(module, module_classes_tuple):
+            # This policy overrides any existing policy
+            if module not in target_module_to_kwargs:
+                # Only inherit from the root kwargs if not already specified
+                target_module_to_kwargs[module] = root_kwargs
+            target_module_to_kwargs[module]["mixed_precision"] = None
+    return target_module_to_kwargs
+
+
+def always_wrap_policy(*args, **kwargs) -> bool:
+    """
+    A simple recursive wrap policy that always returns ``True``. This means
+    that every submodule is wrapped by the wrapper class in
+    :func:`_recursive_wrap`.
+    """
+    return True
+
+
+class _Policy(ABC):
+    """
+    This defines an abstract base class that represents a policy for applying
+    a module-level API.
+    """
+
+    @abstractmethod
+    def _run_policy(
+        self,
+        root_module: nn.Module,
+        ignored_modules: set[nn.Module],
+        root_kwargs: dict[str, Any],
+    ) -> dict[nn.Module, dict[str, Any]]:
+        """
+        This should return a dict ``target_module_to_kwargs`` that maps from
+        each target module to wrap to its kwargs.
+        """
+        ...
+
+
+def _module_wrap_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    module_classes: set[type[nn.Module]],
+) -> bool:
+    """
+    This auto wrap policy wraps every module that is an instance of any type in
+    ``module_classes`` as its own FSDP instance. The root module given by
+    ``module`` is always wrapped as an FSDP instance regardless. Since the
+    wrapping proceeds bottom up, each FSDP instance manages the parameters in
+    its subtree excluding any already managed by a child FSDP instance.
+
+    Args:
+        module (nn.Module): Current module being considered.
+        recurse (bool): If ``False``, then this function must decide whether
+            ``module`` should be wrapped as an FSDP instance or not. If
+            ``True``, then the function is still recursing down the module
+            tree as a part of the DFS.
+        nonwrapped_numel (int): Parameter numel not yet wrapped.
+        module_classes (Set[Type[nn.Module]]): Set of module classes that are
+            wrapped as FSDP instances.
+
+    Returns:
+        ``True`` if ``recurse=True``, and whether ``module`` should be wrapped
+        if ``recurse=False``.
+    """
+    if recurse:
+        return True  # always recurse
+    return isinstance(module, tuple(module_classes))
+
+
+class ModuleWrapPolicy(_Policy):
+    """
+    This policy applies to every module of the specified module classes,
+    passing in the kwargs given to the root.
+    """
+
+    def __init__(self, module_classes: Iterable[type[nn.Module]]):
+        module_classes_set = set(module_classes)
+        self._module_classes = module_classes_set
+        self._module_classes_str = str(module_classes_set)
+
+    def _run_policy(
+        self,
+        root_module: nn.Module,
+        ignored_modules: set[nn.Module],
+        root_kwargs: dict[str, Any],
+    ) -> dict[nn.Module, dict[str, Any]]:
+        module_classes = tuple(self._module_classes)
+        target_module_to_kwargs: dict[nn.Module, dict[str, Any]] = {}
+        for module in root_module.modules():
+            if module in ignored_modules:
+                continue
+            elif isinstance(module, module_classes):
+                # Shallow copy to avoid coupling changes across modules
+                target_module_to_kwargs[module] = copy.copy(root_kwargs)
+        return target_module_to_kwargs
+
+    def __call__(self, module, recurse, *args, **kwargs):
+        # nonwrapped_numel is not used.
+        return _module_wrap_policy(
+            module, recurse, nonwrapped_numel=-1, module_classes=self._module_classes
+        )
+
+    def __repr__(self) -> str:
+        return super().__repr__() + f"({self._module_classes_str})"
+
+
+class CustomPolicy(_Policy):
+    """
+    This policy takes in a lambda function that maps a given ``nn.Module`` to
+    either ``False``, ``True``, or a kwarg dictionary.
+    - If the function returns ``False`` or an empty dictionary, then the module
+      does not have the API applied.
+    - If the function returns ``True``, then the module has the API applied
+      with the root's kwargs.
+    - If the function returns a non-empty dictionary, then the module has the
+      API applied, and the dictionary overrides the root's kwargs.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> model = init_transformer_model(...)
+        >>> def lambda_fn(module: nn.Module):
+        >>>     if module is model.lm_head:
+        >>>         return {"sharding_strategy": ShardingStrategy.SHARD_GRAD_OP}
+        >>>     elif isinstance(module, TransformerBlock):
+        >>>         return True
+        >>>     return False
+        >>> policy = CustomPolicy(lambda_fn)
+        >>> fsdp_model = FSDP(model, auto_wrap_policy=policy)
+    """
+
+    def __init__(self, lambda_fn: Callable[[nn.Module], Union[bool, dict[str, Any]]]):
+        self._lambda_fn = lambda_fn
+
+    def _run_policy(
+        self,
+        root_module: nn.Module,
+        ignored_modules: set[nn.Module],
+        root_kwargs: dict[str, Any],
+    ) -> dict[nn.Module, dict[str, Any]]:
+        target_module_to_kwargs: dict[nn.Module, dict[str, Any]] = {}
+        for module in root_module.modules():
+            if module in ignored_modules:
+                continue
+            res = self._lambda_fn(module)
+            if not isinstance(res, (dict, bool)):
+                raise ValueError(
+                    "The lambda_fn passed to CustomPolicy should return "
+                    f"False/True or a kwarg dict, but it returned {res}"
+                )
+            if not res:
+                continue
+            kwargs = copy.copy(root_kwargs)
+            if isinstance(res, dict):
+                # Override the root kwargs with the ones specified by the
+                # lambda function
+                kwargs.update(res)
+            target_module_to_kwargs[module] = kwargs
+        return target_module_to_kwargs
+
+
+def lambda_auto_wrap_policy(
+    module: nn.Module, recurse: bool, nonwrapped_numel: int, lambda_fn: Callable
+) -> bool:
+    """
+    A convenient auto wrap policy to wrap submodules based on an arbitrary user
+    function. If `lambda_fn(submodule) == True``, the submodule will be wrapped as
+    a `wrapper_cls` unit.
+
+    Return if a module should be wrapped during auto wrapping.
+
+    The first three parameters are required by :func:`_recursive_wrap`.
+
+    Args:
+        module (nn.Module): Current module being considered.
+        recurse (bool): If ``False``, then this function must decide whether
+            ``module`` should be wrapped as an FSDP instance or not. If
+            ``True``, then the function is still recursing down the module
+            tree as a part of the DFS.
+        nonwrapped_numel (int): Parameter numel not yet wrapped.
+
+        lambda_fn (Callable[[nn.Module], bool]): If this returns ``True``, then
+            this module will be wrapped.
+    """
+    if recurse:
+        return True  # always recurse
+    return lambda_fn(module)
+
+
+def transformer_auto_wrap_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    transformer_layer_cls: set[type[nn.Module]],
+) -> bool:
+    """
+    See :func:`_module_wrap_policy`, where ``transformer_layer_cls`` is the
+    same as ``module_classes``. Note that shared parameters must be wrapped in
+    the same FSDP instance, so this auto wrap policy can help wrap shared
+    embeddings into the same FSDP instance for transformer models.
+    """
+    return _module_wrap_policy(module, recurse, nonwrapped_numel, transformer_layer_cls)
+
+
+def _wrap_module_cls_individually(
+    module: nn.Module, module_classes: Sequence[type], recurse: bool, *args, **kwargs
+):
+    if recurse:
+        # always recurse
+        return True
+    else:
+        # if not recursing, decide whether we should wrap based on whether the type of module
+        # is in `module_classes`.
+        return isinstance(module, tuple(module_classes))
+
+
+def _or_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    policies,
+) -> bool:
+    """
+    A policy that wraps ``module`` if any policy in the passed in iterable of
+    ``policies`` returns ``True``.
+    """
+    return any(
+        policy(module=module, recurse=recurse, nonwrapped_numel=nonwrapped_numel)
+        for policy in policies
+    )
+
+
+def size_based_auto_wrap_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    # Additional custom arguments
+    min_num_params: int = int(1e8),
+    force_leaf_modules: Optional[set[type[nn.Module]]] = None,
+    exclude_wrap_modules: Optional[set[type[nn.Module]]] = None,
+) -> bool:
+    """
+    A size-based auto wrap policy.
+
+    Args:
+        module (nn.Module): Current module being considered.
+        recurse (bool): If ``False``, then this function must decide whether
+            ``module`` should be wrapped as an FSDP instance or not. If
+            ``True``, then the function is still recursing down the module
+            tree as a part of the DFS.
+        nonwrapped_numel (int): Parameter numel not yet wrapped.
+
+        min_num_params (int): Customizable policy input that controls the size
+            threshold over which a module is ready to be wrapped. This is in
+            units of numel.
+        force_leaf_modules (Optional[set[type[nn.Module]]]): Set of module types to keep
+            as leaves, i.e. their children will never be wrapped.
+        exclude_wrap_modules (Optional[set[type[nn.Module]]]): Set of module types to be
+            excluded in wrapping.
+
+    Returns:
+        Whether ``module`` should be wrapped.
+    """
+    force_leaf_modules = (
+        size_based_auto_wrap_policy.FORCE_LEAF_MODULES  # type: ignore[attr-defined]
+        if force_leaf_modules is None
+        else force_leaf_modules
+    )
+    exclude_wrap_modules = (
+        size_based_auto_wrap_policy.EXCLUDE_WRAP_MODULES  # type: ignore[attr-defined]
+        if exclude_wrap_modules is None
+        else exclude_wrap_modules
+    )
+
+    # Keep the argument `min_num_params` for BC for now, but it represents the
+    # minimum non-wrapped *numel* before triggering a wrapping
+    min_nonwrapped_numel = min_num_params
+    is_large = nonwrapped_numel >= min_nonwrapped_numel
+    if recurse:
+        # We should recurse if the module is big enough but not in force_leaf_modules list.
+        return is_large and not isinstance(module, tuple(force_leaf_modules))
+    else:
+        # If we are not recursing, determine if we should wrap.
+        return is_large and not isinstance(module, tuple(exclude_wrap_modules))
+
+
+# Set those defaults to the size_based_auto_wrap_policy function. Make them easy to be imported.
+size_based_auto_wrap_policy.EXCLUDE_WRAP_MODULES = {nn.ModuleList, nn.ModuleDict}  # type: ignore[attr-defined]
+size_based_auto_wrap_policy.FORCE_LEAF_MODULES = {nn.MultiheadAttention}  # type: ignore[attr-defined]
+
+
+@contextlib.contextmanager
+def enable_wrap(
+    *, wrapper_cls: Any, **wrapper_kwargs: Any
+) -> Generator[None, None, None]:
+    """
+    Context manager to wrap modules using a wrapper.
+
+    Useful for when you'd like to apply the same configuration arguments to all
+    child modules that you wrap. A particularly important use case is wrapping
+    large layers so that they get sharded (in-place) during initialization, to
+    avoid running out of system memory. Large layers can indicate that they
+    should be sharded via the ``wrap`` annotation and this context manager can
+    provide the exact configuration for these nested instances.
+
+    Usage::
+
+        with enable_wrap(wrapper_cls, **params):
+            # Wraps layer in FSDP by default if within context
+            self.l1 = wrap(torch.nn.Linear(5, 5))
+
+    Args:
+        wrapper_cls:
+            Class that `wrap` annotation will `wrap` modules with, such as
+            `FullyShardedDataParallel`.
+        **wrapper_kwargs:
+            Configuration settings that will be passed to all ``wrap``
+            instances inside the context
+    """
+    kwargs = {
+        "wrapper_cls": wrapper_cls,
+        **wrapper_kwargs,
+    }
+    with _ConfigAutoWrap(**kwargs):
+        yield
+
+
+def wrap(module: nn.Module, **wrap_overrides: Any) -> nn.Module:
+    """
+    Annotate that a module should be wrapped. Annotated modules will only be
+    wrapped if inside of an :func:`enable_wrap` context manager. This allows
+    a module to be initialized both with and without a wrapper without code
+    change.
+
+    The class that this function wraps the passed in ``nn.Module`` with is the
+    passed in ``wrapper_cls`` argument into ``enable_wrap``. Both
+    ``enable_wrap`` and ``wrap`` can take in kwargs specifying how to construct
+    the ``wrapper_cls`` instance. In the case of duplicate kwargs in
+    ``enable_wrap`` and ``wrap``, the argument passed into ``wrap`` will be
+    respected.
+
+    Usage::
+
+        with enable_wrap(wrapper_cls=FSDP, **fsdp_config):
+            # Wraps layer in FSDP by default if within context
+            self.l1 = wrap(torch.nn.Linear(5, 5))
+
+    Args:
+        module (nn.Module): module to wrap (if in :func:`enable_wrap` context)
+        **wrap_overrides: configuration overrides that will take priority over
+            the values provided by the :func:`enable_wrap` context
+    """
+    if _ConfigAutoWrap.in_autowrap_context:
+        assert _ConfigAutoWrap.wrapper_cls is not None
+
+        wrap_overrides = {**_ConfigAutoWrap.kwargs, **wrap_overrides}
+        return _wrap(
+            module,
+            _ConfigAutoWrap.wrapper_cls,
+            **wrap_overrides,
+        )
+    return module
+
+
+def _wrap(module: nn.Module, wrapper_cls: Callable, **kwargs) -> nn.Module:
+    assert wrapper_cls is not None
+    if hasattr(module, "_wrap_overrides"):
+        # If module has a _wrap_overrides attribute, we force overriding the
+        # FSDP config with these attributes for this module. Currently this
+        # is only used to disable mixed precision for BatchNorm when
+        # auto_wrapping.
+        overrides = {**kwargs, **module._wrap_overrides}  # type: ignore[arg-type, dict-item]
+        return wrapper_cls(module, **overrides)
+
+    return wrapper_cls(module, **kwargs)
+
+
+def _recursive_wrap(
+    module: nn.Module,
+    auto_wrap_policy: Callable,
+    wrapper_cls: Callable,
+    ignored_modules: set[nn.Module],
+    ignored_params: set[nn.Parameter],
+    only_wrap_children: bool = False,
+    **kwargs: Any,
+) -> tuple[nn.Module, int]:
+    """
+    Wraps submodules of ``module`` for which ``auto_wrap_policy`` returns
+    ``True`` with ``wrapper_cls``.
+
+    Args:
+        module (nn.Module): Module to recursively wrap.
+        auto_wrap_policy (Callable): A callable representing a policy that
+            determines which modules to recursively wrap with ``wrapper_cls``.
+        ignored_modules (set[torch.nn.Module]): Modules to ignore when
+            wrapping.
+        ignored_params (set[torch.nn.Parameter]): Parameters to ignore when
+            wrapping; these should be the parameters contained in the modules
+            in ``ignored_modules``.
+    Returns:
+        (nn.Module, int):
+            ``module`` after wrapping and the numel recursively wrapped.
+    """
+    assert auto_wrap_policy is not None, "Must specify auto_wrap_policy."
+    assert wrapper_cls is not None, "Must specify wrapper_cls"
+    # Make sure no child is already wrapped.
+    for _, child in module.named_modules():
+        if child in ignored_modules:
+            continue
+        try:
+            assert not isinstance(child, cast(type, wrapper_cls))
+        except TypeError:
+            # wrapper_cls is a function as opposed to a class type, just bypass above check.
+            pass
+
+    # We count all params, assuming none of them are already wrapped.
+    nonwrapped_numel = sum(
+        p.numel() for p in module.parameters() if p not in ignored_params
+    )
+
+    assert auto_wrap_policy is not None
+    if auto_wrap_policy(module=module, recurse=True, nonwrapped_numel=nonwrapped_numel):
+        total_wrapped_numel = 0
+        # Iterate through the children, recursively wrap if necessary
+        for name, child in module.named_children():
+            if child in ignored_modules:
+                continue
+            wrapped_child, num_wrapped_params = _recursive_wrap(
+                module=child,
+                auto_wrap_policy=auto_wrap_policy,
+                wrapper_cls=wrapper_cls,
+                ignored_modules=ignored_modules,
+                ignored_params=ignored_params,
+                **kwargs,
+            )
+            setattr(module, name, wrapped_child)
+            # Keep track of how many parameters have been wrapped
+            total_wrapped_numel += num_wrapped_params
+        # decide if we need to wrap the current module,
+        # since the left over parameters exceed the number of params to wrap
+        remainder = nonwrapped_numel - total_wrapped_numel
+        if not only_wrap_children and auto_wrap_policy(
+            module=module, recurse=False, nonwrapped_numel=remainder
+        ):
+            # Leaf node or final wrapping of the remainder both happen here.
+            return _wrap(module, wrapper_cls, **kwargs), nonwrapped_numel
+        else:
+            return module, total_wrapped_numel
+    return module, 0
+
+
+class _ConfigAutoWrap:
+    """
+    Helper class to wrap modules based on default config args via a context manager.
+    See :func:`enable_wrap` for more information.
+    """
+
+    in_autowrap_context: bool = False  # Context flag
+    wrapper_cls: Optional[Callable] = None  # The wrapper class
+    kwargs: dict[str, Any] = {}  # Wrapper's args
+
+    def __init__(self, **kwargs: dict[str, Any]):
+        self.kwargs = kwargs
+
+    @staticmethod
+    def enable_autowrap_context(kwargs: Any) -> None:
+        if _ConfigAutoWrap.in_autowrap_context:
+            raise NotImplementedError(
+                "You are already within an autowrap context and we currently do not supported nested autowrap."
+            )
+        _ConfigAutoWrap.in_autowrap_context = True
+        # Get and save the wrapper cls for the context.
+        assert "wrapper_cls" in kwargs.keys(), (
+            "Expected to pass in wrapper_cls arg into _ConfigAutoWrap."
+        )
+        _ConfigAutoWrap.wrapper_cls = cast(Callable, kwargs["wrapper_cls"])
+        del kwargs["wrapper_cls"]
+        # Save the rest.
+        _ConfigAutoWrap.kwargs = kwargs
+
+    @staticmethod
+    def disable_autowrap_context() -> None:
+        _ConfigAutoWrap.in_autowrap_context = False
+        _ConfigAutoWrap.wrapper_cls = None
+        _ConfigAutoWrap.kwargs = {}
+
+    def __enter__(self) -> None:
+        self.enable_autowrap_context(self.kwargs)
+
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
+        self.disable_autowrap_context()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/launch.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/launch.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad3307c13303d0319af710923669d119b4cff30c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/launch.py
@@ -0,0 +1,207 @@
+# mypy: allow-untyped-defs
+r"""
+Module ``torch.distributed.launch``.
+
+``torch.distributed.launch`` is a module that spawns up multiple distributed
+training processes on each of the training nodes.
+
+.. warning::
+
+    This module is going to be deprecated in favor of :ref:`torchrun `.
+
+The utility can be used for single-node distributed training, in which one or
+more processes per node will be spawned. The utility can be used for either
+CPU training or GPU training. If the utility is used for GPU training,
+each distributed process will be operating on a single GPU. This can achieve
+well-improved single-node training performance. It can also be used in
+multi-node distributed training, by spawning up multiple processes on each node
+for well-improved multi-node distributed training performance as well.
+This will especially be beneficial for systems with multiple Infiniband
+interfaces that have direct-GPU support, since all of them can be utilized for
+aggregated communication bandwidth.
+
+In both cases of single-node distributed training or multi-node distributed
+training, this utility will launch the given number of processes per node
+(``--nproc-per-node``). If used for GPU training, this number needs to be less
+or equal to the number of GPUs on the current system (``nproc_per_node``),
+and each process will be operating on a single GPU from *GPU 0 to
+GPU (nproc_per_node - 1)*.
+
+**How to use this module:**
+
+1. Single-Node multi-process distributed training
+
+::
+
+    python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
+               YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
+               arguments of your training script)
+
+2. Multi-Node multi-process distributed training: (e.g. two nodes)
+
+
+Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
+
+::
+
+    python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
+               --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
+               --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
+               and all other arguments of your training script)
+
+Node 2:
+
+::
+
+    python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
+               --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
+               --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
+               and all other arguments of your training script)
+
+3. To look up what optional arguments this module offers:
+
+::
+
+    python -m torch.distributed.launch --help
+
+
+**Important Notices:**
+
+1. This utility and multi-process distributed (single-node or
+multi-node) GPU training currently only achieves the best performance using
+the NCCL distributed backend. Thus NCCL backend is the recommended backend to
+use for GPU training.
+
+2. In your training program, you must parse the command-line argument:
+``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
+If your training program uses GPUs, you should ensure that your code only
+runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
+
+Parsing the local_rank argument
+
+::
+
+    >>> # xdoctest: +SKIP
+    >>> import argparse
+    >>> parser = argparse.ArgumentParser()
+    >>> parser.add_argument("--local-rank", "--local_rank", type=int)
+    >>> args = parser.parse_args()
+
+Set your device to local rank using either
+
+::
+
+    >>> torch.cuda.set_device(args.local_rank)  # before your code runs
+
+or
+
+::
+
+    >>> with torch.cuda.device(args.local_rank):
+    >>>    # your code to run
+    >>>    ...
+
+.. versionchanged:: 2.0.0
+
+    The launcher will passes the ``--local-rank=`` argument to your script.
+    From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
+    previously used underscored ``--local_rank``.
+
+    For backward compatibility, it may be necessary for users to handle both
+    cases in their argument parsing code. This means including both ``"--local-rank"``
+    and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
+    provided, the launcher will trigger an error: "error: unrecognized arguments:
+    --local-rank=". For training code that only supports PyTorch 2.0.0+,
+    including ``"--local-rank"`` should be sufficient.
+
+3. In your training program, you are supposed to call the following function
+at the beginning to start the distributed backend. It is strongly recommended
+that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
+but ``env://`` is the one that is officially supported by this module.
+
+::
+
+    >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
+    >>>                                      init_method='env://')
+
+4. In your training program, you can either use regular distributed functions
+or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
+training program uses GPUs for training and you would like to use
+:func:`torch.nn.parallel.DistributedDataParallel` module,
+here is how to configure it.
+
+::
+
+    >>> model = torch.nn.parallel.DistributedDataParallel(model,
+    >>>                                                   device_ids=[args.local_rank],
+    >>>                                                   output_device=args.local_rank)
+
+Please ensure that ``device_ids`` argument is set to be the only GPU device id
+that your code will be operating on. This is generally the local rank of the
+process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
+and ``output_device`` needs to be ``args.local_rank`` in order to use this
+utility
+
+5. Another way to pass ``local_rank`` to the subprocesses via environment variable
+``LOCAL_RANK``. This behavior is enabled when you launch the script with
+``--use-env=True``. You must adjust the subprocess example above to replace
+``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
+will not pass ``--local-rank`` when you specify this flag.
+
+.. warning::
+
+    ``local_rank`` is NOT globally unique: it is only unique per process
+    on a machine.  Thus, don't use it to decide if you should, e.g.,
+    write to a networked filesystem.  See
+    https://github.com/pytorch/pytorch/issues/12042 for an example of
+    how things can go wrong if you don't do this correctly.
+
+
+
+"""
+
+from typing_extensions import deprecated as _deprecated
+
+from torch.distributed.run import get_args_parser, run
+
+
+def parse_args(args):
+    parser = get_args_parser()
+    parser.add_argument(
+        "--use-env",
+        "--use_env",
+        default=False,
+        action="store_true",
+        help="Use environment variable to pass "
+        "'local rank'. For legacy reasons, the default value is False. "
+        "If set to True, the script will not pass "
+        "--local-rank as argument, and will instead set LOCAL_RANK.",
+    )
+    return parser.parse_args(args)
+
+
+def launch(args):
+    if args.no_python and not args.use_env:
+        raise ValueError(
+            "When using the '--no-python' flag, you must also set the '--use-env' flag."
+        )
+    run(args)
+
+
+@_deprecated(
+    "The module torch.distributed.launch is deprecated\n"
+    "and will be removed in future. Use torchrun.\n"
+    "Note that --use-env is set by default in torchrun.\n"
+    "If your script expects `--local-rank` argument to be set, please\n"
+    "change it to read from `os.environ['LOCAL_RANK']` instead. See \n"
+    "https://pytorch.org/docs/stable/distributed.html#launch-utility for \n"
+    "further instructions\n",
+    category=FutureWarning,
+)
+def main(args=None):
+    args = parse_args(args)
+    launch(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/launcher/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/launcher/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb744a2b93615b703eb0dafb7c8e6c71bc1ad5d2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/launcher/__init__.py
@@ -0,0 +1,14 @@
+#!/usr/bin/env/python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+from torch.distributed.launcher.api import (  # noqa: F401
+    elastic_launch,
+    launch_agent,
+    LaunchConfig,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/launcher/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/launcher/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8e2017e7e1511517a4a89e8e33fae832ec6592a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/launcher/api.py
@@ -0,0 +1,290 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import sys
+import uuid
+from dataclasses import dataclass, field
+from typing import Any, Callable, Optional, Union
+
+import torch.distributed.elastic.rendezvous.registry as rdzv_registry
+from torch.distributed.elastic import events, metrics
+from torch.distributed.elastic.agent.server.api import WorkerSpec
+from torch.distributed.elastic.agent.server.local_elastic_agent import LocalElasticAgent
+from torch.distributed.elastic.multiprocessing import (
+    DefaultLogsSpecs,
+    LogsSpecs,
+    SignalException,
+)
+from torch.distributed.elastic.multiprocessing.errors import ChildFailedError
+from torch.distributed.elastic.rendezvous import RendezvousParameters
+from torch.distributed.elastic.rendezvous.utils import parse_rendezvous_endpoint
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+__all__ = ["LaunchConfig", "elastic_launch", "launch_agent"]
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class LaunchConfig:
+    """
+    Creates a rendezvous config.
+
+    Args:
+        min_nodes: Minimum amount of nodes that the user function will
+                        be launched on. Elastic agent ensures that the user
+                        function start only when the min_nodes amount enters
+                        the rendezvous.
+        max_nodes: Maximum amount of nodes that the user function
+                        will be launched on.
+        nproc_per_node: On each node the elastic agent will launch
+                            this amount of workers that will execute user
+                            defined function.
+        rdzv_backend: rdzv_backend to use in the rendezvous (zeus-adapter, etcd).
+        rdzv_endpoint: The endpoint of the rdzv sync. storage.
+        rdzv_configs: Key, value pair that specifies rendezvous specific configuration.
+        rdzv_timeout: Legacy argument that specifies timeout for the rendezvous. It is going
+            to be removed in future versions, see the note below. The default timeout is 900 seconds.
+        run_id: The unique run id of the job (if not passed a unique one will be
+                deduced from run environment - flow workflow id in flow - or auto generated).
+        role: User defined role of the worker (defaults to "trainer").
+        max_restarts: The maximum amount of restarts that elastic agent will conduct
+                    on workers before failure.
+        monitor_interval: The interval in seconds that is used by the elastic_agent
+                        as a period of monitoring workers.
+        start_method: The method is used by the elastic agent to start the
+                    workers (spawn, fork, forkserver).
+        metrics_cfg: configuration to initialize metrics.
+        local_addr: address of the local node if any. If not set, a lookup on the local
+                machine's FQDN will be performed.
+        local_ranks_filter: ranks for which to show logs in console. If not set, show from all.
+
+    .. note::
+        `rdzv_timeout` is a legacy argument that will be removed in future.
+        Set the timeout via `rdzv_configs['timeout']`
+
+    """
+
+    min_nodes: int
+    max_nodes: int
+    nproc_per_node: int
+    logs_specs: Optional[LogsSpecs] = None
+    run_id: str = ""
+    role: str = "default_role"
+    rdzv_endpoint: str = ""
+    rdzv_backend: str = "etcd"
+    rdzv_configs: dict[str, Any] = field(default_factory=dict)
+    rdzv_timeout: int = -1
+    max_restarts: int = 3
+    monitor_interval: float = 0.1
+    start_method: str = "spawn"
+    log_line_prefix_template: Optional[str] = None
+    metrics_cfg: dict[str, str] = field(default_factory=dict)
+    local_addr: Optional[str] = None
+
+    def __post_init__(self):
+        default_timeout = 900
+        if self.rdzv_timeout != -1:
+            self.rdzv_configs["timeout"] = self.rdzv_timeout
+        elif "timeout" not in self.rdzv_configs:
+            self.rdzv_configs["timeout"] = default_timeout
+
+        # Post-processing to enable refactoring to introduce logs_specs due to non-torchrun API usage
+        if self.logs_specs is None:
+            self.logs_specs = DefaultLogsSpecs()
+
+
+class elastic_launch:
+    """
+    Launches an torchelastic agent on the container that invoked the entrypoint.
+
+        1. Pass the ``entrypoint`` arguments as non ``kwargs`` (e.g. no named parameters)/
+           ``entrypoint`` can be a function or a command.
+        2. The return value is a map of each worker's output mapped
+           by their respective global rank.
+
+    Usage
+
+    ::
+
+    def worker_fn(foo):
+        # ...
+
+    def main():
+        # entrypoint is a function.
+        outputs = elastic_launch(LaunchConfig, worker_fn)(foo)
+        # return rank 0's output
+        return outputs[0]
+
+        # entrypoint is a command and ``script.py`` is the python module.
+        outputs = elastic_launch(LaunchConfig, "script.py")(args)
+        outputs = elastic_launch(LaunchConfig, "python")("script.py")
+    """
+
+    def __init__(
+        self,
+        config: LaunchConfig,
+        entrypoint: Union[Callable, str, None],
+    ):
+        self._config = config
+        self._entrypoint = entrypoint
+
+    def __call__(self, *args):
+        return launch_agent(self._config, self._entrypoint, list(args))
+
+
+def _get_entrypoint_name(
+    entrypoint: Union[Callable, str, None], args: list[Any]
+) -> str:
+    """Retrieve entrypoint name with the rule:
+    1. If entrypoint is a function, use ``entrypoint.__qualname__``.
+    2. If entrypoint is a string, check its value:
+        2.1 if entrypoint equals to ``sys.executable`` (like "python"), use the first element from ``args``
+            which does not start with hifen letter (for example, "-u" will be skipped).
+        2.2 otherwise, use ``entrypoint`` value.
+    3. Otherwise, return empty string.
+    """
+    if isinstance(entrypoint, Callable):  # type: ignore[arg-type]
+        return entrypoint.__name__  # type: ignore[union-attr]
+    elif isinstance(entrypoint, str):
+        if entrypoint == sys.executable:
+            return next((arg for arg in args if arg[0] != "-"), "")
+        else:
+            return entrypoint
+    else:
+        return ""
+
+
+def _get_addr_and_port(
+    rdzv_parameters: RendezvousParameters,
+) -> tuple[Optional[str], Optional[int]]:
+    if rdzv_parameters.backend != "static":
+        return (None, None)
+    endpoint = rdzv_parameters.endpoint
+    endpoint = endpoint.strip()
+    if not endpoint:
+        raise ValueError(
+            "Endpoint is missing in endpoint. Try to add --master-addr and --master-port"
+        )
+    master_addr, master_port = parse_rendezvous_endpoint(endpoint, default_port=-1)
+    if master_port == -1:
+        raise ValueError(
+            f"port is missing in endpoint: {endpoint}. Try to specify --master-port"
+        )
+    return (master_addr, master_port)
+
+
+def launch_agent(
+    config: LaunchConfig,
+    entrypoint: Union[Callable, str, None],
+    args: list[Any],
+) -> dict[int, Any]:
+    if not config.run_id:
+        run_id = str(uuid.uuid4().int)
+        logger.warning("config has no run_id, generated a random run_id: %s", run_id)
+        config.run_id = run_id
+
+    entrypoint_name = _get_entrypoint_name(entrypoint, args)
+
+    logger.info(
+        "Starting elastic_operator with launch configs:\n"
+        "  entrypoint       : %(entrypoint)s\n"
+        "  min_nodes        : %(min_nodes)s\n"
+        "  max_nodes        : %(max_nodes)s\n"
+        "  nproc_per_node   : %(nproc_per_node)s\n"
+        "  run_id           : %(run_id)s\n"
+        "  rdzv_backend     : %(rdzv_backend)s\n"
+        "  rdzv_endpoint    : %(rdzv_endpoint)s\n"
+        "  rdzv_configs     : %(rdzv_configs)s\n"
+        "  max_restarts     : %(max_restarts)s\n"
+        "  monitor_interval : %(monitor_interval)s\n"
+        "  log_dir          : %(log_dir)s\n"
+        "  metrics_cfg      : %(metrics_cfg)s\n",
+        {
+            "entrypoint": entrypoint_name,
+            "min_nodes": config.min_nodes,
+            "max_nodes": config.max_nodes,
+            "nproc_per_node": config.nproc_per_node,
+            "run_id": config.run_id,
+            "rdzv_backend": config.rdzv_backend,
+            "rdzv_endpoint": config.rdzv_endpoint,
+            "rdzv_configs": config.rdzv_configs,
+            "max_restarts": config.max_restarts,
+            "monitor_interval": config.monitor_interval,
+            "log_dir": config.logs_specs.root_log_dir,  # type: ignore[union-attr]
+            "metrics_cfg": config.metrics_cfg,
+        },
+    )
+
+    rdzv_parameters = RendezvousParameters(
+        backend=config.rdzv_backend,
+        endpoint=config.rdzv_endpoint,
+        run_id=config.run_id,
+        min_nodes=config.min_nodes,
+        max_nodes=config.max_nodes,
+        local_addr=config.local_addr,
+        **config.rdzv_configs,
+    )
+
+    master_addr, master_port = _get_addr_and_port(rdzv_parameters)
+
+    spec = WorkerSpec(
+        role=config.role,
+        local_world_size=config.nproc_per_node,
+        entrypoint=entrypoint,
+        args=tuple(args),
+        rdzv_handler=rdzv_registry.get_rendezvous_handler(rdzv_parameters),
+        max_restarts=config.max_restarts,
+        monitor_interval=config.monitor_interval,
+        master_addr=master_addr,
+        master_port=master_port,
+        local_addr=config.local_addr,
+    )
+
+    agent = LocalElasticAgent(
+        spec=spec,
+        logs_specs=config.logs_specs,  # type: ignore[arg-type]
+        start_method=config.start_method,
+        log_line_prefix_template=config.log_line_prefix_template,
+    )
+
+    shutdown_rdzv = True
+    try:
+        metrics.initialize_metrics(metrics.MetricsConfig(config.metrics_cfg))
+
+        result = agent.run()
+        # records that agent.run() has succeeded NOT that workers have succeeded
+        events.record(agent.get_event_succeeded())
+
+        if result.is_failed():
+            # ChildFailedError is treated specially by @record
+            # if the error files for the failed children exist
+            # @record will copy the first error (root cause)
+            # to the error file of the launcher process.
+            raise ChildFailedError(
+                name=entrypoint_name,
+                failures=result.failures,
+            )
+
+        return result.return_values
+    except ChildFailedError:
+        raise
+    except SignalException:
+        # when the agent dies with a signal do NOT shutdown the rdzv_handler
+        # since this closes the rendezvous on this rdzv_id permanently and
+        # prevents any additional scaling events
+        shutdown_rdzv = False
+        events.record(agent.get_event_failed())
+        raise
+    except Exception:
+        events.record(agent.get_event_failed())
+        raise
+    finally:
+        if shutdown_rdzv:
+            spec.rdzv_handler.shutdown()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/logging_handlers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/logging_handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed6832fd1ae834b6365a6b005b07bbbfffe90726
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/logging_handlers.py
@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+
+__all__: list[str] = []
+
+_log_handlers: dict[str, logging.Handler] = {
+    "default": logging.NullHandler(),
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e15fb517052e4aefeb7377d1f0ca63cf2b2da753
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/__init__.py
@@ -0,0 +1,7 @@
+import torch
+
+from .functional import *  # noqa: F403
+
+
+if torch.distributed.rpc.is_available():
+    from .api.remote_module import RemoteModule
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..e08b9cad1b031f31fe4ef7c4b7246626c951a871
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py
@@ -0,0 +1,753 @@
+#!/usr/bin/python3
+# mypy: allow-untyped-defs
+import collections
+import io
+import sys
+import types
+from collections.abc import Iterator, Mapping
+from typing import Any, Callable, Optional, TypeVar, Union
+
+import torch
+import torch.distributed.rpc as rpc
+from torch import device, dtype, nn, Tensor
+from torch.distributed import _remote_device
+from torch.distributed.nn.jit import instantiator
+from torch.distributed.rpc.internal import _internal_rpc_pickler
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+from torch.utils.hooks import RemovableHandle
+
+
+__all__ = ["RemoteModule"]
+
+_grad_t = Union[tuple[Tensor, ...], Tensor]
+# See https://mypy.readthedocs.io/en/latest/generics.html#generic-methods-and-generic-self for the use
+# of `T` to annotate `self`. Many methods of `Module` return `self` and we want those return values to be
+# the type of the subclass, not the looser type of `Module`.
+T = TypeVar("T", bound="Module")
+
+_NON_SCRIPTABLE_REMOTE_MODULE_MODULE = (
+    instantiator.instantiate_non_scriptable_remote_module_template()
+)
+
+_REMOTE_MODULE_PICKLED_ATTRIBUTES = (
+    "on",
+    "device",
+    "is_device_map_set",
+    "is_scriptable",
+    "generated_methods",
+    "module_rref",
+)
+
+_SerializedRemoteModule = collections.namedtuple(  # type: ignore[misc]
+    "_SerializedRemoteModule",
+    _REMOTE_MODULE_PICKLED_ATTRIBUTES,
+)
+
+# These attributes are mostly from RemoteModule's parent class and are intentionally not pickled.
+# A new attribute of RemoteModule should be either in _REMOTE_MODULE_PICKLED_ATTRIBUTES
+# or _REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING.
+# Otherwise, it will not be pickled.
+_REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING = (
+    "training",
+    "_parameters",
+    "_buffers",
+    "_non_persistent_buffers_set",
+    "_backward_hooks",
+    "_backward_pre_hooks",
+    "_is_full_backward_hook",
+    "_forward_hooks",
+    "_forward_hooks_with_kwargs",
+    "_forward_hooks_always_called",
+    "_forward_pre_hooks",
+    "_forward_pre_hooks_with_kwargs",
+    "_state_dict_hooks",
+    "_state_dict_pre_hooks",
+    "_load_state_dict_pre_hooks",
+    "_load_state_dict_post_hooks",
+    "_state_dict_pre_hooks",
+    "_modules",
+    # The two attributes below are generated methods, not available at pickling time.
+    "forward_async",
+    "forward",
+)
+
+
+# RPC handler.
+def _instantiate_template(module_interface_cls, enable_moving_cpu_tensors_to_cuda):
+    instantiator.instantiate_scriptable_remote_module_template(
+        module_interface_cls, enable_moving_cpu_tensors_to_cuda
+    )
+
+
+def _create_module(module_cls, args, kwargs, device):
+    module = module_cls(*args, **kwargs)
+    if not isinstance(module, nn.Module):
+        raise ValueError(
+            "Expect `module_cls(*args, **kwargs)` returns an instance of , "
+            f"but it returns an instance of {type(module)}."
+        )
+    module.to(device)
+    return module
+
+
+def _create_module_with_interface(
+    module_cls, args, kwargs, device, module_interface_cls
+):
+    module = _create_module(module_cls, args, kwargs, device)
+    if module_interface_cls is not None:
+        module = torch.jit.script(module)
+    return rpc.RRef(module, module_interface_cls)
+
+
+def _param_rrefs(module_rref, recurse) -> list[rpc.RRef[Parameter]]:
+    ret: list[rpc.RRef[Parameter]] = [
+        rpc.RRef(param) for param in module_rref.local_value().parameters(recurse)
+    ]
+    return ret
+
+
+def _raise_not_supported(name: str) -> None:
+    raise ValueError(f"Method ``{name}`` not supported for RemoteModule")
+
+
+class _RemoteModule(nn.Module):
+    def __new__(cls, *args, **kwargs):
+        # Use __new__ for logging purposes.
+        torch._C._log_api_usage_once("torch.distributed.nn.api.remote_module")
+        return super().__new__(cls)
+
+    def __init__(
+        self,
+        remote_device: str,
+        module_cls: type[nn.Module],
+        args: Optional[tuple] = None,
+        kwargs: Optional[dict[str, Any]] = None,
+        _module_interface_cls: Any = None,
+    ):
+        """
+        RemoteModule instance can only be created after RPC initialization.
+
+        It creates a user-specified module on a specified remote node.
+        It behaves like a regular ``nn.Module`` except that the ``forward`` method is
+        executed on the remote node.
+        It takes care of autograd recording to ensure the backward pass propagates
+        gradients back to the corresponding remote module.
+        It can be shared across processors using `RPC framework `__,
+        without incurring any overheads of copying the actual module,
+        which is equivalent to an :class:`~torch.distributed.rpc.RRef`
+        pointing to the remote module.
+
+        The arguments of ``forward_async`` and ``forward`` are the same as
+        the ``forward`` method of the module returned by the ``module_cls``.
+
+        Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now.
+
+        Particularly, to create a hybrid model, typically the local modules should be
+        created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``).
+        Hybrid Example:
+                >>> class HybridModel(nn.Module):
+                >>>     def __init__(self) -> None:
+                >>>         nn.Module.__init__(self)
+                >>>         self.remote_embedding = RemoteModule(...)
+                >>>         self.local_linear = nn.Linear(...)
+
+        For example, if ``module_cls`` returns an instance of ``nn.Linear``,
+        that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``,
+        the generated ``RemoteModule`` will have 2 methods in signature of
+        ``def forward(input: Tensor) -> Tensor:`` and
+        ``def forward_async(input: Tensor) -> Future[Tensor]:``.
+
+        .. note::
+            If the remote module is placed on a cuda device,
+            any input CPU tensors will be automatically moved to the same cuda device,
+            and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend.
+
+        Args:
+            remote_device (str): Device on the destination worker where we'd like to place this module.
+                The device can be a local device or a remote device specified by one of the following remote
+                formats:
+
+                    1. "rank:/" (ex: "rank:0/cuda:0").
+                    2. "/" (ex: "trainer0/cuda:0").
+
+                In addition, the device field can be optional and the default value is "cpu".
+            module_cls (nn.Module): For example,
+                >>> class MyModule(nn.Module):
+                >>>     def forward(input):
+                >>>         return input + 1
+                >>>
+                >>> module_cls = MyModule
+            args (Sequence, optional): args to be passed to ``module_cls``.
+            kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
+            _module_interface_cls (type, optional): The TorchScript interface type for the module
+                to be created. The type object should be decorated by @torch.jit.interface.
+                If not provided, the generated RemoteModule is not torchscript-able.
+                Warning, this is an experimental API and susceptible to frequent changes.
+
+        Returns:
+            A remote module instance which wraps the :class:`~nn.Module` created by the
+            user-provided ``module_cls``, it has a blocking ``forward`` method and an
+            asynchronous ``forward_async`` method that returns a future of the ``forward`` call
+            on the user-provided module on the remote side.
+
+        Example::
+            Run the following code in two different processes:
+
+            >>> # xdoctest: +SKIP("distributed")
+            >>> # On worker 0:
+            >>> import torch
+            >>> import torch.distributed.rpc as rpc
+            >>> from torch import nn, Tensor
+            >>> from torch.distributed.nn.api.remote_module import RemoteModule
+            >>>
+            >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+            >>> remote_linear_module = RemoteModule(
+            >>>     "worker1/cpu", nn.Linear, args=(20, 30),
+            >>> )
+            >>> input = torch.randn(128, 20)
+            >>> ret_fut = remote_linear_module.forward_async(input)
+            >>> ret = ret_fut.wait()
+            >>> rpc.shutdown()
+
+            >>> # On worker 1:
+            >>> import torch
+            >>> import torch.distributed.rpc as rpc
+            >>>
+            >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+            >>> rpc.shutdown()
+        """
+        super().__init__()
+
+        enable_moving_cpu_tensors_to_cuda = self._prepare_init(remote_device)
+
+        # Default arguments preparation.
+        args = args if args is not None else ()
+        kwargs = kwargs if kwargs is not None else {}
+
+        if _module_interface_cls is not None:
+            # Users reply on this field to know if this generated RemoteModule is TorchScript-able.
+            self.is_scriptable = True
+
+            # Instantiate template on remote side.
+            fut = rpc.rpc_async(
+                self.on,
+                _instantiate_template,
+                (_module_interface_cls, enable_moving_cpu_tensors_to_cuda),
+            )
+
+            self._init_template(
+                _module_interface_cls, enable_moving_cpu_tensors_to_cuda
+            )
+
+            # Instantiate template on remote side.
+            fut = rpc.rpc_async(
+                self.on,
+                _instantiate_template,
+                (_module_interface_cls, enable_moving_cpu_tensors_to_cuda),
+            )
+
+            # Create the module on the remote side.
+            fut.wait()  # Ensure remote_module_cls is available on remote side.
+
+            # TODO: We need to change this to rpc.remote, and make it async (see the else branch below).
+            # For that we need to be able to apply _module_interface_cls to the RRef returned by rpc.remote
+            # See https://github.com/pytorch/pytorch/issues/58098 for more context.
+            self.module_rref = rpc.rpc_sync(
+                self.on,
+                _create_module_with_interface,
+                (module_cls, args, kwargs, self.device, _module_interface_cls),
+            )
+        else:
+            self.is_scriptable = False
+            self.generated_methods = (
+                _NON_SCRIPTABLE_REMOTE_MODULE_MODULE._generated_methods
+            )
+            # Create the module on the remote side.
+            self.module_rref = rpc.remote(
+                self.on,
+                _create_module,
+                (module_cls, args, kwargs, self.device),
+            )
+
+        self._install_generated_methods()
+        self._check_attribute_picklability()
+
+    def remote_parameters(self, recurse: bool = True) -> list[rpc.RRef[Parameter]]:
+        """
+        Return a list of :class:`~torch.distributed.rpc.RRef` pointing to the remote module's parameters.
+
+        This can typically be used in conjunction
+        with :class:`~torch.distributed.optim.DistributedOptimizer`.
+
+        Args:
+            recurse (bool): if True, then returns parameters of the remote
+                module and all submodules of the remote module. Otherwise,
+                returns only parameters that are direct members of the
+                remote module.
+
+        Returns:
+            A list of :class:`~torch.distributed.rpc.RRef` (``List[RRef[nn.Parameter]]``)
+            to remote module's parameters.
+        """
+        return rpc.rpc_sync(self.on, _param_rrefs, args=(self.module_rref, recurse))
+
+    def get_module_rref(self) -> rpc.RRef[nn.Module]:
+        """Return an :class:`~torch.distributed.rpc.RRef` (``RRef[nn.Module]``) pointing to the remote module."""
+        return self.module_rref
+
+    @torch.jit.export
+    def __getstate__(self):
+        raise RuntimeError(
+            "Cannot pickle RemoteModule in python pickler. RemoteModule can only be pickled when using RPC"
+        )
+
+    @torch.jit.export
+    def __setstate__(self, state):
+        raise RuntimeError(
+            "Cannot unpickle RemoteModule in python pickler. RemoteModule can only be unpickled when using RPC"
+        )
+
+    def register_buffer(
+        self, name: str, tensor: Optional[Tensor], persistent: bool = True
+    ) -> None:
+        _raise_not_supported(self.register_buffer.__name__)
+
+    def register_parameter(self, name: str, param: Optional[Parameter]) -> None:
+        _raise_not_supported(self.register_parameter.__name__)
+
+    def add_module(self, name: str, module: Optional[Module]) -> None:
+        _raise_not_supported(self.add_module.__name__)
+
+    def apply(self: T, fn: Callable[[Module], None]) -> T:  # type: ignore[return]
+        _raise_not_supported(self.apply.__name__)
+
+    def cuda(self: T, device: Optional[Union[int, device]] = None) -> T:  # type: ignore[return]
+        _raise_not_supported(self.cuda.__name__)
+
+    def ipu(self: T, device: Optional[Union[int, device]] = None) -> T:  # type: ignore[return]
+        _raise_not_supported(self.ipu.__name__)
+
+    def xpu(self: T, device: Optional[Union[int, device]] = None) -> T:  # type: ignore[return]
+        _raise_not_supported(self.xpu.__name__)
+
+    def cpu(self: T) -> T:  # type: ignore[return]
+        _raise_not_supported(self.cpu.__name__)
+
+    def type(self: T, dst_type: Union[dtype, str]) -> T:  # type: ignore[return]
+        _raise_not_supported(self.type.__name__)
+
+    def float(self: T) -> T:  # type: ignore[return]
+        _raise_not_supported(self.float.__name__)
+
+    def double(self: T) -> T:  # type: ignore[return]
+        _raise_not_supported(self.double.__name__)
+
+    def half(self: T) -> T:  # type: ignore[return]
+        _raise_not_supported(self.half.__name__)
+
+    def bfloat16(self: T) -> T:  # type: ignore[return]
+        _raise_not_supported(self.bfloat16.__name__)
+
+    def to(self, *args, **kwargs) -> T:  # type: ignore[misc, return, type-var]
+        _raise_not_supported(self.to.__name__)
+
+    def register_backward_hook(  # type: ignore[return]
+        self, hook: Callable[[Module, _grad_t, _grad_t], Union[None, _grad_t]]
+    ) -> RemovableHandle:
+        _raise_not_supported(self.register_backward_hook.__name__)
+
+    def register_forward_pre_hook(  # type: ignore[return]
+        self,
+        hook: Union[
+            Callable[[T, tuple[Any, ...]], Optional[Any]],
+            Callable[
+                [T, tuple[Any, ...], dict[str, Any]],
+                Optional[tuple[Any, dict[str, Any]]],
+            ],
+        ],
+        prepend: bool = False,
+        with_kwargs: bool = False,
+    ) -> RemovableHandle:
+        _raise_not_supported(self.register_forward_pre_hook.__name__)
+
+    def register_forward_hook(  # type: ignore[return, override]
+        self,
+        hook: Union[
+            Callable[[T, tuple[Any, ...], Any], Optional[Any]],
+            Callable[[T, tuple[Any, ...], dict[str, Any], Any], Optional[Any]],
+        ],
+        prepend: bool = False,
+        with_kwargs: bool = False,
+    ) -> RemovableHandle:
+        _raise_not_supported(self.register_forward_hook.__name__)
+
+    def state_dict(self, *args, **kwargs):
+        _raise_not_supported(self.state_dict.__name__)
+
+    def load_state_dict(
+        self,
+        state_dict: Mapping[str, Any],
+        strict: bool = True,
+        assign: bool = False,
+    ):
+        _raise_not_supported(self.load_state_dict.__name__)
+
+    def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
+        raise ValueError(
+            "Method ``parameters`` not supported for RemoteModule. Please use ``remote_parameters`` instead."
+        )
+
+    def named_parameters(  # type: ignore[return]
+        self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True
+    ) -> Iterator[tuple[str, Parameter]]:
+        _raise_not_supported(self.named_parameters.__name__)
+
+    def buffers(self, recurse: bool = True) -> Iterator[Tensor]:  # type: ignore[return]
+        _raise_not_supported(self.buffers.__name__)
+
+    def named_buffers(  # type: ignore[return]
+        self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True
+    ) -> Iterator[tuple[str, Tensor]]:
+        _raise_not_supported(self.named_buffers.__name__)
+
+    def children(self) -> Iterator[Module]:  # type: ignore[return]
+        _raise_not_supported(self.children.__name__)
+
+    def named_children(self) -> Iterator[tuple[str, Module]]:  # type: ignore[return]
+        _raise_not_supported(self.named_children.__name__)
+
+    def modules(self) -> Iterator[Module]:  # type: ignore[return]
+        _raise_not_supported(self.modules.__name__)
+
+    def named_modules(
+        self,
+        memo: Optional[set[Module]] = None,
+        prefix: str = "",
+        remove_duplicate: bool = True,
+    ):
+        _raise_not_supported(self.named_modules.__name__)
+
+    def train(self: T, mode: bool = True) -> T:
+        return self.module_rref.rpc_sync().train()  # type: ignore[operator, union-attr]
+
+    def eval(self: T) -> T:
+        return self.module_rref.rpc_sync().eval()  # type: ignore[operator, union-attr]
+
+    def requires_grad_(self: T, requires_grad: bool = True) -> T:  # type: ignore[return]
+        _raise_not_supported(self.requires_grad_.__name__)
+
+    def zero_grad(self, set_to_none: bool = True) -> None:
+        _raise_not_supported(self.zero_grad.__name__)
+
+    def share_memory(self: T) -> T:  # type: ignore[return]
+        _raise_not_supported(self.share_memory.__name__)
+
+    def extra_repr(self) -> str:  # type: ignore[return]
+        _raise_not_supported(self.extra_repr.__name__)
+
+    def _prepare_init(self, remote_device_str: str) -> bool:
+        """Prepare the initialization and returns whether to enable automatically moving CPU tensors to CUDA devices."""
+        # Sanity check.
+        assert rpc._is_current_rpc_agent_set(), "RemoteModule only works in RPC."
+
+        remote_device = _remote_device(remote_device_str)
+        self.on = (
+            remote_device.worker_name()
+            if remote_device.worker_name() is not None
+            else remote_device.rank()
+        )
+        self.device = str(remote_device.device())
+        agent = rpc._get_current_rpc_agent()
+        # If the device map of the remote worker is set,
+        # then enable moving any input CPU tensors to the same cuda device.
+        self.is_device_map_set = bool(
+            agent._get_device_map(agent.get_worker_info(self.on))  # type: ignore[arg-type]
+        )
+        # ``enable_moving_cpu_tensors_to_cuda`` is less strict than ``is_device_map_set``:
+        # If ``enable_moving_cpu_tensors_to_cuda`` is true, but the device map is not set,
+        # then any CPU tensors can still be moved to a cuda device to run forward,
+        # but the output must be moved back to CPU before being sent over the wire.
+        enable_moving_cpu_tensors_to_cuda = torch.device(self.device).type == "cuda"
+        return enable_moving_cpu_tensors_to_cuda
+
+    def _init_template(self, module_interface_cls, enable_moving_cpu_tensors_to_cuda):
+        """Instantiate template on local side."""
+        generated_module = instantiator.instantiate_scriptable_remote_module_template(
+            module_interface_cls, enable_moving_cpu_tensors_to_cuda
+        )
+        self.generated_methods = generated_module._generated_methods
+
+    def _check_attribute_picklability(self):
+        """Check if all the attribute has explicitly defined whether to be pickled (i.e., picklability)."""
+        for k in self.__dict__.keys():
+            if (
+                k not in _REMOTE_MODULE_PICKLED_ATTRIBUTES
+                and k not in _REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING
+            ):
+                raise AttributeError(
+                    f"Attribute {k} must be either in ``_REMOTE_MODULE_PICKLED_ATTRIBUTES`` or "
+                    "``_REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING``."
+                )
+
+    def _install_generated_methods(self):
+        for method in self.generated_methods:
+            method_name = method.__name__
+            method = torch.jit.export(method)
+            setattr(self, method_name, types.MethodType(method, self))
+
+    @staticmethod
+    def init_from_module_rref(
+        remote_device: str,
+        module_rref: rpc.RRef[nn.Module],
+        _module_interface_cls: Any = None,
+    ):
+        """
+        Besides the constructor, a RemoteModule instance can also be initialized given a module RRef.
+
+        This alternate initialization method can be particularly useful if we want to create multiple
+        RemoteModule instances that share the same underlying module and reduce memory consumption.
+
+        Moreover, this also provides a workaround for passing script RemoteModule over RPC,
+        which is not supported. The recommended way is as follows:
+
+            1. the sender creates a RemoteModule;
+            2. the sender sends its ``module_rref`` over RPC;
+            3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``.
+
+        Example::
+            Run the following code in two different processes:
+
+            >>> # xdoctest: +SKIP("distributed")
+            >>> # On worker 0:
+            >>> import torch
+            >>> import torch.distributed.rpc as rpc
+            >>> from torch import nn, Tensor
+            >>> from torch.distributed.nn.api.remote_module import RemoteModule
+            >>>
+            >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+            >>> remote_module = RemoteModule(
+            >>>     "worker1/cpu", nn.Linear, args=(20, 30),
+            >>> )
+            >>>
+            >>> remote_module1 = rpc.rpc_sync(
+            >>>     "worker1/cpu",
+            >>>     RemoteModule.init_from_module_rref,
+            >>>     ("worker1/cpu", remote_module1.get_module_rref()),
+            >>> )
+            >>> rpc.shutdown()
+
+            >>> # On worker 1:
+            >>> import torch
+            >>> import torch.distributed.rpc as rpc
+            >>>
+            >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+            >>> rpc.shutdown()
+
+        Args:
+            remote_device (str): Device on the destination worker where we'd like to place this module.
+                The device can be a local device or a remote device specified by one of the following remote
+                formats:
+
+                    1. "rank:/" (ex: "rank:0/cuda:0").
+                    2. "/" (ex: "trainer0/cuda:0").
+
+                In addition, the device field can be optional and the default value is "cpu".
+            module_rref (RRef[nn.Module]): The module reference shared by both the caller and
+                the created remote module.
+            _module_interface_cls (type, optional): The TorchScript interface type for the module
+                to be created. The type object should be decorated by @torch.jit.interface.
+                If not provided, the generated RemoteModule is not torchscript-able.
+                Warning, this is an experimental API and susceptible to frequent changes.
+
+        Returns:
+            A remote module instance which wraps the :class:`~nn.Module` created by the
+            user-provided ``module_rref``, it has a blocking ``forward`` method and an
+            asynchronous ``forward_async`` method that returns a future of the ``forward`` call
+            on the user-provided module on the remote side.
+        """
+        # NOTE: if a new attribute is added to this class, also need to add it
+        # to ``_REMOTE_MODULE_PICKLED_ATTRIBUTES`` for pickling/unpickling.
+
+        remote_module = object.__new__(RemoteModule)
+
+        enable_moving_cpu_tensors_to_cuda = remote_module._prepare_init(remote_device)
+
+        if _module_interface_cls is not None:
+            # Users reply on this field to know if this generated RemoteModule is TorchScript-able.
+            remote_module.is_scriptable = True
+
+            remote_module._init_template(
+                _module_interface_cls, enable_moving_cpu_tensors_to_cuda
+            )
+        else:
+            remote_module.is_scriptable = False
+            remote_module.generated_methods = (
+                _NON_SCRIPTABLE_REMOTE_MODULE_MODULE._generated_methods
+            )
+        remote_module.module_rref = module_rref
+
+        remote_module._install_generated_methods()
+        remote_module._check_attribute_picklability()
+
+        return remote_module
+
+
+class RemoteModule(_RemoteModule):
+    """
+        A RemoteModule instance can only be created after RPC initialization.
+
+        It creates a user-specified module on a specified remote node.
+        It behaves like a regular ``nn.Module`` except that the ``forward`` method is
+        executed on the remote node.
+        It takes care of autograd recording to ensure the backward pass propagates
+        gradients back to the corresponding remote module.
+
+        It generates two methods ``forward_async`` and ``forward`` based on the
+        signature of the ``forward`` method of ``module_cls``. ``forward_async``
+        runs asynchronously and returns a Future. The arguments of ``forward_async``
+        and ``forward`` are the same as the ``forward`` method of the module
+        returned by the ``module_cls``.
+
+        For example, if ``module_cls`` returns an instance of ``nn.Linear``,
+        that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``,
+        the generated ``RemoteModule`` will have 2 methods with the signatures:
+
+        | ``def forward(input: Tensor) -> Tensor:``
+        | ``def forward_async(input: Tensor) -> Future[Tensor]:``
+
+    Args:
+        remote_device (str): Device on the destination worker where we'd like to place this module.
+            The format should be "/", where the device field can be parsed as torch.device type.
+            E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
+            In addition, the device field can be optional and the default value is "cpu".
+        module_cls (nn.Module): Class for the module to be created remotely. For example,
+
+            >>> class MyModule(nn.Module):
+            >>>     def forward(input):
+            >>>         return input + 1
+            >>>
+            >>> module_cls = MyModule
+
+        args (Sequence, optional): args to be passed to ``module_cls``.
+        kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
+
+    Returns:
+        A remote module instance which wraps the :class:`~nn.Module` created by the
+        user-provided ``module_cls``, it has a blocking ``forward`` method and an
+        asynchronous ``forward_async`` method that returns a future of the ``forward`` call
+        on the user-provided module on the remote side.
+
+    Example::
+        Run the following code in two different processes:
+
+        >>> # xdoctest: +SKIP("distributed")
+        >>> # On worker 0:
+        >>> import torch
+        >>> import torch.distributed.rpc as rpc
+        >>> from torch import nn, Tensor
+        >>> from torch.distributed.nn.api.remote_module import RemoteModule
+        >>>
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> remote_linear_module = RemoteModule(
+        >>>     "worker1/cpu", nn.Linear, args=(20, 30),
+        >>> )
+        >>> input = torch.randn(128, 20)
+        >>> ret_fut = remote_linear_module.forward_async(input)
+        >>> ret = ret_fut.wait()
+        >>> rpc.shutdown()
+
+        >>> # On worker 1:
+        >>> import torch
+        >>> import torch.distributed.rpc as rpc
+        >>>
+        >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+        >>> rpc.shutdown()
+
+        Furthermore, a more practical example that is combined with
+        `DistributedDataParallel `__ (DDP)
+        can be found in this `tutorial `__.
+    """
+
+    def __init__(
+        self,
+        remote_device: str,
+        module_cls: type[nn.Module],
+        args: Optional[tuple] = None,
+        kwargs: Optional[dict[str, Any]] = None,
+    ):
+        super().__init__(remote_device, module_cls, args, kwargs)
+
+
+def _remote_module_receiver(
+    *remote_module_pickled_attrs,
+):
+    """Deserializes a RemoteModule."""
+    serialized_remote_module = _SerializedRemoteModule._make(
+        remote_module_pickled_attrs
+    )
+    m = object.__new__(RemoteModule)
+    m.__dict__.update(serialized_remote_module._asdict())
+
+    # Unpickling the attribute `module_rref` must invoke RRef's `_deserialize()` method.
+    m.module_rref = rpc.PyRRef._deserialize(m.module_rref)
+
+    # Install generated methods when unpickled.
+    for method in m.generated_methods:
+        method_name = method.__name__
+        method = torch.jit.export(method)
+        setattr(m, method_name, types.MethodType(method, m))
+
+    return m
+
+
+def _remote_module_reducer(remote_module):
+    """Serialize a RemoteModule."""
+    pickled_attrs = {}
+    for k, v in remote_module.__dict__.items():
+        # Pickling the attribute `module_rref` must invoke RRef's `_serialize()` method.
+        if k == "module_rref":
+            pickled_attrs[k] = v._serialize()
+        elif k in _REMOTE_MODULE_PICKLED_ATTRIBUTES:
+            pickled_attrs[k] = v
+        # Check if unpickled attributes are all in _REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING.
+        elif k not in _REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING:
+            print(
+                f"The new attribute ``{k}`` of RemoteModule is ignored during RPC pickling. "
+                "To pickle this attribute, please add it to ``_REMOTE_MODULE_PICKLED_ATTRIBUTES``. "
+                "Otherwise, please explicitly add it to ``_REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING``.",
+                file=sys.stderr,
+            )
+
+    return (
+        _remote_module_receiver,
+        tuple(pickled_attrs.values()),
+    )
+
+
+def _recursive_script_module_receiver(
+    recursive_script_module_serialized,
+):
+    """Deserializes a RecursiveScriptModule that does not contain a script RemoteModule."""
+    f = io.BytesIO(recursive_script_module_serialized)
+    m = torch.jit.load(f)
+    return m
+
+
+def _recursive_script_module_reducer(recursive_script_module):
+    """Serialize a RecursiveScriptModule that does not contain a script RemoteModule, and raises an error otherwise."""
+    if hasattr(recursive_script_module._c, "module_rref"):
+        raise RuntimeError(
+            "Passing a script RemoteModule over RPC is not supported. Please create a RemoteModule in the sender, "
+            "send the `module_rref` to the receiver, and create a new instance on the receiver end by passing this `module_rref`."
+        )
+
+    f = io.BytesIO()
+    torch.jit.save(recursive_script_module, f)
+    return (_recursive_script_module_receiver, (f.getvalue(),))
+
+
+_internal_rpc_pickler._register_reducer(RemoteModule, _remote_module_reducer)
+_internal_rpc_pickler._register_reducer(
+    torch.jit.RecursiveScriptModule, _recursive_script_module_reducer
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/functional.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..eeff877260bcc083051c6622a2894677cb6324e4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/functional.py
@@ -0,0 +1,452 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.distributed as dist
+from torch.autograd import Function
+
+# The two imports below are not always available depending on the
+# USE_DISTRIBUTED compile flag. Make sure they raise import error
+# if we're trying to use them.
+from torch.distributed import group, ReduceOp
+
+
+def broadcast(tensor, src, group=group.WORLD):
+    """
+    Broadcasts the tensor to the whole group.
+
+    ``tensor`` must have the same number of elements in all processes
+    participating in the collective.
+
+    Arguments:
+        tensor (Tensor): Data to be sent if ``src`` is the rank of current
+            process.
+        src (int): Source rank.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        Tensor: Received tensor from the broadcast op.
+
+    """
+    return _Broadcast.apply(src, group, tensor)
+
+
+def gather(tensor, dst=0, group=group.WORLD):
+    """
+    Gathers a list of tensors in a single process.
+
+    Arguments:
+        tensor (Tensor): Input tensor.
+        dst (int, optional): Destination rank (default is 0).
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        tuple[Tensor]: List of appropriately-sized tensors with the gathered data.
+    """
+    return _Gather.apply(dst, group, tensor)
+
+
+def scatter(tensors, src=0, group=group.WORLD):
+    """
+    Scatters a list of tensors to all processes in a group.
+
+    Each process will receive exactly one tensor and store its data in the
+    ``tensor`` argument.
+
+    Arguments:
+        tensors (list[Tensor]): List of tensors to scatter on the source rank.
+            Receivers must pass ``None`.
+        src (int, optional): Source rank (default is 0).
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        Tensor: Output tensor from the scatter operation.
+
+    """
+    return _Scatter.apply(src, group, *tensors)
+
+
+def reduce(tensor, dst, op=ReduceOp.SUM, group=group.WORLD):
+    """
+    Reduces the tensor data across all machines.
+
+    Only the process with rank ``dst`` is going to receive the final result.
+
+    Arguments:
+        tensor (Tensor): Input of the collective.
+        dst (int): Destination rank.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        Tensor: Output of the collective.
+
+    """
+    return _Reduce.apply(dst, op, group, tensor)
+
+
+def reduce_scatter(output, input_list, op=ReduceOp.SUM, group=group.WORLD):
+    """
+    Reduces, then scatters a list of tensors to all processes in a group.
+
+    Arguments:
+        output (Tensor): Output tensor.
+        input_list (list[Tensor]): List of tensors to reduce and scatter.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        Tensor: Output of the collective.
+
+    """
+    return _Reduce_Scatter.apply(op, group, output, *input_list)
+
+
+def all_gather(tensor, group=group.WORLD):
+    """
+    Gathers tensors from the whole group in a list.
+
+    Arguments:
+        tensor (Tensor): Tensor to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        tuple([Tensor]): Output of the collective.
+
+    """
+    return _AllGather.apply(group, tensor)
+
+
+def _all_gather_base(output_tensor, input_tensor, group=group.WORLD):
+    """
+    Single tensor all gather. Gathers a single tensor from all ranks, and puts them in a single output tensor.
+
+    Args:
+        output_tensor (Tensor): Output tensor. It should contain
+            correctly-sized tensors to be used for output of the collective.
+        input_tensor (Tensor): Tensor to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Examples:
+        >>> # All tensors below are of torch.int64 dtype.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> # xdoctest: +SKIP("incorrect want text")
+        >>> output_tensor = torch.zeros(2, dtype=torch.int64)
+        >>> output_tensor
+        [tensor([0, 0])] # Rank 0 and 1
+        >>> tensor = torch.arange(1, dtype=torch.int64) + 1 + rank
+        >>> tensor
+        tensor([1]) # Rank 0
+        tensor([2]) # Rank 1
+        >>> dist.all_gather_base(output_tensor, tensor)
+        >>> output_tensor
+        tensor([1,2]) # Rank 0
+        tensor([1,2]) # Rank 1
+
+    .. warning::
+        `_all_gather_base` is experimental and subject to change.
+        It is the caller's responsibility to ensure the output_tensor
+        is correctly sized.
+
+    """
+    return _AllGatherBase.apply(output_tensor, input_tensor, group)
+
+
+def all_to_all(output_tensor_list, input_tensor_list, group=group.WORLD):
+    """
+    Each process scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
+
+    Arguments:
+        output_tensor_list (list[Tensor]): list of tensors to gather one per rank.
+        input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        tuple([Tensor]): Output of the collective.
+
+    """
+    return _AlltoAll.apply(group, output_tensor_list, *input_tensor_list)
+
+
+def all_to_all_single(
+    output,
+    input,
+    output_split_sizes=None,
+    input_split_sizes=None,
+    group=group.WORLD,
+):
+    """
+    Each process splits input tensor and then scatters the split list to all processes in a group.
+
+    Then concatenate the received tensors from all the processes in the group and return single output tensor.
+
+    Arguments:
+        output (Tensor): Gathered concatenated output tensor.
+        input (Tensor): Input tensor to scatter.
+        output_split_sizes: (list[Int], optional): Output split sizes for dim 0
+            if specified None or empty, dim 0 of ``output`` tensor must divide
+            equally by ``world_size``.
+        input_split_sizes: (list[Int], optional): Input split sizes for dim 0
+            if specified None or empty, dim 0 of ``input`` tensor must divide
+            equally by ``world_size``.
+
+    Returns:
+        Tensor: Output of the collective.
+
+    """
+    return _AlltoAllSingle.apply(
+        group, output, output_split_sizes, input_split_sizes, input
+    )
+
+
+def all_reduce(tensor, op=ReduceOp.SUM, group=group.WORLD):
+    """
+    Reduces the tensor data across all machines in such a way that all get the final result.
+
+    After the call the returned tensor is going to be bitwise
+    identical in all processes.
+
+    Arguments:
+        tensor (Tensor): Input of the collective.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        Tensor: Output of the collective
+
+    """
+    return _AllReduce.apply(op, group, tensor)
+
+
+class _Broadcast(Function):
+    @staticmethod
+    def forward(ctx, src, group, tensor):
+        ctx.src = src
+        ctx.group = group
+        ctx.rank = dist.get_rank(group=group)
+        # torch.distributed makes all the calls in place
+        # we allocate new tensors to avoid this
+        tensor = tensor.clone()
+        dist.broadcast(tensor, src, group=group)
+        return tensor
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        gx = _Reduce.apply(ctx.src, ReduceOp.SUM, ctx.group, grad_output)
+        if ctx.src != ctx.rank:
+            gx.zero_()
+        return (None, None, gx)
+
+
+class _Gather(Function):
+    @staticmethod
+    def forward(ctx, dst, group, tensor):
+        ctx.dst = dst
+        ctx.group = group
+        # Need to create a list of tensors here to do the
+        # aggregation, get it from the group size
+        # tensor should be correctly sized for the method
+        # gathering
+        tensor_list = [
+            torch.zeros_like(tensor) for i in range(dist.get_world_size(group=group))
+        ]
+
+        tensor = tensor.contiguous()
+        if dist.get_rank(group=group) == dst:
+            dist.gather(tensor, tensor_list, dst, group=group)
+        else:
+            dist.gather(tensor, None, dst, group=group)
+        return tuple(tensor_list)
+
+    @staticmethod
+    def backward(ctx, *grad_outputs):
+        return (None, None) + (_Scatter.apply(ctx.dst, ctx.group, *grad_outputs),)
+
+
+class _Scatter(Function):
+    @staticmethod
+    def forward(ctx, src, group, *tensors):
+        ctx.src = src
+        ctx.group = group
+        assert all(t.size() == tensors[0].size() for t in tensors)
+        output = torch.zeros_like(tensors[0])
+        if dist.get_rank(group=group) == src:
+            dist.scatter(output, list(tensors), src, group=group)
+        else:
+            dist.scatter(output, None, src, group=group)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return (None, None) + _Gather.apply(ctx.src, ctx.group, grad_output)
+
+
+class _Reduce(Function):
+    @staticmethod
+    def forward(ctx, src, op, group, tensor):
+        ctx.src = src
+        ctx.group = group
+        tensor = tensor.clone()
+        dist.reduce(tensor, src, op=op, group=group)
+        return tensor
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return (None, None, None) + (_Broadcast.apply(ctx.src, ctx.group, grad_output),)
+
+
+class _Reduce_Scatter(Function):
+    @staticmethod
+    def forward(ctx, op, group, tensor, *input_tensor_list):
+        ctx.group = group
+        # Need contiguous tensors for collectives.
+        tensor = tensor.contiguous()
+        input_tensor_list = tuple(t.contiguous() for t in input_tensor_list)
+        dist.reduce_scatter(tensor, list(input_tensor_list), op=op, group=group)
+        return tensor
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return (None, None, None) + _AllGather.apply(ctx.group, grad_output)
+
+
+class _AllGather(Function):
+    @staticmethod
+    def forward(ctx, group, tensor):
+        # Need contiguous tensors for collectives.
+        tensor = tensor.contiguous()
+
+        ctx.group = group
+        out_tensor_list = [
+            torch.empty_like(tensor) for _ in range(dist.get_world_size(group=group))
+        ]
+
+        dist.all_gather(out_tensor_list, tensor, group=group)
+        return tuple(out_tensor_list)
+
+    @staticmethod
+    def backward(ctx, *grad_outputs):
+        if dist.get_backend(group=ctx.group) is dist.Backend.NCCL:
+            rank = dist.get_rank(group=ctx.group)
+            gx = torch.empty_like(grad_outputs[rank])
+            gx = _Reduce_Scatter.apply(ReduceOp.SUM, ctx.group, gx, *grad_outputs)
+        else:
+            # As many backends doesn't support ReduceScatter, we use AlltoAll with .sum()
+            # to emulate the ReduceScatter behavior
+            tensor_list = [torch.empty_like(tensor) for tensor in grad_outputs]
+            gxs = _AlltoAll.apply(ctx.group, tensor_list, *grad_outputs)
+            gx = torch.sum(torch.stack(gxs), dim=0)
+        return (None, gx)
+
+
+class _AllGatherBase(Function):
+    @staticmethod
+    def forward(ctx, output_tensor, input_tensor, group):
+        ctx.group = group
+        dist._all_gather_base(output_tensor, input_tensor.contiguous(), group=group)
+        return output_tensor
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if dist.get_backend(group=ctx.group) is dist.Backend.NCCL:
+            world_size = dist.get_world_size(group=ctx.group)
+            out_size = list(grad_output.size())
+            if out_size[0] % world_size != 0:
+                raise RuntimeError(
+                    f"Tensor with dimensions: {out_size} does "
+                    f"not have first dimension divisible by world_size: {world_size}"
+                )
+            out_size[0] = out_size[0] // dist.get_world_size(group=ctx.group)
+            gx = torch.empty(
+                out_size, device=grad_output.device, dtype=grad_output.dtype
+            )
+            dist._reduce_scatter_base(gx, grad_output, ReduceOp.SUM, ctx.group)
+        else:
+            raise RuntimeError("Backend not supported!")
+        return (None, gx, None)
+
+
+class _AlltoAll(Function):
+    @staticmethod
+    def forward(ctx, group, out_tensor_list, *tensors):
+        ctx.group = group
+        ctx.input_tensor_size_list = [
+            tensors[i].size() for i in range(dist.get_world_size(group=group))
+        ]
+        my_rank = dist.get_rank(group=group)
+        tensors = tuple(t.contiguous() for t in tensors)
+        # Implement it on means of scatter/gather, send/recv async operations have issues
+        if dist.get_backend(group=group) is dist.Backend.GLOO:
+            for i in range(dist.get_world_size(group=group)):
+                to_send = None
+                if i == my_rank:
+                    to_send = list(tensors)
+                dist.scatter(out_tensor_list[i], to_send, i, group=group)
+        else:
+            dist.all_to_all(
+                out_tensor_list,
+                list(tensors),
+                group=group,
+            )
+        return tuple(out_tensor_list)
+
+    @staticmethod
+    def backward(ctx, *grad_outputs):
+        tensor_list = [
+            torch.empty(
+                size, device=grad_outputs[0].device, dtype=grad_outputs[0].dtype
+            )
+            for size in ctx.input_tensor_size_list
+        ]
+        return (None, None) + _AlltoAll.apply(ctx.group, tensor_list, *grad_outputs)
+
+
+class _AlltoAllSingle(Function):
+    @staticmethod
+    def forward(ctx, group, output, output_split_sizes, input_split_sizes, input):
+        ctx.group = group
+        ctx.input_size = input.size()
+        ctx.output_split_sizes = input_split_sizes
+        ctx.input_split_sizes = output_split_sizes
+        dist.all_to_all_single(
+            output,
+            input,
+            output_split_sizes=output_split_sizes,
+            input_split_sizes=input_split_sizes,
+            group=group,
+        )
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        tensor = torch.empty(
+            ctx.input_size, device=grad_output.device, dtype=grad_output.dtype
+        )
+        return (None, None, None, None) + (
+            _AlltoAllSingle.apply(
+                ctx.group,
+                tensor,
+                ctx.output_split_sizes,
+                ctx.input_split_sizes,
+                grad_output.contiguous(),
+            ),
+        )
+
+
+class _AllReduce(Function):
+    @staticmethod
+    def forward(ctx, op, group, tensor):
+        ctx.group = group
+        ctx.op = op
+        tensor = tensor.clone(memory_format=torch.contiguous_format)
+        dist.all_reduce(tensor, op=op, group=group)
+        return tensor
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return (None, None) + (_AllReduce.apply(ctx.op, ctx.group, grad_output),)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/jit/instantiator.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/jit/instantiator.py
new file mode 100644
index 0000000000000000000000000000000000000000..9465eb036daab4b81c82abee1eb38f92ac4d037c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/jit/instantiator.py
@@ -0,0 +1,156 @@
+#!/usr/bin/python3
+# mypy: allow-untyped-defs
+import atexit
+import importlib
+import logging
+import os
+import sys
+import tempfile
+from typing import Optional
+
+import torch
+from torch.distributed.nn.jit.templates.remote_module_template import (
+    get_remote_module_template,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+_FILE_PREFIX = "_remote_module_"
+_TEMP_DIR = tempfile.TemporaryDirectory()
+INSTANTIATED_TEMPLATE_DIR_PATH = _TEMP_DIR.name
+atexit.register(_TEMP_DIR.cleanup)
+logger.info("Created a temporary directory at %s", INSTANTIATED_TEMPLATE_DIR_PATH)
+sys.path.append(INSTANTIATED_TEMPLATE_DIR_PATH)
+
+
+def get_arg_return_types_from_interface(module_interface):
+    assert getattr(module_interface, "__torch_script_interface__", False), (
+        "Expect a TorchScript class interface decorated by @torch.jit.interface."
+    )
+    qualified_name = torch._jit_internal._qualified_name(module_interface)
+    cu = torch.jit._state._python_cu
+    module_interface_c = cu.get_interface(qualified_name)
+    assert "forward" in module_interface_c.getMethodNames(), (
+        f"Expect forward in interface methods, while it has {module_interface_c.getMethodNames()}"
+    )
+    method_schema = module_interface_c.getMethod("forward")
+
+    arg_str_list = []
+    arg_type_str_list = []
+    assert method_schema is not None
+    for argument in method_schema.arguments:
+        arg_str_list.append(argument.name)
+
+        if argument.has_default_value():
+            default_value_str = f" = {argument.default_value}"
+        else:
+            default_value_str = ""
+        arg_type_str = f"{argument.name}: {argument.type}{default_value_str}"
+        arg_type_str_list.append(arg_type_str)
+
+    arg_str_list = arg_str_list[1:]  # Remove "self".
+    args_str = ", ".join(arg_str_list)
+
+    arg_type_str_list = arg_type_str_list[1:]  # Remove "self".
+    arg_types_str = ", ".join(arg_type_str_list)
+
+    assert len(method_schema.returns) == 1
+    argument = method_schema.returns[0]
+    return_type_str = str(argument.type)
+
+    return args_str, arg_types_str, return_type_str
+
+
+def _write(out_path, text):
+    old_text: Optional[str]
+    try:
+        with open(out_path) as f:
+            old_text = f.read()
+    except OSError:
+        old_text = None
+    if old_text != text:
+        with open(out_path, "w") as f:
+            logger.info("Writing %s", out_path)
+            f.write(text)
+    else:
+        logger.info("Skipped writing %s", out_path)
+
+
+def _do_instantiate_remote_module_template(
+    generated_module_name, str_dict, enable_moving_cpu_tensors_to_cuda
+):
+    generated_code_text = get_remote_module_template(
+        enable_moving_cpu_tensors_to_cuda
+    ).format(**str_dict)
+    out_path = os.path.join(
+        INSTANTIATED_TEMPLATE_DIR_PATH, f"{generated_module_name}.py"
+    )
+    _write(out_path, generated_code_text)
+
+    # From importlib doc,
+    # > If you are dynamically importing a module that was created since
+    # the interpreter began execution (e.g., created a Python source file),
+    # you may need to call invalidate_caches() in order for the new module
+    # to be noticed by the import system.
+    importlib.invalidate_caches()
+    generated_module = importlib.import_module(f"{generated_module_name}")
+    return generated_module
+
+
+def instantiate_scriptable_remote_module_template(
+    module_interface_cls, enable_moving_cpu_tensors_to_cuda=True
+):
+    if not getattr(module_interface_cls, "__torch_script_interface__", False):
+        raise ValueError(
+            f"module_interface_cls {module_interface_cls} must be a type object decorated by "
+            "@torch.jit.interface"
+        )
+
+    # Generate the template instance name.
+    module_interface_cls_name = torch._jit_internal._qualified_name(
+        module_interface_cls
+    ).replace(".", "_")
+    generated_module_name = f"{_FILE_PREFIX}{module_interface_cls_name}"
+
+    # Generate type annotation strs.
+    assign_module_interface_cls_str = (
+        f"from {module_interface_cls.__module__} import "
+        f"{module_interface_cls.__name__} as module_interface_cls"
+    )
+    args_str, arg_types_str, return_type_str = get_arg_return_types_from_interface(
+        module_interface_cls
+    )
+    kwargs_str = ""
+    arrow_and_return_type_str = f" -> {return_type_str}"
+    arrow_and_future_return_type_str = f" -> Future[{return_type_str}]"
+
+    str_dict = dict(
+        assign_module_interface_cls=assign_module_interface_cls_str,
+        arg_types=arg_types_str,
+        arrow_and_return_type=arrow_and_return_type_str,
+        arrow_and_future_return_type=arrow_and_future_return_type_str,
+        args=args_str,
+        kwargs=kwargs_str,
+        jit_script_decorator="@torch.jit.script",
+    )
+    return _do_instantiate_remote_module_template(
+        generated_module_name, str_dict, enable_moving_cpu_tensors_to_cuda
+    )
+
+
+def instantiate_non_scriptable_remote_module_template():
+    generated_module_name = f"{_FILE_PREFIX}non_scriptable"
+    str_dict = dict(
+        assign_module_interface_cls="module_interface_cls = None",
+        args="*args",
+        kwargs="**kwargs",
+        arg_types="*args, **kwargs",
+        arrow_and_return_type="",
+        arrow_and_future_return_type="",
+        jit_script_decorator="",
+    )
+    # For a non-scriptable template, always enable moving CPU tensors to a cuda device,
+    # because there is no syntax limitation on the extra handling caused by the script.
+    return _do_instantiate_remote_module_template(generated_module_name, str_dict, True)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/jit/templates/remote_module_template.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/jit/templates/remote_module_template.py
new file mode 100644
index 0000000000000000000000000000000000000000..07b055774b36af4835e308c8a1f85afd0ab35f0f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/nn/jit/templates/remote_module_template.py
@@ -0,0 +1,108 @@
+#!/usr/bin/python3
+# mypy: allow-untyped-defs
+
+
+def get_remote_module_template(enable_moving_cpu_tensors_to_cuda: bool):
+    return _TEMPLATE_PREFIX + (
+        _REMOTE_FORWARD_TEMPLATE_ENABLE_MOVING_CPU_TENSORS_TO_CUDA
+        if enable_moving_cpu_tensors_to_cuda
+        else _REMOTE_FORWARD_TEMPLATE
+    )
+
+
+_TEMPLATE_PREFIX = """from typing import *
+
+import torch
+import torch.distributed.rpc as rpc
+from torch import Tensor
+from torch._jit_internal import Future
+from torch.distributed.rpc import RRef
+from typing import Tuple  # pyre-ignore: unused import
+
+
+{assign_module_interface_cls}
+
+
+def forward_async(self, {arg_types}){arrow_and_future_return_type}:
+    args = (self.module_rref, self.device, self.is_device_map_set, {args})
+    kwargs = {{{kwargs}}}
+    return rpc.rpc_async(
+        self.module_rref.owner(),
+        _remote_forward,
+        args,
+        kwargs,
+    )
+
+
+def forward(self, {arg_types}){arrow_and_return_type}:
+    args = (self.module_rref, self.device, self.is_device_map_set, {args})
+    kwargs = {{{kwargs}}}
+    ret_fut = rpc.rpc_async(
+        self.module_rref.owner(),
+        _remote_forward,
+        args,
+        kwargs,
+    )
+    return ret_fut.wait()
+
+
+_generated_methods = [
+    forward_async,
+    forward,
+]
+
+
+{jit_script_decorator}
+"""
+
+# This template may cause typing error (the mismatch between ``Tuple[()]`` and ``Tuple[Any]``)
+# even if the code is only used for instantiation but not execution.
+# Therefore, only include handling moving CPU tensors to a cuda device if necessary.
+# TODO: Merge these two templates together in the future once TorchScript syntax is improved.
+_REMOTE_FORWARD_TEMPLATE_ENABLE_MOVING_CPU_TENSORS_TO_CUDA = """
+def _remote_forward(
+    module_rref: RRef[module_interface_cls], device: str, is_device_map_set: bool, {arg_types}){arrow_and_return_type}:
+    module = module_rref.local_value()
+    device = torch.device(device)
+
+    if device.type != "cuda":
+        return module.forward({args}, {kwargs})
+
+    # If the module is on a cuda device,
+    # move any CPU tensor in args or kwargs to the same cuda device.
+    # Since torch script does not support generator expression,
+    # have to use concatenation instead of
+    # ``tuple(i.to(device) if isinstance(i, Tensor) else i for i in *args)``.
+    args = ({args},)
+    out_args: Tuple[()] = ()
+    for arg in args:
+        arg = (arg.to(device),) if isinstance(arg, Tensor) else (arg,)
+        out_args = out_args + arg
+
+    kwargs = {{{kwargs}}}
+    for k, v in kwargs.items():
+        if isinstance(v, Tensor):
+            kwargs[k] = kwargs[k].to(device)
+
+    if is_device_map_set:
+        return module.forward(*out_args, {kwargs})
+
+    # If the device map is empty, then only CPU tensors are allowed to send over wire,
+    # so have to move any GPU tensor to CPU in the output.
+    # Since torch script does not support generator expression,
+    # have to use concatenation instead of
+    # ``tuple(i.cpu() if isinstance(i, Tensor) else i for i in module.forward(*out_args, {kwargs}))``.
+    ret: Tuple[()] = ()
+    for i in module.forward(*out_args, {kwargs}):
+        i = (i.cpu(),) if isinstance(i, Tensor) else (i,)
+        ret = ret + i
+    return ret
+"""
+
+_REMOTE_FORWARD_TEMPLATE = """
+def _remote_forward(
+    module_rref: RRef[module_interface_cls], device: str, is_device_map_set: bool, {arg_types}){arrow_and_return_type}:
+    module = module_rref.local_value()
+
+    return module.forward({args}, {kwargs})
+"""
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..faac68bb632934ba730ba7c5ce3cf7fe934a58cf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/__init__.py
@@ -0,0 +1,44 @@
+"""
+:mod:`torch.distributed.optim` exposes DistributedOptimizer, which takes a list
+of remote parameters (:class:`~torch.distributed.rpc.RRef`) and runs the
+optimizer locally on the workers where the parameters live.  The distributed
+optimizer can use any of the local optimizer :ref:`optimizer-algorithms` to
+apply the gradients on each worker.
+"""
+
+import warnings
+
+import torch
+from torch import optim
+
+from .apply_optimizer_in_backward import (
+    _apply_optimizer_in_backward,
+    _get_in_backward_optimizers,
+)
+from .functional_adadelta import _FunctionalAdadelta
+from .functional_adagrad import _FunctionalAdagrad
+from .functional_adam import _FunctionalAdam
+from .functional_adamax import _FunctionalAdamax
+from .functional_adamw import _FunctionalAdamW
+from .functional_rmsprop import _FunctionalRMSprop
+from .functional_rprop import _FunctionalRprop
+from .functional_sgd import _FunctionalSGD
+from .named_optimizer import _NamedOptimizer
+from .utils import as_functional_optim
+
+
+# DistributedOptimizer imports torch.distributed.rpc names, so gate availability
+# based on RPC being available.
+if hasattr(torch._C, "_rpc_init"):
+    from .optimizer import DistributedOptimizer
+
+from .post_localSGD_optimizer import PostLocalSGDOptimizer
+from .zero_redundancy_optimizer import ZeroRedundancyOptimizer
+
+
+__all__ = [
+    "as_functional_optim",
+    "DistributedOptimizer",
+    "PostLocalSGDOptimizer",
+    "ZeroRedundancyOptimizer",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/_deprecation_warning.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/_deprecation_warning.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3434a4cd4f081843295e488c18a67a5c297fcbf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/_deprecation_warning.py
@@ -0,0 +1,16 @@
+import warnings
+
+import torch
+
+
+@torch.jit.ignore  # type: ignore[misc]
+def _scripted_functional_optimizer_deprecation_warning(stacklevel: int = 0) -> None:
+    with warnings.catch_warnings():
+        warnings.simplefilter("always")
+        warnings.warn(
+            "`TorchScript` support for functional optimizers is deprecated "
+            "and will be removed in a future PyTorch release. "
+            "Consider using the `torch.compile` optimizer instead.",
+            DeprecationWarning,
+            stacklevel=stacklevel + 2,
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ff9854793df1aa96a27cb105a1afd1190df942a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py
@@ -0,0 +1,121 @@
+from collections.abc import Iterable
+from typing import Any, no_type_check
+
+import torch
+
+
+__all__: list[str] = []
+
+# WeakTensorKeyDictionary to store relevant meta-data for the Tensor/Parameter
+# without changing it's life-time.
+# NOTE: Alternative is to add the meta-data as an attribute to the tensor,
+#       but that will serialize the meta-data if Tensor is serialized.
+param_to_optim_hook_handle_map = torch.utils.weak.WeakTensorKeyDictionary()
+param_to_acc_grad_map = torch.utils.weak.WeakTensorKeyDictionary()
+
+
+@no_type_check
+def _apply_optimizer_in_backward(
+    optimizer_class: type[torch.optim.Optimizer],
+    params: Iterable[torch.nn.Parameter],
+    optimizer_kwargs: dict[str, Any],
+    register_hook: bool = True,
+) -> None:
+    """
+    Upon ``backward()``, the optimizer specified for each parameter will fire after
+    the gradient has been accumulated into the parameter.
+
+    Note - gradients for these parameters will be set to None after ``backward()``.
+    This means that any other optimizer not specified via `_apply_optimizer_in_backward`
+    over this parameter will be a no-op.
+
+    Args:
+        optimizer_class: (Type[torch.optim.Optimizer]): Optimizer to apply to parameter
+        params: (Iterator[nn.Parameter]): parameters to apply optimizer state to
+        optimizer_kwargs: (Dict[str, Any]): kwargs to pass to optimizer constructor
+        register_hook: (bool): whether to register a hook that runs the optimizer
+            after gradient for this parameter is accumulated. This is the default
+            way that optimizer in backward is implemented, but specific use cases
+            (such as DDP) may wish to override this to implement custom behavior.
+            (Default = True)
+
+    Example::
+        params_generator = model.parameters()
+        param_1 = next(params_generator)
+        remainder_params = list(params_generator)
+
+        apply_optimizer_in_backward(torch.optim.SGD, [param_1], {"lr": 0.02})
+        apply_optimizer_in_backward(torch.optim.Adam, remainder_params, {"lr": 0.04})
+
+        model(...).sum().backward()  # after backward, parameters will already
+        # have their registered optimizer(s) applied.
+
+    """
+    torch._C._log_api_usage_once("torch.distributed.optim.apply_optimizer_in_backward")
+
+    @no_type_check
+    def _apply_optimizer_in_backward_to_param(param: torch.nn.Parameter) -> None:
+        # view_as creates a node in autograd graph that allows us access to the
+        # parameter's AccumulateGrad autograd function object. We register a
+        # hook on this object to fire the optimizer when the gradient for
+        # this parameter is ready (has been accumulated into .grad field)
+
+        # Don't create a new acc_grad if we already have one
+        # i.e. for shared parameters or attaching multiple optimizers to a param.
+        if param not in param_to_acc_grad_map:
+            param_to_acc_grad_map[param] = param.view_as(param).grad_fn.next_functions[
+                0
+            ][0]
+
+        optimizer = optimizer_class([param], **optimizer_kwargs)
+
+        if not hasattr(param, "_in_backward_optimizers"):
+            param._in_backward_optimizers = []  # type: ignore[attr-defined]
+            # TODO: Remove these attributes once we have a better way of accessing
+            # optimizer classes and kwargs for a parameter.
+            param._optimizer_classes = []  # type: ignore[attr-defined]
+            param._optimizer_kwargs = []  # type: ignore[attr-defined]
+
+        param._in_backward_optimizers.append(optimizer)  # type: ignore[attr-defined]
+        param._optimizer_classes.append(optimizer_class)  # type: ignore[attr-defined]
+        param._optimizer_kwargs.append(optimizer_kwargs)  # type: ignore[attr-defined]
+
+        if not register_hook:
+            return
+
+        def optimizer_hook(*_unused) -> None:
+            for opt in param._in_backward_optimizers:  # type: ignore[attr-defined]
+                opt.step()
+
+            param.grad = None
+
+        handle = param_to_acc_grad_map[param].register_hook(optimizer_hook)  # type: ignore[attr-defined]
+        if param not in param_to_optim_hook_handle_map:
+            param_to_optim_hook_handle_map[param] = []
+        param_to_optim_hook_handle_map[param].append(handle)
+
+    for param in params:
+        _apply_optimizer_in_backward_to_param(param)
+
+
+def _get_in_backward_optimizers(module: torch.nn.Module) -> list[torch.optim.Optimizer]:
+    """
+    Return a list of in-backward optimizers applied to ``module``'s parameters. Note that these
+    optimizers are not intended to directly have their ``step`` or ``zero_grad`` methods called
+    by the user and are intended to be used for things like checkpointing.
+
+    Args:
+        module: (torch.nn.Module): model to retrieve in-backward optimizers for
+
+    Returns:
+        List[torch.optim.Optimizer]: the in-backward optimizers.
+
+    Example::
+        _apply_optimizer_in_backward(torch.optim.SGD, model.parameters(), {"lr": 0.01})
+        optims = _get_optimizers_in_backward(model)
+    """
+    optims: list[torch.optim.Optimizer] = []
+    for param in module.parameters():
+        optims.extend(getattr(param, "_in_backward_optimizers", []))
+
+    return optims
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adadelta.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adadelta.py
new file mode 100644
index 0000000000000000000000000000000000000000..9af7bba4680dc668dcee8a7330a0447511fcf209
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adadelta.py
@@ -0,0 +1,111 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional Adadelta Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdadelta:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1.0,
+        rho: float = 0.9,
+        eps: float = 1e-6,
+        weight_decay: float = 0.0,
+        foreach: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+            "rho": rho,
+            "eps": eps,
+            "weight_decay": weight_decay,
+        }
+        self.foreach = foreach
+        self.maximize = maximize
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+    def step(self, gradients: list[Optional[Tensor]]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        square_avgs = []
+        acc_deltas = []
+        state_steps = []
+        lr = self.defaults["lr"]
+        rho = self.defaults["rho"]
+        eps = self.defaults["eps"]
+        weight_decay = self.defaults["weight_decay"]
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+        has_complex = False
+        for param, gradient in zip(params, gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    state["square_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    state["acc_delta"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+
+                state = self.state[param]
+                square_avgs.append(state["square_avg"])
+                acc_deltas.append(state["acc_delta"])
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adadelta(
+                params_with_grad,
+                grads,
+                square_avgs,
+                acc_deltas,
+                state_steps,
+                lr=lr,
+                rho=rho,
+                eps=eps,
+                weight_decay=weight_decay,
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adagrad.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adagrad.py
new file mode 100644
index 0000000000000000000000000000000000000000..5820a94183c724c38b2d9c8ffb1cc7290ad46b1d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adagrad.py
@@ -0,0 +1,115 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional Adagrad Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly let the user pass gradients to the `step` function
+# this is so that we could separate the gradients and parameters
+# and allow multithreaded trainer to update the parameters
+# without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdagrad:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-2,
+        lr_decay: float = 0.0,
+        weight_decay: float = 0.0,
+        initial_accumulator_value: float = 0.0,
+        warmup_lr_multiplier: float = 1.0,
+        warmup_num_iters: float = 0.0,
+        eps: float = 1e-10,
+        coalesce_grad: bool = True,
+        foreach: bool = False,
+        fused: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+            "lr_decay": lr_decay,
+            "eps": eps,
+            "weight_decay": weight_decay,
+            "initial_accumulator_value": initial_accumulator_value,
+            "warmup_lr_multiplier": warmup_lr_multiplier,
+            "warmup_num_iters": warmup_num_iters,
+        }
+        self.coalesce_grad = coalesce_grad
+        self.foreach = foreach
+        self.fused = fused
+        self.maximize = maximize
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+        # TODO: no union or any types in TorchScript, make step a scalar tensor instead
+        # This is also needed by if we want to share_memory on the step across processes
+        for p in self.param_group["params"]:
+            self.state[p] = {
+                "sum": torch.full_like(p.data, initial_accumulator_value),
+                "step": torch.tensor(0.0),
+            }
+
+    def step(self, gradients: list[Optional[Tensor]]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        state_sums = []
+        state_steps: list[Tensor] = []
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_sparse_grad, has_complex = False, False
+        for param, gradient in zip(self.param_group["params"], gradients):
+            if gradient is not None:
+                has_sparse_grad |= gradient.is_sparse
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                state = self.state[param]
+                state_sums.append(state["sum"])
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adagrad(
+                params,
+                grads,
+                state_sums,
+                state_steps,
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                lr_decay=self.defaults["lr_decay"],
+                eps=self.defaults["eps"],
+                has_sparse_grad=has_sparse_grad,
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adam.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adam.py
new file mode 100644
index 0000000000000000000000000000000000000000..b736cd4d164f73a93f1e0d0992e56db4f94c41d0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adam.py
@@ -0,0 +1,202 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional Adam Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdam:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-3,
+        betas: tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 0.0,
+        amsgrad: bool = False,
+        maximize: bool = False,
+        foreach: bool = False,
+        fused: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        self.defaults = {
+            "lr": lr,
+            "eps": eps,
+            "beta1": betas[0],
+            "beta2": betas[1],
+            "weight_decay": weight_decay,
+        }
+        self.amsgrad = amsgrad
+        self.maximize = maximize
+        self.foreach = foreach
+        self.fused = fused
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+    def step_param(self, param: Tensor, grad: Optional[Tensor]):
+        """
+        Similar to step, but operates on a single parameter and optionally a
+        gradient tensor.
+        """
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_avg_sqs = []
+        max_exp_avg_sqs = []
+        state_steps: list[Tensor] = []
+        has_complex = torch.is_complex(param)
+        if grad is not None:
+            params_with_grad.append(param)
+            grads.append(grad)
+        if param not in self.state:
+            self.state[param] = {}
+            state = self.state[param]
+            state["step"] = torch.tensor(0.0)
+            state["exp_avg"] = torch.zeros_like(
+                param, memory_format=torch.preserve_format
+            )
+            state["exp_avg_sq"] = torch.zeros_like(
+                param, memory_format=torch.preserve_format
+            )
+            if self.amsgrad:
+                state["max_exp_avg_sq"] = torch.zeros_like(
+                    param, memory_format=torch.preserve_format
+                )
+
+        state = self.state[param]
+        exp_avgs.append(state["exp_avg"])
+        exp_avg_sqs.append(state["exp_avg_sq"])
+
+        if self.amsgrad:
+            max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+
+        state_steps.append(state["step"])
+        with torch.no_grad():
+            F.adam(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=self.amsgrad,
+                has_complex=has_complex,
+                maximize=self.maximize,
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                eps=self.defaults["eps"],
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+            )
+
+    def step(self, gradients: list[Optional[Tensor]]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_avg_sqs = []
+        max_exp_avg_sqs = []
+        state_steps: list[Tensor] = []
+        has_complex = False
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        for param, gradient in zip(self.param_group["params"], gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    if self.amsgrad:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state["max_exp_avg_sq"] = torch.zeros_like(
+                            param, memory_format=torch.preserve_format
+                        )
+
+                state = self.state[param]
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+
+                if self.amsgrad:
+                    max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adam(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=self.amsgrad,
+                has_complex=has_complex,
+                maximize=self.maximize,
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                eps=self.defaults["eps"],
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adamax.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adamax.py
new file mode 100644
index 0000000000000000000000000000000000000000..9327eca3abfbb5e05ef5b88f211cda87f2e91e24
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adamax.py
@@ -0,0 +1,123 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional Adamax Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdamax:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-3,
+        betas: tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 0.0,
+        foreach: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        self.defaults = {
+            "lr": lr,
+            "eps": eps,
+            "beta1": betas[0],
+            "beta2": betas[1],
+            "weight_decay": weight_decay,
+        }
+        self.foreach = foreach
+        self.maximize = maximize
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+    def step(self, gradients: list[Optional[Tensor]]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_infs = []
+        state_steps: list[Tensor] = []
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_complex = False
+        for param, gradient in zip(self.param_group["params"], gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_inf"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+
+                state = self.state[param]
+
+                exp_avgs.append(state["exp_avg"])
+                exp_infs.append(state["exp_inf"])
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adamax(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_infs,
+                state_steps,
+                eps=self.defaults["eps"],
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adamw.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adamw.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d79cc0f27f0eb1c4a4d9af92281b255754b57ed
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adamw.py
@@ -0,0 +1,203 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional AdamW Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdamW:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-3,
+        betas: tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 1e-2,
+        amsgrad: bool = False,
+        maximize: bool = False,
+        foreach: bool = False,
+        fused: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        self.defaults = {
+            "lr": lr,
+            "eps": eps,
+            "beta1": betas[0],
+            "beta2": betas[1],
+            "weight_decay": weight_decay,
+        }
+        self.amsgrad = amsgrad
+        self.maximize = maximize
+        self.foreach = foreach
+        self.fused = fused
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+    def step_param(self, param: Tensor, grad: Optional[Tensor]):
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_avg_sqs = []
+        max_exp_avg_sqs = []
+        state_steps: list[Tensor] = []
+        has_complex = torch.is_complex(param)
+        if grad is not None:
+            params_with_grad.append(param)
+            grads.append(grad)
+        # Lazy state initialization
+        if param not in self.state:
+            self.state[param] = {}
+            state = self.state[param]
+            state["step"] = torch.tensor(0.0)
+            # Exponential moving average of gradient values
+            state["exp_avg"] = torch.zeros_like(
+                param, memory_format=torch.preserve_format
+            )
+            # Exponential moving average of squared gradient values
+            state["exp_avg_sq"] = torch.zeros_like(
+                param, memory_format=torch.preserve_format
+            )
+            if self.amsgrad:
+                # Maintains max of all exp. moving avg. of sq. grad. values
+                state["max_exp_avg_sq"] = torch.zeros_like(
+                    param, memory_format=torch.preserve_format
+                )
+
+        state = self.state[param]
+
+        exp_avgs.append(state["exp_avg"])
+        exp_avg_sqs.append(state["exp_avg_sq"])
+
+        if self.amsgrad:
+            max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+
+        state_steps.append(state["step"])
+        with torch.no_grad():
+            F.adamw(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=self.amsgrad,
+                maximize=self.maximize,
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                eps=self.defaults["eps"],
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+                has_complex=has_complex,
+            )
+
+    def step(self, gradients: list[Optional[Tensor]]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_avg_sqs = []
+        max_exp_avg_sqs = []
+        state_steps: list[Tensor] = []
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_complex = False
+        for param, gradient in zip(self.param_group["params"], gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    if self.amsgrad:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state["max_exp_avg_sq"] = torch.zeros_like(
+                            param, memory_format=torch.preserve_format
+                        )
+
+                state = self.state[param]
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+
+                if self.amsgrad:
+                    max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adamw(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=self.amsgrad,
+                maximize=self.maximize,
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                eps=self.defaults["eps"],
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+                has_complex=has_complex,
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_rmsprop.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_rmsprop.py
new file mode 100644
index 0000000000000000000000000000000000000000..424c2276bff085c9b5a962d3e7378d8a5a8c7edb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_rmsprop.py
@@ -0,0 +1,130 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional RMSprop Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalRMSprop:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-2,
+        alpha: float = 0.99,
+        eps: float = 1e-8,
+        weight_decay: float = 0.0,
+        momentum: float = 0.0,
+        centered: bool = False,
+        foreach: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+            "alpha": alpha,
+            "eps": eps,
+            "weight_decay": weight_decay,
+            "momentum": momentum,
+        }
+        self.centered = centered
+        self.foreach = foreach
+        self.maximize = maximize
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+    def step(self, gradients: list[Optional[Tensor]]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        square_avgs = []
+        grad_avgs = []
+        momentum_buffer_list = []
+        state_steps = []
+        lr = self.defaults["lr"]
+        alpha = self.defaults["alpha"]
+        eps = self.defaults["eps"]
+        momentum = self.defaults["momentum"]
+        weight_decay = self.defaults["weight_decay"]
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_complex = False
+        for param, gradient in zip(params, gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    state["square_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    if momentum > 0:
+                        state["momentum_buffer"] = torch.zeros_like(
+                            param, memory_format=torch.preserve_format
+                        )
+                    if self.centered:
+                        state["grad_avg"] = torch.zeros_like(
+                            param, memory_format=torch.preserve_format
+                        )
+
+                state = self.state[param]
+                square_avgs.append(state["square_avg"])
+                if momentum > 0:
+                    momentum_buffer_list.append(state["momentum_buffer"])
+                if self.centered:
+                    grad_avgs.append(state["grad_avg"])
+
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.rmsprop(
+                params_with_grad,
+                grads,
+                square_avgs,
+                grad_avgs,
+                momentum_buffer_list,
+                state_steps,
+                lr=lr,
+                alpha=alpha,
+                eps=eps,
+                weight_decay=weight_decay,
+                momentum=momentum,
+                centered=self.centered,
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_rprop.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_rprop.py
new file mode 100644
index 0000000000000000000000000000000000000000..877ea6bddef4792649389f5e883d41323cd10fd1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_rprop.py
@@ -0,0 +1,107 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional Rprop Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalRprop:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-2,
+        etas: tuple[float, float] = (0.5, 1.2),
+        step_sizes: tuple[float, float] = (1e-6, 50),
+        foreach: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+        }
+        self.etas = etas
+        self.step_sizes = step_sizes
+        self.foreach = foreach
+        self.maximize = maximize
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+    def step(self, gradients: list[Optional[Tensor]]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        prevs = []
+        step_sizes = []
+        state_steps = []
+        lr = self.defaults["lr"]
+        etaminus, etaplus = self.etas
+        step_size_min, step_size_max = self.step_sizes
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_complex = False
+        for param, gradient in zip(params, gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    state["prev"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    state["step_size"] = torch.full_like(gradient, lr)
+
+                state = self.state[param]
+                prevs.append(state["prev"])
+                step_sizes.append(state["step_size"])
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.rprop(
+                params_with_grad,
+                grads,
+                prevs,
+                step_sizes,
+                state_steps,
+                step_size_min=step_size_min,
+                step_size_max=step_size_max,
+                etaminus=etaminus,
+                etaplus=etaplus,
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_sgd.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_sgd.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0a00cf02e976365373c7c7183b2056bd62cc304
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_sgd.py
@@ -0,0 +1,166 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional SGD Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalSGD:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-2,
+        momentum: float = 0.0,
+        dampening: float = 0.0,
+        weight_decay: float = 0.0,
+        nesterov: bool = False,
+        maximize: bool = False,
+        foreach: bool = False,
+        fused: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+            "momentum": momentum,
+            "dampening": dampening,
+            "weight_decay": weight_decay,
+        }
+        self.nesterov = nesterov
+        self.maximize = maximize
+        self.foreach = foreach
+        self.fused = fused
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+    def step_param(self, param: Tensor, grad: Optional[Tensor]):
+        """Similar to self.step, but operates on a single parameter and
+        its gradient.
+        """
+        # TODO: Once step_param interface is robust, refactor step to call
+        # step param on each param.
+        weight_decay = self.defaults["weight_decay"]
+        momentum = self.defaults["momentum"]
+        dampening = self.defaults["dampening"]
+        lr = self.defaults["lr"]
+        params = [param]
+        momentum_buffer_list: list[Optional[Tensor]] = []
+        grads = []
+
+        has_sparse_grad = False
+        if grad is not None:
+            grads.append(grad)
+            if grad.is_sparse:
+                has_sparse_grad = True
+            if param not in self.state:
+                self.state[param] = {}
+            state = self.state[param]
+            if "momentum_buffer" not in state:
+                momentum_buffer_list.append(None)
+            else:
+                momentum_buffer_list.append(state["momentum_buffer"])
+
+        with torch.no_grad():
+            F.sgd(
+                params,
+                grads,
+                momentum_buffer_list,
+                weight_decay=weight_decay,
+                momentum=momentum,
+                lr=lr,
+                dampening=dampening,
+                nesterov=self.nesterov,
+                maximize=self.maximize,
+                has_sparse_grad=has_sparse_grad,
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+            )
+        # update momentum_buffer in state
+        state = self.state[param]
+        momentum_buffer = momentum_buffer_list[0]
+        if momentum_buffer is not None:
+            state["momentum_buffer"] = momentum_buffer
+
+    def step(self, gradients: list[Optional[Tensor]]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        momentum_buffer_list: list[Optional[Tensor]] = []
+        lr = self.defaults["lr"]
+        weight_decay = self.defaults["weight_decay"]
+        momentum = self.defaults["momentum"]
+        dampening = self.defaults["dampening"]
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_sparse_grad = False
+        for param, gradient in zip(params, gradients):
+            if gradient is not None:
+                params_with_grad.append(param)
+                grads.append(gradient)
+                if gradient.is_sparse:
+                    has_sparse_grad = True
+
+                if param not in self.state:
+                    self.state[param] = {}
+
+                state = self.state[param]
+                if "momentum_buffer" not in state:
+                    momentum_buffer_list.append(None)
+                else:
+                    momentum_buffer_list.append(state["momentum_buffer"])
+
+        with torch.no_grad():
+            F.sgd(
+                params_with_grad,
+                grads,
+                momentum_buffer_list,
+                weight_decay=weight_decay,
+                momentum=momentum,
+                lr=lr,
+                dampening=dampening,
+                nesterov=self.nesterov,
+                maximize=self.maximize,
+                has_sparse_grad=has_sparse_grad,
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+            )
+
+        # update momentum_buffers in state
+        for i, p in enumerate(params_with_grad):
+            state = self.state[p]
+            momentum_buffer = momentum_buffer_list[i]
+            if momentum_buffer is not None:
+                state["momentum_buffer"] = momentum_buffer
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/named_optimizer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/named_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8be46e6d155104e01a13f5bf09c1e73101c4a92
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/named_optimizer.py
@@ -0,0 +1,328 @@
+# mypy: allow-untyped-defs
+import logging
+import warnings
+from collections.abc import Collection, Mapping
+from copy import deepcopy
+from typing import Any, Callable, Optional, overload, Union
+
+import torch
+import torch.nn as nn
+from torch import optim
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+
+
+__all__: list[str] = []
+
+logger = logging.getLogger(__name__)
+
+
+class _NamedOptimizer(optim.Optimizer):
+    """
+    ``_NamedOptimizer`` takes a dict of parameters and exposes ``state_dict`` by parameter key.
+
+    We replace the original key (number) in an optim to the
+    fully qualified name (FQN) string. User can initialize the optim as they
+    initialize a PyTorch optim, the only difference is that they also need to
+    pass in the FQN of each parameters.
+
+    Args:
+        named_parameters (Mapping[str, Union[torch.Tensor, ShardedTensor]]):
+            Mapping from FQN to parameter.
+        optimizer_class (optim.Optimizer):
+            The class of optimizer to instantiate.
+        param_groups (Collection[Mapping[str, Any]]):
+            `param_groups` to pass to optimizer if specified.
+            The key of the inner map needs to be FQNs.
+            Default: None
+        module (nn.Module): the module whose parameters to updated
+            by the optimizer.
+        args: arguments to pass to the optimizer constructor.
+        kwargs: arguments to pass to the optimizer constructor.
+
+    Example::
+        >>> # xdoctest: +SKIP("distributed")
+        >>> from torch import optim
+        >>> from torch.distributed.optim import _NamedOptimizer
+        >>>
+        >>> # Define the named optimizer.
+        >>> m = Model(...)
+        >>> named_optim = _NamedOptimizer(m.named_parameters(), optim.SGD)
+        >>> # Forward pass + backward pass.
+        >>> named_optim.step()
+        >>> ...
+        >>> # Call state_dict for the named optimizer returns a FQN state_dict.
+        >>> named_optim.state_dict()
+
+    Warning: This API is still in development and subject to change.
+
+    TODO: Add tutorial for _NamedOptimizer.
+    TODO: Add documentation in the docstring for the public attributes
+          like self.param_groups and self.named_parameters.
+    """
+
+    def __init__(
+        self,
+        named_parameters: Mapping[str, Union[torch.Tensor, ShardedTensor]],
+        optimizer_class: optim.Optimizer,
+        param_groups: Optional[Collection[Mapping[str, Any]]] = None,
+        module: Optional[nn.Module] = None,
+        *args,
+        **kwargs,
+    ) -> None:
+        torch._C._log_api_usage_once("torch.distributed.optim._NamedOptimizer")
+        self.param_groups: Collection[Mapping[str, Any]] = param_groups  # type: ignore[assignment]
+        self._param_groups_check()
+        self.named_parameters = dict(named_parameters)
+        params_for_optimizer = (
+            self.named_parameters.values() if param_groups is None else param_groups
+        )
+        self._optimizer = optimizer_class(  # type: ignore[operator]
+            params_for_optimizer,
+            *args,
+            **kwargs,
+        )
+        self.module = module
+        if param_groups is None:
+            self.ordered_param_keys = list(self.named_parameters.keys())
+        else:
+            warnings.warn(
+                "Since we pass in param_groups, we will use param_groups to "
+                "initialize the optimizer, not all parameters of the module."
+            )
+            param_to_key = {param: key for key, param in self.named_parameters.items()}  # type: ignore[misc, has-type]
+            ordered_param_keys = []
+            for group in param_groups:
+                for param in group["params"]:
+                    if param not in param_to_key:
+                        raise ValueError(
+                            f"Expect param name {param} found in param group but is missing."
+                        )
+                    ordered_param_keys.append(param_to_key[param])
+            self.ordered_param_keys = ordered_param_keys
+        # Update param_groups from optimizer.
+        self.param_groups = self._optimizer.param_groups
+
+    def _param_groups_check(self):
+        if self.param_groups is not None:
+            for param_group in self.param_groups:
+                assert isinstance(param_group, dict), "param group must be a dict"
+                assert "params" in param_group, "param group must contain key params"
+                params = param_group["params"]
+                if isinstance(params, torch.Tensor):
+                    params = [params]
+                params = list(params)
+                for param in params:
+                    if not isinstance(param, torch.Tensor):
+                        raise TypeError(
+                            "optimizer can only optimize Tensors, "
+                            "but one of the params is " + torch.typename(param)
+                        )
+                param_group["params"] = params
+
+    def state_dict(self) -> dict[str, Any]:
+        """
+        Return the ``state_dict`` of the optimizer.
+
+        Instead of using number to index
+        parameters, we will use module fully qualified name (FQN) as the key.
+        """
+        state_dict = self._optimizer.state_dict()
+        param_groups = state_dict["param_groups"]
+
+        ret_state = {
+            self.ordered_param_keys[st_key]: state_val
+            for st_key, state_val in state_dict["state"].items()
+        }
+
+        ret_groups = []
+        for group in param_groups:
+            param_keys = [self.ordered_param_keys[param] for param in group["params"]]
+            ret_group = {"params": sorted(param_keys)}
+            for k, v in group.items():
+                if k != "params":
+                    ret_group[k] = deepcopy(v)
+            ret_groups.append(ret_group)
+
+        return self._post_state_dict({"state": ret_state, "param_groups": ret_groups})
+
+    @overload
+    def step(self, closure: None = ...) -> None: ...
+
+    @overload
+    def step(self, closure: Callable[[], float]) -> float: ...
+
+    def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
+        """
+        Perform a single optimization step.
+
+        This will call :meth:`torch.optim.Optimizer.step` on the wrapped
+        optimizer.
+        """
+        return self._optimizer.step(closure=closure)
+
+    @property
+    def state(self) -> Mapping[torch.Tensor, Any]:  # type: ignore[override]
+        return self._optimizer.state
+
+    def load_state_dict(self, state_dict: Mapping[str, Any]) -> None:
+        """
+        Define the default behavior to load a state_dict for ``_NamedOptimizer``.
+
+        Sample Code
+        ```
+            my_model = MyModule()
+            optimizer = _NamedOptimizer(my_model.named_parameters(), Adagrad)
+            ...
+
+            optim_state_dict = optimizer.state_dict()
+            ...
+            ...
+
+            optimizer.load_state_dict(optim_state_dict)
+            ...
+        ```
+        Args:
+            state_dict (Dict[str, Any]) : A ``state_dict`` to load into the optimizer.
+                Note that this state dict update is performed in place.
+
+        .. note:: PyTorch is using lazy init to initialize the optim states.
+            So it is possible that there is no optim state when user call
+            ``load_state_dict`` and for ``_NamedOptimizer`` we make it stricter
+            that users can only call ``load_state_dict`` after the state is initialized.
+            By doing this, we can validate the optim ``state_dict`` to be loaded.
+        """
+        new_state_dict = self._optimizer.state_dict()
+        state_dict = self._pre_load_state_dict(state_dict)
+        state = state_dict["state"]
+        new_state = new_state_dict["state"]
+        if len(new_state) == 0:
+            raise ValueError(
+                "Expects the optim to be initialized before load but found not initialized."
+            )
+
+        for idx, param_key in enumerate(self.ordered_param_keys):
+            # When the conditional training is performed, not all parameters are updated in the optim.
+            if param_key not in state.keys():
+                continue
+            if len(state[param_key]) != len(new_state[idx]):
+                raise ValueError(
+                    f"Expects equal length as {len(new_state[idx])} for parameter {param_key} but found: {len(state[param_key])}"
+                )
+            # Iterate through all optimizer states.
+            for state_key, state_val in new_state[idx].items():
+                if state_key not in state[param_key]:
+                    raise ValueError(
+                        f"Expects state {state_key} for parameter {param_key} but not found."
+                    )
+
+                src_state_val = state[param_key][state_key]
+                if isinstance(state_val, ShardedTensor):
+                    assert isinstance(src_state_val, ShardedTensor)
+                    num_shards = len(state_val.local_shards())
+                    num_new_shards = len(src_state_val.local_shards())
+                    if num_shards != num_new_shards:
+                        raise ValueError(
+                            f"Expects equal number of shards as {num_new_shards} but found {num_shards} for {param_key}/{state_key}"
+                        )
+                    for shard, src_shard in zip(
+                        state_val.local_shards(), src_state_val.local_shards()
+                    ):
+                        shard.tensor.detach().copy_(src_shard.tensor)
+                elif isinstance(state_val, torch.Tensor):
+                    assert isinstance(src_state_val, torch.Tensor)
+                    state_val.detach().copy_(src_state_val)
+                else:
+                    new_state[idx][state_key] = deepcopy(src_state_val)
+
+        # Load param_groups of state_dict
+        src_param_groups = state_dict["param_groups"]
+        new_param_groups = new_state_dict["param_groups"]
+
+        src_group_map = {}
+        for group in src_param_groups:
+            param_keys = list(group["params"])
+            src_group_map[_gen_param_group_key(param_keys)] = group
+        new_group_map = {}
+        for new_group in new_param_groups:
+            param_keys = []
+            for param_key in new_group["params"]:
+                param_keys.append(self.ordered_param_keys[param_key])  # type: ignore[call-overload]
+            new_group_map[_gen_param_group_key(param_keys)] = new_group
+        for group_key, new_group in new_group_map.items():
+            # When not all parameters are used in training or receive gradient, aka., not all parameters
+            # would be in the param_group. Thus we skip the group_key here.
+            if group_key not in src_group_map:
+                continue
+            src_group = src_group_map[group_key]
+            if len(src_group) != len(new_group):
+                raise ValueError(
+                    f"Expects equal param_group size as {len(new_group)} for group {group_key} but found {len(src_group)}."
+                )
+            for k in src_group:
+                if k not in new_group:
+                    raise ValueError(
+                        f"Expects group key {k} to be in group {group_key} in `state_dict` but is missing."
+                    )
+                if k != "params":
+                    new_group[k] = deepcopy(src_group[k])
+
+        self._optimizer.load_state_dict(new_state_dict)
+
+    def add_param_group(self, param_group: Mapping[str, Any]) -> None:
+        """
+        Add a param group to the :class:`_NamedOptimizer` s `param_groups`.
+
+        Warning: This API is still in development and subject to change.
+        """
+        assert isinstance(param_group, dict), "param group must be a dict"
+
+        params = param_group["params"]
+        if isinstance(params, torch.Tensor):
+            param_group["params"] = [params]
+        else:
+            param_group["params"] = list(params)
+
+        param_to_key = {param: key for key, param in self.named_parameters.items()}  # type: ignore[misc, has-type]
+        for param in param_group["params"]:
+            if param not in param_to_key:
+                raise ValueError("some parameters are not in the module")
+            self.ordered_param_keys.append(param_to_key[param])
+
+        self._optimizer.add_param_group(param_group)
+        # Update param_groups from optimizer.
+        self.param_groups = self._optimizer.param_groups
+
+    def init_state(self) -> None:
+        """
+        Run a dummy optimizer step, which allows to initialize optimizer state because we do lazy init for most optimizers.
+
+        This allows doing in-place loading of optimizer state from a checkpoint.
+        """
+        for param in self.named_parameters.values():
+            if param.requires_grad:
+                t = torch.zeros_like(param)
+                param.grad = torch.autograd.Variable(t)
+        # Calling ``step`` will load the initial state for optimizer states.
+        self.step(closure=None)
+
+    def _pre_load_state_dict(self, state_dict) -> dict[str, Any]:
+        # TODO(chienchin): This API should be FSDP agnostic and should support
+        # general user hooks.
+        if isinstance(self.module, FSDP):
+            return FSDP.optim_state_dict_to_load(
+                self.module, self._optimizer, state_dict, is_named_optimizer=True
+            )
+        return state_dict
+
+    def _post_state_dict(self, state_dict) -> dict[str, Any]:
+        # TODO(chienchin): This API should be FSDP agnostic and should support
+        # general user hooks.
+        if isinstance(self.module, FSDP):
+            FSDP.optim_state_dict(self.module, self._optimizer, state_dict)
+        return state_dict
+
+
+def _gen_param_group_key(param_keys: list[str]) -> str:
+    """Concatenate all param keys as a unique indentifier for one param group."""
+    return "/".join(sorted(param_keys))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/optimizer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb7fb8a26a262a0d54683cb6b53a5be07d211e4f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/optimizer.py
@@ -0,0 +1,256 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import logging
+from collections import defaultdict
+from threading import Lock
+from typing import Optional
+
+import torch
+import torch.distributed.autograd as dist_autograd
+import torch.distributed.rpc as rpc
+import torch.jit as jit
+import torch.nn as nn
+from torch import Tensor
+from torch.distributed.rpc import RRef
+
+from .utils import functional_optim_map
+
+
+__all__ = ["DistributedOptimizer"]
+
+logger = logging.getLogger(__name__)
+
+
+# XXX: we define a _ScriptModuleOptimizer here to explicitly
+# compile the FunctionalOptimizer class into TorchScript
+# This is because ScriptClass instance still lives in
+# python unless you explicitly compile it as an attribute
+# in ScriptModule or pass it to a ScriptFunction
+# _ScriptLocalOptimizerInterface serves as a common
+# interface type for Optimizer ScriptModules.
+#
+# TODO (wanchaol): remove this once we added TorchScript
+# class reference semantics
+@jit.interface
+class _ScriptLocalOptimizerInterface:
+    def step(self, autograd_ctx_id: int) -> None:
+        pass
+
+
+class _ScriptLocalOptimizer(nn.Module):
+    # TorchScript does not support multithread concurrent compiling.
+    # request_callback might invoke concurrent compiling, so we
+    # serialize the compiling with a lock
+    compile_lock = Lock()
+
+    def __init__(self, optim_cls, local_params_rref, *args, **kwargs):
+        super().__init__()
+        self._local_params = [rref.local_value() for rref in local_params_rref]
+        self.optim = optim_cls(self._local_params, *args, **kwargs)
+
+    @jit.export
+    def step(self, autograd_ctx_id: int):
+        all_local_grads = dist_autograd.get_gradients(autograd_ctx_id)
+        # apply functional optimizer step with a list of gradients
+        grads: list[Optional[Tensor]] = [
+            all_local_grads[p] if p in all_local_grads else None
+            for p in self._local_params
+        ]
+
+        self.optim.step(grads)
+
+
+# TODO (wanchaol): remove/merge this with ScriptLocalOptimizer once
+# we have converted all to functional optimizer in distributed.optim
+class _LocalOptimizer:
+    # Ideally we would only need to share a lock for instances of
+    # _LocalOptimizer that deal with the same parameters. We are
+    # making a simplifying assumption here that if there is more
+    # than one instance of _LocalOptimizer per worker, they will
+    # be optimizing the same parameters (e.g. each data parallel
+    # trainer will create its own instance of _LocalOptimizer but
+    # they will all optimize the same parameters on each worker)
+    global_lock = Lock()
+
+    def __init__(self, optim_cls, local_params_rref, *args, **kwargs):
+        self._local_params = [rref.local_value() for rref in local_params_rref]
+        self.optim = optim_cls(self._local_params, *args, **kwargs)
+
+    def step(self, autograd_ctx_id):
+        all_local_grads = dist_autograd.get_gradients(autograd_ctx_id)
+
+        with _LocalOptimizer.global_lock:
+            for param, grad in all_local_grads.items():
+                param.grad = grad
+            self.optim.step()
+
+
+def _new_local_optimizer(optim_cls, local_params_rref, *args, **kwargs):
+    return rpc.RRef(_LocalOptimizer(optim_cls, local_params_rref, *args, **kwargs))
+
+
+def _local_optimizer_step(local_optim_rref, autograd_ctx_id):
+    local_optim = local_optim_rref.local_value()
+    local_optim.step(autograd_ctx_id)
+
+
+# new/step functions combined with _ScriptLocalOptimizer to provide GIL-free optimizer
+def _new_script_local_optimizer(optim_cls, local_params_rref, *args, **kwargs):
+    optim = _ScriptLocalOptimizer(optim_cls, local_params_rref, *args, **kwargs)
+
+    with _ScriptLocalOptimizer.compile_lock:
+        script_optim = jit.script(optim)
+        return rpc.RRef(script_optim, _ScriptLocalOptimizerInterface)
+
+
+@jit.script
+def _script_local_optimizer_step(
+    local_optim_rref: RRef[_ScriptLocalOptimizerInterface], autograd_ctx_id: int
+) -> None:
+    local_optim = local_optim_rref.local_value()
+    local_optim.step(autograd_ctx_id)
+
+
+def _wait_for_all(rpc_futs):
+    # TODO: improve error propagation
+    exception = None
+    results = []
+    for fut in rpc_futs:
+        try:
+            results.append(fut.wait())
+        except Exception as e:
+            results.append(e)
+            exception = e
+    if exception is not None:
+        raise exception
+    return results
+
+
+class DistributedOptimizer:
+    """
+    DistributedOptimizer takes remote references to parameters scattered
+    across workers and applies the given optimizer locally for each parameter.
+
+    This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
+    to retrieve the gradients for specific parameters.
+
+    Concurrent calls to
+    :meth:`~torch.distributed.optim.DistributedOptimizer.step`,
+    either from the same or different clients, will
+    be serialized on each worker -- as each worker's optimizer can only work
+    on one set of gradients at a time. However, there is no guarantee that
+    the full forward-backward-optimizer sequence will execute for one client
+    at a time. This means that the gradients being applied may not correspond
+    to the latest forward pass executed on a given worker. Also, there is no
+    guaranteed ordering across workers.
+
+    `DistributedOptimizer` creates the local optimizer with TorchScript enabled
+    by default, so that optimizer updates are not blocked by the Python Global
+    Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
+    Model Parallel). This feature is currently enabled for most optimizers. You
+    can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
+    for your own custom optimizers.
+
+    Args:
+        optimizer_class (optim.Optimizer): the class of optimizer to
+            instantiate on each worker.
+        params_rref (list[RRef]): list of RRefs to local or remote parameters
+            to optimize.
+        args: arguments to pass to the optimizer constructor on each worker.
+        kwargs: arguments to pass to the optimizer constructor on each worker.
+
+    Example::
+        >>> # xdoctest: +SKIP("distributed")
+        >>> import torch.distributed.autograd as dist_autograd
+        >>> import torch.distributed.rpc as rpc
+        >>> from torch import optim
+        >>> from torch.distributed.optim import DistributedOptimizer
+        >>>
+        >>> with dist_autograd.context() as context_id:
+        >>>   # Forward pass.
+        >>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
+        >>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
+        >>>   loss = rref1.to_here() + rref2.to_here()
+        >>>
+        >>>   # Backward pass.
+        >>>   dist_autograd.backward(context_id, [loss.sum()])
+        >>>
+        >>>   # Optimizer.
+        >>>   dist_optim = DistributedOptimizer(
+        >>>      optim.SGD,
+        >>>      [rref1, rref2],
+        >>>      lr=0.05,
+        >>>   )
+        >>>   dist_optim.step(context_id)
+
+    __ https://github.com/pytorch/tutorials/pull/1465
+    """
+
+    def __init__(self, optimizer_class, params_rref, *args, **kwargs):
+        torch._C._log_api_usage_once("torch.distributed.optim.DistributedOptimizer")
+        per_worker_params_rref = defaultdict(list)
+        for param in params_rref:
+            per_worker_params_rref[param.owner()].append(param)
+
+        if optimizer_class in functional_optim_map and jit._state._enabled:
+            optim_ctor = functional_optim_map.get(optimizer_class)
+        else:
+            optim_ctor = optimizer_class
+        self.is_functional_optim = optim_ctor != optimizer_class
+
+        if self.is_functional_optim:
+            optimizer_new_func = _new_script_local_optimizer
+        else:
+            logger.warning(
+                "Creating the optimizer %s without TorchScript support, "
+                "this might result in slow computation time in multithreading environment"
+                "(i.e. Distributed Model Parallel training on CPU) due to the Python's "
+                "Global Interpreter Lock (GIL). Please file an issue if you need this "
+                "optimizer in TorchScript. ",
+                optimizer_class,
+            )
+            optimizer_new_func = _new_local_optimizer
+
+        remote_optim_futs = []
+        for worker, param_rrefs in per_worker_params_rref.items():
+            remote_optim_rref_fut = rpc.rpc_async(
+                worker,
+                optimizer_new_func,
+                args=(optim_ctor, param_rrefs) + args,
+                kwargs=kwargs,
+            )
+            remote_optim_futs.append(remote_optim_rref_fut)
+
+        self.remote_optimizers = _wait_for_all(remote_optim_futs)
+
+    def step(self, context_id):
+        """
+        Performs a single optimization step.
+
+        This will call :meth:`torch.optim.Optimizer.step` on each worker
+        containing parameters to be optimized, and will block until all workers
+        return. The provided ``context_id`` will be used to retrieve the
+        corresponding :class:`~torch.distributed.autograd.context` that
+        contains the gradients that should be applied to the parameters.
+
+        Args:
+            context_id: the autograd context id for which we should run the
+                optimizer step.
+        """
+        dist_autograd._is_valid_context(context_id)
+
+        optimizer_step_func = (
+            _script_local_optimizer_step
+            if self.is_functional_optim
+            else _local_optimizer_step
+        )
+
+        rpc_futs = [
+            rpc.rpc_async(
+                optimizer.owner(),
+                optimizer_step_func,
+                args=(optimizer, context_id),
+            )
+            for optimizer in self.remote_optimizers
+        ]
+        _wait_for_all(rpc_futs)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/post_localSGD_optimizer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/post_localSGD_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c0027d112407396c86a6f9966ac3f4e7a5660a3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/post_localSGD_optimizer.py
@@ -0,0 +1,110 @@
+# mypy: allow-untyped-defs
+import warnings
+
+import torch
+import torch.distributed.algorithms.model_averaging.averagers as averagers
+
+
+class PostLocalSGDOptimizer(torch.optim.Optimizer):
+    r"""
+    Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_,
+    This optimizer runs local optimizer at every step.
+    After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.
+
+    Args:
+        optim: The local optimizer.
+        averager: A model averager instance to run post-localSGD algorithm.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> import torch
+        >>> import torch.distributed as dist
+        >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
+        >>> import torch.nn as nn
+        >>> from torch.distributed.optim import PostLocalSGDOptimizer
+        >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
+        >>>   PostLocalSGDState,
+        >>>   post_localSGD_hook,
+        >>> )
+        >>>
+        >>> model = nn.parallel.DistributedDataParallel(
+        >>>    module, device_ids=[rank], output_device=rank
+        >>> )
+        >>>
+        >>> # Register a post-localSGD communication hook.
+        >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
+        >>> model.register_comm_hook(state, post_localSGD_hook)
+        >>>
+        >>> # Create a post-localSGD optimizer that wraps a local optimizer.
+        >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
+        >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
+        >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
+        >>> opt = PostLocalSGDOptimizer(
+        >>>     optim=local_optim,
+        >>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
+        >>> )
+        >>>
+        >>> # In the first 100 steps, DDP runs global gradient averaging at every step.
+        >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
+        >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
+        >>> for step in range(0, 200):
+        >>>    opt.zero_grad()
+        >>>    loss = loss_fn(output, labels)
+        >>>    loss.backward()
+        >>>    opt.step()
+    """
+
+    def __init__(self, optim: torch.optim.Optimizer, averager: averagers.ModelAverager):
+        self.optim = optim
+        self.param_groups = self.optim.param_groups
+        self.averager = averager
+
+    @property
+    def state(self):
+        return self.optim.state
+
+    def __repr__(self):
+        return self.optim.__repr__()
+
+    def state_dict(self):
+        r"""
+        This is the same as :class:`torch.optim.Optimizer` :meth:`state_dict`,
+        but adds an extra entry to record model averager's step to the checkpoint
+        to ensure reload does not cause unnecessary warm up again.
+        """
+        optim_state_dict = self.optim.state_dict()
+        optim_state_dict["step"] = self.averager.step
+        return optim_state_dict
+
+    def load_state_dict(self, state_dict):
+        r"""
+        This is the same as :class:`torch.optim.Optimizer` :meth:`load_state_dict`,
+        but also restores model averager's step value to the one
+        saved in the provided ``state_dict``.
+
+        If there is no ``"step"`` entry in ``state_dict``,
+        it will raise a warning and initialize the model averager's step to 0.
+        """
+        self.optim.load_state_dict(state_dict)
+        if "step" in state_dict:
+            self.averager.step = state_dict["step"]
+        else:
+            warnings.warn(
+                "Loaded state dict does not contain a step counter for an averager. "
+                "Setting step counter to 0."
+            )
+            self.averager.step = 0
+
+    def step(self):  # type: ignore[override]
+        r"""
+        Performs a single optimization step (parameter update).
+        """
+        self.optim.step()
+        self.averager.average_parameters(params=self.param_groups)
+
+    def zero_grad(self, set_to_none: bool = True):  # type: ignore[override]
+        self.optim.zero_grad(set_to_none=set_to_none)
+
+    def add_param_group(self, param_group):
+        self.optim.add_param_group(param_group)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7075edd2e5210f1dc3d50aaa09688a4a4e1d09c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/utils.py
@@ -0,0 +1,65 @@
+# mypy: allow-untyped-defs
+
+from torch import optim
+
+from .functional_adadelta import _FunctionalAdadelta
+from .functional_adagrad import _FunctionalAdagrad
+from .functional_adam import _FunctionalAdam
+from .functional_adamax import _FunctionalAdamax
+from .functional_adamw import _FunctionalAdamW
+from .functional_rmsprop import _FunctionalRMSprop
+from .functional_rprop import _FunctionalRprop
+from .functional_sgd import _FunctionalSGD
+
+
+# dict to map a user passed in optimizer_class to a functional
+# optimizer class if we have already defined inside the
+# distributed.optim package, this is so that we hide the
+# functional optimizer to user and still provide the same API.
+functional_optim_map = {
+    optim.Adagrad: _FunctionalAdagrad,
+    optim.Adam: _FunctionalAdam,
+    optim.AdamW: _FunctionalAdamW,
+    optim.SGD: _FunctionalSGD,
+    optim.Adadelta: _FunctionalAdadelta,
+    optim.RMSprop: _FunctionalRMSprop,
+    optim.Rprop: _FunctionalRprop,
+    optim.Adamax: _FunctionalAdamax,
+}
+
+
+def register_functional_optim(key, optim):
+    """
+    Interface to insert a new functional optimizer to functional_optim_map
+    ``fn_optim_key`` and ``fn_optimizer`` are user defined. The optimizer and key
+    need not be of :class:`torch.optim.Optimizer` (e.g. for custom optimizers)
+    Example::
+        >>> # import the new functional optimizer
+        >>> # xdoctest: +SKIP
+        >>> from xyz import fn_optimizer
+        >>> from torch.distributed.optim.utils import register_functional_optim
+        >>> fn_optim_key = "XYZ_optim"
+        >>> register_functional_optim(fn_optim_key, fn_optimizer)
+    """
+    if key not in functional_optim_map:
+        functional_optim_map[key] = optim
+
+
+def as_functional_optim(optim_cls: type, *args, **kwargs):
+    try:
+        functional_cls = functional_optim_map[optim_cls]
+    except KeyError as e:
+        raise ValueError(
+            f"Optimizer {optim_cls} does not have a functional counterpart!"
+        ) from e
+
+    return _create_functional_optim(functional_cls, *args, **kwargs)
+
+
+def _create_functional_optim(functional_optim_cls: type, *args, **kwargs):
+    return functional_optim_cls(
+        [],
+        *args,
+        **kwargs,
+        _allow_empty_param_list=True,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8414fd1374bc40c5cb68ae10a22bef6b46ce927
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py
@@ -0,0 +1,1660 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+r"""Zero Redundancy Optimizer."""
+
+import collections
+import copy
+import enum
+import inspect
+import io
+import logging
+from itertools import chain
+from typing import Any, Callable, Optional, Union
+
+import torch
+import torch.distributed as dist
+from torch.distributed.algorithms.join import Join, Joinable, JoinHook
+from torch.distributed.optim.utils import functional_optim_map
+from torch.optim import Optimizer
+
+
+__all__ = ["ZeroRedundancyOptimizer"]
+
+
+logger = logging.getLogger(__name__)
+
+
+# Credits:  classy_vision/generic/distributed_util.py
+def _recursive_copy_to_device(
+    value: Any,
+    non_blocking: bool,
+    device: torch.device,
+) -> Any:
+    r"""
+    Recursively searches lists, tuples, dicts and copies tensors to device if possible.
+
+    Non-tensor values are passed as-is in the result.
+
+    .. note::
+        These are all copies, so if there are two objects that reference
+        the same object, then after this call, there will be two different objects
+        referenced on the device.
+    """
+    if isinstance(value, torch.Tensor):
+        return value.to(device, non_blocking=non_blocking)
+
+    if isinstance(value, (list, tuple)):
+        values = [
+            _recursive_copy_to_device(val, non_blocking=non_blocking, device=device)
+            for val in value
+        ]
+        return values if isinstance(value, list) else tuple(values)
+
+    if isinstance(value, collections.abc.Mapping):
+        return {
+            key: _recursive_copy_to_device(
+                val, non_blocking=non_blocking, device=device
+            )
+            for key, val in value.items()
+        }
+
+    return value
+
+
+def _is_trainable(param: torch.Tensor) -> bool:
+    r"""Return if a parameter is trainable, where trainability is equivalent to requiring a gradient."""
+    return param.requires_grad
+
+
+def _broadcast_object(
+    obj: Any,
+    src_rank: int,
+    group: object = dist.group.WORLD,
+    device: torch.device = torch.device("cpu"),
+) -> Any:
+    r"""
+    Broadcasts an object to the given group.
+
+    It will be sending the object if called from the source rank and receiving
+    the object otherwise.
+
+    Arguments:
+        obj: object to broadcast; only used if called on the source rank.
+        src_rank (int): source rank.
+        group (``ProcessGroup``, optional): group used for the broadcast
+            (default: ``dist.group.WORLD``).
+        device (``torch.device``, optional): device to send from or receive
+            to (default: ``torch.device("cpu")``).
+
+    Returns:
+        The broadcasted object.
+    """
+    if dist.get_rank() == src_rank:
+        # Send the object
+        buffer = io.BytesIO()
+        torch.save(obj, buffer)
+        data = bytearray(buffer.getbuffer())
+        length_tensor = torch.LongTensor([len(data)]).to(device)
+        data_send_tensor = torch.ByteTensor(data).to(device)
+        dist.broadcast(length_tensor, src=src_rank, group=group, async_op=False)
+        dist.broadcast(data_send_tensor, src=src_rank, group=group, async_op=False)
+    else:
+        # Receive the object
+        length_tensor = torch.LongTensor([0]).to(device)
+        dist.broadcast(length_tensor, src=src_rank, group=group, async_op=False)
+        data_recv_tensor = torch.empty(
+            [int(length_tensor.item())], dtype=torch.uint8, device=device
+        )
+        dist.broadcast(data_recv_tensor, src=src_rank, group=group, async_op=False)
+        buffer = io.BytesIO(data_recv_tensor.cpu().numpy())
+        obj = torch.load(buffer, map_location=device, weights_only=False)
+    return obj
+
+
+class _ZeROJoinHook(JoinHook):
+    def __init__(self, zero):
+        assert isinstance(zero, ZeroRedundancyOptimizer), (
+            "ZeRO join hook requires passing in a ZeroRedundancyOptimizer "
+            "instance as the state"
+        )
+        self.zero = zero
+        super().__init__()
+
+    def main_hook(self):
+        """
+        Perform an optimizer step.
+
+        This step updates the joined process's shard of
+        the parameters and broadcasts those parameters.
+        """
+        self.zero.step()
+
+
+class _DDPBucketAssignment:
+    r"""
+    Represent a :class:`DistributedDataParallel` bucket assignment.
+
+    This means that a (possibly non-strict) subset of the parameters corresponding to
+    a DDP bucket assigned to a rank to update.
+
+    Attributes:
+        bucket_index (int): index of the bucket determined by the DDP gradient
+            bucket all-reduce order.
+        parameters (List[torch.Tensor]): model parameters in the bucket
+            assigned to this rank.
+        offset (int): offset into the :class:`GradBucket` 's :meth:`parameters`
+            giving the index of the first element in the passed-in
+            ``parameters``; this equivalently indexes into the
+            :class:`GradBucket` 's :meth:`gradients`.
+        device (torch.device): device on which the parameters are stored.
+        tensor (torch.Tensor): flattened tensor giving the data of the
+            parameter subset assigned to the rank.
+    """
+
+    def __init__(
+        self,
+        bucket_index: int,
+        parameters: list[torch.Tensor],
+        offset: int,
+    ):
+        self.bucket_index = bucket_index
+        self.parameters = parameters
+        self.offset = offset
+        if len(self.parameters) == 0:
+            raise ValueError("Empty bucket assignment")
+        # DDP guarantees all parameters in the bucket have the same device
+        self.device: torch.device = self.parameters[0].device
+        self.tensor: Optional[torch.Tensor] = None
+
+
+class _OverlapStatus(enum.IntEnum):
+    r"""
+    Define possible statuses that :class:`ZeroRedundancyOptimizer` can be in when overlapping with :class:`DistributedDataParallel`.
+
+    Attributes:
+        ``UNINITIALIZED``: The ZeRO instance is effectively uninitialized and
+            is waiting for DDP to finalize its bucketing.
+        ``DDP_HAS_REBUILT_BUCKETS``: DDP has rebuilt its buckets, meaning that
+            its bucketing is finalized. The ZeRO instance can now collect the
+            necessary information about the DDP bucketing.
+        ``INITIALIZED``: The ZeRO instance is fully initialized and can now
+            optimize parameters.
+    """
+
+    UNINITIALIZED = 0
+    DDP_HAS_REBUILT_BUCKETS = 1
+    INITIALIZED = 2
+
+
+class _OverlapInfo:
+    r"""
+    Information needed by :class:`ZeroRedundancyOptimizer` to overlap with :class:`DistributedDataParallel`.
+
+    Arguments:
+        world_size (int): world size of the process group being used.
+
+    Attributes:
+        shard_buckets (bool): if ``True``, then the assignment of each
+            :class:`DistributedDataParallel` bucket is partitioned across
+            possibly multiple :class:`ZeroRedundancyOptimizer` instances (i.e.
+            across possibly multiple ranks) to approximate uniformity following
+            a threshold given by the total parameter size divided by the world
+            size; if ``False``, then each bucket is wholly assigned to a single
+            :class:`ZeroRedundancyOptimizer` instance (i.e. to a single rank);
+            this should be set to the value passed into the hook constructor.
+        status (_OverlapStatus): current status; see :class:`_OverlapStatus`
+            for more information.
+        params_per_bucket (List[List[torch.Tensor]]): ``params_per_bucket[i]``
+            gives the model parameters in the ``i``th bucket.
+        params_per_rank (List[List[torch.Tensor]]): ``params_per_rank[i]``
+            gives the model parameters assigned to the ``i``th rank, where the
+            parameters are grouped by increasing bucket indices.
+        offsets (Dict[int, int]): maps from bucket index to the offset in
+            ``self.params_per_rank[rank]`` giving the index of the first
+            parameter in that bucket, where ``rank`` is this process's own
+            rank; the keys of this :class:`dict` are the bucket indices
+            assigned to this rank.
+        num_bucket_assignments (int): total number of bucket assignments across
+            all ranks; this is equal to the number of
+            :class:`DistributedDataParallel` gradient buckets if
+            ``shard_buckets=False`` and possibly greater otherwise.
+        total_size (int, optional): total size of all buckets (i.e. sum of
+            ``param.numel()`` for all ``param`` across all buckets) if
+            ``shard_buckets=True``; otherwise, ``None``.
+        broadcast_handles (List[Work]): :class:`list` of async work handles for
+            the parameter broadcasts.
+        bucket_index_to_future (Dict[int, torch.futures.Future]):
+            :class:`dict` mapping bucket index to the corresponding all-reduce
+            future.
+        bucket_index_to_bucket (Dict[int, dist.GradBucket]): :class:`dict`
+            mapping bucket index to the corresponding bucket.
+        bucket_indices_seen (List[int]): :class:`list` of the bucket indices
+            seen on this iteration.
+    """
+
+    def __init__(self, world_size) -> None:
+        self.status: _OverlapStatus = _OverlapStatus.UNINITIALIZED
+        self.shard_buckets: bool = False
+
+        # Modified per bucket reconstruction
+        self.params_per_bucket: list[list[torch.Tensor]] = []
+        self.params_per_rank: list[list[torch.Tensor]] = [[] for _ in range(world_size)]
+        self.offsets: dict[int, int] = {}
+        # Group Ranks
+        self.assigned_ranks_per_bucket: list[set[int]] = []
+        self.num_bucket_assignments: int = 0
+        self.total_size: Optional[int] = None
+
+        # Modified per iteration
+        self.broadcast_handles: list[Any] = []
+        self.bucket_indices_seen: list[int] = []
+        # Used by `hook_with_zero_step()`
+        self.bucket_index_to_future: dict[int, torch.futures.Future] = {}
+        self.bucket_index_to_bucket: dict[int, dist.GradBucket] = {}
+
+    def wait_for_broadcasts(self) -> None:
+        r"""
+        Wait for all parameter broadcasts.
+
+        This function should be called once all broadcasts have been scheduled,
+        meaning ``self.broadcast_handles`` is filled. This clears ``self.broadcast_handles``
+        in preparation for the next iteration.
+        """
+        assert len(self.broadcast_handles) == self.num_bucket_assignments, (
+            f"Missing at least one broadcast handle on rank {dist.get_rank()}"
+        )
+        _ = [x.wait() for x in self.broadcast_handles]
+        self.broadcast_handles.clear()
+
+    def clear_per_iter_info(self) -> None:
+        r"""
+        Clear the data structures that are modified per-iteration.
+
+        This function should be called at the end of an iteration.
+        """
+        self.bucket_indices_seen.clear()
+        self.bucket_index_to_future.clear()
+        self.bucket_index_to_bucket.clear()
+
+
+class ZeroRedundancyOptimizer(Optimizer, Joinable):
+    r"""
+    Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group.
+
+    The sharing is done as described by ZeRO_.
+
+    The local optimizer instance in each rank is only
+    responsible for updating approximately ``1 / world_size`` parameters and
+    hence only needs to keep ``1 / world_size`` optimizer states. After
+    parameters are updated locally, each rank will broadcast its parameters to
+    all other peers to keep all model replicas in the same state.
+    ``ZeroRedundancyOptimizer`` can be used in conjunction with
+    :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak
+    memory consumption.
+
+    ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number
+    of parameters at each rank. Each parameter belongs to a single rank and is
+    not divided among ranks. The partition is arbitrary and might not match the
+    the parameter registration or usage order.
+
+    Arguments:
+        params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s
+            or :class:`dict` s giving all parameters, which will be sharded
+            across ranks.
+
+    Keyword Args:
+        optimizer_class (:class:`torch.nn.Optimizer`): the class of the local
+            optimizer.
+        process_group (``ProcessGroup``, optional): ``torch.distributed``
+            ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by
+            :meth:`torch.distributed.init_process_group`).
+        parameters_as_bucket_view (bool, optional): if ``True``, parameters are
+            packed into buckets to speed up communication, and ``param.data``
+            fields point to bucket views at different offsets; if ``False``,
+            each individual parameter is communicated separately, and each
+            ``params.data`` stays intact (default: ``False``).
+        overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is
+            overlapped with :class:`DistributedDataParallel` 's gradient
+            synchronization; this requires (1) either a functional optimizer
+            for the ``optimizer_class`` argument or one with a functional
+            equivalent and (2) registering a DDP communication hook
+            constructed from one of the functions in ``ddp_zero_hook.py``;
+            parameters are packed into buckets matching those in
+            :class:`DistributedDataParallel`, meaning that the
+            ``parameters_as_bucket_view`` argument is ignored.
+            If ``False``, :meth:`step` runs disjointly after the backward pass
+            (per normal).
+            (default: ``False``)
+        **defaults: any trailing arguments, which are forwarded to the local
+            optimizer.
+
+    Example::
+
+        >>> # xdoctest: +SKIP
+        >>> import torch.nn as nn
+        >>> from torch.distributed.optim import ZeroRedundancyOptimizer
+        >>> from torch.nn.parallel import DistributedDataParallel as DDP
+        >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
+        >>> ddp = DDP(model, device_ids=[rank])
+        >>> opt = ZeroRedundancyOptimizer(
+        >>>     ddp.parameters(),
+        >>>     optimizer_class=torch.optim.Adam,
+        >>>     lr=0.01
+        >>> )
+        >>> ddp(inputs).sum().backward()
+        >>> opt.step()
+
+    .. warning::
+        Currently, ``ZeroRedundancyOptimizer`` requires that all of the
+        passed-in parameters are the same dense type.
+
+    .. warning::
+        If you pass ``overlap_with_ddp=True``, be wary of the following: Given
+        the way that overlapping :class:`DistributedDataParallel` with
+        :class:`ZeroRedundancyOptimizer` is currently implemented, the first
+        two or three training iterations do not perform parameter updates in
+        the optimizer step, depending on if ``static_graph=False`` or
+        ``static_graph=True``, respectively. This is because it needs
+        information about the gradient bucketing strategy used by
+        :class:`DistributedDataParallel`, which is not finalized until the
+        second forward pass if ``static_graph=False`` or until the third
+        forward pass if ``static_graph=True``. To adjust for this, one option
+        is to prepend dummy inputs.
+
+    .. warning:: ZeroRedundancyOptimizer is experimental and subject to change.
+
+    .. _ZeRO: https://arxiv.org/abs/1910.02054
+
+    """
+
+    def __init__(
+        self,
+        params,
+        optimizer_class: type[Optimizer],
+        process_group: Optional[Any] = None,
+        parameters_as_bucket_view: bool = False,
+        overlap_with_ddp: bool = False,
+        **defaults: Any,
+    ):
+        r"""Init."""
+        # Perform type and assumption checks on the input parameters
+        params = self._verify_and_init_params(params)
+        self._verify_same_dense_param_type()
+
+        # NOTE: The parent constructor uses `add_param_group()` which is
+        # partially overloaded in ZeroRedundancyOptimizer, so we use the
+        # `initialized` flag to dissociate the behaviour of `add_param_group()`
+        # between the parent and child.
+        self.initialized = False
+
+        Optimizer.__init__(self, params, defaults)
+        Joinable.__init__(self)
+        # Now, all parameters are held in both `self._all_params` and
+        # `self.param_groups`
+
+        # Internal data structures (`_cache` indicates lazily evaluated)
+        self._param_to_rank_cache: dict[torch.Tensor, int] = {}
+        self._param_to_index_cache: dict[torch.Tensor, int] = {}
+        self._partition_parameters_cache: list[list[dict]] = []
+        self._index_to_param_cache: list[torch.Tensor] = []
+        self._device_to_params_per_rank_cache: dict[
+            torch.device, list[list[torch.Tensor]]
+        ] = {}
+        self._bucket_assignments_per_rank_cache: list[
+            dict[int, _DDPBucketAssignment]
+        ] = []
+        self._is_trainable_mask = self._get_is_trainable_mask()
+
+        # Default device for collective communication and buckets
+        self._default_device = self._all_params[0].device
+
+        self.process_group = (
+            process_group if process_group is not None else dist.group.WORLD
+        )
+        self.world_size: int = dist.get_world_size(self.process_group)
+        self.rank: int = dist.get_rank(self.process_group)
+        self.global_rank: int = dist.distributed_c10d.get_global_rank(
+            self.process_group, self.rank
+        )
+
+        self._overlap_with_ddp: bool = overlap_with_ddp
+        self._optim_defaults = defaults
+        self._optim_constructor = self._get_optimizer_constructor(optimizer_class)
+
+        # If `overlap_with_ddp=True`, local optimizer initialization is delayed
+        # to run time after the necessary information has been collected
+        if not overlap_with_ddp:
+            self._init_local_optimizer()
+        else:
+            self._overlap_info: _OverlapInfo = _OverlapInfo(self.world_size)
+            if parameters_as_bucket_view:
+                logger.warning(
+                    "`parameters_as_bucket_view=True` will be ignored since "
+                    "`overlap_with_ddp=True`; instead, a different bucketing "
+                    "strategy will be used"
+                )
+
+        # `self._buckets` is used if `parameters_as_bucket_view=True`, in
+        # which case parameter data is flattened into contiguous bucket tensors
+        self.parameters_as_bucket_view = parameters_as_bucket_view
+        self._buckets: list[list[torch.Tensor]] = []
+        self._build_param_buckets()
+
+        # Optional consolidated optimizer state, only populated if this rank
+        # is the target in `consolidate_state_dict()`
+        self._all_state_dicts: list[dict[str, Any]] = []
+
+        self.initialized = True
+
+    def _clear_cache(self) -> None:
+        r"""Clear the cached data structures giving partition information."""
+        self._partition_parameters_cache.clear()
+        self._param_to_rank_cache.clear()
+        self._index_to_param_cache.clear()
+        self._param_to_index_cache.clear()
+        self._device_to_params_per_rank_cache.clear()
+        self._bucket_assignments_per_rank_cache.clear()
+
+    def add_param_group(self, param_group: dict[str, Any]) -> None:
+        r"""
+        Add a parameter group to the :class:`Optimizer` 's ``param_groups``.
+
+        This can be useful when fine tuning a pre-trained network, as frozen
+        layers can be made trainable and added to the :class:`Optimizer` as
+        training progresses.
+
+        Arguments:
+            param_group (dict): specifies the parameters to be optimized and
+                group-specific optimization options.
+
+        .. warning:: This method handles updating the shards on all partitions
+            but needs to be called on all ranks. Calling this on a subset of
+            the ranks will cause the training to hang because communication
+            primitives are called depending on the managed parameters and
+            expect all the ranks to participate on the same set of parameters.
+        """
+        if self.initialized and self._overlap_with_ddp:
+            raise RuntimeError(
+                "ZeroRedundancyOptimizer with `overlap_with_ddp=True` only "
+                "supports a single parameter group"
+            )
+
+        super().add_param_group(param_group)
+        # NOTE: The rest of the method assumes that the call to the parent's
+        # `add_param_group()` appends the new parameter group and preserves
+        # the previous parameter-group ordering
+
+        if self.initialized:
+            # Force a re-partitioning of the parameters
+            self._clear_cache()
+            param_groups = self._partition_parameters()[self.rank]
+            # NOTE: All parameters in the old parameter groups should be
+            # assigned to the same ranks so that the local optimizers do not
+            # need to be reinitialized
+
+            # Add the parameters assigned to this rank from the new parameter
+            # group to the local optimizer, if any
+            if len(param_groups) == len(self.optim.param_groups) + 1:
+                self.optim.add_param_group(param_groups[-1])
+
+            # Update the bucketing strategy accordingly
+            if self.parameters_as_bucket_view:
+                self._build_param_buckets()
+
+    def consolidate_state_dict(self, to: int = 0) -> None:
+        r"""
+        Consolidate a list of ``state_dict`` s (one per rank) on the target rank.
+
+        Arguments:
+            to (int): the rank that receives the optimizer states (default: 0).
+
+        Raises:
+            RuntimeError: if ``overlap_with_ddp=True`` and this method is
+                called before this :class:`ZeroRedundancyOptimizer` instance
+                has been fully initialized, which happens once
+                :class:`DistributedDataParallel` gradient buckets have been
+                rebuilt.
+
+        .. warning:: This needs to be called on all ranks.
+        """
+        self._check_overlap_initialized()
+
+        # Sync the exposed `param_groups` attributes to the local optimizer in
+        # case they have been updated
+        self._sync_param_groups(self.param_groups, self.optim.param_groups)
+
+        # Pull the sharded state from all ranks and store them in rank order
+        empty_messenger = torch.tensor(
+            [0], dtype=torch.uint8, device=self._default_device
+        )
+
+        # NOTE: We wastefully use `broadcast()` (e.g. instead of `gather()`)
+        # due to compatibility issues with NCCL backend; a possible follow-up
+        # is to move all sharded state management to RPC RRef
+        self._all_state_dicts = []
+        for rank in range(self.world_size):
+            global_rank = dist.distributed_c10d.get_global_rank(
+                self.process_group, rank
+            )
+            if self.rank == to:
+                # Consolidate all local `state_dict`s on this rank, storing on
+                # CPU to save GPU memory
+                if rank == self.rank:
+                    # Directly append own optimizer state
+                    self._all_state_dicts.append(
+                        _recursive_copy_to_device(
+                            self.optim.state_dict(),
+                            non_blocking=True,
+                            device=torch.device("cpu"),
+                        )
+                    )
+                else:
+                    # Receive the optimizer state from the source rank
+                    local_state_dict = _broadcast_object(
+                        empty_messenger,
+                        src_rank=global_rank,
+                        group=self.process_group,
+                        device=self._default_device,
+                    )
+                    self._all_state_dicts.append(
+                        _recursive_copy_to_device(
+                            local_state_dict,
+                            non_blocking=True,
+                            device=torch.device("cpu"),
+                        )
+                    )
+            else:
+                if rank == self.rank:
+                    # Send the optimizer state to the target rank
+                    _ = _broadcast_object(
+                        self.optim.state_dict(),
+                        src_rank=self.global_rank,
+                        group=self.process_group,
+                        device=self._default_device,
+                    )
+                elif rank != to:
+                    # Discard the received object; `broadcast()` is used for
+                    # compatibility reasons
+                    _ = _broadcast_object(
+                        empty_messenger,
+                        src_rank=global_rank,
+                        group=self.process_group,
+                        device=self._default_device,
+                    )
+
+    def _verify_params_per_rank(
+        self,
+        params_per_rank: list[list[torch.Tensor]],
+    ) -> None:
+        r"""
+        Verify ``params_per_rank`` for :meth:`_partition_parameters`.
+
+        The verification is done by checking that ``params_per_rank`` has length equal
+        to the world size and that it does not contain any parameters not passed into the
+        :class:`ZeroRedundancyOptimizer` constructor.
+
+        The parameters in ``params_per_rank`` being a strict subset of those
+        passed into the constructor is valid since some parameters may be
+        frozen.
+
+        Raises:
+            ValueError: if ``params_per_rank`` does not have length equal to
+                the world size or if it contains a parameter that was not
+                passed into the :class:`ZeroRedundancyOptimizer` constructor.
+        """
+        if len(params_per_rank) != self.world_size:
+            raise ValueError(
+                "`params_per_rank` must have length equal to the world size"
+            )
+        all_params_set = set(self._all_params)
+        for params in params_per_rank:
+            for param in params:
+                if param not in all_params_set:
+                    raise ValueError(
+                        "Passing a new parameter in `params_per_rank` that "
+                        "was not passed into the ZeroRedundancyOptimizer "
+                        "constructor"
+                    )
+
+    def _partition_param_group(
+        self, param_group: dict[str, Any], params_per_rank: list[list[torch.Tensor]]
+    ) -> None:
+        r"""
+        Partition the parameter group ``param_group`` according to ``params_per_rank``.
+
+        The partition will modify the ``self._partition_parameters_cache``. This method should
+        only be used as a subroutine for :meth:`_partition_parameters`.
+
+        Arguments:
+            param_group (dict[str, Any]): a parameter group as normally defined
+                in an optimizer state.
+            params_per_rank (list[list[torch.Tensor]]): a :class:`list` of
+                length world size containing :class:`list` s of parameters to
+                assign to each rank.
+        """
+        for rank, params in enumerate(params_per_rank):
+            rank_param_group = copy.copy(param_group)
+            rank_param_group["params"] = params
+            self._partition_parameters_cache[rank].append(rank_param_group)
+
+    def _partition_parameters(
+        self,
+        params_per_rank: Optional[list[list[torch.Tensor]]] = None,
+    ) -> list[list[dict]]:
+        r"""
+        Partitions parameters across distributed data parallel ranks.
+
+        Arguments:
+            params_per_rank (list[list[torch.Tensor]], optional): a
+                :class:`list` of length world size containing :class:`list` s
+                of parameters to assign to each rank; this provides a way to
+                specify a partition manually.
+                If ``None``, the parameters are partitioned according to an
+                internal algorithm.
+                (default: ``None``)
+
+        Returns:
+            A :class:`list` where each element of the list contains the
+            ``param_groups`` for a rank (which itself is a :class:`list` of
+            :class:`dict`); element 0 corresponds to rank 0, etc.; each rank
+            stores the ``param_groups`` for all ranks for the collective
+            communication in :meth:`step`.
+
+        Raises:
+            ValueError: see :meth:`_validate_params_per_rank`.
+            RuntimeError: if ``params_per_rank`` is not ``None`` and this
+                :class:`ZeroRedundancyOptimizer` instance is using more than
+                one parameter group.
+        """
+        if params_per_rank is None:
+            # Partition the parameters optimizing for uniformity
+            if len(self._partition_parameters_cache) == 0:
+                self._partition_parameters_cache = [[] for _ in range(self.world_size)]
+                sizes = [0] * self.world_size
+                for param_group in self.param_groups:
+                    param_group_params_per_rank: list[list] = [
+                        [] for _ in range(self.world_size)
+                    ]
+                    # Sort the parameters by size (largest first)
+                    params_sorted = sorted(
+                        param_group["params"], key=lambda t: t.numel(), reverse=True
+                    )
+                    for param in params_sorted:
+                        # Greedily add the parameter to rank with smallest size so far
+                        rank = self._get_min_index(sizes)
+                        param_group_params_per_rank[rank].append(param)
+                        sizes[rank] += param.numel()
+                    # Apply the constructed partition of the parameter group
+                    self._partition_param_group(
+                        param_group, param_group_params_per_rank
+                    )
+
+            return self._partition_parameters_cache
+
+        # Partition the parameters according to `params_per_rank`
+        assert len(self._partition_parameters_cache) == 0, (
+            "Specifying `params_per_rank` should only be done when the "
+            "parameters have not been partitioned yet"
+        )
+        if len(self.param_groups) != 1:
+            raise RuntimeError(
+                "Specifying `params_per_rank` only supports a single parameter group"
+            )
+        self._verify_params_per_rank(params_per_rank)
+        self._partition_parameters_cache = [[] for _ in range(self.world_size)]
+
+        # Apply the passed-in partition of the parameter group
+        param_group = self.param_groups[0]
+        self._partition_param_group(param_group, params_per_rank)
+
+        return self._partition_parameters_cache
+
+    @property
+    def _param_to_rank(self) -> dict[torch.Tensor, int]:
+        r""":class:`dict` mapping parameters to their assigned data parallel rank in the partition."""
+        if len(self._param_to_rank_cache) == 0:
+            for rank, param_groups in enumerate(self._partition_parameters()):
+                for param_group in param_groups:
+                    for param in param_group["params"]:
+                        self._param_to_rank_cache[param] = rank
+        return self._param_to_rank_cache
+
+    @property
+    def _param_to_index(self) -> dict[torch.Tensor, int]:
+        r"""
+        :class:`dict` mapping parameters to their indices in the global optimizer state.
+
+        NOTE: This assumes that the global optimizer state's indexing (in
+        ``state_dict``) follows a linear ordering over the parameter groups.
+        """
+        if len(self._param_to_index_cache) == 0:
+            self._param_to_index_cache = {
+                p: i
+                for i, p in enumerate(
+                    chain.from_iterable(g["params"] for g in self.param_groups)
+                )
+            }
+        return self._param_to_index_cache
+
+    @property
+    def _index_to_param(self) -> list[torch.Tensor]:
+        r"""List mapping parameter indices in the global optimizer scheme to the actual params."""
+        if len(self._index_to_param_cache) == 0:
+            self._index_to_param_cache = list(
+                chain.from_iterable(g["params"] for g in self.param_groups)
+            )
+        return self._index_to_param_cache
+
+    def _broadcast_params_from_rank(self, rank: int):
+        r"""
+        Broadcast the shard of parameters from a given rank to all other ranks asynchronously.
+
+        Arguments:
+            rank (int): the source rank.
+
+        Returns:
+            A :class:`list` of async work handles for the ``broadcast()`` s
+            performed to synchronize the parameters.
+        """
+        assert not self._overlap_with_ddp, (
+            "`_broadcast_params_from_rank()` should not be used if "
+            "`overlap_with_ddp=True`; instead, the broadcasting should "
+            "happen in the DDP communication hook"
+        )
+        handles = []
+        if self.parameters_as_bucket_view:
+            for dev_i_buckets in self._buckets:
+                bucket = dev_i_buckets[rank]
+                global_rank = dist.distributed_c10d.get_global_rank(
+                    self.process_group, rank
+                )
+                handles.append(
+                    dist.broadcast(
+                        tensor=bucket,
+                        src=global_rank,
+                        group=self.process_group,
+                        async_op=True,
+                    )
+                )
+        else:
+            param_groups = self._partition_parameters()[rank]
+            global_rank = dist.distributed_c10d.get_global_rank(
+                self.process_group, rank
+            )
+            for param_group in param_groups:
+                handles.extend(
+                    dist.broadcast(
+                        tensor=param.data,
+                        src=global_rank,
+                        group=self.process_group,
+                        async_op=True,
+                    )
+                    for param in param_group["params"]
+                )
+        return handles
+
+    def _sync_params(self):
+        r"""
+        Sync all parameter shards across the ranks.
+
+        This rank sends its shard of the parameters to all other ranks and
+        receives a shard from each other rank. This is done using
+        ``broadcast()``. Parameters are sent bucket-by-bucket if
+        ``parameters_as_bucket_view=True``and sent parameter-by-parameter
+        otherwise.
+        """
+        handles = []
+        for rank in range(self.world_size):
+            handles.extend(self._broadcast_params_from_rank(rank))
+        _ = [x.wait() for x in handles]
+
+    @property
+    def _device_to_params_per_rank(
+        self,
+    ) -> dict[torch.device, list[list[torch.Tensor]]]:
+        r"""
+        Return device parameters assigned per rank.
+
+        :class:`dict` mapping each device to a :class:`list` of the per-rank parameter
+        lists filtered to only include the parameters stored on that device.
+        Each per-rank parameter list gives the parameters assigned to that rank
+        to update.
+
+        This is used for constructing the parameter buckets if
+        ``parameters_as_bucket_view=True``.
+
+        Let ``dev_i`` denote the ``i``th device for this rank. Then:
+        ``dev_0`` maps to a list containing:
+            rank 0's assigned parameters stored on ``dev_0``,
+            rank 1's assigned parameters stored on ``dev_0``,
+            ...
+        ``dev_1`` maps to a list containing:
+            rank 0's assigned parameters stored on ``dev_1``,
+            rank 1's assigned parameters stored on ``dev_1``,
+            ...
+        ...
+        """
+        assert self.parameters_as_bucket_view, (
+            "`_device_to_params_per_rank` should only be used if "
+            "`parameters_as_bucket_view=True`"
+        )
+        if len(self._device_to_params_per_rank_cache) == 0:
+            for rank, param_groups in enumerate(self._partition_parameters()):
+                for param_group in param_groups:
+                    for param in param_group["params"]:
+                        device = param.device
+                        if device not in self._device_to_params_per_rank_cache:
+                            self._device_to_params_per_rank_cache[device] = [
+                                [] for _ in range(self.world_size)
+                            ]
+                        self._device_to_params_per_rank_cache[device][rank].append(
+                            param
+                        )
+        return self._device_to_params_per_rank_cache
+
+    def _get_min_index(
+        self,
+        values: list[int],
+        disallowed_indices: Optional[set[int]] = None,
+    ) -> int:
+        r"""
+        Return ``values.index(min(values))``, except only uses one pass.
+
+        It also excludes any indices in ``disallowed_indices`` if provided.
+
+        Arguments:
+            values: (List[int]): :class:`list` of values.
+            disallowed_indices (Optional[set[int]]): indices that are
+                disallowed from being the returned min index.
+        """
+        min_index = -1
+        min_value = float("inf")
+        for i, value in enumerate(values):
+            if disallowed_indices and i in disallowed_indices:
+                continue
+            if value < min_value:
+                min_value = value
+                min_index = i
+        assert min_index >= 0, "All indices are disallowed"
+        return min_index
+
+    def _assign_bucket_subset_to_rank(
+        self,
+        bucket_index: int,
+        bucket_params: list[torch.Tensor],
+        bucket_offset: int,
+        assigned_rank: int,
+        assigned_ranks_per_bucket: list[set[int]],
+    ) -> None:
+        r"""
+        Assign ``bucket_params`` to the rank with the least size assigned so far and collects relevant information.
+
+        The model parameters given by ``bucket_params`` represents a (possibly non-strict)
+        subset of the parameters corresponding to a :class:`DistributedDataParallel` bucket.
+
+        Arguments:
+            bucket_index (int): index of the :class:`DistributedDataParallel`
+                gradient bucket.
+            bucket_params (List[torch.Tensor]): subset of the parameters
+                corresponding to the bucket to assign.
+            bucket_offset (int): offset giving the index of the first element
+                in ``bucket_params`` in the bucket's full parameter list.
+            assigned_rank (int): group rank to assign to.
+            assigned_ranks_per_bucket (list[set[int]]): :class:`set` of group ranks
+                assigned to each bucket.
+        """
+        overlap_info = self._overlap_info
+        if len(bucket_params) == 0:
+            raise ValueError("Empty bucket assignment")
+        params_per_rank = overlap_info.params_per_rank
+        offsets = overlap_info.offsets
+
+        self._bucket_assignments_per_rank_cache[assigned_rank][bucket_index] = (
+            _DDPBucketAssignment(bucket_index, bucket_params, bucket_offset)
+        )
+        if self.global_rank == assigned_rank:
+            offsets[bucket_index] = len(params_per_rank[assigned_rank])
+        params_per_rank[assigned_rank].extend(bucket_params)
+        assigned_ranks_per_bucket[bucket_index].add(assigned_rank)
+        self._overlap_info.num_bucket_assignments += 1
+
+    @property
+    def _bucket_assignments_per_rank(self) -> list[dict[int, _DDPBucketAssignment]]:
+        r"""
+        Return DDP bucket parameters assigned per rank.
+
+        :class:`list` of length world size consisting of :class:`dict` s
+        mapping bucket indices to :class:`_DDPBucketAssignment` s for each
+        rank.
+        """
+        assert self._overlap_with_ddp, (
+            "`_bucket_assignments_per_rank` only be used if `overlap_with_ddp=True`"
+        )
+        if len(self._bucket_assignments_per_rank_cache) > 0:
+            return self._bucket_assignments_per_rank_cache
+
+        overlap_info = self._overlap_info
+        assert overlap_info.status == _OverlapStatus.INITIALIZED
+
+        self._bucket_assignments_per_rank_cache = [{} for _ in range(self.world_size)]
+        params_per_bucket = overlap_info.params_per_bucket
+
+        if overlap_info.shard_buckets:
+            # Define the assignment threshold to approximate uniformity
+            assert overlap_info.total_size is not None, "`total_size` was not computed"
+            threshold = overlap_info.total_size / self.world_size  # type: ignore[operator]
+            size_per_rank = [0 for _ in range(self.world_size)]
+
+        num_buckets = len(params_per_bucket)
+        overlap_info.assigned_ranks_per_bucket = [set() for _ in range(num_buckets)]
+        assigned_ranks_per_bucket = overlap_info.assigned_ranks_per_bucket
+        if not overlap_info.shard_buckets:
+            # Assign each DDP bucket entirely to a single rank
+            for bucket_index, bucket_params in enumerate(params_per_bucket):
+                assert len(bucket_params) > 0, "Empty bucket"
+                assigned_rank = self._get_assigned_rank(bucket_index)
+                self._assign_bucket_subset_to_rank(
+                    bucket_index,
+                    bucket_params,
+                    0,
+                    assigned_rank,
+                    assigned_ranks_per_bucket,
+                )
+        else:
+            # Assign each DDP bucket to possibly multiple ranks
+            # Specifically, sort the DDP buckets by increasing size, and for
+            # each bucket, iteratively assign the maximal unassigned subset
+            # with size less than `threshold` to the rank with the least total
+            # size so far -- each such assignment is represented by a
+            # `_DDPBucketAssignment` instance and only contains parameters from
+            # a single DDP bucket
+            params_per_bucket_enum = sorted(
+                enumerate(params_per_bucket), key=lambda x: sum(p.numel() for p in x[1])
+            )
+            for bucket_index, bucket_params in params_per_bucket_enum:
+                assert len(bucket_params) > 0, "Empty bucket"
+                bucket_offset = 0
+                assignment_size = 0
+                for param_index, param in enumerate(bucket_params):
+                    param_numel = param.numel()
+                    if (
+                        assignment_size + param_numel >= threshold
+                        and param_index > bucket_offset
+                    ):
+                        assigned_rank = self._get_min_index(
+                            size_per_rank, assigned_ranks_per_bucket[bucket_index]
+                        )
+                        # Include up to but not including the parameter that
+                        # exceeded the threshold
+                        self._assign_bucket_subset_to_rank(
+                            bucket_index,
+                            bucket_params[bucket_offset:param_index],
+                            bucket_offset,
+                            assigned_rank,
+                            assigned_ranks_per_bucket,
+                        )
+                        size_per_rank[assigned_rank] += assignment_size
+                        bucket_offset = param_index
+                        assignment_size = 0
+                    assignment_size += param_numel
+                # Assign the remainder of the bucket so that no assignment
+                # spans across two buckets
+                assigned_rank = self._get_min_index(
+                    size_per_rank, assigned_ranks_per_bucket[bucket_index]
+                )
+                self._assign_bucket_subset_to_rank(
+                    bucket_index,
+                    bucket_params[bucket_offset:],
+                    bucket_offset,
+                    assigned_rank,
+                    assigned_ranks_per_bucket,
+                )
+                size_per_rank[assigned_rank] += assignment_size
+
+        return self._bucket_assignments_per_rank_cache
+
+    def _local_step(
+        self,
+        gradients: Optional[list[Optional[torch.Tensor]]] = None,
+        closure: Optional[Callable[[], float]] = None,
+        **kwargs: Any,
+    ) -> Optional[float]:
+        r"""
+        Perform a single optimizer step without syncing parameters across ranks.
+
+        Arguments:
+            gradients (list[Optional[torch.Tensor]], optional): a :class:`list`
+                of length equal to the number of parameters assigned to this
+                rank containing gradient tensors or ``None`` as its elements;
+                a ``None`` in the :class:`list` indicates that the
+                corresponding parameter should not be updated.
+                If the argument itself is ``None``, then all parameters are
+                updated, and the gradients are assumed to be already populated.
+                (default: ``None``)
+            closure (Callable): a closure that re-evaluates the model and
+                returns the loss; optional for most optimizers and should be
+                ``None`` if ``gradients`` is not ``None``; (default: ``None``)
+        Returns:
+            Optional loss depending on the underlying local optimizer.
+
+        .. warning::
+            The argument ``gradients`` should only be specified (i.e. not
+            ``None``) if ``overlap_with_ddp=True``, in which case
+            :class:`ZeroRedundancyOptimizer` wraps a functional optimizer.
+        """
+        Join.notify_join_context(self)
+        # Check if the model trainability has changed
+        is_trainable_mask = self._get_is_trainable_mask()
+        if is_trainable_mask != self._is_trainable_mask:
+            if self._overlap_with_ddp:
+                raise RuntimeError(
+                    "ZeroRedundancyOptimizer with `overlap_with_ddp=True` "
+                    "does not support changing parameter trainability at run "
+                    "time"
+                )
+            logger.warning(
+                "ZeroRedundancyOptimizer detected that the trainable "
+                "parameters changed; rebuilding the parameter buckets if "
+                "enabled"
+            )
+            self._build_param_buckets()
+            self._is_trainable_mask = is_trainable_mask
+
+        # Sync the exposed `param_groups` attributes to the local optimizer in
+        # case they have been updated
+        self._sync_param_groups(self.param_groups, self.optim.param_groups)
+
+        # Run the optimizer step on this shard only
+        if gradients is None:
+            loss = (
+                self.optim.step(**kwargs)
+                if closure is None
+                else self.optim.step(closure=closure, **kwargs)
+            )
+        else:
+            assert self._overlap_with_ddp, (
+                "Specifying `gradients` should not "
+                "be used when `overlap_with_ddp=False`"
+            )
+            assert closure is None, (
+                "`closure` is not supported when using a local functional optimizer"
+            )
+            loss = self.optim.step(gradients=gradients)
+
+        # Sync any updated attributes in the local optimizer to the exposed
+        # `param_groups`
+        self._sync_param_groups(self.optim.param_groups, self.param_groups)
+
+        return loss
+
+    def step(
+        self,
+        closure: Optional[Callable[[], float]] = None,
+        **kwargs: Any,
+    ) -> Optional[float]:
+        r"""
+        Perform a single optimizer step and syncs parameters across all ranks.
+
+        Arguments:
+            closure (Callable): a closure that re-evaluates the model and
+                returns the loss; optional for most optimizers.
+        Returns:
+            Optional loss depending on the underlying local optimizer.
+
+        .. note:: Any extra parameters are passed to the base optimizer as-is.
+        """
+        if self._overlap_with_ddp:
+            logger.warning(
+                "`step()` should not be included in the training loop when "
+                "`overlap_with_ddp=True`"
+            )
+            return None
+
+        # Perform the local optimizer step
+        loss = self._local_step(closure=closure, **kwargs)
+
+        # Sync all of the updated parameter shards across the ranks
+        self._sync_params()
+
+        return loss
+
+    def join_hook(self, **kwargs):
+        r"""
+        Return the ZeRO join hook.
+
+        It enables training on uneven inputs by
+        shadowing the collective communications in the optimizer step.
+
+        Gradients must be properly set before this hook is called.
+
+        Arguments:
+            kwargs (dict): a :class:`dict` containing any keyword arguments
+                to modify the behavior of the join hook at run time; all
+                :class:`Joinable` instances sharing the same join context
+                manager are forwarded the same value for ``kwargs``.
+
+        This hook does not support any keyword arguments; i.e. ``kwargs`` is
+        unused.
+        """
+        return _ZeROJoinHook(self)
+
+    @property
+    def join_device(self) -> torch.device:
+        r"""Return default device."""
+        return self._default_device
+
+    @property
+    def join_process_group(self) -> Any:
+        r"""Return process group."""
+        return self.process_group
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        r"""
+        Load the state pertaining to the given rank from the input ``state_dict``, updating the local optimizer as needed.
+
+        Arguments:
+            state_dict (dict): optimizer state; should be an object returned
+                from a call to :meth:`state_dict`.
+
+        Raises:
+            RuntimeError: if ``overlap_with_ddp=True`` and this method is
+                called before this :class:`ZeroRedundancyOptimizer` instance
+                has been fully initialized, which happens once
+                :class:`DistributedDataParallel` gradient buckets have been
+                rebuilt.
+        """
+        self._check_overlap_initialized()
+
+        for index, value in state_dict["state"].items():
+            param = self._index_to_param[index]
+            if self._param_to_rank[param] != self.rank:
+                # Clear any state irrelevant to this rank
+                state_dict["state"][index] = None
+            else:
+                # Load the parameter state to the local optimizer
+                self.optim.state[param] = _recursive_copy_to_device(
+                    value, non_blocking=True, device=param.device
+                )
+                # Force zero-dimensional tensors (like Adam "step") on CPU
+                for state_name, state_value in self.optim.state[param].items():
+                    if torch.is_tensor(state_value) and state_value.dim() == 0:
+                        self.optim.state[param][state_name] = state_value.cpu()
+
+        super().load_state_dict(state_dict)
+
+        # Sync the input state with the exposed and local optimizer states
+        self._sync_param_groups(state_dict["param_groups"], self.param_groups)
+        self._sync_param_groups(self.param_groups, self.optim.param_groups)
+
+    def state_dict(self) -> dict[str, Any]:
+        r"""
+        Return the last global optimizer state known to this rank.
+
+        .. warning:
+            If the state has not been consolidated to this rank, this raises a
+            runtime error, and even if it has, the state may not be up-to-date,
+            depending on when :meth:`consolidate_state_dict` was last called.
+
+        Raises:
+            RuntimeError: if ``overlap_with_ddp=True`` and this method is
+                called before this :class:`ZeroRedundancyOptimizer` instance
+                has been fully initialized, which happens once
+                :class:`DistributedDataParallel` gradient buckets have been
+                rebuilt; or if this method is called without a preceding call
+                to :meth:`consolidate_state_dict`.
+        """
+        self._check_overlap_initialized()
+
+        if len(self._all_state_dicts) == 0:
+            raise RuntimeError(
+                "Optimizer state has not been consolidated on this rank. "
+                f"Please call `consolidate_state_dict(to={self.rank})` on "
+                "all ranks beforehand if you meant to save the global state."
+            )
+
+        # Get the possibly-stale global optimizer state that uses global
+        # parameter indexing
+        state_dict = super().state_dict()
+
+        # Update the global optimizer state with local state information,
+        # factoring in the translation from local to global indexing
+        for rank, local_state_dict in enumerate(self._all_state_dicts):
+            local_param_groups = local_state_dict["param_groups"]
+            global_param_groups = self._partition_parameters()[rank]
+            assert len(local_param_groups) == len(global_param_groups), (
+                "Mismatch between number of local and global parameter groups"
+            )
+
+            for local_param_group, global_param_group in zip(
+                local_param_groups, global_param_groups
+            ):
+                # `local_param_group` stores local indices, while
+                # `global_param_group` stores the tensors directly
+                local_param_indices = local_param_group["params"]
+                global_params = global_param_group["params"]
+
+                assert len(local_param_indices) == len(global_params), (
+                    "Mismatch between number of local and global parameters in parameter group"
+                )
+                for local_param_index, global_param in zip(
+                    local_param_indices, global_params
+                ):
+                    # Update the global parameter state, if any
+                    if local_param_index in local_state_dict["state"]:
+                        global_param_index = self._param_to_index[global_param]
+                        state_dict["state"][global_param_index] = local_state_dict[
+                            "state"
+                        ][local_param_index]
+
+        # Sort the parameters in the state
+        state_dict["state"] = dict(sorted(state_dict["state"].items()))
+        return state_dict
+
+    @staticmethod
+    def _sync_param_groups(
+        src_param_groups: list[dict[Any, Any]],
+        dst_param_groups: list[dict[Any, Any]],
+    ) -> None:
+        r"""
+        Sync the attributes from the source parameter groups to the destination parameter groups.
+
+        Example attributes include learning rate or scheduler attributes. The
+        two parameter groups should have the same length (i.e. same number of
+        parameter groups).
+
+        Arguments:
+            src_param_groups (list[dict]): parameter groups giving the
+                attribute settings to copy.
+            dst_param_groups (list[dict]): parameter groups giving the
+                attribute settings to set.
+        """
+        assert len(src_param_groups) == len(dst_param_groups), (
+            "Mismatch between number of source and destination parameter groups"
+        )
+        for src_param_group, dst_param_group in zip(src_param_groups, dst_param_groups):
+            # Sync all attributes except the parameters
+            for attr in filter(lambda x: x != "params", src_param_group.keys()):
+                dst_param_group[attr] = src_param_group[attr]
+
+    def _build_param_buckets(self) -> None:
+        r"""
+        Build parameter buckets if ``parameters_as_bucket_view=True``.
+
+        For each device that stores this rank's parameters, there is a
+        bucket (represented as a tensor) containing all of the parameters on
+        that device that are assigned to a given rank in the parameter update
+        partition.
+
+        This method is called in the constructor and any time parameter
+        trainability is changed.
+
+        .. warning::
+            The current implementation assumes that all of the parameters in a
+            bucket are of the same dense type when allocating the bucket's
+            tensor.
+
+        .. warning::
+            If the model parameters are stored across more than one device,
+            then the storage partitioning must be the same across all
+            processes in order for parameter synchronization to work.
+        """
+        if not self.parameters_as_bucket_view or self._overlap_with_ddp:
+            return
+
+        # `self._buckets[i][j]` are the parameters stored on device i and
+        # assigned to rank j
+        num_devices = len(self._device_to_params_per_rank)
+        self._buckets = [[] for _ in range(num_devices)]  # type: ignore[assignment]
+
+        for dev_i, (device, params_per_rank) in enumerate(
+            self._device_to_params_per_rank.items()
+        ):
+            for params in params_per_rank:
+                bucket_size = 0
+                dtype = None
+                trainable_params = []
+                for param in params:
+                    if not _is_trainable(param):
+                        # Clone in case the parameter was previously part of
+                        # a bucket to avoid the data from being destroyed
+                        param.data = param.data.detach().clone()
+                    else:
+                        bucket_size += param.numel()
+                        trainable_params.append(param)
+                    dtype = param.dtype  # assumes all same dtype
+
+                if bucket_size == 0:
+                    # Create a dummy bucket if there are no parameters
+                    bucket = torch.zeros(1, device=device)
+                else:
+                    # Construct the bucket (assuming all dense and same dtype)
+                    bucket = torch.empty(bucket_size, dtype=dtype, device=device)
+                    offset = 0
+                    for param in trainable_params:
+                        offset_next = offset + param.numel()
+                        bucket[offset:offset_next].copy_(param.data.flatten())
+                        param.data = bucket[offset:offset_next].view_as(param.data)
+                        offset = offset_next
+                self._buckets[dev_i].append(bucket)  # type: ignore[arg-type]
+
+    def _build_ddp_param_buckets(self) -> None:
+        r"""
+        Build the DDP bucket with parameters assigned to this rank.
+
+        For each DDP bucket with parameters assigned to this rank, flattens the
+        data of those parameters into a single tensor and saves the tensor to
+        the ``tensor`` attribute in the corresponding
+        :class:`_DDPBucketAssignment` instance stored in
+        ``self._bucket_assignments_per_rank``.
+
+        :class:`DistributedDataParallel` guarantees that the parameters
+        corresponding to a gradient bucket have the same device and the same
+        dtype.
+        """
+        for bucket_assignments in self._bucket_assignments_per_rank:
+            for bucket_assignment in bucket_assignments.values():
+                params = bucket_assignment.parameters
+                bucket_size = 0
+                dtype = None
+                for param in params:
+                    assert _is_trainable(param), (
+                        "Model parameter "
+                        "corresponding to a gradient in a DDP bucket should "
+                        "require a gradient"
+                    )
+                    bucket_size += param.numel()
+                    dtype = param.dtype  # assumes all same dtype
+                assert bucket_size > 0, "Empty bucket"
+
+                # Construct the bucket tensor (assuming all dense and same dtype)
+                tensor = torch.empty(
+                    bucket_size, dtype=dtype, device=bucket_assignment.device
+                )
+                offset = 0
+                for param in params:
+                    offset_next = offset + param.numel()
+                    tensor[offset:offset_next].copy_(param.data.flatten())
+                    param.data = tensor[offset:offset_next].view_as(param.data)
+                    offset = offset_next
+                bucket_assignment.tensor = tensor
+
+    def _verify_and_init_params(
+        self,
+        params: Any,
+    ) -> Union[list[torch.Tensor], list[dict]]:
+        r"""
+        Verify the type of ``params`` and initializes ``self._all_params`` as a :class:`list` of all parameters.
+
+        The initializagtion will first make sure that provided ``params`` is valid.
+
+        Arguments:
+            params (Any): Candidate parameter list or parameter groups to verify.
+
+        Raises:
+            TypeError: ``params`` has an invalid type.
+            ValueError: ``params`` is empty.
+
+        Returns:
+            The persistent form of ``params`` to be passed into the parent
+            :class:`Optimizer` constructor -- i.e. returns ``params`` as a
+            :class:`list` to ensure that it can be iterated over again.
+        """
+        if isinstance(params, torch.Tensor):
+            raise TypeError(
+                "`params` argument should be an iterable of "
+                f"Tensors, but got {torch.typename(params)}"
+            )
+        try:
+            all_params = list(params)
+        except TypeError as e:
+            raise TypeError(
+                "`params` argument should be an iterable of Tensors"
+                f" or dicts, but got {torch.typename(params)}"
+            ) from e
+        if len(all_params) == 0:
+            raise ValueError("ZeroRedundancyOptimizer got an empty parameter list")
+        all_tensors = True
+        all_dicts = True
+        for param in all_params:
+            all_tensors &= isinstance(param, torch.Tensor)
+            all_dicts &= isinstance(param, dict)
+        if not all_tensors and not all_dicts:
+            raise TypeError(
+                "`params` argument should be an iterable of Tensors or dicts"
+            )
+        # Ensure that `self._all_params` contains a list of all parameters
+        if all_tensors:
+            self._all_params = all_params
+        elif all_dicts:
+            self._all_params = []
+            # `all_params` contains parameter groups (not parameters)
+            for param_group in all_params:
+                if "params" not in param_group:
+                    raise ValueError(
+                        "Each parameter group passed-in via `params` must "
+                        "have a 'params' key mapping to the parameters in "
+                        "the group"
+                    )
+                self._all_params.extend(param_group["params"])
+        return all_params
+
+    def _verify_same_dense_param_type(self) -> None:
+        r"""
+        Verify that all parameters are of the same dense type.
+
+        The method assumes that ``self._all_params`` has been initialized
+        and is non-empty.
+
+        Raises:
+            ValueError: ``params`` contains sparse parameters or parameters
+            of varying dense types.
+
+        NOTE: This method can be removed once support for sparse parameters
+        and varying parameter types is added.
+        """
+        typename = torch.typename(self._all_params[0])
+        if self._all_params[0].is_sparse:
+            raise ValueError(
+                "ZeroRedundancyOptimizer only supports using "
+                "the same dense type for all parameters but got "
+                f"{typename}"
+            )
+        for param in self._all_params[1:]:
+            other_typename = torch.typename(param)
+            if other_typename != typename:
+                raise ValueError(
+                    "ZeroRedundancyOptimizer only supports "
+                    "using the same dense type for all "
+                    f"parameters but got both {typename} and "
+                    f"{other_typename}"
+                )
+
+    def _get_is_trainable_mask(self) -> list[bool]:
+        r"""Return a boolean mask indicating if each parameter is trainable (``requires_grad``) or not."""
+        return list(map(_is_trainable, self._all_params))
+
+    def _init_local_optimizer(self) -> None:
+        r"""
+        Initialize this rank's local optimizer, responsible for its subset of the parameters.
+
+        The local optimizer is saved in ``self.optim``.
+        """
+        assert self._optim_constructor is not None, (
+            "The local optimizer class has not been set"
+        )
+
+        param_groups = self._partition_parameters()[self.rank]
+        # `overlap_with_ddp=True` requires a local functional optimizer
+        if self._overlap_with_ddp:
+            # Functional optimizers only support a single parameter group and
+            # require passing in the parameters as a list
+            assert len(param_groups) == 1, (
+                "Initializing the local "
+                "functional optimizer with more than one parameter group"
+            )
+            params = param_groups[0]["params"]
+            # Try to pass `_allow_empty_param_list=True` to avoid erroring
+            if (
+                "_allow_empty_param_list"
+                in inspect.signature(self._optim_constructor).parameters
+            ):
+                self.optim: Any = self._optim_constructor(
+                    params, **self._optim_defaults, _allow_empty_param_list=True
+                )
+            else:
+                logger.warning(
+                    "%s does not support the argument "
+                    "`_allow_empty_param_list`; ZeroRedundancyOptimizer may "
+                    "error due to an empty parameter list",
+                    self._optim_constructor,
+                )
+                self.optim: Any = self._optim_constructor(
+                    params, **self._optim_defaults
+                )  # type: ignore[no-redef]
+
+            # Log information about the DDP and ZeRO bucketing
+            if dist.get_debug_level() != dist.DebugLevel.OFF:
+                local_numel = sum(p.numel() for p in params)
+                num_assigned_buckets = len(
+                    self._bucket_assignments_per_rank[self.global_rank]
+                )
+                logger.info(
+                    "rank %s with %s parameters across %s buckets",
+                    self.global_rank,
+                    local_numel,
+                    num_assigned_buckets,
+                )
+                if self.global_rank == 0:
+                    logger.info(
+                        "%s DDP buckets and %s bucket assignments",
+                        len(self._overlap_info.params_per_bucket),
+                        self._overlap_info.num_bucket_assignments,
+                    )
+        else:
+            # NOTE: Passing `param_groups` into the local optimizer constructor
+            # bypasses the empty parameter list check
+            self.optim: Optimizer = self._optim_constructor(
+                param_groups, **self._optim_defaults
+            )  # type: ignore[no-redef]
+
+        # TODO: Manually add `self.param_groups` if using a functional
+        # optimizer; remove this if/when the functional optimizers support
+        # multiple parameter groups
+        if self._overlap_with_ddp and not hasattr(self.optim, "param_groups"):
+            assert hasattr(self.optim, "param_group"), (
+                "The functional optimizer should set at least one of the "
+                "attributes `param_group` or `param_groups`"
+            )
+            self.optim.param_groups = [self.optim.param_group]  # type: ignore[attr-defined]
+
+        self._sync_param_groups(self.optim.param_groups, self.param_groups)
+
+    def _init_zero_for_overlap(self) -> None:
+        r"""Perform a delayed initialization of the local optimizer and the supporting data structures."""
+        assert self._overlap_with_ddp, (
+            "`_init_zero_for_overlap()` should only be called when "
+            "`overlap_with_ddp=True`"
+        )
+        self._overlap_info.status = _OverlapStatus.INITIALIZED
+        self._clear_cache()
+        self._partition_parameters(self._overlap_info.params_per_rank)
+        self._build_ddp_param_buckets()
+        self._init_local_optimizer()
+
+    def _get_assigned_rank(self, bucket_index: int) -> int:
+        r"""
+        Return the single rank assigned to a :class:`DistributedDataParallel` gradient bucket.
+
+        Arguments:
+            bucket_index (int): index of the :class:`DistributedDataParallel`
+                bucket for which to get the assigned rank.
+        """
+        assert not self._overlap_info.shard_buckets, (
+            "The bucket assignment requires global bucket information and "
+            "will be computed later; there should be no need to use this "
+            "method"
+        )
+        return bucket_index % self.world_size
+
+    def _check_overlap_initialized(self):
+        r"""
+        Check the delayed initialization depending on the value of ``overlap_with_ddp``.
+
+        The delayed initialization has occurred (see
+        :meth:`_init_zero_for_overlap`) if ``overlap_with_ddp=True``, and
+        raises a ``RuntimeError`` if not. This should preface methods that
+        should not be run before that delayed initialization.
+
+        Raises:
+            RuntimeError: if ``overlap_with_ddp=True`` and
+                :meth:`_init_zero_for_overlap` has not been called.
+        """
+        if (
+            self._overlap_with_ddp
+            and self._overlap_info.status != _OverlapStatus.INITIALIZED
+        ):
+            raise RuntimeError(
+                "This method should not be called until this "
+                "ZeroRedundancyOptimizer instance has been fully "
+                "initialized"
+            )
+
+    def _get_optimizer_constructor(self, optimizer_class: Any) -> Any:
+        r"""
+        Return the optimizer constructor using validation and transformation depending on ``overlap_with_ddp``.
+
+        Returns:
+            - ``optimizer_class`` if ``overlap_with_ddp=False`` and
+                ``optimizer_class`` is not a functional optimizer.
+            - ``optimizer_class`` if ``overlap_with_ddp=True`` and
+                ``optimizer_class`` is already a functional optimizer.
+            - The functional equivalent of ``optimizer_class`` if
+                ``overlap_with_ddp=True`` and ``optimizer_class`` is not
+                already a functional optimizer (assuming the equivalent
+                exists).
+
+        Raises:
+            ValueError:
+
+                - if ``overlap_with_ddp=True`` but ``optimizer_class`` is
+                    neither a functional optimizer nor translatable to a
+                    functional optimizer.
+                - if ``overlap_with_ddp=False`` and ``optimizer_class`` is a
+                    functional optimizer.
+        """
+        functional_optims = functional_optim_map.values()
+        if not self._overlap_with_ddp:
+            if optimizer_class in functional_optims:
+                # Using a functional optimizer is only supported when
+                # `overlap_with_ddp=True`
+                raise ValueError(
+                    f"Passing in a functional optimizer {optimizer_class} "
+                    "when `overlap_with_ddp=False`"
+                )
+            else:
+                return optimizer_class
+        else:
+            if optimizer_class in functional_optims:
+                # Already a functional optimizer
+                return optimizer_class
+            elif optimizer_class in functional_optim_map:
+                # Translate the passed-in optimizer class to its functional
+                # equivalent if `overlap_with_ddp=True`
+                optim_constructor = functional_optim_map[optimizer_class]
+                logger.info(
+                    "Using the functional optimizer %s "
+                    "instead of %s since "
+                    "`overlap_with_ddp=True`",
+                    optim_constructor,
+                    optimizer_class,
+                )
+                return optim_constructor
+            else:
+                raise ValueError(
+                    "Using `ddp_with_overlap=True` requires using a "
+                    "functional optimizer, but there is no supported functional "
+                    f"optimizer equivalent for {optimizer_class}"
+                )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d69b365f2342ce1ec97dce9b563949c6bd53e1d2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.pyi
@@ -0,0 +1,84 @@
+# mypy: allow-untyped-defs
+import enum
+from typing import Any, Callable, overload
+
+import torch
+from torch.distributed.algorithms.join import Joinable, JoinHook
+from torch.optim import Optimizer
+
+class _ZeROJoinHook(JoinHook):
+    zero: Any = ...
+    def __init__(self, zero: Any) -> None: ...
+    def main_hook(self) -> None: ...
+
+class _DDPBucketAssignment:
+    bucket_index: int
+    parameters: list[torch.Tensor]
+    offset: int
+    device: torch.device
+    tensor: torch.Tensor | None
+
+class _OverlapStatus(enum.IntEnum):
+    UNINITIALIZED = ...
+    DDP_HAS_REBUILT_BUCKETS = ...
+    INITIALIZED = ...
+
+class _OverlapInfo:
+    status: Any = ...
+    params_per_bucket: Any = ...
+    params_per_rank: Any = ...
+    offsets: Any = ...
+    broadcast_handles: Any = ...
+    bucket_index_to_future: Any = ...
+    bucket_index_to_bucket: Any = ...
+    bucket_indices_seen: Any = ...
+    assigned_ranks_per_bucket: list[set[int]] = ...
+    total_size: int = ...
+    shard_buckets: bool = ...
+    def __init__(self) -> None: ...
+    def wait_for_broadcasts(self) -> None: ...
+    def clear_per_iter_info(self) -> None: ...
+
+class ZeroRedundancyOptimizer(Optimizer, Joinable):
+    functional_optim_map: Any = ...
+    initialized: bool = ...
+    process_group: Any = ...
+    world_size: int = ...
+    rank: int = ...
+    global_rank: int = ...
+    parameters_as_bucket_view: bool = ...
+    optim: Any = ...
+    _device_to_device_index: dict[torch.device, int] = ...
+    _overlap_with_ddp: bool = ...
+    _overlap_info: _OverlapInfo = ...
+    _buckets: list[list[torch.Tensor]] = ...
+    _bucket_assignments_per_rank: list[dict[int, _DDPBucketAssignment]] = ...
+    def __init__(
+        self,
+        params: Any,
+        optimizer_class: type[Optimizer],
+        process_group: Any | None = ...,
+        parameters_as_bucket_view: bool = ...,
+        overlap_with_ddp: bool = ...,
+        **defaults: Any,
+    ) -> None: ...
+    def add_param_group(self, param_group: dict[str, Any]) -> None: ...
+    def consolidate_state_dict(self, to: int = ...) -> None: ...
+    @overload
+    def step(self, closure: None = ..., **kwargs: Any) -> None: ...
+    @overload
+    def step(self, closure: Callable[[], float], **kwargs: Any) -> float: ...
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None: ...
+    def state_dict(self) -> dict[str, Any]: ...
+    def _local_step(
+        self,
+        gradients: list[torch.Tensor | None] | None = None,
+        closure: Callable[[], float] | None = None,
+        **kwargs: Any,
+    ) -> float | None: ...
+    def _get_assigned_rank(self, bucket_index: int) -> int: ...
+    def _init_zero_for_overlap(self) -> None: ...
+    def join_hook(self, **kwargs): ...
+    @property
+    def join_device(self) -> torch.device: ...
+    def join_process_group(self) -> Any: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_IR.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_IR.py
new file mode 100644
index 0000000000000000000000000000000000000000..416965e80ba3b8209e35c6fc3d928e39d08248f0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_IR.py
@@ -0,0 +1,1248 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import copy
+import logging
+import operator
+from collections import defaultdict
+from enum import Enum
+from inspect import Parameter, Signature, signature
+from types import MethodType
+from typing import Any, Callable, Optional, Union
+
+import torch
+import torch.fx as fx
+from torch.distributed import ProcessGroup
+from torch.export import ExportedProgram
+from torch.export.unflatten import (
+    _assign_attr,
+    _AttrKind,
+    _sink_params,
+    InterpreterModule,
+)
+from torch.fx.node import map_aggregate
+from torch.fx.passes.split_module import split_module
+
+from ._backward import _null_coalesce_accumulate, stage_backward
+from ._unflatten import _outline_submodules
+from ._utils import PipeInfo
+from .stage import _PipelineStage
+
+
+logger = logging.getLogger(__name__)
+
+# TODO:
+# 1. investigate gradient sync for shared parameters. how does DDP do it?
+# 2. Add parameter movement to split_module
+
+
+def _find_loss_from_output_and_spec(output_val, spec_val):
+    if spec_val is False:
+        return None
+    if spec_val is True:
+        if not isinstance(output_val, fx.Node):
+            raise RuntimeError(
+                f"Loss spec must specify a dynamic value but got {output_val}"
+            )
+        return output_val
+
+    if isinstance(spec_val, (tuple, list)):
+        if not isinstance(output_val, (tuple, list)):
+            raise RuntimeError(
+                f"Output value {output_val} must match type of loss specification "
+                f"{spec_val}"
+            )
+        if len(output_val) != len(spec_val):
+            raise RuntimeError(
+                f"Output value {output_val} must match length of loss specification "
+                f"{spec_val}"
+            )
+        for out, spec in zip(output_val, spec_val):
+            loss_val = _find_loss_from_output_and_spec(out, spec)
+            if loss_val is not None:
+                return loss_val
+        raise RuntimeError(f"Did not find loss value in specification {spec_val}")
+
+    if isinstance(spec_val, dict):
+        if not isinstance(output_val, dict):
+            raise RuntimeError(
+                f"Output value {output_val} must match type of loss specification "
+                f"{spec_val}"
+            )
+        if set(output_val.keys()) != set(spec_val.keys()):
+            raise RuntimeError(
+                f"Output value {output_val} must match keys of loss specification "
+                f"{spec_val}"
+            )
+        for k in spec_val:
+            loss_val = _find_loss_from_output_and_spec(output_val[k], spec_val[k])
+            if loss_val is not None:
+                return loss_val
+        raise RuntimeError(f"Did not find loss value in specification {spec_val}")
+
+    raise RuntimeError(f"Unsupported type {type(spec_val)} in loss specification")
+
+
+def _find_loss_output(mod: torch.nn.Module, g: fx.Graph, output_loss_value_spec):
+    output_nodes = [n for n in g.nodes if n.op == "output"]
+    assert len(output_nodes) == 1
+    output_node = output_nodes[0]
+    output_val = output_node.args[0]
+    generated_spec: Any = None
+
+    if isinstance(mod, TrivialLossWrapper):
+        # TrivialLossWrapper is pre-defined by PiPPy.
+        # It has loss as the only output so we can safely assume the first output arg is the loss.
+        assert len(output_node.args) == 1
+        loss_node = output_val
+        generated_spec = TrivialLossWrapper.loss_spec
+    elif output_loss_value_spec is None:
+        # Use default spec, i.e. search for "loss" in output values
+        if isinstance(output_val, dict) and "loss" in output_val.keys():
+            loss_node = output_val["loss"]
+            generated_spec = {k: k == "loss" for k in output_val}
+        else:
+            loss_node = None
+            generated_spec = None
+    else:
+        loss_node = _find_loss_from_output_and_spec(output_val, output_loss_value_spec)
+        generated_spec = output_loss_value_spec
+
+    return loss_node, output_node, generated_spec
+
+
+def _insert_stage_symbolic_backward(
+    g: fx.Graph,
+    loss_node: fx.Node,
+    output_node: fx.Node,
+):
+    # Collect metadata about tuple output values. TODO: move this to split_module or FX IR
+    tuples: dict[fx.Node, tuple] = {}
+    for node in reversed(g.nodes):
+        if node.op == "call_function":
+            # In the forward pass, only emit placeholder, module calls, and
+            # getitem calls. If we have a target other than getitem in this
+            # (forward-only) code, there is a bug.
+            assert node.target == operator.getitem, (
+                "Found non-getitem call in forward pass. Please report a bug to PiPPy"
+            )
+            assert len(node.args) == 2, (
+                "Found malformed getitem call. Please report a bug to PiPPy"
+            )
+            indexed_value, node_idx = tuple(node.args)
+
+            # indexed_value is a collection that we are indexing into. It could
+            # exist in the tuples map if we've processed another `getitem`
+            # already.
+            existing_list_size = (
+                len(tuples[indexed_value]) if indexed_value in tuples else -1
+            )
+            new_list_size = max(node_idx + 1, existing_list_size)
+
+            reconstructed_list = [None for _ in range(new_list_size)]
+
+            # Copy over existing elements if present
+            if indexed_value in tuples:
+                for i, val in enumerate(tuples[indexed_value]):
+                    reconstructed_list[i] = val
+
+            # Populate value represented by this node
+            reconstructed_list[node_idx] = node
+
+            tuples[indexed_value] = tuple(reconstructed_list)
+
+    # Keep track of nodes that dominate the loss node.
+    # We will only emit backward operations for nodes that can contribute
+    # to the specified loss value.
+    live_nodes = {loss_node: None}
+    val_to_grad: dict[fx.Node, Optional[fx.Node]] = {loss_node: None}
+
+    def assign_or_accumulate_grad(forward_node, grad_value):
+        if forward_node in val_to_grad and forward_node.op != "placeholder":
+            grad_value = g.call_function(
+                _null_coalesce_accumulate,
+                (val_to_grad[forward_node], grad_value),
+            )
+        val_to_grad[forward_node] = grad_value
+
+    with g.inserting_before(output_node):
+        for node in reversed(g.nodes):
+            if node not in live_nodes:
+                continue
+
+            def add_to_live_nodes(n):
+                live_nodes.setdefault(n, None)
+
+            fx.node.map_arg(node.args, add_to_live_nodes)
+            fx.node.map_arg(node.kwargs, add_to_live_nodes)
+            if node.op == "call_module":
+                output_grads: Union[tuple[Optional[fx.Node], ...], Optional[fx.Node]]
+                if node in tuples:
+                    stage_output = tuples[node]
+                    output_grads = tuple(val_to_grad.get(n, None) for n in tuples[node])
+                    outputs_with_grads_idxs = [
+                        i for i, n in enumerate(tuples[node]) if n in live_nodes
+                    ]
+                else:
+                    stage_output = (node,)
+                    output_grads = val_to_grad[node]
+                    outputs_with_grads_idxs = [0]
+
+                output_grads = (
+                    (output_grads,)
+                    if not isinstance(output_grads, tuple)
+                    else output_grads
+                )
+
+                grad_call = g.call_function(
+                    stage_backward,
+                    kwargs={
+                        "stage_output": stage_output,
+                        "output_grads": output_grads,
+                        "input_values": list(node.all_input_nodes),
+                        "outputs_with_grads_idxs": outputs_with_grads_idxs,
+                    },
+                )
+                # Insert backward stage debug info
+                kwargs_copy = dict(grad_call.kwargs)
+                grad_call.kwargs = kwargs_copy
+
+                grad_call_proxy = fx.Proxy(grad_call)
+                grads = grad_call_proxy.node
+
+                input_nodes = list(node.all_input_nodes)
+                grads_proxy = fx.Proxy(grads)
+                for i, input_node in enumerate(input_nodes):
+                    assign_or_accumulate_grad(input_node, grads_proxy[i].node)  # type: ignore[index]
+
+    return g
+
+
+class PipeSequential(torch.nn.Sequential):
+    @staticmethod
+    def from_sequential(sequential_instance: torch.nn.Sequential):
+        return PipeSequential(*[copy.copy(m) for m in sequential_instance])
+
+    def forward(self, input):
+        for i, module in enumerate(self):
+            input = module(input)
+            if i != len(self) - 1:
+                pipe_split()
+        return input
+
+
+class LossWrapper(torch.nn.Module):
+    """
+    LossWrapper is a convenient abstract class that allows you to wrap up both
+    your model as well as its loss function and specify the connectivity between
+    the inputs, model, loss function, and output value. Example::
+
+        class MyModelWrapper(LossWrapper):
+            def forward(self, x, targets):
+                model_out = self.module(x)
+                loss_value = self.loss_fn(model_out, targets)
+                return loss_value
+
+    The above example defines a connectivity where we expect the forward/loss/backward
+    training procedure to take two arguments (x and targets), pass x into the module
+    to get the output of the feedforward computation, pass the model output and the
+    targets value into the loss function, and get and return the loss value, which will
+    be backpropagated by PiPPy. The above class would then be instantiated like::
+
+        model = ...  # instantiate the model
+        loss_fn = torch.nn.MSELoss()  # for the sake of demonstration
+
+        wrapper = MyModelWrapper(model, loss_fn)
+        pipe = Pipe.from_tracing(wrapper, ...)
+
+    """
+
+    def __init__(self, module, loss_fn):
+        super().__init__()
+        self.module = module
+        self.loss_fn = loss_fn
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError(
+            "This instance of LossWrapper does not have an overridden"
+            "forward(). Please implement forward() to specify the arguments, "
+            "connection between the module and loss, and loss output "
+            "value."
+        )
+
+
+class TrivialLossWrapper(LossWrapper):
+    def forward(self, x, targets):
+        model_out = self.module(x)
+        return self.loss_fn(model_out, targets)
+
+    loss_spec = True
+
+
+# Pipe model representation
+#
+# Pipe can be thought of as an `nn.Sequential++`. That is to say: it specifies
+# a single topological ordering of pipeline "stages" that, when run in series,
+# constitutes all of the operations of the program. However, unlike `nn.Sequential`,
+# Pipe allows non-local usages of values, so long as those uses still respect
+# topological ordering. In particular:
+#
+# 1. Non-local activations. This type of usage can appear in, for example, skip
+#    connections. These values will be directly transmitted from the "def" stage
+#    to all stages that use them skipping intermediate stages. During autograd,
+#    gradients will be propagated back through this skip connection reverse
+#    to how activations propagated in the forward pass.
+# 2. Non-local parameter/module invocations. This occurs when a parameter is used
+#    in a stage downstream of where it is resident. These values can be carried
+#    forward similarly to (1), but in addition one might want to replicate the
+#    value on multiple stages. Gradients for these shared parameters will be
+#    accumulated separately on each stage, but there will be an additional
+#    gradient accumulation before the optimizer step.
+
+
+# Register `_pipe_split()` as an ATen operator. This is required for Export to
+# preserve this marker in the graph.
+torch.library.define("pippy::_pipe_split", "() -> ()")
+
+
+@torch.library.impl("pippy::_pipe_split", "BackendSelect")
+def _pipe_split():
+    return None
+
+
+@torch.library.register_fake("pippy::_pipe_split")  # type: ignore[no-redef]
+def _pipe_split():  # noqa: F811
+    return None
+
+
+# Add an alias for convenience
+aten_pipe_split_alias = torch.ops.pippy._pipe_split.default
+
+# Ask Export to preserve the `_pipe_split` op.
+# See examples in pytorch/torch/fx/node.py
+fx.node._side_effectful_functions.add(aten_pipe_split_alias)
+
+
+# User facing API
+def pipe_split():
+    """
+    pipe_split is a special operator that is used to mark the boundary between
+    stages in a module. It is used to split the module into stages. It is a
+    no-op if your annotated module is run eagerly.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> def forward(self, x):
+        >>>     x = torch.mm(x, self.mm_param)
+        >>>     x = torch.relu(x)
+        >>>     pipe_split()
+        >>>     x = self.lin(x)
+        >>>     return x
+
+    The above example will be split into two stages.
+    """
+    return torch.ops.pippy._pipe_split()
+
+
+class MultiUseParameterConfig(Enum):
+    TRANSMIT = 1
+    REPLICATE = 2
+
+
+MultiUseParamSpec = Union[MultiUseParameterConfig, dict[str, MultiUseParameterConfig]]
+
+
+class DetachExecutor(fx.Interpreter):
+    """
+    Special interpreter to run the split_gm in testing that detaches all inputs to
+    a module invocation. This is needed so that the values at the boundary are
+    leaf modules in autograd execution.
+    """
+
+    def __init__(self, module, garbage_collect_values=True):
+        garbage_collect_values = False
+        super().__init__(module, garbage_collect_values)
+        self.value_remap = {}
+
+    def run(self, *args, initial_env=None):  # type: ignore[override]
+        self.value_remap = {}
+        return super().run(*args, initial_env=initial_env)
+
+    def call_module(self, target, args, kwargs):
+        def detach_tensors(a):
+            if isinstance(a, torch.Tensor) and a.requires_grad:
+                if a not in self.value_remap:
+                    new_val = a.detach().requires_grad_(True)
+                    self.value_remap[a] = new_val
+                return self.value_remap[a]
+            else:
+                return a
+
+        """
+        def dont_traverse_size(a):
+            return type(a) != torch.Size
+        """
+
+        args = map_aggregate(
+            args,
+            detach_tensors,  # dont_traverse_size
+        )
+        kwargs = map_aggregate(
+            kwargs,
+            detach_tensors,  # dont_traverse_size
+        )
+
+        return super().call_module(target, args, kwargs)
+
+    def call_function(self, target, args, kwargs):
+        # HACK to reroute saved input tensors to point to the detach()ed version
+        if target == stage_backward:
+            kwargs = dict(kwargs)
+            kwargs["input_values"] = [
+                self.value_remap.get(v, v) for v in kwargs["input_values"]
+            ]
+        return super().call_function(target, args, kwargs)
+
+
+class _NodeReference:
+    def __init__(self, name):
+        self.name = name
+
+    name: str
+
+
+class _LinearNodeList:
+    def __init__(self, node_list):
+        self.serialize_node_list = []
+        for node in node_list:
+            node_args = fx.node.map_arg(node.args, lambda n: _NodeReference(n.name))  # type: ignore[arg-type,return-value]
+            node_kwargs = fx.node.map_arg(node.kwargs, lambda n: _NodeReference(n.name))  # type: ignore[arg-type,return-value]
+            serialize_node = fx.Node(
+                graph=None,  # type: ignore[arg-type]
+                name=node.name,
+                op=node.op,
+                target=node.target,
+                args=node_args,  # type: ignore[arg-type]
+                kwargs=node_kwargs,  # type: ignore[arg-type]
+                return_type=node.type,
+            )
+            serialize_node.meta = copy.copy(node.meta)
+            self.serialize_node_list.append(serialize_node)
+
+    def to_graph(self):
+        graph = fx.Graph()
+
+        ref_str_to_node: dict[str, fx.Node] = {}
+
+        def ref_to_node(arg):
+            if isinstance(arg, _NodeReference):
+                return ref_str_to_node[arg.name]
+            else:
+                return arg
+
+        for node in self.serialize_node_list:
+            node_args = map_aggregate(node.args, ref_to_node)
+            node_kwargs = map_aggregate(node.kwargs, ref_to_node)
+            deser_node = graph.create_node(
+                op=node.op,
+                target=node.target,
+                args=node_args,  # type: ignore[arg-type]
+                kwargs=node_kwargs,  # type: ignore[arg-type]
+                name=node.name,
+                type_expr=node.type,
+            )
+            ref_str_to_node[node.name] = deser_node
+
+        return graph
+
+
+def _direct_serialization_deserialize(body, nodes):
+    """
+    Custom `__reduce__` method for serialization.
+    DO AS I SAY -- NOT AS I DO. This violates the principle that
+    GraphModules serialize via code export & re-tracing. We allow
+    for this here because **PIPE STAGES SHOULD NOT BE PERSISTED
+    TO DISK -- THIS IS ONLY FOR TRANSMISSION VIA RPC**. Persisting
+    these instances to disk will expose internal implementation
+    details of `fx.Graph` and related data structures and is
+    NOT advised.
+    """
+
+    class DummyModule(torch.nn.Module):
+        def __init__(self, body):
+            super().__init__()
+            self.__dict__.update(body)
+
+    dummy = DummyModule(body)
+
+    return fx.GraphModule(dummy, nodes.to_graph())
+
+
+def _direct_serialization_reduce(self):
+    serialization_dict = dict(self.__dict__)
+    serialization_dict.pop("_graph")
+    return (
+        _direct_serialization_deserialize,
+        (serialization_dict, _LinearNodeList(self.graph.nodes)),
+    )
+
+
+def _modify_graph_op_device(
+    gm: torch.fx.GraphModule,
+    new_device: torch.device,
+):
+    """
+    Modify the device argument of all "call_function" nodes in the graph.  This
+    is useful for moving the graph to a different device. In particular for
+    generator ops, like torch.ones.
+    """
+    modified = False
+    for node in gm.graph.nodes:
+        if node.op == "call_function":
+            if "device" in node.kwargs and node.kwargs["device"] != new_device:
+                logger.debug(
+                    f"Changing device of Node {node.name} from {node.kwargs['device']} to {new_device}"  # noqa: G004
+                )
+                node.update_kwarg("device", new_device)
+                modified = True
+        elif node.op == "call_module":
+            # Recursively modify "device" in submodules
+            submod = gm.get_submodule(node.target)
+            if isinstance(submod, torch.fx.GraphModule):
+                _modify_graph_op_device(submod, new_device)
+            elif isinstance(submod, InterpreterModule):
+                # If unflattening has been performed, we need to access its graph module by `.graph_module`
+                _modify_graph_op_device(submod.graph_module, new_device)  # type: ignore[arg-type]
+            else:
+                logger.warning(
+                    f"Skipping device modification for submodule {node.target} because it is a {type(submod)}"  # noqa: G004
+                )
+
+    if modified:
+        gm.recompile()
+
+
+class Pipe(torch.nn.Module):
+    def __init__(
+        self,
+        split_gm: fx.GraphModule,
+        num_stages: int,
+        has_loss_and_backward: bool,
+        loss_spec,
+    ):
+        # TODO: is there a way not to hard wire init?
+        torch.nn.Module.__init__(self)
+        self.split_gm: fx.GraphModule = split_gm
+        self.executor: DetachExecutor = DetachExecutor(self.split_gm)
+        self.num_stages: int = num_stages
+        self.has_loss_and_backward = has_loss_and_backward
+        self.loss_spec = loss_spec
+
+        for node in split_gm.graph.nodes:
+            assert (
+                node.op in {"call_module", "placeholder", "output"}
+                or (node.op, node.target) == ("call_function", operator.getitem)
+                or (node.op, node.target) == ("call_method", "backward")
+                or (node.op, node.target) == ("call_function", stage_backward)
+                or (node.op, node.target)
+                == ("call_function", _null_coalesce_accumulate)
+            ), node
+
+        # Detect replicated parameters so we know that we have to do an additional allreduce
+        # before applying the optimizer
+        #
+        # Note that this also handles the case where there were multiple calls to a single
+        # module from different stages, regardless of whether that module invocation
+        # was handled by the logic above.
+
+        # Map parameter value to a dictionary that maps the user pipeline module
+        # to the local qualname within that module
+        params_to_users: dict[torch.nn.Parameter, dict[str, str]] = {}
+
+        for m_qualname, mod in self.split_gm.named_children():
+            for p_qualname, param in mod.named_parameters():
+                params_to_users.setdefault(param, {})
+                params_to_users[param][m_qualname] = p_qualname
+
+        self.replicated_params: list[dict[str, str]] = [
+            use_mapping
+            for _, use_mapping in params_to_users.items()
+            if len(use_mapping) > 1
+        ]
+
+        # We must break the aliasing relationship between the replicated parameters for correct
+        # numerics in reference runs. If we do not do this, the autograd tape in separate stages
+        # will have a reference to the same tensor value and will erroneously apply gradient
+        # updates multiple times. Therefore, for each replicated parameter set, we deepcopy the
+        # values so that we have separate instances.
+        for param_mapping in self.replicated_params:
+            for submod_name, param_qualname in param_mapping.items():
+                submod = getattr(self.split_gm, submod_name)
+                atoms = param_qualname.split(".")
+                for atom in atoms[:-1]:
+                    submod = getattr(submod, atom)
+                setattr(submod, atoms[-1], copy.deepcopy(getattr(submod, atoms[-1])))
+
+        def throw(self, *args, **kwargs):
+            raise RuntimeError(
+                "To run pipeline locally, invoke the Pipe object directly, not `split_gm`"
+            )
+
+        self.split_gm.forward = throw
+
+        # Make submodules use custom direct-serialized GraphModule
+        i = 0
+        while True:
+            try:
+                name = f"submod_{i}"
+                submod = getattr(self.split_gm, name)
+                submod.__class__.__reduce__ = _direct_serialization_reduce
+                i += 1
+            except AttributeError:
+                break
+
+    def forward(self, *args, **kwargs):
+        executor_args = args
+        if len(kwargs) > 0:
+            parameters = []
+            for node in self.split_gm.graph.nodes:
+                if node.op == "placeholder":
+                    if node.args and len(node.args) > 0:
+                        parameters.append(
+                            Parameter(
+                                node.target,
+                                Parameter.POSITIONAL_OR_KEYWORD,
+                                default=node.args[0],
+                            )
+                        )
+                    else:
+                        parameter_kind = Parameter.POSITIONAL_OR_KEYWORD
+                        param_name = node.target
+                        if node.target.startswith("**"):
+                            parameter_kind = Parameter.VAR_KEYWORD  # type: ignore[assignment]
+                            param_name = param_name[2:]
+                        elif node.target.startswith("*"):
+                            parameter_kind = Parameter.VAR_POSITIONAL  # type: ignore[assignment]
+                            param_name = param_name[1:]
+                        parameters.append(Parameter(param_name, parameter_kind))
+            signature = Signature(parameters)
+            ba = signature.bind(*args, **kwargs)
+            ba.apply_defaults()
+            executor_args = ba.arguments.values()  # type: ignore[assignment]
+
+        res = self.executor.run(*executor_args)
+
+        return res
+
+    def get_stage_module(self, stage_idx: int) -> torch.nn.Module:
+        """
+        Return a stage module corresponding to `stage_idx` of the `pipe`.
+        """
+        if stage_idx < 0 or stage_idx >= self.num_stages:
+            raise ValueError(f"Invalid stage index {stage_idx}!")
+        return getattr(self.split_gm, f"submod_{stage_idx}")
+
+    @staticmethod
+    def _number_and_count_forward_stages(gm: fx.GraphModule):
+        num_stages = 0
+        found_idxs: dict[int, None] = {}
+        for node in gm.graph.nodes:
+            if node.op == "call_module" and node.target.startswith("submod_"):
+                node.meta["stage_idx"] = int(node.target[len("submod_") :])
+                found_idxs.setdefault(node.meta["stage_idx"])
+                num_stages += 1
+
+        # this assert will fail if a split point is inserted before the first layer, which creates empty first submodule
+        # Update: the following assert may fail against some torch versions >=
+        # 2.2.0, as:
+        # submod_0, submod_1, submod_2, ...
+        # may be named as
+        # submod_0, submod_2, submod_4, ...
+        # TODO: investigate
+        # assert all(i in found_idxs for i in range(num_stages))
+
+        return num_stages
+
+    @staticmethod
+    def _from_traced(
+        mod: torch.nn.Module,
+        exported_program: ExportedProgram,
+        multi_use_param_spec: Optional[MultiUseParamSpec] = None,
+        output_loss_value_spec=None,
+        split_policy: Optional[
+            Callable[[torch.fx.GraphModule], torch.fx.GraphModule]
+        ] = None,
+    ):
+        """
+        Additionally, the ``output_loss_value_spec`` value can be specified to disambiguate
+        which value in the output of `forward` is the loss value on which PiPPy should apply
+        backpropagation. For example, if your ``forward`` returns a tuple ``(loss, model_out)``,
+        you can specify ``output_loss_value_spec=(True, False)``. Or, if your ``forward`` returns
+        a dict ``{'loss': loss_value, 'model_out': model_out}``, you can specify
+        ``output_loss_value_spec={'loss': True, 'model_out': False}``
+        """
+
+        traced = exported_program.module()
+
+        if split_policy is not None:
+            logger.info("Auto-splitting model")
+            traced = split_policy(traced)  # type: ignore[arg-type]
+
+        logger.debug(traced.print_readable(print_output=False))  # type: ignore[operator]
+
+        # Deduplicate `get_attr` nodes that refer to the same parameter . Downstream code for moving
+        # parameters relies on the invariant that parameter accesses happen once. This is not necessarily
+        # the case (especially with custom tracers), so fix that up here.
+        get_attr_nodes: dict[str, fx.Node] = {}
+        for node in traced.graph.nodes:  # type: ignore[union-attr]
+            if node.op == "get_attr":
+                get_attr_nodes.setdefault(node.target, node)
+
+                if get_attr_nodes[node.target] != node:
+                    node.replace_all_uses_with(get_attr_nodes[node.target])
+                    traced.graph.erase_node(node)  # type: ignore[operator, union-attr]
+
+        # avoid looking at next node by keeping track of previous pipe_split
+        prev_pipe_split_idx = -1
+        pipe_split_nodes_to_erase = set()
+        for i, node in enumerate(traced.graph.nodes):  # type: ignore[arg-type, union-attr]
+            if (node.op, node.target) == ("call_function", pipe_split):
+                if prev_pipe_split_idx == i - 1:
+                    pipe_split_nodes_to_erase.add(node)
+                prev_pipe_split_idx = i
+
+        for node in pipe_split_nodes_to_erase:
+            traced.graph.erase_node(node)  # type: ignore[operator, union-attr]
+
+        traced.recompile()  # type: ignore[operator]
+
+        part_idx = 0
+
+        def split_callback(n: fx.Node):
+            nonlocal part_idx
+            if (n.op, n.target) == (
+                "call_function",
+                aten_pipe_split_alias,
+            ):
+                logger.debug(f"Found pipe_split {part_idx}")  # noqa: G004
+                part_idx += 1
+            return part_idx
+
+        # TODO: what does split do with module invocations? does it move the modules
+        # into the submodules?
+        split = split_module(traced, mod, split_callback)  # type: ignore[arg-type]
+        # a (custom) tracer can produce dead code like orphan get_attr nodes
+        split.graph.eliminate_dead_code()
+
+        # peephole to remove pipe_split
+        for submodule in split.modules():
+            if isinstance(submodule, fx.GraphModule):
+                for node in submodule.graph.nodes:
+                    if (node.op, node.target) == (
+                        "call_function",
+                        aten_pipe_split_alias,
+                    ):
+                        submodule.graph.erase_node(node)
+                submodule.recompile()
+
+        for name, submodule in split.named_children():
+            if isinstance(submodule, fx.GraphModule):
+                new_submod = _outline_submodules(submodule.graph)
+                # Replace old submod
+                split.register_module(name, new_submod)
+
+        # TODO: backport this into split_module
+        def delete_user_reference(node, user):
+            """
+            Delete reference of `node` from `user`'s arg list.
+            Args:
+                - node: a `get_attr` node at root.
+                - user: a submodule node that uses `node`.
+            """
+            assert len(user.kwargs) == 0
+            use_idxs = [i for i, arg in enumerate(user.args) if arg == node]
+            assert len(use_idxs) == 1
+            args_copy = list(user.args)
+            args_copy.pop(use_idxs[0])
+            user.args = tuple(args_copy)
+            logger.debug(
+                f"Deleted {node} from user {user}, arg index = {use_idxs[0]}"  # noqa: G004
+            )
+
+        # A list of param referrals for deferred deletion.
+        # To be accumulated in `move_param_to_callee`.
+        to_delete = []
+
+        def _recursive_getattr_with_parent(mod, fqn):
+            # Returns getattr call given a nested FQN, and the last parent
+            atoms = fqn.split(".")
+            for atom in atoms[:-1]:
+                if not hasattr(mod, atom):
+                    return None, None
+                mod = getattr(mod, atom)
+            if not hasattr(mod, atoms[-1]):
+                return mod, None
+            attr = getattr(mod, atoms[-1])
+            return mod, attr
+
+        def move_param_to_callee(
+            root,
+            callee_name,
+            param_fqn,
+        ):
+            """
+            Move a parameter from the root module to a submodule.
+            Args:
+                root: The root module.
+                callee_name: The name of the submodule to move the parameter to.
+                param_fqn: The fully qualified name of the parameter to move.
+            """
+            # `atoms` is a list of strings representing the path to the
+            # parameter in the original model
+            atoms = param_fqn.split(".")
+            mod_itr, param_val = _recursive_getattr_with_parent(split, param_fqn)
+            # Check whether the parameter is a buffer or a parameter
+            is_buffer = atoms[-1] in mod_itr._buffers
+
+            # Check whether the parameter is a tensor
+            assert isinstance(param_val, torch.Tensor), (
+                f"Expected '{param_fqn}' to be {torch.Tensor} but got {type(param_val)}."
+                + (
+                    f" It might happen if module '{param_fqn}' was passed to some 'leaf function'"
+                    f"(see https://pytorch.org/docs/stable/fx.html#fx.wrap). Please inspect "
+                    f"usages of '{param_fqn}' in the traced graph."
+                    if isinstance(param_val, torch.nn.Module)
+                    else ""
+                )
+            )
+
+            # Get submodule
+            callee = root.get_submodule(callee_name)
+            assert not hasattr(callee, param_fqn), (
+                f"Module {callee_name} already has a parameter named {param_fqn}"
+            )
+
+            # Assign the parameter to the submodule
+            if is_buffer:
+                _assign_attr(
+                    param_val,
+                    callee,
+                    param_fqn,
+                    attr_kind=_AttrKind.BUFFER,
+                    persistent=True,  # TODO: handle non-persistent buffer
+                )
+            else:
+                _assign_attr(
+                    param_val,
+                    callee,
+                    param_fqn,
+                    attr_kind=_AttrKind.PARAMETER,
+                )
+            logger.debug(f"Moved parameter {param_fqn} to {callee_name}")  # noqa: G004
+
+            # Next step is to replace placeholder of submodule with a get_attr.
+            # Those placeholders are created by `split_module` inside each
+            # submodule.
+            # Update: this step is now moved to `_sink_params` because
+            # `_sink_params` can do it recursively (i.e. for modules inside
+            # submodule)
+
+            to_delete.append((mod_itr, atoms[-1]))
+
+        # Get the list of all parameters in the root module
+        attr_nodes = list(filter(lambda n: n.op == "get_attr", split.graph.nodes))
+        for node in attr_nodes:
+            # Check whether the parameter is used in only one submodule
+            if len(node.users) > 1:
+                logger.info(
+                    f"Parameter {node.target} used in multiple stages: {node.users}."  # noqa: G004
+                )
+            for user in node.users:
+                assert user.op == "call_module"
+                # Move parameter into submodule
+                move_param_to_callee(
+                    split,
+                    user.target,
+                    node.target,
+                )
+
+        # [aliasing] store tensor id -> list of FQNs, built from state dict
+        # Also assign non-persistent buffers
+        id_to_fqns: dict[int, set[str]] = defaultdict(set)
+        for fqn, tensor in mod.state_dict(keep_vars=True).items():
+            id_to_fqns[id(tensor)].add(fqn)
+        for fqn, tensor in mod.named_buffers():
+            id_to_fqns[id(tensor)].add(fqn)
+
+        # After moving the params to their corresponding hierarchies, we also
+        # need to move the `get_attr` nodes from the root of the graph to those
+        # hierarchies.
+        # [aliasing] use id -> fqn mapping to list out all valid FQNs
+        inputs_to_state: dict[str, list[str]] = {}
+        for attr in attr_nodes:
+            _, tensor = _recursive_getattr_with_parent(mod, attr.target)
+            fqns = list(id_to_fqns[id(tensor)])
+            if fqns:
+                inputs_to_state[attr.name] = fqns
+            elif attr.target in exported_program.constants:  # lifted constants
+                inputs_to_state[attr.name] = [attr.target]
+
+        # [aliasing] for each submodule split, assign attributes on FQNs that may be used.
+        # We determine this based on whether or not the FQN attribute parent exists.
+        # i.e. if the last submodule exists, assign the attribute.
+        added_attributes: dict[str, list[str]] = defaultdict(list)
+        for fqn, tensor in mod.state_dict(keep_vars=True).items():
+            for name, submod in split.named_children():
+                if isinstance(submod, fx.GraphModule):
+                    parent, child = _recursive_getattr_with_parent(submod, fqn)
+                    if (
+                        parent and child is None
+                    ):  # parent exists, attribute doesn't -> assign
+                        added_attributes[name].append(fqn)
+                        setattr(parent, fqn.split(".")[-1], tensor)
+
+        # Deferral deletion: Remove the original attributes (to params) from the
+        # root GraphModule
+        for mod_itr, last_atom in to_delete:
+            try:
+                delattr(mod_itr, last_atom)
+            except AttributeError:
+                # This is expected if the parameter is used in multiple stages
+                pass
+
+        # This is done by (1) `_sink_params` at each submodule;
+        for name, submod in split.named_children():
+            if isinstance(submod, fx.GraphModule):
+                _sink_params(submod, inputs_to_state, [])
+                submod.graph.lint()
+                submod.recompile()
+
+        # [aliasing] This step is not super necessary, but helps reduce parameter usage/memory.
+        # After _sink_params() routine has run, clean up unused attributes that we previously added.
+        # Determine this based on the get_attr nodes - if not used, remove it.
+        for name, attributes in added_attributes.items():
+            submod = getattr(split, name)
+            unused_attributes = set(attributes)
+            # track used attributes in the submodule, running DFS on subgraph hierarchy
+            stack = [("", submod)]  # (scope, submodule)
+            while stack:
+                scope, _mod = stack.pop()
+                if isinstance(_mod, (fx.GraphModule, InterpreterModule)):
+                    for node in _mod.graph.nodes:
+                        if node.op == "get_attr":
+                            # get_attr might get access deeper level attribute
+                            fqn = scope + "." + node.target if scope else node.target
+                            unused_attributes.discard(fqn)
+                for _name, _submod in _mod.named_children():
+                    stack.append((scope + "." + _name if scope else _name, _submod))
+            # delete unused attributes
+            for attr in unused_attributes:
+                mod_itr, atoms = submod, attr.split(".")
+                for atom in atoms[:-1]:
+                    mod_itr = getattr(mod_itr, atom)
+                delattr(mod_itr, atoms[-1])
+
+        for node in attr_nodes:
+            # And (2): remove `get_attr` node from submod's arg list
+            for user in copy.copy(node.users):
+                assert user.op == "call_module"
+                delete_user_reference(node, user)
+            # And (3): remove the `get_attr` node from the root graph.
+            split.graph.erase_node(node)
+
+        split.delete_all_unused_submodules()
+        split.graph.lint()
+        split.recompile()
+
+        num_stages = Pipe._number_and_count_forward_stages(split)
+
+        has_loss_and_backward = False
+        generated_loss_spec = output_loss_value_spec
+
+        if output_loss_value_spec is not None:
+            loss_node, output_node, generated_loss_spec = _find_loss_output(
+                mod, split.graph, output_loss_value_spec
+            )
+            if loss_node is not None:
+                _insert_stage_symbolic_backward(
+                    split.graph,
+                    loss_node,
+                    output_node,
+                )
+                split.recompile()
+                has_loss_and_backward = True
+                logger.debug("Pipeline is in training mode, backward pass generated")
+            else:
+                raise RuntimeError(
+                    f"Did not find any loss value according to {output_loss_value_spec=}"
+                )
+        else:
+            logger.debug("Pipeline is in inference mode, backward pass not generated")
+
+        logger.debug(f"Full pipe model:\n{split}")  # noqa: G004
+
+        return Pipe(
+            split,
+            num_stages,
+            has_loss_and_backward,
+            generated_loss_spec,
+        )
+
+    def print_readable(self):
+        """
+        Print the pipe in a human-readable format.
+        This will print both the root pipe and each stage module.
+        """
+        self.split_gm.print_readable()
+
+    @staticmethod
+    def _trace_with_export(
+        mod: torch.nn.Module,
+        example_args: tuple[Any, ...],
+        example_kwargs: Optional[dict[str, Any]] = None,
+    ) -> ExportedProgram:
+        logger.info("Tracing model ...")
+        try:
+            ep = torch.export.export_for_training(
+                mod,
+                example_args,
+                example_kwargs,
+            )
+        except Exception as e:
+            raise RuntimeError(
+                "It seems that we cannot capture your model as a full graph. "
+                "Typical reasons include graph breaks, data/shape-dependent "
+                "control flow, or missing meta kernels for custom operators. "
+                "You can use our manual pipeline interfaces, or try to fix the "
+                "graph breaks, see https://pytorch.org/docs/stable/export.html"
+            ) from e
+
+        return ep
+
+    @staticmethod
+    def from_tracing(
+        mod: torch.nn.Module,
+        example_args: tuple[Any, ...],
+        example_kwargs: Optional[dict[str, Any]] = None,
+        split_policy: Optional[Callable[[fx.GraphModule], fx.GraphModule]] = None,
+    ):
+        # If a param will be used in multiple pipeline stages, we default the strategy to REPLICATE'ing the param across
+        # stages instead of TRANSMIT'ting it
+        multi_use_param_spec = MultiUseParameterConfig.REPLICATE
+
+        # Figure out which output is loss from output_chunk_spec
+        output_loss_value_spec: Any = None
+        # Deprecated
+        """
+        if output_chunk_spec is not None:
+            output_loss_value_spec = map_aggregate(
+                output_chunk_spec, lambda v: isinstance(v, _LossReducer)
+            )
+        """
+
+        # Trace with export
+        exported_program = Pipe._trace_with_export(
+            mod,
+            example_args,
+            example_kwargs,
+        )
+
+        pipe = Pipe._from_traced(
+            mod,
+            exported_program,
+            multi_use_param_spec,
+            output_loss_value_spec=output_loss_value_spec,
+            split_policy=split_policy,
+        )
+
+        # Users want the first pipeline stage to accept kwargs if the original
+        # program does. This is controlled by the `_codegen` field of the graph,
+        # so we make a copy here. Note: we only want the input spec and not the
+        # output spec, because the output spec is for the last stage. Maybe a
+        # TODO? Not sure yet.
+        split = pipe.split_gm
+        traced = exported_program.module()
+        submod0 = next(iter(split.children()))
+        submod0_sign = signature(submod0.forward)
+        model_sign = signature(traced.forward)
+        if len(model_sign.parameters) != len(submod0_sign.parameters):
+            # We don't change the signature of the first stage if it takes
+            # different number of args than original model
+            logger.info(
+                f"Original model takes {len(model_sign.parameters)} args but the "  # noqa: G004
+                f"first pipeline stage takes {len(submod0_sign.parameters)}. "
+                "Please provide args to respective pipeline stages."
+            )
+        else:
+            # Support kwargs for the first stage
+            submod0.graph._codegen = copy.deepcopy(traced.graph._codegen)  # type: ignore[union-attr]
+            # `_replace` is actually not "private" or internal. based on this doc:
+            # To prevent conflicts with field names, the method and attribute names
+            # start with an underscore
+            submod0.graph._codegen.pytree_info = (  # type: ignore[union-attr]
+                submod0.graph._codegen.pytree_info._replace(out_spec=None)  # type: ignore[operator, union-attr]
+            )
+            submod0.recompile()
+
+        return pipe
+
+    def __str__(self):
+        return self.split_gm.__str__()
+
+    def __repr__(self):
+        return self.split_gm.__repr__()
+
+    def info(self) -> PipeInfo:
+        """
+        Get information about the pipe.
+
+        Returns
+        -------
+        PipeInfo
+            A dataclass containing information about the pipe.
+        """
+        return PipeInfo(
+            graph=self.split_gm.graph,
+            num_stages=self.num_stages,
+            has_loss_and_backward=self.has_loss_and_backward,
+        )
+
+    def build_stage(
+        self,
+        stage_index: int,
+        device: torch.device,
+        group: Optional[ProcessGroup] = None,
+    ) -> _PipelineStage:
+        """
+        Create a `PipelineStage` given a stage index and distributed group.
+        The `PipelineStage` can run with `PipelineSchedule`s.
+        """
+        # Find stage module
+        stage_module = self.get_stage_module(stage_index)
+
+        # Move ops argument to device
+        # Today PT2 tracer does not treat `x.device` as a symbolic device;
+        # instead, the device of tracing time got burned into the generated
+        # code.  Here we provide a workaround for users to manually modify the
+        # "device" kwarg of operations. Such operation may include:
+        # `torch.ones`, `torch.zeros`, `torch.rand`, etc.
+        if isinstance(stage_module, torch.fx.GraphModule):
+            _modify_graph_op_device(stage_module, device)
+        else:
+            logger.warning(
+                f"Expected a `torch.fx.GraphModule` but got {type(stage_module)}"  # noqa: G004
+            )
+
+        # Detach pipe info
+        # Note: be careful what's included in `pipe_info`. We don't want to keep
+        # a reference to `Pipe` or `Pipe.split_gm` which stops python from
+        # recycling them. When python recycles them, other stage modules (which
+        # are irrelevant to current rank) can be automatically freed.
+        pipe_info = self.info()
+        return _PipelineStage(stage_module, stage_index, pipe_info, device, group)
+
+
+class SplitPoint(Enum):
+    """
+    Enum representing the points at which a split can occur in the execution of a submodule.
+    Attributes:
+        BEGINNING: Represents adding a split point *before* the execution of a certain submodule in the `forward` function.
+        END: Represents adding a split point *after* the execution of a certain submodule in the `forward` function.
+    """
+
+    BEGINNING = 1
+    END = 2
+
+
+# For backward compatibility, we kept the PipeSplitWrapper class because `class
+# SplitPoint` used to be defined in this class.
+class PipeSplitWrapper:
+    # Create a class alias for BC
+    SplitPoint = SplitPoint
+
+
+def _split_before_forward(self, *args, **kwargs):
+    pipe_split()
+    return self._orig_forward(*args, **kwargs)
+
+
+def _split_after_forward(self, *args, **kwargs):
+    try:
+        return self._orig_forward(*args, **kwargs)
+    finally:
+        pipe_split()
+
+
+def annotate_split_points(mod: torch.nn.Module, spec: dict[str, SplitPoint]):
+    # TODO: make this implementation out-of-place?
+    for qualname, split_type in spec.items():
+        atoms = qualname.split(".")
+        predecessor_module = mod
+        for i, atom in enumerate(atoms[:-1]):
+            try:
+                predecessor_module = getattr(predecessor_module, atom)
+            except AttributeError as e:
+                raise AttributeError(
+                    f"Specified target {qualname} referenced "
+                    f"nonexistent module {'.'.join(atoms[: i + 1])}"
+                ) from e
+
+        mod_to_wrap = getattr(predecessor_module, atoms[-1])
+        mod_to_wrap._orig_forward = mod_to_wrap.forward
+        if split_type == SplitPoint.BEGINNING:
+            mod_to_wrap.forward = MethodType(_split_before_forward, mod_to_wrap)
+        elif split_type == SplitPoint.END:
+            mod_to_wrap.forward = MethodType(_split_after_forward, mod_to_wrap)
+        else:
+            raise ValueError("Unknown split point type.")
+
+
+def pipeline(
+    module: torch.nn.Module,
+    mb_args: tuple[Any, ...],
+    mb_kwargs: Optional[dict[str, Any]] = None,
+    split_spec: Optional[dict[str, SplitPoint]] = None,
+    split_policy: Optional[Callable[[fx.GraphModule], fx.GraphModule]] = None,
+) -> Pipe:
+    """
+    Split a module based on a specification.
+
+    See `Pipe` for more details.
+
+    Arguments
+    ---------
+    module:
+        The module to be splitted.
+    mb_args:
+        Example positional inputs, in micro-batch form.
+    mb_kwargs:
+        Example keyword inputs, in micro-batch form. (default: `None`)
+    split_spec:
+        A dictionary using submodule names as split marker. (default: `None`)
+    split_policy:
+        The policy to use for splitting the module. (default: `None`)
+
+    Returns
+    -------
+    A pipeline representation of class `Pipe`.
+    """
+    if split_spec is not None and split_policy is not None:
+        raise ValueError(
+            "Cannot specify both `split_spec` and `split_policy`. Please use only one of them."
+        )
+
+    if split_spec is not None:
+        # Annotate split points in the module based on user spec
+        annotate_split_points(module, split_spec)
+        return Pipe.from_tracing(
+            mod=module,
+            example_args=mb_args,
+            example_kwargs=mb_kwargs,
+        )
+    else:
+        # Use split policy
+        return Pipe.from_tracing(
+            mod=module,
+            example_args=mb_args,
+            example_kwargs=mb_kwargs,
+            split_policy=split_policy,
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e715a839997ecc7cd18f8ae095ff097cdf3b8702
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/__init__.py
@@ -0,0 +1,28 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from ._IR import Pipe, pipe_split, pipeline, SplitPoint
+from .schedules import (
+    _ScheduleForwardOnly,
+    Schedule1F1B,
+    ScheduleGPipe,
+    ScheduleInterleaved1F1B,
+    ScheduleInterleavedZeroBubble,
+    ScheduleLoopedBFS,
+    ScheduleZBVZeroBubble,
+)
+from .stage import build_stage, PipelineStage
+
+
+__all__ = [
+    "Pipe",
+    "pipe_split",
+    "SplitPoint",
+    "pipeline",
+    "PipelineStage",
+    "build_stage",
+    "Schedule1F1B",
+    "ScheduleGPipe",
+    "ScheduleInterleaved1F1B",
+    "ScheduleLoopedBFS",
+    "ScheduleInterleavedZeroBubble",
+    "ScheduleZBVZeroBubble",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_backward.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_backward.py
new file mode 100644
index 0000000000000000000000000000000000000000..4269375a1c6f64192aef77f960c58a39e0a039a2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_backward.py
@@ -0,0 +1,403 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import collections
+import logging
+from collections.abc import Iterator
+from typing import Any, Optional, Union
+
+import torch
+from torch.autograd.graph import GradientEdge, Node
+from torch.nn import Parameter
+
+from ._debug import map_debug_info
+
+
+logger = logging.getLogger(__name__)
+
+
+def _get_grad_fn_or_grad_acc(t: torch.Tensor) -> Union[Node, None]:
+    """
+    Get the grad function or grad accumulator for a tensor.
+
+    Accumulate grad nodes are lazily created, so we need to a
+    dummy view in order to trigger its creation.
+    """
+    if t.requires_grad and t.grad_fn is None:
+        # if no grad function (leaf tensors) we use view
+        viewed_t = t.view_as(t)
+        grad_fn = viewed_t.grad_fn
+        if grad_fn is not None:
+            return grad_fn.next_functions[0][0]
+        else:
+            raise RuntimeError(
+                "Attempted to get grad_fn, but got None."
+                "Is this being created in a no-grad context?"
+            )
+    else:
+        return t.grad_fn
+
+
+def reverse_closure(
+    roots: list[Node], target_nodes: set[Node], reverse_edges_dict
+) -> tuple[set[Node], set[Node]]:
+    """
+    This function returns the reverse closure of the given roots,
+    i.e. the set of nodes that can be reached from the roots by following the
+    reverse edges of the graph. The target_nodes are the nodes that we want to
+    include in the closure.
+    """
+    # Recurse until we reach a target node
+    closure: set[Node] = set()
+    visited_target_nodes = set()
+    q: collections.deque[Node] = collections.deque()
+    for node in roots:
+        if node is not None and node not in closure:
+            closure.add(node)
+            q.append(node)
+    while q:
+        node = q.popleft()
+        reverse_edges = reverse_edges_dict[node]
+        for fn in reverse_edges:
+            if fn in closure or fn is None:
+                continue
+            if fn in target_nodes:
+                visited_target_nodes.add(fn)
+                continue
+            closure.add(fn)
+            q.append(fn)
+    return closure, visited_target_nodes
+
+
+def construct_reverse_graph(roots: list[Node]) -> dict[Node, list[Node]]:
+    q: collections.deque[Node] = collections.deque()
+    root_seen: set[Node] = set()
+    reverse_edges_dict: dict[Node, list[Node]] = collections.defaultdict(list)
+    for node in roots:
+        if node is not None and node not in root_seen:
+            q.append(node)
+            root_seen.add(node)
+    while q:
+        node = q.popleft()
+        for fn, _ in node.next_functions:
+            if fn is not None:
+                if len(reverse_edges_dict[fn]) == 0:
+                    q.append(fn)
+                reverse_edges_dict[fn].append(node)
+    return reverse_edges_dict
+
+
+def get_param_groups(
+    inputs: list[Node], params: list[Node], reverse_edges_dict
+) -> list[dict[str, Any]]:
+    """
+    Given a list of inputs and a list of parameters, return a list of parameter
+    groups, where each group contains the parameters and the intermediates that
+    are connected to the parameters.
+
+    The returned list of parameter groups is a list of dictionaries, where each
+    dictionary contains the following keys:
+    - "params": a set of parameters
+    - "intermediates": a set of intermediates
+
+    The returned list of parameter groups is a list of dictionaries,
+    """
+    # reverse graph that starts with inputs, and goes up to the dOutput or the loss,
+    # but omits weights and any subgraphs connecting weights to this closure
+    inputs_closure, _ = reverse_closure(inputs, set(), reverse_edges_dict)
+    param_groups: dict[Node, dict[str, set]] = dict()  # keyed on intermediates
+    for param in params:
+        closure, intersected = reverse_closure(
+            [param], inputs_closure, reverse_edges_dict
+        )
+        param_group: dict[str, set] = {
+            "params": {param},
+            "intermediates": intersected,
+        }
+        for input_node in intersected:
+            existing = param_groups.get(input_node, None)
+            if existing is not None:
+                existing["params"] = existing["params"].union(param_group["params"])
+                existing["intermediates"] = existing["intermediates"].union(
+                    param_group["intermediates"]
+                )
+                param_group = existing
+            else:
+                param_groups[input_node] = param_group
+
+    # Sanity check: union of all param_groups params should be equal to all params
+    union_params: set[Node] = set()
+    seen_ids: set[int] = set()
+    unique_param_groups = []
+    for param_group in param_groups.values():
+        if id(param_group) not in seen_ids:
+            seen_ids.add(id(param_group))
+            unique_param_groups.append(param_group)
+            union_params = union_params.union(param_group["params"])
+
+    # The assert will only be true if the input tensor requires gradients,
+    # otherwise the autograd graph will miss the first layer of inputs
+    # assert union_params == set(params)
+    return unique_param_groups
+
+
+def stage_backward_input(
+    stage_outputs_or_loss: list[torch.Tensor],
+    output_grads: Optional[list[torch.Tensor]],
+    input_values: list[torch.Tensor],
+    weights: Iterator[Parameter],
+) -> tuple[tuple[Optional[torch.Tensor], ...], list[dict[str, Any]]]:
+    """
+    Compute the gradients for only the stage inputs with
+    respect to the stage outputs (if non-last stage) or loss (if last stage)
+
+    After computing input gradients, we save the intermediate nodes in `param_groups`
+    for later use in stage_backward_weight. We don't need to save any other intermediate nodes
+    that aren't needed for dW because when we do dW calculation, we start from saved intermediates.
+    Detaching the stage_outputs_or_loss at the end of this function is important as
+    it frees up the memory that the autograd graph is anticipating to be used later (but doesn't actually need).
+    """
+    stage_output_grad_fns: list[Node] = list(
+        filter(None, map(_get_grad_fn_or_grad_acc, stage_outputs_or_loss))
+    )
+    stage_input_grad_fns: list[Node] = list(
+        filter(None, map(_get_grad_fn_or_grad_acc, input_values))
+    )
+    weight_grad_fns: list[Node] = list(
+        filter(None, map(_get_grad_fn_or_grad_acc, weights))
+    )
+
+    reverse_edges_dict = construct_reverse_graph(stage_output_grad_fns)
+    param_groups = get_param_groups(
+        stage_input_grad_fns, weight_grad_fns, reverse_edges_dict
+    )
+
+    handles = []
+    for param_group in param_groups:
+        for i, intermediate in enumerate(param_group["intermediates"]):
+
+            def get_hook(param_group, i):
+                def hook(grad_inputs):
+                    if param_group.get("grads", None) is None:
+                        param_group["grads"] = [None] * len(
+                            param_group["intermediates"]
+                        )
+                    param_group["grads"][i] = grad_inputs
+
+                return hook
+
+            # These are always "split" nodes that we need to recompute, so
+            # save their inputs.
+            handle = intermediate.register_prehook(get_hook(param_group, i))
+            handles.append(handle)
+
+    if output_grads is None:
+        # In case this is the loss and there are no output_grads, then we just use 1s
+        output_grads = [
+            torch.ones_like(stage_output) for stage_output in stage_outputs_or_loss
+        ]
+
+    dinputs = torch.autograd.grad(
+        stage_outputs_or_loss,
+        inputs=input_values,
+        grad_outputs=output_grads,
+        retain_graph=True,
+    )
+
+    # update the gradients for inputs
+    for i, inp in enumerate(input_values):
+        if inp.grad is None:
+            inp.grad = dinputs[i]
+        else:
+            inp.grad += dinputs[i]
+
+    # stage_outputs_or_loss are not used in backwards after this point, so we can safely remove it from the autograd graph
+    # this allows autograd to clear up the graph dedicated for this tensor and free up significant memory
+    for t in stage_outputs_or_loss:
+        t.detach_()
+
+    # hooks are no longer necessary, clean up for consistency
+    for handle in handles:
+        handle.remove()
+
+    return dinputs, param_groups
+
+
+def stage_backward_weight(
+    weights: Iterator[Parameter], param_groups: list[dict[str, Any]], retain_graph=False
+) -> tuple[Optional[torch.Tensor], ...]:
+    # map weights to param_group_weights
+    grad_acc_to_weight = {}
+    weight_grads: list[Optional[torch.Tensor]] = []
+    for index, weight in enumerate(weights):
+        grad_acc = _get_grad_fn_or_grad_acc(weight)
+        grad_acc_to_weight[grad_acc] = weight, index
+        weight_grads.append(weight.grad)
+
+    for param_group in param_groups:
+        # TODO: Handle case where intermediate can have multiple outputs
+        intermediate_edges = tuple(
+            GradientEdge(i, 0) for i in param_group["intermediates"]
+        )
+        weights_edges = tuple(GradientEdge(w, 0) for w in param_group["params"])
+
+        # Break a reference cycle caused inside stage_backward_input->get_hook->hook
+        # The summarized cycle is:
+        # `hook` -> cell -> param_group -> intermediates -> `hook`
+        # becuase we install the hook function onto each of the intermediate autograd nodes.
+        # We need to keep intermediates alive up until backward_weight, but we can free it now.
+        del param_group["intermediates"]
+
+        assert all(len(g) == 1 for g in param_group["grads"])
+        # [NEW!] Able to pass a GradientEdge to autograd.grad as output
+        # We do not need to retain_graph because... guarantee no overlap?
+        # print("trying to execute: ", intermediate_edges, weights_edges)
+        dweights = torch.autograd.grad(
+            intermediate_edges,
+            weights_edges,
+            grad_outputs=sum(param_group["grads"], tuple()),
+            retain_graph=retain_graph,
+        )
+        # release grad memory early after use
+        del param_group["grads"]
+
+        for grad_acc, dw in zip(param_group["params"], dweights):
+            weight, index = grad_acc_to_weight[grad_acc]
+            if weight.grad is None:
+                weight.grad = dw
+            else:
+                weight.grad += dw
+    # return grads in the original order weights were provided in
+    return tuple(weight_grads)
+
+
+def stage_backward(
+    stage_output,
+    output_grads,
+    input_values,
+    outputs_with_grads_idxs: Optional[list[int]] = None,  # deprecated, not used
+) -> tuple[Optional[torch.Tensor], ...]:
+    """
+    This is a helper function to:
+    1. compute the gradients for the stage inputs, and
+    2. accumulate gradients for the stage module's parameters.
+
+    Given the input value(s) and the corresponding gradient for the output
+    value(s), compute and accumulate gradients for all parameter values (leaves
+    in the autograd trace) as well as return a list of the gradients for the
+    input values
+    """
+    if outputs_with_grads_idxs is not None:
+        # Deprecated, not used in runtime calls, only exists in compiler
+        stage_output = [stage_output[i] for i in outputs_with_grads_idxs]
+        output_grads = [output_grads[i] for i in outputs_with_grads_idxs]
+
+    try:
+        # stage_output may be a composite datatype like dict. Extract all individual
+        # tensor values here
+        stage_output_tensors: list[torch.Tensor] = []
+        output_grad_tensors: list[Optional[torch.Tensor]] = []
+
+        def extract_tensors_with_grads(
+            output_val,
+            grad_val,
+            # Don't delete me- see [Note: ref cycle]
+            extract_tensors_with_grads,
+        ):
+            if isinstance(output_val, torch.Tensor):
+                if not output_val.requires_grad and output_val.grad_fn is None:
+                    return
+                assert isinstance(grad_val, (torch.Tensor, type(None))), (
+                    f"Expected Tensor or None gradient but got {type(grad_val)}"
+                )
+                stage_output_tensors.append(output_val)
+                output_grad_tensors.append(grad_val)
+            elif isinstance(output_val, (tuple, list)):
+                if grad_val is None:
+                    return
+                assert isinstance(grad_val, (tuple, list)), (
+                    f"grad_value expected to have type {type(output_val)} but got {type(grad_val)}"
+                )
+                assert len(output_val) == len(grad_val)
+                for ov, gv in zip(output_val, grad_val):
+                    extract_tensors_with_grads(
+                        ov,
+                        gv,
+                        extract_tensors_with_grads,
+                    )
+            elif isinstance(output_val, dict):
+                if grad_val is None:
+                    return
+                assert isinstance(grad_val, dict)
+                assert set(output_val.keys()) == set(grad_val.keys())
+                for k in output_val.keys():
+                    extract_tensors_with_grads(
+                        output_val[k], grad_val[k], extract_tensors_with_grads
+                    )
+            else:
+                # Output is a non-tensor type; just ignore it
+                pass
+
+        # Note: ref cycle
+        # break a ref cycle that would keep tensors alive until GC runs
+        # 1. extract_tensors_with_grads refers to a cell that holds refs to any vars defined in stage_backward
+        #    and used in extract_tensors_with_grads
+        # 2. extract_tensors_with_grads referred to both stage_output_tensors, output_grad_tensors,
+        #    and to itself (extract_tensors_with_grads) since it makes a recursive call
+        # 3. stage_output_tensors was kept alive by the above refcycle, and it holds activation tensors, which is bad
+        # fix -> explicitly pass in the ref to the fn, so there is no gc cycle anymore
+        extract_tensors_with_grads(
+            stage_output, output_grads, extract_tensors_with_grads
+        )
+
+        torch.autograd.backward(
+            stage_output_tensors,
+            grad_tensors=output_grad_tensors,  # type: ignore[arg-type]
+        )
+
+        # Extract gradients wrt the input values
+        grad_inputs: list[Optional[torch.Tensor]] = []
+        for val in input_values:
+            if isinstance(val, torch.Tensor):
+                grad_inputs.append(val.grad)
+            else:
+                grad_inputs.append(None)
+
+        # Alternative impl: `torch.autograd.grad`.
+        # Note that `torch.autograd.grad` will not accumulate gradients into the
+        # model's parameters.
+        """
+        inputs_with_grad = []
+        for val in input_values:
+            if isinstance(val, torch.Tensor) and val.requires_grad:
+                inputs_with_grad.append(val)
+
+        grad_inputs = torch.autograd.grad(
+            stage_output_tensors, inputs_with_grad, output_grad_tensors,  # type: ignore[arg-type]
+        )
+        """
+
+    except Exception as e:
+        exc_msg = f"""
+        Failed to run stage backward:
+        Stage output: {map_debug_info(stage_output)}
+        Output gradient: {map_debug_info(output_grads)}
+        Input: {map_debug_info(input_values)}
+        """
+        raise RuntimeError(exc_msg) from e
+
+    return tuple(grad_inputs)
+
+
+# TODO: handling requires_grad=False dynamically. Can we analyze this during initial
+# IR emission?
+def _null_coalesce_accumulate(lhs, rhs):
+    """
+    Coalesce two values, even if one of them is null, returning the non-null
+    value.
+    """
+    if lhs is None:
+        return rhs
+    elif rhs is None:
+        return lhs
+    else:
+        return torch.add(lhs, rhs)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_debug.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_debug.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3201d2d3adf1d05921e070d14b4e544844df88f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_debug.py
@@ -0,0 +1,22 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import torch
+from torch.fx.node import Argument
+
+
+def friendly_debug_info(v: object) -> Argument:
+    """
+    Helper function to print out debug info in a friendly way.
+    """
+    if isinstance(v, torch.Tensor):
+        return f"Tensor({v.shape}, grad={v.requires_grad}, dtype={v.dtype})"
+    else:
+        return str(v)
+
+
+def map_debug_info(a: Argument) -> Argument:
+    """
+    Helper function to apply `friendly_debug_info` to items in `a`.
+    `a` may be a list, tuple, or dict.
+    """
+    return torch.fx.node.map_aggregate(a, friendly_debug_info)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_unflatten.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_unflatten.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ed592f2f8d832de0703fbfa296225f17698afbf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/_unflatten.py
@@ -0,0 +1,30 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from collections import defaultdict
+
+import torch
+from torch.export.unflatten import _ModuleFrame, _SubmoduleEntry
+
+
+def _outline_submodules(orig_graph: torch.fx.Graph) -> torch.fx.GraphModule:
+    # Create an empty GraphModule to hold the outlined modules
+    new_module = torch.fx.GraphModule(torch.nn.Module(), torch.fx.Graph())
+    seen_nodes: dict[str, torch.fx.Node] = {}
+    seen_modules: dict[int, list[_SubmoduleEntry]] = defaultdict(list)
+    seen_attrs: dict[str, set[str]] = defaultdict(set)
+    created_modules: dict[str, torch.nn.Module] = {}
+    _ModuleFrame(
+        orig_graph,
+        tuple(orig_graph.nodes),
+        seen_nodes,
+        seen_modules,
+        seen_attrs,
+        created_modules,
+        None,
+        [("", None, 0)],
+        "",
+        {},
+        module=new_module,
+    ).run_outer()
+    new_module.graph.lint()
+    new_module.recompile()
+    return new_module
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/schedules.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/schedules.py
new file mode 100644
index 0000000000000000000000000000000000000000..e431e29b77e6c0518fee0a3c4e045fd2cc84b264
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/schedules.py
@@ -0,0 +1,2652 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import copy
+import csv
+import itertools
+import logging
+import re
+from abc import ABC, abstractmethod
+from collections import Counter, defaultdict
+from enum import Enum
+from typing import Any, Callable, NamedTuple, Optional, TYPE_CHECKING, Union
+
+import torch
+import torch.distributed as dist
+from torch._dynamo import OptimizedModule
+from torch.distributed.fsdp import FSDPModule, UnshardHandle
+from torch.nn.modules.loss import _Loss
+from torch.profiler import record_function
+
+from ._utils import generate_stage_to_rank_mapping
+from .microbatch import merge_chunks, split_args_kwargs_into_chunks, TensorChunkSpec
+from .stage import _PipelineStageBase
+
+
+if TYPE_CHECKING:
+    from torch.distributed import Work
+
+__all__ = [
+    "get_schedule_class",
+    "PipelineScheduleSingle",
+    "PipelineScheduleMulti",
+    "Schedule1F1B",
+    "ScheduleGPipe",
+    "ScheduleInterleaved1F1B",
+    "ScheduleLoopedBFS",
+    "ScheduleInterleavedZeroBubble",
+    "ScheduleZBVZeroBubble",
+]
+
+logger = logging.getLogger(__name__)
+
+
+class _ComputationType(Enum):
+    # TODO(whc) rename to _ActType?
+    FORWARD = 1
+    BACKWARD_INPUT = 2
+    BACKWARD_WEIGHT = 3
+    UNSHARD = 4
+    RESHARD = 5
+    SEND_F = 6
+    RECV_F = 7
+    SEND_B = 8
+    RECV_B = 9
+    FULL_BACKWARD = 10
+
+    def __str__(self):
+        str_map = {
+            _ComputationType.FORWARD: "F",
+            _ComputationType.BACKWARD_INPUT: "I",
+            _ComputationType.BACKWARD_WEIGHT: "W",
+            _ComputationType.UNSHARD: "UNSHARD",
+            _ComputationType.RESHARD: "RESHARD",
+            _ComputationType.SEND_F: "SEND_F",
+            _ComputationType.RECV_F: "RECV_F",
+            _ComputationType.SEND_B: "SEND_B",
+            _ComputationType.RECV_B: "RECV_B",
+            _ComputationType.FULL_BACKWARD: "B",
+        }
+        return str_map[self]
+
+    @staticmethod
+    def from_str(action):
+        if action == "F":
+            return _ComputationType.FORWARD
+        elif action == "I":
+            return _ComputationType.BACKWARD_INPUT
+        elif action == "W":
+            return _ComputationType.BACKWARD_WEIGHT
+        elif action == "UNSHARD":
+            return _ComputationType.UNSHARD
+        elif action == "RESHARD":
+            return _ComputationType.RESHARD
+        elif action == "SEND_F":
+            return _ComputationType.SEND_F
+        elif action == "RECV_F":
+            return _ComputationType.RECV_F
+        elif action == "SEND_B":
+            return _ComputationType.SEND_B
+        elif action == "RECV_B":
+            return _ComputationType.RECV_B
+        elif action == "B":
+            return _ComputationType.FULL_BACKWARD
+        else:
+            raise RuntimeError(f"Invalid computation type {action}")
+
+
+FORWARD = _ComputationType.FORWARD
+BACKWARD_INPUT = _ComputationType.BACKWARD_INPUT
+BACKWARD_WEIGHT = _ComputationType.BACKWARD_WEIGHT
+UNSHARD = _ComputationType.UNSHARD
+RESHARD = _ComputationType.RESHARD
+SEND_F = _ComputationType.SEND_F
+RECV_F = _ComputationType.RECV_F
+SEND_B = _ComputationType.SEND_B
+RECV_B = _ComputationType.RECV_B
+FULL_BACKWARD = _ComputationType.FULL_BACKWARD
+
+# Convenience shorthand for compute actions only since they are used in 'simple schedule format'
+F = FORWARD
+I = BACKWARD_INPUT
+W = BACKWARD_WEIGHT
+B = FULL_BACKWARD
+
+# Helper to parse an action string like 1F0 into a tuple of (stage_index, computation_type, microbatch_index)
+_action_regex = re.compile(
+    r"(\d+)(F|I|B|W|UNSHARD|RESHARD|SEND_F|RECV_F|SEND_B|RECV_B)(\d*)"
+)
+
+
+class _Action(NamedTuple):
+    stage_index: int
+    computation_type: _ComputationType
+    microbatch_index: Optional[int] = None
+
+    def __repr__(self):
+        repr = str(self.stage_index)
+        repr += str(self.computation_type)
+        if self.microbatch_index is not None:
+            repr += str(self.microbatch_index)
+        return repr
+
+    @staticmethod
+    def from_str(action_string: str):
+        """
+        Reverse of __repr__
+
+        String should be formatted as [stage][action type][(microbatch)]
+            e.g. `2F0`, `1UNSHARD`, `3SEND_F1`
+        """
+        action_string = action_string.strip()
+        if match := _action_regex.match(action_string):
+            stage_index, computation_type, microbatch_index = match.groups()
+            return _Action(
+                int(stage_index),
+                _ComputationType.from_str(computation_type),
+                int(microbatch_index) if len(microbatch_index) else None,
+            )
+        elif action_string == "":
+            return None
+        raise RuntimeError(
+            f"Invalid action string: {action_string}, should be formatted as [stage][action type][(microbatch)] e.g. 2F0"
+        )
+
+
+def _format_pipeline_order(
+    pipeline_order: dict[int, list[Optional[_Action]]],
+    error_step_number: Optional[int] = None,
+) -> str:
+    """
+    Formats the pipeline order in a timestep (row) x rank (column) grid of actions
+    and returns the formatted string.
+
+    If `error_step_number` is passed in, an additional label will be added to signify which step
+    that it is erroring on.
+    """
+
+    # don't mutate the original
+    pipeline_order = copy.deepcopy(pipeline_order)
+
+    # Replace None with ""
+    for rank in pipeline_order:
+        for i in range(len(pipeline_order[rank])):
+            if pipeline_order[rank][i] is None:
+                # TODO make a real 'None action' that prints as empty string and make mypy happy
+                pipeline_order[rank][i] = ""  # type: ignore[call-overload]
+
+    # Calculate the maximum number of steps across all ranks
+    num_steps = max(len(actions) for actions in pipeline_order.values())
+    step_labels = [
+        "Step " + str(i).zfill(len(str(num_steps - 1))) for i in range(num_steps)
+    ]
+    # Sorting the dictionary by keys and retrieving values in that order
+    rank_actions = [
+        pipeline_order.get(key, [""] * num_steps) for key in sorted(pipeline_order)
+    ]
+    # Transpose the list of lists (rows to columns)
+    transposed_actions = list(itertools.zip_longest(*rank_actions, fillvalue=""))
+    # Generate column labels for ranks
+    num_ranks = len(pipeline_order)
+    rank_labels = ["Rank " + str(i) for i in range(num_ranks)]
+    # Calculate the maximum length of each column, considering labels
+    max_lengths = [
+        max(len(str(item)) if item is not None else 0 for item in col)
+        for col in zip(step_labels, *transposed_actions)
+    ]
+    # Format the header row with rank labels
+    header_row = " " * (len(step_labels[0]) + 2) + " ".join(
+        f"{label:<{max_lengths[i]}}" for i, label in enumerate(rank_labels)
+    )
+    # Format each row with its corresponding label
+    formatted_rows = [
+        f"{label}: "
+        + " ".join(f"{str(item):<{max_lengths[i]}}" for i, item in enumerate(row))
+        + (
+            " <-- ERROR HERE"
+            if error_step_number is not None
+            and int(label.split()[1]) == error_step_number
+            else ""
+        )
+        for label, row in zip(step_labels, transposed_actions)
+    ]
+    # Join the rows into a single string
+    formatted_table = header_row + "\n" + "\n".join(formatted_rows) + "\n"
+    return formatted_table
+
+
+class _PipelineSchedule(ABC):
+    def __init__(
+        self,
+        n_microbatches: int,
+        loss_fn: Optional[Callable[..., torch.Tensor]] = None,
+        args_chunk_spec: Optional[tuple[TensorChunkSpec, ...]] = None,
+        kwargs_chunk_spec: Optional[dict[str, TensorChunkSpec]] = None,
+        output_merge_spec: Optional[Union[dict[str, Any], tuple[Any]]] = None,
+        scale_grads: bool = True,
+    ):
+        # From arguments
+        self._n_microbatches = n_microbatches
+        self._loss_fn = loss_fn
+
+        # See documentation in `PipelineScheduleSingle` / `PipelineScheduleMulti`
+        self.scale_grads = scale_grads
+
+        # Chunking specification for positional inputs. (default: `None`)
+        self._args_chunk_spec = args_chunk_spec
+        # Chunking specification for keyword inputs. (default: `None`)
+        self._kwargs_chunk_spec = kwargs_chunk_spec
+        self._output_merge_spec = output_merge_spec
+        """
+        # args_chunk_spec and kwargs_chunk_spec specify how to chunk inputs.
+        # They are used to convert batch to microbatches in `step(x)`.  See
+        # `TensorChunkSpec` for helper methods for creating them.
+        """
+
+        # Derived
+        self._has_backward = self._loss_fn is not None
+
+        # Holds the losses for each microbatch.
+        self._internal_losses: list[torch.Tensor] = []
+        logger.info("Using %s", self.__class__.__name__)
+
+    def _maybe_compute_loss(self, stage, output, target_mbs, mb_index):
+        if stage.is_last and self._has_backward:
+            loss = self._compute_loss(output, target_mbs[mb_index])  # type: ignore[index]
+            self._internal_losses.append(loss)
+
+    def _maybe_get_loss(self, stage, mb_index):
+        valid_index = 0 <= mb_index < len(self._internal_losses)
+        if stage.is_last and self._has_backward and valid_index:
+            return self._internal_losses[mb_index]
+        elif len(self._internal_losses) != 0 and not valid_index:
+            raise RuntimeError(
+                f"Loss for microbatch {mb_index} is not available. "
+                f"Available losses for microbatches: {self._internal_losses}"
+            )
+        else:
+            return None
+
+    def _update_losses(self, stages, losses):
+        """
+        Update the losses to those in the internal state
+        """
+        # if stages not a list turn into a list
+        if not isinstance(stages, list):
+            stages = [stages]
+        contains_last_stage = any(stage.is_last for stage in stages)
+
+        # Return losses if there is a container passed in
+        if contains_last_stage and losses is not None:
+            if len(self._internal_losses) != self._n_microbatches:
+                raise RuntimeError(
+                    f"Expecting {self._n_microbatches} losses but got {len(self._internal_losses)}"
+                )
+
+            # Clean external container first
+            losses.clear()
+            # Copy internal losses to external container
+            losses.extend(self._internal_losses)
+
+        self._internal_losses.clear()
+
+    @abstractmethod
+    def _step_microbatches(
+        self,
+        arg_mbs: Optional[list] = None,
+        kwarg_mbs: Optional[list] = None,
+        target_mbs: Optional[list] = None,
+        losses: Optional[list] = None,
+    ):
+        """
+        Run one iteration of the pipeline schedule with list of microbatches.
+        Will go through all the microbatches according to the schedule
+        implementation.
+
+        Args:
+            microbatches: list of microbatch args.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def step(self, *args, target=None, losses: Optional[list] = None, **kwargs):
+        """
+        Run one iteration of the pipeline schedule with *whole-batch* input.
+        Will chunk the input into microbatches automatically, and go through the
+        microbatches according to the schedule implementation.
+
+        args: positional arguments to the model (as in non-pipeline case).
+        kwargs: keyword arguments to the model (as in non-pipeline case).
+        target: target for the loss function.
+        losses: a list to store the losses for each microbatch.
+        """
+        raise NotImplementedError
+
+    def _check_inputs(
+        self,
+        arg_mbs: Optional[list] = None,
+        kwarg_mbs: Optional[list] = None,
+        target_mbs: Optional[list] = None,
+        losses: Optional[list] = None,
+    ):
+        """
+        Pre-process/check inputs
+        """
+
+        def check_type_and_len(mbs, name: str):
+            if not isinstance(mbs, list):
+                raise TypeError(f"{name} must be a list but got a {type(mbs)}")
+            if len(mbs) != self._n_microbatches:
+                raise ValueError(
+                    f"Expecting {self._n_microbatches} {name} but got {len(mbs)}"
+                )
+
+        if arg_mbs is not None:
+            check_type_and_len(arg_mbs, "arg_mbs")
+        else:
+            arg_mbs = [()] * self._n_microbatches
+
+        if kwarg_mbs is not None:
+            check_type_and_len(kwarg_mbs, "kwarg_mbs")
+        else:
+            kwarg_mbs = [{}] * self._n_microbatches
+
+        if target_mbs is not None:
+            check_type_and_len(target_mbs, "target_mbs")
+
+        if losses is not None:
+            if not isinstance(losses, list):
+                raise TypeError(f"losses must be a list but got a {type(losses)}")
+
+        return arg_mbs, kwarg_mbs
+
+    def _compute_loss(self, output, target):
+        return self._loss_fn(output, target)  # type: ignore[misc]
+
+    def _split_inputs(
+        self,
+        args: tuple[Any, ...],
+        kwargs: Optional[dict[str, Any]] = None,
+    ):
+        """
+        Splits a full-batch input into chunks (i.e. microbatches) and returns
+        the chunks
+        """
+        if args or kwargs:
+            args_split, kwargs_split = split_args_kwargs_into_chunks(
+                args,
+                kwargs,
+                self._n_microbatches,
+                self._args_chunk_spec,
+                self._kwargs_chunk_spec,
+            )
+            return args_split, kwargs_split
+        else:
+            # Empty inputs (e.g. when called on middle stages)
+            # Return a list of empty tuples/dicts with matching length as chunks
+            return [()] * self._n_microbatches, [{}] * self._n_microbatches
+
+    def _merge_outputs(self, output_chunks: list[Any]) -> Any:
+        """
+        Merge output chunks back to a batch state.
+        If output_merge_spec is None, the utility will merge output chunks by dimension 0 (batch dim).
+        """
+        return merge_chunks(
+            output_chunks,
+            self._output_merge_spec,
+        )
+
+
+def _batch_p2p(p2p_ops: list[dist.P2POp], desc: Optional[str] = None):
+    """
+    Simple wrapper over batch_isend_irecv from torch.distributed, which just adds a descriptive logger on top.
+    """
+    if len(p2p_ops) == 0:
+        return None
+    desc_str = f"{desc}, " if desc else ""
+    logger.debug("batch_p2p %s%s", desc_str, p2p_ops)
+    return dist.batch_isend_irecv(p2p_ops).pop()
+
+
+def _sorted_batch_p2p(
+    p2p_ops: list[dist.P2POp], desc: Optional[str] = None
+) -> dict[int, dist.Work]:
+    """
+    Sorts the list of P2P ops by the peer rank, and then calls
+    batch_isend_irecv. Return a dictionary of works by peer rank. This function
+    helps us avoid hangs in case of skip connections.
+    """
+    # Arrange p2p_ops by peer rank:
+    #   int is the peer rank;
+    #   List is the list of ops towards the peer
+    ops_by_peer: dict[int, list[dist.P2POp]] = defaultdict(list)
+    work_by_peer: dict[int, dist.Work] = {}
+    if len(p2p_ops) == 0:
+        return work_by_peer
+
+    # Classify the ops by peer rank
+    for op in p2p_ops:
+        ops_by_peer[op.peer].append(op)
+
+    # Call batch_isend_irecv per peer, in sorted order of the peers (to avoid hangs)
+    for peer, ops in sorted(ops_by_peer.items()):
+        work_by_peer[peer] = _batch_p2p(ops, desc=desc)
+
+    return work_by_peer
+
+
+class PipelineScheduleSingle(_PipelineSchedule):
+    """
+    Base class for single-stage schedules.
+    Implements the `step` method.
+    Derived classes should implement `_step_microbatches`.
+
+    Gradients are scaled by num_microbatches depending on the `scale_grads` argument, defaulting to True.  This setting
+    should match the configuration of your loss_fn, which may either average losses (scale_grads=True)
+    or sum losses (scale_grads=False).
+    """
+
+    def __init__(
+        self,
+        stage: _PipelineStageBase,
+        n_microbatches: int,
+        loss_fn: Optional[Callable] = None,
+        args_chunk_spec: Optional[tuple[TensorChunkSpec, ...]] = None,
+        kwargs_chunk_spec: Optional[dict[str, TensorChunkSpec]] = None,
+        output_merge_spec: Optional[Union[dict[str, Any], tuple[Any]]] = None,
+        scale_grads: bool = True,
+    ):
+        # Init parent
+        super().__init__(
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+        )
+        # Self attributes
+        self._stage = stage
+        self._num_stages = stage.num_stages
+        # Set the same has_backward flag for stage object
+        self._stage.has_backward = self._has_backward
+        self._stage_initialized = False
+
+        if n_microbatches < self._num_stages:
+            raise ValueError(
+                f"Number of microbatches ({n_microbatches}) must be greater than \
+or equal to the number of stages ({self._num_stages})."
+            )
+
+    def _initialize_stage(self, args, kwargs):
+        self._stage._prepare_forward_infra(self._n_microbatches, args, kwargs)
+        if self._has_backward:
+            self._stage._prepare_backward_infra(self._n_microbatches)
+        self._stage_initialized = True
+
+    def step(self, *args, target=None, losses: Optional[list] = None, **kwargs):
+        """
+        Run one iteration of the pipeline schedule with *whole-batch* input.
+        Will chunk the input into microbatches automatically, and go through the
+        microbatches according to the schedule implementation.
+
+        args: positional arguments to the model (as in non-pipeline case).
+        kwargs: keyword arguments to the model (as in non-pipeline case).
+        target: target for the loss function.
+        losses: a list to store the losses for each microbatch.
+        """
+
+        # Clean per iteration
+        self._stage.clear_runtime_states()
+
+        # Split inputs into microbatches
+        args_split, kwargs_split = self._split_inputs(args, kwargs)
+
+        # Split target into microbatches
+        if target is not None:
+            targets_split = list(torch.tensor_split(target, self._n_microbatches))
+        else:
+            targets_split = None
+
+        # Run microbatches
+        self._step_microbatches(args_split, kwargs_split, targets_split, losses)
+
+        # Return merged results per original format
+        if self._stage.is_last:
+            return self._merge_outputs(self._stage.output_chunks)
+        else:
+            return None
+
+
+class _ScheduleForwardOnly(PipelineScheduleSingle):
+    """
+    The forward-only schedule.
+    Will go through all the microbatches and perform only the forward pass
+    """
+
+    def _step_microbatches(
+        self,
+        arg_mbs: Optional[list] = None,
+        kwarg_mbs: Optional[list] = None,
+        target_mbs: Optional[list] = None,
+        losses: Optional[list] = None,
+    ):
+        """
+        Run one iteration of the pipeline schedule
+        """
+        if target_mbs is not None or losses is not None:
+            raise RuntimeError(
+                "Forward-only schedule does not support loss computation"
+            )
+
+        arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
+        if not self._stage_initialized:
+            self._initialize_stage(arg_mbs[0], kwarg_mbs[0])
+
+        # Delay send waits
+        fwd_sends_to_wait: list[dist.Work] = []
+
+        # Run microbatches
+        for i in range(self._n_microbatches):
+            with record_function(f"Forward {i}"):
+                ops = self._stage.get_fwd_recv_ops(i)
+                works = _sorted_batch_p2p(ops, desc="fwd_recv")
+                for work in works.values():
+                    work.wait()
+
+                self._stage.forward_one_chunk(i, arg_mbs[i], kwarg_mbs[i])  # type: ignore[index]
+
+                ops = self._stage.get_fwd_send_ops(i)
+                works = _sorted_batch_p2p(ops, desc="fwd_send")
+                fwd_sends_to_wait.extend(works.values())
+
+            logger.debug("[%s] Forwarded microbatch %s", self._stage.stage_index, i)
+
+        # Wait for all forward sends to finish
+        # This should not have performance impact because by the time the first
+        # backward arrives all the forward sends should have been finished.
+        for work in fwd_sends_to_wait:
+            work.wait()
+
+
+class ScheduleGPipe(PipelineScheduleSingle):
+    """
+    The GPipe schedule.
+    Will go through all the microbatches in a fill-drain manner.
+    """
+
+    def _step_microbatches(
+        self,
+        arg_mbs: Optional[list] = None,
+        kwarg_mbs: Optional[list] = None,
+        target_mbs: Optional[list] = None,
+        losses: Optional[list] = None,
+    ):
+        """
+        Run one iteration of the pipeline schedule with list of microbatches.
+        Will go through all the microbatches according to the GPipe schedule.
+
+        Args:
+            microbatches: list of microbatch args.
+        """
+        arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
+
+        if not self._stage_initialized:
+            self._initialize_stage(arg_mbs[0], kwarg_mbs[0])
+
+        # Delay send waits
+        fwd_sends_to_wait: list[dist.Work] = []
+
+        # Run microbatches
+        for i in range(self._n_microbatches):
+            with record_function(f"Forward {i}"):
+                ops = self._stage.get_fwd_recv_ops(i)
+                works = _sorted_batch_p2p(ops, desc="fwd_recv")
+                for work in works.values():
+                    work.wait()
+
+                output = self._stage.forward_one_chunk(i, arg_mbs[i], kwarg_mbs[i])  # type: ignore[index]
+
+                ops = self._stage.get_fwd_send_ops(i)
+                works = _sorted_batch_p2p(ops, desc="fwd_send")
+                fwd_sends_to_wait.extend(works.values())
+
+            logger.debug("[%s] Forwarded microbatch %s", self._stage.stage_index, i)
+
+            self._maybe_compute_loss(self._stage, output, target_mbs, i)
+
+        # Wait for all forward sends to finish
+        # This should not have performance impact because by the time the first
+        # backward arrives all the forward sends should have been finished.
+        for work in fwd_sends_to_wait:
+            work.wait()
+
+        # No loss function, no need to run backward
+        if not self._has_backward:
+            return
+
+        # Run backward
+        # Delay send waits
+        bwd_sends_to_wait: list[dist.Work] = []
+        for i in range(self._n_microbatches):
+            with record_function(f"Backward {i}"):
+                ops = self._stage.get_bwd_recv_ops(i)
+                works = _sorted_batch_p2p(ops, desc="bwd_recv")
+                for work in works.values():
+                    work.wait()
+
+                loss = self._maybe_get_loss(self._stage, i)
+                self._stage.backward_one_chunk(
+                    i,
+                    loss=loss,
+                    last_backward=i == self._n_microbatches - 1,
+                )
+
+                ops = self._stage.get_bwd_send_ops(i)
+                works = _sorted_batch_p2p(ops, desc="bwd_send")
+                bwd_sends_to_wait.extend(works.values())
+
+            logger.debug("[%s] Backwarded microbatch %s", self._stage.stage_index, i)
+
+        self._stage.scale_grads(
+            grad_scale_factor=self._n_microbatches if self.scale_grads else 1
+        )
+
+        # Return losses if there is a container passed in
+        self._update_losses(self._stage, losses)
+
+        # Wait for all backward sends to finish
+        for work in bwd_sends_to_wait:
+            work.wait()
+
+
+class Schedule1F1B(PipelineScheduleSingle):
+    """
+    The 1F1B schedule.
+    Will perform one forward and one backward on the microbatches in steady state.
+    """
+
+    def _step_microbatches(
+        self,
+        arg_mbs: Optional[list] = None,
+        kwarg_mbs: Optional[list] = None,
+        target_mbs: Optional[list] = None,
+        losses: Optional[list] = None,
+    ):
+        """
+        Run one iteration of the pipeline schedule with list of microbatches.
+        Will go through all the microbatches according to the 1F1B schedule.
+
+        Args:
+            microbatches: list of microbatch args.
+        """
+        arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
+
+        if not self._stage_initialized:
+            self._initialize_stage(arg_mbs[0], kwarg_mbs[0])
+
+        # Last stage has 1 warmup, second-to-last 2 warmups, ...
+        # first stage `num_stages` warmups
+        warmup_chunks = min(
+            self._n_microbatches,
+            self._num_stages - self._stage.stage_index,
+        )
+
+        # Chunk counters
+        fwd_mb_index = 0
+        bwd_mb_index = 0
+
+        # Warmup phase
+        send_work = None
+        fwd_sends = []
+        for _ in range(warmup_chunks):
+            # Receive activations
+            fwd_recvs = self._stage.get_fwd_recv_ops(fwd_mb_index)
+            if recv_work := _batch_p2p(fwd_recvs, desc="fwd_recv"):
+                recv_work.wait()
+
+            # Compute
+            output = self._stage.forward_one_chunk(
+                fwd_mb_index, arg_mbs[fwd_mb_index], kwarg_mbs[fwd_mb_index]
+            )  # type: ignore[index]
+
+            # Clear previous chunk's forward sends (hopefully they have well
+            # finished, otherwise, we are heavily communication bound, in which
+            # case it doesn't create a lot of benefit to compute next chunk
+            # eagerly either)
+            if send_work:
+                send_work.wait()
+
+            # Send activations
+            fwd_sends = self._stage.get_fwd_send_ops(fwd_mb_index)
+            if fwd_mb_index != warmup_chunks - 1:
+                # Safe to fire
+                send_work = _batch_p2p(fwd_sends, desc="fwd_send")
+            # otherwise:
+            #   The last foward send is left for fuse with first 1B in 1B1F below
+
+            # Compute loss
+            self._maybe_compute_loss(self._stage, output, target_mbs, fwd_mb_index)
+            fwd_mb_index += 1
+
+        # Now we should have send ops left over, to be fused with first 1B of 1B1F phase below.
+
+        # 1B1F phase
+        while True:  # Don't worry, we have a break inside
+            # We actually do 1B first as the `1B1F` name indicates, so prepare its recv ops
+            bwd_recvs = self._stage.get_bwd_recv_ops(bwd_mb_index)
+
+            # Now, we need to fire the fwd_sends and bwd_recvs together
+            if fuse_work := _batch_p2p(fwd_sends + bwd_recvs, desc="fwd_send_bwd_recv"):
+                fuse_work.wait()
+
+            # Backward one chunk
+            loss = self._maybe_get_loss(self._stage, bwd_mb_index)
+            self._stage.backward_one_chunk(
+                bwd_mb_index,
+                loss=loss,
+                last_backward=bwd_mb_index == self._n_microbatches - 1,
+            )
+
+            # Get the bwd send ops, but don't fire, to be fused with the 1F below
+            bwd_sends = self._stage.get_bwd_send_ops(bwd_mb_index)
+            bwd_mb_index += 1
+
+            if fwd_mb_index == self._n_microbatches:
+                # We are done with 1B1F, so break with some left-over bwd_sends
+                break
+
+            # We prepare 1F of the `1B1F`
+            fwd_recvs = self._stage.get_fwd_recv_ops(fwd_mb_index)
+
+            # Fuse it with bwd_sends above
+            if fuse_work := _batch_p2p(bwd_sends + fwd_recvs, desc="bwd_send_fwd_recv"):
+                fuse_work.wait()
+
+            # Now do the fwd
+            output = self._stage.forward_one_chunk(
+                fwd_mb_index, arg_mbs[fwd_mb_index], kwarg_mbs[fwd_mb_index]
+            )  # type: ignore[index]
+
+            # Compute loss
+            self._maybe_compute_loss(self._stage, output, target_mbs, fwd_mb_index)
+
+            # Get the fwd send ops, but don't fire, leave it for the next iter (wrap-around)
+            fwd_sends = self._stage.get_fwd_send_ops(fwd_mb_index)
+            fwd_mb_index += 1
+
+        # Remember we still have some bwd_sends left over after the break? Now it is time to fire it
+        send_work = _batch_p2p(bwd_sends, desc="bwd_send")
+
+        # Cooldown
+        while bwd_mb_index < self._n_microbatches:
+            # prepare bwd recv ops
+            bwd_recvs = self._stage.get_bwd_recv_ops(bwd_mb_index)
+            if recv_work := _batch_p2p(bwd_recvs, desc="bwd_recv"):
+                recv_work.wait()
+
+            # Backward one chunk
+            loss = self._maybe_get_loss(self._stage, bwd_mb_index)
+            self._stage.backward_one_chunk(
+                bwd_mb_index,
+                loss=loss,
+                last_backward=bwd_mb_index == self._n_microbatches - 1,
+            )
+
+            # Clear previous chunk's backward sends (hopefully they have well finished)
+            if send_work:
+                send_work.wait()
+
+            # Get the bwd send ops, fire it
+            bwd_sends = self._stage.get_bwd_send_ops(bwd_mb_index)
+            send_work = _batch_p2p(bwd_sends, desc="bwd_send")
+            bwd_mb_index += 1
+
+        self._stage.scale_grads(
+            grad_scale_factor=self._n_microbatches if self.scale_grads else 1
+        )
+
+        # Wait for the last backward send to finish
+        if send_work:
+            send_work.wait()
+
+        # Return losses if there is a container passed in
+        self._update_losses(self._stage, losses)
+
+
+def _add_unshard_reshard(
+    compute_actions: list[Optional[_Action]],
+    max_active_stages: int = 3,
+) -> list[_Action]:
+    """Given a basic schedule involving only compute actions (F,B,W), add UNSHARD/RESHARD actions for FSDP.
+
+    UNSHARD refers to fetching the full contents of an FSDP-sharded layer, requiring an all-gather operation.
+    RESHARD does the opposite, releasing memory (but doing no commmunication)
+
+    We abandon the "timestep lock"  during lowering
+
+    max_active_stages controls how many prefetches we allow. It should be measured in mb and tuneable but in practice
+    3 stages is probably the thing we want?
+    (to account for having one f and one b active, and something else prefetching?)
+    """
+
+    def next_stage_indices(
+        count: int, next_actions: list[Optional[_Action]]
+    ) -> list[int]:
+        """Remove duplicates (same stage, different microbatch), find next 'count' stages that will do compute."""
+        seen: set[int] = set()
+        ret: list[int] = []
+
+        for a in next_actions:
+            if a is not None and a.stage_index not in seen:
+                seen.add(a.stage_index)
+                ret.append(a.stage_index)
+                if len(ret) == count:
+                    break
+        return ret
+
+    active_stages: set[int] = set()
+    fsdp_aware_actions: list[_Action] = []
+
+    def _unshard(stage_index: int):
+        active_stages.add(stage_index)
+        fsdp_aware_actions.append(_Action(stage_index, UNSHARD, None))
+
+    def _reshard(stage_index: int):
+        active_stages.remove(stage_index)
+        fsdp_aware_actions.append(_Action(stage_index, RESHARD, None))
+
+    for i, action in enumerate(compute_actions):
+        if action is None:
+            continue
+
+        # We prefetch the next N stages we'll see, dropping existing stages to make room
+        next_n = next_stage_indices(max_active_stages, compute_actions[i:])
+        # Fetch needs to be ordered correctly, so don't use a set
+        fetch = list(filter(lambda s: s not in active_stages, next_n))
+        # Unclear what the best policy is for eviction, but we can maintain order so we do
+        evict = list(filter(lambda s: s not in next_n, active_stages))
+
+        # logger.debug(
+        #     "_add_unshard_reshard Step %d active: %s fetch %s, evict %s",
+        #     i,
+        #     active_stages,
+        #     fetch,
+        #     evict,
+        # )
+
+        for stage in evict:
+            _reshard(stage)
+        for stage in fetch:
+            _unshard(stage)
+        fsdp_aware_actions.append(action)
+
+    return fsdp_aware_actions
+
+
+def _merge_bw(
+    compute_actions: list[Optional[_Action]],
+) -> list[_Action]:
+    """Given a basic schedule involving only compute actions (F,I,W), merge adjacent I and W ops into B ops.
+    (note: I = BACKWARD_INPUT, W = BACKWARD_WEIGHT, B = FULL_BACKWARD)
+
+    B refers to running the whole backward (not separating grad_input and grad_weight), which can be more efficient
+    in some cases.
+    """
+    merged_actions = []
+    while compute_actions:
+        action = compute_actions.pop(0)
+        if action is None:
+            continue
+
+        while len(compute_actions) and (next_action := compute_actions[0]) is None:
+            # remove any None actions between 'action' and 'next_action'
+            compute_actions.pop(0)
+
+        if (
+            action.computation_type == BACKWARD_INPUT
+            and next_action is not None
+            and next_action.computation_type == BACKWARD_WEIGHT
+            and action.stage_index == next_action.stage_index
+            and action.microbatch_index == next_action.microbatch_index
+        ):
+            merged_actions.append(
+                _Action(action.stage_index, FULL_BACKWARD, action.microbatch_index)
+            )
+            compute_actions.pop(0)
+        else:
+            merged_actions.append(action)
+    return merged_actions
+
+
+def _add_send_recv(
+    compute_actions: dict[int, list[_Action]],
+    stage_to_rank: Callable[[int], int],
+    num_stages: int,
+) -> dict[int, list[_Action]]:
+    comm_actions: dict[int, list[_Action]] = {rank: [] for rank in compute_actions}
+    prev_actions: dict[int, set[_Action]] = {rank: set() for rank in compute_actions}
+
+    def _has_comms(action: _Action) -> bool:
+        if action.computation_type == F:
+            return action.stage_index != num_stages - 1 and stage_to_rank(
+                action.stage_index + 1
+            ) != stage_to_rank(action.stage_index)
+        elif action.computation_type in (BACKWARD_INPUT, FULL_BACKWARD):
+            return action.stage_index != 0 and stage_to_rank(
+                action.stage_index - 1
+            ) != stage_to_rank(action.stage_index)
+        return False
+
+    def _get_comms(action: _Action) -> tuple[_Action, _Action]:
+        assert _has_comms(action), f"{action} is not a valid comm action"
+        stage_idx = action.stage_index
+        ctype = action.computation_type
+        mb_idx = action.microbatch_index
+        send = _Action(stage_idx, SEND_F if ctype == F else SEND_B, mb_idx)
+        recv_stage_idx = stage_idx + 1 if ctype == F else stage_idx - 1
+        recv = _Action(recv_stage_idx, RECV_F if ctype == F else RECV_B, mb_idx)
+        return send, recv
+
+    def _ready_to_schedule(
+        action: Optional[_Action], prev_actions: set[_Action]
+    ) -> bool:
+        """We don't put our own recv ops in the schedule, we let a sender on another rank put our recv ops in place.
+        This helps ensure a sane (non-hanging) ordering of sends and recvs.
+        But it also means we might not be able to schedule our next compute action yet.
+        """
+        if action is None:
+            return True
+        elif action.computation_type == F and not action.stage_index == 0:
+            if (
+                _Action(action.stage_index, RECV_F, action.microbatch_index)
+                in prev_actions
+            ):
+                return True
+            elif (
+                _Action(action.stage_index - 1, F, action.microbatch_index)
+                in prev_actions
+            ):
+                return True
+            return False
+        elif (
+            action.computation_type in (BACKWARD_INPUT, FULL_BACKWARD)
+            and not action.stage_index == num_stages - 1
+        ):
+            if (
+                _Action(action.stage_index, RECV_B, action.microbatch_index)
+                in prev_actions
+            ):
+                return True
+            elif (
+                _Action(action.stage_index + 1, BACKWARD_INPUT, action.microbatch_index)
+                in prev_actions
+            ):
+                return True
+            elif (
+                _Action(action.stage_index + 1, FULL_BACKWARD, action.microbatch_index)
+                in prev_actions
+            ):
+                return True
+            return False
+        else:
+            return True
+
+    while compute_actions:
+        progress = False
+        # go in order of ranks even if dict keys aren't ordered
+        for rank in sorted(compute_actions):
+            assert len(compute_actions[rank]) > 0, (
+                f"{rank=}, {len(compute_actions[rank])=}"
+            )
+            action = compute_actions[rank][0]
+
+            if not _ready_to_schedule(action, prev_actions[rank]):
+                continue
+
+            if action is not None:
+                comm_actions[rank].append(action)
+                prev_actions[rank].add(action)
+                if _has_comms(action):
+                    send, recv = _get_comms(action)
+                    # TODO we can avoid send/recv if the 2 stages are on the same rank.
+                    # should we avoid that in the runtime or here?
+                    comm_actions[rank].append(send)
+                    prev_actions[rank].add(send)
+                    comm_actions[stage_to_rank(recv.stage_index)].append(recv)
+                    prev_actions[stage_to_rank(recv.stage_index)].add(recv)
+
+            compute_actions[rank].pop(0)
+            if len(compute_actions[rank]) == 0:
+                del compute_actions[rank]
+            progress = True
+        assert progress, "Malformed compute schedule, can't schedule sends/recvs"
+    return comm_actions
+
+
+def _validate_schedule(
+    actions: dict[int, list[Optional[_Action]]],
+    pp_group_size: int,
+    num_stages: int,
+    num_microbatches: int,
+) -> dict[int, int]:
+    assert len(actions) == pp_group_size, (
+        f"Schedule has incorrect number of ranks - expected {pp_group_size}, actual {len(actions)}"
+    )
+    for rank in range(pp_group_size):
+        assert rank in actions, f"Schedule is missing actions for rank {rank}"
+
+    # We will count all the actions per stage and ensure they happen in a valid order
+    # (e.g. F before (B, I) before W for a given microbatch)
+    stage_actions: dict[int, dict[_ComputationType, set]] = {
+        stage_id: {
+            F: set(),
+            B: set(),
+            I: set(),
+            W: set(),
+        }
+        for stage_id in range(num_stages)
+    }
+    stage_index_to_rank_mapping = {}
+    for rank in actions:
+        for action in actions[rank]:
+            if action is None:
+                continue
+            assert isinstance(action, _Action), (
+                f"Got an invalid action: {action}, expected instance of _Action"
+            )
+            s_id = action.stage_index
+            ctype = action.computation_type
+            mb_id = action.microbatch_index
+            if ctype == F:
+                stage_actions[s_id][F].add(mb_id)
+            elif ctype == B:
+                assert mb_id in stage_actions[s_id][F], (
+                    f"Running Full Backward for stage {s_id}, microbatch {mb_id} without first running Forward"
+                )
+                stage_actions[s_id][B].add(mb_id)
+            elif ctype == I:
+                assert mb_id in stage_actions[s_id][F], (
+                    f"Running Backward Input for stage {s_id}, microbatch {mb_id} without first running Forward"
+                )
+                stage_actions[s_id][I].add(mb_id)
+            elif ctype == W:
+                assert mb_id in stage_actions[s_id][I], (
+                    f"Running Backward Weight for stage {s_id}, microbatch {mb_id} without first running Backward Input"
+                )
+                stage_actions[s_id][W].add(mb_id)
+            if s_id not in stage_index_to_rank_mapping:
+                stage_index_to_rank_mapping[s_id] = rank
+            else:
+                existing_rank = stage_index_to_rank_mapping[s_id]
+                assert rank == existing_rank, (
+                    f"Stage {s_id} is assigned to both rank {rank} and rank {existing_rank}"
+                )
+
+    for s_id in stage_actions:
+        f_mb = len(stage_actions[s_id][F])
+        b_mb = len(stage_actions[s_id][B])
+        i_mb = len(stage_actions[s_id][I])
+        w_mb = len(stage_actions[s_id][W])
+
+        assert f_mb == num_microbatches, (
+            f"Got {f_mb} {F} microbatches for stage {s_id}, expected {num_microbatches}"
+        )
+
+        assert b_mb + (i_mb + w_mb) // 2 == num_microbatches, (
+            f"Invalid backward microbatches for stage {s_id}: expected {num_microbatches} total backwards, \
+            but got B={b_mb}, I={i_mb}, W={w_mb}"
+        )
+    return stage_index_to_rank_mapping
+
+
+class PipelineScheduleMulti(_PipelineSchedule):
+    """
+    Base class for multi-stage schedules.
+    Implements the `step` method.
+
+    Gradients are scaled by num_microbatches depending on the `scale_grads` argument, defaulting to True.  This setting
+    should match the configuration of your loss_fn, which may either average losses (scale_grads=True)
+    or sum losses (scale_grads=False).
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Optional[Callable] = None,
+        args_chunk_spec: Optional[tuple[TensorChunkSpec, ...]] = None,
+        kwargs_chunk_spec: Optional[dict[str, TensorChunkSpec]] = None,
+        output_merge_spec: Optional[Union[dict[str, Any], tuple[Any]]] = None,
+        use_full_backward: Optional[bool] = None,
+        scale_grads: bool = True,
+    ):
+        # Init parent
+        super().__init__(
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+        )
+        # Self attributes
+        self._stages = stages
+        self._num_stages = stages[0].num_stages
+        self.pp_group_size = stages[0].group_size
+        self.rank = stages[0].group_rank
+        # Set the pipeline stage states
+        self.stage_index_to_group_rank = generate_stage_to_rank_mapping(
+            self.pp_group_size, self._num_stages
+        )
+        for stage in self._stages:
+            stage.stage_index_to_group_rank = self.stage_index_to_group_rank
+
+        # Set the same has_backward flag for stage object
+        for stage in self._stages:
+            stage.has_backward = self._has_backward
+        self._stages_initialized = False
+
+        # avoid putting a reference to 'self' inside the lambda, it creates a ref cycle
+        has_loss: bool = self._loss_fn is not None
+        self._should_compute_loss = lambda stage: stage.is_last and has_loss
+
+        # This will be set during init of derived schedules
+        self.pipeline_order: dict[int, list[Optional[_Action]]] = {}
+
+        if use_full_backward is not None:
+            logger.warning(
+                "Deprecation warning: 'use_full_backward' is no longer supported. "
+                "Simply stop passing it, and everything should still work fine."
+            )
+
+    def _initialize_stages(self, args: tuple[Any, ...], kwargs):
+        # may be 'none' value (if this stage sends its output shapes to the next stage via P2P)
+        # or real value (if this stage and next stage are on the same device)
+        next_stage_args: tuple[Any, ...] = tuple()
+        for stage in self._stages:
+            if stage.is_first:
+                next_stage_args = stage._prepare_forward_infra(
+                    self._n_microbatches, args, kwargs
+                )
+            else:
+                next_stage_args = stage._prepare_forward_infra(
+                    self._n_microbatches, next_stage_args, kwargs
+                )
+
+            if self._has_backward:
+                stage._prepare_backward_infra(self._n_microbatches)
+        self._stages_initialized = True
+
+    def _validate_and_set_stage_mapping(
+        self, actions: dict[int, list[Optional[_Action]]]
+    ) -> None:
+        """
+        Allocates the stage index to rank mapping which is needed for communication
+        """
+        self.stage_index_to_group_rank = _validate_schedule(
+            actions,
+            self.pp_group_size,
+            self._num_stages,
+            self._n_microbatches,
+        )
+        for stage in self._stages:
+            stage.stage_index_to_group_rank = self.stage_index_to_group_rank
+
+    def _dump_csv(self, filename):
+        """Dump a CSV representation of the schedule into a file with the provided filename."""
+        with open(filename, "w", newline="") as csvfile:
+            writer = csv.writer(csvfile)
+            for rank in self.pipeline_order:
+                writer.writerow(self.pipeline_order[rank])
+
+    def _load_csv(self, filename, format="compute_only"):
+        """Load a CSV representation of the schedule from a file with the provided filename.
+        This API will most likely get renamed/refactored so is marked as internal for now.
+
+        format must be "compute_only" for PipelineScheduleMulti.
+        """
+        assert format == "compute_only"
+        with open(filename, newline="") as csvfile:
+            reader = csv.reader(csvfile)
+            for rank, row in enumerate(reader):
+                self.pipeline_order[rank] = [_Action.from_str(s) for s in row]
+
+        # Validates the order of the pipeline actions and infers the stage_to_rank_mapping.
+        # This will overwrite the default stage_to_rank_mapping created in the constructor
+        self._validate_and_set_stage_mapping(self.pipeline_order)
+
+    def step(self, *args, target=None, losses: Optional[list] = None, **kwargs):
+        """
+        Run one iteration of the pipeline schedule with *whole-batch* input.
+        Will chunk the input into microbatches automatically, and go through the
+        microbatches according to the schedule implementation.
+
+        args: positional arguments to the model (as in non-pipeline case).
+        kwargs: keyword arguments to the model (as in non-pipeline case).
+        target: target for the loss function.
+        losses: a list to store the losses for each microbatch.
+        """
+        # Clean per iteration
+        for stage in self._stages:
+            stage.clear_runtime_states()
+
+        # Split inputs into microbatches
+        args_split, kwargs_split = self._split_inputs(args, kwargs)
+
+        # Split target into microbatches
+        if target is not None:
+            targets_split = list(torch.tensor_split(target, self._n_microbatches))
+        else:
+            targets_split = None
+
+        # Run microbatches
+        self._step_microbatches(args_split, kwargs_split, targets_split, losses)
+
+        # Return merged results per original format
+        for stage in self._stages:
+            if stage.is_last:
+                return self._merge_outputs(stage.output_chunks)
+        # Does not contain the last stage
+        return None
+
+    def _step_microbatches(
+        self,
+        arg_mbs: Optional[list] = None,
+        kwarg_mbs: Optional[list] = None,
+        target_mbs: Optional[list] = None,
+        losses: Optional[list] = None,
+    ):
+        """
+        Operate on the microbatches for looped schedules (multiple stages on each rank).
+
+        TODO: Does not use sorted_batch_isend_irecv(). As a result, this schedule does
+        not support models with skip connections.
+        """
+        arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
+
+        if not self._stages_initialized:
+            self._initialize_stages(arg_mbs[0], kwarg_mbs[0])
+
+        # Based on the plan in Step 1 created in __init__:
+        # 2. Perform communication based on the pipeline_order
+        stage_index_to_stage: dict[int, _PipelineStageBase] = {
+            stage.stage_index: stage for stage in self._stages
+        }
+
+        # determine prev_rank and next_rank based on which ranks are next to
+        # the stages in the pipeline_order
+        all_prev_ranks: set[int] = set()
+        all_next_ranks: set[int] = set()
+        for stage_index in stage_index_to_stage.keys():
+            # TODO: assumption that stages only communicate from distances of +1/-1 (no skip connections)
+            if stage_index > 0:
+                all_prev_ranks.add(self.stage_index_to_group_rank[stage_index - 1])
+            if stage_index < self._num_stages - 1:
+                all_next_ranks.add(self.stage_index_to_group_rank[stage_index + 1])
+        # count either full_backward or backward_weight together, to determine when to sync DP grads
+        backward_counter: Counter[int] = Counter()
+        for time_step, action in enumerate(self.pipeline_order[self.rank]):
+            try:
+                ops: list[dist.P2POp] = []
+                if action is not None:
+                    computation_type = action.computation_type
+                    mb_index = action.microbatch_index
+                    stage_index = action.stage_index
+                    assert mb_index is not None, (
+                        "All currently supported action types require valid microbatch_index"
+                    )
+                    if computation_type == _ComputationType.FORWARD:
+                        # perform forward computation
+                        stage = stage_index_to_stage[stage_index]
+                        output = stage.forward_one_chunk(
+                            mb_index, arg_mbs[mb_index], kwarg_mbs[mb_index]
+                        )
+                        self._maybe_compute_loss(stage, output, target_mbs, mb_index)
+                        ops.extend(stage.get_fwd_send_ops(mb_index))
+                    elif computation_type == _ComputationType.FULL_BACKWARD:
+                        # perform backward computation
+                        stage = stage_index_to_stage[stage_index]
+                        loss = self._maybe_get_loss(stage, mb_index)
+                        backward_counter[stage_index] += 1
+                        last_backward = (
+                            backward_counter[stage_index] == self._n_microbatches
+                        )
+                        grad_scale_factor = (
+                            self._n_microbatches if self.scale_grads else 1
+                        )
+                        stage.backward_one_chunk(
+                            mb_index,
+                            loss=loss,
+                            full_backward=True,
+                            last_backward=last_backward,
+                        )
+                        if last_backward:
+                            stage.scale_grads(grad_scale_factor)
+
+                        ops.extend(stage.get_bwd_send_ops(mb_index))
+                    elif computation_type == _ComputationType.BACKWARD_INPUT:
+                        # perform backward computation
+                        stage = stage_index_to_stage[stage_index]
+                        loss = self._maybe_get_loss(stage, mb_index)
+                        stage.backward_one_chunk(
+                            mb_index,
+                            loss=loss,
+                            full_backward=False,
+                            last_backward=False,
+                        )
+                        ops.extend(stage.get_bwd_send_ops(mb_index))
+                    elif computation_type == _ComputationType.BACKWARD_WEIGHT:
+                        # perform weight update
+                        stage = stage_index_to_stage[stage_index]
+                        backward_counter[stage_index] += 1
+                        last_backward = (
+                            backward_counter[stage_index] == self._n_microbatches
+                        )
+                        grad_scale_factor = (
+                            self._n_microbatches if self.scale_grads else 1
+                        )
+                        stage.backward_weight_one_chunk(
+                            mb_index,
+                            last_backward=last_backward,
+                        )
+                        if last_backward:
+                            stage.scale_grads(grad_scale_factor)
+                    else:
+                        raise ValueError(f"Unknown computation type {computation_type}")
+
+                # Look at the neighboring ranks for this current timestep and determine whether
+                # this current rank needs to do any recv communication
+                for prev_rank in all_prev_ranks:
+                    prev_rank_ops = self.pipeline_order[prev_rank]
+                    prev_rank_action = None
+                    if time_step < len(prev_rank_ops):
+                        prev_rank_action = prev_rank_ops[time_step]
+                    if prev_rank_action is not None:
+                        computation_type = prev_rank_action.computation_type
+                        mb_index = prev_rank_action.microbatch_index
+                        stage_index = prev_rank_action.stage_index
+                        assert mb_index is not None, (
+                            "All currently supported action types require valid microbatch_index"
+                        )
+                        # Only handle sends for the forward from a previous rank
+                        if computation_type == _ComputationType.FORWARD:
+                            # If not the last stage, then receive fwd activations
+                            if stage_index + 1 in stage_index_to_stage:
+                                # TODO: We are assuming that stage will always receive from stage-1
+                                # however that is not necessarily true of get_fwd_recv_ops
+                                stage = stage_index_to_stage[stage_index + 1]
+                                ops.extend(stage.get_fwd_recv_ops(mb_index))
+                        elif computation_type in (
+                            FULL_BACKWARD,
+                            BACKWARD_INPUT,
+                            BACKWARD_WEIGHT,
+                        ):
+                            # Previous rank doing backward has no influence for the current rank forward recv
+                            pass
+                        else:
+                            raise ValueError(
+                                f"Unknown computation type {computation_type}"
+                            )
+                for next_rank in all_next_ranks:
+                    next_rank_ops = self.pipeline_order[next_rank]
+                    next_rank_action = None
+                    if time_step < len(next_rank_ops):
+                        next_rank_action = next_rank_ops[time_step]
+                    if next_rank_action is not None:
+                        computation_type = next_rank_action.computation_type
+                        mb_index = next_rank_action.microbatch_index
+                        stage_index = next_rank_action.stage_index
+                        assert mb_index is not None, (
+                            "All currently supported action types require valid microbatch_index"
+                        )
+                        # Only handle receives for the backwards from a next rank
+                        if computation_type in (FORWARD, BACKWARD_WEIGHT):
+                            # Next rank doing forward or weight update has no influence for the current rank backward recv
+                            pass
+                        elif computation_type in (BACKWARD_INPUT, FULL_BACKWARD):
+                            # If not the first stage, then receive bwd gradients
+                            if stage_index - 1 in stage_index_to_stage:
+                                # TODO: We are assuming that stage will always receive from stage+1
+                                # however that is not necessarily true of get_bwd_recv_ops
+                                stage = stage_index_to_stage[stage_index - 1]
+                                ops.extend(stage.get_bwd_recv_ops(mb_index))
+                        else:
+                            raise ValueError(
+                                f"Unknown computation type {computation_type}"
+                            )
+
+                # do the communication
+                if ops:
+                    _batch_p2p(ops).wait()
+            except Exception as e:
+                logger.error(
+                    "[Rank %s] pipeline schedule %s caught the following exception \
+                     at time_step %s when running action %s",
+                    self.rank,
+                    self.__class__.__name__,
+                    time_step,
+                    action,
+                )
+                logger.error(
+                    "%s",
+                    _format_pipeline_order(
+                        self.pipeline_order, error_step_number=time_step
+                    ),
+                )
+                raise e
+        # Return losses if there is a container passed in
+        self._update_losses(self._stages, losses)
+
+
+class _PipelineScheduleRuntime(PipelineScheduleMulti):
+    """
+    Provides a simple runtime that requires a 'schedule IR' including specified communication operations.
+
+    Can be instantiated directly by creating _PipelineScheduleRuntime and calling load_csv, or can be
+    subclassed and the subclass can be responsible for creating a schedule IR.
+    """
+
+    def _load_actions(
+        self,
+        actions: dict[int, list[Optional[_Action]]],
+        format: str = "compute_only",
+    ):
+        """
+        Given an in-memory representation for a simple compute-only schedule, lower it to a complex schedule including
+        communication actions.  Stores the schedule in self, and must be called before running step_mo()
+        """
+        # validate the provided actions are valid and overrides the default stage_index_to_group_rank
+        super()._validate_and_set_stage_mapping(actions)
+
+        self.pipeline_order_with_comms: dict[int, list[_Action]] = {}
+        if format == "compute_comms":
+            for rank in actions:
+                self.pipeline_order_with_comms[rank] = []
+                for action in actions[rank]:
+                    assert action is not None
+                    self.pipeline_order_with_comms[rank].append(action)
+            # TODO what level of validation should we offer for compute+comms schedule?
+        elif format == "compute_only":
+            # Perform schedule lowering
+            for rank in actions:
+                self.pipeline_order_with_comms[rank] = _add_unshard_reshard(
+                    actions[rank]
+                )
+
+            self.pipeline_order_with_comms = _add_send_recv(
+                self.pipeline_order_with_comms,
+                stage_to_rank=lambda s: self.stage_index_to_group_rank[s],
+                num_stages=self._num_stages,
+            )
+        else:
+            raise NotImplementedError(f"{format=} is not implemented")
+
+    def _load_csv(self, filename: str, format: str = "compute_only"):
+        """Loads a csv in simple format and then lowers it to include comunication actions
+
+        format must be either "compute_only" or "compute_comms".  If compute_only, the lowering passes
+        will automatically be run to generate a compute_comms schedule.
+        """
+        if format == "compute_only":
+            # this will populate self.pipeline_order
+            super()._load_csv(filename)
+            # this will populate self.pipeline_order_with_comms
+            self._load_actions(self.pipeline_order)
+        elif format == "compute_comms":
+            actions = {}
+            with open(filename, newline="") as csvfile:
+                reader = csv.reader(csvfile)
+                for rank, row in enumerate(reader):
+                    actions[rank] = [_Action.from_str(s) for s in row]
+                self._load_actions(actions, format=format)
+        else:
+            raise NotImplementedError(f"{format=} is not implemented")
+
+    def _dump_csv(self, filename: str):
+        """Dump a CSV representation of the compute + comms schedule into a file with the provided filename."""
+        # TODO should there be an option to dump the compute_only schedule from PipelineScheduleRuntime? It's possible
+        # that it does not exist if it was created from a compute_comms schedule.
+        assert self.pipeline_order_with_comms is not None, (
+            "Must initialize compute_comms schedule before dump_csv"
+        )
+        with open(filename, "w", newline="") as csvfile:
+            writer = csv.writer(csvfile)
+            for rank in self.pipeline_order_with_comms:
+                writer.writerow(self.pipeline_order_with_comms[rank])
+
+    def _simulate(self):
+        return _simulate_comms_compute(
+            self.pipeline_order_with_comms,
+            lambda s: self.stage_index_to_group_rank[s],
+            self._num_stages,
+        )
+
+    def _step_microbatches(
+        self,
+        arg_mbs: Optional[list] = None,
+        kwarg_mbs: Optional[list] = None,
+        target_mbs: Optional[list] = None,
+        losses: Optional[list] = None,
+    ):
+        """
+        Operate on the microbatches for looped schedules (multiple stages on each rank).
+
+        TODO: Does not use sorted_batch_isend_irecv(). As a result, this schedule does
+        not support models with skip connections.
+        """
+        arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
+        if not self._stages_initialized:
+            self._initialize_stages(arg_mbs[0], kwarg_mbs[0])
+
+        # Based on the plan in Step 1 created in __init__:
+        # 2. Perform communication based on the pipeline_order
+        stage_index_to_stage: dict[int, _PipelineStageBase] = {
+            stage.stage_index: stage for stage in self._stages
+        }
+
+        assert self.pipeline_order_with_comms is not None, (
+            "Must call _load_actions() before calling _step_microbatches()"
+        )
+
+        # recv ops indexed by (stage_idx, mb_idx) need to be waited on before use
+        bwd_recv_ops: dict[tuple[int, int], Work] = {}
+        fwd_recv_ops: dict[tuple[int, int], Work] = {}
+
+        # send ops should be waited on before step() exists, mainly for hygeine
+        send_ops: list[Work] = []
+
+        # we track which stages are 'active' when used with FSDP, and wait on unshard ops before computing on stages
+        unshard_ops: dict[int, UnshardHandle] = {}
+        unsharded_stages = set()
+
+        def _assert_unsharded(stage_idx: int):
+            """If an unshard is active for `stage_idx`, wait() it and mark `stage_idx` unshared."""
+            if stage_idx in unshard_ops:
+                unshard_ops[stage_idx].wait()
+                del unshard_ops[stage_idx]
+                unsharded_stages.add(stage_idx)
+            assert stage_idx in unsharded_stages, (
+                f"Attempted to compute on sharded {stage_idx=}"
+            )
+
+        # count either full_backward or backward_weight together, to determine when to sync DP grads
+        backward_counter: Counter[int] = Counter()
+        for time_step, action in enumerate(self.pipeline_order_with_comms[self.rank]):
+            try:
+                comp_type = action.computation_type
+                mb_index: int = (
+                    action.microbatch_index
+                    if action.microbatch_index is not None
+                    else -1
+                )
+                assert mb_index >= 0 or comp_type in (
+                    UNSHARD,
+                    RESHARD,
+                ), f"{action=} missing mb_index"
+                stage_idx = action.stage_index
+                stage = stage_index_to_stage[stage_idx]
+                stage_uses_fsdp = isinstance(stage.submod, FSDPModule)
+                # see [Note: V-schedule special case]
+                is_next_stage_on_this_rank = stage_idx + 1 in stage_index_to_stage
+                is_prev_stage_on_this_rank = stage_idx - 1 in stage_index_to_stage
+
+                logger.debug(
+                    "_PipelineScheduleRuntime running time_step %d, action %s",
+                    time_step,
+                    action,
+                )
+
+                # TODO(whc) it's not actually safe to use _batch_p2p here in the uncommon case the model has skip-connections,
+                # since we do not want to batch up ops between more than a pair of ranks.  _sorted_batch_p2p would be
+                # safe to use instead.
+                # However, I was wondering if I should avoid calling batched operators at all in the case that there is
+                # only one operator per batch.  I could iterate through the 'fwd_send_ops' one by one and run them.
+                if comp_type == SEND_F:
+                    send_ops.append(_batch_p2p(stage.get_fwd_send_ops(mb_index)))
+                elif comp_type == SEND_B:
+                    send_ops.append(_batch_p2p(stage.get_bwd_send_ops(mb_index)))
+                elif comp_type == RECV_F:
+                    assert (
+                        stage_idx,
+                        mb_index,
+                    ) not in fwd_recv_ops, (
+                        "Recv twice for {stage_idx=} {mb_index=} without executing forward"
+                    )
+                    fwd_recv_ops[(stage_idx, mb_index)] = _batch_p2p(
+                        stage.get_fwd_recv_ops(mb_index)
+                    )
+                elif comp_type == RECV_B:
+                    assert (
+                        stage_idx,
+                        mb_index,
+                    ) not in bwd_recv_ops, (
+                        "Recv twice for {stage_idx=} {mb_index=} without executing backward"
+                    )
+                    bwd_recv_ops[(stage_idx, mb_index)] = _batch_p2p(
+                        stage.get_bwd_recv_ops(mb_index)
+                    )
+                elif comp_type == UNSHARD:
+                    if stage_uses_fsdp:
+                        assert (
+                            stage_idx not in unsharded_stages
+                            and stage_idx not in unshard_ops
+                        ), f"Unsharding the same {stage_idx=} twice"
+                        unshard_ops[stage_idx] = stage.submod.unshard(async_op=True)  # type: ignore[operator]
+                elif comp_type == RESHARD:
+                    if stage_uses_fsdp:
+                        assert stage_idx in unsharded_stages, (
+                            f"Resharding {stage_idx=} without unsharding"
+                        )
+                        assert stage_idx not in unshard_ops, (
+                            f"Resharding {stage_idx=} before finishing unshard"
+                        )
+                        stage.submod.reshard()  # type: ignore[operator]
+                elif comp_type == FORWARD:
+                    if stage_uses_fsdp:
+                        _assert_unsharded(stage_idx)
+
+                    if (
+                        not stage.is_first
+                        # no recv op expected for V-schedule special case (see [Note: V-schedule special case])
+                        and not is_prev_stage_on_this_rank
+                    ):
+                        assert (
+                            stage_idx,
+                            mb_index,
+                        ) in fwd_recv_ops, f"Computing {action=} before receiving input"
+                        fwd_recv_ops.pop((stage_idx, mb_index)).wait()
+
+                    output = stage.forward_one_chunk(
+                        mb_index, arg_mbs[mb_index], kwarg_mbs[mb_index]
+                    )
+                    self._maybe_compute_loss(stage, output, target_mbs, mb_index)
+
+                    # SEND/RECV op are avoided for special case with 2 adjacent stages on same rank
+                    # see [Note: V-schedule special case]
+                    if is_next_stage_on_this_rank:
+                        stage_index_to_stage[stage_idx + 1].set_local_fwd_input(
+                            output, mb_index
+                        )
+
+                elif comp_type == FULL_BACKWARD:
+                    if stage_uses_fsdp:
+                        _assert_unsharded(stage_idx)
+
+                    if (
+                        not stage.is_last
+                        # no recv op expected for V-schedule special case (see [Note: V-schedule special case])
+                        and not is_next_stage_on_this_rank
+                    ):
+                        assert (
+                            stage_idx,
+                            mb_index,
+                        ) in bwd_recv_ops, (
+                            f"Attempted to run compute {action=} before receiving input"
+                        )
+                        bwd_recv_ops.pop((stage_idx, mb_index)).wait()
+                    loss = self._maybe_get_loss(stage, mb_index)
+                    backward_counter[stage_idx] += 1
+                    last_backward = backward_counter[stage_idx] == self._n_microbatches
+                    grad_scale_factor = self._n_microbatches if self.scale_grads else 1
+                    stage.backward_one_chunk(
+                        mb_index,
+                        loss=loss,
+                        full_backward=True,
+                        last_backward=last_backward,
+                    )
+                    if last_backward:
+                        stage.scale_grads(grad_scale_factor)
+                    # SEND/RECV op are avoided for special case with 2 adjacent stages on same rank
+                    # see [Note: V-schedule special case]
+                    if is_prev_stage_on_this_rank:
+                        stage_index_to_stage[stage_idx - 1].set_local_bwd_input(
+                            stage.get_local_bwd_output(mb_index), mb_index
+                        )
+                elif comp_type == BACKWARD_INPUT:
+                    if stage_uses_fsdp:
+                        _assert_unsharded(stage_idx)
+
+                    if not stage.is_last and not is_next_stage_on_this_rank:
+                        assert (
+                            stage_idx,
+                            mb_index,
+                        ) in bwd_recv_ops, (
+                            f"Attempted to run compute {action=} before receiving input"
+                        )
+                        bwd_recv_ops.pop((stage_idx, mb_index)).wait()
+                    loss = self._maybe_get_loss(stage, mb_index)
+                    stage.backward_one_chunk(
+                        mb_index,
+                        loss=loss,
+                        full_backward=False,
+                        last_backward=False,
+                    )
+                    # SEND/RECV op are avoided for special case with 2 adjacent stages on same rank
+                    # see [Note: V-schedule special case]
+                    if is_prev_stage_on_this_rank:
+                        stage_index_to_stage[stage_idx - 1].set_local_bwd_input(
+                            stage.get_local_bwd_output(mb_index), mb_index
+                        )
+                elif comp_type == BACKWARD_WEIGHT:
+                    if stage_uses_fsdp:
+                        _assert_unsharded(stage_idx)
+                    backward_counter[stage_idx] += 1
+                    stage.backward_weight_one_chunk(
+                        mb_index,
+                        last_backward=backward_counter[stage_idx]
+                        == self._n_microbatches,
+                    )
+                else:
+                    raise ValueError(f"{action=} is unknown or unsupported")
+            except Exception as e:
+                logger.error(
+                    "_PipelineScheduleRuntime caught exception at step %s when running action %s.  Full Schedule:",
+                    time_step,
+                    action,
+                )
+                # TODO(whc) what is the best practice for printing a multiline log?
+                # logger will split it into multiple log lines, but this makes it hard to read (too wide)
+                print(
+                    _format_pipeline_order(
+                        self.pipeline_order_with_comms,  # type: ignore[arg-type]
+                        error_step_number=time_step,
+                    )
+                )
+                raise e
+
+        # Mostly these operations should have finished long ago, but there isn't an obvious time when to wait for them
+        while len(send_ops):
+            send_ops.pop().wait()
+
+        assert len(unshard_ops) == 0, "Unused unshard operations"
+
+        # Return losses if there is a container passed in
+        self._update_losses(self._stages, losses)
+
+
+class ScheduleLoopedBFS(PipelineScheduleMulti):
+    """
+    Breadth-First Pipeline Parallelism.
+    See https://arxiv.org/abs/2211.05953 for details.
+    Simliar to Interleaved 1F1B, Looped BFS supports multiple stages per rank.
+    What is different is that when microbatches are ready for multiple local
+    stages, Loops BFS will prioritizes the earlier stage, running all available
+    microbatches at once.
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Optional[Union[Callable, _Loss]] = None,
+        output_merge_spec: Optional[Union[dict[str, Any], tuple[Any]]] = None,
+        scale_grads: bool = True,
+    ):
+        super().__init__(
+            stages=stages,
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+        )
+
+        # 1. Create the pipeline_order (all ranks do this calculation)
+        # This will be used to keep track of the current state of the entire pipeline
+        # pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
+        self.pipeline_order: dict[int, list[Optional[_Action]]] = {}
+        # ========================================================================
+        for rank in range(self.pp_group_size):
+            rank_ops = self._calculate_single_rank_operations(rank)
+            self.pipeline_order[rank] = rank_ops
+
+    def _calculate_single_rank_operations(self, rank):
+        n_local_stages = len(self._stages)
+        stage_indices = range(
+            rank, self.pp_group_size * n_local_stages, self.pp_group_size
+        )
+
+        # Store the list of operations used for that rank
+        # Pre-padding, rank starts with no-ops based on the warmup.
+        rank_ops: list[Optional[_Action]] = [None for _ in range(rank)]
+
+        for stage_index in stage_indices:
+            rank_ops.extend(
+                _Action(stage_index, _ComputationType.FORWARD, mb_index)
+                for mb_index in range(self._n_microbatches)
+            )
+
+        # wait for the first backward to trickle up
+        # which is 2 for every hop away
+        post_warmup_ops = 2 * (self.pp_group_size - 1 - rank)
+        rank_ops.extend([None] * post_warmup_ops)
+
+        for stage_index in reversed(stage_indices):
+            rank_ops.extend(
+                _Action(stage_index, _ComputationType.FULL_BACKWARD, mb_index)
+                for mb_index in reversed(range(self._n_microbatches))
+            )
+        return rank_ops
+
+
+def _get_1f1b_rank_ops(
+    n_local_stages,
+    pp_group_size,
+    warmup_ops,
+    fwd_bwd_ops,
+    cooldown_ops,
+    rank,
+    forward_stage_index,
+    backward_stage_index,
+    num_1f1b_microbatches=0,
+    enable_zero_bubble=False,
+):
+    # All stages start with handling microbatch 0
+    fwd_stage_mb_index: dict[int, int] = defaultdict(int)
+    bwd_stage_mb_index: dict[int, int] = defaultdict(int)
+    weight_stage_mb_index: dict[int, int] = defaultdict(int)
+
+    # Store the list of operations used for that rank
+    # Pre-padding, rank starts with no-ops based on the warmup.
+    rank_ops: list[Optional[_Action]] = [None for _ in range(rank)]
+    # These are used to calculate the number of slots to fill with no-ops, to account for the delay in warmup
+    # when we want to wait for the backward to trickle back up and start 1f1b to align all ranks.
+    # Formula:
+    # pre-padding + warmup_ops + post_warmup_ops = earliest time step of first backward
+    # post_warmup_ops = [earliest time step of first backward] - (warmup_ops + pre-padding)
+    # earliest time step of first backward = [local_stages * group_size + 2 * (group_size - 1 - rank)]
+    # warmup_ops = calculated above
+    post_warmup_ops = (
+        n_local_stages * pp_group_size + 2 * (pp_group_size - 1 - rank)
+    ) - (warmup_ops + rank)
+
+    if enable_zero_bubble:
+        post_warmup_ops = pp_group_size - rank - 1
+
+    total_ops = warmup_ops + fwd_bwd_ops + cooldown_ops
+
+    backward_op_ids = []
+    weight_op_count = 0
+
+    FULL_BACKWARD_OR_BACKWARD_INPUT = (
+        BACKWARD_INPUT if enable_zero_bubble else FULL_BACKWARD
+    )
+
+    for op in range(total_ops):
+        # Warmup phase
+        if op < warmup_ops:
+            fwd_stage_index = forward_stage_index(op)
+            # This will assign the current microbatch index and update it as well
+            fwd_stage_mb_index[fwd_stage_index] = (
+                mb_index := fwd_stage_mb_index[fwd_stage_index]
+            ) + 1
+            rank_ops.append(
+                _Action(fwd_stage_index, _ComputationType.FORWARD, mb_index)
+            )
+            if op == warmup_ops - 1:
+                # This is the last step in the warmup phase, so we need to wait for the backward to trickle back up
+                rank_ops.extend([None] * post_warmup_ops)
+        # 1F1B Phase (forward and backward)
+        elif warmup_ops <= op < warmup_ops + fwd_bwd_ops:
+            fwd_stage_index = forward_stage_index(op)
+            fwd_stage_mb_index[fwd_stage_index] = (
+                fwd_mb_index := fwd_stage_mb_index[fwd_stage_index]
+            ) + 1
+            rank_ops.append(
+                _Action(fwd_stage_index, _ComputationType.FORWARD, fwd_mb_index)
+            )
+            bwd_stage_index = backward_stage_index(op)
+            bwd_stage_mb_index[bwd_stage_index] = (
+                bwd_mb_index := bwd_stage_mb_index[bwd_stage_index]
+            ) + 1
+            rank_ops.append(
+                _Action(bwd_stage_index, FULL_BACKWARD_OR_BACKWARD_INPUT, bwd_mb_index)
+            )
+            backward_op_ids.append(op)
+
+            if enable_zero_bubble and op - warmup_ops >= num_1f1b_microbatches:
+                weight_stage_index = backward_stage_index(
+                    backward_op_ids[weight_op_count]
+                )
+                weight_stage_mb_index[weight_stage_index] = (
+                    weight_mb_index := weight_stage_mb_index[weight_stage_index]
+                ) + 1
+                rank_ops.append(
+                    _Action(
+                        weight_stage_index,
+                        _ComputationType.BACKWARD_WEIGHT,
+                        weight_mb_index,
+                    )
+                )
+                weight_op_count += 1
+        # Cooldown phase
+        else:
+            # During cooldown phase, we need steps to align with 1f1b happening in other ranks
+            # TODO: we don't need to always append, after all 1f1b are finished we can stop appending None
+            if not enable_zero_bubble:
+                rank_ops.append(None)
+
+            bwd_stage_index = backward_stage_index(op)
+            bwd_stage_mb_index[bwd_stage_index] = (
+                bwd_mb_index := bwd_stage_mb_index[bwd_stage_index]
+            ) + 1
+            rank_ops.append(
+                _Action(bwd_stage_index, FULL_BACKWARD_OR_BACKWARD_INPUT, bwd_mb_index)
+            )
+            backward_op_ids.append(op)
+
+            if enable_zero_bubble and op - warmup_ops >= num_1f1b_microbatches:
+                weight_stage_index = backward_stage_index(
+                    backward_op_ids[weight_op_count]
+                )
+                weight_stage_mb_index[weight_stage_index] = (
+                    weight_mb_index := weight_stage_mb_index[weight_stage_index]
+                ) + 1
+                rank_ops.append(
+                    _Action(
+                        weight_stage_index,
+                        _ComputationType.BACKWARD_WEIGHT,
+                        weight_mb_index,
+                    )
+                )
+                weight_op_count += 1
+
+    while enable_zero_bubble and weight_op_count < len(backward_op_ids):
+        weight_stage_index = backward_stage_index(backward_op_ids[weight_op_count])
+        weight_stage_mb_index[weight_stage_index] = (
+            weight_mb_index := weight_stage_mb_index[weight_stage_index]
+        ) + 1
+        rank_ops.append(
+            _Action(
+                weight_stage_index, _ComputationType.BACKWARD_WEIGHT, weight_mb_index
+            )
+        )
+        weight_op_count += 1
+
+    return rank_ops
+
+
+class ScheduleInterleaved1F1B(PipelineScheduleMulti):
+    """
+    The Interleaved 1F1B schedule.
+    See https://arxiv.org/pdf/2104.04473 for details.
+    Will perform one forward and one backward on the microbatches in steady
+    state and supports multiple stages per rank. When microbatches are ready for
+    multiple local stages, Interleaved 1F1B prioritizes the earlier microbatch
+    (also called "depth first").
+
+    This schedule is mostly similar to the original paper.
+    It differs by being relaxing the requirement of num_microbatch % pp_size == 0.
+    Using the flex_pp schedule, we will have num_rounds = max(1, n_microbatches // pp_group_size) and
+    it works as long as n_microbatches % num_rounds is 0. As a few examples, support
+
+    1. pp_group_size = 4, n_microbatches = 10. We will have num_rounds = 2 and n_microbatches % 2 is 0.
+    2. pp_group_size = 4, n_microbatches = 3. We will have num_rounds = 1 and n_microbatches % 1 is 0.
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Optional[Callable] = None,
+        args_chunk_spec: Optional[tuple[TensorChunkSpec, ...]] = None,
+        kwargs_chunk_spec: Optional[dict[str, TensorChunkSpec]] = None,
+        output_merge_spec: Optional[Union[dict[str, Any], tuple[Any]]] = None,
+        scale_grads: bool = True,
+    ):
+        self.pp_group_size = stages[0].group_size
+        super().__init__(
+            stages=stages,
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+        )
+        self.n_local_stages = len(stages)
+        self.rank = stages[0].group_rank
+        self.number_of_rounds = max(1, n_microbatches // self.pp_group_size)
+        self.microbatches_per_round = n_microbatches // self.number_of_rounds
+        if n_microbatches % self.number_of_rounds != 0:
+            raise ValueError(
+                "Interleaved 1F1B requires the number of microbatches to be a "
+                f"multiple of the number of rounds ({self.number_of_rounds}), "
+                f"but got {n_microbatches}."
+            )
+        # 1. Create the pipeline_order (all ranks do this calculation)
+        # This will be used to keep track of the current state of the entire pipeline
+        # pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
+        self.pipeline_order: dict[int, list[Optional[_Action]]] = {}
+        for rank in range(self.pp_group_size):
+            rank_ops = self._calculate_single_rank_operations(rank)
+            self.pipeline_order[rank] = rank_ops
+
+    def _calculate_single_rank_operations(self, rank) -> list[Optional[_Action]]:
+        def get_rank_warmup_ops(rank):
+            # Warms up operations for last stage
+            warmups_ops_last_stage = (
+                self.n_local_stages - 1
+            ) * self.microbatches_per_round
+            # Increment warmup operations by 2 for each hop away from the last stage
+            multiply_factor = 2
+            warmup_ops = warmups_ops_last_stage + multiply_factor * (
+                (self.pp_group_size - 1) - rank
+            )
+
+            # We cannot have more warmup operations than there are number of microbatches, so cap it there
+            return min(warmup_ops, self._n_microbatches * self.n_local_stages)
+
+        warmup_ops = get_rank_warmup_ops(rank)
+        microbatch_ops = self.n_local_stages * self._n_microbatches
+        # fwd_bwd_ops should encompass the remaining forwards
+        fwd_bwd_ops = microbatch_ops - warmup_ops
+        # cooldown_ops should encompass the remaining backwards
+        cooldown_ops = microbatch_ops - fwd_bwd_ops
+        # total ops encompass both forward and backward ops
+        total_ops = warmup_ops + fwd_bwd_ops + cooldown_ops
+        # warmup_ops + fwd_bwd_ops * 2 + cooldown_ops == microbatch_ops * 2
+        logger.debug(
+            "rank %s, warmup_ops %s, 1f1b %s, cooldown_ops %s total_ops %s",
+            rank,
+            warmup_ops,
+            fwd_bwd_ops,
+            cooldown_ops,
+            total_ops,
+        )
+
+        # Calculates the stage index based on step and pp_group_size
+        def forward_stage_index(step):
+            # Get the local index from 0 to n_local_stages-1
+            local_index = (step // self.microbatches_per_round) % self.n_local_stages
+            return (local_index * self.pp_group_size) + rank
+
+        def backward_stage_index(step):
+            local_index = (
+                self.n_local_stages
+                - 1
+                - ((step - warmup_ops) // self.microbatches_per_round)
+                % self.n_local_stages
+            )
+            return (local_index * self.pp_group_size) + rank
+
+        return _get_1f1b_rank_ops(
+            self.n_local_stages,
+            self.pp_group_size,
+            warmup_ops,
+            fwd_bwd_ops,
+            cooldown_ops,
+            rank,
+            forward_stage_index,
+            backward_stage_index,
+        )
+
+
+class ScheduleInterleavedZeroBubble(PipelineScheduleMulti):
+    """
+    The Interleaved Zero Bubble schedule.
+    See https://arxiv.org/pdf/2401.10241 for details.
+    Will perform one forward and one backward on inputs for the microbatches in steady
+    state and supports multiple stages per rank. Uses the backward for weights to fill in
+    the pipeline bubble.
+
+    In particular this is implementing the ZB1P schedule in the paper.
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Optional[Callable] = None,
+        args_chunk_spec: Optional[tuple[TensorChunkSpec, ...]] = None,
+        kwargs_chunk_spec: Optional[dict[str, TensorChunkSpec]] = None,
+        output_merge_spec: Optional[Union[dict[str, Any], tuple[Any]]] = None,
+        scale_grads: bool = True,
+    ):
+        # TODO: we don't support Zero Bubble with torch.compile so we
+        # should disable it for now
+        for stage in stages:
+            if isinstance(stage.submod, OptimizedModule):
+                raise RuntimeError(
+                    "The Zero Bubble schedule is not supported with \
+stage modules that have used torch.compile"
+                )
+
+        self.pp_group_size = stages[0].group_size
+        super().__init__(
+            stages=stages,
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+        )
+        self.n_local_stages = len(stages)
+        self.rank = stages[0].group_rank
+        self.number_of_rounds = max(1, n_microbatches // self.pp_group_size)
+        self.microbatches_per_round = n_microbatches // self.number_of_rounds
+        if n_microbatches % self.number_of_rounds != 0:
+            raise ValueError(
+                "Zero bubble requires the number of microbatches to be a "
+                f"multiple of the number of rounds ({self.number_of_rounds}), "
+                f"but got {n_microbatches}."
+            )
+        # 1. Create the pipeline_order (all ranks do this calculation)
+        # This will be used to keep track of the current state of the entire pipeline
+        # pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
+        self.pipeline_order: dict[int, list[Optional[_Action]]] = {}
+        for rank in range(self.pp_group_size):
+            rank_ops = self._calculate_single_rank_operations(rank)
+            self.pipeline_order[rank] = rank_ops
+
+        # This function add bubbles to the generated schedule based on dependencies of actions
+        # Note that the ZB1P schedule will not require bubbles to be manually added and it is
+        # only useful when n_microbatches <= microbatches_per_round
+        self.pipeline_order = self._add_bubbles_to_actions(
+            self.n_local_stages * self.pp_group_size,
+        )
+
+    def _calculate_single_rank_operations(self, rank) -> list[Optional[_Action]]:
+        def get_rank_warmup_ops(rank):
+            # Warms up operations for last stage
+            warmups_ops_last_stage = (
+                self.n_local_stages - 1
+            ) * self.microbatches_per_round
+            # Increment warmup operations by 2 for each hop away from the last stage
+            multiply_factor = 1
+            warmup_ops = warmups_ops_last_stage + multiply_factor * (
+                (self.pp_group_size - 1) - rank
+            )
+
+            # We cannot have more warmup operations than there are number of microbatches, so cap it there
+            return min(warmup_ops, self._n_microbatches * self.n_local_stages)
+
+        warmup_ops = get_rank_warmup_ops(rank)
+        microbatch_ops = self.n_local_stages * self._n_microbatches
+        # fwd_bwd_ops should encompass the remaining forwards
+        fwd_bwd_ops = microbatch_ops - warmup_ops
+        # cooldown_ops should encompass the remaining backwards
+        cooldown_ops = microbatch_ops - fwd_bwd_ops
+        # total ops encompass both forward and backward ops
+        total_ops = warmup_ops + fwd_bwd_ops + cooldown_ops
+        # warmup_ops + fwd_bwd_ops * 2 + cooldown_ops == microbatch_ops * 2
+        logger.debug(
+            "rank %s, warmup_ops %s, 1f1b %s, cooldown_ops %s total_ops %s",
+            rank,
+            warmup_ops,
+            fwd_bwd_ops,
+            cooldown_ops,
+            total_ops,
+        )
+
+        # Calculates the stage index based on step and pp_group_size
+
+        def forward_stage_index(step):
+            # Get the local index from 0 to n_local_stages-1
+            local_index = (step // self.microbatches_per_round) % self.n_local_stages
+            return (local_index * self.pp_group_size) + rank
+
+        def backward_stage_index(step):
+            local_index = (
+                self.n_local_stages
+                - 1
+                - ((step - warmup_ops) // self.microbatches_per_round)
+                % self.n_local_stages
+            )
+            return (local_index * self.pp_group_size) + rank
+
+        num_1f1b_microbatches = rank
+
+        return _get_1f1b_rank_ops(
+            self.n_local_stages,
+            self.pp_group_size,
+            warmup_ops,
+            fwd_bwd_ops,
+            cooldown_ops,
+            rank,
+            forward_stage_index,
+            backward_stage_index,
+            num_1f1b_microbatches,
+            enable_zero_bubble=True,
+        )
+
+    def _add_bubbles_to_actions(self, num_stages_global):
+        actions = self.pipeline_order
+
+        def need_bubble(stage, op, microbatch, num_stages_global, seen_ops):
+            if op == _ComputationType.FORWARD:
+                if stage != 0 and (stage - 1, op, microbatch) not in seen_ops:
+                    return True
+            elif op == _ComputationType.FULL_BACKWARD:
+                if stage == num_stages_global - 1:
+                    return (stage, _ComputationType.FORWARD, microbatch) not in seen_ops
+                return (stage + 1, op, microbatch) not in seen_ops
+            return False
+
+        seen_ops: set[tuple[int, _ComputationType, int]] = set()
+        result: dict[int, list[Optional[_Action]]] = {}
+        next_pointer: dict[int, int] = {}
+        bubbles_added: dict[int, int] = {}
+        total_bubbles_added = 0
+
+        for rank in range(self.pp_group_size):
+            result[rank] = []
+            next_pointer[rank] = 0
+            bubbles_added[rank] = 0
+
+        while True:
+            should_stop = True
+
+            temp_seen_ops: set[tuple[int, _ComputationType, int]] = set()
+
+            for rank in range(self.pp_group_size):
+                timestamp = next_pointer[rank]
+                if timestamp >= len(actions[rank]):
+                    continue
+
+                should_stop = False
+
+                if actions[rank][timestamp] is not None:
+                    temp_action = actions[rank][timestamp]
+                    assert temp_action is not None
+                    stage_index, op, microbatch = temp_action
+                    if not need_bubble(
+                        stage_index, op, microbatch, num_stages_global, seen_ops
+                    ):
+                        result[rank].append(actions[rank][timestamp])
+                        if microbatch is not None:
+                            temp_seen_ops.add((stage_index, op, microbatch))
+                        next_pointer[rank] += 1
+                    else:
+                        result[rank].append(None)
+                        bubbles_added[rank] += 1
+                else:
+                    next_pointer[rank] += 1
+                    result[rank].append(None)
+
+            seen_ops.update(temp_seen_ops)
+            if should_stop:
+                break
+
+        if total_bubbles_added > 0:
+            logger.warning(
+                "Non zero bubbles added: total_bubbles_added=%s bubbles_added=%s",
+                total_bubbles_added,
+                bubbles_added,
+            )
+        return result
+
+
+class ScheduleZBVZeroBubble(PipelineScheduleMulti):
+    """
+    The Zero Bubble schedule (ZBV variant).
+    See https://arxiv.org/pdf/2401.10241 Section 6 for details.
+
+    This schedules requires exactly two stages per rank.
+
+    This schedule will perform one forward and one backward on inputs for the microbatches in steady
+    state and supports multiple stages per rank. Uses backward with respect to weights to fill in
+    the pipeline bubble.
+
+    This ZB-V schedule would have the "zero bubble" property only if time forward == time backward input == time backward weights.
+    In practice, this is not likely true for real models so alternatively
+    a greedy scheduler could be implemented for unequal/unbalanced time.
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Optional[Callable] = None,
+        args_chunk_spec: Optional[tuple[TensorChunkSpec, ...]] = None,
+        kwargs_chunk_spec: Optional[dict[str, TensorChunkSpec]] = None,
+        output_merge_spec: Optional[Union[dict[str, Any], tuple[Any]]] = None,
+        scale_grads: bool = True,
+    ):
+        self.pp_group_size = stages[0].group_size
+        super().__init__(
+            stages=stages,
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+        )
+        self.stage_index_to_group_rank = generate_stage_to_rank_mapping(
+            self.pp_group_size, self._num_stages, style="v"
+        )
+        for stage in self._stages:
+            stage.stage_index_to_group_rank = self.stage_index_to_group_rank
+
+        self.n_local_stages = len(stages)
+        if self.n_local_stages != 2:
+            raise ValueError(
+                "ZBV requires exactly 2 stages per rank, but got "
+                f"{self.n_local_stages}."
+            )
+
+        self.rank = stages[0].group_rank
+        self.num_stages = stages[0].num_stages
+
+        # 1. Create the pipeline_order (all ranks do this calculation)
+        # This will be used to keep track of the current state of the entire pipeline
+        # pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
+        self.pipeline_order: dict[int, list[Optional[_Action]]] = {}
+        for rank in range(self.pp_group_size):
+            rank_ops = self._calculate_single_rank_operations(rank)
+            self.pipeline_order[rank] = rank_ops
+
+    def _calculate_single_rank_operations(self, rank) -> list[Optional[_Action]]:
+        # max(2 * self.pp_group_size - 1, ...) ensure the number of microbatches is at least
+        # as large of the number of microbatches needed to fully utilize the pipeline
+        n_micro = max(2 * self.pp_group_size - 1, self._n_microbatches)
+        rank_ops: list[Optional[_Action]] = [None for _ in range(rank)]
+
+        # Forward and backward action counts for stage chunk 0 and chunk 1
+        f0_cnt, f1_cnt, b0_cnt, b1_cnt = 0, 0, 0, 0
+        # warm-up phase
+        warmup_n1 = 2 * (self.pp_group_size - rank) - 1
+        stage_id_chunk0 = rank
+        stage_id_chunk1 = self.num_stages - 1 - rank
+
+        for _ in range(warmup_n1):
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=F, microbatch_index=f0_cnt)
+            )
+            f0_cnt += 1
+        warmup_n2 = rank
+        for _ in range(warmup_n2):
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=F, microbatch_index=f1_cnt)
+            )
+            f1_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=F, microbatch_index=f0_cnt)
+            )
+            f0_cnt += 1
+        warmup_n3 = self.pp_group_size - rank
+        for _ in range(warmup_n3):
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=F, microbatch_index=f1_cnt)
+            )
+            f1_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=I, microbatch_index=b1_cnt)
+            )
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=W, microbatch_index=b1_cnt)
+            )
+            b1_cnt += 1
+        # stable phase
+        while f1_cnt < f0_cnt or f0_cnt < n_micro:
+            if f0_cnt < n_micro:
+                rank_ops.append(
+                    _Action(
+                        stage_id_chunk0, computation_type=F, microbatch_index=f0_cnt
+                    )
+                )
+                f0_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=I, microbatch_index=b0_cnt)
+            )
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=W, microbatch_index=b0_cnt)
+            )
+            b0_cnt += 1
+
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=F, microbatch_index=f1_cnt)
+            )
+            f1_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=I, microbatch_index=b1_cnt)
+            )
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=W, microbatch_index=b1_cnt)
+            )
+            b1_cnt += 1
+        # cool-down phase
+        w0_cnt, w1_cnt = b0_cnt, b1_cnt
+        cooldown_n1 = rank
+        for _ in range(cooldown_n1):
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=I, microbatch_index=b0_cnt)
+            )
+            b0_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=I, microbatch_index=b1_cnt)
+            )
+            b1_cnt += 1
+        cooldown_n2 = self.pp_group_size - rank
+        for _ in range(cooldown_n2):
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=I, microbatch_index=b0_cnt)
+            )
+            b0_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=W, microbatch_index=w0_cnt)
+            )
+            w0_cnt += 1
+        while w1_cnt < b1_cnt:
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=W, microbatch_index=w1_cnt)
+            )
+            w1_cnt += 1
+        while w0_cnt < b0_cnt:
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=W, microbatch_index=w0_cnt)
+            )
+            w0_cnt += 1
+
+        assert w0_cnt == b0_cnt and b0_cnt == f0_cnt
+        assert w1_cnt == b1_cnt and b1_cnt == f1_cnt
+        # We use max() in the n_micro computation above, so we may need to
+        # remove redundant microbatches
+        rank_ops = [
+            (
+                action
+                if action is not None
+                and action.microbatch_index is not None
+                and action.microbatch_index < self._n_microbatches
+                else None
+            )
+            for action in rank_ops
+        ]
+        return rank_ops
+
+
+def get_schedule_class(schedule_name: str):
+    """
+    Maps a schedule name (case insensitive) to its corresponding class object.
+
+    Args:
+        schedule_name (str): The name of the schedule.
+    """
+    schedule_map = {
+        "1F1B": Schedule1F1B,
+        "Interleaved1F1B": ScheduleInterleaved1F1B,
+        "GPipe": ScheduleGPipe,
+        "LoopedBFS": ScheduleLoopedBFS,
+        "InterleavedZeroBubble": ScheduleInterleavedZeroBubble,
+        "PipelineScheduleSingle": PipelineScheduleSingle,
+        "PipelineScheduleMulti": PipelineScheduleMulti,
+        "ZBVZeroBubble": ScheduleZBVZeroBubble,
+    }
+    lowercase_keys = {k.lower(): k for k in schedule_map.keys()}
+    lowercase_schedule_name = schedule_name.lower()
+    if lowercase_schedule_name not in lowercase_keys:
+        raise ValueError(
+            f"Unknown schedule name '{schedule_name}'. The valid options are {list(schedule_map.keys())}"
+        )
+    return schedule_map[lowercase_keys[lowercase_schedule_name]]
+
+
+def _simulate_comms_compute(
+    pipeline_order, stage_to_rank: Callable[[int], int], num_stages: int
+):
+    """This function dry-run simulates the actions in the schedule from the perspective of all ranks, and flags
+    any deadlocks caused by missing or misordered communications.  It also simulates any bubbles in time where a rank
+    can not execute any action due to waiting for unmet dependencies.  The total number of simulator steps can be used
+    as a metric for unit tests involving IR optimization passes as reordering and merging of IR can reduce the number
+    of simulated steps.
+
+    The simulation is not high-fidelity and does not model overlapping of compute and communication, or cuda streams.
+    Future work may be to enhance this and model the compute time, comms overlap, and even memory.
+    """
+    pipeline_order = {
+        rank: [a for a in pipeline_order[rank] if a is not None]
+        for rank in sorted(pipeline_order)
+    }
+    _schedule: dict[int, list[_Action | None]] = {
+        rank: [] for rank in sorted(pipeline_order)
+    }
+
+    _prev_ops_rank: dict[int, set[_Action]] = {rank: set() for rank in _schedule}
+
+    def add_to_schedule(rank: int, action: Optional[_Action]):
+        _schedule[rank].append(action)
+        if action is not None:
+            _prev_ops_rank[rank].add(action)
+
+    def _ready_to_schedule(action: Optional[_Action]) -> bool:
+        if action is None:
+            return True
+
+        stage_idx = action.stage_index
+        prev_ops = _prev_ops_rank[stage_to_rank(stage_idx)]
+        if action.computation_type == F:
+            if action.stage_index == 0:
+                return True
+            elif (
+                _Action(action.stage_index, RECV_F, action.microbatch_index) in prev_ops
+            ):
+                return True
+            elif (
+                _Action(action.stage_index - 1, F, action.microbatch_index) in prev_ops
+            ):
+                return True
+            return False
+        elif action.computation_type in (BACKWARD_INPUT, FULL_BACKWARD):
+            if action.stage_index == num_stages - 1:
+                return True
+            if _Action(action.stage_index, RECV_B, action.microbatch_index) in prev_ops:
+                return True
+            if (
+                _Action(action.stage_index + 1, BACKWARD_INPUT, action.microbatch_index)
+                in prev_ops
+            ):
+                return True
+            if (
+                _Action(action.stage_index + 1, FULL_BACKWARD, action.microbatch_index)
+                in prev_ops
+            ):
+                return True
+            return False
+        elif action.computation_type == BACKWARD_WEIGHT:
+            return True
+        elif action.computation_type == SEND_F:
+            expected_f = _Action(action.stage_index, F, action.microbatch_index)
+            return expected_f in prev_ops
+        elif action.computation_type == RECV_F:
+            peer_stage_idx = stage_idx - 1
+            expected_send = _Action(peer_stage_idx, SEND_F, action.microbatch_index)
+            return expected_send in _prev_ops_rank[stage_to_rank(peer_stage_idx)]
+        elif action.computation_type == SEND_B:
+            expected_b = _Action(
+                action.stage_index, BACKWARD_INPUT, action.microbatch_index
+            )
+            expected_bw = _Action(
+                action.stage_index, FULL_BACKWARD, action.microbatch_index
+            )
+            return expected_b in prev_ops or expected_bw in prev_ops
+        elif action.computation_type == RECV_B:
+            peer_stage_idx = stage_idx + 1
+            expected_send = _Action(peer_stage_idx, SEND_B, action.microbatch_index)
+            return expected_send in _prev_ops_rank[stage_to_rank(peer_stage_idx)]
+        else:
+            raise ValueError(f"Unsupported action type {action}")
+
+    while pipeline_order:
+        progress = False
+        for rank in sorted(pipeline_order):
+            if len(pipeline_order[rank]) == 0:
+                continue
+
+            action = pipeline_order[rank][0]
+            if _ready_to_schedule(action):
+                if action is not None:
+                    add_to_schedule(rank, action)
+                pipeline_order[rank].pop(0)
+                progress = True
+            else:
+                add_to_schedule(rank, None)
+
+        for i in sorted(pipeline_order, reverse=True):
+            if len(pipeline_order[i]) == 0:
+                del pipeline_order[i]
+
+        # hacky, but do a second pass to replace any 'none' at this timestep with a real action, if it got unblocked
+        # by one of the later ranks
+        for rank in sorted(pipeline_order):
+            if len(pipeline_order[rank]) == 0:
+                continue
+
+            if _schedule[rank][-1] is not None:
+                continue
+
+            action = pipeline_order[rank][0]
+            if _ready_to_schedule(action):
+                if action is not None:
+                    _schedule[rank][-1] = action
+                    _prev_ops_rank[rank].add(action)
+                pipeline_order[rank].pop(0)
+
+        for i in sorted(pipeline_order, reverse=True):
+            if len(pipeline_order[i]) == 0:
+                del pipeline_order[i]
+
+        if not progress:
+            print("WIP comms schedule:\n", _format_pipeline_order(_schedule))
+            for rank in pipeline_order:
+                print(f"{rank=} next action= {pipeline_order[rank][0]}")
+            raise ValueError("Schedule is not progressing")
+
+    return _schedule
+
+
+def _dump_chrometrace(schedule, filename):
+    """
+    This function dumps a schedule IR into a chrometrace format so it can be visualized.
+
+    It is currently very basic and only serves as a graphical alternative to dumping the schedule IR as text.
+
+    As future work we may extend this to include more accurate heuristics for durations, or let users input durations,
+    add 'flow events' to let the UI show the connection between sends and recvs, and model cuda streams for comm/compute
+    as separate streams on the chrometrace view.
+    """
+    events = []
+    for rank in sorted(schedule):
+        for timestep, action in enumerate(schedule[rank]):
+            if action is None:
+                continue
+            events.append(
+                {
+                    "name": str(action),
+                    "cat": (
+                        "computation"
+                        if action.computation_type in (F, B, W)
+                        else "communication"
+                    ),
+                    "ph": "X",
+                    "pid": rank,
+                    "tid": rank,
+                    "ts": timestep,
+                    "dur": 1,
+                }
+            )
+    import json
+
+    with open(filename, "w") as f:
+        json.dump({"traceEvents": events}, f)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/stage.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/stage.py
new file mode 100644
index 0000000000000000000000000000000000000000..71260fcae51704431227c40b11fd85504c41232e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/pipelining/stage.py
@@ -0,0 +1,1510 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import logging
+import operator
+from abc import ABC, abstractmethod
+from typing import Any, Callable, cast, Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.fx as fx
+import torch.nn as nn
+from torch._subclasses.fake_tensor import FakeTensor
+from torch.distributed.fsdp import FSDPModule, fully_shard
+from torch.fx.node import Argument, map_aggregate
+from torch.nn.parallel import DistributedDataParallel
+from torch.utils._pytree import tree_map_only
+
+from ._backward import stage_backward, stage_backward_input, stage_backward_weight
+from ._debug import map_debug_info
+from ._utils import flatten_args, PipeInfo, validate_tensors_metadata
+
+
+__all__ = [
+    "PipelineStage",
+    "build_stage",
+]
+
+logger = logging.getLogger(__name__)
+
+
+def _normalize_model_output_as_tuple(output: Any) -> tuple[Any]:
+    """[Note: pipeline model output type]
+
+    The output of the model passed to pipelining can be any type, controlled by the user.
+
+    However, there are 2 API surfaces that complicate this.
+    (1) the outputs of intermediate stages are passed via Send/Recv ops to subsequent stages. The implicit assumption
+    is that each element of the outputs is a tensor.  Otherwise, Send/Recv would not be supported.  The exception
+    is the last layer of the model, which can output anything any which won't be communicated via Send/Recv.
+    (2) the outputs of the last layer of the model are returned to the user, or, passed to the loss function.
+    The loss function can be written in any way, such that its inputs match the outputs of the model.
+
+    It would be convenient if we could strictly type the output signature of the pipeline stage wrapping the model,
+    but we do not want to impose an unnecessary constraint on user provided models.
+
+    Currently, we let user provided models return either a Tensor or a tuple of Tensors from each stage. Due to
+    torch.export tracing, compiled models may also return a list instead of a Tuple, which we will normalize back to a
+    tuple for consistency.
+
+    TODO: should we be stricter about asserting that stage modules (intermediate and output) all return only Tensor
+    values?
+    """
+    if type(output) is list:
+        # HACK: this is a hacky workaround for the fact that export creates
+        # output in list format
+        output = tuple(output)
+
+    # Unify output form to tuple for easy correspondance with
+    # `act_send_info`
+    output_tuple = output if type(output) is tuple else (output,)
+    return output_tuple
+
+
+class _RootArgPlaceholder:
+    """
+    Placeholder for model-level inputs.
+    """
+
+    def __init__(self, tensor):
+        self.meta = tensor.to("meta")
+
+
+class _RecvInfo:
+    """
+    Represents a stage input.
+    """
+
+    def __init__(
+        self,
+        input_name: str,
+        source: int,
+        buffer: torch.Tensor,
+    ):
+        # Name of this input
+        self.input_name = input_name
+        # Stage index of the source of this input
+        self.source = source
+        # Buffer to receive the input into.
+        self.buffer = buffer
+
+    def __repr__(self):
+        return f"_RecvInfo(input={self.input_name}, source={self.source}, shape={self.buffer.size()})"
+
+
+# An input can be either a received activation or a model input
+InputInfo = Union[_RecvInfo, _RootArgPlaceholder]
+
+
+def _make_tensor_from_meta(
+    example: Union[torch.Tensor, FakeTensor],
+    device: torch.device,
+) -> torch.Tensor:
+    """
+    Create a real tensor from a tensor.
+    """
+    return torch.empty(
+        example.size(),
+        dtype=example.dtype,
+        layout=example.layout,
+        device=device,
+    )
+
+
+class _PipelineStageBase(ABC):
+    """
+    Base class for pipeline stages.
+    Defines or implements common methods used by the `_PipelineStage` used by
+    the tracing frontend and `PipelineStage` used by manual frontend.
+    """
+
+    def __init__(
+        self,
+        submodule: torch.nn.Module,
+        stage_index: int,
+        num_stages: int,
+        device: torch.device,
+        group: Optional[dist.ProcessGroup] = None,
+        dw_builder: Optional[Callable[[], Callable[..., None]]] = None,
+    ):
+        """
+        Args:
+            submodule (torch.nn.Module): The module to be executed in this stage.
+            stage_index (int): The index of this stage.
+            num_stages (int): The total number of stages in this pipeline.
+            device (torch.device): The device to run this stage on.
+            group (Optional[dist.ProcessGroup]): The process group to use for communication.
+                If `None`, the default process group will be used.
+                Default: `None`.
+            dw_builder (Optional[Callable[[], Callable[..., None]]): If provided, dw_builder is a builder function
+                that will build a new dw_runner function that will run parts of module backward that were intentionally
+                skipped during the module's actual backward pass. The builder must be invoked by stage after stage runs
+                model backwards, and stage should save the latest dw_runner to run during weight pas (W).
+                If not provided, a dw_runner will be generated automatically by traversing the autograd graph.
+                When used with schedules that only have F and B steps, the fresh dw_runner function will be called as
+                part of I (input backwards). When used with F,I,W schedules, the dw_runner function implements 'W'.
+        """
+        super().__init__()
+        if stage_index >= num_stages:
+            raise ValueError(
+                f"Stage index {stage_index} is out of range of {num_stages}"
+            )
+
+        self.submod = submodule
+        self.stage_index = stage_index
+        self.num_stages = num_stages
+        self.device = device
+        self.group = group
+
+        self.dw_builder = dw_builder
+
+        # backward state
+        self.backward_state: dict[int, tuple[Any, ...]] = {}
+
+        # store dw_runner per microbatch_id
+        self.dw_runner: dict[int, Callable[..., None]] = {}
+
+        # `group_rank` is rank in process group `group`.
+        self.group_rank = dist.get_rank(self.group)
+        self.group_size = dist.get_world_size(self.group)
+        if self.group_size > self.num_stages:
+            raise RuntimeError(
+                f"Pipeline group size {self.group_size} cannot be larger than number of stages {self.num_stages}"
+            )
+
+        # Run time states
+        self._outputs_meta: Optional[tuple[torch.Tensor, ...]] = None
+        # map microbatch ID to list of forward tensor args
+        self.fwd_cache: dict[int, tuple[Any, list[torch.Tensor]]] = {}
+        # map microbatch ID to list of backward grad tensor args
+        self.bwd_cache: dict[int, tuple[Optional[torch.Tensor], ...]] = {}
+        # Caching chunk outputs for final output merge or reduction
+        self.output_chunks: list[Any] = []
+
+        # Initialize has_backward to false; this will be set to true if loss
+        # function is passed to pipeline schedule
+        self.has_backward = False
+        # Log prefix
+        self.log_prefix = f"[Stage {self.stage_index}]"
+
+        # Forward infra
+        self.args_recv_info: dict[int, tuple[InputInfo, ...]] = {}
+        self.act_send_info: dict[int, list] = {}
+
+        # Backward infra will created lazily
+        self.grad_recv_info: dict = {}
+        self.grad_send_info: Optional[list] = None
+
+        # To be populated later by the Schedule
+        self.chunks: Optional[int] = None
+        self.stage_index_to_group_rank: dict[int, int] = {
+            i: i % self.group_size for i in range(self.num_stages)
+        }
+
+    @property
+    def has_backward(self) -> bool:
+        """
+        Returns true if this stage has a backward pass.
+        """
+        return self._has_backward
+
+    @has_backward.setter
+    def has_backward(self, has_backward: bool):
+        self._has_backward = has_backward
+
+    @property
+    def is_first(self):
+        """
+        Returns true if this stage is the first stage in the pipeline.
+        """
+        return self.stage_index == 0
+
+    @property
+    def is_last(self):
+        """
+        Returns true if this stage is the last stage in the pipeline.
+        """
+        return self.stage_index == self.num_stages - 1
+
+    def _check_chunk_id(self, chunk_id: int):
+        if self.chunks is None:
+            raise RuntimeError(
+                "Attempted to access chunk_id before chunks have been configured."
+            )
+        if chunk_id >= self.chunks:
+            raise RuntimeError(
+                f"Chunk id {chunk_id} is out of range [0, {self.chunks})"
+            )
+
+    def _configure_outputs_meta(self, outputs_meta: tuple[torch.Tensor, ...]):
+        """
+        Track the output shapes/dtype of this stage since they determine the send operation(s) which must match
+        recv operations of the next stage.  The next stage _will_ be freezing its recv buffers based on its initial
+        configuration, so it's important to also freeze/validate the output side to avoid any send/recv mismatches
+        which could show up as hangs, silent corruption, or other errors.
+        """
+        assert self._outputs_meta is None, (
+            "Attempting to reconfigure output_meta, which is not supported"
+        )
+        self._outputs_meta = tuple(outputs_meta)  # type: ignore[assignment]
+
+    def get_outputs_meta(self) -> tuple[torch.Tensor, ...]:
+        """Get the output metadata (meta tensors) reprensenting the outputs of this stage"""
+        assert self._outputs_meta is not None, (
+            "Attempted to get_outputs_meta() without configuring output meta"
+        )
+        return self._outputs_meta
+
+    def _create_grad_send_info(
+        self,
+        args_recv_info: tuple,
+    ) -> list[Optional[int]]:
+        """
+        Create a list of stage indices to send gradients to.
+        """
+        grad_send_info: list[Optional[int]] = []
+
+        def map_recv_to_send(a):
+            # Note: we send gradients back to previous stage as long as in
+            # forward it is a received input, regardless of whether it requires
+            # grad. It is up to the previous stage to disgard this gradient.
+            if isinstance(a, _RecvInfo):
+                grad_send_info.append(a.source)
+                return a.source
+            else:
+                grad_send_info.append(None)
+                return None
+
+        map_aggregate(args_recv_info, map_recv_to_send)
+
+        logger.debug("%s Grad send info: %s", self.log_prefix, grad_send_info)
+        return grad_send_info
+
+    @abstractmethod
+    def _prepare_forward_infra(
+        self,
+        num_microbatches: int,
+        args: tuple[Any, ...],
+        kwargs: Optional[dict[str, Any]] = None,
+    ) -> tuple[Any, ...]:
+        raise NotImplementedError
+
+    def _prepare_backward_infra(self, num_microbatches: int):
+        # TODO: this is needed for backward_maybe_with_nosync
+        self.chunks = num_microbatches
+
+        for mb_index in range(num_microbatches):
+            # `grad_recv_info` is a mirror of `act_send_info`
+            self.grad_recv_info[mb_index] = self._create_grad_recv_info(
+                self.act_send_info
+            )
+
+    @abstractmethod
+    def _create_grad_recv_info(
+        self,
+        act_send_info: dict,
+    ) -> tuple[_RecvInfo, ...]:
+        raise NotImplementedError
+
+    def _get_recv_ops(
+        self,
+        recv_infos: tuple[InputInfo, ...],
+    ) -> list[dist.P2POp]:
+        """
+        Helper function shared by `get_fwd_recv_ops` and `get_bwd_recv_ops`.
+        Returns a list of ops that correspond to the recv infos.
+        """
+        ops: list[dist.P2POp] = []
+        for info in recv_infos:
+            if not isinstance(info, _RecvInfo):
+                continue
+
+            peer_rank = self.stage_index_to_group_rank[info.source]
+            peer_global_rank = (
+                peer_rank
+                if self.group is None
+                else dist.get_global_rank(self.group, peer_rank)
+            )
+            ops.append(
+                dist.P2POp(dist.irecv, info.buffer, peer_global_rank, self.group)
+            )
+
+        return ops
+
+    """[Note: V-schedule special case]
+
+    V-Schedules have a special case where 2 stages with adjacent stage_id are on the same rank.
+
+    ex: 2 ranks, 4 stages forms a simple V:
+    rank0:  stage 0                   stage 3
+    rank1:          stage 1  stage 2
+
+    stage 0,1 and 2,3 communicate activations using send/recv as usual, but stage 1,2 do not need to
+    use communication ops.  Instead, they should pass tensor data directly via function call.
+
+    set_local_fwd_input and (get_local_bwd_output + set_local_bwd_input) facilitate this optimization, and
+    should be called at the appropriate time during the pipeline schedule (after forward or backward execution).
+    """
+
+    def set_local_fwd_input(self, prev_stage_outputs: Any, mb_index: int) -> None:
+        """
+        Moves 'prev_stage_outputs' from another stage on the same rank into place as inputs for this stage. Avoids
+        copying tensor data or using send/recv op.  Detaches original tensor and sets requires_grad so the
+        tensor can serve as a leaf for autograd and gradients can be collected from it during backward.
+        """
+        recv_infos: tuple[InputInfo, ...] = self.args_recv_info[mb_index]
+
+        # See [Note: pipeline model output type]
+        prev_stage_outputs = _normalize_model_output_as_tuple(prev_stage_outputs)
+
+        for info, tensor in zip(recv_infos, prev_stage_outputs):
+            assert isinstance(tensor, torch.Tensor), (
+                f"expected tensor values as outputs from prev stage, got {type(tensor)}"
+            )
+            assert isinstance(info, _RecvInfo), (
+                "set_local_Fwd_input should only be called on non-first stage, which should always have RecvInfo"
+            )
+
+            # We don't need to do a data copy here, since we can directly pass the activation tensor reference from
+            # one stage to the next.  However, we do need to mark the activation as a leaf tensor since it will serve
+            # as the input tensor for a fresh autograd graph, not part of the previous stage's autograd graph.
+            # TODO: confirm, do we use this activation as the root of the backward call for the previous stage? does
+            # detach have any affect on that?
+            info.buffer = tensor.detach().requires_grad_(True)
+
+    def get_local_bwd_output(self, mb_index):
+        """
+        Returns the input grad tensors for this stage, which correspond to the stage inputs during forward.
+        """
+        assert self.has_backward, (
+            "can't steal_bwd_input if this stage doesn't have backward"
+        )
+        assert not self.is_first, "can't get bwd output if this stage is first"
+
+        self._check_chunk_id(mb_index)
+        return self.bwd_cache.pop(mb_index)
+
+    def set_local_bwd_input(
+        self, next_stage_bwd_outputs: tuple[Optional[torch.Tensor], ...], mb_index: int
+    ) -> None:
+        """
+        Moves 'grad input' tensors from the next stage to 'grad_output' on this stage, avoiding a copy or send/recv.
+        Does not detach or set '_requires_grad'.
+        """
+        assert isinstance(next_stage_bwd_outputs, tuple), (
+            f"Expected tuple, got {type(next_stage_bwd_outputs)}"
+        )
+
+        assert self.has_backward, (
+            "can't set bwd input if this stage doesn't have backward"
+        )
+        assert not self.is_last, "can't set bwd input if this stage is last"
+        recv_infos = self.grad_recv_info[mb_index]
+        for info, tensor in zip(recv_infos, next_stage_bwd_outputs):
+            assert isinstance(tensor, torch.Tensor), (
+                f"expected tensor values as outputs from prev stage, got {type(tensor)}"
+            )
+            assert isinstance(info, _RecvInfo), (
+                f"Expected a recv info, got {type(info)}"
+            )
+            info.buffer = tensor
+
+    def get_fwd_recv_ops(self, fwd_chunk_id: int) -> list[dist.P2POp]:
+        """
+        Returns a list of ops that are needed to receive the input arguments
+        for this stage.
+        """
+        recv_infos: tuple[InputInfo, ...] = self.args_recv_info[fwd_chunk_id]
+
+        return self._get_recv_ops(recv_infos)
+
+    def get_bwd_recv_ops(self, bwd_chunk_id: int) -> list[dist.P2POp]:
+        """
+        Returns a list of ops that are needed to receive the gradients
+        for this stage.
+        """
+        if not self.has_backward or self.is_last:
+            return []
+
+        recv_infos = self.grad_recv_info[bwd_chunk_id]
+        return self._get_recv_ops(recv_infos)
+
+    def get_fwd_send_ops(self, fwd_chunk_id: int) -> list[dist.P2POp]:
+        """
+        Get the activation send ops for current stage's forward.
+        """
+        output = self.output_chunks[fwd_chunk_id]
+        # Unify output form to tuple for easy correspondance with
+        # `act_send_info`
+        output_tuple = output if type(output) is tuple else (output,)
+
+        ops: list[dist.P2POp] = []
+
+        for idx, out in enumerate(output_tuple):
+            dst_stages = self.act_send_info[idx]
+            for dst in dst_stages:
+                if dst is None:
+                    continue
+                logger.debug(
+                    "%s Sending tensor to Stage %s: %s",
+                    self.log_prefix,
+                    dst,
+                    out.size(),
+                )
+                peer_rank = self.stage_index_to_group_rank[dst]
+                peer_global_rank = (
+                    peer_rank
+                    if self.group is None
+                    else dist.get_global_rank(self.group, peer_rank)
+                )
+                ops.append(dist.P2POp(dist.isend, out, peer_global_rank, self.group))
+
+        return ops
+
+    def get_bwd_send_ops(self, bwd_chunk_id: int) -> list[dist.P2POp]:
+        """
+        Get the gradient send ops for current stage's backward.
+        """
+        self._check_chunk_id(bwd_chunk_id)
+
+        if not self.has_backward or self.is_first:
+            return []
+
+        # Create bwd send infra lazily
+        if self.grad_send_info is None:
+            # Send info for input grads during backward:
+            # List of destinations corresponding to input grads
+            # Can be None if an input has no grad
+            # `grad_send_info` is a mirror of `args_recv_info`
+            self.grad_send_info = self._create_grad_send_info(self.args_recv_info[0])
+
+        ops: list[dist.P2POp] = []
+        grads_input = self.bwd_cache.pop(bwd_chunk_id)
+        for grad, grad_recv_stage in zip(grads_input, self.grad_send_info):
+            if isinstance(grad, torch.Tensor) and grad_recv_stage is not None:
+                logger.debug(
+                    "%s Sending gradient to Stage %s: %s",
+                    self.log_prefix,
+                    grad_recv_stage,
+                    grad.size(),
+                )
+                peer_rank = self.stage_index_to_group_rank[grad_recv_stage]
+                peer_global_rank = (
+                    peer_rank
+                    if self.group is None
+                    else dist.get_global_rank(self.group, peer_rank)
+                )
+                ops.append(dist.P2POp(dist.isend, grad, peer_global_rank, self.group))
+            else:
+                if not (grad is None and grad_recv_stage is None):
+                    raise RuntimeError(
+                        f"[{self.stage_index}] for chunk {bwd_chunk_id} has gradients {grad} "
+                        f"and is expecting to send gradients to stage {grad_recv_stage}"
+                    )
+        return ops
+
+    def clear_runtime_states(self) -> None:
+        """
+        Clear runtime states of the stage.
+        """
+        # map microbatch ID to list of forward tensor args
+        self.fwd_cache.clear()
+        # Caching chunk outputs for final output merge or reduction
+        self.output_chunks.clear()
+
+        # Clear grad of input buffers in between schedule steps. This is because
+        # `torch.autograd.backward()` will accumulate gradients into leaf
+        # tensors by default. For gradients to pass back to previous stages, we
+        # don't want such accumulation.
+        for recv_tuple in self.args_recv_info.values():  # iterate over all chunks
+            for a in recv_tuple:  # iterate over all input args
+                if isinstance(a, _RecvInfo):
+                    # Set to None is the newer and recommended way to clear grads, compared to `zero_()`.
+                    # See https://github.com/pytorch/pytorch/pull/92731
+                    a.buffer.grad = None
+
+    def _map_tensor_from_recv_info(
+        self,
+        recv_infos: tuple[InputInfo, ...],
+    ):
+        """
+        Map tensors from recv infos to a list.
+        """
+
+        def get_recv_tensor(info):
+            if isinstance(info, _RecvInfo):
+                return info.buffer
+            else:
+                raise AssertionError(f"Expected _RecvInfo but got {type(info)}")
+
+        return map_aggregate(cast(Argument, recv_infos), get_recv_tensor)
+
+    def _retrieve_recv_activations(self, fwd_chunk_id: int):
+        """
+        Retrieve the activations received for the current stage during forward.
+        """
+        recv_infos = self.args_recv_info[fwd_chunk_id]
+        activations = self._map_tensor_from_recv_info(recv_infos)
+        return activations
+
+    def _retrieve_recv_grads(
+        self,
+        bwd_chunk_id: int,
+    ):
+        """
+        Retrieve the gradients received for the current stage during backward.
+        """
+        recv_infos = self.grad_recv_info[bwd_chunk_id]
+        grads = self._map_tensor_from_recv_info(recv_infos)
+        return grads
+
+    def forward_maybe_with_nosync(self, *args, **kwargs):
+        # If submod is wrapped with DDP, we use the `no_sync` context manager to
+        # avoid gradient all-reduce per microbatch
+        if isinstance(self.submod, DistributedDataParallel):
+            with self.submod.no_sync():  # type: ignore[operator]
+                out_val = self.submod(*args, **kwargs)
+        else:
+            out_val = self.submod(*args, **kwargs)
+        return out_val
+
+    def scale_grads(self, grad_scale_factor: int) -> None:
+        """Scale gradients model gradients by `grad_scale_factor`, which should be specified in coordination with the
+        loss function used with pipelining.  For loss functions which perform 'mean' loss reduction, `grad_scale_factor`
+        should be set to num_microbatches.  For loss functions that use `sum` reduction, `grad_scale_factor` should
+        be set to 1.
+
+        Should only be called once per pipeline schedule step, after all backwards passes have completed.
+        """
+
+        # PP scales only for its own contribution (microbatches), but relies on DP to scale further
+        # for DP degree.
+        if grad_scale_factor != 1:
+            for p in self.submod.parameters():
+                if p.grad is not None:
+                    p.grad.div_(grad_scale_factor)
+
+    def backward_maybe_with_nosync(
+        self,
+        backward_type,
+        bwd_kwargs: dict,
+        last_backward: bool = False,
+    ) -> tuple[tuple[Optional[torch.Tensor], ...], Optional[list[dict[str, Any]]]]:
+        """
+        Whether using PP with FSDP or DDP, there are some runtime differences between the last backward step and the
+        other steps.  Namely, we need to accumulate gradients on previous steps and reduce them on the last step, but
+        there are additional state-variables and performance considerations depending on the data parallelism used.
+        This helper should adapt any pipeline parallel schedule to work with common/supported data parallel libraries.
+        """
+
+        def perform_backward(
+            backward_type,
+        ) -> Callable[
+            [],
+            tuple[tuple[Optional[torch.Tensor], ...], Optional[list[dict[str, Any]]]],
+        ]:
+            if backward_type == "full":
+                return lambda: (
+                    stage_backward(
+                        bwd_kwargs["stage_output"],
+                        bwd_kwargs["output_grads"],
+                        bwd_kwargs["input_values"],
+                    ),
+                    None,
+                )
+            elif backward_type == "input":
+                return lambda: stage_backward_input(
+                    bwd_kwargs["stage_output"],
+                    bwd_kwargs["output_grads"],
+                    bwd_kwargs["input_values"],
+                    self.submod.parameters(),
+                )
+            elif backward_type == "weight":
+                return lambda: (
+                    stage_backward_weight(
+                        self.submod.parameters(), bwd_kwargs["param_groups"]
+                    ),
+                    None,
+                )
+            else:
+                raise RuntimeError(f"Unknown backward type: {backward_type}")
+
+        # If submod is wrapped by DDP
+        if isinstance(self.submod, DistributedDataParallel):
+            if last_backward:
+                # Last chunk, prepare for gradient reduction
+                # HACK: reaching into DDP implementation details here. Is there a better way?
+                self.submod.reducer.prepare_for_backward(  # type: ignore[union-attr, operator]
+                    list(
+                        torch.nn.parallel.distributed._find_tensors(  # type: ignore[attr-defined]
+                            bwd_kwargs["stage_output"]
+                        )
+                    )
+                )
+                result = perform_backward(backward_type)()
+            else:
+                with self.submod.no_sync():  # type: ignore[operator]
+                    result = perform_backward(backward_type)()
+        # If submod is a FSDP module
+        elif isinstance(self.submod, FSDPModule):
+            self.submod.set_is_last_backward(False)
+            self.submod.set_reshard_after_backward(False)
+            self.submod.set_requires_gradient_sync(False)
+            result = perform_backward(backward_type)()
+            if last_backward:
+                # Manually call post backward for FSDP
+                def run_post_backward(fsdp_module: FSDPModule) -> None:
+                    fsdp_module.set_is_last_backward(True)
+                    fsdp_module.set_reshard_after_backward(True)
+                    fsdp_module.set_requires_gradient_sync(True)
+                    fsdp_state = fully_shard.state(fsdp_module)  # type: ignore[attr-defined]
+                    for state in fsdp_state._state_ctx.all_states:
+                        if state._fsdp_param_group:
+                            state._fsdp_param_group.post_backward()
+
+                    # it would be much better if pipelining backward invoked .backward so autograd hooks
+                    # worked and modules like DDP/FSDP behaved as expected.  Working around this for the time being,
+                    # we need to call this too to ensure FSDP syncs its grad reduction ops back to the default stream.
+                    fsdp_state._root_post_backward_final_callback()
+
+                run_post_backward(self.submod)
+
+        else:
+            # Non-DP submodule, regular backward
+            result = perform_backward(backward_type)()
+
+        grads, param_groups = result
+        return grads, param_groups
+
+    def forward_one_chunk(
+        self,
+        fwd_chunk_id: int,
+        args: tuple[Any, ...],
+        kwargs: Optional[dict[str, Any]] = None,
+    ):
+        """
+        Perform forward pass on the stage with one microbatch.
+        `args` and `kwargs` are the inputs from *external* to this stage.
+        As of Sept 2024:
+        - `args` applies to the first stage only, other stages receives args
+          through activation transmission.
+        - `kwargs` can be passed to all stages via respective `step` calls.
+        """
+
+        if self.is_first:
+            # First stage doesn't need to receive anything
+            composite_args = args
+        else:
+            # Receive activations for this chunk
+            # Activations only come in args form
+            composite_args = self._retrieve_recv_activations(fwd_chunk_id)
+
+        composite_kwargs = kwargs or {}
+
+        self._validate_fwd_input(args, kwargs)
+
+        # Compute forward
+        try:
+            output = self.forward_maybe_with_nosync(*composite_args, **composite_kwargs)
+
+        except Exception as e:
+            exc_msg = f"""
+            {self.log_prefix} failed to run forward:
+            args: {map_debug_info(composite_args)}
+            kwargs: {map_debug_info(composite_kwargs)}
+            """
+            raise RuntimeError(exc_msg) from e
+
+        # See [Note: pipeline model output type]
+        output_tuple = _normalize_model_output_as_tuple(output)
+
+        # Prepare for final output merge or reduction
+        self.output_chunks.append(output)
+
+        # Save activations and inputs for backward
+        flat_args = flatten_args(composite_args)
+        flat_kwargs = flatten_args(composite_kwargs)
+        flatten_input_tensors = flat_args + flat_kwargs
+        self.fwd_cache[fwd_chunk_id] = (
+            output_tuple,  # stage_output
+            flatten_input_tensors,  # input_values
+        )
+
+        logger.debug(
+            "%s Forwarded chunk %s, outputs: %s",
+            self.log_prefix,
+            fwd_chunk_id,
+            map_debug_info(output),
+        )
+        self._validate_fwd_outputs(output_tuple)
+
+        # We return the original user-provied output, not normalized to tuple.
+        # See [Note: pipeline model output type]
+        return output
+
+    def backward_one_chunk(
+        self,
+        bwd_chunk_id: int,
+        loss=None,
+        full_backward: bool = True,
+        last_backward=False,
+    ):
+        """
+        Perform backward pass on the module.
+        This should only be called once per microbatch.
+
+        If full_backward is True (the default), the full backward pass including weight and input gradients will be run,
+        and it is an error to call `backward_weight_one_chunk` for this bwd_chunk_id.
+
+        If full_backward is False, it is optional that `dw_runner` was provided to the PipelineStage at __init__ time,
+        and a subsequent call to `backward_weight_one_chunk` is required to invoke dw_runner and complete the backward.
+
+        last_backward is controlled by the schedule and signals synchronization of gradients across DP groups
+        after the last backward.
+        """
+        self._check_chunk_id(bwd_chunk_id)
+
+        (
+            stage_output,
+            input_values,
+        ) = self.fwd_cache.pop(bwd_chunk_id)
+
+        # Compute backward
+        if self.is_last:
+            # Last stage computes gradients from loss and has no gradients from
+            # next stage
+            bwd_kwargs = {
+                "stage_output": loss,
+                "output_grads": None,
+                "input_values": input_values,
+            }
+        else:
+            # Otherwise, receive gradients from next stage
+            grads_output = self._retrieve_recv_grads(bwd_chunk_id)
+            # If an input to the pipeline requires gradient,
+            # `torch.autograd.backward` will accumulate the gradient into the
+            # `.grad` field of such input
+            bwd_kwargs = {
+                "stage_output": stage_output,
+                "output_grads": grads_output,
+                "input_values": input_values,
+            }
+
+        grads_input: tuple[Optional[torch.Tensor], ...] = ()
+
+        # Custom backward function
+        if self.dw_builder:
+            # TODO: We may want to change our semantics so we are allowed to ignore
+            # the 'dw_builder' and call full_backward directly when it is a full_backward op.
+            grads_input, _ = self.backward_maybe_with_nosync(
+                "full",
+                bwd_kwargs,
+                last_backward=last_backward,
+            )
+            if full_backward:
+                self.dw_builder()()
+            else:
+                self.dw_runner[bwd_chunk_id] = self.dw_builder()
+        else:
+            if full_backward:
+                grads_input, _ = self.backward_maybe_with_nosync(
+                    "full", bwd_kwargs, last_backward=last_backward
+                )
+            else:
+                param_groups: list[dict[str, Any]] | None = None
+                # Skip the backward for the first stage since we will perform the weight update with
+                # autograd.backward in backward_weight_one_chunk
+                if not self.is_first:
+                    if isinstance(bwd_kwargs["stage_output"], torch.Tensor):
+                        bwd_kwargs["stage_output"] = (bwd_kwargs["stage_output"],)
+
+                    # perform the partial backwards for the inputs with a custom backward function
+                    # when the "stage_ouput" is a loss, then it is a tensor, otherwise it is a tuple of tensors
+                    grads_input, param_groups = self.backward_maybe_with_nosync(
+                        "input", bwd_kwargs, last_backward=last_backward
+                    )
+
+                # TODO: we dont need to save this, add to dw_runner?
+                self.backward_state[bwd_chunk_id] = (
+                    bwd_kwargs["input_values"],
+                    param_groups,
+                    bwd_kwargs["stage_output"],
+                    bwd_kwargs["output_grads"],
+                )
+                # Save a placeholder for the dw_runner
+                self.dw_runner[bwd_chunk_id] = lambda: None
+
+        self.bwd_cache[bwd_chunk_id] = grads_input
+
+        if self.is_last and not self.is_first:
+            # Autograd dependencies:
+            #    rest_of_autograd_graph -> stage_output -> loss
+            # stage_output is no longer used in the last stage for backward and only needed
+            # to return to the user in merge_output_chunks, therefore
+            # this should be detached to release autograd graph context and free memory earlier
+            for t in stage_output:
+                if not t._is_view():  # views are not detachable in-place
+                    t.detach_()
+
+        logger.debug("%s Backwarded chunk %s", self.log_prefix, bwd_chunk_id)
+
+    def backward_weight_one_chunk(self, bwd_chunk_id: int, last_backward=False):
+        assert bwd_chunk_id in self.dw_runner, (
+            f"{self.log_prefix} Attempted to run backward_weight_one_chunk for chunk {bwd_chunk_id}"
+            " without first calling `backward_one_chunk(full_backward=False)`"
+        )
+
+        if self.dw_builder is not None:
+            self.dw_runner.pop(bwd_chunk_id)()
+        else:
+            (
+                input_values,
+                param_groups,
+                stage_output,
+                output_grads,
+            ) = self.backward_state.pop(bwd_chunk_id)
+
+            if self.stage_index != 0:
+                bwd_kwargs = {
+                    "stage_output": stage_output,
+                    "param_groups": param_groups,
+                }
+                self.backward_maybe_with_nosync(
+                    "weight", bwd_kwargs, last_backward=last_backward
+                )
+            else:
+                # TODO: figure out a better way to do this:
+                # if inputs does not require gradient,
+                # then the parameter group will not be fully captured during stage_backward_input
+                # in this case, we need call grad directly on the parameters
+                # To solve: make input fn do the intersect compute and then finish it off during W
+                bwd_kwargs = {
+                    "stage_output": stage_output,
+                    "output_grads": output_grads,
+                    "input_values": input_values,
+                }
+                self.backward_maybe_with_nosync(
+                    "full", bwd_kwargs, last_backward=last_backward
+                )
+
+    def _validate_fwd_input(self, args, kwargs):
+        """Raises a RuntimeError if shapes of input args/kwargs do not match the shapes configured for this stage."""
+
+        if self.is_first:
+            # TODO why is there a separate recv_info for each pipeline chunk?
+            # kwen2501: to avoid passing a `fwd_chunk_id` to this function, we
+            # check all chunks against args_recv_info[0]
+            expected_args = self.args_recv_info[0]
+        else:
+            # We don't check inputs for non-0 stages assuming they don't accept
+            # user inputs in canonical pipeline scenarios
+            return
+
+        if len(kwargs):
+            # TODO- need a mapping of kwarg to position in self.args_recv_info
+            # Without it, we are not 100% sure how to match the args and
+            # expected_args.
+            return
+
+        # TODO- need a mapping of kwarg to position in self.args_recv_info
+        # maybe it's impossible to tell whether the len mismatches because
+        # (a) the user passed an extra arg or missed an arg
+        # (b) the user did not pass a kwarg, which has a default value baked into expected_args
+        expected_tensors_meta = [
+            e.meta if isinstance(e, _RootArgPlaceholder) else e.buffer
+            for e in expected_args
+        ]
+        validate_tensors_metadata(
+            f"Stage {self.stage_index} forward inputs", expected_tensors_meta, args
+        )
+
+    def _validate_fwd_outputs(self, outputs: tuple[torch.Tensor, ...]):
+        """Raises a RuntimeError if this stage produces an output of unexpected shape/dtype.
+        Most likely, this could be cause either by incorrect user specification of output shapes, or becuase
+        shape inference was done on the original model but then at runtime the model is wrapped with something like
+        mixed precision which changes output dtype.
+        """
+        expected_tensors_meta = self.get_outputs_meta()
+        validate_tensors_metadata(
+            f"Stage {self.stage_index} forward outputs", expected_tensors_meta, outputs
+        )
+
+
+class _PipelineStage(_PipelineStageBase):
+    def __init__(
+        self,
+        stage_module: torch.nn.Module,
+        stage_index: int,
+        pipe_info: PipeInfo,
+        device: torch.device,
+        group: Optional[dist.ProcessGroup] = None,
+    ):
+        """
+        Create a pipeline stage given a stage_module to be wrapped by this stage
+        and a `pipe_info` describing the stage relationship of the pipeline.
+
+        Args:
+            stage_module (torch.nn.Module): the module to be wrapped by this stage
+            stage_index (int): the index of this stage in the pipeline
+            pipe_info (PipeInfo): information about the pipeline, can be retrieved by `pipe.info()`
+            device (torch.device): the device to be used by this stage
+            group (Optional[dist.ProcessGroup]): the process group to be used by this stage
+        """
+        _PipelineStageBase.__init__(
+            self,
+            stage_module,
+            stage_index,
+            pipe_info.num_stages,
+            device,
+            group,
+        )
+        self.pipe_info = pipe_info
+
+        # Find stage nodes in graph
+        submod_nodes = [
+            node for node in pipe_info.graph.nodes if node.op == "call_module"
+        ]
+        if len(submod_nodes) != self.num_stages:
+            raise AssertionError(
+                f"Number of submodules in pipe graph {len(submod_nodes)} does not match number of stages {self.num_stages}"
+            )
+
+        # Find my stage node in graph
+        self.node = submod_nodes[self.stage_index]
+        self.name = self.node.name
+        logger.info(
+            "[%s] Creating PipelineStage %s for %s",
+            self.group_rank,
+            stage_index,
+            self.name,
+        )
+
+        # Create mapping from stage name to stage index
+        self.submod_to_stage_index: dict[str, int] = {}
+        for i, node in enumerate(submod_nodes):
+            self.submod_to_stage_index.setdefault(node.name, i)
+
+        # Cast submodule to device
+        self._move_submod_to_device()
+
+    def _move_submod_to_device(self):
+        # Move submodule to indicated device if possible
+        # Note: we cannot move meta module to real devices because meta tensors
+        # do not support to() method. One needs to do an in-place tensor swap in
+        # that case.
+        has_meta_param = any(
+            isinstance(p, FakeTensor) or p.is_meta for p in self.submod.parameters()
+        )
+        if has_meta_param:
+            logger.debug("%s Found meta parameters!", self.log_prefix)
+        else:
+            self.submod.to(self.device)
+
+    def _prepare_forward_infra(
+        self,
+        num_microbatches: int,
+        args: tuple[Any, ...],
+        kwargs: Optional[dict[str, Any]] = None,
+    ) -> tuple[Any, ...]:
+        """
+        Create send/recv infrastructures for activations (during forward)
+        """
+        # TODO(whc)
+        # this method should be deleted once lazy buffer allocation is implemented
+        # for now, it ignores args/kwargs becuase it should not need to do shape inference
+        for chunk in range(num_microbatches):
+            self.args_recv_info[chunk] = self._create_act_recv_info()
+
+        # Send info during forward for each activation
+        self.act_send_info = self._create_act_send_info()
+        return tuple()
+
+    def get_stage_index_of_submod(
+        self,
+        submod_name: str,
+    ):
+        """
+        Given a submodule name, return the stage index of the submodule.
+        """
+        if submod_name not in self.submod_to_stage_index:
+            raise AssertionError(f"Stage id of {submod_name} not found")
+
+        return self.submod_to_stage_index[submod_name]
+
+    def _create_act_recv_info(
+        self,
+    ):
+        """
+        Create a tuple of `_RecvInfo` for inputs to the stage.
+        """
+
+        def create_recv_tensor(placeholder, arg_node):
+            """
+            Create a receive buffer for a placeholder.
+            """
+            example_value = placeholder.meta["val"]
+            if arg_node.op == "placeholder":
+                # This is a root level placeholder, thus an input argument to the entire model.
+                # We are likely at stage 0, hence no need to create a receive buffer.
+                return _RootArgPlaceholder(example_value)
+
+            # Figure out the source stage of this input
+            while arg_node.target is operator.getitem:
+                # If the input is a getitem, we need to go deeper
+                arg_node = arg_node.args[0]
+
+            assert arg_node.op == "call_module", (
+                f"Expecting call_module, got {arg_node.op}"
+            )
+            src_stage = self.get_stage_index_of_submod(arg_node.name)
+
+            # Create a receive buffer for this placeholder
+            logger.debug(
+                "%s Creating recv buffer for input '%s' : %s, %s",
+                self.log_prefix,
+                placeholder.name,
+                example_value.shape,
+                example_value.dtype,
+            )
+            buffer = _make_tensor_from_meta(example_value, self.device)
+            # In case there is backward pass, set requires_grad for receive buffers
+            # before first forward
+            if self.has_backward:
+                buffer.requires_grad_(True)
+
+            return _RecvInfo(
+                arg_node.name,
+                src_stage,
+                buffer,
+            )
+
+        args_recv_info: list[InputInfo] = []
+        # Filter out placeholder nodes from `self.submod` (a GraphModule)
+        placeholders = filter(  # type: ignore[var-annotated]
+            lambda node: node.op == "placeholder",  # type: ignore[arg-type]
+            self.submod.graph.nodes,  # type: ignore[arg-type,union-attr]
+        )
+        # `placeholders` are nodes internal to submod.
+        # `self.node.args` are dependency nodes in the outer graph.
+        # The two are 1:1.
+        for placeholder, arg_node in zip(placeholders, self.node.args):
+            # Create a receive buffer for this placeholder
+            recv_info = create_recv_tensor(placeholder, arg_node)
+            args_recv_info.append(recv_info)
+
+        logger.debug(
+            "%s Activation recv / args info: %s", self.log_prefix, args_recv_info
+        )
+        # `args` is a Tuple, hence we will return a Tuple[InputInfo]
+        return tuple(args_recv_info)
+
+    def find_dst_rank(
+        self,
+        user: fx.Node,
+    ) -> Optional[int]:
+        """
+        Find the destination rank of a `user` node.
+        If the `user` is not a submod, `None` may be returned.
+        """
+        if user.op == "call_module":
+            # User is a stage (`call_module`)
+            return self.get_stage_index_of_submod(user.name)
+        else:
+            # - If user.op == "output":
+            #   No need to send back to rank 0
+            # - If user.target is stage_backward:
+            #   No need to send assuming submod output is stored locally or
+            #   should be re-calucated in case of activation checkpointing
+            return None
+
+    def _create_act_send_info(self):
+        """
+        Create a dict of send info for activations.
+        The dict is of the form:
+        {
+            output_index: [dst_rank_0, dst_rank_1, ...],
+            ...
+        }
+        where the list of `dst_rank`s covers the case where an output value may
+        be consumed by multiple stages.
+        """
+        # Output index: List of receiver ranks
+        act_send_info: dict[int, list] = {}
+        out_idx = 0
+
+        for user in self.node.users:
+            if user.target is operator.getitem:
+                # Recursively find the real destination
+                gi_dsts = act_send_info.setdefault(out_idx, [])
+                for gi_user in user.users:
+                    dst_rank = self.find_dst_rank(gi_user)
+                    if dst_rank is not None:
+                        gi_dsts.append(dst_rank)
+                # Next `getitem` will point to the next output index
+                out_idx += 1
+            else:
+                # In case of single output value, `out_idx` will not increase
+                dsts = act_send_info.setdefault(out_idx, [])
+                dst_rank = self.find_dst_rank(user)
+                if dst_rank is not None:
+                    dsts.append(dst_rank)
+
+        output_node = self._get_output_node()
+        output_vals: tuple[torch.Tensor] = tuple(
+            v.meta["val"] for v in flatten_args(output_node.args)
+        )
+        self._configure_outputs_meta(output_vals)
+
+        logger.debug("%s Send info: %s", self.log_prefix, act_send_info)
+        return act_send_info
+
+    def _get_output_node(self):
+        output_nodes = [node for node in self.submod.graph.nodes if node.op == "output"]  # type: ignore[union-attr]
+        assert len(output_nodes) == 1
+        output_node = output_nodes[0]
+        return output_node
+
+    def _create_grad_recv_info(
+        self,
+        act_send_info: dict,
+    ) -> tuple[_RecvInfo, ...]:
+        """
+        Create a tuple of `_RecvInfo` for gradients.
+        """
+        # Dict[output_index, _RecvInfo]
+        grad_recv_info: dict[int, _RecvInfo] = {}
+        output_node = self._get_output_node()
+
+        # The output node may take multiple args, meaning the submod having multiple output values.
+        output_vals = flatten_args(output_node.args)
+
+        for out_idx, dst_list in act_send_info.items():
+            if not dst_list:
+                # No actual receiver for activation so no grad coming back
+                continue
+
+            output = output_vals[out_idx]
+            example_value = output.meta["val"]
+            logger.debug(
+                f"{self.log_prefix} Creating grad recv buffer for output {output.name} "  # noqa: G004
+                f": {example_value.shape}, {example_value.dtype}"
+            )
+
+            # TODO: otherwise needs grad accumulation
+            assert len(dst_list) == 1, "Backward of skip connections not supported yet"
+            grad_src = dst_list[0]
+            grad_recv_info[out_idx] = _RecvInfo(
+                f"{grad_src}",  # noqa: G004
+                grad_src,
+                _make_tensor_from_meta(example_value, self.device),
+            )
+
+        # Convert to tuple for convenience in get_ops and retrieve tensor
+        grad_recv_info_tuple = tuple(grad_recv_info.values())
+        logger.debug("%s Grad recv info: %s", self.log_prefix, grad_recv_info_tuple)
+        return grad_recv_info_tuple
+
+
+# A helper function to create a pipeline stage based on traced pipeline information
+def build_stage(
+    stage_module: torch.nn.Module,
+    stage_index: int,
+    pipe_info: PipeInfo,
+    device: torch.device,
+    group: Optional[dist.ProcessGroup] = None,
+) -> _PipelineStage:
+    """
+    Create a pipeline stage given a stage_module to be wrapped by this stage
+    and pipeline information.
+
+    Args:
+        stage_module (torch.nn.Module): the module to be wrapped by this stage
+        stage_index (int): the index of this stage in the pipeline
+        pipe_info (PipeInfo): information about the pipeline, can be retrieved by `pipe.info()`
+        device (torch.device): the device to be used by this stage
+        group (Optional[dist.ProcessGroup]): the process group to be used by this stage
+
+    Returns:
+        _PipelineStage: a pipeline stage that can run with `PipelineSchedules`.
+    """
+    return _PipelineStage(
+        stage_module,
+        stage_index,
+        pipe_info,
+        device,
+        group,
+    )
+
+
+class PipelineStage(_PipelineStageBase):
+    """
+    A class representing a pipeline stage in a pipeline parallelism setup.
+
+    PipelineStage assumes sequential partitioning of the model, i.e. the model is split into chunks where outputs from
+    one chunk feed into inputs of the next chunk, with no skip connections.
+
+    PipelineStage performs runtime shape/dtype inference automatically by propagating the outputs from stage0 to
+    stage1 and so forth, in linear order.  To bypass shape inference, pass the `input_args` and `output_args` to each
+    PipelineStage instance.
+
+    Args:
+        submodule (nn.Module): The PyTorch module wrapped by this stage.
+        stage_index (int): The ID of this stage.
+        num_stages (int): The total number of stages.
+        device (torch.device): The device where this stage is located.
+        input_args (Union[torch.Tensor, Tuple[torch.tensor]], optional): The input arguments for the submodule.
+        output_args (Union[torch.Tensor, Tuple[torch.tensor]], optional): The output arguments for the submodule.
+        group (dist.ProcessGroup, optional): The process group for distributed training. If None, default group.
+        dw_builder (Optional[Callable[[], Callable[..., None]]): If provided, dw_builder will build a new dw_runner function
+            that will the W action (input weights) for F, I, W (Fwd, Input, Weight) zero bubble schedules.
+    """
+
+    def __init__(
+        self,
+        submodule: nn.Module,
+        stage_index: int,
+        num_stages: int,
+        device: torch.device,
+        input_args: Optional[Union[torch.Tensor, tuple[torch.Tensor, ...]]] = None,
+        output_args: Optional[Union[torch.Tensor, tuple[torch.Tensor, ...]]] = None,
+        group: Optional[dist.ProcessGroup] = None,
+        dw_builder: Optional[Callable[[], Callable[..., None]]] = None,
+    ):
+        super().__init__(submodule, stage_index, num_stages, device, group, dw_builder)
+        self.inputs: Optional[list[torch.Tensor]] = None
+        self.inputs_meta: Optional[tuple[torch.Tensor, ...]] = None
+        # Note: inputs and submod should ideally be on meta device. We decided not to assert this (yet) becuase it
+        # might be breaking for existing users.
+        if input_args is None:
+            assert output_args is None, (
+                "If specifying output_args, input_args must also be specified. "
+                "Otherwise, shape inference will be performed at runtime"
+            )
+        else:
+            self.inputs_meta = (
+                (input_args,) if isinstance(input_args, torch.Tensor) else input_args
+            )
+            if output_args is None:
+                logger.warning(
+                    "Deprecation warning: passing input_args and performing init-time shape inference is deprecated. "
+                    "PipelineStage now supports runtime shape inference using the real inputs provided to schedule step(). "
+                    "Either delete `input_args` arg to `PipelineStage` to opt-into runtime shape inference, "
+                    "or additionally pass `output_args` to `PipelineStage` to fully override shape inference. "
+                )
+                try:
+                    with torch.no_grad():
+                        output_args = submodule(*self.inputs_meta)
+                    output_args = tree_map_only(
+                        torch.Tensor, lambda x: x.to("meta"), output_args
+                    )
+                except Exception as e:
+                    raise RuntimeError(
+                        "Failed to perform pipeline shape inference- are your inputs on the same device as your module?"
+                    ) from e
+            assert output_args is not None, (
+                "If passing input_args, also pass output_args to override shape inference"
+            )
+            self._configure_outputs_meta(
+                (output_args,) if isinstance(output_args, torch.Tensor) else output_args
+            )
+
+        # these are the buffers used in backwards send/recv, they are allocated later
+        self.outputs_grad: list[torch.Tensor] = []
+
+        dbg_str = (
+            f"Finished pipeline stage init, {self.stage_index=}, {self.is_first=}, "  # noqa: G004
+            f"{self.is_last=}, {self.num_stages=}, "
+        )
+        if self.inputs_meta is not None:
+            dbg_str += (
+                f"inputs: {[inp.shape for inp in self.inputs_meta]}, "
+                f"output: {[output.shape for output in self.get_outputs_meta()]}"
+            )
+        else:
+            dbg_str += " running shape-inference at runtime"
+
+        logger.debug(dbg_str)
+
+    def _shape_inference(
+        self,
+        args: tuple[Any, ...],
+        kwargs: Optional[dict[str, Any]] = None,
+    ):
+        if kwargs is None:
+            kwargs = {}
+        assert args is not None, "Args may be an empty tuple but not None"
+
+        # We skip recv communication if we're the first stage, but also if the previous stage is on the same rank
+        # and can pass its output shapes in as args instead of using send/recv.
+        if (
+            self.is_first
+            # if not first stage, then check if prev stage is on the same rank
+            or self.stage_index_to_group_rank[self.stage_index - 1] == self.group_rank
+        ):
+            logger.debug(
+                "Shape inference: stage %s skipping recv, because shape info passed in via `args`",
+                self.stage_index,
+            )
+            args = tree_map_only(torch.Tensor, lambda x: x.to("meta"), args)
+        else:
+            assert len(args) == 0, (
+                "Can't supply input args for shape inference on non-first stage"
+            )
+            objects = [None]
+            logger.debug(
+                "Shape inference: stage %s receiving from stage %s",
+                self.stage_index,
+                self.stage_index - 1,
+            )
+            dist.recv_object_list(
+                objects,
+                src=dist.get_global_rank(
+                    self.group or dist.distributed_c10d._get_default_group(),
+                    self.stage_index_to_group_rank[self.stage_index - 1],
+                ),
+                group=self.group,
+                device=self.device,
+            )
+            recv_args = objects[0]
+            assert isinstance(recv_args, tuple), type(recv_args)
+            args = recv_args
+
+        # cache input shapes for use during recv buffer allocation
+        self.inputs_meta = args
+        args = tree_map_only(
+            torch.Tensor, lambda x: torch.zeros_like(x, device=self.device), args
+        )
+
+        # set attributes needed for forward
+        with torch.no_grad():
+            outputs = self.submod(*args, **kwargs)
+
+        # if single tensor, convert so it is always a list
+        if isinstance(outputs, torch.Tensor):
+            outputs = [outputs]
+
+        # communicate meta outputs not real outputs for two reasons
+        # 1 - its faster (esp. since obj coll pickles tensor data!)
+        # 2 - avoid activating a cuda context for the src rank when unpickling on the recv end!
+        outputs_meta = tuple(
+            tree_map_only(torch.Tensor, lambda x: x.to("meta"), outputs)
+        )
+        logger.debug(
+            "Shape inference: stage %s inputs %s, outputs %s",
+            self.stage_index,
+            self.inputs_meta,
+            outputs_meta,
+        )
+        self._configure_outputs_meta(outputs_meta)
+
+        # Passing outputs to the next stage:
+        # two cases-
+        # 1. Usually: use send/recv communication to pass the output
+        # 2. Special case: for V-schedules, 2 'adjacent' stages (e.g. stage 3, 4 in an 8-stage 4-rank V)
+        #    pass their shape info via return value and function args rather than send/recv.
+        if (
+            self.is_last
+            # if not last stage, then check if next stage is on the same rank
+            or self.stage_index_to_group_rank[self.stage_index + 1] == self.group_rank
+        ):
+            # Case (2) above: pass shape info via return value and caller passes it as args to next stage's
+            # _shape_inference call
+            logger.debug(
+                "Shape inference: stage %s skipping send to next stage",
+                self.stage_index,
+            )
+
+        else:
+            # Case (1): send shapes via send operation, and ensure not to return it to the caller
+            logger.debug(
+                "Shape inference: stage %s sending to stage %s",
+                self.stage_index,
+                self.stage_index + 1,
+            )
+            dist.send_object_list(
+                [outputs_meta],
+                dst=dist.get_global_rank(
+                    self.group or dist.distributed_c10d._get_default_group(),
+                    self.stage_index_to_group_rank[self.stage_index + 1],
+                ),
+                group=self.group,
+                device=self.device,
+            )
+            outputs_meta = tuple()
+
+        return outputs_meta
+
+    def _prepare_forward_infra(
+        self,
+        num_microbatches: int,
+        args: tuple[Any, ...],
+        kwargs: Optional[dict[str, Any]] = None,
+    ) -> tuple[Any, ...]:
+        # TODO move self.device to an argument from step API (from its input tensors)?
+        assert num_microbatches is not None, "TODO fix num_microbatches"
+
+        outputs: tuple[Any, ...] = tuple()
+        if self.inputs_meta is None:
+            outputs = self._shape_inference(args, kwargs)
+
+        assert self.inputs_meta is not None
+        # Receive info during forward
+        # TODO: create args_recv_info lazily? (same needed for PipelineStage)
+        for chunk_id in range(num_microbatches):
+            if not self.is_first:
+                # We assume that we always receive from stage - 1
+                recv_infos = tuple(
+                    [
+                        _RecvInfo(
+                            f"recv_for_{self.stage_index}_from_{self.stage_index - 1}",
+                            self.stage_index - 1,
+                            _make_tensor_from_meta(inp, self.device),
+                        )
+                        for inp in self.inputs_meta
+                    ]
+                )
+                # In case there is backward pass, set requires_grad for receive buffers
+                if self.has_backward:
+                    for r in recv_infos:
+                        r.buffer.requires_grad_(True)
+
+                self.args_recv_info[chunk_id] = recv_infos
+            else:
+                self.args_recv_info[chunk_id] = tuple(
+                    [_RootArgPlaceholder(i) for i in self.inputs_meta]
+                )
+
+        # Send info during forward for each activation
+        # only need the rank that is being sent to
+        self.act_send_info: dict[int, list] = {}
+
+        for idx in range(len(self.get_outputs_meta())):
+            # We assume we always send to stage + 1
+            if not self.is_last:
+                self.act_send_info[idx] = [self.stage_index + 1]
+            else:
+                self.act_send_info[idx] = []
+
+        return outputs
+
+    def _create_grad_recv_info(
+        self,
+        act_send_info: dict,
+    ) -> tuple[_RecvInfo, ...]:
+        grad_recv_info: tuple[_RecvInfo, ...] = ()
+        if not self.is_last:
+            # Receiving gradients from multiple sources is not supported
+            # hence we only take the first destination
+            grad_recv_info = tuple(
+                [
+                    _RecvInfo(
+                        f"recv_grad_for_{self.stage_index}_from_{dst_list[0]}",
+                        dst_list[0],
+                        _make_tensor_from_meta(
+                            self.get_outputs_meta()[idx], self.device
+                        ),
+                    )
+                    for idx, dst_list in act_send_info.items()
+                ]
+            )
+        return grad_recv_info
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/remote_device.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/remote_device.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab5215e2f83a71e56c6f638bd1453341da671d37
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/remote_device.py
@@ -0,0 +1,120 @@
+# mypy: allow-untyped-defs
+from typing import Optional, Union
+
+import torch
+
+
+class _remote_device:
+    """
+    Represents a device on a remote worker.
+
+    Args:
+        remote_device (str or torch.device): Represents a device on a remote worker.
+            The string format should be one of the following:
+
+                1. "/", where the device field can be parsed as torch.device type.
+                   E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
+                   In addition, the device field can be optional and the default value is "cpu".
+                2. "rank:/", where  is the rank of the
+                   process and device can be parsed as torch.device type.
+                   E.g., "rank:0/cpu", "rank:0", "rank:0/cuda:0"
+                3.  and  are optional and formats like "cpu"
+                    and "cuda:1", just represent local devices.
+    """
+
+    def __init__(self, remote_device: Union[str, torch.device]):
+        PARSE_ERROR = (
+            f"Could not parse remote_device: {remote_device}. The valid format is "
+            "'/' or 'rank:/' or ''"
+        )
+        self._worker_name = None
+        self._rank = None
+        self._device: Optional[Union[str, int, torch.device]] = None
+
+        if isinstance(remote_device, torch.device):
+            self._device = remote_device
+        elif isinstance(remote_device, str):
+            fields = remote_device.split("/")
+            if len(fields) == 2:
+                self._worker_name, self._device = fields
+            elif len(fields) == 1:
+                # Check if this is a valid device.
+                if _remote_device._is_valid_local_device(fields[0]):
+                    self._device = fields[0]
+                else:
+                    self._worker_name = fields[0]
+                    self._device = "cpu"
+            else:
+                raise ValueError(PARSE_ERROR)
+        else:
+            raise TypeError(f"Invalid type for remote_device: {type(remote_device)}")
+
+        # Do some basic sanity check (no empty string)
+        if self._worker_name is not None and not self._worker_name:
+            raise ValueError(PARSE_ERROR)
+
+        # Validate the device.
+        self._device = torch.device(self._device)
+
+        # Check for rank based format.
+        if self._worker_name is not None:
+            fields = self._worker_name.split(":")
+            if len(fields) == 2:
+                # rank:/device format, extract rank
+                if fields[0] == "rank" and fields[1].isdigit():
+                    self._rank = int(fields[1])  # type: ignore[assignment]
+                    self._worker_name = None
+                else:
+                    raise ValueError(PARSE_ERROR)
+            elif len(fields) > 2:
+                raise ValueError(PARSE_ERROR)
+
+    @staticmethod
+    def _is_valid_local_device(device):
+        # Check for torch.device
+        try:
+            torch.device(device)
+            return True
+        except Exception:
+            return False
+
+    def worker_name(self) -> Optional[str]:
+        """Return the name of remote worker representing the remote device and ``None`` if no worker name is available."""
+        return self._worker_name
+
+    def rank(self) -> Optional[int]:
+        """
+        Returns the rank of remote worker representing the remote device.
+        Returns ``None`` if no rank is available.
+        """
+        return self._rank
+
+    def device(self) -> torch.device:
+        """Return the local device on the remote worker."""
+        return self._device  # type: ignore[return-value]
+
+    def __repr__(self):
+        if self._device is not None:
+            if self._worker_name is not None:
+                return f"{self._worker_name}/{self._device}"
+            elif self._rank is not None:
+                return f"rank:{self._rank}/{self._device}"
+            else:
+                return str(self._device)
+        else:
+            if self._worker_name is not None:
+                return f"{self._worker_name}"
+            elif self._rank is not None:
+                return f"{self._rank}"
+            else:
+                raise RuntimeError("Invalid state!")
+
+    def __eq__(self, other):
+        return isinstance(other, _remote_device) and (
+            self._worker_name == other._worker_name
+            and self._device == other._device
+            and self._rank == other._rank
+        )
+
+    def __hash__(self):
+        return hash(self._worker_name) ^ hash(self._device) ^ hash(self._rank)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/rendezvous.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/rendezvous.py
new file mode 100644
index 0000000000000000000000000000000000000000..497d1579ad591e13becd7d47e6fc8696bf6cae58
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/rendezvous.py
@@ -0,0 +1,290 @@
+# mypy: allow-untyped-defs
+try:
+    from urllib.parse import urlparse, urlunparse
+except ImportError as e:
+    raise ImportError(
+        "urllib cannot be found, urlparse from python2 is no longer supported."
+    ) from e
+
+import numbers
+import os
+import sys
+from collections.abc import Iterator
+from datetime import timedelta
+from typing import Callable, Optional
+
+from torch.distributed import FileStore, Store, TCPStore
+
+from .constants import default_pg_timeout
+
+
+_rendezvous_handlers: dict[str, Callable[..., Iterator[tuple[Store, int, int]]]] = {}
+
+__all__ = ["register_rendezvous_handler", "rendezvous"]
+
+
+def register_rendezvous_handler(scheme, handler):
+    """
+    Register a new rendezvous handler.
+
+    Before we can run collective algorithms, participating processes
+    need to find each other and exchange information to be able to
+    communicate. We call this process rendezvous.
+
+    The outcome of the rendezvous process is a triplet containing a
+    shared key/value store, the rank of the process, and the total
+    number of participating processes.
+
+    If none of the bundled rendezvous methods apply to your execution
+    environment you can opt to register your own rendezvous handler.
+    Pick a unique name and use the URL scheme to identify it when
+    calling the `rendezvous()` function.
+
+    Args:
+        scheme (str): URL scheme to identify your rendezvous handler.
+        handler (function): Handler that is invoked when the
+            `rendezvous()` function is called with a URL that uses
+            the corresponding scheme. It must be a generator function
+            that yields the triplet.
+    """
+    global _rendezvous_handlers
+    if scheme in _rendezvous_handlers:
+        raise RuntimeError(f"Rendezvous handler for {scheme}:// already registered")
+    _rendezvous_handlers[scheme] = handler
+
+
+# Query will have format "rank=0&world_size=1" and is
+# converted into {"rank": 0, "world_size": 1}
+def _query_to_dict(query: str) -> dict[str, str]:
+    return {
+        pair[0]: pair[1]
+        for pair in (pair.split("=") for pair in filter(None, query.split("&")))
+    }
+
+
+def _get_use_libuv_from_query_dict(query_dict: dict[str, str]) -> bool:
+    # libuv is the default backend for TCPStore. To enable the non-libuv backend,
+    # user can explicitly specify ``use_libuv=0`` in the URL parameter.
+    if sys.platform == "win32":
+        #  PyTorch is built without libuv support on windows, so default to 0
+        return query_dict.get("use_libuv", os.environ.get("USE_LIBUV", "0")) == "1"
+    return query_dict.get("use_libuv", os.environ.get("USE_LIBUV", "1")) == "1"
+
+
+def _rendezvous_helper(url: str, rank: int, world_size_opt: Optional[int], **kwargs):
+    result = urlparse(url)
+    if world_size_opt is None:
+        world_size = -1
+        if result.scheme == "env":
+            rank = int(os.environ.get("RANK", rank))
+            # If the world_size env variable is not present then it is a dynamic group
+            world_size = int(os.environ.get("WORLD_SIZE", world_size))
+    else:
+        world_size = world_size_opt
+    if rank != -1 or world_size != -1 or world_size_opt is None:
+        query_dict = _query_to_dict(result.query)
+        assert "rank" not in query_dict and "world_size" not in query_dict, (
+            f"The url: {url} has node-specific arguments(rank, world_size) already."
+        )
+        if rank != -1:
+            query_dict["rank"] = str(rank)
+        if world_size != -1 or world_size_opt is None:
+            query_dict["world_size"] = str(world_size)
+        result = result._replace(
+            query=f"{'&'.join([f'{k}={v}' for k, v in query_dict.items()])}"
+        )
+        url = urlunparse(result)
+
+    if result.scheme not in _rendezvous_handlers:
+        raise RuntimeError(f"No rendezvous handler for {result.scheme}://")
+    return _rendezvous_handlers[result.scheme](url, **kwargs)
+
+
+def rendezvous(url: str, rank: int = -1, world_size: int = -1, **kwargs):
+    if not isinstance(url, (str, bytes)):
+        raise RuntimeError(f"`url` must be a string. {type(url)}: {url}")
+
+    if not isinstance(rank, numbers.Integral):
+        raise RuntimeError(f"`rank` must be an integer. {rank}")
+
+    if not isinstance(world_size, numbers.Integral):
+        raise RuntimeError(f"`world_size` must be an integer. {world_size}")
+
+    return _rendezvous_helper(url, rank, world_size, **kwargs)
+
+
+def _create_store_from_options(backend_options, rank):
+    store, _, _ = next(_rendezvous_helper(backend_options.init_method, rank, None))
+    return store
+
+
+def _rendezvous_error(msg):
+    return ValueError("Error initializing torch.distributed using " + msg)
+
+
+def _file_rendezvous_handler(url: str, **kwargs):
+    def _error(msg):
+        return _rendezvous_error("file:// rendezvous: " + msg)
+
+    result = urlparse(url)
+    path = result.path
+    if sys.platform == "win32":
+        import urllib.request
+
+        full_path = result.netloc + result.path
+        path = urllib.request.url2pathname(full_path)
+        if path:
+            # Normalizing an empty string produces ".", which is not expected.
+            path = os.path.normpath(path)
+
+    if not path:
+        raise _error("path missing")
+    query_dict = _query_to_dict(result.query)
+    if "rank" not in query_dict:
+        raise _error("rank parameter missing")
+    if "world_size" not in query_dict:
+        raise _error("world size parameter missing")
+
+    rank = int(query_dict["rank"])
+    world_size = int(query_dict["world_size"])
+    store = FileStore(path, world_size)
+    yield (store, rank, world_size)
+
+    # If this configuration is invalidated, there is nothing we can do about it
+    raise RuntimeError("Unable to perform rerendezvous using file:// method")
+
+
+def _torchelastic_use_agent_store() -> bool:
+    return os.environ.get("TORCHELASTIC_USE_AGENT_STORE", None) == str(True)
+
+
+def _create_c10d_store(
+    hostname, port, rank, world_size, timeout, use_libuv=True
+) -> Store:
+    """
+    Smartly creates a c10d Store object on ``rank`` based on whether we need to re-use agent store.
+
+    The TCPStore server is assumed to be hosted
+    on ``hostname:port``.
+
+    By default, the TCPStore server uses the asynchronous implementation
+    ``LibUVStoreDaemon`` which utilizes libuv.
+
+    If ``torchelastic_use_agent_store()`` is ``True``, then it is assumed that
+    the agent leader (node rank 0) hosts the TCPStore server (for which the
+    endpoint is specified by the given ``hostname:port``). Hence
+    ALL ranks will create and return a TCPStore client (e.g. ``start_daemon=False``).
+
+    If ``torchelastic_use_agent_store()`` is ``False``, then rank 0 will host
+    the TCPStore (with multi-tenancy) and it is assumed that rank 0's hostname
+    and port are correctly passed via ``hostname`` and ``port``. All
+    non-zero ranks will create and return a TCPStore client.
+    """
+    # check if port is uint16_t
+    if not 0 <= port < 2**16:
+        raise ValueError(f"port must have value from 0 to 65535 but was {port}.")
+
+    if _torchelastic_use_agent_store():
+        # We create a new TCPStore for every retry so no need to add prefix for each attempt.
+        return TCPStore(
+            host_name=hostname,
+            port=port,
+            world_size=world_size,
+            is_master=False,
+            timeout=timeout,
+        )
+    else:
+        start_daemon = rank == 0
+        return TCPStore(
+            host_name=hostname,
+            port=port,
+            world_size=world_size,
+            is_master=start_daemon,
+            timeout=timeout,
+            multi_tenant=True,
+            use_libuv=use_libuv,
+        )
+
+
+def _tcp_rendezvous_handler(
+    url: str, timeout: timedelta = default_pg_timeout, **kwargs
+):
+    def _error(msg):
+        return _rendezvous_error("tcp:// rendezvous: " + msg)
+
+    result = urlparse(url)
+    if not result.port:
+        raise _error("port number missing")
+    query_dict = _query_to_dict(result.query)
+    if "rank" not in query_dict:
+        raise _error("rank parameter missing")
+    if "world_size" not in query_dict:
+        raise _error("world size parameter missing")
+
+    rank = int(query_dict["rank"])
+    world_size = int(query_dict["world_size"])
+    use_libuv = _get_use_libuv_from_query_dict(query_dict)
+
+    assert result.hostname is not None
+
+    store = _create_c10d_store(
+        result.hostname, result.port, rank, world_size, timeout, use_libuv
+    )
+
+    yield (store, rank, world_size)
+
+    # If this configuration is invalidated, there is nothing we can do about it
+    raise RuntimeError("Unable to perform re-rendezvous using tcp:// method")
+
+
+def _env_rendezvous_handler(
+    url: str, timeout: timedelta = default_pg_timeout, **kwargs
+):
+    def _error(msg):
+        return _rendezvous_error("env:// rendezvous: " + msg)
+
+    def _env_error(var):
+        return _error(f"environment variable {var} expected, but not set")
+
+    def _get_env_or_raise(env_var: str) -> str:
+        env_val = os.environ.get(env_var, None)
+        if not env_val:
+            raise _env_error(env_var)
+        else:
+            return env_val
+
+    result = urlparse(url)
+    query_dict = _query_to_dict(result.query)
+
+    rank: int
+    world_size: int
+    master_port: int
+    master_addr: str
+
+    if "rank" in query_dict:
+        rank = int(query_dict["rank"])
+    else:
+        rank = int(_get_env_or_raise("RANK"))
+
+    if "world_size" in query_dict:
+        world_size = int(query_dict["world_size"])
+    else:
+        world_size = int(_get_env_or_raise("WORLD_SIZE"))
+
+    master_addr = _get_env_or_raise("MASTER_ADDR")
+    master_port = int(_get_env_or_raise("MASTER_PORT"))
+    use_libuv = _get_use_libuv_from_query_dict(query_dict)
+
+    store = _create_c10d_store(
+        master_addr, master_port, rank, world_size, timeout, use_libuv
+    )
+
+    yield (store, rank, world_size)
+
+    # If this configuration is invalidated, there is nothing we can do about it
+    raise RuntimeError("Unable to perform re-rendezvous using env:// method")
+
+
+register_rendezvous_handler("tcp", _tcp_rendezvous_handler)
+register_rendezvous_handler("env", _env_rendezvous_handler)
+register_rendezvous_handler("file", _file_rendezvous_handler)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/run.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1c073dc861f3c52f0ce13eeea376d6bd3a66104
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/run.py
@@ -0,0 +1,896 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Module ``torch.distributed.run``.
+
+``torch.distributed.run`` is a module that spawns up multiple distributed
+training processes on each of the training nodes.
+
+``torchrun`` is a python
+`console script `_
+to the main module
+`torch.distributed.run `_
+declared in the ``entry_points`` configuration in
+`setup.py `_.
+It is equivalent to invoking ``python -m torch.distributed.run``.
+
+``torchrun`` can be used for single-node distributed training, in which one or
+more processes per node will be spawned. It can be used for either
+CPU training or GPU training. If it is used for GPU training,
+each distributed process will be operating on a single GPU. This can achieve
+well-improved single-node training performance. ``torchrun`` can also be used in
+multi-node distributed training, by spawning up multiple processes on each node
+for well-improved multi-node distributed training performance as well.
+This will especially be beneficial for systems with multiple Infiniband
+interfaces that have direct-GPU support, since all of them can be utilized for
+aggregated communication bandwidth.
+
+In both cases of single-node distributed training or multi-node distributed
+training, ``torchrun`` will launch the given number of processes per node
+(``--nproc-per-node``). If used for GPU training, this number needs to be less
+or equal to the number of GPUs on the current system (``nproc_per_node``),
+and each process will be operating on a single GPU from *GPU 0 to
+GPU (nproc_per_node - 1)*.
+
+.. versionchanged:: 2.0.0
+
+    ``torchrun`` will pass the ``--local-rank=`` argument to your script.
+    From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
+    previously used underscored ``--local_rank``.
+
+    For backward compatibility, it may be necessary for users to handle both
+    cases in their argument parsing code. This means including both ``"--local-rank"``
+    and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
+    provided, ``torchrun`` will trigger an error: "error: unrecognized arguments:
+    --local-rank=". For training code that only supports PyTorch 2.0.0+,
+    including ``"--local-rank"`` should be sufficient.
+
+    ::
+
+        >>> # xdoctest: +SKIP
+        >>> import argparse
+        >>> parser = argparse.ArgumentParser()
+        >>> parser.add_argument("--local-rank", "--local_rank", type=int)
+        >>> args = parser.parse_args()
+
+Usage
+-----
+
+Single-node multi-worker
+++++++++++++++++++++++++
+
+::
+
+    torchrun
+        --standalone
+        --nnodes=1
+        --nproc-per-node=$NUM_TRAINERS
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+.. note:: ``--nproc-per-node`` may be
+          ``"gpu"`` (spawn one process per GPU),
+          ``"cpu"`` (spawn one process per CPU),
+          ``"auto"`` (equivalent to ``"gpu"`` if CUDA is available,
+          else equivalent to ``"cpu"``),
+          or an integer specifying the number of processes.
+          See `torch.distributed.run.determine_local_world_size
+          `_
+          for more details.
+
+Stacked single-node multi-worker
+++++++++++++++++++++++++++++++++
+
+To run multiple instances (separate jobs) of single-node, multi-worker on the
+same host, we need to make sure that each instance (job) is
+setup on different ports to avoid port conflicts (or worse, two jobs being merged
+as a single job). To do this you have to run with ``--rdzv-backend=c10d``
+and specify a different port by setting ``--rdzv-endpoint=localhost:$PORT_k``.
+For ``--nodes=1``, its often convenient to let ``torchrun`` pick a free random
+port automatically instead of manually assigning different ports for each run.
+
+::
+
+    torchrun
+        --rdzv-backend=c10d
+        --rdzv-endpoint=localhost:0
+        --nnodes=1
+        --nproc-per-node=$NUM_TRAINERS
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+
+Fault tolerant (fixed sized number of workers, no elasticity, tolerates 3 failures)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+::
+
+    torchrun
+        --nnodes=$NUM_NODES
+        --nproc-per-node=$NUM_TRAINERS
+        --max-restarts=3
+        --rdzv-id=$JOB_ID
+        --rdzv-backend=c10d
+        --rdzv-endpoint=$HOST_NODE_ADDR
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+``HOST_NODE_ADDR``, in form [:] (e.g. node1.example.com:29400), specifies the node and
+the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
+node in your training cluster, but ideally you should pick a node that has a high bandwidth.
+
+.. note::
+   If no port number is specified ``HOST_NODE_ADDR`` defaults to 29400.
+
+Elastic (``min=1``, ``max=4``, tolerates up to 3 membership changes or failures)
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+::
+
+    torchrun
+        --nnodes=1:4
+        --nproc-per-node=$NUM_TRAINERS
+        --max-restarts=3
+        --rdzv-id=$JOB_ID
+        --rdzv-backend=c10d
+        --rdzv-endpoint=$HOST_NODE_ADDR
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+``HOST_NODE_ADDR``, in form [:] (e.g. node1.example.com:29400), specifies the node and
+the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
+node in your training cluster, but ideally you should pick a node that has a high bandwidth.
+
+.. note::
+   If no port number is specified ``HOST_NODE_ADDR`` defaults to 29400.
+
+Note on rendezvous backend
+--------------------------
+
+For multi-node training you need to specify:
+
+1. ``--rdzv-id``: A unique job id (shared by all nodes participating in the job)
+2. ``--rdzv-backend``: An implementation of
+   :py:class:`torch.distributed.elastic.rendezvous.RendezvousHandler`
+3. ``--rdzv-endpoint``: The endpoint where the rendezvous backend is running; usually in form
+   ``host:port``.
+
+Currently ``c10d`` (recommended), ``etcd-v2``, and ``etcd`` (legacy)  rendezvous backends are
+supported out of the box. To use ``etcd-v2`` or ``etcd``, setup an etcd server with the ``v2`` api
+enabled (e.g. ``--enable-v2``).
+
+.. warning::
+   ``etcd-v2`` and ``etcd`` rendezvous use etcd API v2. You MUST enable the v2 API on the etcd
+   server. Our tests use etcd v3.4.3.
+
+.. warning::
+   For etcd-based rendezvous we recommend using ``etcd-v2`` over ``etcd`` which is functionally
+   equivalent, but uses a revised implementation. ``etcd`` is in maintenance mode and will be
+   removed in a future version.
+
+Definitions
+-----------
+
+1. ``Node`` - A physical instance or a container; maps to the unit that the job manager works with.
+
+2. ``Worker`` - A worker in the context of distributed training.
+
+3. ``WorkerGroup`` - The set of workers that execute the same function (e.g. trainers).
+
+4. ``LocalWorkerGroup`` - A subset of the workers in the worker group running on the same node.
+
+5. ``RANK`` - The rank of the worker within a worker group.
+
+6. ``WORLD_SIZE`` - The total number of workers in a worker group.
+
+7. ``LOCAL_RANK`` - The rank of the worker within a local worker group.
+
+8. ``LOCAL_WORLD_SIZE`` - The size of the local worker group.
+
+9. ``rdzv_id`` - A user-defined id that uniquely identifies the worker group for a job. This id is
+   used by each node to join as a member of a particular worker group.
+
+9. ``rdzv_backend`` - The backend of the rendezvous (e.g. ``c10d``). This is typically a strongly
+   consistent key-value store.
+
+10. ``rdzv_endpoint`` - The rendezvous backend endpoint; usually in form ``:``.
+
+A ``Node`` runs ``LOCAL_WORLD_SIZE`` workers which comprise a ``LocalWorkerGroup``. The union of
+all ``LocalWorkerGroups`` in the nodes in the job comprise the ``WorkerGroup``.
+
+Environment Variables
+---------------------
+
+The following environment variables are made available to you in your script:
+
+1. ``LOCAL_RANK`` -  The local rank.
+
+2. ``RANK`` -  The global rank.
+
+3. ``GROUP_RANK`` - The rank of the worker group. A number between 0 and ``max_nnodes``. When
+   running a single worker group per node, this is the rank of the node.
+
+4. ``ROLE_RANK`` -  The rank of the worker across all the workers that have the same role. The role
+   of the worker is specified in the ``WorkerSpec``.
+
+5. ``LOCAL_WORLD_SIZE`` - The local world size (e.g. number of workers running locally); equals to
+   ``--nproc-per-node`` specified on ``torchrun``.
+
+6. ``WORLD_SIZE`` - The world size (total number of workers in the job).
+
+7. ``ROLE_WORLD_SIZE`` - The total number of workers that was launched with the same role specified
+   in ``WorkerSpec``.
+
+8. ``MASTER_ADDR`` - The FQDN of the host that is running worker with rank 0; used to initialize
+   the Torch Distributed backend.
+
+9. ``MASTER_PORT`` - The port on the ``MASTER_ADDR`` that can be used to host the C10d TCP store.
+
+10. ``TORCHELASTIC_RESTART_COUNT`` - The number of worker group restarts so far.
+
+11. ``TORCHELASTIC_MAX_RESTARTS`` - The configured maximum number of restarts.
+
+12. ``TORCHELASTIC_RUN_ID`` - Equal to the rendezvous ``run_id`` (e.g. unique job id).
+
+13. ``PYTHON_EXEC`` - System executable override. If provided, the python user script will
+    use the value of ``PYTHON_EXEC`` as executable. The `sys.executable` is used by default.
+
+Deployment
+----------
+
+1. (Not needed for the C10d backend) Start the rendezvous backend server and get the endpoint (to be
+   passed as ``--rdzv-endpoint`` to ``torchrun``)
+
+2. Single-node multi-worker: Start ``torchrun`` on the host to start the agent process which
+   creates and monitors a local worker group.
+
+3. Multi-node multi-worker: Start ``torchrun`` with the same arguments on all the nodes
+   participating in training.
+
+When using a job/cluster manager, the entry point command to the multi-node job should be ``torchrun``.
+
+Failure Modes
+-------------
+
+1. Worker failure: For a training job with ``n`` workers, if ``k<=n`` workers fail all workers
+   are stopped and restarted up to ``max_restarts``.
+
+2. Agent failure: An agent failure results in a local worker group failure. It is up to the job
+   manager to fail the entire job (gang semantics) or attempt to replace the node. Both behaviors
+   are supported by the agent.
+
+3. Node failure: Same as agent failure.
+
+Membership Changes
+------------------
+
+1. Node departure (scale-down): The agent is notified of the departure, all existing workers are
+   stopped, a new ``WorkerGroup`` is formed, and all workers are started with a new ``RANK`` and
+   ``WORLD_SIZE``.
+
+2. Node arrival (scale-up): The new node is admitted to the job, all existing workers are stopped,
+   a new ``WorkerGroup`` is formed, and all workers are started with a new ``RANK`` and
+   ``WORLD_SIZE``.
+
+Important Notices
+-----------------
+
+1. This utility and multi-process distributed (single-node or
+   multi-node) GPU training currently only achieves the best performance using
+   the NCCL distributed backend. Thus NCCL backend is the recommended backend to
+   use for GPU training.
+
+2. The environment variables necessary to initialize a Torch process group are provided to you by
+   this module, no need for you to pass ``RANK`` manually.  To initialize a process group in your
+   training script, simply run:
+
+::
+
+    >>> # xdoctest: +SKIP("stub")
+    >>> import torch.distributed as dist
+    >>> dist.init_process_group(backend="gloo|nccl")
+
+3. In your training program, you can either use regular distributed functions
+   or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
+   training program uses GPUs for training and you would like to use
+   :func:`torch.nn.parallel.DistributedDataParallel` module,
+   here is how to configure it.
+
+::
+
+    local_rank = int(os.environ["LOCAL_RANK"])
+    model = torch.nn.parallel.DistributedDataParallel(
+        model, device_ids=[local_rank], output_device=local_rank
+    )
+
+Please ensure that ``device_ids`` argument is set to be the only GPU device id
+that your code will be operating on. This is generally the local rank of the
+process. In other words, the ``device_ids`` needs to be ``[int(os.environ("LOCAL_RANK"))]``,
+and ``output_device`` needs to be ``int(os.environ("LOCAL_RANK"))`` in order to use this
+utility
+
+
+4. On failures or membership changes ALL surviving workers are killed immediately. Make sure to
+   checkpoint your progress. The frequency of checkpoints should depend on your job's tolerance
+   for lost work.
+
+5. This module only supports homogeneous ``LOCAL_WORLD_SIZE``. That is, it is assumed that all
+   nodes run the same number of local workers (per role).
+
+6. ``RANK`` is NOT stable. Between restarts, the local workers on a node can be assigned a
+   different range of ranks than before. NEVER hard code any assumptions about the stable-ness of
+   ranks or some correlation between ``RANK`` and ``LOCAL_RANK``.
+
+7. When using elasticity (``min_size!=max_size``) DO NOT hard code assumptions about
+   ``WORLD_SIZE`` as the world size can change as nodes are allowed to leave and join.
+
+8. It is recommended for your script to have the following structure:
+
+::
+
+    def main():
+        load_checkpoint(checkpoint_path)
+        initialize()
+        train()
+
+
+    def train():
+        for batch in iter(dataset):
+            train_step(batch)
+
+            if should_checkpoint:
+                save_checkpoint(checkpoint_path)
+
+9. (Recommended) On worker errors, this tool will summarize the details of the error
+   (e.g. time, rank, host, pid, traceback, etc). On each node, the first error (by timestamp)
+   is heuristically reported as the "Root Cause" error. To get tracebacks as part of this
+   error summary print out, you must decorate your main entrypoint function in your
+   training script as shown in the example below. If not decorated, then the summary
+   will not include the traceback of the exception and will only contain the exitcode.
+   For details on torchelastic error handling see: https://pytorch.org/docs/stable/elastic/errors.html
+
+::
+
+    from torch.distributed.elastic.multiprocessing.errors import record
+
+
+    @record
+    def main():
+        # do train
+        pass
+
+
+    if __name__ == "__main__":
+        main()
+"""  # noqa: E501
+
+import os
+import sys
+import uuid
+from argparse import ArgumentParser, REMAINDER
+from importlib import metadata
+from typing import Callable, Optional, Union
+
+import torch
+from torch.distributed.argparse_util import check_env, env
+from torch.distributed.elastic.multiprocessing import DefaultLogsSpecs, LogsSpecs, Std
+from torch.distributed.elastic.multiprocessing.errors import record
+from torch.distributed.elastic.rendezvous.utils import _parse_rendezvous_config
+from torch.distributed.elastic.utils import macros
+from torch.distributed.elastic.utils.logging import get_logger
+from torch.distributed.launcher.api import elastic_launch, LaunchConfig
+from torch.utils.backend_registration import _get_custom_mod_func
+
+
+logger = get_logger(__name__)
+
+
+def get_args_parser() -> ArgumentParser:
+    """Parse the command line options."""
+    parser = ArgumentParser(description="Torch Distributed Elastic Training Launcher")
+
+    #
+    # Worker/node size related arguments.
+    #
+
+    parser.add_argument(
+        "--nnodes",
+        action=env,
+        type=str,
+        default="1:1",
+        help="Number of nodes, or the range of nodes in form :.",
+    )
+    parser.add_argument(
+        "--nproc-per-node",
+        "--nproc_per_node",
+        action=env,
+        type=str,
+        default="1",
+        help="Number of workers per node; supported values: [auto, cpu, gpu, int].",
+    )
+
+    #
+    # Rendezvous related arguments
+    #
+
+    parser.add_argument(
+        "--rdzv-backend",
+        "--rdzv_backend",
+        action=env,
+        type=str,
+        default="static",
+        help="Rendezvous backend.",
+    )
+    parser.add_argument(
+        "--rdzv-endpoint",
+        "--rdzv_endpoint",
+        action=env,
+        type=str,
+        default="",
+        help="Rendezvous backend endpoint; usually in form :.",
+    )
+    parser.add_argument(
+        "--rdzv-id",
+        "--rdzv_id",
+        action=env,
+        type=str,
+        default="none",
+        help="User-defined group id.",
+    )
+    parser.add_argument(
+        "--rdzv-conf",
+        "--rdzv_conf",
+        action=env,
+        type=str,
+        default="",
+        help="Additional rendezvous configuration (=,=,...).",
+    )
+    parser.add_argument(
+        "--standalone",
+        action=check_env,
+        help="Start a local standalone rendezvous backend that is represented by a C10d TCP store "
+        "on a free port. Useful when launching single-node, multi-worker job. If specified "
+        "--rdzv-backend, --rdzv-endpoint, --rdzv-id are auto-assigned and any explicitly set values "
+        "are ignored.",
+    )
+
+    #
+    # User-code launch related arguments.
+    #
+
+    parser.add_argument(
+        "--max-restarts",
+        "--max_restarts",
+        action=env,
+        type=int,
+        default=0,
+        help="Maximum number of worker group restarts before failing.",
+    )
+    parser.add_argument(
+        "--monitor-interval",
+        "--monitor_interval",
+        action=env,
+        type=float,
+        default=0.1,
+        help="Interval, in seconds, to monitor the state of workers.",
+    )
+    parser.add_argument(
+        "--start-method",
+        "--start_method",
+        action=env,
+        type=str,
+        default="spawn",
+        choices=["spawn", "fork", "forkserver"],
+        help="Multiprocessing start method to use when creating workers.",
+    )
+    parser.add_argument(
+        "--role",
+        action=env,
+        type=str,
+        default="default",
+        help="User-defined role for the workers.",
+    )
+    parser.add_argument(
+        "-m",
+        "--module",
+        action=check_env,
+        help="Change each process to interpret the launch script as a Python module, executing "
+        "with the same behavior as 'python -m'.",
+    )
+    parser.add_argument(
+        "--no-python",
+        "--no_python",
+        action=check_env,
+        help="Skip prepending the training script with 'python' - just execute it directly. Useful "
+        "when the script is not a Python script.",
+    )
+
+    parser.add_argument(
+        "--run-path",
+        "--run_path",
+        action=check_env,
+        help="Run the training script with runpy.run_path in the same interpreter."
+        " Script must be provided as an abs path (e.g. /abs/path/script.py)."
+        " Takes precedence over --no-python.",
+    )
+    parser.add_argument(
+        "--log-dir",
+        "--log_dir",
+        action=env,
+        type=str,
+        default=None,
+        help="Base directory to use for log files (e.g. /var/log/torch/elastic). The same "
+        "directory is re-used for multiple runs (a unique job-level sub-directory is created with "
+        "rdzv_id as the prefix).",
+    )
+    parser.add_argument(
+        "-r",
+        "--redirects",
+        action=env,
+        type=str,
+        default="0",
+        help="Redirect std streams into a log file in the log directory (e.g. [-r 3] redirects "
+        "both stdout+stderr for all workers, [-r 0:1,1:2] redirects stdout for local rank 0 and "
+        "stderr for local rank 1).",
+    )
+    parser.add_argument(
+        "-t",
+        "--tee",
+        action=env,
+        type=str,
+        default="0",
+        help="Tee std streams into a log file and also to console (see --redirects for format).",
+    )
+
+    parser.add_argument(
+        "--local-ranks-filter",
+        "--local_ranks_filter",
+        action=env,
+        type=str,
+        default="",
+        help="Only show logs from specified ranks in console (e.g. [--local_ranks_filter=0,1,2] will "
+        "only show logs from rank 0, 1 and 2). This will only apply to stdout and stderr, not to"
+        "log files saved via --redirect or --tee",
+    )
+
+    #
+    # Backwards compatible parameters with caffe2.distributed.launch.
+    #
+
+    parser.add_argument(
+        "--node-rank",
+        "--node_rank",
+        type=int,
+        action=env,
+        default=0,
+        help="Rank of the node for multi-node distributed training.",
+    )
+    parser.add_argument(
+        "--master-addr",
+        "--master_addr",
+        default="127.0.0.1",
+        type=str,
+        action=env,
+        help="Address of the master node (rank 0) that only used for static rendezvous. It should "
+        "be either the IP address or the hostname of rank 0. For single node multi-proc training "
+        "the --master-addr can simply be 127.0.0.1; IPv6 should have the pattern "
+        "`[0:0:0:0:0:0:0:1]`.",
+    )
+    parser.add_argument(
+        "--master-port",
+        "--master_port",
+        default=29500,
+        type=int,
+        action=env,
+        help="Port on the master node (rank 0) to be used for communication during distributed "
+        "training. It is only used for static rendezvous.",
+    )
+    parser.add_argument(
+        "--local-addr",
+        "--local_addr",
+        default=None,
+        type=str,
+        action=env,
+        help="Address of the local node. If specified, will use the given address for connection. "
+        "Else, will look up the local node address instead. Else, it will be default to local "
+        "machine's FQDN.",
+    )
+
+    parser.add_argument(
+        "--logs-specs",
+        "--logs_specs",
+        default=None,
+        type=str,
+        help="torchrun.logs_specs group entrypoint name, value must be type of LogsSpecs. "
+        "Can be used to override custom logging behavior.",
+    )
+
+    #
+    # Positional arguments.
+    #
+
+    parser.add_argument(
+        "training_script",
+        type=str,
+        help="Full path to the (single GPU) training program/script to be launched in parallel, "
+        "followed by all the arguments for the training script.",
+    )
+
+    # Rest from the training program.
+    parser.add_argument("training_script_args", nargs=REMAINDER)
+
+    return parser
+
+
+def parse_args(args):
+    parser = get_args_parser()
+    return parser.parse_args(args)
+
+
+def parse_min_max_nnodes(nnodes: str):
+    arr = nnodes.split(":")
+
+    if len(arr) == 1:
+        min_nodes = max_nodes = int(arr[0])
+    elif len(arr) == 2:
+        min_nodes = int(arr[0])
+        max_nodes = int(arr[1])
+    else:
+        raise RuntimeError(f'nnodes={nnodes} is not in "MIN:MAX" format')  # noqa: E231
+
+    return min_nodes, max_nodes
+
+
+def determine_local_world_size(nproc_per_node: str):
+    try:
+        logger.info("Using nproc_per_node=%s.", nproc_per_node)
+        return int(nproc_per_node)
+    except ValueError as e:
+        if nproc_per_node == "cpu":
+            num_proc = os.cpu_count()
+            device_type = "cpu"
+        elif nproc_per_node == "gpu":
+            if not torch.cuda.is_available():
+                raise ValueError("Cuda is not available.") from e
+            device_type = "gpu"
+            num_proc = torch.cuda.device_count()
+        elif nproc_per_node == torch._C._get_privateuse1_backend_name():
+            if not _get_custom_mod_func("is_available")():
+                raise ValueError(f"{nproc_per_node} is not available.") from e
+            device_type = nproc_per_node
+            num_proc = _get_custom_mod_func("device_count")()
+        elif nproc_per_node == "auto":
+            if torch.cuda.is_available():
+                num_proc = torch.cuda.device_count()
+                device_type = "gpu"
+            elif (
+                hasattr(torch, torch._C._get_privateuse1_backend_name())
+                and _get_custom_mod_func("is_available")()
+            ):
+                num_proc = _get_custom_mod_func("device_count")()
+                device_type = torch._C._get_privateuse1_backend_name()
+            else:
+                num_proc = os.cpu_count()
+                device_type = "cpu"
+        else:
+            raise ValueError(
+                f"Unsupported nproc_per_node value: {nproc_per_node}"
+            ) from e
+
+        logger.info(
+            "Using nproc_per_node=%s, setting nproc_per_node to %s since the instance has %s %s",
+            nproc_per_node,
+            num_proc,
+            num_proc,
+            device_type,
+        )
+        return num_proc
+
+
+def get_rdzv_endpoint(args):
+    if args.rdzv_backend == "static" and not args.rdzv_endpoint:
+        return f"{args.master_addr}:{args.master_port}"  # noqa: E231
+    return args.rdzv_endpoint
+
+
+def get_use_env(args) -> bool:
+    """
+    Retrieve ``use_env`` from the args.
+
+    ``use_env`` is a legacy argument, if ``use_env`` is False, the
+    ``--node-rank`` argument will be transferred to all worker processes.
+    ``use_env`` is only used by the ``torch.distributed.launch`` and will
+    be deprecated in future releases.
+    """
+    if not hasattr(args, "use_env"):
+        return True
+    return args.use_env
+
+
+def _get_logs_specs_class(logs_specs_name: Optional[str]) -> type[LogsSpecs]:
+    """
+    Attemps to load `torchrun.logs_spec` entrypoint with key of `logs_specs_name` param.
+    Provides plugin mechanism to provide custom implementation of LogsSpecs.
+
+    Returns `DefaultLogsSpecs` when logs_spec_name is None.
+    Raises ValueError when entrypoint for `logs_spec_name` can't be found in entrypoints.
+    """
+    logs_specs_cls = None
+    if logs_specs_name is not None:
+        eps = metadata.entry_points()
+        if hasattr(eps, "select"):  # >= 3.10
+            group = eps.select(group="torchrun.logs_specs")
+            if group.select(name=logs_specs_name):
+                logs_specs_cls = group[logs_specs_name].load()
+
+        elif specs := eps.get("torchrun.logs_specs"):  # < 3.10
+            if entrypoint_list := [ep for ep in specs if ep.name == logs_specs_name]:
+                logs_specs_cls = entrypoint_list[0].load()
+
+        if logs_specs_cls is None:
+            raise ValueError(
+                f"Could not find entrypoint under 'torchrun.logs_specs[{logs_specs_name}]' key"
+            )
+
+        logger.info(
+            "Using logs_spec '%s' mapped to %s", logs_specs_name, str(logs_specs_cls)
+        )
+    else:
+        logs_specs_cls = DefaultLogsSpecs
+
+    return logs_specs_cls
+
+
+def config_from_args(args) -> tuple[LaunchConfig, Union[Callable, str], list[str]]:
+    # If ``args`` not passed, defaults to ``sys.argv[:1]``
+    min_nodes, max_nodes = parse_min_max_nnodes(args.nnodes)
+    assert 0 < min_nodes <= max_nodes
+    assert args.max_restarts >= 0
+
+    if (
+        hasattr(args, "master_addr")
+        and args.rdzv_backend != "static"
+        and not args.rdzv_endpoint
+    ):
+        logger.warning(
+            "master_addr is only used for static rdzv_backend and when rdzv_endpoint "
+            "is not specified."
+        )
+
+    nproc_per_node = determine_local_world_size(args.nproc_per_node)
+    if "OMP_NUM_THREADS" not in os.environ and nproc_per_node > 1:
+        omp_num_threads = 1
+        logger.warning(
+            "\n*****************************************\n"
+            "Setting OMP_NUM_THREADS environment variable for each process to be "
+            "%s in default, to avoid your system being overloaded, "
+            "please further tune the variable for optimal performance in "
+            "your application as needed. \n"
+            "*****************************************",
+            omp_num_threads,
+        )
+        # This env variable will be passed down to the subprocesses
+        os.environ["OMP_NUM_THREADS"] = str(omp_num_threads)
+
+    log_line_prefix_template = os.getenv("TORCHELASTIC_LOG_LINE_PREFIX_TEMPLATE")
+
+    rdzv_configs = _parse_rendezvous_config(args.rdzv_conf)
+
+    if args.rdzv_backend == "static":
+        rdzv_configs["rank"] = args.node_rank
+
+    rdzv_endpoint = get_rdzv_endpoint(args)
+
+    ranks: Optional[set[int]] = None
+    if args.local_ranks_filter:
+        try:
+            ranks = set(map(int, args.local_ranks_filter.split(",")))
+            assert ranks
+        except Exception as e:
+            raise ValueError(
+                "--local_ranks_filter must be a comma-separated list of integers e.g. --local_ranks_filter=0,1,2"
+            ) from e
+
+    logs_specs_cls: type[LogsSpecs] = _get_logs_specs_class(args.logs_specs)
+    logs_specs = logs_specs_cls(
+        log_dir=args.log_dir,
+        redirects=Std.from_str(args.redirects),
+        tee=Std.from_str(args.tee),
+        local_ranks_filter=ranks,
+    )
+
+    config = LaunchConfig(
+        min_nodes=min_nodes,
+        max_nodes=max_nodes,
+        nproc_per_node=nproc_per_node,
+        run_id=args.rdzv_id,
+        role=args.role,
+        rdzv_endpoint=rdzv_endpoint,
+        rdzv_backend=args.rdzv_backend,
+        rdzv_configs=rdzv_configs,
+        max_restarts=args.max_restarts,
+        monitor_interval=args.monitor_interval,
+        start_method=args.start_method,
+        log_line_prefix_template=log_line_prefix_template,
+        local_addr=args.local_addr,
+        logs_specs=logs_specs,
+    )
+
+    with_python = not args.no_python
+    cmd: Union[Callable, str]
+    cmd_args = []
+    use_env = get_use_env(args)
+    if args.run_path:
+        cmd = run_script_path
+        cmd_args.append(args.training_script)
+    else:
+        if with_python:
+            cmd = os.getenv("PYTHON_EXEC", sys.executable)
+            cmd_args.append("-u")
+            if args.module:
+                cmd_args.append("-m")
+            cmd_args.append(args.training_script)
+        else:
+            if args.module:
+                raise ValueError(
+                    "Don't use both the '--no-python' flag"
+                    " and the '--module' flag at the same time."
+                )
+            cmd = args.training_script
+    if not use_env:
+        cmd_args.append(f"--local-rank={macros.local_rank}")
+    cmd_args.extend(args.training_script_args)
+
+    return config, cmd, cmd_args
+
+
+def run_script_path(training_script: str, *training_script_args: str):
+    """
+    Run the provided `training_script` from within this interpreter.
+
+    Usage: `script_as_function("/abs/path/to/script.py", "--arg1", "val1")`
+    """
+    import runpy
+    import sys
+
+    sys.argv = [training_script] + [*training_script_args]
+    runpy.run_path(sys.argv[0], run_name="__main__")
+
+
+def run(args):
+    torch.multiprocessing._set_thread_name("pt_elastic")
+
+    if args.standalone:
+        args.rdzv_backend = "c10d"
+        args.rdzv_endpoint = "localhost:0"
+        args.rdzv_id = str(uuid.uuid4())
+        logger.info(
+            "\n**************************************\n"
+            "Rendezvous info:\n"
+            "--rdzv-backend=%s "
+            "--rdzv-endpoint=%s "
+            "--rdzv-id=%s\n"
+            "**************************************\n",
+            args.rdzv_backend,
+            args.rdzv_endpoint,
+            args.rdzv_id,
+        )
+
+    config, cmd, cmd_args = config_from_args(args)
+    elastic_launch(
+        config=config,
+        entrypoint=cmd,
+    )(*cmd_args)
+
+
+@record
+def main(args=None):
+    args = parse_args(args)
+    run(args)
+
+
+if __name__ == "__main__":
+    main()
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+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_api.py
@@ -0,0 +1,1301 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import inspect
+import warnings
+from collections.abc import Sequence
+from typing import Any, Callable, cast, Optional
+from typing_extensions import deprecated
+
+import torch
+import torch.distributed.tensor._dispatch as op_dispatch
+import torch.distributed.tensor._random as random
+import torch.nn as nn
+from torch.distributed.device_mesh import _mesh_resources, DeviceMesh
+from torch.distributed.tensor._collective_utils import check_tensor_meta, mesh_broadcast
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor._redistribute import (
+    Redistribute,
+    redistribute_local_tensor,
+)
+from torch.distributed.tensor._utils import (
+    compute_global_tensor_info,
+    compute_local_shape_and_global_offset,
+    normalize_to_torch_size,
+)
+from torch.distributed.tensor.placement_types import (
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+__all__ = [
+    "DTensor",
+    "distribute_tensor",
+    "distribute_module",
+    "ones",
+    "empty",
+    "full",
+    "rand",
+    "randn",
+    "zeros",
+]
+
+aten = torch.ops.aten
+
+
+# NOTE [Autograd interaction between torch.Tensor]
+#
+# The autograd functions defined below are being used by the public
+# facing APIs (i.e. from_local, to_local) to ensure DTensor to work
+# together with torch.Tensor within the autograd engine. This
+# allows DTensor to only exist on part of the module hierarchy.
+#
+# As an example, we have the a module that consists of submodules
+# A, B, and C, the execution flow would be like:
+#  input(torch.Tensor) -> Module A -> Module B -> Module C -> output (torch.Tensor)
+#
+# Suppose I only want to make Module B be a sharded module with
+# DTensor params, the following forward/backward should work:
+#
+#  input(torch.Tensor) -> Module A
+#       -> DTensor input (from_local) -> Sharded Module B -> DTensor output
+#           -> torch.Tensor output (to_local) -> Module C
+#
+# So from_local/to_local must be Autograd functions.
+#
+class _ToTorchTensor(torch.autograd.Function):
+    @staticmethod
+    def forward(  # type: ignore[override]
+        ctx,
+        input: "DTensor",
+        grad_placements: Optional[Sequence[Placement]],
+    ):
+        ctx.dtensor_spec = input._spec
+        ctx.grad_placements = grad_placements
+        local_tensor = input._local_tensor
+
+        # We need to return a fresh Tensor object there as autograd metadata
+        # will be inplaced into it. So we don't want to pollute the Tensor
+        # object stored in the _local_tensor of this DTensor.
+        return local_tensor.view_as(local_tensor)
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor):  # type: ignore[override]
+        dtensor_spec = ctx.dtensor_spec
+        mesh = dtensor_spec.mesh
+        grad_placements = ctx.grad_placements
+        dtensor_meta = dtensor_spec.tensor_meta
+
+        _, tensor_stride = compute_global_tensor_info(
+            grad_output, mesh, dtensor_spec.placements
+        )
+        tensor_stride = tuple(tensor_stride)
+        grad_placements = grad_placements or dtensor_spec.placements
+        grad_spec = DTensorSpec(
+            mesh,
+            grad_placements,
+            tensor_meta=TensorMeta(
+                shape=dtensor_meta.shape,
+                stride=tensor_stride,
+                dtype=dtensor_meta.dtype,
+            ),
+        )
+
+        return (
+            DTensor(
+                grad_output,
+                grad_spec,
+                requires_grad=grad_output.requires_grad,
+            ),
+            None,
+        )
+
+
+class _FromTorchTensor(torch.autograd.Function):
+    @staticmethod
+    def forward(  # type: ignore[override]
+        ctx,  # pyre-ignore[2]: Parameter must be annotated.
+        input: torch.Tensor,
+        device_mesh: DeviceMesh,
+        placements: tuple[Placement, ...],
+        run_check: bool,
+        shape: Optional[torch.Size] = None,
+        stride: Optional[tuple[int, ...]] = None,
+    ) -> "DTensor":
+        ctx.previous_placement = placements
+        ctx.previous_device_mesh = device_mesh
+
+        if shape and stride:
+            tensor_shape, tensor_stride = shape, stride
+        elif not shape and not stride:
+            # if it's not by default run_check, we assume user is certain that each
+            # rank has the same tensor shape, and we just use that to calculate the
+            # global shape
+            global_shape, global_stride = compute_global_tensor_info(
+                input, device_mesh, placements
+            )
+            tensor_shape, tensor_stride = torch.Size(global_shape), tuple(global_stride)
+        else:
+            raise RuntimeError(
+                f"Found shape:{shape}, stride:{stride}.",
+                "Please pass both shape and stride at the same time.",
+            )
+
+        if device_mesh.get_coordinate() is None:
+            # if the global rank is not participating in the device mesh, we
+            # simply set the local tensor to an empty tensor
+            input = input.new_empty(0, requires_grad=input.requires_grad)
+        elif run_check:
+            # TODO: support uneven sharding when global shape/stride not passed, by
+            # building the global TensorMeta during check_tensor_meta
+            check_shape_stride = not shape and not stride
+            check_tensor_meta(input, check_shape_stride=check_shape_stride)
+            # TODO: See if we need to make this run_check logic
+            # have a corresponding backward.
+            for idx, placement in enumerate(placements):
+                if placement.is_replicate():
+                    # broadcast rank 0 tensor to all ranks
+                    # only broadcast if run_check is True
+                    input = input.contiguous()
+                    mesh_broadcast(input, device_mesh, mesh_dim=idx)
+
+        dist_spec = DTensorSpec(
+            device_mesh,
+            placements,
+            tensor_meta=TensorMeta(
+                tensor_shape,
+                tensor_stride,
+                input.dtype,
+            ),
+        )
+
+        # We want a fresh Tensor object that shares memory with the input tensor
+        dist_tensor = DTensor(
+            input.view_as(input),
+            dist_spec,
+            # requires_grad of the dist tensor depends on if input
+            # requires_grad or not
+            requires_grad=input.requires_grad,
+        )
+        return dist_tensor
+
+    @staticmethod
+    def backward(ctx, grad_output: "DTensor"):  # type: ignore[override]
+        previous_placement = ctx.previous_placement
+        previous_device_mesh = ctx.previous_device_mesh
+
+        # reshard to the placement when creating DistributedTensor
+        # so that the gradient layout matches, and we could return
+        # local gradients directly
+        if grad_output.placements != previous_placement:
+            current_spec = grad_output._spec
+            target_spec = DTensorSpec(
+                previous_device_mesh,
+                previous_placement,
+                tensor_meta=grad_output._spec.tensor_meta,
+            )
+            local_tensor = grad_output._local_tensor
+            output = redistribute_local_tensor(
+                local_tensor, current_spec, target_spec, is_backward=True
+            )
+            # TODO: return the redistributed local tensor directly without
+            # differentiable backward. see if this make sense for all cases.
+            return output, None, None, None, None, None
+
+        # TODO: backward is also differentiable now, add a test
+        # to test higher level gradients.
+        return grad_output.to_local(), None, None, None, None, None
+
+
+class DTensor(torch.Tensor):
+    """
+    ``DTensor`` (Distributed Tensor) is a subclass of ``torch.Tensor`` that provides single-device like
+    abstraction to program with multi-device ``torch.Tensor``. It describes the distributed tensor sharding
+    layout (DTensor Layout) through the :class:`DeviceMesh` and following types of :class:`Placement`:
+
+    * :class:`Shard`: Tensor sharded on the tensor dimension ``dim`` on the devices of the ``DeviceMesh`` dimension
+    * :class:`Replicate`: Tensor replicated on the devices of the ``DeviceMesh`` dimension
+    * :class:`Partial`: Tensor is pending reduction on the devices of the ``DeviceMesh`` dimension
+
+    When calling PyTorch operators, ``DTensor`` overrides the PyTorch operators to perform sharded computation and issue
+    communications whenever necessary. Along with the operator computation, ``DTensor`` will transform or propagate the
+    placements (DTensor Layout) properly (based on the operator semantic itself) and generate new ``DTensor`` outputs.
+
+    To ensure numerical correctness of the ``DTensor`` sharded computation when calling PyTorch operators, ``DTensor``
+    requires every Tensor argument of the operator be DTensor.
+
+    .. note:: Directly using the Tensor subclass constructor here is not the recommended way to create a ``DTensor``
+        (i.e. it does not handle autograd correctly hence is not the public API). Please refer to the `create_dtensor`_
+        section to see how to create a ``DTensor``.
+    """
+
+    _local_tensor: torch.Tensor
+    _spec: DTensorSpec
+    __slots__ = ["_local_tensor", "_spec"]
+
+    # _op_dispatcher instance as a class attribute to handle runtime dispatching logic
+    _op_dispatcher: op_dispatch.OpDispatcher = op_dispatch.OpDispatcher()
+
+    @staticmethod
+    @torch._disable_dynamo
+    def __new__(
+        cls,
+        local_tensor: torch.Tensor,
+        spec: DTensorSpec,
+        *,
+        requires_grad: bool,
+    ) -> "DTensor":
+        """
+        Construct a DTensor from a local tensor, device mesh, and placement and
+        other tensor properties (i.e. shape, requires_grad, strides, etc).
+
+        .. note:: This is not a public API and it's only supposed to be used by the
+            operator implementations and internals. If you want to construct a
+            DTensor from a local tensor, consider using ``DTensor.from_local``, if
+            you want to construct a DTensor from a "global" tensor (where you
+            already have tensor initialized and want to shard this tensor),
+            consider using ``distribute_tensor``.
+        """
+        if local_tensor.requires_grad and not requires_grad:
+            warnings.warn(
+                "To construct DTensor from torch.Tensor, it's recommended to "
+                "use local_tensor.detach() and make requires_grad consistent."
+            )
+
+        # new method instruct wrapper tensor from local_tensor and add
+        # placement spec, it does not do actual distribution
+        assert spec.tensor_meta is not None, "TensorMeta should not be None!"
+        r = torch.Tensor._make_wrapper_subclass(  # type: ignore[attr-defined]
+            cls,
+            spec.tensor_meta.shape,
+            strides=spec.tensor_meta.stride,
+            dtype=local_tensor.dtype,
+            device=local_tensor.device,
+            layout=local_tensor.layout,
+            requires_grad=requires_grad,
+        )
+
+        r._spec = spec
+        r._local_tensor = local_tensor
+        return r
+
+    # pyre-fixme[14]: `__repr__` overrides method defined in `DTensor` inconsistently.
+    # pyre-fixme[3]: Return type must be annotated.
+    def __repr__(self):  # type: ignore[override]
+        # TODO: consider all_gather the local tensors for better debugging
+        return f"DTensor(local_tensor={self._local_tensor}, device_mesh={self._spec.mesh}, placements={self._spec.placements})"
+
+    def __tensor_flatten__(self):
+        """
+        protocol to inform how to flatten a DTensor to local tensor
+        for PT2 tracing
+        """
+        return ["_local_tensor"], (self._spec, self.requires_grad)
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors, flatten_spec, outer_size, outer_stride):
+        assert flatten_spec is not None, (
+            "Expecting spec to be not None from `__tensor_flatten__` return value!"
+        )
+        local_tensor = inner_tensors["_local_tensor"]
+        spec, requires_grad = flatten_spec
+        unflatten_tensor_meta = TensorMeta(
+            shape=outer_size,
+            stride=outer_stride,
+            dtype=spec.tensor_meta.dtype,
+        )
+        unflatten_spec = DTensorSpec(
+            spec.mesh,
+            spec.placements,
+            tensor_meta=unflatten_tensor_meta,
+        )
+        return DTensor(
+            local_tensor,
+            unflatten_spec,
+            requires_grad=requires_grad,
+        )
+
+    def __coerce_tangent_metadata__(self):
+        if not any(isinstance(p, Partial) for p in self.placements):
+            return self
+        placements = [
+            Replicate() if isinstance(p, Partial) else p for p in self.placements
+        ]
+        return self.redistribute(device_mesh=self.device_mesh, placements=placements)
+
+    def __coerce_same_metadata_as_tangent__(self, flatten_spec, expected_type=None):
+        if expected_type is not None:
+            return None
+
+        (spec, _) = flatten_spec  # Result of tensor_flatten()
+        return self.redistribute(
+            device_mesh=self.device_mesh,
+            placements=spec.placements,
+        )
+
+    @classmethod
+    @torch._disable_dynamo
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+        return DTensor._op_dispatcher.dispatch(
+            func,
+            args,
+            kwargs or {},
+        )
+
+    @staticmethod
+    def from_local(
+        local_tensor: torch.Tensor,
+        device_mesh: Optional[DeviceMesh] = None,
+        placements: Optional[Sequence[Placement]] = None,
+        *,
+        run_check: bool = False,
+        shape: Optional[torch.Size] = None,
+        stride: Optional[tuple[int, ...]] = None,
+    ) -> "DTensor":
+        """
+        Create a :class:`DTensor` from a local torch.Tensor on each rank
+        according to the ``device_mesh`` and ``placements`` specified.
+
+        Args:
+            local_tensor (torch.Tensor): local torch.Tensor on each rank.
+            device_mesh (:class:`DeviceMesh`, optional): DeviceMesh to place the
+                tensor, if not specified, must be called under a DeviceMesh
+                context manager, default: None
+            placements (List[:class:`Placement`], optional): the placements that
+                describes how to place the local torch.Tensor on DeviceMesh, must
+                have the same number of elements as ``device_mesh.ndim``.
+
+        Keyword args:
+            run_check (bool, optional): at a cost of extra communications, perform
+                sanity check across ranks to check each local tensor's meta information
+                to ensure correctness. If have :class:`Replicate` in ``placements``, the
+                data on first rank of the device mesh dimension will be broadcasted
+                to other ranks. default: False
+            shape (torch.Size, optional): A List of int which specifies the size of
+                DTensor which build on top of `local_tensor`. Note this needs to be
+                provided if the shape of ``local_tensor`` are different across the ranks.
+                If not provided, ``shape`` will be computed assuming the given distributed
+                tensor is evenly sharded across ranks. default: None
+            stride (tuple, optional): A List of int which specifies the stride of DTensor.
+                If not provided, ``stride`` will be computed assuming the given distributed
+                tensor is evenly sharded across ranks. default: None
+
+        Returns:
+            A :class:`DTensor` object
+
+        .. note:: When ``run_check=False``, it is the user's responsibility to ensure the
+            local tensor passed in is correct across ranks (i.e. the tensor is sharded for
+            the ``Shard(dim)`` placement or replicated for the ``Replicate()`` placement).
+            If not, the behavior of the created DTensor is undefined.
+
+        .. note:: ``from_local`` is differentiable, the `requires_grad` of the created
+            `DTensor` object will depend on if `local_tensor` requires_grad or not.
+        """
+        # if same shape/dtype, no need to run_check, if not, must allgather
+        # the metadatas to check the size/dtype across ranks
+        # There should be no data communication unless there's replication
+        # strategy, where we broadcast the replication from the first rank
+        # in the mesh dimension
+        device_mesh = device_mesh or _mesh_resources.get_current_mesh()
+        device_type = device_mesh.device_type
+
+        # convert the local tensor to desired device base on device mesh's device_type
+        if device_type != local_tensor.device.type and not local_tensor.is_meta:
+            local_tensor = local_tensor.to(device_type)
+
+        # set default placements to replicated if not specified
+        if placements is None:
+            placements = [Replicate() for _ in range(device_mesh.ndim)]
+        else:
+            placements = list(placements)
+            for idx, placement in enumerate(placements):
+                # normalize shard dim to be positive
+                if placement.is_shard():
+                    placement = cast(Shard, placement)
+                    if placement.dim < 0:
+                        placements[idx] = Shard(placement.dim + local_tensor.ndim)
+
+        # `from_local` is differentiable, and the gradient of the dist tensor this function
+        # created should flow back the gradients to the local_tensor, so we call an autograd
+        # function to construct the dist tensor instead.
+        return _FromTorchTensor.apply(  # pyre-ignore[16]: autograd func
+            local_tensor,
+            device_mesh,
+            tuple(placements),
+            run_check,
+            shape,
+            stride,
+        )
+
+    def to_local(
+        self, *, grad_placements: Optional[Sequence[Placement]] = None
+    ) -> torch.Tensor:
+        """
+        Get the local tensor of this DTensor on its current rank. For sharding it returns
+        a local shard of the logical tensor view, for replication it returns the replica on
+        its current rank.
+
+        Keyword args:
+            grad_placements (List[:class:`Placement`], optional): the placements describes
+                the future layout of any gradient layout of the Tensor returned from this
+                function.
+                `to_local` converts DTensor to local tensor and the returned local tensor
+                might not be used as the original DTensor layout later in the code. This
+                argument is the hint that user can give to autograd in case the gradient
+                layout of the returned tensor does not match the original DTensor layout.
+                If not specified, we will assume the gradient layout remains the same
+                as the original DTensor and use that for gradient computation.
+
+        Returns:
+            A :class:`torch.Tensor` or ``AsyncCollectiveTensor`` object. it represents the
+            local tensor on its current rank. When an ``AsyncCollectiveTensor`` object is returned,
+            it means the local tensor is not ready yet (i.e. communication is not finished). In this
+            case, user needs to call ``wait`` to wait the local tensor to be ready.
+
+        .. note:: ``to_local`` is differentiable, the ``requires_grad`` of the local tensor returned
+            will depend on if the `DTensor` requires_grad or not.
+        """
+        if not torch.is_grad_enabled():
+            return self._local_tensor
+
+        if grad_placements is not None and not isinstance(grad_placements, tuple):
+            grad_placements = tuple(grad_placements)
+        return _ToTorchTensor.apply(
+            self, grad_placements
+        )  # pyre-ignore[16]: autograd func
+
+    def redistribute(
+        self,
+        device_mesh: Optional[DeviceMesh] = None,
+        placements: Optional[Sequence[Placement]] = None,
+        *,
+        async_op: bool = False,
+    ) -> "DTensor":
+        """
+        ``redistribute`` performs necessary collective operations that redistribute the current
+        DTensor from its current placements to a new placements, or from is current DeviceMesh
+        to a new DeviceMesh. i.e. we can turn a Sharded DTensor to a Replicated DTensor by
+        specifying a Replicate placement for each dimension of the DeviceMesh.
+
+        When redistributing from current to the new placements on one device mesh dimension, we
+        will perform the following operations including communication collective or local operation:
+
+        1. ``Shard(dim)`` -> ``Replicate()``: ``all_gather``
+        2. ``Shard(src_dim)`` -> ``Shard(dst_dim)``: ``all_to_all``
+        3. ``Replicate()`` -> ``Shard(dim)``: local chunking (i.e. ``torch.chunk``)
+        4. ``Partial()`` -> ``Replicate()``: ``all_reduce``
+        5. ``Partial()`` -> ``Shard(dim)``: ``reduce_scatter``
+
+
+        ``redistribute`` would correctly figure out the necessary redistribute steps for DTensors
+        that are created either on 1-D or N-D DeviceMesh.
+
+        Args:
+            device_mesh (:class:`DeviceMesh`, optional): DeviceMesh to place the
+                DTensor. If not specified, it would use the current DTensor's DeviceMesh.
+                default: None
+            placements (List[:class:`Placement`], optional): the new placements that
+                describes how to place the DTensor into the DeviceMesh, must
+                have the same number of elements as ``device_mesh.ndim``.
+                default: replicate on all mesh dimensions
+
+        Keyword args:
+            async_op (bool, optional): whether to perform the DTensor redistribute operation
+                asynchronously or not. Default: False
+
+        Returns:
+            A :class:`DTensor` object
+
+        .. note:: ``redistribute`` is differentiable, which means user do not need to worry about
+            the backward formula of the redistribute operation.
+
+        .. note:: ``redistribute`` currently only supports redistributing DTensor on the same DeviceMesh,
+            Please file an issue if you need to redistribute DTensor to different DeviceMesh.
+        """
+        # NOTE: This redistribute API currently only supports out
+        # of place redistribution, i.e. it always create a new
+        # DTensor object and leave the original one unchanged.
+
+        # if device_mesh is not specified, use the current device_mesh
+        device_mesh = device_mesh or self.device_mesh
+        # raise error if new placements not specified
+        if placements is None:
+            raise RuntimeError("placements is needed for redistribute!")
+
+        placements = list(placements)
+        for i, placement in enumerate(placements):
+            if placement.is_partial():
+                raise RuntimeError(
+                    "Can not redistribute to Partial, redistributing to Partial is for internal use only!"
+                )
+            elif isinstance(placement, Shard) and placement.dim < 0:
+                # normalize shard dim to be positive
+                placements[i] = Shard(placement.dim + self.ndim)
+        placements = tuple(placements)
+
+        # pyre-fixme[16]: `Redistribute` has no attribute `apply`.
+        return Redistribute.apply(self, device_mesh, placements, async_op)
+
+    def full_tensor(
+        self, *, grad_placements: Optional[Sequence[Placement]] = None
+    ) -> torch.Tensor:
+        """
+        Return the full tensor of this DTensor. It will perform necessary collectives
+        to gather the local tensors from other ranks in its DeviceMesh and concatenate
+        them together. It's a syntatic sugar of the following code:
+
+        ``dtensor.redistribute(placements=[Replicate()] * mesh.ndim).to_local()``
+
+        Keyword args:
+            grad_placements (List[:class:`Placement`], optional): the placements describes
+                the future layout of any gradient layout of the full Tensor returned from this
+                function.
+                `full_tensor` converts DTensor to a full torch.Tensor and the returned torch.tensor
+                might not be used as the original replicated DTensor layout later in the code. This
+                argument is the hint that user can give to autograd in case the gradient
+                layout of the returned tensor does not match the original replicated DTensor layout.
+                If not specified, we will assume the gradient layout of the full tensor be replicated.
+
+        Returns:
+            A :class:`torch.Tensor` object that represents the full tensor of this DTensor.
+
+        .. note:: ``full_tensor`` is differentiable.
+        """
+
+        redist_res = self.redistribute(
+            placements=[Replicate()] * self.device_mesh.ndim, async_op=False
+        )
+        return _ToTorchTensor.apply(redist_res, grad_placements)
+
+    @property
+    def device_mesh(self) -> DeviceMesh:
+        """
+        The :class:`DeviceMesh` attribute that associates with this DTensor object.
+
+        .. note:: ``device_mesh`` is a read-only property, it can not be set.
+        """
+        return self._spec.mesh
+
+    @property
+    def placements(self) -> tuple[Placement, ...]:
+        """
+        The placements attribute of this DTensor that describes the layout of this
+        DTensor on the its DeviceMesh.
+
+        .. note:: ``placements`` is a read-only property, it can not be set.
+        """
+        return self._spec.placements
+
+    def __create_write_items__(self, fqn: str, object: Any):
+        from torch.distributed.checkpoint.planner_helpers import (
+            _create_write_items_for_dtensor,
+        )
+
+        if hasattr(self._local_tensor, "__create_write_items__"):
+            return self._local_tensor.__create_write_items__(fqn, object)  # type: ignore[attr-defined]
+        elif isinstance(self._local_tensor, torch.Tensor):
+            return [_create_write_items_for_dtensor(fqn, object)]
+        else:
+            raise RuntimeError("Unsupported tensor type!")
+
+    def __create_chunk_list__(self):
+        """
+        Return a list of ChunkStorageMetadata, which is a dataclass that describes the size/offset of the local shard/replica
+        on current rank. For DTensor, each rank will have a single local shard/replica, so the returned list usually only
+        has one element.
+
+        This dunder method is primariy used for distributed checkpoint purpose.
+
+        Returns:
+            A List[:class:`ChunkStorageMetadata`] object that represents the shard size/offset on the current rank.
+        """
+        from torch.distributed.checkpoint.planner_helpers import (
+            _create_chunk_from_dtensor,
+        )
+
+        if hasattr(self._local_tensor, "__create_chunk_list__"):
+            return self._local_tensor.__create_chunk_list__()  # type: ignore[attr-defined]
+        elif isinstance(self._local_tensor, torch.Tensor):
+            return [_create_chunk_from_dtensor(self)]
+        else:
+            raise RuntimeError("Unsupported tensor type!")
+
+    def __get_tensor_shard__(self, index):
+        if hasattr(self._local_tensor, "__get_tensor_shard__"):
+            return self._local_tensor.__get_tensor_shard__(index)  # type: ignore[attr-defined]
+        elif isinstance(self._local_tensor, torch.Tensor):
+            return self.to_local()
+        else:
+            raise RuntimeError("Unsupported tensor type!")
+
+
+def distribute_tensor(
+    tensor: torch.Tensor,
+    device_mesh: Optional[DeviceMesh] = None,
+    placements: Optional[Sequence[Placement]] = None,
+    *,
+    src_data_rank: Optional[int] = 0,
+) -> DTensor:
+    """
+    Distribute a leaf ``torch.Tensor`` (i.e. nn.Parameter/buffers) to the ``device_mesh`` according
+    to the ``placements`` specified. The rank of ``device_mesh`` and ``placements`` must be the
+    same. The ``tensor`` to distribute is the logical or "global" tensor, and the API would use
+    the ``tensor`` from first rank of the DeviceMesh dimension as the source of truth to preserve
+    the single-device semantic. If you want to construct a DTensor in the middle of the Autograd
+    computation, please use :meth:`DTensor.from_local` instead.
+
+    Args:
+        tensor (torch.Tensor): torch.Tensor to be distributed. Note that if you
+            want to shard a tensor on a dimension that is not evenly divisible by
+            the number of devices in that mesh dimension, we use ``torch.chunk``
+            semantic to shard the tensor and scatter the shards. The uneven sharding
+            behavior is experimental and subject to change.
+        device_mesh (:class:`DeviceMesh`, optional): DeviceMesh to distribute the
+            tensor, if not specified, must be called under a DeviceMesh context
+            manager, default: None
+        placements (List[:class:`Placement`], optional): the placements that
+            describes how to place the tensor on DeviceMesh, must have the same
+            number of elements as ``device_mesh.ndim``. If not specified, we will
+            by default replicate the tensor across the ``device_mesh`` from the
+            first rank of each dimension of the `device_mesh`.
+
+    Keyword args:
+        src_data_rank (int, optional): the rank of the source data for the logical/global tensor, it is
+            used by :meth:`distribute_tensor` to scatter/broadcast the shards/replicas to other ranks.
+            By default, we use ``group_rank=0`` on each DeviceMesh dimension as the source data to preserve
+            the single-device semantic. If passing ``None`` explicitly, :meth:`distribute_tensor` simply uses
+            its local data instead of trying to preserve the single-device semantic via scatter/broadcast.
+            Default: 0
+
+    Returns:
+        A :class:`DTensor` or ``XLAShardedTensor`` object.
+
+    .. note::
+        When initialize the DeviceMesh with the ``xla`` device_type, ``distribute_tensor``
+        return `XLAShardedTensor` instead. see `this issue `__
+        for more details. The XLA integration is experimental and subject to change.
+    """
+
+    torch._C._log_api_usage_once("torch.dtensor.distribute_tensor")
+
+    # get default device mesh if there's nothing specified
+    device_mesh = device_mesh or _mesh_resources.get_current_mesh()
+    device_type = device_mesh.device_type
+    if device_type == "xla":
+        try:
+            # call PyTorch/XLA SPMD for `xla` backend type device mesh.
+            # This returns XLAShardedTensor
+            from torch_xla.distributed.spmd import (  # type:ignore[import]
+                xla_distribute_tensor,
+            )
+
+            return xla_distribute_tensor(tensor, device_mesh, placements)  # type:ignore[return-value]
+        except ImportError as e:
+            msg = "To use DTensor API with xla, you must install the torch_xla package!"
+            raise ImportError(msg) from e
+
+    if not tensor.is_leaf:
+        raise RuntimeError(
+            "`distribute_tensor` should be used to distribute leaf tensors! but found non-leaf tensor!"
+        )
+
+    # convert tensor to the corresponding device type if it's not in that device type
+    if device_type != tensor.device.type and not tensor.is_meta:
+        tensor = tensor.to(device_type)
+
+    # set default placements to replicated if not specified
+    if placements is None:
+        placements = [Replicate() for _ in range(device_mesh.ndim)]
+
+    if len(placements) != device_mesh.ndim:
+        raise ValueError(
+            f"`placements` must have the same length as `device_mesh.ndim`! "
+            f"Found placements length: {len(placements)}, and device_mesh.ndim: {device_mesh.ndim}."
+        )
+    if isinstance(tensor, DTensor):
+        # if the tensor is already a DTensor, we need to check:
+        # 1. if the we can further shard this DTensor if the two device mesh belong to
+        #   the same parenet mesh and further sharding is possible.
+        # 2. check if device mesh and placements are the same
+        if tensor.device_mesh != device_mesh:
+            raise ValueError(
+                f"Cannot distribute a DTensor with device mesh {tensor.device_mesh} "
+                f"to a different device mesh {device_mesh}."
+            )
+        if tensor.placements != tuple(placements):
+            raise ValueError(
+                f"Cannot distribute a DTensor with placements {tensor.placements} "
+                f"to a different placements {placements}. do you want to call "
+                f"`redistribute` instead?"
+            )
+        return tensor
+
+    local_tensor = tensor.detach()
+
+    # TODO(xilun): address sharding order
+    # distribute the tensor according to the placements.
+    placements = list(placements)
+    for idx, placement in enumerate(placements):
+        if placement.is_shard():
+            placement = cast(Shard, placement)
+            if placement.dim < 0:
+                # normalize shard placement dim
+                placement = Shard(placement.dim + tensor.ndim)
+                placements[idx] = placement
+            local_tensor = placement._shard_tensor(
+                local_tensor, device_mesh, idx, src_data_rank
+            )
+        elif placement.is_replicate():
+            placement = cast(Replicate, placement)
+            local_tensor = placement._replicate_tensor(
+                local_tensor, device_mesh, idx, src_data_rank
+            )
+        else:
+            raise RuntimeError(
+                f"Trying to distribute tensor with unsupported placements {placement} on device mesh dimension {idx}!"
+            )
+    placements = tuple(placements)
+
+    assert local_tensor is not None, "distributing a tensor should not be None"
+    # detach the local tensor passed to DTensor since after the construction
+    # of DTensor, autograd would work on top of DTensor instead of local tensor
+    spec = DTensorSpec(
+        mesh=device_mesh,
+        placements=placements,
+        tensor_meta=TensorMeta(
+            shape=tensor.size(),
+            stride=tensor.stride(),
+            dtype=tensor.dtype,
+        ),
+    )
+    return DTensor(
+        local_tensor.requires_grad_(tensor.requires_grad),
+        spec,
+        requires_grad=tensor.requires_grad,
+    )
+
+
+@deprecated("Please use `distribute_tensor` with `src_data_rank=None` instead.")
+def _shard_tensor(
+    full_tensor: torch.Tensor,
+    placements: Sequence[Shard],
+    device_mesh: Optional[DeviceMesh] = None,
+) -> "DTensor":
+    """
+    Locally shards a full tensor based on indicated sharding arrangement, and
+    returns a DTensor containing the local shard.
+
+    .. warning:: This is a private API that is subject to change. It skips the
+        communication otherwise required by `distribute_tensor`. It is only
+        applicable to cases where all ranks have the same `full_tensor`. For
+        example, in distributed inference all ranks load from the same
+        checkpoint. This API will not check for data equality between ranks, it
+        is thus user's responsibility to ensure the `full_tensor` is the same
+        across ranks.
+
+    Args:
+        full_tensor (torch.Tensor): the full tensor to be sharded.
+        placements (Sequence[:class:`Shard`]): the placements that
+            describes how to place the local tensor on DeviceMesh.
+        device_mesh (:class:`DeviceMesh`, optional): DeviceMesh to place the
+            DTensor.  Must have same dimension as the number of placements.
+            If not specified, would be retrieve from current context.
+
+    Returns:
+        A :class:`DTensor` object with the shard as its local tensor.
+
+    Examples:
+        >>> # xdoctest: +SKIP("need world_size and rank")
+        >>> device_mesh = dist.init_device_mesh("cuda", (world_size,))
+        >>> full_tensor = torch.arange(world_size, device=f"cuda:{rank}")
+        >>> dtensor = _shard_tensor(full_tensor, [Shard(1)], device_mesh)
+    """
+    return distribute_tensor(full_tensor, device_mesh, placements, src_data_rank=None)
+
+
+def distribute_module(
+    module: nn.Module,
+    device_mesh: Optional[DeviceMesh] = None,
+    partition_fn: Optional[Callable[[str, nn.Module, DeviceMesh], None]] = None,
+    input_fn: Optional[Callable[[nn.Module, Any, DeviceMesh], None]] = None,
+    output_fn: Optional[Callable[[nn.Module, Any, DeviceMesh], None]] = None,
+) -> nn.Module:
+    """
+    This function expose three functions to control the parameters/inputs/outputs of the module:
+
+    1. To perform sharding on the module before runtime execution by specifying the
+    ``partition_fn`` (i.e. allow user to convert Module parameters to :class:`DTensor`
+    parameters according to the `partition_fn` specified).
+    2. To control the inputs or outputs of the module during runtime execution by
+    specifying the ``input_fn`` and ``output_fn``. (i.e. convert the input to
+    :class:`DTensor`, convert the output back to ``torch.Tensor``)
+
+    Args:
+        module (:class:`nn.Module`): user module to be partitioned.
+        device_mesh (:class:`DeviceMesh`): the device mesh to place the module.
+        partition_fn (Callable): the function to partition parameters (i.e. shard certain
+            parameters across the ``device_mesh``). If ``partition_fn`` is not specified,
+            by default we replicate all module parameters of ``module`` across the mesh.
+        input_fn (Callable): specify the input distribution, i.e. could control how the
+            input of the module is sharded. ``input_fn`` will be installed as a module
+            ``forward_pre_hook`` (pre forward hook).
+        output_fn (Callable): specify the output distribution, i.e. could control how the
+            output is sharded, or convert it back to torch.Tensor. ``output_fn`` will be
+            installed as a module ``forward_hook`` (post forward hook).
+
+    Returns:
+        A module that contains parameters/buffers that are all ``DTensor`` s.
+
+    .. note::
+        When initialize the DeviceMesh with the ``xla`` device_type, ``distribute_module``
+        return nn.Module with PyTorch/XLA SPMD annotated parameters. See
+        `this issue `__
+        for more details. The XLA integration is experimental and subject to change.
+
+    """
+
+    torch._C._log_api_usage_once("torch.dtensor.distribute_module")
+
+    already_distributed = getattr(module, "_distribute_module_applied", False)
+    if already_distributed:
+        raise RuntimeError(
+            "distribute_module should only be called once on a module, "
+            "but it has already been called on this module!"
+        )
+
+    device_mesh = device_mesh or _mesh_resources.get_current_mesh()
+    device_type = device_mesh.device_type
+    if device_type == "xla":
+        try:
+            # This function annotates all module parameters for auto-partitioning with
+            # PyTorch/XLA SPMD or explicitly partition to :class:`XLAShardedTensor` parameters
+            # according to the `partition_fn` specified.
+            from torch_xla.distributed.spmd import (  # type:ignore[import]
+                xla_distribute_module,
+            )
+
+            return xla_distribute_module(
+                module, device_mesh, partition_fn, input_fn, output_fn
+            )  # type:ignore[return-value]
+        except ImportError as e:
+            msg = "To use DTensor API with xla, you must install the torch_xla package!"
+            raise ImportError(msg) from e
+
+    def replicate_module_params_buffers(m: nn.Module, mesh: DeviceMesh) -> None:
+        # This function loop over the immediate module parameters and
+        # buffers, replicate all non DTensor params/buffers to DTensor
+        # parameters/buffers, if they have not been partitioned in the
+        # partition_fn, we can't easily use `module._apply` here
+        # because we don't know what happened inside partition_fn as
+        # user could do anything, i.e. install hooks, and we want to
+        # preserve those.
+        full_replicate = [Replicate()] * mesh.ndim
+        for key, param in m._parameters.items():
+            if param is not None and not isinstance(param, DTensor):
+                m.register_parameter(
+                    key,
+                    nn.Parameter(distribute_tensor(param.data, mesh, full_replicate)),
+                )
+        for key, buffer in m._buffers.items():
+            if buffer is not None and not isinstance(buffer, DTensor):
+                m._buffers[key] = distribute_tensor(buffer, mesh, full_replicate)
+
+    if partition_fn is None:
+        # if partition_fn not specified, we by default replicate
+        # all module params/buffers
+        for name, submod in module.named_modules():
+            replicate_module_params_buffers(submod, device_mesh)
+    else:
+        # apply partition_fun to submodules
+        for name, submod in module.named_modules():
+            partition_fn(name, submod, device_mesh)
+            replicate_module_params_buffers(submod, device_mesh)
+
+    # register input_fn as module forward pre hook
+    if input_fn is not None:
+        # check the input_fn signature
+        num_args = len(inspect.signature(input_fn).parameters)
+        if num_args == 2:
+            # input_fn only takes in inputs and device mesh
+            warnings.warn(
+                "Deprecating input_fn that takes two arguments (inputs, device_mesh), "
+                "please use input_fn that takes in (module, inputs, device_mesh) instead!",
+                FutureWarning,
+                stacklevel=2,
+            )
+            module.register_forward_pre_hook(
+                lambda _, inputs: input_fn(inputs, device_mesh)  # type: ignore[call-arg]
+            )
+        elif num_args == 3:
+            # input_fn takes in module, inputs, device mesh
+            module.register_forward_pre_hook(
+                lambda mod, inputs: input_fn(mod, inputs, device_mesh)
+            )
+        else:
+            raise ValueError(
+                f"input_fn should take in 3 arguments, but got {num_args} arguments!"
+            )
+    # register output_fn as module forward hook
+    if output_fn is not None:
+        num_args = len(inspect.signature(output_fn).parameters)
+        if num_args == 2:
+            # output_fn only takes in outputs and device mesh
+            warnings.warn(
+                "Deprecating output_fn that takes two arguments (inputs, device_mesh), "
+                "please use output_fn that takes in (module, inputs, device_mesh) instead!",
+                FutureWarning,
+                stacklevel=2,
+            )
+            module.register_forward_hook(
+                lambda mod, inputs, outputs: output_fn(outputs, device_mesh)  # type: ignore[call-arg]
+            )
+        elif num_args == 3:
+            module.register_forward_hook(
+                lambda mod, inputs, outputs: output_fn(mod, outputs, device_mesh)
+            )
+        else:
+            raise ValueError(
+                f"output_fn should take in 3 arguments, but got {num_args} arguments!"
+            )
+
+    module._distribute_module_applied = True  # type: ignore[assignment]
+    return module
+
+
+# Below are tensor factory function APIs, which are used to create a DTensor directly. We need
+# to make separate factory function APIs because tensor subclass could not override the tensor
+# factory methods, and we need user to call the factory functions with user intended device_mesh
+# and placements to create a proper DTensor.
+
+
+def _dtensor_init_helper(  # type: ignore[no-untyped-def]
+    init_op,
+    size: torch.Size,
+    device_mesh: Optional[DeviceMesh] = None,
+    placements: Optional[Sequence[Placement]] = None,
+    **kwargs,
+) -> DTensor:
+    # from torch.distributed._tensor.placement_types import DTensorSpec, TensorMeta
+
+    # if device_mesh is None, use the one from mesh resources
+    device_mesh = device_mesh or _mesh_resources.get_current_mesh()
+    kwargs["device"] = device_mesh.device_type
+
+    # set default placements to replicated if not specified
+    placements = placements or tuple(Replicate() for _ in range(device_mesh.ndim))
+
+    # check device_mesh againts placements
+    assert device_mesh.ndim == len(placements), (
+        "mesh dimension does not match the length of placements"
+    )
+
+    assert kwargs["layout"] == torch.strided, "layout value not supported!"
+    torch_stride = torch._prims_common.make_contiguous_strides_for(size)
+
+    # get local tensor shape
+    local_shape, _ = compute_local_shape_and_global_offset(
+        size, device_mesh, placements
+    )
+
+    # initialize the local tensor
+    if init_op == torch.full:
+        fill_value = kwargs.pop("fill_value", 0)
+        local_tensor = init_op(local_shape, fill_value, **kwargs)
+    elif init_op == torch.rand or init_op == torch.randn:
+        # this tensor meta is not used except `shape`
+        dtype = kwargs.get("dtype", torch.get_default_dtype())
+
+        tensor_meta = TensorMeta(size, (0,), dtype)
+        spec = DTensorSpec(device_mesh, tuple(placements), tensor_meta=tensor_meta)
+
+        if random.is_rng_supported_mesh(device_mesh) and not random._rng_tracker:
+            random._rng_tracker = random.OffsetBasedRNGTracker(device_mesh)
+
+        assert random._rng_tracker is not None
+        with random._rng_tracker._distribute_region(spec):
+            local_tensor = init_op(local_shape, **kwargs)
+    else:
+        local_tensor = init_op(local_shape, **kwargs)
+
+    spec = DTensorSpec(
+        device_mesh,
+        tuple(placements),
+        tensor_meta=TensorMeta(
+            size,
+            torch_stride,
+            local_tensor.dtype,
+        ),
+    )
+
+    return DTensor(
+        local_tensor,
+        spec,
+        requires_grad=kwargs["requires_grad"],
+    )
+
+
+def ones(  # type: ignore[no-untyped-def]
+    *size,
+    dtype: Optional[torch.dtype] = None,
+    layout: torch.layout = torch.strided,
+    requires_grad: bool = False,
+    device_mesh: Optional[DeviceMesh] = None,
+    placements: Optional[Sequence[Placement]] = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with the scalar value 1, with the shape defined
+    by the variable argument ``size``.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned DTensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.ones,
+        torch_size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
+
+
+def empty(  # type: ignore[no-untyped-def]
+    *size,
+    dtype: Optional[torch.dtype] = None,
+    layout: torch.layout = torch.strided,
+    requires_grad: bool = False,
+    device_mesh: Optional[DeviceMesh] = None,
+    placements: Optional[Sequence[Placement]] = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with uninitialized data. The shape of the :class:`DTensor`
+    is defined by the variable argument ``size``.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: empty(1,2,3..) or empty([1,2,3..]) or empty((1,2,3..))
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).\
+        layout (:class:`torch.layout`, optional): the desired layout of returned :class:`DTensor`.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.empty,
+        torch_size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
+
+
+def full(  # type: ignore[no-untyped-def]
+    size,
+    fill_value,
+    *,
+    dtype: Optional[torch.dtype] = None,
+    layout: torch.layout = torch.strided,
+    requires_grad: bool = False,
+    device_mesh: Optional[DeviceMesh] = None,
+    placements: Optional[Sequence[Placement]] = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with ``fill_value`` according to ``device_mesh`` and
+    ``placements``, with the shape defined by the argument ``size``.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))
+        fill_value(Scalar): the value to fill the output tensor with.
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned DTensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks.
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.full,
+        torch_size,
+        fill_value=fill_value,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
+
+
+def rand(  # type: ignore[no-untyped-def]
+    *size,
+    requires_grad: bool = False,
+    dtype: Optional[torch.dtype] = None,
+    layout: torch.layout = torch.strided,
+    device_mesh: Optional[DeviceMesh] = None,
+    placements: Optional[Sequence[Placement]] = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with random numbers from a uniform distribution
+    on the interval ``[0, 1)``. The shape of the tensor is defined by the variable
+    argument ``size``.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned DTensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks.
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.rand,
+        torch_size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
+
+
+def randn(  # type: ignore[no-untyped-def]
+    *size,
+    requires_grad: bool = False,
+    dtype: Optional[torch.dtype] = None,
+    layout: torch.layout = torch.strided,
+    device_mesh: Optional[DeviceMesh] = None,
+    placements: Optional[Sequence[Placement]] = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with random numbers from a normal distribution
+    with mean 0 and variance 1. The shape of the tensor is defined by the variable
+    argument ``size``.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned DTensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks.
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.randn,
+        torch_size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
+
+
+def zeros(  # type: ignore[no-untyped-def]
+    *size,
+    requires_grad: bool = False,
+    dtype: Optional[torch.dtype] = None,
+    layout: torch.layout = torch.strided,
+    device_mesh: Optional[DeviceMesh] = None,
+    placements: Optional[Sequence[Placement]] = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with the scalar value 0.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: zeros(1,2,3..) or zeros([1,2,3..]) or zeros((1,2,3..))
+    Keyword args:
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned :class:`DTensor`.
+            Default: ``torch.strided``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.zeros,
+        torch_size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_dtensor_spec.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_dtensor_spec.py
new file mode 100644
index 0000000000000000000000000000000000000000..360f1a0ea0168fb9b77b3f41d9d8457dbd2ee837
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_dtensor_spec.py
@@ -0,0 +1,276 @@
+from dataclasses import dataclass
+from typing import Any, cast, NamedTuple, Optional
+
+import torch
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor.placement_types import (
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+class TensorMeta(NamedTuple):
+    # simple named tuple to represent tensor metadata
+    # intentionally to stay simple only for sharding
+    # propagation purposes.
+    shape: torch.Size
+    stride: tuple[int, ...]
+    dtype: torch.dtype
+
+
+# used internally to propagate the placements
+@dataclass
+class DTensorSpec:
+    mesh: DeviceMesh
+    placements: tuple[Placement, ...]
+
+    # tensor meta will only be set during sharding propagation
+    tensor_meta: Optional[TensorMeta] = None
+
+    def __post_init__(self) -> None:
+        if not isinstance(self.placements, tuple):
+            self.placements = tuple(self.placements)
+        self._hash: Optional[int] = None
+
+    def __setattr__(self, attr: str, value: Any) -> None:
+        super().__setattr__(attr, value)
+        # Make sure to recompute the hash in case any of the hashed attributes
+        # change (though we do not expect `mesh` or `placements` to change)
+        if hasattr(self, "_hash") and attr in ("mesh", "placements", "tensor_meta"):
+            self._hash = None
+
+    def _hash_impl(self) -> int:
+        # hashing and equality check for DTensorSpec are used to cache the sharding
+        # propagation results. We only need to consider the mesh, placements, shape
+        # dtype and stride.
+        # Caveat: we need to keep this in mind and sync hash and eq if we add more
+        # fields to them.
+        if self.tensor_meta is not None:
+            return hash(
+                (
+                    self.mesh,
+                    self.placements,
+                    self.tensor_meta.shape,
+                    self.tensor_meta.stride,
+                    self.tensor_meta.dtype,
+                )
+            )
+        return hash((self.mesh, self.placements))
+
+    def __hash__(self) -> int:
+        # We lazily cache the spec to avoid recomputing the hash upon each
+        # use, where we make sure to update the hash when the `tensor_meta`
+        # changes by overriding `__setattr__`. This must be lazy so that Dynamo
+        # does not try to hash non-singleton `SymInt`s for the stride.
+        if self._hash is None:
+            self._hash = self._hash_impl()
+        return self._hash
+
+    def __eq__(self, other: object, /) -> bool:
+        if not (
+            isinstance(other, DTensorSpec)
+            and self.mesh == other.mesh
+            and self.placements == other.placements
+        ):
+            return False
+        if self.tensor_meta is None or other.tensor_meta is None:
+            return self.tensor_meta == other.tensor_meta
+
+        return (
+            self.tensor_meta.shape == other.tensor_meta.shape  # type: ignore[union-attr]
+            and self.tensor_meta.stride == other.tensor_meta.stride  # type: ignore[union-attr]
+            and self.tensor_meta.dtype == other.tensor_meta.dtype  # type: ignore[union-attr]
+        )
+
+    def __str__(self) -> str:
+        """
+        human readable representation of the DTensorSpec
+        """
+        if len(self.placements) == 1:
+            placement_str = str(self.placements[0])
+        else:
+            placement_str = str(self.placements)
+
+        if self.tensor_meta is not None:
+            tensor_shape = str(tuple(self.tensor_meta.shape))
+        else:
+            tensor_shape = "unknown shape"
+
+        return f"Spec({placement_str} on {tensor_shape})"
+
+    @property
+    def shape(self) -> torch.Size:
+        if self.tensor_meta is None:
+            raise ValueError("tensor_meta is not set")
+        return self.tensor_meta.shape
+
+    @property
+    def stride(self) -> tuple[int, ...]:
+        if self.tensor_meta is None:
+            raise ValueError("tensor_meta is not set")
+        return self.tensor_meta.stride
+
+    @property
+    def ndim(self) -> int:
+        if self.tensor_meta is None:
+            raise ValueError("tensor_meta is not set")
+        return len(self.tensor_meta.shape)
+
+    @property
+    def num_shards(self) -> int:
+        num_shards = 1
+        for i, placement in enumerate(self.placements):
+            if placement.is_shard():
+                num_shards *= self.mesh.size(i)
+        return num_shards
+
+    @property
+    def device_mesh(self) -> DeviceMesh:
+        # simple aliasing for the mesh field, make some
+        # checks that mixes DTensor/DTensorSpec easier
+        return self.mesh
+
+    @property
+    def dim_map(self) -> list[int]:
+        """
+        dim_map is a property we derive from `placements` of
+        the distributed tensor. It simply return a list of ints
+        where dim_map[i] denotes the sharding mapping to the mesh
+        dimension, and len(dim_map) == dist_tensor.ndim
+        dim_map[i] = -1: means tensor dim i replicate on mesh
+        dim_map[i] = j: means tensor dim i shard on mesh dim j
+
+        For example, we have a dist tensor that have the shape of
+        [18, 20, 30], and device_mesh([0, 1, 2, 3]), placements:
+        [Shard(1)], the dim_map of this placement would be:
+        [-1, 0, -1]. This representation is pretty helpful during
+        sharding propagation where we could know exactly each
+        tensor dimension is sharded or not.
+
+        Note that if placements contains `_Partial`, we have to
+        explicitly deal with it, so that when we create a DTensorSpec
+        with dim_map, we could properly record the pending sums.
+        """
+        # dims mapping of dist tensor sharding
+        # return size of tensor ndim, -1 represent replicate
+        # and int >=0 represent shard on that device mesh dim
+        r = [-1] * self.ndim
+        for i, placement in enumerate(self.placements):
+            if placement.is_shard():
+                shard_dim = cast(Shard, placement).dim
+                if r[shard_dim] > -1:
+                    raise ValueError(
+                        f"Tensor dim {shard_dim} is already sharded on mesh dim {r[shard_dim]},"
+                        " DTensor operator implementation does not support things like hybrid"
+                        " sharding strategies yet (i.e. [Shard(0), Shard(0)])"
+                    )
+                r[shard_dim] = i
+        return r
+
+    @property
+    def num_shards_map(self) -> list[int]:
+        """
+        dim_map is a property we derive from `placements` of
+        the distributed tensor. Unlike `dim_map`, `num_shards_map`
+        denotes how many shards each tensor dim has. Like `dim_map`:
+            len(num_shards_map) == dist_tensor.ndim
+            num_shards_map[i] = 1: means tensor dim i is not sharded
+            num_shards_map[i] = j: means tensor dim i has j shards in total
+
+        For example, we have a dist tensor of shape [18, 20, 30],
+        a device_mesh ([[0, 1, 2, 3], [4, 5, 6, 7]]), and placements
+        ([Shard(1), Shard(0)]), the num_shards_map of this distributed tensor
+        would be: [4, 2, 1].
+        """
+        r = [1] * self.ndim
+        for i, placement in enumerate(self.placements):
+            if placement.is_shard():
+                shard_dim = cast(Shard, placement).dim
+                r[shard_dim] *= self.mesh.size(i)
+
+        return r
+
+    @property
+    def sums(self) -> list[int]:
+        """
+        sums is a property we derive from `placements` of the
+        distributed tensor. It simply return a list of ints where
+        sums[i] denotes the pending sum (partial) on mesh dim i
+        """
+        return [
+            idx
+            for idx, placement in enumerate(self.placements)
+            if placement.is_partial()
+        ]
+
+    @classmethod
+    def from_dim_map(
+        cls,
+        mesh: DeviceMesh,
+        dim_map: list[int],
+        sums: list[int],
+        tensor_meta: Optional[TensorMeta] = None,
+    ) -> "DTensorSpec":
+        """
+        Construct a DTensorSpec from dim_map list and pending sum.
+
+        Args:
+            mesh (class:`DeviceMesh`): device mesh to be used in the DTensorSpec
+            dim_map (List[int]): a list of integer that represents sharding on each
+                tensor dimension, see `dim_map` property doc for details
+            sums (List[int]): a list of integer that represents the dist tensor have
+                pending sum on which device mesh dimension.
+            tensor meta (TensorMeta): DTensor metadata
+
+        Return:
+            a class:`DTensorSpec` object
+        """
+        # by default replicate on device mesh dims
+        placements: list[Placement] = [Replicate() for _ in range(mesh.ndim)]
+
+        # find all mesh dims that need pending reductions
+        for s in sums:
+            placements[s] = Partial()
+
+        for i, m in enumerate(dim_map):
+            if m >= 0:
+                placement = placements[m]
+                if placement.is_shard():
+                    placement = cast(Shard, placement)
+                    raise RuntimeError(
+                        f"DeviceMesh dimension cann't be mapped to two dimension of the same tensor: {i} and {placement.dim}"
+                    )
+                elif placement.is_partial():
+                    raise RuntimeError(
+                        f"DeviceMesh dimension {m} cannot be both shard and partial!"
+                    )
+                placements[m] = Shard(i)
+
+        return cls(mesh, tuple(placements), tensor_meta=tensor_meta)
+
+    def is_replicated(self) -> bool:
+        """
+        return True if the current DTensorSpec replicates on all mesh dims (devices)
+        """
+        return all(placement.is_replicate() for placement in self.placements)
+
+    def is_sharded(self) -> bool:
+        """
+        return True if the current DTensorSpec is sharded on any mesh dims (devices)
+        """
+        return any(placement.is_shard() for placement in self.placements)
+
+    def shallow_copy_with_tensor_meta(
+        self, tensor_meta: Optional[TensorMeta]
+    ) -> "DTensorSpec":
+        """
+        Shallow copy the DTensorSpec with a new tensor_meta.
+        """
+        assert tensor_meta is not None, "shallow copy with no tensor_meta!"
+        return DTensorSpec(
+            self.mesh,
+            self.placements,
+            tensor_meta=tensor_meta,
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_op_schema.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_op_schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c12f4ba033a04e08362171d5d4dae862fe924f3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_op_schema.py
@@ -0,0 +1,528 @@
+# mypy: allow-untyped-defs
+from collections.abc import Sequence
+from dataclasses import dataclass
+from functools import cached_property
+from typing import Any, Optional, Union
+
+import torch
+from torch._ops import OpOverload
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor.placement_types import Placement
+
+
+try:
+    from torch.utils._cxx_pytree import tree_leaves, tree_map_only, TreeSpec
+except ImportError:
+    from torch.utils._pytree import (  # type: ignore[no-redef, assignment]
+        tree_leaves,
+        tree_map_only,
+        TreeSpec,
+    )
+
+
+# Common type aliases
+ArgsType = tuple[object, ...]
+KwargsType = dict[str, object]
+
+PlacementList = list[Optional[Placement]]
+
+# ATen op schemas could have Tensor, Tuple[Tensor] and List[Tensor], so output type sould
+# be the same set of possibilities.
+OutputSpecType = Optional[Union[DTensorSpec, Sequence[Optional[DTensorSpec]]]]
+
+
+def _rebuild_tensor_from_dtensor_meta(arg) -> object:
+    """
+    This is used to propagate tensor metadata, must be under fake mode
+    """
+    assert arg.tensor_meta is not None, "DTensorSpec does not contain tensor_meta."
+    return torch.empty_strided(
+        arg.tensor_meta.shape,
+        arg.tensor_meta.stride,
+        dtype=arg.tensor_meta.dtype,
+    )
+
+
+def _is_inplace_op(op: OpOverload):
+    # simple analysis of function schema to determine
+    # if this is an inplace variant, it might not
+    # be entirely correct, but it's good enough for now.
+    return op._schema.name[-1] == "_"
+
+
+def _is_out_variant_op(op: OpOverload):
+    # simple analysis of function schema to determine
+    # if this is an out variant, it might not
+    # be entirely correct, but it's good enough for now.
+    return "out" in op._schema.overload_name
+
+
+def _pretty_print_spec(spec: object) -> str:
+    if spec is None:
+        return "None"
+    elif isinstance(spec, DTensorSpec):
+        return "".join([str(p) for p in spec.placements])
+    elif isinstance(spec, Sequence):
+        return "(" + ", ".join([_pretty_print_spec(s) for s in spec]) + ")"
+    else:
+        raise RuntimeError(f"Unknown spec type to print: spec={spec}")
+
+
+@dataclass
+class PlacementStrategy:
+    """
+    A placement strategy describes acceptable sharding placements of the output
+    and the tensor arguments of an operation.
+
+    note: when the op return value is a single DTensor object, output_specs is
+    DTensorSpec; when the return value is a tuple of Optional[DTensor],
+    output_specs is a tuple of Optional[DTensorSpec].
+    """
+
+    output_specs: Union[DTensorSpec, tuple[Optional[DTensorSpec], ...]]
+    input_specs: Optional[Sequence[DTensorSpec]] = None
+
+    # redistribute costs for this op placement strategy
+    # we need a nested list to record the cost for each
+    # operand of this operator, and for each operand of
+    # this operator it might have multiple placement strategies
+    redistribute_cost: Optional[list[list[float]]] = None
+
+    @cached_property
+    def output_spec(self) -> DTensorSpec:
+        """
+        This function requires that the strategy have exactly one DTensorSpec as the
+        output spec. If the output_specs is a tuple, we throw an exception.
+        """
+        if isinstance(self.output_specs, DTensorSpec):
+            return self.output_specs
+        else:
+            raise ValueError(
+                f"function output_spec expects a single DTensorSpec but got: {self.output_specs}"
+            )
+
+    @cached_property
+    def mesh(self):
+        if isinstance(self.output_specs, DTensorSpec):
+            return self.output_specs.mesh
+        elif isinstance(self.output_specs, tuple):
+            out_spec = self.output_specs[0]
+            assert isinstance(out_spec, DTensorSpec)
+            return out_spec.mesh
+        else:
+            raise ValueError(
+                f"function output_spec expects a single DTensorSpec or a tuple of DTensorSpec but got: {self.output_specs}"
+            )
+
+    def input_spec(self, index: int = 0) -> DTensorSpec:
+        assert self.input_specs is not None, "input_specs of PlacementStrategy is None!"
+        assert len(self.input_specs) > index, (
+            f"Invalid index {index} for input_specs of length "
+            f"{len(self.input_specs)}: {self.input_specs}"
+        )
+        return self.input_specs[index]
+
+    def __str__(self) -> str:
+        if self.input_specs is not None:
+            input_specs_str = f"{_pretty_print_spec(self.input_specs)} -> "
+        else:
+            input_specs_str = ""
+        output_spec_str = _pretty_print_spec(self.output_specs)
+        return f"{input_specs_str}{output_spec_str}"
+
+
+class StrategyType:
+    """
+    Base class type for op strategy, We have two StrategyType:
+        OpStrategy and TupleStrategy
+    """
+
+
+class OpStrategy(StrategyType):
+    """
+    OpStrategy that consists of a list of placement strategies associated with the op
+    """
+
+    def __init__(self, strategies: list[PlacementStrategy]) -> None:
+        super().__init__()
+        self.strategies: list[PlacementStrategy] = strategies
+
+    def __str__(self) -> str:
+        strategy_list_str = ", ".join([str(strategy) for strategy in self.strategies])
+        mesh_shape = self.mesh_shape
+        return f"[{strategy_list_str}] @ mesh: {mesh_shape}"
+
+    def max_num_shards(self) -> int:
+        """
+        Returns the max number of shards across all placement strategies
+        """
+        return max(strategy.output_spec.num_shards for strategy in self.strategies)
+
+    @property
+    def mesh(self):
+        return self.strategies[0].mesh
+
+    @property
+    def mesh_shape(self):
+        return self.strategies[0].mesh.shape
+
+    @property
+    def ndim(self):
+        return self.strategies[0].output_spec.ndim
+
+    @property
+    def shape(self):
+        return self.strategies[0].output_spec.shape
+
+
+class TupleStrategy(StrategyType):
+    """
+    TupleStrategy represents the output strategy of this op is a tuple
+    of strategy, i.e. If the output of this op is a tuple of tensors or list of tensors
+    with possibly different placement strategies, we should return a TupleStrategy that
+    contains a tuple of OpStrategy, where each child represents the sharding strategy
+    of "each element" of the tuple/list of tensors the op returns.
+
+    NOTE: if the output of the op is a List[Tensor] and they share the same placement
+    strategy, then we should return a single OpStrategy instead of a TupleStrategy
+    """
+
+    def __init__(self, childs: Sequence[StrategyType]) -> None:
+        super().__init__()
+        self.childs: Sequence[StrategyType] = childs
+
+    def child_mesh(self, index: int) -> DeviceMesh:
+        op_strategy = self.childs[index]
+        assert isinstance(op_strategy, OpStrategy)
+        return op_strategy.mesh
+
+    def __str__(self) -> str:
+        child_strategies_str = ", ".join(
+            [f"{str(strat)}" for idx, strat in enumerate(self.childs)]
+        )
+        return f"TupleStrategy({child_strategies_str})"
+
+
+@dataclass
+class RuntimeSchemaInfo:
+    """
+    RuntimeSchemaInfo stores the operator schema related information for runtime (eager)
+    execution. This is mainly used for two ways: 1. to generate hash for args to determine
+    whether to re-run sharding prop or not 2. to determine if we need pytree
+    """
+
+    # This static_argnum records static arg "starting index" for ops that have non-tensor
+    # args/kwargs which would affect sharding propagation results. All args starting from
+    # this index would be hashed to our sharding cache.
+    # Note that only a few ops need this information, e.g. view, transpose, var.dim, etc.
+    static_argnum: int = 100
+    # This static_kwargkey records static kwarg names which would affect sharding prop
+    static_kwargkey: Optional[list[str]] = None
+    # each op can decide if it wants to use pytree flatten/unflatten during operator
+    # eager execution, by default we don't need to do flatten/unflatten, only if the
+    # op indicate it needs to, this is to accelerate eager performance.
+    needs_pytree: bool = False
+
+
+@dataclass
+class OpSchema:
+    """
+    OpSchema is a data class that describes an operator input schemas, it includes
+    DTensorSpecs (instead of DTensor) and non-tensor args/kwargs (positional order
+    preserved). It is mainly used by the DTensor's dispatching logic to perform various
+    actions (i.e. sharding propagation, caching sharding decisions, redistribute, etc.)
+
+    NOTE: this should be used as a read only data class
+    TODO: make this a frozen dataclass
+
+    Args:
+        op: the operator overload we are intercepting
+        args_schema: contains args except that the DTensor args have been replaced
+            with its DTensorSpec or OpStrategy
+        kwargs_schema: contains kwargs except that the DTensor kwargs have been replaced
+            with its DTensorSpec or OpStrategy
+    """
+
+    op: OpOverload
+    args_schema: ArgsType
+    kwargs_schema: KwargsType
+
+    schema_info: Optional[RuntimeSchemaInfo] = None
+
+    @property
+    def args_spec(self) -> tuple[DTensorSpec, ...]:
+        """
+        args_spec: Tuple[DTensorSpec, ...]: contains a clean list of args spec list
+            with NO non-DTensor positional arguments (i.e. int/float/tuple, etc)
+            mainly used by sharding propagation to propagate the output spec
+        """
+        args = (
+            tree_leaves(self.args_schema)
+            if self.schema_info is not None and self.schema_info.needs_pytree
+            else self.args_schema
+        )
+        return tuple(item for item in args if isinstance(item, DTensorSpec))
+
+    @property
+    def args_strategy(self) -> tuple[OpStrategy, ...]:
+        # filter out non-relevant values from args schema to get a clean OpStrategy list
+        # separate with args_spec for the ease of type annotation
+        # TODO: see if we should merge this with args_spec
+        args = (
+            tree_leaves(self.args_schema)
+            if self.schema_info is not None and self.schema_info.needs_pytree
+            else self.args_schema
+        )
+        return tuple(item for item in args if isinstance(item, OpStrategy))
+
+    def __repr__(self) -> str:
+        args_schema = ", ".join([str(arg_schema) for arg_schema in self.args_schema])
+        return (
+            f"OpSchema(op={self.op},"
+            f" args_schema=({args_schema}),"
+            f" kwargs_schema={self.kwargs_schema})"
+        )
+
+    def __str__(self) -> str:
+        args_schema: list[str] = []
+        mesh_shape = None
+        for arg in self.args_schema:
+            if isinstance(arg, DTensorSpec):
+                args_schema.append(str(arg))
+                mesh_shape = arg.mesh.shape
+            elif isinstance(arg, OpStrategy):
+                assert len(arg.strategies) == 1
+                args_schema.append(_pretty_print_spec(arg.strategies[0].output_specs))
+                mesh_shape = arg.mesh_shape
+            elif isinstance(arg, TupleStrategy):
+                first_op_strtgy = arg.childs[0]
+                assert isinstance(first_op_strtgy, OpStrategy)
+                mesh_shape = first_op_strtgy.mesh_shape
+                args_schema.append(str(arg))
+            else:
+                args_schema.append(str(arg))
+        return f"Op(op={self.op}, args_schema={', '.join(args_schema)} @ mesh: {mesh_shape})"
+
+    def __post_init__(self) -> None:
+        has_symints = False
+        for a in self.args_schema:
+            if isinstance(a, DTensorSpec) and a.tensor_meta is not None:
+                if any(isinstance(s, torch.SymInt) for s in a.tensor_meta.shape):
+                    has_symints = True
+                    break
+        self.has_symints = has_symints
+
+    def arg_type_tensor_or_tensor_list_like(self, arg_idx: int) -> bool:
+        arg = self.args_schema[arg_idx]
+        is_tensor = isinstance(arg, DTensorSpec)
+        if is_tensor:
+            return True
+
+        if not isinstance(arg, list):
+            return False
+
+        return all(isinstance(e, DTensorSpec) or e is None for e in arg)
+
+    def return_type_tuple_tensor_like(self) -> bool:
+        # all dispatch ops could only return Tuple[Tensor] or have None/ints/floats
+        # in the tuple, but the first element must be a Tensor, so this check is enough
+        return_types = self.op._schema.returns
+        return len(return_types) > 1 and isinstance(
+            return_types[0].type, torch.TensorType
+        )
+
+    def return_type_tensor(self) -> bool:
+        return_types = self.op._schema.returns
+        # all dispatch ops only return Tensor or Tuple[Tensor] for tensor like
+        # return types, so this check is enough for tensor like types
+        return isinstance(return_types[0].type, torch.TensorType)
+
+    def get_mesh_from_args(self, validate: bool = True) -> DeviceMesh:
+        """
+        This util can be used to get a mesh from the OpSchema that contains multiple
+        DTensors as arguments. When `validate` is True, it will try to validate that all the
+        arguments have the same mesh to avoid unexpected cross mesh errors.
+
+        NOTE: this util currently does not handle TupleStrategy when `validate=True`,
+        this is because for TupleStrategy there could be different types of checks, i.e.:
+            - for stack and cat like op, we need to check within a TupleStrategy is every
+              input is on the same mesh
+            - for foreach like ops we need to check "zipped" inputs are on the same mesh
+              for each index.
+        """
+        first_arg = self.args_schema[0]
+        if isinstance(first_arg, (DTensorSpec, OpStrategy)):
+            mesh = first_arg.mesh
+        elif isinstance(first_arg, (list, tuple, TupleStrategy)):
+            first_elem = (
+                first_arg.childs[0]
+                if isinstance(first_arg, TupleStrategy)
+                else first_arg[0]
+            )
+            assert isinstance(first_elem, (DTensorSpec, OpStrategy))
+            mesh = first_elem.mesh
+        else:
+            raise ValueError(f"Cannot find device mesh from args for op : {self.op}.")
+
+        if validate:
+            for arg in self.args_schema[1:]:
+                if isinstance(arg, (DTensorSpec, OpStrategy)) and arg.mesh != mesh:
+                    raise RuntimeError(
+                        f"DTensor does not support cross-mesh operation on {self.op}! "
+                        f"Got meshes: {mesh} {arg.mesh}. "
+                        f"Please make sure all the arguments have the same DeviceMesh."
+                    )
+
+        return mesh
+
+    def __hash__(self) -> int:
+        # Only hash args and kwargs that op indicates to hash
+        if not self.schema_info:
+            static_argnum = len(self.args_schema)
+            static_kwargkey = None
+        else:
+            static_argnum = self.schema_info.static_argnum
+            static_kwargkey = self.schema_info.static_kwargkey
+
+        args_to_hash = tuple(
+            tuple(e) if isinstance(e, list) else e
+            for i, e in enumerate(self.args_schema)
+            if self.arg_type_tensor_or_tensor_list_like(i) or i >= static_argnum
+        )
+        if static_kwargkey is not None:
+            kwargs_to_hash = tuple(
+                self.kwargs_schema.get(k, None) for k in static_kwargkey
+            )
+            return hash((self.op, args_to_hash, kwargs_to_hash))
+        else:
+            return hash((self.op, args_to_hash))
+
+    def __eq__(self, other: object) -> bool:
+        # early return checks
+        if not isinstance(other, OpSchema):
+            return False
+
+        if self.op != other.op:
+            return False
+
+        if len(self.args_schema) != len(other.args_schema):
+            return False
+
+        # compare each element and early return if any of them is different
+        if not self.schema_info:
+            static_argnum = len(self.args_schema)
+            static_kwargkey = None
+        else:
+            static_argnum = self.schema_info.static_argnum
+            static_kwargkey = self.schema_info.static_kwargkey
+
+        for i, (self_arg, other_arg) in enumerate(
+            zip(self.args_schema, other.args_schema)
+        ):
+            if isinstance(self_arg, DTensorSpec) and self_arg != other_arg:
+                return False
+            elif i >= static_argnum and self_arg != other_arg:
+                return False
+
+        # check kwarg equality when there's a static kwarg key
+        if static_kwargkey:
+            for key in static_kwargkey:
+                if self.kwargs_schema.get(key, None) != other.kwargs_schema.get(
+                    key, None
+                ):
+                    return False
+
+        return True
+
+    def gen_fake_args(self) -> ArgsType:
+        """
+        gen_fake_args: generate fake args for the operator, this is mainly used
+            by sharding propagation rules to generate fake args for the operator
+            to run the local tensor operator and get the output spec.
+        """
+        return tree_map_only(
+            DTensorSpec, _rebuild_tensor_from_dtensor_meta, self.args_schema
+        )
+
+    def gen_fake_kwargs(self) -> KwargsType:
+        """
+        gen_fake_kwargs: generate fake kwargs for the operator, this is mainly used
+            by sharding propagation rules to generate fake kwargs for the operator
+            to run the local tensor operator and get the output spec.
+        """
+        return tree_map_only(
+            DTensorSpec, _rebuild_tensor_from_dtensor_meta, self.kwargs_schema
+        )
+
+    def _inplace_rewrap_schema_suggestion(self, origin_schema: "OpSchema") -> None:
+        suggestion_args_spec = self.args_spec
+        new_arg_schema: list[object] = []
+        idx_of_args_spec = 0
+        if (
+            origin_schema.schema_info is not None
+            and origin_schema.schema_info.needs_pytree
+        ):
+            args_schema: Sequence[Any] = tree_leaves(origin_schema.args_schema)
+        else:
+            args_schema = origin_schema.args_schema
+        for arg in args_schema:
+            if isinstance(arg, DTensorSpec):
+                new_arg_schema.append(suggestion_args_spec[idx_of_args_spec])
+                idx_of_args_spec += 1
+            else:
+                new_arg_schema.append(arg)
+        self.args_schema = tuple(new_arg_schema)
+        self.kwargs_schema = origin_schema.kwargs_schema
+
+
+@dataclass
+class OutputSharding:
+    """
+    OutputSharding is a data class that is used by the sharding propagation,
+    it could set the output_spec upon successful propagation. If needs_redistribute
+    is set to True, a redistribute_schema would be returned together to indicate
+    the input arguments needs to be redistributed before the op execution.
+
+    NOTE: the redistribute_schema generated by sharding propagation should be
+    exactly the same as the operator OpSchema, except the DTensorSpecs
+    """
+
+    output_spec: OutputSpecType
+    redistribute_schema: Optional[OpSchema] = None
+    needs_redistribute: bool = False
+
+    @cached_property
+    def mesh(self):
+        if isinstance(self.output_spec, DTensorSpec):
+            return self.output_spec.mesh
+        elif isinstance(self.output_spec, tuple):
+            out_spec = self.output_spec[0]
+            if isinstance(out_spec, DTensorSpec):
+                return out_spec.mesh
+            else:
+                raise ValueError(f"Unknown output spec type: {type(out_spec)}")
+        else:
+            raise ValueError(f"Unknown output spec type: {type(self.output_spec)}")
+
+
+@dataclass
+class OpInfo:
+    """
+    All Runtime Op execution info are packed here
+    """
+
+    # The first compute device mesh recorded from args
+    # NOTE: one op could have multiple meshes from its args. We just record the first
+    # mesh here to check if current rank should participate in computation or not.
+    compute_mesh: DeviceMesh
+
+    # compete runtime operator infos
+    schema: OpSchema
+    flat_args_schema: list[object]
+    local_args: Sequence[object]
+    local_kwargs: dict[str, object]
+    args_tree_spec: Optional[TreeSpec] = None
+
+    # the output sharding info
+    output_sharding: Optional[OutputSharding] = None
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dec4665b1c8b957daa4a5f5d15d988ce00cdc79d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from ._conv_ops import *  # noqa: F403
+from ._embedding_ops import *  # noqa: F403
+from ._experimental_ops import *  # noqa: F403
+from ._math_ops import *  # noqa: F403
+from ._matrix_ops import *  # noqa: F403
+from ._pointwise_ops import *  # noqa: F403
+from ._random_ops import *  # noqa: F403
+from ._tensor_ops import *  # noqa: F403
+from ._view_ops import *  # noqa: F403
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_common_rules.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_common_rules.py
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index 0000000000000000000000000000000000000000..6a5b472685e6c336cbd5065d0b4047a7d6c89c3e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_common_rules.py
@@ -0,0 +1,289 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import string
+from typing import cast, Optional
+
+import torch
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor._op_schema import (
+    _is_inplace_op,
+    _is_out_variant_op,
+    OpSchema,
+    OutputSharding,
+)
+from torch.distributed.tensor._ops.utils import prod
+from torch.distributed.tensor._utils import compute_local_shape_and_global_offset
+
+
+def _replace_char_in_str(string: str, new_char: str, idx: int) -> str:
+    return string[:idx] + new_char + string[idx + 1 :]
+
+
+def _gen_reshard_suggestions(
+    op_schema: OpSchema,
+    input_dims: list[str],
+    input_specs: tuple[DTensorSpec, ...],
+    dim_to_sharding: dict[str, int],
+    pending_sum: list[int],
+) -> OutputSharding:
+    suggested_arg_specs: list[DTensorSpec] = []
+    for input_dim, input_spec in zip(input_dims, input_specs):
+        dim_map = [dim_to_sharding[dim] for dim in input_dim]
+        suggested_arg_specs.append(
+            DTensorSpec.from_dim_map(
+                mesh=input_spec.mesh,
+                dim_map=dim_map,
+                sums=pending_sum,
+                tensor_meta=input_spec.tensor_meta,
+            )
+        )
+    suggested_schema = OpSchema(op_schema.op, tuple(suggested_arg_specs), {})
+    suggested_schema._inplace_rewrap_schema_suggestion(op_schema)
+    return OutputSharding(
+        None,
+        redistribute_schema=suggested_schema,
+    )
+
+
+def einop_rule(
+    equation: str,
+    op_schema: OpSchema,
+    *,
+    linearity: bool = False,
+    enforce_sharding: Optional[dict[str, int]] = None,
+) -> OutputSharding:
+    """
+    Propagate the sharding of inputs to output for ops whose data moves according to einsum notation.
+
+    This is mostly borrowed from @zdevito's sharding simulator. Examples:
+        mk,kn->mn - einsum
+        ij,ij->ij - addition
+        ij,j->ij - broadcasted addition
+        ij->i - reduction
+    Other ops could use this propagation algorithm when applied, note
+    that einsum propagation only deal with list of specs (DTensor specs)
+    as it only works on list of tensors!
+
+    linearity in einop_rule means that the calling op `f` follows this rule:
+        f(a + b) = f(a) + f(b)
+
+    In this case we can propagate the partial sum, note that linearity in einop
+    only applies to partial sum, not other operations like min/max (which are
+    associative but not linear).
+    """
+    # parse einop equation and extract arg specs
+    inputs, outputs = equation.split("->")
+    input_dims, output_dims = inputs.split(","), outputs.split(",")
+    input_specs = op_schema.args_spec
+    # NOTE: only support single output unless needed in future
+    output_dim = output_dims[0]
+
+    dim_to_sharding: dict[str, int] = {}
+    dim_to_size: dict[str, int] = {}
+    # record pending sum, key is mesh dimension, value is pending sum
+    # counter across input specs
+    pending_sums_counter: dict[int, int] = {}
+    seen_shardings: dict[int, str] = {}
+    needs_reshard = False
+
+    def merge_sharding(dim: str, a: int, b: int) -> int:
+        # merge the sharding of inputs if it's able to merge, i.e. we can merge
+        # replicate and shard to shard, but this will trigger an reshard operation
+        if a != b:
+            if a == -1 or b == -1:
+                # reshard the replicate to match the sharded one
+                nonlocal needs_reshard
+                needs_reshard = True
+                return a if a != -1 else b
+            else:
+                # TODO: further merge the sharding properly (i.e. reshard one input to replicate)
+                raise RuntimeError(
+                    f"{equation}: dim {dim} sharded two different ways: {a} and {b}"
+                )
+        else:
+            return a
+
+    for input_dim, input_spec in zip(input_dims, input_specs):
+        # deal with partial sums
+        input_sums = input_spec.sums
+        for sum_dim in input_sums:
+            if sum_dim not in pending_sums_counter:
+                seen_shardings[sum_dim] = "+"
+            # update pending sum counter for pending sum mesh
+            # dimension with the occurrence from each input
+            pending_sums_counter[sum_dim] = pending_sums_counter.get(sum_dim, 0) + 1
+
+        for idx, (dim, mesh_dim) in enumerate(zip(input_dim, input_spec.dim_map)):
+            if enforce_sharding and dim in enforce_sharding:
+                if enforce_sharding[dim] != mesh_dim:
+                    needs_reshard = True
+                dim_to_sharding[dim] = enforce_sharding[dim]
+                dim_to_size[dim] = input_spec.shape[idx]
+            elif dim not in dim_to_sharding:
+                dim_to_sharding[dim] = mesh_dim
+                dim_to_size[dim] = input_spec.shape[idx]
+            else:
+                dim_to_sharding[dim] = merge_sharding(
+                    dim, dim_to_sharding[dim], mesh_dim
+                )
+                assert dim_to_size[dim] == input_spec.shape[idx]
+
+            # after merging sharding, we check if there're multiple
+            # sharding on the same mesh dim.
+            merged_sharding_for_dim = dim_to_sharding[dim]
+            if merged_sharding_for_dim != -1:
+                if (
+                    merged_sharding_for_dim in seen_shardings
+                    and dim != seen_shardings[merged_sharding_for_dim]
+                ):
+                    needs_reshard = True
+                    seen_shardings[merged_sharding_for_dim] += dim
+                else:
+                    seen_shardings[merged_sharding_for_dim] = dim
+
+    if pending_sums_counter and not linearity:
+        # return reshard suggestion with no pending sum, because we already properly
+        # merge the sharding, this reshard suggestion is legit to use
+        return _gen_reshard_suggestions(
+            op_schema, input_dims, input_specs, dim_to_sharding, []
+        )
+    else:
+        # It's a op that support linearity, but not all input arguments are partial
+        # we fail the sharding propagation with suggestion to make all inputs be
+        # partial on the corresponding mesh dim (all inputs should be partial for
+        # the mesh dims in order to execute locally and delay the sum reduction)
+        for value in pending_sums_counter.values():
+            if value != len(input_specs):
+                needs_reshard = True
+
+    for mesh_dim, dims in seen_shardings.items():
+        if len(dims) > 1:
+            # we found different input dims are being sharded on the same mesh dim
+            # in order to perform local op computation, we need to reshard inputs
+            # base on some simple heuristics, now we simply pick the one with least comm
+            # volume. (i.e. the input with least size)
+            # TODO: consider a more advanced heuristic to pick the best sharding
+            costs = []
+            for d in dims:
+                cost = 0
+                for input_dim, input_spec in zip(input_dims, input_specs):
+                    if (
+                        d in input_dim
+                        and input_spec.dim_map[input_dim.index(d)] == mesh_dim
+                    ):
+                        assert input_spec.tensor_meta is not None
+                        global_shape = input_spec.tensor_meta.shape
+                        local_shape, _ = compute_local_shape_and_global_offset(
+                            global_shape, input_spec.mesh, input_spec.placements
+                        )
+                        cost += prod(local_shape) * input_spec.mesh.size(mesh_dim)
+                costs.append(cost)
+            d_to_keep_sharding = dims[costs.index(max(costs))]
+            for d in dims:
+                # update dim_to_sharding to keep the sharding of the dim with
+                # highest comm and make the rest of the dims to replicate
+                if d != d_to_keep_sharding:
+                    dim_to_sharding[d] = -1
+
+    pending_sums = list(pending_sums_counter.keys())
+    if needs_reshard:
+        return _gen_reshard_suggestions(
+            op_schema, input_dims, input_specs, dim_to_sharding, pending_sums
+        )
+
+    # generate output pending sum if a dim is sharded, and it appears in input
+    # but not output
+    for dim, shard_on_mesh in dim_to_sharding.items():
+        if dim not in output_dims[0] and shard_on_mesh != -1:
+            pending_sums.append(shard_on_mesh)
+
+    # if no need to reshard, we directly generate the output sharding
+    output_dim_map = []
+    output_shape = []
+    for dim in output_dim:
+        if dim == "1":
+            # find output dim that is a singleton dimension, mark sharding and shape
+            output_dim_map.append(-1)
+            output_shape.append(1)
+        else:
+            output_dim_map.append(dim_to_sharding[dim])
+            output_shape.append(dim_to_size[dim])
+
+    # XXX: since we still need to have intermediate shape calculation, we need
+    # to pass in the shape here. We should remove this once sharding decomp works
+    # for ops like addmm
+    assert input_specs[0].tensor_meta is not None
+    tensor_meta = TensorMeta(
+        torch.Size(output_shape),
+        input_specs[0].tensor_meta.stride,
+        input_specs[0].tensor_meta.dtype,
+    )
+    return OutputSharding(
+        DTensorSpec.from_dim_map(
+            input_specs[0].mesh,
+            output_dim_map,
+            pending_sums,
+            tensor_meta=tensor_meta,
+        )
+    )
+
+
+def pointwise_rule(op_schema: OpSchema, linearity: bool = False) -> OutputSharding:
+    """
+    Propagate the sharding for pointwise operations.
+
+    Examples:
+        ij,ij->ij - addition/mul
+        ij,j->ij - broadcasted addition
+    """
+    alphabet = string.ascii_lowercase
+    # find the max_dim first in case we need to broadcasting
+    input_specs = op_schema.args_spec
+    max_dim = max(input.ndim for input in input_specs)
+    dimchars = []
+    singleton_counter: list[int] = [0] * max_dim
+    for input in input_specs:
+        start_dim = max_dim - input.ndim
+        p = alphabet[start_dim:max_dim]
+        # handle the "broadcasting to a common shape case"
+        # see https://pytorch.org/docs/stable/notes/broadcasting.html
+        # If any of the dimensions is singleton dimension (i.e. 1).
+        # we mark the dim char as a special "1" to distinguish with
+        # the non-singleton dimension, so that sharding propagation
+        # should just ignore the singleton dimension.
+        if len(input_specs) > 1:
+            for i in range(max_dim):
+                if i < start_dim:
+                    # treat the leading miss dim chars as singleton
+                    singleton_counter[i] += 1
+                elif input.shape[i - start_dim] == 1:
+                    # mark singleton dim char as a special "1" in einop rule
+                    singleton_counter[i] += 1
+                    p = _replace_char_in_str(p, "1", (i - start_dim))
+
+        dimchars.append(p)
+    out_dimchars = alphabet[:max_dim]
+    # check if we replace the all inputs dim char with singleton dimension,
+    # if we replace all inputs, we also need to replace the output dimension.
+    for output_dim_idx in range(len(out_dimchars)):
+        out_dimchar = out_dimchars[output_dim_idx]
+        if singleton_counter[output_dim_idx] == len(input_specs):
+            out_dimchars = _replace_char_in_str(out_dimchars, "1", output_dim_idx)
+
+    fmt = f"{','.join(p for p in dimchars)}->{out_dimchars}"
+
+    enforce_sharding: dict[str, int] = {}
+    if _is_inplace_op(op_schema.op):
+        # inplace op should keep the input sharding it writes to
+        for out_dimchar, mesh_dim in zip(out_dimchars, input_specs[0].dim_map):
+            enforce_sharding[out_dimchar] = mesh_dim
+    elif _is_out_variant_op(op_schema.op):
+        out_spec = cast(DTensorSpec, op_schema.kwargs_schema["out"])
+        for out_dimchar, mesh_dim in zip(out_dimchars, out_spec.dim_map):
+            enforce_sharding[out_dimchar] = mesh_dim
+
+    return einop_rule(
+        fmt,
+        op_schema,
+        linearity=linearity,
+        enforce_sharding=enforce_sharding,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_conv_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_conv_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..2198986d50c5730c1a8a2ce17d9b6acb8935aec2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_conv_ops.py
@@ -0,0 +1,112 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+# implement matrix related ops for distributed tensor
+
+import torch
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor._op_schema import OpSchema, OutputSharding
+from torch.distributed.tensor._ops.utils import register_prop_rule
+
+
+aten = torch.ops.aten
+
+
+@register_prop_rule(aten.convolution.default)
+def convolution_rules(op_schema: OpSchema) -> OutputSharding:
+    (
+        input_spec,
+        weight_spec,
+        bias_spec,
+        stride,
+        padding,
+        dilation,
+        _transposed,
+        _output_padding,
+        _groups,
+    ) = op_schema.args_schema
+
+    assert isinstance(input_spec, DTensorSpec)
+    assert isinstance(weight_spec, DTensorSpec)
+    assert isinstance(bias_spec, DTensorSpec)
+    assert input_spec.tensor_meta is not None
+    assert weight_spec.tensor_meta is not None
+    in_shape = input_spec.tensor_meta.shape
+    weight_shape = weight_spec.tensor_meta.shape
+    assert isinstance(stride, list)
+    assert isinstance(padding, list)
+    assert isinstance(dilation, list)
+    assert isinstance(weight_shape, torch.Size)
+    N, H_in, W_in = in_shape[0], in_shape[2], in_shape[3]
+    C_out = weight_shape[0]
+    H_out = (H_in + 2 * padding[0] - dilation[0] * (weight_shape[2] - 1) - 1) // stride[
+        0
+    ] + 1
+    W_out = (W_in + 2 * padding[1] - dilation[1] * (weight_shape[3] - 1) - 1) // stride[
+        1
+    ] + 1
+    output_shape = [N, C_out, H_out, W_out]
+    output_stride = (C_out * H_out * W_out, H_out * W_out, W_out, 1)
+    output_dim_map = input_spec.dim_map
+    pending_sums = input_spec.sums
+
+    tensor_meta = TensorMeta(
+        torch.Size(output_shape),
+        output_stride,
+        input_spec.tensor_meta.dtype,
+    )
+    return OutputSharding(
+        DTensorSpec.from_dim_map(
+            input_spec.mesh,
+            output_dim_map,
+            pending_sums,
+            tensor_meta=tensor_meta,
+        )
+    )
+
+
+@register_prop_rule(aten.convolution_backward.default)
+def convolution_backward_rules(op_schema: OpSchema) -> OutputSharding:
+    input_spec = op_schema.args_schema[0]
+    (
+        grad_output_spec,
+        input_spec,
+        weight_spec,
+        bias_shape_opt,
+        _stride,
+        _padding,
+        _dilation,
+        _transposed,
+        _output_padding,
+        _groups,
+        _output_mask,
+    ) = op_schema.args_schema
+
+    assert isinstance(grad_output_spec, DTensorSpec)
+    assert isinstance(input_spec, DTensorSpec)
+    assert isinstance(weight_spec, DTensorSpec)
+    assert isinstance(bias_shape_opt, list)
+    assert input_spec.tensor_meta is not None
+    weight_tensor_meta = weight_spec.tensor_meta
+    bias_tensor_meta = TensorMeta(
+        torch.Size(bias_shape_opt),
+        (1,),
+        input_spec.tensor_meta.dtype,
+    )
+
+    grad_input_spec = input_spec
+    grad_weight_spec = DTensorSpec.from_dim_map(
+        input_spec.mesh,
+        [-1, -1, -1, -1],
+        [0],
+        tensor_meta=weight_tensor_meta,
+    )
+    grad_bias_spec = DTensorSpec.from_dim_map(
+        input_spec.mesh,
+        [-1],
+        [0],
+        tensor_meta=bias_tensor_meta,
+    )
+    # TODO: actually the output_mask is not respected here, we should
+    # set the corresponding spec to `None` if the output_mask is not `False`
+    # for a certain output Tensor. This also applies to the conv handler
+    # in torch/distributed/tensor/_tp_conv.py
+    return OutputSharding([grad_input_spec, grad_weight_spec, grad_bias_spec])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_einsum_strategy.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_einsum_strategy.py
new file mode 100644
index 0000000000000000000000000000000000000000..5953721d219c86825901f4f0226bdc51ccdee7bf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_einsum_strategy.py
@@ -0,0 +1,173 @@
+import itertools
+from dataclasses import dataclass
+
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor._op_schema import OpStrategy, PlacementStrategy
+from torch.distributed.tensor.placement_types import (
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+@dataclass
+class EinsumDims:
+    contracting_dims: list[str]
+    batch_dims: list[str]
+    lhs_out_only_dims: list[str]
+    rhs_out_only_dims: list[str]
+
+    @classmethod
+    def parse_equation(cls, equation: str) -> tuple[list[str], str]:
+        # parse einop equation and extract arg specs
+        """
+        Parse the einsum equation str to input dim chars and output dim char
+        """
+        inputs, outputs = equation.split("->")
+        input_dims, output_dims = inputs.split(","), outputs.split(",")
+
+        # NOTE: only support at most two inputs, and single output
+        # extend to support more inputs if needed in future
+        assert len(input_dims) <= 2, "Only support at most two inputs"
+        assert len(output_dims) == 1, "Only support single output"
+        output_dim = output_dims[0]
+        return input_dims, output_dim
+
+    @classmethod
+    def parse_dims(cls, input_dims: list[str], output_dim: str) -> "EinsumDims":
+        """
+        Parse the dims and extract the contracting, batch, and free dimensions
+        for the left and right hand sides.
+        """
+        dim_char_set: set[str] = set()
+        for input_dim in input_dims:
+            dim_char_set.update(input_dim)
+
+        # get a determinisitc order of all dim chars
+        all_dim_chars = sorted(dim_char_set)
+
+        # parse input and output dimensions
+        lhs_out_only_dims, rhs_out_only_dims = [], []
+        batch_dims, contracting_dims = [], []
+
+        for dim_char in all_dim_chars:
+            if dim_char not in output_dim:
+                contracting_dims.append(dim_char)
+            else:
+                is_batch_dim = True
+                for input_dim in input_dims:
+                    is_batch_dim = is_batch_dim and dim_char in input_dim
+
+                if is_batch_dim:
+                    batch_dims.append(dim_char)
+                else:
+                    assert len(input_dims) == 2, (
+                        "free dimension only supported for two inputs!"
+                    )
+                    lhs, rhs = input_dims
+                    if dim_char in lhs:
+                        lhs_out_only_dims.append(dim_char)
+                    elif dim_char in rhs:
+                        rhs_out_only_dims.append(dim_char)
+                    else:
+                        raise RuntimeError("Invalid dimension character")
+
+        return cls(
+            contracting_dims=contracting_dims,
+            batch_dims=batch_dims,
+            lhs_out_only_dims=lhs_out_only_dims,
+            rhs_out_only_dims=rhs_out_only_dims,
+        )
+
+
+def gen_einsum_strategies(
+    equation: str,
+    mesh: DeviceMesh,
+    *,
+    linearity: bool = False,
+) -> OpStrategy:
+    """
+    Generate a strategy list for the ops that follow einsum style notation.
+    """
+    # parse einop equation and extract dims
+    input_dims, output_dim = EinsumDims.parse_equation(equation)
+    edims = EinsumDims.parse_dims(input_dims, output_dim)
+
+    all_mesh_dim_strategies = []
+
+    # generate strategies for each mesh dim
+    for mesh_dim in range(mesh.ndim):
+        mesh_dim_strategies = []
+
+        # placement list stores placements of [output, input1, input2, ...]
+        # first we always have replicate all for inputs and output
+        placement_list: list[Placement] = [Replicate()] * (len(input_dims) + 1)
+        mesh_dim_strategies.append(placement_list)
+
+        # split batch dim
+        for batch_dim in edims.batch_dims:
+            output_batch_dim = output_dim.index(batch_dim)
+            placement_list = [Shard(output_batch_dim)]
+            for input_dim in input_dims:
+                input_batch_dim = input_dim.index(batch_dim)
+                placement_list.append(Shard(input_batch_dim))
+
+            mesh_dim_strategies.append(placement_list)
+
+        # split contracting dim
+        for contracting_dim in edims.contracting_dims:
+            placement_list = [Partial()]
+            for input_dim in input_dims:
+                input_contracting_dim = input_dim.index(contracting_dim)
+                placement_list.append(Shard(input_contracting_dim))
+
+            mesh_dim_strategies.append(placement_list)
+
+        # split lhs free dim
+        for lhs_dim in edims.lhs_out_only_dims:
+            lhs_free_dim = output_dim.index(lhs_dim)
+            # this means split the lhs input and output
+            # i.e. S(0), R -> S(0)
+            lhs_placement_list: list[Placement] = [
+                Shard(lhs_free_dim),
+                Shard(lhs_free_dim),
+                Replicate(),
+            ]
+            mesh_dim_strategies.append(lhs_placement_list)
+
+        # split rhs free dim
+        for rhs_dim in edims.rhs_out_only_dims:
+            rhs_free_dim = output_dim.index(rhs_dim)
+            rhs_placement_list: list[Placement] = [
+                Shard(rhs_free_dim),
+                Replicate(),
+                Shard(rhs_free_dim),
+            ]
+            mesh_dim_strategies.append(rhs_placement_list)
+
+        # linearity strategy
+        if linearity:
+            linearity_placement_list: list[Placement] = [Partial()]
+            for input_dim in input_dims:
+                linearity_placement_list.append(Partial())
+            mesh_dim_strategies.append(linearity_placement_list)
+
+        all_mesh_dim_strategies.append(mesh_dim_strategies)
+
+    # generate strategies for entire mesh
+    strategy_combs = itertools.product(*all_mesh_dim_strategies)
+
+    # TODO: filter out invalid strategies, at this point we generate
+    # all possible strategies without considering the whether the tensor
+    # dim could be sharded or not, we would need to filter out invalid
+    # strategies base on the actual tensor shape
+    # (i.e. for Shard, tensor dim size must > mesh size)
+    all_strategies = []
+    for strategy_comb in strategy_combs:
+        spec_list = [DTensorSpec(mesh, tuple(specs)) for specs in zip(*strategy_comb)]
+        strat = PlacementStrategy(output_specs=spec_list[0], input_specs=spec_list[1:])
+        all_strategies.append(strat)
+
+    return OpStrategy(all_strategies)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_embedding_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_embedding_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..41eb21d6baa131cdeb0bf12daca934287375272b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_embedding_ops.py
@@ -0,0 +1,273 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+# implement matrix related ops for distributed tensor
+from dataclasses import dataclass, field
+from typing import cast, Optional
+
+import torch
+import torch.distributed._functional_collectives as funcol
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor._op_schema import (
+    OpSchema,
+    OpStrategy,
+    PlacementList,
+    StrategyType,
+)
+from torch.distributed.tensor._ops.utils import (
+    expand_to_full_mesh_op_strategy,
+    register_op_strategy,
+)
+from torch.distributed.tensor.placement_types import (
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+aten = torch.ops.aten
+
+
+@dataclass
+class MaskBuffer:
+    data: Optional[torch.Tensor] = None
+    # refcount allows shared usage of the MaskBuffer, as long as all users have the same data
+    refcount: int = 0
+
+    def materialize_mask(self, mask):
+        if self.refcount == 0:
+            self.data = mask
+        else:
+            assert self.data is not None
+            if not torch.equal(self.data, mask):
+                raise RuntimeError(
+                    "MaskBuffer has been materialized with conflicting data"
+                )
+        self.refcount += 1
+
+    def release_mask(self):
+        if self.refcount == 0 or self.data is None:
+            raise RuntimeError("MaskBuffer has not been materialized")
+        self.refcount -= 1
+        if self.refcount == 0:
+            self.data = None
+
+    def apply_mask(self, tensor):
+        if self.refcount == 0 or self.data is None:
+            raise RuntimeError("MaskBuffer has not been materialized")
+
+        # NOTE: _MaskPartial is being used by the embedding op and the gather op.
+        # For gather, the mask has the same dimension as the output tensor, whereas
+        # the output of the embedding op has an additional dimension compare to the input,
+        # hence the output masking logic below having two different cases.
+        if tensor.ndim == self.data.ndim:
+            tensor[self.data] = 0.0
+        else:
+            tensor[self.data, :] = 0.0
+
+
+@dataclass(frozen=True)
+class _MaskPartial(Partial):
+    """
+    A partial mask placement devised for rowwise sharded embedding op, where we need
+    to mask and adjust the indices to the local embedding shard, embedding masking
+    is a special type of the Partial placement
+
+    NOTE: the lifecycle of this MaskPartial placement follows the corresponding DTensor
+    lifecycle, i.e. the indices_mask would only be alive during the lifetime of the DTensor.
+    """
+
+    mask_buffer: MaskBuffer = field(default_factory=MaskBuffer)
+
+    # required fields for computing the local offset and deriving the mask
+    offset_shape: Optional[torch.Size] = None
+    offset_dim: int = 0
+
+    def _partition_value(
+        self, tensor: torch.Tensor, mesh: DeviceMesh, mesh_dim: int
+    ) -> torch.Tensor:
+        # override parent logic to perform partial mask for embedding
+        num_chunks = mesh.size(mesh_dim)
+        # get local shard size and offset on the embedding_dim
+        assert self.offset_shape is not None, (
+            "offset_shape needs to be set for _MaskPartial"
+        )
+        local_shard_size, local_offset_on_dim = Shard._local_shard_size_on_dim(
+            self.offset_shape[self.offset_dim],
+            num_chunks,
+            mesh.get_local_rank(mesh_dim),
+            return_offset=True,
+        )
+        # Build the input mask and save it for the current partial placement
+        # this is so that the output of embedding op can reuse the same partial
+        # placement saved mask to perform mask + reduction
+        mask = (tensor < local_offset_on_dim) | (
+            tensor >= local_offset_on_dim + local_shard_size
+        )
+        # mask the input tensor
+        masked_tensor = tensor.clone() - local_offset_on_dim
+        masked_tensor[mask] = 0
+        # materialize the mask buffer to be used for reduction
+        self.mask_buffer.materialize_mask(mask)
+        return masked_tensor
+
+    def _reduce_value(
+        self, tensor: torch.Tensor, mesh: DeviceMesh, mesh_dim: int
+    ) -> torch.Tensor:
+        # by the time we ned reduction, we should have already saved the mask
+        assert self.mask_buffer.data is not None
+
+        # apply the mask to the tensor that pending reduction
+        self.mask_buffer.apply_mask(tensor)
+
+        # clear the mask buffer
+        self.mask_buffer.release_mask()
+
+        # perform sum reduction
+        return funcol.all_reduce(
+            tensor, reduceOp=self.reduce_op, group=(mesh, mesh_dim)
+        )
+
+    def _reduce_shard_value(
+        self,
+        tensor: torch.Tensor,
+        mesh: DeviceMesh,
+        mesh_dim: int,
+        shard_spec: Placement,
+    ) -> torch.Tensor:
+        # by the time we ned reduction, we should have already saved the mask
+        assert self.mask_buffer.data is not None
+
+        # apply the mask to the tensor that pending reduction
+        self.mask_buffer.apply_mask(tensor)
+
+        # clear the mask buffer
+        self.mask_buffer.release_mask()
+
+        # call reduce_shard_tensor of the shard_spec.
+        shard_spec = cast(Shard, shard_spec)
+        return shard_spec._reduce_shard_tensor(tensor, mesh, self.reduce_op, mesh_dim)
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, _MaskPartial):
+            return False
+
+        # if either data is not None, we invalidate the sharding cache, as this indicates
+        # the current MaskPartial placement is still in use and should not be used for cache hit.
+        if self.mask_buffer.data is not None or other.mask_buffer.data is not None:
+            return False
+
+        return (
+            self.reduce_op == other.reduce_op
+            and self.offset_shape == other.offset_shape
+            and self.offset_dim == other.offset_dim
+        )
+
+    def __hash__(self) -> int:
+        return 1 + hash(
+            (
+                self.reduce_op,
+                self.offset_shape,
+                self.offset_dim,
+            )
+        )
+
+    def __repr__(self) -> str:
+        """
+        machine readable representation of the MaskPartial placement
+        """
+        return f"_MaskPartial(offset_shape={self.offset_shape}, offset_dim={self.offset_dim})"
+
+    def __str__(self) -> str:
+        """
+        human readable representation of the MaskPartial placement
+        """
+        return "MaskP"
+
+
+@register_op_strategy(aten.embedding.default)
+def embedding_strategy(op_schema: OpSchema) -> StrategyType:
+    """
+    This strategy handles embedding op. We have two possible embedding shardings:
+    rowwise and colwise
+    """
+    weight_strategy = cast(OpStrategy, op_schema.args_schema[0])
+    indices_strategy = cast(OpStrategy, op_schema.args_schema[1])
+    mesh = op_schema.get_mesh_from_args()
+
+    weight_shape = weight_strategy.shape
+    indices_shape = indices_strategy.shape
+    output_emd_dim = len(indices_shape)
+
+    single_mesh_dim_strategies = []
+
+    # placement list stores placements of [output, weight, input_indices]
+    # first we always have replicate all for inputs and output
+    all_replicate: PlacementList = [Replicate()] * 3
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # colwise sharding, output shard on last dim, weight shard on dim 1, input replicate
+    colwise_sharding: PlacementList = [Shard(output_emd_dim), Shard(1), Replicate()]
+    single_mesh_dim_strategies.append(colwise_sharding)
+
+    # rowwise sharding, output is embedding partial, weight shard on dim 0, input accepts embedding partial
+    embedding_partial_placement = _MaskPartial(offset_shape=weight_shape, offset_dim=0)
+
+    # NOTE we want to reuse the same mask partial placement so that we can reuse the same mask that generates
+    # from the input indices and use it for output reduction
+    rowwise_sharding: PlacementList = [
+        embedding_partial_placement,
+        Shard(0),
+        embedding_partial_placement,
+    ]
+    single_mesh_dim_strategies.append(rowwise_sharding)
+
+    # batch dim sharding, weight replicated, input can shard on any dim, output follows input
+    for input_dim in range(len(indices_shape)):
+        batch_sharding: PlacementList = [
+            Shard(input_dim),
+            Replicate(),
+            Shard(input_dim),
+        ]
+        single_mesh_dim_strategies.append(batch_sharding)
+
+    return expand_to_full_mesh_op_strategy(mesh, op_schema, single_mesh_dim_strategies)
+
+
+@register_op_strategy(aten.embedding_dense_backward.default)
+def embedding_dense_backward_strategy(op_schema: OpSchema) -> StrategyType:
+    """
+    This strategy handles embedding op. We have two possible embedding shardings:
+    rowwise and colwise
+    """
+    grad_out_strategy = cast(OpStrategy, op_schema.args_schema[0])
+    indices_strategy = cast(OpStrategy, op_schema.args_schema[1])
+    mesh = op_schema.get_mesh_from_args()
+
+    grad_out_shape = grad_out_strategy.shape
+    indices_shape = indices_strategy.shape
+    grad_out_ndim = len(grad_out_shape)
+
+    single_mesh_dim_strategies = []
+
+    # placement list stores placements of [output, weight, input_indices]
+    # first we always have replicate all for inputs and output
+    all_replicate: PlacementList = [Replicate()] * 3
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # colwise sharding backward, grad_out shard on last dim, input replicate,
+    # weight grad shard colwise
+    colwise_sharding: PlacementList = [Shard(1), Shard(grad_out_ndim - 1), Replicate()]
+    single_mesh_dim_strategies.append(colwise_sharding)
+
+    # batch dim sharding, weight replicated, grad_out/input have same sharding
+    # that can shard on any dim, weight grad partial
+    for input_dim in range(len(indices_shape)):
+        batch_sharding: PlacementList = [Partial(), Shard(input_dim), Shard(input_dim)]
+        single_mesh_dim_strategies.append(batch_sharding)
+
+    # grad_out partial, input replicate, weight grad keep partial
+    partial_sharding: PlacementList = [Partial(), Partial(), Replicate()]
+    single_mesh_dim_strategies.append(partial_sharding)
+
+    return expand_to_full_mesh_op_strategy(mesh, op_schema, single_mesh_dim_strategies)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_experimental_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_experimental_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..59e907dc5ba1a9cb68128e1e4d01b24a1ca66640
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_experimental_ops.py
@@ -0,0 +1,27 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+# implement matrix related ops for distributed tensor
+
+import torch
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor._op_schema import (
+    OpSchema,
+    OpStrategy,
+    PlacementStrategy,
+    StrategyType,
+)
+from torch.distributed.tensor._ops.utils import register_op_strategy
+from torch.distributed.tensor.placement_types import Replicate
+
+
+aten = torch.ops.aten
+
+
+@register_op_strategy(aten.slice_backward.default)
+def slice_backward_rules(op_schema: OpSchema) -> StrategyType:
+    """
+    slice_backward is a new_zeros + slice_scatter, we only allow replication
+    on the input/output for now since new_zeros would produce replication
+    """
+    mesh = op_schema.get_mesh_from_args(validate=False)
+    replicate_spec = DTensorSpec(mesh, tuple([Replicate()] * mesh.ndim))
+    return OpStrategy([PlacementStrategy(replicate_spec)])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_math_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_math_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5d30a9e6605a2ac6857e2d8db1673200c829d72
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_math_ops.py
@@ -0,0 +1,1079 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import math
+from collections.abc import Sequence
+from dataclasses import dataclass
+from enum import Enum
+from typing import cast, Optional, Union
+
+import torch
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor._op_schema import (
+    OpSchema,
+    OpStrategy,
+    PlacementList,
+    PlacementStrategy,
+    RuntimeSchemaInfo,
+    TupleStrategy,
+)
+from torch.distributed.tensor._ops.utils import (
+    as_list,
+    expand_to_full_mesh_op_strategy,
+    generate_redistribute_costs,
+    is_tensor_evenly_shardable,
+    normalize_dim,
+    normalize_dims,
+    register_op_strategy,
+)
+from torch.distributed.tensor._utils import normalize_to_torch_size
+from torch.distributed.tensor.placement_types import (
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+aten = torch.ops.aten
+
+
+class Reduction(Enum):
+    NONE = 0
+    MEAN = 1
+    SUM = 2
+
+
+@dataclass(frozen=True)
+class NormReduction:
+    norm_type: Union[int, float, str]
+
+
+ReductionOpType = Union[NormReduction, str]
+
+
+@dataclass(frozen=True)
+class _NormPartial(Partial):
+    """
+    This placement is used for partial vector norm.
+
+    For p-norms (where p not inf or -inf), the p-norm over n elements computes
+        (sum_i x_i^p)^(1/p)
+    where the sum is from i=1 to n. The reduction op is the p-norm itself.
+    For example, consider 2 ranks, a (4,) tensor sharded on dim-0, and 2-norm:
+        Rank 0: [t1, t2] | Rank 1: [t3, t4]
+    After computing 2-norm per gradient (partial placement):
+        Rank 0: [sqrt(t1^2 + t2^2)] | Rank 1: [sqrt(t3^2 + t4^2)]
+    Converting from partial to replicate wants to ultimately get:
+        Rank 0/1: [sqrt(t1^2 + t2^2 + t3^2 + t4^2)]
+    This can be achieved by computing 2-norm on each rank's result. This holds
+    similarly for inf and -inf norm. For 0-norm, the reduction op is sum.
+    """
+
+    norm_type: Union[int, float, str] = 2
+
+    def __post_init__(self):
+        """Set the appropriate reduce op based on the norm type."""
+        # Use `object.__setattr__` to bypass frozen checks
+        if self.norm_type in (float("inf"), "inf"):
+            object.__setattr__(self, "reduce_op", "max")
+        elif self.norm_type in (float("-inf"), "-inf"):
+            object.__setattr__(self, "reduce_op", "min")
+        elif isinstance(self.norm_type, (int, float)):
+            object.__setattr__(self, "reduce_op", "sum")
+        else:
+            raise NotImplementedError(f"Unsupported norm type: {self.norm_type}")
+
+    def _partition_value(
+        self, tensor: torch.Tensor, mesh: DeviceMesh, mesh_dim: int
+    ) -> torch.Tensor:
+        """
+        For example, consider 4 ranks, a (3,) replicated tensor, and 2-norm:
+            Ranks 0 and 1: sqrt(t1^2 + t2^2 + t3^3)
+        To convert from replicated to partial, we want f(x) such that
+            sqrt(t1^2 + t2^2 + t3^3) = sqrt(4f(t1)^2 + 4f(t2)^2 + 4f(t3)^2)
+                                     = sqrt(4) sqrt(f(t1)^2 + f(t2)^2 + f(t3)^2).
+        One such f(x) is f(x) = x / sqrt(4). This generalizes to d ranks and
+        p-norm as f(x) = x / d^(1/p).
+        """
+        if self.reduce_op in ("max", "min"):
+            return tensor
+        elif self.reduce_op == "sum":
+            if self.norm_type == 0:
+                raise NotImplementedError(f"Unsupported norm type:: {self.norm_type}")
+            elif self.norm_type == 1:
+                return tensor / mesh.size(mesh_dim)
+            assert isinstance(self.norm_type, (int, float))
+            return tensor / math.pow(mesh.size(mesh_dim), 1 / self.norm_type)
+        raise NotImplementedError(self.reduce_op)
+
+    def _reduce_shard_value(
+        self,
+        tensor: torch.Tensor,
+        mesh: DeviceMesh,
+        mesh_dim: int,
+        shard_spec: Placement,
+    ) -> torch.Tensor:
+        assert isinstance(shard_spec, Shard), f"{shard_spec}"
+        tensor = self._pre_reduce_transform(tensor)
+        reduced_tensor = super()._reduce_shard_value(tensor, mesh, mesh_dim, shard_spec)
+        return self._post_reduce_transform(reduced_tensor)
+
+    def _reduce_value(
+        self, tensor: torch.Tensor, mesh: DeviceMesh, mesh_dim: int
+    ) -> torch.Tensor:
+        tensor = self._pre_reduce_transform(tensor)
+        reduced_tensor = super()._reduce_value(tensor, mesh, mesh_dim)
+        return self._post_reduce_transform(reduced_tensor)
+
+    def _pre_reduce_transform(self, tensor: torch.Tensor) -> torch.Tensor:
+        if self.reduce_op == "sum":
+            assert isinstance(self.norm_type, (int, float)), f"{self.norm_type}"
+            if self.norm_type != 0 and self.norm_type != 1:
+                return tensor**self.norm_type
+        return tensor
+
+    def _post_reduce_transform(self, tensor: torch.Tensor) -> torch.Tensor:
+        if self.reduce_op == "sum":
+            assert isinstance(self.norm_type, (int, float)), f"{self.norm_type}"
+            if self.norm_type != 0 and self.norm_type != 1:
+                return tensor ** (1.0 / self.norm_type)
+        return tensor
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, _NormPartial):
+            return False
+        return self.norm_type == other.norm_type
+
+    def __hash__(self) -> int:
+        return 1 + hash(self.norm_type)
+
+
+def _infer_reduction_dims(dims_arg: object, ndim: int) -> Optional[list[int]]:
+    if dims_arg is None:
+        return None
+    dims = cast(list[int], as_list(dims_arg))
+    dims = cast(list[int], normalize_dims(dims, ndim))
+    empty_dims = [[0], [-1], []]
+    if ndim == 0 and dims_arg in empty_dims:
+        return None
+    return dims
+
+
+def _infer_reduce_dims_map(
+    reduction_dims: list[int], input_ndim: int, keep_dim=False
+) -> list[int]:
+    reduction_dims_map = []
+    new_dim_count = 0
+    for input_dim in range(input_ndim):
+        if input_dim in reduction_dims and not keep_dim:
+            # if input dim in reduction dims, mark it as -1
+            reduction_dims_map.append(-1)
+        else:
+            # otherwise mark it as the new dim
+            reduction_dims_map.append(new_dim_count)
+            new_dim_count += 1
+
+    return reduction_dims_map
+
+
+def _replicate_dims_start_at(
+    placements: Sequence[Placement], start_dim: int = 0
+) -> tuple[Placement, ...]:
+    new_placements: list[Placement] = []
+    for p in placements:
+        if p.is_partial() or (isinstance(p, Shard) and p.dim >= start_dim):
+            new_placements.append(Replicate())  # make it replicate
+        else:
+            new_placements.append(p)  # keep the placement
+    return tuple(new_placements)
+
+
+# return new_placements which align with placements but skip the skipped_dim
+def _skip_dim(
+    placements: tuple[Placement, ...], skipped_dim: int
+) -> tuple[Placement, ...]:
+    new_placements: list[Placement] = []
+    for p in placements:
+        if isinstance(p, Shard) and p.dim >= skipped_dim:
+            new_placements.append(Shard(p.dim - 1))
+        else:
+            new_placements.append(p)
+    return tuple(new_placements)
+
+
+def replicate_reduction_dims(
+    placements: tuple[Placement, ...], reduction_dims: list[int]
+) -> tuple[Placement, ...]:
+    # replicate the reduction dims if not reduction_linear
+    new_placements: list[Placement] = []
+
+    for p in placements:
+        if p.is_partial():
+            new_placements.append(Replicate())
+        elif isinstance(p, Shard) and p.dim in reduction_dims:
+            new_placements.append(Replicate())
+        else:
+            new_placements.append(p)
+
+    return tuple(new_placements)
+
+
+def map_placements_after_reduction(
+    placements: tuple[Placement, ...],
+    reduction_dims: list[int],
+    reduction_dims_map: list[int],
+    reduction_op: ReductionOpType,
+) -> tuple[Placement, ...]:
+    """
+    Map each placement based on the output shape after reduction.
+    """
+    new_placements: list[Placement] = []
+    for placement in placements:
+        if isinstance(placement, (Replicate, Partial)):
+            new_placements.append(placement)
+        else:
+            assert isinstance(placement, Shard)
+            shard_dim = placement.dim
+            new_shard_dim = reduction_dims_map[shard_dim]
+            if new_shard_dim == -1 or shard_dim in reduction_dims:
+                # if new_shard_dim collapsed or its in the reduction dims
+                # (i.e. for the case where keepdims=True), we generate partial
+                new_placements.append(get_placement_from_reduction_op(reduction_op))
+            else:
+                new_placements.append(Shard(new_shard_dim))
+    return tuple(new_placements)
+
+
+def get_placement_from_reduction_op(reduction_op: ReductionOpType) -> Placement:
+    if isinstance(reduction_op, NormReduction):
+        return _NormPartial(norm_type=reduction_op.norm_type)
+    return Partial(reduction_op)
+
+
+def common_reduction_strategy(
+    input_strategy: OpStrategy,
+    reduce_dims: list[int],
+    keep_dim: bool = False,
+    reduction_linear: bool = True,
+    reduction_op: ReductionOpType = "sum",
+) -> OpStrategy:
+    """
+    reduction_linear means that the reduction `f` follows this rule:
+        f([f(a), f(b)]) = f([a, b])
+
+    reduction linear should be super set of linearity.
+    """
+    # by default follow reduction input strategy
+    reduction_strategy = OpStrategy([])
+
+    for strtg in input_strategy.strategies:
+        if not reduction_linear:
+            # input placements for this strategy should clear out pending sum and sharding
+            # on the reduction dimension
+            input_placements = replicate_reduction_dims(
+                strtg.output_spec.placements, reduce_dims
+            )
+        else:
+            input_placements = strtg.output_spec.placements
+
+        input_spec = DTensorSpec(
+            mesh=input_strategy.mesh,
+            placements=input_placements,
+            tensor_meta=strtg.output_spec.tensor_meta,
+        )
+
+        reduce_dims_map = _infer_reduce_dims_map(reduce_dims, input_spec.ndim, keep_dim)
+        out_placements = map_placements_after_reduction(
+            input_spec.placements, reduce_dims, reduce_dims_map, reduction_op
+        )
+        redistribute_cost = [generate_redistribute_costs(input_strategy, input_spec)]
+        reduction_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=DTensorSpec(
+                    mesh=input_strategy.mesh,
+                    placements=out_placements,
+                ),
+                input_specs=(input_spec,),
+                redistribute_cost=redistribute_cost,
+            )
+        )
+
+    return reduction_strategy
+
+
+LINEAR_REDUCTION_OP_MAP = {
+    aten.all.default: "sum",
+    aten.all.dim: "sum",
+    aten.sum.default: "sum",
+    aten.sum.dim_IntList: "sum",
+    aten.prod.default: "product",
+    aten.prod.dim_int: "product",
+    aten.prod.int_out: "product",
+    aten.mean.default: "avg",
+    aten.mean.dim: "avg",
+    aten.mean.out: "avg",
+    aten.max.default: "max",
+    aten.max.dim: "max",
+    aten.max.out: "max",
+    aten.min.default: "min",
+    aten.min.dim: "min",
+    aten.min.out: "min",
+    aten.any.default: "sum",
+    aten.any.dim: "sum",
+    aten.any.out: "sum",
+    aten.amax.default: "max",
+    aten.amax.out: "max",
+    aten.amin.default: "min",
+    aten.amin.out: "min",
+}
+
+
+@register_op_strategy(
+    list(LINEAR_REDUCTION_OP_MAP.keys()), schema_info=RuntimeSchemaInfo(1)
+)
+def linear_reduction_strategy(op_schema: OpSchema) -> OpStrategy:
+    args_schema = op_schema.args_schema
+    input_strategy = args_schema[0]
+    assert isinstance(input_strategy, OpStrategy)
+
+    dims = None
+    if len(op_schema.args_schema) > 1:
+        dims = _infer_reduction_dims(args_schema[1], input_strategy.ndim)
+
+    reduce_dims = list(range(input_strategy.ndim)) if dims is None else dims
+
+    keep_dim = len(op_schema.args_schema) > 2 and bool(op_schema.args_schema[2])
+    reduction_op = LINEAR_REDUCTION_OP_MAP[op_schema.op]
+    return common_reduction_strategy(
+        input_strategy,
+        reduce_dims,
+        keep_dim=keep_dim,
+        reduction_linear=True,
+        reduction_op=reduction_op,
+    )
+
+
+@register_op_strategy(
+    [aten.var.correction, aten.var.correction_out],
+    schema_info=RuntimeSchemaInfo(1, ["keepdim"]),
+)
+def var_reduction_strategy(op_schema: OpSchema) -> OpStrategy:
+    args_schema = op_schema.args_schema
+    input_strategy = args_schema[0]
+    assert isinstance(input_strategy, OpStrategy)
+    dims = None
+    if len(op_schema.args_schema) > 1:
+        dims = _infer_reduction_dims(args_schema[1], input_strategy.ndim)
+
+    reduce_dims = list(range(input_strategy.ndim)) if dims is None else dims
+
+    keep_dim = cast(bool, op_schema.kwargs_schema.get("keepdim", False))
+    return common_reduction_strategy(
+        input_strategy, reduce_dims, keep_dim=keep_dim, reduction_linear=False
+    )
+
+
+@register_op_strategy(
+    [aten.linalg_vector_norm.default], schema_info=RuntimeSchemaInfo(1)
+)
+def vector_norm_strategy(op_schema: OpSchema) -> OpStrategy:
+    args_schema = op_schema.args_schema
+    input_strategy = args_schema[0]
+    assert isinstance(input_strategy, OpStrategy)
+
+    norm_type = args_schema[1] if len(args_schema) > 1 else 2
+    assert isinstance(norm_type, (int, float, str)), f"{norm_type}"
+    dim = args_schema[2] if len(args_schema) > 2 else None
+    keepdim = args_schema[3] if len(args_schema) > 3 else False
+    dims = _infer_reduction_dims(dim, input_strategy.ndim)
+    reduce_dims = list(range(input_strategy.ndim)) if dims is None else dims
+    return common_reduction_strategy(
+        input_strategy,
+        reduce_dims,
+        keep_dim=cast(bool, keepdim),
+        reduction_linear=True,
+        reduction_op=NormReduction(norm_type),
+    )
+
+
+@register_op_strategy(
+    [aten._foreach_norm.Scalar], schema_info=RuntimeSchemaInfo(1, needs_pytree=True)
+)
+def foreach_norm_strategy(op_schema: OpSchema) -> TupleStrategy:
+    args_schema = op_schema.args_schema
+    input_tuple_strategy = args_schema[0]
+    assert isinstance(input_tuple_strategy, TupleStrategy)
+    norm_type = args_schema[1] if len(args_schema) > 1 else 2
+    assert isinstance(norm_type, (int, float, str)), f"{norm_type}"
+    output_tuple_strategy_childs: list[OpStrategy] = []
+    for op_strategy in input_tuple_strategy.childs:
+        assert isinstance(op_strategy, OpStrategy), f"{op_strategy}"
+        reduce_dims = list(range(op_strategy.ndim))
+        output_strategy = common_reduction_strategy(
+            op_strategy,
+            reduce_dims,
+            reduction_linear=True,
+            reduction_op=NormReduction(norm_type),
+        )
+        output_tuple_strategy_childs.append(output_strategy)
+    return TupleStrategy(output_tuple_strategy_childs)
+
+
+@register_op_strategy(
+    [
+        aten._linalg_svd.default,
+        aten.linalg_qr.default,
+        # TODO: The diagonal ops can have an improved sharding strategy for
+        # shard placements that does not require redistributing to replicate.
+        aten.diagonal_copy.default,
+        aten.diag_embed.default,
+        aten.diag.default,
+        aten.diagonal.default,
+        aten.tril.default,
+        aten.triu.default,
+        aten._linalg_eigh.default,
+        aten.upsample_bicubic2d.default,
+        aten.upsample_bilinear2d.default,
+        aten.upsample_linear1d.default,
+        aten.upsample_nearest2d.default,
+        aten.upsample_trilinear3d.default,
+        # TODO: support the full F.interpolate set of options.
+    ],
+    schema_info=RuntimeSchemaInfo(1),
+)
+def linalg_replicate_strategy(op_schema: OpSchema) -> OpStrategy:
+    """
+    Since we do not have a simple way to compute some linear algebra operations
+    like SVD or QR decomposition, always fall back to replicate.
+    """
+    args_schema = op_schema.args_schema
+    input_strategy = args_schema[0]
+    assert isinstance(input_strategy, OpStrategy), f"{input_strategy}"
+    mesh = input_strategy.mesh
+
+    output_strategies: list[PlacementStrategy] = []
+    for placement_strategy in input_strategy.strategies:
+        replicate_placements = tuple(Replicate() for _ in range(mesh.ndim))
+        replicate_spec = DTensorSpec(
+            mesh=mesh,
+            placements=replicate_placements,
+            tensor_meta=placement_strategy.output_spec.tensor_meta,
+        )
+        redistribute_cost = [
+            generate_redistribute_costs(input_strategy, replicate_spec)
+        ]
+        replicate_strategy = PlacementStrategy(
+            output_specs=replicate_spec,
+            input_specs=(replicate_spec,),
+            redistribute_cost=redistribute_cost,
+        )
+        output_strategies.append(replicate_strategy)
+    return OpStrategy(output_strategies)
+
+
+@register_op_strategy(
+    [aten._log_softmax.default, aten._softmax.default, aten._safe_softmax.default],
+    schema_info=RuntimeSchemaInfo(1),
+)
+def softmax_strategy(op_schema: OpSchema) -> OpStrategy:
+    input_strategy, softmax_dim, *_ = op_schema.args_schema
+    input_strategy = cast(OpStrategy, input_strategy)
+
+    softmax_dim = cast(int, softmax_dim)
+    softmax_dim = normalize_dim(softmax_dim, input_strategy.ndim)
+
+    output_strategy = OpStrategy([])
+    for input_placement_strategy in input_strategy.strategies:
+        redistribute_costs = []
+        input_src_spec = input_placement_strategy.output_spec
+
+        # make sure input is replicated along the softmax dim
+        input_target_spec = DTensorSpec(
+            mesh=input_strategy.mesh,
+            placements=replicate_reduction_dims(
+                input_src_spec.placements, [softmax_dim]
+            ),
+            tensor_meta=input_src_spec.tensor_meta,
+        )
+        redistribute_costs.append(
+            generate_redistribute_costs(input_strategy, input_target_spec)
+        )
+        output_target_spec = input_target_spec
+        output_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=output_target_spec,
+                input_specs=[input_target_spec],
+                redistribute_cost=redistribute_costs,
+            )
+        )
+
+    return output_strategy
+
+
+@register_op_strategy(
+    [
+        aten._log_softmax_backward_data.default,
+        aten._softmax_backward_data.default,
+    ],
+    schema_info=RuntimeSchemaInfo(2),
+)
+def softmax_backward_strategy(op_schema: OpSchema) -> OpStrategy:
+    grad_out_strategy, out_strategy, softmax_dim, _ = op_schema.args_schema
+    grad_out_strategy = cast(OpStrategy, grad_out_strategy)
+    out_strategy = cast(OpStrategy, out_strategy)
+    softmax_dim = cast(int, softmax_dim)
+    softmax_dim = normalize_dim(softmax_dim, grad_out_strategy.ndim)
+
+    grad_in_strategy = OpStrategy([])
+    for grad_out_placement_strat, out_placement_strat in zip(
+        grad_out_strategy.strategies, out_strategy.strategies
+    ):
+        # follow the sharding of the grad_out or out depending on which has more shards
+        grad_out_src_spec = grad_out_placement_strat.output_spec
+        out_src_spec = out_placement_strat.output_spec
+        src_spec = (
+            grad_out_src_spec
+            if grad_out_src_spec.num_shards >= out_src_spec.num_shards
+            else out_src_spec
+        )
+
+        # make sure inputs are replicated along the softmax dim
+        tgt_spec = DTensorSpec(
+            mesh=grad_out_strategy.mesh,
+            placements=replicate_reduction_dims(src_spec.placements, [softmax_dim]),
+        )
+        redist_grad_out_cost = generate_redistribute_costs(grad_out_strategy, tgt_spec)
+        redist_out_cost = generate_redistribute_costs(out_strategy, tgt_spec)
+        grad_in_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=tgt_spec,
+                redistribute_cost=[redist_grad_out_cost, redist_out_cost],
+            )
+        )
+
+    return grad_in_strategy
+
+
+@register_op_strategy(
+    [aten.nll_loss_forward.default, aten.nll_loss2d_forward.default],
+    schema_info=RuntimeSchemaInfo(3),
+)
+def nll_loss_forward_strategy(op_schema: OpSchema) -> OpStrategy:
+    mesh = op_schema.get_mesh_from_args()
+
+    assert len(op_schema.args_schema) == 5
+
+    (
+        input_strategy,
+        target_strategy,
+        weight_strategy,
+        reduction,
+        _,
+    ) = op_schema.args_schema
+    input_strategy = cast(OpStrategy, input_strategy)
+    target_strategy = cast(OpStrategy, target_strategy)
+    reduction = cast(int, reduction)
+
+    input_shape = input_strategy.shape
+    channel_dim = 1 if len(input_shape) >= 2 else 0
+
+    output_strategy = OpStrategy([])
+    for idx, input_placement_strategy in enumerate(input_strategy.strategies):
+        op_args_target_specs = []
+        redistribute_costs = []
+
+        # make sure input is replicated along the channel dim
+        input_src_spec = input_placement_strategy.output_spec
+        input_expected_spec = DTensorSpec(
+            mesh=mesh,
+            placements=replicate_reduction_dims(
+                input_src_spec.placements, [channel_dim]
+            ),
+            tensor_meta=input_src_spec.tensor_meta,
+        )
+        op_args_target_specs.append(input_expected_spec)
+        redistribute_costs.append(
+            generate_redistribute_costs(input_strategy, input_expected_spec)
+        )
+
+        # target doesn't have channel dim, and it follows input on other dims
+        target_src_spec = target_strategy.strategies[idx].output_spec
+        target_expected_spec = DTensorSpec(
+            mesh=mesh,
+            placements=_skip_dim(input_expected_spec.placements, channel_dim),
+            tensor_meta=target_src_spec.tensor_meta,
+        )
+        op_args_target_specs.append(target_expected_spec)
+        redistribute_costs.append(
+            generate_redistribute_costs(target_strategy, target_expected_spec)
+        )
+
+        # weight tensor, if given, has to be a Tensor of size input_shape[channel_dim]
+        # make sure it is replicated
+        if weight_strategy is not None:
+            assert isinstance(weight_strategy, OpStrategy)
+            weight_src_spec = weight_strategy.strategies[idx].output_spec
+            weight_expected_spec = DTensorSpec(
+                mesh=mesh,
+                placements=_replicate_dims_start_at(weight_src_spec.placements),
+                tensor_meta=weight_src_spec.tensor_meta,
+            )
+            op_args_target_specs.append(weight_expected_spec)
+            redistribute_costs.append(
+                generate_redistribute_costs(weight_strategy, weight_expected_spec)
+            )
+
+        if reduction == Reduction.NONE.value:
+            output_expected_spec = target_expected_spec
+            total_weight_expected_spec = DTensorSpec(
+                mesh=mesh, placements=tuple([Replicate()] * mesh.ndim)
+            )
+        else:
+            if reduction == Reduction.MEAN.value:
+                reduction_op = "avg"
+                if not is_tensor_evenly_shardable(
+                    target_expected_spec.shape, target_expected_spec
+                ):
+                    raise ValueError(
+                        "The intermediate results of nll_loss cannot be evenly sharded, \
+                        resulting in biased mean result."
+                    )
+            else:  # reduction == Reduction.SUM.value:
+                reduction_op = "sum"
+            reduce_dims = list(range(target_expected_spec.ndim))
+            reduce_dims_map = _infer_reduce_dims_map(
+                reduce_dims, target_expected_spec.ndim, keep_dim=False
+            )
+            out_placements = map_placements_after_reduction(
+                target_expected_spec.placements,
+                reduce_dims,
+                reduce_dims_map,
+                reduction_op,
+            )
+            output_expected_spec = DTensorSpec(
+                mesh=mesh,
+                placements=out_placements,
+            )
+
+            # whether reduction is sum or mean, the total weight has to be summed up if not replicated
+            total_weight_placements = map_placements_after_reduction(
+                target_expected_spec.placements,
+                reduce_dims,
+                reduce_dims_map,
+                "sum",
+            )
+            total_weight_expected_spec = DTensorSpec(
+                mesh=mesh,
+                placements=total_weight_placements,
+            )
+
+        output_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=(output_expected_spec, total_weight_expected_spec),
+                input_specs=op_args_target_specs,
+                redistribute_cost=redistribute_costs,
+            )
+        )
+
+    return output_strategy
+
+
+@register_op_strategy(
+    [aten.nll_loss_backward.default, aten.nll_loss2d_backward.default],
+    schema_info=RuntimeSchemaInfo(4),
+)
+def nll_loss_backward_strategy(op_schema: OpSchema) -> OpStrategy:
+    # backward op does not need to validate the mesh since forward op has already done it
+    mesh = op_schema.get_mesh_from_args(validate=False)
+
+    assert len(op_schema.args_schema) == 7
+    (
+        grad_out_strategy,
+        input_strategy,
+        target_strategy,
+        weight_strategy,
+        reduction,
+        _,
+        total_weight_strategy,
+    ) = op_schema.args_schema
+    grad_out_strategy = cast(OpStrategy, grad_out_strategy)
+    input_strategy = cast(OpStrategy, input_strategy)
+    target_strategy = cast(OpStrategy, target_strategy)
+    reduction = cast(int, reduction)
+    total_weight_strategy = cast(OpStrategy, total_weight_strategy)
+
+    input_shape = input_strategy.shape
+    channel_dim = 1 if len(input_shape) >= 2 else 0
+
+    grad_in_strategy = OpStrategy([])
+    for idx, input_placement_strategy in enumerate(input_strategy.strategies):
+        op_args_target_specs = []
+        redistribute_costs = []
+
+        # make sure input is replicated along the channel dim
+        input_src_spec = input_placement_strategy.output_spec
+        input_expected_spec = DTensorSpec(
+            mesh=mesh,
+            placements=replicate_reduction_dims(
+                input_src_spec.placements, [channel_dim]
+            ),
+            tensor_meta=input_src_spec.tensor_meta,
+        )
+        op_args_target_specs.append(input_expected_spec)
+        redistribute_costs.append(
+            generate_redistribute_costs(input_strategy, input_expected_spec)
+        )
+
+        # target doesn't have channel dim, and it follows input on other dims
+        target_src_spec = target_strategy.strategies[idx].output_spec
+        target_expected_spec = DTensorSpec(
+            mesh=mesh,
+            placements=_skip_dim(input_expected_spec.placements, channel_dim),
+            tensor_meta=target_src_spec.tensor_meta,
+        )
+        op_args_target_specs.append(target_expected_spec)
+        redistribute_costs.append(
+            generate_redistribute_costs(target_strategy, target_expected_spec)
+        )
+
+        # grad_out follows target if there is no reduction;
+        # otherwise, it should be a replicated scalar.
+        grad_out_src_spec = grad_out_strategy.strategies[idx].output_spec
+        if reduction == Reduction.NONE.value:
+            grad_out_expected_spec = target_expected_spec
+        else:
+            grad_out_expected_spec = DTensorSpec(
+                mesh=mesh,
+                placements=_replicate_dims_start_at(grad_out_src_spec.placements),
+                tensor_meta=grad_out_src_spec.tensor_meta,
+            )
+        op_args_target_specs.insert(0, grad_out_expected_spec)
+        redistribute_costs.insert(
+            0, generate_redistribute_costs(grad_out_strategy, grad_out_expected_spec)
+        )
+
+        # weight tensor, if given, has to be a Tensor of size input_shape[channel_dim]
+        # make sure it is replicated
+        if weight_strategy is not None:
+            assert isinstance(weight_strategy, OpStrategy)
+            weight_src_spec = weight_strategy.strategies[idx].output_spec
+            weight_expected_spec = DTensorSpec(
+                mesh=mesh,
+                placements=_replicate_dims_start_at(weight_src_spec.placements),
+                tensor_meta=weight_src_spec.tensor_meta,
+            )
+            op_args_target_specs.append(weight_expected_spec)
+            redistribute_costs.append(
+                generate_redistribute_costs(weight_strategy, weight_expected_spec)
+            )
+
+        # total_weight should always be replicated
+        total_weight_src_spec = total_weight_strategy.strategies[idx].output_spec
+        total_weight_expected_spec = DTensorSpec(
+            mesh=mesh,
+            placements=_replicate_dims_start_at(total_weight_src_spec.placements),
+            tensor_meta=total_weight_src_spec.tensor_meta,
+        )
+        op_args_target_specs.append(total_weight_expected_spec)
+        redistribute_costs.append(
+            generate_redistribute_costs(
+                total_weight_strategy, total_weight_expected_spec
+            )
+        )
+
+        grad_in_expected_spec = input_expected_spec
+        grad_in_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=grad_in_expected_spec,
+                input_specs=op_args_target_specs,
+                redistribute_cost=redistribute_costs,
+            )
+        )
+
+    return grad_in_strategy
+
+
+@register_op_strategy(
+    [aten.native_layer_norm.default],
+    schema_info=RuntimeSchemaInfo(1),
+)
+def layer_norm_strategy(op_schema: OpSchema) -> OpStrategy:
+    mesh = op_schema.get_mesh_from_args()
+
+    # args must be: input, normalized_shape, weight, bias, eps
+    # for None weight and bias, their corresponding objects will
+    # be None as well. layer_norm_strategy returns one OpStrategy
+    # for the triple return values (out, mean, rstd).
+    assert len(op_schema.args_schema) == 5
+    (
+        input_strategy,
+        normalized_shape,
+        weight_strategy,
+        bias_strategy,
+        _,
+    ) = op_schema.args_schema
+
+    # the current layer norm implementation requires that all
+    # input DTensor's sharding must be in form of OpStrategy
+    assert isinstance(input_strategy, OpStrategy)
+    assert isinstance(normalized_shape, (int, Sequence, torch.Size))
+    normalized_size = normalize_to_torch_size(normalized_shape)
+
+    input_ndim = input_strategy.ndim
+    axis = input_ndim - len(normalized_size)
+
+    # we use OpStrategy because the output (out, mean, rstd)
+    # should have the same placements
+    output_strategy = OpStrategy([])
+    for idx, input_placement_strategy in enumerate(input_strategy.strategies):
+        op_args_target_specs = []
+        redistribute_costs = []
+        input_src_spec = input_placement_strategy.output_spec
+
+        # for the input tensor, we replicate it on the inner dims if necessary
+        # TODO: we can avoid forcing the redistribution once we figure out
+        # how to decompose layer norm
+        input_target_spec = DTensorSpec(
+            mesh=mesh,
+            placements=_replicate_dims_start_at(input_src_spec.placements, axis),
+            tensor_meta=input_src_spec.tensor_meta,
+        )
+        op_args_target_specs.append(input_target_spec)
+        redistribute_costs.append(
+            generate_redistribute_costs(input_strategy, input_target_spec)
+        )
+
+        if weight_strategy is not None:
+            assert isinstance(weight_strategy, OpStrategy)
+            weight_src_spec = weight_strategy.strategies[idx].output_spec
+
+            # for the weight tensor, we replicate it on all dims if necessary
+            # TODO: we can avoid forcing the redistribution once we figure out
+            # how to decompose layer norm
+            weight_target_spec = DTensorSpec(
+                mesh=mesh,
+                placements=_replicate_dims_start_at(weight_src_spec.placements),
+                tensor_meta=weight_src_spec.tensor_meta,
+            )
+            op_args_target_specs.append(weight_target_spec)
+            redistribute_costs.append(
+                generate_redistribute_costs(weight_strategy, weight_target_spec)
+            )
+
+        if bias_strategy is not None:
+            assert isinstance(bias_strategy, OpStrategy)
+            bias_src_spec = bias_strategy.strategies[idx].output_spec
+
+            # for the bias tensor, we replicate it on all dims if necessary
+            # TODO: we can avoid forcing the redistribution once we figure out
+            # how to decompose layer norm
+            bias_target_spec = DTensorSpec(
+                mesh=mesh,
+                placements=_replicate_dims_start_at(bias_src_spec.placements),
+                tensor_meta=bias_src_spec.tensor_meta,
+            )
+            op_args_target_specs.append(bias_target_spec)
+            redistribute_costs.append(
+                generate_redistribute_costs(bias_strategy, bias_target_spec)
+            )
+
+        # the output spec is the same as input spec
+        output_target_spec = input_target_spec
+        output_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=output_target_spec,
+                input_specs=op_args_target_specs,
+                redistribute_cost=redistribute_costs,
+            )
+        )
+
+    return output_strategy
+
+
+@register_op_strategy(
+    [aten.native_layer_norm_backward.default],
+    schema_info=RuntimeSchemaInfo(2),
+)
+def layer_norm_bwd_strategy(op_schema: OpSchema) -> OpStrategy:
+    # backward op does not need to validate the mesh since forward op has already done it
+    mesh = op_schema.get_mesh_from_args(validate=False)
+
+    # args must be: grad_out, input, normalized_shape, mean, rstd,
+    # weight, bias, output_mask. For None weight and bias, their
+    # corresponding objects will be None as well.
+
+    assert len(op_schema.args_schema) == 8
+    (
+        grad_out_strategy,
+        input_strategy,
+        normalized_shape,
+        mean_strategy,
+        rstd_strategy,
+        weight_strategy,
+        bias_strategy,
+        output_mask,
+    ) = op_schema.args_schema
+
+    assert isinstance(grad_out_strategy, OpStrategy)
+    assert isinstance(input_strategy, OpStrategy)
+    assert isinstance(mean_strategy, OpStrategy)
+    assert isinstance(rstd_strategy, OpStrategy)
+
+    assert isinstance(normalized_shape, (int, Sequence, torch.Size))
+    normalized_size = normalize_to_torch_size(normalized_shape)
+    input_ndim = input_strategy.ndim
+    axis = input_ndim - len(normalized_size)
+    outer_dims = list(range(axis))
+
+    assert isinstance(output_mask, list) and len(output_mask) == 3
+
+    # output triple: (d_input, d_weight, d_bias)
+    out_tuple_strategy = OpStrategy([])
+    for idx, input_placement_strategy in enumerate(input_strategy.strategies):
+        # args for PlacementStrategy
+        output_specs_list: list[Optional[DTensorSpec]] = []
+        input_specs_list: list[DTensorSpec] = []
+        redistribute_costs = []
+
+        input_src_spec = input_placement_strategy.output_spec
+        # arg: grad_out
+        # TODO: change the strategy to the following rule.
+        # d_input is basically a product of element-wise mul of
+        # grad_out, rstd, and normalized input, among which rstd
+        # and normalized input (x_hat) should have the same sharding
+        # placements, and grad_out's sharding is determined by the
+        # pointwise result of x_hat and weight/bias.
+        # TODO: now grad_out spec follows input spec. we may need
+        # to change it to apply a pointwise rule over grad_out,
+        # input, and weight.
+        grad_out_target_spec = DTensorSpec(
+            mesh=mesh,
+            placements=_replicate_dims_start_at(input_src_spec.placements, axis),
+            tensor_meta=input_src_spec.tensor_meta,
+        )
+        input_specs_list.append(grad_out_target_spec)
+        redistribute_costs.append(
+            generate_redistribute_costs(grad_out_strategy, grad_out_target_spec)
+        )
+        output_specs_list.append(grad_out_target_spec if output_mask[0] else None)
+
+        # arg: input
+        input_target_spec = DTensorSpec(
+            mesh=mesh,
+            placements=_replicate_dims_start_at(input_src_spec.placements, axis),
+            tensor_meta=input_src_spec.tensor_meta,
+        )
+        input_specs_list.append(input_target_spec)
+        redistribute_costs.append(
+            generate_redistribute_costs(input_strategy, input_target_spec)
+        )
+
+        # arg: mean, rstd
+        mean_src_spec = mean_strategy.strategies[idx].output_spec
+        input_specs_list.append(mean_src_spec)
+        redistribute_costs.append([0.0 for _ in mean_strategy.strategies])
+        rstd_src_spec = rstd_strategy.strategies[idx].output_spec
+        input_specs_list.append(rstd_src_spec)
+        redistribute_costs.append([0.0 for _ in rstd_strategy.strategies])
+
+        def _add_target_input_spec(strategy) -> DTensorSpec:
+            # shared logic for setting the weight and bias target input specs
+            assert isinstance(strategy, OpStrategy)
+            src_spec = strategy.strategies[idx].output_spec
+            # no need to redistribute since they should be replicated in forward pass
+            input_specs_list.append(src_spec)
+            redistribute_costs.append([0.0 for _ in strategy.strategies])
+            return src_spec
+
+        # arg: weight
+        # d_weight = sum(grad_out * (input - mean) / rstd, outer_dim, keepdim=False)
+        if weight_strategy is not None:
+            weight_src_spec = _add_target_input_spec(weight_strategy)
+            # TODO: now d_weight spec follows input spec w/ a reduction.
+            # we may need to change to a pointwise rule over grad_out and
+            # input, then apply a reduction.
+            inp_placements = _replicate_dims_start_at(input_src_spec.placements, axis)
+            reduce_dims_map = _infer_reduce_dims_map(
+                outer_dims, input_src_spec.ndim, False
+            )
+            out_placements = map_placements_after_reduction(
+                inp_placements, outer_dims, reduce_dims_map, "sum"
+            )
+            weight_out_spec = DTensorSpec(
+                mesh=mesh,
+                placements=out_placements,
+                tensor_meta=weight_src_spec.tensor_meta,
+            )
+            output_specs_list.append(weight_out_spec if output_mask[1] else None)
+        else:
+            assert output_mask[1] is False, (
+                "output_mask[1] should not be `True` while weight argument is `None` in native_layer_norm_backward."
+            )
+            output_specs_list.append(None)
+
+        # arg: bias
+        # d_bias = sum(grad_out, outer_dim, keepdim=False)
+        if bias_strategy is not None:
+            bias_src_spec = _add_target_input_spec(bias_strategy)
+            # d_bias spec follows a reduction over grad_out
+            inp_placements = _replicate_dims_start_at(
+                grad_out_target_spec.placements, axis
+            )
+            reduce_dims_map = _infer_reduce_dims_map(
+                outer_dims, grad_out_target_spec.ndim, False
+            )
+            out_placements = map_placements_after_reduction(
+                inp_placements, outer_dims, reduce_dims_map, "sum"
+            )
+            bias_out_spec = DTensorSpec(
+                mesh=mesh,
+                placements=out_placements,
+                tensor_meta=bias_src_spec.tensor_meta,
+            )
+            output_specs_list.append(bias_out_spec if output_mask[2] else None)
+        else:
+            assert output_mask[2] is False, (
+                "output_mask[2] should not be `True` while bias argument is `None` in native_layer_norm_backward."
+            )
+            output_specs_list.append(None)
+
+        out_tuple_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=tuple(output_specs_list),
+                input_specs=input_specs_list,
+                redistribute_cost=redistribute_costs,
+            )
+        )
+
+    return out_tuple_strategy
+
+
+@register_op_strategy(
+    [aten.topk.default],
+    schema_info=RuntimeSchemaInfo(2),
+)
+def topk_strategy(op_schema: OpSchema) -> OpStrategy:
+    input_strategy = cast(OpStrategy, op_schema.args_schema[0])
+    topk_dim = (
+        cast(int, op_schema.args_schema[2]) if len(op_schema.args_schema) > 2 else -1
+    )
+    topk_dim = normalize_dim(topk_dim, input_strategy.ndim)
+
+    single_mesh_dim_strategies = []
+
+    # two outputs (values, indices), 1 input
+    # replicate always works
+    all_replicate: PlacementList = [Replicate()] * 3
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # every dim except topk dim should work
+    for dim in range(input_strategy.ndim):
+        if dim != topk_dim:
+            dim_shardings: PlacementList = [Shard(dim)] * 3
+            single_mesh_dim_strategies.append(dim_shardings)
+    # TODO: topk on sharded dim requries non-trival reduction, address it later
+
+    return expand_to_full_mesh_op_strategy(
+        input_strategy.mesh, op_schema, single_mesh_dim_strategies, input_index=2
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_matrix_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_matrix_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe0a9dfaa301d0372d7e74aafe8d22fb690400d1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_matrix_ops.py
@@ -0,0 +1,772 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+# implement matrix related ops for distributed tensor
+
+
+from typing import Optional
+
+import torch
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor._op_schema import (
+    OpSchema,
+    OpStrategy,
+    PlacementList,
+    PlacementStrategy,
+    RuntimeSchemaInfo,
+)
+from torch.distributed.tensor._ops._einsum_strategy import gen_einsum_strategies
+from torch.distributed.tensor._ops.utils import (
+    expand_to_full_mesh_op_strategy,
+    generate_redistribute_costs,
+    infer_broadcast_dims_map,
+    is_tensor_shardable,
+    map_placements_after_broadcast,
+    prod,
+    register_op_strategy,
+)
+from torch.distributed.tensor.placement_types import Placement, Replicate, Shard
+
+
+aten = torch.ops.aten
+
+
+@register_op_strategy(aten.t.default)
+def transpose_strategy(op_schema: OpSchema) -> OpStrategy:
+    self_strategy = op_schema.args_schema[0]
+    assert isinstance(self_strategy, OpStrategy)
+
+    transpose_strategies = []
+    for input_strategy in self_strategy.strategies:
+        input_spec = input_strategy.output_spec
+        # follow the input spec but transpose the Shard placements
+        output_placements = [
+            Shard(1 - p.dim) if isinstance(p, Shard) else p
+            for p in input_spec.placements
+        ]
+        transpose_strategy = PlacementStrategy(
+            output_specs=DTensorSpec(
+                mesh=input_strategy.mesh,
+                placements=tuple(output_placements),
+            ),
+            input_specs=(input_strategy.output_spec,),
+        )
+        transpose_strategies.append(transpose_strategy)
+
+    return OpStrategy(strategies=transpose_strategies)
+
+
+def _mm_like_strategy(
+    mm_equation: str, mesh: DeviceMesh, op_schema: OpSchema
+) -> OpStrategy:
+    self_strategy, mat2_strategy = op_schema.args_schema
+    assert isinstance(self_strategy, OpStrategy)
+    assert isinstance(mat2_strategy, OpStrategy)
+    # generate all possible strategies for mm
+    mm_strategy = gen_einsum_strategies(mm_equation, mesh)
+    # filter out invalid strategies and associate costs
+    strategies = mm_strategy.strategies
+    filtered_strategies = []
+    for strtg in strategies:
+        assert strtg.input_specs is not None
+        self_spec = strtg.input_specs[0]
+        mat2_spec = strtg.input_specs[1]
+        if is_tensor_shardable(self_strategy.shape, self_spec) and is_tensor_shardable(
+            mat2_strategy.shape, mat2_spec
+        ):
+            redistribute_cost = [
+                generate_redistribute_costs(self_strategy, self_spec),
+                generate_redistribute_costs(mat2_strategy, mat2_spec),
+            ]
+            strtg.redistribute_cost = redistribute_cost
+            filtered_strategies.append(strtg)
+
+    mm_strategy.strategies = filtered_strategies
+
+    return mm_strategy
+
+
+def _addmm_like_strategy(
+    mm_equation: str, mesh: DeviceMesh, op_schema: OpSchema
+) -> OpStrategy:
+    self_strategy, mat1_strategy, mat2_strategy = op_schema.args_schema
+    assert isinstance(self_strategy, OpStrategy)
+    assert isinstance(mat1_strategy, OpStrategy)
+    assert isinstance(mat2_strategy, OpStrategy)
+    self_shape = self_strategy.shape
+    mm_out_shape = torch.Size(
+        [
+            mat2_strategy.shape[-1] if i == len(mat1_strategy.shape) - 1 else dim_size
+            for i, dim_size in enumerate(mat1_strategy.shape)
+        ]
+    )
+    # generate all possible strategies for mm
+    mm_strategy = gen_einsum_strategies(mm_equation, mesh)
+    # filter out invalid strategies and associate costs
+    strategies = mm_strategy.strategies
+    filtered_strategies = []
+    for strtg in strategies:
+        # construct new strategy by consider the self arg
+        assert strtg.input_specs is not None
+        mat1_spec = strtg.input_specs[0]
+        mat2_spec = strtg.input_specs[1]
+        out_spec = strtg.output_spec
+
+        # self arg's spec should follow the output of mm, but need
+        # to consider broadcast for the self arg
+        broadcast_dims_map = infer_broadcast_dims_map(mm_out_shape, self_shape)
+        self_placements = map_placements_after_broadcast(
+            out_spec.placements, mm_out_shape, broadcast_dims_map
+        )
+        self_spec = DTensorSpec(mesh=mesh, placements=self_placements)
+
+        if is_tensor_shardable(mat1_strategy.shape, mat1_spec) and is_tensor_shardable(
+            mat2_strategy.shape, mat2_spec
+        ):
+            # update input specs with new self spec
+            strtg.input_specs = (self_spec, mat1_spec, mat2_spec)
+
+            # associate costs
+            redistribute_cost = [
+                generate_redistribute_costs(self_strategy, self_spec),
+                generate_redistribute_costs(mat1_strategy, mat1_spec),
+                generate_redistribute_costs(mat2_strategy, mat2_spec),
+            ]
+            strtg.redistribute_cost = redistribute_cost
+            filtered_strategies.append(strtg)
+
+    mm_strategy.strategies = filtered_strategies
+
+    return mm_strategy
+
+
+def _scaled_mm_like_strategy(
+    mm_equation: str, mesh: DeviceMesh, op_schema: OpSchema
+) -> OpStrategy:
+    (
+        self_strategy,
+        mat2_strategy,
+        scale_self_strategy,
+        scale_mat2_strategy,
+        bias_strategy,
+        scale_result_strategy,
+        *_,
+    ) = op_schema.args_schema
+    assert isinstance(self_strategy, OpStrategy)
+    assert isinstance(mat2_strategy, OpStrategy)
+    assert isinstance(scale_self_strategy, OpStrategy)
+    assert isinstance(scale_mat2_strategy, OpStrategy)
+    # TODO: add support for these later
+    assert bias_strategy is None, "_scaled_mm on DTensors doesn't support bias"
+    assert scale_result_strategy is None, (
+        "_scaled_mm on DTensors doesn't support scale_result"
+    )
+    # generate all possible strategies for mm
+    mm_strategy = gen_einsum_strategies(mm_equation, mesh)
+    # filter out invalid strategies and associate costs
+    strategies = mm_strategy.strategies
+    filtered_strategies = []
+    for strtg in strategies:
+        assert strtg.input_specs is not None
+        self_spec = strtg.input_specs[0]
+        mat2_spec = strtg.input_specs[1]
+        # propagate the operands' specs to their scales, except for tensor-wise
+        # scaling which can have any numbers of dims (legacy...), hence sharding
+        # dims won't map. for tensor-wise, anyways, we can only do replication.
+        scale_self_spec = (
+            DTensorSpec(self_spec.mesh, (Replicate(),))
+            if prod(scale_self_strategy.shape) == 1
+            else self_spec
+        )
+        scale_mat2_spec = (
+            DTensorSpec(mat2_spec.mesh, (Replicate(),))
+            if prod(scale_mat2_strategy.shape) == 1
+            else mat2_spec
+        )
+        strtg.input_specs = list(strtg.input_specs) + [scale_self_spec, scale_mat2_spec]
+        if (
+            is_tensor_shardable(self_strategy.shape, self_spec)
+            and is_tensor_shardable(mat2_strategy.shape, mat2_spec)
+            and is_tensor_shardable(scale_self_strategy.shape, scale_self_spec)
+            and is_tensor_shardable(scale_mat2_strategy.shape, scale_mat2_spec)
+        ):
+            redistribute_cost = [
+                generate_redistribute_costs(self_strategy, self_spec),
+                generate_redistribute_costs(mat2_strategy, mat2_spec),
+                generate_redistribute_costs(scale_self_strategy, scale_self_spec),
+                generate_redistribute_costs(scale_mat2_strategy, scale_mat2_spec),
+            ]
+            strtg.redistribute_cost = redistribute_cost
+            filtered_strategies.append(strtg)
+
+    mm_strategy.strategies = filtered_strategies
+
+    return mm_strategy
+
+
+@register_op_strategy(aten.mm.default)
+def mm_strategy(op_schema: OpSchema) -> OpStrategy:
+    mesh = op_schema.get_mesh_from_args()
+    return _mm_like_strategy("mk,kn->mn", mesh, op_schema)
+
+
+@register_op_strategy(aten.addmm.default)
+def addmm_strategy(op_schema: OpSchema) -> OpStrategy:
+    mesh = op_schema.get_mesh_from_args()
+    return _addmm_like_strategy("mk,kn->mn", mesh, op_schema)
+
+
+@register_op_strategy(aten.bmm.default)
+def bmm_strategy(op_schema: OpSchema) -> OpStrategy:
+    mesh = op_schema.get_mesh_from_args()
+    return _mm_like_strategy("bmk,bkn->bmn", mesh, op_schema)
+
+
+@register_op_strategy(aten.baddbmm.default)
+def baddmm_strategy(op_schema: OpSchema) -> OpStrategy:
+    mesh = op_schema.get_mesh_from_args()
+    return _addmm_like_strategy("bmk,bkn->bmn", mesh, op_schema)
+
+
+@register_op_strategy(aten._scaled_mm.default)
+def scaled_mm_strategy(op_schema: OpSchema) -> OpStrategy:
+    mesh = op_schema.get_mesh_from_args()
+    return _scaled_mm_like_strategy("mk,kn->mn", mesh, op_schema)
+
+
+@register_op_strategy(
+    aten._scaled_dot_product_flash_attention.default, schema_info=RuntimeSchemaInfo(5)
+)
+def scaled_dot_product_flash_attention_strategy(op_schema: OpSchema) -> OpStrategy:
+    # NOTE: currently we only support some simple strategies to support tensor parallelism
+    # TODO: sdpa might be a good candidate for us to explore decomposed sharding propagation
+    # as it involves: matmul, pointwise, reduction ops together.
+
+    mesh = op_schema.get_mesh_from_args()
+
+    return_debug_mask = len(op_schema.args_schema) >= 6 and op_schema.args_schema[5]
+    q_input_strategy = op_schema.args_schema[0]
+    assert isinstance(q_input_strategy, OpStrategy)
+    # assuming q/k/v have the same shape
+
+    single_mesh_dim_strategies = []
+
+    # placement list stores placements of [outputs, inputs]
+    # in the spda case, we have 3 valid tensor outputs and 3 tensor inputs
+    # first we can always accept full replication for both inputs and outputs
+    all_replicate: PlacementList = [
+        Replicate(),
+        Replicate(),
+        None,  # cum_seq_q
+        None,  # cum_seq_k
+        None,  # max_q
+        None,  # max_k
+        Replicate(),  # rng_state
+        None,  # unused
+        Replicate(),
+        Replicate(),
+        Replicate(),
+        Replicate(),
+    ]
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # second we can accept the sharding pattern of tensor parallelism, which
+    # shard on the num of head dim
+    qkv_sharding = Shard(1)  # num head dim
+    output_sharding = Shard(1)  # num head dim
+    logsumexp_sharding = Shard(1)  # num head dim
+    if return_debug_mask:
+        debug_attn_mask_sharding: Placement = Shard(1)  # num head dim
+    else:
+        # empty debug mask, replicated
+        debug_attn_mask_sharding = Replicate()
+
+    num_heads_dim_sharding: PlacementList = [
+        output_sharding,
+        logsumexp_sharding,
+        None,  # cum_seq_q
+        None,  # cum_seq_k
+        None,  # max_q
+        None,  # max_k
+        Replicate(),  # rng_state
+        None,  # unused
+        debug_attn_mask_sharding,
+        qkv_sharding,
+        qkv_sharding,
+        qkv_sharding,
+    ]
+    single_mesh_dim_strategies.append(num_heads_dim_sharding)
+
+    # Context Parallelism: shards on the sequence dim
+    single_mesh_dim_strategies.append(
+        [
+            Shard(2),  # output
+            Shard(2),  # logsumexp
+            None,  # cum_seq_q
+            None,  # cum_seq_k
+            None,  # max_q
+            None,  # max_k
+            Replicate(),  # rng_state
+            None,  # unused
+            Shard(2),  # debugattn
+            Shard(2),  # q
+            Shard(2),  # k
+            Shard(2),  # v
+        ]
+    )
+    return expand_to_full_mesh_op_strategy(
+        mesh, op_schema, single_mesh_dim_strategies, input_index=9
+    )
+
+
+@register_op_strategy(aten._scaled_dot_product_flash_attention_backward.default)
+def scaled_dot_product_flash_attention_backward_strategy(
+    op_schema: OpSchema,
+) -> OpStrategy:
+    # backward op does not need to validate the mesh since forward op has already done it
+    mesh = op_schema.get_mesh_from_args(validate=False)
+
+    q_input_strategy = op_schema.args_schema[1]
+    assert isinstance(q_input_strategy, OpStrategy)
+    # assuming q/k/v have the same shape
+
+    tensor_input_indices = [
+        i
+        for i, arg_spec in enumerate(op_schema.args_schema)
+        if isinstance(arg_spec, OpStrategy)
+    ]
+    num_tensor_inputs = len(tensor_input_indices)
+
+    single_mesh_dim_strategies = []
+
+    # placement list stores placements of [outputs, inputs]
+    # in the spda backward case, we have 3 tensor outputs and 6 to 10 tensor inputs
+    # first we can always accept full replication for both inputs and outputs
+    all_replicate: PlacementList = [Replicate()] * (3 + num_tensor_inputs)
+
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # second we can accept the sharding pattern of tensor parallelism, which
+    # shard on the num of head dim
+    grad_output_sharding = Shard(1)  # num head dim
+    qkv_sharding = Shard(1)  # num head dim
+    output_sharding = Shard(1)  # num head dim
+    logsumexp_sharding = Shard(1)  # num head dim
+    grad_qkv_sharding = Shard(1)  # num head dim
+
+    num_heads_dim_sharding: PlacementList = [
+        grad_qkv_sharding,
+        grad_qkv_sharding,
+        grad_qkv_sharding,
+        grad_output_sharding,
+        qkv_sharding,
+        qkv_sharding,
+        qkv_sharding,
+        output_sharding,
+        logsumexp_sharding,
+    ]
+    # accept replicate on the rest tensor inputs, potentially
+    # cum_seq_q, cum_seq_k, philox_seed, philox_offset
+    # at indices 6, 7, 12, 13, respectively
+    num_heads_dim_sharding.extend([Replicate()] * (num_tensor_inputs - 6))
+    single_mesh_dim_strategies.append(num_heads_dim_sharding)
+
+    # Context Parallelism: shards on the sequence dim
+    seq_dim_sharding: PlacementList = [
+        Shard(2),  # grad_q
+        Shard(2),  # grad_k
+        Shard(2),  # grad_v
+        Shard(2),  # grad_output
+        Shard(2),  # q
+        Shard(2),  # k
+        Shard(2),  # v
+        Shard(2),  # output
+        Shard(2),  # logsumexp
+    ]
+    # accept replicate on the rest tensor inputs, potentially
+    # cum_seq_q, cum_seq_k, philox_seed, philox_offset
+    # at indices 6, 7, 12, 13, respectively
+    seq_dim_sharding.extend([Replicate()] * (num_tensor_inputs - 6))
+    single_mesh_dim_strategies.append(seq_dim_sharding)
+
+    return expand_to_full_mesh_op_strategy(
+        mesh, op_schema, single_mesh_dim_strategies, input_index=3
+    )
+
+
+@register_op_strategy(aten.constant_pad_nd.default)
+def constant_pad_nd_strategy(op_schema: OpSchema) -> OpStrategy:
+    mesh = op_schema.get_mesh_from_args(validate=False)
+
+    # TODO(d4l3k); implement a more correct strategy for constant_pad_nd
+    return OpStrategy(
+        [
+            PlacementStrategy(
+                output_specs=DTensorSpec(mesh, (Replicate(),)),
+                input_specs=(
+                    DTensorSpec(mesh, (Replicate(),)),
+                    DTensorSpec(mesh, (Replicate(),)),
+                ),
+                redistribute_cost=[[1]],
+            )
+        ]
+    )
+
+
+@register_op_strategy(
+    aten._scaled_dot_product_efficient_attention.default,
+    schema_info=RuntimeSchemaInfo(4),
+)
+def scaled_dot_product_efficient_attention_strategy(op_schema: OpSchema) -> OpStrategy:
+    # NOTE: currently we only support some simple strategies to support tensor parallelism
+    mesh = op_schema.get_mesh_from_args()
+    q_input_strategy = op_schema.args_schema[0]
+    assert isinstance(q_input_strategy, OpStrategy)
+    # assuming q/k/v have the same shape
+
+    has_attn_bias = op_schema.args_schema[3] is not None
+    compute_log_sumexp = op_schema.args_schema[4]
+
+    single_mesh_dim_strategies: list[PlacementList] = []
+
+    # placement list stores placements of [outputs, inputs]
+    # in the spda case, we have 2 valid tensor outputs and 3 or 4 tensor inputs
+    # first we can always accept full replication for both inputs and outputs
+    all_replicate: PlacementList = [
+        Replicate(),
+        Replicate(),
+        None,
+        None,
+        Replicate(),
+        Replicate(),
+        Replicate(),
+    ]
+    if has_attn_bias:
+        all_replicate.append(Replicate())  # attn bias
+
+    # Context Parallelism: shards on the sequence dim
+    single_mesh_dim_strategies.append(
+        [
+            Shard(2),  # output
+            Shard(2),  # logsumexp
+            None,  # philox_seed
+            None,  # philox_offset
+            Shard(2),  # q
+            Shard(2),  # k
+            Shard(2),  # v
+        ]
+    )
+
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # second we can accept the sharding pattern of tensor parallelism, which
+    # shard on the heads dimension
+    qkv_sharding = Shard(1)
+    output_sharding = Shard(1)
+    if compute_log_sumexp:
+        logsumexp_sharding: Placement = Shard(1)
+    else:
+        # empty logsumexp, replicated
+        logsumexp_sharding = Replicate()
+
+    num_heads_dim_sharding = [
+        output_sharding,
+        logsumexp_sharding,
+        None,
+        None,
+        qkv_sharding,
+        qkv_sharding,
+        qkv_sharding,
+    ]
+    if has_attn_bias:
+        num_heads_dim_sharding.append(Shard(1))
+    single_mesh_dim_strategies.append(num_heads_dim_sharding)
+
+    return expand_to_full_mesh_op_strategy(
+        mesh,
+        op_schema,
+        single_mesh_dim_strategies,
+        input_index=4,
+    )
+
+
+@register_op_strategy(aten._scaled_dot_product_efficient_attention_backward.default)
+def scaled_dot_product_efficient_attention_backward_strategy(
+    op_schema: OpSchema,
+) -> OpStrategy:
+    # backward op does not need to validate the mesh since forward op has already done it
+    mesh = op_schema.get_mesh_from_args(validate=False)
+
+    q_input_strategy = op_schema.args_schema[1]
+    assert isinstance(q_input_strategy, OpStrategy)
+    # assuming q/k/v have the same shape
+    has_attn_bias = op_schema.args_schema[4] is not None
+
+    single_mesh_dim_strategies = []
+
+    # placement list stores placements of [outputs, inputs]
+    # in the spda backward case, we have 4 tensor outputs and 8 or 9 tensor inputs
+    # NOTE: Output sharding of grad_bias on heads dim if attn_bias is present;
+    #       otherwise grad_bias will be empty and its DTensorSpec will be removed.
+    # first we can always accept full replication for both inputs and outputs
+    all_replicate: PlacementList = [Replicate()] * (12 + has_attn_bias)
+
+    if not has_attn_bias:
+        all_replicate[3] = None  # grad bias is None if attn_bias is not present
+
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # second we can accept the sharding pattern of tensor parallelism, which
+    # shard on the heads dimension
+    grad_output_sharding = Shard(1)
+    qkv_sharding = Shard(1)
+    output_sharding = Shard(1)
+    logsumexp_sharding = Shard(1)
+    grad_qkv_sharding = Shard(1)
+    grad_bias_sharding = Shard(1) if has_attn_bias else None
+
+    num_heads_dim_sharding: PlacementList = [
+        grad_qkv_sharding,
+        grad_qkv_sharding,
+        grad_qkv_sharding,
+        grad_bias_sharding,
+        grad_output_sharding,
+        qkv_sharding,
+        qkv_sharding,
+        qkv_sharding,
+        # the place for optional input attn_bias,
+        output_sharding,
+        logsumexp_sharding,
+    ]
+    # input sharding of attn_bias on heads dim if present
+    if has_attn_bias:
+        num_heads_dim_sharding.insert(8, Shard(1))
+    # accept replicate on the rest scalar tensor inputs
+    # namely philox_seed and philox_offset
+    num_heads_dim_sharding.extend([Replicate(), Replicate()])
+    single_mesh_dim_strategies.append(num_heads_dim_sharding)
+
+    # Context Parallelism: shards on the sequence dim
+    seq_dim_sharding: PlacementList = [
+        Shard(2),  # grad_q
+        Shard(2),  # grad_k
+        Shard(2),  # grad_v
+        Shard(1) if has_attn_bias else None,  # grad_bias
+        Shard(2),  # grad_output
+        Shard(2),  # q
+        Shard(2),  # k
+        Shard(2),  # v
+        Shard(2),  # output
+        Shard(2),  # logsumexp
+    ]
+    # accept replicate on the rest tensor inputs, potentially
+    # cum_seq_q, cum_seq_k, philox_seed, philox_offset
+    # at indices 6, 7, 12, 13, respectively
+    if has_attn_bias:
+        num_heads_dim_sharding.insert(8, Shard(1))
+    seq_dim_sharding.extend([Replicate(), Replicate()])
+    single_mesh_dim_strategies.append(seq_dim_sharding)
+
+    return expand_to_full_mesh_op_strategy(
+        mesh,
+        op_schema,
+        single_mesh_dim_strategies,
+        input_index=4,
+    )
+
+
+@register_op_strategy(
+    aten._scaled_dot_product_cudnn_attention.default,
+    schema_info=RuntimeSchemaInfo(4),
+)
+def scaled_dot_product_cudnn_attention_strategy(op_schema: OpSchema) -> OpStrategy:
+    mesh = op_schema.get_mesh_from_args()
+
+    (
+        query_strategy,  # query
+        _,  # key
+        _,  # value
+        attn_bias_strategy,
+        compute_log_sumexp,  # compute_log_sumexp
+        *rest_args,  # optional args: dropout_p, is_causal, return_debug_mask, scale
+    ) = op_schema.args_schema
+    return_debug_mask = len(op_schema.args_schema) >= 8 and rest_args[2]
+    has_attn_bias = attn_bias_strategy is not None
+    debug_attn_mask_sharding: Optional[Placement] = (
+        Replicate() if return_debug_mask else None
+    )
+
+    assert isinstance(query_strategy, OpStrategy)
+    # assuming q/k/v have the same shape
+
+    single_mesh_dim_strategies = []
+
+    # placement list stores placements of [outputs, inputs]
+    # in the spda case, we have 2 valid tensor outputs and 3 tensor inputs
+    # first we can always accept full replication for both inputs and outputs
+    all_replicate: PlacementList = [
+        Replicate(),  # output
+        Replicate(),  # logsumexp
+        None,  # cum_seq_q
+        None,  # cum_seq_k
+        None,  # max_q
+        None,  # max_k
+        None,  # philox_seed
+        None,  # philox_offset
+        # NOTE: debug_attn_mask is not supproted by pytorch and is always an empty tensor
+        # https://github.com/pytorch/pytorch/blob/60205b0eb2602317856312a66d955c88334ade0b/aten/src/ATen/native/transformers/cuda/attention.cu#L839-L840
+        debug_attn_mask_sharding,  # debug_attn_mask
+        Replicate(),  # q
+        Replicate(),  # k
+        Replicate(),  # v
+    ]
+    if has_attn_bias:
+        all_replicate.append(Replicate())  # attn bias
+
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # second we can accept the sharding pattern of tensor parallelism, which
+    # shard on the num of head dim
+    tp_sharding = Shard(1)  # num head dim
+    qkv_sharding = tp_sharding
+    output_sharding = tp_sharding
+    logsumexp_sharding = tp_sharding if compute_log_sumexp else Replicate()
+    debug_attn_mask_sharding = tp_sharding if return_debug_mask else None
+
+    num_heads_dim_sharding: PlacementList = [
+        output_sharding,
+        logsumexp_sharding,
+        None,  # cum_seq_q
+        None,  # cum_seq_k
+        None,  # max_q
+        None,  # max_k
+        None,  # philox_seed
+        None,  # philox_offset
+        debug_attn_mask_sharding,
+        qkv_sharding,
+        qkv_sharding,
+        qkv_sharding,
+    ]
+    single_mesh_dim_strategies.append(num_heads_dim_sharding)
+
+    # Context Parallelism: shards on the sequence dim
+    cp_sharding = Shard(2)  # seq dim
+    logsumexp_sharding = cp_sharding if compute_log_sumexp else Replicate()
+    debug_attn_mask_sharding = cp_sharding if return_debug_mask else None
+
+    single_mesh_dim_strategies.append(
+        [
+            cp_sharding,  # output
+            logsumexp_sharding,  # logsumexp
+            None,  # cum_seq_q
+            None,  # cum_seq_k
+            None,  # max_q
+            None,  # max_k
+            None,  # philox_seed
+            None,  # philox_offset
+            debug_attn_mask_sharding,  # debug_attn_mask
+            cp_sharding,  # q
+            cp_sharding,  # k
+            cp_sharding,  # v
+        ]
+    )
+    return expand_to_full_mesh_op_strategy(
+        mesh, op_schema, single_mesh_dim_strategies, input_index=9
+    )
+
+
+@register_op_strategy(aten._scaled_dot_product_cudnn_attention_backward.default)
+def scaled_scaled_dot_product_cudnn_attention_backward_strategy(
+    op_schema: OpSchema,
+) -> OpStrategy:
+    # backward op does not need to validate the mesh since forward op has already done it
+    mesh = op_schema.get_mesh_from_args(validate=False)
+
+    assert len(op_schema.args_schema) >= 15
+    has_attn_bias = op_schema.args_schema[8] is not None
+    has_scale = len(op_schema.args_schema) >= 16 and False
+
+    query_strategy = op_schema.args_schema[1]
+    assert isinstance(query_strategy, OpStrategy)
+    # assuming q/k/v have the same shape
+
+    single_mesh_dim_strategies = []
+
+    # placement list stores placements of [outputs, inputs]
+    # cudnn outputs: (Tensor dq, Tensor dk, Tensor dv)
+    # cudnn inputs: (
+    #   Tensor grad_out,
+    #   Tensor query,
+    #   Tensor key,
+    #   Tensor value,
+    #   Tensor out,
+    #   Tensor logsumexp,
+    #   Tensor philox_seed,
+    #   Tensor philox_offset,
+    #   Tensor attn_bias,
+    #   Tensor cum_seq_q,
+    #   Tensor cum_seq_k,
+    #   SymInt max_q,
+    #   SymInt max_k,
+    #   float dropout_p,
+    #   bool is_causal,
+    #   int? scale,
+    # )
+
+    # case 1: we can always accept full replication for both inputs and outputs
+    all_replicate_out: PlacementList = [
+        Replicate(),  # dq
+        Replicate(),  # dk
+        Replicate(),  # dv
+    ]
+    all_replicate_inp: PlacementList = [Replicate()] * 6
+    all_replicate_inp += [
+        Replicate()
+    ] * 2  # philox_seed, philox_offset is casted to Replicate() in DTensor
+    all_replicate_inp += [Replicate() if has_attn_bias else None]
+    all_replicate_inp += [None] * 6
+    if has_scale:
+        all_replicate_inp.append(None)
+
+    all_replicate: PlacementList = all_replicate_out + all_replicate_inp
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # case 2: we can accept the sharding pattern of tensor parallelism, which
+    #   shards on the num of head dim
+    qkv_sharding = Shard(1)  # num head dim
+    output_sharding = Shard(1)  # num head dim
+    logsumexp_sharding = Shard(1)  # num head dim
+
+    num_heads_dim_sharding_out: PlacementList = [qkv_sharding] * 3
+    num_heads_dim_sharding_inp: PlacementList = [qkv_sharding] * 4
+    num_heads_dim_sharding_inp += [output_sharding]
+    num_heads_dim_sharding_inp += [logsumexp_sharding]
+    num_heads_dim_sharding_inp += [
+        Replicate()
+    ] * 2  # philox_seed, philox_offset is casted to Replicate() in DTensor
+    num_heads_dim_sharding_inp += [Shard(1) if has_attn_bias else None]
+    num_heads_dim_sharding_inp += [None] * 6
+    if has_scale:
+        num_heads_dim_sharding_inp.append(None)
+
+    num_heads_dim_sharding = num_heads_dim_sharding_out + num_heads_dim_sharding_inp
+    single_mesh_dim_strategies.append(num_heads_dim_sharding)
+
+    # case 3: Context Parallelism which shards on the sequence dim
+    context_parallel_sharding_out: PlacementList = [Shard(2)] * 3
+    context_parallel_sharding_inp: PlacementList = [Shard(2)] * 6
+    context_parallel_sharding_inp += [
+        Replicate()
+    ] * 2  # philox_seed, philox_offset is casted to Replicate() in DTensor
+    context_parallel_sharding_inp += [Shard(2) if has_attn_bias else None]
+    context_parallel_sharding_inp += [None] * 6
+    if has_scale:
+        context_parallel_sharding_inp.append(None)
+
+    context_parallel_sharding = (
+        context_parallel_sharding_out + context_parallel_sharding_inp
+    )
+    single_mesh_dim_strategies.append(context_parallel_sharding)
+
+    return expand_to_full_mesh_op_strategy(
+        mesh, op_schema, single_mesh_dim_strategies, input_index=3
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_pointwise_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_pointwise_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..4144b937544d8cee7b9cfe83f9d9daa09a05cd3b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_pointwise_ops.py
@@ -0,0 +1,695 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from collections.abc import Sequence
+from typing import cast
+
+import torch
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor._op_schema import (
+    _is_inplace_op,
+    _is_out_variant_op,
+    OpSchema,
+    OpStrategy,
+    PlacementStrategy,
+    RuntimeSchemaInfo,
+    StrategyType,
+    TupleStrategy,
+)
+from torch.distributed.tensor._ops.utils import (
+    generate_redistribute_costs,
+    infer_broadcast_dims_map,
+    map_placements_after_broadcast,
+    normalize_dim,
+    register_op_strategy,
+)
+from torch.distributed.tensor.placement_types import (
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+aten = torch.ops.aten
+# leave the remaining pointwise_ops list here for convenience,
+# Below ops are some pointwise ops that are yet to be supported,
+# they might not be a complete list.
+# pointwise_ops = [
+#     "fake_quantize_per_channel_affine",
+#     "fake_quantize_per_tensor_affine",
+#     "floor_divide",  # floor_divide is deprecated
+#     "frexp",  # multiple output pointwise op, need to add support
+#     "gradient",  #  need investigation on this op
+#     "imag",  # complex data type only
+#     "quantized_batch_norm",
+#     "quantized_max_pool1d",
+#     "quantized_max_pool2d",
+#     "real",  # complex data type only
+# ]
+
+
+linear_pointwise_ops = [
+    aten.div.Scalar,  # this op is linear on the first argument, and the second argument is scalar, so it fits as a linear op.
+    aten.div_.Scalar,  # this op is linear on the first argument, and the second argument is scalar, so it fits as a linear op.
+    aten.to.dtype,
+    aten.add.Tensor,
+    aten.add_.Tensor,
+]
+
+
+pointwise_ops = [
+    # please keep the entries below alphabetically sorted
+    aten.__ilshift__.Scalar,
+    aten.__ilshift__.Tensor,
+    aten.__irshift__.Scalar,
+    aten.__irshift__.Tensor,
+    aten.__lshift__.Scalar,
+    aten.__lshift__.Tensor,
+    aten.__rshift__.Scalar,
+    aten.__rshift__.Tensor,
+    aten._conj.default,
+    aten.abs.default,
+    aten.abs.out,
+    aten.abs_.default,
+    aten.acos.default,
+    aten.acos.out,
+    aten.acos_.default,
+    aten.acosh.default,
+    aten.acosh.out,
+    aten.acosh_.default,
+    aten.add.Scalar,
+    aten.add.out,
+    aten.add_.Scalar,
+    aten.addcdiv.default,
+    aten.addcdiv.out,
+    aten.addcdiv_.default,
+    aten.addcmul.default,
+    aten.addcmul.out,
+    aten.addcmul_.default,
+    aten.angle.default,
+    aten.angle.out,
+    aten.asin.default,
+    aten.asin.out,
+    aten.asin_.default,
+    aten.asinh.default,
+    aten.asinh.out,
+    aten.asinh_.default,
+    aten.atan.default,
+    aten.atan.out,
+    aten.atan2.default,
+    aten.atan2.out,
+    aten.atan2_.default,
+    aten.atan_.default,
+    aten.atanh.default,
+    aten.atanh.out,
+    aten.atanh_.default,
+    aten.bitwise_and.Scalar,
+    aten.bitwise_and.Scalar_Tensor,
+    aten.bitwise_and.Scalar_out,
+    aten.bitwise_and.Tensor,
+    aten.bitwise_and.Tensor_out,
+    aten.bitwise_and_.Scalar,
+    aten.bitwise_and_.Tensor,
+    aten.bitwise_left_shift.Scalar_Tensor,
+    aten.bitwise_left_shift.Tensor,
+    aten.bitwise_left_shift.Tensor_Scalar,
+    aten.bitwise_left_shift.Tensor_Scalar_out,
+    aten.bitwise_left_shift.Tensor_out,
+    aten.bitwise_left_shift_.Tensor,
+    aten.bitwise_left_shift_.Tensor_Scalar,
+    aten.bitwise_not.default,
+    aten.bitwise_not.out,
+    aten.bitwise_not_.default,
+    aten.bitwise_or.Scalar,
+    aten.bitwise_or.Scalar_Tensor,
+    aten.bitwise_or.Scalar_out,
+    aten.bitwise_or.Tensor,
+    aten.bitwise_or.Tensor_out,
+    aten.bitwise_or_.Scalar,
+    aten.bitwise_or_.Tensor,
+    aten.bitwise_right_shift.Scalar_Tensor,
+    aten.bitwise_right_shift.Tensor,
+    aten.bitwise_right_shift.Tensor_Scalar,
+    aten.bitwise_right_shift.Tensor_Scalar_out,
+    aten.bitwise_right_shift.Tensor_out,
+    aten.bitwise_right_shift_.Tensor,
+    aten.bitwise_right_shift_.Tensor_Scalar,
+    aten.bitwise_xor.Scalar,
+    aten.bitwise_xor.Scalar_Tensor,
+    aten.bitwise_xor.Scalar_out,
+    aten.bitwise_xor.Tensor,
+    aten.bitwise_xor.Tensor_out,
+    aten.bitwise_xor_.Scalar,
+    aten.bitwise_xor_.Tensor,
+    aten.ceil.default,
+    aten.ceil.out,
+    aten.ceil_.default,
+    aten.clamp.default,
+    aten.clamp.out,
+    aten.clamp_.default,
+    aten.clip.default,
+    aten.clip.out,
+    aten.clip_.default,
+    aten.conj_physical.default,
+    aten.conj_physical.out,
+    aten.conj_physical_.default,
+    aten.copysign.Scalar,
+    aten.copysign.Scalar_out,
+    aten.copysign.Tensor,
+    aten.copysign.out,
+    aten.copysign_.Scalar,
+    aten.copysign_.Tensor,
+    aten.cos.default,
+    aten.cos.out,
+    aten.cos_.default,
+    aten.cosh.default,
+    aten.cosh.out,
+    aten.cosh_.default,
+    aten.deg2rad.default,
+    aten.deg2rad.out,
+    aten.deg2rad_.default,
+    aten.digamma.default,
+    aten.digamma.out,
+    aten.digamma_.default,
+    aten.div.Tensor,
+    aten.div.Tensor_mode,
+    aten.div.out,
+    aten.div.out_mode,
+    aten.div_.Tensor,
+    aten.div_.Tensor_mode,
+    aten.eq.Tensor,
+    aten.eq.Tensor_out,
+    aten.eq.Scalar,
+    aten.eq.Scalar_out,
+    aten.erf.default,
+    aten.erf.out,
+    aten.erf_.default,
+    aten.erfc.default,
+    aten.erfc.out,
+    aten.erfc_.default,
+    aten.erfinv.default,
+    aten.erfinv.out,
+    aten.erfinv_.default,
+    aten.exp.default,
+    aten.exp.out,
+    aten.exp2.default,
+    aten.exp2.out,
+    aten.exp2_.default,
+    aten.exp_.default,
+    aten.expm1.default,
+    aten.expm1.out,
+    aten.expm1_.default,
+    aten.float_power.Scalar,
+    aten.float_power.Scalar_out,
+    aten.float_power.Tensor_Scalar,
+    aten.float_power.Tensor_Scalar_out,
+    aten.float_power.Tensor_Tensor,
+    aten.float_power.Tensor_Tensor_out,
+    aten.float_power_.Scalar,
+    aten.float_power_.Tensor,
+    aten.floor.default,
+    aten.floor.out,
+    aten.floor_.default,
+    aten.fmod.Scalar,
+    aten.fmod.Scalar_out,
+    aten.fmod.Tensor,
+    aten.fmod.Tensor_out,
+    aten.fmod_.Scalar,
+    aten.fmod_.Tensor,
+    aten.frac.default,
+    aten.frac.out,
+    aten.frac_.default,
+    aten.ge.Scalar,
+    aten.ge.Tensor,
+    aten.gelu.default,
+    aten.gt.Tensor,
+    aten.gt.Tensor_out,
+    aten.gt.Scalar,
+    aten.gt.Scalar_out,
+    aten.gt.Scalar,
+    aten.gt.Tensor,
+    aten.hypot.default,
+    aten.hypot.out,
+    aten.hypot_.default,
+    aten.i0.default,
+    aten.i0.out,
+    aten.i0_.default,
+    aten.igamma.default,
+    aten.igamma.out,
+    aten.igamma_.default,
+    aten.igammac.default,
+    aten.igammac.out,
+    aten.igammac_.default,
+    aten.isinf.default,
+    aten.isnan.default,
+    aten.isneginf.default,
+    aten.isneginf.out,
+    aten.isposinf.default,
+    aten.isposinf.out,
+    aten.ldexp.default,
+    aten.ldexp.out,
+    aten.ldexp_.default,
+    aten.lt.Tensor,
+    aten.lt.Tensor_out,
+    aten.lt.Scalar,
+    aten.lt.Scalar_out,
+    aten.le.Scalar,
+    aten.le.Tensor,
+    aten.lerp.Scalar,
+    aten.lerp.Scalar_out,
+    aten.lerp.Tensor,
+    aten.lerp.Tensor_out,
+    aten.lerp_.Scalar,
+    aten.lerp_.Tensor,
+    aten.lgamma.default,
+    aten.lgamma.out,
+    aten.lgamma_.default,
+    aten.log.default,
+    aten.log.out,
+    aten.log10.default,
+    aten.log10.out,
+    aten.log10_.default,
+    aten.log1p.default,
+    aten.log1p.out,
+    aten.log1p_.default,
+    aten.log2.default,
+    aten.log2.out,
+    aten.log2_.default,
+    aten.log_.default,
+    aten.logaddexp.default,
+    aten.logaddexp.out,
+    aten.logaddexp2.default,
+    aten.logaddexp2.out,
+    aten.logical_and.default,
+    aten.logical_and.out,
+    aten.logical_and_.default,
+    aten.logical_not.default,
+    aten.logical_not.out,
+    aten.logical_not_.default,
+    aten.logical_or.default,
+    aten.logical_or.out,
+    aten.logical_or_.default,
+    aten.logical_xor.default,
+    aten.logical_xor.out,
+    aten.logical_xor_.default,
+    aten.logit.default,
+    aten.logit.out,
+    aten.logit_.default,
+    aten.masked_fill.Scalar,
+    aten.maximum.default,
+    aten.maximum.out,
+    aten.minimum.default,
+    aten.minimum.out,
+    aten.mul.Scalar,
+    aten.mul.Tensor,
+    aten.mul.out,
+    aten.mul_.Scalar,
+    aten.mul_.Tensor,
+    aten.mvlgamma.default,
+    aten.mvlgamma.out,
+    aten.mvlgamma_.default,
+    aten.native_dropout_backward.default,
+    aten.native_dropout_backward.out,
+    aten.nan_to_num.default,
+    aten.nan_to_num.out,
+    aten.nan_to_num_.default,
+    aten.ne.Scalar,
+    aten.neg.default,
+    aten.neg.out,
+    aten.neg_.default,
+    aten.nextafter.default,
+    aten.nextafter.out,
+    aten.nextafter_.default,
+    aten.polygamma.default,
+    aten.polygamma.out,
+    aten.polygamma_.default,
+    aten.positive.default,
+    aten.pow.Scalar,
+    aten.pow.Scalar_out,
+    aten.pow.Tensor_Scalar,
+    aten.pow.Tensor_Scalar_out,
+    aten.pow.Tensor_Tensor,
+    aten.pow.Tensor_Tensor_out,
+    aten.pow_.Scalar,
+    aten.pow_.Tensor,
+    aten.reciprocal.default,
+    aten.reciprocal.out,
+    aten.reciprocal_.default,
+    aten.rad2deg.default,
+    aten.rad2deg.out,
+    aten.rad2deg_.default,
+    aten.relu.default,
+    aten.relu_.default,
+    aten.remainder.Scalar,
+    aten.remainder.Scalar_Tensor,
+    aten.remainder.Scalar_out,
+    aten.remainder.Tensor,
+    aten.remainder.Tensor_out,
+    aten.remainder_.Scalar,
+    aten.remainder_.Tensor,
+    aten.round.decimals,
+    aten.round.decimals_out,
+    aten.round.default,
+    aten.round.out,
+    aten.round_.decimals,
+    aten.round_.default,
+    aten.rsqrt.default,
+    aten.rsqrt.out,
+    aten.rsqrt_.default,
+    aten.rsub.Scalar,
+    aten.sgn.default,
+    aten.sgn.out,
+    aten.sgn_.default,
+    aten.sigmoid.default,
+    aten.sigmoid.out,
+    aten.sigmoid_.default,
+    aten.sign.default,
+    aten.sign.out,
+    aten.sign_.default,
+    aten.signbit.default,
+    aten.signbit.out,
+    aten.silu.default,
+    aten.silu.out,
+    aten.sin.default,
+    aten.sin.out,
+    aten.sin_.default,
+    aten.sinc.default,
+    aten.sinc.out,
+    aten.sinc_.default,
+    aten.sinh.default,
+    aten.sinh.out,
+    aten.sinh_.default,
+    aten.sqrt.default,
+    aten.sqrt.out,
+    aten.sqrt_.default,
+    aten.square.default,
+    aten.square.out,
+    aten.square_.default,
+    aten.sub.Scalar,
+    aten.sub.Tensor,
+    aten.sub.out,
+    aten.sub_.Scalar,
+    aten.sub_.Tensor,
+    aten.tan.default,
+    aten.tan.out,
+    aten.tan_.default,
+    aten.tanh.default,
+    aten.tanh.out,
+    aten.tanh_.default,
+    aten.true_divide.Tensor,
+    aten.trunc.default,
+    aten.trunc.out,
+    aten.trunc_.default,
+    aten.where.self,
+    aten.where.self_out,
+    aten.xlogy.OutScalar_Self,
+    aten.xlogy.OutScalar_Other,
+    aten.xlogy.OutTensor,
+    aten.xlogy.Scalar_Other,
+    aten.xlogy.Scalar_Self,
+    aten.xlogy.Tensor,
+    aten.xlogy_.Scalar_Other,
+    aten.xlogy_.Tensor,
+    # backward point-wise ops
+    # please keep the entries below alphabetically sorted
+    aten.gelu_backward.default,
+    aten.sigmoid_backward.default,
+    aten.silu_backward.default,
+    aten.tanh_backward.default,
+    aten.threshold_backward.default,
+]
+
+
+def pointwise_strategy(op_schema: OpSchema, linearity: bool = False) -> OpStrategy:
+    max_shards_strategy_index = -1
+    max_shards = -1
+
+    if _is_inplace_op(op_schema.op):
+        # inplace op should follow the first arg strategy
+        followed_strategy = op_schema.args_schema[0]
+    elif _is_out_variant_op(op_schema.op):
+        # out variant op should follow the out kwarg strategy
+        followed_strategy = op_schema.kwargs_schema["out"]
+    else:
+        # normal pointwise op, we choose to follow the arg with
+        # the max shards in case operands needs reshard
+        for idx, arg_strategy in enumerate(op_schema.args_schema):
+            if not isinstance(arg_strategy, OpStrategy):
+                continue
+
+            arg_max_shards = arg_strategy.max_num_shards()
+            if arg_max_shards > max_shards:
+                max_shards_strategy_index = idx
+                max_shards = arg_max_shards
+
+        followed_strategy = op_schema.args_schema[max_shards_strategy_index]
+
+    assert isinstance(followed_strategy, OpStrategy), (
+        f"no strategy to follow for {op_schema}!"
+    )
+    return common_pointwise_strategy(
+        op_schema.args_schema, followed_strategy, linearity
+    )
+
+
+def common_pointwise_strategy(
+    args_schema: Sequence[object],
+    followed_strategy: OpStrategy,
+    linearity: bool,
+) -> OpStrategy:
+    # handle broadcasting
+    common_shape = torch.broadcast_shapes(
+        *[arg.shape for arg in args_schema if isinstance(arg, OpStrategy)]
+    )
+    pointwise_strategy = OpStrategy([])
+
+    for placement_strategy in followed_strategy.strategies:
+        spec_to_follow = placement_strategy.output_spec
+        out_placements: list[Placement] = []
+        for placement in spec_to_follow.placements:
+            if isinstance(placement, Shard):
+                shard_dim = normalize_dim(placement.dim, len(spec_to_follow.shape))
+                common_ndim = len(common_shape)
+                new_shard_dim = common_ndim - len(spec_to_follow.shape) + shard_dim
+                out_placements.append(Shard(new_shard_dim))
+            elif isinstance(placement, Partial) and not linearity:
+                # clear the partial placemnet if op does not support linearity
+                # by default we just replicate the partial, need to see if this
+                # is optimal for all cases
+                out_placements.append(Replicate())
+            else:
+                out_placements.append(placement)
+
+        input_specs: list[DTensorSpec] = []
+        redistribute_costs: list[list[float]] = []
+        for arg_idx, input_arg in enumerate(args_schema):
+            if isinstance(input_arg, OpStrategy):
+                # sanity check that all args that follow the same strategy
+                # are on the same DeviceMesh
+                if input_arg.mesh != followed_strategy.mesh:
+                    raise ValueError(
+                        f"Could not run pointwise computation across different mesh: "
+                        f"Found {input_arg.mesh} and {followed_strategy.mesh}!"
+                    )
+
+                # every arg follow the out_placements, but need to handle broadcasting
+                input_arg_spec = input_arg.strategies[0].output_spec
+                input_arg_dims_map = infer_broadcast_dims_map(
+                    common_shape, input_arg_spec.shape
+                )
+                input_target_placements = map_placements_after_broadcast(
+                    tuple(out_placements),
+                    common_shape,
+                    input_arg_dims_map,
+                )
+                input_arg_target_spec = DTensorSpec(
+                    mesh=followed_strategy.mesh,
+                    placements=input_target_placements,
+                    tensor_meta=input_arg_spec.tensor_meta,
+                )
+                input_specs.append(input_arg_target_spec)
+                redistribute_costs.append(
+                    generate_redistribute_costs(input_arg, input_arg_target_spec)
+                )
+
+        pointwise_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=DTensorSpec(
+                    mesh=followed_strategy.mesh,
+                    placements=tuple(out_placements),
+                ),
+                input_specs=input_specs,
+                redistribute_cost=redistribute_costs,
+            )
+        )
+    return pointwise_strategy
+
+
+def linear_pointwise_strategy(op_schema: OpSchema) -> StrategyType:
+    """
+    Linear pointwise operators can propagate pending reductions.
+    For example, c = add(a, b); if a is pending sum, then c will be
+    pending sum as well without any communication overhead.
+    """
+    return pointwise_strategy(op_schema, linearity=True)
+
+
+for op in linear_pointwise_ops:
+    register_op_strategy(op, schema_info=RuntimeSchemaInfo(static_kwargkey=["out"]))(
+        linear_pointwise_strategy
+    )
+
+for op in pointwise_ops:
+    register_op_strategy(op, schema_info=RuntimeSchemaInfo(static_kwargkey=["out"]))(
+        pointwise_strategy
+    )
+
+
+# TODO: add all for_each ops
+for_each_ops = [
+    aten._foreach_abs.default,
+    aten._foreach_abs_.default,
+    aten._foreach_addcdiv_.Scalar,
+    aten._foreach_addcdiv_.ScalarList,
+    aten._foreach_addcdiv_.Tensor,
+    aten._foreach_addcmul.Scalar,
+    aten._foreach_addcmul_.Scalar,
+    aten._foreach_addcmul_.ScalarList,
+    aten._foreach_addcmul_.Tensor,
+    aten._foreach_clamp_max_.Scalar,
+    aten._foreach_clamp_min_.Scalar,
+    aten._foreach_div_.List,
+    aten._foreach_div_.Scalar,
+    aten._foreach_div_.ScalarList,
+    aten._foreach_div_.Tensor,
+    aten._foreach_div.List,
+    aten._foreach_div.Scalar,
+    aten._foreach_div.ScalarList,
+    aten._foreach_div.Tensor,
+    aten._foreach_lerp_.Scalar,
+    aten._foreach_maximum_.List,
+    aten._foreach_mul.Scalar,
+    aten._foreach_mul.ScalarList,
+    aten._foreach_mul.Tensor,
+    aten._foreach_mul.List,
+    aten._foreach_mul_.Scalar,
+    aten._foreach_mul_.ScalarList,
+    aten._foreach_mul_.Tensor,
+    aten._foreach_mul_.List,
+    aten._foreach_neg.default,
+    aten._foreach_neg_.default,
+    aten._foreach_reciprocal_.default,
+    aten._foreach_sub.Scalar,
+    aten._foreach_sub_.Scalar,
+    aten._foreach_sub.List,
+    aten._foreach_sub_.List,
+    aten._foreach_sub.ScalarList,
+    aten._foreach_sub_.ScalarList,
+    aten._foreach_sqrt.default,
+    aten._foreach_sqrt_.default,
+    aten._foreach_zero_.default,
+    aten._foreach_exp.default,
+    aten._foreach_exp_.default,
+    aten._foreach_cos.default,
+    aten._foreach_cos_.default,
+    aten._foreach_log.default,
+    aten._foreach_log_.default,
+    aten._amp_foreach_non_finite_check_and_unscale_.default,
+]
+
+for_each_linearity_ops = [
+    aten._foreach_add.Scalar,
+    aten._foreach_add_.Scalar,
+    aten._foreach_add_.ScalarList,
+    aten._foreach_add.List,
+    aten._foreach_add_.List,
+]
+
+
+def list_pointwise_strategy(
+    op_schema: OpSchema, linearity: bool = False
+) -> StrategyType:
+    """
+    Apply the pointwise strategy to the zipped arguments. For example, if we
+    run a foreach add of two lists l1 and l2, then we apply the pointwise
+    strategy on each pair (l1[i], l2[i]). If the first argument is a list but
+    the second (or later) one is a tensor, then we broadcast the tensor by
+    replicating it into a list with the length of the first argument.
+
+    Args:
+        mesh (DeviceMesh): device mesh for pointwise ops
+        op_schema (OpSchema): schema of the operator to generate strategy for
+        linearity (bool): specify whether op(a) + op(b) = op(a + b)
+
+    Returns:
+        OpStrategy: generated strategy
+    """
+
+    def args_tuple_strategies(args_schema: tuple[object, ...]) -> list[TupleStrategy]:
+        first_arg = args_schema[0]
+        assert isinstance(first_arg, TupleStrategy)
+        strategy_len = len(first_arg.childs)
+        tuple_strategies: list[TupleStrategy] = []
+        for arg_idx, arg in enumerate(args_schema):
+            if isinstance(arg, TupleStrategy):
+                # every tuple strategy should have the same length
+                assert len(arg.childs) == strategy_len
+                tuple_strategies.append(arg)
+            elif isinstance(arg, OpStrategy):
+                if arg_idx > 0:  # implicitly broadcast
+                    tuple_strategies.append(
+                        TupleStrategy([arg for _ in range(strategy_len)])
+                    )
+                else:
+                    raise RuntimeError(
+                        f"list op only supports tuple strategy! {op_schema}"
+                    )
+        return tuple_strategies
+
+    args_strategies = args_tuple_strategies(op_schema.args_schema)
+    follow_strategy: TupleStrategy = args_strategies[0]
+    list_strategy: list[OpStrategy] = []
+    for child_idx, child_strtgy in enumerate(follow_strategy.childs):
+        assert isinstance(child_strtgy, OpStrategy)
+        args_schema: list[OpStrategy] = [
+            cast(OpStrategy, arg_strategy.childs[child_idx])
+            for arg_strategy in args_strategies
+        ]
+        pointwise_strategy: OpStrategy = common_pointwise_strategy(
+            args_schema, child_strtgy, linearity
+        )
+        list_strategy.append(pointwise_strategy)
+    return TupleStrategy(list_strategy)
+
+
+def list_linear_pointwise_strategy(op_schema: OpSchema) -> StrategyType:
+    """
+    for each list op stratgy that supports linearity
+    """
+    return list_pointwise_strategy(op_schema, linearity=True)
+
+
+for op in for_each_ops:
+    register_op_strategy(op, schema_info=RuntimeSchemaInfo(needs_pytree=True))(
+        list_pointwise_strategy
+    )
+
+for op in for_each_linearity_ops:
+    register_op_strategy(op, schema_info=RuntimeSchemaInfo(needs_pytree=True))(
+        list_linear_pointwise_strategy
+    )
+
+fused_ops = [
+    aten._fused_adam_.default,
+    aten._fused_adam.default,
+    aten._fused_adam.tensor_lr,
+    aten._fused_adam_.tensor_lr,
+    aten._fused_adamw_.default,
+    aten._fused_adamw.default,
+    aten._fused_adamw.tensor_lr,
+    aten._fused_adamw_.tensor_lr,
+]
+
+for op in fused_ops:
+    register_op_strategy(op, schema_info=RuntimeSchemaInfo(needs_pytree=True))(
+        list_pointwise_strategy
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_random_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_random_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..51b1faed14ea49596f4cfe6d1c886ebf790fa943
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_random_ops.py
@@ -0,0 +1,36 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import torch
+from torch.distributed.tensor._op_schema import (
+    OpSchema,
+    OpStrategy,
+    PlacementStrategy,
+    StrategyType,
+)
+from torch.distributed.tensor._ops.utils import is_tensor_partial, register_op_strategy
+
+
+aten = torch.ops.aten
+
+
+@register_op_strategy(
+    [
+        aten.normal_.default,
+        aten.uniform_.default,
+        aten.native_dropout.default,
+        aten.bernoulli_.float,
+        aten.bernoulli.default,
+    ]
+)
+def random_op_strategy(op_schema: OpSchema) -> StrategyType:
+    self_strategy = op_schema.args_schema[0]
+    assert isinstance(self_strategy, OpStrategy)
+
+    random_strategy = OpStrategy([])
+    for arg_strategy in self_strategy.strategies:
+        arg_spec = arg_strategy.output_spec
+        if is_tensor_partial(arg_spec):
+            # TODO: figure out how inplace random op should behave when it's partial
+            raise RuntimeError(f"{op_schema.op} with Partial is not supported yet!")
+        random_strategy.strategies.append(PlacementStrategy(output_specs=arg_spec))
+
+    return random_strategy
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_tensor_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_tensor_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bc0aad734594a32d3e11719c1d94e102a2dc195
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_tensor_ops.py
@@ -0,0 +1,806 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from collections.abc import Sequence, Sized
+from typing import cast, Optional
+
+import torch
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor._op_schema import (
+    _is_inplace_op,
+    OpSchema,
+    OpStrategy,
+    OutputSharding,
+    PlacementList,
+    PlacementStrategy,
+    RuntimeSchemaInfo,
+    StrategyType,
+    TupleStrategy,
+)
+from torch.distributed.tensor._ops._common_rules import pointwise_rule
+from torch.distributed.tensor._ops._embedding_ops import _MaskPartial
+from torch.distributed.tensor._ops.utils import (
+    expand_to_full_mesh_op_strategy,
+    is_tensor_dim_sharded,
+    is_tensor_evenly_shardable,
+    is_tensor_partial,
+    normalize_dim,
+    register_op_strategy,
+    register_prop_rule,
+)
+from torch.distributed.tensor.placement_types import (
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+aten = torch.ops.aten
+
+
+def default_strategy(op_schema: OpSchema) -> StrategyType:
+    # Default strategy by default just propagate the first input strategy
+    select_strategy = op_schema.args_schema[0]
+    assert isinstance(select_strategy, OpStrategy)
+    # we create new DTensorSpecs even for default strategy to assure that
+    # the tensor metas are distinct between the arguments and outputs
+    default_strategy = [
+        PlacementStrategy(
+            output_specs=DTensorSpec(
+                mesh=select_strategy.mesh,
+                placements=strategy.output_spec.placements,
+            )
+        )
+        for strategy in select_strategy.strategies
+    ]
+    return OpStrategy(default_strategy)
+
+
+register_op_strategy(
+    [
+        aten.clone.default,
+        aten.contiguous.default,
+        aten.copy_.default,
+        aten.detach.default,
+        aten.fill_.Scalar,
+        aten.view.dtype,
+        aten.zero_.default,
+    ]
+)(default_strategy)
+
+register_op_strategy(
+    aten._to_copy.default, schema_info=RuntimeSchemaInfo(static_kwargkey=["dtype"])
+)(default_strategy)
+
+
+@register_op_strategy(
+    [
+        aten.equal.default,
+        aten.is_same_size.default,
+    ]
+)
+def equal_strategy(op_schema: OpSchema) -> StrategyType:
+    # equal_strategy deals with ops that comparing two tensor, we need to make sure
+    # sharding layout the same with two operands, we choose to follow the arg with max
+    # num of shards, still keep is_same_size here for completeness as they share the
+    # same strategy in theory.
+    mesh = op_schema.get_mesh_from_args()
+    self_strategy, other_strategy = op_schema.args_schema
+    assert isinstance(self_strategy, OpStrategy)
+    assert isinstance(other_strategy, OpStrategy)
+
+    select_strategy = (
+        self_strategy
+        if self_strategy.max_num_shards() >= other_strategy.max_num_shards()
+        else other_strategy
+    )
+    equal_strategy = OpStrategy([])
+
+    for arg_strategy in select_strategy.strategies:
+        arg_spec = arg_strategy.output_spec
+        if is_tensor_partial(arg_spec):
+            # if the arg_spec have partial, reshard to replicate
+            # otherwise local shard tensor comparison would be invalid
+            output_spec = DTensorSpec(
+                mesh=mesh,
+                placements=tuple(
+                    Replicate() if isinstance(p, Partial) else p
+                    for p in arg_spec.placements
+                ),
+            )
+            equal_strategy.strategies.append(
+                PlacementStrategy(output_specs=output_spec)
+            )
+        else:
+            equal_strategy.strategies.append(PlacementStrategy(arg_spec))
+    return equal_strategy
+
+
+@register_op_strategy(
+    [
+        aten.empty_like.default,
+        aten.ones_like.default,
+        aten.rand_like.default,
+        aten.randn_like.default,
+        aten.zeros_like.default,
+    ],
+    schema_info=RuntimeSchemaInfo(1, ["dtype"]),
+)
+@register_op_strategy(
+    [aten.full_like.default],
+    schema_info=RuntimeSchemaInfo(2, ["dtype"]),
+)
+@register_op_strategy(
+    [
+        aten.randint_like.default,
+        aten.randint_like.low_dtype,
+        aten.randint_like.low_dtype_out,
+    ],
+    schema_info=RuntimeSchemaInfo(3, ["dtype"]),
+)
+def create_like_strategy(op_schema: OpSchema) -> StrategyType:
+    # create_like_strategy deals with ops that creating tensors with same
+    # shape as input, but with specific content that does not depend on
+    # the input, we can propagate sharding, but we have to make sure we
+    # move from partial to replicated.
+    select_strategy = op_schema.args_schema[0]
+    create_like_strategy = OpStrategy([])
+    assert isinstance(select_strategy, OpStrategy)
+    for arg_strategy in select_strategy.strategies:
+        arg_spec = arg_strategy.output_spec
+        output_spec = DTensorSpec(
+            mesh=select_strategy.mesh,
+            placements=tuple(
+                Replicate() if isinstance(p, Partial) else p
+                for p in arg_spec.placements
+            ),
+        )
+        create_like_strategy.strategies.append(
+            PlacementStrategy(output_specs=output_spec, input_specs=(arg_spec,))
+        )
+
+    return create_like_strategy
+
+
+@register_op_strategy(
+    [
+        aten.new_empty.default,
+        aten.new_full.default,
+        aten.new_ones.default,
+        aten.new_zeros.default,
+        aten.new_empty_strided.default,
+    ],
+    schema_info=RuntimeSchemaInfo(1, ["dtype"]),
+)
+def new_factory_strategy(op_schema: OpSchema) -> StrategyType:
+    # Currently there are two strategies:
+    # 1. let the output be replicated
+    # 2. let the output follow the input if input and output have the same shape
+    input_strategy = op_schema.args_schema[0]
+    assert isinstance(input_strategy, OpStrategy)
+
+    mesh = input_strategy.mesh
+    input_shape = input_strategy.shape
+    output_shape = op_schema.args_schema[1]
+    assert isinstance(output_shape, list)
+
+    new_factory_strategy = OpStrategy([])
+    for arg_strategy in input_strategy.strategies:
+        input_spec = arg_strategy.output_spec
+        replica_spec = DTensorSpec(mesh, tuple([Replicate()] * mesh.ndim))
+        new_factory_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=replica_spec,
+                input_specs=(input_spec,),
+                redistribute_cost=[[0.0] * mesh.ndim],
+            )
+        )
+
+        if tuple(input_shape) == tuple(output_shape) and input_spec.is_sharded():
+            # NOTE: for new_empty_strided, currently the non-replicate sharding
+            #       is supported only when the shape is evenly shardable
+            if (
+                op_schema.op == aten.new_empty_strided.default
+                and not is_tensor_evenly_shardable(input_shape, input_spec)
+            ):
+                continue
+
+            new_factory_strategy.strategies.append(
+                PlacementStrategy(
+                    output_specs=input_spec,
+                    input_specs=(input_spec,),
+                    # encouraging new tensor placement to be the same as input
+                    redistribute_cost=[[-0.1] * mesh.ndim],
+                )
+            )
+
+    return new_factory_strategy
+
+
+@register_op_strategy(aten.bucketize.Tensor)
+def gen_bucketize_strategy(op_schema: OpSchema) -> StrategyType:
+    """Just propagate input sharding, but expect replicated for boundaries input."""
+    mesh = op_schema.get_mesh_from_args()
+    input_strategy = op_schema.args_schema[0]
+    bucketize_strategy = OpStrategy([])
+    assert isinstance(input_strategy, OpStrategy)
+    for arg_strategy in input_strategy.strategies:
+        arg_spec = DTensorSpec(mesh, arg_strategy.output_spec.placements)
+        replica_spec = DTensorSpec(mesh, tuple([Replicate()] * mesh.ndim))
+        bucketize_strategy.strategies.append(
+            PlacementStrategy(
+                output_specs=arg_spec, input_specs=(arg_spec, replica_spec)
+            )
+        )
+
+    return bucketize_strategy
+
+
+@register_op_strategy(aten.slice.Tensor, schema_info=RuntimeSchemaInfo(1))
+def gen_slice_strategy(op_schema: OpSchema) -> StrategyType:
+    """Forward all shardings except the slice dimension."""
+    defaults = (None, 0, None, None, 1)
+    input_strategy, dim, start, end, step = (
+        op_schema.args_schema + defaults[len(op_schema.args_schema) :]
+    )
+    assert isinstance(input_strategy, OpStrategy)
+
+    mesh = input_strategy.mesh
+    input_shape = input_strategy.shape
+    input_ndim = input_strategy.ndim
+    assert isinstance(dim, int)
+    if start is None:
+        start = 0
+    if end is None or end > input_shape[dim]:
+        end = input_shape[dim]
+    assert isinstance(start, int)
+    assert isinstance(end, int)
+    assert isinstance(step, int)
+
+    # normalize args
+    slice_dim = normalize_dim(dim, input_ndim)
+    start = normalize_dim(start, input_shape[dim])
+    end = normalize_dim(end, input_shape[dim])
+
+    redundant_slice = start == 0 and end == input_shape[dim] and step == 1
+
+    slice_strategy = OpStrategy([])
+
+    for arg_strategy in input_strategy.strategies:
+        arg_spec = arg_strategy.output_spec
+        if not is_tensor_dim_sharded(arg_spec, dim=slice_dim) or redundant_slice:
+            # only add the strategy if the slice dim is not sharded
+            out_spec = DTensorSpec(mesh, arg_spec.placements)
+            slice_strategy.strategies.append(PlacementStrategy(output_specs=out_spec))
+    if not slice_strategy.strategies:
+        # if all strategies are filtered out, unsharding all specs on slice dim
+        # of the input strategy, and use that as the op strategy
+        for arg_strategy in input_strategy.strategies:
+            arg_spec = arg_strategy.output_spec
+            unshard_spec = DTensorSpec(
+                mesh, unshard_tensor_dim(arg_spec.placements, dim=slice_dim)
+            )
+            slice_strategy.strategies.append(
+                PlacementStrategy(output_specs=unshard_spec)
+            )
+    return slice_strategy
+
+
+def unshard_tensor_dim(
+    placements: Sequence[Placement], dim: int
+) -> tuple[Placement, ...]:
+    """Disallow the given tensor dimension to be sharded."""
+    return tuple(
+        p if (not isinstance(p, Shard) or p.dim != dim) else Replicate()
+        for p in placements
+    )
+
+
+def replicate_tensor_dim(
+    placements: Sequence[Placement], dim: int
+) -> tuple[Placement, ...]:
+    """Force the given tensor dimension to be replicated."""
+    # Not using p.is_shard() to avoid mypy complain about Placement not having
+    # attribute dim.
+    return tuple(
+        Replicate() if p.is_partial() or isinstance(p, Shard) and p.dim == dim else p
+        for p in placements
+    )
+
+
+@register_op_strategy(aten.slice_scatter.default, schema_info=RuntimeSchemaInfo(2))
+def gen_slice_scatter_strategy(op_schema: OpSchema) -> StrategyType:
+    # 1. number of dimensions in input and src need to match.
+    # 2. number of elements on all non-dim need to match between input and src.
+    # 3. numer of elements in src in dim need to match the slice size.
+    # Given the above:
+    # - We suggest for src to follow the sharding of input, except on the scatter dimension,
+    #   where our best bet for now is to make them replicated as a fall-back.
+    #   TODO: Ideally we'd like to make sure the output is re-sharded afterwards to keep input sharding.
+    mesh = op_schema.get_mesh_from_args()
+    input_strategy = op_schema.args_schema[0]
+    assert isinstance(input_strategy, OpStrategy)
+    input_ndim = input_strategy.ndim
+    slice_dim = (
+        cast(int, op_schema.args_schema[2]) if len(op_schema.args_schema) > 2 else 0
+    )
+    slice_dim = normalize_dim(slice_dim, input_ndim)
+
+    slice_scatter_strategy = OpStrategy([])
+    # by default follow the input strategy for both input and src
+    for arg_strategy in input_strategy.strategies:
+        arg_spec = arg_strategy.output_spec
+        if not (
+            is_tensor_dim_sharded(arg_spec, dim=slice_dim)
+            or is_tensor_partial(arg_spec)
+        ):
+            # only add the strategy if the slice_scatter dim is not sharded or partial
+            slice_scatter_strategy.strategies.append(
+                PlacementStrategy(output_specs=arg_spec)
+            )
+
+    if not slice_scatter_strategy.strategies:
+        # if all strategies are filtered out, replicating all specs on slice_scatter dim
+        # of the input strategy, and use that as the op strategy
+        for arg_strategy in input_strategy.strategies:
+            arg_spec = arg_strategy.output_spec
+            replicate_spec = DTensorSpec(
+                mesh, replicate_tensor_dim(arg_spec.placements, dim=slice_dim)
+            )
+            slice_scatter_strategy.strategies.append(
+                PlacementStrategy(output_specs=replicate_spec)
+            )
+    return slice_scatter_strategy
+
+
+@register_op_strategy(aten._local_scalar_dense.default)
+def replica_only_strategy(op_schema: OpSchema) -> StrategyType:
+    """Only allow replication on the input/output."""
+    input_strategy = op_schema.args_schema[0]
+    assert isinstance(input_strategy, OpStrategy)
+    mesh = input_strategy.mesh
+    replicate_spec = DTensorSpec(mesh, tuple([Replicate()] * mesh.ndim))
+    return OpStrategy([PlacementStrategy(replicate_spec)])
+
+
+@register_op_strategy(
+    [aten.scatter_.value, aten.scatter.value, aten.scatter_.src, aten.scatter.src],
+    schema_info=RuntimeSchemaInfo(1),
+)
+def scatter_strategy(op_schema: OpSchema) -> StrategyType:
+    mesh = op_schema.get_mesh_from_args()
+    single_mesh_dim_strategies = []
+
+    # placement list stores placements of [output, input, index, src]
+    # first we always have replicate all for inputs and output
+    if len(op_schema.args_strategy) < 3:
+        # scatter_.src/scatter.src with src be float number instead of tensor
+        all_replicate: PlacementList = [Replicate()] * 3
+    else:
+        all_replicate = [Replicate()] * 4
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # TODO: see if we can support input sharding pattern
+    inplace_op = _is_inplace_op(op_schema.op)
+
+    op_strategy = expand_to_full_mesh_op_strategy(
+        mesh, op_schema, single_mesh_dim_strategies, inplace_op=inplace_op
+    )
+    return op_strategy
+
+
+@register_op_strategy(aten.gather.default)
+def gather_strategy(op_schema: OpSchema) -> StrategyType:
+    mesh = op_schema.get_mesh_from_args()
+    input_strategy = cast(OpStrategy, op_schema.args_schema[0])
+    dim = cast(int, op_schema.args_schema[1])
+    index_strategy = cast(OpStrategy, op_schema.args_schema[2])
+
+    input_shape = input_strategy.shape
+    index_shape = index_strategy.shape
+
+    single_mesh_dim_strategies = []
+
+    # placement list stores placements of [output, input, index]
+    # first we always have replicate all for inputs and output
+    all_replicate: PlacementList = [Replicate()] * 3
+    single_mesh_dim_strategies.append(all_replicate)
+
+    # input sharding, input sharded, index accepts mask partial, output follows index
+    # this only works when the input is sharded on the gather dimension, and
+    # index has size 1 on the gather dimension
+    if index_shape[dim] == 1:
+        index_partial_placement = _MaskPartial(offset_shape=input_shape, offset_dim=dim)
+        input_sharding: PlacementList = [
+            index_partial_placement,
+            Shard(dim),
+            index_partial_placement,
+        ]
+        single_mesh_dim_strategies.append(input_sharding)
+
+    # index sharding, input replicated, index sharded, output follows index
+    # this only works when the sharding dimension is the gather dimension
+    index_sharding: PlacementList = [Shard(dim), Replicate(), Shard(dim)]
+    single_mesh_dim_strategies.append(index_sharding)
+
+    return expand_to_full_mesh_op_strategy(
+        mesh, op_schema, single_mesh_dim_strategies, input_index=1
+    )
+
+
+def _derive_follow_placements_from_tuple_strategy(
+    op: torch._ops.OpOverload,
+    tuple_strategy: TupleStrategy,
+) -> Sequence[Placement]:
+    """
+    derive the placements to follow from the tuple strategy, mainly used by
+    aten.stack, aten.cat, where each operand have the same shape, and correspondingly
+    expecting the same sharding
+    """
+
+    def merge_placement(
+        cur_placement: Placement, new_placement: Placement
+    ) -> Placement:
+        # semantic if we already have a follow placement, we
+        # check each placement for the current arg placement
+        # to see if we want to merge/adjust the placement to follow
+        # the priority: Partial -> Shard -> Replicate
+        if cur_placement == new_placement:
+            return cur_placement
+
+        if cur_placement.is_partial():
+            if new_placement.is_shard():
+                # follow new placement
+                return new_placement
+            elif new_placement.is_partial():
+                # different partial types, we can't merge and have to replicate all here
+                return Replicate()
+            else:
+                # follow partial
+                return cur_placement
+        elif cur_placement.is_shard():
+            if new_placement.is_shard():
+                # cur/new placement are different sharding (i.e. different shard dim)
+                # currently fallback to replicate all args
+                return Replicate()
+            else:
+                # for partial/replicate, follow the current shard placement
+                return cur_placement
+        else:
+            # current replicate, just follow new placement
+            return new_placement
+
+    follow_placements: Optional[list[Placement]] = None
+    mesh = tuple_strategy.child_mesh(0)
+    for arg_strategy in tuple_strategy.childs:
+        assert isinstance(arg_strategy, OpStrategy)
+        if arg_strategy.mesh != mesh:
+            raise ValueError(
+                f"All operands in {op} must have the same mesh, "
+                f"but got {arg_strategy.mesh} and {mesh}."
+            )
+
+        for placement_strategy in arg_strategy.strategies:
+            arg_placements = placement_strategy.output_spec.placements
+            if follow_placements is None:
+                follow_placements = list(arg_placements)
+                continue
+            assert follow_placements is not None
+            for mesh_idx in range(mesh.ndim):
+                # merge placements with the priority
+                follow_placements[mesh_idx] = merge_placement(
+                    follow_placements[mesh_idx], arg_placements[mesh_idx]
+                )
+    assert follow_placements is not None, "follow placements should not be None!"
+    return follow_placements
+
+
+def normalize_shard_for_stack(
+    placements: Sequence[Placement], insert_dim: int = 0
+) -> Sequence[Placement]:
+    # stack op would "insert" new dim, so all sharded dim >= the inserted dim need to
+    # be normalized with the new Shard placement
+    normalized_placements: list[Placement] = []
+    for placement in placements:
+        if isinstance(placement, Shard) and placement.dim >= insert_dim:
+            normalized_placements.append(Shard(placement.dim + 1))
+        else:
+            normalized_placements.append(placement)
+    return normalized_placements
+
+
+@register_op_strategy(aten.stack.default, RuntimeSchemaInfo(1, needs_pytree=True))
+def stack_strategy(op_schema: OpSchema) -> StrategyType:
+    args_schema = op_schema.args_schema
+    input_tuple_strategy = args_schema[0]
+    assert isinstance(input_tuple_strategy, TupleStrategy), f"{input_tuple_strategy}"
+    first_input_strategy = input_tuple_strategy.childs[0]
+    assert isinstance(first_input_strategy, OpStrategy), f"{first_input_strategy}"
+    common_input_ndim = first_input_strategy.ndim
+    dim = cast(int, args_schema[1]) if len(args_schema) > 1 else 0
+    # normalize the dim to be within the common input ndim
+    dim = normalize_dim(dim, common_input_ndim)
+
+    mesh = first_input_strategy.mesh
+
+    follow_placements = _derive_follow_placements_from_tuple_strategy(
+        op_schema.op, input_tuple_strategy
+    )
+
+    # create op strategy base on the follow placements
+    op_strategy = OpStrategy([])
+
+    input_specs = tuple(
+        DTensorSpec(mesh, tuple(follow_placements))
+        for _ in range(len(input_tuple_strategy.childs))
+    )
+
+    follow_placements = normalize_shard_for_stack(follow_placements, dim)
+
+    op_strategy.strategies.append(
+        PlacementStrategy(
+            output_specs=DTensorSpec(mesh, tuple(follow_placements)),
+            input_specs=input_specs,
+        )
+    )
+    return op_strategy
+
+
+@register_op_strategy(aten.cat.default, RuntimeSchemaInfo(1, needs_pytree=True))
+def cat_strategy(op_schema: OpSchema) -> StrategyType:
+    args_schema = op_schema.args_schema
+    input_tuple_strategy = args_schema[0]
+    assert isinstance(input_tuple_strategy, TupleStrategy), f"{input_tuple_strategy}"
+    first_input_strategy = input_tuple_strategy.childs[0]
+    assert isinstance(first_input_strategy, OpStrategy), f"{first_input_strategy}"
+    common_input_ndim = first_input_strategy.ndim
+    dim = cast(int, args_schema[1]) if len(args_schema) > 1 else 0
+    # normalize the dim to be within the common input ndim
+    dim = normalize_dim(dim, common_input_ndim)
+
+    mesh = first_input_strategy.mesh
+
+    follow_placements = _derive_follow_placements_from_tuple_strategy(
+        op_schema.op, input_tuple_strategy
+    )
+    # for cat we unshard the cat dim if it is sharded
+    follow_placements = unshard_tensor_dim(follow_placements, dim)
+
+    # create op strategy base on the follow placements
+    op_strategy = OpStrategy([])
+
+    input_specs = tuple(
+        DTensorSpec(mesh, tuple(follow_placements))
+        for _ in range(len(input_tuple_strategy.childs))
+    )
+    op_strategy.strategies.append(
+        PlacementStrategy(
+            output_specs=DTensorSpec(mesh, tuple(follow_placements)),
+            input_specs=input_specs,
+        )
+    )
+    return op_strategy
+
+
+@register_prop_rule(aten.index_select.default, schema_info=RuntimeSchemaInfo(1))
+def prop_index_select(op_schema: OpSchema) -> OutputSharding:
+    values_spec, dim, indices_spec = op_schema.args_schema
+
+    assert isinstance(values_spec, DTensorSpec)
+    assert isinstance(dim, int)
+    assert isinstance(indices_spec, DTensorSpec)
+
+    all_indices_spec: list[Optional[DTensorSpec]] = [
+        indices_spec if dim == i else None for i in range(values_spec.ndim)
+    ]
+
+    result = prop_index(
+        OpSchema(
+            op=op_schema.op,
+            args_schema=(values_spec, all_indices_spec),
+            kwargs_schema=op_schema.kwargs_schema,
+        )
+    )
+    if result.redistribute_schema:
+        schema_suggestion = result.redistribute_schema
+        result.redistribute_schema = OpSchema(
+            op=op_schema.op,
+            args_schema=(
+                schema_suggestion.args_schema[0],
+                dim,
+                schema_suggestion.args_schema[1][dim],  # type: ignore[index]
+            ),
+            kwargs_schema=op_schema.kwargs_schema,
+        )
+    return result
+
+
+@register_prop_rule(aten.index.Tensor, schema_info=RuntimeSchemaInfo(needs_pytree=True))
+def prop_index(op_schema: OpSchema) -> OutputSharding:
+    """
+    Expect replicated on the first input; _mostly_ pointwise on the second input.
+
+    TODO: exception: when the dtype of second input is "bool", then a torch.nonzero needs to be triggered first.
+    """
+    # Current sharding constraints:
+    # For values:
+    #   1. We currently require that the dimension of values_spec be replicated or partial
+    #      if they are being indexed on.
+    #   2. Other dimensions of values_spec can remain sharded if they are so.
+    # For indices:
+    #   Indices can be either sharded or replicated. All index tensors need to be sharded
+    #   in a compatible way, following the pointwise rule (including resolving Partial
+    #   into either sharded or replicated)
+
+    values_spec, multi_indices_spec = op_schema.args_schema
+    assert isinstance(values_spec, DTensorSpec)
+    assert isinstance(multi_indices_spec, list)
+    multi_indices_spec = cast(list[Optional[DTensorSpec]], multi_indices_spec)
+    valid_indices_spec: list[tuple[int, DTensorSpec]] = [
+        (i, a) for i, a in enumerate(multi_indices_spec) if a is not None
+    ]
+
+    # 1. All indices have to be sharded equally. Moreover, indices can be broadcast.
+    #    Here, we piggyback on the pointwise sharding rule for indices.
+    indices_out = pointwise_rule(
+        OpSchema(
+            op=op_schema.op,
+            args_schema=tuple(v[1] for v in valid_indices_spec),
+            kwargs_schema={},
+        )
+    )
+    need_reshard_on_indices = indices_out.output_spec is None
+
+    if not need_reshard_on_indices:
+        # this means that our inputs are already sharded properly and we will use that as our indices_spec
+        assert isinstance(indices_out.output_spec, DTensorSpec)
+        indices_spec: DTensorSpec = indices_out.output_spec
+    else:
+        assert indices_out.redistribute_schema is not None
+        valid_indices_suggestion = indices_out.redistribute_schema
+        for i, v in enumerate(valid_indices_suggestion.args_spec):
+            multi_indices_spec[valid_indices_spec[i][0]] = v
+        # we'll need to call pointwise_rule again to see what's our ideal indices_spec and then
+        # use that to compute our ideal values_spec
+        indices_output_spec = pointwise_rule(valid_indices_suggestion).output_spec
+        assert isinstance(indices_output_spec, DTensorSpec)
+        indices_spec = indices_output_spec
+
+    lookup_dims = {v[0] for v in valid_indices_spec}
+
+    need_reshard_on_values = tuple(
+        (isinstance(vp, Shard) and (vp.dim in lookup_dims or isinstance(ip, Shard)))
+        for vp, ip in zip(values_spec.placements, indices_spec.placements)
+    )
+
+    if not need_reshard_on_indices and not any(need_reshard_on_values):
+        value_placements = values_spec.placements
+
+        all_dims_consecutive = all(
+            b[0] - a[0] == 1
+            for b, a in zip(valid_indices_spec[1:], valid_indices_spec[:-1])
+        )
+        if all_dims_consecutive:
+            # if all index vectors are consecutives, insert at the dimension of the first index
+            insert_dim: int = valid_indices_spec[0][0]
+        else:
+            # else, insert on the first dimension
+            insert_dim = 0
+
+        def place(vp: Placement, ip: Placement) -> Placement:
+            if isinstance(vp, Shard):
+                return Shard(
+                    vp.dim
+                    if vp.dim < insert_dim
+                    # accounts for the offset in output dimensions
+                    else vp.dim
+                    + indices_spec.ndim
+                    - sum(1 if vp.dim > v[0] else 0 for v in valid_indices_spec)
+                )
+            if isinstance(ip, Shard):
+                return Shard(ip.dim + insert_dim)
+            # Partial or Replicated
+            return vp
+
+        value_placements = tuple(
+            place(vp, ip)
+            for vp, ip in zip(values_spec.placements, indices_spec.placements)
+        )
+        result = OutputSharding(
+            output_spec=DTensorSpec(
+                mesh=values_spec.mesh,
+                placements=value_placements,
+            )
+        )
+        return result
+    else:
+        result = OutputSharding(
+            output_spec=None,
+            redistribute_schema=OpSchema(
+                op=op_schema.op,
+                args_schema=(
+                    DTensorSpec(
+                        mesh=values_spec.mesh,
+                        placements=tuple(
+                            [
+                                Replicate() if need_reshard_on_values[i] else v
+                                for i, v in enumerate(values_spec.placements)
+                            ]
+                        ),
+                        tensor_meta=values_spec.tensor_meta,
+                    ),
+                    multi_indices_spec,
+                ),
+                kwargs_schema=op_schema.kwargs_schema,
+            ),
+        )
+        return result
+
+
+@register_prop_rule(
+    [
+        aten.split.Tensor,
+        aten.split_with_sizes.default,
+        aten.split_with_sizes_copy.default,
+    ],
+    schema_info=RuntimeSchemaInfo(1),
+)
+def split_rule(op_schema: OpSchema) -> OutputSharding:
+    output_spec_list: list[DTensorSpec] = []
+    input_spec = cast(DTensorSpec, op_schema.args_schema[0])
+    ndim = input_spec.ndim
+    split_size_or_sections = op_schema.args_schema[1]
+    dim = cast(int, op_schema.args_schema[2]) if len(op_schema.args_schema) > 2 else 0
+    dim = normalize_dim(dim, ndim)
+
+    # TODO: tensor to split cannot have Partial
+    # in its placements for now. Will need to
+    # support in future.
+    if input_spec.sums:
+        raise NotImplementedError(
+            f"splitting distributed tensor with "
+            f"Partial placement is not implemented!\n"
+            f"DTensorSpec={input_spec}"
+        )
+
+    # TODO: just like slice op, split replicates before
+    # splitting on a sharded dimension
+    need_reshard = False
+    if is_tensor_dim_sharded(input_spec, dim=dim):
+        need_reshard = True
+        input_spec = DTensorSpec(
+            mesh=input_spec.mesh,
+            placements=unshard_tensor_dim(input_spec.placements, dim=dim),
+            tensor_meta=input_spec.tensor_meta,
+        )
+
+    if need_reshard:
+        return OutputSharding(
+            None,
+            redistribute_schema=OpSchema(
+                op=op_schema.op,
+                args_schema=(input_spec,) + op_schema.args_schema[1:],
+                kwargs_schema=op_schema.kwargs_schema,
+            ),
+        )
+
+    def size_split(N, i) -> list:
+        # Last chunk will be smaller if the tensor size N
+        # along the given dimension dim is not divisible by i.
+        assert i > 0
+        return [i] * (N // i) + ([N % i] if N % i != 0 else [])
+
+    output_size_list = (
+        size_split(input_spec.shape[dim], split_size_or_sections)
+        if isinstance(split_size_or_sections, int)
+        else split_size_or_sections
+    )
+    assert isinstance(output_size_list, Sized)
+    output_spec_list = [
+        DTensorSpec(
+            mesh=input_spec.mesh,
+            placements=input_spec.placements,
+        )
+        for _ in range(len(output_size_list))
+    ]
+    return OutputSharding(output_spec_list)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_view_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_view_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..20e7ee7fc2ed17548cce19c8254225fe149dccd2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/_view_ops.py
@@ -0,0 +1,656 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from collections.abc import Iterable, Sequence
+from dataclasses import dataclass
+from typing import Callable, cast, Optional, Union
+
+import torch
+from torch import Tensor
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor._op_schema import (
+    OpSchema,
+    OpStrategy,
+    PlacementStrategy,
+    RuntimeSchemaInfo,
+    StrategyType,
+)
+from torch.distributed.tensor._ops.utils import (
+    generate_redistribute_costs,
+    normalize_dim,
+    normalize_dims,
+    prod,
+    register_op_strategy,
+)
+from torch.distributed.tensor.placement_types import Placement, Replicate, Shard
+
+
+aten = torch.ops.aten
+
+Shape = tuple[int, ...]
+
+
+@dataclass
+class DimSpec:
+    """Specifies how an output dimension maps to an input dimension."""
+
+    def inputs(self) -> Iterable["DimSpec"]:
+        return ()
+
+
+# Rules that map each dimension of the output to dimensions of the input tensor
+DimMap = tuple[DimSpec, ...]
+
+
+@dataclass
+class Singleton(DimSpec):
+    """Output dimension is a singleton."""
+
+
+@dataclass
+class InputDim(DimSpec):
+    """Output dimension maps directly to an input dimension."""
+
+    input_dim: int
+
+
+@dataclass
+class Broadcast(DimSpec):
+    """Output is the broadcast of a singleton input dimension."""
+
+    dim: DimSpec
+    dim_size: int
+
+    @classmethod
+    def new(cls, dim: DimSpec, dim_size: int) -> DimSpec:
+        return Broadcast(dim, dim_size)
+
+    def inputs(self) -> Iterable[DimSpec]:
+        return (self.dim,)
+
+
+@dataclass
+class NewDim(DimSpec):
+    """This is a new dimension created by the op."""
+
+    size: int
+
+    @classmethod
+    def new(cls, size: int) -> DimSpec:
+        return Singleton() if size == 1 else NewDim(size)
+
+
+@dataclass
+class Repeat(DimSpec):
+    """Output dimension is the input dimension repeated n-times."""
+
+    input_dim: DimSpec
+    times: int
+
+    @classmethod
+    def new(cls, dim: DimSpec, times: int) -> DimSpec:
+        if times == 1:
+            return dim
+        elif isinstance(dim, Singleton):
+            # repeating a singleton is the same as broadcasting it
+            return Broadcast(dim, times)
+        else:
+            return Repeat(dim, times)
+
+    def inputs(self) -> Iterable[DimSpec]:
+        return (self.input_dim,)
+
+
+@dataclass
+class Flatten(DimSpec):
+    """Flatten a set of input dimensions, ensuring right-most adjacent elements remain adjacent in the output."""
+
+    input_dims: Sequence[DimSpec]
+
+    @classmethod
+    def new(cls, dims: Sequence[DimSpec]) -> DimSpec:
+        if len(dims) == 0:
+            # flattening a scalar leads to a singleton
+            return Singleton()
+        elif len(dims) == 1:
+            # flattening a single dimension is no-op
+            return dims[0]
+        else:
+            return Flatten(dims)
+
+    def inputs(self) -> Iterable[DimSpec]:
+        return self.input_dims
+
+
+@dataclass
+class Split(DimSpec):
+    """
+    This dimension is a member of a decomposition of the input dim.
+
+    Note that input_dim itself could be a Flattened set of input dims.
+    """
+
+    input_dim: DimSpec
+    group_shape: Shape
+    split_id: int
+
+    @classmethod
+    def new(cls, dim: DimSpec, group_shape: tuple[int, ...], idx: int) -> DimSpec:
+        assert len(group_shape) > 0
+        if len(group_shape) == 1:
+            # not really a group, just return the input dim back
+            assert idx == 0
+            return dim
+        elif group_shape[idx] == 1:
+            return Singleton()
+        else:
+            # remove singletons from group
+            # group_mapping = [(new_index, (shape, old_index)) ...]
+            group_mapping = list(
+                enumerate((s, i) for i, s in enumerate(group_shape) if s != 1)
+            )
+            new_group_shape = tuple(m[1][0] for m in group_mapping)
+            new_idx = next(filter(lambda x: x[1][1] == idx, group_mapping))[0]
+            return Split(dim, new_group_shape, new_idx)
+
+    def inputs(self) -> Iterable[DimSpec]:
+        return (self.input_dim,)
+
+
+def dim_pad_left(ndim: int, min_dims: int) -> DimMap:
+    return (Singleton(),) * max(0, min_dims - ndim) + tuple(
+        InputDim(i) for i in range(ndim)
+    )
+
+
+def dim_atleast_3d(ndim: int) -> DimMap:
+    if ndim == 0:
+        return (Singleton(), Singleton(), Singleton())
+    elif ndim == 1:
+        return (Singleton(), InputDim(0), Singleton())
+    elif ndim == 2:
+        return (InputDim(0), InputDim(1), Singleton())
+    else:
+        return tuple(InputDim(i) for i in range(ndim))
+
+
+def expand(input_shape: Shape, shape: Shape) -> DimMap:
+    """Implement broadcast on multiple dimensions."""
+    assert len(shape) >= len(input_shape)
+
+    # 1. create padded input dimensions
+    padded_input = dim_pad_left(len(input_shape), len(shape))
+    # 2. check that input shapes are compatible
+    mapping = []
+    for p, desired_s in zip(padded_input, shape):
+        if isinstance(p, Singleton):
+            actual_s = 1
+            assert desired_s >= 0
+        else:
+            assert isinstance(p, InputDim), f"DimSpec not supported in expand: {p}"
+            actual_s = input_shape[p.input_dim]
+            assert actual_s == 1 or desired_s == -1 or desired_s == actual_s
+        mapping.append(
+            p
+            if desired_s in (1, -1) or desired_s == actual_s
+            else Broadcast.new(p, desired_s)
+        )
+    return tuple(mapping)
+
+
+def normalize_sizes(sizes: Union[Shape, tuple[Shape]]) -> Shape:
+    if isinstance(sizes[0], int):
+        return cast(Shape, sizes)
+    elif len(sizes) == 1:
+        return sizes[0]
+    else:
+        raise RuntimeError("Size must be int... or tuple")
+
+
+def dim_flatten(ndim: int, start_dim=0, end_dim=-1) -> DimMap:
+    if ndim == 0:
+        return (Singleton(),)
+    elif ndim == 1:
+        return (InputDim(0),)
+    else:
+        # only flattening dims from start_dim to end_dim (inclusive)
+        # other dims are passed through
+        if end_dim < 0:
+            end_dim += ndim
+        results: list[DimSpec] = [InputDim(i) for i in range(start_dim)]
+        results.append(
+            Flatten.new(tuple(InputDim(i) for i in range(start_dim, end_dim + 1)))
+        )
+        results.extend([InputDim(i) for i in range(end_dim + 1, ndim)])
+        return tuple(results)
+
+
+def dim_movedim(
+    ndim: int,
+    input: Union[int, Sequence[int]],
+    destination: Union[int, Sequence[int]],
+) -> DimMap:
+    input = normalize_dims(input, ndim)
+    destination = normalize_dims(destination, ndim)
+
+    assert len(input) == len(destination)
+    input_set = set(input)
+    assert len(input_set) == len(input), "Found repeated input dims"
+    assert len(set(destination)) == len(destination), "Found repeated output dims"
+    assert max(input) < ndim
+    assert max(destination) < ndim
+
+    dest = [-1] * ndim
+    for i, d in zip(input, destination):
+        dest[d] = i
+
+    unused_inputs_iter = iter(i for i in range(ndim) if i not in input_set)
+    for i in range(ndim):
+        if dest[i] == -1:
+            dest[i] = next(unused_inputs_iter)
+
+    return tuple(InputDim(i) for i in dest)
+
+
+def dim_repeat(ndim: int, sizes: Shape) -> DimMap:
+    sizes = normalize_sizes(sizes)
+    assert len(sizes) >= ndim, (
+        f"Number of dimensions of repeat dims {sizes} can not be smaller than number of dimensions of tensor {ndim}."
+    )
+    pad = len(sizes) - ndim
+    return tuple(Repeat.new(Singleton(), s) for s in sizes[:pad]) + tuple(
+        Repeat.new(InputDim(i), s) for i, s in enumerate(sizes[pad:])
+    )
+
+
+def infer_size(total_size: int, sizes: Shape) -> Shape:
+    """
+    One dimension input to view may be "-1".
+
+    Infer the size of this dimension given the total_size.
+    """
+    infers = [i for i, s in enumerate(sizes) if s == -1]
+    size = prod(sizes)
+    assert len(infers) <= 1, "can only infer one size"
+    if infers:
+        size = -size
+        missing_size = total_size // size
+        assert total_size % size == 0, (
+            f"size inferred for -1 is not integral {sizes} should have {total_size} elements."
+        )
+        return tuple(s if s != -1 else missing_size for s in sizes)
+    assert size == total_size, f"sizes do not match {total_size} vs {size}"
+    return sizes
+
+
+def view_groups(from_size: Shape, to_size: Shape) -> DimMap:
+    """
+    Decompose a reshape operation into forwarding, flattening, or splitting dimensions for each output dimension.
+
+    A view or reshape operation can be decomposed into a set of 3 types of smaller operations:
+    1) Forward a dimension from input to output
+    2) Flatten a set of dimensions into a single dimension
+    3) Split one dimension into multiple dimensions
+
+    view_groups identifies these operations and returns, for each output dimension, what
+    is operation was performed in the input dimension. For example:
+
+        view_groups([2, 3, 4], [2, 12]) -> (
+            InputDim(0),
+            Flatten((InputDim(1), InputDim(2)))
+        )
+
+    - ouptut dimension 0 maps to input dimension 0
+    - output dimension 1 maps to a flattened input dimensions 1 and 2
+
+
+        view_groups([2, 3], [3, 2]) -> (
+            Split(Flatten((InputDim(0), InputDim(1))), (3, 2), 0),
+            Split(Flatten((InputDim(0), InputDim(1))), (3, 2), 1),
+        )
+
+    - in the above, input is flattened into a single dimension and then split
+      into two separate dimensions with different sizes from the input.
+    """
+    from_nelem = prod(from_size)
+    to_size = infer_size(from_nelem, normalize_sizes(to_size))
+
+    assert from_nelem == prod(to_size), "Total view shape does not add up"
+
+    from_idx = 0
+    to_idx = 0
+    from_len = len(from_size)
+    to_len = len(to_size)
+
+    result_pp = []
+
+    while from_idx < from_len or to_idx < to_len:
+        from_group_dim, to_group_shape = [], []
+
+        if from_idx >= from_len:
+            f = 1
+        else:
+            f = from_size[from_idx]
+            from_group_dim.append(from_idx)
+            from_idx += 1
+
+        if to_idx >= to_len:
+            t = 1
+        else:
+            t = to_size[to_idx]
+            to_group_shape.append(t)
+            to_idx += 1
+
+        # if any of the groups is singleton, great, we need to backtrack though
+        if f == 1 and t != 1:
+            # produces ([1], [])
+            to_idx -= 1
+            to_group_shape = []
+        elif f != 1 and t == 1:
+            # produces ([], [1])
+            from_idx -= 1
+            from_group_dim = []
+        else:
+            # produces ([1], [1]),  ([2], [2]), ([2,3], [6])
+            while f != t:
+                if f < t:
+                    nf = from_size[from_idx]
+                    from_group_dim.append(from_idx)
+                    from_idx += 1
+                    f *= nf
+                else:
+                    nt = to_size[to_idx]
+                    to_group_shape.append(nt)
+                    to_idx += 1
+                    t *= nt
+
+        if len(to_group_shape) > 0:
+            flattened = Flatten.new(
+                tuple(InputDim(fi) for fi in from_group_dim if from_size[fi] >= 1)
+            )
+            result_pp += [
+                Split.new(flattened, tuple(to_group_shape), i)
+                for i in range(len(to_group_shape))
+            ]
+
+    return tuple(result_pp)
+
+
+def dim_tile(ndim: int, dims: tuple[int, ...]) -> DimMap:
+    if len(dims) < ndim:
+        dims = (1,) * (ndim - len(dims)) + dims
+    return dim_repeat(ndim, dims)
+
+
+def dim_transpose(ndim: int, dim1: int, dim2: int) -> DimMap:
+    dim1 = normalize_dim(dim1, ndim)
+    dim2 = normalize_dim(dim2, ndim)
+    assert dim1 < ndim
+    assert dim2 < ndim
+    dimmap = [InputDim(i) for i in range(ndim)]
+    swapdim = dimmap[dim1]
+    dimmap[dim1] = dimmap[dim2]
+    dimmap[dim2] = swapdim
+    return tuple(dimmap)
+
+
+def dim_squeeze(shape: Shape, dim: Optional[int] = None) -> DimMap:
+    # FIXME: this is wrong when dim=None and one of the dimensions
+    # equals size of the mesh. For example squeeze(DTensor(tensor(4), Shard[0])) could
+    # end up as squeeze(tensor(1)) if we have 4 devices; this would lead to
+    # removal of a dimension that is not actually a singleton.
+    return tuple(
+        InputDim(i)
+        for i, s in enumerate(shape)
+        if s > 1 or (dim is not None and i != normalize_dim(dim, len(shape)))
+    )
+
+
+def dim_unsqueeze(ndim: int, dim: int) -> DimMap:
+    dims = tuple(InputDim(i) for i in range(ndim))
+    if dim < 0:
+        dim += ndim + 1
+    return dims[:dim] + (Singleton(),) + dims[dim:]
+
+
+def dim_view_as_real(shape: Shape) -> DimMap:
+    ndim = len(shape)
+    results: list[DimSpec] = [InputDim(i) for i in range(ndim - 1)]
+    # each complex number is split into two real numbers,
+    # resulting in one more dimension of size 2
+    results.append(Split(InputDim(ndim - 1), (shape[-1], 2), 0))
+    results.append(Split(InputDim(ndim - 1), (shape[-1], 2), 1))
+    return tuple(results)
+
+
+def dim_reduction(
+    ndim: int, dim_or_dims: Optional[Union[int, Sequence[int]]], keepdim: bool
+) -> DimMap:
+    """
+    General fallback for reduction ops where Partial() does not apply.
+
+    This will cause incoming tensor to be replicated on the reducing dimensions.
+    """
+    if dim_or_dims is None:
+        dim_or_dims = tuple(range(ndim))
+    if isinstance(dim_or_dims, int):
+        dim_or_dims = (dim_or_dims,)
+    dim_or_dims = tuple(d if d >= 0 else d + ndim for d in dim_or_dims)
+    return tuple(
+        InputDim(i) if i not in dim_or_dims else Singleton()
+        for i in range(ndim)
+        if i not in dim_or_dims or keepdim
+    )
+
+
+dim_maps: dict[Callable[..., torch.Tensor], Callable[..., DimMap]] = {
+    torch.atleast_1d: lambda x: dim_pad_left(x.ndim, 1),
+    torch.atleast_2d: lambda x: dim_pad_left(x.ndim, 2),
+    torch.atleast_3d: lambda x: dim_atleast_3d(x.ndim),
+    torch.broadcast_to: lambda input, shape: expand(input.shape, shape),
+    Tensor.expand: lambda self, *sizes: expand(self.shape, normalize_sizes(sizes)),
+    torch.flatten: lambda tensor: dim_flatten(tensor.ndim),
+    torch.movedim: lambda input, source, destination: dim_movedim(
+        input.ndim, source, destination
+    ),
+    torch.permute: lambda input, dims: tuple(
+        InputDim(i) for i in normalize_dims(dims, input.ndim)
+    ),
+    torch.ravel: lambda tensor: dim_flatten(tensor.ndim),
+    Tensor.repeat: lambda self, *sizes: dim_repeat(self.ndim, sizes),
+    torch.reshape: lambda input, shape: view_groups(input.shape, shape),
+    torch.squeeze: lambda input, dim=None: dim_squeeze(input.shape, dim),
+    torch.tile: lambda input, dims: dim_tile(input.ndim, dims),
+    torch.transpose: lambda input, dim0, dim1: dim_transpose(input.ndim, dim0, dim1),
+    torch.unsqueeze: lambda input, dim: dim_unsqueeze(input.ndim, dim),
+    Tensor.view: lambda input, *shape: view_groups(input.shape, shape),
+    torch.view_as_complex: lambda input: dim_flatten(input.ndim, input.ndim - 2),
+    torch.view_as_real: lambda input: dim_view_as_real(input.shape),
+}
+
+
+def propagate_shape_and_sharding(
+    input_src_placements: Sequence[Placement],
+    local_in_shape: Shape,
+    rule: DimMap,
+    mesh_sizes: Shape,
+) -> tuple[Sequence[Placement], Sequence[Placement]]:
+    """
+    Determine input target sharding and output sharding based on
+    given global tensor shape and input source sharding.
+
+    Sharding propagation follows mapped dimensions:
+    - An output dimension that maps directly to an input dimension is sharded equally
+    - An output dimension that is a flattened set of input dimensions can only be
+      sharded if only the leftmost flattened dimension is sharded.
+    - An output dimension that is a split of the input dimension can only be sharded
+      if the leftmost split size is divisible by the mesh dimension
+    """
+    assert len(input_src_placements) == len(mesh_sizes)
+    # for each input dim, for each mesh dim, provides a list of possible shardable dimensions
+    mesh_ndim = len(mesh_sizes)
+    shardable_dims: dict[int, list[bool]] = {}
+
+    # in case an input dimension disappears (e.g. collapsing, reduction)
+    # we cannot shard in that dimension (we need a replication fall-back rule)
+    seen_input_dims: set[int] = set()
+
+    def collect_used_inputs(cmd: DimSpec) -> None:
+        if isinstance(cmd, InputDim):
+            seen_input_dims.add(cmd.input_dim)
+        for inp in cmd.inputs():
+            collect_used_inputs(inp)
+
+    for cmd in rule:
+        collect_used_inputs(cmd)
+    for dim in range(len(local_in_shape)):
+        shardable_dims[dim] = [dim in seen_input_dims] * mesh_ndim
+
+    def get_in_dim_to_shard(cmd: DimSpec) -> Optional[InputDim]:
+        if isinstance(cmd, InputDim):
+            return cmd
+        elif isinstance(cmd, Flatten):
+            for dim in cmd.input_dims[1:]:
+                if isinstance(dim, InputDim):
+                    shardable_dims[dim.input_dim] = [False] * mesh_ndim
+            dim0 = cmd.input_dims[0]
+            return dim0 if isinstance(dim0, InputDim) else None
+        elif isinstance(cmd, Split):
+            in_dim = get_in_dim_to_shard(cmd.input_dim)
+            out_size = cmd.group_shape[cmd.split_id]
+            if cmd.split_id == 0 and in_dim is not None:
+                # we need to check that the input dimension is divisible
+                # by the size of the submesh we're sharding it on
+                # NOTE: it would be possible to shard the same input dimension
+                # on more than one mesh dimension. In that case, the dimension
+                # needs to be divisible by the product of mesh sizes.
+                # In order to keep the problem more tractable, we will not consider
+                # double resharding as a suggestion (e.g. [Shard(0), Shard(0) ])
+                # but we will allow it if that's the input and it's compatible
+
+                # 1. is this dimension shardable on each individual mesh dim?
+                shardable_dims[in_dim.input_dim] = [
+                    out_size % mesh_dim_size == 0 for mesh_dim_size in mesh_sizes
+                ]
+
+                # 2. here we special case things like [Shard(0), Shard(0)]
+                submesh_size = 1
+                for size, shard in zip(mesh_sizes, input_src_placements):
+                    if isinstance(shard, Shard) and shard.dim == in_dim:
+                        submesh_size *= size
+                assert out_size % submesh_size == 0, (
+                    f"Resulting dimension size {out_size} is not divisible by its mesh dimension {submesh_size}."
+                )
+
+            # we will only shard our first component of the split
+            return in_dim if cmd.split_id == 0 else None
+        elif isinstance(cmd, Repeat):
+            in_dim = get_in_dim_to_shard(cmd.input_dim)
+            if in_dim is not None:
+                shardable_dims[in_dim.input_dim] = [False] * mesh_ndim
+            return None
+        else:
+            return None
+
+    # for each output dim, find the corresponding input dim in terms of sharding prop
+    shard_dim_map = {}
+    for dim, cmd in enumerate(rule):
+        in_dim = get_in_dim_to_shard(cmd)
+        if in_dim is not None:
+            shard_dim_map[in_dim.input_dim] = dim
+
+    input_tgt_placements = [
+        Replicate()
+        if isinstance(p, Shard) and not shardable_dims[p.dim][mesh_dim]
+        else p
+        for mesh_dim, p in enumerate(input_src_placements)
+    ]
+    output_placements = [
+        Shard(shard_dim_map[p.dim]) if isinstance(p, Shard) else p
+        for p in input_tgt_placements
+    ]
+
+    return input_tgt_placements, output_placements
+
+
+def register_op_strategy_map(
+    aten_op_overload: torch._ops.OpOverload,
+    local_op_name: Callable[..., torch.Tensor],
+    schema_info: Optional[RuntimeSchemaInfo] = None,
+) -> None:
+    dim_map: Callable[..., DimMap] = dim_maps[local_op_name]
+
+    @register_op_strategy(aten_op_overload, schema_info=schema_info)
+    def reshape_strategy(op_schema: OpSchema) -> StrategyType:
+        rules = dim_map(*op_schema.args_schema, **op_schema.kwargs_schema)
+        input_strategy = cast(OpStrategy, op_schema.args_schema[0])
+        mesh = op_schema.get_mesh_from_args(validate=False)
+
+        global_in_shape = input_strategy.shape
+        assert global_in_shape is not None, "Shape required."
+
+        output_strategy = OpStrategy([])
+        for input_placement_strategy in input_strategy.strategies:
+            input_src_spec = input_placement_strategy.output_spec
+
+            input_tgt_placements, output_placements = propagate_shape_and_sharding(
+                input_src_spec.placements,
+                tuple(global_in_shape),
+                rules,
+                mesh.shape,
+            )
+
+            # TODO: optimize this. we shouldn't simply blindly replicate
+            #       unshardable dims ...
+            # FIXME: this can be wrong for situations where we have
+            #        [Shard(0), Shard(0)]
+            input_tgt_spec = DTensorSpec(
+                placements=tuple(input_tgt_placements),
+                mesh=mesh,
+                tensor_meta=input_src_spec.tensor_meta,
+            )
+            redistribute_costs = [
+                generate_redistribute_costs(input_strategy, input_tgt_spec)
+            ]
+
+            output_spec = DTensorSpec(mesh=mesh, placements=tuple(output_placements))
+            output_strategy.strategies.append(
+                PlacementStrategy(
+                    output_specs=output_spec,
+                    input_specs=(input_tgt_spec,),
+                    redistribute_cost=redistribute_costs,
+                )
+            )
+
+        return output_strategy
+
+
+register_op_strategy_map(aten.squeeze.default, torch.squeeze)
+register_op_strategy_map(
+    aten.squeeze.dim, torch.squeeze, schema_info=RuntimeSchemaInfo(1)
+)
+register_op_strategy_map(
+    aten.view.default, Tensor.view, schema_info=RuntimeSchemaInfo(1)
+)
+register_op_strategy_map(
+    aten.reshape.default, torch.reshape, schema_info=RuntimeSchemaInfo(1)
+)
+register_op_strategy_map(
+    aten._unsafe_view.default, Tensor.view, schema_info=RuntimeSchemaInfo(1)
+)
+register_op_strategy_map(
+    aten.unsqueeze.default, torch.unsqueeze, schema_info=RuntimeSchemaInfo(1)
+)
+register_op_strategy_map(
+    aten.expand.default, Tensor.expand, schema_info=RuntimeSchemaInfo(1)
+)
+register_op_strategy_map(
+    aten.permute.default, torch.permute, schema_info=RuntimeSchemaInfo(1)
+)
+register_op_strategy_map(
+    aten.repeat.default, Tensor.repeat, schema_info=RuntimeSchemaInfo(1)
+)
+register_op_strategy_map(
+    aten.transpose.int, torch.transpose, schema_info=RuntimeSchemaInfo(1)
+)
+register_op_strategy_map(aten.view_as_complex.default, torch.view_as_complex)
+register_op_strategy_map(aten.view_as_real.default, torch.view_as_real)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f1894ed73ff570c045b80871ed814f273b98ff2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_ops/utils.py
@@ -0,0 +1,297 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import functools
+import itertools
+import operator
+from collections.abc import Iterable, Sequence
+from typing import Callable, cast, Optional, TypeVar, Union
+from typing_extensions import ParamSpec
+
+import torch
+from torch.distributed.tensor._api import DTensor
+from torch.distributed.tensor._collective_utils import redistribute_cost
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor._op_schema import (
+    OpSchema,
+    OpStrategy,
+    OutputSharding,
+    PlacementList,
+    PlacementStrategy,
+    RuntimeSchemaInfo,
+)
+from torch.distributed.tensor.device_mesh import DeviceMesh
+from torch.distributed.tensor.placement_types import (
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+
+# convenient wrapper to register sharding propagation rules
+# pyre-fixme[3]: Return type must be annotated.
+# pyre-fixme[2]: Parameter must be annotated.
+def register_prop_rule(
+    op: Union[torch._ops.OpOverload, list[torch._ops.OpOverload]],
+    schema_info: Optional[RuntimeSchemaInfo] = None,
+) -> Callable[
+    [Callable[[OpSchema], OutputSharding]], Callable[[OpSchema], OutputSharding]
+]:
+    # pyre-fixme[53]: Captured variable `func` is not annotated.
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+    def wrapper(
+        impl: Callable[[OpSchema], OutputSharding],
+    ) -> Callable[[OpSchema], OutputSharding]:
+        overloads = op if isinstance(op, list) else [op]
+        for overload in overloads:
+            DTensor._op_dispatcher.sharding_propagator.register_sharding_prop_rule(
+                overload, impl, schema_info
+            )
+        return impl
+
+    return wrapper
+
+
+def register_op_strategy(
+    op, schema_info=None
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
+    # pyre-fixme[53]: Captured variable `func` is not annotated.
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+
+    # For every ATen op that accepts any args in this list,
+    # the arg itself can impact the strides (and potentially the sharding strategy)
+    # of the output tensor.
+    # thus, we will detect ATen schemas with any of these args and ensure
+    # that they get specialized here.
+    arg_names_that_require_specializing_cache_strategy = [
+        "memory_format",
+    ]
+
+    def wrapper(impl):
+        if isinstance(op, list):
+            overloads = op
+        else:
+            overloads = [op]
+
+        for overload in overloads:
+            curr_schema_info = None
+            if schema_info is None:
+                specialized_args = [
+                    a.name
+                    for a in overload._schema.arguments
+                    if a.name in arg_names_that_require_specializing_cache_strategy
+                ]
+                if any(specialized_args):
+                    curr_schema_info = RuntimeSchemaInfo(
+                        static_kwargkey=specialized_args
+                    )
+            else:
+                curr_schema_info = schema_info
+            DTensor._op_dispatcher.sharding_propagator.register_op_strategy(
+                overload, impl, curr_schema_info
+            )
+        return impl
+
+    return wrapper
+
+
+def as_list(
+    x: Union[list[object], object],
+    # pyre-fixme[11]: Annotation `immutable_list` is not defined as a type.
+) -> Union[list[object], torch.fx.immutable_collections.immutable_list]:  # type: ignore[valid-type]
+    # During tracing, `aten.sum.dim_IntList` uses `immutable_list` for its args,
+    # which is an object but treated as a list by the tracer. Therefore, keep
+    # `immutable_list` intact here as well.
+    if type(x) is list or isinstance(x, torch.fx.immutable_collections.immutable_list):
+        return x
+    else:
+        return [x]
+
+
+def normalize_dim(dim: int, ndim: int) -> int:
+    return dim if dim >= 0 else dim + ndim
+
+
+def normalize_dims(dims: Union[int, Sequence[int]], ndim: int) -> Sequence[int]:
+    """Normalize a dim or a sequence of dims, so that they are all positive."""
+    if isinstance(dims, int):
+        dims = (normalize_dim(dims, ndim),)
+    elif isinstance(dims, list):
+        dims = [normalize_dim(dim, ndim) for dim in dims]
+    elif isinstance(dims, tuple):
+        dims = tuple([normalize_dim(dim, ndim) for dim in dims])
+    return dims
+
+
+def prod(xs: Iterable[int]) -> int:
+    return functools.reduce(operator.mul, xs, 1)
+
+
+def is_tensor_shardable(shape: Sequence[int], spec: DTensorSpec) -> bool:
+    """Check if the shape is shardable according to the spec."""
+    # number of shards in each tensor dimension
+    shards_map = [1] * len(shape)
+    for i, placement in enumerate(spec.placements):
+        if placement.is_shard():
+            shard_dim = cast(Shard, placement).dim
+            shards_map[shard_dim] *= spec.mesh.size(i)
+
+    for i, dim_size in enumerate(shape):
+        # TODO: maybe we should determine is_shardable based on
+        #       whether it's evenly sharded or not
+        if shards_map[i] > 1 and dim_size < shards_map[i]:
+            return False
+
+    return True
+
+
+def is_tensor_evenly_shardable(shape: Sequence[int], spec: DTensorSpec) -> bool:
+    """Check if the shape is evenly shardable according to the spec."""
+    # number of shards in each tensor dimension
+    shards_map = [1] * len(shape)
+    for i, placement in enumerate(spec.placements):
+        if placement.is_shard():
+            shard_dim = cast(Shard, placement).dim
+            shards_map[shard_dim] *= spec.mesh.size(i)
+
+    for i, dim_size in enumerate(shape):
+        if shards_map[i] > 1 and (dim_size % shards_map[i] != 0):
+            return False
+
+    return True
+
+
+def is_tensor_dim_sharded(spec: DTensorSpec, dim: int) -> bool:
+    """Return True if tensor dim is sharded."""
+    return any(p.is_shard(dim) for p in spec.placements)
+
+
+def is_tensor_partial(spec: DTensorSpec) -> bool:
+    """Return True if tensor is partial on the mesh."""
+    return any(p.is_partial() for p in spec.placements)
+
+
+def infer_broadcast_dims_map(
+    common_shape: torch.Size, input_shape: torch.Size
+) -> list[int]:
+    # infer the broadcast dims map, where it maps from the common shape dim to the input shape dim
+    # this is aligned with the broadcast semantics
+    common_ndim = len(common_shape)
+    input_ndim = len(input_shape)
+    broadcast_dims_map = [-1] * common_ndim
+    for idx in range(-1, -1 - input_ndim, -1):
+        if input_shape[idx] == common_shape[idx]:
+            broadcast_dims_map[common_ndim + idx] = input_ndim + idx
+    return broadcast_dims_map
+
+
+def map_placements_after_broadcast(
+    placements: tuple[Placement, ...],
+    shape: torch.Size,
+    broadcast_dims_map: list[int],
+) -> tuple[Placement, ...]:
+    """Map each placement based on the output shape after broadcast."""
+    new_placements: list[Placement] = []
+    for placement in placements:
+        if isinstance(placement, (Replicate, Partial)):
+            new_placements.append(placement)
+        else:
+            assert isinstance(placement, Shard)
+            shard_dim = normalize_dim(placement.dim, len(shape))
+            new_shard_dim = broadcast_dims_map[shard_dim]
+            if new_shard_dim != -1:
+                # there's a map from the common shape shard dim to
+                # the input shape shard dim before broadcasting,
+                # use that instead
+                new_placements.append(Shard(new_shard_dim))
+            else:
+                # there's no map between common shape shard dim and
+                # the input shape shard dim before broadcasting,
+                # in this case it means implicit broadcasting happen
+                # in this dim, so we can just mark it as replicate
+                # and implict broadcast will broadcast automatically
+                # to the sharded shape
+                new_placements.append(Replicate())
+
+    return tuple(new_placements)
+
+
+def generate_redistribute_costs(
+    src_strategy: OpStrategy, dst_spec: DTensorSpec
+) -> list[float]:
+    redistribute_costs: list[float] = [
+        redistribute_cost(strat.output_spec, dst_spec)
+        for strat in src_strategy.strategies
+    ]
+
+    return redistribute_costs
+
+
+def expand_to_full_mesh_op_strategy(
+    mesh: DeviceMesh,
+    op_schema: OpSchema,
+    single_mesh_dim_strategies: list[PlacementList],
+    *,
+    input_index: int = 1,
+    inplace_op: bool = False,
+) -> OpStrategy:
+    # Expand the single_mesh_dim_strategies to full mesh dim strategies.
+    all_mesh_dim_strategies = [single_mesh_dim_strategies] * mesh.ndim
+
+    strategy_combs = itertools.product(*all_mesh_dim_strategies)
+
+    all_strategies = []
+    for strategy_comb in strategy_combs:
+        spec_list: list[Optional[DTensorSpec]] = []
+        for specs in zip(*strategy_comb):
+            if specs[0] is not None:
+                spec_list.append(DTensorSpec(mesh, specs))
+            else:
+                spec_list.append(None)
+
+        input_specs: list[DTensorSpec] = [
+            s for s in spec_list[input_index:] if isinstance(s, DTensorSpec)
+        ]
+
+        input_args_strategy = op_schema.args_strategy
+        assert len(input_specs) == len(input_args_strategy)
+        self_spec = input_args_strategy[0].strategies[0].output_spec
+
+        if inplace_op and self_spec.placements != input_specs[0].placements:
+            # if it's inplace op, we would only allow the placement strategy to be added when the
+            # input_spec matches the first argument's runtime sharding, otherwise we skip
+            continue
+
+        # check inputs shardable
+        inputs_shardable = all(
+            is_tensor_shardable(inp.shape, s)
+            for inp, s in zip(input_args_strategy, input_specs)
+        )
+
+        # only add to the all_strategies list when all inputs are shardable
+        if inputs_shardable:
+            redistribute_cost = [
+                generate_redistribute_costs(input_strategy, input_spec)
+                for input_strategy, input_spec in zip(input_args_strategy, input_specs)
+            ]
+            if input_index > 1:
+                output_specs = tuple(spec_list[:input_index])
+            else:
+                if spec_list[0] is not None:
+                    output_specs = spec_list[0]  # type: ignore[assignment]
+                else:
+                    raise RuntimeError("output spec is None")
+            strategy = PlacementStrategy(
+                output_specs=output_specs,
+                input_specs=input_specs,
+                redistribute_cost=redistribute_cost,
+            )
+            all_strategies.append(strategy)
+
+    return OpStrategy(all_strategies)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_sharding_prop.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_sharding_prop.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5bb22a92b7deddeaeb473e7d1b7ed5f5b1bb5fb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_sharding_prop.py
@@ -0,0 +1,530 @@
+# mypy: allow-untyped-defs
+import threading
+from collections.abc import Sequence
+from functools import lru_cache
+from itertools import chain
+from typing import Callable, cast, Optional, Union
+
+import torch
+from torch._ops import OpOverload
+from torch._subclasses import FakeTensorMode
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor._op_schema import (
+    OpInfo,
+    OpSchema,
+    OpStrategy,
+    OutputSharding,
+    OutputSpecType,
+    PlacementStrategy,
+    RuntimeSchemaInfo,
+    StrategyType,
+    TupleStrategy,
+)
+from torch.distributed.tensor._utils import (
+    compute_local_shape_and_global_offset,
+    compute_local_stride,
+)
+
+
+aten = torch.ops.aten
+
+
+def _length(obj) -> int:
+    if obj is None:
+        return 0
+    if not isinstance(obj, Sequence):
+        return 1
+    return len(obj)
+
+
+class LocalLRUCache(threading.local):
+    def __init__(self, user_function: Callable) -> None:
+        self.cache = lru_cache(None)(user_function)
+
+    def __call__(self, *args, **kwargs) -> object:
+        return self.cache(*args, **kwargs)
+
+    def cache_info(self):
+        return self.cache.cache_info()
+
+
+class ShardingPropagator:
+    def __init__(self) -> None:
+        self.op_to_rules: dict[OpOverload, Callable[[OpSchema], OutputSharding]] = {}
+        self.op_strategy_funcs: dict[
+            OpOverload,
+            Callable[[OpSchema], StrategyType],
+        ] = {}
+        # op map to save static argnum to decide to reuse sharding prop cache or
+        # re-run sharding prop
+        self.op_to_schema_info: dict[OpOverload, RuntimeSchemaInfo] = {}
+        self.propagate_op_sharding = LocalLRUCache(
+            self.propagate_op_sharding_non_cached
+        )
+        # op map to save indices of shape (and stride) args which may need to be
+        # modified in sharding prop
+        self.op_to_shape_and_stride_idx: dict[
+            OpOverload, Union[int, tuple[int, int]]
+        ] = {
+            # new factory ops
+            aten.new_empty.default: 1,
+            aten.new_full.default: 1,
+            aten.new_ones.default: 1,
+            aten.new_zeros.default: 1,
+            aten.new_empty_strided.default: (1, 2),
+            # view ops
+            aten.expand.default: 1,
+            aten.reshape.default: 1,
+            aten.view.default: 1,
+            aten._unsafe_view.default: 1,
+        }
+
+    def register_sharding_prop_rule(
+        self,
+        op_overload: OpOverload,
+        rule_func: Callable[[OpSchema], OutputSharding],
+        schema_info: Optional[RuntimeSchemaInfo] = None,
+    ):
+        """
+        Register a sharding propagation rule for an operator.
+        """
+        self.op_to_rules[op_overload] = rule_func
+        if schema_info is not None:
+            self.op_to_schema_info[op_overload] = schema_info
+
+    def register_op_strategy(
+        self,
+        op_overload: OpOverload,
+        strategy_func: Callable[[OpSchema], StrategyType],
+        schema_info: Optional[RuntimeSchemaInfo] = None,
+    ):
+        """
+        Register a sharding strategy generator for an operator.
+        """
+        self.op_strategy_funcs[op_overload] = strategy_func
+        if schema_info is not None:
+            self.op_to_schema_info[op_overload] = schema_info
+
+    def _propagate_tensor_meta_non_cached(
+        self, op_schema: OpSchema
+    ) -> Union[None, TensorMeta, Sequence[Optional[TensorMeta]]]:
+        """
+        Propagate the tensor metadata, it could either return a TensorMeta
+        or a list/tuple of TensorMetas
+        """
+        if op_schema.op == aten.equal.default:
+            # data dependent ops can't be used for fake propagation
+            return None
+
+        # NOTE: We must call the tracing in fake tensor mode so that it
+        # avoids materializing memory
+        with FakeTensorMode():
+            fake_args = op_schema.gen_fake_args()
+            fake_kwargs = op_schema.gen_fake_kwargs()
+            fake_out = op_schema.op(*fake_args, **fake_kwargs)
+
+        if isinstance(fake_out, torch.Tensor):
+            return TensorMeta(
+                shape=fake_out.shape, stride=fake_out.stride(), dtype=fake_out.dtype
+            )
+
+        elif isinstance(fake_out, (tuple, list)):
+            tensor_meta_list: list[Optional[TensorMeta]] = []
+            for fake_out_item in fake_out:
+                if isinstance(fake_out_item, torch.Tensor):
+                    tensor_meta_list.append(
+                        TensorMeta(
+                            shape=fake_out_item.shape,
+                            stride=fake_out_item.stride(),
+                            dtype=fake_out_item.dtype,
+                        )
+                    )
+                else:
+                    tensor_meta_list.append(None)
+            return (
+                tuple(tensor_meta_list)
+                if isinstance(fake_out, tuple)
+                else tensor_meta_list
+            )
+        else:
+            # if fake is not a tensor or tuple of tensor, return as none
+            return None
+
+    @lru_cache  # noqa: B019
+    def _propagate_tensor_meta(
+        self, op_schema: OpSchema
+    ) -> Union[None, TensorMeta, Sequence[Optional[TensorMeta]]]:
+        return self._propagate_tensor_meta_non_cached(op_schema)
+
+    def _wrap_output_spec_tensor_meta(
+        self,
+        op: OpOverload,
+        output_specs: OutputSpecType,
+        output_tensor_meta: Union[None, TensorMeta, Sequence[Optional[TensorMeta]]],
+    ) -> None:
+        """
+        Wrap the output_specs with the tensor metadata from the output.
+        """
+
+        if isinstance(output_specs, DTensorSpec):
+            if not isinstance(output_tensor_meta, TensorMeta):
+                # Either error due to ShardingPropagator or due to incorrect OutputSpec
+                if not isinstance(output_tensor_meta, (tuple, list)):
+                    raise ValueError(
+                        "ShardingPropagator error: output does not have an associated "
+                        "TensorMeta"
+                    )
+                raise ValueError(
+                    f"For the op {op.name()}, `output_specs` has 1 output which does "
+                    "not equal the "
+                    f"number of op outputs: {len(output_tensor_meta)}."
+                )
+            output_specs.tensor_meta = output_tensor_meta
+        elif isinstance(output_specs, (tuple, list)):
+            if not isinstance(output_tensor_meta, (tuple, list)) or len(
+                output_specs
+            ) != len(output_tensor_meta):
+                raise ValueError(
+                    f"For the op {op.name()}, `output_specs` has {len(output_specs)} "
+                    "outputs which does not equal the "
+                    f"number of op outputs {_length(output_tensor_meta)}."
+                )
+
+            for i, spec in enumerate(output_specs):
+                if isinstance(spec, DTensorSpec):
+                    output_tensor_meta_i = output_tensor_meta[i]
+                    if not isinstance(output_tensor_meta_i, TensorMeta):
+                        # NOTE: aten.convolution_backward.default is an exception and it
+                        # needs extra handling because the first Tensor in the output
+                        # tuple can be `None` if the input Tensor to convolution op has
+                        # `requires_grad=False` (e.g. convolution layer is the first
+                        # layer in the model). We explicitly allow its corresponding
+                        # TensorMeta to be `None`.
+                        if (
+                            op == aten.convolution_backward.default
+                            and i == 0
+                            and output_tensor_meta_i is None
+                        ):
+                            assert isinstance(output_specs, list)
+                            output_specs[i] = None
+                            continue
+                        else:
+                            raise ValueError(
+                                f"ShardingPropagator error: output {i} of {op.name()} "
+                                "does not have an associated TensorMeta"
+                            )
+
+                    spec.tensor_meta = output_tensor_meta_i
+
+    def _wrap_with_op_strategy(self, op_schema: OpSchema) -> OpSchema:
+        """
+        wrap a op_schema that contains DTensorSpec to another op_schema that contains
+        OpStrategy/TupleStrategy, the returned op_schema is then used for sharding
+        strategy propagation on pytorch operators.
+        """
+
+        def spec_to_strategy(spec: object) -> object:
+            if isinstance(spec, DTensorSpec):
+                return OpStrategy([PlacementStrategy(spec)])
+            elif (
+                isinstance(spec, (list, tuple))
+                and len(spec) > 0
+                and isinstance(spec[0], DTensorSpec)
+            ):
+                # tensor list create tuple strategy
+                tuple_strategy = [spec_to_strategy(s) for s in spec]
+                tuple_strategy = cast(Sequence[StrategyType], tuple_strategy)
+                return TupleStrategy(
+                    tuple(tuple_strategy) if isinstance(spec, tuple) else tuple_strategy
+                )
+            else:
+                return spec
+
+        args_op_strategy = [spec_to_strategy(i) for i in op_schema.args_schema]
+
+        kwargs_op_strategy = {
+            k: spec_to_strategy(v) for k, v in op_schema.kwargs_schema.items()
+        }
+
+        return OpSchema(
+            op=op_schema.op,
+            args_schema=tuple(args_op_strategy),
+            kwargs_schema=kwargs_op_strategy,
+        )
+
+    def propagate(self, op_info: OpInfo) -> None:
+        # We cannot use an lru cache if we know that inputs will have dynamic shapes,
+        # because SymInts are not hashable.
+        # This is generally ok because this only happens during tracing in torch.compile,
+        # and tracing does not need to be as fast as eagermode DTensor usages.
+        if op_info.schema.has_symints:
+            output_sharding = self.propagate_op_sharding_non_cached(op_info.schema)
+        else:
+            output_sharding = cast(
+                OutputSharding, self.propagate_op_sharding(op_info.schema)
+            )
+        op_info.output_sharding = output_sharding
+
+    def propagate_op_sharding_non_cached(self, op_schema: OpSchema) -> OutputSharding:
+        """
+        Propagate the sharding for an operator given the op_schema.
+        """
+        # special case op, we don't need to propagate for local
+        # scalar. TODO: figure out a better way to handle this
+        if op_schema.op is aten._local_scalar_dense.default:
+            return OutputSharding(None, op_schema)
+
+        out_tensor_meta = self._propagate_tensor_meta_non_cached(op_schema)
+
+        if op_schema.op in self.op_strategy_funcs:
+            # wrap the op_schema with op strategy for sharding strategy propagation
+            strategy_schema = self._wrap_with_op_strategy(op_schema)
+
+            # run sharding strategy propagation/generation
+            op_strategy = self.op_strategy_funcs[op_schema.op](strategy_schema)
+
+            if isinstance(op_strategy, OpStrategy):
+                # single Op strategy
+                output_strategy = self._select_strategy(op_strategy)
+
+                # check if we need to redistribute the input
+                needs_redistribute = False
+                expected_input_specs: list[DTensorSpec] = []
+
+                # in case where the op does not specify input_specs and output_specs
+                # is a DTensorSpec, we use output_specs as the spec for each DTensor
+                # input arg.
+                if output_strategy.input_specs is None:
+                    assert isinstance(output_strategy.output_specs, DTensorSpec)
+
+                for idx, input_spec in enumerate(op_schema.args_spec):
+                    desired_spec = (
+                        output_strategy.output_spec
+                        if output_strategy.input_specs is None
+                        else output_strategy.input_specs[idx]
+                    )
+                    expected_input_specs.append(
+                        desired_spec.shallow_copy_with_tensor_meta(
+                            input_spec.tensor_meta
+                        )
+                    )
+                    if input_spec.placements != desired_spec.placements:
+                        needs_redistribute = True
+
+                suggestion_schema = None
+                if needs_redistribute:
+                    suggestion_schema = OpSchema(
+                        op_schema.op, tuple(expected_input_specs), {}
+                    )
+                    suggestion_schema._inplace_rewrap_schema_suggestion(op_schema)
+
+                # shape and stride args need to be modified for
+                # view ops and new factory ops, potentially
+                if op_schema.op in self.op_to_shape_and_stride_idx:
+                    assert isinstance(output_strategy.output_spec, DTensorSpec)
+                    # It happens when the output has the same shape as the input
+                    # and the input placements are not all Replicate().
+                    if output_strategy.output_spec.is_sharded():
+                        schema = suggestion_schema or op_schema
+                        assert isinstance(out_tensor_meta, TensorMeta)
+                        suggestion_schema = self._adjust_shape_and_stride_args(
+                            out_tensor_meta, schema, output_strategy.output_spec
+                        )
+                        needs_redistribute = True
+
+                # construct output spec for the op
+                if op_schema.return_type_tuple_tensor_like():
+                    # for ops that return multiple tensors and the output_specs is not
+                    # a tuple, we use a tuple of that single output spec as the new
+                    # output_specs
+                    output_specs: OutputSpecType = output_strategy.output_specs
+                    if isinstance(output_specs, DTensorSpec):
+                        output_specs = tuple(
+                            [
+                                # create a new DTensorSpec with the same placement as the
+                                # output_specs in output_strategy
+                                DTensorSpec(
+                                    mesh=output_specs.mesh,
+                                    placements=output_specs.placements,
+                                    tensor_meta=output_specs.tensor_meta,
+                                )
+                                for _ in range(len(op_schema.op._schema.returns))
+                            ]
+                        )
+                elif op_schema.return_type_tensor():
+                    output_specs = output_strategy.output_specs
+                else:
+                    output_specs = None
+
+                output_sharding = OutputSharding(
+                    output_specs,
+                    suggestion_schema,
+                    needs_redistribute=needs_redistribute,
+                )
+            elif isinstance(op_strategy, TupleStrategy):
+                # tuple strategy output sharding processing
+                # runtime selected placement strategy for each TupleStrategy input arg
+                selected_strategies: list[PlacementStrategy] = []
+                out_spec_list: list[DTensorSpec] = []
+                for strategy in op_strategy.childs:
+                    assert isinstance(strategy, OpStrategy)
+                    selected_strategy = self._select_strategy(strategy)
+                    selected_strategies.append(selected_strategy)
+                    out_spec_list.append(selected_strategy.output_spec)
+
+                needs_redistribute = False
+                suggestion_args: list[object] = []
+                tensor_or_list_tensor_arg_idx = 0
+
+                for arg in op_schema.args_schema:
+                    if (
+                        arg
+                        and isinstance(arg, (list, tuple))
+                        and isinstance(arg[0], DTensorSpec)
+                    ):
+                        expected_input_spec_list: list[DTensorSpec] = []
+                        for idx, arg_spec in enumerate(arg):
+                            expected_input_spec = selected_strategies[idx].input_spec(
+                                tensor_or_list_tensor_arg_idx
+                            )
+                            expected_input_spec = (
+                                expected_input_spec.shallow_copy_with_tensor_meta(
+                                    arg_spec.tensor_meta
+                                )
+                            )
+                            if arg_spec.placements != expected_input_spec.placements:
+                                needs_redistribute = True
+                            expected_input_spec_list.append(expected_input_spec)
+                        suggestion_args.append(
+                            tuple(expected_input_spec_list)
+                            if isinstance(arg, tuple)
+                            else expected_input_spec_list
+                        )
+                        tensor_or_list_tensor_arg_idx += 1
+
+                    elif isinstance(arg, DTensorSpec):
+                        expected_input_spec = selected_strategies[0].input_spec(
+                            tensor_or_list_tensor_arg_idx
+                        )
+                        expected_input_spec = (
+                            expected_input_spec.shallow_copy_with_tensor_meta(
+                                arg.tensor_meta
+                            )
+                        )
+                        if arg.placements != expected_input_spec.placements:
+                            needs_redistribute = True
+                        suggestion_args.append(expected_input_spec)
+                        tensor_or_list_tensor_arg_idx += 1
+                    else:
+                        suggestion_args.append(arg)
+
+                suggestion_schema = None
+                if needs_redistribute:
+                    suggestion_schema = OpSchema(
+                        op_schema.op, tuple(suggestion_args), op_schema.kwargs_schema
+                    )
+
+                output_sharding = OutputSharding(
+                    tuple(out_spec_list) if out_tensor_meta is not None else None,
+                    suggestion_schema,
+                    needs_redistribute=needs_redistribute,
+                )
+            else:
+                raise ValueError("Unsupported op strategy type")
+
+            # associate the output sharding with the output tensor metadata
+            self._wrap_output_spec_tensor_meta(
+                op_schema.op, output_sharding.output_spec, out_tensor_meta
+            )
+            return output_sharding
+        elif op_schema.op in self.op_to_rules:
+            # propagate the sharding with rule
+            sharding_prop_func = self.op_to_rules[op_schema.op]
+
+            # step 1. there's sharding propagation rule, run
+            # sharding propagation to get the output sharding
+            try:
+                output_sharding = sharding_prop_func(op_schema)
+            except NotImplementedError as e:
+                raise e
+            except Exception as e:
+                raise RuntimeError(
+                    f"Sharding propagation failed on op {op_schema}.\nError: {e}"
+                ) from e
+
+            # step 2. if can't get output_spec from sharding
+            # propagation (i.e. no rules apply for input
+            # placements), we return the output sharding
+            # with schema suggestions, which can be used to
+            # decide how to do redistribute on inputs
+            if output_sharding.output_spec is None:
+                if output_sharding.redistribute_schema is None:
+                    raise RuntimeError(
+                        f"Sharding propagation failed on op {op_schema}!"
+                    )
+                else:
+                    # we do auto redistribute on inputs if necessary
+                    # run sharding propagation again with suggested schema
+                    propagation_res = sharding_prop_func(
+                        output_sharding.redistribute_schema
+                    )
+                    # we set the output sharding with the new propagation result
+                    # so that dispatching know both output_spec and redistribute_schema
+                    # exist, which indicates a reshard is needed
+                    output_sharding.output_spec = propagation_res.output_spec
+                    output_sharding.needs_redistribute = True
+
+            # associate the output sharding with the output tensor metadata
+            self._wrap_output_spec_tensor_meta(
+                op_schema.op, output_sharding.output_spec, out_tensor_meta
+            )
+
+            return output_sharding
+        else:
+            raise NotImplementedError(
+                f"Operator {op_schema.op} does not have a sharding strategy registered."
+            )
+
+    def _select_strategy(self, strategy: OpStrategy) -> PlacementStrategy:
+        if len(strategy.strategies) == 1:
+            # short cut with only one possible strategy
+            return strategy.strategies[0]
+
+        strategy_costs: list[float] = []
+        for strtg in strategy.strategies:
+            assert strtg.redistribute_cost is not None, (
+                "must set redistribute cost each strategy!"
+            )
+            redistribute_cost = sum(chain.from_iterable(strtg.redistribute_cost))
+            strategy_costs.append(redistribute_cost)
+
+        # for eager execution, we just select the one with the minimal redistribute cost
+        return strategy.strategies[strategy_costs.index(min(strategy_costs))]
+
+    def _adjust_shape_and_stride_args(
+        self,
+        out_tensor_meta: TensorMeta,
+        schema: OpSchema,
+        spec: DTensorSpec,
+    ) -> OpSchema:
+        shape_stride_idx = self.op_to_shape_and_stride_idx[schema.op]
+        if isinstance(shape_stride_idx, tuple):
+            shape_idx, stride_idx = shape_stride_idx
+        else:
+            shape_idx = shape_stride_idx
+            stride_idx = None
+
+        expected_input_schema = list(schema.args_schema)
+        # adjust shape to be the same as that of the _local_tensor
+        # of the DTensor input arg at index 0, which is inferred
+        expected_input_schema[shape_idx], _ = compute_local_shape_and_global_offset(
+            out_tensor_meta.shape, spec.mesh, spec.placements
+        )
+
+        # adjust the stride arg for aten.new_empty_strided.default
+        if stride_idx:
+            expected_input_schema[stride_idx] = compute_local_stride(
+                out_tensor_meta.stride, spec.mesh, spec.placements
+            )
+
+        return OpSchema(schema.op, tuple(expected_input_schema), schema.kwargs_schema)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_shards_wrapper.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_shards_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..11bdb4ec2ef245cc137eba472f4b31cb158c70a3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_shards_wrapper.py
@@ -0,0 +1,316 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Any
+
+import torch
+from torch.distributed.checkpoint.metadata import (
+    ChunkStorageMetadata,
+    MetadataIndex,
+    TensorProperties,
+    TensorStorageMetadata,
+)
+from torch.distributed.checkpoint.planner import (
+    TensorWriteData,
+    WriteItem,
+    WriteItemType,
+)
+
+
+aten = (
+    torch.ops.aten
+)  # pyre-ignore[5]: Globally accessible variable `aten` has no type specified.
+
+
+class LocalShardsWrapper(torch.Tensor):  # pyre-ignore[13]: pyre is bad at __new__
+    """
+    A wrapper class to hold local shards of a DTensor.
+    This class is used largely for checkpointing purposes and implicity subtypes
+    the _Checkpointable protocol.
+    """
+
+    __slots__ = ["_local_shards", "_storage_meta"]
+    _local_shards: list[torch.Tensor]
+    _storage_meta: TensorStorageMetadata
+
+    @staticmethod
+    def __new__(
+        cls, local_shards: list[torch.Tensor], local_offsets: list[tuple[int, ...]]
+    ) -> "LocalShardsWrapper":
+        assert len(local_shards) > 0
+        assert len(local_shards) == len(local_offsets)
+        assert all(
+            tensor.device == local_shards[0].device for tensor in local_shards[1:]
+        )
+
+        # we calculate the total tensor size by "concat" on second tensor dimension
+        cat_tensor_shape = list(local_shards[0].size())
+        if len(local_shards) > 1:  # column-wise sharding
+            for shard in local_shards[1:]:
+                cat_tensor_shape[1] += shard.size()[1]
+
+        wrapper_properties = TensorProperties.create_from_tensor(local_shards[0])
+        wrapper_shape = torch.Size(cat_tensor_shape)
+        chunks_meta = [
+            ChunkStorageMetadata(
+                offsets=torch.Size(offset),
+                sizes=shard.size(),
+            )
+            for shard, offset in zip(local_shards, local_offsets)
+        ]
+
+        r = torch.Tensor._make_wrapper_subclass(  # type: ignore[attr-defined]
+            cls,
+            torch.Size(cat_tensor_shape),
+        )
+        r._local_shards = local_shards
+        r._storage_meta = TensorStorageMetadata(
+            properties=wrapper_properties,
+            size=wrapper_shape,
+            chunks=chunks_meta,
+        )
+
+        return r
+
+    # necessary for ops dispatching from this subclass to its local shards
+    @classmethod
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+
+        dispatcher = {
+            torch.ops._c10d_functional.all_gather_into_tensor.default: cls.handle_all_gather_into_tensor,
+            torch.ops._c10d_functional.wait_tensor.default: cls.handle_wait_tensor,
+            aten._to_copy.default: cls.handle_to_copy,
+            aten.view.default: cls.handle_view,
+            aten.equal.default: cls.handle_equal,
+            aten.detach.default: cls.handle_detach,
+            aten.clone.default: cls.handle_clone,
+        }
+
+        if func in dispatcher:
+            return dispatcher[func](
+                args, kwargs
+            )  # pyre-ignore [29] - `Variable[_VT]` is not a function.
+        else:
+            raise NotImplementedError(
+                f"{func} is not supported for LocalShardsWrapper!"
+            )
+
+    @staticmethod
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+    def handle_all_gather_into_tensor(args, kwargs):
+        dim = args[0].local_sizes()[0][1]
+        cat_tensor = torch.cat(
+            [t.view(-1) for t in args[0].local_shards()], dim=0
+        ).view(-1, dim)
+        return torch.ops._c10d_functional.all_gather_into_tensor.default(
+            cat_tensor, *args[1:], **kwargs
+        )
+
+    @staticmethod
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+    def handle_wait_tensor(args, kwargs):
+        return torch.ops._c10d_functional.wait_tensor(args[0])
+
+    @staticmethod
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+    def handle_to_copy(args, kwargs):
+        res_shards_list = [
+            aten._to_copy.default(shard, *args[1:], **kwargs)
+            for shard in args[0].local_shards()
+        ]
+        return LocalShardsWrapper(res_shards_list, args[0].local_offsets())
+
+    @staticmethod
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+    def handle_view(args, kwargs):
+        # TODO, do we need to change the shape of associated offsets?
+        res_shards_list = [
+            aten.view.default(shard, args[1], **kwargs)
+            for shard in args[0].local_shards()
+        ]
+        return LocalShardsWrapper(res_shards_list, args[0].local_offsets())
+
+    @staticmethod
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+    def handle_equal(args, kwargs):
+        """
+        LocalShardsWrapper equal impl also checks for equality of storage metadata
+        and the order of shards
+        """
+        a, b = args[0], args[1]
+        if len(a.local_shards()) != len(b.local_shards()):
+            return False
+        if not all(
+            aten.equal.default(x, y) for x, y in zip(a.local_shards(), b.local_shards())
+        ):
+            return False
+        if not a.storage_metadata() == b.storage_metadata():
+            return False
+        return True
+
+    @staticmethod
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+    def handle_detach(args, kwargs):
+        self_ls = args[0]
+        deatched_local_shards = [
+            aten.detach.default(shard) for shard in self_ls.local_shards()
+        ]
+        self_ls._local_shards = deatched_local_shards
+        self_ls._storage_meta.properties.requires_grad = False
+        return self_ls
+
+    @staticmethod
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
+    def handle_clone(args, kwargs):
+        self_ls = args[0]
+        desired_memory_format = kwargs.get("memory_format", None)
+        if desired_memory_format and desired_memory_format != torch.preserve_format:
+            raise NotImplementedError(
+                f"{desired_memory_format} is not supported for LocalShardsWrapper!"
+            )
+        cloned_local_shards = [
+            shard.clone(memory_format=desired_memory_format)
+            for shard in self_ls._local_shards
+        ]
+        return LocalShardsWrapper(cloned_local_shards, self_ls.local_offsets())
+
+    @property
+    def device(self) -> torch._C.device:  # type: ignore[override]
+        return self._local_shards[0].device
+
+    @property
+    def is_meta(self) -> bool:  # type: ignore[override]
+        return self._local_shards[0].is_meta
+
+    # pyre-ignore[14]
+    def is_pinned(self) -> bool:  # type: ignore[override]
+        return self._storage_meta.properties.pin_memory
+
+    # pyre-ignore[14]
+    def requires_grad_(self, requires_grad: bool = True) -> "LocalShardsWrapper":
+        self._storage_meta.properties.requires_grad = requires_grad
+        [shard.requires_grad_(requires_grad) for shard in self._local_shards]
+        return self
+
+    def local_shards(self) -> list[torch.Tensor]:
+        """
+        Returns a list of :class:`torch.Tensor' corresponding to the
+        local shards for this rank. Returns an empty list if the current rank
+        does not host any shards for this Tensor.
+        """
+        return self._local_shards
+
+    def local_sizes(self) -> list[torch.Size]:
+        """
+        Returns a list of :class:`torch.Size' corresponding to the
+        local sizes for the shards on this rank. Returns an empty list if the current rank
+        does not host any shards for this Tensor.
+        """
+        return [chunk.sizes for chunk in self._storage_meta.chunks]
+
+    def local_offsets(self) -> list[torch.Size]:
+        """
+        Returns a list of :class:`torch.Size' corresponding to the
+        local offsets for the shards on this rank. Returns an empty list if the current rank
+        does not host any shards for this Tensor.
+        """
+        return [chunk.offsets for chunk in self._storage_meta.chunks]
+
+    @property
+    def local_chunks(self) -> list[ChunkStorageMetadata]:
+        """
+        Returns a :class:`List[ChunkStorageMetadata]` object corresponding to the
+        metadata for each tensor shard
+        """
+        return self._storage_meta.chunks
+
+    def storage_metadata(self) -> TensorStorageMetadata:
+        """
+        Returns a :class:`TensorStorageMetadata` object corresponding to the
+        metadata for the local tensor on current rank
+        """
+        return self._storage_meta
+
+    def __create_write_items__(
+        self, fqn: str, object: Any
+    ) -> list[WriteItem]:  # pyre-ignore[2]
+        """
+        For compatibility with DCP, we support creation of WriteItems
+        such that they can be saved properly.
+        """
+        return [
+            WriteItem(
+                index=MetadataIndex(fqn, chunks.offsets),
+                type=WriteItemType.SHARD,
+                tensor_data=TensorWriteData(
+                    chunk=ChunkStorageMetadata(
+                        offsets=chunks.offsets,
+                        sizes=chunks.sizes,
+                    ),
+                    properties=self._storage_meta.properties,
+                    size=object.size(),
+                ),
+            )
+            for tensor, chunks in zip(self.local_shards(), self.local_chunks)
+        ]
+
+    def __create_chunk_list__(self) -> list[ChunkStorageMetadata]:
+        """
+        For compatibility with DCP, we support creation of chunk lists
+        such that they can be saved properly.
+        """
+        return self._storage_meta.chunks
+
+    def __get_tensor_shard__(self, index: MetadataIndex) -> torch.Tensor:
+        """
+        For compatibility with DCP, we support finding shard based on index
+        Return a 'torch.Tensor' shard based on 'MetadataIndex'.
+        """
+        # Fast lookup path
+        if index.index is not None:
+            if (
+                len(self._local_shards) > index.index
+                and self._storage_meta.chunks[index.index].offsets == index.offset
+            ):
+                return self._local_shards[index.index]
+
+        if index.offset is not None:
+            for shard, chunk in zip(self._local_shards, self._storage_meta.chunks):
+                if chunk.offsets == index.offset:
+                    return shard
+
+        raise ValueError(
+            f"Could not find shard at '{index.offset}' for FQN: '{index.fqn}'"
+        )
+
+    def _get_tensor_size_bytes(self) -> int:
+        object_size = 0
+        for shard in self.local_shards():
+            object_size += shard.nelement() * shard.element_size()
+        return object_size
+
+    # pyre-fixme[3]: Return type must be annotated.
+    def __hash__(self):
+        return id(self)
+
+    # pyre-fixme[14]: `__repr__` overrides method defined in `torch._tensor.Tensor` inconsistently.
+    # pyre-fixme[3]: Return type must be annotated.
+    def __repr__(self) -> str:  # type: ignore[override]
+        return f"LocalShardsWrapper:{self._local_shards} {self._storage_meta}"
+
+    def __str__(self) -> str:
+        return f"LocalShardsWrapper:{self._local_shards} {self._storage_meta}"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_tp_conv.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_tp_conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3e908f3e7a228952f618724eea3b292c51865a0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_tp_conv.py
@@ -0,0 +1,279 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+# implement matrix related ops for distributed tensor
+from typing import cast
+
+import torch
+import torch.distributed as dist
+import torch.distributed.tensor._api as dtensor
+
+
+aten = torch.ops.aten
+
+
+def _requires_data_exchange(padding):
+    # TODO: whether there requires data exchange is currently determined by padding
+    return padding[1] != 0
+
+
+def _is_supported(input_size, kernel_size, stride, padding, dilation):
+    if dilation[1] != 1:
+        raise RuntimeError("Dilation must be 1 for tensor parallel convolution.")
+    if padding[1] != 0:
+        if stride[1] != 1:
+            raise RuntimeError(
+                "Stride must be 1 when there is padding for tensor parallel convolution."
+            )
+        if kernel_size[3] // 2 > input_size[3]:
+            raise RuntimeError(
+                "kernel_size[3] // 2 should be less than or equal to input_size[3] for tensor parallel convolution."
+            )
+    else:
+        if not (input_size[3] % stride[1] == 0 and stride[1] == kernel_size[3]):
+            raise RuntimeError(
+                "It requires that input_size[3] is divisible by stride[1] and stride[1] equals kernel_size[3] "
+                "when there is padding for tensor parallel convolution."
+            )
+    return True
+
+
+def _ring_send_recv_construct(in_tensor, d1, d2, left, right, rank, size):
+    # dist comms and reconstruct local input tensor
+    send_to_right = in_tensor[:, :, :, -d1:].contiguous()
+    send_to_left = in_tensor[:, :, :, :d2].contiguous()
+    recv_from_right = torch.zeros_like(send_to_left)
+    recv_from_left = torch.zeros_like(send_to_right)
+
+    send_op_right = dist.P2POp(dist.isend, send_to_right, right)
+    send_op_left = dist.P2POp(dist.isend, send_to_left, left)
+    recv_op_right = dist.P2POp(dist.irecv, recv_from_right, right)
+    recv_op_left = dist.P2POp(dist.irecv, recv_from_left, left)
+
+    reqs = dist.batch_isend_irecv(
+        [send_op_right, send_op_left, recv_op_left, recv_op_right]
+    )
+    for req in reqs:
+        req.wait()
+
+    if rank == 0:
+        in_tensor = torch.cat([in_tensor, recv_from_right], dim=-1)
+    elif rank == size - 1:
+        in_tensor = torch.cat([recv_from_left, in_tensor], dim=-1)
+    else:
+        in_tensor = torch.cat([recv_from_left, in_tensor, recv_from_right], dim=-1)
+
+    return in_tensor
+
+
+def _ring_send_recv_aggregate(grad_in_tensor, d1, d2, left, right, rank, size):
+    # dist comms and aggregate gradients for edge pixels
+    send_to_right = grad_in_tensor[:, :, :, -d2:].contiguous()
+    send_to_left = grad_in_tensor[:, :, :, :d1].contiguous()
+    recv_from_right = torch.zeros_like(send_to_left)
+    recv_from_left = torch.zeros_like(send_to_right)
+
+    send_op_right = dist.P2POp(dist.isend, send_to_right, right)
+    send_op_left = dist.P2POp(dist.isend, send_to_left, left)
+    recv_op_right = dist.P2POp(dist.irecv, recv_from_right, right)
+    recv_op_left = dist.P2POp(dist.irecv, recv_from_left, left)
+
+    reqs = dist.batch_isend_irecv(
+        [send_op_right, send_op_left, recv_op_left, recv_op_right]
+    )
+    for req in reqs:
+        req.wait()
+
+    if rank == 0:
+        grad_in_tensor = grad_in_tensor[:, :, :, :-d2]
+        grad_in_tensor[:, :, :, -d1:] = torch.add(
+            grad_in_tensor[:, :, :, -d1:], recv_from_right
+        )
+    elif rank == size - 1:
+        grad_in_tensor = grad_in_tensor[:, :, :, d1:]
+        grad_in_tensor[:, :, :, :d2] = torch.add(
+            grad_in_tensor[:, :, :, :d2], recv_from_left
+        )
+    else:
+        grad_in_tensor = grad_in_tensor[:, :, :, d1:-d2]
+        grad_in_tensor[:, :, :, -d1:] = torch.add(
+            grad_in_tensor[:, :, :, -d1:], recv_from_right
+        )
+        grad_in_tensor[:, :, :, :d2] = torch.add(
+            grad_in_tensor[:, :, :, :d2], recv_from_left
+        )
+
+
+def tp_convolution(
+    op_call: torch._ops.OpOverload,
+    local_tensor_args: tuple[object, ...],
+    local_tensor_kwargs: dict[str, object],
+) -> object:
+    assert op_call == aten.convolution.default
+    assert len(local_tensor_args) == 9
+
+    rank = dist.get_rank()
+    size = dist.get_world_size()
+    in_tensor = cast(torch.Tensor, local_tensor_args[0])
+    weight = cast(torch.Tensor, local_tensor_args[1])
+    stride, padding, dilation = local_tensor_args[3:6]
+
+    assert _is_supported(in_tensor.shape, weight.shape, stride, padding, dilation)
+    assert isinstance(padding, list)
+
+    if not _requires_data_exchange(padding):
+        local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
+        return local_results
+    else:
+        # step 0 compute the overlap pixels of the input tensor
+        d = weight.shape[3] - 1
+        d1 = d // 2
+        d2 = d - d1
+        assert d1 + d2 == d
+        right = (rank + 1) % size
+        left = (rank - 1 + size) % size
+
+        # step1 reconstruct local input tensor
+        in_tensor = _ring_send_recv_construct(
+            in_tensor, d1, d2, left, right, rank, size
+        )
+
+        # step2 feed local input tensor to op_call
+        local_tensor_args_list = list(local_tensor_args)
+        local_tensor_args_list[0] = in_tensor
+        local_tensor_args = cast(tuple[object, ...], local_tensor_args_list)
+        local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
+
+        # step3 remove extra outputs from the results
+        padding_w = padding[1]
+        w = local_results.size(3)
+        if rank == 0:
+            local_results = local_results[:, :, :, : w - padding_w]
+        elif rank == size - 1:
+            local_results = local_results[:, :, :, padding_w:]
+        else:
+            local_results = local_results[:, :, :, padding_w : w - padding_w]
+
+        return local_results
+
+
+def tp_convolution_backward(
+    op_call: torch._ops.OpOverload,
+    local_tensor_args: tuple[object, ...],
+    local_tensor_kwargs: dict[str, object],
+) -> object:
+    assert op_call == aten.convolution_backward.default
+    assert len(local_tensor_args) == 11
+
+    rank = dist.get_rank()
+    size = dist.get_world_size()
+    grad_out_tensor = cast(torch.Tensor, local_tensor_args[0])
+    in_tensor = cast(torch.Tensor, local_tensor_args[1])
+    weight = cast(torch.Tensor, local_tensor_args[2])
+    stride, padding, dilation = local_tensor_args[4:7]
+
+    assert _is_supported(in_tensor.shape, weight.shape, stride, padding, dilation)
+    assert isinstance(padding, list)
+
+    if not _requires_data_exchange(padding):
+        local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
+        return local_results
+    else:
+        # step 0 compute the overlap pixels of the input tensor
+        d = weight.shape[3] - 1
+        d1 = d // 2
+        d2 = d - d1
+        assert d1 + d2 == d
+        right = (rank + 1) % size
+        left = (rank - 1 + size) % size
+
+        # step1 reconstruct local input tensor
+        in_tensor = _ring_send_recv_construct(
+            in_tensor, d1, d2, left, right, rank, size
+        )
+
+        # step2 reconstruct local gradient output tensor
+        padding_w = padding[1]
+        if rank == 0:
+            grad_out_tensor = torch.nn.functional.pad(
+                grad_out_tensor, (0, padding_w), "constant", 0
+            )
+        elif rank == size - 1:
+            grad_out_tensor = torch.nn.functional.pad(
+                grad_out_tensor, (padding_w, 0), "constant", 0
+            )
+        else:
+            grad_out_tensor = torch.nn.functional.pad(
+                grad_out_tensor, (padding_w, padding_w), "constant", 0
+            )
+
+        # step3 feed local input tensor to op_call
+        local_tensor_args_list = list(local_tensor_args)
+        local_tensor_args_list[0] = grad_out_tensor
+        local_tensor_args_list[1] = in_tensor
+        local_tensor_args = cast(tuple[object, ...], local_tensor_args_list)
+        local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
+
+        # step4 aggregate gradients for edge pixels
+        grad_in_tensor = local_results[0]
+        if grad_in_tensor is not None:
+            grad_in_tensor = _ring_send_recv_aggregate(
+                grad_in_tensor, d1, d2, left, right, rank, size
+            )
+            local_results = list(local_results)
+            local_results[0] = grad_in_tensor
+
+        local_results = cast(tuple[object, ...], local_results)
+
+        return local_results
+
+
+def convolution_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    # extract local tensor and sharding infos to a OpInfo
+    op_info = dtensor.DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
+
+    # sharding propagation
+    dtensor.DTensor._op_dispatcher.sharding_propagator.propagate(op_info)
+    output_sharding = op_info.output_sharding
+    assert output_sharding is not None, "output sharding should not be None"
+
+    # local propagation
+    local_results = tp_convolution(
+        op_call, tuple(op_info.local_args), op_info.local_kwargs
+    )
+
+    return dtensor.DTensor._op_dispatcher.wrap(
+        local_results, output_sharding.output_spec
+    )
+
+
+def convolution_backward_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    # Redistribute grad_output tensor to the same placement as input tensor
+    args = list(args)
+    assert isinstance(args[0], dtensor.DTensor) and isinstance(args[1], dtensor.DTensor)
+    args[0] = args[0].redistribute(args[1].device_mesh, args[1].placements)
+    args = tuple(args)
+
+    # extract local tensor and sharding infos to a OpInfo
+    op_info = dtensor.DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
+
+    # sharding propagation
+    dtensor.DTensor._op_dispatcher.sharding_propagator.propagate(op_info)
+    output_sharding = op_info.output_sharding
+    assert output_sharding is not None, "output sharding should not be None"
+
+    # local propagation
+    local_results = tp_convolution_backward(
+        op_call, tuple(op_info.local_args), op_info.local_kwargs
+    )
+
+    return dtensor.DTensor._op_dispatcher.wrap(
+        local_results, output_sharding.output_spec
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..61705610f08f219d6d2093ad6170edb12efda9b7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/_utils.py
@@ -0,0 +1,284 @@
+from collections.abc import Sequence
+from typing import cast
+
+import torch
+import torch.distributed.tensor._api as dtensor
+from torch._prims_common import ShapeType
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor.placement_types import (
+    _StridedShard,
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+def compute_local_shape_and_global_offset(
+    global_shape: ShapeType, mesh: DeviceMesh, placements: Sequence[Placement]
+) -> tuple[tuple[int, ...], tuple[int, ...]]:
+    """
+    Compute the local tensor shape and the global offsets into the original tensor
+    of a DTensor on its current global rank. This is useful for checkpointing purpose.
+
+    Example (2 host with 4GPUs each):
+    # Below is a DeviceMesh with mesh_shape of (2, 4)
+    mesh = DeviceMesh(device_type="cuda",
+                        mesh=[
+                        [0, 1, 2, 3],
+                        [4, 5, 6, 7]
+                        ],
+    )
+
+    Let's say we distribute a global_tensor of shape (8,4) over the above DeviceMesh
+    with a placements of [Shard(0), Shard(0)].
+    The local shape and global offset will be as follows:
+    rank0 -- local_shape:[1, 4], global_offset:[0, 0]
+    rank1 -- local_shape:[1, 4], global_offset:[1, 0]
+    rank2 -- local_shape:[1, 4], global_offset:[2, 0]
+    rank5 -- local_shape:[1, 4], global_offset:[5, 0]
+    rank3 -- local_shape:[1, 4], global_offset:[3, 0]
+    rank4 -- local_shape:[1, 4], global_offset:[4, 0]
+    rank6 -- local_shape:[1, 4], global_offset:[6, 0]
+    rank7 -- local_shape:[1, 4], global_offset:[7, 0]
+
+    Let's say we distribute a global_tensor of shape (2) over the above DeviceMesh with
+    a placements of [Shard(0)]. We will not have non-empty local tensor for all the ranks.
+    The local shape and global offset will be as follows:
+    rank0 -- local_shape:[1,], global_offset:[0,]
+    rank1 -- local_shape:[1,], global_offset:[1,]
+    rank2 -- local_shape:[0,], global_offset:[2,]
+    rank5 -- local_shape:[0,], global_offset:[2,]
+    rank3 -- local_shape:[0,], global_offset:[2,]
+    rank4 -- local_shape:[0,], global_offset:[2,]
+    rank6 -- local_shape:[0,], global_offset:[2,]
+    rank7 -- local_shape:[0,], global_offset:[2,]
+    """
+    my_coordinate = mesh.get_coordinate()
+
+    if my_coordinate is None:
+        # if rank not in the mesh, return empty offset
+        return ((0,), ())
+    else:
+        local_shape = list(global_shape)
+        global_offset = [0] * len(global_shape)
+        shard_idx_stride_by_mesh_dim = [
+            [0] * mesh.ndim for _ in range(len(global_shape))
+        ]  # index by (shard_dim, mesh_dim)
+        num_shards_by_tensor_dim = [1] * len(global_shape)
+
+        for idx, placement in enumerate(placements):
+            mesh_dim_size = mesh.size(idx)
+            if isinstance(placement, Shard):
+                shard_dim = placement.dim
+                local_offset = [0] * len(global_shape)
+                assert shard_dim < len(local_shape), (
+                    f"Sharding dim {shard_dim} greater than tensor ndim {len(local_shape)}"
+                )
+                shard_size, shard_offset = placement._local_shard_size_on_dim(
+                    local_shape[shard_dim],
+                    mesh_dim_size,
+                    my_coordinate[idx],
+                    return_offset=True,
+                )
+
+                local_shape[shard_dim] = shard_size
+                local_offset[shard_dim] = shard_offset
+
+                # On a given dimension, if the local_offset[shard_dim] is smaller than global_offset[shard_dim],
+                # it means that this dimension has been already sharded in previous placement.
+                # Therefore, we cannot simply replace the global_offset[shard_dim] with local_offset[shard_dim].
+                # Instead, for the given shard_dim, we need to add local_offset[shard_dim] to existing global_offset[shard_dim].
+                if global_offset[shard_dim] <= local_offset[shard_dim]:
+                    global_offset[shard_dim] = local_offset[shard_dim]
+                else:
+                    global_offset[shard_dim] += local_offset[shard_dim]
+
+                num_shards_by_tensor_dim[shard_dim] *= mesh_dim_size
+
+        # NOTE: the offset compute relies on the local shard index and it has no
+        # problem when strided sharding is not present. To correctly compute, we assume
+        # that the ``_StridedShard.split_factor`` field encodes how many partitions
+        # each local tensor will be further split into when sharding on higher mesh
+        # dimensions. However, this number is only correct if the DTensor is not
+        # sharded after the strided sharding completes. For example,
+        # [Shard(0), _StridedShard(0, split_factor=2), Shard(0)] is the placements
+        # where the DTensor's dim-0 is first sharded on device mesh dim-0, then on
+        # device mesh dim-2, and last on mesh dim-1. We define the
+        # "_StridedShard(0, split_factor=2), Shard(0)" part as the strided sharding
+        # part because strided sharding happens on mesh dim-1 and it was caused by
+        # the fact that sharding on dim-2 occurred ahead. In this case, there's no
+        # further sharding after this strided sharding part and ``split_factor``
+        # correctly encodes the number. Another example is
+        # [_StridedShard(0, split_factor=2), Shard(0), Shard(0)] where the DTensor's
+        # dim-0 is first sharded on mesh dim-1, then on mesh dim-0, and last on mesh
+        # dim-2. This violates our assumption that no further sharding shall occur
+        # after the strided sharding part and ``split_factor`` won't correctly
+        # encode the number of further split. So far, the only case where _StridedShard
+        # placement would appear is FSDP2 + TP on 2D mesh and the above case could only
+        # happen on mesh of 3 or more dimensions.
+        # TODO: change this function to correctly address this.
+        # TODO: this logic can be applied to contiguous sharding as well
+        strided_sharding = any(isinstance(p, _StridedShard) for p in placements)
+        if strided_sharding:
+            strided_part_seen = [False] * len(global_shape)
+            strided_part_end = [False] * len(global_shape)
+            for idx, placement in enumerate(placements):
+                mesh_dim_size = mesh.size(idx)
+                if isinstance(placement, Shard):
+                    shard_dim = placement.dim
+
+                    if strided_part_end[shard_dim]:
+                        raise NotImplementedError(
+                            f"Strided sharding does not allow Shard() to appear after "
+                            f"the strided part has ended. {placement} at idx {idx} in "
+                            f"{placements} violates this assumption."
+                        )
+
+                    if strided_part_seen[shard_dim]:
+                        strided_part_end[shard_dim] = True
+
+                    if isinstance(placement, _StridedShard):
+                        strided_part_seen[shard_dim] = True
+                        shard_idx_stride_by_mesh_dim[shard_dim][idx] = (
+                            num_shards_by_tensor_dim[shard_dim]
+                            // (placement.split_factor * mesh_dim_size)
+                        )
+                    else:
+                        num_shards_by_tensor_dim[shard_dim] //= mesh_dim_size
+                        shard_idx_stride_by_mesh_dim[shard_dim][idx] = (
+                            num_shards_by_tensor_dim[shard_dim]
+                        )
+
+            shard_idx = [
+                sum([x * y for x, y in zip(shard_idx_stride, my_coordinate)])
+                for shard_dim, shard_idx_stride in enumerate(
+                    shard_idx_stride_by_mesh_dim
+                )
+            ]
+
+            global_offset = [x * y for x, y in zip(local_shape, shard_idx)]
+
+        return tuple(local_shape), tuple(global_offset)
+
+
+def compute_global_tensor_info(
+    tensor: torch.Tensor, mesh: DeviceMesh, placements: Sequence[Placement]
+) -> tuple[list[int], list[int]]:
+    """
+    Compute the global size and stride of a DTensor from the given local tensor.
+    The local size is multiplited by `world_size` per Sharding dim.
+    The local stride is multiplited by `world_size` per Sharding dim, as long as the
+    dimension is outside sharding dim.
+
+    For example, if we have a local tensor with size (4, 8, 2) and stride (16, 1, 8).
+    If the DTensor placements are [Shard(2)] and world_size is 2;
+    then the global size is (4, 8, 4) and stride is (16 * 2, 1, 8).
+
+    Args:
+        tensor (:class:`torch.Tensor`):
+            Local tensor which DTensor will be constructed from.
+        mesh (:class:`DeviceMesh`):
+            Object which describes the mesh topology
+            of devices for the DTensor.
+        placements (Sequence[:class:`Placement`]]):
+            The attribute of the DTensor that describes its layout
+            on the mesh topology.
+
+    Return:
+        tensor_shape: A List of int which specifies the size of DTensor which build
+            on top of the local tensor.
+        tensor_stride: A List of int which specifies the stride of DTensor.
+    """
+    tensor_shape = list(tensor.size())
+    tensor_stride = list(tensor.stride())
+    for idx, placement in enumerate(placements):
+        mesh_dim_size = mesh.size(idx)
+        if placement.is_shard():
+            shard_placement = cast(Shard, placement)
+            if shard_placement.dim < 0:
+                raise AssertionError(
+                    "Shard placements should have negative dims normalized in "
+                    f"the user-facing APIs: {shard_placement}"
+                )
+            shard_dim = shard_placement.dim
+
+            assert shard_dim < tensor.ndim, (
+                f"Sharding dim {shard_dim} greater than tensor ndim {tensor.ndim} for placement number {idx}."
+            )
+
+            local_dim_size = tensor_shape[shard_dim]
+            tensor_shape[shard_dim] = local_dim_size * mesh_dim_size
+
+            # recover tensor stride by modifying the stride that larger than
+            # the current stride on the shard_dim
+            for i in range(len(tensor_stride)):
+                if i != shard_dim and tensor_stride[i] >= tensor_stride[shard_dim]:
+                    # rescale the stride by the shard size
+                    tensor_stride[i] = tensor_stride[i] * mesh_dim_size
+        elif not isinstance(placement, (Replicate, Partial)):
+            raise RuntimeError(f"placement type {type(placement)} not supported!")
+    return tensor_shape, tensor_stride
+
+
+def try_find_mesh_from_args(
+    op_call: torch._ops.OpOverload, args: Sequence[object]
+) -> DeviceMesh:
+    """
+    Find the device mesh object from args.
+    It returns None if no mesh is found.
+    NOTE: we can optimize this search if needed
+    """
+    for arg in args:
+        if isinstance(arg, (dtensor.DTensor, DTensorSpec)):
+            return arg.device_mesh
+        elif (
+            isinstance(arg, (list, tuple))
+            and len(arg) > 0
+            and isinstance(arg[0], (dtensor.DTensor, DTensorSpec))
+        ):
+            return arg[0].device_mesh
+
+    raise ValueError(f"Cannot find device mesh from args for op : {op_call}.")
+
+
+def compute_local_stride(
+    global_stride: ShapeType, mesh: DeviceMesh, placements: Sequence[Placement]
+) -> tuple[int, ...]:
+    """
+    Compute the stride of a local tensor shard, given the global stride of the DTensor.
+    NOTE: Currently this function is assuming the DTensor is evenly shardable.
+    """
+    stride_divisors = [1] * len(global_stride)
+    for mesh_idx, p in enumerate(placements):
+        if p.is_shard():
+            i = cast(Shard, p).dim
+            # tensor dimension i is sharded on mesh dimension mesh_idx,
+            # so we need to divide all the strides larger than stride[i]
+            # (by the submesh size)
+            for j in range(len(global_stride)):
+                if global_stride[j] > global_stride[i]:
+                    stride_divisors[j] *= mesh.size(mesh_idx)
+    return tuple(
+        global_stride[i] // stride_divisors[i] for i in range(len(global_stride))
+    )
+
+
+def normalize_to_torch_size(size) -> torch.Size:  # type: ignore[no-untyped-def]
+    """
+    Unify variable types of size argument to torch.Size
+    Acceptable types include:
+        int, Sequence[int], Tuple[int], Tuple[Sequence[int]],
+        or torch.Size
+    """
+    if isinstance(size, torch.Size):
+        return size
+
+    if isinstance(size, int):
+        torch_size = [size]
+    elif len(size) == 1 and isinstance(size[0], Sequence):
+        torch_size = list(size[0])
+    else:
+        torch_size = list(size)
+    return torch.Size(torch_size)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5bf3b833fe4786eddcc9f6812faa3ee6a5f3e1a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/__init__.py
@@ -0,0 +1,24 @@
+# mypy: allow-untyped-defs
+from torch.distributed.tensor.debug._comm_mode import CommDebugMode
+from torch.distributed.tensor.debug._visualize_sharding import visualize_sharding
+
+
+__all__ = ["CommDebugMode", "visualize_sharding"]
+
+
+def _get_sharding_prop_cache_info():
+    """
+    Get the cache info for the sharding propagation cache, used for debugging purpose only.
+    This would return a named tuple showing hits, misses, maxsize and cursize of the sharding
+    propagator cache.
+    """
+    from torch.distributed.tensor._api import DTensor
+
+    return (
+        DTensor._op_dispatcher.sharding_propagator.propagate_op_sharding.cache_info()  # type:ignore[attr-defined]
+    )
+
+
+# Set namespace for exposed private names
+CommDebugMode.__module__ = "torch.distributed.tensor.debug"
+visualize_sharding.__module__ = "torch.distributed.tensor.debug"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/_comm_mode.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/_comm_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..570161b676823382608d8a721b953b474cca1465
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/_comm_mode.py
@@ -0,0 +1,735 @@
+# mypy: allow-untyped-defs
+import copy
+import json
+import re
+import weakref
+from collections import defaultdict
+from typing import Any
+
+import torch
+import torch.nn
+from torch._guards import detect_fake_mode
+from torch.autograd.graph import register_multi_grad_hook
+from torch.distributed._tools.mod_tracker import ModTracker
+from torch.distributed.tensor._api import DTensor
+from torch.nn.modules.module import (
+    register_module_forward_hook,
+    register_module_forward_pre_hook,
+    register_module_full_backward_pre_hook,
+)
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils._pytree import tree_flatten
+
+
+__all__ = ["CommDebugMode"]
+
+funcol_native = torch.ops._c10d_functional
+funcol_py = torch.ops.c10d_functional
+funcol_autograd = torch.ops._c10d_functional_autograd
+c10d_ops = torch.ops.c10d
+
+NATIVE_TO_PY_MAPPING = {
+    funcol_native.all_gather_into_tensor: funcol_py.all_gather_into_tensor,
+    funcol_native.all_gather_into_tensor_coalesced: funcol_py.all_gather_into_tensor_coalesced,
+    funcol_native.all_reduce: funcol_py.all_reduce,
+    funcol_native.all_reduce_coalesced: funcol_py.all_reduce_coalesced,
+    funcol_native.all_to_all_single: funcol_py.all_to_all_single,
+    funcol_native.broadcast: funcol_py.broadcast,
+    funcol_native.reduce_scatter_tensor: funcol_py.reduce_scatter_tensor,
+    funcol_native.reduce_scatter_tensor_coalesced: funcol_py.reduce_scatter_tensor_coalesced,
+    # functional ops
+    funcol_autograd.all_to_all_single: funcol_py.all_to_all_single,
+}
+
+c10d_collective_ops = {
+    c10d_ops._allgather_base_,
+    c10d_ops._reduce_scatter_base_,
+    c10d_ops.allgather_,
+    c10d_ops.allgather_coalesced_,
+    c10d_ops.allgather_into_tensor_coalesced_,
+    c10d_ops.allreduce_,
+    c10d_ops.allreduce_coalesced_,
+    c10d_ops.alltoall_,
+    c10d_ops.alltoall_base_,
+    c10d_ops.broadcast_,
+    c10d_ops.gather_,
+    c10d_ops.scatter_,
+    c10d_ops.reduce_,
+    c10d_ops.reduce_scatter_,
+    c10d_ops.reduce_scatter_tensor_coalesced_,
+}
+
+trivial_ops = {
+    "aten.detach.default",
+    "aten.t.default",
+    "aten.view.default",
+    "aten._to_copy.default",
+    "aten.as_strided.default",
+    "aten.transpose.int",
+}
+
+
+class _CommModeModuleTracker(ModTracker):
+    """
+    Inherits ModuleTracker and expands on its functionality to track the
+    parameters and sharding information of a model at a module-level
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.module_helper_dict = {}
+        self.module_parameters_dict = {}
+        self.module_parents_dict = {}
+        self.register_forward_hook_handles = {}
+        self.parent_dict = {}
+        self.parent_list = []
+        self.sharding_dict = {}
+        self.activation_checkpointing = False
+        self.name = ""
+
+    def _fw_set_module_hook(self, mod, input, output):
+        """
+        Updates the current module after module finishes running and
+        all other hooks are resolved
+        """
+
+        if self.is_bw:
+            self.activation_checkpointing = True
+        else:
+            self.activation_checkpointing = False
+
+        if not self.activation_checkpointing:
+            # module is no longer parent of next modules
+            self.parent_list.pop()
+
+            # set current module to previous parent module
+            self.name = self.parent_list[-1]
+
+    def _fw_pre_hook(self, mod, input):
+        """
+        This function is called before the forward pass of a module. It
+        collects the parameters and sharding information of a module and
+        stores it in a dictionary.
+        """
+        if self.is_bw:
+            self.activation_checkpointing = True
+        else:
+            self.activation_checkpointing = False
+
+        self.name = super()._get_mod_name(mod)
+        w_mod = weakref.ref(mod)
+
+        # adds current sub-module to module tracker parent class
+        super()._get_append_fn(w_mod, self.name, False)()
+
+        args, _ = tree_flatten(input)
+        tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad]
+        if not self.is_bw and tensors:
+            register_multi_grad_hook(
+                tensors, super()._get_pop_fn(w_mod, self.name, True)
+            )
+
+        if not self.activation_checkpointing:
+            # contains information about module ordering and depth in the module tree
+            if self.name not in self.module_helper_dict:
+                self.module_helper_dict[self.name] = {}
+
+            self.module_helper_dict[self.name]["module_type"] = (
+                str(type(mod)).replace("<", "").replace(">", "")
+            )
+            self.module_helper_dict[self.name]["depth"] = len(self.parents) - 1
+
+            for param_name, param in mod.named_parameters(recurse=False):
+                if self.name not in self.module_parameters_dict:
+                    self.module_parameters_dict[self.name] = {}
+
+                self.module_parameters_dict[self.name][param_name] = param.data
+
+                if isinstance(param.data, DTensor):
+                    key_name = self.name + "." + param_name
+                    self.sharding_dict[key_name] = param.data.placements
+
+                    if "parameters" not in self.module_helper_dict[self.name]:
+                        self.module_helper_dict[self.name]["parameters"] = {}
+
+                    self.module_helper_dict[self.name]["parameters"][param_name] = str(
+                        param.data.placements
+                    )
+
+            # used to store module's parents to ensure correctness in backward pass/checkpointing
+            if self.name not in self.module_parents_dict:
+                self.module_parents_dict[self.name] = copy.deepcopy(self.parents)
+
+            # used to create parent-child module associations for json dumps
+            parent = self.parent_list[-1]
+            if parent not in self.parent_dict:
+                self.parent_dict[parent] = []
+
+            self.parent_dict[parent].append(self.name)
+            self.parent_list.append(self.name)
+
+            self.register_forward_hook_handles[self.name] = mod.register_forward_hook(
+                self._fw_set_module_hook
+            )
+
+    def _fw_post_hook(self, mod, input, output):
+        """
+        This function is called when the forward pass of a module is called.
+        It updates the module tracker and removes the module from parent data
+        """
+
+        super()._fw_post_hook(mod, input, output)
+
+    def _bw_hook(self, mod, output):
+        """
+        This function is called when the backward pass of a module is called. It
+        updates the current module for backward passes
+        """
+        self.activation_checkpointing = False
+        self.name = super()._get_mod_name(mod)
+
+    def __enter__(self):
+        self.activation_checkpointing = False
+        self.module_parameters_dict.clear()
+        self.sharding_dict.clear()
+        self.parent_dict.clear()
+        self.parent_list = ["Global"]
+        self.module_helper_dict.clear()
+        self.module_helper_dict["Global"] = {"depth": 0}
+        self.module_parents_dict.clear()
+        self.module_parents_dict["Global"] = set()
+        self._fw_pre_handle = register_module_forward_pre_hook(self._fw_pre_hook)
+        self._fw_post_handle = register_module_forward_hook(self._fw_post_hook)
+        self.register_forward_hook_handles.clear()
+        self._bw_handle = register_module_full_backward_pre_hook(self._bw_hook)
+        self.name = "Global"
+
+    def __exit__(self, *args):
+        super().__exit__(*args)
+        self._bw_handle.remove()
+
+        # removes all forward_hook handles added in the pre-hook
+        for handle in self.register_forward_hook_handles.values():
+            handle.remove()
+
+    def print_paramater_info(self):
+        print(self.module_parameters_dict)
+
+    def print_sharding_info(self):
+        for key, value in self.sharding_dict.items():
+            print(key + ": " + str(value))
+
+
+class CommDebugMode(TorchDispatchMode):
+    """
+    :class:`CommDebugMode` is a context manager that counts the number of
+    functional collectives within its context. It does this using a
+    ``TorchDispatchMode``.
+
+    .. note:: Not all collectives are supported yet.
+
+    Example usage
+
+    .. code-block:: python
+
+        mod = ...
+        comm_mode = CommDebugMode()
+        with comm_mode:
+            mod.sum().backward()
+        print(comm_mode.get_comm_counts())
+    """
+
+    def __init__(self):
+        self.comm_counts: dict[Any, int] = defaultdict(int)
+        self.comm_module_counts = {}
+        self.comm_module_operation_counts = {}
+        self.comm_registry = set()
+        for native_op, py_op in NATIVE_TO_PY_MAPPING.items():
+            self.comm_registry.add(native_op)
+            self.comm_registry.add(py_op)
+
+        self.comm_registry.add(torch.ops._dtensor.shard_dim_alltoall)
+        self.advanced_module_tracker = _CommModeModuleTracker()
+
+    def generate_json_dump(self, file_name="comm_mode_log.json", noise_level=3):
+        """
+        Creates json file used to build browser visual
+        0. prints module-level collective counts
+        1. prints dTensor operations not included in trivial operations
+        2. prints operations not included in trivial operations
+        3. prints all operations
+        """
+
+        (
+            include_DTensor_ops,
+            include_module_data,
+            include_ops,
+            include_trivial_ops,
+        ) = self._set_noise_parameters(noise_level)
+
+        # recursively builds json data
+        def add_json_information(json_dict, fqn):
+            json_dict["fqn"] = fqn
+            json_dict["module_type"] = ""
+            json_dict["parameters"] = []
+            json_dict["children"] = []
+            json_dict["collectives_forward"] = []
+            json_dict["collectives_backward"] = []
+            json_dict["operations_forward"] = []
+            json_dict["operations_backward"] = []
+
+            # adds module layer type and parameters, and their sharding
+            if (
+                "module_type" in self.advanced_module_tracker.module_helper_dict[fqn]
+                and include_module_data
+            ):
+                json_dict["module_type"] = (
+                    self.advanced_module_tracker.module_helper_dict[fqn]["module_type"]
+                )
+
+                if "parameters" in self.advanced_module_tracker.module_helper_dict[fqn]:
+                    for (
+                        param_name,
+                        placement,
+                    ) in self.advanced_module_tracker.module_helper_dict[fqn][
+                        "parameters"
+                    ].items():
+                        json_dict["parameters"].append((param_name, placement))
+
+            # adds module collective information
+            if fqn in self.comm_module_counts:
+                for collective, count in self.comm_module_counts[fqn][
+                    "forward"
+                ].items():
+                    json_dict["collectives_forward"].append((str(collective), count))
+
+                for collective, count in self.comm_module_counts[fqn][
+                    "backward"
+                ].items():
+                    json_dict["collectives_backward"].append((str(collective), count))
+
+            # adds module operation information
+            forward_operations = []
+            backward_operations = []
+            checkpointing_operations = []
+
+            # only get operations if the minimum operation noise level is set to true
+            if include_DTensor_ops:
+                if fqn in self.comm_module_operation_counts:
+                    (
+                        forward_operations,
+                        backward_operations,
+                        checkpointing_operations,
+                    ) = self._get_operations_list(
+                        self.comm_module_operation_counts[fqn]
+                    )
+
+            # remove all operations who don't have DTensor inputs
+            if not include_ops:
+                forward_operations = [
+                    op for op in forward_operations if len(op["input_sharding"])
+                ]
+                backward_operations = [
+                    op for op in backward_operations if len(op["input_sharding"])
+                ]
+                checkpointing_operations = [
+                    op for op in checkpointing_operations if len(op["input_sharding"])
+                ]
+
+            # remove all operations in trivial operations set
+            if not include_trivial_ops:
+                forward_operations = [
+                    op
+                    for op in forward_operations
+                    if str(op["name"]) not in trivial_ops
+                ]
+                backward_operations = [
+                    op
+                    for op in backward_operations
+                    if str(op["name"]) not in trivial_ops
+                ]
+                checkpointing_operations = [
+                    op
+                    for op in checkpointing_operations
+                    if str(op["name"]) not in trivial_ops
+                ]
+
+            # converts operation information into string format for json.dumps()
+            forward_operations = copy.deepcopy(forward_operations)
+            for op in forward_operations:
+                op["name"] = str(op["name"])
+
+                for i in range(len(op["input_sharding"])):
+                    op["input_sharding"][i] = str(op["input_sharding"][i])
+                    op["input_shape"][i] = str(op["input_shape"][i])
+
+            backward_operations = copy.deepcopy(backward_operations)
+            for op in backward_operations:
+                op["name"] = str(op["name"])
+
+                for i in range(len(op["input_sharding"])):
+                    op["input_sharding"][i] = str(op["input_sharding"][i])
+                    op["input_shape"][i] = str(op["input_shape"][i])
+
+            checkpointing_operations = copy.deepcopy(checkpointing_operations)
+            for op in checkpointing_operations:
+                op["name"] = str(op["name"])
+
+                for i in range(len(op["input_sharding"])):
+                    op["input_sharding"][i] = str(op["input_sharding"][i])
+                    op["input_shape"][i] = str(op["input_shape"][i])
+
+            json_dict["operations_forward"] = forward_operations
+            json_dict["operations_backward"] = backward_operations
+            json_dict["operations_checkpointing"] = checkpointing_operations
+
+            if fqn not in self.advanced_module_tracker.parent_dict:
+                return json_dict
+
+            # recursively adds module's children
+            for ele in self.advanced_module_tracker.parent_dict[fqn]:
+                json_dict["children"].append(add_json_information({}, ele))
+
+            return json_dict
+
+        json_dict: dict[str, Any] = {}
+        add_json_information(json_dict, "Global")
+
+        # converts dictonary into json file
+        with open(file_name, "w") as json_file:
+            json.dump(json_dict, json_file, indent=4)
+
+    def generate_comm_debug_tracing_table(self, noise_level=3):
+        """
+        Generates detailed table displaying operations and collective tracing information
+        on a module level. Amount of information is dependent on noise_level
+
+        0. prints module-level collective counts
+        1. prints dTensor operations not included in trivial operations, module information
+        2. prints operations not included in trivial operations
+        3. prints all operations
+        """
+
+        (
+            include_DTensor_ops,
+            include_module_data,
+            include_ops,
+            include_trivial_ops,
+        ) = self._set_noise_parameters(noise_level)
+
+        table = ""
+        for fqn in self.advanced_module_tracker.module_helper_dict:
+            # setting up indentations for table formatting
+            indent = "  " * (
+                2 * self.advanced_module_tracker.module_helper_dict[fqn]["depth"]
+            )
+            table += f"{indent}{fqn}\n"
+
+            if include_module_data:
+                if (
+                    "module_type"
+                    in self.advanced_module_tracker.module_helper_dict[fqn]
+                ):
+                    module_type = self.advanced_module_tracker.module_helper_dict[fqn][
+                        "module_type"
+                    ]
+                    table += f"{indent}*module type: {module_type}\n"
+
+                if "parameters" in self.advanced_module_tracker.module_helper_dict[fqn]:
+                    table += f"{indent}*Parameter List\n"
+                    for (
+                        param_name,
+                        placement,
+                    ) in self.advanced_module_tracker.module_helper_dict[fqn][
+                        "parameters"
+                    ].items():
+                        table += f"{indent} *{param_name}: {placement}\n"
+
+            indent += "  "
+            collective_indent = "  " * (
+                2 * self.advanced_module_tracker.module_helper_dict[fqn]["depth"] + 2
+            )
+            operation_indent = "  " * (
+                2 * self.advanced_module_tracker.module_helper_dict[fqn]["depth"] + 3
+            )
+
+            # separate the module's collective and operations by forward and backward
+            forward_collectives = {}
+            backward_collectives = {}
+            if fqn in self.comm_module_counts:
+                forward_collectives = self.comm_module_counts[fqn]["forward"]
+                backward_collectives = self.comm_module_counts[fqn]["backward"]
+
+            forward_operations = []
+            backward_operations = []
+            checkpointing_operations = []
+
+            if include_DTensor_ops:
+                if fqn in self.comm_module_operation_counts:
+                    (
+                        forward_operations,
+                        backward_operations,
+                        checkpointing_operations,
+                    ) = self._get_operations_list(
+                        self.comm_module_operation_counts[fqn]
+                    )
+
+            def add_tracing_information(table, collectives_dict, operation_list):
+                """
+                adds tracing information for module's forward or backward
+                """
+                for collective, count in collectives_dict.items():
+                    table += (
+                        f"\033[1;33m{collective_indent}*{collective}: {count}\033[0m\n"
+                    )
+
+                def add_operations(
+                    table, operation, collective_indent, operation_indent
+                ):
+                    """
+                    adds operation information to the table
+                    """
+                    table += f"\033[1;33m{collective_indent}**{operation_name}\033[0m\n"
+
+                    if len(operation["input_shape"]):
+                        operation_shape = operation["input_shape"]
+                        operation_sharding = operation["input_sharding"]
+                        operation_device_mesh = operation["device_mesh"]
+
+                        table += f"\033[1;31m{operation_indent}shape: {operation_shape}\033[0m\n"
+                        table += f"\033[1;31m{operation_indent}sharding: {operation_sharding}\033[0m\n"
+                        table += f"\033[1;31m{operation_indent}device mesh: {operation_device_mesh}\033[0m\n"
+
+                    return table
+
+                for operation in operation_list:
+                    operation_name = str(operation["name"])
+
+                    # include all operations
+                    if include_trivial_ops:
+                        table = add_operations(
+                            table, operation, collective_indent, operation_indent
+                        )
+
+                    # include all operations not in trivial operations
+                    elif include_ops and operation_name not in trivial_ops:
+                        table = add_operations(
+                            table, operation, collective_indent, operation_indent
+                        )
+
+                    # only include dTensor operations not in trivial set
+                    elif (
+                        include_DTensor_ops
+                        and (operation_name not in trivial_ops)
+                        and len(operation["input_shape"])
+                    ):
+                        table = add_operations(
+                            table, operation, collective_indent, operation_indent
+                        )
+
+                return table
+
+            if len(forward_collectives) or len(forward_operations):
+                table += f"{indent}FORWARD PASS\n"
+                table = add_tracing_information(
+                    table, forward_collectives, forward_operations
+                )
+
+            if len(backward_collectives) or len(backward_operations):
+                table += f"{indent}BACKWARD PASS\n"
+                table = add_tracing_information(
+                    table, backward_collectives, backward_operations
+                )
+
+            if len(checkpointing_operations):
+                table += f"{indent}ACTIVATION CHECKPOINTING\n"
+                table = add_tracing_information(table, {}, checkpointing_operations)
+
+        return table
+
+    def _get_operations_list(self, module_operation_counts):
+        forward_operations = [
+            op for op in module_operation_counts["operations_list"] if not op["is_bw"]
+        ]
+        backward_operations = [
+            op
+            for op in module_operation_counts["operations_list"]
+            if op["is_bw"] and not op["is_activation_checkpointing"]
+        ]
+        checkpointing_operations = [
+            op
+            for op in module_operation_counts["operations_list"]
+            if op["is_activation_checkpointing"]
+        ]
+
+        return forward_operations, backward_operations, checkpointing_operations
+
+    def get_total_counts(self) -> int:
+        return sum(self.comm_counts.values())
+
+    def get_comm_counts(self) -> dict[Any, int]:
+        """Returns the communication counts as a dictionary.
+
+        Returns:
+            Dict[Any, int]: The communication counts as a dictionary.
+        """
+        return self.comm_counts
+
+    def get_parameter_info(self) -> dict[str, dict[str, Any]]:
+        return self.advanced_module_tracker.module_parameters_dict
+
+    def get_sharding_info(self) -> dict[str, dict[str, Any]]:
+        return self.advanced_module_tracker.sharding_dict
+
+    def __enter__(self):
+        self.comm_counts.clear()
+        self.comm_module_counts.clear()
+        self.comm_module_counts["Global"] = {}
+        self.comm_module_counts["Global"]["forward"] = defaultdict(int)
+        self.comm_module_counts["Global"]["backward"] = defaultdict(int)
+
+        self.comm_module_operation_counts.clear()
+
+        super().__enter__()
+        self.advanced_module_tracker.__enter__()
+        return self
+
+    def __exit__(self, *args):
+        self.advanced_module_tracker.__exit__()
+        super().__exit__(*args)
+
+    def log_comm_debug_tracing_table_to_file(
+        self, file_name="comm_mode_log.txt", noise_level=3
+    ):
+        """
+        Alternative to console CommDebugMode output, writes to file specified by the user
+        """
+        ansi_escape = re.compile(r"\x1B\[[0-?]*[ -/]*[@-~]")
+        table = ansi_escape.sub("", self.generate_comm_debug_tracing_table(noise_level))
+
+        with open(file_name, "w") as log_file:
+            log_file.write(table)
+
+    def _set_noise_parameters(self, noise_level):
+        """
+        sets variables controlling what information displays based on noise level
+        """
+        include_DTensor_ops = False
+        include_module_data = False
+        include_ops = False
+        include_trivial_ops = False
+
+        if noise_level > 0:
+            include_DTensor_ops = True
+            include_module_data = True
+
+        if noise_level > 1:
+            include_ops = True
+
+        if noise_level > 2:
+            include_trivial_ops = True
+
+        return (
+            include_DTensor_ops,
+            include_module_data,
+            include_ops,
+            include_trivial_ops,
+        )
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        # When running this mode with DTensor, ordinarily all modes will
+        # run **before** subclasses get a chance to run.
+        # Returning NotImplemented here gives us a chance to let DTensor
+        # run and desugar into comms ops, before CommDebugMode sees them.
+
+        # sets up operation-level collective count
+        if self.advanced_module_tracker.name not in self.comm_module_operation_counts:
+            # dictionary should hold module input and output shape, operations list and collective counter
+            self.comm_module_operation_counts[self.advanced_module_tracker.name] = {
+                "operations_list": []
+            }
+        operation_dict = {}
+        operation_dict["name"] = func
+
+        operation_dict["input_shape"] = []
+        operation_dict["input_sharding"] = []
+        operation_dict["device_mesh"] = ""
+
+        # tracks if the operation is part of the backward pass
+        operation_dict["is_bw"] = self.advanced_module_tracker.is_bw
+
+        # tracks if the operation is part of activation checkpointing
+        operation_dict["is_activation_checkpointing"] = (
+            self.advanced_module_tracker.activation_checkpointing
+        )
+
+        if any(t == DTensor for t in types):
+            for ele in args:
+                if isinstance(ele, DTensor):
+                    # saves shapes and placements of all DTensor args
+                    operation_dict["input_shape"].append(ele.shape)
+                    operation_dict["input_sharding"].append(ele.placements)
+                    operation_dict["device_mesh"] = str(ele.device_mesh)
+
+            self.comm_module_operation_counts[self.advanced_module_tracker.name][
+                "operations_list"
+            ].append(operation_dict)
+
+            return NotImplemented
+
+        kwargs = kwargs if kwargs else {}
+        out = func(*args, **kwargs)
+        func_packet = func._overloadpacket
+
+        # We have many tests that use CommDebugMode to verify the occurrence of
+        # collectives. These tests do so by querying comm_counts with legacy
+        # funcol ops as key. For the purpose of native funcol migration, we
+        # need these tests to work for both legacy and native funcol. To avoid
+        # the need to modify all tests to accommodate the two implementations,
+        # we make CommDebugMode translate native funcol ops into legacy funcol
+        # ops until the migration finishes.
+
+        if func_packet in self.comm_registry or func_packet in c10d_collective_ops:
+            if func_packet in NATIVE_TO_PY_MAPPING:
+                func_packet = NATIVE_TO_PY_MAPPING[func_packet]
+            self.comm_counts[func_packet] += 1
+
+            key = "forward"
+            if self.advanced_module_tracker.is_bw:
+                key = "backward"
+
+            # adds collective count to current module
+            if self.advanced_module_tracker.name not in self.comm_module_counts:
+                self.comm_module_counts[self.advanced_module_tracker.name] = {}
+                self.comm_module_counts[self.advanced_module_tracker.name][
+                    "forward"
+                ] = defaultdict(int)
+                self.comm_module_counts[self.advanced_module_tracker.name][
+                    "backward"
+                ] = defaultdict(int)
+            self.comm_module_counts[self.advanced_module_tracker.name][key][
+                func_packet
+            ] += 1
+
+            # adds collective count to parent modules
+            for par in self.advanced_module_tracker.module_parents_dict[
+                self.advanced_module_tracker.name
+            ]:
+                # makes sure we aren't double counting when current sub-module hasn't been removed from parents
+                if par != self.advanced_module_tracker.name:
+                    if par not in self.comm_module_counts:
+                        self.comm_module_counts[par] = {}
+                        self.comm_module_counts[par]["forward"] = defaultdict(int)
+                        self.comm_module_counts[par]["backward"] = defaultdict(int)
+                    self.comm_module_counts[par][key][func_packet] += 1
+
+        # if tensor op uses fake tensors, return
+        if detect_fake_mode(args):
+            return out
+
+        # add tensor operation to module operation list
+        self.comm_module_operation_counts[self.advanced_module_tracker.name][
+            "operations_list"
+        ].append(operation_dict)
+
+        return out
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/_op_coverage.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/_op_coverage.py
new file mode 100644
index 0000000000000000000000000000000000000000..b43acaa9b196258c8d12e1f046691bff3a8ef30d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/_op_coverage.py
@@ -0,0 +1,104 @@
+# mypy: allow-untyped-defs
+from operator import itemgetter
+
+import torch
+import torch.fx
+import torch.nn as nn
+from functorch.compile import make_boxed_func
+from torch._functorch.compilers import aot_module
+from torch._inductor.decomposition import select_decomp_table
+from torch.distributed.tensor import DTensor
+
+
+inductor_decomps = select_decomp_table()
+
+graphs: list[torch.fx.GraphModule] = []
+
+
+def fwd_bwd_compiler(fx_g, _):
+    graphs.append(fx_g)
+    return make_boxed_func(fx_g)
+
+
+def get_inductor_decomp_graphs(model: nn.Module, args, kwargs):
+    """
+    Obtain forward and backward graphs of a model with inductor decompositions using tracing and aot_module.
+
+    Convenient util to get the fwd and bwd graphs of an arbitrary model
+    with inductor decompositions. Note that this would simply do tracing
+    with aot_module and don't ensure correctness. This is useful to track
+    the ops needed in DTensor.
+    """
+    compiled_mod = aot_module(
+        model, fw_compiler=fwd_bwd_compiler, decompositions=inductor_decomps
+    )
+    output = compiled_mod(*args, **kwargs)
+
+    if output.ndim != 0:
+        # if output is not a scalar tensor, by default sum it in order to
+        # run backward
+        output = output.sum()
+
+    output.backward()
+
+    # one fwd, one bwd graph
+    assert len(graphs) == 2
+    return graphs
+
+
+def print_op_coverage_summary(model: nn.Module, args, kwargs, *, output_csv=False):
+    """
+    Util to print the operator coverage summary of a certain model with tabulute.
+
+    Must have tabulate module installed.
+    """
+    # python module required for summary
+    import csv
+
+    from tabulate import tabulate
+
+    fwd_graph, bwd_graph = get_inductor_decomp_graphs(model, args, kwargs)
+
+    op_counts = {}
+
+    for node in fwd_graph.graph.nodes:
+        if node.op == "call_function" and isinstance(
+            node.target, torch._ops.OpOverload
+        ):
+            if node.target not in op_counts:
+                op_counts[node.target] = 0
+
+            op_counts[node.target] += 1
+
+    for node in bwd_graph.graph.nodes:
+        if node.op == "call_function" and isinstance(
+            node.target, torch._ops.OpOverload
+        ):
+            if node.target not in op_counts:
+                op_counts[node.target] = 0
+
+            op_counts[node.target] += 1
+
+    op_infos = []
+
+    for op, count in op_counts.items():
+        supported = op in DTensor._op_dispatcher.sharding_propagator.op_to_rules
+        op_infos.append([op, str(op._schema), count, supported])
+
+    # sort the op info base on the total count index
+    count_idx = 2
+    op_infos.sort(key=itemgetter(count_idx), reverse=True)
+
+    headers = ["Operator", "Schema", "Total Count", "Supported"]
+    print(tabulate(op_infos, headers=headers))
+
+    if output_csv:
+        # Open a CSV file for writing
+        with open("op_summary.csv", "w", newline="") as csv_file:
+            # Create a CSV writer object
+            csv_writer = csv.writer(csv_file)
+
+            csv_writer.writerow(headers)
+            # Write each table row to the CSV file
+            for row in op_infos:
+                csv_writer.writerow(row)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/_visualize_sharding.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/_visualize_sharding.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc476514bf5537fa0325efb9ca9c64f10ef8a61a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/debug/_visualize_sharding.py
@@ -0,0 +1,178 @@
+# mypy: allow-untyped-defs
+from collections.abc import Sequence
+
+import numpy as np
+
+from torch._prims_common import ShapeType
+from torch.distributed.tensor import DeviceMesh
+from torch.distributed.tensor.placement_types import Placement, Shard
+
+
+__all__ = ["visualize_sharding"]
+
+
+def _mesh_to_coordinate(mesh, device_type):
+    """
+    Given a n-dimensional list of device mesh, this function creates a map of
+    device and its coordinate
+    """
+    # Convert the n-dimensional list to a NumPy array
+    np_mesh = np.array(mesh.mesh.tolist())
+
+    # Create a dictionary to map each value to its coordinate
+    device_to_coordinate_map = {}
+    for coord, value in np.ndenumerate(np_mesh):
+        # device is unique in device_mesh
+        device_to_coordinate_map[f"{device_type}:{str(value)}"] = list(coord)
+
+    return device_to_coordinate_map
+
+
+def _convert_offset_to_ranges(all_offsets):
+    """
+    Using tabulate package to create a table is easier when we specify row and col ranges
+    This function converts offsets to ranges.
+    """
+    converted_blocks = []
+
+    for offset in all_offsets:
+        shape, offset, value = offset
+
+        # Calculate row_range and column_range
+        row_range = (offset[0], offset[0] + shape[0] - 1)
+        column_range = (offset[1], offset[1] + shape[1] - 1)
+
+        # Convert value to string to match your desired format
+        converted_block = {
+            "row_range": row_range,
+            "column_range": column_range,
+            "value": str(value),
+        }
+        converted_blocks.append(converted_block)
+
+    return converted_blocks
+
+
+def _create_table(blocks):
+    """
+    Creates a tabulate table given row and column ranges with device name
+    """
+    try:
+        from tabulate import tabulate
+    except ImportError as e:
+        raise ImportError("tabulate package is required to visualize sharding") from e
+
+    # Extract unique row and column ranges
+    row_ranges = sorted({block["row_range"] for block in blocks})
+    col_ranges = sorted({block["column_range"] for block in blocks})
+
+    # Create a matrix initialized with empty strings
+    matrix = [["" for _ in col_ranges] for _ in row_ranges]
+
+    # Fill the matrix with values
+    for block in blocks:
+        row_index = row_ranges.index(block["row_range"])
+        col_index = col_ranges.index(block["column_range"])
+        if matrix[row_index][col_index] == "":
+            matrix[row_index][col_index] = block["value"]
+        else:
+            matrix[row_index][col_index] += ", " + block["value"]
+
+    # Prepare headers
+    row_headers = [f"Row {r[0]}-{r[1]}" for r in row_ranges]
+    col_headers = [f"Col {c[0]}-{c[1]}" for c in col_ranges]
+
+    return tabulate(matrix, headers=col_headers, showindex=row_headers)
+
+
+def _compute_local_shape_and_global_offset(
+    global_shape: ShapeType,
+    mesh: DeviceMesh,
+    placements: Sequence[Placement],
+    my_coordinate: list[int],
+) -> tuple[tuple[int, ...], tuple[int, ...]]:
+    """
+    Same as torch.distributed._tensor._utils.compute_local_shape_and_global_offset but
+    with custom my_coordinate input. This is the modified implementation for visualize_sharding.
+    """
+
+    if my_coordinate is None:
+        # if rank not in the mesh, return empty offset
+        return ((), ())
+    else:
+        local_shape = list(global_shape)
+        global_offset = [0] * len(global_shape)
+
+        for idx, placement in enumerate(placements):
+            mesh_dim_size = mesh.size(idx)
+            if isinstance(placement, Shard):
+                shard_dim = placement.dim
+                local_offset = [0] * len(global_shape)
+                assert shard_dim < len(local_shape), (
+                    f"Sharding dim {shard_dim} greater than tensor ndim {len(local_shape)}"
+                )
+                shard_size, shard_offset = placement._local_shard_size_on_dim(
+                    local_shape[shard_dim],
+                    mesh_dim_size,
+                    my_coordinate[idx],
+                    return_offset=True,
+                )
+
+                local_shape[shard_dim] = shard_size
+                local_offset[shard_dim] = shard_offset
+
+                # On a given dimension, if the local_offset[shard_dim] is smaller than global_offset[shard_dim],
+                # it means that this dimension has been already sharded in previous placement.
+                # Therefore, we cannot simply replace the global_offset[shard_dim] with local_offset[shard_dim].
+                # Instead, for the given shard_dim, we need to add local_offset[shard_dim] to existing global_offset[shard_dim].
+                if global_offset[shard_dim] <= local_offset[shard_dim]:
+                    global_offset[shard_dim] = local_offset[shard_dim]
+                else:
+                    global_offset[shard_dim] += local_offset[shard_dim]
+
+        return tuple(local_shape), tuple(global_offset)
+
+
+def visualize_sharding(dtensor, header=""):
+    """
+    Visualizes sharding in the terminal for :class:`DTensor` that are 1D or 2D.
+
+    .. note:: This requires the ``tabulate`` package. No sharding info will be printed for empty tensors
+    """
+    if dtensor.numel() == 0:  # we do not print for empty dtensors
+        return
+
+    if len(dtensor.shape) >= 3:
+        raise RuntimeError(
+            "visualize sharding is only implemented for 1D or 2D dtensor"
+        )
+    placements = dtensor.placements
+    device_mesh = dtensor.device_mesh
+    device_type = dtensor.device_mesh.device_type
+
+    if device_mesh.get_coordinate() is None:  # current rank is not in the mesh
+        return
+
+    # Only display the visualization once for each DTensor, on the rank whose
+    # coordinate is 0 on all dimensions. For example, if the mesh is a full mesh,
+    # we will only print on rank 0.
+    local_rank_zero_on_all_dim = all(
+        device_mesh.get_local_rank(mesh_dim=dim) == 0 for dim in range(device_mesh.ndim)
+    )
+    if not local_rank_zero_on_all_dim:
+        return
+
+    device_map = _mesh_to_coordinate(device_mesh, device_type)
+    all_offsets = []
+    for device in device_map:
+        local_shape, global_offset = _compute_local_shape_and_global_offset(
+            dtensor.shape, device_mesh, placements, device_map[device]
+        )
+        all_offsets.append([local_shape, global_offset, device])
+
+    # Convert offsets to blocks with row_ranges for tabulate
+    blocks = _convert_offset_to_ranges(all_offsets)
+
+    # Print the table
+    print(header)
+    print(_create_table(blocks))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/device_mesh.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/device_mesh.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca59ded5eb52bc0a3878e76077ad2879df4bf499
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/device_mesh.py
@@ -0,0 +1,9 @@
+from torch.distributed.device_mesh import (  # noqa: F401
+    _get_device_handle,
+    _mesh_resources,
+    DeviceMesh,
+    init_device_mesh,
+)
+
+
+__all__ = ["init_device_mesh", "DeviceMesh"]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0012040d74a3e0caaf23a71c138681b9c372e591
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/__init__.py
@@ -0,0 +1,34 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from collections.abc import Iterator
+from contextlib import contextmanager
+
+from torch.distributed.tensor._api import DTensor
+from torch.distributed.tensor.experimental._attention import context_parallel
+from torch.distributed.tensor.experimental._func_map import local_map
+from torch.distributed.tensor.experimental._register_sharding import register_sharding
+
+
+__all__ = ["context_parallel", "implicit_replication", "local_map", "register_sharding"]
+
+
+@contextmanager
+def implicit_replication() -> Iterator[None]:
+    """
+    This context manager allows :class:`DTensor` to implicitly treat all non-DTensors (``torch.Tensor``)
+    in the program be replicate :class:`DTensor` s during the operator computation.
+
+    .. warning:: This might possible lead to incorrect results if ``torch.Tensor`` s are not replicated
+        in practice, please use it at your discretion.
+    """
+    try:
+        DTensor._op_dispatcher._allow_implicit_replication = True
+        yield
+    finally:
+        DTensor._op_dispatcher._allow_implicit_replication = False
+
+
+# Set namespace for exposed private names
+context_parallel.__module__ = "torch.distributed.tensor.experimental"
+implicit_replication.__module__ = "torch.distributed.tensor.experimental"
+local_map.__module__ = "torch.distributed.tensor.experimental"
+register_sharding.__module__ = "torch.distributed.tensor.experimental"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_attention.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..c323ea1593e0f92458e641abdf8b618aee49695b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_attention.py
@@ -0,0 +1,1410 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import contextlib
+import itertools
+import logging
+import types
+import weakref
+from abc import ABC, abstractmethod
+from collections.abc import Generator
+from dataclasses import dataclass
+from enum import auto, Enum
+from typing import Any, Callable, Optional, Protocol, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed._functional_collectives as ft_c
+import torch.nn.functional as F
+from torch import nn
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor import distribute_module, DTensor, Replicate, Shard
+from torch.distributed.tensor.parallel.style import ParallelStyle
+
+
+__all__ = ["context_parallel", "set_rotate_method"]
+
+
+class _CausalBehavior(Enum):
+    SKIP = None
+    NOT_IS_CAUSAL = False
+    IS_CAUSAL = True
+
+
+class _RotateMethod(Enum):
+    ALL_TO_ALL = auto()
+    ALL_GATHER = auto()
+
+
+aten = torch.ops.aten
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class _ContextParallelOptions:
+    # Whether to upcast parameters and gradients to float32 to avoid accumulation
+    # errors. It is likely this is always True but we currently keep this variable
+    # for the experimental purpose.
+    convert_to_f32: bool = True
+    enable_load_balance = True
+    rotate_method: _RotateMethod = _RotateMethod.ALL_GATHER
+
+
+_cp_options = _ContextParallelOptions()
+
+
+def _is_causal_behavior(
+    rank: int, world_size: int, i: int, is_causal: bool
+) -> _CausalBehavior:
+    """
+    Calculate is_causal behavior for each KV block. The attention can either be
+    calculated in full, not at all or with the causal mask applied.
+    """
+    if not is_causal:
+        return _CausalBehavior.NOT_IS_CAUSAL
+
+    if i == 0:
+        return _CausalBehavior.IS_CAUSAL
+
+    source_rank = (rank - i) % world_size
+    if source_rank < rank or _cp_options.enable_load_balance:
+        return _CausalBehavior.NOT_IS_CAUSAL
+    else:
+        return _CausalBehavior.SKIP
+
+
+def _maybe_wait(tensor: torch.Tensor) -> torch.Tensor:
+    """
+    When tracing the code, the result tensor is not an AsyncCollectiveTensor,
+    so we cannot call ``wait()``.
+    """
+    if isinstance(tensor, ft_c.AsyncCollectiveTensor):
+        return tensor.wait()
+    return tensor
+
+
+def _partial_update(
+    original: torch.Tensor,
+    new: torch.Tensor,
+    dim: int,
+    n_chunks: int,
+    idx: int,
+    add: bool,
+) -> torch.Tensor:
+    """
+    This API partially update a chunk of ``original`` tensor. The ``original``
+    tensor will be first chunked along ``dim`` dimension then the ``idx`` chunk
+    will be updated with ``new``. If ``add`` is True, the chunk will be added
+    with ``new``, otherwise the chunk with be replaced by ``add``.
+
+    The result is a tensor that is the same size as ``original``.
+    """
+    chunks = list(original.chunk(n_chunks, dim=dim))
+    assert chunks[idx].shape == new.shape, (original.shape, new.shape, idx)
+    if add:
+        chunks[idx] += new
+    else:
+        chunks[idx] = new
+    return torch.cat(chunks, dim=dim)
+
+
+class _SDPAMerger:
+    """A class to help to merge the local SDPA result."""
+
+    def __init__(self, convert_to_f32: bool, seq_dim: int):
+        self._seq_dim = seq_dim
+        self._out: Optional[torch.Tensor] = None
+        self._lse: Optional[torch.Tensor] = None
+        self._convert_to_f32 = convert_to_f32
+        self._out_dtype = torch.float32
+        self._lse_dtype = torch.float32
+
+    def _merge_one(
+        self, block_out: torch.Tensor, block_lse: torch.Tensor, partial: bool
+    ) -> None:
+        block_lse = block_lse.unsqueeze(dim=-1)
+        if self._lse is None:
+            self._lse = block_lse
+            self._out = block_out
+        else:
+            ROUND_ROBIN_CYCLE = 2
+            assert self._lse is not None
+            assert self._out is not None
+            lse = (
+                self._lse.chunk(ROUND_ROBIN_CYCLE, dim=self._seq_dim)[1]
+                if partial
+                else self._lse
+            )
+            out = (
+                self._out.chunk(ROUND_ROBIN_CYCLE, dim=self._seq_dim)[1]
+                if partial
+                else self._out
+            )
+
+            # The algorithm from
+            # github.com/zhuzilin/ring-flash-attention/pull/34#issuecomment-2076126795
+            # gives a relatively stable result.
+            out = out - F.sigmoid(block_lse - lse) * (out - block_out)
+            lse = lse - F.logsigmoid(lse - block_lse)
+            if partial:
+                self._lse = _partial_update(
+                    self._lse,
+                    lse,
+                    dim=self._seq_dim,
+                    n_chunks=ROUND_ROBIN_CYCLE,
+                    idx=1,
+                    add=False,
+                )
+                self._out = _partial_update(
+                    self._out,
+                    out,
+                    dim=self._seq_dim,
+                    n_chunks=ROUND_ROBIN_CYCLE,
+                    idx=1,
+                    add=False,
+                )
+            else:
+                self._lse = lse
+                self._out = out
+
+    def step(self, out: torch.Tensor, lse: torch.Tensor, partial: bool) -> None:
+        self._out_dtype = out.dtype
+        self._lse_dtype = lse.dtype
+
+        if self._convert_to_f32:
+            out = out.to(torch.float32)
+            lse = lse.to(torch.float32)
+
+        self._merge_one(out, lse, partial)
+
+    def results(self) -> tuple[torch.Tensor, torch.Tensor]:
+        assert self._out is not None
+        assert self._lse is not None
+        out, lse = self._out, self._lse.squeeze(-1)
+        return out.to(self._out_dtype), lse.to(self._lse_dtype)
+
+
+def _scaled_dot_product_ring_flash_attention(
+    mesh: DeviceMesh,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    return_debug_mask: bool = False,
+    *,
+    scale: Optional[float] = None,
+) -> tuple[torch.Tensor, ...]:
+    if return_debug_mask:
+        raise NotImplementedError("return_debug_mask is not supported yet")
+
+    seq_dim = 2
+    return _templated_ring_attention(
+        mesh,
+        seq_dim,
+        aten._scaled_dot_product_flash_attention,
+        query=query,
+        key=key,
+        value=value,
+        is_causal=is_causal,
+        dropout_p=dropout_p,
+        scale=scale,
+    )
+
+
+def _scaled_dot_product_ring_efficient_attention(
+    mesh: DeviceMesh,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_bias: Optional[torch.Tensor] = None,
+    compute_log_sumexp: bool = True,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    *,
+    scale: Optional[float] = None,
+) -> tuple[torch.Tensor, ...]:
+    if attn_bias is not None:
+        raise NotImplementedError("attn_bias is not supported yet")
+
+    if not compute_log_sumexp:
+        # CP requires compute_log_sumexp to be True because it always merges LSE
+        compute_log_sumexp = True
+
+    seq_dim = 2
+    return _templated_ring_attention(
+        mesh,
+        seq_dim,
+        aten._scaled_dot_product_efficient_attention,
+        query=query,
+        key=key,
+        value=value,
+        is_causal=is_causal,
+        attn_bias=attn_bias,
+        dropout_p=dropout_p,
+        scale=scale,
+        compute_log_sumexp=compute_log_sumexp,
+    )
+
+
+def _scaled_dot_product_ring_cudnn_attention(
+    mesh: DeviceMesh,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_bias: Optional[torch.Tensor] = None,
+    compute_log_sumexp: bool = True,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    return_debug_mask: bool = False,
+    *,
+    scale: Optional[float] = None,
+) -> tuple[torch.Tensor, ...]:
+    if attn_bias is not None:
+        raise NotImplementedError("attn_bias is not supported yet")
+
+    if not compute_log_sumexp:
+        # CP requires compute_log_sumexp to be True because it always merges LSE
+        compute_log_sumexp = True
+
+    seq_dim = 2
+    return _templated_ring_attention(
+        mesh,
+        seq_dim,
+        aten._scaled_dot_product_cudnn_attention,
+        query=query,
+        key=key,
+        value=value,
+        attn_bias=attn_bias,
+        compute_log_sumexp=compute_log_sumexp,
+        dropout_p=dropout_p,
+        is_causal=is_causal,
+        return_debug_mask=return_debug_mask,
+        scale=scale,
+    )
+
+
+class _AttentionOp(Protocol):
+    def __call__(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        **kwargs: object,
+    ) -> tuple[torch.Tensor, ...]: ...
+
+
+class _RingRotater(ABC):
+    @abstractmethod
+    def __init__(self, pg: dist.ProcessGroup, seq_dim: int) -> None: ...
+
+    @abstractmethod
+    def exchange_buffers(self, curr_buffer: torch.Tensor) -> None: ...
+
+    @abstractmethod
+    def next_buffer(self) -> torch.Tensor: ...
+
+
+class _AllToAllRotater(_RingRotater):
+    """Use all_to_all to send the kv to the next rank"""
+
+    def __init__(self, pg: dist.ProcessGroup, seq_dim: int) -> None:
+        self._pg = pg
+        self._seq_dim = seq_dim
+        self._buffer: Optional[torch.Tensor] = None
+
+    def exchange_buffers(self, curr_buffer: torch.Tensor) -> None:
+        curr_buffer = curr_buffer.contiguous()
+        size = dist.get_world_size(self._pg)
+        dsts = list(range(1, size)) + [0]
+        self._buffer = ft_c.permute_tensor(curr_buffer, dsts, self._pg)
+
+    def next_buffer(self) -> torch.Tensor:
+        assert self._buffer is not None
+        return _maybe_wait(self._buffer)
+
+
+class _AllGatherRotater(_RingRotater):
+    """
+    Allgather the kv and return the only the requried kv.
+    Only one communication will be done.
+    """
+
+    def __init__(self, pg: dist.ProcessGroup, seq_dim: int) -> None:
+        self._pg = pg
+        self._seq_dim = seq_dim
+        self._aggregated_buffer: Optional[torch.Tensor] = None
+        self._idx = 0
+
+    def exchange_buffers(self, curr_buffer: torch.Tensor) -> None:
+        # We only need to perform the allgather once.
+        self._idx += 1
+        if self._aggregated_buffer is None:
+            self._aggregated_buffer = ft_c.all_gather_tensor(
+                curr_buffer.contiguous(), gather_dim=0, group=self._pg
+            )
+
+    def next_buffer(self) -> torch.Tensor:
+        rank = dist.get_rank(self._pg)
+        idx = rank - self._idx
+
+        assert self._aggregated_buffer is not None
+        self._aggregated_buffer = _maybe_wait(self._aggregated_buffer)
+        return self._aggregated_buffer.chunk(dist.get_world_size(self._pg))[idx]
+
+
+def _create_rotater(
+    pg: dist.ProcessGroup, seq_dim: int, method: Optional[_RotateMethod] = None
+) -> _RingRotater:
+    if method is None:
+        method = _cp_options.rotate_method
+
+    if method == _RotateMethod.ALL_TO_ALL:
+        return _AllToAllRotater(pg, seq_dim)
+    elif method == _RotateMethod.ALL_GATHER:
+        return _AllGatherRotater(pg, seq_dim)
+    else:
+        raise NotImplementedError(f"Unkonwn method {method}")
+
+
+def _ring_rotate(
+    block: torch.Tensor, pg: dist.ProcessGroup, send_to_next: bool
+) -> torch.Tensor:
+    block = block.contiguous()
+    size = dist.get_world_size(pg)
+    dsts = (
+        list(range(1, size)) + [0]
+        if send_to_next
+        else [size - 1] + list(range(0, size - 1))
+    )
+    return ft_c.permute_tensor(block, dsts, pg)
+
+
+def _templated_ring_attention(
+    mesh: DeviceMesh,
+    seq_dim: int,
+    op: _AttentionOp,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    is_causal: bool = False,
+    **kwargs: object,
+) -> tuple[torch.Tensor, ...]:
+    """
+    This is a generalized ring attention implementation that can support multiple attention ops.
+
+    Note [Context parallelism load balance algorithm for causal masking]
+    =====================
+    This explanation uses an example to illustrate the CP algorithm with causal
+    masking.
+
+    Consider a scenario where the sequence length of q, k, and v is 4 (e.g.,
+    q = (q0, q1, q2, q3)), and there are two ranks. For simplicity, we will discuss
+    only q and k, as v follows the same pattern as k.
+
+    The diagram below represents a complete QK^T operation without parallelism.
+    The `****` entries indicate that the result is not required due to causal
+    masking (e.g., q0k1 is marked as `****`).
+
+    +----+------------------------+
+    |    |  k0    k1   k2     k3  |
+    +----+------------------------+
+    | q0 | q0k0, ****, ****, **** |
+    | q1 | q1k0, q1k1, ****, **** |
+    | q2 | q2k0, q2k1, q2k2, **** |
+    | q3 | q3k0, q3k1, q3k2, q3k3 |
+    +----+------------------------+
+
+    ### No Load Balance:
+
+    In this scenario, each rank owns a local chunk of q, k, and v, with each chunk
+    containing two elements. Rank0 is responsible for managing (q0, q1) and (k0, k1),
+    while rank1 manages (q2, q3) and (k2, k3).
+
+    First Iteration: Both rank0 and rank1 perform SDPA with their local qkv pairs.
+    Causal masking is enabled as some results are not required (e.g., q0k1).
+
+    Second Iteration: Local queries remain the same, but local kv pairs are exchanged.
+    Rank0 now has (q0, q1) and (k2, k3); rank1 has (q2, q3) and (k0, k1). Rank0 performs
+    no computation, while rank1 computes locally without causal masking since all results
+    (q2k0, q2k1, q3k0, q3k1) are needed.
+
+    ### Round-robin Load Balance:
+
+    In this setup, each rank owns two local chunks of q, k, and v, with each chunk
+    containing one element. Rank0 manages (q0, q3) and (k0, k3); Rank1 manages (q1, q2)
+    and (k1, k2). Although the local chunks are not consecutive, they are concatenated to
+    enable SDPA to be performed in a single call for each step. Consequently, the chunk()
+    function may be required to prepare the correct q, k, and v configurations.
+
+    First Iteration: Both ranks perform SDPA with their local qkv pairs, similar to the
+    no-load-balance case. This iteration corresponds to the `if` of the
+    (`if, `elif`, `else`) in the implemementation.
+
+    Second Iteration: Rank0 now has (q0, q3) and (k1, k2); rank1 has (q1, q2) and
+    (k0, k3). For rank0, no computation is needed for q0. However, computations for
+    q3k1 and q3k2 are required, so only q3 is used for SDPA. This corresponds to the
+    `else` of the (`if`, `elif`, `else`) in the implemementation.
+    For rank1, k0 is not needed for q1 and q2, so only k3 is used for SDPA. This
+    corresponds to the `elif` of (`if`, `elif`, `else`) in the implementation.
+
+    Parameters
+    ----------
+    op:
+        The attention op to use
+    *args:
+        additional args are passed to the op
+    **kwargs:
+        additional kwargs are passed to the op
+
+    Returns
+    -------
+    out:
+        The merged attention output
+    softmax_lse:
+        The logsumexp of the merged attention output
+    """
+    if is_causal and (query.size(2) != key.size(2)):
+        raise NotImplementedError(
+            "is_causal requires the same query and context sequence lengths"
+        )
+    if not is_causal and _cp_options.enable_load_balance:
+        raise RuntimeError("Load balancing requires `is_causal=True`.")
+
+    if isinstance(mesh, dist.ProcessGroup):
+        pg: Union[dist.ProcessGroup, list[dist.ProcessGroup]] = mesh
+    else:
+        pg = mesh.get_group()
+    assert isinstance(pg, dist.ProcessGroup), "process group must be single dimension"
+    rank = dist.get_rank(pg)
+    size = dist.get_world_size(pg)
+
+    next_kv = None
+
+    # Without making key and value contiguous(), the lose curve is bad.
+    # TODO(fegin): figure out why this is a requirement since SDPA does not have
+    # this requirement.
+    key = key.contiguous()
+    value = value.contiguous()
+
+    sdpa_merger = _SDPAMerger(_cp_options.convert_to_f32, seq_dim=seq_dim)
+
+    rest: list[Any]
+    out: torch.Tensor
+    logsumexp: torch.Tensor
+
+    rotater = _create_rotater(pg, 2)
+
+    for i in range(size):
+        if i > 0:
+            # Wait for the kv from the (cp_rank - 1) rank.
+            next_kv = rotater.next_buffer()
+            key = next_kv[: key.numel()].reshape(key.shape)
+            value = next_kv[key.numel() :].reshape(value.shape)
+
+        if i < (size - 1):
+            # Send the k, v to the next rank
+            next_kv = torch.cat([key.flatten(), value.flatten()])
+            next_kv = rotater.exchange_buffers(next_kv)
+
+        is_causal_behavior = _is_causal_behavior(
+            rank=rank, world_size=size, i=i, is_causal=is_causal
+        )
+
+        # For a detailed understanding of the load balancing algorithm, see
+        # Note [Context parallelism load balance algorithm for causal masking]
+        if is_causal_behavior == _CausalBehavior.SKIP:
+            # If i > rank and load balancing is not turned on.
+            continue
+
+        if i == 0 or (not _cp_options.enable_load_balance or not is_causal):
+            # When local balance is enabled, we still need to do SDPA with
+            # the both local chunks of q, k, v for the first iteration.
+            q, k, v, partial = (query, key, value, False)
+        elif i <= rank:
+            # Round-robin load balancing case, and i <= rank.
+            # We need to do SPDA, with only the first local chunk of the k, v.
+            # Note that q, k, v, each contains two local chunks.
+            ROUND_ROBIN_CYCLE = 2
+            q, k, v, partial = (
+                query,
+                key.chunk(ROUND_ROBIN_CYCLE, dim=2)[0],
+                value.chunk(ROUND_ROBIN_CYCLE, dim=2)[0],
+                False,
+            )
+        else:
+            # Round-robin load balancing case, and i > rank.
+            # We need to do SPDA with only the second half of the q, and update
+            # only the the second part of  logsumexp. So partial is True.
+            # Note that q, k, v, each contains two chunks.
+            q, k, v, partial = query.chunk(2, dim=2)[1], key, value, True
+
+        # See https://github.com/pytorch/pytorch/blob/release/2.4/aten/src/ATen/native/native_functions.yaml#L14695
+        # for the SDPA kernel definitions.
+        out, logsumexp, *rest = op(
+            q,
+            k,
+            v,
+            is_causal=is_causal_behavior.value,
+            **kwargs,
+        )
+        sdpa_merger.step(out, logsumexp, partial)
+
+    return *sdpa_merger.results(), *rest
+
+
+def _sdpa_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    # extract local tensor and sharding infos to a OpInfo
+    op_info = DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
+    logger.debug("Dispatching op_call: %s", op_info.schema)
+
+    # sharding propagation
+    # TODO: remove the context parallel strategy from the default propagation
+    # rule. Either figure out how to dynamically enable it or just don't call
+    # propagate.
+    DTensor._op_dispatcher.sharding_propagator.propagate(op_info)
+    output_sharding = op_info.output_sharding
+    assert output_sharding is not None, "output sharding should not be None"
+    assert not output_sharding.needs_redistribute, "inputs need to be redistributed"
+
+    if op_call == aten._scaled_dot_product_flash_attention.default:
+        local_results = _scaled_dot_product_ring_flash_attention(
+            op_info.compute_mesh,
+            *op_info.local_args,  # type: ignore[arg-type]
+            **op_info.local_kwargs,  # type: ignore[arg-type]
+        )
+    elif op_call == aten._scaled_dot_product_efficient_attention.default:
+        local_results = _scaled_dot_product_ring_efficient_attention(
+            op_info.compute_mesh,
+            *op_info.local_args,  # type: ignore[arg-type]
+            **op_info.local_kwargs,  # type: ignore[arg-type]
+        )
+    elif op_call == aten._scaled_dot_product_cudnn_attention.default:
+        local_results = _scaled_dot_product_ring_cudnn_attention(
+            op_info.compute_mesh,
+            *op_info.local_args,  # type: ignore[arg-type]
+            **op_info.local_kwargs,  # type: ignore[arg-type]
+        )
+    else:
+        raise NotImplementedError(
+            "CP only supports flash attention and memory efficient attention now."
+        )
+
+    return DTensor._op_dispatcher.wrap(local_results, output_sharding.output_spec)
+
+
+def _sdpa_backward_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    # Redistribute grad_output tensor to the same placement as output tensor
+    args = list(args)
+    args = tuple(args)
+
+    # extract local tensor and sharding infos to a OpInfo
+    op_info = DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
+    logger.debug("Dispatching op_call: %s", op_info.schema)
+
+    # sharding propagation
+    DTensor._op_dispatcher.sharding_propagator.propagate(op_info)
+    output_sharding = op_info.output_sharding
+    assert output_sharding is not None, "output sharding should not be None"
+    assert not output_sharding.needs_redistribute, "inputs need to be redistributed"
+
+    if op_call == aten._scaled_dot_product_flash_attention_backward.default:
+        local_results = _scaled_dot_product_ring_flash_attention_backward(
+            op_info.compute_mesh,
+            *op_info.local_args,  # type: ignore[arg-type]
+            **op_info.local_kwargs,  # type: ignore[arg-type]
+        )
+    elif op_call == aten._scaled_dot_product_efficient_attention_backward.default:
+        local_results = _scaled_dot_product_ring_efficient_attention_backward(
+            op_info.compute_mesh,
+            *op_info.local_args,  # type: ignore[arg-type]
+            **op_info.local_kwargs,  # type: ignore[arg-type]
+        )
+    elif op_call == aten._scaled_dot_product_cudnn_attention_backward.default:
+        local_results = _scaled_dot_product_ring_cudnn_attention_backward(
+            op_info.compute_mesh,
+            *op_info.local_args,  # type: ignore[arg-type]
+            **op_info.local_kwargs,  # type: ignore[arg-type]
+        )
+    else:
+        raise NotImplementedError(f"{op_call=}")
+
+    return DTensor._op_dispatcher.wrap(local_results, output_sharding.output_spec)
+
+
+def _templated_ring_attention_backward(
+    mesh: DeviceMesh,
+    seq_dim: int,
+    op: _AttentionOp,
+    grad_out: torch.Tensor,
+    grad_out_name: str,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    out: torch.Tensor,
+    logsumexp: torch.Tensor,
+    is_causal: bool,
+    **kwargs: Any,
+) -> tuple[torch.Tensor, ...]:
+    """This API implements the backward of the ring attention."""
+    if not is_causal and _cp_options.enable_load_balance:
+        raise RuntimeError("Load balancing requires `is_causal=True`.")
+    pg = mesh.get_group()
+    assert isinstance(pg, dist.ProcessGroup), "must be single dimension"
+    rank = dist.get_rank(pg)
+    size = dist.get_world_size(pg)
+    next_kv = None
+    next_grad_kv = None
+    rest: list[Any]
+    grad_query_, grad_key_, grad_value_ = None, None, None
+
+    accum_dtype = torch.float32 if _cp_options.convert_to_f32 else query.dtype
+    grad_query = torch.zeros_like(query, dtype=accum_dtype)
+    grad_key = torch.zeros_like(key, dtype=accum_dtype)
+    grad_value = torch.zeros_like(value, dtype=accum_dtype)
+
+    key = key.contiguous()
+    value = value.contiguous()
+    kv_rotater = _create_rotater(pg, 2)
+    dkv_rotater = _create_rotater(pg, 2, method=_RotateMethod.ALL_TO_ALL)
+    for i in range(size):
+        if i > 0:
+            # Wait for the kv from the (cp_rank - 1) rank.
+            buffer = kv_rotater.next_buffer()
+            pointer = 0
+            key = buffer[pointer : pointer + key.numel()].reshape(key.shape)
+            pointer += key.numel()
+            value = buffer[pointer : pointer + value.numel()].reshape(value.shape)
+            pointer += value.numel()
+
+        if i != size - 1:
+            # Send the kv to the next rank.
+            next_kv = torch.cat([key.flatten(), value.flatten()])
+            kv_rotater.exchange_buffers(next_kv)
+
+        is_causal_behavior = _is_causal_behavior(
+            rank=rank, world_size=size, i=i, is_causal=is_causal
+        )
+
+        if is_causal_behavior != _CausalBehavior.SKIP:
+            if i == 0 or (not _cp_options.enable_load_balance or not is_causal):
+                # We need to do SDPA with the full local q, k, v.
+                q, k, v, out_, dout, lse = (query, key, value, out, grad_out, logsumexp)
+            elif i <= rank:
+                # Round-robin load balancing case, and i <= rank.
+                # We need to do SPDA with only the first half of the k, v.
+                # Note that q, k, v, each contains two chunks.
+                q, k, v, out_, dout, lse = (
+                    query,
+                    key.chunk(2, dim=seq_dim)[0],
+                    value.chunk(2, dim=seq_dim)[0],
+                    out,
+                    grad_out,
+                    logsumexp,
+                )
+            else:
+                # Round-robin load balancing case, and i > rank.
+                # We need to do SPDA with only the second half of the q
+                # Note that q, k, v, each contains two chunks.
+                q, k, v, out_, dout, lse = (
+                    query.chunk(2, dim=seq_dim)[1],
+                    key,
+                    value,
+                    out.chunk(2, dim=seq_dim)[1],
+                    grad_out.chunk(2, dim=seq_dim)[1],
+                    # Need to make logsumexp contiguous, otherwise there will
+                    # be numerical error.
+                    logsumexp.chunk(2, dim=seq_dim)[1].contiguous(),
+                )
+
+            kwargs[grad_out_name] = dout
+            # See https://github.com/pytorch/pytorch/blob/release/2.4/aten/src/ATen/native/native_functions.yaml#L14695
+            # for the SDPA kernel definitions.
+            grad_query_, grad_key_, grad_value_, *rest = op(
+                query=q,
+                key=k,
+                value=v,
+                out=out_,
+                logsumexp=lse,
+                is_causal=is_causal_behavior.value,
+                **kwargs,
+            )
+        else:
+            grad_query_ = torch.zeros_like(query, dtype=accum_dtype)
+            grad_key_ = torch.zeros_like(key, dtype=accum_dtype)
+            grad_value_ = torch.zeros_like(value, dtype=accum_dtype)
+
+        ROUND_ROBIN_CYCLE = 2
+        if i == 0:
+            grad_key += grad_key_
+            grad_value += grad_value_
+        else:
+            pointer = 0
+            # Wait for the kv gradient from (cp_rank - 1) rank.
+            next_grad_kv = dkv_rotater.next_buffer()
+            grad_key = next_grad_kv[pointer : pointer + grad_key.numel()].reshape(
+                grad_key.shape
+            )
+            pointer += grad_key.numel()
+            grad_value = next_grad_kv[pointer : pointer + grad_value.numel()].reshape(
+                grad_value.shape
+            )
+
+            if i <= rank and _cp_options.enable_load_balance:
+                grad_key = _partial_update(
+                    grad_key,
+                    grad_key_,
+                    dim=seq_dim,
+                    n_chunks=ROUND_ROBIN_CYCLE,
+                    idx=0,
+                    add=True,
+                )
+                grad_value = _partial_update(
+                    grad_value,
+                    grad_value_,
+                    dim=seq_dim,
+                    n_chunks=ROUND_ROBIN_CYCLE,
+                    idx=0,
+                    add=True,
+                )
+            else:
+                grad_key += grad_key_
+                grad_value += grad_value_
+
+        next_grad_kv = torch.cat([grad_key.flatten(), grad_value.flatten()])
+        # Send the grad key, and grad value to the next rank.
+        dkv_rotater.exchange_buffers(next_grad_kv)
+
+        if i <= rank or not _cp_options.enable_load_balance:
+            grad_query += grad_query_
+        else:
+            grad_query = _partial_update(
+                grad_query,
+                grad_query_,
+                dim=seq_dim,
+                n_chunks=ROUND_ROBIN_CYCLE,
+                idx=1,
+                add=True,
+            )
+
+    assert grad_key_ is not None
+    assert grad_value_ is not None
+    grad_query = grad_query.to(query.dtype)
+    next_grad_kv = dkv_rotater.next_buffer().to(key.dtype)
+    grad_key = next_grad_kv[: grad_key.numel()].reshape(grad_key.shape)
+    grad_value = next_grad_kv[grad_value.numel() :].reshape(grad_value.shape)
+    return (
+        grad_query,
+        grad_key,
+        grad_value,
+        *rest,
+    )
+
+
+def _scaled_dot_product_ring_flash_attention_backward(
+    mesh: DeviceMesh,
+    grad_out: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    out: torch.Tensor,
+    logsumexp: torch.Tensor,
+    cum_seq_q: torch.Tensor,
+    cum_seq_k: torch.Tensor,
+    max_q: int,
+    max_k: int,
+    dropout_p: float,
+    is_causal: bool,
+    philox_seed: torch.Tensor,
+    philox_offset: torch.Tensor,
+    *,
+    scale: Optional[float] = None,
+) -> tuple[torch.Tensor, ...]:
+    seq_dim = 2
+    return _templated_ring_attention_backward(
+        mesh,
+        seq_dim,
+        aten._scaled_dot_product_flash_attention_backward.default,
+        grad_out=grad_out,
+        grad_out_name="grad_out",
+        query=query,
+        key=key,
+        value=value,
+        out=out,
+        logsumexp=logsumexp,
+        is_causal=is_causal,
+        cum_seq_q=cum_seq_q,
+        cum_seq_k=cum_seq_k,
+        max_q=max_q,
+        max_k=max_k,
+        dropout_p=dropout_p,
+        philox_seed=philox_seed,
+        philox_offset=philox_offset,
+        scale=scale,
+    )
+
+
+def _scaled_dot_product_ring_efficient_attention_backward(
+    mesh: DeviceMesh,
+    grad_out: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    bias: torch.Tensor,
+    out: torch.Tensor,
+    logsumexp: torch.Tensor,
+    philox_seed: torch.Tensor,
+    philox_offset: torch.Tensor,
+    dropout_p: float,
+    grad_input_mask: tuple[bool, ...],
+    is_causal: bool = False,
+    *,
+    scale: Optional[float] = None,
+) -> tuple[torch.Tensor, ...]:
+    seq_dim = 2
+    return _templated_ring_attention_backward(
+        mesh,
+        seq_dim,
+        aten._scaled_dot_product_efficient_attention_backward.default,
+        grad_out=grad_out,
+        grad_out_name="grad_out_",
+        query=query,
+        key=key,
+        value=value,
+        attn_bias=bias,
+        out=out,
+        logsumexp=logsumexp,
+        philox_seed=philox_seed,
+        philox_offset=philox_offset,
+        dropout_p=dropout_p,
+        grad_input_mask=grad_input_mask,
+        is_causal=is_causal,
+        scale=scale,
+    )
+
+
+def _scaled_dot_product_ring_cudnn_attention_backward(
+    mesh: DeviceMesh,
+    grad_out: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    out: torch.Tensor,
+    logsumexp: torch.Tensor,
+    philox_seed: torch.Tensor,
+    philox_offset: torch.Tensor,
+    attn_bias: torch.Tensor,
+    cum_seq_q: torch.Tensor,
+    cum_seq_k: torch.Tensor,
+    max_q: int,
+    max_k: int,
+    dropout_p: float,
+    is_causal: bool,
+    *,
+    scale: Optional[float] = None,
+) -> tuple[torch.Tensor, ...]:
+    seq_dim = 2
+    return _templated_ring_attention_backward(
+        mesh,
+        seq_dim,
+        aten._scaled_dot_product_cudnn_attention_backward.default,
+        grad_out=grad_out,
+        grad_out_name="grad_out",
+        query=query,
+        key=key,
+        value=value,
+        out=out,
+        logsumexp=logsumexp,
+        philox_seed=philox_seed,
+        philox_offset=philox_offset,
+        attn_bias=attn_bias,
+        cum_seq_q=cum_seq_q,
+        cum_seq_k=cum_seq_k,
+        max_q=max_q,
+        max_k=max_k,
+        dropout_p=dropout_p,
+        is_causal=is_causal,
+        scale=scale,
+    )
+
+
+customized_ops = {
+    aten._scaled_dot_product_flash_attention.default: _sdpa_handler,
+    aten._scaled_dot_product_flash_attention_backward.default: _sdpa_backward_handler,
+    aten._scaled_dot_product_efficient_attention.default: _sdpa_handler,
+    aten._scaled_dot_product_efficient_attention_backward.default: _sdpa_backward_handler,
+    aten._scaled_dot_product_cudnn_attention.default: _sdpa_handler,
+    aten._scaled_dot_product_cudnn_attention_backward.default: _sdpa_backward_handler,
+}
+
+
+_replaced_functions: dict[Callable, tuple[str, Callable]] = {}
+
+
+def _distribute_function(
+    fn: Callable,
+    fn_module: types.ModuleType,
+    device_mesh: DeviceMesh,
+    input_fn: Optional[Callable] = None,
+    output_fn: Optional[Callable] = None,
+) -> None:
+    """
+    ``distribute_function`` is an experimental API that allows users to "distribute"
+    the inputs and outputs of a function. Similar to ``distribute_module``, this API
+    installs hooks to the ``fn`` to convert the inputs and outputs. There are two
+    major differences between ``distribute_function`` and ``distribute_module``.
+    First, a function does not have parammeters and buffers, as a result,
+    ``distribute_function`` itself won't convert any parameters/buffers but simply
+    install the input and output hooks.  The tensor conversion will happen in the hooks.
+    Another difference is an nn.Module subclass can have several instances and each
+    instance be fed into ``distribute_module`` independently with affecting other
+    instance. On the other hand, function is a singleton object. So if a function
+    is distributed by ``distribute_function`` all subsequent calls to the function
+    will invoke the installed hooks.
+
+    Args:
+        fn (Callable): the function to be distributed.
+        fn_module (types.ModuleType): the Python module that the function is declared.
+            e.g., if ``fn`` is ``torch.nn.functional.scaled_dot_product_attention``,
+            ``fn_module`` is ``torch.nn.functional``.
+        device_mesh (:class:`DeviceMesh`): the device mesh that will be used by the
+            input and output hooks to distribute the tensors.
+        input_fn (Optioinal[Callable]): the hook to distribute or convert the input
+            arguments of ``fn``.
+        output_fn (Optioinal[Callable]): the hook to distribute or convert the output
+            arguments of ``fn``.
+    """
+
+    def wrapper(
+        target_fn: Callable, input_fn: Optional[Callable], output_fn: Optional[Callable]
+    ) -> Callable:
+        def inner_fn(*args: tuple[Any, ...], **kwargs: dict[str, Any]) -> Any:
+            if input_fn is not None:
+                args, kwargs = input_fn(device_mesh, *args, **kwargs)
+            output = target_fn(*args, **kwargs)
+            if output_fn is not None:
+                output = output_fn(device_mesh, output)
+            return output
+
+        return inner_fn
+
+    global _replaced_functions
+
+    if fn in _replaced_functions:
+        return
+
+    wrapper_fn = wrapper(fn, input_fn, output_fn)
+    setattr(fn_module, fn.__name__, wrapper_fn)
+    _replaced_functions[wrapper_fn] = (fn.__name__, fn)
+
+
+def _restore_function(fn: Callable, fn_module: types.ModuleType) -> None:
+    """Restore the function that is replaced by _distribute_function."""
+    global _original_functions
+    global _wrapper_functions
+
+    if fn not in _replaced_functions:
+        return
+
+    original_name, original_fn = _replaced_functions[fn]
+    setattr(fn_module, original_name, original_fn)
+
+
+@contextlib.contextmanager
+def _enable_cp_dispatcher() -> Generator[None, None, None]:
+    """Enables DTensor dispatcher to dispatch SDPA to CP."""
+    old_handlers = DTensor._op_dispatcher._custom_op_handlers
+    DTensor._op_dispatcher._custom_op_handlers = {**old_handlers, **customized_ops}
+
+    yield
+
+    DTensor._op_dispatcher._custom_op_handlers = old_handlers
+
+
+class _AttentionContextParallel(ParallelStyle):
+    """
+    Applies context parallel optimizations to the attention layer.
+
+    This will work for nn.MultiHeadedAttention and custom attention layers that
+    call F.scaled_dotproduct_attention with a simliar signature.
+
+    This expects the `forward` method consumes either:
+
+    * a single tensor for self attention
+    * one argument for each of: query, key, value
+
+    This currently only supports ring attention and the
+    SDPBackend.FLASH_ATTENTION backend. See sdpa_kernel.
+
+    Non-flash attention backends will result in incorrect results.
+    """
+
+    # use a weakref dictionary to store context managers for each nn.Module
+    _CONTEXT_MANAGERS: "weakref.WeakKeyDictionary[nn.Module, Any]" = (
+        weakref.WeakKeyDictionary()
+    )
+
+    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
+        if not isinstance(device_mesh, DeviceMesh):
+            raise ValueError(
+                f"{type(device_mesh)} is not supported by {type(self)} yet."
+            )
+
+        if not device_mesh.ndim == 1:
+            raise ValueError
+
+        return distribute_module(
+            module,
+            device_mesh,
+            input_fn=self._input_fn,  # type: ignore[arg-type]
+            output_fn=self._output_fn,  # type: ignore[arg-type]
+        )
+
+    @classmethod
+    def _input_fn(
+        cls,
+        module: nn.Module,
+        inputs: tuple[Union[torch.Tensor, int, float], ...],
+        device_mesh: DeviceMesh,
+    ) -> tuple[Union[torch.Tensor, int, float], ...]:
+        # TODO(d4l3k); this should be Shard(2), need to fix Linear layer rules
+        placement = [Replicate()]
+
+        def backward_hook(grad: torch.Tensor) -> None:
+            if module in cls._CONTEXT_MANAGERS:
+                cls._CONTEXT_MANAGERS[module].__exit__(None, None, None)
+                del cls._CONTEXT_MANAGERS[module]
+
+        # convert inputs to DTensor
+        inp = []
+        for input in inputs:
+            if isinstance(input, torch.Tensor) and not isinstance(input, DTensor):
+                input = DTensor.from_local(
+                    input.contiguous(), device_mesh, placement, run_check=False
+                )
+
+            if isinstance(input, torch.Tensor) and input.requires_grad:
+                input.register_hook(backward_hook)
+
+            inp.append(input)
+
+        manager = _enable_cp_dispatcher()
+        manager.__enter__()
+        cls._CONTEXT_MANAGERS[module] = manager
+
+        return tuple(inp)
+
+    @classmethod
+    def _output_fn(
+        cls,
+        module: nn.Module,
+        outputs: Union[torch.Tensor, tuple[Union[torch.Tensor, int, float], ...]],
+        device_mesh: DeviceMesh,
+    ) -> Union[
+        Union[torch.Tensor, int, float], tuple[Union[torch.Tensor, int, float], ...]
+    ]:
+        cls._CONTEXT_MANAGERS[module].__exit__(None, None, None)
+        del cls._CONTEXT_MANAGERS[module]
+
+        def backward_hook(grad: torch.Tensor) -> None:
+            if module not in cls._CONTEXT_MANAGERS:
+                manager = _enable_cp_dispatcher()
+                manager.__enter__()
+                cls._CONTEXT_MANAGERS[module] = manager
+
+        # back to local tensor
+        out = []
+        for output in [outputs] if isinstance(outputs, torch.Tensor) else outputs:
+            output = output.to_local() if isinstance(output, DTensor) else output
+
+            if isinstance(output, torch.Tensor) and output.requires_grad:
+                output.register_hook(backward_hook)
+
+            out.append(output)
+
+        if isinstance(outputs, torch.Tensor):
+            return out[0]
+
+        return tuple(out)
+
+
+@contextlib.contextmanager
+def _context_parallel(seq_dim: int, mesh: DeviceMesh) -> Generator[None, None, None]:
+    """Replace SDPA with the CP-wrapped version and enable DTensor CP dispatcher."""
+
+    def attention_input_fn(
+        mesh: DeviceMesh, *args: tuple[Any, ...], **kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        placement = [Shard(seq_dim)]
+        all_args = []
+
+        for arg in itertools.chain(args, kwargs.values()):
+            if isinstance(arg, torch.Tensor) and not isinstance(arg, DTensor):
+                arg = DTensor.from_local(arg, mesh, placement, run_check=False)
+
+            all_args.append(arg)
+
+        new_args = tuple(all_args[0 : len(args)])
+        new_kwargs = dict(zip(kwargs.keys(), all_args[len(args) :]))
+        return new_args, new_kwargs
+
+    def attention_output_fn(mesh: DeviceMesh, outputs: Any) -> Any:
+        new_outputs = []
+        for output in [outputs] if isinstance(outputs, torch.Tensor) else outputs:
+            output = output.to_local() if isinstance(output, DTensor) else output
+            new_outputs.append(output)
+
+        if isinstance(outputs, torch.Tensor):
+            return new_outputs[0]
+
+        return tuple(new_outputs)
+
+    # TODO: provide a more robust way to replace SDPA.
+    # Currently we use monkey patch to replace scaled_dot_product_attention with the
+    # wrapped fn. This is okay if users do `import torch.nn.functional` but will not
+    # work if users do `import torch.nn.functional.scaled_dot_product_attention`.
+    _distribute_function(
+        F.scaled_dot_product_attention,
+        F,
+        mesh,
+        attention_input_fn,
+        attention_output_fn,
+    )
+
+    with _enable_cp_dispatcher():
+        yield
+
+    _restore_function(F.scaled_dot_product_attention, F)
+
+
+class _LoadBalancer(ABC):
+    @classmethod
+    @abstractmethod
+    def shard(
+        cls, buffer: torch.Tensor, mesh: DeviceMesh, seq_dim: int
+    ) -> torch.Tensor: ...
+
+    @classmethod
+    @abstractmethod
+    def unshard(
+        cls, buffer: torch.Tensor, mesh: DeviceMesh, seq_dim: int
+    ) -> torch.Tensor: ...
+
+
+class _SequentialSharder(_LoadBalancer):
+    """
+    This load balancer chunks the buffer into cp_world_size and rank0 gets
+    0th shard, rank1 gets 1st shard, ...
+    So this doesn't have any load balancing effect when using the causal masking.
+    """
+
+    @classmethod
+    def shard(
+        cls, buffer: torch.Tensor, mesh: DeviceMesh, seq_dim: int
+    ) -> torch.Tensor:
+        assert buffer.size()[seq_dim] % mesh.size() == 0
+        return buffer.chunk(mesh.size(), dim=seq_dim)[mesh.get_local_rank()]
+
+    @classmethod
+    def unshard(
+        cls, buffer: torch.Tensor, mesh: DeviceMesh, seq_dim: int
+    ) -> torch.Tensor:
+        buffer = buffer.contiguous()
+        all_buffers = [torch.empty_like(buffer) for _ in range(mesh.size())]
+        ft_c.all_gather_inplace(all_buffers, buffer, mesh)
+        return torch.cat(all_buffers, dim=seq_dim)
+
+
+class _RoundRobinLoadBalancer(_LoadBalancer):
+    """
+    This load balancer chunk the buffer into cp_world_size * ROUND_ROBIN_CYCLE
+    shards, and uses a round robin approach to achieve load balancing.
+    Since ROUND_ROBIN_CYCLE being 2 will achieve perfect load balancing for
+    causal masking, we assume ROUND_ROBIN_CYCLE is always 2 to simplify the
+    implementation.
+    """
+
+    ROUND_ROBIN_CYCLE = 2
+
+    @classmethod
+    def shard(
+        cls, buffer: torch.Tensor, mesh: DeviceMesh, seq_dim: int
+    ) -> torch.Tensor:
+        assert cls.ROUND_ROBIN_CYCLE == 2, (
+            "The current implementation only works if ROUND_ROBIN_CYCLE is 2."
+        )
+        cp_world_size = mesh.size()
+        cp_rank = mesh.get_local_rank()
+        assert buffer.size()[seq_dim] % (cp_world_size * 2) == 0
+        chunks = buffer.chunk(cp_world_size * 2, dim=seq_dim)
+        return torch.cat(
+            (chunks[cp_rank], chunks[cp_world_size * 2 - cp_rank - 1]),
+            dim=seq_dim,
+        )
+
+    @classmethod
+    def unshard(
+        cls, buffer: torch.Tensor, mesh: DeviceMesh, seq_dim: int
+    ) -> torch.Tensor:
+        assert cls.ROUND_ROBIN_CYCLE == 2, (
+            "The current implementation only works if ROUND_ROBIN_CYCLE is 2."
+        )
+        buffer = buffer.contiguous()
+        cp_world_size = mesh.size()
+
+        all_buffers = [torch.empty_like(buffer) for _ in range(cp_world_size)]
+        ft_c.all_gather_inplace(all_buffers, buffer, mesh)
+        sliced_buffers = [sb for b in all_buffers for sb in b.chunk(2, dim=seq_dim)]
+        ordered_buffers = list(sliced_buffers)
+        for i, b in enumerate(sliced_buffers):
+            if i % 2 == 0:
+                ordered_buffers[i // 2] = b
+            else:
+                ordered_buffers[cp_world_size * 2 - (i // 2) - 1] = b
+        return torch.cat(ordered_buffers, dim=seq_dim)
+
+
+def _context_parallel_buffers(
+    mesh: DeviceMesh,
+    buffers: list[torch.Tensor],
+    buffer_seq_dims: list[int],
+) -> list[torch.Tensor]:
+    """Shard the buffers along the sequence dimensions according to CP rules."""
+    new_buffers = []
+    sharder = (
+        _RoundRobinLoadBalancer
+        if _cp_options.enable_load_balance
+        else _SequentialSharder
+    )
+    for buffer, seq_dim in zip(buffers, buffer_seq_dims):
+        new_buffers.append(sharder.shard(buffer, mesh, seq_dim))
+
+    return new_buffers
+
+
+@contextlib.contextmanager
+@torch.no_grad()
+def context_parallel(
+    mesh: DeviceMesh,
+    *,
+    buffers: Optional[list[torch.Tensor]] = None,
+    buffer_seq_dims: Optional[list[int]] = None,
+    no_restore_buffers: Optional[set[torch.Tensor]] = None,
+) -> Generator[None, None, None]:
+    """
+
+    ``context_parallel`` is an experimental API to enable context
+    parallelism (CP). This API performs two actions: 1) patch the SDPA
+    (``torch.nn.functional.scaled_dot_product_attention``) with the CP-enabled
+    one, 2) shard ``buffers`` along the sequence dimension and each rank will
+    preserve the corresponding shard according ``mesh``.
+
+    Args:
+        mesh (:class:`DeviceMesh`): the device mesh for the context parallelism.
+        buffers (Optional[List[torch.Tensor]]): buffers that the usage depend
+            on the sequence dimension. Examples are input batch, labels and
+            positional embedding buffers. These buffers must be sharded along
+            the sequence dimension to ensure the accuracy. The sharding will
+            happen in-place, the buffer's shape will change within the context.
+            The buffers will be restored after the context finishes.
+            ``no_restore_buffers`` can be used to specify which buffers don't
+            need to be restored. Note that ``buffers`` should not contain any
+            nn.Parameter.
+        buffer_seq_dims (Optional[List[int]]): the sequence dimensions of ``buffers``.
+        no_restore_buffers (Optional[Set[torch.Tensor]]): buffers in these set
+            won't be restored after the context exits. This set must be a subset
+            of ``buffers``. If the buffers won't be used after the context exits,
+            these buffers can be put in this list to avoid extra restore time.
+
+    .. warning::
+        `torch.distributed._tensor.experimental.attention.context_parallel` is a
+        prototype feature in PyTorch. The API is subject to change.
+    """
+    buffers = [] if buffers is None else buffers
+    buffer_seq_dims = [] if buffer_seq_dims is None else buffer_seq_dims
+    no_restore_buffers = set() if no_restore_buffers is None else no_restore_buffers
+
+    if len(buffers) != len(buffer_seq_dims):
+        raise ValueError(
+            "`seq_dims` must have the same number of elements as `buffers`."
+        )
+
+    for buffer in no_restore_buffers:
+        # Cannot use `if not buffer in buffers` which will incur tensor comparison.
+        if not any(b is buffer for b in buffers):
+            raise ValueError("`no_restore_buffers` must be a subset of `buffers`.")
+
+    original_buffers = [None if b in no_restore_buffers else b.clone() for b in buffers]
+    chunks = _context_parallel_buffers(mesh, buffers, buffer_seq_dims)
+    for buffer, chunk in zip(buffers, chunks):
+        chunk = chunk.clone()
+        buffer.resize_(chunk.shape)
+        buffer.copy_(chunk)
+
+    with _context_parallel(seq_dim=2, mesh=mesh):
+        yield
+
+    for buffer, original_buffer in zip(buffers, original_buffers):
+        if original_buffer is not None:
+            buffer.resize_(original_buffer.shape)
+            buffer.copy_(original_buffer)
+
+
+@torch.no_grad()
+def context_parallel_unshard(
+    mesh: DeviceMesh,
+    buffers: list[torch.Tensor],
+    seq_dims: list[int],
+) -> list[torch.Tensor]:
+    """
+    Unshard the tensors (e.g., output) that are sharded due to context parallelism.
+
+    Args:
+        mesh (:class:`DeviceMesh`): the device mesh for the context parallelism.
+        buffers (List[torch.Tensor]): the buffers to be unsharded.
+        seq_dims (List[int]): the sequence dimensions of ``buffers``. This list
+            must have the same length as ``buffers``.
+
+    Returns:
+        List[torch.Tensor]: the unsharded buffers.
+    """
+    sharder = (
+        _RoundRobinLoadBalancer
+        if _cp_options.enable_load_balance
+        else _SequentialSharder
+    )
+    return [sharder.unshard(b, mesh, dim) for b, dim in zip(buffers, seq_dims)]
+
+
+def set_rotate_method(rotate_method: str) -> None:
+    """
+    Context Parallel SDPA requires the rotation of kv shards. Users can call this
+    API to specify which rotation method to use. "alltoall" shuffles the kv shards
+    using all-to-all collective. While "allgather" gathers the kv shards using
+    all-gather collective after the first sub-SDPA computation. If this API has not
+    been called, the default rotate method is "allgather".
+
+    Args:
+        rotate_method (str): the rotate method to use. Currently only supports
+        "allgather" and "alltoall". If a different string other than these two
+        is passed in, the function will raise an error.
+
+    Returns:
+        None
+    """
+    if rotate_method == "allgather":
+        _cp_options.rotate_method = _RotateMethod.ALL_GATHER
+    elif rotate_method == "alltoall":
+        _cp_options.rotate_method = _RotateMethod.ALL_TO_ALL
+    else:
+        raise NotImplementedError(
+            "Context Parallel does not support "
+            f"using {rotate_method} for kv shards rotation"
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_func_map.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_func_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..51861141af5b8ea0f5138117cb29e8fc38f2b4b9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_func_map.py
@@ -0,0 +1,235 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from collections.abc import Sequence
+from typing import Callable, Optional, Union
+
+import torch
+from torch.distributed._functional_collectives import AsyncCollectiveTensor
+from torch.distributed.tensor import DeviceMesh, DTensor
+from torch.distributed.tensor.placement_types import Placement
+
+
+try:
+    from torch.utils import _cxx_pytree as pytree
+except ImportError:
+    from torch.utils import _pytree as pytree  # type: ignore[no-redef]
+
+
+__all__ = ["local_map"]
+
+PlacementType = Optional[Sequence[Placement]]
+InputPlacements = Optional[tuple[PlacementType, ...]]
+OutputPlacements = Union[PlacementType, tuple[PlacementType, ...]]
+
+
+def local_map(
+    func: Callable,
+    out_placements: OutputPlacements,
+    in_placements: Optional[InputPlacements] = None,
+    device_mesh: Optional[DeviceMesh] = None,
+    *,
+    redistribute_inputs: bool = False,
+):
+    """
+    :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s
+    to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting
+    the local components of :class:`DTensor`, call the function, and wrap the outputs to
+    :class:`DTensor` according to the ``out_placements``.
+
+    Args:
+        func (Callable): the function to be applied on each local shard of
+            :class:`DTensor` s.
+        out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]):
+            the desired placements of the :class:`DTensor` s in ``func``'s flattened output.
+            If the flattened ``output`` is a single value, the ``out_placements`` should be
+            of type `PlacementType`. Otherwise if the flattened ``output`` has multiple
+            values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1
+            mapping to the flattened ``output``.
+            Besides, for :class:`Tensor` output, we use `PlacementType` as its
+            placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType`
+            should be `None`.
+            Note that the only exception is when no :class:`DTensor` argument is passed
+            in. In this case, even if `out_placements` is not `None`, the result function
+            should ignore the desired placements because the function is not running with
+            :class:`DTensor` s.
+        in_placements (Tuple[`PlacementType`, ...], optional):
+            the required placements of the :class:`DTensor` s in the flattened inputs of ``func``.
+            If ``in_placements`` is specified, :meth:`local_map` would examine whether the
+            placements of each :class:`DTensor` argument is the same as the required
+            placements or not. If the placements are not the same and
+            ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if
+            ``redistribute_inputs`` is ``True``, the argument will be first redistributed to
+            the required sharding placements before passing its local tensor to ``func``.
+            The only exception is when required placements are not ``None`` and the
+            argument is a :class:`torch.Tensor`. In this case, the placements examination
+            will be skipped and the argument will be directly passed to ``func``.
+            If ``in_placements`` is ``None``, no placements examination will be performed.
+            Default: None
+        device_mesh (:class:`DeviceMesh`, optional):
+            the device mesh that all the :class:`DTensor` s are placed on. If not
+            specified, this will be inferred from the input :class:`DTensor` s' device
+            mesh. `local_map` requires every :class:`DTensor` s to be placed on the same
+            device mesh. Default: None.
+        redistribute_inputs (bool, optional):
+            the bool value indicating whether to reshard the input :class:`DTensor` s when
+            their placements are different from the required input placements. If this
+            value is ``False`` and some :class:`DTensor` input has a different placement,
+            an exception will be raised. Default: False.
+
+    Returns:
+        A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor`
+        and returns a :class:`DTensor` constructed from the return value of ``func``.
+
+    Raises:
+        AssertionError: If the input :class:`DTensor` is not placed on the same device
+            mesh, or if they are placed on a different device mesh than the ``device_mesh``
+            argument passed in.
+
+        AssertionError: For any non-DTensor output, we require its corresponding
+            output placement in ``out_placements`` be None. An AssertionError will be raised
+            if this is not the case.
+
+        ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs
+            a redistribution according to ``in_placements``.
+
+    Example:
+        >>> # xdoctest: +SKIP("distributed")
+        >>> def mm_allreduce_forward(device_mesh, W, X):
+        >>>     partial_sum_tensor = torch.mm(W, X)
+        >>>     reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh)
+        >>>     return reduced_tensor
+        >>>
+        >>> W = torch.randn(12, 8, requires_grad=False)
+        >>> X = torch.randn(8, 16, requires_grad=False)
+        >>> Y = torch.mm(W, X)
+        >>> row_wise = [Shard(0)]  # row-wise sharding placements on 1-d mesh
+        >>> col_wise = [Shard(1)]  # col-wise sharding placements on 1-d mesh
+        >>>
+        >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion
+        >>> local_mm_allreduce_forward = local_map(
+        >>>     mm_allreduce_forward,
+        >>>     out_placements=[Replicate()],
+        >>>     in_placements=[col_wise, row_wise],
+        >>>     device_mesh=device_mesh,
+        >>> )
+        >>>
+        >>> W_dt = distribute_tensor(
+        ...     W, device_mesh, (col_wise)
+        ... )  # col-wisely sharded W tensor
+        >>> X_dt = distribute_tensor(
+        ...     X, device_mesh, (row_wise)
+        ... )  # row-wisely sharded X tensor
+        >>> Y_dt = local_mm_allreduce_forward(
+        ...     device_mesh, W_dt, X_dt
+        ... )  # apply local_mm_allreduce_forward to DTensors
+
+    .. note:: This API is currently experimental and subject to change
+    """
+
+    def wrapped(*args, **kwargs):
+        # process input args
+        flat_args, args_spec = pytree.tree_flatten(args)
+        if in_placements is not None:
+            assert len(in_placements) == len(flat_args), (
+                f"in_placements length {len(in_placements)} does not match the number "
+                f"of input args {len(flat_args)}!"
+            )
+
+        # we assume every DTensor object is placed on the same device mesh
+        flat_local_args = []
+        nonlocal device_mesh  # access var device_mesh from the outer scope
+        seen_dtensor_arg = False
+        for idx, arg in enumerate(flat_args):
+            if isinstance(arg, DTensor):
+                # TODO: the current code doesn't consider the uneven sharding case
+                # Need to think about what the consequence is when the input DTensor
+                # is uneven sharded.
+                if device_mesh is None:  # infer device mesh from the DTensor arg
+                    device_mesh = arg.device_mesh
+
+                # this function is applied to at least one DTensor argument
+                seen_dtensor_arg = True
+
+                assert arg.device_mesh == device_mesh, (
+                    f"arg {arg} in local_map has a mismatched device mesh: "
+                    f"{arg} has device mesh {arg.device_mesh} while "
+                    f"the expected device mesh is {device_mesh}!"
+                )
+                if in_placements is not None:
+                    spec = in_placements[idx]
+                    assert spec is not None, (
+                        f"DTensor input {arg} expects placements but received {spec}!"
+                    )
+
+                    if not isinstance(spec, tuple):
+                        spec = tuple(spec)
+
+                    if arg.placements != spec:
+                        if redistribute_inputs:
+                            # redistribute to input placements
+                            arg = arg.redistribute(device_mesh, spec)
+                        else:
+                            raise ValueError(
+                                f"arg {arg} in local_map has a mismatched placements: "
+                                f"arg placements is {arg.placements} but the input "
+                                f"placements is {spec}! "
+                                "If redistribute_inputs is wanted, set "
+                                "redistribute_inputs=True to local_map."
+                            )
+
+                local_arg = arg.to_local()
+                if isinstance(local_arg, AsyncCollectiveTensor):
+                    local_arg = local_arg.wait()
+
+                flat_local_args.append(local_arg)
+            else:
+                # Non-Tensor input must have None in `in_placements`
+                if in_placements is not None and not isinstance(arg, torch.Tensor):
+                    spec = in_placements[idx]
+                    assert spec is None, (
+                        f"Non-Tensor input {arg} expects None placements "
+                        f"but received {spec}!"
+                    )
+
+                flat_local_args.append(arg)
+
+        local_args = pytree.tree_unflatten(flat_local_args, args_spec)
+
+        out = func(*local_args, **kwargs)
+
+        if seen_dtensor_arg:
+            # process output
+            flat_out, out_spec = pytree.tree_flatten(out)
+
+            flat_dist_out = []
+            out_placements_tuple = (
+                out_placements
+                if isinstance(out_placements, tuple)
+                else (out_placements,)
+            )
+            assert len(flat_out) == len(out_placements_tuple), (
+                "local_map requires one PlacementType be provided for each output value,"
+                f" received {len(out_placements_tuple)} out_placements but"
+                f" {len(flat_out)} is expected!"
+            )
+            for out, spec in zip(flat_out, out_placements_tuple):
+                if isinstance(out, torch.Tensor):
+                    assert not isinstance(out, DTensor), (
+                        f"torch.Tensor output expected but received {type(out)}: {out}"
+                    )
+
+                    flat_dist_out.append(
+                        DTensor.from_local(out, device_mesh, spec, run_check=False)
+                    )
+                else:
+                    assert spec is None, (
+                        f"Non-tensor output {out} expects None placements but received {spec}!"
+                    )
+
+                    flat_dist_out.append(out)
+
+            return pytree.tree_unflatten(flat_dist_out, out_spec)
+        else:
+            return out
+
+    return wrapped
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_register_sharding.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_register_sharding.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d817912ac9f2315f880263ab79719301a2f983c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_register_sharding.py
@@ -0,0 +1,138 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from collections.abc import Sequence
+from functools import partial
+from typing import Callable, Union
+
+import torch
+from torch._ops import OpOverload
+from torch.distributed.tensor import DTensor
+from torch.distributed.tensor._op_schema import (
+    _is_inplace_op,
+    OpSchema,
+    OpStrategy,
+    PlacementList,
+    RuntimeSchemaInfo,
+    StrategyType,
+    TupleStrategy,
+)
+from torch.distributed.tensor._ops.utils import expand_to_full_mesh_op_strategy
+
+
+__all__ = ["register_sharding"]
+
+
+def register_sharding(op: Union[OpOverload, list[OpOverload]]):
+    """
+    :meth:`register_sharding` is an experimental API that allows users to register sharding
+    strategies for an operator when the tensor inputs and outputs are DTensor.
+    It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``,
+    e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2)
+    when users would like to overwrite default sharding strategies of existing operators.
+
+    Args:
+        op (Union[OpOverload, List[OpOverload]]):
+            An op or a list of ops to register the customized sharding function.
+
+    Returns:
+        A function decorator which can be used to wrap a function that defines the sharding
+        strategy for the operator specified in ``op``. The defined sharding strategy will be
+        registered to DTensor and will override the default sharding strategy if DTensor has
+        already implemented the operator. The customized sharding function takes the same inputs
+        as the original op (except that if an arg is a :class:`torch.Tensor`, it will be
+        replaced by a tensor-like object that DTensor uses internally). The function should
+        return a sequence of 2-tuples, each specifying acceptable output placements and its
+        corresponding intput placements.
+
+    Example:
+        >>> # xdoctest: +SKIP("distributed")
+        >>> @register_sharding(aten._softmax.default)
+        >>> def custom_softmax_sharding(x, dim, half_to_float):
+        >>>     softmax_dim = dim if dim >= 0 else dim + x.ndim
+        >>>     acceptable_shardings = []
+        >>>
+        >>>     all_replicate = ([Replicate()], [Replicate(), None, None])
+        >>>     acceptable_shardings.append(all_replicate)
+        >>>
+        >>>     for sharding_dim in range(x.ndim):
+        >>>         if sharding_dim != softmax_dim:
+        >>>             all_sharded = (
+        >>>                 [Shard(sharding_dim)],
+        >>>                 [Shard(sharding_dim), None, None],
+        >>>             )
+        >>>             acceptable_shardings.append(all_sharded)
+        >>>
+        >>>     return acceptable_shardings
+
+    .. note:: This API is currently experimental and subject to change
+    """
+
+    def custom_strategy(
+        custom_sharding_fn: Callable[
+            ..., Sequence[tuple[PlacementList, PlacementList]]
+        ],
+        op_schema: OpSchema,
+    ) -> StrategyType:
+        def strategy_to_spec(strategy: object) -> object:
+            if isinstance(strategy, OpStrategy):
+                # take the output spec from the first strategy
+                return strategy.strategies[0].output_spec
+            elif isinstance(strategy, TupleStrategy):
+                return tuple(strategy_to_spec(s) for s in strategy.childs)
+            else:
+                return strategy
+
+        mesh = op_schema.get_mesh_from_args()
+
+        args_schema = tuple(strategy_to_spec(i) for i in op_schema.args_schema)
+        kwargs_schema = {
+            k: strategy_to_spec(v) for k, v in op_schema.kwargs_schema.items()
+        }
+
+        acceptable_shardings = custom_sharding_fn(*args_schema, **kwargs_schema)
+
+        single_mesh_dim_strategies: list[PlacementList] = []
+        for output_specs, input_specs in acceptable_shardings:
+            single_mesh_dim_strategies.append(output_specs + input_specs)
+
+        # TODO: handle out variant ops
+        return expand_to_full_mesh_op_strategy(
+            mesh,
+            op_schema,
+            single_mesh_dim_strategies,
+            input_index=len(op_schema.op._schema.returns),
+            inplace_op=_is_inplace_op(op_schema.op),
+        )
+
+    def wrapper(custom_sharding_fn):
+        def derive_schema_info(op):
+            # NOTE: without user directly providing RuntimeSchemaInfo, for now
+            #       we create it in a conservative fashion as follows:
+            #       1. let static_argnum be the first int argument
+            #       2. let static_kwargkey include all the int type kwargs
+            #       3. always set needs_pytree=True
+            static_argnum = 100
+            static_kwargkey: list[str] = []
+            for i, arg in enumerate(op._schema.arguments):
+                if isinstance(arg.type, torch.IntType) or (
+                    isinstance(arg.type, torch.OptionalType)
+                    and isinstance(arg.type.getElementType(), torch.IntType)
+                ):
+                    static_argnum = min(i, static_argnum)
+                    if arg.kwarg_only:
+                        static_kwargkey.append(arg.name)
+            return RuntimeSchemaInfo(
+                static_argnum, static_kwargkey or None, needs_pytree=True
+            )
+
+        overloads = op if isinstance(op, list) else [op]
+        for overload in overloads:
+            DTensor._op_dispatcher.sharding_propagator.register_op_strategy(
+                overload,
+                partial(custom_strategy, custom_sharding_fn),
+                derive_schema_info(overload),
+            )
+
+        return custom_sharding_fn
+
+    return wrapper
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_tp_transform.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_tp_transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..52de6cebe684a63baf04fd6f7c29fdd975b5af78
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/experimental/_tp_transform.py
@@ -0,0 +1,558 @@
+# mypy: allow-untyped-defs
+import copy
+import operator
+from collections.abc import Sequence
+from typing import Any, cast, Optional
+
+import torch
+from torch._subclasses.fake_tensor import FakeTensor
+from torch.distributed.tensor import DeviceMesh, distribute_tensor, DTensor
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor._op_schema import (
+    OpSchema,
+    OutputSharding,
+    OutputSpecType,
+    PlacementStrategy,
+)
+from torch.distributed.tensor._redistribute import redistribute_local_tensor
+from torch.distributed.tensor.parallel.style import ColwiseParallel, ParallelStyle
+from torch.distributed.tensor.placement_types import Placement, Replicate, Shard
+from torch.export import ExportedProgram
+from torch.export.exported_program import ExportGraphSignature
+from torch.fx import GraphModule
+from torch.fx.experimental.proxy_tensor import make_fx
+from torch.fx.node import Node
+from torch.fx.passes.infra.pass_base import PassBase, PassResult
+from torch.fx.passes.shape_prop import _extract_tensor_metadata
+from torch.utils import _pytree as pytree
+
+
+__all__ = ["tensor_parallel_transformation"]
+
+aten = torch.ops.aten
+
+
+def tensor_parallel_transformation(
+    exported_program: ExportedProgram,
+    rank: int,
+    world_size: int,
+    device_type: str,
+    parallel_strategies: dict[str, ParallelStyle],
+) -> ExportedProgram:
+    """
+    The entry point function to perform graph transformations on an exported program
+    to transform a single-device graph into a tensor parallel graph.
+
+    .. warning::
+        This API is experimental and subject to change.
+    """
+
+    gm = exported_program.graph_module
+    sig = copy.deepcopy(exported_program.graph_signature)
+    state_dict = copy.copy(exported_program.state_dict)
+
+    with gm._set_replace_hook(sig.get_replace_hook()):
+        res = _TensorParallelTransformPass(
+            rank,
+            world_size,
+            device_type,
+            state_dict,
+            exported_program.graph_signature,
+            parallel_strategies,
+        )(gm)
+        assert res is not None
+        gm = res.graph_module
+
+    return exported_program._update(gm, sig, state_dict=state_dict)
+
+
+class _TensorParallelTransformPass(PassBase):
+    """
+    This pass is responsible for transforming a single-device graph into a tensor parallel
+    graph. It will mark the placement strategy of each node in the graph,
+    partition the graph into distributed graph, then shard the parameters/buffers accordingly.
+    """
+
+    def __init__(
+        self,
+        rank: int,
+        world_size: int,
+        device_type: str,
+        state_dict: dict[str, torch.Tensor],
+        graph_signature: ExportGraphSignature,
+        parallel_strategies: dict[str, ParallelStyle],
+    ) -> None:
+        super().__init__()
+        self.rank = rank
+        self.mesh = DeviceMesh(device_type, torch.arange(world_size))
+        self.state_dict: dict[str, torch.Tensor] = state_dict
+        self.graph_signature = graph_signature
+        self.parallel_strategies = parallel_strategies
+
+    def call(self, graph_module) -> PassResult:
+        gm = copy.deepcopy(graph_module)
+
+        parameter_placements = _generate_parameter_and_buffer_placements(
+            list(self.state_dict.keys()), self.parallel_strategies
+        )
+        placement_strategies = _mark_sharding(
+            gm, self.graph_signature, self.mesh, parameter_placements
+        )
+        _partitioner(gm)
+        _shard_state_dict(
+            self.state_dict, placement_strategies, self.graph_signature, self.mesh
+        )
+        return PassResult(gm, True)
+
+
+def _generate_parameter_and_buffer_placements(
+    params_and_buffers: list[str],
+    parallel_strategies: dict[str, ParallelStyle],
+) -> dict[str, Placement]:
+    """
+    Build parameter placements based on the give parallel style of linear layers.
+    """
+    parameter_placements: dict[str, Placement] = {}
+    for linear_fqn, parallel_style in parallel_strategies.items():
+        weight_fqn = f"{linear_fqn}.weight"
+        bias_fqn = f"{linear_fqn}.bias"
+        assert weight_fqn in params_and_buffers
+        parameter_placements[weight_fqn] = (
+            Shard(0) if parallel_style == ColwiseParallel else Shard(1)
+        )
+        if bias_fqn in params_and_buffers:
+            parameter_placements[bias_fqn] = (
+                Shard(0) if parallel_style == ColwiseParallel else Replicate()
+            )
+    return parameter_placements
+
+
+def _mark_tensor_parallel_shardings(
+    gm: GraphModule,
+    graph_signature: ExportGraphSignature,
+    mesh: DeviceMesh,
+    parameter_placements: dict[str, Placement],
+) -> dict[Node, PlacementStrategy]:
+    """
+    Mark the placement strategies of the parameter and buffer placeholder nodes.
+    """
+    placement_strategies: dict[Node, PlacementStrategy] = {}
+    num_params_and_buffers = len(graph_signature.inputs_to_parameters) + len(
+        graph_signature.inputs_to_buffers
+    )
+    placeholder_idx: int = 0
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            if placeholder_idx < num_params_and_buffers:
+                fqn: str = _get_input_node_fqn(node.name, graph_signature)
+                placement: Placement = (
+                    parameter_placements[fqn]
+                    if fqn in parameter_placements
+                    else Replicate()
+                )
+                placement_strategies[node] = _create_placement_strategy(
+                    node,
+                    mesh,
+                    placements=(placement,),
+                )
+                placeholder_idx += 1
+            else:
+                placement_strategies[node] = _create_placement_strategy(
+                    node,
+                    mesh,
+                    placements=(Replicate(),),
+                )
+    return placement_strategies
+
+
+def _get_input_node_fqn(input_name: str, graph_signature: ExportGraphSignature) -> str:
+    """
+    Return the FQN of an input node.
+    """
+    if input_name in graph_signature.inputs_to_parameters:
+        return graph_signature.inputs_to_parameters[input_name]
+    elif input_name in graph_signature.inputs_to_buffers:
+        return graph_signature.inputs_to_buffers[input_name]
+    else:
+        raise ValueError(
+            f"{input_name} not found in inputs_to_parameters or inputs_to_buffers"
+        )
+
+
+def _mark_sharding(
+    gm: GraphModule,
+    graph_signature: ExportGraphSignature,
+    mesh: DeviceMesh,
+    parameter_placements: dict[str, Placement],
+) -> dict[Node, PlacementStrategy]:
+    """
+    Mark the sharding strategy for each node in the graph module.
+    """
+    placement_strategies: dict[Node, PlacementStrategy] = (
+        _mark_tensor_parallel_shardings(
+            gm,
+            graph_signature,
+            mesh,
+            parameter_placements,
+        )
+    )
+
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            if node not in placement_strategies:
+                placement_strategies[node] = _create_placement_strategy(
+                    node, mesh, placements=(Replicate(),)
+                )
+            node.meta["sharding"] = placement_strategies[node]
+        elif node.op == "call_function":
+            if node.target == operator.getitem:
+                input_nodes = node.all_input_nodes
+                assert len(input_nodes) == 1, (
+                    f"non-compute op only support one input now, found node: {node} with length of inputs: {len(node.args)}"
+                )
+                arg_strategy = placement_strategies[input_nodes[0]]
+                placement_strategies[node] = _create_placement_strategy(
+                    node,
+                    mesh,
+                    placements=arg_strategy.output_spec.placements,
+                    input_specs=_get_input_node_specs(node, placement_strategies),
+                )
+                node.meta["sharding"] = placement_strategies[node]
+            else:
+                op_schema = _get_op_schema(node, placement_strategies)
+
+                # get DTensor specs for inputs and outputs
+                if (
+                    op_schema.op
+                    not in DTensor._op_dispatcher.sharding_propagator.op_strategy_funcs
+                    and op_schema.op
+                    not in DTensor._op_dispatcher.sharding_propagator.op_to_rules
+                ):
+                    # Mark all as replicated
+                    output_sharding = _generate_default_output_sharding(
+                        node,
+                        mesh,
+                        op_schema,
+                    )
+                else:
+                    output_sharding = DTensor._op_dispatcher.sharding_propagator.propagate_op_sharding(  # type: ignore[assignment]
+                        op_schema,
+                    )
+                placement_strategies[node] = PlacementStrategy(
+                    output_specs=_get_output_spec_from_output_sharding(output_sharding),
+                    input_specs=output_sharding.redistribute_schema.args_spec
+                    if output_sharding.redistribute_schema is not None
+                    else _get_input_node_specs(node, placement_strategies),
+                )
+                node.meta["sharding"] = placement_strategies[node]
+        elif node.op == "output":
+            node.meta["sharding"] = None
+        else:
+            raise RuntimeError(f"op code {node.op} not supported")
+    return placement_strategies
+
+
+def _get_output_spec_from_output_sharding(
+    output_sharding: OutputSharding,
+) -> DTensorSpec:
+    """
+    Util function to extract output spec from output sharding.
+    """
+    if isinstance(output_sharding.output_spec, DTensorSpec):
+        return output_sharding.output_spec
+    else:
+        # For ops that return multiple outputs, the outputs should have the same output spec
+        assert isinstance(output_sharding.output_spec, Sequence)
+        assert output_sharding.output_spec[0] is not None
+        output_sharding.output_spec[0].tensor_meta = None
+        return output_sharding.output_spec[0]
+
+
+def _create_placement_strategy(
+    node: Node,
+    mesh: DeviceMesh,
+    placements: tuple[Placement, ...],
+    input_specs: Optional[Sequence[DTensorSpec]] = None,
+) -> PlacementStrategy:
+    """
+    Util function to construct a placement strategy for a given node.
+    """
+    placement = PlacementStrategy(
+        input_specs=input_specs,
+        output_specs=DTensorSpec(
+            mesh=mesh,
+            placements=placements,
+        ),
+    )
+    _populate_tensor_meta(node, placement.output_specs)
+    return placement
+
+
+def _populate_tensor_meta(node: Node, output_spec: OutputSpecType) -> None:
+    """
+    Util function to populate tensor meta of output_spec based on node metadata.
+    """
+    if isinstance(node.meta["val"], Sequence):
+        assert isinstance(output_spec, Sequence)
+        for spec, fake_tensor in zip(output_spec, node.meta["val"]):
+            assert spec is not None
+            spec.tensor_meta = TensorMeta(
+                shape=fake_tensor.shape,
+                stride=fake_tensor.stride(),
+                dtype=fake_tensor.dtype,
+            )
+    else:
+        assert isinstance(output_spec, DTensorSpec)
+        output_spec.tensor_meta = TensorMeta(
+            shape=node.meta["val"].shape,
+            stride=node.meta["val"].stride(),
+            dtype=node.meta["val"].dtype,
+        )
+
+
+def _generate_default_output_sharding(
+    node: Node,
+    mesh: DeviceMesh,
+    op_schema: OpSchema,
+) -> OutputSharding:
+    """
+    Util function to create a default output sharding that suggests Replicate placement for both args and outputs.
+    """
+
+    def update_arg_spec(arg_spec: DTensorSpec) -> DTensorSpec:
+        return DTensorSpec(
+            mesh=arg_spec.mesh,
+            placements=(Replicate(),),
+            tensor_meta=arg_spec.tensor_meta,
+        )
+
+    new_op_schema = OpSchema(
+        op=op_schema.op,
+        args_schema=pytree.tree_map_only(
+            DTensorSpec, update_arg_spec, op_schema.args_schema
+        ),
+        kwargs_schema=op_schema.kwargs_schema,
+    )
+
+    def create_output_spec(tensor: FakeTensor) -> DTensorSpec:
+        return DTensorSpec(
+            mesh=mesh,
+            placements=(Replicate(),),
+            tensor_meta=TensorMeta(
+                shape=tensor.shape,
+                stride=tensor.stride(),
+                dtype=tensor.dtype,
+            ),
+        )
+
+    return OutputSharding(
+        output_spec=pytree.tree_map_only(
+            FakeTensor, create_output_spec, node.meta["val"]
+        ),
+        redistribute_schema=new_op_schema,
+        needs_redistribute=True,
+    )
+
+
+def _partitioner(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
+    """
+    Graph partitioner that partitions the single device graph
+    to distributed graph
+    """
+    for node in gm.graph.nodes:
+        node_sharding = node.meta["sharding"]
+        if node.op == "placeholder":
+            out_spec = node_sharding.output_spec
+            local_val = _partition_val(node.meta["val"], out_spec)
+            # update node value
+            node.meta["val"] = local_val
+        elif node.op == "call_function":
+            out_spec = node_sharding.output_spec
+            # check if there's misaligned sharding, insert reshard if there is
+            expected_input_specs = node_sharding.input_specs
+            for idx, input_arg in enumerate(node.all_input_nodes):
+                input_arg_sharding = input_arg.meta["sharding"]
+                input_arg_spec = input_arg_sharding.output_spec
+                desired_spec = (
+                    out_spec
+                    if expected_input_specs is None
+                    else expected_input_specs[idx]
+                )
+                if input_arg_spec != desired_spec:
+                    _insert_reshard_gm(
+                        gm, node, input_arg, input_arg_spec, desired_spec
+                    )
+            # convert output val to its local component
+            output_val = node.meta["val"]
+            node.meta["val"] = _partition_val(output_val, out_spec)
+        elif node.op == "output":
+            for input_arg in node.all_input_nodes:
+                # input args of output should be Replicate, otherwise redistribution is needed.
+                input_args_to_check: Sequence[Node] = (
+                    input_arg if isinstance(input_arg, Sequence) else [input_arg]
+                )
+                for arg in input_args_to_check:
+                    arg_sharding = arg.meta["sharding"]
+                    arg_spec = arg_sharding.output_spec
+                    desired_spec = copy.copy(arg_spec)
+                    desired_spec.placements = (Replicate(),)
+                    if arg_spec != desired_spec:
+                        _insert_reshard_gm(gm, node, arg, arg_spec, desired_spec)
+        else:
+            raise RuntimeError(f"op code {node} not supported")
+
+    _clean_up_graph_metadata(gm)
+    gm.graph.lint()
+    gm.recompile()
+    return gm
+
+
+def _partition_val(val: Any, spec: DTensorSpec) -> Any:
+    """
+    util function to convert a full tensor val to its local component
+    """
+    if isinstance(val, torch.Tensor):
+        local_shard = val
+        if val.ndim == 0:
+            # If it's already a scalar tensor, it is already local, we don't
+            # need to do anything
+            return local_shard
+
+        for idx, placement in enumerate(spec.placements):
+            if placement.is_shard():
+                placement = cast(Shard, placement)
+                num_chunks = spec.mesh.size(mesh_dim=idx)
+                my_coord = spec.mesh.get_coordinate()
+                assert my_coord is not None, "current rank not in mesh!"
+                my_coord_on_mesh_dim = my_coord[idx]
+                local_shard = placement._split_tensor(
+                    local_shard, num_chunks, with_padding=False, contiguous=True
+                )[0][my_coord_on_mesh_dim]
+        return local_shard
+    elif isinstance(val, (list, tuple)):
+        return val.__class__(_partition_val(v, spec) for v in val)
+    else:
+        raise RuntimeError(f"val type {type(val)} not supported")
+
+
+def _insert_reshard_gm(
+    gm: torch.fx.GraphModule,
+    node: Node,
+    input_arg: Node,
+    input_arg_spec: DTensorSpec,
+    desired_spec: DTensorSpec,
+) -> None:
+    """
+    Transform the graph for tensor redistribution.
+    """
+    input_arg_spec.tensor_meta = input_arg.meta["tensor_meta"]
+    desired_spec.tensor_meta = input_arg.meta["tensor_meta"]
+    input_arg_tensor = input_arg.meta["val"]
+
+    # insert reshard operation
+    def reshard_fn(local_tensor: torch.Tensor) -> torch.Tensor:
+        return redistribute_local_tensor(
+            local_tensor,
+            input_arg_spec,
+            desired_spec,
+        )
+
+    reshard_gm = make_fx(reshard_fn)(input_arg_tensor)
+    reshard_gm_nodes = list(reshard_gm.graph.nodes)
+    input_node = reshard_gm_nodes[0]
+    with gm.graph.inserting_before(node):
+        # copy nn_module_stack metadata for output, all-reduce nodes
+        for reshard_node in reshard_gm.graph.nodes:
+            if reshard_node.op not in ["placeholder", "output"]:
+                reshard_node.meta["nn_module_stack"] = (
+                    copy.copy(input_arg.meta["nn_module_stack"])
+                    if not input_arg.op == "placeholder"
+                    else copy.copy(node.meta["nn_module_stack"])
+                )
+        output_node = gm.graph.graph_copy(
+            reshard_gm.graph,
+            val_map={
+                input_node: input_arg,
+            },
+        )
+    node.replace_input_with(input_arg, output_node)  # type: ignore[arg-type]
+
+
+def _clean_up_graph_metadata(gm: torch.fx.GraphModule) -> None:
+    """
+    Clean up the graph by removing sharding and partitioning related metadata
+    """
+    for node in gm.graph.nodes:
+        if "sharding" in node.meta:
+            del node.meta["sharding"]
+        if "val" in node.meta and isinstance(node.meta["val"], torch.Tensor):
+            local_tensor_meta = _extract_tensor_metadata(node.meta["val"])
+            node.meta["tensor_meta"] = local_tensor_meta
+
+
+def _get_input_node_specs(
+    node: Node, placement_strategies: dict[Node, PlacementStrategy]
+) -> tuple[DTensorSpec, ...]:
+    """
+    Get the input specs of a node.
+    """
+    input_specs_list: list[DTensorSpec] = []
+    for input_arg in node.all_input_nodes:
+        if input_arg in placement_strategies:
+            output_spec = placement_strategies[input_arg].output_specs
+            assert isinstance(output_spec, DTensorSpec)
+            input_specs_list.append(output_spec)
+        else:
+            raise ValueError(f"{input_arg} does not have output_spec populated.")
+    return tuple(input_specs_list)
+
+
+def _get_op_schema(
+    node: Node, placement_strategies: dict[Node, PlacementStrategy]
+) -> OpSchema:
+    """
+    Util function to construct the operator schema of a node.
+    """
+    args_schema_list = pytree.tree_map_only(
+        Node, lambda arg: placement_strategies[arg].output_specs, node.args
+    )
+    op_schema = OpSchema(
+        op=cast(torch._ops.OpOverload, node.target),
+        args_schema=tuple(args_schema_list),
+        kwargs_schema=cast(dict[str, object], node.kwargs),
+    )
+    return op_schema
+
+
+def _shard_state_dict(
+    state_dict: dict[str, torch.Tensor],
+    placement_strategies: dict[Node, PlacementStrategy],
+    graph_signature: ExportGraphSignature,
+    mesh: DeviceMesh,
+) -> None:
+    """
+    Inplace partition the weights based on the placement strategy
+    """
+    for node, placement_strategy in placement_strategies.items():
+        if node.op != "placeholder":
+            continue
+        if node.name in graph_signature.inputs_to_parameters:
+            fqn = graph_signature.inputs_to_parameters[node.name]
+        elif node.name in graph_signature.inputs_to_buffers:
+            fqn = graph_signature.inputs_to_buffers[node.name]
+        else:
+            continue
+        assert fqn in state_dict, f"{fqn} not found in state dict: {state_dict.keys()}"
+
+        original_param = state_dict[fqn]
+        dtensor_param = distribute_tensor(
+            original_param,
+            mesh,
+            placement_strategy.output_spec.placements,
+        )
+        local_param = dtensor_param.to_local()
+        state_dict[fqn] = (
+            torch.nn.Parameter(local_param)
+            if isinstance(original_param, torch.nn.Parameter)
+            else local_param
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fe378c51b0d4061ef07e0c3f94bde81fc6ed6c2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/__init__.py
@@ -0,0 +1,23 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from torch.distributed.tensor.parallel.api import parallelize_module
+from torch.distributed.tensor.parallel.loss import loss_parallel
+from torch.distributed.tensor.parallel.style import (
+    ColwiseParallel,
+    ParallelStyle,
+    PrepareModuleInput,
+    PrepareModuleOutput,
+    RowwiseParallel,
+    SequenceParallel,
+)
+
+
+__all__ = [
+    "ColwiseParallel",
+    "ParallelStyle",
+    "PrepareModuleInput",
+    "PrepareModuleOutput",
+    "RowwiseParallel",
+    "SequenceParallel",
+    "parallelize_module",
+    "loss_parallel",
+]
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_data_parallel_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_data_parallel_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6513123e24628471a807b9488dace9625b092049
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_data_parallel_utils.py
@@ -0,0 +1,51 @@
+from functools import partial
+from typing import no_type_check, Optional
+
+import torch
+from torch.distributed._functional_collectives import AsyncCollectiveTensor
+from torch.distributed.tensor import DTensor
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+
+
+@no_type_check
+def sync_grad_hook(grad, *, device_handle=None, compute_stream=None):
+    if isinstance(grad, AsyncCollectiveTensor):
+        if compute_stream is not None:
+            with device_handle.stream(compute_stream):
+                grad = grad.wait()
+        else:
+            grad = grad.wait()
+
+    return grad
+
+
+def _flatten_tensor(
+    tensor: torch.Tensor,
+) -> tuple[torch.Tensor, Optional[DTensorSpec]]:
+    if isinstance(tensor, DTensor):
+        tensor._local_tensor.requires_grad_()
+        return tensor._local_tensor, tensor._spec
+    return tensor, None
+
+
+@no_type_check
+def _unflatten_tensor(tensor, spec, *, device_handle=None, compute_stream=None):
+    # unflatten would mainly be called everytime FSDP allgather parameters.
+    result = DTensor.from_local(
+        tensor,
+        spec.mesh,
+        spec.placements,
+        run_check=False,
+        shape=spec.shape,
+        stride=spec.stride,
+    )
+    if tensor.requires_grad:
+        # only register the hook if the tensor requires grad
+        tensor.register_hook(
+            partial(
+                sync_grad_hook,
+                device_handle=device_handle,
+                compute_stream=compute_stream,
+            )
+        )
+    return result
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a78872f57d8b0a2221fca18601331257611500e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/_utils.py
@@ -0,0 +1,67 @@
+# mypy: allow-untyped-defs
+import warnings
+from typing import Union
+
+from torch.distributed.device_mesh import _mesh_resources
+from torch.distributed.tensor import DeviceMesh
+from torch.distributed.tensor.placement_types import Placement
+
+
+try:
+    from torch._dynamo.external_utils import is_compiling as is_torchdynamo_compiling
+except Exception:
+
+    def is_torchdynamo_compiling():  # type: ignore[misc]
+        return False
+
+
+LayoutsType = Union[Placement, tuple[Placement, ...]]
+
+
+def _deprecate_warnings(func_name: str, extra_msg: str) -> None:
+    """
+    Inject common validation logics for `_prepare_input` funcs via this decorator.
+
+    Include verifying that input needs to be either a :class:`Tensor` or :class:`DTensor`
+    and only 1D :class:`DeviceMesh` is passed in.
+    """
+    # TODO: Will follow up with dynamo POC to make warnings.warn working with dynamo.
+    if not is_torchdynamo_compiling():
+        warnings.warn(
+            f"{func_name} is deprecated and will be removed soon. {extra_msg}",
+            FutureWarning,
+            stacklevel=3,
+        )
+
+
+def _validate_tp_mesh_dim(
+    device_mesh: DeviceMesh,
+) -> None:
+    """
+    Check whether TP mesh dimension is valid or not.
+
+    Args:
+        device_mesh (:class:`DeviceMesh`):
+            The `device_mesh` where we perform
+            Tensor Parallelism on.
+
+    Return:
+        `True` if the mesh dimension
+        is valid, `False` otherwise.
+    """
+    if device_mesh.ndim > 1:
+        raise ValueError(
+            f"Tensor Parallel only accepts a 1D DeviceMesh, but found {device_mesh.ndim}D!"
+            'If you have a 2-D or N-D device_mesh, consider passing in device_mesh["tp"]'
+        )
+
+    root_mesh = _mesh_resources.get_root_mesh(device_mesh)
+    # if a root mesh is not the same as device_mesh,
+    # meaning the device_mesh is sliced out from the root mesh.
+    if root_mesh and root_mesh != device_mesh:
+        tp_mesh_dim_in_root = _mesh_resources.get_root_mesh_dim(device_mesh)
+        if tp_mesh_dim_in_root != root_mesh.ndim - 1:
+            raise RuntimeError(
+                f"Found TP device_mesh on the {tp_mesh_dim_in_root} dimension of its parent mesh.",
+                "Currently we only support intranode TP and TP needs to be the innermost dimension on its parent mesh.",
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/api.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea578239960e501ad2fe3f94e94bec294eacea87
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/api.py
@@ -0,0 +1,129 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import warnings
+from fnmatch import fnmatch
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+from torch.distributed.device_mesh import _mesh_resources, DeviceMesh
+from torch.distributed.tensor.parallel._utils import _validate_tp_mesh_dim
+from torch.distributed.tensor.parallel.style import ParallelStyle
+
+
+__all__ = ["parallelize_module"]
+
+
+def parallelize_module(  # type: ignore[return]
+    module: nn.Module,
+    device_mesh: Optional[DeviceMesh] = None,
+    parallelize_plan: Optional[Union[ParallelStyle, dict[str, ParallelStyle]]] = None,
+    *,
+    src_data_rank: Optional[int] = 0,
+) -> nn.Module:
+    """
+    Apply Tensor Parallelism in PyTorch by parallelizing modules or sub-modules based on a user-specified plan.
+
+    We parallelize module or sub_modules based on a parallelize_plan. The parallelize_plan contains
+    :class:`ParallelStyle`, which indicates how user wants the module or sub_module
+    to be parallelized.
+
+    User can also specify different parallel style per module fully qualified name (FQN).
+
+    Note that ``parallelize_module`` only accepts a 1-D :class:`DeviceMesh`, if you have a 2-D or N-D :class:`DeviceMesh`,
+    slice the DeviceMesh to a 1-D sub DeviceMesh first then pass to this API(i.e. ``device_mesh[\"tp\"]``)
+
+    Args:
+        module (:class:`nn.Module`):
+            Module to be parallelized.
+        device_mesh (:class:`DeviceMesh`, optional):
+            Object which describes the mesh topology of devices for the DTensor.
+            If not specified, the call must be under a DeviceMesh context.
+        parallelize_plan (Union[:class:`ParallelStyle`, Dict[str, :class:`ParallelStyle`]], optional):
+            The plan used to parallelize the module. It can be either a
+            :class:`ParallelStyle` object which contains how we prepare
+            input/output for Tensor Parallelism or it can be a dict of module
+            FQN and its corresponding :class:`ParallelStyle` object. If not
+            specified, the call will do nothing at the moment.
+    Keyword args:
+        src_data_rank (int, optional): the rank of the source data for the logical/global tensor, it is used by
+            :meth:`distribute_tensor` to scatter/broadcast the shards/replicas to other ranks. By default,
+            we use ``group_rank=0`` on each DeviceMesh dimension as the source data to preserve the single-device
+            semantic. If passing ``None`` explicitly, :meth:`parallelize_module` simply uses its local data instead
+            of trying to preserve the single-device semantic via scatter/broadcast. Default: 0
+    Return:
+        A :class:`nn.Module` object parallelized.
+
+    Example::
+        >>> # xdoctest: +SKIP("distributed")
+        >>> from torch.distributed.tensor.parallel import parallelize_module, ColwiseParallel
+        >>> from torch.distributed.device_mesh import init_device_mesh
+        >>>
+        >>> # Define the module.
+        >>> m = Model(...)
+        >>> tp_mesh = init_device_mesh("cuda", (8,))
+        >>> m = parallelize_module(m, tp_mesh, {"w1": ColwiseParallel(), "w2": RowwiseParallel()})
+        >>>
+
+    .. note:: For complex module architecture like Attention, MLP layers, we recommend composing
+        different ParallelStyles together (i.e. ``ColwiseParallel`` and ``RowwiseParallel``) and pass
+        as a parallelize_plan, to achieves the desired sharding computation.
+    """
+    torch._C._log_api_usage_once("torch.distributed.tensor.parallel.parallelize_module")
+
+    device_mesh = device_mesh or _mesh_resources.get_current_mesh()
+    _validate_tp_mesh_dim(device_mesh)
+
+    if parallelize_plan is None:
+        warnings.warn(
+            "No parallelize_plan is provided and auto-parallel is not supported "
+            "at the moment, so this parallelize_module call will do nothing."
+        )
+        return module
+
+    # note: The RNG tracker will be initialized in distribute_tensor() call if it hasn't
+    # been initialized.
+
+    if isinstance(parallelize_plan, ParallelStyle):
+        parallelize_plan.src_data_rank = src_data_rank
+        return parallelize_plan._apply(module, device_mesh)
+    elif isinstance(parallelize_plan, dict):
+        for module_path, parallelize_style in parallelize_plan.items():
+            path_splits = module_path.split(".")
+            if len(path_splits) == 0:
+                raise ValueError(
+                    "Expect module path to be non-empty, but got empty string!"
+                )
+            while path_splits:
+                atom = path_splits.pop(0)
+                matched_children = filter(
+                    # `t[0]` is child name
+                    lambda t: fnmatch(t[0], atom),
+                    module.named_children(),
+                )
+                # apply the plan to all matched submodules
+                for _, submodule in matched_children:
+                    if path_splits:
+                        # we haven't reached the leaf, apply in dict style
+                        leaf_path = ".".join(
+                            path_splits
+                        )  # rest of the path after `atom`
+                        parallelize_module(
+                            submodule,
+                            device_mesh,
+                            {leaf_path: parallelize_style},
+                            src_data_rank=src_data_rank,
+                        )
+                    else:
+                        # otherwise, directly apply style to this submodule
+                        parallelize_module(
+                            submodule,
+                            device_mesh,
+                            parallelize_style,
+                            src_data_rank=src_data_rank,
+                        )
+        return module
+    else:
+        raise TypeError(  # pyre-ignore[7]
+            "Expect Union[ParallelStyle, Dict[str, ParallelStyle]] for"
+            f" parallelize_plan, {type(parallelize_plan)} found!"
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/ddp.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/ddp.py
new file mode 100644
index 0000000000000000000000000000000000000000..39ab299b4f79fc331218d77e347853fa7404db6c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/ddp.py
@@ -0,0 +1,104 @@
+# mypy: allow-untyped-defs
+from typing import Any, Optional
+
+import torch.nn as nn
+from torch.distributed.tensor.parallel._data_parallel_utils import (
+    _flatten_tensor,
+    _unflatten_tensor,
+)
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+def _get_submodule_n_params(module: nn.Module, path: str):
+    """
+    Get submodule and the direct path of parameter from the module
+    """
+    if "." in path:
+        path_list = path.split(".")
+        parent_module_path = ".".join(path_list[:-1])
+        module = module.get_submodule(parent_module_path)
+        path = path_list[-1]
+    return module, path
+
+
+def _update_module_param(param_list: list[tuple[nn.Module, str, nn.Parameter]]):
+    """
+    Update parameters within the module
+    """
+    for item in param_list:
+        parent_module, module_path, t = item
+        assert hasattr(parent_module, module_path)
+        delattr(parent_module, module_path)
+        setattr(parent_module, module_path, t)
+
+
+def _reconstruct_dtensor(module: nn.Module, _input: Any):
+    """
+    Recontruct DTensor parameters from local tensors
+    """
+    param_list = []
+    # TODO: To add perf optimizations to this iterations
+    for name, t in module.named_parameters():
+        if hasattr(t, "_st_info"):
+            dtensor = _unflatten_tensor(t, t._st_info)
+            param_list.append((*_get_submodule_n_params(module, name), dtensor))
+    _update_module_param(param_list)  # type: ignore[arg-type]
+
+
+def _localize_dtensor(
+    module: nn.Module, *_: Any, ignored_params: Optional[set[nn.Parameter]] = None
+):
+    """
+    Convert DTensor parameters to local tensors
+    """
+    if ignored_params is None:
+        ignored_params = set()
+    param_list = []
+    for name, param in module.named_parameters():
+        if param in ignored_params:
+            continue
+        t, sharding_info = _flatten_tensor(param)
+        if sharding_info is not None:
+            t = nn.Parameter(t)
+            t._st_info = sharding_info  # type: ignore[attr-defined]
+            param_list.append((*_get_submodule_n_params(module, name), t))
+    _update_module_param(param_list)  # type: ignore[arg-type]
+
+
+def _pre_dp_module_transform(module: nn.Module):
+    """
+    Enable the composability between Tensor Parallelism (TP) and Data
+    Parallelism(DP) in PyTorch when using DDP. We need to convert Parameters which
+    are DTensors to local tensors before wrapping with data parallelism API.
+    We then register two hooks, one for converting local tensors back to DTensor
+    preforward and one to convert DTensors back to tensors after Forward. By
+    integrating this way, we avoid any special handling of DTensor parameters by DDP
+    and get DTensor's gradients propagated back to DP, e.g. gradient buckets of DDP.
+
+    For now, this API only works with ``DistributedDataParallel``. It will later support
+    other DP methods such as FSDP.
+
+    Args:
+        module (:class:`nn.Module`):
+            Module which has been applied TP on.
+
+    Example::
+        >>> # xdoctest: +SKIP("distributed")
+        >>> from torch.distributed.tensor.parallel import parallelize_module, PairwiseParallel
+        >>> from torch.nn.parallel import DistributedDataParallel as DDP
+        >>> from torch.distributed.tensor.parallel.ddp import pre_dp_module_transform
+        >>>
+        >>> # Define the module.
+        >>> m = module(...)
+        >>> parallelize_module(m, PairwiseParallel())
+        >>> m = pre_dp_module_transform(m)
+        >>> m = DDP(m)
+        >>>
+    """
+
+    _localize_dtensor(module, None, None)
+    # TODO: To add test cases and ensure that it works for nested modules
+    module.register_forward_pre_hook(_reconstruct_dtensor)
+    module.register_forward_hook(_localize_dtensor)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/fsdp.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/fsdp.py
new file mode 100644
index 0000000000000000000000000000000000000000..5282542950c4d360620c6172f27c86b8ed07868e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/fsdp.py
@@ -0,0 +1,390 @@
+# mypy: allow-untyped-defs
+import copy
+from typing import Any, cast, Optional
+
+import torch
+import torch.distributed as dist
+import torch.distributed._shard.sharding_spec as shard_spec
+import torch.distributed.distributed_c10d as c10d
+from torch.distributed._shard.sharded_tensor import (
+    Shard,
+    ShardedTensor,
+    ShardedTensorMetadata,
+    TensorProperties,
+)
+from torch.distributed._shard.sharding_spec import ShardMetadata
+from torch.distributed._shard.sharding_spec.chunk_sharding_spec import ChunkShardingSpec
+from torch.distributed.device_mesh import _mesh_resources
+from torch.distributed.fsdp._common_utils import _set_fsdp_flattened
+from torch.distributed.fsdp._fsdp_extensions import FSDPExtensions
+from torch.distributed.fsdp._shard_utils import _create_chunk_sharded_tensor
+from torch.distributed.remote_device import _remote_device
+from torch.distributed.tensor import DeviceMesh, DTensor, Replicate, Shard as DShard
+from torch.distributed.tensor.parallel._data_parallel_utils import (
+    _flatten_tensor,
+    _unflatten_tensor,
+)
+
+
+__all__ = ["DTensorExtensions"]
+
+
+def _get_box(tensor: DTensor) -> tuple[torch.Size, torch.Size]:
+    device_mesh = tensor.device_mesh
+    assert device_mesh.ndim == 1, "Only 1D DeviceMeshes currently handled"
+
+    placement = tensor.placements[0]
+    offsets = [0] * len(tensor.size())
+    num_chunks = device_mesh.size(mesh_dim=0)
+
+    if tensor.placements[0].is_shard():
+        shard_dim = cast(DShard, placement).dim
+        chunk_size = tensor.size(shard_dim) // num_chunks
+        offsets[shard_dim] = chunk_size
+
+    return (torch.Size(offsets), tensor._local_tensor.size())
+
+
+def _get_box_for(tensor: DTensor, idx: int) -> tuple[torch.Size, torch.Size]:
+    offsets, size = _get_box(tensor)
+    return (torch.Size([val * idx for val in offsets]), size)
+
+
+def _get_local_box(tensor: DTensor) -> tuple[torch.Size, torch.Size]:
+    device_mesh = tensor.device_mesh
+    coord = device_mesh.get_coordinate()
+    assert coord is not None
+    return _get_box_for(tensor, coord[0])
+
+
+def _create_shard_md_from_dt(dt: DTensor, current_rank: int) -> ShardMetadata:
+    mesh = dt.device_mesh
+    assert mesh.ndim == 1, "Only 1D DeviceMeshes currently handled"
+
+    offsets, sizes = _get_local_box(dt)
+    return ShardMetadata(
+        shard_offsets=list(offsets),
+        shard_sizes=list(sizes),
+        placement=f"rank:{current_rank}/{dt._local_tensor.device}",
+    )
+
+
+def _create_sharded_tensor_md_from_dt(
+    dt: DTensor, dt_pg: c10d.ProcessGroup
+) -> ShardedTensorMetadata:
+    # This is where it gets tricky, we have to produce a ShardedTensor that has full coverage
+    # and yet has only one valid shard for the current rank.
+
+    shards_md = []
+    my_rank = dist.get_rank(dt_pg)
+    scapegoat_rank = 0 if my_rank > 0 else 1
+
+    if dt.placements[0].is_shard():
+        shard_count = dt_pg.size()
+    else:
+        shard_count = 1
+
+    for i in range(shard_count):
+        offsets, sizes = _get_box_for(dt, i)
+        shards_md.append(
+            ShardMetadata(
+                shard_offsets=list(offsets),
+                shard_sizes=list(sizes),
+                placement=(
+                    f"rank:{scapegoat_rank if i > 0 else my_rank}/{dt._local_tensor.device}"
+                ),
+            )
+        )
+
+    return ShardedTensorMetadata(
+        shards_metadata=shards_md,
+        size=dt.size(),
+        tensor_properties=TensorProperties(
+            dtype=dt.dtype,
+            layout=dt.layout,
+            requires_grad=dt.requires_grad,
+            # ignore memory_format and pin_memory as those are not supported by DT
+        ),
+    )
+
+
+def _get_dt_pg(dt: DTensor) -> c10d.ProcessGroup:
+    mesh = dt.device_mesh
+    assert mesh.ndim == 1, "Only 1D DeviceMeshes currently handled"
+    return mesh.get_group()
+
+
+def _rewrite_spec_if_needed(
+    spec: shard_spec.ShardingSpec, tensor: torch.Tensor, rank: int
+) -> shard_spec.ShardingSpec:
+    """
+    Rewrite ``spec`` to match the device of ``tensor``.
+
+    FSDP.sharded_optim_state_dict sneakly ships optimizer state to CPU so if the original ShardingSpec
+    produces CUDA metadata, ST construction bombs.
+    """
+    if not isinstance(spec, ChunkShardingSpec):
+        return spec
+
+    # let's see if we need
+    rewrite = False
+    for p in spec.placements:
+        p = cast(_remote_device, p)
+        if p.rank() == rank and p.device() != tensor.device:
+            rewrite = True
+            break
+    if rewrite:
+        spec = copy.deepcopy(spec)
+        for i, placement in enumerate(spec.placements):
+            placement = cast(_remote_device, placement)
+            if placement.rank() == rank and placement.device() != tensor.device:
+                spec.placements[i] = _remote_device(f"rank:{rank}/{tensor.device}")
+
+    return spec
+
+
+def _chunk_tensor(
+    tensor: torch.Tensor,
+    rank: int,
+    world_size: int,
+    num_devices_per_node: int,
+    pg: dist.ProcessGroup,
+) -> torch.Tensor:
+    if type(tensor) is ShardedTensor:
+        assert len(tensor.local_shards()) == 1
+
+        inner_param = tensor.local_tensor()
+        inner_st = _create_chunk_sharded_tensor(
+            inner_param,
+            rank,
+            world_size,
+            num_devices_per_node,
+            pg,
+        )
+
+        outer_local_shard = tensor.local_shards()[0]
+        shards: list[Shard] = [
+            Shard(inner_st, copy.deepcopy(outer_local_shard.metadata))
+        ]
+        st_meta = copy.deepcopy(tensor.metadata())
+        st_meta.tensor_properties.requires_grad = False
+
+        st_outer = ShardedTensor._init_from_local_shards_and_global_metadata(
+            shards,
+            sharded_tensor_metadata=st_meta,
+            process_group=tensor._process_group,
+            init_rrefs=False,
+        )
+        return st_outer
+    elif type(tensor) is DTensor:
+        device_mesh = tensor.device_mesh
+        assert device_mesh.ndim == 1, "Only 1D DeviceMeshes currently handled"
+
+        inner_param = tensor._local_tensor
+
+        inner_st = _create_chunk_sharded_tensor(
+            inner_param,
+            rank,
+            world_size,
+            torch.accelerator.device_count(),
+            pg,
+        )
+
+        dt_pg = _get_dt_pg(tensor)
+        # We do this differently here, we create a ST with no local shards then patch it
+        shards = [
+            Shard(inner_st, _create_shard_md_from_dt(tensor, dist.get_rank(dt_pg)))
+        ]
+
+        st_meta = _create_sharded_tensor_md_from_dt(tensor, dt_pg)
+        st_meta.tensor_properties.requires_grad = False
+
+        st_outer = ShardedTensor._init_from_local_shards_and_global_metadata(
+            shards,
+            sharded_tensor_metadata=st_meta,
+            process_group=dt_pg,
+            init_rrefs=False,
+        )
+
+        return st_outer
+    else:
+        return _create_chunk_sharded_tensor(
+            tensor,
+            rank,
+            world_size,
+            num_devices_per_node,
+            pg,
+        )
+
+
+def _chunk_dtensor(
+    tensor: torch.Tensor,
+    rank: int,
+    device_mesh: DeviceMesh,
+) -> DTensor:
+    """
+    Shard a tensor to chunks along the first dimension.
+
+    The local rank will gets its corresponding chunk as the local tensor to create a DTensor.
+    """
+    root_mesh = _mesh_resources.get_root_mesh(device_mesh)
+    if root_mesh is None:
+        raise RuntimeError("No parent device_mesh is found for FSDP device_mesh.")
+    if root_mesh.ndim < 2:
+        raise RuntimeError(
+            f"Found parent device_mesh of ndim={root_mesh.ndim},",
+            "but meshes must be at least 2D.",
+        )
+
+    # We need to explicitly call .detach() to return a new tensor detached from the current graph.
+    tensor = tensor.detach().clone()
+
+    # When a layer is not involved in TP, then the tensor will not be a DTensor.
+    # e.g. When a layer is not sppecified in the parallelize_plan, TP will have no effect on the layer.
+    # e.g. When you do PairwiseParallel on a 3 layer model, TP will have no effect on the third layer.
+    if isinstance(tensor, torch.Tensor) and not isinstance(tensor, DTensor):
+        # For tensors, it is replicated across tp dimension and sharded across FSDP dimension.
+        # TP is the inner dimension and FSDP is the outer dimension.
+        # Therefore, shard placements for tensor is (Shard(0), Replicate()).
+        replicate_placements = [Replicate() for _ in range(root_mesh.ndim)]
+        shard_placements = [Replicate() for _ in range(root_mesh.ndim)]
+        shard_placements[0] = DShard(0)  # type: ignore[call-overload]
+
+        return DTensor.from_local(
+            tensor, root_mesh, replicate_placements, run_check=False
+        ).redistribute(
+            device_mesh=root_mesh,
+            placements=shard_placements,
+        )
+
+    else:
+        tp_placements = tensor.placements
+        tp_placement = tp_placements[0]
+
+        tensor = tensor.to_local()
+
+        # For DTensors, it is sharded across tp dimension first and then sharded across FSDP dimension.
+        # TP is the inner dimension and FSDP is the outer dimension.
+        # Therefore, shard placements for tensor is (Shard(0), tp_placement).
+        # For higher dimensional meshes, it is replicated across other dimensions. For example, with
+        # HSDP the shard placements for tensor is (Replicate, Shard(0), tp_placement).
+        replicate_placements = [Replicate() for _ in range(root_mesh.ndim)]
+        replicate_placements[-1] = tp_placement  # type: ignore[call-overload]
+        shard_placements = [Replicate() for i in range(root_mesh.ndim)]  # type: ignore[misc]
+        shard_placements[-2] = DShard(0)  # type: ignore[call-overload]
+        shard_placements[-1] = tp_placement  # type: ignore[call-overload]
+
+        return DTensor.from_local(
+            tensor, root_mesh, replicate_placements, run_check=False
+        ).redistribute(
+            device_mesh=root_mesh,
+            placements=shard_placements,
+        )
+
+
+def _pre_load_state_dict(
+    tensor: torch.Tensor,
+) -> tuple[torch.Tensor, list[Shard]]:
+    shards = cast(ShardedTensor, tensor).local_shards()
+    if len(shards) == 1 and type(shards[0].tensor) is ShardedTensor:
+        inner_tensor = shards[0].tensor
+        shards = inner_tensor.local_shards()  # pyre-ignore[16]
+        tensor = inner_tensor
+
+    return (tensor, shards if len(shards) > 0 else [])
+
+
+def _all_gather_dtensor(
+    tensor: DTensor,
+    parent_mesh: Optional[DeviceMesh],
+) -> torch.Tensor:
+    """All gather a DTensor in its FSDP dimension and return the local tensor."""
+    assert parent_mesh == tensor.device_mesh
+
+    placements = list(copy.deepcopy(tensor.placements))
+    # FSDP + TP: [Shard(0), tp_placement] -> [Replicate(), tp_placement]
+    # HSDP + TP: [Replicate(), Shard(0), tp_placement] -> [Replicate(), Replicate(), tp_placement]
+    for i in range(0, len(placements) - 1):
+        placements[i] = Replicate()
+    tensor = tensor.redistribute(
+        device_mesh=tensor.device_mesh,
+        placements=placements,
+    )
+
+    return tensor.to_local()
+
+
+class DTensorExtensions(FSDPExtensions):
+    """
+    DTensorExtension is the TensorFlattener extension needed for 2D FSDP + TP.
+
+    This is the implementation for FSDPExtensions defined in
+    https://github.com/pytorch/pytorch/blob/main/torch/distributed/fsdp/_fsdp_extensions.py
+    """
+
+    def __init__(self, device_handle) -> None:
+        super().__init__()
+        self.compute_stream = None
+        self.device_handle = device_handle
+        # we have to use the dynamo disable this way to disable dynamo as the decorater way would
+        # trigger build failure with torch deploy...
+        self.post_unflatten_transform = torch._dynamo.disable(  # type: ignore[method-assign]
+            self.post_unflatten_transform
+        )
+
+    def pre_flatten_transform(
+        self,
+        tensor: torch.Tensor,
+    ) -> tuple[torch.Tensor, Optional[Any]]:
+        return _flatten_tensor(tensor)
+
+    def post_unflatten_transform(
+        self, tensor: torch.Tensor, param_extension: Any
+    ) -> torch.Tensor:
+        stream = self.compute_stream or self.device_handle.current_stream()
+        with self.device_handle.stream(stream):
+            # runtime we put the unflattened tensor call on the compute stream since
+            # the unflattened tensor might contain computations in fwd/bwd where we
+            # need to sync properly.
+            # TODO: this is a short term fix and we should make the get_unflat_views
+            # directly happen in the compute stream.
+            result = _unflatten_tensor(
+                tensor,
+                param_extension,
+                device_handle=self.device_handle,
+                compute_stream=self.compute_stream,
+            )
+            _set_fsdp_flattened(result)
+            return result
+
+    def chunk_tensor(
+        self,
+        tensor: torch.Tensor,
+        rank: int,
+        world_size: int,
+        num_devices_per_node: int,
+        pg: dist.ProcessGroup,
+        device: Optional[torch.device] = None,
+    ) -> torch.Tensor:
+        return _chunk_tensor(tensor, rank, world_size, num_devices_per_node, pg)
+
+    def chunk_dtensor(
+        self,
+        tensor: torch.Tensor,
+        rank: int,
+        device_mesh: DeviceMesh,
+    ) -> torch.Tensor:
+        return _chunk_dtensor(tensor, rank, device_mesh)
+
+    def pre_load_state_dict_transform(
+        self,
+        tensor: torch.Tensor,
+    ) -> tuple[torch.Tensor, list[Shard]]:
+        return _pre_load_state_dict(tensor)
+
+    def all_gather_dtensor(
+        self,
+        tensor: DTensor,
+        parent_mesh: Optional[DeviceMesh],
+    ) -> torch.Tensor:
+        return _all_gather_dtensor(tensor, parent_mesh)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/input_reshard.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/input_reshard.py
new file mode 100644
index 0000000000000000000000000000000000000000..de003c599468412495680c059b597ad67e510964
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/input_reshard.py
@@ -0,0 +1,106 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from functools import partial
+from typing import Any, Optional
+
+import torch
+from torch.distributed.tensor import DeviceMesh, DTensor, Replicate, Shard
+
+
+__all__ = [
+    "input_reshard",
+]
+
+
+def input_reshard(
+    module: torch.nn.Module,
+    tp_device_mesh: DeviceMesh,
+    input_reshard_dim: Optional[int] = None,
+) -> torch.nn.Module:
+    """
+    Register hooks to an nn.Module for input resharding, enabling sharding and restoration during backward computation.
+
+    Register hooks to an nn.Module with input resharding so that we can shard
+    per the given `tp_device_mesh` and `input_reshard_dim` and restore the
+    input back when recomputing the activations in the backward. The reason
+    why we can do this is that for Tensor Parallel(TP), the input are same
+    across all TP ranks.
+
+    Args:
+        module (:class:`nn.Module`):
+            Module to be registered with input resharding.
+        tp_device_mesh (:class:`DeviceMesh`):
+            Object which describes the mesh topology
+            of devices for Tensor Parallel.
+        input_reshard_dim (Optional[int]):
+            The dimension of where we perform the sharding
+            of input. If set None, there is no sharding of input.
+            Default: None
+
+    Return:
+        A :class:`nn.Module` object registered with TP input resharding.
+    """
+    if input_reshard_dim is None:
+        return module
+
+    cx: Optional[torch.autograd.graph.saved_tensors_hooks] = None
+
+    def input_reshard_forward_pre_hook(_: torch.nn.Module, _i: tuple[Any, ...]) -> None:
+        saved_tensor_hooks = torch.autograd.graph.saved_tensors_hooks(
+            partial(_pack_hook_tp, tp_device_mesh, input_reshard_dim),
+            partial(_unpack_hook_tp, tp_device_mesh, input_reshard_dim),
+        )
+        saved_tensor_hooks.__enter__()
+        nonlocal cx
+        cx = saved_tensor_hooks  # type: ignore[name-defined]
+
+    def input_reshard_backward_hook(
+        _: torch.nn.Module, _i: tuple[Any, ...], _o: Any
+    ) -> Any:
+        nonlocal cx
+        cx.__exit__()  # type: ignore[name-defined, union-attr]
+
+    module.register_forward_pre_hook(input_reshard_forward_pre_hook)
+    module.register_forward_hook(input_reshard_backward_hook)
+    return module
+
+
+def _pack_hook_tp(mesh: DeviceMesh, input_reshard_dim: int, x: torch.Tensor) -> Any:  # noqa: D401
+    """Hook function called after FWD to shard input."""
+    if isinstance(x, DTensor) and all(p.is_replicate() for p in x._spec.placements):
+        return x.redistribute(device_mesh=mesh, placements=[Shard(input_reshard_dim)])
+    elif (
+        not isinstance(x, DTensor)
+        and isinstance(x, torch.Tensor)
+        and x.numel() >= mesh.size()
+    ):
+        return (
+            DTensor.from_local(x, device_mesh=mesh)
+            .redistribute(device_mesh=mesh, placements=[Shard(input_reshard_dim)])
+            .to_local()
+        )
+    else:
+        return x
+
+
+def _unpack_hook_tp(mesh: DeviceMesh, input_reshard_dim: int, x: Any) -> torch.Tensor:  # noqa: D401
+    """Hook function called before activation recomputing in BWD to restore input."""
+    if (
+        isinstance(x, DTensor)
+        and len(x._spec.placements) == 1
+        and x._spec.placements[0].is_shard()
+    ):
+        return x.redistribute(device_mesh=mesh, placements=[Replicate()])
+    elif (
+        not isinstance(x, DTensor)
+        and isinstance(x, torch.Tensor)
+        and x.numel() >= mesh.size()
+    ):
+        return (
+            DTensor.from_local(
+                x, device_mesh=mesh, placements=[Shard(input_reshard_dim)]
+            )
+            .redistribute(device_mesh=mesh, placements=[Replicate()])
+            .to_local()
+        )
+    else:
+        return x
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/loss.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c01e4b15ef7612eeed8e3864a43666a5060b8b6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/loss.py
@@ -0,0 +1,488 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import contextlib
+from typing import cast, Optional
+
+import torch
+import torch._prims_common as utils
+import torch.distributed._functional_collectives as funcol
+import torch.distributed.distributed_c10d as c10d
+from torch import Tensor
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor import DTensor, Replicate, Shard
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor._ops._embedding_ops import _MaskPartial
+from torch.distributed.tensor._ops._math_ops import (
+    _skip_dim,
+    Reduction,
+    replicate_reduction_dims,
+)
+from torch.distributed.tensor.placement_types import Placement
+
+
+aten = torch.ops.aten
+
+
+__all__ = ["loss_parallel"]
+
+
+@contextlib.contextmanager
+def loss_parallel():
+    """
+    A context manager that enables loss parallelism, where efficient parallelized loss computation
+    can be performed when the input is sharded on the class dimension. Currently only the cross-entropy
+    loss is supported.
+
+    Within this context manager, one can use :func:`~torch.nn.functional.cross_entropy` or
+    :class:`~torch.nn.CrossEntropyLoss` as usual, with the following assumptions on the input parameters.
+    The corresponding ``backward()`` call, if any, also needs to happen under this context manager.
+
+    Args:
+        input (:class:`DTensor`):
+            Input logits. Assumed to be sharded on the class dimension.
+        target (Union[:class:`torch.Tensor`, :class:`DTensor`]):
+            Must be ground truth class indices (class probabilities currently not supported).
+            Assumed to be replicated across the ``DeviceMesh``.
+        weight (Union[:class:`torch.Tensor`, :class:`DTensor`], optional):
+            If given, assumed to be replicated across the ``DeviceMesh``.
+        label_smoothing:
+            Currently not supported.
+
+    Returns:
+        A replicated :class:`DTensor`.
+
+    Example:
+        A sharded DTensor is manually created here to showcase the usage.
+        In practice, it is usually the output of a TP module.
+
+        >>> # xdoctest: +SKIP("distributed")
+        >>> from torch.distributed.tensor.parallel import loss_parallel
+        >>> from torch.distributed.device_mesh import init_device_mesh
+        >>> ...
+        >>> device_mesh = init_device_mesh("cuda", (8,))
+        >>> input = torch.randn(4, 16, device="cuda", requires_grad=True)
+        >>> dist_input = distribute_tensor(input, device_mesh, placements=[Shard(1)])
+        >>> target = torch.randint(16, (4,), device="cuda")
+        >>> with loss_parallel():
+        >>>     loss = F.cross_entropy(dist_input, target, reduction="mean")
+        >>>     loss.backward()
+        >>> ...
+    """
+    _enable_custom_loss_ops()
+
+    yield
+
+    _disable_custom_loss_ops()
+
+
+# Currently only needs to support one dimensional DeviceMesh; in general return
+# the mesh_dim with placements[mesh_dim].is_shard(dim)
+def _find_all_reduce_mesh_dim(placements: tuple[Placement, ...], dim: int) -> int:
+    if not len(placements) == 1:
+        raise ValueError(
+            "Currently loss_parallel() only supports input on one-dimensional DeviceMesh."
+        )
+    if not placements[0].is_shard(dim):
+        raise ValueError(
+            f"loss_parallel() should be enabled only when the input tensor is sharded on dimension {dim}."
+        )
+    return 0
+
+
+def _cast_to_dtensor(
+    tensor, placements: tuple[Placement, ...], mesh: DeviceMesh
+) -> DTensor:
+    if isinstance(tensor, DTensor):
+        if tensor.placements == placements:
+            return tensor
+        else:
+            raise RuntimeError(f"Expected {placements} but got {tensor.placements}.")
+    elif isinstance(tensor, torch.Tensor):
+        return DTensor.from_local(
+            tensor, device_mesh=mesh, placements=placements, run_check=False
+        )
+    else:
+        raise TypeError(f"Unsupported type {type(tensor)}")
+
+
+def _propagate_tensor_meta(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> TensorMeta:
+    op_info = DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
+    tensor_meta = DTensor._op_dispatcher.sharding_propagator._propagate_tensor_meta(
+        op_info.schema
+    )
+    if isinstance(tensor_meta, TensorMeta):
+        return tensor_meta
+    elif isinstance(tensor_meta, tuple):
+        return tensor_meta[0]
+    else:
+        raise RuntimeError(f"Unexpected tensor meta type: {type(tensor_meta)}.")
+
+
+# NOTE: The implementation follows torch._decomp.decomposition._log_softmax,
+# with all_reduce manually inserted to perform distributed computation.
+def _log_softmax(x, dim, half_to_float, mesh, mesh_dim):
+    if half_to_float:
+        assert x.dtype == torch.half
+    computation_dtype, result_dtype = utils.elementwise_dtypes(
+        x, type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
+    )
+    x = x.to(dtype=computation_dtype, memory_format=torch.contiguous_format)
+    if x.numel() == 0:
+        shifted = x
+    else:
+        x_max = torch.amax(x, dim, keepdim=True)
+        x_max = funcol.all_reduce(
+            x_max, reduceOp=c10d.ReduceOp.MAX.name, group=(mesh, mesh_dim)
+        )
+        shifted = x - x_max
+    shifted_sumexp = torch.sum(torch.exp(shifted), dim, keepdim=True)
+    shifted_sumexp = funcol.all_reduce(
+        shifted_sumexp, reduceOp=c10d.ReduceOp.SUM.name, group=(mesh, mesh_dim)
+    )
+    shifted_logsumexp = torch.log(shifted_sumexp)
+    result = shifted - shifted_logsumexp
+    if not half_to_float:
+        result = result.to(result_dtype)
+    return result
+
+
+def _log_softmax_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    x = cast(DTensor, args[0])
+    dim = cast(int, args[1])
+    half_to_float = cast(bool, args[2])
+
+    spec = x._spec
+    mesh_dim = _find_all_reduce_mesh_dim(spec.placements, dim)
+
+    output_tensor_meta = _propagate_tensor_meta(op_call, args, kwargs)
+
+    res = _log_softmax(x._local_tensor, dim, half_to_float, spec.mesh, mesh_dim)
+
+    res_spec = DTensorSpec(
+        spec.mesh,
+        spec.placements,
+        tensor_meta=output_tensor_meta,
+    )
+
+    return DTensor(
+        res,
+        res_spec,
+        requires_grad=res.requires_grad,
+    )
+
+
+# NOTE: As explained below at _nll_loss_and_log_softmax_backward, the
+# _log_softmax_backward_handler does not actually do any computation.
+def _log_softmax_backward_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    grad_output = cast(DTensor, args[0])
+    input_dtype = cast(torch.dtype, args[3])
+    return grad_output.to(input_dtype)
+
+
+# NOTE: The implementation follows torch._decomp.decomposition._nll_loss_forward,
+# with customized communication inserted to perform distributed computation.
+def _nll_loss_forward(
+    x: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor],
+    local_weight: Optional[Tensor],
+    reduction: int,
+    ignore_index: int,
+    input_shape: torch.Size,
+    channel_dim: int,
+    mesh: DeviceMesh,
+    mesh_dim: int,
+) -> tuple[Tensor, Tensor]:
+    n_dims = x.dim()
+    channel_dim = 1
+    if n_dims < 2:
+        channel_dim = 0
+
+    def _weight_view(weight: Tensor) -> Tensor:
+        if n_dims > 1:
+            shape = [
+                1,
+            ] * n_dims
+            shape[channel_dim] = weight.shape[0]
+            w = weight.view(shape)
+        else:
+            w = weight
+        return w
+
+    if weight is not None:
+        w = _weight_view(weight)
+        assert local_weight is not None
+        local_w = _weight_view(local_weight)
+        x = x * local_w
+    safe_target = torch.where(target != ignore_index, target, 0)
+    safe_target_ = safe_target.unsqueeze(channel_dim)
+
+    # The following code block is a distributed version of
+    # result = -torch.gather(self, channel_dim, safe_target_).squeeze(channel_dim)
+    partial_placement = _MaskPartial(offset_shape=input_shape, offset_dim=channel_dim)
+    safe_target_partial_ = partial_placement._partition_value(
+        safe_target_, mesh, mesh_dim
+    )
+    result_partial = torch.gather(x, channel_dim, safe_target_partial_)
+    # an all_reduce happens here
+    result_reduced = partial_placement._reduce_value(result_partial, mesh, mesh_dim)
+    result = -result_reduced.squeeze(channel_dim)
+
+    result = torch.where(target != ignore_index, result, 0)
+
+    if reduction == Reduction.NONE.value and n_dims > 1:
+        total_weight = x.new_full((), 0.0)
+        return result, total_weight
+
+    if weight is not None:
+        new_shape = list(x.shape)
+        new_shape[channel_dim] = -1
+        w = w.expand(new_shape)
+        wsum = torch.gather(w, channel_dim, safe_target_).squeeze(channel_dim)
+        wsum = torch.where(target != ignore_index, wsum, 0)
+        total_weight = wsum.sum()
+    else:
+        total_weight = (target != ignore_index).sum().to(x)
+
+    # NOTE: this is correct only on 1D DeviceMesh; o/w additional
+    #       all-reduce on result and total_weight is needed
+    if reduction == Reduction.SUM.value:
+        result = result.sum()
+    elif reduction == Reduction.MEAN.value:
+        result = result.sum() / total_weight
+
+    return result, total_weight
+
+
+def _nll_loss_forward_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    x = cast(DTensor, args[0])
+    target = args[1]
+    weight = args[2]
+    reduction = cast(int, args[3])
+    ignore_index = cast(int, args[4])
+
+    channel_dim = 1 if x.dim() >= 2 else 0
+    spec = x._spec
+    mesh_dim = _find_all_reduce_mesh_dim(spec.placements, channel_dim)
+
+    # Check user input: if target and weight are not DTensors, convert them to DTensors;
+    # if they are DTensors, check that they have the desired placements.
+    target_placements = _skip_dim(
+        replicate_reduction_dims(spec.placements, [channel_dim]), channel_dim
+    )
+    all_replicate_placements = (Replicate(),) * spec.mesh.ndim
+    target = _cast_to_dtensor(target, target_placements, spec.mesh)
+    local_weight = None
+    if weight is not None:
+        weight = _cast_to_dtensor(weight, all_replicate_placements, spec.mesh)
+        # For local computation, both (replicated) weight and (sharded) local_weight
+        # are needed in _nll_loss_forward(). local_weight is generated here using
+        # DTensor API, without incurring any communication.
+        sharded_placements = [
+            Shard(0) if i == mesh_dim else Replicate() for i in range(spec.mesh.ndim)
+        ]
+        local_weight = weight.redistribute(spec.mesh, sharded_placements)._local_tensor
+        assert local_weight.shape[0] == x._local_tensor.shape[channel_dim]
+
+    if reduction == Reduction.NONE.value:
+        output_placements = target_placements
+    else:
+        output_placements = all_replicate_placements
+
+    # tensor inputs to _propagate_tensor_meta need to be DTensors
+    args = list(args)
+    args[1], args[2] = target, weight
+    output_tensor_meta = _propagate_tensor_meta(op_call, tuple(args), kwargs)
+
+    result, total_weight = _nll_loss_forward(
+        x._local_tensor,
+        target._local_tensor,
+        weight._local_tensor if weight is not None else None,
+        local_weight,
+        reduction,
+        ignore_index,
+        x.shape,
+        channel_dim,
+        spec.mesh,
+        mesh_dim,
+    )
+    out_spec = DTensorSpec(spec.mesh, output_placements, tensor_meta=output_tensor_meta)
+
+    return (
+        DTensor(
+            result,
+            out_spec,
+            requires_grad=result.requires_grad,
+        ),
+        total_weight,
+    )
+
+
+# NOTE: The backward computation of cross_entropy goes through two steps:
+# backward for nll_loss and then backward for log_softmax. In loss parallel,
+# the two steps are fused into the following function (called by _nll_loss_backward_handler)
+# to avoid communication when target contains class indices not class probabilities.
+# Also note that the _log_softmax_backward_handler does not perform computation.
+# The implementation resembles _nll_loss_backward and _log_softmax_backward_data
+# from torch._decomp.decomposition.
+def _nll_loss_and_log_softmax_backward(
+    grad_output: Tensor,
+    x: Tensor,
+    target: Tensor,
+    weight: Optional[Tensor],
+    reduction: int,
+    ignore_index: int,
+    total_weight: Tensor,
+    input_shape: torch.Size,
+    channel_dim: int,
+    mesh: DeviceMesh,
+    mesh_dim: int,
+) -> Tensor:
+    channel_dim = 0 if x.dim() < 2 else 1
+    if reduction == Reduction.MEAN.value:
+        grad_output = grad_output / total_weight
+
+    target = target.unsqueeze(channel_dim)
+    safe_target = torch.where(target != ignore_index, target, 0)
+    grad_input = torch.zeros_like(x)
+
+    # The following code block is a distributed version of
+    # grad_input = torch.scatter(grad_input, channel_dim, safe_target, -1.0)
+    partial_placement = _MaskPartial(offset_shape=input_shape, offset_dim=channel_dim)
+    safe_target = safe_target.squeeze(channel_dim).flatten()
+    masked_safe_target = partial_placement._partition_value(safe_target, mesh, mesh_dim)
+    # only update grad_input to -1 if not masked
+    assert partial_placement.mask_buffer.data is not None
+    grad_update = partial_placement.mask_buffer.data.to(grad_input.dtype) - 1.0
+    arange_1d = torch.arange(
+        masked_safe_target.shape[0], device=masked_safe_target.device
+    )
+    # The first two cases with x.dim() <= 2 are for aten.nll_loss_backward.default;
+    # the last case is for aten.nll_loss2d_backward.default.
+    if x.dim() == 1:
+        grad_input[masked_safe_target] = grad_update
+    elif x.dim() == 2:
+        grad_input[arange_1d, masked_safe_target] = grad_update
+    else:
+        grad_input_t = grad_input.transpose(channel_dim, -1)
+        intermidate_shape = grad_input_t.shape
+        grad_input_2d = grad_input_t.reshape(-1, x.shape[channel_dim])
+        grad_input_2d[arange_1d, masked_safe_target] = grad_update
+        grad_input = grad_input_2d.view(intermidate_shape).transpose(channel_dim, -1)
+
+    if grad_input.dim() > grad_output.dim() > 0:
+        grad_output = grad_output.unsqueeze(channel_dim)
+
+    if weight is not None:
+        new_shape = [1 for _ in range(x.dim())]
+        new_shape[channel_dim] = weight.shape[0]
+        weight = weight.reshape(new_shape)
+        # In order for fused computation to work, the following line is rewritten.
+        # grad_output = grad_output * weight
+        new_shape = list(x.shape)
+        new_shape[channel_dim] = -1
+        w = weight.expand(new_shape)
+        w_target = torch.gather(w, channel_dim, target)
+        grad_output = grad_output * w_target
+
+    grad_output = torch.where(target != ignore_index, grad_output, 0)
+
+    # NOTE: Instead of directly returning the grad_input as grad_output for log_softmax,
+    # here we perform backward computation for log_softmax altogether to avoid the
+    # otherwise extra all_gather communication.
+    # return grad_input * grad_output
+    return (grad_input + torch.exp(x)) * grad_output
+
+
+def _nll_loss_backward_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    grad_output = cast(DTensor, args[0])
+    x = cast(DTensor, args[1])
+    target = args[2]
+    weight = args[3]
+    reduction = cast(int, args[4])
+    ignore_index = cast(int, args[5])
+    total_weight = cast(Tensor, args[6])
+
+    channel_dim = 1 if x.dim() >= 2 else 0
+    spec = x._spec
+    mesh_dim = _find_all_reduce_mesh_dim(spec.placements, channel_dim)
+
+    # if target and weight are not DTensors, convert them to DTensors
+    target_placements = _skip_dim(
+        replicate_reduction_dims(spec.placements, [channel_dim]), channel_dim
+    )
+    all_replicate_placements = (Replicate(),) * spec.mesh.ndim
+    target = _cast_to_dtensor(target, target_placements, spec.mesh)
+    if weight is not None:
+        weight = _cast_to_dtensor(weight, all_replicate_placements, spec.mesh)
+
+    # tensor inputs to _propagate_tensor_meta need to be DTensors
+    args = list(args)
+    args[2], args[3] = target, weight
+    args[6] = _cast_to_dtensor(total_weight, all_replicate_placements, spec.mesh)
+    output_tensor_meta = _propagate_tensor_meta(op_call, tuple(args), kwargs)
+
+    result = _nll_loss_and_log_softmax_backward(
+        grad_output._local_tensor,
+        x._local_tensor,
+        target._local_tensor,
+        weight._local_tensor if weight is not None else None,
+        reduction,
+        ignore_index,
+        total_weight,
+        x.shape,
+        channel_dim,
+        spec.mesh,
+        mesh_dim,
+    )
+    # the output sharding is the same as input sharding: Shard(channel_dim) on mesh_dim
+    out_spec = DTensorSpec(
+        spec.mesh,
+        spec.placements,
+        tensor_meta=output_tensor_meta,
+    )
+
+    return DTensor(
+        result,
+        out_spec,
+        requires_grad=result.requires_grad,
+    )
+
+
+customized_loss_ops = {
+    aten._log_softmax.default: _log_softmax_handler,
+    aten._log_softmax_backward_data.default: _log_softmax_backward_handler,
+    aten.nll_loss_forward.default: _nll_loss_forward_handler,
+    aten.nll_loss2d_forward.default: _nll_loss_forward_handler,
+    aten.nll_loss_backward.default: _nll_loss_backward_handler,
+    aten.nll_loss2d_backward.default: _nll_loss_backward_handler,
+}
+
+
+def _enable_custom_loss_ops():
+    DTensor._op_dispatcher._custom_op_handlers.update(customized_loss_ops)
+
+
+def _disable_custom_loss_ops():
+    for custom_op in customized_loss_ops:
+        DTensor._op_dispatcher._custom_op_handlers.pop(custom_op)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/style.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/style.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca0ba2b7d296caafeadff6b2b9105b79b6102960
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/parallel/style.py
@@ -0,0 +1,658 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from abc import ABC, abstractmethod
+from functools import partial
+from typing import Any, Optional, Union
+
+import torch
+import torch.nn as nn
+from torch.distributed.tensor import (
+    DeviceMesh,
+    distribute_module,
+    distribute_tensor,
+    DTensor,
+    Replicate,
+    Shard,
+)
+from torch.distributed.tensor.placement_types import Placement
+
+
+__all__ = [
+    "ParallelStyle",
+    "RowwiseParallel",
+    "SequenceParallel",
+    "ColwiseParallel",
+    "PrepareModuleInput",
+    "PrepareModuleOutput",
+]
+
+
+class ParallelStyle(ABC):
+    """
+    The parallel style contract defines how the module or submodule should be parallelized.
+
+    It only defines the ``apply`` method for ``parallelize_module`` to use, this allows maximum
+    flexibility for different kind of style implementations.
+    """
+
+    src_data_rank: Optional[int] = 0
+
+    @abstractmethod
+    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module: ...
+
+
+class ColwiseParallel(ParallelStyle):
+    """
+    Partition a compatible nn.Module in a column-wise fashion. Currently supports nn.Linear and nn.Embedding.
+    Users can compose it together with RowwiseParallel to achieve the sharding of more complicated modules.
+    (i.e. MLP, Attention)
+
+    Keyword Args:
+        input_layouts (Placement, optional):
+            The DTensor layout of input tensor for the nn.Module, this is used to annotate the input tensor to
+            become a DTensor. If not specified, we assume the input tensor to be replicated.
+        output_layouts (Placement, optional):
+            The DTensor layout of the output for the nn.Module, this is used to ensure the output of the nn.Module
+            with the user desired layout. If not specified, the output tensor is sharded on the last dimension.
+        use_local_output (bool, optional):
+            Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module output, default: True.
+    Returns:
+        A :class:`ParallelStyle` object that represents Colwise sharding of the nn.Module.
+
+    Example::
+        >>> # xdoctest: +SKIP(failing)
+        >>> from torch.distributed.tensor.parallel import parallelize_module, ColwiseParallel
+        >>> from torch.distributed.device_mesh import init_device_mesh
+        >>> ...
+        >>> m = Model(...)  # m is a nn.Module that contains a "w1" nn.Linear submodule
+        >>> tp_mesh = init_device_mesh("cuda", (8,))
+        >>>
+        >>> # By default, the input of the "w1" Linear will be converted to Replicated DTensor
+        >>> # and the output of "w1" will return :class:`torch.Tensor` that shards on the last dim.
+        >>>
+        >>> sharded_mod = parallelize_module(m, tp_mesh, {"w1": ColwiseParallel()})
+        >>> ...
+
+    .. note:: By default ``ColwiseParallel`` output is sharded on the last dimension if the ``output_layouts`` not
+        specified, if there're operators that require specific tensor shape (i.e. before the paired ``RowwiseParallel``),
+        keep in mind that if the output is sharded the operator might need to be adjusted to the sharded size.
+    """
+
+    def __init__(
+        self,
+        *,
+        input_layouts: Optional[Placement] = None,
+        output_layouts: Optional[Placement] = None,
+        use_local_output: bool = True,
+    ):
+        super().__init__()
+        self.input_layouts = (input_layouts or Replicate(),)
+        self.output_layouts = (output_layouts or Shard(-1),)
+        # colwise linear runtime sharding (desired sharding):
+        # 1. requires replicate input
+        # 2. shard output on last dim
+        self.desired_input_layouts = (Replicate(),)
+        self.use_local_output = use_local_output
+
+    @staticmethod
+    def _prepare_input_fn(
+        input_layouts, desired_input_layouts, mod, inputs, device_mesh
+    ):
+        # TODO: figure out dynamo support for instance method and switch this to instance method
+
+        # annotate module input placements/sharding with input_layouts
+        input_tensor = inputs[0]
+        if not isinstance(input_tensor, DTensor):
+            input_tensor = DTensor.from_local(
+                input_tensor, device_mesh, input_layouts, run_check=False
+            )
+
+        # transform the input layouts to the desired layouts of ColwiseParallel
+        if input_layouts != desired_input_layouts:
+            input_tensor = input_tensor.redistribute(
+                placements=desired_input_layouts, async_op=True
+            )
+        return input_tensor
+
+    def _partition_linear_fn(self, name, module, device_mesh):
+        # colwise shard weight/bias to Shard(0), weight be Shard(0)
+        # means Colwise as Linear is input * weight^T + bias, where
+        # weight would become Shard(1)
+        for name, param in module.named_parameters():
+            dist_param = nn.Parameter(
+                distribute_tensor(
+                    param, device_mesh, [Shard(0)], src_data_rank=self.src_data_rank
+                )
+            )
+            module.register_parameter(name, dist_param)
+
+    def _partition_embedding_fn(self, name, module, device_mesh):
+        # colwise shard embedding.weight is straight forward as Shard(1)
+        for name, param in module.named_parameters():
+            dist_param = nn.Parameter(
+                distribute_tensor(
+                    param, device_mesh, [Shard(1)], src_data_rank=self.src_data_rank
+                )
+            )
+            module.register_parameter(name, dist_param)
+
+    @staticmethod
+    def _prepare_output_fn(output_layouts, use_local_output, mod, outputs, device_mesh):
+        # outputs is a shard on last dimension DTensor, i.e. Shard(-1)
+        if outputs.placements != output_layouts:
+            outputs = outputs.redistribute(placements=output_layouts, async_op=True)
+        # back to local tensor
+        return outputs.to_local() if use_local_output else outputs
+
+    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
+        if isinstance(module, nn.Linear):
+            partition_fn = self._partition_linear_fn
+        elif isinstance(module, nn.Embedding):
+            partition_fn = self._partition_embedding_fn
+        else:
+            raise NotImplementedError(
+                "ColwiseParallel currently only support nn.Linear and nn.Embedding!"
+            )
+
+        return distribute_module(
+            module,
+            device_mesh,
+            partition_fn,
+            partial(
+                self._prepare_input_fn, self.input_layouts, self.desired_input_layouts
+            ),
+            partial(
+                self._prepare_output_fn, self.output_layouts, self.use_local_output
+            ),
+        )
+
+
+class RowwiseParallel(ParallelStyle):
+    """
+    Partition a compatible nn.Module in a row-wise fashion. Currently supports nn.Linear and nn.Embedding.
+    Users can compose it with ColwiseParallel to achieve the sharding of more complicated modules.
+    (i.e. MLP, Attention)
+
+    Keyword Args:
+        input_layouts (Placement, optional):
+            The DTensor layout of input tensor for the nn.Module, this is used to annotate the input tensor to
+            become a DTensor. If not specified, we assume the input tensor to be sharded on the last dimension.
+        output_layouts (Placement, optional):
+            The DTensor layout of the output for the nn.Module, this is used to ensure the output of the nn.Module
+            with the user desired layout. If not specified, the output tensor is replicated.
+        use_local_output (bool, optional):
+            Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module output, default: True.
+    Returns:
+        A :class:`ParallelStyle` object that represents Rowwise sharding of the nn.Module.
+
+    Example::
+        >>> # xdoctest: +SKIP(failing)
+        >>> from torch.distributed.tensor.parallel import parallelize_module, RowwiseParallel
+        >>> from torch.distributed.device_mesh import init_device_mesh
+        >>> ...
+        >>> m = Model(...)  # m is a nn.Module that contains a "w2" nn.Linear submodule
+        >>> tp_mesh = init_device_mesh("cuda", (8,))
+        >>>
+        >>> # By default, the input of the "w2" Linear will be converted to DTensor that shards on the last dim
+        >>> # and the output of "w2" will return a replicated :class:`torch.Tensor`.
+        >>>
+        >>> sharded_mod = parallelize_module(m, tp_mesh, {"w2": RowwiseParallel()}),
+        >>> ...
+    """
+
+    def __init__(
+        self,
+        *,
+        input_layouts: Optional[Placement] = None,
+        output_layouts: Optional[Placement] = None,
+        use_local_output: bool = True,
+    ):
+        super().__init__()
+        self.input_layouts = (input_layouts or Shard(-1),)
+        self.output_layouts = (output_layouts or Replicate(),)
+        self.use_local_output = use_local_output
+
+    @staticmethod
+    def _prepare_input_fn(
+        input_layouts, desired_input_layouts, mod, inputs, device_mesh
+    ):
+        input_tensor = inputs[0]
+        if not isinstance(input_tensor, DTensor):
+            input_tensor = DTensor.from_local(
+                input_tensor, device_mesh, input_layouts, run_check=False
+            )
+
+        if input_layouts != desired_input_layouts:
+            input_tensor = input_tensor.redistribute(
+                placements=desired_input_layouts, async_op=True
+            )
+        return input_tensor
+
+    def _partition_linear_fn(self, name, module, device_mesh):
+        # Rowwise shard weight to Shard(1), bias to Replicate(), weight be Shard(1)
+        # means Rowwise as nn.Linear is input * weight^T + bias, where
+        # weight would become Shard(0)
+        module.register_parameter(
+            "weight",
+            nn.Parameter(
+                distribute_tensor(
+                    module.weight,
+                    device_mesh,
+                    [Shard(1)],
+                    src_data_rank=self.src_data_rank,
+                )
+            ),
+        )
+        if getattr(module, "bias", None) is not None:
+            # The Linear module has bias
+            module.register_parameter(
+                "bias",
+                nn.Parameter(
+                    distribute_tensor(
+                        module.bias,
+                        device_mesh,
+                        [Replicate()],
+                        src_data_rank=self.src_data_rank,
+                    )
+                ),
+            )
+
+    def _partition_embedding_fn(self, name, module, device_mesh):
+        # rowwise shard embedding.weight is Shard(0)
+        for name, param in module.named_parameters():
+            dist_param = nn.Parameter(
+                distribute_tensor(
+                    param, device_mesh, [Shard(0)], src_data_rank=self.src_data_rank
+                )
+            )
+            module.register_parameter(name, dist_param)
+
+    @staticmethod
+    def _prepare_output_fn(output_layouts, use_local_output, mod, outputs, device_mesh):
+        # Rowwise sharding produces partial output, depending on output layouts:
+        # 1. to replicate -> allreduce
+        # 2. to shard -> reduce_scatter
+        if outputs.placements != output_layouts:
+            outputs = outputs.redistribute(placements=output_layouts, async_op=True)
+        # back to local tensor if use_local_output is True
+        return outputs.to_local() if use_local_output else outputs
+
+    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
+        if isinstance(module, nn.Linear):
+            partition_fn = self._partition_linear_fn
+            # rowwise linear runtime sharding requires input tensor shard on last dim
+            self.desired_input_layouts: tuple[Placement, ...] = (Shard(-1),)
+        elif isinstance(module, nn.Embedding):
+            partition_fn = self._partition_embedding_fn
+            # rowwise embedding runtime sharding requires input tensor replicated
+            self.desired_input_layouts = (Replicate(),)
+        else:
+            raise NotImplementedError(
+                "RowwiseParallel currently only support nn.Linear and nn.Embedding!"
+            )
+
+        return distribute_module(
+            module,
+            device_mesh,
+            partition_fn,
+            partial(
+                self._prepare_input_fn, self.input_layouts, self.desired_input_layouts
+            ),
+            partial(
+                self._prepare_output_fn, self.output_layouts, self.use_local_output
+            ),
+        )
+
+
+class SequenceParallel(ParallelStyle):
+    """
+    SequenceParallel replicates a compatible ``nn.Module`` parameters and runs the sharded computation with
+    input sharded on the sequence dimension. This currently supports ``nn.LayerNorm``, ``nn.Dropout``, and the
+    `RMSNorm python implementation `__
+
+    This style implements the operation that is described in the paper
+    `Reducing Activation Recomputation in Large Transformer Models `__
+
+    If the input passed in to this ``nn.Module`` is a :class:`torch.Tensor`, it assumes that the input is already sharded
+    on the sequence dimension and converts the input to a :class:`DTensor` sharded on the sequence dimension. If the input
+    passed in to this ``nn.Module`` is already a :class:`DTensor` but is not sharded on the sequence dimension, it would
+    redistribute the input to be sharded on the sequence dimension.
+
+    The output of the ``nn.Module`` will be sharded on the sequence dimension.
+
+    Keyword Args:
+        sequence_dim (int, optional):
+            The sequence dimension of the input tensor for the ``nn.Module``, this is used to annotate the input tensor to
+            become a DTensor that is sharded on the sequence dimension, default: 1.
+        use_local_output (bool, optional):
+            Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module output, default: False.
+    Returns:
+        A :class:`ParallelStyle` object that represents Sequence Parallel of the ``nn.Module``.
+
+    Example::
+        >>> # xdoctest: +SKIP(failing)
+        >>> from torch.distributed.tensor.parallel import parallelize_module, SequenceParallel
+        >>> from torch.distributed.device_mesh import init_device_mesh
+        >>> ...
+        >>> m = Model(...)  # m is a nn.Module that contains a "norm" nn.LayerNorm submodule
+        >>> tp_mesh = init_device_mesh("cuda", (8,))
+        >>>
+        >>> # By default, the input of the "norm" will be converted to DTensor that shards on the sequence dim
+        >>> # and the output of "norm" will return a sharded on sequence dimension :class:`DTensor`.
+        >>>
+        >>> sharded_mod = parallelize_module(m, tp_mesh, {"norm": SequenceParallel()}),
+        >>> ...
+
+    .. note:: SequenceParallel style assumes ones initialization if there are weights in the nn.Module (i.e.
+        ``nn.LayerNorm`` or ``RMSNorm``, and they by default have ones initialization). If you have custom
+        inits for the weights on those modules, you need to broadcast the weights before/after parallelizing
+        to ensure that they are replicated.
+    """
+
+    def __init__(self, *, sequence_dim: int = 1, use_local_output: bool = False):
+        super().__init__()
+        self.sequence_sharding = (Shard(sequence_dim),)
+        self.use_local_output = use_local_output
+
+    def _replicate_module_fn(
+        self, name: str, module: nn.Module, device_mesh: DeviceMesh
+    ):
+        for p_name, param in module.named_parameters():
+            # simple replication with fixed ones_ init from LayerNorm/RMSNorm, which allow
+            # us to simply just use from_local
+            replicated_param = torch.nn.Parameter(
+                DTensor.from_local(param, device_mesh, [Replicate()], run_check=False)
+            )
+            module.register_parameter(p_name, replicated_param)
+
+    @staticmethod
+    def _prepare_input_fn(sequence_sharding, mod, inputs, device_mesh):
+        input_tensor = inputs[0]
+        if isinstance(input_tensor, DTensor):
+            # if the passed in input DTensor is not sharded on the sequence dim, we need to redistribute it
+            if input_tensor.placements != sequence_sharding:
+                input_tensor = input_tensor.redistribute(
+                    placements=sequence_sharding, async_op=True
+                )
+            return input_tensor
+        elif isinstance(input_tensor, torch.Tensor):
+            # assume the input passed in already sharded on the sequence dim and create the DTensor
+            return DTensor.from_local(
+                input_tensor, device_mesh, sequence_sharding, run_check=False
+            )
+        else:
+            raise ValueError(
+                f"expecting input of {mod} to be a torch.Tensor or DTensor, but got {input_tensor}"
+            )
+
+    @staticmethod
+    def _prepare_output_fn(use_local_output, mod, outputs, device_mesh):
+        return outputs.to_local() if use_local_output else outputs
+
+    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
+        return distribute_module(
+            module,
+            device_mesh,
+            self._replicate_module_fn,
+            partial(self._prepare_input_fn, self.sequence_sharding),
+            partial(self._prepare_output_fn, self.use_local_output),
+        )
+
+
+class PrepareModuleInput(ParallelStyle):
+    """
+    Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
+    ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``.
+
+    Keyword Args:
+        input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
+            The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
+            DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
+            as a placeholder. default: None.
+        desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
+            The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
+            have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
+        input_kwarg_layouts (Dict[str, Placement]):
+            The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
+            default: None
+        desired_input_kwarg_layouts: (Dict[str, Placement]):
+            The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
+            have the desired DTensor layouts. default: None.
+        use_local_output (bool, optional):
+            Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
+    Returns:
+        A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs.
+
+    Example::
+        >>> # xdoctest: +SKIP(failing)
+        >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput
+        >>> from torch.distributed.device_mesh import init_device_mesh
+        >>> ...
+        >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
+        >>> tp_mesh = init_device_mesh("cuda", (8,))
+        >>>
+        >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor
+        >>> # and then redistributed to Replicated DTensor.
+        >>> parallelize_module(
+        >>>     block, # this can be a submodule or module
+        >>>     tp_mesh,
+        >>>     parallelize_plan={
+        >>>         "attn": PrepareModuleInput(
+        >>>             input_layouts=(Shard(0), None, None, ...),
+        >>>             desired_input_layouts=(Replicate(), None, None, ...)
+        >>>         ),
+        >>>     }
+        >>> )
+    """
+
+    def __init__(
+        self,
+        *,
+        input_layouts: Optional[Union[Placement, tuple[Optional[Placement]]]] = None,
+        desired_input_layouts: Optional[
+            Union[Placement, tuple[Optional[Placement]]]
+        ] = None,
+        input_kwarg_layouts: Optional[dict[str, Placement]] = None,
+        desired_input_kwarg_layouts: Optional[dict[str, Placement]] = None,
+        use_local_output: bool = False,
+    ):
+        self.input_layouts = (
+            (input_layouts,) if isinstance(input_layouts, Placement) else input_layouts
+        )
+        self.desired_input_layouts = (
+            (desired_input_layouts,)
+            if isinstance(desired_input_layouts, Placement)
+            else desired_input_layouts
+        )
+        self.use_local_output = use_local_output
+        if self.input_layouts is not None:
+            assert self.desired_input_layouts is not None, (
+                "desired module inputs should not be None!"
+            )
+            assert len(self.input_layouts) == len(self.desired_input_layouts), (
+                "input_layouts and desired_input_layouts should have same length!"
+            )
+        self.with_kwargs = input_kwarg_layouts is not None
+        self.input_kwarg_layouts = input_kwarg_layouts or {}
+        self.desired_input_kwarg_layouts = desired_input_kwarg_layouts or {}
+        if self.with_kwargs:
+            assert len(self.input_kwarg_layouts) == len(
+                self.desired_input_kwarg_layouts
+            ), (
+                "input_kwarg_layouts and desired_input_kwarg_layouts should have same length!"
+            )
+
+    def _prepare_input_arg(
+        self,
+        input: Any,
+        mesh: DeviceMesh,
+        input_layout: Optional[Placement],
+        desired_layout: Optional[Placement],
+    ):
+        if input_layout is not None:
+            if isinstance(input, DTensor):
+                # TODO: re-enable the check once we fix the compile path
+                # assert inp.placements[0] == input_layout
+                dt_inp = input
+            else:
+                assert isinstance(input, torch.Tensor), (
+                    "expecting input to be a torch.Tensor!"
+                )
+                dt_inp = DTensor.from_local(
+                    input, mesh, (input_layout,), run_check=False
+                )
+
+            if desired_layout is not None and input_layout != desired_layout:
+                dt_inp = dt_inp.redistribute(placements=(desired_layout,))
+
+            return dt_inp.to_local() if self.use_local_output else dt_inp
+        else:
+            return input
+
+    def _prepare_input_fn(self, inputs, device_mesh):
+        if self.input_layouts is None:
+            return inputs
+        prepared_inputs = []
+        if not isinstance(inputs, tuple):
+            inputs = (inputs,)
+        if len(inputs) != len(self.input_layouts):
+            raise ValueError("module inputs and input_layouts should have same length!")
+
+        assert self.desired_input_layouts is not None, (
+            "desired module inputs should not be None!"
+        )
+        for inp, input_layout, desired_layout in zip(
+            inputs, self.input_layouts, self.desired_input_layouts
+        ):
+            prepared_inputs.append(
+                self._prepare_input_arg(inp, device_mesh, input_layout, desired_layout)
+            )
+        return tuple(prepared_inputs)
+
+    def _prepare_input_kwarg_fn(self, inputs, kwarg_inputs, device_mesh):
+        prepared_arg_inputs = self._prepare_input_fn(inputs, device_mesh)
+        prepared_kwarg_inputs = {}
+        for kwarg_key in kwarg_inputs.keys():
+            kwarg_val = kwarg_inputs[kwarg_key]
+            input_layout = self.input_kwarg_layouts.get(kwarg_key)
+            desired_input_layout = self.desired_input_kwarg_layouts.get(kwarg_key)
+
+            prepared_kwarg_inputs[kwarg_key] = self._prepare_input_arg(
+                kwarg_val, device_mesh, input_layout, desired_input_layout
+            )
+
+        return (prepared_arg_inputs, prepared_kwarg_inputs)
+
+    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
+        if self.with_kwargs:
+            module.register_forward_pre_hook(
+                lambda _, inputs, kwargs: self._prepare_input_kwarg_fn(
+                    inputs, kwargs, device_mesh
+                ),
+                with_kwargs=True,
+            )  # type: ignore[misc]
+        else:
+            module.register_forward_pre_hook(
+                lambda _, inputs: self._prepare_input_fn(inputs, device_mesh)
+            )  # type: ignore[misc, call-arg]
+        return module
+
+
+class PrepareModuleOutput(ParallelStyle):
+    """
+    Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
+    ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``.
+
+    Keyword Args:
+        output_layouts (Union[Placement, Tuple[Placement]]):
+            The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
+            DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
+            ``None`` need to be specified as a placeholder.
+        desired_output_layouts (Union[Placement, Tuple[Placement]]):
+            The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
+            have the desired DTensor layouts.
+        use_local_output (bool, optional):
+            Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
+    Returns:
+        A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs.
+
+    Example::
+        >>> # xdoctest: +SKIP(failing)
+        >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput
+        >>> from torch.distributed.device_mesh import init_device_mesh
+        >>> ...
+        >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
+        >>> tp_mesh = init_device_mesh("cuda", (8,))
+        >>>
+        >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor
+        >>> # and then redistributed to Sharded DTensor.
+        >>> parallelize_module(
+        >>>     block, # this can be a submodule or module
+        >>>     tp_mesh,
+        >>>     parallelize_plan = PrepareModuleOutput(
+        >>>         output_layouts=Replicate(),
+        >>>         desired_output_layouts=Shard(0)
+        >>>     )
+        >>> )
+    """
+
+    def __init__(
+        self,
+        *,
+        output_layouts: Union[Placement, tuple[Placement]],
+        desired_output_layouts: Union[Placement, tuple[Placement]],
+        use_local_output: bool = True,
+    ):
+        self.output_layouts = (
+            (output_layouts,)
+            if isinstance(output_layouts, Placement)
+            else output_layouts
+        )
+        self.desired_output_layouts = (
+            (desired_output_layouts,)
+            if isinstance(desired_output_layouts, Placement)
+            else desired_output_layouts
+        )
+        self.use_local_output = use_local_output
+        assert len(self.output_layouts) == len(self.desired_output_layouts), (
+            "output_layouts and desired_output_layouts should have same length!"
+        )
+
+    def _prepare_out_fn(self, outputs, device_mesh):
+        prepared_outputs = []
+        if not isinstance(outputs, tuple):
+            outputs = (outputs,)
+        if len(outputs) != len(self.output_layouts):
+            raise ValueError(
+                "module outputs and output_layouts should have same length!"
+            )
+        for out, out_layout, desired_out_layout in zip(
+            outputs, self.output_layouts, self.desired_output_layouts
+        ):
+            if out_layout is not None:
+                if isinstance(out, DTensor):
+                    # TODO: re-enable the check once we fix the compile path
+                    # assert out.placements[0] == out_layout
+                    dt_out = out
+                else:
+                    dt_out = DTensor.from_local(
+                        out, device_mesh, (out_layout,), run_check=False
+                    )
+
+                if out_layout != desired_out_layout:
+                    dt_out = dt_out.redistribute(placements=(desired_out_layout,))
+                prepared_outputs.append(
+                    dt_out.to_local() if self.use_local_output else dt_out
+                )
+            else:
+                prepared_outputs.append(out)
+        if len(prepared_outputs) == 1:
+            return prepared_outputs[0]
+        else:
+            return tuple(prepared_outputs)
+
+    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
+        module.register_forward_hook(
+            lambda _, inputs, outputs: self._prepare_out_fn(outputs, device_mesh)
+        )  # type: ignore[misc, call-arg]
+        return module
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/placement_types.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/placement_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..ceb9f170fd3e634c3ac7982777110253af339234
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/tensor/placement_types.py
@@ -0,0 +1,675 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+from dataclasses import dataclass
+from typing import cast, Optional
+
+import torch
+import torch.distributed._functional_collectives as funcol
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor._collective_utils import (
+    fill_empty_tensor_to_shards,
+    mesh_broadcast,
+    mesh_scatter,
+    pad_tensor,
+    shard_dim_alltoall,
+    unpad_tensor,
+)
+
+
+__all__ = ["Placement", "Shard", "Replicate", "Partial"]
+
+
+class Placement:
+    """
+    The base class for the Placement type, where it describes how a DTensor is placed onto the
+    ``DeviceMesh``. ``Placement`` and ``DeviceMesh`` together could describe the DTensor Layout.
+    It is the base class of the three main DTensor Placement types: ``Shard``, ``Replicate``,
+    and ``Partial``.
+
+    This class is not meant to be used directly, mainly served as a typing stub.
+    """
+
+    # convenient utils to check for placement types
+    def is_shard(self, dim: Optional[int] = None) -> bool:
+        is_shard_instance = isinstance(self, Shard)
+        if dim is not None and is_shard_instance:
+            return cast(Shard, self).dim == dim
+        else:
+            return is_shard_instance
+
+    def is_replicate(self) -> bool:
+        return isinstance(self, Replicate)
+
+    def is_partial(self) -> bool:
+        return isinstance(self, Partial)
+
+
+@dataclass(frozen=True)
+class Shard(Placement):
+    """
+    The ``Shard(dim)`` placement describes the DTensor sharding on tensor dimension
+    ``dim`` over a corresponding ``DeviceMesh`` dimension, where each rank on the
+    DeviceMesh dimension only holds a shard/piece of the global Tensor. The
+    ``Shard(dim)`` placement follows the ``torch.chunk(dim)`` semantic, where the
+    last few shards on the DeviceMesh dimension might be empty when the tensor dimension
+    is not evenly divisible on the DeviceMesh dimension. The ``Shard`` placement can be
+    used by all DTensor APIs (i.e. distribute_tensor, from_local, etc.)
+
+    Args:
+        dim (int): The tensor dimension that describes the DTensor is sharded over its
+            corresponding DeviceMesh dimension.
+
+    .. warning:: sharding on a tensor dimension where the tensor dimension size is not
+        evenly divisible on a DeviceMesh dimension is currently experimental and subject to change.
+    """
+
+    dim: int
+
+    def _split_tensor(
+        self,
+        tensor: torch.Tensor,
+        num_chunks: int,
+        *,
+        with_padding: bool = True,
+        contiguous: bool = True,
+    ) -> tuple[list[torch.Tensor], list[int]]:
+        """
+        This function uses torch.chunk to split a tensor into num_chunks shards along
+        the Shard placement dimension, and return a list of shards with their pad sizes.
+
+        Keyword args:
+            with_padding (bool, optional): when True, we pad the tensor on the last
+            few ranks before calling the collectives (i.e. scatter/all_gather, etc.).
+            This is because collectives usually require equal size tensor inputs
+        """
+        assert self.dim <= tensor.ndim, (
+            f"Sharding dim {self.dim} greater than tensor ndim {tensor.ndim}"
+        )
+
+        # chunk tensor over dimension `dim` into n slices
+        tensor_list = list(torch.chunk(tensor, num_chunks, dim=self.dim))
+        num_empty_tensors = num_chunks - len(tensor_list)
+
+        # if no need to have padding or tensor dim size is evenly sharded already
+        # we can return early.
+        if not with_padding or tensor.size(self.dim) % num_chunks == 0:
+            if contiguous:
+                tensor_list = [t.contiguous() for t in tensor_list]
+            return (
+                fill_empty_tensor_to_shards(tensor_list, self.dim, num_empty_tensors),
+                [],
+            )
+
+        # compute the chunk size inline with ``torch.chunk`` to calculate padding
+        full_chunk_size = (tensor.size(self.dim) + num_chunks - 1) // num_chunks
+
+        # Compute chunk size for each chunk for ``self.dim``
+        chunk_sizes = [
+            tensor_list[idx].size(self.dim) if idx < len(tensor_list) else 0
+            for idx in range(num_chunks)
+        ]
+        # Compute pad size on each chunk
+        pad_sizes = [full_chunk_size - chunk_size for chunk_size in chunk_sizes]
+
+        # Reuse tensor to fill empty chunk with empty tensor
+        tensor_list = fill_empty_tensor_to_shards(
+            tensor_list, self.dim, num_empty_tensors
+        )
+        shard_list = []
+        for shard, pad_size in zip(tensor_list, pad_sizes):
+            # Fill the empty tensor with zeroes with padding.
+            if with_padding and pad_size > 0:
+                shard = pad_tensor(shard, self.dim, pad_size)
+            shard = shard.contiguous() if contiguous else shard
+            shard_list.append(shard)
+        return shard_list, pad_sizes
+
+    @staticmethod
+    def _local_shard_size_on_dim(
+        size_on_dim: int,
+        num_chunks: int,
+        rank: int,
+        return_offset: bool = False,
+    ) -> tuple[int, int]:
+        """
+        returns the local shard size and offset on a given tensor dim
+        """
+        # Compute the chunk size inline with ``torch.chunk``
+        if size_on_dim % num_chunks == 0:
+            full_chunk_size = size_on_dim // num_chunks
+            return full_chunk_size, full_chunk_size * rank if return_offset else -1
+
+        # uneven sharding case
+        full_chunk_size = (size_on_dim + num_chunks - 1) // num_chunks
+        shard_starting_idx = full_chunk_size * rank
+
+        if size_on_dim < shard_starting_idx:
+            return 0, size_on_dim if return_offset else -1
+        else:
+            local_shard_size = (
+                min(size_on_dim, shard_starting_idx + full_chunk_size)
+                - shard_starting_idx
+            )
+            return local_shard_size, shard_starting_idx if return_offset else -1
+
+    def _shard_tensor(
+        self,
+        tensor: torch.Tensor,
+        mesh: DeviceMesh,
+        mesh_dim: int,
+        src_data_rank: Optional[int] = 0,
+    ) -> torch.Tensor:
+        """
+        shard and scatter a tensor on a mesh dimension (use coordinate
+        0 on the mesh dimension as source of truth)
+        """
+        my_coordinate = mesh.get_coordinate()
+        num_chunks = mesh.size(mesh_dim=mesh_dim)
+
+        if my_coordinate is None:
+            # if rank is not part of mesh, we simply return an empty tensor
+            return tensor.new_empty(0, requires_grad=tensor.requires_grad)
+
+        mesh_dim_local_rank = my_coordinate[mesh_dim]
+
+        if src_data_rank is None:
+            # src_data_rank specified as None explicitly means to skip the
+            # communications, simply split
+            scatter_list, _ = self._split_tensor(
+                tensor, num_chunks, with_padding=False, contiguous=True
+            )
+
+            return scatter_list[mesh_dim_local_rank]
+
+        scatter_list, pad_sizes = self._split_tensor(
+            tensor, num_chunks, with_padding=True, contiguous=True
+        )
+        output = torch.empty_like(scatter_list[mesh_dim_local_rank])
+        # perform scatter from the src_data_rank as data source when it is not None
+        mesh_scatter(
+            output, scatter_list, mesh, mesh_dim=mesh_dim, group_src=src_data_rank
+        )
+
+        # Only unpad if the local_tensor was padded on the dimension.
+        if pad_sizes and pad_sizes[mesh_dim_local_rank] > 0:
+            output = unpad_tensor(output, self.dim, pad_sizes[mesh_dim_local_rank])
+        return output
+
+    def _reduce_shard_tensor(
+        self,
+        tensor: torch.Tensor,
+        mesh: DeviceMesh,
+        reduce_op: str,
+        mesh_dim: int,
+    ) -> torch.Tensor:
+        """
+        reduce and scatter a tensor on a mesh dimension
+        """
+        my_coordinate = mesh.get_coordinate()
+        num_chunks = mesh.size(mesh_dim=mesh_dim)
+
+        if my_coordinate is None:
+            # if rank is not part of mesh, we simply return local_tensor,
+            # which should be an empty tensor
+            return tensor
+
+        is_padded = tensor.size(self.dim) % num_chunks != 0
+        if is_padded:
+            scattered_list, pad_sizes = self._split_tensor(
+                tensor, num_chunks, with_padding=True, contiguous=True
+            )
+            tensor = torch.cat(scattered_list, dim=self.dim)
+        elif not tensor.is_contiguous():
+            tensor = tensor.contiguous()
+
+        output = funcol.reduce_scatter_tensor(
+            tensor, reduce_op, scatter_dim=self.dim, group=(mesh, mesh_dim)
+        )
+
+        if is_padded:
+            output = unpad_tensor(output, self.dim, pad_sizes[my_coordinate[mesh_dim]])  # type: ignore[possibly-undefined]
+        return output
+
+    def _to_replicate_tensor(
+        self,
+        local_tensor: torch.Tensor,
+        mesh: DeviceMesh,
+        mesh_dim: int,
+        current_logical_shape: list[int],
+    ) -> torch.Tensor:
+        """
+        This function all_gather all shards and return a tensor that
+        is replicated on the previously sharded mesh dimension
+        """
+        num_chunks = mesh.size(mesh_dim=mesh_dim)
+        # check if it's uneven, so we need to pad input tensor before all_gather
+        local_shape = list(local_tensor.size())
+
+        logical_dim_size = current_logical_shape[self.dim]
+        is_padded = logical_dim_size % num_chunks != 0
+
+        if is_padded:
+            full_chunk_size = (logical_dim_size + num_chunks - 1) // num_chunks
+            pad_size = full_chunk_size - local_shape[self.dim]
+            local_tensor = pad_tensor(local_tensor, self.dim, pad_size)
+
+        if not local_tensor.is_contiguous():
+            local_tensor = local_tensor.contiguous()
+
+        result = funcol.all_gather_tensor(
+            local_tensor,
+            gather_dim=self.dim,
+            group=(mesh, mesh_dim),
+        )
+        if is_padded:
+            unpad_size = full_chunk_size * num_chunks - logical_dim_size  # type: ignore[possibly-undefined]
+            result = unpad_tensor(result, self.dim, unpad_size)
+        return result
+
+    def _replicate_to_shard(
+        self,
+        local_tensor: torch.Tensor,
+        mesh: DeviceMesh,
+        mesh_dim: int,
+        shard_index: int,
+    ) -> torch.Tensor:
+        """
+        transform from replicated tensor to a sharded tensor on
+        the current rank, which would perform a local chunk
+        """
+        num_chunks = mesh.size(mesh_dim=mesh_dim)
+        shards, _ = self._split_tensor(
+            local_tensor,
+            num_chunks,
+            with_padding=False,
+            contiguous=False,
+        )
+        return shards[shard_index].clone()
+
+    def _to_new_shard_dim(
+        self,
+        local_tensor: torch.Tensor,
+        mesh: DeviceMesh,
+        mesh_dim: int,
+        current_logical_shape: list[int],
+        new_shard_dim: int,
+    ) -> torch.Tensor:
+        """
+        transform from existing sharded tensor to a new sharded tensor on
+        that shard on a new dimension, which performs an alltoall
+        """
+        my_coordinate = mesh.get_coordinate()
+        if my_coordinate is None:
+            # if rank is not part of mesh, we simply return local_tensor,
+            # which should be an empty tensor
+            return local_tensor
+
+        num_chunks = mesh.size(mesh_dim=mesh_dim)
+
+        old_dim_logical_size = current_logical_shape[self.dim]
+        new_dim_logical_size = current_logical_shape[new_shard_dim]
+        old_dim_padding = old_dim_logical_size % num_chunks != 0
+        new_dim_padding = new_dim_logical_size % num_chunks != 0
+        if old_dim_padding:
+            old_dim_full_chunk_size = (
+                old_dim_logical_size + num_chunks - 1
+            ) // num_chunks
+            old_dim_pad_size = old_dim_full_chunk_size - local_tensor.size(self.dim)
+            local_tensor = pad_tensor(local_tensor, self.dim, old_dim_pad_size)
+        if new_dim_padding:
+            new_dim_full_chunk_size = (
+                new_dim_logical_size + num_chunks - 1
+            ) // num_chunks
+            new_dim_pad_size = new_dim_full_chunk_size * num_chunks - local_tensor.size(
+                new_shard_dim
+            )
+            local_tensor = pad_tensor(local_tensor, new_shard_dim, new_dim_pad_size)
+
+        if not local_tensor.is_contiguous():
+            local_tensor = local_tensor.contiguous()
+
+        new_tensor = shard_dim_alltoall(
+            local_tensor, self.dim, new_shard_dim, mesh, mesh_dim
+        )
+
+        if old_dim_padding:
+            old_dim_unpad_size = (
+                old_dim_full_chunk_size * num_chunks - current_logical_shape[self.dim]  # type: ignore[possibly-undefined]
+            )
+            new_tensor = unpad_tensor(new_tensor, self.dim, old_dim_unpad_size)  # type: ignore[possibly-undefined]
+
+        if new_dim_padding:
+            local_shard_size_on_new_dim = self._local_shard_size_on_dim(
+                new_dim_logical_size, num_chunks, my_coordinate[mesh_dim]
+            )[0]
+            new_dim_unpad_size = new_dim_full_chunk_size - local_shard_size_on_new_dim  # type: ignore[possibly-undefined]
+            new_tensor = unpad_tensor(new_tensor, new_shard_dim, new_dim_unpad_size)  # type: ignore[possibly-undefined]
+
+        return new_tensor
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Shard):
+            return False
+        return self.dim == other.dim
+
+    def __hash__(self) -> int:
+        return hash(self.dim)
+
+    def __repr__(self) -> str:
+        """
+        machine readable representation of the Shard placement
+        """
+        return f"Shard(dim={self.dim})"
+
+    def __str__(self) -> str:
+        """human readable representation of the Shard placement"""
+        return f"S({self.dim})"
+
+
+# kw_only is only available in python >= 3.10
+kw_only_dataclass = dict(kw_only=True) if "kw_only" in dataclass.__kwdefaults__ else {}
+
+
+@dataclass(frozen=True, **kw_only_dataclass)
+class _StridedShard(Shard):
+    """
+    _StridedShard is only introduced to support 2D FSDP2 + TP sharding where the tensor
+    is sharded on the TP mesh dimension first, then sharded on the FSDP mesh dimension.
+    We call this right-to-left sharding which is the opposite of the default
+    left-to-right sharding. See the example below:
+        tensor shape: [8, 8]
+        mesh: [[0, 1], [2, 3]], names=("dp", "tp")
+        placements: [Shard(0), Shard(0)]
+
+    The default sharding behavior shards the tensor on "dp" mesh dimension first then
+    "tp" dimension. The sharding result will be:
+        Rank    |   Mesh Coordinate |   Shard Index
+        ------------------------------------------------
+        0       |   (0, 0)          |   0 (row 0-1)
+        1       |   (0, 1)          |   1 (row 2-3)
+        2       |   (1, 0)          |   2 (row 4-5)
+        3       |   (1, 1)          |   3 (row 6-7)
+
+    While the FSDP2 + TP sharding behavior does the opposite: it shards the tensor on
+    "tp" mesh dim first then "dp" dim. This right-to-left sharding will produce the
+    result:
+        Rank    |   Mesh Coordinate |   Shard Index
+        ------------------------------------------------
+        0       |   (0, 0)          |   0 (row 0-1)
+        1       |   (0, 1)          |   2 (row 4-5)
+        2       |   (1, 0)          |   1 (row 2-3)
+        3       |   (1, 1)          |   3 (row 6-7)
+
+    The consequence is, any attempt to redistribute this DTensor to a full replica will
+    produce a wrong result because the shard-to-replicate redistribution always happens
+    right-to-left, regardless it's left-to-right sharding or right-to-left. To address
+    this, we use _StridedShard placement to make this right-to-left sharding compatible
+    with our left-to-right convention on both tensor distribution and redistribution.
+
+    Now with _StridedShard, the right-to-left sharding above can be represented as:
+        tensor shape: [8, 8]
+        mesh: [[0, 1], [2, 3]], names=("dp", "tp")
+        placements: [_StridedShard(0, split_factor=2), Shard(0)]
+
+    And a left-to-right processing of `placements` will produce the same result, which is
+    different from using the `Shard` placement:
+        Rank    |   Mesh Coordinate |   Shard Index
+        ------------------------------------------------
+        0       |   (0, 0)          |   0 (row 0-1)
+        1       |   (0, 1)          |   2 (row 4-5)
+        2       |   (1, 0)          |   1 (row 2-3)
+        3       |   (1, 1)          |   3 (row 6-7)
+
+    The argument `split_factor` is the number of existing shards over the tensor sharding
+    dimension before processing the _StridedShard placement, as if the sharding happened
+    right-to-left. In the example above, the tensor should first be sharded on the "tp"
+    dimension into 2 shards before being sharded on the "dp" dimension. Therefore, the
+    `split_factor` of the _StridedShard placement on "dp" dim is 2.
+
+    TODO: strided sharding needs to work fine with uneven sharding. Now it forbids
+    resharding if the tensor is unevenly sharded.
+    TODO: we should remove _StridedShard placement once we can unify it with Shard
+    """
+
+    split_factor: int
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, _StridedShard):
+            return self.dim == other.dim and self.split_factor == other.split_factor
+        elif isinstance(other, Shard):
+            # TODO: this is to avoid extra all-gather in dtensor op dispatch
+            # note that sharding prop would not produce _StridedShard and an
+            # placement inequality would introduce an all-gather for resharding
+            return self.dim == other.dim
+        return False
+
+    def __hash__(self) -> int:
+        return hash((self.dim, self.split_factor))
+
+    def __repr__(self) -> str:
+        """
+        machine readable representation of the _StridedShard placement
+        """
+        return f"_StridedShard(dim={self.dim}, sf={self.split_factor})"
+
+    def __str__(self) -> str:
+        """human readable representation of the _StridedShard placement"""
+        return f"_S({self.dim}, {self.split_factor})"
+
+    def _split_tensor(
+        self,
+        tensor: torch.Tensor,
+        num_chunks: int,
+        *,
+        with_padding: bool = True,
+        contiguous: bool = True,
+    ) -> tuple[list[torch.Tensor], list[int]]:
+        """
+        TODO: currently _StridedShard does not support padding
+        """
+        assert self.dim <= tensor.ndim, (
+            f"Sharding dim {self.dim} greater than tensor ndim {tensor.ndim}"
+        )
+
+        total_split = num_chunks * self.split_factor
+        assert tensor.size(self.dim) % total_split == 0, (
+            "_StridedShard currently only allows even sharding but got tensor size"
+            f" {tensor.size(self.dim)} on dim {self.dim} and total split"
+            f" {total_split}={num_chunks} * {self.split_factor}"
+        )
+
+        group_size = self.split_factor
+        total_split_tensor_list = list(torch.chunk(tensor, total_split, dim=self.dim))
+        tensor_list = [
+            torch.cat(
+                [
+                    total_split_tensor_list[i + j * num_chunks]  # stride is num_chunks
+                    for j in range(group_size)
+                ],
+                dim=self.dim,
+            )
+            for i in range(num_chunks)
+        ]
+
+        if contiguous:
+            tensor_list = [t.contiguous() for t in tensor_list]
+
+        return tensor_list, []
+
+    def _to_replicate_tensor(
+        self,
+        local_tensor: torch.Tensor,
+        mesh: DeviceMesh,
+        mesh_dim: int,
+        current_logical_shape: list[int],
+    ) -> torch.Tensor:
+        """
+        Note: currently _StridedShard does not support padding
+        """
+        num_chunks = mesh.size(mesh_dim=mesh_dim)
+        total_split = num_chunks * self.split_factor
+        # NOTE: we require Strided Sharding to be even for now
+        assert current_logical_shape[self.dim] % total_split == 0, (
+            "_StridedShard requires even sharding but got tensor size "
+            f"{current_logical_shape[self.dim]} on dim {self.dim} and "
+            f"total split {total_split}=num_chunks {num_chunks} "
+            f"* split_factor {self.split_factor}"
+        )
+
+        result = funcol.all_gather_tensor(
+            local_tensor,
+            gather_dim=self.dim,
+            group=(mesh, mesh_dim),
+        )
+        if isinstance(result, funcol.AsyncCollectiveTensor):
+            result = result.wait()
+
+        tensor_shard_list = torch.chunk(result, total_split, dim=self.dim)
+        # rearrange the order
+        new_tensor_shard_list = []
+        for idx in range(len(tensor_shard_list)):
+            # the shard split of index `idx` is assigned a new index within
+            # _StridedShard._split_tensor:
+            # the original tensor was split into `total_split` chunks,
+            # all chunks with the same `idx % num_chunks` are merged into one
+            # new shard and placed on mesh's local rank `idx % num_chunks`
+            idx_after_split = idx % num_chunks * self.split_factor + idx // num_chunks
+            new_tensor_shard_list.append(tensor_shard_list[idx_after_split])
+
+        return torch.cat(new_tensor_shard_list, dim=self.dim).contiguous()
+
+
+@dataclass(frozen=True)
+class Replicate(Placement):
+    """
+    The ``Replicate()`` placement describes the DTensor replicating on a corresponding
+    ``DeviceMesh`` dimension, where each rank on the DeviceMesh dimension holds a
+    replica of the global Tensor. The ``Replicate`` placement can be used by all
+    DTensor APIs (i.e. ``distribute_tensor``, ``DTensor.from_local``, etc.)
+    """
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, Replicate)
+
+    def __hash__(self) -> int:
+        # every replicate placement is the same
+        return -1
+
+    def __repr__(self) -> str:
+        """
+        machine readable representation of the Replicate placement
+        """
+        return "Replicate()"
+
+    def __str__(self) -> str:
+        """
+        human readable representation of the Replicate placement
+        """
+        return "R"
+
+    def _replicate_tensor(
+        self,
+        tensor: torch.Tensor,
+        mesh: DeviceMesh,
+        mesh_dim: int,
+        src_data_rank: Optional[int] = 0,
+    ) -> torch.Tensor:
+        """
+        Replicate (broadcast) a torch.Tensor on a mesh dimension (use
+        the first coordinate on the mesh dimension as source of truth)
+        """
+        my_coordinate = mesh.get_coordinate()
+        if my_coordinate is None:
+            # if rank is not part of mesh, we simply return an empty tensor
+            return tensor.new_empty(0, requires_grad=tensor.requires_grad)
+
+        tensor = tensor.contiguous()
+
+        if src_data_rank is not None:
+            # perform broadcast from the src_data_rank as data source when it is not None
+            mesh_broadcast(tensor, mesh, mesh_dim=mesh_dim, group_src=src_data_rank)
+        return tensor
+
+
+@dataclass(frozen=True)
+class Partial(Placement):
+    """
+    The ``Partial(reduce_op)`` placement describes the DTensor that is pending
+    reduction on a specified ``DeviceMesh`` dimension, where each rank on the
+    DeviceMesh dimension holds the partial value of the global Tensor. User can
+    redistribute the ``Partial`` DTensor to a ``Replicate`` or ``Shard(dim)``
+    placement on the specified ``DeviceMesh`` dimension using ``redistribute``,
+    which would trigger necessary communication operations under the hood (i.e.
+    ``allreduce``, ``reduce_scatter``).
+
+    Args:
+        reduce_op (str, optional): The reduction op to be used for the partial DTensor
+            to produce Replicated/Sharded DTensor. Only element-wise reduction operations
+            are supported, including: "sum", "avg", "product", "max", "min", default: "sum".
+
+    .. note:: The ``Partial`` placement can be generated as a result of the DTensor operators,
+        and can only be used by the ``DTensor.from_local`` API.
+    """
+
+    reduce_op: str = "sum"
+
+    def _reduce_value(
+        self, tensor: torch.Tensor, mesh: DeviceMesh, mesh_dim: int
+    ) -> torch.Tensor:
+        # Partial placement contract #1:
+        # _reduce_value: reduce the value of the tensor on the mesh dimension
+        return funcol.all_reduce(
+            tensor, reduceOp=self.reduce_op, group=(mesh, mesh_dim)
+        )
+
+    def _reduce_shard_value(
+        self,
+        tensor: torch.Tensor,
+        mesh: DeviceMesh,
+        mesh_dim: int,
+        shard_spec: Placement,
+    ) -> torch.Tensor:
+        # Partial placement contract #2:
+        # _reduce_shard_value: reduce_scatter the value of the tensor over the mesh dimension
+        shard_spec = cast(Shard, shard_spec)
+        return shard_spec._reduce_shard_tensor(tensor, mesh, self.reduce_op, mesh_dim)
+
+    def _partition_value(
+        self, tensor: torch.Tensor, mesh: DeviceMesh, mesh_dim: int
+    ) -> torch.Tensor:
+        # Partial placement contract #3:
+        # _partition_value: partition the value of a replicated tensor on the mesh dimension
+
+        # _partition_value is the conjugate operation of _reduce_value
+        # - i.e. _partition_value on a sum reduce op is just a divison operation
+        # - the _reduce_value on a sum reduce op would just be a sum(allreduce) operation
+        # TODO: if the reduce_op is min/max, etc. the _partition_value should be a
+        # different operation
+        assert self.reduce_op == "sum", "only support replicate to PartialSUM for now!"
+        num_chunks = mesh.size(mesh_dim=mesh_dim)
+        return tensor / num_chunks
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Partial):
+            return False
+        return self.reduce_op == other.reduce_op
+
+    def __hash__(self) -> int:
+        return 1 + hash(self.reduce_op)
+
+    def __repr__(self) -> str:
+        """
+        machine readable representation of the Partial placement
+        """
+        return f"Partial({self.reduce_op})"
+
+    def __str__(self) -> str:
+        """
+        human readable representation of the Partial placement
+        """
+        return "P"
+
+
+# We keep the old _Partial name for a while for BC reason
+_Partial = Partial
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebe36d0eb1bb9fb80fbb79c2b266ccf54e2035a5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/distributed/utils.py
@@ -0,0 +1,377 @@
+# mypy: allow-untyped-defs
+import dataclasses
+import traceback
+from collections import OrderedDict
+from collections.abc import Container
+from typing import Any, Callable, Optional, overload, TypeVar
+
+import torch
+import torch.distributed as dist
+from torch import nn
+from torch.nn.utils.rnn import PackedSequence
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+def _pack_kwargs(*args: Any, **kwargs: Any) -> tuple[tuple[Any, ...], tuple[str, ...]]:
+    """
+    Turn argument list into separate key list and value list (unpack_kwargs does the opposite).
+
+    Inspiration: https://github.com/facebookresearch/fairscale/blob/eeb6684/fairscale/internal/containers.py#L70
+    Usage::
+
+        kwarg_keys, flat_args = pack_kwargs(1, 2, a=3, b=4)
+        assert kwarg_keys == ("a", "b")
+        assert flat_args == (1, 2, 3, 4)
+        args, kwargs = unpack_kwargs(kwarg_keys, flat_args)
+        assert args == (1, 2)
+        assert kwargs == {"a": 3, "b": 4}
+    Returns:
+        Tuple[Tuple[Any, ...], Tuple[str, ...]]: The first tuple element gives
+        gives both positional args and kwarg values, where the positional args
+        proceed kwarg values and kwarg values are ordered consistently with the
+        kwarg keys. The second tuple element gives the kwarg keys.
+        The second tuple element's length is at most the first tuple element's length.
+    """
+    kwarg_keys: list[str] = []
+    flat_args: list[Any] = list(args)
+    for k, v in kwargs.items():
+        kwarg_keys.append(k)
+        flat_args.append(v)
+
+    return tuple(flat_args), tuple(kwarg_keys)
+
+
+def _cast_forward_inputs(
+    dtype: Optional[torch.dtype],
+    *args: Any,
+    **kwargs: Any,
+) -> tuple[Any, Any]:
+    """
+    Cast floating point tensors in ``args`` and ``kwargs`` to ``input_dtype``.
+
+    This respects the existing ``requires_grad`` on the tensors.
+    """
+    if dtype is None:
+        return args, kwargs
+
+    def cast_fn(x: torch.Tensor) -> torch.Tensor:
+        if not torch.is_floating_point(x) or x.dtype == dtype:
+            return x
+        return x.to(dtype)
+
+    return (_apply_to_tensors(cast_fn, args), _apply_to_tensors(cast_fn, kwargs))
+
+
+def _unpack_kwargs(
+    flat_args: tuple[Any, ...], kwarg_keys: tuple[str, ...]
+) -> tuple[tuple[Any, ...], dict[str, Any]]:
+    """See _pack_kwargs."""
+    assert len(kwarg_keys) <= len(flat_args), (
+        f"too many keys {len(kwarg_keys)} vs. {len(flat_args)}"
+    )
+    if len(kwarg_keys) == 0:
+        return flat_args, {}
+    args = flat_args[: -len(kwarg_keys)]
+    kwargs = dict(zip(kwarg_keys, flat_args[-len(kwarg_keys) :]))
+    return args, kwargs
+
+
+S = TypeVar("S", dict, list, tuple)
+T = TypeVar("T", torch.Tensor, PackedSequence)
+
+
+@overload
+def _recursive_to(
+    inputs: S, target_device: torch.device, use_side_stream_for_tensor_copies: bool
+) -> list[S]: ...
+
+
+@overload
+def _recursive_to(
+    inputs: T, target_device: torch.device, use_side_stream_for_tensor_copies: bool
+) -> tuple[T]: ...
+
+
+def _recursive_to(inputs, target_device, use_side_stream_for_tensor_copies):
+    r"""Recursively moves input to the target_device."""
+
+    def to_map(obj):
+        if isinstance(obj, (torch.Tensor, PackedSequence)):
+            device = obj.data.device if isinstance(obj, PackedSequence) else obj.device
+            if device == target_device:
+                return (obj,)
+            if not use_side_stream_for_tensor_copies:
+                return (obj.to(target_device),)
+            else:
+                # If the custom module is not registered to torch, stream is not used for acceleration
+                device_mod = getattr(torch, device.type, None)
+                if device.type == "cpu" or device_mod is None:
+                    return (obj.to(target_device),)
+
+                from torch.nn.parallel._functions import _get_stream
+
+                # Perform CPU -> target_device copies in a background stream. This code is
+                # motivated from similar logic in torch/nn/parallel/_functions.py
+                stream = _get_stream(target_device)
+                with device_mod.stream(stream):
+                    output = obj.to(target_device)
+                # synchronize with the copy stream
+                with device_mod.device(target_device.index):
+                    current_stream = device_mod.current_stream()
+                    # Sync the current stream with the copy stream
+                    current_stream.wait_stream(stream)
+                    # Ensure tensor memory is not reused until work on
+                    # main stream is complete
+                    if isinstance(obj, PackedSequence):
+                        output.data.record_stream(current_stream)  # type: ignore[arg-type]
+                    else:
+                        assert isinstance(output, torch.Tensor)
+                        output.record_stream(current_stream)  # type: ignore[arg-type]
+                return (output,)
+
+        from torch.nn.parallel.scatter_gather import _is_namedtuple
+
+        if _is_namedtuple(obj):
+            return [type(obj)(*args) for args in zip(*map(to_map, obj))]
+        if isinstance(obj, tuple) and len(obj) > 0:
+            return list(zip(*map(to_map, obj)))
+        if isinstance(obj, list) and len(obj) > 0:
+            return [list(i) for i in zip(*map(to_map, obj))]
+        if isinstance(obj, dict) and len(obj) > 0:
+            return [type(obj)(i) for i in zip(*map(to_map, obj.items()))]
+        return [obj]
+
+    # Avoid reference cycle
+    try:
+        res = to_map(inputs)
+    finally:
+        to_map = None  # type: ignore[assignment]
+    return res
+
+
+def _p_assert(cond: Any, s: str, raise_assertion_error: bool = True) -> None:
+    """Alternate to ``assert`` when in the backward context to print the error message ``s`` since otherwise, it is swallowed."""
+    if not cond:
+        print(s)
+        traceback.print_stack()
+        if raise_assertion_error:
+            raise AssertionError(s)
+
+
+def _alloc_storage(tensor: torch.Tensor, size: torch.Size) -> None:
+    """
+    Allocate storage for ``tensor`` with the given size.
+
+    Returns:
+        bool: ``True`` if this method allocated storage and ``False`` if the
+        storage was already allocated.
+    """
+    with torch.no_grad():
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            already_allocated = tensor._typed_storage()._size() == size.numel()
+            if not already_allocated:
+                tensor_storage_size = tensor._typed_storage()._size()
+                _p_assert(
+                    tensor_storage_size == 0,
+                    "Tensor storage should have been resized to be 0 but got PLACEHOLDEr",
+                )
+                tensor._typed_storage()._resize_(size.numel())
+
+
+def _free_storage(tensor: torch.Tensor):
+    """
+    Frees the underlying storage of ``tensor``.
+
+    Returns:
+        bool: ``True`` if the method freed the storage and ``False`` if the
+        storage was already freed.
+    """
+    with torch.no_grad():
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            already_freed = tensor._typed_storage()._size() == 0
+            if not already_freed:
+                _p_assert(
+                    tensor.storage_offset() == 0,
+                    "Freeing a tensor's storage is unsafe when it is not the sole occupant\n"
+                    f"storage offset: {tensor.storage_offset()}\n"
+                    f"storage size: {tensor._typed_storage()._size()}\n"
+                    f"tensor shape: {tensor.shape}",
+                )
+                tensor._typed_storage()._resize_(0)
+
+
+Q = TypeVar("Q")
+R = TypeVar("R", dict, list, tuple, set, OrderedDict, PackedSequence, Any)
+
+
+@overload
+def _apply_to_tensors(
+    fn: Callable[[torch.Tensor], Q], container: torch.Tensor
+) -> Q: ...
+
+
+@overload
+def _apply_to_tensors(fn: Callable[[torch.Tensor], Any], container: R) -> R: ...
+
+
+def _apply_to_tensors(fn, container):
+    """Recursively apply to all tensor in different kinds of container types."""
+
+    def apply(x):
+        from torch.nn.parallel.scatter_gather import _is_namedtuple
+
+        if isinstance(x, torch.Tensor):
+            return fn(x)
+        elif hasattr(x, "__dataclass_fields__"):
+            dc = dataclasses.replace(x)
+            changes = {
+                f.name: apply(getattr(dc, f.name)) for f in dataclasses.fields(dc)
+            }
+            return dataclasses.replace(dc, **changes)
+        elif isinstance(x, OrderedDict):
+            od = x.__class__()
+            for key, value in x.items():
+                od[key] = apply(value)
+            return od
+        elif isinstance(x, PackedSequence):
+            apply(x.data)
+            return x
+        elif isinstance(x, dict):
+            return {key: apply(value) for key, value in x.items()}
+        elif _is_namedtuple(x):
+            res = (apply(el) for el in x)
+            return type(x)(*res)
+        elif isinstance(x, (list, tuple, set)):
+            return type(x)(apply(el) for el in x)
+        else:
+            return x
+
+    return apply(container)
+
+
+def _to_kwargs(
+    inputs: tuple[Any, ...],
+    kwargs: Optional[dict[str, Any]],
+    target_device: torch.device,
+    use_side_stream_for_tensor_copies: bool,
+) -> tuple[tuple[Any, ...], tuple[dict[str, Any], ...]]:
+    moved_inputs = (
+        _recursive_to(inputs, target_device, use_side_stream_for_tensor_copies)
+        if inputs
+        else []
+    )
+    moved_kwargs = (
+        _recursive_to(kwargs, target_device, use_side_stream_for_tensor_copies)
+        if kwargs
+        else []
+    )
+    if len(moved_inputs) < len(moved_kwargs):
+        moved_inputs.extend([() for _ in range(len(moved_kwargs) - len(inputs))])
+    elif len(moved_kwargs) < len(moved_inputs):
+        moved_kwargs.extend([{} for _ in range(len(moved_inputs) - len(moved_kwargs))])
+    return tuple(moved_inputs), tuple(moved_kwargs)
+
+
+def _verify_param_shape_across_processes(
+    process_group: dist.ProcessGroup,
+    tensors: list[torch.Tensor],
+    logger: Optional["dist.Logger"] = None,
+):
+    return dist._verify_params_across_processes(process_group, tensors, logger)
+
+
+def _sync_module_states(
+    module: nn.Module,
+    process_group: dist.ProcessGroup,
+    broadcast_bucket_size: int,
+    src: int,
+    params_and_buffers_to_ignore: Container[str],
+    broadcast_buffers: bool = True,
+) -> None:
+    """
+    Sync ``module``'s parameters and buffers state.
+
+    Syncs ``module``'s parameters and buffers state so that all ranks contain
+    the same module state across all ranks. Note that this API assumes that all
+    parameter shapes are consistent before running the synchronization. This can
+    be checked with ``_verify_param_shape_across_processes``.
+    """
+    module_states: list[torch.Tensor] = []
+    for name, param in module.named_parameters():
+        if name not in params_and_buffers_to_ignore:
+            module_states.append(param.detach())
+
+    if broadcast_buffers:
+        for name, buffer in module.named_buffers():
+            if name not in params_and_buffers_to_ignore:
+                module_states.append(buffer.detach())
+
+    _sync_params_and_buffers(process_group, module_states, broadcast_bucket_size, src)
+
+
+def _sync_params_and_buffers(
+    process_group: dist.ProcessGroup,
+    module_states: list[torch.Tensor],
+    broadcast_bucket_size: int,
+    src: int,
+) -> None:
+    """Synchronize ``module_states`` (list of tensors) across all processes by broadcasting them from rank 0."""
+    if len(module_states) > 0:
+        dist._broadcast_coalesced(
+            process_group, module_states, broadcast_bucket_size, src
+        )
+
+
+def _replace_by_prefix(
+    state_dict: dict[str, Any],
+    old_prefix: str,
+    new_prefix: str,
+) -> None:
+    """
+    Replace all keys that match a given old_prefix with a new_prefix (in-place).
+
+    Usage::
+
+        state_dict = {"layer.xyz": torch.tensor(1)}
+        replace_by_prefix_(state_dict, "layer.", "module.layer.")
+        assert state_dict == {"module.layer.xyz": torch.tensor(1)}
+    """
+    if old_prefix == new_prefix:
+        raise ValueError("old_prefix and new_prefix must be distinct")
+    for key in list(state_dict.keys()):
+        if not key.startswith(old_prefix):
+            continue
+        new_key = new_prefix + key[len(old_prefix) :]
+        state_dict[new_key] = state_dict[key]
+        del state_dict[key]
+
+
+def _data_ptr_allocated(tensor: torch.Tensor) -> bool:
+    return tensor.untyped_storage().data_ptr() > 0
+
+
+def _get_root_modules(modules: list[nn.Module]) -> list[nn.Module]:
+    """
+    Returns the modules in ``modules`` that are root modules (i.e.
+    parent-less) with respect to the set ``modules``. In other words, these
+    are the modules in ``modules`` that are the not child of any other
+    module in ``modules``.
+    """
+    root_modules: list[nn.Module] = []
+    module_to_modules: dict[nn.Module, set[nn.Module]] = {
+        module: set(module.modules()) for module in modules
+    }
+    for candidate_module in modules:
+        is_root_module = True
+        for module, _modules in module_to_modules.items():
+            is_child_module = (
+                candidate_module is not module and candidate_module in _modules
+            )
+            if is_child_module:
+                is_root_module = False
+                break
+        if is_root_module:
+            root_modules.append(candidate_module)
+    return root_modules
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fft/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fft/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ad1748bab1a82e67a1d7317231ccc896ace324d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fft/__init__.py
@@ -0,0 +1,1482 @@
+import torch
+from torch._C import _add_docstr, _fft  # type: ignore[attr-defined]
+from torch._torch_docs import common_args, factory_common_args
+
+
+__all__ = [
+    "fft",
+    "ifft",
+    "fft2",
+    "ifft2",
+    "fftn",
+    "ifftn",
+    "rfft",
+    "irfft",
+    "rfft2",
+    "irfft2",
+    "rfftn",
+    "irfftn",
+    "hfft",
+    "ihfft",
+    "fftfreq",
+    "rfftfreq",
+    "fftshift",
+    "ifftshift",
+    "Tensor",
+]
+
+Tensor = torch.Tensor
+
+# Note: This not only adds the doc strings for the spectral ops, but
+# connects the torch.fft Python namespace to the torch._C._fft builtins.
+
+fft = _add_docstr(
+    _fft.fft_fft,
+    r"""
+fft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the one dimensional discrete Fourier transform of :attr:`input`.
+
+Note:
+    The Fourier domain representation of any real signal satisfies the
+    Hermitian property: `X[i] = conj(X[-i])`. This function always returns both
+    the positive and negative frequency terms even though, for real inputs, the
+    negative frequencies are redundant. :func:`~torch.fft.rfft` returns the
+    more compact one-sided representation where only the positive frequencies
+    are returned.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+
+Args:
+    input (Tensor): the input tensor
+    n (int, optional): Signal length. If given, the input will either be zero-padded
+        or trimmed to this length before computing the FFT.
+    dim (int, optional): The dimension along which to take the one dimensional FFT.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.fft`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal)
+
+        Calling the backward transform (:func:`~torch.fft.ifft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ifft`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.arange(4)
+    >>> t
+    tensor([0, 1, 2, 3])
+    >>> torch.fft.fft(t)
+    tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j])
+
+    >>> t = torch.tensor([0.+1.j, 2.+3.j, 4.+5.j, 6.+7.j])
+    >>> torch.fft.fft(t)
+    tensor([12.+16.j, -8.+0.j, -4.-4.j,  0.-8.j])
+""".format(
+        **common_args
+    ),
+)
+
+ifft = _add_docstr(
+    _fft.fft_ifft,
+    r"""
+ifft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the one dimensional inverse discrete Fourier transform of :attr:`input`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+
+Args:
+    input (Tensor): the input tensor
+    n (int, optional): Signal length. If given, the input will either be zero-padded
+        or trimmed to this length before computing the IFFT.
+    dim (int, optional): The dimension along which to take the one dimensional IFFT.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ifft`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal)
+
+        Calling the forward transform (:func:`~torch.fft.fft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ifft`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j])
+    >>> torch.fft.ifft(t)
+    tensor([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j])
+""".format(
+        **common_args
+    ),
+)
+
+fft2 = _add_docstr(
+    _fft.fft_fft2,
+    r"""
+fft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the 2 dimensional discrete Fourier transform of :attr:`input`.
+Equivalent to :func:`~torch.fft.fftn` but FFTs only the last two dimensions by default.
+
+Note:
+    The Fourier domain representation of any real signal satisfies the
+    Hermitian property: ``X[i, j] = conj(X[-i, -j])``. This
+    function always returns all positive and negative frequency terms even
+    though, for real inputs, half of these values are redundant.
+    :func:`~torch.fft.rfft2` returns the more compact one-sided representation
+    where only the positive frequencies of the last dimension are returned.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.fft2`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.ifft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n``
+        between the two transforms. This is required to make
+        :func:`~torch.fft.ifft2` the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> x = torch.rand(10, 10, dtype=torch.complex64)
+    >>> fft2 = torch.fft.fft2(x)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.fft2`
+    here is equivalent to two one-dimensional :func:`~torch.fft.fft` calls:
+
+    >>> two_ffts = torch.fft.fft(torch.fft.fft(x, dim=0), dim=1)
+    >>> torch.testing.assert_close(fft2, two_ffts, check_stride=False)
+
+""".format(
+        **common_args
+    ),
+)
+
+ifft2 = _add_docstr(
+    _fft.fft_ifft2,
+    r"""
+ifft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the 2 dimensional inverse discrete Fourier transform of :attr:`input`.
+Equivalent to :func:`~torch.fft.ifftn` but IFFTs only the last two dimensions by default.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the IFFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ifft2`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.fft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ifft2`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> x = torch.rand(10, 10, dtype=torch.complex64)
+    >>> ifft2 = torch.fft.ifft2(x)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.ifft2`
+    here is equivalent to two one-dimensional :func:`~torch.fft.ifft` calls:
+
+    >>> two_iffts = torch.fft.ifft(torch.fft.ifft(x, dim=0), dim=1)
+    >>> torch.testing.assert_close(ifft2, two_iffts, check_stride=False)
+
+""".format(
+        **common_args
+    ),
+)
+
+fftn = _add_docstr(
+    _fft.fft_fftn,
+    r"""
+fftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the N dimensional discrete Fourier transform of :attr:`input`.
+
+Note:
+    The Fourier domain representation of any real signal satisfies the
+    Hermitian property: ``X[i_1, ..., i_n] = conj(X[-i_1, ..., -i_n])``. This
+    function always returns all positive and negative frequency terms even
+    though, for real inputs, half of these values are redundant.
+    :func:`~torch.fft.rfftn` returns the more compact one-sided representation
+    where only the positive frequencies of the last dimension are returned.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.fftn`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.ifftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n``
+        between the two transforms. This is required to make
+        :func:`~torch.fft.ifftn` the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> x = torch.rand(10, 10, dtype=torch.complex64)
+    >>> fftn = torch.fft.fftn(x)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.fftn`
+    here is equivalent to two one-dimensional :func:`~torch.fft.fft` calls:
+
+    >>> two_ffts = torch.fft.fft(torch.fft.fft(x, dim=0), dim=1)
+    >>> torch.testing.assert_close(fftn, two_ffts, check_stride=False)
+
+""".format(
+        **common_args
+    ),
+)
+
+ifftn = _add_docstr(
+    _fft.fft_ifftn,
+    r"""
+ifftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the N dimensional inverse discrete Fourier transform of :attr:`input`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the IFFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ifftn`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.fftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ifftn`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> x = torch.rand(10, 10, dtype=torch.complex64)
+    >>> ifftn = torch.fft.ifftn(x)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.ifftn`
+    here is equivalent to two one-dimensional :func:`~torch.fft.ifft` calls:
+
+    >>> two_iffts = torch.fft.ifft(torch.fft.ifft(x, dim=0), dim=1)
+    >>> torch.testing.assert_close(ifftn, two_iffts, check_stride=False)
+
+""".format(
+        **common_args
+    ),
+)
+
+rfft = _add_docstr(
+    _fft.fft_rfft,
+    r"""
+rfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the one dimensional Fourier transform of real-valued :attr:`input`.
+
+The FFT of a real signal is Hermitian-symmetric, ``X[i] = conj(X[-i])`` so
+the output contains only the positive frequencies below the Nyquist frequency.
+To compute the full output, use :func:`~torch.fft.fft`
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+
+Args:
+    input (Tensor): the real input tensor
+    n (int, optional): Signal length. If given, the input will either be zero-padded
+        or trimmed to this length before computing the real FFT.
+    dim (int, optional): The dimension along which to take the one dimensional real FFT.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.rfft`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal)
+
+        Calling the backward transform (:func:`~torch.fft.irfft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfft`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.arange(4)
+    >>> t
+    tensor([0, 1, 2, 3])
+    >>> torch.fft.rfft(t)
+    tensor([ 6.+0.j, -2.+2.j, -2.+0.j])
+
+    Compare against the full output from :func:`~torch.fft.fft`:
+
+    >>> torch.fft.fft(t)
+    tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j])
+
+    Notice that the symmetric element ``T[-1] == T[1].conj()`` is omitted.
+    At the Nyquist frequency ``T[-2] == T[2]`` is it's own symmetric pair,
+    and therefore must always be real-valued.
+""".format(
+        **common_args
+    ),
+)
+
+irfft = _add_docstr(
+    _fft.fft_irfft,
+    r"""
+irfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the inverse of :func:`~torch.fft.rfft`.
+
+:attr:`input` is interpreted as a one-sided Hermitian signal in the Fourier
+domain, as produced by :func:`~torch.fft.rfft`. By the Hermitian property, the
+output will be real-valued.
+
+Note:
+    Some input frequencies must be real-valued to satisfy the Hermitian
+    property. In these cases the imaginary component will be ignored.
+    For example, any imaginary component in the zero-frequency term cannot
+    be represented in a real output and so will always be ignored.
+
+Note:
+    The correct interpretation of the Hermitian input depends on the length of
+    the original data, as given by :attr:`n`. This is because each input shape
+    could correspond to either an odd or even length signal. By default, the
+    signal is assumed to be even length and odd signals will not round-trip
+    properly. So, it is recommended to always pass the signal length :attr:`n`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+    With default arguments, size of the transformed dimension should be (2^n + 1) as argument
+    `n` defaults to even output size = 2 * (transformed_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor representing a half-Hermitian signal
+    n (int, optional): Output signal length. This determines the length of the
+        output signal. If given, the input will either be zero-padded or trimmed to this
+        length before computing the real IFFT.
+        Defaults to even output: ``n=2*(input.size(dim) - 1)``.
+    dim (int, optional): The dimension along which to take the one dimensional real IFFT.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.irfft`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real IFFT orthonormal)
+
+        Calling the forward transform (:func:`~torch.fft.rfft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfft`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.linspace(0, 1, 5)
+    >>> t
+    tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])
+    >>> T = torch.fft.rfft(t)
+    >>> T
+    tensor([ 2.5000+0.0000j, -0.6250+0.8602j, -0.6250+0.2031j])
+
+    Without specifying the output length to :func:`~torch.fft.irfft`, the output
+    will not round-trip properly because the input is odd-length:
+
+    >>> torch.fft.irfft(T)
+    tensor([0.1562, 0.3511, 0.7812, 1.2114])
+
+    So, it is recommended to always pass the signal length :attr:`n`:
+
+    >>> roundtrip = torch.fft.irfft(T, t.numel())
+    >>> torch.testing.assert_close(roundtrip, t, check_stride=False)
+
+""".format(
+        **common_args
+    ),
+)
+
+rfft2 = _add_docstr(
+    _fft.fft_rfft2,
+    r"""
+rfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the 2-dimensional discrete Fourier transform of real :attr:`input`.
+Equivalent to :func:`~torch.fft.rfftn` but FFTs only the last two dimensions by default.
+
+The FFT of a real signal is Hermitian-symmetric, ``X[i, j] = conj(X[-i, -j])``,
+so the full :func:`~torch.fft.fft2` output contains redundant information.
+:func:`~torch.fft.rfft2` instead omits the negative frequencies in the last
+dimension.
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the real FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.rfft2`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.irfft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfft2`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.rand(10, 10)
+    >>> rfft2 = torch.fft.rfft2(t)
+    >>> rfft2.size()
+    torch.Size([10, 6])
+
+    Compared against the full output from :func:`~torch.fft.fft2`, we have all
+    elements up to the Nyquist frequency.
+
+    >>> fft2 = torch.fft.fft2(t)
+    >>> torch.testing.assert_close(fft2[..., :6], rfft2, check_stride=False)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.rfft2`
+    here is equivalent to a combination of :func:`~torch.fft.fft` and
+    :func:`~torch.fft.rfft`:
+
+    >>> two_ffts = torch.fft.fft(torch.fft.rfft(t, dim=1), dim=0)
+    >>> torch.testing.assert_close(rfft2, two_ffts, check_stride=False)
+
+""".format(
+        **common_args
+    ),
+)
+
+irfft2 = _add_docstr(
+    _fft.fft_irfft2,
+    r"""
+irfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the inverse of :func:`~torch.fft.rfft2`.
+Equivalent to :func:`~torch.fft.irfftn` but IFFTs only the last two dimensions by default.
+
+:attr:`input` is interpreted as a one-sided Hermitian signal in the Fourier
+domain, as produced by :func:`~torch.fft.rfft2`. By the Hermitian property, the
+output will be real-valued.
+
+Note:
+    Some input frequencies must be real-valued to satisfy the Hermitian
+    property. In these cases the imaginary component will be ignored.
+    For example, any imaginary component in the zero-frequency term cannot
+    be represented in a real output and so will always be ignored.
+
+Note:
+    The correct interpretation of the Hermitian input depends on the length of
+    the original data, as given by :attr:`s`. This is because each input shape
+    could correspond to either an odd or even length signal. By default, the
+    signal is assumed to be even length and odd signals will not round-trip
+    properly. So, it is recommended to always pass the signal shape :attr:`s`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+    With default arguments, the size of last dimension should be (2^n + 1) as argument
+    `s` defaults to even output size = 2 * (last_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the real FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Defaults to even output in the last dimension:
+        ``s[-1] = 2*(input.size(dim[-1]) - 1)``.
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        The last dimension must be the half-Hermitian compressed dimension.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.irfft2`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.rfft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfft2`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.rand(10, 9)
+    >>> T = torch.fft.rfft2(t)
+
+    Without specifying the output length to :func:`~torch.fft.irfft2`, the output
+    will not round-trip properly because the input is odd-length in the last
+    dimension:
+
+    >>> torch.fft.irfft2(T).size()
+    torch.Size([10, 8])
+
+    So, it is recommended to always pass the signal shape :attr:`s`.
+
+    >>> roundtrip = torch.fft.irfft2(T, t.size())
+    >>> roundtrip.size()
+    torch.Size([10, 9])
+    >>> torch.testing.assert_close(roundtrip, t, check_stride=False)
+
+""".format(
+        **common_args
+    ),
+)
+
+rfftn = _add_docstr(
+    _fft.fft_rfftn,
+    r"""
+rfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the N-dimensional discrete Fourier transform of real :attr:`input`.
+
+The FFT of a real signal is Hermitian-symmetric,
+``X[i_1, ..., i_n] = conj(X[-i_1, ..., -i_n])`` so the full
+:func:`~torch.fft.fftn` output contains redundant information.
+:func:`~torch.fft.rfftn` instead omits the negative frequencies in the
+last dimension.
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the real FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.rfftn`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.irfftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfftn`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.rand(10, 10)
+    >>> rfftn = torch.fft.rfftn(t)
+    >>> rfftn.size()
+    torch.Size([10, 6])
+
+    Compared against the full output from :func:`~torch.fft.fftn`, we have all
+    elements up to the Nyquist frequency.
+
+    >>> fftn = torch.fft.fftn(t)
+    >>> torch.testing.assert_close(fftn[..., :6], rfftn, check_stride=False)
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.rfftn`
+    here is equivalent to a combination of :func:`~torch.fft.fft` and
+    :func:`~torch.fft.rfft`:
+
+    >>> two_ffts = torch.fft.fft(torch.fft.rfft(t, dim=1), dim=0)
+    >>> torch.testing.assert_close(rfftn, two_ffts, check_stride=False)
+
+""".format(
+        **common_args
+    ),
+)
+
+irfftn = _add_docstr(
+    _fft.fft_irfftn,
+    r"""
+irfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the inverse of :func:`~torch.fft.rfftn`.
+
+:attr:`input` is interpreted as a one-sided Hermitian signal in the Fourier
+domain, as produced by :func:`~torch.fft.rfftn`. By the Hermitian property, the
+output will be real-valued.
+
+Note:
+    Some input frequencies must be real-valued to satisfy the Hermitian
+    property. In these cases the imaginary component will be ignored.
+    For example, any imaginary component in the zero-frequency term cannot
+    be represented in a real output and so will always be ignored.
+
+Note:
+    The correct interpretation of the Hermitian input depends on the length of
+    the original data, as given by :attr:`s`. This is because each input shape
+    could correspond to either an odd or even length signal. By default, the
+    signal is assumed to be even length and odd signals will not round-trip
+    properly. So, it is recommended to always pass the signal shape :attr:`s`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+    With default arguments, the size of last dimension should be (2^n + 1) as argument
+    `s` defaults to even output size = 2 * (last_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the real FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Defaults to even output in the last dimension:
+        ``s[-1] = 2*(input.size(dim[-1]) - 1)``.
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        The last dimension must be the half-Hermitian compressed dimension.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.irfftn`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.rfftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.irfftn`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.rand(10, 9)
+    >>> T = torch.fft.rfftn(t)
+
+    Without specifying the output length to :func:`~torch.fft.irfft`, the output
+    will not round-trip properly because the input is odd-length in the last
+    dimension:
+
+    >>> torch.fft.irfftn(T).size()
+    torch.Size([10, 8])
+
+    So, it is recommended to always pass the signal shape :attr:`s`.
+
+    >>> roundtrip = torch.fft.irfftn(T, t.size())
+    >>> roundtrip.size()
+    torch.Size([10, 9])
+    >>> torch.testing.assert_close(roundtrip, t, check_stride=False)
+
+""".format(
+        **common_args
+    ),
+)
+
+hfft = _add_docstr(
+    _fft.fft_hfft,
+    r"""
+hfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the one dimensional discrete Fourier transform of a Hermitian
+symmetric :attr:`input` signal.
+
+Note:
+
+    :func:`~torch.fft.hfft`/:func:`~torch.fft.ihfft` are analogous to
+    :func:`~torch.fft.rfft`/:func:`~torch.fft.irfft`. The real FFT expects
+    a real signal in the time-domain and gives a Hermitian symmetry in the
+    frequency-domain. The Hermitian FFT is the opposite; Hermitian symmetric in
+    the time-domain and real-valued in the frequency-domain. For this reason,
+    special care needs to be taken with the length argument :attr:`n`, in the
+    same way as with :func:`~torch.fft.irfft`.
+
+Note:
+    Because the signal is Hermitian in the time-domain, the result will be
+    real in the frequency domain. Note that some input frequencies must be
+    real-valued to satisfy the Hermitian property. In these cases the imaginary
+    component will be ignored. For example, any imaginary component in
+    ``input[0]`` would result in one or more complex frequency terms which
+    cannot be represented in a real output and so will always be ignored.
+
+Note:
+    The correct interpretation of the Hermitian input depends on the length of
+    the original data, as given by :attr:`n`. This is because each input shape
+    could correspond to either an odd or even length signal. By default, the
+    signal is assumed to be even length and odd signals will not round-trip
+    properly. So, it is recommended to always pass the signal length :attr:`n`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+    With default arguments, size of the transformed dimension should be (2^n + 1) as argument
+    `n` defaults to even output size = 2 * (transformed_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor representing a half-Hermitian signal
+    n (int, optional): Output signal length. This determines the length of the
+        real output. If given, the input will either be zero-padded or trimmed to this
+        length before computing the Hermitian FFT.
+        Defaults to even output: ``n=2*(input.size(dim) - 1)``.
+    dim (int, optional): The dimension along which to take the one dimensional Hermitian FFT.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.hfft`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian FFT orthonormal)
+
+        Calling the backward transform (:func:`~torch.fft.ihfft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfft`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    Taking a real-valued frequency signal and bringing it into the time domain
+    gives Hermitian symmetric output:
+
+    >>> t = torch.linspace(0, 1, 5)
+    >>> t
+    tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])
+    >>> T = torch.fft.ifft(t)
+    >>> T
+    tensor([ 0.5000-0.0000j, -0.1250-0.1720j, -0.1250-0.0406j, -0.1250+0.0406j,
+            -0.1250+0.1720j])
+
+    Note that ``T[1] == T[-1].conj()`` and ``T[2] == T[-2].conj()`` is
+    redundant. We can thus compute the forward transform without considering
+    negative frequencies:
+
+    >>> torch.fft.hfft(T[:3], n=5)
+    tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])
+
+    Like with :func:`~torch.fft.irfft`, the output length must be given in order
+    to recover an even length output:
+
+    >>> torch.fft.hfft(T[:3])
+    tensor([0.1250, 0.2809, 0.6250, 0.9691])
+""".format(
+        **common_args
+    ),
+)
+
+ihfft = _add_docstr(
+    _fft.fft_ihfft,
+    r"""
+ihfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor
+
+Computes the inverse of :func:`~torch.fft.hfft`.
+
+:attr:`input` must be a real-valued signal, interpreted in the Fourier domain.
+The IFFT of a real signal is Hermitian-symmetric, ``X[i] = conj(X[-i])``.
+:func:`~torch.fft.ihfft` represents this in the one-sided form where only the
+positive frequencies below the Nyquist frequency are included. To compute the
+full output, use :func:`~torch.fft.ifft`.
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimension.
+
+Args:
+    input (Tensor): the real input tensor
+    n (int, optional): Signal length. If given, the input will either be zero-padded
+        or trimmed to this length before computing the Hermitian IFFT.
+    dim (int, optional): The dimension along which to take the one dimensional Hermitian IFFT.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ihfft`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal)
+
+        Calling the forward transform (:func:`~torch.fft.hfft`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfft`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> t = torch.arange(5)
+    >>> t
+    tensor([0, 1, 2, 3, 4])
+    >>> torch.fft.ihfft(t)
+    tensor([ 2.0000-0.0000j, -0.5000-0.6882j, -0.5000-0.1625j])
+
+    Compare against the full output from :func:`~torch.fft.ifft`:
+
+    >>> torch.fft.ifft(t)
+    tensor([ 2.0000-0.0000j, -0.5000-0.6882j, -0.5000-0.1625j, -0.5000+0.1625j,
+            -0.5000+0.6882j])
+""".format(
+        **common_args
+    ),
+)
+
+hfft2 = _add_docstr(
+    _fft.fft_hfft2,
+    r"""
+hfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the 2-dimensional discrete Fourier transform of a Hermitian symmetric
+:attr:`input` signal. Equivalent to :func:`~torch.fft.hfftn` but only
+transforms the last two dimensions by default.
+
+:attr:`input` is interpreted as a one-sided Hermitian signal in the time
+domain. By the Hermitian property, the Fourier transform will be real-valued.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+    With default arguments, the size of last dimension should be (2^n + 1) as argument
+    `s` defaults to even output size = 2 * (last_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the Hermitian FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Defaults to even output in the last dimension:
+        ``s[-1] = 2*(input.size(dim[-1]) - 1)``.
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        The last dimension must be the half-Hermitian compressed dimension.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.hfft2`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.ihfft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfft2`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    Starting from a real frequency-space signal, we can generate a
+    Hermitian-symmetric time-domain signal:
+    >>> T = torch.rand(10, 9)
+    >>> t = torch.fft.ihfft2(T)
+
+    Without specifying the output length to :func:`~torch.fft.hfftn`, the
+    output will not round-trip properly because the input is odd-length in the
+    last dimension:
+
+    >>> torch.fft.hfft2(t).size()
+    torch.Size([10, 10])
+
+    So, it is recommended to always pass the signal shape :attr:`s`.
+
+    >>> roundtrip = torch.fft.hfft2(t, T.size())
+    >>> roundtrip.size()
+    torch.Size([10, 9])
+    >>> torch.allclose(roundtrip, T)
+    True
+
+""".format(
+        **common_args
+    ),
+)
+
+ihfft2 = _add_docstr(
+    _fft.fft_ihfft2,
+    r"""
+ihfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor
+
+Computes the 2-dimensional inverse discrete Fourier transform of real
+:attr:`input`. Equivalent to :func:`~torch.fft.ihfftn` but transforms only the
+two last dimensions by default.
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the Hermitian IFFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: last two dimensions.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ihfft2`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.hfft2`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfft2`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> T = torch.rand(10, 10)
+    >>> t = torch.fft.ihfft2(t)
+    >>> t.size()
+    torch.Size([10, 6])
+
+    Compared against the full output from :func:`~torch.fft.ifft2`, the
+    Hermitian time-space signal takes up only half the space.
+
+    >>> fftn = torch.fft.ifft2(t)
+    >>> torch.allclose(fftn[..., :6], rfftn)
+    True
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.ihfft2`
+    here is equivalent to a combination of :func:`~torch.fft.ifft` and
+    :func:`~torch.fft.ihfft`:
+
+    >>> two_ffts = torch.fft.ifft(torch.fft.ihfft(t, dim=1), dim=0)
+    >>> torch.allclose(t, two_ffts)
+    True
+
+""".format(
+        **common_args
+    ),
+)
+
+hfftn = _add_docstr(
+    _fft.fft_hfftn,
+    r"""
+hfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the n-dimensional discrete Fourier transform of a Hermitian symmetric
+:attr:`input` signal.
+
+:attr:`input` is interpreted as a one-sided Hermitian signal in the time
+domain. By the Hermitian property, the Fourier transform will be real-valued.
+
+Note:
+    :func:`~torch.fft.hfftn`/:func:`~torch.fft.ihfftn` are analogous to
+    :func:`~torch.fft.rfftn`/:func:`~torch.fft.irfftn`. The real FFT expects
+    a real signal in the time-domain and gives Hermitian symmetry in the
+    frequency-domain. The Hermitian FFT is the opposite; Hermitian symmetric in
+    the time-domain and real-valued in the frequency-domain. For this reason,
+    special care needs to be taken with the shape argument :attr:`s`, in the
+    same way as with :func:`~torch.fft.irfftn`.
+
+Note:
+    Some input frequencies must be real-valued to satisfy the Hermitian
+    property. In these cases the imaginary component will be ignored.
+    For example, any imaginary component in the zero-frequency term cannot
+    be represented in a real output and so will always be ignored.
+
+Note:
+    The correct interpretation of the Hermitian input depends on the length of
+    the original data, as given by :attr:`s`. This is because each input shape
+    could correspond to either an odd or even length signal. By default, the
+    signal is assumed to be even length and odd signals will not round-trip
+    properly. It is recommended to always pass the signal shape :attr:`s`.
+
+Note:
+    Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+    With default arguments, the size of last dimension should be (2^n + 1) as argument
+    `s` defaults to even output size = 2 * (last_dim_size - 1)
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the real FFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Defaults to even output in the last dimension:
+        ``s[-1] = 2*(input.size(dim[-1]) - 1)``.
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        The last dimension must be the half-Hermitian compressed dimension.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the forward transform
+        (:func:`~torch.fft.hfftn`), these correspond to:
+
+        * ``"forward"`` - normalize by ``1/n``
+        * ``"backward"`` - no normalization
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian FFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical FFT size.
+        Calling the backward transform (:func:`~torch.fft.ihfftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfftn`
+        the exact inverse.
+
+        Default is ``"backward"`` (no normalization).
+
+Keyword args:
+    {out}
+
+Example:
+
+    Starting from a real frequency-space signal, we can generate a
+    Hermitian-symmetric time-domain signal:
+    >>> T = torch.rand(10, 9)
+    >>> t = torch.fft.ihfftn(T)
+
+    Without specifying the output length to :func:`~torch.fft.hfftn`, the
+    output will not round-trip properly because the input is odd-length in the
+    last dimension:
+
+    >>> torch.fft.hfftn(t).size()
+    torch.Size([10, 10])
+
+    So, it is recommended to always pass the signal shape :attr:`s`.
+
+    >>> roundtrip = torch.fft.hfftn(t, T.size())
+    >>> roundtrip.size()
+    torch.Size([10, 9])
+    >>> torch.allclose(roundtrip, T)
+    True
+
+""".format(
+        **common_args
+    ),
+)
+
+ihfftn = _add_docstr(
+    _fft.fft_ihfftn,
+    r"""
+ihfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
+
+Computes the N-dimensional inverse discrete Fourier transform of real :attr:`input`.
+
+:attr:`input` must be a real-valued signal, interpreted in the Fourier domain.
+The n-dimensional IFFT of a real signal is Hermitian-symmetric,
+``X[i, j, ...] = conj(X[-i, -j, ...])``. :func:`~torch.fft.ihfftn` represents
+this in the one-sided form where only the positive frequencies below the
+Nyquist frequency are included in the last signal dimension. To compute the
+full output, use :func:`~torch.fft.ifftn`.
+
+Note:
+    Supports torch.half on CUDA with GPU Architecture SM53 or greater.
+    However it only supports powers of 2 signal length in every transformed dimensions.
+
+Args:
+    input (Tensor): the input tensor
+    s (Tuple[int], optional): Signal size in the transformed dimensions.
+        If given, each dimension ``dim[i]`` will either be zero-padded or
+        trimmed to the length ``s[i]`` before computing the Hermitian IFFT.
+        If a length ``-1`` is specified, no padding is done in that dimension.
+        Default: ``s = [input.size(d) for d in dim]``
+    dim (Tuple[int], optional): Dimensions to be transformed.
+        Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given.
+    norm (str, optional): Normalization mode. For the backward transform
+        (:func:`~torch.fft.ihfftn`), these correspond to:
+
+        * ``"forward"`` - no normalization
+        * ``"backward"`` - normalize by ``1/n``
+        * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian IFFT orthonormal)
+
+        Where ``n = prod(s)`` is the logical IFFT size.
+        Calling the forward transform (:func:`~torch.fft.hfftn`) with the same
+        normalization mode will apply an overall normalization of ``1/n`` between
+        the two transforms. This is required to make :func:`~torch.fft.ihfftn`
+        the exact inverse.
+
+        Default is ``"backward"`` (normalize by ``1/n``).
+
+Keyword args:
+    {out}
+
+Example:
+
+    >>> T = torch.rand(10, 10)
+    >>> ihfftn = torch.fft.ihfftn(T)
+    >>> ihfftn.size()
+    torch.Size([10, 6])
+
+    Compared against the full output from :func:`~torch.fft.ifftn`, we have all
+    elements up to the Nyquist frequency.
+
+    >>> ifftn = torch.fft.ifftn(t)
+    >>> torch.allclose(ifftn[..., :6], ihfftn)
+    True
+
+    The discrete Fourier transform is separable, so :func:`~torch.fft.ihfftn`
+    here is equivalent to a combination of :func:`~torch.fft.ihfft` and
+    :func:`~torch.fft.ifft`:
+
+    >>> two_iffts = torch.fft.ifft(torch.fft.ihfft(t, dim=1), dim=0)
+    >>> torch.allclose(ihfftn, two_iffts)
+    True
+
+""".format(
+        **common_args
+    ),
+)
+
+fftfreq = _add_docstr(
+    _fft.fft_fftfreq,
+    r"""
+fftfreq(n, d=1.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
+
+Computes the discrete Fourier Transform sample frequencies for a signal of size :attr:`n`.
+
+Note:
+    By convention, :func:`~torch.fft.fft` returns positive frequency terms
+    first, followed by the negative frequencies in reverse order, so that
+    ``f[-i]`` for all :math:`0 < i \leq n/2`` in Python gives the negative
+    frequency terms. For an FFT of length :attr:`n` and with inputs spaced in
+    length unit :attr:`d`, the frequencies are::
+
+        f = [0, 1, ..., (n - 1) // 2, -(n // 2), ..., -1] / (d * n)
+
+Note:
+    For even lengths, the Nyquist frequency at ``f[n/2]`` can be thought of as
+    either negative or positive. :func:`~torch.fft.fftfreq` follows NumPy's
+    convention of taking it to be negative.
+
+Args:
+    n (int): the FFT length
+    d (float, optional): The sampling length scale.
+        The spacing between individual samples of the FFT input.
+        The default assumes unit spacing, dividing that result by the actual
+        spacing gives the result in physical frequency units.
+
+Keyword Args:
+    {out}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Example:
+
+    >>> torch.fft.fftfreq(5)
+    tensor([ 0.0000,  0.2000,  0.4000, -0.4000, -0.2000])
+
+    For even input, we can see the Nyquist frequency at ``f[2]`` is given as
+    negative:
+
+    >>> torch.fft.fftfreq(4)
+    tensor([ 0.0000,  0.2500, -0.5000, -0.2500])
+
+""".format(
+        **factory_common_args
+    ),
+)
+
+rfftfreq = _add_docstr(
+    _fft.fft_rfftfreq,
+    r"""
+rfftfreq(n, d=1.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
+
+Computes the sample frequencies for :func:`~torch.fft.rfft` with a signal of size :attr:`n`.
+
+Note:
+    :func:`~torch.fft.rfft` returns Hermitian one-sided output, so only the
+    positive frequency terms are returned. For a real FFT of length :attr:`n`
+    and with inputs spaced in length unit :attr:`d`, the frequencies are::
+
+        f = torch.arange((n + 1) // 2) / (d * n)
+
+Note:
+    For even lengths, the Nyquist frequency at ``f[n/2]`` can be thought of as
+    either negative or positive. Unlike :func:`~torch.fft.fftfreq`,
+    :func:`~torch.fft.rfftfreq` always returns it as positive.
+
+Args:
+    n (int): the real FFT length
+    d (float, optional): The sampling length scale.
+        The spacing between individual samples of the FFT input.
+        The default assumes unit spacing, dividing that result by the actual
+        spacing gives the result in physical frequency units.
+
+Keyword Args:
+    {out}
+    {dtype}
+    {layout}
+    {device}
+    {requires_grad}
+
+Example:
+
+    >>> torch.fft.rfftfreq(5)
+    tensor([0.0000, 0.2000, 0.4000])
+
+    >>> torch.fft.rfftfreq(4)
+    tensor([0.0000, 0.2500, 0.5000])
+
+    Compared to the output from :func:`~torch.fft.fftfreq`, we see that the
+    Nyquist frequency at ``f[2]`` has changed sign:
+    >>> torch.fft.fftfreq(4)
+    tensor([ 0.0000,  0.2500, -0.5000, -0.2500])
+
+""".format(
+        **factory_common_args
+    ),
+)
+
+fftshift = _add_docstr(
+    _fft.fft_fftshift,
+    r"""
+fftshift(input, dim=None) -> Tensor
+
+Reorders n-dimensional FFT data, as provided by :func:`~torch.fft.fftn`, to have
+negative frequency terms first.
+
+This performs a periodic shift of n-dimensional data such that the origin
+``(0, ..., 0)`` is moved to the center of the tensor. Specifically, to
+``input.shape[dim] // 2`` in each selected dimension.
+
+Note:
+    By convention, the FFT returns positive frequency terms first, followed by
+    the negative frequencies in reverse order, so that ``f[-i]`` for all
+    :math:`0 < i \leq n/2` in Python gives the negative frequency terms.
+    :func:`~torch.fft.fftshift` rearranges all frequencies into ascending order
+    from negative to positive with the zero-frequency term in the center.
+
+Note:
+    For even lengths, the Nyquist frequency at ``f[n/2]`` can be thought of as
+    either negative or positive. :func:`~torch.fft.fftshift` always puts the
+    Nyquist term at the 0-index. This is the same convention used by
+    :func:`~torch.fft.fftfreq`.
+
+Args:
+    input (Tensor): the tensor in FFT order
+    dim (int, Tuple[int], optional): The dimensions to rearrange.
+        Only dimensions specified here will be rearranged, any other dimensions
+        will be left in their original order.
+        Default: All dimensions of :attr:`input`.
+
+Example:
+
+    >>> f = torch.fft.fftfreq(4)
+    >>> f
+    tensor([ 0.0000,  0.2500, -0.5000, -0.2500])
+
+    >>> torch.fft.fftshift(f)
+    tensor([-0.5000, -0.2500,  0.0000,  0.2500])
+
+    Also notice that the Nyquist frequency term at ``f[2]`` was moved to the
+    beginning of the tensor.
+
+    This also works for multi-dimensional transforms:
+
+    >>> x = torch.fft.fftfreq(5, d=1/5) + 0.1 * torch.fft.fftfreq(5, d=1/5).unsqueeze(1)
+    >>> x
+    tensor([[ 0.0000,  1.0000,  2.0000, -2.0000, -1.0000],
+            [ 0.1000,  1.1000,  2.1000, -1.9000, -0.9000],
+            [ 0.2000,  1.2000,  2.2000, -1.8000, -0.8000],
+            [-0.2000,  0.8000,  1.8000, -2.2000, -1.2000],
+            [-0.1000,  0.9000,  1.9000, -2.1000, -1.1000]])
+
+    >>> torch.fft.fftshift(x)
+    tensor([[-2.2000, -1.2000, -0.2000,  0.8000,  1.8000],
+            [-2.1000, -1.1000, -0.1000,  0.9000,  1.9000],
+            [-2.0000, -1.0000,  0.0000,  1.0000,  2.0000],
+            [-1.9000, -0.9000,  0.1000,  1.1000,  2.1000],
+            [-1.8000, -0.8000,  0.2000,  1.2000,  2.2000]])
+
+    :func:`~torch.fft.fftshift` can also be useful for spatial data. If our
+    data is defined on a centered grid (``[-(N//2), (N-1)//2]``) then we can
+    use the standard FFT defined on an uncentered grid (``[0, N)``) by first
+    applying an :func:`~torch.fft.ifftshift`.
+
+    >>> x_centered = torch.arange(-5, 5)
+    >>> x_uncentered = torch.fft.ifftshift(x_centered)
+    >>> fft_uncentered = torch.fft.fft(x_uncentered)
+
+    Similarly, we can convert the frequency domain components to centered
+    convention by applying :func:`~torch.fft.fftshift`.
+
+    >>> fft_centered = torch.fft.fftshift(fft_uncentered)
+
+    The inverse transform, from centered Fourier space back to centered spatial
+    data, can be performed by applying the inverse shifts in reverse order:
+
+    >>> x_centered_2 = torch.fft.fftshift(torch.fft.ifft(torch.fft.ifftshift(fft_centered)))
+    >>> torch.testing.assert_close(x_centered.to(torch.complex64), x_centered_2, check_stride=False)
+
+
+""",
+)
+
+ifftshift = _add_docstr(
+    _fft.fft_ifftshift,
+    r"""
+ifftshift(input, dim=None) -> Tensor
+
+Inverse of :func:`~torch.fft.fftshift`.
+
+Args:
+    input (Tensor): the tensor in FFT order
+    dim (int, Tuple[int], optional): The dimensions to rearrange.
+        Only dimensions specified here will be rearranged, any other dimensions
+        will be left in their original order.
+        Default: All dimensions of :attr:`input`.
+
+Example:
+
+    >>> f = torch.fft.fftfreq(5)
+    >>> f
+    tensor([ 0.0000,  0.2000,  0.4000, -0.4000, -0.2000])
+
+    A round-trip through :func:`~torch.fft.fftshift` and
+    :func:`~torch.fft.ifftshift` gives the same result:
+
+    >>> shifted = torch.fft.fftshift(f)
+    >>> torch.fft.ifftshift(shifted)
+    tensor([ 0.0000,  0.2000,  0.4000, -0.4000, -0.2000])
+
+""",
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fft/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fft/__pycache__/__init__.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/func/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/func/__init__.py
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+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/func/__init__.py
@@ -0,0 +1,31 @@
+from torch._functorch.apis import grad, grad_and_value, vmap
+from torch._functorch.batch_norm_replacement import replace_all_batch_norm_modules_
+from torch._functorch.eager_transforms import (
+    debug_unwrap,
+    functionalize,
+    hessian,
+    jacfwd,
+    jacrev,
+    jvp,
+    linearize,
+    vjp,
+)
+from torch._functorch.functional_call import functional_call, stack_module_state
+
+
+__all__ = [
+    "grad",
+    "grad_and_value",
+    "vmap",
+    "replace_all_batch_norm_modules_",
+    "functionalize",
+    "hessian",
+    "jacfwd",
+    "jacrev",
+    "jvp",
+    "linearize",
+    "vjp",
+    "functional_call",
+    "stack_module_state",
+    "debug_unwrap",
+]
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@@ -0,0 +1,331 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from typing import Callable, cast, Generic, Optional, TypeVar, Union
+
+import torch
+
+
+__all__ = ["Future", "collect_all", "wait_all"]
+
+
+T = TypeVar("T")
+S = TypeVar("S")
+
+
+class _PyFutureMeta(type(torch._C.Future), type(Generic)):  # type: ignore[misc, no-redef]
+    pass
+
+
+class Future(torch._C.Future, Generic[T], metaclass=_PyFutureMeta):
+    r"""
+    Wrapper around a ``torch._C.Future`` which encapsulates an asynchronous
+    execution of a callable, e.g. :meth:`~torch.distributed.rpc.rpc_async`. It
+    also exposes a set of APIs to add callback functions and set results.
+
+    .. warning:: GPU support is a beta feature, subject to changes.
+    """
+
+    def __init__(
+        self, *, devices: Optional[list[Union[int, str, torch.device]]] = None
+    ):
+        r"""
+        Create an empty unset ``Future``. If the future is intended to hold
+        values containing CUDA tensors, (a superset of) their CUDA devices must
+        be specified at construction. (This is only supported if
+        ``torch.cuda.is_available()`` returns ``True``). This is needed to
+        ensure proper CUDA stream synchronization. The child futures, returned
+        by the ``then`` method, will inherit these devices.
+
+        Args:
+            devices(``List[Union[int, str, torch.device]]``, optional): the set
+                of devices on which tensors contained in this future's value are
+                allowed to reside and on which callbacks are allowed to operate.
+        """
+        if devices is None:
+            devices = []
+        super().__init__([torch.device(d) for d in devices])
+
+    def done(self) -> bool:
+        r"""
+        Return ``True`` if this ``Future`` is done. A ``Future`` is done if it
+        has a result or an exception.
+
+        If the value contains tensors that reside on GPUs, ``Future.done()``
+        will return ``True`` even if the asynchronous kernels that are
+        populating those tensors haven't yet completed running on the device,
+        because at such stage the result is already usable, provided one
+        performs the appropriate synchronizations (see :meth:`wait`).
+        """
+        return super().done()
+
+    def wait(self) -> T:
+        r"""
+        Block until the value of this ``Future`` is ready.
+
+        If the value contains tensors that reside on GPUs, then an additional
+        synchronization is performed with the kernels (executing on the device)
+        which may be asynchronously populating those tensors. Such sync is
+        non-blocking, which means that ``wait()`` will insert the necessary
+        instructions in the current streams to ensure that further operations
+        enqueued on those streams will be properly scheduled after the async
+        kernels but, once that is done, ``wait()`` will return, even if those
+        kernels are still running. No further synchronization is required when
+        accessing and using the values, as long as one doesn't change streams.
+
+        Returns:
+            The value held by this ``Future``. If the function (callback or RPC)
+            creating the value has thrown an error, this ``wait`` method will
+            also throw an error.
+        """
+        return super().wait()
+
+    def value(self) -> T:
+        r"""
+        Obtain the value of an already-completed future.
+
+        This method should only be called after a call to :meth:`wait` has
+        completed, or inside a callback function passed to :meth:`then`. In
+        other cases this ``Future`` may not yet hold a value and calling
+        ``value()`` could fail.
+
+        If the value contains tensors that reside on GPUs, then this method will
+        *not* perform any additional synchronization. This should be done
+        beforehand, separately, through a call to :meth:`wait` (except within
+        callbacks, for which it's already being taken care of by :meth:`then`).
+
+        Returns:
+            The value held by this ``Future``. If the function (callback or RPC)
+            creating the value has thrown an error, this ``value()`` method will
+            also throw an error.
+        """
+        return super().value()
+
+    def then(self, callback: Callable[[Future[T]], S]) -> Future[S]:
+        r"""
+        Append the given callback function to this ``Future``, which will be run
+        when the ``Future`` is completed.  Multiple callbacks can be added to
+        the same ``Future``, but the order in which they will be executed cannot
+        be guaranteed (to enforce a certain order consider chaining:
+        ``fut.then(cb1).then(cb2)``). The callback must take one argument, which
+        is the reference to this ``Future``. The callback function can use the
+        :meth:`value` method to get the value. Note that if this ``Future`` is
+        already completed, the given callback will be run immediately inline.
+
+        If the ``Future``'s value contains tensors that reside on GPUs, the
+        callback might be invoked while the async kernels that are populating
+        those tensors haven't yet finished executing on the device. However, the
+        callback will be invoked with some dedicated streams set as current
+        (fetched from a global pool) which will be synchronized with those
+        kernels. Hence any operation performed by the callback on these tensors
+        will be scheduled on the device after the kernels complete. In other
+        words, as long as the callback doesn't switch streams, it can safely
+        manipulate the result without any additional synchronization. This is
+        similar to the non-blocking behavior of :meth:`wait`.
+
+        Similarly, if the callback returns a value that contains tensors that
+        reside on a GPU, it can do so even if the kernels that are producing
+        these tensors are still running on the device, as long as the callback
+        didn't change streams during its execution. If one wants to change
+        streams, one must be careful to re-synchronize them with the original
+        streams, that is, those that were current when the callback was invoked.
+
+        Args:
+            callback(``Callable``): a ``Callable`` that takes this ``Future`` as
+                                    the only argument.
+
+        Returns:
+            A new ``Future`` object that holds the return value of the
+            ``callback`` and will be marked as completed when the given
+            ``callback`` finishes.
+
+        .. note:: Note that if the callback function throws, either
+            through the original future being completed with an exception and
+            calling ``fut.wait()``, or through other code in the callback, the
+            future returned by ``then`` will be marked appropriately with the
+            encountered error. However, if this callback later completes
+            additional futures, those futures are not marked as completed with
+            an error and the user is responsible for handling completion/waiting
+            on those futures independently.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
+            >>> def callback(fut):
+            ...     print(f"RPC return value is {fut.wait()}.")
+            >>> fut = torch.futures.Future()
+            >>> # The inserted callback will print the return value when
+            >>> # receiving the response from "worker1"
+            >>> cb_fut = fut.then(callback)
+            >>> chain_cb_fut = cb_fut.then(
+            ...     lambda x : print(f"Chained cb done. {x.wait()}")
+            ... )
+            >>> fut.set_result(5)
+            RPC return value is 5.
+            Chained cb done. None
+        """
+        return cast(Future[S], super().then(callback))
+
+    def add_done_callback(self, callback: Callable[[Future[T]], None]) -> None:
+        r"""
+        Append the given callback function to this ``Future``, which will be run
+        when the ``Future`` is completed.  Multiple callbacks can be added to
+        the same ``Future``, but the order in which they will be executed cannot
+        be guaranteed. The callback must take one argument, which is the
+        reference to this ``Future``. The callback function can use the
+        :meth:`value` method to get the value. Note that if this ``Future`` is
+        already completed, the given callback will be run inline.
+
+        We recommend that you use the :meth:`then` method as it provides a way
+        to synchronize after your callback has completed. ``add_done_callback``
+        can be cheaper if your callback does not return anything. But both
+        :meth:`then` and ``add_done_callback`` use the same callback
+        registration API under the hood.
+
+        With respect to GPU tensors, this method behaves in the same way as
+        :meth:`then`.
+
+        Args:
+            callback(``Future``): a ``Callable`` that takes in one argument,
+                which is the reference to this ``Future``.
+
+        .. note:: Note that if the callback function throws, either
+            through the original future being completed with an exception and
+            calling ``fut.wait()``, or through other code in the callback,
+            error handling must be carefully taken care of. For example, if
+            this callback later completes additional futures, those futures are
+            not marked as completed with an error and the user is responsible
+            for handling completion/waiting on those futures independently.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
+            >>> def callback(fut):
+            ...     print("This will run after the future has finished.")
+            ...     print(fut.wait())
+            >>> fut = torch.futures.Future()
+            >>> fut.add_done_callback(callback)
+            >>> fut.set_result(5)
+            This will run after the future has finished.
+            5
+        """
+        super().add_done_callback(callback)
+
+    def set_result(self, result: T) -> None:
+        r"""
+        Set the result for this ``Future``, which will mark this ``Future`` as
+        completed and trigger all attached callbacks. Note that a ``Future``
+        cannot be marked completed twice.
+
+        If the result contains tensors that reside on GPUs, this method can be
+        called even if the asynchronous kernels that are populating those
+        tensors haven't yet completed running on the device, provided that the
+        streams on which those kernels were enqueued are set as the current ones
+        when this method is called. Put simply, it's safe to call this method
+        immediately after launching those kernels, without any additional
+        synchronization, as long as one doesn't change streams in between. This
+        method will record events on all the relevant current streams and will
+        use them to ensure proper scheduling for all the consumers of this
+        ``Future``.
+
+        Args:
+            result (object): the result object of this ``Future``.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
+            >>> import threading
+            >>> import time
+            >>> def slow_set_future(fut, value):
+            ...     time.sleep(0.5)
+            ...     fut.set_result(value)
+            >>> fut = torch.futures.Future()
+            >>> t = threading.Thread(
+            ...     target=slow_set_future,
+            ...     args=(fut, torch.ones(2) * 3)
+            ... )
+            >>> t.start()
+            >>> print(fut.wait())
+            tensor([3., 3.])
+            >>> t.join()
+        """
+        super().set_result(result)
+
+    def set_exception(self, result: T) -> None:
+        r"""
+        Set an exception for this ``Future``, which will mark this ``Future`` as
+        completed with an error and trigger all attached callbacks. Note that
+        when calling wait()/value() on this ``Future``, the exception set here
+        will be raised inline.
+
+        Args:
+            result (BaseException): the exception for this ``Future``.
+
+        Example::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
+            >>> fut = torch.futures.Future()
+            >>> fut.set_exception(ValueError("foo"))
+            >>> fut.wait()
+            Traceback (most recent call last):
+            ...
+            ValueError: foo
+        """
+        assert isinstance(
+            result, Exception
+        ), f"{result} is of type {type(result)}, not an Exception."
+
+        def raise_error(fut_result):
+            raise fut_result
+
+        super()._set_unwrap_func(raise_error)
+        self.set_result(result)  # type: ignore[arg-type]
+
+
+def collect_all(futures: list[Future]) -> Future[list[Future]]:
+    r"""
+    Collects the provided :class:`~torch.futures.Future` objects into a single
+    combined :class:`~torch.futures.Future` that is completed when all of the
+    sub-futures are completed.
+
+    Args:
+        futures (list): a list of :class:`~torch.futures.Future` objects.
+
+    Returns:
+        Returns a :class:`~torch.futures.Future` object to a list of the passed
+        in Futures.
+
+    Example::
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
+        >>> fut0 = torch.futures.Future()
+        >>> fut1 = torch.futures.Future()
+        >>> fut = torch.futures.collect_all([fut0, fut1])
+        >>> fut0.set_result(0)
+        >>> fut1.set_result(1)
+        >>> fut_list = fut.wait()
+        >>> print(f"fut0 result = {fut_list[0].wait()}")
+        fut0 result = 0
+        >>> print(f"fut1 result = {fut_list[1].wait()}")
+        fut1 result = 1
+    """
+    return cast(
+        Future[list[Future]],
+        torch._C._collect_all(cast(list[torch._C.Future], futures)),
+    )
+
+
+def wait_all(futures: list[Future]) -> list:
+    r"""
+    Waits for all provided futures to be complete, and returns
+    the list of completed values. If any of the futures encounters an error,
+    the method will exit early and report the error not waiting for other
+    futures to complete.
+
+    Args:
+        futures (list): a list of :class:`~torch.futures.Future` object.
+
+    Returns:
+        A list of the completed :class:`~torch.futures.Future` results. This
+        method will throw an error if ``wait`` on any
+        :class:`~torch.futures.Future` throws.
+    """
+    return [
+        fut.wait()
+        for fut in torch._C._collect_all(cast(list[torch._C.Future], futures)).wait()
+    ]
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_backward_state.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_backward_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c742431857c33af22dbc1ad73b5bdfcf6124b9c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_backward_state.py
@@ -0,0 +1,27 @@
+import torch.fx
+
+
+class BackwardState:
+    """
+    BackwardState is used to pass Python hooks from the forwards pass
+    into the backwards pass in Dynamo+Compiled Autograd.
+
+    It is created by TorchDynamo and has special handling there.
+    Dynamo will pass an empty BackwardState to the forwards, then populate
+    members on it (via setattr) only after the forwards graph is finished.
+    Later on, in CompileAutograd we will inline and add the needed guards
+    on the BackwardState.
+
+    BackwardState is identified and has special handling in AOTAutograd.
+    During AOTAutograd:
+        1) BackwardState is an input to the forwards graph
+        2) It must only be used in the backwards
+        3) It will be empty in the forwards
+        4) In the forwards we add a wrapper to save it
+        5) In the backwards it becomes an input
+        6) There can only be one per graph
+
+    BackwardState requires CompiledAutograd.
+    """
+
+    proxy: torch.fx.Proxy
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_config.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..58859607eee29a9c4d94b14d99dbd0d8cc7120ad
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_config.py
@@ -0,0 +1,103 @@
+import os
+import sys
+from typing import Optional
+
+
+# [@compile_ignored: debug] Fails hard instead of graph breaking on guard on data dependent errors.
+no_data_dependent_graph_break = (
+    os.environ.get("TORCHDYNAMO_NO_DATA_DEPENDENT_GRAPH_BREAK", "0") == "1"
+)
+# [@compile_ignored: debug] Uses z3 for validating the guard optimizations transformations.
+translation_validation = (
+    os.environ.get("TORCHDYNAMO_TRANSLATION_VALIDATION", "0") == "1"
+)
+# Timeout (in milliseconds) for z3 finding a solution.
+# [@compile_ignored: debug]
+translation_validation_timeout = int(
+    os.environ.get("TORCHDYNAMO_TRANSLATION_VALIDATION_TIMEOUT", "600000")
+)
+# Disables bisection for translation validation.
+#
+# Translation validation bisection is enabled by default, if translation validation
+# is also enabled. This should help finding guard simplification issues. However,
+# since validation uses Z3 for bisecting, it might take a lot of time.
+#
+# Set this configuration option so as to avoid bisecting.
+# [@compile_ignored: debug]
+translation_validation_no_bisect = (
+    os.environ.get("TORCHDYNAMO_TRANSLATION_NO_BISECT", "0") == "1"
+)
+# Checks whether replaying ShapeEnv events on a freshly constructed one yields
+# the a ShapeEnv with the same state. This should be used only in testing.
+check_shape_env_recorded_events = False
+
+# TODO: Perhaps consider allowing unions for the configs below (so you can hit
+# multiple reps at the same time)
+
+# Give extended debug information if the string representation of a guard
+# matches this.  For example, set this to "Ne(s0, 10)" and whenever we issue
+# this guard, we will generate full Python and C++ backtrace
+# [@compile_ignored: debug]
+extended_debug_guard_added = os.environ.get(
+    "TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED", None
+)
+
+# Give extended debug information when a particular symbol is allocated.  For
+# example, set this to "u2" and whenever we create this symbol, we will
+# generate full Python and C++ backtrace
+# [@compile_ignored: debug]
+extended_debug_create_symbol = os.environ.get(
+    "TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL", None
+)
+
+# Give extended debug information (C++ backtrace) for all extended debug
+# settings as well as errors.  The C++ backtrace is slow and very spammy so we
+# don't include it by default even when you're requesting extended debug.
+# [@compile_ignored: debug]
+extended_debug_cpp = os.environ.get("TORCHDYNAMO_EXTENDED_DEBUG_CPP", "") != ""
+
+# Give extended debug information (line of code) when a torch function
+# is called during export.  This is useful for showing progress and detecting
+# where export might be stuck. Currently only works for strict=False.
+# [@compile_ignored: debug]
+extended_debug_current_loc = (
+    os.environ.get("TORCHEXPORT_EXTENDED_DEBUG_CURRENT_LOC", "0") == "1"
+)
+
+# [@compile_ignored: debug] Show a warning for every specialization
+print_specializations = False
+
+# wraps (un)equalities with 'Not' class after recording the correct expression
+# in the FX graph. This should incorrectly construct the divisible and replacement
+# lists, and incorrectly issue guards.
+inject_EVALUATE_EXPR_flip_equality_TESTING_ONLY = False
+
+# [@compile_ignored: debug] Validate that ShapeEnv's version key is updated correctly
+validate_shape_env_version_key = False
+
+# If we produce more than this many guards on a symbol, force the symbol to
+# get specialized and bail out if this many guards mention this particular
+# symbol.  This may be slightly more aggressive than the true number of guards
+# issued (as we test if we've hit the limit on-the-fly, whereas we may
+# do further simplifications at final guard issuance time that make guards
+# irrelevant.)
+symbol_guard_limit_before_specialize: Optional[int] = None
+
+# This flag changes whether we should use the same symbolic variable to represent input sizes that are the same.
+use_duck_shape = True
+
+# Controls the registration of torch.nonzero() on the meta device.
+# When True, nonzero returns a tensor with shape (self.numel(), self.dim())
+# assuming all elements are none-zero.
+# Default is False to prevent unintended registration. Set to True to enable.
+meta_nonzero_assume_all_nonzero = False
+
+# Applies size-oblivious reasoning to backed symbols. This allocates a [0, inf] range for backed size symbols,
+# and relies on size-oblivious semantics to avoid 0/1 specialization guards by marking them size-like.
+# Currently an experimental option for export.
+backed_size_oblivious = False
+
+from torch.utils._config_module import install_config_module
+
+
+install_config_module(sys.modules[__name__])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_constant_symnode.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_constant_symnode.py
new file mode 100644
index 0000000000000000000000000000000000000000..c45728d24d1ddba4f315f8cfd13b7827b4fbbb16
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_constant_symnode.py
@@ -0,0 +1,69 @@
+from typing import *  # noqa: F403
+
+
+# Python version of c10/core/ConstantSymNodeImpl.cpp
+# This needs to exist because the Python version of nested int is not compatible
+# with the C++ version of constant symnode.
+class ConstantIntNode:
+    def __init__(self, val: int):
+        self.val = val
+
+    def is_constant(self) -> bool:
+        return True
+
+    def maybe_as_int(self) -> int:
+        return self.val
+
+    def is_int(self) -> bool:
+        return True
+
+    def is_float(self) -> bool:
+        return False
+
+    def is_bool(self) -> bool:
+        return False
+
+    def is_nested_int(self) -> bool:
+        return False
+
+    def clone(self) -> "ConstantIntNode":
+        return self
+
+    def _str(self) -> str:
+        return str(self.val)
+
+    def __str__(self) -> str:
+        return self._str()
+
+    def __repr__(self) -> str:
+        return self._str()
+
+    def _graph_repr(self) -> str:
+        return self._str()
+
+    def mul(self, other: Any) -> Any:
+        return other.mul(self)
+
+    def eq(self, other: Any) -> Any:
+        return other.eq(self)
+
+    def ne(self, other: Any) -> Any:
+        return other.ne(self)
+
+    def gt(self, other: Any) -> Any:
+        return other.lt(self)
+
+    def lt(self, other: Any) -> Any:
+        return other.gt(self)
+
+    def le(self, other: Any) -> Any:
+        return other.ge(self)
+
+    def ge(self, other: Any) -> Any:
+        return other.le(self)
+
+    def is_symbolic(self) -> bool:
+        return False
+
+    def constant_int(self) -> int:
+        return self.val
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_dynamism.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_dynamism.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6cfdef6147e1c8a170ea9b297f2bcef20827810
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/_dynamism.py
@@ -0,0 +1,111 @@
+import re
+from typing import Any, Callable, Union
+
+import torch
+from torch.utils._pytree import tree_flatten_with_path, tree_map
+
+
+KeyPath = tuple[Any, ...]
+NonTensorShapeFn = Callable[[Union[int, float]], tuple[Any, ...]]
+
+__all__ = [
+    "normalize_source_name",
+    "module_to_nested_dict",
+    "track_dynamism_across_examples",
+    "clone_and_convert_to_meta",
+]
+
+
+def normalize_source_name(name: str) -> str:
+    # Match attribute access like .x and replace with ['x']
+    return re.sub(r"\.([a-zA-Z_][a-zA-Z0-9_]*)", r"['\1']", name)
+
+
+def module_to_nested_dict(module: torch.nn.Module) -> dict[str, Any]:
+    """Recursively converts an nn.Module into a nested dictionary with explicit 'parameters' and 'modules' keys."""
+    self_dict: dict[str, Any] = {}
+
+    self_dict["_parameters"] = {}
+    self_dict["_modules"] = {}
+
+    for attr_name in dir(module):
+        if not attr_name.startswith("_") and not callable(getattr(module, attr_name)):
+            attr_value = getattr(module, attr_name)
+            if (
+                not isinstance(attr_value, torch.nn.Module)
+                and isinstance(attr_value, (int, float, torch.Tensor))
+                and type(attr_value) is not bool
+            ):
+                self_dict[attr_name] = attr_value
+
+    for name, param in module.named_parameters(recurse=False):
+        self_dict["_parameters"][name] = param
+    for name, buffer in module.named_buffers(recurse=False):
+        self_dict["_parameters"][name] = buffer
+
+    for name, submodule in module.named_children():
+        self_dict["_modules"][name] = module_to_nested_dict(submodule)
+
+    return self_dict
+
+
+def track_dynamism_across_examples(
+    example_inputs: list[Any],
+) -> dict[Any, Any]:
+    """
+    This function analyzes a list of example inputs to determine the dynamism of their shapes.
+    It tracks whether the dimensions of tensors or non-tensor values change across
+    different examples. The function returns a dictionary where each key represents
+    a path to a value in the input examples, and the corresponding value is a tuple
+    indicating which dimensions are dynamic (i.e., change across examples). This
+    helps in understanding how the structure of data varies across different instances.
+    """
+    tracking: dict[KeyPath, tuple[list[set[Any]], bool]] = {}
+
+    for ex in example_inputs:
+        if "self" in ex and isinstance(ex["self"], torch.nn.Module):
+            ex["self"] = module_to_nested_dict(ex["self"])
+        leaves_with_paths, _ = tree_flatten_with_path(ex)
+        for key_path, value in leaves_with_paths:
+            if not isinstance(value, (int, float, torch.Tensor)):
+                continue
+            if isinstance(value, torch.Tensor):
+                shape: tuple[int | float, ...] = tuple(value.shape)
+                is_tensor = True
+            else:
+                shape = (value,)
+                is_tensor = False
+            if key_path not in tracking:
+                tracking[key_path] = ([set() for _ in range(len(shape))], is_tensor)
+            else:
+                dim_sets, flag = tracking[key_path]
+                if flag != is_tensor:
+                    pass
+                while len(dim_sets) < len(shape):
+                    dim_sets.append(set())
+            for i, dim in enumerate(shape):
+                tracking[key_path][0][i].add(dim)
+
+    output: dict[Any, Any] = {}
+    for key_path, (dim_sets, _is_tensor) in tracking.items():
+        final_dyn = tuple(len(s) > 1 for s in dim_sets)
+        key_str = "L" + "".join(f"{str(k)}" for k in key_path)
+        key = key_path[0].key  # type: ignore[attr-defined]
+        if key not in output:
+            output[key] = {}
+        output[key][key_str] = final_dyn
+    return output
+
+
+def clone_and_convert_to_meta(example_input: Any) -> Any:
+    """
+    This function takes a list of example inputs and for each tensor, clones it and converts it to device=meta.
+    For non-tensor values, it keeps the reference. It uses pytree to handle nested structures recursively.
+    """
+
+    def transform_fn(value: Any) -> Any:
+        if isinstance(value, torch.Tensor):
+            return value.clone().to(device="meta")
+        return value
+
+    return tree_map(transform_fn, example_input)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/accelerator_partitioner.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/accelerator_partitioner.py
new file mode 100644
index 0000000000000000000000000000000000000000..29b8d4541b8185a3e2f3a1ff3d25951900baf6e7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/accelerator_partitioner.py
@@ -0,0 +1,1080 @@
+# mypy: allow-untyped-defs
+import operator
+from collections import deque
+from typing import NamedTuple
+
+import torch
+from torch.fx.experimental.partitioner_utils import (
+    Device,
+    get_extra_size_of,
+    get_latency_of_partitioned_graph,
+    get_partition_to_latency_mapping,
+    NodeLatency,
+    Partition,
+    PartitionerConfig,
+    PartitionMode,
+)
+from torch.fx.graph_module import GraphModule
+from torch.fx.node import map_arg, Node
+from torch.fx.passes.graph_manipulation import get_size_of_all_nodes
+from torch.fx.passes.split_module import split_module
+
+
+class DAGNode:
+    """DAGNode class maintains useful information for a partition (submodule),
+    and its input submodules and output submodules.
+    """
+
+    def __init__(
+        self,
+        submodule_node: Node,
+        input_nodes: list[Node],
+        output_nodes: list[Node],
+        logical_device_ids: list[int],
+        size_bytes: int,
+    ) -> None:
+        self.submodule_node: Node = submodule_node
+        self.input_nodes: list[Node] = input_nodes
+        self.output_nodes: list[Node] = output_nodes
+        self.logical_device_ids: list[int] = logical_device_ids
+        self.size_bytes = size_bytes
+
+    def __str__(self) -> str:
+        return str(self.submodule_node)
+
+
+class DAG:
+    """DAG class contains all the DAG nodes"""
+
+    def __init__(self) -> None:
+        self.nodes: list[DAGNode] = []
+
+    def create_node(
+        self,
+        submodule_node: Node,
+        input_nodes: list[Node],
+        output_nodes: list[Node],
+        logical_devices: list[int],
+        size_bytes: int,
+    ) -> None:
+        node = DAGNode(
+            submodule_node, input_nodes, output_nodes, logical_devices, size_bytes
+        )
+        self.nodes.append(node)
+
+
+class PartitionResult(NamedTuple):
+    """NameTuple used for returning DAG and a new fx module"""
+
+    dag: DAG
+    module_with_submodules: GraphModule
+
+
+"""Followings are some helper functions for partition manipulation"""
+
+
+def reset_partition_device(partitions):
+    for partition in partitions:
+        partition.logical_device_ids = []
+
+
+def combine_two_partitions(
+    partition_0: Partition, partition_1: Partition, partitions: list[Partition]
+) -> None:
+    """Given a list of partitions and its two partitions,
+    combine these two partitions into a new one appending to the partitions
+    and remove the previous two partitions from the list of partitions
+    """
+    partition = Partition(len(partitions))
+    partition.nodes = partition_0.nodes.union(partition_1.nodes)
+    partition.recalculate_mem_size()
+    partitions.append(partition)
+    partitions.remove(partition_0)
+    partitions.remove(partition_1)
+    reorganize_partitions(partitions)
+    return
+
+
+def set_parents_and_children(partitions: list[Partition]) -> None:
+    """Given a list of partitions, mark parents and children for each partition"""
+    # Go through all nodes in a partition.
+    # If a node's user is in other partition,
+    # then the other partition is this partition's children.
+    # This partition is the other partition's parent
+    for partition in partitions:
+        partition.children = set()
+        partition.parents = set()
+    for partition in partitions:
+        for node in partition.nodes:
+            # For each node in the current partition, find its users
+            users = node.users
+            for n in users:
+                # Find which the partition the user node belongs to.
+                # Note that if the node itself is also belongs to that partition,
+                # that partition is not the child of the current partition
+                for p in partitions:
+                    if p != partition and n in p.nodes and node not in p.nodes:
+                        partition.children.add(p)
+                        p.parents.add(partition)
+    return
+
+
+def reorganize_partitions(partitions: list[Partition]) -> None:
+    """Given a list of partitions, reorganize partition id,
+    its parents and its children for each partition
+    """
+    # Rearrange partition ids
+    for i, partition in enumerate(partitions):
+        partition.partition_id = i
+    set_parents_and_children(partitions)
+    return
+
+
+def get_bfs_level_partition(partitions: list[Partition]) -> None:
+    """Given a list of partitions,
+    mark the bfs level for each partition
+    """
+    current_level: set[Partition] = set()
+    visited: set[Partition] = set()
+    for partition in partitions:
+        # If a partition has no parent, it should be in root level
+        if len(partition.parents) == 0:
+            current_level.add(partition)
+    next_level: set[Partition] = set()
+    level = 0
+    # bfs
+    while current_level:
+        partition = current_level.pop()
+        partition.bfs_level = level
+        visited.add(partition)
+        children = partition.children
+        for child in children:
+            if child not in next_level:
+                next_level.add(child)
+        if not current_level:
+            current_level = next_level.copy()
+            next_level = set()
+            level += 1
+    return
+
+
+def get_node_to_partition_mapping(partitions: list[Partition]) -> dict[Node, int]:
+    """Given a list of partitions,return node to partition mapping"""
+    node_to_partition: dict[Node, int] = {}
+    for partition in partitions:
+        for node in partition.nodes:
+            node_to_partition[node] = partition.partition_id
+    return node_to_partition
+
+
+def get_logical_id_to_device(devices: list[Device]) -> dict[int, Device]:
+    """Get a mapping from device logical ID to Device object."""
+    logical_id_to_device: dict[int, Device] = {}
+    for d in devices:
+        logical_id_to_device[d.logical_id] = d
+    return logical_id_to_device
+
+
+def get_device_partition_stats(
+    partitions: list[Partition], devices: list[Device]
+) -> tuple[dict[Device, list[Partition]], dict[Device, int], list[Partition]]:
+    """Given a list of partitions and a list of devices, returns:
+    1. A mapping from device to partitions on it;
+    2. A mapping from device to its remaining memory size;
+    3. A list of partitions that do not have a device.
+    """
+    # logical id to device
+    logical_id_to_device = get_logical_id_to_device(devices)
+    # Track partitions on device
+    device_to_partitions: dict[Device, list[Partition]] = {}
+    # Track device's left mem size
+    device_to_left_mem_bytes: dict[Device, int] = {}
+    for d in devices:
+        device_to_partitions[d] = []
+        device_to_left_mem_bytes[d] = d.available_mem_bytes
+
+    # Deal with the partitions that already have a device
+    # and also collect all partitions without a device (no_device_partitions)
+    no_device_partitions = []
+    for partition in partitions:
+        if partition.logical_device_ids != []:
+            for logical_id in partition.logical_device_ids:
+                device = logical_id_to_device[logical_id]
+                device_to_partitions[device].append(partition)
+                device_to_left_mem_bytes[device] -= partition.used_mem_bytes
+        else:
+            no_device_partitions.append(partition)
+
+    return (
+        device_to_partitions,
+        device_to_left_mem_bytes,
+        no_device_partitions,
+    )
+
+
+def get_device_to_partitions_mapping(
+    partitions: list[Partition], devices: list[Device]
+):
+    """Given a list of partitions and a list of devices,
+    map each partition into a device.
+    """
+
+    def calculate_extra_mem_bytes_needed_for(
+        partition: Partition, partitions: list[Partition]
+    ):
+        all_nodes: set[Node] = set()
+        for p in partitions:
+            all_nodes = all_nodes.union(p.nodes)
+        if len(all_nodes) == 0:
+            return partition.used_mem_bytes
+        all_nodes = all_nodes.union(partition.nodes)
+        extra_size_needed = 0
+        for node in partition.nodes:
+            extra_size_needed += get_extra_size_of(node, all_nodes)
+        return extra_size_needed
+
+    def find_device_for(partition: Partition):
+        """Given a partition, find a logical device for the partition
+        The algorithm is to put the partition on the device
+        that has just enough mem left for that partition.
+        device_to_left_mem_bytes is a dictionary between device and its left mem size
+        sorted by its left mem size
+        """
+        for d in device_to_left_mem_bytes:
+            extra_size_needed = calculate_extra_mem_bytes_needed_for(
+                partition, device_to_partitions[d]
+            )
+            if extra_size_needed < device_to_left_mem_bytes[d]:
+                device_to_partitions[d].append(partition)
+                partition.logical_device_ids.append(d.logical_id)
+                device_to_left_mem_bytes[d] -= extra_size_needed
+                return True
+        return False
+
+    (
+        device_to_partitions,
+        device_to_left_mem_bytes,
+        no_device_partitions,
+    ) = get_device_partition_stats(partitions, devices)
+
+    # Find devices for all the partitions without a device
+    found_device = True
+    for partition in no_device_partitions:
+        device_to_left_mem_bytes = dict(
+            sorted(device_to_left_mem_bytes.items(), key=operator.itemgetter(1))
+        )
+        found_device = find_device_for(partition)
+        if not found_device:
+            break
+    return found_device
+
+
+def check_dependency(partition):
+    """Given a partition,check if there is a circular dependency on
+    this partition using bfs
+    """
+    visited: set[Partition] = {partition}
+    queue: deque[Partition] = deque([partition])
+    while queue:
+        p = queue.popleft()
+        for child in p.children:
+            if child == partition:
+                return True
+            else:
+                if child not in visited:
+                    visited.add(child)
+                    queue.append(child)
+    return False
+
+
+class Partitioner:
+    """A fx module may not fit into one device.
+    Partitioner class helps partition one fx module into submodules (partitions),
+    so that the submodules can be executed crossing different accelerators.
+    The main function of this class is self.partition_graph.
+    It partitions the fx module based on the scheme specified in partition_config
+    A DAG structure is returned
+    along with a new fx module with submodule nodes.
+    """
+
+    def __init__(self) -> None:
+        self.partitions: list[Partition] = []
+        self.node_to_partition: dict[Node, int] = {}
+        self.devices: list[Device] = []
+
+    def partition_graph(
+        self,
+        fx_module: GraphModule,
+        torch_module: torch.nn.Module,
+        partitioner_config: PartitionerConfig,
+    ) -> PartitionResult:
+        """Given the fx module, torch module and partitioner_config,
+        find the partitions, do the partitions,
+        and then return a DAG and a new fx module with submodule nodes (partitions)
+        """
+        self.graph_module = fx_module
+        self.torch_module = torch_module
+        self.devices = partitioner_config.devices
+        if len(self.devices) == 0:
+            raise RuntimeError("No devices")
+        # Tag the size in bytes to all nodes in the graph_module.
+        get_size_of_all_nodes(self.graph_module)
+        # Check if there are op nodes in the fx module
+        nodes = self.graph_module.graph.nodes
+        if all(node.op in {"placeholder", "get_attr", "output"} for node in nodes):
+            raise RuntimeError("No Partition since no operations in the module")
+        # Calculate total size of the fx module
+        total_size_of_graph = 0
+        for node in nodes:
+            if node.op == "output":
+                break
+            total_size_of_graph += node.size_bytes.total_size
+        # Find the device with the max mem size
+        device_with_max_mem = max(self.devices, key=lambda d: d.available_mem_bytes)
+        # AOT based partition
+        if partitioner_config.mode == PartitionMode.aot_based:
+            self.aot_based_partition(
+                partitioner_config.node_to_partition_mapping,
+                partitioner_config.partition_to_logical_device_mapping,
+            )
+        # Single partition if the whole module can be fit into one device
+        elif total_size_of_graph <= device_with_max_mem.available_mem_bytes:
+            self.find_single_partition(
+                total_size_of_graph, logical_device_id=device_with_max_mem.logical_id
+            )
+        elif total_size_of_graph > sum(d.available_mem_bytes for d in self.devices):
+            raise RuntimeError("Devices have no enough memory for the module")
+        else:
+            # Sparse nn based partition
+            if partitioner_config.mode == PartitionMode.sparse_nn:
+                available_mem_bytes = self.devices[0].available_mem_bytes
+                if not all(
+                    device.available_mem_bytes == available_mem_bytes
+                    for device in self.devices
+                ):
+                    raise RuntimeError("All devices must have same memory size!")
+                # sparse_nn_partition only support same memory size
+                # TODO: add different size support for sparse_nn_partition
+                self.sparse_nn_partition(available_mem_bytes)
+            # Cost aware partition
+            elif partitioner_config.mode == PartitionMode.cost_aware:
+                self.cost_aware_partition(
+                    partitioner_config.transfer_rate_bytes_per_sec,
+                    partitioner_config.node_to_latency_mapping,
+                )
+            # KL based partition
+            elif partitioner_config.mode == PartitionMode.kl_based:
+                self.kl_based_partition(
+                    partitioner_config.transfer_rate_bytes_per_sec,
+                    partitioner_config.node_to_latency_mapping,
+                )
+            else:
+                self.size_based_partition()
+
+        # Saturate host if possible.
+        if partitioner_config.saturate_host:
+            self.saturate_host()
+
+        # Partition the graph module based on the partition assignment.
+        module_with_submodules = self.do_partition()
+
+        # The DAG contains DAGNodes with info of each partition's input nodes, output nodes
+        # and how partitions are connected.
+        dag = self.dump_dag(module_with_submodules)
+        ret = PartitionResult(dag, module_with_submodules)
+        return ret
+
+    def find_single_partition(
+        self, total_size_of_graph, logical_device_id: int = 0
+    ) -> None:
+        """Fit the whole fx module into one device"""
+        partition_0 = self.create_partition()
+        for node in self.graph_module.graph.nodes:
+            if node.op == "output":
+                # Skip the output node, but there can
+                # be nodes after the output in certain cases.
+                continue
+            partition_0.nodes.add(node)
+        partition_0.used_mem_bytes = total_size_of_graph
+        partition_0.logical_device_ids = [logical_device_id]
+        # Get the node to partition mapping
+        self.node_to_partition = get_node_to_partition_mapping(self.partitions)
+        return
+
+    def size_based_partition(self) -> None:
+        """This method is to partition the fx module based on memory size.
+        It uses greedy approach. The result may not be the best.
+        The basic idea is:
+        Step 1:
+        Find a device which has enough memory to fit the current node, create a empty partition
+        with the size of that device.
+        Then keep adding the following nodes into the partition until the partition is full.
+        Step 2:
+        Repeat Step 1 until no device left
+        Step 3:
+        If some nodes are left, create a partition for each left node (single node partition).
+        and then try to map those partitions into logical devices with enough mem left.
+        """
+
+        def find_device_based_on_size(node) -> Device:
+            """Given a node, this function is to find a logical device
+            that could fit the node.
+            """
+            mem_size_needed = get_extra_size_of(node, set())
+            device = Device("", -1, -1)
+            for d in self.devices:
+                if (
+                    d not in occupied_devices
+                    and d.available_mem_bytes >= mem_size_needed
+                ):
+                    device = d
+                    break
+            if device.available_mem_bytes < 0:
+                raise RuntimeError(str(node) + "is too large to fit any device")
+            occupied_devices.append(device)
+            return device
+
+        # Track partition and its left mem size
+        partition_to_left_mem_bytes: dict[Partition, int] = {}
+        # Track all the devices that have been used
+        occupied_devices: list[Device] = []
+        partition = self.create_partition()
+        for node in self.graph_module.graph.nodes:
+            if node.op in {"call_module", "call_method", "call_function"}:
+                # Check if there are devices left
+                if len(self.partitions) <= len(self.devices):
+                    total_size_of_input_nodes = get_extra_size_of(node, partition.nodes)
+                    # Check if the current partition is the very first partition
+                    if partition.used_mem_bytes == 0:
+                        # Find a device to fit the first node, return available mem size
+                        device = find_device_based_on_size(node)
+                        occupied_devices.append(device)
+                        # Update partition and its left mem size
+                        partition_to_left_mem_bytes[
+                            partition
+                        ] = device.available_mem_bytes
+                        # Update available mem for the current partition
+                        partition.logical_device_ids.append(device.logical_id)
+                    else:
+                        # The current partition is not the first partition
+                        # Check if the current node can fit into current partition
+                        if (
+                            partition_to_left_mem_bytes[partition]
+                            < total_size_of_input_nodes
+                        ):
+                            # Check if no device is left
+                            if len(self.partitions) == len(self.devices):
+                                # No device is left
+                                # Create the first single node partition for the current node
+                                self.create_single_node_partition(node)
+                                continue
+                            # Some devices are still left
+                            # Create a new partition with a mem size that is enough for the current node
+                            device = find_device_based_on_size(node)
+                            partition = self.create_partition()
+                            total_size_of_input_nodes = get_extra_size_of(
+                                node, partition.nodes
+                            )
+                            partition_to_left_mem_bytes[
+                                partition
+                            ] = device.available_mem_bytes
+                            partition.logical_device_ids.append(device.logical_id)
+                    partition.add_node(node)
+                    partition_to_left_mem_bytes[partition] -= total_size_of_input_nodes
+                # Create single node partitions if no device is left
+                else:
+                    self.create_single_node_partition(node)
+        reorganize_partitions(self.partitions)
+        # Get the node to partition mapping
+        self.node_to_partition = get_node_to_partition_mapping(self.partitions)
+        # Mapping all partitions into device
+        found_partition_to_device_mapping = get_device_to_partitions_mapping(
+            self.partitions, self.devices
+        )
+        if not found_partition_to_device_mapping:
+            raise RuntimeError("Cannot Get a Valid Partition to Logical Device Mapping")
+        return
+
+    def saturate_host(self) -> None:
+        """Saturate host by assigning replicates to unused devices with enough memory.
+        It uses a greedy approach to find a next available set of devices to place all split
+        partitions: For each used device, it searches for an idle device with minimal memory
+        size that can hold all the partition located on that device; If the search is successful
+        for all used devices, it then assigns the new devices' logical ID to the corresponding
+        partition.
+        """
+        (
+            device_to_partitions,
+            device_to_left_mem_bytes,
+            no_device_partitions,
+        ) = get_device_partition_stats(self.partitions, self.devices)
+
+        assert (
+            len(no_device_partitions) == 0
+        ), f"Expect no_device_partitions has 0 device, but get {len(no_device_partitions)}"
+
+        # Devices that hold partitions
+        used_devices = [d for d in self.devices if len(device_to_partitions[d]) > 0]
+        # Track replicates of the assigned devices
+        replicated_device_to_used_device: dict[Device, Device] = {}
+
+        while len(used_devices) * 2 + len(replicated_device_to_used_device) <= len(
+            self.devices
+        ):
+            # Success flag for this round
+            success = True
+            # Devices that have not been assigned
+            idle_devices = [
+                d
+                for d in self.devices
+                if d not in used_devices and d not in replicated_device_to_used_device
+            ]
+            # Temporary mapping from replicated device to original device
+            temp_replicate_mapping = {}
+
+            # Find a new device to replicate all partitions on an used device
+            for used_device in used_devices:
+                # Idle devices that have enough memory
+                available_devices = [
+                    d
+                    for d in idle_devices
+                    if d.available_mem_bytes
+                    >= used_device.available_mem_bytes
+                    - device_to_left_mem_bytes[used_device]
+                ]
+                if len(available_devices) == 0:
+                    success = False
+                    break
+                new_device = min(available_devices, key=lambda d: d.available_mem_bytes)
+                idle_devices.remove(new_device)
+                temp_replicate_mapping[new_device] = used_device
+
+            if not success:
+                break
+            replicated_device_to_used_device.update(temp_replicate_mapping)
+
+        # Update logical device IDs assigned to the partitions
+        for (
+            replicate_device,
+            original_device,
+        ) in replicated_device_to_used_device.items():
+            logical_id = replicate_device.logical_id
+            for partition in device_to_partitions[original_device]:
+                partition.logical_device_ids.append(logical_id)
+        for p in self.partitions:
+            print(p.logical_device_ids)
+
+    def do_partition(self) -> GraphModule:
+        """Return a new fx module with submodule nodes (partitions)."""
+        module_with_submodules = split_module(
+            self.graph_module,
+            self.torch_module,
+            lambda node: self.node_to_partition[node],
+        )
+        return module_with_submodules
+
+    def dump_dag(self, module_with_submodules: GraphModule) -> DAG:
+        """Return the dag structure and the new fx module with submodules."""
+        dag = DAG()
+        for node in module_with_submodules.graph.nodes:
+            if node.op == "output":
+                break
+            if node.op in {"placeholder", "get_attr"}:
+                continue
+            if node.target == operator.__getitem__:
+                continue
+            input_nodes: dict[Node, None] = {}
+            map_arg(node.args, input_nodes.setdefault)
+            map_arg(node.kwargs, input_nodes.setdefault)
+            # When a node has two or more output nodes,
+            # it outputs its result to 'getitem' nodes.
+            # Those 'getitem' nodes are the output node for this node.
+            # Otherwise, the output node is this node itself.
+            if len(node.users) > 1:
+                output_nodes = list(node.users)
+            else:
+                output_nodes = [node]
+            partition_id = int(node.name.rsplit("_", 1)[-1])
+            device_ids = self.partitions[partition_id].logical_device_ids
+            size_bytes = self.partitions[partition_id].used_mem_bytes
+            dag.create_node(
+                node, list(input_nodes), output_nodes, device_ids, size_bytes
+            )
+        return dag
+
+    def create_partition(self) -> Partition:
+        """Create a partition and append it to self.partitions."""
+        partition_id = len(self.partitions)
+        partition = Partition(partition_id)
+        self.partitions.append(partition)
+        return partition
+
+    def create_single_node_partition(self, node):
+        """Create a partition for a single node"""
+        partition = self.create_partition()
+        partition.add_node(node)
+        return
+
+    def sparse_nn_partition(self, available_mem_bytes: int) -> None:
+        """This method partition a sparse nn module.
+        It is size based partition but different from size_based_partition,
+        it only works when all the devices have same memory size (available_mem_bytes).
+        In the future, devices with different mem sizes will be supported like size_based_partition.
+        It first traverse all the nodes and do the partitions based on the same memory size.
+        If the current partition has no enough memory left for a new op node
+        (call_module, call_method, call_function), a new partition is created.
+        When crossing the boundary between non-embedding nodes and embedding nodes,
+        a new partition is created regardlessly.
+        For example, if the current node is a non-embedding node but the next node is an
+        embedding node, a new partition is created for the next node.
+        After the partition, the partitions are combined as much as possible.
+        The rule is that a non-embedding partition only
+        combines with another non-embedding one.
+        So as the embedding partitions.
+        """
+
+        def combine_partitions_based_on_size(
+            partitions: list[Partition], available_mem_bytes: int
+        ) -> None:
+            """Combining small partitions together to keep as less partitions as possible.
+            Here is an example of the algorithm to do this:
+            Assume some partitions, we first sort them based on partition used memory size.
+            [(partition_4, 1), (partition_3, 1), (partition_2, 2), (partition_1, 7), (partition_0, 9)]
+            The available memory is 10.
+            step 1: self.find_partition_to_combine_based_on_size()
+            First, mark bfs level for each partition
+            Second, look the smallest partition, partition_4: 10 - 1 = 9
+            It means any partition has a used memory equal or less than 9 could combine this partition
+            We go from the largest and selection partition_0.
+            Check the bfs level for two partitions, if the level difference is less than 2,
+            it can be combined.
+            step 2: repeat step 1 until no partitions can be combined
+            """
+            find_combination = True
+            while find_combination:
+                # Sort partitions based on memory size
+                sorted_partitions = sorted(partitions, key=lambda p: p.used_mem_bytes)
+                # Mark bfs level
+                get_bfs_level_partition(self.partitions)
+                find_combination, partitions = find_partition_to_combine_based_on_size(
+                    sorted_partitions, available_mem_bytes, partitions
+                )
+            return
+
+        def calculate_mem_bytes_needed(p1, p2):
+            """Given two partitions, calculate how many mem bytes
+            are needed if two partitions are combined
+            """
+            nodes = p1.nodes.union(p2.nodes)
+            mem_bytes_needed = 0
+            for node in nodes:
+                mem_bytes_needed += get_extra_size_of(node, nodes)
+            return mem_bytes_needed
+
+        def find_partition_to_combine_based_on_size(
+            sorted_partitions: list[Partition],
+            available_mem_bytes: int,
+            partitions: list[Partition],
+        ) -> tuple[bool, list[Partition]]:
+            """step 1 in combine_partition_based_on_size()"""
+            find_combination = False
+            smallest_partition = sorted_partitions.pop(0)
+            for p in sorted_partitions[::-1]:
+                if abs(smallest_partition.bfs_level - p.bfs_level) <= 1:
+                    # Calculate how many bytes needed if combined
+                    mem_bytes_needed = calculate_mem_bytes_needed(p, smallest_partition)
+                    if mem_bytes_needed <= available_mem_bytes:
+                        combine_two_partitions(p, smallest_partition, self.partitions)
+                        partitions.remove(smallest_partition)
+                        partitions.remove(p)
+                        partitions.append(self.partitions[-1])
+                        find_combination = True
+                        break
+            return find_combination, partitions
+
+        def reset_partition_in_sparse_nn(partition, new_partition=True):
+            """If crossing the boundary between non-embedding nodes and
+            embedding nodes, create a new partition
+            """
+            if in_embedding_region:
+                embedding_partitions.append(partition)
+            else:
+                non_embedding_partitions.append(partition)
+            if new_partition:
+                partition = self.create_partition()
+                partition.left_mem_bytes = available_mem_bytes
+                return partition
+            return None
+
+        def is_embedding_node(node: Node) -> bool:
+            """Check if a node is an embedding node"""
+            if node.op == "call_module":
+                submodule = self.graph_module
+                for atom in str(node.target).split("."):
+                    if not hasattr(submodule, atom):
+                        raise RuntimeError(
+                            f"Module {submodule} has no attribute {atom}"
+                        )
+                    submodule = getattr(submodule, atom)
+                    if "Embedding" in str(submodule):
+                        return True
+            return False
+
+        # Track embedding partitions and non-embedding partitions separately
+        embedding_partitions: list[Partition] = []
+        non_embedding_partitions: list[Partition] = []
+        # A Flag to check the boundary
+        in_embedding_region: bool = False
+        partition = self.create_partition()
+        for node in self.graph_module.graph.nodes:
+            if node.op in {"call_module", "call_method", "call_function"}:
+                # Check if crossing the boundary between embedding nodes and non embedding nodes
+                if is_embedding_node(node) != in_embedding_region:
+                    # Crossing the boundary
+                    # Check if the current partition is an empty partition
+                    if partition.used_mem_bytes != 0:
+                        # The current partition isn't an empty partition. Create a new one.
+                        partition = reset_partition_in_sparse_nn(partition)
+                    in_embedding_region = not in_embedding_region
+                total_size_of_input_nodes = get_extra_size_of(node, partition.nodes)
+                if (
+                    total_size_of_input_nodes + partition.used_mem_bytes
+                    > available_mem_bytes
+                ):
+                    partition = reset_partition_in_sparse_nn(partition)
+                    total_size_of_input_nodes = get_extra_size_of(node, partition.nodes)
+                    if total_size_of_input_nodes > available_mem_bytes:
+                        raise RuntimeError(
+                            node.target + "is too large to fit into a device"
+                        )
+                partition.add_node(node)
+        reset_partition_in_sparse_nn(partition, new_partition=False)
+        # Set parents and children for partitions
+        set_parents_and_children(self.partitions)
+        # Combining non-embedding partitions
+        combine_partitions_based_on_size(non_embedding_partitions, available_mem_bytes)
+        # Combining embedding partitions
+        combine_partitions_based_on_size(embedding_partitions, available_mem_bytes)
+        total_size_of_non_embedding_partitions = 0
+        for partition in non_embedding_partitions:
+            total_size_of_non_embedding_partitions += partition.used_mem_bytes
+        # Check if devices are enough for all partitions
+        if len(embedding_partitions) > len(self.devices):
+            msg = (
+                "Need "
+                + str(len(embedding_partitions))
+                + " devices, but only "
+                + str(len(self.devices))
+                + " provided"
+            )
+            raise RuntimeError(msg)
+        occupied_devices = []
+        for i, partition in enumerate(embedding_partitions):
+            # Check if all non-embedding partitions can fit into embedding partition devices
+            if (
+                total_size_of_non_embedding_partitions + partition.used_mem_bytes
+                > available_mem_bytes
+            ):
+                raise RuntimeError(
+                    "partition_"
+                    + str(partition.partition_id)
+                    + "(embedding partition) and non embedding partitions can not fit into one device"
+                )
+            else:
+                # Add logical device to the partition
+                partition.logical_device_ids = [self.devices[i].logical_id]
+                occupied_devices.append(self.devices[i].logical_id)
+        # Add logical devices to the non_embedding_partitions
+        for partition in non_embedding_partitions:
+            partition.logical_device_ids = occupied_devices
+        # Get the node to partition mapping
+        self.node_to_partition = get_node_to_partition_mapping(self.partitions)
+        return
+
+    def cost_aware_partition(
+        self,
+        transfer_rate_bytes_per_sec: float,
+        node_to_latency_mapping: dict[Node, NodeLatency],
+    ) -> None:
+        """This method is to partition the fx module based on the cost.
+        The cost is the total latency of running the whole fx module.
+        In partitioner_utils.py, the cost model is built.
+        The cost aware partition algorithm is:
+        #1. At every beginning, each node is a partition.
+            Then we map all the partitions to the devices
+            and calculate the cost
+        #2. Then try to pre-combine any two of the partitions if the two
+            partitions can be combined.
+            (the bfs level is less than 2 or two partitions are connected and
+            can find partition to device mapping)
+            See if any partition pair could reduce the current cost.
+            Choose the pair that shows the minimum cost and then combine them
+        #3. Repeat #2 until the cost cannot be reduced.
+        """
+
+        def try_combining_partitions(p0_index, p1_index, partitions) -> float:
+            """Given two partitions and a list of partitions, combine these two partitions
+            and see what is the cost of the modified partition list
+            """
+            p0 = partitions[p0_index]
+            p1 = partitions[p1_index]
+            """If two partitions' bfs level are less than 2 or two partitions are connected to each other,
+               then they can be combined
+            """
+            if (
+                (abs(p0.bfs_level - p1.bfs_level) <= 1)
+                or (p0 in p1.parents)
+                or p0 in (p1.children)
+            ):
+                combine_two_partitions(p0, p1, partitions)
+                # Check if a circular dependency exists after combining
+                if check_dependency(partitions[-1]):
+                    return float("inf")
+                # Check if the modified partition list can be mapped to devices after combination
+                reset_partition_device(partitions)
+                found_deivce = get_device_to_partitions_mapping(
+                    partitions, self.devices
+                )
+                if not found_deivce:
+                    return float("inf")
+                # Calculate the new cost
+                partition_to_latency_mapping = get_partition_to_latency_mapping(
+                    partitions, node_to_latency_mapping
+                )
+                cost = get_latency_of_partitioned_graph(
+                    partitions,
+                    partition_to_latency_mapping,
+                    transfer_rate_bytes_per_sec,
+                )
+                return cost
+            # If two partition can not be combined, the cost is inf
+            return float("inf")
+
+        def search_combination(
+            transfer_rate_bytes_per_sec, node_to_latency_mapping
+        ) -> bool:
+            """Given transfer rate between partitions and each node's latency,
+            find two partitions to combine so the cost of the partitions can
+            be reduced.
+            The algorithm is :
+            1. Go through all the partition pairs and see
+            if any pair of partitions can be combined.
+            2. Calculate the cost after the combination.
+            3. Select the minimum cost and combine its corresponding partition pair.
+            """
+            partition_to_latency_mapping = get_partition_to_latency_mapping(
+                self.partitions, node_to_latency_mapping
+            )
+            cost = get_latency_of_partitioned_graph(
+                self.partitions,
+                partition_to_latency_mapping,
+                transfer_rate_bytes_per_sec,
+            )
+            if len(self.partitions) == 1:
+                return False
+            partition_pair: list[int] = []
+            for i in range(len(self.partitions) - 1):
+                for j in range(i + 1, len(self.partitions)):
+                    # Try to combine the partition pair
+                    # and see the new cost after combination
+                    new_cost = try_combining_partitions(i, j, self.partitions[:])
+                    if new_cost <= cost:
+                        partition_pair = [i, j]
+                        cost = new_cost
+                    reorganize_partitions(self.partitions)
+            # If a partition pair is found, combine them
+            if len(partition_pair) != 0:
+                p0 = self.partitions[partition_pair[0]]
+                p1 = self.partitions[partition_pair[1]]
+                combine_two_partitions(p0, p1, self.partitions)
+            get_bfs_level_partition(self.partitions)
+            reset_partition_device(self.partitions)
+            get_device_to_partitions_mapping(self.partitions, self.devices)
+            return len(partition_pair) != 0
+
+        for node in self.graph_module.graph.nodes:
+            if node.op not in {"placeholder", "get_attr", "output"}:
+                self.create_single_node_partition(node)
+        # Set up parent partitions and children partitions for each partition
+        set_parents_and_children(self.partitions)
+        # Get bfs level for each partition
+        get_bfs_level_partition(self.partitions)
+        find_combination = True
+        while find_combination:
+            # Search for a pair partition to generate the minimum new cost,
+            # then combine them
+            find_combination = search_combination(
+                transfer_rate_bytes_per_sec, node_to_latency_mapping
+            )
+        # Make sure all partitions are set up correctly
+        reorganize_partitions(self.partitions)
+        # Set up node to partition mapping
+        self.node_to_partition = get_node_to_partition_mapping(self.partitions)
+        return
+
+    def kl_based_partition(
+        self,
+        transfer_rate_bytes_per_sec: float,
+        node_to_latency_mapping: dict[Node, NodeLatency],
+    ) -> None:
+        """This function is a cost aware partition based
+        on Kernighan-Lin algorithm.
+        First, the graph is partitioned using size_based_partition.
+        Then, each node is swapped with any other node in a different
+        partition, and at the same time, the cost is estimated after
+        the swapping.
+        For example, we have nodes n0, n1, n2, n3 and n4.
+        Using size_based_partition, n0 and n1 are in Partition p0.
+        n2, n3 and n4 in Partition p1. The current cost is estimated.
+        We first tried using n0 to swap with n2 from the other partition.
+        Then we see that swapping n0 and n2 shows a lower cost
+        than the current cost and it is the minimum among other pairs like
+        (n0, None)(This means moving n0 to Partition without swapping other nodes),
+        (n0, n3) and (n0, n4). We swap n0 and n2 and set the new cost
+        as the current cost.
+        Then We repeat this process for all the other nodes until all swapping pairs
+        are tried.
+        """
+
+        def swap_nodes(n0, n1, p0, p1):
+            # Either n0 or n1 could be None
+            # That means we simply move the node
+            # to another partition
+            if n0 is not None:
+                p0.remove_node(n0)
+                p1.add_node(n0)
+            if n1 is not None:
+                p0.add_node(n1)
+                p1.remove_node(n1)
+
+        def try_swap_nodes(
+            n0, n1, p0, p1, node_to_latency_mapping, transfer_rate_per_sec
+        ):
+            cost = float("inf")
+            swap_nodes(n0, n1, p0, p1)
+            # Reorganize partitions after swapping
+            reorganize_partitions(self.partitions)
+            # Check if there is a circular dependency after swapping
+            if (not check_dependency(p0)) and (not check_dependency(p1)):
+                reset_partition_device(self.partitions)
+                partition_to_latency_mapping = get_partition_to_latency_mapping(
+                    self.partitions, node_to_latency_mapping
+                )
+                # Check if all partitions can be mapped to logical devices after swapping
+                found_device = get_device_to_partitions_mapping(
+                    self.partitions, self.devices
+                )
+                if not found_device:
+                    cost = float("inf")
+                else:
+                    cost = get_latency_of_partitioned_graph(
+                        self.partitions,
+                        partition_to_latency_mapping,
+                        transfer_rate_bytes_per_sec,
+                    )
+            # Swap back and reset all partitions back to original
+            swap_nodes(n1, n0, p0, p1)
+            reorganize_partitions(self.partitions)
+            reset_partition_device(self.partitions)
+            get_device_to_partitions_mapping(self.partitions, self.devices)
+            return cost
+
+        def swap_node_to_partition(
+            node, p0, p1, node_to_latency_mapping, transfer_rate_per_sec
+        ):
+            """This function helps to swap one node from partition p0
+            with all the nodes in another partition p1
+            """
+            p1_nodes = list(p1.nodes) + [None]
+            min_cost = float("inf")
+            node_pair: list[Node] = []
+            for n1 in p1_nodes:
+                # Ignore the node if it is not a op node
+                if n1 is not None and n1.op in {"placeholder", "get_attr"}:
+                    continue
+                # Try swapping node in p0 with n1 in p1
+                cost = try_swap_nodes(
+                    node, n1, p0, p1, node_to_latency_mapping, transfer_rate_per_sec
+                )
+                if cost < min_cost:
+                    node_pair = [node, n1]
+                    min_cost = cost
+            return cost, node_pair  # type: ignore[possibly-undefined]
+
+        # First use size_base_partition
+        self.size_based_partition()
+        partition_to_latency_mapping = get_partition_to_latency_mapping(
+            self.partitions, node_to_latency_mapping
+        )
+        # Calculate the cost of the partitions
+        cost = get_latency_of_partitioned_graph(
+            self.partitions, partition_to_latency_mapping, transfer_rate_bytes_per_sec
+        )
+        # Keep tracking the node pair that shows the better cost
+        node_pair: list[Node] = []
+        # Keep tracking the partition pair of node pair
+        partition_pair: list[Partition] = []
+        # Collect all the op nodes from the graph
+        op_nodes = [
+            n
+            for n in self.graph_module.graph.nodes
+            if n.op not in {"placeholder", "get_attr", "output"}
+        ]
+        for node in op_nodes:
+            # Find which partition the current node belongs
+            p0_index = self.node_to_partition[node]
+            p0 = self.partitions[p0_index]
+            # Go through all the other partitions to swap
+            # with other nodes from those partitions
+            for p1_index, _ in enumerate(self.partitions):
+                if p0_index != p1_index:
+                    p1 = self.partitions[p1_index]
+                    new_cost, new_node_pair = swap_node_to_partition(
+                        node,
+                        p0,
+                        p1,
+                        node_to_latency_mapping,
+                        transfer_rate_bytes_per_sec,
+                    )
+                    # Update the cost
+                    # Track the swapped node pair and their partitions
+                    if new_cost < cost:
+                        cost = new_cost
+                        node_pair = new_node_pair
+                        partition_pair = [p0, p1]
+            # Do the swapping after trying all the nodes from a partition
+            if len(node_pair) != 0:
+                swap_nodes(
+                    node_pair[0], node_pair[1], partition_pair[0], partition_pair[1]
+                )
+                reorganize_partitions(self.partitions)
+                get_device_to_partitions_mapping(self.partitions, self.devices)
+        reorganize_partitions(self.partitions)
+        # Mapping the device to the partition
+        get_device_to_partitions_mapping(self.partitions, self.devices)
+        return
+
+    def aot_based_partition(
+        self, node_to_partition_mapping, partition_to_logical_device_mapping
+    ):
+        """This function helps to rebuild the partitions given the nodes and its
+        corresponding partition id
+        """
+        partition_id_to_partition_mapping: dict[int, Partition] = {}
+        self.node_to_partition = node_to_partition_mapping
+        for node in self.node_to_partition:
+            partition_id = self.node_to_partition[node]
+            # If the requested partition has not been created, create the partition
+            if partition_id not in partition_id_to_partition_mapping:
+                partition = Partition(partition_id)
+                self.partitions.append(partition)
+                partition_id_to_partition_mapping[partition_id] = partition
+                partition.logical_device_ids = partition_to_logical_device_mapping[
+                    partition_id
+                ]
+            else:
+                partition = partition_id_to_partition_mapping[
+                    self.node_to_partition[node]
+                ]
+            # Add the current node into the partition
+            partition.add_node(node)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/const_fold.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/const_fold.py
new file mode 100644
index 0000000000000000000000000000000000000000..483b7e8b2ea2e7f0ffcbf52a64964fc30468ef05
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/const_fold.py
@@ -0,0 +1,304 @@
+# mypy: allow-untyped-defs
+import re
+from typing import Callable, Optional, Union
+
+import torch.fx
+from torch.fx.node import map_arg
+from torch.fx.passes.split_module import split_module
+
+
+__all__ = [
+    "FoldedGraphModule",
+    "get_unique_attr_name_in_module",
+    "split_const_subgraphs",
+]
+
+
+class FoldedGraphModule(torch.fx.GraphModule):
+    """
+    FoldedGraphModule is a GraphModule which also contains another
+    `const_subgraph_module` representing a subgraph which has all const attr
+    inputs and which can be run once before running the main standard
+    `graph`. The `const_output_names` are the ordered list names of attrs which
+    represent what each respective output from the const_subgraph should be set
+    on which attrs.
+    """
+
+    def __init__(
+        self,
+        root: torch.nn.Module,
+        graph: torch.fx.Graph,
+        const_subgraph: Optional[torch.fx.Graph] = None,
+        fx_const_folded_attrs_name: Optional[str] = None,
+        device_for_folded_attrs: str = "cuda",
+    ):
+        super().__init__(root, graph)
+        self.const_subgraph_module = (
+            None
+            if const_subgraph is None
+            else torch.fx.GraphModule(root, const_subgraph)
+        )
+        self.has_folding_been_run = False
+        self.fx_const_folded_attrs_name = fx_const_folded_attrs_name
+        self.device_for_folded_attrs = device_for_folded_attrs
+
+    def __call__(self, *args, **kwargs):
+        if not self.has_folding_been_run:
+            self.run_folding()
+        return super().__call__(*args)
+
+    def run_folding(self):
+        # If there's no const subgraph module or attr output names to use, return
+        # early as there is no const folding to perform.
+        if (
+            self.const_subgraph_module is None
+            or self.fx_const_folded_attrs_name is None
+        ):
+            return
+
+        assert not self.has_folding_been_run
+        self.has_folding_been_run = True
+
+        # Actually run const folding subgraph. Note that single attr const fold
+        # subgraphs output a single Tensor while multiple outputs are returned as
+        # Tuple[Tensor,].
+        folded_attrs = self.const_subgraph_module()
+
+        def _create_param(i):
+            return torch.nn.Parameter(
+                i.detach().clone()
+                if not isinstance(i, int)
+                else torch.Tensor([i]).to(device=self.device_for_folded_attrs),
+                requires_grad=i.requires_grad if isinstance(i, torch.Tensor) else False,
+            )
+
+        params = (
+            torch.nn.ParameterList([_create_param(i) for i in folded_attrs])
+            if isinstance(folded_attrs, tuple)
+            else _create_param(folded_attrs)
+        )
+        setattr(self, self.fx_const_folded_attrs_name, params)
+
+
+def _inline_module(gm: torch.fx.GraphModule, inline_mod_name: str):
+    """
+    Given `gm` and some graph module which is called with target name `inline_mod_name`,
+    this helper will inline all of the nodes from that called graph module into `gm`.
+    """
+    # Fetch the inner graph module that we want to inline inside `gm`.
+    inline_mod = dict(gm.named_modules())[inline_mod_name]
+    assert isinstance(inline_mod, torch.fx.GraphModule)
+    call_mod_node_to_replace = None
+    for node in gm.graph.nodes:
+        if node.op == "call_module" and node.target == inline_mod_name:
+            call_mod_node_to_replace = node
+            break
+    assert call_mod_node_to_replace is not None
+
+    # Now actually do the swap. Note that we have to keep track of new nodes that are
+    # copied into `gm` -- we do this via replacement_mapping.
+    call_mod_args = call_mod_node_to_replace.args
+    call_mod_kwargs = call_mod_node_to_replace.kwargs
+
+    replacement_mapping: dict[torch.fx.Node, torch.fx.Node] = {}
+    ph_count = 0
+
+    def replacement_fn(node):
+        new_node = replacement_mapping[node]
+        new_node.meta = node.meta.copy()
+        return new_node
+
+    for inline_node in inline_mod.graph.nodes:
+        if inline_node.op == "placeholder":
+            replacement_mapping[inline_node] = (
+                call_mod_kwargs[inline_node.name]
+                if inline_node.name in call_mod_kwargs
+                else call_mod_args[ph_count]
+            )
+
+            ph_count += 1
+            continue
+
+        if inline_node.op == "output":
+            outputs = inline_node.args[0]
+            output_replacements = map_arg(outputs, replacement_fn)
+            call_mod_node_to_replace.replace_all_uses_with(output_replacements)
+            continue
+
+        with gm.graph.inserting_before(call_mod_node_to_replace):
+            new_node = gm.graph.node_copy(inline_node, replacement_fn)
+        replacement_mapping[inline_node] = new_node
+
+    gm.graph.eliminate_dead_code()
+
+
+def get_unique_attr_name_in_module(mod_traced: torch.fx.GraphModule, name: str) -> str:
+    """
+    Make sure the name is unique (in a module) and can represents an attr.
+    """
+    # Delete all characters that are illegal in a Python identifier.
+    name = re.sub("[^0-9a-zA-Z_]+", "_", name)
+    if name[0].isdigit():
+        name = f"_{name}"
+    # Now make sure it is in fact unique to the module by incrementing suffix value.
+    while hasattr(mod_traced, name):
+        match = re.match(r"(.*)_(\d+)$", name)
+        if match is None:
+            name = name + "_1"
+        else:
+            base, num = match.group(1, 2)
+            name = f"{base}_{int(num) + 1}"
+
+    return name
+
+
+def split_const_subgraphs(
+    module: Union[torch.nn.Module, torch.fx.GraphModule],
+    skip_folding_node_fn: Optional[Callable[[torch.fx.Node], bool]] = None,
+    device_for_folded_attrs: str = "cpu",
+) -> FoldedGraphModule:
+    """
+    Looks through `module` for any nodes that have all constant attribute inputs
+    and separates them out into their own constant subgraph, and returns a
+    FoldedGraphModule which runs that constant subgraph on the first run to set
+    attributes on the module prior to running the non-constant portion of the
+    graph.
+    """
+    if not isinstance(module, torch.fx.GraphModule):
+        mod_traced = torch.fx.symbolic_trace(module)
+    else:
+        mod_traced = module
+
+    # Build up a list of const_nodes, defined as nodes that are themselves
+    # get_attrs, or have all get_attr or other constant node inputs.
+    const_nodes: set[torch.fx.Node] = set()
+    found_const_folding = False
+    for node in mod_traced.graph.nodes:
+        # Skip over placeholders/outputs because they can't be const folded and
+        # we don't want to add tags to them.
+        if node.op in {"placeholder", "output"}:
+            continue
+
+        # If the node itself is constant, or all of its inputs are constant,
+        # then tag it as constant.
+        if node.op != "get_attr" and not set(node.all_input_nodes).issubset(
+            const_nodes
+        ):
+            continue
+
+        # If provided skip folding function says to skip, then skip.
+        if skip_folding_node_fn and skip_folding_node_fn(node):
+            continue
+
+        # Skip folding side-effectful functions
+        if node.is_impure():
+            continue
+
+        # Must be a constant foldable node at this point.
+        const_nodes.add(node)
+        if node.op != "get_attr":
+            found_const_folding = True
+
+    # If we did not find any const folding then return early without a const fold subgraph.
+    if not found_const_folding:
+        return FoldedGraphModule(mod_traced, mod_traced.graph)
+
+    # Partition the module into two: submod_0 for constant folding subgraph, and
+    # submod_1 for the rest.
+    def mod_partition(node: torch.fx.Node):
+        return 0 if node in const_nodes else 1
+
+    split = split_module(mod_traced, module, mod_partition)
+
+    const_mod_name, non_const_mod_name = "submod_0", "submod_1"
+    # Safely get submod_1 in case there are no non-const nodes
+    const_gm, non_const_gm = split.submod_0, getattr(split, non_const_mod_name, None)
+
+    # The module that a call_module node refers to gets copied to submodules during split.
+    # The path to the module also gets inlined, i.e. mod.a.b -> mod_a_b. Here we need to
+    # attach inlined modules to `split` as it's the owning module now.
+    for node in non_const_gm.graph.nodes if non_const_gm else []:
+        if node.op == "call_module":
+            setattr(split, node.target, getattr(non_const_gm, node.target))
+    for node in const_gm.graph.nodes:
+        if node.op == "call_module":
+            setattr(split, node.target, getattr(const_gm, node.target))
+
+    # split_module currently does not use get_attrs for attrs. Instead it passes
+    # them in as args from the parent module, which used get_attrs. Here we set
+    # them as get_attrs inside const_gm, allowing for running folding without
+    # somehow a priori knowing the attrs that should be passed as args. We can
+    # unconditionally do this for all placeholders because we know all
+    # placeholders to const_gm must be constants accessible via get_attr.
+    call_const_gm_args = None
+    for node in split.graph.nodes:
+        if node.op == "call_module":
+            if node.target == const_mod_name:
+                call_const_gm_args = node.args
+                break
+    assert call_const_gm_args is not None
+
+    # Here we do the actual replacement of placeholders to get_attrs. Note that here we
+    # set the const_gm.graph into a new root_const_gm with split as the root module,
+    # because we are fetching attributes directly from the root module, instead of
+    # fetching them from const_gm. Example: The const_gm must have some format like:
+    # graph():
+    #    %inp : [num_users=1] = placeholder[target=const_inp]
+    #    %add : [num_users=1] = call_function[target=operator.add](args = (%inp, %inp), kwargs = {})
+    #    return add
+    # We replace that with the following, which does not have any placeholders:
+    # graph():
+    #    %inp_1 : [num_users=1] = get_attr[target=const_inp]
+    #    %add : [num_users=1] = call_function[target=operator.add](args = (%inp_1, %inp_1), kwargs = {})
+    #    return add
+    root_const_gm = torch.fx.GraphModule(split, const_gm.graph)
+    for node in root_const_gm.graph.nodes:
+        if node.op == "output":
+            multiple_outputs = isinstance(node.args[0], tuple)
+            continue
+        if node.op != "placeholder":
+            continue
+        in_node = next(n for n in call_const_gm_args if n.name == node.target)
+        assert in_node.op == "get_attr"
+        with root_const_gm.graph.inserting_before(node):
+            new_node = root_const_gm.graph.get_attr(in_node.target)
+        new_node.meta = node.meta.copy()
+        node.replace_all_uses_with(new_node)
+        root_const_gm.graph.erase_node(node)
+    assert "multiple_outputs" in locals()
+
+    # Now find the call to const_gm inside split, and replace it with a getattr to the
+    # folded tensor(s) that result from constant folding. Note that we don't need to
+    # worry about whether this is one or more tensors because the original graph
+    # correctly uses getitem to extract individual tensors if there are multiple folded.
+    fx_const_folded_attrs_name = get_unique_attr_name_in_module(
+        mod_traced, "_FX_CONST_FOLDED_ATTRS"
+    )
+    setattr(
+        split,
+        fx_const_folded_attrs_name,
+        torch.nn.ParameterList() if multiple_outputs else torch.nn.Parameter(),  # type: ignore[possibly-undefined]
+    )
+    for node in split.graph.nodes:
+        if node.op == "call_module" and node.target == const_mod_name:
+            with node.graph.inserting_before(node):
+                folded_attrs = node.graph.get_attr(fx_const_folded_attrs_name)
+            folded_attrs.meta = node.meta.copy()
+            node.replace_all_uses_with(folded_attrs)
+            break
+
+    # Finally, inline the non-constant submod (if it exists) into the split submod.
+    # This is so that the original caller who may have passed in a graph module will
+    # get back out a graph module whose graph is traced to the same granularity.
+    if hasattr(split, non_const_mod_name):
+        _inline_module(split, non_const_mod_name)
+
+    split.graph.eliminate_dead_code()
+
+    return FoldedGraphModule(
+        split,
+        split.graph,
+        root_const_gm.graph,
+        fx_const_folded_attrs_name,
+        device_for_folded_attrs,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/debug.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/debug.py
new file mode 100644
index 0000000000000000000000000000000000000000..b87dee9db9c73f0b4ea1a0a27682a167e125a71d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/debug.py
@@ -0,0 +1,33 @@
+from collections.abc import Sequence
+
+import torch.fx as fx
+
+
+__all__ = ["set_trace"]
+
+
+def set_trace(gm: fx.GraphModule) -> fx.GraphModule:
+    """
+    Sets a breakpoint in `gm`'s generated python code. It drops into pdb when
+    `gm` gets run.
+
+    Args:
+        gm: graph module to insert breakpoint. It is then recompiled for it to
+            take effect.
+
+    Returns:
+        the `gm` with breakpoint inserted.
+    """
+
+    def insert_pdb(body: Sequence[str]) -> list[str]:
+        return ["import pdb; pdb.set_trace()\n", *body]
+
+    with gm.graph.on_generate_code(
+        make_transformer=lambda cur_transform: (
+            # new code transformer to register
+            lambda body: (insert_pdb(cur_transform(body) if cur_transform else body))
+        )
+    ):
+        gm.recompile()
+
+    return gm
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/graph_gradual_typechecker.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/graph_gradual_typechecker.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b15ae0a6739cf91b0bbe3cd8ef7da0cefe091d5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/graph_gradual_typechecker.py
@@ -0,0 +1,1024 @@
+# mypy: allow-untyped-defs
+import itertools
+import operator
+from functools import reduce
+from typing import Callable, TypeVar
+from typing_extensions import ParamSpec
+
+import sympy
+
+import torch
+from torch.fx.experimental.refinement_types import Equality
+from torch.fx.experimental.unification import Var  # type: ignore[attr-defined]
+from torch.fx.node import Node, Target
+from torch.fx.tensor_type import Dyn, is_consistent, is_more_precise, TensorType
+from torch.nn.modules.batchnorm import BatchNorm2d
+from torch.nn.modules.conv import Conv2d
+
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+_INFERENCE_RULES: dict[Target, Callable] = {}
+_REFINEMENT_RULES: dict[Target, Callable] = {}
+_RULES: dict[Target, Callable] = {}
+
+__all__ = [
+    "GraphTypeChecker",
+    "Refine",
+    "adaptiveavgpool2d_check",
+    "adaptiveavgpool2d_inference_rule",
+    "add_inference_rule",
+    "all_eq",
+    "bn2d_inference_rule",
+    "broadcast_types",
+    "calculate_out_dimension",
+    "conv2d_inference_rule",
+    "conv_refinement_rule",
+    "conv_rule",
+    "element_wise_eq",
+    "expand_to_tensor_dim",
+    "first_two_eq",
+    "flatten_check",
+    "flatten_inference_rule",
+    "flatten_refinement_rule",
+    "get_attr_inference_rule",
+    "get_greatest_upper_bound",
+    "get_parameter",
+    "linear_check",
+    "linear_inference_rule",
+    "linear_refinement_rule",
+    "maxpool2d_check",
+    "maxpool2d_inference_rule",
+    "register_algebraic_expressions_inference_rule",
+    "register_inference_rule",
+    "register_refinement_rule",
+    "relu_inference_rule",
+    "reshape_inference_rule",
+    "transpose_inference_rule",
+]
+
+
+def expand_to_tensor_dim(t, n):
+    """
+    Expand a type to the desired tensor dimension if possible
+    Raise an error otherwise.
+    - t is the given type
+    - n is a number of dimensions to expand to
+    """
+    if t == Dyn:
+        dims = [Dyn] * n
+        return TensorType(tuple(dims))
+    elif isinstance(t, TensorType):
+        if len(t.__args__) != n:
+            raise TypeError(
+                f"Cannot extend tensor. Tensor {t} has rank {len(t.__args__)}. It should have rank {n}"
+            )
+        return t
+    else:
+        raise TypeError(f"Cannot match the type {t}")
+
+
+def broadcast_types(t1, t2):
+    """
+    Applies broadcasting to both given types such that they
+    become consistent with eachother and returns two new
+    resulting types
+    """
+
+    # if either type is Dyn, do nothing since the types are already consistent
+    if t1 == Dyn or t2 == Dyn or isinstance(t1, Var) or isinstance(t2, Var):
+        return t1, t2
+
+    if isinstance(t1, TensorType) and isinstance(t2, TensorType):
+        s1 = len(t1.__args__)
+        s2 = len(t2.__args__)
+
+        new_t1 = list(t1.__args__)
+        new_t2 = list(t2.__args__)
+
+        # We make the types the same length which is the first requirement
+        # for consistency
+        if s1 > s2:
+            for i in range(s1 - s2):
+                new_t2.insert(0, 1)
+
+        elif s2 > s1:
+            for i in range(s2 - s1):
+                new_t1.insert(0, 1)
+
+        # we replace occurrences of "1" with each tensor with
+        # the corresponding type from the other tensor
+        for i, (x, y) in enumerate(zip(new_t1, new_t2)):
+            if x == 1:
+                new_t1[i] = y
+            elif y == 1:
+                new_t2[i] = x
+
+        # at this point our tensors should be consistent
+        # and we can apply the element-wise operation and find the right dimension
+        # for the output of the operation
+        (t1, t2) = TensorType(tuple(new_t1)), TensorType(tuple(new_t2))
+        return (t1, t2)
+    else:
+        raise TypeError(f"Cannot broadcast types {t1} and {t2}")
+
+
+def register_inference_rule(
+    call_target: Target,
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
+    def register(fn: Callable[_P, _T]) -> Callable[_P, _T]:
+        if call_target in _INFERENCE_RULES:
+            raise RuntimeError(f"Inference rule already registered for {call_target}!")
+        _INFERENCE_RULES[call_target] = fn
+        return fn
+
+    return register
+
+
+def register_refinement_rule(
+    call_target: Target,
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
+    def register(fn: Callable[_P, _T]) -> Callable[_P, _T]:
+        if call_target in _REFINEMENT_RULES:
+            raise RuntimeError(f"Refinement rule already registered for {call_target}!")
+        _REFINEMENT_RULES[call_target] = fn
+        return fn
+
+    return register
+
+
+def register_algebraic_expressions_inference_rule(
+    call_target: Target,
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
+    def register(fn: Callable[_P, _T]) -> Callable[_P, _T]:
+        if call_target in _RULES:
+            raise RuntimeError(f"Rule already registered for {call_target}!")
+        _RULES[call_target] = fn
+        return fn
+
+    return register
+
+
+@register_inference_rule(torch.add)
+@register_inference_rule(operator.add)
+def add_inference_rule(n: Node):
+    """
+    Apply the addition inference rule. This includes:
+    - scalar addition
+    - broadcasting semantics
+
+    Note that we always return the least precise type between
+    the operands (after applying broadcasting) to be the final type of the operation
+
+    Note that we do not modify the operand types themselves after applying broadcasting
+    to them. We only use them to calculate the final type
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], Node)
+    t1 = n.args[0].type
+    t2 = n.args[1].type
+
+    # handle scalar addition
+    if t1 == int and isinstance(t2, TensorType):
+        n.type = t2
+        return n.type
+
+    # handle scalar addition
+    elif t2 == int and isinstance(t1, TensorType):
+        n.type = t1
+        return n.type
+
+    # we bring the new types to the point where
+    # we can check for consistency
+    # any inconsistency would not have been caused
+    # by broadcasting at this point
+    (new_t1, new_t2) = broadcast_types(t1, t2)
+
+    if new_t1 != t1 or new_t2 != t2:
+        n.meta["broadcast"] = True
+        n.meta[str(n.args[0])] = new_t1
+        n.meta[str(n.args[1])] = new_t2
+
+    else:
+        n.meta["broadcast"] = False
+
+    new_t1 = t1 if not n.meta["broadcast"] else new_t1
+    new_t2 = t2 if not n.meta["broadcast"] else new_t2
+
+    # we check for consistency between the new types
+    if is_consistent(new_t1, new_t2):
+        # we return the less precise type because
+        # broadcasting may have happened
+        # for operands with shape [1,2,Dyn] and [1,2,1]
+        # we have to assign the node [1,2,Dyn]
+        if is_more_precise(new_t1, new_t2):
+            n.type = new_t2
+        else:
+            n.type = new_t1
+        return n.type
+    else:
+        raise TypeError(
+            f"Cannot add arguments {n.args[0]} ({n.args[0].type}) and {n.args[1]} ({n.args[1].type}) in node {n}."
+            f" Types should match "
+        )
+
+
+@register_inference_rule(getattr)
+def get_attr_inference_rule(n: Node, traced):
+    """
+    The current getattr rule only handles the shape attribute
+    Can be extended to other attributes
+    The most representitive type we have is "Dyn" but the system
+    can be extended with more types, such as a type to represent shapes
+    """
+    attr_name = n.args[1]
+
+    if attr_name == "shape":
+        n.type = Dyn
+    else:
+        raise TypeError("Not yet implemented")
+
+    # TODO. We leave it like this till we add a type to represent tensor sizes
+    return n.type
+
+
+@register_inference_rule(torch.transpose)
+def transpose_inference_rule(n: Node):
+    """
+    We check that dimensions for the transpose operations
+    are within range of the tensor type of the node
+    """
+    if n.target == torch.transpose:
+        assert isinstance(n.args[0], Node)
+        t = n.args[0].type
+
+        assert isinstance(n.args[1], int)
+        assert isinstance(n.args[2], int)
+        dim1, dim2 = n.args[1], n.args[2]
+
+        if t == Dyn:
+            n.type = Dyn
+            return n.type
+
+        elif isinstance(t, TensorType):
+            if 0 <= dim1 < len(t.__args__) and 0 <= dim2 < len(t.__args__):
+                new_type = list(t.__args__)
+                new_type[dim1], new_type[dim2] = new_type[dim2], new_type[dim1]
+                final = TensorType(new_type)
+                n.type = get_greatest_upper_bound(n.type, final)
+                return n.type
+            else:
+                raise TypeError(
+                    f"Cannot transpose {dim1} and {dim2} in type {t} for node {n}"
+                )
+        else:
+            raise TypeError(
+                f"Cannot transpose {dim1} and {dim2} in type {t} for node {n}"
+            )
+
+
+@register_inference_rule(torch.reshape)
+def reshape_inference_rule(n: Node):
+    """
+    Without dynamism, the rule checks that the
+    product of the elements of the argument tensor
+    type is equal to the product of the elements
+    of the required shape. We gradualize this rule
+    by adding a case to handle fully dynamic input
+    as well as input where some of the tensor dimensions
+    are unknown. In this case we check for divisibility
+    """
+    assert isinstance(n.args[0], Node)
+    t1 = n.args[0].type
+
+    assert isinstance(n.args[1], list)
+    t2 = n.args[1]
+    t2_type = TensorType([Dyn if elem == -1 else elem for elem in t2])
+
+    # if we do not know the original tensor dimension,
+    # we return the required dimension
+    if t1 == Dyn:
+        n.type = t2_type
+        return t2_type
+
+    # if any of the dimensions are unknown,
+    # we check for divisibility
+    elif isinstance(t1, TensorType):
+        assert isinstance(t1, TensorType)
+        a = [e if e != Dyn else 1 for e in t1.__args__]
+        p1 = reduce(operator.mul, a)
+        p2 = reduce(operator.mul, t2)
+        if p1 % p2 == 0 or p2 % p1 == 0:
+            n.type = t2_type
+            return t2_type
+        else:
+            raise TypeError(f"Cannot reshape in node {n} from {t1} to {t2_type}")
+    else:
+        raise TypeError(f"Cannot reshape in node {n} from {t1} to {t2_type}")
+
+
+@register_inference_rule(BatchNorm2d)
+def bn2d_inference_rule(n: Node, module_instance):
+    """
+    Given a BatchNorm2D instance and a node check the following conditions:
+    - the input type can be expanded to a size 4 tensor: t =  (x_1, x_2, x_3, x_4)
+    - the current node type can be expanded to a size 4 tensor: t' =  (x_1', x_2', x_3', x_4')
+    - t is consistent with t'
+    - x_2 is consistent with the module's num_features
+    - x_2' is consistent with the module's num_features
+    output type: the more precise type of t and t'
+    """
+    assert isinstance(n.args[0], Node)
+    n.args[0].type = expand_to_tensor_dim(n.args[0].type, 4)
+    arg_type = n.args[0].type
+    n.type = expand_to_tensor_dim(n.type, 4)
+
+    # we check the conditions on the incoming argument
+    # and any existing annotation
+    # we also check for consistency between both annotations
+    if (
+        is_consistent(arg_type.__args__[1], module_instance.num_features)
+        and is_consistent(n.type.__args__[1], module_instance.num_features)
+        and is_consistent(arg_type, n.type)
+    ):
+        # we choose the more precise type
+        # to be the node type
+        # so if an incoming argument has more type information
+        # we set this node's type to be the argument type
+        n.type = get_greatest_upper_bound(arg_type, n.type)
+        return n.type
+    else:
+        raise TypeError(
+            f"Cannot apply {module_instance} with input type {arg_type} and existing type {n.type} on {n}"
+        )
+
+
+def calculate_out_dimension(d_in, module_instance, index):
+    """
+    For calculating h_in and w_out according to the conv2D documentation
+    """
+    padding = (
+        (module_instance.padding, module_instance.padding)
+        if isinstance(module_instance.padding, int)
+        else module_instance.padding
+    )
+    kernel_size = (
+        (module_instance.kernel_size, module_instance.kernel_size)
+        if isinstance(module_instance.kernel_size, int)
+        else module_instance.kernel_size
+    )
+    stride = (
+        (module_instance.stride, module_instance.stride)
+        if isinstance(module_instance.stride, int)
+        else module_instance.stride
+    )
+    dilation = (
+        (module_instance.dilation, module_instance.dilation)
+        if isinstance(module_instance.dilation, int)
+        else module_instance.dilation
+    )
+
+    DIMENSION_TYPES = (int, sympy.Symbol)
+
+    if d_in == Dyn:
+        return Dyn
+
+    elif isinstance(d_in, DIMENSION_TYPES):
+        n = d_in + 2 * padding[index] - dilation[index] * (kernel_size[index] - 1) - 1
+
+        return (n // stride[0]) + 1
+
+    else:
+        raise TypeError(
+            f"{d_in} in {module_instance} must be a number or Dyn. Received {type(d_in)}"
+        )
+
+
+def get_greatest_upper_bound(type1, type2):
+    """
+    Get the most precise type that's consistent with the given types
+    """
+    if type1 == Dyn:
+        return type2
+    elif type2 == Dyn:
+        return type1
+    elif isinstance(type1, TensorType) and isinstance(type2, TensorType):
+        if not is_consistent(type1, type2):
+            raise TypeError(f"Inconsistent types {type1}, {type2}")
+        gub = [
+            t1 if is_more_precise(t1, t2) else t2
+            for (t1, t2) in zip(type1.__args__, type2.__args__)
+        ]
+        return TensorType(tuple(gub))
+
+
+@register_inference_rule(Conv2d)
+def conv2d_inference_rule(n: Node, module_instance):
+    """
+    Given a Conv2D instance and a node check the following conditions:
+    - the input type can be expanded to a size 4 tensor: t =  (x_1, x_2, H, W)
+    - the current node type can be expanded to a size 4 tensor: t' =  (x_1', x_2', x_3', x_4')
+    - x_2 is consistent with the module's in_channels
+    - let o = (x_1, out_channels, H_out, W_out)
+    then the output is the greatest upper bound of o and the existing node type t'.
+    """
+    assert isinstance(n.args[0], Node)
+    n.args[0].type = expand_to_tensor_dim(n.args[0].type, 4)
+    arg_type = n.args[0].type
+    curr_node_type = expand_to_tensor_dim(n.type, 4)
+
+    if is_consistent(arg_type.__args__[1], module_instance.in_channels):
+        w_in = arg_type.__args__[3]
+        h_in = arg_type.__args__[2]
+        h_out = calculate_out_dimension(h_in, module_instance, 0)
+        w_out = calculate_out_dimension(w_in, module_instance, 1)
+        new_type = TensorType(
+            (arg_type.__args__[0], module_instance.out_channels, h_out, w_out)
+        )
+        gub = get_greatest_upper_bound(new_type, curr_node_type)
+        n.type = gub
+        return n.type
+    else:
+        raise TypeError(
+            f"Cannot apply {module_instance} with input type {arg_type} and existing type {n.type} on {n}"
+        )
+
+
+@register_inference_rule(torch.nn.ReLU)
+def relu_inference_rule(n: Node, module_instance):
+    """
+    Input and output shapes should be equal.
+    """
+    assert isinstance(n.args[0], Node)
+
+    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
+        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
+
+    if isinstance(n.args[0].type, TensorType):
+        n.type = get_greatest_upper_bound(n.args[0].type, n.type)
+    return n.type
+
+
+def maxpool2d_check(typ, module_instance):
+    """
+    Applies the maxpool2d shape information to the input
+    this affects the last two dimensions
+    """
+    new_type_list = list(typ.__args__)
+    if len(new_type_list) == 4 or len(new_type_list) == 3:
+        w_in = new_type_list[-1]
+        h_in = new_type_list[-2]
+
+        h_out = calculate_out_dimension(h_in, module_instance, 0)
+        w_out = calculate_out_dimension(w_in, module_instance, 1)
+
+        new_type_list[-1] = w_out
+        new_type_list[-2] = h_out
+        return TensorType(tuple(new_type_list))
+
+    else:
+        raise TypeError(f"Wrong size {typ} for {module_instance}")
+
+
+@register_inference_rule(torch.nn.MaxPool2d)
+def maxpool2d_inference_rule(n: Node, module_instance):
+    """
+    Given a MaxPool2D instance and a node check the following conditions:
+    - Input size matches size 3 or 4
+    - Current node type is consistent with the output type we will calculate
+    - Input size matches output size and the last two dimensions of the output
+      are w_out and h_out. The remaining dimensions are the same as the input
+    - Our final result is the greatest upper bound of the output we calculate
+      and the current node type.
+    """
+    assert isinstance(n.args[0], Node)
+
+    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
+        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
+    if isinstance(n.args[0].type, TensorType):
+        output = maxpool2d_check(n.args[0].type, module_instance)
+        n.type = get_greatest_upper_bound(output, n.type)
+    return n.type
+
+
+def linear_check(tensor_type, module_instance):
+    """
+    Checks that an input tensor type satisfies the conditions for linear operation
+    and returns the output type based on in and out features given by module_instance
+    """
+    if len(tensor_type.__args__) >= 2:
+        if is_consistent(module_instance.in_features, tensor_type.__args__[-1]):
+            new_type_args = list(tensor_type.__args__)
+            new_type_args[-1] = module_instance.out_features
+            return TensorType(tuple(new_type_args))
+        else:
+            raise TypeError(
+                f"Inconsistent {module_instance.in_features} and {tensor_type.__args__[-1]} in {module_instance}"
+            )
+    else:
+        raise TypeError(f"Type {tensor_type} must have rank 2 or more.")
+
+
+@register_inference_rule(torch.nn.Linear)
+def linear_inference_rule(n: Node, module_instance):
+    """
+    Applies the shape information to the input then gets the greatest upper bound
+    of the resulting type and the existing type
+    """
+    assert isinstance(n.args[0], Node)
+    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
+        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
+    if isinstance(n.args[0].type, TensorType):
+        output_type = linear_check(n.args[0].type, module_instance)
+        n.type = get_greatest_upper_bound(output_type, n.type)
+    return n.type
+
+
+def adaptiveavgpool2d_check(tensor_type, module_instance):
+    output_size = module_instance.output_size
+    if isinstance(output_size, int):
+        output_size = [output_size, output_size]
+    elif isinstance(output_size, tuple):
+        output_size = list(output_size)
+        if output_size[0] is None:
+            output_size[0] = output_size[1]
+        if output_size[1] is None:
+            output_size[1] = output_size[0]
+
+    new_type_list = list(tensor_type.__args__)
+
+    if len(tensor_type.__args__) == 4 or len(tensor_type.__args__) == 3:
+        new_type_list[-1] = output_size[1]
+        new_type_list[-2] = output_size[0]
+
+        return TensorType(tuple(new_type_list))
+
+    else:
+        raise TypeError(f"Tensor ranks must be 3 or 4. Got {tensor_type}")
+
+
+@register_inference_rule(torch.nn.AdaptiveAvgPool2d)
+def adaptiveavgpool2d_inference_rule(n: Node, module_instance):
+    """
+    The input and output sizes should be the same except for the last
+    two dimensions taken from the input, which represent width and height
+    """
+    assert isinstance(n.args[0], Node)
+    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
+        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
+    if isinstance(n.args[0].type, TensorType):
+        output_type = adaptiveavgpool2d_check(n.args[0].type, module_instance)
+        n.type = get_greatest_upper_bound(n.type, output_type)
+    return n.type
+
+
+def flatten_check(tensor_type, start_dim, end_dim):
+    l = len(tensor_type.__args__)
+
+    start_dim = l if start_dim == -1 else abs(start_dim)
+    end_dim = l + end_dim + 1 if end_dim < 0 else end_dim + 1
+
+    if 0 <= start_dim <= (l - 1) and 0 <= end_dim <= l and start_dim < end_dim:
+        my_args = list(tensor_type.__args__)
+        lhs = my_args[0:start_dim]
+        rhs = my_args[end_dim:]
+        mid = my_args[start_dim:end_dim]
+        if Dyn in mid:
+            mid = [Dyn]
+        else:
+            mid = [reduce(operator.mul, my_args[start_dim:end_dim])]
+        new_type_list = lhs + mid + rhs
+        return TensorType(tuple(new_type_list))
+    else:
+        raise TypeError(
+            f"Incompatible dimensions {start_dim}, {end_dim - 1} in type {tensor_type}"
+        )
+
+
+@register_inference_rule(torch.flatten)
+def flatten_inference_rule(n: Node):
+    """
+    Applies the flatten shape information to the input then gets the
+    greatest upper bound of the resulting type and the existing type
+    """
+    assert isinstance(n.args[0], Node)
+
+    # set the default start and end dims
+    start_dim = 1
+    end_dim = -1
+
+    if len(n.args) > 1:
+        assert isinstance(n.args[1], int)
+        start_dim = n.args[1]
+
+    if len(n.args) > 2:
+        assert isinstance(n.args[2], int)
+        end_dim = n.args[2]
+
+    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
+        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
+
+    if isinstance(n.args[0].type, TensorType):
+        output_type = flatten_check(n.args[0].type, start_dim, end_dim)
+        n.type = get_greatest_upper_bound(output_type, n.type)
+
+    return n.type
+
+
+class GraphTypeChecker:
+    def __init__(self, env, traced):
+        self.env = env
+        self.traced = traced
+
+    def type_check(self):
+        """
+        A gradual type checker for graphs
+        Effect: every node's field type will be
+        populated with a type after type-checking is done
+        """
+        graph = self.traced.graph
+
+        # type check every node with gradual type rules
+        # if any node does not type check return false
+        for n in graph.nodes:
+            self.type_check_node(n)
+        return True
+
+    def type_check_node(self, n: Node):
+        """
+        Type check a given fx node.
+        Current operations:
+        - Reshape
+        - Transpose
+        - Add
+        - Relu
+        - conv2d
+        - batchnorm2d
+        - flatten
+        - maxpool2d
+        - adaptiveavgpool2d
+        - linear
+        """
+        if n.type is None:
+            n.type = Dyn
+
+        if n.op == "placeholder":
+            return n.type
+
+        elif n.op == "get_attr":
+            t = get_parameter(self.traced, n.target)  # type: ignore[arg-type]
+            if isinstance(t.data, torch.Tensor):
+                n.type = TensorType(t.data.shape)
+            return n.type
+
+        elif n.op == "call_function":
+            if n.target == getattr:
+                assert getattr in _INFERENCE_RULES
+                return _INFERENCE_RULES[n.target](n, self.traced)
+
+            elif n.target in _INFERENCE_RULES:
+                return _INFERENCE_RULES[n.target](n)
+            else:
+                raise RuntimeError(
+                    f"No inference rule registered for target {n.target}!"
+                )
+
+        elif n.op == "call_module":
+            module_instance = self.traced.get_submodule(n.target)
+            if type(module_instance) in _INFERENCE_RULES:
+                return _INFERENCE_RULES[type(module_instance)](n, module_instance)
+            else:
+                raise RuntimeError(
+                    f"No inference rule registered for class {type(module_instance)}!"
+                )
+
+        elif n.op == "output":
+
+            def get_node_type(a):
+                return a.type
+
+            n.type = torch.fx.node.map_arg(n.args[0], get_node_type)
+            return n.type
+
+        else:
+            raise NotImplementedError(f"Method {n.op} not yet implemented")
+
+
+@register_refinement_rule(Conv2d)
+def conv_refinement_rule(n: Node):
+    """
+    The equality constraints are between the first dimension of
+    the input and output
+    """
+    res = []
+    assert isinstance(n.args[0], Node)
+    arg_type = n.args[0].type
+    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
+        res = [Equality(arg_type.__args__[0], n.type.__args__[0])]
+        return res
+
+
+@register_refinement_rule(torch.nn.Linear)
+def linear_refinement_rule(n: Node):
+    """
+    The equality constraints are between the first dimension of
+    the input and output
+    """
+    res = []
+    assert isinstance(n.args[0], Node)
+    arg_type = n.args[0].type
+    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
+        res = [Equality(arg_type.__args__[0], n.type.__args__[0])]
+    return res
+
+
+@register_refinement_rule(BatchNorm2d)
+@register_refinement_rule(torch.nn.ReLU)
+def all_eq(n: Node):
+    """
+    For operations where the input shape is equal to the output shape
+    """
+    res = []
+    assert isinstance(n.args[0], Node)
+    arg_type = n.args[0].type
+    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
+        args1 = arg_type.__args__
+        args2 = n.type.__args__
+        res = [Equality(args1[i], args2[i]) for i in range(len(args1))]
+    return res
+
+
+@register_refinement_rule(torch.nn.AdaptiveAvgPool2d)
+@register_refinement_rule(torch.nn.MaxPool2d)
+def first_two_eq(n: Node):
+    """
+    For operations where the first two dimensions of the input and output shape
+    are equal
+    """
+    res = []
+    assert isinstance(n.args[0], Node)
+    arg_type = n.args[0].type
+    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
+        args1 = arg_type.__args__
+        args2 = n.type.__args__
+        res = [Equality(args1[0], args2[0]), Equality(args1[1], args2[1])]
+    return res
+
+
+@register_refinement_rule(torch.add)
+@register_refinement_rule(operator.add)
+def element_wise_eq(n: Node):
+    """
+    For element-wise operations and handles broadcasting.
+    Note that after applying broadcasting to the arguments
+    we are able to determine if certain dimensions have not been broadcast
+    if they are symbolicallu equal.
+
+    in this case, we can establish equality between those dimensions and the
+    corresponding output dimensions.
+
+    Note that it takes two iterations for this result. One iteration to establish
+    equality between certain dimensions of the operands (requiring the whole solver
+    including unification) and another iteration to establish equality between the operands
+    and the resulting type, requiring another round of constraint generation and unificaiton.
+    """
+    res = []
+    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
+        arg_type1 = n.args[0].type
+        arg_type2 = n.args[1].type
+        if (
+            isinstance(arg_type1, TensorType)
+            and isinstance(arg_type2, TensorType)
+            and isinstance(n.type, TensorType)
+        ):
+            args1, args2 = broadcast_types(arg_type1, arg_type2)
+            # by this point, we know that args1 and args2 are the same size.
+            a1 = args1.__args__
+            a2 = args2.__args__
+            a3 = n.type.__args__
+
+            # we would be here in the second iteration where we establish equality
+            # between operand type dimensions and the resulting type dimensions
+            r = []
+            for x, y, z in zip(a1, a2, a3):
+                if x == y:
+                    r.append(Equality(x, z))
+            res = r
+    return res
+
+
+@register_refinement_rule(torch.flatten)
+def flatten_refinement_rule(n: Node):
+    """
+    Generates equality constraints between the dimensions of the input and output
+    that will not be involved in the flatten operation
+    """
+    assert isinstance(n.args[0], Node)
+
+    eq_const = []
+
+    start_dim = 1
+    end_dim = -1
+
+    if len(n.args) > 1:
+        assert isinstance(n.args[1], int)
+        start_dim = n.args[1]
+
+    if len(n.args) > 2:
+        assert isinstance(n.args[2], int)
+        end_dim = n.args[2]
+
+    if isinstance(n.type, TensorType) and isinstance(n.args[0].type, TensorType):
+        l = len(n.type.__args__)
+        arg_type = n.args[0].type
+        start_dim = l if start_dim == -1 else start_dim
+        end_dim = l + end_dim + 1 if end_dim < 0 else end_dim + 1
+
+        for t1, t2 in zip(n.type.__args__[0:start_dim], arg_type.__args__[0:start_dim]):
+            eq_const.append(Equality(t1, t2))
+
+        for t1, t2 in zip(n.type.__args__[end_dim:], arg_type.__args__[end_dim:]):
+            eq_const.append(Equality(t1, t2))
+    return eq_const
+
+
+@register_algebraic_expressions_inference_rule(Conv2d)
+def conv_rule(n: Node, module_instance):
+    """
+    Represents the outout in terms of an algrbraic expression w.r.t
+    the input when possible
+    """
+    assert isinstance(n.args[0], Node)
+    arg_type = n.args[0].type
+    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
+        w_in = arg_type.__args__[3]
+        h_in = arg_type.__args__[2]
+        h_out = calculate_out_dimension(h_in, module_instance, 0)
+        w_out = calculate_out_dimension(w_in, module_instance, 1)
+        new_type = TensorType((n.type.__args__[0], n.type.__args__[1], h_out, w_out))
+        n.type = new_type
+        return new_type
+
+
+class Refine:
+    """
+    Symbolic shape inference.
+    Generates constraints over type variables.
+    Currently all constraints are equality constraints.
+    """
+
+    def __init__(self, traced):
+        self.constraints = []
+        self.traced = traced
+        self.symbol_iter = itertools.count(start=0, step=1)
+
+    def refine(self):
+        """
+        Generates constraints for
+        every node in the graph based on
+        the operation.
+        """
+        graph = self.traced.graph
+        for n in graph.nodes:
+            self.refine_node(n)
+        return True
+
+    def symbolic_relations(self):
+        """
+        Infers algebraic relations
+        """
+        graph = self.traced.graph
+        for n in graph.nodes:
+            self.infer_symbolic_relations(n)
+        return True
+
+    def replace_dyn_with_fresh_var(self, typ):
+        """
+        Replace all unknown types with fresh type variables.
+        """
+        if typ == Dyn:
+            new_symbol = Var(next(self.symbol_iter))
+            return new_symbol
+        elif isinstance(typ, TensorType):
+            new_args = [self.replace_dyn_with_fresh_var(a) for a in typ.__args__]
+            return TensorType(tuple(new_args))
+        elif isinstance(typ, list):
+            return [self.replace_dyn_with_fresh_var(t) for t in typ]
+        elif isinstance(typ, tuple):
+            return (self.replace_dyn_with_fresh_var(t) for t in typ)
+        else:
+            return typ
+
+    def convert_to_sympy_symbols(self, typ):
+        """
+        Replace all unknown types with fresh type variables.
+        """
+        if isinstance(typ, Var):
+            return sympy.symbols(str(typ))
+        elif isinstance(typ, TensorType):
+            new_args = [self.convert_to_sympy_symbols(a) for a in typ.__args__]
+            return TensorType(tuple(new_args))
+        elif isinstance(typ, list):
+            return [self.convert_to_sympy_symbols(t) for t in typ]
+        elif isinstance(typ, tuple):
+            return (self.convert_to_sympy_symbols(t) for t in typ)
+        else:
+            return typ
+
+    def refine_node(self, n: Node):
+        """
+        Returns a list of equality constraints for
+        call_module and call_function nodes.
+        Models the relation between input and output dimensions
+        using constraints in case they are both tensors.
+        All operations used in resnet50 are defined.
+        """
+        if n.type is None:
+            n.type = Dyn
+
+        n.type = self.replace_dyn_with_fresh_var(n.type)
+
+        if n.op == "call_function":
+            if n.target in _REFINEMENT_RULES:
+                self.constraints += _REFINEMENT_RULES[n.target](n)
+            else:
+                pass
+
+        if n.op == "call_module":
+            module_instance = self.traced.get_submodule(n.target)
+            if type(module_instance) in _REFINEMENT_RULES:
+                self.constraints += _REFINEMENT_RULES[type(module_instance)](n)
+            else:
+                pass
+
+        if n.op == "output":
+
+            def get_node_type(a):
+                return a.type
+
+            n.type = torch.fx.node.map_arg(n.args[0], get_node_type)
+            return n.type
+
+        else:
+            pass
+
+    def infer_symbolic_relations(self, n: Node):
+        n.type = self.convert_to_sympy_symbols(n.type)
+        if n.op == "call_function":
+            if n.target in _RULES:
+                return _RULES[n.target](n)
+            else:
+                pass
+
+        if n.op == "call_module":
+            module_instance = self.traced.get_submodule(n.target)
+            if type(module_instance) in _RULES:
+                return _RULES[type(module_instance)](n, module_instance)
+            else:
+                pass
+
+        if n.op == "output":
+
+            def get_node_type(a):
+                return a.type
+
+            n.type = torch.fx.node.map_arg(n.args[0], get_node_type)
+            return n.type
+
+        else:
+            pass
+
+
+def get_parameter(traced, target: str):
+    """
+    Returns the parameter given by ``target`` if it exists,
+    otherwise throws an error.
+
+    See the docstring for ``get_submodule`` for a more detailed
+    explanation of this method's functionality as well as how to
+    correctly specify ``target``.
+
+    Args:
+        target: The fully-qualified string name of the Parameter
+            to look for. (See ``get_submodule`` for how to specify a
+            fully-qualified string.)
+
+    Returns:
+        torch.nn.Parameter: The Parameter referenced by ``target``
+
+    Raises:
+        AttributeError: If the target string references an invalid
+            path or resolves to something that is not an
+            ``nn.Parameter``
+    """
+    module_path, _, param_name = target.rpartition(".")
+
+    mod: torch.nn.Module = traced.get_submodule(module_path)
+
+    if not hasattr(mod, param_name):
+        raise AttributeError(mod._get_name() + " has no attribute `" + param_name + "`")
+
+    param: torch.nn.Parameter = getattr(mod, param_name)
+
+    return param
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/merge_matmul.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/merge_matmul.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6a51918f930ffdcdff63dc33ac1aac0a4e98bd0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/merge_matmul.py
@@ -0,0 +1,177 @@
+# mypy: allow-untyped-defs
+import itertools
+import operator
+
+import torch
+from torch.fx._symbolic_trace import symbolic_trace
+from torch.fx.node import Node
+from torch.fx.passes.tools_common import legalize_graph
+
+
+def split_result_tensors(
+    result: torch.Tensor, inputs: list[torch.Tensor]
+) -> tuple[torch.Tensor, ...]:
+    """
+    A free function for use in the merge_matmul graph transformation below that
+    splits the output from a merged matmul into the individual results for each
+    input tensor.
+
+    Arguments:
+        result: The merged matmul result tensor.
+        inputs: The list of inputs that were merged into one for the matmul.
+
+    Returns:
+        List of matmul results for each input tensor.
+    """
+    # When fx tracer is running, x.shape[0] will be torch.fx.Attribute but we
+    # need an int even when tracing
+    if isinstance(result, torch.fx.Proxy):
+        splits = [0] * len(inputs)
+    else:
+        splits = [x.shape[0] for x in inputs]
+
+    return torch.split(result, splits)
+
+
+def may_depend_on(a: Node, b: Node, search_depth: int = 6):
+    """
+    Determine if one node depends on another in a torch.fx.Graph.
+
+    Arguments:
+        a: The node that may have a dependency on b.
+        b: The node that a may have a dependency on.
+        search_depth: In the case of an indirect dependency, this function
+                        searches upto this many nodes away in search of a
+                        data dependency. If none is found, the function
+                        makes the conservative assumption that there is a
+                        dependency.
+
+    Returns:
+        True if a may depend on b, False if it definitely does not.
+    """
+    # Equivalence is defined as dependence.
+    if a == b:
+        return True
+
+    # If a has no inputs, it cannot depend on b.
+    if len(a.all_input_nodes) == 0:
+        return False
+
+    # If the search depth has been exhausted and no conclusion has been
+    # reached, assume that there is a data dependency.
+    if search_depth == 0:
+        return True
+
+    # Recursively check all inputs of a.
+    for inp in a.all_input_nodes:
+        if may_depend_on(inp, b, search_depth - 1):
+            return True
+
+    return False
+
+
+def are_nodes_independent(nodes: list[Node]):
+    """
+    Check if all of the given nodes are pairwise-data independent.
+
+    Arguments:
+        nodes: The nodes to check for data dependencies.
+
+    Returns:
+        True if any pair in nodes has a data dependency.
+    """
+    # For each pair in nodes:
+    for i, j in itertools.combinations(nodes, 2):
+        if may_depend_on(i, j) or may_depend_on(j, i):
+            return False
+
+    return True
+
+
+def merge_matmul(in_mod: torch.nn.Module):
+    """
+    A graph transformation that merges matrix multiplication operations that share the same right-hand
+    side operand into one large matrix multiplication.
+               ____      _________        _________
+      ----    |    |    |         |     M|  A * C  |
+    M| A  |  T| B  | * K|    C    | =    |---------|
+      ---- ,  |    |    |         |     T|  B * C  |
+       K       ----      ---------        ---------
+                K            R                R
+    """
+    gm = symbolic_trace(in_mod)
+
+    rhs_users: dict[Node, list[Node]] = {}
+    lhs_users: dict[Node, list[Node]] = {}
+
+    # Populate rhs_users and lhs_users - maps from LHS/RHS matrix multiply operands to
+    # the matmul of which they are the LHS/RHS.
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target is not torch.matmul:
+            continue
+
+        lhs, rhs = node.args
+
+        # TODO: Properly handle aliasing caused by get_attr. For now,
+        # use the attribute name as the operand if the node is a
+        # get_attr.
+        lhs = lhs.target if lhs.op == "get_attr" else lhs
+        rhs = rhs.target if rhs.op == "get_attr" else rhs
+
+        lhs_users.setdefault(lhs, []).append(node)
+        rhs_users.setdefault(rhs, []).append(node)
+
+    for rhs, mms in rhs_users.items():
+        # There must be at least matmuls for a merge to make sense.
+        if len(mms) < 2:
+            continue
+
+        # All matmuls must not depend on each other directly or indirectly
+        # in order for the merge to be possible.
+        if not are_nodes_independent(mms):
+            continue
+
+        lhs_vals = [mm.args[0] for mm in mms]
+
+        # Merge the matmul.
+        # Collect a list of LHS operands and the single RHS operand.
+        lhs = [gm.graph.get_attr(l) if isinstance(l, str) else l for l in lhs_vals]
+        rhs = gm.graph.get_attr(rhs) if isinstance(rhs, str) else rhs
+
+        # Concatenate all the LHS operands.
+        merge_mm_cat = gm.graph.call_function(torch.cat, (lhs,), {})
+
+        # Multiply the concatenated LHS operands with the one RHS. This will produce
+        # the same results as all the individual matmuls involving rhs in the original graph,
+        # but they will all be concatenated together.
+        merge_mm = gm.graph.call_function(
+            torch.matmul,
+            (
+                merge_mm_cat,
+                rhs,
+            ),
+            {},
+        )
+
+        # Split the result of the merged matmul using the shapes of the LHS operands
+        # to ascertain how large each chunk should be.
+        merge_mm_split = gm.graph.call_function(
+            split_result_tensors, (merge_mm, lhs), {}
+        )
+        merge_mm_res = [
+            gm.graph.call_function(operator.getitem, (merge_mm_split, out), {})
+            for out in range(len(lhs))
+        ]
+
+        # Replace all uses of the original, unmerged matmuls with the equivalent split chunk from the merged matmul.
+        for old, new in zip(mms, merge_mm_res):
+            old.replace_all_uses_with(new)
+            gm.graph.erase_node(old)
+
+        # All of the new nodes created above were inserted at the end, so we need to sort
+        # the nodes topologically to make sure all definitions precede uses.
+        legalize_graph(gm)
+
+    gm.recompile()
+    gm.graph.lint()
+    return gm
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/meta_tracer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/meta_tracer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2fc033e0b8dbd17cfdaf614f05f4c61f0351448
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/meta_tracer.py
@@ -0,0 +1,311 @@
+# mypy: allow-untyped-defs
+import builtins
+import functools
+import warnings
+from typing import Any, Callable, Optional, Union
+
+import torch
+import torch.fx
+
+
+def embedding_override(self, input):
+    return torch.empty(*input.shape, self.weight.shape[-1], device="meta")
+
+
+def nn_layernorm_override(self, input):
+    return input
+
+
+def torch_relu_override(x):
+    return x
+
+
+def torch_nn_relu_override(self, x):
+    return x
+
+
+def functional_relu_override(x, inplace=False):
+    assert not inplace, "dont support inplace functional.relu for metatensor analysis"
+    return x
+
+
+def torch_where_override(condition, x, y):
+    # torch.where returns the broadcasted tensor of condition, x, and y,
+    # so hack it by using addition
+    return condition.to(device="meta") + x.to(device="meta") + y.to(device="meta")
+
+
+def torch_abs_override(input, *, out=None):
+    assert out is None, "Dont support in-place abs for MetaTensor analysis"
+    return input
+
+
+manual_meta_overrides: dict[Callable, Callable] = {
+    torch.nn.Embedding: embedding_override,
+    torch.nn.LayerNorm: nn_layernorm_override,
+    torch.relu: torch_relu_override,
+    torch.nn.functional.relu: functional_relu_override,
+    torch.nn.ReLU: torch_nn_relu_override,
+    torch.where: torch_where_override,
+    torch.abs: torch_abs_override,
+}
+
+
+def gen_constructor_wrapper(target):
+    @functools.wraps(target)
+    def wrapper(*args, **kwargs):
+        proxy = None
+
+        def check_has_proxy(v):
+            if isinstance(v, torch.fx.Proxy):
+                nonlocal proxy
+                proxy = v
+
+        torch.fx.node.map_aggregate(args, check_has_proxy)
+        torch.fx.node.map_aggregate(kwargs, check_has_proxy)
+
+        if proxy is not None:
+            return proxy.tracer.create_proxy("call_function", target, args, kwargs)
+        else:
+            return target(*args, **kwargs)
+
+    return wrapper, target
+
+
+class MetaProxy(torch.fx.Proxy):
+    def install_tensor_meta(self, tensor_meta):
+        self._tensor_meta = tensor_meta
+
+    def size(self, dim=None):
+        if hasattr(self, "_tensor_meta") and self._tensor_meta is not None:
+            return self._tensor_meta.size(*[dim] if dim else [])
+        return self.tracer.create_proxy(
+            "call_method", "size", (self, dim) if dim else (self,), {}
+        )
+
+    def dim(self):
+        if hasattr(self, "_tensor_meta") and self._tensor_meta is not None:
+            return self._tensor_meta.dim()
+        return self.tracer.create_proxy("call_method", "dim", (self,), {})
+
+    @property
+    def shape(self):
+        if hasattr(self, "_tensor_meta") and self._tensor_meta is not None:
+            return self._tensor_meta.shape
+        return self.tracer.create_proxy(
+            "call_function", builtins.getattr, (self, "shape"), {}
+        )
+
+    @property
+    def dtype(self):
+        if hasattr(self, "_tensor_meta") and self._tensor_meta is not None:
+            return self._tensor_meta.dtype
+        return self.tracer.create_proxy(
+            "call_function", builtins.getattr, (self, "dtype"), {}
+        )
+
+    @property
+    def device(self):
+        # Hack so we can track when devices are used. During meta-tensor propagation,
+        # replace these values with a constant 'meta'
+        return MetaDeviceAttribute(self, "device")
+
+    def __getattr__(self, k):
+        if k == "_tensor_meta":
+            return self.__getattribute__(k)
+        # note: not added to the graph yet, if this is a method call
+        # we peephole optimize to the method invocation
+        return MetaAttribute(self, k)
+
+
+class MetaAttribute(MetaProxy):
+    def __init__(self, root, attr: str):
+        self.root = root
+        self.attr = attr
+        self.tracer = root.tracer
+        self._node = None
+
+    @property
+    def node(self):
+        # the node for attributes is added lazily, since most will just be method calls
+        # which do not rely on the getitem call
+        if self._node is None:
+            self._node = self.tracer.create_proxy(
+                "call_function", getattr, (self.root, self.attr), {}
+            ).node
+        return self._node
+
+    def __call__(self, *args, **kwargs):
+        return self.tracer.create_proxy(
+            "call_method", self.attr, (self.root,) + args, kwargs
+        )
+
+
+class MetaDeviceAttribute(MetaAttribute):
+    pass
+
+
+def proxys_to_metas(v):
+    if isinstance(v, MetaDeviceAttribute):
+        return "meta"
+    if isinstance(v, torch.fx.Proxy):
+        assert isinstance(v, MetaProxy), f"Expected MetaProxy but got {type(v)}"
+        assert hasattr(v, "_tensor_meta"), "MetaProxy does not have an associated meta"
+        return v._tensor_meta
+    return v
+
+
+class MetaTracer(torch.fx.Tracer):
+    allow_insert_stateless_mods: bool = True
+
+    _TORCH_METHODS_TO_PATCH = ["arange", "zeros", "ones", "full_like", "eye"]
+
+    def create_proxy(
+        self,
+        kind,
+        target,
+        args,
+        kwargs,
+        name=None,
+        type_expr=None,
+        proxy_factory_fn=None,
+    ):
+        rv = super().create_proxy(
+            kind, target, args, kwargs, name, type_expr, proxy_factory_fn
+        )
+
+        if kind == "placeholder" and target in self.meta_args:
+            rv.install_tensor_meta(self.meta_args[target])
+            return rv
+
+        if target in self.orig_fns:
+            # NOTE: tensor constructors in PyTorch define the `device` argument as
+            # *kwargs-only*. That is why this works. If you add methods to
+            # _TORCH_METHODS_TO_PATCH that do not define `device` as kwarg-only,
+            # this will break and you will likely see issues where we cannot infer
+            # the size of the output.
+            if "device" in kwargs:
+                kwargs["device"] = "meta"
+
+        try:
+            args_metas = torch.fx.node.map_aggregate(args, proxys_to_metas)
+            kwargs_metas = torch.fx.node.map_aggregate(kwargs, proxys_to_metas)
+
+            if kind == "call_function":
+                meta_target = manual_meta_overrides.get(target, target)
+                meta_out = meta_target(*args_metas, **kwargs_metas)
+            elif kind == "call_method":
+                meta_target = getattr(args_metas[0], target)  # type: ignore[index]
+                meta_out = meta_target(*args_metas[1:], **kwargs_metas)  # type: ignore[index]
+            elif kind == "call_module":
+                assert hasattr(self, "orig_forward")
+                self._disable_module_getattr = True
+                try:
+                    mod = self.root.get_submodule(target)
+                    mod_type = type(mod)
+                    if mod_type in manual_meta_overrides:
+                        meta_out = manual_meta_overrides[mod_type](
+                            mod, *args_metas, **kwargs_metas
+                        )  # type: ignore[misc, arg-type]
+                    else:
+                        meta_out = self.orig_forward(*args_metas, **kwargs_metas)
+                finally:
+                    self._disable_module_getattr = False
+            elif kind == "get_attr":
+                self._disable_module_getattr = True
+                try:
+                    attr_itr = self.root
+                    atoms = target.split(".")
+                    for atom in atoms:
+                        attr_itr = getattr(attr_itr, atom)
+                    assert isinstance(attr_itr, torch.Tensor)
+                    meta_out = attr_itr.to(device="meta")
+                finally:
+                    self._disable_module_getattr = False
+            else:
+                return rv
+
+            # TODO
+            assert isinstance(rv, torch.fx.Proxy), "Dont support composite output yet"
+            rv.install_tensor_meta(meta_out)
+        except Exception as e:
+            warnings.warn(f"Could not compute metadata for {kind} target {target}: {e}")
+
+        return rv
+
+    def getattr(self, attr, attr_val, parameter_proxy_cache):
+        if getattr(self, "_disable_module_getattr", False):
+            return attr_val
+        else:
+            return super().getattr(attr, attr_val, parameter_proxy_cache)
+
+    def call_module(self, m, forward, args, kwargs):
+        self.orig_forward = forward
+        return super().call_module(m, forward, args, kwargs)
+
+    def _insert_module_as_submodule(self, mod: torch.nn.Module) -> str:
+        """
+        Helper method which tries to insert a module that was not declared as submodule.
+        """
+        idx = 0
+        mod_name = mod.__class__.__name__.lower()
+        path = f"{mod_name}_{idx}"
+        while hasattr(self.root, path):
+            path = f"{mod_name}_{idx}"
+            idx += 1
+
+        self.root.add_module(path, mod)
+        return path
+
+    def path_of_module(self, mod: torch.nn.Module) -> str:
+        try:
+            return super().path_of_module(mod)
+        except NameError:
+            if (
+                self.allow_insert_stateless_mods
+                and len(list(mod.parameters())) == 0
+                and len(list(mod.buffers())) == 0
+            ):
+                path = self._insert_module_as_submodule(mod)
+                self.prev_module = path
+                return path
+            raise
+
+    def proxy(self, node):
+        return MetaProxy(node, self)
+
+    def trace(self, root, meta_args: dict[str, torch.Tensor], concrete_args=None):  # type: ignore[override]
+        assert isinstance(meta_args, dict)
+        self.meta_args = meta_args
+
+        self.patched_torch_methods = {
+            target: gen_constructor_wrapper(getattr(torch, target))
+            for target in self._TORCH_METHODS_TO_PATCH
+        }
+        self.orig_fns = set()
+
+        for name, (wrapper, orig) in self.patched_torch_methods.items():
+            setattr(torch, name, wrapper)
+            self.orig_fns.add(orig)
+
+        try:
+            graph = super().trace(root, concrete_args)
+            graph._tracer_extras = {"meta_args": meta_args}
+            return graph
+        finally:
+            for name, (_, orig) in self.patched_torch_methods.items():
+                setattr(torch, name, orig)
+
+
+def symbolic_trace(
+    root: Union[torch.nn.Module, Callable[..., Any]],
+    meta_args: Optional[dict[str, torch.Tensor]] = None,
+    concrete_args: Optional[dict[str, Any]] = None,
+) -> torch.fx.GraphModule:
+    tracer = MetaTracer()
+    graph = tracer.trace(root, meta_args, concrete_args)  # type: ignore[arg-type]
+    name = (
+        root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__
+    )
+    gm = torch.fx.GraphModule(tracer.root, graph, name)
+    return gm
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint.py
new file mode 100644
index 0000000000000000000000000000000000000000..8aca3e482c95f75506621475935a9d3b8b879d4e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint.py
@@ -0,0 +1,643 @@
+# mypy: allow-untyped-defs
+from torch.fx.experimental.migrate_gradual_types.operation import (
+    op_add,
+    op_div,
+    op_eq,
+    op_gt,
+    op_lt,
+    op_mod,
+    op_mul,
+    op_neq,
+    op_sub,
+)
+from torch.fx.tensor_type import Dyn, TensorType
+
+
+class Constraint:
+    pass
+
+
+class Conj(Constraint):
+    def __init__(self, conjuncts):
+        """
+        :param conjuncts: Conjunction of constraints
+        """
+        self.conjucts = conjuncts
+
+    def __eq__(self, other):
+        if isinstance(other, Conj):
+            return self.conjucts == other.conjucts and self.conjucts == other.conjucts
+        else:
+            return False
+
+    def __repr__(self):
+        return f"And({self.conjucts})"
+
+
+class Disj(Constraint):
+    def __init__(self, disjuncts):
+        """
+        :param disjuncts: Disjunction of constraints
+        """
+        self.disjuncts = disjuncts
+
+    def __eq__(self, other):
+        if isinstance(other, Disj):
+            return (
+                self.disjuncts == other.disjuncts and self.disjuncts == other.disjuncts
+            )
+        else:
+            return False
+
+    def __repr__(self):
+        return f"Or({self.disjuncts})"
+
+
+class Prod(Constraint):
+    def __init__(self, products):
+        """
+        :param products: lists of dimensions to multiply
+        """
+        self.products = products
+
+    def __eq__(self, other):
+        if isinstance(other, Prod):
+            return self.products == other.products and self.products == other.products
+        else:
+            return False
+
+    def __repr__(self):
+        return f"Product({self.products})"
+
+
+class T(Constraint):
+    """
+    True
+    """
+
+    def __init__(self) -> None:
+        pass
+
+    def __eq__(self, other):
+        return isinstance(other, T)
+
+    def __repr__(self):
+        return "True"
+
+
+class F(Constraint):
+    """
+    False
+    """
+
+    def __init__(self) -> None:
+        pass
+
+    def __eq__(self, other):
+        return isinstance(other, F)
+
+    def __repr__(self):
+        return "False"
+
+
+class BinaryConstraint(Constraint):
+    """
+    Represents all binary operations
+    """
+
+    def __init__(self, lhs, rhs, op):
+        """
+        :param lhs: lhs of the constraint
+        :param rhs: rhs of the constraint
+        :param op: string representing the operation
+        """
+        self.lhs = lhs
+        self.rhs = rhs
+        self.op = op
+
+    def __eq__(self, other):
+        if isinstance(other, BinaryConstraint):
+            return (
+                self.lhs == other.lhs and self.rhs == other.rhs and self.op == other.op
+            )
+        else:
+            return False
+
+    def __repr__(self):
+        return f"({self.lhs} {self.op} {self.rhs})"
+
+
+class BinConstraintT(BinaryConstraint):
+    """
+    Binary constraints about tensors
+    """
+
+    def __init__(self, lhs, rhs, op):
+        assert (isinstance(lhs, (TVar, TensorType, int)) or lhs == Dyn) and (
+            isinstance(rhs, (TVar, TensorType, int)) or rhs == Dyn
+        )
+        super().__init__(lhs, rhs, op)
+
+    def __eq__(self, other):
+        return super().__eq__(other)
+
+
+class BinConstraintD(BinaryConstraint):
+    """
+    Binary constraints about dimensions
+    """
+
+    def __init__(self, lhs, rhs, op):
+        assert is_algebraic_expression(lhs) or is_dim(lhs) or is_bool_expr(lhs)
+        assert is_algebraic_expression(rhs) or is_dim(rhs) or is_bool_expr(rhs)
+
+        super().__init__(lhs, rhs, op)
+
+    def __eq__(self, other):
+        return super().__eq__(other)
+
+
+class TGreatestUpperBound(Constraint):
+    """
+    Greatest Upper bound for tensors with dynamic type
+    """
+
+    def __init__(self, res, rhs1, rhs2):
+        """
+        :param res: tensor variable that stores the result of the outout
+        :param rhs1: tensor or tensor variable
+        :param rhs2: tensor or tensor variabke
+        """
+        self.res = res
+        self.rhs1 = rhs1
+        self.rhs2 = rhs2
+
+    def __repr__(self):
+        return f"{self.res} = {self.rhs1}\u2294*{self.rhs2}"
+
+    def __eq__(self, other):
+        if isinstance(other, TGreatestUpperBound):
+            return (
+                self.res == other.res
+                and self.rhs1 == other.rhs1
+                and self.rhs2 == other.rhs2
+            )
+        else:
+            return False
+
+
+class DGreatestUpperBound(Constraint):
+    """
+    Greatest Upper bound for dimensions
+    """
+
+    def __init__(self, res, rhs1, rhs2):
+        """
+        :param res: Dimension variable to store the result
+        :param rhs1: dimension variable 1
+        :param rhs2: dimension variable 2
+        """
+        assert is_dim(res)
+        assert is_dim(rhs1)
+        assert is_dim(rhs2)
+
+        self.res = res
+        self.rhs1 = rhs1
+        self.rhs2 = rhs2
+
+    def __repr__(self):
+        return f"{self.res} = {self.rhs1}\u2294{self.rhs2}"
+
+    def __eq__(self, other):
+        if isinstance(other, DGreatestUpperBound):
+            return (
+                self.res == other.res
+                and self.rhs1 == other.rhs1
+                and self.rhs2 == other.rhs2
+            )
+        else:
+            return False
+
+
+class CanReshape(Constraint):
+    """
+    can_reshape constraint
+    """
+
+    def __init__(self, src, target):
+        """
+        :param src: tensor variable
+        :param target: tensor
+        """
+        self.src = src
+        self.target = target
+
+    def __repr__(self):
+        return f"can-reshape({self.src}, {self.target})"
+
+    def __eq__(self, other):
+        if isinstance(other, CanReshape):
+            return self.src == other.src and self.target == other.target
+        else:
+            return False
+
+
+class IndexSelect(Constraint):
+    def __init__(self, tensor_size, input_var, dim_replace, index, output):
+        """
+        Args:
+            input_var: input to index_select
+            tensor_size: tensor size we are considering
+            dim_replace: the dimension of the output at "index"
+            index: location of the dimensions to replace in the input
+            output: variable to store the result
+        """
+        assert isinstance(input_var, TVar)
+        assert isinstance(output, TVar)
+        assert isinstance(dim_replace, DVar) or dim_replace == Dyn
+        assert isinstance(index, int)
+
+        self.input_var = input_var
+        self.tensor_size = tensor_size
+        self.dim_replace = dim_replace
+        self.index = index
+        self.output = output
+
+    def __repr__(self):
+        return (
+            f" {self.output} = "
+            f"IndexSelect({self.input_var}, "
+            f"tensor_size: {self.tensor_size}, "
+            f"{self.dim_replace}, "
+            f"{self.index})"
+        )
+
+    def __eq__(self, other):
+        if isinstance(other, IndexSelect):
+            return (
+                self.tensor_size == other.tensor_size
+                and self.dim_replace == other.dim_replace
+                and self.index == other.index
+                and self.output == other.output
+                and self.input_var == other.input_var
+            )
+        else:
+            return False
+
+
+class Transpose(Constraint):
+    def __init__(self, tensor_size, input_var, index1, index2, output):
+        """
+        Args:
+            tensor_size: current tensor size
+            input_var: variable to hold input
+            index1: dimension 1
+            index2: dimension 2
+            output: output that stores result
+        """
+        assert isinstance(input_var, TVar)
+        assert isinstance(output, TVar)
+        assert isinstance(index1, int)
+        assert isinstance(index2, int)
+
+        self.input_var = input_var
+        self.tensor_size = tensor_size
+        self.index1 = index1
+        self.index2 = index2
+        self.output = output
+
+    def __repr__(self):
+        return (
+            f" {self.output} = "
+            f"Transpose({self.input_var}, "
+            f"tensor_size: {self.tensor_size}, "
+            f"{self.index1}, "
+            f"{self.index2})"
+        )
+
+    def __eq__(self, other):
+        if isinstance(other, Transpose):
+            return (
+                self.tensor_size == other.tensor_size
+                and self.index1 == other.index1
+                and self.index2 == other.index2
+                and self.output == other.output
+                and self.input_var == other.input_var
+            )
+        else:
+            return False
+
+
+class GetItem(Constraint):
+    def __init__(self, tensor_size, index, res, input_var):
+        """
+        Constraint for getting item given a tensor size
+        :param tensor_size: actual number
+        :param index: actual number representing the index
+        :param res: dimension variable to carry the item we get
+        :param input_var: a tensor variable from which we will get item
+        """
+        assert isinstance(res, DVar)
+
+        self.res = res
+        self.tensor_size = tensor_size
+        self.index = index
+        self.input_var = input_var
+
+    def __repr__(self):
+        return f" {self.res} = GetItem({self.input_var}, tensor_size: {self.tensor_size}, {self.index})"
+
+    def __eq__(self, other):
+        if isinstance(other, GetItem):
+            return (
+                self.res == other.res
+                and self.tensor_size == other.tensor_size
+                and self.index == other.index
+                and self.input_var == other.input_var
+            )
+        else:
+            return False
+
+
+class GetItemTensor(Constraint):
+    def __init__(self, tensor_size, index_tuple, res, input_var):
+        """
+        Constraint for getting item given a tensor size
+        However, when the argument is a tuple, we will
+        expect a tensor
+        :param tensor_size: actual number representing the rank
+        :param index_tuple: tuple for indexing
+        :param res: tensor variable to carry the item we get
+        :param input_var: a tensor variable from which we will get item
+        """
+        assert isinstance(res, TVar)
+
+        self.res = res
+        self.tensor_size = tensor_size
+        self.index_tuple = index_tuple
+        self.input_var = input_var
+
+    def __repr__(self):
+        return f" {self.res} = GetItemT({self.input_var}, tensor_size: {self.tensor_size}, {self.index_tuple})"
+
+    def __eq__(self, other):
+        if isinstance(other, GetItemTensor):
+            return (
+                self.res == other.res
+                and self.tensor_size == other.tensor_size
+                and self.index_tuple == other.index_tuple
+                and self.input_var == other.input_var
+            )
+        else:
+            return False
+
+
+class CalcConv(Constraint):
+    def __init__(
+        self,
+        conv_result,
+        input_var,
+        c_out,
+        kernel,
+        padding,
+        stride,
+        dilation,
+        matching_constraint_vars,
+    ):
+        """
+        :param conv_result: the convolution result
+        :param input_var: input to convolution
+        :param c_out: output chanel type
+        :param kernel: kernel tuple
+        """
+        self.conv_result = conv_result
+        self.input_var = input_var
+        self.c_out = c_out
+        self.kernel = kernel
+        self.padding = padding
+        self.stride = stride
+        self.dilation = dilation
+        self.matching_constraint = matching_constraint_vars
+
+    def __repr__(self):
+        return (
+            f"{self.conv_result} ="
+            f" calc-conv({self.input_var},"
+            f" {self.c_out}, {self.kernel}, "
+            f"{self.padding}, {self.stride},"
+            f" {self.dilation})"
+        )
+
+    def __eq__(self, other):
+        if isinstance(other, CalcConv):
+            return (
+                self.conv_result == other.conv_result
+                and self.input_var == other.input_var
+                and self.c_out == other.c_out
+                and self.kernel == other.kernel
+                and self.padding == other.padding
+                and self.stride == other.stride
+                and self.dilation == other.dilation
+                and self.matching_constraint == other.matching_constraint
+            )
+        else:
+            return False
+
+
+class CalcMaxPool(Constraint):
+    def __init__(
+        self,
+        maxpool_result,
+        input_var,
+        kernel,
+        padding,
+        stride,
+        dilation,
+        matching_constraint_vars,
+    ):
+        """
+        :param maxpool_result: the result of maxpool
+        :param input_var: input to convolution
+        :param kernel: kernel tuple
+        """
+        self.maxpool_result = maxpool_result
+        self.input_var = input_var
+        self.kernel = kernel
+        self.padding = padding
+        self.stride = stride
+        self.dilation = dilation
+        self.matching_constraint = matching_constraint_vars
+
+    def __repr__(self):
+        return (
+            f"{self.maxpool_result} ="
+            f" calc-maxpool({self.input_var},"
+            f"  {self.kernel}, "
+            f"{self.padding}, {self.stride},"
+            f" {self.dilation})"
+        )
+
+    def __eq__(self, other):
+        if isinstance(other, CalcMaxPool):
+            return (
+                self.maxpool_result == other.maxpool_result
+                and self.input_var == other.input_var
+                and self.kernel == other.kernel
+                and self.padding == other.padding
+                and self.stride == other.stride
+                and self.dilation == other.dilation
+                and self.matching_constraint == other.matching_constraint
+            )
+        else:
+            return False
+
+
+class ApplyBroadcasting(Constraint):
+    def __init__(self, res1, res2, input1, input2):
+        """
+        :param res1: resulting tensor 1
+        :param res2: resulting tensor 2
+        :param input1: tensor variable 1
+        :param input2: tensor variable 2
+        """
+        self.res1 = res1
+        self.res2 = res2
+        self.input1 = input1
+        self.input2 = input2
+
+    def __eq__(self, other):
+        if isinstance(other, ApplyBroadcasting):
+            return (
+                self.res1 == other.res1
+                and self.res2 == other.res2
+                and self.input1 == other.input1
+                and self.input2 == other.input2
+            )
+        else:
+            return False
+
+    def __repr__(self):
+        return (
+            f"{self.res1}, {self.res2} ="
+            f" apply-broadcasting({self.input1},"
+            f" {self.input2})"
+        )
+
+
+class CalcProduct(Constraint):
+    """
+    Given correct dimensions, calculate the product for flatten accounting for Dyn
+    """
+
+    def __init__(self, start, end, flattened, dims_to_flatten):
+        """
+        :param start: start index
+        :param end: end index
+        :param flattened: variable to store the product
+        :param dims_to_flatten: the type which we will flatten
+        """
+        assert isinstance(dims_to_flatten, list)
+        assert isinstance(flattened, TVar)
+        assert isinstance(start, int)
+        assert isinstance(end, int)
+
+        self.start = start
+        self.end = end
+        self.dims_to_flatten = dims_to_flatten
+        self.flattened = flattened
+
+    def __eq__(self, other):
+        if isinstance(other, CalcProduct):
+            return (
+                self.start == other.start
+                and self.end == other.end
+                and self.dims_to_flatten == other.dims_to_flatten
+                and self.flattened == other.flattened
+            )
+
+        else:
+            return False
+
+    def __repr__(self):
+        return f"{self.flattened} = CalcProduct({self.start}, {self.end}, {self.dims_to_flatten})"
+
+
+class TVar:
+    """
+    Tensor variable with no tensor constructor
+    """
+
+    def __init__(self, tvar):
+        """
+        :param tvar: tensor variable
+        """
+        self.tvar = tvar
+
+    def __repr__(self):
+        return f"TV({self.tvar})"
+
+    def __eq__(self, other):
+        if isinstance(other, TVar):
+            return self.tvar == other.tvar
+        else:
+            return False
+
+
+class DVar:
+    """
+    Dimension variable
+    """
+
+    def __init__(self, c):
+        """
+        :param c: character or number
+        """
+        self.c = c
+
+    def __repr__(self):
+        return f"DV({self.c})"
+
+    def __eq__(self, other):
+        if isinstance(other, DVar):
+            return self.c == other.c
+        else:
+            return False
+
+
+class BVar:
+    """
+    Boolean variable
+    """
+
+    def __init__(self, c):
+        """
+        :param c: character or number
+        """
+        self.c = c
+
+    def __repr__(self):
+        return f"BV({self.c})"
+
+    def __eq__(self, other):
+        if isinstance(other, BVar):
+            return self.c == other.c
+        else:
+            return False
+
+
+def is_algebraic_expression(constraint):
+    if isinstance(constraint, BinConstraintD):
+        return constraint.op in [op_add, op_sub, op_div, op_mul, op_mod]
+    else:
+        return isinstance(constraint, Prod)
+
+
+def is_bool_expr(constraint):
+    if isinstance(constraint, BinConstraintD):
+        return constraint.op in [op_gt, op_lt, op_neq, op_eq]
+    else:
+        return isinstance(constraint, (BVar, Conj, Disj))
+
+
+def is_dim(d):
+    return isinstance(d, (DVar, int)) or d == Dyn
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_generator.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..03346b800924e5db336579e724bd823757acb9b5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_generator.py
@@ -0,0 +1,1562 @@
+# mypy: allow-untyped-defs
+import operator
+import warnings
+from collections.abc import Iterable
+from typing import Callable, TypeVar
+from typing_extensions import ParamSpec
+
+import torch
+from torch.fx._symbolic_trace import _assert_is_none
+from torch.fx.experimental.migrate_gradual_types.constraint import (
+    ApplyBroadcasting,
+    BinConstraintD,
+    BinConstraintT,
+    CalcConv,
+    CalcMaxPool,
+    CalcProduct,
+    CanReshape,
+    Conj,
+    DGreatestUpperBound,
+    Disj,
+    DVar,
+    F,
+    GetItem,
+    GetItemTensor,
+    IndexSelect,
+    T,
+    TGreatestUpperBound,
+    Transpose,
+    TVar,
+)
+from torch.fx.experimental.migrate_gradual_types.operation import (
+    op_add,
+    op_consistency,
+    op_div,
+    op_eq,
+    op_gt,
+    op_leq,
+    op_lt,
+    op_matching,
+    op_mul,
+    op_neq,
+    op_precision,
+    op_sub,
+)
+from torch.fx.experimental.migrate_gradual_types.util import (
+    gen_bvar,
+    gen_dvar,
+    gen_nat_constraints,
+    gen_tensor_dims,
+    gen_tvar,
+)
+from torch.fx.node import Node, Target
+from torch.fx.tensor_type import Dyn, TensorType
+from torch.nn.modules.batchnorm import BatchNorm2d
+from torch.nn.modules.conv import Conv2d
+
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+_INFERENCE_RULES: dict[Target, Callable] = {}
+
+MAX_TENSOR_RANK = 4
+
+__all__ = [
+    "ConstraintGenerator",
+    "adaptive_inference_rule",
+    "add_layer_norm_constraints",
+    "add_linear_constraints",
+    "arange_inference_rule",
+    "assert_inference_rule",
+    "batchnorm_inference_rule",
+    "bmm_inference_rule",
+    "broadcasting_inference_rule",
+    "conv2d_inference_rule",
+    "cumsum_inference_rule",
+    "embedding_inference_rule",
+    "embedding_inference_rule_functional",
+    "eq_inference_rule",
+    "equality_inference_rule",
+    "expand_inference_rule",
+    "flatten_inference_rule",
+    "full_inference_rule",
+    "gen_broadcasting_constraints",
+    "gen_embedding_rules",
+    "gen_layer_norm_constraints",
+    "generate_flatten_constraints",
+    "get_attr_inference_rule",
+    "getitem_inference_rule",
+    "gt_inference_rule",
+    "index_select_inference_rule",
+    "layer_norm_functional",
+    "layer_norm_inference_rule",
+    "linear_constraints",
+    "linear_inference_rule",
+    "lt_inference_rule",
+    "masked_fill_inference_rule",
+    "maxpool_inference_rule",
+    "neq_inference_rule",
+    "range_check",
+    "register_inference_rule",
+    "relu_inference_rule",
+    "reshape_inference_rule",
+    "size_inference_rule",
+    "tensor_inference_rule",
+    "torch_dim_inference_rule",
+    "torch_linear_inference_rule",
+    "transpose_inference_rule",
+    "type_inference_rule",
+    "view_inference_rule",
+]
+
+
+def register_inference_rule(
+    call_target: Target,
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
+    def register(fn: Callable[_P, _T]) -> Callable[_P, _T]:
+        if call_target in _INFERENCE_RULES:
+            raise RuntimeError(f"Inference rule already registered for {call_target}!")
+        _INFERENCE_RULES[call_target] = fn
+        return fn
+
+    return register
+
+
+def generate_flatten_constraints(start_dim, end_dim, input, flattened, n, counter):
+    d, counter = gen_tensor_dims(n, counter)
+    c1 = BinConstraintT(input, TensorType(d), op_eq)
+    start_dim = n if start_dim == -1 else abs(start_dim)
+    end_dim = n + end_dim + 1 if end_dim < 0 else end_dim + 1
+    c2 = CalcProduct(start_dim, end_dim, flattened, d)
+    nat_constraints = gen_nat_constraints(d)
+    return Conj([c1, c2, *nat_constraints]), counter
+
+
+@register_inference_rule(getattr)
+def get_attr_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    If the attribute is "device" then the tensor shape is preserved
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], str)
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+
+    input = symbols[n.args[0]]
+    attr = n.args[1]
+
+    if attr == "device":
+        return [BinConstraintT(input, output, op_eq)], counter
+    else:
+        raise NotImplementedError("Not yet implemented")
+
+
+@register_inference_rule(torch.bmm)
+def bmm_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Constraints that match the input to a size 3 tensor
+    and switch the dimensions according to the rules
+    of batch multiplication
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], Node)
+
+    bmm_output, counter = gen_tvar(counter)
+    symbols[n] = bmm_output
+
+    bmm_input1 = symbols[n.args[0]]
+    bmm_input2 = symbols[n.args[1]]
+
+    dims_input1, counter = gen_tensor_dims(3, counter)
+    dims_input2, counter = gen_tensor_dims(3, counter)
+
+    inputs_dyn = Conj(
+        [
+            BinConstraintT(bmm_input1, Dyn, op_eq),
+            BinConstraintT(bmm_input2, Dyn, op_eq),
+            BinConstraintT(bmm_output, Dyn, op_eq),
+        ]
+    )
+
+    input1_dyn = Conj(
+        [
+            BinConstraintT(bmm_input1, Dyn, op_eq),
+            BinConstraintT(bmm_input2, TensorType(dims_input2), op_eq),
+            BinConstraintT(
+                bmm_output, TensorType([dims_input2[0], Dyn, dims_input2[2]]), op_eq
+            ),
+        ]
+    )
+
+    input2_dyn = Conj(
+        [
+            BinConstraintT(bmm_input2, Dyn, op_eq),
+            BinConstraintT(bmm_input1, TensorType(dims_input1), op_eq),
+            BinConstraintT(
+                bmm_output, TensorType([dims_input1[0], dims_input1[1], Dyn]), op_eq
+            ),
+        ]
+    )
+
+    consistency_constraints = [
+        BinConstraintD(dims_input1[0], dims_input2[0], op_consistency)
+    ]
+
+    batch_size, counter = gen_dvar(counter)
+
+    inputs_are_tensors = Conj(
+        [
+            BinConstraintT(bmm_input1, TensorType(dims_input1), op_eq),
+            BinConstraintT(bmm_input2, TensorType(dims_input2), op_eq),
+            BinConstraintT(
+                bmm_output,
+                TensorType([batch_size, dims_input1[1], dims_input2[2]]),
+                op_eq,
+            ),
+            *consistency_constraints,
+            DGreatestUpperBound(batch_size, dims_input1[0], dims_input2[0]),
+        ]
+    )
+
+    return [Disj([inputs_dyn, input1_dyn, input2_dyn, inputs_are_tensors])], counter
+
+
+@register_inference_rule("index_select")
+def index_select_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    We constrain the second argument to a vector or Dyn.
+    The output replaces the input with the shape of the vector
+    at the position given by the index (first argument)
+    """
+    # print(n.args)
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], int)
+    assert isinstance(n.args[2], Node)
+
+    index_select, counter = gen_tvar(counter)
+    symbols[n] = index_select
+
+    dims, counter = gen_tensor_dims(1, counter)
+
+    # equality constraint
+    is_size_1 = BinConstraintT(symbols[n.args[2]], TensorType(dims), op_eq)
+    is_dyn = BinConstraintT(symbols[n.args[2]], Dyn, op_eq)
+
+    c2 = Conj(
+        [
+            is_size_1,
+            Disj(
+                [
+                    IndexSelect(
+                        i + 1, symbols[n.args[0]], dims[0], n.args[1], index_select
+                    )
+                    for i in range(MAX_TENSOR_RANK)
+                ]
+            ),
+        ]
+    )
+    c3 = Conj(
+        [
+            is_dyn,
+            Disj(
+                [
+                    IndexSelect(i + 1, symbols[n.args[0]], Dyn, n.args[1], index_select)
+                    for i in range(MAX_TENSOR_RANK)
+                ]
+            ),
+        ]
+    )
+
+    return [Disj([c2, c3])], counter
+
+
+@register_inference_rule("expand")
+def expand_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    We generate the exact constraints as we do for tensor additions but we constraint
+    the rank of this expression to be equal to len(n.args[1:]) so that only
+    those cases get considered for the output
+    """
+    assert isinstance(n.args[0], Node)
+
+    # define the output for expand
+    expand, counter = gen_tvar(counter)
+    symbols[n] = expand
+
+    # since we do not have two nodes here, we will construct an argument variable
+    e1 = symbols[n.args[0]]
+    e2, counter = gen_tvar(counter)
+
+    e2_nat_constraints = []
+    for arg in n.args[1:]:
+        assert isinstance(arg, (Node, int))
+        if isinstance(arg, Node):
+            assert isinstance(symbols[arg], DVar)
+            e2_nat_constraints.append(BinConstraintD(0, symbols[arg], op_leq))
+
+    e2_constraint = BinConstraintT(
+        e2,
+        TensorType(
+            [arg if isinstance(arg, int) else symbols[arg] for arg in n.args[1:]]
+        ),
+        op_eq,
+    )
+
+    constraints, counter = gen_broadcasting_constraints(
+        e1, e2, symbols, counter, expand
+    )
+
+    # constraint the output size
+    dims, counter = gen_tensor_dims(len(n.args[1:]), counter)
+    nat_constraints = gen_nat_constraints(dims)
+    c = [
+        BinConstraintT(expand, TensorType(dims), op_eq),
+        *nat_constraints,
+        e2_constraint,
+        *e2_nat_constraints,
+    ]
+    constraints += c
+
+    return constraints, counter
+
+
+@register_inference_rule(torch.nn.functional.gelu)
+@register_inference_rule(torch.nn.functional.dropout)
+@register_inference_rule(torch.nn.functional.softmax)
+@register_inference_rule("detach")
+@register_inference_rule("to")
+@register_inference_rule("int")
+@register_inference_rule("long")
+@register_inference_rule("contiguous")
+@register_inference_rule(torch.ones)
+@register_inference_rule(torch.zeros)
+def equality_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    We generate the constraint: input = output
+    """
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+
+    if isinstance(n.args[0], Node):
+        input = symbols[n.args[0]]
+        if isinstance(input, TVar):
+            return [BinConstraintT(input, output, op_eq)], counter
+
+        # then we have dimension variables
+        else:
+            for arg in n.args:
+                assert isinstance(symbols[arg], DVar)
+        my_size = [symbols[arg] for arg in n.args]
+        return [BinConstraintT(output, TensorType(my_size), op_eq)], counter
+
+    elif isinstance(n.args[0], tuple):
+        # then the tuple is the size
+        assert len(n.args[0]) <= 4
+        my_size = [symbols[arg] for arg in n.args[0]]
+        return [BinConstraintT(output, TensorType(my_size), op_eq)], counter
+    else:
+        raise NotImplementedError("Method not yet implemented")
+
+
+@register_inference_rule("transpose")
+def transpose_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Can be considered as a sequence of two index selects, so we generate constraints accordingly
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], int)
+    assert isinstance(n.args[2], int)
+
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+
+    from_arg = symbols[n.args[0]]
+    assert isinstance(from_arg, TVar)
+
+    # input and output are dyn
+    is_dyn = Conj(
+        [BinConstraintT(from_arg, Dyn, op_eq), BinConstraintT(output, Dyn, op_eq)]
+    )
+
+    # or input is a tensor and we actually do the replacement
+    c3 = Disj(
+        [
+            Transpose(i + 1, from_arg, n.args[1], n.args[2], output)
+            for i in range(MAX_TENSOR_RANK)
+        ]
+    )
+
+    return [Disj([is_dyn, c3])], counter
+
+
+@register_inference_rule("type_as")
+def type_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    We generate the constraint: input = output
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], Node)
+
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+
+    from_arg = symbols[n.args[0]]
+    to_arg = symbols[n.args[1]]
+
+    assert isinstance(from_arg, TVar)
+    assert isinstance(to_arg, TVar)
+
+    return [
+        BinConstraintT(from_arg, to_arg, op_consistency),
+        BinConstraintT(output, to_arg, op_eq),
+    ], counter
+
+
+@register_inference_rule("masked_fill_")
+def masked_fill_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Similar to addition. For now we implement the constraints when
+    the argument is a boolean tensor. There is also a case for when
+    it is a condition. We will leave this out for now.
+    """
+
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], Node)
+
+    # We will retrieve the type variables from the symbol table
+    # and confirm they are tensor variables
+
+    e1 = symbols[n.args[0]]
+    e2 = symbols[n.args[1]]
+
+    if isinstance(e1, TVar) and isinstance(e2, TVar):
+        masked_fill_tensor, counter = gen_tvar(counter)
+        symbols[n] = masked_fill_tensor
+        return gen_broadcasting_constraints(
+            e1, e2, symbols, counter, masked_fill_tensor
+        )
+    else:
+        raise NotImplementedError("Not yet implemented")
+
+
+@register_inference_rule(torch.nn.functional.embedding)
+def embedding_inference_rule_functional(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    embedding_dim_weights = symbols[n.args[1]]
+
+    # will treat this as a static shape. So we will not use matching.
+    weight_dims, counter = gen_tensor_dims(2, counter)
+    equality_constraint = BinConstraintT(
+        embedding_dim_weights, TensorType(weight_dims), op_eq
+    )
+    embedding_dim = weight_dims[1]
+    constraints, counter = gen_embedding_rules(n, symbols, embedding_dim, counter)
+    return [equality_constraint] + constraints, counter
+
+
+@register_inference_rule(torch.nn.modules.sparse.Embedding)
+def embedding_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    """
+    The output shape differs from the input shape in the last dimension
+    """
+    assert isinstance(n.args[0], Node)
+    return gen_embedding_rules(n, symbols, module_instance.embedding_dim, counter)
+
+
+def gen_embedding_rules(n: Node, symbols, embedding_dim, counter):
+    embedding_output, counter = gen_tvar(counter)
+    symbols[n] = embedding_output
+    embedding_input = symbols[n.args[0]]
+
+    input_dyn = BinConstraintT(embedding_input, Dyn, op_eq)
+    output_dyn = BinConstraintT(embedding_output, Dyn, op_eq)
+
+    c1 = Conj([input_dyn, output_dyn])
+    c2 = []
+
+    for i in range(1, MAX_TENSOR_RANK):
+        new_dims, counter = gen_tensor_dims(i, counter)
+        nat_constraints = gen_nat_constraints(new_dims)
+
+        # we consider all tensor sizes and append embedding_dim to the end of the output dimension in all cases
+        c_tensor_i = Conj(
+            [
+                BinConstraintT(embedding_input, TensorType(new_dims), op_eq),
+                BinConstraintT(
+                    embedding_output, TensorType(new_dims + [embedding_dim]), op_eq
+                ),
+            ]
+            + nat_constraints
+        )
+        c2.append(c_tensor_i)
+
+    return [Disj([c1, Disj(c2)])], counter
+
+
+@register_inference_rule(torch.tensor)
+def tensor_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    If the tensor is a scalar, we will skip it since we
+    do not support scalars yet. We will add support in the future
+    if it's needed. For our examples so far, scalars are not needed.
+    """
+    return [], counter
+
+
+@register_inference_rule("reshape")
+@register_inference_rule("view")
+def view_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Similar to reshape but with an extra condition on the strides
+    """
+    assert isinstance(n.args[0], Node)
+
+    # generate the new variable
+    my_view, counter = gen_tvar(counter)
+    symbols[n] = my_view
+
+    src_var = symbols[n.args[0]]
+    t2 = [
+        symbols[elem] if isinstance(elem, Node) else elem for elem in n.args[1:]
+    ]  # target shape
+    t2_type = []
+    num_constraints = []
+
+    for t in t2:
+        if t == -1:
+            var, counter = gen_dvar(counter)
+            t2_type.append(var)
+            num_constraints.append(BinConstraintD(var, Dyn, op_neq))
+
+        else:
+            num_constraints.append(BinConstraintD(t, Dyn, op_neq))
+            t2_type.append(t)
+
+    t2_type = TensorType(t2_type)  # type: ignore[assignment]
+
+    c1 = BinConstraintT(my_view, t2_type, op_eq)
+    c2 = CanReshape(src_var, t2_type)
+
+    # TODO: add the extra check mentioned here:
+    # https://pytorch.org/docs/stable/generated/torch.Tensor.view.html#torch.Tensor.view
+
+    return [c1, c2] + num_constraints, counter  # type: ignore[operator]
+
+
+@register_inference_rule("size")
+def size_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    The constraint is just lhs = rhs.
+    Ex: size = input_ids.size()
+    """
+
+    if len(n.args) == 1:
+        # generate the new variable
+        size, counter = gen_tvar(counter)
+        symbols[n] = size
+        input = symbols[n.args[0]]
+        c = BinConstraintT(input, size, op_eq)
+        return [c], counter
+
+    elif len(n.args) == 2:
+        # TODO: review this rule; should input = dyn; output = dyn be included here?
+        if isinstance(n.args[1], int):
+            # generate the new variable
+            size_index, counter = gen_dvar(counter)
+            symbols[n] = size_index
+            input = symbols[n.args[0]]
+            c2 = [
+                GetItem(i + 1, n.args[1], size_index, input)
+                for i in range(MAX_TENSOR_RANK)
+            ]
+            c3 = BinConstraintD(0, size_index, op_leq)
+
+            input_dyn = BinConstraintT(input, Dyn, op_eq)
+            output_dyn = BinConstraintD(size_index, Dyn, op_eq)
+            c1 = Conj([input_dyn, output_dyn])
+
+            return [Disj([c1, Conj([Disj(c2), c3])])], counter
+
+        else:
+            raise NotImplementedError
+
+    else:
+        raise NotImplementedError
+
+
+def range_check(i, n):
+    """
+    Checks if an index i is within range of a size n list
+    Args:
+        i: index
+        n: list size
+
+    Returns: Boolean
+    """
+    if i >= 0:
+        return T() if i < n else F()
+    else:
+        return T() if i >= n else F()
+
+
+@register_inference_rule(torch.cumsum)
+def cumsum_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Input and output shapes should be equal
+    We should verify that the index is valid
+    """
+    assert isinstance(n.args[0], Node)
+    arg_1 = n.args[1] if len(n.args) > 1 else n.kwargs["dim"]
+    assert isinstance(arg_1, int)
+
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+    input = symbols[n.args[0]]
+
+    input_dyn = BinConstraintT(input, Dyn, op_eq)
+    output_dyn = BinConstraintT(output, Dyn, op_eq)
+    c1 = Conj([input_dyn, output_dyn])
+    c2 = []
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        new_dims, counter = gen_tensor_dims(i, counter)
+
+        nat_constraints = gen_nat_constraints(new_dims)
+
+        c_tensor_i = Conj(
+            [
+                BinConstraintT(input, TensorType(new_dims), op_eq),
+                BinConstraintT(output, TensorType(new_dims), op_eq),
+            ]
+            + [range_check(arg_1, i)]
+            + nat_constraints
+        )
+
+        c2.append(c_tensor_i)
+    dyn_or_tensor = Disj([c1, Disj(c2)])
+    return [dyn_or_tensor], counter
+
+
+@register_inference_rule(_assert_is_none)
+def assert_inference_rule(n: Node, symbols, constraints, counter):
+    assert len(n.users) == 0
+    return [], counter
+
+
+@register_inference_rule(operator.getitem)
+def getitem_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    # dimension output case
+    if isinstance(n.args[1], int):
+        # create and store the new dimension variable
+        get_item_output, counter = gen_dvar(counter)
+        symbols[n] = get_item_output
+
+        # retrieve arg variables
+        get_item_arg = symbols[n.args[0]]
+        assert isinstance(get_item_arg, TVar)
+
+        # if the input is dynamic, we accept any index and return
+        # a dynamic dimension as output
+        input_dyn = BinConstraintT(get_item_arg, Dyn, op_eq)
+        output_dyn = BinConstraintD(get_item_output, Dyn, op_eq)
+        c1 = Conj([input_dyn, output_dyn])
+
+        # if the input is a tensor,
+        # generate a getItem constraint which will be expanded based on the
+        # tensor dimension.
+
+        c2 = [
+            GetItem(i + 1, n.args[1], get_item_output, get_item_arg)
+            for i in range(MAX_TENSOR_RANK)
+        ]
+
+        # since the output is a dimension, we make sure it's a natural number
+        # added as a conjunction to the disjunction of c2
+        c3 = BinConstraintD(0, get_item_output, op_leq)
+        return [Disj([c1, Conj([Disj(c2), c3])])], counter
+
+    # tensor output case
+    elif isinstance(n.args[1], tuple):
+        # create and store the new tensor variable
+        get_item_output, counter = gen_tvar(counter)
+        symbols[n] = get_item_output
+
+        # retrieve arg variables
+        if n.args[0] in symbols:
+            get_item_arg = symbols[n.args[0]]
+            assert isinstance(get_item_arg, TVar)
+
+            input_dyn = BinConstraintT(get_item_arg, Dyn, op_eq)
+            output_dyn = BinConstraintT(get_item_output, Dyn, op_eq)  # type: ignore[assignment]
+            c1 = Conj([input_dyn, output_dyn])
+
+            c2 = [
+                GetItemTensor(i + 1, n.args[1], get_item_output, get_item_arg)  # type: ignore[misc]
+                for i in range(MAX_TENSOR_RANK)
+            ]
+        else:
+            # TODO: we should figure out why there is a key-error here.
+            return [], counter
+
+        return [Disj([c1, *c2])], counter
+
+    else:
+        raise RuntimeError("Method not yet implemented")
+
+
+@register_inference_rule(operator.gt)
+def gt_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], (Node, int))
+    assert isinstance(n.args[1], (Node, int))
+
+    # We make sure this node will not be used again. We do not
+    # generate a constraint about that node. Only about the operands.
+
+    e1 = symbols[n.args[0]] if isinstance(n.args[0], Node) else n.args[0]
+    e2 = symbols[n.args[1]] if isinstance(n.args[1], Node) else n.args[1]
+
+    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
+        if isinstance(e1, TVar) and isinstance(e2, TVar):
+            gt_tensor, counter = gen_tvar(counter)
+            symbols[n] = gt_tensor
+            return gen_broadcasting_constraints(e1, e2, symbols, counter, gt_tensor)
+
+        elif isinstance(e1, DVar) and isinstance(e2, DVar):
+            # This is meant to be used for flow analysis only
+            gt_constraint = BinConstraintD(e1, e2, op_gt)
+
+            my_gt, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_gt, gt_constraint, op_eq)
+            return [equality_constraint], counter
+
+        else:
+            raise RuntimeError("Sort Mismatch")
+
+    elif isinstance(n.args[0], Node) and not isinstance(n.args[1], Node):
+        if isinstance(e1, DVar):
+            # This is meant to be used for flow analysis only
+            gt_constraint = BinConstraintD(e1, e2, op_gt)
+
+            my_gt, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_gt, gt_constraint, op_eq)
+            return [equality_constraint], counter
+
+        elif isinstance(e1, TVar) and isinstance(e2, int):
+            # then we made the wrong assumption about the argument being a tensor
+            # so we should fix the assumption
+            warnings.warn(
+                f"Made the wrong assumption for node {n}. Correctness not guaranteed."
+            )
+
+            new_e1, counter = gen_dvar(counter)
+            symbols[n.args[0]] = new_e1
+            symbols[n.args[0]]
+
+            gt_constraint = BinConstraintD(new_e1, e2, op_gt)
+
+            my_gt, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_gt, gt_constraint, op_eq)
+            return [equality_constraint], counter
+
+        else:
+            raise NotImplementedError("Method not yet implemented")
+
+    else:
+        raise NotImplementedError("Method not yet implemented")
+
+
+@register_inference_rule(operator.eq)
+def eq_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], (Node, int))
+    assert isinstance(n.args[1], (Node, int))
+
+    e1 = symbols[n.args[0]] if isinstance(n.args[0], Node) else n.args[0]
+    e2 = symbols[n.args[1]] if isinstance(n.args[1], Node) else n.args[1]
+
+    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
+        if isinstance(e1, TVar) and isinstance(e2, TVar):
+            eq_tensor, counter = gen_tvar(counter)
+            symbols[n] = eq_tensor
+            return gen_broadcasting_constraints(e1, e2, symbols, counter, eq_tensor)
+
+        elif isinstance(e1, DVar) and isinstance(e2, DVar):
+            # This is meant to be used for flow analysis only
+            eq_constraint = BinConstraintD(e1, e2, op_eq)
+
+            my_eq, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_eq, eq_constraint, op_eq)
+            return [equality_constraint], counter
+
+        else:
+            raise RuntimeError("Sort Mismatch")
+
+    elif isinstance(n.args[0], Node) and not isinstance(n.args[1], Node):
+        if isinstance(e1, DVar):
+            # This is meant to be used for flow analysis only
+            eq_constraint = BinConstraintD(e1, e2, op_eq)
+
+            my_eq, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_eq, eq_constraint, op_eq)
+            return [equality_constraint], counter
+        else:
+            raise NotImplementedError("Method not yet implemented")
+    else:
+        raise NotImplementedError("Method not yet implemented")
+
+
+@register_inference_rule(operator.ne)
+def neq_inference_rule(n: Node, symbols, constraints, counter):
+    """
+    Translates to inconsistent in gradual types.
+    To prove inequality, we should prove that
+    tensors are either different sizes or
+    disagree on at least one dimension
+
+    This is a WIP (works when the condition
+    is false. We are working on making this operation work
+    when the condition is true as well)
+    """
+    assert isinstance(n.args[0], Node)
+    assert isinstance(n.args[1], tuple)
+
+    # implementing for size 3 and 4
+    if len(n.args[1]) == 3:
+        assert isinstance(n.args[1][0], (Node, int))
+        assert isinstance(n.args[1][1], (Node, int))
+        assert isinstance(n.args[1][2], (Node, int))
+
+        lhs = symbols[n.args[0]]
+
+        b, counter = gen_tensor_dims(4, counter)
+        input_is_size3 = BinConstraintT(lhs, TensorType([b[0], b[1], b[2]]), op_eq)
+
+        d1 = n.args[1][0] if isinstance(n.args[1][0], int) else symbols[n.args[1][0]]
+        d2 = n.args[1][1] if isinstance(n.args[1][1], int) else symbols[n.args[1][1]]
+        d3 = n.args[1][2] if isinstance(n.args[1][2], int) else symbols[n.args[1][2]]
+
+        # dimensions not equal
+        my_ne, counter = gen_bvar(counter)
+        neq_1 = BinConstraintD(d1, b[0], op_neq)
+        neq_2 = BinConstraintD(d2, b[1], op_neq)
+        neq_3 = BinConstraintD(d3, b[2], op_neq)
+
+        # dimensions inconsistent
+        dims_inconsistent1 = Conj(
+            [BinConstraintD(d1, Dyn, op_neq), BinConstraintD(b[0], Dyn, op_neq), neq_1]
+        )
+        dims_inconsistent2 = Conj(
+            [BinConstraintD(d2, Dyn, op_neq), BinConstraintD(b[1], Dyn, op_neq), neq_2]
+        )
+        dims_inconsistent3 = Conj(
+            [BinConstraintD(d3, Dyn, op_neq), BinConstraintD(b[2], Dyn, op_neq), neq_3]
+        )
+
+        dims_inconsistent = Disj(
+            [dims_inconsistent1, dims_inconsistent2, dims_inconsistent3]
+        )
+
+        # we are covering size 3 and 4 only for now
+        ne_constraint = Conj([input_is_size3, dims_inconsistent])
+
+        my_ne, counter = gen_bvar(counter)
+        equality_constraint = BinConstraintD(my_ne, ne_constraint, op_eq)
+
+    elif len(n.args[1]) == 4:
+        assert isinstance(n.args[1][0], (Node, int))
+        assert isinstance(n.args[1][1], (Node, int))
+        assert isinstance(n.args[1][2], (Node, int))
+        assert isinstance(n.args[1][3], (Node, int))
+
+        lhs = symbols[n.args[0]]
+
+        b1, counter = gen_dvar(counter)
+        b2, counter = gen_dvar(counter)
+        b3, counter = gen_dvar(counter)
+        b4, counter = gen_dvar(counter)
+
+        input_is_size4 = BinConstraintT(lhs, TensorType([b1, b2, b3, b4]), op_eq)
+
+        d1 = n.args[1][0] if isinstance(n.args[1][0], int) else symbols[n.args[1][0]]
+        d2 = n.args[1][1] if isinstance(n.args[1][1], int) else symbols[n.args[1][1]]
+        d3 = n.args[1][2] if isinstance(n.args[1][2], int) else symbols[n.args[1][2]]
+        d4 = n.args[1][3] if isinstance(n.args[1][3], int) else symbols[n.args[1][3]]
+
+        # dimensions not equal
+        my_ne, counter = gen_bvar(counter)
+        neq_1 = BinConstraintD(d1, b1, op_neq)
+        neq_2 = BinConstraintD(d2, b2, op_neq)
+        neq_3 = BinConstraintD(d3, b3, op_neq)
+        neq_4 = BinConstraintD(d4, b4, op_neq)
+
+        # dimensions to inconsistent
+        dims_inconsistent1 = Conj(
+            [BinConstraintD(d1, Dyn, op_neq), BinConstraintD(b1, Dyn, op_neq), neq_1]
+        )
+        dims_inconsistent2 = Conj(
+            [BinConstraintD(d2, Dyn, op_neq), BinConstraintD(b2, Dyn, op_neq), neq_2]
+        )
+        dims_inconsistent3 = Conj(
+            [BinConstraintD(d3, Dyn, op_neq), BinConstraintD(b3, Dyn, op_neq), neq_3]
+        )
+        dims_inconsistent4 = Conj(
+            [BinConstraintD(d4, Dyn, op_neq), BinConstraintD(b3, Dyn, op_neq), neq_4]
+        )
+
+        dims_inconsistent = Disj(
+            [
+                dims_inconsistent1,
+                dims_inconsistent2,
+                dims_inconsistent3,
+                dims_inconsistent4,
+            ]
+        )
+
+        ne_constraint = Conj([input_is_size4, dims_inconsistent])
+
+        my_ne, counter = gen_bvar(counter)
+
+        equality_constraint = BinConstraintD(my_ne, ne_constraint, op_eq)
+
+    else:
+        raise NotImplementedError("Method not yet implemented")
+
+    return [equality_constraint], counter
+
+
+@register_inference_rule(operator.lt)
+def lt_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], (Node, int))
+    assert isinstance(n.args[1], (Node, int))
+
+    # We make sure this node will not be used again. We do not
+    # generate a constraint about that node. Only about the operands.
+
+    e1 = symbols[n.args[0]] if isinstance(n.args[0], Node) else n.args[0]
+    e2 = symbols[n.args[1]] if isinstance(n.args[1], Node) else n.args[1]
+
+    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
+        if isinstance(e1, TVar) and isinstance(e2, TVar):
+            lt_tensor, counter = gen_tvar(counter)
+            symbols[n] = lt_tensor
+            return gen_broadcasting_constraints(e1, e2, symbols, counter, lt_tensor)
+
+        elif isinstance(e1, DVar) and isinstance(e2, DVar):
+            # This is meant to be used for flow analysis only
+            lt_constraint = BinConstraintD(e1, e2, op_lt)
+
+            my_lt, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_lt, lt_constraint, op_eq)
+            return [equality_constraint], counter
+
+        else:
+            raise RuntimeError("Sort Mismatch")
+
+    elif isinstance(n.args[0], Node) and not isinstance(n.args[1], Node):
+        if isinstance(e1, DVar):
+            # This is meant to be used for flow analysis only
+            lt_constraint = BinConstraintD(e1, e2, op_lt)
+
+            my_lt, counter = gen_bvar(counter)
+            equality_constraint = BinConstraintD(my_lt, lt_constraint, op_eq)
+            return [equality_constraint], counter
+        else:
+            raise NotImplementedError("Method not yet implemented")
+
+    else:
+        raise NotImplementedError("Method not yet implemented")
+
+
+@register_inference_rule(torch.full)
+def full_inference_rule(n: Node, symbols, constraints, counter):
+    full, counter = gen_tvar(counter)
+    symbols[n] = full
+    res = []
+
+    assert isinstance(n.args[0], Iterable)
+    for arg in n.args[0]:
+        dim = arg if isinstance(arg, int) else symbols[arg]
+        res.append(dim)
+    c = BinConstraintT(full, TensorType(list(res)), op_eq)  # type: ignore[arg-type]
+    return [c], counter
+
+
+# TODO normalize index
+@register_inference_rule(torch.arange)
+def arange_inference_rule(n: Node, symbols, constraints, counter):
+    start = 0
+    step = 1
+
+    if len(n.args) == 1:
+        end = symbols[n.args[0]]
+    else:
+        raise NotImplementedError("Not yet implemented")
+
+    # int((end - start) / step)
+    d1, counter = gen_dvar(counter)
+    size_constraint = BinConstraintD(
+        d1, BinConstraintD(BinConstraintD(end, start, op_sub), step, op_div), op_eq
+    )
+    arange, counter = gen_tvar(counter)
+    symbols[n] = arange
+
+    # either the a parameter is a number or it is Dyn
+    c1 = Disj(
+        [
+            BinConstraintD(end, Dyn, op_eq),
+            BinConstraintD(start, Dyn, op_eq),
+            BinConstraintD(step, Dyn, op_eq),
+        ]
+    )
+    c2 = BinConstraintD(d1, Dyn, op_eq)
+    both_dyn = Conj([c1, c2])
+
+    c11 = Conj(
+        [
+            BinConstraintD(end, Dyn, op_neq),
+            BinConstraintD(start, Dyn, op_neq),
+            BinConstraintD(step, Dyn, op_neq),
+        ]
+    )
+    c22 = BinConstraintD(d1, Dyn, op_neq)
+    both_numbers = Conj([c11, c22, size_constraint])
+
+    return [
+        BinConstraintT(arange, TensorType([d1]), op_eq),
+        Disj([both_dyn, both_numbers]),
+    ], counter
+
+
+def gen_broadcasting_constraints(e1, e2, symbols, counter, output_var):
+    # additional vars that don't correspond to expressions
+    e11, counter = gen_tvar(counter)
+    e22, counter = gen_tvar(counter)
+
+    # generate constraints
+    c1 = TGreatestUpperBound(output_var, e11, e22)
+    c2 = ApplyBroadcasting(e11, e22, e1, e2)
+    c3 = BinConstraintT(e11, e22, op_consistency)
+    return [c1, c2, c3], counter
+
+
+@register_inference_rule(operator.mul)
+@register_inference_rule(torch.ne)
+@register_inference_rule("ne")
+@register_inference_rule(torch.add)
+@register_inference_rule(operator.add)
+def broadcasting_inference_rule(n: Node, symbols, constraints, counter):
+    op_code = None
+    if n.target == operator.add or n.target == torch.add:
+        op_code = op_add
+    elif n.target == operator.mul:
+        op_code = op_mul
+
+    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
+        if isinstance(symbols[n.args[0]], TVar) and isinstance(
+            symbols[n.args[1]], TVar
+        ):
+            my_output, counter = gen_tvar(counter)
+            symbols[n] = my_output
+            e1 = symbols[n.args[0]]
+            e2 = symbols[n.args[1]]
+
+            return gen_broadcasting_constraints(e1, e2, symbols, counter, my_output)
+        else:
+            raise NotImplementedError("Method not yet implemented")
+
+    elif isinstance(n.args[0], Node) and isinstance(n.args[1], (int, float)):
+        if isinstance(symbols[n.args[0]], TVar):
+            my_output, counter = gen_tvar(counter)
+            symbols[n] = my_output
+            e1 = symbols[n.args[0]]
+            return [BinConstraintT(my_output, e1, op_eq)], counter
+        elif isinstance(symbols[n.args[0]], DVar):
+            my_output, counter = gen_dvar(counter)
+            symbols[n] = my_output
+            e1 = symbols[n.args[0]]
+
+            # we will propagate the runtime value here since this is regular addition
+            c = Conj(
+                [
+                    BinConstraintD(
+                        my_output, BinConstraintD(e1, n.args[1], op_code), op_eq
+                    ),
+                    BinConstraintD(0, my_output, op_leq),
+                ]
+            )
+            return [c], counter
+
+    elif isinstance(n.args[1], Node) and isinstance(n.args[0], (int, float)):
+        if isinstance(symbols[n.args[1]], TVar):
+            my_output, counter = gen_tvar(counter)
+            symbols[n] = my_output
+            e2 = symbols[n.args[1]]
+            return [BinConstraintT(my_output, e2, op_eq)], counter
+        elif isinstance(symbols[n.args[1]], DVar):
+            my_output, counter = gen_dvar(counter)
+            symbols[n] = my_output
+            e2 = symbols[n.args[1]]
+
+            # we will propagate the runtime value here since this is regular addition
+            c = Conj(
+                [
+                    BinConstraintD(
+                        my_output, BinConstraintD(e2, n.args[0], op_code), op_eq
+                    ),
+                    BinConstraintD(0, my_output, op_leq),
+                ]
+            )
+            return [c], counter
+
+        else:
+            raise NotImplementedError("Method not yet implemented")
+
+    else:
+        # TODO generate add constraints for scalar addition
+        raise NotImplementedError("Addition not yet implemented")
+
+
+@register_inference_rule(torch.flatten)
+def flatten_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    # generate the new variable
+    flattened, counter = gen_tvar(counter)
+    symbols[n] = flattened
+
+    input = symbols[n.args[0]]
+
+    # set the default start and end dims
+    start_dim = 1
+    end_dim = -1
+
+    if len(n.args) > 1:
+        assert isinstance(n.args[1], int)
+        start_dim = n.args[1]
+
+    if len(n.args) > 2:
+        assert isinstance(n.args[2], int)
+        end_dim = n.args[2]
+
+    c1 = BinConstraintT(input, Dyn, op_eq)
+    c2 = BinConstraintT(flattened, Dyn, op_eq)
+    both_dyn = Conj([c1, c2])
+
+    const = []
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        c, counter = generate_flatten_constraints(
+            start_dim, end_dim, input, flattened, i, counter
+        )
+        const.append(c)
+
+    return [Disj([both_dyn, *const])], counter
+
+
+@register_inference_rule(torch.nn.functional.layer_norm)
+def layer_norm_functional(n: Node, symbols, constraints, counter):
+    """
+    We generate the constraint: input = output
+    """
+    assert isinstance(n.args[0], Node)
+    return gen_layer_norm_constraints(n, n.args[1], symbols, counter)
+
+
+@register_inference_rule(torch.nn.LayerNorm)
+def layer_norm_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    """
+    Input and output shapes should be equal.
+    Input should be consistent with the normalized_shape
+    """
+    assert isinstance(n.args[0], Node)
+    return gen_layer_norm_constraints(
+        n, module_instance.normalized_shape, symbols, counter
+    )
+
+
+def gen_layer_norm_constraints(n: Node, normalized_shape, symbols, counter):
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+    input = symbols[n.args[0]]
+
+    input_dyn = BinConstraintT(input, Dyn, op_eq)
+    output_dyn = BinConstraintT(output, Dyn, op_eq)
+
+    c1 = Conj([input_dyn, output_dyn])
+
+    c2 = []
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        new_dims_rhs, counter = gen_tensor_dims(i, counter)
+        nat_constraints = gen_nat_constraints(new_dims_rhs)
+
+        c_tensor_i = Conj(
+            [
+                BinConstraintT(input, TensorType(new_dims_rhs), op_eq),
+                BinConstraintT(output, TensorType(new_dims_rhs), op_eq),
+            ]
+            + add_layer_norm_constraints(new_dims_rhs, list(normalized_shape))
+            + nat_constraints
+        )
+        c2.append(c_tensor_i)
+    return [Disj([c1, Disj(c2)])], counter
+
+
+@register_inference_rule(torch.nn.Dropout)
+@register_inference_rule(torch.nn.ReLU)
+def relu_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    """
+    Input and output shapes should be equal.
+    """
+    assert isinstance(n.args[0], Node)
+    output, counter = gen_tvar(counter)
+    symbols[n] = output
+    input = symbols[n.args[0]]
+    assert isinstance(input, TVar)
+    return [BinConstraintT(input, output, op_eq)], counter
+
+
+@register_inference_rule(torch.nn.Linear)
+def linear_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    """
+    Input and output sizes should be the same except for the last dimension
+    If the input is Dyn, then so should the output
+    """
+    assert isinstance(n.args[0], Node)
+    return linear_constraints(
+        n, module_instance.in_features, module_instance.out_features, symbols, counter
+    )
+
+
+@register_inference_rule("dim")
+def torch_dim_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+    my_dim, counter = gen_dvar(counter)
+    symbols[n] = my_dim
+    input = symbols[n.args[0]]
+
+    input_dyn = BinConstraintT(input, Dyn, op_eq)
+    output_dyn = BinConstraintD(my_dim, Dyn, op_eq)
+
+    c1 = []
+
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        new_dims_rhs_1, counter = gen_tensor_dims(i, counter)
+
+        c_tensor_i = Conj(
+            [
+                BinConstraintT(input, TensorType(new_dims_rhs_1), op_eq),
+                BinConstraintD(my_dim, i, op_eq),
+            ]
+        )
+        c1.append(c_tensor_i)
+
+    return [Disj([Conj([input_dyn, output_dyn]), Disj(c1)])], counter
+
+
+@register_inference_rule(torch._C._nn.linear)
+def torch_linear_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+    weight_dims, counter = gen_tensor_dims(2, counter)
+    equality_constraint = BinConstraintT(
+        symbols[n.args[1]], TensorType(weight_dims), op_eq
+    )
+    constraints, counter = linear_constraints(
+        n, weight_dims[1], weight_dims[0], symbols, counter
+    )
+    return [equality_constraint] + constraints, counter
+
+
+def linear_constraints(n: Node, in_features, out_features, symbols, counter):
+    linear_output, counter = gen_tvar(counter)
+    symbols[n] = linear_output
+    linear_input = symbols[n.args[0]]
+
+    input_dyn = BinConstraintT(linear_input, Dyn, op_eq)
+    output_dyn = BinConstraintT(linear_output, Dyn, op_eq)
+
+    c1 = Conj([input_dyn, output_dyn])
+
+    c2 = []
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        new_dims_rhs_1, counter = gen_tensor_dims(i, counter)
+        new_dims_rhs_2, counter = gen_tensor_dims(i, counter)
+
+        nat_constraints = gen_nat_constraints(new_dims_rhs_1 + new_dims_rhs_2)
+
+        c_tensor_i = Conj(
+            [
+                BinConstraintT(linear_input, TensorType(new_dims_rhs_1), op_eq),
+                BinConstraintT(linear_output, TensorType(new_dims_rhs_2), op_eq),
+            ]
+            + add_linear_constraints(
+                new_dims_rhs_1, new_dims_rhs_2, in_features, out_features
+            )
+            + nat_constraints
+        )
+        c2.append(c_tensor_i)
+    return [Disj([c1, Disj(c2)])], counter
+
+
+def add_layer_norm_constraints(input_dim, normalized_dim):
+    """
+    The constraints say that the type has te form: [*, 1024, 1024]
+     while the normalized_dim have the form [1024, 1024]
+    Args:
+        input_dim: Input shape of layer norm
+        normalized_dim: normalized_dim parameter of the module instance
+
+    """
+
+    # in this case we return false since there's a pattern mismatch
+    if len(normalized_dim) > len(input_dim):
+        return [F()]
+
+    else:
+        constraints = []
+        for i, n in zip(reversed(input_dim), reversed(normalized_dim)):
+            constraints.append(BinConstraintD(i, n, op_consistency))
+        return constraints
+
+
+def add_linear_constraints(dims1, dims2, in_features, out_features):
+    assert len(dims1) == len(dims2)
+    constraints = []
+    for i in range(len(dims1)):
+        if i == len(dims1) - 1:
+            constraints.append(BinConstraintD(dims1[i], in_features, op_consistency))
+            constraints.append(BinConstraintD(dims2[i], out_features, op_eq))
+        else:
+            constraints.append(BinConstraintD(dims1[i], dims2[i], op_eq))
+
+    return constraints
+
+
+@register_inference_rule(torch.reshape)
+def reshape_inference_rule(n: Node, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    # generate the new variable
+    my_reshape, counter = gen_tvar(counter)
+    symbols[n] = my_reshape
+
+    src_var = symbols[n.args[0]]
+    t2 = n.args[1]
+    t2_type = TensorType([Dyn if elem == -1 else elem for elem in t2])  # type: ignore[union-attr]
+    c1 = BinConstraintT(my_reshape, t2_type, op_eq)  # type: ignore[union-attr]
+    c2 = CanReshape(src_var, t2_type)
+
+    return [c1, c2], counter
+
+
+@register_inference_rule(BatchNorm2d)
+def batchnorm_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    # generate the new variable
+    batchnorm_output, counter = gen_tvar(counter)
+    symbols[n] = batchnorm_output
+    batchnorm_input = symbols[n.args[0]]
+
+    # dim vars
+    d1, counter = gen_dvar(counter)
+    d2, counter = gen_dvar(counter)
+    d3, counter = gen_dvar(counter)
+    d4, counter = gen_dvar(counter)
+
+    nat_constraints = gen_nat_constraints([d1, d2, d3, d4])
+
+    c1 = BinConstraintT(batchnorm_input, TensorType([d1, d2, d3, d4]), op_matching)
+    c2 = BinConstraintT(batchnorm_input, batchnorm_output, op_eq)
+    return [c1, c2, *nat_constraints], counter
+
+
+@register_inference_rule(torch.nn.AdaptiveAvgPool2d)
+def adaptive_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    avg_pool, counter = gen_tvar(counter)
+
+    symbols[n] = avg_pool
+    input_var = symbols[n.args[0]]
+
+    # dim vars
+    d1, counter = gen_dvar(counter)
+    d2, counter = gen_dvar(counter)
+    d3, counter = gen_dvar(counter)
+    d4, counter = gen_dvar(counter)
+    nat_constraints = gen_nat_constraints([d1, d2, d3, d4])
+    c1 = BinConstraintT(input_var, TensorType([d1, d2, d3, d4]), op_matching)
+    c2 = BinConstraintT(
+        avg_pool,
+        TensorType(
+            [d1, d2, module_instance.output_size[0], module_instance.output_size[1]]
+        ),
+        op_eq,
+    )
+
+    return [c1, c2, *nat_constraints], counter
+
+
+@register_inference_rule(Conv2d)
+def conv2d_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+
+    my_conv, counter = gen_tvar(counter)
+    symbols[n] = my_conv
+    input_var = symbols[n.args[0]]
+
+    # dim vars
+    [d1, d2, d3, d4], counter = gen_tensor_dims(MAX_TENSOR_RANK, counter)
+
+    # c1 = Matching(input_var, TensorType([d1, d2, d3, d4]))
+    c1 = BinConstraintT(input_var, TensorType([d1, d2, d3, d4]), op_matching)
+
+    # c2 = DConsistency(module_instance.in_channels, d2)
+    c2 = BinConstraintD(module_instance.in_channels, d2, op_consistency)
+
+    c3 = CalcConv(
+        my_conv,
+        input_var,
+        module_instance.out_channels,
+        module_instance.kernel_size,
+        module_instance.padding,
+        module_instance.stride,
+        module_instance.dilation,
+        [d1, d2, d3, d4],
+    )
+
+    nat_constraints = gen_nat_constraints([d1, d2, d3, d4])
+
+    return [c1, c2, c3, *nat_constraints], counter
+
+
+@register_inference_rule(torch.nn.MaxPool2d)
+def maxpool_inference_rule(n: Node, module_instance, symbols, constraints, counter):
+    assert isinstance(n.args[0], Node)
+    maxpool, counter = gen_tvar(counter)
+    symbols[n] = maxpool
+    input_var = symbols[n.args[0]]
+
+    # dim vars
+    [d1, d2, d3, d4], counter = gen_tensor_dims(MAX_TENSOR_RANK, counter)
+
+    c1 = BinConstraintT(input_var, TensorType([d1, d2, d3, d4]), op_matching)
+
+    c2 = CalcMaxPool(
+        maxpool,
+        input_var,
+        module_instance.kernel_size,
+        module_instance.padding,
+        module_instance.stride,
+        module_instance.dilation,
+        [d1, d2, d3, d4],
+    )
+
+    nat_constraints = gen_nat_constraints([d1, d2, d3, d4])
+
+    return [c1, c2, *nat_constraints], counter
+
+
+class ConstraintGenerator:
+    def __init__(self, traced, graph=None):
+        self.traced = traced  # traced or tracer.root
+        self.traced_params = dict(self.traced.named_parameters())
+        self.constraints = []
+        self.symbol_dict = {}
+        self.graph = traced.graph if hasattr(traced, "graph") else graph
+
+    def generate_constraints(self, counter=0):
+        """
+        Iterate through every node and generate constraints
+        Effect: self.constraints will be populated with the final constraints
+        """
+        graph = self.graph
+
+        all_constraints = []
+
+        for n in graph.nodes:
+            (constraints, counter) = self.generate_constraints_node(n, counter)
+            all_constraints += constraints
+
+        return Conj(all_constraints), counter
+
+    def generate_constraints_node(self, n: Node, counter):
+        """
+        Generate constraints the given node:
+        Currently supported operations:
+        - Reshape
+        - Add
+        - conv2d
+        """
+
+        if n.op == "placeholder":
+            x, counter = gen_tvar(counter)
+            self.symbol_dict[n] = x
+
+            my_type = n.type
+
+            if n.type != Dyn and (not isinstance(n.type, TensorType)):
+                if n.type == torch.nn.parameter.Parameter:
+                    # since we have a parameter, the shape must be static
+                    assert "example_value" in n.meta
+                    my_type = TensorType(n.meta["example_value"].size())
+                else:
+                    my_type = Dyn
+
+            c1 = BinConstraintT(my_type, x, op_precision)
+            c2 = BinConstraintT(x, MAX_TENSOR_RANK, op_leq)
+            return [c1, c2], counter
+
+        elif n.op == "call_function":
+            if n.target in _INFERENCE_RULES:
+                return _INFERENCE_RULES[n.target](
+                    n, self.symbol_dict, self.constraints, counter
+                )
+            else:
+                raise RuntimeError(
+                    f"No inference rule registered for target {n.target}!"
+                )
+
+        elif n.op == "call_module":
+            module_instance = self.traced.get_submodule(n.target)
+            if type(module_instance) in _INFERENCE_RULES:
+                return _INFERENCE_RULES[type(module_instance)](
+                    n, module_instance, self.symbol_dict, self.constraints, counter
+                )
+            else:
+                raise RuntimeError(
+                    f"No inference rule registered for class {type(module_instance)}!"
+                )
+
+        elif n.op == "call_method":
+            if n.target in _INFERENCE_RULES:
+                return _INFERENCE_RULES[n.target](
+                    n, self.symbol_dict, self.constraints, counter
+                )
+            else:
+                raise RuntimeError(
+                    f"No inference rule registered for target {n.target}!"
+                )
+
+        elif n.op == "get_attr":
+            t = self.traced_params.get(n.target, None)
+
+            if isinstance(t, torch.Tensor):
+                if len(t.shape) > 0:
+                    res = list(t.shape)
+                    attr_type = TensorType(res)
+                    output, counter = gen_tvar(counter)
+                    self.symbol_dict[n] = output
+                    return [BinConstraintT(output, attr_type, op_eq)], counter
+                else:
+                    # scalar?
+                    return [], counter
+            else:
+                return [], counter
+
+        elif n.op == "output":
+            return [], counter
+
+        else:
+            raise NotImplementedError(f"Method {n.op} not yet implemented")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_transformation.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_transformation.py
new file mode 100644
index 0000000000000000000000000000000000000000..11ebff0102093c035576eda29e71bfd9ccb87ce6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_transformation.py
@@ -0,0 +1,1322 @@
+# mypy: ignore-errors
+import copy
+import itertools
+from typing import Callable
+
+from torch.fx.experimental.migrate_gradual_types.constraint import (
+    ApplyBroadcasting,
+    BinConstraintD,
+    CalcConv,
+    CalcMaxPool,
+    CalcProduct,
+    CanReshape,
+    Conj,
+    Constraint,
+    DGreatestUpperBound,
+    Disj,
+    DVar,
+    F,
+    GetItem,
+    GetItemTensor,
+    IndexSelect,
+    Prod,
+    T,
+    TGreatestUpperBound,
+    Transpose,
+    TVar,
+)
+from torch.fx.experimental.migrate_gradual_types.constraint_generator import (
+    BinConstraintT,
+    MAX_TENSOR_RANK,
+)
+from torch.fx.experimental.migrate_gradual_types.operation import (
+    op_add,
+    op_consistency,
+    op_div,
+    op_eq,
+    op_leq,
+    op_matching,
+    op_mod,
+    op_mul,
+    op_neq,
+    op_precision,
+    op_sub,
+)
+from torch.fx.experimental.migrate_gradual_types.util import (
+    gen_dvar,
+    gen_nat_constraints,
+    gen_tensor_dims,
+)
+from torch.fx.tensor_type import Dyn, TensorType
+
+
+_TRANSFORMATION_RULES: dict[Constraint, Callable] = {}
+
+
+def register_transformation_rule(call_target):
+    def register(fn):
+        if call_target in _TRANSFORMATION_RULES:
+            raise RuntimeError(
+                f"Transformation rule already registered for {call_target}!"
+            )
+        _TRANSFORMATION_RULES[call_target] = fn
+        return fn
+
+    return register
+
+
+def valid_index(index, dims):
+    """
+    Given a list of dimensions, checks if an index is valid in the list
+    """
+    try:
+        dims[index]
+        return T()
+    except IndexError:
+        return F()
+
+
+@register_transformation_rule(Transpose)
+def transform_transpose(constraint, counter):
+    """
+    Similar to a sequence of two index-selects
+    """
+    dims, counter = gen_tensor_dims(constraint.tensor_size, counter)
+    is_valid_index1 = valid_index(constraint.index1, dims)
+    is_valid_index2 = valid_index(constraint.index2, dims)
+    new_dims = copy.deepcopy(dims)
+    nat_constraints = gen_nat_constraints(dims)
+
+    if is_valid_index1 == T() and is_valid_index2 == T():
+        new_dims[constraint.index1] = dims[constraint.index2]
+        new_dims[constraint.index2] = dims[constraint.index1]
+
+    transformed_constraint = Conj(
+        [
+            BinConstraintT(constraint.input_var, TensorType(dims), op_eq),
+            *nat_constraints,
+            is_valid_index1,
+            is_valid_index2,
+            BinConstraintT(constraint.output, TensorType(new_dims), op_eq),
+        ]
+    )
+    return transformed_constraint, counter
+
+
+@register_transformation_rule(IndexSelect)
+def transform_index_select(constraint, counter):
+    """
+    The constraints consider the given tensor size, checks if the index is valid
+    and if so, generates a constraint for replacing the input dimension
+    with the required dimension
+    """
+    dims, counter = gen_tensor_dims(constraint.tensor_size, counter)
+    is_valid_index = valid_index(constraint.index, dims)
+    nat_constraints = gen_nat_constraints(dims)
+
+    # if the index is valid then replace the input dimension with the new dimension
+    # otherwise the dimension will not be replaced and the clause will contain False
+    if is_valid_index == T():
+        new_dims = copy.deepcopy(dims)
+        new_dims[constraint.index] = constraint.dim_replace
+
+    transformed_constraint = Conj(
+        [
+            BinConstraintT(constraint.input_var, TensorType(dims), op_eq),
+            *nat_constraints,
+            is_valid_index,
+            BinConstraintT(constraint.output, TensorType(new_dims), op_eq),
+        ]
+    )
+
+    # print(constraints)
+    return transformed_constraint, counter
+
+
+@register_transformation_rule(GetItem)
+def transform_get_item(constraint, counter):
+    """
+    generate an equality of the form:
+    t = [a1, ..., an]
+    then generate constraints that check if the given index is valid
+    given this particular tensor size.
+    If the index is valid, generate a constraint to get the item
+    Note that we already handled the Dyn input case in the previous
+    step.
+    Args:
+        constraint: GetItem which assumes we are getting an item from a tensor (not Dyn)
+        counter: variable tracking
+    Returns: simplified constraints for GetItem
+
+    """
+    dims, counter = gen_tensor_dims(constraint.tensor_size, counter)
+    nat_constraints = gen_nat_constraints(dims)
+
+    is_valid_index = valid_index(constraint.index, dims)
+
+    all_constraints = [
+        BinConstraintT(constraint.input_var, TensorType(dims), op_eq),
+        *nat_constraints,
+        is_valid_index,
+    ]
+
+    # if the index is valid, we generate a constraint for getting an item
+    # otherwise this clause will have been UNSAT due to the wrong index
+    if is_valid_index == T():
+        all_constraints.append(
+            BinConstraintD(constraint.res, dims[constraint.index], op_eq)
+        )
+
+    return Conj(all_constraints), counter
+
+
+def valid_index_tensor(index, dims):
+    """
+    if the slice instances exceed the length of the dimensions
+    then this is a type error so we return False
+    """
+    slice_count = 0
+    for s in index:
+        if isinstance(s, slice):
+            slice_count += 1
+    if slice_count > len(dims):
+        return F()
+    else:
+        return T()
+
+
+@register_transformation_rule(GetItemTensor)
+def transform_get_item_tensor(constraint, counter):
+    """
+    When the index is a tuple, then the output will be a tensor
+    TODO: we have to check if this is the case for all HF models
+
+    The cases we are covering here are a tuple with one of:
+     - slice with default argument
+     - None
+
+     None appends 1 to the input tensor dimensions
+     so each occurrence of 'None' increases the rank by 1
+
+     slice with default arguments does not change the rank
+    """
+    assert isinstance(constraint.index_tuple, tuple)
+
+    # generate a result tensor of the expected size
+    dims, counter = gen_tensor_dims(constraint.tensor_size, counter)
+    nat_constraints = gen_nat_constraints(dims)
+
+    # generate a place-holder list of the right rank
+    # where "slice" does not contribute to the rank and "None" does
+    none_c = constraint.index_tuple.count(None)
+    resulting_tensor_dims = (none_c + len(dims)) * [None]
+
+    dim_index = 0
+    for i in range(len(constraint.index_tuple)):
+        # append 1 to the right location of the resulting tensor
+        if constraint.index_tuple[i] is None:
+            resulting_tensor_dims[i] = 1
+
+        elif constraint.index_tuple[i] == slice(None, None, None):
+            pass
+
+        else:
+            raise NotImplementedError("Method not yet implemented")
+
+    # append the remaining dimensions to the right location
+    dim_index = 0
+    for i in range(len(resulting_tensor_dims)):
+        if resulting_tensor_dims[i] is None:
+            resulting_tensor_dims[i] = dims[dim_index]
+            dim_index += 1
+
+    # check if the index is valid
+    is_valid_index = valid_index_tensor(constraint.index_tuple, dims)
+
+    # check if the resulting tensor is within bounds
+    if len(resulting_tensor_dims) > 4:
+        return F(), counter
+
+    else:
+        constraints = [
+            BinConstraintT(constraint.input_var, TensorType(dims), op_eq),
+            BinConstraintT(constraint.res, TensorType(resulting_tensor_dims), op_eq),
+            *nat_constraints,
+            is_valid_index,
+        ]
+        return Conj(constraints), counter
+
+
+@register_transformation_rule(BinConstraintT)
+def generate_binconstraint_t(constraint, counter):
+    """
+    Transform binary constraints for tensors
+    """
+
+    # precision constraints
+    if constraint.op == op_precision:
+        if constraint.lhs == Dyn:
+            return T(), counter
+        elif isinstance(constraint.lhs, TensorType):
+            is_fully_static = all(d != Dyn for d in constraint.lhs.__args__)
+            if is_fully_static:
+                return BinConstraintT(constraint.lhs, constraint.rhs, op_eq), counter
+            else:
+                new_dims = []
+
+                for _ in range(len(constraint.lhs.__args__)):
+                    dim, counter = gen_dvar(counter)
+                    new_dims.append(dim)
+
+                new_dim_constraints = (
+                    [
+                        BinConstraintD(old_dim, new_dim, op_precision)
+                        for new_dim, old_dim in zip(new_dims, constraint.lhs.__args__)
+                    ]
+                    + [BinConstraintT(constraint.rhs, TensorType(new_dims), op_eq)]
+                    + [BinConstraintD(1, new_dim, op_leq) for new_dim in new_dims]
+                )
+                return Conj(new_dim_constraints), counter
+
+    # matching
+    elif constraint.op == op_matching:
+        assert isinstance(constraint.rhs, TensorType)
+        d1 = constraint.rhs.__args__[0]
+        d2 = constraint.rhs.__args__[1]
+        d3 = constraint.rhs.__args__[2]
+        d4 = constraint.rhs.__args__[3]
+
+        conj = [
+            BinConstraintT(constraint.lhs, Dyn, op_eq),
+            BinConstraintD(d1, Dyn, op_eq),
+            BinConstraintD(d2, Dyn, op_eq),
+            BinConstraintD(d3, Dyn, op_eq),
+            BinConstraintD(d4, Dyn, op_eq),
+        ]
+        return (
+            Disj(
+                [
+                    Conj(conj),
+                    BinConstraintT(constraint.lhs, TensorType([d1, d2, d3, d4]), op_eq),
+                ]
+            ),
+            counter,
+        )
+
+    elif constraint.op == op_consistency:
+        c_dyn = Disj(
+            [
+                BinConstraintT(constraint.lhs, Dyn, op_eq),
+                BinConstraintT(constraint.rhs, Dyn, op_eq),
+            ]
+        )
+        (
+            (
+                c_tensor_1,
+                c_tensor_2,
+                c_tensor_3,
+                c_tensor_4,
+            ),
+            counter,
+        ) = gen_consistency_constraints(constraint, counter)
+
+        return Disj([c_dyn, c_tensor_1, c_tensor_2, c_tensor_3, c_tensor_4]), counter
+
+    elif constraint.op == op_leq:
+        assert isinstance(constraint.rhs, int)
+        disj = [BinConstraintT(constraint.lhs, Dyn, op_eq)]
+        for i in range(1, constraint.rhs + 1):
+            dims = []
+            for _ in range(1, i + 1):
+                dim_var, counter = gen_dvar(counter)
+                dims.append(dim_var)
+            disj.append(BinConstraintT(constraint.lhs, TensorType(dims), op_eq))
+        return Disj(disj), counter
+    else:
+        return constraint, counter
+
+
+@register_transformation_rule(BinConstraintD)
+def generate_binconstraint_d(constraint, counter):
+    """
+    Transform binary constraints for dimensions
+    """
+    if constraint.op == op_precision:
+        if isinstance(constraint.lhs, int):
+            return BinConstraintD(constraint.lhs, constraint.rhs, op_eq), counter
+        elif constraint.lhs == Dyn:
+            return T(), counter
+
+    elif constraint.op == op_consistency:
+        return (
+            Disj(
+                [
+                    BinConstraintD(constraint.lhs, constraint.rhs, op_eq),
+                    BinConstraintD(constraint.rhs, Dyn, op_eq),
+                    BinConstraintD(constraint.lhs, Dyn, op_eq),
+                ]
+            ),
+            counter,
+        )
+
+    else:
+        return constraint, counter
+
+
+@register_transformation_rule(Conj)
+def generate_conj(constraint, counter):
+    """
+    Transform conjunctions
+    """
+    new = []
+    for c in constraint.conjucts:
+        new_c, counter = transform_constraint(c, counter)
+        new.append(new_c)
+    return Conj(new), counter
+
+
+@register_transformation_rule(Disj)
+def generate_disj(constraint, counter):
+    """
+    Transform disjunctions
+    """
+    new = []
+    for c in constraint.disjuncts:
+        new_c, counter = transform_constraint(c, counter)
+        new.append(new_c)
+    return Disj(new), counter
+
+
+@register_transformation_rule(TGreatestUpperBound)
+def generate_gub(constraint, counter):
+    """
+    Transform greatest upper bound for tensors. Results in equality and Greatest Upper Bound
+    on dimensions
+    """
+    c1 = Conj(
+        [
+            Disj(
+                [
+                    BinConstraintT(constraint.rhs1, Dyn, op_eq),
+                    BinConstraintT(constraint.rhs2, Dyn, op_eq),
+                ]
+            ),
+            BinConstraintT(constraint.res, Dyn, op_eq),
+        ]
+    )
+
+    [c2, c3, c4, c5], counter = gen_greatest_upper_bound(constraint, counter)
+
+    return Disj([c1, c2, c3, c4, c5]), counter
+
+
+@register_transformation_rule(DGreatestUpperBound)
+def generate_d_gub(constraint, counter):
+    """
+    Transform greatest upper bound for dimensions into equality constraints
+    """
+    c1 = Conj(
+        [
+            BinConstraintD(constraint.rhs1, Dyn, op_eq),
+            BinConstraintD(constraint.res, constraint.rhs2, op_eq),
+        ]
+    )
+    c2 = Conj(
+        [
+            BinConstraintD(constraint.rhs2, Dyn, op_eq),
+            BinConstraintD(constraint.res, constraint.rhs1, op_eq),
+        ]
+    )
+    c3 = Conj(
+        [
+            BinConstraintD(constraint.rhs2, constraint.rhs1, op_eq),
+            BinConstraintD(constraint.res, constraint.rhs1, op_eq),
+        ]
+    )
+    return Disj([c1, c2, c3]), counter
+
+
+@register_transformation_rule(CalcConv)
+def generate_calc_conv(constraint, counter):
+    d, counter = gen_tensor_dims(4, counter)
+    conv_result = TensorType([d[0], d[1], d[2], d[3]])
+
+    # the convolution result is a tensor of size 4
+    c1 = BinConstraintT(constraint.conv_result, conv_result, op_eq)
+
+    # the second dimension of the output is equal to the output channels
+    c2 = Conj(
+        [
+            BinConstraintD(d[1], constraint.c_out, op_eq),
+            BinConstraintD(d[1], Dyn, op_neq),
+        ]
+    )
+
+    # the input corresponds to the output in the first dimension of the convolution
+    c3 = BinConstraintD(constraint.matching_constraint[0], d[0], op_eq)
+
+    c4, c5 = calc_last_two_dims(constraint, d)
+
+    leq_constraints = Conj(
+        [
+            BinConstraintD(0, d[0], op_leq),
+            BinConstraintD(0, d[1], op_leq),
+            BinConstraintD(0, d[2], op_leq),
+            BinConstraintD(0, d[3], op_leq),
+        ]
+    )
+
+    return Conj([c1, c2, c3, c4, c5, leq_constraints]), counter
+
+
+@register_transformation_rule(CalcMaxPool)
+def generate_calc_maxpool(constraint, counter):
+    """
+    Transform maxpool constraints
+    """
+    d, counter = gen_tensor_dims(4, counter)
+    maxpool_result = TensorType([d[0], d[1], d[2], d[3]])
+
+    # the maxpool result is a tensor of size 4
+    c1 = BinConstraintT(constraint.maxpool_result, maxpool_result, op_eq)
+
+    # the input corresponds to the output in the first and second dimension of maxpool
+    c2 = BinConstraintD(constraint.matching_constraint[1], d[1], op_eq)
+    c3 = BinConstraintD(constraint.matching_constraint[0], d[0], op_eq)
+    c4, c5 = calc_last_two_dims(constraint, d)
+
+    leq_constraints = Conj(
+        [
+            BinConstraintD(0, d[0], op_leq),
+            BinConstraintD(0, d[1], op_leq),
+            BinConstraintD(0, d[2], op_leq),
+            BinConstraintD(0, d[3], op_leq),
+        ]
+    )
+
+    return Conj([c1, c2, c3, c4, c5, leq_constraints]), counter
+
+
+@register_transformation_rule(CalcProduct)
+def generate_calc_product(constraint, counter):
+    """
+    Transform flatten constraints
+    """
+    start = constraint.start
+    end = constraint.end
+    dims = constraint.dims_to_flatten
+    flattened = constraint.flattened
+    n = len(constraint.dims_to_flatten)
+
+    # this will be evaluated right here
+    boundary_check = 0 <= start and start < end and end <= n
+
+    c_boundary = T() if boundary_check else F()
+
+    lhs = dims[0:start]
+    rhs = dims[end:]
+    mid = dims[start:end]
+
+    all_possibilities = generate_all_int_dyn_dim_possibilities(mid)
+
+    all_constraints = []
+
+    for p in all_possibilities:
+        p = list(p)
+        # this tells us there is a dynamic variable
+        contains_dyn = not all(constraint.op == op_neq for constraint in p)
+        if contains_dyn:
+            mid_var = [Dyn]
+            total_constraints = lhs + mid_var + rhs
+            if len(total_constraints) > 4:
+                all_constraints.append(F())
+            else:
+                all_constraints.append(
+                    Conj(
+                        [
+                            BinConstraintT(
+                                flattened, TensorType(lhs + mid_var + rhs), op_eq
+                            )
+                        ]
+                        + p
+                    )
+                )
+        else:
+            new_var, counter = gen_dvar(counter)
+            mid_eq_prod = Conj(
+                [
+                    BinConstraintD(new_var, Prod(mid), op_eq),
+                    BinConstraintD(new_var, Dyn, op_neq),
+                ]
+            )
+            mid_var = [new_var]
+            total_constraints = lhs + mid_var + rhs
+            if len(total_constraints) > 4:
+                all_constraints.append(F())
+            else:
+                all_constraints.append(
+                    Conj(
+                        [
+                            BinConstraintT(
+                                flattened, TensorType(lhs + mid_var + rhs), op_eq
+                            ),
+                            mid_eq_prod,
+                        ]
+                        + p
+                    )
+                )
+
+    return Conj([Disj(all_constraints), c_boundary]), counter
+
+
+@register_transformation_rule(CanReshape)
+def generate_reshape(constraint, counter):
+    """
+    Transform reshape constraints
+    """
+    d, counter = gen_tensor_dims(4, counter)
+
+    d1 = d[0]
+    d2 = d[1]
+    d3 = d[2]
+    d4 = d[3]
+
+    target = constraint.target.__args__
+
+    is_fully_static = all(d != Dyn for d in target)
+
+    # dynamic tensor
+    c1_dyn = BinConstraintT(constraint.src, Dyn, op_eq)
+    c2_tensor1 = BinConstraintT(constraint.src, TensorType([d1]), op_eq)
+    c2_tensor2 = BinConstraintT(constraint.src, TensorType([d1, d2]), op_eq)
+    c2_tensor3 = BinConstraintT(constraint.src, TensorType([d1, d2, d3]), op_eq)
+    c2_tensor4 = BinConstraintT(constraint.src, TensorType([d1, d2, d3, d4]), op_eq)
+
+    d1_eq_dyn = BinConstraintD(d1, Dyn, op_eq)
+    d1_neq_dyn = BinConstraintD(d1, Dyn, op_neq)
+
+    d2_eq_dyn = BinConstraintD(d2, Dyn, op_eq)
+    d2_neq_dyn = BinConstraintD(d2, Dyn, op_neq)
+
+    d3_eq_dyn = BinConstraintD(d3, Dyn, op_eq)
+    d3_neq_dyn = BinConstraintD(d3, Dyn, op_neq)
+
+    d4_eq_dyn = BinConstraintD(d3, Dyn, op_eq)
+    d4_neq_dyn = BinConstraintD(d3, Dyn, op_neq)
+
+    nat_d1 = BinConstraintD(0, d1, op_leq)
+    nat_d2 = BinConstraintD(0, d2, op_leq)
+    nat_d3 = BinConstraintD(0, d3, op_leq)
+    nat_d4 = BinConstraintD(0, d4, op_leq)
+
+    if is_fully_static:
+        # size 1 tensor
+        c3_tensor1 = Disj(
+            [d1_eq_dyn, (Conj([d1_neq_dyn, BinConstraintD(d1, Prod(target), op_eq)]))]
+        )
+        all_tensor_1 = Conj([c2_tensor1, c3_tensor1])
+
+        # size 2 tensor
+        all_tensor_2 = Conj(
+            [c2_tensor2, gen_all_reshape_possibilities([d1, d2], target)]
+        )
+
+        # size 3 tensor
+        all_tensor_3 = Conj(
+            [c2_tensor3, gen_all_reshape_possibilities([d1, d2, d3], target)]
+        )
+
+        # size 4 tensor
+        all_tensor_4 = Conj(
+            [c2_tensor4, gen_all_reshape_possibilities([d1, d2, d3, d4], target)]
+        )
+
+        return (
+            Conj(
+                [
+                    Disj(
+                        [c1_dyn, all_tensor_1, all_tensor_2, all_tensor_3, all_tensor_4]
+                    ),
+                    nat_d1,
+                    nat_d2,
+                    nat_d3,
+                    nat_d4,
+                ]
+            ),
+            counter,
+        )
+
+    # then there must be exactly one occurrence of dyn
+    else:
+        new_target = [n for n in target if n != Dyn]
+
+        # tensor 1
+        c3_tensor1 = Disj(
+            [d1_eq_dyn, (Conj([d1_neq_dyn, is_dim_div_by_target(new_target, d1)]))]
+        )
+        all_tensor_1 = Conj([c2_tensor1, c3_tensor1])
+
+        # tensor 2
+        c21 = Disj([d1_eq_dyn, d2_eq_dyn])
+        c22 = Conj(
+            [d1_neq_dyn, d2_neq_dyn, is_dim_div_by_target(new_target, Prod([d1, d2]))]
+        )
+        all_tensor_2 = Conj([c2_tensor2, Disj([c21, c22])])
+
+        # tensor 3
+        c31 = Disj([d1_eq_dyn, d2_eq_dyn, d3_eq_dyn])
+        c32 = Conj(
+            [
+                d1_neq_dyn,
+                d2_neq_dyn,
+                d3_neq_dyn,
+                is_dim_div_by_target(new_target, Prod([d1, d2, d3])),
+            ]
+        )
+        all_tensor_3 = Conj([c2_tensor3, Disj([c31, c32])])
+
+        # tensor 4
+        c41 = Disj([d1_eq_dyn, d2_eq_dyn, d3_eq_dyn, d4_eq_dyn])
+        c42 = Conj(
+            [
+                d1_neq_dyn,
+                d2_neq_dyn,
+                d3_neq_dyn,
+                d4_neq_dyn,
+                is_dim_div_by_target(new_target, Prod([d1, d2, d3, d4])),
+            ]
+        )
+        all_tensor_4 = Conj([c2_tensor4, Disj([c41, c42])])
+
+        return (
+            Conj(
+                [
+                    Disj(
+                        [c1_dyn, all_tensor_1, all_tensor_2, all_tensor_3, all_tensor_4]
+                    ),
+                    nat_d1,
+                    nat_d2,
+                    nat_d3,
+                    nat_d4,
+                ]
+            ),
+            counter,
+        )
+
+
+@register_transformation_rule(ApplyBroadcasting)
+def generate_broadcasting(constraint, counter):
+    """
+    Transform broadcasting constraints
+    """
+    e11, e12 = constraint.res1, constraint.res2
+    e1, e2 = constraint.input1, constraint.input2
+
+    e1_dyn = BinConstraintT(e1, Dyn, op_eq)
+    e2_dyn = BinConstraintT(e2, Dyn, op_eq)
+
+    # Introduce dimensions
+    e1_equal_e11 = BinConstraintT(e1, e11, op_eq)
+    e2_equal_e12 = BinConstraintT(e2, e12, op_eq)
+
+    # dyn possibility
+    e1_dyn_constraint = Conj([e1_dyn, e1_equal_e11, e2_equal_e12])
+    e2_dyn_constraint = Conj([e2_dyn, e1_equal_e11, e2_equal_e12])
+
+    # tensor possibility
+    # generate dimensions to create tensors of size 1
+    final_tensor_1_constraint, _, _, nat_dims_1, counter = gen_broadcasting_constraints(
+        e1, e2, e11, e12, 1, counter
+    )
+
+    # generate dimensions to create tensors of size 2
+    (
+        final_tensor_2_constraint_no_padding,
+        final_tensor_2_constraint_padding_arg1,
+        final_tensor_2_constraint_padding_arg2,
+        nat_dims_2,
+        counter,
+    ) = gen_broadcasting_constraints(e1, e2, e11, e12, 2, counter)
+
+    # generate dimensions to create tensors of size 3
+    (
+        final_tensor_3_constraint_no_padding,
+        final_tensor_3_constraint_padding_arg1,
+        final_tensor_3_constraint_padding_arg2,
+        nat_dims_3,
+        counter,
+    ) = gen_broadcasting_constraints(e1, e2, e11, e12, 3, counter)
+
+    # generate dimensions to create tensors of size 4
+    (
+        final_tensor_4_constraint_no_padding,
+        final_tensor_4_constraint_padding_arg1,
+        final_tensor_4_constraint_padding_arg2,
+        nat_dims_4,
+        counter,
+    ) = gen_broadcasting_constraints(e1, e2, e11, e12, 4, counter)
+
+    final_result = Disj(
+        [
+            e1_dyn_constraint,
+            e2_dyn_constraint,
+            final_tensor_1_constraint,
+            final_tensor_2_constraint_no_padding,
+            final_tensor_2_constraint_padding_arg1,
+            final_tensor_2_constraint_padding_arg2,
+            final_tensor_3_constraint_no_padding,
+            final_tensor_3_constraint_padding_arg1,
+            final_tensor_3_constraint_padding_arg2,
+            final_tensor_4_constraint_no_padding,
+            final_tensor_4_constraint_padding_arg1,
+            final_tensor_4_constraint_padding_arg2,
+        ]
+    )
+
+    return (
+        Conj([final_result, *nat_dims_1, *nat_dims_2, *nat_dims_3, *nat_dims_4]),
+        counter,
+    )
+
+
+def transform_constraint(constraint: Constraint, counter: int):
+    """
+    Transforms a constraint into a simpler constraint.
+    Ex: precision and consistency are transformed to equality
+    Args:
+        constraint: constraint to be transformed
+        counter: for variable tracking
+
+    Returns: Constraint
+
+    """
+    if type(constraint) in _TRANSFORMATION_RULES:
+        return _TRANSFORMATION_RULES[type(constraint)](constraint, counter)
+
+    else:
+        return constraint, counter
+
+
+def calc_last_two_dims(constraint, d: list[DVar]):
+    """
+    Generates constraints for the last two dimensions of a convolution or a maxpool output
+    Args:
+        constraint: CalcConv or CalcMaxPool
+        d: The list of output dimensions
+
+    Returns: Constraints for calculating the last two dimensions of the output
+
+    """
+
+    assert isinstance(constraint, (CalcConv, CalcMaxPool))
+
+    b3 = constraint.matching_constraint[2]
+    b4 = constraint.matching_constraint[3]
+
+    b3_dyn = Conj([BinConstraintD(d[2], Dyn, op_eq), BinConstraintD(b3, Dyn, op_eq)])
+    b4_dyn = Conj([BinConstraintD(d[3], Dyn, op_eq), BinConstraintD(b4, Dyn, op_eq)])
+
+    d3_not_dyn = Conj(
+        [BinConstraintD(d[2], Dyn, op_neq), BinConstraintD(b3, Dyn, op_neq)]
+    )
+    d4_not_dyn = Conj(
+        [BinConstraintD(d[3], Dyn, op_neq), BinConstraintD(b4, Dyn, op_neq)]
+    )
+
+    # transform parameters into tuples incase they are not already
+    padding = (
+        (constraint.padding, constraint.padding)
+        if isinstance(constraint.padding, int)
+        else constraint.padding
+    )
+    kernel = (
+        (constraint.kernel, constraint.kernel)
+        if isinstance(constraint.kernel, int)
+        else constraint.kernel
+    )
+    stride = (
+        (constraint.stride, constraint.stride)
+        if isinstance(constraint.stride, int)
+        else constraint.stride
+    )
+    dilation = (
+        (constraint.dilation, constraint.dilation)
+        if isinstance(constraint.dilation, int)
+        else constraint.dilation
+    )
+
+    f1 = BinConstraintD(b3, BinConstraintD(2, padding[0], op_mul), op_add)
+    f2 = BinConstraintD(dilation[0], BinConstraintD(kernel[0], 1, op_sub), op_mul)
+    f3 = BinConstraintD(
+        BinConstraintD(BinConstraintD(f1, f2, op_sub), 1, op_sub), stride[0], op_div
+    )
+    f4 = BinConstraintD(f3, 1, op_add)
+
+    c4 = Disj([b3_dyn, Conj([d3_not_dyn, BinConstraintD(d[2], f4, op_eq)])])
+
+    f11 = BinConstraintD(b4, BinConstraintD(2, padding[1], op_mul), op_add)
+    f22 = BinConstraintD(dilation[1], BinConstraintD(kernel[1], 1, op_sub), op_mul)
+    f33 = BinConstraintD(
+        BinConstraintD(BinConstraintD(f11, f22, op_sub), 1, op_sub), stride[1], op_div
+    )
+    f44 = BinConstraintD(f33, 1, op_add)
+
+    c5 = Disj([b4_dyn, Conj([d4_not_dyn, BinConstraintD(d[3], f44, op_eq)])])
+
+    return c4, c5
+
+
+def generate_all_int_dyn_dim_possibilities(my_list: list[DVar]):
+    """
+    Generate all possibilities of being equal or not equal to dyn for my_list
+    Args:
+        my_list: List of tensor dimensions
+
+    Returns: A list of a list of constraints. Each list of constraints corresponds to
+    one possibility about the values of the dimension variables
+    """
+    # generate all possibilities of being equal or not equal to dyn for my_list
+    eq_possibilities = [
+        BinConstraintD(my_list[i], Dyn, op_eq) for i in range(len(my_list))
+    ]
+    neq_possibilities = [
+        BinConstraintD(my_list[i], Dyn, op_neq) for i in range(len(my_list))
+    ]
+
+    d_possibilities = [list(i) for i in zip(eq_possibilities, neq_possibilities)]
+    all_possibilities = list(itertools.product(*d_possibilities))
+    return all_possibilities
+
+
+def is_target_div_by_dim(target: list[int], dim: list[DVar]):
+    """
+    Generate constraints to check if the target dimensions are divisible by the input dimensions
+    Args:
+        target: Target dimensions
+        dim: Input dimensions
+
+    Returns: Constraints to check divisibility
+
+    """
+    return BinConstraintD(BinConstraintD(Prod(target), dim, op_mod), 0, op_eq)
+
+
+def is_dim_div_by_target(target: list[int], dim: list[DVar]):
+    """
+    Generate constraints to check if the input dimensions is divisible by the target dimensions
+    Args:
+        target: Target dimensions
+        dim:  Input dimensions
+
+    Returns: Constraints to check divisibility
+
+    """
+    return BinConstraintD(BinConstraintD(dim, Prod(target), op_mod), 0, op_eq)
+
+
+def gen_all_reshape_possibilities(list_of_dims, target):
+    """
+    Consider all possibilities what the input dimensions could be (number or dynamic)
+    Then generate the appropriate constraints using multiplication or mod depending on the possibility
+    The possibilities we consider here are the cross product of being equal to dyn or not equal to dyn
+    for the input. Target is fixed because at most one dimension could be dyn.
+    We have different cases for this.
+
+    Args:
+        list_of_dims: The input list of dimensions
+        target: The tensor we want to reshape to
+
+    Returns: A disjunction of transformed reshape constraints
+
+    """
+    all_possibilities = generate_all_int_dyn_dim_possibilities(list_of_dims)
+
+    all_constraints = []
+
+    for p in all_possibilities:
+        to_multiply = []
+
+        p = list(p)
+
+        for constraint in p:
+            assert isinstance(constraint, BinConstraintD)
+            if constraint.op == op_neq:
+                to_multiply.append(constraint.lhs)
+
+        if not to_multiply:
+            all_constraints.append(Conj(p))
+
+        elif len(to_multiply) < len(list_of_dims):
+            all_constraints.append(
+                Conj(p + [is_target_div_by_dim(target, Prod(to_multiply))])
+            )
+        else:
+            all_constraints.append(
+                Conj(p + [BinConstraintD(Prod(list_of_dims), Prod(target), op_eq)])
+            )
+
+    return Disj(all_constraints)
+
+
+def broadcast_dim(tensor_input1, tensor_input2, res1, res2, index, padding=False):
+    """
+    Apply broadcasting to the 'index' dimension of tensor_input1.
+    Args:
+        tensor_input1: should represent [d1, ..., d_index, ...] where d_index = 1
+        tensor_input2: represents the second input
+        res1: broadcasted result 1
+        res2: broadcasted result 2
+        index: the index to broadcast
+        padding: If padding was used, then tensor_input1[index] does not exist
+
+    Returns:
+
+    """
+    if tensor_input1[index] is None:
+        assert padding
+
+    if not padding:
+        # then the inputs are the same length so they all have dimensions at "index"
+        return Conj(
+            [
+                BinConstraintD(tensor_input1[index], 1, op_eq),
+                BinConstraintD(res1[index], res2[index], op_eq),
+                BinConstraintD(res2[index], tensor_input2[index], op_eq),
+            ]
+        )
+
+    else:
+        # we don't set the input dimension to 1, since it doesn't exist.
+        return Conj(
+            [
+                BinConstraintD(res1[index], res2[index], op_eq),
+                BinConstraintD(res2[index], tensor_input2[index], op_eq),
+            ]
+        )
+
+
+def apply_padding(
+    e1_var: TVar,
+    e11: BinConstraintT,
+    e2: BinConstraintT,
+    e12: BinConstraintT,
+    d2: list[DVar],
+    d11: list[DVar],
+    d12: list[DVar],
+    counter: int,
+):
+    """
+    We are considering the possibility where one input has less dimensions than
+    another input, so we apply padding to the broadcasted results
+
+    Args:
+        e1_var: Variable representing the first input where padding will be
+        e11: constraint of the form e11 = Tensortype[d1, ..., dn]
+        e2:  constraint of the form e2 = Tensortype[d1, ..., dn]
+        e12: constraint of the form e11 = Tensortype[d1, ..., dn]
+        d2: Tensor variables for the second input
+        d11: Tensor variables for the broadcasted first input
+        d12: Tensor variables for the broadcasted second input
+        counter: variable tracking
+
+    Returns: A new constraint whose goal is to apply padding to the broadcasted result
+
+    """
+
+    res = []
+
+    # pad the shorter input with None so we can pass it to the broadcasting helper function
+    for i in range(1, len(d2)):
+        d1, counter = gen_tensor_dims(i, counter)
+
+        nat_constraints = gen_nat_constraints(d1 + d2 + d11 + d12)
+
+        e1 = BinConstraintT(e1_var, TensorType(d1), op_eq)
+
+        simulate_padding = [None] * (len(d2) - i)
+
+        assert len(simulate_padding + d1) == len(d2)
+
+        # for every padding size, we also consider broadcasting
+        broadcast_padding = [
+            broadcast_dim(simulate_padding, d2, d11, d12, j, True)
+            for j in range(len(d2) - i)
+        ]
+
+        # we consider the possibilities for broadcasting for every dimension. Since we already
+        # padded d1, we do not consider it while broadcasting
+        all_broadcasting_possibilities = (
+            generate_all_broadcasting_possibilities_no_padding(
+                d1, d2[(len(d2) - i) :], d11[(len(d2) - i) :], d12[(len(d2) - i) :]
+            )
+        )
+        # combine all constraints into a conjunction
+        c = Conj(
+            [
+                e1,
+                e11,
+                e2,
+                e12,
+                *broadcast_padding,
+                all_broadcasting_possibilities,
+                *nat_constraints,
+            ]
+        )
+        res.append(c)
+
+    return Disj(res), counter
+
+
+def no_broadcast_dim_with_index(
+    d1: list[DVar], d2: list[DVar], d3: list[DVar], d4: list[DVar], i: int
+):
+    """
+    Args:
+        d1: input 1
+        d2: input 2
+        d3: simulated broadcasting for input 1
+        d4: simulated broadcasting for input 2
+        i: the rank of the resulting tensor addition
+
+    Returns: Constraints for when no broadcasting occurs
+    """
+    return Conj(
+        [
+            Disj(
+                [
+                    Conj(
+                        [
+                            BinConstraintD(d1[i], 1, op_eq),
+                            BinConstraintD(d2[i], 1, op_eq),
+                        ]
+                    ),
+                    Conj(
+                        [
+                            BinConstraintD(d1[i], 1, op_neq),
+                            BinConstraintD(d2[i], 1, op_neq),
+                        ]
+                    ),
+                ]
+            ),
+            BinConstraintD(d1[i], d3[i], op_eq),
+            BinConstraintD(d2[i], d4[i], op_eq),
+        ]
+    )
+
+
+def gen_lists_of_dims(num_tensors: int, dim_size: int, counter: int):
+    """
+    Generate lists of DVar to represent tensor dimensions
+    Args:
+        num_tensors: the required number of tensors
+        dim_size: the number of dimensions for each tensor
+        counter: variable tracking
+
+    Returns: A list of a list of tensor dimensions
+
+    """
+    res = []
+
+    for _ in range(num_tensors):
+        dims, counter = gen_tensor_dims(dim_size, counter)
+        res.append(dims)
+
+    return res, counter
+
+
+def create_equality_constraints_for_broadcasting(
+    e1: TVar,
+    e2: TVar,
+    e11: TVar,
+    e12: TVar,
+    d1: list[DVar],
+    d2: list[DVar],
+    d11: list[DVar],
+    d12: list[DVar],
+):
+    """
+    Create equality constraints for when no broadcasting occurs
+    Args:
+        e1: Input 1
+        e2: Input 2
+        e11: Broadcasted input 1
+        e12: Broadcasted input 2
+        d1: Variables that store dimensions for e1
+        d2: Variables that store dimensions for e2
+        d11: Variables that store dimensions for e11
+        d12: Variables that store dimensions for e22
+
+    Returns: Four equality constraints
+
+    """
+
+    e1_tensor = BinConstraintT(e1, TensorType(d1), op_eq)
+    e11_tensor = BinConstraintT(e11, TensorType(d11), op_eq)
+    e2_tensor = BinConstraintT(e2, TensorType(d2), op_eq)
+    e12_tensor = BinConstraintT(e12, TensorType(d12), op_eq)
+    return [e1_tensor, e11_tensor, e2_tensor, e12_tensor]
+
+
+def gen_consistency_constraints(constraint: Constraint, counter: int):
+    """
+    Args:
+        constraint: Consistency constraint on tensors
+        counter: for variable tracking
+
+    Returns: Equality and consistency constraints on dimensions
+
+    """
+
+    all_constraints = []
+
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        new_dims_rhs_1, counter = gen_tensor_dims(i, counter)
+        new_dims_rhs_2, counter = gen_tensor_dims(i, counter)
+
+        nat_constraints = gen_nat_constraints(new_dims_rhs_1 + new_dims_rhs_2)
+
+        c_tensor_i = Conj(
+            [
+                BinConstraintT(constraint.lhs, TensorType(new_dims_rhs_1), op_eq),
+                BinConstraintT(constraint.rhs, TensorType(new_dims_rhs_2), op_eq),
+            ]
+            + [
+                BinConstraintD(d1, d2, op_consistency)
+                for d1, d2 in zip(new_dims_rhs_1, new_dims_rhs_2)
+            ]
+            + nat_constraints
+        )
+
+        all_constraints.append(c_tensor_i)
+
+    return all_constraints, counter
+
+
+def gen_greatest_upper_bound(constraint: TGreatestUpperBound, counter: int):
+    """
+    Args:
+        constraint: Greatest upper bound on tensors
+        counter: variable tracking
+
+    Returns: A set of equality constraints and DGreatestUpperBound constraints
+
+    """
+
+    all_constraints = []
+
+    for i in range(1, MAX_TENSOR_RANK + 1):
+        c = []
+        dims1, counter = gen_tensor_dims(i, counter)
+        c1tensor = TensorType(dims1)
+
+        dims2, counter = gen_tensor_dims(i, counter)
+        c2tensor = TensorType(dims2)
+
+        dims3, counter = gen_tensor_dims(i, counter)
+        c3tensor = TensorType(dims3)
+
+        c += [
+            BinConstraintT(constraint.rhs1, c1tensor, op_eq),
+            BinConstraintT(constraint.rhs2, c2tensor, op_eq),
+            BinConstraintT(constraint.res, c3tensor, op_eq),
+        ] + gen_nat_constraints(dims1 + dims2 + dims3)
+
+        assert (
+            len(c3tensor.__args__) == len(c1tensor.__args__) == len(c2tensor.__args__)
+        )
+        for i in range(len(c3tensor.__args__)):
+            c.append(
+                DGreatestUpperBound(
+                    c3tensor.__args__[i], c1tensor.__args__[i], c2tensor.__args__[i]
+                )
+            )
+
+        all_constraints.append(Conj(c))
+    return all_constraints, counter
+
+
+def generate_all_broadcasting_possibilities_no_padding(
+    d1: list[DVar], d2: list[DVar], d11: list[DVar], d12: list[DVar]
+):
+    """
+    Generate broadcasting constraints assuming no padding. Broadcasting can happen at any dimension.
+    We look at all combinations for all dimensions in d1 and d2
+    Args:
+        d1: input1 dimensions
+        d2: input2 dimensions
+        d11: broadcasted input1 dimensions
+        d12: broadcasted input2 dimensions
+
+    Returns: broadcasting constraints relating the input dimensions to the broadcasted dimensions
+
+    """
+
+    size = len(d1)
+
+    res2 = []
+
+    for i in range(size):
+        t1 = broadcast_dim(d1, d2, d11, d12, i)
+        t2 = broadcast_dim(d2, d1, d12, d11, i)
+        t3 = no_broadcast_dim_with_index(d1, d2, d11, d12, i)
+
+        res2.append(Disj([t1, t2, t3]))
+
+    return Conj(res2)
+
+
+def gen_broadcasting_constraints(
+    e1: TVar, e2: TVar, e11: TVar, e12: TVar, i: int, counter: int
+):
+    """
+    Simulates broadcasting on e1 and e2 and returns the results
+    respectively in e11 and e12. Because of gradual types,
+    e1 and e2 may not be equal. Similarly, e11 and e12 may not
+    be equal. e11 and e12 should be guaranteed to be consistent
+    as they represent the shapes of the tensors to be added after
+    broadcasting.
+    Args:
+        e1: TVar representing the type of input 1
+        e2: TVar representing the type of input 2
+        e11: TVar representing the representing broadcasted input 1
+        e12: TVar representing the representing broadcasted input 2
+        i: The rank of the resulting type of addition
+        counter: for variable tracking
+
+    Returns: Simplified broadcasting constraints
+
+    """
+    dims, counter = gen_lists_of_dims(4, i, counter)
+    [d1, d2, d3, d4] = dims
+    nat_dims_i = gen_nat_constraints(list(itertools.chain.from_iterable(dims)))
+
+    initialize_tensors_constraints = create_equality_constraints_for_broadcasting(
+        e1, e2, e11, e12, d1, d2, d3, d4
+    )
+
+    [e1_tensor, e11_tensor, e2_tensor, e12_tensor] = initialize_tensors_constraints
+
+    # without padding, broadcast all possibilities for tensors of size i
+    final_tensor_constraint_no_padding = Conj(
+        [
+            *initialize_tensors_constraints,
+            generate_all_broadcasting_possibilities_no_padding(d1, d2, d3, d4),
+        ]
+    )
+
+    # with padding, broadcast all possibilities for tensors of size i
+    final_tensor_constraint_padding_arg1, counter = apply_padding(
+        e1, e11_tensor, e2_tensor, e12_tensor, d2, d3, d4, counter
+    )
+
+    final_tensor_constraint_padding_arg2, counter = apply_padding(
+        e2, e12_tensor, e1_tensor, e11_tensor, d1, d4, d3, counter
+    )
+
+    return (
+        final_tensor_constraint_no_padding,
+        final_tensor_constraint_padding_arg1,
+        final_tensor_constraint_padding_arg2,
+        nat_dims_i,
+        counter,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/operation.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/operation.py
new file mode 100644
index 0000000000000000000000000000000000000000..267100c8545c8b2310299337ecf64211f633f6ce
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/operation.py
@@ -0,0 +1,14 @@
+op_add = "+"
+op_sub = "-"
+op_mul = "*"
+op_div = "/"
+op_eq = "="
+op_neq = "!="
+op_imp = "=>"
+op_matching = "\u22b3"  # (contains)
+op_consistency = "~"
+op_precision = "\u2291"  # (square image of or equal to)
+op_leq = "\u2264"  # less-than or equal to
+op_lt = "<"
+op_gt = ">"
+op_mod = "%"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/transform_to_z3.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/transform_to_z3.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1f9f33965e07551c651fa560a80c5e263dd5b85
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/transform_to_z3.py
@@ -0,0 +1,446 @@
+# mypy: allow-untyped-defs
+from torch.fx.experimental.migrate_gradual_types.constraint import (
+    BinConstraintD,
+    BinConstraintT,
+    BVar,
+    Conj,
+    Disj,
+    DVar,
+    F,
+    is_algebraic_expression,
+    is_bool_expr,
+    is_dim,
+    Prod,
+    T,
+    TVar,
+)
+from torch.fx.experimental.migrate_gradual_types.constraint_generator import (
+    ConstraintGenerator,
+)
+from torch.fx.experimental.migrate_gradual_types.constraint_transformation import (
+    transform_constraint,
+)
+from torch.fx.experimental.migrate_gradual_types.operation import (
+    op_add,
+    op_div,
+    op_eq,
+    op_gt,
+    op_leq,
+    op_lt,
+    op_mod,
+    op_mul,
+    op_neq,
+    op_sub,
+)
+from torch.fx.tensor_type import Dyn, TensorType
+
+
+try:
+    import z3  # type: ignore[import]
+
+    from torch.fx.experimental.migrate_gradual_types.z3_types import (
+        D,
+        tensor_type,
+        z3_dyn,
+    )
+
+    HAS_Z3 = True
+
+    def transform_to_z3(constraint, counter, dimension_dict):
+        if isinstance(constraint, Conj):
+            conjuncts = []
+            for c in constraint.conjucts:
+                new_c, counter = transform_to_z3(c, counter, dimension_dict)
+                conjuncts.append(new_c)
+            return z3.And(conjuncts), counter
+
+        elif isinstance(constraint, Disj):
+            disjuncts = []
+            for c in constraint.disjuncts:
+                new_c, counter = transform_to_z3(c, counter, dimension_dict)
+                disjuncts.append(new_c)
+            return z3.Or(disjuncts), counter
+
+        elif isinstance(constraint, T):
+            return True, counter
+
+        elif isinstance(constraint, F):
+            return False, counter
+
+        elif isinstance(constraint, BinConstraintT):
+            if constraint.op == op_eq:
+                lhs, counter = transform_var(constraint.lhs, counter, dimension_dict)
+                rhs, counter = transform_var(constraint.rhs, counter, dimension_dict)
+                return (lhs == rhs), counter
+
+            else:
+                raise NotImplementedError("Method not yet implemented")
+
+        elif isinstance(constraint, BinConstraintD):
+            if constraint.op == op_eq:
+                if isinstance(constraint.lhs, BVar) and is_bool_expr(constraint.rhs):
+                    transformed_rhs, counter = transform_to_z3(
+                        constraint.rhs, counter, dimension_dict
+                    )
+                    transformed_lhs = z3.Bool(constraint.lhs.c)
+                    return transformed_lhs == transformed_rhs, counter
+
+                elif is_dim(constraint.lhs) and is_dim(constraint.rhs):
+                    # with dimension transformations we consider the encoding
+                    lhs, counter = transform_dimension(
+                        constraint.lhs, counter, dimension_dict
+                    )
+                    rhs, counter = transform_dimension(
+                        constraint.rhs, counter, dimension_dict
+                    )
+                    return lhs == rhs, counter
+
+                else:
+                    # then we have an algebraic expression which means that we disregard the
+                    # first element of the encoding
+                    lhs, counter = transform_algebraic_expression(
+                        constraint.lhs, counter, dimension_dict
+                    )
+                    rhs, counter = transform_algebraic_expression(
+                        constraint.rhs, counter, dimension_dict
+                    )
+                    return lhs == rhs, counter
+
+            # The assumption here is that the LHS and RHS must be dimensions
+            elif constraint.op == op_neq:
+                assert is_dim(constraint.lhs)
+                assert is_dim(constraint.rhs)
+                lhs, counter = transform_dimension(
+                    constraint.lhs, counter, dimension_dict
+                )
+                rhs, counter = transform_dimension(
+                    constraint.rhs, counter, dimension_dict
+                )
+                if constraint.rhs == Dyn or constraint.lhs == Dyn:
+                    if constraint.rhs == Dyn:
+                        return lhs.arg(0) == 1, counter
+                    elif constraint.lhs == Dyn:
+                        return rhs.arg(0) == 1, counter
+
+                # if one of the instances is a number
+                elif isinstance(constraint.lhs, int) or isinstance(constraint.rhs, int):
+                    if isinstance(constraint.lhs, int):
+                        return (
+                            z3.Or(
+                                [
+                                    rhs.arg(0) == 0,
+                                    z3.And([rhs.arg(0) == 1, lhs.arg(1) != rhs.arg(1)]),
+                                ]
+                            ),
+                            counter,
+                        )
+
+                    elif isinstance(constraint.rhs, int):
+                        return (
+                            z3.Or(
+                                [
+                                    lhs.arg(0) == 0,
+                                    z3.And([lhs.arg(0) == 1, lhs.arg(1) != rhs.arg(1)]),
+                                ]
+                            ),
+                            counter,
+                        )
+
+                else:
+                    return (
+                        z3.Or(
+                            [
+                                z3.And([lhs.arg(0) == 0, rhs.arg(0) != 0]),
+                                z3.And([lhs.arg(0) != 0, rhs.arg(0) == 0]),
+                                z3.And(
+                                    [
+                                        lhs.arg(0) != 0,
+                                        rhs.arg(0) != 0,
+                                        lhs.arg(1) != rhs.arg(1),
+                                    ]
+                                ),
+                            ]
+                        ),
+                        counter,
+                    )
+
+            elif constraint.op == op_leq:
+                # if the dimensions are not dyn, this will come into effect
+                # there would have been another constraint specifying if a given dimension
+                # is dyn or not
+                assert is_dim(constraint.lhs) and is_dim(constraint.rhs)
+                lhs, counter = transform_algebraic_expression(
+                    constraint.lhs, counter, dimension_dict
+                )
+                rhs, counter = transform_algebraic_expression(
+                    constraint.rhs, counter, dimension_dict
+                )
+                return lhs <= rhs, counter
+
+            elif constraint.op == op_gt:
+                assert is_dim(constraint.lhs) and is_dim(constraint.rhs)
+                lhs, counter = transform_algebraic_expression(
+                    constraint.lhs, counter, dimension_dict
+                )
+                rhs, counter = transform_algebraic_expression(
+                    constraint.rhs, counter, dimension_dict
+                )
+                return lhs > rhs, counter
+
+            elif constraint.op == op_lt:
+                assert is_dim(constraint.lhs) and is_dim(constraint.rhs)
+                lhs, counter = transform_algebraic_expression(
+                    constraint.lhs, counter, dimension_dict
+                )
+                rhs, counter = transform_algebraic_expression(
+                    constraint.rhs, counter, dimension_dict
+                )
+                return lhs < rhs, counter
+
+            else:
+                raise NotImplementedError("operation not yet implemented")
+
+        else:
+            raise NotImplementedError("Operation not yet implemented")
+
+    def transform_var(tensor, counter, dimension_dict):
+        """
+        Transforms tensor variables to a format understood by z3
+        Args:
+            tensor: Tensor variable or a tensor type potentially with variable dimensions
+        Returns: Transformed variable to a z3 format
+
+        """
+        if isinstance(tensor, TensorType):
+            res = []
+            for t in tensor.__args__:
+                transformed, counter = transform_dimension(t, counter, dimension_dict)
+                res.append(transformed)
+
+            assert len(res) <= 4
+            if len(tensor.__args__) == 1:
+                return tensor_type.tensor1(res[0]), counter
+            elif len(tensor.__args__) == 2:
+                return tensor_type.tensor2(res[0], res[1]), counter
+            elif len(tensor.__args__) == 3:
+                return tensor_type.tensor3(res[0], res[1], res[2]), counter
+            elif len(tensor.__args__) == 4:
+                return tensor_type.tensor4(res[0], res[1], res[2], res[3]), counter
+
+        elif tensor == Dyn:
+            return z3_dyn, counter
+
+        elif isinstance(tensor, TVar):
+            return z3.Const(tensor.tvar, tensor_type), counter
+
+    def transform_dimension(dimension, counter, dimension_dict):
+        """
+        Takes a dimension variable or a number and transforms it to a tuple
+        according to our scheme
+        Args:
+            dimension: The dimension to be transformed
+            counter: variable tracking
+
+        Returns:  tuple and the current counter
+
+        """
+        if dimension == Dyn:
+            counter += 1
+            return D(0, z3.Int(counter)), counter
+        elif isinstance(dimension, int):
+            return D(1, dimension), counter
+        elif isinstance(dimension, DVar):
+            if dimension.c in dimension_dict:
+                return (
+                    D(z3.Int(dimension_dict[dimension.c]), z3.Int(dimension.c)),
+                    counter,
+                )
+            else:
+                counter += 1
+                dimension_dict[dimension.c] = counter
+                return D(z3.Int(counter), z3.Int(dimension.c)), counter
+
+    def transform_algebraic_expression(expr, counter, dimension_dict):
+        """
+        Transforms an algebraic expression to z3 format
+        Args:
+            expr: An expression is either a dimension variable or an algebraic-expression
+
+
+        Returns: the transformed expression
+
+        """
+        assert is_algebraic_expression(expr) or is_dim(expr)
+
+        if is_dim(expr):
+            transformed, counter = transform_dimension(expr, counter, dimension_dict)
+            return transformed.arg(1), counter
+
+        elif isinstance(expr, Prod):
+            dims = []
+            for dim in expr.products:
+                assert is_dim(dim)
+                d, counter = transform_dimension(dim, counter, dimension_dict)
+                dims.append(d.arg(1))
+            return z3.Product(dims), counter
+
+        elif is_algebraic_expression(expr):
+            lhs, counter = transform_algebraic_expression(
+                expr.lhs, counter, dimension_dict
+            )
+            rhs, counter = transform_algebraic_expression(
+                expr.rhs, counter, dimension_dict
+            )
+
+            if expr.op == op_sub:
+                c = lhs - rhs
+
+            elif expr.op == op_add:
+                c = lhs + rhs
+
+            elif expr.op == op_div:
+                c = lhs / rhs
+
+            elif expr.op == op_mul:
+                c = lhs * rhs
+
+            elif expr.op == op_mod:
+                c = lhs % rhs
+
+            else:
+                raise NotImplementedError("operation not yet implemented")
+
+            return c, counter
+
+        else:
+            raise RuntimeError
+
+    def transform_all_constraints(traced, counter=0):
+        """
+        Given a trace, generates constraints and transforms them to z3 format
+
+        """
+        dimension_dict = {}  # type: ignore[var-annotated]
+
+        generator = ConstraintGenerator(traced)
+        new_constraints, counter = generator.generate_constraints(counter)
+
+        # print(new_constraints.conjucts[0])
+        # print(*new_constraints.conjucts, sep='\n')
+
+        # transform precision, matching, consistency till obtaining a fixed point
+        new_constraints, counter = iterate_till_fixed_point(new_constraints, counter)
+        # print(new_constraints)
+        # print(new_constraints.conjucts)
+        # new_constraints.conjucts = new_constraints.conjucts[:-1]
+        # print(*new_constraints.conjucts, sep='\n')
+
+        transformed, counter = transform_to_z3(new_constraints, counter, dimension_dict)
+        # print(transformed)
+        return transformed
+
+    def iterate_till_fixed_point(constraints, counter):
+        """
+        Transform constraints till reaching a fixed point
+        """
+        old_c = None
+        while old_c != constraints:
+            old_c = constraints
+            constraints, counter = transform_constraint(constraints, counter)
+        return constraints, counter
+
+    def transform_all_constraints_trace_time(tracer_root, graph, node, counter=0):
+        """
+        Takes a node and a graph and generates two sets of constraints.
+        One set constraints the node's constraints and another set
+        constraints the negation of the node's constraints
+        Args:
+            tracer_root: the root for getting the module instances
+            graph: the graph so far in the tracing process
+            node: node that represents a conditional
+            counter: variable tracking
+
+        Returns: Two sets of constraints. One with a conjunction with the
+        the conditional constraint and the other with a conjunction with
+        its negation.
+
+        """
+        dimension_dict = {}  # type: ignore[var-annotated]
+
+        generator = ConstraintGenerator(tracer_root, graph)
+        new_constraints, counter = generator.generate_constraints(counter)
+
+        condition_constraint = new_constraints.conjucts[-1]
+
+        # we know the constraint is a conjunction where the last constraint is about the conditional
+        # so remove the last constraint
+        new_constraints.conjucts = new_constraints.conjucts[:-1]
+
+        # transform precision, matching, consistency till obtaining a fixed point
+        new_constraints, counter = iterate_till_fixed_point(new_constraints, counter)
+
+        # since the function returns a list of one element, we get the first element
+        # we are only interested in the RHS in this case because the LHS just stores
+        # the result
+
+        # we make sure the constraint is of the form:
+        # c = b where b is a boolean expression
+        # and we consider b (constraint.rhs) for transformation
+        assert isinstance(condition_constraint.lhs, BVar)
+        assert is_bool_expr(condition_constraint.rhs)
+        condition_constraint_rhs = condition_constraint.rhs
+
+        # transform the condition constraint
+        condition_constraint_rhs, counter = iterate_till_fixed_point(
+            condition_constraint_rhs, counter
+        )
+
+        transformed, counter = transform_to_z3(new_constraints, counter, dimension_dict)
+
+        transformed_condition_constraint, counter = transform_to_z3(
+            condition_constraint_rhs, counter, dimension_dict
+        )
+
+        negation_transformed_condition_constraint = z3.Not(
+            transformed_condition_constraint
+        )
+
+        return z3.And([transformed, transformed_condition_constraint]), z3.And(
+            [transformed, negation_transformed_condition_constraint]
+        )
+
+    def evaluate_conditional_with_constraints(
+        tracer_root, graph, node, counter=0, user_constraints=None
+    ):
+        """
+        Given an IR and a node representing a conditional, evaluate the conditional
+        and its negation
+        Args:
+            tracer_root: Tracer root for module instances
+            node: The node to be evaluated
+
+        Returns: the results of evaluating the condition and the negation with
+        the rest of the constraints
+
+        """
+
+        (
+            transformed_positive,
+            transformed_negative,
+        ) = transform_all_constraints_trace_time(tracer_root, graph, node, counter)
+
+        s = z3.Solver()
+        s.add(transformed_positive)
+        if user_constraints is not None:
+            s.add(user_constraints)
+        condition = s.check()
+
+        s = z3.Solver()
+        s.add(transformed_negative)
+        if user_constraints is not None:
+            s.add(user_constraints)
+        negation = s.check()
+        return condition, negation
+
+except ImportError:
+    HAS_Z3 = False
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/util.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd40d2a463f5e78e3548df224ecd15e22813a3c6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/util.py
@@ -0,0 +1,59 @@
+# mypy: allow-untyped-defs
+from torch.fx.experimental.migrate_gradual_types.constraint import (
+    BinConstraintD,
+    BVar,
+    DVar,
+    TVar,
+)
+from torch.fx.experimental.migrate_gradual_types.operation import op_leq
+
+
+def gen_tvar(curr):
+    """
+    Generate a tensor variable
+    :param curr: The current counter
+    :return: a tensor variable and the updated counter
+    """
+    curr += 1
+    return TVar(curr), curr
+
+
+def gen_dvar(curr):
+    """
+    Generate a dimension variable
+    :param curr: the current counter
+    :return: a dimension variable and an updated counter
+    """
+    curr += 1
+    return DVar(curr), curr
+
+
+def gen_bvar(curr):
+    """
+    Generate a boolean variable
+    :param curr: the current counter
+    :return: a boolean variable and an updated counter
+    """
+    curr += 1
+    return BVar(curr), curr
+
+
+def gen_tensor_dims(n, curr):
+    """
+    Generate a list of tensor dimensions
+    :param n:  the number of dimensions
+    :param curr: the current counter
+    :return: a list of dimension variables and an updated counter
+    """
+    dims = []
+    for _ in range(n):
+        dvar, curr = gen_dvar(curr)
+        dims.append(dvar)
+    return dims, curr
+
+
+def gen_nat_constraints(list_of_dims):
+    """
+    Generate natural number constraints for dimensions
+    """
+    return [BinConstraintD(0, d, op_leq) for d in list_of_dims]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/z3_types.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/z3_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..939f4865ab7d982289303093db2024eda6603521
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/z3_types.py
@@ -0,0 +1,30 @@
+try:
+    import z3  # type: ignore[import]
+
+    HAS_Z3 = True
+    # dynamic type
+    dyn = z3.DeclareSort("Dyn")
+    dyn_type = z3.Const("dyn", dyn)
+
+    # dimension
+    dim = z3.Datatype("dim")
+    dim.declare("dim", ("0", z3.IntSort()), ("1", z3.IntSort()))
+    dim = dim.create()
+
+    # tensors
+    tensor_type = z3.Datatype("TensorType")
+    tensor_type.declare("Dyn", ("dyn", dyn))
+    tensor_type.declare("tensor1", ("0", dim))
+    tensor_type.declare("tensor2", ("0", dim), ("1", dim))
+    tensor_type.declare("tensor3", ("0", dim), ("1", dim), ("2", dim))
+    tensor_type.declare("tensor4", ("0", dim), ("1", dim), ("2", dim), ("3", dim))
+    tensor_type = tensor_type.create()
+
+    # create dimension
+    D = dim.dim
+
+    z3_dyn = tensor_type.Dyn(dyn_type)
+
+
+except ImportError:
+    HAS_Z3 = False
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/normalize.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/normalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..73cce6017bf1b5cd944ebff1f26781ef26fb6638
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/normalize.py
@@ -0,0 +1,163 @@
+# mypy: allow-untyped-defs
+import operator
+from typing import Any, Callable, Optional
+
+import torch
+import torch.fx
+import torch.fx as fx
+from torch.fx import Proxy, Transformer
+from torch.fx.node import Argument, map_aggregate, Node, Target
+from torch.fx.operator_schemas import (
+    create_type_hint,
+    normalize_function,
+    normalize_module,
+)
+
+from .schema_type_annotation import AnnotateTypesWithSchema
+
+
+class NormalizeArgs(Transformer):
+    """
+    Normalize arguments to Python targets. This means that
+    `args/kwargs` will be matched up to the module/functional's
+    signature and rewritten to exclusively kwargs in positional order
+    if `normalize_to_only_use_kwargs` is true. Also populates default
+    values. Does not support positional-only parameters or varargs
+    parameters (*args, **kwargs).
+
+    If the nodes have 'type' metadata, it will use it to disambiguate
+    overloads. Otherwise, it will throw an error.
+
+    Example usage:
+        m = torchvision.models.resnet18()
+        traced = torch.fx.symbolic_trace(m)
+        traced = NormalizeArgs(traced).transform()
+    """
+
+    def __init__(
+        self, module: torch.fx.GraphModule, normalize_to_only_use_kwargs: bool = True
+    ):
+        super().__init__(module)
+        self.node_map: dict[Proxy, Node] = {}
+        self.normalize_to_only_use_kwargs = normalize_to_only_use_kwargs
+
+    def run_node(self, n: Node) -> Any:
+        args, kwargs = self.fetch_args_kwargs_from_env(n)
+
+        def get_type(arg):
+            if isinstance(arg, fx.Node):
+                return n.meta["type"] if "type" in n.meta else None
+            return type(arg)
+
+        arg_types = map_aggregate(n.args, get_type)
+        assert isinstance(arg_types, tuple)
+        arg_types = tuple([create_type_hint(i) for i in arg_types])
+        kwarg_types = {k: get_type(v) for k, v in kwargs.items()}
+        if n.op == "call_function":
+            out = self.call_function(n.target, args, kwargs, arg_types, kwarg_types)
+        else:
+            out = super().run_node(n)
+        if n.op != "output":
+            self.node_map[out] = n
+            out.node.meta = n.meta
+            out.node.type = n.type
+        return out
+
+    def call_function(
+        self,
+        target: Target,
+        args: tuple[Argument, ...],
+        kwargs: dict[str, Any],
+        arg_types: Optional[tuple[Any, ...]] = None,
+        kwarg_types: Optional[dict[str, Any]] = None,
+    ):
+        assert callable(target)
+        new_args_and_kwargs = normalize_function(
+            target,
+            args,  # type: ignore[arg-type]
+            kwargs,
+            arg_types,  # type: ignore[arg-type]
+            kwarg_types,
+            self.normalize_to_only_use_kwargs,
+        )
+        if new_args_and_kwargs:
+            new_args, new_kwargs = new_args_and_kwargs
+            return self.tracer.create_proxy(
+                "call_function", target, new_args, new_kwargs
+            )
+        else:
+            return super().call_function(target, args, kwargs)
+
+    def call_module(
+        self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any]
+    ):
+        assert isinstance(target, str)
+        new_args_and_kwargs = normalize_module(
+            self.module,
+            target,
+            args,  # type: ignore[arg-type]
+            kwargs,
+            self.normalize_to_only_use_kwargs,
+        )
+        if new_args_and_kwargs:
+            new_args, new_kwargs = new_args_and_kwargs
+            return super().call_module(target, new_args, new_kwargs)
+        else:
+            return super().call_module(target, args, kwargs)
+
+
+class NormalizeOperators(AnnotateTypesWithSchema):
+    """
+    Normalize callsites that are different ways of "spelling" the same
+    invocation into a single, canonical call. Currently supports:
+
+    1. Normalize operators (e.g. operator.add) to the `torch` ops they
+       ultimately invoke (e.g. torch.add) when it is possible to statically
+       reason that
+
+    Example usage:
+
+        m = torchvision.models.resnet18()
+
+        traced = torch.fx.symbolic_trace(m)
+
+        traced = NormalizeOperators(traced).transform()
+    """
+
+    binary_magic_method_remap: dict[
+        Callable[[Any, Any], Any], Callable[[Any, Any], Any]
+    ] = {
+        torch.add: operator.add,
+        torch.mul: operator.mul,
+        torch.sub: operator.sub,
+        torch.div: operator.truediv,
+        torch.floor_divide: operator.floordiv,
+        torch.remainder: operator.mod,
+        torch.eq: operator.eq,
+        torch.ne: operator.ne,
+        torch.lt: operator.lt,
+        torch.le: operator.le,
+        torch.gt: operator.gt,
+        torch.ge: operator.ge,
+    }
+
+    def call_function(
+        self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any]
+    ):
+        # Normalize operators according to the magic methods implemented on tensors here:
+        # https://github.com/pytorch/pytorch/blob/28c5d90b679c6b38bf4183ec99f16d933c2f1bcd/tools/autograd/templates/python_variable_methods.cpp#L1137 # noqa: B950
+
+        assert callable(target)
+
+        if target in self.binary_magic_method_remap:
+            if len(args) != 2:
+                return super().call_function(target, args, kwargs)
+            lhs, rhs = args
+
+            return super().call_function(
+                target=self.binary_magic_method_remap[target],
+                args=(lhs, rhs),
+                kwargs={},
+            )
+
+        return super().call_function(target, args, kwargs)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/optimization.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/optimization.py
new file mode 100644
index 0000000000000000000000000000000000000000..13d9c2d9ac77963d1a262b83e52b032d4b7c1f2c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/optimization.py
@@ -0,0 +1,486 @@
+# mypy: allow-untyped-defs
+import copy
+import logging
+import operator
+import time
+from collections import defaultdict
+from collections.abc import Iterable
+from enum import Enum
+from typing import Any, cast, Optional
+
+import torch
+import torch.fx as fx
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.mkldnn as th_mkldnn
+from torch.fx.node import Argument, Target
+from torch.fx.passes.shape_prop import ShapeProp
+from torch.nn.utils.fusion import fuse_conv_bn_eval, fuse_linear_bn_eval
+
+
+__all__ = [
+    "matches_module_pattern",
+    "replace_node_module",
+    "fuse",
+    "remove_dropout",
+    "extract_subgraph",
+    "modules_to_mkldnn",
+    "reset_modules",
+    "MklSubgraph",
+    "gen_mkl_autotuner",
+    "use_mkl_length",
+    "UnionFind",
+    "optimize_for_inference",
+]
+
+
+def _parent_name(target: str) -> tuple[str, str]:
+    """
+    Splits a qualname into parent path and last atom.
+    For example, `foo.bar.baz` -> (`foo.bar`, `baz`)
+    """
+    *parent, name = target.rsplit(".", 1)
+    return parent[0] if parent else "", name
+
+
+# Works for length 2 patterns with 2 modules
+def matches_module_pattern(
+    pattern: Iterable[type], node: fx.Node, modules: dict[str, Any]
+):
+    if len(node.args) == 0:
+        return False
+    nodes: tuple[Any, fx.Node] = (node.args[0], node)
+    for expected_type, current_node in zip(pattern, nodes):
+        if not isinstance(current_node, fx.Node):
+            return False
+        if current_node.op != "call_module":
+            return False
+        if not isinstance(current_node.target, str):
+            return False
+        if current_node.target not in modules:
+            return False
+        if type(modules[current_node.target]) is not expected_type:
+            return False
+    return True
+
+
+def replace_node_module(
+    node: fx.Node, modules: dict[str, Any], new_module: torch.nn.Module
+):
+    assert isinstance(node.target, str)
+    parent_name, name = _parent_name(node.target)
+    modules[node.target] = new_module
+    setattr(modules[parent_name], name, new_module)
+
+
+def fuse(model: torch.nn.Module, inplace=False, no_trace=False) -> torch.nn.Module:
+    """
+    Fuses convolution/BN and linear/BN layers for inference purposes.
+    Will deepcopy your model by default, but can modify the model inplace as well.
+    """
+    patterns = [
+        (nn.Conv1d, nn.BatchNorm1d),
+        (nn.Conv2d, nn.BatchNorm2d),
+        (nn.Conv3d, nn.BatchNorm3d),
+        (nn.Linear, nn.BatchNorm1d),
+    ]
+    if not inplace:
+        model = copy.deepcopy(model)
+    if not no_trace or not isinstance(model, torch.fx.GraphModule):
+        fx_model = fx.symbolic_trace(model)
+    else:
+        fx_model = model
+    modules = dict(fx_model.named_modules())
+    new_graph = copy.deepcopy(fx_model.graph)
+
+    for pattern in patterns:
+        for node in new_graph.nodes:
+            if matches_module_pattern(pattern, node, modules):
+                if len(node.args[0].users) > 1:
+                    # Output of conv/linear is used by other nodes
+                    continue
+                first_layer = modules[node.args[0].target]
+                bn = modules[node.target]
+                if not bn.track_running_stats:
+                    continue
+                if pattern[0] in [nn.Conv1d, nn.Conv2d, nn.Conv3d]:
+                    fused_layer = fuse_conv_bn_eval(first_layer, bn)
+                else:  # nn.Linear
+                    fused_layer = fuse_linear_bn_eval(first_layer, bn)
+                replace_node_module(node.args[0], modules, fused_layer)
+                node.replace_all_uses_with(node.args[0])
+                new_graph.erase_node(node)
+    return fx.GraphModule(fx_model, new_graph)
+
+
+def remove_dropout(model: nn.Module) -> nn.Module:
+    """
+    Removes all dropout layers from the module.
+    """
+    fx_model = fx.symbolic_trace(model)
+
+    class DropoutRemover(torch.fx.Transformer):
+        def call_module(
+            self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any]
+        ) -> Any:
+            if isinstance(self.submodules[target], nn.Dropout):
+                assert len(args) == 1
+                return args[0]
+            else:
+                return super().call_module(target, args, kwargs)
+
+    return DropoutRemover(fx_model).transform()
+
+
+def extract_subgraph(
+    orig_module: nn.Module,
+    nodes: list[fx.Node],
+    inputs: list[fx.Node],
+    outputs: list[fx.Node],
+):
+    """
+    Given lists of nodes from an existing graph that represent a subgraph, returns a submodule that executes that subgraph.
+    """
+    new_graph = fx.Graph()
+    env: dict[fx.Node, fx.Node] = {}
+    for input in inputs:
+        new_node = new_graph.placeholder(input.name)
+        env[input] = new_node
+    for node in nodes:
+        new_node = new_graph.node_copy(node, lambda x: env[x])
+        env[node] = new_node
+    new_graph.output([env[output] for output in outputs])
+    new_graph.lint()
+    return fx.GraphModule(orig_module, new_graph)
+
+
+mkldnn_supported = [
+    nn.Conv2d,
+    nn.Linear,
+    nn.BatchNorm2d,
+    nn.ReLU,
+    nn.MaxPool2d,
+    nn.AvgPool2d,
+    nn.AdaptiveAvgPool2d,
+    torch.relu,
+    torch.transpose,
+    torch.sigmoid,
+    F.relu,
+    F.avg_pool2d,
+    F.adaptive_avg_pool2d,
+]
+# These are operators that may not be convertible into MKLDNN ops (e.g. the
+# args are scalar values). Thus, we only include them in the subgraph if their
+# arguments are already in MKLDNN.
+# TODO: Determine whether this can be removed after type inference.
+mkldnn_supported_unknown = [operator.add, operator.mul]
+mkldnn_map = {
+    nn.Conv2d: th_mkldnn.MkldnnConv2d,
+    nn.Linear: th_mkldnn.MkldnnLinear,
+    nn.BatchNorm2d: lambda a, _: th_mkldnn.MkldnnBatchNorm(a),
+}
+
+
+def modules_to_mkldnn(nodes: list[fx.Node], modules: dict[str, nn.Module]):
+    """
+    For each node, if it's a module that can be preconverted into MKLDNN,
+    then we do so and create a mapping to allow us to convert from the MKLDNN
+    version of the module to the original.
+    """
+    old_modules: dict[nn.Module, nn.Module] = {}
+    for node in nodes:
+        if node.op == "call_module":
+            assert isinstance(node.target, str)
+            cur_module = modules[node.target]
+            if type(cur_module) in mkldnn_map:
+                new_module = mkldnn_map[type(cur_module)](cur_module, torch.float)
+                assert isinstance(new_module, nn.Module)
+                old_modules[new_module] = copy.deepcopy(cur_module)
+                replace_node_module(node, modules, new_module)
+    return old_modules
+
+
+def reset_modules(
+    nodes: list[fx.Node],
+    modules: dict[str, nn.Module],
+    old_modules: dict[nn.Module, nn.Module],
+):
+    """
+    Maps each module that's been changed with `modules_to_mkldnn` back to its
+    original.
+    """
+    for node in nodes:
+        if node.op == "call_module":
+            assert isinstance(node.target, str)
+            cur_module = modules[node.target]
+            if cur_module in old_modules:
+                replace_node_module(node, modules, old_modules[cur_module])
+
+
+class MklSubgraph:
+    def __init__(self, fx_graph: fx.Graph):
+        self.fx_graph = fx_graph
+        self.nodes: list[fx.Node] = []
+        self.start_nodes: list[fx.Node] = []
+        self.end_nodes: list[fx.Node] = []
+
+
+def gen_mkl_autotuner(example_inputs, iters=10, warmup=1):
+    """
+    This generates a heuristic that can be passed into `optimize_for_inference` that
+    determines whether a subgraph should be run in MKL by running it with the example_inputs.
+
+    Example usage:
+        heuristic = gen_mkl_autotuner(example_inputs, iters=10)
+        fast_model = optimization.optimize_for_inference(model, heuristic)
+    """
+    fx_model = None
+    old_modules = None
+
+    def use_mkl_heuristic(graph: MklSubgraph) -> bool:
+        nonlocal fx_model, old_modules
+        input_nodes = graph.start_nodes
+        if fx_model is None:
+            fx_model = graph.fx_graph.owning_module
+            old_modules = graph.fx_graph.old_modules  # type: ignore[attr-defined]
+            ShapeProp(fx_model).propagate(example_inputs)
+        sample_inputs = [torch.randn(node.shape) for node in input_nodes]  # type: ignore[attr-defined]
+        output_args = cast(list[fx.Node], [node.args[0] for node in graph.end_nodes])
+        submodule = extract_subgraph(fx_model, graph.nodes, input_nodes, output_args)
+
+        def benchmark(f):
+            for _ in range(warmup):
+                f()
+            begin = time.time()
+            for _ in range(iters):
+                f()
+            return time.time() - begin
+
+        mkl_time = benchmark(
+            lambda: [
+                i.to_dense() for i in submodule(*[i.to_mkldnn() for i in sample_inputs])
+            ]
+        )
+
+        reset_modules(
+            submodule.graph.nodes, dict(submodule.named_modules()), old_modules
+        )
+        no_mkl_time = benchmark(lambda: submodule(*sample_inputs))
+        return mkl_time < no_mkl_time
+
+    return use_mkl_heuristic
+
+
+def use_mkl_length(graph: MklSubgraph) -> bool:
+    """
+    This is a heuristic that can be passed into `optimize_for_inference` that
+    determines whether a subgraph should be run in MKL by checking if there
+    are more than 2 nodes in it
+    """
+    return len(graph.nodes) > 2
+
+
+class UnionFind:
+    def __init__(self, n):
+        self.parent: list[Optional[int]] = [None] * n
+        self.size: list[int] = [0] * n
+
+    def make_set(self, v: int):
+        self.parent[v] = v
+        self.size[v] = 1
+
+    def find(self, v: int) -> int:
+        par = self.parent[v]
+        if v == par:
+            return v
+        assert par is not None
+        self.parent[v] = self.find(par)
+        return cast(int, self.parent[v])
+
+    def join(self, a: int, b: int):
+        a, b = self.find(a), self.find(b)
+        if a == b:
+            return a
+        if self.size[a] < self.size[b]:
+            a, b = b, a
+        self.parent[b] = a
+        self.size[a] += self.size[b]
+
+
+def optimize_for_inference(
+    model: torch.nn.Module,
+    pass_config: Optional[dict[str, Any]] = None,
+    tracer: type[fx.Tracer] = fx.Tracer,
+) -> torch.nn.Module:
+    """
+    Performs a set of optimization passes to optimize a model for the
+    purposes of inference. Specifically, the passes that are run are:
+    1. Conv/BN fusion
+    2. Dropout removal
+    3. MKL layout optimizations
+
+    The third optimization takes a function `use_mkl_heuristic` that's used
+    to determine whether a subgraph should be explicitly run in MKL layout.
+
+    Note: As FX does not currently handle aliasing, this pass currently
+    assumes nothing aliases. If that isn't true, use at your own risk.
+    """
+    default_pass_config = {
+        "conv_bn_fuse": True,
+        "remove_dropout": True,
+        "mkldnn_layout_optimize": {"heuristic": use_mkl_length},
+    }
+    if pass_config is None:
+        pass_config = {}
+    default_pass_config.update(pass_config)
+
+    if default_pass_config["conv_bn_fuse"]:
+        model = fuse(model)
+    if default_pass_config["remove_dropout"]:
+        model = remove_dropout(model)
+    if default_pass_config["mkldnn_layout_optimize"] is False:
+        return model
+    if not isinstance(default_pass_config["mkldnn_layout_optimize"], dict):
+        raise RuntimeError("mkldnn_layout_optimize config is not a dict")
+    if "heuristic" not in default_pass_config["mkldnn_layout_optimize"]:
+        raise RuntimeError("Heuristic not found in mkldnn_layout_optimize config")
+    use_mkl_heuristic = default_pass_config["mkldnn_layout_optimize"]["heuristic"]
+
+    cur_tracer = tracer()
+    fx_graph = cur_tracer.trace(copy.deepcopy(model))
+    fx.GraphModule(cur_tracer.root, fx_graph)
+    modules: dict[str, nn.Module] = dict(model.named_modules())
+
+    class MklSupport(Enum):
+        NO = 1
+        YES = 2
+        UNKNOWN = 3
+
+    # Inserts to_mkldnn and to_dense around every node we want to be a MKLDNN node.
+    # If the op is in `mkldnn_supported` then we always treat it as a MKLDNN node.
+    # However, if it's in `mkldnn_supported_unknown`, then we only treat it as
+    # a MKLDNN node if its inputs are MKLDNN nodes.
+    for node in list(fx_graph.nodes):
+        supports_mkldnn = MklSupport.NO
+        if node.op == "call_module":
+            cur_module = modules[node.target]
+            if type(cur_module) in mkldnn_supported:
+                supports_mkldnn = MklSupport.YES
+                sample_parameter = next(cur_module.parameters(), None)
+                if sample_parameter is not None:
+                    assert (
+                        sample_parameter.dtype == torch.float
+                    ), "this pass is only for torch.float modules"
+                    assert sample_parameter.device == torch.device(
+                        "cpu"
+                    ), "this pass is only for CPU modules"
+        elif node.op == "call_function":
+            if node.target in mkldnn_supported:
+                supports_mkldnn = MklSupport.YES
+            elif node.target in mkldnn_supported_unknown:
+                supports_mkldnn = MklSupport.UNKNOWN
+
+        if supports_mkldnn != MklSupport.NO:
+            if supports_mkldnn == MklSupport.UNKNOWN:
+                if not any(arg.target == "to_dense" for arg in node.args):
+                    continue
+            with fx_graph.inserting_before(node):
+                mkldnn_args = fx.map_arg(
+                    node.args, lambda n: fx_graph.call_method("to_mkldnn", (n,))
+                )
+
+            node.args = cast(tuple[fx.node.Argument], mkldnn_args)
+
+            with fx_graph.inserting_after(node):
+                dense_x = fx_graph.create_node("call_method", "to_dense", (node,))
+                node.replace_all_uses_with(dense_x)
+                dense_x.args = (node,)
+
+    # Does pre-conversion of all modules into MKLDNN (when possible)
+    old_modules = modules_to_mkldnn(list(fx_graph.nodes), modules)
+    fx_graph.old_modules = old_modules  # type: ignore[attr-defined]
+
+    # optimizes all a -> to_dense -> to_mkldnn -> b patterns into a -> b
+    for node in fx_graph.nodes:
+        if node.op == "call_method" and node.target == "to_dense":
+            prv_node = node.args[0]
+            users = list(node.users)
+            for user in users:
+                if user.op == "call_method" and user.target == "to_mkldnn":
+                    user.replace_all_uses_with(prv_node)
+                    fx_graph.erase_node(user)
+            if len(node.users) == 0:
+                fx_graph.erase_node(node)
+
+    num_nodes = len(fx_graph.nodes)
+    uf = UnionFind(num_nodes)
+
+    def get_color(n):
+        if hasattr(n, "color"):  # Current node is part of a MKL subgraph
+            return uf.find(n.color)
+        if hasattr(n, "start_color"):  # Current node is input to MKL subgraph
+            return uf.find(n.start_color)
+        return None
+
+    # This code is to find each MKLDNN subgraph. Each MKLDNN subgraph consists
+    # of input nodes (which are only `to_mkldnn` calls), output nodes
+    # (`to_dense` calls), and intermediate nodes, which are run entirely on
+    # MKLDNN layout tensors.
+    #
+    # Specifically, this code does a flood fill on a directed acyclic graph
+    # (DAG), starting from each possible "start node" (i.e: `to_mkldnn` nodes).
+    # If every node only had one input, this would be sufficient. However, in
+    # the case that a node has multiple inputs coming from different start
+    # nodes (i.e. colors), we need to join these 2 colors into 1. That's done
+    # using a Disjoint Set Union.
+    for cur_idx, node in enumerate(fx_graph.nodes):
+        if node.op == "call_method" and node.target == "to_mkldnn":
+            node.start_color = cur_idx
+            uf.make_set(cur_idx)
+        elif node.op == "call_method" and node.target == "to_dense":
+            assert get_color(node.args[0]) is not None
+            node.end_color = get_color(node.args[0])
+        else:
+            cur_colors = [
+                get_color(i)
+                for i in node.all_input_nodes
+                if isinstance(i, fx.Node)
+                if get_color(i) is not None
+            ]
+
+            if len(cur_colors) == 0:
+                continue
+            assert not any(i is None for i in cur_colors)
+            cur_colors = sorted(cur_colors)
+            node.color = cur_colors[0]
+            for other_color in cur_colors[1:]:
+                uf.join(cur_colors[0], other_color)
+
+    mkldnn_graphs: dict[int, MklSubgraph] = defaultdict(lambda: MklSubgraph(fx_graph))
+    for node in fx_graph.nodes:
+        if hasattr(node, "color"):
+            mkldnn_graphs[uf.find(node.color)].nodes.append(node)
+        if hasattr(node, "start_color"):
+            mkldnn_graphs[uf.find(node.start_color)].start_nodes.append(node)
+        if hasattr(node, "end_color"):
+            mkldnn_graphs[uf.find(node.end_color)].end_nodes.append(node)
+
+    # Now that we have all the subgraphs, we need to decide which MKLDNN
+    # subgraphs we actually want to keep in MKLDNN.
+    for graph in mkldnn_graphs.values():
+        if not use_mkl_heuristic(graph):
+            for node in graph.start_nodes + graph.end_nodes:
+                prv = node.args[0]
+                node.replace_all_uses_with(prv)
+                fx_graph.erase_node(node)
+            reset_modules(graph.nodes, modules, old_modules)
+
+    mkldnn_conversions = 0
+    for node in fx_graph.nodes:
+        if node.target == "to_mkldnn" or node.target == "to_dense":
+            mkldnn_conversions += 1
+
+    logging.getLogger(__name__).info("mkldnn conversions: %s", mkldnn_conversions)
+    fx_graph.lint()
+    result = fx.GraphModule(model, fx_graph)
+    return result
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/partitioner_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/partitioner_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3658dd1a9ce96aff26adbc5f47818e9e57e13d35
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/partitioner_utils.py
@@ -0,0 +1,317 @@
+# mypy: allow-untyped-defs
+from enum import Enum
+from typing import NamedTuple
+
+from torch.fx.node import map_arg, Node
+
+
+class Partition:
+    """Partition class contains all the information about an individual partition.
+    It also provides necessary methods for manipulation the partition.
+    """
+
+    def __init__(self, partition_id: int) -> None:
+        self.nodes: set[Node] = set()
+        self.partition_id = partition_id
+        self.parents: set[Partition] = set()
+        self.children: set[Partition] = set()
+        self.bfs_level: int = -1
+        self.used_mem_bytes: int = 0
+        self.logical_device_ids: list[int] = []
+
+    def __str__(self):
+        return str(self.partition_id)
+
+    def recalculate_mem_size(self):
+        self.used_mem_bytes = 0
+        for node in self.nodes:
+            self.used_mem_bytes += get_extra_size_of(node, self.nodes)
+
+    def add_node(self, node):
+        input_nodes: dict[Node, None] = {}
+        map_arg(node.args, input_nodes.setdefault)
+        map_arg(node.kwargs, input_nodes.setdefault)
+        # Add current node's input nodes if they are placeholder or constants
+        for n in input_nodes:
+            if n.op in {"placeholder", "get_attr"}:
+                self.nodes.add(n)
+        self.nodes.add(node)
+        self.recalculate_mem_size()
+
+    def remove_node(self, node):
+        # Remove a node only if the node is in the partition
+        if node in self.nodes:
+            self.nodes.remove(node)
+            # Collect the node's input nodes
+            input_nodes: dict[Node, None] = {}
+            map_arg(node.args, input_nodes.setdefault)
+            map_arg(node.kwargs, input_nodes.setdefault)
+            # Check if an input node is a placeholder or get_attr,
+            # and this input node is not used by some other nodes in this partition,
+            # the remove this input node
+            for input_node in input_nodes:
+                if all(
+                    n not in self.nodes for n in input_node.users
+                ) and input_node.op in {"placeholder", "get_attr"}:
+                    self.nodes.remove(input_node)
+            self.recalculate_mem_size()
+
+
+class Device(NamedTuple):
+    name: str
+    available_mem_bytes: int
+    logical_id: int
+
+
+class NodeLatency(NamedTuple):
+    # Latency due to the memory bandwidth
+    mem_latency_sec: float
+    # Latency due to the computation
+    computer_latency_sec: float
+
+
+class PartitionLatency(NamedTuple):
+    # Sum of all nodes' memory latency on the critical path
+    mem_latency_sec: float
+    # Sum of all nodes' compute latency on the critical path
+    computer_latency_sec: float
+    # Latency of the critical path
+    overall_latency_sec: float
+
+
+class PartitionMode(Enum):
+    size_based = 0
+    sparse_nn = 1
+    cost_aware = 2
+    kl_based = 3
+    aot_based = 4
+
+
+class PartitionerConfig(NamedTuple):
+    devices: list[Device]
+    mode: PartitionMode = PartitionMode.size_based
+    transfer_rate_bytes_per_sec: float = 0.0
+    node_to_latency_mapping: dict[Node, NodeLatency] = {}
+    node_to_partition_mapping: dict[Node, int] = {}
+    partition_to_logical_device_mapping: dict[int, list[int]] = {}
+    # Saturate host by replicating partitions to the remaining idle devices.
+    saturate_host: bool = False
+
+
+def get_extra_size_of(node: Node, nodes: set[Node]) -> int:
+    """Given a node and a set of nodes,
+    this function return the extra size that needed
+    if this node is included in this set.
+    """
+    # Find all its input nodes
+    input_nodes: dict[Node, None] = {}
+    map_arg(node.args, input_nodes.setdefault)
+    map_arg(node.kwargs, input_nodes.setdefault)
+    # Calculate total size of related nodes
+    total_size_of_input_nodes = 0
+    for n in input_nodes:
+        # Make sure this node hasn't been in this set yet
+        if n not in nodes:
+            size_bytes = getattr(n, "size_bytes", None)
+            if size_bytes:
+                total_size_of_input_nodes += size_bytes.output_size
+            else:
+                raise RuntimeError("node has no size_bytes attr")
+    # Don't forget the op node itself
+    size_bytes = getattr(node, "size_bytes", None)
+    if size_bytes:
+        total_size_of_input_nodes += size_bytes.total_size
+    else:
+        raise RuntimeError("node has no size_bytes attr")
+    return total_size_of_input_nodes
+
+
+def get_latency_of_one_partition(
+    partition: Partition, node_to_latency_mapping: dict[Node, NodeLatency]
+) -> PartitionLatency:
+    """Given a partition and its nodes' latency, return a PartitionLatency for this partition"""
+
+    def get_top_nodes(partition: Partition) -> list[Node]:
+        """Given a partition, return a list of nodes on the top bfs level"""
+        top_nodes: list[Node] = []
+        for node in partition.nodes:
+            # Skip placeholder and get_attr nodes
+            if node.op in {"placeholder", "get_attr"}:
+                continue
+            input_nodes: dict[Node, None] = {}
+            map_arg(node.args, input_nodes.setdefault)
+            map_arg(node.kwargs, input_nodes.setdefault)
+            # If a node has no input nodes in this partition,
+            # or its input nodes in this partition are placeholders and get_attrs
+            # this node is on the top bfs level in this partition
+            if not any(
+                n in partition.nodes and n.op not in {"placeholder", "get_attr"}
+                for n in input_nodes
+            ):
+                top_nodes.append(node)
+        return top_nodes
+
+    def dfs_helper(node: Node, partition_latency) -> PartitionLatency:
+        """Given a top node of a partition, this function returns
+        the latency of the critical path in the partition
+        """
+        node_latency = node_to_latency_mapping[node]
+        # Calculate the current overall latency of the partition
+        overall_latency_sec = partition_latency.overall_latency_sec + max(
+            node_latency.computer_latency_sec, node_latency.mem_latency_sec
+        )
+        # Update the mem latency of this path
+        mem_latency_sec = (
+            partition_latency.mem_latency_sec + node_latency.mem_latency_sec
+        )
+        # Update the compute latency of this path
+        computer_latency_sec = (
+            partition_latency.computer_latency_sec + node_latency.computer_latency_sec
+        )
+        # Get all users of this node that are in this partition
+        users = set(node.users).intersection(partition.nodes)
+        if users:
+            max_latency = PartitionLatency(
+                mem_latency_sec=0.0, computer_latency_sec=0.0, overall_latency_sec=0.0
+            )
+            for n in users:
+                # Get new partition latency recursively
+                new_partition_latency = dfs_helper(
+                    n,
+                    PartitionLatency(
+                        mem_latency_sec, computer_latency_sec, overall_latency_sec
+                    ),
+                )
+                if (
+                    new_partition_latency.overall_latency_sec
+                    > max_latency.overall_latency_sec
+                ):
+                    max_latency = new_partition_latency
+            return max_latency
+        # If there is no user, the node is at bottom of the partition
+        return PartitionLatency(
+            mem_latency_sec, computer_latency_sec, overall_latency_sec
+        )
+
+    # Main part starts
+    # Get all top level nodes of this partition
+    top_nodes = get_top_nodes(partition)
+    critical_path_latency = PartitionLatency(
+        mem_latency_sec=0.0, computer_latency_sec=0.0, overall_latency_sec=0.0
+    )
+    # Go through all top nodes and find the largest latency (critical pass latency)
+    for node in top_nodes:
+        partition_latency = dfs_helper(
+            node,
+            PartitionLatency(
+                mem_latency_sec=0.0, computer_latency_sec=0.0, overall_latency_sec=0.0
+            ),
+        )
+        if (
+            partition_latency.overall_latency_sec
+            > critical_path_latency.overall_latency_sec
+        ):
+            critical_path_latency = partition_latency
+    return critical_path_latency
+
+
+def get_partition_to_latency_mapping(
+    partitions: list[Partition], node_to_latency_mapping: dict[Node, NodeLatency]
+) -> dict[Partition, PartitionLatency]:
+    """Given all the partitions and node_to_latency_mapping dictionary,
+    return a mapping dictionary of each partition to its overall latency
+    """
+    partition_to_latency_mapping: dict[Partition, PartitionLatency] = {}
+    # Go through each partition and get its latency
+    for partition in partitions:
+        partition_latency = get_latency_of_one_partition(
+            partition, node_to_latency_mapping
+        )
+        partition_to_latency_mapping[partition] = partition_latency
+    return partition_to_latency_mapping
+
+
+def get_comm_latency_between(
+    parent_partition: Partition,
+    child_partition: Partition,
+    transfer_rate_bytes_per_sec: float,
+):
+    """Given two partitions (parent and child),
+    calculate the communication latency between the two.
+    """
+    # If two partitions are on the same device, the comm latency is 0.
+    if (
+        parent_partition.logical_device_ids != []
+        and child_partition.logical_device_ids != []
+        and parent_partition.logical_device_ids == child_partition.logical_device_ids
+    ):
+        return 0.0
+    # Keep tracking the communication size between parent and child
+    comm_size = 0
+    # Keep tracking all the counted node
+    visited_nodes = set()
+    # Go through all nodes in the child partition
+    # If a node has input nodes from the parent partition,
+    # the output size of those input nodes will be counted
+    # and added to comm_size
+    for node in child_partition.nodes:
+        input_nodes: dict[Node, None] = {}
+        map_arg(node.args, input_nodes.setdefault)
+        map_arg(node.kwargs, input_nodes.setdefault)
+        for n in input_nodes:
+            if n in parent_partition.nodes and n not in visited_nodes:
+                size_bytes = getattr(n, "size_bytes", None)
+                if size_bytes is not None:
+                    comm_size += size_bytes.output_size
+                visited_nodes.add(n)
+    return comm_size / transfer_rate_bytes_per_sec
+
+
+def get_latency_of_partitioned_graph(
+    partitions: list[Partition],
+    partition_to_latency_mapping: dict[Partition, PartitionLatency],
+    transfer_rate_bytes_per_sec: float,
+):
+    """Given all partitions in a graph, find the critical path among all partitions
+    and return its latency as the latency of the whole graph
+    """
+
+    def dfs_helper(partition: Partition, latency_so_far_sec: float) -> float:
+        """This function helps to recursively get the latency of a path of partitions"""
+        # Update latency by adding current partition's latency
+        latency_so_far_sec += partition_to_latency_mapping[
+            partition
+        ].overall_latency_sec
+
+        if partition.children:
+            max_latency_sec = 0.0
+            for child in partition.children:
+                # Calculate latency between
+                comm_latency_sec = get_comm_latency_between(
+                    partition, child, transfer_rate_bytes_per_sec
+                )
+                new_latency_sec = dfs_helper(
+                    child, latency_so_far_sec + comm_latency_sec
+                )
+                if new_latency_sec > max_latency_sec:
+                    max_latency_sec = new_latency_sec
+            return max_latency_sec
+        return latency_so_far_sec
+
+    def get_top_partitions(partitions: list[Partition]) -> list[Partition]:
+        """This function is to return all the partitions without parents
+        as the starting points of all the paths
+        """
+        # If a partition has no parents, then it is a top partition
+        top_partitions = [
+            partition for partition in partitions if len(partition.parents) == 0
+        ]
+        return top_partitions
+
+    top_partitions = get_top_partitions(partitions)
+    critical_path_latency_sec = 0.0
+    for partition in top_partitions:
+        latency_sec = dfs_helper(partition, 0.0)
+        if latency_sec > critical_path_latency_sec:
+            critical_path_latency_sec = latency_sec
+    return critical_path_latency_sec
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3e7212937c358e720912dc8c13390879e333eed
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py
@@ -0,0 +1,2352 @@
+# mypy: allow-untyped-decorators
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import annotations
+
+import functools
+import inspect
+import logging
+import operator
+import traceback
+import typing
+import typing_extensions
+import warnings
+import weakref
+from collections import defaultdict, OrderedDict
+from collections.abc import Generator, Mapping, Sequence
+from contextlib import _GeneratorContextManager, contextmanager, ExitStack, nullcontext
+from dataclasses import dataclass
+from typing import (
+    Any,
+    Callable,
+    Optional,
+    overload,
+    Protocol,
+    TYPE_CHECKING,
+    TypeVar,
+    Union,
+)
+from typing_extensions import Concatenate, ParamSpec, Self
+from weakref import WeakKeyDictionary
+
+import torch
+import torch._ops
+import torch.fx as fx
+import torch.fx.traceback as fx_traceback
+import torch.utils._pytree as pytree
+from torch import SymBool, SymInt, Tensor
+from torch._dispatch.python import enable_python_dispatcher
+from torch._library.fake_class_registry import FakeScriptObject
+from torch._logging import trace_structured
+from torch._subclasses.fake_impls import fast_detach
+from torch._subclasses.fake_tensor import (
+    FakeTensor,
+    FakeTensorMode,
+    is_fake,
+    unset_fake_temporarily,
+)
+from torch._subclasses.meta_utils import is_sparse_any
+from torch.fx import GraphModule, Proxy, Tracer
+from torch.fx.graph_module import _assign_attr
+from torch.fx.node import (
+    _side_effectful_need_to_be_preserved_pre_dispatch,
+    Argument,
+    Target,
+)
+from torch.fx.passes.shape_prop import _extract_tensor_metadata
+from torch.nn import Module
+from torch.overrides import TorchFunctionMode
+from torch.utils._python_dispatch import (
+    _disable_infra_mode,
+    _push_mode,
+    _unset_infra_mode,
+    TorchDispatchMode,
+)
+from torch.utils._stats import count
+from torch.utils._thunk import Thunk
+from torch.utils._traceback import CapturedTraceback
+from torch.utils.weak import _WeakHashRef, WeakIdKeyDictionary, WeakTensorKeyDictionary
+
+from ._backward_state import BackwardState
+from .sym_node import SymNode
+
+
+if TYPE_CHECKING:
+    import types
+    from collections.abc import MutableMapping
+
+    import sympy
+
+    from torch._ops import OpOverload
+    from torch.fx._symbolic_trace import PHBase
+    from torch.types import IntLikeType
+
+__all__ = [
+    "PythonKeyTracer",
+    "dispatch_trace",
+    "make_fx",
+    "DecompositionInterpreter",
+    "py_sym_types",
+    "get_innermost_proxy_mode",
+    "get_proxy_mode",
+    "handle_sym_dispatch",
+    "maybe_enable_thunkify",
+    "maybe_disable_thunkify",
+]
+
+_ProxyTracer = Union["PythonKeyTracer", "_GraphAppendingTracerEx"]
+
+_AnyScriptObject = (torch.ScriptObject, FakeScriptObject)
+_AnyScriptObjectType = Union[torch.ScriptObject, FakeScriptObject]
+
+aten = torch.ops.aten
+prim = torch.ops.prim
+
+log = logging.getLogger(__name__)
+not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented")
+
+CURRENT_DECOMPOSITION_TABLE: Mapping[OpOverload, Callable] = {}
+
+CONSTANT_NUMEL_LIMIT = 1
+
+T = TypeVar("T")
+U = TypeVar("U")
+_P = ParamSpec("_P")
+R = TypeVar("R")
+
+null_ctx_type = type(nullcontext)
+# We currently convert all SymInt to proxies before we use them.
+# This could plausibly be handled at the Dynamo level.
+pytree.register_pytree_node(
+    torch.Size,
+    lambda xs: (list(xs), None),
+    lambda xs, _: tuple(xs),
+    flatten_with_keys_fn=lambda xs: (
+        [(pytree.SequenceKey(i), x) for i, x in enumerate(xs)],
+        None,
+    ),
+    serialized_type_name="torch.Size",
+)
+
+
+def fake_signature(fn: Callable[_P, R], nargs: int) -> Callable[_P, R]:
+    """FX gets confused by varargs, de-confuse it"""
+    argnames = ",".join(f"arg{i}" for i in range(nargs))
+    return eval(f"lambda {argnames}: fn({argnames})", {"fn": fn})
+
+
+@contextmanager
+def decompose(
+    decomposition_table: Optional[Mapping[OpOverload, Callable]]
+) -> Generator[Mapping[OpOverload, Callable], None, None]:
+    global CURRENT_DECOMPOSITION_TABLE
+    old_decomposition_table = CURRENT_DECOMPOSITION_TABLE
+    CURRENT_DECOMPOSITION_TABLE = decomposition_table or {}
+    try:
+        yield CURRENT_DECOMPOSITION_TABLE
+    finally:
+        CURRENT_DECOMPOSITION_TABLE = old_decomposition_table
+
+
+# ensure we cannot collide with other properties
+proxy_slot = object()
+
+
+class _NoDefault:
+    pass
+
+
+no_default = _NoDefault()
+
+from torch.types import py_sym_types, PySymType
+
+
+class _HasMeta(Protocol):
+    meta: dict[str, PySymType]
+
+
+def is_sym_node(node: _HasMeta) -> bool:
+    assert hasattr(node, "meta"), "All nodes traced with proxy_tensor should have meta"
+    return "val" in node.meta and isinstance(node.meta["val"], py_sym_types)
+
+
+@overload
+def set_proxy_slot(obj: Tensor, tracer: _ProxyTracer, proxy: _ProxyTensor) -> None:
+    ...
+
+
+@overload
+def set_proxy_slot(
+    obj: _AnyScriptObjectType, tracer: _ProxyTracer, proxy: Proxy
+) -> None:
+    ...
+
+
+@overload
+def set_proxy_slot(
+    obj: PySymType, tracer: _ProxyTracer, proxy: _PySymProxyType
+) -> None:
+    ...
+
+
+def set_proxy_slot(
+    obj: Union[PySymType, _AnyScriptObjectType, Tensor],
+    tracer: _ProxyTracer,
+    proxy: object,
+) -> None:
+    log.debug("set_proxy_slot %s (%s) %s", obj, id(obj), proxy)
+    if isinstance(obj, Tensor):
+        # We DO want to clobber proxies whenever we run an inplace operation
+        # on a tensor, and it affects the metadata on the proxy.
+        assert isinstance(proxy, _ProxyTensor)
+        tracer.tensor_tracker[obj] = proxy
+    elif isinstance(obj, (_AnyScriptObject)):
+        # We DO want to clobber proxies, with a similar rationale as for tensors.
+        assert isinstance(proxy, Proxy)
+        tracer.script_object_tracker[obj] = proxy
+    else:
+        # NB: Never clobber pre-existing proxy.  Although the proxies
+        # are in principle equivalent, when we do graph partitioning
+        # we need there not to be spurious dependencies on tangent inputs.
+        # This works because primals get their SymInts set first, and
+        # THEN later we allocate tangent inputs.  Make sure if a SymInt
+        # is derivable from a primal that we use that.
+        assert isinstance(obj, py_sym_types), type(obj)
+        if obj not in tracer.symnode_tracker:
+            tracer.symnode_tracker[obj] = typing.cast(_PySymProxyType, proxy)
+
+            # WAR: python test/dynamo/test_subclasses.py
+            # TestNestedTensor.test_basic_autograd
+            #
+            # AOTAutograd doesn't pass the "outer sizes" as an actual argument
+            # to make_fx, but it is made use of internally in AOTAutograd's
+            # call to tensor unflatten.  Because the outer sizes isn't passed
+            # as an argument, it is therefore untracked.  However, it turns
+            # out you luck out, because *Dynamo* will manually add the outer
+            # sizes as an argument so you can fix up the proxy'ness.
+            #
+            # This is probably fixed in
+            # https://github.com/pytorch/pytorch/pull/125941/
+            import sympy
+
+            if isinstance(obj.node.expr, sympy.Symbol):
+                tracer.sympy_expr_tracker[obj.node.expr] = proxy
+
+
+def has_proxy_slot(obj: Tensor, tracer: _ProxyTracer) -> bool:
+    assert isinstance(obj, (Tensor, SymNode)), type(obj)
+    return bool(get_proxy_slot(obj, tracer, False, lambda _: True))
+
+
+_PySymProxyType = Thunk[Proxy]
+
+
+@overload
+def get_proxy_slot(
+    obj: Tensor,
+    tracer: _ProxyTracer,
+) -> _ProxyTensor:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: Tensor,
+    tracer: _ProxyTracer,
+    default: U,
+) -> Union[_ProxyTensor, U]:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: Tensor,
+    tracer: _ProxyTracer,
+    default: U,
+    transform: Callable[[_ProxyTensor], R],
+) -> Union[R, U]:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: _AnyScriptObjectType,
+    tracer: _ProxyTracer,
+) -> Proxy:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: _AnyScriptObjectType,
+    tracer: _ProxyTracer,
+    default: U,
+) -> Union[Proxy, U]:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: _AnyScriptObjectType,
+    tracer: _ProxyTracer,
+    default: U,
+    transform: Callable[[Proxy], R],
+) -> Union[R, U]:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: PySymType,
+    tracer: _ProxyTracer,
+) -> _PySymProxyType:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: PySymType,
+    tracer: _ProxyTracer,
+    default: T,
+) -> Union[T, _PySymProxyType]:
+    ...
+
+
+@overload
+def get_proxy_slot(
+    obj: PySymType,
+    tracer: _ProxyTracer,
+    default: U,
+    transform: Callable[[_PySymProxyType], R],
+) -> Union[R, U]:
+    ...
+
+
+# the default argument is what to return if the slot is not set.
+# the transform argument is handy if you need to extract a subfield from
+# the successfully looked up result (but NOT the default.)
+def get_proxy_slot(
+    obj: Union[Tensor, _AnyScriptObjectType, PySymType],
+    tracer: _ProxyTracer,
+    default: object = no_default,
+    transform: Callable = lambda x: x,
+) -> object:
+    tracker: Any
+    if isinstance(obj, Tensor):
+        tracker = tracer.tensor_tracker
+    elif isinstance(obj, _AnyScriptObject):
+        tracker = tracer.script_object_tracker
+    else:
+        assert isinstance(obj, py_sym_types), type(obj)
+        tracker = tracer.symnode_tracker
+
+    if obj not in tracker:
+        # Last ditch
+        if isinstance(obj, py_sym_types) and obj.node.expr in tracer.sympy_expr_tracker:
+            value = tracer.sympy_expr_tracker[obj.node.expr]
+        else:
+            if isinstance(default, _NoDefault):
+                raise RuntimeError(
+                    f"{obj} ({id(obj)})is not tracked with proxy for {tracer}"
+                )
+            return default
+    else:
+        value = tracker[obj]
+    res = transform(value)
+    return res
+
+
+def snapshot_fake(val: Tensor) -> Optional[Tensor]:
+    # val.detach() will also eventually call fast_detach(),
+    # but this saves us a full trip into __torch_dispatch__
+    # (snapshot_fake is called a lot)
+    if isinstance(val, FakeTensor):
+        return fast_detach(val.fake_mode, val)
+    else:
+        return val.detach()
+
+
+_ExtractValType = Optional[
+    Union[
+        PySymType,
+        _AnyScriptObjectType,
+        BackwardState,
+        list["_ExtractValType"],
+        tuple["_ExtractValType", ...],
+        dict[str, "_ExtractValType"],
+        Tensor,
+        int,
+        float,
+        bool,
+    ]
+]
+
+
+def extract_val(val: _ExtractValType) -> _ExtractValType:
+    if is_fake(val):
+        return snapshot_fake(val)
+    elif isinstance(val, py_sym_types):
+        return val
+    elif isinstance(val, _AnyScriptObject):
+        return val
+    elif isinstance(val, BackwardState):
+        return val
+    elif isinstance(val, (list, tuple)):
+        return val.__class__([extract_val(x) for x in val])
+    elif isinstance(val, dict):
+        return {k: extract_val(v) for k, v in val.items()}
+    elif isinstance(val, Tensor):
+        if not val.is_sparse:
+            # NB: Kinda hacky, but we should try to get val as the metadata
+            # everywhere
+            # TODO: This doesn't properly track storages.  A more robust
+            # approach would be to maintain a per-trace FakeTensorMode and
+            # from_real_tensor to create fake values (don't forget to
+            # snapshot_fake)
+            from torch._guards import detect_fake_mode
+
+            fake_tensor_mode = detect_fake_mode(val)
+            if not fake_tensor_mode:
+                fake_tensor_mode = FakeTensorMode(allow_fallback_kernels=True)
+            with fake_tensor_mode:
+                return torch.empty_strided(
+                    val.shape, val.stride(), device=val.device, dtype=val.dtype
+                )
+        else:
+            return None
+    elif isinstance(val, (int, float, bool)):
+        return val
+    elif val is None:
+        return None
+
+    typing_extensions.assert_never(val)
+
+
+@contextmanager
+def _enable_thunkify(
+    tracer: _ProxyTracer, *, enable: bool = True
+) -> Generator[None, None, None]:
+    """
+    Enable thunkification inside the context manager.  Thunkification prevents
+    SymNode computation from directly being traced into an FX graph; instead,
+    the compute is only added to the graph if it is actually used.  This helps
+    us track SymNode compute when it is computed (since we need /something/
+    to put in the tracker) even if it is unlikely to be used.
+    """
+    old = tracer.enable_thunkify
+    tracer.enable_thunkify = enable
+    try:
+        yield
+    finally:
+        tracer.enable_thunkify = old
+
+
+@contextmanager
+def maybe_disable_thunkify() -> Generator[None, None, None]:
+    """Within a context, disable thunkification.  See :func:`maybe_enable_thunkify`
+    for more details.  This is helpful if you have a wrapper function which
+    you want to enable thunkification on, but in some segment on the inside (say,
+    the original user function), you want to disable thunkification as you know
+    it is not needed there.
+    """
+    proxy_mode = get_proxy_mode()
+    if proxy_mode is not None:
+        with _enable_thunkify(proxy_mode.tracer, enable=False):
+            yield
+    else:
+        yield
+
+
+@contextmanager
+def maybe_enable_thunkify() -> Generator[None, None, None]:
+    """Within this context manager, if you are doing make_fx tracing, we will thunkify
+    all SymNode compute and avoid tracing it into the graph unless it is actually needed.
+    You should prefer to avoid using this as much as possible, as lazy evaluation of
+    SymNode tracing can lead to long chains of thunks which will stack overflow
+    if you evaluate them.  However, this is currently sometimes necessary as there
+    are buggy parts of PT2 which will fail with "s0 is not tracked with proxy" error
+    due to insufficient tracing of SymNode computation.
+    """
+    proxy_mode = get_proxy_mode()
+    if proxy_mode is not None:
+        with _enable_thunkify(proxy_mode.tracer):
+            yield
+    else:
+        yield
+
+
+# Note [invariants for node meta 'val']
+# What invariants do we have for the 'val' set on the FX node?  It has accurate
+# metadata... but only for metadata that exists "below" all other subsystems
+# (most notably autograd, but also vmap, functorch transforms, etc).  This means
+# you can get the dtype, shape, stride, storage, but you CANNOT get requires_grad,
+# grad_fn, _base (_base actually may be set due to recursive call to
+# ADInplaceOrView, but you shouldn't rely on it.)
+def set_meta(proxy: Proxy, val: _ExtractValType) -> Proxy:
+    proxy.node.meta["val"] = extract_val(val)
+
+    with _enable_thunkify(proxy.tracer):  # type: ignore[arg-type]
+        # Best effort tensor_meta setting; prefer using val!
+        if is_fake(val):
+            proxy.node.meta["tensor_meta"] = _extract_tensor_metadata(val)
+        elif isinstance(val, Tensor) and not val.is_sparse:
+            proxy.node.meta["tensor_meta"] = _extract_tensor_metadata(val)
+    return proxy
+
+
+def thunkify(
+    tracer: _ProxyTracer, f: Callable[_P, R], *args: _P.args, **kwargs: _P.kwargs
+) -> Thunk[R]:
+    """
+    Delays computation of f until it's called again
+    Also caches the result
+    """
+    if tracer.enable_thunkify:
+        return Thunk(functools.partial(f, *args, **kwargs))
+    else:
+        r = f(*args, **kwargs)
+        return Thunk(lambda: r)
+
+
+def track_tensor(
+    tensor: Tensor, proxy: Proxy, *, constant: Optional[Tensor], tracer: _ProxyTracer
+) -> None:
+    def try_set_proxy_slot(
+        outer_s: IntLikeType,
+        proxy_callable: Callable[Concatenate[PySymType, _P], Proxy],
+        *args: _P.args,
+        **kwargs: _P.kwargs,
+    ) -> None:
+        assert callable(proxy_callable)
+        if isinstance(outer_s, SymInt):
+            with _enable_thunkify(tracer):
+                set_proxy_slot(
+                    outer_s,
+                    tracer,
+                    thunkify(tracer, proxy_callable, outer_s, *args, **kwargs),
+                )
+
+    # The basic idea is that we need to associate each tensor/SymInt
+    # with a Proxy.  How do we setup this association?  We just store
+    # the proxy on the proxy slot of the object, keyed on the tracer
+    # (so that if we have multiple tracers at the same time, they
+    # don't clobber each other.)
+    for i, s in enumerate(tensor.shape):
+        try_set_proxy_slot(
+            s,
+            lambda x, i: set_meta(
+                tracer.create_proxy(
+                    "call_function", torch.ops.aten.sym_size.int, (proxy, i), {}
+                ),
+                x,
+            ),
+            i,
+        )
+
+    if not is_sparse_any(tensor):
+        for i, s in enumerate(tensor.stride()):
+            try_set_proxy_slot(
+                s,
+                lambda x, i: set_meta(
+                    tracer.create_proxy(
+                        "call_function", torch.ops.aten.sym_stride.int, (proxy, i), {}
+                    ),
+                    x,
+                ),
+                i,
+            )
+
+    try_set_proxy_slot(
+        tensor.numel(),
+        lambda x: set_meta(
+            tracer.create_proxy(
+                "call_function", torch.ops.aten.sym_numel.default, (proxy,), {}
+            ),
+            x,
+        ),
+    )
+    if not is_sparse_any(tensor):
+        try_set_proxy_slot(
+            tensor.storage_offset(),
+            lambda x: set_meta(
+                tracer.create_proxy(
+                    "call_function",
+                    torch.ops.aten.sym_storage_offset.default,
+                    (proxy,),
+                    {},
+                ),
+                x,
+            ),
+        )
+    set_proxy_slot(tensor, tracer, _ProxyTensor(proxy, constant))
+
+
+_NestedProxys = Union[
+    Proxy, Sequence["_NestedProxys"], Mapping[object, "_NestedProxys"]
+]
+_NestedTensors = Union[
+    Tensor, Sequence["_NestedTensors"], Mapping[object, "_NestedTensors"]
+]
+
+
+def track_tensor_tree(
+    inner_res: T,
+    proxy_res: _NestedProxys,
+    *,
+    constant: Optional[_NestedTensors],
+    tracer: _ProxyTracer,
+) -> T:
+    # NB: We call set_unbacked_bindings only on the *topmost* call to
+    # track_tensor_tree, not recursive calls.  This is because there must
+    # be only ONE unbacked_binding proxy call, and it should be the one
+    # where all of the unbacked SymInts actually first come into existence.
+    # If you call this again on the inner proxies for the tuple projections,
+    # you will have multiple unbacked_bindings for the same symbol, but
+    # they're not going to show up anywhere.
+    #
+    # I was briefly deceived into setting unbacked bindings recursively when
+    # working on https://github.com/pytorch/pytorch/pull/133585 because I
+    # observed that some extra unbacked bindings were needed to handle some
+    # higher order operator code.  But actually it looks like this was
+    # just an unrelated bug that needed to be fixed separately.
+    _set_unbacked_bindings(inner_res, proxy_res)
+
+    def wrap_with_proxy(
+        e: object, proxy: _NestedProxys, constant: Optional[_NestedTensors]
+    ) -> None:
+        if isinstance(e, Tensor):
+            assert isinstance(proxy, Proxy)
+            assert constant is None or isinstance(constant, Tensor)
+            track_tensor(e, proxy, tracer=tracer, constant=constant)
+            set_meta(proxy, e)
+        elif isinstance(e, py_sym_types):
+            assert isinstance(proxy, Proxy)
+            # NB: eagerly set meta here, so that the numbering is in order
+            set_meta(proxy, e)
+            set_proxy_slot(e, tracer, thunkify(tracer, lambda: proxy))
+        elif isinstance(e, _AnyScriptObject):
+            assert isinstance(proxy, Proxy)
+            set_proxy_slot(e, tracer, proxy)
+            set_meta(proxy, e)
+        elif isinstance(e, (tuple, list)):
+            # example use case: allreduce_ returns ([tensor], work)
+            if isinstance(proxy, fx.Proxy):
+                set_meta(proxy, e)
+
+            def get_constant(
+                c: Optional[_NestedTensors], idx: int
+            ) -> Optional[_NestedTensors]:
+                if c is None:
+                    return None
+                else:
+                    assert isinstance(c, (list, tuple))
+                    return c[idx]
+
+            for idx, ee in enumerate(e):
+                # Use an indexer here - if proxy is a List then it will unwrap
+                # it. If it's a Proxy then it will proxy the getelem.
+                wrap_with_proxy(ee, proxy[idx], get_constant(constant, idx))  # type: ignore[index]
+
+        elif isinstance(e, dict):
+            # example use case: triton_kernel_wrapper takes arguments as kwargs
+
+            # In theory we could support const-prop when proxy-tensor-tracing
+            # operators that returns dicts of tensors, but we have no use case
+            # for it today (since the only op we currently trace that can
+            # return a dict is triton_kernel_wrapper_functional/mutation,
+            # which does not participate in const-prop)
+            assert constant is None
+
+            if isinstance(proxy, fx.Proxy):
+                set_meta(proxy, e)
+
+            for key, val in e.items():
+                wrap_with_proxy(val, proxy[key], None)  # type: ignore[index]
+
+        elif isinstance(e, BackwardState):
+            assert isinstance(proxy, Proxy)
+            set_meta(proxy, e)
+            e.proxy = proxy
+        else:
+            # intentionally pass on primitives
+            pass
+
+    wrap_with_proxy(inner_res, proxy_res, constant)
+
+    return inner_res
+
+
+@dataclass
+class _ProxyTensor:
+    proxy: Proxy
+    constant: Optional[Tensor]
+
+
+def fetch_sym_proxy(
+    tracer: _ProxyTracer,
+) -> Callable[[PySymType], Union[bool, int, float, Proxy]]:
+    def inner(e: PySymType) -> Union[int, bool, float, Proxy]:
+        n = e.node
+        if n.constant is not None:
+            return n.constant
+        if e.node.expr.is_number:
+            if isinstance(e, SymBool):
+                return bool(e.node.expr)
+            elif isinstance(e, SymInt):
+                return int(e.node.expr)
+            return float(e.node.expr)
+        else:
+            assert isinstance(e, py_sym_types)
+            # NB: we REQUIRE all symints to be tracked
+            return get_proxy_slot(e, tracer).force()
+
+    return inner
+
+
+@overload
+def fetch_object_proxy(tracer: _ProxyTracer, t: Tensor) -> Union[_ProxyTensor, Tensor]:
+    ...
+
+
+@overload
+def fetch_object_proxy(
+    tracer: _ProxyTracer, t: _AnyScriptObjectType
+) -> Union[Proxy, _AnyScriptObjectType]:
+    ...
+
+
+@overload
+def fetch_object_proxy(
+    tracer: _ProxyTracer, t: PySymType
+) -> Union[_PySymProxyType, PySymType]:
+    ...
+
+
+def fetch_object_proxy(
+    tracer: _ProxyTracer, t: Union[Tensor, _AnyScriptObjectType, PySymType]
+) -> object:
+    return get_proxy_slot(t, tracer, t)
+
+
+HANDLED_TYPES = (Tensor, torch.nn.Parameter, FakeTensor)
+
+
+def _maybe_record_pointwise_barrier(
+    func: object, proxy_mode: ProxyTorchDispatchMode
+) -> None:
+    """
+    Records pointwise operators in user program (non decomposed) that were output in fp16/bf16
+    """
+    if proxy_mode.decomp_layers or not proxy_mode.emulate_precision_casts:
+        return
+
+    if (
+        not isinstance(func, torch._ops.OpOverload)
+        or torch.Tag.pointwise not in func.tags
+    ):
+        return
+
+    last_node = next(iter(reversed(proxy_mode.tracer.graph.nodes)))
+    t = last_node.meta.get("val")
+    if not isinstance(t, torch.Tensor) or t.dtype not in (
+        torch.bfloat16,
+        torch.float16,
+    ):
+        return
+
+    last_node.meta["low_precision_pointwise_barrier"] = True
+
+
+def proxy_call(
+    proxy_mode: ProxyTorchDispatchMode,
+    func: OpOverload,
+    pre_dispatch: bool,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    unrecognized_types: list[type] = []
+    flat_args_kwargs, spec = pytree.tree_flatten((args, kwargs))
+
+    def can_handle_tensor(x: Tensor) -> bool:
+        r = type(x) in HANDLED_TYPES or has_proxy_slot(x, proxy_mode.tracer)
+        if proxy_mode._allow_fake_constant:
+            r = r or type(x) in (torch._subclasses.FakeTensor,)
+        if not r:
+            unrecognized_types.append(type(x))
+        return r
+
+    # If there are any tensor subclasses, we need to handle those tensor subclasses first
+    # TODO: we could use types to test this
+    if not all(can_handle_tensor(x) for x in flat_args_kwargs if isinstance(x, Tensor)):
+        not_implemented_log.debug(
+            "ProxyTensorMode tensors without proxy had unrecognized subclasses: %s",
+            unrecognized_types,
+        )
+        return NotImplemented
+
+    r = maybe_handle_decomp(proxy_mode, func, args, kwargs)
+    if r is not NotImplemented:
+        _maybe_record_pointwise_barrier(func, proxy_mode)
+        return r
+
+    # For pre-autograd tracing, we do not want to run CompositeImplicit decomps.
+    if not pre_dispatch and func not in [
+        torch.ops.aten.size.default,
+        torch.ops.aten.stride.default,
+        torch.ops.aten.storage_offset.default,
+    ]:
+        with proxy_mode:
+            r = func.decompose(*args, **kwargs)
+            if r is not NotImplemented:
+                return r
+
+    if func is torch.ops.aten.is_nonzero.default:
+        with proxy_mode:
+            return (args[0] != 0).item()  # type: ignore[attr-defined]
+
+    tracer = proxy_mode.tracer
+    f_flat_args_kwargs = [
+        (
+            fetch_object_proxy(tracer, x)
+            if isinstance(x, (Tensor, _AnyScriptObject))
+            else x
+        )
+        for x in flat_args_kwargs
+    ]
+
+    # If there are SymInts, we also should not consider this constant.
+    # However, fake tensor handling of SymInts is sufficiently broken that
+    # I couldn't write a test for this case
+    all_constant = (
+        not any(
+            t.constant is None
+            for t in f_flat_args_kwargs
+            if isinstance(t, _ProxyTensor)
+        )
+        # TODO: maybe constant SymInts should also be allowed?  Not sure if
+        # this can happen
+        and not any(isinstance(x, py_sym_types) for x in flat_args_kwargs)
+    )
+
+    if torch.Tag.data_dependent_output in func.tags:
+        # Check if all of the Tensor inputs are constants
+        if all_constant:
+            const_flat_args_kwargs = [
+                t.constant if isinstance(t, _ProxyTensor) else t
+                for t in f_flat_args_kwargs
+            ]
+            const_args, const_kwargs = pytree.tree_unflatten(
+                const_flat_args_kwargs, spec
+            )
+            with unset_fake_temporarily():
+                return func(*const_args, **const_kwargs)
+        # If any of the Tensor inputs are "real" (not FakeTensor), we may
+        # incorrectly burn in constants by allowing this access.  Raise
+        # an error in this case
+        if proxy_mode._error_on_data_dependent_ops and pytree.tree_all_only(
+            Tensor, lambda t: not is_fake(t), (args, kwargs)
+        ):
+            raise RuntimeError(
+                f"It appears that you're trying to get value out of a tracing tensor with {func} - erroring out! "
+                "It's likely that this is caused by data-dependent control flow or similar.  "
+                "It may be possible to trace this with dynamic shapes; try setting tracing_mode='symbolic' "
+                "in your make_fx call."
+            )
+
+    proxy_flat_args_kwargs = [
+        e.proxy if isinstance(e, _ProxyTensor) else e for e in f_flat_args_kwargs
+    ]
+    proxy_flat_args_kwargs = [
+        (fetch_sym_proxy(proxy_mode.tracer)(e) if isinstance(e, py_sym_types) else e)
+        for e in proxy_flat_args_kwargs
+    ]
+    proxy_args, proxy_kwargs = pytree.tree_unflatten(proxy_flat_args_kwargs, spec)
+
+    # When we trace through a torch.tensor invocation, you never actually
+    # see a torch.ops.aten.tensor call. Instead, the way this function is
+    # implemented internally is that we allocate a plain tensor (this is
+    # *guaranteed* to be a plain tensor, we disable all modes when doing
+    # so), and then call at::lift_fresh on it (to give modes a chance to do
+    # their stuff).  Furthermore, the tensor argument to lift_fresh is guaranteed
+    # to be freshly allocated, so we want lift_fresh to be a no-op (directly
+    # returning the input argument).
+    #
+    # Here is the basic problem: when we trace this sequence of executions
+    # into an FX graph, what happens to this call sequence?  Traditionally,
+    # tensor constants get interned as buffers on the FX GraphModule.  But
+    # this is dangerous.  Consider:
+    #
+    #       x = torch.tensor(1)
+    #       x.add_(2)
+    #
+    # Naively, this traces into:
+    #
+    #       t = self._tensor_constant0  # initialized to torch.tensor(1)
+    #       x = torch.ops.aten.lift_fresh(t)
+    #       x.add_(2)
+    #
+    # If lift_fresh returns t directly, the subsequent add_ call will
+    # modify the tensor constant. Really, the problem is we've violated
+    # the invariant the argument to lift is fresh.  So what we should
+    # preserve the invariant by replacing lift_fresh with lift_fresh_copy:
+    #
+    #       t = self._tensor_constant0  # initialized to torch.tensor(1)
+    #       x = torch.ops.aten.lift_fresh_copy(t)
+    #       x.add_(2)
+    #
+    # This is what the overload modification does.
+    if func is torch.ops.aten.lift_fresh.default:
+        func = torch.ops.aten.lift_fresh_copy.default
+
+    proxy_out = proxy_mode.tracer.create_proxy(
+        "call_function",
+        func,
+        proxy_args,
+        proxy_kwargs,
+        name=proxy_mode.tracer.graph._target_to_str(func.overloadpacket.__name__),
+    )
+
+    with _enable_thunkify(proxy_mode.tracer):
+        out = func(*args, **kwargs)
+
+    # In some circumstances, we will be tracing in a situation where a tensor
+    # is *statically* known to be a constant (currently, this only happens if
+    # you run torch.tensor; deterministic factory functions like torch.arange
+    # don't get this treatment).  When the tensor in question is small, it's
+    # helpful to due constant propagation in case we call item() (in which
+    # case we can return the constant value that is known, rather than give
+    # an error.)  The logic here tests if constant propagation is possible
+    # (because all of the inputs are constant).  If so, we disable fake tensor
+    # mode (if it is on) and do true compute on the constant.
+    #
+    # It's worth highlighting that we're making a policy decision here.
+    # There is a potential that the tensor is actually quite large, and we
+    # don't actually want to run the compute.  The tensor being quite large
+    # is one of the reasons why factory functions don't get this treatment
+    # (since they can be quite large; if a parameter is initialized to a
+    # constant value it will be!)  Similarly, there is also a potential
+    # to run an operator that blows up the size of a small tensor; we don't
+    # protect against this case, but we could force, e.g., only single
+    # element constant computation by testing the numel of the result before
+    # propagating const-ness.  Similarly, we don't require the constant to
+    # live on CPU, but we could.
+    any_constant = any(
+        t.constant is not None
+        for t in f_flat_args_kwargs
+        if isinstance(t, _ProxyTensor)
+    )
+
+    constant = None
+
+    def tensor_numel_in_limit(t: Tensor) -> bool:
+        return t.numel() <= CONSTANT_NUMEL_LIMIT
+
+    # If this is a lift, the input tensor is guaranteed to be a
+    # constant, so we keep a copy of the original argument along so
+    # we can query it if we're asked to item() it at some later point
+    if (
+        func is torch.ops.aten.lift_fresh_copy.default
+        and out.numel() <= CONSTANT_NUMEL_LIMIT
+    ):
+        with unset_fake_temporarily():
+            assert isinstance(args[0], (Proxy, Tensor)), type(args[0])
+            constant = args[0].clone()
+    elif (
+        torch.Tag.nondeterministic_seeded not in func.tags
+        and all_constant
+        and any_constant
+        and pytree.tree_all_only(Tensor, tensor_numel_in_limit, out)
+    ):
+        # NB: do NOT include factories as constants
+        with unset_fake_temporarily():
+            const_flat_args_kwargs = [
+                t.constant if isinstance(t, _ProxyTensor) else t
+                for t in f_flat_args_kwargs
+            ]
+            const_args, const_kwargs = pytree.tree_unflatten(
+                const_flat_args_kwargs, spec
+            )
+            constant = func(*const_args, **const_kwargs)
+    else:
+        constant = None
+
+    track_tensor_tree(out, proxy_out, constant=constant, tracer=tracer)
+    _maybe_record_pointwise_barrier(func, proxy_mode)
+    return out
+
+
+class _SymNodeDict:
+    """
+    Wrapper around a dictionary that will hash SymInts with their nodes
+    """
+
+    def __init__(self) -> None:
+        self.sym_node_dict: dict[PySymType, _PySymProxyType] = {}
+
+    def __setitem__(self, key: PySymType, value: _PySymProxyType) -> None:
+        self.sym_node_dict[key.node] = value
+
+    def __getitem__(self, key: PySymType) -> _PySymProxyType:
+        return self.sym_node_dict[key.node]
+
+    def __contains__(self, key: PySymType) -> bool:
+        return key.node in self.sym_node_dict
+
+    def get(
+        self, key: PySymType, default: Optional[_PySymProxyType] = None
+    ) -> _PySymProxyType:
+        # dict.get()'s annotation doesn't accept `None` when the value type
+        # isn't Optional.
+        return self.sym_node_dict.get(key.node, default)  # type: ignore[arg-type]
+
+    def __iter__(self) -> Any:
+        raise NotImplementedError
+
+    def __len__(self) -> int:
+        return len(self.sym_node_dict)
+
+
+class PythonKeyTracer(Tracer):
+    script_object_tracker: MutableMapping[_AnyScriptObjectType, Proxy]
+    symnode_tracker: _SymNodeDict
+    sympy_expr_tracker: dict[sympy.Symbol, object]
+    tensor_tracker: MutableMapping[Tensor, _ProxyTensor]
+    torch_fn_counts: dict[OpOverload, int]
+    enable_thunkify: bool = False
+
+    def __init__(self) -> None:
+        super().__init__(autowrap_modules=())  # type: ignore[arg-type]
+        self.tensor_tracker = WeakTensorKeyDictionary()
+        self.symnode_tracker = _SymNodeDict()
+        self.script_object_tracker = WeakIdKeyDictionary(
+            dict=None, ref_type=_WeakHashRef
+        )
+        self.sympy_expr_tracker = dict()
+
+        # Stores the torch function that was called during tracing
+        self.torch_fn_metadata = None
+        # Stores the counts for every torch function called. This is to help
+        # distinguish between different calls to the same torch function.
+        self.torch_fn_counts = {}
+        self.enable_thunkify = False
+
+    # In general, we don't want to make modules leaves. In principle, users of
+    # this tracer might want to override this in order to turn a couple specific
+    # modules into leaves in the traced graph.
+    def call_module(
+        self,
+        m: Module,
+        forward: Callable[..., Any],
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any],
+    ) -> Any:
+        return forward(*args, **kwargs)
+
+    # We don't want to turn getattr calls into proxies. So we just return the actual value.
+    def getattr(
+        self, attr: str, attr_val: object, parameter_proxy_cache: dict[str, Proxy]
+    ) -> object:
+        return attr_val
+
+    def create_arg(self, a: object) -> fx.node.Node:
+        if isinstance(a, torch.nn.Parameter):
+            for n, p in self.root.named_parameters():
+                if a is p:
+                    return self.create_node("get_attr", n, (), {})
+
+            qualname = self.get_fresh_qualname("_param_constant")
+            setattr(self.root, qualname, a)
+
+            return self.create_node("get_attr", qualname, (), {})
+        elif isinstance(a, py_sym_types):
+            assert a.node.constant is not None
+            return a.node.constant
+        return super().create_arg(a)  # type: ignore[return-value]
+
+    @overload
+    def unwrap_proxy(self, e: Tensor) -> Union[Proxy, Tensor]:
+        ...
+
+    @overload
+    def unwrap_proxy(self, e: PySymType) -> Union[Proxy, PySymType]:
+        ...
+
+    @overload
+    def unwrap_proxy(
+        self, e: _AnyScriptObjectType
+    ) -> Union[Proxy, _AnyScriptObjectType]:
+        ...
+
+    def unwrap_proxy(self, e: T) -> object:
+        if isinstance(e, Tensor):
+            return get_proxy_slot(e, self, e, lambda x: x.proxy)
+        elif isinstance(e, py_sym_types):
+            return get_proxy_slot(e, self, e, lambda e: e.force())
+        elif isinstance(e, _AnyScriptObject):
+            return get_proxy_slot(e, self, e)
+        else:
+            return e
+
+    def create_node(
+        self,
+        kind: str,
+        target: Target,
+        args: tuple[Argument, ...],
+        kwargs: dict[str, Argument],
+        name: Optional[str] = None,
+        type_expr: Optional[Any] = None,
+    ) -> torch.fx.Node:
+        node = super().create_node(kind, target, args, kwargs, name, type_expr)  # type: ignore[arg-type]
+
+        def map_fn(v: Any) -> Optional[_ExtractValType]:
+            if not isinstance(v, torch.fx.Node) or "val" not in v.meta:
+                return None
+            val = v.meta["val"]
+            # other subclasses like FunctionalTensor error on `extract_val`
+            # "Attempting to use FunctionalTensor on its own." just store FakeTensors for now
+            if isinstance(val, torch.Tensor) and not isinstance(val, FakeTensor):
+                return None
+            return extract_val(v.meta["val"])
+
+        # TODO: opt-in mechanism ?
+        if isinstance(
+            target,
+            (
+                torch._higher_order_ops.triton_kernel_wrap.TritonKernelWrapperFunctional,
+                torch._higher_order_ops.triton_kernel_wrap.TritonKernelWrapperMutation,
+            ),
+        ):
+            arg_inp, kwarg_inp = torch.fx.node.map_aggregate((args, kwargs), map_fn)  # type: ignore[misc, arg-type]
+            node.meta["arg_kwarg_vals"] = (arg_inp, kwarg_inp)
+
+        return node
+
+
+def _make_temp_remove_mode_context_manager(
+    mode_ty: type[TorchFunctionMode],
+) -> Callable[[], _GeneratorContextManager[Optional[TorchFunctionMode]]]:
+    @contextmanager
+    def context_manager_fn() -> Generator[Optional[TorchFunctionMode], None, None]:
+        from torch.overrides import _len_torch_function_stack, _pop_mode, _push_mode
+
+        temp_elements = []
+        removed_mode = None
+
+        while _len_torch_function_stack() > 0:
+            mode = _pop_mode()
+            if isinstance(mode, mode_ty):
+                removed_mode = mode
+                break
+            else:
+                temp_elements.append(mode)
+
+        for mode in reversed(temp_elements):
+            _push_mode(mode)
+
+        try:
+            yield removed_mode
+
+        finally:
+            if removed_mode is not None:
+                count = len(temp_elements)
+                while count > 0:
+                    mode = _pop_mode()
+                    count -= 1
+
+                temp_elements.append(removed_mode)
+
+                for mode in reversed(temp_elements):
+                    _push_mode(mode)
+
+    return context_manager_fn
+
+
+@torch._disable_dynamo
+def dispatch_trace(
+    root: Union[Module, Callable],
+    tracer: Tracer,
+    concrete_args: Optional[tuple[Any, ...]] = None,
+) -> GraphModule:
+    graph = tracer.trace(root, concrete_args)  # type: ignore[arg-type]
+
+    # NB: be careful not to DCE .item() calls
+    def impure_pred(n: fx.Node) -> bool:
+        from .symbolic_shapes import is_accessor_node
+
+        # Always defer to the built-in notion of impure
+        if n.is_impure():
+            return True
+
+        # Accessors always OK to DCE
+        if is_accessor_node(n):
+            return False
+
+        # If the operator in question takes SymInt args to SymInt output,
+        # we assume it's pure and OK to DCE
+        if (
+            isinstance(n.meta.get("val"), py_sym_types)
+            and
+            # NB: constant args ok
+            all(
+                isinstance(a.meta.get("val"), py_sym_types)
+                for a in n.args
+                if isinstance(a, fx.Node)
+            )
+        ):
+            return False
+
+        # No idea, just assume it's not OK
+        return True
+
+    graph.eliminate_dead_code(impure_pred)
+    from torch._inductor.fx_passes.dedupe_symint_uses import dedupe_symints
+
+    dedupe_symints(graph)
+    name = root.__class__.__name__ if isinstance(root, Module) else root.__name__
+    return fx._lazy_graph_module._make_graph_module(tracer.root, graph, name)
+
+
+def wrap_key(
+    f: Callable[_P, R], tensors: _P.args, tracer: _ProxyTracer, pre_dispatch: bool
+) -> Callable[_P, R]:
+    flat_tensors, _tensors_spec = pytree.tree_flatten(tensors)
+
+    @functools.wraps(f)
+    def wrapped(*proxies: _P.args, **_unused: _P.kwargs) -> R:
+        flat_proxies, _proxies_spec = pytree.tree_flatten(proxies)
+        assert len(flat_proxies) == len(flat_tensors)
+        with disable_proxy_modes_tracing() as m:
+            assert isinstance(m, ProxyTorchDispatchMode)
+            track_tensor_tree(flat_tensors, flat_proxies, constant=None, tracer=tracer)
+
+        def get_tensor_proxy_slot(t: Tensor) -> Union[Tensor, Proxy]:
+            return get_proxy_slot(t, tracer, t, lambda x: x.proxy)  # type: ignore[attr-defined]
+
+        out = f(*tensors)  # type:ignore[call-arg]
+        out = pytree.tree_map_only(Tensor, get_tensor_proxy_slot, out)
+        out = pytree.tree_map_only(
+            _AnyScriptObject, lambda t: get_proxy_slot(t, tracer, t, lambda x: x), out
+        )
+
+        def get_sym_proxy_slot(t: PySymType) -> Proxy:
+            return get_proxy_slot(t, tracer).force()
+
+        out = pytree.tree_map_only(py_sym_types, get_sym_proxy_slot, out)
+        return out
+
+    return wrapped
+
+
+# TODO: Make downstream users of this work with OperatorBase
+ORIGINAL_ATEN: Optional[object] = None
+
+
+@contextmanager
+def set_original_aten_op(func: OpOverload) -> Generator[None, None, None]:
+    global ORIGINAL_ATEN
+    if ORIGINAL_ATEN is None and fx_traceback.has_preserved_node_meta():
+        ORIGINAL_ATEN = func
+        fx_traceback.current_meta["original_aten"] = func
+        try:
+            yield
+        finally:
+            ORIGINAL_ATEN = None
+            fx_traceback.current_meta["original_aten"] = None
+    else:
+        yield
+
+
+class TorchFunctionMetadataMode(TorchFunctionMode):
+    def __init__(self, tracer: _ProxyTracer) -> None:
+        self.tracer = tracer
+
+    def __torch_function__(
+        self,
+        func: OpOverload,
+        types: tuple[torch._C._TensorMeta, ...],
+        args: tuple[object, ...] = (),
+        kwargs: Optional[dict[str, object]] = None,
+    ) -> object:
+        kwargs = kwargs or {}
+        self.tracer.torch_fn_metadata = func
+        self.tracer.torch_fn_counts[func] = self.tracer.torch_fn_counts.get(func, 0) + 1
+        return func(*args, **kwargs)
+
+
+_temp_remove_metadata_torch_function_mode = _make_temp_remove_mode_context_manager(
+    TorchFunctionMetadataMode
+)
+
+
+# This mode is **only** used for pre_dispatch tracing.
+# In particular, we need to make sure that autograd/autocast API's
+# that do not desugar into dispatcher operators stay in the graph.
+class PreDispatchTorchFunctionMode(TorchFunctionMode):
+    def __init__(self, tracer: _ProxyTracer) -> None:
+        self.tracer = tracer
+        # The input to torch.amp.autocast_mode._exit_autocast graph node should be the
+        # enter_autocast node. So we have to save the enter autocast node here, and assign it
+        # to the exit_autocast call_function node.
+        self.enter_autocast_nodes: list[torch.fx.Node] = []
+
+    def __torch_function__(
+        self,
+        func: Union[OpOverload, Callable],
+        types: tuple[torch._C._TensorMeta, ...],
+        args: tuple[object, ...] = (),
+        kwargs: Optional[dict[str, object]] = None,
+    ) -> object:
+        kwargs = kwargs or {}
+        if func in _side_effectful_need_to_be_preserved_pre_dispatch:
+            # It's for passing the export verifier which needs to verify the meta['val']
+            # TODO(tmanlaibaatar): we should systematically couple it with expoert verifier,
+            # instead of hardcoding it here.
+            # T203648563
+            if func == torch.amp.autocast_mode._exit_autocast:
+                enter_node = self.enter_autocast_nodes.pop()
+                args = (enter_node,)
+            node = self.tracer.create_node("call_function", func, args, {})  # type: ignore[arg-type]
+            if func == torch.amp.autocast_mode._enter_autocast:
+                self.enter_autocast_nodes.append(node)
+            if func in [
+                torch._C._set_grad_enabled,
+                torch.amp.autocast_mode._enter_autocast,
+                torch.amp.autocast_mode._exit_autocast,
+            ]:
+                node.meta["val"] = None
+            return node
+            # Don't actually run the function! We just want to trace the calls
+            # into a graph. We don't actualy want to change global autograd state.
+        return func(*args, **kwargs)
+
+
+_temp_remove_pre_dispatch_torch_function_mode = _make_temp_remove_mode_context_manager(
+    PreDispatchTorchFunctionMode
+)
+
+
+class ProxyTorchDispatchMode(TorchDispatchMode):
+    # Ensure this is read-only; this exists only for legacy reasons
+    @property
+    def enable_tracing(self) -> bool:
+        return True
+
+    def __init__(
+        self,
+        tracer: _ProxyTracer,
+        tracing_mode: str,
+        pre_dispatch: bool = False,
+        _allow_fake_constant: bool = False,
+        _error_on_data_dependent_ops: bool = True,
+    ) -> None:
+        dk = torch._C.DispatchKey.PreDispatch if pre_dispatch else None
+        super().__init__(dk)
+        self.tracer = tracer
+        self.tracing_mode = tracing_mode
+        self.pre_dispatch = pre_dispatch
+        self._allow_fake_constant = _allow_fake_constant
+        self._error_on_data_dependent_ops = _error_on_data_dependent_ops
+        # Indicates to our torch_dispatch dispatching infra that
+        # this is an "infra" mode with lower dispatching precedence.
+        self._mode_key = torch._C._TorchDispatchModeKey.PROXY
+        # Every time we enter a mode, we maintain a stack telling us what the previous
+        # ProxyTorchDispatchMode state was (if there was any).
+        # This lets us properly reset the state on exit.
+        self.enter_stack: list[Optional[ProxyTorchDispatchMode]] = []
+        self.decomp_layers = 0
+        from torch._inductor import config
+
+        self.emulate_precision_casts = config.emulate_precision_casts
+
+    @count
+    def __torch_dispatch__(
+        self,
+        func: OpOverload,
+        types: tuple[torch._C._TensorMeta, ...],
+        args: tuple[object, ...] = (),
+        kwargs: Optional[dict[str, object]] = None,
+    ) -> object:
+        with set_original_aten_op(func):
+            kwargs = kwargs or {}
+
+            if func in (prim.device.default,):
+                return func(*args, **kwargs)
+
+            return proxy_call(self, func, self.pre_dispatch, args, kwargs)
+
+    def __enter__(self) -> Self:
+        # Stash and store the previous proxy mode (there may or may not be one)
+        maybe_prev_proxy_mode = _unset_infra_mode(torch._C._TorchDispatchModeKey.PROXY)
+        self.enter_stack.append(maybe_prev_proxy_mode)
+        return super().__enter__()
+
+    def __exit__(
+        self,
+        exc_type: Optional[type[BaseException]],
+        exc_value: Optional[BaseException],
+        traceback: Optional[types.TracebackType],
+    ) -> Optional[bool]:
+        b = super().__exit__(exc_type, exc_value, traceback)
+
+        # Re-enable the previous proxy mode, if there was one.
+        mb_previous_proxy_mode = self.enter_stack.pop()
+        if mb_previous_proxy_mode is not None:
+            _push_mode(mb_previous_proxy_mode)
+
+        return b
+
+    @classmethod
+    def is_infra_mode(cls) -> bool:
+        return True
+
+    def _compute_proxy(
+        self, func: OpOverload, args: tuple[object, ...], out: PySymType
+    ) -> Proxy:
+        # Handle torch.sym_sum
+        n_args: tuple[object, ...]
+        if len(args) == 1 and isinstance(args[0], (list, tuple)):
+            n_args = (
+                tuple(
+                    get_proxy_slot(a, self.tracer).force().node
+                    if isinstance(a, py_sym_types)
+                    else a
+                    for a in args[0]
+                ),
+            )
+        else:
+            n_args = tuple(
+                get_proxy_slot(a, self.tracer).force().node
+                if isinstance(a, py_sym_types)
+                else a
+                for a in args
+            )
+
+        # func doesn't have a __torch_function__ that Proxy can interpose, so
+        # we gotta do it manually
+        n_out = self.tracer.create_node("call_function", func, n_args, {})  # type: ignore[arg-type]
+        p_out = fx.Proxy(n_out, self.tracer)
+        set_meta(p_out, out)
+        return p_out
+
+    def __sym_dispatch__(
+        self,
+        func: OpOverload,
+        types: tuple[torch._C._TensorMeta, ...],
+        args: tuple[object, ...],
+        kwargs: dict[str, object],
+    ) -> object:
+        # Peephole optimize multiply by one
+        # NB: be careful not to trigger guards here!
+        if func == operator.mul:
+            if isinstance(args[1], int) and args[1] == 1:
+                return args[0]
+            elif isinstance(args[0], int) and args[0] == 1:
+                return args[1]
+
+        # For speed, we assume there are no nested data structures
+        # (otherwise we could use tree_map)
+        # We also assume there are no keyword arguments.
+        assert not kwargs
+        out = func(*args, **kwargs)
+
+        # If func returned a constant, we don't need to trace; we have
+        # determined that the result is constant (no matter if the inputs
+        # were symbolic) and it is no longer necessary to trace the
+        # computation.  This could occur if func triggered some guards.
+        if isinstance(out, py_sym_types):
+            p_out_thunk = thunkify(
+                self.tracer, self._compute_proxy, func=func, args=args, out=out
+            )
+            set_proxy_slot(out, self.tracer, p_out_thunk)
+
+        return out
+
+
+class _GraphAppendingTracerEx(fx.proxy.GraphAppendingTracer):
+    script_object_tracker: MutableMapping[_AnyScriptObjectType, Proxy]
+    symnode_tracker: MutableMapping[PySymType, _PySymProxyType]
+    tensor_tracker: MutableMapping[Tensor, _ProxyTensor]
+    sympy_expr_tracker: dict[sympy.Symbol, object]
+    torch_fn_metadata: Optional[OpOverload]
+    torch_fn_counts: dict[OpOverload, int]
+    enable_thunkify: bool = False
+
+    def __init__(self, graph: fx.graph.Graph) -> None:
+        super().__init__(graph)
+        self.symnode_tracker = weakref.WeakKeyDictionary()
+        self.tensor_tracker = WeakTensorKeyDictionary()
+        self.sympy_expr_tracker = {}
+        self.script_object_tracker = WeakIdKeyDictionary(
+            dict=None, ref_type=_WeakHashRef
+        )
+        # Stores the torch function that was called during tracing
+        self.torch_fn_metadata = None
+        # Stores the counts for every torch function called. This is to help
+        # distinguish between different calls to the same torch function.
+        self.torch_fn_counts = {}
+
+
+# TODO: I'm not sure what the point of this class is; you can just
+# make_fx through a regular Interpreter
+class DecompositionInterpreter(fx.Interpreter):
+    def __init__(
+        self,
+        module: fx.GraphModule,
+        new_graph: fx.Graph,
+        decomposition_table: Optional[Mapping[OpOverload, Callable]] = None,
+        **kwargs: object,
+    ) -> None:
+        super().__init__(module, **kwargs)  # type: ignore[arg-type]
+        self.new_graph = new_graph
+        self.tracer = _GraphAppendingTracerEx(self.new_graph)
+        # Blegh
+        self.decomposition_table = decomposition_table or {}
+        self.mode = ProxyTorchDispatchMode(self.tracer, tracing_mode="real")
+
+    def placeholder(
+        self, target: str, args: tuple[object, ...], kwargs: dict[str, object]  # type: ignore[override]
+    ) -> object:
+        out = super().placeholder(target, args, kwargs)  # type: ignore[arg-type]
+        proxy = fx.Proxy(self.new_graph.placeholder(target), self.tracer)
+        track_tensor_tree(out, proxy, constant=None, tracer=self.tracer)
+        # TODO handle case where the first character of target is '*'
+        return out
+
+    def get_attr(
+        self, target: str, args: tuple[object, ...], kwargs: dict[str, object]  # type: ignore[override]
+    ) -> object:
+        out = super().get_attr(target, args, kwargs)  # type: ignore[arg-type]
+        proxy = fx.Proxy(self.new_graph.get_attr(target), self.tracer)
+        track_tensor_tree(out, proxy, constant=None, tracer=self.tracer)
+        return out
+
+    # call_function, call_method, call_module get traced automatically by the outer mode.
+
+    def output(
+        self, target: str, args: tuple[object, ...], kwargs: dict[str, object]  # type: ignore[override]
+    ) -> object:
+        out = super().output(target, args, kwargs)  # type: ignore[arg-type]
+
+        def get_proxy_node(x: _ProxyTensor) -> fx.node.Node:
+            return x.proxy.node
+
+        def unwrap(e: Tensor) -> Union[Tensor, fx.Node]:
+            return get_proxy_slot(e, self.tracer, e, get_proxy_node)
+
+        self.new_graph.output(pytree.tree_map(unwrap, out))
+        return out
+
+    def run(self, *args: object, **kwargs: object) -> object:
+        # Should enter the mode at least once for being able to restore it later
+        # See: https://github.com/pytorch/pytorch/pull/82549#discussion_r934782025
+        with decompose(self.decomposition_table), self.mode:
+            return super().run(*args, **kwargs)  # type: ignore[arg-type]
+
+
+def wrapper_and_args_for_make_fx(
+    func: Callable[..., R], args: tuple[object, ...], kwargs: dict[str, object]
+) -> tuple[Callable[[list[object]], R], list[object]]:
+    # make_fx doesn't support kwargs, so we need to do this flattening
+    # and then unflatten the args before calling func
+    flat_args, spec = pytree.tree_flatten((args, kwargs))
+
+    def wrapped(flat_args: list[object]) -> R:
+        fn_args, fn_kwargs = pytree.tree_unflatten(flat_args, spec)
+        return func(*fn_args, **fn_kwargs)
+
+    return wrapped, flat_args
+
+
+@contextmanager
+def disable_autocast_cache() -> Generator[None, None, None]:
+    old_value = torch.is_autocast_cache_enabled()
+    torch.set_autocast_cache_enabled(False)
+    try:
+        yield
+    finally:
+        torch.set_autocast_cache_enabled(old_value)
+
+
+class _ModuleNotInstalledAsSubmoduleError(NameError):
+    pass
+
+
+# Base class for inline _ModuleStackTracer.__init__.AttrProxy
+class _AttrProxy:
+    def reset_proxy_mapping(self, base: Module, path: str) -> None:
+        pass
+
+
+class _ModuleStackTracer(PythonKeyTracer):
+    r"""Customized version of PythonKeyTracer that retains module stack
+    information in node.meta["nn_module_stack"].
+
+    FX symbolic trace actually does this already, but it relies on `self.root`
+    being the actual module being traced. Since make_fx traces a lambda of our
+    creation, things don't work properly.
+
+    So for this version we hold onto a reference to the original module
+    (scope_root) and use that to match the path. Also when we see,
+            A
+           / \
+          B   C
+           \ /
+            D
+    we want to record the path as A.B.D by recording only one path.
+    See Note [Preserving the nn module stack metadata during export non-strict mode]  # noqa: W605
+    """
+
+    def __init__(self, scope_root: GraphModule) -> None:
+        super().__init__()
+        self.scope_root = scope_root
+        self.enable_attr_proxy = False
+        self.submodule_paths = {}
+        for name, m in self.scope_root.named_modules(remove_duplicate=False):
+            if m in self.submodule_paths:
+                log.info(
+                    "Shared module found between %s and %s, AttrProxy is enabled.",
+                    self.submodule_paths[m],
+                    name,
+                )
+                self.enable_attr_proxy = True
+            else:
+                self.submodule_paths[m] = name
+
+        self.proxy_paths: WeakKeyDictionary[_AttrProxy, str] = WeakKeyDictionary()
+        self.attr_proxy_map: WeakKeyDictionary[Module, _AttrProxy] = WeakKeyDictionary()
+        self.proxy_modules: WeakKeyDictionary[_AttrProxy, Module] = WeakKeyDictionary()
+        self.counter = 0
+
+        self.module_id_cache = defaultdict(list)
+        for name, mod in self.scope_root.named_modules(remove_duplicate=False):
+            self.module_id_cache[id(mod)].append(name)
+
+        # Build a wrapper around _AttrProxy to provide the tracer. We can't
+        # store it on _AttrProxy itself beceause we mimic the underlying class
+        # (including its attributes).
+        tracer = self
+
+        class AttrProxy(_AttrProxy):
+            def __init__(self, base: Union[Module, _AttrProxy], path: str) -> None:
+                if isinstance(base, _AttrProxy):
+                    base = base.get_base()  # type: ignore[attr-defined]
+
+                assert isinstance(base, Module)
+                # Class is modified to be a subclass of torch.nn.Module
+                # Warning: We blow away our own attributes here to mimic the base class
+                # - so don't expect `self.x` to do anything useful.
+                self.__class__ = type(
+                    base.__class__.__name__,
+                    (self.__class__, base.__class__),
+                    {},
+                )
+                self.__dict__ = base.__dict__
+                self.__class__.__module__ = base.__class__.__module__
+                self.__class__.__qualname__ = base.__class__.__qualname__
+
+                # This overwrites any existing paths if `base` is an AttrProxy
+                tracer.proxy_paths[self] = path
+                tracer.proxy_modules[self] = base
+
+            def __getattr__(self, name: str) -> AttrProxy:
+                assert isinstance(self, Module)
+                # Calling into torch.nn.Module.__getattr__ with super(),
+                # That __getattr__ is patched to be module_getattr_wrapper in _symbolic_trace.py.
+                # which then calls into _ModuleStackTracer.getattr
+                attr_val = super().__getattr__(name)  # type: ignore[misc]
+                if not isinstance(attr_val, Module):
+                    return attr_val
+
+                return AttrProxy(attr_val, tracer.proxy_paths[self] + "." + name)
+
+            def get_base(self) -> Module:
+                return tracer.proxy_modules[self]
+
+            def __getitem__(self, idx: Union[int, slice]) -> AttrProxy:
+                if isinstance(idx, slice):
+                    if isinstance(self, torch.nn.Sequential):
+                        # Copied from nn/modules/container.py
+                        res = torch.nn.Sequential(
+                            OrderedDict(list(self._modules.items())[idx])
+                        )
+                        return AttrProxy(res, f"{tracer.proxy_paths[self]}.{idx}")
+                    elif isinstance(self, torch.nn.ModuleList):
+                        # Copied from nn/modules/container.py
+                        res = torch.nn.ModuleList(list(self._modules.values())[idx])
+                        return AttrProxy(res, f"{tracer.proxy_paths[self]}.{idx}")
+
+                return super().__getitem__(idx)  # type: ignore[misc]
+
+            @property
+            def _modules(self) -> dict[str, AttrProxy]:
+                assert "_modules" in self.__dict__
+                submodules = self.__dict__["_modules"]
+                assert isinstance(submodules, dict)
+                return {
+                    key: (
+                        AttrProxy(value, tracer.proxy_paths[self] + "." + str(key))  # type: ignore[misc]
+                        if value is not None
+                        else value
+                    )
+                    for key, value in submodules.items()
+                }
+
+        self.proxy_type = AttrProxy
+
+    def path_of_module(self, mod: Module) -> str:
+        """
+        Use tracked access path during tracing instead of the default BFS behavior.
+        Still use all the possible module paths to verify the result.
+        """
+        if mod is self.scope_root:
+            return ""
+
+        if isinstance(mod, _AttrProxy):
+            return self.proxy_paths[mod]
+
+        try:
+            return Tracer.path_of_module(self, mod)
+        except NameError as e:
+            raise _ModuleNotInstalledAsSubmoduleError from e
+
+    def getattr(
+        self, attr: str, attr_val: object, parameter_proxy_cache: dict[str, Proxy]
+    ) -> object:
+        if (
+            not isinstance(attr_val, Module)
+            or isinstance(attr_val, fx.GraphModule)
+            or not self.enable_attr_proxy
+        ):
+            return super().getattr(attr, attr_val, parameter_proxy_cache)
+        if isinstance(attr_val, _AttrProxy):
+            return attr_val
+
+        # See NOTE [caching AttrProxy].
+        if attr_val not in self.attr_proxy_map:
+            self.attr_proxy_map[attr_val] = self.proxy_type(attr_val, attr)
+        else:
+            self.attr_proxy_map[attr_val].reset_proxy_mapping(attr_val, attr)
+        return self.attr_proxy_map[attr_val]
+
+    def trace(  # type: ignore[override]
+        self, root: Union[Module, Callable], concrete_args: Optional[dict[str, object]]
+    ) -> fx.Graph:
+        res = super().trace(root, concrete_args)
+
+        # Since we are making _AttrProxy mimic the original
+        # submodule, when someone registers a module directly
+        # to the tracer while tracing, the proxy object gets registered
+        # first. So we need to replace the proxy modules with the real ones
+        # This can happen during HOO tracing
+        proxy_module_names_to_be_replaced: list[tuple[str, _AttrProxy]] = []
+        for name, module in self.root.named_modules():
+            if module in self.proxy_modules:
+                proxy_module_names_to_be_replaced.append((name, module))
+
+        def _delete_proxy_attr(obj: Module, target: str) -> bool:
+            # Copied from fx/graph_module.py
+            # Customized it for proxy type
+            atoms = target.split(".")
+            path, target_submod = atoms[:-1], atoms[-1]
+            assert isinstance(obj, Module)
+            mod = obj
+
+            # Get the parent module
+            for item in path:
+                if not hasattr(mod, item):
+                    return False
+
+                mod = getattr(mod, item)
+
+                if not isinstance(mod, (_AttrProxy, Module)):
+                    return False
+
+            if not hasattr(mod, target_submod):
+                return False
+
+            # At least the leaf module should be proxy type.
+            if not isinstance(getattr(mod, target_submod), _AttrProxy):
+                return False
+
+            delattr(mod, target_submod)
+            return True
+
+        for proxy_module_name, proxy_module in proxy_module_names_to_be_replaced:
+            _delete_proxy_attr(self.root, proxy_module_name)
+            actual_module = self.proxy_modules[proxy_module]
+            _assign_attr(actual_module, self.root, proxy_module_name)
+
+        return res
+
+    def call_module(
+        self,
+        m: Module,
+        forward: Callable,
+        args: tuple[object, ...],
+        kwargs: dict[str, object],
+    ) -> None:
+        """PythonKeyTracer overrides call_module to avoid the scope handling,
+        but we actually want it.
+        """
+        from torch._dynamo import OptimizedModule
+
+        # FIXME (tmanlaibaatar)
+        # When we call torch.compile inside HOO, we will end up
+        # invoking a module that is not registered on the root. For
+        # now, we just inline them. But once we start supporting
+        # mark_strict in export, we do need to properly handle this.
+        # Right now, it doesn't matter because current non-strict
+        # use cases don't need to work with HOO.
+        if isinstance(m, (OptimizedModule, GraphModule)):
+            return forward(*args, **kwargs)
+
+        try:
+            return Tracer.call_module(self, m, forward, args, kwargs)
+        except _ModuleNotInstalledAsSubmoduleError:
+            warnings.warn(
+                f"Unable to find the path of the module {m}. "
+                "This might be because the module was not properly registered "
+                "as a submodule, which is not good practice. We will trace "
+                "through the module without recording stack information."
+            )
+            return forward(*args, **kwargs)
+
+    def is_leaf_module(self, m: Module, module_qualified_name: str) -> bool:
+        return False
+
+    def create_node(self, *args: object, **kwargs: object) -> fx.node.Node:
+        """
+        Create node and add on metadata.
+        Add nn_module_stack here instead of TracerBase,
+        since calls to make_fx() might not want to record module stack metadata.
+        Add torch_fn by looking at torch_fn_metadata and torch_fn_counts.
+        Add stack_trace by filtering out forward() stack frames.
+        """
+        node = super().create_node(*args, **kwargs)  # type: ignore[arg-type]
+
+        # nn_module_stack
+        if node.op not in ["placeholder", "output"]:
+            if "nn_module_stack" not in node.meta:
+                node.meta["nn_module_stack"] = self.module_stack
+            # convert nn_module_stack from Dict[key, (FQN, class)] -> Dict[str, Tuple[str, str]]
+            for key, (fqn, mod_cls) in node.meta["nn_module_stack"].items():
+                if isinstance(mod_cls, type):
+                    node.meta["nn_module_stack"][key] = (
+                        fqn,
+                        mod_cls.__module__ + "." + mod_cls.__qualname__,
+                    )
+
+        # torch_fn
+        if (
+            node.op == "call_function"
+            and self.torch_fn_metadata is not None
+            and "torch_fn" not in node.meta
+        ):
+            node.meta["torch_fn"] = (
+                f"{self.torch_fn_metadata.__name__}_{self.torch_fn_counts[self.torch_fn_metadata]}",
+                f"{self.torch_fn_metadata.__class__.__name__}.{self.torch_fn_metadata.__name__}",
+            )
+
+        # stack_trace
+        if "stack_trace" not in node.meta and node.op not in ["placeholder", "output"]:
+            user_frame_summary = CapturedTraceback.extract().summary()
+            if user_frame_summary:
+                # we retain frames from forward() calls, or ops
+                # located in torch/__init__.py (e.g. sym_int, sym_constrain_range, vmap)
+                stack_trace = [
+                    frame
+                    for frame in user_frame_summary
+                    if (
+                        frame.name == "forward"
+                        or frame.filename.endswith("torch/__init__.py")
+                    )
+                ]
+                # filter out forward() frames from fx/_symbolic_trace.py, export/_trace.py
+                # this is hardcoded, but leads to a much cleaner stack trace
+                stack_trace = [
+                    frame
+                    for frame in stack_trace
+                    if not (
+                        frame.filename.endswith("fx/_symbolic_trace.py")
+                        or frame.filename.endswith("export/_trace.py")
+                    )
+                ]
+                if (
+                    stack_trace
+                ):  # empty list for strict mode, dynamo should handle stack_trace
+                    stack_trace = traceback.StackSummary.from_list(stack_trace)
+                    node.meta["stack_trace"] = "".join(stack_trace.format()).strip()
+
+        return node
+
+
+class _MakefxTracer:
+    def __init__(
+        self,
+        decomposition_table: Optional[Mapping[OpOverload, Callable]],
+        tracing_mode: str,
+        _allow_non_fake_inputs: bool,
+        pre_dispatch: bool,
+        record_module_stack: bool,
+        _allow_fake_constant: bool,
+        _error_on_data_dependent_ops: bool,
+    ) -> None:
+        # Configurations that are used to initialize the context managers and their states.
+        # Should not modify them during tracing.
+        self.decomposition_table: dict[OpOverload, Callable] = dict(
+            decomposition_table or {}
+        )
+        self.decomposition_table.setdefault(
+            torch.ops.aten.sym_numel.default, torch._decomp.decompositions.sym_numel
+        )
+        self.tracing_mode: str = tracing_mode
+        self._allow_non_fake_inputs: bool = _allow_non_fake_inputs
+        self.pre_dispatch: bool = pre_dispatch
+        self.record_module_stack: bool = record_module_stack
+        self._allow_fake_constant: bool = _allow_fake_constant
+        self._error_on_data_dependent_ops: bool = _error_on_data_dependent_ops
+
+        # All context managers and their states should be initialized before tracing based on the inputs
+        # and configurations. After tracing, their states should be cleaned except for shape_env.
+        # Remember to specify how to intialize it from user inputs and from parent tracer whenever
+        # adding new modes in _MakefxTracer.
+        self.fake_tensor_mode: Optional[FakeTensorMode] = None
+        self.proxy_mode: Union[nullcontext, ProxyTorchDispatchMode] = nullcontext()
+        self.proxy_function_mode: Union[
+            nullcontext, PreDispatchTorchFunctionMode
+        ] = nullcontext()
+        self.fx_tracer: Optional[PythonKeyTracer] = None
+        self.python_dispatcher_mode: Union[nullcontext, Any] = nullcontext()
+        self.torch_fn_metadata_mode: Union[
+            nullcontext, TorchFunctionMetadataMode
+        ] = nullcontext()
+
+    def _checkpoint_modes(self) -> list[Any]:
+        return [
+            self.fake_tensor_mode,
+            self.proxy_mode,
+            self.proxy_function_mode,
+            self.fx_tracer,
+            self.python_dispatcher_mode,
+            self.torch_fn_metadata_mode,
+        ]
+
+    def _restore_modes(
+        self,
+        prev_fake_tensor_mode: Optional[FakeTensorMode],
+        prev_proxy_mode: Union[nullcontext, ProxyTorchDispatchMode],
+        prev_proxy_function_mode: Union[nullcontext, PreDispatchTorchFunctionMode],
+        prev_fx_tracer: Optional[PythonKeyTracer],
+        prev_python_dispatcher_mode: Union[nullcontext, Any],
+        prev_torch_fn_metadata_mode: Union[nullcontext, TorchFunctionMetadataMode],
+    ) -> None:
+        self.fake_tensor_mode = prev_fake_tensor_mode
+        self.proxy_mode = prev_proxy_mode
+        self.proxy_function_mode = prev_proxy_function_mode
+        self.fx_tracer = prev_fx_tracer
+        self.python_dispatcher_mode = prev_python_dispatcher_mode
+        self.torch_fn_metadata_mode = prev_torch_fn_metadata_mode
+
+    @contextmanager
+    def _init_modes_from_inputs(
+        self, f: Callable, args: tuple[object, ...]
+    ) -> Generator[None, None, None]:
+        prev_modes = self._checkpoint_modes()
+        try:
+            # Avoid importing sympy at a module level
+            from .symbolic_shapes import ShapeEnv
+
+            if hasattr(f, "_orig_mod") and self.record_module_stack:
+                scope_root = f._orig_mod
+                self.fx_tracer = _ModuleStackTracer(scope_root)
+            else:
+                self.fx_tracer = PythonKeyTracer()
+
+            if self.tracing_mode == "fake":
+                import torch._dynamo
+
+                fake_tensor_mode = torch._dynamo.utils.detect_fake_mode(args)
+                if fake_tensor_mode is None:
+                    import torch._functorch.config as _config
+
+                    with _config.patch(fake_tensor_allow_unsafe_data_ptr_access=False):
+                        fake_tensor_mode = FakeTensorMode(
+                            allow_fallback_kernels=True,
+                            allow_non_fake_inputs=self._allow_non_fake_inputs,
+                            shape_env=ShapeEnv(),
+                            static_shapes=True,
+                        )
+                self.fake_tensor_mode = fake_tensor_mode
+            elif self.tracing_mode == "symbolic":
+                import torch._dynamo
+
+                fake_tensor_mode = torch._dynamo.utils.detect_fake_mode(args)
+                if fake_tensor_mode is None:
+                    shape_env = ShapeEnv()
+                    import torch._functorch.config as _config
+
+                    with _config.patch(fake_tensor_allow_unsafe_data_ptr_access=False):
+                        fake_tensor_mode = FakeTensorMode(
+                            allow_fallback_kernels=False,
+                            allow_non_fake_inputs=self._allow_non_fake_inputs,
+                            shape_env=shape_env,
+                        )
+                assert (
+                    fake_tensor_mode.shape_env is not None
+                ), "shape_env should be set if tracing with 'symbolic'"
+                self.fake_tensor_mode = fake_tensor_mode
+            else:
+                if not self.tracing_mode == "real":
+                    raise AssertionError(
+                        f"Unexpected tracing type: {self.tracing_mode}"
+                    )
+
+            self._construct_modes_with_fx_tracer(self.fx_tracer)
+            yield
+        finally:
+            self._restore_modes(*prev_modes)
+
+    def _construct_modes_with_fx_tracer(self, fx_tracer: _ProxyTracer) -> None:
+        self.proxy_mode = ProxyTorchDispatchMode(
+            fx_tracer,
+            self.tracing_mode,
+            pre_dispatch=self.pre_dispatch,
+            _allow_fake_constant=self._allow_fake_constant,
+            _error_on_data_dependent_ops=self._error_on_data_dependent_ops,
+        )
+
+        if self.pre_dispatch:
+            self.proxy_function_mode = PreDispatchTorchFunctionMode(fx_tracer)
+
+        # pre-autograd tracing uses per-dispatch-key modes,
+        # which requires the python dispatcher
+        if self.tracing_mode == "symbolic" or self.pre_dispatch:
+            self.python_dispatcher_mode = enable_python_dispatcher()
+
+        self.torch_fn_metadata_mode = TorchFunctionMetadataMode(fx_tracer)
+
+    @contextmanager
+    def _init_modes_from_parent(
+        self, parent_tracer: _MakefxTracer
+    ) -> Generator[None, None, None]:
+        # By default, subtracer creates new modes based on parent tracer's config.
+        # However, there are cases where we want to share the same modes with parent tracer
+        # For example, fake_tensor_mode, we want the example value's fake_mode of parent graph and subgraphs to be the same.
+        prev_modes = self._checkpoint_modes()
+        try:
+            self.fake_tensor_mode = parent_tracer.fake_tensor_mode
+
+            def _create_sub_fx_tracer(parent_tracer: _ProxyTracer) -> PythonKeyTracer:
+                if type(parent_tracer) == PythonKeyTracer:
+                    return PythonKeyTracer()
+                elif type(parent_tracer) == _ModuleStackTracer:
+                    return _ModuleStackTracer(parent_tracer.scope_root)
+                else:
+                    raise RuntimeError(
+                        f"Unexpected tracer type: {type(parent_tracer)}."
+                    )
+
+            assert parent_tracer.fx_tracer is not None
+            self.fx_tracer = _create_sub_fx_tracer(parent_tracer.fx_tracer)
+            self._construct_modes_with_fx_tracer(self.fx_tracer)
+            yield
+        finally:
+            self._restore_modes(*prev_modes)
+
+    def _trace_inner(self, f: Callable, *args: object) -> GraphModule:
+        # TODO: We need to explicitly import torch._dynamo before calling dispatch_trace,
+        # because dispatch_trace will introduce the lazy import of torch._dynamo,
+        # and some contexts set before calling dispatch_trace will cause problems with the import of torch._dynamo,
+        # such as some torch API(torch.ones and so on) in populate_builtin_to_tensor_fn_map() will be affected
+        # by the context set before dispatch_trace.
+        import torch._dynamo
+
+        phs = pytree.tree_map(lambda _: torch.fx._symbolic_trace.PH, args)
+
+        def _wrap_fake(args: T) -> T:
+            arg_count = 0
+
+            def inner_wrap_fake(x: object) -> object:
+                nonlocal arg_count
+                # TODO: it would be nice to line these up with the names
+                # FX will choose for the placeholders, but we don't
+                # actually know what the names will be at this point yet
+                # NB: the Source here is actually meaningless
+                from torch._dynamo.source import ConstantSource
+
+                assert self.fake_tensor_mode is not None
+                source = ConstantSource(f"input{arg_count}")
+                if isinstance(x, Tensor):
+                    arg_count += 1
+                    return self.fake_tensor_mode.from_tensor(x, source=source)
+                # NB: don't match on bools
+                elif type(x) is int and self.tracing_mode == "symbolic":
+                    assert (
+                        self.fake_tensor_mode.shape_env is not None
+                    ), "shape_env should be set if tracing with 'symbolic'"
+                    return self.fake_tensor_mode.shape_env.create_symintnode(
+                        self.fake_tensor_mode.shape_env.create_symbol(
+                            x, source, positive=None
+                        ),
+                        hint=x,
+                        source=source,
+                    )
+                elif isinstance(x, torch.ScriptObject):
+                    return torch._library.fake_class_registry.maybe_to_fake_obj(
+                        self.fake_tensor_mode, x
+                    )
+
+                assert not isinstance(
+                    x, FakeScriptObject
+                ), f"ScriptObject {x} has been fakified. Cannot wrap_fake it again."
+                return x
+
+            wrap_fn_map = {
+                "real": lambda x: x,
+                "fake": inner_wrap_fake,
+                "symbolic": inner_wrap_fake,
+            }
+            return pytree.tree_map(wrap_fn_map[self.tracing_mode], args)
+
+        def _wrap_func(f: Callable[_P, R], phs: Sequence[PHBase]) -> Callable[_P, R]:
+            if (
+                not hasattr(inspect.unwrap(f), "__code__")
+                or inspect.unwrap(f).__code__.co_flags & inspect.CO_VARARGS
+            ):
+                # FX doesn't support varargs, so we gotta fake up a wrapper
+                # TODO: Would be nice to fix this at the source...
+                return fake_signature(f, len(phs))
+            return f
+
+        args = _wrap_fake(args)
+        func = _wrap_func(f, phs)
+        # We disable the autocast cache as the autocast cache causes type conversions on parameters to
+        # check a cache, which introduces untracked tensors into the graph
+        #
+        # We also disable tracing by any other tensor proxy-based tracers except the current. The
+        # purpose of `make_fx` is to produce graphmodules as a side effect; its internal execution is
+        # thus irrelevant to any external functional trace.
+        proxy_mode: ProxyTorchDispatchMode = typing.cast(
+            ProxyTorchDispatchMode, self.proxy_mode
+        )
+        with ExitStack() as stack:
+            stack.enter_context(decompose(self.decomposition_table))
+            if self.fake_tensor_mode:
+                stack.enter_context(self.fake_tensor_mode)
+            stack.enter_context(self.python_dispatcher_mode)
+            stack.enter_context(self.proxy_function_mode)
+            stack.enter_context(self.torch_fn_metadata_mode)
+            stack.enter_context(proxy_mode)
+            stack.enter_context(disable_autocast_cache())
+            stack.enter_context(_set_make_fx_tracer(self))
+
+            assert self.fx_tracer is not None
+            try:
+                t = dispatch_trace(
+                    wrap_key(func, args, self.fx_tracer, self.pre_dispatch),
+                    tracer=self.fx_tracer,
+                    concrete_args=tuple(phs),
+                )
+            except Exception:
+                trace_structured(
+                    "artifact",
+                    metadata_fn=lambda: {
+                        "name": "make_fx_fail_partial",
+                        "encoding": "string",
+                    },
+                    payload_fn=lambda: self.fx_tracer.graph.python_code(  # type: ignore[union-attr]
+                        root_module="self",
+                        verbose=True,
+                        include_stride=True,
+                        include_device=True,
+                    ).src,
+                )
+                raise
+
+        # TODO: kind of a bad way to do it, should maybe figure out a better way
+        if self.tracing_mode == "symbolic":
+            assert self.fake_tensor_mode is not None
+            t.shape_env = self.fake_tensor_mode.shape_env  # type: ignore[assignment]
+        return t
+
+    def trace(self, f: Callable, *args: object) -> fx.GraphModule:
+        with self._init_modes_from_inputs(f, args):
+            return self._trace_inner(f, *args)
+
+    def trace_subgraph(self, f: Callable, *args: object) -> GraphModule:
+        # Create a new tracer based on parent's config
+        sub_tracer = _MakefxTracer(
+            self.decomposition_table,
+            "real",
+            self._allow_non_fake_inputs,
+            self.pre_dispatch,
+            self.record_module_stack,
+            self._allow_fake_constant,
+            self._error_on_data_dependent_ops,
+        )
+        with sub_tracer._init_modes_from_parent(self):
+            return sub_tracer._trace_inner(f, *args)
+
+
+_CURRENT_MAKE_FX_TRACER: Optional[_MakefxTracer] = None
+
+
+@contextmanager
+def _set_make_fx_tracer(tracer: _MakefxTracer) -> Generator[None, None, None]:
+    global _CURRENT_MAKE_FX_TRACER
+    prev_tracer = _CURRENT_MAKE_FX_TRACER
+    try:
+        _CURRENT_MAKE_FX_TRACER = tracer
+        yield
+    finally:
+        _CURRENT_MAKE_FX_TRACER = prev_tracer
+
+
+def make_fx(
+    f: Callable,
+    decomposition_table: Optional[Mapping[OpOverload, Callable]] = None,
+    tracing_mode: str = "real",
+    _allow_non_fake_inputs: bool = False,
+    *,
+    pre_dispatch: bool = False,
+    record_module_stack: bool = False,
+    _allow_fake_constant: bool = False,
+    _error_on_data_dependent_ops: bool = True,
+) -> Callable[..., GraphModule]:
+    """
+    Given a function f, return a new function which when executed with valid
+    arguments to f, returns an FX GraphModule representing the set of operations that
+    were executed during the course of execution.
+    """
+
+    assert tracing_mode in ["real", "fake", "symbolic"]
+
+    make_fx_tracer = _MakefxTracer(
+        decomposition_table,
+        tracing_mode,
+        _allow_non_fake_inputs,
+        pre_dispatch,
+        record_module_stack,
+        _allow_fake_constant,
+        _error_on_data_dependent_ops,
+    )
+
+    @functools.wraps(f)
+    def wrapped(*args: object) -> GraphModule:
+        return make_fx_tracer.trace(f, *args)
+
+    return wrapped
+
+
+def get_torch_dispatch_modes() -> list[TorchDispatchMode]:
+    return torch.utils._python_dispatch._get_current_dispatch_mode_stack()
+
+
+# TODO: this is a legacy name, there is only ever one proxy mode as it's an
+# infra mode
+def get_innermost_proxy_mode() -> Optional[ProxyTorchDispatchMode]:
+    return get_proxy_mode()
+
+
+def get_proxy_mode() -> Optional[ProxyTorchDispatchMode]:
+    """
+    Current the currently active proxy tracing mode, or None if
+    we are not currently tracing.  This includes pre-dispatch proxy
+    tracing.
+    """
+    pre_dispatch_mode = torch._ops._get_dispatch_mode_pre_dispatch(
+        torch._C._TorchDispatchModeKey.PROXY
+    )
+    mode = torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.PROXY)
+    assert (
+        pre_dispatch_mode is None or mode is None
+    ), f"pre_dispatch_mode={pre_dispatch_mode}, mode={mode}"
+    return pre_dispatch_mode or mode
+
+
+def handle_sym_dispatch(func: Callable[_P, R], args: _P.args, kwargs: _P.kwargs) -> R:
+    """
+    Call into the currently active proxy tracing mode to do a
+    SymInt/SymFloat/SymBool dispatch trace on a function that operates on
+    these arguments.
+    """
+    mode = get_proxy_mode()
+    assert mode
+    # Have to do it manually, because we're not doing the normal torch
+    # dispatch machinery which disables it for us
+    with disable_proxy_modes_tracing():
+        # TODO: properly compute types
+        types: list[type] = []
+        return mode.__sym_dispatch__(func, types, args, kwargs)  # type: ignore[arg-type, return-value]
+
+
+@contextmanager
+def disable_proxy_modes_tracing() -> Generator[ProxyTorchDispatchMode, None, None]:
+    return _disable_infra_mode(torch._C._TorchDispatchModeKey.PROXY)
+
+
+def maybe_handle_decomp(
+    proxy_mode: ProxyTorchDispatchMode,
+    op: OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    from torch._inductor.compiler_bisector import CompilerBisector
+
+    if op in CURRENT_DECOMPOSITION_TABLE:
+        if CompilerBisector.disable_subsystem(
+            "aot_eager_decomp_partition", "decomposition", lambda: repr(op)
+        ):
+            return NotImplemented
+
+        with proxy_mode:
+            proxy_mode.decomp_layers += 1
+            out = CURRENT_DECOMPOSITION_TABLE[op](*args, **kwargs)
+            proxy_mode.decomp_layers -= 1
+            return out
+
+    return NotImplemented
+
+
+def get_isolated_graphmodule(
+    func: Callable,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+    tracing_mode: str = "real",
+    decomposition_table: Optional[Mapping[OpOverload, Callable]] = None,
+) -> GraphModule:
+    """A helper function used to get the GraphModule for the given func.
+
+    It's expected to be used in the ProxyTensor tracing context.
+    It detaches the args and kwargs from the current tracer so that the trace of
+    the current graph module can be created without any side-effects.
+    """
+    wrapped, all_args = wrapper_and_args_for_make_fx(func, args, kwargs)
+
+    with disable_proxy_modes_tracing():
+        gm = make_fx(
+            wrapped, decomposition_table=decomposition_table, tracing_mode=tracing_mode
+        )(all_args)
+    return gm
+
+
+def _set_unbacked_bindings(out: object, out_proxy: _NestedProxys) -> None:
+    """A helper function for setting up unbacked_bindings on the destination FX graph."""
+    from .symbolic_shapes import compute_unbacked_bindings
+
+    # Can't use detect_fake_mode here,
+    #
+    # python test/distributed/_tensor/test_dtensor_compile.py -k
+    # test_tp_compile_fullgraph_is_seq_parallel_False
+    #
+    # will fail.  Very strange, it probably isn't right for them to be using
+    # two fake modes there...
+    fake_mode = torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE)
+    if fake_mode and fake_mode.shape_env:
+        if symbol_to_path := compute_unbacked_bindings(fake_mode.shape_env, out):
+            assert isinstance(out_proxy, Proxy), out_proxy
+            out_proxy.node.meta["unbacked_bindings"] = symbol_to_path
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/recording.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/recording.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcaa6659571f8233b23cff3febb51d0f52b23b80
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/recording.py
@@ -0,0 +1,513 @@
+# mypy: allow-untyped-defs
+import functools
+import inspect
+import itertools
+import logging
+from dataclasses import dataclass
+from typing import Any, Callable, Optional, Union
+
+import torch
+import torch.utils._pytree as pytree
+
+
+log = logging.getLogger(__name__)
+trace_shape_events_log = torch._logging.getArtifactLogger(
+    __name__, "trace_shape_events"
+)
+
+
+__all__ = [
+    "ShapeEnvEvent",
+    "record_shapeenv_event",
+    "replay_shape_env_events",
+    "FakeTensorMeta",
+    "shape_env_check_state_equal",
+    "NotEqualError",
+]
+
+# [Note: Recording ShapeEnv Events]
+# =================================
+#
+# What is a ShapeEnv event?
+# -------------------------
+# We consider a ShapeEnv event every function call (ShapeEnv method or
+# independent function) that modifies the state of the ShapeEnv instance.
+# Such calls are recorded alongside their positional and keyword arguments,
+# so that it may be replayed over a different ShapeEnv instance.
+#
+# See [Note: ShapeEnv State Equality] for what is considered the state
+# of a ShapeEnv instance.
+#
+# What is it for?
+# ---------------
+# ShapeEnv events recording is used for reconstructing the ShapeEnv in an
+# arbitrary state in time.
+#
+# Being able to arbitrarily replay events like so is useful, mainly for
+# translation validation bisection. i.e. if a ValidationException has been
+# raised, find the earliest point in time where the translation validation
+# fails.
+#
+# Besides that, it also allows us to inspect the given instance and,
+# for example, check the guards that would actually be issued at that point.
+#
+# What kind of arguments can be stored in an event?
+# -------------------------------------------------
+# There's no specific rule for what cannot be used as an argument.
+# That said, pay special attention to the following cases:
+#
+#   1. Tensor inputs: there are some tests that check whether the inputs
+#      were garbage collected after execution. These will fail if there's
+#      an event that is holding a reference to those inputs.
+#
+#   2. ShapeEnv arguments: if there is an argument of ShapeEnv type, that
+#      will be automatically replaced by the new given ShapeEnv instance.
+#
+#   3. SymTypes arguments: they also hold references to ShapeEnv. So,
+#      whenever we see them, we create a new instance, replacing the
+#      ShapeEnv reference.
+#
+#   4. FX nodes: specifically, FX nodes from the FX graph for symbolic
+#      shapes. That argument must be replaced when replaying the event at
+#      ShapeEnvEvent.run, since it has to reference a node from the given
+#      instance, and not from the recorded instance.
+
+
+# Event class for reconstructing ShapeEnv at arbitrary time.
+#
+# Represents a method call that mutates ShapeEnv in a way that affects the
+# issued guards, when ShapeEnv.produce_guards is called.
+@dataclass
+class ShapeEnvEvent:
+    # ShapeEnv method.
+    f: Callable
+
+    # Arguments and keyword arguments called with.
+    args: Optional[list[Any]] = None
+    kwargs: Optional[dict[str, Any]] = None
+
+    # List of tracked_fakes at the time the method was called.
+    tracked_fakes: Optional[list[Any]] = None
+
+    # Name of the captured event.
+    # Used for special handling of particular methods.
+    name: Optional[str] = None
+
+    # Replay itself, but using shape_env as self.
+    def run(self, shape_env=None) -> Any:
+        from torch.fx.experimental.symbolic_shapes import (
+            is_symbolic,
+            ShapeEnv,
+            SymTypes,
+        )
+
+        # Special handling for the constructor event.
+        if self.f is ShapeEnv:
+            assert shape_env is None and self.args is None and self.kwargs is not None
+            return ShapeEnv(**self.kwargs)
+
+        assert shape_env is not None
+        args = list(self.args or [])
+        kwargs = dict(self.kwargs or {})
+
+        # Replace any argument of type ShapeEnv by the given one.
+        args, kwargs = pytree.tree_map_only(
+            ShapeEnv, lambda _: shape_env, (args, kwargs)
+        )
+
+        # Replace any argument of type SymTypes by a new instance,
+        # replacing its ShapeEnv reference.
+        args, kwargs = pytree.tree_map_only(
+            lambda x: isinstance(x, SymTypes) and is_symbolic(x),
+            lambda a: type(a)(a.node.with_shape_env(shape_env)),
+            (args, kwargs),
+        )
+
+        # Converts FX nodes using the mapping argument.
+        def maybe_convert_node(x: Any) -> Any:
+            if not isinstance(x, torch.fx.Node):
+                # Don't do anything to x if it's not an FX node.
+                return x
+
+            # If, at some point, we created an FX node, it means that translation validation is on.
+            # It also means we are building an FX graph for symbolic shapes at shape_env.graph, and
+            # we are tracking node names at shape_env.name_to_node.
+            assert hasattr(shape_env, "name_to_node")
+            name_to_node = shape_env.name_to_node  # type: ignore[attr-defined]
+            assert x.name in name_to_node
+            return name_to_node[x.name]
+
+        # Replaces the value of an specific argument by the result of fn.
+        def replacearg(index: int, key: str, fn: Callable):
+            if index < len(args):
+                args[index] = fn(args[index])
+            if key in kwargs:
+                kwargs[key] = fn(kwargs[key])
+
+        if self.is_create_fx_call_function():
+            # ShapeEnv.create_fx_call_function:
+            # "args" parameter is a tuple of FX nodes from the FX graph of the old ShapeEnv.
+            # They must be replaced, since a "call_function" FX node with this tuple as argument
+            # will be added to the FX graph of the new shape_env.
+            replacearg(
+                index=2,
+                key="args",
+                fn=lambda args: tuple(maybe_convert_node(a) for a in args),
+            )
+        if self.is_evaluate_expr() or self.is_defer_runtime_assert():
+            # ShapeEnv.evaluate_expr and ShapeEnv.defer_runtime_assert:
+            # "fx_node" parameter is an (optional) FX node that represents the evaluate expression.
+            # They must be replaced, since it will be part of a "call_function" FX node for
+            # torch._assert, which will be added to the FX graph of the new shape_env.
+            replacearg(index=3, key="fx_node", fn=maybe_convert_node)
+
+        # Actually call the method with the converted arguments.
+        return self.f(*args, **kwargs)
+
+    def __str__(self) -> str:
+        name = self.name if self.name is not None else self.f.__name__
+        return f"event: {name} ({self.args}, {self.kwargs})"
+
+    def is_create_fx_call_function(self) -> bool:
+        return self.name == "_create_fx_call_function"
+
+    def is_evaluate_expr(self) -> bool:
+        return self.name == "evaluate_expr"
+
+    def is_defer_runtime_assert(self) -> bool:
+        return self.name == "defer_runtime_assert"
+
+
+NEST = 0
+
+
+# Extracts a ShapeEnv instance inside args and kwargs.
+# Specifically, it looks for:
+#   1. ShapeEnv arguments
+#   2. SymInt, SymFloat, or SymBool arguments
+# If we find more than one object of any of the above types, we
+# also check that the ShapeEnv instance is the same for all of them.
+def _extract_shape_env_and_assert_equal(args, kwargs):
+    from torch.fx.experimental.symbolic_shapes import is_symbolic, ShapeEnv, SymTypes
+
+    def assert_equal(old: Optional[ShapeEnv], new: ShapeEnv) -> ShapeEnv:
+        if old is not None:
+            assert old is new, "call with different ShapeEnv"
+        return new
+
+    shape_env = None
+    for val in itertools.chain(args, kwargs.values()):
+        if isinstance(val, ShapeEnv):
+            shape_env = assert_equal(shape_env, val)
+        if isinstance(val, SymTypes) and is_symbolic(val):
+            shape_env = assert_equal(shape_env, val.node.shape_env)
+
+    return shape_env
+
+
+# Decorator for recording the given function as a replayable event.
+#
+# This decorator should be used at every function that mutates the state of
+# ShapeEnv in some way that affects the resulting issued guards (i.e. when
+# ShapeEnv.produce_guards is called).
+#
+# save_tracked_fakes: saves a snapshot of the TrackedFake list.
+# This is used when calling ShapeEnv.produce_guards at arbitrary points in time.
+#
+# When to save the list of TrackedFake?
+# =====================================
+# We should save the list of TrackedFake whenever the translation validation
+# bisection may actually stop and call the produce_guards method at the moment
+# right after the recorded function was played. In other words, since the
+# bisection bisects through torch._assert calls, we should save in all methods
+# that adds a torch._assert call to the symbolic shapes FX graph.
+#
+# At the moment, there are 2 methods that save the list:
+#   - ShapeEnv.evaluate_expr
+#   - ShapeEnv.defer_runtime_assert
+def record_shapeenv_event(*, save_tracked_fakes: bool = False) -> Callable:
+    def decorator(fn: Callable) -> Callable:
+        assert callable(fn)
+        args = inspect.getfullargspec(fn).args
+        assert args and args[0] == "self", (
+            "record_shapeenv_event should only wrap methods on ShapeEnv; refactor your "
+            "code so that it calls into a method on ShapeEnv"
+        )
+        name = fn.__name__
+
+        @functools.wraps(fn)
+        def wrapper(*args, **kwargs):
+            from torch.fx.experimental.symbolic_shapes import ShapeEnv
+
+            assert isinstance(args[0], ShapeEnv)
+
+            global NEST
+
+            trace_shape_events_log.debug(
+                "%scall %s(*%r, **%r)", " " * NEST, name, args[1:], kwargs
+            )
+            NEST += 1
+
+            def retlog(r):
+                trace_shape_events_log.debug("%s-> %s", " " * (NEST - 1), r)
+                return r
+
+            try:
+                shape_env = args[0]
+                if not shape_env.should_record_events or shape_env.is_recording:  # type: ignore[has-type]
+                    # If ShapeEnv is already recording an event, call the wrapped
+                    # function directly.
+                    #
+                    # NB: here, we skip the check of whether all ShapeEnv instances
+                    # are equal, in favor of a faster dispatch.
+                    return retlog(fn(*args, **kwargs))
+
+                # Retrieve an instance of ShapeEnv.
+                # Assumption: the collection of args and kwargs may not reference
+                # different ShapeEnv instances.
+                self = _extract_shape_env_and_assert_equal(args, kwargs)
+
+                # If we are calling this function without any ShapeEnv instance
+                # alive in its arguments, we don't record and call the original.
+                if self is None:
+                    return retlog(fn(*args, **kwargs))
+
+                # Otherwise, start recording and call the function.
+                with self._recording():
+                    # Take a snapshot of the current tracked_fakes.
+                    tracked_fakes = (
+                        self._snapshot_tracked_fakes() if save_tracked_fakes else None
+                    )
+                    # Record the event for 'fn'.
+                    event = ShapeEnvEvent(
+                        fn, list(args), kwargs, tracked_fakes, name=fn.__name__
+                    )
+                    # Play the event on this ShapeEnv.
+                    # NB: It's important to put the event first, because running
+                    # the event can trigger internal events that must be ordered
+                    # after this event.  However, if an exception happens, we do
+                    # NOT want to have the event in the list, so pop it off from
+                    # the record if an error happened
+                    self.events.append(event)
+                    try:
+                        return retlog(event.run(self))
+                    except Exception:
+                        self.events.pop()
+                        raise
+
+            except Exception:
+                log.error(  # noqa: G201
+                    "failed while running %s(*%s, **%s)",
+                    name,
+                    args[1:],
+                    kwargs,
+                    exc_info=log.isEnabledFor(logging.INFO),
+                )
+                raise
+
+            finally:
+                NEST -= 1
+
+        return wrapper
+
+    return decorator
+
+
+# Replays the ShapeEnvEvents list.
+# It assumes the first event is the constructor call.
+#
+# fn: transforms an old FX node into one corresponding to the newly created ShapeEnv.
+def replay_shape_env_events(events):
+    from torch.fx.experimental.symbolic_shapes import ShapeEnv
+
+    constructor_event = events[0]
+    assert constructor_event.f == ShapeEnv
+
+    # Constructs the new ShapeEnv.
+    shape_env = constructor_event.run()
+
+    for event in events[1:]:
+        try:
+            # Actually replays each event.
+            # We need to call create_mapping_fn every time, since the node list might
+            # change after each event is replayed.
+            event.run(shape_env)
+        except Exception:
+            log.error("failed when running event: %s", event)
+            raise
+
+    return shape_env
+
+
+# FakeTensor metadata.
+# This is to be used in place of FakeTensor placeholders when calling
+# ShapeEnv.produce_guards.
+@dataclass
+class FakeTensorMeta:
+    tensor_size: tuple[Union[int, torch.SymInt], ...]
+    tensor_stride: tuple[Union[int, torch.SymInt], ...]
+    tensor_storage_offset: Union[int, torch.SymInt]
+    is_nested: bool
+
+    def size(self) -> tuple[Union[int, torch.SymInt], ...]:
+        return self.tensor_size
+
+    def stride(self) -> tuple[Union[int, torch.SymInt], ...]:
+        return self.tensor_stride
+
+    def storage_offset(self) -> Union[int, torch.SymInt]:
+        return self.tensor_storage_offset
+
+    def dim(self) -> int:
+        return len(self.tensor_size)
+
+    @staticmethod
+    def from_fake(fake) -> "FakeTensorMeta":
+        return FakeTensorMeta(
+            fake.size(), fake.stride(), fake.storage_offset(), fake.is_nested
+        )
+
+
+# [Note: ShapeEnv State Equality]
+# ===============================
+#
+# What is considered ShapeEnv state?
+# ----------------------------------
+# We consider to be the state of a ShapeEnv instance everything that
+# is not in the inline tuple inside remove_nonstate_variables function.
+# That is: the fields within ShapeEnv that modify the flow of execution
+# of the program.
+#
+# So, for example: the replacements field might influence on how an
+# expression is simplified. That, in turn, may result in a guard being
+# statically known (i.e. not added).
+#
+# On the other hand, var_to_stack serves only changes what is printed
+# in the screen, i.e. used only for debugging purposes. Therefore, we
+# should not consider it when comparing states.
+#
+# What to do on NotEqualError?
+# ----------------------------
+# Here are a few possible causes for getting a NotEqualError raised:
+#
+#   1. New field that does not belong in the ShapeEnv state.
+#      For example: log field of type ShapeEnvLoggerAdapter. Different
+#      ShapeEnv instances will always have different ShapeEnvLoggerAdapter
+#      instances, i.e. equality comparison would fail.
+#      Solution: add it to the inlined tuple inside remove_nonstate_variables
+#      function inside check_equal method.
+#
+#   2. New field that is not directly comparable across instances.
+#      For example: guards field of type List[ShapeGuard]. More specifically,
+#      the ShapeGuard type holds an expression and a stack information
+#      for debugging purposes. When replaying the even on a new ShapeEnv
+#      instance, the stack would be different, which would trigger this error.
+#      Solution: add a special case to the map_value function inside
+#      check_equal function.
+#
+#   3. Mutation of ShapeEnv on some not recorded function.
+#      If a mutation of the state of ShapeEnv happens inside a function
+#      that is not recorded (or that no caller in the stack is recorded),
+#      then, the replayed ShapeEnv won't catch that.
+#      Solution: decorate the function with record_shape_env_event.
+
+
+# Checks whether the state of two ShapeEnv are equal w.r.t. the guards
+# returned by ShapeEnv.produce_guards.
+def shape_env_check_state_equal(env1, env2, non_state_variable_names, map_value):
+    # Collect and remove variables that don't necessarily represent the state
+    # of a ShapeEnv. Note: we copy the dictionary so that we don't modify the
+    # instance itself.
+    env1_vars = vars(env1).copy()
+    env2_vars = vars(env2).copy()
+
+    for v in non_state_variable_names:
+        if v in env1_vars:
+            env1_vars.pop(v)
+        if v in env2_vars:
+            env2_vars.pop(v)
+
+    # Function for transforming the mismatched values into string.
+    # Needed, since dict and set entries order might not be the same every time.
+    def value_to_str(value: Any) -> str:
+        if isinstance(value, dict):
+            return (
+                "{"
+                + ", ".join(f"{k}: {value[k]}" for k in sorted(value.keys(), key=str))
+                + "}"
+            )
+        if isinstance(value, set):
+            return "{" + ", ".join(f"{v}" for v in sorted(value)) + "}"
+        return str(value)
+
+    # Compares env1_vars with env2_vars.
+    # Here, we allow the value of each field to be mapped, so that we appropriately
+    # compare the two values.
+    def compare_vars(
+        map_value: Callable[[str, Any], Any]
+    ) -> list[tuple[str, str, str]]:
+        env1_set, env2_set = set(env1_vars), set(env2_vars)
+
+        # First, compare the set of keys in each vars dictionary.
+        if env1_set != env2_set:
+            raise NotEqualError(
+                "field set mismatch:",
+                [
+                    (
+                        "found unique fields:",
+                        str(sorted(env1_set - env2_set)),
+                        str(sorted(env2_set - env1_set)),
+                    ),
+                ],
+            )
+
+        # Then, sort the keys, and compare the mapped values of each key.
+        sorted_keys = list(env1_set)
+        sorted_keys.sort()
+
+        mapped_dict = [
+            (k, map_value(k, env1_vars[k]), map_value(k, env2_vars[k]))
+            for k in sorted_keys
+        ]
+
+        # Return a list of tuples representing the fields that did not match
+        # alongside their respective mapped values.
+        return [
+            (f"{k}: values don't match.", value_to_str(val1), value_to_str(val2))
+            for k, val1, val2 in mapped_dict
+            if val1 != val2
+        ]
+
+    # Accumulate the mismatching fields.
+    errors = compare_vars(map_value)
+
+    if len(errors) > 0:
+        raise NotEqualError("field values don't match:", errors)
+
+
+class NotEqualError(Exception):
+    def __init__(
+        self,
+        msg: str,
+        mismatched: list[tuple[str, str, str]],
+    ) -> None:
+        details = "\n".join(
+            [
+                "\n".join(
+                    [
+                        f"==> {inner_msg}",
+                        f"  >  Left: {str1}",
+                        f"  > Right: {str2}",
+                    ]
+                )
+                for inner_msg, str1, str2 in mismatched
+            ]
+        )
+
+        super().__init__(
+            f"""\
+ShapeEnv not equal: {msg}
+
+{details}
+"""
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/refinement_types.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/refinement_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e92163a2139caab2fd2a690d810f52073e75644
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/refinement_types.py
@@ -0,0 +1,16 @@
+class Equality:
+    def __init__(self, lhs: object, rhs: object):
+        self.lhs = lhs
+        self.rhs = rhs
+
+    def __str__(self) -> str:
+        return f"{self.lhs} = {self.rhs}"
+
+    def __repr__(self) -> str:
+        return f"{self.lhs} = {self.rhs}"
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, Equality):
+            return self.lhs == other.lhs and self.rhs == other.rhs
+        else:
+            return False
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/rewriter.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/rewriter.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e635a525f6f09c2759c8d3fa105068f70ac6094
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/rewriter.py
@@ -0,0 +1,143 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import ast
+import copy
+import functools
+import inspect
+import textwrap
+from types import FunctionType
+from typing import Any, Callable, cast, Optional, Union
+
+import torch
+from torch._sources import normalize_source_lines
+from torch.fx._symbolic_trace import Tracer
+from torch.fx.graph import Graph
+
+
+class AST_Rewriter(ast.NodeTransformer):
+    """
+    Take a FunctionType object representing a `forward` method, then
+    perform an AST rewrite to swap out nodes that are not symbolically
+    traceable with a callsite to the FX alternative.
+
+    To support swapping out an AST node, define a new `visit` method on
+    that node. For more details, see:
+    https://docs.python.org/3/library/ast.html#ast.NodeTransformer
+    """
+
+    # This function checks for new keys added in the globals dict. TorchDynamo
+    # can insert new keys in the global dict and upset the check. Therefore, put
+    # a disable here. This function is an optimization pass and not really
+    # suitable for dynamo tracing anyways.
+    @torch._dynamo.disable
+    def rewrite(self, fn: FunctionType):
+        # Normalize the source lines
+        sourcelines, _ = inspect.getsourcelines(fn)
+        sourcelines = normalize_source_lines(sourcelines)
+        source = "".join(sourcelines)
+        normalized_str = textwrap.dedent(source)
+
+        # Rewrite the original AST
+        source_ast = ast.parse(normalized_str)
+        dest_ast = ast.fix_missing_locations(self.visit(source_ast))
+
+        # Pull out the compiled function from the newly-created Module
+        code = compile(dest_ast, "", "exec")
+        globals_dict = copy.copy(fn.__globals__)
+        keys_before = set(globals_dict.keys())
+        exec(code, globals_dict)
+        new_keys = list(set(globals_dict.keys()) - keys_before)
+        assert len(new_keys) == 1
+        fn_compiled = globals_dict[new_keys[0]]
+
+        # return the compiled function with the original globals
+        def change_func_globals(f, globals):
+            """Based on https://stackoverflow.com/a/13503277/2988730 (@unutbu)"""
+            # __globals__ is a private member of the function class
+            # so we have to copy the function, f, all of its member, except f.__globals__
+            g = FunctionType(
+                f.__code__,
+                globals,
+                name=f.__name__,
+                argdefs=f.__defaults__,
+                closure=f.__closure__,
+            )
+            g = functools.update_wrapper(g, f)
+            g.__kwdefaults__ = copy.copy(f.__kwdefaults__)  # type:ignore[attr-defined]
+            return g
+
+        # Return the correct FunctionType object
+        return change_func_globals(fn_compiled, globals=fn.__globals__)
+
+    def visit_Assert(self, node):
+        """
+        Swap out the Assert node (Python's `assert`) with a callsite to the
+        symbolically-traceable torch._assert function
+        """
+        # Create the Call node
+        n = ast.parse("torch._assert()", mode="eval")
+        assert isinstance(n, ast.Expression)
+        call_node = n.body
+        assert isinstance(call_node, ast.Call)
+        msg = node.msg if node.msg else ast.Constant(value="", kind=None)
+        call_node.args = [node.test, msg]
+
+        # Ensure that the new node conforms to the Python AST grammar
+        expr_wrapper = ast.Expr(value=call_node)
+
+        # Return the new Call node to signify that we want to use it as
+        # a replacement for the original _assert node
+        return ast.copy_location(expr_wrapper, node)
+
+    def visit_AnnAssign(self, node):
+        """
+        Swap out Python's AnnAssign with an Assign node where the annotation function is called.
+        Example:
+             Original:
+             y: Tensor_Type(1,2,3, Dyn) = f2(x)
+            Output:
+             y = annotate(f2(x),Tensor_Type((1,2,3,Dyn)))
+        """
+        return ast.Assign(
+            targets=[node.target],
+            value=ast.Call(
+                func=ast.Name(id="annotate", ctx=ast.Load()),
+                args=[node.value, node.annotation],
+                keywords=[],
+            ),
+        )
+
+
+class RewritingTracer(Tracer):
+    def trace(
+        self,
+        root: Union[torch.nn.Module, Callable],
+        concrete_args: Optional[dict[str, Any]] = None,
+    ) -> Graph:
+        return super().trace(_rewrite(root), concrete_args)
+
+
+def _rewrite(fn: Union[torch.nn.Module, Callable]) -> Union[torch.nn.Module, Callable]:
+    if isinstance(fn, torch.nn.Module):
+        # Rewrite this module's `forward` as well as the `forward`s of
+        # all of this module's recursive descendents. Return the new,
+        # rewritten module hierarchy.
+        def rewrite_module(m: torch.nn.Module):
+            class RewrittenModule(torch.nn.Module):
+                def __init__(self, orig):
+                    super().__init__()
+                    for k, v in orig.__dict__.items():
+                        if isinstance(v, torch.nn.Module):
+                            self.__dict__[k] = copy.copy(rewrite_module(v))
+                        else:
+                            self.__dict__[k] = copy.copy(v)
+
+            RewrittenModule.forward = AST_Rewriter().rewrite(
+                cast(FunctionType, m.forward)
+            )
+            return RewrittenModule(m)
+
+        return rewrite_module(fn)
+    else:
+        # Rewrite this single free function
+        return AST_Rewriter().rewrite(cast(FunctionType, fn))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/schema_type_annotation.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/schema_type_annotation.py
new file mode 100644
index 0000000000000000000000000000000000000000..335c027c9321b8f9f1e097c560c5e7efbbff1f9d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/schema_type_annotation.py
@@ -0,0 +1,145 @@
+# mypy: allow-untyped-defs
+import inspect
+from typing import Any, Optional
+
+import torch
+import torch.fx
+from torch._jit_internal import boolean_dispatched
+from torch.fx import Transformer
+from torch.fx.node import Argument, Target
+from torch.fx.operator_schemas import _torchscript_type_to_python_type
+
+
+class AnnotateTypesWithSchema(Transformer):
+    """
+    Use Python function signatures to annotate types for `Nodes` within an FX graph.
+    This pulls out Python function signatures for:
+
+        1. Standard `torch.nn` Module calls
+        2. `torch.nn.functional` calls
+        3. Attribute fetches via `get_attr`
+
+    Example usage:
+
+        m = torchvision.models.resnet18()
+
+        traced = torch.fx.symbolic_trace(m)
+
+        traced = AnnotateTypesWithSchema(traced).transform()
+
+    """
+
+    def __init__(
+        self,
+        module: torch.nn.Module,
+        annotate_functionals: bool = True,
+        annotate_modules: bool = True,
+        annotate_get_attrs: bool = True,
+    ):
+        super().__init__(module)
+        self.annotate_functionals = annotate_functionals
+        self.annotate_modules = annotate_modules
+        self.annotate_get_attrs = annotate_get_attrs
+
+    def call_function(
+        self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any]
+    ):
+        python_ret_type = None
+        if self.annotate_functionals and target.__module__ == "torch.nn.functional":
+            target_for_analysis = target
+            if target in boolean_dispatched:
+                # HACK: `boolean_dispatch` as used in `torch.nn.functional` makes it so that we have
+                # a 2-way dispatch based on a boolean value. Here we check that the `true` and `false`
+                # branches of the dispatch have exactly the same signature. If they do, use the `true`
+                # branch signature for analysis. Otherwise, leave this un-normalized
+                assert not isinstance(target, str)
+                dispatched = boolean_dispatched[target]
+                if_true, if_false = dispatched["if_true"], dispatched["if_false"]
+                # TODO: can we emit the union of these? What are the implications on TorchScript
+                # compilation?
+                if (
+                    inspect.signature(if_true).return_annotation
+                    != inspect.signature(if_false).return_annotation
+                ):
+                    return super().call_function(target, args, kwargs)
+                target_for_analysis = if_true
+
+            python_ret_type = self._extract_python_return_type(target_for_analysis)
+
+        return_proxy = super().call_function(target, args, kwargs)
+        return_proxy.node.type = (
+            return_proxy.node.type if return_proxy.node.type else python_ret_type
+        )
+        return return_proxy
+
+    def call_module(
+        self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any]
+    ):
+        python_ret_type = None
+        assert isinstance(target, str)
+        submod = self.fetch_attr(target)
+        if self.annotate_modules and hasattr(submod.__class__, "__name__"):
+            classname = submod.__class__.__name__
+            if getattr(torch.nn, classname, None) == submod.__class__:
+                python_ret_type = self._extract_python_return_type(submod.forward)
+        return_proxy = super().call_module(target, args, kwargs)
+        return_proxy.node.type = (
+            return_proxy.node.type if return_proxy.node.type else python_ret_type
+        )
+        return return_proxy
+
+    def get_attr(
+        self,
+        target: torch.fx.node.Target,
+        args: tuple[Argument, ...],
+        kwargs: dict[str, Any],
+    ):
+        attr_proxy = super().get_attr(target, args, kwargs)
+
+        if self.annotate_get_attrs:
+            module_itr = self.module
+            assert isinstance(target, str)
+            atoms = target.split(".")
+            for i, atom in enumerate(atoms):
+                if not hasattr(module_itr, atom):
+                    raise RuntimeError(
+                        f'Node referenced nonextent target {".".join(atoms[:i])}!'
+                    )
+                module_itr = getattr(module_itr, atom)
+
+            maybe_inferred_ts_type = torch._C._jit_try_infer_type(module_itr)
+            if maybe_inferred_ts_type.success():
+                python_type = _torchscript_type_to_python_type(
+                    maybe_inferred_ts_type.type()
+                )
+                attr_proxy.node.type = (
+                    python_type if not attr_proxy.node.type else attr_proxy.node.type
+                )
+
+        return attr_proxy
+
+    def _extract_python_return_type(self, target: Target) -> Optional[Any]:
+        """
+        Given a Python call target, try to extract the Python return annotation
+        if it is available, otherwise return None
+
+        Args:
+
+            target (Callable): Python callable to get return annotation for
+
+        Returns:
+
+            Optional[Any]: Return annotation from the `target`, or None if it was
+                not available.
+        """
+        assert callable(target)
+        try:
+            sig = inspect.signature(target)
+        except (ValueError, TypeError):
+            return None
+
+        return (
+            sig.return_annotation
+            if sig.return_annotation is not inspect.Signature.empty
+            else None
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/sym_node.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/sym_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fe1421bc06363f543374327f6d3b5e9262098c2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/sym_node.py
@@ -0,0 +1,1822 @@
+# mypy: allow-untyped-defs
+
+from __future__ import annotations
+
+
+"""
+This file does three things:
+- Contains the definition of SymNode
+- Installs all the magic methods into SymBool, SymFloat, SymFloat at import time
+- Does not depend on sympy at import time
+
+As this file is imported from within torch/__init__.py we do not want it to depend on SymPy
+to avoid having to load SymPy at import time, as doing so is *very* slow.
+"""
+
+
+import builtins
+import functools
+import inspect
+import itertools
+import logging
+import math
+import operator
+import sys
+from functools import lru_cache, update_wrapper
+from typing import Optional, TYPE_CHECKING, Union
+
+import torch
+import torch._logging.structured as structured
+
+# NB: The sym_* functions are used via getattr() and must be imported here.
+from torch import (  # noqa: F401
+    sym_float,
+    sym_ite,
+    sym_max,
+    sym_min,
+    sym_not,
+    SymBool,
+    SymFloat,
+    SymInt,
+)
+from torch._logging import dtrace_structured
+
+
+if TYPE_CHECKING:
+    from torch.fx.experimental.symbolic_shapes import ShapeEnv
+
+log = logging.getLogger(__name__)
+sym_node_log = torch._logging.getArtifactLogger(__name__, "sym_node")
+
+
+__all__ = ["SymNode", "method_to_operator", "magic_methods"]
+
+
+from torch.types import py_sym_types as SymTypes
+
+
+def _to_symtype(t):
+    if t is bool:
+        return SymBool
+    if t is int:
+        return SymInt
+    if t is float:
+        return SymFloat
+    return t
+
+
+# TODO: An incomplete list
+# 1. Set variables to be equal when we do equality
+# 2. Specialize on 0/1 when we do subtraction
+class SymNode:
+    """
+    This is a type erased SymInt/SymFloat which we use to do actual operations.
+    End users don't touch this.  Magic methods are NOT defined on this object.
+    """
+
+    # Note [optimized_summation]: indicates that SymNode is an Add expression of the form
+    # a + b + c + d... etc where all terms are unique symbols. This allows us to do some optimizations
+    # for common patterns see _optimized_add.
+
+    # The unfortunate reason we have this here is because sympy sets  __slots__ = () for add expression,
+    # so we cannot add the attribute directly to the sympy expression. Furthermore, we cannot use it as
+    # a weak dictionary key either! So instead, we attach the attribute here to the SymNode.
+    _optimized_summation: bool = False
+
+    def __init__(
+        self,
+        expr,
+        shape_env,
+        pytype,
+        hint: Optional[Union[int, float, bool]],
+        constant=None,
+        fx_node=None,
+        optimized_summation=False,
+    ):
+        self._expr = expr
+        self.shape_env = shape_env
+        self.pytype = pytype
+        self._optimized_summation = optimized_summation
+
+        # What's the difference between hint and constant?
+        #
+        # - A constant is known to be invariant across invocations of the model;
+        #   it will always be this value.  We only really know this when we
+        #   encounter an honest-to-goodness literal (when wrapping it into
+        #   a SymNode, we set constant.)  Most of the time, constant is None
+        #
+        # - A hint is a *particular* value from the particular run we are
+        #   tracing, but it may vary the next time around.  It's useful to
+        #   keep this around, as if we need a concrete value from a SymNode,
+        #   we will return the hint and guard on the expression that produced
+        #   it giving the same hint next time around.  The hint is not
+        #   guaranteed to be set either: if you have an unbacked SymNode,
+        #   there won't be any hint; it was the result of some tensor-dependent
+        #   computation, but we don't know what it actually is because we
+        #   haven't actually run the tensor computation.
+        #
+        # If _hint is None, we will query maybe_evaluate_static(compute_hint=True)
+        # in hopes that we've learned enough about the unbacked symints to
+        # discharge the hint; otherwise, you're likely to just error out.
+        #
+        # (A previous version of this system had some optimizations to only
+        # recompute when it was possible we had learned enough about the
+        # unbacked symint that a hint was now possible, but as we added more
+        # potential refinements to unbacked symints this got harder to keep
+        # in sync, so we've deleted it for now.)
+
+        def compute_hint():
+            from torch.fx.experimental.symbolic_shapes import has_free_unbacked_symbols
+
+            # This occasionally gets exercised by, e.g.,
+            # convert_shape_to_symint.  It's just a nicety so you don't HAVE
+            # to have a correct hint on hand when making a SymNode.
+            # Don't attempt to compute for unbacked, this can be quite
+            # expensive.
+            if has_free_unbacked_symbols(self.expr):
+                return None
+            hint = self.shape_env._maybe_evaluate_static(self.expr, compute_hint=True)
+            if hint is not None:
+                hint = self.pytype(hint) if not isinstance(hint, SymTypes) else hint
+            return hint
+
+        if hint is not None:
+            assert type(hint) is pytype or type(hint) is _to_symtype(pytype), (
+                "Cannot create SymNode of type "
+                f"{pytype} with incompatible hint of type {type(hint)}"
+            )
+            if self.shape_env and self.shape_env._translation_validation_enabled:
+                # This is technically not TV, but this assert is expensive so
+                # let's only do it when we're already doing expensive things
+                computed_hint = compute_hint()
+                assert (
+                    hint == computed_hint
+                ), f"{hint} != {computed_hint} (for {self.expr})"
+        else:
+            hint = compute_hint()
+        self._hint = hint
+        self.constant: Optional[Union[int, float, bool]] = constant
+
+        # Record the FX node of the current node if we are doing translation
+        # validation. They will be used for building the input assertions for
+        # the translation validation problem.
+        tx_validation_en = (
+            self.shape_env and self.shape_env._translation_validation_enabled
+        )
+        self.fx_node = tx_validation_en and fx_node
+
+    def with_shape_env(self, shape_env: ShapeEnv) -> SymNode:
+        return SymNode(
+            self._expr, shape_env, self.pytype, self._hint, self.constant, self.fx_node
+        )
+
+    def _value_eq(self, other: SymNode) -> bool:
+        # Purposely don't include the shape_env in the eq.
+        return (
+            self._expr == other._expr
+            and self.pytype == other.pytype
+            and self._hint == other._hint
+            and self.constant == other.constant
+            and self.fx_node == other.fx_node
+        )
+
+    def _value_hash(self) -> int:
+        # Purposely don't include the shape_env in the hash.
+        return hash((self._expr, self.pytype, self._hint, self.constant, self.fx_node))
+
+    @property
+    def expr(self):
+        return self.shape_env.replace(self._expr)
+
+    @property
+    def hint(self):
+        return self._hint
+
+    def has_hint(self):
+        return self._hint is not None
+
+    def require_hint(self, fallback=None):
+        from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols
+
+        if self._hint is None:
+            if fallback is not None:
+                # Say we have some expr like 2*u0 + s0
+                # The hint will be None, since the expr contains at least 1 unbacked.
+                # We will:
+                # - replace every backed free symbol with its corresponding hint
+                # - replace every unbacked free symbol with the fallback
+                # - regenerate the expression with those symbol replacements
+                # Note: this is not really complete either, since right now
+                # this logic does not take into account any value ranges
+                # for the unbacked symints, we may need to beef it up at some point.
+                unbacked_symbols = free_unbacked_symbols(self.expr)
+                replacements = {
+                    s: 4096 if s in unbacked_symbols else self.shape_env.var_to_val[s]
+                    for s in self.expr.free_symbols
+                }
+                return self.expr.xreplace(replacements)
+            # NB: we expect this to raise
+            return self.shape_env.size_hint(self.expr)
+        return self._hint
+
+    def maybe_as_int(self):
+        if self.expr.is_number:
+            return int(self.expr)
+        else:
+            return None
+
+    # NB: This does conversions, not sure if this is good or not
+    def maybe_as_float(self):
+        import sympy
+
+        if isinstance(self.expr, sympy.Float):
+            return float(self.expr)
+        else:
+            return None
+
+    def maybe_as_bool(self):
+        import sympy
+
+        if self.expr is sympy.true:
+            return True
+        elif self.expr is sympy.false:
+            return False
+        else:
+            return None
+
+    def is_int(self):
+        return self.pytype is int
+
+    def is_float(self):
+        return self.pytype is float
+
+    def is_bool(self):
+        return self.pytype is bool
+
+    def is_nested_int(self):
+        # Unbacked SymInts cannot be nested int today
+        return (
+            self._hint is not None
+            and isinstance(self._hint, SymInt)
+            and self._hint.node.is_nested_int()
+        )
+
+    def wrap_int(self, num):
+        assert type(num) is int
+        import sympy
+
+        return SymNode(
+            sympy.Integer(num), self.shape_env, int, num, constant=num, fx_node=num
+        )
+
+    def wrap_float(self, num):
+        assert type(num) is float
+        import sympy
+
+        return SymNode(
+            sympy.Float(num), self.shape_env, float, num, constant=num, fx_node=num
+        )
+
+    def wrap_bool(self, num):
+        assert type(num) is bool
+        import sympy
+
+        return SymNode(
+            sympy.true if num else sympy.false,
+            self.shape_env,
+            bool,
+            num,
+            constant=num,
+            fx_node=num,
+        )
+
+    def clone(self):
+        return self
+
+    def str(self):
+        return f"{self.expr}"
+
+    def __str__(self):
+        return self.str()
+
+    def __repr__(self):
+        rep = [
+            f"SymNode({self._expr}, shape_env={self.shape_env}, pytype={self.pytype}",
+        ]
+        if self._hint is not None:
+            rep.append(f"hint={self._hint}")
+        if self.constant is not None:
+            rep.append(f"constant={self.constant}")
+        if self.fx_node is not None:
+            rep.append(f"fx_node={self.fx_node}")
+        return ", ".join(rep) + ")"
+
+    def _graph_repr(self) -> builtins.str:
+        # Representation used by GraphModule to create a pythonic version of a graph
+        return self.str()
+
+    # These methods call the metaprogrammed methods, they're hand written
+    # here so we get good stack traces
+    def abs(self) -> SymNode:
+        return self._abs()  # type: ignore[attr-defined]
+
+    def pos(self) -> SymNode:
+        return self._pos()  # type: ignore[attr-defined]
+
+    def round(self, ndigits=None) -> SymNode:
+        return self._round(ndigits)  # type: ignore[attr-defined]
+
+    def trunc(self) -> SymNode:
+        return self._trunc()  # type: ignore[attr-defined]
+
+    def add(self, other) -> SymNode:
+        return self._add(other)  # type: ignore[attr-defined]
+
+    def sub(self, other) -> SymNode:
+        return self._sub(other)  # type: ignore[attr-defined]
+
+    def mul(self, other) -> SymNode:
+        return self._mul(other)  # type: ignore[attr-defined]
+
+    def mod(self, other) -> SymNode:
+        return self._mod(other)  # type: ignore[attr-defined]
+
+    def float_pow(self, other) -> SymNode:
+        return self._float_pow(other)  # type: ignore[attr-defined]
+
+    def pow_by_natural(self, other) -> SymNode:
+        return self._pow_by_natural(other)  # type: ignore[attr-defined]
+
+    def and_(self, other) -> SymNode:
+        return self._and_(other)  # type: ignore[attr-defined]
+
+    def or_(self, other) -> SymNode:
+        return self._or_(other)  # type: ignore[attr-defined]
+
+    def float_truediv(self, other) -> SymNode:
+        return self._float_truediv(other)  # type: ignore[attr-defined]
+
+    def int_truediv(self, other) -> SymNode:
+        return self._int_truediv(other)  # type: ignore[attr-defined]
+
+    def int_floordiv(self, other) -> SymNode:
+        return self._int_floordiv(other)  # type: ignore[attr-defined]
+
+    def lshift(self, other) -> SymNode:
+        return self._lshift(other)  # type: ignore[attr-defined]
+
+    def rshift(self, other) -> SymNode:
+        return self._rshift(other)  # type: ignore[attr-defined]
+
+    def sym_not(self) -> SymNode:  # noqa: F811
+        return self._sym_not()  # type: ignore[attr-defined]
+
+    def eq(self, other) -> SymNode:
+        return self._eq(other)  # type: ignore[attr-defined]
+
+    def ne(self, other) -> SymNode:
+        return self._ne(other)  # type: ignore[attr-defined]
+
+    def gt(self, other) -> SymNode:
+        return self._gt(other)  # type: ignore[attr-defined]
+
+    def lt(self, other) -> SymNode:
+        return self._lt(other)  # type: ignore[attr-defined]
+
+    def le(self, other) -> SymNode:
+        return self._le(other)  # type: ignore[attr-defined]
+
+    def ge(self, other) -> SymNode:
+        return self._ge(other)  # type: ignore[attr-defined]
+
+    def floor(self) -> SymNode:
+        return self._floor()  # type: ignore[attr-defined]
+
+    def is_integer(self) -> SymNode:
+        return self._is_integer()  # type: ignore[attr-defined]
+
+    def sym_float(self) -> SymNode:  # noqa: F811
+        return self._sym_float()  # type: ignore[attr-defined]
+
+    def sym_int(self) -> SymNode:
+        return self._sym_int()  # type: ignore[attr-defined]
+
+    def ceil(self) -> SymNode:
+        return self._ceil()  # type: ignore[attr-defined]
+
+    def neg(self) -> SymNode:
+        return self._neg()  # type: ignore[attr-defined]
+
+    def sym_min(self, other) -> SymNode:  # noqa: F811
+        return self._sym_min(other)  # type: ignore[attr-defined]
+
+    def sym_max(self, other) -> SymNode:  # noqa: F811
+        return self._sym_max(other)  # type: ignore[attr-defined]
+
+    def sym_ite(self, then_val, else_val) -> SymNode:
+        return self._sym_ite(then_val, else_val)  # type: ignore[attr-defined]
+
+    def is_contiguous(self, sizes, strides) -> SymNode:
+        return self._is_contiguous(sizes, strides)  # type: ignore[attr-defined]
+
+    def is_channels_last_contiguous_2d(self, sizes, strides) -> SymNode:
+        return self._is_channels_last_contiguous_2d(sizes, strides)  # type: ignore[attr-defined]
+
+    def is_channels_last_contiguous_3d(self, sizes, strides) -> SymNode:
+        return self._is_channels_last_contiguous_3d(sizes, strides)  # type: ignore[attr-defined]
+
+    def is_channels_last_strides_2d(self, sizes, strides) -> SymNode:
+        return self._is_channels_last_strides_2d(sizes, strides)  # type: ignore[attr-defined]
+
+    def is_channels_last_strides_3d(self, sizes, strides) -> SymNode:
+        return self._is_channels_last_strides_3d(sizes, strides)  # type: ignore[attr-defined]
+
+    def is_non_overlapping_and_dense_indicator(self, sizes, strides) -> SymNode:
+        return self._is_non_overlapping_and_dense_indicator(sizes, strides)  # type: ignore[attr-defined]
+
+    # Make C++ happy
+    def sym_or(self, other):
+        return self.or_(other)
+
+    def sym_and(self, other):
+        return self.and_(other)
+
+    # Integer bitwise ops
+    def bitwise_and(self, other):
+        return self._bitwise_and(other)  # type: ignore[attr-defined]
+
+    def bitwise_or(self, other):
+        return self._bitwise_or(other)  # type: ignore[attr-defined]
+
+    # There is no int_truediv available from C++
+    def truediv(self, other):
+        return self.float_truediv(other)
+
+    def floordiv(self, other) -> SymNode:
+        return self.int_floordiv(other)
+
+    # We didn't bind integer pow in C++
+    def pow(self, other):
+        return self.float_pow(other)
+
+    def is_non_overlapping_and_dense(self, sizes, strides):
+        return self.is_non_overlapping_and_dense_indicator(sizes, strides).eq(to_node(self, 1))  # type: ignore[attr-defined]
+
+    def int_(self):
+        return self.guard_int("", 0)  # NB: uses Python backtrace
+
+    # This one is currently done by hand, but if we add other variadic
+    # functions consider factoring it out to be metaprogrammed too.  Note that
+    # some load bearing logic is directly in torch.sym_sum
+
+    def sym_sum(self, args) -> SymNode:
+        import sympy
+
+        # Inner impl
+        from torch.fx.experimental.proxy_tensor import (
+            get_proxy_mode,
+            handle_sym_dispatch,
+        )
+
+        if get_proxy_mode():
+            return to_node(
+                self,
+                handle_sym_dispatch(
+                    torch.sym_sum,
+                    (tuple(wrap_node(a) for a in args),),
+                    {},
+                ),
+            )
+        exprs = [a.expr for a in args]
+        out = sympy.Add(*exprs)
+
+        size_hints = []
+        out_hint = None
+        for a in args:
+            if a.hint is None:
+                break
+            size_hints.append(a.hint)
+        else:
+            out_hint = sum(size_hints)
+
+        fx_node, _ = self.shape_env._create_fx_call_function(
+            torch.sym_sum, (tuple(a.fx_node for a in args),)
+        )
+
+        # NB: Only for integers!
+        return SymNode(out, self.shape_env, int, out_hint, fx_node=fx_node)
+
+    def evaluate(self, size_oblivious=False):
+        return self.shape_env.evaluate_sym_node(self, size_oblivious)
+
+    # You can manually trigger a guard with this function
+    def guard_int(self, file, line):
+        # TODO: use the file/line for some useful diagnostic on why a
+        # guard occurred
+        r = self.evaluate()
+        try:
+            return int(r)
+        except Exception:
+            log.warning("Failed to convert to int: %s", r)
+            raise
+
+    def guard_float(self, file, line):
+        # TODO: use the file/line for some useful diagnostic on why a
+        # guard occurred
+        r = self.evaluate()
+        try:
+            return float(r)
+        except Exception:
+            log.warning("Failed to convert to float: %s", r)
+            raise
+
+    def guard_bool(self, file, line):
+        # TODO: use the file/line for some useful diagnostic on why a
+        # guard occurred
+        r = self.evaluate()
+        try:
+            return bool(r)
+        except Exception:
+            log.warning("Failed to convert to bool: %s", r)
+            raise
+
+    def expect_true(self, file, line):
+        from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols
+
+        if (
+            self.has_hint()
+            and not free_unbacked_symbols(self.expr)
+            and not self.shape_env.prefer_deferred_runtime_asserts_over_guards
+        ):
+            # OK to generate guards
+            return self.guard_bool(file, line)
+        # Generate a deferred runtime assert (this might actually end up doing
+        # a regular guard if we can!)
+        # TODO: file/line here is very important, because the assert has been
+        # deferred so you can't backtrace easily
+        return self.shape_env.defer_runtime_assert(
+            self.expr, f"{file}:{line}", fx_node=self.fx_node
+        )
+
+    def expect_size(self, file, line):
+        from torch.fx.experimental.symbolic_shapes import _advise_is_size
+
+        b = self.ge(self.wrap_int(0))
+        # Generate a deferred runtime assert
+        r = b.expect_true(file, line)
+        # Refine compile time range, but only if it's unbacked.
+        # If you refine range for hinted variables, you can end up making
+        # improper deductions since compile time reasoning may be
+        # incompatible with runtime reasoning.
+        if r and not self.has_hint():
+            _advise_is_size(SymInt(self))
+        return r
+
+    def guard_size_oblivious(self, file, line):
+        """
+        Like guard_bool, but if we encounter unbacked symbols, if those symbols
+        are size-like, we will treat them as >= 2 for the purposes of the analysis.
+
+        This CHANGES the runtime semantics, but all size-oblivious sites have been
+        audited to ensure that the runtime semantics don't change in a material way.
+        Acceptable runtime semantic changes are, e.g., squeeze() no longer dropping
+        an unbacked one size, or a tensor reporting as non-contiguous even if it's
+        contiguous if it would have been reported contiguous due to being empty.
+        """
+        # TODO: use the file/line for some useful diagnostic on why a
+        # guard occurred
+        r = self.evaluate(size_oblivious=True)
+        try:
+            return bool(r)
+        except Exception:
+            log.warning("Failed to convert to bool: %s", r)
+            raise
+
+    def bool_(self):
+        return self.guard_bool("", 0)
+
+    def is_symbolic(self):
+        return True
+
+    def nested_int(self):
+        return None
+
+    def is_constant(self):
+        return False
+
+
+# TODO: this probably needs the sizes-strides eval functions
+METHOD_TO_OPERATOR = {
+    "pos": operator.pos,
+    "abs": operator.abs,
+    "add": operator.add,
+    "and": operator.and_,
+    "bitwise_and": operator.and_,
+    "ceil": math.ceil,
+    "eq": operator.eq,
+    "floor": math.floor,
+    "trunc": math.trunc,
+    "int_floordiv": operator.floordiv,
+    "ge": operator.ge,
+    "gt": operator.gt,
+    "is_integer": lambda x: x.is_integer(),
+    "le": operator.le,
+    "lshift": operator.lshift,
+    "lt": operator.lt,
+    "mod": operator.mod,
+    "mul": operator.mul,
+    "ne": operator.ne,
+    "neg": operator.neg,
+    "or": operator.or_,
+    "bitwise_or": operator.or_,
+    "float_pow": operator.pow,
+    "pow_by_natural": operator.pow,
+    "round": builtins.round,
+    "rshift": operator.rshift,
+    "sub": operator.sub,
+    "sym_float": sym_float,
+    "sym_ite": sym_ite,
+    "sym_max": sym_max,
+    "sym_min": sym_min,
+    "sym_not": sym_not,
+    "float_truediv": operator.truediv,
+    "int_truediv": operator.truediv,
+}
+
+unary_magic_methods = {
+    "abs",
+    "sym_float",
+    "sym_int",
+    "ceil",
+    "floor",
+    "neg",
+    "sym_not",
+    "pos",
+    "trunc",
+}
+
+
+# Adding math ops: sqrt, cos, sin, ...
+def _get_sym_node_fn(name):
+    def fn(self):
+        return getattr(self, f"_sym_{name}")()
+
+    return fn
+
+
+math_op_names = (
+    "sqrt",
+    "cos",
+    "cosh",
+    "sin",
+    "sinh",
+    "tan",
+    "tanh",
+    "asin",
+    "acos",
+    "atan",
+    "log2",
+)
+for name in math_op_names:
+    sym_name = f"sym_{name}"
+    priv_sym_name = f"_{sym_name}"
+    setattr(SymNode, sym_name, _get_sym_node_fn(name))
+    METHOD_TO_OPERATOR[sym_name] = getattr(torch, priv_sym_name)
+    unary_magic_methods.add(sym_name)
+    __all__.append(sym_name)
+
+
+# Unary methods that are not magic methods
+unary_nonmagic_methods = {
+    "is_integer",
+}
+
+unary_methods = unary_magic_methods | unary_nonmagic_methods
+
+# Most methods are only registered on SymInt and SymFloat
+# Some methods are only be registered on SymBool
+only_bool_magic_methods = {"and", "or", "sym_not", "sym_ite"}
+# Methods that implicitly convert SymBool into SymInt
+bool_becomes_int_magic_methods = {"add", "sub", "mul"}
+# Methods that are also on SymBool, in addition to on SymInt and SymFloat
+also_bool_magic_methods = {"eq"}
+bool_magic_methods = only_bool_magic_methods | also_bool_magic_methods
+
+# Methods that are only for float
+only_float_magic_methods = {"is_integer", "round", "sym_int", "sym_log2"}
+
+
+magic_methods_on_operator_with_trailing_underscore = {"and", "or"}
+# remap necessary because an op name can have a bitwise and boolean implementation
+bitwise_ops = {
+    "bitwise_and": "and",
+    "bitwise_or": "or",
+}
+
+
+always_float_magic_methods = {"int_truediv", "float_truediv", "sym_float", "float_pow"}
+
+for name in math_op_names:
+    sym_name = f"sym_{name}"
+    always_float_magic_methods.add(sym_name)
+
+
+always_int_magic_methods = {"ceil", "floor", "trunc", "pow_by_natural"}
+always_bool_magic_methods = {
+    "eq",
+    "ne",
+    "gt",
+    "lt",
+    "le",
+    "ge",
+    "and",
+    "or",
+    "sym_not",
+    "is_non_overlapping_and_dense",
+    "is_integer",
+}
+
+# Methods that have a `__foo__` as well as `__rfoo__`
+
+
+def _sympy_float_truediv(a, b):
+    from torch.utils._sympy.functions import FloatTrueDiv
+
+    return FloatTrueDiv(a, b)
+
+
+def _sympy_int_truediv(a, b):
+    from torch.utils._sympy.functions import IntTrueDiv
+
+    return IntTrueDiv(a, b)
+
+
+def _sympy_floordiv(a, b):
+    from torch.utils._sympy.functions import FloorDiv
+
+    return FloorDiv(a, b)
+
+
+def _sympy_mod(a, b):
+    from torch.utils._sympy.functions import Mod, PythonMod
+
+    if a.is_nonnegative and b.is_nonnegative:
+        return Mod(a, b)
+    else:
+        return PythonMod(a, b)
+
+
+def _sympy_pow_by_natural(a, b):
+    from torch.utils._sympy.functions import PowByNatural
+
+    return PowByNatural(a, b)
+
+
+def _sympy_float_pow(a, b):
+    from torch.utils._sympy.functions import FloatPow
+
+    return FloatPow(a, b)
+
+
+def _sympy_and(a, b):
+    import sympy
+
+    return sympy.And(a, b)
+
+
+def _sympy_or(a, b):
+    import sympy
+
+    return sympy.Or(a, b)
+
+
+def _sympy_lshift(a, b):
+    from torch.utils._sympy.functions import LShift
+
+    return LShift(a, b)
+
+
+def _sympy_rshift(a, b):
+    from torch.utils._sympy.functions import RShift
+
+    return RShift(a, b)
+
+
+def _binary_search_insert_arg(ordered_args, new_arg):
+    """
+    If new_arg is found in ordered_args None is returned, else the new
+    ordered_args with new_arg inserted
+    """
+    if len(ordered_args) == 0:
+        return [new_arg]
+
+    from sympy.core.basic import _args_sortkey as sort_key, Basic
+
+    # Fast path when new_arg > ordered_args[-1].
+    if sort_key(ordered_args[-1]) < sort_key(new_arg):
+        return ordered_args + [new_arg]
+
+    # Fast path when new_arg < ordered_args[0].
+    if sort_key(ordered_args[0]) > sort_key(new_arg):
+        return [new_arg] + ordered_args
+
+    low, high = 0, len(ordered_args) - 1
+
+    while low <= high:
+        mid = (low + high) // 2
+        compare_result = Basic.compare(ordered_args[mid], new_arg)
+        if compare_result == 0:
+            return None
+        elif compare_result < 0:
+            low = mid + 1
+        else:
+            high = mid - 1
+
+    ordered_args.insert(low, new_arg)
+    return ordered_args
+
+
+def _optimized_add(
+    lhs, rhs, lhs_is_optimized_summation=False, rhs_is_optimized_summation=False
+):
+    """
+    Custom optimization for Add used to optimize incremental binary summations of certain properties. The idea
+    is when we know the expression is a summation of unique symbols all we need to know is the correct order of symbols,
+    and no other optimizations are needed. We pass evaluate=false, with the correct order of args and save the following.
+    1. Avoid running other optimizations when the Add is constructed.
+    2. Manually figure out the order of the args for the new expression in log(n) comparisons instead of nLog(n)
+    (comparing terms is expensive and shows in the profiles).
+    The function returns a tuple of (1) a boolean that indicates whether the output is a summation of unique symbols,
+    (2) the result sympy expression.
+    """
+    import sympy
+    from sympy.core.basic import _args_sortkey as sortkey
+
+    def make_optimized(ordered_args):
+        result = sympy.Add(*ordered_args, evaluate=False)
+        return (True, result)
+
+    from torch.utils._sympy.functions import _is_symbols_binary_summation
+
+    lhs_is_optimized_summation |= _is_symbols_binary_summation(lhs)
+    rhs_is_optimized_summation |= _is_symbols_binary_summation(rhs)
+
+    if lhs_is_optimized_summation and rhs_is_optimized_summation:
+        # (a0+a1..) + (a2+a3..) => (a0+a1+a2+a3)
+        if sortkey(lhs._args[-1]) < sortkey(rhs._args[0]):
+            return make_optimized(lhs._args + rhs._args)
+        #  (a2+a3..) + (a0+a1..) => (a0+a1+a2+a3)
+        if sortkey(lhs._args[0]) > sortkey(rhs._args[-1]):
+            return make_optimized(rhs._args + lhs._args)
+
+    # (a0+a2) + a1 => (a0+a1+a2)
+    if lhs_is_optimized_summation and rhs.is_symbol:
+        new_args = _binary_search_insert_arg(list(lhs._args), rhs)
+        if new_args is not None:
+            return make_optimized(new_args)
+
+    # a1 + (a0+a2)=> (a0+a1+a2)
+    if rhs_is_optimized_summation and lhs.is_symbol:
+        new_args = _binary_search_insert_arg(list(rhs._args), lhs)
+        if new_args is not None:
+            return make_optimized(new_args)
+
+    result = sympy.Add(lhs, rhs)
+    return (_is_symbols_binary_summation(result), result)
+
+
+def _bitwise_and(a, b):
+    from torch.utils._sympy.functions import BitwiseFn_bitwise_and
+
+    return BitwiseFn_bitwise_and(a, b)
+
+
+def _bitwise_or(a, b):
+    from torch.utils._sympy.functions import BitwiseFn_bitwise_or
+
+    return BitwiseFn_bitwise_or(a, b)
+
+
+reflectable_magic_methods = {
+    "add": _optimized_add,
+    "sub": operator.sub,
+    "mul": operator.mul,
+    "mod": _sympy_mod,
+    "pow_by_natural": _sympy_pow_by_natural,
+    "float_pow": _sympy_float_pow,
+    "and": _sympy_and,
+    "bitwise_and": _bitwise_and,
+    "or": _sympy_or,
+    "bitwise_or": _bitwise_or,
+    "float_truediv": _sympy_float_truediv,
+    "int_truediv": _sympy_int_truediv,
+    "int_floordiv": _sympy_floordiv,
+    "lshift": _sympy_lshift,
+    "rshift": _sympy_rshift,
+}
+
+
+def _floor_ceil_helper(a, fn):
+    import sympy
+
+    if isinstance(a, sympy.Mul):
+        aa = a.args
+        if len(aa) == 2 and isinstance(aa[0], sympy.Float) and aa[1].is_integer:
+            coef = sympy.Integer(aa[0])
+            if aa[0] == coef:  # structural equality test
+                return coef * aa[1]
+    if (
+        isinstance(a, sympy.Float)
+        and a == sympy.Integer(a)
+        or isinstance(a, sympy.Integer)
+    ):
+        return sympy.Integer(a)
+    return fn(a)
+
+
+def _sympy_floor(a):
+    from torch.utils._sympy.functions import FloorToInt
+
+    return FloorToInt(a)
+
+
+# NB: this is Python trunc semantics which returns an int.  Do NOT use this to
+# represent torch.trunc (which is float to float)
+def _sympy_trunc(a):
+    from torch.utils._sympy.functions import TruncToInt
+
+    return TruncToInt(a)
+
+
+def _sympy_ceil(a):
+    from torch.utils._sympy.functions import CeilToInt
+
+    return CeilToInt(a)
+
+
+def _sympy_eq(a, b):
+    import sympy
+
+    return sympy.Eq(a, b)
+
+
+def _sympy_ne(a, b):
+    import sympy
+
+    return sympy.Ne(a, b)
+
+
+def _sympy_gt(a, b):
+    import sympy
+
+    return sympy.Gt(a, b)
+
+
+def _sympy_lt(a, b):
+    import sympy
+
+    return sympy.Lt(a, b)
+
+
+def _sympy_le(a, b):
+    import sympy
+
+    return sympy.Le(a, b)
+
+
+def _sympy_ge(a, b):
+    import sympy
+
+    return sympy.Ge(a, b)
+
+
+def _sympy_min(a, b):
+    from torch.utils._sympy.functions import Min
+
+    return Min(a, b)
+
+
+def _sympy_max(a, b):
+    from torch.utils._sympy.functions import Max
+
+    return Max(a, b)
+
+
+def _sympy_ite(a, t, f):
+    import sympy
+
+    return sympy.Piecewise((t, a), (f, True))
+
+
+current_module = sys.modules[__name__]
+
+
+def _get_sym_math_fn(name):
+    def fn(a):
+        import torch.utils._sympy.functions
+
+        return getattr(torch.utils._sympy.functions, f"OpaqueUnaryFn_{name}")(a)
+
+    return fn
+
+
+for name in math_op_names:
+    priv_sympy_name = f"_sympy_{name}"
+    fn = _get_sym_math_fn(name)
+    fn.__qualname__ = fn.__name__ = priv_sympy_name
+    setattr(current_module, priv_sympy_name, fn)
+
+del fn, name, priv_sympy_name  # type: ignore[possibly-undefined]
+
+
+def _sympy_abs(a):
+    import sympy
+
+    return sympy.Abs(a)
+
+
+def _sympy_round(number, ndigits=None):
+    from torch.utils._sympy.functions import RoundDecimal, RoundToInt
+
+    if ndigits is None:
+        return RoundToInt(number)
+    else:
+        return RoundDecimal(number, ndigits)
+
+
+def _sympy_sym_float(a):
+    from torch.utils._sympy.functions import ToFloat
+
+    # NB: Cannot use a * 1.0 here, because 0 * 1.0 is 0 which incorrectly
+    # reports that it is an integer
+    return ToFloat(a)
+
+
+def _sympy_is_integer(a):
+    import sympy
+
+    from torch.utils._sympy.functions import ToFloat
+
+    return sympy.Eq(ToFloat(sympy.floor(a)), a)
+
+
+magic_methods = {
+    **reflectable_magic_methods,
+    "sym_not": operator.invert,
+    "pos": operator.pos,
+    "eq": _sympy_eq,
+    "ne": _sympy_ne,
+    "gt": _sympy_gt,
+    "lt": _sympy_lt,
+    "le": _sympy_le,
+    "ge": _sympy_ge,
+    "floor": _sympy_floor,
+    "trunc": _sympy_trunc,
+    "sym_float": _sympy_sym_float,
+    "ceil": _sympy_ceil,
+    "neg": operator.neg,
+    "sym_min": _sympy_min,
+    "sym_max": _sympy_max,
+    "sym_ite": _sympy_ite,
+    "abs": _sympy_abs,
+    "round": _sympy_round,
+    "is_integer": _sympy_is_integer,
+}
+
+
+for name in math_op_names:
+    sym_name = f"sym_{name}"
+    magic_methods[sym_name] = getattr(current_module, f"_sympy_{name}")
+
+del name, sym_name, math_op_names, current_module  # type: ignore[possibly-undefined]
+
+
+def sympy_is_contiguous(sizes, strides):
+    dim = len(sizes)
+    return sympy_is_contiguous_generic(sizes, strides, list(range(dim - 1, -1, -1)))
+
+
+def sympy_is_contiguous_generic(sizes, strides, dim_order):
+    import sympy
+
+    dim = len(sizes)
+
+    if len(dim_order) != dim:
+        return sympy.false
+
+    is_contiguous = sympy.true
+    z = sympy.S.One
+    # Contiguous if the strides make sense (or the dim is size 1)
+    for d in dim_order:
+        is_contiguous &= sympy.Eq(sizes[d], sympy.S.One) | sympy.Eq(strides[d], z)
+        z *= sizes[d]
+    # OR if any size is zero
+    for d in range(dim):
+        is_contiguous |= sympy.Eq(sizes[d], sympy.S.Zero)
+    return is_contiguous
+
+
+# NB: There is a TODO in C++ to allow omitting the batch dim.  If that
+# happens you will need to refactor this
+
+
+def sympy_is_channels_last_contiguous_2d(sizes, strides):
+    return sympy_is_contiguous_generic(sizes, strides, [1, 3, 2, 0])
+
+
+def sympy_is_channels_last_contiguous_3d(sizes, strides):
+    return sympy_is_contiguous_generic(sizes, strides, [1, 4, 3, 2, 0])
+
+
+def sympy_is_channels_last_strides_generic(sizes, strides, dim_order):
+    import sympy
+
+    from torch.utils._sympy.functions import Max
+
+    dim = len(sizes)
+
+    if dim != len(dim_order):
+        return sympy.false
+
+    m = sympy.S.Zero
+    r = sympy.true
+
+    # special case for trivial C dimension. default to NCHW
+    r &= sympy.Ne(strides[1], 0)
+
+    for d in dim_order:
+        r &= sympy.Ne(sizes[d], 0) & (strides[d] >= m)
+        # Fallback to NCHW as default layout for ambiguous cases
+        # This is the flaw of implicit memory_format from strides.
+        # N111 tensor with identical strides for size 1 dimension;
+        # Two cases could lead us here:
+        # a. N111 contiguous Tensor ([N,1,1,1]@[1,1,1,1])
+        # b. N11W contiguous Tensor sliced on the W-dimension.
+        # ([N,1,1,1]@[W,W,W,W])
+        if d == 0:
+            r &= sympy.Ne(m, strides[1])
+        # This is necessary to:
+        # 1. distinguish the memory_format of N1H1;
+        #     [H, 1, 1, 1] channels_last stride
+        #     [H, H, 1, 1] contiguous stride
+        # 2. permutation of 1C1W:
+        #     [1, C, 1, H]@[HC, H, H, 1] transpose(1, 3)
+        #     [1, H, 1, C]@[HC, 1, H, H] shouldn't be identified as
+        #     channels_last
+        m = strides[d] * Max(sizes[d], 1)
+
+    return r
+
+
+def sympy_is_channels_last_strides_2d(sizes, strides):
+    return sympy_is_channels_last_strides_generic(sizes, strides, [1, 3, 2, 0])
+
+
+def sympy_is_channels_last_strides_3d(sizes, strides):
+    return sympy_is_channels_last_strides_generic(sizes, strides, [1, 4, 3, 2, 0])
+
+
+def _sympy_is_non_overlapping_and_dense_indicator(sizes, strides):
+    from torch.utils._sympy.functions import IsNonOverlappingAndDenseIndicator
+
+    return IsNonOverlappingAndDenseIndicator(*sizes, *strides)
+
+
+sizes_strides_methods = {
+    # TODO: These could also be done with indicators, maybe it is better
+    # for reasoning to do it that way
+    "is_contiguous": sympy_is_contiguous,
+    "is_channels_last_contiguous_2d": sympy_is_channels_last_contiguous_2d,
+    "is_channels_last_contiguous_3d": sympy_is_channels_last_contiguous_3d,
+    "is_channels_last_strides_2d": sympy_is_channels_last_strides_2d,
+    "is_channels_last_strides_3d": sympy_is_channels_last_strides_3d,
+    "is_non_overlapping_and_dense_indicator": _sympy_is_non_overlapping_and_dense_indicator,
+}
+
+alternate_impl_if_hinted_methods = {
+    "sym_min": builtins.min,
+    "sym_max": builtins.max,
+}
+
+
+def to_node(self, num):
+    if isinstance(num, SymTypes):
+        return num.node
+    elif type(num) is bool:
+        return self.wrap_bool(num)
+    elif type(num) is int:
+        return self.wrap_int(num)
+    elif type(num) is float:
+        return self.wrap_float(num)
+    else:
+        # NotImplemented is important so that Python tries the
+        # other magic method
+        return NotImplemented
+
+
+def wrap_node(x):
+    # TODO: let C++ also take advantage of this
+    if isinstance(x, SymNode) and x.constant is not None:
+        return x.constant
+    if x.is_int():
+        return SymInt(x)
+    elif x.is_float():
+        return SymFloat(x)
+    elif x.is_bool():
+        return SymBool(x)
+    else:
+        raise AssertionError(f"unrecognized return type {x}")
+
+
+def method_to_operator(method):
+    return METHOD_TO_OPERATOR[method]
+
+
+def _make_node_magic(method, func):
+    func = lru_cache(256)(func)
+
+    if method in magic_methods_on_operator_with_trailing_underscore:
+        method_attr = f"{method}_"
+    else:
+        method_attr = method
+
+    def uninteresting_files() -> set[str]:
+        import torch
+
+        mods = [
+            torch._dynamo.eval_frame,
+            torch._dynamo.utils,
+            torch.fx.experimental.sym_node,
+            torch,
+        ]
+        import torch._dynamo.guards
+
+        return (
+            {inspect.getfile(m) for m in mods}
+            | torch._dynamo.guards.uninteresting_files()
+            | {""}
+        )
+
+    def capture_provenance(fn):
+        @functools.wraps(fn)
+        def wrapper(self, other=None):
+            if other is None:
+                result = fn(self)
+            else:
+                result = fn(self, other)
+            if torch._logging._internal.GET_DTRACE_STRUCTURED:
+                if other is not None:
+                    arguments = [self, other]
+                else:
+                    arguments = [self]
+
+                def get_id(sym_node) -> Optional[int]:
+                    # We don't want to return an ID if the input is a constant
+                    import sympy
+
+                    if sym_node.constant is not None:
+                        return None
+                    elif id(sym_node) == id(result):
+                        return None
+                    elif isinstance(sym_node.expr, (sympy.Integer, sympy.Float)):
+                        return None
+                    elif sym_node.expr in (sympy.true, sympy.false):
+                        return None
+                    return id(sym_node)
+
+                dtrace_structured(
+                    "expression_created",
+                    metadata_fn=lambda: {
+                        "method": method,
+                        "result": str(result),
+                        "result_id": id(result),
+                        "arguments": [str(a) for a in arguments],
+                        "argument_ids": [
+                            get_id(i) for i in arguments if get_id(i) is not None
+                        ],
+                        "user_stack": structured.get_user_stack(3),
+                        "stack": structured.get_framework_stack(3),
+                    },
+                )
+
+            return result
+
+        return wrapper
+
+    @capture_provenance
+    def binary_magic_impl(self, other):
+        from torch.fx.experimental.proxy_tensor import (
+            get_proxy_mode,
+            handle_sym_dispatch,
+        )
+
+        op = method_to_operator(method)
+
+        out_hint = None
+        if self.hint is not None and other.hint is not None:
+            out_hint = op(self.hint, other.hint)
+
+        alternate_impl = alternate_impl_if_hinted_methods.get(method)
+        if alternate_impl and out_hint is not None:
+            return to_node(self, alternate_impl(wrap_node(self), wrap_node(other)))
+
+        if get_proxy_mode():
+            return to_node(
+                self, handle_sym_dispatch(op, (wrap_node(self), wrap_node(other)), {})
+            )
+        assert isinstance(other, SymNode)
+        optimized_summation = False
+        try:
+            if method == "mod":
+                from torch.utils._sympy.functions import Mod, PythonMod
+
+                # Special handling for mod that requires access to the value
+                # ranges
+                shape_env = self.shape_env
+                if (
+                    self.expr.is_nonnegative
+                    or shape_env.bound_sympy(self.expr).lower >= 0
+                ) and (
+                    other.expr.is_nonnegative
+                    or shape_env.bound_sympy(other.expr).lower >= 0
+                ):
+                    out = Mod(self.expr, other.expr)
+                else:
+                    out = PythonMod(self.expr, other.expr)
+            elif method == "add":
+                # see Note [optimized_summation]
+                (optimized_summation, out) = func(
+                    self.expr,
+                    other.expr,
+                    self._optimized_summation,
+                    other._optimized_summation,
+                )
+            else:
+                # TODO: consider constant prop here
+                out = func(self.expr, other.expr)
+        except Exception:
+            log.warning("failed to eval %s(%s, %s)", method, self.expr, other.expr)
+            raise
+        sym_node_log.debug("%s %s %s -> %s", method, self.expr, other.expr, out)
+        pytype: type
+        # This is not strictly correct. In Python, a**b may return complex when
+        # a < 0 and b is a float: (-1)**2.1. Same for sympy.sqrt(-3.14). This
+        # returns a float while both arguments are ints: 2**(-1). Also, max and
+        # min do not type promote. To avoid having data-dependent control flow
+        # here, we just set the type to float if one of the args is a float. In
+        # case of a type mismatch, we assume that it will be detected during
+        # evaluation.
+        if method in always_float_magic_methods:
+            pytype = float
+        elif method in always_bool_magic_methods:
+            pytype = bool
+        elif self.pytype is float or other.pytype is float:
+            pytype = float
+        else:
+            pytype = self.pytype
+
+        if (
+            pytype is not None
+            and out_hint is not None
+            and not isinstance(out_hint, SymTypes)
+        ):
+            out_hint = pytype(out_hint)
+
+        # Create a FX node that corresponds to the operation being applied to
+        # this node.
+        fx_node, _ = self.shape_env._create_fx_call_function(
+            op, (self.fx_node, other.fx_node)
+        )
+
+        result = SymNode(
+            out,
+            self.shape_env,
+            pytype,
+            out_hint,
+            fx_node=fx_node,
+            optimized_summation=optimized_summation,  # see Note [optimized_summation]
+        )
+        return result
+
+    @capture_provenance
+    def unary_magic_impl(self):
+        from torch.fx.experimental.proxy_tensor import (
+            get_proxy_mode,
+            handle_sym_dispatch,
+        )
+
+        op = method_to_operator(method)
+        if get_proxy_mode():
+            return to_node(self, handle_sym_dispatch(op, (wrap_node(self),), {}))
+        # TODO: consider constant prop here
+        expr = self.expr
+        if method == "floor" or method == "ceiling":
+            expr = self.shape_env._simplify_floor_div(expr)
+
+        try:
+            out = func(expr)
+        except Exception:
+            log.warning("failed to eval %s(%s)", method, expr)
+            raise
+        sym_node_log.debug("%s %s -> %s", func, expr, out)
+        out_hint = None
+        if self.hint is not None:
+            out_hint = op(self.hint)
+        pytype: type
+        if method in always_int_magic_methods:
+            pytype = int
+        elif method in always_bool_magic_methods:
+            pytype = bool
+        elif method in always_float_magic_methods:
+            pytype = float
+        else:
+            pytype = self.pytype
+
+        fx_node, _ = self.shape_env._create_fx_call_function(op, (self.fx_node,))
+        return SymNode(out, self.shape_env, pytype, out_hint, fx_node=fx_node)
+
+    if method in unary_methods:
+        setattr(SymNode, f"_{method_attr}", unary_magic_impl)
+    elif method == "sym_ite":
+
+        def sym_ite_impl(pred_node, then_node, else_node):
+            from torch.fx.experimental.proxy_tensor import (
+                get_proxy_mode,
+                handle_sym_dispatch,
+            )
+
+            out_hint = then_node.hint if pred_node.hint else else_node.hint
+            if get_proxy_mode():
+                return to_node(
+                    pred_node,
+                    handle_sym_dispatch(
+                        sym_ite,
+                        (
+                            wrap_node(pred_node),
+                            wrap_node(then_node),
+                            wrap_node(else_node),
+                        ),
+                        {},
+                    ),
+                )
+
+            try:
+                out = func(pred_node.expr, then_node.expr, else_node.expr)
+            except Exception:
+                log.warning(
+                    "failed to eval %s(%s, %s, %s)",
+                    method,
+                    pred_node.expr,
+                    then_node.expr,
+                    else_node.expr,
+                )
+                raise
+
+            fx_node, _ = pred_node.shape_env._create_fx_call_function(
+                sym_ite, (pred_node.fx_node, then_node.fx_node, else_node.fx_node)
+            )
+            return SymNode(
+                out, pred_node.shape_env, then_node.pytype, out_hint, fx_node=fx_node
+            )
+
+        setattr(SymNode, f"_{method_attr}", sym_ite_impl)
+    elif method == "round":
+
+        def round_impl(self, ndigits=None):
+            from torch.fx.experimental.proxy_tensor import (
+                get_proxy_mode,
+                handle_sym_dispatch,
+            )
+
+            op = builtins.round
+            if get_proxy_mode():
+                return to_node(
+                    self, handle_sym_dispatch(op, (wrap_node(self), ndigits), {})
+                )
+
+            expr = self.expr
+            try:
+                out = func(expr, ndigits)
+            except Exception:
+                log.warning("failed to eval %s(%s, ndigits=%s)", method, expr, ndigits)
+                raise
+
+            if ndigits is None:
+                pytype = int
+            else:
+                pytype = self.pytype
+
+            out_hint = None
+            if self.hint is not None:
+                out_hint = op(self.hint, ndigits)
+
+            # Internally, None is used as sentinel to indicate that a something is not a node on an FX graph. At the
+            # same time, there is no way to wrap a plain None into an FX node. Thus, there is no way to pass None here
+            # without triggering some asserts that check whether we are mixing FX nodes with untracked arguments. The
+            # hack down below works, because all round function down the line all take ndigits=None as default in their
+            # signature.
+            # TODO: Remove the args construction below if a different sentinel is used by FX.
+            # ezyang(May 2024): LOL
+            args = [self.fx_node]
+            if ndigits is not None:
+                args.append(ndigits)
+            fx_node, _ = self.shape_env._create_fx_call_function(op, tuple(args))
+            return SymNode(out, self.shape_env, pytype, out_hint, fx_node=fx_node)
+
+        setattr(SymNode, f"_{method_attr}", round_impl)
+    else:
+        setattr(SymNode, f"_{method_attr}", binary_magic_impl)
+
+
+def _make_node_sizes_strides(method, func):
+    # NB: don't LRU cache, lots of arguments
+
+    def sizes_strides_impl(self, sizes, strides):
+        from torch.fx.experimental.proxy_tensor import (
+            get_proxy_mode,
+            handle_sym_dispatch,
+        )
+
+        op = getattr(sys.modules[__name__], method)
+        if get_proxy_mode():
+            return to_node(
+                self,
+                handle_sym_dispatch(
+                    op,
+                    ([wrap_node(s) for s in sizes], [wrap_node(s) for s in strides]),
+                    {},
+                ),
+            )
+        size_exprs = [s.expr for s in sizes]
+        stride_exprs = [s.expr for s in strides]
+        try:
+            out = func(size_exprs, stride_exprs)
+        except Exception:
+            log.warning("failed to eval %s(%s, %s)", method, size_exprs, stride_exprs)
+            raise
+        # bool is never expandable
+
+        size_hints = []
+        out_hint = None
+        for s in sizes:
+            if s.hint is None:
+                break
+            size_hints.append(s.hint)
+        else:
+            stride_hints = []
+            for s in strides:
+                if s.hint is None:
+                    break
+                stride_hints.append(s.hint)
+            else:
+                out_hint = op(size_hints, stride_hints)
+
+        # NB: This is the indicator function, not the actual bool!
+        pytype: type
+        if method.endswith("_indicator"):
+            pytype = int
+        else:
+            pytype = bool
+        return SymNode(out, self.shape_env, pytype, out_hint)
+
+    setattr(SymNode, f"_{method}", sizes_strides_impl)
+
+    # TODO: This is technically hotpath, but in the ideal end state
+    # guards on this will resolve at a higher level so you never
+    # spend time in this code
+    def sizes_strides_user(sizes, strides):
+        import sympy
+
+        from torch.fx.experimental.symbolic_shapes import (
+            eval_is_non_overlapping_and_dense,
+        )
+
+        for a in itertools.chain(sizes, strides):
+            if isinstance(a, SymInt):
+                return wrap_node(
+                    getattr(a.node, method)(
+                        [to_node(a.node, b) for b in sizes],
+                        [to_node(a.node, b) for b in strides],
+                    )
+                )
+        if method == "is_non_overlapping_and_dense_indicator":
+            return eval_is_non_overlapping_and_dense(sizes, strides)
+        else:
+            # TODO: this is an awful implementation
+            return bool(
+                func(
+                    [sympy.sympify(a) for a in sizes],
+                    [sympy.sympify(a) for a in strides],
+                )
+            )
+
+    # Skip for is_non_overlapping_and_dense_indicator
+    if not hasattr(sys.modules[__name__], method):
+        setattr(sys.modules[__name__], method, sizes_strides_user)
+
+
+for method, func in magic_methods.items():
+    _make_node_magic(method, func)
+
+for method, func in sizes_strides_methods.items():
+    _make_node_sizes_strides(method, func)
+
+
+def _make_user_magic(method, user_type):
+    # User magic takes care of wrapping the other operand into a node,
+    # so that our internal logic can assume everything is nodes
+
+    if method in magic_methods_on_operator_with_trailing_underscore:
+        method_attr = f"sym_{method}"
+    else:
+        method_attr = method
+
+    def get_constant(x: Union[SymInt, int, SymFloat, float, SymBool, bool]):
+        if isinstance(x, (int, float, bool)):
+            return x
+        if isinstance(x, SymBool):
+            return x.node.guard_bool("", 0)
+        raise AssertionError("expect to be called with constant SymBools")
+
+    def is_constant(x):
+        if isinstance(x, (int, float, bool)):
+            return True
+        if isinstance(x, (SymInt, SymFloat, SymBool)):
+            return x.node.is_constant()
+        return False
+
+    # Promotion rules for binary operations.  NB: we preserve PYTHON semantics
+    #   - if args are same type, do nothing
+    #   - if one arg is float, promote other arg to float
+    #       - nb: this applies to floordiv, even though output is integral
+    #       (it's still float)
+    #   - pow is funny business
+    #       - if both ints
+    #       - trigger a guard on exponent >= 0
+    #           - if non-negative, output is int
+    #           - otherwise, output is float
+    #   - otherwise, promote other arg to float
+    #       - nb: complex is impossible to handle correctly lol, with
+    #       negative base and integral float need to diverge semantics and
+    #       just always return complex.  Neener neener pretend this problem
+    #       doesn't exist
+    #   - equality is pain: Python does the fancy thing where it unpacks the
+    #     mantissa from the float and then compares that against the int.
+    #     Which means it is able to tell that
+    #     9007199254740993 != 9007199254740992. (rather than if the LHS was
+    #     promoted to float, in which case it would have truncated to the RHS
+    #     and subsequently been equal).  We'll model this exactly by having
+    #     special mixed type equality operations.  Unfortunately, we need to
+    #     do this for all comparison operations (maybe I'll only implement
+    #     compare)
+    #   - sym_ite mumble mumble really shouldn't allow mixed but whatever
+
+    if method in bool_becomes_int_magic_methods:
+
+        def promote(x):
+            """Implements True+True=2, which works in python but not sympy"""
+            if isinstance(x, SymBool):
+                return SymInt(x.node.wrap_int(int(x)))
+            return x
+
+    else:
+
+        def promote(x):
+            return x
+
+    def promote2(self, other):
+        # TODO: Remove eq and other relations from this list.
+        # CPython has fancy implementations for these to get as much precision
+        # as possible instead of just promoting to float64 and praying, so we
+        # need to handle them specially too.
+        # Also, note that int_truediv doesn't go through this path: both
+        # arguments are "int" so there isn't any promotion
+        if method not in [
+            "add",
+            "sub",
+            "mul",
+            "mod",
+            "float_pow",
+            "float_truediv",
+            "int_floordiv",
+            "sym_min",
+            "sym_max",
+            # TODO: remove these
+            "eq",
+            "ne",
+            "gt",
+            "lt",
+            "le",
+            "ge",
+        ]:
+            return self, other
+        f_self = isinstance(self, (float, torch.SymFloat))
+        f_other = isinstance(other, (float, torch.SymFloat))
+        if f_self or f_other:
+            if not f_self:
+                self = torch.sym_float(self)
+            if not f_other:
+                other = torch.sym_float(other)
+        return self, other
+
+    # Before and after performing the operation, check if any operands are constant.
+    # If so, extract out the constant values first. If `self` itself is a
+    # constant, then "redispatch" by calling back into the operator. Sometimes
+    # this means that operations involving SymBool return plain bools.
+    # Alternatively, we could also rewrap into constant Symbool (i.e. by
+    # implementing wrap_bool in ConstantSymNodeImpl), but we're not doing that
+    # today for no particular reason.
+    def unary_magic_impl(self):
+        self = promote(self)
+        if is_constant(self):
+            return (method_to_operator(method))(get_constant(self))
+        return wrap_node(getattr(self.node, method_attr)())
+
+    def binary_magic_impl(self, other):
+        if not isinstance(other, (int, float, bool, SymInt, SymFloat, SymBool)):
+            return NotImplemented
+        sym_node_log.debug("MAGIC %s %s %s", method, self, other)
+        self = promote(self)
+        other = promote(other)
+        self, other = promote2(self, other)
+        if is_constant(self):
+            return (method_to_operator(method))(get_constant(self), other)
+        if is_constant(other):
+            other = get_constant(other)
+        other_node = to_node(self.node, other)
+        if other_node is NotImplemented:
+            return NotImplemented
+        ret = wrap_node(getattr(self.node, method_attr)(other_node))
+        return get_constant(ret) if is_constant(ret) else ret
+
+    def rbinary_magic_impl(self, other):
+        if not isinstance(other, (int, float, bool, SymInt, SymFloat, SymBool)):
+            return NotImplemented
+        self = promote(self)
+        other = promote(other)
+        self, other = promote2(self, other)
+        if is_constant(self):
+            return (method_to_operator(method))(get_constant(self), other)
+        if is_constant(other):
+            other = get_constant(other)
+        other_node = to_node(self.node, other)
+        if other_node is NotImplemented:
+            return NotImplemented
+        ret = wrap_node(getattr(other_node, method_attr)(self.node))
+        return get_constant(ret) if is_constant(ret) else ret
+
+    if method in unary_magic_methods:
+        setattr(user_type, f"__{method}__", unary_magic_impl)
+    elif method in unary_nonmagic_methods:
+        orig = getattr(user_type, method)
+        setattr(user_type, method, update_wrapper(unary_magic_impl, orig))
+    elif method == "sym_ite":
+
+        def sym_ite_magic_impl(pred, then_val, else_val):
+            pred_node = pred.node
+            then_node = to_node(pred_node, then_val)
+            else_node = to_node(pred_node, else_val)
+            if then_node is NotImplemented or else_node is NotImplemented:
+                return NotImplemented
+            assert (
+                isinstance(then_node, SymNode)
+                and isinstance(else_node, SymNode)
+                and then_node.pytype == else_node.pytype
+            )
+            ret = wrap_node(getattr(pred.node, method_attr)(then_node, else_node))
+            return get_constant(ret) if ret.node.is_constant() else ret
+
+        setattr(user_type, f"__{method}__", sym_ite_magic_impl)
+    elif method == "round":
+
+        def round_magic_impl(self, ndigits=None):
+            if is_constant(self):
+                return builtins.round(get_constant(self), ndigits)
+
+            return wrap_node(getattr(self.node, method)(ndigits))
+
+        setattr(user_type, f"__{method}__", round_magic_impl)
+    else:
+        method_name = method
+        if method in bitwise_ops:
+            method_name = bitwise_ops[method]
+        setattr(user_type, f"__{method_name}__", binary_magic_impl)
+        if method in reflectable_magic_methods:
+            setattr(user_type, f"__r{method_name}__", rbinary_magic_impl)
+
+
+for method, func in magic_methods.items():  # type: ignore[assignment]
+    if method in only_bool_magic_methods:
+        _make_user_magic(method, SymBool)
+        continue
+    if method in only_float_magic_methods:
+        _make_user_magic(method, SymFloat)
+        continue
+    if method in also_bool_magic_methods or method in bool_becomes_int_magic_methods:
+        _make_user_magic(method, SymBool)
+    _make_user_magic(method, SymInt)
+    if method not in bitwise_ops:
+        _make_user_magic(method, SymFloat)
+
+del method
+del func
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py
new file mode 100644
index 0000000000000000000000000000000000000000..9be16476d40dfb27b8a6a236e5c2d33a54c391f1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py
@@ -0,0 +1,7294 @@
+from __future__ import annotations
+
+
+"""
+``torch.fx.experimental.symbolic_shapes`` provides interfaces for interacting with
+our symbolic shapes reasoning system that is used heavily in torch.compile.  Although
+this is not generally considered public API, when writing framework code in PyTorch
+as well as extensions to PyTorch (e.g., in custom operator implementations), you may
+need to make use of these APIs to setup dynamic shapes support appropriately.
+"""
+
+import abc
+import atexit
+import collections
+import dis
+import functools
+import inspect
+import itertools
+import logging
+import math
+import operator
+import os
+import re
+import sys
+import threading
+import traceback
+from collections import Counter, defaultdict
+from collections.abc import Iterator, Mapping, Sequence
+from contextlib import _GeneratorContextManager, contextmanager
+from dataclasses import asdict, dataclass, field
+from enum import Enum
+from typing import (
+    Any,
+    Callable,
+    cast,
+    NamedTuple,
+    NoReturn,
+    Optional,
+    TYPE_CHECKING,
+    TypeVar,
+    Union,
+)
+from typing_extensions import deprecated, TypeAlias, TypeGuard
+
+import torch
+import torch.fx
+import torch.fx.traceback as fx_traceback
+import torch.utils._pytree as pytree
+
+# NB: The sym_* functions are used via getattr() and must be imported here.
+from torch import SymBool, SymFloat, SymInt
+from torch._guards import ShapeGuard, SLoc, Source, TracingContext
+from torch._logging import dtrace_structured, LazyString, structured, trace_structured
+from torch._subclasses.meta_utils import is_sparse_any
+from torch._utils_internal import signpost_event
+from torch.fx.experimental import _config as config
+from torch.fx.experimental.recording import (
+    FakeTensorMeta,
+    record_shapeenv_event,
+    replay_shape_env_events,
+    shape_env_check_state_equal,
+    ShapeEnvEvent,
+)
+from torch.fx.experimental.sym_node import SymNode, SymTypes
+from torch.utils._ordered_set import OrderedSet
+from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+from torch.utils._sympy.functions import (
+    Application,
+    CeilToInt,
+    CleanDiv,
+    FloorDiv,
+    FloorToInt,
+    IsNonOverlappingAndDenseIndicator,
+    Max,
+    Mod,
+    PythonMod,
+)
+from torch.utils._sympy.numbers import int_oo
+from torch.utils._sympy.printers import CppPrinter, PythonPrinter
+from torch.utils._sympy.singleton_int import SingletonInt
+from torch.utils._sympy.solve import try_solve
+from torch.utils._sympy.symbol import make_symbol, symbol_is_type, SymT
+from torch.utils._sympy.value_ranges import (
+    bound_sympy,
+    SymPyValueRangeAnalysis,
+    ValueRangeError,
+    ValueRanges,
+)
+from torch.utils._traceback import CapturedTraceback, format_frame
+
+
+if TYPE_CHECKING:
+    import types
+
+    from torch import Tensor
+    from torch._subclasses.fake_tensor import FakeTensor
+    from torch.types import BoolLikeType
+
+
+InputList = list
+DimList = list
+
+log = logging.getLogger(__name__)
+
+import sympy
+from sympy import Add, S
+
+
+class GuardOnDataDependentSymNode(RuntimeError):
+    cond: sympy.Basic
+
+    def __init__(self, cond: sympy.Basic, *args: Any) -> None:
+        super().__init__(*args)
+        self.cond = cond
+
+
+class PendingUnbackedSymbolNotFound(RuntimeError):
+    pass
+
+
+aten = torch._ops.ops.aten  # type: ignore[has-type]
+
+__all__ = [
+    "has_symbolic_sizes_strides",
+    "create_contiguous",
+    "ShapeEnv",
+    "is_concrete_int",
+    "is_concrete_float",
+    "guard_int",
+    "guard_float",
+    "guard_scalar",
+    "canonicalize_bool_expr",
+    "hint_int",
+    "SYMPY_INTERP",
+    "free_symbols",
+    "is_symbol_binding_fx_node",
+    "is_concrete_bool",
+    "is_nested_int",
+    "SHAPEENV_EVENT_KEY",
+    "CURRENT_NODE_KEY",
+    "has_free_symbols",
+    "has_free_unbacked_symbols",
+    "sym_eq",
+    "SymbolicContext",
+    "StatelessSymbolicContext",
+    "StatefulSymbolicContext",
+    "SubclassSymbolicContext",
+    "statically_known_true",
+    "guard_size_oblivious",
+    "check_consistent",
+    "compute_unbacked_bindings",
+    "ConvertIntKey",
+    "rebind_unbacked",
+    "resolve_unbacked_bindings",
+    "is_accessor_node",
+    "ValueRangesSLoc",
+    "SymIntEqByExpr",
+]
+
+# FX node metadata keys for symbolic shape FX graph.
+SHAPEENV_EVENT_KEY = "shapeenv_event"
+CURRENT_NODE_KEY = "current_node"
+
+
+def log_lru_cache_stats(wrapped_f: functools._lru_cache_wrapper[object]) -> None:
+    log.debug(
+        "lru_cache_stats %s: %s", wrapped_f.__name__, wrapped_f.cumulative_cache_info()  # type: ignore[attr-defined]
+    )
+
+
+# Note about Sympy Expr/SympyBoolean/Basic typing: the Sympy hierarchy is
+#
+#   Basic
+#       Expr
+#       SympyBoolean
+#           Relational
+#
+# Notably, Expr and SympyBoolean are not related.  So use Basic when the
+# expression could denote int, float OR bool, and otherwise use the more
+# specific Expr for int/float and SympyBoolean for bool.
+#
+# In obscure Meta only situations, sympy.logic.boolalg doesn't exist at runtime.
+# So make sure only type checker evaluates this alias.
+# Xref: https://www.internalfb.com/diff/D53324783
+SympyBoolean: TypeAlias = "sympy.logic.boolalg.Boolean"
+
+
+_T = TypeVar("_T")
+_SympyT = TypeVar("_SympyT", sympy.Expr, SympyBoolean, sympy.Basic)
+
+
+class SymIntEqByExpr:
+    """
+    This is a wrapper around SymInt which has alternative semantics for
+    equality.  Specifically, instead of erroring or guarding, we
+    instead will hash/compare equality based on the underlying sympy
+    expression; e.g., s0 and s1 will always compare as False.
+
+    NB: This does NOT do fancy analysis that maybe_evaluate_static does;
+    we can only reason through equalities that occur because to expressions
+    canonicalize to the same expression via regular simplification.
+    """
+
+    val: Union[torch.SymInt, int]
+
+    def __init__(self, val: Union[torch.SymInt, int]) -> None:
+        self.val = val
+
+    def __repr__(self) -> str:
+        return repr(self.val)
+
+    def _extract(self) -> sympy.Expr:
+        if isinstance(self.val, torch.SymInt):
+            return self.val.node.expr
+        else:
+            return sympy.Integer(self.val)
+
+    def __eq__(self, other: object) -> bool:
+        assert isinstance(other, SymIntEqByExpr)
+
+        # int equality fastpath
+        if type(self.val) is int and type(other.val) is int:
+            return self.val == other.val
+
+        return self._extract() == other._extract()
+
+    def __hash__(self) -> int:
+        return hash(self._extract())
+
+
+def _nested_int_aware_sort(
+    tup: tuple[Union[SymInt, int], int]
+) -> tuple[int, Union[SymInt, int], int]:
+    return (
+        # Order nested ints by their coefficients.
+        # 1 here to order nested ints after non-nested-ints.
+        (1, tup[0].node.nested_int_coeff(), tup[1])
+        if is_nested_int(tup[0])
+        else (0, *tup)
+    )
+
+
+# Wrapper on lru_cache that reports statistics at process end
+def lru_cache(
+    maxsize: Optional[int],
+) -> Callable[[Callable[..., _T]], functools._lru_cache_wrapper[_T]]:
+    def inner(f: Callable[..., _T]) -> functools._lru_cache_wrapper[_T]:
+        wrapped_f = functools.lru_cache(maxsize)(f)
+        old_cache_clear = wrapped_f.cache_clear
+        prev_hits = 0
+        prev_misses = 0
+
+        # TODO: There's a ref-cycle here (wrapped_f -> cumulative_cache_info
+        # -> wrapped_f) but cannot be solved with weakref as wrapped_f is not
+        # weakref'able on some versions of Python
+
+        def cumulative_cache_info() -> functools._CacheInfo:
+            cur = wrapped_f.cache_info()
+            return functools._CacheInfo(
+                prev_hits + cur.hits,
+                prev_misses + cur.misses,
+                cur.maxsize,
+                cur.currsize,
+            )
+
+        def new_cache_clear() -> None:
+            nonlocal prev_hits, prev_misses
+            cur = wrapped_f.cache_info()
+            prev_hits += cur.hits
+            prev_misses += cur.misses
+            old_cache_clear()
+
+        wrapped_f.cache_clear = new_cache_clear  # type: ignore[attr-defined, method-assign]
+        wrapped_f.cumulative_cache_info = cumulative_cache_info  # type: ignore[attr-defined, method-assign]
+        if log.isEnabledFor(logging.DEBUG):
+            atexit.register(log_lru_cache_stats, wrapped_f)  # type: ignore[arg-type]
+        return wrapped_f
+
+    return inner
+
+
+# These are modules that contain generic code for interacting with ShapeEnv
+# which are unlikely to identify a particular interesting guard statement
+@lru_cache(None)
+def uninteresting_files() -> set[str]:
+    import torch._compile
+    import torch._dynamo.eval_frame
+    import torch._inductor.sizevars
+    import torch._library.custom_ops
+    import torch._library.fake_impl
+    import torch._logging
+    import torch._subclasses.fake_tensor
+    import torch._subclasses.meta_utils
+
+    mods = [
+        sys.modules[__name__],
+        torch.fx.experimental.recording,
+        torch.fx.experimental.sym_node,
+        torch.fx.interpreter,
+        torch,
+        torch._compile,
+        torch._dynamo.eval_frame,
+        torch._inductor.sizevars,
+        torch._library.custom_ops,
+        torch._library.fake_impl,
+        torch._subclasses.meta_utils,
+        torch._subclasses.fake_tensor,
+        torch._logging._internal,
+        torch._logging.structured,
+    ]
+    import torch._dynamo.guards
+
+    return (
+        {inspect.getfile(m) for m in mods}
+        | torch._dynamo.guards.uninteresting_files()
+        | {""}
+    )
+
+
+class ConstraintViolationError(RuntimeError):
+    pass
+
+
+def has_symbolic_sizes_strides(elem: torch.Tensor) -> bool:
+    return elem._has_symbolic_sizes_strides
+
+
+Int: TypeAlias = Union[torch.SymInt, int]
+
+
+def create_contiguous(shape: Sequence[Int]) -> list[Int]:
+    strides: list[Int] = [1]
+    for dim in reversed(shape[:-1]):
+        strides.append(dim * strides[-1])  # type: ignore[operator]
+    return list(reversed(strides))
+
+
+def hint_int(a: Union[torch.SymInt, int], fallback: Optional[int] = None) -> int:
+    """
+    Retrieve the hint for an int (based on the underlying real values as observed
+    at runtime).  If no hint is available (e.g., because data dependent shapes),
+    if fallback is not None, use that instead (otherwise raise an error).
+    """
+    if isinstance(a, torch.SymInt):
+        return a.node.require_hint(fallback)
+    assert type(a) is int, a
+    return a
+
+
+Scalar: TypeAlias = Union[torch.SymInt, torch.SymFloat, torch.SymBool, int, float, bool]
+
+
+def has_hint(a: Scalar) -> bool:
+    if isinstance(a, SymTypes):
+        return a.node.has_hint()
+    return True
+
+
+def is_concrete_int(a: Union[int, SymInt]) -> bool:
+    """
+    Utility to check if underlying object
+    in SymInt is concrete value. Also returns
+    true if integer is passed in.
+
+    Args:
+        a (SymInt or int): Object to test if it int
+    """
+    assert isinstance(a, (SymInt, int))
+
+    if isinstance(a, int):
+        return True
+
+    if isinstance(a.node.expr, sympy.core.numbers.Integer):
+        return True
+
+    return False
+
+
+def is_concrete_float(a: Union[float, SymFloat]) -> bool:
+    r"""Utility to check if underlying object
+    in SymInt is concrete value. Also returns
+    true if integer is passed in.
+
+    Args:
+        a (SymInt or float): Object to test if it float
+    """
+    assert isinstance(a, (SymFloat, float))
+
+    if isinstance(a, float):
+        return True
+
+    if isinstance(a.node.expr, sympy.core.numbers.Float):
+        return True
+
+    return False
+
+
+def guard_size_oblivious(expr: Union[torch.SymBool, bool]) -> bool:
+    """
+    Perform a guard on a symbolic boolean expression in a size oblivious way.
+    This is typically used when a non-oblivious test would result in a guard
+    on a data dependent value of which we don't know the value of at compile time.
+    When a guard is tested this way, we may diverge in behavior from how regular
+    PyTorch semantics would treat it.  For more information, see
+    https://github.com/pytorch/pytorch/pull/118579
+    """
+    if isinstance(expr, torch.SymBool):
+        return expr.node.guard_size_oblivious("", 0)
+    else:
+        assert isinstance(expr, bool), expr
+        return expr
+
+
+def _guard_sizes_oblivious(
+    lhs_sizes: Sequence[Union[torch.SymInt, bool]],
+    rhs_sizes: Sequence[Union[torch.SymInt, bool]],
+) -> bool:
+    """
+    Leverage guard_size_oblivious to compare if two lists of int/symint are equal.
+    Useful to compare sizes, strides etc.
+    """
+
+    return len(lhs_sizes) == len(rhs_sizes) and all(
+        guard_size_oblivious(lhs_item == rhs_item)
+        for lhs_item, rhs_item in zip(lhs_sizes, rhs_sizes)
+    )
+
+
+def check_consistent(new: _T, old: _T) -> None:
+    """
+    Test that two "meta" values (typically either Tensor or SymInt) have
+    the same values, e.g., after retracing.  If we don't understand the
+    quantities in question, we'll just skip the consistency check.
+    """
+    # TODO: do boolean equality test too, see
+    # https://github.com/pytorch/pytorch/issues/124110
+    scalar_types = (torch.SymInt, torch.SymFloat, int, float)
+
+    if isinstance(new, torch.Tensor):
+        assert isinstance(old, torch.Tensor)
+        torch._check(
+            old.dim() == new.dim(), lambda: f"{old.shape} != {new.shape} (old != new)"
+        )
+        # Do this manually so that each individual test is irrefutable
+        # (TODO: should be a helper for this, maybe sym_eq?  That
+        # gives us a compound expression and I'm not sure it
+        # simplifies right now)
+        for i, j in zip(old.shape, new.shape):
+            torch._check(i == j, lambda: f"{old.shape} != {new.shape} (old != new)")
+    # NB: bool is subclass of int
+    elif isinstance(new, scalar_types) and not isinstance(new, bool):
+        assert isinstance(old, scalar_types) and not isinstance(
+            old, bool
+        ), f"{old} != {new}"
+        torch._check(old == new, lambda: f"{old} != {new} (old != new)")
+
+
+def resolve_unbacked_bindings(
+    shape_env: Optional[ShapeEnv],
+    bindings: Optional[dict[sympy.Symbol, pytree.KeyPath]],
+) -> Optional[dict[sympy.Symbol, pytree.KeyPath]]:
+    if bindings is None:
+        return None
+    assert shape_env is not None
+    return {shape_env.unbacked_renamings.get(k, k): v for k, v in bindings.items()}
+
+
+Result: TypeAlias = Union[torch.Tensor, tuple[torch.Tensor, ...]]
+
+
+def rebind_unbacked(
+    shape_env: Optional[ShapeEnv], n: torch.fx.Node, result: Result
+) -> None:
+    """
+    Suppose we are retracing a pre-existing FX graph that previously had
+    fake tensor propagation (and therefore unbacked SymInts).  When we retrace,
+    we re-propagate fake tensors, which results in new unbacked SymInts.
+    When this happens, we need to tell the shape environment about the equivalence
+    of the old and new unbacked SymInts.  Pass us the old torch.fx.Node (which
+    has the old binding information) and the new result (which we can extract the
+    new unbacked SymInts out from).
+    """
+
+    # Inputs never need rebinding
+    if n.op == "placeholder":
+        return
+
+    if bindings := resolve_unbacked_bindings(
+        shape_env, n.meta.get("unbacked_bindings")
+    ):
+        assert shape_env is not None
+        for raw_u0, path in bindings.items():
+            u1 = pytree.key_get(result, path)
+            # Sometimes, things were previously unbacked bindings become constants.
+            # There are two situations this can happen.
+            #
+            # First, you might have a runtime assert that causes the
+            # constant-ification.  In this case, the /binding/ itself will
+            # still be an unbacked symbol (because we will only force it
+            # to be a constant later in fake tensor propagation).  In this
+            # case, u1 is a SymInt and we still do all our work as normal.
+            #
+            # But second, it might be that fake tensor propagation DIRECTLY
+            # converted the unbacked SymInt into a constant.  This happens
+            # more rarely, but we have identified two situations it can
+            # validly occur:
+            #
+            # - If you have a tensor_version operator, these are initially
+            #   allocated as unbacked SymInts, but after AOTAutograd they
+            #   get forced specialized to specific values.  In this case,
+            #   there is no reason to do runtime asserts on them, this is
+            #   just a hack to properly keep track of them to start.
+            #
+            # - If you have an item() call on a constant tensor, the result
+            #   of the item() call is constant and we do not need runtime
+            #   asserts on this symbol.  In
+            #   https://github.com/pytorch/pytorch/issues/140625 we have a
+            #   case where in the initial trace of the program we are unable
+            #   to determine that torch.tensor is constant, but then
+            #   subsequent passes cause torch.tensor to become a constant and
+            #   then the unbacked symbol goes poof.
+            #
+            # In all of these cases, it is no longer necessary to generate
+            # deferred runtime asserts, since other subsystems (e.g., the
+            # constant-ification pass) ensure that the quantity is now truly
+            # static and cannot change at runtime.  So it's OK to discard
+            # in these situations.
+            #
+            # There is one more hazard (re
+            # https://github.com/pytorch/pytorch/issues/141248), the problem
+            # is that you can end up with "dangling" unbacked symbols that
+            # exist in the ShapeEnv but are never bound anywhere.  You might
+            # like an invariant that unbacked symbols never get lost.  But
+            # we do not have this invariant, so do not try to enforce it.
+            if isinstance(u1, int):
+                log.info(
+                    "rebind_unbacked: discard %s %s %s -> %s",
+                    n.target,
+                    raw_u0,
+                    path,
+                    u1,
+                )
+                continue
+
+            # We only care about rebinding unbacked things
+            if u1.node.hint is not None:
+                continue
+
+            raw_u1 = u1.node.expr
+            # Simplify SymBool binding
+            if (
+                isinstance(raw_u1, sympy.Piecewise)
+                and len(raw_u1.args) == 2
+                and (
+                    raw_u1_args0 := cast(
+                        tuple[sympy.Basic, sympy.Basic], raw_u1.args[0]
+                    )
+                )
+                and raw_u1_args0[0] == 1
+                and isinstance(eq := raw_u1_args0[1], sympy.Eq)
+                and isinstance(new_raw_u1 := eq.lhs, sympy.Symbol)
+                and shape_env.var_to_range[new_raw_u1].issubset(ValueRanges(0, 1))
+                and eq.rhs == 1
+                and cast(tuple[sympy.Basic, sympy.Basic], raw_u1.args[1]) == (0, True)
+            ):
+                # This is what the pattern match above is testing
+                repacked = _sympy_cast_symbool_to_symint_guardless(
+                    sympy.Eq(new_raw_u1, 1)
+                )
+                assert repacked == raw_u1, f"{repacked} != {raw_u1}"
+                # Cancel the to_int(to_bool(x)). This is sound because x in
+                # [0, 1]
+                raw_u1 = new_raw_u1
+
+            if not isinstance(raw_u1, sympy.Symbol):
+                assert (
+                    not raw_u1.free_symbols
+                ), f"should have been constant, but got {raw_u1}"
+                continue
+
+            # The old and new could be the same if you improperly hit the memo
+            # while retracing.  Make sure you updated FakeTensorMode.epoch
+            assert raw_u0 != raw_u1, f"{raw_u0} possible memo disaster"
+            # Reuse the OLD symbol name
+            shape_env._rename_unbacked_to(raw_u1, raw_u0)
+
+
+# NB: You could try to expand this to cover more cases by simply
+# detecting whenever you have an int output, but this is a bit
+# dangerous in case someone adds a function that returns an int but is
+# mutating.  So manually whitelist for now.
+def is_accessor_node(node: torch.fx.Node) -> bool:
+    # Dynamo only exercised condition
+    if (
+        node.op == "call_method"
+        and isinstance(node.args[0], torch.fx.Node)
+        and isinstance(node.args[0].meta.get("example_value"), torch.Tensor)
+        and node.target in ["size", "stride", "storage_offset", "item"]
+    ):
+        return True
+    if node.op == "call_function" and node.target in [
+        torch.ops.aten.sym_size,
+        torch.ops.aten.sym_size.default,
+        torch.ops.aten.sym_size.int,
+        torch.ops.aten.sym_stride,
+        torch.ops.aten.sym_stride.default,
+        torch.ops.aten.sym_stride.int,
+        torch.ops.aten.sym_storage_offset,
+        torch.ops.aten.sym_storage_offset.default,
+        torch.ops.aten.sym_numel.default,
+    ]:
+        return True
+    return False
+
+
+def canonicalize_bool_expr(expr: _T) -> _T:
+    """
+    Canonicalize a boolean expression by transforming it into a lt / le
+    inequality and moving all the non-constant terms to the rhs.
+    We canonicalize And / Ors / Not via cnf and then canonicalize their subexpr
+    recursively
+    nb. sympy.Rel.canonical is not good enough https://github.com/sympy/sympy/issues/25924
+
+    Args:
+        expr (sympy.Expr): Expression to canonicalize
+    """
+    # Canonicalise an inequality by transforming it into a lt / le
+    # inequality and moving all the non-constant terms to the rhs
+    # We canonicalise And / Ors / Not via cnf
+    # nb. Relational.canonical in sympy is broken
+    # https://github.com/sympy/sympy/issues/25924
+
+    if not isinstance(
+        expr, (sympy.Rel, sympy.And, sympy.Or, sympy.Not, sympy.Eq, sympy.Ne)
+    ):
+        return expr
+
+    if isinstance(expr, (sympy.And, sympy.Or, sympy.Not)):
+        expr = sympy.logic.boolalg.to_cnf(expr)
+    return _canonicalize_bool_expr_impl(expr)  # type: ignore[arg-type, return-value]
+
+
+def _sympy_from_args(
+    cls: type[Union[sympy.Add, sympy.Mul]],
+    args: list[sympy.Expr],
+    sort: bool = True,
+    is_commutative: Optional[bool] = None,
+) -> sympy.Expr:
+    if not args:
+        return cls.identity  # type: ignore[union-attr]
+    # These args are already in canonical form, so we avoid calling
+    # Add(*args) to avoid expensive Add.flatten operation
+    if sort:
+        if cls is sympy.Add:
+            sort_fn = sympy.core.add._addsort
+        elif cls is sympy.Mul:
+            sort_fn = sympy.core.mul._mulsort
+        else:
+            raise ValueError(f"Unknown cls: {cls}")
+
+        # we don't support non commutative with sort
+        assert is_commutative is True
+        if args[0].is_Number:
+            rest = args[1:]
+            sort_fn(rest)
+            return cls._from_args([args[0]] + rest, is_commutative=is_commutative)  # type: ignore[attr-defined]
+        else:
+            args = args.copy()
+            sort_fn(args)
+            return cls._from_args(args, is_commutative=is_commutative)  # type: ignore[attr-defined]
+    else:
+        # if the args are already sorted, we create directly
+        return cls._from_args(args, is_commutative=is_commutative)  # type: ignore[attr-defined]
+
+
+def _canonicalize_bool_expr_impl(expr: SympyBoolean) -> SympyBoolean:
+    """
+    After canonicalization, we are guaranteed to have eliminated Ge/Gt relations
+    (rewriting them to Le/Lt, respectively).
+    """
+    if isinstance(expr, (sympy.And, sympy.Or)):
+        return type(expr)(*map(canonicalize_bool_expr, expr.args))
+
+    opposite = {sympy.Gt: sympy.Lt, sympy.Ge: sympy.Le}
+    t: Union[type[Any]]
+    if isinstance(expr, tuple(opposite.keys())):
+        rhs = expr.lhs - expr.rhs  # type: ignore[attr-defined]
+        t = opposite[type(expr)]  # type: ignore[index]
+    else:
+        assert isinstance(expr, (sympy.Lt, sympy.Le, sympy.Eq, sympy.Ne))
+        rhs = expr.rhs - expr.lhs
+        t = type(expr)
+
+    def is_neg(t: sympy.Expr) -> bool:
+        return (t.is_Number and t.is_negative) or (
+            isinstance(t, sympy.Mul) and t.args[0].is_Number and t.args[0].is_negative
+        )
+
+    lhs = S.Zero
+    rhs = _reduce_to_lowest_terms(rhs)
+    if isinstance(rhs, sympy.Add):
+        pos = []
+        neg = []
+        for term in rhs.args:
+            if is_neg(term):
+                neg.append(-term)
+            else:
+                pos.append(term)
+        # these are already sorted
+        rhs = _sympy_from_args(sympy.Add, pos, sort=False, is_commutative=True)
+        # the terms were changed, so needs a sorting
+        lhs = _sympy_from_args(sympy.Add, neg, sort=True, is_commutative=True)
+    elif is_neg(rhs):
+        # lhs == 0
+        lhs, rhs = -rhs, S.Zero
+    # We don't have to evaluate here because lhs, rhs came from a Boolean
+    # and it was already simplified
+    return t(lhs, rhs, evaluate=False)
+
+
+def _reduce_to_lowest_terms(expr: sympy.Expr) -> sympy.Expr:
+    """
+    Eliminates any integer factor from a given expression.
+    E.g., 6x + 4y reduces to 3x + 2y.
+
+    Useful when an expression is == or != to 0.
+    """
+
+    def integer_coefficient(x: sympy.Expr) -> int:
+        if x.is_Integer:
+            return abs(int(x))
+        elif x.is_Mul:
+            # If one of the args of a Mul is an Integer, it is the
+            # first arg. eg: args(2*x*3*y) == (6, x, y)
+            return abs(int(x.args[0])) if x.args[0].is_Integer else 1  # type: ignore[call-overload]
+        else:
+            return 1
+
+    def div_by_factor(x: sympy.Expr, factor: int) -> sympy.Expr:
+        if x.is_Integer:
+            return x / factor
+        elif x.is_Mul:
+            if x.args[0] != factor:
+                args = [x.args[0] / sympy.Integer(factor), *x.args[1:]]
+            else:
+                # Mul._from_args require a canonical list of args
+                # so we remove the first arg (x.args[0] / factor) if it was 1
+                args = list(x.args[1:])
+            return _sympy_from_args(sympy.Mul, args, is_commutative=x.is_commutative)
+        else:
+            raise AssertionError(f"illegal arg to div_by_factor: {x}")
+
+    if expr.is_Add:
+        atoms = cast(Sequence[sympy.Expr], expr.args)
+        factor = functools.reduce(math.gcd, map(integer_coefficient, atoms))
+        if factor == 1:
+            return expr
+        atoms = [div_by_factor(x, factor) for x in atoms]
+        return _sympy_from_args(
+            sympy.Add, atoms, sort=True, is_commutative=expr.is_commutative
+        )
+    elif expr.is_Integer:
+        return S.One
+    elif expr.is_Mul:
+        return div_by_factor(expr, integer_coefficient(expr))
+    return expr
+
+
+def is_concrete_bool(a: Union[bool, SymBool]) -> bool:
+    """
+    Utility to check if underlying object
+    in SymBool is concrete value. Also returns
+    true if integer is passed in.
+
+    Args:
+        a (SymBool or bool): Object to test if it bool
+    """
+    assert isinstance(a, (SymBool, bool))
+
+    if isinstance(a, bool):
+        return True
+
+    if isinstance(
+        a.node.expr, (sympy.logic.boolalg.BooleanTrue, sympy.logic.boolalg.BooleanFalse)
+    ):
+        return True
+
+    return False
+
+
+def is_nested_int(s: Union[int, SymInt]) -> TypeGuard[SymInt]:
+    return isinstance(s, torch.SymInt) and s.node.is_nested_int()
+
+
+IterateExprsAtom: TypeAlias = Union[
+    SymInt, SymFloat, SymBool, int, float, bool, sympy.Basic, torch.Tensor
+]
+IterateExprs: TypeAlias = Union[IterateExprsAtom, Sequence[IterateExprsAtom]]
+
+
+def _iterate_exprs(val: IterateExprs) -> Iterator[sympy.Basic]:
+    if isinstance(val, SymTypes):
+        # This allow applies to the jagged layout NestedTensor case as
+        # nested ints are not symbolic
+        if is_symbolic(val):
+            yield val.node.expr
+    elif isinstance(val, sympy.Basic):
+        yield val
+    elif isinstance(val, (int, float, bool)):
+        pass
+    elif isinstance(val, (tuple, list)):
+        for s in val:
+            yield from _iterate_exprs(s)
+    elif is_sparse_any(val):
+        yield from _iterate_exprs(val.size())
+    elif isinstance(val, torch.Tensor):
+        yield from _iterate_exprs(val.size())
+        yield from _iterate_exprs(val.stride())
+        yield from _iterate_exprs(val.storage_offset())
+    elif val is None:
+        pass
+    # see Note: [Generator arguments in AOTDispatcher]
+    elif isinstance(val, torch.Generator):
+        pass
+    else:
+        raise AssertionError(f"cannot extract sympy expressions from {val} {type(val)}")
+
+
+def free_symbols(val: IterateExprs) -> OrderedSet[sympy.Symbol]:
+    if val is None:
+        return OrderedSet()
+    itr = _iterate_exprs(val)
+    # we need at least 1 to call union, so we hand code the identity
+    try:
+        first_expr = next(itr)
+    except StopIteration:
+        return OrderedSet()
+
+    # TODO: Apparently, returning an OrderedSet here breaks
+    # python test/distributed/tensor/test_dtensor_compile.py TestDTensorCompile.test_dtensor_dynamic
+    return first_expr.free_symbols.union(*(e.free_symbols for e in itr))  # type: ignore[return-value]
+
+
+def has_free_symbols(val: IterateExprs) -> bool:
+    """Faster version of bool(free_symbols(val))"""
+    return not all(e.is_number for e in _iterate_exprs(val))
+
+
+def has_free_unbacked_symbols(x: IterateExprs) -> bool:
+    """Faster version of bool(free_unbacked_symbols(val))"""
+    from sympy.core.traversal import iterargs
+
+    for s in _iterate_exprs(x):
+        for arg in iterargs(s):
+            if arg.is_Symbol and symbol_is_type(
+                arg, (SymT.UNBACKED_INT, SymT.UNBACKED_FLOAT)
+            ):
+                return True
+    return False
+
+
+# Like free_symbols, but filtered to only report unbacked symbols
+def free_unbacked_symbols(x: IterateExprs) -> OrderedSet[sympy.Symbol]:
+    # NB: keep synced with is_unbacked_symint
+    return OrderedSet(
+        s
+        for s in free_symbols(x)
+        if symbol_is_type(s, (SymT.UNBACKED_INT, SymT.UNBACKED_FLOAT))
+    )
+
+
+# WARNING: Don't use this on Dynamo produced graphs, they don't have meta
+# setup!
+def is_symbol_binding_fx_node(node: torch.fx.Node) -> Optional[sympy.Symbol]:
+    if (
+        "val" in node.meta
+        and isinstance(node.meta["val"], torch.SymInt)
+        and isinstance(node.meta["val"].node.expr, sympy.Symbol)
+        and (
+            node.op == "placeholder"
+            or free_unbacked_symbols(node.meta["val"].node.expr)
+        )
+    ):
+        return node.meta["val"].node.expr
+    return None
+
+
+def find_symbol_binding_fx_nodes(
+    graph: torch.fx.Graph,
+) -> dict[sympy.Symbol, torch.fx.Node]:
+    r = {}
+    # NB: Prefer first occurrence of symbol
+    for node in graph.nodes:
+        if (s := is_symbol_binding_fx_node(node)) is not None and s not in r:
+            r[s] = node
+    return r
+
+
+# Analogous to ConvertIntSource
+@dataclass(frozen=True)
+class ConvertIntKey:
+    def __str__(self) -> str:
+        return ".cast_symbool_to_symint_guardless()"
+
+    def get(self, b: bool) -> Union[int, SymInt]:
+        """Get the int value from bool"""
+        return cast_symbool_to_symint_guardless(b)
+
+
+@dataclass(frozen=True)
+class CallMethodKey:
+    name: str
+
+    def __str__(self) -> str:
+        return f".{self.name}()"
+
+    def get(self, o: Any) -> Any:
+        """Call the method on object"""
+        return getattr(o, self.name)()
+
+
+@dataclass(frozen=True)
+class InnerTensorKey:
+    inner_name: str
+
+    def __str__(self) -> str:
+        return f".{self.inner_name}"
+
+    def get(self, o: Any) -> Any:
+        """Get the inner tensor attribute"""
+        return getattr(o, self.inner_name)
+
+
+@dataclass(frozen=True)
+class DivideByKey:
+    divisor: Union[int, SymInt]
+
+    def __str__(self) -> str:
+        return f".__floordiv__({self.divisor})"
+
+    def get(self, o: int) -> int:
+        """Divide object by divisor"""
+        return o // self.divisor
+
+
+def _free_unbacked_symbols_with_path(
+    a: object,
+    path: pytree.KeyPath,
+    real: Optional[object] = None,
+    shape_env: Optional[ShapeEnv] = None,
+    pending: Optional[set[sympy.Symbol]] = None,
+    simplify: bool = False,
+) -> dict[sympy.Symbol, pytree.KeyPath]:
+    go = functools.partial(
+        _free_unbacked_symbols_with_path,
+        shape_env=shape_env,
+        pending=pending,
+        simplify=simplify,
+    )
+
+    def expr(s: Union[SymInt, SymFloat, SymBool]) -> sympy.Expr:
+        if simplify:
+            return s.node.expr
+        # (When called from compute_unbacked_bindings)
+        # NB: Intentionally access _expr, not expr, do not want
+        # simplification!
+        return s.node._expr
+
+    if pending is None:
+        pending = set()
+    r = {}
+    if isinstance(a, (tuple, list)):
+        # NB: real is apparently not always a tuple/list here
+        # python test/inductor/test_torchinductor.py CpuTests.test_index_propagation_nested_indirect_indexing_cpu
+        for i in range(len(a)):
+            r.update(
+                go(
+                    a[i],
+                    path + (pytree.SequenceKey(i),),
+                    real=real[i] if real is not None else None,  # type: ignore[index]
+                )
+            )
+    elif is_traceable_wrapper_subclass(a):
+        # TODO: Determine if this is correct
+        attrs, _ = a.__tensor_flatten__()
+        for attr in attrs:
+            sub = getattr(a, attr)
+            r.update(go(sub, path + (InnerTensorKey(attr),)))
+    elif isinstance(a, torch.Tensor):
+        from torch._subclasses.fake_tensor import FakeTensor
+
+        assert isinstance(a, FakeTensor)
+        r.update(
+            go(
+                a.size(),
+                path + (CallMethodKey("size"),),
+                real=a.real_tensor.size() if a.real_tensor is not None else None,
+            )
+        )
+        if a.layout not in [
+            torch.sparse_csr,
+            torch.sparse_csc,
+            torch.sparse_bsr,
+            torch.sparse_bsc,
+        ]:
+            r.update(
+                go(
+                    a.stride(),
+                    path + (CallMethodKey("stride"),),
+                    real=a.real_tensor.stride() if a.real_tensor is not None else None,
+                )
+            )
+        r.update(
+            go(
+                a.storage_offset(),
+                path + (CallMethodKey("storage_offset"),),
+                real=(
+                    a.real_tensor.storage_offset()
+                    if a.real_tensor is not None
+                    else None
+                ),
+            )
+        )
+
+    elif (
+        isinstance(a, (torch.SymInt, torch.SymFloat))
+        and isinstance(s := expr(a), sympy.Symbol)
+        and s in pending
+    ):
+        r[s] = path
+        if shape_env and real is not None:
+            assert isinstance(real, (int, float))
+            shape_env.set_unbacked_var_to_val(s, real)
+        pending.remove(s)
+    # When an unbacked SymInt is perfectly divisible by an integer
+    # constant, we replace it with the integer constant to improve
+    # reasoning capabilities.  However, in synthetic examples, it is
+    # then possible that the factor never is explicitly allocated.
+    # Fortunately, we can compute it by division.
+    elif (
+        isinstance(a, torch.SymInt)
+        and isinstance(s := expr(a), sympy.Mul)
+        and len(s.args) == 2
+        and isinstance(lhs := s.args[0], (sympy.Integer, sympy.Symbol))
+        and isinstance(rhs := s.args[1], sympy.Symbol)
+        # support exactly one unbacked for now
+        and ((rhs in pending) ^ (lhs in pending))
+        # support constant coefficient or backed symbolic coefficient
+        and (
+            isinstance(coeff := lhs if lhs not in pending else rhs, sympy.Integer)
+            or shape_env
+            and coeff in shape_env.var_to_val
+        )
+    ):
+
+        def _symint_wrap(s: sympy.Symbol) -> SymInt:
+            return shape_env.create_symintnode(  # type: ignore[union-attr]
+                s,
+                hint=int(shape_env.var_to_val[s]),  # type: ignore[union-attr]
+                source=shape_env.var_to_sources.get(s, [None])[0],  # type: ignore[union-attr]
+            )
+
+        unbacked = lhs if lhs in pending else rhs
+        divisor: Union[int, SymInt] = (
+            int(coeff)
+            if shape_env and isinstance(coeff, sympy.Integer)
+            else _symint_wrap(coeff)
+        )
+        # TODO: DivideByKey needs to test divisibility at runtime!
+        r[unbacked] = path + (DivideByKey(divisor),)
+        if real is not None:
+            assert isinstance(real, int)
+            val = (
+                real // int(coeff)
+                if isinstance(coeff, sympy.Integer)
+                else CleanDiv(real, coeff)
+            )
+            if shape_env:
+                shape_env.set_unbacked_var_to_val(unbacked, val)
+        pending.remove(unbacked)
+    # The annoyance here arises from the fact that SymBool is
+    # allocated by allocating a SymInt and then testing if it's equal
+    # to one.  So you have a complicated binding site logic for this.
+    elif (
+        isinstance(a, torch.SymBool)
+        and isinstance(s := expr(a), sympy.Eq)
+        # This must match create_unbacked_symbool EXACTLY
+        and isinstance(s.lhs, sympy.Symbol)
+        and s.rhs == 1
+        and s.lhs in pending
+    ):
+        r[s.lhs] = path + (ConvertIntKey(),)
+        if real is not None:
+            assert type(real) is bool
+            if shape_env:
+                shape_env.set_unbacked_var_to_val(s, int(real))
+        pending.remove(s.lhs)
+
+    return r
+
+
+def compute_unbacked_bindings(
+    shape_env: Optional[ShapeEnv],
+    example_value: object,
+    old_example_value: Optional[object] = None,
+    peek: bool = False,
+) -> Optional[dict[sympy.Symbol, pytree.KeyPath]]:
+    """
+    After having run fake tensor propagation and producing example_value
+    result, traverse example_value looking for freshly bound unbacked
+    symbols and record their paths for later.  It is an error if
+    we have allocated an unbacked SymInt but it cannot be found in
+    example_value.  (NB: this means if you have a multi-output
+    function, you must call this on the tuple of tensor output, you
+    cannot wait!)
+
+    The peek parameter lets you check out what the bindings are without
+    changing the affected list.  This is primarily useful for ensuring
+    unbacked_var_to_val is promptly populated when propagate_real_tensors is on.
+    """
+    if shape_env is None:
+        return None
+
+    fs = shape_env.pending_fresh_unbacked_symbols
+    pending = set(fs)
+    if not pending:
+        return None
+
+    if not peek:
+        log.info("compute_unbacked_bindings %s", fs)
+        fs.clear()
+
+    symbol_to_path = _free_unbacked_symbols_with_path(
+        example_value, (), shape_env=shape_env, pending=pending, simplify=False
+    )
+    if not peek and pending:
+        extra = (
+            repr((example_value.stride(), example_value.storage_offset()))
+            if isinstance(example_value, torch.Tensor)
+            else ""
+        )
+        raise PendingUnbackedSymbolNotFound(
+            f"Pending unbacked symbols {pending} not in returned outputs {example_value} {extra}.\n"
+            "Did you accidentally call new_dynamic_size() or item() more times "
+            "than you needed to in your fake implementation?\n"
+            "For more help, see https://docs.google.com/document/d/1RWrH-3wLEpzR9kCS6gGBNen_-Fs-8PVbWWFE5AcgeWE/edit"
+        )
+
+    # Why do we have to do some rebinding here?  If the original FX node
+    # wasn't a binding site because you had a memo hit, but post
+    # translation you aren't a memo hit anymore, there's now a new binding
+    # site... but we know (because it's the same FX node) that the value
+    # is actually the same, they're just not obviously equal anymore.
+    #
+    # The logic here is written carefully, because unlike the
+    # bind_unbacked case, we are not guaranteed to have a symbol for
+    # old_sym.  If we have a symbol, do regular rename unbacked to; but if
+    # we don't, we need to specially eliminate the fresh unbacked symbol
+    # (NB: we are /trusting/ that the memoization is correct, and that we
+    # don't need to generate a new runtime assert.  This is load bearing,
+    # as repropagation can happen after we've frozen runtime asserts.)
+    if old_example_value is not None:
+        for keypath in symbol_to_path.values():
+            old_sym = pytree.key_get(old_example_value, keypath)
+            new_sym = pytree.key_get(example_value, keypath)
+            if isinstance(new_sym, SymTypes) and isinstance(
+                new_s := new_sym.node.expr, sympy.Symbol
+            ):
+                if (
+                    isinstance(old_sym, SymTypes)
+                    and (old_s := old_sym.node.expr) != new_s
+                ):
+                    if isinstance(old_s, sympy.Symbol):
+                        shape_env._rename_unbacked_to(new_s, old_s)
+                    else:
+                        shape_env._eliminate_unbacked(new_s, old_s)
+                elif not isinstance(old_sym, SymTypes):
+                    shape_env._eliminate_unbacked(new_s, sympy.sympify(old_sym))
+
+    return symbol_to_path
+
+
+def definitely_true(a: BoolLikeType) -> bool:
+    """
+    Returns True only if we can tell that a is True, possibly introducing
+    a guard in the process.  If a depends on some unbacked SymInt, we may
+    return False even though there may exist a possible value of the SymInt
+    that would cause the expression to return True.
+
+    When is it appropriate to use definitely_true?  First, if you can use
+    a higher level combinator prefer using those instead, they are definitely
+    safe (modulo short-circuiting).
+    Second, it can be used if the program would behave equivalently if
+    definitely_true always returned False. Finally, it even
+    be OK if the program wouldn't behave equivalently, so long as the
+    change is semantics preserving.  It can be semantics preserving if
+    the program errors in more cases than it did previously (but otherwise
+    behaves identically), or if it changes some quantity in a way that
+    doesn't matter (e.g., strides often fall in this bucket.)
+    """
+    if isinstance(a, SymBool):
+        if a.node.has_hint():
+            return guard_bool(a)
+        else:
+            return False
+    return bool(a)
+
+
+def definitely_false(a: BoolLikeType) -> bool:
+    """
+    Returns True only if we can tell that a is False, possibly introducing
+    a guard in the process.  If a depends on some unbacked SymInt, we may
+    return False even though there may exist a possible value of the SymInt
+    that would cause the expression a to be False.  See definitely_true
+    for more usage guidance.
+    """
+    if isinstance(a, SymBool):
+        if a.node.has_hint():
+            return not guard_bool(a)
+        else:
+            return False
+    return not bool(a)
+
+
+def statically_known_true(x: Union[bool, SymBool]) -> bool:
+    """
+    Returns True if x can be simplified to a constant and is true.
+
+    .. note::
+        This function doesn't introduce new guards, so the expression may end
+        up evaluating to true at runtime even if this function returns False.
+
+    Args:
+        x (bool, SymBool): The expression to try statically evaluating
+    """
+    if isinstance(x, SymBool):
+        expr = x.node.expr
+        shape_env = x.node.shape_env
+        try:
+            simplified = shape_env._maybe_evaluate_static(expr)
+            if simplified is not None:
+                return bool(simplified)
+        except Exception:
+            log.debug("Could not simplify %s", expr)
+        return False
+    assert isinstance(x, bool)
+    return x
+
+
+def sym_eq(x: _T, y: _T) -> Union[bool, SymBool]:
+    """
+    Like ==, but when run on list/tuple, it will recursively test equality
+    and use sym_and to join the results together, without guarding.
+    """
+    if (isinstance(x, tuple) and isinstance(y, tuple)) or (
+        isinstance(x, list) and isinstance(y, list)
+    ):
+        if len(x) != len(y):
+            return False
+        return functools.reduce(operator.and_, map(sym_eq, x, y), True)
+    elif isinstance(x, (int, torch.SymInt)) and isinstance(y, (int, torch.SymInt)):
+        return x == y
+    else:
+        raise AssertionError(f"unexpected sym_eq between {type(x)} {type(y)}")
+
+
+def guard_scalar(
+    a: Union[SymBool, SymInt, SymFloat, int, bool, float]
+) -> Union[bool, int, float]:
+    if isinstance(a, (SymBool, bool)):
+        return guard_bool(a)
+    elif isinstance(a, (SymInt, int)):
+        return guard_int(a)
+    elif isinstance(a, (SymFloat, float)):
+        return guard_float(a)
+    else:
+        raise AssertionError(f"unrecognized scalar {a}")
+
+
+def _constrain_symbol_range(
+    shape_env: ShapeEnv, s: sympy.Symbol, compiler_min: int, compiler_max: int
+) -> None:
+    shape_env.constrain_symbol_range(s, compiler_min, compiler_max)
+
+
+def _advise_is_size(a: SymInt) -> None:
+    """
+    Don't use this directly; use torch._check_is_size instead.
+
+    This is a softer version of _constrain_range_for_size (with min=0,
+    max=Inf).  Instead of forcibly constraining a variable (and erroring if we
+    failed to constrain it), it will simply advise us that a size is
+    constrained in some way.  We will always defer a runtime assert for this
+    constraint if we cannot prove it at compile-time, but we we only
+    *sometimes* learn useful extra information at compile-time with this
+    information.  This is in contrast to constrain_range_for_size, where if
+    you don't call that on a fresh unbacked symint, chances are we will choke.
+
+    TODO: Make Dynamo handle this appropriately if this is seen in Dynamo-ed
+    code.  Right now this is only really used in code with AOTAutograd trace
+    through, so it is not a big problem that this isn't supported, but in
+    principle all of this code should be Dynamo'able too.
+
+    TODO: I didn't support min/max because I didn't have a use case where this
+    actually helped.  In principle we can support it, it just makes the
+    implementation below more complicated.
+    """
+
+    # This must always succeed, because the sole allowed caller _check_is_size
+    # was responsible for expect_true'ing this
+    # This assert triggers expensive sym compute, do not do it until its cheap.
+    # assert a >= 0
+
+    # NB: it's important not to constrain range for size for *hinted* SymInts,
+    # because it is not only unsound, it will immediately trip our asserts
+    # that hints have to be consistent with static analysis!  If you somehow
+    # have an unbounded SymInt that later constrains to 1, this will be
+    # inconsistent with the range
+    if (
+        isinstance(a, SymInt)
+        and isinstance(a.node, SymNode)
+        and isinstance(a.node.expr, sympy.Symbol)
+        and a.node.shape_env.is_unbacked_symint(a.node.expr)
+    ):
+        _constrain_range_for_size(a)
+
+
+def _advise_is_bounded(a: SymInt, upper_bound: Union[int, SymInt]) -> None:
+    if (
+        isinstance(a, SymInt)
+        and isinstance(a.node, SymNode)
+        and isinstance(a.node.expr, sympy.Symbol)
+        and a.node.shape_env.is_unbacked_symint(a.node.expr)
+        and isinstance(upper_bound, int)  # TODO: relax
+    ):
+        a.node.shape_env._constrain_is_bounded(a.node.expr, upper_bound)
+
+
+def _constrain_range_for_size(
+    a: SymInt, min: Optional[int] = None, max: Optional[int] = None
+) -> None:
+    """
+    This function is NOT INTENDED to be used by itself.
+    """
+
+    if isinstance(a, (SymFloat, SymBool)):
+        raise ValueError("Constraining SymFloat/SymBool is nyi")
+
+    assert isinstance(a, SymInt), "can only constrain range for SymInt"
+    assert isinstance(a.node.expr, sympy.Symbol), f"constraining non-Symbols NYI: {a}"
+
+    a.node.shape_env._constrain_range_for_size(a.node.expr, min, max)
+
+
+# inclusive both ways
+def constrain_range(
+    a: SymInt, *, min: Optional[int], max: Optional[int] = None
+) -> None:
+    """
+    Applies a constraint that the passed in SymInt must lie between min-max
+    inclusive-inclusive, WITHOUT introducing a guard on the SymInt (meaning
+    that it can be used on unbacked SymInts).  If min/max are None, we assume
+    that the dimension is unbounded in that direction.  Repeated application
+    of constrain_range intersects the ranges.  This is a fairly low level API
+    that doesn't have a lot of safety guarantees (TODO: provide higher level
+    APIs).
+
+    Currently, we use this API in the following circumstance: when we allocate
+    an unbacked SymInt, denoting an integer quantity which is data dependent,
+    we ordinarily do not know anything about what values it may take.  This
+    means that any sort of guard on it will immediately fail.  However, in
+    many cases, we know something about the unbacked SymInt: for example, we
+    know that nonzero(x).size(0) must be >= 0.  We use constrain_range to
+    narrow the possible range, declaring that negative symbols are impossible.
+    This permits to definitely answer True to queries like 'nnz >= 0', even if
+    we don't know what the actual (hinted) value of 'nnz' is.  In fact, we
+    actually use constrain_range to unsoundly discharge common guards: for an
+    unbacked SymInt produced by nonzero, we will also assume that it is not
+    equal to 0/1 (even though these are perfectly possible values at runtime),
+    because we generally expect graphs that are valid for N=2 to also be valid
+    for N=1.
+    """
+    if min is None:
+        min = -int_oo
+    if max is None:
+        max = int_oo
+
+    if max < min:
+        raise ValueError(
+            "Maximum value to constrain_as_size can't be less than the specified min value, "
+            "received min={min} and max={max}"
+        )
+
+    if isinstance(a, int):
+        if not (min <= a <= max):
+            raise ValueError(f"Invalid value {a} for range [{min}:{max}]")
+        return
+
+    a.node.shape_env._constrain_range(a.node.expr, min, max)
+
+
+def constrain_unify(a: torch.SymInt, b: torch.SymInt) -> None:
+    """
+    Given two SymInts, constrain them so that they must be equal.  NB:
+    this will not work with SymInts that represent nontrivial expressions
+    (yet!)
+    """
+    if not isinstance(a, SymInt):
+        if not isinstance(b, SymInt):
+            assert a == b
+            return
+        else:
+            shape_env = b.node.shape_env
+    else:
+        shape_env = a.node.shape_env
+
+    shape_env._constrain_unify(a, b)
+
+
+# Assume that a boolean is true for the purposes of subsequent symbolic
+# reasoning.  This will keep track of corresponding runtime checks to verify
+# that the result is upheld: either as a regular guard, or as a special set
+# of asserts which are triggered when an unbacked SymInt is allocated.
+#
+# DO NOT use this function for these cases:
+#
+#  - This is inappropriate for "branching" conditions (where both
+#    true and false result in valid programs).  We will always assume
+#    the condition evaluates true, and so it will never be possible
+#    to trace the false condition when you use it.  For true branching
+#    on unbacked SymInts, you must use torch.cond; if you incorrectly
+#    use expect_true in this case, you will make the false branch
+#    unreachable (as we will simply assume that only the true branch
+#    is ever exercised).
+#
+#  - This is inappropriate for situations where you know some other system
+#    invariant guarantees that this property holds, since you don't
+#    really need to insert a runtime check in that case.  Use something
+#    like constrain_range in that case.
+#
+# This API has a hitch.  To avoid having to reimplement error reporting
+# capabilities, this function CAN return False.  The invariant is that
+# the surrounding code must raise an error when this function returns
+# False.  This is quite low level, so we recommend using other functions
+# like check() which enforce this in a more intuitive way.
+#
+# By the way, this name is a nod to the __builtin_expect macro,
+# which is used similarly (but unlike __builtin_expect, you MUST fail
+# in the unlikely branch.)  (I think expect is a good name; in recent
+# versions of C++, this is replaced with [[likely]], which is weaker
+# and not accurate for this function!)
+def expect_true(a: Union[SymBool, bool], skip: int = 0) -> bool:
+    if isinstance(a, SymBool):
+        # TODO: check perf implications of this
+        frame = inspect.currentframe()
+        for _ in range(skip + 1):  # always run this loop at least once
+            if frame is None:
+                break
+            frame = frame.f_back
+        return a.node.expect_true(
+            frame.f_code.co_filename if frame else "", frame.f_lineno if frame else 0
+        )
+    assert type(a) is bool, a
+    return a
+
+
+def guard_bool(a: Union[SymBool, bool]) -> bool:
+    if isinstance(a, SymBool):
+        return a.node.guard_bool("", 0)  # NB: uses Python backtrace
+    assert type(a) is bool, a
+    return a
+
+
+def guard_int(a: Union[SymInt, int]) -> int:
+    if isinstance(a, SymInt):
+        return a.node.guard_int("", 0)  # NB: uses Python backtrace
+    assert type(a) is int, a
+    return a
+
+
+def guard_float(a: Union[SymFloat, float]) -> float:
+    if isinstance(a, SymFloat):
+        return a.node.guard_float("", 0)  # NB: uses Python backtrace
+    assert isinstance(a, float), a
+    return a
+
+
+# Given a GraphModule, return all the FakeTensors for all the placeholders
+def fx_placeholder_vals(gm: torch.fx.GraphModule) -> list[object]:
+    return [n.meta["val"] for n in gm.graph.nodes if n.op == "placeholder"]
+
+
+def fx_placeholder_targets(gm: torch.fx.GraphModule) -> list[str]:
+    return [n.target for n in gm.graph.nodes if n.op == "placeholder"]
+
+
+# Given a GraphModule and arguments to run it with, evaluate that the guards
+# for its associated ShapeEnv are satisfied by the passed arguments.  This
+# WILL check for duck sizing.
+def eval_guards(
+    gm: torch.fx.GraphModule, *args: Tensor, ignore_static: bool = True
+) -> bool:
+    return gm.shape_env.evaluate_guards_for_args(  # type: ignore[operator, union-attr]
+        fx_placeholder_vals(gm), args, ignore_static=ignore_static
+    )
+
+
+def bind_symbols(gm: torch.fx.GraphModule, *args: Tensor) -> dict[sympy.Symbol, int]:
+    return gm.shape_env.bind_symbols(fx_placeholder_vals(gm), args)  # type: ignore[operator, union-attr]
+
+
+class DimDynamic(Enum):
+    """
+    Controls how to perform symbol allocation for a dimension.  It is always
+    sound to default this to DYNAMIC, but the policies DUCK and STATIC can
+    result in better trace-time and compile-time performance, as they reduce
+    the number of allocated symbols and generally make your graph more static.
+
+    NB: If we notice you've applied a constraint to the dimension, we will
+    force it to DYNAMIC for simplicity.
+
+    DimDynamic is controlled by a variety of higher level UX features.
+    Currently:
+
+    - In eager mode, the default policy is DUCK.
+        - The default is changed to STATIC with assume_static_by_default.
+        - An individual dim is marked DYNAMIC if you mark_dynamic_dim.
+    - In export mode, the default policy is STATIC.
+        - An individual dim is marked DYNAMIC if you specify it in
+          dynamic_shapes passed to export.
+    """
+
+    # Treat the dimension symbolically
+    DYNAMIC = 0
+    # Treat the dimension symbolically, but if its hint matches another
+    # dynamic dimension, unify the two symbols ("duck sizing")
+    DUCK = 1
+    # Treat the dimension statically based on its hint
+    STATIC = 2
+    # Treat the dimension as a size-like unbacked
+    SIZE_LIKE_UNBACKED = 3
+    # Infer the strides from stride. If size is static, strides will be static as well.
+    INFER_STRIDE = 4
+    # Like SIZE_LIKE_UNBACKED, but there's a hint
+    OBLIVIOUS_SIZE = 5
+
+
+# NB: These constraints affect both clients and backends: given some
+# constraint C, the client must pass inputs that satisfy the constraint,
+# while a backend must not introduce guards BEYOND this constraint.
+# For clarity, we document the implications on both sides for both the client
+# and the backend.
+#
+# NB: These constraints are on a *single* dimension.  In principle, we could
+# also have multi-dimension constraints, but our guess is that this is not
+# actually useful and so we are not supporting it right now.
+#
+# NB: Strict constraints are typically only suitable for export, as in eager
+# a backend like inductor may validly introduce extra, discretionary guards
+# to improve performance of code.  A StrictMinMaxConstraint would be brittle
+# under future optimizations performed by inductor; we don't guarantee
+# eager code with StrictMinMaxConstraint will keep working in the future!
+
+
+@dataclass(frozen=True)
+class Constraint:
+    warn_only: bool
+
+
+@dataclass(frozen=True)
+class StrictMinMaxConstraint(Constraint):
+    """
+    For clients: the size at this dimension must be within 'vr' (which
+    specifies a lower and upper bound, inclusive-inclusive) AND it
+    must be non-negative and should not be 0 or 1 (but see NB below).
+
+    For backends: there must not be any guards on this dimension which
+    are not implied by the given lower and upper bound.  Regardless of
+    the lower bound, the backend can assume the size is non-negative
+    and that it is not 0 or 1.
+
+    An unbounded StrictMinMaxConstraint can be thought of as a strict version
+    of "RelaxedUnspecConstraint".
+
+    NB: Export will often unsoundly assume that a graph works for 0/1, even
+    though at trace time we assumed size is not 0 or 1.  The idea is that
+    if we produce a graph that works for a range of values, it will be OK
+    for N=0/1 too.
+    """
+
+    vr: ValueRanges
+
+    def render(self, source: Source) -> str:
+        """Format the constrain equation"""
+        # TODO: better printing for -oo and oo
+        return f"{self.vr.lower} <= {source.name()} <= {self.vr.upper}"
+
+
+@dataclass(frozen=True)
+class RelaxedUnspecConstraint(Constraint):
+    """
+    For clients: no explicit constraint; constraint is whatever is implicitly
+    inferred by guards from tracing.
+
+    For backends: there must exist at least TWO possible values for the
+    size at this dimension which satisfy the guards for this dimension.
+
+    In other words, this constraint helps us distinguish between "we don't
+    care if this dimension specializes or not" versus "this dimension must be
+    unspecialized."  However, this constraint doesn't say very much about what
+    specialization is permitted; for example, if we guard on a size being
+    even, this would still be acceptable under an unspec constraint.  This
+    makes RelaxedUnspecConstraint useful for eager mode, where your backend compiler
+    may add constraints to otherwise dynamic dimensions; we can't assert that
+    there are NO guards as this is brittle because compilers should be able to
+    add extra constraints.  If you want to assert that there are no guards,
+    use StrictMinMaxConstraint with an unbounded ValueRanges.
+    """
+
+    def render(self, source: Source) -> str:
+        return f"RelaxedUnspecConstraint({source.name()})"
+
+
+# NB: None here indicates the client constraint is whatever is implicitly
+# inferred by guards from tracing, and that a backend can add whatever guards
+# it wants (including fully specializing the value).
+DimConstraint = Union[StrictMinMaxConstraint, RelaxedUnspecConstraint, None]
+
+
+@dataclass(frozen=True)
+class EqualityConstraint(Constraint):
+    """
+    Represent and decide various kinds of equality constraints between input sources.
+
+    A "source pair" is a pair of input sources for dynamic dimensions that
+    are specified equal. We represent `source_pairs` in a union-find forest
+    so that we can efficiently check whether two such sources are transitively equal.
+
+    A "derived equality" relates an input source to an expression over a root.
+    The root can be another input source, corresponding to some dynamic dimension,
+    or a phantom symbol that does not directly represent any dynamic dimension. We
+    represent `derived_equalities` involving input sources in a transitively-closed map
+    so that we can efficiently check whether an input source is transitively equal to
+    a given expression over another input source.
+    (NOTE: In contrast, it is easy to decide whether an input source is transitively equal
+    to a given expression over a phantom symbol; such expressions are already in canonical
+    form and so the problem reduces to symbolic expression equality.)
+    """
+
+    source_pairs: list[tuple[Source, Source]]
+    derived_equalities: list[
+        tuple[Source, Union[Source, sympy.Symbol], Callable[[sympy.Expr], sympy.Expr]]
+    ]
+    phantom_symbols: list[sympy.Symbol]
+    relaxed_sources: set[Source]
+
+    _parents: dict[Source, Source] = field(init=False)
+    _defs: dict[Source, sympy.Expr] = field(init=False)
+
+    def __post_init__(self) -> None:
+        """
+        Pre-processing to answer queries `is_equal` and `is_derived` below.
+
+        Example: Suppose we are given:
+          source_pairs [a = b, b = c]
+          derived_equalities [d = c + 1, e = d - 1]
+        We first construct a union find with source_pairs:
+          _parents = {a: a, b: a, c: a}
+        Then we compute canonical symbolic expressions, recursively applying derived_equalities
+        until we bottom out:
+          _defs = {d: c + 1, e: (c + 1) - 1 aka c}
+        """
+
+        # self._parents is a map from input sources to input sources where, conceptually,
+        # these are directed edges in a union-find forest
+        _parents: dict[Source, Source] = {}
+        object.__setattr__(self, "_parents", _parents)
+        # self._defs is a map from input sources to "canonical" symbolic expressions,
+        # i.e., unary expressions with symbols that corresponds to regular Dims (i.e.,
+        # not derived Dims)
+        _defs: dict[Source, sympy.Expr] = {}
+        object.__setattr__(self, "_defs", _defs)
+
+        for source1, source2 in self.source_pairs:
+            # preprocess into a union-find forest
+            self._union(self._find(source1), self._find(source2))
+        for source, root, fn in self.derived_equalities:
+            # preprocess into a transitively-closed map
+            # NOTE(avik): we reuse the union-find forest for canonicalizing input sources
+            if isinstance(root, sympy.Symbol):
+                self._defs[self._find(source)] = fn(root)
+            else:
+                self._defs[self._find(source)] = fn(self._rewrite(root))
+
+    def _find(self, source: Source) -> Source:
+        # chase edges to find the root of this equivalence class
+        if source in self._parents:
+            return self._find(self._parents[source])
+        else:
+            return source
+
+    def _union(self, root1: Source, root2: Source) -> None:
+        # merge two equivalence classes by adding an edge from one root to the other
+        if root1 != root2:
+            self._parents[root1] = root2
+
+    def _rewrite(self, src: Source) -> sympy.Expr:
+        # always represent the given source by the root of its equivalence class
+        src = self._find(src)
+        if src in self._defs:
+            # simply look up the definition if it exists
+            # NOTE(avik): This works because definitions are always transitively-closed;
+            # otherwise we would have to do recursive rewriting.
+            return self._defs[src]
+        else:
+            # otherwise, create a symbol representing the source
+            return sympy.Symbol(src.name())
+
+    def is_equal(self, source1: Source, source2: Source) -> bool:
+        return (
+            # check whether source1 and source2 have the same root
+            # or are relaxed
+            (src1 := self._find(source1)) in self.relaxed_sources
+            or (src2 := self._find(source2)) in self.relaxed_sources
+            or src1 == src2
+            # check whether source1 is derived equal to source2
+            or self.is_derived(source1, source2, lambda x: x)
+        )
+
+    def is_derived(
+        self, src: Source, symbol_src: Source, fn: Callable[[sympy.Expr], sympy.Expr]
+    ) -> bool:
+        # check whether both src and symbol_src have the same definition
+        return self._rewrite(src) == fn(self._rewrite(symbol_src))
+
+
+def _assert_symbol_context(symbolic_context: object) -> TypeGuard[SymbolicContext]:
+    assert isinstance(
+        symbolic_context, SymbolicContext
+    ), "Invalid symbolic_context object"
+    assert (
+        type(symbolic_context) is not SymbolicContext
+    ), "Illegal usage of symbolic_context ABC"
+    return True
+
+
+def _is_supported_equivalence(expr: sympy.Expr) -> bool:
+    # Currently supported Dim ops are linear expressions with integer coefficients.
+    # So check that expr only contains +, *, ints, and a single occurrence of a symbol.
+    # (See also documentation of dynamic_shapes._DerivedDim.)
+    if isinstance(expr, (sympy.Add, sympy.Mul)):
+        if len(expr.args) > 2:
+            return False
+        lhs, rhs = expr.args
+        return (_is_supported_equivalence(lhs) and isinstance(rhs, sympy.Integer)) or (
+            isinstance(lhs, sympy.Integer) and _is_supported_equivalence(rhs)
+        )
+    return isinstance(expr, sympy.Symbol)
+
+
+def _has_uninterpretable_sympy_function(expr: sympy.Basic) -> bool:
+    """
+    Add functions that our sympy interpreter can't reify into FX nodes
+    """
+    return expr.has(
+        torch.utils._sympy.functions.ToFloat,
+        torch.utils._sympy.functions.TruncToInt,
+        torch.utils._sympy.functions.CeilToInt,
+    )
+
+
+@dataclass(frozen=True)
+class SymbolicContext:
+    """
+    Data structure specifying how we should create symbols in
+    ``create_symbolic_sizes_strides_storage_offset``; e.g., should
+    they be static or dynamic.
+
+    This is an abstract base class because we are probably going to add
+    another version of this that says "use exactly these SymInts, don't
+    allocate fresh symbols."
+    """
+
+
+@dataclass(frozen=True)
+class StatelessSymbolicContext(SymbolicContext):
+    """
+    Create symbols in ``create_symbolic_sizes_strides_storage_offset`` via
+    a symbolic_context determination as given by ``DimDynamic`` and ``DimConstraint``.
+    This will cause fresh symbols to be allocated
+    """
+
+    dynamic_sizes: DimList[DimDynamic]
+    dynamic_strides: DimList[DimDynamic] = None  # type: ignore[assignment]
+    constraint_sizes: DimList[DimConstraint] = None  # type: ignore[assignment]
+    constraint_strides: DimList[DimConstraint] = None  # type: ignore[assignment]
+    # If the tensor is a view, this should be populated for the base. It contains
+    # information on how to allocate symbols when recursively fakeifying the base
+    # during view fake-ification.
+    view_base_context: Optional[SymbolicContext] = None
+    # TODO: add storage offset and stride symbolic_context
+
+    def __post_init__(self) -> None:
+        if self.dynamic_strides is None:
+            object.__setattr__(
+                self,
+                "dynamic_strides",
+                [DimDynamic.INFER_STRIDE] * len(self.dynamic_sizes),
+            )
+        if self.constraint_sizes is None:
+            object.__setattr__(
+                self, "constraint_sizes", [None] * len(self.dynamic_sizes)
+            )
+        if self.constraint_strides is None:
+            object.__setattr__(
+                self, "constraint_strides", [None] * len(self.dynamic_sizes)
+            )
+        assert all(
+            stride in (DimDynamic.INFER_STRIDE, DimDynamic.DYNAMIC, DimDynamic.DUCK)
+            for stride in self.dynamic_strides
+        )
+
+
+# note [Tensor Fakification and Symbol Caching]
+#
+# As of the time of this note, dynamo creates a fresh fake tensor mode for backends.
+# The reason we do this is because there are certain classes of operations, namely,
+# metadata mutations, that change tensor size, stride, etc. This means that the fake tensor
+# state at the end of a dynamo trace is different than the fake tensor state at the beginning
+# of a trace. Backends like aot_autograd need a fresh fake tensor to correctly track metadata mutation,
+# view relationships, etc.
+#
+# As we create a new fake mode, we also lose the memoization that comes with it. Rather than
+# transfer the memoization cache, we instead transfer the shape env. However, with this
+# comes nuance - as dynamo is selective in how it makes symbolic shapes. Due to strategies in
+# automatic dynamic and constraints, the policy for which dims are dynamic is nuanced and varies across
+# recompilations.
+#
+# In order to preserve the symbolic decisions made during dynamo tensor fakification, we pass
+# a StatefulSymbolicContext at creation time. This object is tracked, per tensor, on the TracingContext.
+# The lifecycle of this object should match the lifecycle of the original dynamo tracked tensor, and it is
+# safe to reuse this object as many times as necessary to create a fake tensor. Fake tensors
+# created with new fake modes should produce the same exact symbols as the original, providing the same shape_env
+# is used.
+# TODO(voz): Shape env validation
+@dataclass(frozen=True)
+class StatefulSymbolicContext(StatelessSymbolicContext):
+    """
+    Create symbols in ``create_symbolic_sizes_strides_storage_offset`` via
+    a symbolic_context determination as given by a cache of Source:Symbol. A cache hit
+    will reuse a stored symbol, and a cache miss will write to this cache.
+
+    This behaves like StatelessSymbolicContext, except the cache supersedes the
+    other values - dynamic_sizes and constraint_sizes will not be read if we cache
+    hit.
+
+    It is the cache owners responsibility to maintain the lifecycle of the cache
+    w/r/t different shape_envs, clearing, etc.
+    """
+
+    tensor_source: Source = None  # type: ignore[assignment]
+    # Why is this keyd on int first?
+    # That integer is actually the id of the shape_env. This cache short-circuits symbol
+    # creation, and we must store it per shape env. Now, while tracing invariants are a single
+    # shape env per tracing context, and every new frame gets a new shape_env. So where would we have
+    # multiple shape envs? The answer lies in recording. When we are replaying, replay_shape_env_events
+    # is invoked, and creates a new shape_env. Replaying events against this new shape_env will
+    # cause it to fail with unknown symbols, as the symbols cached here will skip creation, and never
+    # get recorded in var_to_val, etc.
+    # TODO(voz): consider a weakref to the shape_env here
+    shape_env_to_source_to_symbol_cache: dict[int, dict[str, sympy.Expr]] = None  # type: ignore[assignment]
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        # The None default is annoying, but required because of dataclass limitations
+        assert self.tensor_source is not None
+        if not self.shape_env_to_source_to_symbol_cache:
+            object.__setattr__(self, "shape_env_to_source_to_symbol_cache", {})
+
+
+@dataclass(frozen=True)
+class SubclassSymbolicContext(StatefulSymbolicContext):
+    """
+    The correct symbolic context for a given inner tensor of a traceable tensor subclass
+    may differ from that of the outer symbolic context. This structure allows for this
+    flexibility, with inner symbolic contexts mapped via attr -> symbolic context.
+    """
+
+    inner_contexts: dict[str, SymbolicContext] = None  # type: ignore[assignment]
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        if self.inner_contexts is None:
+            self.inner_contexts = {}
+
+
+def is_symbolic(
+    val: Union[int, SymInt, float, SymFloat, bool, SymBool]
+) -> TypeGuard[Union[SymInt, SymFloat, SymBool]]:
+    if isinstance(val, (int, float, bool)):
+        return False
+    return val.node.is_symbolic()
+
+
+IndicatorTypes = (IsNonOverlappingAndDenseIndicator,)
+
+
+def _expandsums(args: list[sympy.Expr]) -> tuple[sympy.Expr, bool]:
+    adds, other = [], []
+    for arg in args:
+        if arg.is_Add:
+            adds.append(arg)
+        else:
+            other.append(arg)
+
+    result = [sympy.Mul(*other)]
+    for add in adds:
+        result = [a * b for a, b in itertools.product(result, add.args)]
+
+    result = sympy.Add(*result)
+    return result, len(adds) > 1 or (len(adds) > 0 and len(other) > 0)
+
+
+def _fast_expand(expr: _SympyT) -> _SympyT:
+    # The expand algorithm in sympy is slow due to all the features is supports
+    # For eg: e^(-x)*(x-1)/(x+1) is expanded to (x-1)/(e^x + e^x*x) if x is
+    # positive and (e^(-x)*x-e^(-x))/(x+1) if x is negative. We do not implement
+    # such features here to avoid expensive checks. We also make sure that we
+    # only re-create the objects if any of the args changed to avoid expensive
+    # checks when re-creating objects.
+    new_args = [_fast_expand(arg) for arg in expr.args]  # type: ignore[arg-type]
+    if any(arg is not new_arg for arg, new_arg in zip(expr.args, new_args)):
+        return _fast_expand(expr.func(*new_args))
+
+    if expr.is_Pow:
+        base: sympy.Expr
+        exp: sympy.Expr
+        base, exp = expr.args  # type: ignore[assignment]
+        if exp.is_Integer and base.is_Add:
+            if exp > 1:
+                return sympy.expand_multinomial(expr, deep=False)
+            elif exp < 0:
+                return S.One / sympy.expand_multinomial(S.One / expr, deep=False)
+    elif expr.is_Mul:
+        num: list[sympy.Expr] = []
+        den: list[sympy.Expr] = []
+        for arg in expr.args:
+            if arg.is_Pow and arg.args[1] == -1:
+                den.append(S.One / arg)  # type: ignore[operator, arg-type]
+            else:
+                num.append(arg)  # type: ignore[arg-type]
+
+        num, num_changed = _expandsums(num)
+        den, den_changed = _expandsums(den)
+        if num_changed or den_changed:
+            return num / den
+
+    return expr
+
+
+@lru_cache(256)
+def safe_expand(r: _SympyT) -> _SympyT:
+    """
+    Expand the given symbolic expression by recursively rewriting product of
+    sums into sum of products (with the product being either a multiplication or
+    exponentiation).
+
+    NOTE: using this on an intermediate expression may prevent simplification
+    down the line, e.g., if we eagerly expand `(a + b)^2` into `a^2 + 2ab + b^2`,
+    we won't be able to simplify `(a^2 + 2ab + b^2) / (a + b)` as easily.
+    """
+    if hasattr(r, "expand"):
+        try:
+            return _fast_expand(r)
+        except RecursionError:
+            log.warning("RecursionError in _fast_expand(%s)", r)
+            return r
+    else:
+        return r
+
+
+class _SymbolInfo(NamedTuple):
+    k: sympy.Symbol
+    vr: Optional[ValueRanges]
+    val: Optional[sympy.Integer]
+    is_size_like: bool
+
+
+@lru_cache(None)
+def _maybe_evaluate_static_worker(
+    expr: _SympyT,
+    # NB: this is a tuple to ensure it can be LRU cached
+    symbol_info: tuple[_SymbolInfo, ...],
+    unbacked_only: bool,
+    size_oblivious: bool,
+) -> Optional[_SympyT]:
+    """
+    This variant of ShapeEnv._maybe_evaluate_static has no dependence on
+    ShapeEnv and thus can be cached indefinitely.  It does the "heavy" lifting
+    for static evaluation, including nontrivial reliance on Sympy simplification
+    that occurs when we reallocate the symbols
+    """
+
+    # Simplify making use of value range lower bound
+    new_shape_env = {}
+    new_range_env = {}
+    for idx, sinfo in enumerate(symbol_info):
+        k, vr, val, is_size_like = sinfo
+        if isinstance(val, SingletonInt):
+            # Skip var_ranges logic for SingletonInt which is only used
+            # for jagged layout NestedTensors today
+            continue
+        assert vr is not None
+        if size_oblivious and is_size_like:
+            lower = max(2, vr.lower)
+            # Clamping size-oblivious to some quantity below sys.maxsize
+            # helps us determine that f(u0) != sys.maxsize, which is a
+            # test that is looking for sys.maxsize as a sentinel, but you
+            # don't really want to worry about it for unbacked SymInts.
+            # This is similar to the flavor where size oblivious omits
+            # 0/1, it changes semantics but in a benign way.
+            upper = min(2**48, vr.upper)
+            # Excluding the very upper bound can be helpful
+            if upper > lower:
+                upper = upper - 1
+            # This is a bit dodgy: what this means is that there was a
+            # size-like unbacked symbol whose upper bound < 2.  This
+            # causes... problems.
+            if lower <= upper:
+                vr = ValueRanges(lower, upper)
+        else:
+            lower = vr.lower
+        # Don't do anything if we don't have a nontrivial lower bound
+        # Also don't do anything if we asked only to simplify unbacked
+        # SymInt
+        if lower is -int_oo or (unbacked_only and val is not None) or not vr.is_int:
+            new_range_env[k] = vr
+            continue
+        # The goal is to take our symbols which have various lower bounds
+        # and reallocate them into new symbols which are exactly positive;
+        # e.g., if we have s0 in [2, inf], we want to turn it into ess0 in
+        # [1, inf], where s0 = ess0 + 1.  This gives the most information
+        # to sympy for subsequent simplifications.
+        #
+        # Positive means >= 1
+        # Positive - 1 means >= 0
+        # Positive + lower - 1 means >= lower
+        # The new symbol 's' is "too low", so when we substitute it in
+        # we have to increase it by offset (and conversely, the new
+        # variables have to have their value range bounds adjusted as
+        # well)
+        s = sympy.Symbol(f"evaluate_static_shape_{idx}", positive=True, integer=True)
+
+        # Note:
+        #   Offset might be a fraction(e.g. aten.split.Tensor), but shapes are always integers.
+        #   Sympy might give unexepected results when comparing an integer with a non-integer
+        #   Therefore, we cast offset to int here.
+        #   For example:
+        #       shape_0 = sympy.Symbol("shape_0", positive=True, integer=True)
+        #       expr = sympy.Eq(shape_0 - 1/3, 4)
+        #       expr.xreplace({}) # False
+        offset = int(lower - 1)
+        new_shape_env[k] = s + offset
+        new_range_env[s] = SymPyValueRangeAnalysis.add(vr, -offset)
+
+    # TODO: remove this try catch (esp for unbacked_only)
+    try:
+        new_expr = expr.xreplace(new_shape_env)
+    except RecursionError:
+        log.warning("RecursionError in sympy.xreplace(%s, %s)", expr, new_shape_env)
+        return None
+
+    # We need to canonicalize, as after expand we may have something like `a + b = a` and
+    # sympy will not simplify the a. The two appeareances of the a will then make value ranges
+    # analysis give lose bounds
+    new_expr = canonicalize_bool_expr(safe_expand(new_expr))
+    if new_expr.is_number:
+        return new_expr
+
+    # Check if the range can solve it statically
+    out = bound_sympy(new_expr, new_range_env)
+    if out.is_singleton():
+        return out.lower
+
+    return new_expr if unbacked_only else None
+
+
+def error() -> NoReturn:
+    raise AssertionError("shouldn't be hit")
+
+
+# TODO: Deduplicate this with torch/_prims_common/__init__.py
+def eval_is_non_overlapping_and_dense(
+    sizes: Sequence[int], strides: Sequence[int]
+) -> int:
+    return int(guard_bool(_eval_is_non_overlapping_and_dense(sizes, strides)))
+
+
+def _eval_is_non_overlapping_and_dense(
+    sizes: Sequence[int], strides: Sequence[int]
+) -> bool:
+    dim = len(sizes)
+
+    # Short-circuits for tensors of rank one, which are
+    # non-overlapping and "dense" if their stride is one
+    # or it is a 0/1 element tensor
+    if dim == 1:
+        return strides[0] == 1 or sizes[0] < 2
+
+    # Checks that there exists a permutation of the strides s.t. the tensor would be contiguous
+    # Sorts (length, stride) pairs by stride
+    lengths_and_strides = sorted(zip(sizes, strides), key=operator.itemgetter(1))
+
+    # Unlike the C++ code, we don't move the 0/1 size dimensions to the
+    # end.  So we have to keep going for this code.
+    expected_stride = 1
+    for length, stride in lengths_and_strides:
+        if length == 1:
+            continue
+
+        if stride != expected_stride:
+            return False
+
+        expected_stride *= length
+
+    return True
+
+
+def _sympy_cast_symbool_to_symint_guardless(x: SympyBoolean) -> sympy.Expr:
+    return sympy.Piecewise((1, x), (0, True))
+
+
+def cast_symbool_to_symint_guardless(
+    symbool: Union[bool, torch.SymBool]
+) -> Union[int, torch.SymInt]:
+    if isinstance(symbool, bool):
+        return 1 if symbool else 0
+    int_sym = _sympy_cast_symbool_to_symint_guardless(symbool.node.expr)
+    return symbool.node.shape_env.create_symintnode(
+        int_sym, hint=int(symbool.node.require_hint()) if has_hint(symbool) else None
+    )
+
+
+SYMPY_INTERP = {
+    "IsNonOverlappingAndDenseIndicator": eval_is_non_overlapping_and_dense,
+    "cast_symbool_to_symint_guardless": cast_symbool_to_symint_guardless,
+    "math": math,
+    "torch": torch,
+}
+
+
+def _lru_cache(
+    fn: Callable[..., _T], maxsize: Optional[int] = None
+) -> functools._lru_cache_wrapper[_T]:
+    """
+    Wrapper around lru_cache that clears when new info about shapes has been
+    updated.
+
+    Use lru_cache if the output is always the same, regardless of the
+    constraints we know now (i.e. evaluate_expr)
+
+    Use _lru_cache otherwise.
+
+    Also note that this depends on _update_version_counter being called on the
+    shape environment whenever the constraints are updated, otherwise the cache
+    will not be cleared.
+    """
+    fn_cache = lru_cache(maxsize)(fn)
+    prior_version = 0
+
+    if config.validate_shape_env_version_key:
+        prior_key = None
+
+        @functools.wraps(fn)
+        def wrapper(self: ShapeEnv, *args: Any, **kwargs: Any) -> _T:
+            nonlocal prior_version, prior_key
+            if prior_key is None:
+                prior_key = self._get_key()
+
+            if prior_version != self._version_counter:
+                fn_cache.cache_clear()
+                prior_version = self._version_counter
+                prior_key = self._get_key()
+            else:
+                assert (
+                    prior_key == self._get_key()
+                ), "ShapeEnv cache key changed without version being updated!"
+
+            return fn_cache(self, *args, **kwargs)
+
+    else:
+
+        @functools.wraps(fn)
+        def wrapper(self: ShapeEnv, *args: Any, **kwargs: Any) -> _T:  # type: ignore[misc]
+            nonlocal prior_version
+            if prior_version != self._version_counter:
+                fn_cache.cache_clear()
+                prior_version = self._version_counter
+
+            return fn_cache(self, *args, **kwargs)
+
+    wrapper.cache_clear = fn_cache.cache_clear  # type: ignore[attr-defined]
+    wrapper.cache_info = fn_cache.cache_info  # type: ignore[attr-defined]
+    return wrapper  # type: ignore[return-value]
+
+
+# This is pretty similar to ShapeGuard but it also comes with a message,
+# and is exclusively used for things that MUST be true (unlike guards,
+# which can evaluate False, in which case you just choose not to use
+# a particular specialization)
+@dataclass(frozen=True)
+class RuntimeAssert:
+    expr: SympyBoolean
+    msg: str = field(repr=False)
+    stack: CapturedTraceback = field(repr=False)
+
+
+# Used for printing SymExprs in compile_fx
+class SymExprPrinter(PythonPrinter):
+    def _print_Float(self, expr: sympy.Float) -> str:
+        return str(float(expr))
+
+
+class _ShapeGuardPrinter(abc.ABC):
+    def __init__(
+        self,
+        symbol_to_source: Mapping[sympy.Symbol, list[Source]],
+        source_ref: Callable[[Source], str],
+        var_to_sources: Mapping[sympy.Symbol, list[Source]],
+    ) -> None:
+        self.symbol_to_source = symbol_to_source
+        self.source_ref = source_ref
+        self.var_to_sources = var_to_sources
+        super().__init__()
+
+    def _print_Float(self, expr: sympy.Float) -> str:
+        return str(float(expr))
+
+    def _print_Symbol(self, expr: sympy.Symbol) -> str:
+        assert isinstance(expr, sympy.Symbol), str(type(expr))
+
+        def repr_symbol_to_source() -> str:
+            return repr(
+                {
+                    symbol: [s.name() for s in sources]
+                    for symbol, sources in self.symbol_to_source.items()
+                }
+            )
+
+        assert self.symbol_to_source.get(expr), (
+            f"{expr} (could be from {[s.name() for s in self.var_to_sources[expr]]}) "
+            f"not in {repr_symbol_to_source()}.  If this assert is failing, it could be "
+            "due to the issue described in https://github.com/pytorch/pytorch/pull/90665"
+        )
+        return self.print_source(self.symbol_to_source[expr][0])
+
+    @abc.abstractmethod
+    def print_source(self, source: Source) -> str:
+        ...
+
+    @abc.abstractmethod
+    def doprint(self, expr: sympy.Expr) -> str:
+        ...
+
+
+class ShapeGuardPythonPrinter(_ShapeGuardPrinter, PythonPrinter):
+    def __init__(self, *args: Any) -> None:
+        super().__init__(*args)
+        self._print_cache: dict[sympy.Expr, str] = {}
+
+    def print_source(self, source: Source) -> str:
+        return self.source_ref(source)
+
+    def doprint(self, expr: sympy.Expr) -> str:
+        val = self._print_cache.get(expr, None)
+        if val is not None:
+            return val
+        else:
+            res = PythonPrinter.doprint(self, expr)
+            self._print_cache[expr] = res
+            return res
+
+
+@deprecated(
+    "`torch.fx.experimental.symbolic_shapes.ShapeGuardPrinter` is deprecated, "
+    "please use `torch.fx.experimental.symbolic_shapes.ShapeGuardPythonPrinter` instead.",
+    category=FutureWarning,
+)
+class ShapeGuardPrinter(ShapeGuardPythonPrinter):
+    pass
+
+
+class _ShapeGuardCppPrinter(_ShapeGuardPrinter, CppPrinter):
+    def __init__(self, *args: Any) -> None:
+        self.all_symbols: set[str] = set()
+        self.source_to_symbol: dict[Source, sympy.Symbol] = {}
+        super().__init__(*args)
+
+    def print_source(self, source: Source) -> str:
+        if source in self.source_to_symbol:
+            return self.source_to_symbol[source].name
+
+        source_name = source.name()
+        mangled_name = re.sub("[^0-9a-zA-Z_]+", "_", source_name)
+        old_mangled_name = mangled_name
+        count = 0
+        while mangled_name in self.all_symbols:
+            mangled_name = f"{old_mangled_name}_{count}"
+            count += 1
+        self.source_to_symbol[source] = sympy.Symbol(mangled_name)
+        self.all_symbols.add(mangled_name)
+        return mangled_name
+
+    def doprint(self, expr: sympy.Expr) -> str:
+        return CppPrinter.doprint(self, expr)
+
+
+# A dataclass for storing shape guards
+@dataclass(frozen=True)
+class _ShapeGuardsHelper:
+    exprs: list[str]
+
+
+# A dataclass for storing C++ expressions and helper variables
+@dataclass(frozen=True)
+class _CppShapeGuardsHelper(_ShapeGuardsHelper):
+    source_to_symbol: dict[Source, sympy.Symbol]
+
+
+class LoggingShapeGuardPrinter(ShapeGuardPythonPrinter):
+    def __init__(self, var_to_sources: Mapping[sympy.Symbol, list[Source]]):
+        super().__init__(var_to_sources, lambda n: n.name(), var_to_sources)
+
+
+class DynamicDimConstraintPrinter(PythonPrinter):
+    """
+    Printer for dynamic dim constraints.
+    - Instead of symbol s_k it prints its source t.size()[i]
+    - Instead of Eq(_, _), Mod(_, _), etc. it prints _ == _, _ % _, etc.
+
+    We use this to suggest code for specifying dynamic dim constraints.
+    """
+
+    def __init__(
+        self,
+        symbol_to_source: dict[sympy.Symbol, list[Source]],
+        source_name_to_debug_name: Mapping[str, str],
+    ):
+        super().__init__()
+        self.symbol_to_source = symbol_to_source
+        self.source_name_to_debug_name = source_name_to_debug_name
+
+    def _print_Symbol(self, expr: sympy.Symbol) -> str:
+        assert isinstance(expr, sympy.Symbol), str(type(expr))
+        assert self.symbol_to_source.get(
+            expr
+        ), f"Unknown symbol {expr} created by constraints solver"
+        return self.symbol_to_source[expr][0].name()
+
+
+class DimConstraints:
+    """
+    Custom solver for a system of constraints on symbolic dimensions.
+    Solutions are "static" values or simplified "dynamic" constraints.
+    """
+
+    def __init__(
+        self,
+        symbol_to_source: dict[sympy.Symbol, list[Source]],
+        var_to_val: Mapping[sympy.Symbol, sympy.Integer],
+        marked_dynamic: set[sympy.Symbol],
+        source_name_to_debug_name: Mapping[str, str],
+    ) -> None:
+        # We try to solve systems of inequalities with 1 free variable.
+        self._univariate_inequalities: dict[
+            sympy.Symbol, set[SympyBoolean]
+        ] = defaultdict(set)
+        # Among them, we prioritize solving for a free variable that has equalities.
+        # NOTE: _symbols_with_equalities is always a subset of _univariate_inequalities.keys()
+        # and removing a symbol from the former => removing it from the latter.
+        self._symbols_with_equalities: set[sympy.Symbol] = set()
+        # A solution of a free variable with equalities becomes a substitution.
+        # We use these substitutions to simplify other constraints.
+        # NOTE: removing a symbol from _symbols_with_equalities => adding it to _substitutions.
+        self._substitutions: dict[sympy.Symbol, sympy.Integer] = {}
+
+        # In general, constraints may have // and % operations.
+        # Of course, // can be expressed in terms of / and %.
+        # Our inequality solver can handle / but not %. So we need to transform them away.
+        # We do so by using the values of variables as hints to evaluate %.
+        # For soundness we record additional congruence guards and solve them separately.
+        self._var_to_val: Mapping[sympy.Symbol, sympy.Integer] = var_to_val
+        self._congruences: defaultdict[sympy.Symbol, set[sympy.Expr]] = defaultdict(set)
+
+        # We do not try to (directly) solve inequalities with > 1 free variables.
+        # NOTE: free variables in these inequalities cannot also be in _substitutions.
+        self._multivariate_inequalities: set[SympyBoolean] = set()
+
+        # We park external equalities between free variables here.
+        self._symbolic_equivalences: list[tuple[Source, sympy.Expr]] = []
+
+        # Solutions come in two forms:
+        # - (static) specializations
+        # - (dynamic) inequalities / congruences
+        self._static_results: set[str] = set()
+        self._dynamic_results: set[str] = set()
+
+        # printer for solutions
+        self._dcp = DynamicDimConstraintPrinter(
+            symbol_to_source, source_name_to_debug_name
+        )
+
+        # inconsistencies found on substituting with concrete values / static solutions
+        self._inconsistencies: list[str] = []
+
+        # symbols that are marked dynamic
+        self._marked_dynamic = marked_dynamic
+
+        # track supported sympy functions and subtract from list of all sympy functions
+        self._supported_sympy_functions: set[sympy.Function] = {
+            Application,
+            Mod,
+            PythonMod,
+            FloorDiv,
+        }
+        self._enumerate_sympy_functions()
+
+    def rewrite_with_congruences(self, s: sympy.Symbol, expr: _SympyT) -> _SympyT:
+        """
+        Eliminate expressions of the form b // d and b % d while adding congruences of the form b % d == k.
+        This leaves rational operators (in particular of the form b / d) that our inequality solver can handle.
+        We solve the added congruences separately (using our congruence solver, see below).
+        """
+
+        def mod_handler(*args: sympy.Expr) -> sympy.Expr:
+            # Suppose that we have an expression of the form b % d with free variable s.
+            # Using the value of s as a "hint," we can evaluate b % d to a value k.
+            # Then we can rewrite b % d to k while adding the guard b % d == k.
+
+            # NOTE(avik): This abstraction is provably sound but, in general, incomplete. It is complete IFF
+            # the original expression always evaluates to a constant value (i.e., it does not vary with s).
+            # In other words,
+            # - solutions of s with the rewritten expression are guaranteed to also be solutions of s with
+            #   the original expression;
+            # - while it may be possible to find solutions of s with the original expression that are not
+            #   solutions with the rewritten expression, in that case the original expression cannot evaluate
+            #   to the same value for all solutions of s.
+            #
+            # Should we be worried about this incompleteness? No, because of the following reasons:
+            # 1. It unblocks dramatic simplification that would not be otherwise possible with current tech
+            #    (i.e., "don't let perfect be the enemy of the good").
+            # 2. We already have a tradition of using hints to add guards in the compiler for making progress.
+            # 3. We have not yet seen a counterexample arise in practice! In particular, any congruence guards
+            #    we generate (or simplify to) seem to be of the form b % d == k where k is a constant.
+            #
+            # Here's a theoretical counterexample: 3*s % (s + 1) == s - 2, that is satisfied by all s >= 2.
+            # With any hint (say) s = k, we'd rewrite this to: 3*s % (s + 1) == k - 2. But, substituting, we
+            # would then get k - 2 == s - 2, and thus s = k as the (only, constant) solution!
+            base, divisor = args
+            base, divisor = self.rewrite_with_congruences(
+                s, base
+            ), self.rewrite_with_congruences(s, divisor)
+            mod_reduced = base.xreplace(self._var_to_val) % divisor.xreplace(
+                self._var_to_val
+            )
+            congruence = (base - mod_reduced) % divisor
+            if congruence != 0:
+                self._congruences[s].add(congruence)
+            return mod_reduced
+
+        def floor_div_handler(*args: sympy.Expr) -> sympy.Expr:
+            # Suppose that we have an expression of the form b // d with free variable s.
+            # Using the value of s, we can evaluate b % d to a value k.
+            # Then we can rewrite b // d to (b - k) / d, while adding the guard b % d == k.
+
+            # NOTE(avik): This is exactly equivalent to rewriting b // d as (b - (b % d)) / d
+            # and eliminating b % d as above.
+            base, divisor = args
+            base, divisor = self.rewrite_with_congruences(
+                s, base
+            ), self.rewrite_with_congruences(s, divisor)
+            mod_reduced = base.xreplace(self._var_to_val) % divisor.xreplace(
+                self._var_to_val
+            )
+            congruence = (base - mod_reduced) % divisor
+            if congruence != 0:
+                self._congruences[s].add(congruence)
+            # NB: Must not be CleanDiv, it needs to be regular sympy division
+            # so inequality solver works.  This is sort of problematic for
+            # is_integer tests though haha
+            return (base - mod_reduced) / divisor
+
+        if expr.has(Mod):
+            expr = expr.replace(Mod, mod_handler)
+        # 7 // -3 is -3, 7 % -3 is -2, and 7 - (-2) / -3 is -3.0 so negative
+        # arguments should be OK.
+        if expr.has(PythonMod):
+            expr = expr.replace(PythonMod, mod_handler)
+        if expr.has(FloorDiv):
+            expr = expr.replace(FloorDiv, floor_div_handler)
+        return expr
+
+    def _enumerate_sympy_functions(self) -> None:
+        module = torch.utils._sympy.functions
+        all_functions = set()
+        for attr in dir(module):
+            if isinstance(func := getattr(module, attr), sympy.FunctionClass):
+                all_functions.add(func)
+        self._unsupported_sympy_functions = all_functions.difference(
+            self._supported_sympy_functions
+        )
+
+    def _has_unsupported_sympy_function(self, expr: sympy.Basic) -> bool:
+        """
+        Tracks list of sympy.Functions the export solver doesn't know how to handle.
+        """
+        return expr.has(*self._unsupported_sympy_functions)
+
+    def add(self, expr: SympyBoolean) -> bool:
+        """Add an expression to the set of constraints.
+
+        Return whether the expression is a trivial constraint (i.e., an obvious tautology).
+        """
+        if expr == sympy.true:
+            return True
+        orig_expr = expr
+        orig_reduced = orig_expr.xreplace(self._var_to_val)
+        # TODO(avik): https://github.com/pytorch/pytorch/issues/101093
+        # It is possible that `expr` will fail the consistency check because of
+        # precision errors. Specifically, on substituting its free symbols with
+        # their concrete values, we might end up comparing floats. Until we have
+        # a fix for this issue, we delay raising such failures. See solve().
+        if orig_reduced == sympy.false:
+            self._inconsistencies.append(f"{orig_expr} is inconsistent!")
+        if isinstance(expr, sympy.Ne) or self._has_unsupported_sympy_function(expr):
+            # we're not going to do anything useful with these, so drop them
+            return False
+        free_symbols = expr.free_symbols
+        assert free_symbols, f"Did not expect constraint with no free variables: {expr}"
+        if len(free_symbols) > 1:
+            # multivariate: record and move on
+            self._multivariate_inequalities.add(expr)
+        else:
+            # univariate: can solve these immediately
+            s = next(iter(free_symbols))
+            # eliminate // and % (see documentation of `rewrite_with_congruences` above)
+            old_n_congruences = len(self._congruences[s])
+            expr = self.rewrite_with_congruences(s, expr)
+            new_n_congruences = len(self._congruences[s])
+            if expr == sympy.true:
+                return old_n_congruences == new_n_congruences
+            reduced = expr.xreplace(self._var_to_val)
+            if reduced == sympy.false:
+                self._inconsistencies.append(
+                    f"{expr}, obtained by rewriting {orig_expr} with congruences, "
+                    "is inconsistent!"
+                )
+            if isinstance(expr, sympy.Eq):
+                # special status for symbols that have equalities (see `solve` below)
+                self._symbols_with_equalities.add(s)
+            self._univariate_inequalities[s].add(expr)
+        return False
+
+    def add_equality(self, source: Source, expr: sympy.Expr) -> None:
+        """Add an equality constraint"""
+        if expr.is_number:
+            # specialization, right here
+            self._static_results.add(f"{source.name()} == {expr}")
+        else:
+            # these will resolve to either specializations or dynamic equality constraints
+            self._symbolic_equivalences.append((source, expr))
+
+    def _reduce_congruences(self) -> dict[sympy.Symbol, set[sympy.Expr]]:
+        reduced_congruences: dict[sympy.Symbol, set[sympy.Expr]] = {}
+        for s, congruences in self._congruences.items():
+            remainder_modulus_pairs = []
+            congruences_to_check = set()
+            for congruence in congruences:
+                base, divisor = congruence.args
+                # We are given a congruence of the form base % divisor == 0 with a free variable s. So:
+                # - we transform this into an equation of the form base = divisor * tmp;
+                # - we solve this equation for s to get a linear solution with free variable tmp.
+                tmp = sympy.Symbol("reduce_congruences_tmp", integer=True)
+                symbol, solution = sympy.solve_linear(base - divisor * tmp, symbols=[s])
+                # See https://docs.sympy.org/latest/modules/solvers/solvers.html#sympy.solvers.solvers.solve_linear
+                # for how to interpret the results.
+                if s == symbol:
+                    # This means the solution is of the form s = modulus*tmp + remainder.
+                    modulus, remainder = sympy.polys.polytools.div(solution, tmp)
+                    if isinstance(modulus, sympy.Integer) and isinstance(
+                        remainder, sympy.Integer
+                    ):
+                        # Make sure 0 <= remainder <= modulus.
+                        remainder = remainder % modulus
+                        remainder_modulus_pairs.append((remainder, modulus))
+                        continue
+                # This means that we did not get a unique solution to the equation.
+                # No problem, we will check it.
+                congruences_to_check.add(congruence)
+            # Finally we solve for a congruence s such that s = r_i mod m_i for each (r_i, m_i).
+            # The solution will be a congruence of the form s = r mod m.
+            # NOTE(avik): Since the given m_i may not be pairwise coprime, we can't just use CRT.
+            if remainder_modulus_pairs:
+                remainder, modulus = sympy.ntheory.modular.solve_congruence(
+                    *remainder_modulus_pairs
+                )
+                reduced_congruences[s] = {(s - remainder) % modulus}
+                substitution = {
+                    s: modulus * sympy.Symbol("tmp", integer=True) + remainder
+                }
+                reduced_congruences[s].update(
+                    congruence
+                    for congruence in congruences_to_check
+                    if not sympy.checksol(congruence, substitution)
+                )
+            else:
+                reduced_congruences[s] = congruences_to_check
+
+        return reduced_congruences
+
+    def _raise_inconsistencies(self) -> None:
+        if self._inconsistencies:
+            msg = "\n".join(self._inconsistencies)
+            self._inconsistencies.clear()
+            raise ValueError(f"The following inconsistencies were found:\n{msg}")
+
+    def solve(self) -> None:
+        """Solve the system of constraint equations to find simplified constraints"""
+        self._raise_inconsistencies()
+        # as long as there are symbols with equalities, solve for them
+        # NOTE(avik): this is guaranteed to terminate (#iterations <= #symbols)
+        while self._symbols_with_equalities:
+            s = self._symbols_with_equalities.pop()
+            exprs = self._univariate_inequalities.pop(s)
+            solution = sympy.solvers.inequalities.reduce_inequalities(exprs, s)
+            if isinstance(solution, sympy.And):
+                solution = next(
+                    (arg for arg in solution.args if isinstance(arg, sympy.Eq)),
+                    solution,
+                )
+            assert isinstance(
+                solution, sympy.Eq
+            ), f"Expected an equality constraint for {s}, got {solution}"
+            symbol, val = solution.args
+            assert symbol == s, f"Expected a constraint on {s} instead of on {symbol}"
+            # because this is univariate, the solution is a specialization
+            self._static_results.add(
+                f"{self._dcp.symbol_to_source[s][0].name()} == {val}"
+            )
+            # add this as a substitution to simplify other constraints
+            self._substitutions[s] = val  # type: ignore[assignment]
+
+            # simplify multivariate inequalities: some of them will now become univariate!
+            multivariate_inequalities = self._multivariate_inequalities
+            self._multivariate_inequalities = set()
+            for expr in multivariate_inequalities:
+                self.add(expr.xreplace({s: self._substitutions[s]}))
+            self._raise_inconsistencies()
+
+        # solve linear congruences
+        # NOTE(avik): We do not need to solve them for symbols that have already been specialized.
+        reduced_congruences = self._reduce_congruences()
+        for s, congruences in reduced_congruences.items():
+            for congruence in congruences:
+                # any congruence that cannot be checked becomes a dynamic constraint as well
+                if s not in self._substitutions or not sympy.checksol(
+                    congruence, {s: self._substitutions[s]}
+                ):
+                    if self._is_supported_congruence(congruence):
+                        base, divisor = congruence.args
+                        tmp_name = "_" + str(
+                            self._dcp.source_name_to_debug_name.get(
+                                self._dcp.symbol_to_source[s][0].name(),
+                                self._dcp.symbol_to_source[s][0].name(),
+                            )
+                        )
+                        tmp = sympy.Symbol(tmp_name, integer=True)
+                        from torch._dynamo.source import ConstantSource
+
+                        self._dcp.symbol_to_source[tmp] = [ConstantSource(tmp_name)]
+                        r = try_solve(sympy.Eq(base, divisor * tmp), s)
+                        assert r is not None
+                        self._dynamic_results.add(self._dcp.doprint(sympy.Eq(s, r[1])))
+
+        # remaining symbols have only pure inequalities (no equalities)
+        for s, exprs in self._univariate_inequalities.items():
+            try:
+                solution = sympy.solvers.inequalities.reduce_inequalities(exprs, s)
+                # because this is univariate, the solution is a dynamic (range) constraint
+                if isinstance(solution, sympy.Or):
+                    solution = next(
+                        iter(
+                            arg
+                            for arg in solution.args
+                            if arg.xreplace(self._var_to_val)
+                        )
+                    )
+                if isinstance(solution, sympy.And):
+                    for arg in solution.args:
+                        self._dynamic_results.add(self._dcp.doprint(arg))
+                else:
+                    self._dynamic_results.add(self._dcp.doprint(solution))
+            except (NotImplementedError, AssertionError) as e:
+                log.warning("Failed to reduce inequalities: %s", e)
+                for expr2 in exprs:
+                    self._dynamic_results.add(self._dcp.doprint(expr2))
+
+        # simplify symbolic equivalences: some of them will now become specializations!
+        symbolic_equivalences = self._symbolic_equivalences
+        self._symbolic_equivalences = []
+        for source, expr3 in symbolic_equivalences:
+            self.add_equality(source, expr3.xreplace(self._substitutions))
+
+        # remaining symbolic equivalences become dynamic equality constraints
+        for source, expr3 in self._symbolic_equivalences:
+            self._dynamic_results.add(f"{source.name()} == {self._dcp.doprint(expr3)}")
+
+    @classmethod
+    def _is_supported_congruence(cls, congruence: sympy.Expr) -> bool:
+        base, divisor = congruence.args
+        # Congruences that can be currently expressed with supported Dim ops are
+        # of the form (x + a) % b == 0, where x is a Dim and a and b are constants.
+        # This allows us to derive x as b*y - a for some Dim y.
+        # (See also documentation of dynamic_shapes._DerivedDim.)
+        if isinstance(base, sympy.Add):
+            lhs, rhs = base.args
+            cond = (
+                isinstance(lhs, sympy.Symbol) and isinstance(rhs, sympy.Integer)
+            ) or (isinstance(lhs, sympy.Integer) and isinstance(rhs, sympy.Symbol))
+        else:
+            cond = isinstance(base, sympy.Symbol)
+        cond = cond and isinstance(divisor, sympy.Integer)
+        return cond
+
+    def forced_specializations(self) -> dict[str, sympy.Expr]:
+        """Returns a dictionary of the names of symbols to their specialized value"""
+
+        def debug_name(src: Source) -> str:
+            name = src.name()
+            if self._dcp.source_name_to_debug_name:
+                return f"{self._dcp.source_name_to_debug_name[name]} = {name}"
+            else:
+                return name
+
+        return {
+            debug_name(self._dcp.symbol_to_source[s][0]): val
+            for s, val in self._substitutions.items()
+            if s in self._marked_dynamic
+        }
+
+    def _is_derived_dim(
+        self, dim: object
+    ) -> TypeGuard[torch.export.dynamic_shapes._DerivedDim]:
+        return isinstance(dim, torch.export.dynamic_shapes._DerivedDim)
+
+    def _is_dim(self, dim: object) -> TypeGuard[torch.export.dynamic_shapes._Dim]:
+        return isinstance(dim, torch.export.dynamic_shapes._Dim) and not isinstance(
+            dim, torch.export.dynamic_shapes._DerivedDim
+        )
+
+    def _process_derived_dim_roots(
+        self,
+        results: dict[str, dict[str, Any]],
+        name_to_dim: dict[str, Any],
+    ) -> None:
+        """
+        Here we resolve 2 concerns with derived dims suggested fixes: 1) newly introduced roots,
+        and 2) root swapping.
+
+        1) Newly introduced roots appear with modulo guards, e.g. Mod(dx, 2) = 0 suggests
+        dx is a derived dim equal to 2 * _dx, introducing a new root _dx. Currently the final
+        suggested fixes handle this correctly, but we can get intermediate results that look like
+        {"dy": {"eq": "dx + 1"}, "dx": {"eq": "2 * _dx + 1, "min": 3, "max": 15}}
+        and this routine prettifies this by unifying to a single root, and making each suggestion
+        either a derived dim or min/max range, not both.
+
+        2) With suggested fixes for derived dims, roots can be swapped,
+        e.g. dx, dx - 1 -> dy + 1, dy. Here we don't want to print out the attached name,
+        since this leads to messages like "dx - 1 = Dim("dx - 1", ...)".
+        Instead we evaluate the new root value, and remove results for its derivations.
+
+        First we find all the original roots (specified in dynamic_shapes), that are found in the
+        values of results (i.e. used for computing suggesting fix values). These original roots
+        (suppose `dx`) are either specialized, unchanged, refined, or swapped
+        (expressed as a derived dim). If any of the first 3 cases happen, we suggest `dx`'s value
+        in results, and remove suggestions for derivations of `dx`, assuming the derived relation
+        is valid. If swapped, we find the new root, and use the fix to evaluate `dx`'s new value,
+        and then do the same with `dx`'s derivations.
+
+        Assuming the originally specified derived relations are correct is valid, because:
+            1) if the relations are plain wrong (e.g. input shape = (6, 4) with spec (dx, dx - 1))
+               produce_guards() will catch this and crash before hand.
+            2) if the relations are numerically correct but do not match the emitted guard,
+               for example:
+
+                    def forward(self, x, y):
+                        return x.reshape([-1]) + y  # guard: s0 * 2 = s1
+                    inputs = (torch.randn(6, 2), torch.randn(12))
+                    dx = Dim("dx", min=2, max=32)
+                    dynamic_shapes={"x": (dx, 2), "y": (dx + 6, )}  # this matches values but not op
+
+               then this leads to 2 linear equations, and a) produce_guards() is able to solve for
+               the unique solution of dx = 6 and specialize, and b) the export constraint solver will
+               raise an issue due to range constraints (a unique solution means not all values in a
+               range satisfy a guard) and also force specializations.
+        """
+        from torch.export.dynamic_shapes import Dim
+
+        def _check_same_range(c: Mapping[str, int], dim: object) -> bool:
+            # returns True if c & dim are both min/max ranges with same values
+            return (
+                self._is_dim(dim)
+                and ("min" in c or "max" in c)
+                and (
+                    (dim.min < 2 and c.get("min", 2) == 2) or dim.min == c.get("min", 2)  # type: ignore[attr-defined]
+                )  # let pass if analysis min = 2 and specified min = 0/1
+                and dim.max == c.get("max", int_oo)  # type: ignore[attr-defined]
+            )
+
+        # 1) newly introduced roots
+        # this part we handle adding newly introduced roots
+        # these arise from guards like "x.shape[0] % 3 == 0"
+        # leading to suggested fixes like "dx = 3*_dx"
+        # extract _dx, and find appropriate min/max values
+        #
+        # before, we have something like:
+        # {"dx": {"eq": 3*_dx+1, "min": 4, "max": 10}, "dy": dx+1, "dz": dx+2}
+        # we want instead:
+        # {"_dx": {"min": 1, "max": 4}, "dx": 3*_dx+1, "dy": 3*_dx+2, "dz": 3*_dx+3}
+        introduced_roots: dict[str, str] = {}  # map new root -> old root
+        for k, c in list(results.items()):
+            if "eq" in c and isinstance(c["eq"], sympy.Expr):  # derived dim
+                root = next(iter(c["eq"].free_symbols))
+                if str(root) not in name_to_dim:
+                    introduced_roots[str(root)] = k
+                    # calculate necessary min & max
+                    modulus, remainder = sympy.polys.polytools.div(c["eq"], root)
+                    c_min = c.get("min", 2)
+                    min_ = math.ceil((c_min - remainder) / modulus)
+                    c_max = c.get("max", int_oo)
+                    max_ = math.floor((c_max - remainder) / modulus)
+                    # create result & dim
+                    results[str(root)] = {"min": min_, "max": max_}
+                    name_to_dim[str(root)] = Dim(str(root), min=min_, max=max_)
+                    # remove old root min/max bounds
+                    c.pop("min", None)
+                    c.pop("max", None)
+
+        # alter derivations that depend on old root, to unify to new root
+        # e.g. dx=3*_dx+1, dy=dx+1 -> dy=3*_dx+2
+        for old_root in introduced_roots.values():
+            for k, c in list(results.items()):
+                if (
+                    "eq" in c
+                    and isinstance(c["eq"], sympy.Expr)
+                    and str(symbol := next(iter(c["eq"].free_symbols))) == old_root
+                ):  # derived dim with root = old_root
+                    new_root_expr = results[str(old_root)]["eq"]  # dx=3*_dx+1
+                    new_expr = c["eq"].subs({symbol: new_root_expr})  # dy=(3*_dx+1)+1
+                    c["eq"] = new_expr
+
+        # 2) root swapping
+        # collect all the original roots that are used for calculating values of suggested fixes
+        # this consists of:
+        # 1) {"dx": {"min": ..., "max": ...}} -> dx: refined root dim
+        # 2) {"dy": "dx + 1"} -> dx: root for suggested fix
+        modified_roots: set[str] = set()
+        for k, c in results.items():
+            if k not in name_to_dim:  # _dynamo.export() may handle source directly
+                continue
+            if self._is_dim(name_to_dim[k]) and ("min" in c or "max" in c):  # case 1)
+                modified_roots.add(k)
+            elif "eq" in c and isinstance(c["eq"], sympy.Expr):  # case 2)
+                root = next(iter(c["eq"].free_symbols))
+                assert root is not None
+                modified_roots.add(str(root))
+
+        # exclude newly introduced roots, we've already processed these
+        modified_roots = modified_roots.difference(introduced_roots)
+
+        # evaluate the new value for each root
+        # this is now either 1) unchanged, 2) refined with a new range,
+        # or 3) specialized to a concrete value
+        modified_root_values: dict[str, dict[str, Any]] = {}
+        for mroot in modified_roots:
+            swapped_root = True
+            if mroot in results:
+                c = results[mroot]
+                if ("min" in c or "max" in c) or isinstance(  # range
+                    c["eq"], int
+                ):  # specialized
+                    # here, the original root is a root Dim or concrete value in results.
+                    # if it is a derived dim, it is swapped, and we handle that below.
+                    if not _check_same_range(
+                        c, name_to_dim[mroot]
+                    ):  # ignore if unchanged
+                        modified_root_values[mroot] = c
+                    swapped_root = False
+
+            if swapped_root:
+                # if the original root has been swapped in results, that means the new root
+                # is a range (if it had specialized, the original root would have too).
+                # find this new root, and solve for the original root's range.
+                for k, c in results.items():
+                    if k not in name_to_dim:
+                        continue
+                    dim = name_to_dim[k]
+                    if (
+                        dim.__class__.__name__ == "_DerivedDim"
+                        and dim.root.__name__ == mroot
+                    ):
+                        # only look for min/max root, otherwise root would have specialized
+                        if "min" in c or "max" in c:
+                            expr = sympy.sympify(k)
+                            s = next(iter(expr.free_symbols))
+                            result = {
+                                "min": try_solve(sympy.Eq(expr, c["min"]), s)[1],  # type: ignore[arg-type, index]
+                                "max": try_solve(sympy.Eq(expr, c["max"]), s)[1],  # type: ignore[arg-type, index]
+                            }
+                            if not _check_same_range(
+                                result, name_to_dim[mroot]  # type: ignore[index, arg-type]
+                            ):  # ignore if unchanged
+                                modified_root_values[mroot] = result  # type: ignore[index]
+                                break
+
+        # filter out results where the key is a derived dim (e.g. {"dx - 1" : 4})
+        # we only want to suggest fixes for the root, to avoid derived names.
+        # also, remove anything in modified_roots, since we either add new modified values after this,
+        # or have decided they are unchanged.
+        for k in list(results.keys()):
+            if k not in name_to_dim:
+                continue
+            if self._is_derived_dim(name_to_dim[k]) or k in modified_roots:
+                del results[k]
+
+        # update results with modified root values
+        # now results has the following properties:
+        # - only contains original roots as keys
+        # - each root is now either specialized, refined, or derived from another original root
+        results.update(modified_root_values)
+
+    def prettify_results(
+        self,
+        original_signature: inspect.Signature,
+        dynamic_shapes: Union[dict[str, Any], tuple[Any], list[Any]],
+        constraint_violation_error: object,
+        forced_specializations: dict[str, str],
+    ) -> str:
+        """Format a message for constraint violation erros"""
+        from torch.export.dynamic_shapes import _get_dim_name_mapping
+
+        if not self._dcp.source_name_to_debug_name:
+            # nothing to do
+            return ""
+
+        def transform(s: str, inverse: bool = False) -> str:
+            for k, v in self._dcp.source_name_to_debug_name.items():
+                s = s.replace(k, v) if not inverse else s.replace(v, k)
+            return s
+
+        results: defaultdict[str, dict[str, Any]] = defaultdict(dict)
+        if dynamic_shapes is None:
+            dynamic_shapes = {}
+
+        def flip(op: str) -> str:
+            if op == "<=":
+                return ">="
+            if op == ">=":
+                return "<="
+            if op == "<":
+                return ">"
+            if op == ">":
+                return "<"
+            assert op == "=="
+            return op
+
+        def relation_with_digit(expr: str, op: str, digit: int) -> None:
+            if op == "<=":
+                results[expr]["max"] = digit
+            elif op == "<":
+                results[expr]["max"] = digit - 1
+            elif op == ">=":
+                results[expr]["min"] = digit
+            elif op == ">":
+                results[expr]["min"] = digit + 1
+            else:
+                assert op == "=="
+                results[expr]["eq"] = digit
+
+        # retrieve dynamic shapes
+        name_to_dim = _get_dim_name_mapping(dynamic_shapes)
+
+        for s in self._static_results.union(self._dynamic_results):
+            t = transform(s)
+            if t == s:
+                continue
+            left, op, right = re.split(r"( == | <= | >= | < | > )", t)
+            op = op.strip()
+            if op == "==" and left == right:
+                continue
+            if right.isdigit():
+                relation_with_digit(left, op, int(right))
+            elif left.isdigit():
+                relation_with_digit(right, flip(op), int(left))
+            else:
+                assert op == "==", t
+                try:
+                    results[left]["eq"] = sympy.sympify(right)
+                except TypeError:  # rhs source is not linked to Dim name
+                    pass
+
+        # order forced specializations based on name
+        forced_specializations = {
+            k: forced_specializations[k]
+            for k in sorted(
+                forced_specializations.keys(),
+                key=lambda x: x.split(" = ")[1],
+            )
+        }
+
+        buf = ""
+        if forced_specializations:
+            debug_names = set()
+            for k in forced_specializations:
+                dim = name_to_dim[k.split(" = ")[0]]
+                if self._is_derived_dim(dim):
+                    debug_names.add(dim.root.__name__)  # type: ignore[attr-defined]
+                else:
+                    debug_names.add(dim.__name__)
+
+            buf += (
+                f"Specializations unexpectedly required ({', '.join(sorted(debug_names))})! "
+                'For more information, run with TORCH_LOGS="+dynamic".\n'
+            )
+            for s, val in forced_specializations.items():
+                buf += f"  - solving the guards generated for {s} resulted in a specialized value of {val}.\n"
+
+        self._process_derived_dim_roots(results, name_to_dim)
+
+        dims = []
+        others = []
+
+        # order results by source name
+        results2 = {
+            k: results[k]
+            for k in sorted(
+                results.keys(),
+                key=lambda x: transform(x, inverse=True),
+            )
+        }
+        for k, c in results2.items():
+            if "eq" in c:
+                other = c["eq"]
+                if isinstance(other, int):
+                    others.append(f"{k} = {other}")
+                elif _is_supported_equivalence(other):
+                    others.append(f"{k} = {other}")
+            else:
+                min_ = c.get("min", None)
+                if min_ == 2:
+                    min_ = None
+                max_ = c.get("max", None)
+                if min_ is not None and max_ is not None:
+                    dims.append(f"{k} = Dim('{k}', min={min_}, max={max_})")
+                elif min_ is not None:
+                    dims.append(f"{k} = Dim('{k}', min={min_})")
+                elif max_ is not None:
+                    dims.append(f"{k} = Dim('{k}', max={max_})")
+                else:
+                    dims.append(f"{k} = Dim('{k}')")
+
+        # results2 will get filtered out if no new suggestions,
+        # this can happen if guards are too complex.
+        # in that case don't suggest fix
+        if dims or others:
+            buf += "\nSuggested fixes:\n  "
+            buf += "\n  ".join(dims + others)
+
+        return buf
+
+
+TLS = threading.local()
+
+
+@dataclass(frozen=True)
+class ShapeEnvSettings:
+    """
+    Encapsulates all shape env settings that could potentially affect
+    FakeTensor dispatch. Used when creating dispatch cache keys.
+    """
+
+    allow_scalar_outputs: bool
+    allow_dynamic_output_shape_ops: bool
+    assume_static_by_default: bool
+    specialize_zero_one: bool
+    duck_shape: bool
+    prefer_deferred_runtime_asserts_over_guards: bool
+    allow_complex_guards_as_runtime_asserts: bool
+
+
+@dataclass
+class ValueRangesSLoc:
+    """
+    Locations of the guards that triggered lower and upper bound.
+    """
+
+    lower: SLoc
+    upper: SLoc
+
+
+@contextmanager
+def _suppress_guards(shape_env: ShapeEnv) -> Iterator[None]:
+    shape_env._suppress_guards_enter()
+    try:
+        yield
+    finally:
+        shape_env._suppress_guards_exit()
+
+
+@dataclass
+class _FrameLocalResult:
+    loc: Optional[str] = None
+    locals: dict[str, Any] = field(default_factory=dict)
+    symbols: dict[str, str] = field(default_factory=dict)
+
+
+class ShapeEnv:
+    # This is a wrapper over the actual __init__ function.
+    #
+    # Where to add a new constructor parameter to ShapeEnv?
+    # =====================================================
+    # This __init__ function should be used only for parameters related to event recording.
+    # These are parameters that we don't wish to pass down the road to new ShapeEnv instances
+    # created from replaying events.
+    #
+    # If you wish to add a parameter to the constructor of ShapeEnv, unrelated to event
+    # recording, do so in the _init function.
+    def __init__(
+        self,
+        *,
+        should_record_events: Optional[bool] = None,
+        tracked_fakes: Optional[list[Any]] = None,
+        **kwargs: Any,
+    ) -> None:
+        self._init(**kwargs)
+
+        # Disable event recording when replaying.
+        kwargs["should_record_events"] = False
+
+        from torch.fx.experimental.validator import translation_validation_enabled
+
+        self._translation_validation_enabled = translation_validation_enabled()
+
+        # If not specified, enable event recording if both:
+        #   - Translation validation is on
+        #   - Translation validation bisection is not disabled
+        self.should_record_events = (
+            should_record_events
+            if should_record_events is not None
+            else (
+                self._translation_validation_enabled
+                and not config.translation_validation_no_bisect
+            )
+        )
+
+        # Enable event recording check if both:
+        #   - It should record events
+        #   - The recording check is enabled
+        self.check_recorded_events = (
+            self.should_record_events and config.check_shape_env_recorded_events
+        )
+
+        # This will make sure we only record the top-level function call.
+        self.is_recording = False
+        # Keep track of the list of tracked fakes.
+        self.tracked_fakes = tracked_fakes
+        # List of events for reconstructing ShapeEnv at arbitrary points in time.
+        self.events: list[ShapeEnvEvent] = (
+            [ShapeEnvEvent(ShapeEnv, kwargs=kwargs)]
+            if self.should_record_events
+            else []
+        )
+
+        # FakeTensor per-ShapeEnv operation cache. This is used for caching
+        # operations that contain symbolic shapes which have guards on the
+        # ShapeEnv (so are ShapeEnv-dependent).
+        #
+        # NOTE: It's important that SymNodes in this cache have their ShapeEnv
+        # stripped otherwise you end up with cycles which can only be cleaned
+        # with the GC.
+        self.fake_tensor_cache: dict[
+            torch._subclasses.fake_tensor._DispatchCacheKey,
+            torch._subclasses.fake_tensor._DispatchCacheEntry,
+        ] = {}
+
+    # Pro-tip: if you add new field to ShapeEnv, this affects some accept
+    # tests.  Accept their output with:
+    #
+    #   EXPECTTEST_ACCEPT=1 python test/dynamo/test_dynamic_shapes.py -k test_shape_env_equal
+    #
+    def _init(
+        self,
+        *,
+        allow_scalar_outputs: bool = True,
+        allow_dynamic_output_shape_ops: bool = True,
+        # NB: These are legacy configuration that help us make good choices
+        # when the constraint/dynamic dims are not explicitly passed to us.
+        # Ideally we will fix all call sites to be explicit and not have
+        # implicit choices, but this apparently was pretty involved.
+        assume_static_by_default: bool = False,
+        # Note - On 0/1 specialization
+        #
+        # The following options affect decisions we make about eager
+        # specialization.  Disabling them will increase trace time (as we do
+        # more symbolic reasoning) and can also harm the quality of generated
+        # code (because inductor may not be able to specialize for bounds
+        # being equal--although if we later respecialize because of a guard,
+        # your code may be just as good as it was before.)
+        #
+        # When True, eagerly specialize input sizes which have 0/1.
+        specialize_zero_one: bool = True,
+        # When True, assume input sizes which have the same size are
+        # symbolically equal.
+        duck_shape: Optional[bool] = None,
+        # For debugging
+        co_fields: Optional[dict[str, str]] = None,
+        # When True, whenever safe, we will generate a deferred runtime assert
+        # instead of a guard whenever we know that an expression must be True,
+        # otherwise it would be an error, even for backed SymInts (where we
+        # could ostensibly unconditionally generate guards).  This is useful
+        # for export, where preventing "error checking" sizes from showing up
+        # in guards is helpful, since these guards in some sense are overly
+        # pedantic.  See also https://github.com/pytorch/pytorch/issues/121749
+        prefer_deferred_runtime_asserts_over_guards: bool = False,
+        # When True, does not emit or raise constraint violation errors on
+        # implicit guards generated by ops, and defers to runtime assertions
+        # in the graph instead. For export.
+        allow_complex_guards_as_runtime_asserts: bool = False,
+        # XXX Add any new settings that could affect FakeTensor evaluation
+        # to: torch._subclasses.fake_tensor._ShapeEnvSettings
+    ) -> None:
+        if duck_shape is None:
+            duck_shape = config.use_duck_shape
+
+        self.settings = ShapeEnvSettings(
+            # Not directly used by ShapeEnv; indirectly used by FakeTensor
+            allow_scalar_outputs=allow_scalar_outputs,
+            allow_dynamic_output_shape_ops=allow_dynamic_output_shape_ops,
+            # End
+            assume_static_by_default=assume_static_by_default,
+            specialize_zero_one=specialize_zero_one,
+            duck_shape=duck_shape,
+            prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards,
+            allow_complex_guards_as_runtime_asserts=allow_complex_guards_as_runtime_asserts,
+        )
+
+        self.guards: list[ShapeGuard] = []
+        self.axioms: dict[sympy.Expr, sympy.Expr] = {}
+        # Maps symbolic ints to their original concrete values
+        # Currently populated from tensors
+        self.var_to_val: dict[sympy.Symbol, sympy.Integer] = {}
+        # Like var_to_val, but only set when propagate_real_tensors is on.
+        # Used as last resort to avoid GuardOnDataDependent error
+        self.unbacked_var_to_val: dict[sympy.Symbol, sympy.Integer] = {}
+        # Like above, but used exclusively for OBLIVIOUS_SIZE.  These
+        # potentially could be put together but I am not sure, writing out
+        # the logic individually before abstracting.
+        self.oblivious_var_to_val: dict[sympy.Symbol, sympy.Integer] = {}
+        # Maps symbolic ints to their min/max range.  These ranges
+        # are conservative: the int MUST fall in the range, but the
+        # range may contain ints which may not actually appear in
+        # practice
+        self.var_to_range: dict[sympy.Symbol, ValueRanges] = {}
+        self.var_to_range_sloc: dict[sympy.Symbol, ValueRangesSLoc] = {}
+        self.source_name_to_debug_name: dict[str, str] = {}
+        self.var_to_sources: dict[sympy.Symbol, list[Source]] = {}
+        self.var_to_stack: dict[sympy.Symbol, CapturedTraceback] = {}
+        # Maps a source to the *original* symbol that was assigned to it
+        self.source_to_var: dict[str, sympy.Symbol] = {}
+        # Maps from sympy ints to expressions representing them
+        # Populated from equality guards (i.e. a.shape[0] == b.shape[0])
+        self.replacements: dict[sympy.Symbol, sympy.Expr] = {}
+        # The sloc of the guard that triggered this replacement to be added
+        self.replacements_slocs: dict[sympy.Symbol, SLoc] = {}
+        self.unbacked_renamings: dict[sympy.Symbol, sympy.Symbol] = {}
+        # Set holds a % b expressions that evaluate to 0.
+        self.divisible: set[sympy.Expr] = set()
+        # Set that holds "size-like" symbols.  When we perform
+        # "size-oblivious" tests, these can be assumed to be >= 2.
+        self.size_like: set[sympy.Symbol] = set()
+        # Duck-shaping says that if two input tensors have the same size,
+        # they get assigned the same symbolic variable
+        self.val_to_var: dict[int, sympy.Symbol] = {}
+        if specialize_zero_one:
+            self.val_to_var = {0: sympy.S.Zero, 1: sympy.S.One}
+        self.unbacked_symfloat_counter = itertools.count()
+        self.unbacked_symint_counter = itertools.count()
+        # Similar to guards, but these MUST evaluate to true and can
+        # only be evaluated at runtime midway through (i.e., they always
+        # involve unbacked symints)
+        #
+        # For efficiency reasons, we index in the following way.  Suppose you have
+        # a runtime assert i0 + i1 <= s1.  We pick the most recently allocated
+        # symbol in the source expression and add the assert to the list for
+        # that symbol e.g., {i1: [i0 + i1 <= s1]}.
+        #
+        # We access the runtime asserts in two situations:
+        #
+        #   - When we are guarding on an expression, we will attempt to
+        #     statically evaluate it, in case the unbacked SymInts can
+        #     simplify away.  If we have a runtime assert, we may be able
+        #     to discharge the guard entirely.  We only need to attempt
+        #     runtime asserts that mention freevars of the expression in
+        #     question.
+        #
+        #   - When we are performing codegen (in Inductor for eager, or
+        #     when finalizing the export FX graph), we need to know what
+        #     extra runtime asserts to insert.  Whenever an unbacked
+        #     SymInt comes into scope, all runtime asserts involving it
+        #     become eligible for insertion (so long as all of their other
+        #     free unbacked symbols are also in scope).  We technically
+        #     can handle any choice of key by kicking inexpressible asserts
+        #     to the next unbacked symbol to wait on, but if we choose the
+        #     latest key, an assert will only show up at the moment when
+        #     we can actually codegen it.
+        self.deferred_runtime_asserts: dict[
+            Optional[sympy.Symbol], list[RuntimeAssert]
+        ] = {}
+        # This exists so we can efficiently invalidate the cache (it's used as
+        # part of the cache key); otherwise we'd have to iterate through
+        # deferred_runtime_asserts to compute its length
+        self.num_deferred_runtime_asserts = 0
+        self.log = log
+        self.log.info("create_env")
+        self.frozen = False
+        self.runtime_asserts_frozen = False
+        self.dim_constraints: Optional[DimConstraints] = None
+        self.counter: Counter[str] = collections.Counter()
+        # Mapping from sympy.Symbol to the number of guards which mention this
+        # symbol
+        self.symbol_guard_counter: Counter[sympy.Symbol] = collections.Counter()
+        # A selection of important fields on co_field; solely used for
+        # signpost_event
+        self.co_fields = co_fields if co_fields else {}
+
+        # Whenever we allocate a fresh unbacked Symbol, we add it to this
+        # pending list.  Unbacked symbol allocation can occur at unpredictable
+        # points during meta tensor propagation, but at some point, we
+        # have to know what the binding site for an unbacked symbol is, and
+        # this is computed when we actually place the node in the graph. The
+        # important thing is that we always actually handle every unaccounted
+        # for unbacked symbol, so this list helps us keep track of them and
+        # then make sure they are all accounted for.
+        #
+        # We could potentially give rise to errors earlier by lexically
+        # scoping when we do propagation, and only allowing unbacked symbols
+        # to be allocated at this point in time.  However this is inconvenient
+        # to do in Dynamo, because fake tensor propagation is far from when we
+        # analyze binding sites (set_example_value), so we do it in a more
+        # mutatey way.
+        #
+        # NB: fresh unbacked symbols NEVER get substitutions applied to them,
+        # they are binding sites!
+        self.pending_fresh_unbacked_symbols: list[sympy.Symbol] = []
+
+        # Version counter used to invalidate cached values
+        self._prev_cache_key = self._get_key()
+        self._version_counter = 0
+
+        # Each time divisible is changed this should be set to True, this is set in _update_version_counter.
+        self._resimplify_floor_div_axioms = True
+
+        # Cache for FX nodes.
+        # Maps an already built node a tuple of:
+        #   1. node's target
+        #   2. list of arguments
+        # This drastically reduces the size of the FX graph, avoiding
+        # duplicated nodes.
+        self.fx_node_cache: dict[tuple[Callable, tuple[Any, ...]], torch.fx.Node] = {}
+        self.source_to_symbol: dict[str, sympy.Symbol] = {}
+
+        # Suppose you want to replace an unbacked symbol with another
+        # unbacked symbol.  This is error prone because you can cause
+        # references to unbacked symbols to time travel backwards.  E.g.,
+        #
+        # u1 = x.item()
+        # ... use of u1 ...
+        # u2 = y.item()
+        # u3 = z.item()
+        # torch._check(u1 == u2 + u3)
+        #
+        # If you replace u1 with u2 + u3, then the use of u1 now
+        # references u2 and u3 prior to them actually being bound at
+        # runtime.
+        #
+        # To control for this, we track the order unbacked symbols
+        # were allocated, and only allow substitutions if they respect
+        # the dependency from this order; an unbacked symbol can only
+        # be substituted with unbacked symbols that come before it in the
+        # order.
+        #
+        # This also imposes an ordering on the unbacked symbol binding
+        # sites themselves: you are not allowed to reorder unbacked symbol
+        # bindings.  At the moment, this is not tracked, but we potentially
+        # could track this at the IR level using a higher order operator
+        # with something like effect token tracking.
+        self.unbacked_alloc_order: dict[sympy.Symbol, int] = {}
+
+        from torch.fx.experimental.validator import translation_validation_enabled
+
+        self._translation_validation_enabled = translation_validation_enabled()
+
+        if self._translation_validation_enabled:
+            from torch.fx.experimental.validator import TranslationValidator
+
+            self.validator = TranslationValidator()
+            self.graph = torch.fx.Graph()
+            # Create an output graph and start inserting before that.
+            # This is needed when 'deepcopy'-ing this object.
+            self.graph.inserting_before(self.graph.output(None))
+
+            # Mapping of each node name to the node itself.
+            #
+            # This is useful for matching an FX node from a recorded ShapeEnv.graph
+            # to the FX node of the ShapeEnv we are running the event on.
+            #
+            # Whenever you add a node to self.graph, you must add a mapping to this
+            # variable. Otherwise, the built FX graph on the replayed ShapeEnv will
+            # not be valid.
+            self.name_to_node: dict[str, torch.fx.Node] = {}
+
+    @property
+    def allow_scalar_outputs(self) -> bool:
+        return self.settings.allow_scalar_outputs
+
+    @property
+    def allow_dynamic_output_shape_ops(self) -> bool:
+        return self.settings.allow_dynamic_output_shape_ops
+
+    @property
+    def assume_static_by_default(self) -> bool:
+        return self.settings.assume_static_by_default
+
+    @property
+    def specialize_zero_one(self) -> bool:
+        return self.settings.specialize_zero_one
+
+    @property
+    def duck_shape(self) -> bool:
+        return self.settings.duck_shape
+
+    @property
+    def prefer_deferred_runtime_asserts_over_guards(self) -> bool:
+        return self.settings.prefer_deferred_runtime_asserts_over_guards
+
+    @property
+    def allow_complex_guards_as_runtime_asserts(self) -> bool:
+        return self.settings.allow_complex_guards_as_runtime_asserts
+
+    def check_equal(self, other: ShapeEnv) -> None:
+        """Compare another ShapeEnv for equivalence"""
+        # ShapeEnv fields that are not relevant for the outcome of
+        # ShapeEnv.produce_guards call:
+        #   - Debugging variables
+        #   - Translation validation related variables
+        #   - Events recording related variables
+        non_state_variable_names = (
+            "counter",
+            "log",
+            "var_to_stack",
+            "fx_node_cache",
+            "graph",
+            "validator",
+            "check_recorded_events",
+            "should_record_events",
+            "is_recording",
+            "tracked_fakes",
+            "events",
+            "source_name_to_debug_name",
+            "_prev_cache_key",
+            "_version_counter",
+            "dim_constraints",
+            # source locations are OK to diverge
+            "var_to_range_sloc",
+            "replacements_slocs",
+            "_resimplify_floor_div_axioms",
+            "_expr_sym_node_id",
+        )
+
+        # Mapping of the value of each to-be-compared field into the values that
+        # should actually be compared.
+        #
+        # You should modify this if, for example, the field that holds state and
+        # debugging information. e.g. ShapeGuard holds the actual guard (sympy.Expr)
+        # and the stack when it was added to the set of guards. In order to compare
+        # it, we throw away the stack information.
+        def map_value(key: str, value: Any) -> Any:
+            if key in ("unbacked_symfloat_counter", "unbacked_symint_counter"):
+                from copy import copy
+
+                # For itertools.count(), we compare the next integer returned
+                # by the count iterators. Not that we need to copy the iterator
+                # first. Otherwise we are mutating the object.
+                return next(copy(value))
+            elif key == "guards":
+                # Transform the list of ShapeGuard into a list of expressions.
+                return [g.expr for g in value]
+            elif key == "deferred_runtime_asserts":
+                # Transform the list of RuntimeAsserts into a list of expressions.
+                return {s: [ra.expr for ra in ras] for s, ras in value.items()}
+            elif key == "name_to_node":
+                # Compare just the set of keys is the same.
+                return set(value.keys())
+            elif key in (
+                "symbol_guard_counter",
+                "pending_fresh_unbacked_symbols",
+                "fake_tensor_cache",
+            ):
+                # Skip this for comparisons
+                return None
+            return value
+
+        shape_env_check_state_equal(self, other, non_state_variable_names, map_value)
+
+    def _snapshot_tracked_fakes(self) -> Optional[list[Any]]:
+        if self.tracked_fakes is None:
+            return None
+
+        from torch._dynamo.variables.builder import TrackedFake
+
+        def maybe_transform_fake(fake: TrackedFake) -> TrackedFake:
+            inner_fake = (
+                fake.fake
+                if isinstance(fake.fake, (torch.SymInt, torch.SymFloat))
+                else FakeTensorMeta.from_fake(fake.fake)
+            )
+            # Even though TrackedFake accepts either a Union[SymInt, FakeTensor], here we give it a
+            # FakeTensorMeta for two reasons:
+            #   1. this is all the information we need when recording ShapeEnvEvents.
+            #   2. it works even if each TrackedFake changes its metadata.
+            return TrackedFake(inner_fake, fake.source, fake.symbolic_context)  # type: ignore[arg-type]
+
+        return [maybe_transform_fake(fake) for fake in self.tracked_fakes]
+
+    def _last_event_index(self) -> int:
+        return len(self.events) - 1
+
+    @contextmanager
+    def _recording(self) -> Iterator[None]:
+        self.is_recording = True
+        try:
+            yield
+        finally:
+            self.is_recording = False
+
+    @record_shapeenv_event()
+    def _eliminate_unbacked(self, orig_s: sympy.Symbol, new_s: sympy.Expr) -> None:
+        self._set_replacement(orig_s, new_s, "eliminate_unbacked")
+
+    @record_shapeenv_event()
+    def set_unbacked_var_to_val(self, k: sympy.Symbol, v: int) -> None:
+        """Used only when propagate_real_tensors; registers a value for an
+        unbacked symbol, which can be used last resort to resolve hints."""
+        log.info("set_unbacked_var_to_val %s = %s", k, v)
+        self.unbacked_var_to_val[k] = sympy.sympify(v)
+
+    # Unlike set_replacement, this records a shapeenv event
+    @record_shapeenv_event()
+    def _rename_unbacked_to(self, orig_s: sympy.Symbol, new_s: sympy.Symbol) -> None:
+        assert isinstance(orig_s, sympy.Symbol), orig_s
+        assert isinstance(new_s, sympy.Symbol), new_s
+        assert free_unbacked_symbols(new_s), new_s
+        assert free_unbacked_symbols(orig_s), orig_s
+        dest = self.replacements.get(orig_s)
+        if dest is not None:
+            assert not free_unbacked_symbols(dest), f"{orig_s} -> {dest}"
+        self._set_replacement(orig_s, new_s, "rename_unbacked_to")
+        self.unbacked_renamings[orig_s] = new_s
+        if dest is not None:
+            self._set_replacement(new_s, dest, "rename_unbacked_to_dest")
+
+    @record_shapeenv_event()
+    def _constrain_is_bounded(self, a: sympy.Symbol, upper_bound: int) -> None:
+        # TODO: Do something nontrivial when upper_bound is expression
+        pass
+
+    @record_shapeenv_event()
+    def _constrain_range_for_size(
+        self, a: sympy.Symbol, min: Optional[int] = None, max: Optional[int] = None
+    ) -> None:
+        if min is None:
+            min = 0
+        if max is None:
+            max = int_oo
+
+        if max < min:
+            raise ValueError(
+                "Maximum value to constrain_as_size can't be less than the specified min value, "
+                "received min={min} and max={max}"
+            )
+
+        self.constrain_symbol_range(
+            a,
+            compiler_min=min,
+            compiler_max=max,
+        )
+        self.size_like.add(a)
+
+    @record_shapeenv_event()
+    def _constrain_range(self, a: sympy.Expr, min: int, max: int) -> None:
+        if isinstance(a, sympy.Integer):
+            if not (min <= int(a) <= max):
+                raise ValueRangeError(f"Invalid value {int(a)} for range [{min}:{max}]")
+            return
+
+        # TODO: Shouldn't we install a guard if the symbol is backed?  Or is the
+        # semantics that this is an "unchecked" assert (but it this actually
+        # something useful?  Might be better to restrict only for unbacked
+        # SymInt).
+        if isinstance(a, sympy.Symbol):
+            self.constrain_symbol_range(
+                a,
+                compiler_min=min,
+                compiler_max=max,
+            )
+
+    @record_shapeenv_event()
+    def _constrain_unify(self, a: SymInt, b: SymInt) -> None:
+        """
+        Given two SymInts, constrain them so that they must be equal.  NB:
+        this will not work with SymInts that represent nontrivial expressions
+        (yet!)
+        """
+        # TODO: this does not install a deferred runtime assert yet
+
+        # TODO: Maybe dedupe this with _maybe_guard_rel?
+        # Update Feb 2024: this is extra important to do, this doesn't handle
+        # unbacked replacements properly nor does it generate deferred runtime
+        # asserts
+        if not isinstance(a, SymInt):
+            if not isinstance(b, SymInt):
+                assert a == b
+            else:
+                assert isinstance(
+                    b.node.expr, sympy.Symbol
+                ), "constraining non-Symbols NYI"
+                assert b.node.shape_env is self
+                self.replacements[b.node.expr] = sympy.Integer(a)
+        else:
+            # TODO: Actually, we can support this as long as one of them is a symbol.
+            # NB: We can't actually do "unification" as our operators are not
+            # injective
+            assert isinstance(a.node.expr, sympy.Symbol), "constraining non-Symbols NYI"
+            assert a.node.shape_env is self
+            if not isinstance(b, SymInt):
+                self.replacements[a.node.expr] = sympy.Integer(b)
+            else:
+                assert a.node.shape_env is b.node.shape_env
+                assert isinstance(
+                    b.node.expr, sympy.Symbol
+                ), "constraining non-Symbols NYI"
+                new_var = self._find(a.node.expr)
+                self.replacements[b.node.expr] = new_var
+
+    def _ignore_fresh_unbacked_symbols_tls(self) -> bool:
+        return getattr(TLS, "ignore_fresh_unbacked_symbols", False)
+
+    @record_shapeenv_event()
+    def _ignore_fresh_unbacked_symbols_set(self, b: bool) -> bool:
+        prev = self._ignore_fresh_unbacked_symbols_tls()
+        TLS.ignore_fresh_unbacked_symbols = b
+        return prev
+
+    @contextmanager
+    def ignore_fresh_unbacked_symbols(self) -> Iterator[None]:
+        """
+        Indicates that the newly allocated unbacked SymInts are being
+        discarded
+        """
+        prev = self._ignore_fresh_unbacked_symbols_set(True)
+        try:
+            yield
+        finally:
+            self._ignore_fresh_unbacked_symbols_set(prev)
+
+    @record_shapeenv_event()
+    def freeze(self) -> None:
+        """Freeze this ShapeEnv to stop accumulating guards
+
+        A frozen ShapeEnv will ignore any further guards generated on it and
+        only emit a warning which may lead to accuracy problems.
+        """
+        self.frozen = True
+
+    @record_shapeenv_event()
+    def freeze_runtime_asserts(self) -> None:
+        """Freeze this ShapeEnv to stop adding deferred runtime asserts.
+
+        We will error if you try to install a new runtime assert when it is
+        frozen.  This would indicate a lowering violation, or perhaps something
+        we know statically is already True but we are checking it again in a way
+        that is not clearly dischargeable.
+        """
+        # self.prefer_deferred_runtime_asserts_over_guards = False
+        self.runtime_asserts_frozen = True
+
+    def _create_symbol_for_source(self, source: Source) -> Optional[sympy.Symbol]:
+        if not self._translation_validation_enabled:
+            return None
+        srcname = source.name()
+        if source not in self.source_to_symbol:
+            self.source_to_symbol[srcname] = sympy.Symbol(srcname, integer=True)
+        return self.source_to_symbol[srcname]
+
+    def _add_z3var(self, symbol: sympy.Symbol, type: type) -> None:
+        if self._translation_validation_enabled:
+            self.validator.add_var(symbol, type)
+
+    def _add_target_expr(self, expr: SympyBoolean) -> None:
+        if self._translation_validation_enabled:
+            self.validator.add_target_expr(expr)
+
+    def _add_assertion(self, expr: SympyBoolean) -> None:
+        if self._translation_validation_enabled:
+            self.validator.add_assertion(expr)
+
+    def _check_translation_validate(self) -> None:
+        if self._translation_validation_enabled:
+            self.validator.validate()
+
+    @record_shapeenv_event()
+    def _create_fx_call_function(
+        self,
+        op: Callable,
+        args: tuple,
+    ) -> tuple[Optional[torch.fx.Node], bool]:
+        # Cache this tuple in order to avoid duplicated nodes.
+        node_key = (op, args)
+        # Flags whether the returned node was cached or not.
+        fresh = False
+
+        if self._translation_validation_enabled and node_key not in self.fx_node_cache:
+            # Presence of None in the arguments implies that we should ignore this operation.
+            if any(a is None for a in args):
+                # We check if we are not mixing SymNode that should not be ignored
+                # (fx_node is not None) with those that should (fx_node is None).
+                assert all(not isinstance(a, torch.fx.Node) for a in args)
+                return None, fresh
+
+            fresh = True
+
+            # If translation validation is enabled, all arguments must have its
+            # own FX node.
+            assert all(
+                a is not None for a in args
+            ), f"missing arg in FX graph ({op.__name__}): {args}"
+            node = self.fx_node_cache[node_key] = self.graph.call_function(op, args)
+            self.name_to_node[node.name] = node
+
+        return self.fx_node_cache.get(node_key, None), fresh
+
+    def _create_fx_placeholder_and_z3var(
+        self,
+        symbol: sympy.Symbol,
+        type: type,
+    ) -> Optional[torch.fx.Node]:
+        if not self._translation_validation_enabled:
+            return None
+
+        node_key = (self.graph.placeholder, (symbol,))
+
+        # Check if we haven't added this symbol already.
+        # If so, skip the placeholder creation, as it
+        # generates invalid Python code.
+        if node_key not in self.fx_node_cache:
+            # Add a Z3 variable according to 'type'.
+            self._add_z3var(symbol, type)
+            # Create the FX placeholder out of a mangled name.
+            mangled_name = re.sub(
+                r"[^a-zA-Z0-9]", "_", re.sub(r"[()]", "", symbol.name)
+            )
+            node = self.fx_node_cache[node_key] = self.graph.placeholder(mangled_name)
+            self.name_to_node[node.name] = node
+            # Attach the 'symbol' to the placeholder so that we can retrieve
+            # the Z3 variable later.
+            node.meta["symbol"] = symbol
+
+        return self.fx_node_cache[node_key]
+
+    def _remove_fx_node(self, node: Optional[torch.fx.Node]) -> None:
+        if self._translation_validation_enabled and node is not None:
+            self.name_to_node.pop(node.name)
+            self.graph.erase_node(node)
+
+    def _add_fx_node_metadata(self, node: torch.fx.Node) -> None:
+        from torch._dynamo.utils import get_current_node
+
+        if self.should_record_events:
+            node.meta[SHAPEENV_EVENT_KEY] = self._last_event_index()
+            node.meta[CURRENT_NODE_KEY] = get_current_node()
+
+    def _suppress_guards_tls(self) -> bool:
+        return getattr(TLS, "suppress_guards", False)
+
+    @record_shapeenv_event()
+    def _suppress_guards_enter(self) -> None:
+        if not hasattr(TLS, "suppress_guards_stack"):
+            TLS.suppress_guards_stack = []
+        old = self._suppress_guards_tls()
+        TLS.suppress_guards_stack.append(old)
+        TLS.suppress_guards = True
+
+    @record_shapeenv_event()
+    def _suppress_guards_exit(self) -> None:
+        old = (
+            TLS.suppress_guards_stack.pop()
+            if len(TLS.suppress_guards_stack) > 0
+            else False
+        )
+        TLS.suppress_guards = old
+
+    def suppress_guards(self) -> _GeneratorContextManager[None]:
+        """Context manager to ignore all guards generated inside"""
+        return _suppress_guards(self)
+
+    def _get_key(self) -> tuple[int, int, int, int]:
+        """
+        Defines the current "state" of the guards we've accumulated in this ShapeEnv.
+        Determines when we need to invalidate our cache
+        """
+        return (
+            len(self.replacements),
+            len(self.divisible),
+            self.num_deferred_runtime_asserts,
+            len(self.unbacked_var_to_val),
+        )
+
+    def _update_version_counter(self) -> None:
+        # if the change to shape env effects self.divisible set
+        # _resimplify_floor_div_axioms.
+        # This is used to trigger a resimplication of FloorDiv to CleanDivs
+        # in implication inside the function resimplify_floor_div.
+        if len(self.divisible) != self._prev_cache_key[1]:
+            self._resimplify_floor_div_axioms = True
+
+        # The shape environment is queried orders of magnitude more often than
+        # it is changed, so we summarise the cache key into a linearly
+        # increasing version counter which is cheaper to check in _lru_cache
+
+        # Only update version counter if the state actually changed
+        cur_key = self._get_key()
+
+        if self._prev_cache_key != cur_key:
+            self._prev_cache_key = cur_key
+            self._version_counter += 1
+
+    def _produce_dyn_sizes(
+        self,
+        ex_size: Sequence[Union[int, SymInt]],
+        source: Source,
+        symbolic_context: SymbolicContext,
+    ) -> list[sympy.Expr]:
+        return self._produce_dyn_sizes_from_int_tuple(
+            tuple(ex_size), source, symbolic_context
+        )
+
+    def _produce_dyn_sizes_from_int_tuple(
+        self,
+        tensor_size: Sequence[Union[int, SymInt]],
+        source: Source,
+        symbolic_context: SymbolicContext,
+    ) -> list[sympy.Expr]:
+        assert all(
+            not is_symbolic(val) for val in tensor_size
+        ), f"Expect size to be a plain tuple of ints but got {tensor_size}"
+        from torch._dynamo.source import TensorProperty, TensorPropertySource
+
+        _assert_symbol_context(symbolic_context)
+        dynamic_dims = symbolic_context.dynamic_sizes  # type: ignore[attr-defined]
+        constraint_dims = symbolic_context.constraint_sizes  # type: ignore[attr-defined]
+        size = []
+        for i, val in enumerate(tensor_size):
+            sym = self.create_symbol(
+                val,
+                TensorPropertySource(source, TensorProperty.SIZE, i),
+                dynamic_dims[i],
+                constraint_dims[i],
+                do_not_specialize_zero_one=config.backed_size_oblivious,
+                symbolic_context=symbolic_context,
+            )
+            if (
+                config.backed_size_oblivious
+                and isinstance(sym, sympy.Symbol)  # could be static
+                and symbol_is_type(sym, SymT.SIZE)
+            ):
+                self.size_like.add(sym)
+            size.append(sym)
+        return size
+
+    def create_symbolic_sizes_strides_storage_offset(
+        self,
+        ex: torch.Tensor,
+        source: Source,
+        *,
+        symbolic_context: Optional[SymbolicContext] = None,
+    ) -> tuple[
+        tuple[Union[int, SymInt], ...],
+        tuple[Union[int, SymInt], ...],
+        Union[int, SymInt],
+    ]:
+        """
+        Returns a list of symbolic sizes and strides for the given tensor.
+        We try our best to express stride in terms of the sizes, so as to not
+        introduce new symbolic variables.
+        """
+
+        ex_size = tuple(
+            self._maybe_specialize_sym_int_with_hint(sz) for sz in ex.size()
+        )
+        ex_stride = tuple(
+            self._maybe_specialize_sym_int_with_hint(sd) for sd in ex.stride()
+        )
+        ex_storage_offset = self._maybe_specialize_sym_int_with_hint(
+            ex.storage_offset()
+        )
+
+        return self._create_symbolic_sizes_strides_storage_offset(
+            ex_size,
+            ex_stride,
+            ex_storage_offset,
+            [_is_dim_dynamic(ex, i) for i in range(ex.dim())],
+            source,
+            symbolic_context=symbolic_context,
+        )
+
+    # Dynamo may want to wrap FakeTensors with SymInt sizes up e.g. make_fx(opt_f(), tracing_mode="symbolic").
+    # We create symbols in shape_env using the backed hints behind SymInt.
+
+    # Case 1: when SymInt is backed, dynamo can proceed with FakeTensors that have concrete shape.
+    # produce_guards will trigger specializations on the outer stuff
+
+    # Case 2: when the SymInt is unbacked, we will throw an data dependent error in require_hint().
+    #
+    # It's probably good for now but it's important to note that this approach has implications for
+    # the original shape_env when checking guards in different order.
+
+    # Example:
+    # ---------
+    # Consider a function "opt_f" as shown below:
+
+    # @torch.compile()
+    # def opt_f(x: bool, y: Tensor):
+    #   if x == True:
+    #     return y + torch.randn([4])
+    #   else:
+    #     return y
+    # Depending on the sequence of calls, we might install two different sets of guards:
+
+    # 1. opt_f(False, y):
+    #    - "x == False" (always works for any size y)
+
+    # 2. opt_f(True, y):
+    #    - Triggers recompilation and results in guards like:
+    #      - "x == True and y.size(0) == 4"
+    #      - (or "y.size(0) == 4 and x == True")
+
+    # The order of checking the guards matters. In this specific example:
+    # If True branch guard check precedes False branch and for True branch, y.size(0) check precedes x == True,
+    # we may have an unnessary shape speciliazation for y.
+    def _maybe_specialize_sym_int_with_hint(
+        self, maybe_sym: Union[int, SymInt]
+    ) -> Union[int, SymInt]:
+        assert isinstance(maybe_sym, (int, torch.SymInt))
+        if is_symbolic(maybe_sym):
+            assert (
+                maybe_sym.node.shape_env is not self
+            ), "expect the symbol is created from an shape env other than current one."
+            return maybe_sym.node.require_hint()
+        return maybe_sym
+
+    @record_shapeenv_event()
+    def _create_symbolic_sizes_strides_storage_offset(
+        self,
+        # NB: SymInt is allowed here due to nested int, normally you don't
+        # actually pass true symbolic sizes to this function
+        ex_size: Sequence[Union[int, SymInt]],
+        ex_stride: Sequence[Union[int, SymInt]],
+        ex_storage_offset: Union[int, SymInt],
+        is_dim_dynamic: Sequence[bool],
+        source: Source,
+        *,
+        symbolic_context: Optional[SymbolicContext] = None,
+    ) -> tuple[
+        tuple[Union[int, SymInt], ...],
+        tuple[Union[int, SymInt], ...],
+        Union[int, SymInt],
+    ]:
+        dim = len(ex_size)
+
+        # Reimplement the legacy behavior
+        if symbolic_context is None:
+            constraint_sizes: list[DimConstraint] = [None] * dim
+            constraint_strides: list[DimConstraint] = [None] * dim
+            dynamic_dims = []
+            dynamic_strides = []
+            for i in range(dim):
+                # NB: This is encapsulation breaking!  Legacy behavior was
+                # bad.
+                if is_dim_dynamic[i]:
+                    r = DimDynamic.DYNAMIC
+                elif self.assume_static_by_default:
+                    r = DimDynamic.STATIC
+                else:
+                    r = DimDynamic.DUCK
+                dynamic_dims.append(r)
+                dynamic_strides.append(r)
+            dynamic_dims = [DimDynamic.DUCK] * dim
+            dynamic_strides = [DimDynamic.INFER_STRIDE] * dim
+            # symbolic_context is None - set one
+            symbolic_context = StatelessSymbolicContext(
+                dynamic_sizes=dynamic_dims,
+                dynamic_strides=dynamic_strides,
+                constraint_sizes=constraint_sizes,
+                constraint_strides=constraint_strides,
+            )
+        # We got a StatelessSymbolicContext
+        _assert_symbol_context(symbolic_context)
+        constraint_sizes = symbolic_context.constraint_sizes  # type: ignore[attr-defined]
+        constraint_strides = symbolic_context.constraint_strides  # type: ignore[attr-defined]
+        dynamic_sizes = symbolic_context.dynamic_sizes  # type: ignore[attr-defined]
+        dynamic_strides = symbolic_context.dynamic_strides  # type: ignore[attr-defined]
+
+        # TODO: make this configurable from outside symbolic_context; we made a symbolic_context
+        # decision here where if all sizes are static, we are going to
+        # specialize all of the inner strides/offset too. We don't have to
+        # do this, and arguably we should ALWAYS allow for dynamic offset,
+        # this is cheap.
+        # TODO: This should be DYNAMIC, using DUCK for BC
+        dynamic_offset = (
+            DimDynamic.STATIC
+            if all(r == DimDynamic.STATIC for r in dynamic_sizes)
+            else DimDynamic.DUCK
+        )
+        are_sizes_static = all(r == DimDynamic.STATIC for r in dynamic_sizes)
+
+        assert len(dynamic_sizes) == dim, f"{len(dynamic_sizes)} != {dim}"
+        assert len(dynamic_strides) == dim, f"{len(dynamic_sizes)} != {dim}"
+        assert len(constraint_sizes) == dim
+        assert len(constraint_strides) == dim
+
+        from torch._dynamo.source import TensorProperty, TensorPropertySource
+
+        size: list[sympy.Expr] = self._produce_dyn_sizes_from_int_tuple(
+            ex_size, source, symbolic_context
+        )
+        stride = self._compute_symbolic_stride(
+            source,
+            size,
+            ex_size,
+            ex_stride,
+            dynamic_strides,
+            constraint_strides,
+            are_sizes_static,
+            symbolic_context,
+        )
+
+        sym_sizes = [
+            self.create_symintnode(
+                sym,
+                hint=hint,
+                source=TensorPropertySource(source, TensorProperty.SIZE, i),
+            )
+            for i, (sym, hint) in enumerate(zip(size, ex_size))
+        ]
+        sym_stride = []
+        for i, stride_expr in enumerate(stride):
+            # NB: Don't duck size the stride; instead use the expression
+            # we computed
+            assert stride_expr is not None
+            sym_stride.append(
+                self.create_symintnode(
+                    stride_expr,
+                    hint=ex_stride[i],
+                    source=TensorPropertySource(source, TensorProperty.STRIDE, i),
+                )
+            )
+        sym_storage_offset = self.create_symintnode(
+            self.create_symbol(
+                ex_storage_offset,
+                TensorPropertySource(source, TensorProperty.STORAGE_OFFSET),
+                dynamic_dim=dynamic_offset,
+                constraint_dim=None,
+                symbolic_context=symbolic_context,
+            ),
+            hint=ex_storage_offset,
+            source=TensorPropertySource(source, TensorProperty.STORAGE_OFFSET),
+        )
+        return tuple(sym_sizes), tuple(sym_stride), sym_storage_offset
+
+    def _compute_symbolic_stride(
+        self,
+        source: Source,
+        size: Sequence[sympy.Expr],
+        ex_size: Sequence[Union[int, SymInt]],
+        ex_stride: Sequence[Union[int, SymInt]],
+        dynamic_strides: Sequence[DimDynamic],
+        constraint_strides: Sequence[
+            Optional[Union[StrictMinMaxConstraint, RelaxedUnspecConstraint]]
+        ],
+        are_sizes_static: bool,
+        symbolic_context: SymbolicContext,
+    ) -> list[sympy.Expr]:
+        from torch._dynamo.source import TensorProperty, TensorPropertySource
+
+        stride: list[Optional[sympy.Expr]] = [None] * len(size)
+        candidates: dict[Union[int, SymInt], sympy.Expr] = {}
+
+        # iterate over unbound strides in val ascending order with
+        # index descending as a tie breaker since for cases like
+        # [(1, 1), (1, 0)], we want to fill in the right most
+        # stride first.
+        val_list = [(val, -i) for i, val in enumerate(ex_stride)]
+        val_list.sort(key=_nested_int_aware_sort)
+
+        for val, neg_i in val_list:
+            i = -neg_i
+            contiguous_stride = (
+                i != len(ex_stride) - 1
+                and ex_stride[i] == ex_size[i + 1] * ex_stride[i + 1]
+            )
+            if val in (0, 1) and not contiguous_stride:
+                out_stride = sympy.Integer(val)
+            else:
+                dynamic_stride = dynamic_strides[i]
+                if dynamic_stride == DimDynamic.INFER_STRIDE and val in candidates:
+                    # Set stride to a candidate only for DimDynamic.INFER_STRIDE
+                    out_stride = candidates[val]
+                else:
+                    # Set INFER_STRIDE to STATIC or DUCK depending on sizes
+                    dyn_stride = dynamic_stride
+                    if dynamic_stride == DimDynamic.INFER_STRIDE:
+                        dyn_stride = (
+                            DimDynamic.STATIC if are_sizes_static else DimDynamic.DUCK
+                        )
+                    out_stride = self.create_symbol(
+                        val,
+                        TensorPropertySource(source, TensorProperty.STRIDE, i),
+                        dynamic_dim=dyn_stride,
+                        constraint_dim=constraint_strides[i],
+                        symbolic_context=symbolic_context,
+                    )
+            stride[i] = out_stride
+            candidates[ex_size[i] * val] = size[i] * out_stride
+
+        assert all(x is not None for x in stride)
+        return stride
+
+    @record_shapeenv_event()
+    def create_symintnode(
+        self,
+        sym: sympy.Expr,
+        *,
+        hint: Optional[int],
+        source: Optional[Source] = None,
+    ) -> Union[int, SymInt]:
+        """Create a SymInt value from a symbolic expression
+
+        If you know what the current hint value of the SymInt to be created
+        is, pass it into hint.  Otherwise, pass None and we will make our best
+        guess
+
+        """
+        if self._translation_validation_enabled and source is not None:
+            # Create a new symbol for this source.
+            symbol = self._create_symbol_for_source(source)
+            assert symbol is not None
+
+            # Create a new FX placeholder and Z3 variable for 'symbol'.
+            fx_node = self._create_fx_placeholder_and_z3var(symbol, int)
+
+            # Add an equality assertion for the newly created symbol and 'sym'.
+            self._add_assertion(sympy.Eq(symbol, sym))
+        else:
+            fx_node = None
+
+        out: Union[int, SymInt]
+        if isinstance(sym, sympy.Integer):
+            if hint is not None:
+                assert int(sym) == hint
+            out = int(sym)
+        else:
+            # How can this occur? When we mark_unbacked, we end up with a real
+            # tensor that has hints for all sizes, but we MUST NOT create a
+            # SymNode with a hint, because we're hiding the hint from our eyes
+            # with the unbacked Symbol.  And in fact, the hint compute may be
+            # inconsistent with size oblivious tests.
+            if free_unbacked_symbols(sym):
+                hint = None
+            out = SymInt(SymNode(sym, self, int, hint, fx_node=fx_node))
+        return out
+
+    @record_shapeenv_event()
+    def create_symfloatnode(
+        self,
+        sym: sympy.Expr,
+        *,
+        hint: Optional[int],
+        source: Optional[Source] = None,
+    ) -> Union[float, SymFloat]:
+        """Create a SymFloat value from a symbolic expression"""
+        if self._translation_validation_enabled and source is not None:
+            # Create a new symbol for this source.
+            symbol = self._create_symbol_for_source(source)
+            assert symbol is not None
+
+            # Create a new FX placeholder and Z3 variable for 'symbol'.
+            fx_node = self._create_fx_placeholder_and_z3var(symbol, float)
+
+            # Add an equality assertion for the newly created symbol and 'sym'.
+            self._add_assertion(sympy.Eq(symbol, sym))
+        else:
+            fx_node = None
+
+        out: Union[float, SymFloat]
+        if isinstance(sym, sympy.Float):
+            if hint is not None:
+                assert float(sym) == hint
+            out = float(sym)
+        else:
+            # You could give this the same treatment as SymInt above if
+            # you supported mark_unbacked on a float, but it's a kind of
+            # strange thing to do though because floats don't get 0/1
+            # specialization anyway
+            if free_unbacked_symbols(sym):
+                assert hint is None, sym
+            out = SymFloat(SymNode(sym, self, float, hint, fx_node=fx_node))
+        return out
+
+    @record_shapeenv_event()
+    def create_unspecified_symint_and_symbol(
+        self, value: int, source: Source, dynamic_dim: DimDynamic
+    ) -> Union[int, SymInt]:
+        """Create a SymInt wrapping a new unspecified symbol"""
+        return self.create_symintnode(
+            self.create_unspecified_symbol(
+                value,
+                source=source,
+                dynamic_dim=dynamic_dim,
+            ),
+            hint=value,
+            source=source,
+        )
+
+    def create_symboolnode(self, sym: sympy.Expr) -> SymBool:
+        """Create a SymBool object from a sympy boolean expression"""
+        # This function is only being used in serialization, so we do not track it
+        # for validation.
+        return SymBool(SymNode(sym, self, bool, None))
+
+    def _log_create_unbacked_symbol(
+        self,
+        prefix: str,
+        symbol: sympy.Symbol,
+        vr: ValueRanges,
+        source: Optional[Source] = None,
+        sym_node: Optional[SymNode] = None,
+    ) -> None:
+        is_debug = config.extended_debug_create_symbol is not None and str(
+            symbol
+        ) in config.extended_debug_create_symbol.split(",")
+        sloc: Union[str, SLoc]
+        if source is None:
+            sloc, maybe_extra_debug = self._get_stack_summary(is_debug)
+        else:
+            sloc, maybe_extra_debug = source.name(), ""
+        log.info(
+            "%s %s [%s, %s] %s%s",
+            prefix,
+            symbol,
+            vr.lower,
+            vr.upper,
+            sloc,
+            maybe_extra_debug,
+            stack_info=is_debug,
+        )
+        trace_structured(
+            "create_unbacked_symbol",
+            metadata_fn=lambda: {
+                "symbol": str(symbol),
+                "node_id": id(sym_node),
+                "vr": f"[{vr.lower}, {vr.upper}]",
+                "user_stack": structured.get_user_stack(3),
+                "stack": structured.get_framework_stack(),
+            },
+        )
+
+    @record_shapeenv_event()
+    def create_unbacked_symfloat(self) -> SymFloat:
+        """Create a symbolic float without a hint value"""
+        symbol: sympy.Symbol = make_symbol(
+            SymT.UNBACKED_FLOAT, next(self.unbacked_symfloat_counter)
+        )
+        self.counter["create_unbacked_symbol"] += 1
+        if not self._ignore_fresh_unbacked_symbols_tls():
+            self.pending_fresh_unbacked_symbols.append(symbol)
+        self.var_to_stack[symbol] = CapturedTraceback.extract(skip=1)
+        vr = self.var_to_range[symbol] = ValueRanges.unknown()
+        assert vr.is_float
+        sloc = self._get_sloc()
+        self.var_to_range_sloc[symbol] = ValueRangesSLoc(sloc, sloc)
+
+        # Create a new FX placeholder and Z3 variable for 'symbol'.
+        fx_node = self._create_fx_placeholder_and_z3var(symbol, float)
+
+        sym_node = SymNode(symbol, self, float, None, fx_node=fx_node)
+        self._log_create_unbacked_symbol(
+            "create_unbacked_symfloat", symbol, vr, sym_node=sym_node
+        )
+
+        return SymFloat(sym_node)
+
+    @record_shapeenv_event()
+    def create_unbacked_symint(self, source: Optional[Source] = None) -> SymInt:
+        """Create a symbolic integer without a hint value"""
+        symbol: sympy.Symbol = make_symbol(
+            SymT.UNBACKED_INT, next(self.unbacked_symint_counter), integer=True
+        )
+        if not self._ignore_fresh_unbacked_symbols_tls():
+            self.pending_fresh_unbacked_symbols.append(symbol)
+        self.counter["create_unbacked_symbol"] += 1
+        self.var_to_stack[symbol] = CapturedTraceback.extract(skip=1)
+        vr = self.var_to_range[symbol] = self._default_unspecified_value_range()
+        assert vr.is_int
+        sloc = self._get_sloc()
+        self.var_to_range_sloc[symbol] = ValueRangesSLoc(sloc, sloc)
+
+        # Create a new FX placeholder and Z3 variable for 'symbol'.
+        fx_node = self._create_fx_placeholder_and_z3var(symbol, int)
+
+        sym_node = SymNode(symbol, self, int, None, fx_node=fx_node)
+        self._log_create_unbacked_symbol(
+            "create_unbacked_symint", symbol, vr, source, sym_node=sym_node
+        )
+
+        return SymInt(sym_node)
+
+    def is_unbacked_symint(self, symbol: sympy.Symbol) -> bool:
+        """Check if a sympy symbol matches the naming convention for unbacked symbols"""
+        return symbol_is_type(symbol, SymT.UNBACKED_INT)
+
+    @record_shapeenv_event()
+    def create_unbacked_symbool(self) -> SymBool:
+        """Create a symbolic boolean without a hint value"""
+        symbol: sympy.Symbol = make_symbol(
+            SymT.UNBACKED_INT, next(self.unbacked_symint_counter), integer=True
+        )
+        if not self._ignore_fresh_unbacked_symbols_tls():
+            self.pending_fresh_unbacked_symbols.append(symbol)
+        self.counter["create_unbacked_symbol"] += 1
+        self.var_to_stack[symbol] = CapturedTraceback.extract(skip=1)
+        vr = self.var_to_range[symbol] = ValueRanges(0, 1)
+        assert vr.is_int
+        sloc = self._get_sloc("default value range for unbacked SymBool")
+        self.var_to_range_sloc[symbol] = ValueRangesSLoc(sloc, sloc)
+
+        # Create a new FX placeholder and Z3 variable for 'symbol'.
+        fx_node = self._create_fx_placeholder_and_z3var(symbol, bool)
+
+        sym_node = SymNode(sympy.Eq(symbol, 1), self, bool, None, fx_node=fx_node)
+        self._log_create_unbacked_symbol(
+            "create_unbacked_symbool", symbol, vr, sym_node=sym_node
+        )
+
+        return SymBool(sym_node)
+
+    @record_shapeenv_event()
+    def create_unspecified_symbol(
+        self,
+        val: Union[int, SymInt, float, SymFloat],
+        source: Source,
+        dynamic_dim: DimDynamic = DimDynamic.DUCK,
+        constraint_dim: DimConstraint = None,  # NB: includes None
+        symbolic_context: Optional[StatelessSymbolicContext] = None,
+    ) -> sympy.Expr:
+        """
+        Create a symbol with an unspecified value
+
+        Compared to standard symbols we do not assume the value is positive,
+        nor do we specialze on zero or one values.
+        """
+        # 'positive' is None for unspecified symbols, since we can't
+        # assume that it will be neither positive nor negative.
+
+        # We don't want to specialize zero one val for unspecified symbol
+        # so that we can always get a new symbol despite val.
+        return self.create_symbol(
+            val,
+            source,
+            dynamic_dim,
+            constraint_dim,
+            positive=None,
+            do_not_specialize_zero_one=True,
+            symbolic_context=symbolic_context,
+        )
+
+    @record_shapeenv_event()
+    def create_symbol(
+        self,
+        val: int,
+        source: Source,
+        dynamic_dim: DimDynamic = DimDynamic.DUCK,
+        constraint_dim: DimConstraint = None,  # NB: includes None
+        positive: Optional[bool] = True,
+        do_not_specialize_zero_one: bool = False,
+        symbolic_context: Optional[StatelessSymbolicContext] = None,
+    ) -> sympy.Expr:
+        """Create a new symbol which is tracked by this ShapeEnv"""
+        # check if constraint_dim is actually static integer
+        if (
+            isinstance(constraint_dim, StrictMinMaxConstraint)
+            and constraint_dim.vr.lower == constraint_dim.vr.upper
+        ):
+            dynamic_dim = DimDynamic.STATIC
+            if constraint_dim.vr.lower != val:
+                raise ConstraintViolationError(
+                    f"Static shape constraint of {constraint_dim.vr.lower} does not match input size of {val}, "
+                    f"for {source.name()}"
+                )
+            if symbolic_context:
+                from torch._dynamo.source import TensorPropertySource
+
+                assert isinstance(source, TensorPropertySource)
+                # TODO: storage_offset handling?
+                assert source.idx is not None
+                symbolic_context.dynamic_sizes[source.idx] = dynamic_dim
+                symbolic_context.constraint_sizes[source.idx] = None
+            constraint_dim = None
+
+        # see note [Tensor Fakification and Symbol Caching]
+        source_name = source.name()
+        if (
+            isinstance(symbolic_context, StatefulSymbolicContext)
+            and id(self) not in symbolic_context.shape_env_to_source_to_symbol_cache
+        ):
+            symbolic_context.shape_env_to_source_to_symbol_cache[id(self)] = {}
+
+        if (
+            isinstance(symbolic_context, StatefulSymbolicContext)
+            and source_name
+            and (
+                source_name
+                in symbolic_context.shape_env_to_source_to_symbol_cache[id(self)]
+            )
+        ):
+            return symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][
+                source_name
+            ]
+
+        if dynamic_dim in (DimDynamic.SIZE_LIKE_UNBACKED, DimDynamic.OBLIVIOUS_SIZE):
+            out = self.create_unbacked_symint(source).node.expr
+            self._constrain_range_for_size(out)
+            if isinstance(symbolic_context, StatefulSymbolicContext) and source_name:
+                symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][
+                    source_name
+                ] = out
+            if dynamic_dim is DimDynamic.OBLIVIOUS_SIZE:
+                self.oblivious_var_to_val[out] = val
+            return out
+
+        if do_not_specialize_zero_one:
+            specialize_zero_one = False
+        else:
+            specialize_zero_one = self.specialize_zero_one
+
+        assert isinstance(source, Source), f"{type(source)} {source}"
+        assert not (positive and val < 0), f"positive set for negative value: {val}"
+        # It's always sound to allocate a symbol as DYNAMIC.  If the user
+        # constrained the symbol, force the symbolic_context to DYNAMIC, because our
+        # constraint code will do weird stuff if, e.g., it's duck shaped
+        if constraint_dim is not None:
+            dynamic_dim = DimDynamic.DYNAMIC
+
+        if dynamic_dim is DimDynamic.STATIC:
+            out = sympy.Integer(val)
+            if isinstance(symbolic_context, StatefulSymbolicContext) and source_name:
+                symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][
+                    source_name
+                ] = out
+            return out
+
+        elif dynamic_dim is DimDynamic.DUCK:
+            # duck_shape can be used to globally turn off duck shaping, even
+            # if it was requested
+            duck = self.duck_shape
+        elif dynamic_dim is DimDynamic.DYNAMIC:
+            duck = False
+        else:
+            raise AssertionError(f"unhandled dynamic_dim {dynamic_dim}")
+
+        sloc = self._get_sloc()
+
+        if val in (0, 1) and specialize_zero_one:
+            r = self.val_to_var[val]
+        elif not duck or val not in self.val_to_var:
+            # If we're not duck shaping, we always create a new symbol
+            # Even if we're duck shaping, if we haven't seen this particular
+            # value before, we also create a new symbol
+            if type(val) is int or is_nested_int(val):
+                sympy_expr = make_symbol(
+                    SymT.SIZE, len(self.var_to_val), positive=positive, integer=True
+                )
+            else:
+                sympy_expr = make_symbol(
+                    SymT.FLOAT, len(self.var_to_val), positive=positive, real=True
+                )
+            self.source_to_var[source_name] = sympy_expr
+            # We always associate vars to vals
+            if isinstance(val, int):
+                self.var_to_val[sympy_expr] = sympy.Integer(val)
+            elif isinstance(val, float):
+                self.var_to_val[sympy_expr] = sympy.Float(val)
+            else:
+                # Only used for jagged layout nested tensors
+                self.var_to_val[sympy_expr] = SingletonInt(
+                    val.node.nested_int(), coeff=val.node.nested_int_coeff()
+                )
+
+            # Do the appending later, because we always want to populate this
+            self.var_to_sources[sympy_expr] = []
+            # Create a Z3 variable for the new symbol.
+            self._add_z3var(sympy_expr, int)
+
+            if duck:
+                # Make sure to reuse this symbol for subsequent duck shaping
+                self.val_to_var[val] = sympy_expr
+
+            if isinstance(val, int):
+                if positive:
+                    # Add assertions for the newly created symbols
+                    self._add_assertion(sympy_expr > 1)
+
+                    # Apply default range, which assumes not zero-one
+                    self.var_to_range[sympy_expr] = self._default_value_range(
+                        do_not_specialize_zero_one
+                    )
+                    self.var_to_range_sloc[sympy_expr] = ValueRangesSLoc(
+                        self._get_sloc(
+                            "user code shown is first use of this value--the guard itself is not "
+                            "due user code but due to 0/1 specialization in the framework; to "
+                            "avoid specialization try torch._dynamo.mark_unbacked(tensor, dim)"
+                            if self.specialize_zero_one
+                            else None
+                        ),
+                        sloc,
+                    )
+                else:
+                    self.var_to_range[
+                        sympy_expr
+                    ] = self._default_unspecified_value_range()
+                    self.var_to_range_sloc[sympy_expr] = ValueRangesSLoc(sloc, sloc)
+
+                # Small performance optimization: if we have a min-max constraint,
+                # we can proactively narrow to that range
+                if isinstance(constraint_dim, StrictMinMaxConstraint):
+                    assert not duck
+                    self._update_var_to_range(
+                        sympy_expr, constraint_dim.vr, is_constraint=True
+                    )
+
+                vr = self.var_to_range[sympy_expr]
+                assert vr.is_int
+
+                if val not in vr:
+                    raise ConstraintViolationError(
+                        f"{val} not in range [{vr.lower}, {vr.upper}]"
+                    )
+
+                range_str = f"[{vr.lower}, {vr.upper}]"
+            elif isinstance(val, float):
+                self.var_to_range[sympy_expr] = vr = ValueRanges(-sympy.oo, sympy.oo)
+                self.var_to_range_sloc[sympy_expr] = ValueRangesSLoc(sloc, sloc)
+                range_str = f"[{vr.lower}, {vr.upper}]"
+                assert vr.is_float
+            else:
+                # Skip var_range logic for SingletonInt
+                # Only used for jagged layout nested tensors
+                range_str = ""
+
+            r = sympy_expr
+
+            is_debug = config.extended_debug_create_symbol is not None and str(
+                sympy_expr
+            ) in config.extended_debug_create_symbol.split(",")
+            maybe_more_info = ""
+            if not is_debug and os.getenv("TORCHDYNAMO_EXTENDED_ADVICE", "1") not in (
+                "0",
+                "",
+            ):
+                maybe_more_info = (
+                    ", for more info run with "
+                    f'TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="{sympy_expr}" '
+                    "or to suppress this message run with "
+                    'TORCHDYNAMO_EXTENDED_ADVICE="0"'
+                )
+            sloc, maybe_extra_debug = self._get_stack_summary(is_debug)
+            self.log.info(
+                "create_symbol %s = %s for %s %s %s%s%s",
+                sympy_expr,
+                val,
+                source.name(),
+                range_str,
+                sloc,
+                maybe_more_info,
+                maybe_extra_debug,
+                stack_info=is_debug,
+            )
+            trace_structured(
+                "create_symbol",
+                metadata_fn=lambda: {
+                    "symbol": str(sympy_expr),
+                    "val": repr(val),
+                    "vr": range_str,
+                    "source": source.name(),
+                    "user_stack": structured.from_traceback(
+                        TracingContext.extract_stack()
+                    ),
+                    "stack": structured.from_traceback(
+                        CapturedTraceback.extract(skip=1).summary()
+                    ),
+                },
+            )
+
+            self.counter["create_symbol"] += 1
+        else:
+            # This implements duck-shaping: input sizes that match are assigned
+            # the same symint
+            r = self.val_to_var[val]
+            self.source_to_var[source_name] = r
+            self.log.debug("create_symbol %s duck sized %s", r, source.name())
+
+        if isinstance(r, sympy.Symbol):
+            r_sources = self.var_to_sources[r]
+            r_sources.append(source)
+            if not source.is_ephemeral() and r_sources[0].is_ephemeral():
+                # prefer non-ephemeral source first since it may be guarded on later
+                r_sources[0], r_sources[-1] = r_sources[-1], r_sources[0]
+
+            # This ensures we get zeros in symbol_guard_counts, which makes
+            # some queries simpler (since we will accumulate mass on 0 this
+            # way)
+            self.symbol_guard_counter[r] = 0
+
+        if isinstance(symbolic_context, StatefulSymbolicContext) and source_name:
+            symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][
+                source_name
+            ] = r
+        return r
+
+    def add_var_to_val(self, expr: sympy.Symbol, val: int) -> None:
+        """Adds a new symbol to the symbolic environment."""
+        log.debug("add_var_to_val %s %s", expr, val, stack_info=True)
+        assert expr not in self.var_to_val, f"{expr} already exists"
+        self.var_to_val[expr] = sympy.Integer(val)
+
+    def _debug_name(self, source: Source) -> str:
+        src_name = source.name()
+        return self.source_name_to_debug_name.get(src_name, src_name)
+
+    def _render_range_for_constraint_violation(
+        self, source: Source, c: Union[StrictMinMaxConstraint, RelaxedUnspecConstraint]
+    ) -> str:
+        if isinstance(c, StrictMinMaxConstraint):
+            lower, upper = c.vr.lower, c.vr.upper
+            default = self._default_value_range()
+            if lower <= default.lower:
+                lower = None
+            if upper >= default.upper:
+                upper = None
+            c_render = (
+                f"{self._debug_name(source)} = {source.name()} in the specified range"
+            )
+            if lower is not None and upper is not None:
+                c_render += f" {lower} <= {self._debug_name(source)} <= {upper}"
+            elif lower is None and upper is not None:
+                c_render += f" {self._debug_name(source)} <= {upper}"
+            elif lower is not None and upper is None:
+                c_render += f" {lower} <= {self._debug_name(source)}"
+            return c_render
+        return c.render(source)
+
+    def produce_guards(self, *args: Any, **kwargs: Any) -> list[str]:
+        """
+        Like produce_guards_verbose, but only returns the non-verbose python guard expressions
+        (no verbose guards produced.)
+        """
+        return self.produce_guards_verbose(*args, **kwargs, langs=("python",))[0].exprs
+
+    def produce_guards_verbose(
+        self,
+        placeholders: Sequence[FakeTensor],
+        sources: Sequence[Source],
+        source_ref: Callable[[Source], str] = lambda n: n.name(),
+        *,
+        guards: Optional[list[ShapeGuard]] = None,
+        input_contexts: Optional[DimList[SymbolicContext]] = None,
+        # Encodes user-specified input shape equations of the form s = s' and s = fn(s').
+        # (See docs on EqualityConstraint for details of the encoding.)
+        equalities_inputs: Optional[EqualityConstraint] = None,
+        _simplified: bool = False,
+        # Indicates if we should produce guards for known static values.
+        ignore_static: bool = True,
+        langs: tuple[str, ...] = ("python", "verbose_python"),
+    ) -> list[_ShapeGuardsHelper]:
+        """
+        Generates a list of guards strings which, when evaluated in a context that
+        defines tensors for all the sources, returns True or False depending
+        on if the guards in the list evaluated to True or not.  Primarily used by Dynamo,
+        but this is also helpful for manual testing of guards (see
+        evaluate_guards_for_args)
+
+        For convenience in testing, a source is allowed to be a str,
+        in which case we will assume it is a LocalSource
+
+        simplified lets you omit duck sizing, equality and 0/1 guards.
+        This is useful for testing when you don't care about the boilerplate
+        guards, and it may be helpful for user output too (be careful though;
+        some equality guards are nontrivial!  It would be nice to get simplified
+        output to print them too).  It's private because it's not
+        intended for normal use
+
+        Returns guards in python and python with verbose comments (verbose) by
+        default.
+        """
+        self.log.info("produce_guards")
+
+        # Check if we get to the same ShapeEnv state by replaying the recorded events.
+        # This will create a new ShapeEnv instance, and call all recorded function
+        # calls on this new instance. Finally, it will check whether this new instance
+        # has equal state.
+        #
+        # It's important that we do it in the begining of this function, since it modifies
+        # self.dim_constraints through its execution. Changes that happen in this method
+        # aren't interesting, since this is the function call we wish to reproduce at the
+        # end. If we wish to simply reproduce ShapeEnv instances even after this call,
+        # this method should also be recorded.
+        if self.check_recorded_events:
+            shape_env = replay_shape_env_events(self.events)
+            self.check_equal(shape_env)
+
+        assert len(placeholders) == len(
+            sources
+        ), f"len({placeholders}) != len({sources})"
+        Tensorlike = (torch.Tensor, FakeTensorMeta)
+
+        def _create_no_constraints_context(t: Tensor) -> StatelessSymbolicContext:
+            return StatelessSymbolicContext(
+                # Ignored; only the constraints part is relevant below.
+                dynamic_sizes=[DimDynamic.DYNAMIC] * t.dim(),
+                dynamic_strides=[DimDynamic.INFER_STRIDE] * t.dim(),
+                constraint_sizes=[None] * t.dim(),
+                constraint_strides=[None] * t.dim(),
+            )
+
+        # Expand optional inputs, or verify invariants are upheld
+        if input_contexts is None:
+            input_contexts = [
+                _create_no_constraints_context(t) if isinstance(t, Tensorlike) else None
+                for t in placeholders
+            ]
+        else:
+            assert len(input_contexts) == len(placeholders)
+            for i, (t, context) in enumerate(zip(placeholders, input_contexts)):
+                if isinstance(t, Tensorlike):
+                    if context is None:
+                        input_contexts[i] = _create_no_constraints_context(t)
+                else:
+                    assert isinstance(t, (SymInt, int, SymFloat, float))
+                    assert not isinstance(context, list)
+
+        # It took a lot of sweat to figure out the algorithm here.  Let's
+        # explain how it works.
+        #
+        # The ShapeEnv lifecycle looks something like this:
+        #
+        # - For each input, you either generate a fresh Sympy symbol (s0) to
+        #   represent its value (a binding site), or you reuse some
+        #   preexisting symbol or expression, skipping the symbol allocation
+        #   (e.g., duck sizing to a preexisting symbol, or expressing a
+        #   stride as a multiplication of a separate stride and size.)
+        #   Naively, you might expect to bind a fresh Sympy symbol for
+        #   every input, but this is fairly wasteful as most of these
+        #   symbols immediately simplify away, and if you don't eagerly
+        #   specialize, e.g., 0/1 symbols, you end up with very complicated
+        #   expressions that are not optimizable in practice.
+        #
+        # - You perform some compute on these symbols, occasionally
+        #   introducing guards on boolean expressions on these symbols.
+        #   In particular, whenever we guard on equality (_maybe_guard_rel),
+        #   we can simplify shapes; e.g., when s0 == s1 * 2, we can now
+        #   replace all occurrences of s0 with s1 * 2.  Sometimes, a
+        #   boolean expression evaluation doesn't introduce a guard, as
+        #   the guard is already entailed by the simplifications we have
+        #   applied.
+        #
+        # - In the end, you have a bunch of replacements (saying how to
+        #   simplify shapes) and a bunch of guards (all the equality guards
+        #   are trivial, because they're covered by the replacements).
+        #
+        # From the ShapeEnv, we must generate a Python expression that, when
+        # evaluated on a set of inputs, tells us whether or not these boolean
+        # expressions would have evaluated in the same way.  However,
+        # we cannot easily compute this, as we elide recording boolean
+        # expressions when we think they are vacuously true.  Thus, we seek
+        # an approximation: we must generate an expression, if true, would have
+        # produced an "equivalent" ShapeEnv, which would answer guard
+        # expressions in the same way.
+        #
+        # Our notion of equivalence is a bit subtle.  For example, consider
+        # the ShapeEnv created from an input of size (5, 4) versus (4, 4)
+        # (no other guards.)  Duck sizing would generate (s0, s1) in the first
+        # case but (s0, s0) in the second.  We do NOT assume that size
+        # variables are disjoint; so in fact a graph that assumes the input
+        # could be (s0, s1) subsumes (s0, s0) (setting s0 == s1), but not
+        # vice versa.  However, consider an analogous case (1,) versus (2,).
+        # Duck sizing generates (1,) and (s0,); the (s0,) graph does NOT
+        # subsume the (1,) graph because we assume that any size variables
+        # is NOT 0/1 (and make simplifications according to this; e.g., if
+        # we queried s0 == 0, we would immediately return False without
+        # returning a guard.)
+        #
+        # So, it is perhaps easier to flip things on their head: the guard
+        # expressions we generate here say what simplifications are valid,
+        # and what are not. Below, we explain each of the guard expressions
+        # we generate
+
+        # TODO: Make this more efficient by binding all the size/stride/offsets
+        # to locals before performing tests on them.
+
+        from torch._dynamo.source import TensorProperty, TensorPropertySource
+
+        # Actual codegen must be delayed as we don't necessarily know what
+        # the symbol mapping is
+        input_guards = []
+
+        symbol_to_source: dict[sympy.Symbol, list[Source]] = collections.defaultdict(
+            list
+        )
+        symbol_to_constraints: defaultdict[
+            sympy.Symbol, set[Constraint]
+        ] = collections.defaultdict(set)
+        constraint_violations: list[tuple[bool, str, Callable[[], str]]] = []
+
+        printers: list[_ShapeGuardPrinter] = []
+        py_printer = ShapeGuardPythonPrinter(
+            symbol_to_source, source_ref, self.var_to_sources
+        )
+        for lang in langs:
+            if lang in ["python", "verbose_python"]:
+                printers.append(py_printer)
+            elif lang == "cpp":
+                printers.append(
+                    _ShapeGuardCppPrinter(
+                        symbol_to_source, source_ref, self.var_to_sources
+                    )
+                )
+            else:
+                raise NotImplementedError(f"Unknown lang: {lang}")
+
+        def record_constraint_violation(
+            warn_only: bool,
+            debug_name: str,
+            msg: str,
+            hint: Optional[Callable[[], str]] = None,
+        ) -> None:
+            constraint_violations.append(
+                (warn_only, debug_name, lambda: f"{msg}{hint()}" if hint else msg)
+            )
+
+        def is_dim(src: object) -> TypeGuard[TensorPropertySource]:
+            return (
+                isinstance(src, TensorPropertySource)
+                and src.prop is TensorProperty.SIZE
+            )
+
+        if equalities_inputs:
+            source_index = {}
+            for i, src in enumerate(sources):
+                source_index[src.name()] = i
+
+            def get_expression(tensor_dim_src: Source) -> sympy.Expr:
+                fake = placeholders[source_index[tensor_dim_src.base.name()]]  # type: ignore[attr-defined]
+                assert tensor_dim_src.idx is not None  # type: ignore[attr-defined]
+                symint = fake.shape[tensor_dim_src.idx]  # type: ignore[attr-defined]
+                if isinstance(symint, torch.SymInt):
+                    return symint.node.expr
+                else:
+                    assert type(symint) is int, f"Expected int, got {type(symint)}"
+                    return sympy.Integer(symint)
+
+            for src1, src2 in equalities_inputs.source_pairs:
+                expr1, expr2 = get_expression(src1), get_expression(src2)  # type: ignore[]
+                # Check whether given input shape values satisfy a specified equation s = s'.
+                # - Raise when the equation was violated by the given input shape values.
+                # - Otherwise issue a guard to constrain them.
+                concrete_val = self.evaluate_expr(sympy.Eq(expr1, expr2))
+                if not concrete_val:
+                    raise ConstraintViolationError(
+                        f"{src1.name()} = {expr1 if isinstance(expr1, int) else expr1.xreplace(self.var_to_val)}"
+                        " is not equal to "
+                        f"{src2.name()} = {expr2 if isinstance(expr2, int) else expr2.xreplace(self.var_to_val)}"
+                    )
+
+            for srcEq, root, fn in equalities_inputs.derived_equalities:
+                expr1 = get_expression(srcEq)
+                # recall that root is either a phantom symbol or an input source
+                expr2, debug_name = (
+                    (root, self.var_to_sources[root][0].name())
+                    if isinstance(root, sympy.Symbol)
+                    else (get_expression(root), self._debug_name(root))
+                )
+                expr2_ = fn(expr2)
+                # Check whether given input shape values satisfy a specified equation s = fn(s').
+                # - Raise when the equation was violated by the given input shape values.
+                # - Otherwise issue a guard to constrain them.
+                concrete_val = self.evaluate_expr(sympy.Eq(expr1, expr2_))
+                if not concrete_val:
+                    raise ConstraintViolationError(
+                        f"Expected input {srcEq.name()} to be equal to "
+                        f"{fn(sympy.Symbol(debug_name))}, "
+                        f"where {debug_name} = {expr2.xreplace(self.var_to_val)}, "
+                        f"but got {expr1.xreplace(self.var_to_val)}"
+                    )
+
+            for phantom_symbol in equalities_inputs.phantom_symbols:
+                # we created additional phantom symbols that are not input shape dimensions
+                symbol_to_source[phantom_symbol].extend(
+                    self.var_to_sources[phantom_symbol]
+                )
+
+        # How do we know what the value of s0 is?  Fresh variables can only be
+        # bound by inputs, so there MUST be some other input which binds the
+        # variable.  If there is no such input, this is an error in our
+        # system.  We record where all symbols come from, to help you diagnose
+        # why those symbols didn't occur.
+        #
+        # In fact, generally speaking it is only possible for the "outermost"
+        # user of a ShapeEnv to evaluate the guards, because some inputs may
+        # not be available to inner levels.  For example, Dynamo can guard on
+        # tensors that never actually become graph arguments (they are
+        # pruned).  In this case, only Dynamo knows about these arguments.
+        def track_symint(
+            source: Source, val: Union[SymInt, int], constraint: DimConstraint = None
+        ) -> None:
+            log.debug("track_symint %s %s %s", LazyString(source.name), val, constraint)
+            assert not isinstance(val, SymInt) or is_symbolic(val)
+
+            if isinstance(val, SymInt) and val.node.maybe_as_int() is not None:
+                val = val.node.maybe_as_int()
+
+            if isinstance(val, SymInt):
+                s = val.node.expr
+                if isinstance(s, sympy.Symbol):
+                    symbol_to_source[s].append(source)
+                    if constraint is not None and not isinstance(
+                        constraint, RelaxedUnspecConstraint
+                    ):
+                        symbol_to_constraints[s].add(constraint)
+                else:
+                    constraint_violated = False
+                    if isinstance(constraint, StrictMinMaxConstraint):
+                        # try inferring the ranges of the expr s
+                        sym_vrs = {
+                            x: self.var_to_range.get(x, None) for x in s.free_symbols
+                        }
+                        if any(vr is None for vr in sym_vrs.values()):
+                            # some of the free symbols in s don't have ranges
+                            constraint_violated = True
+                    elif isinstance(constraint, RelaxedUnspecConstraint):
+                        if s.is_number:
+                            i = int(s)
+                            # Don't complain about 0/1 specialization, we
+                            # expect to have to compile in this case anyway
+                            if i not in (0, 1):
+                                constraint_violated = True
+                    if constraint_violated:
+                        assert constraint is not None
+
+                        def hint(s: sympy.Expr) -> str:
+                            sexpr = py_printer.doprint(s)
+                            return f"{sexpr}."
+
+                        var_with_range = self._render_range_for_constraint_violation(
+                            source, constraint
+                        )
+                        msg = (
+                            f"Not all values of {var_with_range} are valid because "
+                            f"{self._debug_name(source)} was inferred to be equal to "
+                        )
+                        record_constraint_violation(
+                            constraint.warn_only,
+                            self._debug_name(source),
+                            msg,
+                            hint=functools.partial(hint, s),
+                        )
+
+                input_guards.append((source, s))
+            else:
+                s = sympy.Integer(val)
+                input_guards.append((source, s))
+                constraint_violated = False
+                if isinstance(constraint, StrictMinMaxConstraint):
+                    if not (
+                        s == constraint.vr.lower == constraint.vr.upper
+                    ):  # allow static constraints
+                        constraint_violated = True
+                elif isinstance(constraint, RelaxedUnspecConstraint):
+                    # Don't complain about 0/1 specialization, we
+                    # expect to have to compile in this case anyway
+                    if val not in (0, 1):
+                        constraint_violated = True
+                if constraint_violated:
+                    assert constraint is not None
+                    var_with_range = self._render_range_for_constraint_violation(
+                        source, constraint
+                    )
+                    msg = (
+                        f"Not all values of {var_with_range} are valid because "
+                        f"{self._debug_name(source)} was inferred to be a constant ({val})."
+                    )
+                    record_constraint_violation(
+                        constraint.warn_only, self._debug_name(source), msg
+                    )
+
+        def track_symfloat(source: Source, val: Union[float, SymFloat]) -> None:
+            log.debug("track_symfloat %s %s", LazyString(source.name), val)
+            assert not isinstance(val, SymFloat) or is_symbolic(val)
+
+            if isinstance(val, SymFloat) and val.node.maybe_as_float() is not None:
+                val = val.node.maybe_as_float()
+
+            if isinstance(val, SymFloat):
+                s = val.node.expr
+                if isinstance(s, sympy.Symbol):
+                    symbol_to_source[s].append(source)
+                input_guards.append((source, s))
+            else:
+                s = sympy.Float(val)
+                input_guards.append((source, s))
+
+        for t, source, context in zip(placeholders, sources, input_contexts):
+            if isinstance(source, str):
+                from torch._dynamo.source import LocalSource
+
+                source = LocalSource(source)
+            assert isinstance(source, Source)
+            if t is None:
+                continue
+            if isinstance(t, (SymInt, int)):
+                track_symint(source, t)
+                continue
+            elif isinstance(t, (SymFloat, float)):
+                track_symfloat(source, t)
+                continue
+            assert isinstance(t, Tensorlike)
+            if is_traceable_wrapper_subclass(t):
+                from torch._dynamo.source import AttrSource
+
+                assert isinstance(context, SubclassSymbolicContext)
+
+                # For subclasses, we need to track symints on BOTH the outer
+                # and inner tensors.
+                # TODO: type this better
+                sources_tensors_constraints: list[tuple[Source, Any, Any, Any]] = [
+                    (source, t, context.constraint_sizes, context.constraint_strides)
+                ]
+                attrs, _ = t.__tensor_flatten__()
+                for attr in attrs:
+                    inner_t = getattr(t, attr)
+                    inner_context = context.inner_contexts[attr]
+                    sources_tensors_constraints.append(
+                        (
+                            AttrSource(source, attr),
+                            inner_t,
+                            inner_context.constraint_sizes,  # type: ignore[attr-defined]
+                            inner_context.constraint_strides,  # type: ignore[attr-defined]
+                        )
+                    )
+            else:
+                sources_tensors_constraints = [
+                    (source, t, context.constraint_sizes, context.constraint_strides)  # type: ignore[attr-defined]
+                ]
+
+            for (
+                src,
+                curr_t,
+                constraint_size,
+                constraint_stride,
+            ) in sources_tensors_constraints:
+                if is_sparse_any(curr_t):
+                    for i, ss in enumerate(curr_t.size()):
+                        property_source = TensorPropertySource(
+                            src, TensorProperty.SIZE, i
+                        )
+                        track_symint(property_source, ss, constraint_size[i])
+                else:
+                    for i, ss in enumerate(curr_t.size()):
+                        property_source = TensorPropertySource(
+                            src, TensorProperty.SIZE, i
+                        )
+                        track_symint(property_source, ss, constraint_size[i])
+                    for i, ss in enumerate(curr_t.stride()):
+                        property_source = TensorPropertySource(
+                            src, TensorProperty.STRIDE, i
+                        )
+                        track_symint(property_source, ss, constraint_stride[i])
+                    track_symint(
+                        TensorPropertySource(src, TensorProperty.STORAGE_OFFSET),
+                        curr_t.storage_offset(),
+                    )
+
+        # 1. Every input must equal the final simplified symbolic expression
+        #    stored on the placeholder.  Given a placeholder (s0*2, s1),
+        #    if we have an input (2, 3), we must show s0*2 == 2 and s1 == 3.
+        #    This does a lot of work: it covers duck sizing and equality guards.
+        all_exprs: list[list[str]] = [[] for _ in langs]
+        self.dim_constraints = DimConstraints(
+            symbol_to_source,
+            self.var_to_val,
+            set(symbol_to_constraints.keys()),
+            self.source_name_to_debug_name,
+        )
+
+        if not _simplified:
+            for source, expr in input_guards:
+                srcname = source.name()
+                if self._translation_validation_enabled:
+                    # Ignore sources that were not turned into SymInts.
+                    if srcname in self.source_to_symbol:
+                        self._add_target_expr(
+                            sympy.Eq(self.source_to_symbol[srcname], expr)
+                        )
+
+                # Small optimization
+                if (
+                    isinstance(expr, sympy.Symbol)
+                    and symbol_to_source.get(expr)
+                    and source == symbol_to_source[expr][0]
+                ):
+                    continue
+
+                # This logic excludes static values found on tensors from guarding, because
+                # dynamo's check_tensor_fn does that (see guards.cpp).
+                # However, for non tensor sources, we still need to guard here.
+                if ignore_static and isinstance(source, TensorPropertySource):
+                    if expr.is_number:
+                        self.log.debug(
+                            "Skipping guard %s", f"{source_ref(source)} == {expr}"
+                        )
+                        continue
+
+                if is_dim(source):
+                    self.dim_constraints.add_equality(source, expr)
+
+                for exprs, printer, lang in zip(all_exprs, printers, langs):
+                    res = f"{printer.print_source(source)} == {printer.doprint(expr)}"
+
+                    if lang == "verbose_python":
+                        if (s0 := self.source_to_var.get(srcname)) is not None:
+                            if source != self.var_to_sources[s0][0]:
+                                res = (
+                                    f"{res}  # duck sizing added this equality because these "
+                                    f"variables had the same size {self.var_to_val[s0]} "
+                                    "(to avoid this specialization, set torch.fx.experimental._config.use_duck_shape = False)"
+                                )
+                            elif (sloc := self.replacements_slocs.get(s0)) is not None:
+                                res = f"{res}  # {sloc}"
+                            else:
+                                res = f"{res}  # (unknown var {s0}, please file a bug)"
+                        else:
+                            res = f"{res}  # (unknown source {srcname}, please file a bug)"
+                    exprs.append(res)
+
+                if (
+                    isinstance(source, TensorPropertySource)
+                    and source.prop is TensorProperty.SIZE
+                    and equalities_inputs
+                    and len(expr.free_symbols) == 1
+                ):
+                    symbol = next(iter(expr.free_symbols))
+                    if (
+                        isinstance(expr, sympy.Symbol)
+                        and expr in symbol_to_constraints
+                        and not equalities_inputs.is_equal(
+                            source, symbol_to_source[expr][0]
+                        )
+                    ):
+                        msg = (
+                            f"The values of {self._debug_name(source)} = {source.name()} and "
+                            f"{self._debug_name(symbol_to_source[expr][0])} = {symbol_to_source[expr][0].name()} "
+                            "must always be equal."
+                        )
+                        record_constraint_violation(
+                            equalities_inputs.warn_only, self._debug_name(source), msg
+                        )
+
+                    if (
+                        not isinstance(expr, sympy.Symbol)
+                        and symbol in symbol_to_constraints
+                        and not equalities_inputs.is_derived(
+                            source,
+                            symbol_to_source[symbol][0],
+                            lambda x: expr.xreplace({symbol: x}),
+                        )
+                    ):
+                        src = symbol_to_source[symbol][0]
+                        msg = (
+                            f"The values of {self._debug_name(source)} = {source.name()} must always be related to "
+                            f"the values of {self._debug_name(src)} = {src.name()} by "
+                            f"{self._debug_name(source)} = {expr.xreplace({symbol: sympy.sympify(self._debug_name(src))})}."
+                        )
+                        record_constraint_violation(
+                            equalities_inputs.warn_only, self._debug_name(source), msg
+                        )
+
+                # NB: Not necessary to report constraint violations here:
+                # constraints are guaranteed to be on symbols (we've already
+                # caught constants and non-atomic expressions), so we only
+                # have relational constraints, but we don't support those
+                # at the moment
+
+        # 2. Every guard must evaluate to True (but remember many guards
+        #    like s0 == s1*2 because trivial due to simplification)
+        issued = set()
+
+        def issue_guard(guard: ShapeGuard) -> None:
+            expr = self.simplify(guard.expr)
+
+            # Avoid re-issueing the same guard.
+            if expr in issued:
+                return
+
+            issued.add(expr)
+
+            try:
+                is_trivial = False
+                if any(
+                    is_dim(source)
+                    for s in expr.free_symbols
+                    for source in symbol_to_source[s]
+                ):
+                    assert self.dim_constraints is not None
+                    is_trivial = self.dim_constraints.add(expr)
+
+                for exprs, printer, lang in zip(all_exprs, printers, langs):
+                    guard_expr = printer.doprint(expr)
+                    if lang == "verbose_python":
+                        guard_expr = f"{guard_expr}  # {guard.sloc}"
+                    exprs.append(guard_expr)
+
+                self._add_target_expr(expr)
+                # A non-relational constraint on a single sizevar can violate
+                # a constraint
+                if not is_trivial and len(expr.free_symbols) == 1:
+                    symbol = next(iter(expr.free_symbols))
+                    source = symbol_to_source[symbol][0]
+                    constraints = symbol_to_constraints[symbol]
+                    for c in constraints:
+                        if isinstance(c, StrictMinMaxConstraint):
+                            var_with_range = (
+                                self._render_range_for_constraint_violation(source, c)
+                            )
+                            msg = (
+                                f"Not all values of {var_with_range} "
+                                f"satisfy the generated guard {py_printer.doprint(expr)}."
+                            )
+                            record_constraint_violation(
+                                c.warn_only, self._debug_name(source), msg
+                            )
+                        elif isinstance(c, RelaxedUnspecConstraint):
+                            # This is fine, we allow guards here as long as it
+                            # didn't constrain it to one value  (we don't
+                            # actually know this; this depends on our
+                            # ValueRanges reasoning capability)
+                            pass
+                        else:
+                            raise AssertionError(f"unrecognized constraint {c}")
+            except Exception:
+                self.log.warning("Failing guard allocated at %s", guard.sloc)
+                raise
+
+        # First, issue all guards.
+        # This removes all the checks that follow from bounds
+        # We could simply emit those and also the bounds 2 <= size when necessary
+        for guard in guards if guards is not None else self.guards:
+            if (
+                self._maybe_evaluate_static(
+                    guard.expr, axioms=(), size_oblivious=guard.size_oblivious
+                )
+                is not None
+            ):
+                continue
+            issue_guard(guard)
+
+        # Because there are guards that export's constraint solver can suggest good fixes for, that we may have
+        # deferred as runtime asserts, and that produce_guards() alone won't do anything with (e.g. divisiblity guards),
+        # we want to send runtime asserts to export's constraint solver too. These will still stay in the graph as asserts,
+        # but export's constraint solver can decide whether to do anything with them (i.e. raise an error and provide
+        # suggested fixes, or decide it's out of scope and leave as a runtime assert in the graph).
+        for ra in self.deferred_runtime_asserts.get(None, []):
+            if self._maybe_evaluate_static(ra.expr, axioms=()) is not None:
+                continue
+            expr = self.simplify(ra.expr)
+            self.dim_constraints.add(expr)
+
+        # 3. Every symbol must be within its value range (this handles 0/1
+        # specialization too).
+        for symbol, sources in symbol_to_source.items():
+            r = self.var_to_range.get(symbol)
+            if r is None:
+                continue
+            vr_sloc = self.var_to_range_sloc[symbol]
+
+            assert sources
+            bounds = []
+            rf = source_ref(sources[0])
+            verbose_expr = ""
+            if r.lower not in (-sympy.oo, -int_oo):
+                if any(is_dim(source) for source in sources):
+                    self.dim_constraints.add(sympy.Ge(symbol, r.lower))
+                # Only print lower bound in simplified mode if it is not the
+                # default
+                if not _simplified or r.lower != self._default_value_range().lower:
+                    bounds.append(sympy.Le(r.lower, symbol, evaluate=False))
+                verbose_expr = f"{r.lower} <= {rf}  # {vr_sloc.lower}"
+            if r.upper not in (sympy.oo, int_oo):
+                if any(is_dim(source) for source in sources):
+                    self.dim_constraints.add(sympy.Le(symbol, r.upper))
+                # nontrivial upper bound is always interesting
+                bounds.append(sympy.Le(symbol, r.upper, evaluate=False))
+                if verbose_expr:
+                    verbose_expr = f"{r.lower} <= {rf} <= {r.upper}  # {vr_sloc.lower} and {vr_sloc.upper}"
+                else:
+                    verbose_expr = f"{rf} <= {r.upper}  # {vr_sloc.upper}"
+            if bounds:
+                bound = sympy.And(*bounds, evaluate=False)
+
+                for exprs, printer, lang in zip(all_exprs, printers, langs):
+                    if lang == "verbose_python":
+                        exprs.append(verbose_expr)
+                    else:
+                        exprs.append(printer.doprint(bound))
+                # NB: verbose_exprs are done above
+
+                # Check constraints
+                constraints = symbol_to_constraints[symbol]
+                for c in constraints:
+                    if isinstance(c, StrictMinMaxConstraint):
+                        # TODO: With int_oo, I think this condition is a noop
+                        # now
+                        if not (c.vr & self._default_value_range()).issubset(r):
+                            source = sources[0]
+
+                            expr = sympy.And(
+                                sympy.Le(r.lower, symbol), sympy.Le(symbol, r.upper)
+                            )
+                            guard_expr = py_printer.doprint(expr)
+                            var_with_range = (
+                                self._render_range_for_constraint_violation(source, c)
+                            )
+                            msg = f"Not all values of {var_with_range} satisfy the generated guard {guard_expr}"
+                            record_constraint_violation(
+                                c.warn_only,
+                                self._debug_name(source),
+                                msg,
+                            )
+            # We NaN specialize, which means similar to 0/1 specialization we
+            # should assume that the float is NOT nan.  This is load bearing
+            # if you have something like an equality guard, nan will play
+            # merry hell with the reasoning.
+            if symbol_is_type(symbol, SymT.FLOAT):
+                res = f"not math.isnan({py_printer.print_source(sources[0])})"
+                for exprs, printer, lang in zip(all_exprs, printers, langs):
+                    if lang == "verbose_python":
+                        exprs.append(
+                            f"{res}  # implicit guard for float input due to NaN specialization in the framework"
+                        )
+                    elif lang == "python":
+                        exprs.append(res)
+                    elif lang == "cpp":
+                        exprs.append(f"~std::isnan({printer.print_source(sources[0])})")
+                    else:
+                        raise NotImplementedError(f"Unimplemented for lang: {lang}")
+
+        if constraint_violations:
+            warn_msgs: list[str] = []
+            error_msgs: list[str] = []
+            debug_names = set()
+            for warn_only, debug_name, msg_cb in constraint_violations:
+                if warn_only:
+                    str_msg = f"  {len(warn_msgs) + 1}. {msg_cb()}"
+                    warn_msgs.append(str_msg)
+                else:
+                    str_msg = f"  - {msg_cb()}"
+                    error_msgs.append(str_msg)
+                    debug_names.add(debug_name)
+            if len(error_msgs) > 0:
+                debug_names_str = ", ".join(sorted(debug_names))
+                err = "\n".join(error_msgs)
+                raise ConstraintViolationError(
+                    f"Constraints violated ({debug_names_str})! "
+                    'For more information, run with TORCH_LOGS="+dynamic".\n'
+                    f"{err}"
+                )
+            elif len(warn_msgs) > 0:
+                log.debug("%s Warning only constraints violated", len(warn_msgs))
+
+        signpost_event(
+            "dynamic",
+            "produce_guards",
+            {
+                **self.co_fields,
+                **self.counter,
+                "num_guards": len(all_exprs[0]),
+                "free_symbols": sum(1 for v in symbol_to_source.values() if v),
+                # The keys are meaningless from an aggregate perspective, so
+                # don't include them.  Biggest first.
+                "symbol_guard_counts": sorted(
+                    self.symbol_guard_counter.values(), reverse=True
+                ),
+            },
+        )
+
+        if self._translation_validation_enabled:
+            from torch.fx.experimental.validator import PopulateValidator
+
+            # Add all deferred runtime assertions; these are not technically
+            # handled by produce_guards but we need to put them in the target
+            # set
+            for ras in self.deferred_runtime_asserts.values():
+                for ra in ras:
+                    self._add_target_expr(ra.expr)
+
+            # Add value range bound guards for all symbols with no trivial bounds.
+            # Reason: '_maybe_evaluate_static' may eliminate guards based on the
+            # refined value ranges.
+            for sym, vr in self.var_to_range.items():
+                if vr.lower not in (-sympy.oo, -int_oo):
+                    self._add_target_expr(sympy.Le(vr.lower, sym))
+                if vr.upper not in (sympy.oo, int_oo):
+                    self._add_target_expr(sympy.Le(sym, vr.upper))
+
+            # Before validating, populate the input of the validator with the
+            # built FX graph.
+            with fx_traceback.preserve_node_meta():
+                PopulateValidator(self.graph, self.validator).run()
+
+        # Only run translation validation when we are not passing custom guards
+        if guards is None:
+            self._check_translation_validate()
+
+        helpers: list[_ShapeGuardsHelper] = []
+        for exprs, printer, lang in zip(all_exprs, printers, langs):
+            if lang == "cpp":
+                assert isinstance(printer, _ShapeGuardCppPrinter)
+                helpers.append(_CppShapeGuardsHelper(exprs, printer.source_to_symbol))
+            else:
+                helpers.append(_ShapeGuardsHelper(exprs))
+        return helpers
+
+    def produce_guards_expression(
+        self,
+        placeholders: Sequence[Union[SymInt, FakeTensor]],
+        *,
+        guards: Optional[list[ShapeGuard]] = None,
+        ignore_static: bool = True,
+    ) -> Optional[str]:
+        """
+        Expected to be used with evaluate_guards_expression(). Produces the guards
+        for the given placeholders and returns a string expression to be evaluated
+        by evaluate_guards_expression given concrete values for the placeholders.
+        """
+        from torch._dynamo.source import LocalSource
+
+        arg_names = [f"t{i}" for i in range(len(placeholders))]
+        produced_guards = self.produce_guards(
+            placeholders,
+            [LocalSource(a) for a in arg_names],
+            guards=guards,
+            ignore_static=ignore_static,
+        )
+        if produced_guards:
+            return " and ".join(produced_guards)
+        return None
+
+    def evaluate_symexpr(self, code: str) -> Union[int, float, bool]:
+        """
+        To be used by compile_fx to evaluate symexprs
+        """
+        args = {str(e): val for e, val in self.var_to_val.items()}
+        return eval(code, SYMPY_INTERP, args)
+
+    def deserialize_symexpr(self, code: str) -> Union[SymInt, SymFloat, SymBool]:
+        """
+        To be used by compile_fx to deserialize symexprs
+        """
+        args = {
+            str(e): SymInt(SymNode(e, self, int, int(val), fx_node=None))
+            for e, val in self.var_to_val.items()
+        }
+        return eval(code, SYMPY_INTERP, args)
+
+    def evaluate_guards_expression(self, code: str, args: Sequence[object]) -> bool:
+        """
+        Expected to be used with produce_guards_expression(). Evaluates an expression
+        generated by produce_guards_expression for the given concrete args.
+        """
+        arg_names = [f"t{i}" for i in range(len(args))]
+        return eval(code, SYMPY_INTERP, {"L": dict(zip(arg_names, args))})
+
+    def evaluate_guards_for_args(
+        self,
+        placeholders: Sequence[FakeTensor],
+        args: Sequence[Tensor],
+        *,
+        ignore_static: bool = True,
+    ) -> bool:
+        """Generate guards for a graph's placeholder values and evaluate the guards with args"""
+        code = self.produce_guards_expression(placeholders, ignore_static=ignore_static)
+        if code:
+            return self.evaluate_guards_expression(code, args)
+        return True
+
+    def get_pruned_guards(self, symints: Sequence[torch.SymInt]) -> list[ShapeGuard]:
+        """
+        Get a list of guards, but pruned so it only provides guards that
+        reference symints from the passed in input
+        """
+        symints = {
+            s.node.expr for s in symints if isinstance(s.node.expr, sympy.Symbol)
+        }
+        guards = [
+            g for g in self.guards if all(s in symints for s in g.expr.free_symbols)
+        ]
+        return guards
+
+    def bind_symbols(
+        self, placeholders: Sequence[FakeTensor], args: Sequence[Tensor]
+    ) -> dict[sympy.Symbol, int]:
+        """
+        Given a paired list of placeholders (fake tensors with
+        symbolic sizes) and concrete arguments (regular tensors
+        with real sizes), returns a dictionary mapping each
+        symbol to its real value.  So for example, if you
+        have a placeholder with size (s0, s1), binding
+        (2, 4) to it will give you {s0: 2, s1: 4}.  This is
+        not guaranteed to bind ALL symbols in the ShapeEnv;
+        we can't bind a symbol if it doesn't occur in any placeholder,
+        and symbols that already have replacements won't get bindings.
+
+        This is a little duplicative with evaluate_guards but
+        it's different enough that it seemed cleanest to make
+        another copy.  This assumes the guards are already checked,
+        though if it's cheap we'll check for shenanigans
+        """
+        bindings: dict[sympy.Symbol, int] = {}
+
+        def bind_symint(arg: object, val: object) -> None:
+            if isinstance(val, SymInt):
+                assert isinstance(arg, int)
+                s = val.node.expr
+
+                if isinstance(s, sympy.Symbol):
+                    if s in bindings:
+                        assert bindings[s] == arg, f"{bindings[s]} != {arg}"
+                    else:
+                        bindings[s] = arg
+                elif isinstance(-s, sympy.Symbol):
+                    if -s in bindings:
+                        assert bindings[-s] == -arg, f"{bindings[-s]} != {-arg}"
+                    else:
+                        bindings[-s] = -arg
+
+        for t, arg in zip(placeholders, args):
+            if t is None:
+                continue
+            if isinstance(t, SymInt):
+                bind_symint(arg, t)
+                continue
+            assert isinstance(t, torch.Tensor)
+            for i, s in enumerate(t.size()):
+                bind_symint(arg.size(i), s)
+            for i, s in enumerate(t.stride()):
+                bind_symint(arg.stride(i), s)
+            bind_symint(arg.storage_offset(), t.storage_offset())
+
+        return bindings
+
+    def get_nontrivial_guards(self) -> list[SympyBoolean]:
+        """Returns a list of guard expressions that aren't statically known (i.e. not trivial)"""
+        return [
+            self.simplify(guard.expr)
+            for guard in self.guards
+            if self._maybe_evaluate_static(
+                guard.expr, axioms=(), size_oblivious=guard.size_oblivious
+            )
+            is None
+        ]
+
+    def format_guards(self, verbose: bool = False) -> str:
+        """Format this shape env's guard expressions with optional traceback info if verbose"""
+
+        return "\n".join(
+            f" - {guard.expr}{' ' + str(guard.sloc) if verbose else ''}"
+            for guard in self.guards
+        )
+
+    def bound_sympy(
+        self, expr: sympy.Expr, size_oblivious: bool = False
+    ) -> ValueRanges:
+        """Given a sympy expression, computes a ValueRanges bound for what values it can be"""
+        # TODO: maybe it's guaranteed x in is var_to_range?
+        var_to_range = {x: self.var_to_range.get(x, None) for x in expr.free_symbols}
+        if size_oblivious:
+            # Clamp values of size-like variables
+            # NB: discarding the old upper bound in intentional, per
+            # https://github.com/pytorch/pytorch/pull/123675
+            for x in self.size_like & var_to_range.keys():
+                if var_to_range[x] is not None:
+                    # NB: do NOT set upper to 2 ** 48, we're using this solely
+                    # to determine if we can do size-like replacement, the
+                    # upper bound is irrelevant here
+                    var_to_range[x] = ValueRanges(2, int_oo)
+        return bound_sympy(expr, var_to_range)  # type: ignore[arg-type]
+
+    @_lru_cache
+    def get_axioms(
+        self,
+        symbols: Optional[tuple[sympy.Symbol]] = None,
+        compute_hint: bool = False,
+    ) -> tuple[SympyBoolean, ...]:
+        """
+        Given the symbols in an expression, it returns all the runtime asserts that have those symbols
+        concatenated with all the guards.
+        If symbols is None, it returns all the runtime asserts (and all the guards)
+        """
+        if symbols is None:
+            runtime_asserts = (
+                r.expr for rs in self.deferred_runtime_asserts.values() for r in rs
+            )
+        else:
+            runtime_asserts = (
+                r.expr
+                for s in symbols
+                if s not in self.var_to_val
+                for r in self.deferred_runtime_asserts.get(s, ())
+            )
+        guards: Iterator[SympyBoolean] = (g.expr for g in self.guards)
+        axioms: Iterator[SympyBoolean] = itertools.chain(guards, runtime_asserts)
+        if compute_hint:
+            axioms = (
+                canonicalize_bool_expr(a.xreplace(self.var_to_val)) for a in axioms
+            )
+        return tuple(dict.fromkeys(axioms).keys())
+
+    @lru_cache(None)
+    def get_implications(
+        self, e: SympyBoolean
+    ) -> tuple[tuple[SympyBoolean, sympy.logic.boolalg.BooleanAtom], ...]:
+        """Given a expression, it returns a list of predicates that follow from it"""
+        equiv: dict[SympyBoolean, sympy.logic.boolalg.BooleanAtom] = {}
+
+        def add_expr(expr: SympyBoolean) -> None:
+            expr = canonicalize_bool_expr(expr)
+            if isinstance(expr, (sympy.Eq, sympy.Ne)):
+                # No need to canonicalize
+                # TODO We could further canonicalize Eq ordering the lhs and rhs somehow
+                # With this, we could remove the need for the commutativity part
+                opposite = sympy.Eq if isinstance(expr, sympy.Ne) else sympy.Ne
+                # Commutativity of == and !=
+                equiv[type(expr)(expr.lhs, expr.rhs, evaluate=False)] = sympy.true
+                equiv[type(expr)(expr.rhs, expr.lhs, evaluate=False)] = sympy.true
+                equiv[opposite(expr.lhs, expr.rhs, evaluate=False)] = sympy.false
+                equiv[opposite(expr.rhs, expr.lhs, evaluate=False)] = sympy.false
+            else:
+                # Expr and negation
+                equiv[expr] = sympy.true
+                # we do not pass evaluate=False like others on purpose here!
+                # we want not(a=b and not ~(a Optional[sympy.Basic]:
+        """
+        Tries to evaluate expr without introducing guards
+
+        If unbacked_only == True, then we only do substitutions on
+        unbacked SymInts (leaving regular hinted integers alone).  This could
+        result in an expression that still contains backed SymInts, which you
+        could then potentially guard on.
+
+        Use compute_hint == True if you are trying to compute a non-binding
+        hint for the particular hint values of backed and unbacked SymInts,
+        e.g., if s0 happens to be 3 this run, compute_hint will subsitute s0 with 3.
+        """
+
+        # axioms with compute hint NYE
+        assert not compute_hint or not axioms
+        expr = self.simplify(expr, size_oblivious)
+
+        if compute_hint:
+            expr = expr.xreplace(self.var_to_val).xreplace(self.unbacked_var_to_val)
+
+        expr = canonicalize_bool_expr(expr)
+
+        def resimplify_floor_div(axioms: dict[sympy.Expr, sympy.Expr]) -> None:
+            if not self._resimplify_floor_div_axioms:
+                return
+            self._resimplify_floor_div_axioms = False
+            new_items = {}
+            for k, v in axioms.items():
+                # A FloorDiv in implications could have became CleanDiv at this point, due to new facts
+                # to the shapeEnv. This handles such issue but its not ideal. This is the only expression
+                # simplification that depends on the global state of shape env.
+                # TODO try to get rid of CleanDiv since it breaks the invariant thats simplifications of sympy
+                # expressions only depend on the expression itself.
+                if k.has(FloorDiv):
+                    new_items.update({self.simplify(k): v})
+            axioms.update(new_items)
+
+        # Pattern matching
+        if axioms is None:
+            resimplify_floor_div(self.axioms)
+            subst = self.axioms
+        else:
+            subst = {}
+            for e in axioms:
+                if e.free_symbols.issubset(expr.free_symbols):
+                    subst.update(dict(self.get_implications(self.simplify(e))))
+
+            resimplify_floor_div(subst)
+
+        expr = expr.xreplace(subst)
+        # TODO: compute hint might have gotten broken here
+
+        fs = expr.free_symbols
+
+        if not fs and (expr.is_number or expr.is_Boolean):
+            return expr
+
+        if var_to_range is None:
+            var_ranges = self.var_to_range
+        else:
+            var_ranges = dict(var_to_range)
+
+        symbol_info = tuple(
+            _SymbolInfo(
+                s,
+                var_ranges.get(s),
+                self.var_to_val.get(s),
+                s in self.size_like,
+            )
+            for s in sorted(fs, key=str)  # TODO: speed up sort?
+        )
+
+        r = _maybe_evaluate_static_worker(
+            expr, symbol_info, unbacked_only, size_oblivious
+        )
+        return r
+
+    @_lru_cache
+    def replace(self, expr: _SympyT) -> _SympyT:
+        """Apply symbol replacements to any symbols in the given expression"""
+        replacements = {}
+        for s in expr.free_symbols:
+            r = self._find(s)
+            # Micro-optimization: only do replacements if r and s are different
+            # Otherwise, xreplace is not a no-op and will trigger expensive
+            # assumption queries if expr has a relational node.
+            if not r.is_Symbol or r != s:
+                replacements[s] = r
+        if replacements:
+            return safe_expand(expr.xreplace(replacements))
+        else:
+            return expr
+
+    @_lru_cache
+    def _update_divisible(self) -> None:
+        new_divisible = set()
+        for k in self.divisible:
+            res = self.replace(k)
+            if not res.is_number:
+                new_divisible.add(k)
+
+        self.divisible = new_divisible
+        self._update_version_counter()
+
+    @_lru_cache
+    def simplify(self, expr: _SympyT, size_oblivious: bool = False) -> _SympyT:
+        """Use known constraints and replacements to simplify the given expr"""
+        expr = safe_expand(expr)
+        expr = self.replace(expr)
+
+        if size_oblivious and expr.has(Max):
+            max_replacements = {}
+            for atom in expr.atoms(Max):
+                a, b = atom.args
+                if b == 1 or b == 0:
+                    a, b = b, a
+                if a == 1 or a == 0:
+                    if (
+                        isinstance(b, Add)
+                        and len(b.free_symbols) == 2  # TODO: expand to N?
+                        and b.free_symbols == set(b.atoms())
+                        and all(x in self.size_like for x in b.free_symbols)
+                    ):
+                        max_replacements[atom] = b
+            if max_replacements:
+                expr = expr.xreplace(max_replacements)
+                expr = safe_expand(expr)
+
+        # TODO it would seem that this pass is not necessary given the
+        # below replacement of // with /, but for nested FloorDivs
+        # the non-recursive replacement doesn't work, and
+        # recursive makes it hard to look up divisibility,
+        # because existing divisibility info has FloorDiv in it, not /
+        # for now just do a separate pass to catch common nested case
+        if expr.has(FloorDiv):
+            self._update_divisible()
+            div_replacements = {}
+            for atom in expr.atoms(FloorDiv):
+                base, divisor = atom.args
+                if isinstance(divisor, FloorDiv):
+                    base1, divisor1 = divisor.args
+                    if (
+                        self.replace(Mod(base, divisor)) in self.divisible
+                        and base == base1
+                        and self.replace(Mod(base1, divisor1)) in self.divisible
+                    ):
+                        div_replacements[atom] = divisor1
+            if div_replacements:
+                expr = expr.xreplace(div_replacements)
+                expr = safe_expand(expr)
+        if expr.has(FloorDiv):
+            div_replacements = {}
+            pows = expr.atoms(sympy.Pow)
+            rationals = expr.atoms(sympy.Rational).difference(expr.atoms(sympy.Integer))
+            for fd in expr.atoms(FloorDiv):
+                base, divisor = fd.args
+                if self.replace(Mod(base, divisor)) in self.divisible:
+                    div_replacements[fd] = CleanDiv(base, divisor)
+            if div_replacements:
+                new_expr = expr.xreplace(div_replacements)
+                new_expr = safe_expand(new_expr)
+                new_pows = new_expr.atoms(sympy.Pow)
+                new_rationals = new_expr.atoms(sympy.Rational).difference(
+                    new_expr.atoms(sympy.Integer)
+                )
+                # divisions simplified away
+                if new_pows.issubset(pows) and new_rationals.issubset(rationals):
+                    expr = new_expr
+        return expr
+
+    # TODO: overload for allow_none literal
+    @lru_cache(256)
+    def size_hint(
+        self, expr: sympy.Basic, *, allow_none: bool = False
+    ) -> Optional[sympy.Basic]:
+        """
+        Gets a size hint for a given expression from the underlying shapes we had.
+        Does not introduce a guard, so only use this when you can guarantee that
+        your code is still valid for arbitrary shapes (such as optimization decisions)
+        """
+        result_expr = safe_expand(expr).xreplace(self.var_to_val)
+        if not result_expr.is_number:
+            from torch.utils._sympy.singleton_int import SingletonInt
+
+            if isinstance(result_expr, SingletonInt):
+                return None
+            r = self._maybe_evaluate_static(result_expr, compute_hint=True)
+            if r is not None:
+                return r
+            if allow_none:
+                return None
+
+            if self.oblivious_var_to_val:
+                # See https://github.com/pytorch/pytorch/issues/137100#issuecomment-2495778113
+                correct_hint = result_expr.xreplace(self.oblivious_var_to_val)
+                counterfactual_hint = result_expr.xreplace(
+                    {k: max(v, 2) for k, v in self.oblivious_var_to_val.items()}
+                )
+                if (
+                    not correct_hint.free_symbols
+                    and not counterfactual_hint.free_symbols
+                ):
+                    if correct_hint == counterfactual_hint:
+                        log.info("oblivious_size hit %s -> %s", expr, correct_hint)
+                        return correct_hint
+                    else:
+                        log.info(
+                            "oblivious_size counterfactual failed %s -> %s != %s",
+                            expr,
+                            correct_hint,
+                            counterfactual_hint,
+                        )
+                else:
+                    log.info(
+                        "oblivious_size miss %s -> %s (counterfactual: %s)",
+                        expr,
+                        correct_hint,
+                        counterfactual_hint,
+                    )
+
+            if self.unbacked_var_to_val:
+                unsound_expr = result_expr.xreplace(self.unbacked_var_to_val)
+                if not unsound_expr.free_symbols:
+                    log.warning(
+                        "propagate_real_tensors size_hint(%s) -> %s", expr, unsound_expr
+                    )
+                    trace_structured(
+                        "propagate_real_tensors",
+                        metadata_fn=lambda: {
+                            "expr": repr(expr),
+                            "result": repr(unsound_expr),
+                            "stack": structured.from_traceback(
+                                CapturedTraceback.extract(skip=1).summary()
+                            ),
+                        },
+                    )
+                    self.defer_runtime_assert(
+                        sympy.Eq(result_expr, unsound_expr),
+                        f"propagate_real_tensors: {result_expr} == {unsound_expr}",
+                    )
+                    return unsound_expr
+
+            raise self._make_data_dependent_error(result_expr, expr)
+        return result_expr
+
+    # NB: keep in sync with size_hint
+    @lru_cache(256)
+    def has_hint(self, expr: sympy.Expr) -> bool:
+        result_expr = safe_expand(expr).xreplace(self.var_to_val)
+        return (
+            result_expr.is_number
+            or self._maybe_evaluate_static(result_expr) is not None
+        )
+
+    def _make_data_dependent_error(
+        self,
+        expr: sympy.Basic,
+        unhinted_expr: sympy.Basic,
+        *,
+        size_oblivious_result: Optional[sympy.Basic] = None,
+        expr_sym_node_id: Optional[int] = None,
+    ) -> GuardOnDataDependentSymNode:
+        # TODO: in a Dynamo context, having user code, and having the
+        # name of the local, will be much better
+        size_like_symbols = []
+        for s in expr.free_symbols:
+            stacktrace = "".join(self.var_to_stack[s].format())
+            self.log.debug(
+                "Data dependent variable '%s' allocated at:\n%s", s, stacktrace
+            )
+            if s in self.size_like:
+                size_like_symbols.append(s)
+        size_oblivious_result_msg = ""
+        if size_oblivious_result is not None:
+            size_oblivious_result_msg = (
+                f"ATTENTION: guard_size_oblivious would fix the error, evaluating expression to {size_oblivious_result}.\n"
+                "Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.\n\n"
+            )
+        sloc, maybe_extra_debug = self._get_stack_summary(True)
+        if expr.is_integer:  # type: ignore[attr-defined]
+            desc = (
+                "Could not extract specialized integer from data-dependent expression"
+            )
+        else:
+            desc = "Could not guard on data-dependent expression"
+        msg = (
+            f"{desc} {expr} (unhinted: {unhinted_expr}).  "
+            f"(Size-like symbols: {', '.join(map(str, size_like_symbols)) or 'none'})\n\n"
+            f"{size_oblivious_result_msg}"
+            f"Caused by: {sloc}\n"
+            'For more information, run with TORCH_LOGS="dynamic"\n'
+            "For extended logs when we create symbols, also add "
+            f"TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL=\"{','.join(map(str, expr.free_symbols))}\"\n"
+            "If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n"
+            "For more debugging help, see "
+            "https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n"
+            + maybe_extra_debug
+            # TODO: Help text about how to use our runtime tests to fix this
+            # problem
+        )
+
+        dtrace_structured(
+            "guard_on_data_dependent_error",
+            metadata_fn=lambda: {
+                "expr": repr(expr),
+                "unhinted_expr": repr(unhinted_expr),
+                "expr_id": self._expr_sym_node_id,
+                "stack": structured.from_traceback(
+                    CapturedTraceback.extract(skip=1).summary()
+                ),
+            },
+        )
+        return GuardOnDataDependentSymNode(expr, msg)
+
+    def _update_var_to_range(
+        self,
+        symbol: sympy.Symbol,
+        vr: ValueRanges,
+        vr_sloc: Optional[ValueRangesSLoc] = None,
+        *,
+        is_constraint: bool = False,
+    ) -> None:
+        lower, upper = vr.lower, vr.upper
+
+        # If we have a size-like unbacked SymInt, refuse to refine the range to be
+        # less than two.  This is because when we intersect this range
+        # with [2, inf] for size oblivious tests, the range would be
+        # unsatisfiable.  In other words, once you have a size-like
+        # unbacked SymInt, we can never learn that it is exactly zero or one,
+        # because we would now give inconsistent results for all size
+        # oblivous tests!
+        if upper < 2 and symbol in self.size_like:
+            vr = ValueRanges(lower, 2)
+
+        # Updates the range and the guards corresponding to each bound of the symbol.
+        if symbol not in self.var_to_range:
+            self.log.debug("_update_var_to_range %s = %s (new)", symbol, vr)
+            self.var_to_range[symbol] = vr
+            if vr_sloc is None:
+                sloc = self._get_sloc()
+                vr_sloc = ValueRangesSLoc(sloc, sloc)
+            self.var_to_range_sloc[symbol] = vr_sloc
+        else:
+            old = self.var_to_range[symbol]
+            new = old & vr
+            if new != old:
+                if vr_sloc is None:
+                    sloc = self._get_sloc()
+                    vr_sloc = ValueRangesSLoc(sloc, sloc)
+                if new.lower != old.lower:
+                    self.var_to_range_sloc[symbol].lower = vr_sloc.lower
+                if new.upper != old.upper:
+                    self.var_to_range_sloc[symbol].upper = vr_sloc.upper
+                self.var_to_range[symbol] = new
+                self.log.debug("_update_var_to_range %s = %s (update)", symbol, new)
+
+        if (v := self.var_to_val.get(symbol)) is not None:
+            r = self.var_to_range[symbol]
+            if v not in r:
+                # For constraint failure, delay this for later
+                # TODO: Rework all of this, the constraint logic is very
+                # duplicative with regular reasoning
+                if not is_constraint:
+                    assert v in r, f"{v} not in {r}"
+
+    def _set_replacement(self, a: sympy.Symbol, tgt: sympy.Expr, msg: str) -> None:
+        """
+        Adds or updates a replacement for a symbol.
+        Use this instead of `self.replacements[a] = tgt`.
+        """
+        if tgt == self.replacements.get(a, None):
+            return
+
+        if a in tgt.free_symbols:
+            return
+
+        # Precondition: a == tgt
+        assert isinstance(a, sympy.Symbol)
+
+        if (
+            self.allow_complex_guards_as_runtime_asserts
+            and not _is_supported_equivalence(tgt)
+        ):
+            return  # continuing leads to placeholder shapes having complex expressions that we can't resolve
+
+        # Handles nested tensor symbolic variables which don't have
+        # var_to_range bounds
+        tgt_bound = None
+        if a in self.var_to_range:
+            src_bound = self.var_to_range[a]
+
+            # First, refine the value range of a based on the computed value range
+            # of tgt.  This is always OK to do, even if we decide not to do the
+            # substitution in the end.  This might be a no-op, if a already has
+            # a tighter bound
+            tgt_bound = self.bound_sympy(tgt)
+            self._update_var_to_range(a, tgt_bound)
+
+            # Next, check if we can update the range of free symbols in tgt
+            # based on the range in a. But only do it if:
+            #  - the source bound non-trivially improves over what we get out of
+            #    the existing bounds.
+            #  - the replacement is univariate and we can invert the tgt expression
+            if not tgt_bound.issubset(src_bound) and len(tgt.free_symbols) == 1:
+                b = next(iter(tgt.free_symbols))
+                # Try to invert the equality
+                r = try_solve(sympy.Eq(a, tgt), b, floordiv_inequality=False)
+                if r is not None:
+                    self.log.debug(
+                        "set_replacement: solve for %s in %s == %s gives %s",
+                        b,
+                        a,
+                        tgt,
+                        r,
+                    )
+                    # The solution here can be non-integral, for example, if
+                    # we have s0 = 2*s1, then s1 = s0/2.  What we would like
+                    # to do is calculated the bounds in arbitrary precision,
+                    # and then requantize the bound to integers when we are
+                    # done.
+                    rat_b_bound = self.bound_sympy(r[1])
+                    b_bound = ValueRanges(
+                        CeilToInt(rat_b_bound.lower), FloorToInt(rat_b_bound.upper)
+                    )
+                    self._update_var_to_range(b, b_bound, self.var_to_range_sloc[a])
+                    tgt_bound = self.bound_sympy(tgt)
+                    assert tgt_bound.issubset(
+                        src_bound
+                    ), f"{tgt_bound=} not a subset of {src_bound=}"
+
+            # TODO: Should we propagate size-like-ness?
+            #
+            # Pros: if u0 is size-like, intuitively u0 == u1 should cause u1
+            # to become size-like.
+            #
+            # Cons: if u0 is size-like, what about u0 - 1 == u1?  You CAN'T
+            # propagate in this case, because what if u0 == 0, then u1 is negative
+            # and clearly isn't a size.  So, at minimum, any f(x) whose value
+            # range isn't [0, inf] given x in [0, inf] cannot propagate
+            # size-like-ness.  But there are many situations where you could
+            # imagine u1 is going to be size-like and actually you just didn't
+            # have a refined enough value range on u0.  Since even innocuous
+            # looking arithmetic operations can destroy size-like-ness, it's
+            # best to not propagate it at all and force the user to annotate it
+            # as necessary.
+            #
+            # Compromise: we preserve size-like-ness only for exact equality
+            # and nothing else.
+            if a in self.size_like and isinstance(tgt, sympy.Symbol):
+                self.size_like.add(tgt)
+            elif isinstance(tgt, sympy.Symbol) and tgt in self.size_like:
+                self.size_like.add(a)
+
+            # Now, decide if we will do the substitution.
+            #
+            #  - If the source has a non-trivial range, only substitute if
+            #    we preserve this range.  Note that we may have propagated
+            #    the src_range to free variables in tgt when tgt is univariate
+            #    and we could find an inverse, which helps us achieve this.
+            #    This ensures we never "forget" about user defined ranges,
+            #    even if they end up being defined on composite formulas
+            #    like s0 + s1.
+            #
+            #  - If the variable is unbacked, only substitute if the substitution
+            #    would preserve the bounds also under size-like-ness conditions.
+
+            if not tgt_bound.issubset(src_bound):
+                self.log.debug(
+                    "skipped set_replacement %s = %s (%s) [%s not subset of %s]",
+                    a,
+                    tgt,
+                    msg,
+                    tgt_bound,
+                    src_bound,
+                )
+                return
+            elif a in self.size_like:
+                tgt_bound_so = self.bound_sympy(tgt, size_oblivious=True)
+                src_bound_so = self.bound_sympy(a, size_oblivious=True)
+                if not tgt_bound_so.issubset(src_bound_so):
+                    self.log.debug(
+                        "skipped set_replacement %s = %s (%s) "
+                        "[%s not subset of %s (size-oblivious conditions)]",
+                        a,
+                        tgt,
+                        msg,
+                        tgt_bound_so,
+                        src_bound_so,
+                    )
+                    return
+
+        if isinstance(tgt, (sympy.Integer, sympy.Float)):
+            # specializing to a constant, which is likely unexpected (unless
+            # you specified dynamic=True)
+
+            user_tb = TracingContext.extract_stack()
+            trace_structured(
+                "symbolic_shape_specialization",
+                metadata_fn=lambda: {
+                    "symbol": repr(a),
+                    "sources": [s.name() for s in self.var_to_sources.get(a, [])],
+                    "value": repr(tgt),
+                    "reason": msg,
+                    "stack": structured.from_traceback(
+                        CapturedTraceback.extract(skip=1).summary()
+                    ),
+                    "user_stack": (
+                        structured.from_traceback(user_tb) if user_tb else None
+                    ),
+                },
+            )
+
+            if config.print_specializations:
+                self.log.warning(
+                    "Specializing %s to %s", self.var_to_sources[a][0].name(), tgt
+                )
+                self.log.debug("SPECIALIZATION", stack_info=True)
+        log.info("set_replacement %s = %s (%s) %s", a, tgt, msg, tgt_bound)
+        self.replacements[a] = tgt
+        # NB: the replacement may get refined, but the user will find the
+        # FIRST one most useful (TODO: Maybe we could consider tracking all of
+        # them)
+        if a not in self.replacements_slocs:
+            self.replacements_slocs[a] = self._get_sloc()
+        self._update_version_counter()
+
+        # When specializing 'a == tgt', the equality should be also conveyed to
+        # Z3, in case an expression uses 'a'.
+        self._add_target_expr(sympy.Eq(a, tgt, evaluate=False))
+
+    def _add_divisible(self, expr: sympy.Expr) -> None:
+        self.divisible.add(expr)
+        self._update_version_counter()
+
+    @_lru_cache
+    @record_shapeenv_event()
+    def _find(self, a: sympy.Symbol) -> sympy.Expr:
+        """
+        Implements a DSU-like algorithm to find the variable that represents a
+        Also handles transitive non-identity replacements.
+
+        a: b + c
+        c: d
+        """
+        if a not in self.replacements:
+            return a
+        res = self.replacements[a]
+        cur_replace = {s: self._find(s) for s in res.free_symbols}
+        replaced, changed = self.replacements[a]._xreplace(cur_replace)
+        if changed:
+            self._set_replacement(a, replaced, "find")
+        return self.replacements[a]
+
+    @lru_cache(256)
+    def _maybe_guard_rel(self, expr: sympy.Rel) -> None:
+        """
+        The relational guard is guarded to be true.  Use this information to
+        simplify shapes (i.e. a == b or a % 5 == 0)
+        """
+        assert isinstance(expr, sympy.Rel)
+
+        # A good example of what goes wrong if you don't do this is
+        # python test/functorch/test_aotdispatch.py -k
+        # test_aot_autograd_symbolic_module_exhaustive_nn_LazyConv3d_cpu_float32
+        if isinstance(expr, sympy.Ne):
+            return
+
+        free = list(expr.free_symbols)
+
+        assert (
+            len(free) > 0
+        ), f"The expression should not be static by this point: {expr}"
+        # In case of really gnarly expression, we don't blow up
+        if len(free) > 5:
+            return
+
+        # Prioritize unbacked symints for solving by ordering them last.
+        # Prefer to simplify out lexicographically higher symbols (i.e. simplify out s4 over s3).
+        #   (NB: this unfortunately isn't strictly equivalent to simplifying out newer symbols)
+        # Prefer to simplify out symbols with ephemeral sources.
+        def _smart_symbol_sort(x: sympy.Symbol) -> tuple[int, int, str]:
+            has_only_ephemeral_sources = x in self.var_to_sources and all(
+                s.is_ephemeral() for s in self.var_to_sources[x]
+            )
+            # NB: size_hint is int, not sympy.Expr, do not use int_oo here
+            hint_size = self.size_hint(x, allow_none=True)
+            if hint_size is None:
+                size = sys.maxsize
+            elif symbol_is_type(x, SymT.SIZE):
+                assert isinstance(hint_size, sympy.Expr)
+                size = int(hint_size)
+            else:
+                size = sys.maxsize
+            name = x.name
+            # 1 puts ephemeral sourced symbols first when sorting in reverse
+            return (1 if has_only_ephemeral_sources else 0, size, name)
+
+        free = sorted(free, key=_smart_symbol_sort, reverse=True)  # type: ignore[attr-defined]
+        lhs = expr.lhs
+        rhs = expr.rhs
+
+        self._refine_ranges(expr)
+
+        # The rest of this stuff is for equality only
+        if not isinstance(expr, sympy.Eq):
+            return
+
+        if not expr.has(Mod):
+            try:
+                floor_div_atoms = lhs.atoms(FloorDiv).union(rhs.atoms(FloorDiv))
+                if len(floor_div_atoms) > 0 and any(
+                    a.divisor != 1 for a in floor_div_atoms
+                ):
+                    raise NotImplementedError
+
+                # Never replace unbacked symbols with other unbacked symbols.
+                # This is error prone because you can cause references to
+                # unbacked symbols to time travel backwards.  E.g.,
+                #
+                # u1 = x.item()
+                # ... use of u1 ...
+                # u2 = y.item()
+                # u3 = z.item()
+                # torch._check(u1 == u2 + u3)
+                #
+                # If you replace u1 with u2 + u3, then the use of u1 now
+                # references u2 and u3 prior to them actually being bound at
+                # runtime.  It's pretty inconvenient to setup control
+                # dependencies for substitutions, so ban it entirely.
+                def trivial_solve(lhs: sympy.Expr, rhs: sympy.Expr) -> bool:
+                    if isinstance(lhs, sympy.Symbol):
+                        if free_unbacked_symbols(lhs) and not free_unbacked_symbols(
+                            rhs
+                        ):
+                            return True
+                        if symbol_is_type(lhs, SymT.FLOAT):
+                            return True
+                        # TODO: Maybe trivial solutions for int should also be
+                        # done?
+                    return False
+
+                # short-circuit when no solving is needed
+                if trivial_solve(lhs, rhs):
+                    self._set_replacement(lhs, self._find(rhs), "trivial_lhs")
+                elif trivial_solve(rhs, lhs):
+                    self._set_replacement(rhs, self._find(lhs), "trivial_rhs")
+                else:
+                    r = try_solve(expr, free[0], floordiv_inequality=False)
+                    if r is not None and all(
+                        t.is_integer for t in sympy.preorder_traversal(r[1])
+                    ):
+                        new_var = self._find(r[1])
+                        ok = len(free_unbacked_symbols(new_var)) == 0
+                        if ok:
+                            self._set_replacement(free[0], new_var, "solve")
+            except NotImplementedError:
+                pass
+        if expr.has(Mod):
+            mod_expr = next(iter(expr.atoms(Mod)))
+            try:
+                r = try_solve(expr, mod_expr, floordiv_inequality=False)
+                if r is not None and r[1] == 0:
+                    self._add_divisible(mod_expr)
+                    # This is a little bit of extra logic to make things like
+                    # torch.empty(i0, q).view(c, -1, q) work out
+                    p, q = mod_expr.args
+                    if (
+                        isinstance(q, sympy.Number)
+                        and isinstance(p, sympy.Mul)
+                        and len(p.args) == 2
+                    ):
+                        c, i0 = p.args
+                        # Given Mod(c * i0, q) == 0
+                        if (
+                            isinstance(c, sympy.Number)
+                            and isinstance(i0, sympy.Symbol)
+                            and self.is_unbacked_symint(i0)
+                        ):
+                            # We have Mod(i0, q / c) == 0, which means we can
+                            # rewrite i0 as (q / gcd(q, c)) * i1
+                            d = q / sympy.gcd(q, c)  # TODO: CleanDiv?
+                            i1 = self.create_unbacked_symint().node.expr
+                            # Propagate the value ranges.  It doesn't really
+                            # matter if we use truediv or floordiv, because we
+                            # have established divisibility.
+                            self._update_var_to_range(
+                                i1,
+                                SymPyValueRangeAnalysis.floordiv(
+                                    self.var_to_range[i0], ValueRanges.wrap(d)
+                                ),
+                            )
+                            # Propagate hints (real tensor tracing)
+                            if i0 in self.unbacked_var_to_val:
+                                self.set_unbacked_var_to_val(
+                                    i1, self.unbacked_var_to_val[i0] // d
+                                )
+                            # Propagate size-like-ness
+                            if i0 in self.size_like:
+                                self.size_like.add(i1)
+                            self._set_replacement(i0, d * i1, "divisibility")
+
+            except NotImplementedError:
+                pass
+        return
+
+    # See: Note - On 0/1 specialization
+    def _default_value_range(
+        self, do_not_specialize_zero_one: bool = False
+    ) -> ValueRanges:
+        lower = 0 if (do_not_specialize_zero_one or not self.specialize_zero_one) else 2
+        return ValueRanges(lower, int_oo)
+
+    def _default_unspecified_value_range(self) -> ValueRanges:
+        return ValueRanges.unknown_int()
+
+    @_lru_cache
+    def _simplify_floor_div(self, expr: sympy.Expr) -> sympy.Expr:
+        floor_divs = tuple(expr.atoms(FloorDiv))
+        # we expect floor_divs to be exact,
+        # and thus add the guards for the exact floordivs,
+        # even if tracing doesn't require them otherwise
+        for fd in reversed(floor_divs):
+            base, divisor = fd.args
+            mod_expr = Mod(base, divisor)
+            eq_expr = sympy.Eq(mod_expr, 0)
+            # add necessary mod guards
+            self.evaluate_expr(eq_expr)
+        return self.simplify(expr)
+
+    # We're about to add a guard/runtime assert, check if the ShapeEnv is frozen
+    # and if so issue a warning
+    def _check_frozen(self, expr: sympy.Basic, concrete_val: sympy.Basic) -> None:
+        if self.frozen:
+            self.counter["ignored_backward_guard"] += 1
+            signpost_event(
+                "dynamic",
+                "evaluate_expr_frozen",
+                {
+                    **self.co_fields,
+                    "ignored_guard": f"{expr} == {concrete_val}",
+                    # no version = original state (this signpost is expected)
+                    # version 2 = dynamic backwards is eagerly compiled
+                    "version": 2,
+                },
+            )
+            log.info(
+                "Ignored guard %s == %s, this could result in accuracy problems",
+                expr,
+                concrete_val,
+                # only print stack trace when debug mode is on (e.g. TORCH_LOGS="dynamic")
+                stack_info=True if log.getEffectiveLevel() < logging.WARNING else False,
+            )
+
+    def _get_stack_summary(
+        self, is_debug: bool = False, framework_loc: Optional[str] = None
+    ) -> tuple[SLoc, str]:
+        floc: Optional[Union[str, traceback.FrameSummary]] = framework_loc
+        if floc is None:
+            frame = inspect.currentframe()
+            try:
+                while frame is not None:
+                    if frame.f_code.co_filename not in uninteresting_files():
+                        floc = traceback.FrameSummary(
+                            frame.f_code.co_filename,
+                            frame.f_lineno,
+                            frame.f_code.co_name,
+                        )
+                        break
+                    frame = frame.f_back
+            finally:
+                del frame
+
+        # NB: this stack is truncated, but it's fine because the main
+        # stack_info will give you the rest of the info you need
+        maybe_user_loc = None
+        user_tb = TracingContext.extract_stack()
+        if user_tb:
+            idx = len(user_tb) - 1
+            while idx > 0 and user_tb[idx].filename in uninteresting_files():
+                idx -= 1
+            maybe_user_loc = format_frame(user_tb[idx], line=True)
+
+        maybe_extra_debug = ""
+        if is_debug and user_tb:
+            maybe_extra_debug = (
+                "\nUser Stack (most recent call last):\n"
+                + "  (snipped, see stack below for prefix)\n"
+                + "".join(traceback.format_list(user_tb))
+            )
+        if is_debug and config.extended_debug_cpp:
+            cpp_stack = CapturedTraceback.extract(cpp=True)
+            maybe_extra_debug += "\nC++ stack trace:\n" + "".join(cpp_stack.format())
+        elif is_debug:
+            maybe_extra_debug += (
+                "\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1"
+            )
+
+        return SLoc(floc, maybe_user_loc), maybe_extra_debug
+
+    # Pass in framework_loc to override the framework location info
+    def _get_sloc(self, framework_loc: Optional[str] = None) -> SLoc:
+        sloc, _ = self._get_stack_summary(framework_loc=framework_loc)
+        return sloc
+
+    def _find_frame_locals(self) -> _FrameLocalResult:
+        """
+        Given the current user code frame, finds the relevant lines of code,
+        values of symbolic locals, and free symbols involved.
+        """
+        frame_locals: dict[str, Any] = {}
+        frame_symbols: dict[str, str] = {}
+
+        if (
+            frame := _find_user_code_frame()
+        ) is None or frame.f_code.co_filename == "":
+            return _FrameLocalResult()
+
+        # find bytecode instructions relevant to the frame
+        instructions = list(dis.Bytecode(frame.f_code))
+        co_lines, offset = inspect.getsourcelines(frame.f_code)
+        start, end, cur = None, None, None
+        for i, instr in enumerate(instructions):
+            if instr.starts_line is not None:
+                cur = instr.starts_line
+            if cur != frame.f_lineno:
+                continue
+            if start is None:
+                start = end = i
+            else:
+                end = i
+
+        if start is None or end is None:  # no instructions found
+            return _FrameLocalResult()
+
+        # track involved locals and free symbols
+        def go(x: Any) -> Optional[str]:
+            if isinstance(x, torch.Tensor):
+                for y in x.size():
+                    go(y)
+                for y in x.stride():
+                    go(y)
+                go(x.storage_offset())
+                return (
+                    f"Tensor(shape: {x.size()}, "
+                    f"stride: {x.stride()}, "
+                    f"storage_offset: {x.storage_offset()})"
+                )
+            elif isinstance(x, (SymBool, SymInt, SymFloat)):
+                for s in x.node.expr.free_symbols:
+                    if str(s) in frame_symbols:  # type: ignore[operator]
+                        continue
+                    if s in self.var_to_sources:
+                        frame_symbols[str(s)] = self.var_to_sources[s][0].name()  # type: ignore[assignment]
+                return str(x)
+            return None
+
+        # go through instructions, seeing linenos & involved locals
+        last_lineno = frame.f_lineno
+        for instr in instructions[start : end + 1]:
+            if (lineno := instr.starts_line) is not None:
+                last_lineno = max(last_lineno, lineno)
+            if isinstance(instr.argval, str) and instr.argval in frame.f_locals:
+                frame_locals[instr.argval] = pytree.tree_map(
+                    go, frame.f_locals[instr.argval]  # type: ignore[index]
+                )
+
+        # store LOC
+        locs = co_lines[frame.f_lineno - offset : last_lineno + 1 - offset]
+        indent = len(locs[0]) - len(locs[0].lstrip())
+        frame_loc = "".join([loc[indent:] for loc in locs]).strip()  # type: ignore[assignment]
+        return _FrameLocalResult(
+            loc=frame_loc, locals=frame_locals, symbols=frame_symbols
+        )
+
+    def _log_guard(self, prefix: str, g: SympyBoolean, forcing_spec: bool) -> None:
+        dtrace_structured(
+            "guard_added",
+            metadata_fn=lambda: {
+                "expr": str(g),
+                "stack": structured.from_traceback(
+                    CapturedTraceback.extract(skip=1).summary()
+                ),
+                "symbol_to_sources": {
+                    str(v): k
+                    for k, v in self.source_to_var.items()
+                    if v in g.free_symbols
+                },
+                "frame_locals": asdict(self._find_frame_locals()),
+            },
+        )
+        trace_structured(
+            "guard_added_fast",
+            metadata_fn=lambda: {
+                "expr": str(g),
+                "user_stack": structured.from_traceback(TracingContext.extract_stack()),
+                "stack": structured.from_traceback(
+                    CapturedTraceback.extract(skip=1).summary()
+                ),
+            },
+        )
+        if self.log.isEnabledFor(logging.INFO):
+            str_g = str(g)
+            is_debug = (
+                config.extended_debug_guard_added is not None
+                and str_g == config.extended_debug_guard_added
+            )
+            sloc, maybe_extra_debug = self._get_stack_summary(is_debug)
+            maybe_more_info = ""
+            if not is_debug:
+                maybe_more_info = (
+                    ", for more info run with "
+                    f'TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED="{str_g}"'
+                )
+            self.log.info(
+                "%s %s [guard added] %s%s%s",
+                prefix if not forcing_spec else f"{prefix} (forcing_spec)",
+                str_g,
+                sloc,
+                maybe_more_info,
+                maybe_extra_debug,
+                stack_info=is_debug,
+            )
+
+    # A local variable to evaluate_expr stored in the class to avoid
+    # using it for the lru_cache that is on top of it since it does
+    # not effect the results. When needed its read directly.
+    _expr_sym_node_id: Optional[int] = None
+
+    def evaluate_sym_node(
+        self,
+        sym_node: SymNode,
+        size_oblivious: bool = False,
+    ) -> sympy.Basic:
+        """
+        Given a a SymNode, evaluates sym_node.expr, adding guards if necessary.
+        """
+
+        self._expr_sym_node_id = id(sym_node)
+        return self.evaluate_expr(
+            sym_node.expr, sym_node.hint, sym_node.fx_node, size_oblivious
+        )
+
+    @lru_cache(256)
+    @record_shapeenv_event(save_tracked_fakes=True)
+    def evaluate_expr(
+        self,
+        orig_expr: sympy.Basic,
+        hint: Optional[Union[int, bool, float]] = None,
+        fx_node: Optional[torch.fx.Node] = None,
+        size_oblivious: bool = False,
+        *,
+        forcing_spec: bool = False,
+    ) -> sympy.Basic:
+        try:
+            return self._evaluate_expr(
+                orig_expr,
+                hint,
+                fx_node,
+                size_oblivious,
+                forcing_spec=forcing_spec,
+            )
+        except Exception:
+            self.log.warning(
+                "failed during evaluate_expr(%s, hint=%s, size_oblivious=%s, forcing_spec=%s",
+                orig_expr,
+                hint,
+                size_oblivious,
+                forcing_spec,
+            )
+            raise
+
+    def _evaluate_expr(
+        self,
+        orig_expr: sympy.Basic,
+        hint: Optional[Union[bool, int, float]] = None,
+        fx_node: Optional[torch.fx.Node] = None,
+        size_oblivious: bool = False,
+        *,
+        forcing_spec: bool = False,
+    ) -> sympy.Basic:
+        """
+        Given an expression, evaluates it, adding guards if necessary
+        """
+
+        # TODO: split conjunctions and evaluate them separately
+
+        if isinstance(
+            orig_expr,
+            (sympy.logic.boolalg.BooleanTrue, sympy.logic.boolalg.BooleanFalse),
+        ):
+            return orig_expr
+
+        # Don't track this one
+        @functools.lru_cache(None)
+        def compute_concrete_val() -> sympy.Basic:
+            if hint is None:
+                # This is only ever called for expressions WITHOUT unbacked
+                # symbols
+                r = self.size_hint(orig_expr)
+                assert r is not None
+                return r
+            else:
+                return sympy.sympify(hint)
+
+        concrete_val: Optional[sympy.Basic]
+
+        # Check if:
+        #   1. 'translation_validation' is set
+        #   2. the corresponding 'fx_node' is not 'None'
+        #   3. the guard should not be suppressed
+        #   4. the guard doesn't contain backed symfloat symbols
+        #      since z3 can't handle floats
+        #
+        # If all of the above check, we create an FX node representing the
+        # actual expression to be guarded.
+        node = None
+        fresh = False
+        if (
+            self._translation_validation_enabled
+            and fx_node is not None
+            and not self._suppress_guards_tls()
+            and not size_oblivious
+            and not any(symbol_is_type(s, SymT.FLOAT) for s in orig_expr.free_symbols)
+        ):
+            # TODO: does this even worked with unbacked :think:
+            concrete_val = compute_concrete_val()
+            if concrete_val is sympy.true:
+                node, fresh = self._create_fx_call_function(torch._assert, (fx_node,))
+            elif concrete_val is sympy.false:
+                neg, _ = self._create_fx_call_function(operator.not_, (fx_node,))
+                node, fresh = self._create_fx_call_function(torch._assert, (neg,))
+            else:
+                eql, _ = self._create_fx_call_function(
+                    operator.eq, (fx_node, concrete_val)
+                )
+                node, fresh = self._create_fx_call_function(torch._assert, (eql,))
+
+            assert node is not None
+            # If this is a fresh node, we have to remember the event index that
+            # corresponds to this assertion node.
+            # Reason: so that, given an assertion node, we can replay the ShapeEnv
+            # events until the point where this assertion node was freshly created.
+            if fresh:
+                self._add_fx_node_metadata(node)
+
+        # After creating the FX node corresponding to orig_expr, we must make sure that
+        # no error will be raised until the end of this function.
+        #
+        # Reason: the translation validation may become invalid otherwise.
+        #
+        # If an error is raised before the end of this function, we remove the FX node
+        # inserted, and re-raise the error.
+        guard = None
+
+        try:
+            if orig_expr.is_number:
+                self.log.debug("eval %s [trivial]", orig_expr)
+                if hint is not None:
+                    if isinstance(hint, bool):
+                        assert orig_expr == hint, f"{orig_expr} != {hint}"
+                    else:
+                        assert sympy.Eq(orig_expr, hint), f"{orig_expr} != {hint}"
+                return orig_expr
+
+            expr = orig_expr
+
+            static_expr = self._maybe_evaluate_static(
+                expr, size_oblivious=size_oblivious
+            )
+            if static_expr is not None:
+                self.log.debug(
+                    "eval %s == %s [statically known]",
+                    f"size_oblivious({orig_expr})"
+                    if size_oblivious
+                    else size_oblivious,
+                    static_expr,
+                )
+                if (
+                    not size_oblivious
+                    and config.backed_size_oblivious
+                    and hint is not None
+                ):
+                    # TODO: maybe reconcile this with use of counterfactual hints
+                    # in unbacked case
+                    assert static_expr == hint, f"{static_expr} != {hint}"
+                return static_expr
+
+            transmute_into_runtime_assert = False
+
+            concrete_val = None
+            if not (expr.free_symbols <= self.var_to_val.keys()):
+                # TODO: dedupe this with _maybe_evaluate_static
+                # Attempt to eliminate the unbacked SymInt
+                new_expr = self._maybe_evaluate_static(expr, unbacked_only=True)
+                assert new_expr is not None
+                if not (new_expr.free_symbols <= self.var_to_val.keys()):
+                    size_oblivious_result = None
+                    if not size_oblivious:
+                        size_oblivious_result = self._maybe_evaluate_static(
+                            expr, size_oblivious=True
+                        )
+
+                    ok = False
+
+                    # Last ditch
+                    if (
+                        self.oblivious_var_to_val
+                        and not (
+                            correct_hint := orig_expr.xreplace(
+                                self.oblivious_var_to_val
+                            )
+                        ).free_symbols
+                        and not (
+                            counterfactual_hint := orig_expr.xreplace(
+                                {
+                                    k: max(2, v)
+                                    for k, v in self.oblivious_var_to_val.items()
+                                }
+                            )
+                        ).free_symbols
+                        and correct_hint == counterfactual_hint
+                    ):
+                        # TODO: better logging
+                        log.info(
+                            "oblivious_size %s -> %s (passed counterfactual)",
+                            orig_expr,
+                            correct_hint,
+                        )
+                        concrete_val = correct_hint
+                        # NB: do NOT transmute into runtime assert
+                        ok = True
+
+                    if (
+                        not ok
+                        and self.unbacked_var_to_val
+                        and not (
+                            unsound_result := orig_expr.xreplace(
+                                self.unbacked_var_to_val
+                            ).xreplace(self.var_to_val)
+                        ).free_symbols
+                    ):
+                        log.warning(
+                            "propagate_real_tensors evaluate_expr(%s) -> %s",
+                            orig_expr,
+                            unsound_result,
+                        )
+                        trace_structured(
+                            "propagate_real_tensors",
+                            metadata_fn=lambda: {
+                                "expr": repr(orig_expr),
+                                "result": repr(unsound_result),
+                                "stack": structured.from_traceback(
+                                    CapturedTraceback.extract(skip=1).summary()
+                                ),
+                            },
+                        )
+                        dtrace_structured(
+                            "propagate_real_tensors_provenance",
+                            metadata_fn=lambda: {
+                                "expr": repr(orig_expr),
+                                "result": repr(unsound_result),
+                                "expr_node_id": self._expr_sym_node_id,
+                                "user_stack": structured.get_user_stack(3),
+                                "stack": structured.get_framework_stack(3),
+                                "symbol_to_sources": {
+                                    str(v): k
+                                    for k, v in self.source_to_var.items()
+                                    if v in orig_expr.free_symbols
+                                },
+                                "frame_locals": asdict(self._find_frame_locals()),
+                            },
+                        )
+                        transmute_into_runtime_assert = True
+                        concrete_val = unsound_result
+                        ok = True
+
+                    if not ok:
+                        raise self._make_data_dependent_error(
+                            expr.xreplace(self.var_to_val),
+                            expr,
+                            size_oblivious_result=size_oblivious_result,
+                            expr_sym_node_id=self._expr_sym_node_id,
+                        )
+                else:
+                    expr = new_expr
+
+            if concrete_val is None:
+                concrete_val = compute_concrete_val()
+            self._check_frozen(expr, concrete_val)
+
+            if (
+                config.inject_EVALUATE_EXPR_flip_equality_TESTING_ONLY
+                and isinstance(hint, bool)
+                and isinstance(expr, (sympy.Eq, sympy.Ne))
+            ):
+                expr = sympy.Not(expr)
+
+            # Turn this into a boolean expression, no longer need to consult
+            # concrete_val
+            if concrete_val is sympy.true:
+                g = cast(SympyBoolean, expr)
+            elif concrete_val is sympy.false:
+                g = sympy.Not(expr)
+            else:
+                g = sympy.Eq(expr, concrete_val)  # type: ignore[arg-type]
+
+            if transmute_into_runtime_assert:
+                self.defer_runtime_assert(
+                    g, f"propagate_real_tensors: {orig_expr} == {concrete_val}"
+                )
+                return concrete_val
+
+            if not self._suppress_guards_tls():
+                self._log_guard("eval", g, forcing_spec=forcing_spec)
+
+                if isinstance(g, sympy.Rel):
+                    # TODO: If we successfully eliminate a symbol via equality, it
+                    # is not actually necessary to save a guard for the equality,
+                    # as we will implicitly generate a guard when we match that
+                    # input against the symbol.  Probably the easiest way to
+                    # implement this is to have maybe_guard_rel return a bool
+                    # saying if it "subsumed" the guard (and therefore the guard
+                    # is no longer necessary)
+                    self._maybe_guard_rel(g)
+
+                if not self.allow_complex_guards_as_runtime_asserts:
+                    # at this point, we've evaluated the concrete expr value, and have
+                    # flipped/negated the guard if necessary. Now we know what to guard
+                    # or defer to runtime assert on.
+                    guard = ShapeGuard(
+                        g, self._get_sloc(), size_oblivious=size_oblivious
+                    )
+                    self.guards.append(guard)
+                    self.axioms.update(dict(self.get_implications(self.simplify(g))))
+                else:
+                    # it's fine to defer simple guards here without checking,
+                    # the _maybe_guard_rel() call above will set replacements if possible,
+                    # and so the result here will be statically known
+                    self.defer_runtime_assert(g, f"evaluate_expr: {orig_expr}")
+            else:
+                self._log_guard("eval [guard suppressed]", g, forcing_spec=forcing_spec)
+
+        except Exception:
+            if fresh:
+                self._remove_fx_node(node)
+            raise
+        else:
+            if not self._suppress_guards_tls():
+                if guard is not None:  # we might have deferred this to runtime assert
+                    for s in g.free_symbols:
+                        self.symbol_guard_counter[s] += 1
+                        # Forcing_spec to avoid infinite recursion
+                        if (
+                            not forcing_spec
+                            and config.symbol_guard_limit_before_specialize is not None
+                            and self.symbol_guard_counter[s]
+                            > config.symbol_guard_limit_before_specialize
+                        ):
+                            # Force specialization
+                            self.log.info(
+                                "symbol_guard_limit_before_specialize=%s exceeded on %s",
+                                config.symbol_guard_limit_before_specialize,
+                                s,
+                            )
+                            self.evaluate_expr(s, forcing_spec=True)
+
+        return concrete_val
+
+    def cleanup(self) -> None:
+        """
+        Break reference cycles.
+
+        This destroys the stacks. If you really want to keep them, we
+        just need some way to break references on code objects.
+        """
+        for s in self.var_to_stack.values():
+            s.cleanup()
+        for ras in self.deferred_runtime_asserts.values():
+            for ra in ras:
+                ra.stack.cleanup()
+
+    @lru_cache(256)
+    @record_shapeenv_event(save_tracked_fakes=True)
+    def defer_runtime_assert(
+        self, orig_expr: SympyBoolean, msg: str, fx_node: Optional[torch.fx.Node] = None
+    ) -> bool:
+        """Create an assert that is checked at runtime
+
+        Args:
+            orig_expr (sympy.Expr): Boolean expression to assert is true
+            msg (str): Message to display on assertion failure
+            fx_node (Optional, torch.fx.Node): node in ``self.graph`` corresponding
+                to the expression, if applicable
+
+        """
+        expr = orig_expr
+
+        # TODO: split conjunctions and evaluate them separately
+
+        static_expr = self._maybe_evaluate_static(expr)
+        if static_expr is not None:
+            self.log.debug(
+                "runtime_assert %s == %s [statically known]", orig_expr, static_expr
+            )
+            # TODO: assert bool(static_expr)
+            return bool(static_expr)
+
+        # Attempt to eliminate the unbacked SymInt
+        new_expr = self._maybe_evaluate_static(expr, unbacked_only=True)
+        assert new_expr is not None
+        if (
+            not self.prefer_deferred_runtime_asserts_over_guards
+            and new_expr.free_symbols <= self.var_to_val.keys()
+        ):
+            # Do a normal guard
+            return self.evaluate_expr(new_expr, fx_node=fx_node)
+        # NB: Don't use new_expr as expr; it could contain gunk like shape0
+        # which we don't want to guard on
+
+        if (
+            self._translation_validation_enabled
+            and fx_node is not None
+            and not self._suppress_guards_tls()
+        ):
+            node, fresh = self._create_fx_call_function(torch._assert, (fx_node,))
+            assert node is not None
+            if fresh:
+                self._add_fx_node_metadata(node)
+
+        if not self._suppress_guards_tls():
+            self._log_guard("runtime_assert", orig_expr, forcing_spec=False)
+            # If you're here because of this assert, read Note [Backwards runtime asserts]
+            # in torch/_inductor/graph.py
+            if self.runtime_asserts_frozen:
+                log.warning("runtime_asserts_frozen but then got %s", expr)
+            self._check_frozen(expr, sympy.true)
+            # eliminate symbols on equality tests / refine ranges
+            if isinstance(expr, sympy.Rel):
+                self._maybe_guard_rel(expr)
+
+            # canonicalise to remove equations that are trivially equal
+            orig_expr = expr
+            expr = canonicalize_bool_expr(expr)
+            stack = CapturedTraceback.extract(skip=1)
+            ra = RuntimeAssert(expr, msg, stack)
+            # TODO: Do this in a way that is less janky than int(s.name[1:])
+            cands = sorted(
+                (s for s in expr.free_symbols if symbol_is_type(s, SymT.UNBACKED_INT)),
+                key=lambda s: int(s.name[1:]),
+            )
+            # Is None when prefer_deferred_runtime_asserts_over_guards=True
+            # and the guard in question has no unbacked SymInts in front
+            ix = cands[-1] if cands else None
+            self.deferred_runtime_asserts.setdefault(ix, []).append(ra)
+            self.axioms.update(dict(self.get_implications(self.simplify(expr))))
+            self.num_deferred_runtime_asserts += 1
+            self._update_version_counter()
+        else:
+            self._log_guard(
+                "runtime_assert [guard suppressed]", orig_expr, forcing_spec=False
+            )
+
+        return True
+
+    # Refines the ranges of the variables present in 'guard'.
+    #
+    # This function tries to refine the range of the variables inside
+    # 'guard' by reasoning about it. Specifically, when 'guard' is a
+    # 'sympy.Relational' operation.
+    #
+    # It does mainly 3 things:
+    #   1. Tries to isolate a variable in the left-hand side
+    #   2. Compute the value range of the right-hand side
+    #   3. Update the value range of the variable, if better
+    def _refine_ranges(self, expr: SympyBoolean) -> None:
+        expr = self.simplify(expr)
+
+        for symbol in expr.free_symbols:
+            assert isinstance(symbol, sympy.Symbol)
+
+            if isinstance(self.var_to_val.get(symbol, None), SingletonInt):
+                # Skip var_to_range logic for SingletonInt which is only used
+                # for jagged layout NestedTensors today
+                continue
+
+            r = try_solve(expr, symbol)
+
+            if r is None or not (symbol.is_integer and r[1].is_integer):
+                # Range refinement only supports integer symbols for now.
+                # There are lots of SymPy bugs when it comes to comparing
+                # reals and integers, so we skip that for now.
+                continue
+
+            r_expr, rhs = r
+            vr = self.var_to_range[symbol]
+            lower, upper = vr.lower, vr.upper
+
+            rhs_vr = bound_sympy(rhs, self.var_to_range)
+
+            # Let's suppose that we have a preexisting range for x [0, 100].
+            # Now, we issue a guard x > y, where the range for y is [50, 150].
+            # Then, lower = 0, rhs_vr.lower = 50 and therefore refinement can happen,
+            # refining x to [51, 100], since x must be greater than y, but the lowest
+            # y could be is 50.
+            #
+            # sympy.Eq may update both lower and upper bounds.
+            # sympy.G{t,e} may update the lower bound, only.
+            # sympy.L{t,e} may update the upper bound, only.
+            if lower < rhs_vr.lower and isinstance(
+                r_expr, (sympy.Eq, sympy.Ge, sympy.Gt)
+            ):
+                # Strictly greater relations allow us to refine a bit more, since
+                # x < y implies that the lower bound for x is: y + 1.
+                lower = rhs_vr.lower + int(isinstance(r_expr, sympy.Gt))
+            if upper > rhs_vr.upper and isinstance(
+                r_expr, (sympy.Eq, sympy.Le, sympy.Lt)
+            ):
+                upper = rhs_vr.upper - int(isinstance(r_expr, sympy.Lt))
+
+            # Do nothing if the new value range is no better than what we already have.
+            if vr == ValueRanges(lower, upper):
+                continue
+
+            # Updates the range and the guards corresponding to each bound of the symbol.
+            self._update_var_to_range(symbol, ValueRanges(lower, upper))
+            # If the range is refined to singleton, set replacement
+            if self.var_to_range[symbol].is_singleton():
+                self._set_replacement(
+                    symbol,
+                    self.var_to_range[symbol].lower,
+                    "range_refined_to_singleton",
+                )
+
+            # Clears the cache, since this update can change the result.
+            self._maybe_evaluate_static.cache_clear()
+
+    @lru_cache(maxsize=None)
+    @record_shapeenv_event()
+    def constrain_symbol_range(
+        self, s: sympy.Symbol, compiler_min: int, compiler_max: int
+    ) -> None:
+        upd_vr = ValueRanges(compiler_min, compiler_max)
+        old_vr = self.var_to_range.get(s, ValueRanges.unknown())
+        self._update_var_to_range(s, upd_vr)
+        if (new_vr := self.var_to_range[s]) != old_vr:
+            log.info(
+                "constrain_symbol_range %s [%s, %s]", s, new_vr.lower, new_vr.upper
+            )
+
+
+def _is_int(expr: object) -> bool:
+    return isinstance(expr, SymInt) and expr.node.expr.is_number
+
+
+# WARNING: This is legacy, DO NOT USE
+def _is_dim_dynamic(t: torch.Tensor, d: int) -> bool:
+    return hasattr(t, "_dynamo_dynamic_indices") and d in t._dynamo_dynamic_indices
+
+
+class PropagateUnbackedSymInts(torch.fx.Interpreter):
+    def run_node(self, n: torch.fx.Node) -> Result:
+        """
+        Run an FX node, propagating unbacked Symbol bindings to the new fake tensor
+        """
+        from torch._guards import detect_fake_mode
+
+        result = super().run_node(n)
+        rebind_unbacked(detect_fake_mode().shape_env, n, result)
+        return result
+
+
+def _find_user_code_frame() -> Optional[types.FrameType]:
+    frame = inspect.currentframe()
+    while frame is not None:
+        if not frame.f_code.co_filename.startswith(
+            os.path.dirname(inspect.getfile(torch)) + os.path.sep
+        ):
+            break
+        frame = frame.f_back
+    return frame
+
+
+def _blame_user_code(e: Exception, frame: types.FrameType) -> None:
+    frame_summary = traceback.FrameSummary(
+        frame.f_code.co_filename,
+        frame.f_lineno,
+        frame.f_code.co_name,
+    )
+    msg = e.args[0]
+    msg += "\n\nThe following call raised this error:\n" + "".join(
+        traceback.StackSummary.from_list([frame_summary]).format()
+    )
+    e.args = (msg,)
+
+
+class _PythonMsgPrinter(PythonPrinter):
+    """
+    Util printer that replaces sympy symbols with their source-level names
+    and renders sympy relational operators (e.g., Eq, Ne, Ge, Le) inline
+    (i.e., as ==, !=, >, <).
+    """
+
+    def __init__(self, src_map: dict[str, list[str]]) -> None:
+        super().__init__()
+        self.src_map = src_map
+
+    def _print_Symbol(self, sym: sympy.Symbol) -> str:
+        return self.src_map[sym.name][0]
+
+
+def _suggest_torch_checks(
+    e: GuardOnDataDependentSymNode, src_map: defaultdict[str, list[str]]
+) -> None:
+    # extract the unresolved condition on unbacked symints in the error
+    cond = e.cond
+    diff = ", ".join(s.name for s in cond.free_symbols if s.name not in src_map)
+    if diff:
+        log.warning("Unable to find user code corresponding to {%s}", diff)
+        return
+    printer = _PythonMsgPrinter(src_map)
+    msg = e.args[0]
+    msg += "\nTo fix the error, insert one of the following checks before this call:"
+    # suggested fixes to resolve `cond`` are to tell the compiler to assume
+    # either `cond` or its negation (the user will need to select which)
+    suggested_fixes = [
+        f"torch._check({printer.doprint(cond)})",
+        f"torch._check({printer.doprint(sympy.Not(cond))})",
+    ]
+    for i, fix in enumerate(suggested_fixes):
+        msg += f"\n  {i + 1}. {fix}"
+    src_mapped = ", ".join(
+        f"`{s}` with {' or '.join(src_map[s])}"
+        for s in sorted(s.name for s in cond.free_symbols)
+    )
+    msg += f"\n\n(These suggested fixes were derived by replacing {src_mapped} in {cond} and its negation.)"
+    e.args = (msg,)
+
+
+def _suggest_fixes_for_data_dependent_error_non_strict(
+    e: GuardOnDataDependentSymNode,
+) -> None:
+    """
+    Given a raised data-dependent error, add the following to the error message:
+    1. the closest user code location that raised the error;
+    2. suggested fixes for the error in terms of live variables at that location.
+    """
+
+    # walk the stack up from the data-dependent error until a non-torch frame is found
+    frame = _find_user_code_frame()
+    if frame is not None:
+        # add frame info to error message
+        _blame_user_code(e, frame)
+
+        # map symbol names reachable via frame locals to their source-level names
+        src_map = defaultdict(list)
+        for var, val in frame.f_locals.items():
+            try:
+                tree_leaves_with_path = pytree.tree_leaves_with_path(val)
+            except ValueError:
+                log.warning(
+                    "pytree.tree_leaves_with_path failed for value of type {%s} in local variable {%s}",
+                    type(val),
+                    var,
+                )
+                continue
+            # figure out how to access any symbol inside `val` through `var`
+            for path, leaf in tree_leaves_with_path:
+                name = var + pytree.keystr(path)
+                if isinstance(leaf, torch.SymInt):
+                    src_map[str(leaf.node.expr)].append(name)
+                elif isinstance(leaf, torch.Tensor):
+                    for i, dim in enumerate(leaf.shape):
+                        if isinstance(dim, torch.SymInt):
+                            src_map[str(dim.node.expr)].append(f"{name}.shape[{i}]")
+
+        # add suggested torch.check()s based on `src_map` to the error message
+        # replacing unbacked symints in the unresolved condition in the error
+        _suggest_torch_checks(e, src_map)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7db0e29d1d4f75c770562c65013c03817643f6b7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/__init__.py
@@ -0,0 +1,4 @@
+# mypy: disable-error-code=attr-defined
+from .core import reify, unify  # noqa: F403
+from .more import unifiable  # noqa: F403
+from .variable import isvar, Var, var, variables, vars  # noqa: F403
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/core.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..e32f42c8968e8bc2efd7e4a8c711026ead7c569b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/core.py
@@ -0,0 +1,141 @@
+# mypy: allow-untyped-defs
+from collections.abc import Iterator  # type: ignore[import]
+from functools import partial
+
+from .dispatch import dispatch
+from .unification_tools import assoc  # type: ignore[import]
+from .utils import transitive_get as walk
+from .variable import isvar
+
+
+__all__ = ["reify", "unify"]
+
+###############
+# Reification #
+###############
+
+
+@dispatch(Iterator, dict)
+def _reify(t, s):
+    return map(partial(reify, s=s), t)
+    # return (reify(arg, s) for arg in t)
+
+
+_reify
+
+
+@dispatch(tuple, dict)  # type: ignore[no-redef]
+def _reify(t, s):
+    return tuple(reify(iter(t), s))
+
+
+_reify
+
+
+@dispatch(list, dict)  # type: ignore[no-redef]
+def _reify(t, s):
+    return list(reify(iter(t), s))
+
+
+_reify
+
+
+@dispatch(dict, dict)  # type: ignore[no-redef]
+def _reify(d, s):
+    return {k: reify(v, s) for k, v in d.items()}
+
+
+_reify
+
+
+@dispatch(object, dict)  # type: ignore[no-redef]
+def _reify(o, s):
+    return o  # catch all, just return the object
+
+
+def reify(e, s):
+    """Replace variables of expression with substitution
+    >>> # xdoctest: +SKIP
+    >>> x, y = var(), var()
+    >>> e = (1, x, (3, y))
+    >>> s = {x: 2, y: 4}
+    >>> reify(e, s)
+    (1, 2, (3, 4))
+    >>> e = {1: x, 3: (y, 5)}
+    >>> reify(e, s)
+    {1: 2, 3: (4, 5)}
+    """
+    if isvar(e):
+        return reify(s[e], s) if e in s else e
+    return _reify(e, s)
+
+
+###############
+# Unification #
+###############
+
+seq = tuple, list, Iterator
+
+
+@dispatch(seq, seq, dict)
+def _unify(u, v, s):
+    if len(u) != len(v):
+        return False
+    for uu, vv in zip(u, v):  # avoiding recursion
+        s = unify(uu, vv, s)
+        if s is False:
+            return False
+    return s
+
+
+#
+# @dispatch((set, frozenset), (set, frozenset), dict)
+# def _unify(u, v, s):
+#     i = u & v
+#     u = u - i
+#     v = v - i
+#     return _unify(sorted(u), sorted(v), s)
+#
+#
+# @dispatch(dict, dict, dict)
+# def _unify(u, v, s):
+#     if len(u) != len(v):
+#         return False
+#     for key, uval in iteritems(u):
+#         if key not in v:
+#             return False
+#         s = unify(uval, v[key], s)
+#         if s is False:
+#             return False
+#     return s
+#
+#
+# @dispatch(object, object, dict)
+# def _unify(u, v, s):
+#     return False  # catch all
+
+
+@dispatch(object, object, dict)
+def unify(u, v, s):  # no check at the moment
+    """Find substitution so that u == v while satisfying s
+    >>> x = var("x")
+    >>> unify((1, x), (1, 2), {})
+    {~x: 2}
+    """
+    u = walk(u, s)
+    v = walk(v, s)
+    if u == v:
+        return s
+    if isvar(u):
+        return assoc(s, u, v)
+    if isvar(v):
+        return assoc(s, v, u)
+    return _unify(u, v, s)
+
+
+unify
+
+
+@dispatch(object, object)  # type: ignore[no-redef]
+def unify(u, v):
+    return unify(u, v, {})
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/dispatch.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/dispatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..82d62e1f161971cc84c2cb85c138838ed488e639
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/dispatch.py
@@ -0,0 +1,8 @@
+from functools import partial
+
+from .multipledispatch import dispatch  # type: ignore[import]
+
+
+namespace = {}  # type: ignore[var-annotated]
+
+dispatch = partial(dispatch, namespace=namespace)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/match.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/match.py
new file mode 100644
index 0000000000000000000000000000000000000000..01861a086f64b6121aa9e174d16176533cd0e1a5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/match.py
@@ -0,0 +1,129 @@
+# mypy: allow-untyped-defs
+from .core import reify, unify  # type: ignore[attr-defined]
+from .unification_tools import first, groupby  # type: ignore[import]
+from .utils import _toposort, freeze
+from .variable import isvar
+
+
+class Dispatcher:
+    def __init__(self, name):
+        self.name = name
+        self.funcs = {}
+        self.ordering = []
+
+    def add(self, signature, func):
+        self.funcs[freeze(signature)] = func
+        self.ordering = ordering(self.funcs)
+
+    def __call__(self, *args, **kwargs):
+        func, _ = self.resolve(args)
+        return func(*args, **kwargs)
+
+    def resolve(self, args):
+        n = len(args)
+        for signature in self.ordering:
+            if len(signature) != n:
+                continue
+            s = unify(freeze(args), signature)
+            if s is not False:
+                result = self.funcs[signature]
+                return result, s
+        raise NotImplementedError(
+            "No match found. \nKnown matches: "
+            + str(self.ordering)
+            + "\nInput: "
+            + str(args)
+        )
+
+    def register(self, *signature):
+        def _(func):
+            self.add(signature, func)
+            return self
+
+        return _
+
+
+class VarDispatcher(Dispatcher):
+    """A dispatcher that calls functions with variable names
+    >>> # xdoctest: +SKIP
+    >>> d = VarDispatcher("d")
+    >>> x = var("x")
+    >>> @d.register("inc", x)
+    ... def f(x):
+    ...     return x + 1
+    >>> @d.register("double", x)
+    ... def f(x):
+    ...     return x * 2
+    >>> d("inc", 10)
+    11
+    >>> d("double", 10)
+    20
+    """
+
+    def __call__(self, *args, **kwargs):
+        func, s = self.resolve(args)
+        d = {k.token: v for k, v in s.items()}
+        return func(**d)
+
+
+global_namespace = {}  # type: ignore[var-annotated]
+
+
+def match(*signature, **kwargs):
+    namespace = kwargs.get("namespace", global_namespace)
+    dispatcher = kwargs.get("Dispatcher", Dispatcher)
+
+    def _(func):
+        name = func.__name__
+
+        if name not in namespace:
+            namespace[name] = dispatcher(name)
+        d = namespace[name]
+
+        d.add(signature, func)
+
+        return d
+
+    return _
+
+
+def supercedes(a, b):
+    """``a`` is a more specific match than ``b``"""
+    if isvar(b) and not isvar(a):
+        return True
+    s = unify(a, b)
+    if s is False:
+        return False
+    s = {k: v for k, v in s.items() if not isvar(k) or not isvar(v)}
+    if reify(a, s) == a:
+        return True
+    if reify(b, s) == b:
+        return False
+
+
+# Taken from multipledispatch
+def edge(a, b, tie_breaker=hash):
+    """A should be checked before B
+    Tie broken by tie_breaker, defaults to ``hash``
+    """
+    if supercedes(a, b):
+        if supercedes(b, a):
+            return tie_breaker(a) > tie_breaker(b)
+        else:
+            return True
+    return False
+
+
+# Taken from multipledispatch
+def ordering(signatures):
+    """A sane ordering of signatures to check, first to last
+    Topological sort of edges as given by ``edge`` and ``supercedes``
+    """
+    signatures = list(map(tuple, signatures))
+    edges = [(a, b) for a in signatures for b in signatures if edge(a, b)]
+    edges = groupby(first, edges)
+    for s in signatures:
+        if s not in edges:
+            edges[s] = []
+    edges = {k: [b for a, b in v] for k, v in edges.items()}  # type: ignore[attr-defined, assignment]
+    return _toposort(edges)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/more.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/more.py
new file mode 100644
index 0000000000000000000000000000000000000000..da2b1773f95ba096fa661cb958d849c3674c835f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/more.py
@@ -0,0 +1,122 @@
+# mypy: allow-untyped-defs
+from .core import reify, unify  # type: ignore[attr-defined]
+from .dispatch import dispatch
+
+
+def unifiable(cls):
+    """Register standard unify and reify operations on class
+    This uses the type and __dict__ or __slots__ attributes to define the
+    nature of the term
+    See Also:
+    >>> # xdoctest: +SKIP
+    >>> class A(object):
+    ...     def __init__(self, a, b):
+    ...         self.a = a
+    ...         self.b = b
+    >>> unifiable(A)
+    
+    >>> x = var("x")
+    >>> a = A(1, 2)
+    >>> b = A(1, x)
+    >>> unify(a, b, {})
+    {~x: 2}
+    """
+    _unify.add((cls, cls, dict), unify_object)
+    _reify.add((cls, dict), reify_object)
+
+    return cls
+
+
+#########
+# Reify #
+#########
+
+
+def reify_object(o, s):
+    """Reify a Python object with a substitution
+    >>> # xdoctest: +SKIP
+    >>> class Foo(object):
+    ...     def __init__(self, a, b):
+    ...         self.a = a
+    ...         self.b = b
+    ...
+    ...     def __str__(self):
+    ...         return "Foo(%s, %s)" % (str(self.a), str(self.b))
+    >>> x = var("x")
+    >>> f = Foo(1, x)
+    >>> print(f)
+    Foo(1, ~x)
+    >>> print(reify_object(f, {x: 2}))
+    Foo(1, 2)
+    """
+    if hasattr(o, "__slots__"):
+        return _reify_object_slots(o, s)
+    else:
+        return _reify_object_dict(o, s)
+
+
+def _reify_object_dict(o, s):
+    obj = object.__new__(type(o))
+    d = reify(o.__dict__, s)
+    if d == o.__dict__:
+        return o
+    obj.__dict__.update(d)
+    return obj
+
+
+def _reify_object_slots(o, s):
+    attrs = [getattr(o, attr) for attr in o.__slots__]
+    new_attrs = reify(attrs, s)
+    if attrs == new_attrs:
+        return o
+    else:
+        newobj = object.__new__(type(o))
+        for slot, attr in zip(o.__slots__, new_attrs):
+            setattr(newobj, slot, attr)
+        return newobj
+
+
+@dispatch(slice, dict)
+def _reify(o, s):
+    """Reify a Python ``slice`` object"""
+    return slice(*reify((o.start, o.stop, o.step), s))
+
+
+#########
+# Unify #
+#########
+
+
+def unify_object(u, v, s):
+    """Unify two Python objects
+    Unifies their type and ``__dict__`` attributes
+    >>> # xdoctest: +SKIP
+    >>> class Foo(object):
+    ...     def __init__(self, a, b):
+    ...         self.a = a
+    ...         self.b = b
+    ...
+    ...     def __str__(self):
+    ...         return "Foo(%s, %s)" % (str(self.a), str(self.b))
+    >>> x = var("x")
+    >>> f = Foo(1, x)
+    >>> g = Foo(1, 2)
+    >>> unify_object(f, g, {})
+    {~x: 2}
+    """
+    if type(u) != type(v):
+        return False
+    if hasattr(u, "__slots__"):
+        return unify(
+            [getattr(u, slot) for slot in u.__slots__],
+            [getattr(v, slot) for slot in v.__slots__],
+            s,
+        )
+    else:
+        return unify(u.__dict__, v.__dict__, s)
+
+
+@dispatch(slice, slice, dict)
+def _unify(u, v, s):
+    """Unify a Python ``slice`` object"""
+    return unify((u.start, u.stop, u.step), (v.start, v.stop, v.step), s)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb7304069243fb45604e165b06b377a5db233a7d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/__init__.py
@@ -0,0 +1,7 @@
+from .core import dispatch
+from .dispatcher import (
+    Dispatcher,
+    halt_ordering,
+    MDNotImplementedError,
+    restart_ordering,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/conflict.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/conflict.py
new file mode 100644
index 0000000000000000000000000000000000000000..44a893ad56a40b69e600dca737860fd3df69e4f4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/conflict.py
@@ -0,0 +1,139 @@
+# mypy: allow-untyped-defs
+import operator
+
+from .utils import _toposort, groupby
+from .variadic import isvariadic
+
+
+__all__ = [
+    "AmbiguityWarning",
+    "supercedes",
+    "consistent",
+    "ambiguous",
+    "ambiguities",
+    "super_signature",
+    "edge",
+    "ordering",
+]
+
+
+class AmbiguityWarning(Warning):
+    pass
+
+
+def supercedes(a, b):
+    """A is consistent and strictly more specific than B"""
+    if len(a) < len(b):
+        # only case is if a is empty and b is variadic
+        return not a and len(b) == 1 and isvariadic(b[-1])
+    elif len(a) == len(b):
+        return all(map(issubclass, a, b))
+    else:
+        # len(a) > len(b)
+        p1 = 0
+        p2 = 0
+        while p1 < len(a) and p2 < len(b):
+            cur_a = a[p1]
+            cur_b = b[p2]
+            if not (isvariadic(cur_a) or isvariadic(cur_b)):
+                if not issubclass(cur_a, cur_b):
+                    return False
+                p1 += 1
+                p2 += 1
+            elif isvariadic(cur_a):
+                assert p1 == len(a) - 1
+                return p2 == len(b) - 1 and issubclass(cur_a, cur_b)
+            elif isvariadic(cur_b):
+                assert p2 == len(b) - 1
+                if not issubclass(cur_a, cur_b):
+                    return False
+                p1 += 1
+        return p2 == len(b) - 1 and p1 == len(a)
+
+
+def consistent(a, b):
+    """It is possible for an argument list to satisfy both A and B"""
+
+    # Need to check for empty args
+    if not a:
+        return not b or isvariadic(b[0])
+    if not b:
+        return not a or isvariadic(a[0])
+
+    # Non-empty args check for mutual subclasses
+    if len(a) == len(b):
+        return all(issubclass(aa, bb) or issubclass(bb, aa) for aa, bb in zip(a, b))
+    else:
+        p1 = 0
+        p2 = 0
+        while p1 < len(a) and p2 < len(b):
+            cur_a = a[p1]
+            cur_b = b[p2]
+            if not issubclass(cur_b, cur_a) and not issubclass(cur_a, cur_b):
+                return False
+            if not (isvariadic(cur_a) or isvariadic(cur_b)):
+                p1 += 1
+                p2 += 1
+            elif isvariadic(cur_a):
+                p2 += 1
+            elif isvariadic(cur_b):
+                p1 += 1
+        # We only need to check for variadic ends
+        # Variadic types are guaranteed to be the last element
+        return (
+            isvariadic(cur_a)  # type: ignore[possibly-undefined]
+            and p2 == len(b)
+            or isvariadic(cur_b)  # type: ignore[possibly-undefined]
+            and p1 == len(a)
+        )
+
+
+def ambiguous(a, b):
+    """A is consistent with B but neither is strictly more specific"""
+    return consistent(a, b) and not (supercedes(a, b) or supercedes(b, a))
+
+
+def ambiguities(signatures):
+    """All signature pairs such that A is ambiguous with B"""
+    signatures = list(map(tuple, signatures))
+    return {
+        (a, b)
+        for a in signatures
+        for b in signatures
+        if hash(a) < hash(b)
+        and ambiguous(a, b)
+        and not any(supercedes(c, a) and supercedes(c, b) for c in signatures)
+    }
+
+
+def super_signature(signatures):
+    """A signature that would break ambiguities"""
+    n = len(signatures[0])
+    assert all(len(s) == n for s in signatures)
+
+    return [max((type.mro(sig[i]) for sig in signatures), key=len)[0] for i in range(n)]
+
+
+def edge(a, b, tie_breaker=hash):
+    """A should be checked before B
+    Tie broken by tie_breaker, defaults to ``hash``
+    """
+    # A either supercedes B and B does not supercede A or if B does then call
+    # tie_breaker
+    return supercedes(a, b) and (
+        not supercedes(b, a) or tie_breaker(a) > tie_breaker(b)
+    )
+
+
+def ordering(signatures):
+    """A sane ordering of signatures to check, first to last
+    Topological sort of edges as given by ``edge`` and ``supercedes``
+    """
+    signatures = list(map(tuple, signatures))
+    edges = [(a, b) for a in signatures for b in signatures if edge(a, b)]
+    edges = groupby(operator.itemgetter(0), edges)
+    for s in signatures:
+        if s not in edges:
+            edges[s] = []
+    edges = {k: [b for a, b in v] for k, v in edges.items()}  # type: ignore[assignment, attr-defined]
+    return _toposort(edges)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/core.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1f09dcf559c7022693bf89e1cd56d5fa01315eb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/core.py
@@ -0,0 +1,84 @@
+# mypy: allow-untyped-defs
+import inspect
+
+from .dispatcher import Dispatcher, MethodDispatcher
+
+
+global_namespace = {}  # type: ignore[var-annotated]
+
+__all__ = ["dispatch", "ismethod"]
+
+
+def dispatch(*types, **kwargs):
+    """Dispatch function on the types of the inputs
+    Supports dispatch on all non-keyword arguments.
+    Collects implementations based on the function name.  Ignores namespaces.
+    If ambiguous type signatures occur a warning is raised when the function is
+    defined suggesting the additional method to break the ambiguity.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> @dispatch(int)
+        ... def f(x):
+        ...     return x + 1
+        >>> @dispatch(float)
+        ... def f(x):
+        ...     return x - 1
+        >>> # xdoctest: +SKIP
+        >>> f(3)
+        4
+        >>> f(3.0)
+        2.0
+        >>> # Specify an isolated namespace with the namespace keyword argument
+        >>> my_namespace = {}
+        >>> @dispatch(int, namespace=my_namespace)
+        ... def foo(x):
+        ...     return x + 1
+        >>> # Dispatch on instance methods within classes
+        >>> class MyClass(object):
+        ...     @dispatch(list)
+        ...     def __init__(self, data):
+        ...         self.data = data
+        ...
+        ...     @dispatch(int)
+        ...     def __init__(self, datum):
+        ...         self.data = [datum]
+        >>> MyClass([1, 2, 3]).data
+        [1, 2, 3]
+        >>> MyClass(3).data
+        [3]
+    """
+    namespace = kwargs.get("namespace", global_namespace)
+
+    types = tuple(types)
+
+    def _df(func):
+        name = func.__name__
+
+        if ismethod(func):
+            dispatcher = inspect.currentframe().f_back.f_locals.get(  # type: ignore[union-attr]
+                name,  # type: ignore[union-attr]
+                MethodDispatcher(name),
+            )
+        else:
+            if name not in namespace:
+                namespace[name] = Dispatcher(name)
+            dispatcher = namespace[name]
+
+        dispatcher.add(types, func)
+        return dispatcher
+
+    return _df
+
+
+def ismethod(func):
+    """Is func a method?
+    Note that this has to work as the method is defined but before the class is
+    defined.  At this stage methods look like functions.
+    """
+    if hasattr(inspect, "signature"):
+        signature = inspect.signature(func)
+        return signature.parameters.get("self", None) is not None
+    else:
+        spec = inspect.getfullargspec(func)  # type: ignore[union-attr, assignment]
+        return spec and spec.args and spec.args[0] == "self"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f160995cce0a4a2324ec3d635a58fd97f99026e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py
@@ -0,0 +1,453 @@
+# mypy: allow-untyped-defs
+import inspect
+import itertools as itl
+from typing_extensions import deprecated
+from warnings import warn
+
+from .conflict import ambiguities, AmbiguityWarning, ordering, super_signature
+from .utils import expand_tuples
+from .variadic import isvariadic, Variadic
+
+
+__all__ = [
+    "MDNotImplementedError",
+    "ambiguity_warn",
+    "halt_ordering",
+    "restart_ordering",
+    "variadic_signature_matches_iter",
+    "variadic_signature_matches",
+    "Dispatcher",
+    "source",
+    "MethodDispatcher",
+    "str_signature",
+    "warning_text",
+]
+
+
+class MDNotImplementedError(NotImplementedError):
+    """A NotImplementedError for multiple dispatch"""
+
+
+def ambiguity_warn(dispatcher, ambiguities):
+    """Raise warning when ambiguity is detected
+    Parameters
+    ----------
+    dispatcher : Dispatcher
+        The dispatcher on which the ambiguity was detected
+    ambiguities : set
+        Set of type signature pairs that are ambiguous within this dispatcher
+    See Also:
+        Dispatcher.add
+        warning_text
+    """
+    warn(warning_text(dispatcher.name, ambiguities), AmbiguityWarning)
+
+
+@deprecated(
+    "`halt_ordering` is deprecated, you can safely remove this call.",
+    category=FutureWarning,
+)
+def halt_ordering():
+    """Deprecated interface to temporarily disable ordering."""
+
+
+@deprecated(
+    "`restart_ordering` is deprecated, if you would like to eagerly order the dispatchers, "
+    "you should call the `reorder()` method on each dispatcher.",
+    category=FutureWarning,
+)
+def restart_ordering(on_ambiguity=ambiguity_warn):
+    """Deprecated interface to temporarily resume ordering."""
+
+
+def variadic_signature_matches_iter(types, full_signature):
+    """Check if a set of input types matches a variadic signature.
+    Notes
+    -----
+    The algorithm is as follows:
+    Initialize the current signature to the first in the sequence
+    For each type in `types`:
+        If the current signature is variadic
+            If the type matches the signature
+                yield True
+            Else
+                Try to get the next signature
+                If no signatures are left we can't possibly have a match
+                    so yield False
+        Else
+            yield True if the type matches the current signature
+            Get the next signature
+    """
+    sigiter = iter(full_signature)
+    sig = next(sigiter)
+    for typ in types:
+        matches = issubclass(typ, sig)
+        yield matches
+        if not isvariadic(sig):
+            # we're not matching a variadic argument, so move to the next
+            # element in the signature
+            sig = next(sigiter)
+    else:
+        try:
+            sig = next(sigiter)
+        except StopIteration:
+            assert isvariadic(sig)
+            yield True
+        else:
+            # We have signature items left over, so all of our arguments
+            # haven't matched
+            yield False
+
+
+def variadic_signature_matches(types, full_signature):
+    # No arguments always matches a variadic signature
+    assert full_signature
+    return all(variadic_signature_matches_iter(types, full_signature))
+
+
+class Dispatcher:
+    """Dispatch methods based on type signature
+    Use ``dispatch`` to add implementations
+    Examples
+    --------
+    >>> # xdoctest: +SKIP("bad import name")
+    >>> from multipledispatch import dispatch
+    >>> @dispatch(int)
+    ... def f(x):
+    ...     return x + 1
+    >>> @dispatch(float)
+    ... def f(x):
+    ...     return x - 1
+    >>> f(3)
+    4
+    >>> f(3.0)
+    2.0
+    """
+
+    __slots__ = "__name__", "name", "funcs", "_ordering", "_cache", "doc"
+
+    def __init__(self, name, doc=None):
+        self.name = self.__name__ = name
+        self.funcs = {}
+        self.doc = doc
+
+        self._cache = {}
+
+    def register(self, *types, **kwargs):
+        """register dispatcher with new implementation
+        >>> # xdoctest: +SKIP
+        >>> f = Dispatcher("f")
+        >>> @f.register(int)
+        ... def inc(x):
+        ...     return x + 1
+        >>> @f.register(float)
+        ... def dec(x):
+        ...     return x - 1
+        >>> @f.register(list)
+        ... @f.register(tuple)
+        ... def reverse(x):
+        ...     return x[::-1]
+        >>> f(1)
+        2
+        >>> f(1.0)
+        0.0
+        >>> f([1, 2, 3])
+        [3, 2, 1]
+        """
+
+        def _df(func):
+            self.add(types, func, **kwargs)  # type: ignore[call-arg]
+            return func
+
+        return _df
+
+    @classmethod
+    def get_func_params(cls, func):
+        if hasattr(inspect, "signature"):
+            sig = inspect.signature(func)
+            return sig.parameters.values()
+
+    @classmethod
+    def get_func_annotations(cls, func):
+        """get annotations of function positional parameters"""
+        params = cls.get_func_params(func)
+        if params:
+            Parameter = inspect.Parameter
+
+            params = (
+                param
+                for param in params
+                if param.kind
+                in (Parameter.POSITIONAL_ONLY, Parameter.POSITIONAL_OR_KEYWORD)
+            )
+
+            annotations = tuple(param.annotation for param in params)
+
+            if all(ann is not Parameter.empty for ann in annotations):
+                return annotations
+
+    def add(self, signature, func):
+        """Add new types/method pair to dispatcher
+        >>> # xdoctest: +SKIP
+        >>> D = Dispatcher("add")
+        >>> D.add((int, int), lambda x, y: x + y)
+        >>> D.add((float, float), lambda x, y: x + y)
+        >>> D(1, 2)
+        3
+        >>> D(1, 2.0)
+        Traceback (most recent call last):
+        ...
+        NotImplementedError: Could not find signature for add: 
+        >>> # When ``add`` detects a warning it calls the ``on_ambiguity`` callback
+        >>> # with a dispatcher/itself, and a set of ambiguous type signature pairs
+        >>> # as inputs.  See ``ambiguity_warn`` for an example.
+        """
+        # Handle annotations
+        if not signature:
+            annotations = self.get_func_annotations(func)
+            if annotations:
+                signature = annotations
+
+        # Handle union types
+        if any(isinstance(typ, tuple) for typ in signature):
+            for typs in expand_tuples(signature):
+                self.add(typs, func)
+            return
+
+        new_signature = []
+
+        for index, typ in enumerate(signature, start=1):
+            if not isinstance(typ, (type, list)):
+                str_sig = ", ".join(
+                    c.__name__ if isinstance(c, type) else str(c) for c in signature
+                )
+                raise TypeError(
+                    f"Tried to dispatch on non-type: {typ}\n"
+                    f"In signature: <{str_sig}>\n"
+                    f"In function: {self.name}"
+                )
+
+            # handle variadic signatures
+            if isinstance(typ, list):
+                if index != len(signature):
+                    raise TypeError("Variadic signature must be the last element")
+
+                if len(typ) != 1:
+                    raise TypeError(
+                        "Variadic signature must contain exactly one element. "
+                        "To use a variadic union type place the desired types "
+                        "inside of a tuple, e.g., [(int, str)]"
+                    )
+                new_signature.append(Variadic[typ[0]])
+            else:
+                new_signature.append(typ)
+
+        self.funcs[tuple(new_signature)] = func
+        self._cache.clear()
+
+        try:
+            del self._ordering
+        except AttributeError:
+            pass
+
+    @property
+    def ordering(self):
+        try:
+            return self._ordering
+        except AttributeError:
+            return self.reorder()
+
+    def reorder(self, on_ambiguity=ambiguity_warn):
+        self._ordering = od = ordering(self.funcs)
+        amb = ambiguities(self.funcs)
+        if amb:
+            on_ambiguity(self, amb)
+        return od
+
+    def __call__(self, *args, **kwargs):
+        types = tuple([type(arg) for arg in args])
+        try:
+            func = self._cache[types]
+        except KeyError as e:
+            func = self.dispatch(*types)
+            if not func:
+                raise NotImplementedError(
+                    f"Could not find signature for {self.name}: <{str_signature(types)}>"
+                ) from e
+            self._cache[types] = func
+        try:
+            return func(*args, **kwargs)
+
+        except MDNotImplementedError as e:
+            funcs = self.dispatch_iter(*types)
+            next(funcs)  # burn first
+            for func in funcs:
+                try:
+                    return func(*args, **kwargs)
+                except MDNotImplementedError:
+                    pass
+
+            raise NotImplementedError(
+                "Matching functions for "
+                f"{self.name}: <{str_signature(types)}> found, but none completed successfully",
+            ) from e
+
+    def __str__(self):
+        return f""
+
+    __repr__ = __str__
+
+    def dispatch(self, *types):
+        """Determine appropriate implementation for this type signature
+        This method is internal.  Users should call this object as a function.
+        Implementation resolution occurs within the ``__call__`` method.
+        >>> # xdoctest: +SKIP
+        >>> from multipledispatch import dispatch
+        >>> @dispatch(int)
+        ... def inc(x):
+        ...     return x + 1
+        >>> implementation = inc.dispatch(int)
+        >>> implementation(3)
+        4
+        >>> print(inc.dispatch(float))
+        None
+        See Also:
+          ``multipledispatch.conflict`` - module to determine resolution order
+        """
+
+        if types in self.funcs:
+            return self.funcs[types]
+
+        try:
+            return next(self.dispatch_iter(*types))
+        except StopIteration:
+            return None
+
+    def dispatch_iter(self, *types):
+        n = len(types)
+        for signature in self.ordering:
+            if len(signature) == n and all(map(issubclass, types, signature)):
+                result = self.funcs[signature]
+                yield result
+            elif len(signature) and isvariadic(signature[-1]):
+                if variadic_signature_matches(types, signature):
+                    result = self.funcs[signature]
+                    yield result
+
+    @deprecated(
+        "`resolve()` is deprecated, use `dispatch(*types)`", category=FutureWarning
+    )
+    def resolve(self, types):
+        """Determine appropriate implementation for this type signature
+        .. deprecated:: 0.4.4
+            Use ``dispatch(*types)`` instead
+        """
+        return self.dispatch(*types)
+
+    def __getstate__(self):
+        return {"name": self.name, "funcs": self.funcs}
+
+    def __setstate__(self, d):
+        self.name = d["name"]
+        self.funcs = d["funcs"]
+        self._ordering = ordering(self.funcs)
+        self._cache = {}
+
+    @property
+    def __doc__(self):
+        docs = [f"Multiply dispatched method: {self.name}"]
+
+        if self.doc:
+            docs.append(self.doc)
+
+        other = []
+        for sig in self.ordering[::-1]:
+            func = self.funcs[sig]
+            if func.__doc__:
+                s = f"Inputs: <{str_signature(sig)}>\n"
+                s += "-" * len(s) + "\n"
+                s += func.__doc__.strip()
+                docs.append(s)
+            else:
+                other.append(str_signature(sig))
+
+        if other:
+            docs.append("Other signatures:\n    " + "\n    ".join(other))
+
+        return "\n\n".join(docs)
+
+    def _help(self, *args):
+        return self.dispatch(*map(type, args)).__doc__
+
+    def help(self, *args, **kwargs):
+        """Print docstring for the function corresponding to inputs"""
+        print(self._help(*args))
+
+    def _source(self, *args):
+        func = self.dispatch(*map(type, args))
+        if not func:
+            raise TypeError("No function found")
+        return source(func)
+
+    def source(self, *args, **kwargs):
+        """Print source code for the function corresponding to inputs"""
+        print(self._source(*args))
+
+
+def source(func):
+    s = f"File: {inspect.getsourcefile(func)}\n\n"
+    s = s + inspect.getsource(func)
+    return s
+
+
+class MethodDispatcher(Dispatcher):
+    """Dispatch methods based on type signature
+    See Also:
+        Dispatcher
+    """
+
+    __slots__ = ("obj", "cls")
+
+    @classmethod
+    def get_func_params(cls, func):
+        if hasattr(inspect, "signature"):
+            sig = inspect.signature(func)
+            return itl.islice(sig.parameters.values(), 1, None)
+
+    def __get__(self, instance, owner):
+        self.obj = instance
+        self.cls = owner
+        return self
+
+    def __call__(self, *args, **kwargs):
+        types = tuple([type(arg) for arg in args])
+        func = self.dispatch(*types)
+        if not func:
+            raise NotImplementedError(
+                f"Could not find signature for {self.name}: <{str_signature(types)}>"
+            )
+        return func(self.obj, *args, **kwargs)
+
+
+def str_signature(sig):
+    """String representation of type signature
+    >>> str_signature((int, float))
+    'int, float'
+    """
+    return ", ".join(cls.__name__ for cls in sig)
+
+
+def warning_text(name, amb):
+    """The text for ambiguity warnings"""
+    text = f"\nAmbiguities exist in dispatched function {name}\n\n"
+    text += "The following signatures may result in ambiguous behavior:\n"
+    for pair in amb:
+        text += "\t" + ", ".join("[" + str_signature(s) + "]" for s in pair) + "\n"
+    text += "\n\nConsider making the following additions:\n\n"
+    text += "\n\n".join(
+        [
+            "@dispatch(" + str_signature(super_signature(s)) + f")\ndef {name}(...)"
+            for s in amb
+        ]
+    )
+    return text
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c91cca2067afcd406aa35c51363417ca4ada2e0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py
@@ -0,0 +1,127 @@
+# mypy: allow-untyped-defs
+from collections import OrderedDict
+
+
+__all__ = ["raises", "expand_tuples", "reverse_dict", "groupby", "typename"]
+
+
+def raises(err, lamda):
+    try:
+        lamda()
+        return False
+    except err:
+        return True
+
+
+def expand_tuples(L):
+    """
+    >>> expand_tuples([1, (2, 3)])
+    [(1, 2), (1, 3)]
+    >>> expand_tuples([1, 2])
+    [(1, 2)]
+    """
+    if not L:
+        return [()]
+    elif not isinstance(L[0], tuple):
+        rest = expand_tuples(L[1:])
+        return [(L[0],) + t for t in rest]
+    else:
+        rest = expand_tuples(L[1:])
+        return [(item,) + t for t in rest for item in L[0]]
+
+
+# Taken from theano/theano/gof/sched.py
+# Avoids licensing issues because this was written by Matthew Rocklin
+def _toposort(edges):
+    """Topological sort algorithm by Kahn [1] - O(nodes + vertices)
+    inputs:
+        edges - a dict of the form {a: {b, c}} where b and c depend on a
+    outputs:
+        L - an ordered list of nodes that satisfy the dependencies of edges
+    >>> _toposort({1: (2, 3), 2: (3,)})
+    [1, 2, 3]
+    >>> # Closely follows the wikipedia page [2]
+    >>> # [1] Kahn, Arthur B. (1962), "Topological sorting of large networks",
+    >>> # Communications of the ACM
+    >>> # [2] http://en.wikipedia.org/wiki/Toposort#Algorithms
+    """
+    incoming_edges = reverse_dict(edges)
+    incoming_edges = OrderedDict((k, set(val)) for k, val in incoming_edges.items())
+    S = OrderedDict.fromkeys(v for v in edges if v not in incoming_edges)
+    L = []
+
+    while S:
+        n, _ = S.popitem()
+        L.append(n)
+        for m in edges.get(n, ()):
+            assert n in incoming_edges[m]
+            incoming_edges[m].remove(n)
+            if not incoming_edges[m]:
+                S[m] = None
+    if any(incoming_edges.get(v, None) for v in edges):
+        raise ValueError("Input has cycles")
+    return L
+
+
+def reverse_dict(d):
+    """Reverses direction of dependence dict
+    >>> d = {"a": (1, 2), "b": (2, 3), "c": ()}
+    >>> reverse_dict(d)  # doctest: +SKIP
+    {1: ('a',), 2: ('a', 'b'), 3: ('b',)}
+    :note: dict order are not deterministic. As we iterate on the
+        input dict, it make the output of this function depend on the
+        dict order. So this function output order should be considered
+        as undeterministic.
+    """
+    result = OrderedDict()  # type: ignore[var-annotated]
+    for key in d:
+        for val in d[key]:
+            result[val] = result.get(val, ()) + (key,)
+    return result
+
+
+# Taken from toolz
+# Avoids licensing issues because this version was authored by Matthew Rocklin
+def groupby(func, seq):
+    """Group a collection by a key function
+    >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"]
+    >>> groupby(len, names)  # doctest: +SKIP
+    {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
+    >>> iseven = lambda x: x % 2 == 0
+    >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
+    {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
+    See Also:
+        ``countby``
+    """
+
+    d = OrderedDict()  # type: ignore[var-annotated]
+    for item in seq:
+        key = func(item)
+        if key not in d:
+            d[key] = []
+        d[key].append(item)
+    return d
+
+
+def typename(type):
+    """Get the name of `type`.
+    Parameters
+    ----------
+    type : Union[Type, Tuple[Type]]
+    Returns
+    -------
+    str
+        The name of `type` or a tuple of the names of the types in `type`.
+    Examples
+    --------
+    >>> typename(int)
+    'int'
+    >>> typename((int, float))
+    '(int, float)'
+    """
+    try:
+        return type.__name__
+    except AttributeError:
+        if len(type) == 1:
+            return typename(*type)
+        return f"({', '.join(map(typename, type))})"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b5604a152480f83916108cb1b02de3bc9b9adb5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py
@@ -0,0 +1,96 @@
+# mypy: allow-untyped-defs
+from .utils import typename
+
+
+__all__ = ["VariadicSignatureType", "isvariadic", "VariadicSignatureMeta", "Variadic"]
+
+
+class VariadicSignatureType(type):
+    # checking if subclass is a subclass of self
+    def __subclasscheck__(cls, subclass):
+        other_type = subclass.variadic_type if isvariadic(subclass) else (subclass,)
+        return subclass is cls or all(
+            issubclass(other, cls.variadic_type)  # type: ignore[attr-defined]
+            for other in other_type
+        )
+
+    def __eq__(cls, other):
+        """
+        Return True if other has the same variadic type
+        Parameters
+        ----------
+        other : object (type)
+            The object (type) to check
+        Returns
+        -------
+        bool
+            Whether or not `other` is equal to `self`
+        """
+        return isvariadic(other) and set(cls.variadic_type) == set(other.variadic_type)  # type: ignore[attr-defined]
+
+    def __hash__(cls):
+        return hash((type(cls), frozenset(cls.variadic_type)))  # type: ignore[attr-defined]
+
+
+def isvariadic(obj):
+    """Check whether the type `obj` is variadic.
+    Parameters
+    ----------
+    obj : type
+        The type to check
+    Returns
+    -------
+    bool
+        Whether or not `obj` is variadic
+    Examples
+    --------
+    >>> # xdoctest: +SKIP
+    >>> isvariadic(int)
+    False
+    >>> isvariadic(Variadic[int])
+    True
+    """
+    return isinstance(obj, VariadicSignatureType)
+
+
+class VariadicSignatureMeta(type):
+    """A metaclass that overrides ``__getitem__`` on the class. This is used to
+    generate a new type for Variadic signatures. See the Variadic class for
+    examples of how this behaves.
+    """
+
+    def __getitem__(cls, variadic_type):
+        if not (isinstance(variadic_type, (type, tuple)) or type(variadic_type)):
+            raise ValueError(
+                "Variadic types must be type or tuple of types"
+                " (Variadic[int] or Variadic[(int, float)]"
+            )
+
+        if not isinstance(variadic_type, tuple):
+            variadic_type = (variadic_type,)
+        return VariadicSignatureType(
+            f"Variadic[{typename(variadic_type)}]",
+            (),
+            dict(variadic_type=variadic_type, __slots__=()),
+        )
+
+
+class Variadic(metaclass=VariadicSignatureMeta):
+    """A class whose getitem method can be used to generate a new type
+    representing a specific variadic signature.
+    Examples
+    --------
+    >>> # xdoctest: +SKIP
+    >>> Variadic[int]  # any number of int arguments
+    
+    >>> Variadic[(int, str)]  # any number of one of int or str arguments
+    
+    >>> issubclass(int, Variadic[int])
+    True
+    >>> issubclass(int, Variadic[(int, str)])
+    True
+    >>> issubclass(str, Variadic[(int, str)])
+    True
+    >>> issubclass(float, Variadic[(int, str)])
+    False
+    """
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..a47d900273f5ea4d1fcbeae1be35f8685f5b0a32
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py
@@ -0,0 +1,419 @@
+# mypy: allow-untyped-defs
+import collections
+import operator
+from collections.abc import Mapping
+from functools import reduce
+
+
+__all__ = [
+    "merge",
+    "merge_with",
+    "valmap",
+    "keymap",
+    "itemmap",
+    "valfilter",
+    "keyfilter",
+    "itemfilter",
+    "assoc",
+    "dissoc",
+    "assoc_in",
+    "update_in",
+    "get_in",
+]
+
+
+def _get_factory(f, kwargs):
+    factory = kwargs.pop("factory", dict)
+    if kwargs:
+        raise TypeError(
+            f"{f.__name__}() got an unexpected keyword argument '{kwargs.popitem()[0]}'"
+        )
+    return factory
+
+
+def merge(*dicts, **kwargs):
+    """Merge a collection of dictionaries
+
+    >>> merge({1: "one"}, {2: "two"})
+    {1: 'one', 2: 'two'}
+
+    Later dictionaries have precedence
+
+    >>> merge({1: 2, 3: 4}, {3: 3, 4: 4})
+    {1: 2, 3: 3, 4: 4}
+
+    See Also:
+        merge_with
+    """
+    if len(dicts) == 1 and not isinstance(dicts[0], Mapping):
+        dicts = dicts[0]
+    factory = _get_factory(merge, kwargs)
+
+    rv = factory()
+    for d in dicts:
+        rv.update(d)
+    return rv
+
+
+def merge_with(func, *dicts, **kwargs):
+    """Merge dictionaries and apply function to combined values
+
+    A key may occur in more than one dict, and all values mapped from the key
+    will be passed to the function as a list, such as func([val1, val2, ...]).
+
+    >>> merge_with(sum, {1: 1, 2: 2}, {1: 10, 2: 20})
+    {1: 11, 2: 22}
+
+    >>> merge_with(first, {1: 1, 2: 2}, {2: 20, 3: 30})  # doctest: +SKIP
+    {1: 1, 2: 2, 3: 30}
+
+    See Also:
+        merge
+    """
+    if len(dicts) == 1 and not isinstance(dicts[0], Mapping):
+        dicts = dicts[0]
+    factory = _get_factory(merge_with, kwargs)
+
+    result = factory()
+    for d in dicts:
+        for k, v in d.items():
+            if k not in result:
+                result[k] = [v]
+            else:
+                result[k].append(v)
+    return valmap(func, result, factory)
+
+
+def valmap(func, d, factory=dict):
+    """Apply function to values of dictionary
+
+    >>> bills = {"Alice": [20, 15, 30], "Bob": [10, 35]}
+    >>> valmap(sum, bills)  # doctest: +SKIP
+    {'Alice': 65, 'Bob': 45}
+
+    See Also:
+        keymap
+        itemmap
+    """
+    rv = factory()
+    rv.update(zip(d.keys(), map(func, d.values())))
+    return rv
+
+
+def keymap(func, d, factory=dict):
+    """Apply function to keys of dictionary
+
+    >>> bills = {"Alice": [20, 15, 30], "Bob": [10, 35]}
+    >>> keymap(str.lower, bills)  # doctest: +SKIP
+    {'alice': [20, 15, 30], 'bob': [10, 35]}
+
+    See Also:
+        valmap
+        itemmap
+    """
+    rv = factory()
+    rv.update(zip(map(func, d.keys()), d.values()))
+    return rv
+
+
+def itemmap(func, d, factory=dict):
+    """Apply function to items of dictionary
+
+    >>> accountids = {"Alice": 10, "Bob": 20}
+    >>> itemmap(reversed, accountids)  # doctest: +SKIP
+    {10: "Alice", 20: "Bob"}
+
+    See Also:
+        keymap
+        valmap
+    """
+    rv = factory()
+    rv.update(map(func, d.items()))
+    return rv
+
+
+def valfilter(predicate, d, factory=dict):
+    """Filter items in dictionary by value
+
+    >>> iseven = lambda x: x % 2 == 0
+    >>> d = {1: 2, 2: 3, 3: 4, 4: 5}
+    >>> valfilter(iseven, d)
+    {1: 2, 3: 4}
+
+    See Also:
+        keyfilter
+        itemfilter
+        valmap
+    """
+    rv = factory()
+    for k, v in d.items():
+        if predicate(v):
+            rv[k] = v
+    return rv
+
+
+def keyfilter(predicate, d, factory=dict):
+    """Filter items in dictionary by key
+
+    >>> iseven = lambda x: x % 2 == 0
+    >>> d = {1: 2, 2: 3, 3: 4, 4: 5}
+    >>> keyfilter(iseven, d)
+    {2: 3, 4: 5}
+
+    See Also:
+        valfilter
+        itemfilter
+        keymap
+    """
+    rv = factory()
+    for k, v in d.items():
+        if predicate(k):
+            rv[k] = v
+    return rv
+
+
+def itemfilter(predicate, d, factory=dict):
+    """Filter items in dictionary by item
+
+    >>> def isvalid(item):
+    ...     k, v = item
+    ...     return k % 2 == 0 and v < 4
+
+    >>> d = {1: 2, 2: 3, 3: 4, 4: 5}
+    >>> itemfilter(isvalid, d)
+    {2: 3}
+
+    See Also:
+        keyfilter
+        valfilter
+        itemmap
+    """
+    rv = factory()
+    for item in d.items():
+        if predicate(item):
+            k, v = item
+            rv[k] = v
+    return rv
+
+
+def assoc(d, key, value, factory=dict):
+    """Return a new dict with new key value pair
+
+    New dict has d[key] set to value. Does not modify the initial dictionary.
+
+    >>> assoc({"x": 1}, "x", 2)
+    {'x': 2}
+    >>> assoc({"x": 1}, "y", 3)  # doctest: +SKIP
+    {'x': 1, 'y': 3}
+    """
+    d2 = factory()
+    d2.update(d)
+    d2[key] = value
+    return d2
+
+
+def dissoc(d, *keys, **kwargs):
+    """Return a new dict with the given key(s) removed.
+
+    New dict has d[key] deleted for each supplied key.
+    Does not modify the initial dictionary.
+
+    >>> dissoc({"x": 1, "y": 2}, "y")
+    {'x': 1}
+    >>> dissoc({"x": 1, "y": 2}, "y", "x")
+    {}
+    >>> dissoc({"x": 1}, "y")  # Ignores missing keys
+    {'x': 1}
+    """
+    factory = _get_factory(dissoc, kwargs)
+    d2 = factory()
+
+    if len(keys) < len(d) * 0.6:
+        d2.update(d)
+        for key in keys:
+            if key in d2:
+                del d2[key]
+    else:
+        remaining = set(d)
+        remaining.difference_update(keys)
+        for k in remaining:
+            d2[k] = d[k]
+    return d2
+
+
+def assoc_in(d, keys, value, factory=dict):
+    """Return a new dict with new, potentially nested, key value pair
+
+    >>> purchase = {
+    ...     "name": "Alice",
+    ...     "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
+    ...     "credit card": "5555-1234-1234-1234",
+    ... }
+    >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00])  # doctest: +SKIP
+    {'credit card': '5555-1234-1234-1234',
+     'name': 'Alice',
+     'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}}
+    """
+    return update_in(d, keys, lambda x: value, value, factory)
+
+
+def update_in(d, keys, func, default=None, factory=dict):
+    """Update value in a (potentially) nested dictionary
+
+    inputs:
+    d - dictionary on which to operate
+    keys - list or tuple giving the location of the value to be changed in d
+    func - function to operate on that value
+
+    If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the
+    original dictionary with v replaced by func(v), but does not mutate the
+    original dictionary.
+
+    If k0 is not a key in d, update_in creates nested dictionaries to the depth
+    specified by the keys, with the innermost value set to func(default).
+
+    >>> inc = lambda x: x + 1
+    >>> update_in({"a": 0}, ["a"], inc)
+    {'a': 1}
+
+    >>> transaction = {
+    ...     "name": "Alice",
+    ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
+    ...     "credit card": "5555-1234-1234-1234",
+    ... }
+    >>> update_in(transaction, ["purchase", "costs"], sum)  # doctest: +SKIP
+    {'credit card': '5555-1234-1234-1234',
+     'name': 'Alice',
+     'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}}
+
+    >>> # updating a value when k0 is not in d
+    >>> update_in({}, [1, 2, 3], str, default="bar")
+    {1: {2: {3: 'bar'}}}
+    >>> update_in({1: "foo"}, [2, 3, 4], inc, 0)
+    {1: 'foo', 2: {3: {4: 1}}}
+    """
+    ks = iter(keys)
+    k = next(ks)
+
+    rv = inner = factory()
+    rv.update(d)
+
+    for key in ks:
+        if k in d:
+            d = d[k]
+            dtemp = factory()
+            dtemp.update(d)
+        else:
+            d = dtemp = factory()
+
+        inner[k] = inner = dtemp
+        k = key
+
+    if k in d:
+        inner[k] = func(d[k])
+    else:
+        inner[k] = func(default)
+    return rv
+
+
+def get_in(keys, coll, default=None, no_default=False):
+    """Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.
+
+    If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
+    ``no_default`` is specified, then it raises KeyError or IndexError.
+
+    ``get_in`` is a generalization of ``operator.getitem`` for nested data
+    structures such as dictionaries and lists.
+
+    >>> transaction = {
+    ...     "name": "Alice",
+    ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
+    ...     "credit card": "5555-1234-1234-1234",
+    ... }
+    >>> get_in(["purchase", "items", 0], transaction)
+    'Apple'
+    >>> get_in(["name"], transaction)
+    'Alice'
+    >>> get_in(["purchase", "total"], transaction)
+    >>> get_in(["purchase", "items", "apple"], transaction)
+    >>> get_in(["purchase", "items", 10], transaction)
+    >>> get_in(["purchase", "total"], transaction, 0)
+    0
+    >>> get_in(["y"], {}, no_default=True)
+    Traceback (most recent call last):
+        ...
+    KeyError: 'y'
+
+    See Also:
+        itertoolz.get
+        operator.getitem
+    """
+    try:
+        return reduce(operator.getitem, keys, coll)
+    except (KeyError, IndexError, TypeError):
+        if no_default:
+            raise
+        return default
+
+
+def getter(index):
+    if isinstance(index, list):
+        if len(index) == 1:
+            index = index[0]
+            return lambda x: (x[index],)
+        elif index:
+            return operator.itemgetter(*index)
+        else:
+            return lambda x: ()
+    else:
+        return operator.itemgetter(index)
+
+
+def groupby(key, seq):
+    """Group a collection by a key function
+
+    >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"]
+    >>> groupby(len, names)  # doctest: +SKIP
+    {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
+
+    >>> iseven = lambda x: x % 2 == 0
+    >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
+    {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
+
+    Non-callable keys imply grouping on a member.
+
+    >>> groupby(
+    ...     "gender",
+    ...     [
+    ...         {"name": "Alice", "gender": "F"},
+    ...         {"name": "Bob", "gender": "M"},
+    ...         {"name": "Charlie", "gender": "M"},
+    ...     ],
+    ... )  # doctest:+SKIP
+    {'F': [{'gender': 'F', 'name': 'Alice'}],
+     'M': [{'gender': 'M', 'name': 'Bob'},
+           {'gender': 'M', 'name': 'Charlie'}]}
+
+    Not to be confused with ``itertools.groupby``
+
+    See Also:
+        countby
+    """
+    if not callable(key):
+        key = getter(key)
+    d = collections.defaultdict(lambda: [].append)  # type: ignore[var-annotated]
+    for item in seq:
+        d[key(item)](item)
+    rv = {}
+    for k, v in d.items():
+        rv[k] = v.__self__  # type: ignore[var-annotated, attr-defined]
+    return rv
+
+
+def first(seq):
+    """The first element in a sequence
+
+    >>> first("ABC")
+    'A'
+    """
+    return next(iter(seq))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7634c9b2ec90b870143954f741c8eb3be01d8d6b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py
@@ -0,0 +1,108 @@
+# mypy: allow-untyped-defs
+__all__ = ["hashable", "transitive_get", "raises", "reverse_dict", "xfail", "freeze"]
+
+
+def hashable(x):
+    try:
+        hash(x)
+        return True
+    except TypeError:
+        return False
+
+
+def transitive_get(key, d):
+    """Transitive dict.get
+    >>> d = {1: 2, 2: 3, 3: 4}
+    >>> d.get(1)
+    2
+    >>> transitive_get(1, d)
+    4
+    """
+    while hashable(key) and key in d:
+        key = d[key]
+    return key
+
+
+def raises(err, lamda):
+    try:
+        lamda()
+        return False
+    except err:
+        return True
+
+
+# Taken from theano/theano/gof/sched.py
+# Avoids licensing issues because this was written by Matthew Rocklin
+def _toposort(edges):
+    """Topological sort algorithm by Kahn [1] - O(nodes + vertices)
+    inputs:
+        edges - a dict of the form {a: {b, c}} where b and c depend on a
+    outputs:
+        L - an ordered list of nodes that satisfy the dependencies of edges
+    >>> # xdoctest: +SKIP
+    >>> _toposort({1: (2, 3), 2: (3,)})
+    [1, 2, 3]
+    Closely follows the wikipedia page [2]
+    [1] Kahn, Arthur B. (1962), "Topological sorting of large networks",
+    Communications of the ACM
+    [2] http://en.wikipedia.org/wiki/Toposort#Algorithms
+    """
+    incoming_edges = reverse_dict(edges)
+    incoming_edges = {k: set(val) for k, val in incoming_edges.items()}
+    S = {v for v in edges if v not in incoming_edges}
+    L = []
+
+    while S:
+        n = S.pop()
+        L.append(n)
+        for m in edges.get(n, ()):
+            assert n in incoming_edges[m]
+            incoming_edges[m].remove(n)
+            if not incoming_edges[m]:
+                S.add(m)
+    if any(incoming_edges.get(v, None) for v in edges):
+        raise ValueError("Input has cycles")
+    return L
+
+
+def reverse_dict(d):
+    """Reverses direction of dependence dict
+    >>> d = {"a": (1, 2), "b": (2, 3), "c": ()}
+    >>> reverse_dict(d)  # doctest: +SKIP
+    {1: ('a',), 2: ('a', 'b'), 3: ('b',)}
+    :note: dict order are not deterministic. As we iterate on the
+        input dict, it make the output of this function depend on the
+        dict order. So this function output order should be considered
+        as undeterministic.
+    """
+    result = {}  # type: ignore[var-annotated]
+    for key in d:
+        for val in d[key]:
+            result[val] = result.get(val, ()) + (key,)
+    return result
+
+
+def xfail(func):
+    try:
+        func()
+        raise Exception("XFailed test passed")  # pragma:nocover  # noqa: TRY002
+    except Exception:
+        pass
+
+
+def freeze(d):
+    """Freeze container to hashable form
+    >>> freeze(1)
+    1
+    >>> freeze([1, 2])
+    (1, 2)
+    >>> freeze({1: 2})  # doctest: +SKIP
+    frozenset([(1, 2)])
+    """
+    if isinstance(d, dict):
+        return frozenset(map(freeze, d.items()))
+    if isinstance(d, set):
+        return frozenset(map(freeze, d))
+    if isinstance(d, (tuple, list)):
+        return tuple(map(freeze, d))
+    return d
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/variable.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/variable.py
new file mode 100644
index 0000000000000000000000000000000000000000..46e59851fdfa8389e29288176a50dc62fb568654
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unification/variable.py
@@ -0,0 +1,90 @@
+# mypy: allow-untyped-defs
+from contextlib import contextmanager
+
+from .dispatch import dispatch
+from .utils import hashable
+
+
+_global_logic_variables = set()  # type: ignore[var-annotated]
+_glv = _global_logic_variables
+
+
+class Var:
+    """Logic Variable"""
+
+    _id = 1
+
+    def __new__(cls, *token):
+        if len(token) == 0:
+            token = f"_{Var._id}"  # type: ignore[assignment]
+            Var._id += 1
+        elif len(token) == 1:
+            token = token[0]
+
+        obj = object.__new__(cls)
+        obj.token = token  # type: ignore[attr-defined]
+        return obj
+
+    def __str__(self):
+        return "~" + str(self.token)  # type: ignore[attr-defined]
+
+    __repr__ = __str__
+
+    def __eq__(self, other):
+        return type(self) == type(other) and self.token == other.token  # type: ignore[attr-defined]
+
+    def __hash__(self):
+        return hash((type(self), self.token))  # type: ignore[attr-defined]
+
+
+def var():
+    return lambda *args: Var(*args)
+
+
+def vars():
+    return lambda n: [var() for i in range(n)]
+
+
+@dispatch(Var)
+def isvar(v):
+    return True
+
+
+isvar
+
+
+@dispatch(object)  # type: ignore[no-redef]
+def isvar(o):
+    return not not _glv and hashable(o) and o in _glv
+
+
+@contextmanager
+def variables(*variables):
+    """
+    Context manager for logic variables
+
+    Example:
+        >>> # xdoctest: +SKIP("undefined vars")
+        >>> from __future__ import with_statement
+        >>> with variables(1):
+        ...     print(isvar(1))
+        True
+        >>> print(isvar(1))
+        False
+        >>> # Normal approach
+        >>> from unification import unify
+        >>> x = var("x")
+        >>> unify(x, 1)
+        {~x: 1}
+        >>> # Context Manager approach
+        >>> with variables("x"):
+        ...     print(unify("x", 1))
+        {'x': 1}
+    """
+    old_global_logic_variables = _global_logic_variables.copy()
+    _global_logic_variables.update(set(variables))
+    try:
+        yield
+    finally:
+        _global_logic_variables.clear()
+        _global_logic_variables.update(old_global_logic_variables)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unify_refinements.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unify_refinements.py
new file mode 100644
index 0000000000000000000000000000000000000000..bab662e0655a2c7c4049ff9b8ae50341567c1259
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/unify_refinements.py
@@ -0,0 +1,124 @@
+# mypy: allow-untyped-defs
+from torch.fx.experimental.graph_gradual_typechecker import Refine
+from torch.fx.experimental.unification import unify, Var  # type: ignore[attr-defined]
+from torch.fx.tensor_type import TensorType
+
+
+def infer_symbolic_types_single_pass(traced):
+    """
+    Calls our symbolic inferencer once.
+    """
+    r = Refine(traced)
+    r.refine()
+    mgu = unify_eq(r.constraints)
+    substitute_all_types(traced.graph, mgu)
+
+
+def infer_symbolic_types(traced):
+    """
+    Calls our symbolic inferencer twice.
+    This is useful when one pass is not enough
+    to infer all the information such as the case
+    for braodcasting.
+    """
+    r = Refine(traced)
+    r.refine()
+    mgu = unify_eq(r.constraints)
+    substitute_all_types(traced.graph, mgu)
+
+    r = Refine(traced)
+    r.refine()
+    mgu = unify_eq(r.constraints)
+    substitute_all_types(traced.graph, mgu)
+
+    r.symbolic_relations()
+
+
+def convert_eq(list_of_eq):
+    """
+    Convert equality constraints in the right format
+    to be used by unification library.
+    """
+    lhs = []
+    rhs = []
+    for eq in list_of_eq:
+        lhs.append(eq.lhs)
+        rhs.append(eq.rhs)
+    return tuple(lhs), tuple(rhs)
+
+
+def unify_eq(list_of_eq):
+    """
+    Apply unification to a set of
+    equality constraints
+    """
+    lhs, rhs = convert_eq(list_of_eq)
+    return unify(lhs, rhs)
+
+
+def substitute_solution_one_type(mapping, t):
+    """
+    Apply the most general unifier to a type
+    """
+    if isinstance(t, Var):
+        if t in mapping.keys():
+            return mapping[t]
+        else:
+            return t
+
+    elif isinstance(t, TensorType):
+        new_type = []
+        for typ in t.__args__:
+            if typ in mapping.keys():
+                new_type.append(mapping[typ])
+            else:
+                new_type.append(typ)
+        return TensorType(tuple(new_type))
+
+    elif isinstance(t, list):
+        new_type = []
+        for typ in t:
+            new_type.append(substitute_solution_one_type(mapping, typ))
+        return new_type
+
+    elif isinstance(t, tuple):
+        new_type = []
+        for typ in t:
+            new_type.append(substitute_solution_one_type(mapping, typ))
+        return tuple(new_type)
+
+    else:
+        return t
+
+
+def substitute_all_types(graph, mapping):
+    """
+    Apply the most general unifier to all types in a graph
+    till reaching a fixed point. If the input and output graph
+    are the same, we converge.
+    """
+    flag = True
+    while flag:
+        flag = False
+        for k in mapping:
+            old_mapping_val = mapping[k]
+            if mapping[k] in mapping.keys():
+                new_key = mapping[k]
+                mapping[k] = mapping[new_key]
+            if old_mapping_val != mapping[k]:
+                flag = True
+
+    for n in graph.nodes:
+        n.type = substitute_solution_one_type(mapping, n.type)
+
+
+def check_for_type_equality(g1, g2):
+    """
+    A check equality to be used in fixed points.
+    We do not use graph equality but instead type
+    equality.
+    """
+    for n, m in zip(g1.nodes, g2.nodes):
+        if n.type != m.type:
+            return False
+    return True
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/validator.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/validator.py
new file mode 100644
index 0000000000000000000000000000000000000000..17a814b233c63fb9755473cc0406834087a80056
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/experimental/validator.py
@@ -0,0 +1,871 @@
+# mypy: allow-untyped-defs
+import builtins
+import functools
+import logging
+import math
+import operator
+from dataclasses import dataclass
+from typing import Any, Callable, Optional, Union
+
+import sympy
+
+import torch
+import torch.fx
+import torch.fx.traceback as fx_traceback
+from torch._dynamo.exc import TorchDynamoException
+from torch._dynamo.utils import dynamo_timed
+from torch.fx.node import Argument, Target
+from torch.utils._sympy.interp import sympy_interp
+
+
+log = logging.getLogger(__name__)
+
+try:
+    import z3  # type: ignore[import]
+
+    # Translation Validation for Dynamo guards
+    # ========================================
+    #
+    # Checks whether optimizations applied to the collected guards are
+    # valid. In other words, whether the guard function we actually run
+    # does not have false positives (unsound).
+    #
+    # In order to do so, we build the guards using 2 different information
+    # attached to each 'SymNode':
+    #   1. SymPy expressions
+    #   2. FX nodes
+    #
+    # SymPy expressions have implicit optimizations baked within itself,
+    # which may have a few bugs. On the other hand, we build the FX graph
+    # manually, with no optimizations enabled. This gives us access to
+    # the "ground truth".
+    #
+    # We then convert into Z3 expressions both the SymPy expressions
+    # (see [Note: SympyToZ3]) that reach 'ShapeEnv.produce_guards' function
+    # and the FX nodes (see [Note: PopulateValidator]) that go through
+    # 'ShapeEnv.evaluate_expr' function. Finally, we run the validation.
+    # (see [Note: TranslationValidator])
+    # Better Z3 to string implementation (for a small fraction of Z3).
+    #
+    # Here are the things we clean before showing the Z3 expression:
+    #   - Rename a few ops (e.g. "Distinct" ==> "!=")
+    #
+    #   - Ignore ToInt and ToReal operations:
+    #     usually they don't really matter
+    #
+    #   - Transform (ToInt (/ ...)) into (idiv ...):
+    #     this is the pattern for floor division
+    #
+    #   - Collect a chain of the same operations into one
+    def z3str(e: z3.ExprRef) -> str:
+        assert z3.is_expr(e), f"unsupported expression type: {e}"
+
+        def get_args_str(e: z3.ExprRef) -> list[str]:
+            return [z3str(e.arg(i)) for i in range(e.num_args())]
+
+        # First, we simplify the given expression.
+        # This is done using rewriting rules, so shouldn't take long.
+        e = z3.simplify(e)
+
+        # Only support function applications.
+        # Even Z3 "variables" are, in fact, function applications.
+        if not z3.is_app(e):
+            raise ValueError(f"can't print Z3 expression: {e}")
+
+        if z3.is_int_value(e) or z3.is_rational_value(e):
+            return e.as_string()  # type: ignore[attr-defined]
+
+        decl = e.decl()
+        kind = decl.kind()
+        op = str(decl)
+        args = get_args_str(e)
+
+        if kind == z3.Z3_OP_POWER:
+            op = "pow"
+
+        elif kind in (z3.Z3_OP_ADD, z3.Z3_OP_MUL):
+            # Collect the arguments of chains of ADD and MUL.
+            # This is safe, since they are associative.
+
+            def collect_str_args(e):
+                if not (z3.is_app(e) and e.decl().kind() == kind):
+                    return [z3str(e)]
+                else:
+                    return [
+                        x
+                        for i in range(e.num_args())
+                        for x in collect_str_args(e.arg(i))
+                    ]
+
+            args = collect_str_args(e)
+
+        elif kind == z3.Z3_OP_NOT:
+            # Revert some conversions that z3.simplify applies:
+            #   - a != b ==> (Not (== a b)) ==> (!= a b)
+            #   - a < b ==> (Not (<= b a)) ==> (> b a)
+            #   - a > b ==> (Not (<= a b)) ==> (> a b)
+
+            assert e.num_args() == 1
+            arg = e.arg(0)
+
+            assert z3.is_app(arg)
+            argkind = arg.decl().kind()
+
+            logic_inverse = {
+                z3.Z3_OP_EQ: "!=",
+                z3.Z3_OP_LE: ">",
+                z3.Z3_OP_GE: "<",
+            }
+
+            if argkind in logic_inverse:
+                op = logic_inverse[argkind]
+                args = get_args_str(arg)
+
+        elif kind in (z3.Z3_OP_TO_INT, z3.Z3_OP_TO_REAL):
+            assert e.num_args() == 1
+            argstr = z3str(e.arg(0))
+
+            # Check if it's the floor division pattern.
+            if argstr.startswith("(/"):
+                return "(idiv" + argstr[2:]
+
+            # Otherwise, just ignore it.
+            return argstr
+
+        elif kind == z3.Z3_OP_UNINTERPRETED:
+            assert e.num_args() == 0
+            return str(decl)
+
+        string = op + " " + " ".join(args)
+        return f"({string.rstrip()})"
+
+    # We need to convert to/from BitVec in order to use z3 bitwise ops.
+    # We assume that integers are 64 bit.
+    # If all args are boolean, then use the boolean bitwise op implementation instead, if provided.
+    def _bitwise_op(bitwise_func, bool_func):
+        @functools.wraps(bitwise_func)
+        def wrapper(self, *args):
+            if bool_func is not None and all(
+                isinstance(arg, z3.BoolRef) for arg in args
+            ):
+                return bool_func(*args)
+
+            wrapped_args = tuple(z3.Int2BV(a, 64) for a in args)
+            return z3.BV2Int(bitwise_func(*wrapped_args))
+
+        return wrapper
+
+    # Implementation of Python semantics as Z3 expressions.
+    #
+    # Z3 Real-Int theory has operators with semantics that differ that of
+    # Python. Therefore, in order to get it right, we need to implement
+    # the (Python) semantics we are relying on in Z3.
+    @dataclass
+    class _Z3Ops:
+        # Validator used for adding assertions as needed.
+        # e.g. div(a, b) requires b != 0.
+        validator: "TranslationValidator"
+
+        # The 2 functions below are used for conditionally casting between
+        # integer and reals.
+        #
+        # Returns a real expression from 'x'.
+        @staticmethod
+        def to_real(x: z3.ArithRef) -> z3.ArithRef:
+            return x if x.is_real() else z3.ToReal(x)
+
+        # Returns an integer expression from 'x'.
+        @staticmethod
+        def to_int(x: z3.ArithRef) -> z3.ArithRef:
+            return x if x.is_int() else z3.ToInt(x)
+
+        def sym_sum(self, args: z3.ArithRef) -> z3.ArithRef:
+            return sum(args)
+
+        # Implements Python division semantics.
+        def div(self, numerator: z3.ArithRef, denominator: z3.ArithRef) -> z3.ArithRef:
+            self.validator.add_assertion(denominator != 0)  # type: ignore[arg-type]
+            return _Z3Ops.to_real(numerator) / _Z3Ops.to_real(denominator)
+
+        def floor(self, number: z3.ArithRef) -> z3.ArithRef:
+            # Z3 ToInt function rounds a real number towards negative infinity.
+            return _Z3Ops.to_int(number)
+
+        # Python semantics for 'FloorDiv' states that before applying the floor
+        # function, the operands are converted to their common type.
+        def floordiv(
+            self, numerator: z3.ArithRef, denominator: z3.ArithRef
+        ) -> z3.ArithRef:
+            cast_result_to_real = numerator.is_real() or denominator.is_real()
+            result = _Z3Ops.to_int(self.div(numerator, denominator))
+            # Since the 'result' is already an integer, we just have to check
+            # whether we should cast it to real.
+            return _Z3Ops.to_real(result) if cast_result_to_real else result
+
+        def ceil(self, number: z3.ArithRef) -> z3.ArithRef:
+            return z3.If(
+                self.floor(number) < number, self.floor(number + 1), number
+            )  # type: ignore[return-value]
+
+        def trunc(self, number: z3.ArithRef) -> z3.ArithRef:
+            return z3.If(number >= 0, self.floor(number), self.ceil(number))  # type: ignore[return-value]
+
+        def max(self, a: z3.ArithRef, b: z3.ArithRef) -> z3.ArithRef:
+            return z3.If(a > b, a, b)  # type: ignore[return-value]
+
+        def min(self, a: z3.ArithRef, b: z3.ArithRef) -> z3.ArithRef:
+            return z3.If(a < b, a, b)  # type: ignore[return-value]
+
+        # Python semantics for 'Mod' is defined as: p % q = p - floordiv(p, q) * q
+        # It should work with both integer and reals.
+        def mod(self, p: z3.ArithRef, q: z3.ArithRef) -> z3.ArithRef:
+            return p - self.floordiv(p, q) * q
+
+        def pow(self, base: z3.ArithRef, exp: z3.ArithRef) -> z3.ArithRef:
+            # Z3 can't handle complex numbers very well.
+            self.validator.add_assertion(z3.Or(base != 0, exp > 0))  # type: ignore[arg-type]
+            return base**exp
+
+        def sqrt(self, number: z3.ArithRef) -> z3.ArithRef:
+            # Square-root:
+            # 1. Only work with reals
+            number = _Z3Ops.to_real(number)
+            # 2. The number should be positive or zero.
+            #    Otherwise, Z3 returns 'unknown'.
+            self.validator.add_assertion(number >= 0)
+            return number**0.5
+
+        def abs(self, number: z3.ArithRef) -> z3.ArithRef:
+            return z3.Abs(number)
+
+        def round_to_int(self, number: z3.ArithRef) -> z3.ArithRef:
+            # Pythons builtin 'round' implements the 'round half to even' strategy
+            # See https://en.wikipedia.org/wiki/Rounding#Rounding_half_to_even
+            # z3 has an equivalent z3.fpRoundToIntegral(z3.RoundNearestTiesToEven(), ...), but this only applies to
+            # floating point numbers, which is different from real numbers that we are dealing with here.
+            # Instead, we implement 'round half to even' in terms of 'round half up' (floor(x + 0.5)) and
+            # 'round half down' (ceil(x - 0.5)).
+            # Assuming 'round half up' is the default case, we need to correct ..., -3.5, -1.5, 0.5, 2.5, 4.5, ...
+            # to round down, i.e. use the 'round half down' strategy
+            return z3.If(
+                self.mod(number, z3.IntVal(2)) == 0.5,
+                self.ceil(number - 0.5),
+                self.floor(number + 0.5),
+            )
+
+        bitwise_and = _bitwise_op(operator.and_, z3.And)
+        bitwise_or = _bitwise_op(operator.or_, z3.Or)
+        lshift = _bitwise_op(operator.lshift, None)
+        rshift = _bitwise_op(operator.rshift, None)
+
+    # Lifts a callable to be used in Z3.
+    #
+    # This function replaces the given 'op' by a function that:
+    #
+    #   1. Lifts the arguments into Z3 (i.e. make them inhabitants of Z3)
+    #
+    #   2. Calls an operation that corresponds to 'op', but works with Z3
+    #      inhabitants (left as is if it works as is)
+    def z3op(op: Callable, validator: "TranslationValidator") -> Callable:
+        # Operations that have booleans as their argument.
+        # This is needed because the argument of some FX nodes were
+        # literal integers, instead of booleans. So, whenever this flag
+        # is set, we also convert ints to booleans.
+        boolean_ops = {operator.not_}
+        as_bool = op in boolean_ops
+
+        # Lifts the function into 'z3.ExprRef' domain.
+        def lift(func):
+            def wrap(a) -> z3.ExprRef:
+                if isinstance(a, (z3.ArithRef, z3.BoolRef)):
+                    return a
+                # Convert it into a Z3 value, if it is some of the supported
+                # types below.
+                if isinstance(a, bool) or (as_bool and isinstance(a, int)):
+                    return z3.BoolVal(bool(a))
+                if isinstance(a, (int, sympy.Integer)):
+                    return z3.IntVal(int(a))
+                if isinstance(a, (float, sympy.Float)):
+                    return z3.RealVal(float(a))
+                raise ValueError(f"can't lift type: {type(a)}")
+
+            @functools.wraps(func)
+            def wrapper(*args):
+                # Lifts the arguments into a list of Z3 inhabitants.
+                if len(args) == 1 and isinstance(args[0], (list, tuple)):
+                    wrapped_args = (tuple(wrap(a) for a in args[0]),)
+                else:
+                    wrapped_args = tuple(wrap(a) for a in args)
+                # Run the function on the Z3 expressions.
+                return func(*wrapped_args)
+
+            return wrapper
+
+        ops = _Z3Ops(validator)
+        replacement_map = {
+            # Operator module.
+            operator.not_: lift(z3.Not),
+            operator.and_: lift(ops.bitwise_and),
+            operator.or_: lift(ops.bitwise_or),
+            operator.lshift: lift(ops.lshift),
+            operator.rshift: lift(ops.rshift),
+            operator.floordiv: lift(ops.floordiv),
+            operator.truediv: lift(ops.div),
+            operator.mod: lift(ops.mod),
+            operator.abs: lift(ops.abs),
+            builtins.round: lift(ops.round_to_int),
+            # Math module.
+            math.ceil: lift(ops.ceil),
+            math.floor: lift(ops.floor),
+            math.trunc: lift(ops.trunc),
+            # Torch module.
+            torch.sym_float: lift(ops.to_real),
+            torch.sym_max: lift(ops.max),
+            torch.sym_min: lift(ops.min),
+            torch.sym_sum: lift(ops.sym_sum),
+            torch.sym_ite: lift(lambda b, t, f: t if b else f),
+            torch._sym_sqrt: lift(ops.sqrt),  # type: ignore[attr-defined]
+            # Not lifted because we only use this function as a
+            # marker for adding the expression as validator input.
+            torch._assert: torch._assert,
+        }
+        return replacement_map[op] if op in replacement_map else lift(op)
+
+    # Processes an FX graph, populating the given validator.
+    #
+    # [Note: PopulateValidator]
+    # This class walks through each node in the FX graph, translating
+    # them into the Z3 world.
+    #
+    # Then, whenever it finds an 'torch._assert' call_function operation,
+    # it adds the Z3 expression corresponding to the argument as validator
+    # input.
+    class PopulateValidator(torch.fx.Interpreter):
+        def __init__(self, graph: torch.fx.Graph, validator: "TranslationValidator"):
+            # Reference to the translation validator.
+            self.validator = validator
+
+            # Build the graph module and call `Interpreter` constructor.
+            module = torch.fx.GraphModule(root={}, graph=graph)
+            super().__init__(module, garbage_collect_values=True)
+
+        def placeholder(
+            self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any]
+        ) -> Any:
+            symbol = fx_traceback.get_current_meta()["symbol"]
+            return self.validator.z3var(symbol)
+
+        def call_function(
+            self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any]
+        ) -> Any:
+            if target != torch._assert:
+                # Lift and runs the node target function
+                return super().call_function(z3op(target, self.validator), args, kwargs)  # type: ignore[arg-type]
+            # Adds the Z3 expression corresponding to the first argument
+            # as a validator input.
+            assert (
+                len(args) == 1
+            ), f"expected 1 argument on assertion. Got: {len(args)} "
+            self.validator.add_source_expr(args[0])  # type: ignore[arg-type]
+
+    # Translates SymPy expressions into Z3 expressions.
+    #
+    # [Note: SympyToZ3]
+    # At the time of the translation, all free variables present in the
+    # SymPy expression being translated must be already mapped to a Z3
+    # integer variable.
+    class SympyToZ3:
+        OPERATOR_HANDLES = {"add", "mul", "eq", "ne", "lt", "gt", "le", "ge"}
+
+        def __init__(
+            self,
+            validator: "TranslationValidator",
+        ) -> None:
+            self._validator = validator
+            self._ops = _Z3Ops(self._validator)
+
+        def constant(self, value: Any, dtype: torch.dtype) -> z3.ExprRef:
+            # TODO: Probably OK to relax this and allow lower precision
+            if dtype is torch.int64:
+                return z3.IntVal(int(value))
+            if dtype is torch.double:
+                return z3.RealVal(float(value))
+            if dtype is torch.bool:
+                return z3.BoolVal(bool(value))
+            raise ValueError(f"unsupported dtype (SympyToZ3): {dtype}")
+
+        def to_dtype(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef:
+            if dtype == torch.float64:
+                return z3.ToReal(x)
+            raise NotImplementedError(f"to_dtype {dtype} NYI")
+
+        def trunc_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef:
+            return z3.ToInt(x)
+
+        def round_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef:
+            return self._ops.round_to_int(x)
+
+        def int_truediv(
+            self, numerator: z3.ArithRef, denominator: z3.ArithRef
+        ) -> z3.ArithRef:
+            return self._ops.div(numerator, denominator)
+
+        def truediv(
+            self, numerator: z3.ArithRef, denominator: z3.ArithRef
+        ) -> z3.ArithRef:
+            return self._ops.div(numerator, denominator)
+
+        def floordiv(
+            self, numerator: z3.ArithRef, denominator: z3.ArithRef
+        ) -> z3.ArithRef:
+            return self._ops.floordiv(numerator, denominator)
+
+        def div(self, numerator: z3.ArithRef, denominator: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.floordiv(numerator, denominator)
+
+        def pow(self, base: z3.ArithRef, exp: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.pow(base, exp)
+
+        def pow_by_natural(self, base: z3.ArithRef, exp: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.pow(base, exp)
+
+        def mod(self, p: z3.ArithRef, q: z3.ArithRef) -> z3.ArithRef:
+            return self._ops.mod(p, q)
+
+        def ceil_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef:
+            return self._ops.ceil(x)
+
+        def floor_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef:
+            return self._ops.floor(x)
+
+        def __getattr__(self, name: str) -> Any:
+            REPLACEMENT = {
+                "and_": z3.And,
+                "or_": z3.Or,
+                "not_": z3.Not,
+                "bitwise_and": self._ops.bitwise_and,
+                "bitwise_or": self._ops.bitwise_or,
+                "lshift": self._ops.lshift,
+                "rshift": self._ops.rshift,
+                "floor": self._ops.floor,
+                "ceil": self._ops.ceil,
+                "minimum": self._ops.min,
+                "maximum": self._ops.max,
+            }
+
+            if name in REPLACEMENT:
+                return REPLACEMENT[name]
+            if name in self.OPERATOR_HANDLES:
+                return getattr(operator, name)
+            raise AttributeError(f"unhandled operator: {name}")
+
+        def run(self, expr: sympy.Basic) -> z3.ExprRef:
+            return sympy_interp(self, self._validator.symbols, expr)  # type: ignore[arg-type]
+
+    # Dynamo guards translation validator.
+    #
+    # [Note: TranslationValidator]
+    # Verifies whether the guards issued by 'ShapeEnv.produce_guards' are sound.
+    # That is: whether those (target) guards only yield TRUE whenever the original,
+    # unoptimized, (source) guards yield TRUE.
+    #
+    # More concretely, given 'source' and 'target' guard expressions, we wish to
+    # check whether the following expression holds:
+    #
+    # Not(And(source)) AND And(target)
+    #
+    # i.e. whether there is an assignment of the free variables where the opposite
+    # happens: target is TRUE, but source is FALSE.
+    class TranslationValidator:
+        def __init__(self) -> None:
+            log.debug("new instance")
+
+            # Mapping of SymPy symbols to Z3 variables.
+            self.symbols: dict[sympy.Symbol, z3.ExprRef] = {}
+
+            # Set of source Z3 expressions.
+            # They represent the generated guards without any kind of
+            # simplification or transformation.
+            self._source_exprs: set[z3.BoolRef] = set()
+
+            # Set of target Z3 expressions.
+            # They represent the actual checked guards at runtime. They might
+            # be simplified or transformed versions of the source guards.
+            self._target_exprs: set[z3.BoolRef] = set()
+
+            # Set of Z3 expressions representing assertions over both the
+            # source and target expressions.
+            self._assertions: set[z3.BoolRef] = set()
+
+        # Retrieves the corresponding Z3 variable.
+        def z3var(self, symbol: sympy.Symbol) -> z3.ExprRef:
+            assert symbol in self.symbols, f"Z3 variable not found for: {symbol}"
+            return self.symbols[symbol]
+
+        # Create a variable in Z3 of 'type' for 'symbol', if it doesn't already exists.
+        def add_var(self, symbol: sympy.Symbol, type: type) -> z3.ExprRef:
+            if symbol in self.symbols:
+                return self.symbols[symbol]
+
+            log.debug("new variable: %s (%s)", symbol.name, type.__name__)
+
+            if type is int:
+                var = z3.Int(symbol.name)
+
+                # If 'symbol' is positive (SymPy assumption), we have to
+                # convey it to Z3 as well.
+                if symbol.is_positive:  # type: ignore[attr-defined]
+                    self._target_exprs.add(var > 0)
+            elif type is float:
+                var = z3.Real(symbol.name)
+            elif type is bool:
+                var = z3.Bool(symbol.name)
+            else:
+                raise RuntimeError(f"unsupported type for Z3 variable: {type}")
+
+            self.symbols[symbol] = var
+            return var
+
+        # Checks whether all symbols were already added.
+        def _check_freesymbols(self, e: sympy.Basic) -> None:
+            for s in e.free_symbols:
+                assert isinstance(s, sympy.Symbol)
+                # Call 'z3var' just to check whether there's already a
+                # Z3 variable corresponding to 's'.
+                self.z3var(s)
+
+        def to_z3_boolean_expr(self, e: sympy.Basic) -> z3.BoolRef:
+            z3expr = SympyToZ3(self).run(e)
+            assert isinstance(
+                z3expr, z3.BoolRef
+            ), f"expected boolean expression. Got: {z3expr}"
+            return z3expr
+
+        def add_source_expr(self, e: z3.BoolRef) -> None:
+            if e not in self._source_exprs:
+                log.debug("add source guard: %s", z3str(e))
+            self._source_exprs.add(e)
+
+        def add_target_expr(self, e: "sympy.logic.boolalg.Boolean") -> None:
+            self._check_freesymbols(e)
+            z3expr = self.to_z3_boolean_expr(e)
+            if e not in self._target_exprs:
+                log.debug("add target guard: %s", z3str(z3expr))
+            self._target_exprs.add(z3expr)
+
+        def add_assertion(self, e: Union[z3.BoolRef, sympy.Basic]) -> None:
+            if isinstance(e, sympy.Basic):
+                self._check_freesymbols(e)
+                ref = self.to_z3_boolean_expr(e)
+            else:
+                ref = e
+            assert isinstance(ref, z3.BoolRef)
+            if ref not in self._assertions:
+                log.debug("add assertion: %s", z3str(ref))
+            self._assertions.add(ref)
+
+        def validate(self) -> None:
+            with dynamo_timed("TranslationValidator.validate"):
+                return self._validate()
+
+        def _validate(self) -> None:
+            if len(self._source_exprs) == 0 or len(self._target_exprs) == 0:
+                # If there are no source/target expressions, there's nothing we really
+                # wish to prove. So, we just return.
+                return None
+
+            # Here, we use "QF_NRA" logic for the solver:
+            #   "Quantifier-free Non-linear Real Arithmetic".
+            #
+            # Most of the guards expressions have:
+            #   1. arithmetic between integer and reals
+            #   2. no quantifiers
+            #   3. potentially non-linear.
+            #
+            # Although there's also "QF_NIRA" (mixed integer-real arithmetic),
+            # "QF_NRA" seems to work better on 'dynamo/test_dynamic_shapes.py'.
+            solver = z3.SolverFor("QF_NRA")
+            # Set a timeout for finding a solution.
+            solver.set(timeout=translation_validation_timeout())
+
+            # Add all the assertions to the solver.
+            for assertion in self._assertions:
+                solver.add(assertion)
+
+            # "Is there any case where it's TRUE for the target expressions,
+            #  but FALSE for the source expressions?"
+            solver.add(z3.Not(z3.And(*self._source_exprs)))
+            solver.add(*self._target_exprs)
+
+            log.debug("translation validation: start")
+            r = solver.check()
+            if r == z3.sat:
+                # Target expressions are unsound.
+                # Log the found model and the source expressions that failed.
+                model = solver.model()
+                raise ValidationException(
+                    model,
+                    self._assertions,
+                    self._target_exprs,
+                    failed_source_exprs=[
+                        inp for inp in self._source_exprs if not model.evaluate(inp)
+                    ],
+                )
+            else:
+                if r == z3.unknown:
+                    # Could not find a solution. It didn't fail, but it also
+                    # didn't succeed. Canceling the validation execution (keyboard
+                    # interrupt) also gets to this branch.
+                    log.warning(
+                        "translation validation: could not validate: got z3.unknown"
+                    )
+                else:
+                    # Target expressions are sound.
+                    assert r == z3.unsat
+                    log.debug("translation validation: success")
+
+except ImportError:
+    _HAS_Z3 = False
+
+    __all__ = [
+        "translation_validation_enabled",
+        "translation_validation_timeout",
+        "ValidationException",
+        "BisectValidationException",
+    ]
+
+else:
+    _HAS_Z3 = True
+
+    __all__ = [
+        "z3str",
+        "z3op",
+        "PopulateValidator",
+        "SympyToZ3",
+        "TranslationValidator",
+        "translation_validation_enabled",
+        "translation_validation_timeout",
+        "ValidationException",
+        "BisectValidationException",
+    ]
+
+from torch.fx.experimental import _config as config
+
+
+def translation_validation_enabled() -> bool:
+    # Checks everytime this function is called, in case the Dynamo
+    # option is set, but Z3 is not installed.
+    _assert_z3_installed_if_tv_set()
+    return _HAS_Z3 and config.translation_validation
+
+
+def translation_validation_timeout() -> int:
+    return config.translation_validation_timeout
+
+
+def _assert_z3_installed_if_tv_set():
+    assert _HAS_Z3 or not config.translation_validation, (
+        "translation validation requires Z3 package. Please, either install "
+        "z3-solver or disable translation validation."
+    )
+
+
+class ValidationException(TorchDynamoException):
+    def __init__(self, model, assertions, target_exprs, failed_source_exprs):
+        assert _HAS_Z3
+
+        def symbolstr(sym) -> str:
+            return f"{sym}: {model[sym]}"
+
+        def joinlines(xs) -> str:
+            return "\n".join(f"  ==> {x}" for x in xs)
+
+        model_str = joinlines(sorted(map(symbolstr, model)))
+        assertions_str = joinlines(sorted(map(z3str, assertions)))
+        target_exprs_str = joinlines(sorted(map(z3str, target_exprs)))
+        failed_source_exprs_str = joinlines(sorted(map(z3str, failed_source_exprs)))
+
+        self.msg = "translation validation failed."
+        self.details = f"""\
+Model:
+{model_str}
+
+Assertions:
+{assertions_str}
+
+Target Expressions:
+{target_exprs_str}
+
+Failed Source Expressions:
+{failed_source_exprs_str}"""
+
+    def __str__(self):
+        return f"{self.msg}\n\n{self.details}"
+
+
+class BisectValidationException(TorchDynamoException):
+    def __init__(self, validation_exc, expr, failed_action, traced_node):
+        self.msg = f"translation validation failed when {failed_action}: {expr}"
+        self.details = f"""\
+Failure occurred while running node:
+    {traced_node.format_node()}
+
+{validation_exc.details}"""
+
+    def __str__(self):
+        return f"{self.msg}\n\n{self.details}"
+
+
+# Checks when this module is loaded.
+_assert_z3_installed_if_tv_set()
+
+
+# Translation validation bisection.
+#
+# Bisect into the torch._assert nodes recorded in the shape_env FX graph, and raise
+# the earliest ValidationException.
+#
+# As guards are added by ShapeEnv.evaluate_expr calls, some simplification errors
+# might be silently happening. This function tries to nail down exactly at which
+# point things went wrong from a validation perspective.
+def bisect(shape_env):
+    from torch.fx.experimental.recording import (
+        FakeTensorMeta,
+        replay_shape_env_events,
+        ShapeEnvEvent,
+    )
+    from torch.fx.experimental.symbolic_shapes import (
+        CURRENT_NODE_KEY,
+        ShapeEnv,
+        SHAPEENV_EVENT_KEY,
+    )
+
+    events = shape_env.events
+
+    # Retrieves the ShapeEnvEvent associated with node.
+    def get_node_event(node: torch.fx.Node) -> ShapeEnvEvent:
+        assert SHAPEENV_EVENT_KEY in node.meta
+        return events[node.meta[SHAPEENV_EVENT_KEY]]
+
+    # Creates a new instance of fake, but updating every symbolic value's ShapeEnv
+    # reference to the one given as argument.
+    #
+    # This is needed so as not to simplify a symbolic expression using a ShapeEnv
+    # "from the future", where it may have a different set of replacements.
+    def new_with_shape_env(shape_env: ShapeEnv, fake) -> Any:
+        if isinstance(fake, int):
+            return fake
+        if isinstance(fake, torch.SymInt):
+            return torch.SymInt(fake.node.with_shape_env(shape_env))
+        if isinstance(fake, torch.SymFloat):
+            return torch.SymFloat(fake.node.with_shape_env(shape_env))
+        assert isinstance(fake, FakeTensorMeta)
+        return FakeTensorMeta(
+            tuple(new_with_shape_env(shape_env, s) for s in fake.size()),
+            tuple(new_with_shape_env(shape_env, s) for s in fake.stride()),
+            new_with_shape_env(shape_env, fake.storage_offset()),
+            fake.is_nested,
+        )
+
+    # Checks whether the given shape_env fails when produce_guards is called.
+    def check_shapeenv_fails(
+        shape_env: ShapeEnv, tracked_fakes: Optional[list[Any]]
+    ) -> Optional[ValidationException]:
+        assert tracked_fakes is not None
+        try:
+            # This produce_guards call is a best-effort replication, since we
+            # don't populate EqualityConstraint list. Reason: we would also have
+            # to save OutputGraph.tracked_fakes_id_to_source.
+            shape_env.produce_guards(
+                [new_with_shape_env(shape_env, a.fake) for a in tracked_fakes],
+                [a.source for a in tracked_fakes],
+                input_contexts=[a.symbolic_context for a in tracked_fakes],
+            )
+            return None
+        except ValidationException as e:
+            return e
+
+    # Checks whether the ShapeEnv reconstructed by replaying the events until
+    # node is created fails when produce_guards is called.
+    def check_node_fails(node: torch.fx.Node) -> Optional[ValidationException]:
+        number = node.meta[SHAPEENV_EVENT_KEY]
+        # Reconstruct shape_env until the event at event_number.
+        shape_env = replay_shape_env_events(events[: number + 1])
+        shape_env.graph.lint()
+        return check_shapeenv_fails(shape_env, events[number].tracked_fakes)
+
+    last_exception = check_shapeenv_fails(
+        shape_env, shape_env._snapshot_tracked_fakes()
+    )
+
+    if not last_exception:
+        # We don't actually fail due to a produce_guards call.
+        # Stop and don't bisect.
+        log.info("translation validation succeeded: no errors found.")
+        return
+
+    if not shape_env.should_record_events or config.translation_validation_no_bisect:
+        # Bisection is off.
+        # Return the last ValidationException we got.
+        raise last_exception
+
+    # Cache the raised exception (if any) at each bisection point.
+    exception = {}
+
+    # Bisection happens on the assertion nodes of the recorded FX graph for
+    # dynamic shapes.
+    assert_nodes = [
+        node for node in shape_env.graph.nodes if node.target == torch._assert
+    ]
+
+    # Preparing the indices for binary search.
+    # The overall invariants are
+    # - for all i < left, assert_node[i] doesn't fail
+    # - for all i >= right, assert_node[i] fails
+    # - `right in exception` always holds
+    # - `left <= right` always holds
+    left, mid, right = 0, 0, len(assert_nodes) - 1
+    exception[right] = check_node_fails(assert_nodes[right])
+
+    while left < right:
+        mid = (left + right) // 2
+
+        node = assert_nodes[mid]
+        log.debug("bisecting at %s: %s", mid, get_node_event(node))
+
+        # Check whether the new shape_env raises a ValidationException or not.
+        exception[mid] = check_node_fails(node)
+
+        if exception[mid]:
+            right = mid
+        else:
+            left = mid + 1
+
+    assert left in exception and isinstance(exception[left], ValidationException)
+
+    node = assert_nodes[left]
+    event = get_node_event(node)
+
+    if event.is_evaluate_expr():
+        failed_action = "evaluating"
+    else:
+        assert event.is_defer_runtime_assert(), f"unexpected event type: {event}"
+        failed_action = "adding runtime assert"
+
+    args = event.args
+    assert args is not None
+    assert len(args) >= 2, (
+        f"bisecting expects {event.name} to have at least 2 positional arguments. "
+        f"Got: {len(args)}"
+    )
+    assert isinstance(args[1], sympy.Basic), (
+        f"bisecting expects {event.name} to have a SymPy expression as its second argument. "
+        f"Got: {type(args[1])}"
+    )
+
+    raise BisectValidationException(
+        exception[left],
+        expr=args[1],
+        failed_action=failed_action,
+        traced_node=node.meta[CURRENT_NODE_KEY],
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..433d8818e259a6c0d8d674d15a0312815010ec7f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/__init__.py
@@ -0,0 +1,14 @@
+from . import (
+    graph_drawer,
+    graph_manipulation,
+    net_min_base,
+    operator_support,
+    param_fetch,
+    reinplace,
+    runtime_assert,
+    shape_prop,
+    split_module,
+    split_utils,
+    splitter_base,
+    tools_common,
+)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/_tensorify_python_scalars.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/_tensorify_python_scalars.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7a2cdfcb17e628fd164822e7552d65905dada11
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/_tensorify_python_scalars.py
@@ -0,0 +1,366 @@
+from __future__ import annotations
+
+import logging
+import os
+from typing import Any, Union
+
+from sympy import Integer, Number, Symbol
+from sympy.logic.boolalg import BooleanAtom
+
+import torch
+import torch.fx as fx
+from torch._dynamo.exc import TensorifyScalarRestartAnalysis
+from torch._dynamo.symbolic_convert import TensorifyState
+from torch._dynamo.utils import get_metrics_context
+from torch._prims_common import get_computation_dtype
+from torch._subclasses import fake_tensor  # noqa: TCH001
+from torch._subclasses.fake_tensor import FakeTensor
+from torch._utils_internal import justknobs_check
+from torch.fx._utils import lazy_format_graph_code
+from torch.fx.experimental.symbolic_shapes import guard_scalar, ShapeEnv  # noqa: TCH001
+from torch.fx.graph_module import GraphModule  # noqa: TCH001
+
+# TODO: refactor
+from torch.fx.passes.runtime_assert import _get_sym_val
+from torch.fx.proxy import MetaProxy
+from torch.utils._sympy.interp import _run_sympy_handler, sympy_interp
+from torch.utils._sympy.reference import TensorReferenceAnalysis
+from torch.utils._sympy.symbol import symbol_is_type, SymT
+
+
+__all__: list[str] = []
+
+log = logging.getLogger(__name__)
+graph_code_log = torch._logging.getArtifactLogger(__name__, "graph_code")
+
+# The general shape of this transformation is to look for Tensor operations
+# that take a backed SymFloat as an argument, and then redo them as tensor
+# compute (with ints and tensors as inputs). For example, add(Tensor, Scalar)
+# can be translated into add(Tensor, Tensor). Because Dynamo has already
+# arranged for floats to be Tensor inputs to the graph, for typical float
+# compute you can entirely translate the Python float operations into Tensor
+# operations with only Tensor inputs.
+#
+# This pass is also responsible for doing CSE on the fly as we do this, since
+# you don't want to keep recomputing the same quantity over and over again if
+# it's used multiple times.
+#
+# This pass runs on the JOINT graph produced by AOT Autograd, prior to partitioning.
+# The primary goal of this pass is to eliminate floats by replacing TensorScalar
+# operations with TensorTensor operations and then Dead Code Elimination (DCE) of
+# the item calls, which effectively removes the floats.
+#
+# This needs to happen before partitioning because it influences partitioning decisions,
+# specifically by ensuring that we don't need to save floats across partitions.
+# Additionally, there is a separate pass that changes which device computations
+# occur on. That pass must be run after this one, but still before partitioning.
+#
+# HISTORY NOTE: Originally, I wanted to formulate this pass as pushing item()
+# calls down, transforming float compute into int compute as we went. If you
+# manage to eliminate all float compute, this ends up being equivalent, but
+# there is a critical difference when some floats cannot be eliminated: when
+# we call item() on them, what should it's SymFloat be? Ideally, it would
+# be the same backed SymFloat we had before. But without symbolic expresssion
+# propogation on tensor quantities, repropagating would instead give you an
+# unbacked SymFloat. Maybe it is a good idea to implement symbolic propagation
+# on 0d scalar tensors, but I decided to go for something simpler to start.
+#
+# The boring stuff:
+#
+# * What operators can I Tensor-ify? (Anything with a Scalar argument)
+# * How do I Tensor-ify a SymFloat sympy expression (Sympy -> Op Handler -> Tensor)
+#
+# TODO: make sure this runs before CPU->CUDA pass for cudagraph friendliness
+
+
+SUPPORTED_OPS = {
+    torch.ops.aten.mul.Tensor: torch.ops.aten.mul.Tensor,
+    torch.ops.aten.add.Tensor: torch.ops.aten.add.Tensor,
+    torch.ops.aten.sub.Tensor: torch.ops.aten.sub.Tensor,
+    torch.ops.aten.div.Tensor: torch.ops.aten.div.Tensor,
+    torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor,
+    torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor,
+    torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor,
+    torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor,
+    torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor,
+    torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor,
+}
+
+
+@torch.fx._compatibility.compatibility(is_backward_compatible=False)
+def tensorify_python_scalars(
+    gm: GraphModule, shape_env: ShapeEnv, fake_mode: fake_tensor.FakeTensorMode
+) -> None:
+    """
+    Converts Python scalar operations into Tensor operations within the graph. This pass looks for
+    Tensor operations that involve SymFloat arguments and transforms them into equivalent operations
+    that use only Tensor inputs.
+
+    Args:
+        gm: The FX graph module representing the computation graph.
+        shape_env: The shape environment responsible for symbolic shape tracking and propagation
+        during graph transformations.
+
+    Returns:
+        None
+    """
+    import sympy
+
+    knob = True
+    if (env := os.getenv("TENSORIFY_PYTHON_SCALARS")) is not None:
+        if env in ("0", "FALSE"):
+            knob = False
+    else:
+        knob = justknobs_check("pytorch/compiler:tensorify_python_scalars")
+    if not knob:
+        return None
+
+    graph = gm.graph
+    tracer = fx.proxy.GraphAppendingTracer(graph)
+    expr_to_sym_proxy: dict[sympy.Expr, MetaProxy] = {}
+    expr_to_tensor_proxy: dict[sympy.Expr, MetaProxy] = {}
+    tensorified_symbols: set[sympy.Symbol] = set()
+    should_restart = False
+
+    first_non_placeholder = None
+    placeholders = set()
+    for node in graph.nodes:
+        if node.op != "placeholder":
+            first_non_placeholder = node
+            break
+        else:
+            placeholders.add(node)
+
+    Analysis = TensorReferenceAnalysis
+
+    def _sympy_interp(expr: sympy.Expr) -> MetaProxy:
+        # sympy_interp() with hash consing, and special handling for
+        # generating constants correctly
+
+        # hash cons
+        if isinstance(expr, Symbol) and expr not in expr_to_tensor_proxy:
+            # This is guaranteed to be populated by invariant established by
+            # insert_deferred_runtime_asserts
+            expr_to_tensor_proxy[expr] = torch.ops.aten.scalar_tensor.default(
+                expr_to_sym_proxy[expr]
+            )
+
+        # cache constants, why not
+        if isinstance(expr, (Integer, Number, BooleanAtom)):
+            dtype = None
+            c: Union[bool, int, float]
+            if isinstance(expr, BooleanAtom):
+                dtype = torch.bool
+                c = bool(expr)
+            elif isinstance(expr, sympy.Integer):
+                dtype = torch.int64
+                c = int(expr)
+            elif isinstance(expr, sympy.Number):
+                dtype = torch.float64
+                c = float(expr)
+
+            node = graph.call_function(
+                torch.ops.aten.scalar_tensor.default, (c,), {"dtype": dtype}
+            )
+            with fake_mode:
+                node.meta["val"] = torch.ops.aten.scalar_tensor.default(c, dtype=dtype)
+            expr_to_tensor_proxy[expr] = MetaProxy(
+                node,
+                tracer=tracer,
+                fake_mode=fake_mode,
+            )
+
+        if expr in expr_to_tensor_proxy:
+            return expr_to_tensor_proxy[expr]
+
+        # don't cache
+        if isinstance(expr, Symbol):
+            return sympy_interp(Analysis, expr_to_tensor_proxy, expr)  # type: ignore[arg-type]
+
+        # hash cons on arguments, run expr handler
+        expr_to_tensor_proxy[expr] = _run_sympy_handler(
+            Analysis,
+            [_sympy_interp(arg) for arg in expr.args],  # type: ignore[arg-type]
+            expr,
+        )
+
+        return expr_to_tensor_proxy[expr]
+
+    nodes = list(graph.nodes)
+    for i, node in enumerate(nodes[:-1]):
+        with graph.inserting_before(
+            nodes[i + 1] if node not in placeholders else first_non_placeholder
+        ):
+            # Look for tensor.item() calls on placeholders
+            if (
+                node is not None
+                and node.op == "call_function"
+                and node.target is torch.ops.aten._local_scalar_dense.default
+            ):
+                dtype = node.args[0].meta["val"].dtype
+                if dtype != torch.float64:
+                    continue
+
+                assert isinstance(node.args[0], fx.Node), node.args[0]
+
+                s = node.meta["val"].node.expr
+                expr_to_tensor_proxy[s] = MetaProxy(
+                    node.args[0], tracer=tracer, fake_mode=fake_mode
+                )
+                expr_to_sym_proxy[s] = MetaProxy(
+                    node, tracer=tracer, fake_mode=fake_mode
+                )
+            elif (sym_expr := _get_sym_val(node)) is not None:
+                if sym_expr not in expr_to_sym_proxy and not isinstance(
+                    sym_expr, (sympy.Number, sympy.logic.boolalg.BooleanAtom)
+                ):
+                    expr_to_sym_proxy[sym_expr] = MetaProxy(
+                        node, tracer=tracer, fake_mode=fake_mode
+                    )
+
+            # Specialize all dimensions that contain symfloats. Here's
+            # an example test that requires this:
+            # PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=4 python test/inductor/test_torchinductor_opinfo.py TestInductorOpInfoCUDA.test_comprehensive_nn_functional_interpolate_bicubic_cuda_float32 # noqa: B950
+            val = node.meta.get("val")
+            if isinstance(val, FakeTensor):
+                for dim in val.shape:
+                    if isinstance(dim, torch.SymInt):
+                        for s in dim.node.expr.free_symbols:
+                            name = str(s)
+                            if symbol_is_type(
+                                s, SymT.FLOAT
+                            ) and not TensorifyState.should_specialize(name):
+                                # In principle, we could support float input that
+                                # is used to do size compute. The problem is that
+                                # we don't actually want to tensorify the compute
+                                # in this case, which means we need codegen support for
+                                # all symfloats.
+                                TensorifyState.specialize(name)
+                                should_restart = True
+
+            # Look for functions to convert
+            if node.op == "call_function" and (
+                replacement_op := SUPPORTED_OPS.get(node.target)
+            ):
+                args: list[Any] = []
+                transform = False
+                compute_dtype = get_computation_dtype(node.meta["val"].dtype)
+
+                for a in node.args:
+                    if (
+                        isinstance(a, fx.Node)
+                        and "val" in a.meta
+                        and isinstance(zf := a.meta["val"], torch.SymFloat)
+                    ):
+                        transform = True
+                        try:
+                            proxy = _sympy_interp(zf.node.expr)
+                        except NotImplementedError:
+                            transform = False
+                            break
+
+                        # We use _expr instead of expr b/c we want the symbol not the replacement
+                        tensorified_symbols.add(a.meta["val"].node._expr)
+
+                        # The upcasting is irrelevant when the compute dtype is bool. This happens
+                        # in cases where we are tensorifying a comparison operator such as
+                        # torch.ops.aten.gt.Tensor
+                        if (
+                            compute_dtype != torch.bool
+                            and proxy.node.meta["val"].dtype != compute_dtype
+                        ):
+                            proxy = torch.ops.prims.convert_element_type.default(
+                                proxy, compute_dtype
+                            )
+
+                        args.append(proxy)
+                    elif isinstance(a, fx.Node):
+                        args.append(MetaProxy(a, tracer=tracer, fake_mode=fake_mode))
+                    else:
+                        args.append(a)
+
+                if transform:
+                    replacement_proxy = replacement_op(*args)
+
+                    if compute_dtype != node.meta["val"].dtype:
+                        replacement_proxy = (
+                            torch.ops.prims.convert_element_type.default(
+                                replacement_proxy,
+                                node.meta["val"].dtype,
+                            )
+                        )
+
+                    node.replace_all_uses_with(replacement_proxy.node)
+                    graph.erase_node(node)
+
+                    metrics_context = get_metrics_context()
+                    if metrics_context.in_progress():
+                        metrics_context.set(
+                            "tensorify_float_success", True, overwrite=True
+                        )
+
+    failed_tensorify_ops: set[str] = set()
+
+    # Now do one more pass that specializes all symfloats we didn't manage
+    # to tensorify away.
+    for node in reversed(graph.nodes):
+        if node.op == "output" or node.op == "placeholder":
+            continue
+
+        with graph.inserting_before(node):
+            if len(node.users) == 0 and not node.is_impure():
+                graph.erase_node(node)
+                continue
+
+            if isinstance(
+                (val := node.meta.get("val")),
+                (torch.SymFloat, torch.SymInt, torch.SymBool),
+            ):
+                if all(
+                    symbol_is_type(s, SymT.FLOAT) for s in val.node.expr.free_symbols
+                ):
+                    # If all symbols are backed symfloats, we can just specialize the whole node
+                    # and get more precise guards. eg.
+                    #
+                    # zf = a.item()
+                    # zf2 = zf // 2
+                    # op(.. zf2 ..)
+                    #
+                    # It's better to guard on zf // 2 == 2.0 than zf == 5.0
+
+                    failed_tensorify_ops.update(str(key) for key in node.users.keys())
+
+                    node.replace_all_uses_with(guard_scalar(val))
+                    graph.erase_node(node)
+
+    # Sometimes by the time we get to tensorify, there have already been
+    # specializations, eg. in python_arg_parser.h. In these cases,
+    # placeholder nodes no longer have a reference to their original
+    # symfloat and thus we need to deduce specializations have happend
+    # via shape_env.replacements. NB: there's an important invariant here
+    # that symfloats keep consistent names across restarts.
+    for k, v in shape_env.var_to_val.items():
+        if symbol_is_type(k, SymT.FLOAT) and isinstance(v, sympy.core.numbers.Float):
+            name = str(k)
+            if (
+                not TensorifyState.should_specialize(name)
+                and k not in tensorified_symbols
+            ):
+                TensorifyState.specialize(name)
+                should_restart = True
+
+    if should_restart:
+        # Sledgehammer time. Restart dynamo analysis, keeping track of which input sources
+        # are no longer needed and should be specialized. Restarting analysis is necessary
+        # because we need to instruct Dynamo to NOT make these as inputs.
+        metrics_context = get_metrics_context()
+        if metrics_context.in_progress():
+            metrics_context.set(
+                "tensorify_float_failure", failed_tensorify_ops, overwrite=True
+            )
+            metrics_context.set("tensorify_float_success", True, overwrite=True)
+        raise TensorifyScalarRestartAnalysis
+
+    graph_code_log.debug(
+        "%s", lazy_format_graph_code("tensorify_python_scalars", gm, colored=True)
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/annotate_getitem_nodes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/annotate_getitem_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a31a76420b34814a6148fa1ffa21f9b0dc897fb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/annotate_getitem_nodes.py
@@ -0,0 +1,59 @@
+import operator
+
+import torch
+
+
+def annotate_getitem_nodes(graph: torch.fx.Graph) -> None:
+    """
+    Annotate the type of getitem nodes, inferred from the type of sequence node.
+    If sequence node is not annotated with a type, do nothing.
+    Currently support getitem nodes from tuple, list, and NamedTuple sequence node.
+
+    This is helpful since annotations on local names within function are lost during FX transforms.
+    Adding back known type annotation for getitem nodes to improve jit scriptability.
+
+    Args:
+        graph (Graph): The graph to be annotated
+    """
+    for node in graph.nodes:
+        if node.target == operator.getitem:
+            sequence_node, index_node = node.args
+            if not sequence_node.type:
+                continue
+            # container types
+            if hasattr(sequence_node.type, "_name"):
+                parameterized_types = sequence_node.type.__args__
+                if sequence_node.type._name == "Tuple":
+                    if len(parameterized_types) == 2 and isinstance(
+                        parameterized_types[1], type(...)
+                    ):
+                        node.type = parameterized_types[0]
+                    else:
+                        assert len(parameterized_types) > index_node
+                        node_type = parameterized_types[index_node]
+                        node.type = node_type
+                elif sequence_node.type._name == "List":
+                    assert len(parameterized_types) == 1
+                    node.type = parameterized_types[0]
+            # Generic Alias Type
+            elif hasattr(sequence_node.type, "__origin__"):
+                parameterized_types = sequence_node.type.__args__
+                if sequence_node.type.__origin__ is tuple:
+                    if len(parameterized_types) == 2 and isinstance(
+                        parameterized_types[1], type(...)
+                    ):
+                        node.type = parameterized_types[0]
+                    else:
+                        assert len(parameterized_types) > index_node
+                        node_type = parameterized_types[index_node]
+                        node.type = node_type
+                elif sequence_node.type.__origin__ is list:
+                    assert len(parameterized_types) == 1
+                    node.type = parameterized_types[0]
+            # NamedTuple type
+            elif hasattr(sequence_node.type, "__annotations__"):
+                if sequence_node.type == torch.Tensor:
+                    continue
+                sequence_node_field_types = sequence_node.type.__annotations__
+                field_name = sequence_node.type._fields[index_node]
+                node.type = sequence_node_field_types[field_name]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/backends/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/backends/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/backends/cudagraphs.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/backends/cudagraphs.py
new file mode 100644
index 0000000000000000000000000000000000000000..b98178f0d5339321673deffdac6f03a96ffbde45
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/backends/cudagraphs.py
@@ -0,0 +1,61 @@
+# mypy: allow-untyped-defs
+import operator
+
+import torch
+from torch.fx.passes.fake_tensor_prop import FakeTensorProp
+from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner
+from torch.fx.passes.operator_support import OperatorSupport
+from torch.fx.passes.tools_common import CALLABLE_NODE_OPS
+from torch.utils import _pytree as pytree
+
+
+class CudaGraphsSupport(OperatorSupport):
+    # TODO: why is submodules passed here
+    def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
+        if node.op not in CALLABLE_NODE_OPS:
+            return False
+
+        if node.target in [torch.ops.aten.embedding_dense_backward.default]:
+            return False
+
+        if node.target in [operator.getitem]:
+            return True
+
+        found_not_cuda = False
+
+        def meta_fk(meta):
+            return meta["val"] if "val" in meta else meta["fake_result"]
+
+        def find_not_cuda(t):
+            nonlocal found_not_cuda
+            if isinstance(t, torch.Tensor) and t.device.type != "cuda":
+                found_not_cuda = True
+
+        for n in node.all_input_nodes:
+            pytree.tree_map_(find_not_cuda, meta_fk(n.meta))
+
+        pytree.tree_map_(find_not_cuda, meta_fk(node.meta))
+
+        # NB: factory function is accounted for because the result would be
+        # cpu or cuda
+
+        return not found_not_cuda
+
+
+def partition_cudagraphs(gm, inputs):
+    """
+    Partition an FX graph into sub-GraphModules that can be validly run under
+    CUDA graphs.  For a subgraph to be runnable under CUDA, all of the operations
+    must involve CUDA tensors only/
+    """
+
+    FakeTensorProp(gm).propagate(*inputs)
+    supported_ops = CudaGraphsSupport()
+    # TODO: single node partition may be wrong due to the pessimization
+    # from copying in and out the data.  Check in benchmarks, perhaps
+    partitioner = CapabilityBasedPartitioner(
+        gm, supported_ops, allows_single_node_partition=True
+    )
+    partitions = partitioner.propose_partitions()
+    fused_graph = partitioner.fuse_partitions(partitions)
+    return fused_graph
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/dialect/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/dialect/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/dialect/common/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/dialect/common/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/dialect/common/cse_pass.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/dialect/common/cse_pass.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5889375bb07ae0f56917aff9950db67ff3f4bec
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/dialect/common/cse_pass.py
@@ -0,0 +1,155 @@
+# mypy: allow-untyped-defs
+from typing import Any
+
+import torch
+from torch.fx import Graph, GraphModule, Node
+from torch.fx.passes.infra.pass_base import PassBase, PassResult
+from torch.utils._pytree import tree_flatten
+
+
+aten = torch.ops.aten
+
+
+# stateful ops are banned from CSE
+rand_ops = {
+    aten.dropout,
+    aten._fused_dropout,
+    aten._standard_gamma,
+    aten.bernoulli,
+    aten.multinomial,
+    aten.native_dropout,
+    aten.normal,
+    aten.poisson,
+    aten.binomial,
+    aten.rrelu,
+    aten.rand_like,
+    aten.rand,
+    aten.randint,
+    aten.randn,
+    aten.randperm,
+}  # noqa: E501,B950
+
+inplace_ops = {
+    aten.add_,
+    aten.sub_,
+    aten.mul_,
+    aten.div_,
+    aten.pow_,
+    aten.lerp_,
+    aten.relu_,
+    aten.sigmoid_,
+    aten.tanh_,
+}  # noqa: E501
+
+
+@torch.fx._compatibility.compatibility(is_backward_compatible=False)
+def get_CSE_banned_ops():
+    return rand_ops.union(inplace_ops)
+
+
+@torch.fx._compatibility.compatibility(is_backward_compatible=False)
+class CSEPass(PassBase):
+    def __init__(self, banned_ops=None):
+        """
+        This version of CSE Pass aims to be dialect agnostic, and it's implemented purely based on the connectivity between fx.Node.
+
+        For functional dialects, user would only need to specify the random ops in ban list.
+
+        Warning: CSE Pass cannot be safely applied on a FX graph in non-functional dialects.
+        If your dialect contains stateful operators, please customized the banned_ops.
+
+        """
+        if banned_ops is None:
+            banned_ops = set()
+        self.banned_ops = banned_ops
+        super().__init__()
+
+    def call(self, graph_module: GraphModule) -> PassResult:
+        """
+        Return a new copy of torch.fx.GraphModule with CSE applied to the input graph
+
+        Example usage:
+
+        from torch.fx.experimental.proxy_tensor import make_fx
+        def f(a):
+            b = a * a
+            c = a * a
+            return b+c
+
+        p = CSEPass()
+        traced_graph = make_fx(f)(torch.tensor(1))
+        print(traced_graph)
+        result = p(traced_graph)
+        print(result.graph_module)
+        """
+
+        def get_aten_target(node):
+            if hasattr(node.target, "overloadpacket"):
+                return node.target.overloadpacket
+            return node.target
+
+        modified = False
+        new_graph = Graph()
+        env: dict[
+            Node, Node
+        ] = {}  # map from node in the old graph to node in the new graph
+        hash_env: dict[
+            tuple[torch._ops.OpOverload, int], Node
+        ] = {}  # map from hash to a node in the new graph
+        token_map: dict[
+            tuple[torch._ops.OpOverload, int], dict[str, Any]
+        ] = {}  # map from hash to token
+        for n in graph_module.graph.nodes:
+            # The placeholder, output, and get_attr nodes are copied to the new graph without change
+            # do not CSE away random operations
+            if (
+                n.op == "placeholder"
+                or n.op == "output"
+                or n.op == "get_attr"
+                or get_aten_target(n) in self.banned_ops
+            ):
+                new_node = new_graph.node_copy(n, lambda x: env[x])
+                env[n] = new_node
+            else:  # n.op == 'call_function', should never see n.op == 'call_module' or 'call_method'
+                # substitute args and kwargs members to their mapping in env if exists
+                # specs can be used to reconstruct nested list/dictionaries
+                def substitute(arg_list):
+                    arg_list, spec = tree_flatten(arg_list)
+                    for i in range(len(arg_list)):
+                        v = arg_list[i]
+                        if isinstance(v, Node) and v in env:
+                            arg_list[i] = env[v]
+                    return tuple(arg_list), spec
+
+                args, args_spec = substitute(n.args)
+                kwargs, kwargs_spec = substitute(n.kwargs)
+
+                # each token corresponds to a unique node
+                # nodes with the same token can be substituted
+                token = {
+                    "target": n.target,
+                    "args": args,
+                    "args_spec": args_spec,
+                    "kwargs": kwargs,
+                    "kwargs_spec": kwargs_spec,
+                }
+
+                # hash substituted args to a number, do not hash specs because specs are not hashable
+                hash_arg = hash((args, kwargs))
+                hash_val = (n.target, hash_arg)
+
+                # check if a node has a substitute and can be eliminated
+                hash_val_in_hash_env = hash_val in hash_env
+                if hash_val_in_hash_env and token_map[hash_val] == token:
+                    modified = True  # substitution happens and the graph is modified
+                    env[n] = hash_env[hash_val]
+                    continue
+
+                new_node = new_graph.node_copy(n, lambda x: env[x])
+                env[n] = new_node
+                if not hash_val_in_hash_env:
+                    hash_env[hash_val] = new_node
+                    token_map[hash_val] = token
+
+        csed_gm = GraphModule(graph_module, new_graph)
+        return PassResult(csed_gm, modified)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/fake_tensor_prop.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/fake_tensor_prop.py
new file mode 100644
index 0000000000000000000000000000000000000000..8036f5d0fd556879ded2d622a284602d240b8534
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/fake_tensor_prop.py
@@ -0,0 +1,80 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch.fx
+from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
+from torch.fx import Node
+from torch.fx._compatibility import compatibility
+from torch.fx.experimental.proxy_tensor import py_sym_types, snapshot_fake
+from torch.fx.node import map_aggregate
+
+
+__all__ = ["FakeTensorProp"]
+
+
+@compatibility(is_backward_compatible=False)
+class FakeTensorProp(torch.fx.Interpreter):
+    """
+    Execute an FX graph Node-by-Node and record a fake tensor representing
+    the metadata for the node.  Unlike ShapeProp, (1) this propagation
+    is cheap--it does the propagation with meta tensors which do not actually
+    store data, and (2) the fake tensors have much more fine grained information,
+    e.g., they have accurate alias information that can be consulted by looking
+    at the storages.
+
+    Args:
+         module (GraphModule): The module to be executed
+         mode (Optional[FakeTensorMode]): The dispatch mode used to execute computation indicated by each FX Node.
+    """
+
+    def __init__(
+        self, module: torch.fx.GraphModule, mode: Optional[FakeTensorMode] = None
+    ):
+        super().__init__(module)
+        if mode is None:
+            mode = FakeTensorMode()
+        self._mode = mode
+        mode.epoch += 1
+        mode.reset_nt_tensor_id_counter()
+
+    def run_node(self, n: Node):
+        from torch.fx.experimental.symbolic_shapes import (
+            compute_unbacked_bindings,
+            rebind_unbacked,
+        )
+
+        result = super().run_node(n)
+        rebind_unbacked(self._mode.shape_env, n, result)
+
+        def extract_val(obj):
+            if isinstance(obj, FakeTensor):
+                return snapshot_fake(obj)
+            elif isinstance(obj, torch.Tensor):
+                # TODO: How is it possible that we get a non fake tensor?  We
+                # should be running under the mode...
+                return snapshot_fake(self._mode.from_tensor(obj, static_shapes=True))
+            elif isinstance(obj, py_sym_types):
+                return obj
+            else:
+                return None
+
+        meta = map_aggregate(result, extract_val)
+        if meta is not None:
+            n.meta["val"] = meta
+            if (shape_env := self._mode.shape_env) and (
+                symbol_to_path := compute_unbacked_bindings(shape_env, result)
+            ):
+                n.meta["unbacked_bindings"] = symbol_to_path
+
+        return result
+
+    def propagate(self, *args):
+        fake_args = [
+            self._mode.from_tensor(a) if isinstance(a, torch.Tensor) else a
+            for a in args
+        ]
+        return self.propagate_dont_convert_inputs(*fake_args)
+
+    def propagate_dont_convert_inputs(self, *args):
+        with self._mode:
+            return super().run(*args)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/graph_drawer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/graph_drawer.py
new file mode 100644
index 0000000000000000000000000000000000000000..275b0d5f6f9e42d5a67fe2cf2c66deafc099bd40
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/graph_drawer.py
@@ -0,0 +1,494 @@
+# mypy: allow-untyped-defs
+
+import hashlib
+from itertools import chain
+from typing import Any, Optional, TYPE_CHECKING
+
+import torch
+import torch.fx
+from torch.fx._compatibility import compatibility
+from torch.fx.graph import _parse_stack_trace
+from torch.fx.node import _format_arg, _get_qualified_name
+from torch.fx.operator_schemas import normalize_function
+from torch.fx.passes.shape_prop import TensorMetadata
+
+
+try:
+    import pydot
+
+    HAS_PYDOT = True
+except ModuleNotFoundError:
+    HAS_PYDOT = False
+    pydot = None
+
+
+__all__ = ["FxGraphDrawer"]
+
+_COLOR_MAP = {
+    "placeholder": '"AliceBlue"',
+    "call_module": "LemonChiffon1",
+    "get_param": "Yellow2",
+    "get_attr": "LightGrey",
+    "output": "PowderBlue",
+}
+
+_HASH_COLOR_MAP = [
+    "CadetBlue1",
+    "Coral",
+    "DarkOliveGreen1",
+    "DarkSeaGreen1",
+    "GhostWhite",
+    "Khaki1",
+    "LavenderBlush1",
+    "LightSkyBlue",
+    "MistyRose1",
+    "MistyRose2",
+    "PaleTurquoise2",
+    "PeachPuff1",
+    "Salmon",
+    "Thistle1",
+    "Thistle3",
+    "Wheat1",
+]
+
+_WEIGHT_TEMPLATE = {
+    "fillcolor": "Salmon",
+    "style": '"filled,rounded"',
+    "fontcolor": "#000000",
+}
+
+if HAS_PYDOT:
+
+    @compatibility(is_backward_compatible=False)
+    class FxGraphDrawer:
+        """
+        Visualize a torch.fx.Graph with graphviz
+        Basic usage:
+            g = FxGraphDrawer(symbolic_traced, "resnet18")
+            g.get_dot_graph().write_svg("a.svg")
+        """
+
+        def __init__(
+            self,
+            graph_module: torch.fx.GraphModule,
+            name: str,
+            ignore_getattr: bool = False,
+            ignore_parameters_and_buffers: bool = False,
+            skip_node_names_in_args: bool = True,
+            parse_stack_trace: bool = False,
+            dot_graph_shape: Optional[str] = None,
+            normalize_args: bool = False,
+        ):
+            self._name = name
+            self.dot_graph_shape = (
+                dot_graph_shape if dot_graph_shape is not None else "record"
+            )
+            self.normalize_args = normalize_args
+            _WEIGHT_TEMPLATE["shape"] = self.dot_graph_shape
+
+            self._dot_graphs = {
+                name: self._to_dot(
+                    graph_module,
+                    name,
+                    ignore_getattr,
+                    ignore_parameters_and_buffers,
+                    skip_node_names_in_args,
+                    parse_stack_trace,
+                )
+            }
+
+            for node in graph_module.graph.nodes:
+                if node.op != "call_module":
+                    continue
+
+                leaf_node = self._get_leaf_node(graph_module, node)
+
+                if not isinstance(leaf_node, torch.fx.GraphModule):
+                    continue
+
+                self._dot_graphs[f"{name}_{node.target}"] = self._to_dot(
+                    leaf_node,
+                    f"{name}_{node.target}",
+                    ignore_getattr,
+                    ignore_parameters_and_buffers,
+                    skip_node_names_in_args,
+                    parse_stack_trace,
+                )
+
+        def get_dot_graph(self, submod_name=None) -> pydot.Dot:
+            """
+            Visualize a torch.fx.Graph with graphviz
+            Example:
+                >>> # xdoctest: +REQUIRES(module:pydot)
+                >>> # xdoctest: +REQUIRES(module:ubelt)
+                >>> # define module
+                >>> class MyModule(torch.nn.Module):
+                >>>     def __init__(self) -> None:
+                >>>         super().__init__()
+                >>>         self.linear = torch.nn.Linear(4, 5)
+                >>>     def forward(self, x):
+                >>>         return self.linear(x).clamp(min=0.0, max=1.0)
+                >>> module = MyModule()
+                >>> # trace the module
+                >>> symbolic_traced = torch.fx.symbolic_trace(module)
+                >>> # setup output file
+                >>> import ubelt as ub
+                >>> dpath = ub.Path.appdir("torch/tests/FxGraphDrawer").ensuredir()
+                >>> fpath = dpath / "linear.svg"
+                >>> # draw the graph
+                >>> g = FxGraphDrawer(symbolic_traced, "linear")
+                >>> g.get_dot_graph().write_svg(fpath)
+            """
+            if submod_name is None:
+                return self.get_main_dot_graph()
+            else:
+                return self.get_submod_dot_graph(submod_name)
+
+        def get_main_dot_graph(self) -> pydot.Dot:
+            return self._dot_graphs[self._name]
+
+        def get_submod_dot_graph(self, submod_name) -> pydot.Dot:
+            return self._dot_graphs[f"{self._name}_{submod_name}"]
+
+        def get_all_dot_graphs(self) -> dict[str, pydot.Dot]:
+            return self._dot_graphs
+
+        def _get_node_style(self, node: torch.fx.Node) -> dict[str, str]:
+            template = {
+                "shape": self.dot_graph_shape,
+                "fillcolor": "#CAFFE3",
+                "style": '"filled,rounded"',
+                "fontcolor": "#000000",
+            }
+            if node.op in _COLOR_MAP:
+                template["fillcolor"] = _COLOR_MAP[node.op]
+            else:
+                # Use a random color for each node; based on its name so it's stable.
+                target_name = node._pretty_print_target(node.target)
+                target_hash = int(
+                    hashlib.md5(
+                        target_name.encode(), usedforsecurity=False
+                    ).hexdigest()[:8],
+                    16,
+                )
+                template["fillcolor"] = _HASH_COLOR_MAP[
+                    target_hash % len(_HASH_COLOR_MAP)
+                ]
+            return template
+
+        def _get_leaf_node(
+            self, module: torch.nn.Module, node: torch.fx.Node
+        ) -> torch.nn.Module:
+            py_obj = module
+            assert isinstance(node.target, str)
+            atoms = node.target.split(".")
+            for atom in atoms:
+                if not hasattr(py_obj, atom):
+                    raise RuntimeError(
+                        str(py_obj) + " does not have attribute " + atom + "!"
+                    )
+                py_obj = getattr(py_obj, atom)
+            return py_obj
+
+        def _typename(self, target: Any) -> str:
+            if isinstance(target, torch.nn.Module):
+                ret = torch.typename(target)
+            elif isinstance(target, str):
+                ret = target
+            else:
+                ret = _get_qualified_name(target)
+
+            # Escape "{" and "}" to prevent dot files like:
+            # https://gist.github.com/SungMinCho/1a017aab662c75d805c5954d62c5aabc
+            # which triggers `Error: bad label format (...)` from dot
+            return ret.replace("{", r"\{").replace("}", r"\}")
+
+        # shorten path to avoid drawing long boxes
+        # for full path = '/home/weif/pytorch/test.py'
+        # return short path = 'pytorch/test.py'
+        def _shorten_file_name(
+            self,
+            full_file_name: str,
+            truncate_to_last_n: int = 2,
+        ):
+            splits = full_file_name.split("/")
+            if len(splits) >= truncate_to_last_n:
+                return "/".join(splits[-truncate_to_last_n:])
+            return full_file_name
+
+        def _get_node_label(
+            self,
+            module: torch.fx.GraphModule,
+            node: torch.fx.Node,
+            skip_node_names_in_args: bool,
+            parse_stack_trace: bool,
+        ) -> str:
+            def _get_str_for_args_kwargs(arg):
+                if isinstance(arg, tuple):
+                    prefix, suffix = r"|args=(\l", r",\n)\l"
+                    arg_strs_list = [_format_arg(a, max_list_len=8) for a in arg]
+                elif isinstance(arg, dict):
+                    prefix, suffix = r"|kwargs={\l", r",\n}\l"
+                    arg_strs_list = [
+                        f"{k}: {_format_arg(v, max_list_len=8)}" for k, v in arg.items()
+                    ]
+                else:  # Fall back to nothing in unexpected case.
+                    return ""
+
+                # Strip out node names if requested.
+                if skip_node_names_in_args:
+                    arg_strs_list = [a for a in arg_strs_list if "%" not in a]
+                if len(arg_strs_list) == 0:
+                    return ""
+                arg_strs = prefix + r",\n".join(arg_strs_list) + suffix
+                if len(arg_strs_list) == 1:
+                    arg_strs = arg_strs.replace(r"\l", "").replace(r"\n", "")
+                return arg_strs.replace("{", r"\{").replace("}", r"\}")
+
+            label = "{" + f"name=%{node.name}|op_code={node.op}\n"
+
+            if node.op == "call_module":
+                leaf_module = self._get_leaf_node(module, node)
+                label += r"\n" + self._typename(leaf_module) + r"\n|"
+                extra = ""
+                if hasattr(leaf_module, "__constants__"):
+                    extra = r"\n".join(
+                        [
+                            f"{c}: {getattr(leaf_module, c)}"
+                            for c in leaf_module.__constants__  # type: ignore[union-attr]
+                        ]  # type: ignore[union-attr]
+                    )
+                label += extra + r"\n"
+            else:
+                label += f"|target={self._typename(node.target)}" + r"\n"
+                if self.normalize_args:
+                    try:
+                        args, kwargs = normalize_function(  # type: ignore[misc]
+                            node.target,  # type: ignore[arg-type]
+                            node.args,  # type: ignore[arg-type]
+                            node.kwargs,
+                            normalize_to_only_use_kwargs=True,
+                        )
+                    except Exception:
+                        # Fallback to not normalizing if there's an exception.
+                        # Some functions need overloads specified to normalize.
+                        args, kwargs = node.args, node.kwargs
+                else:
+                    args, kwargs = node.args, node.kwargs
+                if len(args) > 0:
+                    label += _get_str_for_args_kwargs(args)
+                if len(kwargs) > 0:
+                    label += _get_str_for_args_kwargs(kwargs)
+                label += f"|num_users={len(node.users)}" + r"\n"
+
+            tensor_meta = node.meta.get("tensor_meta")
+            label += self._tensor_meta_to_label(tensor_meta)
+
+            # for original fx graph
+            # print buf=buf0, n_origin=6
+            buf_meta = node.meta.get("buf_meta", None)
+            if buf_meta is not None:
+                label += f"|buf={buf_meta.name}" + r"\n"
+                label += f"|n_origin={buf_meta.n_origin}" + r"\n"
+
+            # for original fx graph
+            # print file:lineno code
+            if parse_stack_trace and node.stack_trace is not None:
+                parsed_stack_trace = _parse_stack_trace(node.stack_trace)
+                fname = self._shorten_file_name(parsed_stack_trace.file)
+                label += (
+                    f"|file={fname}:{parsed_stack_trace.lineno} {parsed_stack_trace.code}"
+                    + r"\n"
+                )
+
+            return label + "}"
+
+        def _tensor_meta_to_label(self, tm) -> str:
+            if tm is None:
+                return ""
+            elif isinstance(tm, TensorMetadata):
+                return self._stringify_tensor_meta(tm)
+            elif isinstance(tm, list):
+                result = ""
+                for item in tm:
+                    result += self._tensor_meta_to_label(item)
+                return result
+            elif isinstance(tm, dict):
+                result = ""
+                for v in tm.values():
+                    result += self._tensor_meta_to_label(v)
+                return result
+            elif isinstance(tm, tuple):
+                result = ""
+                for item in tm:
+                    result += self._tensor_meta_to_label(item)
+                return result
+            else:
+                raise RuntimeError(f"Unsupported tensor meta type {type(tm)}")
+
+        def _stringify_tensor_meta(self, tm: TensorMetadata) -> str:
+            result = ""
+            if not hasattr(tm, "dtype"):
+                print("tm", tm)
+            result += "|" + "dtype" + "=" + str(tm.dtype) + r"\n"
+            result += "|" + "shape" + "=" + str(tuple(tm.shape)) + r"\n"
+            result += "|" + "requires_grad" + "=" + str(tm.requires_grad) + r"\n"
+            result += "|" + "stride" + "=" + str(tm.stride) + r"\n"
+            if tm.is_quantized:
+                assert tm.qparams is not None
+                assert "qscheme" in tm.qparams
+                qscheme = tm.qparams["qscheme"]
+                if qscheme in {
+                    torch.per_tensor_affine,
+                    torch.per_tensor_symmetric,
+                }:
+                    result += "|" + "q_scale" + "=" + str(tm.qparams["scale"]) + r"\n"
+                    result += (
+                        "|"
+                        + "q_zero_point"
+                        + "="
+                        + str(tm.qparams["zero_point"])
+                        + r"\n"
+                    )
+                elif qscheme in {
+                    torch.per_channel_affine,
+                    torch.per_channel_symmetric,
+                    torch.per_channel_affine_float_qparams,
+                }:
+                    result += (
+                        "|"
+                        + "q_per_channel_scale"
+                        + "="
+                        + str(tm.qparams["scale"])
+                        + r"\n"
+                    )
+                    result += (
+                        "|"
+                        + "q_per_channel_zero_point"
+                        + "="
+                        + str(tm.qparams["zero_point"])
+                        + r"\n"
+                    )
+                    result += (
+                        "|"
+                        + "q_per_channel_axis"
+                        + "="
+                        + str(tm.qparams["axis"])
+                        + r"\n"
+                    )
+                else:
+                    raise RuntimeError(f"Unsupported qscheme: {qscheme}")
+                result += "|" + "qscheme" + "=" + str(tm.qparams["qscheme"]) + r"\n"
+            return result
+
+        def _get_tensor_label(self, t: torch.Tensor) -> str:
+            return str(t.dtype) + str(list(t.shape)) + r"\n"
+
+        # when parse_stack_trace=True
+        # print file:lineno code
+        def _to_dot(
+            self,
+            graph_module: torch.fx.GraphModule,
+            name: str,
+            ignore_getattr: bool,
+            ignore_parameters_and_buffers: bool,
+            skip_node_names_in_args: bool,
+            parse_stack_trace: bool,
+        ) -> pydot.Dot:
+            """
+            Actual interface to visualize a fx.Graph. Note that it takes in the GraphModule instead of the Graph.
+            If ignore_parameters_and_buffers is True, the parameters and buffers
+            created with the module will not be added as nodes and edges.
+            """
+
+            # "TB" means top-to-bottom rank direction in layout
+            dot_graph = pydot.Dot(name, rankdir="TB")
+
+            buf_name_to_subgraph = {}
+
+            for node in graph_module.graph.nodes:
+                if ignore_getattr and node.op == "get_attr":
+                    continue
+
+                style = self._get_node_style(node)
+                dot_node = pydot.Node(
+                    node.name,
+                    label=self._get_node_label(
+                        graph_module, node, skip_node_names_in_args, parse_stack_trace
+                    ),
+                    **style,
+                )
+
+                current_graph = dot_graph
+
+                buf_meta = node.meta.get("buf_meta", None)
+                if buf_meta is not None and buf_meta.n_origin > 1:
+                    buf_name = buf_meta.name
+                    if buf_name not in buf_name_to_subgraph:
+                        buf_name_to_subgraph[buf_name] = pydot.Cluster(
+                            buf_name, label=buf_name
+                        )
+                    current_graph = buf_name_to_subgraph.get(buf_name)
+
+                current_graph.add_node(dot_node)
+
+                def get_module_params_or_buffers():
+                    for pname, ptensor in chain(
+                        leaf_module.named_parameters(), leaf_module.named_buffers()
+                    ):
+                        pname1 = node.name + "." + pname
+                        label1 = (
+                            pname1 + "|op_code=get_" + "parameter"
+                            if isinstance(ptensor, torch.nn.Parameter)
+                            else "buffer" + r"\l"
+                        )
+                        dot_w_node = pydot.Node(
+                            pname1,
+                            label="{" + label1 + self._get_tensor_label(ptensor) + "}",
+                            **_WEIGHT_TEMPLATE,
+                        )
+                        dot_graph.add_node(dot_w_node)
+                        dot_graph.add_edge(pydot.Edge(pname1, node.name))
+
+                if node.op == "call_module":
+                    leaf_module = self._get_leaf_node(graph_module, node)
+
+                    if not ignore_parameters_and_buffers and not isinstance(
+                        leaf_module, torch.fx.GraphModule
+                    ):
+                        get_module_params_or_buffers()
+
+            for subgraph in buf_name_to_subgraph.values():
+                subgraph.set("color", "royalblue")
+                subgraph.set("penwidth", "2")
+                dot_graph.add_subgraph(subgraph)
+
+            for node in graph_module.graph.nodes:
+                if ignore_getattr and node.op == "get_attr":
+                    continue
+
+                for user in node.users:
+                    dot_graph.add_edge(pydot.Edge(node.name, user.name))
+
+            return dot_graph
+
+else:
+    if not TYPE_CHECKING:
+
+        @compatibility(is_backward_compatible=False)
+        class FxGraphDrawer:
+            def __init__(
+                self,
+                graph_module: torch.fx.GraphModule,
+                name: str,
+                ignore_getattr: bool = False,
+                ignore_parameters_and_buffers: bool = False,
+                skip_node_names_in_args: bool = True,
+                parse_stack_trace: bool = False,
+                dot_graph_shape: Optional[str] = None,
+                normalize_args: bool = False,
+            ):
+                raise RuntimeError(
+                    "FXGraphDrawer requires the pydot package to be installed. Please install "
+                    "pydot through your favorite Python package manager."
+                )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/graph_manipulation.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/graph_manipulation.py
new file mode 100644
index 0000000000000000000000000000000000000000..f559aa0bfcb3d96733f479d864aaab40923c473c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/graph_manipulation.py
@@ -0,0 +1,113 @@
+# mypy: allow-untyped-defs
+from typing import Any, NamedTuple, Optional
+
+import torch
+from torch.fx._compatibility import compatibility
+from torch.fx.graph import Graph
+from torch.fx.graph_module import GraphModule
+from torch.fx.node import map_arg, Node, Target
+from torch.fx.passes.shape_prop import ShapeProp
+
+
+__all__ = [
+    "replace_target_nodes_with",
+    "size_bytes",
+    "get_size_of_all_nodes",
+    "get_tensor_meta",
+    "get_size_of_node",
+]
+
+
+@compatibility(is_backward_compatible=False)
+def replace_target_nodes_with(
+    fx_module: GraphModule,
+    old_op: str,
+    old_target: Target,
+    new_op: str,
+    new_target: Target,
+):
+    """Modifies all nodes in fx_module.graph.nodes which match the specified op code and target,
+    and updates them to match the new op code and target"""
+    new_graph = Graph()
+    val_map: dict[Node, Node] = {}
+    for node in fx_module.graph.nodes:
+        if node.op == old_op and node.target == old_target:
+            args = map_arg(node.args, lambda n: val_map[n])
+            kwargs = map_arg(node.kwargs, lambda n: val_map[n])
+            assert isinstance(args, tuple)
+            assert isinstance(kwargs, dict)
+            val_map[node] = new_graph.create_node(
+                new_op, new_target, args, kwargs, node.name
+            )
+        else:
+            val_map[node] = new_graph.node_copy(node, lambda n: val_map[n])
+    fx_module.graph = new_graph
+
+
+@compatibility(is_backward_compatible=False)
+class size_bytes(NamedTuple):
+    output_size: int
+    total_size: int
+
+
+@compatibility(is_backward_compatible=False)
+def get_size_of_all_nodes(
+    fx_module: GraphModule, args: Optional[list[torch.Tensor]] = None
+) -> None:
+    """Given a fx graph module, update each node with its total size (weights + bias + output)
+    and its output_size(output). For a non-module node, the total size is the output size.
+    return total size"""
+    if args is not None:
+        # Mark shape and dtype for each node (node.shape and node.dtype)
+        ShapeProp(fx_module).propagate(*args)
+    # Calculate the total size of the whole fx graph
+    for node in fx_module.graph.nodes:
+        if node.op == "output":
+            break
+        node.size_bytes = get_size_of_node(fx_module, node)
+    return
+
+
+@compatibility(is_backward_compatible=False)
+def get_tensor_meta(node: Node) -> Any:
+    tensor_meta = node.meta.get("tensor_meta")
+
+    if not tensor_meta:
+        raise RuntimeError(
+            f"Node {node} has no tensor metadata associated with it! "
+            f"Check that shape propagation has run."
+        )
+
+    return tensor_meta
+
+
+@compatibility(is_backward_compatible=False)
+def get_size_of_node(fx_module: GraphModule, node: Node) -> size_bytes:
+    """Given a node with node.dtype and node.shape, return its total size and its output size.
+    total_size = weights + bias + output_size
+    """
+    # Total num of elements
+    total_num_of_elems = 0
+    # For a module, conside all parameters
+    if node.op == "call_module":
+        submodule_dict = dict(fx_module.named_modules())
+        submodule = submodule_dict[node.target]
+        parameters = submodule.named_parameters()
+        # Parameters are named tuples
+        for _name, p in parameters:
+            total_num_of_elems += p.numel()
+    # Don't forget the output size
+    # node.shape is the shape of this node's output
+    tensor_meta = get_tensor_meta(node)
+    output_elem = tensor_meta.shape.numel()
+    total_num_of_elems += output_elem
+    # Assume for now if it's quantized then it's qint8 or quint8
+    if tensor_meta.is_quantized:
+        size_per_elem_bytes = torch._empty_affine_quantized(
+            [], dtype=tensor_meta.dtype
+        ).element_size()
+    else:
+        size_per_elem_bytes = torch.tensor([], dtype=tensor_meta.dtype).element_size()
+    total_size = size_per_elem_bytes * total_num_of_elems
+    output_size = size_per_elem_bytes * output_elem
+    return size_bytes(output_size, total_size)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/graph_transform_observer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/graph_transform_observer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e19abc7ad3d8b726adeb22082b4c11dfe1cbefc2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/graph_transform_observer.py
@@ -0,0 +1,219 @@
+# mypy: allow-untyped-defs
+import os
+from typing import Callable, Optional, TypeVar
+
+from torch.fx import Graph, Node
+from torch.fx._compatibility import compatibility
+from torch.fx.graph_module import GraphModule
+from torch.fx.traceback import NodeSource, NodeSourceAction
+
+
+T = TypeVar("T")
+
+
+from .graph_drawer import FxGraphDrawer
+
+
+__all__ = ["GraphTransformObserver"]
+
+
+@compatibility(is_backward_compatible=False)
+class GraphTransformObserver:
+    __pass_count = 0
+
+    def __init__(
+        self,
+        gm: GraphModule,
+        passname: str,
+        subsystem: Optional[str] = None,
+        log_url: Optional[str] = None,
+    ):
+        """
+        log_url is inferred to be torch._inductor.config.trace.log_url_for_graph_xform unless otherwise specified
+        """
+        from torch._inductor.config import trace
+
+        self.gm = gm
+        self.passname = passname
+        self.subsystem = subsystem
+
+        if log_url is None:
+            log_url = trace.log_url_for_graph_xform
+
+        self.log_url = log_url
+
+        self.active = trace.enabled or self.log_url is not None
+
+        if self.active:
+            self.erased_nodes: set[str] = set()
+            self.created_nodes: set[str] = set()
+            self.name_to_node: dict[str, Node] = {}
+            # record graph modules deepcopied from self.gm, so we can remove hoooks on them when exiting the context
+            self.copied_gms: list[GraphModule] = []
+
+            self._node_creation_hook = self.get_node_creation_hook()
+            self._node_erase_hook = self.get_node_erase_hook()
+            self._node_replace_hook = self.get_node_replace_hook()
+            self._deepcopy_hook = self.get_deepcopy_hook()
+
+        # If log_url is None, we don't log anything
+        if self.log_url is None:
+            return
+        GraphTransformObserver.__pass_count += 1
+
+        self.input_dot_graph = FxGraphDrawer(
+            self.gm,
+            self.passname,
+            ignore_getattr=True,
+            ignore_parameters_and_buffers=True,
+        ).get_dot_graph()
+
+    @classmethod
+    def get_current_pass_count(cls):
+        return cls.__pass_count
+
+    def apply_gm_pass(self, pass_fn: Callable[[GraphModule], T]) -> Optional[T]:
+        with self:
+            if not self._check_disable_pass():
+                return pass_fn(self.gm)
+
+        return None
+
+    def apply_graph_pass(self, pass_fn: Callable[[Graph], T]) -> Optional[T]:
+        with self:
+            if not self._check_disable_pass():
+                return pass_fn(self.gm.graph)
+
+        return None
+
+    def _check_disable_pass(self):
+        if self.subsystem is None:
+            return False
+
+        debug_info = lambda: self.passname  # noqa: E731
+        from torch._inductor.compiler_bisector import CompilerBisector
+
+        return CompilerBisector.disable_subsystem(
+            "inductor", self.subsystem, debug_info
+        )
+
+    def __enter__(self):
+        if not self.active:
+            return self
+        self.gm._register_create_node_hook(self._node_creation_hook)
+        self.gm._register_erase_node_hook(self._node_erase_hook)
+        self.gm._register_replace_node_hook(self._node_replace_hook)
+        self.gm._register_deepcopy_hook(self._deepcopy_hook)
+
+        self.erased_nodes.clear()
+        self.created_nodes.clear()
+        self.name_to_node.clear()
+        self.copied_gms.clear()
+
+        for node in self.gm.graph.nodes:
+            self.name_to_node[node.name] = node
+
+        return self
+
+    def __exit__(self, type, value, tb):
+        if not self.active:
+            return
+        for gm in self.copied_gms + [self.gm]:
+            gm._unregister_create_node_hook(self._node_creation_hook)
+            gm._unregister_erase_node_hook(self._node_erase_hook)
+            gm._unregister_replace_node_hook(self._node_replace_hook)
+            gm._unregister_deepcopy_hook(self._deepcopy_hook)
+
+        if self.log_url is None:
+            return
+
+        if len(self.created_nodes) > 0 or len(self.erased_nodes) > 0:
+            for e in self.input_dot_graph.get_node_list():
+                if e.get_name() in self.erased_nodes:
+                    e.obj_dict["attributes"]["fillcolor"] = "yellow"
+                else:
+                    e.obj_dict["attributes"]["fillcolor"] = "grey"
+            assert self.log_url is not None
+            self.input_dot_graph.write(
+                os.path.join(
+                    self.log_url,
+                    f"pass_{GraphTransformObserver.__pass_count}_{self.passname}_input_graph.dot",
+                )
+            )
+
+            output_dot_graph = FxGraphDrawer(
+                self.gm,
+                self.passname,
+                ignore_getattr=True,
+                ignore_parameters_and_buffers=True,
+            ).get_dot_graph()
+            for e in output_dot_graph.get_node_list():
+                if e.get_name() in self.created_nodes:
+                    e.obj_dict["attributes"]["fillcolor"] = "yellow"
+                else:
+                    e.obj_dict["attributes"]["fillcolor"] = "grey"
+            output_dot_graph.write(
+                os.path.join(
+                    self.log_url,
+                    f"pass_{GraphTransformObserver.__pass_count}_{self.passname}_output_graph.dot",
+                )
+            )
+
+    def get_node_creation_hook(self):
+        # We have to return a function instead of using a class method directly
+        # to avoid max recursion issue when deepcopy a graph module within the context manager.
+        def on_node_creation(node):
+            self.created_nodes.add(node.name)
+            self.name_to_node[node.name] = node
+            source = NodeSource(None, self.passname, NodeSourceAction.CREATE)
+            if "from_node" not in node.meta:
+                node.meta["from_node"] = [source]
+            else:
+                node.meta["from_node"].append(source)
+
+        return on_node_creation
+
+    def get_node_erase_hook(self):
+        def on_node_erase(node):
+            self.erased_nodes.add(node.name)
+            self.name_to_node.pop(node.name, None)
+
+        return on_node_erase
+
+    def get_node_replace_hook(self):
+        def on_node_replace(old: Node, new: str, user: Node):
+            # Update node meta when replacing old node with new node
+            new_node = self.name_to_node.get(new, None)
+
+            if not new_node:
+                return
+
+            assert isinstance(new_node, Node)
+
+            action = [NodeSourceAction.REPLACE]
+            if new_node.name in self.created_nodes:
+                action.append(NodeSourceAction.CREATE)
+
+            def created_this_pass(source):
+                return source.pass_name == self.passname and source.action == [
+                    NodeSourceAction.CREATE
+                ]
+
+            # remove redundant source added on node creation
+            new_from_node = new_node.meta.get("from_node", [])
+            new_from_node = [
+                source for source in new_from_node if not created_this_pass(source)
+            ]
+
+            # add new source
+            new_node_source = NodeSource(old, self.passname, action)
+            new_from_node.append(new_node_source)
+            new_node.meta["from_node"] = new_from_node
+
+        return on_node_replace
+
+    def get_deepcopy_hook(self):
+        def on_deepcopy(gm):
+            self.copied_gms.append(gm)
+
+        return on_deepcopy
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..939157f1302e75e3cf17ec3c1e93d1b8993d67a0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/__init__.py
@@ -0,0 +1 @@
+from . import pass_manager
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/__pycache__/__init__.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/partitioner.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/partitioner.py
new file mode 100644
index 0000000000000000000000000000000000000000..7867a0a7a6ae7117ffba25505de44de491293aa6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/partitioner.py
@@ -0,0 +1,388 @@
+# mypy: allow-untyped-defs
+import collections
+import itertools
+import logging
+from collections.abc import Iterable, Sequence
+from copy import copy
+from typing import Optional
+
+from torch.fx.graph_module import GraphModule
+from torch.fx.node import _get_qualified_name, Node
+from torch.fx.passes.operator_support import OperatorSupportBase
+from torch.fx.passes.utils.fuser_utils import fuse_by_partitions
+
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
+
+
+class Partition:
+    def __init__(
+        self, id: Optional[int] = None, nodes: Optional[Iterable[Node]] = None
+    ):
+        self.id = id
+        self.nodes = dict.fromkeys(nodes) if nodes is not None else {}
+
+    def __repr__(self) -> str:
+        return str(self.nodes)
+
+    def add_node(self, node: Node):
+        self.nodes.update({node: None})
+
+    def remove_node(self, node: Node):
+        del self.nodes[node]
+
+    def size(self):
+        return len(self.nodes)
+
+
+class _DependencyViewer:
+    def __init__(self, graph_module: GraphModule):
+        self.upstreams = collections.defaultdict(set)
+        self.downstreams = collections.defaultdict(set)
+
+        for node in graph_module.graph.nodes:
+            for input_node in node.all_input_nodes:
+                # add input_node and input_node's upstream dependency
+                self.upstreams[node].add(input_node)
+                self.upstreams[node].update(self.upstreams[input_node])
+
+        for node in reversed(graph_module.graph.nodes):
+            for output_node in node.users:
+                # add output_node and output_node's downstream dependency
+                self.downstreams[node].add(output_node)
+                self.downstreams[node].update(self.downstreams[output_node])
+
+    def downstreams_of(self, node: Node) -> set[Node]:
+        return self.downstreams[node]
+
+    def upstreams_of(self, node: Node) -> set[Node]:
+        return self.upstreams[node]
+
+
+class CapabilityBasedPartitioner:
+    def __init__(
+        self,
+        graph_module: GraphModule,
+        operator_support: OperatorSupportBase,
+        allows_single_node_partition: bool = False,
+        non_compute_ops: Optional[Sequence[str]] = None,
+        allowed_single_node_partition_ops: Optional[Sequence[str]] = None,
+    ) -> None:
+        self.graph_module = graph_module
+        self.operator_support = operator_support
+        self.allows_single_node_partition = allows_single_node_partition
+        self.non_compute_ops = non_compute_ops if non_compute_ops is not None else []
+        self.allowed_single_node_partition_ops = (
+            allowed_single_node_partition_ops
+            if allowed_single_node_partition_ops is not None
+            else []
+        )
+        self.dependency_viewer = _DependencyViewer(graph_module)
+
+    def __is_node_supported(self, node: Node) -> bool:
+        return self.operator_support.is_node_supported(
+            dict(self.graph_module.named_modules()), node
+        )
+
+    def propose_partitions(self) -> list[Partition]:
+        # partition_map is a mapping from partition id to a set of partition id's.
+        # The value set contains all the partition ids that can be reached by doing a
+        # DFS starting from the partition id in the key.
+        partition_map: dict[int, set] = collections.defaultdict(set)
+
+        # assumptions: nodes in candidate list is sorted in topological order
+        assignment: dict[Node, int] = {}  # mapping from node to partition_id
+        partitions_by_id: dict[
+            int, Partition
+        ] = {}  # mapping from partition_id to partition
+        nodes_order: dict[
+            Node, int
+        ] = {}  # mapping from nodes to reversed topological order
+        partitions_order: dict[
+            int, int
+        ] = {}  # mapping from partition_id to minimum topo order of nodes in partition
+        new_partition_id = itertools.count()
+
+        # try to merge partition other_id into partition self_id
+        # merge only happens if the end graph doesn't contain cyclic dependency
+        # returns `True` when merge happens, `False` otherwise.
+        def maybe_merge_partition(self_id: int, other_id: int):
+            # merged_nodes is the union of nodes in two partition to-be-merged
+            merged_nodes = copy(partitions_by_id[self_id].nodes)
+            merged_nodes.update(partitions_by_id[other_id].nodes)
+
+            def dfs_iter_find_cycle(all_user_nodes: set[Node]):
+                for user_node in all_user_nodes:
+                    visited_partition_ids = set()
+
+                    for path_node in self.dependency_viewer.downstreams_of(user_node):
+                        # If any of the nodes in the dfs path of this node are in the merged_nodes
+                        # list then there is a cycle in the graph.
+                        if path_node in merged_nodes:
+                            return True
+
+                        # If any of the nodes in the dfs path of this node are in the assignment
+                        # map then we have to make sure that the partitions that these nodes belong
+                        # to do not form a cycle with the current partitions being merged. This means
+                        # iterating through all the nodes in all the parititons that are traversed in
+                        # the dfs path and checking if they are in the merged_nodes list.
+                        if path_node in assignment:
+                            partition_id = assignment[path_node]
+                            # If the partition id has already been visited then we know that it doesn't
+                            # form a cycle with the current partitions being merged.
+                            if partition_id in visited_partition_ids:
+                                continue
+                            p_map = partition_map[partition_id]
+                            if self_id in p_map or other_id in p_map:
+                                return True
+
+                            visited_partition_ids.add(partition_id)
+
+                return False
+
+            # check if merge would create cyclic dependency.
+            all_user_nodes = set()
+            for node in merged_nodes:
+                for user_node in node.users:
+                    if user_node not in merged_nodes:
+                        all_user_nodes.add(user_node)
+
+            if dfs_iter_find_cycle(all_user_nodes):
+                # return false indicating cyclic dependency found and
+                # merge is aborted
+                return False
+
+            # no cyclic dependency found, move forward with the merge
+            # updating partition nodes
+            partitions_by_id[self_id].nodes = merged_nodes
+            # updating assignment map
+            for node in partitions_by_id[other_id].nodes:
+                assignment[node] = self_id
+            # delete other partition
+            del partitions_by_id[other_id]
+
+            partitions_order[self_id] = min(
+                partitions_order[self_id], partitions_order[other_id]
+            )
+            del partitions_order[other_id]
+
+            partition_map[self_id] = partition_map[self_id].union(
+                partition_map[other_id]
+            )
+            del partition_map[other_id]
+
+            return True
+
+        def merge_single_node(node: Node, id: Optional[int]):
+            def _update_partition_map(node: Node, id: int):
+                # Iterate through all the users of this node and update the partition map to indicate
+                # that there is a path from the partition id of this node to the target partition id.
+                for user_node in node.users:
+                    target_id = assignment.get(user_node, None)
+                    if target_id is not None:
+                        partition_map[id].add(target_id)
+                        partition_map[id].update(partition_map[target_id])
+
+                # Iterate through all the upstream nodes of this node and update the partition map
+                # to indicate that there is a path from the partition id of the upstream node to the
+                # current node's partition id.
+                upstream_nodes = self.dependency_viewer.upstreams_of(node)
+                for curr_node in upstream_nodes:
+                    source_id = assignment.get(curr_node, None)
+                    if source_id is not None:
+                        partition_map[source_id].add(id)
+
+            if node in assignment:
+                partitions_by_id[assignment[node]].remove_node(node)
+
+            if id is None:
+                assignment.pop(node)
+            elif id not in partitions_by_id:
+                assignment[node] = id
+                partitions_by_id[id] = Partition(id=id, nodes=[node])
+                _update_partition_map(node, id)
+            else:
+                assignment[node] = id
+                partitions_by_id[id].add_node(node)
+                _update_partition_map(node, id)
+
+        logger.debug("Proposing partitions...")
+
+        for node in reversed(self.graph_module.graph.nodes):
+            # use Dict as an ordered set to ensure deterministic partitioning result, don't care value
+            merge_candidates: dict[int, None] = {}
+
+            # Note a limited horizontal fusion is enabled:
+            #   when `node` is not supported, the code below attempts to fuse consumer of `node`.
+            #
+            # I don't see a need to add a knob to disable horizontal fusion yet, we can short-cut
+            # the fusion by adding an `else` block here to skip horizontal fusion.
+            if self.__is_node_supported(node) and node not in assignment:
+                partition_id = next(new_partition_id)
+                nodes_order[node] = partition_id
+                partitions_order[partition_id] = partition_id
+                merge_single_node(node, partition_id)
+                merge_candidates[partition_id] = None
+
+            # merge all possible partitions
+            for partition_id, _ in sorted(
+                partitions_order.items(), key=lambda item: item[1]
+            ):
+                merge_candidates[partition_id] = None
+
+            merge_candidates_list = list(merge_candidates.keys())
+            if len(merge_candidates_list) > 1:
+                self_id = merge_candidates_list[0]
+                for other_id in merge_candidates_list[1:]:
+                    # note: merge partition `other_id` into partition `self_id` if
+                    # it doesn't create cyclic dependency in the graph, otherwise,
+                    # this is a no-op
+                    maybe_merge_partition(self_id, other_id)
+
+        # post processing to re-assign "getitem" nodes into upstream partition
+        logger.debug("Reassigning getitem nodes to its producer node's partition...")
+        nodes_reassignment: dict[Node, int] = {}
+        for node in self.graph_module.graph.nodes:
+            is_tuple_output = True
+            for user in node.users:
+                if (
+                    user.op != "call_function"
+                    or _get_qualified_name(user.target) != "_operator.getitem"
+                ):  # type: ignore[arg-type]
+                    is_tuple_output = False
+                    break
+
+            # node has tuple outputs, re-assign all following getitem node into node's partition
+            if is_tuple_output:
+                id = assignment.get(node, None)  # type: ignore[arg-type]
+                for user in node.users:
+                    if assignment.get(user, None) != id:  # type: ignore[arg-type]
+                        nodes_reassignment[user] = id  # type: ignore[assignment]
+        for node, id in nodes_reassignment.items():
+            merge_single_node(node, id)
+
+        # filter out single node partitions
+        if not self.allows_single_node_partition:
+            logger.debug("Filtering out single node partitions...")
+            default_non_compute_ops = {"torch.ops.aten.view", "_operator.getitem"}
+            non_compute_ops = default_non_compute_ops.union(set(self.non_compute_ops))
+            partitions_to_remove: list[int] = []
+            for id, partition in partitions_by_id.items():
+                compute_node_count = 0
+                for node in partition.nodes:
+                    if node.op == "call_function":
+                        assert callable(node.target)
+                        if _get_qualified_name(node.target) not in non_compute_ops:
+                            compute_node_count += 1
+                        if (
+                            _get_qualified_name(node.target)
+                            in self.allowed_single_node_partition_ops
+                        ):
+                            compute_node_count += 1
+                if compute_node_count <= 1:
+                    partitions_to_remove.append(id)
+            for id in partitions_to_remove:
+                del partitions_by_id[id]
+
+        logger.debug("Partitions proposed:")
+        for id, partition in partitions_by_id.items():
+            logger.debug(
+                "partition #%s: %s", id, [node.name for node in partition.nodes]
+            )
+
+        return [
+            partition for partition in partitions_by_id.values() if partition.size() > 0
+        ]
+
+    def fuse_partitions(
+        self, partitions: list[Partition], prefix: str = "fused_"
+    ) -> GraphModule:
+        logger.debug("Fusing partitions...")
+        # fuse_by_partitions expects partitions in List[Dict[Node, None]]: [ {node0 : None}, {node1 : None} ]
+        return fuse_by_partitions(
+            self.graph_module,
+            [partition.nodes for partition in partitions],
+            prefix=prefix,
+        )
+
+    # remove non-compute-ops that sits at the boundary of a partition.
+    def remove_bookend_non_compute_ops(self, partitions: list[Partition]):
+        non_compute_ops = set(self.non_compute_ops)
+
+        def is_non_compute_node(node: Node):
+            return (
+                node.op == "call_function"
+                and _get_qualified_name(node.target) in non_compute_ops  # type: ignore[arg-type]
+            )
+
+        # cache transparent nodes
+        transparent_input_nodes: dict[Node, bool] = {}
+        transparent_output_nodes: dict[Node, bool] = {}
+
+        def is_transparent_input_node(
+            node: Node, partition: set[Node], removed_nodes: set[Node]
+        ):
+            if (
+                node.op == "placeholder"
+                or (node not in partition)
+                or (node in removed_nodes)
+            ):
+                return True
+            if node in transparent_input_nodes:
+                return transparent_input_nodes[node]
+            if is_non_compute_node(node):
+                for input_n in node.all_input_nodes:
+                    if not is_transparent_input_node(input_n, partition, removed_nodes):
+                        transparent_input_nodes[node] = False
+                        return False
+                transparent_input_nodes[node] = True
+                return True
+            transparent_input_nodes[node] = False
+            return False
+
+        def is_transparent_output_node(
+            node: Node, partition: set[Node], removed_nodes: set[Node]
+        ):
+            if (
+                node.op == "placeholder"
+                or (node not in partition)
+                or (node in removed_nodes)
+            ):
+                return True
+            if node in transparent_output_nodes:
+                return transparent_output_nodes[node]
+            if is_non_compute_node(node):
+                for output_n in node.users:
+                    if not is_transparent_output_node(
+                        output_n, partition, removed_nodes
+                    ):
+                        transparent_output_nodes[node] = False
+                        return False
+                transparent_output_nodes[node] = True
+                return True
+            transparent_output_nodes[node] = False
+            return False
+
+        for partition in partitions:
+            # Note it's ok to use `set` here, since we are only query if a node
+            # has been removed. We are NEVER going to iterate on nodes inside
+            # the set.
+            remove_node: set[Node] = set()
+            for node in partition.nodes:
+                if is_non_compute_node(node) and (
+                    is_transparent_input_node(node, set(partition.nodes), remove_node)
+                    or is_transparent_output_node(
+                        node, set(partition.nodes), remove_node
+                    )
+                ):
+                    remove_node.add(node)
+
+            if len(remove_node) != 0:
+                for node in remove_node:
+                    partition.nodes.pop(node, None)
+
+    def partition_and_fuse(self, prefix: str = "fused_") -> GraphModule:
+        partitions = self.propose_partitions()
+        fused_gm = self.fuse_partitions(partitions, prefix=prefix)
+        return fused_gm
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/pass_base.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/pass_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..957b8145f995dedb7d40f7d63ba555d40173a53d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/pass_base.py
@@ -0,0 +1,78 @@
+# mypy: allow-untyped-defs
+import abc
+from collections import namedtuple
+from typing import Optional
+
+from torch.fx._compatibility import compatibility
+from torch.fx.graph_module import GraphModule
+
+
+__all__ = ["PassResult", "PassBase"]
+
+
+@compatibility(is_backward_compatible=False)
+class PassResult(namedtuple("PassResult", ["graph_module", "modified"])):
+    """
+    Result of a pass:
+        graph_module: The modified graph module
+        modified: A flag for if the pass has modified the graph module
+    """
+
+    __slots__ = ()
+
+    def __new__(cls, graph_module, modified):
+        return super().__new__(cls, graph_module, modified)
+
+
+@compatibility(is_backward_compatible=False)
+class PassBase(abc.ABC):
+    """
+    Base interface for implementing passes.
+
+    It is required to implement the `call` function so that we can directly
+    pass instances of the Pass directly to the PassManager and call them as a
+    function.
+
+    We can directly pass an instance of a class implementing this interface into
+    the PassManager's `passes` attribute.
+    """
+
+    def __call__(self, graph_module: GraphModule) -> Optional[PassResult]:
+        """
+        Runs the precondition check, the pass itself, and the postcondition check.
+        """
+
+        self.requires(graph_module)
+        res = self.call(graph_module)
+        self.ensures(graph_module)
+        return res
+
+    @abc.abstractmethod
+    def call(self, graph_module: GraphModule) -> Optional[PassResult]:
+        """
+        The pass that is run through the given graph module. To implement a
+        pass, it is required to implement this function.
+
+        Args:
+            graph_module: The graph module we will run a pass on
+        """
+
+    def requires(self, graph_module: GraphModule) -> None:  # noqa: B027
+        """
+        This function will be called before the pass is run and will check that
+        the given graph module contains the preconditions needed to run the
+        pass. It is not required to implement this function.
+
+        Args:
+            graph_module: The graph module we will run checks on
+        """
+
+    def ensures(self, graph_module: GraphModule) -> None:  # noqa: B027
+        """
+        This function will be called after the pass is run and will check that
+        the given graph module contains the postconditions needed to run the
+        pass. It is not required to implement this function.
+
+        Args:
+            graph_module: The graph module we will run checks on
+        """
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/pass_manager.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/pass_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..68753d9351f103003c9a8fcac402900ad63d1658
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/infra/pass_manager.py
@@ -0,0 +1,309 @@
+# mypy: allow-untyped-defs
+import inspect
+import logging
+from functools import wraps
+from queue import Queue
+from typing import Callable
+
+import torch.nn as nn
+from torch.fx._compatibility import compatibility
+from torch.fx.graph_module import GraphModule
+from torch.fx.passes.infra.pass_base import PassResult
+
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
+
+__all__ = ["pass_result_wrapper", "this_before_that_pass_constraint", "PassManager"]
+
+
+@compatibility(is_backward_compatible=False)
+def pass_result_wrapper(fn: Callable) -> Callable:
+    """
+    Wrapper for passes which currently do not return a PassResult.
+    This wrapper makes them return a PassResult containing the modified object
+    and True for the "modified" flag.
+
+    Args:
+        fn (Callable[Module, Any])
+
+    Returns:
+        wrapped_fn (Callable[Module, PassResult])
+    """
+    if fn is None:
+        return None
+
+    @wraps(fn)
+    def wrapped_fn(gm):
+        res = fn(gm)
+        if res is None:
+            return PassResult(gm, True)
+        if isinstance(res, PassResult):
+            return res
+        elif isinstance(res, nn.Module):
+            return PassResult(res, True)
+
+    if not inspect.isfunction(fn):
+        wrapped_fn.__name__ = type(fn).__name__
+
+    return wrapped_fn
+
+
+def _validate_pass_schedule_constraint(
+    constraint: Callable[[Callable, Callable], bool], passes: list[Callable]
+) -> None:
+    for i, a in enumerate(passes):
+        for j, b in enumerate(passes[i + 1 :]):
+            if constraint(a, b):
+                continue
+            raise RuntimeError(
+                f"pass schedule constraint violated. Expected {a} before {b}"
+                f" but found {a} at index {i} and {b} at index{j} in pass"
+                f" list."
+            )
+
+
+def _topological_sort_passes(
+    passes: list[Callable], constraints: list[Callable]
+) -> list[Callable]:
+    """
+    Args
+        passes: Passes that we are ordering
+        constraints: Constraints applied on these passes
+
+    Returns
+        A sorted list of callables and a boolean of if a circular dependency
+        existed
+    """
+    if len(constraints) == 0:
+        return passes
+
+    # Contruct a graph mapping nodes to a list of their users
+    graph: dict[Callable, list[Callable]] = {p: [] for p in passes}
+    indegree_map: dict[Callable, int] = dict.fromkeys(passes, 0)
+    candidates: Queue = Queue()
+    for a in passes:
+        for b in passes:
+            if a == b:
+                continue
+
+            for constraint in constraints:
+                if not constraint(a, b):
+                    graph[b].append(a)
+                    indegree_map[a] += 1
+
+        if indegree_map[a] == 0:
+            candidates.put(a)
+
+    visited: dict[Callable, bool] = dict.fromkeys(passes, False)
+    sorted_passes: list[Callable] = []
+
+    while not candidates.empty():
+        p = candidates.get()
+        sorted_passes.append(p)
+        visited[p] = True
+
+        for n in graph[p]:
+            if not visited[n]:
+                indegree_map[n] -= 1
+                if indegree_map[n] == 0:
+                    candidates.put(n)
+
+    # Check if there are unvisited nodes (aka cycles in the graph)
+    cycle_passes = list(filter(lambda p: indegree_map[p] != 0, indegree_map.keys()))
+    if len(cycle_passes) != 0:
+        error = (
+            f"Circular dependency detected within the following passes: {cycle_passes}"
+        )
+        raise RuntimeError(error)
+
+    return sorted_passes
+
+
+@compatibility(is_backward_compatible=False)
+def this_before_that_pass_constraint(this: Callable, that: Callable) -> Callable:
+    """
+    Defines a partial order ('depends on' function) where `this` must occur
+    before `that`.
+
+    For example, the following pass list and constraint list would be invalid.
+    ```
+    passes = [pass_b, pass_a]
+
+    constraints = [this_before_that_pass_constraint(pass_a, pass_b)]
+    ```
+
+    Args:
+        this (Callable): pass which should occur first
+        that (Callable): pass which should occur later
+
+    Returns:
+        depends_on (Callable[[Object, Object], bool]
+    """
+
+    def depends_on(a: Callable, b: Callable):
+        return a != that or b != this
+
+    return depends_on
+
+
+@compatibility(is_backward_compatible=False)
+class PassManager:
+    """
+    Construct a PassManager.
+
+    Collects passes and constraints. This defines the pass schedule, manages
+    pass constraints and pass execution.
+
+    Args:
+        passes (Optional[List[Callable]]): List of passes. A pass is a
+            callable which modifies an object and returns a PassResult
+        constraint (Optional[List[Callable]]): List of constraints. A
+            constraint is a callable which takes two passes (A, B) and returns
+            True if A depends on B and False otherwise. See implementation of
+            `this_before_that_pass_constraint` for example.
+        steps (int): Max number of times we run the passes (default = 1).
+        run_checks_after_each_pass (bool): Whether to run checks and linting
+            after each pass
+        suppress_check_failures (bool): Whether to raise errors when running
+            checks
+    """
+
+    passes: list[Callable[[nn.Module], PassResult]]
+    constraints: list[Callable[[Callable, Callable], bool]]
+    _validated: bool = False
+    steps: int = 1
+
+    def __init__(
+        self,
+        passes=None,
+        constraints=None,
+        steps=None,
+        run_checks_after_each_pass: bool = False,
+        suppress_check_failures: bool = False,
+    ):
+        self.passes = passes or []
+        self.constraints = constraints or []
+        if steps:
+            self.steps = steps
+
+        self.run_checks_after_each_pass = run_checks_after_each_pass
+        self.suppress_check_failures = suppress_check_failures
+
+    def add_pass(self, _pass: Callable):
+        """
+        Adds a pass into the current list of passes.
+        """
+        self.passes.append(_pass)
+        self._validated = False
+
+    def add_constraint(self, constraint: Callable):
+        """
+        Adds a constraint into the current list of constraints.
+        """
+        self.constraints.append(constraint)
+        self._validated = False
+
+    def validate_constraints(self):
+        """
+        Validates that current pass schedule defined by `self.passes` is valid
+        according to all constraints in `self.constraints`
+        """
+        if self._validated:
+            return
+        for constraint in self.constraints:
+            _validate_pass_schedule_constraint(constraint, self.passes)
+        self._validated = True
+
+    def solve_constraints(self):
+        """
+        Finds a valid traversal order based on the given constraints and orders
+        the passes based on this order.
+
+        If a circular dependency exists between the constraints and steps = 1,
+        then we will raise an error because if steps != 1 this means that we
+        will re-run the passes, allowing for circular dependencies.
+        """
+        self.passes = _topological_sort_passes(self.passes, self.constraints)
+        self._validated = True
+
+    def add_checks(self, check: Callable) -> None:
+        """
+        Adds a function which takes runs various checks on a given graph module.
+        This function is run before and after each pass if the
+        `run_checks_after_each_pass` flag is enabled.
+        """
+        sig = inspect.signature(check)
+
+        if len(list(sig.parameters.values())) != 1:
+            raise TypeError(
+                "PassManager check function should only take in one variable, a module"
+            )
+
+        setattr(self, "check", check)  # noqa: B010
+
+    def check(self, module: nn.Module) -> None:
+        pass
+
+    def __call__(self, module: nn.Module) -> PassResult:
+        """
+        Runs a list of passes in the order based on `self.passes` on the given
+        graph module. Each time a pass is run, checks and linting will be run on
+        the graph module if `run_checks_after_each_pass` is set.
+
+        If the module is a graph module, we will run the list of passes until
+        the graph stops changing, or until `steps` number of times.
+        """
+        # Order the passes based on the constraints
+        if not self._validated:
+            self.solve_constraints()
+
+        # Check graph invariants
+        self.check(module)
+
+        # Run the set of passes `steps` number of times or until the graph stops
+        # changing
+        overall_modified = False
+        for _ in range(self.steps):
+            modified = False
+
+            # Run the set of passes on the graph module
+            for i, fn in enumerate(self.passes):
+                fn_name = fn.__name__ if inspect.isfunction(fn) else type(fn).__name__
+                logger.debug("Running pass '%s'", fn_name)
+
+                try:
+                    res = fn(module)
+
+                    if not isinstance(res, PassResult) and not hasattr(
+                        res, "graph_module"
+                    ):
+                        raise TypeError(
+                            f"The result of the pass {fn_name} should be type PassResult."
+                            + "Please wrap it with pass_result_wrapper()"
+                        )
+                    module = res.graph_module
+                    modified = modified or res.modified
+
+                    if isinstance(module, GraphModule):
+                        logger.debug("Graph after pass '%s': %s", fn_name, module.graph)
+                        module.recompile()
+
+                    # Check graph invariants
+                    if self.run_checks_after_each_pass:
+                        self.check(module)
+
+                except Exception as e:
+                    prev_pass_names = [
+                        p.__name__ if inspect.isfunction(p) else type(p).__name__
+                        for p in self.passes[:i]
+                    ]
+                    msg = f"An error occurred when running the '{fn_name}' pass after the following passes: {prev_pass_names}"
+                    raise Exception(msg) from e  # noqa: TRY002
+
+            # If the graph no longer changes, then we can stop running these passes
+            overall_modified = overall_modified or modified
+            if not modified:
+                break
+
+        return PassResult(module, overall_modified)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/net_min_base.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/net_min_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bfd7cdd8258317ff142990484c5e9300490aa72
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/net_min_base.py
@@ -0,0 +1,951 @@
+# mypy: allow-untyped-defs
+import logging
+from dataclasses import dataclass
+from typing import Any, Callable, Optional
+
+import torch
+import torch.fx
+from torch.fx._compatibility import compatibility
+from torch.fx.node import map_arg
+
+from .shape_prop import ShapeProp
+from .split_utils import split_by_tags
+from .tools_common import (
+    CALLABLE_NODE_OPS,
+    FxNetAccFusionsFinder,
+    Names,
+    NodeList,
+    NodeSet,
+    TensorOrTensors,
+    Tensors,
+)
+
+
+__all__ = [
+    "FxNetMinimizerBadModuleError",
+    "FxNetMinimizerRunFuncError",
+    "FxNetMinimizerResultMismatchError",
+]
+
+_LOGGER = logging.getLogger(__name__)
+
+
+@compatibility(is_backward_compatible=False)
+class FxNetMinimizerBadModuleError(Exception):
+    """
+    Raised if failed to split out a minimize module
+    """
+
+
+@compatibility(is_backward_compatible=False)
+class FxNetMinimizerRunFuncError(Exception):
+    """
+    Raised if error occurs during run_a or run_b functions
+    """
+
+
+@compatibility(is_backward_compatible=False)
+class FxNetMinimizerResultMismatchError(Exception):
+    """
+    Raised if comparing function thinks the results are mismatching.
+    """
+
+
+@dataclass
+class _MinimizerSettingBase:
+    """
+    Args:
+    `accumulate_error`: Instead of using a's input for both converted module to verify
+    , use the previous outputs of each converted module as input to accumulate the
+    errors.
+
+    `traverse_method`: "sequential" or "binary" or "accumulate"
+    Determine the way of traverse the nodes in FX module.
+
+    `find_all`: Minimizer will go through the entire model and return all problematic nodes.
+
+    `return_intermediate`: If true, when using `run_nodes()` function to run the
+    model, intermediate results of all the ops will be returned as output.
+
+    `all_outputs`: If true, when using `_run_and_compare()` function,
+    all the output nodes in the subgraph will be used for comparison.
+    """
+
+    accumulate_error: bool = False
+    traverse_method: str = "sequential"
+    find_all: bool = False
+    return_intermediate: bool = False
+    all_outputs: bool = False
+
+    def __str__(self):
+        settings_str = "FX Minimizer Settings:\n"
+
+        for k, v in vars(self).items():
+            settings_str += f"\t{k}: {v}\n"
+
+        return settings_str
+
+
+class _MinimizerBase:
+    """
+    This class is used to automatically find problematic nodes in a model. It takes a FX
+    graphmodule and generate some submodules while traverse the graph. Then two functions
+    `run_a` and `run_b` will be used to run the same submodule and a function `compare_fn`
+    will be used to compare the results.
+
+    Currently we provides two ways to traverse the graph and generate submodules.
+        1. Sequential traversal: this will traverse the graph node by node and generate
+           one submodule with one sigle node.
+        2. Binary searching: this will do a binary search style traversal on the graph.
+
+    For internal Users, a guide can be found here https://fb.quip.com/HDtuAgiKGfkP.
+    """
+
+    def __init__(
+        self,
+        module: torch.fx.GraphModule,
+        sample_input: Tensors,
+        compare_fn: Callable[
+            [TensorOrTensors, TensorOrTensors, Names], tuple[float, bool]
+        ],
+        settings: _MinimizerSettingBase,
+        module_exporter: Optional[
+            Callable[[Tensors, torch.fx.GraphModule, str], None]
+        ] = None,
+        exclusion_fn: Optional[Callable[[NodeList, int, int], None]] = None,
+    ):
+        assert isinstance(module, torch.fx.GraphModule)
+
+        self.module = module
+        self.sample_input = sample_input
+        self.compare_fn = compare_fn
+        self.module_exporter = module_exporter
+        self.settings = settings
+        self.exclusion_fn = exclusion_fn
+
+        # Stores outputs of run_a function
+        self.a_outputs: dict[str, Any] = {}
+
+        # Stores outputs of run_b function
+        self.b_outputs: dict[str, Any] = {}
+
+        # Stores the results of compare_fn
+        self.results: dict[Any, Any] = {}
+
+        # Stores the report for the runs
+        self.reports: list[list[str]] = []
+
+        # Current iteration
+        self.iteration: int = 0
+
+        callable_nodes = {
+            node for node in self.module.graph.nodes if node.op in CALLABLE_NODE_OPS
+        }
+        self.run_shape_prop()
+        self.fusions = FxNetAccFusionsFinder(self.module, callable_nodes)()
+
+        # Check if number of input in sample_input matches the number of placeholders
+        placeholders = [
+            node.name for node in self.module.graph.nodes if node.op == "placeholder"
+        ]
+        assert len(placeholders) == len(self.sample_input)
+
+        # Store sample_input
+        for i, name in enumerate(placeholders):
+            self.a_outputs[name] = sample_input[i]
+            self.b_outputs[name] = sample_input[i]
+
+    def run_shape_prop(self) -> None:
+        """
+        Helper function to run shape propagation on module. Can be overridden by
+        subclasses for custom shape propagation logic.
+        """
+        ShapeProp(self.module).propagate(*self.sample_input)
+
+    def run_a(
+        self, mod: torch.fx.GraphModule, inputs: Tensors, report_idx: int = -1
+    ) -> TensorOrTensors:
+        """
+        Run `mod` with `inputs` and generate output. The output will be compared with
+        output of run_b().
+        """
+        raise RuntimeError("run_a() is not implemented.")
+
+    def run_b(
+        self, mod: torch.fx.GraphModule, inputs: Tensors, report_idx: int = -1
+    ) -> TensorOrTensors:
+        """
+        Run `mod` with `inputs` and generate output. The output will be compared with
+        output of run_a().
+        """
+        raise RuntimeError("run_b() is not implemented.")
+
+    def _store_outputs(
+        self,
+        a_result: TensorOrTensors,
+        b_result: TensorOrTensors,
+        submodule: torch.fx.GraphModule,
+    ):
+        """
+        Store the outputs of self.run_a() and self.run_b() into self.a_outputs and
+        self.b_outputs, so that we can use them when execute preceding nodes that
+        use those outputs as inputs.
+
+        Args:
+            a_result: Output of self.run_a(). Could be a tensor or tensors.
+            b_result: Output of self.run_b(). Could be a tensor or tensors.
+            submodule: The module that generates a_result and b_result.
+        """
+        output_node = next(
+            node for node in submodule.graph.nodes if node.op == "output"
+        )
+
+        # Only one output
+        if isinstance(output_node.args[0], torch.fx.Node):
+            self.a_outputs[output_node.args[0].name] = a_result
+            self.b_outputs[output_node.args[0].name] = b_result
+        # Multiple outputs
+        else:
+            for i, arg in enumerate(output_node.args[0]):
+                self.a_outputs[arg.name] = a_result[i]
+                self.b_outputs[arg.name] = b_result[i]
+
+    def _get_submod_inputs(
+        self, main_module: torch.fx.GraphModule, submod_path: str
+    ) -> tuple[Tensors, Tensors]:
+        """
+        Try get submodule inputs from stored outputs. If not found then use
+        torch_glow.get_submod_inputs to get the inputs.
+
+        If accumulate_error is False, use a_input for run_a() and run_b()
+        otherwise use a_input for run_a and b_input for run_b.
+
+        Args:
+            main_module: Top-levlel fx module.
+            submod_path: Path to the submodule we want to run and compare results.
+
+        Returns:
+            a_input: List of tensor(s) that will be used by run_a() as submodule inputs.
+            b_input: List of tensor(s) that will be used by run_b() as submodule inputs.
+        """
+        a_input = []
+        b_input = []
+        submodule = getattr(main_module, submod_path)
+        placeholders = [
+            node.name for node in submodule.graph.nodes if node.op == "placeholder"
+        ]
+
+        # If all placeholder can be found in stored outputs, use stored
+        # outputs as inputs. Otherwise, use `torch_glow.get_submod_inputs`
+        # to get the inputs.
+        if set(placeholders) <= self.a_outputs.keys():
+            for name in placeholders:
+                a_input.append(self.a_outputs[name])
+                b_input.append(self.b_outputs[name])
+        else:
+            if self.settings.accumulate_error:
+                print(f"Can't find previous stored outputs named {placeholders}!")
+
+            def get_inputs(self: torch.nn.Module, inputs: Any):
+                nonlocal a_input
+                a_input = inputs
+
+            # Use forward hook to get the inputs to the submodule
+            handle = submodule.register_forward_pre_hook(get_inputs)
+            main_module(*self.sample_input)
+            handle.remove()
+
+            b_input = a_input
+
+        if not self.settings.accumulate_error:
+            return a_input, a_input
+
+        return a_input, b_input
+
+    def _tag_nodes(self, selected_nodes: NodeSet):
+        """
+        Tag selected nodes with tag "minimize". Nodes with the same tags will
+        be split to the same submodule afterwards.
+
+        Args:
+            selected_nodes: Nodes that we want to minimize. We will tag those nodes
+                with "minimize", all preceding nodes with "main_0" and all following
+                nodes with "main_1".
+        """
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+
+            if node in selected_nodes:
+                node.tag = "minimize"
+            elif any(
+                n.tag in {"minimize", "main_1"}
+                for n in node.all_input_nodes
+                if n.op in CALLABLE_NODE_OPS
+            ):
+                node.tag = "main_1"
+            else:
+                node.tag = "main_0"
+
+    def _build_submodule(self, nodes: NodeSet) -> tuple[torch.fx.GraphModule, str]:
+        """
+        Split self.module so that one submodule consists of `nodes` and only `nodes`.
+
+        Args:
+            nodes: Nodes that we want to include in the minimize submodule.
+
+        Returns:
+            split_module (torch.fx.GraphModule): the module after split.
+            submodule_name (str): the name of the submodule that consists of `nodes`.
+        """
+        # Color provided nodes
+        self._tag_nodes(nodes)
+
+        # Split module based on coloring
+        split_module = split_by_tags(self.module, ["main_0", "minimize", "main_1"])
+
+        # Find submodule containing colored nodes
+        submodule_name: str = ""
+        for child_name, _ in split_module.named_children():  # type: ignore[union-attr]
+            # Skip submodules we're not interested in at the moment
+            if "minimize" not in child_name:
+                continue
+
+            if submodule_name == "":
+                submodule_name = child_name
+            else:
+                raise FxNetMinimizerBadModuleError(
+                    f"Expected only one minimize submodule with nodes {nodes}"
+                )
+
+        if submodule_name == "":
+            raise FxNetMinimizerBadModuleError(
+                f"Minimize submodule was not found with nodes {nodes}"
+            )
+
+        return split_module, submodule_name  # type: ignore[return-value]
+
+    def _run_and_compare(
+        self,
+        split_module: torch.fx.GraphModule,
+        submod_name: str,
+        output_names: Names,
+        report_idx: int = -1,
+    ):
+        """
+        Run the submodule in `split_module` that has name `submod_name`
+        using `self.run_a` and `self.run_b` and compare their results.
+
+        Args:
+            split_module: Main module that contains the minimize submodule.
+            submod_name: Name of the minimize submodule.
+            output_names: Names of the node we want to output. If None, we
+                will use the original output.
+        """
+        submodule = getattr(split_module, submod_name)
+        a_input, b_input = self._get_submod_inputs(split_module, submod_name)
+
+        if len(self.reports) == 0:
+            self.reports.append([])
+            self.iteration = 1
+
+        report = self.reports[report_idx if report_idx >= 0 else self.iteration - 1]
+        report.append("Run and compare ...")
+
+        if output_names and not self.settings.all_outputs:
+            output_nodes: NodeList = []
+            for node in submodule.graph.nodes:
+                if node.op == "output":
+                    submodule.graph.erase_node(node)
+
+                if node.name in output_names:
+                    output_nodes.append(node)
+
+            submodule.graph.output(
+                output_nodes[0] if len(output_nodes) == 1 else tuple(output_nodes)
+            )
+            submodule.graph.lint()
+            submodule.recompile()
+
+        # Use name of args in output node as key to store comparison result
+        for node in submodule.graph.nodes:
+            if node.op == "output":
+                result_key = map_arg(node.args, lambda x: x.name)
+
+        try:
+            a_result = self.run_a(submodule, a_input, report_idx)
+            b_result = self.run_b(submodule, b_input, report_idx)
+            self._store_outputs(a_result, b_result, submodule)
+        except Exception as e:
+            report.append(f"Exception raised when running {submod_name}: {e}")
+            raise FxNetMinimizerRunFuncError(  # noqa: B904
+                f"Exception raised when running {submod_name}: {e}"
+            )
+
+        # Compare results
+        names: Names = output_names
+        if output_names is None:
+            names = [str(v) for v in result_key]  # type: ignore[possibly-undefined]
+
+        numeric_result, bool_result = self.compare_fn(a_result, b_result, names)
+
+        self.results[result_key] = numeric_result  # type: ignore[possibly-undefined]
+        report.append(f"Numerical accuracy = {numeric_result}")
+        if not bool_result:
+            report.append(f"Result mismatch for {result_key}")  # type: ignore[possibly-undefined]
+            if self.module_exporter:
+                if isinstance(result_key, tuple):  # type: ignore[possibly-undefined]
+                    result_key = result_key[-1]
+                # pyre-ignore[29]: not a function
+                self.module_exporter(
+                    a_input,
+                    submodule,
+                    str(result_key[0]) + "_cpu",  # type: ignore[index]
+                )
+                # pyre-ignore[29]: not a function
+                self.module_exporter(
+                    b_input,
+                    submodule,
+                    str(result_key[0]) + "_acc",  # type: ignore[index]
+                )
+            raise FxNetMinimizerResultMismatchError(f"Result mismatch for {result_key}")  # type: ignore[possibly-undefined]
+
+    def _binary_search_impl(
+        self, all_nodes: NodeList, start_idx: int, end_idx: int
+    ) -> NodeSet:
+        """
+        Recursive binary search implementation.
+        """
+        culprits: NodeSet = set()
+        nodes: NodeList = all_nodes[start_idx:end_idx]
+
+        report: list[str] = []
+        if self.exclusion_fn is not None:
+            self.exclusion_fn(nodes, start_idx, end_idx)
+            if len(nodes) == 0:
+                report = ["All nodes are excluded by user"]
+                self.reports.append(report)
+                return culprits
+
+        first_node_name = nodes[0].name
+        output_node_name = nodes[-1].name
+        self.iteration += 1
+        self.reports.append(report)
+        report.append(f"Binary search iteration {self.iteration}")
+        report.append(
+            f"From node index {start_idx}:{first_node_name} to {end_idx - 1}:{output_node_name}. "
+            f"Size of the interested node list is {len(nodes)}"
+        )
+        cur_nodes: NodeSet = set(nodes)
+
+        try:
+            split_module, submod_name = self._build_submodule(cur_nodes)
+            self._run_and_compare(split_module, submod_name, [output_node_name])
+
+        except (FxNetMinimizerRunFuncError, FxNetMinimizerResultMismatchError):
+            if len(nodes) == 1:
+                report.append(
+                    f"This is the last node in the sub-module. "
+                    f"Search in the current branch is successful with culprit = {cur_nodes}."
+                )
+                self.print_report(report)
+                return cur_nodes
+
+            report.append(
+                "Proceed to split and lower the halves of the current "
+                "sub-module individually."
+            )
+            self.print_report(report)
+
+            mid = len(nodes) // 2
+            culprits = self._binary_search_impl(all_nodes, start_idx, start_idx + mid)
+
+            if len(culprits) != 0 and not self.settings.find_all:
+                return culprits
+
+            culprits = self._binary_search_impl(all_nodes, start_idx + mid, end_idx)
+
+            if len(culprits) == 0:
+                report.append(
+                    f"Further split and lowering found no errors. "
+                    f"Unable to minimize the submodule with list of nodes: {nodes}"
+                )
+                self.print_report(report)
+
+            return culprits
+        else:
+            report.append("No discrepancy found.")
+            self.print_report(report)
+            return set()
+
+    def _binary_traverse(self, nodes: NodeList) -> NodeSet:
+        """
+        Binary search on `nodes` for culprit.
+        """
+        return self._binary_search_impl(nodes, 0, len(nodes))
+
+    def _sequential_traverse(self, nodes: NodeList) -> NodeSet:
+        """
+        Traverse `nodes` one by one and determine if any of them is a culprit.
+        """
+        culprits: NodeSet = set()
+
+        for node in nodes:
+            report: list[str] = []
+            self.reports.append(report)
+            self.iteration += 1
+            report.append(f"Sequential traverse iteration {self.iteration}.")
+            report.append(f"Visit node: {node.name}")
+
+            _LOGGER.info("Visit node: %s", node.name)
+            node_list: NodeList = [node]
+            if self.exclusion_fn is not None:
+                self.exclusion_fn(node_list, -1, -1)
+                if len(node_list) == 0:
+                    report.append(f"User exclusion : {node.name}")
+                    self.print_report(report)
+                    if not self.settings.find_all:
+                        return culprits
+                    else:
+                        continue
+
+            cur_nodes: NodeSet = {node}
+
+            if node in self.fusions:
+                cur_nodes = self.fusions[node]
+
+            try:
+                split_module, submod_name = self._build_submodule(cur_nodes)
+                self._run_and_compare(split_module, submod_name, [node.name])
+                self.print_report(report)
+            except FxNetMinimizerResultMismatchError:
+                culprits.add(node)
+                report.append(f"Found culprit from numeric error: {node}")
+                self.print_report(report)
+                if not self.settings.find_all:
+                    return culprits
+            except FxNetMinimizerRunFuncError:
+                culprits.update(cur_nodes)
+                report.append(f"Found culprit from run error: {node}")
+                self.print_report(report)
+                if not self.settings.find_all:
+                    return culprits
+
+        return culprits
+
+    def _block_traverse_impl(
+        self, nodes: NodeList, start_idx: int, end_idx: int, find_last_node: bool
+    ) -> int:
+        """
+        Recursive block search implementation.
+        find_last_node: If True, search for the last node which result in numerics difference
+        if False: find first node in sorted node list
+        """
+        report: list[str] = []
+
+        mid = (start_idx + end_idx) // 2
+        cur_nodes_list: NodeList = nodes[: mid + 1] if find_last_node else nodes[mid:]
+
+        if self.exclusion_fn:
+            self.exclusion_fn(cur_nodes_list, -1, -1)
+
+        cur_nodes = set(cur_nodes_list)
+
+        first_node_name = cur_nodes_list[0].name
+        last_node_name = cur_nodes_list[-1].name
+        target_node_name = last_node_name if find_last_node else first_node_name
+
+        self.iteration += 1
+        self.reports.append(report)
+        report.extend(
+            [
+                "=" * 30,
+                f"Block search iteration {self.iteration}",
+            ]
+        )
+        report.extend(
+            [
+                f"Search for {'last' if find_last_node else 'first'} node in culprits",
+                f"From node index {start_idx}:{nodes[start_idx].name} to {end_idx}:{nodes[end_idx].name}. ",
+                f"Subgraph constructed by {first_node_name} to {last_node_name}",
+                f"Targeting node: {target_node_name}",
+                f"Size of the interested node list is {end_idx - start_idx + 1}",
+            ]
+        )
+        report_idx = len(self.reports) - 1
+
+        try:
+            split_module, submod_name = self._build_submodule(cur_nodes)
+            self._run_and_compare(
+                split_module, submod_name, [last_node_name], report_idx
+            )
+        except (FxNetMinimizerResultMismatchError, FxNetMinimizerRunFuncError):
+            report.append(
+                f"Culprits found from node {first_node_name} to {last_node_name}."
+            )
+
+            if start_idx == mid:
+                report.extend(
+                    [
+                        "This is the last node in the sub-module. ",
+                        "Search in the current branch is successful with node :",
+                        f"{start_idx}, node name: {nodes[start_idx].name}.",
+                    ]
+                )
+                self.print_report(report)
+                return start_idx
+
+            report.append(
+                "Proceed to split and lower the halves of the current "
+                "sub-module individually."
+            )
+            self.print_report(report)
+
+            if find_last_node:
+                return self._block_traverse_impl(nodes, start_idx, mid, find_last_node)
+            else:
+                return self._block_traverse_impl(
+                    nodes, mid + 1, end_idx, find_last_node
+                )
+        else:
+            report.append(
+                f"Culprits not found from node start to {mid}:{nodes[mid].name}."
+            )
+
+            if start_idx == mid:
+                report.extend(
+                    [
+                        "This is the last node in the sub-module. ",
+                        "Search in the current branch is successful with node",
+                        f"{start_idx}, node name: {nodes[start_idx].name}.",
+                    ]
+                )
+                self.print_report(report)
+                return start_idx + 1 if find_last_node else start_idx - 1
+
+            report.append(
+                "Proceed to split and lower the halves of the current "
+                "sub-module individually."
+            )
+            self.print_report(report)
+
+            if find_last_node:
+                return self._block_traverse_impl(
+                    nodes, mid + 1, end_idx, find_last_node
+                )
+            else:
+                return self._block_traverse_impl(nodes, start_idx, mid, find_last_node)
+
+    def _block_traverse(
+        self, nodes: NodeList, find_last_node: Optional[bool]
+    ) -> NodeSet:
+        """
+        Traverse topologically sorted node list
+        Find minimium block (start_idx, end_idx) which contains the culprit
+        1st pass: search for end_idx by finding the last node in culprit block
+        where Numerical accuracy (0, end_idx) > threshold
+        2nd pass: search for start_idx by finding the first node in culprit block
+        where Numerical accuracy (start_idx, end_idx) < threshold
+        Form minimum block by (start_idx - 1, end_idx)
+        """
+        culprits: NodeSet = set()
+        first_node_name = nodes[0].name
+        last_node_name = nodes[-1].name
+        last_node_report = [f"Block search from {first_node_name} to {last_node_name}"]
+        last_node_report.append("*" * 50)
+        self.reports.append(last_node_report)
+
+        start_idx = 0
+        end_idx = len(nodes) - 1
+        run_both = True if find_last_node is None else False
+
+        # step 1: find (0, end_idx) of culprit block
+        if run_both or find_last_node:
+            last_node_report.append("Start searching for last node in culprit")
+            self.print_report(last_node_report)
+            end_idx = self._block_traverse_impl(nodes, start_idx, end_idx, True)
+            last_node_report.extend(
+                ["Finish Pass 1", f"Find end_idx = {end_idx}:{nodes[end_idx].name}"]
+            )
+            self.print_report(last_node_report)
+
+        # step 2: reduce culprit block to (start_idx, end_idx)
+        if run_both or not find_last_node:
+            first_node_report = ["Start searching for first node in culprit"]
+            self.print_report(first_node_report)
+            start_idx = self._block_traverse_impl(
+                nodes[0 : end_idx + 1], start_idx, end_idx, False
+            )
+            first_node_report.append("*" * 50)
+            self.reports.append(first_node_report)
+            first_node_report.extend(
+                [
+                    "Finish Pass 2",
+                    f"Find start_idx = {start_idx}:{nodes[start_idx].name}",
+                ]
+            )
+            self.print_report(first_node_report)
+
+        # step 3: form module with minimum culprits
+        culprits.update(nodes[start_idx : end_idx + 1])
+        result_report = [
+            f"Finish searching, found minimum block ({nodes[start_idx]},{nodes[end_idx]})"
+        ]
+        self.reports.append(result_report)
+        self.print_report(result_report)
+        return culprits
+
+    def _defined_traverse(self, nodes: NodeList) -> NodeSet:
+        """
+        run user defined `nodes` and determine if it is a culprit.
+        """
+        culprits: NodeSet = set()
+        if self.exclusion_fn is not None:
+            self.exclusion_fn(nodes, -1, -1)
+        if len(nodes) == 0:
+            report = ["All nodes are excluded by user"]
+            self.reports.append(report)
+            return culprits
+
+        first_node_name = nodes[0].name
+        output_node_name = nodes[-1].name
+        report = [f"Defined graph from {first_node_name} to {output_node_name}"]
+        cur_nodes: NodeSet = set(nodes)
+        try:
+            split_module, submod_name = self._build_submodule(cur_nodes)
+            self._run_and_compare(split_module, submod_name, [output_node_name])
+            self.print_report(report)
+        except (FxNetMinimizerResultMismatchError, FxNetMinimizerRunFuncError):
+            report.append(f"Found culprit {cur_nodes}")
+            self.print_report(report)
+            return culprits
+
+        return culprits
+
+    def _accumulate_traverse(self, nodes: NodeList) -> NodeSet:
+        culprits: NodeSet = set()
+        nodes_to_run: NodeSet = set()
+
+        # find_all is not supported for accumulate traversal because all the
+        # ops run on NNPI. So we return after the first op that raises error.
+        if self.settings.find_all:
+            print("'Find All' mode is not supported in accumulate traversal.")
+            return culprits
+
+        for node in nodes:
+            report: list[str] = []
+            self.reports.append(report)
+            self.iteration += 1
+            report.append(f"Accumulate traverse iteration {self.iteration}.")
+
+            nodes_to_run.add(node)
+
+            node_name = node.name
+            if node_name is not None and isinstance(node_name, tuple):
+                node_name = node_name[0]
+            assert node_name is not None and isinstance(
+                node_name, str
+            ), f"minimize: node_name: {node_name}"
+
+            report.append(f"Add node: {node_name}")
+
+            try:
+                split_module, submod_name = self._build_submodule(nodes_to_run)
+                self._run_and_compare(split_module, submod_name, [node_name])
+                self.print_report(report)
+            except (FxNetMinimizerResultMismatchError, FxNetMinimizerRunFuncError):
+                culprits.add(node)
+                report.append(f"Found culprit {node}")
+                self.print_report(report)
+                return culprits
+
+        return culprits
+
+    def _skip_traverse_impl(
+        self, all_nodes: NodeList, start_idx: int, end_idx: int
+    ) -> NodeSet:
+        """
+        Skip certain nodes in graph based on settings
+        """
+        culprits: NodeSet = set()
+        nodes: NodeList = all_nodes[start_idx:end_idx]
+        cur_nodes: NodeSet = set(nodes)
+        if self.exclusion_fn is not None:
+            self.exclusion_fn(nodes, start_idx, end_idx)
+            cur_nodes = set(nodes)
+        else:
+            for node in nodes:
+                if node in self.fusions:
+                    cur_nodes.update(self.fusions[node])
+        report: list[str] = []
+        self.reports.append(report)
+        self.iteration += 1
+        report.append(f" Nodes block {self.iteration}.")
+        report.append(
+            f"From node index {start_idx} to {end_idx - 1}. "
+            f"Size of the interested node list is {len(nodes)}"
+        )
+
+        try:
+            split_module, submod_name = self._build_submodule(cur_nodes)
+            self._run_and_compare(split_module, submod_name, [])
+        except FxNetMinimizerResultMismatchError:
+            culprits.update(cur_nodes)
+            report.append(f"Found culprit from numeric error: {cur_nodes}")
+            self.print_report(report)
+            return culprits
+        except FxNetMinimizerRunFuncError:
+            culprits.update(cur_nodes)
+            report.append(f"Found culprit from run error: {cur_nodes}")
+            self.print_report(report)
+            return culprits
+        else:
+            report.append("No discrepancy found.")
+            self.print_report(report)
+            return set()
+
+    def _skip_traverse(self, all_nodes: NodeList, skip_nodes: list) -> NodeSet:
+        """
+        Skip certain nodes in graph based on settings
+        """
+        start_idx = 0
+        num_nodes = len(all_nodes)
+        idx = 0
+        culprits = set()
+        while idx < num_nodes:
+            node = all_nodes[idx]
+            if node.name in skip_nodes:  # skip the node
+                if idx > start_idx:
+                    culprits = self._skip_traverse_impl(all_nodes, start_idx, idx)
+                start_idx = idx + 1
+            elif idx == num_nodes - 1 and start_idx <= idx:  # last node
+                culprits = self._skip_traverse_impl(all_nodes, start_idx, idx + 1)
+            idx += 1
+
+        return culprits
+
+    def _collect_nodes(self, start: Optional[str], end: Optional[str]) -> NodeList:
+        """
+        Collect nodes in the model that between nodes with name of `start` and `end`.
+        These two nodes are also included.
+        """
+        nodes: NodeList = []
+        add_node = start is None
+
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+
+            if node.name == start:
+                add_node = True
+
+            if add_node:
+                nodes.append(node)
+
+            if node.name == end:
+                break
+
+        return nodes
+
+    def run_nodes(self, start: Optional[str] = None, end: Optional[str] = None):
+        """
+        Run part of the model from `start` node to `end` node. If `start` is None
+        then we start from the beginning of the model. If `end` is None then we
+        stop at the end of the model.
+
+        Args:
+            start: The name of the node which is the first node of the submodule
+                we want to run. If set to None, then we'll start with the first
+                node of the model.
+            end: The name of the node which is the last node of the submodule we
+                want to run. If set to None, we'll end with the last node of the
+                model.
+        """
+        nodes = self._collect_nodes(start, end)
+        cur_nodes = set(nodes)
+
+        for node in nodes:
+            if node in self.fusions:
+                cur_nodes.update(self.fusions[node])
+
+        output_names = []
+        if self.settings.return_intermediate:
+            output_names = [node.name for node in nodes]
+
+        try:
+            split_module, submod_name = self._build_submodule(cur_nodes)
+            self._run_and_compare(split_module, submod_name, output_names)
+        except (
+            FxNetMinimizerRunFuncError,
+            FxNetMinimizerResultMismatchError,
+        ) as e:
+            print(e)
+
+    def print_report(self, report: list[str]):
+        for i in range(len(report)):
+            if i > 0:
+                print(" . " + report[i])
+            else:
+                print(report[i])
+
+    def print_reports(self):
+        for report in self.reports:
+            self.print_report(report)
+
+    def minimize(
+        self,
+        start: Optional[str] = None,
+        end: Optional[str] = None,
+        skip_nodes: Optional[list] = None,
+        find_last_node: Optional[bool] = None,
+    ) -> NodeSet:
+        """
+        Minimizing the model from node with name `start` to node with name `end` base
+        on self.settings. Find culprits that causes FxNetMinimizerRunFuncError or
+        FxNetMinimizerResultMismatchError errors.
+
+        Args:
+            start: The name of the node where we want to start minimizing. If set
+                to None, then we'll start with the first node of the model.
+            end: The name of the node where we want to terminate minimizing. If
+                set to None, we'll end with the last node of the model.
+            skip_nodes: The names of nodes where we want to skip during minimizing.
+                It'll create subgraphs without these skip nodes under the hood.
+                Only applicable in mode "skip".
+            find_last_node: True if only last_node of a culprits is needed in mode "block".
+                False if only the first_node of a culprits is needed.
+                Only applicable in mode "block".
+
+        Returns:
+            nodes: A list of nodes that causes FxNetMinimizerRunFuncError or
+                FxNetMinimizerResultMismatchError errors during minimizing.
+        """
+
+        print(self.settings)
+        print(self.module.graph)
+
+        nodes = self._collect_nodes(start, end)
+
+        if self.settings.traverse_method == "sequential":
+            return self._sequential_traverse(nodes)
+
+        if self.settings.traverse_method == "binary":
+            return self._binary_traverse(nodes)
+
+        if self.settings.traverse_method == "accumulate":
+            return self._accumulate_traverse(nodes)
+
+        if self.settings.traverse_method == "skip":
+            if skip_nodes is None:
+                raise RuntimeError(
+                    "'skip_nodes' can't be None when 'traverse_method' is 'skip'."
+                )
+            return self._skip_traverse(nodes, skip_nodes)
+
+        if self.settings.traverse_method == "defined":
+            return self._defined_traverse(nodes)
+
+        if self.settings.traverse_method == "block":
+            return self._block_traverse(nodes, find_last_node)
+
+        raise RuntimeError(f"Unknown traverse method {self.settings.traverse_method}!")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/operator_support.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/operator_support.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cb14d312b60b0209195706488dd48a359c40b3f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/operator_support.py
@@ -0,0 +1,229 @@
+# mypy: allow-untyped-defs
+import abc
+import typing as t
+
+import torch
+import torch.fx
+from torch.fx._compatibility import compatibility
+
+from .shape_prop import TensorMetadata
+from .tools_common import CALLABLE_NODE_OPS, get_node_target
+
+
+__all__ = [
+    "OperatorSupportBase",
+    "OperatorSupport",
+    "create_op_support",
+    "chain",
+    "OpSupports",
+    "any_chain",
+]
+
+# fx.Node.target typename, as returned by `get_node_target()`
+TargetTypeName = str
+
+# Arguments' dtypes for a given node, see `OperatorSupport`
+SupportedArgumentDTypes = t.Optional[
+    tuple[
+        t.Sequence[t.Sequence[torch.dtype]],
+        dict[str, t.Sequence[torch.dtype]],
+    ]
+]
+
+SupportDict = t.Mapping[TargetTypeName, SupportedArgumentDTypes]
+
+
+@compatibility(is_backward_compatible=False)
+class OperatorSupportBase(abc.ABC):
+    """Interface for determining if a fx.Node is supported by a backend"""
+
+    @abc.abstractmethod
+    def is_node_supported(
+        self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node
+    ) -> bool:
+        raise NotImplementedError
+
+
+@compatibility(is_backward_compatible=False)
+class OperatorSupport(OperatorSupportBase):
+    """
+    `_support_dict` maps node.target typename to supported inputs dtypes.
+
+    node.target typename is retrieved using helper function `get_node_target()`
+
+    If supported inputs dtypes is None, it means any dtype is supported, else
+    we should see a tuple like (([dtypes], ...), {"name":[dtypes], ...}).
+
+    The first tuple ([dtypes], ...) indicates what dtypes are supported for
+    inputs in node.args and the second dict {"name": [dtypes], ...} indicates
+    what dtypes are supported for inputs in node.kwargs.
+
+    For inputs in args, if we don't want to check it, we can put None there,
+    e.g. (None, [torch.float]) indicates that we don't care about the type of
+    the first input in args. And for inputs in kwargs, if not listed, will not
+    be checked.
+    """
+
+    _support_dict: SupportDict
+
+    def __init__(self, support_dict: t.Optional[SupportDict] = None):
+        self._support_dict = support_dict or {}
+
+    def is_node_supported(
+        self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node
+    ) -> bool:
+        """
+        Args:
+            `submodules`: mapping from module name to the module. This can be
+                          retrieved by calling model.named_modules().
+
+            `node`: a Fx node that we want to determine whether it's supported.
+
+        Returns:
+            `is_supported`: whether the arg `node` is supported.
+        """
+        if node.op not in CALLABLE_NODE_OPS:
+            return True
+
+        target = get_node_target(submodules, node)
+
+        # Target not found in _support_dict meaning that we don't support this op at all
+        if target not in self._support_dict:
+            return False
+
+        # The rule for target is None meaning that we accept any dtype
+        if self._support_dict[target] is None:
+            return True
+
+        args_dtypes, kwargs_dtypes = self._support_dict[target]  # type: ignore[misc]
+
+        # Check args dtypes
+        for i, dtypes in enumerate(args_dtypes):
+            if len(node.args) <= i:
+                break
+
+            # None indicates we don't care about the dtype of args[i]
+            if dtypes is None:
+                continue
+
+            # If arg is not a node then we don't check it
+            if not isinstance(node.args[i], torch.fx.Node):
+                continue
+
+            arg_dtype = _get_arg_dtype(node.args[i])  # type: ignore[arg-type]
+            if arg_dtype not in dtypes:
+                return False
+
+        # Check kwargs dtypes
+        for k, dtypes in kwargs_dtypes.items():
+            if k not in node.kwargs:
+                continue
+
+            # If arg is not a node then we don't check it
+            if not isinstance(node.kwargs[k], torch.fx.Node):
+                continue
+
+            kwarg_dtype = _get_arg_dtype(node.kwargs[k])  # type: ignore[arg-type]
+            if kwarg_dtype not in dtypes:
+                return False
+
+        return True
+
+
+# ======================================================================
+# Functional interfaces and utils for defining basic operator support logic
+# and composing them into more complex ones
+# ======================================================================
+
+IsNodeSupported = t.Callable[[t.Mapping[str, torch.nn.Module], torch.fx.Node], bool]
+
+
+@compatibility(is_backward_compatible=False)
+def create_op_support(is_node_supported: IsNodeSupported) -> OperatorSupportBase:
+    """Wraps a `IsNodeSupported` function into an `OperatorSupportBase` instance
+
+    `IsNodeSupported` has the same call signature as
+    `OperatorSupportBase.is_node_supported`
+    """
+
+    class FunctionalOperatorSupport(OperatorSupportBase):
+        def is_node_supported(
+            self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node
+        ) -> bool:
+            return is_node_supported(submodules, node)
+
+    return FunctionalOperatorSupport()
+
+
+@compatibility(is_backward_compatible=False)
+def chain(*op_support: OperatorSupportBase) -> OperatorSupportBase:
+    """Combines a sequence of `OperatorSupportBase` instances to form a single `OperatorSupportBase`
+    instance by evaluating each input `OperatorSupportBase` instance, and returns False if
+    any of it reports False.
+    """
+
+    def _chain(submods, node) -> bool:
+        return all(x.is_node_supported(submods, node) for x in op_support)
+
+    return create_op_support(_chain)
+
+
+@compatibility(is_backward_compatible=False)
+def any_chain(*op_support: OperatorSupportBase) -> OperatorSupportBase:
+    """Combines a sequence of `OperatorSupportBase` instances to form a single `OperatorSupportBase`
+    instance by evaluating each input `OperatorSupportBase` instance, and returns True if
+    any of it reports True.
+    """
+
+    def _any_chain(submods, node) -> bool:
+        return any(x.is_node_supported(submods, node) for x in op_support)
+
+    return create_op_support(_any_chain)
+
+
+@compatibility(is_backward_compatible=False)
+class OpSupports:
+    """A set of atomic `OperatorSupportBase` instances that can be combined together
+    to form more complex operator support logic.
+    """
+
+    @classmethod
+    def decline_if_input_dtype(cls, dtype: torch.dtype) -> OperatorSupportBase:
+        """Report a node as non-supported, if any of its arguments is of dtype"""
+
+        def _decline_if_input_dtype(
+            submodules: t.Mapping[str, torch.nn.Module],
+            node: torch.fx.Node,
+        ) -> bool:
+            for arg in node.all_input_nodes:
+                arg_dtype = _get_arg_dtype(arg)
+                if arg_dtype == dtype:
+                    return False
+            return True
+
+        return create_op_support(_decline_if_input_dtype)
+
+    @classmethod
+    def decline_if_node_in_names(cls, disallow_set: set[str]) -> OperatorSupportBase:
+        """
+        If a node has a name that is in the disallow set, reported it as non-supported.
+        """
+
+        def _decline_if_node_in_names(
+            submodules: t.Mapping[str, torch.nn.Module],
+            node: torch.fx.Node,
+        ) -> bool:
+            return node.name not in disallow_set
+
+        return create_op_support(_decline_if_node_in_names)
+
+
+def _get_arg_dtype(arg: torch.fx.Node) -> t.Any:
+    assert isinstance(arg, torch.fx.Node)
+    tensor_meta = arg.meta.get("tensor_meta")  # type: ignore[union-attr]
+    dtype = (
+        tensor_meta.dtype
+        if isinstance(tensor_meta, TensorMetadata)
+        else arg.meta["type"]
+    )
+    return dtype
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/param_fetch.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/param_fetch.py
new file mode 100644
index 0000000000000000000000000000000000000000..02904b8e403e51a6cb00fae1dcdd4bbfbe2a66a6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/param_fetch.py
@@ -0,0 +1,96 @@
+from typing import Any, Callable
+
+import torch
+import torch.nn as nn
+from torch.fx._compatibility import compatibility
+from torch.fx.graph_module import GraphModule
+
+
+__all__ = [
+    "default_matching",
+    "extract_attrs_for_lowering",
+    "lift_lowering_attrs_to_nodes",
+]
+
+
+# Matching method matches the attribute name of current version to the attribute name of `target_version`
+@compatibility(is_backward_compatible=False)
+def default_matching(name: str, target_version: int) -> str:
+    """Default matching method"""
+    return name
+
+
+# This dict maps the nn.Module class name to the attribute name list that we want to fetch for lowering.
+# The first integer in the tuple is the version number of the nn.Module class when we create the parameter list.
+# If there's a version mismatch then it means the parameter names in the book might be mismatched with nn.Module.
+module_fetch_book: dict[type, tuple[int, list[str], Callable[[str, int], str]]] = {
+    torch.nn.modules.linear.Linear: (1, ["weight", "bias"], default_matching),
+    torch.nn.modules.conv.Conv2d: (
+        1,
+        [
+            "weight",
+            "bias",
+            "kernel_size",
+            "stride",
+            "padding",
+            "dilation",
+            "groups",
+            "padding_mode",
+        ],
+        default_matching,
+    ),
+    torch.nn.modules.batchnorm.BatchNorm2d: (
+        2,
+        ["weight", "bias", "running_mean", "running_var", "eps"],
+        default_matching,
+    ),
+    torch.nn.modules.pooling.AdaptiveAvgPool2d: (1, [], default_matching),
+    torch.nn.modules.pooling.MaxPool2d: (
+        1,
+        ["kernel_size", "stride", "padding", "dilation", "return_indices", "ceil_mode"],
+        default_matching,
+    ),
+    torch.nn.modules.activation.ReLU: (1, ["inplace"], default_matching),
+}
+
+
+@compatibility(is_backward_compatible=False)
+def extract_attrs_for_lowering(mod: nn.Module) -> dict[str, Any]:
+    """If `mod` is in `module_fetch_book`, fetch the mod's attributes that in the `module_fetch_book`
+    after checking module's version is compatible with the `module_fetch_book`.
+    """
+    attrs_for_lowering: dict[str, Any] = {}
+    attrs_for_lowering["name"] = torch.typename(mod)
+
+    if type(mod) in module_fetch_book:
+        version, param_to_fetch, matching_method = module_fetch_book[type(mod)]
+        if version < mod._version:
+            raise RuntimeError(
+                f"Fetcher version {version} try to fetch {torch.typename(mod)} version {mod._version}, "
+                "please upgrade the module_fetch_book, open an issue and @842974287 "
+                "or report a bug to AIACC team directly."
+            )
+        for attr in param_to_fetch:
+            attrs_for_lowering[attr] = getattr(mod, matching_method(attr, mod._version))
+    else:
+        raise RuntimeError(
+            f"{torch.typename(mod)} is not in the module_fetch_book yet, "
+            "please add it to the module_fetch_book, open an issue and @842974287 "
+            "or report a bug to AIACC team directly."
+        )
+    return attrs_for_lowering
+
+
+@compatibility(is_backward_compatible=False)
+def lift_lowering_attrs_to_nodes(fx_module: GraphModule) -> None:
+    """Recursively traverse all `fx_module` nodes and fetch the module's attributes if the node is a leaf module."""
+    submodules = dict(fx_module.named_modules())
+
+    for node in fx_module.graph.nodes:
+        if node.op == "call_module":
+            if isinstance(submodules[node.target], GraphModule):
+                lift_lowering_attrs_to_nodes(submodules[node.target])
+            else:
+                node.attrs_for_lowering = extract_attrs_for_lowering(
+                    submodules[node.target]
+                )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/pass_manager.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/pass_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddb1410f684066f27b4b9b14bcb06966654a0ea5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/pass_manager.py
@@ -0,0 +1,253 @@
+# mypy: allow-untyped-defs
+import logging
+from functools import wraps
+from inspect import unwrap
+from typing import Callable, Optional
+
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "PassManager",
+    "inplace_wrapper",
+    "log_hook",
+    "loop_pass",
+    "this_before_that_pass_constraint",
+    "these_before_those_pass_constraint",
+]
+
+
+# for callables which modify object inplace and return something other than
+# the object on which they act
+def inplace_wrapper(fn: Callable) -> Callable:
+    """
+    Convenience wrapper for passes which modify an object inplace. This
+    wrapper makes them return the modified object instead.
+
+    Args:
+        fn (Callable[Object, Any])
+
+    Returns:
+        wrapped_fn (Callable[Object, Object])
+    """
+
+    @wraps(fn)
+    def wrapped_fn(gm):
+        fn(gm)
+        return gm
+
+    return wrapped_fn
+
+
+def log_hook(fn: Callable, level=logging.INFO) -> Callable:
+    """
+    Logs callable output.
+
+    This is useful for logging output of passes. Note inplace_wrapper replaces
+    the pass output with the modified object. If we want to log the original
+    output, apply this wrapper before inplace_wrapper.
+
+
+    ```
+    def my_pass(d: Dict) -> bool:
+        changed = False
+        if "foo" in d:
+            d["foo"] = "bar"
+            changed = True
+        return changed
+
+
+    pm = PassManager(passes=[inplace_wrapper(log_hook(my_pass))])
+    ```
+
+    Args:
+        fn (Callable[Type1, Type2])
+        level: logging level (e.g. logging.INFO)
+
+    Returns:
+        wrapped_fn (Callable[Type1, Type2])
+    """
+
+    @wraps(fn)
+    def wrapped_fn(gm):
+        val = fn(gm)
+        logger.log(level, "Ran pass %s\t Return value: %s", fn, val)
+        return val
+
+    return wrapped_fn
+
+
+def loop_pass(
+    base_pass: Callable,
+    n_iter: Optional[int] = None,
+    predicate: Optional[Callable] = None,
+):
+    """
+    Convenience wrapper for passes which need to be applied multiple times.
+
+    Exactly one of `n_iter`or `predicate` must be specified.
+
+    Args:
+        base_pass (Callable[Object, Object]): pass to be applied in loop
+        n_iter (int, optional): number of times to loop pass
+        predicate (Callable[Object, bool], optional):
+
+    """
+    assert (n_iter is not None) ^ (
+        predicate is not None
+    ), "Exactly one of `n_iter`or `predicate` must be specified."
+
+    @wraps(base_pass)
+    def new_pass(source):
+        output = source
+        if n_iter is not None and n_iter > 0:
+            for _ in range(n_iter):
+                output = base_pass(output)
+        elif predicate is not None:
+            while predicate(output):
+                output = base_pass(output)
+        else:
+            raise RuntimeError(
+                f"loop_pass must be given positive int n_iter (given "
+                f"{n_iter}) xor predicate (given {predicate})"
+            )
+        return output
+
+    return new_pass
+
+
+# Pass Schedule Constraints:
+#
+# Implemented as 'depends on' operators. A constraint is satisfied iff a list
+# has a valid partial ordering according to this comparison operator.
+def _validate_pass_schedule_constraint(
+    constraint: Callable[[Callable, Callable], bool], passes: list[Callable]
+):
+    for i, a in enumerate(passes):
+        for j, b in enumerate(passes[i + 1 :]):
+            if constraint(a, b):
+                continue
+            raise RuntimeError(
+                f"pass schedule constraint violated. Expected {a} before {b}"
+                f" but found {a} at index {i} and {b} at index{j} in pass"
+                f" list."
+            )
+
+
+def this_before_that_pass_constraint(this: Callable, that: Callable):
+    """
+    Defines a partial order ('depends on' function) where `this` must occur
+    before `that`.
+    """
+
+    def depends_on(a: Callable, b: Callable):
+        return a != that or b != this
+
+    return depends_on
+
+
+def these_before_those_pass_constraint(these: Callable, those: Callable):
+    """
+    Defines a partial order ('depends on' function) where `these` must occur
+    before `those`. Where the inputs are 'unwrapped' before comparison.
+
+    For example, the following pass list and constraint list would be invalid.
+    ```
+    passes = [
+        loop_pass(pass_b, 3),
+        loop_pass(pass_a, 5),
+    ]
+
+    constraints = [these_before_those_pass_constraint(pass_a, pass_b)]
+    ```
+
+    Args:
+        these (Callable): pass which should occur first
+        those (Callable): pass which should occur later
+
+    Returns:
+        depends_on (Callable[[Object, Object], bool]
+    """
+
+    def depends_on(a: Callable, b: Callable):
+        return unwrap(a) != those or unwrap(b) != these
+
+    return depends_on
+
+
+class PassManager:
+    """
+    Construct a PassManager.
+
+    Collects passes and constraints. This defines the pass schedule, manages
+    pass constraints and pass execution.
+
+    Args:
+        passes (Optional[List[Callable]]): list of passes. A pass is a
+            callable which modifies an object and returns modified object
+        constraint (Optional[List[Callable]]): list of constraints. A
+            constraint is a callable which takes two passes (A, B) and returns
+            True if A depends on B and False otherwise. See implementation of
+            `this_before_that_pass_constraint` for example.
+    """
+
+    passes: list[Callable]
+    constraints: list[Callable]
+    _validated: bool = False
+
+    def __init__(
+        self,
+        passes=None,
+        constraints=None,
+    ):
+        self.passes = passes or []
+        self.constraints = constraints or []
+
+    @classmethod
+    def build_from_passlist(cls, passes):
+        pm = PassManager(passes)
+        # TODO(alexbeloi): add constraint management/validation
+        return pm
+
+    def add_pass(self, _pass: Callable):
+        self.passes.append(_pass)
+        self._validated = False
+
+    def add_constraint(self, constraint):
+        self.constraints.append(constraint)
+        self._validated = False
+
+    def remove_pass(self, _passes: list[str]):
+        if _passes is None:
+            return
+        passes_left = [ps for ps in self.passes if ps.__name__ not in _passes]
+        self.passes = passes_left
+        self._validated = False
+
+    def replace_pass(self, _target, _replacement):
+        passes_left = []
+        for ps in self.passes:
+            if ps.__name__ == _target.__name__:
+                passes_left.append(_replacement)
+            else:
+                passes_left.append(ps)
+        self.passes = passes_left
+        self._validated = False
+
+    def validate(self):
+        """
+        Validates that current pass schedule defined by `self.passes` is valid
+        according to all constraints in `self.constraints`
+        """
+        if self._validated:
+            return
+        for constraint in self.constraints:
+            _validate_pass_schedule_constraint(constraint, self.passes)
+        self._validated = True
+
+    def __call__(self, source):
+        self.validate()
+        out = source
+        for _pass in self.passes:
+            out = _pass(out)
+        return out
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/reinplace.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/reinplace.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fcd72938367c5272edea4e6804f9d10582a0ba8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/reinplace.py
@@ -0,0 +1,752 @@
+# mypy: allow-untyped-defs
+import _operator
+import itertools
+from collections import defaultdict
+from enum import Enum
+
+import torch
+from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
+from torch.fx import Node
+from torch.fx._compatibility import compatibility
+from torch.multiprocessing.reductions import StorageWeakRef
+from torch.utils import _pytree as pytree
+from torch.utils._pytree import tree_map_only
+
+
+__all__ = ["reinplace"]
+
+
+class _ViewType(Enum):
+    NonView = 0
+    SingleOutputView = 1
+    MultiOutputView = 2
+
+
+def _is_view_op(tgt):
+    if tgt is not None and isinstance(tgt, torch._ops.OpOverload):
+        schema = tgt._schema
+        if len(schema.arguments) > 0:
+            first_arg = schema.arguments[0]
+            # check if op is a view
+            return (
+                first_arg.alias_info is not None and not first_arg.alias_info.is_write
+            )
+
+
+def _get_view_type(tgt) -> _ViewType:
+    if tgt is not None and isinstance(tgt, torch._ops.OpOverload):
+        schema = tgt._schema
+        if len(schema.arguments) > 0:
+            first_arg = schema.arguments[0]
+            # check if op is a view
+            if first_arg.alias_info is not None and not first_arg.alias_info.is_write:
+                # check if op is a multi-output view
+                if "*" in first_arg.alias_info.after_set:
+                    return _ViewType.MultiOutputView
+                else:
+                    return _ViewType.SingleOutputView
+    return _ViewType.NonView
+
+
+# Stores a bunch of metadata related to functionalization each node.
+# Relevant metadata:
+# n.meta['fake_result']: FakeTensor (same type as the output of the node, but with FakeTenors instead of Tensors)
+#   The fake tensor output from running the current node
+# n.meta['view_of']: Node
+#   If the current node n is a view of some base tensor, the 'view_of' field tells us which
+#   view node was used to generate the current node (a view tensor).
+#   This information actually makes `fake_result` redundant, but we can use `fake_result`
+#   to sanity check that our aliasing information is correct.
+@compatibility(is_backward_compatible=False)
+class _FunctionalizationMetadataProp(torch.fx.Interpreter):
+    def run_node(self, node: Node):
+        self.node_counter += 1
+        result = super().run_node(node)
+        node.meta["fake_result"] = result
+        node.meta["node_idx"] = self.node_counter
+
+        # (1) Update metadata with the list of nodes that are used by this node
+        # copy_() doesn't read from its first argument; it writes to it, overwriting previous data.
+        # We don't want to treat it as "being used as an input".
+        node_args = node.args
+        if node.target is torch.ops.aten.copy_.default:
+            node_args = node_args[1:]
+
+        # (2) Update metadata to track aliasing information about view tensor nodes.
+        if node.op == "call_function":
+            view_type = _get_view_type(node.target)
+            if view_type == _ViewType.SingleOutputView:
+                assert isinstance(node.args[0], Node)
+                node.meta["view_of"] = node.args[0]
+            elif view_type == _ViewType.MultiOutputView:
+                self.multi_output_view_nodes[node] = node.args[0]
+
+            # Check if we returned a multi-output view,
+            # and we're now grabbing the individual views from the output.
+            #
+            # For multi-output views, we want to map each output view to the base,
+            # but this mapping involves two separate nodes in FX IR.
+            # e.g. "a, b = x_1.split(...)" becomes:
+            #    %split_tensor : [num_users=2] = call_function[target=torch.ops.aten.split.Tensor](args = (%x_1, 2), kwargs = {})
+            #    %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%split_tensor, 0), kwargs = {})
+            #    %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%split_tensor, 1), kwargs = {})
+            # And we'd like to set:
+            #    getitem1.meta['view_of'] = x_1
+            elif node.target is _operator.getitem:
+                list_arg = node.args[0]
+                maybe_base_of_view = self.multi_output_view_nodes.get(list_arg, None)
+                if maybe_base_of_view is not None:
+                    # Note: we could also track indexing info here for multi-output views.
+                    # I don't think this metadata is strictly needed for de-functionalization.
+                    assert isinstance(maybe_base_of_view, Node)
+                    node.meta["view_of"] = maybe_base_of_view
+
+        if "view_of" in node.meta:
+            # We're linking the current node with its first argument as views.
+            # Assert here that this is actually the case, and their storages are the same.
+            assert isinstance(node.meta["fake_result"], FakeTensor)
+            assert isinstance(node.meta["view_of"].meta["fake_result"], FakeTensor)
+            view_storage = StorageWeakRef(node.meta["fake_result"]._typed_storage())
+            base_storage = StorageWeakRef(
+                node.meta["view_of"].meta["fake_result"]._typed_storage()
+            )
+            assert view_storage == base_storage
+        return result
+
+    def propagate(self, *args):
+        self.multi_output_view_nodes = {}
+        self.node_counter = -1
+
+        with FakeTensorMode() as mode:
+            fake_args = [
+                mode.from_tensor(a) if isinstance(a, torch.Tensor) else a for a in args
+            ]
+            return super().run(*fake_args)
+
+
+def _schemas_match(functional_schema, inplace_schema):
+    names_match = (
+        inplace_schema.name.endswith("_")
+        and inplace_schema.name[:-1] == functional_schema.name
+    )
+    arg_types_match = len(functional_schema.arguments) == len(
+        inplace_schema.arguments
+    ) and all(
+        a1.type == a2.type
+        for a1, a2 in zip(functional_schema.arguments, inplace_schema.arguments)
+    )
+    # for the inplace op, its first argument should be mutable
+    assert (
+        inplace_schema.arguments[0].alias_info is not None
+        and inplace_schema.arguments[0].alias_info.is_write
+    )
+    # and its remaining arguments shouldn't be.
+    assert all(a.alias_info is None for a in inplace_schema.arguments[1:])
+    return names_match and arg_types_match
+
+
+# TODO: this should be beefed up to be able to properly re-inplace with:
+# - mutating ops (e.g. _fused_moving_avg_obs_fq_helper)
+# - out= ops (e.g. angle -> angle.out)
+# TODO: we should also figure this info out using torchgen.
+def _maybe_get_inplace_op(op):
+    # __module__ seems broken; it returns torch._ops.aten which doesn't exist
+    if not isinstance(op, torch._ops.OpOverload):
+        return None
+    # Some view ops have inplace variants (as_strided_, etc),
+    # but we do NOT want the reinplacing pass to directly add these into the program.
+    # (they'll require extra special handling, aren't aren't really useful for perf anyway)
+    if _is_view_op(op):
+        return None
+    op_namespace = op.__module__.split(".")[-1]
+    op_base_name = op.overloadpacket.__name__
+    maybe_namespace_module = getattr(torch.ops, op_namespace)
+    maybe_inplace_op = (
+        None
+        if maybe_namespace_module is None
+        else getattr(maybe_namespace_module, f"{op_base_name}_", None)
+    )
+    if maybe_inplace_op is None:
+        return None
+
+    inplace_overloads = [
+        getattr(maybe_inplace_op, overload_name)
+        for overload_name in maybe_inplace_op.overloads()
+    ]
+    inplace_overloads_with_matching_schemas = [
+        f for f in inplace_overloads if _schemas_match(op._schema, f._schema)
+    ]
+    # Just because foo() and foo_() are both existing operators,
+    # They aren't guaranteed to have compatible schemas.
+    # For example, pow.Scalar(Scalar self, Tensor exponent) has no valid inplace variant,
+    # Even though several overloads of pow_ exist.
+    if len(inplace_overloads_with_matching_schemas) == 0:
+        return None
+    assert len(inplace_overloads_with_matching_schemas) == 1
+    inplace_op = inplace_overloads_with_matching_schemas[0]
+    return inplace_op
+
+
+_VIEW_INVERSE_MAP = {
+    torch.ops.aten.diagonal_scatter.default: torch.ops.aten.diagonal.default,
+    torch.ops.aten.select_scatter.default: torch.ops.aten.select.int,
+    torch.ops.aten.slice_scatter.default: torch.ops.aten.slice.Tensor,
+    torch.ops.aten.as_strided_scatter.default: torch.ops.aten.as_strided.default,
+}
+
+
+# This function, given a set of set of (aliased) tensor nodes,
+# Returns any nodes in the graph that *use* any of the aliases, that occur *after* op_index
+# in the node ordering.
+def _get_all_later_node_usages(tensor_aliases: set[Node], op_index: int):
+    def _add_if_tensor(x, set_):
+        if isinstance(x, FakeTensor):
+            set_.add(StorageWeakRef(x._typed_storage()))
+
+    nodes_used_after = set()
+    for t in tensor_aliases:
+        # get all nodes that use the current alias
+        usage_nodes = t.users
+        for n in usage_nodes:
+            # We only care about usages after the current node
+            if "node_idx" not in n.meta or n.meta["node_idx"] <= op_index:
+                continue
+            # We also don't care about intermediate view ops.
+            # They only matter if their output is then used elsewhere
+            # (either in an out-of-place op, or as an output to the function).
+            if n in tensor_aliases:
+                if (
+                    isinstance(n.target, torch._ops.OpOverload)
+                    or n.target == _operator.getitem
+                ):
+                    continue
+            nodes_used_after.add(n)
+    return nodes_used_after
+
+
+# Given an op that we're trying to re-inplace, "b = foo(a)",
+# And given a {view}_scatter op that shows up later in the graph, "y = {view}_scatter(base, x, args...)"
+# Then re-inplacing `foo()` would allow us to remove the `{view}_scatter` op entirely, IF:
+# If there are any aliases in the alias_set(a) that satisfy:
+# (1) The base of "alias", "alias_base", has the same size/stride/offset metadata as "base"
+# (2) The output of running {view}(alias, args...) gives you the same size/stride/offset metadata
+#     as "alias"
+def _get_view_inverse_node_usages(
+    later_node_usages: set[Node], self_aliases: set[Node]
+) -> set[Node]:
+    def matching_view_metadata(a, b):
+        return (
+            a.size() == b.size()
+            and a.stride() == b.stride()
+            and a.storage_offset() == b.storage_offset()
+        )
+
+    view_inverse_nodes = set()
+    # Go through them in node order, so we can see chains of view_scatter ops.
+    for n in sorted(later_node_usages, key=lambda x: x.meta["node_idx"]):
+        if n.target not in _VIEW_INVERSE_MAP:
+            continue
+        base = n.args[0]
+        mutated_view = n.args[1]
+        assert isinstance(base, Node)
+        assert isinstance(base.meta["fake_result"], FakeTensor)
+        assert isinstance(mutated_view, Node)
+        assert isinstance(mutated_view.meta["fake_result"], FakeTensor)
+        # Check that this view_inverse op actually corresponds to taking doing the inverse
+        # of one of our existing self_alias nodes.
+        original_view = _VIEW_INVERSE_MAP[n.target]
+        for self_alias in self_aliases:
+            # We're looking for some alias of the self arg, "alias",
+            # that was created from some op `alias = foo(base, args...)`
+            # such that the current _scatter op "inverts" that foo call.
+            # We can check that by running the original op again, and checking that the strides match.
+            if "view_of" not in self_alias.meta:
+                continue
+            self_alias_base = self_alias.meta["view_of"]
+            try:
+                # The we're trying to re-use the args from the view_scatter call inside of the corresponding
+                # view op, which might throw. This just indicates that view_scatter op isn't a valid inverse
+                # of the current alias we're looking at.
+                view_replay_metadata = original_view(
+                    self_alias_base.meta["fake_result"], *n.args[2:], **n.kwargs
+                )
+                expected_metadata = self_alias.meta["fake_result"]
+                # If the alias and its base both have matching metadata, then this view_scatter op is valid to re-inplace.
+                if matching_view_metadata(
+                    self_alias_base.meta["fake_result"], base.meta["fake_result"]
+                ) and matching_view_metadata(view_replay_metadata, expected_metadata):
+                    view_inverse_nodes.add(n)
+            except Exception:
+                continue
+
+    return view_inverse_nodes
+
+
+@compatibility(is_backward_compatible=True)
+def reinplace(gm, *sample_args):
+    """
+    Given an fx.GraphModule, modifies it to perform "reinplacing",
+    mutating the nodes of the graph.
+    We look for out-of-place op call sites like `b = a.add(...)`,
+    and convert them to be inplace (`b = a.add_(...)`),
+    as long as the input to the current operator ("a") isn't re-used
+    anywhere later in the graph.
+
+    This pass currently expects to operate on a **functional, ATen** graph.
+    This can be obtained by running `make_fx(functionalize(f))`.
+
+    Sample inputs are needed to determine aliasing relationships of the inputs.
+    In general, we can't reinplace node `b = a.add(...)` if "a" aliases any of the
+    inputs to the program.
+
+    Given a node "b = foo(a, args...) the algorithm for re-inplacing is as follows:
+
+    (1) Perform some initial checks on the metadata of "a" and "args..."
+        that can disqualify them from being reinplaced.
+
+      (1a) Check that the self argument we're attempting to reinplace
+           has acceptable dtype/size metadata to reinplace with.
+
+           For example, if we have:
+             a = torch.ones(1)
+             b = torch.ones(10)
+             out = torch.add(a, b)
+           We can't turn that into
+             a.add_(b)
+           Because that would require resizing "a".
+
+           Similarly, we can't convert torch.ge(a, b) into a.ge_(b),
+           because that would require changing a's dtype (from e.g. float32 to bool).
+           Note that in this specific example, we could technically do better..
+
+           If we see the pattern:
+             a_1 = a.ge(b)
+             a_2 = aten._to_copy(a_1, a.dtype)
+           Then we this should be valid to completely re-inplace
+           (this is exactly what functionalization will emit when it sees a.ge_(b)).
+
+           This optimization is only really important for user programs
+           that directly use inplace comparison ops though.
+
+           We also cannot re-inplace on tensors that have overlapping memory,
+           e.g. torch.ones(1).expand(4, 4).add_(1)
+
+      (1b) Check if "a" is an alias of any of the program inputs.
+
+          If it is, skip and move to the next node.
+          Inplace'ing an op that would cause it to mutate a program is not sound,
+          because that would be a side effect visible to the user.
+
+          NOTE: there's a future optimization that we should make:
+          if "a" is a (alias of a)  program input, but later in the program
+          there is a node that looks like "a.copy_(...)",
+          Then re-inplacing is ok to do - we are temporarily re-using a's buffer,
+          which will later be overwritten by the copy_() call.
+
+          This will be an important optimization to have for programs that mutate
+          their inputs. It currently isn't implemented though.
+
+      (1c) Check if "a" and "args..." alias
+
+          For example, re-inplacing to create code like the below
+          isn't guaranteed to be sound:
+
+            aten.mul_(a, a)
+
+    (2) Check that "a" and all of its outstanding aliases are not used anywhere
+        later in the graph. If this is the case, then it's safe to re-inplace
+        to "b = foo_(a)".
+
+        There are a few caveats to this, explained in more detail below:
+        (a) If "a" is used later as an argument to a view op, that is okay.
+            It's only a problem if "a" (or that view) is later passed
+            into a normal operator, or if it is returned as the program output.
+        (b) If "a" is a repeat argument in `foo()`, then don't reinplace.
+            Most ATen kernels don't make any guarantees that this is sound,
+            e.g. if you do aten.mul_(a, a).
+            So we'll just ban re-inplacing in this case.
+            It's only a problem if "a" (or that view) is later passed
+        (c) If "a" is used as an input into a view "inverse" / "scatter"
+            operator, it is potentially fine to re-inplace
+            (and remove that scatter operator from the graph).
+            See below for a more detailed example.
+
+        NOTE: there is an optimization in this step that is crucial
+        to fully recovering performance from functionalization.
+
+        Given this program:
+        def f(x):
+            a = torch.ops.aten.add(x, x)
+            b = torch.ops.aten.diagonal(a)
+            torch.ops.aten.fill_(b, 0)
+            return d
+
+        Functionalization will emit the following:
+        def f(x):
+            a = torch.ops.aten.add(x, x)
+            b = torch.ops.aten.diagonal(a, 0, 1)
+            b_updated = torch.ops.aten.fill(b, 0)
+            a_updated = torch.ops.aten.diagonal_scatter(a, b_updated, 0, 1)
+            return a_updated
+
+        Ordinarily, we would not be able to reinplace the fill,
+        because "b" aliases with "a" which is used by the diagonal_scatter call.
+
+        "re-inplacing" is on the hook for figuring out that it is ok to
+        completely, the expensive diagonal_scatter call, if we re-inplace the add().
+
+        So, for every `alias in alias_set(a)`, instead of checking
+        that "alias" is not used anywhere later in the graph,
+        we check that
+            EITHER:
+          (a) alias is not used anywhere later in the graph
+            OR:
+          (b) alias is used exactly once later on in the graph,
+              in the following op:
+
+                out = foo_scatter(alias, x, args...)
+
+              where the following must hold:
+                (i) "foo_scatter" is the "inverse" operator for foo.
+                    This only applies to "foo" ops that are view operators,
+                    which view into a subset of the original tensor's memory.
+                    In practice, there are ~4 operators where this applies:
+                      diagonal -> diagonal_scatter
+                      slice -> slice_scatter
+                      select -> select_scatter
+                      as_strided -> as_strided_scatter
+                (ii) "args..." are the same between the foo() and foo_scatter() calls.
+
+    (3) Perform the actual re-inplacing on foo!
+
+      (3b) is the common case, but special care is needed for {view}_scatter (3a)
+
+      (3a) {view}_scatter ops.
+
+        Consider this program:
+          a = torch.zeros(2, 2)
+          b = torch.ones(2)
+          a[0] = b
+
+        Post functionalization, that will look like:
+          a = torch.zeros(2)
+          b = torch.ones(1)
+          a_updated = torch.select_scatter(a, b, 0, 0)
+
+        In this case though, there is no "functional" op to re-inplace!
+        Instead, we'd like to directly remove toe select_scatter call.
+        We already know from (3) that this is valid,
+        because "a" has no later usages in the graph.
+
+        We perform the re-inplacing on the {view}_scatter op like so
+        Before:
+          a_updated = torch.select_scatter(a, b, args...)
+        After:
+          a_slice = a.select(a, args...)
+          a_slice.copy_(b)
+
+      (3b) Otherwise, replace the functional op with its inplace variant.
+        Before:
+          b = foo(a, args...)
+        After:
+          a.foo_(args...)
+
+    (4) Finally, after converting either:
+          Before:
+            b = foo(a)
+          After:
+            foo_(a)
+        or
+          Before:
+            b = {slice}_scatter(a, mutated_slice, args...)
+          After:
+            slice = {slice}(a, args...)
+            slice.copy_(mutated_slice)
+
+        We now need to find all later nodes that use "b" as an argument
+        and update them to take in "a" instead.
+
+        Note that for the majority of inplace ops, this isn't actually necessary
+        (because most inplace ops return "self" as their output).
+        This isn't generally true for all mutable ops though, which is why
+        we need to actually replace all of the arguments.
+
+        We also need to update our metadata of Dict[StorageWeakRef, Set[Node]],
+        That maps a given tensor storage to the set of all nodes that take in that storage
+        as an input.
+        Specifically, re-inplacing `b = foo(a)` causes "a" and "b"'s sets to get fused
+        together.
+
+    (5) Any "view_inverse/scatter" nodes that were identified as "it's ok to ignore them"
+        during step (3) get manually deleted from the graph.
+        Their outputs are no longer used, so technically standard DCE would be able
+        to do this, but we can no longer run FX's DCE pass now that we have mutable
+        ops in the graph.
+    """
+    _FunctionalizationMetadataProp(gm).propagate(*sample_args)
+
+    # Useful debug printing
+    # def _print(x):
+    # if isinstance(x, FakeTensor):
+    # print(f'fake_result: {StorageWeakRef(x._typed_storage()).cdata}')
+
+    # for n in gm.graph.nodes:
+    # print(n.format_node())
+    # if hasattr(n, 'meta'):
+    # print(f'node_idx: {n.meta["node_idx"]}')
+    # if 'fake_result' in n.meta:
+    # tree_map(_print, n.meta['fake_result'])
+    # if 'view_of' in n.meta:
+    # print(f'view_of: {str(n.meta["view_of"])}')
+    # print()
+
+    # We need to know which nodes correspond to inputs (or their aliases)
+    # so we know not to re-inplace them.
+    # NOTE: later, we'll need to add an optimization for fully recovering performance
+    # on programs that mutate inputs.
+    input_storages = {
+        StorageWeakRef(node.meta["fake_result"]._typed_storage())
+        for node in gm.graph.nodes
+        if (
+            node.op == "placeholder"
+            and isinstance(node.meta["fake_result"], torch.Tensor)
+        )
+    }
+
+    # We also need to know for a given node, what are all of its aliasing nodes.
+    storage_to_nodes: dict[StorageWeakRef, set[Node]] = defaultdict(set)
+    for n in gm.graph.nodes:
+        if "fake_result" in n.meta:
+            # Tree-mapping because some ops can return lists of tensors.
+            def _add_to_map(x):
+                if isinstance(x, FakeTensor):
+                    storage_to_nodes[StorageWeakRef(x._typed_storage())].add(n)
+
+            pytree.tree_map_(_add_to_map, n.meta["fake_result"])
+
+    # inplace-ify functional ops, subject to the constraints written below.
+    all_later_view_inverse_nodes_to_delete = set()
+    for node in gm.graph.nodes:
+        if node.op == "call_function":
+            # Today, the re-inplace pass on directly acts on:
+            # - functional ops with an inplace variant
+            # - {view}_scatter ops that can be potentially removed from the graph.
+            # Both of these ops take in tensor first args, so filtering on this condition
+            # makes the later code simpler.
+            # We should revisit this at some point though, particularly when we also want
+            # the reinplacer to be able to handle out= and mutable operators
+            # and tensorlist first args (like `_foreach_` ops).
+            if not isinstance(node.target, torch._ops.OpOverload):
+                continue
+            if len(node.target._schema.arguments) < 1:
+                continue
+            if type(node.target._schema.arguments[0].type) != torch.TensorType:
+                continue
+
+            # Step 1a: Check that the self argument we're attempting to reinplace
+            # has the same size/stride as the output.
+            # For example, we shouldn't try to reinplace torch.add(scalar_tensor, larger_tensor)
+            # As it would require resizing scalar_tensor.
+            # (We could potentially swizzle this into larger_tensor.add_(scalar_tensor),
+            # this is probably an optimization to revisit later).
+            self_arg = node.args[0]
+            self_flattened = pytree.tree_leaves(self_arg.meta["fake_result"])
+            node_flattened = pytree.tree_leaves(node.meta["fake_result"])
+            self_has_wrong_metadata = False
+            if len(self_flattened) == len(node_flattened):
+                for self_meta, node_meta in zip(self_flattened, node_flattened):
+                    if self_meta.numel() != node_meta.numel():
+                        self_has_wrong_metadata = True
+                    if self_meta.dtype != node_meta.dtype:
+                        self_has_wrong_metadata = True
+                    # We also cannot re-inplace on tensors that have internal memory overlap.
+                    # e.g. torch.ones(1).expand(4, 4).add_(1)
+                    if torch._debug_has_internal_overlap(self_meta) == 1:
+                        self_has_wrong_metadata = True
+            # Here, we (optimistically) assume that a.resize(b) is valid to re-inplace,
+            # Since users should never really be calling the functional "torch.ops.aten.resize"
+            # op directly in their programs.
+            if self_has_wrong_metadata and node.target != torch.ops.aten.resize.default:
+                continue
+
+            # Step 1b: ensure that the op we're trying to re-inplace isn't a program input
+            self_arg_storage = StorageWeakRef(
+                self_arg.meta["fake_result"]._typed_storage()
+            )
+            if self_arg_storage in input_storages:
+                # TODO: later, add the optimization for handling `copy_()` calls in the graph.
+                continue
+            if len([x for x in node.args if x is self_arg]) > 1:
+                # Step 1c:
+                # Calling stuff like aten.mul_(a, a) isn't guaranteed to be sound,
+                # so we prevent re-inplacing in this case.
+                continue
+
+            self_arg_storage = StorageWeakRef(
+                self_arg.meta["fake_result"]._typed_storage()
+            )
+            self_aliases = storage_to_nodes[self_arg_storage]
+
+            # First, we find all later usages of any of the aliases of self_arg.
+            later_node_usages = _get_all_later_node_usages(
+                self_aliases, node.meta["node_idx"]
+            )
+            # Then, we check if any of those later usages are actually view_scatter ops
+            # that are safe to fully remove.
+            later_view_inverse_node_usages = _get_view_inverse_node_usages(
+                later_node_usages, self_aliases
+            )
+
+            # Step 2: Check to see if the input to the op is re-used later in the graph.
+            # If not (same goes for its aliases), then this op is safe to re-in place.
+            # This is a slightly roundabout way to check that there are no later usages of the current self argument.
+            # (later_view_inverse_node_usages corresponds to "view_scatter" nodes that we are allowed to delete)
+            can_reinplace = len(later_node_usages - later_view_inverse_node_usages) == 0
+            if not can_reinplace:
+                continue
+
+            # Step 3a: Special handling for when we see *_scatter operators.
+            # When we see an operator like `b = torch.slice_scatter(a, ...)`,
+            # instead of trying to "inplace" it into a.slice_scatter_(..._),
+            # we would prefer to remove it from the graph entirely,
+            # and instead copy_() the slice directly into the larger tensor.
+            # See the description of the algorithm for a full example.
+            if (
+                node.target in _VIEW_INVERSE_MAP
+                and node not in all_later_view_inverse_nodes_to_delete
+            ):
+                view_op = _VIEW_INVERSE_MAP[node.target]
+                # Before:
+                #   base_updated = torch.ops.aten.slice_scatter.default(base, mutated_slice, args...)
+                # After:
+                #   slice = torch.ops.aten.slice.default(base, args...)
+                #   slice.copy_(mutated_slice)
+                with gm.graph.inserting_before(node):
+                    mutated_slice_node = node.args[1]
+                    remaining_slice_args = node.args[2:]
+                    slice_node = gm.graph.create_node(
+                        "call_function",
+                        view_op,
+                        (self_arg,) + tuple(remaining_slice_args),
+                        node.kwargs,
+                    )
+                    gm.graph.create_node(
+                        "call_function",
+                        torch.ops.aten.copy_.default,
+                        (
+                            slice_node,
+                            mutated_slice_node,
+                        ),
+                        {},
+                    )
+                # Add the slice_scatter node to our "nodes to delete" list.
+                all_later_view_inverse_nodes_to_delete.add(node)
+
+            else:
+                # Step 3b: Check to see if this operator has an inplace variant.
+                maybe_inplace_op = _maybe_get_inplace_op(node.target)
+                if maybe_inplace_op is None:
+                    continue
+                # And if so, replace it with its inplace variant.
+                node.target = maybe_inplace_op
+
+            # At this point, 'storage_to_nodes' will be stale.
+            # Now that we're inplacing `b = foo(a)`, we need to effectively
+            # union together the dict values for b and a's storage.
+            # Hmm... morally I think we also want to keep the `fake_result` metadata
+            # up to date here, but I'm not sure how easy it is to do.
+            # Maybe it's fine to wait until the end of the pass to update it.
+            curr_node_storage = StorageWeakRef(
+                node.meta["fake_result"]._typed_storage()
+            )
+            storage_to_nodes[self_arg_storage].update(
+                storage_to_nodes[curr_node_storage]
+            )
+            storage_to_nodes[curr_node_storage].update(
+                storage_to_nodes[self_arg_storage]
+            )
+
+            # Need to remember the view_scatter view nodes we found so we can remove them alter.
+            all_later_view_inverse_nodes_to_delete.update(
+                later_view_inverse_node_usages
+            )
+
+            # Step 4:
+            # Now that we've replaced b = a.foo() with a.foo_(),
+            # We need to replace any later usages of "b" with "a"
+            for old in itertools.chain([node], later_view_inverse_node_usages):
+                new = old.args[0]
+                nodes_to_update = [
+                    n for n in old.users if n.meta["node_idx"] > node.meta["node_idx"]
+                ]
+                for node_to_update in nodes_to_update:
+
+                    def replace_arg(a):
+                        if a == old:
+                            return new
+                        return a
+
+                    # First, replace usages of "b" with "a"
+                    node_to_update.args = tree_map_only(
+                        Node, replace_arg, node_to_update.args
+                    )
+                    node_to_update.kwargs = tree_map_only(
+                        Node, replace_arg, node_to_update.kwargs
+                    )
+
+                    # Second, update our storage_to_nodes data structure.
+                    old_flattened_res = pytree.tree_leaves(old.meta["fake_result"])
+                    node_flattened_res = pytree.tree_leaves(
+                        node_to_update.meta["fake_result"]
+                    )
+
+                    old_res_storage = {
+                        StorageWeakRef(x._typed_storage())
+                        for x in old_flattened_res
+                        if isinstance(x, FakeTensor)
+                    }
+                    node_res_storage = {
+                        StorageWeakRef(x._typed_storage())
+                        for x in node_flattened_res
+                        if isinstance(x, FakeTensor)
+                    }
+
+                    # This will happen if we're updating a view op, e.g.
+                    # e.g. replacing
+                    #     x = view(old)
+                    #     x = view(new)
+                    # When that happens, we need to make sure to keep our
+                    # storage mapping up to date.
+                    #
+                    # We're checking for len(...) == 1 here because all view ops are guaranteed to return either a single tensor,
+                    # or multiple tensors that all share the same storage.
+                    # We can't just check equality because we might encounter FX nodes that return zero tensor outputs.
+                    if (
+                        len(old_res_storage) == 1
+                        and len(node_res_storage) == 1
+                        and old_res_storage == node_res_storage
+                    ):
+                        new_flattened_res = pytree.tree_leaves(new.meta["fake_result"])
+                        new_res_storage = {
+                            StorageWeakRef(x._typed_storage())
+                            for x in new_flattened_res
+                            if isinstance(x, FakeTensor)
+                        }
+                        assert len(new_res_storage) == 1
+                        (new_ref,) = new_res_storage
+                        (node_ref,) = node_res_storage
+                        # Technically, "old_ref" and all its aliases will remain
+                        # in our mapping.
+                        # That should be fine though, since we deleted "old"
+                        # from the graph at this point.
+                        storage_to_nodes[node_ref].update(storage_to_nodes[new_ref])
+                        storage_to_nodes[new_ref].update(storage_to_nodes[node_ref])
+
+    # Step 4: delete any _scatter nodes that we de-functionalized
+    # Need to take care not to delete any of these nodes until after *all* modifications
+    # to the graph are finished.
+    for to_delete in all_later_view_inverse_nodes_to_delete:
+        gm.graph.erase_node(to_delete)
+
+    gm.recompile()
+    return gm
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/runtime_assert.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/runtime_assert.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8c12327f31861ad54863a2578076b6a6a6c51b5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/runtime_assert.py
@@ -0,0 +1,630 @@
+# mypy: allow-untyped-defs
+import functools
+import logging
+import operator
+import sys
+from typing import Any, Optional, TYPE_CHECKING
+
+
+# Import sympy and ShapeEnv during TYPE_CHECKING since importing sympy is slow
+if TYPE_CHECKING:
+    import sympy
+
+    from torch.fx.experimental.symbolic_shapes import ShapeEnv
+else:
+    ShapeEnv = Any
+
+import torch
+import torch.utils._pytree as pytree
+from torch import fx
+from torch._subclasses.meta_utils import is_sparse_any
+from torch.fx._compatibility import compatibility
+from torch.fx._utils import lazy_format_graph_code
+from torch.fx.experimental.proxy_tensor import py_sym_types
+from torch.fx.experimental.sym_node import SymNode
+from torch.fx.graph_module import GraphModule
+
+
+__all__ = ["insert_deferred_runtime_asserts"]
+
+log = logging.getLogger(__name__)
+graph_code_log = torch._logging.getArtifactLogger(__name__, "graph_code")
+
+
+def _get_example_value(node: fx.Node) -> Optional[str]:
+    """
+    Get the example value key for a node, since dynamo uses "example_value"
+    while non-strict export uses "val.
+    """
+    if "example_value" in node.meta:
+        return node.meta["example_value"]
+    elif "val" in node.meta:
+        return node.meta["val"]
+    else:
+        return None
+
+
+def _get_sym_val(node: fx.Node) -> Optional["sympy.Expr"]:
+    val = _get_example_value(node)
+    if isinstance(val, py_sym_types):
+        return val.node.expr
+    return None
+
+
+@compatibility(is_backward_compatible=True)
+def insert_deferred_runtime_asserts(
+    gm: GraphModule,
+    shape_env: ShapeEnv,
+    name: str,
+    export: bool = False,
+) -> None:
+    """
+    During tracing, we may have discovered that some data-dependent values
+    had runtime assert on them; e.g., torch.empty(x.item()) induces a runtime
+    that x.item() >= 0.  This asserts can happen unpredictably during fake
+    tensor propagation, so we cannot conveniently insert them into the FX graph
+    when they occur.  Instead, we accumulate them in the ShapeEnv, and in this
+    pass insert them into the graph as proper tests.
+
+    This pass also deduplicates size-related computation, CSE-ing ops that produce
+    symbolic values and/or are involved in runtime asserts. Additionally, shape calls
+    (size/stride/storage_offset) are turned into compute on input sizes if possible,
+    allowing intermediate tensors to be freed earlier. For example, here dynamo will
+    DCE the cat and repeat calls:
+
+        z = torch.cat([x, x], dim=0)  # 2*s0
+        w = z.repeat(y.shape[0])  # 2*s0*s1
+        _w = w.shape[0]
+        # something with _w, but not w ...
+
+        # turns into ->
+        _w0 = 2 * s0
+        _w = _w0 * s1
+
+        # where s0, s1 are either SymInt graph inputs, or the result of added size calls
+
+    Redundant torch._check or torch.ops.aten._assert_scalar.default calls that assert
+    the same expression, and redundant constrain_range calls are also deduplicated.
+    Additionally, because single-symbol bound checks (e.g. u0 >= 0, u0 <= 5) accumulate
+    information in the ShapeEnv, the ShapeEnv contains min/max bounds for each symbol,
+    and we delete all previous calls, adding bound checks at the end of this pass.
+    """
+
+    # Import sympy locally
+    import sympy
+
+    from torch._export.passes._node_metadata_hook import _set_node_metadata_hook
+    from torch.fx.experimental.symbolic_shapes import (
+        _has_uninterpretable_sympy_function,
+        CallMethodKey,
+        cast_symbool_to_symint_guardless,
+        ConvertIntKey,
+        DivideByKey,
+        free_symbols,
+        InnerTensorKey,
+        resolve_unbacked_bindings,
+    )
+    from torch.utils._sympy.numbers import int_oo
+    from torch.utils._sympy.reference import (
+        OptimizedPythonReferenceAnalysis,
+        PythonReferenceAnalysis,
+    )
+    from torch.utils._sympy.value_ranges import ValueRanges
+
+    # TODO: Request simplification on runtime asserts before emitting them
+    ras_by_symbol = shape_env.deferred_runtime_asserts.copy()
+    graph = gm.graph
+    tracer = fx.proxy.GraphAppendingTracer(graph)
+    graph_code_log.debug(
+        "%s",
+        lazy_format_graph_code(
+            f"pre insert_deferred_runtime_asserts {name}", gm, colored=True
+        ),
+    )
+
+    # We are going to mutate the dict
+    expr_to_proxy: dict[sympy.Expr, fx.Proxy] = {}
+    placeholders = set()
+    first_non_placeholder = None
+    for node in graph.nodes:
+        if node.op != "placeholder":
+            first_non_placeholder = node
+            break
+        else:
+            placeholders.add(node)
+
+    def _is_intermediate_tensor_sym_call(node: fx.Node) -> bool:
+        """
+        If a size/stride/storage offset call on an intermediate tensor,
+        we can try to compute the value from input shapes instead.
+        """
+        return (
+            (val := _get_sym_val(node)) is not None
+            and not isinstance(val, sympy.Number)
+            # this holds back from reifying anything in torch.utils._sympy.functions.py that's unsupported
+            and not _has_uninterpretable_sympy_function(val)
+            and any(
+                isinstance(arg, fx.Node)
+                and isinstance(_get_example_value(arg), (torch.Tensor, torch.Size))
+                and arg.op != "placeholder"
+                for arg in node.args
+            )
+        )
+
+    # Figure out what key to use, val or example_value
+    val_key = "val"
+    for node in graph.nodes:
+        if "example_value" in node.meta:
+            val_key = "example_value"
+            break
+        elif "val" in node.meta:
+            break
+
+    def _node_metadata_hook(
+        node: torch.fx.Node,
+        stack_trace: Optional[str] = None,
+        nn_module_stack: Optional[dict[str, Any]] = None,
+    ) -> None:
+        fake_args = pytree.tree_map(
+            lambda arg: (
+                _get_example_value(arg) if isinstance(arg, torch.fx.Node) else arg
+            ),
+            node.args,
+        )
+        try:
+            target = node.target
+            if node.op == "call_method":
+                assert isinstance(node.target, str)
+                target = getattr(fake_args[0], node.target)
+                fake_args = fake_args[1:]
+            node.meta[val_key] = target(*fake_args)  # type: ignore[operator]
+        except NotImplementedError:
+            # This can happen when attempting to reify a symbol with an unsupported call_function node,
+            # e.g. with NestedTensors + sym_size.int via match_symbol().
+            # This seems to be fine, as the node gets CSE'd and deleted later in favor of a SymInt graph input.
+            pass
+        if stack_trace is not None:
+            node.meta["stack_trace"] = stack_trace
+        if nn_module_stack is not None:
+            node.meta["nn_module_stack"] = nn_module_stack
+
+    # Track asserts/checks we've added
+    added_asserts: set[sympy.Expr] = set()
+    constrained_unbacked_symbols: set[sympy.Symbol] = set()
+
+    Analysis = PythonReferenceAnalysis if export else OptimizedPythonReferenceAnalysis
+
+    def _sympy_interp(expr_to_proxy, expr):
+        # sympy_interp() with hash consing
+        from sympy import Integer, Number, Symbol
+        from sympy.logic.boolalg import BooleanAtom
+
+        from torch.utils._sympy.interp import _run_sympy_handler, sympy_interp
+
+        # hash cons
+        if expr in expr_to_proxy:
+            return expr_to_proxy[expr]
+        # base cases, don't cache
+        if isinstance(expr, (Integer, Number, Symbol, BooleanAtom)):
+            return sympy_interp(Analysis, expr_to_proxy, expr)
+
+        # hash cons on arguments, run expr handler
+        expr_to_proxy[expr] = _run_sympy_handler(
+            Analysis,
+            [_sympy_interp(expr_to_proxy, arg) for arg in expr.args],
+            expr,
+        )
+        return expr_to_proxy[expr]
+
+    def _is_bound_expr_for_symbol(expr: "sympy.Expr") -> bool:
+        # This is probably unnecessary, but since torch._check() calls for single-symbol bounds
+        # like u0 >= 0, 10 >= u0 accumulate range info in the ShapeEnv, we designate these calls as redundant
+        # and instead add 2 runtime asserts at the end of this pass, if the min/max bounds are non-trivial.
+        if len(expr.args) != 2 or expr.func not in (sympy.LessThan, sympy.GreaterThan):
+            return False
+        lhs, rhs = expr.args
+        return (isinstance(lhs, sympy.Symbol) and isinstance(rhs, sympy.Number)) or (
+            isinstance(rhs, sympy.Symbol) and isinstance(lhs, sympy.Number)
+        )
+
+    def add_runtime_asserts(ras):
+        for ra in ras:
+            if (
+                # redundant
+                ra.expr in added_asserts
+                # if we've already added a constrain_range call for this symbol,
+                # then single-symbol bound asserts like u0 >= 0, u0 <= 5 are redundant.
+                or (
+                    len(ra.expr.free_symbols) == 1
+                    and next(iter(ra.expr.free_symbols)) in constrained_unbacked_symbols
+                    and _is_bound_expr_for_symbol(ra.expr)
+                )
+                # don't try to reify sympy functions we can't turn into FX nodes
+                or _has_uninterpretable_sympy_function(ra.expr)
+            ):
+                continue
+
+            log.debug("inserting runtime assert %s", ra.expr)
+            # Need to process ALL free symbols, not just unbacked ones
+            fvs = free_symbols(ra.expr)
+            missing = fvs - expr_to_proxy.keys()
+            if missing:
+                i1 = min(missing, key=str)
+                # TODO: Remove relaxing assert on unbacked_symint https://github.com/pytorch/pytorch/issues/119689
+                # assert shape_env.is_unbacked_symint(i1), i1
+                ras_by_symbol.setdefault(i1, []).append(ra)
+            else:
+                # Convert the sympy expression into a sequence of FX
+                # nodes
+                with _set_node_metadata_hook(gm, _node_metadata_hook):
+                    res = _sympy_interp(expr_to_proxy, ra.expr).node
+                    graph.call_function(
+                        torch.ops.aten._assert_scalar.default,
+                        # TODO: use ra.msg here, but it's pretty
+                        # useless right now
+                        (
+                            res,
+                            f"Runtime assertion failed for expression {ra.expr} on node '{res}'",
+                        ),
+                    )
+                added_asserts.add(ra.expr)
+
+    nodes = list(graph.nodes)
+    for i, node in enumerate(nodes[:-1]):
+        # Placeholders can match symbols, but when we destructure them
+        # with size we have to make sure we insert the nodes after all
+        # the placeholders
+        with graph.inserting_before(
+            nodes[i + 1] if node not in placeholders else first_non_placeholder
+        ):
+            # Unfortunately, this logic still must remain because manual
+            # make_fx calls may not explicitly bind all symbolic ints as
+            # arguments to the function, so we must infer it from the other
+            # arguments
+            if (
+                node in placeholders
+                and (example_value := _get_example_value(node)) is not None
+            ):
+
+                def match_symbol(symint, cb):
+                    if (
+                        isinstance(symint, torch.SymInt)
+                        and isinstance(symint.node, SymNode)
+                        and isinstance(s := symint.node.expr, sympy.Symbol)
+                        and s not in expr_to_proxy
+                    ):
+                        with _set_node_metadata_hook(gm, _node_metadata_hook):
+                            expr_to_proxy[s] = fx.Proxy(cb(), tracer=tracer)
+                        log.debug("expr_to_proxy[%s] = %s", s, expr_to_proxy[s])
+
+                match_symbol(example_value, lambda: node)
+                if isinstance(t := example_value, torch.Tensor):
+                    for i, s in enumerate(t.size()):
+                        match_symbol(
+                            s,
+                            lambda: graph.call_function(
+                                torch.ops.aten.sym_size.int, (node, i)
+                            ),
+                        )
+                    if not is_sparse_any(t):
+                        for i, s in enumerate(t.stride()):
+                            match_symbol(
+                                s,
+                                lambda: graph.call_function(
+                                    torch.ops.aten.sym_stride.int, (node, i)
+                                ),
+                            )
+                        match_symbol(
+                            t.storage_offset(),
+                            lambda: graph.call_function(
+                                torch.ops.aten.sym_storage_offset.default, (node,)
+                            ),
+                        )
+
+            # Handle asserts that aren't associated with any symbol.  This
+            # doesn't really have to be in the loop as it will only run once,
+            # it just needs to happen right after the placeholders.
+            # insert this after placeholders & added sym nodes, and before non-placeholders.
+            if node == first_non_placeholder:
+                add_runtime_asserts(ras_by_symbol.pop(None, []))  # type: ignore[call-overload]
+
+            # deduplicate asserts already present in graph
+            if node.target in (
+                torch._check,
+                torch.ops.aten._assert_scalar.default,
+            ):
+                if (
+                    node.args[0] == True  # noqa: E712
+                    or (assert_expr := _get_sym_val(node.args[0])) in expr_to_proxy
+                    or (
+                        assert_expr is not None
+                        and _is_bound_expr_for_symbol(assert_expr)
+                    )
+                ):
+                    arg = node.args[0]
+                    gm.graph.erase_node(node)
+                    if isinstance(arg, fx.Node) and not arg.users:
+                        gm.graph.erase_node(arg)
+                else:
+                    added_asserts.add(assert_expr)  # type: ignore[arg-type]
+
+            # hash cons, replace function calls that return torch.SymInts with direct references to
+            # FX nodes built up to reify the sympy expression.
+            if (
+                node.op != "placeholder"
+                and (sym_expr := _get_sym_val(node)) is not None
+            ):
+                # this guards against deleting calls like item() that produce new untracked symbols
+                def has_new_untracked_symbols():
+                    for symbol in sym_expr.free_symbols:
+                        if symbol not in expr_to_proxy:
+                            return True
+                    return False
+
+                # this guards against deleting calls that produce unbacked bindings we haven't yet seen.
+                # in this case looking at sym_expr.free_symbols might not be enough, if the example value has a hint
+                # (is backed), but produces an unbacked symbol. In this case keep the node alive.
+                resolved_unbacked_bindings = resolve_unbacked_bindings(
+                    shape_env, node.meta.get("unbacked_bindings", {})
+                )
+
+                assert resolved_unbacked_bindings is not None
+
+                def has_new_unbacked_bindings():
+                    for key in resolved_unbacked_bindings.keys():
+                        if key not in expr_to_proxy:
+                            return True
+                    return False
+
+                # maybe re-reify expression, replace current node
+                if (
+                    sym_expr in expr_to_proxy
+                    or (  # example value is redundant
+                        _is_intermediate_tensor_sym_call(node)
+                        # shape call on intermediate tensor, turn into computation on input shapes
+                        and not has_new_untracked_symbols()
+                    )
+                ) and not has_new_unbacked_bindings():
+                    if _is_intermediate_tensor_sym_call(
+                        node
+                    ):  # reify from input shapes
+                        with _set_node_metadata_hook(
+                            gm,
+                            functools.partial(
+                                _node_metadata_hook,
+                                stack_trace=node.meta.get("stack_trace"),
+                                nn_module_stack=node.meta.get("nn_module_stack"),
+                            ),
+                        ):
+                            expr_to_proxy[sym_expr] = _sympy_interp(expr_to_proxy, sym_expr)  # type: ignore[arg-type]
+                        # won't try DCE-ing tensor compute here
+                    hash_node = expr_to_proxy[sym_expr].node  # type: ignore[arg-type]
+                    node.replace_all_uses_with(hash_node)
+                    gm.graph.erase_node(node)
+                    log.debug(
+                        "CSE node %s -> %s for expr %s", node, hash_node, sym_expr
+                    )
+
+                # store node in hash cons, don't delete/replace
+                elif sym_expr not in expr_to_proxy and not isinstance(
+                    sym_expr, (sympy.Number, sympy.logic.boolalg.BooleanAtom)
+                ):  # don't hash cons primitives
+                    expr_to_proxy[sym_expr] = fx.Proxy(node, tracer=tracer)  # type: ignore[arg-type]
+
+            # We add sym_constrain_range calls for symbols later in any case if they're size-like or range-constrained,
+            # so calls before that are redundant.
+            if node.target in (
+                torch.ops.aten.sym_constrain_range.default,
+                torch.ops.aten.sym_constrain_range_for_size.default,
+            ):
+                gm.graph.erase_node(node)
+
+            defs = []
+
+            # AOTAutograd will create new symbols as the unbacked_bindings keys, which PropagateSymInts will set as
+            # equivalent, but the refinement calls we perform in this pass may struggle with associating the two.
+            # More concretely, when re-exporting/tracing, constraining only the new symbol may not communicate enough
+            # information about the old symbol when we re-export, raising errors on data-dependent guards.
+            # Call resolve_unbacked_bindings() to get the original symbol if present, otherwise we take it as is.
+            if unbacked_bindings := resolve_unbacked_bindings(
+                shape_env, node.meta.get("unbacked_bindings")
+            ):
+                for s, keypath in unbacked_bindings.items():
+                    defs.append(s)
+
+                    # TODO: some CSE when generating these nodes can probably
+                    # help reduce graph size and improve compile time
+                    def go(node, keypath):
+                        if keypath == ():
+                            return node
+                        if (
+                            len(keypath) >= 2
+                            and isinstance(keypath[0], CallMethodKey)
+                            and isinstance(keypath[1], pytree.SequenceKey)
+                        ):
+                            if keypath[0].name == "size":
+                                return go(
+                                    graph.call_function(
+                                        torch.ops.aten.sym_size.int,
+                                        (node, keypath[1].idx),
+                                    ),
+                                    keypath[2:],
+                                )
+                            if keypath[0].name == "stride":
+                                return go(
+                                    graph.call_function(
+                                        torch.ops.aten.sym_stride.int,
+                                        (node, keypath[1].idx),
+                                    ),
+                                    keypath[2:],
+                                )
+                            return go(
+                                graph.call_method(
+                                    keypath[0].name, (node, keypath[1].idx)
+                                ),
+                                keypath[2:],
+                            )
+                        elif isinstance(keypath[0], CallMethodKey):
+                            return go(
+                                graph.call_method(keypath[0].name, (node,)), keypath[1:]
+                            )
+                        elif isinstance(keypath[0], pytree.SequenceKey):
+                            return go(
+                                graph.call_function(
+                                    operator.getitem, (node, keypath[0].idx)
+                                ),
+                                keypath[1:],
+                            )
+                        elif isinstance(keypath[0], ConvertIntKey):
+                            return go(
+                                graph.call_function(
+                                    cast_symbool_to_symint_guardless, (node,)
+                                ),
+                                keypath[1:],
+                            )
+                        elif isinstance(keypath[0], DivideByKey):
+                            # TODO: need to assert divisibility
+                            return go(
+                                graph.call_function(
+                                    operator.floordiv, (node, keypath[0].divisor)
+                                ),
+                                keypath[1:],
+                            )
+                        elif isinstance(keypath[0], InnerTensorKey):
+                            return go(
+                                graph.call_function(
+                                    getattr, (node, keypath[0].inner_name)
+                                ),
+                                keypath[1:],
+                            )
+                        else:
+                            raise AssertionError(f"unrecognized keypath {keypath}")
+
+                    if s not in expr_to_proxy:
+                        with _set_node_metadata_hook(gm, _node_metadata_hook):
+                            expr_to_proxy[s] = fx.Proxy(
+                                go(node, keypath), tracer=tracer
+                            )
+                        log.debug("expr_to_proxy[%s] = %s", s, expr_to_proxy[s])
+
+            for i0 in defs:
+                ras = ras_by_symbol.pop(i0, [])
+                # Before we perform any asserts, first apply range
+                # refinement.  This is important, because if we are going
+                # to retrace the graph (and we typically are if we send
+                # the graph to AOTAutograd), we need to make sure we apply
+                # range refinement (ala _check_is_size) first, BEFORE we
+                # run any of the asserts.  Otherwise, we may decide to
+                # perform substitutions based on the asserts which we then
+                # can't back out, because value ranges can only be applied
+                # to asserts.)
+                #
+                # A perhaps better long term plan is to avoid this order
+                # dependence by making it possible to refine ranges on
+                # arbitrary expressions, not just symbols.  But it is not
+                # so easy to make use of this information, see
+                # https://twitter.com/ezyang/status/1745801370299482492
+                # We actually made an attempt at this in
+                # https://github.com/pytorch/pytorch/pull/119043
+                # which didn't work.
+                #
+                # Another ideas for how to do this:
+                # - Have bound_sympy be the source of truth of the ranges of any expression
+                # - Cache intermediate results for every subexpression of bound_sympy
+                # - This cache should be possible to edit to refine ranges
+                #
+                # One issue with this proposal is that if
+                # we have a bound on 2x, we are not going to be able to
+                # apply it for 4x.  Similarly, we may have bounds for an
+                # equivalent expression that we are not applying because
+                # it's not a perfect match (e.g. x < y vs y > x)".
+                #
+                # The first issue we already have it and it's impossible
+                # to solve in general, so any implementation on a best
+                # effort basis should do.
+                #
+                # The second issue is a preexisting one. It can be mitigated
+                # with a normalization algorithm. In general, it may also
+                # be on a best effort basis, but since our grammar is not
+                # terribly difficult, chances are we could even fully
+                # normalize SymPy expressions... who knows.
+                if i0 in constrained_unbacked_symbols:
+                    continue  # constrain symbol just once
+
+                if i0 in shape_env.size_like:
+                    if export:
+                        graph.call_function(
+                            torch.ops.aten.sym_constrain_range_for_size.default,
+                            (expr_to_proxy[i0].node,),
+                        )
+                    else:
+                        graph.call_function(
+                            torch._check_is_size, (expr_to_proxy[i0].node,)
+                        )
+
+                vr = shape_env.var_to_range[i0]
+                if vr.is_int and vr.upper == sys.maxsize - 1:
+                    # treat upper bound == sys.maxsize - 1 for int symbols as +oo
+                    # to avoid redundant runtime assert
+                    vr = ValueRanges(vr.lower, int_oo)
+                if not shape_env._default_unspecified_value_range().issubset(vr):
+                    # The runtime range is constrained, so add a runtime
+                    # assert and also explicitly refine the range
+                    # (refinement should not be necessary once runtime
+                    # asserts cause refinement, but that's NYI)
+                    def convert(s):
+                        if s in (int_oo, -int_oo):
+                            return None
+                        try:
+                            return int(s)
+                        except TypeError:
+                            return None
+
+                    if (
+                        expr_to_proxy[i0].node.target
+                        != cast_symbool_to_symint_guardless
+                    ):
+                        # TODO(pianpwk): calling sym_constrain_range_for_size or adding bound asserts
+                        # raises AOTAutograd errors on cast_symbool_to_symint_guardless
+
+                        with _set_node_metadata_hook(
+                            gm,
+                            functools.partial(
+                                _node_metadata_hook,
+                                stack_trace=node.meta.get("stack_trace"),
+                                nn_module_stack=node.meta.get("nn_module_stack"),
+                            ),
+                        ):
+                            if (min_val := convert(vr.lower)) is not None:
+                                ge = _sympy_interp(expr_to_proxy, i0 >= min_val).node
+                                graph.call_function(
+                                    torch.ops.aten._assert_scalar.default,
+                                    (
+                                        ge,
+                                        f"Runtime assertion failed for expression {i0 >= min_val} on node '{ge}'",
+                                    ),
+                                )
+                                added_asserts.add(i0 >= min_val)
+                            if (max_val := convert(vr.upper)) is not None:
+                                le = _sympy_interp(expr_to_proxy, i0 <= max_val).node
+                                graph.call_function(
+                                    torch.ops.aten._assert_scalar.default,
+                                    (
+                                        le,
+                                        f"Runtime assertion failed for expression {i0 <= max_val} on node '{le}'",
+                                    ),
+                                )
+                                added_asserts.add(i0 <= max_val)
+
+                constrained_unbacked_symbols.add(i0)
+                add_runtime_asserts(ras)
+
+    # delete unused reified symbols
+    for expr, proxy in expr_to_proxy.items():
+        if (
+            isinstance(expr, sympy.Symbol)
+            and proxy.node.op != "placeholder"  # keep placeholders intact
+            and not proxy.node.users
+        ):
+            log.debug("deleting unused reified symbol for %s", expr)
+            gm.graph.erase_node(proxy.node)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/shape_prop.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/shape_prop.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a88b73bba184741ada401572c96c684abd5bab5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/shape_prop.py
@@ -0,0 +1,223 @@
+# mypy: ignore-errors
+
+import traceback
+from typing import Any, NamedTuple, Optional
+
+import torch
+import torch.fx
+from torch._dispatch.python import enable_python_dispatcher
+from torch._guards import detect_fake_mode
+from torch._subclasses.meta_utils import is_sparse_any
+from torch.fx._compatibility import compatibility
+from torch.fx.node import map_aggregate, Node
+
+
+__all__ = ["TensorMetadata", "ShapeProp"]
+
+
+@compatibility(is_backward_compatible=True)
+class TensorMetadata(NamedTuple):
+    # TensorMetadata is a structure containing pertinent information
+    # about a tensor within a PyTorch program.
+
+    # General Tensor metadata
+    shape: torch.Size
+    dtype: torch.dtype
+    requires_grad: bool
+    stride: tuple[int, ...]
+    memory_format: Optional[torch.memory_format]
+
+    # Quantization metadata
+    is_quantized: bool
+    qparams: dict[str, Any]
+
+
+def _extract_tensor_metadata(
+    result: torch.Tensor, include_contiguity=True
+) -> TensorMetadata:
+    """
+    Extract a TensorMetadata NamedTuple describing `result`.
+    """
+    shape = result.shape
+    dtype = result.dtype
+    requires_grad = result.requires_grad
+    stride = result.stride() if not is_sparse_any(result) else ()
+
+    memory_format = None
+
+    if include_contiguity and not is_sparse_any(result):
+        memory_formats = {
+            torch.contiguous_format,
+            torch.channels_last,
+            torch.channels_last_3d,
+        }
+        for query_format in memory_formats:
+            if result.is_contiguous(memory_format=query_format):
+                memory_format = query_format
+                break
+
+    is_quantized = result.is_quantized
+    qparams: dict[str, Any] = {}
+    if is_quantized:
+        qscheme = result.qscheme()
+        qparams["qscheme"] = qscheme
+        if qscheme in {torch.per_tensor_affine, torch.per_tensor_symmetric}:
+            qparams["scale"] = result.q_scale()  # type: ignore[assignment]
+            qparams["zero_point"] = result.q_zero_point()  # type: ignore[assignment]
+        elif qscheme in {
+            torch.per_channel_affine,
+            torch.per_channel_affine_float_qparams,
+            torch.per_channel_symmetric,
+        }:
+            # In this branch, scale and zero_point are expected to be tensors,
+            # we store the values as immutable_list in TensorMetadata for
+            # easier serialization downstream
+            qparams["scale"] = result.q_per_channel_scales().tolist()  # type: ignore[assignment]
+            qparams["zero_point"] = result.q_per_channel_zero_points().tolist()  # type: ignore[assignment]
+            qparams["axis"] = result.q_per_channel_axis()  # type: ignore[assignment]
+
+    return TensorMetadata(
+        shape, dtype, requires_grad, stride, memory_format, is_quantized, qparams
+    )
+
+
+@compatibility(is_backward_compatible=True)
+class ShapeProp(torch.fx.Interpreter):
+    """
+    Execute an FX graph Node-by-Node and
+    record the shape and type of the result
+    into the corresponding node.
+
+    Example:
+         In this example, we record the shape
+         and data type of a module given
+         an example input ``torch.randn(50, D_in)``.
+         We print the name, shape and dtype of each node.
+
+        class TwoLayerNet(torch.nn.Module):
+            def __init__(self, D_in, H, D_out):
+                super().__init__()
+                self.linear1 = torch.nn.Linear(D_in, H)
+                self.linear2 = torch.nn.Linear(H, D_out)
+            def forward(self, x):
+                h_relu = self.linear1(x).clamp(min=0)
+                y_pred = self.linear2(h_relu)
+                return y_pred
+        N, D_in, H, D_out = 64, 1000, 100, 10
+        x = torch.randn(N, D_in)
+        y = torch.randn(N, D_out)
+        model = TwoLayerNet(D_in, H, D_out)
+        gm = torch.fx.symbolic_trace(model)
+        sample_input = torch.randn(50, D_in)
+        ShapeProp(gm).propagate(sample_input)
+
+        for node in gm.graph.nodes:
+            print(node.name, node.meta['tensor_meta'].dtype,
+                node.meta['tensor_meta'].shape)
+
+        The output of this code is:
+
+        x torch.float32 torch.Size([50, 1000])
+        linear1 torch.float32 torch.Size([50, 100])
+        clamp_1 torch.float32 torch.Size([50, 100])
+        linear2 torch.float32 torch.Size([50, 10])
+        output torch.float32 torch.Size([50, 10])
+
+    Args:
+         module (GraphModule): The module to be executed
+         fake_mode (FakeTensorMode): A fake mode for copying the gm
+
+    """
+
+    def __init__(self, gm, fake_mode=None):
+        super().__init__(gm)
+        if fake_mode is None:
+            fake_mode = detect_fake_mode()
+        if fake_mode is not None:
+            from torch._dynamo.utils import deepcopy_to_fake_tensor
+
+            # Note:
+            # We need fake execution cause the inputs are fake, however, we cannot fakify the module
+            # - because we need to write to the tensor_meta of the real module. So we fakify to
+            # produce a result (L131 below), to extract tensor meta, and then keep going.
+            #
+            # If we were to fakify, we would write to the wrong node, and then downstream fusion
+            # would be missing the tensor_meta.
+            #
+            # See torch/_inductor/overrides.py for where this is called upstream of fusion.
+            self.fake_module = deepcopy_to_fake_tensor(self.module, fake_mode)
+            self.fake_mode = fake_mode
+        else:
+            self.fake_module = None
+            self.fake_mode = None
+
+        self.real_module = self.module
+
+    def run_node(self, n: Node) -> Any:
+        from torch.fx.experimental.symbolic_shapes import (
+            compute_unbacked_bindings,
+            rebind_unbacked,
+        )
+
+        try:
+            if self.fake_module is not None:
+                # Hacky swap. Alternatively, we could do this with overriding
+                # call_module and get_attr.
+                self.module = self.fake_module
+            try:
+                if self.fake_mode is not None:
+                    with self.fake_mode, enable_python_dispatcher():
+                        result = super().run_node(n)
+                        rebind_unbacked(self.fake_mode.shape_env, n, result)
+                else:
+                    result = super().run_node(n)
+            finally:
+                self.module = self.real_module
+        except Exception as e:
+            traceback.print_exc()
+            raise RuntimeError(
+                f"ShapeProp error for: node={n.format_node()} with meta={n.meta}"
+            ) from e
+
+        found_tensor = False
+
+        def extract_tensor_meta(obj):
+            if isinstance(obj, torch.Tensor):
+                nonlocal found_tensor
+                found_tensor = True
+                return _extract_tensor_metadata(obj)
+            else:
+                return obj
+
+        meta = map_aggregate(result, extract_tensor_meta)
+        if found_tensor:
+            n.meta["tensor_meta"] = meta
+
+        if self.fake_mode:
+            if (shape_env := self.fake_mode.shape_env) and (
+                symbol_to_path := compute_unbacked_bindings(shape_env, result)
+            ):
+                n.meta["unbacked_bindings"] = symbol_to_path
+
+        n.meta["type"] = type(result)
+        return result
+
+    def propagate(self, *args):
+        """
+        Run `module` via interpretation and return the result and
+        record the shape and type of each node.
+
+        Args:
+            *args (Tensor): the sample input.
+
+        Returns:
+            Any: The value returned from executing the Module
+        """
+        if self.fake_mode is not None:
+            fake_args = [
+                self.fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t
+                for t in args
+            ]
+        else:
+            fake_args = args
+        return super().run(*fake_args)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/split_module.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/split_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..59c560423d40167623a33bc71ab30c4c1a93e10d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/split_module.py
@@ -0,0 +1,626 @@
+# mypy: allow-untyped-defs
+import inspect
+import logging
+from collections import OrderedDict
+from typing import Any, Callable, Optional
+
+import torch
+from torch.fx._compatibility import compatibility
+from torch.fx._utils import lazy_format_graph_code
+from torch.fx.graph_module import GraphModule
+from torch.fx.node import Node
+
+
+__all__ = ["Partition", "split_module"]
+log = _LOGGER = logging.getLogger(__name__)
+
+
+@compatibility(is_backward_compatible=True)
+class Partition:
+    def __init__(self, name: str):
+        self.name: str = name
+        self.submod_name = f"submod_{name}"
+        self.node_names: list[str] = []
+        self.inputs: dict[str, None] = {}
+        self.outputs: dict[str, None] = {}
+        self.dependencies: dict[str, None] = {}
+        self.dependents: dict[str, None] = {}
+        self.graph: torch.fx.graph.Graph = torch.fx.graph.Graph()
+        self.environment: dict[Node, Node] = {}
+        self.targets: dict[str, Any] = {}
+
+    def __repr__(self) -> str:
+        return (
+            f"name: {self.name},\n"
+            f" nodes: {self.node_names},\n"
+            f" inputs: {self.inputs},\n"
+            f" outputs: {self.outputs},\n"
+            f" partitions depended on: {self.dependencies},\n"
+            f" partition dependents: {self.dependents}"
+        )
+
+
+def _get_attr_from_qualname(mod: torch.nn.Module, qualname: str) -> Any:
+    attr_val = mod
+    for atom in qualname.split("."):  # type: ignore[union-attr]
+        if not hasattr(attr_val, atom):
+            raise AttributeError(f"Node target {qualname} not found!")
+        attr_val = getattr(attr_val, atom)
+    return attr_val
+
+
+# Creates subgraphs out of main graph
+@compatibility(is_backward_compatible=True)
+def split_module(
+    m: GraphModule,
+    root_m: torch.nn.Module,
+    split_callback: Callable[[Node], int],
+    qualname_map: Optional[dict[str, str]] = None,
+    keep_original_order: Optional[bool] = False,
+    keep_original_node_name: Optional[bool] = False,
+):
+    """
+    Creates subgraphs out of main graph
+
+    Args:
+        m (GraphModule): Graph module to split
+        root_m (torch.nn.Module): root nn module. Not currently used. Included
+            because the root nn module is usually transformed via
+            torch.fx._symbolic_trace.symbolic_trace (see example below)
+        split_callback (Callable[[Node], int]): Callable function
+            that maps a given Node instance to a numeric partition identifier.
+            split_module will use this function as the policy for which operations
+            appear in which partitions in the output Module.
+        qualname_map: Optional[Dict[str, str]]: optional output parameter that returns a
+            mapping from new target names in the module after split to old target
+            names in the original module.
+        keep_original_order: Optional[bool]: keep the original order of the GraphModule
+            or use the Topological order of the new constructed GraphModule
+
+
+    Returns:
+        GraphModule: the module after split.
+
+    Example:
+
+        This is a sample setup:
+
+            import torch
+            from torch.fx.symbolic_trace import symbolic_trace
+            from torch.fx.graph_module import GraphModule
+            from torch.fx.node import Node
+            from torch.fx.passes.split_module import split_module
+
+            class MyModule(torch.nn.Module):
+                def __init__(self) -> None:
+                    super().__init__()
+                    self.param = torch.nn.Parameter(torch.rand(3, 4))
+                    self.linear = torch.nn.Linear(4, 5)
+
+                def forward(self, x, y):
+                    z = self.linear(x + self.param).clamp(min=0.0, max=1.0)
+                    w = self.linear(y).clamp(min=0.0, max=1.0)
+                    return z + w
+
+            # symbolically trace model
+            my_module = MyModule()
+            my_module_traced = symbolic_trace(my_module)
+
+            # random mod partitioning
+            partition_counter = 0
+            NPARTITIONS = 3
+
+            def mod_partition(node: Node):
+                global partition_counter
+                partition = partition_counter % NPARTITIONS
+                partition_counter = (partition_counter + 1) % NPARTITIONS
+                return partition
+
+            # split module in module with submodules
+            module_with_submodules = split_module(
+                my_module_traced, my_module, mod_partition
+            )
+
+        Output looks like this. Original graph is broken into partitions
+
+            > print(module_with_submodules)
+            GraphModule(
+                (submod_0): GraphModule(
+                    (linear): Linear(in_features=4, out_features=5, bias=True)
+                )
+                (submod_1): GraphModule(
+                    (linear): Linear(in_features=4, out_features=5, bias=True)
+                )
+                (submod_2): GraphModule()
+            )
+
+            def forward(self, x, y):
+                param = self.param
+                submod_0 = self.submod_0(x, param, y);  x = param = y = None
+                getitem = submod_0[0]
+                getitem_1 = submod_0[1];  submod_0 = None
+                submod_1 = self.submod_1(getitem, getitem_1);  getitem = getitem_1 = None
+                getitem_2 = submod_1[0]
+                getitem_3 = submod_1[1];  submod_1 = None
+                submod_2 = self.submod_2(getitem_2, getitem_3);  getitem_2 = getitem_3 = None
+                return submod_2
+
+        Output of split module is the same as output of input traced module.
+        This is an example within a test setting:
+
+            > orig_out = my_module_traced(x, y)
+            > submodules_out = module_with_submodules(x, y)
+            > self.assertEqual(orig_out, submodules_out)
+            True
+    """
+
+    log.debug(
+        "%s",
+        lazy_format_graph_code("pre split_module", m, colored=True),
+    )
+
+    def construct_graph(
+        node: Node,
+        base_mod_env: dict[str, Node],
+        base_mod_attrs: dict[str, torch.fx.graph_module.GraphModule],
+    ):
+        if node.op == "placeholder":
+            default_value = (
+                node.args[0] if len(node.args) > 0 else inspect.Signature.empty
+            )
+            if keep_original_node_name:
+                args = (
+                    () if default_value is inspect.Signature.empty else (default_value,)
+                )
+                base_mod_env[node.name] = base_mod_graph.create_node(
+                    "placeholder",
+                    node.name,
+                    args=args,  # type: ignore[arg-type]
+                    type_expr=node.type,
+                )
+            else:
+                base_mod_env[node.name] = base_mod_graph.placeholder(
+                    node.target,  # type: ignore[arg-type]
+                    type_expr=node.type,
+                    default_value=default_value,
+                )
+            base_mod_env[node.name].meta = node.meta.copy()
+        elif node.op == "get_attr":
+            base_mod_env[node.name] = base_mod_graph.get_attr(node.target)  # type: ignore[arg-type]
+            base_mod_env[node.name].meta = node.meta.copy()
+            assert isinstance(node.target, str)
+            attr_val = _get_attr_from_qualname(m, node.target)
+            base_mod_attrs[node.target] = attr_val  # type: ignore[index]
+        return base_mod_env, base_mod_attrs
+
+    import sympy
+
+    partitions: dict[str, Partition] = {}
+    orig_nodes: dict[str, Node] = {}
+    symbol_to_node: dict[sympy.Symbol, Node] = {}
+
+    def record_cross_partition_use(def_node: Node, use_node: Optional[Node]):
+        from torch.fx.experimental.symbolic_shapes import free_symbols
+
+        defined = getattr(def_node, "_fx_partition", None)
+        used = getattr(use_node, "_fx_partition", None)
+
+        log.debug(
+            "record_cross_partition_use %s (%s) %s (%s)",
+            def_node.name,
+            defined,
+            use_node.name if use_node is not None else "-",
+            used,
+        )
+
+        if defined != used:
+            if defined is not None:
+                def_partition = partitions[defined]
+                def_partition.outputs.setdefault(def_node.name)
+                if used is not None:
+                    def_partition.dependents.setdefault(used)
+
+            if used is not None:
+                use_partition = partitions[used]
+                use_partition.inputs.setdefault(def_node.name)
+                # We have made def_node an input to the use_partition.  If
+                # this input has symbolic symbols in its size, those also must
+                # be made as inputs to the partition
+                if (def_val := def_node.meta.get("example_value")) is not None:
+                    for s in sorted(free_symbols(def_val), key=str):
+                        s_node = symbol_to_node[s]
+                        use_partition.inputs.setdefault(s_node.name)
+                        if symbol_to_node[s].op != "placeholder":
+                            # If the node that defines the symbol is not a
+                            # placeholder, we must make it an output of the
+                            # partition.  Note that this may be in a different
+                            # partition than defined!  Although, this doesn't
+                            # really make a difference for correctness, since
+                            # defined is guaranteed to have the symbol in
+                            # scope and can return it; you just get less
+                            # optimal codegen in this case.
+                            s_defined = getattr(s_node, "_fx_partition", None)
+                            if s_defined is not None:
+                                s_def_partition = partitions[s_defined]
+                                s_def_partition.outputs.setdefault(s_node.name)
+                                s_def_partition.dependents.setdefault(used)
+                if defined is not None:
+                    use_partition.dependencies.setdefault(defined)
+
+    def instantiate_node_partition_mapping(node):
+        partition_name = str(split_callback(node))
+        log.debug(
+            "instantiate_node_partition_mapping %s (%s)", node.name, partition_name
+        )
+
+        # add node to partitions
+        partition = partitions.get(partition_name)
+        if partition is None:
+            partitions[partition_name] = partition = Partition(partition_name)
+
+        partition.node_names.append(node.name)
+        node._fx_partition = partition_name
+
+    # Global State Nodes are nodes which by their global state effects,
+    # "taint" all downstream nodes while they are active.
+    GLOBAL_STATE_NODES = [
+        torch.amp._enter_autocast,
+        torch.amp._exit_autocast,
+        torch._C._set_grad_enabled,
+    ]
+
+    # For grad regions:
+    # ------------------------
+    # 1. first region: we do nothing
+    # 2. subsequent regions: we insert the set_grad at the beginning
+    grad_regions: OrderedDict[Node, set[int]] = OrderedDict()
+
+    # For autocast regions:
+    # ------------------------
+    # 1. first region: we will only insert the _exit at the end
+    # 2. intermediate regions: we will insert both the
+    #    _enter at the beginning and _exit at the end
+    # 3. last region: we will only insert _enter at the beginning
+    # We will do so in the order in which the autocasts were instantiated.
+    autocast_regions: OrderedDict[Node, set[int]] = OrderedDict()
+    autocast_exits: dict[Node, Optional[Node]] = {}
+
+    active_grad = None
+    active_autocasts = set()
+
+    for node in m.graph.nodes:
+        # This will prefer placeholder bindings, because those come first.
+        # This is a little dangerous though: it is possible that an unbacked
+        # symbol is used without any binding site for it, in which case we
+        # will get a KeyError not able to find it.  I'd like to fix this by
+        # having passes.runtime_assert establish some invariants that I can
+        # rely on later, but this needs some extra work.  Quick fix first.
+        # See https://github.com/pytorch/pytorch/issues/130534
+        if (
+            (val := node.meta.get("example_value")) is not None
+            and isinstance(val, (torch.SymInt, torch.SymFloat))
+            and isinstance(s0 := val.node.expr, sympy.Symbol)
+            and s0 not in symbol_to_node
+        ):
+            symbol_to_node[val.node.expr] = node
+
+        if node.op in ["placeholder", "get_attr", "output"]:
+            continue
+
+        instantiate_node_partition_mapping(node)
+
+        if node.op == "call_function" and node.target in GLOBAL_STATE_NODES:
+            if node.target == torch._C._set_grad_enabled:
+                assert len(node.args) == 1
+                assert isinstance(node.args[0], bool)
+                active_grad = node
+                grad_regions[active_grad] = set({split_callback(node)})
+            elif node.target == torch.amp._enter_autocast:
+                # Should all be python constants
+                assert all(not isinstance(arg, Node) for arg in node.args)
+                active_autocasts.add(node)
+                autocast_regions[node] = set({split_callback(node)})
+                autocast_exits[node] = None
+            elif node.target == torch.amp._exit_autocast:
+                assert len(node.args) == 1
+                autocast_regions[node.args[0]].add(split_callback(node))
+                active_autocasts.remove(node.args[0])
+                autocast_exits[node.args[0]] = node
+
+        if active_grad is not None:
+            grad_regions[active_grad].add(split_callback(node))
+
+        for a in active_autocasts:
+            autocast_regions[a].add(split_callback(node))
+
+    assert all(v is not None for v in autocast_exits.values()), "autocast must exit"
+
+    autocast_regions = {k: sorted(v) for k, v in autocast_regions.items()}
+    grad_regions = {k: sorted(v) for k, v in grad_regions.items()}
+
+    if _LOGGER.isEnabledFor(logging.DEBUG):
+        _LOGGER.debug("autocast_regions: %s", autocast_regions)
+        _LOGGER.debug("grad_regions: %s", grad_regions)
+
+    assert_monotonically_increasing = bool(autocast_regions) or bool(grad_regions)
+
+    # split nodes into partitions
+    highest_partition = -1
+    for node in m.graph.nodes:
+        orig_nodes[node.name] = node
+
+        # TODO currently placeholders/parameters aren't put into random partitions,
+        # rather they're added to the graphs where they are used down below
+        if node.op in ["placeholder", "get_attr"]:
+            continue
+        if node.op == "output":
+            torch.fx.graph.map_arg(
+                node.args[0], lambda n: record_cross_partition_use(n, None)
+            )
+            continue
+
+        if assert_monotonically_increasing:
+            pid = split_callback(node)
+            assert highest_partition <= pid, (
+                "autocast or set_grad_enabled require monotonically increasing partitions:"
+                f"highest: {highest_partition}, this node's: {pid}"
+            )
+            highest_partition = pid
+
+        # do not capture cross-partition dependencies for global state nodes as they will be
+        # self-contained - their setup and unwind will be isolated to each partition submodule.
+        if node.target not in GLOBAL_STATE_NODES:
+            torch.fx.graph.map_arg(
+                node.args, lambda def_node: record_cross_partition_use(def_node, node)
+            )
+            torch.fx.graph.map_arg(
+                node.kwargs, lambda def_node: record_cross_partition_use(def_node, node)
+            )  # noqa: B950
+
+    original_partition_order = list(partitions.keys())
+    # find partitions with no dependencies
+    root_partitions: list[str] = []
+    for partition_name, partition in partitions.items():
+        if not len(partition.dependencies):
+            root_partitions.append(partition_name)
+
+    # check partitions for circular dependencies and create topological partition ordering
+    sorted_partitions: list[str] = []
+    while root_partitions:
+        root_partition = root_partitions.pop()
+        sorted_partitions.append(root_partition)
+        for dependent in partitions[root_partition].dependents:
+            partitions[dependent].dependencies.pop(root_partition)
+            if not partitions[dependent].dependencies:
+                root_partitions.append(dependent)
+    if len(sorted_partitions) != len(partitions):
+        raise RuntimeError("cycle exists between partitions!")
+
+    # Enter prelude
+    for regions_mapping in [autocast_regions, grad_regions]:
+        for node, regions in regions_mapping.items():
+            assert len(regions) > 0
+            partitions[str(regions[0])].environment[node] = node
+            for r in regions[1:]:
+                partition = partitions[str(r)]
+                new_node = partition.graph.create_node(
+                    op=node.op,
+                    target=node.target,
+                    args=tuple(arg for arg in node.args),
+                    kwargs={},
+                    type_expr=node.type,
+                )
+                new_node.meta = (
+                    node.meta.copy()
+                )  # is it really a good idea to copy this?
+                partition.environment[node] = new_node
+
+    # add placeholders to partition inputs
+    for partition_name in sorted_partitions:
+        partition = partitions[partition_name]
+        new_inputs: dict[str, None] = {}
+        for inp in partition.inputs:
+            orig_node = orig_nodes[inp]
+            # We don't pass in get_attr nodes as inputs to the partition, but
+            # instead set them as targets and use getattr within the module
+
+            if orig_node.op == "get_attr":
+                assert isinstance(orig_node.target, str)
+
+                orig_attr = _get_attr_from_qualname(m, orig_node.target)
+                if isinstance(orig_attr, torch.nn.Module):
+                    placeholder = partition.graph.get_attr(orig_node.target)
+                    partition.targets[orig_node.target] = orig_attr
+                else:
+                    placeholder = partition.graph.placeholder(
+                        inp,
+                        type_expr=orig_nodes[inp].type,
+                    )
+                    new_inputs[inp] = None
+            else:
+                placeholder = partition.graph.placeholder(
+                    inp,
+                    type_expr=orig_nodes[inp].type,
+                )
+                new_inputs[inp] = None
+            placeholder.meta = orig_nodes[inp].meta.copy()
+            partition.environment[orig_nodes[inp]] = placeholder
+        partition.inputs = new_inputs
+
+    # Transform nodes and collect targets for partition's submodule
+    for node in m.graph.nodes:
+        if hasattr(node, "_fx_partition"):
+            partition = partitions[node._fx_partition]
+
+            # swap out old graph nodes in kw/args with references to new nodes in this submodule
+            environment = partition.environment
+            gathered_args = torch.fx.graph.map_arg(node.args, lambda n: environment[n])
+            gathered_kwargs = torch.fx.graph.map_arg(
+                node.kwargs, lambda n: environment[n]
+            )
+
+            if node.op not in ["call_module", "get_attr"]:
+                target = node.target
+            else:
+                target_attr = _get_attr_from_qualname(m, node.target)
+                target = node.target.replace(".", "_")
+                partition.targets[target] = target_attr
+                # Fill in the passed-in mapping from new qualname to old qualname
+                if qualname_map is not None:
+                    # When creating the split module later, the submodules will have
+                    # path prefix matching the corresponding partition's submod_name
+                    qualname = f"{partition.submod_name}.{target}"
+                    qualname_map[qualname] = node.target
+
+            assert isinstance(gathered_args, tuple)
+            assert isinstance(gathered_kwargs, dict)
+            name = node.name if keep_original_node_name else None
+            new_node = partition.graph.create_node(
+                op=node.op,
+                target=target,
+                args=gathered_args,
+                kwargs=gathered_kwargs,
+                type_expr=node.type,
+                name=name,
+            )
+            new_node.meta = node.meta.copy()
+            partition.environment[node] = new_node
+
+    # Exit epilogue
+    for regions_mapping in [autocast_regions]:
+        for node in reversed(regions_mapping):
+            regions = regions_mapping[node]
+            assert len(regions) > 0
+            for r in regions[:-1]:
+                partition = partitions[str(r)]
+                exit_node = autocast_exits[node]
+                assert exit_node is not None, "Missing exit node"
+                new_node = partition.graph.create_node(
+                    op=exit_node.op,
+                    target=exit_node.target,
+                    args=(partition.environment[node],),
+                    kwargs={},
+                    type_expr=exit_node.type,
+                )
+                new_node.meta = (
+                    exit_node.meta.copy()
+                )  # is it really a good idea to copy this?
+
+    # original module environment dict mapping node names to nodes
+    orig_mod_env: dict[str, Node] = {}
+    # Set up values to construct base module
+    base_mod_env: dict[str, Node] = {}
+    base_mod_graph: torch.fx.graph.Graph = torch.fx.graph.Graph()
+    base_mod_attrs: dict[str, torch.fx.graph_module.GraphModule] = {}
+    if not keep_original_order:
+        for node in m.graph.nodes:
+            base_mod_env, base_mod_attrs = construct_graph(
+                node, base_mod_env, base_mod_attrs
+            )
+
+    else:
+        # Go through the graph to construct the mapping dict
+        for node in m.graph.nodes:
+            orig_mod_env[node.name] = node
+
+    # Do some things iterating over the partitions in topological order again:
+    # 1) Finish off submodule Graphs by setting corresponding outputs
+    # 2) Construct GraphModules for each submodule
+    # 3) Construct the base graph by emitting calls to those submodules in
+    #    topological order or original order specified by keep_original_order
+
+    construct_order_partitions = (
+        sorted_partitions if not keep_original_order else original_partition_order
+    )
+
+    already_constructed_attr_nodes = set()
+
+    # We actually need to insert the placeholder nodes in the original order
+    # otherwise graph signature will be wrong.
+    original_order = [node for node in m.graph.nodes if node.op == "placeholder"]
+
+    for partition_name in construct_order_partitions:
+        partition = partitions[partition_name]
+
+        # Set correct output values
+        output_vals = tuple(
+            partition.environment[orig_nodes[name]] for name in partition.outputs
+        )
+
+        # skip output node generation if there are no output values
+        num_output_vals = len(output_vals)
+        if num_output_vals == 1:
+            partition.graph.output(output_vals[0])
+        elif num_output_vals > 1:
+            partition.graph.output(output_vals)
+        else:
+            # Invariant - Graph should always have an output node.
+            partition.graph.output(())
+
+        if keep_original_order:
+            # first get the attr nodes required by this partition
+            orig_mod_attr_nodes: list[Node] = [
+                orig_mod_env[key]
+                for key in partition.inputs
+                if key not in original_order
+            ]
+
+            for node in original_order:
+                if node in already_constructed_attr_nodes:
+                    continue  # already added this attr to the base graph
+                base_mod_env, _based_mod_attrs = construct_graph(
+                    node, base_mod_env, base_mod_attrs
+                )
+                already_constructed_attr_nodes.add(node)
+
+            # Construct GraphModule for this partition
+            for node in orig_mod_attr_nodes:  # type: ignore[attr-defined]
+                if node in already_constructed_attr_nodes:
+                    continue
+                base_mod_env, base_mod_attrs = construct_graph(
+                    node, base_mod_env, base_mod_attrs
+                )
+                already_constructed_attr_nodes.add(node)
+
+        base_mod_attrs[partition.submod_name] = torch.fx.graph_module.GraphModule(
+            partition.targets, partition.graph
+        )  # noqa: B950
+
+        # Emit call in base graph to this submodule
+        output_val = base_mod_graph.call_module(
+            partition.submod_name,
+            tuple(base_mod_env[name] for name in partition.inputs),
+        )
+
+        num_outputs = len(partition.outputs)
+        if num_outputs > 1:
+            # Unpack multiple return values from submodule
+            output_val_proxy = torch.fx.proxy.Proxy(output_val)
+            for i, output_name in enumerate(partition.outputs):
+                base_mod_env[output_name] = output_val_proxy[i].node  # type: ignore[index]
+        elif num_outputs == 1:
+            base_mod_env[next(iter(partition.outputs))] = output_val
+
+    # When keep_original_order=True and if the graph doesn't have any
+    # `call_function` node then `base_mod_graph`, `base_mod_env` and `base_mod_attrs`
+    # are never populated.
+    # For this case, we call `construct_graph` here which takes care of updating them.
+    if keep_original_order and not base_mod_env:
+        for node in m.graph.nodes:
+            base_mod_env, base_mod_attrs = construct_graph(
+                node, base_mod_env, base_mod_attrs
+            )
+
+    # Add output node to `base_mod_graph` (i.e. the split graph) which will be returned.
+    for node in m.graph.nodes:
+        if node.op == "output":
+            base_mod_graph.output(
+                torch.fx.graph.map_arg(node.args[0], lambda n: base_mod_env[n.name])
+            )  # noqa: B950
+
+    ret = torch.fx.graph_module.GraphModule(base_mod_attrs, base_mod_graph)
+    log.debug(
+        "%s",
+        lazy_format_graph_code("post split_module", ret, colored=True),
+    )
+    return ret
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/split_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/split_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..926747b2a41ff23b7a0a268e73e5a219e8266b22
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/split_utils.py
@@ -0,0 +1,307 @@
+# mypy: allow-untyped-defs
+import copy
+from dataclasses import dataclass, field
+from typing import Optional, Union
+
+import torch.fx
+from torch.fx._compatibility import compatibility
+from torch.fx.graph import map_arg
+from torch.fx.passes.utils import HolderModule, lift_subgraph_as_module
+
+from .tools_common import NodeList
+
+
+__all__ = ["getattr_recursive", "setattr_recursive", "Component", "split_by_tags"]
+
+
+@compatibility(is_backward_compatible=False)
+def getattr_recursive(obj, name):
+    for layer in name.split("."):
+        if hasattr(obj, layer):
+            obj = getattr(obj, layer)
+        else:
+            return None
+    return obj
+
+
+@compatibility(is_backward_compatible=False)
+def setattr_recursive(obj, attr, value):
+    if "." not in attr:
+        setattr(obj, attr, value)
+    else:
+        layer = attr.split(".")
+        setattr_recursive(getattr(obj, layer[0]), ".".join(layer[1:]), value)
+
+
+@compatibility(is_backward_compatible=False)
+@dataclass
+class Component:
+    """
+    A component serves as a container for a subgraph we want to create afterwards.
+    """
+
+    graph: torch.fx.Graph
+    order: int
+    name: str
+
+    # Stores the placeholder nodes in `graph`.
+    input_placeholders: list = field(default_factory=list)
+
+    # Store the nodes in original graph that are placeholder in `graph`.
+    orig_inputs: list = field(default_factory=list)
+
+    # Store the nodes in original graph that are outputs in `graph`.
+    orig_outputs: list = field(default_factory=list)
+
+    # Mapping from get_attr node in original graph to get_attr node in `graph`.
+    getattr_maps: dict[torch.fx.Node, torch.fx.Node] = field(default_factory=dict)
+    constructor_args: list[str] = field(default_factory=list)
+    gm: Optional[torch.fx.GraphModule] = None
+
+
+@compatibility(is_backward_compatible=False)
+def split_by_tags(
+    gm: torch.fx.GraphModule,
+    tags: list[str],
+    return_fqn_mapping: bool = False,
+    return_tuple: bool = False,
+    GraphModuleCls: type[torch.fx.GraphModule] = torch.fx.GraphModule,
+) -> Union[torch.fx.GraphModule, tuple[torch.fx.GraphModule, dict[str, str]]]:
+    """
+    Splits a GraphModule using tags on its graph nodes. We honor the order of
+    tags. For example, we have tags = ["a", "b", "c"], the function will create
+    the initial submodules in the order of "a", "b", "c".
+
+    To set a tag:
+    gm.graph.nodes[idx].tag = "mytag"
+
+    This will result in all nodes with the same tag being extracted and placed in their
+    own submodule. For placeholder, output and get_attr node, the tag is ignored. placeholder
+    and output nodes are created when needed while get_attr nodes get copied to submodules
+    where they are used.
+
+    Given the following module def:
+
+    class SimpleModule(torch.nn.Module):
+        def __init__(self) -> None:
+            super().__init__()
+            self.linear1 = torch.nn.Linear(...)
+            self.linear2 = torch.nn.Linear(...)
+            self.linear3 = torch.nn.Linear(...)
+
+        def forward(self, in1, in2):
+            r1 = self.linear1(in1)
+            r2 = self.linear2(in2)
+            r3 = torch.cat([r1, r2])
+            return self.linear3(r3)
+
+    Marking the node corresponding to in1 with the tag sc.REQUEST_ONLY.lower() results in the following split:
+
+    ro:
+    def forward(self, in1):
+        self = self.root
+        linear1 = self.linear1(in1)
+        return linear1
+
+    main:
+    def forward(self, in2, linear1):
+        self = self.root
+        linear2 = self.linear2(in2)
+        cat_1 = torch.cat([linear1, linear2])
+        linear3 = self.linear3(cat_1)
+        return linear3
+
+    main:
+    def forward(self, in1, in2):
+        self = self.root
+        ro_0 = self.ro_0(in1)
+        main_1 = self.main_1(in2, ro_0)
+        return main_1
+
+    Returns:
+        split_gm: torch fx graph after split
+        orig_to_split_fqn_mapping: a map between the original fqn and the fqn
+            after split for call_module and get_attr.
+    """
+
+    def flatten(x: torch.fx.node.Argument) -> NodeList:
+        """
+        Stores nodes in x to a list and returns the list.
+        """
+        r: NodeList = []
+        map_arg(x, r.append)
+        return r
+
+    # Mapping from node in original module to node in created submodule.
+    node_remapping: dict[torch.fx.Node, torch.fx.Node] = {}
+
+    # Mapping from node in original module or created submodules to
+    # corresponding component.
+    node_to_component: dict[torch.fx.Node, Component] = {}
+
+    # Mapping from tag to the corresponding component.
+    tag_to_component: dict[str, Component] = {}
+
+    # Stores all components.
+    all_components: list[Component] = []
+
+    # Stores nodes that will be used in main graph.
+    used_in_main: dict[torch.fx.Node, None] = {}
+
+    # Main graph after split.
+    main_g = torch.fx.Graph()
+
+    # Mapping from node in original module to node in main graph after split.
+    main_remapping: dict[torch.fx.Node, torch.fx.Node] = {}
+
+    # Output node of original module.
+    output_node: Optional[torch.fx.Node] = None
+
+    # Create a component for each tag, we don't expect to create other components afterwards.
+    for tag in tags:
+        comp = Component(torch.fx.Graph(), len(all_components), f"{tag}")
+        all_components.append(comp)
+        tag_to_component[tag] = comp
+
+    # Traverse the nodes in original graph and take care of them.
+    for node in gm.graph.nodes:
+        if node.op == "output":
+            if output_node is not None:
+                raise RuntimeError("Multiple output nodes in graph!")
+            output_node = node
+            continue
+
+        # Placeholders in the original graph get copied to main graph.
+        if node.op == "placeholder":
+            main_remapping[node] = main_g.placeholder(node.name, type_expr=node.type)
+            main_remapping[node].meta = copy.copy(node.meta)
+            continue
+
+        # Get_attr nodes are ignored because we are not tagging them.
+        # Instead, we copy them directly to the submodules use them afterwards.
+        if node.op == "get_attr":
+            continue
+
+        # Now we process callable nodes which are nodes with op of call_module,
+        # call_function or call_method. Every callable nodes should be tagged.
+        assert hasattr(node, "tag"), f"Node does not have tag: {node.format_node()}"
+
+        upstream_components = [
+            node_to_component[x]
+            for x in flatten(node.args) + flatten(node.kwargs)
+            if x.op not in {"placeholder", "get_attr"}
+        ]
+
+        comp = tag_to_component[node.tag]
+        node_to_component[node] = comp
+
+        # Max order of upperstream components.
+        mx = max((c.order for c in upstream_components), default=0)
+
+        # Expect the component for `node` has higher order then its upstream components.
+        assert (
+            comp.order >= mx
+        ), f"Component {comp.name} order must be >= max of its upstream components, order={comp.order} and max={mx}"
+
+        # Map a input of `node` to nodes in the component's graph.
+        def remap_func(x):
+            # If input is a get_attr node, copy it to current component's graph.
+            # Returns the get_attr node in current component's graph.
+            if x.op == "get_attr":
+                if x not in comp.getattr_maps:
+                    comp.getattr_maps[x] = comp.graph.get_attr(
+                        x.target, type_expr=x.type
+                    )
+                    comp.getattr_maps[x].meta = copy.copy(x.meta)
+                return comp.getattr_maps[x]
+
+            # If input is not a placeholder, it should have been put into a component
+            # already. If it's the current component then we return the corresponding
+            # node in the component.
+            if x.op != "placeholder" and node_to_component[x] == comp:
+                return node_remapping[x]
+
+            # If input is a placeholder or it's in other components, we want to make it
+            # as a placeholder in current component's graph.
+            if x not in comp.orig_inputs:
+                comp.orig_inputs.append(x)
+                placeholder = comp.graph.placeholder(x.name, type_expr=x.type)
+                placeholder.meta = copy.copy(x.meta)
+                comp.input_placeholders.append(placeholder)
+                used_in_main[x] = None
+
+            return comp.input_placeholders[comp.orig_inputs.index(x)]
+
+        n = comp.graph.node_copy(node, remap_func)
+        n.tag = node.tag  # type: ignore[attr-defined]
+        node_remapping[node] = n
+        node_to_component[n] = comp
+
+    if output_node is None:
+        raise RuntimeError("Graph had no output node!")
+
+    for x in flatten(output_node.args[0]):
+        if x.op == "get_attr":
+            # We don't need components mapping for nodes of type "get_attr"
+            # that are consumed by the output. Only need to make sure we create
+            # corresponding counterparts in the resulting graph.
+            main_remapping[x] = main_g.get_attr(x.name, type_expr=x.type)
+        else:
+            # All component results consumed by the output node should be
+            # marked as "used in main".
+            used_in_main[x] = None
+
+    # If a node is used in main graph then we mark it as an output in the component
+    # it belongs to.
+    for n in used_in_main:
+        if n.op != "placeholder":
+            node_to_component[n].orig_outputs.append(n)
+
+    # Now we create a graphmodule for each component.
+    orig_to_split_fqn_mapping: dict[str, str] = {}
+    for comp in all_components:
+        outs = tuple(map(node_remapping.__getitem__, comp.orig_outputs))
+
+        if return_tuple:
+            comp.graph.output(outs)
+        else:
+            # Take care of the args of FX output node. If there's a single
+            # output then the output node args is like (output_single), else
+            # if there're multiple outputs then the output node args is like
+            # ((output_0, output_1, ...)).
+            comp.graph.output(outs[0] if len(outs) == 1 else outs)
+
+        comp.gm, comp_orig_to_split_fqn_mapping = lift_subgraph_as_module(
+            gm, subgraph=comp.graph, comp_name=comp.name
+        )
+        orig_to_split_fqn_mapping.update(comp_orig_to_split_fqn_mapping)
+
+        # Create a call_module node in main graph.
+        main_node = main_g.call_module(
+            comp.name,
+            args=tuple(map(main_remapping.__getitem__, comp.orig_inputs)),
+            kwargs=None,
+        )
+
+        if len(outs) == 1 and not return_tuple:
+            main_remapping[comp.orig_outputs[0]] = main_node
+        else:
+            for i, o in enumerate(comp.orig_outputs):
+                # Use Proxy to record getitem access.
+                main_remapping[o] = torch.fx.Proxy(main_node)[i].node  # type: ignore[index]
+
+    main_g.output(map_arg(output_node.args[0], main_remapping.__getitem__))
+    main_root = HolderModule({comp.name: comp.gm for comp in all_components})
+    main_g._codegen = gm.graph._codegen
+
+    # If the output nodes consumes get_attr directly in the original graph,
+    # then we need to make sure get_attr is copied to the new graph.
+    for x in flatten(output_node.args[0]):
+        if x.op == "get_attr":
+            setattr(main_root, x.name, getattr_recursive(gm, x.target))  # type: ignore[arg-type]
+
+    result_gm = GraphModuleCls(main_root, main_g)
+    if return_fqn_mapping:
+        return result_gm, orig_to_split_fqn_mapping
+
+    return result_gm
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/splitter_base.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/splitter_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ca9da390f35c7406bcacef7a8938c009115d2fd
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/splitter_base.py
@@ -0,0 +1,925 @@
+# mypy: allow-untyped-defs
+import argparse
+import copy
+import logging
+from collections import defaultdict
+from collections.abc import Iterable, Sequence
+from dataclasses import dataclass
+from typing import Any, NamedTuple, Optional
+
+import torch
+from torch.fx._compatibility import compatibility
+from torch.fx.node import map_arg
+from torch.fx.passes.graph_manipulation import get_size_of_node
+
+from .graph_drawer import FxGraphDrawer
+from .operator_support import get_node_target, OperatorSupportBase
+from .shape_prop import ShapeProp
+from .split_utils import split_by_tags
+from .tools_common import (
+    CALLABLE_NODE_OPS,
+    FxNetAccFusionsFinder,
+    is_node_output_tensor,
+    NodeList,
+    NodeSet,
+    Tensors,
+)
+
+
+__all__ = [
+    "FxNetAccNodesFinder",
+    "FxNetSplitterInternalError",
+    "Subgraph",
+    "SplitResult",
+    "generate_inputs_for_submodules",
+]
+_LOGGER = logging.getLogger(__name__)
+
+DEFAULT_MIN_ACC_MODULE_SIZE = 1
+DEFAULT_SKIP_FUSION = False
+DEFAULT_ALLOW_NON_TENSOR = False
+
+
+class _SplitterSettingBase:
+    def __init__(
+        self,
+        min_acc_module_size=DEFAULT_MIN_ACC_MODULE_SIZE,
+        skip_fusion=DEFAULT_SKIP_FUSION,
+        allow_non_tensor=DEFAULT_ALLOW_NON_TENSOR,
+        max_acc_splits: int = -1,
+    ):
+        parser = argparse.ArgumentParser()
+        parser.add_argument(
+            "--min-acc-module-size",
+            "--min_acc_module_size",
+            required=False,
+            type=int,
+            help="Minimum size limit of an accelerator subgraph.",
+        )
+        parser.add_argument(
+            "--max-acc-splits",
+            "--max_acc_splits",
+            required=False,
+            type=int,
+            help="Enforce a maximum number of split subgraphs.",
+        )
+        parser.add_argument(
+            "--skip-fusion",
+            "--skip_fusion",
+            default=False,
+            action="store_true",
+            help="If true then no fusion groups. Fusion group is used to "
+            "enforce no non-tensor data flow between submodules. If we don't "
+            "have this constrain, setting this to false is recommended as it "
+            "can reduce overhead.",
+        )
+        parser.add_argument(
+            "--allow-non-tensor",
+            "--allow_non_tensor",
+            default=False,
+            action="store_true",
+            help="For some backends non-tensor data flow between cpu and them "
+            "are not allowed. Therefore, if a node supported by accelerator but "
+            "it has non-tensor inputs or outputs to a cpu node we would want to "
+            "consider it as a cpu node during splitting. However, for some backends "
+            "we might not care about non-tensor data flow and we can set this option "
+            "to true to disable the functionality that prevent non-tensor data flow.",
+        )
+        args, _unknown = parser.parse_known_args()
+
+        self.min_acc_module_size: int = (
+            args.min_acc_module_size
+            if args.min_acc_module_size
+            else min_acc_module_size
+        )
+        self.skip_fusion: bool = args.skip_fusion if args.skip_fusion else skip_fusion
+        self.allow_non_tensor: bool = (
+            args.allow_non_tensor if args.allow_non_tensor else allow_non_tensor
+        )
+        self.max_acc_splits: int = max_acc_splits
+
+
+@compatibility(is_backward_compatible=False)
+class FxNetAccNodesFinder:
+    """
+    Finds a set of nodes that can be supported on ACC, excluding nodes that have non-tensor
+    input/output to cpu nodes to prevent non-tensor data flow between backends and cpu.
+
+    I.e. if we have a chain:
+
+    ACC_NODE_1 -> ACC_NODE_2 -> ACC_NODE_3 -> CPU_NODE_1
+
+    where every ACC node produces non-tensor output, then they all should be treated as CPU nodes.
+
+    This behavior can be turned off by passing allow_non_tensor=True.
+    """
+
+    def __init__(
+        self,
+        module: torch.fx.GraphModule,
+        operator_support: OperatorSupportBase,
+        allow_non_tensor: bool,
+    ):
+        self.module = module
+        self.operator_support = operator_support
+        self.allow_non_tensor = allow_non_tensor
+        self.acc_nodes: NodeSet = set()
+
+    def reduce_acc_nodes_non_tensor_input_helper(self, cpu_worklist: NodeList):
+        """
+        Transitively excludes nodes from ACC supported set.
+        For every node in the worklist:
+        - removes its downstream ACC nodes from ACC supported set,
+        - if any downstream ACC node produces non-tensor output,
+          then it gets added into the worklist.
+        """
+        while cpu_worklist:
+            node = cpu_worklist.pop(0)
+
+            for user in node.users:
+                if user in self.acc_nodes:
+                    self.acc_nodes.remove(user)
+                    if not is_node_output_tensor(user):
+                        cpu_worklist.append(user)
+
+    def reduce_acc_nodes_non_tensor_input(self):
+        """
+        Excludes nodes from ACC supported set that have direct
+        upstream CPU nodes that produce non-tensor outputs.
+        """
+        non_tensor_cpu_nodes: NodeList = []
+
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+            if node in self.acc_nodes:
+                continue
+            if is_node_output_tensor(node):
+                continue
+            non_tensor_cpu_nodes.append(node)
+
+        self.reduce_acc_nodes_non_tensor_input_helper(non_tensor_cpu_nodes)
+
+    def reduce_acc_nodes_non_tensor_output(self):
+        """
+        Excludes nodes from ACC supported set that produce non-tensor
+        outputs and have downstream CPU nodes.
+        """
+        while True:
+            new_cpu_nodes: NodeList = []
+
+            for acc_node in self.acc_nodes:
+                if is_node_output_tensor(acc_node):
+                    continue
+                for user in acc_node.users:
+                    if user not in self.acc_nodes:
+                        new_cpu_nodes.append(acc_node)
+                        break
+
+            if not new_cpu_nodes:
+                break
+
+            for new_cpu_node in new_cpu_nodes:
+                self.acc_nodes.remove(new_cpu_node)
+
+            self.reduce_acc_nodes_non_tensor_input_helper(new_cpu_nodes)
+
+    def __call__(self) -> NodeSet:
+        submodules = dict(self.module.named_modules())
+        self.acc_nodes = {
+            n
+            for n in self.module.graph.nodes
+            if n.op in CALLABLE_NODE_OPS
+            and self.operator_support.is_node_supported(submodules, n)
+        }
+
+        if not self.allow_non_tensor:
+            self.reduce_acc_nodes_non_tensor_input()
+            self.reduce_acc_nodes_non_tensor_output()
+
+        return self.acc_nodes
+
+
+@compatibility(is_backward_compatible=False)
+class FxNetSplitterInternalError(Exception):
+    pass
+
+
+@compatibility(is_backward_compatible=False)
+@dataclass
+class Subgraph:
+    is_acc: bool
+    nodes: NodeList
+    device_ordinal: Optional[int] = None
+
+
+@compatibility(is_backward_compatible=False)
+class SplitResult(NamedTuple):
+    """
+    Stores the results of the splitter.
+
+    Attributes:
+        split_module: root module after splitting.
+        submodule_inputs: a dict that maps submodule name to its inputs.
+        non_acc_submodule_prefix: the prefix for non acc submodules. For
+            acc submodule the prefix is alwasy "_run_on_acc_".
+    """
+
+    split_module: torch.fx.GraphModule
+    submodule_inputs: dict[str, Any]
+    non_acc_submodule_prefix: str
+
+
+@compatibility(is_backward_compatible=False)
+def generate_inputs_for_submodules(
+    model: torch.nn.Module,
+    inputs: Sequence[Any],
+    target_submodules: Iterable[str],
+    deepcopy: bool = False,
+) -> dict[str, Any]:
+    """
+    Generate inputs for targeting submdoules in the given model. Note that if two submodules refer to the same obj, this
+    function doesn't work.
+
+    Args:
+        model: root model.
+        inputs: inputs to the root model.
+        target_submodules: submodules that we want to generate inputs for.
+
+    Returns:
+        A dict that maps from submodule name to its inputs.
+    """
+
+    handles = []
+    results = {}
+    submodule_to_names = {mod: name for name, mod in model.named_modules()}
+
+    def pre_forward(module, module_inputs):
+        results[submodule_to_names[module]] = (
+            copy.deepcopy(module_inputs) if deepcopy else module_inputs
+        )
+
+    for name, mod in model.named_modules():
+        if name in target_submodules:
+            handles.append(mod.register_forward_pre_hook(pre_forward))
+
+    def clean_up_handles():
+        for h in handles:
+            h.remove()
+
+    try:
+        with torch.no_grad():
+            model(*inputs)
+    except Exception as e:
+        clean_up_handles()
+        raise e
+
+    clean_up_handles()
+    return results
+
+
+class _SplitterBase:
+    """
+    Splits a GraphModule into sub-GraphModules for execution on CPU or the accelerator.
+    Output is a GraphModule with supported and unsupported operators grouped into as few sub-GraphModules as possible.
+    Assumes that only "call_module", "call_function" and "call_method" from FX IR can potentially be executed on the accelerator.
+
+    Given the following graph:
+          ==> b ==>
+        //         \\
+       a             d
+        \\         //
+          ==> c ==>
+
+    class SimpleModule(torch.nn.Module):
+        def forward(self, a):
+            b = torch.sin(a)
+            c = torch.cos(a)
+            d = b + c
+            return d
+
+    and providing "operator_support" that indicates that 'b' and 'c' can be executed on the accelerator,
+    we will get the following split result:
+
+    main:
+    def forward(self, a):
+        run_on_acc_0_0 = self._run_on_acc_0_0(a)
+        getitem = run_on_acc_0_0[0]
+        getitem_1 = run_on_acc_0_0[1]
+        run_on_cpu_1_1 = self._run_on_cpu_1_1(getitem, getitem_1)
+        return run_on_cpu_1_1
+
+    _run_on_acc_0_0:
+    def forward(self, a):
+        sin_1 = torch.sin(a)
+        cos_1 = torch.cos(a)
+        return (sin_1, cos_1)
+
+    _run_on_cpu_1_1:
+    def forward(self, sin_1, cos_1):
+        add_1 = sin_1 + cos_1
+        return add_1
+    """
+
+    # PCIe bandwidth for the backend, default to 100 GB/s
+    PCIe_BW = 100 * 2**30
+
+    def __init__(
+        self,
+        module: torch.fx.GraphModule,
+        sample_input: Sequence[Any],
+        operator_support: OperatorSupportBase,
+        settings: _SplitterSettingBase,
+        non_acc_submodule_name: str = "_run_on_cpu_",
+        return_tuple: bool = False,
+        nodes_finder: Optional[FxNetAccNodesFinder] = None,
+    ):
+        """
+        Preprocesses graph before splitting:
+        - finds nodes supported by ACC,
+        - finds fusion groups for ACC nodes having non-tensor IO,
+        - builds a graph of direct dependencies,
+        - builds a map of fused nodes to their fusions.
+        As a result we get self.acc_nodes, self.deps and self.fusions.
+        """
+        assert isinstance(module, torch.fx.GraphModule)
+
+        self.module = module
+        ShapeProp(self.module).propagate(*sample_input)
+
+        self.settings = settings
+        self.operator_support = operator_support
+        self.sample_input = sample_input
+        if nodes_finder is None:
+            nodes_finder = FxNetAccNodesFinder(
+                self.module, self.operator_support, self.settings.allow_non_tensor
+            )
+        self.acc_nodes = nodes_finder()
+
+        if self.settings.skip_fusion:
+            self.fusions = {}
+        else:
+            self.fusions = FxNetAccFusionsFinder(module, self.acc_nodes)()
+
+        # Modify deps to add more deps for fused nodes
+        self.deps = self.find_deps()
+        self.update_deps_for_fusions()
+
+        self.non_acc_submodule_name = non_acc_submodule_name
+        self._node_submodule_map: dict[str, str] = {}
+        self._return_tuple = return_tuple
+
+        self.tags: list[str] = []
+
+    # ===============================================================
+    # Helpers for ctor and initial state
+    # ===============================================================
+
+    def get_node_submodule_map(self) -> dict[str, str]:
+        """Returns a map from node name to submodule name, e.g.
+        node: main_module_impl_impl_over_arch_unary_multiple_embedding
+          _pooling_embedding_pooling_sparse_entity_equivalence_key
+          _proxy_embedding_bag
+        maps to submodule name of: _run_on_acc_1
+        """
+        return self._node_submodule_map
+
+    def find_deps(self) -> dict[torch.fx.Node, NodeSet]:
+        """
+        Builds a graph of node dependencies. Leaf nodes don't have any
+        dependencies and the "output" node doesn't have nodes depending on it.
+
+        Resulting graph has only direct dependencies, i.e. there are no
+        transitive dependencies.
+        """
+        deps: dict[torch.fx.Node, NodeSet] = defaultdict(set)
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+
+            for user in node.users:
+                if user.op != "output":
+                    deps[user].add(node)
+        return deps
+
+    def update_deps_for_fusions(self):
+        """
+        Updates graph of dependencies so that:
+        - nodes from the same fusion depend on the same set of outer nodes,
+        - outer nodes depending on a fusion depend on all nodes in that fusion.
+        """
+        for node in self.fusions:
+            fusion = self.fusions[node]
+            for fused_neighbor in fusion:
+                self.deps[node].update(self.deps[fused_neighbor] - fusion)
+
+                for user in fused_neighbor.users:
+                    if user not in fusion:
+                        self.deps[user].add(node)
+
+    # ===============================================================
+    # Helpers for preview
+    # ===============================================================
+
+    def _lower_model_to_backend(
+        self, mod: torch.fx.GraphModule, inputs: Tensors
+    ) -> torch.nn.Module:
+        """
+        Lower the model to a backend.
+        """
+
+        return mod
+
+    def _find_culprit(self, mod: torch.fx.GraphModule, inputs: Tensors) -> str:
+        """
+        When an error occurs during lowering or running the lowered mod, we use this
+        function to find culprits in the `mod` that causes the error.
+        """
+
+        return "Unable to find a culprit because _find_culprit() function is not implemented."
+
+    def _draw_graph_based_on_node_support(
+        self, mod: torch.fx.GraphModule, supported_nodes: NodeList
+    ):
+        color_map = {
+            "default": "AliceBlue",
+            "supported": "chartreuse1",
+            "unsupported": "crimson",
+        }
+
+        class CustomDrawer(FxGraphDrawer):
+            def _get_node_style(self, node):
+                template = super()._get_node_style(node)
+                if node in supported_nodes:
+                    template["fillcolor"] = color_map["supported"]
+                elif node.op in CALLABLE_NODE_OPS:
+                    template["fillcolor"] = color_map["unsupported"]
+                else:
+                    template["fillcolor"] = color_map["default"]
+
+                return template
+
+        drawer = CustomDrawer(mod, "node_support", ignore_getattr=True)
+        dot_graph = drawer.get_main_dot_graph()
+        # pyre-fixme[16]: `pydot.Dot` has no attribute `write_raw`.
+        dot_graph.write_raw("node_support.dot")
+
+    def node_support_preview(self, dump_graph: bool = False):
+        submodules = dict(self.module.named_modules())
+
+        supported_nodes: NodeList = []
+        supported_node_types = defaultdict(set)
+        unsupported_node_types = defaultdict(set)
+
+        def get_dtype(arg):
+            tensor_meta = arg.meta.get("tensor_meta")
+            return getattr(tensor_meta, "dtype", None)
+
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+
+            target = get_node_target(submodules, node)
+
+            # Store dtype of arg in node.args. If arg doesn't have dtype, i.e. not a tensor, we'll store None.
+            arg_dtypes = [
+                get_dtype(arg) if isinstance(arg, torch.fx.Node) else None
+                for arg in node.args
+            ]
+
+            # Find last non-None element. If all elements are None, return max_len.
+            last_index = len(arg_dtypes) - next(
+                (
+                    i
+                    for i, dtype in enumerate(reversed(arg_dtypes))
+                    if dtype is not None
+                ),
+                len(arg_dtypes),
+            )
+
+            # Strip None elements at the end.
+            arg_dtypes_tuple = tuple(arg_dtypes[:last_index])
+            kwarg_dtypes_tuple = tuple(
+                (k, get_dtype(arg))
+                for k, arg in node.kwargs.items()
+                if isinstance(arg, torch.fx.Node)
+            )
+
+            if self.operator_support.is_node_supported(submodules, node):
+                supported_nodes.append(node)
+                supported_node_types[target].add((arg_dtypes_tuple, kwarg_dtypes_tuple))
+            else:
+                unsupported_node_types[target].add(
+                    (arg_dtypes_tuple, kwarg_dtypes_tuple)
+                )
+
+        if dump_graph:
+            self._draw_graph_based_on_node_support(self.module, supported_nodes)
+
+        reports = "\nSupported node types in the model:\n"
+        for t, dtypes in supported_node_types.items():
+            for arg_dtypes_tuple, kwarg_dtypes_tuple in dtypes:
+                reports += f"{t}: ({arg_dtypes_tuple}, {dict(kwarg_dtypes_tuple)})\n"
+
+        reports += "\nUnsupported node types in the model:\n"
+        for t, dtypes in unsupported_node_types.items():
+            for arg_dtypes_tuple, kwarg_dtypes_tuple in dtypes:
+                reports += f"{t}: ({arg_dtypes_tuple}, {dict(kwarg_dtypes_tuple)})\n"
+
+        print(reports)
+
+        # Return reports for testing purpose
+        return reports
+
+    def split_preview(self, dump_graph: bool = False):
+        reports = ""
+        subgraphs = self.put_nodes_into_subgraphs()
+        acc_subgraphs_num = len([g for g in subgraphs if g.is_acc])
+        cpu_subgraphs_num = len(subgraphs) - acc_subgraphs_num
+        reports += f"Before removing small acc subgraphs, total {len(subgraphs)} subgraphs are created:"
+        reports += f" {acc_subgraphs_num} acc subgraphs and {cpu_subgraphs_num} cpu subgraphs.\n"
+
+        subgraphs = self.remove_small_acc_subgraphs(subgraphs)
+        acc_subgraphs_num = len([g for g in subgraphs if g.is_acc])
+        cpu_subgraphs_num = len(subgraphs) - acc_subgraphs_num
+        reports += f"After removing small acc subgraphs, total {len(subgraphs)} subgraphs are created:"
+        reports += f" {acc_subgraphs_num} acc subgraphs and {cpu_subgraphs_num} cpu subgraphs.\n"
+
+        for i, subgraph in enumerate(subgraphs):
+            reports += (
+                f"_run_on_acc_{i}: "
+                if subgraph.is_acc
+                else f"{self.non_acc_submodule_name}{i}: "
+            )
+            reports += f"{len(subgraph.nodes)} node(s)\n"
+
+        self.tag(subgraphs)
+        split_mod = self.split(remove_tag=True)
+        split_mod.eval()
+
+        if dump_graph:
+            drawer = FxGraphDrawer(split_mod, "preview", ignore_getattr=True)
+            dot_graphs = drawer.get_all_dot_graphs()
+            for name, dot_graph in dot_graphs.items():
+                # pyre-fixme[16]: `pydot.Dot` has no attribute `write_raw`.
+                dot_graph.write_raw(f"{name}.dot")
+
+        max_qps: float = self.PCIe_BW
+        bottleneck_module = ""
+
+        for node in split_mod.graph.nodes:
+            if node.op == "call_module" and "acc" in node.target:
+                reports += f"\nProcessing acc submodule {node.target}\n"
+
+                submod = getattr(split_mod, node.target)
+
+                def get_submod_inputs(main_mod, submod, example_inputs):
+                    sub_inputs = None
+
+                    def get_inputs(self, inputs):
+                        nonlocal sub_inputs
+                        sub_inputs = inputs
+
+                    handle = submod.register_forward_pre_hook(get_inputs)
+                    main_mod(*example_inputs)
+                    handle.remove()
+                    return sub_inputs
+
+                submod_inputs = get_submod_inputs(split_mod, submod, self.sample_input)
+                ShapeProp(submod).propagate(*submod_inputs)
+
+                total_input_bytes = 0
+                total_output_bytes = 0
+
+                reports += "Checking inputs...\n"
+                for n in submod.graph.nodes:
+                    if n.op == "placeholder":
+                        if not is_node_output_tensor(n):
+                            reports += f"Input {n.name} is not a tensor, this might cause problems during lowering!\n"
+                        else:
+                            total_input_bytes += get_size_of_node(submod, n)[0]
+                    if n.op == "output":
+                        output_node = n
+
+                reports += "Checking outputs...\n"
+
+                def get_bytes(node: torch.fx.Node):
+                    nonlocal total_output_bytes
+                    nonlocal reports
+                    if not is_node_output_tensor(node):
+                        reports += f"Output {node.name} is not a tensor, this might cause problems during lowering!\n"
+                    else:
+                        total_output_bytes += get_size_of_node(submod, node)[0]
+
+                map_arg(output_node.args, get_bytes)  # type: ignore[possibly-undefined]
+                qps = self.PCIe_BW / max(total_input_bytes, total_output_bytes)
+                reports += f"Total input size in bytes is {total_input_bytes}, total output size in bytes is {total_output_bytes},"
+                reports += f" theoretical max qps (bounds by PCIe bandwidth) for this submodule is {qps}.\n"
+
+                if qps < max_qps:
+                    max_qps = qps
+                    bottleneck_module = node.target
+
+                try:
+                    lowered_submod = self._lower_model_to_backend(submod, submod_inputs)
+                except RuntimeError:
+                    reports += "Run into an error during lowering!\n"
+                    reports += self._find_culprit(submod, submod_inputs)
+                    continue
+
+                try:
+                    lowered_submod(*submod_inputs)
+                except RuntimeError:
+                    reports += "Run into an error during inference!\n"
+                    reports += self._find_culprit(submod, submod_inputs)
+                else:
+                    reports += "Lowering and running succeed!\n"
+
+        reports += f"\nTheoretical max qps (bounds by PCIe bandwidth) for this model is {max_qps},"
+        reports += f" bottleneck is submodule {bottleneck_module}."
+        print(reports)
+
+        # return the reports for testing purposes
+        return reports
+
+    # ===============================================================
+    # Helpers for extend_acc_subgraph() method
+    # ===============================================================
+
+    def find_reverse_deps(
+        self, tag_id: Optional[int] = None
+    ) -> dict[torch.fx.Node, NodeSet]:
+        """
+        Builds reversed topological node dependencies, if tag_id is specified,
+        we ignore nodes that are in later subgraph i.e. nodes have greater tag_id.
+        """
+        result: dict[torch.fx.Node, NodeSet] = defaultdict(set)
+
+        for node in self.module.graph.nodes:
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+
+            for user in node.users:
+                if user.op not in CALLABLE_NODE_OPS:
+                    continue
+
+                if tag_id is None or (int(user.tag.split("_")[-1]) < tag_id):
+                    result[node].add(user)
+
+        return result
+
+    def update_reverse_deps_for_fusions(self, deps: dict[torch.fx.Node, NodeSet]):
+        processed_node = set()
+
+        for node, fusion in self.fusions.items():
+            if node in processed_node:
+                continue
+
+            new_dep = set()
+
+            # Create a new dependency set which include all the
+            # dependencies of the nodes in the fusion group
+            for n in fusion:
+                new_dep.update(deps[n])
+
+            # Exclude nodes in the fusion
+            new_dep.difference_update(fusion)
+
+            # Update dependency
+            for n in fusion:
+                deps[n] = new_dep
+
+                for arg in n.all_input_nodes:
+                    if arg not in fusion:
+                        deps[arg].update(fusion)
+
+                processed_node.add(n)
+
+    def find_parent_nodes_of_subgraph(self, tag: str) -> NodeSet:
+        """
+        Finds parent nodes of the `tag` subgraph.
+
+        Traverse the inputs of nodes in the subgraph, if input doesn't belong to the subgraph
+        and is not a placeholder, we consider it as the parent node of the subgraph.
+        """
+        parent_nodes = set()
+
+        for node in self.module.graph.nodes:
+            if node.op in CALLABLE_NODE_OPS and node.tag == tag:
+                for arg in node.all_input_nodes:
+                    if arg.op in CALLABLE_NODE_OPS and arg.tag != tag:
+                        parent_nodes.add(arg)
+
+        return parent_nodes
+
+    def extend_acc_subgraph(self, tag: str):
+        """
+        Extend the acc subgraph with `tag` going the reversed topological direction.
+        """
+        # Dict that maps node to its users and ignore users that
+        # are in the subgraph that has greater tag
+        deps = self.find_reverse_deps(tag_id=int(tag.split("_")[-1]))
+        self.update_reverse_deps_for_fusions(deps)
+
+        # Parent nodes of the subgraph
+        parent_nodes = self.find_parent_nodes_of_subgraph(tag)
+
+        visited_nodes: NodeSet = set()
+
+        while parent_nodes:
+            node = None
+
+            # Find a acc node that depends on visited nodes only
+            for n in parent_nodes:
+                if deps[n] <= visited_nodes and n in self.acc_nodes:
+                    node = n
+                    break
+
+            if node is None:
+                break
+
+            # Put the node into `tag` subgraph
+            node.tag = tag  # type: ignore[attr-defined]
+            parent_nodes.remove(node)
+            visited_nodes.add(node)
+
+            # If node is in a fusion group, add all fusion buddies to parent nodes
+            if node in self.fusions:
+                for fusion_node in self.fusions[node]:
+                    if fusion_node not in visited_nodes:
+                        parent_nodes.add(fusion_node)
+
+            # Add inputs of the node to parent nodes
+            for arg in node.all_input_nodes:
+                if arg.op in CALLABLE_NODE_OPS and arg not in visited_nodes:
+                    parent_nodes.add(arg)
+
+    # ===============================================================
+    # Helpers for split() method
+    # ===============================================================
+
+    def starter_nodes(self) -> tuple[NodeSet, NodeSet]:
+        """
+        Finds nodes that consume module inputs or get_attr nodes.
+        """
+        starter_cpu_nodes: NodeSet = set()
+        starter_acc_nodes: NodeSet = set()
+        for node in self.module.graph.nodes:
+            if node.op not in {"placeholder", "get_attr"}:
+                continue
+            for user in node.users:
+                if user in self.acc_nodes:
+                    starter_acc_nodes.add(user)
+                else:
+                    starter_cpu_nodes.add(user)
+        return starter_cpu_nodes, starter_acc_nodes
+
+    def put_nodes_into_subgraphs(self) -> list[Subgraph]:
+        # We start graph traversal from leaf nodes
+        current_cpu_nodes, current_acc_nodes = self.starter_nodes()
+        visited_nodes: NodeSet = set()
+
+        # Determine which subgraph to start from based on which subgraph has
+        # 0-dep node
+        acc_subgraph: bool = not any(len(self.deps[n]) == 0 for n in current_cpu_nodes)
+
+        current_subgraph_nodes: NodeList = []
+
+        # Result accumulator
+        subgraphs: list[Subgraph] = []
+        while current_cpu_nodes or current_acc_nodes:
+            # Find the first node that should belong to the current subgraph and has all dependencies resolved
+            current_nodes = current_acc_nodes if acc_subgraph else current_cpu_nodes
+            node = next(
+                (n for n in current_nodes if self.deps[n] <= visited_nodes),
+                None,
+            )
+
+            # If nothing was found, then it's time to flip the mode and start a new subgraph
+            if node is None:
+                if not current_subgraph_nodes:
+                    raise FxNetSplitterInternalError("Subgraph can't be empty")
+
+                subgraphs.append(
+                    Subgraph(is_acc=acc_subgraph, nodes=current_subgraph_nodes)
+                )
+                acc_subgraph = not acc_subgraph
+                current_subgraph_nodes = []
+                continue
+
+            current_nodes.remove(node)
+            visited_nodes.add(node)
+            current_subgraph_nodes.append(node)
+
+            # Add fusion buddies
+            if node in self.fusions:
+                if node in self.acc_nodes:
+                    current_acc_nodes.update(self.fusions[node] - visited_nodes)
+                else:
+                    current_cpu_nodes.update(self.fusions[node] - visited_nodes)
+
+            # Put depending nodes into the queue
+            for user in node.users:
+                if user.op not in CALLABLE_NODE_OPS:
+                    continue
+
+                # Add downstream nodes
+                if user in self.acc_nodes:
+                    current_acc_nodes.add(user)
+                else:
+                    current_cpu_nodes.add(user)
+
+        # Check if the last subgraph was not created
+        if current_subgraph_nodes:
+            subgraphs.append(
+                Subgraph(is_acc=acc_subgraph, nodes=current_subgraph_nodes)
+            )
+
+        if not subgraphs:
+            raise FxNetSplitterInternalError("Couldn't create subgraphs")
+
+        return subgraphs
+
+    def remove_small_acc_subgraphs(self, subgraphs: list[Subgraph]) -> list[Subgraph]:
+        """
+        This pass finds ACC submodules with less than specified size and merges
+        them with adjacent CPU submodules.
+        """
+        result: list[Subgraph] = []
+        for subgraph in subgraphs:
+            if subgraph.is_acc:
+                if len(subgraph.nodes) >= self.settings.min_acc_module_size:
+                    result.append(subgraph)
+                else:
+                    print(
+                        "Eliminating acc subgraph because it's smaller than the threshold: "
+                        f"{len(subgraph.nodes)} < {self.settings.min_acc_module_size}"
+                    )
+                    if result:
+                        result[-1].nodes.extend(subgraph.nodes)
+                    else:
+                        subgraph.is_acc = False
+                        result.append(subgraph)
+            else:
+                if result and not result[-1].is_acc:
+                    result[-1].nodes.extend(subgraph.nodes)
+                else:
+                    result.append(subgraph)
+        return result
+
+    def tag(self, subgraphs: list[Subgraph]):
+        self.tags = []
+        for subgraph in subgraphs:
+            tag = (
+                f"_run_on_acc_{len(self.tags)}"
+                if subgraph.is_acc
+                else f"{self.non_acc_submodule_name}{len(self.tags)}"
+            )
+            self.tags.append(tag)
+            for node in subgraph.nodes:
+                if hasattr(node, "tag"):
+                    raise FxNetSplitterInternalError(f"Node {node} was already tagged")
+
+                node.tag = tag  # type: ignore[attr-defined]
+                self._node_submodule_map[node.name] = tag
+
+    def split(self, remove_tag: bool = False) -> torch.fx.GraphModule:
+        split_module = split_by_tags(
+            self.module, self.tags, return_tuple=self._return_tuple
+        )
+        if remove_tag:
+            for node in self.module.graph.nodes:
+                if hasattr(node, "tag"):
+                    del node.tag
+        return split_module  # type: ignore[return-value]
+
+    def __call__(self) -> torch.fx.GraphModule:
+        subgraphs = self.put_nodes_into_subgraphs()
+        subgraphs = self.remove_small_acc_subgraphs(subgraphs)
+        acc_subgraphs_count = len([s for s in subgraphs if s.is_acc])
+        non_acc_subgraphs_count = len(subgraphs) - acc_subgraphs_count
+        print(
+            f"Got {acc_subgraphs_count} acc subgraphs and {non_acc_subgraphs_count} non-acc subgraphs"
+        )
+        self.tag(subgraphs)
+        return self.split()
+
+    def generate_split_results(self) -> SplitResult:
+        split_module = self()
+        submodule_names = []
+        for name, _mod in split_module.named_children():
+            submodule_names.append(name)
+        if (
+            self.settings.max_acc_splits > 0
+            and len(submodule_names) > self.settings.max_acc_splits
+        ):
+            raise ValueError(
+                "Cannot fulfill max_acc_splits limit. "
+                "This may cause split fragmentation and "
+                "result in performance issues."
+            )
+
+        submodule_inputs = generate_inputs_for_submodules(
+            split_module, self.sample_input, submodule_names
+        )
+        return SplitResult(split_module, submodule_inputs, self.non_acc_submodule_name)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/tests/test_pass_manager.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/tests/test_pass_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..157dc4017eda576f10793ef46b78cd97b0f5074b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/tests/test_pass_manager.py
@@ -0,0 +1,56 @@
+import unittest
+
+from ..pass_manager import (
+    inplace_wrapper,
+    PassManager,
+    these_before_those_pass_constraint,
+    this_before_that_pass_constraint,
+)
+
+
+class TestPassManager(unittest.TestCase):
+    def test_pass_manager_builder(self) -> None:
+        passes = [lambda x: 2 * x for _ in range(10)]
+        pm = PassManager(passes)
+        pm.validate()
+
+    def test_this_before_that_pass_constraint(self) -> None:
+        passes = [lambda x: 2 * x for _ in range(10)]
+        pm = PassManager(passes)
+
+        # add unfulfillable constraint
+        pm.add_constraint(this_before_that_pass_constraint(passes[-1], passes[0]))
+
+        self.assertRaises(RuntimeError, pm.validate)
+
+    def test_these_before_those_pass_constraint(self) -> None:
+        passes = [lambda x: 2 * x for _ in range(10)]
+        constraint = these_before_those_pass_constraint(passes[-1], passes[0])
+        pm = PassManager([inplace_wrapper(p) for p in passes])
+
+        # add unfulfillable constraint
+        pm.add_constraint(constraint)
+
+        self.assertRaises(RuntimeError, pm.validate)
+
+    def test_two_pass_managers(self) -> None:
+        """Make sure we can construct the PassManager twice and not share any
+        state between them"""
+
+        passes = [lambda x: 2 * x for _ in range(3)]
+        constraint = these_before_those_pass_constraint(passes[0], passes[1])
+        pm1 = PassManager()
+        for p in passes:
+            pm1.add_pass(p)
+        pm1.add_constraint(constraint)
+        output1 = pm1(1)
+        self.assertEqual(output1, 2**3)
+
+        passes = [lambda x: 3 * x for _ in range(3)]
+        constraint = these_before_those_pass_constraint(passes[0], passes[1])
+        pm2 = PassManager()
+        for p in passes:
+            pm2.add_pass(p)
+        pm2.add_constraint(constraint)
+        output2 = pm2(1)
+        self.assertEqual(output2, 3**3)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/tools_common.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/tools_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..212b094e86e3536c2d135be4e93fb6368871ce7a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/tools_common.py
@@ -0,0 +1,319 @@
+# mypy: allow-untyped-defs
+import collections
+import operator
+from collections.abc import Mapping
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+import torch
+import torch.fx
+from torch.fx._compatibility import compatibility
+from torch.fx.node import _get_qualified_name
+
+
+__all__ = [
+    "get_acc_ops_name",
+    "get_node_target",
+    "is_node_output_tensor",
+    "FxNetAccFusionsFinder",
+    "legalize_graph",
+]
+
+Tensors = Union[tuple[torch.Tensor], list[torch.Tensor]]
+TensorOrTensors = Union[torch.Tensor, Tensors]
+NodeList = list[torch.fx.Node]
+NodeSet = set[torch.fx.Node]
+Names = list[str]
+CALLABLE_NODE_OPS = {"call_module", "call_function", "call_method"}
+
+
+@compatibility(is_backward_compatible=False)
+def get_acc_ops_name(k):
+    if isinstance(k, str):
+        return k
+    elif k.__module__ and "acc_ops" in k.__module__:
+        return f"acc_ops.{k.__name__}"
+    else:
+        module = k.__module__.replace(
+            "torch._ops", "torch.ops"
+        )  # WAR for bug in how torch.ops assigns module
+        return f"{module if module else ''}.{k.__name__}"
+
+
+@compatibility(is_backward_compatible=False)
+def get_node_target(
+    submodules: Mapping[str, torch.nn.Module], node: torch.fx.Node
+) -> str:
+    """
+    Given a `node` returns its target typename.
+
+    For "call_method" node, return node.target which is the name of that method being called.
+    This could potential lead to conflict but should be okay because normally it's on a tensor.
+
+    For "call_function" node, return typename of node.target.
+
+    For "call_module" node, return typename of the module that node.target point to.
+
+    If seeing "_VariableFunctionsClass" in the target name string, it will be replaced by
+    "torch". e.g. _VariableFunctionsClass.relu would become torch.relu.
+    """
+
+    assert node.op in CALLABLE_NODE_OPS, (
+        "Expect op types of " + ", ".join(CALLABLE_NODE_OPS) + f", but found {node.op}"
+    )
+
+    if node.op == "call_module":
+        assert isinstance(node.target, str)
+        submod = submodules[node.target]
+        submod_type = getattr(submod, "_base_class_origin", type(submod))
+        return get_acc_ops_name(submod_type)
+    elif node.op == "call_function":
+        target: Any = node.target
+        return (
+            f"acc_ops.{target.__name__}"
+            if target.__module__ is not None and "acc_ops" in target.__module__
+            else _get_qualified_name(target)
+        )
+    else:
+        assert isinstance(node.target, str)
+        return node.target
+
+
+@compatibility(is_backward_compatible=False)
+def is_node_output_tensor(node: torch.fx.Node) -> bool:
+    """Checks if the node output produces a Tensor or not.
+
+    NOTE: This requires to run `ShapeProp` on the containing fx graph before
+    calling this function. This is because it works by checking the `type`
+    metadata on the node. This metadata is produced by the `ShapeProp`.
+    """
+    type_ = node.meta.get("type", None)
+    return type_ is not None and issubclass(type_, torch.Tensor)
+
+
+@compatibility(is_backward_compatible=False)
+class FxNetAccFusionsFinder:
+    """
+    Finds groups of connected ACC nodes that pass non-tensor data between each other.
+    Such groups are called fusion groups.
+    """
+
+    def __init__(self, module: torch.fx.GraphModule, acc_nodes: NodeSet):
+        self.module = module
+        self.nodes = list(module.graph.nodes)
+        self.acc_nodes = acc_nodes
+
+    @dataclass
+    class FusionGroup:
+        # The smallest idx of nodes in the fusion group after topological sorting all the nodes in the model.
+        top_node_idx: int
+
+        # Nodes in this fusion group.
+        nodes: NodeSet
+
+        # Inputs to this fusion group.
+        inputs: NodeSet
+
+        # Nodes that in the fusion group that haven't been processed yet.
+        nodes_need_process: NodeSet
+
+        def add_node(self, node):
+            """
+            Add a node to fusion group.
+            """
+            if node in self.nodes:
+                return
+
+            self.nodes_need_process.add(node)
+            self.nodes.add(node)
+            self.inputs.discard(node)
+            self.inputs.update(
+                {
+                    n
+                    for n in node.all_input_nodes
+                    if n.op in CALLABLE_NODE_OPS and n not in self.nodes
+                }
+            )
+
+    def recursive_add_node(
+        self,
+        fusion_group: "FxNetAccFusionsFinder.FusionGroup",
+        inputs: Union[NodeSet, NodeList],
+        visited: Optional[NodeSet] = None,
+    ):
+        """
+        Start from inputs and going reverse topological order. If any upstream node
+        is in the fusion group, add all the nodes in this path to fusion group.
+        """
+        for arg in inputs:
+            # skip the node if already seen
+            if visited is not None:
+                if arg in visited:
+                    continue
+                visited.add(arg)
+
+            # Skip placeholder and get_attr because they won't be in the fusion group.
+            if arg.op not in CALLABLE_NODE_OPS:
+                continue
+
+            # If the node has smaller idx, it's already an upstream node of the fusion
+            # group. We don't need to check it anymore.
+            if self.nodes.index(arg) < fusion_group.top_node_idx:
+                continue
+
+            # If the node is in the fusion group, return True.
+            if arg in fusion_group.nodes:
+                return True
+
+            # Check the upstream nodes of the node, if any of them is in the fusion group
+            # we'll add this node to fusion group and return True.
+            if self.recursive_add_node(fusion_group, arg.all_input_nodes, visited):
+                fusion_group.add_node(arg)
+                return True
+
+        return False
+
+    def __call__(self) -> dict[torch.fx.Node, NodeSet]:
+        result: dict[torch.fx.Node, NodeSet] = {}
+        acc_nodes = list(self.acc_nodes)
+
+        for node in acc_nodes:
+            if node in result:
+                continue
+            if node.op not in CALLABLE_NODE_OPS:
+                continue
+            if "tensor_meta" in node.meta:
+                continue
+            if node not in self.acc_nodes:
+                continue
+
+            fusion_group: FxNetAccFusionsFinder.FusionGroup = self.FusionGroup(
+                top_node_idx=self.nodes.index(node),
+                nodes={node},
+                inputs=set(node.all_input_nodes),
+                nodes_need_process={node},
+            )
+            while fusion_group.nodes_need_process:
+                node = fusion_group.nodes_need_process.pop()
+                self.recursive_add_node(
+                    fusion_group,
+                    fusion_group.inputs,
+                    visited=set(),
+                )
+
+                # Optionally add downstream nodes
+                if "tensor_meta" not in node.meta:
+                    for user in node.users:
+                        if user.op not in CALLABLE_NODE_OPS:
+                            continue
+                        if user in fusion_group.nodes:
+                            continue
+
+                        fusion_group.add_node(user)
+                        self.recursive_add_node(
+                            fusion_group,
+                            fusion_group.inputs,
+                            visited=set(),
+                        )
+
+                # Add some upstream nodes
+                for arg in node.all_input_nodes:
+                    if arg.op not in CALLABLE_NODE_OPS:
+                        continue
+                    if "tensor_meta" in arg.meta:
+                        continue
+                    if arg in fusion_group.nodes:
+                        continue
+
+                    fusion_group.add_node(arg)
+                    fusion_group.top_node_idx = min(
+                        fusion_group.top_node_idx, self.nodes.index(arg)
+                    )
+                    self.recursive_add_node(
+                        fusion_group,
+                        fusion_group.inputs,
+                        visited=set(),
+                    )
+
+            if not (set(fusion_group.nodes) <= self.acc_nodes):
+                self.acc_nodes -= fusion_group.nodes
+            else:
+                for n in fusion_group.nodes:
+                    result[n] = fusion_group.nodes
+
+        return result
+
+
+@compatibility(is_backward_compatible=False)
+def legalize_graph(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
+    """
+    Replace the graph of the given GraphModule with one that contains the same nodes as the
+    original, but in topologically sorted order.
+
+    This is used by the merge_matmul transformation below, which disturbs the topologically sorted
+    order of its input GraphModule, so that this order is restored before further transformation.
+
+    Arguments:
+        gm: The graph module to topologically sort. It is modified in-place.
+
+    Returns:
+        The graph module in-place sorted
+    """
+
+    # These operators are used for making runtime assertions before any
+    # data-dependent operators occur. We want to prioritize sorting these to
+    # ensure that these assertions appear before any data-dependent operations
+    # in the graph.
+    PRIORITIZED_OPS = [
+        operator.add,
+        operator.mul,
+        operator.sub,
+        operator.floordiv,
+        operator.truediv,
+        operator.mod,
+        operator.le,
+        operator.lt,
+        operator.ge,
+        operator.gt,
+        operator.eq,
+        operator.ne,
+        torch.ops.aten.sym_constrain_range.default,
+        torch.ops.aten.sym_constrain_range_for_size.default,
+        torch.ops.aten._assert_async.msg,
+        torch.ops.aten.scalar_tensor.default,
+        torch.ops.aten._assert_scalar.default,
+    ]
+
+    indeg = dict.fromkeys(gm.graph.nodes, 0)
+    new_graph = torch.fx.Graph()
+    # Track how many unfulfilled dependencies each node has
+    for node in gm.graph.nodes:
+        for user in node.users:
+            indeg[user] += 1
+    queue: collections.deque = collections.deque()
+    # Add all nodes with no dependencies to the queue
+    for node in gm.graph.nodes:
+        if indeg[node] == 0:
+            queue.append(node)
+    env: dict[torch.fx.Node, torch.fx.Node] = {}
+    # Pop nodes from the queue, and add nodes that have had all their
+    # dependencies fulfilled
+    while len(queue) > 0:
+        cur = queue.popleft()
+        env[cur] = new_graph.node_copy(cur, lambda x: env[x])
+        for user in cur.users:
+            indeg[user] -= 1
+            if indeg[user] == 0:
+                if user.op == "call_function" and user.target in PRIORITIZED_OPS:
+                    queue.appendleft(user)
+                else:
+                    queue.append(user)
+    # If the new graph's size is not as large as the old one, then there must be
+    # a cycle (i.e. some node's dependencies were not satisfied.)
+    if len(new_graph.nodes) < len(gm.graph.nodes):
+        raise RuntimeError(
+            f"Input graph has cycles, unable to add {[node for node in indeg if indeg[node] != 0]}"
+        )
+    new_graph._codegen = gm.graph._codegen
+    gm.graph = new_graph
+    return gm
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee5e7e66868a0776609ff7ffff458f6a91ccf98a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/__init__.py
@@ -0,0 +1 @@
+from .common import compare_graphs, HolderModule, lift_subgraph_as_module
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/common.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..17362c9eec1254305527714234846943460c45fb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/common.py
@@ -0,0 +1,94 @@
+# mypy: allow-untyped-defs
+
+from torch.fx._compatibility import compatibility
+from torch.fx.graph import Graph
+from torch.fx.graph_module import GraphModule
+from torch.fx.passes.utils.matcher_utils import SubgraphMatcher
+from torch.nn import Module
+
+
+__all__ = ["HolderModule", "lift_subgraph_as_module", "compare_graphs"]
+
+
+@compatibility(is_backward_compatible=False)
+class HolderModule(Module):
+    """
+    HolderModule is used to copy all the attributes from original module to submodules
+    that uses the attributes
+    """
+
+    def __init__(self, d):
+        super().__init__()
+        for k, v in d.items():
+            self.add_module(k, v)
+
+
+@compatibility(is_backward_compatible=False)
+def lift_subgraph_as_module(
+    gm: GraphModule,
+    subgraph: Graph,
+    comp_name: str = "",
+    class_name: str = "GraphModule",
+) -> tuple[GraphModule, dict[str, str]]:
+    """
+    Create a GraphModule for subgraph, which copies the necessary attributes from the original parent graph_module.
+
+    Args:
+        gm (GraphModule): parent graph module
+
+        subgraph (Graph): a valid subgraph that contains copied nodes from the parent graph
+
+        comp_name (str): name for the new component
+
+        class_name (str): name for the submodule
+
+    """
+
+    # Loop through all module calls (call_module) and param fetches (get_attr)
+    # in this component, creating HolderModules as necessary to match the path.
+    # e.g. if in the original module there's a get_attr node fetches "conv.weight".
+    # We create a HolderModule as root -> add a HolderModule named "conv" ->
+    # make "weight" a attribute of "conv" HolderModule and point to conv.weight in
+    # the original module.
+    submodule = HolderModule({})
+    orig_to_split_fqn_mapping: dict[str, str] = {}
+    for n in subgraph.nodes:
+        if n.op not in ("call_module", "get_attr"):
+            continue
+
+        target = n.target
+        assert isinstance(target, str)
+        target_name_parts = target.split(".")
+        curr = submodule
+        orig_gm = gm
+
+        for name in target_name_parts[:-1]:
+            if not hasattr(curr, name):
+                curr.add_module(name, HolderModule({}))
+
+            curr = getattr(curr, name)
+            orig_gm = getattr(orig_gm, name)
+
+        leaf_node_name = target_name_parts[-1]
+        leaf_node = getattr(orig_gm, leaf_node_name)
+
+        orig_to_split_fqn_mapping[target] = f"{comp_name}.{target}"
+        # Relies on custom __setattr__ magic.
+        setattr(curr, leaf_node_name, leaf_node)
+
+    return GraphModule(submodule, subgraph, class_name), orig_to_split_fqn_mapping
+
+
+@compatibility(is_backward_compatible=False)
+def compare_graphs(left: Graph, right: Graph) -> bool:
+    """
+    Return True if two graphs are identical, i.e they
+        - have the same number of outputs in the same order
+        - have the same number of inputs in the same order
+        - have the same set of nodes, and identical connectivity
+    """
+
+    matcher = SubgraphMatcher(left, match_output=True, match_placeholder=True)
+    matches = matcher.match(right)
+
+    return len(matches) > 0
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/fuser_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/fuser_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7487bc2c6631bf4a8d36b37ab0039701b563c4d1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/fuser_utils.py
@@ -0,0 +1,276 @@
+# mypy: allow-untyped-defs
+import copy
+from queue import SimpleQueue
+from typing import Optional as _Optional
+
+import torch.fx
+from torch.fx._compatibility import compatibility
+from torch.fx.graph import Graph
+from torch.fx.graph_module import GraphModule
+from torch.fx.node import Node
+from torch.fx.passes.tools_common import legalize_graph, NodeList, NodeSet
+from torch.fx.passes.utils import lift_subgraph_as_module
+
+
+@compatibility(is_backward_compatible=False)
+def topo_sort(nodes: NodeList) -> NodeList:
+    # sort nodes according to the topological order
+    indegree_map = dict.fromkeys(nodes, 0)
+    candidates: SimpleQueue = SimpleQueue()
+
+    for node in nodes:
+        for n in node.all_input_nodes:
+            if n in indegree_map:
+                indegree_map[node] += 1
+        if indegree_map[node] == 0:
+            candidates.put(node)
+
+    sorted_nodes: NodeList = []
+    while not candidates.empty():
+        node = candidates.get()
+        sorted_nodes.append(node)
+
+        for n in node.users:
+            if n in indegree_map:
+                indegree_map[n] -= 1
+                if indegree_map[n] == 0:
+                    candidates.put(n)
+
+    assert len(nodes) == len(
+        sorted_nodes
+    ), "topological sorted nodes doesn't have same length as input nodes"
+
+    return sorted_nodes
+
+
+@compatibility(is_backward_compatible=False)
+def validate_partition(partition: NodeList) -> bool:
+    # verify the partition does't form a dependency cycle in the original graph
+    # returns True for valid partition, False for invalid
+
+    partition_set = set(partition)
+
+    outputs: NodeList = []
+    for node in partition_set:
+        for user_node in node.users:
+            if user_node not in partition_set:
+                # external user node, need to expose as an output
+                outputs.append(user_node)
+
+    # Perform BFS on the partition outputs.
+    # If it reaches a node within the partition, then it found a cycle.
+    # This function takes the ownership of `root_nodes` and may modify it.
+    def bfs_find_cycle(root_nodes: NodeList) -> bool:
+        # Set used to exclude nodes that have already been visited.
+        # If a node has been visited, that node and all its children have
+        # been checked for cycles.
+        visited: NodeSet = set()
+
+        # Start with `root_nodes` and traverse through (toward child nodes)
+        # their connected sub-graph. Nodes in `visited` won't be added
+        # to `queue` again.
+        queue: NodeList = root_nodes
+        while queue:
+            current = queue.pop()
+            visited.add(current)
+            if current in partition_set:
+                # Started from partition's `output` nodes, and reached
+                # another node in partition. Cycle!
+                return True
+            for user_node in current.users:
+                if user_node in visited:
+                    continue
+                queue.append(user_node)
+        # `root_nodes` don't cause cycle.
+        return False
+
+    # Use all output nodes as roots to traverse
+    # the graph to check cycles.
+    if bfs_find_cycle(outputs):
+        return False
+
+    return True
+
+
+@compatibility(is_backward_compatible=False)
+def fuse_as_graphmodule(
+    gm: GraphModule,
+    nodes: NodeList,
+    module_name: str,
+    partition_lookup_table: _Optional[dict[Node, None]] = None,
+    *,
+    always_return_tuple: bool = False,
+) -> tuple[GraphModule, tuple[Node, ...], tuple[Node, ...]]:
+    """
+    Fuse nodes in graph_module into a GraphModule.
+
+    Args:
+        gm (GraphModule): target graph_module
+
+        nodes (List[Node]): list of nodes in `gm` to fuse, where the node must be topologically sorted
+
+        module_name: class name for the fused GraphModule
+
+        partition_lookup_table (Optional[Dict[Node, None]]): optional dict of nodes to speed up lookup
+
+        always_return_tuple (bool): whether to always return a tuple, even if there is only one output
+
+    Returns:
+        fused_gm (GraphModule): fused graph module, where its node is a copy of `nodes` in `gm`
+
+        original_inputs (Tuple[Node, ...]): input nodes to `nodes` in original `gm`
+
+        original_outputs (Tuple[Node, ...]): consumer nodes of `nodes` in original `gm`
+
+    """
+
+    # assumption: nodes are already sorted in topo order
+
+    for node in nodes:
+        assert (
+            node.graph.owning_module is gm
+        ), f"{node} doesn't belong to passed in graph module {gm._get_name()}"
+        assert not node._erased, f"{node} has been removed from owning graph"
+        assert (
+            node in gm.graph._find_nodes_lookup_table
+        ), f"{node} is not found in graph module {gm._get_name()}"
+
+    # validates partition doesn't introduce dependency circles in the graph
+    assert validate_partition(nodes), "Invalid partition, found dependency cycles"
+
+    # if no dict of partition nodes is provided, reconstruct it by nodes list to reduce lookup time
+    if partition_lookup_table is None:
+        partition_lookup_table = dict.fromkeys(nodes)
+
+    subgraph = Graph()
+
+    node_to_placeholder: dict[
+        Node, Node
+    ] = {}  # mapping of nodes from old graph to placeholder in new graph
+    node_map: dict[Node, Node] = {}  # mapping of nodes from old graph to new graph
+
+    # handles inputs through graph.node_copy's arg_transform functions
+    def remap_inputs(x):
+        if x.op == "get_attr":
+            # TODO: do we really need copy the get_attr node into the graph?
+            # do something here
+            pass
+
+        if x in partition_lookup_table:
+            # x is inside subgraph, return the copied node
+            # the node should have been copied aleady, as we are copying graph in the topological order
+            return node_map[x]
+
+        if x not in node_to_placeholder:
+            # x is not in subgraph, create a new placeholder for subgraph
+            placeholder_node = subgraph.placeholder(x.name, type_expr=x.type)
+            # copy all meta fields, even if some fields might be irrelvant for the placeholder node
+            placeholder_node.meta = copy.copy(x.meta)
+            node_to_placeholder[x] = placeholder_node
+
+        return node_to_placeholder[x]
+
+    # copy nodes in topological order
+    for node in nodes:
+        new_node = subgraph.node_copy(node, remap_inputs)
+        node_map[node] = new_node
+
+    # handles outputs
+    output_mapping: dict[Node, Node] = {}  # mapping from old output to new outputs
+
+    for node in nodes:
+        for user_node in node.users:
+            if user_node not in partition_lookup_table:
+                # external user node, need to expose as an output
+                output_mapping[node] = node_map[node]
+
+    # outs contain nodes in the new subgraph
+    outs = tuple(output_mapping.values())
+
+    if always_return_tuple:
+        # always return a tuple, even if there is only one output
+        subgraph.output(outs)
+    else:
+        # If there's a single output then return it directly, otherwise return a tuple.
+        subgraph.output(outs[0] if len(outs) == 1 else outs)
+
+    # lint to ensure correctness
+    subgraph.lint()
+    fused_gm: GraphModule
+    fused_gm, _ = lift_subgraph_as_module(
+        gm, subgraph, comp_name="", class_name=module_name
+    )
+
+    # sub_gm's input nodes in the original module
+    original_inputs: tuple[Node, ...] = tuple(node_to_placeholder.keys())
+
+    # sub_gm's outputs node in the original module
+    original_outputs: tuple[Node, ...] = tuple(output_mapping.keys())
+
+    return fused_gm, original_inputs, original_outputs
+
+
+@compatibility(is_backward_compatible=False)
+def insert_subgm(
+    gm: GraphModule,
+    sub_gm: GraphModule,
+    orig_inputs: tuple[Node, ...],
+    orig_outputs: tuple[Node, ...],
+):
+    # add sub_gm into gm
+    submodule_name = sub_gm.__class__.__name__
+    gm.add_submodule(submodule_name, sub_gm)
+
+    # Create a call_module node in main graph.
+    module_node = gm.graph.call_module(submodule_name, args=orig_inputs, kwargs=None)
+
+    output_node = sub_gm.graph.output_node()
+    if len(orig_outputs) == 1 and not isinstance(output_node.args[0], tuple):
+        # main_remapping[comp.orig_outputs[0]] = module_node
+        orig_outputs[0].replace_all_uses_with(module_node, propagate_meta=True)
+    else:
+        for i, orig_output in enumerate(orig_outputs):
+            # Use Proxy to record getitem access.
+            proxy_out = torch.fx.Proxy(module_node)[i].node  # type: ignore[index]
+            orig_output.replace_all_uses_with(proxy_out, propagate_meta=True)
+
+        module_node.meta["val"] = tuple(
+            orig_output.meta.get("val", None) for orig_output in orig_outputs
+        )
+    return gm
+
+
+@compatibility(is_backward_compatible=False)
+def erase_nodes(gm: GraphModule, nodes: NodeList):
+    # erase original nodes in inversed topological order
+    for node in reversed(nodes):
+        gm.graph.erase_node(node)
+
+
+@compatibility(is_backward_compatible=False)
+def fuse_by_partitions(
+    gm: GraphModule,
+    partitions: list[dict[Node, None]],
+    prefix: str = "fused_",
+    always_return_tuple: bool = False,
+) -> GraphModule:
+    for partition_id, partition in enumerate(partitions):
+        sorted_nodes = topo_sort(list(partition))
+
+        submodule_name = prefix + str(partition_id)
+        sub_gm, orig_inputs, orig_outputs = fuse_as_graphmodule(
+            gm,
+            sorted_nodes,
+            submodule_name,
+            partition,
+            always_return_tuple=always_return_tuple,
+        )
+
+        insert_subgm(gm, sub_gm, orig_inputs, orig_outputs)
+
+        erase_nodes(gm, sorted_nodes)
+
+    # topological sort original gm with newly created sub_gm
+    legalize_graph(gm)
+
+    return gm
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/matcher_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/matcher_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..27d24ed29945ded5171604b4b1eaf19fab06b69a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/matcher_utils.py
@@ -0,0 +1,440 @@
+# mypy: allow-untyped-defs
+import copy
+import logging
+import os
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Any, Union
+
+import torch
+from torch.fx import Graph, Node
+from torch.fx._compatibility import compatibility
+
+
+__all__ = ["SubgraphMatcher", "InternalMatch"]
+
+
+# Set`PYTORCH_MATCHER_LOGLEVEL=INFO` to see debug logs
+def _init_logger():
+    logger = logging.getLogger(__name__)
+
+    level = os.environ.get("PYTORCH_MATCHER_LOGLEVEL", "WARNING").upper()
+    logger.setLevel(level)
+    console = logging.StreamHandler()
+    formatter = logging.Formatter("%(filename)s > %(message)s")
+    console.setFormatter(formatter)
+    console.setLevel(level)
+    # add the handlers to the logger
+    logger.addHandler(console)
+    logger.propagate = False
+    return logger
+
+
+logger = _init_logger()
+
+
+@compatibility(is_backward_compatible=False)
+@dataclass
+class InternalMatch:
+    # Nodes from which the match was found
+    anchors: list[Node]
+    # Maps nodes in the pattern subgraph to nodes in the larger graph
+    nodes_map: dict[Node, Node] = field(default_factory=dict)
+
+    # nodes in target graph that are matched placeholder in pattern
+    placeholder_nodes: list[Node] = field(default_factory=list)
+
+    # nodes in matched subgraph returned by output
+    returning_nodes: list[Node] = field(default_factory=list)
+
+    # map from a string name to a node in the target graph
+    # only available if the matcher is `SubgraphMatcherWithNameNodesMap`
+    name_node_map: dict[str, Node] = field(default_factory=dict)
+
+    def __copy__(self):
+        return InternalMatch(
+            anchors=self.anchors,
+            nodes_map=self.nodes_map.copy(),
+            placeholder_nodes=self.placeholder_nodes.copy(),
+            returning_nodes=self.returning_nodes.copy(),
+        )
+
+
+@compatibility(is_backward_compatible=False)
+class SubgraphMatcher:
+    def __init__(
+        self,
+        pattern: Graph,
+        match_output: bool = False,
+        match_placeholder: bool = False,
+        remove_overlapping_matches: bool = True,
+        ignore_literals: bool = False,
+    ) -> None:
+        """
+        Args:
+            pattern: the targeted matching pattern, represented in fx.Graph.
+            match_output: If True, output node in the pattern graph will be treated as a part of the targeted pattern.
+                If False, output node is ignored during match.
+            match_placeholder: If True, placeholder node in the pattern graph will be treated as a part of
+                the targeted pattern. If False, placeholder nodes will be used a wildcard.
+            remove_overlapping_matches: If True, in the case of overlapping matches, only the first match
+                will be returned.
+            ignore_literals: If True, will not check if literals are equal and
+                will instead treat them as wildcards.
+        """
+
+        self.pattern = pattern
+        self.match_output = match_output
+        self.match_placeholder = match_placeholder
+        self.remove_overlapping_matches = remove_overlapping_matches
+        self.ignore_literals = ignore_literals
+
+        if len(pattern.nodes) == 0:
+            raise ValueError(
+                "SubgraphMatcher cannot be initialized with an empty pattern"
+            )
+
+        for node in pattern.nodes:
+            if node.op != "output":
+                assert (
+                    len(node.users) > 0
+                ), "SubgraphMatcher cannot be initialized with an pattern with dead code"
+
+        # TODO: assert pattern is a connected graph
+
+        self.pattern_placeholder_nodes = [
+            n for n in pattern.nodes if n.op == "placeholder"
+        ]
+        output_node = next(iter(reversed(pattern.nodes)))
+        # nodes returned by outputs
+        self.pattern_returning_nodes: list[Node] = output_node.all_input_nodes
+
+        self.pattern_anchors: list[Node] = []
+        if match_output:
+            self.pattern_anchors = [output_node]
+        else:
+            # If a node has output_node as the ONLY user, then this node is a graph sink,
+            # and should be matched against as an anchor
+            self.pattern_anchors = [
+                n for n in output_node.all_input_nodes if len(n.users) == 1
+            ]
+
+    def _match_attributes(self, pn: Node, gn: Node) -> bool:
+        # Attributes matching is complicated. Right now we only support matching constant tensor
+        assert isinstance(pn.target, str), f"pn.target {pn.target} must be a string."
+        assert isinstance(gn.target, str), f"gn.target {gn.target} must be a string."
+
+        pn_value = torch.fx.graph_module._get_attr(pn.graph.owning_module, pn.target)
+        gn_value = torch.fx.graph_module._get_attr(gn.graph.owning_module, gn.target)
+
+        if type(pn_value) != type(gn_value):
+            return False
+
+        # Don't require exact match on tensor values.
+        if isinstance(pn_value, torch.Tensor):
+            return isinstance(gn_value, torch.Tensor)
+        else:
+            raise RuntimeError(f"Unsupported type {pn_value} when matching attributes")
+        return False
+
+    def _nodes_are_equal(self, pn: Node, gn: Node) -> bool:
+        # if exact match for placeholder is not required, then use placeholder as a wildcard
+        if not self.match_placeholder and pn.op == "placeholder":
+            return True
+
+        if pn.op == gn.op:
+            if pn.op == "placeholder" or pn.op == "output":
+                return True
+            elif pn.op == "get_attr":
+                return self._match_attributes(pn, gn)
+            return pn.target == gn.target
+        return False
+
+    def _is_contained(self, nodes_map: dict[Node, Node]) -> bool:
+        # `lookup` represents all the nodes in `original_graph`
+        # that are part of `pattern`
+
+        # Placeholders can be used by other nodes in the graphs
+        lookup: dict[Node, Node] = {
+            gn: pn for pn, gn in nodes_map.items() if pn.op != "placeholder"
+        }
+
+        for gn, pn in lookup.items():
+            # nodes returned by output are allowed to be used in other areas of the graph
+            if pn in self.pattern_returning_nodes:
+                continue
+
+            for user in gn.users:
+                # If this node has users that were not in `lookup`, then it must leak out of the
+                # pattern subgraph
+                if user not in lookup:
+                    return False
+        return True
+
+    def _remove_overlapping_matches(
+        self, matches: list[InternalMatch]
+    ) -> list[InternalMatch]:
+        non_overlapping_matches: list[InternalMatch] = []
+        nodes_matched: set[Node] = set()
+
+        for match in matches:
+            found_overlap = False
+            for pn, gn in match.nodes_map.items():
+                if pn.op not in {"placeholder", "output"} and gn in nodes_matched:
+                    found_overlap = True
+                    break
+
+            if not found_overlap:
+                non_overlapping_matches.append(match)
+                for pn, gn in match.nodes_map.items():
+                    if pn.op not in {"placeholder", "output"}:
+                        nodes_matched.add(gn)
+        return non_overlapping_matches
+
+    def _match_literals(self, pn: Any, gn: Any, match: InternalMatch) -> bool:
+        assert not (
+            isinstance(pn, Node) and isinstance(gn, Node)
+        ), "pn and gn cannot both be Node"
+
+        if isinstance(pn, Node) and not isinstance(gn, Node):
+            if pn.op == "placeholder":
+                # Check if we've already matched these nodes in the current
+                # traversal
+                if pn in match.nodes_map:
+                    return match.nodes_map[pn] == gn
+
+                match.nodes_map[pn] = gn
+                return True
+            else:
+                return False
+        elif not isinstance(pn, Node) and isinstance(gn, Node):
+            return False
+        else:
+            return type(gn) == type(pn) and gn == pn
+
+    def _match_nodes(self, pn: Node, gn: Node, match: InternalMatch) -> bool:
+        logger.info("  matching %s to %s", pn, gn)
+
+        assert isinstance(pn, Node) and isinstance(gn, Node), str(
+            f"pn and gn must be Node, pn: {pn}, gn: {gn}"
+        )
+
+        # Check if we've already matched these nodes in the current
+        # traversal
+        if pn in match.nodes_map:
+            return match.nodes_map[pn] == gn
+
+        # TODO: use a more efficient way to check if gn is matched before: two-way dict
+        if gn in match.nodes_map.values():
+            return False
+
+        if not self._nodes_are_equal(pn, gn):
+            return False
+
+        # Optimistically mark `pn` as a match for `gn`, and save a local copy of match
+        saved_match = copy.copy(match)
+        match.nodes_map[pn] = gn
+
+        # Placeholder is a wildcard and can be matched with any python object
+        # (including list/tuple)
+        if pn.op == "placeholder":
+            return True
+
+        # Recursively traverse upwards to check if `pn` is a true
+        # match for `gn`
+        match_found = True
+
+        def _match_args(args1: Union[list, tuple], args2: Union[list, tuple]) -> bool:
+            if len(args1) != len(args2):
+                return False
+
+            for a1, a2 in zip(args1, args2):
+                if isinstance(a1, Node) and isinstance(a2, Node):
+                    matched = self._match_nodes(a1, a2, match)
+                elif isinstance(a1, (list, tuple)) and isinstance(a2, (list, tuple)):
+                    matched = _match_args(a1, a2)
+                else:
+                    matched = (
+                        self._match_literals(a1, a2, match) or self.ignore_literals
+                    )
+
+                if not matched:
+                    return False
+
+            return True
+
+        # Flatten all args/kwargs into 1 list of args
+        pn_args, gn_args = None, None
+        if (
+            (
+                len(pn.args) != len(gn.args)
+                or list(pn.kwargs.keys()) != list(gn.kwargs.keys())
+            )
+            and pn.op == "call_function"
+            and isinstance(pn.target, torch._ops.OpOverload)
+        ):
+            args_schema = pn.target._schema.arguments
+
+            def get_all_arguments(orig_args, orig_kwargs):
+                all_args = []
+                for i, schema in enumerate(args_schema):
+                    if schema.name in orig_kwargs:
+                        all_args.append(orig_kwargs[schema.name])
+                    elif not schema.kwarg_only and i < len(orig_args):
+                        all_args.append(orig_args[i])
+                    else:
+                        all_args.append(schema.default_value)
+                return all_args
+
+            pn_args = get_all_arguments(pn.args, pn.kwargs)
+            gn_args = get_all_arguments(gn.args, gn.kwargs)
+
+        elif len(pn.args) == len(gn.args) and list(pn.kwargs.keys()) == list(
+            gn.kwargs.keys()
+        ):
+            pn_args = list(pn.args)
+            gn_args = list(gn.args)
+            pn_args.extend(list(pn.kwargs.values()))
+            gn_args.extend(list(gn.kwargs.values()))
+        else:
+            match_found = False
+
+        match_found = (
+            match_found
+            and pn_args is not None
+            and gn_args is not None
+            and _match_args(pn_args, gn_args)
+        )
+
+        if not match_found:
+            # revert to saved_match before matching with current node
+            match = copy.copy(saved_match)
+            return False
+
+        return True
+
+    def match(self, graph: Graph) -> list[InternalMatch]:
+        """
+        Returns:
+            The matched subgraphs.
+            Thre returned subgraph would be fully self-contained, meaning the nodes (except placeholder
+            and nodes returned by output) can only be consumed by nodes within the matched subgraph.
+
+        Subgraph pattern matcher is implemented with the backtracking style in the following steps:
+
+        1. We first identify all the anchor nodes in the pattern graph. The anchor nodes
+        are the "sinks" (nodes with no user other than the output node) of the pattern graph.
+        One pattern graph could have multiple anchors if it has multiple return values.
+
+        2. In the target graph, we identify the potential candidate nodes that can be matched
+        with each anchor. These anchor-candidate pairs are the starting points for
+        pairwise per-node matching.
+
+        3. For each anchor-candidate pair, we simultaneously traverse backwards (DFS) in both
+        pattern and target graphs. For every pattern nodes along traversal path, we compare it
+        against the target nodes. In case any comparison failed, the match for this anchor-candidate
+        pair fails. A match is found when DFS completes traversing the graph. See `self._match_nodes`
+        for more details.
+
+        4. In the case of multiple anchors, every anchor will need to find a match using step 3.
+        In addition, the matches found between anchors need to have a common intersection node
+        in order for the match to be valid. This is implemented with backtracking. See `backtracking`
+        for more details.
+
+        Notice: graph traversal must be done in the reverser order because a tensor can have multiple
+        consumers, but can only have a single producer. Only with reverser order, we can we jointly
+        traverse the pattern and target graph in a deterministic path.
+
+        Warning: In theory, this backtracking algorithm have an **exponential** time complexity. However,
+        in practice, it's unlikely to blow up.
+
+        """
+        from torch.fx.passes.utils.fuser_utils import validate_partition
+
+        # find candidate nodes to match with pattern anchors
+        match_candidates: dict[Node, list[Node]] = defaultdict(list)
+        for pattern_anchor in self.pattern_anchors:
+            for node in graph.nodes:
+                if self._nodes_are_equal(pattern_anchor, node):
+                    match_candidates[pattern_anchor].append(node)
+        match_candidates_list = list(match_candidates.items())
+
+        logger.info("Initial match_candidates_list: %s\n", match_candidates_list)
+
+        matches: list[InternalMatch] = []
+
+        def backtracking(anchor_index, match):
+            if anchor_index == len(match_candidates_list):
+                match.placeholder_nodes = [
+                    match.nodes_map[pn] for pn in self.pattern_placeholder_nodes
+                ]
+                match.returning_nodes = [
+                    match.nodes_map[pn] for pn in self.pattern_returning_nodes
+                ]
+                matches.append(match)
+
+                logger.info("Found a match: %s\n", match)
+                return
+
+            pattern_anchor, candidate_nodes = match_candidates_list[anchor_index]
+            saved_match = copy.copy(match)
+
+            for node in candidate_nodes:
+                logger.info("Trying to match anchor %s to %s", pattern_anchor, node)
+
+                match_found = self._match_nodes(pattern_anchor, node, match)
+                if match_found:
+                    # match next anchor
+                    backtracking(anchor_index + 1, match)
+                else:
+                    logger.info(
+                        "Failed to match anchor %s to %s\n", pattern_anchor, node
+                    )
+
+                # revert to saved_match before matching with current anchor
+                match = copy.copy(saved_match)
+
+        match = InternalMatch(anchors=self.pattern_anchors)
+        if match_candidates_list:
+            backtracking(0, match)
+
+        # filter out the matches where the subgraph is not fully_contained
+        before = len(matches)
+        matches = [match for match in matches if self._is_contained(match.nodes_map)]
+        after = len(matches)
+        if before != after:
+            logger.info(
+                "Filtered out %s matches because they are not fully contained",
+                before - after,
+            )
+
+        # filter out the matches that form a cycle if the subgraph is fused
+        valid_matches = []
+        for match in matches:
+            matched_compute_nodes = [
+                gn
+                for pn, gn in match.nodes_map.items()
+                if pn.op not in {"placeholder", "output"}
+            ]
+            if validate_partition(matched_compute_nodes):
+                valid_matches.append(match)
+        if len(valid_matches) != len(matches):
+            logger.info(
+                "Filtered out %s matches because \
+                          matched subgraph would form a cycle if fused",
+                len(matches) - len(valid_matches),
+            )
+
+        if self.remove_overlapping_matches:
+            before = len(valid_matches)
+            matches = self._remove_overlapping_matches(valid_matches)
+            after = len(matches)
+            if before != after:
+                logger.info(
+                    "Filtered out %s matches because matched subgraphs are overlapping",
+                    before - after,
+                )
+
+        logger.info("Matches returned: %s", matches)
+
+        return matches
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fa9b721e9ccdb83f835916485abcdd8a9c2e86e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py
@@ -0,0 +1,112 @@
+from torch.fx import Graph, GraphModule, Node
+from torch.fx._compatibility import compatibility
+
+from .matcher_utils import InternalMatch, SubgraphMatcher
+
+
+__all__ = ["SubgraphMatcherWithNameNodeMap"]
+
+
+def _split_to_graph_and_name_node_map(
+    gm: GraphModule,
+) -> tuple[GraphModule, dict[str, Node]]:
+    from torch.fx.graph import _PyTreeInfo
+    from torch.utils._pytree import tree_flatten, tree_unflatten
+
+    name_node_map = {}
+    for n in gm.graph.nodes:
+        if n.op == "output":
+            assert gm._out_spec is not None
+            output = tree_unflatten(n.args[0], gm._out_spec)
+            assert isinstance(
+                output, tuple
+            ), "Expecting the pattern graph to return a tuple"
+            assert (
+                len(output) >= 2
+            ), "Expecting the pattern graph to have at least two outputs"
+            *out, name_node_map = output
+            flattened, out_spec = tree_flatten(out)
+            assert isinstance(
+                name_node_map, dict
+            ), "Expecting the input graph to have a dict output as the last element"
+            n.args = (flattened,)
+            orig_pytree_info = gm._graph._codegen.pytree_info  # type: ignore[attr-defined]
+            gm._graph._codegen.pytree_info = _PyTreeInfo(  # type: ignore[attr-defined]
+                orig_pytree_info.orig_args, orig_pytree_info.in_spec, out_spec
+            )
+    gm.recompile()
+    return gm, name_node_map
+
+
+@compatibility(is_backward_compatible=False)
+class SubgraphMatcherWithNameNodeMap(SubgraphMatcher):
+    """Extends SubgraphMatcher to support querying the matched subgraph nodes through node name,
+    this requires pattern to have specific format (returning and additional dictionary at the output,
+    that has node name as key, and the node in the pattern graph as value, see Example for more details)
+
+    Difference with SubgraphMatcher is that it takes a `pattern_gm` GraphModule as input during
+    initialization since we need to modify the graph (which requires `recompile` the GraphModule)
+
+    Example::
+        def pattern(x, weight):
+            conv = F.conv2d(x, weight)
+            relu = F.relu(conv)
+            return relu, {"conv": conv, "relu": relu}
+
+        def target_graph(x, weight):
+            conv = F.conv2d(x, weight)
+            relu = F.relu(conv)
+            relu *= 2
+            return relu
+
+        pattern_gm = export_for_training(pattern, example_inputs).module()
+        target_gm = export_for_training(target_graph, example_inputs).module()
+        matcher = SubgraphMatcherWithNameNodeMap(pattern_gm)
+        matches = matcher.match(target_gm)
+        for match in matches:
+            match.name_node_map["conv"].meta["annotation"] = ...
+
+    """
+
+    def __init__(
+        self,
+        pattern_gm: GraphModule,
+        match_output: bool = False,
+        match_placeholder: bool = False,
+        remove_overlapping_matches: bool = True,
+        ignore_literals: bool = False,
+    ) -> None:
+        pattern_gm, name_node_map = _split_to_graph_and_name_node_map(pattern_gm)
+        self.name_node_map = name_node_map
+        super().__init__(
+            pattern_gm.graph,
+            match_output,
+            match_placeholder,
+            remove_overlapping_matches,
+            ignore_literals,
+        )
+
+    def match(self, graph: Graph) -> list[InternalMatch]:
+        """The returned InternalMatch will have name_node_map populated with a map
+        from node name (str) to the target node, e.g.
+        {"conv": target_conv_ndoe, "relu": target_relu_node}
+
+        this requires the pattern graph returns an additional
+        output of node name to node, e.g. instead of:
+        ```
+        def pattern(...):
+            ...
+            return relu
+        ```
+        we should do:
+        ```
+        def pattern(...):
+            ...
+            return relu, {"conv": conv, "relu": relu}
+        ``` instead
+        """
+        internal_matches = super().match(graph)
+        for internal_match in internal_matches:
+            for k, n in self.name_node_map.items():
+                internal_match.name_node_map[k] = internal_match.nodes_map[n]
+        return internal_matches
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/source_matcher_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/source_matcher_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..97a60b06694c0ba9b03651ecf422574895ba8283
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/fx/passes/utils/source_matcher_utils.py
@@ -0,0 +1,162 @@
+import logging
+import os
+from dataclasses import dataclass, field
+from typing import Any, Callable, Optional
+
+from torch.fx._compatibility import compatibility
+from torch.fx.graph import Graph
+from torch.fx.node import Node
+
+
+__all__ = ["get_source_partitions", "check_subgraphs_connected", "SourcePartition"]
+
+
+# Set`PYTORCH_MATCHER_LOGLEVEL=INFO` to see debug logs
+def _init_logger() -> logging.Logger:
+    logger = logging.getLogger(__name__)
+
+    level = os.environ.get("PYTORCH_MATCHER_LOGLEVEL", "WARNING").upper()
+    logger.setLevel(level)
+    console = logging.StreamHandler()
+    formatter = logging.Formatter("%(filename)s > %(message)s")
+    console.setFormatter(formatter)
+    console.setLevel(level)
+    # add the handlers to the logger
+    logger.addHandler(console)
+    logger.propagate = False
+    return logger
+
+
+logger = _init_logger()
+
+
+@compatibility(is_backward_compatible=False)
+@dataclass
+class SourcePartition:
+    # Nodes in a particular partition
+    nodes: list[Node]
+
+    # The source these nodes decomposed from
+    source: Any
+
+    # Nodes in the graph that are needed as inputs to the partition
+    # These do not include the params of the partition
+    input_nodes: list[Node] = field(default_factory=list)
+
+    # Nodes in the partition that are being used by nodes outside of the
+    # partition
+    output_nodes: list[Node] = field(default_factory=list)
+
+    # Parameters that are being used
+    params: list[Node] = field(default_factory=list)
+
+
+@compatibility(is_backward_compatible=False)  # type: ignore[misc]
+def get_source_partitions(
+    graph: Graph,
+    wanted_sources: list[Any],
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> dict[Any, list[SourcePartition]]:
+    """
+    Args:
+        graph: The graph we want to partition
+        wanted_sources: List of sources of nodes that were decomposed from this
+            source. This can be a function (ex. torch.nn.functional.linear) or a
+            leaf module type (ex. torch.nn.Linear).
+
+    Returns:
+        Dictionary mapping sources that were given to a list of SourcePartitions
+        that correspond to the list of nodes that were decomposed from the given
+        source.
+    """
+    modules: dict[type, dict[str, list[Node]]] = {}
+
+    for node in graph.nodes:
+        # The metadata source_fn should contain a tuple of a unique name for the
+        # source, and the source function if the node is decomposed from a
+        # function, or the type of module if the node is decomposed from a leaf
+        # module
+
+        # TODO: Bypass "torch_fn" when "source_fn_stack" because now "torch_fn" can
+        # be different from "source_fn_stack", for example for the add_ node
+        # decomposed from batch norm. We should remove the check on "source_fn_stack"
+        # after we fix "torch_fn". T199561090
+        if (source_fn_st := node.meta.get("source_fn_stack", None)) is None and (
+            torch_fn := node.meta.get("torch_fn", None)
+        ) is not None:
+            node_fqn, source_fn = torch_fn
+            source_fn_name = source_fn.split(".")[1]
+            if source_fn_name in wanted_sources:
+                diff_modules = modules.setdefault(source_fn_name, {})
+                partition = diff_modules.setdefault(node_fqn, [])
+                partition.append(node)
+
+        if (source_fn_st := node.meta.get("source_fn_stack", None)) is not None:
+            source_fn = source_fn_st[-1]
+            if source_fn[1] in wanted_sources:
+                diff_modules = modules.setdefault(source_fn[1], {})
+                partition = diff_modules.setdefault(source_fn[0], [])
+                partition.append(node)
+
+    def make_partition(nodes: list[Node], module_type: type) -> SourcePartition:
+        input_nodes = set()
+        output_nodes = set()
+        params = set()
+        for node in nodes:
+            for arg in node.args:
+                if isinstance(arg, Node) and arg not in nodes and arg.op != "get_attr":
+                    input_nodes.add(arg)
+
+            if node.op == "get_attr":
+                params.add(node)
+                # get_attr nodes won't be output nodes
+                continue
+
+            for user in node.users.keys():
+                if user not in nodes:
+                    output_nodes.add(node)
+
+        return SourcePartition(
+            nodes,
+            module_type,
+            list(input_nodes),
+            list(output_nodes),
+            list(params),  # type: ignore[arg-type]
+        )
+
+    ret: dict[type[Any], list[SourcePartition]] = {}
+
+    if filter_fn:
+        # for each partition, we apply filter_fn to filter out all partitions that doesn't satisfy the
+        # filter condition
+        filtered_modules = {}
+        for tp, name_to_partition in modules.items():
+            filtered_name_to_partition = {
+                name: partition
+                for name, partition in name_to_partition.items()
+                if all(map(filter_fn, partition))
+            }
+            filtered_modules[tp] = filtered_name_to_partition
+        modules = filtered_modules
+
+    for k, v in modules.items():
+        ret[k] = [make_partition(partition, k) for partition in v.values()]
+
+    return ret
+
+
+@compatibility(is_backward_compatible=False)  # type: ignore[misc]
+def check_subgraphs_connected(
+    subgraph1: SourcePartition, subgraph2: SourcePartition
+) -> bool:
+    """
+    Given two subgraphs A and B (in the form of a list of nodes), checks if
+    A has nodes connecting to at least one node in B -- aka there exists a node
+    in B that uses a node in A (not the other way around).
+    """
+
+    for node in reversed(subgraph1.nodes):
+        for user in node.users.keys():
+            if user in subgraph2.nodes:
+                return True
+    return False
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f62ec3afe125a6502cb1eacf600879f10975ea61
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/__init__.py
@@ -0,0 +1,295 @@
+# mypy: allow-untyped-defs
+import warnings
+from collections.abc import Iterator
+from contextlib import contextmanager
+from typing import Any
+
+import torch._C
+
+# These are imported so users can access them from the `torch.jit` module
+from torch._jit_internal import (
+    _Await,
+    _drop,
+    _IgnoreContextManager,
+    _isinstance,
+    _overload,
+    _overload_method,
+    export,
+    Final,
+    Future,
+    ignore,
+    is_scripting,
+    unused,
+)
+from torch.jit._async import fork, wait
+from torch.jit._await import _awaitable, _awaitable_nowait, _awaitable_wait
+from torch.jit._decomposition_utils import _register_decomposition
+from torch.jit._freeze import freeze, optimize_for_inference, run_frozen_optimizations
+from torch.jit._fuser import (
+    fuser,
+    last_executed_optimized_graph,
+    optimized_execution,
+    set_fusion_strategy,
+)
+from torch.jit._ir_utils import _InsertPoint
+from torch.jit._script import (
+    _ScriptProfile,
+    _unwrap_optional,
+    Attribute,
+    CompilationUnit,
+    interface,
+    RecursiveScriptClass,
+    RecursiveScriptModule,
+    script,
+    script_method,
+    ScriptFunction,
+    ScriptModule,
+    ScriptWarning,
+)
+from torch.jit._serialization import (
+    jit_module_from_flatbuffer,
+    load,
+    save,
+    save_jit_module_to_flatbuffer,
+)
+from torch.jit._trace import (
+    _flatten,
+    _get_trace_graph,
+    _script_if_tracing,
+    _unique_state_dict,
+    is_tracing,
+    ONNXTracedModule,
+    TopLevelTracedModule,
+    trace,
+    trace_module,
+    TracedModule,
+    TracerWarning,
+    TracingCheckError,
+)
+from torch.utils import set_module
+
+
+__all__ = [
+    "Attribute",
+    "CompilationUnit",
+    "Error",
+    "Future",
+    "ScriptFunction",
+    "ScriptModule",
+    "annotate",
+    "enable_onednn_fusion",
+    "export",
+    "export_opnames",
+    "fork",
+    "freeze",
+    "interface",
+    "ignore",
+    "isinstance",
+    "load",
+    "onednn_fusion_enabled",
+    "optimize_for_inference",
+    "save",
+    "script",
+    "script_if_tracing",
+    "set_fusion_strategy",
+    "strict_fusion",
+    "trace",
+    "trace_module",
+    "unused",
+    "wait",
+]
+
+# For backwards compatibility
+_fork = fork
+_wait = wait
+_set_fusion_strategy = set_fusion_strategy
+
+
+def export_opnames(m):
+    r"""
+    Generate new bytecode for a Script module.
+
+    Returns what the op list would be for a Script Module based off the current code base.
+
+    If you have a LiteScriptModule and want to get the currently present
+    list of ops call _export_operator_list instead.
+    """
+    return torch._C._export_opnames(m._c)
+
+
+# torch.jit.Error
+Error = torch._C.JITException
+set_module(Error, "torch.jit")
+# This is not perfect but works in common cases
+Error.__name__ = "Error"
+Error.__qualname__ = "Error"
+
+
+# for use in python if using annotate
+def annotate(the_type, the_value):
+    """Use to give type of `the_value` in TorchScript compiler.
+
+    This method is a pass-through function that returns `the_value`, used to hint TorchScript
+    compiler the type of `the_value`. It is a no-op when running outside of TorchScript.
+
+    Though TorchScript can infer correct type for most Python expressions, there are some cases where
+    type inference can be wrong, including:
+
+    - Empty containers like `[]` and `{}`, which TorchScript assumes to be container of `Tensor`
+    - Optional types like `Optional[T]` but assigned a valid value of type `T`, TorchScript would assume
+      it is type `T` rather than `Optional[T]`
+
+    Note that `annotate()` does not help in `__init__` method of `torch.nn.Module` subclasses because it
+    is executed in eager mode. To annotate types of `torch.nn.Module` attributes,
+    use :meth:`~torch.jit.Attribute` instead.
+
+    Example:
+
+    .. testcode::
+
+        import torch
+        from typing import Dict
+
+        @torch.jit.script
+        def fn():
+            # Telling TorchScript that this empty dictionary is a (str -> int) dictionary
+            # instead of default dictionary type of (str -> Tensor).
+            d = torch.jit.annotate(Dict[str, int], {})
+
+            # Without `torch.jit.annotate` above, following statement would fail because of
+            # type mismatch.
+            d["name"] = 20
+
+    .. testcleanup::
+
+        del fn
+
+    Args:
+        the_type: Python type that should be passed to TorchScript compiler as type hint for `the_value`
+        the_value: Value or expression to hint type for.
+
+    Returns:
+        `the_value` is passed back as return value.
+    """
+    return the_value
+
+
+def script_if_tracing(fn):
+    """
+    Compiles ``fn`` when it is first called during tracing.
+
+    ``torch.jit.script`` has a non-negligible start up time when it is first called due to
+    lazy-initializations of many compiler builtins. Therefore you should not use
+    it in library code. However, you may want to have parts of your library work
+    in tracing even if they use control flow. In these cases, you should use
+    ``@torch.jit.script_if_tracing`` to substitute for
+    ``torch.jit.script``.
+
+    Args:
+        fn: A function to compile.
+
+    Returns:
+        If called during tracing, a :class:`ScriptFunction` created by `torch.jit.script` is returned.
+        Otherwise, the original function `fn` is returned.
+    """
+    return _script_if_tracing(fn)
+
+
+# for torch.jit.isinstance
+def isinstance(obj, target_type):
+    """
+    Provide container type refinement in TorchScript.
+
+    It can refine parameterized containers of the List, Dict, Tuple, and Optional types. E.g. ``List[str]``,
+    ``Dict[str, List[torch.Tensor]]``, ``Optional[Tuple[int,str,int]]``. It can also
+    refine basic types such as bools and ints that are available in TorchScript.
+
+    Args:
+        obj: object to refine the type of
+        target_type: type to try to refine obj to
+    Returns:
+        ``bool``: True if obj was successfully refined to the type of target_type,
+            False otherwise with no new type refinement
+
+
+    Example (using ``torch.jit.isinstance`` for type refinement):
+    .. testcode::
+
+        import torch
+        from typing import Any, Dict, List
+
+        class MyModule(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+
+            def forward(self, input: Any): # note the Any type
+                if torch.jit.isinstance(input, List[torch.Tensor]):
+                    for t in input:
+                        y = t.clamp(0, 0.5)
+                elif torch.jit.isinstance(input, Dict[str, str]):
+                    for val in input.values():
+                        print(val)
+
+        m = torch.jit.script(MyModule())
+        x = [torch.rand(3,3), torch.rand(4,3)]
+        m(x)
+        y = {"key1":"val1","key2":"val2"}
+        m(y)
+    """
+    return _isinstance(obj, target_type)
+
+
+class strict_fusion:
+    """
+    Give errors if not all nodes have been fused in inference, or symbolically differentiated in training.
+
+    Example:
+    Forcing fusion of additions.
+
+    .. code-block:: python
+
+        @torch.jit.script
+        def foo(x):
+            with torch.jit.strict_fusion():
+                return x + x + x
+
+    """
+
+    def __init__(self) -> None:
+        if not torch._jit_internal.is_scripting():
+            warnings.warn("Only works in script mode")
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, type: Any, value: Any, tb: Any) -> None:
+        pass
+
+
+# Context manager for globally hiding source ranges when printing graphs.
+# Note that these functions are exposed to Python as static members of the
+# Graph class, so mypy checks need to be skipped.
+@contextmanager
+def _hide_source_ranges() -> Iterator[None]:
+    old_enable_source_ranges = torch._C.Graph.global_print_source_ranges  # type: ignore[attr-defined]
+    try:
+        torch._C.Graph.set_global_print_source_ranges(False)  # type: ignore[attr-defined]
+        yield
+    finally:
+        torch._C.Graph.set_global_print_source_ranges(old_enable_source_ranges)  # type: ignore[attr-defined]
+
+
+def enable_onednn_fusion(enabled: bool):
+    """Enable or disables onednn JIT fusion based on the parameter `enabled`."""
+    torch._C._jit_set_llga_enabled(enabled)
+
+
+def onednn_fusion_enabled():
+    """Return whether onednn JIT fusion is enabled."""
+    return torch._C._jit_llga_enabled()
+
+
+del Any
+
+if not torch._C._jit_init():
+    raise RuntimeError("JIT initialization failed")
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_async.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_async.py
new file mode 100644
index 0000000000000000000000000000000000000000..533064b6cd76f39e3934b5ca378023068c0762cb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_async.py
@@ -0,0 +1,115 @@
+# mypy: allow-untyped-defs
+"""Async API.
+
+This module contains the API for parallelism in TorchScript, notably:
+    * torch.jit.fork
+    * torch.jit.wait
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+import torch
+from torch._jit_internal import Future
+from torch.jit._builtins import _register_builtin
+from torch.utils import set_module
+
+
+set_module(Future, "torch.jit")
+
+
+def fork(func, *args, **kwargs):
+    r"""
+    Create an asynchronous task executing `func` and a reference to the value of the result of this execution.
+
+    `fork` will return immediately, so the return value of `func` may not have been computed yet. To force completion
+    of the task and access the return value invoke `torch.jit.wait` on the Future. `fork` invoked
+    with a `func` which returns `T` is typed as `torch.jit.Future[T]`. `fork` calls can be arbitrarily
+    nested, and may be invoked with positional and keyword arguments.
+    Asynchronous execution will only occur when run in TorchScript. If run in pure python,
+    `fork` will not execute in parallel. `fork` will also not execute in parallel when invoked
+    while tracing, however the `fork` and `wait` calls will be captured in the exported IR Graph.
+
+    .. warning::
+        `fork` tasks will execute non-deterministically. We recommend only spawning
+        parallel fork tasks for pure functions that do not modify their inputs,
+        module attributes, or global state.
+
+    Args:
+        func (callable or torch.nn.Module):  A Python function or `torch.nn.Module`
+            that will be invoked. If executed in TorchScript, it will execute asynchronously,
+            otherwise it will not. Traced invocations of fork will be captured in the IR.
+        ``*args``, ``**kwargs``: arguments to invoke `func` with.
+    Returns:
+        `torch.jit.Future[T]`: a reference to the execution of `func`. The value `T`
+        can only be accessed by forcing completion of `func` through `torch.jit.wait`.
+
+    Example (fork a free function):
+
+    .. code-block:: python
+
+        import torch
+        from torch import Tensor
+
+
+        def foo(a: Tensor, b: int) -> Tensor:
+            return a + b
+
+
+        def bar(a):
+            fut: torch.jit.Future[Tensor] = torch.jit.fork(foo, a, b=2)
+            return torch.jit.wait(fut)
+
+
+        script_bar = torch.jit.script(bar)
+        input = torch.tensor(2)
+        # only the scripted version executes asynchronously
+        assert script_bar(input) == bar(input)
+        # trace is not run asynchronously, but fork is captured in IR
+        graph = torch.jit.trace(bar, (input,)).graph
+        assert "fork" in str(graph)
+
+    Example (fork a module method):
+
+    .. code-block:: python
+
+        import torch
+        from torch import Tensor
+
+
+        class AddMod(torch.nn.Module):
+            def forward(self, a: Tensor, b: int):
+                return a + b
+
+
+        class Mod(torch.nn.Module):
+            def __init__(self) -> None:
+                super(self).__init__()
+                self.mod = AddMod()
+
+            def forward(self, input):
+                fut = torch.jit.fork(self.mod, a, b=2)
+                return torch.jit.wait(fut)
+
+
+        input = torch.tensor(2)
+        mod = Mod()
+        assert mod(input) == torch.jit.script(mod).forward(input)
+    """
+    return torch._C.fork(func, *args, **kwargs)
+
+
+def wait(future):
+    r"""
+    Force completion of a `torch.jit.Future[T]` asynchronous task, returning the result of the task.
+
+    See :func:`~fork` for docs and examples.
+    Args:
+        future (torch.jit.Future[T]): an asynchronous task reference, created through `torch.jit.fork`
+    Returns:
+        `T`: the return value of the completed task
+    """
+    return torch._C.wait(future)
+
+
+_register_builtin(wait, "aten::wait")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_await.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_await.py
new file mode 100644
index 0000000000000000000000000000000000000000..b00da7b3384b7a263aeab613b3e613c4bd3c3667
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_await.py
@@ -0,0 +1,27 @@
+# mypy: allow-untyped-defs
+import torch
+from torch._jit_internal import _Await
+from torch.jit._builtins import _register_builtin
+from torch.utils import set_module
+
+
+set_module(_Await, "torch.jit")
+
+
+def _awaitable(func, *args, **kwargs):
+    r"""Create Await object that will call specified functioni with specified args, when it is requested for the result."""
+    return torch._C._awaitable(func, *args, **kwargs)
+
+
+def _awaitable_wait(aw):
+    r"""Request await the result of execution, if Await is not completed yet, the func will be called immediately."""
+    return torch._C._awaitable_wait(aw)
+
+
+def _awaitable_nowait(o):
+    r"""Create completed Await with specified result."""
+    return torch._C._awaitable_nowait(o)
+
+
+_register_builtin(_awaitable_wait, "prim::awaitable_wait")
+_register_builtin(_awaitable_nowait, "prim::awaitable_nowait")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_builtins.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_builtins.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb8ac26471a95846dfda2ddefb705dbb51e87f25
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_builtins.py
@@ -0,0 +1,193 @@
+# mypy: allow-untyped-defs
+import cmath
+import math
+import warnings
+from collections import OrderedDict
+from typing import Optional
+
+import torch
+import torch.backends.cudnn as cudnn
+from torch.nn.modules.utils import (
+    _list_with_default,
+    _pair,
+    _quadruple,
+    _single,
+    _triple,
+)
+
+
+_builtin_table: Optional[dict[int, str]] = None
+
+_modules_containing_builtins = (torch, torch._C._nn, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._sparse, torch._C._special)  # type: ignore[attr-defined] # noqa: B950
+
+_builtin_ops = [
+    # Pairs of (function, op_name)
+    (_pair, "aten::_pair"),
+    (_quadruple, "aten::_quadruple"),
+    (_single, "aten::_single"),
+    (_triple, "aten::_triple"),
+    (_list_with_default, "aten::list_with_default"),
+    (OrderedDict, "aten::dict"),
+    (dict, "aten::dict"),
+    (cudnn.is_acceptable, "aten::cudnn_is_acceptable"),
+    (math.ceil, "aten::ceil"),
+    (math.copysign, "aten::copysign"),
+    (math.erf, "aten::erf"),
+    (math.erfc, "aten::erfc"),
+    (math.exp, "aten::exp"),
+    (math.expm1, "aten::expm1"),
+    (math.fabs, "aten::fabs"),
+    (math.floor, "aten::floor"),
+    (math.gamma, "aten::gamma"),
+    (math.lgamma, "aten::lgamma"),
+    (math.log, "aten::log"),
+    (math.log10, "aten::log10"),
+    (math.log1p, "aten::log1p"),
+    (math.pow, "aten::pow"),
+    (math.sqrt, "aten::sqrt"),
+    (math.isnan, "aten::isnan"),
+    (math.asinh, "aten::asinh"),
+    (math.atanh, "aten::atanh"),
+    (math.cosh, "aten::cosh"),
+    (math.sinh, "aten::sinh"),
+    (math.tanh, "aten::tanh"),
+    (math.acos, "aten::acos"),
+    (math.asin, "aten::asin"),
+    (math.atan, "aten::atan"),
+    (math.atan2, "aten::atan2"),
+    (math.cos, "aten::cos"),
+    (math.sin, "aten::sin"),
+    (math.tan, "aten::tan"),
+    (math.asinh, "aten::asinh"),
+    (math.atanh, "aten::atanh"),
+    (math.acosh, "aten::acosh"),
+    (math.fmod, "aten::fmod"),
+    (math.modf, "aten::modf"),
+    (math.factorial, "aten::factorial"),
+    (math.frexp, "aten::frexp"),
+    (math.isinf, "aten::isinf"),
+    (math.degrees, "aten::degrees"),
+    (math.radians, "aten::radians"),
+    (cmath.isnan, "aten::isnan"),
+    (cmath.isfinite, "aten::isfinite"),
+    (cmath.isinf, "aten::isinf"),
+    (cmath.phase, "aten::angle"),
+    (cmath.rect, "aten::polar"),
+    (cmath.log, "aten::log"),
+    (cmath.log10, "aten::log10"),
+    (cmath.sqrt, "aten::sqrt"),
+    (cmath.exp, "aten::exp"),
+    (cmath.sin, "aten::sin"),
+    (cmath.tan, "aten::tan"),
+    (cmath.cos, "aten::cos"),
+    (cmath.asin, "aten::asin"),
+    (cmath.acos, "aten::acos"),
+    (cmath.atan, "aten::atan"),
+    (cmath.sinh, "aten::sinh"),
+    (cmath.cosh, "aten::cosh"),
+    (cmath.tanh, "aten::tanh"),
+    (cmath.asinh, "aten::asinh"),
+    (cmath.acosh, "aten::acosh"),
+    (cmath.atanh, "aten::atanh"),
+    (math.ldexp, "aten::ldexp"),
+    (torch._assert, "aten::_assert"),
+    (torch.autograd.grad, "aten::grad"),
+    (torch.autograd.backward, "aten::backward"),
+    (torch._C._infer_size, "aten::_infer_size"),
+    (torch.nn.functional._no_grad_embedding_renorm_, "aten::_no_grad_embedding_renorm_"),  # type: ignore[attr-defined]
+    (torch.nn.functional.assert_int_or_pair, "aten::_assert_int_or_pair"),
+    (torch.nn.init._no_grad_fill_, "aten::_no_grad_fill_"),
+    (torch.nn.init._no_grad_normal_, "aten::_no_grad_normal_"),
+    (torch.nn.init._no_grad_uniform_, "aten::_no_grad_uniform_"),
+    (torch.nn.init._no_grad_zero_, "aten::_no_grad_zero_"),
+    (torch._C._get_tracing_state, "aten::_get_tracing_state"),
+    (torch._C._get_cpu_capability, "aten::_get_cpu_capability"),
+    (warnings.warn, "aten::warn"),
+    (torch._VF.stft, "aten::stft"),  # type: ignore[attr-defined]
+    (torch._VF.istft, "aten::istft"),  # type: ignore[attr-defined]
+    (torch._VF.cdist, "aten::cdist"),  # type: ignore[attr-defined]
+    (torch._VF.norm, "aten::norm"),  # type: ignore[attr-defined]
+    (torch._VF.unique_dim, "aten::unique_dim"),
+    (torch._VF.unique_consecutive, "aten::unique_consecutive"),  # type: ignore[attr-defined]
+    (torch._VF.nuclear_norm, "aten::nuclear_norm"),
+    (torch._VF.frobenius_norm, "aten::frobenius_norm"),
+    (torch._VF.tensordot, "aten::tensordot"),  # type: ignore[attr-defined]
+]
+
+# ops in torch.functional are bound to torch
+# in these cases, we want to resolve the function to their python implementation
+# instead looking up a builtin "aten::" schema
+
+
+def _gen_torch_functional_registered_ops():
+    # eventually ops should encompass all of torch/functional.py, (torch.functional.__all__)
+    # but we are currently only able to compile some of the functions. additionally,
+    # some functions directly map to their aten:: implementations.
+    # TODO: add support for more ops
+    ops = [
+        "stft",
+        "istft",
+        "lu",
+        "cdist",
+        "norm",
+        "unique",
+        "unique_consecutive",
+        "tensordot",
+    ]
+    return {getattr(torch.functional, name) for name in ops}
+
+
+_functional_registered_ops = _gen_torch_functional_registered_ops()
+
+
+def _is_special_functional_bound_op(fn):
+    return fn in _functional_registered_ops
+
+
+# lazily built to ensure the correct initialization order
+def _get_builtin_table():
+    global _builtin_table
+    if _builtin_table is not None:
+        return _builtin_table
+    _builtin_table = {}
+
+    def register_all(mod):
+        for name in dir(mod):
+            v = getattr(mod, name)
+            if (
+                callable(v)
+                and not _is_special_functional_bound_op(v)
+                and v is not torch.no_grad
+                and v is not torch.autocast
+            ):
+                # Fixup inconsistency in segment_reduce
+                if name == "_segment_reduce":
+                    name = name[1:]
+                _builtin_ops.append((v, "aten::" + name))
+
+    for mod in _modules_containing_builtins:
+        register_all(mod)
+
+    _builtin_ops.append((math.gcd, "aten::gcd"))
+    _builtin_ops.append((math.isfinite, "aten::isfinite"))
+    _builtin_ops.append((math.remainder, "aten::mathremainder"))  # type: ignore[attr-defined]
+
+    import torch.distributed.autograd as dist_autograd
+
+    if dist_autograd.is_available():
+        _builtin_ops.append((dist_autograd.get_gradients, "aten::get_gradients"))
+        _builtin_ops.append((dist_autograd.backward, "aten::dist_backward"))
+
+    # populate the _builtin_table from _builtin_ops
+    for builtin, aten_op in _builtin_ops:
+        _builtin_table[id(builtin)] = aten_op
+
+    return _builtin_table
+
+
+def _register_builtin(fn, op):
+    _get_builtin_table()[id(fn)] = op
+
+
+def _find_builtin(fn):
+    return _get_builtin_table().get(id(fn))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_check.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_check.py
new file mode 100644
index 0000000000000000000000000000000000000000..f708ee87f3089a199232a353572cdd84258e21c2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_check.py
@@ -0,0 +1,249 @@
+# mypy: allow-untyped-defs
+import ast
+import inspect
+import textwrap
+import warnings
+
+import torch
+
+
+class AttributeTypeIsSupportedChecker(ast.NodeVisitor):
+    """Check the ``__init__`` method of a given ``nn.Module``.
+
+    It ensures that all instance-level attributes can be properly initialized.
+
+    Specifically, we do type inference based on attribute values...even
+    if the attribute in question has already been typed using
+    Python3-style annotations or ``torch.jit.annotate``. This means that
+    setting an instance-level attribute to ``[]`` (for ``List``),
+    ``{}`` for ``Dict``), or ``None`` (for ``Optional``) isn't enough
+    information for us to properly initialize that attribute.
+
+    An object of this class can walk a given ``nn.Module``'s AST and
+    determine if it meets our requirements or not.
+
+    Known limitations
+    1. We can only check the AST nodes for certain constructs; we can't
+    ``eval`` arbitrary expressions. This means that function calls,
+    class instantiations, and complex expressions that resolve to one of
+    the "empty" values specified above will NOT be flagged as
+    problematic.
+    2. We match on string literals, so if the user decides to use a
+    non-standard import (e.g. `from typing import List as foo`), we
+    won't catch it.
+
+    Example:
+        .. code-block:: python
+
+            class M(torch.nn.Module):
+                def fn(self):
+                    return []
+
+                def __init__(self) -> None:
+                    super().__init__()
+                    self.x: List[int] = []
+
+                def forward(self, x: List[int]):
+                    self.x = x
+                    return 1
+
+        The above code will pass the ``AttributeTypeIsSupportedChecker``
+        check since we have a function call in ``__init__``. However,
+        it will still fail later with the ``RuntimeError`` "Tried to set
+        nonexistent attribute: x. Did you forget to initialize it in
+        __init__()?".
+
+    Args:
+        nn_module - The instance of ``torch.nn.Module`` whose
+            ``__init__`` method we wish to check
+    """
+
+    def check(self, nn_module: torch.nn.Module) -> None:
+        source_lines = inspect.getsource(nn_module.__class__.__init__)
+
+        # Ignore comments no matter the indentation
+        def is_useless_comment(line):
+            line = line.strip()
+            return line.startswith("#") and not line.startswith("# type:")
+
+        source_lines = "\n".join(
+            [l for l in source_lines.split("\n") if not is_useless_comment(l)]
+        )
+
+        # This AST only contains the `__init__` method of the nn.Module
+        init_ast = ast.parse(textwrap.dedent(source_lines))
+
+        # Get items annotated in the class body
+        self.class_level_annotations = list(nn_module.__annotations__.keys())
+
+        # Flag for later
+        self.visiting_class_level_ann = False
+
+        self.visit(init_ast)
+
+    def _is_empty_container(self, node: ast.AST, ann_type: str) -> bool:
+        if ann_type == "List":
+            # Assigning `[]` to a `List` type gives you a Node where
+            # value=List(elts=[], ctx=Load())
+            if not isinstance(node, ast.List):
+                return False
+            if node.elts:
+                return False
+        elif ann_type == "Dict":
+            # Assigning `{}` to a `Dict` type gives you a Node where
+            # value=Dict(keys=[], values=[])
+            if not isinstance(node, ast.Dict):
+                return False
+            if node.keys:
+                return False
+        elif ann_type == "Optional":
+            # Assigning `None` to an `Optional` type gives you a
+            # Node where value=Constant(value=None, kind=None)
+            if not isinstance(node, ast.Constant):
+                return False
+            if node.value:  # type: ignore[attr-defined]
+                return False
+
+        return True
+
+    def visit_Assign(self, node):
+        """Store assignment state when assigning to a Call Node.
+
+        If we're visiting a Call Node (the right-hand side of an
+        assignment statement), we won't be able to check the variable
+        that we're assigning to (the left-hand side of an assignment).
+        Because of this, we need to store this state in visitAssign.
+        (Luckily, we only have to do this if we're assigning to a Call
+        Node, i.e. ``torch.jit.annotate``. If we're using normal Python
+        annotations, we'll be visiting an AnnAssign Node, which has its
+        target built in.)
+        """
+        try:
+            if (
+                isinstance(node.value, ast.Call)
+                and node.targets[0].attr in self.class_level_annotations
+            ):
+                self.visiting_class_level_ann = True
+        except AttributeError:
+            return
+        self.generic_visit(node)
+        self.visiting_class_level_ann = False
+
+    def visit_AnnAssign(self, node):
+        """Visit an AnnAssign node in an ``nn.Module``'s ``__init__`` method.
+
+        It checks if it conforms to our attribute annotation rules."""
+        # If we have a local variable
+        try:
+            if node.target.value.id != "self":
+                return
+        except AttributeError:
+            return
+
+        # If we have an attribute that's already been annotated at the
+        # class level
+        if node.target.attr in self.class_level_annotations:
+            return
+
+        # TODO @ansley: add `Union` once landed
+
+        # NB: Even though `Tuple` is a "container", we don't want to
+        # check for it here. `Tuple` functions as an type with an
+        # "infinite" number of subtypes, in the sense that you can have
+        # `Tuple[())]`, `Tuple[T1]`, `Tuple[T2]`, `Tuple[T1, T2]`,
+        # `Tuple[T2, T1]` and so on, and none of these subtypes can be
+        # used in place of the other. Therefore, assigning an empty
+        # tuple in `__init__` CORRECTLY means that that variable
+        # cannot be reassigned later to a non-empty tuple. Same
+        # deal with `NamedTuple`
+
+        containers = {"List", "list", "Dict", "dict", "Optional"}
+
+        # If we're not evaluating one of the specified problem types
+        try:
+            if node.annotation.value.id not in containers:
+                return
+        except AttributeError:
+            # To evaluate a base type (`str`, `int`, etc.), we would
+            # have needed to get the name through `node.annotation.id`
+            # instead of `node.annotation.value.id`. Seems that we're
+            # not evaluating one of our "containers"
+            return
+
+        # Check if the assigned variable is empty
+        ann_type = node.annotation.value.id
+        if not self._is_empty_container(node.value, ann_type):
+            return
+
+        warnings.warn(
+            "The TorchScript type system doesn't support "
+            "instance-level annotations on empty non-base "
+            "types in `__init__`. Instead, either 1) use a "
+            "type annotation in the class body, or 2) wrap "
+            "the type in `torch.jit.Attribute`."
+        )
+
+    def visit_Call(self, node):
+        """Determine if a Call node is 'torch.jit.annotate' in __init__.
+
+        Visit a Call node in an ``nn.Module``'s ``__init__``
+        method and determine if it's ``torch.jit.annotate``. If so,
+        see if it conforms to our attribute annotation rules.
+        """
+        # If we have an attribute that's already been annotated at the
+        # class level
+        if self.visiting_class_level_ann:
+            return
+
+        # If this isn't a call to `torch.jit.annotate`
+        try:
+            if (
+                node.func.value.value.id != "torch"
+                or node.func.value.attr != "jit"
+                or node.func.attr != "annotate"
+            ):
+                self.generic_visit(node)
+            elif (
+                node.func.value.value.id != "jit" or node.func.value.attr != "annotate"
+            ):
+                self.generic_visit(node)
+        except AttributeError:
+            # Looks like we didn't even have the right node structure
+            # to check for `torch.jit.annotate` in the first place
+            self.generic_visit(node)
+
+        # Invariant: we have a `torch.jit.annotate` or a
+        # `torch.annotate` call
+
+        # A Call Node for `torch.jit.annotate` should have an `args`
+        # list of length 2 where args[0] represents the annotation and
+        # args[1] represents the actual value
+        if len(node.args) != 2:
+            return
+
+        if not isinstance(node.args[0], ast.Subscript):
+            return
+
+        # See notes in `visit_AnnAssign` r.e. containers
+
+        containers = {"List", "Dict", "Optional"}
+
+        try:
+            ann_type = node.args[0].value.id  # type: ignore[attr-defined]
+        except AttributeError:
+            return
+
+        if ann_type not in containers:
+            return
+
+        # Check if the assigned variable is empty
+        if not self._is_empty_container(node.args[1], ann_type):
+            return
+
+        warnings.warn(
+            "The TorchScript type system doesn't support "
+            "instance-level annotations on empty non-base "
+            "types in `__init__`. Instead, either 1) use a "
+            "type annotation in the class body, or 2) wrap "
+            "the type in `torch.jit.Attribute`."
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_dataclass_impls.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_dataclass_impls.py
new file mode 100644
index 0000000000000000000000000000000000000000..58abc91da0440ba334a054cfdbcb9d4cb9623f14
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_dataclass_impls.py
@@ -0,0 +1,190 @@
+# mypy: allow-untyped-defs
+# Functions for synthesizing magic methods for JIT-compiled dataclasses
+import ast
+import dataclasses
+import inspect
+import os
+from functools import partial
+from typing import Callable
+
+from torch._jit_internal import FAKE_FILENAME_PREFIX, is_optional
+from torch._sources import ParsedDef, SourceContext
+
+
+def _get_fake_filename(cls, method_name):
+    return os.path.join(FAKE_FILENAME_PREFIX, cls.__name__, method_name)
+
+
+def compose_fn(cls, name: str, body_lines: list[str], signature: str) -> ParsedDef:
+    body = "\n".join(f"  {b}" for b in body_lines)
+    decl = f"def {name}{signature}:\n{body}"
+
+    # Parse the function declaration
+    try:
+        py_ast = ast.parse(decl)
+    except SyntaxError as e:
+        # This should only happen if there's some unforeseeable change
+        # in the dataclasses module that makes our synthesized code fail
+        raise RuntimeError(
+            f"TorchScript failed to synthesize dataclass method '{name}' for class '{cls.__name__}'. "
+            "Please file a bug report at "
+        ) from e
+    fake_filename = _get_fake_filename(cls, name)
+    # Parse the function
+    return ParsedDef(
+        py_ast,
+        ctx=SourceContext(
+            source=decl, filename=fake_filename, file_lineno=0, leading_whitespace_len=0
+        ),
+        source=decl,
+        filename=fake_filename,
+        file_lineno=0,
+    )
+
+
+def synthesize__init__(cls) -> ParsedDef:
+    # Supporting default factories in the way that people expect would sort of require us to
+    # allow compiling lambda functions, which is not currently supported.
+    if any(
+        field.default_factory is not dataclasses.MISSING
+        for field in dataclasses.fields(cls)
+    ):
+        raise NotImplementedError(
+            "Default factory initializers are not supported in TorchScript dataclasses"
+        )
+
+    # Simply read off the generated __init__ signature from CPython's implementation. It'll be
+    # almost correct except for InitVar annotations, which we need to handle specially.
+    signature = inspect.signature(cls.__init__)
+
+    # Handle InitVars if needed (only works on Python 3.8+, when a `type` attribute was added to InitVar);
+    # see CPython commit here https://github.com/python/cpython/commit/01ee12ba35a333e8a6a25c4153c4a21838e9585c
+    init_vars: list[str] = []
+    params = []
+    for name, param in signature.parameters.items():
+        ann = param.annotation
+
+        if isinstance(ann, dataclasses.InitVar):
+            # The TorchScript interpreter can't handle InitVar annotations, so we unwrap the underlying type here
+            init_vars.append(name)
+            params.append(param.replace(annotation=ann.type))  # type: ignore[attr-defined]
+        else:
+            params.append(param)
+
+    signature = signature.replace(parameters=params)
+
+    body = [
+        # Assign all attributes to self
+        f"self.{field.name} = {field.name}"
+        for field in dataclasses.fields(cls)
+        if field.init and field.name not in init_vars
+    ]
+    # Call user's impl of __post_init__ if it exists
+    if hasattr(cls, "__post_init__"):
+        body.append("self.__post_init__(" + ", ".join(init_vars) + ")")
+
+    return compose_fn(cls, "__init__", body or ["pass"], signature=str(signature))
+
+
+# This is a placeholder at the moment since the TorchScript interpreter doesn't call __repr__
+def synthesize__repr__(cls) -> ParsedDef:
+    return compose_fn(
+        cls,
+        "__repr__",
+        [
+            f"return '{cls.__name__}("
+            + ", ".join(
+                [
+                    f"{field.name}=self.{field.name}"
+                    for field in dataclasses.fields(cls)
+                    if field.repr
+                ]
+            )
+            + ")'"
+        ],
+        signature="(self) -> str",
+    )
+
+
+def synthesize__hash__(cls) -> ParsedDef:
+    return compose_fn(
+        cls,
+        "__hash__",
+        [
+            # This is just a placeholder to prevent compilation from failing; this won't even get called at
+            # all right now because the TorchScript interpreter doesn't call custom __hash__ implementations
+            "raise NotImplementedError('__hash__ is not supported for dataclasses in TorchScript')"
+        ],
+        signature="(self) -> int",
+    )
+
+
+# Implementation for __eq__ and __ne__
+def synthesize_equality(cls, name: str, converse: str) -> ParsedDef:
+    return synthesize_comparison(
+        cls,
+        name,
+        allow_eq=True,
+        raise_on_none=False,
+        inner=[f"if val1 {converse} val2: return False"],
+    )
+
+
+def synthesize_inequality(cls, name: str, op: str, allow_eq: bool) -> ParsedDef:
+    return synthesize_comparison(
+        cls,
+        name,
+        allow_eq,
+        raise_on_none=True,
+        inner=[
+            f"if val1 {op} val2: return True",
+            f"elif val2 {op} val1: return False",
+        ],
+    )
+
+
+def synthesize_comparison(
+    cls, name: str, allow_eq: bool, raise_on_none: bool, inner: list[str]
+) -> ParsedDef:
+    body = []
+    for field in dataclasses.fields(cls):
+        if not field.compare:
+            continue
+
+        body.extend(
+            [
+                f"val1 = self.{field.name}",
+                f"val2 = other.{field.name}",
+            ]
+        )
+        body.extend(
+            inner
+            if not is_optional(field.type)
+            else [
+                # Type refinement for optional fields; we need this to avoid type errors from the interpreter
+                "if val1 is not None and val2 is not None:",
+                *["  " + line for line in inner],
+                "elif (val1 is None) != (val2 is None):",
+                f"  raise TypeError('Cannot compare {cls.__name__} with None')"
+                if raise_on_none
+                else "  return False",
+            ]
+        )
+
+    body.append(f"return {allow_eq}")
+    return compose_fn(
+        cls, name, body, signature=f"(self, other: {cls.__name__}) -> bool"
+    )
+
+
+DATACLASS_MAGIC_METHODS: dict[str, Callable] = {
+    "__init__": synthesize__init__,
+    "__repr__": synthesize__repr__,
+    "__hash__": synthesize__hash__,
+    "__eq__": partial(synthesize_equality, name="__eq__", converse="!="),
+    "__ne__": partial(synthesize_equality, name="__ne__", converse="=="),
+    "__lt__": partial(synthesize_inequality, name="__lt__", op="<", allow_eq=False),
+    "__le__": partial(synthesize_inequality, name="__le__", op="<", allow_eq=True),
+    "__gt__": partial(synthesize_inequality, name="__gt__", op=">", allow_eq=False),
+    "__ge__": partial(synthesize_inequality, name="__ge__", op=">", allow_eq=True),
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_decomposition_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_decomposition_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..795f9da8e073a1197f81ddf379738a7f93605d7c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_decomposition_utils.py
@@ -0,0 +1,12 @@
+# mypy: allow-untyped-defs
+import torch
+from torch._ops import OpOverload, OpOverloadPacket
+
+
+def _register_decomposition(op: OpOverload, graph: torch._C.Graph):
+    assert not isinstance(
+        op, OpOverloadPacket
+    ), f"Must pass specific op overload, not overload packet, found {op}"
+    assert isinstance(op, OpOverload)
+
+    torch._C._jit_register_decomposition_for_schema(op._schema, graph)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_decompositions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_decompositions.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba37fe5f0cac26d38ae28d5501ed092f991e9602
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_decompositions.py
@@ -0,0 +1,137 @@
+# mypy: allow-untyped-defs
+import torch
+from torch import Tensor
+
+
+aten = torch.ops.aten
+import inspect
+import warnings
+from typing import Callable, Optional, TypeVar
+from typing_extensions import ParamSpec
+
+from torch.types import Number
+
+
+decomposition_table: dict[str, torch.jit.ScriptFunction] = {}
+function_name_set: set[str] = set()
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+
+def check_decomposition_has_type_annotations(f):
+    inspect_empty = inspect._empty  # type: ignore[attr-defined]
+    sig = inspect.signature(f)
+    for param in sig.parameters.values():
+        assert (
+            param.annotation != inspect_empty
+        ), f"No signature on param {param.name} for function {f.name}"
+
+    assert (
+        sig.return_annotation != inspect_empty
+    ), f"No return annotation for function {f.name}"
+
+
+def signatures_match(decomposition_sig, torch_op_sig):
+    decomp_params = decomposition_sig.parameters
+    op_params = torch_op_sig.parameters
+
+    if len(decomp_params) != len(op_params):
+        return False
+
+    for decomp_param, op_param in zip(decomp_params.values(), op_params.values()):
+        # can't check full equality yet because not all fields are correcly deduced
+        # in the torch_op_sig - like default value
+        # can't check 'kind' bc
+        # kwarg-only values with defaults not yet supported in TS
+        inspect_empty = inspect._empty  # type: ignore[attr-defined]
+        for field in ["name", "annotation"]:
+            if field == "name" and decomp_param.name == "self":
+                warnings.warn("PyTorch uses 'input' instead of 'self' on public api")
+
+            if getattr(decomp_param, field) != getattr(op_param, field):
+                return False
+
+        decomp_default = decomp_param.default
+        op_default = op_param.default
+        # default value not always correctly inferred as being present on torch schema,
+        # but if specified on both they should be equal
+        if decomp_default != inspect_empty and op_default != inspect_empty:
+            if decomp_default != op_default:
+                return False
+
+    return decomposition_sig.return_annotation == torch_op_sig.return_annotation
+
+
+def register_decomposition(
+    aten_op: torch._ops.OpOverload,
+    registry: Optional[dict[str, torch.jit.ScriptFunction]] = None,
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
+    def decomposition_decorator(f: Callable[_P, _T]) -> Callable[_P, _T]:
+        nonlocal registry
+        if registry is None:
+            registry = decomposition_table
+
+        assert isinstance(aten_op, torch._ops.OpOverload)
+
+        # Need unique name for jit function serialization
+        assert (
+            f.__name__ not in function_name_set
+        ), f"Duplicated function name {f.__name__}"
+        function_name_set.add(f.__name__)
+
+        scripted_func = torch.jit.script(f)
+        torch._C._jit_pass_inline(scripted_func.graph)
+
+        for _ in range(2):
+            torch._C._jit_pass_peephole(scripted_func.graph)
+            torch._C._jit_pass_constant_propagation(scripted_func.graph)
+
+        registry[str(aten_op._schema)] = scripted_func
+        return f
+
+    return decomposition_decorator
+
+
+# TODO: replace torch.sigmoid -> aten.sigmoid
+
+
+@register_decomposition(aten.var.correction)
+def var_decomposition(
+    input: Tensor,
+    dim: Optional[list[int]] = None,
+    correction: Optional[Number] = None,
+    keepdim: bool = False,
+) -> Tensor:
+    if dim is None:
+        dim_i: list[int] = []
+        dim = dim_i
+
+    if isinstance(dim, (tuple, list)) and len(dim) == 0:
+        n = input.numel()
+    else:
+        n = 1
+        for dim_i in dim:  # type: ignore[assignment]
+            n *= input.shape[dim_i]  # type: ignore[call-overload]
+
+    mean = aten.mean(input, dim, True)
+    sub = input - mean
+    sq = sub * sub
+    sum = aten.sum(sq, dim, keepdim)
+
+    if correction is None:
+        denom = float(n - 1)
+    else:
+        if isinstance(correction, int):
+            denom = float(n - correction)
+        elif isinstance(correction, float):
+            denom = float(n) - correction
+        else:
+            raise RuntimeError("correction must be int or float")
+
+    return sum / max(0, denom)
+
+
+@register_decomposition(aten.var.default)
+def var(input: Tensor, unbiased: bool = True) -> Tensor:
+    return var_decomposition(input, correction=(1 if unbiased else 0))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_freeze.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_freeze.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d2db0a4f14246c4e0dc2f0acc031d93fa090c78
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_freeze.py
@@ -0,0 +1,234 @@
+# mypy: allow-untyped-defs
+"""Freezing.
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+from typing import Optional
+
+import torch
+from torch.jit._script import RecursiveScriptModule, ScriptModule
+
+
+def freeze(
+    mod, preserved_attrs: Optional[list[str]] = None, optimize_numerics: bool = True
+):
+    r"""Freeze ScriptModule, inline submodules, and attributes as constants.
+
+    Freezing a :class:`ScriptModule` will clone it and attempt to inline the cloned
+    module's submodules, parameters, and attributes as constants in the TorchScript IR Graph.
+    By default, `forward` will be preserved, as well as attributes & methods specified in
+    `preserved_attrs`. Additionally, any attribute that is modified within a preserved
+    method will be preserved.
+
+    Freezing currently only accepts ScriptModules that are in eval mode.
+
+    Freezing applies generic optimization that will speed up your model regardless of machine.
+    To further optimize using server-specific settings, run `optimize_for_inference` after
+    freezing.
+
+    Args:
+        mod (:class:`ScriptModule`): a module to be frozen
+        preserved_attrs (Optional[List[str]]): a list of attributes to preserve in addition to the forward method.
+            Attributes modified in preserved methods will also be preserved.
+        optimize_numerics (bool): If ``True``, a set of optimization passes will be run that does not strictly
+            preserve numerics. Full details of optimization can be found at `torch.jit.run_frozen_optimizations`.
+
+    Returns:
+        Frozen :class:`ScriptModule`.
+
+    Example (Freezing a simple module with a Parameter):
+
+    .. testcode::
+        import torch
+        class MyModule(torch.nn.Module):
+            def __init__(self, N, M):
+                super().__init__()
+                self.weight = torch.nn.Parameter(torch.rand(N, M))
+                self.linear = torch.nn.Linear(N, M)
+
+            def forward(self, input):
+                output = self.weight.mm(input)
+                output = self.linear(output)
+                return output
+
+        scripted_module = torch.jit.script(MyModule(2, 3).eval())
+        frozen_module = torch.jit.freeze(scripted_module)
+        # parameters have been removed and inlined into the Graph as constants
+        assert len(list(frozen_module.named_parameters())) == 0
+        # See the compiled graph as Python code
+        print(frozen_module.code)
+
+    Example (Freezing a module with preserved attributes)
+
+    .. testcode::
+        import torch
+        class MyModule2(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.modified_tensor = torch.tensor(10.)
+                self.version = 1
+
+            def forward(self, input):
+                self.modified_tensor += 1
+                return input + self.modified_tensor
+
+        scripted_module = torch.jit.script(MyModule2().eval())
+        frozen_module = torch.jit.freeze(scripted_module, preserved_attrs=["version"])
+        # we've manually preserved `version`, so it still exists on the frozen module and can be modified
+        assert frozen_module.version == 1
+        frozen_module.version = 2
+        # `modified_tensor` is detected as being mutated in the forward, so freezing preserves
+        # it to retain model semantics
+        assert frozen_module(torch.tensor(1)) == torch.tensor(12)
+        # now that we've run it once, the next result will be incremented by one
+        assert frozen_module(torch.tensor(1)) == torch.tensor(13)
+
+    Note:
+        Freezing submodule attributes is also supported:
+        frozen_module = torch.jit.freeze(scripted_module, preserved_attrs=["submodule.version"])
+
+    Note:
+        If you're not sure why an attribute is not being inlined as a constant, you can run
+        `dump_alias_db` on frozen_module.forward.graph to see if freezing has detected the
+        attribute is being modified.
+
+    Note:
+        Because freezing makes weights constants and removes module hierarchy, `to` and other
+        nn.Module methods to manipulate device or dtype no longer work. As a workaround,
+        You can remap devices by specifying `map_location` in `torch.jit.load`, however
+        device-specific logic may have been baked into the model.
+    """
+    if not isinstance(mod, ScriptModule):
+        raise RuntimeError(
+            "Freezing expects a ScriptModule as input. "
+            "Please use torch.jit.script or torch.jit.trace to script your 'nn.Module'."
+        )
+
+    if mod.training:
+        raise RuntimeError(
+            "Freezing is currently only implemented for modules in eval mode. "
+            "Please call .eval() on your module before freezing."
+        )
+
+    preserved_attrs = preserved_attrs if preserved_attrs is not None else []
+
+    out = RecursiveScriptModule(torch._C._freeze_module(mod._c, preserved_attrs))
+    RecursiveScriptModule._finalize_scriptmodule(out)
+
+    preserved_methods = [x for x in preserved_attrs if mod._c._has_method(x)]
+    run_frozen_optimizations(out, optimize_numerics, preserved_methods)
+
+    return out
+
+
+def run_frozen_optimizations(
+    mod, optimize_numerics: bool = True, preserved_methods: Optional[list[str]] = None
+):
+    r"""
+    Run a series of optimizations looking for patterns that occur in frozen graphs.
+
+    The current set of optimizations includes:
+        - Dropout Removal
+        - Pretranspose Linear Layers
+        - Concat Linear Layers with same input Tensor
+        - Conv -> Batchnorm folding
+        - Conv -> Add/Sub folding
+        - Conv -> Mul/Div folding
+
+    Args:
+        mod (:class:`ScriptModule`): a frozen module to be optimized
+
+        optimize_numerics (bool): If ``True``, a set of optimization passes will be run that does not strictly
+        preserve numerics. These optimizations preserve default rtol and atol of `torch.testing.assert_close`
+        when applied on a single transformation, however in a module where many transformations are applied
+        the rtol or atol may no longer fall within the default `assert_close` tolerance. Conv -> Batchnorm folding,
+        Conv-Add/Sub, and Conv -> Mul/Div folding all may alter numerics.
+
+    Returns:
+        None
+
+    Note:
+        In rare occassions, this can result in slower execution.
+
+    Example (Freezing a module with Conv->Batchnorm)
+    .. code-block:: python
+        import torch
+
+        in_channels, out_channels = 3, 32
+        conv = torch.nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=2, bias=True
+        )
+        bn = torch.nn.BatchNorm2d(out_channels, eps=0.001)
+        mod = torch.nn.Sequential(conv, bn)
+        # set optimize to False here, by default freezing runs run_frozen_optimizations
+        frozen_mod = torch.jit.freeze(torch.jit.script(mod.eval()), optimize=False)
+        # inspect frozen mod
+        assert "batch_norm" in str(frozen_mod.graph)
+        torch.jit.run_frozen_optimizations(frozen_mod)
+        assert "batch_norm" not in str(frozen_mod.graph)
+
+    """
+    if mod._c._has_method("forward"):
+        torch._C._jit_pass_optimize_frozen_graph(mod.graph, optimize_numerics)
+
+    if preserved_methods is None:
+        preserved_methods = []
+
+    for method in preserved_methods:
+        torch._C._jit_pass_optimize_frozen_graph(
+            mod.__getattr__(method).graph, optimize_numerics
+        )
+
+
+def optimize_for_inference(
+    mod: ScriptModule, other_methods: Optional[list[str]] = None
+) -> ScriptModule:
+    """
+    Perform a set of optimization passes to optimize a model for the purposes of inference.
+
+    If the model is not already frozen, optimize_for_inference
+    will invoke `torch.jit.freeze` automatically.
+
+    In addition to generic optimizations that should speed up your model regardless
+    of environment, prepare for inference will also bake in build specific settings
+    such as the presence of CUDNN or MKLDNN, and may in the future make transformations
+    which speed things up on one machine but slow things down on another. Accordingly,
+    serialization is not implemented following invoking `optimize_for_inference` and
+    is not guaranteed.
+
+    This is still in prototype, and may have the potential to slow down your model.
+    Primary use cases that have been targeted so far have been vision models on cpu
+    and gpu to a lesser extent.
+
+    Example (optimizing a module with Conv->Batchnorm)::
+
+        import torch
+
+        in_channels, out_channels = 3, 32
+        conv = torch.nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=2, bias=True
+        )
+        bn = torch.nn.BatchNorm2d(out_channels, eps=0.001)
+        mod = torch.nn.Sequential(conv, bn)
+        frozen_mod = torch.jit.optimize_for_inference(torch.jit.script(mod.eval()))
+        assert "batch_norm" not in str(frozen_mod.graph)
+        # if built with MKLDNN, convolution will be run with MKLDNN weights
+        assert "MKLDNN" in frozen_mod.graph
+    """
+    if not isinstance(mod, ScriptModule):
+        raise RuntimeError(
+            "optimize_for_inference expects a ScriptModule as input. "
+            "Please use torch.jit.script or torch.jit.trace to script your 'nn.Module'."
+        )
+
+    if other_methods is None:
+        other_methods = []
+
+    if hasattr(mod, "training"):
+        mod = freeze(mod.eval(), preserved_attrs=other_methods)
+
+    torch._C._jit_pass_optimize_for_inference(mod._c, other_methods)
+
+    return mod
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_fuser.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_fuser.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc5dd80362971bc7cdcb65e9619571c9eb600daf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_fuser.py
@@ -0,0 +1,160 @@
+# mypy: allow-untyped-defs
+import contextlib
+
+import torch
+
+
+@contextlib.contextmanager
+def optimized_execution(should_optimize):
+    """Context manager that controls whether the JIT's executor will run optimizations before executing a function."""
+    stored_flag = torch._C._get_graph_executor_optimize()
+    torch._C._set_graph_executor_optimize(should_optimize)
+    try:
+        yield
+    finally:
+        torch._C._set_graph_executor_optimize(stored_flag)
+
+
+@contextlib.contextmanager
+def fuser(name):
+    """Context manager that facilitates switching between backend fusers.
+
+    Valid names:
+    * ``fuser0`` - enables only legacy fuser
+    * ``fuser1`` - enables only NNC
+    * ``fuser2`` - enables only nvFuser
+    * ``fuser3`` - enables oneDNN Graph
+    """
+    old_cpu_fuse = torch._C._jit_can_fuse_on_cpu()
+    old_gpu_fuse = torch._C._jit_can_fuse_on_gpu()
+    old_texpr_fuser_state = torch._C._jit_texpr_fuser_enabled()
+    old_nvfuser_state = torch._C._jit_nvfuser_enabled()
+    old_llga_state = torch._C._jit_llga_enabled()
+    if name == "fuser0":  # legacy fuser
+        torch._C._jit_override_can_fuse_on_cpu(True)
+        torch._C._jit_override_can_fuse_on_gpu(True)
+        torch._C._jit_set_texpr_fuser_enabled(False)
+        torch._C._jit_set_nvfuser_enabled(False)
+        torch._C._jit_set_llga_enabled(False)
+    elif name == "fuser1":  # NNC
+        old_profiling_executor = torch._C._jit_set_profiling_executor(True)
+        old_profiling_mode = torch._C._get_graph_executor_optimize(True)
+        torch._C._jit_override_can_fuse_on_cpu(True)
+        torch._C._jit_override_can_fuse_on_gpu(True)
+        torch._C._jit_set_texpr_fuser_enabled(True)
+        torch._C._jit_set_nvfuser_enabled(False)
+        torch._C._jit_set_llga_enabled(False)
+    elif name == "fuser2":  # nvFuser
+        torch._C._jit_override_can_fuse_on_cpu(False)
+        torch._C._jit_override_can_fuse_on_gpu(False)
+        torch._C._jit_set_texpr_fuser_enabled(False)
+        torch._C._jit_set_nvfuser_enabled(True)
+        torch._C._jit_set_llga_enabled(False)
+    elif name == "fuser3":  # oneDNN Graph
+        old_profiling_executor = torch._C._jit_set_profiling_executor(True)
+        old_profiling_mode = torch._C._get_graph_executor_optimize(True)
+        torch._C._jit_override_can_fuse_on_cpu(True)
+        torch._C._jit_override_can_fuse_on_gpu(False)
+        torch._C._jit_set_texpr_fuser_enabled(True)
+        torch._C._jit_set_nvfuser_enabled(False)
+        torch._C._jit_set_llga_enabled(True)
+    elif name == "none":  # Turn Pytorch fuser off
+        torch._C._jit_override_can_fuse_on_cpu(False)
+        torch._C._jit_override_can_fuse_on_gpu(False)
+        torch._C._jit_set_texpr_fuser_enabled(False)
+        torch._C._jit_set_nvfuser_enabled(False)
+        torch._C._jit_set_llga_enabled(False)
+    else:
+        raise Exception(f"unrecognized fuser option (name: {name})")  # noqa: TRY002
+    try:
+        yield
+    finally:
+        if name in ["fuser1", "fuser3"]:  # NNC or oneDNN Graph
+            torch._C._jit_set_profiling_executor(old_profiling_executor)  # type: ignore[possibly-undefined]
+            torch._C._get_graph_executor_optimize(old_profiling_mode)  # type: ignore[possibly-undefined]
+        # recover the previous values
+        torch._C._jit_override_can_fuse_on_cpu(old_cpu_fuse)
+        torch._C._jit_override_can_fuse_on_gpu(old_gpu_fuse)
+        torch._C._jit_set_texpr_fuser_enabled(old_texpr_fuser_state)
+        torch._C._jit_set_nvfuser_enabled(old_nvfuser_state)
+        torch._C._jit_set_llga_enabled(old_llga_state)
+
+
+last_executed_optimized_graph = torch._C._last_executed_optimized_graph
+
+
+def _get_differentiable_graph_node(node, diff_node):
+    if node.kind() == "prim::DifferentiableGraph":
+        diff_node.append(node)
+    else:
+        for block in node.blocks():
+            for n in block.nodes():
+                _get_differentiable_graph_node(n, diff_node)
+
+
+def _graph_for(self, *args, **kwargs):
+    return _script_method_graph_for(self, self, *args, **kwargs)
+
+
+def _script_method_graph_for(self, parent, *args, **kwargs):
+    try:
+        dbs = parent.get_debug_state()
+        eps = list(dbs.execution_plans.values())
+        assert len(eps) == 1
+        graph = eps[0].graph.copy()
+
+        # graph_executor_states for differentiable node
+        fw_states = eps[0].code.differentiable_op_executor_states()
+        diff_nodes: list[torch._C.Node] = []
+        for n in graph.nodes():
+            _get_differentiable_graph_node(n, diff_nodes)
+
+        assert len(fw_states) == len(diff_nodes)
+        # swap each differentiable graph with optimized graph in their execution plan
+        for n, state in zip(diff_nodes, fw_states):
+            fw_execution_plans = list(state.execution_plans.values())
+            # we can only update the subgraph when there's a unique execution
+            # plan. Avoid assert here so we would skip the ones that can't be
+            # updated while try the best effort to update other nodes.
+            if len(fw_execution_plans) == 1:
+                n.g_("Subgraph", fw_execution_plans[0].graph)
+
+        return graph
+    except Exception:
+        # fallback approach, we just ran the graph and return the recorded optimized
+        # graph
+        self(*args, **kwargs)
+        return last_executed_optimized_graph()
+
+
+def set_fusion_strategy(strategy: list[tuple[str, int]]):
+    """Set the type and number of specializations that can occur during fusion.
+
+    Usage: provide a list of pairs (type, depth) where type is one of "STATIC" or "DYNAMIC"
+    and depth is an integer.
+
+    Behavior - static vs dynamic:
+        In STATIC fusion, fused ops are compiled to have fixed input shapes. The shape is determined
+        based on some initial profiling runs.
+        In DYNAMIC fusion, fused ops are compiled to have variable input shapes, so that multiple
+        shapes are possible.
+
+    In both cases, we also recompile on new striding behavior, device, or dtype.
+
+    Behavior - fallback functions & depth:
+        When an input doesn't match the format required by the specialized compiled op, it will run
+        a fallback function. Fallback functions are recursively be compiled and specialized based
+        on the observed tensor shapes. Since compilation can be slow, the "depth" parameter is provided to
+        limit the number of specializations that can be compiled, before giving up on recompiling and
+        falling back to a completely un-fused, un-specialized implementation.
+
+    The list of (type, depth) pairs controls the type of specializations and the number of
+    specializations. For example: [("STATIC", 2), ("DYNAMIC", 2)] indicates that the first
+    two specializations will use static fusions, the following two specializations will use
+    dynamic fusion, and any inputs that satisfy none of the 4 options will run an
+    unfused implementation.
+
+    NB: in the future, if more as more fusion backends are added there may be more granular
+    apis for specific fusers.
+    """
+    return torch._C._jit_set_fusion_strategy(strategy)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_ir_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_ir_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7f03ee3bc86830509e1adf3293ca1e84c671949
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_ir_utils.py
@@ -0,0 +1,33 @@
+from types import TracebackType
+from typing import Optional, Union
+
+import torch
+
+
+class _InsertPoint:
+    def __init__(
+        self,
+        insert_point_graph: torch._C.Graph,
+        insert_point: Union[torch._C.Node, torch._C.Block],
+    ):
+        self.insert_point = insert_point
+        self.g = insert_point_graph
+        self.guard = None
+
+    def __enter__(self) -> None:
+        self.prev_insert_point = self.g.insertPoint()
+        self.g.setInsertPoint(self.insert_point)
+
+    def __exit__(
+        self,
+        exc_type: Optional[type[BaseException]],
+        exc_val: Optional[BaseException],
+        exc_tb: Optional[TracebackType],
+    ) -> None:
+        self.g.setInsertPoint(self.prev_insert_point)
+
+
+def insert_point_guard(
+    self: torch._C.Graph, insert_point: Union[torch._C.Node, torch._C.Block]
+) -> _InsertPoint:
+    return _InsertPoint(self, insert_point)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_logging.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0687023386ee7745af4e2cb0731f76528125477
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_logging.py
@@ -0,0 +1,11 @@
+import torch
+
+
+add_stat_value = torch.ops.prim.AddStatValue
+
+set_logger = torch._C._logging_set_logger
+LockingLogger = torch._C.LockingLogger
+AggregationType = torch._C.AggregationType
+NoopLogger = torch._C.NoopLogger
+
+time_point = torch.ops.prim.TimePoint
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_monkeytype_config.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_monkeytype_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a324fda8af88050469a9e68b13492be746303c3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_monkeytype_config.py
@@ -0,0 +1,195 @@
+# mypy: allow-untyped-defs
+import inspect
+import sys
+import typing
+from collections import defaultdict
+from collections.abc import Iterable
+from pathlib import Path
+from types import CodeType
+from typing import Optional
+
+import torch
+
+
+_IS_MONKEYTYPE_INSTALLED = True
+try:
+    import monkeytype  # type: ignore[import]
+    from monkeytype import trace as monkeytype_trace
+    from monkeytype.config import _startswith, LIB_PATHS  # type: ignore[import]
+    from monkeytype.db.base import (  # type: ignore[import]
+        CallTraceStore,
+        CallTraceStoreLogger,
+        CallTraceThunk,
+    )
+    from monkeytype.tracing import CallTrace, CodeFilter  # type: ignore[import]
+except ImportError:
+    _IS_MONKEYTYPE_INSTALLED = False
+
+
+# Checks whether a class is defind in `torch.*` modules
+def is_torch_native_class(cls):
+    if not hasattr(cls, "__module__"):
+        return False
+
+    parent_modules = cls.__module__.split(".")
+    if not parent_modules:
+        return False
+
+    root_module = sys.modules.get(parent_modules[0])
+    return root_module is torch
+
+
+def get_type(type):
+    """Convert the given type to a torchScript acceptable format."""
+    if isinstance(type, str):
+        return type
+    elif inspect.getmodule(type) == typing:
+        # If the type is a type imported from typing
+        # like Tuple, List, Dict then replace `typing.`
+        # with a null string. This needs to be done since
+        # typing.List is not accepted by TorchScript.
+        type_to_string = str(type)
+        return type_to_string.replace(type.__module__ + ".", "")
+    elif is_torch_native_class(type):
+        # If the type is a subtype of torch module, then TorchScript expects a fully qualified name
+        # for the type which is obtained by combining the module name and type name.
+        return type.__module__ + "." + type.__name__
+    else:
+        # For all other types use the name for the type.
+        return type.__name__
+
+
+def get_optional_of_element_type(types):
+    """Extract element type, return as `Optional[element type]` from consolidated types.
+
+    Helper function to extracts the type of the element to be annotated to Optional
+    from the list of consolidated types and returns `Optional[element type]`.
+    TODO: To remove this check once Union support lands.
+    """
+    elem_type = types[1] if type(None) == types[0] else types[0]
+    elem_type = get_type(elem_type)
+
+    # Optional type is internally converted to Union[type, NoneType], which
+    # is not supported yet in TorchScript. Hence, representing the optional type as string.
+    return "Optional[" + elem_type + "]"
+
+
+def get_qualified_name(func):
+    return func.__qualname__
+
+
+if _IS_MONKEYTYPE_INSTALLED:
+
+    class JitTypeTraceStoreLogger(CallTraceStoreLogger):
+        """A JitTypeCallTraceLogger that stores logged traces in a CallTraceStore."""
+
+        def __init__(self, store: CallTraceStore):
+            super().__init__(store)
+
+        def log(self, trace: CallTrace) -> None:
+            self.traces.append(trace)
+
+    class JitTypeTraceStore(CallTraceStore):
+        def __init__(self) -> None:
+            super().__init__()
+            # A dictionary keeping all collected CallTrace
+            # key is fully qualified name of called function
+            # value is list of all CallTrace
+            self.trace_records: dict[str, list] = defaultdict(list)
+
+        def add(self, traces: Iterable[CallTrace]):
+            for t in traces:
+                qualified_name = get_qualified_name(t.func)
+                self.trace_records[qualified_name].append(t)
+
+        def filter(
+            self,
+            qualified_name: str,
+            qualname_prefix: Optional[str] = None,
+            limit: int = 2000,
+        ) -> list[CallTraceThunk]:
+            return self.trace_records[qualified_name]
+
+        def analyze(self, qualified_name: str) -> dict:
+            # Analyze the types for the given module
+            # and create a dictionary of all the types
+            # for arguments.
+            records = self.trace_records[qualified_name]
+            all_args = defaultdict(set)
+            for record in records:
+                for arg, arg_type in record.arg_types.items():
+                    all_args[arg].add(arg_type)
+            return all_args
+
+        def consolidate_types(self, qualified_name: str) -> dict:
+            all_args = self.analyze(qualified_name)
+            # If there are more types for an argument,
+            # then consolidate the type to `Any` and replace the entry
+            # by type `Any`.
+            for arg, types in all_args.items():
+                types = list(types)
+                type_length = len(types)
+                if type_length == 2 and type(None) in types:
+                    # TODO: To remove this check once Union suppport in TorchScript lands.
+                    all_args[arg] = get_optional_of_element_type(types)
+                elif type_length > 1:
+                    all_args[arg] = "Any"
+                elif type_length == 1:
+                    all_args[arg] = get_type(types[0])
+            return all_args
+
+        def get_args_types(self, qualified_name: str) -> dict:
+            return self.consolidate_types(qualified_name)
+
+    class JitTypeTraceConfig(monkeytype.config.Config):
+        def __init__(self, s: JitTypeTraceStore):
+            super().__init__()
+            self.s = s
+
+        def trace_logger(self) -> JitTypeTraceStoreLogger:
+            """Return a JitCallTraceStoreLogger that logs to the configured trace store."""
+            return JitTypeTraceStoreLogger(self.trace_store())
+
+        def trace_store(self) -> CallTraceStore:
+            return self.s
+
+        def code_filter(self) -> Optional[CodeFilter]:
+            return jit_code_filter
+
+else:
+    # When MonkeyType is not installed, we provide dummy class definitions
+    # for the below classes.
+    class JitTypeTraceStoreLogger:  # type:  ignore[no-redef]
+        def __init__(self) -> None:
+            pass
+
+    class JitTypeTraceStore:  # type:  ignore[no-redef]
+        def __init__(self) -> None:
+            self.trace_records = None
+
+    class JitTypeTraceConfig:  # type:  ignore[no-redef]
+        def __init__(self) -> None:
+            pass
+
+    monkeytype_trace = None  # type: ignore[assignment]  # noqa: F811
+
+
+def jit_code_filter(code: CodeType) -> bool:
+    """Codefilter for Torchscript to trace forward calls.
+
+    The custom CodeFilter is required while scripting a FX Traced forward calls.
+    FX Traced forward calls have `code.co_filename` start with '<' which is used
+    to exclude tracing of stdlib and site-packages in the default code filter.
+    Since we need all forward calls to be traced, this custom code filter
+    checks for code.co_name to be 'forward' and enables tracing for all such calls.
+    The code filter is similar to default code filter for monkeytype and
+    excludes tracing of stdlib and site-packages.
+    """
+    # Filter code without a source file and exclude this check for 'forward' calls.
+    if code.co_name != "forward" and (
+        not code.co_filename or code.co_filename[0] == "<"
+    ):
+        return False
+
+    filename = Path(code.co_filename).resolve()
+    return not any(_startswith(filename, lib_path) for lib_path in LIB_PATHS)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_passes/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_passes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_passes/_property_propagation.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_passes/_property_propagation.py
new file mode 100644
index 0000000000000000000000000000000000000000..c410b8fbb7fd329442aa867c0e39c03cd4f15199
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_passes/_property_propagation.py
@@ -0,0 +1,46 @@
+"""
+Tools to help with tensor property propagation.
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+from typing import Any
+
+import torch
+from torch import TensorType
+from torch._C import Graph
+
+
+def apply_input_props_using_example(graph: Graph, example_input: list[Any]) -> None:
+    """
+    Applies properties for each tensor in the graph inputs
+    using the example supplied.
+    """
+    graph_inputs = list(graph.inputs())
+    if len(graph_inputs) == 0:
+        return
+
+    # Strip self args off for methods
+    in_0 = graph_inputs[0]
+    if isinstance(in_0.type(), torch._C.ClassType) and in_0.debugName() == "self":
+        graph_inputs = graph_inputs[1:]
+
+    if not len(graph_inputs) == len(example_input):
+        raise RuntimeError(
+            "Number of inputs in graph does not match number of inputs in the example"
+        )
+
+    for i, (graph_i, example_i) in enumerate(zip(graph_inputs, example_input)):
+        if example_i is None:
+            continue  # Skip the type check
+
+        if isinstance(example_i, torch.Tensor) != isinstance(
+            graph_i.type(), TensorType
+        ):
+            raise RuntimeError(
+                f"Input {i} does not match type of example", graph_i, example_i
+            )
+
+        if isinstance(example_i, torch.Tensor):
+            graph_i.setType(TensorType.create_from_tensor(example_i))  # type: ignore[arg-type]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_pickle.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d6b64a52a9838babc6aff508489a43d645ac0d0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_pickle.py
@@ -0,0 +1,40 @@
+# These functions are referenced from the pickle archives produced by
+# ScriptModule.save()
+
+
+# These (`build_*`) functions used to be used by `pickler.cpp` to specify
+# the type of the list for certain special types, but now all lists get
+# a type attached and restored via `restore_type_tag` below. The legacy
+# functions should stick around for backwards-compatibility.
+
+from typing import Union
+
+
+def build_intlist(data: list[int]) -> list[int]:
+    return data
+
+
+def build_tensorlist(data: list[object]) -> list[object]:
+    return data
+
+
+def build_doublelist(data: list[float]) -> list[float]:
+    return data
+
+
+def build_boollist(data: list[bool]) -> list[bool]:
+    return data
+
+
+def build_tensor_from_id(data: Union[int, object]) -> Union[int, None]:
+    if isinstance(data, int):
+        # just the id, can't really do anything
+        return data
+    return None
+
+
+def restore_type_tag(value: object, type_str: str) -> object:
+    # The type_ptr is used by the jit unpickler to restore the full static type
+    # to container types like list when they are re-loaded, but this doesn't
+    # matter for Python, so just return the plain value
+    return value
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_recursive.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_recursive.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6d6c4a673a767bacccd2f0e9bfdf75f5d1c66d2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_recursive.py
@@ -0,0 +1,1062 @@
+# mypy: allow-untyped-defs
+import collections
+import functools
+import inspect
+import sys
+import textwrap
+import types
+import warnings
+
+import torch
+import torch._jit_internal as _jit_internal
+from torch._sources import fake_range
+from torch.jit._builtins import _find_builtin
+from torch.jit._check import AttributeTypeIsSupportedChecker
+from torch.jit._state import _add_script_class, _get_script_class, _python_cu
+from torch.jit.frontend import (
+    get_class_properties,
+    get_default_args,
+    get_jit_class_def,
+    get_jit_def,
+)
+from torch.nn import Module
+
+
+ScriptMethodStub = collections.namedtuple(
+    "ScriptMethodStub", ("resolution_callback", "def_", "original_method")
+)
+PropertyStub = collections.namedtuple("PropertyStub", ("resolution_callback", "def_"))
+
+
+# TODO: there should be a more principled way of doing this.
+ignored_attributes = [
+    "_version",
+    "_parameters",
+    "_buffers",
+    "_non_persistent_buffers_set",
+    "_backward_hooks",
+    "_backward_pre_hooks",
+    "_forward_hooks",
+    "_forward_hooks_with_kwargs",
+    "_forward_pre_hooks",
+    "_forward_pre_hooks_with_kwargs",
+    "_forward_hooks_always_called",
+    "_state_dict_hooks",
+    "_state_dict_pre_hooks",
+    "_load_state_dict_pre_hooks",
+    "_load_state_dict_post_hooks",
+    "_modules",
+    "_initializing",
+    "dump_patches",
+]
+
+
+def _compile_and_register_class(obj, rcb, qualified_name):
+    script_class = _get_script_class(obj)
+
+    if not script_class:
+        ast = get_jit_class_def(obj, obj.__name__)
+        defaults = torch.jit.frontend.get_default_args_for_class(obj)
+        script_class = torch._C._jit_script_class_compile(
+            qualified_name, ast, defaults, rcb
+        )
+        _add_script_class(obj, script_class)
+
+    return script_class
+
+
+def make_stub(func, name):
+    rcb = _jit_internal.createResolutionCallbackFromClosure(func)
+    ast = get_jit_def(func, name, self_name="RecursiveScriptModule")
+    return ScriptMethodStub(rcb, ast, func)
+
+
+def make_stub_from_method(nn_module, method_name):
+    func = getattr(nn_module, method_name)
+    if isinstance(func, ScriptMethodStub):
+        return func
+    # Make sure the name present in the resulting AST will match the name
+    # requested here. The only time they don't match is if you do something
+    # like:
+    #   def _forward(self):
+    #       pass
+    #   forward = _forward
+    # In this case, the actual function object will have the name `_forward`,
+    # even though we requested a stub for `forward`.
+    return make_stub(func, method_name)
+
+
+def make_stubs_from_exported_methods(mod):
+    stubs = []
+    for name in dir(mod):
+        item = getattr(mod, name, None)
+        if (
+            _jit_internal.get_torchscript_modifier(item)
+            is _jit_internal.FunctionModifiers.EXPORT
+        ):
+            stubs.append(make_stub_from_method(mod, name))
+
+    return stubs
+
+
+def jit_ignored_properties(module):
+    user_annotated_ignored_attributes = getattr(
+        module, "__jit_ignored_attributes__", []
+    )
+
+    def get_properties_names(module):
+        return {k for k, v in vars(module).items() if isinstance(v, property)}
+
+    properties = get_properties_names(type(module))
+    user_annoted_ignored_properties = set()
+
+    for ignored_attr in user_annotated_ignored_attributes:
+        if ignored_attr in properties:
+            user_annoted_ignored_properties.add(ignored_attr)
+    return user_annoted_ignored_properties
+
+
+# base types that can be constants
+# in addition, tuples and lists of these base types are also considered constants
+# If you edit this list, then you also need to edit the handlers in
+# ConstantValue in jit/script/init.cpp
+_constant_types = (
+    bool,
+    float,
+    int,
+    str,
+    type(None),
+    torch.device,
+    torch.layout,
+    torch.dtype,
+)
+
+
+def _get_valid_constant(attr, v, owner_type):
+    if isinstance(v, _constant_types):
+        return v
+    elif isinstance(v, (tuple, list)):
+        return tuple(_get_valid_constant(attr, x, owner_type) for x in v)
+    constants = ", ".join(torch.typename(typ) for typ in _constant_types)
+    raise TypeError(
+        textwrap.dedent(
+            f"""
+        '{torch.typename(type(v))}' object in attribute '{owner_type}.{attr}' is not a valid constant.
+        Valid constants are:
+        1. a nn.ModuleList
+        2. a value of type {{{constants}}}
+        3. a list or tuple of (2)
+        """
+        )
+    )
+
+
+class SourceContext(torch._C._jit_tree_views.SourceRangeFactory):
+    def __init__(self, source, filename, file_lineno, leading_whitespace_len):
+        super().__init__(source, filename, file_lineno, leading_whitespace_len)
+
+
+def get_annotations(obj):
+    if sys.version_info < (3, 10):
+        return getattr(obj, "__annotations__", {})
+    # In Python-3.10+ it is recommended to use inspect.get_annotations
+    # See https://docs.python.org/3.10/howto/annotations.html
+    # But also, in 3.10 annotations from base class are not inherited
+    # by unannotated derived one, so they must be manually extracted
+    annotations = inspect.get_annotations(obj)
+    if annotations:
+        return annotations
+
+    def get_cls_annotations(cls):
+        cls_annotations = inspect.get_annotations(cls)
+        if cls_annotations:
+            return cls_annotations
+        for base in cls.__bases__:
+            cls_annotations = get_cls_annotations(base)
+            if cls_annotations:
+                return cls_annotations
+        return {}
+
+    cls = obj if isinstance(obj, type) else type(obj)
+    return get_cls_annotations(cls)
+
+
+def infer_concrete_type_builder(nn_module, share_types=True):
+    """
+    Build a ConcreteModuleTypeBuilder from an nn.Module.
+
+    This ConcreteModuleType doesn't have a JIT type associated with it yet, it
+    must be filled in by the caller.
+    """
+    concrete_type_builder = torch._C.ConcreteModuleTypeBuilder(type(nn_module))
+    if isinstance(nn_module, (torch.nn.ModuleDict)):
+        concrete_type_builder.set_module_dict()
+    if isinstance(nn_module, (torch.nn.ModuleList, torch.nn.Sequential)):
+        concrete_type_builder.set_module_list()
+    if isinstance(nn_module, (torch.nn.ParameterList)):
+        concrete_type_builder.set_parameter_list()
+    if isinstance(nn_module, (torch.nn.ParameterDict)):
+        concrete_type_builder.set_parameter_dict()
+
+    class_annotations = get_annotations(nn_module)
+    if isinstance(nn_module, (torch.ao.quantization.QuantWrapper)):
+        class_annotations = {}
+
+    # Get user-annotated ignored attributes.
+    user_annotated_ignored_attributes = getattr(
+        nn_module, "__jit_ignored_attributes__", []
+    )
+    concrete_type_builder.add_ignored_attributes(user_annotated_ignored_attributes)
+    ignored_properties = jit_ignored_properties(nn_module)
+
+    # try to infer the type from type annotation or from the object itself
+    def infer_type(name, item):
+        # The forward function from Module is special; never use this annotations; we
+        # need to infer type directly using JIT.  I originally wanted to write
+        # this test as isinstance(class_annotations[name], Callable) but
+        # isinstance on typing things doesn't seem to work: isinstance(list, Callable)
+        # is also true!
+        inferred = False
+        try:
+            if (
+                name in class_annotations
+                and class_annotations[name]
+                != torch.nn.Module.__annotations__["forward"]
+            ):
+                ann_to_type = torch.jit.annotations.ann_to_type(
+                    class_annotations[name], fake_range()
+                )
+                attr_type = torch._C.InferredType(ann_to_type)
+            elif isinstance(item, torch.jit.Attribute):
+                ann_to_type = torch.jit.annotations.ann_to_type(item.type, fake_range())
+                attr_type = torch._C.InferredType(ann_to_type)
+            else:
+                attr_type = torch._C._jit_try_infer_type(item)
+                inferred = True
+        except RuntimeError as re:
+            raise RuntimeError(f"Error inferring type for {name}: {item}: {re}") from re
+
+        return attr_type, inferred
+
+    added_names = set()
+
+    for name, item in nn_module._parameters.items():
+        if name in user_annotated_ignored_attributes:
+            continue
+
+        assert item is None or isinstance(item, torch.Tensor)
+        attr_type, _ = infer_type(name, item)
+        # We currently have the invariant in various places in our code
+        # that parameters must be Tensors. However, the nn.Module API also
+        # allows NoneType parameters. These parameters are not returned as
+        # part of `parameters()` and its variants, but are available
+        # through direct attribute access.
+        concrete_type_builder.add_attribute(name, attr_type.type(), True, False)
+        added_names.add(name)
+
+    for name, item in nn_module._buffers.items():
+        if name in user_annotated_ignored_attributes:
+            continue
+
+        assert item is None or isinstance(item, torch.Tensor)
+        attr_type, _ = infer_type(name, item)
+        concrete_type_builder.add_attribute(name, attr_type.type(), False, True)
+        added_names.add(name)
+
+    for name, item in nn_module._modules.items():
+        if name in user_annotated_ignored_attributes:
+            continue
+
+        attr_type, _ = infer_type(name, item)
+        if item is None:
+            # Modules can be None. We don't have direct support for optional
+            # Modules, so the register it as an NoneType attribute instead.
+            concrete_type_builder.add_attribute(name, attr_type.type(), False, False)
+            continue
+        if attr_type.success():
+            assert attr_type.type().is_interface_type()
+            # if the type can be inferred, it should be a module interface type
+            sub_concrete_type = torch._C.ConcreteModuleType.from_jit_type(
+                attr_type.type()
+            )
+        else:
+            # otherwise we get the concrete module type for item and add it to concrete_type
+            sub_concrete_type = get_module_concrete_type(item, share_types)
+        concrete_type_builder.add_module(name, sub_concrete_type)
+
+        added_names.add(name)
+
+    # populate constants_set
+    constants_set = set(getattr(nn_module, "__constants__", ()))
+
+    # Constants annotated via `Final[T]` rather than being added to `__constants__`
+    for name, ann in class_annotations.items():
+        if torch._jit_internal.is_final(ann):
+            constants_set.add(name)
+
+    for name in constants_set:
+        if name in added_names:
+            # TODO: We should really error in this case, but its bc-breaking so
+            # we need to warn for at least one release
+            if name in nn_module._modules:
+                hint = "submodule"
+            elif name in nn_module._buffers:
+                hint = "buffer"
+            elif name in nn_module._parameters:
+                hint = "parameter"
+            else:
+                raise AssertionError(
+                    "added_names must be submodule, parameter, or buffer"
+                )
+
+            warnings.warn(
+                f"'{name}' was found in ScriptModule constants, "
+                f" but it is a non-constant {hint}. Consider removing it."
+            )
+            continue
+        if not hasattr(nn_module, name):
+            # TODO: We should really error in this case, but its bc-breaking so
+            # we need to warn for at least one release
+            warnings.warn(
+                f"'{name}' was found in ScriptModule constants, "
+                "but was not actually set in __init__. "
+                "Consider removing it."
+            )
+            continue
+        value = getattr(nn_module, name)
+        concrete_type_builder.add_constant(
+            name, _get_valid_constant(name, value, type(nn_module).__name__)
+        )
+        added_names.add(name)
+
+    # populate overloads
+    overloads = getattr(nn_module, "__overloads__", {})
+    # update with any annotated overloads
+    overloads.update(
+        get_overload_name_mapping(
+            get_overload_annotations(nn_module, ignored_properties)
+        )
+    )
+    for name, overloaded_names in overloads.items():
+        concrete_type_builder.add_overload(name, overloaded_names)
+
+    for name, value in nn_module.__dict__.items():
+        if name in ignored_attributes or name.startswith("__"):
+            # Python objects have lots of random attributes attached to them;
+            # PyTorch adds a few more. Prevent these from getting compiled.
+            continue
+
+        if name in user_annotated_ignored_attributes:
+            continue
+
+        if name in added_names:
+            # Don't re-add anything we already added
+            continue
+
+        isoverloadpacket = isinstance(value, torch._ops.OpOverloadPacket)
+        if isoverloadpacket:
+            value = value.op
+        # Handle Python function attributes
+        if inspect.isfunction(value):
+            try:
+                scripted_fn = torch.jit.script(value)
+                concrete_type_builder.add_function_attribute(
+                    name, torch._C._jit_try_infer_type(scripted_fn).type(), value
+                )
+            except Exception as e:
+                # If we fail to script the function, it isn't a hard error.
+                # Instead, we will add it to the list of attributes we failed
+                # to convert, with the compilation error.
+                hint = (
+                    "(This function exists as an attribute on the Python module, "
+                    "but we failed to compile it to a TorchScript function. "
+                    f"\nThe error stack is reproduced here:\n{e}"
+                )
+                concrete_type_builder.add_failed_attribute(name, hint)
+
+            continue
+
+        # Handle calls to builtin functions (either bespoke builtins from torch.jit._builtins or
+        # a call to an aten function like torch.add)
+        builtin_symbol_name = _find_builtin(value)
+        if builtin_symbol_name:
+            concrete_type_builder.add_builtin_function(name, builtin_symbol_name)
+            continue
+
+        # Handle Script function attributes
+        if isinstance(value, torch.jit.ScriptFunction):
+            concrete_type_builder.add_function_attribute(
+                name, torch._C._jit_try_infer_type(value).type(), value
+            )
+            continue
+
+        # If we got here, this is a regular "data" attribute, add it to the concrete type
+        attr_type, inferred = infer_type(name, value)
+        if attr_type.success():
+            concrete_type_builder.add_attribute(name, attr_type.type(), False, False)
+        else:
+            # TODO: could add more detail here. For example, what the user should do
+            # when the pytype is `list` or `NoneType`
+            inferred_msg = (
+                "Its type was inferred; try adding a type annotation for the attribute."
+                if inferred
+                else ""
+            )
+            additional_info = f"{attr_type.reason()}. {inferred_msg}"
+            hint = (
+                "(This attribute exists on the Python module, "
+                f"but we failed to convert Python type: '{torch.typename(type(value))}' "
+                f"to a TorchScript type. {additional_info})"
+            )
+            concrete_type_builder.add_failed_attribute(name, hint)
+
+    # add hooks to concrete type
+    for hook in nn_module._forward_hooks.values():
+        concrete_type_builder.add_forward_hook(hook)
+    for pre_hook in nn_module._forward_pre_hooks.values():
+        concrete_type_builder.add_forward_pre_hook(pre_hook)
+
+    return concrete_type_builder
+
+
+class ConcreteTypeStore:
+    type_store: dict[type[Module], list[torch._C.ConcreteModuleType]]
+    methods_compiled: set[torch._C.ConcreteModuleType]
+
+    def __init__(self) -> None:
+        # Python module type => List[ConcreteModuleType)]
+        self.type_store = {}
+        # ConcreteTypes that have had their methods already compiled
+        self.methods_compiled = set()
+
+    def get_or_create_concrete_type(self, nn_module):
+        """Infer a ConcreteType from this `nn.Module` instance. Underlying JIT types are re-used if possible."""
+        concrete_type_builder = infer_concrete_type_builder(nn_module)
+
+        nn_module_type = type(nn_module)
+        if nn_module_type not in self.type_store:
+            self.type_store[nn_module_type] = []
+
+        # Search the type store for an already-available JIT type
+        known_types = self.type_store[nn_module_type]
+        for known_type in known_types:
+            if known_type.equals(concrete_type_builder):
+                return known_type
+
+        # We didn't find anything; generate a new JIT type from this concrete type
+        concrete_type = concrete_type_builder.build()
+        self.type_store[nn_module_type].append(concrete_type)
+        return concrete_type
+
+
+concrete_type_store = ConcreteTypeStore()
+
+
+def create_methods_and_properties_from_stubs(
+    concrete_type, method_stubs, property_stubs
+):
+    method_defs = [m.def_ for m in method_stubs]
+    method_rcbs = [m.resolution_callback for m in method_stubs]
+    method_defaults = [get_default_args(m.original_method) for m in method_stubs]
+
+    property_defs = [p.def_ for p in property_stubs]
+    property_rcbs = [p.resolution_callback for p in property_stubs]
+
+    concrete_type._create_methods_and_properties(
+        property_defs, property_rcbs, method_defs, method_rcbs, method_defaults
+    )
+
+
+def create_hooks_from_stubs(concrete_type, hook_stubs, pre_hook_stubs):
+    hook_defs = [h.def_ for h in hook_stubs]
+    hook_rcbs = [h.resolution_callback for h in hook_stubs]
+
+    pre_hook_defs = [h.def_ for h in pre_hook_stubs]
+    pre_hook_rcbs = [h.resolution_callback for h in pre_hook_stubs]
+
+    concrete_type._create_hooks(hook_defs, hook_rcbs, pre_hook_defs, pre_hook_rcbs)
+
+
+def get_module_concrete_type(nn_module, share_types=True):
+    """
+    Get a concrete type for nn_modules.
+
+    If share_types is True, the concrete type is fetched from concrete_type_store.
+    If it is False, a new concrete type is created without first searching concrete_type_store.
+
+    Args:
+        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
+        share_types = Whether to share underlying JIT types between modules (if possible).
+
+    Returns:
+        A concrete type for nn_module.
+    """
+    assert isinstance(nn_module, Module)
+    if isinstance(nn_module, torch.jit.ScriptModule) and hasattr(
+        nn_module, "_concrete_type"
+    ):
+        return nn_module._concrete_type
+
+    if share_types:
+        # Look into the store of cached JIT types
+        concrete_type = concrete_type_store.get_or_create_concrete_type(nn_module)
+    else:
+        # Get a concrete type directly, without trying to re-use an existing JIT
+        # type from the type store.
+        concrete_type_builder = infer_concrete_type_builder(nn_module, share_types)
+        concrete_type_builder.set_poisoned()
+        concrete_type = concrete_type_builder.build()
+
+    return concrete_type
+
+
+def create_script_class(obj):
+    """
+    Create and return a RecursiveScriptClass instance from a Python object.
+
+    Arguments:
+        obj: A Python object.
+    """
+    qualified_class_name = _jit_internal._qualified_name(type(obj))
+    rcb = _jit_internal.createResolutionCallbackForClassMethods(type(obj))
+    # Script the type of obj if it hasn't already been scripted.
+    _compile_and_register_class(type(obj), rcb, qualified_class_name)
+    class_ty = _python_cu.get_class(qualified_class_name)
+    # Create an empty torch._C.ScriptObject with the scripted type.
+    cpp_object = torch._C._create_object_with_type(class_ty)
+    # Copy all of the attributes over to the torch._C.ScriptObject.
+    for name, value in obj.__dict__.items():
+        cpp_object.setattr(name, value)
+
+    # Wrap the torch._C.ScriptObject in a RecursiveScriptClass instance.
+    return wrap_cpp_class(cpp_object)
+
+
+def create_script_module(nn_module, stubs_fn, share_types=True, is_tracing=False):
+    """
+    Create a new ScriptModule from an nn.Module.
+
+    Args:
+        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
+        stubs_fn:  Lambda that takes an nn.Module and generates a list of ScriptMethodStubs to compile.
+        share_types:  Whether to share underlying JIT types between modules (if possible).
+            NOTE: Only set to False this when we cannot guarantee type sharing will work
+                correctly. This only happens today for traced modules, where the same
+                module can produce different traced methods depending on the inputs.
+        is_tracing: Whether this function is called during tracing or scripting. If tracing,
+                we don't need to do AttributeTypeIsSupportedChecker because all the unsupported
+                attributes will be baked as constant in the tracing graph. In addition,
+                this check significantly slows down the traced modules when the module size is big.
+    """
+    assert not isinstance(nn_module, torch.jit.RecursiveScriptModule)
+    check_module_initialized(nn_module)
+    concrete_type = get_module_concrete_type(nn_module, share_types)
+    if not is_tracing:
+        AttributeTypeIsSupportedChecker().check(nn_module)
+    return create_script_module_impl(nn_module, concrete_type, stubs_fn)
+
+
+def create_script_module_impl(nn_module, concrete_type, stubs_fn):
+    """
+    Convert an nn.Module to a RecursiveScriptModule.
+
+    Args:
+        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
+        concrete_type:  The fully initialized ConcreteType of the module.
+        stubs_fn:  Lambda that takes an nn.Module and generates a list of ScriptMethodStubs to compile.
+    """
+    cpp_module = torch._C._create_module_with_type(concrete_type.jit_type)
+    method_stubs = stubs_fn(nn_module)
+    property_stubs = get_property_stubs(nn_module)
+    hook_stubs, pre_hook_stubs = get_hook_stubs(nn_module)
+    ignored_properties = jit_ignored_properties(nn_module)
+
+    def init_fn(script_module):
+        # Initialize the ScriptModule:
+        # 1. Copy the attributes/parameters/buffers from the original `nn_module` to the new ScriptModule.
+        for name in concrete_type.get_attributes().keys():
+            orig_value = getattr(nn_module, name)
+            orig_value = (
+                orig_value.value
+                if isinstance(orig_value, torch.jit.Attribute)
+                else orig_value
+            )
+            cpp_module.setattr(name, orig_value)
+
+        # 2. Copy the submodules from the original `nn_module` to the new ScriptModule,
+        #    recursively scripting them.
+        for name, sub_concrete_type in concrete_type.get_modules():
+            orig_value = getattr(nn_module, name)
+            assert isinstance(
+                orig_value, Module
+            ), f"Expected Module but got {type(orig_value)}"
+            module_type = sub_concrete_type.jit_type
+            if isinstance(module_type, torch._C.InterfaceType):
+                # use the interface inference rule to compile the module
+                scripted = interface_script(module_type, orig_value)
+            elif isinstance(orig_value, torch.jit.ScriptModule):
+                scripted = orig_value
+            else:
+                # always reuse the provided stubs_fn to infer the methods to compile
+                scripted = create_script_module_impl(
+                    orig_value, sub_concrete_type, stubs_fn
+                )
+
+            cpp_module.setattr(name, scripted)
+            script_module._modules[name] = scripted
+
+        # 3. Copy @ignored/@unused methods and attrs from the original `nn_module` to the new ScriptModule.
+        #    This ensures we can access these Python methods on the ScriptModule.
+        for name in dir(nn_module):
+            if name in ignored_properties:
+                continue
+            item = getattr(nn_module, name, None)
+            if inspect.ismethod(item) and _jit_internal.is_ignored_fn(item):
+                unbound_function = getattr(nn_module, name).__func__
+                bound_method = unbound_function.__get__(script_module)
+                setattr(script_module, name, bound_method)
+            elif concrete_type.is_ignored_attribute(name):
+                setattr(script_module, name, item)
+
+        # For convenience, attach the concrete type to the new ScriptModule
+        script_module._concrete_type = concrete_type
+
+    # Actually create the ScriptModule, initializing it with the function we just defined
+    script_module = torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)
+
+    # Compile methods if necessary
+    if concrete_type not in concrete_type_store.methods_compiled:
+        create_methods_and_properties_from_stubs(
+            concrete_type, method_stubs, property_stubs
+        )
+        # Create hooks after methods to ensure no name collisions between hooks and methods.
+        # If done before, hooks can overshadow methods that aren't exported.
+        create_hooks_from_stubs(concrete_type, hook_stubs, pre_hook_stubs)
+        torch._C._run_emit_module_hook(cpp_module)
+        concrete_type_store.methods_compiled.add(concrete_type)
+
+    # Copy the forward hooks and pre-hooks to the new ScriptModule
+    # to allow the hooks to be run from eager as ScriptFunctions
+    for idx, fn in enumerate(script_module._c._get_forward_pre_hooks()):
+        script_module._forward_pre_hooks[idx] = fn
+    for idx, fn in enumerate(script_module._c._get_forward_hooks()):
+        script_module._forward_hooks[idx] = fn
+
+    # Special handling so methods like __len__ work in script methods on classes derived from containers
+    if (
+        isinstance(
+            nn_module, (torch.nn.ModuleList, torch.nn.Sequential, torch.nn.ModuleDict)
+        )
+        and "__len__" not in cpp_module._method_names()
+    ):
+        script_module.define(f"def __len__(self):\n   return {len(nn_module)}\n")
+    if (
+        isinstance(nn_module, torch.nn.ModuleDict)
+        and "__contains__" not in cpp_module._method_names()
+    ):
+        if len(nn_module.keys()):
+            keys = repr(list(nn_module.keys()))
+            script_module.define(
+                f"def __contains__(self, key: str):\n   return key in {keys}\n"
+            )
+        else:
+            script_module.define("def __contains__(self, key: str):\n   return False\n")
+
+    # Make the compiled methods available to the Python ScriptModule class.
+    for method_stub in method_stubs:
+        if method_stub.original_method is None:
+            # define()'d methods don't have an Python original_method, so we
+            # don't need to do any Python re-wrapping stuff
+            continue
+
+        name = method_stub.original_method.__name__
+        if name != method_stub.def_.name().name:
+            # TODO: Why skip this? Because @torch.jit._overload_method will
+            # mangle the name of the function.
+            continue
+        script_method = cpp_module._get_method(name)
+
+        # Wrap the original to propagate docstrings and such.
+        # TODO: we don't currently do this functions that are recursively
+        # compiled, we should.
+        wrapped_script_method = functools.wraps(method_stub.original_method)(
+            script_method
+        )
+
+        # Add the methods to the script_module directly. This ensures they will
+        # be found first when `name` is looked up (as opposed to the stubs or
+        # nn.Module.forward)
+        script_module.__dict__[name] = wrapped_script_method
+
+    # Make module properties available on the Python ScriptModule class.
+    for property_stub in property_stubs:
+        property_name = property_stub.def_.name().name
+        fget = cpp_module._get_method(property_stub.def_.getter_name().name)
+        # Setter is optional, so it may not exist.
+        setter_name = property_stub.def_.setter_name()
+        fset = cpp_module._get_method(setter_name.name) if setter_name else None
+        script_module.__dict__[property_name] = property(property_name, fget, fset)  # type: ignore[arg-type]
+
+    # copy over python methods to script module if they aren't defined on the script module
+    # this is currently an internal api used only on module containers
+    for name in dir(nn_module):
+        if name in ignored_properties:
+            continue
+        item = getattr(nn_module, name, None)
+        if (
+            _jit_internal.get_torchscript_modifier(item)
+            is _jit_internal.FunctionModifiers.COPY_TO_SCRIPT_WRAPPER
+        ):
+            add_python_attr_to_scripted_model(script_module, nn_module, name)
+
+    return script_module
+
+
+# We define shims of certain attributes on the RecursiveScriptModule to support
+# magic methods. To check if a script model defines an attribute we need
+# to also check that the attribute is not the shim
+def script_model_defines_attr(script_model, attr):
+    script_attr = getattr(script_model, attr, None)
+    if script_attr is None:
+        return False
+    default_attr = getattr(torch.jit.RecursiveScriptModule, attr, None)
+    if default_attr is None:
+        return False
+    return script_attr != default_attr
+
+
+def add_python_attr_to_scripted_model(script_model, orig, attr):
+    if hasattr(orig, attr) and script_model_defines_attr(script_model, attr):
+        setattr(script_model, attr, getattr(orig, attr))
+
+
+def get_overload_annotations(mod, jit_ignored_properties):
+    # original function => [(mangled overload name, overload function)]
+    overloads = {}
+
+    for name in dir(type(mod)):
+        if name in jit_ignored_properties:
+            continue
+        item = getattr(mod, name, None)
+        if not callable(item):
+            continue
+
+        # builtin functions like repr() in python 2 do not have __module__ defined
+        if hasattr(item, "__module__") and item.__module__ is not None:
+            method_overloads = _jit_internal._get_overloaded_methods(
+                item, mod.__class__
+            )
+            if method_overloads is None:
+                continue
+
+            if item.__func__ in method_overloads:
+                raise RuntimeError(
+                    _jit_internal.get_overload_no_implementation_error_message(
+                        "method", item.__func__
+                    )
+                )
+
+            names = [name + "__" + str(i) for i in range(len(method_overloads))]
+            overloads[item] = list(zip(names, method_overloads))
+
+    return overloads
+
+
+def get_overload_name_mapping(overload_info):
+    # Same format as __overloads__
+    # original function => [overload names]
+    overload_name_mappings: dict[str, list[str]] = {}
+    for orig_fn, overloads in overload_info.items():
+        original_name = orig_fn.__name__
+        if original_name not in overload_name_mappings:
+            overload_name_mappings[original_name] = []
+
+        for overload_name, _ in overloads:
+            overload_name_mappings[original_name].append(overload_name)
+    return overload_name_mappings
+
+
+def _check_no_signature(func):
+    signature = torch.jit.annotations.get_signature(
+        func, None, fake_range(), inspect.ismethod(func)
+    )
+    if signature is None:
+        qual_name = _jit_internal._qualified_name(func)
+        raise RuntimeError(
+            f"Must explicitly add type annotations to overloaded functions: {qual_name}"
+        )
+
+
+def make_stubs_for_overloads(overload_info):
+    overload_stubs = []
+    for orig_fn, overloads in overload_info.items():
+        orig_ast = get_jit_def(
+            orig_fn, orig_fn.__name__, self_name="RecursiveScriptModule"
+        )
+        for overload_name, overload_fn in overloads:
+            _check_no_signature(overload_fn)
+            over_ast = get_jit_def(
+                overload_fn, overload_fn.__name__, self_name="RecursiveScriptModule"
+            )
+            new_ast = torch._C._replace_overloaded_method_decl(
+                over_ast.decl(), orig_ast, overload_name
+            )
+            _rcb = _jit_internal.createResolutionCallbackFromClosure(orig_fn)
+            overload_stubs.append(ScriptMethodStub(_rcb, new_ast, overload_fn))
+    return overload_stubs
+
+
+def check_module_initialized(mod):
+    assert isinstance(mod, torch.nn.Module)
+    if not hasattr(mod, "_parameters"):
+        raise RuntimeError(
+            f"'{torch.typename(type(mod))}' has not been initialized, did you forget to call 'super()'?"
+        )
+
+    # This is to avoid importing torch.distributed.nn
+    if not hasattr(mod, "remote_parameters"):
+        for name, param in mod._parameters.items():
+            if param is not None and torch.nn.parameter.is_lazy(param):
+                raise RuntimeError(
+                    f"'{torch.typename(type(mod))}' has uninitialized parameters {name}. Did you forget to run a forward pass?"
+                )
+        for name, buf in mod._buffers.items():
+            if buf is not None and torch.nn.parameter.is_lazy(buf):
+                raise RuntimeError(
+                    f"'{torch.typename(type(mod))}' has uninitialized buffers {name}. Did you forget to run a forward pass?"
+                )
+
+
+def infer_methods_to_compile(nn_module):
+    """Implement the default rules for which methods should act as starting points for compilation.
+
+    (TODO add a link when the rules are published).
+    """
+    check_module_initialized(nn_module)
+    ignored_properties = jit_ignored_properties(nn_module)
+
+    methods: list[str] = []
+    if hasattr(nn_module, "forward") and not _jit_internal.is_ignored_fn(
+        nn_module.forward
+    ):
+        forward_func = getattr(nn_module.forward, "__func__", None)
+        module_forward = getattr(torch.nn.Module, "forward", None)
+        if forward_func != module_forward:
+            methods = ["forward"]
+
+    exported = []
+    for name in dir(nn_module):
+        if name in ignored_properties:
+            continue
+        item = getattr(nn_module, name, None)
+        if (
+            _jit_internal.get_torchscript_modifier(item)
+            is _jit_internal.FunctionModifiers.EXPORT
+        ):
+            exported.append(name)
+
+    methods = methods + exported
+
+    overload_name_mappings = dict(getattr(nn_module, "__overloads__", {}))
+    overload_info = get_overload_annotations(nn_module, ignored_properties)
+    overload_name_mappings.update(get_overload_name_mapping(overload_info))
+    overload_stubs = make_stubs_for_overloads(overload_info)
+
+    nn_module.__overloads__ = overload_name_mappings
+
+    # we shouldn't directly compile overloaded methods, just its overloads
+    def ignore_overloaded(method_name):
+        return method_name not in overload_name_mappings
+
+    filtered_methods = filter(ignore_overloaded, methods)
+
+    # Unique the methods. We don't want to use a set to store the methods because it
+    # introduces non-determinism to compile order.
+    uniquer: set[str] = set()
+    uniqued_methods = []
+    for name in filtered_methods:
+        if name in uniquer:
+            continue
+        uniqued_methods.append(name)
+        uniquer.add(name)
+
+    stubs = [make_stub_from_method(nn_module, method) for method in uniqued_methods]
+    return overload_stubs + stubs
+
+
+def get_hook_stubs(nn_module):
+    """Return forward hook and pre_hook ScriptModuleStubs."""
+    check_module_initialized(nn_module)
+    hook_map: dict = {}
+
+    hook_stubs = []
+    for hook in nn_module._forward_hooks.values():
+        if hook.__name__ in hook_map:
+            if id(hook) != id(hook_map[hook.__name__]):
+                raise RuntimeError(
+                    f"Hook '{hook.__name__}' on {type(nn_module).__name__} "
+                    "has at least two different python definitions."
+                    " Please use unique names for all hooks."
+                )
+        else:
+            hook_map[hook.__name__] = hook
+        hook_stubs.append(make_stub(hook, hook.__name__))
+
+    pre_hook_stubs = []
+    for pre_hook in nn_module._forward_pre_hooks.values():
+        if pre_hook.__name__ in hook_map:
+            if id(pre_hook) != id(hook_map[pre_hook.__name__]):
+                raise RuntimeError(
+                    f"Pre-hook '{pre_hook.__name__}' on {type(nn_module).__name__} "
+                    "has at least two different python definitions."
+                    " Please use unique names for all hooks."
+                )
+        else:
+            hook_map[pre_hook.__name__] = pre_hook
+        pre_hook_stubs.append(make_stub(pre_hook, pre_hook.__name__))
+
+    return hook_stubs, pre_hook_stubs
+
+
+def get_property_stubs(nn_module):
+    """Create property stubs for the properties of the module by creating method stubs for the getter and setter."""
+    module_ty = type(nn_module)
+    properties_asts = get_class_properties(module_ty, self_name="RecursiveScriptModule")
+    rcbs = {}
+
+    for name in dir(module_ty):
+        item = getattr(module_ty, name, None)
+        if isinstance(item, property):
+            if not item.fget:
+                raise RuntimeError(
+                    f"Property {name} of {nn_module.__name__} must have a getter"
+                )
+
+            rcbs[name] = _jit_internal.createResolutionCallbackFromClosure(item.fget)
+
+    stubs = [PropertyStub(rcbs[ast.name().name], ast) for ast in properties_asts]
+    return stubs
+
+
+def interface_script(mod_interface, nn_module):
+    """
+    Make a ScriptModule from an nn.Module, using the interface methods rule for determining which methods to compile.
+
+    Args:
+        mod_interface: the interface type that the module have
+        nn_module:  The original Python nn.Module that we are creating a ScriptModule for.
+    """
+    if isinstance(nn_module, torch.jit.ScriptModule):
+        return nn_module
+
+    check_module_initialized(nn_module)
+
+    def infer_interface_methods_to_compile(nn_module):
+        """Rule to infer the methods from the interface type.
+
+        It is used to know which methods need to act as starting points for compilation.
+        """
+        stubs = [
+            make_stub_from_method(nn_module, method)
+            for method in mod_interface.getMethodNames()
+        ]
+        return stubs
+
+    return create_script_module(nn_module, infer_interface_methods_to_compile)
+
+
+def try_compile_fn(fn, loc):
+    if _jit_internal.is_ignored_fn(fn):
+        # Don't do anything for @ignore'd functions
+        return None
+
+    if isinstance(fn, torch.nn.Module):
+        # Since modules are callable pybind recognizes them as functions, but
+        # don't do anything for them
+        return None
+
+    if not inspect.isfunction(fn) and not inspect.ismethod(fn):
+        raise RuntimeError(
+            f"`{fn}` is not a function. Recursive scripting only supports "
+            "Python functions or methods currently.\n"
+            f"Consider manually annotating `{fn}` with @torch.jit.script."
+        )
+
+    # The object returned by __prepare_scriptable__ might have a different closure.
+    # Resolve it here to get the right resolution callback.
+    fn = fn.__prepare_scriptable__() if hasattr(fn, "__prepare_scriptable__") else fn  # type: ignore[operator]
+
+    # We don't have the actual scope where the function was defined, but we can
+    # extract the necessary info from the closed over variables on the function
+    # object
+    rcb = _jit_internal.createResolutionCallbackFromClosure(fn)
+    return torch.jit.script(fn, _rcb=rcb)
+
+
+def wrap_cpp_class(cpp_class):
+    """Wrap this torch._C.Object in a Python RecursiveScriptClass."""
+    return torch.jit.RecursiveScriptClass(cpp_class)
+
+
+def wrap_cpp_module(cpp_module):
+    """Wrap this torch._C.ScriptModule in a Python ScriptModule, recursively for all submodules."""
+
+    def init_fn(script_module):
+        for name, cpp_module in torch._C.ModuleDict(script_module._c).items():
+            setattr(script_module, name, wrap_cpp_module(cpp_module))
+        script_module._concrete_type = torch._C.ConcreteModuleType.from_jit_type(
+            script_module._c._type()
+        )
+
+        for idx, fn in enumerate(script_module._c._get_forward_pre_hooks()):
+            script_module._forward_pre_hooks[idx] = fn
+        for idx, fn in enumerate(script_module._c._get_forward_hooks()):
+            script_module._forward_hooks[idx] = fn
+
+    return torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)
+
+
+def compile_unbound_method(concrete_type, fn):
+    if _jit_internal.is_ignored_fn(fn):
+        return None
+    stub = make_stub(fn, fn.__name__)
+    with torch._jit_internal._disable_emit_hooks():
+        # We don't want to call the hooks here since the graph that is calling
+        # this function is not yet complete
+        create_methods_and_properties_from_stubs(concrete_type, (stub,), ())
+    return stub
+
+
+def lazy_bind(concrete_type, unbound_method):
+    """
+    Return a function that lazily binds `unbound_method` to a provided Module IValue, then invokes the method.
+
+    We do this so that any Python shenanigans that
+    will poison type sharing are impossible at compile time.
+    """
+
+    def lazy_binding_method(cpp_module, *args):
+        def init_fn(script_module):
+            orig_class = concrete_type.py_class
+
+            # Copy @ignored/@unused methods from the original module to the new one.
+            # This ensures they are available during execution.
+            for name in dir(orig_class):
+                item = getattr(orig_class, name, None)
+                if _jit_internal.is_ignored_fn(item):
+                    setattr(script_module, name, item)
+
+            # Copy constants over so they are available during execution.
+            for name, value in concrete_type.get_constants().items():
+                setattr(script_module, name, value)
+
+        script_module = torch.jit.RecursiveScriptModule._construct(cpp_module, init_fn)
+        method = types.MethodType(unbound_method, script_module)
+        return method(*args)
+
+    # make the lazy binding method "look like" the original method
+    lazy_binding_method.original_fn = unbound_method  # type: ignore[attr-defined]
+    lazy_binding_method.__name__ = unbound_method.__name__
+    torch._jit_internal.copy_torchscript_modifier(unbound_method, lazy_binding_method)
+
+    return lazy_binding_method
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_script.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_script.py
new file mode 100644
index 0000000000000000000000000000000000000000..5777b047e74ef0000f0fc2ccf5881b77e8055a5f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_script.py
@@ -0,0 +1,1740 @@
+"""TorchScript.
+
+This module contains functionality to support the JIT's scripting frontend, notably:
+    - torch.jit.script
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+import collections
+import copy
+import enum
+import functools
+import inspect
+import pickle
+import warnings
+from typing import Any, Callable, Union
+
+import torch
+import torch._jit_internal as _jit_internal
+from torch._classes import classes
+from torch._jit_internal import _get_model_id, _qualified_name
+from torch._utils_internal import log_torchscript_usage
+from torch.jit._builtins import _register_builtin
+from torch.jit._fuser import _graph_for, _script_method_graph_for
+from torch.jit._monkeytype_config import (
+    JitTypeTraceConfig,
+    JitTypeTraceStore,
+    monkeytype_trace,
+)
+from torch.jit._recursive import (
+    _compile_and_register_class,
+    infer_methods_to_compile,
+    ScriptMethodStub,
+    wrap_cpp_module,
+)
+from torch.jit._state import (
+    _enabled,
+    _set_jit_function_cache,
+    _set_jit_overload_cache,
+    _try_get_jit_cached_function,
+    _try_get_jit_cached_overloads,
+)
+from torch.jit.frontend import get_default_args, get_jit_class_def, get_jit_def
+from torch.nn import Module
+from torch.overrides import (
+    has_torch_function,
+    has_torch_function_unary,
+    has_torch_function_variadic,
+)
+from torch.package import PackageExporter, PackageImporter
+from torch.utils import set_module
+
+from ._serialization import validate_map_location
+
+
+type_trace_db = JitTypeTraceStore()  # DB to hold all call traces from MonkeyType
+
+torch._C.ScriptMethod.graph_for = _script_method_graph_for  # type: ignore[attr-defined]
+torch._C.ScriptFunction.graph_for = _graph_for  # type: ignore[attr-defined]
+ScriptFunction = torch._C.ScriptFunction
+ScriptFunction.__doc__ = """
+Functionally equivalent to a :class:`ScriptModule`, but represents a single
+function and does not have any attributes or Parameters.
+"""
+ScriptFunction.__name__ = "ScriptFunction"
+ScriptFunction.__qualname__ = "torch.jit.ScriptFunction"
+set_module(ScriptFunction, "torch.jit")
+
+
+# Throws an error if a jit function is pickled.
+# Helps to avoid Python crashes for Python versions 3.9.5 + when protocol 0 or 1 is given as an argument.
+def _reduce(cls):
+    raise pickle.PickleError("ScriptFunction cannot be pickled")
+
+
+ScriptFunction.__reduce__ = _reduce  # type: ignore[assignment]
+
+
+if _enabled:
+    Attribute = collections.namedtuple("Attribute", ["value", "type"])
+else:
+
+    def Attribute(value, type):  # type: ignore[no-redef]
+        return value
+
+
+Attribute.__doc__ = """
+    This method is a pass-through function that returns `value`, mostly
+    used to indicate to the TorchScript compiler that the left-hand side
+    expression is a class instance attribute with type of `type`. Note that
+    `torch.jit.Attribute` should only be used in `__init__` method of `jit.ScriptModule`
+    subclasses.
+
+    Though TorchScript can infer correct type for most Python expressions, there are some cases where
+    type inference can be wrong, including:
+
+    - Empty containers like `[]` and `{}`, which TorchScript assumes to be container of `Tensor`
+    - Optional types like `Optional[T]` but assigned a valid value of type `T`, TorchScript would assume
+      it is type `T` rather than `Optional[T]`
+
+    In eager mode, it is simply a pass-through function that returns `value`
+    without other implications.
+
+    Example:
+
+    .. testcode::
+
+        import torch
+        from typing import Dict
+
+        class AttributeModule(torch.jit.ScriptModule):
+            def __init__(self) -> None:
+                super().__init__()
+                self.foo = torch.jit.Attribute(0.1, float)
+
+                # we should be able to use self.foo as a float here
+                assert 0.0 < self.foo
+
+                self.names_ages = torch.jit.Attribute({}, Dict[str, int])
+                self.names_ages["someone"] = 20
+                assert isinstance(self.names_ages["someone"], int)
+
+        m = AttributeModule()
+        # m will contain two attributes
+        # 1. foo of type float
+        # 2. names_ages of type Dict[str, int]
+
+    .. testcleanup::
+
+        del AttributeModule
+        del m
+
+    Note: it's now preferred to instead use type annotations instead of `torch.jit.Attribute`:
+
+    .. testcode::
+
+        import torch
+        from typing import Dict
+
+        class AttributeModule(torch.nn.Module):
+            names: Dict[str, int]
+
+            def __init__(self) -> None:
+                super().__init__()
+                self.names = {}
+
+        m = AttributeModule()
+
+    .. testcleanup::
+
+        del AttributeModule
+        del m
+
+    Args:
+        value: An initial value to be assigned to attribute.
+        type: A Python type
+
+    Returns:
+        Returns `value`
+"""
+
+
+def _get_type_trace_db():
+    # This is a private API. Use of this for external purposes is discouraged.
+    return type_trace_db
+
+
+# Gets a function from the name of a method on a type
+def _get_function_from_type(cls, name):
+    return getattr(cls, name, None)
+
+
+# ScriptClasses must be new-style classes because we construct them using their
+# __new__ method.
+def _is_new_style_class(cls):
+    if hasattr(cls, "__class__"):
+        return "__dict__" in dir(cls) or hasattr(cls, "__slots__")
+
+
+# These OrderedDictWrapper classes replace the actual OrderedDicts in
+# module with versions that get/set properties inside of Module.
+# This allows us to reuse most of nn.Module while still storing the
+# data in C++.
+# Each OrderedDict needs to support:
+#  x not in view
+#  x in view
+#  view[name] = ...
+#  view.values()
+#  del view[name]
+#  view.items()
+#  view.keys()
+#  len(view)
+
+
+class OrderedDictWrapper:
+    def __init__(self, _c):
+        self._c = _c
+
+    def keys(self):
+        return [k for k, v in self.items()]
+
+    def values(self):
+        return [v for k, v in self.items()]
+
+    def __len__(self):
+        return len(self.values())
+
+    def __delitem__(self, k):
+        raise RuntimeError("cannot delete methods or parameters of a script module")
+
+    def items(self):
+        return self._c.items()
+
+    def __setitem__(self, k, v):
+        if k not in self:
+            raise RuntimeError(
+                f"Can't add a new parameter after ScriptModule construction. Tried to add '{k}"
+            )
+        self._c.setattr(k, v)
+
+    def __contains__(self, k):
+        return self._c.contains(k)
+
+    def __getitem__(self, k):
+        if k not in self:
+            raise KeyError(k)
+        return self._c.getattr(k)
+
+
+class OrderedModuleDict(OrderedDictWrapper):
+    def __init__(self, module, python_dict):
+        super().__init__(torch._C.ModuleDict(module))
+        # contains _both_ script modules and non-script python-only modules
+
+        # because script modules are subclassed in python and the
+        # C++ Module class will not hold references to them,
+        # to ensure that you always get the same python value here
+        # we store it in the python dict as well
+        self._python_modules = python_dict
+
+    def items(self):
+        r = self._python_modules.items()
+        return r
+
+    def __contains__(self, k):
+        return k in self._python_modules
+
+    def __setitem__(self, k, v):
+        # Cases where sub-module can be re-assigned after ScriptModule construction
+        # 1. If the attr is an module interface type, it's guaranteed that the module is
+        #    not inlined in the graph, so it's safe to swap a new ScriptModule in.
+        # 2. if the new value if a ScriptModule with the same JIT type, IR won't change
+        #    and it's legit to swap a new module in.
+        # In these two cases we allow swapping a new scripted module and update the
+        # corresponding python module dict to keep sync.
+        # Note: the value to be swapped in has to be ScriptModule instead of nn.Module,
+        # otherwise it's illegal and we throw error.
+        if isinstance(v, ScriptModule):
+            self._c.setattr(k, v)
+            self._python_modules[k] = v
+        else:
+            raise RuntimeError(
+                "Cannot re-assign modules in a ScriptModule with non-scripted "
+                f"module, tried to replace existing module '{k}': {v}"
+            )
+
+    def __getitem__(self, k):
+        return self._python_modules[k]
+
+
+# For each user-defined class that subclasses ScriptModule, this meta-class:
+# (1) finds all the methods annotated with @script_method in a ScriptModule and
+#     removes them from the class attributes
+# (2) puts a wrapper around the class's __init__ method to recursively compile
+#     all of the script_methods with the module after the original __init__ has
+#     run. This has to occur after the user-defined __init__ so that submodules and
+#     parameters are initialized _before_ the script compiler resolve references to
+#     `self.param` or `self.module`.
+class ScriptMeta(type):
+    def __init__(cls, name, bases, attrs):  # noqa: B902
+        # Aggregate all the ScriptMethods and constants from superclasses
+        cls._methods: dict[str, Any] = {}
+        cls._constants_set = set(getattr(cls, "__constants__", ()))
+        for base in reversed(bases):
+            for k, v in getattr(base, "_methods", {}).items():
+                cls._methods[k] = v
+            base_constants: set = getattr(base, "_constants_set", set())
+            cls._constants_set = cls._constants_set.union(base_constants)
+
+        # find all the script methods of the current class
+        for k, v in sorted(attrs.items()):
+            if isinstance(v, ScriptMethodStub):
+                delattr(cls, k)
+                cls._methods[v.original_method.__name__] = v
+
+        if getattr(cls, "_disable_script_meta", False):
+            # We leave built-in ScriptModule types alone, since this metaclass
+            # is only for compiling user classes that inherit from
+            # ScriptModule.
+            super().__init__(name, bases, attrs)
+            return
+
+        original_init = getattr(cls, "__init__", lambda self: None)
+
+        @functools.wraps(original_init)
+        def init_then_script(self, *args, **kwargs):
+            num_methods = len(cls._methods)
+            original_init(self, *args, **kwargs)
+            added_methods_in_init = len(cls._methods) > num_methods
+
+            if type(self) == cls:
+
+                def make_stubs(module):
+                    cls = type(module)
+                    if hasattr(cls, "_methods"):
+                        return [v for k, v in sorted(cls._methods.items())]
+                    else:
+                        return infer_methods_to_compile(module)
+
+                self.__dict__[
+                    "_actual_script_module"
+                ] = torch.jit._recursive.create_script_module(
+                    self, make_stubs, share_types=not added_methods_in_init
+                )
+
+                # Delete the Python attributes that now shadow the ScriptModule
+                # ones, so that __getattr__ and __setattr__ will properly find
+                # the scripted versions.
+                concrete_type = self._actual_script_module._concrete_type
+                for name in concrete_type.get_attributes():
+                    delattr(self, name)
+                for name, _ in concrete_type.get_modules():
+                    delattr(self, name)
+                for name in ("_parameters", "_buffers", "_modules"):
+                    delattr(self, name)
+
+        cls.__init__ = init_then_script  # type: ignore[misc]
+        super().__init__(name, bases, attrs)
+
+
+class _CachedForward:
+    def __get__(self, obj, cls):
+        return self.__getattr__("forward")  # type: ignore[attr-defined]
+
+
+class ScriptWarning(Warning):
+    pass
+
+
+def script_method(fn):
+    if not _enabled:
+        return fn
+    # NOTE: we need to traverse two frames here because the meta-class frame
+    # for ScriptModule will be present, as opposed to invoking @script on a
+    # a function or invoking define() on a CompilationUnit.
+    # The stack will look like:
+    #
+    # 0. createResolutionCallback()
+    # 1. script_method()
+    # 2. ScriptModule metaclass frame
+    # 3. Surrounding scope
+    #
+    # createResolutionCallback internally adds 1 to get us to the scope of this
+    # function (the calling function). Adding 2 gets us to the proper surrounding scope.
+    _rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=2)
+    ast = get_jit_def(fn, fn.__name__, self_name="ScriptModule")
+    return ScriptMethodStub(_rcb, ast, fn)
+
+
+class ConstMap:
+    def __init__(self, const_mapping):
+        self.const_mapping = const_mapping
+
+    def __getattr__(self, attr):
+        return self.const_mapping[attr]
+
+
+def unpackage_script_module(
+    importer: PackageImporter, script_module_id: str
+) -> torch.nn.Module:
+    """
+    Call by ``torch.package.PackageImporter``'s Pickler's ``persistent_load`` function.
+
+    Performs work of loading and returning a ScriptModule from a ``torch.package`` archive.
+    """
+    if not isinstance(importer.zip_reader, torch._C.PyTorchFileReader):
+        raise RuntimeError(
+            "Loading ScriptObjects from a PackageImporter created from a "
+            "directory is not supported. Use a package archive file instead."
+        )
+    cu = torch._C.CompilationUnit()
+    cpp_module = torch._C._import_ir_module_from_package(
+        cu,
+        importer.zip_reader,
+        importer.storage_context,
+        validate_map_location(importer.last_map_location),
+        script_module_id,
+    )
+    return wrap_cpp_module(cpp_module)
+
+
+if _enabled:
+    _magic_methods = [
+        "__iter__",
+        "__len__",
+        "__neg__",
+        "__mul__",
+        "__contains__",
+        "__add__",
+        "__sub__",
+        "__pow__",
+        "__truediv__",
+        "__mod__",
+        "__ne__",
+        "__eq__",
+        "__lt__",
+        "__gt__",
+        "__le__",
+        "__ge__",
+        "__and__",
+        "__or__",
+        "__xor__",
+        "__getitem__",
+        "__setitem__",
+        "__call__",
+        "__int__",
+        "__float__",
+        "__bool__",
+        "__str__",
+        "__enter__",
+        "__exit__",
+    ]
+
+    class RecursiveScriptClass:
+        """Wrapper for a TorchScript class instance for use in Python.
+
+        An analogue of RecursiveScriptModule for regular objects that are not modules.
+        This class is a wrapper around a torch._C.ScriptObject that represents an instance
+        of a TorchScript class and allows it to be used in Python.
+
+        Attributes:
+            _c [torch._C.ScriptObject]: The C++ object to which attribute lookups and method
+                calls are forwarded.
+            _props [Dict[str, property]]: A dictionary of properties fetched from self._c and
+                exposed on this wrppaer.
+        """
+
+        def __init__(self, cpp_class):
+            super().__init__()
+            self.__dict__["_initializing"] = True
+            self._c = cpp_class
+
+            # Add wrapped object's properties to this class instance.
+            self._props = {
+                prop.name: property(prop.getter, prop.setter)
+                for prop in self._c._properties()
+            }
+
+            self.__dict__["_initializing"] = False
+
+        def __getattr__(self, attr):
+            if self.__dict__.get("_initializing"):
+                return super().__getattr__(attr)  # type: ignore[misc]
+
+            if attr in self._props:
+                return self._props[attr].fget()  # type: ignore[call-arg, misc]
+
+            return getattr(self._c, attr)
+
+        def __setattr__(self, attr, value):
+            if self.__dict__.get("_initializing"):
+                return super().__setattr__(attr, value)
+
+            if attr in self._props:
+                return self._props[attr].fset(value)  # type: ignore[call-arg, misc]
+
+            setattr(self._c, attr, value)
+
+        # Delegate calls to magic methods like __len__ to the C++ module backing the
+        # RecursiveScriptClass.
+        def forward_magic_method(self, method_name, *args, **kwargs):
+            if not self._c._has_method(method_name):
+                raise TypeError
+
+            self_method = self.__getattr__(method_name)
+            return self_method(*args, **kwargs)
+
+        def __getstate__(self):
+            raise pickle.PickleError("ScriptClasses cannot be pickled")
+
+        def __iadd__(self, other):
+            if self._c._has_method("__iadd__"):
+                return self.forward_magic_method("__iadd__", other)
+            else:
+                return self.forward_magic_method("__add__", other)
+
+    for method_name in _magic_methods:
+
+        def method_template(self, *args, **kwargs):
+            return self.forward_magic_method(method_name, *args, **kwargs)
+
+        setattr(RecursiveScriptClass, method_name, method_template)
+
+    # this is a Python 'non-data descriptor' that causes the first access
+    # to ScriptModule's forward to look up the forward method and stash
+    # it in the objects dict. Due to the standard rules for attribute lookup,
+    # subsequent lookups will just directly return the previously looked up method.
+    # This is necessary because nn.Module defines forward as a method. If we
+    # did nothing, __getattr__ would not be called. Instead we'd get nn.Module.forward
+    # which always throws an exception.
+
+    class ScriptModule(Module, metaclass=ScriptMeta):
+        r"""Wrapper for C++ torch::jit::Module with methods, attributes, and parameters.
+
+        A wrapper around C++ ``torch::jit::Module``. ``ScriptModule``\s
+        contain methods, attributes, parameters, and
+        constants. These can be accessed the same way as on a normal ``nn.Module``.
+        """
+
+        __jit_unused_properties__ = [
+            "code",
+            "code_with_constants",
+            "graph",
+            "inlined_graph",
+            "original_name",
+        ]
+
+        def __init__(self) -> None:
+            super().__init__()
+
+        forward: Callable[..., Any] = _CachedForward()  # type: ignore[assignment]
+
+        def __getattr__(self, attr):
+            if "_actual_script_module" not in self.__dict__:
+                return super().__getattr__(attr)
+            return getattr(self._actual_script_module, attr)
+
+        def __setattr__(self, attr, value):
+            if "_actual_script_module" not in self.__dict__:
+                # Unwrap torch.jit.Attribute into a regular setattr + record
+                # the provided type in __annotations__.
+                #
+                # This ensures that if we use the attr again in `__init__`, it
+                # will look like the actual value, not an instance of Attribute.
+                if isinstance(value, Attribute):
+                    # NB: Ensure that we set __annotations__ on the specific
+                    # class in question, and not on a superclass (which would
+                    # be wrong wrong wrong!).
+                    # See also https://github.com/pytorch/pytorch/issues/39463
+                    if "__annotations__" not in self.__class__.__dict__:
+                        self.__class__.__annotations__ = {}
+                    self.__annotations__[attr] = value.type
+                    value = value.value
+                return super().__setattr__(attr, value)
+
+            setattr(self._actual_script_module, attr, value)
+
+        def define(self, src):
+            if "_actual_script_module" in self.__dict__:
+                # If we have completed initialization, just defer to the
+                # backing RecursiveScriptModule to eagerly compile the provided
+                # source.
+                return self._actual_script_module.define(src)
+
+            # Otherwise, we are still in the object's __init__.
+            # In that case, add `src` as a stub to be compiled.
+            #
+            # We use frames_up=1 to get to the proper surrounding scope. The stack
+            # will look like:
+            # 0. createResolutionCallback
+            # 1. define()
+            # 2. surrounding scope.
+            #
+            # createResolutionCallback internally adds 1 to get us to our frame, then
+            # we add 1 to get to the proper surrounding scope.
+            rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
+            ast = torch._C._parse_source_def(src)
+            self._methods[ast.name().name] = ScriptMethodStub(rcb, ast, None)
+
+        def _replicate_for_data_parallel(self):
+            return self._actual_script_module._replicate_for_data_parallel()
+
+        def __reduce_package__(self, exporter: PackageExporter):
+            """Save a ScriptModule inside of a ``torch.package`` archive.
+
+            Called by ``torch.package.PackageExporter``'s Pickler's ``persistent_id`` when
+            saving TorchScript objects. Performs act of saving a ScriptModule inside of
+            a ``torch.package`` archive.
+
+            Returns method to load the ScriptModule from a ``torch.package.PackageImporter``'s
+            Pickler's ``persistent_load`` function.
+            """
+            script_module_id = exporter.get_unique_id()
+            exporter.script_module_serializer.serialize(self._c, int(script_module_id))
+            return (unpackage_script_module, (script_module_id,))
+
+    class RecursiveScriptModule(ScriptModule):
+        # XXX: RecursiveScriptModule inherits from ScriptModule for the sole
+        # reason that it retains the existing isinstance(ScriptModule)
+        # behavior.
+        r"""Retain the existing isinstance(ScriptModule) behavior.
+
+        The core data structure in TorchScript is the ``ScriptModule``. It is an
+        analogue of torch's ``nn.Module`` and represents an entire model as a tree of
+        submodules. Like normal modules, each individual module in a ``ScriptModule`` can
+        have submodules, parameters, and methods. In ``nn.Module``\s methods are implemented
+        as Python functions, but in ``ScriptModule``\s methods are implemented as
+        TorchScript functions, a statically-typed subset of Python that contains all
+        of PyTorch's built-in Tensor operations. This difference allows your
+        ``ScriptModule``\s code to run without the need for a Python interpreter.
+
+        ``ScriptModule``\s should not be created manually, instead use
+        either :func:`tracing ` or :func:`scripting `.
+        Tracing and scripting can be applied incrementally and :ref:`composed as necessary `.
+
+        * Tracing records the tensor operations as executed with a set of example inputs and uses these
+          operations to construct a computation graph. You can use the full dynamic behavior of Python with tracing,
+          but values other than Tensors and control flow aren't captured in the graph.
+
+        * Scripting inspects the Python code of the model
+          and compiles it to TorchScript. Scripting allows the use of many `types`_ of values and supports dynamic control flow.
+          Many, but not all features of Python are supported by the compiler, so changes to the source code may be necessary.
+        """
+
+        _disable_script_meta = True
+
+        def __init__(self, cpp_module):
+            self.__dict__["_initializing"] = True
+            self._c = cpp_module
+            super().__init__()
+            # Delete the 'training' attribute set up by `Module.__init__`. It
+            # will get set on the underlying cpp module, so we delete it here
+            # to avoid this version shadowing the cpp module version.
+            delattr(self, "training")
+
+        @staticmethod
+        def _construct(cpp_module, init_fn):
+            """
+            Construct a RecursiveScriptModule that's ready for use.
+
+            PyTorch code should use this to construct a RecursiveScriptModule instead
+            of instead of calling `__init__` directly, as it makes sure the
+            object is properly finalized (and in the future, we may take
+            control of how the RecursiveScriptModule instance is created).
+
+            Args:
+                cpp_module:  The C++ Module that will hold the actual state of
+                             this RecursiveScriptModule instance.
+                init_fn:  Lambda that initializes the RecursiveScriptModule passed to it.
+            """
+            script_module = RecursiveScriptModule(cpp_module)
+            init_fn(script_module)
+
+            # Finalize the ScriptModule: replace the nn.Module state with our
+            # custom implementations and flip the _initializing bit.
+            RecursiveScriptModule._finalize_scriptmodule(script_module)
+            return script_module
+
+        @staticmethod
+        def _finalize_scriptmodule(script_module):
+            script_module._parameters = OrderedDictWrapper(
+                torch._C.ParameterDict(script_module._c)
+            )
+            script_module._buffers = OrderedDictWrapper(
+                torch._C.BufferDict(script_module._c)
+            )
+            script_module._modules = OrderedModuleDict(
+                script_module._c, script_module._modules
+            )
+            script_module._initializing = False
+
+        def _reconstruct(self, cpp_module):
+            """
+            Re-construct an instance of RecursiveScriptModule using an instance of a C++ module.
+
+            Args:
+                cpp_module: The C++ module that this RecursiveScriptModule will be rebuilt around.
+            """
+            self.__init__(cpp_module)  # type: ignore[misc]
+
+            # Copy the concrete type from the C++ module to this ScriptModule.
+            self._concrete_type = torch._C.ConcreteModuleType.from_jit_type(
+                self._c._type()
+            )
+
+            # Copy submodules from the C++ module to this ScriptModule.
+            modules = {}
+            for name, cpp_module in torch._C.ModuleDict(self._c).items():
+                modules[name] = wrap_cpp_module(cpp_module)
+            self._modules = OrderedModuleDict(self._c, modules)  # type: ignore[assignment]
+
+            # Copy parameters and buffers.
+            self._parameters = OrderedDictWrapper(torch._C.ParameterDict(self._c))  # type: ignore[assignment]
+            self._buffers = OrderedDictWrapper(torch._C.BufferDict(self._c))  # type: ignore[assignment]
+
+            # Get rid of the functions from the old C++ module.
+            self.__dict__ = {
+                k: v
+                for k, v in self.__dict__.items()
+                if not isinstance(v, torch._C.ScriptMethod)
+            }
+            self.__dict__["_initializing"] = False
+
+        @property
+        def graph(self):
+            r"""Return a string representation of the internal graph for the ``forward`` method.
+
+            See :ref:`interpreting-graphs` for details.
+            """
+            return self._c._get_method("forward").graph
+
+        @property
+        def inlined_graph(self):
+            r"""
+            Return a string representation of the internal graph for the ``forward`` method.
+
+            This graph will be preprocessed to inline all function and method calls.
+            See :ref:`interpreting-graphs` for details.
+            """
+            return self.forward.inlined_graph  # type: ignore[attr-defined]
+
+        @property
+        def code(self):
+            r"""
+            Return a pretty-printed representation (as valid Python syntax) of the internal graph for the ``forward`` method.
+
+            See :ref:`inspecting-code` for details.
+            """
+            return self.forward.code  # type: ignore[attr-defined]
+
+        @property
+        def code_with_constants(self):
+            r"""Return a tuple.
+
+            Returns a tuple of:
+
+            [0] a pretty-printed representation (as valid Python syntax) of
+            the internal graph for the ``forward`` method. See `code`.
+            [1] a ConstMap following the CONSTANT.cN format of the output in [0].
+            The indices in the [0] output are keys to the underlying constant's values.
+
+            See :ref:`inspecting-code` for details.
+            """
+            r = self.forward.code_with_constants  # type: ignore[attr-defined]
+            return (r[0], ConstMap(r[1]))
+
+        def save(self, f, **kwargs):
+            r"""Save with a file-like object.
+
+            save(f, _extra_files={})
+
+            See :func:`torch.jit.save ` which accepts a file-like object.
+            This function, torch.save(), converts the object to a string, treating it as a path.
+            DO NOT confuse these two functions when it comes to the 'f' parameter functionality.
+            """
+            return self._c.save(str(f), **kwargs)
+
+        def _save_for_lite_interpreter(self, *args, **kwargs):
+            r"""Add (or update) the bytecode session to the script model.
+
+            _save_for_lite_interpreter(f)
+
+            The updated model is used
+            in lite interpreter for mobile applications.
+
+            Args:
+                f: a string containing a file name.
+                _extra_files: Map from filename to contents which will be stored as part of 'f'.
+
+            """
+            return self._c._save_for_mobile(*args, **kwargs)
+
+        def _save_to_buffer_for_lite_interpreter(self, *args, **kwargs):
+            return self._c._save_to_buffer_for_mobile(*args, **kwargs)
+
+        def save_to_buffer(self, *args, **kwargs):
+            return self._c.save_to_buffer(*args, **kwargs)
+
+        def get_debug_state(self, *args, **kwargs):
+            return self._c.get_debug_state()
+
+        def extra_repr(self):
+            return f"original_name={self.original_name}"
+
+        def graph_for(self, *args, **kwargs):
+            return self.forward.graph_for(self, *args, **kwargs)  # type: ignore[attr-defined]
+
+        @property
+        def original_name(self):
+            if type(self) == str(self._c._type().name()):
+                return ""
+            return str(self._c._type().name())
+
+        def define(self, src):
+            # We use frames_up=1 to get to the proper surrounding scope. The stack
+            # will look like:
+            # 0. createResolutionCallback
+            # 1. define()
+            # 2. surrounding scope.
+            #
+            # createResolutionCallback internally adds 1 to get us to our frame, then
+            # we add 1 to get to the proper surrounding scope.
+            rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
+            self._c._define(self._concrete_type, src, rcb)
+
+        def __getattr__(self, attr):
+            if "_initializing" not in self.__dict__:
+                raise RuntimeError(
+                    "ScriptModule has not been initialized, did you forget to call super's init?"
+                )
+
+            if self._initializing:
+                return super().__getattr__(attr)
+
+            # _modules check is before hasattr since modules are included as attributes in _c,
+            # but we want to get the python wrapper from _modules instead of the raw _c object.
+            if attr in self._modules:
+                return self._modules[attr]
+            elif self._c.hasattr(attr):
+                return self._c.getattr(attr)
+            elif self._c._has_method(attr):
+                script_method = self._c._get_method(attr)
+                # cache method so future calls do not go through __getattr__
+                # to improve invocation performance
+                self.__dict__[attr] = script_method
+                return script_method
+
+            return super().__getattr__(attr)
+
+        def __setattr__(self, attr, value):
+            if self._initializing:
+                return super().__setattr__(attr, value)
+
+            if attr in self._modules:
+                self._modules[attr] = value
+            elif self._c.hasattr(attr):
+                self._c.setattr(attr, value)
+            elif (
+                hasattr(self, "_concrete_type")
+                and attr in self._concrete_type.get_constants().keys()
+            ):
+                # TODO: we don't have _concrete_type set after load(), and in general we lose constant information.
+                # We should encode constants as class type attributes (or something) so it persists across save/load.
+                raise AttributeError(
+                    f"Cannot mutate TorchScript constant value: '{attr}'. Value: '{value}'"
+                )
+            else:
+                # We allow setting Python attributes on the ScriptModule, for
+                # when people want to stash some convenience info on it.
+                # TODO: it's possible that the following is confusing:
+                #   s = torch.jit.script(...)
+                #   s.python_attr = ...
+                #   s.save()   <--- this doesn't have `python_attr`
+                # It's fairly trivial to save enough info to warn in this case.
+                return super().__setattr__(attr, value)
+
+        def __copy__(self):
+            return torch.jit._recursive.wrap_cpp_module(copy.copy(self._c))
+
+        def __deepcopy__(self, memo):
+            return torch.jit._recursive.wrap_cpp_module(copy.deepcopy(self._c, memo))
+
+        # Python magic methods do method lookups on an object's class type, instead of looking up
+        # the method defines on the class instance. In order to continue to expose the magic methods
+        # of builtin-containers (ModuleList, Sequential, ModuleDict) to Python, we
+        # define magic methods here as a shim to the correct attribute.
+        def forward_magic_method(self, method_name, *args, **kwargs):
+            self_method = getattr(self, method_name)
+            if getattr(self_method, "__func__", None) == getattr(
+                RecursiveScriptModule, method_name
+            ):
+                raise NotImplementedError
+            return self_method(*args, **kwargs)
+
+        def __iter__(self):
+            return self.forward_magic_method("__iter__")
+
+        def __getitem__(self, idx):
+            return self.forward_magic_method("__getitem__", idx)
+
+        def __len__(self):
+            return self.forward_magic_method("__len__")
+
+        def __contains__(self, key):
+            return self.forward_magic_method("__contains__", key)
+
+        # dir is defined by the base nn.Module, so instead of throwing if
+        # it is not overridden, we call into the nn.Module __dir__ method
+        def __dir__(self):
+            self_method = self.__dir__
+            if (
+                self_method.__func__  # type: ignore[attr-defined]
+                == _get_function_from_type(RecursiveScriptModule, "__dir__")
+            ):
+                return super().__dir__()
+            return self_method()
+
+        # to resolve bool(value), Python looks if __bool__ is defined then __iter__
+        # is defined then returns true for classes. Since __iter__() on this
+        # class throws if it isn't overridden, we define __bool__ to preserve default behavior
+        def __bool__(self):
+            self_method = self.__bool__
+            if (
+                self_method.__func__  # type: ignore[attr-defined]
+                == _get_function_from_type(RecursiveScriptModule, "__bool__")
+            ):
+                return True
+            return self_method()
+
+        def _replicate_for_data_parallel(self):
+            # we have to initialize ScriptModule properly so that
+            # it works with pybind11
+            def init_fn(script_module):
+                # Don't do anything here, we'll initialize the ScriptModule below
+                return
+
+            return RecursiveScriptModule._construct(
+                self._c._replicate_for_data_parallel(), init_fn
+            )
+
+    # Need to copy all RecursiveScriptModule methods to ScriptModule.
+    #
+    # This is because `super().foo()` does not use
+    # `__getattr__` to look up `foo`. So we need to make each method available on
+    # the ScriptModule manually.
+    for name, item in RecursiveScriptModule.__dict__.items():
+        if not callable(item) and not isinstance(item, property):
+            continue
+        if name.startswith("__") or hasattr(ScriptModule, name):
+            continue
+        # We can copy over the implementation wholesale because besides the
+        # `super()` thing above, ScriptModule behaves exactly like
+        # RecursiveScriptModule
+        setattr(ScriptModule, name, item)
+
+    def _get_methods(cls):
+        import inspect
+
+        # In Python 3 unbound methods are functions, but in Python 2 they are methods
+        return inspect.getmembers(
+            cls, predicate=lambda x: inspect.isfunction(x) or inspect.ismethod(x)
+        )
+
+    _compiled_methods_allowlist = {
+        "forward",
+        "register_buffer",
+        "register_parameter",
+        "register_module",
+        "add_module",
+        "_apply",
+        "apply",
+        "cuda",
+        "cpu",
+        "to",
+        "type",
+        "float",
+        "double",
+        "half",
+        "state_dict",
+        "_save_to_state_dict",
+        "load_state_dict",
+        "_load_from_state_dict",
+        "_named_members",
+        "parameters",
+        "named_parameters",
+        "buffers",
+        "named_buffers",
+        "children",
+        "named_children",
+        "modules",
+        "named_modules",
+        "zero_grad",
+        "share_memory",
+        "_get_name",
+        "extra_repr",
+        "_slow_forward",
+        "_tracing_name",
+        "eval",
+        "train",
+        "get_extra_state",
+        "set_extra_state",
+    }
+
+    def _make_fail(name):
+        def fail(self, *args, **kwargs):
+            raise RuntimeError(name + " is not supported on ScriptModules")
+
+        return fail
+
+    for name, method in _get_methods(torch.nn.Module):
+        if name.startswith("__") or name.endswith("_call_impl"):
+            continue
+        if (
+            name not in RecursiveScriptModule.__dict__
+            and name not in _compiled_methods_allowlist
+        ):
+            setattr(RecursiveScriptModule, method.__name__, _make_fail(name))
+
+
+else:
+    # TODO MAKE SURE THAT DISABLING WORKS
+    class RecursiveScriptClass:  # type: ignore[no-redef]
+        pass
+
+    class ScriptModule(torch.nn.Module):  # type: ignore[no-redef]
+        def __init__(self, arg=None):
+            super().__init__()
+
+    class RecursiveScriptModule(ScriptModule):  # type: ignore[no-redef]
+        def __init__(self, arg=None):
+            super().__init__()
+
+
+def call_prepare_scriptable_func_impl(obj, memo):
+    if not isinstance(obj, torch.nn.Module):
+        return obj
+
+    obj_id = id(obj)
+
+    # If obj_id is in memo, obj has already been prepared or is being
+    # prepared in another call up the stack.
+    if obj_id in memo:
+        return memo[id(obj)]
+
+    obj = (
+        obj.__prepare_scriptable__() if hasattr(obj, "__prepare_scriptable__") else obj
+    )  # type: ignore[operator]
+    # Record obj in memo to avoid infinite recursion in the case of cycles in the module
+    # hierarchy when recursing below.
+    memo[obj_id] = obj
+
+    new_obj_dict = {}
+
+    for name, sub_module in obj.__dict__.items():
+        if name == "_modules":
+            for k, v in sub_module.items():
+                sub_module[k] = call_prepare_scriptable_func_impl(v, memo)
+            new_obj_dict[name] = sub_module
+        elif isinstance(sub_module, torch.nn.Module) and not isinstance(
+            sub_module, ScriptModule
+        ):
+            new_obj_dict[name] = call_prepare_scriptable_func_impl(sub_module, memo)
+        else:
+            new_obj_dict[name] = sub_module
+
+    for k, v in new_obj_dict.items():
+        obj.__dict__[name] = v
+
+    return obj
+
+
+def call_prepare_scriptable_func(obj):
+    memo: dict[int, torch.nn.Module] = {}
+    return call_prepare_scriptable_func_impl(obj, memo)
+
+
+def create_script_dict(obj):
+    """
+    Create a ``torch._C.ScriptDict`` instance with the data from ``obj``.
+
+    Args:
+        obj (dict): The Python dictionary that is used to initialize the ``ScriptDict``
+                    returned by this function.
+
+    Returns:
+        An instance of ``torch._C.ScriptDict`` that has the same data as ``obj``
+        and can be passed between Python and TorchScript with reference semantics and
+        zero copy overhead.
+    """
+    return torch._C.ScriptDict(obj)  # type: ignore[attr-defined]
+
+
+def create_script_list(obj, type_hint=None):
+    """
+    Create a ``torch._C.ScriptList`` instance with the data from ``obj``.
+
+    Args:
+        obj (dict): The Python list that is used to initialize the ``ScriptList``
+                    returned by this function.
+    Returns:
+        An instance of ``torch._C.ScriptList`` that has the same data as ``obj``
+        and can be passed between Python and TorchScript with reference semantics and
+        zero copy overhead.
+    """
+    return torch._C.ScriptList(obj)  # type: ignore[attr-defined]
+
+
+_TOPLEVEL: bool = True
+
+
+def _script_impl(
+    obj,
+    optimize=None,
+    _frames_up=0,
+    _rcb=None,
+    example_inputs: Union[list[tuple], dict[Callable, list[tuple]], None] = None,
+):
+    global type_trace_db
+
+    if optimize is not None:
+        warnings.warn(
+            "`optimize` is deprecated and has no effect. "
+            "Use `with torch.jit.optimized_execution()` instead",
+            FutureWarning,
+            stacklevel=3,
+        )
+
+    # No-op for modules, functions, class instances that are already scripted
+    if isinstance(obj, RecursiveScriptClass):
+        return obj
+    if isinstance(obj, ScriptModule):
+        return obj
+    if isinstance(obj, ScriptFunction):
+        return obj
+
+    if example_inputs:
+        # If MonkeyType is installed, enable profile directed type annotation
+        # Check if example_inputs are defined and generate call traces
+        # for the method by running eager mode version of the method with
+        # the provide example inputs. This logs all the traces in type_trace_db
+        type_trace_db = JitTypeTraceStore()
+        if monkeytype_trace:
+            monkeytype_config = JitTypeTraceConfig(type_trace_db)
+            with monkeytype_trace(monkeytype_config):
+                if isinstance(example_inputs, dict):
+                    # If the obj is an nn.Module or a class, then each method is
+                    # executed with the arguments provided in the example inputs.
+                    # example inputs here will be of type Dict(class.method, (arguments))
+                    # This is used to infer type annotations for those methods
+                    # which are not called directly under the hood of monkeytype.
+                    for module, example_input in example_inputs.items():
+                        for example in example_input:
+                            module(*example)
+                elif isinstance(example_inputs, list):
+                    for examples in example_inputs:
+                        obj(*examples)
+                else:
+                    raise ValueError(
+                        "Error: Unable to infer types. Please format the inputs to type `List[Tuple]`"
+                        " or `Dict[Callable, List[Tuple]]` to be run with MonkeyType."
+                    )
+        else:
+            warnings.warn(
+                "Warning: monkeytype is not installed. Please install https://github.com/Instagram/MonkeyType "
+                "to enable Profile-Directed Typing in TorchScript. Refer to "
+                "https://github.com/Instagram/MonkeyType/blob/master/README.rst to install MonkeyType. "
+            )
+
+    if isinstance(obj, torch.nn.Module):
+        obj = call_prepare_scriptable_func(obj)
+        return torch.jit._recursive.create_script_module(
+            obj, torch.jit._recursive.infer_methods_to_compile
+        )
+    else:
+        obj = (
+            obj.__prepare_scriptable__()
+            if hasattr(obj, "__prepare_scriptable__")
+            else obj
+        )  # type: ignore[operator]
+
+    if isinstance(obj, dict):
+        return create_script_dict(obj)
+    if isinstance(obj, list):
+        return create_script_list(obj)
+
+    if inspect.isclass(obj):
+        qualified_name = _qualified_name(obj)
+        # If this type is a `nn.Module` subclass, they probably meant to pass
+        # an instance instead of a Module
+        if issubclass(obj, torch.nn.Module):
+            raise RuntimeError(
+                f"Type '{obj}' cannot be compiled since it inherits from nn.Module, pass an instance instead"
+            )
+
+        # Enums are automatically usable in TorchScript, explicitly scripting
+        # is not necessary, but not harmful either.
+        if issubclass(obj, enum.Enum):
+            return obj
+
+        if not _is_new_style_class(obj):
+            raise RuntimeError(
+                "TorchScript classes must be new-style classes. "
+                "Please inherit from 'object'."
+            )
+        if len(obj.mro()) > 2:
+            raise RuntimeError(
+                "TorchScript classes does not support inheritance yet. "
+                "Please directly inherit from 'object'."
+            )
+        if _rcb is None:
+            _rcb = _jit_internal.createResolutionCallbackFromFrame(_frames_up + 1)
+        _compile_and_register_class(obj, _rcb, qualified_name)
+        return obj
+    elif inspect.isfunction(obj) or inspect.ismethod(obj):
+        qualified_name = _qualified_name(obj)
+        # this is a decorated fn, and we need to the underlying fn and its rcb
+        if hasattr(obj, "__script_if_tracing_wrapper"):
+            obj = obj.__original_fn  # type: ignore[union-attr]
+            _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
+
+        # some functions are explicitly marked as not supported in script mode
+        if hasattr(obj, "__script_unsupported"):
+            raise RuntimeError("TorchScript error: " + obj.__script_unsupported)
+
+        _check_directly_compile_overloaded(obj)
+        maybe_already_compiled_fn = _try_get_jit_cached_function(obj)
+        if maybe_already_compiled_fn:
+            maybe_already_compiled_fn._torchdynamo_inline = obj  # type: ignore[attr-defined]
+            return maybe_already_compiled_fn
+        ast = get_jit_def(obj, obj.__name__)
+        if _rcb is None:
+            _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
+        fn = torch._C._jit_script_compile(
+            qualified_name, ast, _rcb, get_default_args(obj)
+        )
+        # Forward docstrings
+        fn.__doc__ = obj.__doc__
+        fn.__name__ = "ScriptFunction"
+        fn.__qualname__ = "torch.jit.ScriptFunction"
+        # Allow torch.compile() to inline
+        fn._torchdynamo_inline = obj  # type: ignore[attr-defined]
+        _set_jit_function_cache(obj, fn)
+        return fn
+    else:
+        return torch.jit._recursive.create_script_class(obj)
+
+
+def script(
+    obj,
+    optimize=None,
+    _frames_up=0,
+    _rcb=None,
+    example_inputs: Union[list[tuple], dict[Callable, list[tuple]], None] = None,
+):
+    r"""Script the function.
+
+    Scripting a function or ``nn.Module`` will inspect the source code, compile
+    it as TorchScript code using the TorchScript compiler, and return a :class:`ScriptModule` or
+    :class:`ScriptFunction`. TorchScript itself is a subset of the Python language, so not all
+    features in Python work, but we provide enough functionality to compute on
+    tensors and do control-dependent operations. For a complete guide, see the
+    :ref:`language-reference`.
+
+    Scripting a dictionary or list copies the data inside it into a TorchScript instance than can be
+    subsequently passed by reference between Python and TorchScript with zero copy overhead.
+
+    ``torch.jit.script`` can be used as a function for modules, functions, dictionaries and lists
+     and as a decorator ``@torch.jit.script`` for :ref:`torchscript-classes` and functions.
+
+    Args:
+        obj (Callable, class, or nn.Module):  The ``nn.Module``, function, class type,
+                                                  dictionary, or list to compile.
+        example_inputs (Union[List[Tuple], Dict[Callable, List[Tuple]], None]): Provide example inputs
+            to annotate the arguments for a function or ``nn.Module``.
+
+    Returns:
+        If ``obj`` is ``nn.Module``, ``script`` returns
+        a :class:`ScriptModule` object. The returned :class:`ScriptModule` will
+        have the same set of sub-modules and parameters as the
+        original ``nn.Module``. If ``obj`` is a standalone function,
+        a :class:`ScriptFunction` will be returned. If ``obj`` is a ``dict``, then
+        ``script`` returns an instance of `torch._C.ScriptDict`. If ``obj`` is a ``list``,
+        then ``script`` returns an instance of `torch._C.ScriptList`.
+
+    **Scripting a function**
+        The ``@torch.jit.script`` decorator will construct a :class:`ScriptFunction`
+        by compiling the body of the function.
+
+        Example (scripting a function):
+
+        .. testcode::
+
+            import torch
+
+            @torch.jit.script
+            def foo(x, y):
+                if x.max() > y.max():
+                    r = x
+                else:
+                    r = y
+                return r
+
+            print(type(foo))  # torch.jit.ScriptFunction
+
+            # See the compiled graph as Python code
+            print(foo.code)
+
+            # Call the function using the TorchScript interpreter
+            foo(torch.ones(2, 2), torch.ones(2, 2))
+
+        .. testoutput::
+            :hide:
+
+            ...
+
+    ****Scripting a function using example_inputs**
+        Example inputs can be used to annotate a function arguments.
+
+        Example (annotating a function before scripting):
+
+        .. testcode::
+
+            import torch
+
+            def test_sum(a, b):
+                return a + b
+
+            # Annotate the arguments to be int
+            scripted_fn = torch.jit.script(test_sum, example_inputs=[(3, 4)])
+
+            print(type(scripted_fn))  # torch.jit.ScriptFunction
+
+            # See the compiled graph as Python code
+            print(scripted_fn.code)
+
+            # Call the function using the TorchScript interpreter
+            scripted_fn(20, 100)
+
+        .. testoutput::
+            :hide:
+
+            ...
+
+    **Scripting an nn.Module**
+        Scripting an ``nn.Module`` by default will compile the ``forward`` method and recursively
+        compile any methods, submodules, and functions called by ``forward``. If a ``nn.Module`` only uses
+        features supported in TorchScript, no changes to the original module code should be necessary. ``script``
+        will construct :class:`ScriptModule` that has copies of the attributes, parameters, and methods of
+        the original module.
+
+        Example (scripting a simple module with a Parameter):
+
+        .. testcode::
+
+            import torch
+
+            class MyModule(torch.nn.Module):
+                def __init__(self, N, M):
+                    super().__init__()
+                    # This parameter will be copied to the new ScriptModule
+                    self.weight = torch.nn.Parameter(torch.rand(N, M))
+
+                    # When this submodule is used, it will be compiled
+                    self.linear = torch.nn.Linear(N, M)
+
+                def forward(self, input):
+                    output = self.weight.mv(input)
+
+                    # This calls the `forward` method of the `nn.Linear` module, which will
+                    # cause the `self.linear` submodule to be compiled to a `ScriptModule` here
+                    output = self.linear(output)
+                    return output
+
+            scripted_module = torch.jit.script(MyModule(2, 3))
+
+        Example (scripting a module with traced submodules):
+
+        .. testcode::
+
+            import torch
+            import torch.nn as nn
+            import torch.nn.functional as F
+
+            class MyModule(nn.Module):
+                def __init__(self) -> None:
+                    super().__init__()
+                    # torch.jit.trace produces a ScriptModule's conv1 and conv2
+                    self.conv1 = torch.jit.trace(nn.Conv2d(1, 20, 5), torch.rand(1, 1, 16, 16))
+                    self.conv2 = torch.jit.trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16))
+
+                def forward(self, input):
+                    input = F.relu(self.conv1(input))
+                    input = F.relu(self.conv2(input))
+                    return input
+
+            scripted_module = torch.jit.script(MyModule())
+
+        To compile a method other than ``forward`` (and recursively compile anything it calls), add
+        the :func:`@torch.jit.export ` decorator to the method. To opt out of compilation
+        use :func:`@torch.jit.ignore ` or :func:`@torch.jit.unused `.
+
+        Example (an exported and ignored method in a module)::
+
+            import torch
+            import torch.nn as nn
+
+
+            class MyModule(nn.Module):
+                def __init__(self) -> None:
+                    super().__init__()
+
+                @torch.jit.export
+                def some_entry_point(self, input):
+                    return input + 10
+
+                @torch.jit.ignore
+                def python_only_fn(self, input):
+                    # This function won't be compiled, so any
+                    # Python APIs can be used
+                    import pdb
+
+                    pdb.set_trace()
+
+                def forward(self, input):
+                    if self.training:
+                        self.python_only_fn(input)
+                    return input * 99
+
+
+            scripted_module = torch.jit.script(MyModule())
+            print(scripted_module.some_entry_point(torch.randn(2, 2)))
+            print(scripted_module(torch.randn(2, 2)))
+
+        Example ( Annotating forward of nn.Module using example_inputs)::
+
+            import torch
+            import torch.nn as nn
+            from typing import NamedTuple
+
+            class MyModule(NamedTuple):
+            result: List[int]
+
+            class TestNNModule(torch.nn.Module):
+                def forward(self, a) -> MyModule:
+                    result = MyModule(result=a)
+                    return result
+
+            pdt_model = TestNNModule()
+
+            # Runs the pdt_model in eager model with the inputs provided and annotates the arguments of forward
+            scripted_model = torch.jit.script(pdt_model, example_inputs={pdt_model: [([10, 20, ], ), ], })
+
+            # Run the scripted_model with actual inputs
+            print(scripted_model([20]))
+    """
+    if not _enabled:
+        return obj
+    try:
+        global _TOPLEVEL
+        prev = _TOPLEVEL
+        _TOPLEVEL = False
+        ret = _script_impl(
+            obj=obj,
+            optimize=optimize,
+            _frames_up=_frames_up + 1,
+            _rcb=_rcb,
+            example_inputs=example_inputs,
+        )
+
+        if prev:
+            log_torchscript_usage("script", model_id=_get_model_id(ret))
+
+        return ret
+    finally:
+        _TOPLEVEL = prev
+
+
+# overloads are registered in _jit_internal and compiled here so that _overload
+# can be used in nn/functional.py without an import cycle
+
+
+def _check_overload_defaults(impl_defaults, overload_defaults, loc):
+    for name, overload_value in overload_defaults.items():
+        if name not in impl_defaults or impl_defaults[name] != overload_value:
+            raise torch.jit.frontend.FrontendError(
+                loc,
+                "Default parameters on overloads do not affect the runtime so they "
+                "must equal to the default parameter on the implementation function. Found on "
+                f"parameter {name}",
+            )
+
+
+def _compile_function_with_overload(overload_fn, qual_name, impl_fn):
+    overload_decl = get_jit_def(overload_fn, overload_fn.__name__).decl()
+    overload_signature = torch.jit.annotations.get_signature(
+        overload_fn, None, None, inspect.ismethod(overload_fn)
+    )
+    impl_ast = get_jit_def(impl_fn, impl_fn.__name__)
+    overload_defaults = get_default_args(overload_fn)
+    implementation_defaults = get_default_args(impl_fn)
+    _rcb = _jit_internal.createResolutionCallbackFromClosure(impl_fn)
+    _check_overload_defaults(
+        implementation_defaults, overload_defaults, overload_decl.range()
+    )
+    fn = torch._C._jit_script_compile_overload(
+        qual_name,
+        overload_decl,
+        impl_ast,
+        _rcb,
+        implementation_defaults,
+        overload_signature,
+    )
+    return fn
+
+
+def _get_overloads(obj):
+    # check for cached compiled fns
+    existing_compiled_fns = _try_get_jit_cached_overloads(obj)
+    qual_name = _qualified_name(obj)
+    uncompiled_overloads = _jit_internal._get_fn_overloads(qual_name)
+    if uncompiled_overloads is None:
+        return existing_compiled_fns
+
+    if obj in uncompiled_overloads:
+        raise RuntimeError(
+            _jit_internal.get_overload_no_implementation_error_message("function", obj)
+        )
+
+    compiled_fns = [
+        _compile_function_with_overload(overload_fn, qual_name, obj)
+        for overload_fn in uncompiled_overloads
+    ]
+
+    if existing_compiled_fns:
+        compiled_fns = existing_compiled_fns + compiled_fns
+
+    # cache compilation, remove information stored to do compilation
+    _set_jit_overload_cache(obj, compiled_fns)
+    _jit_internal._clear_fn_overloads(qual_name)
+    return compiled_fns
+
+
+def _check_directly_compile_overloaded(obj):
+    qual_name = _qualified_name(obj)
+    if _jit_internal._get_fn_overloads(qual_name) or _try_get_jit_cached_overloads(obj):
+        raise RuntimeError(
+            f"Function {qual_name} cannot be directly compiled because it"
+            " is overloaded. It must be used in a context of a function"
+            " where its inputs can determine which overload to call."
+        )
+
+
+def interface(obj):
+    r"""Decorate to annotate classes or modules of different types.
+
+    This decorator can be used to define an interface that can be used to annotate
+    classes or modules of different types. This can be used for to annotate a submodule
+    or attribute class that could have different types that implement the same
+    interface, or which could be swapped at runtime; or to store a list of modules or
+    classes of varying types.
+
+    It is sometimes used to implement "Callables" - functions or modules that implement
+    an interface but whose implementations differ and which can be swapped out.
+
+    Example:
+    .. testcode::
+
+        import torch
+        from typing import List
+
+        @torch.jit.interface
+        class InterfaceType:
+            def run(self, x: torch.Tensor) -> torch.Tensor:
+                pass
+
+        # implements InterfaceType
+        @torch.jit.script
+        class Impl1:
+            def run(self, x: torch.Tensor) -> torch.Tensor:
+                return x.relu()
+
+        class Impl2(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.val = torch.rand(())
+
+            @torch.jit.export
+            def run(self, x: torch.Tensor) -> torch.Tensor:
+                return x + self.val
+
+        def user_fn(impls: List[InterfaceType], idx: int, val: torch.Tensor) -> torch.Tensor:
+            return impls[idx].run(val)
+
+        user_fn_jit = torch.jit.script(user_fn)
+
+        impls = [Impl1(), torch.jit.script(Impl2())]
+        val = torch.rand(4, 4)
+        user_fn_jit(impls, 0, val)
+        user_fn_jit(impls, 1, val)
+    """
+    if not inspect.isclass(obj):
+        raise RuntimeError("interface must be applied to a class")
+    if not _is_new_style_class(obj):
+        raise RuntimeError("TorchScript interfaces must inherit from 'object'")
+
+    # Expected MRO is:
+    #   User module
+    #   torch.nn.modules.module.Module
+    #   object
+    is_module_interface = issubclass(obj, torch.nn.Module) and len(obj.mro()) == 3
+
+    if not is_module_interface and len(obj.mro()) > 2:
+        raise RuntimeError(
+            "TorchScript interface does not support inheritance yet. "
+            "Please directly inherit from 'object' or 'nn.Module'."
+        )
+
+    qualified_name = _qualified_name(obj)
+    rcb = _jit_internal.createResolutionCallbackFromFrame(1)
+    # if this type is a `nn.Module` subclass, generate a module interface type
+    # instead of a class interface type; a module interface type only compiles
+    # the user provided methods as part of the interface
+    ast = get_jit_class_def(obj, obj.__name__)
+    mangled_classname = torch._C._jit_script_interface_compile(
+        qualified_name, ast, rcb, is_module_interface
+    )
+    obj.__torch_script_interface__ = mangled_classname
+    return obj
+
+
+def _recursive_compile_class(obj, loc):
+    _qual_name = _qualified_name(obj)
+    # We're starting a new compilation, so update the error call stack in
+    # case it fails
+    error_stack = torch._C.CallStack(_qual_name, loc)  # noqa: F841
+    rcb = _jit_internal.createResolutionCallbackForClassMethods(obj)
+    return _compile_and_register_class(obj, rcb, _qual_name)
+
+
+CompilationUnit = torch._C.CompilationUnit
+set_module(CompilationUnit, "torch.jit")
+
+
+def pad(s: str, padding: int, offset: int = 0, char: str = " "):
+    if padding >= len(s):
+        padding -= len(s)
+    return "".join([char for _ in range(padding + offset)]) + s
+
+
+class _ScriptProfileColumn:
+    def __init__(self, header: str, alignment: int = 4, offset: int = 0):
+        self.header = header
+        self.alignment = alignment
+        self.offset = offset
+        self.rows: dict[int, Any] = {}
+
+    def add_row(self, lineno: int, value: Any):
+        self.rows[lineno] = value
+
+    def materialize(self):
+        max_length = len(self.header)
+        rows: list[tuple[int, str]] = []
+        for key, value in self.rows.items():
+            cell = str(value)
+            rows.append((key, cell))
+            max_length = max(len(cell), max_length)
+
+        if self.alignment > 0:
+            padding = max_length + self.alignment
+            padding -= padding % self.alignment
+        else:
+            padding = 0
+
+        rows = [(key, pad(cell, padding, self.offset)) for key, cell in rows]
+        return pad(self.header, padding, self.offset), rows
+
+
+class _ScriptProfileTable:
+    def __init__(self, cols: list[_ScriptProfileColumn], source_range: list[int]):
+        self.cols = cols
+        self.source_range = source_range
+
+    def dump_string(self):
+        outputs: list[str] = []
+        cells: list[tuple[str, dict[int, str]]] = []
+        header_buffer = ""
+        for col in self.cols:
+            header, rows = col.materialize()
+            header_buffer += header
+            cells.append((header, dict(rows)))
+
+        outputs.append(header_buffer)
+        outputs.append(pad("", len(header_buffer), 0, "="))
+        for line in self.source_range:
+            row_buffer = ""
+            for header, rows in cells:
+                cell = rows.get(line)
+                if cell is None:
+                    row_buffer += pad("", len(header))
+                else:
+                    row_buffer += cell
+            outputs.append(row_buffer)
+        return "\n".join(outputs)
+
+
+class _ScriptProfile:
+    def __init__(self) -> None:
+        self.profile = classes.profiling._ScriptProfile()
+
+    def enable(self):
+        self.profile.enable()
+
+    def disable(self):
+        self.profile.disable()
+
+    def dump_string(self) -> str:
+        outputs: list[str] = []
+        for source_stats in self.profile._dump_stats():
+            source_ref = source_stats.source()
+            source_lines = source_ref.text().splitlines()
+            dedent = min(len(line) - len(line.lstrip(" ")) for line in source_lines)
+            source_lines = [line[dedent:] for line in source_lines]
+
+            start_line = source_ref.starting_lineno()
+            end_line = start_line + len(source_lines)
+            source_range = range(start_line, end_line)
+            lineno = _ScriptProfileColumn("Line #")
+            hits = _ScriptProfileColumn("Hits")
+            time_ns = _ScriptProfileColumn("Time (ns)")
+            line_contents = _ScriptProfileColumn("Line Contents", 0, 1)
+            stats = source_stats.line_map()
+            for line in source_range:
+                lineno.add_row(line, line)
+                line_contents.add_row(line, source_lines[line - start_line])
+                stat = stats.get(line)
+                if stat is not None:
+                    hits.add_row(line, stat.count())
+                    time_ns.add_row(line, stat.duration_ns())
+
+            table = _ScriptProfileTable(
+                [lineno, hits, time_ns, line_contents], list(source_range)
+            )
+            outputs.append(table.dump_string())
+        return "\n\n".join(outputs)
+
+    def dump(self):
+        print(self.dump_string())
+
+
+def _unwrap_optional(x):
+    assert x is not None, "Unwrapping null optional"
+    return x
+
+
+_register_builtin(_unwrap_optional, "aten::_unwrap_optional")
+_register_builtin(_jit_internal.is_scripting, "aten::is_scripting")
+_register_builtin(has_torch_function, "aten::has_torch_function")
+_register_builtin(has_torch_function_unary, "aten::has_torch_function")
+_register_builtin(has_torch_function_variadic, "aten::has_torch_function")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_script.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_script.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f6727f9198cac4d787e5221b2ef63c89dedc8fa4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_script.pyi
@@ -0,0 +1,296 @@
+# mypy: allow-untyped-defs
+# mypy: disable-error-code="type-arg"
+from typing import Any, Callable, NamedTuple, overload, TypeVar
+from typing_extensions import Never, TypeAlias
+
+from _typeshed import Incomplete
+
+import torch
+from torch._classes import classes as classes
+from torch._jit_internal import _qualified_name as _qualified_name
+from torch.jit._builtins import _register_builtin as _register_builtin
+from torch.jit._fuser import (
+    _graph_for as _graph_for,
+    _script_method_graph_for as _script_method_graph_for,
+)
+from torch.jit._monkeytype_config import (
+    JitTypeTraceConfig as JitTypeTraceConfig,
+    JitTypeTraceStore as JitTypeTraceStore,
+    monkeytype_trace as monkeytype_trace,
+)
+from torch.jit._recursive import (
+    _compile_and_register_class as _compile_and_register_class,
+    infer_methods_to_compile as infer_methods_to_compile,
+    ScriptMethodStub as ScriptMethodStub,
+    wrap_cpp_module as wrap_cpp_module,
+)
+from torch.jit._serialization import validate_map_location as validate_map_location
+from torch.jit._state import (
+    _enabled as _enabled,
+    _set_jit_function_cache as _set_jit_function_cache,
+    _set_jit_overload_cache as _set_jit_overload_cache,
+    _try_get_jit_cached_function as _try_get_jit_cached_function,
+    _try_get_jit_cached_overloads as _try_get_jit_cached_overloads,
+)
+from torch.jit.frontend import (
+    get_default_args as get_default_args,
+    get_jit_class_def as get_jit_class_def,
+    get_jit_def as get_jit_def,
+)
+from torch.nn import Module as Module
+from torch.overrides import (
+    has_torch_function as has_torch_function,
+    has_torch_function_unary as has_torch_function_unary,
+    has_torch_function_variadic as has_torch_function_variadic,
+)
+from torch.package import (
+    PackageExporter as PackageExporter,
+    PackageImporter as PackageImporter,
+)
+from torch.utils import set_module as set_module
+
+ScriptFunction = torch._C.ScriptFunction
+
+type_trace_db: JitTypeTraceStore
+
+# Defined in torch/csrc/jit/python/script_init.cpp
+ResolutionCallback: TypeAlias = Callable[[str], Callable[..., Any]]
+_ClassVar = TypeVar("_ClassVar", bound=type)
+
+def _reduce(cls) -> None: ...
+
+class Attribute(NamedTuple):
+    value: Incomplete
+    type: Incomplete
+
+def _get_type_trace_db(): ...
+def _get_function_from_type(cls, name): ...
+def _is_new_style_class(cls): ...
+
+class OrderedDictWrapper:
+    _c: Incomplete
+    def __init__(self, _c) -> None: ...
+    def keys(self): ...
+    def values(self): ...
+    def __len__(self) -> int: ...
+    def __delitem__(self, k) -> None: ...
+    def items(self): ...
+    def __setitem__(self, k, v) -> None: ...
+    def __contains__(self, k) -> bool: ...
+    def __getitem__(self, k): ...
+
+class OrderedModuleDict(OrderedDictWrapper):
+    _python_modules: Incomplete
+    def __init__(self, module, python_dict) -> None: ...
+    def items(self): ...
+    def __contains__(self, k) -> bool: ...
+    def __setitem__(self, k, v) -> None: ...
+    def __getitem__(self, k): ...
+
+class ScriptMeta(type):
+    def __init__(cls, name, bases, attrs) -> None: ...
+
+class _CachedForward:
+    def __get__(self, obj, cls): ...
+
+class ScriptWarning(Warning): ...
+
+def script_method(fn): ...
+
+class ConstMap:
+    const_mapping: Incomplete
+    def __init__(self, const_mapping) -> None: ...
+    def __getattr__(self, attr): ...
+
+def unpackage_script_module(
+    importer: PackageImporter,
+    script_module_id: str,
+) -> torch.nn.Module: ...
+
+_magic_methods: Incomplete
+
+class RecursiveScriptClass:
+    _c: Incomplete
+    _props: Incomplete
+    def __init__(self, cpp_class) -> None: ...
+    def __getattr__(self, attr): ...
+    def __setattr__(self, attr, value) -> None: ...
+    def forward_magic_method(self, method_name, *args, **kwargs): ...
+    def __getstate__(self) -> None: ...
+    def __iadd__(self, other): ...
+
+def method_template(self, *args, **kwargs): ...
+
+class ScriptModule(Module, metaclass=ScriptMeta):
+    __jit_unused_properties__: Incomplete
+    def __init__(self) -> None: ...
+    forward: Callable[..., Any]
+    def __getattr__(self, attr): ...
+    def __setattr__(self, attr, value) -> None: ...
+    def define(self, src): ...
+    def _replicate_for_data_parallel(self): ...
+    def __reduce_package__(self, exporter: PackageExporter): ...
+    # add __jit_unused_properties__
+    @property
+    def code(self) -> str: ...
+    @property
+    def code_with_constants(self) -> tuple[str, ConstMap]: ...
+    @property
+    def graph(self) -> torch.Graph: ...
+    @property
+    def inlined_graph(self) -> torch.Graph: ...
+    @property
+    def original_name(self) -> str: ...
+
+class RecursiveScriptModule(ScriptModule):
+    _disable_script_meta: bool
+    _c: Incomplete
+    def __init__(self, cpp_module) -> None: ...
+    @staticmethod
+    def _construct(cpp_module, init_fn): ...
+    @staticmethod
+    def _finalize_scriptmodule(script_module) -> None: ...
+    _concrete_type: Incomplete
+    _modules: Incomplete
+    _parameters: Incomplete
+    _buffers: Incomplete
+    __dict__: Incomplete
+    def _reconstruct(self, cpp_module) -> None: ...
+    def save(self, f, **kwargs): ...
+    def _save_for_lite_interpreter(self, *args, **kwargs): ...
+    def _save_to_buffer_for_lite_interpreter(self, *args, **kwargs): ...
+    def save_to_buffer(self, *args, **kwargs): ...
+    def get_debug_state(self, *args, **kwargs): ...
+    def extra_repr(self): ...
+    def graph_for(self, *args, **kwargs): ...
+    def define(self, src) -> None: ...
+    def __getattr__(self, attr): ...
+    def __setattr__(self, attr, value) -> None: ...
+    def __copy__(self): ...
+    def __deepcopy__(self, memo): ...
+    def forward_magic_method(self, method_name, *args, **kwargs): ...
+    def __iter__(self): ...
+    def __getitem__(self, idx): ...
+    def __len__(self) -> int: ...
+    def __contains__(self, key) -> bool: ...
+    def __dir__(self): ...
+    def __bool__(self) -> bool: ...
+    def _replicate_for_data_parallel(self): ...
+
+def _get_methods(cls): ...
+
+_compiled_methods_allowlist: Incomplete
+
+def _make_fail(name): ...
+def call_prepare_scriptable_func_impl(obj, memo): ...
+def call_prepare_scriptable_func(obj): ...
+def create_script_dict(obj): ...
+def create_script_list(obj, type_hint: Incomplete | None = ...): ...
+@overload
+def script(
+    obj: type[Module],
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> Never: ...
+@overload
+def script(
+    obj: dict,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> torch.ScriptDict: ...
+@overload
+def script(
+    obj: list,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> torch.ScriptList: ...
+@overload
+def script(  # type: ignore[overload-overlap]
+    obj: Module,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> RecursiveScriptModule: ...
+@overload
+def script(  # type: ignore[overload-overlap]
+    obj: _ClassVar,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> _ClassVar: ...
+@overload
+def script(
+    obj: Callable,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> ScriptFunction: ...
+@overload
+def script(
+    obj: Any,
+    optimize: bool | None = None,
+    _frames_up: int = 0,
+    _rcb: ResolutionCallback | None = None,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = None,
+) -> RecursiveScriptClass: ...
+@overload
+def script(
+    obj,
+    optimize: Incomplete | None = ...,
+    _frames_up: int = ...,
+    _rcb: Incomplete | None = ...,
+    example_inputs: list[tuple] | dict[Callable, list[tuple]] | None = ...,
+): ...
+def _check_overload_defaults(impl_defaults, overload_defaults, loc) -> None: ...
+def _compile_function_with_overload(overload_fn, qual_name, impl_fn): ...
+def _get_overloads(obj): ...
+def _check_directly_compile_overloaded(obj) -> None: ...
+def interface(obj): ...
+def _recursive_compile_class(obj, loc): ...
+
+CompilationUnit: Incomplete
+
+def pad(s: str, padding: int, offset: int = ..., char: str = ...): ...
+
+class _ScriptProfileColumn:
+    header: Incomplete
+    alignment: Incomplete
+    offset: Incomplete
+    rows: Incomplete
+    def __init__(
+        self,
+        header: str,
+        alignment: int = ...,
+        offset: int = ...,
+    ) -> None: ...
+    def add_row(self, lineno: int, value: Any): ...
+    def materialize(self): ...
+
+class _ScriptProfileTable:
+    cols: Incomplete
+    source_range: Incomplete
+    def __init__(
+        self,
+        cols: list[_ScriptProfileColumn],
+        source_range: list[int],
+    ) -> None: ...
+    def dump_string(self): ...
+
+class _ScriptProfile:
+    profile: Incomplete
+    def __init__(self) -> None: ...
+    def enable(self) -> None: ...
+    def disable(self) -> None: ...
+    def dump_string(self) -> str: ...
+    def dump(self) -> None: ...
+
+def _unwrap_optional(x): ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_serialization.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_serialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..57cb6a8475d9859621433864a62955243b824981
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_serialization.py
@@ -0,0 +1,280 @@
+# mypy: allow-untyped-defs
+"""Serialization.
+
+This module contains functionality for serializing TorchScript modules, notably:
+    * torch.jit.save
+    * torch.jit.load
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+import os
+
+import torch
+from torch._jit_internal import _get_model_id
+from torch._utils_internal import log_torchscript_usage
+from torch.jit._recursive import wrap_cpp_module
+from torch.serialization import validate_cuda_device
+
+
+def save(m, f, _extra_files=None):
+    r"""
+    Save an offline version of this module for use in a separate process.
+
+    The saved module serializes all of the methods, submodules, parameters, and
+    attributes of this module. It can be loaded into the C++ API using
+    ``torch::jit::load(filename)`` or into the Python API with
+    :func:`torch.jit.load `.
+
+    To be able to save a module, it must not make any calls to native Python
+    functions.  This means that all submodules must be subclasses of
+    :class:`ScriptModule` as well.
+
+    .. DANGER::
+        All modules, no matter their device, are always loaded onto the CPU
+        during loading.  This is different from :func:`torch.load`'s semantics
+        and may change in the future.
+
+    Args:
+        m: A :class:`ScriptModule` to save.
+        f: A file-like object (has to implement write and flush) or a string
+           containing a file name.
+        _extra_files: Map from filename to contents which will be stored as part of `f`.
+
+    .. note::
+        torch.jit.save attempts to preserve the behavior of some operators
+        across versions. For example, dividing two integer tensors in
+        PyTorch 1.5 performed floor division, and if the module
+        containing that code is saved in PyTorch 1.5 and loaded in PyTorch 1.6
+        its division behavior will be preserved. The same module saved in
+        PyTorch 1.6 will fail to load in PyTorch 1.5, however, since the
+        behavior of division changed in 1.6, and 1.5 does not know how to
+        replicate the 1.6 behavior.
+
+    Example:
+    .. testcode::
+
+        import torch
+        import io
+
+        class MyModule(torch.nn.Module):
+            def forward(self, x):
+                return x + 10
+
+        m = torch.jit.script(MyModule())
+
+        # Save to file
+        torch.jit.save(m, 'scriptmodule.pt')
+        # This line is equivalent to the previous
+        m.save("scriptmodule.pt")
+
+        # Save to io.BytesIO buffer
+        buffer = io.BytesIO()
+        torch.jit.save(m, buffer)
+
+        # Save with extra files
+        extra_files = {'foo.txt': b'bar'}
+        torch.jit.save(m, 'scriptmodule.pt', _extra_files=extra_files)
+    """
+    log_torchscript_usage("save", model_id=_get_model_id(m))
+    if _extra_files is None:
+        _extra_files = {}
+    if isinstance(f, (str, os.PathLike)):
+        m.save(f, _extra_files=_extra_files)
+    else:
+        ret = m.save_to_buffer(_extra_files=_extra_files)
+        f.write(ret)
+
+
+def load(f, map_location=None, _extra_files=None, _restore_shapes=False):
+    r"""
+    Load a :class:`ScriptModule` or :class:`ScriptFunction` previously saved with :func:`torch.jit.save `.
+
+    All previously saved modules, no matter their device, are first loaded onto CPU,
+    and then are moved to the devices they were saved from. If this fails (e.g.
+    because the run time system doesn't have certain devices), an exception is
+    raised.
+
+    Args:
+        f: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+        map_location (string or torch.device): A simplified version of
+            ``map_location`` in `torch.jit.save` used to dynamically remap
+            storages to an alternative set of devices.
+        _extra_files (dictionary of filename to content): The extra
+            filenames given in the map would be loaded and their content
+            would be stored in the provided map.
+        _restore_shapes (bool): Whether or not to retrace the module on load using stored inputs
+
+    Returns:
+        A :class:`ScriptModule` object.
+
+    .. warning::
+        It is possible to construct malicious pickle data which will execute arbitrary code
+        during func:`torch.jit.load`. Never load data that could have come from an untrusted
+        source, or that could have been tampered with. **Only load data you trust**.
+
+    Example:
+    .. testcode::
+
+        import torch
+        import io
+
+        torch.jit.load('scriptmodule.pt')
+
+        # Load ScriptModule from io.BytesIO object
+        with open('scriptmodule.pt', 'rb') as f:
+            buffer = io.BytesIO(f.read())
+
+        # Load all tensors to the original device
+        torch.jit.load(buffer)
+
+        # Load all tensors onto CPU, using a device
+        buffer.seek(0)
+        torch.jit.load(buffer, map_location=torch.device('cpu'))
+
+        # Load all tensors onto CPU, using a string
+        buffer.seek(0)
+        torch.jit.load(buffer, map_location='cpu')
+
+        # Load with extra files.
+        extra_files = {'foo.txt': ''}  # values will be replaced with data
+        torch.jit.load('scriptmodule.pt', _extra_files=extra_files)
+        print(extra_files['foo.txt'])
+
+    .. testoutput::
+        :hide:
+
+        ...
+
+    .. testcleanup::
+
+        import os
+        os.remove("scriptmodule.pt")
+    """
+    if isinstance(f, (str, os.PathLike)):
+        if not os.path.exists(f):
+            raise ValueError(f"The provided filename {f} does not exist")
+        if os.path.isdir(f):
+            raise ValueError(f"The provided filename {f} is a directory")
+
+    map_location = validate_map_location(map_location)
+    if _extra_files is None:
+        _extra_files = {}
+
+    cu = torch._C.CompilationUnit()
+    if isinstance(f, (str, os.PathLike)):
+        cpp_module = torch._C.import_ir_module(
+            cu, os.fspath(f), map_location, _extra_files, _restore_shapes
+        )  # type: ignore[call-arg]
+    else:
+        cpp_module = torch._C.import_ir_module_from_buffer(
+            cu, f.read(), map_location, _extra_files, _restore_shapes
+        )  # type: ignore[call-arg]
+
+    # TODO: Pretty sure this approach loses ConstSequential status and such
+    ret = wrap_cpp_module(cpp_module)
+    log_torchscript_usage("load", model_id=_get_model_id(ret))
+    return ret
+
+
+def validate_map_location(map_location=None):
+    if isinstance(map_location, str):
+        map_location = torch.device(map_location)
+    elif not (map_location is None or isinstance(map_location, torch.device)):
+        raise ValueError(
+            "map_location should be either None, string or torch.device, "
+            "but got type: " + str(type(map_location))
+        )
+
+    if str(map_location).startswith("cuda"):
+        validate_cuda_device(map_location)
+
+    return map_location
+
+
+def jit_module_from_flatbuffer(f):
+    if isinstance(f, (str, os.PathLike)):
+        f = os.fspath(f)
+        return wrap_cpp_module(torch._C._load_jit_module_from_file(f))
+    else:
+        return wrap_cpp_module(torch._C._load_jit_module_from_bytes(f.read()))
+
+
+def save_jit_module_to_flatbuffer(m, f, _extra_files=None):
+    r"""
+    Save an offline version of this module for use in a separate process.
+
+    The saved module serializes all of the methods, submodules, parameters, and
+    attributes of this module. It can be loaded into the C++ API using
+    ``torch::jit::load_jit_module_from_file(filename)`` or into the Python API with
+    :func:`torch.jit.jit_module_from_flatbuffer`.
+
+    To be able to save a module, it must not make any calls to native Python
+    functions.  This means that all submodules must be subclasses of
+    :class:`ScriptModule` as well.
+
+    .. DANGER::
+        All modules, no matter their device, are always loaded onto the CPU
+        during loading.  This is different from :func:`torch.load`'s semantics
+        and may change in the future.
+
+    Args:
+        m: A :class:`ScriptModule` to save.
+        f: A string for file path
+
+
+    Example:
+    .. testcode::
+
+        import torch
+        import io
+
+        class MyModule(torch.nn.Module):
+            def forward(self, x):
+                return x + 10
+
+        m = torch.jit.script(MyModule())
+
+        # Save to file
+        torch.jit.save_jit_module_to_flatbuffer(m, 'scriptmodule.ff')
+    """
+    extra_files = _extra_files
+    if extra_files is None:
+        extra_files = {}
+
+    if isinstance(f, (str, os.PathLike)):
+        f = os.fspath(f)
+        torch._C._save_jit_module(m._c, f, extra_files)
+    else:
+        s = torch._C._save_jit_module_to_bytes(m._c, extra_files)
+        f.write(s)
+
+
+def get_flatbuffer_module_info(path_or_file):
+    r"""Get some information regarding a model file in flatbuffer format.
+
+    Args:
+        path_or_file: Either str, Path or file like object (BytesIO OK).
+            If it's str or Path, we will read the file referenced by that
+            path as Bytes.
+
+    Returns:
+        A dict with metadata on what that file contains, currently looks like
+        this:
+        {
+            'bytecode_version': 4,  # int
+            'operator_version': 4,  # int
+            'function_names': {
+                '__torch__.___torch_mangle_0.Foo.forward'}, # set
+            'type_names': set(),  # set
+            'opname_to_num_args': {'aten::linear': 3} # Dict[str, int]
+        }
+    """
+    if isinstance(path_or_file, (str, os.PathLike)):
+        with open(path_or_file, "rb") as f:
+            all_bytes = f.read()
+    else:
+        all_bytes = path_or_file.read()
+    return torch._C._get_module_info_from_flatbuffer(all_bytes)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_shape_functions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_shape_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa0dc2b82d541d80f84081a3bcdab877a86f0de0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_shape_functions.py
@@ -0,0 +1,1474 @@
+# mypy: allow-untyped-defs
+import math
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+
+number = Union[int, float]
+# flake8: noqa
+
+###
+# There are generated files that depend on this file
+# To re-generate, please run from the root of the repo:
+# python torchgen/shape_functions/gen_jit_shape_functions.py
+
+# How to test:
+# After regenerating files, compile PyTorch.
+# Then run: ./build/bin/test_jit --gtest_filter=TestShapeGraphLinting.Basic
+# If you have enabled opinfo testing for the op, also run:
+# python test/test_ops_jit.py TestJitCPU.test_variant_consistency_jit_[FAILING_OP]_cpu_float32
+# to reproduce errors from opinfo tests.
+
+# Example PR: https://github.com/pytorch/pytorch/pull/80860/files
+####
+
+import torch
+
+
+def broadcast(a: list[int], b: list[int]):
+    dimsA = len(a)
+    dimsB = len(b)
+    ndim = max(dimsA, dimsB)
+    expandedSizes: list[int] = []
+
+    for i in range(ndim):
+        offset = ndim - 1 - i
+        dimA = dimsA - 1 - offset
+        dimB = dimsB - 1 - offset
+        sizeA = a[dimA] if (dimA >= 0) else 1
+        sizeB = b[dimB] if (dimB >= 0) else 1
+
+        if sizeA != sizeB and sizeA != 1 and sizeB != 1:
+            # TODO: only assertion error is bound in C++ compilation right now
+            raise AssertionError(
+                f"The size of tensor a {sizeA} must match the size of tensor b ({sizeB}) at non-singleton dimension {i}"
+            )
+
+        expandedSizes.append(sizeB if sizeA == 1 else sizeA)
+
+    return expandedSizes
+
+
+def broadcast_three(a: list[int], b: list[int], c: list[int]):
+    return broadcast(broadcast(a, b), c)
+
+
+def broadcast_one_three(a: list[int], b: Any, c: list[int]):
+    return broadcast(a, c)
+
+
+def adaptive_avg_pool2d(self: list[int], out: list[int]):
+    assert len(out) == 2
+    assert len(self) == 3 or len(self) == 4
+    for i in range(1, len(self)):
+        assert self[i] != 0
+
+    shape: list[int] = []
+    for i in range(0, len(self) - 2):
+        shape.append(self[i])
+    for elem in out:
+        shape.append(elem)
+    return shape
+
+
+def _copy(self: list[int]):
+    out: list[int] = []
+    for elem in self:
+        out.append(elem)
+    return out
+
+
+def unary(self: list[int]):
+    return _copy(self)
+
+
+def broadcast_inplace(a: list[int], b: list[int]):
+    dimsA = len(a)
+    dimsB = len(b)
+    if dimsB > dimsA:
+        raise AssertionError(
+            f"The dims of tensor b ({dimsB}) must be less than or equal tothe dims of tensor a ({dimsA}) "
+        )
+    for dimA in range(dimsA):
+        dimB = dimsB - dimsA + dimA
+        sizeA = a[dimA]
+        sizeB = b[dimB] if (dimB >= 0) else 1
+        if sizeA != sizeB and sizeB != 1:
+            # TODO: only assertion error is bound in C++ compilation right now
+            raise AssertionError(
+                "The size of tensor a {} must match the size of tensor b ("
+                "{}) at non-singleton dimension {}".format(sizeA, sizeB, dimA)
+            )
+    return _copy(a)
+
+
+def expand(self: list[int], sizes: list[int]):
+    assert len(sizes) >= len(self)
+    ndim = len(sizes)
+    tensor_dim = len(self)
+    if ndim == 0:
+        return _copy(sizes)
+    out: list[int] = []
+    for i in range(ndim):
+        offset = ndim - 1 - i
+        dim = tensor_dim - 1 - offset
+        size = self[dim] if dim >= 0 else 1
+        targetSize = sizes[i]
+        if targetSize == -1:
+            assert dim >= 0
+            targetSize = size
+        if size != targetSize:
+            assert size == 1
+            size = targetSize
+        out.append(size)
+    return out
+
+
+def expand_one_unused(self: list[int], sizes: list[int], inp0: Any):
+    return expand(self, sizes)
+
+
+def infer_size_impl(shape: list[int], numel: int) -> list[int]:
+    newsize = 1
+    infer_dim: Optional[int] = None
+    for dim in range(len(shape)):
+        if shape[dim] == -1:
+            if infer_dim is not None:
+                raise AssertionError("only one dimension can be inferred")
+            infer_dim = dim
+        elif shape[dim] >= 0:
+            newsize *= shape[dim]
+        else:
+            raise AssertionError("invalid shape dimensions")
+    if not (
+        numel == newsize
+        or (infer_dim is not None and newsize > 0 and numel % newsize == 0)
+    ):
+        raise AssertionError("invalid shape")
+    out = _copy(shape)
+    if infer_dim is not None:
+        out[infer_dim] = numel // newsize
+    return out
+
+
+def numel(sizes: list[int]):
+    numel = 1
+    for elem in sizes:
+        numel *= elem
+    return numel
+
+
+def view(self: list[int], sizes: list[int]):
+    return infer_size_impl(sizes, numel(self))
+
+
+def view_one_unused(self: list[int], sizes: list[int], *, implicit: bool = False):
+    return view(self, sizes)
+
+
+def sum_mean_dim(
+    self: list[int], opt_dims: Optional[list[int]], keep_dim: bool, dt: Any
+):
+    out: list[int] = []
+    if opt_dims is None or len(opt_dims) == 0:
+        dims: list[int] = list(range(len(self)))
+    else:
+        dims = opt_dims
+
+    for idx in range(len(self)):
+        is_mean_dim: bool = False
+        for reduce_dim in dims:
+            if idx == maybe_wrap_dim(reduce_dim, len(self)):
+                is_mean_dim = True
+        if is_mean_dim:
+            if keep_dim:
+                out.append(1)
+        else:
+            out.append(self[idx])
+    return out
+
+
+def max_dim(self: list[int], dim: int, keep_dim: bool):
+    out = sum_mean_dim(self, [dim], keep_dim, None)
+    return out, out
+
+
+# note: python already rounds down towards negative infinity on integer division, special arithmetic not needed
+def div_rtn(x: int, y: int):
+    return x // y
+
+
+def pooling_output_shape_pad_lr(
+    inputSize: int,
+    kernelSize: int,
+    pad_l: int,
+    pad_r: int,
+    stride: int,
+    dilation: int,
+    ceil_mode: bool,
+):
+    outputSize = (
+        div_rtn(
+            inputSize
+            + pad_l
+            + pad_r
+            - dilation * (kernelSize - 1)
+            - 1
+            + (stride - 1 if ceil_mode else 0),
+            stride,
+        )
+        + 1
+    )
+    if ceil_mode:
+        if (outputSize - 1) * stride >= inputSize + pad_l:
+            outputSize = outputSize - 1
+    return outputSize
+
+
+def pooling_output_shape(
+    inputSize: int,
+    kernelSize: int,
+    pad_l: int,
+    stride: int,
+    dilation: int,
+    ceil_mode: bool,
+):
+    assert stride != 0, "stride should not be zeero"
+    return pooling_output_shape_pad_lr(
+        inputSize, kernelSize, pad_l, pad_l, stride, dilation, ceil_mode
+    )
+
+
+def pool2d_shape_check(
+    input: list[int],
+    kH: int,
+    kW: int,
+    dH: int,
+    dW: int,
+    padH: int,
+    padW: int,
+    dilationH: int,
+    dilationW: int,
+    nInputPlane: int,
+    inputHeight: int,
+    inputWidth: int,
+    outputHeight: int,
+    outputWidth: int,
+):
+    ndim = len(input)
+
+    assert kW > 0 and kH > 0
+    assert dW > 0 and dH > 0
+    assert dilationH > 0 and dilationW > 0
+
+    valid_dims = input[1] != 0 and input[2] != 0
+    assert (
+        ndim == 3
+        and input[0] != 0
+        and valid_dims
+        or (ndim == 4 and valid_dims and input[3] != 0)
+    )
+
+    assert kW // 2 >= padW and kH // 2 >= padH
+    assert outputWidth >= 1 and outputHeight >= 1
+
+
+def max_pool2d(
+    input: list[int],
+    kernel_size: list[int],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    ceil_mode: bool,
+):
+    assert (
+        len(kernel_size) == 1 or len(kernel_size) == 2
+    ), "max_pool2d: kernel_size must either be a single int, or a tuple of two ints"
+    kH = kernel_size[0]
+    kW = kH if len(kernel_size) == 1 else kernel_size[1]
+
+    assert (
+        len(stride) == 0 or len(stride) == 1 or len(stride) == 2
+    ), "max_pool2d: stride must either be omitted, a single int, or a tuple of two ints"
+    dH = kH if len(stride) == 0 else stride[0]
+    if len(stride) == 0:
+        dW = kW
+    elif len(stride) == 1:
+        dW = dH
+    else:
+        dW = stride[1]
+
+    assert (
+        len(padding) == 1 or len(padding) == 2
+    ), "max_pool2d: padding must either be a single int, or a tuple of two ints"
+    padH = padding[0]
+    padW = padH if len(padding) == 1 else padding[1]
+
+    assert (
+        len(dilation) == 1 or len(dilation) == 2
+    ), "max_pool2d: dilation must be either a single int, or a tuple of two ints"
+    dilationH = dilation[0]
+    dilationW = dilationH if len(dilation) == 1 else dilation[1]
+
+    assert len(input) == 3 or len(input) == 4
+
+    nbatch = input[-4] if len(input) == 4 else 1
+    nInputPlane = input[-3]
+    inputHeight = input[-2]
+    inputWidth = input[-1]
+
+    outputHeight = pooling_output_shape(inputHeight, kH, padH, dH, dilationH, ceil_mode)
+    outputWidth = pooling_output_shape(inputWidth, kW, padW, dW, dilationW, ceil_mode)
+
+    pool2d_shape_check(
+        input,
+        kH,
+        kW,
+        dH,
+        dW,
+        padH,
+        padW,
+        dilationH,
+        dilationW,
+        nInputPlane,
+        inputHeight,
+        inputWidth,
+        outputHeight,
+        outputWidth,
+    )
+
+    if len(input) == 3:
+        return [nInputPlane, outputHeight, outputWidth]
+    else:
+        return [nbatch, nInputPlane, outputHeight, outputWidth]
+
+
+def max_pool2d_with_indices(
+    input: list[int],
+    kernel_size: list[int],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    ceil_mode: bool,
+):
+    out = max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
+    return (out, out)
+
+
+def upsample_nearest2d(
+    input: list[int],
+    output_size: Optional[list[int]],
+    scale_factors: Optional[list[float]],
+):
+    out: list[int] = []
+    out.append(input[0])
+    out.append(input[1])
+
+    if scale_factors is None and output_size is None:
+        assert 0, "Either output_size or scale_factors must be presented"
+
+    if output_size is not None:
+        assert (
+            scale_factors is None
+        ), "Must specify exactly one of output_size and scale_factors"
+        assert len(output_size) == 2
+        out.append(output_size[0])
+        out.append(output_size[1])
+
+    if scale_factors is not None:
+        assert (
+            output_size is None
+        ), "Must specify exactly one of output_size and scale_factors"
+        assert len(scale_factors) == 2
+        out.append(int(input[2] * scale_factors[0]))
+        out.append(int(input[3] * scale_factors[1]))
+
+    return out
+
+
+def mm(self: list[int], mat2: list[int]):
+    assert len(self) == 2, "self must be a matrix"
+    assert len(mat2) == 2, "mat2 must be a matrix"
+
+    assert self[1] == mat2[0]
+    return [self[0], mat2[1]]
+
+
+def dot(self: list[int], tensor: list[int]):
+    assert len(self) == 1 and len(tensor) == 1
+    assert self[0] == tensor[0]
+    out: list[int] = []
+    return out
+
+
+def mv(self: list[int], vec: list[int]):
+    assert len(self) == 2 and len(vec) == 1
+    assert self[1] == vec[0]
+    # TODO: return self
+    return [self[0]]
+
+
+def unsqueeze(li: list[int], dim: int):
+    dim = maybe_wrap_dim(dim, len(li) + 1)
+    out = _copy(li)
+    out.insert(dim, 1)
+    return out
+
+
+def squeeze_nodim(li: list[int]):
+    out: list[int] = []
+    for i in range(len(li)):
+        if li[i] != 1:
+            out.append(li[i])
+    return out
+
+
+def squeeze(li: list[int], dim: int):
+    out: list[int] = []
+    wrapped_dim = maybe_wrap_dim(dim, len(li))
+    for i in range(len(li)):
+        if i == wrapped_dim:
+            if li[i] != 1:
+                out.append(li[i])
+        else:
+            out.append(li[i])
+    return out
+
+
+def squeeze_dims(li: list[int], dims: list[int]):
+    if len(dims) == 0:
+        return li
+    wrapped_dims = _copy(dims)
+    for i in range(len(dims)):
+        wrapped_dims[i] = maybe_wrap_dim(wrapped_dims[i], len(li))
+    result: list[int] = []
+    for i in range(len(li)):
+        if li[i] == 1:
+            if i not in wrapped_dims:
+                result.append(li[i])
+        else:
+            result.append(li[i])
+    return result
+
+
+def index_select(self: list[int], dim: int, index: list[int]):
+    dim = maybe_wrap_dim(dim, len(self))
+    numel = multiply_integers(index)
+    assert len(index) <= 1
+    assert dim == 0 or dim < len(self)
+    result_size: list[int] = []
+    for i in range(len(self)):
+        if dim == i:
+            result_size.append(numel)
+        else:
+            result_size.append(self[i])
+    return result_size
+
+
+def embedding(
+    weight: list[int],
+    indices: list[int],
+    padding_idx: int = -1,
+    scale_grad_by_freq: bool = False,
+    sparse: bool = False,
+):
+    assert len(weight) == 2
+    if len(indices) == 1:
+        return index_select(weight, 0, indices)
+    size = _copy(indices)
+    size.append(weight[1])
+    return size
+
+
+def max_int():
+    return 9223372036854775807
+
+
+def slice(
+    self: list[int], dim: int, start: Optional[int], end: Optional[int], step: int
+):
+    ndim = len(self)
+    assert ndim != 0
+    dim = maybe_wrap_dim(dim, ndim)
+    start_val = start if start is not None else 0
+    end_val = end if end is not None else max_int()
+    assert step > 0
+    if start_val == max_int():
+        start_val = 0
+    if start_val < 0:
+        start_val += self[dim]
+    if end_val < 0:
+        end_val += self[dim]
+    if start_val < 0:
+        start_val = 0
+    elif start_val > self[dim]:
+        start_val = self[dim]
+    if end_val < start_val:
+        end_val = start_val
+    elif end_val >= self[dim]:
+        end_val = self[dim]
+    slice_len = end_val - start_val
+    out = _copy(self)
+    out[dim] = (slice_len + step - 1) // step
+    return out
+
+
+def check_cat_no_zero_dim(tensors: list[list[int]]):
+    for tensor in tensors:
+        assert len(tensor) > 0
+
+
+def legacy_cat_wrap_dim(dim: int, tensor_sizes: list[list[int]]):
+    out_dim: Optional[int] = None
+    for size in tensor_sizes:
+        if not (len(size) == 1 and size[0] == 0):
+            if out_dim is None:
+                out_dim = maybe_wrap_dim(dim, len(size))
+    if out_dim is None:
+        out_dim = dim
+    return out_dim
+
+
+def should_skip(tensor: list[int]):
+    return numel(tensor) == 0 and len(tensor) == 1
+
+
+def check_cat_shape_except_dim(
+    first: list[int], second: list[int], dimension: int, index: int
+):
+    first_dims = len(first)
+    second_dims = len(second)
+    assert first_dims == second_dims, "Tensors must have same number of dimensions"
+    for dim in range(0, first_dims):
+        if dim != dimension:
+            assert (
+                first[dim] == second[dim]
+            ), "Sizes of tensors must match except in dimension"
+
+
+def cat(tensors: list[list[int]], dim: int):
+    check_cat_no_zero_dim(tensors)
+    dim = legacy_cat_wrap_dim(dim, tensors)
+    assert len(tensors) > 0
+    not_skipped_tensor: Optional[list[int]] = None
+    for tensor in tensors:
+        if not should_skip(tensor):
+            not_skipped_tensor = tensor
+    if not_skipped_tensor is None:
+        return [0]
+
+    cat_dim_size = 0
+
+    for i in range(len(tensors)):
+        tensor = tensors[i]
+        if not should_skip(tensor):
+            check_cat_shape_except_dim(not_skipped_tensor, tensor, dim, i)
+            cat_dim_size = cat_dim_size + tensor[dim]
+
+    result_size = _copy(not_skipped_tensor)
+    result_size[dim] = cat_dim_size
+    return result_size
+
+
+def stack(tensors: list[list[int]], dim: int):
+    unsqueezed_tensors: list[list[int]] = []
+    for tensor in tensors:
+        unsqueezed = unsqueeze(tensor, dim)
+        unsqueezed_tensors.append(unsqueezed)
+    return cat(unsqueezed_tensors, dim)
+
+
+def select(self: list[int], dim: int, index: int):
+    ndim = len(self)
+    assert ndim != 0
+    dim = maybe_wrap_dim(dim, ndim)
+    size = self[dim]
+    assert not (index < -size or index >= size)
+    if index < 0:
+        index += size
+    out: list[int] = []
+    for i in range(ndim):
+        if i != dim:
+            out.append(self[i])
+    return out
+
+
+def matmul(tensor1: list[int], tensor2: list[int]):
+    dim_tensor1 = len(tensor1)
+    dim_tensor2 = len(tensor2)
+    if dim_tensor1 == 1 and dim_tensor2 == 1:
+        return dot(tensor1, tensor2)
+    elif dim_tensor1 == 2 and dim_tensor2 == 1:
+        return mv(tensor1, tensor2)
+    elif dim_tensor1 == 1 and dim_tensor2 == 2:
+        return squeeze(mm(unsqueeze(tensor1, 0), tensor2), 0)
+    elif dim_tensor1 == 2 and dim_tensor2 == 2:
+        return mm(tensor1, tensor2)
+    elif dim_tensor1 >= 1 and dim_tensor2 >= 1:
+        # We are multiplying b1 x n x m1 by x2 x m2 x p (where b1 can be a list);
+        # we track m1 vs m2 separately even though they must match for nicer error messages
+        n = tensor1[-2] if dim_tensor1 > 1 else 1
+        batch_tensor1: list[int] = []
+        # TODO: handling of slice
+        for i in range(dim_tensor1 - 2):
+            batch_tensor1.append(tensor1[i])
+        p = tensor2[-1]
+        batch_tensor2: list[int] = []
+        # TODO: handling of slice
+        for i in range(dim_tensor2 - 2):
+            batch_tensor2.append(tensor2[i])
+
+        # expand the batch portion (i.e. cut off matrix dimensions and expand rest)
+        expand_batch_portion = broadcast(batch_tensor1, batch_tensor2)
+
+        # todo: copy ?
+        output_shape = expand_batch_portion
+        if dim_tensor1 > 1:
+            output_shape.append(n)
+
+        if dim_tensor2 > 1:
+            output_shape.append(p)
+
+        return output_shape
+    else:
+        assert False, "both  arguments to matmul need to be at least 1D"
+
+
+def t(self: list[int]):
+    assert len(self) <= 2
+    self_len = len(self)
+    if self_len == 0:
+        out: list[int] = []
+        return out
+    elif self_len == 1:
+        return [self[0]]
+    else:
+        return [self[1], self[0]]
+
+
+def transpose(self: list[int], dim0: int, dim1: int):
+    ndims = len(self)
+    dim0 = maybe_wrap_dim(dim0, ndims)
+    dim1 = maybe_wrap_dim(dim1, ndims)
+    if dim0 == dim1:
+        return _copy(self)
+    out: list[int] = []
+    for i in range(ndims):
+        if i == dim0:
+            out.append(self[dim1])
+        elif i == dim1:
+            out.append(self[dim0])
+        else:
+            out.append(self[i])
+    return out
+
+
+def linear(input: list[int], weight: list[int], bias: Optional[list[int]]):
+    out = matmul(input, t(weight))
+    if bias is not None:
+        assert broadcast(bias, out) == out
+    return out
+
+
+def addmm(self: list[int], mat1: list[int], mat2: list[int], beta: Any, alpha: Any):
+    return broadcast(self, mm(mat1, mat2))
+
+
+def check_non_negative(array: list[int]) -> bool:
+    # TODO: look into rewriting with early return and getting loop unrolling to fire
+    non_negative = False
+    for val in array:
+        if val < 0:
+            non_negative = True
+    return non_negative
+
+
+def check_shape_forward(
+    input: list[int],
+    weight_sizes: list[int],
+    bias: Optional[list[int]],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    groups: int,
+):
+    k = len(input)
+    weight_dim = len(weight_sizes)
+
+    # TODO: assertions could be expanded with the error messages
+    assert not check_non_negative(padding)
+    assert not check_non_negative(stride)
+
+    assert weight_dim == k
+    assert weight_sizes[0] >= groups
+    assert (weight_sizes[0] % groups) == 0
+    # only handling not transposed
+    assert input[1] == weight_sizes[1] * groups
+    assert bias is None or (len(bias) == 1 and bias[0] == weight_sizes[0])
+
+    for i in range(2, k):
+        assert (input[i] + 2 * padding[i - 2]) >= (
+            dilation[i - 2] * (weight_sizes[i] - 1) + 1
+        )
+
+    # this is not handling transposed convolution yet
+
+
+def conv_output_size(
+    input_size: list[int],
+    weight_size: list[int],
+    bias: Optional[list[int]],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    groups: int,
+):
+    check_shape_forward(
+        input_size, weight_size, bias, stride, padding, dilation, groups
+    )
+
+    has_dilation = len(dilation) > 0
+    dim = len(input_size)
+    output_size: list[int] = []
+    input_batch_size_dim = 0
+    weight_output_channels_dim = 0
+    output_size.append(input_size[input_batch_size_dim])
+    output_size.append(weight_size[weight_output_channels_dim])
+
+    for d in range(2, dim):
+        dilation_ = dilation[d - 2] if has_dilation else 1
+        kernel = dilation_ * (weight_size[d] - 1) + 1
+        output_size.append(
+            (input_size[d] + (2 * padding[d - 2]) - kernel) // stride[d - 2] + 1
+        )
+    return output_size
+
+
+def conv1d(
+    input: list[int],
+    weight: list[int],
+    bias: Optional[list[int]],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    groups: int,
+):
+    assert len(weight) == 3
+    assert len(input) == 3
+    return conv_output_size(input, weight, bias, stride, padding, dilation, groups)
+
+
+def conv2d(
+    input: list[int],
+    weight: list[int],
+    bias: Optional[list[int]],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    groups: int,
+):
+    assert len(weight) == 4
+    assert len(input) == 4
+    return conv_output_size(input, weight, bias, stride, padding, dilation, groups)
+
+
+def conv_backwards(
+    grad_output: list[int],
+    input: list[int],
+    weight: list[int],
+    biases: Optional[list[int]],
+):
+    # Bias gradient is always generated regardess of if biases is supplied
+    return _copy(input), _copy(weight), [grad_output[1]]
+
+
+def conv_transpose2d_input(
+    input: list[int],
+    weight: list[int],
+    bias: Optional[list[int]] = None,
+    stride: Optional[list[int]] = None,
+    padding: Optional[list[int]] = None,
+    output_padding: Optional[list[int]] = None,
+    groups: int = 1,
+    dilation: Optional[list[int]] = None,
+) -> list[int]:
+    if stride is None:
+        stride = [1, 1]
+    if padding is None:
+        padding = [0, 0]
+    if output_padding is None:
+        output_padding = [0, 0]
+    if dilation is None:
+        dilation = [1, 1]
+    has_dilation = len(dilation) > 0
+    dim = len(input)
+    output_size: list[int] = []
+    input_batch_size_dim = 0
+    weight_output_channels_dim = 1
+    output_size.append(input[input_batch_size_dim])
+    output_size.append(weight[weight_output_channels_dim] * groups)
+
+    for d in range(2, dim):
+        dilation_ = dilation[d - 2] if has_dilation else 1
+        kernel = dilation_ * (weight[d] - 1)
+        output_size.append(
+            (input[d] - 1) * stride[d - 2]
+            - 2 * padding[d - 2]
+            + kernel
+            + output_padding[d - 2]
+            + 1
+        )
+    return output_size
+
+
+def conv_forwards(
+    input: list[int],
+    weight: list[int],
+    bias: Optional[list[int]],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    transposed: bool,
+    output_padding: list[int],
+    groups: int,
+) -> list[int]:
+    has_dilation = len(dilation) > 0
+    has_output_padding = len(output_padding) > 0
+    dim = len(input)
+    output_size: list[int] = []
+    input_batch_size_dim = 0
+    weight_output_channels_dim = 1 if transposed else 0
+    output_size.append(input[input_batch_size_dim])
+    if transposed:
+        output_size.append(weight[weight_output_channels_dim] * groups)
+    else:
+        output_size.append(weight[weight_output_channels_dim])
+
+    for d in range(2, dim):
+        dilation_ = dilation[d - 2] if has_dilation else 1
+        output_padding_ = output_padding[d - 2] if has_output_padding else 0
+        if transposed:
+            kernel = dilation_ * (weight[d] - 1)
+            output_size.append(
+                (input[d] - 1) * stride[d - 2]
+                - 2 * padding[d - 2]
+                + kernel
+                + output_padding_
+                + 1
+            )
+        else:
+            kernel = dilation_ * (weight[d] - 1) + 1
+            output_size.append(
+                (input[d] + (2 * padding[d - 2]) - kernel) // stride[d - 2] + 1
+            )
+    return output_size
+
+
+def _conv_forwards(
+    input: list[int],
+    weight: list[int],
+    bias: Optional[list[int]],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    transposed: bool,
+    output_padding: list[int],
+    groups: int,
+    benchmark: bool,
+    deterministic: bool,
+    cudnn_enabled: bool,
+    allow_tf32: bool,
+) -> list[int]:
+    return conv_forwards(
+        input,
+        weight,
+        bias,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+    )
+
+
+def batch_norm(
+    input: list[int],
+    weight: Optional[list[int]],
+    bias: Optional[list[int]],
+    running_mean: Optional[list[int]],
+    running_var: Optional[list[int]],
+    training: bool,
+    momentum: float,
+    eps: float,
+    cudnn_enabled: bool,
+):
+    out: list[int] = []
+    for elem in input:
+        out.append(elem)
+    return out
+
+
+def conv3d(
+    input: list[int],
+    weight: list[int],
+    bias: Optional[list[int]],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    groups: int,
+):
+    assert len(weight) == 5
+    assert len(input) == 5
+    return conv_output_size(input, weight, bias, stride, padding, dilation, groups)
+
+
+def maybe_wrap_dim(dim: int, dim_post_expr: int, wrap_scalar: bool = True):
+    if dim_post_expr <= 0:
+        assert wrap_scalar
+        dim_post_expr = 1
+    min = -dim_post_expr
+    max = dim_post_expr - 1
+    assert not (dim < min or dim > max)
+    if dim < 0:
+        dim += dim_post_expr
+    return dim
+
+
+def zero_dim_tensor(input: Any):
+    out: list[int] = []
+    return out
+
+
+def multiply_integers(li: list[int]):
+    out = 1
+    for elem in li:
+        out = out * elem
+    return out
+
+
+def arange_end(end: number, inp0: Any, inp1: Any, inp2: Any, inp3: Any):
+    assert end >= 0
+    return [int(math.ceil(end))]
+
+
+def arange_start(
+    start: number, end: number, inp0: Any, inp1: Any, inp2: Any, inp3: Any
+):
+    assert end >= 0
+    assert end >= start
+    return [int(math.ceil(end - start))]
+
+
+def arange_start_step(
+    start: number, end: number, step: number, inp0: Any, inp1: Any, inp2: Any, inp3: Any
+):
+    assert step != 0
+    if step < 0:
+        assert start >= end
+    else:
+        assert end >= start
+    return [int(math.ceil((end - start) / step))]
+
+
+def permute(input: list[int], dims: list[int]):
+    assert len(input) == len(dims)
+    ndim = len(dims)
+    seen_dims: list[int] = []
+    newSizes: list[int] = []
+    for i in range(ndim):
+        dim = maybe_wrap_dim(dims[i], ndim)
+        seen_dims.append(dim)
+        newSizes.append(input[dim])
+    for i in range(1, ndim):
+        for j in range(i):
+            assert seen_dims[i] != seen_dims[j]
+    return newSizes
+
+
+def movedim(self: list[int], source: list[int], destination: list[int]) -> list[int]:
+    self_dim = len(self)
+    if self_dim <= 1:
+        return self
+    normalized_src: list[int] = []
+    normalized_dst: list[int] = []
+    for i in range(len(source)):
+        normalized_src.append(maybe_wrap_dim(source[i], self_dim))
+        normalized_dst.append(maybe_wrap_dim(destination[i], self_dim))
+    order = [-1 for i in range(self_dim)]
+    src_dims = [i for i in range(self_dim)]
+    dst_dims = [i for i in range(self_dim)]
+
+    for i in range(len(source)):
+        order[normalized_dst[i]] = normalized_src[i]
+        src_dims[normalized_src[i]] = -1
+        dst_dims[normalized_dst[i]] = -1
+
+    source_dims: list[int] = []
+    destination_dims: list[int] = []
+    for ele in src_dims:
+        if ele != -1:
+            source_dims.append(ele)
+    for ele in dst_dims:
+        if ele != -1:
+            destination_dims.append(ele)
+
+    rest_dim = self_dim - len(source)
+    for i in range(rest_dim):
+        order[destination_dims[i]] = source_dims[i]
+    return permute(self, order)
+
+
+def flatten(input: list[int], start_dim: int, end_dim: int):
+    start_dim = maybe_wrap_dim(start_dim, len(input))
+    end_dim = maybe_wrap_dim(end_dim, len(input))
+    assert start_dim <= end_dim
+    if len(input) == 0:
+        return [1]
+    if start_dim == end_dim:
+        # TODO: return self
+        out: list[int] = []
+        for elem in input:
+            out.append(elem)
+        return out
+    slice_numel = 1
+    for i in range(start_dim, end_dim + 1):
+        slice_numel *= input[i]
+    # TODO: use slicing when slice optimization has landed
+    # slice_numel = multiply_integers(input[start_dim:end_dim - start_dim + 1])
+    shape: list[int] = []
+    for i in range(start_dim):
+        shape.append(input[i])
+    shape.append(slice_numel)
+    for i in range(end_dim + 1, len(input)):
+        shape.append(input[i])
+    return shape
+
+
+def nonzero_lower_bound(input: list[int]):
+    return [0, len(input)]
+
+
+def nonzero_upper_bound(input: list[int]):
+    return [numel(input), len(input)]
+
+
+def _reduce_along_dim(self: list[int], dim: int, keepdim: bool):
+    dim = maybe_wrap_dim(dim, len(self))
+    out: list[int] = []
+    for i, self_dim in enumerate(self):
+        if i == dim:
+            if keepdim:
+                out.append(1)
+        else:
+            out.append(self_dim)
+    return out
+
+
+def argmax(
+    self: list[int], dim: Optional[int] = None, keepdim: bool = False
+) -> list[int]:
+    if dim is None:
+        return []
+    return _reduce_along_dim(self, dim, keepdim)
+
+
+def bmm(self: list[int], mat2: list[int]) -> list[int]:
+    assert len(self) == 3, "bmm only supports 3D tensors"
+    assert len(mat2) == 3, "bmm only supports 3D tensors"
+    assert self[0] == mat2[0], "mismatching batch dimension"
+    assert self[2] == mat2[1], "mismatching contracting dimension"
+    return [self[0], self[1], mat2[2]]
+
+
+def _shape_as_tensor(self: list[int]) -> list[int]:
+    return [len(self)]
+
+
+def topk(self: list[int], k: int, dim: int = -1) -> tuple[list[int], list[int]]:
+    if len(self) == 0:
+        result: list[int] = []
+    else:
+        assert (
+            k <= self[dim]
+        ), f"k ({k}) is too big for dimension {dim} of size {self[dim]}"
+        result = _copy(self)
+        result[dim] = k
+    return result, result
+
+
+def nll_loss_forward(
+    self: list[int], target: list[int], weight: Optional[list[int]], reduction: int
+) -> tuple[list[int], list[int]]:
+    # This is taken shamelessly from the meta function in LossNLL.cpp
+    self_dim = len(self)
+    target_dim = len(target)
+    assert 0 < self_dim <= 2
+    assert target_dim <= 1
+    no_batch_dim = self_dim == 1 and target_dim == 0
+    assert no_batch_dim or (self[0] == target[0])
+    n_classes = self[-1]
+    scalar_shape: list[int] = []
+    assert weight is None or (len(weight) == 1 and weight[0] == n_classes)
+    if reduction == 0 and self_dim == 2:
+        reduction_shape = [self[0]]
+    else:
+        reduction_shape = scalar_shape
+    return reduction_shape, scalar_shape
+
+
+def native_layer_norm(
+    input: list[int], normalized_shape: list[int]
+) -> tuple[list[int], list[int], list[int]]:
+    reduction_shape: list[int] = []
+    num_unreduced_dimensions = len(input) - len(normalized_shape)
+    assert num_unreduced_dimensions >= 0
+    for i in range(num_unreduced_dimensions):
+        reduction_shape.append(input[i])
+    for i in range(num_unreduced_dimensions, len(input)):
+        reduction_shape.append(1)
+    return _copy(input), reduction_shape, reduction_shape
+
+
+def native_batch_norm(
+    input: list[int],
+    weight: Optional[list[int]],
+    bias: Optional[list[int]],
+    running_mean: Optional[list[int]],
+    running_var: Optional[list[int]],
+    training: bool,
+) -> tuple[list[int], list[int], list[int]]:
+    if training:
+        _size = [input[1]]
+    else:
+        _size = [0]
+    return _copy(input), _size, _size
+
+
+def _batch_norm_with_update(
+    input: list[int],
+    weight: Optional[list[int]],
+    bias: Optional[list[int]],
+    running_mean: Optional[list[int]],
+    running_var: Optional[list[int]],
+) -> tuple[list[int], list[int], list[int], list[int]]:
+    _size = [input[1]]
+    return _copy(input), _size, _size, [0]
+
+
+def cross_entropy_loss(
+    self: list[int],
+    target: list[int],
+    weight: Optional[list[int]] = None,
+    reduction: int = 1,
+    ignore_index: int = -100,
+    label_smoothing: float = 0.0,
+) -> list[int]:
+    result_shape = nll_loss_forward(self, target, weight, reduction)[0]
+    return result_shape
+
+
+"""
+Currently deferring the enabling of this, as part of the propoasal to suspend
+adding ops.
+There are currently cases in the test case where this is being called
+in the SSA opinfo tests with with unexpected values (eg list of two ints, see the first
+opinfo test). The behavoir of index is significantly dependent on the inputs.
+
+This could be an error with how we are matching up shape functions, or that this
+function needs to just implement everything.
+
+def index_Tensor(self: List[int], indices: List[Optional[List[int]]]) -> List[int]:
+    assert len(indices) <= len(self), "More indices than dimensions to index"
+    broadcasted_shape: List[int] = []
+    for index_tensor_shape in indices:
+        if index_tensor_shape is not None:
+            broadcasted_shape = broadcast(broadcasted_shape, index_tensor_shape)
+    return broadcasted_shape
+"""
+
+ScriptFn = torch._C.ScriptFunction
+shape_compute_graph_mapping: dict[str, ScriptFn] = {}
+bounded_compute_graph_mapping: dict[str, tuple[ScriptFn, ScriptFn]] = {}
+script_func_map: dict[Callable, ScriptFn] = {}
+
+
+def process_func(func: Callable):
+    if func not in script_func_map:
+        scripted_func = torch.jit.script(func)
+
+        torch._C._jit_pass_inline(scripted_func.graph)
+
+        for _ in range(2):
+            torch._C._jit_pass_peephole(scripted_func.graph)
+            torch._C._jit_pass_constant_propagation(scripted_func.graph)
+
+        script_func_map[func] = scripted_func
+    return script_func_map[func]
+
+
+def add_shape_compute_mapping(operator_schema: str, func: Callable):
+    global shape_compute_graph_mapping
+
+    shape_compute_graph_mapping[operator_schema] = process_func(func)
+
+
+def add_bounded_compute_mapping(
+    operator_schema: str, lower_bound_func: Callable, upper_bound_func: Callable
+):
+    # Adds a shape compute function for both upper and lower bounds
+    fns = (process_func(lower_bound_func), process_func(upper_bound_func))
+    bounded_compute_graph_mapping[operator_schema] = fns
+
+
+add_shape_compute_mapping(
+    "aten::contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a)",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::rsub.Tensor(Tensor self, Scalar other, Scalar alpha=1) -> Tensor", unary
+)
+add_shape_compute_mapping(
+    "aten::dropout(Tensor input, float p, bool train) -> Tensor", unary
+)
+add_shape_compute_mapping(
+    "aten::adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor",
+    adaptive_avg_pool2d,
+)
+add_shape_compute_mapping(
+    "prim::NumToTensor.Scalar(Scalar a) -> Tensor", zero_dim_tensor
+)
+add_shape_compute_mapping("prim::NumToTensor.bool(bool a) -> Tensor", zero_dim_tensor)
+add_shape_compute_mapping(
+    "aten::zeros(int[] size, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::to.dtype(Tensor(a) self, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -> (Tensor(a))",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::arange(Scalar end, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)",
+    arange_end,
+)
+add_shape_compute_mapping(
+    "aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor",
+    arange_start,
+)
+add_shape_compute_mapping(
+    "aten::arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor",
+    arange_start_step,
+)
+add_shape_compute_mapping("aten::squeeze(Tensor(a) self) -> Tensor(a)", squeeze_nodim)
+add_shape_compute_mapping(
+    "aten::squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)", squeeze
+)
+add_shape_compute_mapping(
+    "aten::squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)", squeeze_dims
+)
+add_shape_compute_mapping(
+    "aten::unsqueeze(Tensor(a) self, int dim) -> Tensor(a)", unsqueeze
+)
+add_shape_compute_mapping(
+    "aten::slice.Tensor(Tensor(a) self, int dim=0, int? start=None, int? end=None, int step=1) -> Tensor(a)",
+    slice,
+)
+add_shape_compute_mapping(
+    "aten::select.int(Tensor(a) self, int dim, int index) -> Tensor(a)", select
+)
+add_shape_compute_mapping(
+    "aten::index_select(Tensor self, int dim, Tensor index) -> Tensor", index_select
+)
+add_shape_compute_mapping(
+    "aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight=None, Tensor? bias=None, "
+    "float eps=1e-05, bool cudnn_enable=True) -> Tensor",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor", unary
+)
+add_shape_compute_mapping(
+    "aten::_no_grad_embedding_renorm_(Tensor weight, Tensor input, float max_norm, float norm_type) -> Tensor",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!)",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor",
+    embedding,
+)
+add_shape_compute_mapping("aten::mm(Tensor self, Tensor mat2) -> Tensor", mm)
+add_shape_compute_mapping("aten::dot(Tensor self, Tensor tensor) -> Tensor", dot)
+add_shape_compute_mapping("aten::mv(Tensor self, Tensor vec) -> Tensor", mv)
+add_shape_compute_mapping("aten::matmul(Tensor self, Tensor other) -> Tensor", matmul)
+add_shape_compute_mapping(
+    "aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor", linear
+)
+add_shape_compute_mapping(
+    "aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor",
+    max_pool2d,
+)
+add_shape_compute_mapping(
+    "aten::max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)",
+    max_pool2d_with_indices,
+)
+add_shape_compute_mapping("aten::t(Tensor(a) self) -> Tensor(a)", t)
+add_shape_compute_mapping(
+    "aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)", transpose
+)
+add_shape_compute_mapping(
+    "aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensor",
+    conv1d,
+)
+add_shape_compute_mapping(
+    "aten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensor",
+    conv2d,
+)
+add_shape_compute_mapping(
+    "aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensor",
+    batch_norm,
+)
+add_shape_compute_mapping(
+    "aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensor",
+    conv3d,
+)
+add_shape_compute_mapping(
+    "aten::convolution_backward(Tensor grad_output, Tensor input, Tensor weight, int[]? bias_sizes, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)",
+    conv_backwards,
+)
+add_shape_compute_mapping(
+    "aten::convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensor",
+    conv_forwards,
+)
+add_shape_compute_mapping(
+    "aten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor",
+    _conv_forwards,
+)
+add_shape_compute_mapping(
+    "aten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int groups=1, int[2] dilation=1) -> Tensor",
+    conv_transpose2d_input,
+)
+add_shape_compute_mapping(
+    "aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)",
+    flatten,
+)
+add_shape_compute_mapping("aten::cat(Tensor[] tensors, int dim=0) -> Tensor", cat)
+add_shape_compute_mapping("aten::stack(Tensor[] tensors, int dim=0) -> Tensor", stack)
+add_shape_compute_mapping(
+    "aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)", permute
+)
+add_shape_compute_mapping(
+    "aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)",
+    movedim,
+)
+add_shape_compute_mapping("aten::view(Tensor(a) self, int[] size) -> Tensor(a)", view)
+add_shape_compute_mapping(
+    "aten::expand_as(Tensor(a) self, Tensor other) -> Tensor(a)", expand
+)
+add_shape_compute_mapping(
+    "aten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> Tensor(a)",
+    expand_one_unused,
+)
+add_shape_compute_mapping(
+    "aten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor",
+    sum_mean_dim,
+)
+add_shape_compute_mapping(
+    "aten::sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor",
+    sum_mean_dim,
+)
+add_shape_compute_mapping(
+    "aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)",
+    max_dim,
+)
+add_shape_compute_mapping(
+    "aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor", zero_dim_tensor
+)
+add_shape_compute_mapping(
+    "aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensor", zero_dim_tensor
+)
+add_shape_compute_mapping(
+    "aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor",
+    addmm,
+)
+add_shape_compute_mapping(
+    "aten::upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> (Tensor)",
+    upsample_nearest2d,
+)
+add_shape_compute_mapping(
+    "aten::quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensor",
+    unary,
+)
+add_shape_compute_mapping(
+    "aten::quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensor",
+    unary,
+)
+add_shape_compute_mapping("aten::dequantize(Tensor self) -> Tensor", unary)
+add_shape_compute_mapping(
+    "quantized::add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc",
+    broadcast,
+)
+add_shape_compute_mapping(
+    "aten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor", argmax
+)
+add_shape_compute_mapping("aten::bmm(Tensor self, Tensor mat2) -> Tensor", bmm)
+add_shape_compute_mapping(
+    "aten::_shape_as_tensor(Tensor self) -> Tensor", _shape_as_tensor
+)
+add_shape_compute_mapping(
+    "aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)",
+    topk,
+)
+add_shape_compute_mapping(
+    "aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)",
+    nll_loss_forward,
+)
+add_shape_compute_mapping(
+    "aten::native_layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)",
+    native_layer_norm,
+)
+add_shape_compute_mapping(
+    "aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)",
+    native_batch_norm,
+)
+add_shape_compute_mapping(
+    "aten::_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)",
+    native_batch_norm,
+)
+add_shape_compute_mapping(
+    "aten::_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)",
+    native_batch_norm,
+)
+add_shape_compute_mapping(
+    "_batch_norm_with_update(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor)",
+    _batch_norm_with_update,
+)
+
+add_shape_compute_mapping(
+    "aten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor",
+    cross_entropy_loss,
+)
+# add_shape_compute_mapping("aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor", index_Tensor)
+
+# TODO: migrate over all of symbolic_shape_registry_util.cpp
+# These are duplicated here so that the functions will be serialiazed
+add_shape_compute_mapping(
+    "aten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor",
+    broadcast_three,
+)
+add_shape_compute_mapping(
+    "aten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor",
+    broadcast_one_three,
+)
+add_shape_compute_mapping(
+    "aten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)",
+    broadcast_inplace,
+)
+
+# quantized_conv_prepack TODO
+
+# Shape Compute Fn with upper and lower bounds
+add_bounded_compute_mapping(
+    "aten::nonzero(Tensor self) -> (Tensor)", nonzero_lower_bound, nonzero_upper_bound
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_state.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c0c58b8c98aae1b494f602cfeef13c994255508
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_state.py
@@ -0,0 +1,128 @@
+# mypy: allow-untyped-defs
+"""JIT-related state.
+
+This module stores various pieces of Python-global state relating to the JIT.
+
+This is not intended to be imported directly; please the exposed
+functionalities in `torch.jit`.
+"""
+
+import os
+import weakref
+from typing import Any
+
+import torch
+
+
+class EnabledProxy:
+    """Stores whether the JIT is enabled or not.
+
+    This is just a wrapper for a bool, so that we get reference semantics
+    """
+
+    def __init__(self) -> None:
+        self.enabled = self.parse_env(
+            "PYTORCH_JIT", True, "> Using PyTorch JIT", "> PyTorch JIT DISABLED"
+        )
+
+    def parse_env(self, name, default, true_message, false_message):
+        value = os.environ.get(name)
+        if value is None:
+            return default
+        if value.lower() in {"1", "true", "yes"}:
+            return True
+        elif value.lower() in {"0", "false", "no"}:
+            return False
+        if value == "1v":
+            print(true_message)
+            return True
+        elif value == "0v":
+            print(false_message)
+            return False
+        raise ValueError(f"Unknown setting of {name}. Try using 0 or 1.")
+
+    def __bool__(self):
+        return self.enabled
+
+
+_enabled = EnabledProxy()
+
+
+def disable():
+    _enabled.enabled = False
+
+
+def enable():
+    _enabled.enabled = True
+
+
+# The Python CompilationUnit. All functions and modules defined in Python will
+# live in here. It's defined in Python because doing in cpp creates static
+# destruction order issues.
+_python_cu = torch._C.CompilationUnit()
+
+
+# python class => ScriptClass mapping
+_script_classes: dict[type[Any], type[Any]] = {}
+_name_to_pyclass: dict[str, type[Any]] = {}
+
+
+def _add_script_class(python_class, script_class):
+    _script_classes[python_class] = script_class
+    _name_to_pyclass[script_class.qualified_name()] = python_class
+
+
+def _get_script_class(python_class):
+    override = getattr(python_class, "_jit_override_qualname", None)
+    if override is not None:
+        python_class = _get_python_class(override)
+    return _script_classes.get(python_class, None)
+
+
+def _get_python_class(qualified_name):
+    return _name_to_pyclass.get(qualified_name, None)
+
+
+def _clear_class_state():
+    _script_classes.clear()
+    _name_to_pyclass.clear()
+
+
+# Caching: we currently cache compilation of free functions and overloaded functions.
+# To cache free functions we hold a weak ref to the function object and
+# map to the compiled fn's qualified name.
+# To cache overloaded functions we hold a weak ref to the function obj and
+# map to all of its overloaded compiled fns.
+# In the future we could consider caching more types of objects so that
+# aliasing is preserved across separate compilations of the same object.
+
+_jit_caching_layer: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
+_jit_function_overload_caching: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
+
+
+def _try_get_jit_cached_overloads(key):
+    qual_names = _jit_function_overload_caching.get(key, None)
+    if qual_names:
+        return [_python_cu.find_function(qual_name) for qual_name in qual_names]
+    else:
+        return None
+
+
+def _set_jit_overload_cache(key, compiled_fns):
+    _jit_function_overload_caching[key] = [fn.qualified_name for fn in compiled_fns]
+
+
+def _try_get_jit_cached_function(key):
+    if getattr(key, "__disable_jit_function_caching__", False) is True:
+        return None
+    qual_name = _jit_caching_layer.get(key, None)
+    if qual_name:
+        return _python_cu.find_function(qual_name)
+    else:
+        return None
+
+
+def _set_jit_function_cache(key, value):
+    # only free functions currently supported
+    assert isinstance(value, torch.jit.ScriptFunction)
+    _jit_caching_layer[key] = value.qualified_name
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_trace.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_trace.py
new file mode 100644
index 0000000000000000000000000000000000000000..eae30f415e9b0fdf4b8d3ed2df7f13c7b36eb9b7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/_trace.py
@@ -0,0 +1,1504 @@
+# mypy: allow-untyped-defs
+"""Tracing.
+
+This module contains functionality to support the JIT's tracing frontend, notably:
+    * torch.jit.trace
+    * torch.jit.trace_module
+
+This is not intended to be imported directly; please use the exposed
+functionalities in `torch.jit`.
+"""
+
+import contextlib
+import copy
+import functools
+import inspect
+import os
+import re
+import warnings
+from enum import Enum
+from typing import Any, Callable, Optional, TypeVar
+from typing_extensions import ParamSpec
+
+import torch
+from torch._jit_internal import (
+    _get_model_id,
+    _qualified_name,
+    get_callable_argument_names,
+    is_scripting,
+)
+from torch.autograd import function
+from torch.jit._script import _CachedForward, script, ScriptModule
+from torch.jit._state import _enabled, _python_cu
+from torch.nn import Module
+from torch.testing._comparison import default_tolerances
+
+
+_flatten = torch._C._jit_flatten
+_unflatten = torch._C._jit_unflatten
+
+R = TypeVar("R", covariant=True)  # return type (always covariant)
+P = ParamSpec("P")
+
+
+def _create_interpreter_name_lookup_fn(frames_up=1):
+    def _get_interpreter_name_for_var(var):
+        frame = inspect.currentframe()
+        if not frame:
+            raise RuntimeError("failed to inspect frame")
+
+        i = 0
+        while i < frames_up + 1:
+            frame = frame.f_back
+            if not frame:
+                raise RuntimeError("failed to get frame")
+            i += 1
+
+        f_locals = frame.f_locals
+
+        for k, v in f_locals.items():
+            if isinstance(v, torch.Tensor) and var is v:
+                return k if k != "self" else ""
+        return ""
+
+    return _get_interpreter_name_for_var
+
+
+def _unique_state_dict(module, keep_vars=False):
+    # since Parameter.detach() always creates a new torch.Tensor instance,
+    # id(v) doesn't work with it. So we always get the Parameter or Buffer
+    # as values, and deduplicate the params using Parameters and Buffers
+    state_dict = module.state_dict(keep_vars=True)
+    filtered_dict = type(state_dict)()
+    seen_ids: set[int] = set()
+    for k, v in state_dict.items():
+        if id(v) in seen_ids:
+            continue
+        seen_ids.add(id(v))
+        if keep_vars:
+            filtered_dict[k] = v
+        else:
+            filtered_dict[k] = v.detach()
+    return filtered_dict
+
+
+class ONNXTracedModule(torch.nn.Module):
+    def __init__(
+        self,
+        inner,
+        strict=True,
+        force_outplace=False,
+        return_inputs=False,
+        return_inputs_states=False,
+    ):
+        super().__init__()
+        # inner may be a Module, or it may be an arbitrary callable
+        # If it's a Module, we get its parameters automatically, which lets
+        # us avoid a special casing functions versus modules.
+        self.inner = inner
+        self.strict = strict
+        self._force_outplace = force_outplace
+        self._return_inputs = return_inputs
+        self._return_inputs_states = return_inputs_states
+
+    def forward(self, *args: torch.Tensor):
+        in_vars, in_desc = _flatten(args)
+        # NOTE: use full state, because we need it for BatchNorm export
+        # This differs from the compiler path, which doesn't support it at the moment.
+        module_state = list(_unique_state_dict(self, keep_vars=True).values())
+
+        ret_inputs = []
+        inputs_states = []
+        outs = []
+
+        def wrapper(*args):
+            in_args: list[torch.Tensor] = []
+            for i in range(len(in_vars)):
+                if not isinstance(args[i], torch.Tensor):
+                    raise RuntimeError("Expected Tensor argument")
+                in_args.append(args[i])
+
+            trace_inputs = _unflatten(in_args, in_desc)
+
+            if self._return_inputs:
+                ret_inputs.append(
+                    tuple(x.clone(memory_format=torch.preserve_format) for x in args)
+                )
+            if self._return_inputs_states:
+                inputs_states.append(_unflatten(in_args, in_desc))
+            outs.append(self.inner(*trace_inputs))
+            if self._return_inputs_states:
+                inputs_states[0] = (inputs_states[0], trace_inputs)
+            out_vars, _ = _flatten(outs)
+            if len(out_vars) == 1:
+                return out_vars[0]
+            else:
+                return tuple(out_vars)
+
+        graph, _out = torch._C._create_graph_by_tracing(
+            wrapper,
+            in_vars + module_state,
+            _create_interpreter_name_lookup_fn(),
+            self.strict,
+            self._force_outplace,
+        )
+
+        if self._return_inputs:
+            return graph, outs[0], ret_inputs[0]
+        if self._return_inputs_states:
+            return graph, outs[0], inputs_states[0]
+        else:
+            return graph, outs[0]
+
+
+def _clone_inputs(args):
+    def clone_input(a):
+        if a is None:
+            return None
+        elif isinstance(a, torch.Tensor):
+            # TODO: figure out one liner to .clone() and set requires_grad
+            v = (
+                a.detach()
+                .clone(memory_format=None if a.is_mkldnn else torch.preserve_format)
+                .requires_grad_(a.requires_grad)
+            )
+            if a.grad is not None:
+                v.grad = clone_input(v.grad)
+            return v
+        else:
+            return a.clone(memory_format=torch.preserve_format)
+
+    return function._nested_map(
+        lambda x: isinstance(x, torch.Tensor), clone_input, condition_msg="tensors"
+    )(args)
+
+
+# This is purely for developer debugging.  We are not going to advertise it.
+_JIT_TIME = os.environ.get("PYTORCH_JIT_TIME", False)  # CUDA-only timing
+_JIT_DISABLE = os.environ.get("PYTORCH_JIT_DISABLE", False)
+_JIT_STATS = os.environ.get("PYTORCH_JIT_STATS", False)
+
+
+@contextlib.contextmanager
+def _time(trace_name, name, time=True):
+    if (not _JIT_TIME and not time) or not torch.cuda.is_available():
+        yield
+        return
+    stream = torch.cuda.current_stream()
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+    stream.record_event(start)
+    try:
+        yield
+    finally:
+        stream.record_event(end)
+        end.synchronize()
+        print(f"{trace_name} {name} time: {start.elapsed_time(end)} ms")
+
+
+def verify(model, args, loss_fn=torch.sum, devices=None):
+    """
+    Verify that a JIT compiled model has the same behavior as its uncompiled version along with its backwards pass.
+
+    If your model returns multiple outputs,
+    you must also specify a `loss_fn` to produce a loss for which
+    the backwards will be computed.
+
+    This function has side-effects (e.g., it executes your model / saves and loads
+    parameters), so don't expect the model to come out exactly the same as what
+    you passed in.
+
+    Args:
+        model (compiled torch.nn.Module or function): the module/function to be
+            verified.  The module/function definition MUST have been decorated with
+            `@torch.jit.compile`.
+        args (tuple or Tensor): the positional arguments to pass to the
+            compiled function/module to be verified.  A non-tuple is assumed to
+            be a single positional argument to be passed to the model.
+        loss_fn (function, optional): the loss function to be applied to
+            the output of the model, before backwards is invoked.  By default,
+            we assume that a model returns a single result, and we :func:`torch.sum`
+            before calling backwards; if this is inappropriate, you can pass your
+            own loss function.  Note that if a model returns a tuple of results,
+            these are passed as separate positional arguments to `loss_fn`.
+        devices (iterable of device IDs, optional): the GPU devices which the
+            compiled module will be run on.  This determines the RNG state we
+            must save when running both compiled and uncompiled versions of the model.
+    """
+    # TODO: In principle, we track device information in our trace, so it
+    # should be possible to check if our execution actually obeyed the 'devices'
+    # the user provided.
+
+    # TODO: Consider adding a utility function to torch.jit to test
+    # for this case
+    if not isinstance(model, torch._C.CompiledFunction):  # type: ignore[attr-defined]
+        raise TypeError(
+            "Cannot verify an uncompiled module.  Add @torch.jit.compile to compile it"
+        )
+    is_module = isinstance(model, Module)
+
+    if not isinstance(args, tuple):
+        args = (args,)
+
+    if is_module:
+        saved_state = copy.deepcopy(model.state_dict())
+
+    def run_fwd_bwd(args, force_trace=False, assert_compiled=False):
+        params = list(model.parameters()) if is_module else []
+        in_vars, _ = _flatten((args, params))
+        # We use a special API to reset the trace and compile it from scratch.
+        compiled_fn = model
+        if force_trace:
+            compiled_fn.clear_cache()
+        if assert_compiled:
+            hits = compiled_fn.hits
+        out = model(*args)
+        if assert_compiled and compiled_fn.hits == hits:  # type: ignore[possibly-undefined]
+            raise RuntimeError("failed to use the compiled function")
+        if not isinstance(out, tuple):
+            out = (out,)
+        if loss_fn == torch.sum and len(out) != 1:
+            raise ValueError(
+                f"Model returns {len(out)} outputs, but default loss function "
+                "(torch.sum) can only handle a single output"
+            )
+        out_vars, _ = _flatten(out)
+        saved_outs = [
+            v.detach().clone(memory_format=torch.preserve_format) for v in out_vars
+        ]
+        loss = loss_fn(*out)
+        grads = torch.autograd.grad([loss], in_vars)
+        # TODO: I'm not sure if the clone here is necessary but it is safer
+        saved_grads = [
+            v.detach().clone(memory_format=torch.preserve_format) for v in grads
+        ]
+        return (saved_outs, saved_grads)
+
+    with torch.random.fork_rng(devices, _caller="torch.jit.verify"):
+        uncompiled_outs, uncompiled_grads = run_fwd_bwd(args, force_trace=True)
+        assert model.has_trace_for(*args)
+
+    if is_module:
+        model.load_state_dict(saved_state)  # type: ignore[possibly-undefined]
+    compiled_outs, compiled_grads = run_fwd_bwd(args, assert_compiled=True)
+
+    _verify_equal(uncompiled_outs, compiled_outs)
+    _verify_equal(uncompiled_grads, compiled_grads)
+
+
+def _verify_equal(xs, ys):
+    for x, y in zip(xs, ys):
+        if x.sub(y).abs().max() > 1e-6:
+            raise RuntimeError("JIT and real computation mismatch")
+
+
+def indent(s):
+    return "\n".join(["\t" + line for line in s.splitlines()])
+
+
+class TracingCheckError(Exception):
+    def __init__(self, graph_diff_error, tensor_compare_error, extra_msg=None):
+        self.message = "Tracing failed sanity checks!\n"
+        if extra_msg is not None:
+            self.message += extra_msg + "\n"
+        if graph_diff_error is not None:
+            self.message += "ERROR: Graphs differed across invocations!\n"
+            self.message += indent(graph_diff_error) + "\n"
+        if tensor_compare_error is not None:
+            self.message += (
+                "ERROR: Tensor-valued Constant nodes differed in value "
+                "across invocations. This often indicates that the tracer has"
+                " encountered untraceable code.\n"
+            )
+            self.message += indent(tensor_compare_error) + "\n"
+        super().__init__(self.message)
+
+
+# Check the traced module against a set of user-provided validation inputs
+@torch.no_grad()
+def _check_trace(
+    check_inputs,
+    func,
+    traced_func,
+    check_tolerance,
+    strict,
+    force_outplace,
+    is_trace_module,
+    _module_class,
+    example_inputs_is_kwarg=False,
+):
+    # Note: tracing is independent of optimizations, which consume the trace
+    for inputs in check_inputs:
+        if isinstance(inputs, torch.Tensor):
+            inputs = (inputs,)
+
+        if is_trace_module:
+            copied_dict = {}
+            for name, data in inputs.items():
+                copied_dict[name] = _clone_inputs(data)
+            check_mod = torch.jit.trace_module(
+                getattr(func, "__self__", func),
+                copied_dict,
+                check_trace=False,
+                strict=strict,
+                _force_outplace=force_outplace,
+                _module_class=_module_class,
+                _compilation_unit=torch._C.CompilationUnit(),
+                example_inputs_is_kwarg=example_inputs_is_kwarg,
+                _store_inputs=False,
+            )
+            check_mod_func = check_mod._c._get_method(traced_func.name)
+            inputs = inputs[traced_func.name]
+            if (
+                isinstance(inputs, (torch.Tensor))
+                or isinstance(inputs, dict)
+                and not example_inputs_is_kwarg
+            ):
+                inputs = (inputs,)
+        else:
+            if example_inputs_is_kwarg:
+                check_mod = torch.jit.trace(
+                    func,
+                    check_trace=False,
+                    strict=strict,
+                    _force_outplace=force_outplace,
+                    _module_class=_module_class,
+                    example_kwarg_inputs=_clone_inputs(inputs),
+                    _store_inputs=False,
+                )
+            else:
+                check_mod = torch.jit.trace(
+                    func,
+                    _clone_inputs(inputs),
+                    check_trace=False,
+                    strict=strict,
+                    _force_outplace=force_outplace,
+                    _module_class=_module_class,
+                    _store_inputs=False,
+                )
+            check_mod_func = check_mod
+
+        def graph_diagnostic_info():
+            mod_canonicalized = torch._C._jit_pass_canonicalize(traced_func.graph)
+            torch._C._jit_pass_inline(mod_canonicalized)
+            torch._C._jit_pass_erase_shape_information(mod_canonicalized)
+            mod_str = str(mod_canonicalized)
+            mod_str = re.sub(r"___torch_mangle_[0-9]+\.", "", mod_str)
+            check_canonicalized = torch._C._jit_pass_canonicalize(check_mod_func.graph)
+            torch._C._jit_pass_inline(check_canonicalized)
+            torch._C._jit_pass_erase_shape_information(check_canonicalized)
+            check_str = str(check_canonicalized)
+            check_str = re.sub(r"___torch_mangle_[0-9]+\.", "", check_str)
+
+            graph_diff_errors = None
+            if mod_str != check_str:
+                import difflib
+
+                graph_diff = difflib.ndiff(
+                    mod_str.splitlines(True), check_str.splitlines(True)
+                )
+                graph_diff_errors = "Graph diff:\n" + indent("".join(graph_diff)) + "\n"
+
+                for n_mod, n_check in zip(
+                    mod_canonicalized.nodes(), check_canonicalized.nodes()
+                ):
+                    if str(n_mod) != str(n_check):
+                        graph_diff_errors += "First diverging operator:\n"
+                        node_diff = difflib.ndiff(
+                            str(n_mod).splitlines(True), str(n_check).splitlines(True)
+                        )
+                        source_printout = (
+                            "Node diff:\n" + indent("".join(node_diff)) + "\n"
+                        )
+                        mod_stack = n_mod.sourceRange()
+                        if mod_stack:
+                            source_printout += (
+                                "Trace source location:\n" + indent(mod_stack) + "\n"
+                            )
+                        check_stack = n_check.sourceRange()
+                        if check_stack:
+                            source_printout += (
+                                "Check source location:\n" + indent(check_stack) + "\n"
+                            )
+                        graph_diff_errors += source_printout
+
+                        break  # For now, only print out the first pair of nodes that diverges
+
+            tensor_compare_errors = None
+            # Check Tensor-valued constant nodes
+            for n_mod, n_check in zip(
+                mod_canonicalized.nodes(), check_canonicalized.nodes()
+            ):
+                if n_mod.kind() != n_check.kind():
+                    break  # Graphs have already diverged
+
+                if n_mod.kind() == "prim::Constant" and not (
+                    n_mod.mustBeNone() or n_check.mustBeNone()
+                ):
+                    if not n_mod.hasAttribute("value"):
+                        continue
+                    if n_mod.kindOf("value") != "t" or n_check.kindOf("value") != "t":
+                        continue
+
+                    mod_tensor_val = n_mod.t("value")
+                    check_tensor_val = n_check.t("value")
+
+                    try:
+                        torch.testing.assert_close(
+                            mod_tensor_val, check_tensor_val, equal_nan=True
+                        )
+                    except (RuntimeError, AssertionError) as e:
+                        if tensor_compare_errors is None:
+                            tensor_compare_errors = ""
+                        tensor_compare_errors += "Node:\n" + indent(str(n_mod)) + "\n"
+                        compare_stack = n_mod.sourceRange()
+                        if compare_stack:
+                            tensor_compare_errors += (
+                                "Source Location:\n" + indent(compare_stack) + "\n"
+                            )
+                        tensor_compare_errors += "Comparison exception: " + indent(
+                            str(e)
+                        )
+
+                        break  # For now, only print the first diverging pair
+
+            return graph_diff_errors, tensor_compare_errors
+
+        def wrap_retval(x):
+            return x if isinstance(x, tuple) else (x,)
+
+        def run_mod_and_filter_tensor_outputs(mod, inputs, running_what):
+            try:
+                if isinstance(inputs, dict) and example_inputs_is_kwarg:
+                    outs = wrap_retval(mod(**inputs))
+                else:
+                    outs = wrap_retval(mod(*_clone_inputs(inputs)))
+                outs = [out for out in outs if isinstance(out, torch.Tensor)]
+                return outs
+            except Exception as e:
+                graph_diff_errors, tensor_compare_errors = graph_diagnostic_info()
+                msg = f"encountered an exception while running the {running_what} with test inputs.\nException:\n{indent(str(e))}"
+                raise TracingCheckError(
+                    graph_diff_errors,
+                    tensor_compare_errors,
+                    extra_msg=msg,
+                ) from e
+
+        has_warned = [False]
+
+        def maybe_warn_nondeterministic():
+            if has_warned[0]:
+                return
+            has_warned[0] = True
+            nondeterm_ops = [
+                op for op in traced_func.graph.nodes() if op.isNondeterministic()
+            ]
+            if len(nondeterm_ops) > 0:
+                nondeterministic_ops_warning = "Trace had nondeterministic nodes. "
+                nondeterministic_ops_warning += (
+                    "Did you forget call .eval() on your model? Nodes:\n"
+                )
+                nondeterministic_ops_warning += "\n".join(
+                    [indent(str(op)) for op in nondeterm_ops][:20]
+                )
+                nondeterministic_ops_warning += (
+                    "\nThis may cause errors in trace checking. To disable trace checking,"
+                    " pass check_trace=False to torch.jit.trace()"
+                )
+                warnings.warn(
+                    nondeterministic_ops_warning, category=TracerWarning, stacklevel=5
+                )
+
+        def compare_outputs(original, reference, match_what):
+            all_ok = True
+            for i, (orig, ref) in enumerate(zip(original, reference)):
+                try:
+                    if orig.is_quantized:
+                        orig = orig.dequantize()
+                    if ref.is_quantized:
+                        ref = ref.dequantize()
+                    if orig.is_mkldnn:
+                        orig = orig.to_dense()
+                    if ref.is_mkldnn:
+                        ref = ref.to_dense()
+                    if ref.is_complex() or orig.is_complex():
+                        torch.testing.assert_close(
+                            orig.to(torch.cdouble),
+                            ref.to(torch.cdouble),
+                            rtol=check_tolerance,
+                            atol=default_tolerances(orig, ref)[1],
+                            equal_nan=True,
+                        )
+                    else:
+                        if orig.is_mps or ref.is_mps:
+                            torch.testing.assert_close(
+                                orig.float(),
+                                ref.float(),
+                                rtol=check_tolerance,
+                                atol=default_tolerances(orig, ref)[1],
+                                equal_nan=True,
+                            )
+                        elif getattr(orig, "is_nested", None) or getattr(
+                            ref, "is_nested", None
+                        ):
+                            assert getattr(orig, "is_nested", None) == getattr(
+                                ref, "is_nested", None
+                            )
+                            for t_orig, t_ref in zip(orig.unbind(), ref.unbind()):
+                                torch.testing.assert_close(
+                                    t_orig.double(),
+                                    t_ref.double(),
+                                    rtol=check_tolerance,
+                                    atol=default_tolerances(t_orig, t_ref)[1],
+                                    equal_nan=True,
+                                )
+                        else:
+                            torch.testing.assert_close(
+                                orig.double(),
+                                ref.double(),
+                                rtol=check_tolerance,
+                                atol=default_tolerances(orig, ref)[1],
+                                equal_nan=True,
+                            )
+                except AssertionError as e:
+                    maybe_warn_nondeterministic()
+                    warnings.warn(
+                        "Output nr "
+                        + str(i + 1)
+                        + ". of the traced function does not match "
+                        "the corresponding output of the "
+                        + match_what
+                        + ". Detailed error:\n"
+                        + str(e),
+                        category=TracerWarning,
+                        stacklevel=4,
+                    )
+                    all_ok = False
+
+            return all_ok
+
+        traced_outs = run_mod_and_filter_tensor_outputs(traced_func, inputs, "trace")
+        fn_outs = run_mod_and_filter_tensor_outputs(func, inputs, "Python function")
+        if compare_outputs(traced_outs, fn_outs, "Python function"):
+            check_outs = run_mod_and_filter_tensor_outputs(
+                check_mod_func, inputs, "repeated trace"
+            )
+            compare_outputs(traced_outs, check_outs, "repeated trace")
+
+        diag_info = graph_diagnostic_info()
+        if any(info is not None for info in diag_info):
+            raise TracingCheckError(*diag_info)
+
+
+class TracerWarning(Warning):
+    @staticmethod
+    def ignore_lib_warnings():
+        # We ignore warnings from all submodules excluding the JIT, because we need them e.g. for _check_trace
+        warnings.filterwarnings(
+            "ignore", category=TracerWarning, module="torch.(?!jit)"
+        )
+        warnings.filterwarnings("ignore", "torch::jit::fuser::cuda")
+
+
+# We ignore the tracer warnings coming form inside the library, because all our shape
+# checks in nn will trigger them.
+TracerWarning.ignore_lib_warnings()
+torch._C._tracer_warn_use_python()
+
+
+def make_tuple(example_inputs):
+    if isinstance(example_inputs, (torch.Tensor, dict)):
+        return (example_inputs,)
+    # done primarily so that weird iterables fail here and not pybind11 code
+    if not isinstance(example_inputs, tuple):
+        return tuple(example_inputs)
+    return example_inputs
+
+
+def make_module(mod, _module_class, _compilation_unit):
+    if isinstance(mod, ScriptModule):
+        return mod
+    elif torch._jit_internal.module_has_exports(mod):
+        infer_methods_stubs_fn = torch.jit._recursive.make_stubs_from_exported_methods
+        return torch.jit._recursive.create_script_module(
+            mod, infer_methods_stubs_fn, share_types=False, is_tracing=True
+        )
+    else:
+        if _module_class is None:
+            _module_class = TopLevelTracedModule
+        return _module_class(mod, _compilation_unit=_compilation_unit)
+
+
+def wrap_check_inputs(check_inputs):
+    if check_inputs is None:
+        return None
+
+    return [{"forward": c} for c in check_inputs]
+
+
+def analyze_ts_result_with_export_result(export, trace):
+    import torch.utils._pytree as pytree
+
+    flat_export = pytree.tree_leaves(export)
+    flat_trace = pytree.tree_leaves(trace)
+
+    for orig, loaded in zip(flat_export, flat_trace):
+        if orig.layout != loaded.layout:
+            return False
+        # mkldnn is not supported for torch.allclose
+        if orig.layout == torch._mkldnn:  # type: ignore[attr-defined]
+            return True
+        if type(orig) != type(loaded):
+            return False
+
+        if isinstance(orig, torch._subclasses.FakeTensor):
+            # Skip for FakeTensor.
+            return True
+        elif isinstance(orig, torch.Tensor):
+            if orig.dtype != loaded.dtype:
+                return False
+            if not torch.allclose(orig, loaded):
+                return False
+        else:
+            if orig != loaded:
+                return False
+    return True
+
+
+def _trace_impl(
+    func,
+    example_inputs=None,
+    optimize=None,
+    check_trace=True,
+    check_inputs=None,
+    check_tolerance=1e-5,
+    strict=True,
+    _force_outplace=False,
+    _module_class=None,
+    _compilation_unit=_python_cu,
+    example_kwarg_inputs=None,
+    _store_inputs=True,
+):
+    if isinstance(func, torch.jit.ScriptModule):
+        # it is hard to trace it because the forward method on ScriptModule is already defined, so it
+        # would result in an error.
+        warnings.warn(
+            "The input to trace is already a ScriptModule, tracing it is a no-op. Returning the object as is."
+        )
+        return func
+
+    if isinstance(func, torch.nn.Module):
+        if example_inputs is None:
+            if isinstance(example_kwarg_inputs, dict):
+                example_inputs = example_kwarg_inputs
+            else:
+                raise RuntimeError("example_kwarg_inputs should be a dict")
+        return trace_module(
+            func,
+            {"forward": example_inputs},
+            None,
+            check_trace,
+            wrap_check_inputs(check_inputs),
+            check_tolerance,
+            strict,
+            _force_outplace,
+            _module_class,
+            example_inputs_is_kwarg=isinstance(example_kwarg_inputs, dict),
+            _store_inputs=_store_inputs,
+        )
+    if (
+        hasattr(func, "__self__")
+        and isinstance(func.__self__, torch.nn.Module)
+        and func.__name__ == "forward"
+    ):
+        if example_inputs is None:
+            if isinstance(example_kwarg_inputs, dict):
+                example_inputs = example_kwarg_inputs
+            else:
+                raise RuntimeError("example_kwarg_inputs should be a dict")
+        return trace_module(
+            func.__self__,
+            {"forward": example_inputs},
+            None,
+            check_trace,
+            wrap_check_inputs(check_inputs),
+            check_tolerance,
+            strict,
+            _force_outplace,
+            _module_class,
+            example_inputs_is_kwarg=isinstance(example_kwarg_inputs, dict),
+            _store_inputs=_store_inputs,
+        )
+
+    # Special case for common case of passing a single Tensor
+    if (
+        isinstance(example_inputs, (torch.Tensor, dict))
+        and example_kwarg_inputs is None
+    ):
+        example_inputs = (example_inputs,)
+    # done primarily so that weird iterables fail here and not pybind11 code
+    elif example_kwarg_inputs is None and not isinstance(example_inputs, tuple):
+        example_inputs = tuple(example_inputs)
+
+    var_lookup_fn = _create_interpreter_name_lookup_fn(0)
+
+    if hasattr(func, "__self__") and isinstance(func.__self__, torch.nn.Module):
+        raise AttributeError(
+            "trace doesn't support compiling individual module's functions.\n"
+            "Please use trace_module"
+        )
+
+    name = _qualified_name(func)
+    if isinstance(example_kwarg_inputs, dict):
+        example_inputs = example_kwarg_inputs
+        traced = torch._C._create_function_from_trace_with_dict(
+            name,
+            func,
+            example_kwarg_inputs,
+            var_lookup_fn,
+            strict,
+            _force_outplace,
+            get_callable_argument_names(func),
+        )
+    else:
+        traced = torch._C._create_function_from_trace(
+            name,
+            func,
+            example_inputs,
+            var_lookup_fn,
+            strict,
+            _force_outplace,
+            get_callable_argument_names(func),
+        )
+
+    # Check the trace against new traces created from user-specified inputs
+    if check_trace:
+        if check_inputs is not None:
+            _check_trace(
+                check_inputs,
+                func,
+                traced,
+                check_tolerance,
+                strict,
+                _force_outplace,
+                False,
+                _module_class,
+                example_inputs_is_kwarg=isinstance(example_kwarg_inputs, dict),
+            )
+        else:
+            _check_trace(
+                [example_inputs],
+                func,
+                traced,
+                check_tolerance,
+                strict,
+                _force_outplace,
+                False,
+                _module_class,
+                example_inputs_is_kwarg=isinstance(example_kwarg_inputs, dict),
+            )
+
+    # Allow torch.compile() to inline
+    traced._torchdynamo_inline = func  # type: ignore[attr-defined]
+    return traced
+
+
+class _ExportType(str, Enum):
+    DIRECT_EXPORT = "DIRECT_EXPORT"
+    TRACE_AND_EXPORT = "TRACE_AND_EXPORT"
+    SOURCE_TO_SOURCE = "SOURCE_TO_SOURCE"
+
+    def __str__(self) -> str:
+        return self.value
+
+
+class _ExportOutcome(str, Enum):
+    SUCCESS = "SUCCESS"
+    FAILED_TO_EXPORT = "FAILED_TO_EXPORT"
+    FAILED_TO_RUN = "FAILED_TO_RUN"
+    ACCURACY_ERROR = "ACCURACY_ERROR"
+
+    def __str__(self) -> str:
+        return self.value
+
+
+def trace(
+    func,
+    example_inputs=None,
+    optimize=None,
+    check_trace=True,
+    check_inputs=None,
+    check_tolerance=1e-5,
+    strict=True,
+    _force_outplace=False,
+    _module_class=None,
+    _compilation_unit=_python_cu,
+    example_kwarg_inputs=None,
+    _store_inputs=True,
+):
+    r"""
+    Trace a function and return an executable  or :class:`ScriptFunction` that will be optimized using just-in-time compilation.
+
+    Tracing is ideal for code that operates only on
+    ``Tensor``\\s and lists, dictionaries, and
+    tuples of ``Tensor``\\s.
+
+    Using `torch.jit.trace` and `torch.jit.trace_module`, you can turn an
+    existing module or Python function into a TorchScript
+    :class:`ScriptFunction` or :class:`ScriptModule`. You must provide example
+    inputs, and we run the function, recording the operations performed on all
+    the tensors.
+
+    * The resulting recording of a standalone function produces `ScriptFunction`.
+    * The resulting recording of `nn.Module.forward` or `nn.Module` produces
+      `ScriptModule`.
+
+    This module also contains any parameters that the original
+    module had as well.
+
+    Warning:
+        Tracing only correctly records functions and modules which are not data
+        dependent (e.g., do not have conditionals on data in tensors) and do not have
+        any untracked external dependencies (e.g., perform input/output or
+        access global variables). Tracing only records operations done when the given
+        function is run on the given tensors. Therefore, the returned
+        `ScriptModule` will always run the same traced graph on any input. This
+        has some important implications when your module is expected to run
+        different sets of operations, depending on the input and/or the module
+        state. For example,
+
+        * Tracing will not record any control-flow like if-statements or loops.
+          When this control-flow is constant across your module, this is fine
+          and it often inlines the control-flow decisions. But sometimes the
+          control-flow is actually part of the model itself. For instance, a
+          recurrent network is a loop over the (possibly dynamic) length of an
+          input sequence.
+        * In the returned :class:`ScriptModule`, operations that have different
+          behaviors in ``training`` and ``eval`` modes will always behave as if
+          it is in the mode it was in during tracing, no matter which mode the
+          `ScriptModule` is in.
+
+        In cases like these, tracing would not be appropriate and
+        :func:`scripting ` is a better choice. If you trace
+        such models, you may silently get incorrect results on subsequent
+        invocations of the model. The tracer will try to emit warnings when
+        doing something that may cause an incorrect trace to be produced.
+
+    Args:
+        func (callable or torch.nn.Module):  A Python function or `torch.nn.Module`
+            that will be run with `example_inputs`. `func` arguments and return
+            values  must be tensors or (possibly nested) tuples that contain
+            tensors. When a module is passed `torch.jit.trace`, only the
+            ``forward`` method is run and traced (see :func:`torch.jit.trace
+            ` for details).
+
+    Keyword arguments:
+        example_inputs (tuple or torch.Tensor or None, optional): A tuple of example
+            inputs that will be passed to the function while tracing.
+            Default: ``None``. Either this argument or ``example_kwarg_inputs``
+            should be specified. The resulting trace can be run with inputs of
+            different types and shapes assuming the traced operations support those
+            types and shapes. `example_inputs` may also be a single Tensor in which
+            case it is automatically wrapped in a tuple. When the value is None,
+            ``example_kwarg_inputs`` should be specified.
+
+        check_trace (``bool``, optional): Check if the same inputs run through
+            traced code produce the same outputs. Default: ``True``. You might want
+            to disable this if, for example, your network contains non-
+            deterministic ops or if you are sure that the network is correct despite
+            a checker failure.
+
+        check_inputs (list of tuples, optional): A list of tuples of input
+            arguments that should be used to check the trace against what is
+            expected. Each tuple is equivalent to a set of input arguments that
+            would be specified in ``example_inputs``. For best results, pass in
+            a set of checking inputs representative of the space of shapes and
+            types of inputs you expect the network to see.  If not specified,
+            the original ``example_inputs`` are used for checking
+        check_tolerance (float, optional): Floating-point comparison tolerance
+            to use in the checker procedure.  This can be used to relax the
+            checker strictness in the event that results diverge numerically
+            for a known reason, such as operator fusion.
+        strict (``bool``, optional): run the tracer in a strict mode or not
+            (default: ``True``). Only turn this off when you want the tracer to
+            record your mutable container types (currently ``list``/``dict``)
+            and you are sure that the container you are using in your
+            problem is a ``constant`` structure and does not get used as
+            control flow (if, for) conditions.
+        example_kwarg_inputs (dict, optional): This parameter is a pack of keyword
+            arguments of example inputs that will be passed to the function while
+            tracing. Default: ``None``. Either this argument or ``example_inputs``
+            should be specified. The dict will be unpacking by the arguments name
+            of the traced function. If the keys of the dict don't not match with
+            the traced function's arguments name, a runtime exception will be raised.
+
+    Returns:
+        If `func` is `nn.Module` or ``forward`` of `nn.Module`, `trace` returns
+        a :class:`ScriptModule` object with a single ``forward`` method
+        containing the traced code.  The returned `ScriptModule` will
+        have the same set of sub-modules and parameters as the original
+        ``nn.Module``.  If ``func`` is a standalone function, ``trace``
+        returns `ScriptFunction`.
+
+    Example (tracing a function):
+
+    .. testcode::
+
+        import torch
+
+        def foo(x, y):
+            return 2 * x + y
+
+        # Run `foo` with the provided inputs and record the tensor operations
+        traced_foo = torch.jit.trace(foo, (torch.rand(3), torch.rand(3)))
+
+        # `traced_foo` can now be run with the TorchScript interpreter or saved
+        # and loaded in a Python-free environment
+
+    Example (tracing an existing module)::
+
+        import torch
+        import torch.nn as nn
+
+
+        class Net(nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.conv = nn.Conv2d(1, 1, 3)
+
+            def forward(self, x):
+                return self.conv(x)
+
+
+        n = Net()
+        example_weight = torch.rand(1, 1, 3, 3)
+        example_forward_input = torch.rand(1, 1, 3, 3)
+
+        # Trace a specific method and construct `ScriptModule` with
+        # a single `forward` method
+        module = torch.jit.trace(n.forward, example_forward_input)
+
+        # Trace a module (implicitly traces `forward`) and construct a
+        # `ScriptModule` with a single `forward` method
+        module = torch.jit.trace(n, example_forward_input)
+
+    """
+    if not _enabled:
+        return func
+    if optimize is not None:
+        warnings.warn(
+            "`optimize` is deprecated and has no effect. "
+            "Use `with torch.jit.optimized_execution()` instead",
+            FutureWarning,
+            stacklevel=2,
+        )
+
+    from torch._utils_internal import (
+        check_if_torch_exportable,
+        log_torch_jit_trace_exportability,
+        log_torchscript_usage,
+    )
+
+    traced_func = _trace_impl(
+        func,
+        example_inputs,
+        optimize,
+        check_trace,
+        check_inputs,
+        check_tolerance,
+        strict,
+        _force_outplace,
+        _module_class,
+        _compilation_unit,
+        example_kwarg_inputs,
+        _store_inputs,
+    )
+    log_torchscript_usage("trace", model_id=_get_model_id(traced_func))
+
+    if check_if_torch_exportable():
+        from torch._export.converter import TS2EPConverter
+        from torch.export._trace import (
+            _convert_ts_to_export_experimental,
+            _process_jit_trace_inputs_for_export,
+        )
+
+        traced_func_for_export = _trace_impl(
+            func,
+            example_inputs=example_inputs,
+            optimize=optimize,
+            check_trace=False,
+            check_inputs=check_inputs,
+            check_tolerance=check_tolerance,
+            strict=strict,
+            _force_outplace=_force_outplace,
+            _module_class=_module_class,
+            _compilation_unit=_compilation_unit,
+            example_kwarg_inputs=example_kwarg_inputs,
+            _store_inputs=_store_inputs,
+        )
+
+        export_args, _ = _process_jit_trace_inputs_for_export(
+            example_inputs, example_kwarg_inputs
+        )
+
+        def _log_exportability(func_to_export, export_func, export_args, export_type):
+            try:
+                traced_result = func_to_export(*export_args)
+            except Exception as e:
+                _ = e
+                log_torch_jit_trace_exportability(
+                    "trace", str(export_type), str(_ExportOutcome.SUCCESS), "succeeded"
+                )
+                return
+
+            try:
+                ep_module = export_func(func_to_export, export_args)
+            except Exception as e:
+                log_torch_jit_trace_exportability(
+                    "trace",
+                    str(export_type),
+                    str(_ExportOutcome.FAILED_TO_EXPORT),
+                    str(e),
+                )
+                return
+
+            try:
+                export = ep_module(*export_args)
+            except Exception as e:
+                log_torch_jit_trace_exportability(
+                    "trace", str(export_type), str(_ExportOutcome.FAILED_TO_RUN), str(e)
+                )
+                return
+
+            if not analyze_ts_result_with_export_result(export, traced_result):
+                log_torch_jit_trace_exportability(
+                    "trace",
+                    str(export_type),
+                    str(_ExportOutcome.ACCURACY_ERROR),
+                    "accuracy error",
+                )
+                return
+
+            log_torch_jit_trace_exportability(
+                "trace", str(export_type), str(_ExportOutcome.SUCCESS), "succeeded"
+            )
+
+        def _direct_export_and_lower(func, export_args):
+            return torch.export.export(func, export_args, strict=False).module()
+
+        def _convert_ts_to_export_source_to_source(func, export_args):
+            return TS2EPConverter(func, export_args).convert().module()
+
+        # torch.jit.trace is noop when the original module is torch.jit.ScriptModule
+        if not isinstance(traced_func_for_export, torch.jit.ScriptModule):
+            _log_exportability(
+                traced_func_for_export,
+                _direct_export_and_lower,
+                export_args,
+                _ExportType.DIRECT_EXPORT,
+            )
+
+        _log_exportability(
+            traced_func_for_export,
+            _convert_ts_to_export_experimental,
+            export_args,
+            _ExportType.TRACE_AND_EXPORT,
+        )
+        _log_exportability(
+            traced_func_for_export,
+            _convert_ts_to_export_source_to_source,
+            export_args,
+            _ExportType.SOURCE_TO_SOURCE,
+        )
+
+    return traced_func
+
+
+_trace_module_map: Optional[dict[Any, Any]] = None
+
+
+def trace_module(
+    mod,
+    inputs,
+    optimize=None,
+    check_trace=True,
+    check_inputs=None,
+    check_tolerance=1e-5,
+    strict=True,
+    _force_outplace=False,
+    _module_class=None,
+    _compilation_unit=_python_cu,
+    example_inputs_is_kwarg=False,
+    _store_inputs=True,
+):
+    """
+    Trace a module and return an executable :class:`ScriptModule` that will be optimized using just-in-time compilation.
+
+    When a module is passed to :func:`torch.jit.trace `, only
+    the ``forward`` method is run and traced. With ``trace_module``, you can specify a dictionary of
+    method names to example inputs to trace (see the ``inputs``) argument below.
+
+    See :func:`torch.jit.trace ` for more information on tracing.
+
+    Args:
+        mod (torch.nn.Module):  A ``torch.nn.Module`` containing methods whose names are
+                                specified in ``inputs``. The given methods will be compiled
+                                as a part of a single `ScriptModule`.
+        inputs (dict):  A dict containing sample inputs indexed by method names in ``mod``.
+                                The inputs will be passed to methods whose names correspond to inputs'
+                                keys while tracing.
+                                ``{ 'forward' : example_forward_input, 'method2': example_method2_input}``
+    Keyword arguments:
+        check_trace (``bool``, optional): Check if the same inputs run through
+                                      traced code produce the same outputs. Default: ``True``. You might want
+                                      to disable this if, for example, your network contains non-
+                                      deterministic ops or if you are sure that the network is correct despite
+                                      a checker failure.
+
+        check_inputs (list of dicts, optional): A list of dicts of input arguments that should be used
+                                                 to check the trace against what is expected. Each tuple
+                                                 is equivalent to a set of input arguments that would
+                                                 be specified in ``inputs``. For best results, pass in a
+                                                 set of checking inputs representative of the space of
+                                                 shapes and types of inputs you expect the network to see.
+                                                 If not specified, the original ``inputs`` are used for checking
+        check_tolerance (float, optional): Floating-point comparison tolerance to use in the checker procedure.
+                                           This can be used to relax the checker strictness in the event that
+                                           results diverge numerically for a known reason, such as operator fusion.
+        example_inputs_is_kwarg (``bool``, optional): This parameter indicate whether the example inputs is a pack
+                                           pack of keyword arguments. Default: ``False``.
+
+    Returns:
+        A :class:`ScriptModule` object with a single ``forward`` method containing the traced code.
+        When ``func`` is a ``torch.nn.Module``, the returned :class:`ScriptModule` will have the same set of
+        sub-modules and parameters as ``func``.
+
+    Example (tracing a module with multiple methods)::
+
+        import torch
+        import torch.nn as nn
+
+
+        class Net(nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.conv = nn.Conv2d(1, 1, 3)
+
+            def forward(self, x):
+                return self.conv(x)
+
+            def weighted_kernel_sum(self, weight):
+                return weight * self.conv.weight
+
+
+        n = Net()
+        example_weight = torch.rand(1, 1, 3, 3)
+        example_forward_input = torch.rand(1, 1, 3, 3)
+
+        # Trace a specific method and construct `ScriptModule` with
+        # a single `forward` method
+        module = torch.jit.trace(n.forward, example_forward_input)
+
+        # Trace a module (implicitly traces `forward`) and construct a
+        # `ScriptModule` with a single `forward` method
+        module = torch.jit.trace(n, example_forward_input)
+
+        # Trace specific methods on a module (specified in `inputs`), constructs
+        # a `ScriptModule` with `forward` and `weighted_kernel_sum` methods
+        inputs = {"forward": example_forward_input, "weighted_kernel_sum": example_weight}
+        module = torch.jit.trace_module(n, inputs)
+
+    """
+    if not _enabled:
+        return mod
+    if optimize is not None:
+        warnings.warn(
+            "`optimize` is deprecated and has no effect. "
+            "Use `with torch.jit.optimized_execution()` instead",
+            FutureWarning,
+            stacklevel=2,
+        )
+
+    var_lookup_fn = _create_interpreter_name_lookup_fn(0)
+
+    if not isinstance(mod, torch.nn.Module):
+        raise AttributeError("expected torch.nn.Module as the first argument")
+
+    if not isinstance(inputs, dict):
+        raise AttributeError("expected a dictionary of (method_name, input) pairs")
+
+    old_module_map = torch.jit._trace._trace_module_map
+    try:
+        trace_module_map: dict[Any, Any] = {}
+
+        def register_submods(mod, prefix):
+            for name, child in mod.named_children():
+                submod_qualname = prefix + "." + name
+                trace_module_map[child] = submod_qualname
+                register_submods(child, submod_qualname)
+
+        trace_module_map["__module"] = mod
+        torch.jit._trace._trace_module_map = trace_module_map
+        register_submods(mod, "__module")
+
+        module = make_module(mod, _module_class, _compilation_unit)
+
+        for method_name, example_inputs in inputs.items():
+            if method_name == "forward":
+                # "forward" is a special case because we need to trace
+                # `Module.__call__`, which sets up some extra tracing, but uses
+                # argument names of the real `Module.forward` method.
+                func = mod
+                forward_method = getattr(mod, method_name)
+                argument_names = get_callable_argument_names(forward_method)
+            else:
+                func = getattr(mod, method_name)
+                argument_names = get_callable_argument_names(func)
+
+            if isinstance(example_inputs, dict) and example_inputs_is_kwarg:
+                # Raise exception when the user provided key names are not aligned with forward() method's arguments' name/
+                for key in example_inputs:
+                    if key not in argument_names:
+                        valid_arguments = "[" + ",".join(argument_names) + "]"
+                        raise NameError(
+                            f"""'{key}' is not in forward() method's arguments,
+                         valid arguments name are {valid_arguments}"""
+                        )
+                module._c._create_method_from_trace_with_dict(
+                    method_name,
+                    func,
+                    example_inputs,
+                    var_lookup_fn,
+                    strict,
+                    _force_outplace,
+                    argument_names,
+                    _store_inputs,
+                )
+            else:
+                example_inputs = make_tuple(example_inputs)
+                module._c._create_method_from_trace(
+                    method_name,
+                    func,
+                    example_inputs,
+                    var_lookup_fn,
+                    strict,
+                    _force_outplace,
+                    argument_names,
+                    _store_inputs,
+                )
+
+            check_trace_method = module._c._get_method(method_name)
+
+            # Check the trace against new traces created from user-specified inputs
+            if check_trace:
+                if check_inputs is not None:
+                    _check_trace(
+                        check_inputs,
+                        func,
+                        check_trace_method,
+                        check_tolerance,
+                        strict,
+                        _force_outplace,
+                        True,
+                        _module_class,
+                        example_inputs_is_kwarg=example_inputs_is_kwarg,
+                    )
+                else:
+                    _check_trace(
+                        [inputs],
+                        func,
+                        check_trace_method,
+                        check_tolerance,
+                        strict,
+                        _force_outplace,
+                        True,
+                        _module_class,
+                        example_inputs_is_kwarg=example_inputs_is_kwarg,
+                    )
+    finally:
+        torch.jit._trace._trace_module_map = old_module_map
+
+    return module
+
+
+def is_tracing():
+    """Return a boolean value.
+
+    Returns ``True`` in tracing (if a function is called during the
+    tracing of code with ``torch.jit.trace``) and ``False`` otherwise.
+    """
+    if is_scripting():
+        return False
+    return torch._C._is_tracing()
+
+
+class TracedModule(ScriptModule):
+    _disable_script_meta = True
+
+    def __init__(self, orig, id_set=None, _compilation_unit=None):
+        # XXX: orig can be a nn.Module or a function!
+        super().__init__()
+        assert isinstance(orig, torch.nn.Module)
+
+        # Copy a subset of `orig` to a temporary nn.Module.
+        # This is a way to customize what will actually get compiled by create_script_module
+        id_set = set()
+
+        # This allows us to preserve the original module's qualified name by defining a new
+        # type with the attribute _jit_override_qualname. In torch._jit_internal._qualified_name
+        # we have a special case that will look up this attribute to override whatever qualname
+        # we would get from the python type system
+        class QualnameWrapper(torch.nn.Module):
+            pass
+
+        QualnameWrapper._jit_override_qualname = torch._jit_internal._qualified_name(  # type: ignore[attr-defined]
+            type(orig)
+        )
+
+        tmp_module = QualnameWrapper()
+
+        def check_unique(param):
+            if param in id_set:
+                raise ValueError(
+                    "TracedModules don't support parameter sharing between modules"
+                )
+            id_set.add(param)
+
+        tmp_module.training = orig.training
+
+        for name, param in orig._parameters.items():
+            if param is not None:
+                tmp_module._parameters[name] = param
+                check_unique(param)
+        for name, buf in orig._buffers.items():
+            if buf is not None:
+                tmp_module._buffers[name] = buf
+                check_unique(buf)
+        for name, val in orig.__dict__.items():
+            if (
+                torch._C._jit_is_script_object(val)
+                and name not in orig._parameters
+                and name not in orig._buffers
+            ):
+                setattr(tmp_module, name, val)
+
+        if orig._backward_hooks:
+            raise ValueError(
+                "Modules that have backward hooks assigned can't be compiled: "
+                + str(orig)
+            )
+
+        for name, submodule in orig._modules.items():
+            if submodule is None:
+                continue
+            tmp_module._modules[name] = make_module(
+                submodule, TracedModule, _compilation_unit=None
+            )
+
+        script_module = torch.jit._recursive.create_script_module(
+            tmp_module, lambda module: (), share_types=False, is_tracing=True
+        )
+
+        self.__dict__["_name"] = type(orig).__name__
+        self.__dict__["_actual_script_module"] = script_module
+        for name in ("_parameters", "_buffers", "_modules", "training"):
+            delattr(self, name)
+
+    def forward(self, *args, **kwargs):
+        raise RuntimeError("Trace submodules cannot be called.")
+
+    def __getattr__(self, attr):
+        if "_actual_script_module" not in self.__dict__:
+            return super().__getattr__(attr)
+        return getattr(self._actual_script_module, attr)
+
+    def __setattr__(self, attr, value):
+        if "_actual_script_module" not in self.__dict__:
+            return super().__setattr__(attr, value)
+        setattr(self._actual_script_module, attr, value)
+
+    def _get_name(self):
+        return self._name
+
+    def extra_repr(self):
+        return f"original_name={self._name}"
+
+
+class TopLevelTracedModule(TracedModule):
+    forward: Callable[..., Any] = _CachedForward()  # type: ignore[assignment]
+
+    def _reconstruct(self, cpp_module):
+        """
+        Re-construct an instance of TopLevelTracedModule using an instance of a C++ module.
+
+        Args:
+            cpp_module: The C++ module that this TopLevelTracedModule will be rebuilt around.
+        """
+        self.__dict__["_actual_script_module"]._reconstruct(cpp_module)
+
+
+def _script_if_tracing(fn: Callable[P, R]) -> Callable[P, R]:
+    @functools.wraps(fn)
+    def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+        if not is_tracing():
+            # Not tracing, don't do anything
+            return fn(*args, **kwargs)
+
+        compiled_fn: Callable[P, R] = script(wrapper.__original_fn)  # type: ignore[attr-defined]
+        return compiled_fn(*args, **kwargs)
+
+    wrapper.__original_fn = fn  # type: ignore[attr-defined]
+    wrapper.__script_if_tracing_wrapper = True  # type: ignore[attr-defined]
+
+    return wrapper
+
+
+def _get_trace_graph(
+    f,
+    args=(),
+    kwargs=None,
+    strict=True,
+    _force_outplace=False,
+    return_inputs=False,
+    _return_inputs_states=False,
+):
+    """Return a tuple on tracing a function or model.
+
+    .. warning::
+        This function is internal-only and should only be used by the ONNX
+        exporter. If you are trying to get a graph through tracing, please go
+        through the public API instead::
+
+            trace = torch.jit.trace(nn.LSTMCell(), (input, hidden))
+            trace_graph = trace.graph
+
+    Trace a function or model, returning a tuple consisting of the both the
+    *trace* of an execution, as well as the original return value. If return_inputs,
+    also returns the trace inputs as part of the tuple
+
+    Tracing is guaranteed not to change the semantics of the function/module
+    that is traced.
+
+    Args:
+        f (torch.nn.Module or function): the function or module
+            to be traced.
+        args (tuple or Tensor): the positional arguments to pass to the
+            function/module to be traced.  A non-tuple is assumed to
+            be a single positional argument to be passed to the model.
+        kwargs (dict): the keyword arguments to pass to the function/module
+            to be traced.
+
+    Example (trace a cell):
+
+    .. testcode::
+
+        trace = torch.jit.trace(nn.LSTMCell(), (input, hidden))
+    """
+    if kwargs is None:
+        kwargs = {}
+    if not isinstance(args, tuple):
+        args = (args,)
+    outs = ONNXTracedModule(
+        f, strict, _force_outplace, return_inputs, _return_inputs_states
+    )(*args, **kwargs)
+    return outs
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/annotations.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/annotations.py
new file mode 100644
index 0000000000000000000000000000000000000000..9df5b7b4fb7c9f0877629ef0e399c4a580da6778
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/annotations.py
@@ -0,0 +1,550 @@
+# mypy: allow-untyped-defs
+import ast
+import builtins
+import dis
+import enum
+import inspect
+import re
+import typing
+import warnings
+from textwrap import dedent
+
+import torch
+from torch._C import (
+    _GeneratorType,
+    AnyType,
+    AwaitType,
+    BoolType,
+    ComplexType,
+    DeviceObjType,
+    DictType,
+    EnumType,
+    FloatType,
+    FutureType,
+    InterfaceType,
+    IntType,
+    ListType,
+    NoneType,
+    NumberType,
+    OptionalType,
+    StreamObjType,
+    StringType,
+    TensorType,
+    TupleType,
+    UnionType,
+)
+from torch._jit_internal import (  # type: ignore[attr-defined]
+    _Await,
+    _qualified_name,
+    Any,
+    BroadcastingList1,
+    BroadcastingList2,
+    BroadcastingList3,
+    Dict,
+    Future,
+    is_await,
+    is_dict,
+    is_future,
+    is_ignored_fn,
+    is_list,
+    is_optional,
+    is_tuple,
+    is_union,
+    List,
+    Optional,
+    Tuple,
+    Union,
+)
+from torch._sources import get_source_lines_and_file
+
+from ._state import _get_script_class
+
+
+if torch.distributed.rpc.is_available():
+    from torch._C import RRefType
+    from torch._jit_internal import is_rref, RRef
+
+from torch._ops import OpOverloadPacket
+
+
+class Module:
+    def __init__(self, name, members):
+        self.name = name
+        self.members = members
+
+    def __getattr__(self, name):
+        try:
+            return self.members[name]
+        except KeyError:
+            raise RuntimeError(
+                f"Module {self.name} has no member called {name}"
+            ) from None
+
+
+class EvalEnv:
+    env = {
+        "torch": Module("torch", {"Tensor": torch.Tensor}),
+        "Tensor": torch.Tensor,
+        "typing": Module("typing", {"Tuple": Tuple}),
+        "Tuple": Tuple,
+        "List": List,
+        "Dict": Dict,
+        "Optional": Optional,
+        "Union": Union,
+        "Future": Future,
+        "Await": _Await,
+    }
+
+    def __init__(self, rcb):
+        self.rcb = rcb
+        if torch.distributed.rpc.is_available():
+            self.env["RRef"] = RRef
+
+    def __getitem__(self, name):
+        if name in self.env:
+            return self.env[name]
+        if self.rcb is not None:
+            return self.rcb(name)
+        return getattr(builtins, name, None)
+
+
+def get_signature(fn, rcb, loc, is_method):
+    if isinstance(fn, OpOverloadPacket):
+        signature = try_real_annotations(fn.op, loc)
+    else:
+        signature = try_real_annotations(fn, loc)
+    if signature is not None and is_method:
+        # If this is a method, then the signature will include a type for
+        # `self`, but type comments do not contain a `self`. So strip it
+        # away here so everything is consistent (`inspect.ismethod` does
+        # not work here since `fn` is unbound at this point)
+        param_types, return_type = signature
+        param_types = param_types[1:]
+        signature = (param_types, return_type)
+
+    if signature is None:
+        type_line, source = None, None
+        try:
+            source = dedent("".join(get_source_lines_and_file(fn)[0]))
+            type_line = get_type_line(source)
+        except TypeError:
+            pass
+        # This might happen both because we failed to get the source of fn, or
+        # because it didn't have any annotations.
+        if type_line is not None:
+            signature = parse_type_line(type_line, rcb, loc)
+
+    return signature
+
+
+def is_function_or_method(the_callable):
+    # A stricter version of `inspect.isroutine` that does not pass for built-in
+    # functions
+    return inspect.isfunction(the_callable) or inspect.ismethod(the_callable)
+
+
+def is_vararg(the_callable):
+    if not is_function_or_method(the_callable) and callable(the_callable):  # noqa: B004
+        # If `the_callable` is a class, de-sugar the call so we can still get
+        # the signature
+        the_callable = the_callable.__call__
+
+    if is_function_or_method(the_callable):
+        return inspect.getfullargspec(the_callable).varargs is not None
+    else:
+        return False
+
+
+def get_param_names(fn, n_args):
+    if isinstance(fn, OpOverloadPacket):
+        fn = fn.op
+
+    if (
+        not is_function_or_method(fn)
+        and callable(fn)
+        and is_function_or_method(fn.__call__)
+    ):  # noqa: B004
+        # De-sugar calls to classes
+        fn = fn.__call__
+
+    if is_function_or_method(fn):
+        if is_ignored_fn(fn):
+            fn = inspect.unwrap(fn)
+        return inspect.getfullargspec(fn).args
+    else:
+        # The `fn` was not a method or function (maybe a class with a __call__
+        # method, so use a default param name list)
+        return [str(i) for i in range(n_args)]
+
+
+def check_fn(fn, loc):
+    # Make sure the function definition is not a class instantiation
+    try:
+        source = dedent("".join(get_source_lines_and_file(fn)[0]))
+    except (OSError, TypeError):
+        return
+    if source is None:
+        return
+
+    py_ast = ast.parse(source)
+    if len(py_ast.body) == 1 and isinstance(py_ast.body[0], ast.ClassDef):
+        raise torch.jit.frontend.FrontendError(
+            loc,
+            f"Cannot instantiate class '{py_ast.body[0].name}' in a script function",
+        )
+    if len(py_ast.body) != 1 or not isinstance(py_ast.body[0], ast.FunctionDef):
+        raise torch.jit.frontend.FrontendError(
+            loc, "Expected a single top-level function"
+        )
+
+
+def _eval_no_call(stmt, glob, loc):
+    """Evaluate statement as long as it does not contain any method/function calls."""
+    bytecode = compile(stmt, "", mode="eval")
+    for insn in dis.get_instructions(bytecode):
+        if "CALL" in insn.opname:
+            raise RuntimeError(
+                f"Type annotation should not contain calls, but '{stmt}' does"
+            )
+    return eval(bytecode, glob, loc)  # type: ignore[arg-type] # noqa: P204
+
+
+def parse_type_line(type_line, rcb, loc):
+    """Parse a type annotation specified as a comment.
+
+    Example inputs:
+        # type: (Tensor, torch.Tensor) -> Tuple[Tensor]
+        # type: (Tensor, Tuple[Tensor, Tensor]) -> Tensor
+    """
+    arg_ann_str, ret_ann_str = split_type_line(type_line)
+
+    try:
+        arg_ann = _eval_no_call(arg_ann_str, {}, EvalEnv(rcb))
+    except (NameError, SyntaxError) as e:
+        raise RuntimeError(
+            "Failed to parse the argument list of a type annotation"
+        ) from e
+
+    if not isinstance(arg_ann, tuple):
+        arg_ann = (arg_ann,)
+
+    try:
+        ret_ann = _eval_no_call(ret_ann_str, {}, EvalEnv(rcb))
+    except (NameError, SyntaxError) as e:
+        raise RuntimeError(
+            "Failed to parse the return type of a type annotation"
+        ) from e
+
+    arg_types = [ann_to_type(ann, loc) for ann in arg_ann]
+    return arg_types, ann_to_type(ret_ann, loc)
+
+
+def get_type_line(source):
+    """Try to find the line containing a comment with the type annotation."""
+    type_comment = "# type:"
+
+    lines = source.split("\n")
+    lines = list(enumerate(lines))
+    type_lines = list(filter(lambda line: type_comment in line[1], lines))
+    # `type: ignore` comments may be needed in JIT'ed functions for mypy, due
+    # to the hack in torch/_VF.py.
+
+    # An ignore type comment can be of following format:
+    #   1) type: ignore
+    #   2) type: ignore[rule-code]
+    # This ignore statement must be at the end of the line
+
+    # adding an extra backslash before the space, to avoid triggering
+    # one of the checks in .github/workflows/lint.yml
+    type_pattern = re.compile("# type:\\ ignore(\\[[a-zA-Z-]+\\])?$")
+    type_lines = list(filter(lambda line: not type_pattern.search(line[1]), type_lines))
+
+    if len(type_lines) == 0:
+        # Catch common typo patterns like extra spaces, typo in 'ignore', etc.
+        wrong_type_pattern = re.compile("#[\t ]*type[\t ]*(?!: ignore(\\[.*\\])?$):")
+        wrong_type_lines = list(
+            filter(lambda line: wrong_type_pattern.search(line[1]), lines)
+        )
+        if len(wrong_type_lines) > 0:
+            raise RuntimeError(
+                "The annotation prefix in line "
+                + str(wrong_type_lines[0][0])
+                + " is probably invalid.\nIt must be '# type:'"
+                + "\nSee PEP 484 (https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code)"  # noqa: B950
+                + "\nfor examples"
+            )
+        return None
+    elif len(type_lines) == 1:
+        # Only 1 type line, quit now
+        return type_lines[0][1].strip()
+
+    # Parse split up argument types according to PEP 484
+    # https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code
+    return_line = None
+    parameter_type_lines = []
+    for line_num, line in type_lines:
+        if "# type: (...) -> " in line:
+            return_line = (line_num, line)
+            break
+        elif type_comment in line:
+            parameter_type_lines.append(line)
+    if return_line is None:
+        raise RuntimeError(
+            "Return type line '# type: (...) -> ...' not found on multiline "
+            "type annotation\nfor type lines:\n"
+            + "\n".join([line[1] for line in type_lines])
+            + "\n(See PEP 484 https://www.python.org/dev/peps/pep-0484/#suggested-syntax-for-python-2-7-and-straddling-code)"
+        )
+
+    def get_parameter_type(line):
+        item_type = line[line.find(type_comment) + len(type_comment) :]
+        return item_type.strip()
+
+    types = map(get_parameter_type, parameter_type_lines)
+    parameter_types = ", ".join(types)
+
+    return return_line[1].replace("...", parameter_types)
+
+
+def split_type_line(type_line):
+    """Split the comment with the type annotation into parts for argument and return types.
+
+    For example, for an input of:
+        # type: (Tensor, torch.Tensor) -> Tuple[Tensor, Tensor]
+
+    This function will return:
+        ("(Tensor, torch.Tensor)", "Tuple[Tensor, Tensor]")
+
+    """
+    start_offset = len("# type:")
+    try:
+        arrow_pos = type_line.index("->")
+    except ValueError:
+        raise RuntimeError(
+            "Syntax error in type annotation (couldn't find `->`)"
+        ) from None
+    return type_line[start_offset:arrow_pos].strip(), type_line[arrow_pos + 2 :].strip()
+
+
+def try_real_annotations(fn, loc):
+    """Try to use the Py3.5+ annotation syntax to get the type."""
+    try:
+        # Note: anything annotated as `Optional[T]` will automatically
+        # be returned as `Union[T, None]` per
+        # https://github.com/python/typing/blob/master/src/typing.py#L850
+        sig = inspect.signature(fn)
+    except ValueError:
+        return None
+
+    all_annots = [sig.return_annotation] + [
+        p.annotation for p in sig.parameters.values()
+    ]
+    if all(ann is sig.empty for ann in all_annots):
+        return None
+
+    arg_types = [ann_to_type(p.annotation, loc) for p in sig.parameters.values()]
+    return_type = ann_to_type(sig.return_annotation, loc)
+    return arg_types, return_type
+
+
+# Finds common type for enum values belonging to an Enum class. If not all
+# values have the same type, AnyType is returned.
+def get_enum_value_type(e: type[enum.Enum], loc):
+    enum_values: List[enum.Enum] = list(e)
+    if not enum_values:
+        raise ValueError(f"No enum values defined for: '{e.__class__}'")
+
+    types = {type(v.value) for v in enum_values}
+    ir_types = [try_ann_to_type(t, loc) for t in types]
+
+    # If Enum values are of different types, an exception will be raised here.
+    # Even though Python supports this case, we chose to not implement it to
+    # avoid overcomplicate logic here for a rare use case. Please report a
+    # feature request if you find it necessary.
+    res = torch._C.unify_type_list(ir_types)
+    if not res:
+        return AnyType.get()
+    return res
+
+
+def is_tensor(ann):
+    if issubclass(ann, torch.Tensor):
+        return True
+
+    if issubclass(
+        ann,
+        (
+            torch.LongTensor,
+            torch.DoubleTensor,
+            torch.FloatTensor,
+            torch.IntTensor,
+            torch.ShortTensor,
+            torch.HalfTensor,
+            torch.CharTensor,
+            torch.ByteTensor,
+            torch.BoolTensor,
+        ),
+    ):
+        warnings.warn(
+            "TorchScript will treat type annotations of Tensor "
+            "dtype-specific subtypes as if they are normal Tensors. "
+            "dtype constraints are not enforced in compilation either."
+        )
+        return True
+
+    return False
+
+
+def _fake_rcb(inp):
+    return None
+
+
+def try_ann_to_type(ann, loc, rcb=None):
+    ann_args = typing.get_args(ann)  # always returns a tuple!
+
+    if ann is inspect.Signature.empty:
+        return TensorType.getInferred()
+    if ann is None:
+        return NoneType.get()
+    if inspect.isclass(ann) and is_tensor(ann):
+        return TensorType.get()
+    if is_tuple(ann):
+        # Special case for the empty Tuple type annotation `Tuple[()]`
+        if len(ann_args) == 1 and ann_args[0] == ():
+            return TupleType([])
+        return TupleType([try_ann_to_type(a, loc) for a in ann_args])
+    if is_list(ann):
+        elem_type = try_ann_to_type(ann_args[0], loc)
+        if elem_type:
+            return ListType(elem_type)
+    if is_dict(ann):
+        key = try_ann_to_type(ann_args[0], loc)
+        value = try_ann_to_type(ann_args[1], loc)
+        # Raise error if key or value is None
+        if key is None:
+            raise ValueError(
+                f"Unknown type annotation: '{ann_args[0]}' at {loc.highlight()}"
+            )
+        if value is None:
+            raise ValueError(
+                f"Unknown type annotation: '{ann_args[1]}' at {loc.highlight()}"
+            )
+        return DictType(key, value)
+    if is_optional(ann):
+        if issubclass(ann_args[1], type(None)):
+            contained = ann_args[0]
+        else:
+            contained = ann_args[1]
+        valid_type = try_ann_to_type(contained, loc)
+        msg = "Unsupported annotation {} could not be resolved because {} could not be resolved. At\n{}"
+        assert valid_type, msg.format(repr(ann), repr(contained), repr(loc))
+        return OptionalType(valid_type)
+    if is_union(ann):
+        # TODO: this is hack to recognize NumberType
+        if set(ann_args) == {int, float, complex}:
+            return NumberType.get()
+        inner: List = []
+        # We need these extra checks because both `None` and invalid
+        # values will return `None`
+        # TODO: Determine if the other cases need to be fixed as well
+        for a in typing.get_args(ann):
+            if a is None:
+                inner.append(NoneType.get())
+            maybe_type = try_ann_to_type(a, loc)
+            msg = "Unsupported annotation {} could not be resolved because {} could not be resolved. At\n{}"
+            assert maybe_type, msg.format(repr(ann), repr(maybe_type), repr(loc))
+            inner.append(maybe_type)
+        return UnionType(inner)  # type: ignore[arg-type]
+    if torch.distributed.rpc.is_available() and is_rref(ann):
+        return RRefType(try_ann_to_type(ann_args[0], loc))
+    if is_future(ann):
+        return FutureType(try_ann_to_type(ann_args[0], loc))
+    if is_await(ann):
+        elementType = try_ann_to_type(ann_args[0], loc) if ann_args else AnyType.get()
+        return AwaitType(elementType)
+    if ann is float:
+        return FloatType.get()
+    if ann is complex:
+        return ComplexType.get()
+    if ann is int or ann is torch.SymInt:
+        return IntType.get()
+    if ann is str:
+        return StringType.get()
+    if ann is bool:
+        return BoolType.get()
+    if ann is Any:
+        return AnyType.get()
+    if ann is type(None):
+        return NoneType.get()
+    if inspect.isclass(ann) and hasattr(ann, "__torch_script_interface__"):
+        return InterfaceType(ann.__torch_script_interface__)
+    if ann is torch.device:
+        return DeviceObjType.get()
+    if ann is torch.Generator:
+        return _GeneratorType.get()
+    if ann is torch.Stream:
+        return StreamObjType.get()
+    if ann is torch.dtype:
+        return IntType.get()  # dtype not yet bound in as its own type
+    if inspect.isclass(ann) and issubclass(ann, enum.Enum):
+        if _get_script_class(ann) is None:
+            scripted_class = torch.jit._script._recursive_compile_class(ann, loc)
+            name = scripted_class.qualified_name()
+        else:
+            name = _qualified_name(ann)
+        return EnumType(name, get_enum_value_type(ann, loc), list(ann))
+    if inspect.isclass(ann):
+        maybe_script_class = _get_script_class(ann)
+        if maybe_script_class is not None:
+            return maybe_script_class
+        if torch._jit_internal.can_compile_class(ann):
+            return torch.jit._script._recursive_compile_class(ann, loc)
+
+    # Maybe resolve a NamedTuple to a Tuple Type
+    if rcb is None:
+        rcb = _fake_rcb
+    return torch._C._resolve_type_from_object(ann, loc, rcb)
+
+
+def ann_to_type(ann, loc, rcb=None):
+    the_type = try_ann_to_type(ann, loc, rcb)
+    if the_type is not None:
+        return the_type
+    raise ValueError(f"Unknown type annotation: '{ann}' at {loc.highlight()}")
+
+
+__all__ = [
+    "Any",
+    "List",
+    "BroadcastingList1",
+    "BroadcastingList2",
+    "BroadcastingList3",
+    "Tuple",
+    "is_tuple",
+    "is_list",
+    "Dict",
+    "is_dict",
+    "is_optional",
+    "is_union",
+    "TensorType",
+    "TupleType",
+    "FloatType",
+    "ComplexType",
+    "IntType",
+    "ListType",
+    "StringType",
+    "DictType",
+    "AnyType",
+    "Module",
+    # TODO: Consider not exporting these during wildcard import (reserve
+    # that for the types; for idiomatic typing code.)
+    "get_signature",
+    "check_fn",
+    "get_param_names",
+    "parse_type_line",
+    "get_type_line",
+    "split_type_line",
+    "try_real_annotations",
+    "try_ann_to_type",
+    "ann_to_type",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/frontend.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/frontend.py
new file mode 100644
index 0000000000000000000000000000000000000000..76682e752299186f6605a03ed97b0b9d20f96d01
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/frontend.py
@@ -0,0 +1,1288 @@
+# mypy: allow-untyped-defs
+import ast
+import copy
+import dataclasses
+import inspect
+import re
+import string
+from collections import namedtuple
+from textwrap import dedent
+
+import torch
+import torch.jit.annotations
+from torch import _jit_internal
+from torch._C._jit_tree_views import (
+    Apply,
+    Assert,
+    Assign,
+    Attribute,
+    AugAssign,
+    BinOp,
+    Break,
+    ClassDef,
+    Const,
+    Continue,
+    Decl,
+    Def,
+    Delete,
+    DictComp,
+    DictLiteral,
+    Dots,
+    EmptyTypeAnnotation,
+    ExprStmt,
+    FalseLiteral,
+    For,
+    Ident,
+    If,
+    ListComp,
+    ListLiteral,
+    NoneLiteral,
+    Param,
+    Pass,
+    Property,
+    Raise,
+    Return,
+    Select,
+    SliceExpr,
+    Starred,
+    Stmt,
+    StringLiteral,
+    Subscript,
+    TernaryIf,
+    TrueLiteral,
+    TupleLiteral,
+    UnaryOp,
+    Var,
+    While,
+    With,
+    WithItem,
+)
+from torch._jit_internal import (  # noqa: F401
+    _is_drop_fn,
+    FunctionModifiers,
+    is_static_fn,
+    should_drop,
+)
+from torch._sources import (
+    get_source_lines_and_file,
+    make_source_context,
+    parse_def,
+    ParsedDef as _ParsedDef,
+)
+from torch.jit._dataclass_impls import DATACLASS_MAGIC_METHODS
+from torch.jit._monkeytype_config import get_qualified_name, monkeytype_trace
+
+
+_IS_ASTUNPARSE_INSTALLED = False
+try:
+    import astunparse  # type: ignore[import]
+
+    _IS_ASTUNPARSE_INSTALLED = True
+except ImportError:
+    pass
+
+# Borrowed from cPython implementation
+# https://github.com/python/cpython/blob/561612d8456cfab5672c9b445521113b847bd6b3/Lib/textwrap.py#L411#
+
+_reserved_prefix = "__jit"
+_reserved_names = {"print"}
+_identifier_chars = set(string.ascii_lowercase + string.ascii_uppercase + string.digits)
+
+
+def is_reserved_name(name):
+    return name.startswith(_reserved_prefix) or name in _reserved_names
+
+
+pretty_node_names = {
+    ast.FunctionDef: "function definitions",
+    ast.For: "for loops",
+    ast.Delete: "del statements",
+    ast.ClassDef: "class definitions",
+    ast.With: "with statements",
+    ast.Raise: "raise statements",
+    ast.Assert: "assertions",
+    ast.Import: "import statements",
+    ast.ImportFrom: "import statements",
+    ast.Global: "global variables",
+    ast.Break: "break statements",
+    ast.Continue: "continue statements",
+}
+
+node_start_tokens = {
+    ast.FunctionDef: "def",
+    ast.For: "for",
+    ast.Delete: "del",
+    ast.ClassDef: "class",
+    ast.With: "with",
+    ast.Raise: "raise",
+    ast.Assert: "assert",
+    ast.Import: "import",
+    ast.ImportFrom: "from",
+    ast.Global: "global",
+    ast.Break: "break",
+    ast.Continue: "continue",
+}
+
+pretty_node_names.update(
+    {
+        ast.AsyncFunctionDef: "async function definitions",
+        ast.AsyncFor: "async for loops",
+        ast.AsyncWith: "async with statements",
+        ast.Try: "try blocks",
+        ast.Nonlocal: "nonlocal variables",
+    }
+)
+
+node_start_tokens.update(
+    {
+        ast.AsyncFunctionDef: "async def",
+        ast.AsyncFor: "async for",
+        ast.AsyncWith: "async with",
+        ast.Try: "try",
+        ast.Nonlocal: "nonlocal",
+    }
+)
+
+pretty_node_names.update(
+    {
+        ast.AnnAssign: "annotated assignments",
+    }
+)
+# NB: no specific token for AnnAssign
+
+
+class FrontendError(Exception):
+    def __init__(self, source_range, msg):
+        self.source_range = source_range
+        self.msg = msg
+
+        # This has to be instantiated here so the ErrorReport is accurate to the
+        # call stack when the FrontendError was raised
+        self.error_report = torch._C.ErrorReport(self.source_range)
+
+    def __str__(self):
+        return self.msg + self.error_report.what().lstrip()
+
+
+class NotSupportedError(FrontendError):
+    pass
+
+
+class UnsupportedNodeError(NotSupportedError):
+    def __init__(self, ctx, offending_node, reason=""):
+        # If we don't have a specific token, we default to length of 1
+        node_type = type(offending_node)
+        range_len = len(node_start_tokens.get(node_type, " "))
+        source_range = ctx.make_range(
+            offending_node.lineno,
+            offending_node.col_offset,
+            offending_node.col_offset + range_len,
+        )
+        feature_name = pretty_node_names.get(node_type, node_type.__name__)
+        msg = f"{feature_name} {reason + ' ' if reason else ''}aren't supported"
+        super().__init__(source_range, msg)
+
+
+class FrontendTypeError(FrontendError):
+    pass
+
+
+def build_withitems(ctx, items):
+    items = [build_withitem(ctx, i) for i in items]
+    return list(items)
+
+
+def build_stmts(ctx, stmts):
+    stmts = [build_stmt(ctx, s) for s in stmts]
+    return list(filter(None, stmts))
+
+
+def get_class_properties(cls, self_name):
+    """
+    Get a list of Property objects representing the properties of a class.
+
+    Args:
+        cls:  The class to get properties of.
+        self_name: The name of the class that the properties should belong to.
+    Returns:
+        A list of Property objects corresponding to the properties of cls. Property
+        here refers to the subclass of TreeView.
+    """
+    props = inspect.getmembers(cls, predicate=lambda m: isinstance(m, property))
+    # Any property that should not compiled must be in this list on the Module.
+    unused_properties = getattr(cls, "__jit_unused_properties__", [])
+
+    # Create Property TreeView objects from inspected property objects.
+    properties = []
+    for prop in props:
+        if prop[0] not in unused_properties and not should_drop(prop[1].fget):
+            getter = get_jit_def(
+                prop[1].fget, f"__{prop[0]}_getter", self_name=self_name
+            )
+            setter = (
+                get_jit_def(prop[1].fset, f"__{prop[0]}_setter", self_name=self_name)
+                if prop[1].fset
+                else None
+            )
+            properties.append(
+                Property(getter.range(), Ident(getter.range(), prop[0]), getter, setter)
+            )
+
+    return properties
+
+
+def get_class_assigns(ctx, cls_ast):
+    assigns = []
+
+    def maybe_build_assign(builder, entry):
+        nonlocal assigns
+        try:
+            assigns.append(builder(ctx, entry))
+        except NotSupportedError:
+            pass
+
+    for entry in cls_ast.body:
+        if isinstance(entry, ast.Assign):
+            maybe_build_assign(StmtBuilder.build_Assign, entry)
+        elif isinstance(entry, ast.AnnAssign):
+            maybe_build_assign(StmtBuilder.build_AnnAssign, entry)
+    return assigns
+
+
+def get_jit_class_def(cls, self_name):
+    """Get definitions for each method within the current class independently.
+
+    Args:
+        cls: The class to get definition of.
+        self_name: The name of the class that the properties should belong to.
+
+    Returns:
+        torch._C._jit_tree_views.ClassDef: A representation of the class,
+            the methods in the class and their definition as a tree.
+    """
+    # TODO: proper overriding analysis when implementing class inheritance
+    methods = inspect.getmembers(
+        cls,
+        predicate=lambda m: (inspect.ismethod(m) or inspect.isfunction(m))
+        and not is_static_fn(cls, m.__name__)
+        and m.__name__ in cls.__dict__
+        and not _is_drop_fn(m),
+    )
+
+    def is_classmethod(fn):
+        return inspect.ismethod(fn) and getattr(fn, "__self__", None) == cls
+
+    # Get and parse the source code for this class
+    sourcelines, file_lineno, filename = get_source_lines_and_file(
+        cls, torch._C.ErrorReport.call_stack()
+    )
+    source = "".join(sourcelines)
+
+    dedent_src = dedent(source)
+    py_ast = ast.parse(dedent_src)
+
+    class_ast = py_ast.body[0]
+    assert isinstance(class_ast, ast.ClassDef)
+
+    # Special case for dataclasses. In general we need access to the source code for
+    # an object in order to JIT compile it. But the dataclasses module dynamically synthesizes
+    # magic methods for classes, and we can't get the source code for these methods. As a
+    # workaround, we synthesize TorchScript-friendly implementations ourselves.
+    if dataclasses.is_dataclass(cls):
+        # Detect whether the user manually implemented any of the magic methods. If they did,
+        # we don't want to synthesize/override them.
+        overrides = {
+            method.name
+            for method in class_ast.body
+            if isinstance(method, ast.FunctionDef)
+            and method.name in DATACLASS_MAGIC_METHODS
+        }
+        for i, (name, _) in enumerate(methods):
+            # Is this a magic method we can synthesize?
+            synthesizer_fn = DATACLASS_MAGIC_METHODS.get(name)
+            if synthesizer_fn and name not in overrides:
+                parsed_def = synthesizer_fn(cls)
+                methods[i] = name, parsed_def
+                func = getattr(cls, name)
+                _jit_internal.loader.cache(func, parsed_def.source)
+
+    method_defs = [
+        get_jit_def(obj, name, self_name=self_name, is_classmethod=is_classmethod(obj))
+        for (name, obj) in methods
+    ]
+    properties = get_class_properties(cls, self_name)
+
+    leading_whitespace_len = len(source.split("\n", 1)[0]) - len(
+        dedent_src.split("\n", 1)[0]
+    )
+    ctx = make_source_context(
+        source, filename, file_lineno, leading_whitespace_len, False
+    )
+    assigns = get_class_assigns(ctx, class_ast)
+
+    return build_class_def(ctx, class_ast, method_defs, properties, self_name, assigns)
+
+
+def get_jit_def(fn, def_name, self_name=None, is_classmethod=False):
+    """
+    Build a JIT AST (TreeView) from the given function.
+
+    Args:
+        fn: A function object to compile or a pre-parsed ParsedDef object
+        def_name: The name to give to the resulting AST object. This is not
+            always the same as `fn.__name__`, for example:
+                def _forward(self):
+                    ...
+                forward = _forward
+            In this case, the `__name__` attribute of the function object is "_forward",
+            but we want the result AST to have the name "forward".
+        self_name: If this function is a method, what the type name of `self` is.
+    """
+    parsed_def = parse_def(fn) if not isinstance(fn, _ParsedDef) else fn
+    type_line = torch.jit.annotations.get_type_line(parsed_def.source)
+    fn_def = parsed_def.ast.body[0]
+
+    if is_classmethod:
+        arg_name = fn_def.args.args[0].arg  # type:ignore[union-attr]
+        # Insert a statement that assigns the first argument to the class
+        assign_stmt = ast.parse(f"{arg_name} = {self_name}").body[0]
+        fn_def.body.insert(0, assign_stmt)  # type:ignore[union-attr]
+
+    # Swap out the function signature and body if it is unused
+    if should_drop(fn):
+        unused_fn_def = ast.parse(
+            'def unused_fn(self: Any):\n\traise RuntimeError("Cannot call @unused methods")'
+        )
+        if len(unused_fn_def.body) != 1 or not isinstance(
+            unused_fn_def.body[0], ast.FunctionDef
+        ):
+            raise RuntimeError(
+                f"Expected a single top-level function: {parsed_def.filename}:{parsed_def.file_lineno}"
+            )
+        unused_def = unused_fn_def.body[0]
+        fn_def.body = unused_def.body  # type:ignore[union-attr]
+        # kwarg/vararg not supported by `build_def`
+        fn_def.args.kwarg = fn_def.args.vararg = None  # type:ignore[union-attr]
+        for arg in fn_def.args.args + fn_def.args.kwonlyargs:  # type:ignore[union-attr]
+            # Replace potentially unsupported type annotations by "Any"
+            arg.annotation = unused_def.args.args[0].annotation
+        if _is_drop_fn(fn):
+            # Dropping potentially unsupported return type annotation for jit._drop
+            fn_def.returns = None  # type:ignore[union-attr]
+            fn_def.type_comment = None  # type:ignore[union-attr]
+
+    # If MonkeyType is installed, get all the consolidated type traces
+    # for the arguments from type_trace_db
+    type_trace_db = torch.jit._script._get_type_trace_db()
+    pdt_arg_types = None
+    if monkeytype_trace and not isinstance(fn, _ParsedDef):  # type: ignore[truthy-function]
+        qualname = get_qualified_name(fn)
+        pdt_arg_types = type_trace_db.get_args_types(qualname)
+
+    return build_def(
+        parsed_def.ctx,
+        fn_def,
+        type_line,
+        def_name,
+        self_name=self_name,
+        pdt_arg_types=pdt_arg_types,
+    )
+
+
+# TODO: more robust handling of recognizing ignore context manager
+def is_torch_jit_ignore_context_manager(stmt):
+    # checks if the statement is torch.jit.ignore context manager
+    if isinstance(stmt.items[0].context_expr, ast.Call):
+        # extract torch part
+        function = stmt.items[0].context_expr.func
+        if isinstance(function, ast.Attribute):
+            attr_name = function.attr
+            attr_value = function.value
+            if attr_name == "_IgnoreContextManager" and isinstance(
+                attr_value, ast.Attribute
+            ):
+                # there should be at most two nested attributes (e.g torch.jit._IgnoreContextManager)
+                if attr_value.attr == "jit" and isinstance(attr_value.value, ast.Name):
+                    if attr_value.value.id == "torch":
+                        return True
+    return False
+
+
+class Builder:
+    def __call__(self, ctx, node):
+        method = getattr(self, "build_" + node.__class__.__name__, None)
+        if method is None:
+            raise UnsupportedNodeError(ctx, node)
+        return method(ctx, node)
+
+
+def build_class_def(ctx, py_def, methods, properties, self_name, assigns):
+    r = ctx.make_range(
+        py_def.lineno, py_def.col_offset, py_def.col_offset + len("class")
+    )
+    return ClassDef(
+        Ident(r, self_name), [Stmt(method) for method in methods], properties, assigns
+    )
+
+
+def build_def(ctx, py_def, type_line, def_name, self_name=None, pdt_arg_types=None):
+    body = py_def.body
+    r = ctx.make_range(py_def.lineno, py_def.col_offset, py_def.col_offset + len("def"))
+
+    param_list = build_param_list(ctx, py_def.args, self_name, pdt_arg_types)
+    return_type = None
+    if getattr(py_def, "returns", None) is not None:
+        return_type = build_expr(ctx, py_def.returns)
+
+    decl = Decl(r, param_list, return_type)
+    is_method = self_name is not None
+    if type_line is not None:
+        type_comment_decl = torch._C.parse_type_comment(type_line)
+        decl = torch._C.merge_type_from_type_comment(decl, type_comment_decl, is_method)
+
+    return Def(Ident(r, def_name), decl, build_stmts(ctx, body))
+
+
+_vararg_kwarg_err = (
+    "Compiled functions can't take variable number of arguments "
+    "or use keyword-only arguments with defaults"
+)
+
+
+def build_param_list(ctx, py_args, self_name, pdt_arg_types=None):
+    if py_args.kwarg is not None:
+        expr = py_args.kwarg
+        ctx_range = ctx.make_range(
+            expr.lineno, expr.col_offset - 1, expr.col_offset + len(expr.arg)
+        )
+        raise NotSupportedError(ctx_range, _vararg_kwarg_err)
+    if py_args.vararg is not None:
+        expr = py_args.vararg
+        ctx_range = ctx.make_range(
+            expr.lineno, expr.col_offset - 1, expr.col_offset + len(expr.arg)
+        )
+        raise NotSupportedError(ctx_range, _vararg_kwarg_err)
+    if len(py_args.kw_defaults) > 0:
+        # kw_defaults is a list of the values for the kwargs (which default to None),
+        # so they don't actually have line numbers.
+        for arg in py_args.kw_defaults:
+            if arg is not None:
+                ctx_range = build_expr(ctx, arg).range()
+                raise NotSupportedError(ctx_range, _vararg_kwarg_err)
+
+    # List of Tuple of args and type as inferred by profile directed typing
+    arg_and_types = [
+        (
+            arg,
+            pdt_arg_types[arg.arg]
+            if pdt_arg_types and bool(pdt_arg_types[arg.arg])
+            else None,
+        )
+        for arg in py_args.args
+    ]
+    arg_and_types_kwonlyargs = [
+        (
+            arg,
+            pdt_arg_types[arg.arg]
+            if pdt_arg_types and bool(pdt_arg_types[arg.arg])
+            else None,
+        )
+        for arg in py_args.kwonlyargs
+    ]
+
+    result = [
+        build_param(ctx, arg, self_name, kwarg_only=False, pdt_arg_type=arg_type)
+        for arg, arg_type in arg_and_types
+    ]
+    result += [
+        build_param(ctx, arg, self_name, kwarg_only=True, pdt_arg_type=arg_type)
+        for arg, arg_type in arg_and_types_kwonlyargs
+    ]
+    return result
+
+
+def build_param(ctx, py_arg, self_name, kwarg_only, pdt_arg_type=None):
+    # NB: In Python3 py_arg is a pair of (str arg, expr? annotation)
+    name = py_arg.arg
+    r = ctx.make_range(py_arg.lineno, py_arg.col_offset, py_arg.col_offset + len(name))
+    if getattr(py_arg, "annotation", None) is not None:
+        annotation_expr = build_expr(ctx, py_arg.annotation)
+    elif pdt_arg_type:
+        annotation_expr = Var(Ident(r, pdt_arg_type))
+    elif self_name is not None and name == "self":
+        annotation_expr = Var(Ident(r, self_name))
+    else:
+        annotation_expr = EmptyTypeAnnotation(r)
+    return Param(annotation_expr, Ident(r, name), kwarg_only)
+
+
+def build_ignore_context_manager(ctx, stmt):
+    InputType = namedtuple("InputType", ["name", "ann"])
+    OutputType = namedtuple("OutputType", ["name", "ann"])
+
+    def process_ins_outs(args):
+        # parse the context manager to figure out inputs and outputs
+        # with their annotated types
+        # TODO: add input, output validator
+        inputs = []
+        outputs = []
+        for arg in args:
+            var_name = arg.arg
+            var_ann = arg.value.value
+            var_decl_type, var_ann = var_ann.split(":")
+            if var_decl_type == "inp":
+                inputs.append(InputType(var_name, var_ann))
+            if var_decl_type == "out":
+                outputs.append(OutputType(var_name, var_ann))
+        return inputs, outputs
+
+    def create_unique_name_ext(ctx, stmt):
+        # extension will be based on the full path filename plus
+        # the line number of original context manager
+        fn = re.sub(r"[^a-zA-Z0-9_]", "_", ctx.filename)
+        return f"{fn}_{stmt.lineno}"
+
+    def build_return_ann_stmt(outputs):
+        return_type_ann = ""
+        return_statement_str = "return "
+        if len(outputs) == 0:
+            return_type_ann += " -> None"
+        if len(outputs) == 1:
+            return_type_ann = " -> " + outputs[0].ann
+            return_statement_str += outputs[0].name
+        if len(outputs) > 1:
+            return_type_ann = " -> tuple"
+            return_type_ann += "[" + ", ".join([var.ann for var in outputs]) + "]"
+            return_statement_str += ", ".join([var.name for var in outputs])
+        return return_type_ann, return_statement_str
+
+    def build_args(args):
+        return ", ".join([arg.name for arg in args])
+
+    inputs, outputs = process_ins_outs(stmt.items[0].context_expr.keywords)
+
+    # build the replacement function str with given inputs and outputs
+    ignore_function_name = "func_ignore_" + create_unique_name_ext(ctx, stmt)
+    ignore_function_str = "\ndef " + ignore_function_name
+    ignore_function_str += (
+        "(" + ", ".join([var.name + " :" + var.ann for var in inputs]) + ")"
+    )
+
+    return_ann, return_stmt = build_return_ann_stmt(outputs)
+    ignore_function_str += return_ann + ": pass"
+
+    # first create the functionDef object from just declaration
+    ignore_function = ast.parse(ignore_function_str).body[0]
+
+    # dump the body of context manager to dummy function
+    ignore_function.body = stmt.body  # type: ignore[attr-defined]
+
+    # insert return statement to the function
+    return_stmt = ast.parse(return_stmt).body[0]
+    ignore_function.body.append(return_stmt)  # type: ignore[attr-defined]
+
+    ignore_func_str = f"""\
+# Backward compat: These used to be imported into the outer global scope so some
+# code may still expect them.
+from typing import List, Dict, Tuple
+
+@torch.jit.ignore
+{astunparse.unparse(ignore_function)}
+"""
+    g = copy.copy(globals())
+    exec(ignore_func_str, g)  # noqa: P204
+    # registers the custom function in the global context
+    globals()[ignore_function_name] = g[ignore_function_name]
+
+    # build the statements as:
+    # , , ... = torch.jit.frontend.(, )
+    assign_str_lhs = build_args(outputs)
+    # this function will be registered in torch.jit.frontend module by default
+    assign_str_rhs = (
+        f"torch.jit.frontend.{ignore_function_name}(" + build_args(inputs) + ")"
+    )
+
+    if len(outputs) > 0:
+        assign_str = assign_str_lhs + " = " + assign_str_rhs
+    else:
+        assign_str = assign_str_rhs
+    assign_ast = ast.parse(assign_str).body[0]
+    return assign_ast
+
+
+def get_default_args(fn):
+    """
+    Get a dictionary of default arguments for a function.
+
+    Args:
+        fn: Callable - The function to inspect for default arguments.
+    Returns:
+        (Dict[str, Any]): mapping argument names to their default values if
+        :attr:`fn` is not None, else empty dictionary.
+    """
+    if fn is None:
+        return {}
+
+    signature = inspect.signature(fn)
+
+    return {
+        k: v.default
+        for k, v in signature.parameters.items()
+        if v.default is not inspect.Parameter.empty
+    }
+
+
+def get_default_args_for_class(cls):
+    """
+    Get default arguments for all methods in a class (except for static methods).
+
+    Args:
+        cls: type - The class type to inspect for default arguments.
+    Returns:
+        A Dict[str, Dict[str, Any]] which maps each method name to a Dict[str, Any]
+        that maps each argument name to its default value.
+    """
+    # Get methods (except static methods because those are compiled separately as
+    # if they were independent script functions).
+    methods = inspect.getmembers(
+        cls,
+        predicate=lambda m: (inspect.ismethod(m) or inspect.isfunction(m))
+        and not is_static_fn(cls, m.__name__)
+        and m.__name__ in cls.__dict__,
+    )
+
+    # Get method defaults. Property defaults do not need to be considered
+    # because setters cannot be invoked without a value.
+    defaults = {
+        method_name: get_default_args(method_impl)
+        for method_name, method_impl in methods
+    }
+
+    return defaults
+
+
+class WithItemBuilder(Builder):
+    @staticmethod
+    def build_withitem(ctx, item):
+        lineno = item.context_expr.lineno
+        start = item.context_expr.col_offset
+        end = start + len(pretty_node_names[ast.With])
+        op_vars = item.optional_vars
+        r = ctx.make_range(lineno, start, end)
+
+        return WithItem(
+            r,
+            build_expr(ctx, item.context_expr),
+            build_expr(ctx, op_vars) if op_vars else None,
+        )
+
+
+class StmtBuilder(Builder):
+    augassign_map = {
+        ast.Add: "+",
+        ast.Sub: "-",
+        ast.Mult: "*",
+        ast.Div: "/",
+        ast.Mod: "%",
+        ast.BitOr: "|",
+        ast.BitAnd: "&",
+        ast.BitXor: "^",
+        ast.LShift: "<<",
+        ast.RShift: ">>",
+        ast.Pow: "**",
+    }
+
+    @staticmethod
+    def build_Expr(ctx, stmt):
+        value = stmt.value
+        if value.__class__.__name__ == "Str":
+            # If a statement is a string literal expression,
+            # then it is a docstring. Just ignore it.
+            return None
+        else:
+            return ExprStmt(build_expr(ctx, value))
+
+    @staticmethod
+    def build_Assign(ctx, stmt):
+        rhs = build_expr(ctx, stmt.value)
+        lhs = [build_expr(ctx, x) for x in stmt.targets]
+        return Assign(lhs, rhs)
+
+    @staticmethod
+    def build_AnnAssign(ctx, stmt):
+        if stmt.value is None:
+            raise UnsupportedNodeError(ctx, stmt, reason="without assigned value")
+
+        # Disallow type annotations on instance attributes outside of __init__
+        if (
+            type(stmt.target) == ast.Attribute
+            and stmt.target.value.id == "self"  # type: ignore[attr-defined]
+            and ctx.funcname != "__init__"
+        ):
+            start = stmt.col_offset
+            end = start + len(f"self.{stmt.target.attr}")
+            if hasattr(stmt.annotation, "id"):
+                end += len(f": {stmt.annotation.id}")
+            sr = ctx.make_range(stmt.lineno, start, end)
+            raise ValueError(
+                "Type annotations on instance attributes must be declared in "
+                f"__init__, not '{ctx.funcname}': {sr}"
+            )
+
+        rhs = build_expr(ctx, stmt.value)
+        lhs = build_expr(ctx, stmt.target)
+        the_type = build_expr(ctx, stmt.annotation)
+        return Assign([lhs], rhs, the_type)
+
+    @staticmethod
+    def build_Delete(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("del"))
+
+        return Delete(r, [build_expr(ctx, target) for target in stmt.targets])
+
+    @staticmethod
+    def build_Return(ctx, stmt):
+        r = ctx.make_range(
+            stmt.lineno, stmt.col_offset, stmt.col_offset + len("return")
+        )
+        return Return(r, None if stmt.value is None else build_expr(ctx, stmt.value))
+
+    @staticmethod
+    def build_Raise(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("raise"))
+        expr = build_expr(ctx, stmt.exc)
+        return Raise(r, expr)
+
+    @staticmethod
+    def build_Assert(ctx, stmt):
+        r = ctx.make_range(
+            stmt.lineno, stmt.col_offset, stmt.col_offset + len("assert")
+        )
+        test = build_expr(ctx, stmt.test)
+        msg = build_expr(ctx, stmt.msg) if stmt.msg is not None else None
+        return Assert(r, test, msg)
+
+    @staticmethod
+    def build_AugAssign(ctx, stmt):
+        lhs = build_expr(ctx, stmt.target)
+        rhs = build_expr(ctx, stmt.value)
+        op = type(stmt.op)
+        if op in StmtBuilder.augassign_map:
+            op_token = StmtBuilder.augassign_map[op]
+        else:
+            raise NotSupportedError(
+                find_before(ctx, rhs.range().start, "=", offsets=(-1, 0)),
+                "unsupported kind of augmented assignment: " + op.__name__,
+            )
+        return AugAssign(lhs, op_token, rhs)
+
+    @staticmethod
+    def build_While(ctx, stmt):
+        if stmt.orelse:
+            # TODO: try to recover the location of else:? Python doesn't give us useful
+            # annotations in this case
+            raise NotSupportedError(
+                None, "else branches of while loops aren't supported"
+            )
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("while"))
+        return While(r, build_expr(ctx, stmt.test), build_stmts(ctx, stmt.body))
+
+    @staticmethod
+    def build_For(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("for"))
+        if stmt.orelse:
+            raise NotSupportedError(r, "else branches of for loops aren't supported")
+
+        return For(
+            r,
+            [build_expr(ctx, stmt.target)],
+            [build_expr(ctx, stmt.iter)],
+            build_stmts(ctx, stmt.body),
+        )
+
+    @staticmethod
+    def build_If(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("if"))
+        return If(
+            r,
+            build_expr(ctx, stmt.test),
+            build_stmts(ctx, stmt.body),
+            build_stmts(ctx, stmt.orelse),
+        )
+
+    @staticmethod
+    def build_Print(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("print"))
+        if stmt.dest:
+            raise NotSupportedError(
+                r, "print statements with non-default destinations aren't supported"
+            )
+        args = [build_expr(ctx, val) for val in stmt.values]
+        return ExprStmt(Apply(Var(Ident(r, "print")), args, []))
+
+    @staticmethod
+    def build_Pass(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("pass"))
+        return Pass(r)
+
+    @staticmethod
+    def build_Break(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("break"))
+        return Break(r)
+
+    @staticmethod
+    def build_Continue(ctx, stmt):
+        r = ctx.make_range(
+            stmt.lineno, stmt.col_offset, stmt.col_offset + len("continue")
+        )
+        return Continue(r)
+
+    @staticmethod
+    def build_With(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset + len("with"))
+        # Handle ignore context manager
+        if is_torch_jit_ignore_context_manager(stmt):
+            if not _IS_ASTUNPARSE_INSTALLED:
+                raise RuntimeError(
+                    "torch.jit._IgnoreContextManager requires installing Python library `astunparse`, \
+                                   please install it in your Python environment"
+                )
+            assign_ast = build_ignore_context_manager(ctx, stmt)
+            return build_stmt(ctx, assign_ast)
+        return With(r, build_withitems(ctx, stmt.items), build_stmts(ctx, stmt.body))
+
+
+class ExprBuilder(Builder):
+    binop_map = {
+        ast.Add: "+",
+        ast.Sub: "-",
+        ast.Mult: "*",
+        ast.Div: "/",
+        ast.Pow: "**",
+        ast.Mod: "%",
+        ast.FloorDiv: "//",
+        ast.BitAnd: "&",
+        ast.BitXor: "^",
+        ast.BitOr: "|",
+        ast.LShift: "<<",
+        ast.RShift: ">>",
+    }
+
+    binop_map[ast.MatMult] = "@"
+
+    unop_map = {
+        ast.Not: "not",
+        ast.USub: "-",
+        ast.Invert: "~",
+    }
+
+    boolop_map = {
+        ast.And: "and",
+        ast.Or: "or",
+    }
+
+    cmpop_map = {
+        ast.Eq: "==",
+        ast.NotEq: "!=",
+        ast.LtE: "<=",
+        ast.Lt: "<",
+        ast.GtE: ">=",
+        ast.Gt: ">",
+        ast.Is: "is",
+        ast.IsNot: "is not",
+        ast.In: "in",
+        ast.NotIn: "not in",
+    }
+
+    @staticmethod
+    def build_Attribute(ctx, expr):
+        base = build_expr(ctx, expr.value)
+        # expr.attr is just a string, so it's not annotated in any way, so we have
+        # to build the range manually
+        source = ctx.source.encode("utf-8")
+
+        def get_char(index):
+            return chr(source[index])
+
+        start_pos = base.range().end + 1
+        while get_char(start_pos) in string.whitespace:  # Skip whitespace
+            start_pos += 1
+        end_pos = start_pos + len(expr.attr)
+        name_range = ctx.make_raw_range(start_pos, end_pos)
+        return Select(base, Ident(name_range, expr.attr))
+
+    @staticmethod
+    def build_Call(ctx, expr):
+        func = build_expr(ctx, expr.func)
+        args = [build_expr(ctx, py_arg) for py_arg in expr.args]
+        if hasattr(expr, "starargs") and expr.starargs:
+            stararg_expr = build_expr(ctx, expr.starargs)
+            args += [Starred(stararg_expr.range(), stararg_expr)]
+        kwargs = []
+        for kw in expr.keywords:
+            kw_expr = build_expr(ctx, kw.value)
+            # XXX: we could do a better job at figuring out the range for the name here
+            if not kw.arg:
+                raise NotSupportedError(
+                    kw_expr.range(), "keyword-arg expansion is not supported"
+                )
+            kwargs.append(Attribute(Ident(kw_expr.range(), kw.arg), kw_expr))
+        return Apply(func, args, kwargs)
+
+    @staticmethod
+    def build_Ellipsis(ctx, expr):
+        r = ctx.make_range(
+            expr.lineno, expr.col_offset, expr.col_offset + 3
+        )  # len("...") == 3
+        return Dots(r)
+
+    @staticmethod
+    def build_Name(ctx, expr):
+        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + len(expr.id))
+        if expr.id.startswith(_reserved_prefix):
+            raise NotSupportedError(
+                r,
+                "names of variables used in JIT-ed functions "
+                "can't start with " + _reserved_prefix,
+            )
+        if expr.id == "True":
+            return TrueLiteral(r)
+        elif expr.id == "False":
+            return FalseLiteral(r)
+        elif expr.id == "None":
+            return NoneLiteral(r)
+        elif expr.id == "Ellipsis":
+            return Dots(r)
+        return Var(Ident(r, expr.id))
+
+    @staticmethod
+    def build_NameConstant(ctx, expr):
+        r = ctx.make_range(
+            expr.lineno, expr.col_offset, expr.col_offset + len(str(expr.value))
+        )
+        if expr.value is True:
+            return TrueLiteral(r)
+        elif expr.value is False:
+            return FalseLiteral(r)
+        elif expr.value is None:
+            return NoneLiteral(r)
+        elif expr.value == Ellipsis:
+            return Dots(r)
+        else:
+            raise ValueError("Name constant value unsupported: " + str(expr.value))
+
+    @staticmethod
+    def build_BinOp(ctx, expr):
+        lhs = build_expr(ctx, expr.left)
+        rhs = build_expr(ctx, expr.right)
+        op = type(expr.op)
+
+        if op == ast.Div and not ctx.uses_true_division:
+            err_range = ctx.make_raw_range(lhs.range().end, rhs.range().start)
+            raise FrontendError(
+                err_range,
+                "Division of ints in TorchScript uses Python 3 true "
+                "division semantics. Please put `from __future__ "
+                "import division` at the top of your file",
+            )
+        op_token = ExprBuilder.binop_map.get(op)
+        if op_token is None:
+            err_range = ctx.make_raw_range(lhs.range().end, rhs.range().start)
+            raise NotSupportedError(
+                err_range, "unsupported binary operator: " + op.__name__
+            )
+        return BinOp(op_token, lhs, rhs)
+
+    @staticmethod
+    def build_UnaryOp(ctx, expr):
+        sub_expr = build_expr(ctx, expr.operand)
+        op = type(expr.op)
+        op_token = ExprBuilder.unop_map.get(op)
+        if op_token is None:
+            raise NotSupportedError(
+                expr.range(), "unsupported unary operator: " + op.__name__
+            )
+        r = ctx.make_range(
+            expr.lineno, expr.col_offset, expr.col_offset + len(op_token)
+        )
+        return UnaryOp(r, op_token, sub_expr)
+
+    @staticmethod
+    def build_BoolOp(ctx, expr):
+        if len(expr.values) < 2:
+            raise AssertionError(
+                "expected at least 2 values in BoolOp, but got " + str(len(expr.values))
+            )
+        sub_exprs = [build_expr(ctx, sub_expr) for sub_expr in expr.values]
+        op = type(expr.op)
+        op_token = ExprBuilder.boolop_map.get(op)
+        if op_token is None:
+            err_range = ctx.make_raw_range(
+                sub_exprs[0].range().end, sub_exprs[1].range().start
+            )
+            raise NotSupportedError(
+                err_range, "unsupported boolean operator: " + op.__name__
+            )
+        lhs = sub_exprs[0]
+        for rhs in sub_exprs[1:]:
+            lhs = BinOp(op_token, lhs, rhs)
+        return lhs
+
+    @staticmethod
+    def build_IfExp(ctx, expr):
+        return TernaryIf(
+            build_expr(ctx, expr.test),
+            build_expr(ctx, expr.body),
+            build_expr(ctx, expr.orelse),
+        )
+
+    @staticmethod
+    def build_Compare(ctx, expr):
+        operands = [build_expr(ctx, e) for e in [expr.left] + list(expr.comparators)]
+        result = None
+        for lhs, op_, rhs in zip(operands, expr.ops, operands[1:]):
+            op = type(op_)
+            op_token = ExprBuilder.cmpop_map.get(op)
+            r = ctx.make_raw_range(lhs.range().end, rhs.range().start)
+            if op_token is None:
+                raise NotSupportedError(
+                    r, "unsupported comparison operator: " + op.__name__
+                )
+
+            if op == ast.NotIn:
+                # NB: `not in` is just `not( in )`, so we don't introduce new tree view
+                # but just make it a nested call in our tree view structure
+                in_expr = BinOp("in", lhs, rhs)
+                cmp_expr = UnaryOp(r, "not", in_expr)
+            else:
+                cmp_expr = BinOp(op_token, lhs, rhs)
+
+            if result is None:
+                result = cmp_expr
+            else:
+                result = BinOp("and", result, cmp_expr)
+        return result
+
+    @staticmethod
+    def build_Subscript(ctx, expr):
+        def build_SliceExpr(ctx, base, slice_expr):
+            lower = (
+                build_expr(ctx, slice_expr.lower)
+                if slice_expr.lower is not None
+                else None
+            )
+            upper = (
+                build_expr(ctx, slice_expr.upper)
+                if slice_expr.upper is not None
+                else None
+            )
+            step = (
+                build_expr(ctx, slice_expr.step)
+                if slice_expr.step is not None
+                else None
+            )
+            return SliceExpr(base.range(), lower, upper, step)
+
+        def build_Index(ctx, base, index_expr):
+            if isinstance(index_expr.value, ast.Tuple):
+                raise NotSupportedError(
+                    base.range(),
+                    "slicing multiple dimensions with tuples not supported yet",
+                )
+            return build_expr(ctx, index_expr.value)
+
+        def build_ExtSlice(ctx, base, extslice):
+            sub_exprs = []
+            for expr in extslice.dims:
+                sub_type = type(expr)
+                if sub_type is ast.Index:
+                    sub_exprs.append(build_Index(ctx, base, expr))
+                elif sub_type is ast.Slice:
+                    sub_exprs.append(build_SliceExpr(ctx, base, expr))
+                elif sub_type is ast.Constant and expr.value is Ellipsis:
+                    sub_exprs.append(Dots(base.range()))
+                else:
+                    raise NotSupportedError(
+                        base.range(),
+                        f"slicing multiple dimensions with {sub_type} not supported",
+                    )
+            return sub_exprs
+
+        base = build_expr(ctx, expr.value)
+        sub_type = type(expr.slice)
+        if sub_type is ast.Index:
+            if isinstance(expr.slice.value, ast.Tuple):
+                # N-dimensional indexing using Tuple: x[(i, j, k)] is equivalent to x[i, j, k]
+                # XXX: Indexing using a list is **different**! It triggers advanced indexing.
+                indices = [
+                    build_expr(ctx, index_expr) for index_expr in expr.slice.value.elts
+                ]
+                if not indices:
+                    # `col_offset` is an int, but `end_col_offset` is
+                    # `Optional[int]`. The magic number is here to make
+                    # sure we can parse `()` on any machine
+                    r = ctx.make_range(
+                        expr.lineno,
+                        expr.slice.value.col_offset,
+                        expr.slice.value.col_offset + 2,
+                    )
+                    tup = TupleLiteral(r, [])
+                    indices.append(tup)
+                return Subscript(base, indices)
+            else:
+                return Subscript(base, [build_expr(ctx, expr.slice.value)])
+        elif sub_type is ast.Slice:
+            return Subscript(base, [build_SliceExpr(ctx, base, expr.slice)])
+        elif sub_type is ast.ExtSlice:
+            return Subscript(base, build_ExtSlice(ctx, base, expr.slice))
+        else:  # In Python3.9 array indicies are not wrapped in ast.Index
+            if sub_type is ast.Tuple:
+                # N-dimensional indexing using Tuple: x[(i, j, k)] is equivalent to x[i, j, k]
+                indices = []
+                for index_expr in expr.slice.elts:
+                    if isinstance(index_expr, ast.Slice):
+                        indices.append(build_SliceExpr(ctx, base, index_expr))
+                    else:
+                        indices.append(build_expr(ctx, index_expr))
+                # Special-case logic for `typing.Tuple[()]`
+                if not indices:
+                    # See note above r.e. magic number
+                    r = ctx.make_range(
+                        expr.lineno, expr.slice.col_offset, expr.slice.col_offset + 2
+                    )
+                    tup = TupleLiteral(r, [])
+                    indices.append(tup)
+                return Subscript(base, indices)
+            return Subscript(base, [build_expr(ctx, expr.slice)])
+
+    @staticmethod
+    def build_List(ctx, expr):
+        return ListLiteral(
+            ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1),
+            [build_expr(ctx, e) for e in expr.elts],
+        )
+
+    @staticmethod
+    def build_Tuple(ctx, expr):
+        return TupleLiteral(
+            ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1),
+            [build_expr(ctx, e) for e in expr.elts],
+        )
+
+    @staticmethod
+    def build_Dict(ctx, expr):
+        range = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1)
+        if expr.keys and not expr.keys[0]:
+            raise NotSupportedError(
+                range, "Dict expansion (e.g. `{**dict}`) is not supported"
+            )
+        return DictLiteral(
+            range,
+            [build_expr(ctx, e) for e in expr.keys],
+            [build_expr(ctx, e) for e in expr.values],
+        )
+
+    @staticmethod
+    def build_Num(ctx, expr):
+        value = str(expr.value)
+        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + len(value))
+        return Const(r, value)
+
+    @staticmethod
+    def build_Constant(ctx, expr):
+        value = expr.value
+        if value is None or isinstance(value, bool):
+            # NB: this check has to happen before the int check because bool is
+            # a subclass of int
+            return ExprBuilder.build_NameConstant(ctx, expr)
+        if isinstance(value, (int, float, complex)):
+            return ExprBuilder.build_Num(ctx, expr)
+        elif isinstance(value, str):
+            return ExprBuilder.build_Str(ctx, expr)
+        elif isinstance(value, type(Ellipsis)):
+            return ExprBuilder.build_Ellipsis(ctx, expr)
+        else:
+            error_range = ctx.make_range(
+                expr.lineno, expr.col_offset, expr.col_offset + len(str(value))
+            )
+            raise FrontendError(error_range, "Unknown Constant expression type")
+
+    @staticmethod
+    def build_Str(ctx, expr):
+        value = str(expr.value)
+        r = ctx.make_range(
+            expr.lineno, expr.col_offset, expr.col_offset + len(value) + 1
+        )
+        return StringLiteral(r, value)
+
+    @staticmethod
+    def build_JoinedStr(ctx, expr):
+        s = ""
+        args = []
+        for value in expr.values:
+            r = ctx.make_range(value.lineno, value.col_offset, value.col_offset + 1)
+            if isinstance(value, ast.FormattedValue):
+                if value.conversion != -1:
+                    raise NotSupportedError(r, "Don't support conversion in JoinedStr")
+                if value.format_spec is not None:
+                    raise NotSupportedError(r, "Don't support formatting in JoinedStr")
+                s += "{}"
+                args.append(build_expr(ctx, value.value))
+            elif isinstance(value, ast.Constant):
+                s += value.value
+            else:
+                raise NotSupportedError(r, "Unsupported value in JoinedStr")
+
+        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1)
+        return Apply(Select(StringLiteral(r, s), Ident(r, "format")), args, [])
+
+    @staticmethod
+    def build_ListComp(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset)
+        if len(stmt.generators) != 1:
+            raise NotSupportedError(r, "Only a single generator is currently supported")
+
+        if len(stmt.generators[0].ifs) != 0:
+            raise NotSupportedError(r, "Comprehension ifs are not supported yet")
+
+        elt_expr = build_expr(ctx, stmt.elt)
+        target_expr = build_expr(ctx, stmt.generators[0].target)
+        iter_expr = build_expr(ctx, stmt.generators[0].iter)
+
+        return ListComp(r, elt_expr, target_expr, iter_expr)
+
+    @staticmethod
+    def build_GeneratorExp(ctx, stmt):
+        # Convert Generator expression to ListComp
+        return ExprBuilder.build_ListComp(ctx, stmt)
+
+    @staticmethod
+    def build_DictComp(ctx, stmt):
+        r = ctx.make_range(stmt.lineno, stmt.col_offset, stmt.col_offset)
+        if len(stmt.generators) != 1:
+            raise NotSupportedError(r, "Only a single generator is currently supported")
+
+        if len(stmt.generators[0].ifs) != 0:
+            raise NotSupportedError(r, "Comprehension ifs are not supported yet")
+
+        key_expr = build_expr(ctx, stmt.key)
+        value_expr = build_expr(ctx, stmt.value)
+        target_expr = build_expr(ctx, stmt.generators[0].target)
+        iter_expr = build_expr(ctx, stmt.generators[0].iter)
+
+        return DictComp(r, key_expr, value_expr, target_expr, iter_expr)
+
+    @staticmethod
+    def build_Starred(ctx, expr):
+        r = ctx.make_range(expr.lineno, expr.col_offset, expr.col_offset + 1)
+        return Starred(r, build_expr(ctx, expr.value))
+
+
+build_expr = ExprBuilder()
+build_stmt = StmtBuilder()
+build_withitem = WithItemBuilder()
+
+
+def find_before(ctx, pos, substr, offsets=(0, 0)):
+    new_pos = ctx.source[:pos].rindex(substr)
+    return ctx.make_raw_range(new_pos + offsets[0], new_pos + len(substr) + offsets[1])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/generate_bytecode.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/generate_bytecode.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebdceaddca92ad8ac40dfdebb50f9eaae596d42c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/generate_bytecode.py
@@ -0,0 +1,33 @@
+# mypy: allow-untyped-defs
+
+from torch._C import _compile_graph_to_code_table, _generate_upgraders_graph
+
+
+def format_bytecode(table):
+    # given a nested tuple, convert it to nested list
+    def listify(content):
+        if not isinstance(content, tuple):
+            return content
+        return [listify(i) for i in content]
+
+    formatted_table = {}
+    for entry in table:
+        identifier = entry[0]
+        content = entry[1]
+        content = listify(content)
+        formatted_table[identifier] = content
+    return formatted_table
+
+
+def generate_upgraders_bytecode() -> list:
+    yaml_content = []
+    upgraders_graph_map = _generate_upgraders_graph()
+    for upgrader_name, upgrader_graph in upgraders_graph_map.items():
+        bytecode_table = _compile_graph_to_code_table(upgrader_name, upgrader_graph)
+        entry = {upgrader_name: format_bytecode(bytecode_table)}
+        yaml_content.append(entry)
+    return yaml_content
+
+
+if __name__ == "__main__":
+    raise RuntimeError("This file is not meant to be run directly")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/mobile/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/mobile/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..593dabf2fc43ca075d604977a0d254ea02f3ba55
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/mobile/__init__.py
@@ -0,0 +1,232 @@
+# mypy: allow-untyped-defs
+import os
+
+import torch
+from torch.jit._serialization import validate_map_location
+
+
+def _load_for_lite_interpreter(f, map_location=None):
+    r"""
+    Load a :class:`LiteScriptModule` saved with :func:`torch.jit._save_for_lite_interpreter`.
+
+    Args:
+        f: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+        map_location: a string or torch.device used to dynamically remap
+            storages to an alternative set of devices.
+
+    Returns:
+        A :class:`LiteScriptModule` object.
+
+    Example:
+
+    .. testcode::
+
+        import torch
+        import io
+
+        # Load LiteScriptModule from saved file path
+        torch.jit._load_for_lite_interpreter('lite_script_module.pt')
+
+        # Load LiteScriptModule from io.BytesIO object
+        with open('lite_script_module.pt', 'rb') as f:
+            buffer = io.BytesIO(f.read())
+
+        # Load all tensors to the original device
+        torch.jit.mobile._load_for_lite_interpreter(buffer)
+    """
+    if isinstance(f, (str, os.PathLike)):
+        if not os.path.exists(f):
+            raise ValueError(f"The provided filename {f} does not exist")
+        if os.path.isdir(f):
+            raise ValueError(f"The provided filename {f} is a directory")
+
+    map_location = validate_map_location(map_location)
+
+    if isinstance(f, (str, os.PathLike)):
+        cpp_module = torch._C._load_for_lite_interpreter(os.fspath(f), map_location)
+    else:
+        cpp_module = torch._C._load_for_lite_interpreter_from_buffer(
+            f.read(), map_location
+        )
+
+    return LiteScriptModule(cpp_module)
+
+
+class LiteScriptModule:
+    def __init__(self, cpp_module):
+        self._c = cpp_module
+        super().__init__()
+
+    def __call__(self, *input):
+        return self._c.forward(input)
+
+    def find_method(self, method_name):
+        return self._c.find_method(method_name)
+
+    def forward(self, *input):
+        return self._c.forward(input)
+
+    def run_method(self, method_name, *input):
+        return self._c.run_method(method_name, input)
+
+
+def _export_operator_list(module: LiteScriptModule):
+    r"""Return a set of root operator names (with overload name) that are used by any method in this mobile module."""
+    return torch._C._export_operator_list(module._c)
+
+
+def _get_model_bytecode_version(f_input) -> int:
+    r"""Take a file-like object to return an integer.
+
+    Args:
+        f_input: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+
+    Returns:
+        version: An integer. If the integer is -1, the version is invalid. A warning
+            will show in the log.
+
+    Example:
+    .. testcode::
+
+        from torch.jit.mobile import _get_model_bytecode_version
+
+        # Get bytecode version from a saved file path
+        version = _get_model_bytecode_version("path/to/model.ptl")
+
+    """
+    if isinstance(f_input, (str, os.PathLike)):
+        if not os.path.exists(f_input):
+            raise ValueError(f"The provided filename {f_input} does not exist")
+        if os.path.isdir(f_input):
+            raise ValueError(f"The provided filename {f_input} is a directory")
+
+    if isinstance(f_input, (str, os.PathLike)):
+        return torch._C._get_model_bytecode_version(os.fspath(f_input))
+    else:
+        return torch._C._get_model_bytecode_version_from_buffer(f_input.read())
+
+
+def _get_mobile_model_contained_types(f_input) -> int:
+    r"""Take a file-like object and return a set of string, like ("int", "Optional").
+
+    Args:
+        f_input: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+
+    Returns:
+        type_list: A set of string, like ("int", "Optional"). These are types used in bytecode.
+
+    Example:
+
+    .. testcode::
+
+        from torch.jit.mobile import _get_mobile_model_contained_types
+
+        # Get type list from a saved file path
+        type_list = _get_mobile_model_contained_types("path/to/model.ptl")
+
+    """
+    if isinstance(f_input, (str, os.PathLike)):
+        if not os.path.exists(f_input):
+            raise ValueError(f"The provided filename {f_input} does not exist")
+        if os.path.isdir(f_input):
+            raise ValueError(f"The provided filename {f_input} is a directory")
+
+    if isinstance(f_input, (str, os.PathLike)):
+        return torch._C._get_mobile_model_contained_types(os.fspath(f_input))
+    else:
+        return torch._C._get_mobile_model_contained_types_from_buffer(f_input.read())
+
+
+def _backport_for_mobile(f_input, f_output, to_version):
+    r"""Take a input string containing a file name (file-like object) and a new destination to return a boolean.
+
+    Args:
+        f_input: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+        f_output: path to new model destination
+        to_version: the expected output model bytecode version
+    Returns:
+        success: A boolean. If backport success, return true, otherwise false
+    """
+    if isinstance(f_input, (str, os.PathLike)):
+        if not os.path.exists(f_input):
+            raise ValueError(f"The provided filename {f_input} does not exist")
+        if os.path.isdir(f_input):
+            raise ValueError(f"The provided filename {f_input} is a directory")
+
+    if (isinstance(f_input, (str, os.PathLike))) and (
+        isinstance(f_output, (str, os.PathLike))
+    ):
+        return torch._C._backport_for_mobile(
+            os.fspath(f_input), os.fspath(f_output), to_version
+        )
+    else:
+        return torch._C._backport_for_mobile_from_buffer(
+            f_input.read(), str(f_output), to_version
+        )
+
+
+def _backport_for_mobile_to_buffer(f_input, to_version):
+    r"""Take a string containing a file name (file-like object).
+
+    Args:
+        f_input: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+
+    """
+    if isinstance(f_input, (str, os.PathLike)):
+        if not os.path.exists(f_input):
+            raise ValueError(f"The provided filename {f_input} does not exist")
+        if os.path.isdir(f_input):
+            raise ValueError(f"The provided filename {f_input} is a directory")
+
+    if isinstance(f_input, (str, os.PathLike)):
+        return torch._C._backport_for_mobile_to_buffer(os.fspath(f_input), to_version)
+    else:
+        return torch._C._backport_for_mobile_from_buffer_to_buffer(
+            f_input.read(), to_version
+        )
+
+
+def _get_model_ops_and_info(f_input):
+    r"""Retrieve the root (top level) operators of a model and their corresponding compatibility info.
+
+    These root operators can call other operators within them (traced ops), and
+    a root op can call many different traced ops depending on internal code paths in the root op.
+    These traced ops are not returned by this function. Those operators are abstracted into the
+    runtime as an implementation detail (and the traced ops themselves can also call other operators)
+    making retrieving them difficult and their value from this api negligible since they will differ
+    between which runtime version the model is run on. Because of this, there is a false positive this
+    api can't prevent in a compatibility usecase. All the root ops of a model are present in a
+    target runtime, but not all the traced ops are which prevents a model from being able to run.
+    Args:
+        f_input: a file-like object (has to implement read, readline, tell, and seek),
+            or a string containing a file name
+
+    Returns:
+        Operators and info: A Dictionary mapping strings (the qualified names of the root operators)
+        of the model to their OperatorInfo structs.
+
+    Example:
+
+    .. testcode::
+
+        from torch.jit.mobile import _get_model_ops_and_info
+
+        # Get bytecode version from a saved file path
+        ops_and_info = _get_model_ops_and_info("path/to/model.ptl")
+
+    """
+    if isinstance(f_input, (str, os.PathLike)):
+        if not os.path.exists(f_input):
+            raise ValueError(f"The provided filename {f_input} does not exist")
+        if os.path.isdir(f_input):
+            raise ValueError(f"The provided filename {f_input} is a directory")
+
+    if isinstance(f_input, (str, os.PathLike)):
+        return torch._C._get_model_ops_and_info(os.fspath(f_input))
+    else:
+        return torch._C._get_model_ops_and_info(f_input.read())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/quantized.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/quantized.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2500c1f1b9fed481ab713953746294dc52668af
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/quantized.py
@@ -0,0 +1,100 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+class QuantizedLinear(torch.jit.ScriptModule):
+    def __init__(self, other):
+        raise RuntimeError(
+            "torch.jit.QuantizedLinear is no longer supported. Please use "
+            "torch.ao.nn.quantized.dynamic.Linear instead."
+        )
+
+
+# FP16 weights
+class QuantizedLinearFP16(torch.jit.ScriptModule):
+    def __init__(self, other):
+        super().__init__()
+        raise RuntimeError(
+            "torch.jit.QuantizedLinearFP16 is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.Linear instead."
+        )
+
+
+# Quantized RNN cell implementations
+class QuantizedRNNCellBase(torch.jit.ScriptModule):
+    def __init__(self, other):
+        raise RuntimeError(
+            "torch.jit.QuantizedRNNCellBase is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.RNNCell instead."
+        )
+
+
+class QuantizedRNNCell(QuantizedRNNCellBase):
+    def __init__(self, other):
+        raise RuntimeError(
+            "torch.jit.QuantizedRNNCell is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.RNNCell instead."
+        )
+
+
+class QuantizedLSTMCell(QuantizedRNNCellBase):
+    def __init__(self, other):
+        super().__init__(other)
+        raise RuntimeError(
+            "torch.jit.QuantizedLSTMCell is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.LSTMCell instead."
+        )
+
+
+class QuantizedGRUCell(QuantizedRNNCellBase):
+    def __init__(self, other):
+        super().__init__(other)
+        raise RuntimeError(
+            "torch.jit.QuantizedGRUCell is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.GRUCell instead."
+        )
+
+
+class QuantizedRNNBase(torch.jit.ScriptModule):
+    def __init__(self, other, dtype=torch.int8):
+        raise RuntimeError(
+            "torch.jit.QuantizedRNNBase is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic instead."
+        )
+
+
+class QuantizedLSTM(QuantizedRNNBase):
+    def __init__(self, other, dtype):
+        raise RuntimeError(
+            "torch.jit.QuantizedLSTM is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.LSTM instead."
+        )
+
+
+class QuantizedGRU(QuantizedRNNBase):
+    def __init__(self, *args, **kwargs):
+        raise RuntimeError(
+            "torch.jit.QuantizedGRU is no longer supported. "
+            "Please use the torch.ao.nn.quantized.dynamic.GRU instead."
+        )
+
+
+def quantize_rnn_cell_modules(module):
+    raise RuntimeError(
+        "quantize_rnn_cell_modules function is no longer supported. "
+        "Please use torch.ao.quantization.quantize_dynamic API instead."
+    )
+
+
+def quantize_linear_modules(module, dtype=torch.int8):
+    raise RuntimeError(
+        "quantize_linear_modules function is no longer supported. "
+        "Please use torch.ao.quantization.quantize_dynamic API instead."
+    )
+
+
+def quantize_rnn_modules(module, dtype=torch.int8):
+    raise RuntimeError(
+        "quantize_rnn_modules function is no longer supported. "
+        "Please use torch.ao.quantization.quantize_dynamic API instead."
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/supported_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/supported_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..791a11a9b3aa7dcac62cc3a2c129985abaf85bc1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/supported_ops.py
@@ -0,0 +1,344 @@
+# mypy: allow-untyped-defs
+import inspect
+import textwrap
+
+import torch.jit
+from torch.jit._builtins import _find_builtin
+
+
+# this file is for generating documentation using sphinx autodoc
+# > help(torch.jit.supported_ops) will also give a nice listed of the
+# supported ops programmatically
+
+
+def _hidden(name):
+    return name.startswith("_") and not name.startswith("__")
+
+
+def _emit_type(type):
+    return str(type)
+
+
+def _emit_arg(indent, i, arg):
+    v = f"{arg.name} : {_emit_type(arg.type)}"
+    default = arg.default_value
+    if default is not None:
+        v = f"{v}={str(default)}"
+    if i > 0:
+        v = f"\n{' ' * indent}{v}"
+    return v
+
+
+def _emit_args(indent, arguments):
+    return ",".join(_emit_arg(indent, i, arg) for i, arg in enumerate(arguments))
+
+
+def _emit_ret(ret):
+    return _emit_type(ret.type)
+
+
+def _emit_rets(returns):
+    if len(returns) == 1:
+        return _emit_ret(returns[0])
+    return f"Tuple[{', '.join(_emit_ret(r) for r in returns)}]"
+
+
+def _emit_schema(mod, name, schema, arg_start=0, padding=4):
+    if mod is None:
+        qualified_name = name
+    else:
+        qualified_name = f"{mod}.{name}"
+    schema_str = (
+        f"{qualified_name}"
+        f"({_emit_args(len(qualified_name) + 1 + padding, schema.arguments[arg_start:])}) "
+        f"-> {_emit_rets(schema.returns)}"
+    )
+    return schema_str
+
+
+def _get_tensor_ops():
+    def is_tensor_method(schema):
+        if len(schema.arguments) == 0:
+            return False
+        self = schema.arguments[0]
+        if self.name != "self":
+            return False
+        if not self.type.isSubtypeOf(torch._C.TensorType.get()):
+            return False
+        return True
+
+    methods = []
+    # discover methods
+    for elem in dir(torch.Tensor):
+        if not _hidden(elem):
+            schemas = torch._C._jit_get_schemas_for_operator("aten::" + elem)
+            for schema in schemas:
+                if is_tensor_method(schema):
+                    methods.append(_emit_schema("Tensor", elem, schema, arg_start=1))
+
+    return "Supported Tensor Methods", methods
+
+
+def _get_nn_functional_ops():
+    functions = []
+
+    # Iterate over torch.nn.functional
+    mod = torch.nn.functional
+    name = mod.__name__
+    for elem in dir(torch.nn.functional):
+        attr = getattr(mod, elem)
+        if not inspect.isfunction(attr) or _hidden(elem[0]):
+            # Ignore non-functions and internal methods
+            continue
+
+        attr_module = inspect.getmodule(attr)
+        if not attr_module:
+            raise RuntimeError(f"Module for {attr} not found")
+
+        if "torch.nn.functional" not in attr_module.__name__:
+            # Ignore functions from outside torch.nn.functional
+            continue
+
+        try:
+            # compile fn, get schema
+            scripted = torch.jit.script(attr)
+            scripted_schema = scripted.schema
+            functions.append(_emit_schema(name, elem, scripted_schema))
+        except:  # noqa: B001,E722
+            # Skip interpolate / boolean dispatched things
+            pass
+
+    # Iterate over modules that we know contain a lot of builtins
+    for mod in torch.jit._builtins._modules_containing_builtins:
+        name = mod.__name__
+        for elem in dir(mod):
+            builtin = _find_builtin(getattr(mod, elem))
+            if builtin is not None:
+                schemas = torch._C._jit_get_schemas_for_operator(builtin)
+                for schema in schemas:
+                    # remove _tan but not __and__
+                    if not _hidden(elem):
+                        functions.append(_emit_schema(name, elem, schema))
+    return "Supported PyTorch Functions", functions
+
+
+def _get_builtins_helper():
+    builtins = []
+    for fn, _builtin_name in torch.jit._builtins._builtin_ops:
+        mod = inspect.getmodule(fn)
+
+        if not hasattr(fn, "__name__"):
+            # typing classes
+            continue
+        if not mod:
+            continue
+        if _hidden(fn.__name__) or _hidden(fn.__qualname__) or _hidden(mod.__name__):
+            # skip internal-only methods
+            continue
+
+        if "torch._C" in mod.__name__:
+            continue
+
+        builtins.append((fn, _builtin_name))
+
+    return builtins
+
+
+def _is_math_fn(fn):
+    mod = inspect.getmodule(fn)
+    if not mod:
+        raise RuntimeError(f"Module for {fn} not found")
+
+    return mod.__name__ == "math"
+
+
+def _get_torchscript_builtins():
+    functions = []
+    builtins = filter(lambda fn: not _is_math_fn(fn[0]), _get_builtins_helper())
+    builtins_list = list(builtins)
+    # Iterate over the specially added builtins
+    for fn, _builtin_name in builtins_list:
+        mod = inspect.getmodule(fn)
+        if not mod:
+            raise RuntimeError(f"Module for {fn} not found")
+        builtin = _find_builtin(fn)
+        if builtin is not None:
+            schemas = torch._C._jit_get_schemas_for_operator(builtin)
+            for schema in schemas:
+                functions.append(_emit_schema(mod.__name__, fn.__name__, schema))
+
+    return "TorchScript Builtin Functions", functions
+
+
+def _get_math_builtins():
+    functions = []
+    builtins = filter(lambda fn: _is_math_fn(fn[0]), _get_builtins_helper())
+    builtins_list = list(builtins)
+    # Iterate over the specially added builtins
+    for fn, _builtin_name in builtins_list:
+        mod = inspect.getmodule(fn)
+        if not mod:
+            raise RuntimeError(f"Module for {fn} not found")
+        builtin = _find_builtin(fn)
+        if builtin is not None:
+            schemas = torch._C._jit_get_schemas_for_operator(builtin)
+            for schema in schemas:
+                schema_str = _emit_schema(mod.__name__, fn.__name__, schema)
+                if "Tensor" in schema_str:
+                    # Skip Tensor ops that have the same name as math functions
+                    # (they will show up in the tensor methods section)
+                    continue
+                functions.append(schema)
+
+    return "``math`` Module", functions
+
+
+def _get_global_builtins():
+    # Taken from the 'globals' map in torch/csrc/jit/frontend/ir_emitter.cpp
+    supported_builtins = [
+        "print",
+        "tuple",
+        "float",
+        "complex",
+        "int",
+        "bool",
+        "str",
+        "getattr",
+        "hasattr",
+        "isinstance",
+        "len",
+        "hex",
+        "oct",
+        "round",
+        "hash",
+        "min",
+        "max",
+        "abs",
+        "all",
+        "divmod",
+        "list",
+        "ord",
+        "chr",
+        "bin",
+        "range",
+        "zip",
+        "enumerate",
+        "sorted",
+    ]
+
+    op_renames = {
+        "bool": "aten::Bool",
+        "int": "aten::Int",
+        "float": "aten::Float",
+        "complex": "aten::Complex",
+        "abs": "prim::abs",
+        "max": "prim::max",
+        "min": "prim::min",
+        "range": "fake::does_not_exist",
+    }
+
+    schemaless_op_explanations = {
+        "print": "Print any value",
+        "tuple": "Lists cannot be converted to tuples with this method since their size is not statically known",
+        "getattr": "Attribute name must be a literal string",
+        "hasattr": "Attribute name must be a literal string",
+        "isinstance": "Result is static",
+        "zip": "Arguments must be iterable. See :ref:`Iterables ` for details.",
+        "enumerate": "Arguments must be iterable. See :ref:`Iterables ` for details.",
+        "range": "Can only be used as an iterator in a for loop",
+    }
+
+    magic_methods = [
+        ("complex", "__complex__"),
+        ("float", "__float__"),
+        ("int", "__int__"),
+        ("bool", "__bool__"),
+        ("str", "__str__"),
+        ("len", "__len__"),
+        ("hex", "__hex__"),
+        ("oct", "__oct__"),
+    ]
+
+    magic_methods_rows = []
+    for fn, magic_method in magic_methods:
+        magic_methods_rows.append(f'"{fn}", "``{magic_method}``"')
+
+    schematized_ops = []
+    schemaless_ops = []
+
+    for fn in supported_builtins:
+        op_name = f"aten::{fn}"
+        if fn in op_renames:
+            op_name = op_renames[fn]
+        schemas = torch._C._jit_get_schemas_for_operator(op_name)
+        for s in schemas:
+            schematized_ops.append(_emit_schema(None, fn, s, padding=0))
+        if len(schemas) > 0:
+            schematized_ops.append("")
+        else:
+            table_row = (
+                f'":external+python:py:obj:`{fn}`", "{schemaless_op_explanations[fn]}"'
+            )
+            schemaless_ops.append(table_row)
+
+    schematized_ops_str = "\n".join(schematized_ops)
+    schemaless_ops_str = "\n".join(schemaless_ops)
+    magic_methods_rows_str = "\n".join(magic_methods_rows)
+    schematized_ops_str = textwrap.indent(schematized_ops_str, "\t")
+    schemaless_ops_str = textwrap.indent(schemaless_ops_str, "\t")
+    magic_methods_rows_str = textwrap.indent(magic_methods_rows_str, "\t")
+    section = f"""
+The functions in the following table are supported but do not have a static schema
+
+.. csv-table::
+    :header: "Function", "Note"
+
+{schemaless_ops_str}
+
+The following functions will use the corresponding magic method on :any:`TorchScript classes`
+
+.. csv-table::
+    :header: "Function", "Magic Method"
+
+{magic_methods_rows_str}
+
+These built-in functions use the schema
+
+.. rst-class:: codeblock-height-limiter
+
+::
+
+{schematized_ops_str}
+    """
+
+    return "Python Built-in Functions", section
+
+
+def _list_supported_ops():
+    def emit_block(decls):
+        return "\n.. rst-class:: codeblock-height-limiter\n\n::\n\n{}\n".format(
+            "".join(f"    {d}\n\n" for d in decls)
+        )
+
+    body = ""
+    op_gathering_fns = (
+        _get_tensor_ops,
+        _get_nn_functional_ops,
+        _get_torchscript_builtins,
+        _get_global_builtins,
+        _get_math_builtins,
+    )
+    for fn in op_gathering_fns:
+        header, items = fn()
+        link_target = header.replace("`", "").replace("-", "").lower().replace(" ", "-")
+        if isinstance(items, str):
+            section = f"{header}\n{'~' * len(header)}\n{items}\n"
+        else:
+            section = f"{header}\n{'~' * len(header)}\n{emit_block(items)}"
+        section = f".. _{link_target}:" + "\n\n" + section
+        body += section
+
+    return body
+
+
+__doc__ = _list_supported_ops()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/unsupported_tensor_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/unsupported_tensor_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..162e4c5320685467fd1b3ef0ea04dc89f6d155c4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/jit/unsupported_tensor_ops.py
@@ -0,0 +1,78 @@
+# mypy: allow-untyped-defs
+from textwrap import dedent
+from typing import Any
+
+import torch.jit
+
+
+def execWrapper(code, glob, loc):
+    exec(code, glob, loc)
+
+
+def _gen_unsupported_methods_properties():
+    tensor_attrs = set(filter(lambda x: x[0] != "_", dir(torch.Tensor)))
+    tensor = torch.tensor([2])
+    funcs_template = dedent(
+        """
+    def func(x):
+        return x.{op}()
+    """
+    )
+
+    deprecated_apis = {
+        "volatile",
+        "resize",
+        "reinforce",
+        "new",
+        "name",
+        "map2_",
+        "has_names",
+        "grad_fn",
+        "resize_as",
+    }
+    tensor_attrs = tensor_attrs - deprecated_apis
+
+    properties = []
+    methods = []
+    sorted_tensor_attrs = sorted(tensor_attrs, key=lambda x: x.lower())
+    for attr in sorted_tensor_attrs:
+        funcs_str = funcs_template.format(op=attr)
+        scope: dict[str, Any] = {}
+        execWrapper(funcs_str, globals(), scope)
+        try:
+            torch.jit.CompilationUnit(funcs_str)
+        except Exception as e:
+            if "nonexistent attribute" not in repr(e):
+                continue
+            attr_repr = repr(getattr(tensor, attr))
+            if "bound method" in attr_repr or "built-in method" in attr_repr:
+                methods.append(attr)
+            else:
+                properties.append(attr)
+
+    mapped_methods = ("\t*  :meth:`~torch.Tensor." + x + r"`" for x in methods)
+    mapped_properties = ("\t*  :attr:`~torch.Tensor." + x + r"`" for x in properties)
+    return "\n".join(mapped_methods), "\n".join(mapped_properties)
+
+
+def _list_unsupported_tensor_ops():
+    header = """\n\n
+Unsupported Tensor Methods
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    """
+    methods, properties = _gen_unsupported_methods_properties()
+    return (
+        header
+        + "\n"
+        + methods
+        + """
+
+Unsupported Tensor Properties
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    """
+        + "\n"
+        + properties
+    )
+
+
+__doc__ = _list_unsupported_tensor_ops()
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/linalg/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/linalg/__init__.py
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+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/linalg/__init__.py
@@ -0,0 +1,3013 @@
+from torch._C import (  # type: ignore[attr-defined]
+    _add_docstr,
+    _linalg,
+    _LinAlgError as LinAlgError,
+)
+
+
+common_notes = {
+    "experimental_warning": """This function is "experimental" and it may change in a future PyTorch release.""",
+    "sync_note": "When inputs are on a CUDA device, this function synchronizes that device with the CPU.",
+    "sync_note_ex": r"When the inputs are on a CUDA device, this function synchronizes only when :attr:`check_errors`\ `= True`.",
+    "sync_note_has_ex": (
+        "When inputs are on a CUDA device, this function synchronizes that device with the CPU. "
+        "For a version of this function that does not synchronize, see :func:`{}`."
+    ),
+}
+
+
+# Note: This not only adds doc strings for functions in the linalg namespace, but
+# also connects the torch.linalg Python namespace to the torch._C._linalg builtins.
+
+cross = _add_docstr(
+    _linalg.linalg_cross,
+    r"""
+linalg.cross(input, other, *, dim=-1, out=None) -> Tensor
+
+
+Computes the cross product of two 3-dimensional vectors.
+
+Supports input of float, double, cfloat and cdouble dtypes. Also supports batches
+of vectors, for which it computes the product along the dimension :attr:`dim`.
+It broadcasts over the batch dimensions.
+
+Args:
+    input (Tensor): the first input tensor.
+    other (Tensor): the second input tensor.
+    dim  (int, optional): the dimension along which to take the cross-product. Default: `-1`.
+
+Keyword args:
+    out (Tensor, optional): the output tensor. Ignored if `None`. Default: `None`.
+
+Example:
+    >>> a = torch.randn(4, 3)
+    >>> a
+    tensor([[-0.3956,  1.1455,  1.6895],
+            [-0.5849,  1.3672,  0.3599],
+            [-1.1626,  0.7180, -0.0521],
+            [-0.1339,  0.9902, -2.0225]])
+    >>> b = torch.randn(4, 3)
+    >>> b
+    tensor([[-0.0257, -1.4725, -1.2251],
+            [-1.1479, -0.7005, -1.9757],
+            [-1.3904,  0.3726, -1.1836],
+            [-0.9688, -0.7153,  0.2159]])
+    >>> torch.linalg.cross(a, b)
+    tensor([[ 1.0844, -0.5281,  0.6120],
+            [-2.4490, -1.5687,  1.9792],
+            [-0.8304, -1.3037,  0.5650],
+            [-1.2329,  1.9883,  1.0551]])
+    >>> a = torch.randn(1, 3)  # a is broadcast to match shape of b
+    >>> a
+    tensor([[-0.9941, -0.5132,  0.5681]])
+    >>> torch.linalg.cross(a, b)
+    tensor([[ 1.4653, -1.2325,  1.4507],
+            [ 1.4119, -2.6163,  0.1073],
+            [ 0.3957, -1.9666, -1.0840],
+            [ 0.2956, -0.3357,  0.2139]])
+""",
+)
+
+cholesky = _add_docstr(
+    _linalg.linalg_cholesky,
+    r"""
+linalg.cholesky(A, *, upper=False, out=None) -> Tensor
+
+Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **Cholesky decomposition** of a complex Hermitian or real symmetric positive-definite matrix
+:math:`A \in \mathbb{K}^{n \times n}` is defined as
+
+.. math::
+
+    A = LL^{\text{H}}\mathrlap{\qquad L \in \mathbb{K}^{n \times n}}
+
+where :math:`L` is a lower triangular matrix with real positive diagonal (even in the complex case) and
+:math:`L^{\text{H}}` is the conjugate transpose when :math:`L` is complex, and the transpose when :math:`L` is real-valued.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note_has_ex"].format("torch.linalg.cholesky_ex")}
+"""
+    + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.cholesky_ex` for a version of this operation that
+        skips the (slow) error checking by default and instead returns the debug
+        information. This makes it a faster way to check if a matrix is
+        positive-definite.
+
+        :func:`torch.linalg.eigh` for a different decomposition of a Hermitian matrix.
+        The eigenvalue decomposition gives more information about the matrix but it
+        slower to compute than the Cholesky decomposition.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of symmetric or Hermitian positive-definite matrices.
+
+Keyword args:
+    upper (bool, optional): whether to return an upper triangular matrix.
+        The tensor returned with upper=True is the conjugate transpose of the tensor
+        returned with upper=False.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if the :attr:`A` matrix or any matrix in a batched :attr:`A` is not Hermitian
+                  (resp. symmetric) positive-definite. If :attr:`A` is a batch of matrices,
+                  the error message will include the batch index of the first matrix that fails
+                  to meet this condition.
+
+Examples::
+
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> A = A @ A.T.conj() + torch.eye(2) # creates a Hermitian positive-definite matrix
+    >>> A
+    tensor([[2.5266+0.0000j, 1.9586-2.0626j],
+            [1.9586+2.0626j, 9.4160+0.0000j]], dtype=torch.complex128)
+    >>> L = torch.linalg.cholesky(A)
+    >>> L
+    tensor([[1.5895+0.0000j, 0.0000+0.0000j],
+            [1.2322+1.2976j, 2.4928+0.0000j]], dtype=torch.complex128)
+    >>> torch.dist(L @ L.T.conj(), A)
+    tensor(4.4692e-16, dtype=torch.float64)
+
+    >>> A = torch.randn(3, 2, 2, dtype=torch.float64)
+    >>> A = A @ A.mT + torch.eye(2)  # batch of symmetric positive-definite matrices
+    >>> L = torch.linalg.cholesky(A)
+    >>> torch.dist(L @ L.mT, A)
+    tensor(5.8747e-16, dtype=torch.float64)
+""",
+)
+
+cholesky_ex = _add_docstr(
+    _linalg.linalg_cholesky_ex,
+    r"""
+linalg.cholesky_ex(A, *, upper=False, check_errors=False, out=None) -> (Tensor, Tensor)
+
+Computes the Cholesky decomposition of a complex Hermitian or real
+symmetric positive-definite matrix.
+
+This function skips the (slow) error checking and error message construction
+of :func:`torch.linalg.cholesky`, instead directly returning the LAPACK
+error codes as part of a named tuple ``(L, info)``. This makes this function
+a faster way to check if a matrix is positive-definite, and it provides an
+opportunity to handle decomposition errors more gracefully or performantly
+than :func:`torch.linalg.cholesky` does.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+If :attr:`A` is not a Hermitian positive-definite matrix, or if it's a batch of matrices
+and one or more of them is not a Hermitian positive-definite matrix,
+then ``info`` stores a positive integer for the corresponding matrix.
+The positive integer indicates the order of the leading minor that is not positive-definite,
+and the decomposition could not be completed.
+``info`` filled with zeros indicates that the decomposition was successful.
+If ``check_errors=True`` and ``info`` contains positive integers, then a RuntimeError is thrown.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note_ex"]}
+
+.. warning:: {common_notes["experimental_warning"]}
+"""
+    + r"""
+
+.. seealso::
+        :func:`torch.linalg.cholesky` is a NumPy compatible variant that always checks for errors.
+
+Args:
+    A (Tensor): the Hermitian `n \times n` matrix or the batch of such matrices of size
+                    `(*, n, n)` where `*` is one or more batch dimensions.
+
+Keyword args:
+    upper (bool, optional): whether to return an upper triangular matrix.
+        The tensor returned with upper=True is the conjugate transpose of the tensor
+        returned with upper=False.
+    check_errors (bool, optional): controls whether to check the content of ``infos``. Default: `False`.
+    out (tuple, optional): tuple of two tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> A = A @ A.t().conj()  # creates a Hermitian positive-definite matrix
+    >>> L, info = torch.linalg.cholesky_ex(A)
+    >>> A
+    tensor([[ 2.3792+0.0000j, -0.9023+0.9831j],
+            [-0.9023-0.9831j,  0.8757+0.0000j]], dtype=torch.complex128)
+    >>> L
+    tensor([[ 1.5425+0.0000j,  0.0000+0.0000j],
+            [-0.5850-0.6374j,  0.3567+0.0000j]], dtype=torch.complex128)
+    >>> info
+    tensor(0, dtype=torch.int32)
+
+""",
+)
+
+inv = _add_docstr(
+    _linalg.linalg_inv,
+    r"""
+linalg.inv(A, *, out=None) -> Tensor
+
+Computes the inverse of a square matrix if it exists.
+Throws a `RuntimeError` if the matrix is not invertible.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+for a matrix :math:`A \in \mathbb{K}^{n \times n}`,
+its **inverse matrix** :math:`A^{-1} \in \mathbb{K}^{n \times n}` (if it exists) is defined as
+
+.. math::
+
+    A^{-1}A = AA^{-1} = \mathrm{I}_n
+
+where :math:`\mathrm{I}_n` is the `n`-dimensional identity matrix.
+
+The inverse matrix exists if and only if :math:`A` is `invertible`_. In this case,
+the inverse is unique.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices
+then the output has the same batch dimensions.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note_has_ex"].format("torch.linalg.inv_ex")}
+"""
+    + r"""
+
+.. note::
+    Consider using :func:`torch.linalg.solve` if possible for multiplying a matrix on the left by
+    the inverse, as::
+
+        linalg.solve(A, B) == linalg.inv(A) @ B  # When B is a matrix
+
+    It is always preferred to use :func:`~solve` when possible, as it is faster and more
+    numerically stable than computing the inverse explicitly.
+
+.. seealso::
+
+        :func:`torch.linalg.pinv` computes the pseudoinverse (Moore-Penrose inverse) of matrices
+        of any shape.
+
+        :func:`torch.linalg.solve` computes :attr:`A`\ `.inv() @ \ `:attr:`B` with a
+        numerically stable algorithm.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of invertible matrices.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if the matrix :attr:`A` or any matrix in the batch of matrices :attr:`A` is not invertible.
+
+Examples::
+
+    >>> A = torch.randn(4, 4)
+    >>> Ainv = torch.linalg.inv(A)
+    >>> torch.dist(A @ Ainv, torch.eye(4))
+    tensor(1.1921e-07)
+
+    >>> A = torch.randn(2, 3, 4, 4)  # Batch of matrices
+    >>> Ainv = torch.linalg.inv(A)
+    >>> torch.dist(A @ Ainv, torch.eye(4))
+    tensor(1.9073e-06)
+
+    >>> A = torch.randn(4, 4, dtype=torch.complex128)  # Complex matrix
+    >>> Ainv = torch.linalg.inv(A)
+    >>> torch.dist(A @ Ainv, torch.eye(4))
+    tensor(7.5107e-16, dtype=torch.float64)
+
+.. _invertible:
+    https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem
+""",
+)
+
+solve_ex = _add_docstr(
+    _linalg.linalg_solve_ex,
+    r"""
+linalg.solve_ex(A, B, *, left=True, check_errors=False, out=None) -> (Tensor, Tensor)
+
+A version of :func:`~solve` that does not perform error checks unless :attr:`check_errors`\ `= True`.
+It also returns the :attr:`info` tensor returned by `LAPACK's getrf`_.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note_ex"]}
+
+.. warning:: {common_notes["experimental_warning"]}
+"""
+    + r"""
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    left (bool, optional): whether to solve the system :math:`AX=B` or :math:`XA = B`. Default: `True`.
+    check_errors (bool, optional): controls whether to check the content of ``infos`` and raise
+                                   an error if it is non-zero. Default: `False`.
+    out (tuple, optional): tuple of two tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(result, info)`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> Ainv, info = torch.linalg.solve_ex(A)
+    >>> torch.dist(torch.linalg.inv(A), Ainv)
+    tensor(0.)
+    >>> info
+    tensor(0, dtype=torch.int32)
+
+.. _LAPACK's getrf:
+    https://www.netlib.org/lapack/explore-html/dd/d9a/group__double_g_ecomputational_ga0019443faea08275ca60a734d0593e60.html
+""",
+)
+
+inv_ex = _add_docstr(
+    _linalg.linalg_inv_ex,
+    r"""
+linalg.inv_ex(A, *, check_errors=False, out=None) -> (Tensor, Tensor)
+
+Computes the inverse of a square matrix if it is invertible.
+
+Returns a namedtuple ``(inverse, info)``. ``inverse`` contains the result of
+inverting :attr:`A` and ``info`` stores the LAPACK error codes.
+
+If :attr:`A` is not an invertible matrix, or if it's a batch of matrices
+and one or more of them is not an invertible matrix,
+then ``info`` stores a positive integer for the corresponding matrix.
+The positive integer indicates the diagonal element of the LU decomposition of
+the input matrix that is exactly zero.
+``info`` filled with zeros indicates that the inversion was successful.
+If ``check_errors=True`` and ``info`` contains positive integers, then a RuntimeError is thrown.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note_ex"]}
+
+.. warning:: {common_notes["experimental_warning"]}
+"""
+    + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.inv` is a NumPy compatible variant that always checks for errors.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                    consisting of square matrices.
+    check_errors (bool, optional): controls whether to check the content of ``info``. Default: `False`.
+
+Keyword args:
+    out (tuple, optional): tuple of two tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> Ainv, info = torch.linalg.inv_ex(A)
+    >>> torch.dist(torch.linalg.inv(A), Ainv)
+    tensor(0.)
+    >>> info
+    tensor(0, dtype=torch.int32)
+
+""",
+)
+
+det = _add_docstr(
+    _linalg.linalg_det,
+    r"""
+linalg.det(A, *, out=None) -> Tensor
+
+Computes the determinant of a square matrix.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+.. seealso::
+
+        :func:`torch.linalg.slogdet` computes the sign and natural logarithm of the absolute
+        value of the determinant of square matrices.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> torch.linalg.det(A)
+    tensor(0.0934)
+
+    >>> A = torch.randn(3, 2, 2)
+    >>> torch.linalg.det(A)
+    tensor([1.1990, 0.4099, 0.7386])
+""",
+)
+
+slogdet = _add_docstr(
+    _linalg.linalg_slogdet,
+    r"""
+linalg.slogdet(A, *, out=None) -> (Tensor, Tensor)
+
+Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.
+
+For complex :attr:`A`, it returns the sign and the natural logarithm of the modulus of the
+determinant, that is, a logarithmic polar decomposition of the determinant.
+
+The determinant can be recovered as `sign * exp(logabsdet)`.
+When a matrix has a determinant of zero, it returns `(0, -inf)`.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+.. seealso::
+
+        :func:`torch.linalg.det` computes the determinant of square matrices.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    out (tuple, optional): output tuple of two tensors. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(sign, logabsdet)`.
+
+    `sign` will have the same dtype as :attr:`A`.
+
+    `logabsdet` will always be real-valued, even when :attr:`A` is complex.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> A
+    tensor([[ 0.0032, -0.2239, -1.1219],
+            [-0.6690,  0.1161,  0.4053],
+            [-1.6218, -0.9273, -0.0082]])
+    >>> torch.linalg.det(A)
+    tensor(-0.7576)
+    >>> torch.logdet(A)
+    tensor(nan)
+    >>> torch.linalg.slogdet(A)
+    torch.return_types.linalg_slogdet(sign=tensor(-1.), logabsdet=tensor(-0.2776))
+""",
+)
+
+eig = _add_docstr(
+    _linalg.linalg_eig,
+    r"""
+linalg.eig(A, *, out=None) -> (Tensor, Tensor)
+
+Computes the eigenvalue decomposition of a square matrix if it exists.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **eigenvalue decomposition** of a square matrix
+:math:`A \in \mathbb{K}^{n \times n}` (if it exists) is defined as
+
+.. math::
+
+    A = V \operatorname{diag}(\Lambda) V^{-1}\mathrlap{\qquad V \in \mathbb{C}^{n \times n}, \Lambda \in \mathbb{C}^n}
+
+This decomposition exists if and only if :math:`A` is `diagonalizable`_.
+This is the case when all its eigenvalues are different.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The returned eigenvalues are not guaranteed to be in any specific order.
+
+.. note:: The eigenvalues and eigenvectors of a real matrix may be complex.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note"]}
+"""
+    + r"""
+
+.. warning:: This function assumes that :attr:`A` is `diagonalizable`_ (for example, when all the
+             eigenvalues are different). If it is not diagonalizable, the returned
+             eigenvalues will be correct but :math:`A \neq V \operatorname{diag}(\Lambda)V^{-1}`.
+
+.. warning:: The returned eigenvectors are normalized to have norm `1`.
+             Even then, the eigenvectors of a matrix are not unique, nor are they continuous with respect to
+             :attr:`A`. Due to this lack of uniqueness, different hardware and software may compute
+             different eigenvectors.
+
+             This non-uniqueness is caused by the fact that multiplying an eigenvector by
+             by :math:`e^{i \phi}, \phi \in \mathbb{R}` produces another set of valid eigenvectors
+             of the matrix.  For this reason, the loss function shall not depend on the phase of the
+             eigenvectors, as this quantity is not well-defined.
+             This is checked when computing the gradients of this function. As such,
+             when inputs are on a CUDA device, the computation of the gradients
+             of this function synchronizes that device with the CPU.
+
+
+.. warning:: Gradients computed using the `eigenvectors` tensor will only be finite when
+             :attr:`A` has distinct eigenvalues.
+             Furthermore, if the distance between any two eigenvalues is close to zero,
+             the gradient will be numerically unstable, as it depends on the eigenvalues
+             :math:`\lambda_i` through the computation of
+             :math:`\frac{1}{\min_{i \neq j} \lambda_i - \lambda_j}`.
+
+.. seealso::
+
+        :func:`torch.linalg.eigvals` computes only the eigenvalues.
+        Unlike :func:`torch.linalg.eig`, the gradients of :func:`~eigvals` are always
+        numerically stable.
+
+        :func:`torch.linalg.eigh` for a (faster) function that computes the eigenvalue decomposition
+        for Hermitian and symmetric matrices.
+
+        :func:`torch.linalg.svd` for a function that computes another type of spectral
+        decomposition that works on matrices of any shape.
+
+        :func:`torch.linalg.qr` for another (much faster) decomposition that works on matrices of
+        any shape.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of diagonalizable matrices.
+
+Keyword args:
+    out (tuple, optional): output tuple of two tensors. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(eigenvalues, eigenvectors)` which corresponds to :math:`\Lambda` and :math:`V` above.
+
+    `eigenvalues` and `eigenvectors` will always be complex-valued, even when :attr:`A` is real. The eigenvectors
+    will be given by the columns of `eigenvectors`.
+
+Examples::
+
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> A
+    tensor([[ 0.9828+0.3889j, -0.4617+0.3010j],
+            [ 0.1662-0.7435j, -0.6139+0.0562j]], dtype=torch.complex128)
+    >>> L, V = torch.linalg.eig(A)
+    >>> L
+    tensor([ 1.1226+0.5738j, -0.7537-0.1286j], dtype=torch.complex128)
+    >>> V
+    tensor([[ 0.9218+0.0000j,  0.1882-0.2220j],
+            [-0.0270-0.3867j,  0.9567+0.0000j]], dtype=torch.complex128)
+    >>> torch.dist(V @ torch.diag(L) @ torch.linalg.inv(V), A)
+    tensor(7.7119e-16, dtype=torch.float64)
+
+    >>> A = torch.randn(3, 2, 2, dtype=torch.float64)
+    >>> L, V = torch.linalg.eig(A)
+    >>> torch.dist(V @ torch.diag_embed(L) @ torch.linalg.inv(V), A)
+    tensor(3.2841e-16, dtype=torch.float64)
+
+.. _diagonalizable:
+    https://en.wikipedia.org/wiki/Diagonalizable_matrix#Definition
+""",
+)
+
+eigvals = _add_docstr(
+    _linalg.linalg_eigvals,
+    r"""
+linalg.eigvals(A, *, out=None) -> Tensor
+
+Computes the eigenvalues of a square matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **eigenvalues** of a square matrix :math:`A \in \mathbb{K}^{n \times n}` are defined
+as the roots (counted with multiplicity) of the polynomial `p` of degree `n` given by
+
+.. math::
+
+    p(\lambda) = \operatorname{det}(A - \lambda \mathrm{I}_n)\mathrlap{\qquad \lambda \in \mathbb{C}}
+
+where :math:`\mathrm{I}_n` is the `n`-dimensional identity matrix.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The returned eigenvalues are not guaranteed to be in any specific order.
+
+.. note:: The eigenvalues of a real matrix may be complex, as the roots of a real polynomial may be complex.
+
+          The eigenvalues of a matrix are always well-defined, even when the matrix is not diagonalizable.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note"]}
+"""
+    + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.eig` computes the full eigenvalue decomposition.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Returns:
+    A complex-valued tensor containing the eigenvalues even when :attr:`A` is real.
+
+Examples::
+
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> L = torch.linalg.eigvals(A)
+    >>> L
+    tensor([ 1.1226+0.5738j, -0.7537-0.1286j], dtype=torch.complex128)
+
+    >>> torch.dist(L, torch.linalg.eig(A).eigenvalues)
+    tensor(2.4576e-07)
+""",
+)
+
+eigh = _add_docstr(
+    _linalg.linalg_eigh,
+    r"""
+linalg.eigh(A, UPLO='L', *, out=None) -> (Tensor, Tensor)
+
+Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **eigenvalue decomposition** of a complex Hermitian or real symmetric matrix
+:math:`A \in \mathbb{K}^{n \times n}` is defined as
+
+.. math::
+
+    A = Q \operatorname{diag}(\Lambda) Q^{\text{H}}\mathrlap{\qquad Q \in \mathbb{K}^{n \times n}, \Lambda \in \mathbb{R}^n}
+
+where :math:`Q^{\text{H}}` is the conjugate transpose when :math:`Q` is complex, and the transpose when :math:`Q` is real-valued.
+:math:`Q` is orthogonal in the real case and unitary in the complex case.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+:attr:`A` is assumed to be Hermitian (resp. symmetric), but this is not checked internally, instead:
+
+- If :attr:`UPLO`\ `= 'L'` (default), only the lower triangular part of the matrix is used in the computation.
+- If :attr:`UPLO`\ `= 'U'`, only the upper triangular part of the matrix is used.
+
+The eigenvalues are returned in ascending order.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note"]}
+"""
+    + r"""
+
+.. note:: The eigenvalues of real symmetric or complex Hermitian matrices are always real.
+
+.. warning:: The eigenvectors of a symmetric matrix are not unique, nor are they continuous with
+             respect to :attr:`A`. Due to this lack of uniqueness, different hardware and
+             software may compute different eigenvectors.
+
+             This non-uniqueness is caused by the fact that multiplying an eigenvector by
+             `-1` in the real case or by :math:`e^{i \phi}, \phi \in \mathbb{R}` in the complex
+             case produces another set of valid eigenvectors of the matrix.
+             For this reason, the loss function shall not depend on the phase of the eigenvectors, as
+             this quantity is not well-defined.
+             This is checked for complex inputs when computing the gradients of this function. As such,
+             when inputs are complex and are on a CUDA device, the computation of the gradients
+             of this function synchronizes that device with the CPU.
+
+.. warning:: Gradients computed using the `eigenvectors` tensor will only be finite when
+             :attr:`A` has distinct eigenvalues.
+             Furthermore, if the distance between any two eigenvalues is close to zero,
+             the gradient will be numerically unstable, as it depends on the eigenvalues
+             :math:`\lambda_i` through the computation of
+             :math:`\frac{1}{\min_{i \neq j} \lambda_i - \lambda_j}`.
+
+.. warning:: User may see pytorch crashes if running `eigh` on CUDA devices with CUDA versions before 12.1 update 1
+             with large ill-conditioned matrices as inputs.
+             Refer to :ref:`Linear Algebra Numerical Stability` for more details.
+             If this is the case, user may (1) tune their matrix inputs to be less ill-conditioned,
+             or (2) use :func:`torch.backends.cuda.preferred_linalg_library` to
+             try other supported backends.
+
+.. seealso::
+
+        :func:`torch.linalg.eigvalsh` computes only the eigenvalues of a Hermitian matrix.
+        Unlike :func:`torch.linalg.eigh`, the gradients of :func:`~eigvalsh` are always
+        numerically stable.
+
+        :func:`torch.linalg.cholesky` for a different decomposition of a Hermitian matrix.
+        The Cholesky decomposition gives less information about the matrix but is much faster
+        to compute than the eigenvalue decomposition.
+
+        :func:`torch.linalg.eig` for a (slower) function that computes the eigenvalue decomposition
+        of a not necessarily Hermitian square matrix.
+
+        :func:`torch.linalg.svd` for a (slower) function that computes the more general SVD
+        decomposition of matrices of any shape.
+
+        :func:`torch.linalg.qr` for another (much faster) decomposition that works on general
+        matrices.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of symmetric or Hermitian matrices.
+    UPLO ('L', 'U', optional): controls whether to use the upper or lower triangular part
+                               of :attr:`A` in the computations. Default: `'L'`.
+
+Keyword args:
+    out (tuple, optional): output tuple of two tensors. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(eigenvalues, eigenvectors)` which corresponds to :math:`\Lambda` and :math:`Q` above.
+
+    `eigenvalues` will always be real-valued, even when :attr:`A` is complex.
+    It will also be ordered in ascending order.
+
+    `eigenvectors` will have the same dtype as :attr:`A` and will contain the eigenvectors as its columns.
+
+Examples::
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> A = A + A.T.conj()  # creates a Hermitian matrix
+    >>> A
+    tensor([[2.9228+0.0000j, 0.2029-0.0862j],
+            [0.2029+0.0862j, 0.3464+0.0000j]], dtype=torch.complex128)
+    >>> L, Q = torch.linalg.eigh(A)
+    >>> L
+    tensor([0.3277, 2.9415], dtype=torch.float64)
+    >>> Q
+    tensor([[-0.0846+-0.0000j, -0.9964+0.0000j],
+            [ 0.9170+0.3898j, -0.0779-0.0331j]], dtype=torch.complex128)
+    >>> torch.dist(Q @ torch.diag(L.cdouble()) @ Q.T.conj(), A)
+    tensor(6.1062e-16, dtype=torch.float64)
+
+    >>> A = torch.randn(3, 2, 2, dtype=torch.float64)
+    >>> A = A + A.mT  # creates a batch of symmetric matrices
+    >>> L, Q = torch.linalg.eigh(A)
+    >>> torch.dist(Q @ torch.diag_embed(L) @ Q.mH, A)
+    tensor(1.5423e-15, dtype=torch.float64)
+""",
+)
+
+eigvalsh = _add_docstr(
+    _linalg.linalg_eigvalsh,
+    r"""
+linalg.eigvalsh(A, UPLO='L', *, out=None) -> Tensor
+
+Computes the eigenvalues of a complex Hermitian or real symmetric matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **eigenvalues** of a complex Hermitian or real symmetric  matrix :math:`A \in \mathbb{K}^{n \times n}`
+are defined as the roots (counted with multiplicity) of the polynomial `p` of degree `n` given by
+
+.. math::
+
+    p(\lambda) = \operatorname{det}(A - \lambda \mathrm{I}_n)\mathrlap{\qquad \lambda \in \mathbb{R}}
+
+where :math:`\mathrm{I}_n` is the `n`-dimensional identity matrix.
+The eigenvalues of a real symmetric or complex Hermitian matrix are always real.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The eigenvalues are returned in ascending order.
+
+:attr:`A` is assumed to be Hermitian (resp. symmetric), but this is not checked internally, instead:
+
+- If :attr:`UPLO`\ `= 'L'` (default), only the lower triangular part of the matrix is used in the computation.
+- If :attr:`UPLO`\ `= 'U'`, only the upper triangular part of the matrix is used.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note"]}
+"""
+    + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.eigh` computes the full eigenvalue decomposition.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of symmetric or Hermitian matrices.
+    UPLO ('L', 'U', optional): controls whether to use the upper or lower triangular part
+                               of :attr:`A` in the computations. Default: `'L'`.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Returns:
+    A real-valued tensor containing the eigenvalues even when :attr:`A` is complex.
+    The eigenvalues are returned in ascending order.
+
+Examples::
+
+    >>> A = torch.randn(2, 2, dtype=torch.complex128)
+    >>> A = A + A.T.conj()  # creates a Hermitian matrix
+    >>> A
+    tensor([[2.9228+0.0000j, 0.2029-0.0862j],
+            [0.2029+0.0862j, 0.3464+0.0000j]], dtype=torch.complex128)
+    >>> torch.linalg.eigvalsh(A)
+    tensor([0.3277, 2.9415], dtype=torch.float64)
+
+    >>> A = torch.randn(3, 2, 2, dtype=torch.float64)
+    >>> A = A + A.mT  # creates a batch of symmetric matrices
+    >>> torch.linalg.eigvalsh(A)
+    tensor([[ 2.5797,  3.4629],
+            [-4.1605,  1.3780],
+            [-3.1113,  2.7381]], dtype=torch.float64)
+""",
+)
+
+householder_product = _add_docstr(
+    _linalg.linalg_householder_product,
+    r"""
+householder_product(A, tau, *, out=None) -> Tensor
+
+Computes the first `n` columns of a product of Householder matrices.
+
+Let :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`, and
+let :math:`A \in \mathbb{K}^{m \times n}` be a matrix with columns :math:`a_i \in \mathbb{K}^m`
+for :math:`i=1,\ldots,m` with :math:`m \geq n`. Denote by :math:`b_i` the vector resulting from
+zeroing out the first :math:`i-1` components of :math:`a_i` and setting to `1` the :math:`i`-th.
+For a vector :math:`\tau \in \mathbb{K}^k` with :math:`k \leq n`, this function computes the
+first :math:`n` columns of the matrix
+
+.. math::
+
+    H_1H_2 ... H_k \qquad\text{with}\qquad H_i = \mathrm{I}_m - \tau_i b_i b_i^{\text{H}}
+
+where :math:`\mathrm{I}_m` is the `m`-dimensional identity matrix and :math:`b^{\text{H}}` is the
+conjugate transpose when :math:`b` is complex, and the transpose when :math:`b` is real-valued.
+The output matrix is the same size as the input matrix :attr:`A`.
+
+See `Representation of Orthogonal or Unitary Matrices`_ for further details.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+.. seealso::
+
+        :func:`torch.geqrf` can be used together with this function to form the `Q` from the
+        :func:`~qr` decomposition.
+
+        :func:`torch.ormqr` is a related function that computes the matrix multiplication
+        of a product of Householder matrices with another matrix.
+        However, that function is not supported by autograd.
+
+.. warning::
+    Gradient computations are only well-defined if :math:`\tau_i \neq \frac{1}{||a_i||^2}`.
+    If this condition is not met, no error will be thrown, but the gradient produced may contain `NaN`.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    tau (Tensor): tensor of shape `(*, k)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if :attr:`A` doesn't satisfy the requirement `m >= n`,
+                  or :attr:`tau` doesn't satisfy the requirement `n >= k`.
+
+Examples::
+
+    >>> A = torch.randn(2, 2)
+    >>> h, tau = torch.geqrf(A)
+    >>> Q = torch.linalg.householder_product(h, tau)
+    >>> torch.dist(Q, torch.linalg.qr(A).Q)
+    tensor(0.)
+
+    >>> h = torch.randn(3, 2, 2, dtype=torch.complex128)
+    >>> tau = torch.randn(3, 1, dtype=torch.complex128)
+    >>> Q = torch.linalg.householder_product(h, tau)
+    >>> Q
+    tensor([[[ 1.8034+0.4184j,  0.2588-1.0174j],
+            [-0.6853+0.7953j,  2.0790+0.5620j]],
+
+            [[ 1.4581+1.6989j, -1.5360+0.1193j],
+            [ 1.3877-0.6691j,  1.3512+1.3024j]],
+
+            [[ 1.4766+0.5783j,  0.0361+0.6587j],
+            [ 0.6396+0.1612j,  1.3693+0.4481j]]], dtype=torch.complex128)
+
+.. _Representation of Orthogonal or Unitary Matrices:
+    https://www.netlib.org/lapack/lug/node128.html
+""",
+)
+
+ldl_factor = _add_docstr(
+    _linalg.linalg_ldl_factor,
+    r"""
+linalg.ldl_factor(A, *, hermitian=False, out=None) -> (Tensor, Tensor)
+
+Computes a compact representation of the LDL factorization of a Hermitian or symmetric (possibly indefinite) matrix.
+
+When :attr:`A` is complex valued it can be Hermitian (:attr:`hermitian`\ `= True`)
+or symmetric (:attr:`hermitian`\ `= False`).
+
+The factorization is of the form the form :math:`A = L D L^T`.
+If :attr:`hermitian` is `True` then transpose operation is the conjugate transpose.
+
+:math:`L` (or :math:`U`) and :math:`D` are stored in compact form in ``LD``.
+They follow the format specified by `LAPACK's sytrf`_ function.
+These tensors may be used in :func:`torch.linalg.ldl_solve` to solve linear systems.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note_has_ex"].format("torch.linalg.ldl_factor_ex")}
+"""
+    + r"""
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of symmetric or Hermitian matrices.
+
+Keyword args:
+    hermitian (bool, optional): whether to consider the input to be Hermitian or symmetric.
+                                For real-valued matrices, this switch has no effect. Default: `False`.
+    out (tuple, optional): tuple of two tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(LD, pivots)`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> A = A @ A.mT # make symmetric
+    >>> A
+    tensor([[7.2079, 4.2414, 1.9428],
+            [4.2414, 3.4554, 0.3264],
+            [1.9428, 0.3264, 1.3823]])
+    >>> LD, pivots = torch.linalg.ldl_factor(A)
+    >>> LD
+    tensor([[ 7.2079,  0.0000,  0.0000],
+            [ 0.5884,  0.9595,  0.0000],
+            [ 0.2695, -0.8513,  0.1633]])
+    >>> pivots
+    tensor([1, 2, 3], dtype=torch.int32)
+
+.. _LAPACK's sytrf:
+    https://www.netlib.org/lapack/explore-html/d3/db6/group__double_s_ycomputational_gad91bde1212277b3e909eb6af7f64858a.html
+""",
+)
+
+ldl_factor_ex = _add_docstr(
+    _linalg.linalg_ldl_factor_ex,
+    r"""
+linalg.ldl_factor_ex(A, *, hermitian=False, check_errors=False, out=None) -> (Tensor, Tensor, Tensor)
+
+This is a version of :func:`~ldl_factor` that does not perform error checks unless :attr:`check_errors`\ `= True`.
+It also returns the :attr:`info` tensor returned by `LAPACK's sytrf`_.
+``info`` stores integer error codes from the backend library.
+A positive integer indicates the diagonal element of :math:`D` that is zero.
+Division by 0 will occur if the result is used for solving a system of linear equations.
+``info`` filled with zeros indicates that the factorization was successful.
+If ``check_errors=True`` and ``info`` contains positive integers, then a `RuntimeError` is thrown.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note_ex"]}
+
+.. warning:: {common_notes["experimental_warning"]}
+"""
+    + r"""
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions
+                consisting of symmetric or Hermitian matrices.
+
+Keyword args:
+    hermitian (bool, optional): whether to consider the input to be Hermitian or symmetric.
+                                For real-valued matrices, this switch has no effect. Default: `False`.
+    check_errors (bool, optional): controls whether to check the content of ``info`` and raise
+                                   an error if it is non-zero. Default: `False`.
+    out (tuple, optional): tuple of three tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(LD, pivots, info)`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> A = A @ A.mT # make symmetric
+    >>> A
+    tensor([[7.2079, 4.2414, 1.9428],
+            [4.2414, 3.4554, 0.3264],
+            [1.9428, 0.3264, 1.3823]])
+    >>> LD, pivots, info = torch.linalg.ldl_factor_ex(A)
+    >>> LD
+    tensor([[ 7.2079,  0.0000,  0.0000],
+            [ 0.5884,  0.9595,  0.0000],
+            [ 0.2695, -0.8513,  0.1633]])
+    >>> pivots
+    tensor([1, 2, 3], dtype=torch.int32)
+    >>> info
+    tensor(0, dtype=torch.int32)
+
+.. _LAPACK's sytrf:
+    https://www.netlib.org/lapack/explore-html/d3/db6/group__double_s_ycomputational_gad91bde1212277b3e909eb6af7f64858a.html
+""",
+)
+
+ldl_solve = _add_docstr(
+    _linalg.linalg_ldl_solve,
+    r"""
+linalg.ldl_solve(LD, pivots, B, *, hermitian=False, out=None) -> Tensor
+
+Computes the solution of a system of linear equations using the LDL factorization.
+
+:attr:`LD` and :attr:`pivots` are the compact representation of the LDL factorization and
+are expected to be computed by :func:`torch.linalg.ldl_factor_ex`.
+:attr:`hermitian` argument to this function should be the same
+as the corresponding arguments in :func:`torch.linalg.ldl_factor_ex`.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+"""
+    + rf"""
+.. warning:: {common_notes["experimental_warning"]}
+"""
+    + r"""
+
+Args:
+    LD (Tensor): the `n \times n` matrix or the batch of such matrices of size
+                      `(*, n, n)` where `*` is one or more batch dimensions.
+    pivots (Tensor): the pivots corresponding to the LDL factorization of :attr:`LD`.
+    B (Tensor): right-hand side tensor of shape `(*, n, k)`.
+
+Keyword args:
+    hermitian (bool, optional): whether to consider the decomposed matrix to be Hermitian or symmetric.
+                                For real-valued matrices, this switch has no effect. Default: `False`.
+    out (tuple, optional): output tensor. `B` may be passed as `out` and the result is computed in-place on `B`.
+                           Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(2, 3, 3)
+    >>> A = A @ A.mT # make symmetric
+    >>> LD, pivots, info = torch.linalg.ldl_factor_ex(A)
+    >>> B = torch.randn(2, 3, 4)
+    >>> X = torch.linalg.ldl_solve(LD, pivots, B)
+    >>> torch.linalg.norm(A @ X - B)
+    >>> tensor(0.0001)
+""",
+)
+
+lstsq = _add_docstr(
+    _linalg.linalg_lstsq,
+    r"""
+torch.linalg.lstsq(A, B, rcond=None, *, driver=None) -> (Tensor, Tensor, Tensor, Tensor)
+
+Computes a solution to the least squares problem of a system of linear equations.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **least squares problem** for a linear system :math:`AX = B` with
+:math:`A \in \mathbb{K}^{m \times n}, B \in \mathbb{K}^{m \times k}` is defined as
+
+.. math::
+
+    \min_{X \in \mathbb{K}^{n \times k}} \|AX - B\|_F
+
+where :math:`\|-\|_F` denotes the Frobenius norm.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+:attr:`driver` chooses the backend function that will be used.
+For CPU inputs the valid values are `'gels'`, `'gelsy'`, `'gelsd`, `'gelss'`.
+To choose the best driver on CPU consider:
+
+- If :attr:`A` is well-conditioned (its `condition number`_ is not too large), or you do not mind some precision loss.
+
+  - For a general matrix: `'gelsy'` (QR with pivoting) (default)
+  - If :attr:`A` is full-rank: `'gels'` (QR)
+
+- If :attr:`A` is not well-conditioned.
+
+  - `'gelsd'` (tridiagonal reduction and SVD)
+  - But if you run into memory issues: `'gelss'` (full SVD).
+
+For CUDA input, the only valid driver is `'gels'`, which assumes that :attr:`A` is full-rank.
+
+See also the `full description of these drivers`_
+
+:attr:`rcond` is used to determine the effective rank of the matrices in :attr:`A`
+when :attr:`driver` is one of (`'gelsy'`, `'gelsd'`, `'gelss'`).
+In this case, if :math:`\sigma_i` are the singular values of `A` in decreasing order,
+:math:`\sigma_i` will be rounded down to zero if :math:`\sigma_i \leq \text{rcond} \cdot \sigma_1`.
+If :attr:`rcond`\ `= None` (default), :attr:`rcond` is set to the machine precision of the dtype of :attr:`A` times `max(m, n)`.
+
+This function returns the solution to the problem and some extra information in a named tuple of
+four tensors `(solution, residuals, rank, singular_values)`. For inputs :attr:`A`, :attr:`B`
+of shape `(*, m, n)`, `(*, m, k)` respectively, it contains
+
+- `solution`: the least squares solution. It has shape `(*, n, k)`.
+- `residuals`: the squared residuals of the solutions, that is, :math:`\|AX - B\|_F^2`.
+  It has shape `(*, k)`.
+  It is computed when `m > n` and every matrix in :attr:`A` is full-rank,
+  otherwise, it is an empty tensor.
+  If :attr:`A` is a batch of matrices and any matrix in the batch is not full rank,
+  then an empty tensor is returned. This behavior may change in a future PyTorch release.
+- `rank`: tensor of ranks of the matrices in :attr:`A`.
+  It has shape equal to the batch dimensions of :attr:`A`.
+  It is computed when :attr:`driver` is one of (`'gelsy'`, `'gelsd'`, `'gelss'`),
+  otherwise it is an empty tensor.
+- `singular_values`: tensor of singular values of the matrices in :attr:`A`.
+  It has shape `(*, min(m, n))`.
+  It is computed when :attr:`driver` is one of (`'gelsd'`, `'gelss'`),
+  otherwise it is an empty tensor.
+
+.. note::
+    This function computes `X = \ `:attr:`A`\ `.pinverse() @ \ `:attr:`B` in a faster and
+    more numerically stable way than performing the computations separately.
+
+.. warning::
+    The default value of :attr:`rcond` may change in a future PyTorch release.
+    It is therefore recommended to use a fixed value to avoid potential
+    breaking changes.
+
+Args:
+    A (Tensor): lhs tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    B (Tensor): rhs tensor of shape `(*, m, k)` where `*` is zero or more batch dimensions.
+    rcond (float, optional): used to determine the effective rank of :attr:`A`.
+                             If :attr:`rcond`\ `= None`, :attr:`rcond` is set to the machine
+                             precision of the dtype of :attr:`A` times `max(m, n)`. Default: `None`.
+
+Keyword args:
+    driver (str, optional): name of the LAPACK/MAGMA method to be used.
+        If `None`, `'gelsy'` is used for CPU inputs and `'gels'` for CUDA inputs.
+        Default: `None`.
+
+Returns:
+    A named tuple `(solution, residuals, rank, singular_values)`.
+
+Examples::
+
+    >>> A = torch.randn(1,3,3)
+    >>> A
+    tensor([[[-1.0838,  0.0225,  0.2275],
+         [ 0.2438,  0.3844,  0.5499],
+         [ 0.1175, -0.9102,  2.0870]]])
+    >>> B = torch.randn(2,3,3)
+    >>> B
+    tensor([[[-0.6772,  0.7758,  0.5109],
+         [-1.4382,  1.3769,  1.1818],
+         [-0.3450,  0.0806,  0.3967]],
+        [[-1.3994, -0.1521, -0.1473],
+         [ 1.9194,  1.0458,  0.6705],
+         [-1.1802, -0.9796,  1.4086]]])
+    >>> X = torch.linalg.lstsq(A, B).solution # A is broadcasted to shape (2, 3, 3)
+    >>> torch.dist(X, torch.linalg.pinv(A) @ B)
+    tensor(1.5152e-06)
+
+    >>> S = torch.linalg.lstsq(A, B, driver='gelsd').singular_values
+    >>> torch.dist(S, torch.linalg.svdvals(A))
+    tensor(2.3842e-07)
+
+    >>> A[:, 0].zero_()  # Decrease the rank of A
+    >>> rank = torch.linalg.lstsq(A, B).rank
+    >>> rank
+    tensor([2])
+
+.. _condition number:
+    https://pytorch.org/docs/main/linalg.html#torch.linalg.cond
+.. _full description of these drivers:
+    https://www.netlib.org/lapack/lug/node27.html
+""",
+)
+
+matrix_power = _add_docstr(
+    _linalg.linalg_matrix_power,
+    r"""
+matrix_power(A, n, *, out=None) -> Tensor
+
+Computes the `n`-th power of a square matrix for an integer `n`.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+If :attr:`n`\ `= 0`, it returns the identity matrix (or batch) of the same shape
+as :attr:`A`. If :attr:`n` is negative, it returns the inverse of each matrix
+(if invertible) raised to the power of `abs(n)`.
+
+.. note::
+    Consider using :func:`torch.linalg.solve` if possible for multiplying a matrix on the left by
+    a negative power as, if :attr:`n`\ `> 0`::
+
+        torch.linalg.solve(matrix_power(A, n), B) == matrix_power(A, -n)  @ B
+
+    It is always preferred to use :func:`~solve` when possible, as it is faster and more
+    numerically stable than computing :math:`A^{-n}` explicitly.
+
+.. seealso::
+
+        :func:`torch.linalg.solve` computes :attr:`A`\ `.inverse() @ \ `:attr:`B` with a
+        numerically stable algorithm.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, m)` where `*` is zero or more batch dimensions.
+    n (int): the exponent.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if :attr:`n`\ `< 0` and the matrix :attr:`A` or any matrix in the
+                  batch of matrices :attr:`A` is not invertible.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> torch.linalg.matrix_power(A, 0)
+    tensor([[1., 0., 0.],
+            [0., 1., 0.],
+            [0., 0., 1.]])
+    >>> torch.linalg.matrix_power(A, 3)
+    tensor([[ 1.0756,  0.4980,  0.0100],
+            [-1.6617,  1.4994, -1.9980],
+            [-0.4509,  0.2731,  0.8001]])
+    >>> torch.linalg.matrix_power(A.expand(2, -1, -1), -2)
+    tensor([[[ 0.2640,  0.4571, -0.5511],
+            [-1.0163,  0.3491, -1.5292],
+            [-0.4899,  0.0822,  0.2773]],
+            [[ 0.2640,  0.4571, -0.5511],
+            [-1.0163,  0.3491, -1.5292],
+            [-0.4899,  0.0822,  0.2773]]])
+""",
+)
+
+matrix_rank = _add_docstr(
+    _linalg.linalg_matrix_rank,
+    r"""
+linalg.matrix_rank(A, *, atol=None, rtol=None, hermitian=False, out=None) -> Tensor
+
+Computes the numerical rank of a matrix.
+
+The matrix rank is computed as the number of singular values
+(or eigenvalues in absolute value when :attr:`hermitian`\ `= True`)
+that are greater than :math:`\max(\text{atol}, \sigma_1 * \text{rtol})` threshold,
+where :math:`\sigma_1` is the largest singular value (or eigenvalue).
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+If :attr:`hermitian`\ `= True`, :attr:`A` is assumed to be Hermitian if complex or
+symmetric if real, but this is not checked internally. Instead, just the lower
+triangular part of the matrix is used in the computations.
+
+If :attr:`rtol` is not specified and :attr:`A` is a matrix of dimensions `(m, n)`,
+the relative tolerance is set to be :math:`\text{rtol} = \max(m, n) \varepsilon`
+and :math:`\varepsilon` is the epsilon value for the dtype of :attr:`A` (see :class:`.finfo`).
+If :attr:`rtol` is not specified and :attr:`atol` is specified to be larger than zero then
+:attr:`rtol` is set to zero.
+
+If :attr:`atol` or :attr:`rtol` is a :class:`torch.Tensor`, its shape must be broadcastable to that
+of the singular values of :attr:`A` as returned by :func:`torch.linalg.svdvals`.
+
+.. note::
+    This function has NumPy compatible variant `linalg.matrix_rank(A, tol, hermitian=False)`.
+    However, use of the positional argument :attr:`tol` is deprecated in favor of :attr:`atol` and :attr:`rtol`.
+
+"""
+    + rf"""
+.. note:: The matrix rank is computed using a singular value decomposition
+          :func:`torch.linalg.svdvals` if :attr:`hermitian`\ `= False` (default) and the eigenvalue
+          decomposition :func:`torch.linalg.eigvalsh` when :attr:`hermitian`\ `= True`.
+          {common_notes["sync_note"]}
+"""
+    + r"""
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    tol (float, Tensor, optional): [NumPy Compat] Alias for :attr:`atol`. Default: `None`.
+
+Keyword args:
+    atol (float, Tensor, optional): the absolute tolerance value. When `None` it's considered to be zero.
+                                    Default: `None`.
+    rtol (float, Tensor, optional): the relative tolerance value. See above for the value it takes when `None`.
+                                    Default: `None`.
+    hermitian(bool): indicates whether :attr:`A` is Hermitian if complex
+                     or symmetric if real. Default: `False`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.eye(10)
+    >>> torch.linalg.matrix_rank(A)
+    tensor(10)
+    >>> B = torch.eye(10)
+    >>> B[0, 0] = 0
+    >>> torch.linalg.matrix_rank(B)
+    tensor(9)
+
+    >>> A = torch.randn(4, 3, 2)
+    >>> torch.linalg.matrix_rank(A)
+    tensor([2, 2, 2, 2])
+
+    >>> A = torch.randn(2, 4, 2, 3)
+    >>> torch.linalg.matrix_rank(A)
+    tensor([[2, 2, 2, 2],
+            [2, 2, 2, 2]])
+
+    >>> A = torch.randn(2, 4, 3, 3, dtype=torch.complex64)
+    >>> torch.linalg.matrix_rank(A)
+    tensor([[3, 3, 3, 3],
+            [3, 3, 3, 3]])
+    >>> torch.linalg.matrix_rank(A, hermitian=True)
+    tensor([[3, 3, 3, 3],
+            [3, 3, 3, 3]])
+    >>> torch.linalg.matrix_rank(A, atol=1.0, rtol=0.0)
+    tensor([[3, 2, 2, 2],
+            [1, 2, 1, 2]])
+    >>> torch.linalg.matrix_rank(A, atol=1.0, rtol=0.0, hermitian=True)
+    tensor([[2, 2, 2, 1],
+            [1, 2, 2, 2]])
+""",
+)
+
+norm = _add_docstr(
+    _linalg.linalg_norm,
+    r"""
+linalg.norm(A, ord=None, dim=None, keepdim=False, *, out=None, dtype=None) -> Tensor
+
+Computes a vector or matrix norm.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+
+Whether this function computes a vector or matrix norm is determined as follows:
+
+- If :attr:`dim` is an `int`, the vector norm will be computed.
+- If :attr:`dim` is a `2`-`tuple`, the matrix norm will be computed.
+- If :attr:`dim`\ `= None` and :attr:`ord`\ `= None`,
+  :attr:`A` will be flattened to 1D and the `2`-norm of the resulting vector will be computed.
+- If :attr:`dim`\ `= None` and :attr:`ord` `!= None`, :attr:`A` must be 1D or 2D.
+
+:attr:`ord` defines the norm that is computed. The following norms are supported:
+
+======================     =========================  ========================================================
+:attr:`ord`                norm for matrices          norm for vectors
+======================     =========================  ========================================================
+`None` (default)           Frobenius norm             `2`-norm (see below)
+`'fro'`                    Frobenius norm             -- not supported --
+`'nuc'`                    nuclear norm               -- not supported --
+`inf`                      `max(sum(abs(x), dim=1))`  `max(abs(x))`
+`-inf`                     `min(sum(abs(x), dim=1))`  `min(abs(x))`
+`0`                        -- not supported --        `sum(x != 0)`
+`1`                        `max(sum(abs(x), dim=0))`  as below
+`-1`                       `min(sum(abs(x), dim=0))`  as below
+`2`                        largest singular value     as below
+`-2`                       smallest singular value    as below
+other `int` or `float`     -- not supported --        `sum(abs(x)^{ord})^{(1 / ord)}`
+======================     =========================  ========================================================
+
+where `inf` refers to `float('inf')`, NumPy's `inf` object, or any equivalent object.
+
+.. seealso::
+
+        :func:`torch.linalg.vector_norm` computes a vector norm.
+
+        :func:`torch.linalg.matrix_norm` computes a matrix norm.
+
+        The above functions are often clearer and more flexible than using :func:`torch.linalg.norm`.
+        For example, `torch.linalg.norm(A, ord=1, dim=(0, 1))` always
+        computes a matrix norm, but with `torch.linalg.vector_norm(A, ord=1, dim=(0, 1))` it is possible
+        to compute a vector norm over the two dimensions.
+
+Args:
+    A (Tensor): tensor of shape `(*, n)` or `(*, m, n)` where `*` is zero or more batch dimensions
+    ord (int, float, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: `None`
+    dim (int, Tuple[int], optional): dimensions over which to compute
+        the vector or matrix norm. See above for the behavior when :attr:`dim`\ `= None`.
+        Default: `None`
+    keepdim (bool, optional): If set to `True`, the reduced dimensions are retained
+        in the result as dimensions with size one. Default: `False`
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+    dtype (:class:`torch.dtype`, optional): If specified, the input tensor is cast to
+        :attr:`dtype` before performing the operation, and the returned tensor's type
+        will be :attr:`dtype`. Default: `None`
+
+Returns:
+    A real-valued tensor, even when :attr:`A` is complex.
+
+Examples::
+
+    >>> from torch import linalg as LA
+    >>> a = torch.arange(9, dtype=torch.float) - 4
+    >>> a
+    tensor([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
+    >>> B = a.reshape((3, 3))
+    >>> B
+    tensor([[-4., -3., -2.],
+            [-1.,  0.,  1.],
+            [ 2.,  3.,  4.]])
+
+    >>> LA.norm(a)
+    tensor(7.7460)
+    >>> LA.norm(B)
+    tensor(7.7460)
+    >>> LA.norm(B, 'fro')
+    tensor(7.7460)
+    >>> LA.norm(a, float('inf'))
+    tensor(4.)
+    >>> LA.norm(B, float('inf'))
+    tensor(9.)
+    >>> LA.norm(a, -float('inf'))
+    tensor(0.)
+    >>> LA.norm(B, -float('inf'))
+    tensor(2.)
+
+    >>> LA.norm(a, 1)
+    tensor(20.)
+    >>> LA.norm(B, 1)
+    tensor(7.)
+    >>> LA.norm(a, -1)
+    tensor(0.)
+    >>> LA.norm(B, -1)
+    tensor(6.)
+    >>> LA.norm(a, 2)
+    tensor(7.7460)
+    >>> LA.norm(B, 2)
+    tensor(7.3485)
+
+    >>> LA.norm(a, -2)
+    tensor(0.)
+    >>> LA.norm(B.double(), -2)
+    tensor(1.8570e-16, dtype=torch.float64)
+    >>> LA.norm(a, 3)
+    tensor(5.8480)
+    >>> LA.norm(a, -3)
+    tensor(0.)
+
+Using the :attr:`dim` argument to compute vector norms::
+
+    >>> c = torch.tensor([[1., 2., 3.],
+    ...                   [-1, 1, 4]])
+    >>> LA.norm(c, dim=0)
+    tensor([1.4142, 2.2361, 5.0000])
+    >>> LA.norm(c, dim=1)
+    tensor([3.7417, 4.2426])
+    >>> LA.norm(c, ord=1, dim=1)
+    tensor([6., 6.])
+
+Using the :attr:`dim` argument to compute matrix norms::
+
+    >>> A = torch.arange(8, dtype=torch.float).reshape(2, 2, 2)
+    >>> LA.norm(A, dim=(1,2))
+    tensor([ 3.7417, 11.2250])
+    >>> LA.norm(A[0, :, :]), LA.norm(A[1, :, :])
+    (tensor(3.7417), tensor(11.2250))
+""",
+)
+
+vector_norm = _add_docstr(
+    _linalg.linalg_vector_norm,
+    r"""
+linalg.vector_norm(x, ord=2, dim=None, keepdim=False, *, dtype=None, out=None) -> Tensor
+
+Computes a vector norm.
+
+If :attr:`x` is complex valued, it computes the norm of :attr:`x`\ `.abs()`
+
+Supports input of float, double, cfloat and cdouble dtypes.
+
+This function does not necessarily treat multidimensional :attr:`x` as a batch of
+vectors, instead:
+
+- If :attr:`dim`\ `= None`, :attr:`x` will be flattened before the norm is computed.
+- If :attr:`dim` is an `int` or a `tuple`, the norm will be computed over these dimensions
+  and the other dimensions will be treated as batch dimensions.
+
+This behavior is for consistency with :func:`torch.linalg.norm`.
+
+:attr:`ord` defines the vector norm that is computed. The following norms are supported:
+
+======================   ===============================
+:attr:`ord`              vector norm
+======================   ===============================
+`2` (default)            `2`-norm (see below)
+`inf`                    `max(abs(x))`
+`-inf`                   `min(abs(x))`
+`0`                      `sum(x != 0)`
+other `int` or `float`   `sum(abs(x)^{ord})^{(1 / ord)}`
+======================   ===============================
+
+where `inf` refers to `float('inf')`, NumPy's `inf` object, or any equivalent object.
+
+:attr:`dtype` may be used to perform the computation in a more precise dtype.
+It is semantically equivalent to calling ``linalg.vector_norm(x.to(dtype))``
+but it is faster in some cases.
+
+.. seealso::
+
+        :func:`torch.linalg.matrix_norm` computes a matrix norm.
+
+Args:
+    x (Tensor): tensor, flattened by default, but this behavior can be
+        controlled using :attr:`dim`.  (Note: the keyword argument
+        `input` can also be used as an alias for `x`.)
+    ord (int, float, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: `2`
+    dim (int, Tuple[int], optional): dimensions over which to compute
+        the norm. See above for the behavior when :attr:`dim`\ `= None`.
+        Default: `None`
+    keepdim (bool, optional): If set to `True`, the reduced dimensions are retained
+        in the result as dimensions with size one. Default: `False`
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+    dtype (:class:`torch.dtype`, optional): type used to perform the accumulation and the return.
+        If specified, :attr:`x` is cast to :attr:`dtype` before performing the operation,
+        and the returned tensor's type will be :attr:`dtype` if real and of its real counterpart if complex.
+        :attr:`dtype` may be complex if :attr:`x` is complex, otherwise it must be real.
+        :attr:`x` should be convertible without narrowing to :attr:`dtype`. Default: None
+
+Returns:
+    A real-valued tensor, even when :attr:`x` is complex.
+
+Examples::
+
+    >>> from torch import linalg as LA
+    >>> a = torch.arange(9, dtype=torch.float) - 4
+    >>> a
+    tensor([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
+    >>> B = a.reshape((3, 3))
+    >>> B
+    tensor([[-4., -3., -2.],
+            [-1.,  0.,  1.],
+            [ 2.,  3.,  4.]])
+    >>> LA.vector_norm(a, ord=3.5)
+    tensor(5.4345)
+    >>> LA.vector_norm(B, ord=3.5)
+    tensor(5.4345)
+""",
+)
+
+matrix_norm = _add_docstr(
+    _linalg.linalg_matrix_norm,
+    r"""
+linalg.matrix_norm(A, ord='fro', dim=(-2, -1), keepdim=False, *, dtype=None, out=None) -> Tensor
+
+Computes a matrix norm.
+
+If :attr:`A` is complex valued, it computes the norm of :attr:`A`\ `.abs()`
+
+Support input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices: the norm will be computed over the
+dimensions specified by the 2-tuple :attr:`dim` and the other dimensions will
+be treated as batch dimensions. The output will have the same batch dimensions.
+
+:attr:`ord` defines the matrix norm that is computed. The following norms are supported:
+
+======================   ========================================================
+:attr:`ord`              matrix norm
+======================   ========================================================
+`'fro'` (default)        Frobenius norm
+`'nuc'`                  nuclear norm
+`inf`                    `max(sum(abs(x), dim=1))`
+`-inf`                   `min(sum(abs(x), dim=1))`
+`1`                      `max(sum(abs(x), dim=0))`
+`-1`                     `min(sum(abs(x), dim=0))`
+`2`                      largest singular value
+`-2`                     smallest singular value
+======================   ========================================================
+
+where `inf` refers to `float('inf')`, NumPy's `inf` object, or any equivalent object.
+
+Args:
+    A (Tensor): tensor with two or more dimensions. By default its
+        shape is interpreted as `(*, m, n)` where `*` is zero or more
+        batch dimensions, but this behavior can be controlled using :attr:`dim`.
+    ord (int, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: `'fro'`
+    dim (Tuple[int, int], optional): dimensions over which to compute the norm. Default: `(-2, -1)`
+    keepdim (bool, optional): If set to `True`, the reduced dimensions are retained
+        in the result as dimensions with size one. Default: `False`
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+    dtype (:class:`torch.dtype`, optional): If specified, the input tensor is cast to
+        :attr:`dtype` before performing the operation, and the returned tensor's type
+        will be :attr:`dtype`. Default: `None`
+
+Returns:
+    A real-valued tensor, even when :attr:`A` is complex.
+
+Examples::
+
+    >>> from torch import linalg as LA
+    >>> A = torch.arange(9, dtype=torch.float).reshape(3, 3)
+    >>> A
+    tensor([[0., 1., 2.],
+            [3., 4., 5.],
+            [6., 7., 8.]])
+    >>> LA.matrix_norm(A)
+    tensor(14.2829)
+    >>> LA.matrix_norm(A, ord=-1)
+    tensor(9.)
+    >>> B = A.expand(2, -1, -1)
+    >>> B
+    tensor([[[0., 1., 2.],
+            [3., 4., 5.],
+            [6., 7., 8.]],
+
+            [[0., 1., 2.],
+            [3., 4., 5.],
+            [6., 7., 8.]]])
+    >>> LA.matrix_norm(B)
+    tensor([14.2829, 14.2829])
+    >>> LA.matrix_norm(B, dim=(0, 2))
+    tensor([ 3.1623, 10.0000, 17.2627])
+""",
+)
+
+matmul = _add_docstr(
+    _linalg.linalg_matmul,
+    r"""
+linalg.matmul(input, other, *, out=None) -> Tensor
+
+Alias for :func:`torch.matmul`
+""",
+)
+
+diagonal = _add_docstr(
+    _linalg.linalg_diagonal,
+    r"""
+linalg.diagonal(A, *, offset=0, dim1=-2, dim2=-1) -> Tensor
+
+Alias for :func:`torch.diagonal` with defaults :attr:`dim1`\ `= -2`, :attr:`dim2`\ `= -1`.
+""",
+)
+
+multi_dot = _add_docstr(
+    _linalg.linalg_multi_dot,
+    r"""
+linalg.multi_dot(tensors, *, out=None)
+
+Efficiently multiplies two or more matrices by reordering the multiplications so that
+the fewest arithmetic operations are performed.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+This function does not support batched inputs.
+
+Every tensor in :attr:`tensors` must be 2D, except for the first and last which
+may be 1D. If the first tensor is a 1D vector of shape `(n,)` it is treated as a row vector
+of shape `(1, n)`, similarly if the last tensor is a 1D vector of shape `(n,)` it is treated
+as a column vector of shape `(n, 1)`.
+
+If the first and last tensors are matrices, the output will be a matrix.
+However, if either is a 1D vector, then the output will be a 1D vector.
+
+Differences with `numpy.linalg.multi_dot`:
+
+- Unlike `numpy.linalg.multi_dot`, the first and last tensors must either be 1D or 2D
+  whereas NumPy allows them to be nD
+
+.. warning:: This function does not broadcast.
+
+.. note:: This function is implemented by chaining :func:`torch.mm` calls after
+          computing the optimal matrix multiplication order.
+
+.. note:: The cost of multiplying two matrices with shapes `(a, b)` and `(b, c)` is
+          `a * b * c`. Given matrices `A`, `B`, `C` with shapes `(10, 100)`,
+          `(100, 5)`, `(5, 50)` respectively, we can calculate the cost of different
+          multiplication orders as follows:
+
+          .. math::
+
+             \begin{align*}
+             \operatorname{cost}((AB)C) &= 10 \times 100 \times 5 + 10 \times 5 \times 50 = 7500 \\
+             \operatorname{cost}(A(BC)) &= 10 \times 100 \times 50 + 100 \times 5 \times 50 = 75000
+             \end{align*}
+
+          In this case, multiplying `A` and `B` first followed by `C` is 10 times faster.
+
+Args:
+    tensors (Sequence[Tensor]): two or more tensors to multiply. The first and last
+        tensors may be 1D or 2D. Every other tensor must be 2D.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> from torch.linalg import multi_dot
+
+    >>> multi_dot([torch.tensor([1, 2]), torch.tensor([2, 3])])
+    tensor(8)
+    >>> multi_dot([torch.tensor([[1, 2]]), torch.tensor([2, 3])])
+    tensor([8])
+    >>> multi_dot([torch.tensor([[1, 2]]), torch.tensor([[2], [3]])])
+    tensor([[8]])
+
+    >>> A = torch.arange(2 * 3).view(2, 3)
+    >>> B = torch.arange(3 * 2).view(3, 2)
+    >>> C = torch.arange(2 * 2).view(2, 2)
+    >>> multi_dot((A, B, C))
+    tensor([[ 26,  49],
+            [ 80, 148]])
+""",
+)
+
+svd = _add_docstr(
+    _linalg.linalg_svd,
+    r"""
+linalg.svd(A, full_matrices=True, *, driver=None, out=None) -> (Tensor, Tensor, Tensor)
+
+Computes the singular value decomposition (SVD) of a matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **full SVD** of a matrix
+:math:`A \in \mathbb{K}^{m \times n}`, if `k = min(m,n)`, is defined as
+
+.. math::
+
+    A = U \operatorname{diag}(S) V^{\text{H}}
+    \mathrlap{\qquad U \in \mathbb{K}^{m \times m}, S \in \mathbb{R}^k, V \in \mathbb{K}^{n \times n}}
+
+where :math:`\operatorname{diag}(S) \in \mathbb{K}^{m \times n}`,
+:math:`V^{\text{H}}` is the conjugate transpose when :math:`V` is complex, and the transpose when :math:`V` is real-valued.
+The matrices  :math:`U`, :math:`V` (and thus :math:`V^{\text{H}}`) are orthogonal in the real case, and unitary in the complex case.
+
+When `m > n` (resp. `m < n`) we can drop the last `m - n` (resp. `n - m`) columns of `U` (resp. `V`) to form the **reduced SVD**:
+
+.. math::
+
+    A = U \operatorname{diag}(S) V^{\text{H}}
+    \mathrlap{\qquad U \in \mathbb{K}^{m \times k}, S \in \mathbb{R}^k, V \in \mathbb{K}^{n \times k}}
+
+where :math:`\operatorname{diag}(S) \in \mathbb{K}^{k \times k}`.
+In this case, :math:`U` and :math:`V` also have orthonormal columns.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The returned decomposition is a named tuple `(U, S, Vh)`
+which corresponds to :math:`U`, :math:`S`, :math:`V^{\text{H}}` above.
+
+The singular values are returned in descending order.
+
+The parameter :attr:`full_matrices` chooses between the full (default) and reduced SVD.
+
+The :attr:`driver` kwarg may be used in CUDA with a cuSOLVER backend to choose the algorithm used to compute the SVD.
+The choice of a driver is a trade-off between accuracy and speed.
+
+- If :attr:`A` is well-conditioned (its `condition number`_ is not too large), or you do not mind some precision loss.
+
+  - For a general matrix: `'gesvdj'` (Jacobi method)
+  - If :attr:`A` is tall or wide (`m >> n` or `m << n`): `'gesvda'` (Approximate method)
+
+- If :attr:`A` is not well-conditioned or precision is relevant: `'gesvd'` (QR based)
+
+By default (:attr:`driver`\ `= None`), we call `'gesvdj'` and, if it fails, we fallback to `'gesvd'`.
+
+Differences with `numpy.linalg.svd`:
+
+- Unlike `numpy.linalg.svd`, this function always returns a tuple of three tensors
+  and it doesn't support `compute_uv` argument.
+  Please use :func:`torch.linalg.svdvals`, which computes only the singular values,
+  instead of `compute_uv=False`.
+
+.. note:: When :attr:`full_matrices`\ `= True`, the gradients with respect to `U[..., :, min(m, n):]`
+          and `Vh[..., min(m, n):, :]` will be ignored, as those vectors can be arbitrary bases
+          of the corresponding subspaces.
+
+.. warning:: The returned tensors `U` and `V` are not unique, nor are they continuous with
+             respect to :attr:`A`.
+             Due to this lack of uniqueness, different hardware and software may compute
+             different singular vectors.
+
+             This non-uniqueness is caused by the fact that multiplying any pair of singular
+             vectors :math:`u_k, v_k` by `-1` in the real case or by
+             :math:`e^{i \phi}, \phi \in \mathbb{R}` in the complex case produces another two
+             valid singular vectors of the matrix.
+             For this reason, the loss function shall not depend on this :math:`e^{i \phi}` quantity,
+             as it is not well-defined.
+             This is checked for complex inputs when computing the gradients of this function. As such,
+             when inputs are complex and are on a CUDA device, the computation of the gradients
+             of this function synchronizes that device with the CPU.
+
+.. warning:: Gradients computed using `U` or `Vh` will only be finite when
+             :attr:`A` does not have repeated singular values. If :attr:`A` is rectangular,
+             additionally, zero must also not be one of its singular values.
+             Furthermore, if the distance between any two singular values is close to zero,
+             the gradient will be numerically unstable, as it depends on the singular values
+             :math:`\sigma_i` through the computation of
+             :math:`\frac{1}{\min_{i \neq j} \sigma_i^2 - \sigma_j^2}`.
+             In the rectangular case, the gradient will also be numerically unstable when
+             :attr:`A` has small singular values, as it also depends on the computation of
+             :math:`\frac{1}{\sigma_i}`.
+
+.. seealso::
+
+        :func:`torch.linalg.svdvals` computes only the singular values.
+        Unlike :func:`torch.linalg.svd`, the gradients of :func:`~svdvals` are always
+        numerically stable.
+
+        :func:`torch.linalg.eig` for a function that computes another type of spectral
+        decomposition of a matrix. The eigendecomposition works just on square matrices.
+
+        :func:`torch.linalg.eigh` for a (faster) function that computes the eigenvalue decomposition
+        for Hermitian and symmetric matrices.
+
+        :func:`torch.linalg.qr` for another (much faster) decomposition that works on general
+        matrices.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    full_matrices (bool, optional): controls whether to compute the full or reduced
+                                    SVD, and consequently,
+                                    the shape of the returned tensors
+                                    `U` and `Vh`. Default: `True`.
+
+Keyword args:
+    driver (str, optional): name of the cuSOLVER method to be used. This keyword argument only works on CUDA inputs.
+        Available options are: `None`, `gesvd`, `gesvdj`, and `gesvda`.
+        Default: `None`.
+    out (tuple, optional): output tuple of three tensors. Ignored if `None`.
+
+Returns:
+    A named tuple `(U, S, Vh)` which corresponds to :math:`U`, :math:`S`, :math:`V^{\text{H}}` above.
+
+    `S` will always be real-valued, even when :attr:`A` is complex.
+    It will also be ordered in descending order.
+
+    `U` and `Vh` will have the same dtype as :attr:`A`. The left / right singular vectors will be given by
+    the columns of `U` and the rows of `Vh` respectively.
+
+Examples::
+
+    >>> A = torch.randn(5, 3)
+    >>> U, S, Vh = torch.linalg.svd(A, full_matrices=False)
+    >>> U.shape, S.shape, Vh.shape
+    (torch.Size([5, 3]), torch.Size([3]), torch.Size([3, 3]))
+    >>> torch.dist(A, U @ torch.diag(S) @ Vh)
+    tensor(1.0486e-06)
+
+    >>> U, S, Vh = torch.linalg.svd(A)
+    >>> U.shape, S.shape, Vh.shape
+    (torch.Size([5, 5]), torch.Size([3]), torch.Size([3, 3]))
+    >>> torch.dist(A, U[:, :3] @ torch.diag(S) @ Vh)
+    tensor(1.0486e-06)
+
+    >>> A = torch.randn(7, 5, 3)
+    >>> U, S, Vh = torch.linalg.svd(A, full_matrices=False)
+    >>> torch.dist(A, U @ torch.diag_embed(S) @ Vh)
+    tensor(3.0957e-06)
+
+.. _condition number:
+    https://pytorch.org/docs/main/linalg.html#torch.linalg.cond
+.. _the resulting vectors will span the same subspace:
+    https://en.wikipedia.org/wiki/Singular_value_decomposition#Singular_values,_singular_vectors,_and_their_relation_to_the_SVD
+""",
+)
+
+svdvals = _add_docstr(
+    _linalg.linalg_svdvals,
+    r"""
+linalg.svdvals(A, *, driver=None, out=None) -> Tensor
+
+Computes the singular values of a matrix.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The singular values are returned in descending order.
+
+.. note:: This function is equivalent to NumPy's `linalg.svd(A, compute_uv=False)`.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note"]}
+"""
+    + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.svd` computes the full singular value decomposition.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    driver (str, optional): name of the cuSOLVER method to be used. This keyword argument only works on CUDA inputs.
+        Available options are: `None`, `gesvd`, `gesvdj`, and `gesvda`.
+        Check :func:`torch.linalg.svd` for details.
+        Default: `None`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Returns:
+    A real-valued tensor, even when :attr:`A` is complex.
+
+Examples::
+
+    >>> A = torch.randn(5, 3)
+    >>> S = torch.linalg.svdvals(A)
+    >>> S
+    tensor([2.5139, 2.1087, 1.1066])
+
+    >>> torch.dist(S, torch.linalg.svd(A, full_matrices=False).S)
+    tensor(2.4576e-07)
+""",
+)
+
+cond = _add_docstr(
+    _linalg.linalg_cond,
+    r"""
+linalg.cond(A, p=None, *, out=None) -> Tensor
+
+Computes the condition number of a matrix with respect to a matrix norm.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **condition number** :math:`\kappa` of a matrix
+:math:`A \in \mathbb{K}^{n \times n}` is defined as
+
+.. math::
+
+    \kappa(A) = \|A\|_p\|A^{-1}\|_p
+
+The condition number of :attr:`A` measures the numerical stability of the linear system `AX = B`
+with respect to a matrix norm.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+:attr:`p` defines the matrix norm that is computed. The following norms are supported:
+
+=========    =================================
+:attr:`p`    matrix norm
+=========    =================================
+`None`       `2`-norm (largest singular value)
+`'fro'`      Frobenius norm
+`'nuc'`      nuclear norm
+`inf`        `max(sum(abs(x), dim=1))`
+`-inf`       `min(sum(abs(x), dim=1))`
+`1`          `max(sum(abs(x), dim=0))`
+`-1`         `min(sum(abs(x), dim=0))`
+`2`          largest singular value
+`-2`         smallest singular value
+=========    =================================
+
+where `inf` refers to `float('inf')`, NumPy's `inf` object, or any equivalent object.
+
+For :attr:`p` is one of `('fro', 'nuc', inf, -inf, 1, -1)`, this function uses
+:func:`torch.linalg.norm` and :func:`torch.linalg.inv`.
+As such, in this case, the matrix (or every matrix in the batch) :attr:`A` has to be square
+and invertible.
+
+For :attr:`p` in `(2, -2)`, this function can be computed in terms of the singular values
+:math:`\sigma_1 \geq \ldots \geq \sigma_n`
+
+.. math::
+
+    \kappa_2(A) = \frac{\sigma_1}{\sigma_n}\qquad \kappa_{-2}(A) = \frac{\sigma_n}{\sigma_1}
+
+In these cases, it is computed using :func:`torch.linalg.svdvals`. For these norms, the matrix
+(or every matrix in the batch) :attr:`A` may have any shape.
+
+.. note :: When inputs are on a CUDA device, this function synchronizes that device with the CPU
+           if :attr:`p` is one of `('fro', 'nuc', inf, -inf, 1, -1)`.
+
+.. seealso::
+
+        :func:`torch.linalg.solve` for a function that solves linear systems of square matrices.
+
+        :func:`torch.linalg.lstsq` for a function that solves linear systems of general matrices.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions
+                    for :attr:`p` in `(2, -2)`, and of shape `(*, n, n)` where every matrix
+                    is invertible for :attr:`p` in `('fro', 'nuc', inf, -inf, 1, -1)`.
+    p (int, inf, -inf, 'fro', 'nuc', optional):
+        the type of the matrix norm to use in the computations (see above). Default: `None`
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Returns:
+    A real-valued tensor, even when :attr:`A` is complex.
+
+Raises:
+    RuntimeError:
+        if :attr:`p` is one of `('fro', 'nuc', inf, -inf, 1, -1)`
+        and the :attr:`A` matrix or any matrix in the batch :attr:`A` is not square
+        or invertible.
+
+Examples::
+
+    >>> A = torch.randn(3, 4, 4, dtype=torch.complex64)
+    >>> torch.linalg.cond(A)
+    >>> A = torch.tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])
+    >>> torch.linalg.cond(A)
+    tensor([1.4142])
+    >>> torch.linalg.cond(A, 'fro')
+    tensor(3.1623)
+    >>> torch.linalg.cond(A, 'nuc')
+    tensor(9.2426)
+    >>> torch.linalg.cond(A, float('inf'))
+    tensor(2.)
+    >>> torch.linalg.cond(A, float('-inf'))
+    tensor(1.)
+    >>> torch.linalg.cond(A, 1)
+    tensor(2.)
+    >>> torch.linalg.cond(A, -1)
+    tensor(1.)
+    >>> torch.linalg.cond(A, 2)
+    tensor([1.4142])
+    >>> torch.linalg.cond(A, -2)
+    tensor([0.7071])
+
+    >>> A = torch.randn(2, 3, 3)
+    >>> torch.linalg.cond(A)
+    tensor([[9.5917],
+            [3.2538]])
+    >>> A = torch.randn(2, 3, 3, dtype=torch.complex64)
+    >>> torch.linalg.cond(A)
+    tensor([[4.6245],
+            [4.5671]])
+""",
+)
+
+pinv = _add_docstr(
+    _linalg.linalg_pinv,
+    r"""
+linalg.pinv(A, *, atol=None, rtol=None, hermitian=False, out=None) -> Tensor
+
+Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.
+
+The pseudoinverse may be `defined algebraically`_
+but it is more computationally convenient to understand it `through the SVD`_
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+If :attr:`hermitian`\ `= True`, :attr:`A` is assumed to be Hermitian if complex or
+symmetric if real, but this is not checked internally. Instead, just the lower
+triangular part of the matrix is used in the computations.
+
+The singular values (or the norm of the eigenvalues when :attr:`hermitian`\ `= True`)
+that are below :math:`\max(\text{atol}, \sigma_1 \cdot \text{rtol})` threshold are
+treated as zero and discarded in the computation,
+where :math:`\sigma_1` is the largest singular value (or eigenvalue).
+
+If :attr:`rtol` is not specified and :attr:`A` is a matrix of dimensions `(m, n)`,
+the relative tolerance is set to be :math:`\text{rtol} = \max(m, n) \varepsilon`
+and :math:`\varepsilon` is the epsilon value for the dtype of :attr:`A` (see :class:`.finfo`).
+If :attr:`rtol` is not specified and :attr:`atol` is specified to be larger than zero then
+:attr:`rtol` is set to zero.
+
+If :attr:`atol` or :attr:`rtol` is a :class:`torch.Tensor`, its shape must be broadcastable to that
+of the singular values of :attr:`A` as returned by :func:`torch.linalg.svd`.
+
+.. note:: This function uses :func:`torch.linalg.svd` if :attr:`hermitian`\ `= False` and
+          :func:`torch.linalg.eigh` if :attr:`hermitian`\ `= True`.
+          For CUDA inputs, this function synchronizes that device with the CPU.
+
+.. note::
+    Consider using :func:`torch.linalg.lstsq` if possible for multiplying a matrix on the left by
+    the pseudoinverse, as::
+
+        torch.linalg.lstsq(A, B).solution == A.pinv() @ B
+
+    It is always preferred to use :func:`~lstsq` when possible, as it is faster and more
+    numerically stable than computing the pseudoinverse explicitly.
+
+.. note::
+    This function has NumPy compatible variant `linalg.pinv(A, rcond, hermitian=False)`.
+    However, use of the positional argument :attr:`rcond` is deprecated in favor of :attr:`rtol`.
+
+.. warning::
+    This function uses internally :func:`torch.linalg.svd` (or :func:`torch.linalg.eigh`
+    when :attr:`hermitian`\ `= True`), so its derivative has the same problems as those of these
+    functions. See the warnings in :func:`torch.linalg.svd` and :func:`torch.linalg.eigh` for
+    more details.
+
+.. seealso::
+
+        :func:`torch.linalg.inv` computes the inverse of a square matrix.
+
+        :func:`torch.linalg.lstsq` computes :attr:`A`\ `.pinv() @ \ `:attr:`B` with a
+        numerically stable algorithm.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    rcond (float, Tensor, optional): [NumPy Compat]. Alias for :attr:`rtol`. Default: `None`.
+
+Keyword args:
+    atol (float, Tensor, optional): the absolute tolerance value. When `None` it's considered to be zero.
+                                    Default: `None`.
+    rtol (float, Tensor, optional): the relative tolerance value. See above for the value it takes when `None`.
+                                    Default: `None`.
+    hermitian(bool, optional): indicates whether :attr:`A` is Hermitian if complex
+                               or symmetric if real. Default: `False`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(3, 5)
+    >>> A
+    tensor([[ 0.5495,  0.0979, -1.4092, -0.1128,  0.4132],
+            [-1.1143, -0.3662,  0.3042,  1.6374, -0.9294],
+            [-0.3269, -0.5745, -0.0382, -0.5922, -0.6759]])
+    >>> torch.linalg.pinv(A)
+    tensor([[ 0.0600, -0.1933, -0.2090],
+            [-0.0903, -0.0817, -0.4752],
+            [-0.7124, -0.1631, -0.2272],
+            [ 0.1356,  0.3933, -0.5023],
+            [-0.0308, -0.1725, -0.5216]])
+
+    >>> A = torch.randn(2, 6, 3)
+    >>> Apinv = torch.linalg.pinv(A)
+    >>> torch.dist(Apinv @ A, torch.eye(3))
+    tensor(8.5633e-07)
+
+    >>> A = torch.randn(3, 3, dtype=torch.complex64)
+    >>> A = A + A.T.conj()  # creates a Hermitian matrix
+    >>> Apinv = torch.linalg.pinv(A, hermitian=True)
+    >>> torch.dist(Apinv @ A, torch.eye(3))
+    tensor(1.0830e-06)
+
+.. _defined algebraically:
+    https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse#Existence_and_uniqueness
+.. _through the SVD:
+    https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse#Singular_value_decomposition_(SVD)
+""",
+)
+
+matrix_exp = _add_docstr(
+    _linalg.linalg_matrix_exp,
+    r"""
+linalg.matrix_exp(A) -> Tensor
+
+Computes the matrix exponential of a square matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+this function computes the **matrix exponential** of :math:`A \in \mathbb{K}^{n \times n}`, which is defined as
+
+.. math::
+    \mathrm{matrix\_exp}(A) = \sum_{k=0}^\infty \frac{1}{k!}A^k \in \mathbb{K}^{n \times n}.
+
+If the matrix :math:`A` has eigenvalues :math:`\lambda_i \in \mathbb{C}`,
+the matrix :math:`\mathrm{matrix\_exp}(A)` has eigenvalues :math:`e^{\lambda_i} \in \mathbb{C}`.
+
+Supports input of bfloat16, float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+
+Example::
+
+    >>> A = torch.empty(2, 2, 2)
+    >>> A[0, :, :] = torch.eye(2, 2)
+    >>> A[1, :, :] = 2 * torch.eye(2, 2)
+    >>> A
+    tensor([[[1., 0.],
+             [0., 1.]],
+
+            [[2., 0.],
+             [0., 2.]]])
+    >>> torch.linalg.matrix_exp(A)
+    tensor([[[2.7183, 0.0000],
+             [0.0000, 2.7183]],
+
+             [[7.3891, 0.0000],
+              [0.0000, 7.3891]]])
+
+    >>> import math
+    >>> A = torch.tensor([[0, math.pi/3], [-math.pi/3, 0]]) # A is skew-symmetric
+    >>> torch.linalg.matrix_exp(A) # matrix_exp(A) = [[cos(pi/3), sin(pi/3)], [-sin(pi/3), cos(pi/3)]]
+    tensor([[ 0.5000,  0.8660],
+            [-0.8660,  0.5000]])
+""",
+)
+
+
+solve = _add_docstr(
+    _linalg.linalg_solve,
+    r"""
+linalg.solve(A, B, *, left=True, out=None) -> Tensor
+
+Computes the solution of a square system of linear equations with a unique solution.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+this function computes the solution :math:`X \in \mathbb{K}^{n \times k}` of the **linear system** associated to
+:math:`A \in \mathbb{K}^{n \times n}, B \in \mathbb{K}^{n \times k}`, which is defined as
+
+.. math:: AX = B
+
+If :attr:`left`\ `= False`, this function returns the matrix :math:`X \in \mathbb{K}^{n \times k}` that solves the system
+
+.. math::
+
+    XA = B\mathrlap{\qquad A \in \mathbb{K}^{k \times k}, B \in \mathbb{K}^{n \times k}.}
+
+This system of linear equations has one solution if and only if :math:`A` is `invertible`_.
+This function assumes that :math:`A` is invertible.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+Letting `*` be zero or more batch dimensions,
+
+- If :attr:`A` has shape `(*, n, n)` and :attr:`B` has shape `(*, n)` (a batch of vectors) or shape
+  `(*, n, k)` (a batch of matrices or "multiple right-hand sides"), this function returns `X` of shape
+  `(*, n)` or `(*, n, k)` respectively.
+- Otherwise, if :attr:`A` has shape `(*, n, n)` and  :attr:`B` has shape `(n,)`  or `(n, k)`, :attr:`B`
+  is broadcasted to have shape `(*, n)` or `(*, n, k)` respectively.
+  This function then returns the solution of the resulting batch of systems of linear equations.
+
+.. note::
+    This function computes `X = \ `:attr:`A`\ `.inverse() @ \ `:attr:`B` in a faster and
+    more numerically stable way than performing the computations separately.
+
+.. note::
+    It is possible to compute the solution of the system :math:`XA = B` by passing the inputs
+    :attr:`A` and :attr:`B` transposed and transposing the output returned by this function.
+
+.. note::
+    :attr:`A` is allowed to be a non-batched `torch.sparse_csr_tensor`, but only with `left=True`.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note_has_ex"].format("torch.linalg.solve_ex")}
+"""
+    + r"""
+
+.. seealso::
+
+        :func:`torch.linalg.solve_triangular` computes the solution of a triangular system of linear
+        equations with a unique solution.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions.
+    B (Tensor): right-hand side tensor of shape `(*, n)` or  `(*, n, k)` or `(n,)` or `(n, k)`
+                according to the rules described above
+
+Keyword args:
+    left (bool, optional): whether to solve the system :math:`AX=B` or :math:`XA = B`. Default: `True`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if the :attr:`A` matrix is not invertible or any matrix in a batched :attr:`A`
+                  is not invertible.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> b = torch.randn(3)
+    >>> x = torch.linalg.solve(A, b)
+    >>> torch.allclose(A @ x, b)
+    True
+    >>> A = torch.randn(2, 3, 3)
+    >>> B = torch.randn(2, 3, 4)
+    >>> X = torch.linalg.solve(A, B)
+    >>> X.shape
+    torch.Size([2, 3, 4])
+    >>> torch.allclose(A @ X, B)
+    True
+
+    >>> A = torch.randn(2, 3, 3)
+    >>> b = torch.randn(3, 1)
+    >>> x = torch.linalg.solve(A, b) # b is broadcasted to size (2, 3, 1)
+    >>> x.shape
+    torch.Size([2, 3, 1])
+    >>> torch.allclose(A @ x, b)
+    True
+    >>> b = torch.randn(3)
+    >>> x = torch.linalg.solve(A, b) # b is broadcasted to size (2, 3)
+    >>> x.shape
+    torch.Size([2, 3])
+    >>> Ax = A @ x.unsqueeze(-1)
+    >>> torch.allclose(Ax, b.unsqueeze(-1).expand_as(Ax))
+    True
+
+.. _invertible:
+    https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem
+""",
+)
+
+solve_triangular = _add_docstr(
+    _linalg.linalg_solve_triangular,
+    r"""
+linalg.solve_triangular(A, B, *, upper, left=True, unitriangular=False, out=None) -> Tensor
+
+Computes the solution of a triangular system of linear equations with a unique solution.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+this function computes the solution :math:`X \in \mathbb{K}^{n \times k}` of the **linear system**
+associated to the triangular matrix :math:`A \in \mathbb{K}^{n \times n}` without zeros on the diagonal
+(that is, it is `invertible`_) and the rectangular matrix , :math:`B \in \mathbb{K}^{n \times k}`,
+which is defined as
+
+.. math:: AX = B
+
+The argument :attr:`upper` signals whether :math:`A` is upper or lower triangular.
+
+If :attr:`left`\ `= False`, this function returns the matrix :math:`X \in \mathbb{K}^{n \times k}` that
+solves the system
+
+.. math::
+
+    XA = B\mathrlap{\qquad A \in \mathbb{K}^{k \times k}, B \in \mathbb{K}^{n \times k}.}
+
+If :attr:`upper`\ `= True` (resp. `False`) just the upper (resp. lower) triangular half of :attr:`A`
+will be accessed. The elements below the main diagonal will be considered to be zero and will not be accessed.
+
+If :attr:`unitriangular`\ `= True`, the diagonal of :attr:`A` is assumed to be ones and will not be accessed.
+
+The result may contain `NaN` s if the diagonal of :attr:`A` contains zeros or elements that
+are very close to zero and :attr:`unitriangular`\ `= False` (default) or if the input matrix
+has very small eigenvalues.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+.. seealso::
+
+        :func:`torch.linalg.solve` computes the solution of a general square system of linear
+        equations with a unique solution.
+
+Args:
+    A (Tensor): tensor of shape `(*, n, n)` (or `(*, k, k)` if :attr:`left`\ `= False`)
+                where `*` is zero or more batch dimensions.
+    B (Tensor): right-hand side tensor of shape `(*, n, k)`.
+
+Keyword args:
+    upper (bool): whether :attr:`A` is an upper or lower triangular matrix.
+    left (bool, optional): whether to solve the system :math:`AX=B` or :math:`XA = B`. Default: `True`.
+    unitriangular (bool, optional): if `True`, the diagonal elements of :attr:`A` are assumed to be
+                                    all equal to `1`. Default: `False`.
+    out (Tensor, optional): output tensor. `B` may be passed as `out` and the result is computed in-place on `B`.
+                            Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3).triu_()
+    >>> B = torch.randn(3, 4)
+    >>> X = torch.linalg.solve_triangular(A, B, upper=True)
+    >>> torch.allclose(A @ X, B)
+    True
+
+    >>> A = torch.randn(2, 3, 3).tril_()
+    >>> B = torch.randn(2, 3, 4)
+    >>> X = torch.linalg.solve_triangular(A, B, upper=False)
+    >>> torch.allclose(A @ X, B)
+    True
+
+    >>> A = torch.randn(2, 4, 4).tril_()
+    >>> B = torch.randn(2, 3, 4)
+    >>> X = torch.linalg.solve_triangular(A, B, upper=False, left=False)
+    >>> torch.allclose(X @ A, B)
+    True
+
+.. _invertible:
+    https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem
+""",
+)
+
+lu_factor = _add_docstr(
+    _linalg.linalg_lu_factor,
+    r"""
+linalg.lu_factor(A, *, bool pivot=True, out=None) -> (Tensor, Tensor)
+
+Computes a compact representation of the LU factorization with partial pivoting of a matrix.
+
+This function computes a compact representation of the decomposition given by :func:`torch.linalg.lu`.
+If the matrix is square, this representation may be used in :func:`torch.linalg.lu_solve`
+to solve system of linear equations that share the matrix :attr:`A`.
+
+The returned decomposition is represented as a named tuple `(LU, pivots)`.
+The ``LU`` matrix has the same shape as the input matrix ``A``. Its upper and lower triangular
+parts encode the non-constant elements of ``L`` and ``U`` of the LU decomposition of ``A``.
+
+The returned permutation matrix is represented by a 1-indexed vector. `pivots[i] == j` represents
+that in the `i`-th step of the algorithm, the `i`-th row was permuted with the `j-1`-th row.
+
+On CUDA, one may use :attr:`pivot`\ `= False`. In this case, this function returns the LU
+decomposition without pivoting if it exists.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note_has_ex"].format("torch.linalg.lu_factor_ex")}
+"""
+    + r"""
+.. warning:: The LU decomposition is almost never unique, as often there are different permutation
+             matrices that can yield different LU decompositions.
+             As such, different platforms, like SciPy, or inputs on different devices,
+             may produce different valid decompositions.
+
+             Gradient computations are only supported if the input matrix is full-rank.
+             If this condition is not met, no error will be thrown, but the gradient may not be finite.
+             This is because the LU decomposition with pivoting is not differentiable at these points.
+
+.. seealso::
+
+        :func:`torch.linalg.lu_solve` solves a system of linear equations given the output of this
+        function provided the input matrix was square and invertible.
+
+        :func:`torch.lu_unpack` unpacks the tensors returned by :func:`~lu_factor` into the three
+        matrices `P, L, U` that form the decomposition.
+
+        :func:`torch.linalg.lu` computes the LU decomposition with partial pivoting of a possibly
+        non-square matrix. It is a composition of :func:`~lu_factor` and :func:`torch.lu_unpack`.
+
+        :func:`torch.linalg.solve` solves a system of linear equations. It is a composition
+        of :func:`~lu_factor` and :func:`~lu_solve`.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    pivot (bool, optional): Whether to compute the LU decomposition with partial pivoting, or the regular LU
+                            decomposition. :attr:`pivot`\ `= False` not supported on CPU. Default: `True`.
+    out (tuple, optional): tuple of two tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(LU, pivots)`.
+
+Raises:
+    RuntimeError: if the :attr:`A` matrix is not invertible or any matrix in a batched :attr:`A`
+                  is not invertible.
+
+Examples::
+
+    >>> A = torch.randn(2, 3, 3)
+    >>> B1 = torch.randn(2, 3, 4)
+    >>> B2 = torch.randn(2, 3, 7)
+    >>> LU, pivots = torch.linalg.lu_factor(A)
+    >>> X1 = torch.linalg.lu_solve(LU, pivots, B1)
+    >>> X2 = torch.linalg.lu_solve(LU, pivots, B2)
+    >>> torch.allclose(A @ X1, B1)
+    True
+    >>> torch.allclose(A @ X2, B2)
+    True
+
+.. _invertible:
+    https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem
+""",
+)
+
+lu_factor_ex = _add_docstr(
+    _linalg.linalg_lu_factor_ex,
+    r"""
+linalg.lu_factor_ex(A, *, pivot=True, check_errors=False, out=None) -> (Tensor, Tensor, Tensor)
+
+This is a version of :func:`~lu_factor` that does not perform error checks unless :attr:`check_errors`\ `= True`.
+It also returns the :attr:`info` tensor returned by `LAPACK's getrf`_.
+
+"""
+    + rf"""
+.. note:: {common_notes["sync_note_ex"]}
+
+.. warning:: {common_notes["experimental_warning"]}
+"""
+    + r"""
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+
+Keyword args:
+    pivot (bool, optional): Whether to compute the LU decomposition with partial pivoting, or the regular LU
+                            decomposition. :attr:`pivot`\ `= False` not supported on CPU. Default: `True`.
+    check_errors (bool, optional): controls whether to check the content of ``infos`` and raise
+                                   an error if it is non-zero. Default: `False`.
+    out (tuple, optional): tuple of three tensors to write the output to. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(LU, pivots, info)`.
+
+.. _LAPACK's getrf:
+    https://www.netlib.org/lapack/explore-html/dd/d9a/group__double_g_ecomputational_ga0019443faea08275ca60a734d0593e60.html
+""",
+)
+
+lu_solve = _add_docstr(
+    _linalg.linalg_lu_solve,
+    r"""
+linalg.lu_solve(LU, pivots, B, *, left=True, adjoint=False, out=None) -> Tensor
+
+Computes the solution of a square system of linear equations with a unique solution given an LU decomposition.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+this function computes the solution :math:`X \in \mathbb{K}^{n \times k}` of the **linear system** associated to
+:math:`A \in \mathbb{K}^{n \times n}, B \in \mathbb{K}^{n \times k}`, which is defined as
+
+.. math:: AX = B
+
+where :math:`A` is given factorized as returned by :func:`~lu_factor`.
+
+If :attr:`left`\ `= False`, this function returns the matrix :math:`X \in \mathbb{K}^{n \times k}` that solves the system
+
+.. math::
+
+    XA = B\mathrlap{\qquad A \in \mathbb{K}^{k \times k}, B \in \mathbb{K}^{n \times k}.}
+
+If  :attr:`adjoint`\ `= True` (and :attr:`left`\ `= True`), given an LU factorization of :math:`A`
+this function function returns the :math:`X \in \mathbb{K}^{n \times k}` that solves the system
+
+.. math::
+
+    A^{\text{H}}X = B\mathrlap{\qquad A \in \mathbb{K}^{k \times k}, B \in \mathbb{K}^{n \times k}.}
+
+where :math:`A^{\text{H}}` is the conjugate transpose when :math:`A` is complex, and the
+transpose when :math:`A` is real-valued. The :attr:`left`\ `= False` case is analogous.
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if the inputs are batches of matrices then
+the output has the same batch dimensions.
+
+Args:
+    LU (Tensor): tensor of shape `(*, n, n)` (or `(*, k, k)` if :attr:`left`\ `= True`)
+                 where `*` is zero or more batch dimensions as returned by :func:`~lu_factor`.
+    pivots (Tensor): tensor of shape `(*, n)` (or `(*, k)` if :attr:`left`\ `= True`)
+                     where `*` is zero or more batch dimensions as returned by :func:`~lu_factor`.
+    B (Tensor): right-hand side tensor of shape `(*, n, k)`.
+
+Keyword args:
+    left (bool, optional): whether to solve the system :math:`AX=B` or :math:`XA = B`. Default: `True`.
+    adjoint (bool, optional): whether to solve the system :math:`AX=B` or :math:`A^{\text{H}}X = B`. Default: `False`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> A = torch.randn(3, 3)
+    >>> LU, pivots = torch.linalg.lu_factor(A)
+    >>> B = torch.randn(3, 2)
+    >>> X = torch.linalg.lu_solve(LU, pivots, B)
+    >>> torch.allclose(A @ X, B)
+    True
+
+    >>> B = torch.randn(3, 3, 2)   # Broadcasting rules apply: A is broadcasted
+    >>> X = torch.linalg.lu_solve(LU, pivots, B)
+    >>> torch.allclose(A @ X, B)
+    True
+
+    >>> B = torch.randn(3, 5, 3)
+    >>> X = torch.linalg.lu_solve(LU, pivots, B, left=False)
+    >>> torch.allclose(X @ A, B)
+    True
+
+    >>> B = torch.randn(3, 3, 4)   # Now solve for A^T
+    >>> X = torch.linalg.lu_solve(LU, pivots, B, adjoint=True)
+    >>> torch.allclose(A.mT @ X, B)
+    True
+
+.. _invertible:
+    https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem
+""",
+)
+
+lu = _add_docstr(
+    _linalg.linalg_lu,
+    r"""
+lu(A, *, pivot=True, out=None) -> (Tensor, Tensor, Tensor)
+
+Computes the LU decomposition with partial pivoting of a matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **LU decomposition with partial pivoting** of a matrix
+:math:`A \in \mathbb{K}^{m \times n}` is defined as
+
+.. math::
+
+    A = PLU\mathrlap{\qquad P \in \mathbb{K}^{m \times m}, L \in \mathbb{K}^{m \times k}, U \in \mathbb{K}^{k \times n}}
+
+where `k = min(m,n)`, :math:`P` is a `permutation matrix`_, :math:`L` is lower triangular with ones on the diagonal
+and :math:`U` is upper triangular.
+
+If :attr:`pivot`\ `= False` and :attr:`A` is on GPU, then the **LU decomposition without pivoting** is computed
+
+.. math::
+
+    A = LU\mathrlap{\qquad L \in \mathbb{K}^{m \times k}, U \in \mathbb{K}^{k \times n}}
+
+When :attr:`pivot`\ `= False`, the returned matrix :attr:`P` will be empty.
+The LU decomposition without pivoting `may not exist`_ if any of the principal minors of :attr:`A` is singular.
+In this case, the output matrix may contain `inf` or `NaN`.
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+.. seealso::
+
+        :func:`torch.linalg.solve` solves a system of linear equations using the LU decomposition
+        with partial pivoting.
+
+.. warning:: The LU decomposition is almost never unique, as often there are different permutation
+             matrices that can yield different LU decompositions.
+             As such, different platforms, like SciPy, or inputs on different devices,
+             may produce different valid decompositions.
+
+.. warning:: Gradient computations are only supported if the input matrix is full-rank.
+             If this condition is not met, no error will be thrown, but the gradient
+             may not be finite.
+             This is because the LU decomposition with pivoting is not differentiable at these points.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    pivot (bool, optional): Controls whether to compute the LU decomposition with partial pivoting or
+        no pivoting. Default: `True`.
+
+Keyword args:
+    out (tuple, optional): output tuple of three tensors. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(P, L, U)`.
+
+Examples::
+
+    >>> A = torch.randn(3, 2)
+    >>> P, L, U = torch.linalg.lu(A)
+    >>> P
+    tensor([[0., 1., 0.],
+            [0., 0., 1.],
+            [1., 0., 0.]])
+    >>> L
+    tensor([[1.0000, 0.0000],
+            [0.5007, 1.0000],
+            [0.0633, 0.9755]])
+    >>> U
+    tensor([[0.3771, 0.0489],
+            [0.0000, 0.9644]])
+    >>> torch.dist(A, P @ L @ U)
+    tensor(5.9605e-08)
+
+    >>> A = torch.randn(2, 5, 7, device="cuda")
+    >>> P, L, U = torch.linalg.lu(A, pivot=False)
+    >>> P
+    tensor([], device='cuda:0')
+    >>> torch.dist(A, L @ U)
+    tensor(1.0376e-06, device='cuda:0')
+
+.. _permutation matrix:
+    https://en.wikipedia.org/wiki/Permutation_matrix
+.. _may not exist:
+    https://en.wikipedia.org/wiki/LU_decomposition#Definitions
+""",
+)
+
+tensorinv = _add_docstr(
+    _linalg.linalg_tensorinv,
+    r"""
+linalg.tensorinv(A, ind=2, *, out=None) -> Tensor
+
+Computes the multiplicative inverse of :func:`torch.tensordot`.
+
+If `m` is the product of the first :attr:`ind` dimensions of :attr:`A` and `n` is the product of
+the rest of the dimensions, this function expects `m` and `n` to be equal.
+If this is the case, it computes a tensor `X` such that
+`tensordot(\ `:attr:`A`\ `, X, \ `:attr:`ind`\ `)` is the identity matrix in dimension `m`.
+`X` will have the shape of :attr:`A` but with the first :attr:`ind` dimensions pushed back to the end
+
+.. code:: text
+
+    X.shape == A.shape[ind:] + A.shape[:ind]
+
+Supports input of float, double, cfloat and cdouble dtypes.
+
+.. note:: When :attr:`A` is a `2`-dimensional tensor and :attr:`ind`\ `= 1`,
+          this function computes the (multiplicative) inverse of :attr:`A`
+          (see :func:`torch.linalg.inv`).
+
+.. note::
+    Consider using :func:`torch.linalg.tensorsolve` if possible for multiplying a tensor on the left
+    by the tensor inverse, as::
+
+        linalg.tensorsolve(A, B) == torch.tensordot(linalg.tensorinv(A), B)  # When B is a tensor with shape A.shape[:B.ndim]
+
+    It is always preferred to use :func:`~tensorsolve` when possible, as it is faster and more
+    numerically stable than computing the pseudoinverse explicitly.
+
+.. seealso::
+
+        :func:`torch.linalg.tensorsolve` computes
+        `torch.tensordot(tensorinv(\ `:attr:`A`\ `), \ `:attr:`B`\ `)`.
+
+Args:
+    A (Tensor): tensor to invert. Its shape must satisfy
+                    `prod(\ `:attr:`A`\ `.shape[:\ `:attr:`ind`\ `]) ==
+                    prod(\ `:attr:`A`\ `.shape[\ `:attr:`ind`\ `:])`.
+    ind (int): index at which to compute the inverse of :func:`torch.tensordot`. Default: `2`.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if the reshaped :attr:`A` is not invertible or the product of the first
+                  :attr:`ind` dimensions is not equal to the product of the rest.
+
+Examples::
+
+    >>> A = torch.eye(4 * 6).reshape((4, 6, 8, 3))
+    >>> Ainv = torch.linalg.tensorinv(A, ind=2)
+    >>> Ainv.shape
+    torch.Size([8, 3, 4, 6])
+    >>> B = torch.randn(4, 6)
+    >>> torch.allclose(torch.tensordot(Ainv, B), torch.linalg.tensorsolve(A, B))
+    True
+
+    >>> A = torch.randn(4, 4)
+    >>> Atensorinv = torch.linalg.tensorinv(A, ind=1)
+    >>> Ainv = torch.linalg.inv(A)
+    >>> torch.allclose(Atensorinv, Ainv)
+    True
+""",
+)
+
+tensorsolve = _add_docstr(
+    _linalg.linalg_tensorsolve,
+    r"""
+linalg.tensorsolve(A, B, dims=None, *, out=None) -> Tensor
+
+Computes the solution `X` to the system `torch.tensordot(A, X) = B`.
+
+If `m` is the product of the first :attr:`B`\ `.ndim`  dimensions of :attr:`A` and
+`n` is the product of the rest of the dimensions, this function expects `m` and `n` to be equal.
+
+The returned tensor `x` satisfies
+`tensordot(\ `:attr:`A`\ `, x, dims=x.ndim) == \ `:attr:`B`.
+`x` has shape :attr:`A`\ `[B.ndim:]`.
+
+If :attr:`dims` is specified, :attr:`A` will be reshaped as
+
+.. code:: text
+
+    A = movedim(A, dims, range(len(dims) - A.ndim + 1, 0))
+
+Supports inputs of float, double, cfloat and cdouble dtypes.
+
+.. seealso::
+
+        :func:`torch.linalg.tensorinv` computes the multiplicative inverse of
+        :func:`torch.tensordot`.
+
+Args:
+    A (Tensor): tensor to solve for. Its shape must satisfy
+                    `prod(\ `:attr:`A`\ `.shape[:\ `:attr:`B`\ `.ndim]) ==
+                    prod(\ `:attr:`A`\ `.shape[\ `:attr:`B`\ `.ndim:])`.
+    B (Tensor): tensor of shape :attr:`A`\ `.shape[:\ `:attr:`B`\ `.ndim]`.
+    dims (Tuple[int], optional): dimensions of :attr:`A` to be moved.
+        If `None`, no dimensions are moved. Default: `None`.
+
+Keyword args:
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Raises:
+    RuntimeError: if the reshaped :attr:`A`\ `.view(m, m)` with `m` as above  is not
+                  invertible or the product of the first :attr:`ind` dimensions is not equal
+                  to the product of the rest of the dimensions.
+
+Examples::
+
+    >>> A = torch.eye(2 * 3 * 4).reshape((2 * 3, 4, 2, 3, 4))
+    >>> B = torch.randn(2 * 3, 4)
+    >>> X = torch.linalg.tensorsolve(A, B)
+    >>> X.shape
+    torch.Size([2, 3, 4])
+    >>> torch.allclose(torch.tensordot(A, X, dims=X.ndim), B)
+    True
+
+    >>> A = torch.randn(6, 4, 4, 3, 2)
+    >>> B = torch.randn(4, 3, 2)
+    >>> X = torch.linalg.tensorsolve(A, B, dims=(0, 2))
+    >>> X.shape
+    torch.Size([6, 4])
+    >>> A = A.permute(1, 3, 4, 0, 2)
+    >>> A.shape[B.ndim:]
+    torch.Size([6, 4])
+    >>> torch.allclose(torch.tensordot(A, X, dims=X.ndim), B, atol=1e-6)
+    True
+""",
+)
+
+qr = _add_docstr(
+    _linalg.linalg_qr,
+    r"""
+qr(A, mode='reduced', *, out=None) -> (Tensor, Tensor)
+
+Computes the QR decomposition of a matrix.
+
+Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`,
+the **full QR decomposition** of a matrix
+:math:`A \in \mathbb{K}^{m \times n}` is defined as
+
+.. math::
+
+    A = QR\mathrlap{\qquad Q \in \mathbb{K}^{m \times m}, R \in \mathbb{K}^{m \times n}}
+
+where :math:`Q` is orthogonal in the real case and unitary in the complex case,
+and :math:`R` is upper triangular with real diagonal (even in the complex case).
+
+When `m > n` (tall matrix), as `R` is upper triangular, its last `m - n` rows are zero.
+In this case, we can drop the last `m - n` columns of `Q` to form the
+**reduced QR decomposition**:
+
+.. math::
+
+    A = QR\mathrlap{\qquad Q \in \mathbb{K}^{m \times n}, R \in \mathbb{K}^{n \times n}}
+
+The reduced QR decomposition agrees with the full QR decomposition when `n >= m` (wide matrix).
+
+Supports input of float, double, cfloat and cdouble dtypes.
+Also supports batches of matrices, and if :attr:`A` is a batch of matrices then
+the output has the same batch dimensions.
+
+The parameter :attr:`mode` chooses between the full and reduced QR decomposition.
+If :attr:`A` has shape `(*, m, n)`, denoting `k = min(m, n)`
+
+- :attr:`mode`\ `= 'reduced'` (default): Returns `(Q, R)` of shapes `(*, m, k)`, `(*, k, n)` respectively.
+  It is always differentiable.
+- :attr:`mode`\ `= 'complete'`: Returns `(Q, R)` of shapes `(*, m, m)`, `(*, m, n)` respectively.
+  It is differentiable for `m <= n`.
+- :attr:`mode`\ `= 'r'`: Computes only the reduced `R`. Returns `(Q, R)` with `Q` empty and `R` of shape `(*, k, n)`.
+  It is never differentiable.
+
+Differences with `numpy.linalg.qr`:
+
+- :attr:`mode`\ `= 'raw'` is not implemented.
+- Unlike `numpy.linalg.qr`, this function always returns a tuple of two tensors.
+  When :attr:`mode`\ `= 'r'`, the `Q` tensor is an empty tensor.
+
+.. warning:: The elements in the diagonal of `R` are not necessarily positive.
+             As such, the returned QR decomposition is only unique up to the sign of the diagonal of `R`.
+             Therefore, different platforms, like NumPy, or inputs on different devices,
+             may produce different valid decompositions.
+
+.. warning:: The QR decomposition is only well-defined if the first `k = min(m, n)` columns
+             of every matrix in :attr:`A` are linearly independent.
+             If this condition is not met, no error will be thrown, but the QR produced
+             may be incorrect and its autodiff may fail or produce incorrect results.
+
+Args:
+    A (Tensor): tensor of shape `(*, m, n)` where `*` is zero or more batch dimensions.
+    mode (str, optional): one of `'reduced'`, `'complete'`, `'r'`.
+                          Controls the shape of the returned tensors. Default: `'reduced'`.
+
+Keyword args:
+    out (tuple, optional): output tuple of two tensors. Ignored if `None`. Default: `None`.
+
+Returns:
+    A named tuple `(Q, R)`.
+
+Examples::
+
+    >>> A = torch.tensor([[12., -51, 4], [6, 167, -68], [-4, 24, -41]])
+    >>> Q, R = torch.linalg.qr(A)
+    >>> Q
+    tensor([[-0.8571,  0.3943,  0.3314],
+            [-0.4286, -0.9029, -0.0343],
+            [ 0.2857, -0.1714,  0.9429]])
+    >>> R
+    tensor([[ -14.0000,  -21.0000,   14.0000],
+            [   0.0000, -175.0000,   70.0000],
+            [   0.0000,    0.0000,  -35.0000]])
+    >>> (Q @ R).round()
+    tensor([[  12.,  -51.,    4.],
+            [   6.,  167.,  -68.],
+            [  -4.,   24.,  -41.]])
+    >>> (Q.T @ Q).round()
+    tensor([[ 1.,  0.,  0.],
+            [ 0.,  1., -0.],
+            [ 0., -0.,  1.]])
+    >>> Q2, R2 = torch.linalg.qr(A, mode='r')
+    >>> Q2
+    tensor([])
+    >>> torch.equal(R, R2)
+    True
+    >>> A = torch.randn(3, 4, 5)
+    >>> Q, R = torch.linalg.qr(A, mode='complete')
+    >>> torch.dist(Q @ R, A)
+    tensor(1.6099e-06)
+    >>> torch.dist(Q.mT @ Q, torch.eye(4))
+    tensor(6.2158e-07)
+""",
+)
+
+vander = _add_docstr(
+    _linalg.linalg_vander,
+    r"""
+vander(x, N=None) -> Tensor
+
+Generates a Vandermonde matrix.
+
+Returns the Vandermonde matrix :math:`V`
+
+.. math::
+
+    V = \begin{pmatrix}
+            1 & x_1 & x_1^2 & \dots & x_1^{N-1}\\
+            1 & x_2 & x_2^2 & \dots & x_2^{N-1}\\
+            1 & x_3 & x_3^2 & \dots & x_3^{N-1}\\
+            \vdots & \vdots & \vdots & \ddots &\vdots \\
+            1 & x_n & x_n^2 & \dots & x_n^{N-1}
+        \end{pmatrix}.
+
+for `N > 1`.
+If :attr:`N`\ `= None`, then `N = x.size(-1)` so that the output is a square matrix.
+
+Supports inputs of float, double, cfloat, cdouble, and integral dtypes.
+Also supports batches of vectors, and if :attr:`x` is a batch of vectors then
+the output has the same batch dimensions.
+
+Differences with `numpy.vander`:
+
+- Unlike `numpy.vander`, this function returns the powers of :attr:`x` in ascending order.
+  To get them in the reverse order call ``linalg.vander(x, N).flip(-1)``.
+
+Args:
+    x (Tensor): tensor of shape `(*, n)` where `*` is zero or more batch dimensions
+                consisting of vectors.
+
+Keyword args:
+    N (int, optional): Number of columns in the output. Default: `x.size(-1)`
+
+Example::
+
+    >>> x = torch.tensor([1, 2, 3, 5])
+    >>> linalg.vander(x)
+    tensor([[  1,   1,   1,   1],
+            [  1,   2,   4,   8],
+            [  1,   3,   9,  27],
+            [  1,   5,  25, 125]])
+    >>> linalg.vander(x, N=3)
+    tensor([[ 1,  1,  1],
+            [ 1,  2,  4],
+            [ 1,  3,  9],
+            [ 1,  5, 25]])
+""",
+)
+
+vecdot = _add_docstr(
+    _linalg.linalg_vecdot,
+    r"""
+linalg.vecdot(x, y, *, dim=-1, out=None) -> Tensor
+
+Computes the dot product of two batches of vectors along a dimension.
+
+In symbols, this function computes
+
+.. math::
+
+    \sum_{i=1}^n \overline{x_i}y_i.
+
+over the dimension :attr:`dim` where :math:`\overline{x_i}` denotes the conjugate for complex
+vectors, and it is the identity for real vectors.
+
+Supports input of half, bfloat16, float, double, cfloat, cdouble and integral dtypes.
+It also supports broadcasting.
+
+Args:
+    x (Tensor): first batch of vectors of shape `(*, n)`.
+    y (Tensor): second batch of vectors of shape `(*, n)`.
+
+Keyword args:
+    dim (int): Dimension along which to compute the dot product. Default: `-1`.
+    out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.
+
+Examples::
+
+    >>> v1 = torch.randn(3, 2)
+    >>> v2 = torch.randn(3, 2)
+    >>> linalg.vecdot(v1, v2)
+    tensor([ 0.3223,  0.2815, -0.1944])
+    >>> torch.vdot(v1[0], v2[0])
+    tensor(0.3223)
+""",
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d00ba1e8d5aff6a18490ac7b16a629ac36e3dcb5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/__init__.py
@@ -0,0 +1,57 @@
+from torch.masked._ops import (
+    _canonical_dim,
+    _combine_input_and_mask,
+    _generate_docstring,
+    _input_mask,
+    _output_mask,
+    _reduction_identity,
+    _where,
+    amax,
+    amin,
+    argmax,
+    argmin,
+    cumprod,
+    cumsum,
+    log_softmax,
+    logaddexp,
+    logsumexp,
+    mean,
+    median,
+    norm,
+    normalize,
+    prod,
+    softmax,
+    softmin,
+    std,
+    sum,
+    var,
+)
+from torch.masked.maskedtensor.core import is_masked_tensor, MaskedTensor
+from torch.masked.maskedtensor.creation import as_masked_tensor, masked_tensor
+
+
+__all__ = [
+    "amax",
+    "amin",
+    "argmax",
+    "argmin",
+    "as_masked_tensor",
+    "cumprod",
+    "cumsum",
+    "is_masked_tensor",
+    "log_softmax",
+    "logaddexp",
+    "logsumexp",
+    "masked_tensor",
+    "MaskedTensor",
+    "mean",
+    "median",
+    "norm",
+    "normalize",
+    "prod",
+    "softmax",
+    "softmin",
+    "std",
+    "sum",
+    "var",
+]
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/_docs.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/_docs.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa130bbefbc5caa7373459ef2fc3dc5292239948
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/_docs.py
@@ -0,0 +1,1177 @@
+# This file is generated, do not modify it!
+#
+# To update this file, run the update masked docs script as follows:
+#
+#   python tools/update_masked_docs.py
+#
+# The script must be called from an environment where the development
+# version of torch package can be imported and is functional.
+#
+
+amax_docstring = """amax(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns maximum of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of maximum operation, which is used to start the
+reduction, depends on input dtype. For instance, for float32, uint8,
+and int32 dtypes, the identity values are ``-inf``, ``0``, and ``-2147483648``, respectively.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in maximum computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of maximum operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.amax(input, 1, mask=mask)
+    tensor([                  -1, -9223372036854775808])
+"""
+
+amin_docstring = """amin(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns minimum of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of minimum operation, which is used to start the
+reduction, depends on input dtype. For instance, for float32, uint8,
+and int32 dtypes, the identity values are ``inf``, ``255``, and ``2147483647``, respectively.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in minimum computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of minimum operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.amin(input, 1, mask=mask)
+    tensor([                 -3, 9223372036854775807])
+"""
+
+argmax_docstring = """argmax(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+Returns argmax of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+The identity value of argmax operation, which is used to start the
+reduction, depends on input dtype. For instance, for float32, uint8,
+and int32 dtypes, the identity values are ``-inf``, ``0``, and ``-2147483648``, respectively.
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in argmax computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of argmax operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which argmax is computed.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.argmax(input, 1, mask=mask)
+    tensor([2, 0])
+"""
+
+argmin_docstring = """argmin(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+Returns argmin of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+The identity value of argmin operation, which is used to start the
+reduction, depends on input dtype. For instance, for float32, uint8,
+and int32 dtypes, the identity values are ``inf``, ``255``, and ``2147483647``, respectively.
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in argmin computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of argmin operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which argmin is computed.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.argmin(input, 1, mask=mask)
+    tensor([0, 0])
+"""
+
+cumprod_docstring = """cumprod(input, dim, *, dtype=None, mask=None) -> Tensor
+
+Returns cumulative_prod of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Cumsum of i-th element in ``x`` is
+defined as ``prod(x[:i])``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+cumulative_prod computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the cumulative_prod output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which cumulative_prod is computed.
+
+Keyword args:
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.cumprod(input, 1, mask=mask)
+    tensor([[-3., -3.,  3.],
+            [ 1.,  1.,  1.]])
+"""
+
+cumsum_docstring = """cumsum(input, dim, *, dtype=None, mask=None) -> Tensor
+
+Returns cumulative_sum of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Cumsum of i-th element in ``x`` is
+defined as ``sum(x[:i])``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+cumulative_sum computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the cumulative_sum output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which cumulative_sum is computed.
+
+Keyword args:
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.cumsum(input, 1, mask=mask)
+    tensor([[-3., -3., -4.],
+            [ 0.,  0.,  0.]])
+"""
+
+log_softmax_docstring = """log_softmax(input, dim, *, dtype=None, mask=None) -> Tensor
+
+Returns log_softmax of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. LogSoftmax of i-th element in ``x`` is
+defined as ``log(exp(x[i])/sum(exp(x)))``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+log_softmax computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the log_softmax output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which log_softmax is computed.
+
+Keyword args:
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.log_softmax(input, 1, mask=mask)
+    tensor([[-2.1269,    -inf, -0.1269],
+            [    nan,     nan,     nan]])
+"""
+
+logsumexp_docstring = """logsumexp(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns logsumexp of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of logsumexp operation, which is used to start the reduction, is ``-2147483648``.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in logsumexp computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of logsumexp operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.logsumexp(input, 1, mask=mask)
+    tensor([                   0, -9223372036854775808])
+"""
+
+mean_docstring = """mean(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns mean of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+By definition, the identity value of a mean operation is the mean
+value of the tensor. If all elements of the input tensor along given
+dimension(s) :attr:`dim` are masked-out, the identity value of the
+mean is undefined.  Due to this ambiguity, the elements of output
+tensor with strided layout, that correspond to fully masked-out
+elements, have ``nan`` values.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in mean computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of mean operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.mean(input, 1, mask=mask)
+    tensor([-2., nan])
+"""
+
+median_docstring = """median(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+Returns median of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+By definition, the identity value of a median operation is the median
+value of the tensor. If all elements of the input tensor along given
+dimension(s) :attr:`dim` are masked-out, the identity value of the
+median is undefined.  Due to this ambiguity, the elements of output
+tensor with strided layout, that correspond to fully masked-out
+elements, have ``nan`` values.
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in median computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of median operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which median is computed.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.median(input, 1, mask=mask)
+    tensor([-3., nan])
+"""
+
+norm_docstring = """norm(input, ord, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns norm of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of norm operation, which is used to start the
+reduction, is ``0.0``, except for ``ord=-inf`` it is
+``inf``.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in norm computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of norm operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    ord (int, float, optional): the order of vector norm. Default: 2.
+      See :func:`torch.linalg.vector_norm` for a list of supported norms.
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.norm(input, 2.0, 1, mask=mask)
+    tensor([3.1623, 0.0000])
+"""
+
+normalize_docstring = """normalize(input, ord, dim, *, eps=1e-12, dtype=None, mask=None) -> Tensor
+
+Returns normalize of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Normalize of i-th element in ``x`` is
+defined as ``x[i]/max(norm(x, p), eps)``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+normalize computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the normalize output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    ord (int, float): the order of vector norm. Default: 2.
+      See :func:`torch.linalg.vector_norm` for a list of supported norms.
+    dim (int): the dimension along which normalize is computed.
+
+Keyword args:
+    eps (float, optional): small value to avoid division by zero. Default: 1e-12.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.normalize(input, 2.0, 1, mask=mask)
+    tensor([[-0.9487,  0.0000, -0.3162],
+            [ 0.0000,  0.0000,  0.0000]])
+"""
+
+prod_docstring = """prod(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns product of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of product operation, which is used to start the reduction, is ``1``.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in product computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of product operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.prod(input, 1, mask=mask)
+    tensor([3, 1])
+"""
+
+softmax_docstring = """softmax(input, dim, *, dtype=None, mask=None) -> Tensor
+
+Returns softmax of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Softmax of i-th element in ``x`` is
+defined as ``exp(x[i])/sum(exp(x))``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+softmax computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the softmax output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which softmax is computed.
+
+Keyword args:
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.softmax(input, 1, mask=mask)
+    tensor([[0.1192, 0.0000, 0.8808],
+            [   nan,    nan,    nan]])
+"""
+
+softmin_docstring = """softmin(input, dim, *, dtype=None, mask=None) -> Tensor
+
+Returns softmin of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Softmin of i-th element in ``x`` is
+defined as ``exp(-x[i])/sum(exp(-x))``.
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+softmin computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the softmin output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int): the dimension along which softmin is computed.
+
+Keyword args:
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3., -2., -1.], [ 0., 1., 2.]])
+    >>> input
+    tensor([[-3., -2., -1.],
+            [ 0.,  1.,  2.]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.softmin(input, 1, mask=mask)
+    tensor([[0.8808, 0.0000, 0.1192],
+            [   nan,    nan,    nan]])
+"""
+
+std_docstring = """std(input, dim, unbiased, *, keepdim=False, dtype=None, mask=None) -> Tensor
+Returns standard_deviation of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+The identity value of sample standard deviation operation is undefined. The
+elements of output tensor with strided layout, that correspond to
+fully masked-out elements, have ``nan`` values.
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in standard_deviation computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of standard_deviation operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+    unbiased (bool): when True, use Bessel's correction, otherwise, compute
+      the uncorrected sample variance.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.std(input, 1, False, mask=mask)
+    tensor([1., nan])
+"""
+
+sum_docstring = """sum(input, dim, *, keepdim=False, dtype=None, mask=None) -> Tensor
+
+Returns sum of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+
+The identity value of sum operation, which is used to start the reduction, is ``0``.
+
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in sum computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of sum operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.sum(input, 1, mask=mask)
+    tensor([-4,  0])
+"""
+
+var_docstring = """var(input, dim, unbiased, *, keepdim=False, dtype=None, mask=None) -> Tensor
+Returns variance of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.
+The identity value of sample variance operation is undefined. The
+elements of output tensor with strided layout, that correspond to
+fully masked-out elements, have ``nan`` values.
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in variance computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of variance operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+      Default: None that is equivalent to ``tuple(range(input.ndim))``.
+    unbiased (bool): when True, use Bessel's correction, otherwise, compute
+      the uncorrected sample variance.
+
+Keyword args:
+    keepdim (bool, optional): whether the output tensor has
+      :attr:`dim` retained or not. Default: False.
+    dtype (:class:`torch.dtype`, optional): the desired data type
+      of returned tensor.  If specified, the input tensor is
+      casted to :attr:`dtype` before the operation is
+      performed. Default: None.
+    mask (:class:`torch.Tensor`, optional): the boolean tensor
+      containing the binary mask of validity of input tensor
+      elements.
+      Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+Example::
+
+    >>> input = tensor([[-3, -2, -1], [ 0, 1, 2]])
+    >>> input
+    tensor([[-3, -2, -1],
+            [ 0,  1,  2]])
+    >>> mask = tensor([[ True, False, True], [False, False, False]])
+    >>> mask
+    tensor([[ True, False,  True],
+            [False, False, False]])
+    >>> torch.masked._ops.var(input, 1, False, mask=mask)
+    tensor([1., nan])
+"""
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4f96c7b1c45d67354f91fcfa3b2a2708fe4c9a2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/_ops.py
@@ -0,0 +1,1806 @@
+# mypy: allow-untyped-defs
+import warnings
+from typing import Any, Callable, Optional, TYPE_CHECKING, TypeVar, Union
+from typing_extensions import ParamSpec
+
+import torch
+from torch import sym_float, Tensor
+from torch._prims_common import corresponding_real_dtype
+from torch.masked import _docs
+from torch.masked.maskedtensor.core import is_masked_tensor, MaskedTensor
+from torch.masked.maskedtensor.creation import as_masked_tensor
+
+
+if TYPE_CHECKING:
+    from torch.types import _dtype as DType
+
+    DimOrDims = Optional[Union[int, tuple[int], list[int]]]
+else:
+    # The JIT doesn't understand Union, nor torch.dtype here
+    DType = int
+    DimOrDims = Optional[tuple[int]]
+
+
+__all__: list[str] = []
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+# All masked reduction/normalization operations have the same
+# signatures. Here we introduce docstring templates that are applied
+# to docstrings of reduction/normalization functions via
+# _apply_docstring_templates decorator.
+
+
+def _apply_docstring_templates(func: Callable[_P, _T]) -> Callable[_P, _T]:
+    """Decorator that applies docstring templates to function docstring
+    and returns the function instance.
+    """
+
+    doc_string = getattr(_docs, f"{func.__name__}_docstring", None)
+    if doc_string is None:
+        warnings.warn(
+            f"No documentation string available for {func.__name__}."
+            " PyTorch team should run `python tools/update_masked_docs.py`"
+            " to generate the missing docstrings."
+        )
+    else:
+        func.__doc__ = doc_string
+
+    # Expose function as public symbol
+    __all__.append(func.__name__)
+
+    return func
+
+
+def _generate_docstring(func):
+    """A utility function called from tools/update_masked_docs.py
+    script to update the module torch.masked._docs.py
+    """
+    docstring_templates = dict(
+        reduction_signature="""\
+{function_name}(input, {operation_args}, *, {operation_kwargs}) -> Tensor""",
+        reduction_descr="""\
+Returns {operation name} of all the elements in the :attr:`input`
+tensor along the given dimension(s) :attr:`dim` while the :attr:`input`
+elements are masked out according to the boolean tensor
+:attr:`mask`.""",
+        reduction_args="""\
+If :attr:`keepdim` is ``True``, the output tensor is of the same size
+as :attr:`input` except in the dimension(s) :attr:`dim` where it is of
+size 1. Otherwise, :attr:`dim` is squeezed (see
+:func:`torch.squeeze`), resulting in the output tensor having 1 (or
+``len(dim)``) fewer dimension(s).
+
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True
+then the corresponding element in :attr:`input` tensor will be
+included in {operation name} computation, otherwise the element is
+ignored.
+
+When all elements of :attr:`input` along the given dimension
+:attr:`dim` are ignored (fully masked-out), the corresponding element
+of the output tensor will have undefined value: it may or may not
+correspond to the identity value of {operation name} operation; the
+choice may correspond to the value that leads to the most efficient
+storage of :attr:`output` tensor.
+
+The mask of the output tensor can be computed as
+``torch.any(torch.broadcast_to(mask, input.shape), dim, keepdim=keepdim,
+dtype=torch.bool)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    {args_declarations}
+
+Keyword args:
+    {kwargs_declarations}""",
+        reduction_example="""\
+Example::
+
+    >>> input = {example_input}
+    >>> input
+    {indent_example_input}
+    >>> mask = {example_mask}
+    >>> mask
+    {indent_example_mask}
+    >>> {full_function_name}(input, {example_args}, mask=mask)
+    {indent_example_output}
+""",
+        reduction_identity="""\
+The identity value of {operation name} operation, which is used to start the reduction, is ``{identity_int32}``.""",
+        reduction_identity_dtype="""\
+The identity value of {operation name} operation, which is used to start the
+reduction, depends on input dtype. For instance, for float32, uint8,
+and int32 dtypes, the identity values are ``{identity_float32}``, ``{identity_uint8}``, and ``{identity_int32}``, respectively.""",
+        normalization_signature="""\
+{function_name}(input, {operation_args}, *, {operation_kwargs}) -> Tensor""",
+        normalization_descr="""\
+Returns {operation name} of all the slices in the :attr:`input` tensor
+along :attr:`dim` while the :attr:`input` elements are masked out
+according to the boolean tensor :attr:`mask`.
+
+{definition}""",
+        normalization_args="""\
+The boolean tensor :attr:`mask` defines the "validity" of
+:attr:`input` tensor elements: if :attr:`mask` element is True then
+the corresponding element in :attr:`input` tensor will be included in
+{operation name} computation, otherwise the element is ignored.
+
+The values of masked-out elements of the output tensor have undefined
+value: it may or may not be set to zero or nan; the choice may correspond to
+the value that leads to the most efficient storage of :attr:`output`
+tensor.
+
+The mask of the {operation name} output tensor can be computed as
+``torch.broadcast_to(mask, input.shape)``.
+
+The shapes of the :attr:`mask` tensor and the :attr:`input` tensor
+don't need to match, but they must be :ref:`broadcastable
+` and the dimensionality of the :attr:`mask`
+tensor must not be greater than of the :attr:`input` tensor.
+
+Args:
+    input (Tensor): the input tensor
+    {args_declarations}
+
+Keyword args:
+    {kwargs_declarations}""",
+        normalization_example="""\
+Example::
+
+    >>> input = {example_input}
+    >>> input
+    {indent_example_input}
+    >>> mask = {example_mask}
+    >>> mask
+    {indent_example_mask}
+    >>> {full_function_name}(input, {example_args}, mask=mask)
+    {indent_example_output}
+""",
+    )
+
+    args_and_kwargs = dict(
+        # argument name sufficies separated by double underscore will
+        # be removed in the final documentation string.
+        sum=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        prod=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        cumsum=(("dim__as_int",), ("dtype=None", "mask=None")),
+        cumprod=(("dim__as_int",), ("dtype=None", "mask=None")),
+        amin=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        amax=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        argmin=(("dim__as_int",), ("keepdim=False", "dtype=None", "mask=None")),
+        argmax=(("dim__as_int",), ("keepdim=False", "dtype=None", "mask=None")),
+        mean=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        median=(("dim__as_int",), ("keepdim=False", "dtype=None", "mask=None")),
+        norm=(
+            (
+                "ord",
+                "dim",
+            ),
+            ("keepdim=False", "dtype=None", "mask=None"),
+        ),
+        var=(("dim", "unbiased"), ("keepdim=False", "dtype=None", "mask=None")),
+        std=(("dim", "unbiased"), ("keepdim=False", "dtype=None", "mask=None")),
+        logsumexp=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
+        softmax=(("dim__as_int",), ("dtype=None", "mask=None")),
+        log_softmax=(("dim__as_int",), ("dtype=None", "mask=None")),
+        softmin=(("dim__as_int",), ("dtype=None", "mask=None")),
+        normalize=(
+            (
+                "ord__required",
+                "dim__as_int",
+            ),
+            ("eps=1e-12", "dtype=None", "mask=None"),
+        ),
+    )
+
+    argument_declarations = dict(
+        dim="""\
+dim (int or tuple of ints, optional): the dimension or dimensions to reduce.
+  Default: None that is equivalent to ``tuple(range(input.ndim))``.""",
+        dim__as_int="""\
+dim (int): the dimension along which {operation name} is computed.""",
+        ord="""\
+ord (int, float, optional): the order of vector norm. Default: 2.
+  See :func:`torch.linalg.vector_norm` for a list of supported norms.""",
+        ord__required="""\
+ord (int, float): the order of vector norm. Default: 2.
+  See :func:`torch.linalg.vector_norm` for a list of supported norms.""",
+        unbiased="""\
+unbiased (bool): when True, use Bessel's correction, otherwise, compute
+  the uncorrected sample variance.""",
+        eps="""\
+eps (float, optional): small value to avoid division by zero. Default: {default}.""",
+        keepdim="""\
+keepdim (bool, optional): whether the output tensor has
+  :attr:`dim` retained or not. Default: {default}.""",
+        dtype="""\
+dtype (:class:`torch.dtype`, optional): the desired data type
+  of returned tensor.  If specified, the input tensor is
+  casted to :attr:`dtype` before the operation is
+  performed. Default: {default}.""",
+        mask="""\
+mask (:class:`torch.Tensor`, optional): the boolean tensor
+  containing the binary mask of validity of input tensor
+  elements.
+  Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.""",
+    )
+
+    definitions = dict(
+        softmax="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Softmax of i-th element in ``x`` is
+defined as ``exp(x[i])/sum(exp(x))``.""",
+        log_softmax="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. LogSoftmax of i-th element in ``x`` is
+defined as ``log(exp(x[i])/sum(exp(x)))``.""",
+        softmin="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Softmin of i-th element in ``x`` is
+defined as ``exp(-x[i])/sum(exp(-x))``.""",
+        normalize="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Normalize of i-th element in ``x`` is
+defined as ``x[i]/max(norm(x, p), eps)``.""",
+        cumsum="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Cumsum of i-th element in ``x`` is
+defined as ``sum(x[:i])``.""",
+        cumprod="""\
+Let ``x`` be a sequence of unmasked elements of one-dimensional slice
+of the :attr:`input` tensor. Cumsum of i-th element in ``x`` is
+defined as ``prod(x[:i])``.""",
+    )
+
+    reduction_names = dict(
+        sum="sum",
+        prod="product",
+        amax="maximum",
+        amin="minimum",
+        argmax="argmax",
+        argmin="argmin",
+        mean="mean",
+        median="median",
+        norm="norm",
+        var="variance",
+        std="standard_deviation",
+        logsumexp="logsumexp",
+    )
+
+    normalization_names = dict(
+        softmax="softmax",
+        log_softmax="log_softmax",
+        softmin="softmin",
+        normalize="normalize",
+        cumsum="cumulative_sum",
+        cumprod="cumulative_prod",
+    )
+
+    operation_names = {}
+    operation_names.update(reduction_names)
+    operation_names.update(normalization_names)
+
+    # Default example data:
+    example_dim = 1
+    example_input = torch.tensor([[-3, -2, -1], [0, 1, 2]])
+    example_mask = torch.tensor([[True, False, True], [False, False, False]])
+    example_args: tuple[Any, ...]
+    if func.__name__ in {"norm", "normalize"}:
+        example_args = (2.0, example_dim)
+        example_input = example_input.to(dtype=torch.float32)
+    elif func.__name__ in {"var", "std"}:
+        example_args = (example_dim, False)
+    elif func.__name__ == "median":
+        example_args = (example_dim,)
+        example_input = example_input.to(dtype=torch.float32)
+    else:
+        example_args = (example_dim,)
+
+    operation_args: tuple[str, ...]
+    operation_kwargs: tuple[str, ...]
+    operation_args, operation_kwargs = args_and_kwargs[func.__name__]
+    arg_declarations = [
+        "\n    ".join(
+            argument_declarations.get(a, f'{a.split("__", 1)[0]}: TBD.').splitlines()
+        )
+        for a in operation_args
+    ]
+    kwarg_declarations = [
+        "\n    ".join(
+            argument_declarations.get(
+                a.split("=", 1)[0], f'{a.split("__", 1)[0]}: TBD.'
+            )
+            .format(default=a.split("=", 1)[1])
+            .splitlines()
+        )
+        for a in operation_kwargs
+    ]
+
+    if func.__name__ in reduction_names:
+        op_kind = "reduction"
+        doc_sections = ["signature", "descr", "identity", "args", "example"]
+    elif func.__name__ in normalization_names:
+        op_kind = "normalization"
+        doc_sections = ["signature", "descr", "args", "example"]
+        example_input = example_input.to(dtype=torch.float32)
+    else:
+        assert 0  # add function name to operation names dictionaries
+    example_output = func(example_input, *example_args, mask=example_mask)
+
+    template_data = {
+        "function_name": func.__name__,
+        "full_function_name": func.__module__ + "." + func.__name__,
+        "operation name": operation_names[func.__name__],
+        "operation_args": ", ".join(a.split("__", 1)[0] for a in operation_args),
+        "operation_kwargs": ", ".join(a.split("__", 1)[0] for a in operation_kwargs),
+        # one-line representation of a tensor:
+        "example_input": " ".join(str(example_input).split()),
+        "example_args": ", ".join(map(str, example_args)),
+        "example_mask": " ".join(str(example_mask).split()),
+        # multi-line representation of a tensor with indent
+        "indent_example_input": ("\n    ").join(str(example_input).splitlines()),
+        "indent_example_mask": ("\n    ").join(str(example_mask).splitlines()),
+        "indent_example_output": ("\n    ").join(str(example_output).splitlines()),
+    }
+
+    if func.__name__ in reduction_names:
+        template_data.update(
+            identity_uint8=_reduction_identity(
+                func.__name__, torch.tensor(0, dtype=torch.uint8)
+            ),
+            identity_int32=_reduction_identity(
+                func.__name__, torch.tensor(0, dtype=torch.int32)
+            ),
+            identity_float32=_reduction_identity(
+                func.__name__, torch.tensor(0, dtype=torch.float32)
+            ),
+        )
+        if func.__name__ == "norm":
+            template_data.update(
+                identity_ord_ninf=_reduction_identity(
+                    func.__name__, torch.tensor(0, dtype=torch.float32), float("-inf")
+                )
+            )
+    elif func.__name__ in normalization_names:
+        template_data.update(definition=definitions[func.__name__])
+    else:
+        assert 0  # add function name to operation names dictionaries
+    template_data.update(
+        args_declarations=("\n    ".join(arg_declarations)).format_map(template_data)
+    )
+    template_data.update(
+        kwargs_declarations=("\n    ".join(kwarg_declarations)).format_map(
+            template_data
+        )
+    )
+
+    # Apply function name info to docstring templates:
+    templates = {
+        k: v.format_map(template_data)
+        for k, v in docstring_templates.items()
+        if k.startswith(op_kind)
+    }
+    templates.update(
+        (k, v.format_map(template_data) if isinstance(v, str) else v)
+        for k, v in template_data.items()
+    )
+
+    # Apply docstring templates to function doctring:
+    if func.__doc__ is None:
+        doc_template = "\n\n".join([f"{{{op_kind}_{sec}}}" for sec in doc_sections])
+    else:
+        doc_template = func.__doc__
+    return doc_template.format_map(templates)
+
+
+def _reduction_identity(op_name: str, input: Tensor, *args):
+    """Return identity value as scalar tensor of a reduction operation on
+    given input, or None, if the identity value cannot be uniquely
+    defined for the given input.
+
+    The identity value of the operation is defined as the initial
+    value to reduction operation that has a property ``op(op_identity,
+    value) == value`` for any value in the domain of the operation.
+    Or put it another way, including or excluding the identity value in
+    a list of operands will not change the reduction result.
+
+    See https://github.com/pytorch/rfcs/pull/27 for more information.
+
+    """
+    dtype: DType = input.dtype
+    device = input.device
+    op_name = op_name.rsplit(".", 1)[-1]  # lstrip module name when present
+    if op_name in {"sum", "cumsum"}:
+        return torch.tensor(0, dtype=dtype, device=device)
+    elif op_name in {"prod", "cumprod"}:
+        return torch.tensor(1, dtype=dtype, device=device)
+    elif op_name in {"amax", "argmax", "logaddexp"}:
+        if torch.is_floating_point(input):
+            return torch.tensor(-torch.inf, dtype=dtype, device=device)
+        elif torch.is_signed(input) or dtype == torch.uint8:
+            return torch.tensor(torch.iinfo(dtype).min, dtype=dtype, device=device)
+    elif op_name in {"logsumexp"}:
+        if torch.is_floating_point(input):
+            return torch.tensor(-torch.inf, dtype=dtype, device=device)
+        elif torch.is_complex(input):
+            return torch.tensor(-torch.inf + 0j, dtype=dtype, device=device)
+        elif torch.is_signed(input) or dtype == torch.uint8:
+            return torch.tensor(torch.iinfo(dtype).min, dtype=dtype, device=device)
+    elif op_name in {"amin", "argmin"}:
+        if torch.is_floating_point(input):
+            return torch.tensor(torch.inf, dtype=dtype, device=device)
+        elif torch.is_signed(input) or dtype == torch.uint8:
+            return torch.tensor(torch.iinfo(dtype).max, dtype=dtype, device=device)
+    elif op_name == "mean":
+        # Strictly speaking, the identity value of the mean operation
+        # is the mean of the input. Since the mean value depends on
+        # the dim argument and it may be a non-scalar tensor, we
+        # consider the identity value of the mean operation ambiguous.
+        # Moreover, the mean value of empty input is undefined.
+        return None
+    elif op_name == "norm":
+        ord = args[0] if args else 2
+        if ord == float("-inf"):
+            assert torch.is_floating_point(input), input.dtype
+            return torch.tensor(torch.inf, dtype=dtype, device=device)
+        return torch.tensor(0, dtype=dtype, device=device)
+    elif op_name == "median":
+        # We use NaN for now because the implementation is currently using torch.nanmedian
+        # and NaN is the identity for that function since it gets ignored
+        dtype = input.dtype if torch.is_floating_point(input) else torch.float
+        return torch.tensor(torch.nan, dtype=dtype, device=device)
+    elif op_name in {"var", "std"}:
+        return None
+    raise NotImplementedError(f"identity of {op_name} on {dtype} input")
+
+
+def _canonical_dim(dim: DimOrDims, ndim: int) -> tuple[int, ...]:
+    """Return dim argument as a tuple of sorted dim values."""
+    dims: list[int] = []
+    if dim == ():
+        # Currently, `dim=()` in reductions operations means "reduce
+        # over all dimensions" while in future, it will read "no
+        # reduce". See https://github.com/pytorch/pytorch/issues/29137
+        # When gh-29137 is resolved, this if-block must be deleted.
+        dim = None
+    if dim is None:
+        return tuple(range(ndim))
+    ndim = max(ndim, 1)
+    dim_ = (dim,) if isinstance(dim, (int, torch.SymInt)) else dim
+    for d in dim_:
+        if d in dims:
+            raise RuntimeError(f"dim={d} appears multiple times in the list of dims")
+        if d >= ndim or d < -ndim:
+            raise IndexError(
+                f"Dimension out of range (expected to be in range of [{-ndim}, {ndim - 1}], but got {d})"
+            )
+        dims.append(d % ndim)
+    return tuple(sorted(dims))
+
+
+def _sparse_coo_flatten_indices(indices: Tensor, shape: tuple):
+    # Flatted N-D indices to 1-D indices
+    flat_indices = indices.new_zeros(indices.size(1))
+    for d, sz in enumerate(shape):
+        flat_indices.mul_(sz)
+        flat_indices.add_(indices[d])
+    return flat_indices
+
+
+def _any(input: Tensor, dim: tuple, keepdim: bool):
+    # Support torch.any with tuple dim argument.
+    # Workaround of https://github.com/pytorch/pytorch/issues/56586
+    r = input
+    for d in reversed(dim):
+        r = r.any(dim=d, keepdim=keepdim)
+    return r
+
+
+def _sparse_coo_where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
+    """Sparse variant of torch.where. Supports sparse COO and hybrid sparse COO tensors.
+
+    _sparse_coo_where implements the following invariant:
+
+      _sparse_coo_where(mask, input, fill_value).to_dense(fill_value) ==
+        torch.where(mask.to_dense(), input.to_dense(), torch.full(input.shape, fill_value))
+
+    where `a == b` means `assertEqual(a, b)`, mask is boolean sparse
+    tensor, and `to_dense(fill_value)` is like `to_dense()` except
+    that the unspecified elements are mapped to `fill_value` rather
+    than to `0`.
+
+    Returns a sparse COO tensor with the following features:
+
+    - all specified elements correspond to masked-in elements that
+      have the values of the input tensor. If there exists a masked-in
+      element (as specified by mask) that is not specified in the
+      input, in the result tensor, the corresponding element has value
+      0. In the dense part of the sparse tensor, the masked-out
+      elements are replaced with fill_value.
+
+    - all unspecified elements correspond to masked-out elements.
+    """
+
+    assert input.layout == torch.sparse_coo
+    assert mask.layout == input.layout
+    assert mask.shape == input.shape
+    assert mask.dense_dim() == input.dense_dim()  # TODO: eliminate this restriction
+
+    input = input.coalesce()
+
+    # For set operations on sparse tensor indices, we'll convert
+    # multi-dimensional indices to 1-D indices for efficiency.
+    input_flat_indices = _sparse_coo_flatten_indices(
+        input.indices(), input.shape[: input.sparse_dim()]
+    )
+    mask_flat_indices = _sparse_coo_flatten_indices(
+        mask.indices(), mask.shape[: mask.sparse_dim()]
+    )
+
+    # the set of mask flat indices that define masked-in elements:
+    if mask.dense_dim() > 0:
+        mask_values = _any(
+            mask.values(), tuple(range(1, input.sparse_dim() + 1)), False
+        )
+    else:
+        mask_values = mask.values()
+    maskin_flat_indices = mask_flat_indices[mask_values.nonzero()[:, 0]]
+
+    def intersection(i1, i2):
+        union, counts = torch.cat([i1, i2]).unique(return_counts=True)
+        return union, torch.where(counts.gt(1))
+
+    def minus(i1, i2):
+        union, counts = torch.cat([i1, i2]).unique(return_counts=True)
+        return intersection(union[torch.where(counts.eq(1))], i1)
+
+    def _apply(a):
+        obj, w = a
+        return obj[w]
+
+    # the set of input flat indices of specified and masked-in elements:
+    maskin_input_flat_indices = _apply(
+        intersection(maskin_flat_indices, input_flat_indices)
+    )
+    _, w = intersection(input_flat_indices, maskin_input_flat_indices)
+
+    # the indices and values of masked-in elements
+    where_input_indices = input.indices()[(slice(None),) + w]
+    where_input_values = input.values()[w]
+
+    if mask.dense_dim() > 0:
+        # apply mask to the dense part of the input values:
+        _, w1 = intersection(mask_flat_indices, maskin_input_flat_indices)
+        where_mask_values = mask.values()[w1]
+        where_input_values = torch.where(
+            where_mask_values, where_input_values, fill_value
+        )
+
+    # the set of flat indices of unspecified input and masked-in elements:
+    maskin_zero_flat_indices = _apply(
+        minus(maskin_flat_indices, maskin_input_flat_indices)
+    )
+
+    # the indices of masked-in zero elements
+    _, w = intersection(mask_flat_indices, maskin_zero_flat_indices)
+    where_zero_indices = mask.indices()[(slice(None),) + w]
+
+    # construct result
+    n = where_zero_indices.size(1)
+    if n == 0:
+        # the input is coalesced, hence input_flat_indices are ordered
+        # and the result is guaranteed to be coalesced:
+        result = torch.sparse_coo_tensor(
+            where_input_indices, where_input_values, input.shape
+        )
+        return result._coalesced_(True)
+
+    where_indices = torch.cat([where_input_indices, where_zero_indices], dim=1)
+    where_values = torch.cat(
+        [
+            where_input_values,
+            where_input_values.new_zeros((n,) + where_input_values.shape[1:]),
+        ]
+    )
+    result = torch.sparse_coo_tensor(where_indices, where_values, input.shape)
+
+    # appending zero elements leads to uncoalesced sparse tensor
+    return result.coalesce()
+
+
+def _sparse_coo_scatter_reduction_helper(
+    op,
+    mask_input: Tensor,
+    dims: tuple[int, ...],
+    keepdim: bool,
+    dtype: Optional[DType] = None,
+) -> Tensor:
+    reduce = op.__name__
+    valid_reductions = ["sum", "prod", "amax", "amin"]
+    if reduce not in valid_reductions:
+        raise ValueError(
+            f"op must be one of {' '.join(valid_reductions)}, but got {reduce} instead"
+        )
+
+    output_dtype = dtype
+    values, indices = mask_input._values(), mask_input._indices()
+    input_dims = mask_input.dim()
+    num_sparse_dims = mask_input.sparse_dim()
+    reduced_sparse_dims = []
+    retained_sparse_dims = []
+    reduced_dense_dims = []
+
+    # promote dtype if specified
+    if values.dtype != output_dtype:
+        values = values.to(output_dtype)
+
+    if keepdim:
+        output_shape = tuple(
+            1 if i in dims else si for (i, si) in enumerate(mask_input.shape)
+        )
+    else:
+        output_shape = tuple(
+            si for (i, si) in enumerate(mask_input.shape) if i not in dims
+        )
+
+    for d in dims:
+        if d >= input_dims:
+            continue
+
+        if d < num_sparse_dims:
+            reduced_sparse_dims.append(d)
+        else:
+            reduced_dense_dims.append(d + 1 - num_sparse_dims)
+
+    # Reduce dense dimensions
+    if len(reduced_dense_dims) > 0:
+        if reduce == "sum":
+            new_values = values
+            new_values = op(new_values, dim=reduced_dense_dims, keepdim=bool(keepdim))
+        else:
+            # FIXME: Implement reductions for dense dimensions for ops with non-zero reduction identities
+            return NotImplemented
+    else:
+        new_values = values.clone()
+
+    # Reduce sparse dimensions
+    if len(reduced_sparse_dims) == num_sparse_dims:
+        if reduce in {"amax", "amin"} and new_values.size(0) == 0:
+            # IndexError: amax(): Expected reduction dim 0 to have non-zero size.
+            # sum()/prod() return the reduction identity when dim has size 0 but amax()/amin() do not
+            # See https://github.com/pytorch/pytorch/issues/61901
+            new_values = _reduction_identity(reduce, new_values)
+        else:
+            new_values = op(new_values, dim=0)
+        if keepdim:
+            for _ in range(num_sparse_dims):
+                new_values = new_values.unsqueeze(0)
+        return new_values.to(dtype=output_dtype).to_sparse()
+    else:
+        new_indices = indices.clone()
+        if keepdim:
+            # zero out reduced sparse dimensions if keepdim = True
+            # ensures that the call to torch.unique folds duplicated indices together while preserving the dimension
+            new_indices[reduced_sparse_dims, :] = 0
+        else:
+            # remove reduced sparse dimensions if keepdim = False
+            if len(reduced_sparse_dims) > 0:
+                retained_sparse_dims = [
+                    i
+                    for i in range(num_sparse_dims)
+                    if i not in set(reduced_sparse_dims)
+                ]
+                new_indices = new_indices.index_select(
+                    0, torch.tensor(retained_sparse_dims).to(mask_input.device)
+                )
+
+    # Use scatter_reduce to reduce items in the new_values tensor that correspond to the same indices in new_indices
+    if new_indices.numel() > 0:
+        # lexsort indices and get index tensor for scatter reduction
+        new_indices, inverse_indices = torch.unique(
+            new_indices, return_inverse=True, dim=1
+        )
+        out_shape = list(new_values.shape)
+        out_shape[0] = new_indices.shape[1]
+        for _ in range(new_values.ndim - 1):
+            inverse_indices = inverse_indices.unsqueeze(-1)
+        scatter_indices = inverse_indices.expand(new_values.shape)
+        # FIXME: temporary workaround for issue with bfloat16/float16 remove when acctype is implemented for scatter_reduce
+        if output_dtype in {torch.bfloat16, torch.float16}:
+            new_values = new_values.to(torch.float)
+            out = new_values.new_empty(out_shape)
+            new_values = out.scatter_reduce_(
+                0, scatter_indices, new_values, reduce=reduce, include_self=False
+            )
+            new_values = new_values.to(dtype=output_dtype)
+        else:
+            out = new_values.new_empty(out_shape)
+            new_values = out.scatter_reduce_(
+                0, scatter_indices, new_values, reduce=reduce, include_self=False
+            )
+
+    return torch.sparse_coo_tensor(
+        new_indices,
+        new_values,
+        output_shape,
+        dtype=output_dtype,
+        device=mask_input.device,
+    )
+
+
+def _sparse_csr_segment_reduction_helper(
+    op,
+    mask_input: Tensor,
+    dims: tuple[int, ...],
+    keepdim: bool,
+    dtype: Optional[DType] = None,
+) -> Tensor:
+    # Currently, while sparse CSR is always 2D with no dense dimensions keepdim must be True
+    # FIXME: when dense dimensions are implemented for CSR tensors
+    assert (
+        keepdim
+    ), "reduction operations on CSR tensors with keepdim=False is unsupported"
+    reduce = op.__name__
+    valid_reductions = ["sum", "prod", "mean", "amax", "amin"]
+    if reduce not in valid_reductions:
+        raise ValueError(
+            f"op must be one of {' '.join(valid_reductions)}, but got {reduce} instead"
+        )
+    device = mask_input.device
+    output_dtype = dtype
+    values, crow_indices, col_indices = (
+        mask_input.values(),
+        mask_input.crow_indices(),
+        mask_input.col_indices(),
+    )
+
+    # promote dtype if specified
+    if values.dtype != output_dtype:
+        values = values.to(output_dtype)
+
+    if len(dims) == 0:
+        return mask_input
+    if len(dims) == 1:
+        if dims[0] == 0:
+            new_col_indices, scatter_indices = torch.unique(
+                col_indices, return_inverse=True
+            )
+            new_nnz = new_col_indices.shape[0]
+            new_crow_indices = torch.tensor([0, new_nnz])
+            new_values = values.new_empty(new_col_indices.shape)
+            new_values.scatter_reduce_(
+                0, scatter_indices, values, reduce, include_self=False
+            )
+            new_shape = [1, mask_input.size(1)]
+        else:
+            assert (
+                dims[0] == 1
+            ), "Sparse CSR tensors are 2D and only support reduction along dim 0 or 1."
+            # all intervals new_crow_indices[i] - new_crow_indices[i-1] are 1
+            # except for where crow_indices[i] == crow_indices[i-1] where the interval remains as 0
+            new_crow_indices = torch.cat(
+                (
+                    crow_indices.new_zeros(1),
+                    torch.cumsum(torch.diff(crow_indices) != 0, 0),
+                ),
+                0,
+            )
+            new_nnz = new_crow_indices[-1]
+            new_col_indices = col_indices.new_zeros(new_nnz)
+            new_values = torch._segment_reduce(values, reduce, offsets=crow_indices)  # type: ignore[attr-defined]
+            new_shape = [mask_input.size(0), 1]
+    else:
+        assert len(dims) == 2
+        nnz = min(1, values.numel())
+        if nnz == 1:
+            op_kwargs = {"keepdim": True, "dtype": output_dtype}
+            # amax and amin do not support dtype kwarg
+            if reduce in ["amax", "amin"]:
+                del op_kwargs["dtype"]
+            new_values = op(values, 0, **op_kwargs)
+        else:
+            new_values = torch.empty(0, dtype=output_dtype)
+        new_col_indices = col_indices.new_zeros(nnz)
+        new_crow_indices = torch.tensor([0, nnz])
+        new_shape = [1, nnz]
+
+    return torch.sparse_csr_tensor(
+        new_crow_indices,
+        new_col_indices,
+        new_values,
+        new_shape,
+        dtype=output_dtype,
+        device=device,
+    )
+
+
+def _sparse_csr_where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
+    """Sparse variant of torch.where. Supports sparse CSR tensors."""
+    # TODO: implement sparse CSR specific where operator for efficiency
+    return _sparse_coo_where(
+        mask.to_sparse_coo(), input.to_sparse_coo(), fill_value
+    ).to_sparse_csr()
+
+
+def _where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
+    """torch.where with sparse inputs support.
+
+    _where implements the following invariant:
+
+      _where(mask, input, fill_value).to_dense(fill_value) ==
+        torch.where(mask.to_dense(), input.to_dense(), torch.full(input.shape, fill_value))
+
+    where `a == b` means `assertEqual(a, b)`, mask is boolean sparse
+    tensor, and `to_dense(fill_value)` is like `to_dense()` except
+    that the unspecified elements are mapped to `fill_value` rather
+    than to `0`.
+
+    Returns a sparse tensor with the following features:
+
+    - all specified elements correspond to masked-in elements that
+      have the values of the input tensor. If there exists a masked-in
+      element (as specified by mask) that is not specified in the
+      input, in the result tensor, the corresponding element has value
+      0. In the dense part of the sparse tensor, the masked-out
+      elements are replaced with fill_value.
+
+    - all unspecified elements correspond to masked-out elements.
+    """
+    if mask.layout == torch.strided:
+        return torch.where(mask, input, fill_value)
+    elif mask.layout == torch.sparse_coo:
+        return _sparse_coo_where(mask, input, fill_value)
+    elif mask.layout == torch.sparse_csr:
+        return _sparse_csr_where(mask, input, fill_value)
+    else:
+        raise ValueError(
+            f"_where expects strided or sparse COO or sparse CSR tensor but got {mask.layout}"
+        )
+
+
+def _input_mask(input: Union[Tensor, MaskedTensor], *args, **kwargs) -> Tensor:
+    """Return canonical input mask.
+
+    A canonical input mask is defined as a boolean mask tensor that
+    shape and layout matches with the shape and the layout of the
+    input.
+
+    The canonical input mask is computed from the :attr:`mask` tensor
+    content to meet the following criteria:
+
+    1. The shape of the canonical input mask is the same as the shape
+       of :attr:`input` tensor. If the mask tensor has a smaller shape
+       than the shape of the :attr:`input`, broadcasting rules will be
+       applied. Downcasting of mask is not supported.
+
+    2. The layout of the canonical input mask is the same as the
+       layout of the :attr:`input` tensor. If the mask has different
+       layout, it will be converted to the expected layout.  In the
+       case of sparse COO layout, the canonical input mask will be
+       coalesced.
+
+    3. The dtype of the canonical input mask is torch.bool. If the
+       mask dtype is not bool then it will be converted to bool dtype
+       using `.to(dtype=bool)` method call.
+
+    4. The elements of the canonical input mask have boolean values
+       copied from the content of the :attr:`mask` tensor (after
+       possible broadcasting and dtype conversion transforms).  In
+       general, the sparsity pattern of the sparse canonical input
+       mask need not to be the same as the sparsity pattern of the
+       sparse :attr:`input` tensor.
+
+    """
+    if input.layout not in {torch.strided, torch.sparse_coo, torch.sparse_csr}:
+        raise ValueError(
+            f"_input_mask expects strided or sparse COO or sparse CSR tensor but got {input.layout}"
+        )
+
+    mask = kwargs.get("mask")
+
+    # default mask
+    if mask is None:
+        raise ValueError("_input_mask requires explicit mask")
+
+    # mask shape must match with input shape
+    if mask.shape != input.shape:
+        if mask.ndim > input.ndim:
+            raise IndexError(
+                "_input_mask expected broadcastable mask (got mask dimensionality higher than of the input)"
+            )
+        if mask.layout == torch.strided:
+            mask = torch.broadcast_to(mask.clone(), input.shape).to(dtype=torch.bool)
+        elif mask.layout == torch.sparse_coo:
+            mask = torch._sparse_broadcast_to(mask, input.shape)
+        else:
+            assert mask.layout == torch.sparse_csr
+            # Broadcasting of CSR tensors is not implemented. Working
+            # around by using COO layout.
+            mask = torch._sparse_broadcast_to(
+                mask.to_sparse(), input.shape
+            ).to_sparse_csr()
+
+    # mask layout must match with input layout
+    if mask.layout != input.layout:
+        if input.layout == torch.strided:
+            mask = mask.to_dense()
+        elif input.layout == torch.sparse_coo:
+            if mask.layout == torch.strided:
+                mask = mask.to_sparse(input.sparse_dim())
+            else:
+                mask = mask.to_sparse()
+        else:
+            assert input.layout == torch.sparse_csr
+            mask = mask.to_sparse_csr()
+
+    # sparse mask must be coalesced
+    if mask.layout == torch.sparse_coo:
+        mask = mask.coalesce()
+
+    # mask is a boolean tensor
+    mask = mask.to(dtype=torch.bool)
+
+    return mask
+
+
+def _output_mask(op, input: Tensor, *args, **kwargs) -> Tensor:
+    """Return output mask of masked operation applied to given arguments."""
+    if callable(op):
+        is_reduction = op.__name__ in {
+            "sum",
+            "prod",
+            "amax",
+            "amin",
+            "argmax",
+            "argmin",
+            "mean",
+            "median",
+            "norm",
+            "var",
+            "std",
+            "logsumexp",
+        }
+        is_normalization = op.__name__ in {
+            "softmax",
+            "log_softmax",
+            "softmin",
+            "normalize",
+            "cumsum",
+            "cumprod",
+        }
+        if is_reduction:
+            if op.__name__ == "norm":
+                if args:
+                    args = args[1:]  # lstrip ord argument
+            dim = args[0] if args else kwargs.get("dim")
+            outmask = _input_mask(input, *args, **kwargs)
+            keepdim = kwargs.get("keepdim", False)
+            dim_ = _canonical_dim(dim, input.ndim)
+            return _any(outmask, dim_, bool(keepdim))
+        elif is_normalization:
+            return _input_mask(input, *args, **kwargs)
+        else:
+            raise ValueError(
+                f"_output_mask expected masked operation (got callable {op.__module__}.{op.__name__})"
+            )
+    else:
+        raise ValueError(
+            f"_output_mask expected masked operation (got {type(op).__name__} object)"
+        )
+
+
+def _combine_input_and_mask(
+    op, input: Union[MaskedTensor, Tensor], mask, *args
+) -> Tensor:
+    def helper(input, mask):
+        if mask is None:
+            return input
+        canonical_mask = _input_mask(input, mask=mask)
+        if callable(op):
+            fill_value = _reduction_identity(op.__name__, input, *args)
+            return _where(canonical_mask, input, fill_value)
+        else:
+            raise ValueError(
+                f"_combine_input_and_mask expected masked operation (got {type(op).__name__} object)"
+            )
+
+    class Combine(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, input, mask):
+            """Return input with masked-out elements eliminated for the given operations."""
+            ctx.save_for_backward(mask)
+
+            if mask is not None:
+                ctx.mark_non_differentiable(mask)
+
+            return helper(input, mask)
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            (mask,) = ctx.saved_tensors
+            grad_data = (
+                grad_output.get_data() if is_masked_tensor(grad_output) else grad_output
+            )
+            result = as_masked_tensor(grad_data, mask)
+            return result, None
+
+    return (
+        Combine.apply(input.get_data(), input.get_mask())  # type: ignore[union-attr]
+        if is_masked_tensor(input)
+        else helper(input, mask)
+    )
+
+
+@_apply_docstring_templates
+def sum(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    # __doc__ is generated by _apply_docstring_templates decorator
+    if dtype is None:
+        # promote integer types to int64 when output dtype is not specified
+        if input.layout == torch.sparse_csr:
+            if input.dtype in {
+                torch.uint8,
+                torch.bool,
+                torch.int8,
+                torch.int16,
+                torch.int32,
+            }:
+                # csr.to(dtype=torch.int64) is not implemented, so
+                # using coo.to on input to ensure the promoted dtype
+                input = input.to_sparse_coo().to(dtype=torch.int64).to_sparse_csr()
+            else:
+                dtype = input.dtype
+        else:
+            dtype = input.dtype
+            if input.dtype in {
+                torch.uint8,
+                torch.bool,
+                torch.int8,
+                torch.int16,
+                torch.int32,
+            }:
+                dtype = torch.int64
+    dim_ = _canonical_dim(dim, input.ndim)
+    mask_input = _combine_input_and_mask(sum, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.sum(mask_input, dim_, bool(keepdim), dtype=dtype)
+    elif mask_input.layout == torch.sparse_coo:
+        return _sparse_coo_scatter_reduction_helper(
+            torch.sum, mask_input, dim_, bool(keepdim), dtype
+        )
+    elif mask_input.layout == torch.sparse_csr:
+        return torch._sparse_csr_sum(
+            mask_input, dim=list(dim_), keepdim=bool(keepdim), dtype=dtype
+        )
+    else:
+        raise ValueError(
+            f"masked sum expects strided, sparse_coo or sparse_csr tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def prod(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    # __doc__ is generated by _apply_docstring_templates decorator
+    if dtype is None:
+        # promote integer types to int64 when output dtype is not specified
+        if input.layout == torch.sparse_csr:
+            if input.dtype in {
+                torch.uint8,
+                torch.bool,
+                torch.int8,
+                torch.int16,
+                torch.int32,
+            }:
+                # csr.to(dtype=torch.int64) is not implemented, so
+                # using coo.to on input to ensure the promoted dtype
+                input = input.to_sparse_coo().to(dtype=torch.int64).to_sparse_csr()
+            else:
+                dtype = input.dtype
+        else:
+            dtype = input.dtype
+            if input.dtype in {
+                torch.uint8,
+                torch.bool,
+                torch.int8,
+                torch.int16,
+                torch.int32,
+            }:
+                dtype = torch.int64
+    dim_ = _canonical_dim(dim, input.ndim)
+    mask_input = _combine_input_and_mask(prod, input, mask)
+    if mask_input.layout == torch.strided:
+        # Workaround https://github.com/pytorch/pytorch/issues/56586
+        result = mask_input
+        result = result.to(dtype=dtype)
+        for d in reversed(dim_):
+            result = result.prod(dim=d, keepdim=bool(keepdim))
+        return result
+    elif mask_input.layout == torch.sparse_coo:
+        if mask is None:
+            # See comment in the sparse_csr branch, the same issue arises for sparse_coo tensors
+            raise ValueError(
+                "masked prod expects explicit mask for sparse_coo tensor input"
+            )
+        return _sparse_coo_scatter_reduction_helper(
+            torch.prod, mask_input, dim_, bool(keepdim), dtype
+        )
+    elif mask_input.layout == torch.sparse_csr:
+        if mask is None:
+            # mask is None corresponds to all-True mask. The
+            # unspecified elements in the CSR tensor correspond to
+            # zero values. Hence, the prod reduction result is
+            # automatically zero unless all elements are specified.
+            # A semi-optimal way to take this into account is to use:
+            #
+            #   masked_prod(csr, ..., mask=None) == torch._sparse_csr_prod(csr, ...) * all(csr.nonzero(), ...)
+            #
+            # but that requires implementing `all` and `nonzero`
+            # support for sparse csr tensors.
+            raise ValueError(
+                "masked prod expects explicit mask for sparse_csr tensor input"
+            )
+        return torch._sparse_csr_prod(
+            mask_input, dim=list(dim_), keepdim=bool(keepdim), dtype=dtype
+        )
+    else:
+        raise ValueError(
+            f"masked prod expects strided, sparse_coo or sparse_csr tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def cumsum(
+    input: Tensor,
+    dim: int,
+    *,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    mask_input = _combine_input_and_mask(sum, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.cumsum(mask_input, dim_, dtype=dtype).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked cumsum expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def cumprod(
+    input: Tensor,
+    dim: int,
+    *,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    mask_input = _combine_input_and_mask(prod, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.cumprod(mask_input, dim_, dtype=dtype).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked cumprod expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def amax(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+
+{reduction_descr}
+
+{reduction_identity_dtype}
+
+{reduction_args}
+
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+
+    mask_input = _combine_input_and_mask(amax, input, mask)
+    dim_ = _canonical_dim(dim, mask_input.ndim)
+    if mask_input.layout == torch.strided:
+        return torch.amax(mask_input, dim_, bool(keepdim)).to(dtype=dtype)
+    elif mask_input.layout == torch.sparse_coo:
+        if mask is None:
+            # See comment in the sparse_csr branch of prod, a similar issue arises here
+            # where unspecified elements along a dimension may need to be reduced with the result
+            raise ValueError(
+                "masked amax expects explicit mask for sparse_coo tensor input"
+            )
+        return _sparse_coo_scatter_reduction_helper(
+            torch.amax, mask_input, dim_, bool(keepdim), dtype
+        )
+    elif mask_input.layout == torch.sparse_csr:
+        if mask is None:
+            raise ValueError(
+                "masked amax expects explicit mask for sparse_csr tensor input"
+            )
+        return _sparse_csr_segment_reduction_helper(
+            torch.amax, mask_input, dim_, bool(keepdim), dtype
+        )
+    else:
+        raise ValueError(
+            f"masked amax expects strided, sparse_coo or sparse_csr tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def amin(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+
+{reduction_descr}
+
+{reduction_identity_dtype}
+
+{reduction_args}
+
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+
+    mask_input = _combine_input_and_mask(amin, input, mask)
+    dim_ = _canonical_dim(dim, mask_input.ndim)
+    if mask_input.layout == torch.strided:
+        return torch.amin(mask_input, dim_, bool(keepdim)).to(dtype=dtype)
+    elif mask_input.layout == torch.sparse_coo:
+        if mask is None:
+            # See comment in the sparse_csr branch of prod, a similar issue arises here
+            # where unspecified elements along a dimension may need to be reduced with the result
+            raise ValueError(
+                "masked amax expects explicit mask for sparse_coo tensor input"
+            )
+        return _sparse_coo_scatter_reduction_helper(
+            torch.amin, mask_input, dim_, bool(keepdim), dtype
+        )
+    elif mask_input.layout == torch.sparse_csr:
+        if mask is None:
+            raise ValueError(
+                "masked amin expects explicit mask for sparse_csr tensor input"
+            )
+        return _sparse_csr_segment_reduction_helper(
+            torch.amin, mask_input, dim_, bool(keepdim), dtype
+        )
+    else:
+        raise ValueError(
+            f"masked amin expects strided, sparse_coo or sparse_csr tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def argmax(
+    input: Union[Tensor, MaskedTensor],
+    dim: Optional[int] = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+{reduction_descr}
+{reduction_identity_dtype}
+{reduction_args}
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+    mask_input = _combine_input_and_mask(argmax, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.argmax(mask_input, dim, bool(keepdim)).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked argmax expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def argmin(
+    input: Union[Tensor, MaskedTensor],
+    dim: Optional[int] = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+{reduction_descr}
+{reduction_identity_dtype}
+{reduction_args}
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+    mask_input = _combine_input_and_mask(argmin, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.argmin(mask_input, dim, bool(keepdim)).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked argmin expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def mean(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+
+{reduction_descr}
+
+By definition, the identity value of a mean operation is the mean
+value of the tensor. If all elements of the input tensor along given
+dimension(s) :attr:`dim` are masked-out, the identity value of the
+mean is undefined.  Due to this ambiguity, the elements of output
+tensor with strided layout, that correspond to fully masked-out
+elements, have ``nan`` values.
+
+{reduction_args}
+
+{reduction_example}"""
+    dtype_source = "Optional"
+    if dtype is None:
+        dtype = input.dtype
+        dtype_source = "Input"
+
+    if not (dtype.is_floating_point or dtype.is_complex):
+        raise ValueError(
+            f"mean(): Could not infer output dtype. {dtype_source} dtype must be either "
+            f"a floating point or complex dtype. Got: {dtype}"
+        )
+    if input.layout == torch.strided:
+        if mask is None:
+            # TODO: compute count analytically
+            count = sum(
+                torch.ones(input.shape, dtype=torch.int64, device=input.device),
+                dim,
+                keepdim=keepdim,
+            )
+            total = sum(input, dim, keepdim=keepdim, dtype=dtype)
+        else:
+            inmask = _input_mask(input, mask=mask)
+            count = inmask.sum(dim=dim, keepdim=bool(keepdim))
+            total = sum(input, dim, keepdim=keepdim, dtype=dtype, mask=inmask)
+        return total / count
+    elif input.layout == torch.sparse_csr:
+        mask_input = _combine_input_and_mask(mean, input, mask)
+        dim_ = _canonical_dim(dim, mask_input.ndim)
+        if mask is None:
+            raise ValueError(
+                "masked mean expects explicit mask for sparse_csr tensor input"
+            )
+        return _sparse_csr_segment_reduction_helper(
+            torch.mean, mask_input, dim_, bool(keepdim), dtype
+        )
+    else:
+        raise ValueError(
+            f"masked mean expects strided or sparse_csr tensor (got {input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def median(
+    input: Union[Tensor, MaskedTensor],
+    dim: int = -1,
+    *,
+    keepdim: bool = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+{reduction_descr}
+By definition, the identity value of a median operation is the median
+value of the tensor. If all elements of the input tensor along given
+dimension(s) :attr:`dim` are masked-out, the identity value of the
+median is undefined.  Due to this ambiguity, the elements of output
+tensor with strided layout, that correspond to fully masked-out
+elements, have ``nan`` values.
+{reduction_args}
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    is_float = torch.is_floating_point(input)
+    if not is_float:
+        input = input.to(dtype=torch.float)
+    mask_input = _combine_input_and_mask(median, input, mask)
+    if mask_input.layout == torch.strided:
+        output = torch.nanmedian(mask_input, dim_, keepdim).values
+        if is_float:
+            return output
+        elif not is_float and not torch.isnan(output).any():
+            return output.to(dtype=dtype)
+        else:
+            raise ValueError(
+                "masked median expects no fully masked out rows if dtype is not floating point"
+            )
+    else:
+        raise ValueError(
+            f"masked median expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def logsumexp(
+    input: Tensor,
+    dim: DimOrDims = None,
+    *,
+    keepdim: bool = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)
+    mask_input = _combine_input_and_mask(logsumexp, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.logsumexp(mask_input, dim_, keepdim=keepdim).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked logsumexp expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+# Cannot use _apply_docstring_templates as it is only set up for reductions and normalizations
+def logaddexp(
+    input: Union[Tensor, MaskedTensor],
+    other: Union[Tensor, MaskedTensor],
+    *,
+    dtype: Optional[DType] = None,
+    input_mask: Optional[Tensor] = None,
+    other_mask: Optional[Tensor] = None,
+) -> Tensor:
+    """logaddexp(input, other, *, dtype=None, input_mask=None, other_mask=None) -> Tensor
+
+    Returns logaddexp of all the elements in the :attr:`input` and the :attr:`other`
+    tensor. The :attr:`input` elements are masked out according to the boolean tensor
+    :attr:`input_mask` and the attr:`other` elements are masked out according to the boolean tensor
+    :attr:`other_mask`.
+
+    The shapes of a mask tensor and the tensor to be masked
+    don't need to match, but they must be :ref:`broadcastable
+    ` and the dimensionality of the mask
+    tensor must not be greater than of the tensor to be masked.
+
+    Args:
+        input (Tensor): the input tensor
+        other (Tensor): the second input tensor
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type
+          of returned tensor.  If specified, the output tensor is
+          casted to :attr:`dtype` after the operation is
+          performed. Default: None.
+        input_mask (:class:`torch.Tensor`, optional): the boolean tensor
+          containing the binary mask of validity of :attr:`input` tensor elements.
+          Default: None that is equivalent to ``torch.ones(input.shape, dtype=torch.bool)``.
+        other_mask (:class:`torch.Tensor`, optional): the boolean tensor
+          containing the binary mask of validity of :attr:`other` tensor elements.
+          Default: None that is equivalent to ``torch.ones(other.shape, dtype=torch.bool)``.
+
+    Example::
+
+        >>> input = torch.tensor([-100.0, -200, -300])
+        >>> input
+        tensor([-100., -200., -300.])
+        >>> other = torch.tensor([-1.0, -2, -3])
+        >>> other
+        tensor([-1., -2., -3.])
+        >>> mask = torch.tensor([True, False, True])
+        >>> mask
+        tensor([ True, False,  True])
+        >>> torch.masked._ops.logaddexp(input, other, input_mask=mask, other_mask=mask)
+        tensor([-1., -inf, -3.])"""
+    if dtype is None:
+        dtype = input.dtype
+    if input.layout == torch.strided and other.layout == torch.strided:
+        mask_input = _combine_input_and_mask(logaddexp, input, input_mask)
+        mask_other = _combine_input_and_mask(logaddexp, other, other_mask)
+        return torch.logaddexp(mask_input, mask_other).to(dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked logaddexp expects strided tensors (got {input.layout} tensor for input, {other.layout} for other)"
+        )
+
+
+@_apply_docstring_templates
+def norm(
+    input: Union[Tensor, MaskedTensor],
+    ord: Optional[float] = 2.0,
+    dim: DimOrDims = None,
+    *,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+
+{reduction_descr}
+
+The identity value of norm operation, which is used to start the
+reduction, is ``{identity_float32}``, except for ``ord=-inf`` it is
+``{identity_ord_ninf}``.
+
+{reduction_args}
+
+{reduction_example}"""
+    if dtype is None:
+        dtype = input.dtype
+    mask_input = _combine_input_and_mask(norm, input, mask, ord)
+    if mask_input.layout == torch.strided:
+        dim_ = _canonical_dim(dim, input.ndim)
+        return torch.linalg.vector_norm(
+            mask_input, ord, dim_, bool(keepdim), dtype=dtype
+        )
+    else:
+        raise ValueError(
+            f"masked norm expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+def _std_var(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims,
+    unbiased: Optional[bool],
+    *,
+    correction_opt: Optional[Union[int, float]],
+    keepdim: Optional[bool],
+    dtype: Optional[DType],
+    mask: Optional[Tensor],
+    take_sqrt: Optional[bool],
+) -> Tensor:
+    assert (
+        unbiased is None or correction_opt is None
+    ), "Only one of unbiased and correction may be given"
+    correction = 1.0
+    if unbiased is not None:
+        correction = 1.0 if unbiased else 0.0
+    if correction_opt is not None:
+        correction = sym_float(correction_opt)
+
+    if dtype is None:
+        dtype = input.dtype
+        if not (dtype.is_floating_point or dtype.is_complex):
+            dtype = torch.float32
+    compute_dtype = dtype
+    if not (compute_dtype.is_floating_point or compute_dtype.is_complex):
+        compute_dtype = torch.float32
+    if input.layout == torch.strided:
+        if mask is None:
+            # TODO: compute count analytically
+            count = sum(
+                torch.ones(input.shape, dtype=torch.int64, device=input.device),
+                dim,
+                keepdim=True,
+            )
+            sample_total = sum(input, dim, keepdim=True, dtype=dtype)
+        else:
+            inmask = _input_mask(input, mask=mask)
+            count = inmask.sum(dim=dim, keepdim=True)
+            sample_total = sum(input, dim, keepdim=True, dtype=dtype, mask=inmask)
+        # TODO: replace torch.subtract/divide/square/maximum with
+        # masked subtract/divide/square/maximum when these will be
+        # available.
+        sample_mean = torch.divide(sample_total, count)
+        x = torch.subtract(input, sample_mean)
+        if mask is None:
+            total = sum(x * x.conj(), dim, keepdim=keepdim, dtype=compute_dtype)
+        else:
+            total = sum(
+                x * x.conj(), dim, keepdim=keepdim, dtype=compute_dtype, mask=inmask  # type: ignore[possibly-undefined]
+            )
+        if not keepdim:
+            count = count.reshape(total.shape)
+        if correction != 0:
+            real_dtype = (
+                corresponding_real_dtype(compute_dtype)
+                if compute_dtype.is_complex
+                else compute_dtype
+            )
+            count = count.to(real_dtype)
+            count = torch.subtract(count, correction)
+            count = torch.maximum(count, count.new_zeros([]))
+        output = torch.divide(total, count).to(dtype=dtype)
+        if take_sqrt:
+            output = torch.sqrt(output)
+        return output
+    else:
+        raise ValueError(
+            f"masked std/var expects strided tensor (got {input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def var(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    unbiased: Optional[bool] = None,
+    *,
+    correction: Optional[Union[int, float]] = None,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+{reduction_descr}
+The identity value of sample variance operation is undefined. The
+elements of output tensor with strided layout, that correspond to
+fully masked-out elements, have ``nan`` values.
+{reduction_args}
+{reduction_example}"""
+    return _std_var(
+        input=input,
+        dim=dim,
+        unbiased=unbiased,
+        correction_opt=correction,
+        keepdim=keepdim,
+        dtype=dtype,
+        mask=mask,
+        take_sqrt=False,
+    )
+
+
+@_apply_docstring_templates
+def std(
+    input: Union[Tensor, MaskedTensor],
+    dim: DimOrDims = None,
+    unbiased: Optional[bool] = None,
+    *,
+    correction: Optional[int] = None,
+    keepdim: Optional[bool] = False,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    """\
+{reduction_signature}
+{reduction_descr}
+The identity value of sample standard deviation operation is undefined. The
+elements of output tensor with strided layout, that correspond to
+fully masked-out elements, have ``nan`` values.
+{reduction_args}
+{reduction_example}"""
+    return _std_var(
+        input=input,
+        dim=dim,
+        unbiased=unbiased,
+        correction_opt=correction,
+        keepdim=keepdim,
+        dtype=dtype,
+        mask=mask,
+        take_sqrt=True,
+    )
+
+
+@_apply_docstring_templates
+def softmax(
+    input: Union[Tensor, MaskedTensor],
+    dim: int,
+    *,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    mask_input = _combine_input_and_mask(amax, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.nn.functional.softmax(mask_input, dim_, dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked softmax expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def log_softmax(
+    input: Union[Tensor, MaskedTensor],
+    dim: int,
+    *,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    mask_input = _combine_input_and_mask(amax, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.nn.functional.log_softmax(mask_input, dim_, dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked log_softmax expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def softmin(
+    input: Union[Tensor, MaskedTensor],
+    dim: int,
+    *,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    dim_ = _canonical_dim(dim, input.ndim)[0]
+    mask_input = _combine_input_and_mask(amin, input, mask)
+    if mask_input.layout == torch.strided:
+        return torch.nn.functional.softmin(mask_input, dim_, dtype=dtype)
+    else:
+        raise ValueError(
+            f"masked softmin expects strided tensor (got {mask_input.layout} tensor)"
+        )
+
+
+@_apply_docstring_templates
+def normalize(
+    input: Union[Tensor, MaskedTensor],
+    ord: float,
+    dim: int,
+    *,
+    eps: float = 1e-12,
+    dtype: Optional[DType] = None,
+    mask: Optional[Tensor] = None,
+) -> Tensor:
+    if dtype is None:
+        dtype = input.dtype
+    # TODO: eliminate mask_input as unnecessary when using masked divide.
+    mask_input = _combine_input_and_mask(sum, input, mask)
+    if mask_input.layout == torch.strided:
+        nrm_ = norm(input, ord, dim, keepdim=True, dtype=dtype, mask=mask)
+        # TODO: replace torch.maximum with masked maximum when available.
+        denom = torch.maximum(nrm_, nrm_.new_full([], eps))
+        # TODO: replace torch.divide with masked divide when available.
+        return torch.divide(mask_input, denom)
+    else:
+        raise ValueError(
+            f"masked normalize expects strided tensor (got {mask_input.layout} tensor)"
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e38e03c87086cf50d031dd5591f64f65399d6ac1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+# flake8: noqa
+
+from .binary import _apply_native_binary, _is_native_binary
+from .core import is_masked_tensor, MaskedTensor
+from .passthrough import _apply_pass_through_fn, _is_pass_through_fn
+from .reductions import _apply_reduction, _is_reduction
+from .unary import _apply_native_unary, _is_native_unary
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/_ops_refs.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/_ops_refs.py
new file mode 100644
index 0000000000000000000000000000000000000000..719df7eac464f7b44499646f0129623b95aa9461
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/_ops_refs.py
@@ -0,0 +1,531 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+from functools import partial
+from typing import Any, Callable, TYPE_CHECKING
+
+import torch
+
+from .binary import _apply_native_binary, NATIVE_BINARY_FNS, NATIVE_INPLACE_BINARY_FNS
+from .core import (
+    _get_data,
+    _masks_match,
+    _maybe_get_mask,
+    is_masked_tensor,
+    MaskedTensor,
+)
+from .passthrough import _apply_pass_through_fn, PASSTHROUGH_FNS
+from .reductions import (
+    _apply_reduction,
+    NATIVE_REDUCE_FNS,
+    TENSOR_REDUCE_FNS,
+    TORCH_REDUCE_FNS,
+)
+from .unary import _apply_native_unary, NATIVE_INPLACE_UNARY_FNS, NATIVE_UNARY_FNS
+
+
+if TYPE_CHECKING:
+    from torch._ops import OpOverload
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+def _check_args_kwargs_length(
+    args, kwargs, error_prefix, len_args=None, len_kwargs=None
+):
+    if len_args is not None and len_args != len(args):
+        raise ValueError(
+            f"{error_prefix}: len(args) must be {len_args} but got {len(args)}"
+        )
+    if len_kwargs is not None and len_kwargs != len(kwargs):
+        raise ValueError(
+            f"{error_prefix}: len(kwargs) must be {len_kwargs} but got {len(kwargs)}"
+        )
+
+
+class _MaskedContiguous(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input):
+        if not is_masked_tensor(input):
+            raise ValueError("MaskedContiguous forward: input must be a MaskedTensor.")
+
+        if input.is_contiguous():
+            return input
+
+        data = input.get_data()
+        mask = input.get_mask()
+
+        return MaskedTensor(data.contiguous(), mask.contiguous())
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output
+
+
+class _MaskedToDense(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input):
+        if not is_masked_tensor(input):
+            raise ValueError("MaskedToDense forward: input must be a MaskedTensor.")
+
+        if input.layout == torch.strided:
+            return input
+
+        ctx.layout = input.layout
+        data = input.get_data()
+        mask = input.get_mask()
+
+        return MaskedTensor(data.to_dense(), mask.to_dense())
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        layout = ctx.layout
+
+        if layout == torch.sparse_coo:
+            return grad_output.to_sparse_coo()
+        elif layout == torch.sparse_csr:
+            return grad_output.to_sparse_csr()
+        elif layout == torch.strided:
+            return grad_output.to_dense()
+        raise ValueError("to_dense: Unsupported input layout: ", layout)
+
+
+class _MaskedToSparse(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input):
+        if not is_masked_tensor(input):
+            raise ValueError("MaskedToSparse forward: input must be a MaskedTensor.")
+
+        # Following the convention from sparse tensors that to_sparse always means that we convert to sparse_coo
+        if input.layout == torch.sparse_coo:
+            return input
+
+        data = input.get_data()
+        mask = input.get_mask()
+        sparse_mask = mask.to_sparse_coo().coalesce()
+        sparse_data = data.sparse_mask(sparse_mask)
+
+        return MaskedTensor(sparse_data, sparse_mask)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output.to_dense()
+
+
+class _MaskedToSparseCsr(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input):
+        if not is_masked_tensor(input):
+            raise ValueError("MaskedToSparseCsr forward: input must be a MaskedTensor.")
+
+        if input._masked_data.ndim != 2:
+            raise ValueError(
+                f"Only 2D tensors can be converted to the SparseCsr layout but got shape: {input._masked_data.size()}"
+            )
+
+        if input.layout == torch.sparse_csr:
+            return input
+
+        data = input.get_data()
+        mask = input.get_mask()
+        sparse_mask = mask.to_sparse_csr()
+        sparse_data = data.sparse_mask(sparse_mask)
+
+        return MaskedTensor(sparse_data, sparse_mask)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output.to_dense()
+
+
+class _MaskedWhere(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, cond, self, other):
+        ctx.mark_non_differentiable(cond)
+        ctx.save_for_backward(cond)
+        return torch.ops.aten.where(cond, self, other)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        (cond,) = ctx.saved_tensors
+
+        def masked_out_like(mt):
+            return MaskedTensor(mt.get_data(), torch.zeros_like(mt.get_mask()).bool())
+
+        return (
+            None,
+            torch.ops.aten.where(cond, grad_output, masked_out_like(grad_output)),
+            torch.ops.aten.where(cond, masked_out_like(grad_output), grad_output),
+        )
+
+
+_MASKEDTENSOR_FUNCTION_TABLE = {}
+
+_function_fn_apply_map = {
+    (
+        tuple(NATIVE_REDUCE_FNS),
+        tuple(TORCH_REDUCE_FNS),
+        tuple(TENSOR_REDUCE_FNS),
+    ): _apply_reduction,
+}
+
+for fn_map_list, apply_fn in _function_fn_apply_map.items():
+    for fn_map in fn_map_list:
+        for fn in fn_map:
+            _MASKEDTENSOR_FUNCTION_TABLE[fn] = partial(apply_fn, fn)
+
+
+def register_function_func(ops):
+    """
+    Used for registering a new __torch_function__ function to MaskedTensor
+    Called via _MASKEDTENSOR_FUNCTION_TABLE[func](*args, **kwargs)
+
+    The code to register a new function looks like:
+
+    @register_function_func(list_of_ops)
+    def foo(func, *args, **kwargs):
+        
+    """
+
+    def wrapper(func):
+        for op in ops:
+            _MASKEDTENSOR_FUNCTION_TABLE[op] = partial(func, op)
+
+    return wrapper
+
+
+@register_function_func(NATIVE_REDUCE_FNS + TORCH_REDUCE_FNS + TENSOR_REDUCE_FNS)
+def _general_function_reductions(func, *args, **kwargs):
+    return _apply_reduction(func, *args, **kwargs)
+
+
+@register_function_func([torch.Tensor.where, torch.where])
+def _function_where(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, "__torch_function__, torch.where", len_args=3, len_kwargs=0
+    )
+    return _MaskedWhere.apply(*args)
+
+
+@register_function_func([torch.Tensor.contiguous])
+def _function_contiguous(func, *args, **kwargs):
+    return _MaskedContiguous.apply(args[0])
+
+
+@register_function_func([torch.Tensor.to_dense])
+def _function_to_dense(func, *args, **kwargs):
+    return _MaskedToDense.apply(args[0])
+
+
+@register_function_func([torch.Tensor.to_sparse])
+def _function_to_sparse(func, *args, **kwargs):
+    return _MaskedToSparse.apply(args[0])
+
+
+@register_function_func([torch.Tensor.to_sparse_csr])
+def _function_to_sparse_csr(func, *args, **kwargs):
+    return _MaskedToSparseCsr.apply(args[0])
+
+
+_MASKEDTENSOR_DISPATCH_TABLE: dict["OpOverload", Callable[..., Any]] = {}
+
+
+def register_dispatch_func(aten_ops):
+    """
+    Used for registering a new __torch_dispatch__ function to MaskedTensor
+    Called via _MASKEDTENSOR_DISPATCH_TABLE[func](*args, **kwargs)
+
+    The code to register a new function looks like:
+
+    @register_dispatch_func(list_of_ops)
+    def foo(func, *args, **kwargs):
+        
+    """
+
+    def wrapper(func):
+        for aten_op in aten_ops:
+            _MASKEDTENSOR_DISPATCH_TABLE[aten_op] = partial(func, aten_op)
+
+    return wrapper
+
+
+@register_dispatch_func(NATIVE_REDUCE_FNS + TORCH_REDUCE_FNS + TENSOR_REDUCE_FNS)
+def _general_reduction(func, *args, **kwargs):
+    return _apply_reduction(func, *args, **kwargs)
+
+
+@register_dispatch_func(PASSTHROUGH_FNS)
+def _general_passthrough(func, *args, **kwargs):
+    return _apply_pass_through_fn(func, *args, **kwargs)
+
+
+@register_dispatch_func(NATIVE_UNARY_FNS + NATIVE_INPLACE_UNARY_FNS)
+def _general_unary(func, *args, **kwargs):
+    return _apply_native_unary(func, *args, **kwargs)
+
+
+@register_dispatch_func(NATIVE_BINARY_FNS + NATIVE_INPLACE_BINARY_FNS)
+def _general_binary(func, *args, **kwargs):
+    return _apply_native_binary(func, *args, **kwargs)
+
+
+@register_dispatch_func([torch.ops.aten.stride])
+def stride(func, *args, **kwargs):
+    return None
+
+
+@register_dispatch_func([torch.ops.aten.sym_stride])
+def sym_stride(func, *args, **kwargs):
+    return None
+
+
+@register_dispatch_func([torch.ops.prim.layout])
+def layout(func, *args, **kwargs):
+    return _get_data(args[0]).layout
+
+
+@register_dispatch_func([torch.ops.aten.is_contiguous])
+def is_contiguous(func, *args, **kwargs):
+    data = _get_data(args[0])
+    if data.is_sparse:
+        raise ValueError("MaskedTensors with sparse data do not have is_contiguous")
+    return func(data, *args[1:], **kwargs)
+
+
+@register_dispatch_func([torch.ops.aten.is_strides_like_format])
+def is_strides_like_format(func, *args, **kwargs):
+    data = _get_data(args[0])
+    if data.is_sparse:
+        raise ValueError(
+            "MaskedTensors with sparse data do not have is_strides_like_format"
+        )
+    return func(data, *args[1:], **kwargs)
+
+
+@register_dispatch_func([torch.ops.aten.is_non_overlapping_and_dense])
+def is_non_overlapping_and_dense(func, *args, **kwargs):
+    data = _get_data(args[0])
+    if data.is_sparse:
+        raise ValueError(
+            "MaskedTensors with sparse data do not have is_non_overlapping_and_dense"
+        )
+    return func(data, *args[1:], **kwargs)
+
+
+@register_dispatch_func([torch.ops.aten.contiguous])
+def contiguous(func, *args, **kwargs):
+    if _get_data(args[0]).is_sparse:
+        raise ValueError("MaskedTensors with sparse data do not have contiguous")
+    return _MaskedContiguous.apply(args[0])
+
+
+@register_dispatch_func([torch.ops.aten.new_empty_strided])
+def new_empty_strided(func, *args, **kwargs):
+    _check_args_kwargs_length(args, kwargs, f"__torch_dispatch__, {func}", len_args=3)
+    data = _get_data(args[0])
+    mask = _maybe_get_mask(args[0])
+    if tuple(args[1]) != tuple(data.size()):
+        raise ValueError(
+            f"__torch_dispatch__, {func}: args[1] expected to be the same as data.size()"
+        )
+    if tuple(args[2]) != tuple(data.stride()):
+        raise ValueError(
+            f"__torch_dispatch__, {func}: args[2] expected to be the same as data.stride()"
+        )
+    return MaskedTensor(func(data, args[1], args[2], **kwargs), mask)
+
+
+@register_dispatch_func([torch.ops.aten._local_scalar_dense])
+def _local_scalar_dense(func, *args, **kwargs):
+    if not _maybe_get_mask(args[0]):
+        raise ValueError(f"__torch_dispatch__, {func}: expected a mask tensor")
+    return torch.ops.aten._local_scalar_dense(_get_data(args[0]))
+
+
+@register_dispatch_func([torch.ops.aten.detach, torch.ops.aten.clone])
+def _apply_fn_on_data(func, *args, **kwargs):
+    return MaskedTensor(func(_get_data(args[0])), _maybe_get_mask(args[0]))
+
+
+@register_dispatch_func([torch.ops.aten._to_copy])
+def _to_copy(func, *args, **kwargs):
+    new_data = func(_get_data(args[0]), *args[1:], **kwargs)
+    return MaskedTensor(new_data, _maybe_get_mask(args[0]))
+
+
+@register_dispatch_func([torch.ops.aten._softmax])
+def _softmax(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=3, len_kwargs=0
+    )
+    data = _get_data(args[0])
+    mask = _maybe_get_mask(args[0])
+    result_data = torch.ops.aten._masked_softmax(data, ~mask, args[1], 2)
+    return MaskedTensor(result_data, mask)
+
+
+@register_dispatch_func([torch.ops.aten.ones_like])
+def ones_like(func, *args, **kwargs):
+    _check_args_kwargs_length(args, kwargs, f"__torch_dispatch__, {func}", len_args=1)
+    result_data = func(_get_data(args[0]), **kwargs)
+    return MaskedTensor(result_data, _maybe_get_mask(args[0]))
+
+
+@register_dispatch_func([torch.ops.aten._softmax_backward_data])
+def _softmax_backward_data(func, *args, **kwargs):
+    _check_args_kwargs_length(args, kwargs, f"__torch_dispatch__, {func}", len_args=4)
+    grad, output, dim, _input_dtype = args
+    if is_masked_tensor(grad) and is_masked_tensor(output):
+        if not _masks_match(grad, output):
+            raise ValueError(
+                "__torch_dispatch__, {func}: expected the masks of grad and output to match"
+            )
+        grad_data = _get_data(grad)
+        new_grad_data = torch.ops.aten._masked_softmax_backward(
+            grad_data,
+            _get_data(output),
+            ~_maybe_get_mask(grad),
+            dim % grad_data.ndim,
+        )
+        res = MaskedTensor(new_grad_data, _maybe_get_mask(grad))
+        return res
+    else:
+        raise ValueError(
+            f"__torch_dispatch__, {func}: grad and output must both be MaskedTensors"
+        )
+
+
+@register_dispatch_func([torch.ops.aten.copy_])
+def copy_(func, *args, **kwargs):
+    _check_args_kwargs_length(args, kwargs, f"__torch_dispatch__, {func}", len_args=2)
+    if not _masks_match(_maybe_get_mask(args[0]), _maybe_get_mask(args[1])):
+        raise ValueError("args[0] mask and args[1] mask must match but do not")
+    func(_get_data(args[0]), _get_data(args[1]))
+    return args[0]
+
+
+@register_dispatch_func([torch.ops.aten.where])
+def where(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=3, len_kwargs=0
+    )
+    if not torch.is_tensor(args[0]):
+        raise ValueError("__torch_dispatch__, {func}: expected args[0] to be a tensor")
+    mx = args[1]
+    my = args[2]
+    if not is_masked_tensor(mx):
+        mx = MaskedTensor(mx, torch.ones_like(mx, dtype=torch.bool))
+    if not is_masked_tensor(my):
+        my = MaskedTensor(my, torch.ones_like(my, dtype=torch.bool))
+    new_data = func(args[0], mx.get_data(), my.get_data())
+    new_mask = func(args[0], mx.get_mask(), my.get_mask())
+    return MaskedTensor(new_data, new_mask)
+
+
+@register_dispatch_func([torch.ops.aten._to_sparse])
+def _to_sparse(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    if not torch.is_tensor(args[0]):
+        raise TypeError("__torch_dispatch__, {func}: expected args[0] to be a tensor")
+    mt = args[0]
+    if not is_masked_tensor(mt):
+        mt = MaskedTensor(mt, torch.ones_like(mt, dtype=torch.bool))
+    if mt.is_sparse_coo():
+        return mt
+    new_mask = func(_maybe_get_mask(args[0])).coalesce()
+    new_data = _get_data(args[0]).sparse_mask(new_mask)
+    return MaskedTensor(new_data, new_mask)
+
+
+@register_dispatch_func([torch.ops.aten._to_sparse_csr])
+def _to_sparse_csr(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    if not torch.is_tensor(args[0]):
+        raise ValueError("__torch_dispatch__, {func}: expected args[0] to be a tensor")
+    mt = args[0]
+    if not is_masked_tensor(mt):
+        mt = MaskedTensor(mt, torch.ones_like(mt).bool())
+    if mt.is_sparse_csr():
+        return mt
+    new_mask = func(_maybe_get_mask(args[0]))
+    new_data = _get_data(args[0]).sparse_mask(new_mask)
+    return MaskedTensor(new_data, new_mask)
+
+
+@register_dispatch_func([torch.ops.aten._to_dense])
+def _to_dense(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    if not torch.is_tensor(args[0]):
+        raise ValueError("__torch_dispatch__, {func}: expected args[0] to be a tensor")
+    mt = args[0]
+    if not is_masked_tensor(mt):
+        mt = MaskedTensor(mt, torch.ones_like(mt).bool())
+    new_data = func(_get_data(args[0]))
+    new_mask = func(_maybe_get_mask(args[0]))
+    return MaskedTensor(new_data, new_mask)
+
+
+@register_dispatch_func([torch.ops.aten._indices])
+def _indices(func, *args, **kwargs):
+    # Assumes data is sparse
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    data = _get_data(args[0]).indices()
+    return MaskedTensor(data, torch.ones_like(data).bool())
+
+
+@register_dispatch_func([torch.ops.aten._values])
+def _values(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    data = _get_data(args[0]).values()
+    return MaskedTensor(data, torch.ones_like(data).bool())
+
+
+@register_dispatch_func([torch.ops.aten._sparse_coo_tensor_with_dims_and_tensors])
+def _sparse_coo_tensor_with_dims_and_tensors(func, *args, **kwargs):
+    new_args = list(args)
+    if is_masked_tensor(args[-1]):
+        new_args[-1] = args[-1].get_data()
+    if is_masked_tensor(args[-2]):
+        new_args[-2] = args[-2].get_data()
+
+    new_data = func(*new_args, **kwargs)
+    new_args[-1] = torch.ones_like(new_args[-1])
+    new_mask = func(*new_args, **kwargs).bool()
+
+    return MaskedTensor(new_data, new_mask)
+
+
+@register_dispatch_func([torch.ops.aten.is_same_size])
+def is_same_size(func, *args, **kwargs):
+    _check_args_kwargs_length(args, kwargs, f"__torch_dispatch__, {func}", len_args=2)
+    return _get_data(args[0]).is_same_size(_get_data(args[1]))
+
+
+@register_dispatch_func([torch.ops.aten._is_any_true])
+def _is_any_true(func, *args, **kwargs):
+    _check_args_kwargs_length(
+        args, kwargs, f"__torch_dispatch__, {func}", len_args=1, len_kwargs=0
+    )
+    data = _get_data(args[0])
+    mask = _maybe_get_mask(args[0])
+    if mask is None:
+        raise ValueError(
+            f"__torch_dispatch__, {func}: expected args[0] to be a MaskedTensor"
+        )
+    if data.dtype != torch.bool:
+        raise ValueError(f"__torch_dispatch__, {func}: expected a boolean tensor")
+    if data.is_sparse:
+        raise ValueError(f"MaskedTensors with sparse data do not have {func}")
+
+    return MaskedTensor(func(data & mask), torch.tensor(True))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/binary.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/binary.py
new file mode 100644
index 0000000000000000000000000000000000000000..8315ae11be7175c2b5aaef178a4bc4785dcbcb29
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/binary.py
@@ -0,0 +1,200 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import torch
+
+from .core import (
+    _map_mt_args_kwargs,
+    _masks_match,
+    _tensors_match,
+    _wrap_result,
+    is_masked_tensor,
+)
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+BINARY_NAMES = [
+    "add",
+    "atan2",
+    "arctan2",
+    "bitwise_and",
+    "bitwise_or",
+    "bitwise_xor",
+    "bitwise_left_shift",
+    "bitwise_right_shift",
+    "div",
+    "divide",
+    "floor_divide",
+    "fmod",
+    "logaddexp",
+    "logaddexp2",
+    "mul",
+    "multiply",
+    "nextafter",
+    "remainder",
+    "sub",
+    "subtract",
+    "true_divide",
+    "eq",
+    "ne",
+    "le",
+    "ge",
+    "greater",
+    "greater_equal",
+    "gt",
+    "less_equal",
+    "lt",
+    "less",
+    "maximum",
+    "minimum",
+    "fmax",
+    "fmin",
+    "not_equal",
+]
+
+INPLACE_BINARY_NAMES = [
+    n + "_"
+    for n in (
+        list(
+            set(BINARY_NAMES)
+            - {
+                "logaddexp",
+                "logaddexp2",
+                "equal",
+                "fmin",
+                "minimum",
+                "maximum",
+                "fmax",
+            }
+        )
+    )
+]
+
+
+def _get_at_least_one_mask(a, b):
+    if not is_masked_tensor(a) and not is_masked_tensor(b):
+        raise TypeError("At least one of `a` and `b` must be a MaskedTensor")
+    if not _masks_match(a, b):
+        raise ValueError("a and b must have matching masks")
+    if is_masked_tensor(a):
+        return a.get_mask()
+    return b.get_mask()
+
+
+def _binary_helper(fn, args, kwargs, inplace):
+    if len(kwargs) != 0:
+        raise ValueError("len(kwargs) must equal 0")
+    for a in args[2:]:
+        if torch.is_tensor(a):
+            raise TypeError(
+                "MaskedTensor binary ops do not support Tensor arguments aside from the lhs and rhs"
+            )
+
+    if not _masks_match(*args[:2]):
+        raise ValueError(
+            "Input masks must match. If you need support for this, please open an issue on Github."
+        )
+
+    data_args, _data_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x.get_data())
+    mask_args, _mask_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x.get_mask())
+
+    args0_layout = data_args[0].layout
+    same_layout = (
+        torch.is_tensor(data_args[1]) or is_masked_tensor(data_args[1])
+    ) and (args0_layout == data_args[1].layout)
+
+    if args0_layout == torch.sparse_coo:
+        if same_layout:
+            if not _tensors_match(data_args[0].indices(), data_args[1].indices()):
+                raise ValueError(
+                    "sparse_coo indices must match. If you need support for this, please open an issue on Github."
+                )
+            if data_args[0].size() != data_args[1].size():
+                raise ValueError(
+                    "input1 and input2 must have the same size for binary functions."
+                )
+
+            data_args[1] = data_args[1].values()
+
+        i = data_args[0].indices()
+        size = data_args[0].size()
+        data_args[0] = data_args[0].values()
+        v = fn(*data_args)
+        result_data = torch.sparse_coo_tensor(i, v, size)
+
+    elif args0_layout == torch.sparse_csr:
+        if same_layout:
+            if not (
+                _tensors_match(data_args[0].crow_indices(), data_args[1].crow_indices())
+                and _tensors_match(
+                    data_args[0].col_indices(), data_args[1].col_indices()
+                )
+            ):
+                raise ValueError(
+                    "sparse_csr indices must match. If you need support for this, please open an issue on Github."
+                )
+
+            data_args[1] = data_args[1].values()
+
+        crow = data_args[0].crow_indices()
+        col = data_args[0].col_indices()
+        size = data_args[0].size()
+        data_args[0] = data_args[0].values()
+        v = fn(*data_args)
+        result_data = torch.sparse_csr_tensor(crow, col, v, size)
+
+    else:
+        result_data = fn(*data_args)
+
+    if inplace:
+        args[0]._set_data_mask(result_data, mask_args[0])
+        return args[0]
+    else:
+        result_mask = _get_at_least_one_mask(*args[:2])
+        # sparse tensors don't have strides so we can only expand if the layout is strided
+        if args0_layout == torch.strided:
+            result_mask = result_mask.expand_as(result_data)
+        return _wrap_result(result_data, result_mask)
+
+
+def _torch_binary(fn_name):
+    fn = getattr(torch.ops.aten, fn_name)
+
+    def binary_fn(*args, **kwargs):
+        return _binary_helper(fn, args, kwargs, inplace=False)
+
+    return binary_fn
+
+
+def _torch_inplace_binary(fn_name):
+    fn = getattr(torch.ops.aten, fn_name)
+
+    def binary_fn(*args, **kwargs):
+        return _binary_helper(fn, args, kwargs, inplace=True)
+
+    return binary_fn
+
+
+NATIVE_BINARY_MAP = {
+    getattr(torch.ops.aten, name): _torch_binary(name) for name in BINARY_NAMES
+}
+NATIVE_INPLACE_BINARY_MAP = {
+    getattr(torch.ops.aten, name): _torch_inplace_binary(name)
+    for name in INPLACE_BINARY_NAMES
+}
+
+NATIVE_BINARY_FNS = list(NATIVE_BINARY_MAP.keys())
+NATIVE_INPLACE_BINARY_FNS = list(NATIVE_INPLACE_BINARY_MAP.keys())
+
+
+def _is_native_binary(fn):
+    return fn in NATIVE_BINARY_FNS or fn in NATIVE_INPLACE_BINARY_FNS
+
+
+def _apply_native_binary(fn, *args, **kwargs):
+    if fn in NATIVE_BINARY_FNS:
+        return NATIVE_BINARY_MAP[fn](*args, **kwargs)
+    if fn in NATIVE_INPLACE_BINARY_FNS:
+        return NATIVE_INPLACE_BINARY_MAP[fn](*args, **kwargs)
+    return NotImplemented
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9867def26a30df7e0f0988271c692b1959de2ea
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/core.py
@@ -0,0 +1,359 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import warnings
+from typing import Any
+from typing_extensions import TypeIs
+
+import torch
+from torch.overrides import get_default_nowrap_functions
+
+
+__all__ = [
+    "MaskedTensor",
+    "is_masked_tensor",
+]
+
+
+def is_masked_tensor(obj: Any, /) -> TypeIs["MaskedTensor"]:
+    r"""Returns True if the input is a MaskedTensor, else False
+
+    Args:
+        a: any input
+
+    Examples:
+
+        >>> # xdoctest: +SKIP
+        >>> from torch.masked import MaskedTensor
+        >>> data = torch.arange(6).reshape(2,3)
+        >>> mask = torch.tensor([[True, False, False], [True, True, False]])
+        >>> mt = MaskedTensor(data, mask)
+        >>> is_masked_tensor(mt)
+        True
+    """
+    return isinstance(obj, MaskedTensor)
+
+
+def _tensors_match(a, b, exact=True, rtol=1e-05, atol=1e-08):
+    if is_masked_tensor(a) or is_masked_tensor(b):
+        raise ValueError("Neither `a` nor `b` can be a MaskedTensor.")
+    if a.layout != b.layout:
+        raise ValueError(
+            f"`a` and `b` must have the same layout. Got {a.layout} and {b.layout}"
+        )
+
+    if a.dtype != b.dtype:
+        b = b.type(a.dtype)
+    if a.layout == b.layout == torch.sparse_coo:
+        return _tensors_match(a.values(), b.values(), exact) and _tensors_match(
+            a.indices(), b.indices(), exact
+        )
+    elif a.layout == b.layout == torch.sparse_csr:
+        return (
+            _tensors_match(a.crow_indices(), b.crow_indices(), exact)
+            and _tensors_match(a.col_indices(), b.col_indices(), exact)
+            and _tensors_match(a.values(), b.values(), exact)
+        )
+    if exact:
+        return (a.dim() == b.dim()) and torch.eq(a, b).all().item()
+    return (a.dim() == b.dim()) and torch.allclose(a, b, rtol=rtol, atol=atol)
+
+
+def _masks_match(a, b):
+    if is_masked_tensor(a) and is_masked_tensor(b):
+        mask_a = a.get_mask()
+        mask_b = b.get_mask()
+        return _tensors_match(mask_a, mask_b, exact=True)
+    return True
+
+
+def _map_mt_args_kwargs(args, kwargs, map_fn):
+    def _helper(a, map_fn):
+        if is_masked_tensor(a):
+            return map_fn(a)
+        elif torch.is_tensor(a):
+            return a
+        elif isinstance(a, list):
+            a_impl, _ = _map_mt_args_kwargs(a, {}, map_fn)
+            return a_impl
+        elif isinstance(a, tuple):
+            a_impl, _ = _map_mt_args_kwargs(a, {}, map_fn)
+            return tuple(a_impl)
+        else:
+            return a
+
+    if kwargs is None:
+        kwargs = {}
+    impl_args = []
+    for a in args:
+        impl_args.append(_helper(a, map_fn))
+    impl_kwargs = {}
+    for k in kwargs.keys():
+        impl_kwargs[k] = _helper(a, map_fn)
+    return impl_args, impl_kwargs
+
+
+def _wrap_result(result_data, result_mask):
+    if isinstance(result_data, list):
+        return [_wrap_result(r, m) for (r, m) in zip(result_data, result_mask)]
+    if isinstance(result_data, tuple):
+        return tuple(_wrap_result(r, m) for (r, m) in zip(result_data, result_mask))
+    if torch.is_tensor(result_data):
+        return MaskedTensor(result_data, result_mask)
+    # Expect result_data and result_mask to be Tensors only
+    return NotImplemented
+
+
+def _masked_tensor_str(data, mask, formatter):
+    if data.layout in {torch.sparse_coo, torch.sparse_csr}:
+        data = data.to_dense()
+        mask = mask.to_dense()
+    if data.dim() == 1:
+        formatted_elements = [
+            formatter.format(d.item()) if isinstance(d.item(), float) else str(d.item())
+            for d in data
+        ]
+        max_len = max(8 if x[1] else len(x[0]) for x in zip(formatted_elements, ~mask))
+        return (
+            "["
+            + ", ".join(
+                [
+                    "--".rjust(max_len) if m else e
+                    for (e, m) in zip(formatted_elements, ~mask)
+                ]
+            )
+            + "]"
+        )
+    sub_strings = [_masked_tensor_str(d, m, formatter) for (d, m) in zip(data, mask)]
+    sub_strings = ["\n".join(["  " + si for si in s.split("\n")]) for s in sub_strings]
+    return "[\n" + ",\n".join(sub_strings) + "\n]"
+
+
+def _get_data(a):
+    if is_masked_tensor(a):
+        return a._masked_data
+    return a
+
+
+def _maybe_get_mask(a):
+    if is_masked_tensor(a):
+        return a.get_mask()
+    return None
+
+
+class MaskedTensor(torch.Tensor):
+    @staticmethod
+    def __new__(cls, data, mask, requires_grad=False):
+        if is_masked_tensor(data) or not torch.is_tensor(data):
+            raise TypeError("data must be a Tensor")
+        if is_masked_tensor(mask) or not torch.is_tensor(mask):
+            raise TypeError("mask must be a Tensor")
+        # Use a Tensor that of the give size for the wrapper.
+        kwargs = {
+            "device": data.device,
+            "dtype": data.dtype,
+            "layout": data.layout,
+            "requires_grad": requires_grad,
+            "dispatch_sizes_strides_policy": "strides",
+            "dispatch_layout": True,
+        }
+        warnings.warn(
+            (
+                "The PyTorch API of MaskedTensors is in prototype stage "
+                "and will change in the near future. Please open a Github issue "
+                "for features requests and see our documentation on the torch.masked "
+                "module for further information about the project."
+            ),
+            UserWarning,
+            stacklevel=2,
+        )
+        if data.requires_grad:
+            warnings.warn(
+                "It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
+                "To avoid this, you can use data.detach().clone()",
+                UserWarning,
+                stacklevel=2,
+            )
+        return torch.Tensor._make_wrapper_subclass(cls, data.size(), **kwargs)  # type: ignore[attr-defined]
+
+    def _preprocess_data(self, data, mask):
+        from .._ops import _sparse_coo_where, _sparse_csr_where
+
+        if data.layout != mask.layout:
+            raise TypeError("data and mask must have the same layout.")
+        if data.layout == torch.sparse_coo:
+            data = data.coalesce()
+            mask = mask.coalesce()
+            if data._nnz() != mask._nnz():
+                data = _sparse_coo_where(mask, data, torch.tensor(0))
+        elif data.layout == torch.sparse_csr:
+            if data._nnz() != mask._nnz():
+                data = _sparse_csr_where(mask, data, torch.tensor(0))
+
+        # Have to pick awkward names to not conflict with existing fields such as data
+        self._masked_data = data.clone()
+        self._masked_mask = mask.clone()
+
+    def _validate_members(self):
+        data = self._masked_data
+        mask = self.get_mask()
+        if type(data) != type(mask):
+            raise TypeError(
+                f"data and mask must have the same type. Got {type(data)} and {type(mask)}"
+            )
+        if data.layout not in {torch.strided, torch.sparse_coo, torch.sparse_csr}:
+            raise TypeError(f"data layout of {data.layout} is not supported.")
+        if data.layout == torch.sparse_coo:
+            if not _tensors_match(data.indices(), mask.indices(), exact=True):
+                raise ValueError(
+                    "data and mask are both sparse COO tensors but do not have the same indices."
+                )
+        elif data.layout == torch.sparse_csr:
+            if not _tensors_match(
+                data.crow_indices(), mask.crow_indices(), exact=True
+            ) or not _tensors_match(data.col_indices(), mask.col_indices(), exact=True):
+                raise ValueError(
+                    "data and mask are both sparse CSR tensors but do not share either crow or col indices."
+                )
+        if mask.dtype != torch.bool:
+            raise TypeError("mask must have dtype bool.")
+        if not (
+            data.dtype == torch.float16
+            or data.dtype == torch.float32
+            or data.dtype == torch.float64
+            or data.dtype == torch.bool
+            or data.dtype == torch.int8
+            or data.dtype == torch.int16
+            or data.dtype == torch.int32
+            or data.dtype == torch.int64
+        ):
+            raise TypeError(f"{data.dtype} is not supported in MaskedTensor.")
+        if data.dim() != mask.dim():
+            raise ValueError("data.dim() must equal mask.dim()")
+        if data.size() != mask.size():
+            raise ValueError("data.size() must equal mask.size()")
+
+    def __init__(self, data, mask, requires_grad=False):
+        self._preprocess_data(data, mask)
+        self._validate_members()
+
+    @staticmethod
+    def _from_values(data, mask):
+        """Differentiable constructor for MaskedTensor"""
+
+        class Constructor(torch.autograd.Function):
+            @staticmethod
+            def forward(ctx, data, mask):
+                return MaskedTensor(data, mask)
+
+            @staticmethod
+            def backward(ctx, grad_output):
+                return grad_output, None
+
+        result = Constructor.apply(data, mask)
+        return result
+
+    def _set_data_mask(self, data, mask):
+        self._masked_data = data
+        self._masked_mask = mask
+        self._validate_members()
+
+    def __repr__(self):  # type: ignore[override]
+        formatter = "{0:8.4f}"
+        if self.dim() == 0:
+            scalar_data = self.get_data().item()
+            data_formatted = (
+                formatter.format(scalar_data)
+                if isinstance(scalar_data, float)
+                else str(scalar_data)
+            )
+            if not self.get_mask().item():
+                data_formatted = "--"
+            return (
+                "MaskedTensor("
+                + data_formatted
+                + ", "
+                + str(self.get_mask().item())
+                + ")"
+            )
+        s = _masked_tensor_str(self.get_data(), self.get_mask(), formatter)
+        s = "\n".join("  " + si for si in s.split("\n"))
+        return "MaskedTensor(\n" + s + "\n)"
+
+    # Seems like this needs to be defined before torch_dispatch to work
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+
+        from ._ops_refs import _MASKEDTENSOR_FUNCTION_TABLE
+
+        if func in _MASKEDTENSOR_FUNCTION_TABLE:
+            return _MASKEDTENSOR_FUNCTION_TABLE[func](*args, **kwargs)
+
+        if not all(issubclass(cls, t) for t in types):
+            return NotImplemented
+        with torch._C.DisableTorchFunctionSubclass():
+            ret = func(*args, **kwargs)
+            if func in get_default_nowrap_functions():
+                return ret
+            else:
+                return torch._tensor._convert(ret, cls)
+
+    @classmethod
+    def unary(cls, fn, data, mask):
+        return MaskedTensor(fn(data), mask)
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args, kwargs):
+        func = func.overloadpacket
+
+        from ._ops_refs import _MASKEDTENSOR_DISPATCH_TABLE
+
+        if func in _MASKEDTENSOR_DISPATCH_TABLE:
+            return _MASKEDTENSOR_DISPATCH_TABLE[func](*args, **kwargs)
+
+        msg = (
+            f"{func.__name__} is not implemented in __torch_dispatch__ for MaskedTensor.\n"
+            "If you would like this operator to be supported, please file an issue for a feature request at "
+            "https://github.com/pytorch/maskedtensor/issues with a minimal reproducible code snippet.\n"
+            "In the case that the semantics for the operator are not trivial, it would be appreciated "
+            "to also include a proposal for the semantics."
+        )
+        warnings.warn(msg)
+        return NotImplemented
+
+    def __lt__(self, other):
+        if is_masked_tensor(other):
+            return MaskedTensor(self.get_data() < _get_data(other), self.get_mask())
+        return MaskedTensor(self.get_data() < other, self.get_mask())
+
+    def to_tensor(self, value):
+        return self.get_data().masked_fill(~self.get_mask(), value)
+
+    def get_data(self):
+        class GetData(torch.autograd.Function):
+            @staticmethod
+            def forward(ctx, self):
+                return self._masked_data.detach()
+
+            @staticmethod
+            def backward(ctx, grad_output):
+                if is_masked_tensor(grad_output):
+                    return grad_output
+                return MaskedTensor(grad_output, self.get_mask())
+
+        return GetData.apply(self)
+
+    def get_mask(self):
+        return self._masked_mask
+
+    def is_sparse_coo(self):
+        return self.layout == torch.sparse_coo
+
+    def is_sparse_csr(self):  # type: ignore[override]
+        return self.layout == torch.sparse_csr
+
+    # Update later to support more sparse layouts
+    @property
+    def is_sparse(self):
+        return self.is_sparse_coo() or self.is_sparse_csr()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/creation.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/creation.py
new file mode 100644
index 0000000000000000000000000000000000000000..35c8e3d2aa9438dbcfc7995a1cdcd3c5cc8dc1fc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/creation.py
@@ -0,0 +1,24 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+from .core import MaskedTensor
+
+
+__all__ = [
+    "as_masked_tensor",
+    "masked_tensor",
+]
+
+
+# These two factory functions are intended to mirror
+#     torch.tensor - guaranteed to be a leaf node
+#     torch.as_tensor - differentiable constructor that preserves the autograd history
+
+
+def masked_tensor(
+    data: object, mask: object, requires_grad: bool = False
+) -> MaskedTensor:
+    return MaskedTensor(data, mask, requires_grad)
+
+
+def as_masked_tensor(data: object, mask: object) -> MaskedTensor:
+    return MaskedTensor._from_values(data, mask)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/passthrough.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/passthrough.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba13f50c1fee9c9fc10563ffc9f4ff3211c0dca6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/passthrough.py
@@ -0,0 +1,50 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+"""
+These are functions that should simply be applied to both mask and data.
+Take select or stack as an example. This operation can be applied to
+both the mask and data of a MaskedTensor and the result wrapped into
+a new MaskedTensor as a result.
+"""
+
+import torch
+
+from .core import _map_mt_args_kwargs, _wrap_result
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+PASSTHROUGH_FNS = [
+    torch.ops.aten.select,
+    torch.ops.aten.transpose,
+    torch.ops.aten.split,
+    torch.ops.aten.t,
+    torch.ops.aten.slice,
+    torch.ops.aten.slice_backward,
+    torch.ops.aten.select_backward,
+    torch.ops.aten.index,
+    torch.ops.aten.expand,
+    torch.ops.aten.view,
+    torch.ops.aten._unsafe_view,
+    torch.ops.aten._reshape_alias,
+    torch.ops.aten.cat,
+    torch.ops.aten.unsqueeze,
+    torch.ops.aten.unfold,
+    torch.ops.aten.unfold_backward,
+    torch.ops.aten.im2col,
+    torch.ops.aten.col2im,
+    torch.ops.aten.stack,
+]
+
+
+def _is_pass_through_fn(fn):
+    return fn in PASSTHROUGH_FNS
+
+
+def _apply_pass_through_fn(fn, *args, **kwargs):
+    data_args, data_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x.get_data())
+    result_data = fn(*data_args, **data_kwargs)
+    mask_args, mask_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x.get_mask())
+    result_mask = fn(*mask_args, **mask_kwargs)
+    return _wrap_result(result_data, result_mask)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/reductions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..fedab1c12a63786fe950edda37ced9637beed83c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/reductions.py
@@ -0,0 +1,176 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import warnings
+
+import torch
+
+from .core import is_masked_tensor
+from .creation import as_masked_tensor, masked_tensor
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+def _masked_all_all(data, mask=None):
+    if mask is None:
+        return data.all()
+    return data.masked_fill(~mask, True).all()
+
+
+def _masked_all_dim(data, dim, keepdim=False, mask=None):
+    if mask is None:
+        return torch.all(data, dim=dim, keepdim=keepdim)
+    return torch.all(data.masked_fill(~mask, True), dim=dim, keepdim=keepdim)
+
+
+def _masked_all(*args, **kwargs):
+    if len(args) == 1 and len(kwargs) == 1:
+        return _masked_all_all(args[0], mask=kwargs["mask"])
+    return _masked_all_dim(*args, **kwargs)
+
+
+def _multidim_any(mask, dim, keepdim):
+    if isinstance(dim, int):
+        return _multidim_any(mask, [dim], keepdim)
+    for d in sorted(dim, reverse=True):
+        mask = torch.any(mask, dim=d, keepdim=keepdim)
+    return mask
+
+
+def _get_masked_fn(fn):
+    if fn == "all":
+        return _masked_all
+    return getattr(torch.masked, fn)
+
+
+def _torch_reduce_all(fn):
+    def reduce_all(self):
+        masked_fn = _get_masked_fn(fn)
+        data = self.get_data()
+        mask = self.get_mask().values() if self.is_sparse else self.get_mask()
+        # When reduction is "all", then torch.argmin/torch.argmax needs to return the index of the
+        # element corresponding to the min/max, but this operation isn't supported correctly for sparse layouts.
+        # Therefore, this implementation calculates it using the strides.
+        if fn == "all":
+            result_data = masked_fn(data, mask=mask)
+
+        elif fn in {"argmin", "argmax"} and self.is_sparse_coo():
+            sparse_idx = masked_fn(data.values(), mask=mask).to(dtype=torch.int)
+            indices = (
+                data.to_sparse_coo().indices()
+                if not self.is_sparse_coo()
+                else data.indices()
+            )
+            idx = indices.unbind(1)[sparse_idx]
+            stride = data.size().numel() / torch.tensor(
+                data.size(), device=data.device
+            ).cumprod(0)
+            result_data = torch.sum(idx * stride)
+
+        # we simply pass in the values for sparse COO/CSR tensors
+        elif self.is_sparse:
+            result_data = masked_fn(masked_tensor(data.values(), mask))
+
+        else:
+            result_data = masked_fn(self, mask=mask)
+
+        return as_masked_tensor(result_data, torch.any(mask))
+
+    return reduce_all
+
+
+def _torch_reduce_dim(fn):
+    def reduce_dim(self, dim, keepdim=False, dtype=None):
+        if self.is_sparse:
+            msg = (
+                f"The sparse version of {fn} is not implemented in reductions.\n"
+                "If you would like this operator to be supported, please file an issue for a feature request at "
+                "https://github.com/pytorch/maskedtensor/issues with a minimal reproducible code snippet.\n"
+                "In the case that the semantics for the operator are not trivial, it would be appreciated "
+                "to also include a proposal for the semantics."
+            )
+            warnings.warn(msg)
+            return NotImplemented
+        if not is_masked_tensor(self):
+            raise TypeError("Input to reduce_dim must be a MaskedTensor")
+
+        masked_fn = _get_masked_fn(fn)
+        data = self.get_data()
+        mask = self.get_mask()
+        if fn == "all":
+            result_data = masked_fn(data, dim=dim, keepdim=keepdim, mask=mask)
+        else:
+            result_data = masked_fn(
+                self, dim=dim, keepdim=keepdim, dtype=dtype, mask=self.get_mask()
+            )
+        return as_masked_tensor(result_data, _multidim_any(mask, dim, keepdim))
+
+    return reduce_dim
+
+
+def _torch_reduce(fn):
+    def reduce_fn(*args, **kwargs):
+        if len(args) == 1 and len(kwargs) == 0:
+            return _torch_reduce_all(fn)(args[0])
+        return _torch_reduce_dim(fn)(*args, **kwargs)
+
+    return reduce_fn
+
+
+def _reduce_dim_args(input, dim, keepdim=False, dtype=None):
+    return input, dim, keepdim, dtype
+
+
+def _torch_grad_reduce(fn):
+    def grad_reduce(*args, **kwargs):
+        if len(args) == 1 and len(kwargs) == 0:
+            return _torch_reduce_all(fn)(args[0])
+        # TODO: autograd.Function doesn't support kwarg
+        input, dim, keepdim, dtype = _reduce_dim_args(*args, **kwargs)
+        return _torch_reduce_dim(fn)(input, dim, keepdim, dtype)
+
+    return grad_reduce
+
+
+REDUCE_NAMES = [
+    "sum",
+    "mean",
+    "amin",
+    "amax",
+    "argmin",
+    "argmax",
+    "prod",
+    "all",
+    "norm",
+    "var",
+    "std",
+]
+
+NATIVE_REDUCE_MAP = {
+    getattr(torch.ops.aten, name): _torch_reduce(name) for name in REDUCE_NAMES
+}
+TORCH_REDUCE_MAP = {
+    getattr(torch, name): _torch_grad_reduce(name) for name in REDUCE_NAMES
+}
+TENSOR_REDUCE_MAP = {
+    getattr(torch.Tensor, name): _torch_grad_reduce(name) for name in REDUCE_NAMES
+}
+
+NATIVE_REDUCE_FNS = list(NATIVE_REDUCE_MAP.keys())
+TORCH_REDUCE_FNS = list(TORCH_REDUCE_MAP.keys())
+TENSOR_REDUCE_FNS = list(TENSOR_REDUCE_MAP.keys())
+
+
+def _is_reduction(fn):
+    return fn in NATIVE_REDUCE_MAP or fn in TORCH_REDUCE_MAP or fn in TENSOR_REDUCE_MAP
+
+
+def _apply_reduction(fn, *args, **kwargs):
+    if fn in NATIVE_REDUCE_MAP:
+        return NATIVE_REDUCE_MAP[fn](*args, **kwargs)
+    if fn in TORCH_REDUCE_MAP:
+        return TORCH_REDUCE_MAP[fn](*args, **kwargs)
+    if fn in TENSOR_REDUCE_MAP:
+        return TENSOR_REDUCE_MAP[fn](*args, **kwargs)
+    return NotImplemented
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/unary.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/unary.py
new file mode 100644
index 0000000000000000000000000000000000000000..e04ee6e810a7418829b68323097612391017b14e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/masked/maskedtensor/unary.py
@@ -0,0 +1,194 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import torch
+
+from .core import _map_mt_args_kwargs, _wrap_result
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+UNARY_NAMES = [
+    "abs",
+    "absolute",
+    "acos",
+    "arccos",
+    "acosh",
+    "arccosh",
+    "angle",
+    "asin",
+    "arcsin",
+    "asinh",
+    "arcsinh",
+    "atan",
+    "arctan",
+    "atanh",
+    "arctanh",
+    "bitwise_not",
+    "ceil",
+    "clamp",
+    "clip",
+    "conj_physical",
+    "cos",
+    "cosh",
+    "deg2rad",
+    "digamma",
+    "erf",
+    "erfc",
+    "erfinv",
+    "exp",
+    "exp2",
+    "expm1",
+    "fix",
+    "floor",
+    "frac",
+    "lgamma",
+    "log",
+    "log10",
+    "log1p",
+    "log2",
+    "logit",
+    "i0",
+    "isnan",
+    "nan_to_num",
+    "neg",
+    "negative",
+    "positive",
+    "pow",
+    "rad2deg",
+    "reciprocal",
+    "round",
+    "rsqrt",
+    "sigmoid",
+    "sign",
+    "sgn",
+    "signbit",
+    "sin",
+    "sinc",
+    "sinh",
+    "sqrt",
+    "square",
+    "tan",
+    "tanh",
+    "trunc",
+]
+
+INPLACE_UNARY_NAMES = [
+    n + "_"
+    for n in (list(set(UNARY_NAMES) - {"angle", "positive", "signbit", "isnan"}))
+]
+
+# Explicitly tracking functions we know are currently not supported
+# This might be due to missing code gen or because of complex semantics
+UNARY_NAMES_UNSUPPORTED = [
+    "atan2",
+    "arctan2",
+    "bitwise_left_shift",
+    "bitwise_right_shift",
+    "copysign",
+    "float_power",
+    "fmod",
+    "frexp",
+    "gradient",
+    "imag",
+    "ldexp",
+    "lerp",
+    "logical_not",
+    "hypot",
+    "igamma",
+    "igammac",
+    "mvlgamma",
+    "nextafter",
+    "polygamma",
+    "real",
+    "remainder",
+    "true_divide",
+    "xlogy",
+]
+
+
+def _unary_helper(fn, args, kwargs, inplace):
+    if len(kwargs) != 0:
+        raise ValueError(
+            "MaskedTensor unary ops require that len(kwargs) == 0. "
+            "If you need support for this, please open an issue on Github."
+        )
+    for a in args[1:]:
+        if torch.is_tensor(a):
+            raise TypeError(
+                "MaskedTensor unary ops do not support additional Tensor arguments"
+            )
+
+    mask_args, _mask_kwargs = _map_mt_args_kwargs(
+        args, kwargs, lambda x: x._masked_mask
+    )
+    data_args, _data_kwargs = _map_mt_args_kwargs(
+        args, kwargs, lambda x: x._masked_data
+    )
+
+    if args[0].layout == torch.sparse_coo:
+        data_args[0] = data_args[0].coalesce()
+        s = data_args[0].size()
+        i = data_args[0].indices()
+        data_args[0] = data_args[0].coalesce().values()
+        v = fn(*data_args)
+        result_data = torch.sparse_coo_tensor(i, v, size=s)
+
+    elif args[0].layout == torch.sparse_csr:
+        crow = data_args[0].crow_indices()
+        col = data_args[0].col_indices()
+        data_args[0] = data_args[0].values()
+        v = fn(*data_args)
+        result_data = torch.sparse_csr_tensor(crow, col, v)
+
+    else:
+        result_data = fn(*data_args)
+
+    if inplace:
+        args[0]._set_data_mask(result_data, mask_args[0])
+        return args[0]
+    else:
+        return _wrap_result(result_data, mask_args[0])
+
+
+def _torch_unary(fn_name):
+    fn = getattr(torch.ops.aten, fn_name)
+
+    def unary_fn(*args, **kwargs):
+        return _unary_helper(fn, args, kwargs, inplace=False)
+
+    return unary_fn
+
+
+def _torch_inplace_unary(fn_name):
+    fn = getattr(torch.ops.aten, fn_name)
+
+    def unary_fn(*args, **kwargs):
+        return _unary_helper(fn, args, kwargs, inplace=True)
+
+    return unary_fn
+
+
+NATIVE_UNARY_MAP = {
+    getattr(torch.ops.aten, name): _torch_unary(name) for name in UNARY_NAMES
+}
+NATIVE_INPLACE_UNARY_MAP = {
+    getattr(torch.ops.aten, name): _torch_inplace_unary(name)
+    for name in INPLACE_UNARY_NAMES
+}
+
+NATIVE_UNARY_FNS = list(NATIVE_UNARY_MAP.keys())
+NATIVE_INPLACE_UNARY_FNS = list(NATIVE_INPLACE_UNARY_MAP.keys())
+
+
+def _is_native_unary(fn):
+    return fn in NATIVE_UNARY_FNS or fn in NATIVE_INPLACE_UNARY_FNS
+
+
+def _apply_native_unary(fn, *args, **kwargs):
+    if fn in NATIVE_UNARY_FNS:
+        return NATIVE_UNARY_MAP[fn](*args, **kwargs)
+    if fn in NATIVE_INPLACE_UNARY_FNS:
+        return NATIVE_INPLACE_UNARY_MAP[fn](*args, **kwargs)
+    return NotImplemented
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ffd93fa0efd484c013397f60fb72920a170cabe
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/__init__.py
@@ -0,0 +1,53 @@
+# mypy: allow-untyped-defs
+r"""
+PyTorch Profiler is a tool that allows the collection of performance metrics during training and inference.
+Profiler's context manager API can be used to better understand what model operators are the most expensive,
+examine their input shapes and stack traces, study device kernel activity and visualize the execution trace.
+
+.. note::
+    An earlier version of the API in :mod:`torch.autograd` module is considered legacy and will be deprecated.
+
+"""
+import os
+
+from torch._C._autograd import _supported_activities, DeviceType, kineto_available
+from torch._C._profiler import _ExperimentalConfig, ProfilerActivity, RecordScope
+from torch._environment import is_fbcode
+from torch.autograd.profiler import KinetoStepTracker, record_function
+from torch.optim.optimizer import register_optimizer_step_post_hook
+
+from .profiler import (
+    _KinetoProfile,
+    ExecutionTraceObserver,
+    profile,
+    ProfilerAction,
+    schedule,
+    supported_activities,
+    tensorboard_trace_handler,
+)
+
+
+__all__ = [
+    "profile",
+    "schedule",
+    "supported_activities",
+    "tensorboard_trace_handler",
+    "ProfilerAction",
+    "ProfilerActivity",
+    "kineto_available",
+    "DeviceType",
+    "record_function",
+    "ExecutionTraceObserver",
+]
+
+from . import itt
+
+
+def _optimizer_post_hook(optimizer, args, kwargs):
+    KinetoStepTracker.increment_step("Optimizer")
+
+
+if os.environ.get("KINETO_USE_DAEMON", "") or (
+    is_fbcode() and os.environ.get("KINETO_FORCE_OPTIMIZER_HOOK", "")
+):
+    _ = register_optimizer_step_post_hook(_optimizer_post_hook)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/_memory_profiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/_memory_profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..f10831ade397b510b50685fcc46c50140d515a66
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/_memory_profiler.py
@@ -0,0 +1,1196 @@
+# mypy: allow-untyped-defs
+import collections
+import dataclasses
+import enum
+import itertools as it
+import logging
+from collections.abc import Iterator
+from typing import Any, cast, Optional, Union
+from typing_extensions import Literal
+
+import torch
+from torch._C import FunctionSchema
+from torch._C._autograd import _ProfilerResult
+from torch._C._profiler import (
+    _EventType,
+    _ExtraFields_Allocation,
+    _ExtraFields_TorchOp,
+    _ProfilerEvent,
+    _TensorMetadata,
+    RecordScope,
+)
+from torch._utils import _element_size
+from torch.profiler import _utils
+
+
+KeyAndID = tuple["Key", int]
+TensorAndID = tuple["TensorKey", int]
+
+log = logging.getLogger(__name__)
+
+
+class Category(enum.Enum):
+    INPUT = enum.auto()
+    TEMPORARY = enum.auto()
+    ACTIVATION = enum.auto()
+    GRADIENT = enum.auto()
+    AUTOGRAD_DETAIL = enum.auto()
+    PARAMETER = enum.auto()
+    OPTIMIZER_STATE = enum.auto()
+
+
+_CATEGORY_TO_COLORS = {
+    Category.PARAMETER: "darkgreen",
+    Category.OPTIMIZER_STATE: "goldenrod",
+    Category.INPUT: "black",
+    Category.TEMPORARY: "mediumpurple",
+    Category.ACTIVATION: "red",
+    Category.GRADIENT: "mediumblue",
+    Category.AUTOGRAD_DETAIL: "royalblue",
+    None: "grey",
+}
+
+_CATEGORY_TO_INDEX = {c: i for i, c in enumerate(_CATEGORY_TO_COLORS)}
+
+
+class Action(enum.Enum):
+    PREEXISTING = enum.auto()
+    CREATE = enum.auto()
+    INCREMENT_VERSION = enum.auto()
+    DESTROY = enum.auto()
+
+
+_ACTION_TO_INDEX = {i: i.value for i in Action}
+
+
+@dataclasses.dataclass(eq=True, unsafe_hash=False, frozen=True)
+class Key:
+    device: torch.device
+
+
+@dataclasses.dataclass
+class _Storage:
+    """Bundle storage pointer and id.
+
+    All profiling logic should use `allocation_id`, however it is useful to
+    print storage pointers for debugging and unit tests sometimes look up
+    values using the storage data pointer of a live Tensor."""
+
+    ptr: int
+    allocation_id: int
+
+    def __repr__(self) -> str:
+        return f"{hex(self.ptr):>18} ({self.allocation_id})"
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, _Storage) and self.allocation_id == other.allocation_id
+
+    def __hash__(self) -> int:
+        return hash(self.allocation_id)
+
+
+@dataclasses.dataclass(eq=True, unsafe_hash=True, frozen=True)
+class TensorKey(Key):
+    """Hashable identifier for a storage which has been asigned an ID.
+
+    A detailed description of Tensor IDs and why they are needed is given in
+    `torch/csrc/profiler/collection.h` when `TensorID` is declared. To
+    summarize, multiple Storage buffers can map to the same logical Tensor.
+    This dataclass is used to refer to a concrete in-memory StorageImpl of
+    a Tensor.
+    """
+
+    id: int
+    storage: _Storage
+
+    def __repr__(self) -> str:
+        return f"id={self.id}: {repr(self.storage):<24} ({self.device})"
+
+    def __lt__(self, other: "TensorKey") -> bool:
+        return self._as_sortable < other._as_sortable
+
+    @staticmethod
+    def _make(
+        tensor_id: Optional[int],
+        storage_ptr: Optional[int],
+        allocation_id: Optional[int],
+        device: torch.device,
+    ) -> Optional["TensorKey"]:
+        if (
+            tensor_id is not None
+            and storage_ptr is not None
+            and allocation_id is not None
+        ):
+            return TensorKey(device, tensor_id, _Storage(storage_ptr, allocation_id))
+        return None
+
+    @classmethod
+    def from_allocation(cls, alloc: _ExtraFields_Allocation) -> Optional["TensorKey"]:
+        return cls._make(alloc.id, alloc.ptr, alloc.allocation_id, alloc.device)
+
+    @classmethod
+    def from_tensor(cls, t: Optional[_TensorMetadata]) -> Optional["TensorKey"]:
+        if t is not None:
+            return cls._make(t.id, t.storage_data_ptr, t.allocation_id, t.device)
+        return None
+
+    @property
+    def _as_sortable(self) -> tuple[int, int, str, int]:
+        return self.id, self.storage.allocation_id, self.device.type, self.device.index
+
+
+def _extract_parameters_and_gradients(
+    node: _ProfilerEvent,
+) -> Iterator[tuple[Optional[TensorKey], Optional[TensorKey]]]:
+    children = node.children
+
+    # AccumulateGrad is used in the Autograd engine to handle gradient updates.
+    # There are two possible cases:
+    # 1) This is a newly created gradient Tensor. In that case there is nothing
+    #    to accumulate, so autograd simply detaches the Tensor.
+    #
+    # 2) There is a preexisting gradient Tensor and we need to add the newly
+    #    computed update. This is done with an in-place add (aten::add_) op.
+    #    (The underscore suffix denotes "in-place".)
+    if (
+        node.typed[0] == _EventType.TorchOp
+        and node.typed[1].scope == RecordScope.BACKWARD_FUNCTION
+        # TODO(robieta): Move away from load bearing names
+        and node.name == "torch::autograd::AccumulateGrad"
+        and children
+        and children[0].typed[0] == _EventType.TorchOp
+        and children[0].name in ("aten::detach", "aten::add_")
+        and children[0].typed[1].inputs
+        and isinstance(children[0].typed[1].inputs[0], _TensorMetadata)
+    ):
+        yield None, TensorKey.from_tensor(children[0].typed[1].inputs[0])
+
+    # We directly instrument `torch.nn.Module` and `torch.optim.Optimizer`
+    # NOTE: The values captured by the python tracer are cached; they can be
+    #       used to build up labels but do not imply that a Tensor was live at
+    #       a particular time.
+    elif node.typed[0] == _EventType.PyCall:
+        typed_fields = node.typed[1]
+        assert typed_fields.module is None or typed_fields.optimizer is None
+        if typed_fields.module is not None:
+            for _, p, p_grad in typed_fields.module.parameters:
+                yield TensorKey.from_tensor(p), TensorKey.from_tensor(p_grad)
+
+        if typed_fields.optimizer is not None:
+            for p, p_grad, _ in typed_fields.optimizer.parameters:
+                yield TensorKey.from_tensor(p), TensorKey.from_tensor(p_grad)
+
+
+def extract_parameters(node: _ProfilerEvent) -> Iterator[TensorKey]:
+    for p, _p_grad in _extract_parameters_and_gradients(node):
+        if p is not None:
+            yield p
+
+
+def extract_gradients(
+    node: _ProfilerEvent,
+) -> Iterator[tuple[Optional[TensorKey], TensorKey]]:
+    for p, p_grad in _extract_parameters_and_gradients(node):
+        if p_grad is not None:
+            yield p, p_grad
+
+
+def get_scopes(event: Optional[_ProfilerEvent]) -> tuple[RecordScope, ...]:
+    scopes = []
+    while event:
+        if event.typed[0] == _EventType.TorchOp:
+            scopes.append(event.typed[1].scope)
+        event = event.parent
+    return tuple(scopes)
+
+
+class SchemaMatcher:
+    """Lookup operator schema based on profiled name.
+
+    When profiling we record the operator's name but not the schema. However
+    some analysis requires that information. Fortunately we can look up
+    registered schema from the recorded name. We do not, however, record the
+    overload and so we must compare the profiled arguments with all overloads
+    to determine viable matches.
+
+    Note: Once https://github.com/pytorch/pytorch/issues/78871 is completed
+    this code will be obsolete.
+    """
+
+    @classmethod
+    def inputs_are_mutable(cls, t: _ExtraFields_TorchOp) -> tuple[Optional[bool], ...]:
+        """Determine which inputs may have mutated based on function schema.
+
+        Note that we don't need to resolve down to a single schema to perform
+        this analysis. An input is mutable if it is mutable in any overload. In
+        practice, however, it is overwhelmingly common to match a single
+        overload. If we cannot find any valid schema then we must be
+        conservative and assume all inputs are mutable.
+        """
+        mutable: Optional[list[bool]] = None
+        for schema in cls.match_schemas(t):
+            mutable = mutable or [False for _ in schema.arguments]
+            for i, arg in enumerate(schema.arguments):
+                mutable[i] |= getattr(arg.alias_info, "is_write", False)
+
+        return tuple(mutable or (None for _ in t.inputs))
+
+    @classmethod
+    def match_schemas(cls, t: _ExtraFields_TorchOp) -> tuple[FunctionSchema, ...]:
+        signature = tuple(
+            # Tensor
+            TensorKey.from_tensor(i) if isinstance(i, _TensorMetadata)
+            #
+            # TensorList
+            else [TensorKey.from_tensor(j) for j in i] if isinstance(i, list)
+            #
+            # Scalar and uncaptured inputs.
+            else i
+            for i in t.inputs
+        )
+
+        def matches(schema) -> bool:
+            return len(schema.arguments) == len(signature) and all(
+                cls._types_match(observed, schema_arg.type)
+                for observed, schema_arg in zip(signature, schema.arguments)
+            )
+
+        return tuple(s for s in cls.lookup_schemas(t.name) or () if matches(s))
+
+    @classmethod
+    def _types_match(cls, observed, schema_type) -> bool:
+        if isinstance(schema_type, torch._C.OptionalType):
+            schema_type = schema_type.getElementType()
+            return observed is None or cls._types_match(observed, schema_type)
+
+        if isinstance(schema_type, torch._C.AnyType):
+            return True
+
+        if schema_type.isSubtypeOf(torch._C.ListType.ofTensors()):
+            return isinstance(observed, list) and all(
+                isinstance(i, TensorKey) for i in observed
+            )
+
+        type_map: tuple[tuple[Any, Union[type, tuple[type, ...]]], ...] = (
+            (torch._C.TensorType, TensorKey),
+            (torch._C.NoneType, type(None)),
+            (torch._C.BoolType, bool),
+            (torch._C.IntType, int),
+            (torch._C.FloatType, float),
+            (torch._C.ComplexType, complex),
+            (torch._C.NumberType, (bool, int, float, complex)),
+        )
+
+        for jit_type, py_types in type_map:
+            if isinstance(schema_type, jit_type):
+                return isinstance(observed, py_types)
+
+        # Profiler only records a subset of possible argument types. If we
+        # reach this point then the schema must call for a type that profiler
+        # does not record. Thus, the schema can only be a match if `observed`
+        # is also None.
+        return observed is None
+
+    @staticmethod
+    def lookup_schemas(name: str) -> Optional[tuple[FunctionSchema, ...]]:
+        # TODO(robieta):
+        #   _jit_get_schemas_for_operator is quite expensive. (~100us / call)
+        #   Consider adding `functools.lru_cache` if that becomes an issue.
+
+        try:
+            # Schema lookup will throw if `name` is malformed. (For example,
+            # schemas must be namespaced and schema lookup will fail if name
+            # does not include "::".) We simply catch the exception and return
+            # `None` to denote that `name` cannot be an operator name.
+            #
+            # Note that record_function annotations also go through this path,
+            # so it is expected that some names will not correspond to PyTorch
+            # operators.
+            if "::" not in name:
+                return None
+            return tuple(torch._C._jit_get_schemas_for_operator(name))
+        except RuntimeError:
+            return None
+
+
+class OpTree:
+    def __init__(self, result: _ProfilerResult) -> None:
+        self._root_nodes = result.experimental_event_tree()
+        self._sorted_nodes = tuple(sorted(self.dfs(), key=lambda x: x.start_time_ns))
+
+    def dfs(self, *args, **kwargs) -> Iterator[_ProfilerEvent]:
+        yield from _utils.traverse_dfs(self._root_nodes, *args, **kwargs)
+
+    @property
+    def sorted_nodes(self) -> tuple[_ProfilerEvent, ...]:
+        return self._sorted_nodes
+
+
+class SizeMap:
+    def __init__(self, op_tree: OpTree) -> None:
+        self._values: dict[TensorKey, int] = {}
+
+        for node in op_tree.sorted_nodes:
+            if node.typed[0] == _EventType.TorchOp:
+                for t in self._flat_tensor_inputs(node.typed[1]):
+                    self._update_values(t)
+
+            elif node.typed[0] == _EventType.PyCall:
+                typed_fields = node.typed[1]
+                assert typed_fields.module is None or typed_fields.optimizer is None
+                if typed_fields.module is not None:
+                    for _, p, p_grad in typed_fields.module.parameters:
+                        self._update_values(p)
+                        self._update_values(p_grad)
+
+                if typed_fields.optimizer is not None:
+                    for p, p_grad, state in typed_fields.optimizer.parameters:
+                        self._update_values(p)
+                        self._update_values(p_grad)
+                        for _, t in state:
+                            self._update_values(t)
+
+        allocations: dict[TensorKey, int] = {}
+        for node in op_tree.sorted_nodes:
+            if node.typed[0] == _EventType.Allocation:
+                alloc_fields = node.typed[1]
+                key = TensorKey.from_allocation(alloc_fields)
+                if key:
+                    new_size = abs(alloc_fields.alloc_size)
+                    prior_size = allocations.setdefault(key, new_size)
+
+                    # It is possible to resize Storage in PyTorch, however we
+                    # key on data pointer so most resizes will be treated as a
+                    # change in storage. The one corner case that cannot be
+                    # handled is `realloc` which successfully resizes the
+                    # storage. At time of writing this is not done anywhere in
+                    # the core PyTorch codebase.
+                    if prior_size != new_size:
+                        delta = f"{prior_size} vs. {new_size}"
+                        log.warning("Mismatch between allocation and free: %s", delta)
+
+        self._values.update(allocations)
+
+    def _update_values(self, t: Optional[_TensorMetadata]) -> None:
+        key = TensorKey.from_tensor(t)
+        if key is not None and t is not None and t.layout == torch.strided:
+            # Scalars are represented as zero dim Tensors
+            n = max(i[0] * i[1] for i in zip(t.sizes or [1], t.strides or [1]))
+
+            num_bytes = n * _element_size(t.dtype)
+            assert num_bytes >= 0, f"{num_bytes}"
+            self._values[key] = max(self._values.get(key, 0), num_bytes)
+
+    @staticmethod
+    def _flat_tensor_inputs(op: _ExtraFields_TorchOp) -> Iterator[_TensorMetadata]:
+        for i in op.inputs:
+            if isinstance(i, _TensorMetadata):
+                yield i
+            elif isinstance(i, list):
+                yield from i
+
+    def __getitem__(self, key: TensorKey):
+        return self._values[key]
+
+
+@dataclasses.dataclass()
+class DataFlowEdge:
+    input_version: Optional[int] = None
+    mutated: Optional[bool] = False
+
+    @property
+    def is_allocation(self) -> bool:
+        return self.input_version is None
+
+    @property
+    def is_deletion(self) -> bool:
+        return self.mutated is None
+
+
+class DataFlowNode:
+    def __init__(self, event: _ProfilerEvent, graph: "DataFlowGraph") -> None:
+        self._event = event
+        self._graph = graph
+        self._edges: dict[TensorKey, DataFlowEdge] = self._determine_edges()
+
+        for key, edge in self._edges.items():
+            if edge.mutated and not edge.is_allocation:
+                self._graph.bump(key)
+
+        # Make sure the version bumping behavior matches what we expect.
+        versions = {k: (v, self._graph.lookup(k)) for k, v in self.outputs.items()}
+        assert all(i == j for i, j in versions.values()), f"{versions}, {self._edges}"
+
+    def _determine_edges(self) -> dict[TensorKey, DataFlowEdge]:
+        subtree = tuple(_utils.traverse_dfs([self._event]))
+
+        # Start by populating edges from op inputs and outputs.
+        mutable_by_key: dict[Optional[TensorKey], set[Optional[bool]]] = {}
+        for op in (i.typed[1] for i in subtree if i.typed[0] == _EventType.TorchOp):
+            for op_input, mutable in zip(
+                op.inputs, SchemaMatcher.inputs_are_mutable(op)
+            ):
+                # Tensor
+                if isinstance(op_input, _TensorMetadata):
+                    key = TensorKey.from_tensor(op_input)
+                    mutable_by_key.setdefault(key, set()).add(mutable)
+
+                # TensorList
+                elif isinstance(op_input, list):
+                    for op_input_i in op_input:
+                        key = TensorKey.from_tensor(op_input_i)
+                        mutable_by_key.setdefault(key, set()).add(mutable)
+
+        edges: collections.defaultdict[Optional[TensorKey], DataFlowEdge]
+        edges = collections.defaultdict(DataFlowEdge)
+        for key, mutable_set in mutable_by_key.items():
+            if key is not None:
+                edges[key].input_version = self._graph.lookup(key) if key else -1
+
+                # We consider an op to be mutated if we encounter a schema where it
+                # is a mutable argument OR if it is ambiguous. (We never explicitly
+                # see it in any schema.)
+                mutated = (True in mutable_set) or (tuple(mutable_set) == (None,))
+                edges[key].mutated = mutated
+
+        # Then handle deletions. Note that deleting a Tensor implicitly adds
+        # it as an input edge.
+        for i in subtree:
+            if i.typed[0] == _EventType.Allocation and i.typed[1].alloc_size < 0:
+                key = TensorKey.from_allocation(i.typed[1])
+                edge = edges[key]
+                assert key is None or edge.mutated is not None, f"Double delete: {key}"
+                edge.mutated = None
+                edge.input_version = self._graph.lookup(key) if key else -1
+
+        # And finally handle allocations. This step must be last, because the
+        # previous two steps optimistically add input edges.
+        for i in subtree:
+            if i.typed[0] == _EventType.Allocation and i.typed[1].alloc_size > 0:
+                edges[TensorKey.from_allocation(i.typed[1])].input_version = None
+
+        # We don't need to sort the inputs, but it makes debugging and unit tests nicer.
+        return dict(sorted((k, v) for k, v in edges.items() if k is not None))
+
+    @property
+    def inputs(self) -> dict[TensorKey, tuple[bool, int]]:
+        return {
+            # MyPy can't see through `is_allocation` to know that
+            # `v.input_version` is not None.
+            k: (bool(v.mutated), cast(int, v.input_version))
+            for k, v in self._edges.items()
+            if not v.is_allocation
+        }
+
+    @property
+    def outputs(self) -> dict[TensorKey, int]:
+        return {
+            k: 0 if v.input_version is None else v.input_version + 1
+            for k, v in self._edges.items()
+            if (v.is_allocation and not v.is_deletion) or v.mutated
+        }
+
+    @property
+    def intermediates(self) -> tuple[TensorKey, ...]:
+        return tuple(
+            k for k, v in self._edges.items() if v.is_allocation and v.is_deletion
+        )
+
+    @property
+    def start_time(self) -> int:
+        return self._event.start_time_ns
+
+
+class DataFlowGraph:
+    def __init__(self, op_tree: OpTree) -> None:
+        self._op_tree = op_tree
+        self._leaf_events = self._extract_leaf_events(op_tree)
+        self._active_version: dict[TensorKey, Optional[int]] = {}
+        self._flow_nodes = [DataFlowNode(e, self) for e in self.leaf_events]
+        self._flow_nodes.sort(key=lambda x: x.start_time)
+        self.validate()
+
+    @property
+    def flow_nodes(self) -> tuple[DataFlowNode, ...]:
+        return tuple(self._flow_nodes)
+
+    def validate(self):
+        # Check that each (Tensor, version) pair has a unique creation node
+        outputs: set[tuple[TensorKey, int]] = set()
+        for node in self.flow_nodes:
+            node_outputs = set(node.outputs.items())
+            duplicates = outputs & node_outputs
+            assert not duplicates, f"{node._event.name} {node._edges} {duplicates}"
+            outputs |= node_outputs
+
+        # And check that `self._nodes` forms a valid topologically sorted DAG.
+        tensor_versions: dict[TensorKey, int] = {}
+        for node in self.flow_nodes:
+            for key, (_, version) in node.inputs.items():
+                expected = tensor_versions.get(key, 0)
+                assert expected == version, (expected, version)
+
+            for key, version in node.outputs.items():
+                prior_version = tensor_versions.get(key, version)
+                assert version >= prior_version, (version, prior_version)
+                tensor_versions[key] = version
+
+    @property
+    def leaf_events(self) -> tuple[_ProfilerEvent, ...]:
+        return self._leaf_events
+
+    @staticmethod
+    def _extract_leaf_events(op_tree: OpTree) -> tuple[_ProfilerEvent, ...]:
+        """Partially traverse the op tree and extract top level ops.
+
+        Consider the following code:
+        ```
+        with record_function("My annotation"):
+            x.zero_()
+            y.zero_()
+        ```
+
+        The op tree (assuming no Autograd) will look like:
+          
+            TorchOp: "My annotation"
+              TorchOp: zero_
+                TorchOp: fill_
+              TorchOp: zero_
+                TorchOp: fill_
+
+        The recursive structure of operator calls makes data flow unwieldy.
+        In order to simplify analysis we would like to select the highest level
+        ops to represent in the graph. In this case those are the `zero_` ops;
+        the fact that `fill_` is called is an implementation detail. We also
+        do not want to group everything under "My annotation" as this could
+        create overly coarse bundles and lose critical semantics.
+
+        To address this issue we walk over the graph and select the topmost
+        torch ops ** which match at least one operator schema **. These form
+        the leaves of the first pass through the op tree. (As well as any
+        allocations or frees which do are not part of a kernel.) These events
+        form the logical nodes in our data flow graph.
+        """
+
+        leaf_events: list[_ProfilerEvent] = []
+
+        def leaf_op(e: _ProfilerEvent) -> bool:
+            return e.typed[0] == _EventType.TorchOp and (
+                e.typed[1].scope == RecordScope.BACKWARD_FUNCTION
+                or bool(SchemaMatcher.match_schemas(e.typed[1]))
+            )
+
+        def children_fn(e: _ProfilerEvent):
+            if leaf_op(e) or e.tag == _EventType.Allocation:
+                leaf_events.append(e)
+                return []
+
+            return e.children
+
+        for _ in op_tree.dfs(children_fn=children_fn):
+            pass
+
+        return tuple(sorted(leaf_events, key=lambda x: x.start_time_ns))
+
+    def lookup(self, key: TensorKey) -> int:
+        version = self._active_version.setdefault(key, 0)
+        assert version is not None
+        return version
+
+    def bump(self, key: TensorKey) -> None:
+        prior_version = self._active_version.get(key, None)
+        assert prior_version is not None
+        self._active_version[key] = prior_version + 1
+
+    def delete(self, key: TensorKey) -> None:
+        assert self._active_version.setdefault(key, 0) is not None
+        self._active_version[key] = None
+
+
+@dataclasses.dataclass
+class CategoryElement:
+    by_id: Optional[Category] = None
+    by_key: dict[TensorKey, Category] = dataclasses.field(default_factory=dict)
+    by_version: dict[TensorAndID, Category] = dataclasses.field(default_factory=dict)
+
+    # Used by unit tests to check internals. (And consequently by
+    # MemoryProfile.lookup) This should not be used in any other capacity.
+    _by_id_keyset: set[TensorKey] = dataclasses.field(default_factory=set)
+
+
+@dataclasses.dataclass
+class CategoryDict:
+    _values: collections.defaultdict[int, CategoryElement] = dataclasses.field(
+        default_factory=lambda: collections.defaultdict(CategoryElement)
+    )
+
+    def set_by_id(self, key: TensorKey, category: Category) -> None:
+        self._values[key.id].by_id = category
+        self._values[key.id]._by_id_keyset.add(key)
+
+    def set_by_key(self, key: TensorKey, category: Category) -> None:
+        self._values[key.id].by_key[key] = category
+
+    def set_by_version(self, key: TensorKey, version: int, category: Category) -> None:
+        self._values[key.id].by_version[(key, version)] = category
+
+    def setdefault_by_version(
+        self, key: TensorKey, version: int, category: Category
+    ) -> None:
+        self._values[key.id].by_version.setdefault((key, version), category)
+
+    def get(self, key: Key, version: int) -> Optional[Category]:
+        if isinstance(key, Key) and not isinstance(key, TensorKey):
+            return None
+        element = self._values[key.id]
+        return (
+            element.by_id
+            or element.by_key.get(key, None)
+            or element.by_version.get((key, version), None)
+        )
+
+
+class MemoryProfile:
+    def __init__(self, result: _ProfilerResult) -> None:
+        self._op_tree = OpTree(result)
+        self._data_flow_graph = DataFlowGraph(self._op_tree)
+        self._size_map = SizeMap(self._op_tree)
+        self._categories = CategoryDict()
+
+        self._set_gradients_and_temporaries()
+        self._set_parameters_using_python_tracer()
+        self._set_inputs()
+        self._set_parameters_using_data_flow()
+        self._set_activations()
+        self._set_optimizer_state()
+        self._set_autograd_detail()
+
+    @property
+    def timeline(self) -> tuple[tuple[int, Action, KeyAndID, int], ...]:
+        output: list[tuple[int, Action, KeyAndID, int]] = []
+        allocation_times: dict[tuple[TensorKey, bool], int] = {}
+        live_unknown: dict[tuple[int, torch.device], Literal[True]] = {}
+        for event in self._op_tree.dfs():
+            if event.typed[0] == _EventType.Allocation:
+                alloc_fields = event.typed[1]
+                alloc_size = alloc_fields.alloc_size
+                is_allocation = alloc_size > 0
+                t = event.start_time_ns
+
+                tkey = TensorKey.from_allocation(alloc_fields)
+                if tkey is not None:
+                    allocation_times[(tkey, is_allocation)] = t
+
+                else:
+                    key = Key(alloc_fields.device)
+                    ptr_and_device = (alloc_fields.ptr, key.device)
+                    if is_allocation:
+                        if ptr_and_device in live_unknown:
+                            output.append(
+                                (t, Action.INCREMENT_VERSION, (key, 0), alloc_size)
+                            )
+                        else:
+                            live_unknown[ptr_and_device] = True
+                            output.append((t, Action.CREATE, (key, 0), alloc_size))
+                    else:
+                        output.append((t, Action.DESTROY, (key, 0), -alloc_size))
+                        if not live_unknown.pop(ptr_and_device, False):
+                            output.append(
+                                (-1, Action.PREEXISTING, (key, 0), -alloc_size)
+                            )
+
+        snapshot = self._category_snapshot()
+        last_version = dict(sorted(snapshot.keys()))
+
+        events: list[tuple[int, Action, TensorAndID]] = [
+            (-1, Action.PREEXISTING, (key, version))
+            for key, version in snapshot.keys()
+            if (key, True) not in allocation_times and version == 0
+        ]
+
+        for node in self._data_flow_graph.flow_nodes:
+            for key, edge in node._edges.items():
+                if edge.is_allocation:
+                    t = allocation_times[(key, True)]
+                    events.append((t, Action.CREATE, (key, 0)))
+
+                elif edge.mutated:
+                    t = node._event.start_time_ns
+                    version = edge.input_version
+                    assert version is not None
+                    events.append((t, Action.INCREMENT_VERSION, (key, version)))
+
+                if edge.is_deletion:
+                    t = allocation_times[(key, False)]
+                    events.append((t, Action.DESTROY, (key, last_version[key])))
+
+        output.extend(
+            (time, action, (key, version), self._size_map[key])
+            for time, action, (key, version) in events
+        )
+
+        output.sort(key=lambda x: (x[0], x[1].value))
+        return tuple(output)
+
+    def _is_gradient(self, *args, **kwargs) -> bool:
+        return self._categories.get(*args, **kwargs) == Category.GRADIENT
+
+    def _category_snapshot(self) -> dict[TensorAndID, Optional[Category]]:
+        all_tensor_versions: set[TensorAndID] = set()
+
+        for node in self._data_flow_graph.flow_nodes:
+            all_tensor_versions.update(((k, v) for k, (_, v) in node.inputs.items()))
+            all_tensor_versions.update((key, 0) for key in node.intermediates)
+            all_tensor_versions.update(node.outputs.items())
+
+        for i in self._categories._values.values():
+            all_tensor_versions.update((key, 0) for key in i._by_id_keyset)
+
+        return {
+            (key, version): self._categories.get(key, version)
+            for key, version in sorted(all_tensor_versions)
+        }
+
+    def _any_version_depends_on_gradient(self) -> set[int]:
+        """Extract IDs of Tensors which depend or will depend on a gradient.
+
+        Note that this weakened definition of "depends" requires us to loop
+        over the data flow graph multiple times because it allows dependency
+        information to flow backward through edges and removes the guarantee
+        that nodes are topologically sorted. (Or indeed, even that a valid
+        topological order exists.) Put another way, we have converted an
+        acyclic data flow graph into a cyclic graph and we are attempting to
+        partition cycles involving a gradient from the rest of the graph.
+        """
+        depends_on_gradient: set[int] = set()
+        while True:
+            start_size = len(depends_on_gradient)
+            for node in self._data_flow_graph.flow_nodes:
+                ids = tuple(
+                    key.id
+                    for key, (_, version) in node.inputs.items()
+                    if self._categories.get(key, version)
+                    in (Category.GRADIENT, Category.PARAMETER)
+                    or key.id in depends_on_gradient
+                )
+
+                if ids:
+                    depends_on_gradient.update(ids)
+                    depends_on_gradient.update(key.id for key in node.outputs)
+
+            # We are guaranteed to exit because there is a finite set of
+            # TensorAndID pairs. In practice we do not expect to loop more than
+            # three times: once to identify the core parameter update loop,
+            # once to fold the first step into that loop, and a third time
+            # where no new elements are added.
+            if len(depends_on_gradient) == start_size:
+                return depends_on_gradient
+
+    def _set_gradients_and_temporaries(self) -> None:
+        """Mark Tensors which are unambiguous and simple to reason about."""
+
+        # Gradients are straightforward to detect. We directly check the
+        # `.grad` property in the Python tracer, and we can detect any new
+        # gradient Tensors from `AccumulateGrad` ops.
+        for event in self._op_tree.dfs():
+            for _, p_grad in extract_gradients(event):
+                self._categories.set_by_id(p_grad, Category.GRADIENT)
+
+        # Similarly, temporary Tensors are easy to identify and are useful to
+        # flag since they can make memory use "spikier" than one would
+        # otherwise expect.
+        for node in self._data_flow_graph.flow_nodes:
+            for i in node.intermediates:
+                self._categories.set_by_key(i, Category.TEMPORARY)
+
+    def _set_parameters_using_python_tracer(self) -> None:
+        for event in self._op_tree.dfs():
+            for p in extract_parameters(event):
+                if p is not None:
+                    self._categories.set_by_id(p, Category.PARAMETER)
+
+    def _set_inputs(self) -> None:
+        """Mark inputs based on which Tensors are updated using gradients.
+
+        The process for differentiating between inputs and activations is more
+        involved. Most Tensors in a training loop depend on at least one
+        gradient: parameters depend on them through updates, and activations
+        and optimizer state depend on them transitively through parameters.
+        Critically, we do not need to know which Tensors are parameters to
+        apply this method; we can simply walk the data flow graph to build the
+        set of all values which depend on a gradient and then obtain the set
+        of inputs from the conjugate set.
+
+        There is, however, one hiccup. The first time we see a parameter is
+        generally on the forward pass of the first step. We know from
+        inspection of the data flow graph that v1 of that Tensor depends on
+        a gradient (provided we profile an optimizer step), but not v0. To
+        address this problem we weaken the definition of "depends on a
+        gradient" to "any version of this Tensor depends on a gradient",
+        which in turn strengthens the criteria for the input set enough to
+        filter the activations in the forward pass of the first step."""
+
+        # All of this analysis is predicated on using at least one training
+        # step (or parameters from the python tracer) to partition the graph.
+        # Absent that we cannot determine which Tensors are inputs and which
+        # ones are part of the model.
+        depends_on_gradient = self._any_version_depends_on_gradient()
+
+        # We only want to annotate Tensors which actually contribute to the
+        # model calculation.
+        produces_gradient: set[TensorAndID] = set()
+        for node in reversed(self._data_flow_graph.flow_nodes):
+            tensors = {(key, version) for key, (_, version) in node.inputs.items()}
+            tensors |= node.outputs.items()
+            if any(
+                self._categories.get(*i) in (Category.GRADIENT, Category.PARAMETER)
+                or i in produces_gradient
+                for i in tensors
+            ):
+                produces_gradient |= tensors
+
+        # Don't include Tensors created in the backward pass, as these are
+        # generally Autograd implementation details rather than proper inputs.
+        input_candidates = produces_gradient.copy()
+        for node in self._data_flow_graph.flow_nodes:
+            if RecordScope.BACKWARD_FUNCTION in get_scopes(node._event):
+                input_candidates -= set(node.outputs.items())
+
+        for key, version in input_candidates:
+            if key.id not in depends_on_gradient:
+                self._categories.setdefault_by_version(key, version, Category.INPUT)
+
+    def _set_parameters_using_data_flow(self) -> None:
+        """Deduce which Tensors are parameters.
+
+        Consider the following code for the step of SGD with momentum
+        (nesterov=False), where `d_p` is the gradient of `param` and `buf` is
+        the momentum buffer.
+        ```
+          buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
+          d_p = buf
+          param.add_(d_p, alpha=-lr)
+        ```
+        Both `param` and `buf` take a gradient and perform an in-place update.
+
+        The python tracer will inspect calls to `nn.Module.forward` and
+        `optim.Optimizer.step` to extract parameter and optimizer state
+        respectively (including parameters), so this is generally a non-issue.
+
+        However as a fallback we can also exploit several properties of
+        parameters to distinguish them from other model state.
+
+        First, they are directly used in the forward pass. (At this point we
+        haven't established which parts of the graph correspond to the forward
+        pass but we can deduce enough to suffice.) Some mutable state such as
+        batch norm moving averages also contribute to the forward pass, but
+        optimizer state does not.
+
+        Second, a parameter is by definition used to compute at least one
+        gradient and depends on at least one gradient.
+        """
+        snapshot = self._category_snapshot()
+
+        # Determine which Tensors might be parameters based on forward pass
+        # data flow. Note this these are only candidates; we filter nodes that
+        # we know are part of the backward pass but that doesn't guarantee that
+        # they are part of the forward pass.
+        candidate_parameters: set[TensorAndID] = set()
+        candidate_fwd_tensors: set[TensorAndID] = {
+            i for i, category in snapshot.items() if category == Category.INPUT
+        }
+
+        for node in self._data_flow_graph.flow_nodes:
+            inputs = {(key, value) for key, (_, value) in node.inputs.items()}
+            if (
+                # Don't check nodes in the backward pass.
+                RecordScope.BACKWARD_FUNCTION not in get_scopes(node._event)
+                and not any(self._is_gradient(*i) for i in inputs)
+                and not any(self._is_gradient(*i) for i in node.outputs.items())
+                #
+                # and only check nodes which depend on an input.
+                and candidate_fwd_tensors.intersection(inputs)
+            ):
+                candidate_fwd_tensors |= node.outputs.items()
+                candidate_parameters |= inputs.difference(candidate_fwd_tensors)
+
+        # Require that each parameter eventually contributes to the value of a gradient
+        used_for_gradient: set[TensorAndID] = set()
+        for node in reversed(self._data_flow_graph.flow_nodes):
+            if any(
+                self._is_gradient(*i) or i in used_for_gradient
+                for i in node.outputs.items()
+            ):
+                used_for_gradient.update(
+                    (key, version) for key, (_, version) in node.inputs.items()
+                )
+        candidate_parameters.intersection_update(used_for_gradient)
+
+        # and depends on a gradient.
+        parameter_keys = {key.id for key, _ in candidate_parameters}
+        parameter_keys &= self._any_version_depends_on_gradient()
+
+        for key, _ in snapshot.keys():
+            if key.id in parameter_keys:
+                self._categories.set_by_id(key, Category.PARAMETER)
+
+    def _set_activations(self) -> None:
+        """Flood the graph to identify activations."""
+
+        required = {Category.INPUT, Category.ACTIVATION}
+        also_allowed = {Category.PARAMETER, Category.TEMPORARY}
+        for node in self._data_flow_graph.flow_nodes:
+            inputs = {(key, value) for key, (_, value) in node.inputs.items()}
+            input_categories = {self._categories.get(*i) for i in inputs}
+
+            if (
+                (input_categories & required)
+                and not (input_categories - (required | also_allowed))
+                #
+                # Stop filling when we reach the backward pass.
+                and RecordScope.BACKWARD_FUNCTION not in get_scopes(node._event)
+            ):
+                for i in node.outputs.items():
+                    self._categories.setdefault_by_version(*i, Category.ACTIVATION)
+
+    def _set_optimizer_state(self) -> None:
+        for event in self._op_tree.dfs():
+            if event.typed[0] == _EventType.PyCall and event.typed[1].optimizer:
+                parameters = event.typed[1].optimizer.parameters
+                for _, t in it.chain.from_iterable(
+                    (state for _, _, state in parameters)
+                ):
+                    key = TensorKey.from_tensor(t)
+                    if key is not None:
+                        self._categories.set_by_id(key, Category.OPTIMIZER_STATE)
+
+    def _set_autograd_detail(self):
+        prior = {None, Category.AUTOGRAD_DETAIL}
+        for node in self._data_flow_graph.flow_nodes:
+            if RecordScope.BACKWARD_FUNCTION in get_scopes(node._event):
+                for key, version in node.outputs.items():
+                    if version == 0 or self._categories.get(key, version - 1) in prior:
+                        self._categories.setdefault_by_version(
+                            key, version, Category.AUTOGRAD_DETAIL
+                        )
+
+
+class MemoryProfileTimeline:
+    def __init__(self, memory_profile):
+        """The minimum representation of the memory profile timeline
+        includes the memory timeline and categories. The timeline
+        consists of [timestamp, action, (TensorKey, version), numbytes]
+        elements, to denote any actions (pre-existing, create, destroy,
+        or increment_version) that occurred to a specific Tensor for a
+        chunk of memory. The categories help map each (TensorKey,
+        version) pair into a category."""
+        self.timeline = memory_profile.timeline
+        self.categories = memory_profile._categories
+
+    def _coalesce_timeline(self, device_str):
+        """Convert the memory timeline and categories into a memory plot
+        consisting of timestamps and their respective sizes by category
+        for a given device.
+
+        Input: device
+        Output: [timestamps, sizes by category]
+        """
+        device = torch.device(device_str)
+        times: list[int] = []
+        sizes: list[list[int]] = []
+
+        def update(key, version, delta):
+            category = (
+                self.categories.get(key, version)
+                if isinstance(key, TensorKey)
+                else None
+            )
+            index = _CATEGORY_TO_INDEX[category] + 1
+            sizes[-1][index] += int(delta)
+
+        t_min = -1
+        for t, action, (key, version), numbytes in self.timeline:
+            if key.device != device:
+                continue
+
+            # Convert timestamps from ns to us, to match trace events.
+            if t != -1:
+                t = int(t / 1000)
+
+            # Save the smallest timestamp to populate pre-existing allocs.
+            if t_min == -1 or (t < t_min and t > 0):
+                t_min = t
+
+            # Handle timestep
+            if len(times) == 0:
+                times.append(t)
+                sizes.append([0] + [0 for _ in _CATEGORY_TO_INDEX])
+
+            elif t != times[-1]:
+                times.append(t)
+                sizes.append(sizes[-1].copy())
+
+            # Handle memory and categories
+            if action in (Action.PREEXISTING, Action.CREATE):
+                update(key, version, numbytes)
+
+            elif action == Action.INCREMENT_VERSION:
+                update(key, version, -numbytes)
+                update(key, version + 1, numbytes)
+
+            elif action == Action.DESTROY:
+                update(key, version, -numbytes)
+
+            else:
+                raise ValueError(f"Unknown action: {action}")
+
+        times = [t_min if t < 0 else t for t in times]
+        return times, sizes
+
+    def export_memory_timeline(self, path, device_str) -> None:
+        """Saves the memory timeline as [times, sizes by category]
+        as a JSON formatted file to the given path for the given
+        device."""
+        times, sizes = self._coalesce_timeline(device_str)
+        # TODO: Write a faster serialize (orjson not available in CI)
+        import json
+
+        with open(path, "w") as f:
+            json.dump([times, sizes], f)
+
+    def export_memory_timeline_raw(self, path, device_str) -> None:
+        """Saves the memory timeline as raw memory event tuples in the
+        form of (timestamp, action, numbytes, category)
+        as a JSON formatted file to the given path for the given
+        device."""
+        device = torch.device(device_str)
+        raw_events: list[tuple[int, int, int, int]] = []
+
+        def get_category_index(key, version):
+            category = (
+                self.categories.get(key, version)
+                if isinstance(key, TensorKey)
+                else None
+            )
+            return _CATEGORY_TO_INDEX[category]
+
+        for t, action, (key, version), numbytes in self.timeline:
+            if key.device != device:
+                continue
+
+            if action in (Action.PREEXISTING, Action.CREATE):
+                raw_events.append(
+                    (
+                        t,
+                        _ACTION_TO_INDEX[action],
+                        numbytes,
+                        get_category_index(key, version),
+                    )
+                )
+
+            elif action == Action.INCREMENT_VERSION:
+                raw_events.append(
+                    (
+                        t,
+                        _ACTION_TO_INDEX[action],
+                        -numbytes,
+                        get_category_index(key, version),
+                    )
+                )
+                raw_events.append(
+                    (
+                        t,
+                        _ACTION_TO_INDEX[action],
+                        numbytes,
+                        get_category_index(key, version + 1),
+                    )
+                )
+
+            elif action == Action.DESTROY:
+                raw_events.append(
+                    (
+                        t,
+                        _ACTION_TO_INDEX[action],
+                        -numbytes,
+                        get_category_index(key, version),
+                    )
+                )
+
+            else:
+                raise ValueError(f"Unknown action: {action}")
+
+        import json
+
+        with open(path, "w") as f:
+            json.dump(raw_events, f)
+
+    def export_memory_timeline_html(
+        self, path, device_str, figsize=(20, 12), title=None
+    ) -> None:
+        """Exports the memory timeline as an HTML file which contains
+        the memory timeline plot embedded as a PNG file."""
+        # Check if user has matplotlib installed, return gracefully if not.
+        import importlib.util
+
+        matplotlib_spec = importlib.util.find_spec("matplotlib")
+        if matplotlib_spec is None:
+            print(
+                "export_memory_timeline_html failed because matplotlib was not found."
+            )
+            return
+
+        from base64 import b64encode
+        from os import remove
+        from tempfile import NamedTemporaryFile
+
+        import matplotlib.pyplot as plt
+        import numpy as np
+
+        mt = self._coalesce_timeline(device_str)
+        times, sizes = np.array(mt[0]), np.array(mt[1])
+        # For this timeline, start at 0 to match Chrome traces.
+        t_min = min(times)
+        times -= t_min
+        stacked = np.cumsum(sizes, axis=1) / 1024**3
+        device = torch.device(device_str)
+        max_memory_allocated = torch.cuda.max_memory_allocated(device)
+        max_memory_reserved = torch.cuda.max_memory_reserved(device)
+
+        # Plot memory timeline as stacked data
+        fig = plt.figure(figsize=figsize, dpi=80)
+        axes = fig.gca()
+        for category, color in _CATEGORY_TO_COLORS.items():
+            i = _CATEGORY_TO_INDEX[category]
+            axes.fill_between(
+                times / 1e3, stacked[:, i], stacked[:, i + 1], color=color, alpha=0.7
+            )
+        fig.legend(["Unknown" if i is None else i.name for i in _CATEGORY_TO_COLORS])
+        # Usually training steps are in magnitude of ms.
+        axes.set_xlabel("Time (ms)")
+        axes.set_ylabel("Memory (GB)")
+        title = "\n\n".join(
+            ([title] if title else [])
+            + [
+                f"Max memory allocated: {max_memory_allocated / (1024**3):.2f} GiB \n"
+                f"Max memory reserved: {max_memory_reserved / (1024**3):.2f} GiB"
+            ]
+        )
+        axes.set_title(title)
+
+        # Embed the memory timeline image into the HTML file
+        tmpfile = NamedTemporaryFile("wb", suffix=".png", delete=False)
+        tmpfile.close()
+        fig.savefig(tmpfile.name, format="png")
+
+        with open(tmpfile.name, "rb") as tmp:
+            encoded = b64encode(tmp.read()).decode("utf-8")
+            html = f"""
+GPU Memory Timeline HTML
+
+  
+
+"""
+
+            with open(path, "w") as f:
+                f.write(html)
+        remove(tmpfile.name)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/_pattern_matcher.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/_pattern_matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..41748ea39545a817865df3c913df904a23a0a1cb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/_pattern_matcher.py
@@ -0,0 +1,662 @@
+# mypy: allow-untyped-defs
+import json
+import math
+import os
+import re
+from typing import Optional
+
+import torch
+import torch.utils.benchmark as benchmark
+from torch._C._profiler import (
+    _EventType,
+    _ExtraFields_PyCall,
+    _ExtraFields_PyCCall,
+    _ExtraFields_TorchOp,
+    _ProfilerEvent,
+)
+from torch.profiler import profile
+from torch.profiler._utils import index_of_first_match, traverse_bfs, traverse_dfs
+
+
+class Pattern:
+    """
+    Base class for all patterns, subclass this class and implement match()
+    to define custom patterns.
+
+    In subclass, define description and skip property.
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        self.prof = prof
+        self.should_benchmark = should_benchmark
+        self.name = "Please specify a name for pattern"
+        self.description = "Please specify a description for pattern"
+        self.url = ""
+        assert prof.profiler is not None and prof.profiler.kineto_results is not None
+        self.event_tree = prof.profiler.kineto_results.experimental_event_tree()
+        self.tid_root: dict[int, list[_ProfilerEvent]] = {}
+        for event in self.event_tree:
+            self.tid_root.setdefault(event.start_tid, []).append(event)
+
+    @property
+    def skip(self):
+        return False
+
+    def report(self, event: _ProfilerEvent):
+        msg = (
+            f"{self.description}\n[Source Code Location] {source_code_location(event)}"
+        )
+        return msg
+
+    def eventTreeTraversal(self):
+        """
+        Traverse the event tree and yield all events.
+        Override this method in subclass to customize the traversal.
+        """
+        yield from traverse_dfs(self.event_tree)
+
+    def summary(self, events: list[_ProfilerEvent]):
+        default_summary = f"{self.name}: {len(events)} events matched."
+        if self.should_benchmark:
+            # If benchmark summary is not empty, use it.
+            return (
+                self.benchmark_summary(events)
+                if hasattr(self, "benchmark")  # type: ignore[attr-defined]
+                else default_summary
+            )
+        return default_summary
+
+    def benchmark_summary(self, events: list[_ProfilerEvent]):
+        def format_time(time_ns: int):
+            unit_lst = ["ns", "us", "ms"]
+            for unit in unit_lst:
+                if time_ns < 1000:
+                    return f"{time_ns:.2f} {unit}"
+                time_ns //= 1000
+            return f"{time_ns:.2f} s"
+
+        assert hasattr(self, "benchmark"), "Please implement benchmark()"
+        shapes_factor_map = self.benchmark(events)  # type: ignore[attr-defined]
+        original_time = sum(event.duration_time_ns for event in events)
+        new_time = sum(
+            shapes_factor_map[input_shapes(event)] * event.duration_time_ns
+            for event in events
+        )
+        return (
+            f"{self.name}: {len(events)} events matched. "
+            f"Total Estimated Speedup: {format_time(original_time - new_time)} ({round(original_time / new_time, 2)}X)"
+        )
+
+    def match(self, event: _ProfilerEvent):
+        """
+        Return True if the event matches the pattern.
+        This method should be overriden in subclass.
+        """
+        raise NotImplementedError
+
+    def matched_events(self):
+        if self.skip:
+            return []
+        matched_events = [
+            event for event in self.eventTreeTraversal() if self.match(event)
+        ]
+        return matched_events
+
+    def root_of(self, event: _ProfilerEvent):
+        while event.parent:
+            event = event.parent
+        return event
+
+    def siblings_of(self, event: _ProfilerEvent):
+        if event.parent:
+            children = event.parent.children
+        else:
+            children = self.tid_root[event.start_tid]
+        index = children.index(event)
+        return children[:index], children[index + 1 :]
+
+    def next_of(self, event: _ProfilerEvent):
+        _, next_events = self.siblings_of(event)
+        return next_events[0] if next_events else None
+
+    def prev_of(self, event: _ProfilerEvent):
+        prev_events, _ = self.siblings_of(event)
+        return prev_events[-1] if prev_events else None
+
+    def go_up_until(self, event: _ProfilerEvent, predicate):
+        if not event:
+            return None
+        while event.parent and not predicate(event):
+            event = event.parent
+        return event
+
+
+# Patterns
+
+
+class NamePattern(Pattern):
+    def __init__(self, prof: profile, name: str, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.description = f"Matched Name Event: {name}"
+        self.name = name
+
+    def match(self, event: _ProfilerEvent):
+        return re.search(self.name, event.name) is not None
+
+
+class ExtraCUDACopyPattern(Pattern):
+    """
+    This pattern identifies if we creates a constant tensor on CPU and immediately moves it to GPU.
+    example: torch.zeros((100, 100)).to("cuda")
+
+    Pattern:
+    build-in method                 |build-in method
+        ...                         |    aten::to
+            aten::fill_/aten::zero_ |        aten::_to_copy
+
+    Algorithm:
+    We start at node aten::to, go parent events' previous events,
+    and check if we have a aten::fill_/aten::zero_ as we keep going down the tree.
+    We always select the last child in the children list when we go down the tree.
+    If at any step we failed, it is not a match.
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Extra CUDA Copy Pattern"
+        self.description = "Filled a CPU tensor and immediately moved it to GPU. Please initialize it on GPU."
+        self.url = "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html#create-tensors-directly-on-the-target-device"
+        self.init_ops = {
+            "aten::fill_",
+            "aten::zero_",
+            "aten::normal_",
+            "aten::uniform_",
+        }
+
+    @property
+    def skip(self):
+        return not self.prof.with_stack or not self.prof.record_shapes
+
+    def match(self, event):
+        # TODO: We should also check tensor identities
+        if event.name != "aten::to":
+            return False
+        to_event = event
+        if not event.children:
+            return False
+        event = event.children[-1]
+        if event.name != "aten::_to_copy":
+            return False
+        if not event.children:
+            return False
+        event = event.children[-1]
+        if event.name != "aten::copy_":
+            return False
+        # aten::copy_ should have the first 2 args dtype the same
+        dtypes = input_dtypes(event)
+        if len(dtypes) < 2:
+            return False
+        if dtypes[0] is None or dtypes[0] != dtypes[1]:
+            return False
+        event = to_event
+        # Up one level
+        event = event.parent
+        if event is None:
+            return False
+        # Check if we have a aten::fill_ in previous leaf
+        event = self.prev_of(event)
+        if event is None:
+            return False
+        while event.children:
+            event = event.children[-1]
+            # aten::zero_ is a special optimzation case where fill_ is not called
+            if event.name in self.init_ops:
+                return True
+        return event.name in self.init_ops
+        # TODO: Check if tensor is reused
+
+    def benchmark(self, events: list[_ProfilerEvent]):
+        shapes_factor_map = {input_shapes(event): 0.0 for event in events}
+        for shape in shapes_factor_map:
+            size = shape[0]
+            to_timer = benchmark.Timer(
+                stmt='torch.ones(size).to("cuda")', globals={"size": size}
+            )
+            de_timer = benchmark.Timer(
+                stmt='torch.ones(size, device="cuda")', globals={"size": size}
+            )
+            to_time = to_timer.timeit(10).mean
+            de_time = de_timer.timeit(10).mean
+            shapes_factor_map[shape] = de_time / to_time
+        return shapes_factor_map
+
+
+class ForLoopIndexingPattern(Pattern):
+    """
+    This pattern identifies if we use a for loop to index a tensor that
+    can be vectorized.
+    example:
+    tensor = torch.empty((100, 100))
+    for i in range(100):
+        tensor[i] = i
+
+    Pattern:
+    aten::select | ... | aten::select | ... (Repeat)
+
+    Algorithm:
+    We start at node aten::select, and we check if we can find this alternating patterns.
+    We also keep a dictionary to avoid duplicate match in the for loop.
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "For Loop Indexing Pattern"
+        self.description = "For loop indexing detected. Vectorization recommended."
+        self.visited: set[int] = set()
+
+    def eventTreeTraversal(self):
+        """
+        We need to use BFS traversal order to avoid duplicate match.
+        """
+        yield from traverse_bfs(self.event_tree)
+
+    def match(self, event: _ProfilerEvent):
+        if event.name != "aten::select":
+            return False
+        if event.id in self.visited:
+            return False
+        repeat_count = 1
+        _, next = self.siblings_of(event)
+        if len(next) <= 1:
+            return False
+
+        # Custom event list matching
+        def same_ops(list1, list2):
+            if len(list1) != len(list2):
+                return False
+            for op1, op2 in zip(list1, list2):
+                if op1.name != op2.name:
+                    return False
+            return True
+
+        # Record the ops between two aten::select
+        next_select_idx = index_of_first_match(next, lambda e: e.name == "aten::select")
+        if next_select_idx is None:
+            return False
+        indexing_ops = [event] + next[:next_select_idx]
+        next = next[len(indexing_ops) - 1 :]
+        for i in range(0, len(next), len(indexing_ops)):
+            if same_ops(indexing_ops, next[i : i + len(indexing_ops)]):
+                repeat_count += 1
+                self.visited.add(next[i].id)
+            else:
+                break
+        return repeat_count >= 10
+
+
+class FP32MatMulPattern(Pattern):
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "FP32 MatMul Pattern"
+        self.description = (
+            "You are currently using GPU that supports TF32. "
+            "Please enable TF32 by setting 'torch.backends.cuda.matmul.allow_tf32 = True'"
+        )
+        self.url = "https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+
+    @property
+    def skip(self):
+        if torch.version.hip is not None:
+            has_tf32 = False
+        else:
+            # Anything less than sm_80 is not Ampere which doesn't support TF32
+            has_tf32 = all(int(arch[3:]) >= 80 for arch in torch.cuda.get_arch_list())
+        return has_tf32 is False or super().skip or not self.prof.record_shapes
+
+    def match(self, event: _ProfilerEvent):
+        # If we saw this pattern once, we don't need to match it again
+        if event.tag != _EventType.TorchOp:
+            return False
+        assert isinstance(event.extra_fields, _ExtraFields_TorchOp)
+        if event.name == "aten::mm":
+            if event.extra_fields.allow_tf32_cublas is False:
+                return True
+        return False
+
+    def report(self, event: _ProfilerEvent):
+        return self.description
+
+    def benchmark(self, events: list[_ProfilerEvent]):
+        shapes_factor_map = {input_shapes(event): 0.0 for event in events}
+        for shape in shapes_factor_map:
+            matrixA = torch.randn(shape[0], device="cuda", dtype=torch.float32)
+            matrixB = torch.randn(shape[1], device="cuda", dtype=torch.float32)
+            fp32_timer = benchmark.Timer(
+                stmt="torch.mm(matrixA, matrixB)",
+                globals={"matrixA": matrixA, "matrixB": matrixB},
+            )
+            tf32_timer = benchmark.Timer(
+                stmt="torch.mm(matrixA, matrixB)",
+                setup="torch.backends.cuda.matmul.allow_tf32 = True",
+                globals={"matrixA": matrixA, "matrixB": matrixB},
+            )
+            torch.backends.cuda.matmul.allow_tf32 = False
+            fp32_time = fp32_timer.timeit(10).mean
+            tf32_time = tf32_timer.timeit(10).mean
+            shapes_factor_map[shape] = tf32_time / fp32_time
+        return shapes_factor_map
+
+
+class OptimizerSingleTensorPattern(Pattern):
+    """
+    This pattern identifies if we are using the single-tensor version of an optimizer.
+    example:
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
+    By adding foreach=True to enable multi-tensor optimizer, we can gain speedup when
+    the kernels are relatively small.
+
+    Pattern:
+    XXXXX: _single_tenser_
+
+    Algorithm:
+    String match
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Optimizer Single Tensor Pattern"
+        self.optimizers_with_foreach = ["adam", "sgd", "adamw"]
+        self.description = (
+            "Deteced optimizer running with single tensor implementation. "
+            "Please enable multi tensor implementation by passing 'foreach=True' into optimizer."
+        )
+        self.url = ""
+
+    def match(self, event: _ProfilerEvent):
+        for optimizer in self.optimizers_with_foreach:
+            if event.name.endswith(f"_single_tensor_{optimizer}"):
+                return True
+        return False
+
+
+class SynchronizedDataLoaderPattern(Pattern):
+    """
+    This pattern identifies if we are using num_workers=0 in DataLoader.
+    example:
+    torch.utils.data.DataLoader(dataset, batch_size=batch_size)
+    Add num_workers=N to the arguments. N depends on system configuration.
+
+    Pattern:
+    dataloader.py(...): __iter__
+        dataloader.py(...): _get_iterator
+            NOT dataloader.py(...): check_worker_number_rationality
+
+    Algorithm:
+    If we don't see check_worker_number_rationality call in the dataloader __iter__,
+    It is not an asynchronous dataloader.
+
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Synchronized DataLoader Pattern"
+        self.description = (
+            "Detected DataLoader running with synchronized implementation. "
+            "Please enable asynchronous dataloading by setting num_workers > 0 when initializing DataLoader."
+        )
+        self.url = (
+            "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html"
+            "#enable-async-data-loading-and-augmentation"
+        )
+
+    def match(self, event: _ProfilerEvent):
+        def is_dataloader_function(name: str, function_name: str):
+            return name.startswith(
+                os.path.join("torch", "utils", "data", "dataloader.py")
+            ) and name.endswith(function_name)
+
+        # TODO: fixme! Due to lifetime issues of the function name, this field might
+        # actually point to an already freed string when the even is a PyCall.
+        # Just silently skip this to unblock testing.
+        try:
+            event.name
+        except UnicodeDecodeError:
+            return False
+
+        if not is_dataloader_function(event.name, "__iter__"):
+            return False
+        if not event.children:
+            return False
+        event = event.children[0]
+        if not is_dataloader_function(event.name, "_get_iterator"):
+            return False
+        if not event.children:
+            return False
+        event = event.children[0]
+        return not is_dataloader_function(event.name, "check_worker_number_rationality")
+        # TODO: We should also check if the loader is bottleneck.
+
+
+class GradNotSetToNonePattern(Pattern):
+    """
+    This pattern identifies if we are not setting grad to None in zero_grad.
+    example:
+    optimizer.zero_grad()
+    By setting set_to_none=True, we can gain speedup
+
+    Pattern:
+    XXXXX: _zero_grad
+        NOT aten::zeros
+            aten::zero_
+
+    aten::zero_ is called on each parameter in the model.
+    We also want to make sure it is not called by aten::zeros.
+
+    Algorithm:
+    String match
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Gradient Set To Zero Instead of None Pattern"
+        self.description = (
+            "Detected gradient set to zero instead of None. "
+            "Please add 'set_to_none=True' when calling zero_grad()."
+        )
+        self.url = (
+            "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html"
+            "#disable-gradient-calculation-for-validation-or-inference"
+        )
+
+    def match(self, event: _ProfilerEvent):
+        if not event.name.endswith(": zero_grad"):
+            return False
+        if not event.children:
+            return False
+
+        for sub_event in traverse_dfs(event.children):
+            if (
+                sub_event.name == "aten::zero_"
+                and sub_event.parent.name != "aten::zeros"
+            ):
+                return True
+        # TODO: We should also check if the optimizer's numerical behavior will change.
+        return False
+
+
+class Conv2dBiasFollowedByBatchNorm2dPattern(Pattern):
+    """
+    This pattern identifies if we are enabling bias in Conv2d which is followed by BatchNorm2d.
+    Bias doesn't do anything when followed by batchnorm.
+    Pattern:
+    nn.Module: Conv2d            | nn.Module: BatchNorm2d
+        ...
+            aten::conv2d AND dtype of third argument is not null
+    The third argument is the bias
+    Algorithm:
+    String match
+    """
+
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Enabling Bias in Conv2d Followed By BatchNorm Pattern"
+        self.description = "Detected bias enabled in Conv2d that is followed by BatchNorm2d. Please set 'bias=False' in Conv2d."
+        self.url = (
+            "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html"
+            "#disable-bias-for-convolutions-directly-followed-by-a-batch-norm"
+        )
+
+    @property
+    def skip(self):
+        return self.prof.record_shapes is False or super().skip
+
+    def match(self, event: _ProfilerEvent):
+        if event.name != "aten::conv2d":
+            return False
+        if len(input_dtypes(event)) < 3 or input_dtypes(event)[2] is None:
+            return False
+        # This means bias=True
+        event = self.go_up_until(
+            event, lambda e: e.name.startswith("nn.Module: Conv2d")
+        )
+        if not event:
+            return False
+        event = self.next_of(event)
+        if not event:
+            return False
+        return event.name.startswith("nn.Module: BatchNorm2d")
+
+
+class MatMulDimInFP16Pattern(Pattern):
+    def __init__(self, prof: profile, should_benchmark: bool = False):
+        super().__init__(prof, should_benchmark)
+        self.name = "Matrix Multiplication Dimension Not Aligned Pattern"
+        self.description = "Detected matmul with dimension not aligned. Please use matmul with aligned dimension."
+        self.url = "https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html#use-mixed-precision-and-amp"
+
+    @property
+    def skip(self):
+        return not self.prof.with_stack or not self.prof.record_shapes
+
+    def match(self, event: _ProfilerEvent):
+        def mutiple_of(shapes, multiple):
+            return all(dim % multiple == 0 for shape in shapes for dim in shape[-2:])
+
+        if event.name not in ("aten::mm", "aten::bmm", "aten::addmm"):
+            return False
+        if not input_dtypes(event):
+            return False
+        arg_dtype = input_dtypes(event)[0]
+        if arg_dtype in (torch.bfloat16, torch.half) and not mutiple_of(
+            input_shapes(event), 8
+        ):
+            return True
+        return False
+
+    def benchmark(self, events: list[_ProfilerEvent]):
+        def closest_multiple(shapes, multiple):
+            return [multiple * math.ceil(shape / multiple) for shape in shapes]
+
+        shapes_factor_map = {input_shapes(event): 0.0 for event in events}
+        for shape in shapes_factor_map:
+            matrixA = torch.randn(shape[0], device="cuda", dtype=torch.float16)
+            matrixB = torch.randn(shape[1], device="cuda", dtype=torch.float16)
+            not_aligned_dim_timer = benchmark.Timer(
+                stmt="torch.mm(matrixA, matrixB)",
+                globals={"matrixA": matrixA, "matrixB": matrixB},
+            )
+            matrixA = torch.randn(
+                closest_multiple(shape[0], 8), device="cuda", dtype=torch.float16
+            )
+            matrixB = torch.randn(
+                closest_multiple(shape[1], 8), device="cuda", dtype=torch.float16
+            )
+            aligned_dim_timer = benchmark.Timer(
+                stmt="torch.mm(matrixA, matrixB)",
+                globals={"matrixA": matrixA, "matrixB": matrixB},
+            )
+            not_aligned_dim_time = not_aligned_dim_timer.timeit(10).mean
+            aligned_dim_time = aligned_dim_timer.timeit(10).mean
+            shapes_factor_map[shape] = aligned_dim_time / not_aligned_dim_time
+        return shapes_factor_map
+
+
+def source_code_location(event: Optional[_ProfilerEvent]):
+    while event:
+        if event.tag == _EventType.PyCall or event.tag == _EventType.PyCCall:
+            assert isinstance(
+                event.extra_fields, (_ExtraFields_PyCall, _ExtraFields_PyCCall)
+            )
+            if not event.extra_fields.caller.file_name.startswith("torch" + os.sep):
+                return f"{event.extra_fields.caller.file_name}:{event.extra_fields.caller.line_number}"
+        event = event.parent
+    return "No source code location found"
+
+
+def input_shapes(event: _ProfilerEvent):
+    assert isinstance(event.extra_fields, _ExtraFields_TorchOp)
+    return tuple(tuple(getattr(i, "sizes", ())) for i in event.extra_fields.inputs)
+
+
+def input_dtypes(event: _ProfilerEvent):
+    assert isinstance(event.extra_fields, _ExtraFields_TorchOp)
+    return tuple(getattr(i, "dtype", None) for i in event.extra_fields.inputs)
+
+
+def report_all_anti_patterns(
+    prof,
+    should_benchmark: bool = False,
+    print_enable: bool = True,
+    json_report_dir: Optional[str] = None,
+):
+    report_dict: dict = {}
+    anti_patterns = [
+        ExtraCUDACopyPattern(prof, should_benchmark),
+        # ForLoopIndexingPattern(prof, should_benchmark),
+        FP32MatMulPattern(prof, should_benchmark),
+        OptimizerSingleTensorPattern(prof, should_benchmark),
+        SynchronizedDataLoaderPattern(prof, should_benchmark),
+        GradNotSetToNonePattern(prof, should_benchmark),
+        Conv2dBiasFollowedByBatchNorm2dPattern(prof, should_benchmark),
+        MatMulDimInFP16Pattern(prof, should_benchmark),
+    ]
+    reported = set()
+    summaries = []
+    message_list = [f"{'-' * 40}TorchTidy Report{'-' * 40}"]
+    message_list.append("Matched Events:")
+
+    for anti_pattern in anti_patterns:
+        matched_events = anti_pattern.matched_events()
+        if not matched_events:
+            continue
+        summaries.append(anti_pattern.summary(matched_events))
+        for event in matched_events:
+            report_msg = anti_pattern.report(event)
+            if report_msg not in reported:
+                message_list.append(report_msg)
+                reported.add(report_msg)
+                src_location, line_no = source_code_location(event).split(":")
+                report_dict.setdefault(src_location, []).append(
+                    {
+                        "line_number": int(line_no),
+                        "name": anti_pattern.name,
+                        "url": anti_pattern.url,
+                        "message": anti_pattern.description,
+                    }
+                )
+
+    if json_report_dir is not None:
+        json_report_path = os.path.join(json_report_dir, "torchtidy_report.json")
+        if os.path.exists(json_report_path):
+            with open(json_report_path) as f:
+                exisiting_report = json.load(f)
+                exisiting_report.update(report_dict)
+                report_dict = exisiting_report
+        with open(json_report_path, "w") as f:
+            json.dump(report_dict, f, indent=4)
+
+    message_list.append("Summary:")
+    message_list += summaries
+    message_list.append(f"{'-' * 40}TorchTidy Report{'-' * 40}")
+    if print_enable:
+        print("\n".join(message_list))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..af693aecdde10f5f128b47a6eaa50807d2091ea7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/_utils.py
@@ -0,0 +1,385 @@
+# mypy: allow-untyped-defs
+import functools
+import operator
+import re
+from collections import deque
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+from torch.autograd.profiler import profile
+from torch.profiler import DeviceType
+
+
+if TYPE_CHECKING:
+    from torch.autograd import _KinetoEvent
+
+
+def _traverse(tree, next_fn, children_fn=lambda x: x.children, reverse: bool = False):
+    order = reversed if reverse else lambda x: x
+    remaining = deque(order(tree))
+    while remaining:
+        curr_event = next_fn(remaining)
+        yield curr_event
+        for child_event in order(children_fn(curr_event)):
+            remaining.append(child_event)
+
+
+traverse_dfs = functools.partial(_traverse, next_fn=lambda x: x.pop(), reverse=True)
+traverse_bfs = functools.partial(
+    _traverse, next_fn=lambda x: x.popleft(), reverse=False
+)
+
+
+@dataclass
+class EventMetrics:
+    duration_time_ns: int = 0
+    self_time_ns: int = 0
+    idle_time_ns: int = 0
+    queue_depth: int = 0
+
+    @property
+    def fraction_idle_time(self):
+        if self.duration_time_ns == 0:
+            return 0.0
+        return self.idle_time_ns / self.duration_time_ns
+
+
+@dataclass
+class Interval:
+    start: int
+    end: int
+    queue_depth: int = 0
+
+
+class EventKey:
+    def __init__(self, event):
+        self.event = event
+
+    def __hash__(self):
+        return hash(self.event.id)
+
+    def __eq__(self, other):
+        return self.event.id == other.event.id
+
+    def __repr__(self):
+        return f"{self.event.name}"
+
+    def intervals_overlap(self, intervals: list[Interval]):
+        overlap_time = 0
+        intervals = sorted(intervals, key=lambda x: x.start)
+
+        if intervals:
+            overlap_start = max(self.event.start_time_ns, intervals[0].start)
+            overlap_end = min(self.event.end_time_ns, intervals[0].end)
+
+            if overlap_start < overlap_end:
+                overlap_time += overlap_end - overlap_start
+
+        i, j = 0, 1
+        while j < len(intervals):
+            prev_interval = intervals[i]
+            curr_interval = intervals[j]
+            j += 1
+            if prev_interval.end > curr_interval.start:
+                # Completely subsumed by previous interval
+                if prev_interval.end > curr_interval.end:
+                    j += 1
+                    continue
+                else:
+                    curr_interval.start = prev_interval.end
+                    i = j
+
+            overlap_start = max(self.event.start_time_ns, curr_interval.start)
+            overlap_end = min(self.event.end_time_ns, curr_interval.end)
+            if overlap_start < overlap_end:
+                overlap_time += overlap_end - overlap_start
+
+        return overlap_time
+
+
+class BasicEvaluation:
+    def __init__(self, prof: profile):
+        self.profile = prof
+        self.metrics: dict[EventKey, EventMetrics] = {}
+        self.compute_self_time()
+        self.event_keys = sorted(
+            (e for e in self.metrics.keys()), key=lambda x: x.event.start_time_ns
+        )
+        self.events = [e.event for e in self.event_keys]
+        self.cuda_events: list[_KinetoEvent] = []
+        self.queue_depth_list = self.compute_queue_depth()
+        self.compute_idle_time()
+
+    def compute_self_time(self):
+        """
+        Computes event's self time(total time - time in child ops).
+        """
+        assert self.profile.kineto_results is not None
+        stack = deque(self.profile.kineto_results.experimental_event_tree())
+
+        # standard iterating dfs
+        while stack:
+            curr_event = stack.pop()
+            self_time = curr_event.duration_time_ns
+            for child_event in curr_event.children:
+                self_time -= child_event.duration_time_ns
+                stack.append(child_event)
+            assert (
+                EventKey(curr_event) not in self.metrics
+            ), f"Duplicate id: {curr_event.id}, {curr_event.name}"
+            self.metrics[EventKey(curr_event)] = EventMetrics(self_time_ns=self_time)
+            self.metrics[
+                EventKey(curr_event)
+            ].duration_time_ns = curr_event.duration_time_ns
+
+    def compute_queue_depth(self):
+        """
+        Computes queue_depth at each event. This will calculate the queue depth data for
+        All the events in the tree.
+        This will return a list of Interval of queue depth data of cuda launch and kernels.
+        """
+        assert self.profile.kineto_results is not None
+        cuda_event_list = self.profile.kineto_results.events()
+
+        def is_cuda_launch_kernel(e):
+            # TODO: find a better way to identify cudaLaunchKernel
+            return e.name == "cudaLaunchKernel"
+
+        def is_cuda_kernel(e):
+            # TODO: find a better way to identify CUDA Kernel
+            return e.device_type() == DeviceType.CUDA and "mem" not in e.name.lower()
+
+        cuda_launch_events = sorted(
+            (e for e in cuda_event_list if is_cuda_launch_kernel(e)),
+            key=lambda x: x.start_ns(),
+        )
+        cuda_kernel_events = sorted(
+            (e for e in cuda_event_list if is_cuda_kernel(e)),
+            key=lambda x: x.start_ns(),
+        )
+
+        self.cuda_events = sorted(
+            cuda_launch_events + cuda_kernel_events, key=lambda x: x.start_ns()
+        )
+
+        kernel_mapping: dict[_KinetoEvent, int] = {}
+        last_mapped_kernel = 0
+        for cuda_launch_event in cuda_launch_events:
+            index = index_of_first_match(
+                cuda_kernel_events,
+                lambda x: x.linked_correlation_id()
+                == cuda_launch_event.linked_correlation_id(),
+                start=last_mapped_kernel,
+            )
+            kernel_mapping[cuda_launch_event] = index
+            last_mapped_kernel = index if index is not None else last_mapped_kernel
+
+        current_kernel_index = 0
+        spawned_kernel_index = -1
+
+        all_events = cuda_launch_events + cuda_kernel_events + self.events
+
+        def new_old_event_comparator(event):
+            if hasattr(event, "start_us"):
+                return event.start_us() * 1000
+            if hasattr(event, "start_ns"):
+                return event.start_ns()
+            if hasattr(event, "start_time_ns"):
+                return event.start_time_ns
+            raise Exception("Unknown Event Type")  # noqa: TRY002
+
+        queue_depth_list: list[Interval] = []
+        all_events.sort(key=new_old_event_comparator)
+        for event in all_events:
+            # Find latest cuda kernel event
+            if hasattr(event, "start_us"):
+                start_time = event.start_us() * 1000
+                end_time = (event.start_us() + event.duration_us()) * 1000
+                # Find current spawned cuda kernel event
+                if event in kernel_mapping and kernel_mapping[event] is not None:
+                    spawned_kernel_index = kernel_mapping[event]
+            if hasattr(event, "start_ns"):
+                start_time = event.start_ns()
+                end_time = event.start_ns() + event.duration_ns()
+                # Find current spawned cuda kernel event
+                if event in kernel_mapping and kernel_mapping[event] is not None:
+                    spawned_kernel_index = kernel_mapping[event]
+            elif hasattr(event, "start_time_ns"):
+                start_time = event.start_time_ns  # type: ignore[attr-defined]
+                end_time = event.end_time_ns  # type: ignore[attr-defined]
+
+            while (
+                current_kernel_index < len(cuda_kernel_events)
+                and (cuda_kernel_events[current_kernel_index].start_ns())
+                <= start_time  # type: ignore[possibly-undefined]
+            ):
+                current_kernel_index += 1
+            current_queue_depth = spawned_kernel_index - current_kernel_index + 1
+            current_queue_depth = max(current_queue_depth, 0)
+
+            if hasattr(event, "start_us") or hasattr(event, "start_ns"):
+                queue_depth_list.append(
+                    Interval(start_time, end_time, current_queue_depth)  # type: ignore[possibly-undefined]
+                )
+            elif hasattr(event, "start_time_ns"):
+                self.metrics[EventKey(event)].queue_depth = current_queue_depth
+
+        return queue_depth_list
+
+    def compute_idle_time(self):
+        """
+        Computes idle time of the profile.
+        """
+        # Based on queue_depth_list, we can calculate idle time for all the events
+        idle = False
+        idle_start = 0
+        idle_intervals: list[Interval] = []
+        if self.queue_depth_list and self.events:
+            idle_intervals += [
+                Interval(self.events[0].start_time_ns, self.queue_depth_list[0].start),
+                Interval(self.queue_depth_list[-1].end, self.events[-1].end_time_ns),
+            ]
+
+        for data_point in self.queue_depth_list:
+            if data_point.queue_depth == 0 and not idle:
+                idle_start = data_point.end
+                idle = True
+            if data_point.queue_depth > 0 and idle:
+                idle_intervals.append(Interval(idle_start, data_point.start))
+                idle = False
+
+        event_list = [e.event for e in self.metrics.keys()]
+        for event in event_list:
+            self.metrics[EventKey(event)].idle_time_ns = EventKey(
+                event
+            ).intervals_overlap(idle_intervals)
+
+    def rank_events(self, length):
+        """
+        Filter and Rank the events based on some heuristics:
+        1) Events that are in the falling phase of the queue depth.
+        2) Events that have a high idle_time, self_time difference.
+
+        Parameters:
+            length: The number of events to return.
+        """
+
+        # Find the interval when qd is falling to 0
+        import torch
+
+        queue_depth_list = list(reversed(self.queue_depth_list))
+        qd_values = [e.queue_depth for e in queue_depth_list]
+
+        bottom_threashold = 0
+        top_threashold = 4
+        decrease_interval = []
+        i = 0
+        while i < len(qd_values):
+            if qd_values[i] > bottom_threashold:
+                i += 1
+                continue
+            for j in range(i + 1, len(qd_values)):
+                # Find next zero and if the max value between them exceeds
+                # the threshold, then we have a falling interval
+                next_minimum_idx = index_of_first_match(
+                    qd_values, lambda x: x <= bottom_threashold, start=j
+                )
+                peak_idx = argmax(qd_values, start=j, end=next_minimum_idx)
+
+                # if is a valid peak, we add to list and continue
+                if peak_idx is not None and qd_values[peak_idx] >= top_threashold:
+                    decrease_interval.append(
+                        Interval(
+                            queue_depth_list[peak_idx].start, queue_depth_list[i].start
+                        )
+                    )
+                    i = next_minimum_idx if next_minimum_idx is not None else i
+                    break
+            i += 1
+        # Filter out events that are not in the decrease interval
+        event_list = [
+            event
+            for event in self.metrics.keys()
+            if event.intervals_overlap(decrease_interval)
+        ]
+        if event_list:
+            self_time = torch.tensor(
+                [self.metrics[event].self_time_ns for event in event_list],
+                dtype=torch.float32,
+            )
+            idle_time = torch.tensor(
+                [self.metrics[event].fraction_idle_time for event in event_list],
+                dtype=torch.float32,
+            )
+            normalized_gain = (idle_time - torch.mean(idle_time)) / torch.std(idle_time)
+            normalized_self = (self_time - torch.mean(self_time)) / torch.std(self_time)
+            heuristic_score_list = normalized_gain + 0.6 * normalized_self
+
+            # Sort events by heuristic
+            event_list = [
+                event
+                for _, event in sorted(
+                    zip(heuristic_score_list, event_list),
+                    key=operator.itemgetter(0),
+                    reverse=True,
+                )
+            ]
+            event_list = event_list[:length]
+        return event_list
+
+    def get_optimizable_events(self, length: int = 1, print_enable: bool = True):
+        event_list = self.rank_events(length)
+        if not print_enable:
+            return event_list
+        output = "Optimizable events:\n" if event_list else "No events to optimize\n"
+
+        output += "\n".join(
+            [
+                f"""{'-' * 80}
+Event:                {event}
+Source code location: {source_code_location(event.event)}
+Percentage idle time: {self.metrics[event].fraction_idle_time * 100:.2f}%
+{'-' * 80}"""
+                for event in event_list
+            ]
+        )
+        if print_enable:
+            print(output)
+        return event_list
+
+
+def index_of_first_match(seq, predicate, start=0, end=None):
+    if end is None or end >= len(seq):
+        end = len(seq)
+    for i in range(start, end):
+        if predicate(seq[i]):
+            return i
+    return None
+
+
+def argmax(seq, key=lambda x: x, start=0, end=None):
+    seq = seq[start:end]
+    if len(seq) == 0:
+        return None
+    return seq.index(max(seq, key=key)) + start
+
+
+def source_code_location(event):
+    while event is not None:
+        match = re.search(r"\.py\(.*\)", event.name)
+        if match is None:
+            event = event.parent
+            continue
+        return event.name
+    return "No source code location found"
+
+
+# Provide an OSS workaround for cudagraphs + CUPTI issue
+# https://github.com/pytorch/pytorch/issues/75504
+# TODO(dberard) - deprecate / remove workaround for CUDA >= 12, when
+# we stop supporting older CUDA versions.
+def _init_for_cuda_graphs():
+    from torch.autograd.profiler import profile
+
+    with profile():
+        pass
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/itt.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/itt.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d4bda2b3420bdb367033aba2c0ef426bdd2a59a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/itt.py
@@ -0,0 +1,80 @@
+# mypy: allow-untyped-defs
+from contextlib import contextmanager
+
+
+try:
+    from torch._C import _itt
+except ImportError:
+
+    class _ITTStub:
+        @staticmethod
+        def _fail(*args, **kwargs):
+            raise RuntimeError(
+                "ITT functions not installed. Are you sure you have a ITT build?"
+            )
+
+        @staticmethod
+        def is_available():
+            return False
+
+        rangePush = _fail
+        rangePop = _fail
+        mark = _fail
+
+    _itt = _ITTStub()  # type: ignore[assignment]
+
+
+__all__ = ["is_available", "range_push", "range_pop", "mark", "range"]
+
+
+def is_available():
+    """
+    Check if ITT feature is available or not
+    """
+    return _itt.is_available()
+
+
+def range_push(msg):
+    """
+    Pushes a range onto a stack of nested range span.  Returns zero-based
+    depth of the range that is started.
+
+    Arguments:
+        msg (str): ASCII message to associate with range
+    """
+    return _itt.rangePush(msg)
+
+
+def range_pop():
+    """
+    Pops a range off of a stack of nested range spans. Returns the
+    zero-based depth of the range that is ended.
+    """
+    return _itt.rangePop()
+
+
+def mark(msg):
+    """
+    Describe an instantaneous event that occurred at some point.
+
+    Arguments:
+        msg (str): ASCII message to associate with the event.
+    """
+    return _itt.mark(msg)
+
+
+@contextmanager
+def range(msg, *args, **kwargs):
+    """
+    Context manager / decorator that pushes an ITT range at the beginning
+    of its scope, and pops it at the end. If extra arguments are given,
+    they are passed as arguments to msg.format().
+
+    Args:
+        msg (str): message to associate with the range
+    """
+    range_push(msg.format(*args, **kwargs))
+    try:
+        yield
+    finally:
+        range_pop()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/profiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecc848c20e7ead1b948abb7f6e49968ca45472b5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/profiler.py
@@ -0,0 +1,1122 @@
+# mypy: allow-untyped-defs
+import gzip
+import json
+import os
+import shutil
+import tempfile
+from abc import ABC, abstractmethod
+from collections.abc import Iterable
+from enum import Enum
+from functools import partial
+from typing import Any, Callable, Optional
+from typing_extensions import Self
+from warnings import warn
+
+import torch
+import torch.autograd.profiler as prof
+from torch._C import _get_privateuse1_backend_name
+from torch._C._profiler import (
+    _add_execution_trace_observer,
+    _disable_execution_trace_observer,
+    _enable_execution_trace_observer,
+    _ExperimentalConfig,
+    _remove_execution_trace_observer,
+)
+from torch._environment import is_fbcode
+from torch.autograd import kineto_available, ProfilerActivity
+from torch.profiler._memory_profiler import MemoryProfile, MemoryProfileTimeline
+
+
+__all__ = [
+    "supported_activities",
+    "ProfilerAction",
+    "schedule",
+    "tensorboard_trace_handler",
+    "profile",
+    "ExecutionTraceObserver",
+]
+PROFILER_STEP_NAME = "ProfilerStep"
+
+
+class _NumpyEncoder(json.JSONEncoder):
+    """
+    Json encoder for numpy types (np.int, np.float, np.array etc.)
+    Returns default encoder if numpy is not available
+    """
+
+    def default(self, obj):
+        """Encode NumPy types to JSON"""
+        try:
+            import numpy as np
+        except ImportError:
+            return json.JSONEncoder.default(self, obj)
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        else:
+            return json.JSONEncoder.default(self, obj)
+
+
+def supported_activities():
+    """
+    Returns a set of supported profiler tracing activities.
+
+    Note: profiler uses CUPTI library to trace on-device CUDA kernels.
+    In case when CUDA is enabled but CUPTI is not available, passing
+    ``ProfilerActivity.CUDA`` to profiler results in using the legacy CUDA
+    profiling code (same as in the legacy ``torch.autograd.profiler``).
+    This, in turn, results in including CUDA time in the profiler table output,
+    but not in the JSON trace.
+    """
+    return torch.autograd._supported_activities()
+
+
+class _ITraceObserver(ABC):
+    """Abstract interface for a Trace observer.
+    This satisfies 3 methods: start, stop and cleanup"""
+
+    @abstractmethod
+    def start(self):
+        pass
+
+    @abstractmethod
+    def stop(self):
+        pass
+
+    @abstractmethod
+    def cleanup(self):
+        pass
+
+
+class _KinetoProfile:
+    """Low-level profiler wrap the autograd profile
+
+    Args:
+        activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:
+            ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``,
+            ``torch.profiler.ProfilerActivity.XPU``.
+            Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA
+            or (when available) ProfilerActivity.XPU.
+        record_shapes (bool): save information about operator's input shapes.
+        profile_memory (bool): track tensor memory allocation/deallocation (see ``export_memory_timeline``
+            for more details).
+        with_stack (bool): record source information (file and line number) for the ops.
+        with_flops (bool): use formula to estimate the FLOPS of specific operators
+            (matrix multiplication and 2D convolution).
+        with_modules (bool): record module hierarchy (including function names)
+            corresponding to the callstack of the op. e.g. If module A's forward call's
+            module B's forward which contains an aten::add op,
+            then aten::add's module hierarchy is A.B
+            Note that this support exist, at the moment, only for TorchScript models
+            and not eager mode models.
+        experimental_config (_ExperimentalConfig) : A set of experimental options
+            used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.
+        execution_trace_observer (ExecutionTraceObserver) : A PyTorch Execution Trace Observer object.
+            `PyTorch Execution Traces `__ offer a graph based
+            representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators.
+            When this argument is included the observer start() and stop() will be called for the
+            same time window as PyTorch profiler.
+        acc_events (bool): Enable the accumulation of FunctionEvents across multiple profiling cycles
+
+
+    .. note::
+        This API is experimental and subject to change in the future.
+
+        Enabling shape and stack tracing results in additional overhead.
+        When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
+        that may further prevent certain optimizations that depend on the reference count and introduce
+        extra tensor copies.
+    """
+
+    def __init__(
+        self,
+        *,
+        activities: Optional[Iterable[ProfilerActivity]] = None,
+        record_shapes: bool = False,
+        profile_memory: bool = False,
+        with_stack: bool = False,
+        with_flops: bool = False,
+        with_modules: bool = False,
+        experimental_config: Optional[_ExperimentalConfig] = None,
+        execution_trace_observer: Optional[_ITraceObserver] = None,
+        acc_events: bool = False,
+        custom_trace_id_callback: Optional[Callable[[], str]] = None,
+    ):
+        self.activities = set(activities) if activities else supported_activities()
+        self.record_shapes = record_shapes
+        self.with_flops = with_flops
+        self.profile_memory = profile_memory
+        self.with_stack = with_stack
+        self.with_modules = with_modules
+        self.experimental_config = experimental_config
+        self.execution_trace_observer = execution_trace_observer
+        self.acc_events = acc_events
+        self.custom_trace_id_callback = custom_trace_id_callback
+        self.profiler: Optional[prof.profile] = None
+        self.mem_tl: Optional[MemoryProfileTimeline] = None
+        self.use_device = None
+        if ProfilerActivity.CUDA in self.activities:
+            self.use_device = "cuda"
+        elif ProfilerActivity.XPU in self.activities:
+            self.use_device = "xpu"
+        elif ProfilerActivity.MTIA in self.activities:
+            self.use_device = "mtia"
+        elif ProfilerActivity.HPU in self.activities:
+            self.use_device = "hpu"
+        elif ProfilerActivity.PrivateUse1 in self.activities:
+            self.use_device = _get_privateuse1_backend_name()
+
+        # user-defined metadata to be amended to the trace
+        self.preset_metadata: dict[str, str] = {}
+
+    def start(self):
+        self.prepare_trace()
+        self.start_trace()
+
+    def stop(self):
+        self.stop_trace()
+
+    def prepare_trace(self):
+        if (self.profiler is None) or (not self.acc_events):
+            self.profiler = prof.profile(
+                use_cpu=(ProfilerActivity.CPU in self.activities),
+                use_device=self.use_device,
+                record_shapes=self.record_shapes,
+                with_flops=self.with_flops,
+                profile_memory=self.profile_memory,
+                with_stack=self.with_stack,
+                with_modules=self.with_modules,
+                use_kineto=True,
+                experimental_config=self.experimental_config,
+                acc_events=self.acc_events,
+                custom_trace_id_callback=self.custom_trace_id_callback,
+            )
+        self.profiler._prepare_trace()
+
+    def start_trace(self):
+        if self.execution_trace_observer:
+            self.execution_trace_observer.start()
+        assert self.profiler is not None
+        self.profiler._start_trace()
+
+        if self.profile_memory:
+            self.add_metadata_json("profile_memory", "1")
+        if self.with_stack:
+            self.add_metadata_json("with_stack", "1")
+        if self.record_shapes:
+            self.add_metadata_json("record_shapes", "1")
+        if self.with_modules:
+            self.add_metadata_json("with_modules", "1")
+        if self.with_flops:
+            self.add_metadata_json("with_flops", "1")
+
+        if kineto_available():
+            dist_info = self._get_distributed_info()
+            if dist_info:
+                self.add_metadata_json(
+                    "distributedInfo", json.dumps(dist_info, cls=_NumpyEncoder)
+                )
+
+            if hasattr(torch, "_inductor"):
+                import torch._inductor.config as inductor_config
+
+                if inductor_config.triton.cudagraphs:
+                    os.environ["DISABLE_CUPTI_LAZY_REINIT"] = "1"
+                    self.add_metadata_json("DISABLE_CUPTI_LAZY_REINIT", "1")
+                    # FIXME: CUDA Graph does not work well with CUPTI teardown.
+                    #   1) crashes on 1st lazy CUPTI re-init after teardown (CUDA 11)
+                    #   2) crashes on 2nd non-lazy CUPTI re-init after teardown (CUDA 12)
+                    # Workaround: turn off CUPTI teardown when using CUDA Graphs.
+                    os.environ["TEARDOWN_CUPTI"] = "0"
+
+            # Insert the preset user metadata to the trace
+            for k, v in self.preset_metadata.items():
+                self.add_metadata_json(k, v)
+
+    def stop_trace(self):
+        if self.execution_trace_observer:
+            self.execution_trace_observer.stop()
+        assert self.profiler is not None
+        self.profiler.__exit__(None, None, None)
+
+    def export_chrome_trace(self, path: str):
+        """
+        Exports the collected trace in Chrome JSON format. If kineto is enabled, only
+        last cycle in schedule is exported.
+        """
+        assert self.profiler
+        if path.endswith(".gz"):
+            fp = tempfile.NamedTemporaryFile("w+b", suffix=".json", delete=False)
+            fp.close()
+            retvalue = self.profiler.export_chrome_trace(fp.name)
+            with open(fp.name, "rb") as fin:
+                with gzip.open(path, "wb") as fout:
+                    fout.writelines(fin)
+            os.remove(fp.name)
+            return retvalue
+        else:
+            return self.profiler.export_chrome_trace(path)
+
+    def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
+        """Save stack traces to a file
+
+        Args:
+            path (str): save stacks file to this location;
+            metric (str): metric to use: "self_cpu_time_total" or "self_cuda_time_total"
+        """
+        assert self.profiler
+        return self.profiler.export_stacks(path, metric)
+
+    def toggle_collection_dynamic(
+        self, enable: bool, activities: Iterable[ProfilerActivity]
+    ):
+        """Toggle collection of activities on/off at any point of collection. Currently supports toggling Torch Ops
+        (CPU) and CUDA activity supported in Kineto
+
+        Args:
+            activities (iterable): list of activity groups to use in profiling, supported values:
+                ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``
+        Examples:
+
+        .. code-block:: python
+
+            with torch.profiler.profile(
+                activities=[
+                    torch.profiler.ProfilerActivity.CPU,
+                    torch.profiler.ProfilerActivity.CUDA,
+                ]
+            ) as p:
+                code_to_profile_0()
+                // turn off collection of all CUDA activity
+                p.toggle_collection_dynamic(False, [torch.profiler.ProfilerActivity.CUDA])
+                code_to_profile_1()
+                // turn on collection of all CUDA activity
+                p.toggle_collection_dynamic(True, [torch.profiler.ProfilerActivity.CUDA])
+                code_to_profile_2()
+            print(p.key_averages().table(
+                sort_by="self_cuda_time_total", row_limit=-1))
+        """
+        if not self.profiler:
+            return
+        self.profiler.toggle_collection_dynamic(enable, activities)
+
+    def key_averages(
+        self,
+        group_by_input_shape: bool = False,
+        group_by_stack_n: int = 0,
+        group_by_overload_name: bool = False,
+    ):
+        """Averages events, grouping them by operator name and (optionally) input shapes, stack
+        and overload name.
+
+        .. note::
+            To use shape/stack functionality make sure to set record_shapes/with_stack
+            when creating profiler context manager.
+        """
+        assert self.profiler
+        return self.profiler.key_averages(
+            group_by_input_shape, group_by_stack_n, group_by_overload_name
+        )
+
+    def events(self):
+        """
+        Returns the list of unaggregated profiler events,
+        to be used in the trace callback or after the profiling is finished
+        """
+        assert self.profiler
+        return self.profiler.function_events
+
+    def add_metadata(self, key: str, value: str):
+        """
+        Adds a user defined metadata with a string key and a string value
+        into the trace file
+        """
+        wrapped_value = '"' + value.replace('"', '\\"') + '"'
+        torch.autograd._add_metadata_json(key, wrapped_value)
+
+    def add_metadata_json(self, key: str, value: str):
+        """
+        Adds a user defined metadata with a string key and a valid json value
+        into the trace file
+        """
+        torch.autograd._add_metadata_json(key, value)
+
+    def preset_metadata_json(self, key: str, value: str):
+        """
+        Preset a user defined metadata when the profiler is not started
+        and added into the trace file later.
+        Metadata is in the format of a string key and a valid json value
+        """
+        self.preset_metadata[key] = value
+
+    def _get_distributed_info(self):
+        import torch.distributed as dist
+
+        if not dist.is_available() or not dist.is_initialized():
+            return None
+
+        backend = dist.get_backend()
+        dist_info = {
+            "backend": backend,
+            "rank": dist.get_rank(),
+            "world_size": dist.get_world_size(),
+            "pg_count": dist.get_pg_count(),
+            "pg_config": dist.distributed_c10d._get_all_pg_configs(),
+        }
+        if backend == "nccl":
+            nccl_version = torch.cuda.nccl.version()
+            dist_info["nccl_version"] = ".".join(str(v) for v in nccl_version)
+        return dist_info
+
+    def _memory_profile(self) -> MemoryProfile:
+        required = ("record_shapes", "profile_memory", "with_stack")
+        missing = [f"{i}=True" for i in required if not getattr(self, i)]
+        if missing:
+            raise ValueError(f"{', '.join(missing)} required for memory profiling.")
+
+        assert self.profiler is not None and self.profiler.kineto_results is not None
+        return MemoryProfile(self.profiler.kineto_results)
+
+    def export_memory_timeline(self, path: str, device: Optional[str] = None) -> None:
+        """Export memory event information from the profiler collected
+        tree for a given device, and export a timeline plot. There are 3
+        exportable files using ``export_memory_timeline``, each controlled by the
+        ``path``'s suffix.
+
+        - For an HTML compatible plot, use the suffix ``.html``, and a memory timeline
+          plot will be embedded as a PNG file in the HTML file.
+
+        - For plot points consisting of ``[times, [sizes by category]]``, where
+          ``times`` are timestamps and ``sizes`` are memory usage for each category.
+          The memory timeline plot will be saved a JSON (``.json``) or gzipped JSON
+          (``.json.gz``) depending on the suffix.
+
+        - For raw memory points, use the suffix ``.raw.json.gz``. Each raw memory
+          event will consist of ``(timestamp, action, numbytes, category)``, where
+          ``action`` is one of ``[PREEXISTING, CREATE, INCREMENT_VERSION, DESTROY]``,
+          and ``category`` is one of the enums from
+          ``torch.profiler._memory_profiler.Category``.
+
+        Output: Memory timeline written as gzipped JSON, JSON, or HTML.
+        """
+        # Default to device 0, if unset. Fallback on cpu.
+        if device is None:
+            if self.use_device and self.use_device != "cuda":
+                device = self.use_device + ":0"
+            else:
+                device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+        # Construct the memory timeline plot data
+        self.mem_tl = MemoryProfileTimeline(self._memory_profile())
+
+        # Depending on the file suffix, save the data as json.gz or json.
+        # For html, we can embed the image into an HTML file.
+        if path.endswith(".html"):
+            self.mem_tl.export_memory_timeline_html(path, device)
+        elif path.endswith(".gz"):
+            fp = tempfile.NamedTemporaryFile("w+t", suffix=".json", delete=False)
+            fp.close()
+            if path.endswith("raw.json.gz"):
+                self.mem_tl.export_memory_timeline_raw(fp.name, device)
+            else:
+                self.mem_tl.export_memory_timeline(fp.name, device)
+            with open(fp.name) as fin:
+                with gzip.open(path, "wt") as fout:
+                    fout.writelines(fin)
+            os.remove(fp.name)
+        else:
+            self.mem_tl.export_memory_timeline(path, device)
+
+
+class ProfilerAction(Enum):
+    """
+    Profiler actions that can be taken at the specified intervals
+    """
+
+    NONE = 0
+    WARMUP = 1
+    RECORD = 2
+    RECORD_AND_SAVE = 3
+
+
+def schedule(
+    *,
+    wait: int,
+    warmup: int,
+    active: int,
+    repeat: int = 0,
+    skip_first: int = 0,
+    skip_first_wait: int = 0,
+) -> Callable:
+    """
+    Returns a callable that can be used as profiler ``schedule`` argument. The profiler will skip
+    the first ``skip_first`` steps, then wait for ``wait`` steps, then do the warmup for the next ``warmup`` steps,
+    then do the active recording for the next ``active`` steps and then repeat the cycle starting with ``wait`` steps.
+    The optional number of cycles is specified with the ``repeat`` parameter, the zero value means that
+    the cycles will continue until the profiling is finished.
+
+    The ``skip_first_wait`` parameter controls whether the first ``wait`` stage should be skipped.
+    This can be useful if a user wants to wait longer than ``skip_first`` between cycles, but not
+    for the first profile. For example, if ``skip_first`` is 10 and ``wait`` is 20, the first cycle will
+    wait 10 + 20 = 30 steps before warmup if ``skip_first_wait`` is zero, but will wait only 10
+    steps if ``skip_first_wait`` is non-zero. All subsequent cycles will then wait 20 steps between the
+    last active and warmup.
+    """
+
+    def schedule_fn(step: int) -> ProfilerAction:
+        assert step >= 0
+        if step < skip_first:
+            return ProfilerAction.NONE
+        else:
+            step -= skip_first
+        # If wait >> skip_first and we want to grab profiling early, shift left by wait if skip_first_wait is True
+        if skip_first_wait != 0:
+            step += wait
+        num_steps = wait + warmup + active
+        if repeat > 0 and step / num_steps >= repeat:
+            return ProfilerAction.NONE
+        mod_step = step % num_steps
+        if mod_step < wait:
+            return ProfilerAction.NONE
+        elif mod_step < wait + warmup:
+            return ProfilerAction.WARMUP
+        else:
+            return (
+                ProfilerAction.RECORD
+                if mod_step < num_steps - 1
+                else ProfilerAction.RECORD_AND_SAVE
+            )
+
+    assert (
+        wait >= 0 and warmup >= 0 and active > 0 and repeat >= 0 and skip_first >= 0
+    ), "Invalid profiler schedule arguments"
+    if warmup == 0:
+        warn("Profiler won't be using warmup, this can skew profiler results")
+    return schedule_fn
+
+
+def _default_schedule_fn(_: int) -> ProfilerAction:
+    """
+    Default profiler behavior - immediately starts recording the events,
+    keeps doing it on every profiler step.
+    """
+    return ProfilerAction.RECORD
+
+
+def tensorboard_trace_handler(
+    dir_name: str, worker_name: Optional[str] = None, use_gzip: bool = False
+):
+    """
+    Outputs tracing files to directory of ``dir_name``, then that directory can be
+    directly delivered to tensorboard as logdir.
+    ``worker_name`` should be unique for each worker in distributed scenario,
+    it will be set to '[hostname]_[pid]' by default.
+    """
+    import os
+    import socket
+    import time
+
+    def handler_fn(prof) -> None:
+        nonlocal worker_name
+        if not os.path.isdir(dir_name):
+            try:
+                os.makedirs(dir_name, exist_ok=True)
+            except Exception as e:
+                raise RuntimeError("Can't create directory: " + dir_name) from e
+        if not worker_name:
+            worker_name = f"{socket.gethostname()}_{os.getpid()}"
+        # Use nanosecond here to avoid naming clash when exporting the trace
+        file_name = f"{worker_name}.{time.time_ns()}.pt.trace.json"
+        if use_gzip:
+            file_name = file_name + ".gz"
+        prof.export_chrome_trace(os.path.join(dir_name, file_name))
+
+    return handler_fn
+
+
+class profile(_KinetoProfile):
+    """Profiler context manager.
+
+    Args:
+        activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:
+            ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``,
+            ``torch.profiler.ProfilerActivity.XPU``.
+            Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA
+            or (when available) ProfilerActivity.XPU.
+        schedule (Callable): callable that takes step (int) as a single parameter and returns
+            ``ProfilerAction`` value that specifies the profiler action to perform at each step.
+        on_trace_ready (Callable): callable that is called at each step when ``schedule``
+            returns ``ProfilerAction.RECORD_AND_SAVE`` during the profiling.
+        record_shapes (bool): save information about operator's input shapes.
+        profile_memory (bool): track tensor memory allocation/deallocation.
+        with_stack (bool): record source information (file and line number) for the ops.
+        with_flops (bool): use formula to estimate the FLOPs (floating point operations) of specific operators
+            (matrix multiplication and 2D convolution).
+        with_modules (bool): record module hierarchy (including function names)
+            corresponding to the callstack of the op. e.g. If module A's forward call's
+            module B's forward which contains an aten::add op,
+            then aten::add's module hierarchy is A.B
+            Note that this support exist, at the moment, only for TorchScript models
+            and not eager mode models.
+        experimental_config (_ExperimentalConfig) : A set of experimental options
+            used for Kineto library features. Note, backward compatibility is not guaranteed.
+        execution_trace_observer (ExecutionTraceObserver) : A PyTorch Execution Trace Observer object.
+            `PyTorch Execution Traces `__ offer a graph based
+            representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators.
+            When this argument is included the observer start() and stop() will be called for the
+            same time window as PyTorch profiler. See the examples section below for a code sample.
+        acc_events (bool): Enable the accumulation of FunctionEvents across multiple profiling cycles
+        use_cuda (bool):
+            .. deprecated:: 1.8.1
+                use ``activities`` instead.
+
+    .. note::
+        Use :func:`~torch.profiler.schedule` to generate the callable schedule.
+        Non-default schedules are useful when profiling long training jobs
+        and allow the user to obtain multiple traces at the different iterations
+        of the training process.
+        The default schedule simply records all the events continuously for the
+        duration of the context manager.
+
+    .. note::
+        Use :func:`~torch.profiler.tensorboard_trace_handler` to generate result files for TensorBoard:
+
+        ``on_trace_ready=torch.profiler.tensorboard_trace_handler(dir_name)``
+
+        After profiling, result files can be found in the specified directory. Use the command:
+
+        ``tensorboard --logdir dir_name``
+
+        to see the results in TensorBoard.
+        For more information, see
+        `PyTorch Profiler TensorBoard Plugin `__
+
+    .. note::
+        Enabling shape and stack tracing results in additional overhead.
+        When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
+        that may further prevent certain optimizations that depend on the reference count and introduce
+        extra tensor copies.
+
+
+    Examples:
+
+    .. code-block:: python
+
+        with torch.profiler.profile(
+            activities=[
+                torch.profiler.ProfilerActivity.CPU,
+                torch.profiler.ProfilerActivity.CUDA,
+            ]
+        ) as p:
+            code_to_profile()
+        print(p.key_averages().table(
+            sort_by="self_cuda_time_total", row_limit=-1))
+
+    Using the profiler's ``schedule``, ``on_trace_ready`` and ``step`` functions:
+
+    .. code-block:: python
+
+        # Non-default profiler schedule allows user to turn profiler on and off
+        # on different iterations of the training loop;
+        # trace_handler is called every time a new trace becomes available
+        def trace_handler(prof):
+            print(prof.key_averages().table(
+                sort_by="self_cuda_time_total", row_limit=-1))
+            # prof.export_chrome_trace("/tmp/test_trace_" + str(prof.step_num) + ".json")
+
+        with torch.profiler.profile(
+            activities=[
+                torch.profiler.ProfilerActivity.CPU,
+                torch.profiler.ProfilerActivity.CUDA,
+            ],
+
+            # In this example with wait=1, warmup=1, active=2, repeat=1,
+            # profiler will skip the first step/iteration,
+            # start warming up on the second, record
+            # the third and the forth iterations,
+            # after which the trace will become available
+            # and on_trace_ready (when set) is called;
+            # the cycle repeats starting with the next step
+
+            schedule=torch.profiler.schedule(
+                wait=1,
+                warmup=1,
+                active=2,
+                repeat=1),
+            on_trace_ready=trace_handler
+            # on_trace_ready=torch.profiler.tensorboard_trace_handler('./log')
+            # used when outputting for tensorboard
+            ) as p:
+                for iter in range(N):
+                    code_iteration_to_profile(iter)
+                    # send a signal to the profiler that the next iteration has started
+                    p.step()
+
+    The following sample shows how to setup up an Execution Trace Observer (`execution_trace_observer`)
+
+    .. code-block:: python
+
+        with torch.profiler.profile(
+            ...
+            execution_trace_observer=(
+                ExecutionTraceObserver().register_callback("./execution_trace.json")
+            ),
+        ) as p:
+            for iter in range(N):
+                code_iteration_to_profile(iter)
+                p.step()
+
+    You can also refer to test_execution_trace_with_kineto() in tests/profiler/test_profiler.py.
+    Note: One can also pass any object satisfying the _ITraceObserver interface.
+    """
+
+    def __init__(
+        self,
+        *,
+        activities: Optional[Iterable[ProfilerActivity]] = None,
+        schedule: Optional[Callable[[int], ProfilerAction]] = None,
+        on_trace_ready: Optional[Callable[..., Any]] = None,
+        record_shapes: bool = False,
+        profile_memory: bool = False,
+        with_stack: bool = False,
+        with_flops: bool = False,
+        with_modules: bool = False,
+        experimental_config: Optional[_ExperimentalConfig] = None,
+        execution_trace_observer: Optional[_ITraceObserver] = None,
+        acc_events: bool = False,
+        # deprecated:
+        use_cuda: Optional[bool] = None,
+        custom_trace_id_callback: Optional[Callable[[], str]] = None,
+    ):
+        activities_set = set(activities) if activities else supported_activities()
+        if use_cuda is not None:
+            warn(
+                "`use_cuda` is deprecated, use `activities` argument instead",
+                FutureWarning,
+                stacklevel=2,
+            )
+            if use_cuda:
+                activities_set.add(ProfilerActivity.CUDA)
+            elif ProfilerActivity.CUDA in activities_set:
+                activities_set.remove(ProfilerActivity.CUDA)
+        assert len(activities_set) > 0, "No valid profiler activities found"
+
+        super().__init__(
+            activities=activities,
+            record_shapes=record_shapes,
+            profile_memory=profile_memory,
+            with_stack=with_stack,
+            with_flops=with_flops,
+            with_modules=with_modules,
+            experimental_config=experimental_config,
+            execution_trace_observer=execution_trace_observer
+            if execution_trace_observer
+            else ExecutionTraceObserver.build_execution_trace_obs_from_env(),
+            acc_events=acc_events,
+            custom_trace_id_callback=custom_trace_id_callback,
+        )
+
+        if schedule:
+            self.schedule = schedule
+            # add step markers into the trace and table view
+            self.record_steps = True
+        else:
+            self.schedule = _default_schedule_fn
+            self.record_steps = False
+        self.on_trace_ready = on_trace_ready
+        self.step_num = 0
+        self.current_action = self.schedule(self.step_num)
+        self.step_rec_fn: Optional[prof.record_function] = None
+
+        self.action_map: dict[
+            tuple[ProfilerAction, Optional[ProfilerAction]], list[Any]
+        ] = {
+            # key is (prev_action, current_action), value is action list corresponding to the state pair.
+            (ProfilerAction.NONE, ProfilerAction.NONE): [],
+            (ProfilerAction.NONE, ProfilerAction.WARMUP): [self.prepare_trace],
+            (ProfilerAction.NONE, ProfilerAction.RECORD): [
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            (ProfilerAction.NONE, ProfilerAction.RECORD_AND_SAVE): [
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            (ProfilerAction.WARMUP, ProfilerAction.NONE): [
+                partial(warn, "Incorrect schedule: WARMUP followed by NONE"),
+                self.start_trace,
+                self.stop_trace,
+            ],
+            (ProfilerAction.WARMUP, ProfilerAction.WARMUP): [],
+            (ProfilerAction.WARMUP, ProfilerAction.RECORD): [self.start_trace],
+            (ProfilerAction.WARMUP, ProfilerAction.RECORD_AND_SAVE): [self.start_trace],
+            (ProfilerAction.RECORD, ProfilerAction.NONE): [
+                partial(warn, "Incorrect schedule: RECORD followed by NONE"),
+                self.stop_trace,
+            ],
+            (ProfilerAction.RECORD, ProfilerAction.WARMUP): [
+                partial(warn, "Incorrect schedule: RECORD followed by WARMUP"),
+                self.stop_trace,
+            ],
+            (ProfilerAction.RECORD, ProfilerAction.RECORD): [],
+            (ProfilerAction.RECORD, ProfilerAction.RECORD_AND_SAVE): [],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.NONE): [
+                self.stop_trace,
+                self._trace_ready,
+            ],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.WARMUP): [
+                self.stop_trace,
+                self._trace_ready,
+                self.prepare_trace,
+            ],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.RECORD): [
+                self.stop_trace,
+                self._trace_ready,
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            (ProfilerAction.RECORD_AND_SAVE, ProfilerAction.RECORD_AND_SAVE): [
+                self.stop_trace,
+                self._trace_ready,
+                self.prepare_trace,
+                self.start_trace,
+            ],
+            # used for exit action
+            (ProfilerAction.WARMUP, None): [self.start_trace, self.stop_trace],
+            (ProfilerAction.RECORD, None): [self.stop_trace, self._trace_ready],
+            (ProfilerAction.RECORD_AND_SAVE, None): [
+                self.stop_trace,
+                self._trace_ready,
+            ],
+        }
+        # Start tracking increments to profiler step, this will be used
+        # by Kineto
+        prof.KinetoStepTracker.init_step_count(PROFILER_STEP_NAME)
+
+    def __enter__(self):
+        self.start()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.stop()
+        prof.KinetoStepTracker.erase_step_count(PROFILER_STEP_NAME)
+        if self.execution_trace_observer:
+            self.execution_trace_observer.cleanup()
+
+    def start(self):
+        self._transit_action(ProfilerAction.NONE, self.current_action)
+        if self.record_steps:
+            self.step_rec_fn = prof.record_function(
+                "ProfilerStep#" + str(self.step_num)
+            )
+            self.step_rec_fn.__enter__()
+
+    def stop(self):
+        if self.record_steps and self.step_rec_fn:
+            self.step_rec_fn.__exit__(None, None, None)
+        self._transit_action(self.current_action, None)
+
+    def step(self):
+        """
+        Signals the profiler that the next profiling step has started.
+        """
+        if self.record_steps and self.step_rec_fn:
+            self.step_rec_fn.__exit__(None, None, None)
+        prev_action = self.current_action
+        self.step_num += 1
+        self.current_action = self.schedule(self.step_num)
+
+        self._transit_action(prev_action, self.current_action)
+        if os.environ.get("KINETO_USE_DAEMON", "") or (
+            is_fbcode() and os.environ.get("KINETO_FORCE_STEP_HOOK", "")
+        ):
+            prof.KinetoStepTracker.increment_step(PROFILER_STEP_NAME)
+
+        if self.record_steps:
+            self.step_rec_fn = prof.record_function(
+                "ProfilerStep#" + str(self.step_num)
+            )
+            self.step_rec_fn.__enter__()
+
+    def set_custom_trace_id_callback(self, callback):
+        """
+        Sets a callback to be called when a new trace ID is generated.
+        """
+        self.custom_trace_id_callback = callback
+
+    def get_trace_id(self):
+        """
+        Returns the current trace ID.
+        """
+        if self.profiler is None:
+            return None
+        return self.profiler.trace_id
+
+    def _trace_ready(self):
+        if self.on_trace_ready:
+            self.on_trace_ready(self)
+
+    def _transit_action(self, prev_action, current_action):
+        action_list = self.action_map.get((prev_action, current_action))
+        if action_list:
+            for action in action_list:
+                action()
+
+    def _stats(self) -> Optional[prof._ProfilerStats]:
+        if self.profiler is None:
+            return None
+        return self.profiler._stats
+
+
+class ExecutionTraceObserver(_ITraceObserver):
+    """Execution Trace Observer
+
+    Each process can have a single ExecutionTraceObserver instance. The observer
+    can be added to record function callbacks via calling register_callback()
+    explicitly. Without calling unregister_callback(), repeated calls to
+    register_callback() will not add additional observers to record function
+    callbacks. Once an ExecutionTraceObserver is created, the start() and stop()
+    methods control when the event data is recorded.
+
+    Deleting or calling unregister_callback() will remove the observer from the
+    record function callbacks, finalize the output file, and will stop
+    incurring any overheads.
+    """
+
+    def __init__(self) -> None:
+        """
+        Initializes the default states.
+        """
+        self._registered = False
+        self._execution_trace_running = False
+        self.extra_resources_collection = False
+        self.resources_dir: str = ""
+        self.output_file_path: str = ""
+        self.output_file_path_observer: str = ""
+
+    def __del__(self):
+        """
+        Calls unregister_callback() to make sure to finalize outputs.
+        """
+        self.unregister_callback()
+
+    @staticmethod
+    def build_execution_trace_obs_from_env() -> Optional["ExecutionTraceObserver"]:
+        """
+        Returns an ExecutionTraceObserver instance if the environment variable
+        ENABLE_PYTORCH_EXECUTION_TRACE is set to 1, otherwise returns None.
+
+        Configures the observer to also collect extra resources if the environment variable
+        ``ENABLE_PYTORCH_EXECUTION_TRACE_EXTRAS=1``. These are resources such as generated kernels,
+        index tensor data etc. that are required to make the Execution Trace replayable.
+        """
+        if os.environ.get("ENABLE_PYTORCH_EXECUTION_TRACE", "0") == "1":
+            try:
+                fp = tempfile.NamedTemporaryFile("w+t", suffix=".et.json", delete=False)
+            except Exception as e:
+                warn(
+                    f"Execution trace will not be recorded. Exception on creating default temporary file: {e}"
+                )
+                return None
+            fp.close()
+            et = ExecutionTraceObserver()
+            et.register_callback(fp.name)
+            # additionally, check if the env requires us to collect extra resources
+            if os.environ.get("ENABLE_PYTORCH_EXECUTION_TRACE_EXTRAS", "0") == "1":
+                et.set_extra_resource_collection(True)
+            else:
+                et.set_extra_resource_collection(False)
+            return et
+        return None
+
+    def set_extra_resource_collection(self, val) -> None:
+        """
+        Collects extra resources such as generated kernels, index tensor data, and any other
+        metadata that is required to complete the Execution Trace content.
+
+        The caller should call this method with val=True after calling register_callback() if they want
+        to collect the extra resources.
+        """
+        self.extra_resources_collection = val
+        if self.extra_resources_collection:
+            self.get_resources_dir(can_create=True)
+        return
+
+    def register_callback(self, output_file_path: str) -> Self:
+        """
+        Adds ET observer to record function callbacks. The data will be
+        written to output_file_path.
+        """
+
+        def get_temp_uncompressed_file() -> str:
+            fp = tempfile.NamedTemporaryFile("w+b", suffix=".json", delete=False)
+            fp.close()
+            return fp.name
+
+        if not self._registered:
+            self.output_file_path = output_file_path
+            if output_file_path.endswith(".gz"):
+                output_file_path = get_temp_uncompressed_file()
+            self.output_file_path_observer = output_file_path
+            self._registered = _add_execution_trace_observer(output_file_path)
+        return self
+
+    def get_resources_dir(self, can_create=False) -> Optional[str]:
+        """
+        Generates the resources directory for the generated kernels,
+        or index tensor data or any other metadata that is required
+        to complete the Execution Trace content.
+
+        The directory is created right where the ET file is being output.
+
+        Only works if the observer has called set_extra_resource_collection(val=True).
+
+        Returns None if the observer is not configured with extra resource collection.
+        """
+        if not self.extra_resources_collection:
+            return None
+        if self.resources_dir:
+            # already created
+            return self.resources_dir
+        generated_path = ExecutionTraceObserver.get_resources_dir_for_et_path(
+            self.output_file_path, create_dir=can_create
+        )
+        if not generated_path:
+            # could not find of create the resources dir
+            return None
+        self.resources_dir = generated_path
+        return self.resources_dir
+
+    @staticmethod
+    def get_resources_dir_for_et_path(
+        trace_path, create_dir: bool = False
+    ) -> Optional[str]:
+        work_dir, file_name = os.path.split(trace_path)
+        resource_dir = os.path.join(
+            work_dir, os.path.splitext(file_name)[0] + "_resources"
+        )
+        if not os.path.exists(resource_dir):
+            if create_dir:
+                try:
+                    os.mkdir(resource_dir)
+                except Exception:
+                    warn(f"Execution trace exception when creating {resource_dir}")
+                    return None
+            else:
+                return None
+        return resource_dir
+
+    def unregister_callback(self):
+        """
+        Removes ET observer from record function callbacks.
+        """
+
+        def _save_triton_kernels() -> None:
+            try:
+                resource_dir = self.get_resources_dir()
+            except Exception as e:
+                warn(
+                    f"Execution trace exception when generating resource directory: {e}"
+                )
+                return
+            if not resource_dir:
+                return
+
+            # Save the kernel paths for the generated kernels
+            from torch._inductor.codecache import PyCodeCache as PyCodeCache
+
+            kernel_files = [
+                v.__file__
+                for v in PyCodeCache.modules
+                if getattr(v, "__file__", None) is not None
+            ]
+
+            for kernel_file in kernel_files:
+                if kernel_file is None:
+                    continue
+                name = os.path.basename(kernel_file)
+                dst = os.path.join(resource_dir, name)
+                shutil.copyfile(kernel_file, dst)
+
+        def _save_gz_file(uncompressed_file: str, output_file: str) -> None:
+            print(f"Execution Trace: compressing {uncompressed_file} to {output_file}")
+            with open(uncompressed_file, "rb") as fin:
+                with gzip.open(output_file, "wb") as fout:
+                    fout.writelines(fin)
+            os.remove(uncompressed_file)
+
+        if self._registered:
+            self.stop()
+
+            try:
+                _save_triton_kernels()
+            except Exception as e:
+                warn(f"Execution trace failed to save kernels: {e}")
+
+            _remove_execution_trace_observer()
+            if self.output_file_path.endswith("gz"):
+                _save_gz_file(self.output_file_path_observer, self.output_file_path)
+
+            self._registered = False
+
+    @property
+    def is_registered(self):
+        """
+        Returns True if the execution trace observer is registered, otherwise False.
+        """
+        return self._registered
+
+    def is_running(self):
+        """
+        Returns True if the observer is running, otherwise False.
+        """
+        return self._execution_trace_running
+
+    def start(self):
+        """
+        Starts to capture.
+        """
+        if self._registered and not self._execution_trace_running:
+            _enable_execution_trace_observer()
+            self._execution_trace_running = True
+            self._record_pg_config()
+
+    def stop(self):
+        """
+        Stops to capture.
+        """
+        if self._execution_trace_running:
+            _disable_execution_trace_observer()
+            self._execution_trace_running = False
+
+    def cleanup(self):
+        """
+        Calls unregister_callback() to make sure to finalize outputs.
+        """
+        self.unregister_callback()
+
+    def get_output_file_path(self) -> Optional[str]:
+        """
+        Returns the output file name or None.
+        """
+        if self.output_file_path:
+            return self.output_file_path
+        else:
+            return None
+
+    def _record_pg_config(self) -> None:
+        # Records the PG config info to the trace as node:
+        #  ## process_group:init ##
+        if (
+            self.is_registered
+            and torch.distributed.is_available()
+            and torch.distributed.is_initialized()
+        ):
+            pg_config_info = torch.distributed.distributed_c10d._world.pg_config_info
+            torch.autograd._record_function_with_args_enter(
+                "## process_group:init ##",
+                json.dumps(pg_config_info, cls=_NumpyEncoder),
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/python_tracer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/python_tracer.py
new file mode 100644
index 0000000000000000000000000000000000000000..aff0fbc32ff3a8870ce81f569daa2587f598394f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/profiler/python_tracer.py
@@ -0,0 +1,19 @@
+import os
+import site
+import sys
+
+import torch
+
+
+def _prefix_regex() -> list[str]:
+    raw_paths = (
+        site.getsitepackages()
+        + sys.path
+        + [site.getuserbase()]
+        + [site.getusersitepackages()]
+        + [os.path.dirname(os.path.dirname(torch.__file__))]
+    )
+
+    path_prefixes = sorted({os.path.abspath(i) for i in raw_paths}, reverse=True)
+    assert all(isinstance(i, str) for i in path_prefixes)
+    return [i + os.sep for i in path_prefixes]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..11114de4313869c02c7df7a7b67a5df0e17adff9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/__init__.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+from .fake_quantize import *  # noqa: F403
+from .fuse_modules import fuse_modules
+from .fuser_method_mappings import *  # noqa: F403
+from .observer import *  # noqa: F403
+from .qconfig import *  # noqa: F403
+from .quant_type import *  # noqa: F403
+from .quantization_mappings import *  # noqa: F403
+from .quantize import *  # noqa: F403
+from .quantize_jit import *  # noqa: F403
+from .stubs import *  # noqa: F403
+
+
+def default_eval_fn(model, calib_data):
+    r"""
+    Default evaluation function takes a torch.utils.data.Dataset or a list of
+    input Tensors and run the model on the dataset
+    """
+    for data, _target in calib_data:
+        model(data)
+
+
+__all__ = [
+    "QuantWrapper",
+    "QuantStub",
+    "DeQuantStub",
+    # Top level API for eager mode quantization
+    "quantize",
+    "quantize_dynamic",
+    "quantize_qat",
+    "prepare",
+    "convert",
+    "prepare_qat",
+    # Top level API for graph mode quantization on TorchScript
+    "quantize_jit",
+    "quantize_dynamic_jit",
+    "_prepare_ondevice_dynamic_jit",
+    "_convert_ondevice_dynamic_jit",
+    "_quantize_ondevice_dynamic_jit",
+    # Top level API for graph mode quantization on GraphModule(torch.fx)
+    # 'fuse_fx', 'quantize_fx',  # TODO: add quantize_dynamic_fx
+    # 'prepare_fx', 'prepare_dynamic_fx', 'convert_fx',
+    "QuantType",  # quantization type
+    # custom module APIs
+    "get_default_static_quant_module_mappings",
+    "get_static_quant_module_class",
+    "get_default_dynamic_quant_module_mappings",
+    "get_default_qat_module_mappings",
+    "get_default_qconfig_propagation_list",
+    "get_default_compare_output_module_list",
+    "get_quantized_operator",
+    "get_fuser_method",
+    # Sub functions for `prepare` and `swap_module`
+    "propagate_qconfig_",
+    "add_quant_dequant",
+    "swap_module",
+    "default_eval_fn",
+    # Observers
+    "ObserverBase",
+    "WeightObserver",
+    "HistogramObserver",
+    "observer",
+    "default_observer",
+    "default_weight_observer",
+    "default_placeholder_observer",
+    "default_per_channel_weight_observer",
+    # FakeQuantize (for qat)
+    "default_fake_quant",
+    "default_weight_fake_quant",
+    "default_fixed_qparams_range_neg1to1_fake_quant",
+    "default_fixed_qparams_range_0to1_fake_quant",
+    "default_per_channel_weight_fake_quant",
+    "default_histogram_fake_quant",
+    # QConfig
+    "QConfig",
+    "default_qconfig",
+    "default_dynamic_qconfig",
+    "float16_dynamic_qconfig",
+    "float_qparams_weight_only_qconfig",
+    # QAT utilities
+    "default_qat_qconfig",
+    "prepare_qat",
+    "quantize_qat",
+    # module transformations
+    "fuse_modules",
+]
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/_numeric_suite.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/_numeric_suite.py
new file mode 100644
index 0000000000000000000000000000000000000000..49ccc8e69523f7dbee2335b788a2cb3a7db618a2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/_numeric_suite.py
@@ -0,0 +1,28 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/ns/_numeric_suite.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.ns._numeric_suite import (
+    _convert_tuple_to_list,
+    _dequantize_tensor_list,
+    _find_match,
+    _get_logger_dict_helper,
+    _is_identical_module_type,
+    compare_model_outputs,
+    compare_model_stub,
+    compare_weights,
+    get_logger_dict,
+    get_matching_activations,
+    Logger,
+    NON_LEAF_MODULE_TO_ADD_OBSERVER_ALLOW_LIST,
+    OutputLogger,
+    prepare_model_outputs,
+    prepare_model_with_stubs,
+    Shadow,
+    ShadowLogger,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/_numeric_suite_fx.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/_numeric_suite_fx.py
new file mode 100644
index 0000000000000000000000000000000000000000..55cd7085740d0ce8de79491acbfc4888ebba21f8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/_numeric_suite_fx.py
@@ -0,0 +1,26 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/ns/_numeric_suite_fx.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.ns._numeric_suite_fx import (
+    _add_loggers_impl,
+    _add_loggers_one_model,
+    _add_shadow_loggers_impl,
+    _extract_logger_info_one_model,
+    _extract_weights_impl,
+    _extract_weights_one_model,
+    add_loggers,
+    add_shadow_loggers,
+    extend_logger_results_with_comparison,
+    extract_logger_info,
+    extract_shadow_logger_info,
+    extract_weights,
+    NSTracer,
+    OutputLogger,
+    RNNReturnType,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/_quantized_conversions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/_quantized_conversions.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d930c366c0dd9857e463005474a2d59c04c4ae6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/_quantized_conversions.py
@@ -0,0 +1,133 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+# Pack pairs of int4 values into int8, in row major order; first int4
+# value goes into lower order bits, and second int4 value into higher
+# order bits of resulting int8 value.
+def pack_int4_to_int8(weight):
+    assert weight.dim() == 2
+    assert weight.shape[1] % 2 == 0
+    assert weight.dtype == torch.int8
+    return ((weight[:, 1::2] & 0xF) << 4) | (weight[:, 0::2] & 0xF)
+
+
+# Unpack quandruples of bits in int8 values into int4 values, in row
+# major order; lower 4 bits go into first int4 value goes, and upper 4
+# bits go into second int4 value.
+def unpack_int8_to_int4(weight):
+    assert weight.dim() == 2
+    assert weight.dtype == torch.int8
+    return torch.stack((weight & 0xF, (weight >> 4) & 0xF), dim=2).view(
+        weight.shape[0], 2 * weight.shape[1]
+    )
+
+
+# Transpose the weight matrix, and then reorder its elements according
+# to underlying requirements of CUTLASS library, so that it could be
+# used for CUTLASS-based mixed datatypes linear operation.
+def quantized_weight_reorder_for_mixed_dtypes_linear_cutlass(
+    weight, dtypeq, transpose=False
+):
+    assert weight.dim() == 2
+    assert weight.dtype == torch.int8
+    assert dtypeq == torch.int8 or dtypeq == torch.quint4x2
+    assert weight.device.type == "cuda"
+
+    device = weight.device
+
+    # subbyte_transpose
+    if not transpose:
+        if dtypeq == torch.int8:
+            outp = weight.T
+        elif dtypeq == torch.quint4x2:
+            outp = pack_int4_to_int8(unpack_int8_to_int4(weight.view(torch.int8)).T)
+    else:
+        outp = weight
+
+    ncols, nrows = outp.shape  # type: ignore[possibly-undefined]
+    assert nrows % (32 if dtypeq == torch.quint4x2 else 64) == 0
+    assert ncols % 64 == 0
+
+    # permute_B_rows_for_mixed_gemm
+    # (permute cols actually, as transpose is applied first here)
+    if dtypeq == torch.quint4x2:
+        cols_permuted = (
+            torch.tensor(
+                [0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15],
+                device=device,
+            )
+            + (torch.arange(0, nrows // 16, device=device).reshape(-1, 1) * 16).expand(
+                nrows // 16, 16
+            )
+        ).view(-1)
+    else:
+        cols_permuted = (
+            torch.tensor(
+                [0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15],
+                device=device,
+            )
+            + (torch.arange(0, nrows // 16, device=device).reshape(-1, 1) * 16).expand(
+                nrows // 16, 16
+            )
+        ).view(-1)
+    outp = outp.index_copy(1, cols_permuted, outp)
+
+    # interleave_column_major_tensor
+    magic0 = 4 if dtypeq == torch.quint4x2 else 2
+    magic1 = 32 // magic0
+
+    tmp0 = (
+        (torch.arange(0, ncols // magic0, device=device) * (nrows // 4 * magic0))
+        .view(-1, 1)
+        .repeat(1, nrows // 4 * magic0)
+        .view(-1)
+    )
+    tmp1 = (
+        (torch.arange(0, nrows // 4 // magic1, device=device) * (magic0 * magic1))
+        .view(-1, 1)
+        .repeat(1, magic1)
+        .view(-1)
+        .repeat(ncols)
+    )
+    tmp2 = (
+        (torch.arange(0, magic0, device=device) * magic1)
+        .view(-1, 1)
+        .repeat(1, nrows // 4)
+        .view(-1)
+        .repeat(ncols // magic0)
+    )
+    tmp3 = torch.arange(0, magic1, device=device).repeat(nrows // 4 * ncols // magic1)
+
+    outp_offsets = tmp0 + tmp1 + tmp2 + tmp3
+
+    tmp = outp.view(-1).view(torch.int32)
+    outp = torch.zeros_like(tmp)
+    outp.scatter_(0, outp_offsets, tmp)
+    outp = outp.view(weight.dtype)
+
+    # add_bias_and_interleave_quantized_tensor_inplace
+    tmp = outp.view(-1)
+
+    outp = torch.empty_like(tmp)
+    if dtypeq == torch.int8:
+        tmp = (tmp.to(torch.int) + 128).to(tmp.dtype)
+        outp[0::4] = tmp[0::4]
+        outp[1::4] = tmp[2::4]
+        outp[2::4] = tmp[1::4]
+        outp[3::4] = tmp[3::4]
+    elif dtypeq == torch.quint4x2:
+        tmp0 = ((tmp & 0xF) + 8) & 0xF
+        tmp0 = (tmp0[1::2] << 4) | tmp0[0::2]
+        tmp1 = (((tmp >> 4) & 0xF) + 8) & 0xF
+        tmp1 = (tmp1[1::2] << 4) | tmp1[0::2]
+        outp[0::4] = tmp0[0::2]
+        outp[1::4] = tmp0[1::2]
+        outp[2::4] = tmp1[0::2]
+        outp[3::4] = tmp1[1::2]
+
+    if dtypeq == torch.quint4x2:
+        nrows *= 2
+        ncols //= 2
+
+    return outp.view(nrows, ncols).view(torch.uint8)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fake_quantize.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fake_quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..69a5d730bfb68e89e24beb04ad13fd3fa5881ae9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fake_quantize.py
@@ -0,0 +1,32 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/fake_quantize.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.fake_quantize import (
+    _is_fake_quant_script_module,
+    _is_per_channel,
+    _is_per_tensor,
+    _is_symmetric_quant,
+    default_fake_quant,
+    default_fixed_qparams_range_0to1_fake_quant,
+    default_fixed_qparams_range_neg1to1_fake_quant,
+    default_fused_act_fake_quant,
+    default_fused_per_channel_wt_fake_quant,
+    default_fused_wt_fake_quant,
+    default_histogram_fake_quant,
+    default_per_channel_weight_fake_quant,
+    default_weight_fake_quant,
+    disable_fake_quant,
+    disable_observer,
+    enable_fake_quant,
+    enable_observer,
+    FakeQuantize,
+    FakeQuantizeBase,
+    FixedQParamsFakeQuantize,
+    FusedMovingAvgObsFakeQuantize,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fuse_modules.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fuse_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b704fa8094e8b367e9eba47102863ba845415b9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fuse_modules.py
@@ -0,0 +1,22 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/fuse_modules.py`, while adding an import statement
+here.
+"""
+
+# TODO: These functions are not used outside the `fuse_modules.py`
+#       Keeping here for now, need to remove them later.
+from torch.ao.quantization.fuse_modules import (
+    _fuse_modules,
+    _get_module,
+    _set_module,
+    fuse_known_modules,
+    fuse_modules,
+    get_fuser_method,
+)
+
+# for backward compatiblity
+from torch.ao.quantization.fuser_method_mappings import fuse_conv_bn, fuse_conv_bn_relu
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fuser_method_mappings.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fuser_method_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfb13ac96271fa7b926cc703918984760e6ede15
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fuser_method_mappings.py
@@ -0,0 +1,15 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/fuser_method_mappings.py`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fuser_method_mappings import (
+    _DEFAULT_OP_LIST_TO_FUSER_METHOD,
+    fuse_conv_bn,
+    fuse_conv_bn_relu,
+    fuse_linear_bn,
+    get_fuser_method,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c01cbd457374c27e40b07daca5ae1644a701767d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/__init__.py
@@ -0,0 +1,15 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.fx.convert import convert
+from torch.ao.quantization.fx.fuse import fuse
+
+# omitting files that's unlikely to be used right now, for example
+# the newly added lower_to_fbgemm etc.
+from torch.ao.quantization.fx.prepare import prepare
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/_equalize.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/_equalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..7acea4f84a2a0a82f134b6790e573f8f1cb677f2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/_equalize.py
@@ -0,0 +1,38 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx._equalize import (
+    _convert_equalization_ref,
+    _InputEqualizationObserver,
+    _WeightEqualizationObserver,
+    calculate_equalization_scale,
+    clear_weight_quant_obs_node,
+    convert_eq_obs,
+    CUSTOM_MODULE_SUPP_LIST,
+    custom_module_supports_equalization,
+    default_equalization_qconfig,
+    EqualizationQConfig,
+    fused_module_supports_equalization,
+    get_equalization_qconfig_dict,
+    get_layer_sqnr_dict,
+    get_op_node_and_weight_eq_obs,
+    input_equalization_observer,
+    is_equalization_observer,
+    maybe_get_next_equalization_scale,
+    maybe_get_next_input_eq_obs,
+    maybe_get_weight_eq_obs_node,
+    nn_module_supports_equalization,
+    node_supports_equalization,
+    remove_node,
+    reshape_scale,
+    scale_input_observer,
+    scale_weight_functional,
+    scale_weight_node,
+    update_obs_for_equalization,
+    weight_equalization_observer,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/convert.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d6ac350602bb7a97c773a3a09fec0780483379f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/convert.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.convert import convert
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/fuse.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/fuse.py
new file mode 100644
index 0000000000000000000000000000000000000000..67527080304fb31ddc54fe254533e2196f77a616
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/fuse.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.fuse import fuse
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/fusion_patterns.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/fusion_patterns.py
new file mode 100644
index 0000000000000000000000000000000000000000..e29337b3f861e5b54dc9f37d39d12ad975ad1315
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/fusion_patterns.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.fuse_handler import DefaultFuseHandler, FuseHandler
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/graph_module.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/graph_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..a71e980a57ba141bdc5bbe9b283d69582eb8fd82
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/graph_module.py
@@ -0,0 +1,17 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.graph_module import (
+    _is_observed_module,
+    _is_observed_standalone_module,
+    FusedGraphModule,
+    GraphModule,
+    ObservedGraphModule,
+    ObservedStandaloneGraphModule,
+    QuantizedGraphModule,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/match_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/match_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b49f7c645d8d1bc3a154d62a1295a90b155f986
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/match_utils.py
@@ -0,0 +1,14 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.match_utils import (
+    _find_matches,
+    _is_match,
+    _MatchResult,
+    MatchAllNode,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/pattern_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/pattern_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a83e180fc4dbaa28d1d41a10037684f0afa6610
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/pattern_utils.py
@@ -0,0 +1,35 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.pattern_utils import (
+    _register_fusion_pattern,
+    _register_quant_pattern,
+    get_default_fusion_patterns,
+    get_default_output_activation_post_process_map,
+    get_default_quant_patterns,
+    QuantizeHandler,
+)
+
+
+# QuantizeHandler.__module__ = _NAMESPACE
+_register_fusion_pattern.__module__ = "torch.ao.quantization.fx.pattern_utils"
+get_default_fusion_patterns.__module__ = "torch.ao.quantization.fx.pattern_utils"
+_register_quant_pattern.__module__ = "torch.ao.quantization.fx.pattern_utils"
+get_default_quant_patterns.__module__ = "torch.ao.quantization.fx.pattern_utils"
+get_default_output_activation_post_process_map.__module__ = (
+    "torch.ao.quantization.fx.pattern_utils"
+)
+
+# __all__ = [
+#     "QuantizeHandler",
+#     "_register_fusion_pattern",
+#     "get_default_fusion_patterns",
+#     "_register_quant_pattern",
+#     "get_default_quant_patterns",
+#     "get_default_output_activation_post_process_map",
+# ]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/prepare.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca65dcc04dd0021f0065892ca86e209a1c218473
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/prepare.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.prepare import prepare
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/quantization_patterns.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/quantization_patterns.py
new file mode 100644
index 0000000000000000000000000000000000000000..20d8cc52ee4fb16843becec5487d9d4ee46681c9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/quantization_patterns.py
@@ -0,0 +1,48 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.quantize_handler import (
+    BatchNormQuantizeHandler,
+    BinaryOpQuantizeHandler,
+    CatQuantizeHandler,
+    ConvReluQuantizeHandler,
+    CopyNodeQuantizeHandler,
+    CustomModuleQuantizeHandler,
+    DefaultNodeQuantizeHandler,
+    EmbeddingQuantizeHandler,
+    FixedQParamsOpQuantizeHandler,
+    GeneralTensorShapeOpQuantizeHandler,
+    LinearReLUQuantizeHandler,
+    QuantizeHandler,
+    RNNDynamicQuantizeHandler,
+    StandaloneModuleQuantizeHandler,
+)
+
+
+QuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+BinaryOpQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+CatQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+ConvReluQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+LinearReLUQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+BatchNormQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+EmbeddingQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+RNNDynamicQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+DefaultNodeQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+FixedQParamsOpQuantizeHandler.__module__ = (
+    "torch.ao.quantization.fx.quantization_patterns"
+)
+CopyNodeQuantizeHandler.__module__ = "torch.ao.quantization.fx.quantization_patterns"
+CustomModuleQuantizeHandler.__module__ = (
+    "torch.ao.quantization.fx.quantization_patterns"
+)
+GeneralTensorShapeOpQuantizeHandler.__module__ = (
+    "torch.ao.quantization.fx.quantization_patterns"
+)
+StandaloneModuleQuantizeHandler.__module__ = (
+    "torch.ao.quantization.fx.quantization_patterns"
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/quantization_types.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/quantization_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..a422cdd3142e04c8d16f495cc6cd65823451810b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/quantization_types.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.utils import Pattern, QuantizerCls
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef35559884b7c430f1d5c72b21f72979108469a5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/fx/utils.py
@@ -0,0 +1,20 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.utils import (
+    all_node_args_have_no_tensors,
+    assert_and_get_unique_device,
+    create_getattr_from_value,
+    get_custom_module_class_keys,
+    get_linear_prepack_op_for_dtype,
+    get_new_attr_name_with_prefix,
+    get_non_observable_arg_indexes_and_types,
+    get_qconv_prepack_op,
+    graph_module_from_producer_nodes,
+    maybe_get_next_module,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/observer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/observer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e6c7c1917c83433fc19f016140b25d060284535
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/observer.py
@@ -0,0 +1,36 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/observer.py`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.observer import (
+    _is_activation_post_process,
+    _is_per_channel_script_obs_instance,
+    _ObserverBase,
+    _PartialWrapper,
+    _with_args,
+    _with_callable_args,
+    ABC,
+    default_debug_observer,
+    default_dynamic_quant_observer,
+    default_float_qparams_observer,
+    default_histogram_observer,
+    default_observer,
+    default_per_channel_weight_observer,
+    default_placeholder_observer,
+    default_weight_observer,
+    get_observer_state_dict,
+    HistogramObserver,
+    load_observer_state_dict,
+    MinMaxObserver,
+    MovingAverageMinMaxObserver,
+    MovingAveragePerChannelMinMaxObserver,
+    NoopObserver,
+    ObserverBase,
+    PerChannelMinMaxObserver,
+    PlaceholderObserver,
+    RecordingObserver,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/qconfig.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/qconfig.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bb7e14110cb9cdc4e9c2c418c6776ea6445f0d3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/qconfig.py
@@ -0,0 +1,30 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/qconfig.py`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.qconfig import (
+    _add_module_to_qconfig_obs_ctr,
+    _assert_valid_qconfig,
+    default_activation_only_qconfig,
+    default_debug_qconfig,
+    default_dynamic_qconfig,
+    default_per_channel_qconfig,
+    default_qat_qconfig,
+    default_qat_qconfig_v2,
+    default_qconfig,
+    default_weight_only_qconfig,
+    float16_dynamic_qconfig,
+    float16_static_qconfig,
+    float_qparams_weight_only_qconfig,
+    get_default_qat_qconfig,
+    get_default_qconfig,
+    per_channel_dynamic_qconfig,
+    QConfig,
+    qconfig_equals,
+    QConfigAny,
+    QConfigDynamic,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quant_type.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quant_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..8555f03792661f39c85c8facf3f911786cc25d0f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quant_type.py
@@ -0,0 +1,10 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/quant_type.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.quant_type import _get_quant_type_to_str, QuantType
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantization_mappings.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantization_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b44a980ce82fbfa5a81ad906499806cf99b876f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantization_mappings.py
@@ -0,0 +1,29 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/quantization_mappings.py`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.quantization_mappings import (
+    _get_special_act_post_process,
+    _has_special_act_post_process,
+    _INCLUDE_QCONFIG_PROPAGATE_LIST,
+    DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS,
+    DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS,
+    DEFAULT_MODULE_TO_ACT_POST_PROCESS,
+    DEFAULT_QAT_MODULE_MAPPINGS,
+    DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS,
+    DEFAULT_STATIC_QUANT_MODULE_MAPPINGS,
+    get_default_compare_output_module_list,
+    get_default_dynamic_quant_module_mappings,
+    get_default_float_to_quantized_operator_mappings,
+    get_default_qat_module_mappings,
+    get_default_qconfig_propagation_list,
+    get_default_static_quant_module_mappings,
+    get_dynamic_quant_module_class,
+    get_quantized_operator,
+    get_static_quant_module_class,
+    no_observer_set,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantize.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..600d3a46fed0346e3ae8909872cd5bf3c733860c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantize.py
@@ -0,0 +1,30 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/quantize.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.quantize import (
+    _add_observer_,
+    _convert,
+    _get_observer_dict,
+    _get_unique_devices_,
+    _is_activation_post_process,
+    _observer_forward_hook,
+    _propagate_qconfig_helper,
+    _register_activation_post_process_hook,
+    _remove_activation_post_process,
+    _remove_qconfig,
+    add_quant_dequant,
+    convert,
+    prepare,
+    prepare_qat,
+    propagate_qconfig_,
+    quantize,
+    quantize_dynamic,
+    quantize_qat,
+    swap_module,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantize_fx.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantize_fx.py
new file mode 100644
index 0000000000000000000000000000000000000000..649142c7a7eee9885d96b37f70e582f3ea9a9f8d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantize_fx.py
@@ -0,0 +1,26 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/quantize_fx.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.fx.graph_module import ObservedGraphModule
+from torch.ao.quantization.quantize_fx import (
+    _check_is_graph_module,
+    _convert_fx,
+    _convert_standalone_module_fx,
+    _fuse_fx,
+    _prepare_fx,
+    _prepare_standalone_module_fx,
+    _swap_ff_with_fxff,
+    convert_fx,
+    fuse_fx,
+    prepare_fx,
+    prepare_qat_fx,
+    QuantizationTracer,
+    Scope,
+    ScopeContextManager,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantize_jit.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantize_jit.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa627dc7bb51ef7ea1fde7e2e5da283c9f6c8900
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/quantize_jit.py
@@ -0,0 +1,26 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/quantize_jit.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.quantize_jit import (
+    _check_forward_method,
+    _check_is_script_module,
+    _convert_jit,
+    _prepare_jit,
+    _prepare_ondevice_dynamic_jit,
+    _quantize_jit,
+    convert_dynamic_jit,
+    convert_jit,
+    fuse_conv_bn_jit,
+    prepare_dynamic_jit,
+    prepare_jit,
+    quantize_dynamic_jit,
+    quantize_jit,
+    script_qconfig,
+    script_qconfig_dict,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/stubs.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/stubs.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3fd5c63683dc572c35cabc202ee4ddb2b0053c6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/stubs.py
@@ -0,0 +1,10 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/stubs.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.stubs import DeQuantStub, QuantStub, QuantWrapper
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d51d58f38d7462713f84ab62427852c1dd8e52c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/quantization/utils.py
@@ -0,0 +1,29 @@
+# flake8: noqa: F401
+r"""
+Utils shared by different modes of quantization (eager/graph)
+
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+`torch/ao/quantization/utils.py`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.utils import (
+    activation_dtype,
+    activation_is_int8_quantized,
+    activation_is_statically_quantized,
+    calculate_qmin_qmax,
+    check_min_max_valid,
+    get_combined_dict,
+    get_qconfig_dtypes,
+    get_qparam_dict,
+    get_quant_type,
+    get_swapped_custom_module_class,
+    getattr_from_fqn,
+    is_per_channel,
+    is_per_tensor,
+    weight_dtype,
+    weight_is_quantized,
+    weight_is_statically_quantized,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/ATen/ATenConfig.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/ATen/ATenConfig.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..0ce7803dbf78897298d81c2679f2cdb3c872bc15
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/ATen/ATenConfig.cmake
@@ -0,0 +1,9 @@
+# Find the TH includes and library
+#
+# ATEN_INCLUDE_DIR -- where to find the includes
+# ATEN_LIBRARIES -- list of libraries to link against
+# ATEN_FOUND -- set to 1 if found
+
+set(ATEN_FOUND 1)
+set(ATEN_INCLUDE_DIR "/pytorch/torch/include")
+set(ATEN_LIBRARIES "")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..84f04397702571e538c48cffaae2bdc157afa2d2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake
@@ -0,0 +1,133 @@
+# - Config file for the Caffe2 package
+# It defines the following variable(s)
+#   CAFFE2_INCLUDE_DIRS     - include directories for FooBar
+# as well as Caffe2 targets for other cmake libraries to use.
+
+# library version information
+
+# Utils functions.
+include("${CMAKE_CURRENT_LIST_DIR}/public/utils.cmake")
+
+# Depending on whether Caffe2 uses gflags during compile time or
+# not, invoke gflags.
+if(OFF)
+  include("${CMAKE_CURRENT_LIST_DIR}/public/gflags.cmake")
+  if(NOT TARGET gflags)
+    message(FATAL_ERROR
+        "Your installed Caffe2 version uses gflags but the gflags library "
+        "cannot be found. Did you accidentally remove it, or have you set "
+        "the right CMAKE_PREFIX_PATH and/or GFLAGS_ROOT_DIR? If you do not "
+        "have gflags, you will need to install gflags and set the library "
+        "path accordingly.")
+  endif()
+endif()
+
+# Depending on whether Caffe2 uses glog during compile time or
+# not, invoke glog.
+if(OFF)
+  include("${CMAKE_CURRENT_LIST_DIR}/public/glog.cmake")
+  if(NOT TARGET glog::glog)
+    message(FATAL_ERROR
+        "Your installed Caffe2 version uses glog but the glog library "
+        "cannot be found. Did you accidentally remove it, or have you set "
+        "the right CMAKE_PREFIX_PATH and/or GFLAGS_ROOT_DIR? If you do not "
+        "have glog, you will need to install glog and set the library "
+        "path accordingly.")
+  endif()
+endif()
+
+# Protobuf
+if(ON)
+  if(NOT TARGET protobuf::libprotobuf)
+    # Define protobuf::libprotobuf as a dummy target to resolve references to
+    # protobuf::libprotobuf in Caffe2Targets.cmake.
+    add_library(dummy INTERFACE)
+    add_library(protobuf::libprotobuf ALIAS dummy)
+  endif()
+else()
+  include("${CMAKE_CURRENT_LIST_DIR}/public/protobuf.cmake")
+  if(NOT TARGET protobuf::libprotobuf)
+    message(FATAL_ERROR
+        "Your installed Caffe2 version uses protobuf but the protobuf library "
+        "cannot be found. Did you accidentally remove it, or have you set "
+        "the right CMAKE_PREFIX_PATH? If you do not have protobuf, you will "
+        "need to install protobuf and set the library path accordingly.")
+  endif()
+  message(STATUS "Caffe2: Protobuf version " ${Protobuf_VERSION})
+  # If during build time we know the protobuf version, we will also do a sanity
+  # check to ensure that the protobuf library that Caffe2 found is consistent
+  # with the compiled version.
+  if(FALSE)
+    if(NOT (${Protobuf_VERSION} VERSION_EQUAL Protobuf_VERSION_NOTFOUND))
+      message(FATAL_ERROR
+          "Your installed Caffe2 is built with protobuf "
+          "Protobuf_VERSION_NOTFOUND"
+          ", while your current cmake setting discovers protobuf version "
+          ${Protobuf_VERSION}
+          ". Please specify a protobuf version that is the same as the built "
+          "version.")
+    endif()
+  endif()
+endif()
+
+if (OFF)
+  include("${CMAKE_CURRENT_LIST_DIR}/public/LoadHIP.cmake")
+endif()
+
+if(ON)
+  # The file public/cuda.cmake exclusively uses CAFFE2_USE_*.
+  # If Caffe2 was compiled with the libraries below, they must
+  # be found again when including the Caffe2 target.
+  set(CAFFE2_USE_CUDA ON)
+
+  # Add current directory to module path so we pick up FindCUDAToolkit.cmake
+  set(old_CMAKE_MODULE_PATH "${CMAKE_MODULE_PATH}")
+  list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}")
+  include("${CMAKE_CURRENT_LIST_DIR}/public/cuda.cmake")
+  set(CMAKE_MODULE_PATH "${old_CMAKE_MODULE_PATH}")
+
+  if(ON AND NOT CAFFE2_USE_CUDA)
+    message(FATAL_ERROR
+      "Your installed Caffe2 version uses CUDA but I cannot find the CUDA "
+      "libraries. Please set the proper CUDA prefixes and / or install "
+      "CUDA.")
+  endif()
+endif()
+
+if(OFF)
+  # Add current directory to module path so we pick up FindSYCLToolkit.cmake
+  set(old_CMAKE_MODULE_PATH "${CMAKE_MODULE_PATH}")
+  list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}")
+  include("${CMAKE_CURRENT_LIST_DIR}/public/xpu.cmake")
+  set(CMAKE_MODULE_PATH "${old_CMAKE_MODULE_PATH}")
+endif()
+
+if(ON)
+  include("${CMAKE_CURRENT_LIST_DIR}/public/mkl.cmake")
+endif()
+
+if(ON)
+  include("${CMAKE_CURRENT_LIST_DIR}/public/mkldnn.cmake")
+endif()
+
+# import targets
+include ("${CMAKE_CURRENT_LIST_DIR}/Caffe2Targets.cmake")
+
+# Interface libraries, that allows one to build proper link flags.
+# We will also define a helper variable, Caffe2_MAIN_LIBS, that resolves to
+# the main caffe2 libraries in cases of cuda presence / absence.
+set(Caffe2_MAIN_LIBS torch_library)
+
+# include directory.
+#
+# Newer versions of CMake set the INTERFACE_INCLUDE_DIRECTORIES property
+# of the imported targets. It is hence not necessary to add this path
+# manually to the include search path for targets which link to gflags.
+# The following lines are here for backward compatibility, in case one
+# would like to use the old-style include path.
+get_filename_component(
+    CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+# Note: the current list dir is _INSTALL_PREFIX/share/cmake/Gloo.
+get_filename_component(
+    _INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+set(CAFFE2_INCLUDE_DIRS "${_INSTALL_PREFIX}/include")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Caffe2Targets-release.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Caffe2Targets-release.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..946f1b44746743e286f5a8d06816572df52b8c47
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Caffe2Targets-release.cmake
@@ -0,0 +1,59 @@
+#----------------------------------------------------------------
+# Generated CMake target import file for configuration "Release".
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Import target "c10_cuda" for configuration "Release"
+set_property(TARGET c10_cuda APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(c10_cuda PROPERTIES
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libc10_cuda.so"
+  IMPORTED_SONAME_RELEASE "libc10_cuda.so"
+  )
+
+list(APPEND _cmake_import_check_targets c10_cuda )
+list(APPEND _cmake_import_check_files_for_c10_cuda "${_IMPORT_PREFIX}/lib/libc10_cuda.so" )
+
+# Import target "c10" for configuration "Release"
+set_property(TARGET c10 APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(c10 PROPERTIES
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libc10.so"
+  IMPORTED_SONAME_RELEASE "libc10.so"
+  )
+
+list(APPEND _cmake_import_check_targets c10 )
+list(APPEND _cmake_import_check_files_for_c10 "${_IMPORT_PREFIX}/lib/libc10.so" )
+
+# Import target "torch_cpu" for configuration "Release"
+set_property(TARGET torch_cpu APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(torch_cpu PROPERTIES
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libtorch_cpu.so"
+  IMPORTED_SONAME_RELEASE "libtorch_cpu.so"
+  )
+
+list(APPEND _cmake_import_check_targets torch_cpu )
+list(APPEND _cmake_import_check_files_for_torch_cpu "${_IMPORT_PREFIX}/lib/libtorch_cpu.so" )
+
+# Import target "torch_cuda" for configuration "Release"
+set_property(TARGET torch_cuda APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(torch_cuda PROPERTIES
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libtorch_cuda.so"
+  IMPORTED_SONAME_RELEASE "libtorch_cuda.so"
+  )
+
+list(APPEND _cmake_import_check_targets torch_cuda )
+list(APPEND _cmake_import_check_files_for_torch_cuda "${_IMPORT_PREFIX}/lib/libtorch_cuda.so" )
+
+# Import target "torch" for configuration "Release"
+set_property(TARGET torch APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(torch PROPERTIES
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libtorch.so"
+  IMPORTED_SONAME_RELEASE "libtorch.so"
+  )
+
+list(APPEND _cmake_import_check_targets torch )
+list(APPEND _cmake_import_check_files_for_torch "${_IMPORT_PREFIX}/lib/libtorch.so" )
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Caffe2Targets.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Caffe2Targets.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..2747e0a5ebf39a71c46489db6aa66b329fc8502a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Caffe2Targets.cmake
@@ -0,0 +1,188 @@
+# Generated by CMake
+
+if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
+   message(FATAL_ERROR "CMake >= 3.0.0 required")
+endif()
+if(CMAKE_VERSION VERSION_LESS "3.0.0")
+   message(FATAL_ERROR "CMake >= 3.0.0 required")
+endif()
+cmake_policy(PUSH)
+cmake_policy(VERSION 3.0.0...3.30)
+#----------------------------------------------------------------
+# Generated CMake target import file.
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Protect against multiple inclusion, which would fail when already imported targets are added once more.
+set(_cmake_targets_defined "")
+set(_cmake_targets_not_defined "")
+set(_cmake_expected_targets "")
+foreach(_cmake_expected_target IN ITEMS c10_cuda c10 torch_cpu torch_cpu_library torch_cuda torch_cuda_library torch torch_library)
+  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
+  if(TARGET "${_cmake_expected_target}")
+    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
+  else()
+    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
+  endif()
+endforeach()
+unset(_cmake_expected_target)
+if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
+  unset(_cmake_targets_defined)
+  unset(_cmake_targets_not_defined)
+  unset(_cmake_expected_targets)
+  unset(CMAKE_IMPORT_FILE_VERSION)
+  cmake_policy(POP)
+  return()
+endif()
+if(NOT _cmake_targets_defined STREQUAL "")
+  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
+  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
+  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
+endif()
+unset(_cmake_targets_defined)
+unset(_cmake_targets_not_defined)
+unset(_cmake_expected_targets)
+
+
+# Compute the installation prefix relative to this file.
+get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+if(_IMPORT_PREFIX STREQUAL "/")
+  set(_IMPORT_PREFIX "")
+endif()
+
+# Create imported target c10_cuda
+add_library(c10_cuda SHARED IMPORTED)
+
+set_target_properties(c10_cuda PROPERTIES
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+  INTERFACE_LINK_LIBRARIES "c10;torch::cudart"
+)
+
+# Create imported target c10
+add_library(c10 SHARED IMPORTED)
+
+set_target_properties(c10 PROPERTIES
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+)
+
+# Create imported target torch_cpu
+add_library(torch_cpu SHARED IMPORTED)
+
+set_target_properties(torch_cpu PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "USE_DISTRIBUTED;USE_C10D_GLOO;USE_RPC;USE_TENSORPIPE"
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+  INTERFACE_LINK_LIBRARIES "protobuf::libprotobuf;c10;caffe2::mkl"
+)
+
+# Create imported target torch_cpu_library
+add_library(torch_cpu_library INTERFACE IMPORTED)
+
+set_target_properties(torch_cpu_library PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "\$"
+  INTERFACE_COMPILE_OPTIONS "\$"
+  INTERFACE_INCLUDE_DIRECTORIES "\$"
+  INTERFACE_LINK_LIBRARIES "-Wl,--no-as-needed,\"\$\" -Wl,--as-needed;\$"
+  INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "\$"
+)
+
+# Create imported target torch_cuda
+add_library(torch_cuda SHARED IMPORTED)
+
+set_target_properties(torch_cuda PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "USE_C10D_NCCL"
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+  INTERFACE_LINK_LIBRARIES "torch::cudart;c10_cuda;torch_cpu_library"
+)
+
+# Create imported target torch_cuda_library
+add_library(torch_cuda_library INTERFACE IMPORTED)
+
+set_target_properties(torch_cuda_library PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "\$"
+  INTERFACE_COMPILE_OPTIONS "\$"
+  INTERFACE_INCLUDE_DIRECTORIES "\$"
+  INTERFACE_LINK_LIBRARIES "-Wl,--no-as-needed,\"\$\" -Wl,--as-needed;\$"
+  INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "\$"
+)
+
+# Create imported target torch
+add_library(torch SHARED IMPORTED)
+
+set_target_properties(torch PROPERTIES
+  INTERFACE_LINK_LIBRARIES "torch_cpu_library;torch_cuda_library"
+)
+
+# Create imported target torch_library
+add_library(torch_library INTERFACE IMPORTED)
+
+set_target_properties(torch_library PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "\$"
+  INTERFACE_COMPILE_OPTIONS "\$"
+  INTERFACE_INCLUDE_DIRECTORIES "\$"
+  INTERFACE_LINK_LIBRARIES "-Wl,--no-as-needed,\"\$\" -Wl,--as-needed;\$"
+  INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "\$"
+)
+
+# Load information for each installed configuration.
+file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/Caffe2Targets-*.cmake")
+foreach(_cmake_config_file IN LISTS _cmake_config_files)
+  include("${_cmake_config_file}")
+endforeach()
+unset(_cmake_config_file)
+unset(_cmake_config_files)
+
+# Cleanup temporary variables.
+set(_IMPORT_PREFIX)
+
+# Loop over all imported files and verify that they actually exist
+foreach(_cmake_target IN LISTS _cmake_import_check_targets)
+  if(CMAKE_VERSION VERSION_LESS "3.28"
+      OR NOT DEFINED _cmake_import_check_xcframework_for_${_cmake_target}
+      OR NOT IS_DIRECTORY "${_cmake_import_check_xcframework_for_${_cmake_target}}")
+    foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
+      if(NOT EXISTS "${_cmake_file}")
+        message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
+   \"${_cmake_file}\"
+but this file does not exist.  Possible reasons include:
+* The file was deleted, renamed, or moved to another location.
+* An install or uninstall procedure did not complete successfully.
+* The installation package was faulty and contained
+   \"${CMAKE_CURRENT_LIST_FILE}\"
+but not all the files it references.
+")
+      endif()
+    endforeach()
+  endif()
+  unset(_cmake_file)
+  unset("_cmake_import_check_files_for_${_cmake_target}")
+endforeach()
+unset(_cmake_target)
+unset(_cmake_import_check_targets)
+
+# Make sure the targets which have been exported in some other
+# export set exist.
+unset(${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets)
+foreach(_target "protobuf::libprotobuf" )
+  if(NOT TARGET "${_target}" )
+    set(${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets "${${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets} ${_target}")
+  endif()
+endforeach()
+
+if(DEFINED ${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets)
+  if(CMAKE_FIND_PACKAGE_NAME)
+    set( ${CMAKE_FIND_PACKAGE_NAME}_FOUND FALSE)
+    set( ${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE "The following imported targets are referenced, but are missing: ${${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets}")
+  else()
+    message(FATAL_ERROR "The following imported targets are referenced, but are missing: ${${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets}")
+  endif()
+endif()
+unset(${CMAKE_FIND_PACKAGE_NAME}_NOT_FOUND_MESSAGE_targets)
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
+cmake_policy(POP)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindCUDAToolkit.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindCUDAToolkit.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..ec9ae530aa6b2bdceb87f966e706fb5c2a36349a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindCUDAToolkit.cmake
@@ -0,0 +1,1081 @@
+
+# This module is back-ported from CMake 3.17 and above to work with CMake 3.10
+
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#[=======================================================================[.rst:
+FindCUDAToolkit
+---------------
+
+.. versionadded:: 3.17
+
+This script locates the NVIDIA CUDA toolkit and the associated libraries, but
+does not require the ``CUDA`` language be enabled for a given project. This
+module does not search for the NVIDIA CUDA Samples.
+
+.. versionadded:: 3.19
+  QNX support.
+
+Search Behavior
+^^^^^^^^^^^^^^^
+
+The CUDA Toolkit search behavior uses the following order:
+
+1. If the ``CUDA`` language has been enabled we will use the directory
+   containing the compiler as the first search location for ``nvcc``.
+
+2. If the ``CUDAToolkit_ROOT`` cmake configuration variable (e.g.,
+   ``-DCUDAToolkit_ROOT=/some/path``) *or* environment variable is defined, it
+   will be searched.  If both an environment variable **and** a
+   configuration variable are specified, the *configuration* variable takes
+   precedence.
+
+   The directory specified here must be such that the executable ``nvcc`` or
+   the appropriate ``version.txt`` file can be found underneath the specified
+   directory.
+
+3. If the CUDA_PATH environment variable is defined, it will be searched
+   for ``nvcc``.
+
+4. The user's path is searched for ``nvcc`` using :command:`find_program`.  If
+   this is found, no subsequent search attempts are performed.  Users are
+   responsible for ensuring that the first ``nvcc`` to show up in the path is
+   the desired path in the event that multiple CUDA Toolkits are installed.
+
+5. On Unix systems, if the symbolic link ``/usr/local/cuda`` exists, this is
+   used.  No subsequent search attempts are performed.  No default symbolic link
+   location exists for the Windows platform.
+
+6. The platform specific default install locations are searched.  If exactly one
+   candidate is found, this is used.  The default CUDA Toolkit install locations
+   searched are:
+
+   +-------------+-------------------------------------------------------------+
+   | Platform    | Search Pattern                                              |
+   +=============+=============================================================+
+   | macOS       | ``/Developer/NVIDIA/CUDA-X.Y``                              |
+   +-------------+-------------------------------------------------------------+
+   | Other Unix  | ``/usr/local/cuda-X.Y``                                     |
+   +-------------+-------------------------------------------------------------+
+   | Windows     | ``C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y`` |
+   +-------------+-------------------------------------------------------------+
+
+   Where ``X.Y`` would be a specific version of the CUDA Toolkit, such as
+   ``/usr/local/cuda-9.0`` or
+   ``C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0``
+
+   .. note::
+
+       When multiple CUDA Toolkits are installed in the default location of a
+       system(e.g., both ``/usr/local/cuda-9.0`` and ``/usr/local/cuda-10.0``
+       exist but the ``/usr/local/cuda`` symbolic link does **not** exist), this
+       package is marked as **not** found.
+
+       There are too many factors involved in making an automatic decision in
+       the presence of multiple CUDA Toolkits being installed.  In this
+       situation, users are encouraged to either (1) set ``CUDAToolkit_ROOT`` or
+       (2) ensure that the correct ``nvcc`` executable shows up in ``$PATH`` for
+       :command:`find_program` to find.
+
+Arguments
+^^^^^^^^^
+
+``[]``
+    The ``[]`` argument requests a version with which the package found
+    should be compatible. See :ref:`find_package version format `
+    for more details.
+
+Options
+^^^^^^^
+
+``REQUIRED``
+    If specified, configuration will error if a suitable CUDA Toolkit is not
+    found.
+
+``QUIET``
+    If specified, the search for a suitable CUDA Toolkit will not produce any
+    messages.
+
+``EXACT``
+    If specified, the CUDA Toolkit is considered found only if the exact
+    ``VERSION`` specified is recovered.
+
+Imported targets
+^^^^^^^^^^^^^^^^
+
+An :ref:`imported target ` named ``CUDA::toolkit`` is provided.
+
+This module defines :prop_tgt:`IMPORTED` targets for each
+of the following libraries that are part of the CUDAToolkit:
+
+- :ref:`CUDA Runtime Library`
+- :ref:`CUDA Driver Library`
+- :ref:`cuBLAS`
+- :ref:`cuFFT`
+- :ref:`cuRAND`
+- :ref:`cuSOLVER`
+- :ref:`cuSPARSE`
+- :ref:`cuPTI`
+- :ref:`NPP`
+- :ref:`nvBLAS`
+- :ref:`nvGRAPH`
+- :ref:`nvJPEG`
+- :ref:`nvidia-ML`
+- :ref:`nvRTC`
+- :ref:`nvToolsExt`
+- :ref:`OpenCL`
+- :ref:`cuLIBOS`
+
+.. _`cuda_toolkit_rt_lib`:
+
+CUDA Runtime Library
+""""""""""""""""""""
+
+The CUDA Runtime library (cudart) are what most applications will typically
+need to link against to make any calls such as `cudaMalloc`, and `cudaFree`.
+
+Targets Created:
+
+- ``CUDA::cudart``
+- ``CUDA::cudart_static``
+
+.. _`cuda_toolkit_driver_lib`:
+
+CUDA Driver Library
+""""""""""""""""""""
+
+The CUDA Driver library (cuda) are used by applications that use calls
+such as `cuMemAlloc`, and `cuMemFree`.
+
+Targets Created:
+
+- ``CUDA::cuda_driver``
+
+.. _`cuda_toolkit_cuBLAS`:
+
+cuBLAS
+""""""
+
+The `cuBLAS `_ library.
+
+Targets Created:
+
+- ``CUDA::cublas``
+- ``CUDA::cublas_static``
+- ``CUDA::cublasLt`` starting in CUDA 10.1
+- ``CUDA::cublasLt_static`` starting in CUDA 10.1
+
+.. _`cuda_toolkit_cuFFT`:
+
+cuFFT
+"""""
+
+The `cuFFT `_ library.
+
+Targets Created:
+
+- ``CUDA::cufft``
+- ``CUDA::cufftw``
+- ``CUDA::cufft_static``
+- ``CUDA::cufft_static_nocallback`` starting in CUDA 9.2, requires CMake 3.23+
+- ``CUDA::cufftw_static``
+
+cuRAND
+""""""
+
+The `cuRAND `_ library.
+
+Targets Created:
+
+- ``CUDA::curand``
+- ``CUDA::curand_static``
+
+.. _`cuda_toolkit_cuSOLVER`:
+
+cuSOLVER
+""""""""
+
+The `cuSOLVER `_ library.
+
+Targets Created:
+
+- ``CUDA::cusolver``
+- ``CUDA::cusolver_static``
+
+.. _`cuda_toolkit_cuSPARSE`:
+
+cuSPARSE
+""""""""
+
+The `cuSPARSE `_ library.
+
+Targets Created:
+
+- ``CUDA::cusparse``
+- ``CUDA::cusparse_static``
+
+.. _`cuda_toolkit_cupti`:
+
+cupti
+"""""
+
+The `NVIDIA CUDA Profiling Tools Interface `_.
+
+Targets Created:
+
+- ``CUDA::cupti``
+- ``CUDA::cupti_static``
+
+.. _`cuda_toolkit_NPP`:
+
+NPP
+"""
+
+The `NPP `_ libraries.
+
+Targets Created:
+
+- `nppc`:
+
+  - ``CUDA::nppc``
+  - ``CUDA::nppc_static``
+
+- `nppial`: Arithmetic and logical operation functions in `nppi_arithmetic_and_logical_operations.h`
+
+  - ``CUDA::nppial``
+  - ``CUDA::nppial_static``
+
+- `nppicc`: Color conversion and sampling functions in `nppi_color_conversion.h`
+
+  - ``CUDA::nppicc``
+  - ``CUDA::nppicc_static``
+
+- `nppicom`: JPEG compression and decompression functions in `nppi_compression_functions.h`
+  Removed starting in CUDA 11.0, use :ref:`nvJPEG` instead.
+
+  - ``CUDA::nppicom``
+  - ``CUDA::nppicom_static``
+
+- `nppidei`: Data exchange and initialization functions in `nppi_data_exchange_and_initialization.h`
+
+  - ``CUDA::nppidei``
+  - ``CUDA::nppidei_static``
+
+- `nppif`: Filtering and computer vision functions in `nppi_filter_functions.h`
+
+  - ``CUDA::nppif``
+  - ``CUDA::nppif_static``
+
+- `nppig`: Geometry transformation functions found in `nppi_geometry_transforms.h`
+
+  - ``CUDA::nppig``
+  - ``CUDA::nppig_static``
+
+- `nppim`: Morphological operation functions found in `nppi_morphological_operations.h`
+
+  - ``CUDA::nppim``
+  - ``CUDA::nppim_static``
+
+- `nppist`: Statistics and linear transform in `nppi_statistics_functions.h` and `nppi_linear_transforms.h`
+
+  - ``CUDA::nppist``
+  - ``CUDA::nppist_static``
+
+- `nppisu`: Memory support functions in `nppi_support_functions.h`
+
+  - ``CUDA::nppisu``
+  - ``CUDA::nppisu_static``
+
+- `nppitc`: Threshold and compare operation functions in `nppi_threshold_and_compare_operations.h`
+
+  - ``CUDA::nppitc``
+  - ``CUDA::nppitc_static``
+
+- `npps`:
+
+  - ``CUDA::npps``
+  - ``CUDA::npps_static``
+
+.. _`cuda_toolkit_nvBLAS`:
+
+nvBLAS
+""""""
+
+The `nvBLAS `_ libraries.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvblas``
+
+.. _`cuda_toolkit_nvGRAPH`:
+
+nvGRAPH
+"""""""
+
+The `nvGRAPH `_ library.
+Removed starting in CUDA 11.0
+
+Targets Created:
+
+- ``CUDA::nvgraph``
+- ``CUDA::nvgraph_static``
+
+
+.. _`cuda_toolkit_nvJPEG`:
+
+nvJPEG
+""""""
+
+The `nvJPEG `_ library.
+Introduced in CUDA 10.
+
+Targets Created:
+
+- ``CUDA::nvjpeg``
+- ``CUDA::nvjpeg_static``
+
+.. _`cuda_toolkit_nvRTC`:
+
+nvRTC
+"""""
+
+The `nvRTC `_ (Runtime Compilation) library.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvrtc``
+
+.. _`cuda_toolkit_nvml`:
+
+nvidia-ML
+"""""""""
+
+The `NVIDIA Management Library `_.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvml``
+
+.. _`cuda_toolkit_nvToolsExt`:
+
+nvToolsExt
+""""""""""
+
+The `NVIDIA Tools Extension `_.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::nvToolsExt``
+
+.. _`cuda_toolkit_opencl`:
+
+OpenCL
+""""""
+
+The `NVIDIA OpenCL Library `_.
+This is a shared library only.
+
+Targets Created:
+
+- ``CUDA::OpenCL``
+
+.. _`cuda_toolkit_cuLIBOS`:
+
+cuLIBOS
+"""""""
+
+The cuLIBOS library is a backend thread abstraction layer library which is
+static only.  The ``CUDA::cublas_static``, ``CUDA::cusparse_static``,
+``CUDA::cufft_static``, ``CUDA::curand_static``, and (when implemented) NPP
+libraries all automatically have this dependency linked.
+
+Target Created:
+
+- ``CUDA::culibos``
+
+**Note**: direct usage of this target by consumers should not be necessary.
+
+.. _`cuda_toolkit_cuRAND`:
+
+
+
+Result variables
+^^^^^^^^^^^^^^^^
+
+``CUDAToolkit_FOUND``
+    A boolean specifying whether or not the CUDA Toolkit was found.
+
+``CUDAToolkit_VERSION``
+    The exact version of the CUDA Toolkit found (as reported by
+    ``nvcc --version`` or ``version.txt``).
+
+``CUDAToolkit_VERSION_MAJOR``
+    The major version of the CUDA Toolkit.
+
+``CUDAToolkit_VERSION_MINOR``
+    The minor version of the CUDA Toolkit.
+
+``CUDAToolkit_VERSION_PATCH``
+    The patch version of the CUDA Toolkit.
+
+``CUDAToolkit_BIN_DIR``
+    The path to the CUDA Toolkit library directory that contains the CUDA
+    executable ``nvcc``.
+
+``CUDAToolkit_INCLUDE_DIRS``
+    The path to the CUDA Toolkit ``include`` folder containing the header files
+    required to compile a project linking against CUDA.
+
+``CUDAToolkit_LIBRARY_DIR``
+    The path to the CUDA Toolkit library directory that contains the CUDA
+    Runtime library ``cudart``.
+
+``CUDAToolkit_LIBRARY_ROOT``
+    .. versionadded:: 3.18
+
+    The path to the CUDA Toolkit directory containing the nvvm directory and
+    version.txt.
+
+``CUDAToolkit_TARGET_DIR``
+    The path to the CUDA Toolkit directory including the target architecture
+    when cross-compiling. When not cross-compiling this will be equivalent to
+    the parent directory of ``CUDAToolkit_BIN_DIR``.
+
+``CUDAToolkit_NVCC_EXECUTABLE``
+    The path to the NVIDIA CUDA compiler ``nvcc``.  Note that this path may
+    **not** be the same as
+    :variable:`CMAKE_CUDA_COMPILER _COMPILER>`.  ``nvcc`` must be
+    found to determine the CUDA Toolkit version as well as determining other
+    features of the Toolkit.  This variable is set for the convenience of
+    modules that depend on this one.
+
+
+#]=======================================================================]
+
+# NOTE: much of this was simply extracted from FindCUDA.cmake.
+
+#   James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#   Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html
+#
+#   Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#   Copyright (c) 2007-2009
+#   Scientific Computing and Imaging Institute, University of Utah
+#
+#   This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#   for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+#
+###############################################################################
+
+# The toolkit is located during compiler detection for CUDA and stored in CMakeCUDACompiler.cmake as
+# CMAKE_CUDA_COMPILER_TOOLKIT_ROOT and CMAKE_CUDA_COMPILER_LIBRARY_ROOT.
+# We compute the rest based on those here to avoid re-searching and to avoid finding a possibly
+# different installation.
+if(CMAKE_CUDA_COMPILER_TOOLKIT_ROOT)
+  set(CUDAToolkit_ROOT_DIR "${CMAKE_CUDA_COMPILER_TOOLKIT_ROOT}")
+  set(CUDAToolkit_LIBRARY_ROOT "${CMAKE_CUDA_COMPILER_LIBRARY_ROOT}")
+  set(CUDAToolkit_VERSION "${CMAKE_CUDA_COMPILER_TOOLKIT_VERSION}")
+
+  if(CUDAToolkit_VERSION MATCHES [=[([0-9]+)\.([0-9]+)\.([0-9]+)]=])
+    set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}")
+    set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}")
+    set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}")
+  endif()
+else()
+  function(_CUDAToolkit_find_root_dir )
+    cmake_parse_arguments(arg "" "" "SEARCH_PATHS;FIND_FLAGS" ${ARGN})
+
+    if(NOT CUDAToolkit_BIN_DIR)
+      if(NOT CUDAToolkit_SENTINEL_FILE)
+        find_program(CUDAToolkit_NVCC_EXECUTABLE
+          NAMES nvcc nvcc.exe
+          PATHS ${arg_SEARCH_PATHS}
+          ${arg_FIND_FLAGS}
+        )
+      endif()
+
+      if(NOT CUDAToolkit_NVCC_EXECUTABLE)
+        find_file(CUDAToolkit_SENTINEL_FILE
+          NAMES version.txt
+          PATHS ${arg_SEARCH_PATHS}
+          NO_DEFAULT_PATH
+        )
+      endif()
+
+      if(EXISTS "${CUDAToolkit_NVCC_EXECUTABLE}")
+        # If NVCC exists  then invoke it to find the toolkit location.
+        # This allows us to support wrapper scripts (e.g. ccache or colornvcc), CUDA Toolkit,
+        # NVIDIA HPC SDK, and distro's splayed layouts
+        execute_process(COMMAND ${CUDAToolkit_NVCC_EXECUTABLE} "-v" "__cmake_determine_cuda"
+          OUTPUT_VARIABLE _CUDA_NVCC_OUT ERROR_VARIABLE _CUDA_NVCC_OUT)
+        if(_CUDA_NVCC_OUT MATCHES "\\#\\$ TOP=([^\r\n]*)")
+          get_filename_component(CUDAToolkit_BIN_DIR "${CMAKE_MATCH_1}/bin" ABSOLUTE)
+        else()
+          get_filename_component(CUDAToolkit_BIN_DIR "${CUDAToolkit_NVCC_EXECUTABLE}" DIRECTORY)
+        endif()
+        unset(_CUDA_NVCC_OUT)
+
+        mark_as_advanced(CUDAToolkit_BIN_DIR)
+        set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}" CACHE PATH "" FORCE)
+      endif()
+
+      if(CUDAToolkit_SENTINEL_FILE)
+        get_filename_component(CUDAToolkit_BIN_DIR ${CUDAToolkit_SENTINEL_FILE} DIRECTORY ABSOLUTE)
+        set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}/bin")
+
+        set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}" CACHE PATH "" FORCE)
+        mark_as_advanced(CUDAToolkit_BIN_DIR)
+      endif()
+    endif()
+
+    if(CUDAToolkit_BIN_DIR)
+      get_filename_component(CUDAToolkit_ROOT_DIR ${CUDAToolkit_BIN_DIR} DIRECTORY ABSOLUTE)
+      set(CUDAToolkit_ROOT_DIR "${CUDAToolkit_ROOT_DIR}" PARENT_SCOPE)
+    endif()
+
+  endfunction()
+
+  # For NVCC we can easily deduce the SDK binary directory from the compiler path.
+  if(CMAKE_CUDA_COMPILER_LOADED AND NOT CUDAToolkit_BIN_DIR AND CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA")
+    get_filename_component(CUDAToolkit_BIN_DIR "${CMAKE_CUDA_COMPILER}" DIRECTORY)
+    set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}" CACHE PATH "")
+    # Try language provided path first.
+    _CUDAToolkit_find_root_dir(SEARCH_PATHS "${CUDAToolkit_BIN_DIR}" FIND_FLAGS NO_DEFAULT_PATH)
+    mark_as_advanced(CUDAToolkit_BIN_DIR)
+  endif()
+
+  # Try user provided path
+  if(NOT CUDAToolkit_ROOT_DIR AND CUDAToolkit_ROOT)
+    _CUDAToolkit_find_root_dir(SEARCH_PATHS "${CUDAToolkit_ROOT}" FIND_FLAGS PATH_SUFFIXES bin NO_DEFAULT_PATH)
+  endif()
+  if(NOT CUDAToolkit_ROOT_DIR)
+    _CUDAToolkit_find_root_dir(FIND_FLAGS PATHS ENV CUDA_PATH PATH_SUFFIXES bin)
+  endif()
+
+  # If the user specified CUDAToolkit_ROOT but the toolkit could not be found, this is an error.
+  if(NOT CUDAToolkit_ROOT_DIR AND (DEFINED CUDAToolkit_ROOT OR DEFINED ENV{CUDAToolkit_ROOT}))
+    # Declare error messages now, print later depending on find_package args.
+    set(fail_base "Could not find nvcc executable in path specified by")
+    set(cuda_root_fail "${fail_base} CUDAToolkit_ROOT=${CUDAToolkit_ROOT}")
+    set(env_cuda_root_fail "${fail_base} environment variable CUDAToolkit_ROOT=$ENV{CUDAToolkit_ROOT}")
+
+    if(CUDAToolkit_FIND_REQUIRED)
+      if(DEFINED CUDAToolkit_ROOT)
+        message(FATAL_ERROR ${cuda_root_fail})
+      elseif(DEFINED ENV{CUDAToolkit_ROOT})
+        message(FATAL_ERROR ${env_cuda_root_fail})
+      endif()
+    else()
+      if(NOT CUDAToolkit_FIND_QUIETLY)
+        if(DEFINED CUDAToolkit_ROOT)
+          message(STATUS ${cuda_root_fail})
+        elseif(DEFINED ENV{CUDAToolkit_ROOT})
+          message(STATUS ${env_cuda_root_fail})
+        endif()
+      endif()
+      set(CUDAToolkit_FOUND FALSE)
+      unset(fail_base)
+      unset(cuda_root_fail)
+      unset(env_cuda_root_fail)
+      return()
+    endif()
+  endif()
+
+  # CUDAToolkit_ROOT cmake / env variable not specified, try platform defaults.
+  #
+  # - Linux: /usr/local/cuda-X.Y
+  # - macOS: /Developer/NVIDIA/CUDA-X.Y
+  # - Windows: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y
+  #
+  # We will also search the default symlink location /usr/local/cuda first since
+  # if CUDAToolkit_ROOT is not specified, it is assumed that the symlinked
+  # directory is the desired location.
+  if(NOT CUDAToolkit_ROOT_DIR)
+    if(UNIX)
+      if(NOT APPLE)
+        set(platform_base "/usr/local/cuda-")
+      else()
+        set(platform_base "/Developer/NVIDIA/CUDA-")
+      endif()
+    else()
+      set(platform_base "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v")
+    endif()
+
+    # Build out a descending list of possible cuda installations, e.g.
+    file(GLOB possible_paths "${platform_base}*")
+    # Iterate the glob results and create a descending list.
+    set(versions)
+    foreach(p ${possible_paths})
+      # Extract version number from end of string
+      string(REGEX MATCH "[0-9][0-9]?\\.[0-9]$" p_version ${p})
+      if(IS_DIRECTORY ${p} AND p_version)
+        list(APPEND versions ${p_version})
+      endif()
+    endforeach()
+
+    # Sort numerically in descending order, so we try the newest versions first.
+    if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.18)
+      list(SORT versions COMPARE NATURAL ORDER DESCENDING)
+    elseif(versions)
+      # Alphabetical sort here is not ideal but better than nothing
+      list(SORT versions)
+      list(REVERSE versions)
+    endif()
+
+    # With a descending list of versions, populate possible paths to search.
+    set(search_paths)
+    foreach(v ${versions})
+      list(APPEND search_paths "${platform_base}${v}")
+    endforeach()
+
+    # Force the global default /usr/local/cuda to the front on Unix.
+    if(UNIX)
+      list(INSERT search_paths 0 "/usr/local/cuda")
+    endif()
+
+    # Now search for the toolkit again using the platform default search paths.
+    _CUDAToolkit_find_root_dir(SEARCH_PATHS "${search_paths}" FIND_FLAGS PATH_SUFFIXES bin)
+
+    # We are done with these variables now, cleanup for caller.
+    unset(platform_base)
+    unset(possible_paths)
+    unset(versions)
+    unset(search_paths)
+
+    if(NOT CUDAToolkit_ROOT_DIR)
+      if(CUDAToolkit_FIND_REQUIRED)
+        message(FATAL_ERROR "Could not find nvcc, please set CUDAToolkit_ROOT.")
+      elseif(NOT CUDAToolkit_FIND_QUIETLY)
+        message(STATUS "Could not find nvcc, please set CUDAToolkit_ROOT.")
+      endif()
+
+      set(CUDAToolkit_FOUND FALSE)
+      return()
+    endif()
+  endif()
+endif()
+
+if(NOT CUDAToolkit_BIN_DIR)
+  set(CUDAToolkit_BIN_DIR "${CUDAToolkit_ROOT_DIR}/bin")
+endif()
+
+if(NOT CUDAToolkit_NVCC_EXECUTABLE)
+  set(CUDAToolkit_NVCC_EXECUTABLE "${CUDAToolkit_BIN_DIR}/nvcc${CMAKE_EXECUTABLE_SUFFIX}")
+endif()
+
+if(CMAKE_CUDA_COMPILER_TOOLKIT_VERSION)
+  set(CUDAToolkit_VERSION "${CMAKE_CUDA_COMPILER_TOOLKIT_VERSION}")
+else()
+  function(_CUDAToolkit_find_version_file result_variable)
+    # We first check for a non-scattered installation to prefer it over a scattered installation.
+    if(CUDAToolkit_ROOT AND EXISTS "${CUDAToolkit_ROOT}/version.txt")
+      set(${result_variable} "${CUDAToolkit_ROOT}/version.txt" PARENT_SCOPE)
+    elseif(CUDAToolkit_ROOT_DIR AND EXISTS "${CUDAToolkit_ROOT_DIR}/version.txt")
+      set(${result_variable} "${CUDAToolkit_ROOT_DIR}/version.txt" PARENT_SCOPE)
+    elseif(CMAKE_SYSROOT_LINK AND EXISTS "${CMAKE_SYSROOT_LINK}/usr/lib/cuda/version.txt")
+      set(${result_variable} "${CMAKE_SYSROOT_LINK}/usr/lib/cuda/version.txt" PARENT_SCOPE)
+    elseif(EXISTS "${CMAKE_SYSROOT}/usr/lib/cuda/version.txt")
+      set(${result_variable} "${CMAKE_SYSROOT}/usr/lib/cuda/version.txt" PARENT_SCOPE)
+    endif()
+  endfunction()
+
+  _CUDAToolkit_find_version_file( _CUDAToolkit_version_file )
+  if(_CUDAToolkit_version_file)
+    # CUDAToolkit_LIBRARY_ROOT contains the device library and version file.
+    get_filename_component(CUDAToolkit_LIBRARY_ROOT "${_CUDAToolkit_version_file}" DIRECTORY ABSOLUTE)
+  endif()
+  unset(_CUDAToolkit_version_file)
+
+  if(CUDAToolkit_NVCC_EXECUTABLE AND
+     CMAKE_CUDA_COMPILER_VERSION AND
+     CUDAToolkit_NVCC_EXECUTABLE STREQUAL CMAKE_CUDA_COMPILER)
+    # Need to set these based off the already computed CMAKE_CUDA_COMPILER_VERSION value
+    # This if statement will always match, but is used to provide variables for MATCH 1,2,3...
+    if(CMAKE_CUDA_COMPILER_VERSION MATCHES [=[([0-9]+)\.([0-9]+)\.([0-9]+)]=])
+      set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}")
+      set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}")
+      set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}")
+      set(CUDAToolkit_VERSION "${CMAKE_CUDA_COMPILER_VERSION}")
+    endif()
+  elseif(CUDAToolkit_NVCC_EXECUTABLE)
+    # Compute the version by invoking nvcc
+    execute_process(COMMAND ${CUDAToolkit_NVCC_EXECUTABLE} "--version" OUTPUT_VARIABLE NVCC_OUT)
+    if(NVCC_OUT MATCHES [=[ V([0-9]+)\.([0-9]+)\.([0-9]+)]=])
+      set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}")
+      set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}")
+      set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}")
+      set(CUDAToolkit_VERSION "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}")
+    endif()
+    unset(NVCC_OUT)
+  else()
+    _CUDAToolkit_find_version_file(version_file)
+    if(version_file)
+      file(READ "${version_file}" VERSION_INFO)
+      if(VERSION_INFO MATCHES [=[CUDA Version ([0-9]+)\.([0-9]+)\.([0-9]+)]=])
+        set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}")
+        set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}")
+        set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}")
+        set(CUDAToolkit_VERSION "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}")
+      endif()
+    endif()
+  endif()
+endif()
+
+# Find target directory when crosscompiling.
+if(CMAKE_CROSSCOMPILING)
+  if(CMAKE_SYSTEM_PROCESSOR STREQUAL "armv7-a")
+    # Support for NVPACK
+    set(CUDAToolkit_TARGET_NAME "armv7-linux-androideabi")
+  elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "arm")
+    set(CUDAToolkit_TARGET_NAME "armv7-linux-gnueabihf")
+  elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64")
+    if(ANDROID_ARCH_NAME STREQUAL "arm64")
+      set(CUDAToolkit_TARGET_NAME "aarch64-linux-androideabi")
+    elseif(CMAKE_SYSTEM_NAME STREQUAL "QNX")
+      set(CUDAToolkit_TARGET_NAME "aarch64-qnx")
+    else()
+      set(CUDAToolkit_TARGET_NAME "aarch64-linux")
+    endif(ANDROID_ARCH_NAME STREQUAL "arm64")
+  elseif(CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64")
+    set(CUDAToolkit_TARGET_NAME "x86_64-linux")
+  endif()
+
+  if(EXISTS "${CUDAToolkit_ROOT_DIR}/targets/${CUDAToolkit_TARGET_NAME}")
+    set(CUDAToolkit_TARGET_DIR "${CUDAToolkit_ROOT_DIR}/targets/${CUDAToolkit_TARGET_NAME}")
+    # add known CUDA target root path to the set of directories we search for programs, libraries and headers
+    list(PREPEND CMAKE_FIND_ROOT_PATH "${CUDAToolkit_TARGET_DIR}")
+
+    # Mark that we need to pop the root search path changes after we have
+    # found all cuda libraries so that searches for our cross-compilation
+    # libraries work when another cuda sdk is in CMAKE_PREFIX_PATH or
+    # PATh
+    set(_CUDAToolkit_Pop_ROOT_PATH True)
+  endif()
+endif()
+
+# If not already set we can simply use the toolkit root or it's a scattered installation.
+if(NOT CUDAToolkit_TARGET_DIR)
+  # Not cross compiling
+  set(CUDAToolkit_TARGET_DIR "${CUDAToolkit_ROOT_DIR}")
+  # Now that we have the real ROOT_DIR, find components inside it.
+  list(APPEND CMAKE_PREFIX_PATH ${CUDAToolkit_ROOT_DIR})
+
+  # Mark that we need to pop the prefix path changes after we have
+  # found the cudart library.
+  set(_CUDAToolkit_Pop_Prefix True)
+endif()
+
+# CUDAToolkit_TARGET_DIR always points to the directory containing the include directory.
+# On a scattered installation /usr, on a non-scattered something like /usr/local/cuda or /usr/local/cuda-10.2/targets/aarch64-linux.
+if(EXISTS "${CUDAToolkit_TARGET_DIR}/include/cuda_runtime.h")
+  set(CUDAToolkit_INCLUDE_DIR "${CUDAToolkit_TARGET_DIR}/include")
+elseif(NOT CUDAToolkit_FIND_QUIETLY)
+  message(STATUS "Unable to find cuda_runtime.h in \"${CUDAToolkit_TARGET_DIR}/include\" for CUDAToolkit_INCLUDE_DIR.")
+endif()
+
+# The NVHPC layout moves math library headers and libraries to a sibling directory.
+# Create a separate variable so this directory can be selectively added to math targets.
+if(NOT EXISTS "${CUDAToolkit_INCLUDE_DIR}/cublas_v2.h")
+  set(CUDAToolkit_MATH_INCLUDE_DIR "${CUDAToolkit_TARGET_DIR}/../../math_libs/include")
+  get_filename_component(CUDAToolkit_MATH_INCLUDE_DIR "${CUDAToolkit_MATH_INCLUDE_DIR}" ABSOLUTE)
+  if(NOT EXISTS "${CUDAToolkit_MATH_INCLUDE_DIR}/cublas_v2.h")
+    if(NOT CUDAToolkit_FIND_QUIETLY)
+      message(STATUS "Unable to find cublas_v2.h in either \"${CUDAToolkit_INCLUDE_DIR}\" or \"${CUDAToolkit_MATH_INCLUDE_DIR}\"")
+    endif()
+    unset(CUDAToolkit_MATH_INCLUDE_DIR)
+  endif()
+endif()
+
+# Find the CUDA Runtime Library libcudart
+find_library(CUDA_CUDART
+  NAMES cudart
+  PATH_SUFFIXES lib64 lib/x64
+)
+find_library(CUDA_CUDART
+  NAMES cudart
+  PATH_SUFFIXES lib64/stubs lib/x64/stubs
+)
+
+if(NOT CUDA_CUDART AND NOT CUDAToolkit_FIND_QUIETLY)
+  message(STATUS "Unable to find cudart library.")
+endif()
+
+if(_CUDAToolkit_Pop_Prefix)
+  list(REMOVE_AT CMAKE_PREFIX_PATH -1)
+  unset(_CUDAToolkit_Pop_Prefix)
+endif()
+
+#-----------------------------------------------------------------------------
+# Perform version comparison and validate all required variables are set.
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(CUDAToolkit
+  REQUIRED_VARS
+    CUDAToolkit_INCLUDE_DIR
+    CUDAToolkit_VERSION
+    CUDA_CUDART
+    CUDAToolkit_BIN_DIR
+  VERSION_VAR
+    CUDAToolkit_VERSION
+)
+
+mark_as_advanced(CUDA_CUDART
+                 CUDAToolkit_INCLUDE_DIR
+                 CUDAToolkit_NVCC_EXECUTABLE
+                 CUDAToolkit_SENTINEL_FILE
+                 )
+
+#-----------------------------------------------------------------------------
+# Construct result variables
+if(CUDAToolkit_FOUND)
+  set(CUDAToolkit_INCLUDE_DIRS ${CUDAToolkit_INCLUDE_DIR})
+  get_filename_component(CUDAToolkit_LIBRARY_DIR ${CUDA_CUDART} DIRECTORY ABSOLUTE)
+endif()
+
+#-----------------------------------------------------------------------------
+# Construct import targets
+if(CUDAToolkit_FOUND)
+
+  function(_CUDAToolkit_find_and_add_import_lib lib_name)
+    cmake_parse_arguments(arg "" "" "ALT;DEPS;EXTRA_HINTS;EXTRA_PATH_SUFFIXES;EXTRA_INCLUDE_DIRS" ${ARGN})
+
+    set(search_names ${lib_name} ${arg_ALT})
+
+    find_library(CUDA_${lib_name}_LIBRARY
+      NAMES ${search_names}
+      HINTS ${CUDAToolkit_LIBRARY_DIR}
+            ENV CUDA_PATH
+            ${arg_EXTRA_HINTS}
+      PATH_SUFFIXES nvidia/current lib64 lib/x64 lib
+                    ${arg_EXTRA_PATH_SUFFIXES}
+    )
+    # Don't try any stub directories until we have exhausted all other
+    # search locations.
+    find_library(CUDA_${lib_name}_LIBRARY
+      NAMES ${search_names}
+      HINTS ${CUDAToolkit_LIBRARY_DIR}
+            ENV CUDA_PATH
+            ${arg_EXTRA_HINTS}
+      PATH_SUFFIXES lib64/stubs lib/x64/stubs lib/stubs stubs
+                    # Support NVHPC splayed math library layout
+                    ../../math_libs/${CUDAToolkit_VERSION_MAJOR}.${CUDAToolkit_VERSION_MINOR}/lib64
+                    ../../math_libs/lib64
+    )
+
+    mark_as_advanced(CUDA_${lib_name}_LIBRARY)
+
+    if(NOT TARGET CUDA::${lib_name} AND CUDA_${lib_name}_LIBRARY)
+      add_library(CUDA::${lib_name} UNKNOWN IMPORTED)
+      set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+          INTERFACE_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}")
+      set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+          INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}")
+      if(DEFINED CUDAToolkit_MATH_INCLUDE_DIR)
+        string(FIND ${CUDA_${lib_name}_LIBRARY} "math_libs" math_libs)
+        if(NOT ${math_libs} EQUAL -1)
+          set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+              INTERFACE_INCLUDE_DIRECTORIES "${CUDAToolkit_MATH_INCLUDE_DIRS}")
+          set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+              INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${CUDAToolkit_MATH_INCLUDE_DIRS}")
+        endif()
+      endif()
+      set_property(TARGET CUDA::${lib_name} PROPERTY IMPORTED_LOCATION "${CUDA_${lib_name}_LIBRARY}")
+      foreach(dep ${arg_DEPS})
+        if(TARGET CUDA::${dep})
+          set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+              INTERFACE_LINK_LIBRARIES CUDA::${dep})
+        endif()
+      endforeach()
+      if(arg_EXTRA_INCLUDE_DIRS)
+        set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+            INTERFACE_INCLUDE_DIRECTORIES "${arg_EXTRA_INCLUDE_DIRS}")
+        set_property(TARGET CUDA::${lib_name} APPEND PROPERTY
+            INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${arg_EXTRA_INCLUDE_DIRS}")
+      endif()
+    endif()
+  endfunction()
+
+  if(NOT TARGET CUDA::toolkit)
+    add_library(CUDA::toolkit IMPORTED INTERFACE)
+    set_property(TARGET CUDA::toolkit APPEND PROPERTY
+        INTERFACE_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}")
+    set_property(TARGET CUDA::toolkit APPEND PROPERTY
+        INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}")
+  endif()
+
+  _CUDAToolkit_find_and_add_import_lib(cuda_driver ALT cuda)
+
+  _CUDAToolkit_find_and_add_import_lib(cudart)
+  _CUDAToolkit_find_and_add_import_lib(cudart_static)
+
+  # setup dependencies that are required for cudart_static when building
+  # on linux. These are generally only required when using the CUDA toolkit
+  # when CUDA language is disabled
+  if(NOT TARGET CUDA::cudart_static_deps
+     AND TARGET CUDA::cudart_static)
+
+    add_library(CUDA::cudart_static_deps IMPORTED INTERFACE)
+    set_property(TARGET CUDA::cudart_static APPEND PROPERTY
+        INTERFACE_LINK_LIBRARIES CUDA::cudart_static_deps)
+
+    if(UNIX AND (CMAKE_C_COMPILER OR CMAKE_CXX_COMPILER))
+      find_package(Threads REQUIRED)
+      set_property(TARGET CUDA::cudart_static_deps APPEND PROPERTY
+          INTERFACE_LINK_LIBRARIES Threads::Threads ${CMAKE_DL_LIBS})
+    endif()
+
+    if(UNIX AND NOT APPLE AND NOT (CMAKE_SYSTEM_NAME STREQUAL "QNX"))
+      # On Linux, you must link against librt when using the static cuda runtime.
+      find_library(CUDAToolkit_rt_LIBRARY rt)
+      mark_as_advanced(CUDAToolkit_rt_LIBRARY)
+      if(NOT CUDAToolkit_rt_LIBRARY)
+        message(WARNING "Could not find librt library, needed by CUDA::cudart_static")
+      else()
+        set_property(TARGET CUDA::cudart_static_deps APPEND PROPERTY
+            INTERFACE_LINK_LIBRARIES ${CUDAToolkit_rt_LIBRARY})
+      endif()
+    endif()
+  endif()
+
+  _CUDAToolkit_find_and_add_import_lib(culibos) # it's a static library
+  foreach(cuda_lib cublasLt cufft curand cusparse nppc nvjpeg)
+    _CUDAToolkit_find_and_add_import_lib(${cuda_lib})
+    _CUDAToolkit_find_and_add_import_lib(${cuda_lib}_static DEPS culibos)
+  endforeach()
+
+  if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 11.0.0)
+    # cublas depends on cublasLt
+    # https://docs.nvidia.com/cuda/archive/11.0/cublas/index.html#static-library
+    _CUDAToolkit_find_and_add_import_lib(cublas DEPS cublasLt)
+    _CUDAToolkit_find_and_add_import_lib(cublas_static DEPS cublasLt_static)
+  else()
+    _CUDAToolkit_find_and_add_import_lib(cublas)
+    _CUDAToolkit_find_and_add_import_lib(cublas_static DEPS culibos)
+  endif()
+
+  if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 11.4)
+    _CUDAToolkit_find_and_add_import_lib(cuFile ALT cufile DEPS culibos)
+    _CUDAToolkit_find_and_add_import_lib(cuFile_static ALT cufile_static DEPS culibos)
+
+    _CUDAToolkit_find_and_add_import_lib(cuFile_rdma ALT cufile_rdma DEPS cuFile culibos)
+    _CUDAToolkit_find_and_add_import_lib(cuFile_rdma_static ALT cufile_rdma_static DEPS cuFile_static culibos)
+  endif()
+
+  # cuFFTW depends on cuFFT
+  _CUDAToolkit_find_and_add_import_lib(cufftw DEPS cufft)
+  _CUDAToolkit_find_and_add_import_lib(cufftw_static DEPS cufft_static)
+  if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 9.2)
+    _CUDAToolkit_find_and_add_import_lib(cufft_static_nocallback DEPS culibos)
+  endif()
+
+  # cuSOLVER depends on cuBLAS, and cuSPARSE
+  _CUDAToolkit_find_and_add_import_lib(cusolver DEPS cublas cusparse)
+  _CUDAToolkit_find_and_add_import_lib(cusolver_static DEPS cublas_static cusparse_static culibos)
+
+
+  if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 10.1.2)
+    # cusolver depends on liblapack_static.a starting with CUDA 10.1 update 2,
+    # https://docs.nvidia.com/cuda/archive/11.5.0/cusolver/index.html#static-link-lapack
+    _CUDAToolkit_find_and_add_import_lib(cusolver_lapack_static ALT lapack_static) # implementation detail static lib
+    _CUDAToolkit_find_and_add_import_lib(cusolver_static DEPS cusolver_lapack_static)
+  endif()
+
+  if(CUDAToolkit_VERSION VERSION_GREATER 11.2.1)
+    # cusolver depends on libcusolver_metis and cublasLt
+    # https://docs.nvidia.com/cuda/archive/11.2.2/cusolver/index.html#link-dependency
+    _CUDAToolkit_find_and_add_import_lib(cusolver DEPS cublasLt)
+
+    _CUDAToolkit_find_and_add_import_lib(cusolver_metis_static ALT metis_static) # implementation detail static lib
+    _CUDAToolkit_find_and_add_import_lib(cusolver_static DEPS cusolver_metis_static cublasLt_static)
+  endif()
+
+  # nvGRAPH depends on cuRAND, and cuSOLVER.
+  _CUDAToolkit_find_and_add_import_lib(nvgraph DEPS curand cusolver)
+  _CUDAToolkit_find_and_add_import_lib(nvgraph_static DEPS curand_static cusolver_static)
+
+  # Process the majority of the NPP libraries.
+  foreach(cuda_lib nppial nppicc nppidei nppif nppig nppim nppist nppitc npps nppicom nppisu)
+    _CUDAToolkit_find_and_add_import_lib(${cuda_lib} DEPS nppc)
+    _CUDAToolkit_find_and_add_import_lib(${cuda_lib}_static DEPS nppc_static)
+  endforeach()
+
+  find_path(CUDAToolkit_CUPTI_INCLUDE_DIR cupti.h PATHS
+      "${CUDAToolkit_ROOT_DIR}/extras/CUPTI/include"
+      "${CUDAToolkit_INCLUDE_DIR}/../extras/CUPTI/include"
+      "${CUDAToolkit_INCLUDE_DIR}"
+      NO_DEFAULT_PATH)
+  mark_as_advanced(CUDAToolkit_CUPTI_INCLUDE_DIR)
+
+  if(CUDAToolkit_CUPTI_INCLUDE_DIR)
+    _CUDAToolkit_find_and_add_import_lib(cupti
+                                        EXTRA_PATH_SUFFIXES ../extras/CUPTI/lib64/
+                                                            ../extras/CUPTI/lib/
+                                        EXTRA_INCLUDE_DIRS "${CUDAToolkit_CUPTI_INCLUDE_DIR}")
+    _CUDAToolkit_find_and_add_import_lib(cupti_static
+                                        EXTRA_PATH_SUFFIXES ../extras/CUPTI/lib64/
+                                                            ../extras/CUPTI/lib/
+                                        EXTRA_INCLUDE_DIRS "${CUDAToolkit_CUPTI_INCLUDE_DIR}")
+  endif()
+
+  _CUDAToolkit_find_and_add_import_lib(nvrtc DEPS cuda_driver)
+
+  _CUDAToolkit_find_and_add_import_lib(nvml ALT nvidia-ml nvml)
+
+  # nvtools can be installed outside the CUDA toolkit directory,
+  # so search the NVTOOLSEXT_PATH windows only environment variable
+  set(nvToolsExt_EXTRA_PATH)
+  if(WIN32)
+     set(nvToolsExt_EXTRA_PATH "C:\\Program Files\\NVIDIA Corporation\\NvToolsExt")
+  endif()
+
+  find_path(CUDAToolkit_nvToolsExt_INCLUDE_DIR nvToolsExt.h
+      PATHS "${CUDAToolkit_INCLUDE_DIR}"
+            "${CUDAToolkit_ROOT_DIR}"
+            ENV NVTOOLSEXT_PATH
+            "${nvToolsExt_EXTRA_PATH}"
+      PATH_SUFFIXES include
+      NO_DEFAULT_PATH)
+  mark_as_advanced(CUDAToolkit_nvToolsExt_INCLUDE_DIR)
+
+  if(CUDAToolkit_nvToolsExt_INCLUDE_DIR)
+    _CUDAToolkit_find_and_add_import_lib(nvToolsExt
+        ALT nvToolsExt64 nvToolsExt64_1
+        EXTRA_HINTS ENV NVTOOLSEXT_PATH
+                    "${nvToolsExt_EXTRA_PATH}"
+        EXTRA_INCLUDE_DIRS "${CUDAToolkit_nvToolsExt_INCLUDE_DIR}")
+  endif()
+
+  _CUDAToolkit_find_and_add_import_lib(OpenCL)
+endif()
+
+unset(CUDAToolkit_ROOT_DIR)
+
+if(_CUDAToolkit_Pop_ROOT_PATH)
+  list(REMOVE_AT CMAKE_FIND_ROOT_PATH 0)
+  unset(_CUDAToolkit_Pop_ROOT_PATH)
+endif()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindCUDSS.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindCUDSS.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..b614e1c492b99f7b3adf456b0b88bdf5cd26fd0b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindCUDSS.cmake
@@ -0,0 +1,67 @@
+# Find the CUDSS library
+#
+# The following variables are optionally searched for defaults
+#  CUDSS_ROOT: Base directory where CUDSS is found
+#  CUDSS_INCLUDE_DIR: Directory where CUDSS header is searched for
+#  CUDSS_LIBRARY: Directory where CUDSS library is searched for
+#
+# The following are set after configuration is done:
+#  CUDSS_FOUND
+#  CUDSS_INCLUDE_PATH
+#  CUDSS_LIBRARY_PATH
+
+include(FindPackageHandleStandardArgs)
+
+set(CUDSS_ROOT $ENV{CUDSS_ROOT_DIR} CACHE PATH "Folder containing NVIDIA CUDSS")
+if (DEFINED $ENV{CUDSS_ROOT_DIR})
+  message(WARNING "CUDSS_ROOT_DIR is deprecated. Please set CUDSS_ROOT instead.")
+endif()
+list(APPEND CUDSS_ROOT $ENV{CUDSS_ROOT_DIR} ${CUDA_TOOLKIT_ROOT_DIR})
+
+# Compatible layer for CMake <3.12. CUDSS_ROOT will be accounted in for searching paths and libraries for CMake >=3.12.
+list(APPEND CMAKE_PREFIX_PATH ${CUDSS_ROOT})
+
+set(CUDSS_INCLUDE_DIR $ENV{CUDSS_INCLUDE_DIR} CACHE PATH "Folder containing NVIDIA CUDSS header files")
+
+find_path(CUDSS_INCLUDE_PATH cudss.h
+  HINTS ${CUDSS_INCLUDE_DIR}
+  PATH_SUFFIXES cuda/include cuda include)
+
+set(CUDSS_LIBRARY $ENV{CUDSS_LIBRARY} CACHE PATH "Path to the CUDSS library file (e.g., libcudss.so)")
+
+set(CUDSS_LIBRARY_NAME "libcudss.so")
+if(MSVC)
+  set(CUDSS_LIBRARY_NAME "cudss.lib")
+endif()
+
+find_library(CUDSS_LIBRARY_PATH ${CUDSS_LIBRARY_NAME}
+  PATHS ${CUDSS_LIBRARY}
+  PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64)
+
+find_package_handle_standard_args(CUDSS DEFAULT_MSG CUDSS_LIBRARY_PATH CUDSS_INCLUDE_PATH)
+
+if(CUDSS_FOUND)
+  # Get CUDSS version
+  file(READ ${CUDSS_INCLUDE_PATH}/cudss.h CUDSS_HEADER_CONTENTS)
+  string(REGEX MATCH "define CUDSS_VER_MAJOR * +([0-9]+)"
+               CUDSS_VERSION_MAJOR "${CUDSS_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDSS_VER_MAJOR * +([0-9]+)" "\\1"
+               CUDSS_VERSION_MAJOR "${CUDSS_VERSION_MAJOR}")
+  string(REGEX MATCH "define CUDSS_VER_MINOR * +([0-9]+)"
+               CUDSS_VERSION_MINOR "${CUDSS_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDSS_VER_MINOR * +([0-9]+)" "\\1"
+               CUDSS_VERSION_MINOR "${CUDSS_VERSION_MINOR}")
+  string(REGEX MATCH "define CUDSS_VER_PATCH * +([0-9]+)"
+               CUDSS_VERSION_PATCH "${CUDSS_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDSS_VER_PATCH * +([0-9]+)" "\\1"
+               CUDSS_VERSION_PATCH "${CUDSS_VERSION_PATCH}")
+  # Assemble CUDSS version. Use minor version since current major version is 0.
+  if(NOT CUDSS_VERSION_MINOR)
+    set(CUDSS_VERSION "?")
+  else()
+    set(CUDSS_VERSION
+        "${CUDSS_VERSION_MAJOR}.${CUDSS_VERSION_MINOR}.${CUDSS_VERSION_PATCH}")
+  endif()
+endif()
+
+mark_as_advanced(CUDSS_ROOT CUDSS_INCLUDE_DIR CUDSS_LIBRARY CUDSS_VERSION)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindCUSPARSELT.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindCUSPARSELT.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..6c15bde147469ddc84980dca0c756e8f26e1ddb1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindCUSPARSELT.cmake
@@ -0,0 +1,67 @@
+# Find the CUSPARSELT library
+#
+# The following variables are optionally searched for defaults
+#  CUSPARSELT_ROOT: Base directory where CUSPARSELT is found
+#  CUSPARSELT_INCLUDE_DIR: Directory where CUSPARSELT header is searched for
+#  CUSPARSELT_LIBRARY: Directory where CUSPARSELT library is searched for
+#
+# The following are set after configuration is done:
+#  CUSPARSELT_FOUND
+#  CUSPARSELT_INCLUDE_PATH
+#  CUSPARSELT_LIBRARY_PATH
+
+include(FindPackageHandleStandardArgs)
+
+set(CUSPARSELT_ROOT $ENV{CUSPARSELT_ROOT_DIR} CACHE PATH "Folder containing NVIDIA cuSPARSELt")
+if (DEFINED $ENV{CUSPARSELT_ROOT_DIR})
+  message(WARNING "CUSPARSELT_ROOT_DIR is deprecated. Please set CUSPARSELT_ROOT instead.")
+endif()
+list(APPEND CUSPARSELT_ROOT $ENV{CUSPARSELT_ROOT_DIR} ${CUDA_TOOLKIT_ROOT_DIR})
+
+# Compatible layer for CMake <3.12. CUSPARSELT_ROOT will be accounted in for searching paths and libraries for CMake >=3.12.
+list(APPEND CMAKE_PREFIX_PATH ${CUSPARSELT_ROOT})
+
+set(CUSPARSELT_INCLUDE_DIR $ENV{CUSPARSELT_INCLUDE_DIR} CACHE PATH "Folder containing NVIDIA cuSPARSELt header files")
+
+find_path(CUSPARSELT_INCLUDE_PATH cusparseLt.h
+  HINTS ${CUSPARSELT_INCLUDE_DIR}
+  PATH_SUFFIXES cuda/include cuda include)
+
+set(CUSPARSELT_LIBRARY $ENV{CUSPARSELT_LIBRARY} CACHE PATH "Path to the cusparselt library file (e.g., libcusparseLt.so)")
+
+set(CUSPARSELT_LIBRARY_NAME "libcusparseLt.so")
+if(MSVC)
+  set(CUSPARSELT_LIBRARY_NAME "cusparseLt.lib")
+endif()
+
+find_library(CUSPARSELT_LIBRARY_PATH ${CUSPARSELT_LIBRARY_NAME}
+  PATHS ${CUSPARSELT_LIBRARY}
+  PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64)
+
+find_package_handle_standard_args(CUSPARSELT DEFAULT_MSG CUSPARSELT_LIBRARY_PATH CUSPARSELT_INCLUDE_PATH)
+
+if(CUSPARSELT_FOUND)
+  # Get cuSPARSELt version
+  file(READ ${CUSPARSELT_INCLUDE_PATH}/cusparseLt.h CUSPARSELT_HEADER_CONTENTS)
+  string(REGEX MATCH "define CUSPARSELT_VER_MAJOR * +([0-9]+)"
+               CUSPARSELT_VERSION_MAJOR "${CUSPARSELT_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUSPARSELT_VER_MAJOR * +([0-9]+)" "\\1"
+               CUSPARSELT_VERSION_MAJOR "${CUSPARSELT_VERSION_MAJOR}")
+  string(REGEX MATCH "define CUSPARSELT_VER_MINOR * +([0-9]+)"
+               CUSPARSELT_VERSION_MINOR "${CUSPARSELT_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUSPARSELT_VER_MINOR * +([0-9]+)" "\\1"
+               CUSPARSELT_VERSION_MINOR "${CUSPARSELT_VERSION_MINOR}")
+  string(REGEX MATCH "define CUSPARSELT_VER_PATCH * +([0-9]+)"
+               CUSPARSELT_VERSION_PATCH "${CUSPARSELT_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUSPARSELT_VER_PATCH * +([0-9]+)" "\\1"
+               CUSPARSELT_VERSION_PATCH "${CUSPARSELT_VERSION_PATCH}")
+  # Assemble cuSPARSELt version. Use minor version since current major version is 0.
+  if(NOT CUSPARSELT_VERSION_MINOR)
+    set(CUSPARSELT_VERSION "?")
+  else()
+    set(CUSPARSELT_VERSION
+        "${CUSPARSELT_VERSION_MAJOR}.${CUSPARSELT_VERSION_MINOR}.${CUSPARSELT_VERSION_PATCH}")
+  endif()
+endif()
+
+mark_as_advanced(CUSPARSELT_ROOT CUSPARSELT_INCLUDE_DIR CUSPARSELT_LIBRARY CUSPARSELT_VERSION)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindSYCLToolkit.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindSYCLToolkit.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..808a3ec33eca5f9070cb1f7badbdb068b60f82fa
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/FindSYCLToolkit.cmake
@@ -0,0 +1,131 @@
+# This will define the following variables:
+# SYCL_FOUND               : True if the system has the SYCL library.
+# SYCL_INCLUDE_DIR         : Include directories needed to use SYCL.
+# SYCL_LIBRARY_DIR         ：The path to the SYCL library.
+# SYCL_LIBRARY             : SYCL library fullname.
+# SYCL_COMPILER_VERSION    : SYCL compiler version.
+
+include(FindPackageHandleStandardArgs)
+
+set(SYCL_ROOT "")
+if(DEFINED ENV{SYCL_ROOT})
+  set(SYCL_ROOT $ENV{SYCL_ROOT})
+elseif(DEFINED ENV{CMPLR_ROOT})
+  set(SYCL_ROOT $ENV{CMPLR_ROOT})
+endif()
+
+string(COMPARE EQUAL "${SYCL_ROOT}" "" nosyclfound)
+if(nosyclfound)
+  set(SYCL_FOUND False)
+  set(SYCL_REASON_FAILURE "SYCL library not set!!")
+  set(SYCL_NOT_FOUND_MESSAGE "${SYCL_REASON_FAILURE}")
+  return()
+endif()
+
+# Find SYCL compiler executable.
+find_program(
+  SYCL_COMPILER
+  NAMES icx
+  PATHS "${SYCL_ROOT}"
+  PATH_SUFFIXES bin bin64
+  NO_DEFAULT_PATH
+  )
+
+function(parse_sycl_compiler_version version_number)
+  # Execute the SYCL compiler with the --version flag to match the version string.
+  execute_process(COMMAND ${SYCL_COMPILER} --version OUTPUT_VARIABLE SYCL_VERSION_STRING)
+  string(REGEX REPLACE "Intel\\(R\\) (.*) Compiler ([0-9]+\\.[0-9]+\\.[0-9]+) (.*)" "\\2"
+               SYCL_VERSION_STRING_MATCH ${SYCL_VERSION_STRING})
+  string(REPLACE "." ";" SYCL_VERSION_LIST ${SYCL_VERSION_STRING_MATCH})
+  # Split the version number list into major, minor, and patch components.
+  list(GET SYCL_VERSION_LIST 0 VERSION_MAJOR)
+  list(GET SYCL_VERSION_LIST 1 VERSION_MINOR)
+  list(GET SYCL_VERSION_LIST 2 VERSION_PATCH)
+  # Calculate the version number in the format XXXXYYZZ, using the formula (major * 10000 + minor * 100 + patch).
+  math(EXPR VERSION_NUMBER_MATCH "${VERSION_MAJOR} * 10000 + ${VERSION_MINOR} * 100 + ${VERSION_PATCH}")
+  set(${version_number} "${VERSION_NUMBER_MATCH}" PARENT_SCOPE)
+endfunction()
+
+parse_sycl_compiler_version(SYCL_COMPILER_VERSION)
+
+if(NOT SYCL_COMPILER_VERSION)
+  set(SYCL_FOUND False)
+  set(SYCL_REASON_FAILURE "Cannot parse sycl compiler version to get SYCL_COMPILER_VERSION!")
+  set(SYCL_NOT_FOUND_MESSAGE "${SYCL_REASON_FAILURE}")
+  return()
+endif()
+
+# Find include path from binary.
+find_file(
+  SYCL_INCLUDE_DIR
+  NAMES include
+  HINTS ${SYCL_ROOT}
+  NO_DEFAULT_PATH
+  )
+
+# Find include/sycl path from include path.
+find_file(
+  SYCL_INCLUDE_SYCL_DIR
+  NAMES sycl
+  HINTS ${SYCL_ROOT}/include/
+  NO_DEFAULT_PATH
+  )
+
+# Due to the unrecognized compilation option `-fsycl` in other compiler.
+list(APPEND SYCL_INCLUDE_DIR ${SYCL_INCLUDE_SYCL_DIR})
+
+# Find library directory from binary.
+find_file(
+  SYCL_LIBRARY_DIR
+  NAMES lib lib64
+  HINTS ${SYCL_ROOT}
+  NO_DEFAULT_PATH
+  )
+
+# Define the old version of SYCL toolkit that is compatible with the current version of PyTorch.
+set(PYTORCH_2_5_SYCL_TOOLKIT_VERSION 20249999)
+
+# By default, we use libsycl.so on Linux and sycl.lib on Windows as the SYCL library name.
+if (SYCL_COMPILER_VERSION VERSION_LESS_EQUAL PYTORCH_2_5_SYCL_TOOLKIT_VERSION)
+  # Don't use if(LINUX) here since this requires cmake>=3.25 and file is installed
+  # and used by other projects.
+  # See: https://cmake.org/cmake/help/v3.25/variable/LINUX.html
+  if(CMAKE_SYSTEM_NAME MATCHES "Linux")
+    set(sycl_lib_suffix "-preview")
+  elseif(CMAKE_SYSTEM_NAME MATCHES "Windows")
+    # On Windows, the SYCL library is named sycl7.lib until PYTORCH_2_5_SYCL_TOOLKIT_VERSION.
+    # sycl.lib is supported in the later version.
+    set(sycl_lib_suffix "7")
+  endif()
+endif()
+
+# Find SYCL library fullname.
+find_library(
+  SYCL_LIBRARY
+  NAMES "sycl${sycl_lib_suffix}"
+  HINTS ${SYCL_LIBRARY_DIR}
+  NO_DEFAULT_PATH
+)
+
+# Find OpenCL library fullname, which is a dependency of oneDNN.
+find_library(
+  OCL_LIBRARY
+  NAMES OpenCL
+  HINTS ${SYCL_LIBRARY_DIR}
+  NO_DEFAULT_PATH
+)
+
+if((NOT SYCL_LIBRARY) OR (NOT OCL_LIBRARY))
+  set(SYCL_FOUND False)
+  set(SYCL_REASON_FAILURE "SYCL library is incomplete!!")
+  set(SYCL_NOT_FOUND_MESSAGE "${SYCL_REASON_FAILURE}")
+  return()
+endif()
+
+find_package_handle_standard_args(
+  SYCL
+  FOUND_VAR SYCL_FOUND
+  REQUIRED_VARS SYCL_INCLUDE_DIR SYCL_LIBRARY_DIR SYCL_LIBRARY
+  REASON_FAILURE_MESSAGE "${SYCL_REASON_FAILURE}"
+  VERSION_VAR SYCL_COMPILER_VERSION
+  )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDA.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDA.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..017ea59578fe34dbca4984d09862d2359361180a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDA.cmake
@@ -0,0 +1,11 @@
+# This is a wrapper of the upstream `./upstream/FindCUDA.cmake` that
+# automatically includes `./upstream/CMakeInitializeConfigs.cmake` before
+# `./upstream/FindCUDA.cmake`. The `CMakeInitializeConfigs.cmake`, which is
+# absent in old CMake versions, creates some necessary variables for the later
+# to run.
+# See ./README.md for details.
+
+set(UPSTREAM_FIND_CUDA_DIR "${CMAKE_CURRENT_LIST_DIR}/upstream/")
+
+include("${UPSTREAM_FIND_CUDA_DIR}/CMakeInitializeConfigs.cmake")
+include("${UPSTREAM_FIND_CUDA_DIR}/FindCUDA.cmake")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDNN.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDNN.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..82134328c803dc87a89564638540a6cbcfa2d906
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/FindCUDNN.cmake
@@ -0,0 +1,78 @@
+# Find the CUDNN libraries
+#
+# The following variables are optionally searched for defaults
+#  CUDNN_ROOT: Base directory where CUDNN is found
+#  CUDNN_INCLUDE_DIR: Directory where CUDNN header is searched for
+#  CUDNN_LIBRARY: Directory where CUDNN library is searched for
+#  CUDNN_STATIC: Are we looking for a static library? (default: no)
+#
+# The following are set after configuration is done:
+#  CUDNN_FOUND
+#  CUDNN_INCLUDE_PATH
+#  CUDNN_LIBRARY_PATH
+#
+
+include(FindPackageHandleStandardArgs)
+
+set(CUDNN_ROOT $ENV{CUDNN_ROOT_DIR} CACHE PATH "Folder containing NVIDIA cuDNN")
+if (DEFINED $ENV{CUDNN_ROOT_DIR})
+  message(WARNING "CUDNN_ROOT_DIR is deprecated. Please set CUDNN_ROOT instead.")
+endif()
+list(APPEND CUDNN_ROOT $ENV{CUDNN_ROOT_DIR} ${CUDA_TOOLKIT_ROOT_DIR})
+
+# Compatible layer for CMake <3.12. CUDNN_ROOT will be accounted in for searching paths and libraries for CMake >=3.12.
+list(APPEND CMAKE_PREFIX_PATH ${CUDNN_ROOT})
+
+set(CUDNN_INCLUDE_DIR $ENV{CUDNN_INCLUDE_DIR} CACHE PATH "Folder containing NVIDIA cuDNN header files")
+
+find_path(CUDNN_INCLUDE_PATH cudnn.h
+  HINTS ${CUDNN_INCLUDE_DIR}
+  PATH_SUFFIXES cuda/include cuda include)
+
+option(CUDNN_STATIC "Look for static CUDNN" OFF)
+if (CUDNN_STATIC)
+  set(CUDNN_LIBNAME "libcudnn_static.a")
+else()
+  set(CUDNN_LIBNAME "cudnn")
+endif()
+
+set(CUDNN_LIBRARY $ENV{CUDNN_LIBRARY} CACHE PATH "Path to the cudnn library file (e.g., libcudnn.so)")
+if (CUDNN_LIBRARY MATCHES ".*cudnn_static.a" AND NOT CUDNN_STATIC)
+  message(WARNING "CUDNN_LIBRARY points to a static library (${CUDNN_LIBRARY}) but CUDNN_STATIC is OFF.")
+endif()
+
+find_library(CUDNN_LIBRARY_PATH ${CUDNN_LIBNAME}
+  PATHS ${CUDNN_LIBRARY}
+  PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64)
+
+find_package_handle_standard_args(CUDNN DEFAULT_MSG CUDNN_LIBRARY_PATH CUDNN_INCLUDE_PATH)
+
+if(CUDNN_FOUND)
+  # Get cuDNN version
+  if(EXISTS ${CUDNN_INCLUDE_PATH}/cudnn_version.h)
+    file(READ ${CUDNN_INCLUDE_PATH}/cudnn_version.h CUDNN_HEADER_CONTENTS)
+  else()
+    file(READ ${CUDNN_INCLUDE_PATH}/cudnn.h CUDNN_HEADER_CONTENTS)
+  endif()
+  string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)"
+               CUDNN_VERSION_MAJOR "${CUDNN_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1"
+               CUDNN_VERSION_MAJOR "${CUDNN_VERSION_MAJOR}")
+  string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)"
+               CUDNN_VERSION_MINOR "${CUDNN_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDNN_MINOR * +([0-9]+)" "\\1"
+               CUDNN_VERSION_MINOR "${CUDNN_VERSION_MINOR}")
+  string(REGEX MATCH "define CUDNN_PATCHLEVEL * +([0-9]+)"
+               CUDNN_VERSION_PATCH "${CUDNN_HEADER_CONTENTS}")
+  string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1"
+               CUDNN_VERSION_PATCH "${CUDNN_VERSION_PATCH}")
+  # Assemble cuDNN version
+  if(NOT CUDNN_VERSION_MAJOR)
+    set(CUDNN_VERSION "?")
+  else()
+    set(CUDNN_VERSION
+        "${CUDNN_VERSION_MAJOR}.${CUDNN_VERSION_MINOR}.${CUDNN_VERSION_PATCH}")
+  endif()
+endif()
+
+mark_as_advanced(CUDNN_ROOT CUDNN_INCLUDE_DIR CUDNN_LIBRARY CUDNN_VERSION)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/CMakeInitializeConfigs.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/CMakeInitializeConfigs.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..5517e8f0624b1e5538b761e1f4891227007d0045
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/CMakeInitializeConfigs.cmake
@@ -0,0 +1,40 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+# Present in upstream, but not supported on versions of cmake we need to support
+# include_guard(GLOBAL)
+
+# Initializes `<_PREFIX>_` variables from the corresponding
+# `<_PREFIX>__INIT`, for the configurations currently used.
+function(cmake_initialize_per_config_variable _PREFIX _DOCSTRING)
+  string(STRIP "${${_PREFIX}_INIT}" _INIT)
+  set("${_PREFIX}" "${_INIT}"
+    CACHE STRING "${_DOCSTRING} during all build types.")
+  mark_as_advanced("${_PREFIX}")
+
+  if (NOT CMAKE_NOT_USING_CONFIG_FLAGS)
+    set(_CONFIGS Debug Release MinSizeRel RelWithDebInfo)
+
+    get_property(_GENERATOR_IS_MULTI_CONFIG GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
+    if (_GENERATOR_IS_MULTI_CONFIG)
+      list(APPEND _CONFIGS ${CMAKE_CONFIGURATION_TYPES})
+    else()
+      if (NOT CMAKE_NO_BUILD_TYPE)
+        set(CMAKE_BUILD_TYPE "${CMAKE_BUILD_TYPE_INIT}" CACHE STRING
+          "Choose the type of build, options are: None Debug Release RelWithDebInfo MinSizeRel ...")
+      endif()
+      list(APPEND _CONFIGS ${CMAKE_BUILD_TYPE})
+    endif()
+
+    list(REMOVE_DUPLICATES _CONFIGS)
+    foreach(_BUILD_TYPE IN LISTS _CONFIGS)
+      if (NOT "${_BUILD_TYPE}" STREQUAL "")
+        string(TOUPPER "${_BUILD_TYPE}" _BUILD_TYPE)
+        string(STRIP "${${_PREFIX}_${_BUILD_TYPE}_INIT}" _INIT)
+        set("${_PREFIX}_${_BUILD_TYPE}" "${_INIT}"
+          CACHE STRING "${_DOCSTRING} during ${_BUILD_TYPE} builds.")
+        mark_as_advanced("${_PREFIX}_${_BUILD_TYPE}")
+      endif()
+    endforeach()
+  endif()
+endfunction()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..f642072bdc51c8511592ae9c679ca94dd063cadc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA.cmake
@@ -0,0 +1,1982 @@
+#.rst:
+# FindCUDA
+# --------
+#
+# .. note::
+#
+#   The FindCUDA module has been superseded by first-class support
+#   for the CUDA language in CMake.  It is no longer necessary to
+#   use this module or call ``find_package(CUDA)``.  This module
+#   now exists only for compatibility with projects that have not
+#   been ported.
+#
+#   Instead, list ``CUDA`` among the languages named in the top-level
+#   call to the :command:`project` command, or call the
+#   :command:`enable_language` command with ``CUDA``.
+#   Then one can add CUDA (``.cu``) sources to programs directly
+#   in calls to :command:`add_library` and :command:`add_executable`.
+#
+# Tools for building CUDA C files: libraries and build dependencies.
+#
+# This script locates the NVIDIA CUDA C tools.  It should work on Linux,
+# Windows, and macOS and should be reasonably up to date with CUDA C
+# releases.
+#
+# This script makes use of the standard :command:`find_package` arguments of
+# ````, ``REQUIRED`` and ``QUIET``.  ``CUDA_FOUND`` will report if an
+# acceptable version of CUDA was found.
+#
+# The script will prompt the user to specify ``CUDA_TOOLKIT_ROOT_DIR`` if
+# the prefix cannot be determined by the location of nvcc in the system
+# path and ``REQUIRED`` is specified to :command:`find_package`.  To use
+# a different installed version of the toolkit set the environment variable
+# ``CUDA_BIN_PATH`` before running cmake (e.g.
+# ``CUDA_BIN_PATH=/usr/local/cuda1.0`` instead of the default
+# ``/usr/local/cuda``) or set ``CUDA_TOOLKIT_ROOT_DIR`` after configuring.  If
+# you change the value of ``CUDA_TOOLKIT_ROOT_DIR``, various components that
+# depend on the path will be relocated.
+#
+# It might be necessary to set ``CUDA_TOOLKIT_ROOT_DIR`` manually on certain
+# platforms, or to use a CUDA runtime not installed in the default
+# location.  In newer versions of the toolkit the CUDA library is
+# included with the graphics driver -- be sure that the driver version
+# matches what is needed by the CUDA runtime version.
+#
+# The following variables affect the behavior of the macros in the
+# script (in alphebetical order).  Note that any of these flags can be
+# changed multiple times in the same directory before calling
+# ``CUDA_ADD_EXECUTABLE``, ``CUDA_ADD_LIBRARY``, ``CUDA_COMPILE``,
+# ``CUDA_COMPILE_PTX``, ``CUDA_COMPILE_FATBIN``, ``CUDA_COMPILE_CUBIN``
+# or ``CUDA_WRAP_SRCS``::
+#
+#   CUDA_64_BIT_DEVICE_CODE (Default matches host bit size)
+#   -- Set to ON to compile for 64 bit device code, OFF for 32 bit device code.
+#      Note that making this different from the host code when generating object
+#      or C files from CUDA code just won't work, because size_t gets defined by
+#      nvcc in the generated source.  If you compile to PTX and then load the
+#      file yourself, you can mix bit sizes between device and host.
+#
+#   CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE (Default ON)
+#   -- Set to ON if you want the custom build rule to be attached to the source
+#      file in Visual Studio.  Turn OFF if you add the same cuda file to multiple
+#      targets.
+#
+#      This allows the user to build the target from the CUDA file; however, bad
+#      things can happen if the CUDA source file is added to multiple targets.
+#      When performing parallel builds it is possible for the custom build
+#      command to be run more than once and in parallel causing cryptic build
+#      errors.  VS runs the rules for every source file in the target, and a
+#      source can have only one rule no matter how many projects it is added to.
+#      When the rule is run from multiple targets race conditions can occur on
+#      the generated file.  Eventually everything will get built, but if the user
+#      is unaware of this behavior, there may be confusion.  It would be nice if
+#      this script could detect the reuse of source files across multiple targets
+#      and turn the option off for the user, but no good solution could be found.
+#
+#   CUDA_BUILD_CUBIN (Default OFF)
+#   -- Set to ON to enable and extra compilation pass with the -cubin option in
+#      Device mode. The output is parsed and register, shared memory usage is
+#      printed during build.
+#
+#   CUDA_BUILD_EMULATION (Default OFF for device mode)
+#   -- Set to ON for Emulation mode. -D_DEVICEEMU is defined for CUDA C files
+#      when CUDA_BUILD_EMULATION is TRUE.
+#
+#   CUDA_LINK_LIBRARIES_KEYWORD (Default "")
+#    -- The  keyword to use for internal
+#       target_link_libraries calls. The default is to use no keyword which
+#       uses the old "plain" form of target_link_libraries. Note that is matters
+#       because whatever is used inside the FindCUDA module must also be used
+#       outside - the two forms of target_link_libraries cannot be mixed.
+#
+#   CUDA_GENERATED_OUTPUT_DIR (Default CMAKE_CURRENT_BINARY_DIR)
+#   -- Set to the path you wish to have the generated files placed.  If it is
+#      blank output files will be placed in CMAKE_CURRENT_BINARY_DIR.
+#      Intermediate files will always be placed in
+#      CMAKE_CURRENT_BINARY_DIR/CMakeFiles.
+#
+#   CUDA_HOST_COMPILATION_CPP (Default ON)
+#   -- Set to OFF for C compilation of host code.
+#
+#   CUDA_HOST_COMPILER (Default CMAKE_C_COMPILER)
+#   -- Set the host compiler to be used by nvcc.  Ignored if -ccbin or
+#      --compiler-bindir is already present in the CUDA_NVCC_FLAGS or
+#      CUDA_NVCC_FLAGS_ variables.  For Visual Studio targets,
+#      the host compiler is constructed with one or more visual studio macros
+#      such as $(VCInstallDir), that expands out to the path when
+#      the command is run from within VS.
+#      If the CUDAHOSTCXX environment variable is set it will
+#      be used as the default.
+#
+#   CUDA_NVCC_FLAGS
+#   CUDA_NVCC_FLAGS_
+#   -- Additional NVCC command line arguments.  NOTE: multiple arguments must be
+#      semi-colon delimited (e.g. --compiler-options;-Wall)
+#
+#   CUDA_PROPAGATE_HOST_FLAGS (Default ON)
+#   -- Set to ON to propagate CMAKE_{C,CXX}_FLAGS and their configuration
+#      dependent counterparts (e.g. CMAKE_C_FLAGS_DEBUG) automatically to the
+#      host compiler through nvcc's -Xcompiler flag.  This helps make the
+#      generated host code match the rest of the system better.  Sometimes
+#      certain flags give nvcc problems, and this will help you turn the flag
+#      propagation off.  This does not affect the flags supplied directly to nvcc
+#      via CUDA_NVCC_FLAGS or through the OPTION flags specified through
+#      CUDA_ADD_LIBRARY, CUDA_ADD_EXECUTABLE, or CUDA_WRAP_SRCS.  Flags used for
+#      shared library compilation are not affected by this flag.
+#
+#   CUDA_PROPAGATE_HOST_FLAGS_BLACKLIST (Default "")
+#   -- A list containing the host flags that should not be propagated when
+#      CUDA_PROPAGATE_HOST_FLAGS is ON.
+#
+#   CUDA_SEPARABLE_COMPILATION (Default OFF)
+#   -- If set this will enable separable compilation for all CUDA runtime object
+#      files.  If used outside of CUDA_ADD_EXECUTABLE and CUDA_ADD_LIBRARY
+#      (e.g. calling CUDA_WRAP_SRCS directly),
+#      CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME and
+#      CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS should be called.
+#
+#   CUDA_SOURCE_PROPERTY_FORMAT
+#   -- If this source file property is set, it can override the format specified
+#      to CUDA_WRAP_SRCS (OBJ, PTX, CUBIN, or FATBIN).  If an input source file
+#      is not a .cu file, setting this file will cause it to be treated as a .cu
+#      file. See documentation for set_source_files_properties on how to set
+#      this property.
+#
+#   CUDA_USE_STATIC_CUDA_RUNTIME (Default ON)
+#   -- When enabled the static version of the CUDA runtime library will be used
+#      in CUDA_LIBRARIES.  If the version of CUDA configured doesn't support
+#      this option, then it will be silently disabled.
+#
+#   CUDA_VERBOSE_BUILD (Default OFF)
+#   -- Set to ON to see all the commands used when building the CUDA file.  When
+#      using a Makefile generator the value defaults to VERBOSE (run make
+#      VERBOSE=1 to see output), although setting CUDA_VERBOSE_BUILD to ON will
+#      always print the output.
+#
+# The script creates the following macros (in alphebetical order)::
+#
+#   CUDA_ADD_CUFFT_TO_TARGET( cuda_target )
+#   -- Adds the cufft library to the target (can be any target).  Handles whether
+#      you are in emulation mode or not.
+#
+#   CUDA_ADD_CUBLAS_TO_TARGET( cuda_target )
+#   -- Adds the cublas library to the target (can be any target).  Handles
+#      whether you are in emulation mode or not.
+#
+#   CUDA_ADD_EXECUTABLE( cuda_target file0 file1 ...
+#                        [WIN32] [MACOSX_BUNDLE] [EXCLUDE_FROM_ALL] [OPTIONS ...] )
+#   -- Creates an executable "cuda_target" which is made up of the files
+#      specified.  All of the non CUDA C files are compiled using the standard
+#      build rules specified by CMAKE and the cuda files are compiled to object
+#      files using nvcc and the host compiler.  In addition CUDA_INCLUDE_DIRS is
+#      added automatically to include_directories().  Some standard CMake target
+#      calls can be used on the target after calling this macro
+#      (e.g. set_target_properties and target_link_libraries), but setting
+#      properties that adjust compilation flags will not affect code compiled by
+#      nvcc.  Such flags should be modified before calling CUDA_ADD_EXECUTABLE,
+#      CUDA_ADD_LIBRARY or CUDA_WRAP_SRCS.
+#
+#   CUDA_ADD_LIBRARY( cuda_target file0 file1 ...
+#                     [STATIC | SHARED | MODULE] [EXCLUDE_FROM_ALL] [OPTIONS ...] )
+#   -- Same as CUDA_ADD_EXECUTABLE except that a library is created.
+#
+#   CUDA_BUILD_CLEAN_TARGET()
+#   -- Creates a convenience target that deletes all the dependency files
+#      generated.  You should make clean after running this target to ensure the
+#      dependency files get regenerated.
+#
+#   CUDA_COMPILE( generated_files file0 file1 ... [STATIC | SHARED | MODULE]
+#                 [OPTIONS ...] )
+#   -- Returns a list of generated files from the input source files to be used
+#      with ADD_LIBRARY or ADD_EXECUTABLE.
+#
+#   CUDA_COMPILE_PTX( generated_files file0 file1 ... [OPTIONS ...] )
+#   -- Returns a list of PTX files generated from the input source files.
+#
+#   CUDA_COMPILE_FATBIN( generated_files file0 file1 ... [OPTIONS ...] )
+#   -- Returns a list of FATBIN files generated from the input source files.
+#
+#   CUDA_COMPILE_CUBIN( generated_files file0 file1 ... [OPTIONS ...] )
+#   -- Returns a list of CUBIN files generated from the input source files.
+#
+#   CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME( output_file_var
+#                                                        cuda_target
+#                                                        object_files )
+#   -- Compute the name of the intermediate link file used for separable
+#      compilation.  This file name is typically passed into
+#      CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS.  output_file_var is produced
+#      based on cuda_target the list of objects files that need separable
+#      compilation as specified by object_files.  If the object_files list is
+#      empty, then output_file_var will be empty.  This function is called
+#      automatically for CUDA_ADD_LIBRARY and CUDA_ADD_EXECUTABLE.  Note that
+#      this is a function and not a macro.
+#
+#   CUDA_INCLUDE_DIRECTORIES( path0 path1 ... )
+#   -- Sets the directories that should be passed to nvcc
+#      (e.g. nvcc -Ipath0 -Ipath1 ... ). These paths usually contain other .cu
+#      files.
+#
+#
+#   CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS( output_file_var cuda_target
+#                                            nvcc_flags object_files)
+#   -- Generates the link object required by separable compilation from the given
+#      object files.  This is called automatically for CUDA_ADD_EXECUTABLE and
+#      CUDA_ADD_LIBRARY, but can be called manually when using CUDA_WRAP_SRCS
+#      directly.  When called from CUDA_ADD_LIBRARY or CUDA_ADD_EXECUTABLE the
+#      nvcc_flags passed in are the same as the flags passed in via the OPTIONS
+#      argument.  The only nvcc flag added automatically is the bitness flag as
+#      specified by CUDA_64_BIT_DEVICE_CODE.  Note that this is a function
+#      instead of a macro.
+#
+#   CUDA_SELECT_NVCC_ARCH_FLAGS(out_variable [target_CUDA_architectures])
+#   -- Selects GPU arch flags for nvcc based on target_CUDA_architectures
+#      target_CUDA_architectures : Auto | Common | All | LIST(ARCH_AND_PTX ...)
+#       - "Auto" detects local machine GPU compute arch at runtime.
+#       - "Common" and "All" cover common and entire subsets of architectures
+#      ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX
+#      NAME: Kepler Maxwell Kepler+Tesla Maxwell+Tegra Pascal Volta Turing
+#      NUM: Any number. Only those pairs are currently accepted by NVCC though:
+#            3.5 3.7 5.0 5.2 5.3 6.0 6.1 6.2 7.0 7.2 7.5
+#      Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable}
+#      Additionally, sets ${out_variable}_readable to the resulting numeric list
+#      Example:
+#       CUDA_SELECT_NVCC_ARCH_FLAGS(ARCH_FLAGS 3.0 3.5+PTX 5.2(5.0) Maxwell)
+#        LIST(APPEND CUDA_NVCC_FLAGS ${ARCH_FLAGS})
+#
+#      More info on CUDA architectures: https://en.wikipedia.org/wiki/CUDA
+#      Note that this is a function instead of a macro.
+#
+#   CUDA_WRAP_SRCS ( cuda_target format generated_files file0 file1 ...
+#                    [STATIC | SHARED | MODULE] [OPTIONS ...] )
+#   -- This is where all the magic happens.  CUDA_ADD_EXECUTABLE,
+#      CUDA_ADD_LIBRARY, CUDA_COMPILE, and CUDA_COMPILE_PTX all call this
+#      function under the hood.
+#
+#      Given the list of files (file0 file1 ... fileN) this macro generates
+#      custom commands that generate either PTX or linkable objects (use "PTX" or
+#      "OBJ" for the format argument to switch).  Files that don't end with .cu
+#      or have the HEADER_FILE_ONLY property are ignored.
+#
+#      The arguments passed in after OPTIONS are extra command line options to
+#      give to nvcc.  You can also specify per configuration options by
+#      specifying the name of the configuration followed by the options.  General
+#      options must precede configuration specific options.  Not all
+#      configurations need to be specified, only the ones provided will be used.
+#
+#         OPTIONS -DFLAG=2 "-DFLAG_OTHER=space in flag"
+#         DEBUG -g
+#         RELEASE --use_fast_math
+#         RELWITHDEBINFO --use_fast_math;-g
+#         MINSIZEREL --use_fast_math
+#
+#      For certain configurations (namely VS generating object files with
+#      CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE set to ON), no generated file will
+#      be produced for the given cuda file.  This is because when you add the
+#      cuda file to Visual Studio it knows that this file produces an object file
+#      and will link in the resulting object file automatically.
+#
+#      This script will also generate a separate cmake script that is used at
+#      build time to invoke nvcc.  This is for several reasons.
+#
+#        1. nvcc can return negative numbers as return values which confuses
+#        Visual Studio into thinking that the command succeeded.  The script now
+#        checks the error codes and produces errors when there was a problem.
+#
+#        2. nvcc has been known to not delete incomplete results when it
+#        encounters problems.  This confuses build systems into thinking the
+#        target was generated when in fact an unusable file exists.  The script
+#        now deletes the output files if there was an error.
+#
+#        3. By putting all the options that affect the build into a file and then
+#        make the build rule dependent on the file, the output files will be
+#        regenerated when the options change.
+#
+#      This script also looks at optional arguments STATIC, SHARED, or MODULE to
+#      determine when to target the object compilation for a shared library.
+#      BUILD_SHARED_LIBS is ignored in CUDA_WRAP_SRCS, but it is respected in
+#      CUDA_ADD_LIBRARY.  On some systems special flags are added for building
+#      objects intended for shared libraries.  A preprocessor macro,
+#      _EXPORTS is defined when a shared library compilation is
+#      detected.
+#
+#      Flags passed into add_definitions with -D or /D are passed along to nvcc.
+#
+#
+#
+# The script defines the following variables::
+#
+#   CUDA_VERSION_MAJOR    -- The major version of cuda as reported by nvcc.
+#   CUDA_VERSION_MINOR    -- The minor version.
+#   CUDA_VERSION
+#   CUDA_VERSION_STRING   -- CUDA_VERSION_MAJOR.CUDA_VERSION_MINOR
+#   CUDA_HAS_FP16         -- Whether a short float (float16,fp16) is supported.
+#
+#   CUDA_TOOLKIT_ROOT_DIR -- Path to the CUDA Toolkit (defined if not set).
+#   CUDA_SDK_ROOT_DIR     -- Path to the CUDA SDK.  Use this to find files in the
+#                            SDK.  This script will not directly support finding
+#                            specific libraries or headers, as that isn't
+#                            supported by NVIDIA.  If you want to change
+#                            libraries when the path changes see the
+#                            FindCUDA.cmake script for an example of how to clear
+#                            these variables.  There are also examples of how to
+#                            use the CUDA_SDK_ROOT_DIR to locate headers or
+#                            libraries, if you so choose (at your own risk).
+#   CUDA_INCLUDE_DIRS     -- Include directory for cuda headers.  Added automatically
+#                            for CUDA_ADD_EXECUTABLE and CUDA_ADD_LIBRARY.
+#   CUDA_LIBRARIES        -- Cuda RT library.
+#   CUDA_CUFFT_LIBRARIES  -- Device or emulation library for the Cuda FFT
+#                            implementation (alternative to:
+#                            CUDA_ADD_CUFFT_TO_TARGET macro)
+#   CUDA_CUBLAS_LIBRARIES -- Device or emulation library for the Cuda BLAS
+#                            implementation (alternative to:
+#                            CUDA_ADD_CUBLAS_TO_TARGET macro).
+#   CUDA_cudart_static_LIBRARY -- Statically linkable cuda runtime library.
+#                                 Only available for CUDA version 5.5+
+#   CUDA_cudadevrt_LIBRARY -- Device runtime library.
+#                             Required for separable compilation.
+#   CUDA_cupti_LIBRARY    -- CUDA Profiling Tools Interface library.
+#                            Only available for CUDA version 4.0+.
+#   CUDA_curand_LIBRARY   -- CUDA Random Number Generation library.
+#                            Only available for CUDA version 3.2+.
+#   CUDA_cusolver_LIBRARY -- CUDA Direct Solver library.
+#                            Only available for CUDA version 7.0+.
+#   CUDA_cusparse_LIBRARY -- CUDA Sparse Matrix library.
+#                            Only available for CUDA version 3.2+.
+#   CUDA_npp_LIBRARY      -- NVIDIA Performance Primitives lib.
+#                            Only available for CUDA version 4.0+.
+#   CUDA_nppc_LIBRARY     -- NVIDIA Performance Primitives lib (core).
+#                            Only available for CUDA version 5.5+.
+#   CUDA_nppi_LIBRARY     -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 5.5 - 8.0.
+#   CUDA_nppial_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppicc_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppicom_LIBRARY  -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppidei_LIBRARY  -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppif_LIBRARY    -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppig_LIBRARY    -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppim_LIBRARY    -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppist_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppisu_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_nppitc_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
+#                            Only available for CUDA version 9.0.
+#   CUDA_npps_LIBRARY     -- NVIDIA Performance Primitives lib (signal processing).
+#                            Only available for CUDA version 5.5+.
+#   CUDA_nvcuvenc_LIBRARY -- CUDA Video Encoder library.
+#                            Only available for CUDA version 3.2+.
+#                            Windows only.
+#   CUDA_nvcuvid_LIBRARY  -- CUDA Video Decoder library.
+#                            Only available for CUDA version 3.2+.
+#                            Windows only.
+#
+
+#   James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#   Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html
+#
+#   Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#   Copyright (c) 2007-2009
+#   Scientific Computing and Imaging Institute, University of Utah
+#
+#   This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#   for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+#
+###############################################################################
+
+# FindCUDA.cmake
+
+# This macro helps us find the location of helper files we will need the full path to
+macro(CUDA_FIND_HELPER_FILE _name _extension)
+  set(_full_name "${_name}.${_extension}")
+  # CMAKE_CURRENT_LIST_FILE contains the full path to the file currently being
+  # processed.  Using this variable, we can pull out the current path, and
+  # provide a way to get access to the other files we need local to here.
+  get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+  set(CUDA_${_name} "${CMAKE_CURRENT_LIST_DIR}/FindCUDA/${_full_name}")
+  if(NOT EXISTS "${CUDA_${_name}}")
+    set(error_message "${_full_name} not found in ${CMAKE_CURRENT_LIST_DIR}/FindCUDA")
+    if(CUDA_FIND_REQUIRED)
+      message(FATAL_ERROR "${error_message}")
+    else()
+      if(NOT CUDA_FIND_QUIETLY)
+        message(STATUS "${error_message}")
+      endif()
+    endif()
+  endif()
+  # Set this variable as internal, so the user isn't bugged with it.
+  set(CUDA_${_name} ${CUDA_${_name}} CACHE INTERNAL "Location of ${_full_name}" FORCE)
+endmacro()
+
+#####################################################################
+## CUDA_INCLUDE_NVCC_DEPENDENCIES
+##
+
+# So we want to try and include the dependency file if it exists.  If
+# it doesn't exist then we need to create an empty one, so we can
+# include it.
+
+# If it does exist, then we need to check to see if all the files it
+# depends on exist.  If they don't then we should clear the dependency
+# file and regenerate it later.  This covers the case where a header
+# file has disappeared or moved.
+
+macro(CUDA_INCLUDE_NVCC_DEPENDENCIES dependency_file)
+  set(CUDA_NVCC_DEPEND)
+  set(CUDA_NVCC_DEPEND_REGENERATE FALSE)
+
+
+  # Include the dependency file.  Create it first if it doesn't exist .  The
+  # INCLUDE puts a dependency that will force CMake to rerun and bring in the
+  # new info when it changes.  DO NOT REMOVE THIS (as I did and spent a few
+  # hours figuring out why it didn't work.
+  if(NOT EXISTS ${dependency_file})
+    file(WRITE ${dependency_file} "#FindCUDA.cmake generated file.  Do not edit.\n")
+  endif()
+  # Always include this file to force CMake to run again next
+  # invocation and rebuild the dependencies.
+  #message("including dependency_file = ${dependency_file}")
+  include(${dependency_file})
+
+  # Now we need to verify the existence of all the included files
+  # here.  If they aren't there we need to just blank this variable and
+  # make the file regenerate again.
+#   if(DEFINED CUDA_NVCC_DEPEND)
+#     message("CUDA_NVCC_DEPEND set")
+#   else()
+#     message("CUDA_NVCC_DEPEND NOT set")
+#   endif()
+  if(CUDA_NVCC_DEPEND)
+    #message("CUDA_NVCC_DEPEND found")
+    foreach(f ${CUDA_NVCC_DEPEND})
+      # message("searching for ${f}")
+      if(NOT EXISTS ${f})
+        #message("file ${f} not found")
+        set(CUDA_NVCC_DEPEND_REGENERATE TRUE)
+      endif()
+    endforeach()
+  else()
+    #message("CUDA_NVCC_DEPEND false")
+    # No dependencies, so regenerate the file.
+    set(CUDA_NVCC_DEPEND_REGENERATE TRUE)
+  endif()
+
+  #message("CUDA_NVCC_DEPEND_REGENERATE = ${CUDA_NVCC_DEPEND_REGENERATE}")
+  # No incoming dependencies, so we need to generate them.  Make the
+  # output depend on the dependency file itself, which should cause the
+  # rule to re-run.
+  if(CUDA_NVCC_DEPEND_REGENERATE)
+    set(CUDA_NVCC_DEPEND ${dependency_file})
+    #message("Generating an empty dependency_file: ${dependency_file}")
+    file(WRITE ${dependency_file} "#FindCUDA.cmake generated file.  Do not edit.\n")
+  endif()
+
+endmacro()
+
+###############################################################################
+###############################################################################
+# Setup variables' defaults
+###############################################################################
+###############################################################################
+
+# Allow the user to specify if the device code is supposed to be 32 or 64 bit.
+if(CMAKE_SIZEOF_VOID_P EQUAL 8)
+  set(CUDA_64_BIT_DEVICE_CODE_DEFAULT ON)
+else()
+  set(CUDA_64_BIT_DEVICE_CODE_DEFAULT OFF)
+endif()
+option(CUDA_64_BIT_DEVICE_CODE "Compile device code in 64 bit mode" ${CUDA_64_BIT_DEVICE_CODE_DEFAULT})
+
+# Attach the build rule to the source file in VS.  This option
+option(CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE "Attach the build rule to the CUDA source file.  Enable only when the CUDA source file is added to at most one target." ON)
+
+# Prints out extra information about the cuda file during compilation
+option(CUDA_BUILD_CUBIN "Generate and parse .cubin files in Device mode." OFF)
+
+# Set whether we are using emulation or device mode.
+option(CUDA_BUILD_EMULATION "Build in Emulation mode" OFF)
+
+# Where to put the generated output.
+set(CUDA_GENERATED_OUTPUT_DIR "" CACHE PATH "Directory to put all the output files.  If blank it will default to the CMAKE_CURRENT_BINARY_DIR")
+
+# Parse HOST_COMPILATION mode.
+option(CUDA_HOST_COMPILATION_CPP "Generated file extension" ON)
+
+# Extra user settable flags
+cmake_initialize_per_config_variable(CUDA_NVCC_FLAGS "Semi-colon delimit multiple arguments.")
+
+if(DEFINED ENV{CUDAHOSTCXX})
+  set(CUDA_HOST_COMPILER "$ENV{CUDAHOSTCXX}" CACHE FILEPATH "Host side compiler used by NVCC")
+elseif(CMAKE_GENERATOR MATCHES "Visual Studio")
+  set(_CUDA_MSVC_HOST_COMPILER "$(VCInstallDir)Tools/MSVC/$(VCToolsVersion)/bin/Host$(Platform)/$(PlatformTarget)")
+  if(MSVC_VERSION LESS 1910)
+   set(_CUDA_MSVC_HOST_COMPILER "$(VCInstallDir)bin")
+  endif()
+
+  set(CUDA_HOST_COMPILER "${_CUDA_MSVC_HOST_COMPILER}" CACHE FILEPATH "Host side compiler used by NVCC")
+
+else()
+  if(APPLE
+      AND "${CMAKE_C_COMPILER_ID}" MATCHES "Clang"
+      AND "${CMAKE_C_COMPILER}" MATCHES "/cc$")
+    # Using cc which is symlink to clang may let NVCC think it is GCC and issue
+    # unhandled -dumpspecs option to clang. Also in case neither
+    # CMAKE_C_COMPILER is defined (project does not use C language) nor
+    # CUDA_HOST_COMPILER is specified manually we should skip -ccbin and let
+    # nvcc use its own default C compiler.
+    # Only care about this on APPLE with clang to avoid
+    # following symlinks to things like ccache
+    if(DEFINED CMAKE_C_COMPILER AND NOT DEFINED CUDA_HOST_COMPILER)
+      get_filename_component(c_compiler_realpath "${CMAKE_C_COMPILER}" REALPATH)
+      # if the real path does not end up being clang then
+      # go back to using CMAKE_C_COMPILER
+      if(NOT "${c_compiler_realpath}" MATCHES "/clang$")
+        set(c_compiler_realpath "${CMAKE_C_COMPILER}")
+      endif()
+    else()
+      set(c_compiler_realpath "")
+    endif()
+    set(CUDA_HOST_COMPILER "${c_compiler_realpath}" CACHE FILEPATH "Host side compiler used by NVCC")
+  elseif(MSVC AND "${CMAKE_C_COMPILER}" MATCHES "clcache|sccache")
+    # NVCC does not think it will work if it is passed clcache.exe or sccache.exe
+    # as the host compiler, which means that builds with CC=cl.exe won't work.
+    # Best to just feed it whatever the actual cl.exe is as the host compiler.
+    set(CUDA_HOST_COMPILER "cl.exe" CACHE FILEPATH "Host side compiler used by NVCC")
+  else()
+    set(CUDA_HOST_COMPILER "${CMAKE_C_COMPILER}"
+      CACHE FILEPATH "Host side compiler used by NVCC")
+  endif()
+endif()
+
+# Propagate the host flags to the host compiler via -Xcompiler
+option(CUDA_PROPAGATE_HOST_FLAGS "Propagate C/CXX_FLAGS and friends to the host compiler via -Xcompile" ON)
+
+# Blacklisted flags to prevent propagation
+set(CUDA_PROPAGATE_HOST_FLAGS_BLACKLIST  "" CACHE STRING "Blacklisted flags to prevent propagation")
+
+# Enable CUDA_SEPARABLE_COMPILATION
+option(CUDA_SEPARABLE_COMPILATION "Compile CUDA objects with separable compilation enabled.  Requires CUDA 5.0+" OFF)
+
+# Specifies whether the commands used when compiling the .cu file will be printed out.
+option(CUDA_VERBOSE_BUILD "Print out the commands run while compiling the CUDA source file.  With the Makefile generator this defaults to VERBOSE variable specified on the command line, but can be forced on with this option." OFF)
+
+mark_as_advanced(
+  CUDA_64_BIT_DEVICE_CODE
+  CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE
+  CUDA_GENERATED_OUTPUT_DIR
+  CUDA_HOST_COMPILATION_CPP
+  CUDA_NVCC_FLAGS
+  CUDA_PROPAGATE_HOST_FLAGS
+  CUDA_PROPAGATE_HOST_FLAGS_BLACKLIST
+  CUDA_BUILD_CUBIN
+  CUDA_BUILD_EMULATION
+  CUDA_VERBOSE_BUILD
+  CUDA_SEPARABLE_COMPILATION
+  )
+
+# Single config generators like Makefiles or Ninja don't usually have
+# CMAKE_CONFIGURATION_TYPES defined (but note that it can be defined if set by
+# projects or developers). Even CMAKE_BUILD_TYPE might not be defined for
+# single config generators (and should not be defined for multi-config
+# generators). To ensure we get a complete superset of all possible
+# configurations, we combine CMAKE_CONFIGURATION_TYPES, CMAKE_BUILD_TYPE and
+# all of the standard configurations, then weed out duplicates with
+# list(REMOVE_DUPLICATES). Looping over the unique set then ensures we have
+# each configuration-specific set of nvcc flags defined and marked as advanced.
+set(CUDA_configuration_types ${CMAKE_CONFIGURATION_TYPES} ${CMAKE_BUILD_TYPE} Debug MinSizeRel Release RelWithDebInfo)
+list(REMOVE_DUPLICATES CUDA_configuration_types)
+
+###############################################################################
+###############################################################################
+# Locate CUDA, Set Build Type, etc.
+###############################################################################
+###############################################################################
+
+macro(cuda_unset_include_and_libraries)
+  unset(CUDA_TOOLKIT_INCLUDE CACHE)
+  unset(CUDA_CUDART_LIBRARY CACHE)
+  unset(CUDA_CUDA_LIBRARY CACHE)
+  # Make sure you run this before you unset CUDA_VERSION.
+  unset(CUDA_cudart_static_LIBRARY CACHE)
+  unset(CUDA_cudadevrt_LIBRARY CACHE)
+  unset(CUDA_cublas_LIBRARY CACHE)
+  unset(CUDA_cublas_device_LIBRARY CACHE)
+  unset(CUDA_cublasemu_LIBRARY CACHE)
+  unset(CUDA_cublasLt_LIBRARY CACHE)
+  unset(CUDA_cufft_LIBRARY CACHE)
+  unset(CUDA_cufftemu_LIBRARY CACHE)
+  unset(CUDA_cupti_LIBRARY CACHE)
+  unset(CUDA_curand_LIBRARY CACHE)
+  unset(CUDA_cusolver_LIBRARY CACHE)
+  unset(CUDA_cusparse_LIBRARY CACHE)
+  unset(CUDA_npp_LIBRARY CACHE)
+  unset(CUDA_nppc_LIBRARY CACHE)
+  unset(CUDA_nppi_LIBRARY CACHE)
+  unset(CUDA_npps_LIBRARY CACHE)
+  unset(CUDA_nvcuvenc_LIBRARY CACHE)
+  unset(CUDA_nvcuvid_LIBRARY CACHE)
+  unset(CUDA_GPU_DETECT_OUTPUT CACHE)
+endmacro()
+
+# Check to see if the CUDA_TOOLKIT_ROOT_DIR and CUDA_SDK_ROOT_DIR have changed,
+# if they have then clear the cache variables, so that will be detected again.
+if(NOT "${CUDA_TOOLKIT_ROOT_DIR}" STREQUAL "${CUDA_TOOLKIT_ROOT_DIR_INTERNAL}")
+  unset(CUDA_TOOLKIT_TARGET_DIR CACHE)
+  unset(CUDA_NVCC_EXECUTABLE CACHE)
+  cuda_unset_include_and_libraries()
+  unset(CUDA_VERSION CACHE)
+endif()
+
+if(NOT "${CUDA_TOOLKIT_TARGET_DIR}" STREQUAL "${CUDA_TOOLKIT_TARGET_DIR_INTERNAL}")
+  cuda_unset_include_and_libraries()
+endif()
+
+#
+#  End of unset()
+#
+
+#
+#  Start looking for things
+#
+
+# Search for the cuda distribution.
+if(NOT CUDA_TOOLKIT_ROOT_DIR AND NOT CMAKE_CROSSCOMPILING)
+  # Search in the CUDA_BIN_PATH first.
+  find_program(CUDA_TOOLKIT_ROOT_DIR_NVCC
+    NAMES nvcc nvcc.exe
+    PATHS
+      ENV CUDA_TOOLKIT_ROOT
+      ENV CUDA_PATH
+      ENV CUDA_BIN_PATH
+    PATH_SUFFIXES bin bin64
+    DOC "Toolkit location."
+    NO_DEFAULT_PATH
+    )
+
+  # Now search default paths
+  find_program(CUDA_TOOLKIT_ROOT_DIR_NVCC
+    NAMES nvcc nvcc.exe
+    PATHS /opt/cuda/bin
+    PATH_SUFFIXES cuda/bin
+    DOC "Toolkit location."
+    )
+
+  if (CUDA_TOOLKIT_ROOT_DIR_NVCC)
+    get_filename_component(CUDA_TOOLKIT_ROOT_DIR_NVCC_PAR "${CUDA_TOOLKIT_ROOT_DIR_NVCC}" DIRECTORY)
+    get_filename_component(CUDA_TOOLKIT_ROOT_DIR "${CUDA_TOOLKIT_ROOT_DIR_NVCC_PAR}" DIRECTORY CACHE)
+    string(REGEX REPLACE "[/\\\\]?bin[64]*[/\\\\]?$" "" CUDA_TOOLKIT_ROOT_DIR ${CUDA_TOOLKIT_ROOT_DIR})
+    # We need to force this back into the cache.
+    set(CUDA_TOOLKIT_ROOT_DIR ${CUDA_TOOLKIT_ROOT_DIR} CACHE PATH "Toolkit location." FORCE)
+    set(CUDA_TOOLKIT_TARGET_DIR ${CUDA_TOOLKIT_ROOT_DIR})
+  endif()
+  unset(CUDA_TOOLKIT_ROOT_DIR_NVCC CACHE)
+
+  if (NOT EXISTS ${CUDA_TOOLKIT_ROOT_DIR})
+    if(CUDA_FIND_REQUIRED)
+      message(FATAL_ERROR "Specify CUDA_TOOLKIT_ROOT_DIR")
+    elseif(NOT CUDA_FIND_QUIETLY)
+      message("CUDA_TOOLKIT_ROOT_DIR not found or specified")
+    endif()
+  endif ()
+endif ()
+
+if(CMAKE_CROSSCOMPILING)
+  SET (CUDA_TOOLKIT_ROOT $ENV{CUDA_TOOLKIT_ROOT})
+  if(CMAKE_SYSTEM_PROCESSOR STREQUAL "armv7-a")
+    # Support for NVPACK
+    set (CUDA_TOOLKIT_TARGET_NAMES "armv7-linux-androideabi")
+  elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "arm")
+    # Support for arm cross compilation
+    set(CUDA_TOOLKIT_TARGET_NAMES "armv7-linux-gnueabihf")
+  elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64")
+    # Support for aarch64 cross compilation
+    if (ANDROID_ARCH_NAME STREQUAL "arm64")
+      set(CUDA_TOOLKIT_TARGET_NAMES "aarch64-linux-androideabi")
+    else()
+      set(CUDA_TOOLKIT_TARGET_NAMES "aarch64-linux" "sbsa-linux")
+    endif (ANDROID_ARCH_NAME STREQUAL "arm64")
+  endif()
+
+  foreach(CUDA_TOOLKIT_TARGET_NAME IN LISTS CUDA_TOOLKIT_TARGET_NAMES)
+    if (EXISTS "${CUDA_TOOLKIT_ROOT}/targets/${CUDA_TOOLKIT_TARGET_NAME}")
+      set(CUDA_TOOLKIT_TARGET_DIR "${CUDA_TOOLKIT_ROOT}/targets/${CUDA_TOOLKIT_TARGET_NAME}" CACHE PATH "CUDA Toolkit target location.")
+      SET (CUDA_TOOLKIT_ROOT_DIR ${CUDA_TOOLKIT_ROOT} CACHE PATH "Toolkit location." FORCE)
+      mark_as_advanced(CUDA_TOOLKIT_TARGET_DIR)
+      break()
+    endif()
+  endforeach()
+
+  # add known CUDA targetr root path to the set of directories we search for programs, libraries and headers
+  set( CMAKE_FIND_ROOT_PATH "${CUDA_TOOLKIT_TARGET_DIR};${CMAKE_FIND_ROOT_PATH}")
+  macro( cuda_find_host_program )
+    if (COMMAND find_host_program)
+      find_host_program( ${ARGN} )
+    else()
+      find_program( ${ARGN} )
+    endif()
+  endmacro()
+else()
+  # for non-cross-compile, find_host_program == find_program and CUDA_TOOLKIT_TARGET_DIR == CUDA_TOOLKIT_ROOT_DIR
+  macro( cuda_find_host_program )
+    find_program( ${ARGN} )
+  endmacro()
+  SET (CUDA_TOOLKIT_TARGET_DIR ${CUDA_TOOLKIT_ROOT_DIR})
+endif()
+
+
+# CUDA_NVCC_EXECUTABLE
+if(DEFINED ENV{CUDA_NVCC_EXECUTABLE})
+  set(CUDA_NVCC_EXECUTABLE "$ENV{CUDA_NVCC_EXECUTABLE}" CACHE FILEPATH "The CUDA compiler")
+else()
+  cuda_find_host_program(CUDA_NVCC_EXECUTABLE
+    NAMES nvcc
+    PATHS "${CUDA_TOOLKIT_ROOT_DIR}"
+    ENV CUDA_PATH
+    ENV CUDA_BIN_PATH
+    PATH_SUFFIXES bin bin64
+    NO_DEFAULT_PATH
+    )
+  # Search default search paths, after we search our own set of paths.
+  cuda_find_host_program(CUDA_NVCC_EXECUTABLE nvcc)
+endif()
+
+if(CUDA_NVCC_EXECUTABLE AND NOT CUDA_VERSION)
+  # Compute the version.
+  execute_process(COMMAND ${CUDA_NVCC_EXECUTABLE} "--version"
+    OUTPUT_VARIABLE NVCC_OUT
+    RESULT_VARIABLE NVCC_RC)
+  if(NOT (${NVCC_RC} EQUAL 0))
+    message(WARNING "Failed to execute '${CUDA_NVCC_EXECUTABLE} --version'")
+    set(CUDA_FOUND FALSE)
+    return()
+  endif()
+  string(REGEX REPLACE ".*release ([0-9]+)\\.([0-9]+).*" "\\1" CUDA_VERSION_MAJOR ${NVCC_OUT})
+  string(REGEX REPLACE ".*release ([0-9]+)\\.([0-9]+).*" "\\2" CUDA_VERSION_MINOR ${NVCC_OUT})
+  set(CUDA_VERSION "${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}" CACHE STRING "Version of CUDA as computed from nvcc.")
+  mark_as_advanced(CUDA_VERSION)
+else()
+  # Need to set these based off of the cached value
+  string(REGEX REPLACE "([0-9]+)\\.([0-9]+).*" "\\1" CUDA_VERSION_MAJOR "${CUDA_VERSION}")
+  string(REGEX REPLACE "([0-9]+)\\.([0-9]+).*" "\\2" CUDA_VERSION_MINOR "${CUDA_VERSION}")
+endif()
+
+# Always set this convenience variable
+set(CUDA_VERSION_STRING "${CUDA_VERSION}")
+
+# CUDA_TOOLKIT_INCLUDE
+find_path(CUDA_TOOLKIT_INCLUDE
+  device_functions.h # Header included in toolkit
+  PATHS ${CUDA_TOOLKIT_TARGET_DIR}
+  ENV CUDA_PATH
+  ENV CUDA_INC_PATH
+  PATH_SUFFIXES include
+  NO_DEFAULT_PATH
+  )
+# Search default search paths, after we search our own set of paths.
+find_path(CUDA_TOOLKIT_INCLUDE device_functions.h)
+mark_as_advanced(CUDA_TOOLKIT_INCLUDE)
+
+set(CUDA_HAS_FP16 TRUE)
+
+# Set the user list of include dir to nothing to initialize it.
+set (CUDA_NVCC_INCLUDE_DIRS_USER "")
+set (CUDA_INCLUDE_DIRS ${CUDA_TOOLKIT_INCLUDE})
+
+macro(cuda_find_library_local_first_with_path_ext _var _names _doc _path_ext )
+  if(CMAKE_SIZEOF_VOID_P EQUAL 8)
+    # CUDA 3.2+ on Windows moved the library directories, so we need the new
+    # and old paths.
+    set(_cuda_64bit_lib_dir "${_path_ext}lib/x64" "${_path_ext}lib64" "${_path_ext}libx64" )
+  endif()
+  # CUDA 3.2+ on Windows moved the library directories, so we need to new
+  # (lib/Win32) and the old path (lib).
+  find_library(${_var}
+    NAMES ${_names}
+    PATHS "${CUDA_TOOLKIT_TARGET_DIR}"
+    ENV CUDA_PATH
+    ENV CUDA_LIB_PATH
+    PATH_SUFFIXES ${_cuda_64bit_lib_dir} "${_path_ext}lib/Win32" "${_path_ext}lib" "${_path_ext}libWin32"
+    DOC ${_doc}
+    NO_DEFAULT_PATH
+    )
+  if (NOT CMAKE_CROSSCOMPILING)
+    # Search default search paths, after we search our own set of paths.
+    find_library(${_var}
+      NAMES ${_names}
+      PATHS "/usr/lib/nvidia-current"
+      DOC ${_doc}
+      )
+  endif()
+endmacro()
+
+macro(cuda_find_library_local_first _var _names _doc)
+  cuda_find_library_local_first_with_path_ext( "${_var}" "${_names}" "${_doc}" "" )
+endmacro()
+
+macro(find_library_local_first _var _names _doc )
+  cuda_find_library_local_first( "${_var}" "${_names}" "${_doc}" "" )
+endmacro()
+
+
+# CUDA_LIBRARIES
+cuda_find_library_local_first(CUDA_CUDART_LIBRARY cudart "\"cudart\" library")
+
+cuda_find_library_local_first(CUDA_cudart_static_LIBRARY cudart_static "static CUDA runtime library")
+mark_as_advanced(CUDA_cudart_static_LIBRARY)
+
+
+if(CUDA_cudart_static_LIBRARY)
+  # If static cudart available, use it by default, but provide a user-visible option to disable it.
+  option(CUDA_USE_STATIC_CUDA_RUNTIME "Use the static version of the CUDA runtime library if available" ON)
+else()
+  # If not available, silently disable the option.
+  set(CUDA_USE_STATIC_CUDA_RUNTIME OFF CACHE INTERNAL "")
+endif()
+
+if(CUDA_USE_STATIC_CUDA_RUNTIME)
+  set(CUDA_CUDART_LIBRARY_VAR CUDA_cudart_static_LIBRARY)
+else()
+  set(CUDA_CUDART_LIBRARY_VAR CUDA_CUDART_LIBRARY)
+endif()
+
+cuda_find_library_local_first(CUDA_cudadevrt_LIBRARY cudadevrt "\"cudadevrt\" library")
+mark_as_advanced(CUDA_cudadevrt_LIBRARY)
+
+if(CUDA_USE_STATIC_CUDA_RUNTIME)
+  if(UNIX)
+    # Check for the dependent libraries.  Here we look for pthreads.
+    if (DEFINED CMAKE_THREAD_PREFER_PTHREAD)
+      set(_cuda_cmake_thread_prefer_pthread ${CMAKE_THREAD_PREFER_PTHREAD})
+    endif()
+    set(CMAKE_THREAD_PREFER_PTHREAD 1)
+
+    # Many of the FindXYZ CMake comes with makes use of try_compile with int main(){return 0;}
+    # as the source file.  Unfortunately this causes a warning with -Wstrict-prototypes and
+    # -Werror causes the try_compile to fail.  We will just temporarily disable other flags
+    # when doing the find_package command here.
+    set(_cuda_cmake_c_flags ${CMAKE_C_FLAGS})
+    set(CMAKE_C_FLAGS "-fPIC")
+    find_package(Threads REQUIRED)
+    set(CMAKE_C_FLAGS ${_cuda_cmake_c_flags})
+
+    if (DEFINED _cuda_cmake_thread_prefer_pthread)
+      set(CMAKE_THREAD_PREFER_PTHREAD ${_cuda_cmake_thread_prefer_pthread})
+      unset(_cuda_cmake_thread_prefer_pthread)
+    else()
+      unset(CMAKE_THREAD_PREFER_PTHREAD)
+    endif()
+
+    if(NOT APPLE)
+      #On Linux, you must link against librt when using the static cuda runtime.
+      find_library(CUDA_rt_LIBRARY rt)
+      if (NOT CUDA_rt_LIBRARY)
+        message(WARNING "Expecting to find librt for libcudart_static, but didn't find it.")
+      endif()
+    endif()
+  endif()
+endif()
+
+cuda_find_library_local_first_with_path_ext(CUDA_cupti_LIBRARY cupti "\"cupti\" library" "extras/CUPTI/")
+mark_as_advanced(CUDA_cupti_LIBRARY)
+
+# Set the CUDA_LIBRARIES variable.  This is the set of stuff to link against if you are
+# using the CUDA runtime.  For the dynamic version of the runtime, most of the
+# dependencies are brough in, but for the static version there are additional libraries
+# and linker commands needed.
+# Initialize to empty
+set(CUDA_LIBRARIES)
+
+# If we are using emulation mode and we found the cudartemu library then use
+# that one instead of cudart.
+if(CUDA_BUILD_EMULATION AND CUDA_CUDARTEMU_LIBRARY)
+  list(APPEND CUDA_LIBRARIES ${CUDA_CUDARTEMU_LIBRARY})
+elseif(CUDA_USE_STATIC_CUDA_RUNTIME AND CUDA_cudart_static_LIBRARY)
+  list(APPEND CUDA_LIBRARIES ${CUDA_cudart_static_LIBRARY} ${CMAKE_THREAD_LIBS_INIT} ${CMAKE_DL_LIBS})
+  if (CUDA_rt_LIBRARY)
+    list(APPEND CUDA_LIBRARIES ${CUDA_rt_LIBRARY})
+  endif()
+  if(APPLE)
+    # We need to add the default path to the driver (libcuda.dylib) as an rpath, so that
+    # the static cuda runtime can find it at runtime.
+    list(APPEND CUDA_LIBRARIES -Wl,-rpath,/usr/local/cuda/lib)
+  endif()
+else()
+  list(APPEND CUDA_LIBRARIES ${CUDA_CUDART_LIBRARY})
+endif()
+
+# 1.1 toolkit on linux doesn't appear to have a separate library on
+# some platforms.
+cuda_find_library_local_first(CUDA_CUDA_LIBRARY cuda "\"cuda\" library (older versions only).")
+
+mark_as_advanced(
+  CUDA_CUDA_LIBRARY
+  CUDA_CUDART_LIBRARY
+  )
+
+#######################
+# Look for some of the toolkit helper libraries
+macro(FIND_CUDA_HELPER_LIBS _name)
+  cuda_find_library_local_first(CUDA_${_name}_LIBRARY ${_name} "\"${_name}\" library")
+  mark_as_advanced(CUDA_${_name}_LIBRARY)
+endmacro()
+
+if(CUDA_BUILD_EMULATION)
+  message(FATAL_ERROR "CUDA_BUILD_EMULATION is not supported in version 3.1 and onwards.  You must disable it to proceed.  You have version ${CUDA_VERSION}.")
+endif()
+
+find_cuda_helper_libs(cufft)
+find_cuda_helper_libs(cublas)
+find_cuda_helper_libs(cublasLt)
+# cusparse showed up in version 3.2
+find_cuda_helper_libs(cusparse)
+find_cuda_helper_libs(curand)
+if (WIN32)
+  find_cuda_helper_libs(nvcuvenc)
+  find_cuda_helper_libs(nvcuvid)
+endif()
+
+# In CUDA 9.0 NPP was nppi was removed
+find_cuda_helper_libs(nppc)
+find_cuda_helper_libs(nppial)
+find_cuda_helper_libs(nppicc)
+find_cuda_helper_libs(nppicom)
+find_cuda_helper_libs(nppidei)
+find_cuda_helper_libs(nppif)
+find_cuda_helper_libs(nppig)
+find_cuda_helper_libs(nppim)
+find_cuda_helper_libs(nppist)
+find_cuda_helper_libs(nppisu)
+find_cuda_helper_libs(nppitc)
+find_cuda_helper_libs(npps)
+set(CUDA_npp_LIBRARY "${CUDA_nppc_LIBRARY};${CUDA_nppial_LIBRARY};${CUDA_nppicc_LIBRARY};${CUDA_nppicom_LIBRARY};${CUDA_nppidei_LIBRARY};${CUDA_nppif_LIBRARY};${CUDA_nppig_LIBRARY};${CUDA_nppim_LIBRARY};${CUDA_nppist_LIBRARY};${CUDA_nppisu_LIBRARY};${CUDA_nppitc_LIBRARY};${CUDA_npps_LIBRARY}")
+# cusolver showed up in version 7.0
+find_cuda_helper_libs(cusolver)
+
+if (CUDA_BUILD_EMULATION)
+  set(CUDA_CUFFT_LIBRARIES ${CUDA_cufftemu_LIBRARY})
+  set(CUDA_CUBLAS_LIBRARIES ${CUDA_cublasemu_LIBRARY})
+else()
+  set(CUDA_CUFFT_LIBRARIES ${CUDA_cufft_LIBRARY})
+  set(CUDA_CUBLAS_LIBRARIES ${CUDA_cublas_LIBRARY} ${CUDA_cublas_device_LIBRARY} ${CUDA_cublasLt_LIBRARY})
+endif()
+
+########################
+# Look for the SDK stuff.  As of CUDA 3.0 NVSDKCUDA_ROOT has been replaced with
+# NVSDKCOMPUTE_ROOT with the old CUDA C contents moved into the C subdirectory
+find_path(CUDA_SDK_ROOT_DIR common/inc/cutil.h
+ HINTS
+  "$ENV{NVSDKCOMPUTE_ROOT}/C"
+  ENV NVSDKCUDA_ROOT
+  "[HKEY_LOCAL_MACHINE\\SOFTWARE\\NVIDIA Corporation\\Installed Products\\NVIDIA SDK 10\\Compute;InstallDir]"
+ PATHS
+  "/Developer/GPU\ Computing/C"
+  )
+
+# Keep the CUDA_SDK_ROOT_DIR first in order to be able to override the
+# environment variables.
+set(CUDA_SDK_SEARCH_PATH
+  "${CUDA_SDK_ROOT_DIR}"
+  "${CUDA_TOOLKIT_ROOT_DIR}/local/NVSDK0.2"
+  "${CUDA_TOOLKIT_ROOT_DIR}/NVSDK0.2"
+  "${CUDA_TOOLKIT_ROOT_DIR}/NV_CUDA_SDK"
+  "$ENV{HOME}/NVIDIA_CUDA_SDK"
+  "$ENV{HOME}/NVIDIA_CUDA_SDK_MACOSX"
+  "/Developer/CUDA"
+  )
+
+# Example of how to find an include file from the CUDA_SDK_ROOT_DIR
+
+# find_path(CUDA_CUT_INCLUDE_DIR
+#   cutil.h
+#   PATHS ${CUDA_SDK_SEARCH_PATH}
+#   PATH_SUFFIXES "common/inc"
+#   DOC "Location of cutil.h"
+#   NO_DEFAULT_PATH
+#   )
+# # Now search system paths
+# find_path(CUDA_CUT_INCLUDE_DIR cutil.h DOC "Location of cutil.h")
+
+# mark_as_advanced(CUDA_CUT_INCLUDE_DIR)
+
+
+# Example of how to find a library in the CUDA_SDK_ROOT_DIR
+
+# # cutil library is called cutil64 for 64 bit builds on windows.  We don't want
+# # to get these confused, so we are setting the name based on the word size of
+# # the build.
+
+# if(CMAKE_SIZEOF_VOID_P EQUAL 8)
+#   set(cuda_cutil_name cutil64)
+# else()
+#   set(cuda_cutil_name cutil32)
+# endif()
+
+# find_library(CUDA_CUT_LIBRARY
+#   NAMES cutil ${cuda_cutil_name}
+#   PATHS ${CUDA_SDK_SEARCH_PATH}
+#   # The new version of the sdk shows up in common/lib, but the old one is in lib
+#   PATH_SUFFIXES "common/lib" "lib"
+#   DOC "Location of cutil library"
+#   NO_DEFAULT_PATH
+#   )
+# # Now search system paths
+# find_library(CUDA_CUT_LIBRARY NAMES cutil ${cuda_cutil_name} DOC "Location of cutil library")
+# mark_as_advanced(CUDA_CUT_LIBRARY)
+# set(CUDA_CUT_LIBRARIES ${CUDA_CUT_LIBRARY})
+
+
+
+#############################
+# Check for required components
+set(CUDA_FOUND TRUE)
+
+set(CUDA_TOOLKIT_ROOT_DIR_INTERNAL "${CUDA_TOOLKIT_ROOT_DIR}" CACHE INTERNAL
+  "This is the value of the last time CUDA_TOOLKIT_ROOT_DIR was set successfully." FORCE)
+set(CUDA_TOOLKIT_TARGET_DIR_INTERNAL "${CUDA_TOOLKIT_TARGET_DIR}" CACHE INTERNAL
+  "This is the value of the last time CUDA_TOOLKIT_TARGET_DIR was set successfully." FORCE)
+set(CUDA_SDK_ROOT_DIR_INTERNAL "${CUDA_SDK_ROOT_DIR}" CACHE INTERNAL
+  "This is the value of the last time CUDA_SDK_ROOT_DIR was set successfully." FORCE)
+
+include(${CMAKE_CURRENT_LIST_DIR}/FindPackageHandleStandardArgs.cmake)
+
+find_package_handle_standard_args(CUDA
+  REQUIRED_VARS
+    CUDA_TOOLKIT_ROOT_DIR
+    CUDA_NVCC_EXECUTABLE
+    CUDA_INCLUDE_DIRS
+    ${CUDA_CUDART_LIBRARY_VAR}
+  VERSION_VAR
+    CUDA_VERSION
+  )
+
+
+
+###############################################################################
+###############################################################################
+# Macros
+###############################################################################
+###############################################################################
+
+###############################################################################
+# Add include directories to pass to the nvcc command.
+macro(CUDA_INCLUDE_DIRECTORIES)
+  foreach(dir ${ARGN})
+    list(APPEND CUDA_NVCC_INCLUDE_DIRS_USER ${dir})
+  endforeach()
+endmacro()
+
+
+##############################################################################
+cuda_find_helper_file(parse_cubin cmake)
+cuda_find_helper_file(make2cmake cmake)
+cuda_find_helper_file(run_nvcc cmake)
+include("${CMAKE_CURRENT_LIST_DIR}/FindCUDA/select_compute_arch.cmake")
+
+##############################################################################
+# Separate the OPTIONS out from the sources
+#
+macro(CUDA_GET_SOURCES_AND_OPTIONS _sources _cmake_options _options)
+  set( ${_sources} )
+  set( ${_cmake_options} )
+  set( ${_options} )
+  set( _found_options FALSE )
+  foreach(arg ${ARGN})
+    if("x${arg}" STREQUAL "xOPTIONS")
+      set( _found_options TRUE )
+    elseif(
+        "x${arg}" STREQUAL "xWIN32" OR
+        "x${arg}" STREQUAL "xMACOSX_BUNDLE" OR
+        "x${arg}" STREQUAL "xEXCLUDE_FROM_ALL" OR
+        "x${arg}" STREQUAL "xSTATIC" OR
+        "x${arg}" STREQUAL "xSHARED" OR
+        "x${arg}" STREQUAL "xMODULE"
+        )
+      list(APPEND ${_cmake_options} ${arg})
+    else()
+      if ( _found_options )
+        list(APPEND ${_options} ${arg})
+      else()
+        # Assume this is a file
+        list(APPEND ${_sources} ${arg})
+      endif()
+    endif()
+  endforeach()
+endmacro()
+
+##############################################################################
+# Parse the OPTIONS from ARGN and set the variables prefixed by _option_prefix
+#
+macro(CUDA_PARSE_NVCC_OPTIONS _option_prefix)
+  set( _found_config )
+  foreach(arg ${ARGN})
+    # Determine if we are dealing with a perconfiguration flag
+    foreach(config ${CUDA_configuration_types})
+      string(TOUPPER ${config} config_upper)
+      if (arg STREQUAL "${config_upper}")
+        set( _found_config _${arg})
+        # Set arg to nothing to keep it from being processed further
+        set( arg )
+      endif()
+    endforeach()
+
+    if ( arg )
+      list(APPEND ${_option_prefix}${_found_config} "${arg}")
+    endif()
+  endforeach()
+endmacro()
+
+##############################################################################
+# Helper to add the include directory for CUDA only once
+function(CUDA_ADD_CUDA_INCLUDE_ONCE)
+  get_directory_property(_include_directories INCLUDE_DIRECTORIES)
+  set(_add TRUE)
+  if(_include_directories)
+    foreach(dir ${_include_directories})
+      if("${dir}" STREQUAL "${CUDA_INCLUDE_DIRS}")
+        set(_add FALSE)
+      endif()
+    endforeach()
+  endif()
+  if(_add)
+    include_directories(${CUDA_INCLUDE_DIRS})
+  endif()
+endfunction()
+
+function(CUDA_BUILD_SHARED_LIBRARY shared_flag)
+  set(cmake_args ${ARGN})
+  # If SHARED, MODULE, or STATIC aren't already in the list of arguments, then
+  # add SHARED or STATIC based on the value of BUILD_SHARED_LIBS.
+  list(FIND cmake_args SHARED _cuda_found_SHARED)
+  list(FIND cmake_args MODULE _cuda_found_MODULE)
+  list(FIND cmake_args STATIC _cuda_found_STATIC)
+  if( _cuda_found_SHARED GREATER -1 OR
+      _cuda_found_MODULE GREATER -1 OR
+      _cuda_found_STATIC GREATER -1)
+    set(_cuda_build_shared_libs)
+  else()
+    if (BUILD_SHARED_LIBS)
+      set(_cuda_build_shared_libs SHARED)
+    else()
+      set(_cuda_build_shared_libs STATIC)
+    endif()
+  endif()
+  set(${shared_flag} ${_cuda_build_shared_libs} PARENT_SCOPE)
+endfunction()
+
+##############################################################################
+# Helper to avoid clashes of files with the same basename but different paths.
+# This doesn't attempt to do exactly what CMake internals do, which is to only
+# add this path when there is a conflict, since by the time a second collision
+# in names is detected it's already too late to fix the first one.  For
+# consistency sake the relative path will be added to all files.
+function(CUDA_COMPUTE_BUILD_PATH path build_path)
+  #message("CUDA_COMPUTE_BUILD_PATH([${path}] ${build_path})")
+  # Only deal with CMake style paths from here on out
+  file(TO_CMAKE_PATH "${path}" bpath)
+  if (IS_ABSOLUTE "${bpath}")
+    # Absolute paths are generally unnessary, especially if something like
+    # file(GLOB_RECURSE) is used to pick up the files.
+
+    string(FIND "${bpath}" "${CMAKE_CURRENT_BINARY_DIR}" _binary_dir_pos)
+    if (_binary_dir_pos EQUAL 0)
+      file(RELATIVE_PATH bpath "${CMAKE_CURRENT_BINARY_DIR}" "${bpath}")
+    else()
+      file(RELATIVE_PATH bpath "${CMAKE_CURRENT_SOURCE_DIR}" "${bpath}")
+    endif()
+  endif()
+
+  # This recipe is from cmLocalGenerator::CreateSafeUniqueObjectFileName in the
+  # CMake source.
+
+  # Remove leading /
+  string(REGEX REPLACE "^[/]+" "" bpath "${bpath}")
+  # Avoid absolute paths by removing ':'
+  string(REPLACE ":" "_" bpath "${bpath}")
+  # Avoid relative paths that go up the tree
+  string(REPLACE "../" "__/" bpath "${bpath}")
+  # Avoid spaces
+  string(REPLACE " " "_" bpath "${bpath}")
+
+  # Strip off the filename.  I wait until here to do it, since removin the
+  # basename can make a path that looked like path/../basename turn into
+  # path/.. (notice the trailing slash).
+  get_filename_component(bpath "${bpath}" PATH)
+
+  set(${build_path} "${bpath}" PARENT_SCOPE)
+  #message("${build_path} = ${bpath}")
+endfunction()
+
+##############################################################################
+# This helper macro populates the following variables and setups up custom
+# commands and targets to invoke the nvcc compiler to generate C or PTX source
+# dependent upon the format parameter.  The compiler is invoked once with -M
+# to generate a dependency file and a second time with -cuda or -ptx to generate
+# a .cpp or .ptx file.
+# INPUT:
+#   cuda_target         - Target name
+#   format              - PTX, CUBIN, FATBIN or OBJ
+#   FILE1 .. FILEN      - The remaining arguments are the sources to be wrapped.
+#   OPTIONS             - Extra options to NVCC
+# OUTPUT:
+#   generated_files     - List of generated files
+##############################################################################
+##############################################################################
+
+macro(CUDA_WRAP_SRCS cuda_target format generated_files)
+
+  # Put optional arguments in list.
+  set(_argn_list "${ARGN}")
+  # If one of the given optional arguments is "PHONY", make a note of it, then
+  # remove it from the list.
+  list(FIND _argn_list "PHONY" _phony_idx)
+  if("${_phony_idx}" GREATER "-1")
+    set(_target_is_phony true)
+    list(REMOVE_AT _argn_list ${_phony_idx})
+  else()
+    set(_target_is_phony false)
+  endif()
+
+  # If CMake doesn't support separable compilation, complain
+  if(CUDA_SEPARABLE_COMPILATION AND CMAKE_VERSION VERSION_LESS "2.8.10.1")
+    message(SEND_ERROR "CUDA_SEPARABLE_COMPILATION isn't supported for CMake versions less than 2.8.10.1")
+  endif()
+
+  # Set up all the command line flags here, so that they can be overridden on a per target basis.
+
+  set(nvcc_flags "")
+
+  # Emulation if the card isn't present.
+  if (CUDA_BUILD_EMULATION)
+    # Emulation.
+    set(nvcc_flags ${nvcc_flags} --device-emulation -D_DEVICEEMU -g)
+  else()
+    # Device mode.  No flags necessary.
+  endif()
+
+  if(CUDA_HOST_COMPILATION_CPP)
+    set(CUDA_C_OR_CXX CXX)
+  else()
+    message(WARNING "--host-compilation flag is deprecated in CUDA version >= 3.0.  Removing --host-compilation C flag" )
+    set(CUDA_C_OR_CXX C)
+  endif()
+
+  set(generated_extension ${CMAKE_${CUDA_C_OR_CXX}_OUTPUT_EXTENSION})
+
+  if(CUDA_64_BIT_DEVICE_CODE)
+    set(nvcc_flags ${nvcc_flags} -m64)
+  else()
+    set(nvcc_flags ${nvcc_flags} -m32)
+  endif()
+
+  if(CUDA_TARGET_CPU_ARCH)
+    set(nvcc_flags ${nvcc_flags} "--target-cpu-architecture=${CUDA_TARGET_CPU_ARCH}")
+  endif()
+
+  # This needs to be passed in at this stage, because VS needs to fill out the
+  # various macros from within VS.  Note that CCBIN is only used if
+  # -ccbin or --compiler-bindir isn't used and CUDA_HOST_COMPILER matches
+  # _CUDA_MSVC_HOST_COMPILER
+  if(CMAKE_GENERATOR MATCHES "Visual Studio")
+    set(ccbin_flags -D "\"CCBIN:PATH=${_CUDA_MSVC_HOST_COMPILER}\"" )
+  else()
+    set(ccbin_flags)
+  endif()
+
+  # Figure out which configure we will use and pass that in as an argument to
+  # the script.  We need to defer the decision until compilation time, because
+  # for VS projects we won't know if we are making a debug or release build
+  # until build time.
+  if(CMAKE_GENERATOR MATCHES "Visual Studio")
+    set( CUDA_build_configuration "$(ConfigurationName)" )
+  else()
+    set( CUDA_build_configuration "${CMAKE_BUILD_TYPE}")
+  endif()
+
+  # Initialize our list of includes with the user ones followed by the CUDA system ones.
+  set(CUDA_NVCC_INCLUDE_DIRS ${CUDA_NVCC_INCLUDE_DIRS_USER} "${CUDA_INCLUDE_DIRS}")
+  if(_target_is_phony)
+    # If the passed in target name isn't a real target (i.e., this is from a call to one of the
+    # cuda_compile_* functions), need to query directory properties to get include directories
+    # and compile definitions.
+    get_directory_property(_dir_include_dirs INCLUDE_DIRECTORIES)
+    get_directory_property(_dir_compile_defs COMPILE_DEFINITIONS)
+
+    list(APPEND CUDA_NVCC_INCLUDE_DIRS "${_dir_include_dirs}")
+    set(CUDA_NVCC_COMPILE_DEFINITIONS "${_dir_compile_defs}")
+  else()
+    # Append the include directories for this target via generator expression, which is
+    # expanded by the FILE(GENERATE) call below.  This generator expression captures all
+    # include dirs set by the user, whether via directory properties or target properties
+    list(APPEND CUDA_NVCC_INCLUDE_DIRS "$")
+
+    # Do the same thing with compile definitions
+    set(CUDA_NVCC_COMPILE_DEFINITIONS "$")
+  endif()
+
+
+  # Reset these variables
+  set(CUDA_WRAP_OPTION_NVCC_FLAGS)
+  foreach(config ${CUDA_configuration_types})
+    string(TOUPPER ${config} config_upper)
+    set(CUDA_WRAP_OPTION_NVCC_FLAGS_${config_upper})
+  endforeach()
+
+  CUDA_GET_SOURCES_AND_OPTIONS(_cuda_wrap_sources _cuda_wrap_cmake_options _cuda_wrap_options ${_argn_list})
+  CUDA_PARSE_NVCC_OPTIONS(CUDA_WRAP_OPTION_NVCC_FLAGS ${_cuda_wrap_options})
+
+  # Figure out if we are building a shared library.  BUILD_SHARED_LIBS is
+  # respected in CUDA_ADD_LIBRARY.
+  set(_cuda_build_shared_libs FALSE)
+  # SHARED, MODULE
+  list(FIND _cuda_wrap_cmake_options SHARED _cuda_found_SHARED)
+  list(FIND _cuda_wrap_cmake_options MODULE _cuda_found_MODULE)
+  if(_cuda_found_SHARED GREATER -1 OR _cuda_found_MODULE GREATER -1)
+    set(_cuda_build_shared_libs TRUE)
+  endif()
+  # STATIC
+  list(FIND _cuda_wrap_cmake_options STATIC _cuda_found_STATIC)
+  if(_cuda_found_STATIC GREATER -1)
+    set(_cuda_build_shared_libs FALSE)
+  endif()
+
+  # CUDA_HOST_FLAGS
+  if(_cuda_build_shared_libs)
+    # If we are setting up code for a shared library, then we need to add extra flags for
+    # compiling objects for shared libraries.
+    set(CUDA_HOST_SHARED_FLAGS ${CMAKE_SHARED_LIBRARY_${CUDA_C_OR_CXX}_FLAGS})
+  else()
+    set(CUDA_HOST_SHARED_FLAGS)
+  endif()
+
+  macro(_filter_blocklisted_host_flags CUDA_FLAGS)
+    string(REGEX REPLACE "[ \t]+" ";" ${CUDA_FLAGS} "${${CUDA_FLAGS}}")
+    foreach(_blacklisted ${CUDA_PROPAGATE_HOST_FLAGS_BLACKLIST})
+      list(REMOVE_ITEM ${CUDA_FLAGS} "${_blacklisted}")
+    endforeach()
+    string(REPLACE ";" " " ${CUDA_FLAGS} "${${CUDA_FLAGS}}")
+  endmacro()
+
+  # Only add the CMAKE_{C,CXX}_FLAGS if we are propagating host flags.  We
+  # always need to set the SHARED_FLAGS, though.
+  if(CUDA_PROPAGATE_HOST_FLAGS)
+    set(_cuda_C_FLAGS "${CMAKE_${CUDA_C_OR_CXX}_FLAGS}")
+    _filter_blocklisted_host_flags(_cuda_C_FLAGS)
+    set(_cuda_host_flags "set(CMAKE_HOST_FLAGS ${_cuda_C_FLAGS} ${CUDA_HOST_SHARED_FLAGS})")
+  else()
+    set(_cuda_host_flags "set(CMAKE_HOST_FLAGS ${CUDA_HOST_SHARED_FLAGS})")
+  endif()
+
+  set(_cuda_nvcc_flags_config "# Build specific configuration flags")
+  # Loop over all the configuration types to generate appropriate flags for run_nvcc.cmake
+  foreach(config ${CUDA_configuration_types})
+    string(TOUPPER ${config} config_upper)
+    # CMAKE_FLAGS are strings and not lists.  By not putting quotes around CMAKE_FLAGS
+    # we convert the strings to lists (like we want).
+
+    if(CUDA_PROPAGATE_HOST_FLAGS)
+      # nvcc chokes on -g3 in versions previous to 3.0, so replace it with -g
+      set(_cuda_fix_g3 FALSE)
+
+      set(_cuda_C_FLAGS "${CMAKE_${CUDA_C_OR_CXX}_FLAGS_${config_upper}}")
+      _filter_blocklisted_host_flags(_cuda_C_FLAGS)
+      if(_cuda_fix_g3)
+        string(REPLACE "-g3" "-g" _cuda_C_FLAGS "${_cuda_C_FLAGS}")
+      endif()
+
+      string(APPEND _cuda_host_flags "\nset(CMAKE_HOST_FLAGS_${config_upper} ${_cuda_C_FLAGS})")
+    endif()
+
+    # Note that if we ever want CUDA_NVCC_FLAGS_ to be string (instead of a list
+    # like it is currently), we can remove the quotes around the
+    # ${CUDA_NVCC_FLAGS_${config_upper}} variable like the CMAKE_HOST_FLAGS_ variable.
+    string(APPEND _cuda_nvcc_flags_config "\nset(CUDA_NVCC_FLAGS_${config_upper} ${CUDA_NVCC_FLAGS_${config_upper}} ;; ${CUDA_WRAP_OPTION_NVCC_FLAGS_${config_upper}})")
+  endforeach()
+
+  # Process the C++14 flag.  If the host sets the flag, we need to add it to nvcc and
+  # remove it from the host. This is because -Xcompile -std=c++ will choke nvcc (it uses
+  # the C preprocessor).  In order to get this to work correctly, we need to use nvcc's
+  # specific c++14 flag.
+  if( "${_cuda_host_flags}" MATCHES "-std=c\\+\\+11")
+    # Add the c++14 flag to nvcc if it isn't already present.  Note that we only look at
+    # the main flag instead of the configuration specific flags.
+    if( NOT "${CUDA_NVCC_FLAGS}" MATCHES "-std=c\\+\\+14" )
+      list(APPEND nvcc_flags --std c++14)
+    endif()
+    string(REGEX REPLACE "[-]+std=c\\+\\+14" "" _cuda_host_flags "${_cuda_host_flags}")
+  endif()
+
+  if(_cuda_build_shared_libs)
+    list(APPEND nvcc_flags "-D${cuda_target}_EXPORTS")
+  endif()
+
+  # Reset the output variable
+  set(_cuda_wrap_generated_files "")
+
+  # Iterate over the macro arguments and create custom
+  # commands for all the .cu files.
+  foreach(file ${_argn_list})
+    # Ignore any file marked as a HEADER_FILE_ONLY
+    get_source_file_property(_is_header ${file} HEADER_FILE_ONLY)
+    # Allow per source file overrides of the format.  Also allows compiling non-.cu files.
+    get_source_file_property(_cuda_source_format ${file} CUDA_SOURCE_PROPERTY_FORMAT)
+    if((${file} MATCHES "\\.cu$" OR _cuda_source_format) AND NOT _is_header)
+
+      if(NOT _cuda_source_format)
+        set(_cuda_source_format ${format})
+      endif()
+      # If file isn't a .cu file, we need to tell nvcc to treat it as such.
+      if(NOT file MATCHES "\\.cu$")
+        set(cuda_language_flag -x=cu)
+      else()
+        set(cuda_language_flag)
+      endif()
+
+      if( ${_cuda_source_format} MATCHES "OBJ")
+        set( cuda_compile_to_external_module OFF )
+      else()
+        set( cuda_compile_to_external_module ON )
+        if( ${_cuda_source_format} MATCHES "PTX" )
+          set( cuda_compile_to_external_module_type "ptx" )
+        elseif( ${_cuda_source_format} MATCHES "CUBIN")
+          set( cuda_compile_to_external_module_type "cubin" )
+        elseif( ${_cuda_source_format} MATCHES "FATBIN")
+          set( cuda_compile_to_external_module_type "fatbin" )
+        else()
+          message( FATAL_ERROR "Invalid format flag passed to CUDA_WRAP_SRCS or set with CUDA_SOURCE_PROPERTY_FORMAT file property for file '${file}': '${_cuda_source_format}'.  Use OBJ, PTX, CUBIN or FATBIN.")
+        endif()
+      endif()
+
+      if(cuda_compile_to_external_module)
+        # Don't use any of the host compilation flags for PTX targets.
+        set(CUDA_HOST_FLAGS)
+        set(CUDA_NVCC_FLAGS_CONFIG)
+      else()
+        set(CUDA_HOST_FLAGS ${_cuda_host_flags})
+        set(CUDA_NVCC_FLAGS_CONFIG ${_cuda_nvcc_flags_config})
+      endif()
+
+      # Determine output directory
+      cuda_compute_build_path("${file}" cuda_build_path)
+      set(cuda_compile_intermediate_directory "${CMAKE_CURRENT_BINARY_DIR}/CMakeFiles/${cuda_target}.dir/${cuda_build_path}")
+      if(CUDA_GENERATED_OUTPUT_DIR)
+        set(cuda_compile_output_dir "${CUDA_GENERATED_OUTPUT_DIR}")
+      else()
+        if ( cuda_compile_to_external_module )
+          set(cuda_compile_output_dir "${CMAKE_CURRENT_BINARY_DIR}")
+        else()
+          set(cuda_compile_output_dir "${cuda_compile_intermediate_directory}")
+        endif()
+      endif()
+
+      # Add a custom target to generate a c or ptx file. ######################
+
+      get_filename_component( basename ${file} NAME )
+      if( cuda_compile_to_external_module )
+        set(generated_file_path "${cuda_compile_output_dir}")
+        set(generated_file_basename "${cuda_target}_generated_${basename}.${cuda_compile_to_external_module_type}")
+        set(format_flag "-${cuda_compile_to_external_module_type}")
+        file(MAKE_DIRECTORY "${cuda_compile_output_dir}")
+      else()
+        set(generated_file_path "${cuda_compile_output_dir}/${CMAKE_CFG_INTDIR}")
+        set(generated_file_basename "${cuda_target}_generated_${basename}${generated_extension}")
+        if(CUDA_SEPARABLE_COMPILATION)
+          set(format_flag "-dc")
+        else()
+          set(format_flag "-c")
+        endif()
+      endif()
+
+      # Set all of our file names.  Make sure that whatever filenames that have
+      # generated_file_path in them get passed in through as a command line
+      # argument, so that the ${CMAKE_CFG_INTDIR} gets expanded at run time
+      # instead of configure time.
+      set(generated_file "${generated_file_path}/${generated_file_basename}")
+      set(cmake_dependency_file "${cuda_compile_intermediate_directory}/${generated_file_basename}.depend")
+      set(NVCC_generated_dependency_file "${cuda_compile_intermediate_directory}/${generated_file_basename}.NVCC-depend")
+      set(generated_cubin_file "${generated_file_path}/${generated_file_basename}.cubin.txt")
+      set(custom_target_script_pregen "${cuda_compile_intermediate_directory}/${generated_file_basename}.cmake.pre-gen")
+      set(custom_target_script "${cuda_compile_intermediate_directory}/${generated_file_basename}$<$>:.$>.cmake")
+
+      # Setup properties for obj files:
+      if( NOT cuda_compile_to_external_module )
+        set_source_files_properties("${generated_file}"
+          PROPERTIES
+          EXTERNAL_OBJECT true # This is an object file not to be compiled, but only be linked.
+          )
+      endif()
+
+      # Don't add CMAKE_CURRENT_SOURCE_DIR if the path is already an absolute path.
+      get_filename_component(file_path "${file}" PATH)
+      if(IS_ABSOLUTE "${file_path}")
+        set(source_file "${file}")
+      else()
+        set(source_file "${CMAKE_CURRENT_SOURCE_DIR}/${file}")
+      endif()
+
+      if( NOT cuda_compile_to_external_module AND CUDA_SEPARABLE_COMPILATION)
+        list(APPEND ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS "${generated_file}")
+      endif()
+
+      # Bring in the dependencies.  Creates a variable CUDA_NVCC_DEPEND #######
+      cuda_include_nvcc_dependencies(${cmake_dependency_file})
+
+      # Convenience string for output #########################################
+      if(CUDA_BUILD_EMULATION)
+        set(cuda_build_type "Emulation")
+      else()
+        set(cuda_build_type "Device")
+      endif()
+
+      # Build the NVCC made dependency file ###################################
+      set(build_cubin OFF)
+      if ( NOT CUDA_BUILD_EMULATION AND CUDA_BUILD_CUBIN )
+         if ( NOT cuda_compile_to_external_module )
+           set ( build_cubin ON )
+         endif()
+      endif()
+
+      # Configure the build script
+      configure_file("${CUDA_run_nvcc}" "${custom_target_script_pregen}" @ONLY)
+      file(GENERATE
+        OUTPUT "${custom_target_script}"
+        INPUT "${custom_target_script_pregen}"
+        )
+
+      # So if a user specifies the same cuda file as input more than once, you
+      # can have bad things happen with dependencies.  Here we check an option
+      # to see if this is the behavior they want.
+      if(CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE)
+        set(main_dep MAIN_DEPENDENCY ${source_file})
+      else()
+        set(main_dep DEPENDS ${source_file})
+      endif()
+
+      if(CUDA_VERBOSE_BUILD)
+        set(verbose_output ON)
+      elseif(CMAKE_GENERATOR MATCHES "Makefiles")
+        set(verbose_output "$(VERBOSE)")
+      # This condition lets us also turn on verbose output when someone
+      # specifies CMAKE_VERBOSE_MAKEFILE, even if the generator isn't
+      # the Makefiles generator (this is important for us, Ninja users.)
+      elseif(CMAKE_VERBOSE_MAKEFILE)
+        set(verbose_output ON)
+      else()
+        set(verbose_output OFF)
+      endif()
+
+      # Create up the comment string
+      file(RELATIVE_PATH generated_file_relative_path "${CMAKE_BINARY_DIR}" "${generated_file}")
+      if(cuda_compile_to_external_module)
+        set(cuda_build_comment_string "Building NVCC ${cuda_compile_to_external_module_type} file ${generated_file_relative_path}")
+      else()
+        set(cuda_build_comment_string "Building NVCC (${cuda_build_type}) object ${generated_file_relative_path}")
+      endif()
+
+      set(_verbatim VERBATIM)
+      if(ccbin_flags MATCHES "\\$\\(VCInstallDir\\)")
+        set(_verbatim "")
+      endif()
+
+      # Build the generated file and dependency file ##########################
+      add_custom_command(
+        OUTPUT ${generated_file}
+        # These output files depend on the source_file and the contents of cmake_dependency_file
+        ${main_dep}
+        DEPENDS ${CUDA_NVCC_DEPEND}
+        DEPENDS ${custom_target_script}
+        # Make sure the output directory exists before trying to write to it.
+        COMMAND ${CMAKE_COMMAND} -E make_directory "${generated_file_path}"
+        COMMAND ${CMAKE_COMMAND} ARGS
+          -D verbose:BOOL=${verbose_output}
+          ${ccbin_flags}
+          -D build_configuration:STRING=${CUDA_build_configuration}
+          -D "generated_file:STRING=${generated_file}"
+          -D "generated_cubin_file:STRING=${generated_cubin_file}"
+          -P "${custom_target_script}"
+        WORKING_DIRECTORY "${cuda_compile_intermediate_directory}"
+        COMMENT "${cuda_build_comment_string}"
+        ${_verbatim}
+        )
+
+      # Make sure the build system knows the file is generated.
+      set_source_files_properties(${generated_file} PROPERTIES GENERATED TRUE)
+
+      list(APPEND _cuda_wrap_generated_files ${generated_file})
+
+      # Add the other files that we want cmake to clean on a cleanup ##########
+      list(APPEND CUDA_ADDITIONAL_CLEAN_FILES "${cmake_dependency_file}")
+      list(REMOVE_DUPLICATES CUDA_ADDITIONAL_CLEAN_FILES)
+      set(CUDA_ADDITIONAL_CLEAN_FILES ${CUDA_ADDITIONAL_CLEAN_FILES} CACHE INTERNAL "List of intermediate files that are part of the cuda dependency scanning.")
+
+    endif()
+  endforeach()
+
+  # Set the return parameter
+  set(${generated_files} ${_cuda_wrap_generated_files})
+endmacro()
+
+function(_cuda_get_important_host_flags important_flags flag_string)
+  if(CMAKE_GENERATOR MATCHES "Visual Studio")
+    string(REGEX MATCHALL "/M[DT][d]?" flags "${flag_string}")
+    list(APPEND ${important_flags} ${flags})
+  else()
+    string(REGEX MATCHALL "-fPIC" flags "${flag_string}")
+    list(APPEND ${important_flags} ${flags})
+  endif()
+  set(${important_flags} ${${important_flags}} PARENT_SCOPE)
+endfunction()
+
+###############################################################################
+###############################################################################
+# Separable Compilation Link
+###############################################################################
+###############################################################################
+
+# Compute the filename to be used by CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS
+function(CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME output_file_var cuda_target object_files)
+  if (object_files)
+    set(generated_extension ${CMAKE_${CUDA_C_OR_CXX}_OUTPUT_EXTENSION})
+    set(output_file "${CMAKE_CURRENT_BINARY_DIR}/CMakeFiles/${cuda_target}.dir/${CMAKE_CFG_INTDIR}/${cuda_target}_intermediate_link${generated_extension}")
+  else()
+    set(output_file)
+  endif()
+
+  set(${output_file_var} "${output_file}" PARENT_SCOPE)
+endfunction()
+
+# Setup the build rule for the separable compilation intermediate link file.
+function(CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS output_file cuda_target options object_files)
+  if (object_files)
+
+    set_source_files_properties("${output_file}"
+      PROPERTIES
+      EXTERNAL_OBJECT TRUE # This is an object file not to be compiled, but only
+                           # be linked.
+      GENERATED TRUE       # This file is generated during the build
+      )
+
+    # For now we are ignoring all the configuration specific flags.
+    set(nvcc_flags)
+    CUDA_PARSE_NVCC_OPTIONS(nvcc_flags ${options})
+    if(CUDA_64_BIT_DEVICE_CODE)
+      list(APPEND nvcc_flags -m64)
+    else()
+      list(APPEND nvcc_flags -m32)
+    endif()
+    # If -ccbin, --compiler-bindir has been specified, don't do anything.  Otherwise add it here.
+    list( FIND nvcc_flags "-ccbin" ccbin_found0 )
+    list( FIND nvcc_flags "--compiler-bindir" ccbin_found1 )
+    if( ccbin_found0 LESS 0 AND ccbin_found1 LESS 0 AND CUDA_HOST_COMPILER )
+      # Match VERBATIM check below.
+      if(CUDA_HOST_COMPILER MATCHES "\\$\\(VCInstallDir\\)")
+        list(APPEND nvcc_flags -ccbin "\"${CUDA_HOST_COMPILER}\"")
+      else()
+        list(APPEND nvcc_flags -ccbin "${CUDA_HOST_COMPILER}")
+      endif()
+    endif()
+
+    # Create a list of flags specified by CUDA_NVCC_FLAGS_${CONFIG} and CMAKE_${CUDA_C_OR_CXX}_FLAGS*
+    set(config_specific_flags)
+    set(flags)
+    foreach(config ${CUDA_configuration_types})
+      string(TOUPPER ${config} config_upper)
+      # Add config specific flags
+      foreach(f ${CUDA_NVCC_FLAGS_${config_upper}})
+        list(APPEND config_specific_flags $<$:${f}>)
+      endforeach()
+      set(important_host_flags)
+      _cuda_get_important_host_flags(important_host_flags "${CMAKE_${CUDA_C_OR_CXX}_FLAGS_${config_upper}}")
+      foreach(f ${important_host_flags})
+        list(APPEND flags $<$:-Xcompiler> $<$:${f}>)
+      endforeach()
+    endforeach()
+    # Add CMAKE_${CUDA_C_OR_CXX}_FLAGS
+    set(important_host_flags)
+    _cuda_get_important_host_flags(important_host_flags "${CMAKE_${CUDA_C_OR_CXX}_FLAGS}")
+    foreach(f ${important_host_flags})
+      list(APPEND flags -Xcompiler ${f})
+    endforeach()
+
+    # Add our general CUDA_NVCC_FLAGS with the configuration specifig flags
+    set(nvcc_flags ${CUDA_NVCC_FLAGS} ${config_specific_flags} ${nvcc_flags})
+
+    file(RELATIVE_PATH output_file_relative_path "${CMAKE_BINARY_DIR}" "${output_file}")
+
+    # Some generators don't handle the multiple levels of custom command
+    # dependencies correctly (obj1 depends on file1, obj2 depends on obj1), so
+    # we work around that issue by compiling the intermediate link object as a
+    # pre-link custom command in that situation.
+    set(do_obj_build_rule TRUE)
+    if (MSVC_VERSION GREATER 1599 AND MSVC_VERSION LESS 1800)
+      # VS 2010 and 2012 have this problem.
+      set(do_obj_build_rule FALSE)
+    endif()
+
+    set(_verbatim VERBATIM)
+    if(nvcc_flags MATCHES "\\$\\(VCInstallDir\\)")
+      set(_verbatim "")
+    endif()
+
+    if (do_obj_build_rule)
+      add_custom_command(
+        OUTPUT ${output_file}
+        DEPENDS ${object_files}
+        COMMAND ${CUDA_NVCC_EXECUTABLE} ${nvcc_flags} -dlink ${object_files} -o ${output_file}
+        ${flags}
+        COMMENT "Building NVCC intermediate link file ${output_file_relative_path}"
+        COMMAND_EXPAND_LISTS
+        ${_verbatim}
+        )
+    else()
+      get_filename_component(output_file_dir "${output_file}" DIRECTORY)
+      add_custom_command(
+        TARGET ${cuda_target}
+        PRE_LINK
+        COMMAND ${CMAKE_COMMAND} -E echo "Building NVCC intermediate link file ${output_file_relative_path}"
+        COMMAND ${CMAKE_COMMAND} -E make_directory "${output_file_dir}"
+        COMMAND ${CUDA_NVCC_EXECUTABLE} ${nvcc_flags} ${flags} -dlink ${object_files} -o "${output_file}"
+        COMMAND_EXPAND_LISTS
+        ${_verbatim}
+        )
+    endif()
+ endif()
+endfunction()
+
+###############################################################################
+###############################################################################
+# ADD LIBRARY
+###############################################################################
+###############################################################################
+macro(CUDA_ADD_LIBRARY cuda_target)
+
+  CUDA_ADD_CUDA_INCLUDE_ONCE()
+
+  # Separate the sources from the options
+  CUDA_GET_SOURCES_AND_OPTIONS(_sources _cmake_options _options ${ARGN})
+  CUDA_BUILD_SHARED_LIBRARY(_cuda_shared_flag ${ARGN})
+  # Create custom commands and targets for each file.
+  CUDA_WRAP_SRCS( ${cuda_target} OBJ _generated_files ${_sources}
+    ${_cmake_options} ${_cuda_shared_flag}
+    OPTIONS ${_options} )
+
+  # Compute the file name of the intermedate link file used for separable
+  # compilation.
+  CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+
+  # Add the library.
+  add_library(${cuda_target} ${_cmake_options}
+    ${_generated_files}
+    ${_sources}
+    ${link_file}
+    )
+
+  # Add a link phase for the separable compilation if it has been enabled.  If
+  # it has been enabled then the ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS
+  # variable will have been defined.
+  CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS("${link_file}" ${cuda_target} "${_options}" "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+
+  target_link_libraries(${cuda_target} ${CUDA_LINK_LIBRARIES_KEYWORD}
+    ${CUDA_LIBRARIES}
+    )
+
+  if(CUDA_SEPARABLE_COMPILATION)
+    target_link_libraries(${cuda_target} ${CUDA_LINK_LIBRARIES_KEYWORD}
+      ${CUDA_cudadevrt_LIBRARY}
+      )
+  endif()
+
+  # We need to set the linker language based on what the expected generated file
+  # would be. CUDA_C_OR_CXX is computed based on CUDA_HOST_COMPILATION_CPP.
+  set_target_properties(${cuda_target}
+    PROPERTIES
+    LINKER_LANGUAGE ${CUDA_C_OR_CXX}
+    )
+
+endmacro()
+
+
+###############################################################################
+###############################################################################
+# ADD EXECUTABLE
+###############################################################################
+###############################################################################
+macro(CUDA_ADD_EXECUTABLE cuda_target)
+
+  CUDA_ADD_CUDA_INCLUDE_ONCE()
+
+  # Separate the sources from the options
+  CUDA_GET_SOURCES_AND_OPTIONS(_sources _cmake_options _options ${ARGN})
+  # Create custom commands and targets for each file.
+  CUDA_WRAP_SRCS( ${cuda_target} OBJ _generated_files ${_sources} OPTIONS ${_options} )
+
+  # Compute the file name of the intermedate link file used for separable
+  # compilation.
+  CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME(link_file ${cuda_target} "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+
+  # Add the library.
+  add_executable(${cuda_target} ${_cmake_options}
+    ${_generated_files}
+    ${_sources}
+    ${link_file}
+    )
+
+  # Add a link phase for the separable compilation if it has been enabled.  If
+  # it has been enabled then the ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS
+  # variable will have been defined.
+  CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS("${link_file}" ${cuda_target} "${_options}" "${${cuda_target}_SEPARABLE_COMPILATION_OBJECTS}")
+
+  target_link_libraries(${cuda_target} ${CUDA_LINK_LIBRARIES_KEYWORD}
+    ${CUDA_LIBRARIES}
+    )
+
+  # We need to set the linker language based on what the expected generated file
+  # would be. CUDA_C_OR_CXX is computed based on CUDA_HOST_COMPILATION_CPP.
+  set_target_properties(${cuda_target}
+    PROPERTIES
+    LINKER_LANGUAGE ${CUDA_C_OR_CXX}
+    )
+
+endmacro()
+
+
+###############################################################################
+###############################################################################
+# (Internal) helper for manually added cuda source files with specific targets
+###############################################################################
+###############################################################################
+macro(cuda_compile_base cuda_target format generated_files)
+  # Update a counter in this directory, to keep phony target names unique.
+  set(_cuda_target "${cuda_target}")
+  get_property(_counter DIRECTORY PROPERTY _cuda_internal_phony_counter)
+  if(_counter)
+    math(EXPR _counter "${_counter} + 1")
+  else()
+    set(_counter 1)
+  endif()
+  string(APPEND _cuda_target "_${_counter}")
+  set_property(DIRECTORY PROPERTY _cuda_internal_phony_counter ${_counter})
+
+  # Separate the sources from the options
+  CUDA_GET_SOURCES_AND_OPTIONS(_sources _cmake_options _options ${ARGN})
+
+  # Create custom commands and targets for each file.
+  CUDA_WRAP_SRCS( ${_cuda_target} ${format} _generated_files ${_sources}
+                  ${_cmake_options} OPTIONS ${_options} PHONY)
+
+  set( ${generated_files} ${_generated_files})
+
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA COMPILE
+###############################################################################
+###############################################################################
+macro(CUDA_COMPILE generated_files)
+  cuda_compile_base(cuda_compile OBJ ${generated_files} ${ARGN})
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA COMPILE PTX
+###############################################################################
+###############################################################################
+macro(CUDA_COMPILE_PTX generated_files)
+  cuda_compile_base(cuda_compile_ptx PTX ${generated_files} ${ARGN})
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA COMPILE FATBIN
+###############################################################################
+###############################################################################
+macro(CUDA_COMPILE_FATBIN generated_files)
+  cuda_compile_base(cuda_compile_fatbin FATBIN ${generated_files} ${ARGN})
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA COMPILE CUBIN
+###############################################################################
+###############################################################################
+macro(CUDA_COMPILE_CUBIN generated_files)
+  cuda_compile_base(cuda_compile_cubin CUBIN ${generated_files} ${ARGN})
+endmacro()
+
+
+###############################################################################
+###############################################################################
+# CUDA ADD CUFFT TO TARGET
+###############################################################################
+###############################################################################
+macro(CUDA_ADD_CUFFT_TO_TARGET target)
+  if (CUDA_BUILD_EMULATION)
+    target_link_libraries(${target} ${CUDA_LINK_LIBRARIES_KEYWORD} ${CUDA_cufftemu_LIBRARY})
+  else()
+    target_link_libraries(${target} ${CUDA_LINK_LIBRARIES_KEYWORD} ${CUDA_cufft_LIBRARY})
+  endif()
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA ADD CUBLAS TO TARGET
+###############################################################################
+###############################################################################
+macro(CUDA_ADD_CUBLAS_TO_TARGET target)
+  if (CUDA_BUILD_EMULATION)
+    target_link_libraries(${target} ${CUDA_LINK_LIBRARIES_KEYWORD} ${CUDA_cublasemu_LIBRARY})
+  else()
+    target_link_libraries(${target} ${CUDA_LINK_LIBRARIES_KEYWORD} ${CUDA_cublas_LIBRARY} ${CUDA_cublas_device_LIBRARY} ${CUDA_cublasLt_LIBRARY})
+  endif()
+endmacro()
+
+###############################################################################
+###############################################################################
+# CUDA BUILD CLEAN TARGET
+###############################################################################
+###############################################################################
+macro(CUDA_BUILD_CLEAN_TARGET)
+  # Call this after you add all your CUDA targets, and you will get a
+  # convenience target.  You should also make clean after running this target
+  # to get the build system to generate all the code again.
+
+  set(cuda_clean_target_name clean_cuda_depends)
+  if (CMAKE_GENERATOR MATCHES "Visual Studio")
+    string(TOUPPER ${cuda_clean_target_name} cuda_clean_target_name)
+  endif()
+  add_custom_target(${cuda_clean_target_name}
+    COMMAND ${CMAKE_COMMAND} -E remove ${CUDA_ADDITIONAL_CLEAN_FILES})
+
+  # Clear out the variable, so the next time we configure it will be empty.
+  # This is useful so that the files won't persist in the list after targets
+  # have been removed.
+  set(CUDA_ADDITIONAL_CLEAN_FILES "" CACHE INTERNAL "List of intermediate files that are part of the cuda dependency scanning.")
+endmacro()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/make2cmake.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/make2cmake.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..580f24a400d8c5662ec572c4631db9e3e47645d9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/make2cmake.cmake
@@ -0,0 +1,106 @@
+#  James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#  Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html
+#
+#  Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#  Copyright (c) 2007-2009
+#  Scientific Computing and Imaging Institute, University of Utah
+#
+#  This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#  for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+#
+
+#######################################################################
+# This converts a file written in makefile syntax into one that can be included
+# by CMake.
+
+# Input variables
+#
+# verbose:BOOL=<>          OFF: Be as quiet as possible (default)
+#                          ON : Extra output
+#
+# input_file:FILEPATH=<>   Path to dependency file in makefile format
+#
+# output_file:FILEPATH=<>  Path to file with dependencies in CMake readable variable
+#
+
+file(READ ${input_file} depend_text)
+
+if (NOT "${depend_text}" STREQUAL "")
+
+  # message("FOUND DEPENDS")
+
+  string(REPLACE "\\ " " " depend_text ${depend_text})
+
+  # This works for the nvcc -M generated dependency files.
+  string(REGEX REPLACE "^.* : " "" depend_text ${depend_text})
+  string(REGEX REPLACE "[ \\\\]*\n" ";" depend_text ${depend_text})
+
+  set(dependency_list "")
+
+  foreach(file ${depend_text})
+
+    string(REGEX REPLACE "^ +" "" file ${file})
+
+    # OK, now if we had a UNC path, nvcc has a tendency to only output the first '/'
+    # instead of '//'.  Here we will test to see if the file exists, if it doesn't then
+    # try to prepend another '/' to the path and test again.  If it still fails remove the
+    # path.
+
+    if(NOT EXISTS "${file}")
+      if (EXISTS "/${file}")
+        set(file "/${file}")
+      else()
+        if(verbose)
+          message(WARNING " Removing non-existent dependency file: ${file}")
+        endif()
+        set(file "")
+      endif()
+    endif()
+
+    # Make sure we check to see if we have a file, before asking if it is not a directory.
+    # if(NOT IS_DIRECTORY "") will return TRUE.
+    if(file AND NOT IS_DIRECTORY "${file}")
+      # If softlinks start to matter, we should change this to REALPATH.  For now we need
+      # to flatten paths, because nvcc can generate stuff like /bin/../include instead of
+      # just /include.
+      get_filename_component(file_absolute "${file}" ABSOLUTE)
+      list(APPEND dependency_list "${file_absolute}")
+    endif()
+
+  endforeach()
+
+else()
+  # message("FOUND NO DEPENDS")
+endif()
+
+# Remove the duplicate entries and sort them.
+list(REMOVE_DUPLICATES dependency_list)
+list(SORT dependency_list)
+
+foreach(file ${dependency_list})
+  string(APPEND cuda_nvcc_depend " \"${file}\"\n")
+endforeach()
+
+file(WRITE ${output_file} "# Generated by: make2cmake.cmake\nSET(CUDA_NVCC_DEPEND\n ${cuda_nvcc_depend})\n\n")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/parse_cubin.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/parse_cubin.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..25ceb49f3dd8e684e35cac49834c4db0aa5c338a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/parse_cubin.cmake
@@ -0,0 +1,109 @@
+#  James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#  Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html
+#
+#  Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#  Copyright (c) 2007-2009
+#  Scientific Computing and Imaging Institute, University of Utah
+#
+#  This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#  for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+#
+
+#######################################################################
+# Parses a .cubin file produced by nvcc and reports statistics about the file.
+
+
+file(READ ${input_file} file_text)
+
+if (NOT "${file_text}" STREQUAL "")
+
+  string(REPLACE ";" "\\;" file_text ${file_text})
+  string(REPLACE "\ncode" ";code" file_text ${file_text})
+
+  list(LENGTH file_text len)
+
+  foreach(line ${file_text})
+
+    # Only look at "code { }" blocks.
+    if(line MATCHES "^code")
+
+      # Break into individual lines.
+      string(REGEX REPLACE "\n" ";" line ${line})
+
+      foreach(entry ${line})
+
+        # Extract kernel names.
+        if (${entry} MATCHES "[^g]name = ([^ ]+)")
+          set(entry "${CMAKE_MATCH_1}")
+
+          # Check to see if the kernel name starts with "_"
+          set(skip FALSE)
+          # if (${entry} MATCHES "^_")
+            # Skip the rest of this block.
+            # message("Skipping ${entry}")
+            # set(skip TRUE)
+          # else ()
+            message("Kernel:    ${entry}")
+          # endif ()
+
+        endif()
+
+        # Skip the rest of the block if necessary
+        if(NOT skip)
+
+          # Registers
+          if (${entry} MATCHES "reg([ ]+)=([ ]+)([^ ]+)")
+            set(entry "${CMAKE_MATCH_3}")
+            message("Registers: ${entry}")
+          endif()
+
+          # Local memory
+          if (${entry} MATCHES "lmem([ ]+)=([ ]+)([^ ]+)")
+            set(entry "${CMAKE_MATCH_3}")
+            message("Local:     ${entry}")
+          endif()
+
+          # Shared memory
+          if (${entry} MATCHES "smem([ ]+)=([ ]+)([^ ]+)")
+            set(entry "${CMAKE_MATCH_3}")
+            message("Shared:    ${entry}")
+          endif()
+
+          if (${entry} MATCHES "^}")
+            message("")
+          endif()
+
+        endif()
+
+
+      endforeach()
+
+    endif()
+
+  endforeach()
+
+else()
+  # message("FOUND NO DEPENDS")
+endif()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/run_nvcc.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/run_nvcc.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..9293df3aafbdefdd8664ae2860d1b5b7fc9bfbfb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/run_nvcc.cmake
@@ -0,0 +1,303 @@
+#  James Bigler, NVIDIA Corp (nvidia.com - jbigler)
+#
+#  Copyright (c) 2008 - 2009 NVIDIA Corporation.  All rights reserved.
+#
+#  This code is licensed under the MIT License.  See the FindCUDA.cmake script
+#  for the text of the license.
+
+# The MIT License
+#
+# License for the specific language governing rights and limitations under
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+
+
+##########################################################################
+# This file runs the nvcc commands to produce the desired output file along with
+# the dependency file needed by CMake to compute dependencies.  In addition the
+# file checks the output of each command and if the command fails it deletes the
+# output files.
+
+# Input variables
+#
+# verbose:BOOL=<>          OFF: Be as quiet as possible (default)
+#                          ON : Describe each step
+#
+# build_configuration:STRING=<> Typically one of Debug, MinSizeRel, Release, or
+#                               RelWithDebInfo, but it should match one of the
+#                               entries in CUDA_HOST_FLAGS. This is the build
+#                               configuration used when compiling the code.  If
+#                               blank or unspecified Debug is assumed as this is
+#                               what CMake does.
+#
+# generated_file:STRING=<> File to generate.  This argument must be passed in.
+#
+# generated_cubin_file:STRING=<> File to generate.  This argument must be passed
+#                                                   in if build_cubin is true.
+
+cmake_policy(PUSH)
+cmake_policy(SET CMP0007 NEW)
+cmake_policy(SET CMP0010 NEW)
+if(NOT generated_file)
+  message(FATAL_ERROR "You must specify generated_file on the command line")
+endif()
+
+# Set these up as variables to make reading the generated file easier
+set(CMAKE_COMMAND "@CMAKE_COMMAND@") # path
+set(source_file "@source_file@") # path
+set(NVCC_generated_dependency_file "@NVCC_generated_dependency_file@") # path
+set(cmake_dependency_file "@cmake_dependency_file@") # path
+set(CUDA_make2cmake "@CUDA_make2cmake@") # path
+set(CUDA_parse_cubin "@CUDA_parse_cubin@") # path
+set(build_cubin @build_cubin@) # bool
+set(CUDA_HOST_COMPILER "@CUDA_HOST_COMPILER@") # path
+# We won't actually use these variables for now, but we need to set this, in
+# order to force this file to be run again if it changes.
+set(generated_file_path "@generated_file_path@") # path
+set(generated_file_internal "@generated_file@") # path
+set(generated_cubin_file_internal "@generated_cubin_file@") # path
+
+set(CUDA_NVCC_EXECUTABLE "@CUDA_NVCC_EXECUTABLE@") # path
+set(CUDA_NVCC_FLAGS @CUDA_NVCC_FLAGS@ ;; @CUDA_WRAP_OPTION_NVCC_FLAGS@) # list
+@CUDA_NVCC_FLAGS_CONFIG@
+set(nvcc_flags @nvcc_flags@) # list
+set(CUDA_NVCC_INCLUDE_DIRS [==[@CUDA_NVCC_INCLUDE_DIRS@]==]) # list (needs to be in lua quotes to address backslashes)
+string(REPLACE "\\" "/" CUDA_NVCC_INCLUDE_DIRS "${CUDA_NVCC_INCLUDE_DIRS}")
+set(CUDA_NVCC_COMPILE_DEFINITIONS [==[@CUDA_NVCC_COMPILE_DEFINITIONS@]==]) # list (needs to be in lua quotes see #16510 ).
+set(format_flag "@format_flag@") # string
+set(cuda_language_flag @cuda_language_flag@) # list
+
+# Clean up list of include directories and add -I flags
+list(REMOVE_DUPLICATES CUDA_NVCC_INCLUDE_DIRS)
+set(CUDA_NVCC_INCLUDE_ARGS)
+foreach(dir ${CUDA_NVCC_INCLUDE_DIRS})
+  # Extra quotes are added around each flag to help nvcc parse out flags with spaces.
+  list(APPEND CUDA_NVCC_INCLUDE_ARGS "-I${dir}")
+endforeach()
+
+# Clean up list of compile definitions, add -D flags, and append to nvcc_flags
+list(REMOVE_DUPLICATES CUDA_NVCC_COMPILE_DEFINITIONS)
+foreach(def ${CUDA_NVCC_COMPILE_DEFINITIONS})
+  list(APPEND nvcc_flags "-D${def}")
+endforeach()
+
+if(build_cubin AND NOT generated_cubin_file)
+  message(FATAL_ERROR "You must specify generated_cubin_file on the command line")
+endif()
+
+# This is the list of host compilation flags.  It C or CXX should already have
+# been chosen by FindCUDA.cmake.
+@CUDA_HOST_FLAGS@
+
+# Take the compiler flags and package them up to be sent to the compiler via -Xcompiler
+set(nvcc_host_compiler_flags "")
+# If we weren't given a build_configuration, use Debug.
+if(NOT build_configuration)
+  set(build_configuration Debug)
+endif()
+string(TOUPPER "${build_configuration}" build_configuration)
+#message("CUDA_NVCC_HOST_COMPILER_FLAGS = ${CUDA_NVCC_HOST_COMPILER_FLAGS}")
+foreach(flag ${CMAKE_HOST_FLAGS} ${CMAKE_HOST_FLAGS_${build_configuration}})
+  # Extra quotes are added around each flag to help nvcc parse out flags with spaces.
+  string(APPEND nvcc_host_compiler_flags ",\"${flag}\"")
+endforeach()
+if (nvcc_host_compiler_flags)
+  set(nvcc_host_compiler_flags "-Xcompiler" ${nvcc_host_compiler_flags})
+endif()
+#message("nvcc_host_compiler_flags = \"${nvcc_host_compiler_flags}\"")
+# Add the build specific configuration flags
+list(APPEND CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS_${build_configuration}})
+
+# Any -ccbin existing in CUDA_NVCC_FLAGS gets highest priority
+list( FIND CUDA_NVCC_FLAGS "-ccbin" ccbin_found0 )
+list( FIND CUDA_NVCC_FLAGS "--compiler-bindir" ccbin_found1 )
+if( ccbin_found0 LESS 0 AND ccbin_found1 LESS 0 AND CUDA_HOST_COMPILER )
+  if (CUDA_HOST_COMPILER STREQUAL "@_CUDA_MSVC_HOST_COMPILER@" AND DEFINED CCBIN)
+    set(CCBIN -ccbin "${CCBIN}")
+  else()
+    set(CCBIN -ccbin "${CUDA_HOST_COMPILER}")
+  endif()
+endif()
+
+# cuda_execute_process - Executes a command with optional command echo and status message.
+#
+#   status  - Status message to print if verbose is true
+#   command - COMMAND argument from the usual execute_process argument structure
+#   ARGN    - Remaining arguments are the command with arguments
+#
+#   CUDA_result - return value from running the command
+#
+# Make this a macro instead of a function, so that things like RESULT_VARIABLE
+# and other return variables are present after executing the process.
+macro(cuda_execute_process status command)
+  set(_command ${command})
+  if(NOT "x${_command}" STREQUAL "xCOMMAND")
+    message(FATAL_ERROR "Malformed call to cuda_execute_process.  Missing COMMAND as second argument. (command = ${command})")
+  endif()
+  if(verbose)
+    execute_process(COMMAND "${CMAKE_COMMAND}" -E echo -- ${status})
+    # Now we need to build up our command string.  We are accounting for quotes
+    # and spaces, anything else is left up to the user to fix if they want to
+    # copy and paste a runnable command line.
+    set(cuda_execute_process_string)
+    foreach(arg ${ARGN})
+      # If there are quotes, excape them, so they come through.
+      string(REPLACE "\"" "\\\"" arg ${arg})
+      # Args with spaces need quotes around them to get them to be parsed as a single argument.
+      if(arg MATCHES " ")
+        list(APPEND cuda_execute_process_string "\"${arg}\"")
+      else()
+        list(APPEND cuda_execute_process_string ${arg})
+      endif()
+    endforeach()
+    # Echo the command
+    execute_process(COMMAND ${CMAKE_COMMAND} -E echo ${cuda_execute_process_string})
+  endif()
+  # Run the command
+  execute_process(COMMAND ${ARGN} RESULT_VARIABLE CUDA_result )
+endmacro()
+
+# Delete the target file
+cuda_execute_process(
+  "Removing ${generated_file}"
+  COMMAND "${CMAKE_COMMAND}" -E remove "${generated_file}"
+  )
+
+# For CUDA 2.3 and below, -G -M doesn't work, so remove the -G flag
+# for dependency generation and hope for the best.
+set(depends_CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS}")
+set(CUDA_VERSION @CUDA_VERSION@)
+
+# nvcc doesn't define __CUDACC__ for some reason when generating dependency files.  This
+# can cause incorrect dependencies when #including files based on this macro which is
+# defined in the generating passes of nvcc invocation.  We will go ahead and manually
+# define this for now until a future version fixes this bug.
+set(CUDACC_DEFINE -D__CUDACC__)
+
+# Generate the dependency file
+cuda_execute_process(
+  "Generating dependency file: ${NVCC_generated_dependency_file}"
+  COMMAND "${CUDA_NVCC_EXECUTABLE}"
+  -M
+  ${CUDACC_DEFINE}
+  "${source_file}"
+  -o "${NVCC_generated_dependency_file}"
+  ${CCBIN}
+  ${nvcc_flags}
+  ${nvcc_host_compiler_flags}
+  ${depends_CUDA_NVCC_FLAGS}
+  -DNVCC
+  ${CUDA_NVCC_INCLUDE_ARGS}
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Generate the cmake readable dependency file to a temp file.  Don't put the
+# quotes just around the filenames for the input_file and output_file variables.
+# CMake will pass the quotes through and not be able to find the file.
+cuda_execute_process(
+  "Generating temporary cmake readable file: ${cmake_dependency_file}.tmp"
+  COMMAND "${CMAKE_COMMAND}"
+  -D "input_file:FILEPATH=${NVCC_generated_dependency_file}"
+  -D "output_file:FILEPATH=${cmake_dependency_file}.tmp"
+  -D "verbose=${verbose}"
+  -P "${CUDA_make2cmake}"
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Copy the file if it is different
+cuda_execute_process(
+  "Copy if different ${cmake_dependency_file}.tmp to ${cmake_dependency_file}"
+  COMMAND "${CMAKE_COMMAND}" -E copy_if_different "${cmake_dependency_file}.tmp" "${cmake_dependency_file}"
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Delete the temporary file
+cuda_execute_process(
+  "Removing ${cmake_dependency_file}.tmp and ${NVCC_generated_dependency_file}"
+  COMMAND "${CMAKE_COMMAND}" -E remove "${cmake_dependency_file}.tmp" "${NVCC_generated_dependency_file}"
+  )
+
+if(CUDA_result)
+  message(FATAL_ERROR "Error generating ${generated_file}")
+endif()
+
+# Generate the code
+cuda_execute_process(
+  "Generating ${generated_file}"
+  COMMAND "${CUDA_NVCC_EXECUTABLE}"
+  "${source_file}"
+  ${cuda_language_flag}
+  ${format_flag} -o "${generated_file}"
+  ${CCBIN}
+  ${nvcc_flags}
+  ${nvcc_host_compiler_flags}
+  ${CUDA_NVCC_FLAGS}
+  -DNVCC
+  ${CUDA_NVCC_INCLUDE_ARGS}
+  )
+
+if(CUDA_result)
+  # Since nvcc can sometimes leave half done files make sure that we delete the output file.
+  cuda_execute_process(
+    "Removing ${generated_file}"
+    COMMAND "${CMAKE_COMMAND}" -E remove "${generated_file}"
+    )
+  message(FATAL_ERROR "Error generating file ${generated_file}")
+else()
+  if(verbose)
+    message("Generated ${generated_file} successfully.")
+  endif()
+endif()
+
+# Cubin resource report commands.
+if( build_cubin )
+  # Run with -cubin to produce resource usage report.
+  cuda_execute_process(
+    "Generating ${generated_cubin_file}"
+    COMMAND "${CUDA_NVCC_EXECUTABLE}"
+    "${source_file}"
+    ${CUDA_NVCC_FLAGS}
+    ${nvcc_flags}
+    ${CCBIN}
+    ${nvcc_host_compiler_flags}
+    -DNVCC
+    -cubin
+    -o "${generated_cubin_file}"
+    ${CUDA_NVCC_INCLUDE_ARGS}
+    )
+
+  # Execute the parser script.
+  cuda_execute_process(
+    "Executing the parser script"
+    COMMAND  "${CMAKE_COMMAND}"
+    -D "input_file:STRING=${generated_cubin_file}"
+    -P "${CUDA_parse_cubin}"
+    )
+
+endif()
+
+cmake_policy(POP)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..febdbb5fe193dc42e052e145071630dfefd2ac18
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake
@@ -0,0 +1,300 @@
+# Synopsis:
+#   CUDA_SELECT_NVCC_ARCH_FLAGS(out_variable [target_CUDA_architectures])
+#   -- Selects GPU arch flags for nvcc based on target_CUDA_architectures
+#      target_CUDA_architectures : Auto | Common | All | LIST(ARCH_AND_PTX ...)
+#       - "Auto" detects local machine GPU compute arch at runtime.
+#       - "Common" and "All" cover common and entire subsets of architectures
+#      ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX
+#      NAME: Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal Volta Turing Ampere
+#      NUM: Any number. Only those pairs are currently accepted by NVCC though:
+#            3.5 3.7 5.0 5.2 5.3 6.0 6.2 7.0 7.2 7.5 8.0
+#      Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable}
+#      Additionally, sets ${out_variable}_readable to the resulting numeric list
+#      Example:
+#       CUDA_SELECT_NVCC_ARCH_FLAGS(ARCH_FLAGS 3.0 3.5+PTX 5.2(5.0) Maxwell)
+#        LIST(APPEND CUDA_NVCC_FLAGS ${ARCH_FLAGS})
+#
+#      More info on CUDA architectures: https://en.wikipedia.org/wiki/CUDA
+#
+
+if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
+  if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA"
+      AND CMAKE_CUDA_COMPILER_VERSION MATCHES "^([0-9]+\\.[0-9]+)")
+    set(CUDA_VERSION "${CMAKE_MATCH_1}")
+  endif()
+endif()
+
+# See: https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list
+
+# This list will be used for CUDA_ARCH_NAME = All option
+set(CUDA_KNOWN_GPU_ARCHITECTURES  "Kepler" "Maxwell")
+
+# This list will be used for CUDA_ARCH_NAME = Common option (enabled by default)
+set(CUDA_COMMON_GPU_ARCHITECTURES "3.5" "5.0")
+
+# This list is used to filter CUDA archs when autodetecting
+set(CUDA_ALL_GPU_ARCHITECTURES "3.5" "5.0")
+
+if(CUDA_VERSION VERSION_GREATER "10.5")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Ampere")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.0")
+
+  if(CUDA_VERSION VERSION_LESS "11.1")
+    set(CUDA_LIMIT_GPU_ARCHITECTURE "8.0")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.0+PTX")
+  endif()
+endif()
+
+if(NOT CUDA_VERSION VERSION_LESS "11.1")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.6")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.6")
+  set(CUDA_LIMIT_GPU_ARCHITECUTRE "8.6")
+
+  if(CUDA_VERSION VERSION_LESS "11.8")
+    set(CUDA_LIMIT_GPU_ARCHITECTURE "8.9")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.6+PTX")
+  endif()
+endif()
+
+if(NOT CUDA_VERSION VERSION_LESS "11.8")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Ada")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Hopper")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.9")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "9.0")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "8.9")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "9.0")
+
+  if(CUDA_VERSION VERSION_LESS "12.0")
+    set(CUDA_LIMIT_GPU_ARCHITECTURE "9.0")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "8.9+PTX")
+    list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "9.0+PTX")
+  endif()
+endif()
+
+if(NOT CUDA_VERSION VERSION_LESS "12.0")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "9.0a")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "9.0a")
+  list(REMOVE_ITEM CUDA_COMMON_GPU_ARCHITECTURES "3.5")
+  list(REMOVE_ITEM CUDA_ALL_GPU_ARCHITECTURES "3.5")
+endif()
+
+if(CUDA_VERSION VERSION_GREATER "12.6")
+  list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Blackwell")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "10.0")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "10.0a")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "10.1a")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "12.0")
+  list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "12.0a")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "10.0")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "10.0a")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "10.1a")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "12.0")
+  list(APPEND CUDA_ALL_GPU_ARCHITECTURES "12.0a")
+endif()
+
+
+################################################################################################
+# A function for automatic detection of GPUs installed  (if autodetection is enabled)
+# Usage:
+#   CUDA_DETECT_INSTALLED_GPUS(OUT_VARIABLE)
+#
+function(CUDA_DETECT_INSTALLED_GPUS OUT_VARIABLE)
+  if(NOT CUDA_GPU_DETECT_OUTPUT)
+    if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
+      set(file "${PROJECT_BINARY_DIR}/detect_cuda_compute_capabilities.cu")
+    else()
+      set(file "${PROJECT_BINARY_DIR}/detect_cuda_compute_capabilities.cpp")
+    endif()
+
+    file(WRITE ${file} ""
+      "#include \n"
+      "#include \n"
+      "int main()\n"
+      "{\n"
+      "  int count = 0;\n"
+      "  if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
+      "  if (count == 0) return -1;\n"
+      "  for (int device = 0; device < count; ++device)\n"
+      "  {\n"
+      "    cudaDeviceProp prop;\n"
+      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
+      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
+      "  }\n"
+      "  return 0;\n"
+      "}\n")
+
+    if(CMAKE_CUDA_COMPILER_LOADED) # CUDA as a language
+      try_run(run_result compile_result ${PROJECT_BINARY_DIR} ${file}
+              RUN_OUTPUT_VARIABLE compute_capabilities)
+    else()
+      try_run(run_result compile_result ${PROJECT_BINARY_DIR} ${file}
+              CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS}"
+              LINK_LIBRARIES ${CUDA_LIBRARIES}
+              RUN_OUTPUT_VARIABLE compute_capabilities)
+    endif()
+
+    # Filter unrelated content out of the output.
+    string(REGEX MATCHALL "[0-9]+\\.[0-9]+" compute_capabilities "${compute_capabilities}")
+
+    if(run_result EQUAL 0)
+      string(REPLACE "2.1" "2.1(2.0)" compute_capabilities "${compute_capabilities}")
+      set(CUDA_GPU_DETECT_OUTPUT ${compute_capabilities}
+        CACHE INTERNAL "Returned GPU architectures from detect_gpus tool" FORCE)
+    endif()
+  endif()
+
+  if(NOT CUDA_GPU_DETECT_OUTPUT)
+    message(STATUS "Automatic GPU detection failed. Building for common architectures.")
+    set(${OUT_VARIABLE} ${CUDA_COMMON_GPU_ARCHITECTURES} PARENT_SCOPE)
+  else()
+    # Filter based on CUDA version supported archs
+    set(CUDA_GPU_DETECT_OUTPUT_FILTERED "")
+    separate_arguments(CUDA_GPU_DETECT_OUTPUT)
+    foreach(ITEM IN ITEMS ${CUDA_GPU_DETECT_OUTPUT})
+        if(CUDA_LIMIT_GPU_ARCHITECTURE AND (ITEM VERSION_GREATER CUDA_LIMIT_GPU_ARCHITECTURE OR
+                                            ITEM VERSION_EQUAL CUDA_LIMIT_GPU_ARCHITECTURE))
+        list(GET CUDA_COMMON_GPU_ARCHITECTURES -1 NEWITEM)
+        string(APPEND CUDA_GPU_DETECT_OUTPUT_FILTERED " ${NEWITEM}")
+      else()
+        string(APPEND CUDA_GPU_DETECT_OUTPUT_FILTERED " ${ITEM}")
+      endif()
+    endforeach()
+
+    set(${OUT_VARIABLE} ${CUDA_GPU_DETECT_OUTPUT_FILTERED} PARENT_SCOPE)
+  endif()
+endfunction()
+
+
+################################################################################################
+# Function for selecting GPU arch flags for nvcc based on CUDA architectures from parameter list
+# Usage:
+#   SELECT_NVCC_ARCH_FLAGS(out_variable [list of CUDA compute archs])
+function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable)
+  set(CUDA_ARCH_LIST "${ARGN}")
+
+  if("X${CUDA_ARCH_LIST}" STREQUAL "X" )
+    set(CUDA_ARCH_LIST "Auto")
+  endif()
+
+  set(cuda_arch_bin)
+  set(cuda_arch_ptx)
+
+  if("${CUDA_ARCH_LIST}" STREQUAL "All")
+    set(CUDA_ARCH_LIST ${CUDA_KNOWN_GPU_ARCHITECTURES})
+  elseif("${CUDA_ARCH_LIST}" STREQUAL "Common")
+    set(CUDA_ARCH_LIST ${CUDA_COMMON_GPU_ARCHITECTURES})
+  elseif("${CUDA_ARCH_LIST}" STREQUAL "Auto")
+    CUDA_DETECT_INSTALLED_GPUS(CUDA_ARCH_LIST)
+    message(STATUS "Autodetected CUDA architecture(s): ${CUDA_ARCH_LIST}")
+  endif()
+
+  # Now process the list and look for names
+  string(REGEX REPLACE "[ \t]+" ";" CUDA_ARCH_LIST "${CUDA_ARCH_LIST}")
+  list(REMOVE_DUPLICATES CUDA_ARCH_LIST)
+  foreach(arch_name ${CUDA_ARCH_LIST})
+    set(arch_bin)
+    set(arch_ptx)
+    set(add_ptx FALSE)
+    # Check to see if we are compiling PTX
+    if(arch_name MATCHES "(.*)\\+PTX$")
+      set(add_ptx TRUE)
+      set(arch_name ${CMAKE_MATCH_1})
+    endif()
+    if(arch_name MATCHES "^([0-9]+\\.[0-9]a?(\\([0-9]+\\.[0-9]\\))?)$")
+      set(arch_bin ${CMAKE_MATCH_1})
+      set(arch_ptx ${arch_bin})
+    else()
+      # Look for it in our list of known architectures
+      if(${arch_name} STREQUAL "Kepler+Tesla")
+        set(arch_bin 3.7)
+      elseif(${arch_name} STREQUAL "Kepler")
+        set(arch_bin 3.5)
+        set(arch_ptx 3.5)
+      elseif(${arch_name} STREQUAL "Maxwell+Tegra")
+        set(arch_bin 5.3)
+      elseif(${arch_name} STREQUAL "Maxwell")
+        set(arch_bin 5.0 5.2)
+        set(arch_ptx 5.2)
+      elseif(${arch_name} STREQUAL "Pascal")
+        set(arch_bin 6.0 6.1)
+        set(arch_ptx 6.1)
+     elseif(${arch_name} STREQUAL "Volta+Tegra")
+        set(arch_bin 7.2)
+      elseif(${arch_name} STREQUAL "Volta")
+        set(arch_bin 7.0 7.0)
+        set(arch_ptx 7.0)
+      elseif(${arch_name} STREQUAL "Turing")
+        set(arch_bin 7.5)
+        set(arch_ptx 7.5)
+      elseif(${arch_name} STREQUAL "Ampere+Tegra")
+        set(arch_bin 8.7)
+      elseif(${arch_name} STREQUAL "Ampere")
+        set(arch_bin 8.0 8.6)
+        set(arch_ptx 8.0 8.6)
+      elseif(${arch_name} STREQUAL "Ada")
+        set(arch_bin 8.9)
+        set(arch_ptx 8.9)
+      elseif(${arch_name} STREQUAL "Hopper")
+        set(arch_bin 9.0)
+        set(arch_ptx 9.0)
+      elseif(${arch_name} STREQUAL "Blackwell+Tegra")
+        set(arch_bin 10.1)
+      elseif(${arch_name} STREQUAL "Blackwell")
+        set(arch_bin 10.0 12.0)
+        set(arch_ptx 10.0 12.0)
+      else()
+        message(SEND_ERROR "Found Unknown CUDA Architecture Name in CUDA_SELECT_NVCC_ARCH_FLAGS: ${arch_name} ")
+      endif()
+    endif()
+    if(NOT arch_bin)
+      message(SEND_ERROR "arch_bin wasn't set for some reason")
+    endif()
+    list(APPEND cuda_arch_bin ${arch_bin})
+    if(add_ptx)
+      if (NOT arch_ptx)
+        set(arch_ptx ${arch_bin})
+      endif()
+      list(APPEND cuda_arch_ptx ${arch_ptx})
+    endif()
+  endforeach()
+
+  # remove dots and convert to lists
+  string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX REPLACE "\\." "" cuda_arch_ptx "${cuda_arch_ptx}")
+  string(REGEX MATCHALL "[0-9()]+a?" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX MATCHALL "[0-9]+a?"   cuda_arch_ptx "${cuda_arch_ptx}")
+
+  if(cuda_arch_bin)
+    list(REMOVE_DUPLICATES cuda_arch_bin)
+  endif()
+  if(cuda_arch_ptx)
+    list(REMOVE_DUPLICATES cuda_arch_ptx)
+  endif()
+
+  set(nvcc_flags "")
+  set(nvcc_archs_readable "")
+
+  # Tell NVCC to add binaries for the specified GPUs
+  foreach(arch ${cuda_arch_bin})
+    if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
+      # User explicitly specified ARCH for the concrete CODE
+      list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
+      list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1})
+    else()
+      # User didn't explicitly specify ARCH for the concrete CODE, we assume ARCH=CODE
+      list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch})
+      list(APPEND nvcc_archs_readable sm_${arch})
+    endif()
+  endforeach()
+
+  # Tell NVCC to add PTX intermediate code for the specified architectures
+  foreach(arch ${cuda_arch_ptx})
+    list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch})
+    list(APPEND nvcc_archs_readable compute_${arch})
+  endforeach()
+
+  string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}")
+  set(${out_variable}          ${nvcc_flags}          PARENT_SCOPE)
+  set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
+endfunction()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageHandleStandardArgs.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageHandleStandardArgs.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..67f6bd6f2bcd1a0313078a28a07cc584df7b885b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageHandleStandardArgs.cmake
@@ -0,0 +1,386 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#[=======================================================================[.rst:
+FindPackageHandleStandardArgs
+-----------------------------
+
+This module provides a function intended to be used in :ref:`Find Modules`
+implementing :command:`find_package()` calls.  It handles the
+``REQUIRED``, ``QUIET`` and version-related arguments of ``find_package``.
+It also sets the ``_FOUND`` variable.  The package is
+considered found if all variables listed contain valid results, e.g.
+valid filepaths.
+
+.. command:: find_package_handle_standard_args
+
+  There are two signatures::
+
+    find_package_handle_standard_args(
+      (DEFAULT_MSG|)
+      ...
+      )
+
+    find_package_handle_standard_args(
+      [FOUND_VAR ]
+      [REQUIRED_VARS ...]
+      [VERSION_VAR ]
+      [HANDLE_COMPONENTS]
+      [CONFIG_MODE]
+      [FAIL_MESSAGE ]
+      )
+
+  The ``_FOUND`` variable will be set to ``TRUE`` if all
+  the variables ``...`` are valid and any optional
+  constraints are satisfied, and ``FALSE`` otherwise.  A success or
+  failure message may be displayed based on the results and on
+  whether the ``REQUIRED`` and/or ``QUIET`` option was given to
+  the :command:`find_package` call.
+
+  The options are:
+
+  ``(DEFAULT_MSG|)``
+    In the simple signature this specifies the failure message.
+    Use ``DEFAULT_MSG`` to ask for a default message to be computed
+    (recommended).  Not valid in the full signature.
+
+  ``FOUND_VAR ``
+    Obsolete.  Specifies either ``_FOUND`` or
+    ``_FOUND`` as the result variable.  This exists only
+    for compatibility with older versions of CMake and is now ignored.
+    Result variables of both names are always set for compatibility.
+
+  ``REQUIRED_VARS ...``
+    Specify the variables which are required for this package.
+    These may be named in the generated failure message asking the
+    user to set the missing variable values.  Therefore these should
+    typically be cache entries such as ``FOO_LIBRARY`` and not output
+    variables like ``FOO_LIBRARIES``.
+
+  ``VERSION_VAR ``
+    Specify the name of a variable that holds the version of the package
+    that has been found.  This version will be checked against the
+    (potentially) specified required version given to the
+    :command:`find_package` call, including its ``EXACT`` option.
+    The default messages include information about the required
+    version and the version which has been actually found, both
+    if the version is ok or not.
+
+  ``HANDLE_COMPONENTS``
+    Enable handling of package components.  In this case, the command
+    will report which components have been found and which are missing,
+    and the ``_FOUND`` variable will be set to ``FALSE``
+    if any of the required components (i.e. not the ones listed after
+    the ``OPTIONAL_COMPONENTS`` option of :command:`find_package`) are
+    missing.
+
+  ``CONFIG_MODE``
+    Specify that the calling find module is a wrapper around a
+    call to ``find_package( NO_MODULE)``.  This implies
+    a ``VERSION_VAR`` value of ``_VERSION``.  The command
+    will automatically check whether the package configuration file
+    was found.
+
+  ``FAIL_MESSAGE ``
+    Specify a custom failure message instead of using the default
+    generated message.  Not recommended.
+
+Example for the simple signature:
+
+.. code-block:: cmake
+
+  find_package_handle_standard_args(LibXml2 DEFAULT_MSG
+    LIBXML2_LIBRARY LIBXML2_INCLUDE_DIR)
+
+The ``LibXml2`` package is considered to be found if both
+``LIBXML2_LIBRARY`` and ``LIBXML2_INCLUDE_DIR`` are valid.
+Then also ``LibXml2_FOUND`` is set to ``TRUE``.  If it is not found
+and ``REQUIRED`` was used, it fails with a
+:command:`message(FATAL_ERROR)`, independent whether ``QUIET`` was
+used or not.  If it is found, success will be reported, including
+the content of the first ````.  On repeated CMake runs,
+the same message will not be printed again.
+
+Example for the full signature:
+
+.. code-block:: cmake
+
+  find_package_handle_standard_args(LibArchive
+    REQUIRED_VARS LibArchive_LIBRARY LibArchive_INCLUDE_DIR
+    VERSION_VAR LibArchive_VERSION)
+
+In this case, the ``LibArchive`` package is considered to be found if
+both ``LibArchive_LIBRARY`` and ``LibArchive_INCLUDE_DIR`` are valid.
+Also the version of ``LibArchive`` will be checked by using the version
+contained in ``LibArchive_VERSION``.  Since no ``FAIL_MESSAGE`` is given,
+the default messages will be printed.
+
+Another example for the full signature:
+
+.. code-block:: cmake
+
+  find_package(Automoc4 QUIET NO_MODULE HINTS /opt/automoc4)
+  find_package_handle_standard_args(Automoc4  CONFIG_MODE)
+
+In this case, a ``FindAutmoc4.cmake`` module wraps a call to
+``find_package(Automoc4 NO_MODULE)`` and adds an additional search
+directory for ``automoc4``.  Then the call to
+``find_package_handle_standard_args`` produces a proper success/failure
+message.
+#]=======================================================================]
+
+include(${CMAKE_CURRENT_LIST_DIR}/FindPackageMessage.cmake)
+
+# internal helper macro
+macro(_FPHSA_FAILURE_MESSAGE _msg)
+  if (${_NAME}_FIND_REQUIRED)
+    message(FATAL_ERROR "${_msg}")
+  else ()
+    if (NOT ${_NAME}_FIND_QUIETLY)
+      message(STATUS "${_msg}")
+    endif ()
+  endif ()
+endmacro()
+
+
+# internal helper macro to generate the failure message when used in CONFIG_MODE:
+macro(_FPHSA_HANDLE_FAILURE_CONFIG_MODE)
+  # _CONFIG is set, but FOUND is false, this means that some other of the REQUIRED_VARS was not found:
+  if(${_NAME}_CONFIG)
+    _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE}: missing:${MISSING_VARS} (found ${${_NAME}_CONFIG} ${VERSION_MSG})")
+  else()
+    # If _CONSIDERED_CONFIGS is set, the config-file has been found, but no suitable version.
+    # List them all in the error message:
+    if(${_NAME}_CONSIDERED_CONFIGS)
+      set(configsText "")
+      list(LENGTH ${_NAME}_CONSIDERED_CONFIGS configsCount)
+      math(EXPR configsCount "${configsCount} - 1")
+      foreach(currentConfigIndex RANGE ${configsCount})
+        list(GET ${_NAME}_CONSIDERED_CONFIGS ${currentConfigIndex} filename)
+        list(GET ${_NAME}_CONSIDERED_VERSIONS ${currentConfigIndex} version)
+        string(APPEND configsText "    ${filename} (version ${version})\n")
+      endforeach()
+      if (${_NAME}_NOT_FOUND_MESSAGE)
+        string(APPEND configsText "    Reason given by package: ${${_NAME}_NOT_FOUND_MESSAGE}\n")
+      endif()
+      _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE} ${VERSION_MSG}, checked the following files:\n${configsText}")
+
+    else()
+      # Simple case: No Config-file was found at all:
+      _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE}: found neither ${_NAME}Config.cmake nor ${_NAME_LOWER}-config.cmake ${VERSION_MSG}")
+    endif()
+  endif()
+endmacro()
+
+
+function(FIND_PACKAGE_HANDLE_STANDARD_ARGS _NAME _FIRST_ARG)
+
+# Set up the arguments for `cmake_parse_arguments`.
+  set(options  CONFIG_MODE  HANDLE_COMPONENTS)
+  set(oneValueArgs  FAIL_MESSAGE  VERSION_VAR  FOUND_VAR)
+  set(multiValueArgs REQUIRED_VARS)
+
+# Check whether we are in 'simple' or 'extended' mode:
+  set(_KEYWORDS_FOR_EXTENDED_MODE  ${options} ${oneValueArgs} ${multiValueArgs} )
+  list(FIND _KEYWORDS_FOR_EXTENDED_MODE "${_FIRST_ARG}" INDEX)
+
+  if(${INDEX} EQUAL -1)
+    set(FPHSA_FAIL_MESSAGE ${_FIRST_ARG})
+    set(FPHSA_REQUIRED_VARS ${ARGN})
+    set(FPHSA_VERSION_VAR)
+  else()
+    cmake_parse_arguments(FPHSA "${options}" "${oneValueArgs}" "${multiValueArgs}"  ${_FIRST_ARG} ${ARGN})
+
+    if(FPHSA_UNPARSED_ARGUMENTS)
+      message(FATAL_ERROR "Unknown keywords given to FIND_PACKAGE_HANDLE_STANDARD_ARGS(): \"${FPHSA_UNPARSED_ARGUMENTS}\"")
+    endif()
+
+    if(NOT FPHSA_FAIL_MESSAGE)
+      set(FPHSA_FAIL_MESSAGE  "DEFAULT_MSG")
+    endif()
+
+    # In config-mode, we rely on the variable _CONFIG, which is set by find_package()
+    # when it successfully found the config-file, including version checking:
+    if(FPHSA_CONFIG_MODE)
+      list(INSERT FPHSA_REQUIRED_VARS 0 ${_NAME}_CONFIG)
+      list(REMOVE_DUPLICATES FPHSA_REQUIRED_VARS)
+      set(FPHSA_VERSION_VAR ${_NAME}_VERSION)
+    endif()
+
+    if(NOT FPHSA_REQUIRED_VARS)
+      message(FATAL_ERROR "No REQUIRED_VARS specified for FIND_PACKAGE_HANDLE_STANDARD_ARGS()")
+    endif()
+  endif()
+
+# now that we collected all arguments, process them
+
+  if("x${FPHSA_FAIL_MESSAGE}" STREQUAL "xDEFAULT_MSG")
+    set(FPHSA_FAIL_MESSAGE "Could NOT find ${_NAME}")
+  endif()
+
+  list(GET FPHSA_REQUIRED_VARS 0 _FIRST_REQUIRED_VAR)
+
+  string(TOUPPER ${_NAME} _NAME_UPPER)
+  string(TOLOWER ${_NAME} _NAME_LOWER)
+
+  if(FPHSA_FOUND_VAR)
+    if(FPHSA_FOUND_VAR MATCHES "^${_NAME}_FOUND$"  OR  FPHSA_FOUND_VAR MATCHES "^${_NAME_UPPER}_FOUND$")
+      set(_FOUND_VAR ${FPHSA_FOUND_VAR})
+    else()
+      message(FATAL_ERROR "The argument for FOUND_VAR is \"${FPHSA_FOUND_VAR}\", but only \"${_NAME}_FOUND\" and \"${_NAME_UPPER}_FOUND\" are valid names.")
+    endif()
+  else()
+    set(_FOUND_VAR ${_NAME_UPPER}_FOUND)
+  endif()
+
+  # collect all variables which were not found, so they can be printed, so the
+  # user knows better what went wrong (#6375)
+  set(MISSING_VARS "")
+  set(DETAILS "")
+  # check if all passed variables are valid
+  set(FPHSA_FOUND_${_NAME} TRUE)
+  foreach(_CURRENT_VAR ${FPHSA_REQUIRED_VARS})
+    if(NOT ${_CURRENT_VAR})
+      set(FPHSA_FOUND_${_NAME} FALSE)
+      string(APPEND MISSING_VARS " ${_CURRENT_VAR}")
+    else()
+      string(APPEND DETAILS "[${${_CURRENT_VAR}}]")
+    endif()
+  endforeach()
+  if(FPHSA_FOUND_${_NAME})
+    set(${_NAME}_FOUND TRUE)
+    set(${_NAME_UPPER}_FOUND TRUE)
+  else()
+    set(${_NAME}_FOUND FALSE)
+    set(${_NAME_UPPER}_FOUND FALSE)
+  endif()
+
+  # component handling
+  unset(FOUND_COMPONENTS_MSG)
+  unset(MISSING_COMPONENTS_MSG)
+
+  if(FPHSA_HANDLE_COMPONENTS)
+    foreach(comp ${${_NAME}_FIND_COMPONENTS})
+      if(${_NAME}_${comp}_FOUND)
+
+        if(NOT DEFINED FOUND_COMPONENTS_MSG)
+          set(FOUND_COMPONENTS_MSG "found components: ")
+        endif()
+        string(APPEND FOUND_COMPONENTS_MSG " ${comp}")
+
+      else()
+
+        if(NOT DEFINED MISSING_COMPONENTS_MSG)
+          set(MISSING_COMPONENTS_MSG "missing components: ")
+        endif()
+        string(APPEND MISSING_COMPONENTS_MSG " ${comp}")
+
+        if(${_NAME}_FIND_REQUIRED_${comp})
+          set(${_NAME}_FOUND FALSE)
+          string(APPEND MISSING_VARS " ${comp}")
+        endif()
+
+      endif()
+    endforeach()
+    set(COMPONENT_MSG "${FOUND_COMPONENTS_MSG} ${MISSING_COMPONENTS_MSG}")
+    string(APPEND DETAILS "[c${COMPONENT_MSG}]")
+  endif()
+
+  # version handling:
+  set(VERSION_MSG "")
+  set(VERSION_OK TRUE)
+
+  # check with DEFINED here as the requested or found version may be "0"
+  if (DEFINED ${_NAME}_FIND_VERSION)
+    if(DEFINED ${FPHSA_VERSION_VAR})
+      set(_FOUND_VERSION ${${FPHSA_VERSION_VAR}})
+
+      if(${_NAME}_FIND_VERSION_EXACT)       # exact version required
+        # count the dots in the version string
+        string(REGEX REPLACE "[^.]" "" _VERSION_DOTS "${_FOUND_VERSION}")
+        # add one dot because there is one dot more than there are components
+        string(LENGTH "${_VERSION_DOTS}." _VERSION_DOTS)
+        if (_VERSION_DOTS GREATER ${_NAME}_FIND_VERSION_COUNT)
+          # Because of the C++ implementation of find_package() ${_NAME}_FIND_VERSION_COUNT
+          # is at most 4 here. Therefore a simple lookup table is used.
+          if (${_NAME}_FIND_VERSION_COUNT EQUAL 1)
+            set(_VERSION_REGEX "[^.]*")
+          elseif (${_NAME}_FIND_VERSION_COUNT EQUAL 2)
+            set(_VERSION_REGEX "[^.]*\\.[^.]*")
+          elseif (${_NAME}_FIND_VERSION_COUNT EQUAL 3)
+            set(_VERSION_REGEX "[^.]*\\.[^.]*\\.[^.]*")
+          else ()
+            set(_VERSION_REGEX "[^.]*\\.[^.]*\\.[^.]*\\.[^.]*")
+          endif ()
+          string(REGEX REPLACE "^(${_VERSION_REGEX})\\..*" "\\1" _VERSION_HEAD "${_FOUND_VERSION}")
+          unset(_VERSION_REGEX)
+          if (NOT ${_NAME}_FIND_VERSION VERSION_EQUAL _VERSION_HEAD)
+            set(VERSION_MSG "Found unsuitable version \"${_FOUND_VERSION}\", but required is exact version \"${${_NAME}_FIND_VERSION}\"")
+            set(VERSION_OK FALSE)
+          else ()
+            set(VERSION_MSG "(found suitable exact version \"${_FOUND_VERSION}\")")
+          endif ()
+          unset(_VERSION_HEAD)
+        else ()
+          if (NOT ${_NAME}_FIND_VERSION VERSION_EQUAL _FOUND_VERSION)
+            set(VERSION_MSG "Found unsuitable version \"${_FOUND_VERSION}\", but required is exact version \"${${_NAME}_FIND_VERSION}\"")
+            set(VERSION_OK FALSE)
+          else ()
+            set(VERSION_MSG "(found suitable exact version \"${_FOUND_VERSION}\")")
+          endif ()
+        endif ()
+        unset(_VERSION_DOTS)
+
+      else()     # minimum version specified:
+        if (${_NAME}_FIND_VERSION VERSION_GREATER _FOUND_VERSION)
+          set(VERSION_MSG "Found unsuitable version \"${_FOUND_VERSION}\", but required is at least \"${${_NAME}_FIND_VERSION}\"")
+          set(VERSION_OK FALSE)
+        else ()
+          set(VERSION_MSG "(found suitable version \"${_FOUND_VERSION}\", minimum required is \"${${_NAME}_FIND_VERSION}\")")
+        endif ()
+      endif()
+
+    else()
+
+      # if the package was not found, but a version was given, add that to the output:
+      if(${_NAME}_FIND_VERSION_EXACT)
+         set(VERSION_MSG "(Required is exact version \"${${_NAME}_FIND_VERSION}\")")
+      else()
+         set(VERSION_MSG "(Required is at least version \"${${_NAME}_FIND_VERSION}\")")
+      endif()
+
+    endif()
+  else ()
+    # Check with DEFINED as the found version may be 0.
+    if(DEFINED ${FPHSA_VERSION_VAR})
+      set(VERSION_MSG "(found version \"${${FPHSA_VERSION_VAR}}\")")
+    endif()
+  endif ()
+
+  if(VERSION_OK)
+    string(APPEND DETAILS "[v${${FPHSA_VERSION_VAR}}(${${_NAME}_FIND_VERSION})]")
+  else()
+    set(${_NAME}_FOUND FALSE)
+  endif()
+
+
+  # print the result:
+  if (${_NAME}_FOUND)
+    FIND_PACKAGE_MESSAGE(${_NAME} "Found ${_NAME}: ${${_FIRST_REQUIRED_VAR}} ${VERSION_MSG} ${COMPONENT_MSG}" "${DETAILS}")
+  else ()
+
+    if(FPHSA_CONFIG_MODE)
+      _FPHSA_HANDLE_FAILURE_CONFIG_MODE()
+    else()
+      if(NOT VERSION_OK)
+        _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE}: ${VERSION_MSG} (found ${${_FIRST_REQUIRED_VAR}})")
+      else()
+        _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE} (missing:${MISSING_VARS}) ${VERSION_MSG}")
+      endif()
+    endif()
+
+  endif ()
+
+  set(${_NAME}_FOUND ${${_NAME}_FOUND} PARENT_SCOPE)
+  set(${_NAME_UPPER}_FOUND ${${_NAME}_FOUND} PARENT_SCOPE)
+endfunction()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageMessage.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageMessage.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..6821cee4f77a9d84c74f2c140870a2163ae5a5f0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindPackageMessage.cmake
@@ -0,0 +1,47 @@
+# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
+# file Copyright.txt or https://cmake.org/licensing for details.
+
+#.rst:
+# FindPackageMessage
+# ------------------
+#
+#
+#
+# FIND_PACKAGE_MESSAGE( "message for user" "find result details")
+#
+# This macro is intended to be used in FindXXX.cmake modules files.  It
+# will print a message once for each unique find result.  This is useful
+# for telling the user where a package was found.  The first argument
+# specifies the name (XXX) of the package.  The second argument
+# specifies the message to display.  The third argument lists details
+# about the find result so that if they change the message will be
+# displayed again.  The macro also obeys the QUIET argument to the
+# find_package command.
+#
+# Example:
+#
+# ::
+#
+#   if(X11_FOUND)
+#     FIND_PACKAGE_MESSAGE(X11 "Found X11: ${X11_X11_LIB}"
+#       "[${X11_X11_LIB}][${X11_INCLUDE_DIR}]")
+#   else()
+#    ...
+#   endif()
+
+function(FIND_PACKAGE_MESSAGE pkg msg details)
+  # Avoid printing a message repeatedly for the same find result.
+  if(NOT ${pkg}_FIND_QUIETLY)
+    string(REPLACE "\n" "" details "${details}")
+    set(DETAILS_VAR FIND_PACKAGE_MESSAGE_DETAILS_${pkg})
+    if(NOT "${details}" STREQUAL "${${DETAILS_VAR}}")
+      # The message has not yet been printed.
+      message(STATUS "${msg}")
+
+      # Save the find details in the cache to avoid printing the same
+      # message again.
+      set("${DETAILS_VAR}" "${details}"
+        CACHE INTERNAL "Details about finding ${pkg}")
+    endif()
+  endif()
+endfunction()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..58c74ddda3505cd3e2de8e61408a2832c486fa51
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake
@@ -0,0 +1,206 @@
+set(PYTORCH_FOUND_HIP FALSE)
+
+# If ROCM_PATH is set, assume intention is to compile with
+# ROCm support and error out if the ROCM_PATH does not exist.
+# Else ROCM_PATH does not exist, assume a default of /opt/rocm
+# In the latter case, if /opt/rocm does not exist emit status
+# message and return.
+if(DEFINED ENV{ROCM_PATH})
+  set(ROCM_PATH $ENV{ROCM_PATH})
+  if(NOT EXISTS ${ROCM_PATH})
+    message(FATAL_ERROR
+      "ROCM_PATH environment variable is set to ${ROCM_PATH} but does not exist.\n"
+      "Set a valid ROCM_PATH or unset ROCM_PATH environment variable to fix.")
+  endif()
+else()
+  if(UNIX)
+    set(ROCM_PATH /opt/rocm)
+  else() # Win32
+    set(ROCM_PATH C:/opt/rocm)
+  endif()
+  if(NOT EXISTS ${ROCM_PATH})
+    message(STATUS
+        "ROCM_PATH environment variable is not set and ${ROCM_PATH} does not exist.\n"
+        "Building without ROCm support.")
+    return()
+  endif()
+endif()
+
+if(NOT DEFINED ENV{ROCM_INCLUDE_DIRS})
+  set(ROCM_INCLUDE_DIRS ${ROCM_PATH}/include)
+else()
+  set(ROCM_INCLUDE_DIRS $ENV{ROCM_INCLUDE_DIRS})
+endif()
+
+# MAGMA_HOME
+if(NOT DEFINED ENV{MAGMA_HOME})
+  set(MAGMA_HOME ${ROCM_PATH}/magma)
+  set(ENV{MAGMA_HOME} ${ROCM_PATH}/magma)
+else()
+  set(MAGMA_HOME $ENV{MAGMA_HOME})
+endif()
+
+# MIOpen isn't a part of HIP-SDK for Windows and hence, may have a different
+# installation directory.
+if(WIN32)
+  if(NOT DEFINED ENV{MIOPEN_PATH})
+    set(miopen_DIR C:/opt/miopen/lib/cmake/miopen)
+  else()
+    set(miopen_DIR $ENV{MIOPEN_PATH}/lib/cmake/miopen)
+  endif()
+endif()
+
+torch_hip_get_arch_list(PYTORCH_ROCM_ARCH)
+if(PYTORCH_ROCM_ARCH STREQUAL "")
+  message(FATAL_ERROR "No GPU arch specified for ROCm build. Please use PYTORCH_ROCM_ARCH environment variable to specify GPU archs to build for.")
+endif()
+message("Building PyTorch for GPU arch: ${PYTORCH_ROCM_ARCH}")
+
+# Add HIP to the CMAKE Module Path
+# needed because the find_package call to this module uses the Module mode search
+# https://cmake.org/cmake/help/latest/command/find_package.html#search-modes
+if(UNIX)
+  set(CMAKE_MODULE_PATH ${ROCM_PATH}/lib/cmake/hip ${CMAKE_MODULE_PATH})
+else() # Win32
+  set(CMAKE_MODULE_PATH ${ROCM_PATH}/cmake/ ${CMAKE_MODULE_PATH})
+endif()
+
+# Add ROCM_PATH to CMAKE_PREFIX_PATH, needed because the find_package
+# call to individual ROCM components uses the Config mode search
+list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH})
+
+macro(find_package_and_print_version PACKAGE_NAME)
+  find_package("${PACKAGE_NAME}" ${ARGN})
+  message("${PACKAGE_NAME} VERSION: ${${PACKAGE_NAME}_VERSION}")
+endmacro()
+
+# Find the HIP Package
+# MODULE argument is added for clarity that CMake is searching
+# for FindHIP.cmake in Module mode
+find_package_and_print_version(HIP 1.0 MODULE)
+
+if(HIP_FOUND)
+  set(PYTORCH_FOUND_HIP TRUE)
+
+  # Find ROCM version for checks
+  if(UNIX)
+    set(ROCM_VERSION_HEADER_PATH ${ROCM_INCLUDE_DIRS}/rocm-core/rocm_version.h)
+  else()
+    set(ROCM_VERSION_HEADER_PATH ${ROCM_INCLUDE_DIRS}/hip/hip_version.h)
+  endif()
+  get_filename_component(ROCM_HEADER_NAME ${ROCM_VERSION_HEADER_PATH} NAME)
+
+  if(EXISTS ${ROCM_VERSION_HEADER_PATH})
+    set(ROCM_HEADER_FILE ${ROCM_VERSION_HEADER_PATH})
+  else()
+    message(FATAL_ERROR "********************* ${ROCM_HEADER_NAME} could not be found ******************\n")
+  endif()
+
+  # Read the ROCM headerfile into a variable
+  file(READ ${ROCM_HEADER_FILE} ROCM_HEADER_CONTENT)
+
+  # Since Windows currently supports only a part of ROCm and names it HIP-SDK,
+  # we need to refer to the HIP-SDK equivalents of entities existing in ROCm lib.
+  if(UNIX)
+    set(ROCM_LIB_NAME "ROCM")
+  else() # Win32
+    set(ROCM_LIB_NAME "HIP")
+  endif()
+  # Below we use a RegEx to find ROCM version numbers.
+  # Note that CMake does not support \s for blank space. That is
+  # why in the regular expressions below we have a blank space in
+  # the square brackets.
+  # There are three steps:
+  # 1. Match regular expression
+  # 2. Strip the non-numerical part of the string
+  # 3. Strip leading and trailing spaces
+
+  string(REGEX MATCH "${ROCM_LIB_NAME}_VERSION_MAJOR[ ]+[0-9]+" TEMP1 ${ROCM_HEADER_CONTENT})
+  string(REPLACE "${ROCM_LIB_NAME}_VERSION_MAJOR" "" TEMP2 ${TEMP1})
+  string(STRIP ${TEMP2} ROCM_VERSION_DEV_MAJOR)
+  string(REGEX MATCH "${ROCM_LIB_NAME}_VERSION_MINOR[ ]+[0-9]+" TEMP1 ${ROCM_HEADER_CONTENT})
+  string(REPLACE "${ROCM_LIB_NAME}_VERSION_MINOR" "" TEMP2 ${TEMP1})
+  string(STRIP ${TEMP2} ROCM_VERSION_DEV_MINOR)
+  string(REGEX MATCH "${ROCM_LIB_NAME}_VERSION_PATCH[ ]+[0-9]+" TEMP1 ${ROCM_HEADER_CONTENT})
+  string(REPLACE "${ROCM_LIB_NAME}_VERSION_PATCH" "" TEMP2 ${TEMP1})
+  string(STRIP ${TEMP2} ROCM_VERSION_DEV_PATCH)
+
+  # Create ROCM_VERSION_DEV_INT which is later used as a preprocessor macros
+  set(ROCM_VERSION_DEV "${ROCM_VERSION_DEV_MAJOR}.${ROCM_VERSION_DEV_MINOR}.${ROCM_VERSION_DEV_PATCH}")
+  math(EXPR ROCM_VERSION_DEV_INT "(${ROCM_VERSION_DEV_MAJOR}*10000) + (${ROCM_VERSION_DEV_MINOR}*100) + ${ROCM_VERSION_DEV_PATCH}")
+
+  message("\n***** ROCm version from ${ROCM_HEADER_NAME} ****\n")
+  message("ROCM_VERSION_DEV: ${ROCM_VERSION_DEV}")
+  message("ROCM_VERSION_DEV_MAJOR: ${ROCM_VERSION_DEV_MAJOR}")
+  message("ROCM_VERSION_DEV_MINOR: ${ROCM_VERSION_DEV_MINOR}")
+  message("ROCM_VERSION_DEV_PATCH: ${ROCM_VERSION_DEV_PATCH}")
+  message("ROCM_VERSION_DEV_INT:   ${ROCM_VERSION_DEV_INT}")
+
+  math(EXPR TORCH_HIP_VERSION "(${HIP_VERSION_MAJOR} * 100) + ${HIP_VERSION_MINOR}")
+  message("HIP_VERSION_MAJOR: ${HIP_VERSION_MAJOR}")
+  message("HIP_VERSION_MINOR: ${HIP_VERSION_MINOR}")
+  message("TORCH_HIP_VERSION: ${TORCH_HIP_VERSION}")
+
+  # Find ROCM components using Config mode
+  # These components will be searced for recursively in ${ROCM_PATH}
+  message("\n***** Library versions from cmake find_package *****\n")
+  find_package_and_print_version(hip REQUIRED CONFIG)
+  find_package_and_print_version(amd_comgr REQUIRED)
+  find_package_and_print_version(rocrand REQUIRED)
+  find_package_and_print_version(hiprand REQUIRED)
+  find_package_and_print_version(rocblas REQUIRED)
+  find_package_and_print_version(hipblas REQUIRED)
+  find_package_and_print_version(miopen REQUIRED)
+  find_package_and_print_version(hipfft REQUIRED)
+  find_package_and_print_version(hipsparse REQUIRED)
+  find_package_and_print_version(rocprim REQUIRED)
+  find_package_and_print_version(hipcub REQUIRED)
+  find_package_and_print_version(rocthrust REQUIRED)
+  find_package_and_print_version(hipsolver REQUIRED)
+  # workaround cmake 4 build issue
+  if(CMAKE_VERSION VERSION_GREATER_EQUAL "4.0.0")
+    message(WARNING "Work around hiprtc cmake failure for cmake >= 4")
+    set(CMAKE_POLICY_VERSION_MINIMUM 3.5)
+    find_package_and_print_version(hiprtc REQUIRED)
+    unset(CMAKE_POLICY_VERSION_MINIMUM)
+  else()
+    find_package_and_print_version(hiprtc REQUIRED)
+  endif()
+  find_package_and_print_version(hipblaslt REQUIRED)
+
+  if(UNIX)
+    find_package_and_print_version(rccl)
+    find_package_and_print_version(hsa-runtime64 REQUIRED)
+
+    # roctx is part of roctracer
+    find_library(ROCM_ROCTX_LIB roctx64 HINTS ${ROCM_PATH}/lib)
+
+    set(PROJECT_RANDOM_BINARY_DIR "${PROJECT_BINARY_DIR}")
+
+    if(ROCM_VERSION_DEV VERSION_GREATER_EQUAL "5.7.0")
+      # check whether hipblaslt provides HIPBLASLT_MATMUL_DESC_A_SCALE_POINTER_VEC_EXT
+      set(file "${PROJECT_BINARY_DIR}/hipblaslt_test_vec_ext.cc")
+      file(WRITE ${file} ""
+        "#define LEGACY_HIPBLAS_DIRECT\n"
+        "#include \n"
+        "int main() {\n"
+        "    hipblasLtMatmulDescAttributes_t attr = HIPBLASLT_MATMUL_DESC_A_SCALE_POINTER_VEC_EXT;\n"
+        "    return 0;\n"
+        "}\n"
+        )
+      try_compile(hipblaslt_compile_result_vec_ext ${PROJECT_RANDOM_BINARY_DIR} ${file}
+        CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${ROCM_INCLUDE_DIRS}"
+        COMPILE_DEFINITIONS -D__HIP_PLATFORM_AMD__ -D__HIP_PLATFORM_HCC__
+        OUTPUT_VARIABLE hipblaslt_compile_output)
+      if(hipblaslt_compile_result_vec_ext)
+        set(HIPBLASLT_VEC_EXT ON)
+        #message("hipblaslt is using scale pointer vec ext: ${hipblaslt_compile_output}")
+        message("hipblaslt is using scale pointer vec ext")
+      else()
+        set(HIPBLASLT_VEC_EXT OFF)
+        message("hipblaslt is NOT using scale pointer vec ext: ${hipblaslt_compile_output}")
+        #message("hipblaslt is NOT using scale pointer vec ext")
+      endif()
+    endif()
+  endif()
+endif()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/cuda.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/cuda.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..c66d32b115c1ae17d2e60375b8f7687c07772a7f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/cuda.cmake
@@ -0,0 +1,387 @@
+# ---[ cuda
+
+# Poor man's include guard
+if(TARGET torch::cudart)
+  return()
+endif()
+
+# sccache is only supported in CMake master and not in the newest official
+# release (3.11.3) yet. Hence we need our own Modules_CUDA_fix to enable sccache.
+list(APPEND CMAKE_MODULE_PATH ${CMAKE_CURRENT_LIST_DIR}/../Modules_CUDA_fix)
+
+# We don't want to statically link cudart, because we rely on it's dynamic linkage in
+# python (follow along torch/cuda/__init__.py and usage of cudaGetErrorName).
+# Technically, we can link cudart here statically, and link libtorch_python.so
+# to a dynamic libcudart.so, but that's just wasteful.
+# However, on Windows, if this one gets switched off, the error "cuda: unknown error"
+# will be raised when running the following code:
+# >>> import torch
+# >>> torch.cuda.is_available()
+# >>> torch.cuda.current_device()
+# More details can be found in the following links.
+# https://github.com/pytorch/pytorch/issues/20635
+# https://github.com/pytorch/pytorch/issues/17108
+if(NOT MSVC)
+  set(CUDA_USE_STATIC_CUDA_RUNTIME OFF CACHE INTERNAL "")
+endif()
+
+# Find CUDA.
+find_package(CUDA)
+if(NOT CUDA_FOUND)
+  message(WARNING
+    "PyTorch: CUDA cannot be found. Depending on whether you are building "
+    "PyTorch or a PyTorch dependent library, the next warning / error will "
+    "give you more info.")
+  set(CAFFE2_USE_CUDA OFF)
+  return()
+endif()
+
+# Enable CUDA language support
+set(CUDAToolkit_ROOT "${CUDA_TOOLKIT_ROOT_DIR}")
+# Pass clang as host compiler, which according to the docs
+# Must be done before CUDA language is enabled, see
+# https://cmake.org/cmake/help/v3.15/variable/CMAKE_CUDA_HOST_COMPILER.html
+if("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang")
+  set(CMAKE_CUDA_HOST_COMPILER "${CMAKE_CXX_COMPILER}")
+endif()
+enable_language(CUDA)
+if("X${CMAKE_CUDA_STANDARD}" STREQUAL "X" )
+  set(CMAKE_CUDA_STANDARD ${CMAKE_CXX_STANDARD})
+endif()
+set(CMAKE_CUDA_STANDARD_REQUIRED ON)
+
+# CMP0074 - find_package will respect _ROOT variables
+cmake_policy(PUSH)
+if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.12.0)
+  cmake_policy(SET CMP0074 NEW)
+endif()
+
+find_package(CUDAToolkit REQUIRED)
+
+cmake_policy(POP)
+
+if(NOT CMAKE_CUDA_COMPILER_VERSION VERSION_EQUAL CUDAToolkit_VERSION)
+  message(FATAL_ERROR "Found two conflicting CUDA versions:\n"
+                      "V${CMAKE_CUDA_COMPILER_VERSION} in '${CUDA_INCLUDE_DIRS}' and\n"
+                      "V${CUDAToolkit_VERSION} in '${CUDAToolkit_INCLUDE_DIRS}'")
+endif()
+
+message(STATUS "PyTorch: CUDA detected: " ${CUDA_VERSION})
+message(STATUS "PyTorch: CUDA nvcc is: " ${CUDA_NVCC_EXECUTABLE})
+message(STATUS "PyTorch: CUDA toolkit directory: " ${CUDA_TOOLKIT_ROOT_DIR})
+if(CUDA_VERSION VERSION_LESS 11.0)
+  message(FATAL_ERROR "PyTorch requires CUDA 11.0 or above.")
+endif()
+
+if(CUDA_FOUND)
+  # Sometimes, we may mismatch nvcc with the CUDA headers we are
+  # compiling with, e.g., if a ccache nvcc is fed to us by CUDA_NVCC_EXECUTABLE
+  # but the PATH is not consistent with CUDA_HOME.  It's better safe
+  # than sorry: make sure everything is consistent.
+  if(MSVC AND CMAKE_GENERATOR MATCHES "Visual Studio")
+    # When using Visual Studio, it attempts to lock the whole binary dir when
+    # `try_run` is called, which will cause the build to fail.
+    string(RANDOM BUILD_SUFFIX)
+    set(PROJECT_RANDOM_BINARY_DIR "${PROJECT_BINARY_DIR}/${BUILD_SUFFIX}")
+  else()
+    set(PROJECT_RANDOM_BINARY_DIR "${PROJECT_BINARY_DIR}")
+  endif()
+  set(file "${PROJECT_BINARY_DIR}/detect_cuda_version.cc")
+  file(WRITE ${file} ""
+    "#include \n"
+    "#include \n"
+    "int main() {\n"
+    "  printf(\"%d.%d\", CUDA_VERSION / 1000, (CUDA_VERSION / 10) % 100);\n"
+    "  return 0;\n"
+    "}\n"
+    )
+  if(NOT CMAKE_CROSSCOMPILING)
+    try_run(run_result compile_result ${PROJECT_RANDOM_BINARY_DIR} ${file}
+      CMAKE_FLAGS "-DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS}"
+      LINK_LIBRARIES ${CUDA_LIBRARIES}
+      RUN_OUTPUT_VARIABLE cuda_version_from_header
+      COMPILE_OUTPUT_VARIABLE output_var
+      )
+    if(NOT compile_result)
+      message(FATAL_ERROR "PyTorch: Couldn't determine version from header: " ${output_var})
+    endif()
+    message(STATUS "PyTorch: Header version is: " ${cuda_version_from_header})
+    if(NOT cuda_version_from_header STREQUAL ${CUDA_VERSION_STRING})
+      # Force CUDA to be processed for again next time
+      # TODO: I'm not sure if this counts as an implementation detail of
+      # FindCUDA
+      set(${cuda_version_from_findcuda} ${CUDA_VERSION_STRING})
+      unset(CUDA_TOOLKIT_ROOT_DIR_INTERNAL CACHE)
+      # Not strictly necessary, but for good luck.
+      unset(CUDA_VERSION CACHE)
+      # Error out
+      message(FATAL_ERROR "FindCUDA says CUDA version is ${cuda_version_from_findcuda} (usually determined by nvcc), "
+        "but the CUDA headers say the version is ${cuda_version_from_header}.  This often occurs "
+        "when you set both CUDA_HOME and CUDA_NVCC_EXECUTABLE to "
+        "non-standard locations, without also setting PATH to point to the correct nvcc.  "
+        "Perhaps, try re-running this command again with PATH=${CUDA_TOOLKIT_ROOT_DIR}/bin:$PATH.  "
+        "See above log messages for more diagnostics, and see https://github.com/pytorch/pytorch/issues/8092 for more details.")
+    endif()
+  endif()
+endif()
+
+# ---[ CUDA libraries wrapper
+
+# find lbnvrtc.so
+set(CUDA_NVRTC_LIB "${CUDA_nvrtc_LIBRARY}" CACHE FILEPATH "")
+if(CUDA_NVRTC_LIB AND NOT CUDA_NVRTC_SHORTHASH)
+  find_package(Python COMPONENTS Interpreter)
+  execute_process(
+    COMMAND Python::Interpreter -c
+    "import hashlib;hash=hashlib.sha256();hash.update(open('${CUDA_NVRTC_LIB}','rb').read());print(hash.hexdigest()[:8])"
+    RESULT_VARIABLE _retval
+    OUTPUT_VARIABLE CUDA_NVRTC_SHORTHASH)
+  if(NOT _retval EQUAL 0)
+    message(WARNING "Failed to compute shorthash for libnvrtc.so")
+    set(CUDA_NVRTC_SHORTHASH "XXXXXXXX")
+  else()
+    string(STRIP "${CUDA_NVRTC_SHORTHASH}" CUDA_NVRTC_SHORTHASH)
+    message(STATUS "${CUDA_NVRTC_LIB} shorthash is ${CUDA_NVRTC_SHORTHASH}")
+  endif()
+endif()
+
+# Create new style imported libraries.
+# Several of these libraries have a hardcoded path if CAFFE2_STATIC_LINK_CUDA
+# is set. This path is where sane CUDA installations have their static
+# libraries installed. This flag should only be used for binary builds, so
+# end-users should never have this flag set.
+
+# cuda
+add_library(caffe2::cuda INTERFACE IMPORTED)
+set_property(
+    TARGET caffe2::cuda PROPERTY INTERFACE_LINK_LIBRARIES
+    CUDA::cuda_driver)
+
+# cudart
+add_library(torch::cudart INTERFACE IMPORTED)
+if(CAFFE2_STATIC_LINK_CUDA)
+    set_property(
+        TARGET torch::cudart PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cudart_static)
+else()
+    set_property(
+        TARGET torch::cudart PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cudart)
+endif()
+
+# nvToolsExt
+if(USE_SYSTEM_NVTX)
+  find_path(nvtx3_dir NAMES nvtx3 PATHS ${CUDA_INCLUDE_DIRS})
+else()
+  find_path(nvtx3_dir NAMES nvtx3 PATHS "${PROJECT_SOURCE_DIR}/third_party/NVTX/c/include" NO_DEFAULT_PATH)
+endif()
+find_package_handle_standard_args(nvtx3 DEFAULT_MSG nvtx3_dir)
+if(nvtx3_FOUND)
+  add_library(torch::nvtx3 INTERFACE IMPORTED)
+  target_include_directories(torch::nvtx3 INTERFACE "${nvtx3_dir}")
+  target_compile_definitions(torch::nvtx3 INTERFACE TORCH_CUDA_USE_NVTX3)
+else()
+  message(WARNING "Cannot find NVTX3, find old NVTX instead")
+  add_library(torch::nvtoolsext INTERFACE IMPORTED)
+  set_property(TARGET torch::nvtoolsext PROPERTY INTERFACE_LINK_LIBRARIES CUDA::nvToolsExt)
+endif()
+
+
+# cublas
+add_library(caffe2::cublas INTERFACE IMPORTED)
+if(CAFFE2_STATIC_LINK_CUDA AND NOT WIN32)
+    set_property(
+        TARGET caffe2::cublas PROPERTY INTERFACE_LINK_LIBRARIES
+        # NOTE: cublas is always linked dynamically
+        CUDA::cublas CUDA::cublasLt)
+    set_property(
+        TARGET caffe2::cublas APPEND PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cudart_static rt)
+else()
+    set_property(
+        TARGET caffe2::cublas PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cublas CUDA::cublasLt)
+endif()
+
+# cudnn interface
+# static linking is handled by USE_STATIC_CUDNN environment variable
+if(CAFFE2_USE_CUDNN)
+  if(USE_STATIC_CUDNN)
+    set(CUDNN_STATIC ON CACHE BOOL "")
+  else()
+    set(CUDNN_STATIC OFF CACHE BOOL "")
+  endif()
+
+  find_package(CUDNN)
+
+  if(NOT CUDNN_FOUND)
+    message(WARNING
+      "Cannot find cuDNN library. Turning the option off")
+    set(CAFFE2_USE_CUDNN OFF)
+  else()
+    if(CUDNN_VERSION VERSION_LESS "8.1.0")
+      message(FATAL_ERROR "PyTorch requires cuDNN 8.1 and above.")
+    endif()
+  endif()
+
+  add_library(torch::cudnn INTERFACE IMPORTED)
+  target_include_directories(torch::cudnn INTERFACE ${CUDNN_INCLUDE_PATH})
+  if(CUDNN_STATIC AND NOT WIN32)
+    target_link_options(torch::cudnn INTERFACE
+        "-Wl,--exclude-libs,libcudnn_static.a")
+  else()
+    target_link_libraries(torch::cudnn INTERFACE ${CUDNN_LIBRARY_PATH})
+  endif()
+else()
+  message(STATUS "USE_CUDNN is set to 0. Compiling without cuDNN support")
+endif()
+
+if(CAFFE2_USE_CUSPARSELT)
+  find_package(CUSPARSELT)
+
+  if(NOT CUSPARSELT_FOUND)
+    message(WARNING
+      "Cannot find cuSPARSELt library. Turning the option off")
+    set(CAFFE2_USE_CUSPARSELT OFF)
+  else()
+    add_library(torch::cusparselt INTERFACE IMPORTED)
+    target_include_directories(torch::cusparselt INTERFACE ${CUSPARSELT_INCLUDE_PATH})
+    target_link_libraries(torch::cusparselt INTERFACE ${CUSPARSELT_LIBRARY_PATH})
+  endif()
+else()
+  message(STATUS "USE_CUSPARSELT is set to 0. Compiling without cuSPARSELt support")
+endif()
+
+if(USE_CUDSS)
+  find_package(CUDSS)
+
+  if(NOT CUDSS_FOUND)
+    message(WARNING
+      "Cannot find CUDSS library. Turning the option off")
+    set(USE_CUDSS OFF)
+  else()
+    add_library(torch::cudss INTERFACE IMPORTED)
+    target_include_directories(torch::cudss INTERFACE ${CUDSS_INCLUDE_PATH})
+    target_link_libraries(torch::cudss INTERFACE ${CUDSS_LIBRARY_PATH})
+  endif()
+else()
+  message(STATUS "USE_CUDSS is set to 0. Compiling without cuDSS support")
+endif()
+
+# cufile
+if(CAFFE2_USE_CUFILE)
+  add_library(torch::cufile INTERFACE IMPORTED)
+  if(CAFFE2_STATIC_LINK_CUDA AND NOT WIN32)
+      set_property(
+          TARGET torch::cufile PROPERTY INTERFACE_LINK_LIBRARIES
+          CUDA::cuFile_static)
+  else()
+      set_property(
+          TARGET torch::cufile PROPERTY INTERFACE_LINK_LIBRARIES
+          CUDA::cuFile)
+  endif()
+else()
+  message(STATUS "USE_CUFILE is set to 0. Compiling without cuFile support")
+endif()
+
+# curand
+add_library(caffe2::curand INTERFACE IMPORTED)
+if(CAFFE2_STATIC_LINK_CUDA AND NOT WIN32)
+    set_property(
+        TARGET caffe2::curand PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::curand_static)
+else()
+    set_property(
+        TARGET caffe2::curand PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::curand)
+endif()
+
+# cufft
+add_library(caffe2::cufft INTERFACE IMPORTED)
+if(CAFFE2_STATIC_LINK_CUDA AND NOT WIN32)
+    set_property(
+        TARGET caffe2::cufft PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cufft_static_nocallback)
+else()
+    set_property(
+        TARGET caffe2::cufft PROPERTY INTERFACE_LINK_LIBRARIES
+        CUDA::cufft)
+endif()
+
+# nvrtc
+add_library(caffe2::nvrtc INTERFACE IMPORTED)
+set_property(
+    TARGET caffe2::nvrtc PROPERTY INTERFACE_LINK_LIBRARIES
+    CUDA::nvrtc caffe2::cuda)
+
+# Add onnx namepsace definition to nvcc
+if(ONNX_NAMESPACE)
+  list(APPEND CUDA_NVCC_FLAGS "-DONNX_NAMESPACE=${ONNX_NAMESPACE}")
+else()
+  list(APPEND CUDA_NVCC_FLAGS "-DONNX_NAMESPACE=onnx_c2")
+endif()
+
+# Don't activate VC env again for Ninja generators with MSVC on Windows if CUDAHOSTCXX is not defined
+# by adding --use-local-env.
+if(MSVC AND CMAKE_GENERATOR STREQUAL "Ninja" AND NOT DEFINED ENV{CUDAHOSTCXX})
+  list(APPEND CUDA_NVCC_FLAGS "--use-local-env")
+endif()
+
+# setting nvcc arch flags
+torch_cuda_get_nvcc_gencode_flag(NVCC_FLAGS_EXTRA)
+# CMake 3.18 adds integrated support for architecture selection, but we can't rely on it
+set(CMAKE_CUDA_ARCHITECTURES OFF)
+list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
+message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA}")
+
+# disable some nvcc diagnostic that appears in boost, glog, glags, opencv, etc.
+foreach(diag cc_clobber_ignored
+             field_without_dll_interface
+             base_class_has_different_dll_interface
+             dll_interface_conflict_none_assumed
+             dll_interface_conflict_dllexport_assumed
+             bad_friend_decl)
+  list(APPEND SUPPRESS_WARNING_FLAGS --diag_suppress=${diag})
+endforeach()
+string(REPLACE ";" "," SUPPRESS_WARNING_FLAGS "${SUPPRESS_WARNING_FLAGS}")
+list(APPEND CUDA_NVCC_FLAGS -Xcudafe ${SUPPRESS_WARNING_FLAGS})
+
+set(CUDA_PROPAGATE_HOST_FLAGS_BLOCKLIST "-Werror")
+if(MSVC)
+  list(APPEND CUDA_NVCC_FLAGS "--Werror" "cross-execution-space-call")
+  list(APPEND CUDA_NVCC_FLAGS "--no-host-device-move-forward")
+endif()
+
+# Debug and Release symbol support
+if(MSVC)
+  if(${CAFFE2_USE_MSVC_STATIC_RUNTIME})
+    string(APPEND CMAKE_CUDA_FLAGS_DEBUG " -Xcompiler /MTd")
+    string(APPEND CMAKE_CUDA_FLAGS_MINSIZEREL " -Xcompiler /MT")
+    string(APPEND CMAKE_CUDA_FLAGS_RELEASE " -Xcompiler /MT")
+    string(APPEND CMAKE_CUDA_FLAGS_RELWITHDEBINFO " -Xcompiler /MT")
+  else()
+    string(APPEND CMAKE_CUDA_FLAGS_DEBUG " -Xcompiler /MDd")
+    string(APPEND CMAKE_CUDA_FLAGS_MINSIZEREL " -Xcompiler /MD")
+    string(APPEND CMAKE_CUDA_FLAGS_RELEASE " -Xcompiler /MD")
+    string(APPEND CMAKE_CUDA_FLAGS_RELWITHDEBINFO " -Xcompiler /MD")
+  endif()
+  if(CUDA_NVCC_FLAGS MATCHES "Zi")
+    list(APPEND CUDA_NVCC_FLAGS "-Xcompiler" "-FS")
+  endif()
+elseif(CUDA_DEVICE_DEBUG)
+  list(APPEND CUDA_NVCC_FLAGS "-g" "-G")  # -G enables device code debugging symbols
+endif()
+
+# Set expt-relaxed-constexpr to suppress Eigen warnings
+list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
+
+# Set expt-extended-lambda to support lambda on device
+list(APPEND CUDA_NVCC_FLAGS "--expt-extended-lambda")
+
+foreach(FLAG ${CUDA_NVCC_FLAGS})
+  string(FIND "${FLAG}" " " flag_space_position)
+  if(NOT flag_space_position EQUAL -1)
+    message(FATAL_ERROR "Found spaces in CUDA_NVCC_FLAGS entry '${FLAG}'")
+  endif()
+  string(APPEND CMAKE_CUDA_FLAGS " ${FLAG}")
+endforeach()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/gflags.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/gflags.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..186cda1a909ab79431114d1c61de895069255389
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/gflags.cmake
@@ -0,0 +1,83 @@
+# ---[ gflags
+
+# We will try to use the config mode first, and then manual find.
+find_package(gflags CONFIG QUIET)
+if(NOT TARGET gflags)
+  find_package(gflags MODULE QUIET)
+endif()
+
+if(TARGET gflags)
+  message(STATUS "Caffe2: Found gflags with new-style gflags target.")
+elseif(GFLAGS_FOUND)
+  message(STATUS "Caffe2: Found gflags with old-style gflag starget.")
+  add_library(gflags UNKNOWN IMPORTED)
+  set_property(
+      TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARY})
+  set_property(
+      TARGET gflags PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+      ${GFLAGS_INCLUDE_DIR})
+else()
+  message(STATUS
+      "Caffe2: Cannot find gflags automatically. Using legacy find.")
+
+  # - Try to find GFLAGS in the legacy way.
+  #
+  # The following variables are optionally searched for defaults
+  #  GFLAGS_ROOT_DIR: Base directory where all GFLAGS components are found
+  #
+  # The following are set after configuration is done:
+  #  GFLAGS_FOUND
+  #  GFLAGS_INCLUDE_DIRS
+  #  GFLAGS_LIBRARIES
+  #  GFLAGS_LIBRARYRARY_DIRS
+  include(FindPackageHandleStandardArgs)
+  set(GFLAGS_ROOT_DIR "" CACHE PATH "Folder contains Gflags")
+
+  # We are testing only a couple of files in the include directories
+  if(WIN32)
+    find_path(GFLAGS_INCLUDE_DIR gflags/gflags.h
+        PATHS ${GFLAGS_ROOT_DIR}/src/windows)
+  else()
+    find_path(GFLAGS_INCLUDE_DIR gflags/gflags.h
+        PATHS ${GFLAGS_ROOT_DIR})
+  endif()
+
+  if(WIN32)
+    find_library(GFLAGS_LIBRARY_RELEASE
+        NAMES libgflags
+        PATHS ${GFLAGS_ROOT_DIR}
+        PATH_SUFFIXES Release)
+
+    find_library(GFLAGS_LIBRARY_DEBUG
+        NAMES libgflags-debug
+        PATHS ${GFLAGS_ROOT_DIR}
+        PATH_SUFFIXES Debug)
+    set(GFLAGS_LIBRARY optimized ${GFLAGS_LIBRARY_RELEASE} debug ${GFLAGS_LIBRARY_DEBUG})
+  else()
+    find_library(GFLAGS_LIBRARY gflags)
+  endif()
+
+  find_package_handle_standard_args(
+      gflags DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
+
+  if(GFLAGS_FOUND)
+    message(
+        STATUS
+        "Caffe2: Found gflags  (include: ${GFLAGS_INCLUDE_DIR}, "
+        "library: ${GFLAGS_LIBRARY})")
+    add_library(gflags UNKNOWN IMPORTED)
+    set_property(
+        TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARY})
+    set_property(
+        TARGET gflags PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+        ${GFLAGS_INCLUDE_DIR})
+  endif()
+endif()
+
+# After above, we should have the gflags target now.
+if(NOT TARGET gflags)
+  message(WARNING
+      "Caffe2: gflags cannot be found. Depending on whether you are building "
+      "Caffe2 or a Caffe2 dependent library, the next warning / error will "
+      "give you more info.")
+endif()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/glog.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/glog.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..bb03e81f29e3afed43ba95260cc5c298be881f72
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/glog.cmake
@@ -0,0 +1,70 @@
+# ---[ glog
+
+# We will try to use the config mode first, and then manual find.
+find_package(glog CONFIG QUIET)
+if(NOT TARGET glog::glog)
+  find_package(glog MODULE QUIET)
+endif()
+
+if(TARGET glog::glog)
+  message(STATUS "Caffe2: Found glog with new-style glog target.")
+elseif(GLOG_FOUND)
+  message(
+      STATUS
+      "Caffe2: Found glog with old-style glog starget. Glog never shipped "
+      "old style glog targets, so somewhere in your cmake path there might "
+      "be a custom Findglog.cmake file that got triggered. We will make a "
+      "best effort to create the new style glog target for you.")
+  add_library(glog::glog UNKNOWN IMPORTED)
+  set_property(
+      TARGET glog::glog PROPERTY IMPORTED_LOCATION ${GLOG_LIBRARY})
+  set_property(
+      TARGET glog::glog PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+      ${GLOG_INCLUDE_DIR})
+else()
+  message(STATUS "Caffe2: Cannot find glog automatically. Using legacy find.")
+
+  # - Try to find Glog
+  #
+  # The following variables are optionally searched for defaults
+  #  GLOG_ROOT_DIR: Base directory where all GLOG components are found
+  #
+  # The following are set after configuration is done:
+  #  GLOG_FOUND
+  #  GLOG_INCLUDE_DIRS
+  #  GLOG_LIBRARIES
+  #  GLOG_LIBRARYRARY_DIRS
+
+  include(FindPackageHandleStandardArgs)
+  set(GLOG_ROOT_DIR "" CACHE PATH "Folder contains Google glog")
+  if(NOT WIN32)
+      find_path(GLOG_INCLUDE_DIR glog/logging.h
+          PATHS ${GLOG_ROOT_DIR})
+  endif()
+
+  find_library(GLOG_LIBRARY glog
+      PATHS ${GLOG_ROOT_DIR}
+      PATH_SUFFIXES lib lib64)
+
+  find_package_handle_standard_args(glog DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
+
+  if(GLOG_FOUND)
+    message(STATUS
+        "Caffe2: Found glog (include: ${GLOG_INCLUDE_DIR}, "
+        "library: ${GLOG_LIBRARY})")
+    add_library(glog::glog UNKNOWN IMPORTED)
+    set_property(
+        TARGET glog::glog PROPERTY IMPORTED_LOCATION ${GLOG_LIBRARY})
+    set_property(
+        TARGET glog::glog PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+        ${GLOG_INCLUDE_DIR})
+  endif()
+endif()
+
+# After above, we should have the glog::glog target now.
+if(NOT TARGET glog::glog)
+  message(WARNING
+      "Caffe2: glog cannot be found. Depending on whether you are building "
+      "Caffe2 or a Caffe2 dependent library, the next warning / error will "
+      "give you more info.")
+endif()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/mkl.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/mkl.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..2f6d1fd905aa303cc240b058318acdfb2483e9ad
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/mkl.cmake
@@ -0,0 +1,40 @@
+find_package(MKL QUIET)
+
+if(TARGET caffe2::mkl)
+  return()
+endif()
+
+add_library(caffe2::mkl INTERFACE IMPORTED)
+target_include_directories(caffe2::mkl INTERFACE ${MKL_INCLUDE_DIR})
+target_link_libraries(caffe2::mkl INTERFACE ${MKL_LIBRARIES})
+foreach(MKL_LIB IN LISTS MKL_LIBRARIES)
+  if(EXISTS "${MKL_LIB}")
+    get_filename_component(MKL_LINK_DIR "${MKL_LIB}" DIRECTORY)
+    if(IS_DIRECTORY "${MKL_LINK_DIR}")
+      target_link_directories(caffe2::mkl INTERFACE "${MKL_LINK_DIR}")
+    endif()
+  endif()
+endforeach()
+
+# TODO: This is a hack, it will not pick up architecture dependent
+# MKL libraries correctly; see https://github.com/pytorch/pytorch/issues/73008
+set_property(
+  TARGET caffe2::mkl PROPERTY INTERFACE_LINK_DIRECTORIES
+  ${MKL_ROOT}/lib ${MKL_ROOT}/lib/intel64 ${MKL_ROOT}/lib/intel64_win ${MKL_ROOT}/lib/win-x64)
+
+if(UNIX)
+  if(USE_STATIC_MKL)
+    foreach(MKL_LIB_PATH IN LISTS MKL_LIBRARIES)
+      if(NOT EXISTS "${MKL_LIB_PATH}")
+        continue()
+      endif()
+
+      get_filename_component(MKL_LIB_NAME "${MKL_LIB_PATH}" NAME)
+
+      # Match archive libraries starting with "libmkl_"
+      if(MKL_LIB_NAME MATCHES "^libmkl_" AND MKL_LIB_NAME MATCHES ".a$")
+        target_link_options(caffe2::mkl INTERFACE "-Wl,--exclude-libs,${MKL_LIB_NAME}")
+      endif()
+    endforeach()
+  endif()
+endif()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/mkldnn.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/mkldnn.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..87935625f9bfb543d1cdc7f2b59f11e8d4a709e7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/mkldnn.cmake
@@ -0,0 +1,18 @@
+set(MKLDNN_USE_NATIVE_ARCH ${USE_NATIVE_ARCH})
+
+if(CPU_AARCH64)
+  include(${CMAKE_CURRENT_LIST_DIR}/ComputeLibrary.cmake)
+endif()
+
+find_package(MKLDNN QUIET)
+
+if(NOT TARGET caffe2::mkldnn)
+  add_library(caffe2::mkldnn INTERFACE IMPORTED)
+endif()
+
+set_property(
+  TARGET caffe2::mkldnn PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+  ${MKLDNN_INCLUDE_DIR})
+set_property(
+  TARGET caffe2::mkldnn PROPERTY INTERFACE_LINK_LIBRARIES
+  ${MKLDNN_LIBRARIES})
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/protobuf.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/protobuf.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..77ec3622b132dc7a7817716dd24ef986e6ac030d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/protobuf.cmake
@@ -0,0 +1,92 @@
+# ---[ Protobuf
+
+# We will try to use the config mode first, and then manual find.
+find_package(Protobuf CONFIG QUIET)
+if(NOT Protobuf_FOUND)
+  find_package(Protobuf MODULE QUIET)
+endif()
+
+if((TARGET protobuf::libprotobuf OR TARGET protobuf::libprotobuf-lite) AND TARGET protobuf::protoc)
+  # Hooray. This is the most ideal situation, meaning that you either have a
+  # Protobuf config file installed (like on Windows), or you are using a
+  # modern CMake that ships with a FindProtobuf.cmake file that produces
+  # modern targets.
+  message(STATUS "Caffe2: Found protobuf with new-style protobuf targets.")
+elseif(Protobuf_FOUND OR PROTOBUF_FOUND)
+  # If the modern targets are not present, we will generate them for you for
+  # backward compatibility. This is backported from CMake's new FindProtobuf.cmake
+  # content.
+  if((NOT PROTOBUF_LIBRARY) AND (NOT PROTOBUF_LITE_LIBRARY))
+    message(FATAL_ERROR
+        "Caffe2: Found protobuf with old style targets, but could not find targets."
+        " PROTOBUF_LIBRARY: " ${PROTOBUF_LIBRARY}
+        " PROTOBUF_LITE_LIBRARY: " ${PROTOBUF_LITE_LIBRARY}
+        " Protobuf_LIBRARY: " ${Protobuf_LIBRARY}
+        " Protobuf_LITE_LIBRARY: " ${Protobuf_LITE_LIBRARY})
+  endif()
+  message(STATUS "Caffe2: Found protobuf with old-style protobuf targets.")
+
+  if(PROTOBUF_LIBRARY)
+    if(NOT TARGET protobuf::libprotobuf)
+      add_library(protobuf::libprotobuf UNKNOWN IMPORTED)
+      set_target_properties(protobuf::libprotobuf PROPERTIES
+          INTERFACE_INCLUDE_DIRECTORIES "${PROTOBUF_INCLUDE_DIRS}")
+    endif()
+    if(EXISTS "${PROTOBUF_LIBRARY}")
+      set_target_properties(protobuf::libprotobuf PROPERTIES
+          IMPORTED_LOCATION "${PROTOBUF_LIBRARY}")
+    endif()
+    if(EXISTS "${PROTOBUF_LIBRARY_RELEASE}")
+      set_property(TARGET protobuf::libprotobuf APPEND PROPERTY
+          IMPORTED_CONFIGURATIONS RELEASE)
+      set_target_properties(protobuf::libprotobuf PROPERTIES
+          IMPORTED_LOCATION_RELEASE "${PROTOBUF_LIBRARY_RELEASE}")
+    endif()
+    if(EXISTS "${PROTOBUF_LIBRARY_DEBUG}")
+      set_property(TARGET protobuf::libprotobuf APPEND PROPERTY
+          IMPORTED_CONFIGURATIONS DEBUG)
+      set_target_properties(protobuf::libprotobuf PROPERTIES
+          IMPORTED_LOCATION_DEBUG "${PROTOBUF_LIBRARY_DEBUG}")
+    endif()
+  endif()
+
+  if(PROTOBUF_LITE_LIBRARY)
+    if(NOT TARGET protobuf::libprotobuf-lite)
+      add_library(protobuf::libprotobuf-lite UNKNOWN IMPORTED)
+      set_target_properties(protobuf::libprotobuf-lite PROPERTIES
+          INTERFACE_INCLUDE_DIRECTORIES "${PROTOBUF_INCLUDE_DIRS}")
+    endif()
+    if(EXISTS "${PROTOBUF_LITE_LIBRARY}")
+      set_target_properties(protobuf::libprotobuf-lite PROPERTIES
+          IMPORTED_LOCATION "${PROTOBUF_LITE_LIBRARY}")
+    endif()
+    if(EXISTS "${PROTOBUF_LITE_LIBRARY_RELEASE}")
+      set_property(TARGET protobuf::libprotobuf-lite APPEND PROPERTY
+          IMPORTED_CONFIGURATIONS RELEASE)
+      set_target_properties(protobuf::libprotobuf-lite PROPERTIES
+          IMPORTED_LOCATION_RELEASE "${PROTOBUF_LITE_LIBRARY_RELEASE}")
+    endif()
+    if(EXISTS "${PROTOBUF_LITE_LIBRARY_DEBUG}")
+      set_property(TARGET protobuf::libprotobuf-lite APPEND PROPERTY
+          IMPORTED_CONFIGURATIONS DEBUG)
+      set_target_properties(protobuf::libprotobuf-lite PROPERTIES
+          IMPORTED_LOCATION_DEBUG "${PROTOBUF_LITE_LIBRARY_DEBUG}")
+    endif()
+  endif()
+
+  if(PROTOBUF_PROTOC_EXECUTABLE)
+    if(NOT TARGET protobuf::protoc)
+      add_executable(protobuf::protoc IMPORTED)
+    endif()
+    set_property(TARGET protobuf::protoc PROPERTY
+        IMPORTED_LOCATION ${PROTOBUF_PROTOC_EXECUTABLE})
+  endif()
+endif()
+
+# After above, we should have the protobuf related target now.
+if((NOT TARGET protobuf::libprotobuf) AND (NOT TARGET protobuf::libprotobuf-lite))
+  message(WARNING
+      "Protobuf cannot be found. Depending on whether you are building Caffe2 "
+      "or a Caffe2 dependent library, the next warning / error will give you "
+      "more info.")
+endif()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/utils.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/utils.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..781a4e6819f83870b58ee3e794caf7ef461ef705
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/utils.cmake
@@ -0,0 +1,512 @@
+################################################################################################
+# Exclude and prepend functionalities
+function(exclude OUTPUT INPUT)
+set(EXCLUDES ${ARGN})
+foreach(EXCLUDE ${EXCLUDES})
+        list(REMOVE_ITEM INPUT "${EXCLUDE}")
+endforeach()
+set(${OUTPUT} ${INPUT} PARENT_SCOPE)
+endfunction(exclude)
+
+function(prepend OUTPUT PREPEND)
+set(OUT "")
+foreach(ITEM ${ARGN})
+        list(APPEND OUT "${PREPEND}${ITEM}")
+endforeach()
+set(${OUTPUT} ${OUT} PARENT_SCOPE)
+endfunction(prepend)
+
+################################################################################################
+# Parses a version string that might have values beyond major, minor, and patch
+# and set version variables for the library.
+# Usage:
+#   caffe2_parse_version_str( )
+function(caffe2_parse_version_str LIBNAME VERSIONSTR)
+  string(REGEX REPLACE "^([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MAJOR "${VERSIONSTR}")
+  string(REGEX REPLACE "^[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MINOR  "${VERSIONSTR}")
+  string(REGEX REPLACE "[0-9]+\\.[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_PATCH "${VERSIONSTR}")
+  set(${LIBNAME}_VERSION_MAJOR ${${LIBNAME}_VERSION_MAJOR} ${ARGN} PARENT_SCOPE)
+  set(${LIBNAME}_VERSION_MINOR ${${LIBNAME}_VERSION_MINOR} ${ARGN} PARENT_SCOPE)
+  set(${LIBNAME}_VERSION_PATCH ${${LIBNAME}_VERSION_PATCH} ${ARGN} PARENT_SCOPE)
+  set(${LIBNAME}_VERSION "${${LIBNAME}_VERSION_MAJOR}.${${LIBNAME}_VERSION_MINOR}.${${LIBNAME}_VERSION_PATCH}" PARENT_SCOPE)
+endfunction()
+
+###
+# Removes common indentation from a block of text to produce code suitable for
+# setting to `python -c`, or using with pycmd. This allows multiline code to be
+# nested nicely in the surrounding code structure.
+#
+# This function respsects Python_EXECUTABLE if it defined, otherwise it uses
+# `python` and hopes for the best. An error will be thrown if it is not found.
+#
+# Args:
+#     outvar : variable that will hold the stdout of the python command
+#     text   : text to remove indentation from
+#
+function(dedent outvar text)
+  # Use Python_EXECUTABLE if it is defined, otherwise default to python
+  if("${Python_EXECUTABLE}" STREQUAL "")
+    set(_python_exe "python3")
+  else()
+    set(_python_exe "${Python_EXECUTABLE}")
+  endif()
+  set(_fixup_cmd "import sys; from textwrap import dedent; print(dedent(sys.stdin.read()))")
+  file(WRITE "${CMAKE_BINARY_DIR}/indented.txt" "${text}")
+  execute_process(
+    COMMAND "${_python_exe}" -c "${_fixup_cmd}"
+    INPUT_FILE "${CMAKE_BINARY_DIR}/indented.txt"
+    RESULT_VARIABLE _dedent_exitcode
+    OUTPUT_VARIABLE _dedent_text)
+  if(NOT _dedent_exitcode EQUAL 0)
+    message(ERROR " Failed to remove indentation from: \n\"\"\"\n${text}\n\"\"\"
+    Python dedent failed with error code: ${_dedent_exitcode}")
+    message(FATAL_ERROR " Python dedent failed with error code: ${_dedent_exitcode}")
+  endif()
+  # Remove supurflous newlines (artifacts of print)
+  string(STRIP "${_dedent_text}" _dedent_text)
+  set(${outvar} "${_dedent_text}" PARENT_SCOPE)
+endfunction()
+
+
+function(pycmd_no_exit outvar exitcode cmd)
+  # Use Python_EXECUTABLE if it is defined, otherwise default to python
+  if("${Python_EXECUTABLE}" STREQUAL "")
+    set(_python_exe "python")
+  else()
+    set(_python_exe "${Python_EXECUTABLE}")
+  endif()
+  # run the actual command
+  execute_process(
+    COMMAND "${_python_exe}" -c "${cmd}"
+    RESULT_VARIABLE _exitcode
+    OUTPUT_VARIABLE _output)
+  # Remove supurflous newlines (artifacts of print)
+  string(STRIP "${_output}" _output)
+  set(${outvar} "${_output}" PARENT_SCOPE)
+  set(${exitcode} "${_exitcode}" PARENT_SCOPE)
+endfunction()
+
+
+###
+# Helper function to run `python -c ""` and capture the results of stdout
+#
+# Runs a python command and populates an outvar with the result of stdout.
+# Common indentation in the text of `cmd` is removed before the command is
+# executed, so the caller does not need to worry about indentation issues.
+#
+# This function respsects Python_EXECUTABLE if it defined, otherwise it uses
+# `python` and hopes for the best. An error will be thrown if it is not found.
+#
+# Args:
+#     outvar : variable that will hold the stdout of the python command
+#     cmd    : text representing a (possibly multiline) block of python code
+#
+function(pycmd outvar cmd)
+  dedent(_dedent_cmd "${cmd}")
+  pycmd_no_exit(_output _exitcode "${_dedent_cmd}")
+
+  if(NOT _exitcode EQUAL 0)
+    message(ERROR " Failed when running python code: \"\"\"\n${_dedent_cmd}\n\"\"\"")
+    message(FATAL_ERROR " Python command failed with error code: ${_exitcode}")
+  endif()
+  # Remove supurflous newlines (artifacts of print)
+  string(STRIP "${_output}" _output)
+  set(${outvar} "${_output}" PARENT_SCOPE)
+endfunction()
+
+
+##############################################################################
+# Macro to update cached options.
+macro(caffe2_update_option variable value)
+  if(CAFFE2_CMAKE_BUILDING_WITH_MAIN_REPO)
+    get_property(__help_string CACHE ${variable} PROPERTY HELPSTRING)
+    set(${variable} ${value} CACHE BOOL ${__help_string} FORCE)
+  else()
+    set(${variable} ${value})
+  endif()
+endmacro()
+
+
+##############################################################################
+# Add an interface library definition that is dependent on the source.
+#
+# It's probably easiest to explain why this macro exists, by describing
+# what things would look like if we didn't have this macro.
+#
+# Let's suppose we want to statically link against torch.  We've defined
+# a library in cmake called torch, and we might think that we just
+# target_link_libraries(my-app PUBLIC torch).  This will result in a
+# linker argument 'libtorch.a' getting passed to the linker.
+#
+# Unfortunately, this link command is wrong!  We have static
+# initializers in libtorch.a that would get improperly pruned by
+# the default link settings.  What we actually need is for you
+# to do -Wl,--whole-archive,libtorch.a -Wl,--no-whole-archive to ensure
+# that we keep all symbols, even if they are (seemingly) not used.
+#
+# What caffe2_interface_library does is create an interface library
+# that indirectly depends on the real library, but sets up the link
+# arguments so that you get all of the extra link settings you need.
+# The result is not a "real" library, and so we have to manually
+# copy over necessary properties from the original target.
+#
+# (The discussion above is about static libraries, but a similar
+# situation occurs for dynamic libraries: if no symbols are used from
+# a dynamic library, it will be pruned unless you are --no-as-needed)
+macro(caffe2_interface_library SRC DST)
+  add_library(${DST} INTERFACE)
+  add_dependencies(${DST} ${SRC})
+  # Depending on the nature of the source library as well as the compiler,
+  # determine the needed compilation flags.
+  get_target_property(__src_target_type ${SRC} TYPE)
+  # Depending on the type of the source library, we will set up the
+  # link command for the specific SRC library.
+  if(${__src_target_type} STREQUAL "STATIC_LIBRARY")
+    # In the case of static library, we will need to add whole-static flags.
+    if(APPLE)
+      target_link_libraries(
+          ${DST} INTERFACE -Wl,-force_load,\"$\")
+    elseif(MSVC)
+      # In MSVC, we will add whole archive in default.
+      target_link_libraries(
+         ${DST} INTERFACE "$")
+      target_link_options(
+         ${DST} INTERFACE "-WHOLEARCHIVE:$")
+    else()
+      # Assume everything else is like gcc
+      target_link_libraries(${DST} INTERFACE
+          "-Wl,--whole-archive,\"$\" -Wl,--no-whole-archive")
+    endif()
+    # Link all interface link libraries of the src target as well.
+    # For static library, we need to explicitly depend on all the libraries
+    # that are the dependent library of the source library. Note that we cannot
+    # use the populated INTERFACE_LINK_LIBRARIES property, because if one of the
+    # dependent library is not a target, cmake creates a $ wrapper
+    # and then one is not able to find target "src". For more discussions, check
+    #   https://gitlab.kitware.com/cmake/cmake/issues/15415
+    #   https://cmake.org/pipermail/cmake-developers/2013-May/019019.html
+    # Specifically the following quote
+    #
+    # """
+    # For STATIC libraries we can define that the PUBLIC/PRIVATE/INTERFACE keys
+    # are ignored for linking and that it always populates both LINK_LIBRARIES
+    # LINK_INTERFACE_LIBRARIES.  Note that for STATIC libraries the
+    # LINK_LIBRARIES property will not be used for anything except build-order
+    # dependencies.
+    # """
+    target_link_libraries(${DST} INTERFACE
+        $)
+  elseif(${__src_target_type} STREQUAL "SHARED_LIBRARY")
+    if("${CMAKE_CXX_COMPILER_ID}" MATCHES "GNU")
+      target_link_libraries(${DST} INTERFACE
+          "-Wl,--no-as-needed,\"$\" -Wl,--as-needed")
+    else()
+      target_link_libraries(${DST} INTERFACE ${SRC})
+    endif()
+    # Link all interface link libraries of the src target as well.
+    # For shared libraries, we can simply depend on the INTERFACE_LINK_LIBRARIES
+    # property of the target.
+    target_link_libraries(${DST} INTERFACE
+        $)
+  else()
+    message(FATAL_ERROR
+        "You made a CMake build file error: target " ${SRC}
+        " must be of type either STATIC_LIBRARY or SHARED_LIBRARY. However, "
+        "I got " ${__src_target_type} ".")
+  endif()
+  # For all other interface properties, manually inherit from the source target.
+  set_target_properties(${DST} PROPERTIES
+    INTERFACE_COMPILE_DEFINITIONS
+    $
+    INTERFACE_COMPILE_OPTIONS
+    $
+    INTERFACE_INCLUDE_DIRECTORIES
+    $
+    INTERFACE_SYSTEM_INCLUDE_DIRECTORIES
+    $)
+endmacro()
+
+
+##############################################################################
+# Creating a Caffe2 binary target with sources specified with relative path.
+# Usage:
+#   caffe2_binary_target(target_name_or_src  [] [] ...)
+# If only target_name_or_src is specified, this target is build with one single
+# source file and the target name is autogen from the filename. Otherwise, the
+# target name is given by the first argument and the rest are the source files
+# to build the target.
+function(caffe2_binary_target target_name_or_src)
+  # https://cmake.org/cmake/help/latest/command/function.html
+  # Checking that ARGC is greater than # is the only way to ensure
+  # that ARGV# was passed to the function as an extra argument.
+  if(ARGC GREATER 1)
+    set(__target ${target_name_or_src})
+    prepend(__srcs "${CMAKE_CURRENT_SOURCE_DIR}/" "${ARGN}")
+  else()
+    get_filename_component(__target ${target_name_or_src} NAME_WE)
+    prepend(__srcs "${CMAKE_CURRENT_SOURCE_DIR}/" "${target_name_or_src}")
+  endif()
+  add_executable(${__target} ${__srcs})
+  target_link_libraries(${__target} torch_library)
+  # If we have Caffe2_MODULES defined, we will also link with the modules.
+  if(DEFINED Caffe2_MODULES)
+    target_link_libraries(${__target} ${Caffe2_MODULES})
+  endif()
+  install(TARGETS ${__target} DESTINATION bin)
+endfunction()
+
+function(caffe2_hip_binary_target target_name_or_src)
+  if(ARGC GREATER 1)
+    set(__target ${target_name_or_src})
+    prepend(__srcs "${CMAKE_CURRENT_SOURCE_DIR}/" "${ARGN}")
+  else()
+    get_filename_component(__target ${target_name_or_src} NAME_WE)
+    prepend(__srcs "${CMAKE_CURRENT_SOURCE_DIR}/" "${target_name_or_src}")
+  endif()
+
+  caffe2_binary_target(${target_name_or_src})
+
+  target_compile_options(${__target} PRIVATE ${HIP_CXX_FLAGS})
+  target_include_directories(${__target} PRIVATE ${Caffe2_HIP_INCLUDE})
+endfunction()
+
+
+##############################################################################
+# Multiplex between adding libraries for CUDA versus HIP (AMD Software Stack).
+# Usage:
+#   torch_cuda_based_add_library(cuda_target)
+#
+macro(torch_cuda_based_add_library cuda_target)
+  if(USE_ROCM)
+    hip_add_library(${cuda_target} ${ARGN})
+  elseif(USE_CUDA)
+    add_library(${cuda_target} ${ARGN})
+  else()
+  endif()
+endmacro()
+
+##############################################################################
+# Get the HIP arch flags specified by PYTORCH_ROCM_ARCH.
+# Usage:
+#   torch_hip_get_arch_list(variable_to_store_flags)
+#
+macro(torch_hip_get_arch_list store_var)
+  if(DEFINED ENV{PYTORCH_ROCM_ARCH})
+    set(_TMP $ENV{PYTORCH_ROCM_ARCH})
+  else()
+    # Use arch of installed GPUs as default
+    execute_process(COMMAND "rocm_agent_enumerator" COMMAND bash "-c" "grep -v gfx000 | sort -u | xargs | tr -d '\n'"
+                    RESULT_VARIABLE ROCM_AGENT_ENUMERATOR_RESULT
+                    OUTPUT_VARIABLE ROCM_ARCH_INSTALLED)
+    if(NOT ROCM_AGENT_ENUMERATOR_RESULT EQUAL 0)
+      message(FATAL_ERROR " Could not detect ROCm arch for GPUs on machine. Result: '${ROCM_AGENT_ENUMERATOR_RESULT}'")
+    endif()
+    set(_TMP ${ROCM_ARCH_INSTALLED})
+  endif()
+  string(REPLACE " " ";" ${store_var} "${_TMP}")
+endmacro()
+
+##############################################################################
+# Get the XPU arch flags specified by TORCH_XPU_ARCH_LIST.
+# Usage:
+#   torch_xpu_get_arch_list(variable_to_store_flags)
+#
+macro(torch_xpu_get_arch_list store_var)
+  if(DEFINED ENV{TORCH_XPU_ARCH_LIST})
+    set(${store_var} $ENV{TORCH_XPU_ARCH_LIST})
+  endif()
+endmacro()
+
+##############################################################################
+# Get the NVCC arch flags specified by TORCH_CUDA_ARCH_LIST and CUDA_ARCH_NAME.
+# Usage:
+#   torch_cuda_get_nvcc_gencode_flag(variable_to_store_flags)
+#
+macro(torch_cuda_get_nvcc_gencode_flag store_var)
+  # setting nvcc arch flags
+  if((NOT DEFINED TORCH_CUDA_ARCH_LIST) AND (DEFINED ENV{TORCH_CUDA_ARCH_LIST}))
+    message(WARNING
+        "In the future we will require one to explicitly pass "
+        "TORCH_CUDA_ARCH_LIST to cmake instead of implicitly setting it as an "
+        "env variable. This will become a FATAL_ERROR in future version of "
+        "pytorch.")
+    set(TORCH_CUDA_ARCH_LIST $ENV{TORCH_CUDA_ARCH_LIST})
+  endif()
+  if(DEFINED CUDA_ARCH_NAME)
+    message(WARNING
+        "CUDA_ARCH_NAME is no longer used. Use TORCH_CUDA_ARCH_LIST instead. "
+        "Right now, CUDA_ARCH_NAME is ${CUDA_ARCH_NAME} and "
+        "TORCH_CUDA_ARCH_LIST is ${TORCH_CUDA_ARCH_LIST}.")
+    set(TORCH_CUDA_ARCH_LIST TORCH_CUDA_ARCH_LIST ${CUDA_ARCH_NAME})
+  endif()
+
+  # Invoke cuda_select_nvcc_arch_flags from proper cmake FindCUDA.
+  cuda_select_nvcc_arch_flags(${store_var} ${TORCH_CUDA_ARCH_LIST})
+endmacro()
+
+
+##############################################################################
+# Add standard compile options.
+# Usage:
+#   torch_compile_options(lib_name)
+function(torch_compile_options libname)
+  set_property(TARGET ${libname} PROPERTY CXX_STANDARD 17)
+
+  # until they can be unified, keep these lists synced with setup.py
+  if(MSVC)
+
+    if(MSVC_Z7_OVERRIDE)
+      set(MSVC_DEBINFO_OPTION "/Z7")
+    else()
+      set(MSVC_DEBINFO_OPTION "/Zi")
+    endif()
+
+    target_compile_options(${libname} PUBLIC
+      $<$:
+        ${MSVC_RUNTIME_LIBRARY_OPTION}
+        $<$,$>:${MSVC_DEBINFO_OPTION}>
+        /EHsc
+        /bigobj>
+      )
+  else()
+    set(private_compile_options
+      -Wall
+      -Wextra
+      -Wdeprecated
+      -Wno-unused-parameter
+      -Wno-missing-field-initializers
+      -Wno-array-bounds
+      -Wno-unknown-pragmas
+      -Wno-strict-overflow
+      -Wno-strict-aliasing
+      )
+    list(APPEND private_compile_options -Wunused-function)
+    list(APPEND private_compile_options -Wunused-variable)
+    if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+      list(APPEND private_compile_options -Wunused-but-set-variable)
+    endif()
+    if(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+      list(APPEND private_compile_options -Wunused-private-field -Wextra-semi -Wno-error=extra-semi)
+    else()
+      list(APPEND private_compile_options
+        # Considered to be flaky.  See the discussion at
+        # https://github.com/pytorch/pytorch/pull/9608
+        -Wno-maybe-uninitialized)
+    endif()
+
+    if(WERROR)
+      list(APPEND private_compile_options
+        -Werror
+        -Werror=inconsistent-missing-override
+        -Werror=inconsistent-missing-destructor-override
+        -Werror=unused-function
+        -Werror=unused-variable
+        -Werror=pedantic
+      )
+      if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+        list(APPEND private_compile_options -Werror=unused-but-set-variable)
+      endif()
+    endif()
+  endif()
+
+
+  target_compile_options(${libname} PRIVATE
+      $<$:${private_compile_options}>)
+  if(USE_CUDA)
+    foreach(option IN LISTS private_compile_options)
+      if(CMAKE_CUDA_HOST_COMPILER_ID STREQUAL "GNU")
+        if("${option}" STREQUAL "-Wextra-semi")
+          continue()
+        endif()
+        if("${option}" STREQUAL "-Wunused-private-field")
+          continue()
+        endif()
+      endif()
+      target_compile_options(${libname} PRIVATE $<$:-Xcompiler ${option}>)
+    endforeach()
+  endif()
+
+  if(NOT WIN32 AND NOT USE_ASAN)
+    # Enable hidden visibility by default to make it easier to debug issues with
+    # TORCH_API annotations. Hidden visibility with selective default visibility
+    # behaves close enough to Windows' dllimport/dllexport.
+    #
+    # Unfortunately, hidden visibility messes up some ubsan warnings because
+    # templated classes crossing library boundary get duplicated (but identical)
+    # definitions. It's easier to just disable it.
+    target_compile_options(${libname} PRIVATE
+        $<$: -fvisibility=hidden>)
+  endif()
+
+  # Use -O2 for release builds (-O3 doesn't improve perf, and -Os results in perf regression)
+  target_compile_options(${libname} PRIVATE
+      $<$,$,$>>:-O2>)
+
+endfunction()
+
+##############################################################################
+# Set old-style FindCuda.cmake compile flags from modern CMake cuda flags.
+# Usage:
+#   torch_update_find_cuda_flags()
+function(torch_update_find_cuda_flags)
+  # Convert -O2 -Xcompiler="-O2 -Wall" to "-O2;-Xcompiler=-O2,-Wall"
+  if(USE_CUDA)
+    separate_arguments(FLAGS UNIX_COMMAND "${CMAKE_CUDA_FLAGS}")
+    string(REPLACE " " "," FLAGS "${FLAGS}")
+    set(CUDA_NVCC_FLAGS ${FLAGS} PARENT_SCOPE)
+
+    separate_arguments(FLAGS_DEBUG UNIX_COMMAND "${CMAKE_CUDA_FLAGS_DEBUG}")
+    string(REPLACE " " "," FLAGS_DEBUG "${FLAGS_DEBUG}")
+    set(CUDA_NVCC_FLAGS_DEBUG "${FLAGS_DEBUG}" PARENT_SCOPE)
+
+    separate_arguments(FLAGS_RELEASE UNIX_COMMAND "${CMAKE_CUDA_FLAGS_RELEASE}")
+    string(REPLACE " " "," FLAGS_RELEASE "${FLAGS_RELEASE}")
+    set(CUDA_NVCC_FLAGS_RELEASE "${FLAGS_RELEASE}" PARENT_SCOPE)
+
+    separate_arguments(FLAGS_MINSIZEREL UNIX_COMMAND "${CMAKE_CUDA_FLAGS_MINSIZEREL}")
+    string(REPLACE " " "," FLAGS_MINSIZEREL "${FLAGS_MINSIZEREL}")
+    set(CUDA_NVCC_FLAGS_MINSIZEREL "${FLAGS_MINSIZEREL}" PARENT_SCOPE)
+
+    separate_arguments(FLAGS_RELWITHDEBINFO UNIX_COMMAND "${CMAKE_CUDA_FLAGS_RELWITHDEBINFO}")
+    string(REPLACE " " "," FLAGS_RELWITHDEBINFO "${FLAGS_RELWITHDEBINFO}")
+    set(CUDA_NVCC_FLAGS_RELWITHDEBINFO "${FLAGS_RELWITHDEBINFO}" PARENT_SCOPE)
+
+    message(STATUS "Converting CMAKE_CUDA_FLAGS to CUDA_NVCC_FLAGS:\n"
+                    "    CUDA_NVCC_FLAGS                = ${FLAGS}\n"
+                    "    CUDA_NVCC_FLAGS_DEBUG          = ${FLAGS_DEBUG}\n"
+                    "    CUDA_NVCC_FLAGS_RELEASE        = ${FLAGS_RELEASE}\n"
+                    "    CUDA_NVCC_FLAGS_RELWITHDEBINFO = ${FLAGS_RELWITHDEBINFO}\n"
+                    "    CUDA_NVCC_FLAGS_MINSIZEREL     = ${FLAGS_MINSIZEREL}")
+  endif()
+endfunction()
+
+include(CheckCXXCompilerFlag)
+
+##############################################################################
+# CHeck if given flag is supported and append it to provided outputvar
+# Also define HAS_UPPER_CASE_FLAG_NAME variable
+# Usage:
+#   append_cxx_flag_if_supported("-Werror" CMAKE_CXX_FLAGS)
+function(append_cxx_flag_if_supported flag outputvar)
+    string(TOUPPER "HAS${flag}" _FLAG_NAME)
+    string(REGEX REPLACE "[=-]" "_" _FLAG_NAME "${_FLAG_NAME}")
+    # GCC silents unknown -Wno-XXX flags, so we detect the corresponding -WXXX.
+    if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+      string(REGEX REPLACE "Wno-" "W" new_flag "${flag}")
+    else()
+      set(new_flag ${flag})
+    endif()
+    check_cxx_compiler_flag("${new_flag}" ${_FLAG_NAME})
+    if(${_FLAG_NAME})
+        string(APPEND ${outputvar} " ${flag}")
+        set(${outputvar} "${${outputvar}}" PARENT_SCOPE)
+    endif()
+endfunction()
+
+function(target_compile_options_if_supported target flag)
+  set(_compile_options "")
+  append_cxx_flag_if_supported("${flag}" _compile_options)
+  if(NOT "${_compile_options}" STREQUAL "")
+    target_compile_options(${target} PRIVATE ${flag})
+  endif()
+endfunction()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/xpu.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/xpu.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..5025da53b5bfabadaf74cf14f1123be087ec4538
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/xpu.cmake
@@ -0,0 +1,56 @@
+# ---[ xpu
+
+# Poor man's include guard
+if(TARGET torch::xpurt)
+  return()
+endif()
+
+set(XPU_HOST_CXX_FLAGS)
+set(XPU_DEVICE_CXX_FLAGS)
+
+# Find SYCL library.
+find_package(SYCLToolkit REQUIRED)
+if(NOT SYCL_FOUND)
+  set(PYTORCH_FOUND_XPU FALSE)
+  return()
+endif()
+set(PYTORCH_FOUND_XPU TRUE)
+
+# SYCL library interface
+add_library(torch::sycl INTERFACE IMPORTED)
+
+set_property(
+    TARGET torch::sycl PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+    ${SYCL_INCLUDE_DIR})
+set_property(
+    TARGET torch::sycl PROPERTY INTERFACE_LINK_LIBRARIES
+    ${SYCL_LIBRARY})
+
+# xpurt
+add_library(torch::xpurt INTERFACE IMPORTED)
+set_property(
+    TARGET torch::xpurt PROPERTY INTERFACE_LINK_LIBRARIES
+    torch::sycl)
+
+# setting xpu arch flags
+torch_xpu_get_arch_list(XPU_ARCH_FLAGS)
+# propagate to torch-xpu-ops
+set(TORCH_XPU_ARCH_LIST ${XPU_ARCH_FLAGS})
+
+if(CMAKE_SYSTEM_NAME MATCHES "Linux" AND SYCL_COMPILER_VERSION VERSION_LESS_EQUAL PYTORCH_2_5_SYCL_TOOLKIT_VERSION)
+  # for ABI compatibility on Linux
+  string(APPEND XPU_HOST_CXX_FLAGS " -D__INTEL_PREVIEW_BREAKING_CHANGES")
+  string(APPEND XPU_DEVICE_CXX_FLAGS " -fpreview-breaking-changes")
+endif()
+
+string(APPEND XPU_HOST_CXX_FLAGS " -DSYCL_COMPILER_VERSION=${SYCL_COMPILER_VERSION}")
+
+if(DEFINED ENV{XPU_ENABLE_KINETO})
+  set(XPU_ENABLE_KINETO TRUE)
+else()
+  set(XPU_ENABLE_KINETO FALSE)
+endif()
+
+if(NOT WIN32)
+  set(XPU_ENABLE_KINETO TRUE)
+endif()
\ No newline at end of file
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets-release.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets-release.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..b59f8ceca10f56aaad16d71c32979919ea0537c1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets-release.cmake
@@ -0,0 +1,39 @@
+#----------------------------------------------------------------
+# Generated CMake target import file for configuration "Release".
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Import target "tensorpipe_uv" for configuration "Release"
+set_property(TARGET tensorpipe_uv APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(tensorpipe_uv PROPERTIES
+  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C"
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib64/libtensorpipe_uv.a"
+  )
+
+list(APPEND _cmake_import_check_targets tensorpipe_uv )
+list(APPEND _cmake_import_check_files_for_tensorpipe_uv "${_IMPORT_PREFIX}/lib64/libtensorpipe_uv.a" )
+
+# Import target "tensorpipe" for configuration "Release"
+set_property(TARGET tensorpipe APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(tensorpipe PROPERTIES
+  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib64/libtensorpipe.a"
+  )
+
+list(APPEND _cmake_import_check_targets tensorpipe )
+list(APPEND _cmake_import_check_files_for_tensorpipe "${_IMPORT_PREFIX}/lib64/libtensorpipe.a" )
+
+# Import target "tensorpipe_cuda" for configuration "Release"
+set_property(TARGET tensorpipe_cuda APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(tensorpipe_cuda PROPERTIES
+  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib64/libtensorpipe_cuda.a"
+  )
+
+list(APPEND _cmake_import_check_targets tensorpipe_cuda )
+list(APPEND _cmake_import_check_files_for_tensorpipe_cuda "${_IMPORT_PREFIX}/lib64/libtensorpipe_cuda.a" )
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..aae4e9095ecf2c2fdf659e6547c867cd631fbabc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Tensorpipe/TensorpipeTargets.cmake
@@ -0,0 +1,122 @@
+# Generated by CMake
+
+if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
+   message(FATAL_ERROR "CMake >= 2.8.12 required")
+endif()
+if(CMAKE_VERSION VERSION_LESS "2.8.12")
+   message(FATAL_ERROR "CMake >= 2.8.12 required")
+endif()
+cmake_policy(PUSH)
+cmake_policy(VERSION 2.8.12...3.30)
+#----------------------------------------------------------------
+# Generated CMake target import file.
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Protect against multiple inclusion, which would fail when already imported targets are added once more.
+set(_cmake_targets_defined "")
+set(_cmake_targets_not_defined "")
+set(_cmake_expected_targets "")
+foreach(_cmake_expected_target IN ITEMS tensorpipe_uv tensorpipe tensorpipe_cuda)
+  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
+  if(TARGET "${_cmake_expected_target}")
+    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
+  else()
+    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
+  endif()
+endforeach()
+unset(_cmake_expected_target)
+if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
+  unset(_cmake_targets_defined)
+  unset(_cmake_targets_not_defined)
+  unset(_cmake_expected_targets)
+  unset(CMAKE_IMPORT_FILE_VERSION)
+  cmake_policy(POP)
+  return()
+endif()
+if(NOT _cmake_targets_defined STREQUAL "")
+  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
+  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
+  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
+endif()
+unset(_cmake_targets_defined)
+unset(_cmake_targets_not_defined)
+unset(_cmake_expected_targets)
+
+
+# Compute the installation prefix relative to this file.
+get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+if(_IMPORT_PREFIX STREQUAL "/")
+  set(_IMPORT_PREFIX "")
+endif()
+
+# Create imported target tensorpipe_uv
+add_library(tensorpipe_uv STATIC IMPORTED)
+
+set_target_properties(tensorpipe_uv PROPERTIES
+  INTERFACE_LINK_LIBRARIES "\$;\$;\$"
+)
+
+# Create imported target tensorpipe
+add_library(tensorpipe STATIC IMPORTED)
+
+set_target_properties(tensorpipe PROPERTIES
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+  INTERFACE_LINK_LIBRARIES "\$"
+)
+
+# Create imported target tensorpipe_cuda
+add_library(tensorpipe_cuda STATIC IMPORTED)
+
+set_target_properties(tensorpipe_cuda PROPERTIES
+  INTERFACE_INCLUDE_DIRECTORIES "/usr/local/cuda/include"
+  INTERFACE_LINK_LIBRARIES "tensorpipe;/usr/local/cuda/lib64/libcudart.so"
+)
+
+# Load information for each installed configuration.
+file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/TensorpipeTargets-*.cmake")
+foreach(_cmake_config_file IN LISTS _cmake_config_files)
+  include("${_cmake_config_file}")
+endforeach()
+unset(_cmake_config_file)
+unset(_cmake_config_files)
+
+# Cleanup temporary variables.
+set(_IMPORT_PREFIX)
+
+# Loop over all imported files and verify that they actually exist
+foreach(_cmake_target IN LISTS _cmake_import_check_targets)
+  if(CMAKE_VERSION VERSION_LESS "3.28"
+      OR NOT DEFINED _cmake_import_check_xcframework_for_${_cmake_target}
+      OR NOT IS_DIRECTORY "${_cmake_import_check_xcframework_for_${_cmake_target}}")
+    foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
+      if(NOT EXISTS "${_cmake_file}")
+        message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
+   \"${_cmake_file}\"
+but this file does not exist.  Possible reasons include:
+* The file was deleted, renamed, or moved to another location.
+* An install or uninstall procedure did not complete successfully.
+* The installation package was faulty and contained
+   \"${CMAKE_CURRENT_LIST_FILE}\"
+but not all the files it references.
+")
+      endif()
+    endforeach()
+  endif()
+  unset(_cmake_file)
+  unset("_cmake_import_check_files_for_${_cmake_target}")
+endforeach()
+unset(_cmake_target)
+unset(_cmake_import_check_targets)
+
+# This file does not depend on other imported targets which have
+# been exported from the same project but in a separate export set.
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
+cmake_policy(POP)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Torch/TorchConfig.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Torch/TorchConfig.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..8791494100d6ad4f2feff69ea2326bf2078faa20
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Torch/TorchConfig.cmake
@@ -0,0 +1,175 @@
+# FindTorch
+# -------
+#
+# Finds the Torch library
+#
+# This will define the following variables:
+#
+#   TORCH_FOUND        -- True if the system has the Torch library
+#   TORCH_INCLUDE_DIRS -- The include directories for torch
+#   TORCH_LIBRARIES    -- Libraries to link against
+#   TORCH_CXX_FLAGS    -- Additional (required) compiler flags
+#
+# and the following imported targets:
+#
+#   torch
+macro(append_torchlib_if_found)
+  foreach (_arg ${ARGN})
+    find_library(${_arg}_LIBRARY ${_arg} PATHS "${TORCH_INSTALL_PREFIX}/lib")
+    if(${_arg}_LIBRARY)
+      list(APPEND TORCH_LIBRARIES ${${_arg}_LIBRARY})
+    else()
+      message(WARNING "static library ${${_arg}_LIBRARY} not found.")
+    endif()
+  endforeach()
+endmacro()
+
+macro(append_wholearchive_lib_if_found)
+  foreach (_arg ${ARGN})
+    find_library(${_arg}_LIBRARY ${_arg} PATHS "${TORCH_INSTALL_PREFIX}/lib")
+    if(${_arg}_LIBRARY)
+      if(APPLE)
+        list(APPEND TORCH_LIBRARIES "-Wl,-force_load,${${_arg}_LIBRARY}")
+      elseif(MSVC)
+        list(APPEND TORCH_LIBRARIES "-WHOLEARCHIVE:${${_arg}_LIBRARY}")
+      else()
+        # Linux
+        list(APPEND TORCH_LIBRARIES "-Wl,--whole-archive ${${_arg}_LIBRARY} -Wl,--no-whole-archive")
+      endif()
+    else()
+      message(WARNING "static library ${${_arg}_LIBRARY} not found.")
+    endif()
+  endforeach()
+endmacro()
+
+include(FindPackageHandleStandardArgs)
+
+if(DEFINED ENV{TORCH_INSTALL_PREFIX})
+  set(TORCH_INSTALL_PREFIX $ENV{TORCH_INSTALL_PREFIX})
+else()
+  # Assume we are in /share/cmake/Torch/TorchConfig.cmake
+  get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+  get_filename_component(TORCH_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+endif()
+
+# Include directories.
+if(EXISTS "${TORCH_INSTALL_PREFIX}/include")
+  set(TORCH_INCLUDE_DIRS
+    ${TORCH_INSTALL_PREFIX}/include
+    ${TORCH_INSTALL_PREFIX}/include/torch/csrc/api/include)
+else()
+  set(TORCH_INCLUDE_DIRS
+    ${TORCH_INSTALL_PREFIX}/include
+    ${TORCH_INSTALL_PREFIX}/include/torch/csrc/api/include)
+endif()
+
+# Library dependencies.
+if(ON)
+  find_package(Caffe2 REQUIRED PATHS ${CMAKE_CURRENT_LIST_DIR}/../Caffe2)
+  set(TORCH_LIBRARIES torch ${Caffe2_MAIN_LIBS})
+  append_torchlib_if_found(c10)
+else()
+  add_library(torch STATIC IMPORTED) # set imported_location at the bottom
+  #library need whole archive
+  append_wholearchive_lib_if_found(torch torch_cpu)
+  if(ON)
+    append_wholearchive_lib_if_found(torch_cuda c10_cuda)
+  endif()
+  if(OFF)
+    append_wholearchive_lib_if_found(torch_xpu c10_xpu)
+  endif()
+
+  # We need manually add dependent libraries when they are not linked into the
+  # shared library.
+  # TODO: this list might be incomplete.
+  append_torchlib_if_found(c10)
+
+  if(ON)
+    append_torchlib_if_found(nnpack)
+  endif()
+
+  if(ON)
+    append_torchlib_if_found(pytorch_qnnpack)
+  endif()
+
+  if(ON)
+    append_torchlib_if_found(XNNPACK)
+    append_torchlib_if_found(microkernels-prod)
+  endif()
+
+  if(OFF)
+    append_torchlib_if_found(kleidiai)
+  endif()
+
+  append_torchlib_if_found(caffe2_protos protobuf-lite protobuf protoc)
+  append_torchlib_if_found(onnx onnx_proto)
+
+  append_torchlib_if_found(fmt)
+  append_torchlib_if_found(cpuinfo clog)
+
+  append_torchlib_if_found(eigen_blas)
+  append_torchlib_if_found(pthreadpool)
+
+  if(ON)
+    append_torchlib_if_found(fbgemm)
+  endif()
+
+  if(ON)
+    append_torchlib_if_found(dnnl mkldnn)
+  endif()
+
+  append_torchlib_if_found(sleef asmjit)
+endif()
+
+if(1)
+  append_torchlib_if_found(kineto)
+endif()
+
+if(ON)
+  if(MSVC)
+    find_library(CAFFE2_NVRTC_LIBRARY caffe2_nvrtc PATHS "${TORCH_INSTALL_PREFIX}/lib")
+    list(APPEND TORCH_CUDA_LIBRARIES ${CAFFE2_NVRTC_LIBRARY})
+  else()
+    set(TORCH_CUDA_LIBRARIES ${CUDA_NVRTC_LIB})
+  endif()
+  if(TARGET torch::nvtoolsext)
+    list(APPEND TORCH_CUDA_LIBRARIES torch::nvtoolsext)
+  endif()
+
+  if(ON)
+    find_library(C10_CUDA_LIBRARY c10_cuda PATHS "${TORCH_INSTALL_PREFIX}/lib")
+    list(APPEND TORCH_CUDA_LIBRARIES ${C10_CUDA_LIBRARY} ${Caffe2_PUBLIC_CUDA_DEPENDENCY_LIBS})
+  endif()
+  list(APPEND TORCH_LIBRARIES ${TORCH_CUDA_LIBRARIES})
+endif()
+
+if(OFF AND ON)
+    append_torchlib_if_found(c10_xpu torch_xpu)
+endif()
+
+# When we build libtorch with the old libstdc++ ABI, dependent libraries must too.
+if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
+  set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=1")
+endif()
+
+find_library(TORCH_LIBRARY torch PATHS "${TORCH_INSTALL_PREFIX}/lib")
+# the statements below changes target properties on
+# - the imported target from Caffe2Targets.cmake in shared library mode (see the find_package above)
+#    - this is untested whether it is the correct (or desired) methodology in CMake
+# - the imported target created in this file in static library mode
+if(NOT ON)
+  # do not set this property on the shared library target, as it will cause confusion in some builds
+  # as the configuration specific property is set in the Caffe2Targets.cmake file
+  set_target_properties(torch PROPERTIES
+      IMPORTED_LOCATION "${TORCH_LIBRARY}"
+  )
+endif()
+set_target_properties(torch PROPERTIES
+    INTERFACE_INCLUDE_DIRECTORIES "${TORCH_INCLUDE_DIRS}"
+    CXX_STANDARD 17
+)
+if(TORCH_CXX_FLAGS)
+  set_property(TARGET torch PROPERTY INTERFACE_COMPILE_OPTIONS "${TORCH_CXX_FLAGS}")
+endif()
+
+find_package_handle_standard_args(Torch DEFAULT_MSG TORCH_LIBRARY TORCH_INCLUDE_DIRS)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Torch/TorchConfigVersion.cmake b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Torch/TorchConfigVersion.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..e8c8acabda585a2dd972db54da9d33c2122ea375
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/share/cmake/Torch/TorchConfigVersion.cmake
@@ -0,0 +1,11 @@
+set(PACKAGE_VERSION "2.7.1")
+
+# Check whether the requested PACKAGE_FIND_VERSION is compatible
+if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
+  set(PACKAGE_VERSION_COMPATIBLE FALSE)
+else()
+  set(PACKAGE_VERSION_COMPATIBLE TRUE)
+  if("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}")
+    set(PACKAGE_VERSION_EXACT TRUE)
+  endif()
+endif()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..23188bba9b8003e9dc44d099ac1ab6099ffff199
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/__init__.py
@@ -0,0 +1,113 @@
+# mypy: allow-untyped-defs
+
+import copyreg
+import os.path as _osp
+import weakref
+
+import torch
+from torch.utils import (
+    backcompat as backcompat,
+    collect_env as collect_env,
+    data as data,
+    deterministic as deterministic,
+    hooks as hooks,
+)
+from torch.utils.backend_registration import (
+    generate_methods_for_privateuse1_backend,
+    rename_privateuse1_backend,
+)
+from torch.utils.cpp_backtrace import get_cpp_backtrace
+from torch.utils.throughput_benchmark import ThroughputBenchmark
+
+
+def set_module(obj, mod):
+    """
+    Set the module attribute on a python object for a given object for nicer printing
+    """
+    if not isinstance(mod, str):
+        raise TypeError("The mod argument should be a string")
+    obj.__module__ = mod
+
+
+if torch._running_with_deploy():
+    # not valid inside torch_deploy interpreter, no paths exists for frozen modules
+    cmake_prefix_path = None
+else:
+    cmake_prefix_path = _osp.join(
+        _osp.dirname(_osp.dirname(__file__)), "share", "cmake"
+    )
+
+
+def swap_tensors(t1, t2):
+    """
+    This function swaps the content of the two Tensor objects.
+    At a high level, this will make t1 have the content of t2 while preserving
+    its identity.
+
+    This will not work if t1 and t2 have different slots.
+    """
+    # Ensure there are no weakrefs
+    if weakref.getweakrefs(t1):
+        raise RuntimeError("Cannot swap t1 because it has weakref associated with it")
+    if weakref.getweakrefs(t2):
+        raise RuntimeError("Cannot swap t2 because it has weakref associated with it")
+    t1_slots = set(copyreg._slotnames(t1.__class__))  # type: ignore[attr-defined]
+    t2_slots = set(copyreg._slotnames(t2.__class__))  # type: ignore[attr-defined]
+    if t1_slots != t2_slots:
+        raise RuntimeError("Cannot swap t1 and t2 if they have different slots")
+
+    def swap_attr(name):
+        tmp = getattr(t1, name)
+        setattr(t1, name, (getattr(t2, name)))
+        setattr(t2, name, tmp)
+
+    def error_pre_hook(grad_outputs):
+        raise RuntimeError(
+            "Trying to execute AccumulateGrad node that was poisoned by swap_tensors "
+            "this can happen when you try to run backward on a tensor that was swapped. "
+            "For a module m with `torch.__future__.set_swap_module_params_on_conversion(True)` "
+            "you should not change the device or dtype of the module (e.g. `m.cpu()` or `m.half()`) "
+            "between running forward and backward. To resolve this, please only change the "
+            "device/dtype before running forward (or after both forward and backward)."
+        )
+
+    def check_use_count(t, name="t1"):
+        use_count = t._use_count()
+        error_str = (
+            f"Expected use_count of {name} to be 1 or 2 with an AccumulateGrad node but got {use_count} "
+            f"make sure you are not holding references to the tensor in other places."
+        )
+        if use_count > 1:
+            if use_count == 2 and t.is_leaf:
+                accum_grad_node = torch.autograd.graph.get_gradient_edge(t).node
+                # Make sure that the accumulate_grad node was not lazy_init-ed by get_gradient_edge
+                if t._use_count() == 2:
+                    accum_grad_node.register_prehook(error_pre_hook)
+                else:
+                    raise RuntimeError(error_str)
+            else:
+                raise RuntimeError(error_str)
+
+    check_use_count(t1, "t1")
+    check_use_count(t2, "t2")
+
+    # Swap the types
+    # Note that this will fail if there are mismatched slots
+    swap_attr("__class__")
+
+    # Swap the dynamic attributes
+    swap_attr("__dict__")
+
+    # Swap the slots
+    for slot in t1_slots:
+        if hasattr(t1, slot) and hasattr(t2, slot):
+            swap_attr(slot)
+        elif hasattr(t1, slot):
+            setattr(t2, slot, (getattr(t1, slot)))
+            delattr(t1, slot)
+        elif hasattr(t2, slot):
+            setattr(t1, slot, (getattr(t2, slot)))
+            delattr(t2, slot)
+
+    # Swap the at::Tensor they point to
+    torch._C._swap_tensor_impl(t1, t2)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_appending_byte_serializer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_appending_byte_serializer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f00ab603b5f9d2b7fdd2904cc5ba5f22c51f8c37
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_appending_byte_serializer.py
@@ -0,0 +1,109 @@
+from collections.abc import Iterable
+from typing import Callable, Generic, TypeVar
+
+
+T = TypeVar("T")
+
+_ENCODING_VERSION: int = 1
+
+__all__ = ["AppendingByteSerializer"]
+
+
+#######################################
+# Helper classes
+#######################################
+
+
+class BytesWriter:
+    def __init__(self, preallocate_size: int) -> None:
+        self._data = bytearray(preallocate_size)
+
+    def write_uint64(self, i: int) -> None:
+        self._data.extend(i.to_bytes(8, byteorder="big", signed=False))
+
+    def write_str(self, s: str) -> None:
+        payload = s.encode("utf-8")
+        self.write_bytes(payload)
+
+    def write_bytes(self, b: bytes) -> None:
+        self.write_uint64(len(b))
+        self._data.extend(b)
+
+    def to_bytes(self) -> bytes:
+        return bytes(self._data)
+
+
+class BytesReader:
+    def __init__(self, data: bytes) -> None:
+        self._data = data
+        self._i = 0
+
+    def is_finished(self) -> bool:
+        return len(self._data) == self._i
+
+    def read_uint64(self) -> int:
+        result = int.from_bytes(
+            self._data[self._i : self._i + 8], byteorder="big", signed=False
+        )
+        self._i += 8
+        return result
+
+    def read_str(self) -> str:
+        return self.read_bytes().decode("utf-8")
+
+    def read_bytes(self) -> bytes:
+        size = self.read_uint64()
+        result = self._data[self._i : self._i + size]
+        self._i += size
+        return result
+
+
+#######################################
+# AppendingByteSerializer
+#######################################
+
+
+class AppendingByteSerializer(Generic[T]):
+    """
+    Provides efficient serialization and deserialization of list of bytes
+    Note that this does not provide any guarantees around byte order
+    """
+
+    _serialize_fn: Callable[[BytesWriter, T], None]
+    _writer: BytesWriter
+    _preallocate_size: int
+
+    def __init__(
+        self,
+        *,
+        serialize_fn: Callable[[BytesWriter, T], None],
+        preallocate_size: int = 0,
+    ) -> None:
+        self._serialize_fn = serialize_fn
+        self._preallocate_size = preallocate_size
+        self.clear()
+
+    def clear(self) -> None:
+        self._writer = BytesWriter(preallocate_size=self._preallocate_size)
+        # First 8-bytes are for version
+        self._writer.write_uint64(_ENCODING_VERSION)
+
+    def append(self, data: T) -> None:
+        self._serialize_fn(self._writer, data)
+
+    def extend(self, elems: Iterable[T]) -> None:
+        for elem in elems:
+            self.append(elem)
+
+    def to_bytes(self) -> bytes:
+        return self._writer.to_bytes()
+
+    @staticmethod
+    def to_list(data: bytes, *, deserialize_fn: Callable[[BytesReader], T]) -> list[T]:
+        reader = BytesReader(data)
+        assert reader.read_uint64() == _ENCODING_VERSION
+
+        result: list[T] = []
+        while not reader.is_finished():
+            result.append(deserialize_fn(reader))
+        return result
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_backport_slots.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_backport_slots.py
new file mode 100644
index 0000000000000000000000000000000000000000..123996a85416504f0a132403e1f7cf69aa4821d5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_backport_slots.py
@@ -0,0 +1,116 @@
+# This code is backported from python 3.10 dataclasses. Once 3.10 becomes the
+# minimum supported we should use dataclass(slots=True) instead.
+
+from __future__ import annotations
+
+import dataclasses
+import itertools
+from typing import TYPE_CHECKING, TypeVar
+
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+    from _typeshed import DataclassInstance
+
+
+__all__ = ["dataclass_slots"]
+
+_T = TypeVar("_T", bound="DataclassInstance")
+
+
+def dataclass_slots(cls: type[_T]) -> type[DataclassInstance]:
+    assert dataclasses.is_dataclass(cls), "Can only be used on dataclasses."
+
+    def _get_slots(cls: type[DataclassInstance]) -> Generator[str, None, None]:
+        slots = cls.__dict__.get("__slots__")
+        # `__dictoffset__` and `__weakrefoffset__` can tell us whether
+        # the base type has dict/weakref slots, in a way that works correctly
+        # for both Python classes and C extension types. Extension types
+        # don't use `__slots__` for slot creation
+        if slots is None:
+            slots = []
+            if getattr(cls, "__weakrefoffset__", -1) != 0:
+                slots.append("__weakref__")
+            if getattr(cls, "__dictrefoffset__", -1) != 0:
+                slots.append("__dict__")
+            yield from slots
+        elif isinstance(slots, str):
+            yield slots
+        # Slots may be any iterable, but we cannot handle an iterator
+        # because it will already be (partially) consumed.
+        elif not hasattr(cls, "__next__"):
+            yield from slots
+        else:
+            raise TypeError(f"Slots of '{cls.__name__}' cannot be determined")
+
+    def _add_slots(
+        cls: type[DataclassInstance], is_frozen: bool, weakref_slot: bool
+    ) -> type[DataclassInstance]:
+        # Need to create a new class, since we can't set __slots__
+        #  after a class has been created.
+
+        # Make sure __slots__ isn't already set.
+        if "__slots__" in cls.__dict__:
+            raise TypeError(f"{cls.__name__} already specifies __slots__")
+
+        # Create a new dict for our new class.
+        cls_dict = dict(cls.__dict__)
+        field_names = tuple(f.name for f in dataclasses.fields(cls))
+        # Make sure slots don't overlap with those in base classes.
+        inherited_slots = set(
+            itertools.chain.from_iterable(map(_get_slots, cls.__mro__[1:-1]))
+        )
+        # The slots for our class.  Remove slots from our base classes.  Add
+        # '__weakref__' if weakref_slot was given, unless it is already present.
+        cls_dict["__slots__"] = tuple(
+            itertools.filterfalse(
+                inherited_slots.__contains__,
+                itertools.chain(
+                    # gh-93521: '__weakref__' also needs to be filtered out if
+                    # already present in inherited_slots
+                    field_names,
+                    ("__weakref__",) if weakref_slot else (),
+                ),
+            ),
+        )
+
+        for field_name in field_names:
+            # Remove our attributes, if present. They'll still be
+            #  available in _MARKER.
+            cls_dict.pop(field_name, None)
+
+        # Remove __dict__ itself.
+        cls_dict.pop("__dict__", None)
+
+        # Clear existing `__weakref__` descriptor, it belongs to a previous type:
+        cls_dict.pop("__weakref__", None)  # gh-102069
+
+        # And finally create the class.
+        qualname = getattr(cls, "__qualname__", None)
+        cls = type(cls.__name__, cls.__bases__, cls_dict)
+        if qualname is not None:
+            cls.__qualname__ = qualname
+
+        def _dataclass_getstate(self: _T) -> object:
+            fields = dataclasses.fields(self)
+            return [getattr(self, f.name) for f in fields]
+
+        def _dataclass_setstate(self: _T, state: list[object]) -> None:
+            fields = dataclasses.fields(self)
+            for field, value in zip(fields, state):
+                # use setattr because dataclass may be frozen
+                object.__setattr__(self, field.name, value)
+
+        if is_frozen:
+            # Need this for pickling frozen classes with slots.
+            if "__getstate__" not in cls_dict:
+                cls.__getstate__ = _dataclass_getstate  # type: ignore[method-assign, assignment]
+            if "__setstate__" not in cls_dict:
+                cls.__setstate__ = _dataclass_setstate  # type: ignore[attr-defined]
+
+        return cls
+
+    params = getattr(cls, dataclasses._PARAMS)  # type: ignore[attr-defined]
+    weakref_slot = getattr(params, "weakref_slot", False)
+    return _add_slots(cls, params.frozen, weakref_slot)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_config_module.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_config_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..48dd1425b37370790eedb86dcf2180b27202ec4d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_config_module.py
@@ -0,0 +1,796 @@
+import contextlib
+import copy
+import hashlib
+import importlib
+import inspect
+import io
+import os
+import pickle
+import sys
+import tokenize
+import unittest
+from dataclasses import dataclass
+from types import FunctionType, ModuleType
+from typing import (
+    Any,
+    Callable,
+    Generic,
+    NoReturn,
+    Optional,
+    TYPE_CHECKING,
+    TypeVar,
+    Union,
+)
+from typing_extensions import deprecated
+from unittest import mock
+
+from torch._utils_internal import justknobs_check
+
+
+# Types saved/loaded in configs
+CONFIG_TYPES = (int, float, bool, type(None), str, list, set, tuple, dict)
+
+
+# Duplicated, because mypy needs these types statically
+T = TypeVar("T", bound=Union[int, float, bool, None, str, list, set, tuple, dict])
+
+
+_UNSET_SENTINEL = object()
+
+
+@dataclass
+class _Config(Generic[T]):
+    """Represents a config with richer behaviour than just a default value.
+    ::
+        i.e.
+        foo = Config(justknob="//foo:bar", default=False)
+        install_config_module(...)
+
+    This configs must be installed with install_config_module to be used
+
+    Precedence Order:
+        alias: If set, the directly use the value of the alias.
+        env_name_force: If set, this environment variable has precedence over
+            everything after this.
+            If multiple env variables are given, the precendence order is from
+            left to right.
+        user_override: If a user sets a value (i.e. foo.bar=True), that
+            has precedence over everything after this.
+        env_name_default: If set, this environment variable will override everything
+            after this.
+            If multiple env variables are given, the precendence order is from
+            left to right.
+        justknob: If this pytorch installation supports justknobs, that will
+            override defaults, but will not override the user_override precendence.
+        default: This value is the lowest precendance, and will be used if nothing is
+            set.
+
+    Environment Variables:
+        These are interpreted to be either "0" or "1" to represent true and false.
+
+    Arguments:
+        justknob: the name of the feature / JK. In OSS this is unused.
+        default: is the value to default this knob to in OSS.
+        alias: The alias config to read instead.
+        env_name_force: The environment variable, or list of, to read that is a FORCE
+            environment variable. I.e. it overrides everything except for alias.
+        env_name_default: The environment variable, or list of, to read that changes the
+            default behaviour. I.e. user overrides take preference.
+    """
+
+    default: Union[T, object]
+    justknob: Optional[str] = None
+    env_name_default: Optional[list[str]] = None
+    env_name_force: Optional[list[str]] = None
+    alias: Optional[str] = None
+
+    def __init__(
+        self,
+        default: Union[T, object] = _UNSET_SENTINEL,
+        justknob: Optional[str] = None,
+        env_name_default: Optional[Union[str, list[str]]] = None,
+        env_name_force: Optional[Union[str, list[str]]] = None,
+        value_type: Optional[type] = None,
+        alias: Optional[str] = None,
+    ):
+        # python 3.9 does not support kw_only on the dataclass :(.
+        self.default = default
+        self.justknob = justknob
+        self.env_name_default = _Config.string_or_list_of_string_to_list(
+            env_name_default
+        )
+        self.env_name_force = _Config.string_or_list_of_string_to_list(env_name_force)
+        self.value_type = value_type
+        self.alias = alias
+        if self.alias is not None:
+            assert (
+                default is _UNSET_SENTINEL
+                and justknob is None
+                and env_name_default is None
+                and env_name_force is None
+            ), "if alias is set, none of {default, justknob and env var} can be set"
+
+    @staticmethod
+    def string_or_list_of_string_to_list(
+        val: Optional[Union[str, list[str]]]
+    ) -> Optional[list[str]]:
+        if val is None:
+            return None
+        if isinstance(val, str):
+            return [val]
+        assert isinstance(val, list)
+        return val
+
+
+# In runtime, we unbox the Config[T] to a T, but typechecker cannot see this,
+# so in order to allow for this dynamic behavior to work correctly with
+# typechecking we are going to lie to the typechecker that Config[T] returns
+# a T.
+if TYPE_CHECKING:
+
+    def Config(
+        default: Union[T, object] = _UNSET_SENTINEL,
+        justknob: Optional[str] = None,
+        env_name_default: Optional[Union[str, list[str]]] = None,
+        env_name_force: Optional[Union[str, list[str]]] = None,
+        value_type: Optional[type] = None,
+        alias: Optional[str] = None,
+    ) -> T:
+        ...
+
+else:
+
+    def Config(
+        default: Union[T, object] = _UNSET_SENTINEL,
+        justknob: Optional[str] = None,
+        env_name_default: Optional[Union[str, list[str]]] = None,
+        env_name_force: Optional[Union[str, list[str]]] = None,
+        value_type: Optional[type] = None,
+        alias: Optional[str] = None,
+    ) -> _Config[T]:
+        return _Config(
+            default, justknob, env_name_default, env_name_force, value_type, alias
+        )
+
+
+def _read_env_variable(name: str) -> Optional[Union[bool, str]]:
+    value = os.environ.get(name)
+    if value == "1":
+        return True
+    if value == "0":
+        return False
+    return value
+
+
+def install_config_module(module: ModuleType) -> None:
+    """
+    Converts a module-level config into a `ConfigModule()`.
+
+    See _config_typing.pyi for instructions on how to get the converted module to typecheck.
+    """
+
+    class ConfigModuleInstance(ConfigModule):
+        # __annotations__ is written to by Sphinx autodoc
+        _bypass_keys = set({"_is_dirty", "_hash_digest", "__annotations__"})
+
+    def visit(
+        source: Union[ModuleType, type],
+        dest: Union[ModuleType, SubConfigProxy],
+        prefix: str,
+    ) -> None:
+        """Walk the module structure and move everything to module._config"""
+        if sys.version_info[:2] < (3, 10):
+            type_hints = getattr(source, "__annotations__", {})
+        else:
+            type_hints = inspect.get_annotations(source)
+        for key, value in list(source.__dict__.items()):
+            if (
+                key.startswith("__")
+                or isinstance(value, (ModuleType, FunctionType))
+                or (hasattr(value, "__module__") and value.__module__ == "typing")
+                # Handle from torch.utils._config_module import Config
+                or (isinstance(value, type) and issubclass(value, _Config))
+            ):
+                continue
+
+            name = f"{prefix}{key}"
+            annotated_type = type_hints.get(key, None)
+            if isinstance(value, CONFIG_TYPES):
+                config[name] = _ConfigEntry(
+                    _Config(default=value, value_type=annotated_type)
+                )
+                if dest is module:
+                    delattr(module, key)
+            elif isinstance(value, _Config):
+                if annotated_type is not None and value.value_type is None:
+                    value.value_type = annotated_type
+
+                config[name] = _ConfigEntry(value)
+
+                if dest is module:
+                    delattr(module, key)
+            elif isinstance(value, type):
+                assert value.__module__ == module.__name__
+                # a subconfig with `class Blah:` syntax
+                proxy = SubConfigProxy(module, f"{name}.")
+                visit(value, proxy, f"{name}.")
+                if dest is module:
+                    setattr(dest, key, proxy)
+                else:
+                    dest.__dict__[key] = proxy
+            else:
+                raise AssertionError(f"Unhandled config {key}={value} ({type(value)})")
+
+    config: dict[str, _ConfigEntry] = {}
+
+    compile_ignored_keys = get_assignments_with_compile_ignored_comments(module)
+
+    visit(module, module, "")
+    module._config = config  # type: ignore[attr-defined]
+    module._compile_ignored_keys = compile_ignored_keys  # type: ignore[attr-defined]
+    module.__class__ = ConfigModuleInstance
+    module._is_dirty = True  # type: ignore[attr-defined]
+    module._hash_digest = None  # type: ignore[attr-defined]
+
+
+COMPILE_IGNORED_MARKER = "@compile_ignored"
+
+
+# Gets all the keys (i.e. assignments) with a @compile_ignored comment
+def get_assignments_with_compile_ignored_comments(module: ModuleType) -> set[str]:
+    source_code = inspect.getsource(module)
+    assignments = set()
+
+    # Tokenize the source code to retrieve comments
+    tokens = tokenize.tokenize(io.BytesIO(source_code.encode("utf-8")).readline)
+    current_comment = "", -1
+    prev_name = ""
+
+    for token in tokens:
+        if token.type == tokenize.COMMENT:
+            prev_name = ""
+            maybe_current = token.string.strip()
+            if COMPILE_IGNORED_MARKER in maybe_current:
+                assert current_comment == (
+                    "",
+                    -1,
+                ), f"unconsumed {COMPILE_IGNORED_MARKER}"
+                current_comment = maybe_current, token.start[0]
+        elif token.type == tokenize.NAME:
+            # Only accept the first name token, to handle if you have
+            # something like foo: Bar = ...
+            if not prev_name:
+                prev_name = token.string
+        elif token.type == tokenize.OP and token.string == "=":
+            # Check if the current assignment follows a comment
+            # with COMPILE_IGNORED_MARKER
+            if (
+                COMPILE_IGNORED_MARKER in current_comment[0]
+                and current_comment[1] == token.start[0] - 1
+            ):
+                assignments.add(prev_name)
+                current_comment = "", -1  # reset
+            prev_name = ""
+    assert current_comment == ("", -1), f"unconsumed {COMPILE_IGNORED_MARKER}"
+    return assignments
+
+
+@dataclass
+class _ConfigEntry:
+    # The default value specified in the configuration
+    default: Any
+    # The type of the configuration value
+    value_type: type
+    # The value specified by the user when they overrode the configuration
+    # _UNSET_SENTINEL indicates the value is not set.
+    user_override: Any = _UNSET_SENTINEL
+    # The justknob to check for this config
+    justknob: Optional[str] = None
+    # environment variables are read at install time
+    env_value_force: Any = _UNSET_SENTINEL
+    env_value_default: Any = _UNSET_SENTINEL
+    # Used to work arounds bad assumptions in unittest.mock.patch
+    # The code to blame is
+    # https://github.com/python/cpython/blob/94a7a4e22fb8f567090514785c69e65298acca42/Lib/unittest/mock.py#L1637
+    # Essentially, mock.patch requires, that if __dict__ isn't accessible
+    # (which it isn't), that after delattr is called on the object, the
+    # object must throw when hasattr is called. Otherwise, it doesn't call
+    # setattr again.
+    # Technically we'll have an intermediate state of hiding the config while
+    # mock.patch is unpatching itself, but it calls setattr after the delete
+    # call so the final state is correct. It's just very unintuitive.
+    # upstream bug - python/cpython#126886
+    hide: bool = False
+    alias: Optional[str] = None
+
+    def __init__(self, config: _Config):
+        self.default = config.default
+        self.value_type = (
+            config.value_type if config.value_type is not None else type(self.default)
+        )
+        self.justknob = config.justknob
+        self.alias = config.alias
+        if config.env_name_default is not None:
+            for val in config.env_name_default:
+                if (env_value := _read_env_variable(val)) is not None:
+                    self.env_value_default = env_value
+                    break
+        if config.env_name_force is not None:
+            for val in config.env_name_force:
+                if (env_value := _read_env_variable(val)) is not None:
+                    self.env_value_force = env_value
+                    break
+
+        # Ensure justknobs and envvars are allowlisted types
+        if self.justknob is not None and self.default is not None:
+            assert isinstance(
+                self.default, bool
+            ), f"justknobs only support booleans, {self.default} is not a boolean"
+        if self.value_type is not None and (
+            config.env_name_default is not None or config.env_name_force is not None
+        ):
+            assert self.value_type in (
+                bool,
+                str,
+                Optional[bool],
+                Optional[str],
+            ), f"envvar configs only support (optional) booleans or strings, {self.value_type} is neither"
+
+
+class ConfigModule(ModuleType):
+    # NOTE: This should be kept in sync with _config_typing.pyi.
+
+    # The actual configuration settings.  E.g., torch._dynamo.config.debug
+    # would live as "debug" in the key, and torch._inductor.config.triton.cudagraphs
+    # maps as "triton.cudagraphs". See discussion on the class for meaning of various sub items
+    _config: dict[str, _ConfigEntry]
+    _bypass_keys: set[str]
+    _compile_ignored_keys: set[str]
+    _is_dirty: bool
+    _hash_digest: Optional[bytes]
+
+    def __init__(self) -> None:
+        raise NotImplementedError(
+            f"use {__name__}.install_config_module(sys.modules[__name__])"
+        )
+
+    def __setattr__(self, name: str, value: object) -> None:
+        if name in self._bypass_keys:
+            super().__setattr__(name, value)
+        elif name not in self._config:
+            raise AttributeError(f"{self.__name__}.{name} does not exist")
+        elif self._config[name].alias is not None:
+            self._set_alias_val(self._config[name], value)
+        else:
+            self._config[name].user_override = value
+            self._is_dirty = True
+            self._config[name].hide = False
+
+    def __getattr__(self, name: str) -> Any:
+        try:
+            config = self._config[name]
+
+            if config.hide:
+                raise AttributeError(f"{self.__name__}.{name} does not exist")
+
+            alias_val = self._get_alias_val(config)
+            if alias_val is not _UNSET_SENTINEL:
+                return alias_val
+
+            if config.env_value_force is not _UNSET_SENTINEL:
+                return config.env_value_force
+
+            if config.user_override is not _UNSET_SENTINEL:
+                return config.user_override
+
+            if config.env_value_default is not _UNSET_SENTINEL:
+                return config.env_value_default
+
+            if config.justknob is not None:
+                # JK only supports bools and ints
+                return justknobs_check(name=config.justknob, default=config.default)
+
+            # Note that reference types can still be modified, so we
+            # copy them to user_overrides in case the user overrides
+            # them
+            if isinstance(config.default, (list, set, dict)):
+                config.user_override = copy.deepcopy(config.default)
+                return config.user_override
+            return config.default
+
+        except KeyError as e:
+            # make hasattr() work properly
+            raise AttributeError(f"{self.__name__}.{name} does not exist") from e
+
+    def __delattr__(self, name: str) -> None:
+        self._is_dirty = True
+        # must support delete because unittest.mock.patch deletes
+        # then recreate things
+        self._config[name].user_override = _UNSET_SENTINEL
+        self._config[name].hide = True
+
+    def _get_alias_module_and_name(
+        self, entry: _ConfigEntry
+    ) -> Optional[tuple[ModuleType, str]]:
+        alias = entry.alias
+        if alias is None:
+            return None
+        module_name, constant_name = alias.rsplit(".", 1)
+        try:
+            module = importlib.import_module(module_name)
+        except ImportError as e:
+            raise AttributeError("config alias {alias} does not exist") from e
+        return module, constant_name
+
+    def _get_alias_val(self, entry: _ConfigEntry) -> Any:
+        data = self._get_alias_module_and_name(entry)
+        if data is None:
+            return _UNSET_SENTINEL
+        module, constant_name = data
+        constant_value = getattr(module, constant_name)
+        return constant_value
+
+    def _set_alias_val(self, entry: _ConfigEntry, val: Any) -> None:
+        data = self._get_alias_module_and_name(entry)
+        assert data is not None
+        module, constant_name = data
+        setattr(module, constant_name, val)
+
+    def _is_default(self, name: str) -> bool:
+        """
+        Returns true if the config is at its default value.
+        configs overriden by the env are not considered default.
+        """
+        config_val = self._config[name]
+        # The config is not overridden by the user, and the env_value_default
+        # is different from the default value (meaning user has set the env to
+        # change the default value).
+        not_set_env_default = (
+            config_val.env_value_default is _UNSET_SENTINEL
+            or config_val.env_value_default == config_val.default
+        )
+        not_set_env_force = (
+            config_val.env_value_force is _UNSET_SENTINEL
+            or config_val.env_value_force == config_val.default
+        )
+
+        unset = config_val.user_override is _UNSET_SENTINEL
+        # Handle reference types specially to avoid spammy warnings
+        if isinstance(config_val.default, (list, set, dict)):
+            unset = unset or config_val.user_override == config_val.default
+        return unset and not_set_env_default and not_set_env_force
+
+    def _get_dict(
+        self,
+        ignored_keys: Optional[list[str]] = None,
+        ignored_prefixes: Optional[list[str]] = None,
+        skip_default: bool = False,
+    ) -> dict[str, Any]:
+        """Export a dictionary of current configuration keys and values.
+
+        This function is design to provide a single point which handles
+        accessing config options and exporting them into a dictionary.
+        This is used by a number of different user facing export methods
+        which all have slightly different semantics re: how and what to
+        skip.
+        If a config is aliased, it skips this config.
+
+        Arguments:
+            ignored_keys are keys that should not be exported.
+            ignored_prefixes are prefixes that if a key matches should
+                not be exported
+            skip_default does two things. One if a key has not been modified
+                it skips it.
+        """
+        config: dict[str, Any] = {}
+        for key in self._config:
+            if ignored_keys and key in ignored_keys:
+                continue
+            if ignored_prefixes:
+                if any(key.startswith(prefix) for prefix in ignored_prefixes):
+                    continue
+            if skip_default and self._is_default(key):
+                continue
+            if self._config[key].alias is not None:
+                continue
+            config[key] = copy.deepcopy(getattr(self, key))
+
+        return config
+
+    def get_type(self, config_name: str) -> type:
+        return self._config[config_name].value_type
+
+    def save_config(self) -> bytes:
+        """Convert config to a pickled blob"""
+        ignored_keys = getattr(self, "_save_config_ignore", [])
+        return pickle.dumps(
+            self._get_dict(ignored_keys=ignored_keys),
+            protocol=2,
+        )
+
+    def save_config_portable(self) -> dict[str, Any]:
+        """Convert config to portable format"""
+        prefixes = ["_"]
+        prefixes.extend(getattr(self, "_cache_config_ignore_prefix", []))
+        return self._get_dict(ignored_prefixes=prefixes)
+
+    def codegen_config(self) -> str:
+        """Convert config to Python statements that replicate current config.
+        This does NOT include config settings that are at default values.
+        """
+
+        # additional imports required
+        imports = set()
+
+        def get_module_name(func: Callable, add_dot: bool) -> str:
+            module_name = func.__module__
+            if module_name == "builtins":
+                module_name = ""
+            if add_dot and module_name != "":
+                module_name += "."
+            return module_name
+
+        def add_import(func: Callable) -> None:
+            module_name = get_module_name(func, False)
+            if module_name:
+                imports.add(module_name)
+
+        def list_of_callables_to_string(v: Union[list, set]) -> list[str]:
+            return [f"{get_module_name(item, True)}{item.__name__}" for item in v]
+
+        def importable_callable(v: Any) -> bool:
+            # functools.partial has no attributes below but is a callable
+            return callable(v) and hasattr(v, "__module__") and hasattr(v, "__name__")
+
+        def get_config_line(mod, k, v) -> str:  # type: ignore[no-untyped-def]
+            """
+            Return a string version of the config line.
+            Handle v when v is a callable, or a list/dict of callables. Add import statements for callables if necessary.
+            We assume that the value of a single config won't be a mix of callables and non-callables.
+
+            Example output:
+                import logging
+                import _warnings
+                torch._dynamo.config.reorderable_logging_functions = { _warnings.warn, logging.warn, print }
+            """
+            if importable_callable(v):
+                add_import(v)
+                return f"{mod}.{k} = {get_module_name(v, True)}{v.__name__}"
+            elif isinstance(v, (list, set)) and all(
+                importable_callable(item) for item in v
+            ):
+                for item in v:
+                    add_import(item)
+                v_list = list_of_callables_to_string(v)
+                if isinstance(v, list):
+                    return f"{mod}.{k} = {v_list}"
+                else:
+                    return f"{mod}.{k} = {{ {', '.join(v_list)} }}"
+            else:
+                return f"{mod}.{k} = {v!r}"
+
+        lines = []
+        mod = self.__name__
+        for k, v in self._get_dict(
+            ignored_keys=getattr(self, "_save_config_ignore", []), skip_default=True
+        ).items():
+            lines.append(get_config_line(mod, k, v))
+        for import_name in imports:
+            lines.insert(0, f"import {import_name}")
+        return "\n".join(lines)
+
+    def get_hash(self) -> bytes:
+        """Hashes the configs that are not compile_ignored"""
+        if self._is_dirty or self._hash_digest is None:
+            dict_to_hash = self._get_dict(ignored_keys=list(self._compile_ignored_keys))
+            string_to_hash = repr(sorted(dict_to_hash.items()))
+            self._hash_digest = hashlib.md5(
+                string_to_hash.encode("utf-8"), usedforsecurity=False
+            ).digest()
+            self._is_dirty = False
+        return self._hash_digest
+
+    @deprecated(
+        "`config.to_dict()` has been deprecated. It no longer changes the underlying config."
+        " use `config.get_config_copy()` instead if you just want a copy of the config, or "
+        "config.load_config if you need mutable access",
+        category=FutureWarning,
+    )
+    def to_dict(self) -> dict[str, Any]:
+        return self.get_config_copy()
+
+    @deprecated(
+        "`config.shallow_copy_dict()` has been deprecated. It no longer changes the underlying config."
+        " use `config.get_config_copy()` instead if you just want a copy of the config, or "
+        "config.load_config if you need mutable access",
+        category=FutureWarning,
+    )
+    def shallow_copy_dict(self) -> dict[str, Any]:
+        return self.get_config_copy()
+
+    def load_config(self, maybe_pickled_config: Union[bytes, dict[str, Any]]) -> None:
+        """Restore from a prior call to save_config() or shallow_copy_dict()"""
+        if not isinstance(maybe_pickled_config, dict):
+            config = pickle.loads(maybe_pickled_config)
+        else:
+            config = maybe_pickled_config
+        for k, v in config.items():
+            if k in self._config:
+                setattr(self, k, v)
+            else:
+                from torch._dynamo.utils import warn_once
+
+                warn_once(f"key {k} with value {v} is not understood by this config")
+
+    def get_config_copy(self) -> dict[str, Any]:
+        return self._get_dict()
+
+    def patch(
+        self,
+        arg1: Optional[Union[str, dict[str, Any]]] = None,
+        arg2: Any = None,
+        **kwargs: dict[str, Any],
+    ) -> "ContextDecorator":
+        """
+        Decorator and/or context manager to make temporary changes to a config.
+
+        As a decorator:
+
+            @config.patch("name", val)
+            @config.patch(name1=val1, name2=val2)
+            @config.patch({"name1": val1, "name2", val2})
+            def foo(...):
+                ...
+
+        As a context manager:
+
+            with config.patch("name", val):
+                ...
+        """
+        changes: dict[str, Any]
+        if arg1 is not None:
+            if arg2 is not None:
+                assert isinstance(arg1, str)
+                # patch("key", True) syntax
+                changes = {arg1: arg2}
+            else:
+                assert isinstance(arg1, dict)
+                # patch({"key": True}) syntax
+                changes = arg1
+            assert not kwargs
+        else:
+            # patch(key=True) syntax
+            changes = kwargs
+            assert arg2 is None
+        assert isinstance(changes, dict), f"expected `dict` got {type(changes)}"
+        prior: dict[str, Any] = {}
+        config = self
+
+        class ConfigPatch(ContextDecorator):
+            def __enter__(self) -> None:
+                assert not prior
+                for key in changes.keys():
+                    # KeyError on invalid entry
+                    prior[key] = config.__getattr__(key)
+                for k, v in changes.items():
+                    config.__setattr__(k, v)
+
+            def __exit__(self, exc_type, exc_val, exc_tb):  # type: ignore[no-untyped-def]
+                for k, v in prior.items():
+                    config.__setattr__(k, v)
+                prior.clear()
+
+        return ConfigPatch()
+
+    def _make_closure_patcher(self, **changes: dict[str, Any]) -> Any:
+        """
+        A lower-overhead version of patch() for things on the critical path.
+
+        Usage:
+
+            # do this off the critical path
+            change_fn = config.make_closure_patcher(foo=True)
+
+            ...
+
+            revert = change_fn()
+            try:
+              ...
+            finally:
+                revert()
+
+        """
+        config = self._config
+
+        def change() -> Callable[[], None]:
+            prior = {k: config[k].user_override for k in changes}
+            for k, v in changes.items():
+                self._config[k].user_override = v
+
+            def revert() -> None:
+                for k, v in prior.items():
+                    self._config[k].user_override = v
+
+            return revert
+
+        return change
+
+
+class ContextDecorator(contextlib.ContextDecorator):
+    """
+    Same as contextlib.ContextDecorator, but with support for
+    `unittest.TestCase`
+    """
+
+    def __enter__(self) -> None:
+        raise NotImplementedError("NYI")
+
+    def __exit__(self, exc_type, exc_val, exc_tb) -> NoReturn:  # type: ignore[no-untyped-def]
+        raise NotImplementedError("NYI")
+
+    def __call__(self, func: Callable[[Any], Any]) -> Any:
+        if isinstance(func, type) and issubclass(func, unittest.TestCase):
+
+            class _TestCase(func):  # type: ignore[valid-type, misc]
+                @classmethod
+                def setUpClass(cls) -> None:
+                    self.__enter__()
+                    try:
+                        super().setUpClass()
+                    except Exception:
+                        self.__exit__(None, None, None)
+                        raise
+
+                @classmethod
+                def tearDownClass(cls) -> None:
+                    try:
+                        super().tearDownClass()
+                    finally:
+                        self.__exit__(None, None, None)
+
+            _TestCase.__name__ = func.__name__
+            _TestCase.__qualname__ = func.__qualname__
+            _TestCase.__module__ = func.__module__
+
+            return _TestCase
+
+        return super().__call__(func)
+
+
+class SubConfigProxy:
+    """
+    Shim to redirect to main config.
+    `config.triton.cudagraphs` maps to _config["triton.cudagraphs"]
+    """
+
+    def __init__(self, config: object, prefix: str):
+        # `super().__setattr__` to bypass custom `__setattr__`
+        super().__setattr__("_config", config)
+        super().__setattr__("_prefix", prefix)
+
+    def __setattr__(self, name: str, value: object) -> None:
+        return self._config.__setattr__(self._prefix + name, value)
+
+    def __getattr__(self, name: str) -> Any:
+        return self._config.__getattr__(self._prefix + name)
+
+    def __delattr__(self, name: str) -> None:
+        return self._config.__delattr__(self._prefix + name)
+
+
+def patch_object(obj: object, name: str, value: object) -> object:
+    """
+    Workaround `mock.patch.object` issue with ConfigModule
+    """
+    if isinstance(obj, ConfigModule):
+        return obj.patch(name, value)
+    return mock.patch.object(obj, name, value)
+
+
+def get_tristate_env(name: str, default: Any = None) -> Optional[bool]:
+    value = os.environ.get(name)
+    if value == "1":
+        return True
+    if value == "0":
+        return False
+    return default
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_config_typing.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_config_typing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8d94d1913b499d453f499b17cad130fd721a978e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_config_typing.pyi
@@ -0,0 +1,34 @@
+# mypy: allow-untyped-defs
+from typing import Any, TYPE_CHECKING
+
+"""
+This was semi-automatically generated by running
+
+    stubgen torch.utils._config_module.py
+
+And then manually extracting the methods of ConfigModule and converting them into top-level functions.
+
+This file should be imported into any file that uses install_config_module like so:
+
+    if TYPE_CHECKING:
+        from torch.utils._config_typing import *  # noqa: F401, F403
+
+    from torch.utils._config_module import install_config_module
+
+    # adds patch, save_config, etc
+    install_config_module(sys.modules[__name__])
+
+Note that the import should happen before the call to install_config_module(), otherwise runtime errors may occur.
+"""
+
+assert TYPE_CHECKING, "Do not use at runtime"
+
+def save_config() -> bytes: ...
+def save_config_portable() -> dict[str, Any]: ...
+def codegen_config() -> str: ...
+def get_hash() -> bytes: ...
+def to_dict() -> dict[str, Any]: ...
+def shallow_copy_dict() -> dict[str, Any]: ...
+def load_config(config: bytes | dict[str, Any]) -> None: ...
+def get_config_copy() -> dict[str, Any]: ...
+def patch(arg1: str | dict[str, Any] | None = None, arg2: Any = None, **kwargs): ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_content_store.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_content_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..fab3730a43c87b77094f513a140bbbaae5ff725f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_content_store.py
@@ -0,0 +1,239 @@
+# mypy: allow-untyped-defs
+# This module provides a FAST (on GPU) content addressable store for storages
+# (and tensors on top of them) with VERY WEAK portability guarantees (e.g.,
+# don't expect CPU/CUDA to address to the same hash, don't expect it to be
+# portable across devices) that is NOT cryptographically secure.  In return,
+# we are able to hash 40G of tensor data on GPU in less than a second,
+# compared to running SHA-1 in CPU which would a minute or so.  The primary
+# use case is for efficiently snapshotting intermediate tensor data for
+# offline debugging, but it's been put in this module in case you think of
+# another use case for it.  The hash function could be replaced with a
+# straight reimplementation of SHA-1, which would give us much stronger
+# portability guarantees.
+#
+# WARNING: THERE IS NO BC/FC GUARANTEE FOR THIS FORMAT!  If you need to format
+# shift the result, consider packing it into a single torch.save object
+# with traditional view sharing.
+#
+# Because of the weak portability guarantees, you can only write to the
+# content store from a single process; we don't provide any capability
+# of "reopening" a content store to add more things to it.  But we don't
+# assume that you can keep all of the tensors you want to add to the store
+# in memory at once, because you probably can't!  Nor do we assume that
+# you know a priori whether or not two storages can be deduplicated or not.
+#
+# Note: only storages are content-addressed; tensors are name addressed
+#
+# Note: our padding strategy means that [1, 0] and [1] int16 tensors would
+# map to the same (padded) storage.  We think this will be immaterial for most
+# users.
+
+import ctypes
+import functools
+import hashlib
+import os.path
+import struct
+from collections import defaultdict
+from typing import Optional
+
+import torch
+import torch._prims as prims
+import torch._utils
+import torch.nn.functional as F
+from torch.multiprocessing.reductions import StorageWeakRef
+
+
+def lazy_compile(**compile_kwargs):
+    """Lazily wrap a function with torch.compile on the first call
+
+    This avoids eagerly importing dynamo.
+    """
+
+    def decorate_fn(fn):
+        @functools.wraps(fn)
+        def compile_hook(*args, **kwargs):
+            compiled_fn = torch.compile(fn, **compile_kwargs)
+            globals()[fn.__name__] = functools.wraps(fn)(compiled_fn)
+            return compiled_fn(*args, **kwargs)
+
+        return compile_hook
+
+    return decorate_fn
+
+
+# Use of torch.compile is mandatory for (1) good memory usage
+# and (2) xor_sum implementation.  This is our first instance of
+# using PT2 to implement a kernel in PyTorch; if we get AOT capabilities
+# it would be good to apply it here.
+@lazy_compile(dynamic=True)
+def hash_storage_kernel(x):
+    # The randint calls are carefully written to hit things we
+    # have lowerings for in inductor.  Lack of unsigned 32-bit integer
+    # is a pain.
+    a = torch.randint(
+        -(2**31), 2**31, x.shape, device=x.device, dtype=torch.int32
+    ).abs()
+    a = ((a % (2**31 - 1)) + 1).long()
+    b = (
+        torch.randint(-(2**31), 2**31, x.shape, device=x.device, dtype=torch.int32)
+        .abs()
+        .long()
+    )
+    # This is a standard shift-multiply universal hash family
+    # plus xor sum hash, using Philox to generate random numbers.
+    # Our Philox RNG is not deterministic across devices so
+    # don't use this for stable hashing.
+    #
+    # This assumes fixed length so you're also obligated to bucket
+    # by the length of tensor as well
+    return prims.xor_sum((a * x + b).int(), [0])
+
+
+# Returns a hex digest of the data in the storage.  Guaranteed to be
+# SHA-1 if stable_hash=True, otherwise it will consistent for a single
+# process run but not necessarily across processes.
+def hash_storage(storage: torch.UntypedStorage, *, stable_hash: bool = False) -> str:
+    import torch._dynamo
+    from torch._dynamo.utils import is_compile_supported
+
+    device_type = storage.device.type
+    if stable_hash or not is_compile_supported(device_type):
+        cpu_storage = storage.cpu()
+        # TODO: make storage support buffer protocol so this isn't
+        # necessary
+        buf = (ctypes.c_byte * cpu_storage.nbytes()).from_address(
+            cpu_storage.data_ptr()
+        )
+        sha1 = hashlib.sha1(usedforsecurity=False)
+        sha1.update(buf)
+        return sha1.hexdigest()
+
+    # TODO: factor this into a random utility
+    if device_type == "cpu":
+        generator = torch._C.default_generator
+    elif device_type == "cuda":
+        generator = torch.cuda.default_generators[storage.device.index]
+    elif device_type == "mps":
+        generator = torch.mps._get_default_mps_generator()
+    elif device_type == "xpu":
+        generator = torch.xpu.default_generators[storage.device.index]
+    else:
+        raise AssertionError(f"unhandled device type {device_type}")
+    state = generator.get_state()
+    try:
+        generator.manual_seed(0)
+        x = torch.empty(0, dtype=torch.uint8, device=storage.device).set_(storage)  # type: ignore[call-overload]
+        # The dtype-casting view cannot be compiled, and so the
+        # padding/reshaping also needs to be done externally even
+        # though it could be profitably fused
+        pad = -x.numel() % 4
+        if pad > 0:
+            x = F.pad(x, (0, pad), "constant", 0)
+        x = x.view(torch.int32)
+        # We run the 32-bit hash five times with differing parameters to
+        # reduce chance of collision
+        ITER = 5
+        cs = [hash_storage_kernel(x).item() for _ in range(ITER)]
+        return struct.pack(">" + "i" * ITER, *cs).hex()
+    finally:
+        generator.set_state(state)
+
+
+class ContentStoreWriter:
+    # Structure:
+    #   storages/
+    #     00/
+    #       0000..00
+    #   tensors/
+    #     name
+    def __init__(self, loc: str, stable_hash: bool = False) -> None:
+        self.loc: str = loc
+        self.seen_storage_hashes: set[str] = set()
+        self.stable_hash = stable_hash
+
+    # TODO: offer some sort of non-blocking API to speed things up
+    def write_storage(self, storage: torch.UntypedStorage) -> str:
+        h = hash_storage(storage, stable_hash=self.stable_hash)
+        if h in self.seen_storage_hashes:
+            return h
+        # TODO: consider not using torch.save for this; we don't actually
+        # need any metadata for the storage
+        subfolder = os.path.join(self.loc, "storages")
+        os.makedirs(subfolder, exist_ok=True)
+        target = os.path.join(subfolder, h)
+        if os.path.exists(target):
+            return h
+        torch.save(storage, target)
+        self.seen_storage_hashes.add(h)
+        return h
+
+    def compute_tensor_metadata(self, t: torch.Tensor, h=None):
+        if h is None:
+            h = hash_storage(t.untyped_storage(), stable_hash=self.stable_hash)
+        return (
+            t.dtype,
+            h,
+            t.storage_offset(),
+            tuple(t.shape),
+            t.stride(),
+            torch._utils.get_tensor_metadata(t),
+        )
+
+    def write_tensor(self, name: str, t: torch.Tensor) -> None:
+        storage = t.untyped_storage()
+        h = self.write_storage(storage)
+        # TODO: Support more advanced snapshotting of requires_grad/grad/etc
+        d, f = os.path.split(name)
+        payload = self.compute_tensor_metadata(t, h=h)
+        subfolder = os.path.join(self.loc, "tensors", d)
+        os.makedirs(subfolder, exist_ok=True)
+        torch.save(payload, os.path.join(subfolder, f))
+
+
+class ContentStoreReader:
+    def __init__(self, loc: str, *, cache=True) -> None:
+        self.loc = loc
+        self.storage_cache: Optional[
+            dict[Optional[torch.device], dict[str, StorageWeakRef]]
+        ] = None
+        if cache:
+            self.storage_cache = defaultdict(dict)
+
+    def read_storage(self, h: str, *, device=None) -> torch.UntypedStorage:
+        if device is not None:
+            device = torch.device(device)
+        ws = (
+            self.storage_cache[device].get(h)
+            if self.storage_cache is not None
+            else None
+        )
+        s: Optional[torch.UntypedStorage]
+        if ws is not None:
+            s = torch.UntypedStorage._new_with_weak_ptr(ws.cdata)
+            if s is not None:
+                return s
+        s = torch.load(
+            os.path.join(self.loc, "storages", h),
+            weights_only=True,
+            map_location=device,
+        )._untyped_storage
+        assert s is not None
+        if self.storage_cache is not None:
+            self.storage_cache[device][h] = StorageWeakRef(s)
+        return s
+
+    def read_tensor_metadata(self, name: str):
+        fn = os.path.join(self.loc, "tensors", name)
+        if not os.path.exists(fn):
+            raise FileNotFoundError(fn)
+        return torch.load(fn, weights_only=True)
+
+    def read_tensor(self, name: str, *, device=None) -> torch.Tensor:
+        dtype, h, storage_offset, size, stride, metadata = self.read_tensor_metadata(
+            name
+        )
+        storage = self.read_storage(h, device=device)
+        t = torch.tensor([], dtype=dtype, device=storage.device)
+        t.set_(storage, storage_offset, size, stride)
+        torch._utils.set_tensor_metadata(t, metadata)
+        return t
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_contextlib.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_contextlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f1b991438c0783ff3b1d7e7ab50bc628cb5cf2c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_contextlib.py
@@ -0,0 +1,157 @@
+# mypy: allow-untyped-defs
+# Extra utilities for working with context managers that should have been
+# in the standard library but are not
+
+import functools
+import inspect
+import warnings
+import sys
+from typing import Any, Callable, TypeVar, cast
+
+# Used for annotating the decorator usage of _DecoratorContextManager (e.g.,
+# 'no_grad' and 'enable_grad').
+# See https://mypy.readthedocs.io/en/latest/generics.html#declaring-decorators
+FuncType = Callable[..., Any]
+F = TypeVar('F', bound=FuncType)
+
+
+def _wrap_generator(ctx_factory, func):
+    """
+    Wrap each generator invocation with the context manager factory.
+
+    The input should be a function that returns a context manager,
+    not a context manager itself, to handle one-shot context managers.
+    """
+    @functools.wraps(func)
+    def generator_context(*args, **kwargs):
+        gen = func(*args, **kwargs)
+
+        # Generators are suspended and unsuspended at `yield`, hence we
+        # make sure the grad mode is properly set every time the execution
+        # flow returns into the wrapped generator and restored when it
+        # returns through our `yield` to our caller (see PR #49017).
+        try:
+            # Issuing `None` to a generator fires it up
+            with ctx_factory():
+                response = gen.send(None)
+
+            while True:
+                try:
+                    # Forward the response to our caller and get its next request
+                    request = yield response
+
+                except GeneratorExit:
+                    # Inform the still active generator about its imminent closure
+                    with ctx_factory():
+                        gen.close()
+                    raise
+
+                except BaseException:
+                    # Propagate the exception thrown at us by the caller
+                    with ctx_factory():
+                        response = gen.throw(*sys.exc_info())
+
+                else:
+                    # Pass the last request to the generator and get its response
+                    with ctx_factory():
+                        response = gen.send(request)
+
+        # We let the exceptions raised above by the generator's `.throw` or
+        # `.send` methods bubble up to our caller, except for StopIteration
+        except StopIteration as e:
+            # The generator informed us that it is done: take whatever its
+            # returned value (if any) was and indicate that we're done too
+            # by returning it (see docs for python's return-statement).
+            return e.value
+
+    return generator_context
+
+
+def context_decorator(ctx, func):
+    """
+    Like contextlib.ContextDecorator.
+
+    But with the following differences:
+    1. Is done by wrapping, rather than inheritance, so it works with context
+       managers that are implemented from C and thus cannot easily inherit from
+       Python classes
+    2. Wraps generators in the intuitive way (c.f. https://bugs.python.org/issue37743)
+    3. Errors out if you try to wrap a class, because it is ambiguous whether
+       or not you intended to wrap only the constructor
+
+    The input argument can either be a context manager (in which case it must
+    be a multi-shot context manager that can be directly invoked multiple times)
+    or a callable that produces a context manager.
+    """
+    assert not (callable(ctx) and hasattr(ctx, '__enter__')), (
+        f"Passed in {ctx} is both callable and also a valid context manager "
+        "(has __enter__), making it ambiguous which interface to use.  If you "
+        "intended to pass a context manager factory, rewrite your call as "
+        "context_decorator(lambda: ctx()); if you intended to pass a context "
+        "manager directly, rewrite your call as context_decorator(lambda: ctx)"
+    )
+
+    if not callable(ctx):
+        def ctx_factory():
+            return ctx
+    else:
+        ctx_factory = ctx
+
+    if inspect.isclass(func):
+        raise RuntimeError(
+            "Cannot decorate classes; it is ambiguous whether or not only the "
+            "constructor or all methods should have the context manager applied; "
+            "additionally, decorating a class at definition-site will prevent "
+            "use of the identifier as a conventional type.  "
+            "To specify which methods to decorate, decorate each of them "
+            "individually."
+        )
+
+    if inspect.isgeneratorfunction(func):
+        return _wrap_generator(ctx_factory, func)
+
+    @functools.wraps(func)
+    def decorate_context(*args, **kwargs):
+        with ctx_factory():
+            return func(*args, **kwargs)
+
+    return decorate_context
+
+
+class _DecoratorContextManager:
+    """Allow a context manager to be used as a decorator."""
+
+    def __call__(self, orig_func: F) -> F:
+        if inspect.isclass(orig_func):
+            warnings.warn(
+                "Decorating classes is deprecated and will be disabled in "
+                "future versions. You should only decorate functions or methods. "
+                "To preserve the current behavior of class decoration, you can "
+                "directly decorate the `__init__` method and nothing else.",
+                FutureWarning,
+                stacklevel=2,
+            )
+            func = cast(F, lambda *args, **kwargs: orig_func(*args, **kwargs))
+        else:
+            func = orig_func
+
+        return cast(F, context_decorator(self.clone, func))
+
+    def __enter__(self) -> None:
+        raise NotImplementedError
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        raise NotImplementedError
+
+    def clone(self):
+        # override this method if your children class takes __init__ parameters
+        return self.__class__()
+
+
+class _NoParamDecoratorContextManager(_DecoratorContextManager):
+    """Allow a context manager to be used as a decorator without parentheses."""
+
+    def __new__(cls, orig_func=None):
+        if orig_func is None:
+            return super().__new__(cls)
+        return cls()(orig_func)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_cpp_embed_headers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_cpp_embed_headers.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cb0fee3a3f8cc3c5b9bd6d3325fd8b3a483a7a5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_cpp_embed_headers.py
@@ -0,0 +1,57 @@
+from collections.abc import Sequence
+from pathlib import Path
+from re import match as _match
+from typing import Optional, Union
+
+
+def read_file(fname: Union[Path, str]) -> list[str]:
+    with open(fname, encoding="utf-8") as f:
+        return f.readlines()
+
+
+def _embed_headers(
+    content: list[str], include_dirs: list[Path], processed_files: set[str]
+) -> str:
+    for line_idx, cur_line in enumerate(content):
+        # Eliminate warning: `#pragma once in main file`
+        if cur_line.startswith("#pragma once"):
+            content[line_idx] = ""
+            continue
+        m = _match('^\\s*#include\\s*[<"]([^>"]+)[>"]', cur_line)
+        if m is None:
+            continue
+        for include_dir in include_dirs:
+            path = include_dir / m[1]
+            if not path.exists():
+                continue
+            if str(path) in processed_files:
+                content[line_idx] = ""
+                continue
+            processed_files.add(str(path))
+            content[line_idx] = _embed_headers(
+                read_file(path), include_dirs, processed_files
+            )
+            break
+    return "".join(content)
+
+
+def embed_headers(
+    fname: str, include_dirs: Optional[Union[Sequence[str], Sequence[Path], str]] = None
+) -> str:
+    if include_dirs is None:
+        include_dirs = [Path(__file__).parent.parent.parent]
+    elif isinstance(include_dirs, str):
+        include_dirs = [Path(include_dirs)]
+    else:
+        include_dirs = [Path(x) for x in include_dirs]
+
+    return _embed_headers(read_file(fname), include_dirs, {fname})
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) < 2:
+        print("Usage:\n {sys.argv[0]} filename")
+        sys.exit(1)
+    print(embed_headers(sys.argv[1]))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_cpp_extension_versioner.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_cpp_extension_versioner.py
new file mode 100644
index 0000000000000000000000000000000000000000..f414ec00ddc23681ca2ed1b2e6627477dc45da2a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_cpp_extension_versioner.py
@@ -0,0 +1,61 @@
+# mypy: allow-untyped-defs
+import collections
+
+
+Entry = collections.namedtuple('Entry', 'version, hash')
+
+
+def update_hash(seed, value):
+    # Good old boost::hash_combine
+    # https://www.boost.org/doc/libs/1_35_0/doc/html/boost/hash_combine_id241013.html
+    return seed ^ (hash(value) + 0x9e3779b9 + (seed << 6) + (seed >> 2))
+
+
+def hash_source_files(hash_value, source_files):
+    for filename in source_files:
+        with open(filename, 'rb') as file:
+            hash_value = update_hash(hash_value, file.read())
+    return hash_value
+
+
+def hash_build_arguments(hash_value, build_arguments):
+    for group in build_arguments:
+        if group:
+            for argument in group:
+                hash_value = update_hash(hash_value, argument)
+    return hash_value
+
+
+class ExtensionVersioner:
+    def __init__(self):
+        self.entries = {}
+
+    def get_version(self, name):
+        entry = self.entries.get(name)
+        return None if entry is None else entry.version
+
+    def bump_version_if_changed(self,
+                                name,
+                                source_files,
+                                build_arguments,
+                                build_directory,
+                                with_cuda,
+                                with_sycl,
+                                is_python_module,
+                                is_standalone):
+        hash_value = 0
+        hash_value = hash_source_files(hash_value, source_files)
+        hash_value = hash_build_arguments(hash_value, build_arguments)
+        hash_value = update_hash(hash_value, build_directory)
+        hash_value = update_hash(hash_value, with_cuda)
+        hash_value = update_hash(hash_value, with_sycl)
+        hash_value = update_hash(hash_value, is_python_module)
+        hash_value = update_hash(hash_value, is_standalone)
+
+        entry = self.entries.get(name)
+        if entry is None:
+            self.entries[name] = entry = Entry(0, hash_value)
+        elif hash_value != entry.hash:
+            self.entries[name] = entry = Entry(entry.version + 1, hash_value)
+
+        return entry.version
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_cxx_pytree.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_cxx_pytree.py
new file mode 100644
index 0000000000000000000000000000000000000000..12dfeb7b883d4d361c4924b7b9857c50840a30d6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_cxx_pytree.py
@@ -0,0 +1,1095 @@
+"""
+Contains utility functions for working with nested python data structures.
+
+A *pytree* is Python nested data structure. It is a tree in the sense that
+nodes are Python collections (e.g., list, tuple, dict) and the leaves are
+Python values. Furthermore, a pytree should not contain reference cycles.
+
+pytrees are useful for working with nested collections of Tensors. For example,
+one can use `tree_map` to map a function over all Tensors inside some nested
+collection of Tensors and `tree_leaves` to get a flat list of all Tensors
+inside some nested collection. pytrees are helpful for implementing nested
+collection support for PyTorch APIs.
+"""
+
+import functools
+import sys
+import types
+from collections.abc import Iterable
+from typing import Any, Callable, Optional, overload, TypeVar, Union
+from typing_extensions import deprecated, TypeIs
+
+import optree
+
+from torch._vendor.packaging.version import Version
+
+
+# Keep the version in sync with torch.utils._cxx_pytree!
+if Version(optree.__version__) < Version("0.13.0"):  # type: ignore[attr-defined]
+    raise ImportError(
+        "torch.utils._cxx_pytree depends on optree, which is an optional dependency "
+        "of PyTorch. To use it, please upgrade your optree package to >= 0.13.0"
+    )
+
+del Version
+
+
+from optree import PyTreeSpec as TreeSpec  # direct import for type annotations
+
+import torch.utils._pytree as python_pytree
+from torch.utils._pytree import KeyEntry as KeyEntry
+
+
+__all__ = [
+    "PyTree",
+    "Context",
+    "FlattenFunc",
+    "UnflattenFunc",
+    "DumpableContext",
+    "ToDumpableContextFn",
+    "FromDumpableContextFn",
+    "TreeSpec",
+    "LeafSpec",
+    "keystr",
+    "key_get",
+    "register_pytree_node",
+    "tree_flatten",
+    "tree_flatten_with_path",
+    "tree_unflatten",
+    "tree_iter",
+    "tree_leaves",
+    "tree_leaves_with_path",
+    "tree_structure",
+    "tree_map",
+    "tree_map_with_path",
+    "tree_map_",
+    "tree_map_only",
+    "tree_map_only_",
+    "tree_all",
+    "tree_any",
+    "tree_all_only",
+    "tree_any_only",
+    "treespec_dumps",
+    "treespec_loads",
+    "treespec_pprint",
+]
+
+
+__TORCH_DICT_SESSION = optree.dict_insertion_ordered(True, namespace="torch")
+__TORCH_DICT_SESSION.__enter__()  # enable globally and permanently
+
+
+T = TypeVar("T")
+S = TypeVar("S")
+U = TypeVar("U")
+R = TypeVar("R")
+
+
+Context = Any
+PyTree = Any
+FlattenFunc = Callable[[PyTree], tuple[list[Any], Context]]
+UnflattenFunc = Callable[[Iterable[Any], Context], PyTree]
+OpTreeUnflattenFunc = Callable[[Context, Iterable[Any]], PyTree]
+DumpableContext = Any  # Any json dumpable text
+ToDumpableContextFn = Callable[[Context], DumpableContext]
+FromDumpableContextFn = Callable[[DumpableContext], Context]
+KeyPath = tuple[KeyEntry, ...]
+FlattenWithKeysFunc = Callable[[PyTree], tuple[list[tuple[KeyEntry, Any]], Any]]
+
+
+def _reverse_args(func: UnflattenFunc) -> OpTreeUnflattenFunc:
+    @functools.wraps(func)
+    def wrapped(*args: Any, **kwargs: Any) -> Any:
+        return func(*reversed(args), **kwargs)
+
+    return wrapped
+
+
+def register_pytree_node(
+    cls: type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+    flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None,
+) -> None:
+    """Register a container-like type as pytree node.
+
+    Args:
+        cls (type): A Python type to treat as an internal pytree node.
+        flatten_fn (callable): A function to be used during flattening, taking an instance of
+            ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
+            recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
+            passed to the ``unflatten_fn``.
+        unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
+            returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
+            The function should return an instance of ``cls``.
+        serialized_type_name (str, optional): A keyword argument used to specify the fully
+            qualified name used when serializing the tree spec.
+        to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
+            to convert the context of the pytree to a custom json dumpable representation. This is
+            used for json serialization, which is being used in :mod:`torch.export` right now.
+        from_dumpable_context (callable, optional): An optional keyword argument to custom specify
+            how to convert the custom json dumpable representation of the context back to the
+            original context. This is used for json deserialization, which is being used in
+            :mod:`torch.export` right now.
+
+    Example::
+
+        >>> # xdoctest: +SKIP
+        >>> # Registry a Python type with lambda functions
+        >>> register_pytree_node(
+        ...     set,
+        ...     lambda s: (sorted(s), None, None),
+        ...     lambda children, _: set(children),
+        ... )
+    """
+    if flatten_with_keys_fn is not None:
+        raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+    _private_register_pytree_node(
+        cls,
+        flatten_fn,
+        unflatten_fn,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=to_dumpable_context,
+        from_dumpable_context=from_dumpable_context,
+    )
+
+    python_pytree._private_register_pytree_node(
+        cls,
+        flatten_fn,
+        unflatten_fn,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=to_dumpable_context,
+        from_dumpable_context=from_dumpable_context,
+    )
+
+
+@deprecated(
+    "`torch.utils._cxx_pytree._register_pytree_node` is deprecated. "
+    "Please use `torch.utils._cxx_pytree.register_pytree_node` instead.",
+    category=FutureWarning,
+)
+def _register_pytree_node(
+    cls: type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+) -> None:
+    """Register a container-like type as pytree node for the C++ pytree only.
+
+    The ``namespace`` argument is used to avoid collisions that occur when different libraries
+    register the same Python type with different behaviors. It is recommended to add a unique prefix
+    to the namespace to avoid conflicts with other libraries. Namespaces can also be used to specify
+    the same class in different namespaces for different use cases.
+
+    .. warning::
+        For safety reasons, a ``namespace`` must be specified while registering a custom type. It is
+        used to isolate the behavior of flattening and unflattening a pytree node type. This is to
+        prevent accidental collisions between different libraries that may register the same type.
+
+    Args:
+        cls (type): A Python type to treat as an internal pytree node.
+        flatten_fn (callable): A function to be used during flattening, taking an instance of
+            ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
+            recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
+            passed to the ``unflatten_fn``.
+        unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
+            returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
+            The function should return an instance of ``cls``.
+        serialized_type_name (str, optional): A keyword argument used to specify the fully
+            qualified name used when serializing the tree spec.
+        to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
+            to convert the context of the pytree to a custom json dumpable representation. This is
+            used for json serialization, which is being used in :mod:`torch.export` right now.
+        from_dumpable_context (callable, optional): An optional keyword argument to custom specify
+            how to convert the custom json dumpable representation of the context back to the
+            original context. This is used for json deserialization, which is being used in
+            :mod:`torch.export` right now.
+    """
+
+    _private_register_pytree_node(
+        cls,
+        flatten_fn,
+        unflatten_fn,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=to_dumpable_context,
+        from_dumpable_context=from_dumpable_context,
+    )
+
+
+def _private_register_pytree_node(
+    cls: type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+) -> None:
+    """This is an internal function that is used to register a pytree node type
+    for the C++ pytree only. End-users should use :func:`register_pytree_node`
+    instead.
+    """
+    # TODO(XuehaiPan): remove this condition when we make Python pytree out-of-box support
+    # PyStructSequence types
+    if not optree.is_structseq_class(cls):
+        optree.register_pytree_node(
+            cls,
+            flatten_fn,
+            _reverse_args(unflatten_fn),
+            namespace="torch",
+        )
+
+
+def _is_pytreespec_instance(obj: Any, /) -> TypeIs[TreeSpec]:
+    return isinstance(obj, TreeSpec)
+
+
+def tree_is_leaf(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    """Check if a pytree is a leaf.
+
+    >>> tree_is_leaf(1)
+    True
+    >>> tree_is_leaf(None)
+    True
+    >>> tree_is_leaf([1, 2, 3])
+    False
+    >>> tree_is_leaf((1, 2, 3), is_leaf=lambda x: isinstance(x, tuple))
+    True
+    >>> tree_is_leaf({'a': 1, 'b': 2, 'c': 3})
+    False
+    >>> tree_is_leaf({'a': 1, 'b': 2, 'c': None})
+    False
+
+    Args:
+        tree (pytree): A pytree to check if it is a leaf node.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A boolean indicating if the pytree is a leaf node.
+    """
+    return optree.tree_is_leaf(
+        tree,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_flatten(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> tuple[list[Any], TreeSpec]:
+    """Flatten a pytree.
+
+    See also :func:`tree_unflatten`.
+
+    The flattening order (i.e., the order of elements in the output list) is deterministic,
+    corresponding to a left-to-right depth-first tree traversal.
+
+    >>> tree = {"b": (2, [3, 4]), "a": 1, "c": None, "d": 5}
+    >>> tree_flatten(tree)
+    ([2, 3, 4, 1, None, 5], PyTreeSpec({'b': (*, [*, *]), 'a': *, 'c': *, 'd': *}, NoneIsLeaf, namespace='torch'))
+    >>> tree_flatten(1)
+    ([1], PyTreeSpec(*, NoneIsLeaf, namespace='torch'))
+    >>> tree_flatten(None)
+    ([None], PyTreeSpec(*, NoneIsLeaf, namespace='torch'))
+    >>> from collections import OrderedDict
+    >>> tree = OrderedDict([("b", (2, [3, 4])), ("a", 1), ("c", None), ("d", 5)])
+    >>> tree_flatten(tree)
+    ([2, 3, 4, 1, None, 5], PyTreeSpec(OrderedDict({'b': (*, [*, *]), 'a': *, 'c': *, 'd': *}), NoneIsLeaf, namespace='torch'))
+
+    Args:
+        tree (pytree): A pytree to flatten.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A pair ``(leaves, treespec)`` where the first element is a list of leaf values and the
+        second element is a treespec representing the structure of the pytree.
+    """
+    return optree.tree_flatten(  # type: ignore[return-value]
+        tree,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_unflatten(leaves: Iterable[Any], treespec: TreeSpec) -> PyTree:
+    """Reconstruct a pytree from the treespec and the leaves.
+
+    The inverse of :func:`tree_flatten`.
+
+    >>> tree = {"b": (2, [3, 4]), "a": 1, "c": None, "d": 5}
+    >>> leaves, treespec = tree_flatten(tree)
+    >>> tree == tree_unflatten(leaves, treespec)
+    True
+
+    Args:
+        leaves (iterable): The list of leaves to use for reconstruction. The list must match the
+            number of leaves of the treespec.
+        treespec (TreeSpec): The treespec to reconstruct.
+
+    Returns:
+        The reconstructed pytree, containing the ``leaves`` placed in the structure described by
+        ``treespec``.
+    """
+    if not _is_pytreespec_instance(treespec):
+        raise TypeError(
+            f"tree_unflatten(leaves, treespec): Expected `treespec` to be instance of "
+            f"PyTreeSpec but got item of type {type(treespec)}."
+        )
+    return optree.tree_unflatten(treespec, leaves)  # type: ignore[arg-type]
+
+
+def tree_iter(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Iterable[Any]:
+    """Get an iterator over the leaves of a pytree.
+
+    See also :func:`tree_flatten`.
+
+    >>> tree = {"b": (2, [3, 4]), "a": 1, "c": None, "d": 5}
+    >>> list(tree_iter(tree))
+    [2, 3, 4, 1, None, 5]
+    >>> list(tree_iter(1))
+    [1]
+    >>> list(tree_iter(None))
+    [None]
+
+    Args:
+        tree (pytree): A pytree to flatten.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        An iterator over the leaf values.
+    """
+    return optree.tree_iter(
+        tree,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_leaves(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> list[Any]:
+    """Get the leaves of a pytree.
+
+    See also :func:`tree_flatten`.
+
+    >>> tree = {"b": (2, [3, 4]), "a": 1, "c": None, "d": 5}
+    >>> tree_leaves(tree)
+    [2, 3, 4, 1, None, 5]
+    >>> tree_leaves(1)
+    [1]
+    >>> tree_leaves(None)
+    [None]
+
+    Args:
+        tree (pytree): A pytree to flatten.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A list of leaf values.
+    """
+    return optree.tree_leaves(
+        tree,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_structure(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> TreeSpec:
+    """Get the treespec for a pytree.
+
+    See also :func:`tree_flatten`.
+
+    >>> tree = {"b": (2, [3, 4]), "a": 1, "c": None, "d": 5}
+    >>> tree_structure(tree)
+    PyTreeSpec({'b': (*, [*, *]), 'a': *, 'c': *, 'd': *}, NoneIsLeaf, namespace='torch')
+    >>> tree_structure(1)
+    PyTreeSpec(*, NoneIsLeaf, namespace='torch')
+    >>> tree_structure(None)
+    PyTreeSpec(*, NoneIsLeaf, namespace='torch')
+
+    Args:
+        tree (pytree): A pytree to flatten.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A treespec object representing the structure of the pytree.
+    """
+    return optree.tree_structure(  # type: ignore[return-value]
+        tree,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_map(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Map a multi-input function over pytree args to produce a new pytree.
+
+    See also :func:`tree_map_`.
+
+    >>> tree_map(lambda x: x + 1, {"x": 7, "y": (42, 64)})
+    {'x': 8, 'y': (43, 65)}
+    >>> tree_map(lambda x: x is None, {"x": 7, "y": (42, 64), "z": None})
+    {'x': False, 'y': (False, False), 'z': True}
+
+    If multiple inputs are given, the structure of the tree is taken from the first input;
+    subsequent inputs need only have ``tree`` as a prefix:
+
+    >>> tree_map(lambda x, y: [x] + y, [5, 6], [[7, 9], [1, 2]])
+    [[5, 7, 9], [6, 1, 2]]
+
+    Args:
+        func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees.
+        tree (pytree): A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A new pytree with the same structure as ``tree`` but with the value at each leaf given by
+        ``func(x, *xs)`` where ``x`` is the value at the corresponding leaf in ``tree`` and ``xs``
+        is the tuple of values at corresponding nodes in ``rests``.
+    """
+    return optree.tree_map(
+        func,
+        tree,
+        *rests,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+def tree_map_(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Like :func:`tree_map`, but do an inplace call on each leaf and return the original tree.
+
+    See also :func:`tree_map`.
+
+    Args:
+        func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees.
+        tree (pytree): A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        The original ``tree`` with the value at each leaf is given by the side-effect of function
+        ``func(x, *xs)`` (not the return value) where ``x`` is the value at the corresponding leaf
+        in ``tree`` and ``xs`` is the tuple of values at values at corresponding nodes in ``rests``.
+    """
+    return optree.tree_map_(
+        func,
+        tree,
+        *rests,
+        is_leaf=is_leaf,
+        none_is_leaf=True,
+        namespace="torch",
+    )
+
+
+Type2 = tuple[type[T], type[S]]
+Type3 = tuple[type[T], type[S], type[U]]
+if sys.version_info >= (3, 10):
+    TypeAny = Union[type[Any], tuple[type[Any], ...], types.UnionType]
+else:
+    TypeAny = Union[type[Any], tuple[type[Any], ...]]
+
+Fn2 = Callable[[Union[T, S]], R]
+Fn3 = Callable[[Union[T, S, U]], R]
+Fn = Callable[[T], R]
+FnAny = Callable[[Any], R]
+
+MapOnlyFn = Callable[[T], Callable[[Any], Any]]
+
+
+# These specializations help with type inference on the lambda passed to this
+# function
+@overload
+def map_only(type_or_types_or_pred: type[T], /) -> MapOnlyFn[Fn[T, Any]]:
+    ...
+
+
+@overload
+def map_only(type_or_types_or_pred: Type2[T, S], /) -> MapOnlyFn[Fn2[T, S, Any]]:
+    ...
+
+
+@overload
+def map_only(type_or_types_or_pred: Type3[T, S, U], /) -> MapOnlyFn[Fn3[T, S, U, Any]]:
+    ...
+
+
+# This specialization is needed for the implementations below that call
+@overload
+def map_only(type_or_types_or_pred: TypeAny, /) -> MapOnlyFn[FnAny[Any]]:
+    ...
+
+
+@overload
+def map_only(type_or_types_or_pred: Callable[[Any], bool], /) -> MapOnlyFn[FnAny[Any]]:
+    ...
+
+
+def map_only(
+    type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]], /
+) -> MapOnlyFn[FnAny[Any]]:
+    """
+    Suppose you are writing a tree_map over tensors, leaving everything
+    else unchanged.  Ordinarily you would have to write:
+
+        def go(t):
+            if isinstance(t, Tensor):
+                return ...
+            else:
+                return t
+
+    With this function, you only need to write:
+
+        @map_only(Tensor)
+        def go(t):
+            return ...
+
+    You can also directly use 'tree_map_only'
+    """
+    if isinstance(type_or_types_or_pred, (type, tuple)) or (
+        sys.version_info >= (3, 10)
+        and isinstance(type_or_types_or_pred, types.UnionType)
+    ):
+
+        def pred(x: Any) -> bool:
+            return isinstance(x, type_or_types_or_pred)  # type: ignore[arg-type]
+
+    elif callable(type_or_types_or_pred):
+        pred = type_or_types_or_pred  # type: ignore[assignment]
+    else:
+        raise TypeError("Argument must be a type, a tuple of types, or a callable.")
+
+    def wrapper(func: Callable[[T], Any]) -> Callable[[Any], Any]:
+        @functools.wraps(func)
+        def wrapped(x: T) -> Any:
+            if pred(x):
+                return func(x)
+            return x
+
+        return wrapped
+
+    return wrapper
+
+
+@overload
+def tree_map_only(
+    type_or_types_or_pred: type[T],
+    /,
+    func: Fn[T, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    type_or_types_or_pred: Type2[T, S],
+    /,
+    func: Fn2[T, S, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    type_or_types_or_pred: Type3[T, S, U],
+    /,
+    func: Fn3[T, S, U, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    type_or_types_or_pred: TypeAny,
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    type_or_types_or_pred: Callable[[Any], bool],
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+def tree_map_only(
+    type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]],
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    return tree_map(map_only(type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
+
+
+@overload
+def tree_map_only_(
+    type_or_types_or_pred: type[T],
+    /,
+    func: Fn[T, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    type_or_types_or_pred: Type2[T, S],
+    /,
+    func: Fn2[T, S, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    type_or_types_or_pred: Type3[T, S, U],
+    /,
+    func: Fn3[T, S, U, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    type_or_types_or_pred: TypeAny,
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    type_or_types_or_pred: Callable[[Any], bool],
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+def tree_map_only_(
+    type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]],
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    return tree_map_(map_only(type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
+
+
+def tree_all(
+    pred: Callable[[Any], bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return all(map(pred, flat_args))
+
+
+def tree_any(
+    pred: Callable[[Any], bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return any(map(pred, flat_args))
+
+
+@overload
+def tree_all_only(
+    type_or_types: type[T],
+    /,
+    pred: Fn[T, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_all_only(
+    type_or_types: Type2[T, S],
+    /,
+    pred: Fn2[T, S, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_all_only(
+    type_or_types: Type3[T, S, U],
+    /,
+    pred: Fn3[T, S, U, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+def tree_all_only(
+    type_or_types: TypeAny,
+    /,
+    pred: FnAny[bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return all(pred(x) for x in flat_args if isinstance(x, type_or_types))
+
+
+@overload
+def tree_any_only(
+    type_or_types: type[T],
+    /,
+    pred: Fn[T, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_any_only(
+    type_or_types: Type2[T, S],
+    /,
+    pred: Fn2[T, S, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_any_only(
+    type_or_types: Type3[T, S, U],
+    /,
+    pred: Fn3[T, S, U, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+def tree_any_only(
+    type_or_types: TypeAny,
+    /,
+    pred: FnAny[bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return any(pred(x) for x in flat_args if isinstance(x, type_or_types))
+
+
+def broadcast_prefix(
+    prefix_tree: PyTree,
+    full_tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> list[Any]:
+    """Return a list of broadcasted leaves in ``prefix_tree`` to match the number of leaves in ``full_tree``.
+
+    If a ``prefix_tree`` is a prefix of a ``full_tree``, this means the ``full_tree`` can be
+    constructed by replacing the leaves of ``prefix_tree`` with appropriate **subtrees**.
+
+    This function returns a list of leaves with the same size as ``full_tree``. The leaves are
+    replicated from ``prefix_tree``. The number of replicas is determined by the corresponding
+    subtree in ``full_tree``.
+
+    >>> broadcast_prefix(1, [1, 2, 3])
+    [1, 1, 1]
+    >>> broadcast_prefix([1, 2, 3], [1, 2, 3])
+    [1, 2, 3]
+    >>> broadcast_prefix([1, 2, 3], [1, 2, 3, 4])
+    Traceback (most recent call last):
+        ...
+    ValueError: list arity mismatch; expected: 3, got: 4; list: [1, 2, 3, 4].
+    >>> broadcast_prefix([1, 2, 3], [1, 2, (3, 4)])
+    [1, 2, 3, 3]
+    >>> broadcast_prefix([1, 2, 3], [1, 2, {"a": 3, "b": 4, "c": (None, 5)}])
+    [1, 2, 3, 3, 3, 3]
+
+    Args:
+        prefix_tree (pytree): A pytree with the same structure as a prefix of ``full_tree``.
+        full_tree (pytree): A pytree with the same structure as a suffix of ``prefix_tree``.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A list of leaves in ``prefix_tree`` broadcasted to match the number of leaves in ``full_tree``.
+    """
+    result: list[Any] = []
+
+    def add_leaves(x: Any, subtree: PyTree) -> None:
+        subtreespec = tree_structure(subtree, is_leaf=is_leaf)
+        result.extend([x] * subtreespec.num_leaves)
+
+    tree_map_(
+        add_leaves,
+        prefix_tree,
+        full_tree,
+        is_leaf=is_leaf,
+    )
+    return result
+
+
+# Broadcasts a pytree to the provided TreeSpec and returns the flattened
+# values. If this is not possible, then this function returns None.
+#
+# For example, given pytree=0 and spec=TreeSpec(list, None, [LeafSpec(), LeafSpec()]),
+# would return [0, 0]. This is useful for part of the vmap implementation:
+# a user can pass in vmap(fn, in_dims)(*inputs). `in_dims` should be
+# broadcastable to the tree structure of `inputs` and we use
+# _broadcast_to_and_flatten to check this.
+def _broadcast_to_and_flatten(
+    tree: PyTree,
+    treespec: TreeSpec,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Optional[list[Any]]:
+    assert _is_pytreespec_instance(treespec)
+    full_tree = tree_unflatten([0] * treespec.num_leaves, treespec)
+    try:
+        return broadcast_prefix(tree, full_tree, is_leaf=is_leaf)
+    except ValueError:
+        return None
+
+
+def treespec_dumps(treespec: TreeSpec, protocol: Optional[int] = None) -> str:
+    """Serialize a treespec to a JSON string."""
+    if not _is_pytreespec_instance(treespec):
+        raise TypeError(
+            f"treespec_dumps(treespec): Expected `treespec` to be instance of "
+            f"PyTreeSpec but got item of type {type(treespec)}."
+        )
+
+    dummy_tree = tree_unflatten([0] * treespec.num_leaves, treespec)
+    orig_treespec = python_pytree.tree_structure(dummy_tree)
+    return python_pytree.treespec_dumps(orig_treespec, protocol=protocol)
+
+
+@functools.lru_cache
+def treespec_loads(serialized: str) -> TreeSpec:
+    """Deserialize a treespec from a JSON string."""
+    orig_treespec = python_pytree.treespec_loads(serialized)
+    dummy_tree = python_pytree.tree_unflatten(
+        [0] * orig_treespec.num_leaves,
+        orig_treespec,
+    )
+    treespec = tree_structure(dummy_tree)
+    return treespec
+
+
+class _DummyLeaf:
+    def __repr__(self) -> str:
+        return "*"
+
+
+def treespec_pprint(treespec: TreeSpec) -> str:
+    dummy_tree = tree_unflatten(
+        [_DummyLeaf() for _ in range(treespec.num_leaves)],
+        treespec,
+    )
+    return repr(dummy_tree)
+
+
+class LeafSpecMeta(type(TreeSpec)):  # type: ignore[misc]
+    def __instancecheck__(self, instance: object) -> bool:
+        return _is_pytreespec_instance(instance) and instance.is_leaf()
+
+
+class LeafSpec(TreeSpec, metaclass=LeafSpecMeta):
+    def __new__(cls) -> "LeafSpec":
+        return optree.treespec_leaf(none_is_leaf=True)  # type: ignore[return-value]
+
+
+def tree_flatten_with_path(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> tuple[list[tuple[KeyPath, Any]], TreeSpec]:
+    """Flattens a pytree like :func:`tree_flatten`, but also returns each leaf's key path.
+
+    Args:
+        tree: a pytree to flatten. If it contains a custom type, that type must be
+            registered with an appropriate `tree_flatten_with_path_fn` when registered
+            with :func:`register_pytree_node`.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+    Returns:
+        A tuple where the first element is a list of (key path, leaf) pairs, and the
+        second element is a :class:`TreeSpec` representing the structure of the flattened
+        tree.
+    """
+    raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+
+def tree_leaves_with_path(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> list[tuple[KeyPath, Any]]:
+    """Gets the leaves of a pytree like ``tree_leaves`` and returns each leaf's key path.
+
+    Args:
+        tree: a pytree. If it contains a custom type, that type must be
+            registered with an appropriate `tree_flatten_with_path_fn` when registered
+            with :func:`register_pytree_node`.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+    Returns:
+        A list of (key path, leaf) pairs.
+    """
+    raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+
+def tree_map_with_path(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Like :func:`tree_map`, but the provided callable takes an additional key path argument.
+
+    Args:
+        func: A function that takes ``2 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees. The first positional argument
+            to ``func`` is the key path of the leaf in question. The second
+            positional argument is the value of the leaf.
+        tree: A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests: A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns
+        A new pytree with the same structure as ``tree`` but with the value at each leaf given by
+        ``func(keypath, x, *xs)`` where ``keypath`` is the key path at the
+        corresponding leaf in ``tree``, ``x`` is the value at that leaf, and
+        ``xs`` is the tuple of values at corresponding nodes in ``rests``.
+    """
+    raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+
+def keystr(kp: KeyPath) -> str:
+    """Given a key path, return a pretty-printed representation."""
+    raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+
+def key_get(obj: Any, kp: KeyPath) -> Any:
+    """Given an object and a key path, return the value at the key path."""
+    raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.")
+
+
+with python_pytree._NODE_REGISTRY_LOCK:
+    python_pytree._cxx_pytree_imported = True
+    args, kwargs = (), {}  # type: ignore[var-annotated]
+    for args, kwargs in python_pytree._cxx_pytree_pending_imports:
+        _private_register_pytree_node(*args, **kwargs)
+    python_pytree._cxx_pytree_pending_imports.clear()
+    del args, kwargs
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_device.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_device.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7903fc3b465a362e050997c6aa30db4fcfd6ab4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_device.py
@@ -0,0 +1,117 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+import torch
+from torch.overrides import TorchFunctionMode, _pop_mode, _push_mode
+from torch.utils._contextlib import context_decorator
+from torch._C import _len_torch_function_stack
+import functools
+
+CURRENT_DEVICE: Optional[torch.device] = None
+
+@functools.lru_cache(1)
+def _device_constructors():
+    return {
+        # standard ones
+        torch.empty,
+        torch.empty_permuted,
+        torch.empty_strided,
+        torch.empty_quantized,
+        torch.ones,
+        torch.arange,
+        torch.bartlett_window,
+        torch.blackman_window,
+        torch.eye,
+        torch.fft.fftfreq,
+        torch.fft.rfftfreq,
+        torch.full,
+        torch.fill,
+        torch.hamming_window,
+        torch.hann_window,
+        torch.kaiser_window,
+        torch.linspace,
+        torch.logspace,
+        torch.nested.nested_tensor,
+        # This function doesn't actually take a device argument
+        # torch.normal,
+        torch.ones,
+        torch.rand,
+        torch.randn,
+        torch.randint,
+        torch.randperm,
+        torch.range,
+        torch.sparse_coo_tensor,
+        torch.sparse_compressed_tensor,
+        torch.sparse_csr_tensor,
+        torch.sparse_csc_tensor,
+        torch.sparse_bsr_tensor,
+        torch.sparse_bsc_tensor,
+        torch.tril_indices,
+        torch.triu_indices,
+        torch.vander,
+        torch.zeros,
+        torch.asarray,
+        # weird ones
+        torch.tensor,
+        torch.as_tensor,
+        torch.scalar_tensor,
+        torch.asarray,
+    }
+
+# NB: This is directly called from C++ in torch/csrc/Device.cpp
+class DeviceContext(TorchFunctionMode):
+    def __init__(self, device):
+        self.device = torch.device(device)
+
+    def __enter__(self):
+        global CURRENT_DEVICE
+        self.old_device = CURRENT_DEVICE
+        CURRENT_DEVICE = self.device
+        # We need to put the device at the bottom of the stack
+        # If we set default device within a function mode context
+        # exiting that context mode will pop the device function mode off
+        # of the stack incorrectly
+        cur_stack = [_pop_mode() for _ in range(_len_torch_function_stack())]
+
+        _push_mode(self)
+
+        for mode in reversed(cur_stack):
+            _push_mode(mode)
+
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        global CURRENT_DEVICE
+        CURRENT_DEVICE = self.old_device
+        cur_stack = []
+        # Invariant: there should only be one DeviceContext on the stack at any time
+        # (At the bottom), pop all mdoes until we hit the bottom, assert it's a DeviceContext
+        # or else someone else has popped it!
+        for _ in range(_len_torch_function_stack() - 1):
+            mode = _pop_mode()
+            assert not isinstance(mode, DeviceContext)
+            cur_stack.append(mode)
+
+        if _len_torch_function_stack() > 0:
+            mode = _pop_mode()
+            assert isinstance(mode, DeviceContext)
+
+        for mode in reversed(cur_stack):
+            _push_mode(mode)
+
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+        if func in _device_constructors() and kwargs.get('device') is None:
+            kwargs['device'] = self.device
+        return func(*args, **kwargs)
+
+# NB: This is directly called from C++ in torch/csrc/Device.cpp
+def device_decorator(device, func):
+    return context_decorator(lambda: device, func)
+
+def set_device(device):
+    """
+    Set the default device inside of the wrapped function by decorating it with this function.
+
+    If you would like to use this as a context manager, use device as a
+    context manager directly, e.g., ``with torch.device(device)``.
+    """
+    return lambda func: device_decorator(torch.device(device), func)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_exposed_in.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_exposed_in.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0963b0e4e6aeb8c31d8f87ff6fa1a91ed5730cc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_exposed_in.py
@@ -0,0 +1,20 @@
+from typing import Callable, TypeVar
+
+
+F = TypeVar("F")
+
+
+# Allows one to expose an API in a private submodule publicly as per the definition
+# in PyTorch's public api policy.
+#
+# It is a temporary solution while we figure out if it should be the long-term solution
+# or if we should amend PyTorch's public api policy. The concern is that this approach
+# may not be very robust because it's not clear what __module__ is used for.
+# However, both numpy and jax overwrite the __module__ attribute of their APIs
+# without problem, so it seems fine.
+def exposed_in(module: str) -> Callable[[F], F]:
+    def wrapper(fn: F) -> F:
+        fn.__module__ = module
+        return fn
+
+    return wrapper
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_filelock.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_filelock.py
new file mode 100644
index 0000000000000000000000000000000000000000..dabf3bdc5fed8b78cc740192f4a3868c67e49012
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_filelock.py
@@ -0,0 +1,42 @@
+from types import TracebackType
+from typing import Optional
+from typing_extensions import Self
+
+from filelock import FileLock as base_FileLock
+
+from torch.monitor import _WaitCounter
+
+
+class FileLock(base_FileLock):
+    """
+    This behaves like a normal file lock.
+
+    However, it adds waitcounters for acquiring and releasing the filelock
+    as well as for the critical region within it.
+
+    pytorch.filelock.enter - While we're acquiring the filelock.
+    pytorch.filelock.region - While we're holding the filelock and doing work.
+    pytorch.filelock.exit - While we're releasing the filelock.
+    """
+
+    def __enter__(self) -> Self:
+        self.region_counter = _WaitCounter("pytorch.filelock.region").guard()
+        with _WaitCounter("pytorch.filelock.enter").guard():
+            result = super().__enter__()
+        self.region_counter.__enter__()
+        return result
+
+    def __exit__(
+        self,
+        exc_type: Optional[type[BaseException]],
+        exc_value: Optional[BaseException],
+        traceback: Optional[TracebackType],
+    ) -> None:
+        self.region_counter.__exit__()
+        with _WaitCounter("pytorch.filelock.exit").guard():
+            # Returns nothing per
+            # https://github.com/tox-dev/filelock/blob/57f488ff8fdc2193572efe102408fb63cfefe4e4/src/filelock/_api.py#L379
+            super().__exit__(exc_type, exc_value, traceback)
+        # Returns nothing per
+        # https://github.com/pytorch/pytorch/blob/0f6bfc58a2cfb7a5c052bea618ab62becaf5c912/torch/csrc/monitor/python_init.cpp#L315
+        return None
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_foreach_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_foreach_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..863921bbf87f0604adf28d7dd05bea3db1bc6da8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_foreach_utils.py
@@ -0,0 +1,44 @@
+from typing import Optional
+
+import torch
+from torch import Tensor
+from torch.autograd.grad_mode import no_grad
+from typing_extensions import TypeAlias
+
+def _get_foreach_kernels_supported_devices() -> list[str]:
+    r"""Return the device type list that supports foreach kernels."""
+    return ["cuda", "xpu", torch._C._get_privateuse1_backend_name()]
+
+def _get_fused_kernels_supported_devices() -> list[str]:
+    r"""Return the device type list that supports fused kernels in optimizer."""
+    return ["mps", "cuda", "xpu", "hpu", "cpu", torch._C._get_privateuse1_backend_name()]
+
+TensorListList: TypeAlias = list[list[Optional[Tensor]]]
+Indices: TypeAlias = list[int]
+_foreach_supported_types = [torch.Tensor]
+
+
+# This util function splits tensors into groups by device and dtype, which is useful before sending
+# tensors off to a foreach implementation, which requires tensors to be on one device and dtype.
+# If tensorlistlist contains more than one tensorlist, the following assumptions are made BUT NOT verified:
+#   - tensorlists CAN be None
+#   - all tensors in the first specified list cannot be None
+#   - given an index i, all specified tensorlist[i]s match in dtype and device
+# with_indices (bool, optional): whether to track previous indices as the last list per dictionary entry.
+#   It comes in handy if there are Nones or literals in the tensorlists that are getting scattered out.
+#   Whereas mutating a tensor in the resulting split-up tensorlists WILL propagate changes back to the
+#   original input tensorlists, changing up Nones/literals WILL NOT propagate, and manual propagation
+#   may be necessary. Check out torch/optim/sgd.py for an example.
+@no_grad()
+def _group_tensors_by_device_and_dtype(
+    tensorlistlist: TensorListList,
+    with_indices: bool = False,
+) -> dict[tuple[torch.device, torch.dtype], tuple[TensorListList, Indices]]:
+    return torch._C._group_tensors_by_device_and_dtype(tensorlistlist, with_indices)
+
+def _device_has_foreach_support(device: torch.device) -> bool:
+    return device.type in (_get_foreach_kernels_supported_devices() + ["cpu"]) and not torch.jit.is_scripting()
+
+
+def _has_foreach_support(tensors: list[Tensor], device: torch.device) -> bool:
+    return _device_has_foreach_support(device) and all(t is None or type(t) in _foreach_supported_types for t in tensors)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_freeze.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_freeze.py
new file mode 100644
index 0000000000000000000000000000000000000000..30a797350d2d28f0ea1ea6df14f96fa7177efaaf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_freeze.py
@@ -0,0 +1,289 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+"""
+Freeze Python packages.
+
+Freezing makes it possible to ship arbitrary Python modules as part of a C++
+library. The Python source of the module is compiled to bytecode and written
+to `.c` files, to be imported by Python's built-in FrozenImporter.
+
+In a normal Python installation, FrozenImporter is only used to bootstrap the
+initialization of the import machinery. Python's importers are defined in
+Python (see `_bootstrap.py` and `_bootstrap_external.py`) but need to be
+retrieved before any importers are available. Freezing the module bytecode
+resolves this circular dependency.
+
+This script will freeze the Python standard library. It produces two things:
+- Bytecode files: A set of `.c` that define C variables containing Python bytecode.
+- Main file: A `main.c` file listing all of these modules in the right form to be
+  consumed by FrozenImporter.
+
+The library that wishes to these modules make them available to the local
+Python instance by extending `PyImport_FrozenModules` appropriately (see
+https://docs.python.org/3/c-api/import.html#c.PyImport_FrozenModules).
+"""
+
+import argparse
+import functools
+import itertools
+import marshal
+import os
+import types
+from dataclasses import dataclass
+from pathlib import Path
+
+
+PATH_MARKER = ""
+MAIN_INCLUDES = """#include 
+
+"""
+
+MAIN_PREFIX_TEMPLATE = """
+// Compiled standard library modules. These should be appended to the existing
+// `PyImport_FrozenModules` that ships with CPython.
+struct _frozen {}[] = {{
+"""
+
+FAKE_PREFIX = MAIN_PREFIX_TEMPLATE.format("_PyImport_FrozenModules")
+
+MAIN_SUFFIX = """\
+    {0, 0, 0} /* sentinel */
+};
+"""
+
+# Exclude some standard library modules to:
+# 1. Slim down the final frozen lib.
+# 2. Remove functionality we don't want to support.
+DENY_LIST = [
+    # Interface to unix databases
+    "dbm",
+    # ncurses bindings (terminal interfaces)
+    "curses",
+    # Tcl/Tk GUI
+    "tkinter",
+    "tkinter",
+    # Tests for the standard library
+    "test",
+    "tests",
+    "idle_test",
+    "__phello__.foo.py",
+    # importlib frozen modules. These are already baked into CPython.
+    "_bootstrap.py",
+    "_bootstrap_external.py",
+]
+
+NUM_BYTECODE_FILES = 5
+
+
+def indent_msg(fn):
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        args[0].indent += 1
+        ret = fn(*args, **kwargs)
+        args[0].indent -= 1
+        return ret
+
+    return wrapper
+
+
+@dataclass
+class FrozenModule:
+    # The fully qualified module name, e.g. 'foo.bar.baz'
+    module_name: str
+    # The name of the C variable that holds the bytecode, e.g. 'M_foo__bar__baz'
+    c_name: str
+    # The size of the C variable. Negative if this module is a package.
+    size: int
+    # The frozen bytecode
+    bytecode: bytes
+
+
+class Freezer:
+    def __init__(self, verbose: bool):
+        self.frozen_modules: list[FrozenModule] = []
+        self.indent: int = 0
+        self.verbose: bool = verbose
+
+    def msg(self, path: Path, code: str):
+        if not self.verbose:
+            return
+        # P: package dir
+        # F: python file
+        # S: skipped (not a package dir)
+        # X: skipped (deny-listed)
+        # N: skipped (not a python file)
+        print("    " * self.indent, end="")
+        print(f"{code} {path}")
+
+    def write_bytecode(self, install_root):
+        """
+        Write the `.c` files containing the frozen bytecode.
+
+        Shared frozen modules evenly across the files.
+        """
+        bytecode_file_names = [f"bytecode_{i}.c" for i in range(NUM_BYTECODE_FILES)]
+        bytecode_files = [
+            open(os.path.join(install_root, name), "w") for name in bytecode_file_names
+        ]
+        it = itertools.cycle(bytecode_files)
+        for m in self.frozen_modules:
+            self.write_frozen(m, next(it))
+
+        for f in bytecode_files:
+            f.close()
+
+    def write_main(self, install_root, oss, symbol_name):
+        """Write the `main.c` file containing a table enumerating all the frozen modules."""
+        with open(os.path.join(install_root, "main.c"), "w") as outfp:
+            outfp.write(MAIN_INCLUDES)
+            for m in self.frozen_modules:
+                outfp.write(f"extern unsigned char {m.c_name}[];\n")
+
+            outfp.write(MAIN_PREFIX_TEMPLATE.format(symbol_name))
+            for m in self.frozen_modules:
+                outfp.write(f'\t{{"{m.module_name}", {m.c_name}, {m.size}}},\n')
+            outfp.write(MAIN_SUFFIX)
+            if oss:
+                outfp.write(FAKE_PREFIX)
+                outfp.write(MAIN_SUFFIX)
+
+    def write_frozen(self, m: FrozenModule, outfp):
+        """Write a single frozen module's bytecode out to a C variable."""
+        outfp.write(f"unsigned char {m.c_name}[] = {{")
+        for i in range(0, len(m.bytecode), 16):
+            outfp.write("\n\t")
+            for c in bytes(m.bytecode[i : i + 16]):
+                outfp.write(f"{c:d},")
+        outfp.write("\n};\n")
+
+    def compile_path(self, path: Path, top_package_path: Path):
+        """Entry point for compiling a Path object."""
+        if path.is_dir():
+            self.compile_package(path, top_package_path)
+        else:
+            self.compile_file(path, top_package_path)
+
+    @indent_msg
+    def compile_package(self, path: Path, top_package_path: Path):
+        """Compile all the files within a Python package dir."""
+        assert path.is_dir()
+        if path.name in DENY_LIST:
+            self.msg(path, "X")
+            return
+
+        # Python packages are directories that have __init__.py in them.
+        is_package_dir = any(child.name == "__init__.py" for child in path.iterdir())
+        if not is_package_dir:
+            self.msg(path, "S")
+            return
+
+        self.msg(path, "P")
+        # Recursively compile all children in this dir
+        for child in path.iterdir():
+            self.compile_path(child, top_package_path)
+
+    def get_module_qualname(self, file_path: Path, top_package_path: Path) -> list[str]:
+        # `path` looks like 'Lib/foo/bar/baz.py'
+
+        # chop off 'Lib/' to get something that represents a Python module hierarchy.
+        # e.g. 'foo/bar/baz.py', which maps to 'foo.bar.baz'
+        normalized_path = file_path.relative_to(top_package_path.parent)
+
+        if normalized_path.name == "__init__.py":
+            # Special handling for `__init__.py`. In this case, this file
+            # specifies that the containing directory should be treated as a package.
+            # For 'foo/bar/baz/__init__.py':
+            # - The module name is 'baz'
+            module_basename = normalized_path.parent.name
+            # - The parent is foo.bar (need to shave off the 'baz')
+            module_parent = normalized_path.parent.parent.parts
+        else:
+            module_basename = normalized_path.stem
+            module_parent = normalized_path.parent.parts
+        return list(module_parent) + [module_basename]
+
+    def compile_string(self, file_content: str) -> types.CodeType:
+        # instead of passing in the real build time path to 'compile', we
+        # pass in a marker instead. This prevents the build time path being
+        # leaked to runtime. That path may not be available at runtime.
+        # Setting the path to a mark make sure it's a hard error rather
+        # than a flaky error when inspect module tries to retrieve python source
+        # code during torchscripting.
+        path_marker = PATH_MARKER
+        return compile(file_content, path_marker, "exec")
+
+    @indent_msg
+    def compile_file(self, path: Path, top_package_path: Path):
+        """
+        Compile a Python source file to frozen bytecode.
+
+        Append the result to `self.frozen_modules`.
+        """
+        assert path.is_file()
+        if path.suffix != ".py":
+            self.msg(path, "N")
+            return
+
+        if path.name in DENY_LIST:
+            self.msg(path, "X")
+            return
+
+        self.msg(path, "F")
+        module_qualname = self.get_module_qualname(path, top_package_path)
+        module_mangled_name = "__".join(module_qualname)
+        c_name = "M_" + module_mangled_name
+
+        with open(path) as src_file:
+            co = self.compile_string(src_file.read())
+
+        bytecode = marshal.dumps(co)
+        size = len(bytecode)
+        if path.name == "__init__.py":
+            # Python packages are signified by negative size.
+            size = -size
+        self.frozen_modules.append(
+            FrozenModule(".".join(module_qualname), c_name, size, bytecode)
+        )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Compile py source")
+    parser.add_argument("paths", nargs="*", help="Paths to freeze.")
+    parser.add_argument("--verbose", action="store_true", help="Print debug logs")
+    parser.add_argument(
+        "--install-dir", "--install_dir", help="Root directory for all output files"
+    )
+    parser.add_argument(
+        "--oss",
+        action="store_true",
+        help="If it's OSS build, add a fake _PyImport_FrozenModules",
+    )
+    parser.add_argument(
+        "--symbol-name",
+        "--symbol_name",
+        help="The name of the frozen module array symbol to generate",
+        default="_PyImport_FrozenModules_torch",
+    )
+
+    args = parser.parse_args()
+
+    f = Freezer(args.verbose)
+
+    for p in args.paths:
+        path = Path(p)
+        if path.is_dir() and not Path.exists(path / "__init__.py"):
+            # this 'top level path p' is a standard directory containing modules,
+            # not a module itself
+            # each 'mod' could be a dir containing __init__.py or .py file
+            # NB: sorted to make sure this is deterministic
+            for mod in sorted(path.glob("*")):
+                f.compile_path(mod, mod)
+        else:
+            f.compile_path(path, path)
+
+    f.write_bytecode(args.install_dir)
+    f.write_main(args.install_dir, args.oss, args.symbol_name)
+
+
+if __name__ == "__main__":
+    main()  # pragma: no cover
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_functools.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_functools.py
new file mode 100644
index 0000000000000000000000000000000000000000..40ffd8f80a9e72928ec6980ee2cfc5620f802213
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_functools.py
@@ -0,0 +1,44 @@
+import functools
+from typing import Callable, TypeVar
+from typing_extensions import Concatenate, ParamSpec
+
+
+_P = ParamSpec("_P")
+_T = TypeVar("_T")
+_C = TypeVar("_C")
+
+# Sentinel used to indicate that cache lookup failed.
+_cache_sentinel = object()
+
+
+def cache_method(
+    f: Callable[Concatenate[_C, _P], _T]
+) -> Callable[Concatenate[_C, _P], _T]:
+    """
+    Like `@functools.cache` but for methods.
+
+    `@functools.cache` (and similarly `@functools.lru_cache`) shouldn't be used
+    on methods because it caches `self`, keeping it alive
+    forever. `@cache_method` ignores `self` so won't keep `self` alive (assuming
+    no cycles with `self` in the parameters).
+
+    Footgun warning: This decorator completely ignores self's properties so only
+    use it when you know that self is frozen or won't change in a meaningful
+    way (such as the wrapped function being pure).
+    """
+    cache_name = "_cache_method_" + f.__name__
+
+    @functools.wraps(f)
+    def wrap(self: _C, *args: _P.args, **kwargs: _P.kwargs) -> _T:
+        assert not kwargs
+        if not (cache := getattr(self, cache_name, None)):
+            cache = {}
+            setattr(self, cache_name, cache)
+        cached_value = cache.get(args, _cache_sentinel)
+        if cached_value is not _cache_sentinel:
+            return cached_value
+        value = f(self, *args, **kwargs)
+        cache[args] = value
+        return value
+
+    return wrap
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_get_clean_triton.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_get_clean_triton.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5e1495e7dc57483bc4936dcf7e96806788aab25
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_get_clean_triton.py
@@ -0,0 +1,150 @@
+# mypy: allow-untyped-defs
+import argparse
+import os
+import re
+from pathlib import Path
+
+
+def remove_triton_function_declaration(source_code: str) -> str:
+    remove_head = re.sub(r"(\n.+\s\'\'\'\n)", "\n", source_code)
+    remove_tail = re.sub(r"(\'\'\'\,.+)", "\n", remove_head)
+    return remove_tail
+
+
+def remove_async_compile(source_code: str) -> str:
+    remove_top_level = str.replace(source_code, "async_compile = AsyncCompile()", "")
+    remove_compile = str.replace(remove_top_level, "async_compile.wait(globals())", "")
+    remove_del = str.replace(remove_compile, "del async_compile", "")
+    return remove_del
+
+
+def rename_kernels(source_code: str) -> str:
+    pattern = r"(\w+)\s*=\s*async_compile\.triton\('triton_',\s"
+    triton_kernel_decl = "def triton_"
+    matches = [
+        (match.end(), match.group(1))
+        for match in re.finditer(pattern, source_code, re.DOTALL)
+    ]
+
+    # Starting from the last match to avoid issues with shifting indices after replacements
+    for end_index, captured_string in reversed(matches):
+        # Find the index of the next "B" after the current match
+        index_of_B = source_code.find(triton_kernel_decl, end_index)
+        if index_of_B != -1:
+            # Replace the triton_kernel_decl with the captured string
+            source_code = (
+                source_code[:index_of_B]
+                + f"def {captured_string}"
+                + source_code[index_of_B + len(triton_kernel_decl) :]
+            )
+        else:
+            # If triton_kernel_decl is not found after the current match, continue to the next
+            continue
+
+    return source_code
+
+
+def merge_params(original_params: list[str], new_params: list[str]) -> list[str]:
+    for idx in range(len(new_params)):
+        if new_params[idx] == "T":
+            new_params[idx] = original_params[idx]
+    return new_params
+
+
+def add_launch_params(
+    original: str, kernel_to_params: dict[str, tuple[str, str]]
+) -> str:
+    # Regex to match the function call in the original string
+    pattern = r"(\w+)\.run\((.*)\)"
+
+    def replace(match) -> str:
+        # Extract parts from the regex match
+        func_name = match.group(1)
+        params = match.group(2)
+        new_params, grid = kernel_to_params[func_name]
+        new_params = merge_params(params.split(", "), new_params.split(", "))
+
+        # Format the new function call
+        new_string = f"{func_name}[{grid}]({', '.join(new_params)})"
+        return new_string
+
+    transformed = re.sub(pattern, replace, original)
+
+    remove_inductor_wrappers = re.sub(
+        r"@triton_heuristics[^@]*@triton.jit",
+        r"@triton.jit",
+        transformed,
+        flags=re.DOTALL,
+    )
+
+    return remove_inductor_wrappers
+
+
+def process_file(input_filename: str, output_filename: str) -> str:
+    with open(input_filename) as file:
+        source_code = file.read()
+
+    transformed_code = source_code
+    if "def triton_(" in source_code:
+        raise RuntimeError(
+            "Need to run original Pytorch code generating kernels with TORCHINDUCTOR_UNIQUE_KERNEL_NAMES=1"
+        )
+    # transformed_code = rename_kernels(transformed_code)
+    transformed_code = remove_triton_function_declaration(transformed_code)
+    transformed_code = remove_async_compile(transformed_code)
+
+    launch_params_filename = f"{input_filename}.launch_params"
+    if not os.path.exists(launch_params_filename):
+        raise RuntimeError(
+            f"Missing {launch_params_filename}. Run `TORCHINDUCTOR_DUMP_LAUNCH_PARAMS=1 python {input_filename} first."
+        )
+
+    with open(launch_params_filename) as f:
+        launch_params_meta = f.readlines()
+
+    split_params = [i.split("|") for i in launch_params_meta]
+    kernel_args_grid = {a.strip(): (b.strip(), c.strip()) for a, b, c in split_params}
+    transformed_code = add_launch_params(transformed_code, kernel_args_grid)
+
+    with open(output_filename, "w") as file:
+        file.write(transformed_code)
+    return transformed_code
+
+
+def get_clean_triton(
+    input_path: Path, output_path: Path = Path("triton_only_repro.py")
+):
+    """Run experiments and output results to file
+
+    Args:
+        input_path (Optional[Path]): Path to inductor generated output codede
+        output_path (Optional[Path]): Path to write out the new python file
+    """
+    return process_file(str(input_path), str(output_path))
+
+
+if __name__ == "__main__":
+    """Sample usage:
+    # Running sweep
+    python inputcode.py
+    """
+    parser = argparse.ArgumentParser(
+        description="Clean Inductor generated code to remove Inductor dependencies"
+    )
+
+    # Add the arguments
+    parser.add_argument(
+        "input_path", type=Path, help="Path to inductor generated output code"
+    )
+    parser.add_argument(
+        "--output_path",
+        type=Path,
+        default=Path("triton_only_repro.py"),
+        help="Path to write out the clean triton output",
+    )
+
+    # Parse the arguments
+    args = parser.parse_args()
+
+    # Call the function with parsed arguments
+    result = get_clean_triton(args.input_path, args.output_path)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_import_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_import_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc2d7d4f0382c9c3287a03ad6f5125a54fd07ddc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_import_utils.py
@@ -0,0 +1,44 @@
+import functools
+import importlib.util
+from types import ModuleType
+from typing import Optional
+
+import torch
+
+
+def _check_module_exists(name: str) -> bool:
+    r"""Returns if a top-level module with :attr:`name` exists *without**
+    importing it. This is generally safer than try-catch block around a
+    `import X`. It avoids third party libraries breaking assumptions of some of
+    our tests, e.g., setting multiprocessing start method when imported
+    (see librosa/#747, torchvision/#544).
+    """
+    try:
+        spec = importlib.util.find_spec(name)
+        return spec is not None
+    except ImportError:
+        return False
+
+
+@functools.lru_cache
+def dill_available() -> bool:
+    return (
+        _check_module_exists("dill")
+        # dill fails to import under torchdeploy
+        and not torch._running_with_deploy()
+    )
+
+
+@functools.lru_cache
+def import_dill() -> Optional[ModuleType]:
+    if not dill_available():
+        return None
+
+    import dill
+
+    # XXX: By default, dill writes the Pickler dispatch table to inject its
+    # own logic there. This globally affects the behavior of the standard library
+    # pickler for any user who transitively depends on this module!
+    # Undo this extension to avoid altering the behavior of the pickler globally.
+    dill.extend(use_dill=False)
+    return dill
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_mode_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_mode_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..91c0e07b3d9345094e532beb4b94c1ff3c4b26a6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_mode_utils.py
@@ -0,0 +1,11 @@
+# mypy: allow-untyped-defs
+import torch
+from typing import TypeVar
+
+T = TypeVar('T')
+
+# returns if all are the same mode
+def all_same_mode(modes):
+    return all(tuple(mode == modes[0] for mode in modes))
+
+no_dispatch = torch._C._DisableTorchDispatch
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_ordered_set.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_ordered_set.py
new file mode 100644
index 0000000000000000000000000000000000000000..29373289c42606c2f3fd08120cc59af8dcf25b1c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_ordered_set.py
@@ -0,0 +1,176 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Iterable,
+    Iterator,
+    MutableSet,
+    Reversible,
+    Set as AbstractSet,
+)
+from typing import Any, cast, Optional, TypeVar
+
+
+T = TypeVar("T")
+T_co = TypeVar("T_co", covariant=True)
+
+__all__ = ["OrderedSet"]
+
+
+class OrderedSet(MutableSet[T], Reversible[T]):
+    """
+    Insertion ordered set, similar to OrderedDict.
+    """
+
+    __slots__ = ("_dict",)
+
+    def __init__(self, iterable: Optional[Iterable[T]] = None):
+        self._dict = dict.fromkeys(iterable, None) if iterable is not None else {}
+
+    @staticmethod
+    def _from_dict(dict_inp: dict[T, None]) -> OrderedSet[T]:
+        s: OrderedSet[T] = OrderedSet()
+        s._dict = dict_inp
+        return s
+
+    #
+    # Required overriden abstract methods
+    #
+    def __contains__(self, elem: object) -> bool:
+        return elem in self._dict
+
+    def __iter__(self) -> Iterator[T]:
+        return iter(self._dict)
+
+    def __len__(self) -> int:
+        return len(self._dict)
+
+    def __reversed__(self) -> Iterator[T]:
+        return reversed(self._dict)
+
+    def add(self, elem: T) -> None:
+        self._dict[elem] = None
+
+    def discard(self, elem: T) -> None:
+        self._dict.pop(elem, None)
+
+    def clear(self) -> None:
+        # overridden because MutableSet impl is slow
+        self._dict.clear()
+
+    # Unimplemented set() methods in _collections_abc.MutableSet
+
+    @classmethod
+    def _wrap_iter_in_set(cls, other: Any) -> Any:
+        """
+        Wrap non-Set Iterables in OrderedSets
+
+        Some of the magic methods are more strict on input types than
+        the public apis, so we need to wrap inputs in sets.
+        """
+
+        if not isinstance(other, AbstractSet) and isinstance(other, Iterable):
+            return cls(other)
+        else:
+            return other
+
+    def pop(self) -> T:
+        if not self:
+            raise KeyError("pop from an empty set")
+        return self._dict.popitem()[0]
+
+    def copy(self) -> OrderedSet[T]:
+        return OrderedSet._from_dict(self._dict.copy())
+
+    def difference(self, *others: Iterable[T]) -> OrderedSet[T]:
+        res = self.copy()
+        res.difference_update(*others)
+        return res
+
+    def difference_update(self, *others: Iterable[T]) -> None:
+        for other in others:
+            self -= other  # type: ignore[arg-type]
+
+    def update(self, *others: Iterable[T]) -> None:
+        for other in others:
+            self |= other
+
+    def intersection(self, *others: Iterable[T]) -> OrderedSet[T]:
+        res = self.copy()
+        for other in others:
+            if other is not self:
+                res &= other  # type: ignore[arg-type]
+        return res
+
+    def intersection_update(self, *others: Iterable[T]) -> None:
+        for other in others:
+            self &= other  # type: ignore[arg-type]
+
+    def issubset(self, other: Iterable[T]) -> bool:
+        return self <= self._wrap_iter_in_set(other)
+
+    def issuperset(self, other: Iterable[T]) -> bool:
+        return self >= self._wrap_iter_in_set(other)
+
+    def symmetric_difference(self, other: Iterable[T]) -> OrderedSet[T]:
+        return self ^ other  # type: ignore[operator]
+
+    def symmetric_difference_update(self, other: Iterable[T]) -> None:
+        self ^= other  # type: ignore[arg-type]
+
+    def union(self, *others: Iterable[T]) -> OrderedSet[T]:
+        res = self.copy()
+        for other in others:
+            if other is self:
+                continue
+            res |= other
+        return res
+
+    # Specify here for correct type inference, otherwise would
+    # return AbstractSet[T]
+    def __sub__(self, other: AbstractSet[T_co]) -> OrderedSet[T]:
+        # following cpython set impl optimization
+        if isinstance(other, OrderedSet) and (len(self) * 4) > len(other):
+            out = self.copy()
+            out -= other
+            return out
+        return cast(OrderedSet[T], super().__sub__(other))
+
+    def __ior__(self, other: Iterable[T]) -> OrderedSet[T]:  # type: ignore[misc, override]   # noqa: PYI034
+        if isinstance(other, OrderedSet):
+            self._dict.update(other._dict)
+            return self
+        return super().__ior__(other)  # type: ignore[arg-type]
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, OrderedSet):
+            return self._dict == other._dict
+        return super().__eq__(other)
+
+    def __ne__(self, other: object) -> bool:
+        if isinstance(other, OrderedSet):
+            return self._dict != other._dict
+        return super().__ne__(other)
+
+    def __or__(self, other: AbstractSet[T_co]) -> OrderedSet[T]:
+        return cast(OrderedSet[T], super().__or__(other))
+
+    def __and__(self, other: AbstractSet[T_co]) -> OrderedSet[T]:
+        # MutableSet impl will iterate over other, iter over smaller of two sets
+        if isinstance(other, OrderedSet) and len(self) < len(other):
+            return other & self
+        return cast(OrderedSet[T], super().__and__(other))
+
+    def __xor__(self, other: AbstractSet[T_co]) -> OrderedSet[T]:
+        return cast(OrderedSet[T], super().__xor__(other))
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({list(self)})"
+
+    def __getstate__(self) -> list[T]:
+        return list(self._dict.keys())
+
+    def __setstate__(self, state: list[T]) -> None:
+        self._dict = dict.fromkeys(state, None)
+
+    def __reduce__(self) -> tuple[type[OrderedSet[T]], tuple[list[T]]]:
+        return (OrderedSet, (list(self),))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_python_dispatch.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_python_dispatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a06fd13ae46b23189aae3f5f5755199060b257f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_python_dispatch.py
@@ -0,0 +1,712 @@
+# mypy: allow-untyped-defs
+import contextlib
+
+import warnings
+from dataclasses import dataclass
+from typing import Any, Optional, Union, Protocol, overload
+from collections.abc import Sequence
+from typing_extensions import TypeIs
+from collections import deque
+
+import torch
+import torchgen
+import torchgen.model
+from torch._C import (
+    _get_dispatch_stack_at,
+    _len_torch_dispatch_stack,
+    _pop_torch_dispatch_stack,
+    _push_on_torch_dispatch_stack,
+    DispatchKey,
+)
+
+
+# TODO: Limitations and things about enable_torch_dispatch_mode we should fix before exposing it:
+# - We need a better user-facing api for _DisableTorchDispatch that
+#   is able to selectively disable __torch_dispatch__ of a particular class.
+# - It doesn't work with the tensor constructors (torch.tensor, torch.Tensor)
+# - Better name (see https://github.com/pytorch/pytorch/pull/63496#discussion_r694091694)
+
+_is_in_torch_dispatch_mode = False
+_is_in_non_infra_torch_dispatch_mode = False
+
+def is_in_torch_dispatch_mode(include_infra_modes=True) -> bool:
+    return _is_in_torch_dispatch_mode if include_infra_modes else _is_in_non_infra_torch_dispatch_mode
+
+
+class TorchDispatchMode:
+    """
+    A ``TorchDispatchMode`` allows you to override the meaning of all
+    ``__torch_dispatch__`` overrideable functions within a dynamic scope,
+    without having to actually create a tensor subclass or manually
+    monkey-patch functions in the PyTorch API.  Some common situations
+    where you should use a mode:
+
+        * You want to override the meaning of factory functions, or other
+          functions that do not otherwise take a tensor as an argument
+          (these cannot be overridden with tensor subclasses).
+
+        * You want to override the behavior of all functions without needing
+          to wrap your inputs in tensor subclasses; e.g., if you are just
+          interested in logging intermediate computations.
+
+        * You want to control the order of execution of various tensor
+          subclasses explicitly, rather than implicitly via the return of
+          ``NotImplemented``.
+
+    Independent subclasses of :class:`TorchDispatchMode` are compositional:
+    modes can be pushed onto a stack using ``with MyMode():``.
+    When you call functions in the PyTorch API inside your
+    ``__torch_dispatch__`` implementation, by default, they will forward on to
+    the next mode on the mode stack.  If you want recursively call back into
+    your current ``__torch_dispatch__`` implementation, either explicitly
+    invoke ``self.__torch_dispatch__(...)``, or use the context manager
+    ``__torch_dispatch__(self)`` to make PyTorch
+    API self-referential (beware of infinite loops, in this case!)
+    """
+
+    def __init__(self, _dispatch_key=None):
+        if _dispatch_key is not None:
+            assert isinstance(_dispatch_key, torch._C.DispatchKey)
+            self.__dict__["_dispatch_key"] = _dispatch_key
+
+        self.old_dispatch_mode_flags: deque[bool] = deque()
+        self.old_non_infra_dispatch_mode_flags: deque[bool] = deque()
+
+    def _lazy_init_old_dispatch_mode_flags(self):
+        if not hasattr(self, "old_dispatch_mode_flags"):
+            self.old_dispatch_mode_flags: deque[bool] = deque()  # type: ignore[no-redef]
+
+        if not hasattr(self, "old_non_infra_dispatch_mode_flags"):
+            self.old_non_infra_dispatch_mode_flags: deque[bool] = deque()  # type: ignore[no-redef]
+
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        raise NotImplementedError
+
+    def __enter__(self):
+        global _is_in_torch_dispatch_mode
+        global _is_in_non_infra_torch_dispatch_mode
+        # Previously, there wasn't any state in this class' constructor
+        # super calls were added to existing modes, but for any new modes
+        # this will replicate the previous behavior of not strictly needing
+        # to call super().__init__()
+        self._lazy_init_old_dispatch_mode_flags()
+        self.old_dispatch_mode_flags.append(_is_in_torch_dispatch_mode)
+        _is_in_torch_dispatch_mode = True
+        self.old_non_infra_dispatch_mode_flags.append(_is_in_non_infra_torch_dispatch_mode)
+        _is_in_non_infra_torch_dispatch_mode = _is_in_non_infra_torch_dispatch_mode or not self.is_infra_mode()
+        _push_mode(self)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        mb_dk_or_mode_key = self.__dict__.get("_dispatch_key", None)
+        if mb_dk_or_mode_key is None:
+            # Today, mode keys are not used at all in the per-dispatch-key-mode logic (for pre-dispatch)
+            # We should probably revisit this.
+            mb_dk_or_mode_key = self.__dict__.get("_mode_key", None)
+        global _is_in_torch_dispatch_mode
+        _is_in_torch_dispatch_mode = self.old_dispatch_mode_flags.pop()
+        global _is_in_non_infra_torch_dispatch_mode
+        _is_in_non_infra_torch_dispatch_mode = self.old_non_infra_dispatch_mode_flags.pop()
+        _pop_mode(mb_dk_or_mode_key)
+
+    @classmethod
+    def push(cls, *args, **kwargs):
+        warnings.warn(
+            "`Mode.push()` is no longer necessary and can be replaced with just `with Mode()`"
+        )
+        instance = cls(*args, **kwargs)
+        return instance
+
+    @classmethod
+    def is_infra_mode(cls):
+        return False
+
+
+
+def _get_current_dispatch_mode():
+    stack_len = _len_torch_dispatch_stack()
+    # Return a user mode on the stack if there are any
+    if stack_len > 0:
+        return _get_dispatch_stack_at(stack_len - 1)
+    return None
+
+
+def _detect_infra_mode(key):
+    assert key in [torch._C._TorchDispatchModeKey.FUNCTIONAL, torch._C._TorchDispatchModeKey.PROXY]
+    from torch._ops import _get_dispatch_mode_pre_dispatch
+
+    pre_dispatch_mode = _get_dispatch_mode_pre_dispatch(
+        key
+    )
+    post_dispatch_mode = torch._C._get_dispatch_mode(
+        key
+    )
+
+    assert (pre_dispatch_mode is None) or (
+        post_dispatch_mode is None
+    )
+
+    if pre_dispatch_mode is None:
+        return post_dispatch_mode
+
+    return pre_dispatch_mode
+
+
+def _unset_infra_mode(key):
+    from torch._ops import _get_dispatch_mode_pre_dispatch, unset_mode_pre_dispatch
+
+    pre_dispatch_mode = _get_dispatch_mode_pre_dispatch(key)
+    post_dispatch_mode = torch._C._get_dispatch_mode(key)
+    if pre_dispatch_mode and post_dispatch_mode:
+        raise AssertionError(
+            "Can't have active infra mode on both pre and post dispatch mode stack"
+        )
+
+    if pre_dispatch_mode:
+        mode = unset_mode_pre_dispatch(key)
+        return mode
+    if post_dispatch_mode:
+        return torch._C._unset_dispatch_mode(key)
+
+
+def _disable_infra_mode(key):
+    assert key in (
+        torch._C._TorchDispatchModeKey.FUNCTIONAL,
+        torch._C._TorchDispatchModeKey.PROXY,
+    )
+    mode_unset = _unset_infra_mode(key)
+    try:
+        yield mode_unset
+    finally:
+        if mode_unset is not None:
+            _push_mode(mode_unset)
+
+
+def _get_current_dispatch_mode_stack():
+    stack_len = _len_torch_dispatch_stack()
+    return [_get_dispatch_stack_at(i) for i in range(stack_len)]
+
+
+def _push_mode(mode: TorchDispatchMode):
+    k = mode._dispatch_key if hasattr(mode, "_dispatch_key") else None
+    assert k is None or k == torch._C.DispatchKey.PreDispatch
+    if k is None:
+        _push_on_torch_dispatch_stack(mode)
+        return
+
+    from torch._ops import _set_mode_pre_dispatch, get_cached_ops
+
+    # See Note [Not Caching Per-Dispatch-Key Mode Handlers]
+    # Clear the cache of every op that has been used so far, for this particular key.
+    ks = torch._C._functionality_to_backend_keys(k)
+    for op in get_cached_ops():
+        for key in ks:
+            op._uncache_dispatch(key)
+    _set_mode_pre_dispatch(mode)
+
+
+def _pop_mode(k: Optional[Union[DispatchKey, torch._C._TorchDispatchModeKey]] = None):
+    if k == torch._C.DispatchKey.PreDispatch:  # type: ignore[attr-defined]
+        from torch._ops import _pop_mode_from_pre_dispatch
+
+        return _pop_mode_from_pre_dispatch()
+
+    if k is None or isinstance(k, torch._C._TorchDispatchModeKey):
+        return _pop_torch_dispatch_stack(k)
+
+
+@contextlib.contextmanager
+def _pop_mode_temporarily(k: Optional[DispatchKey] = None):
+    old = _pop_mode(k)
+    try:
+        yield old
+    finally:
+        _push_mode(old)
+
+
+@contextlib.contextmanager
+def _disable_current_modes():
+    from torch._ops import (
+        _len_torch_dispatch_stack_pre_dispatch,
+        _pop_mode_from_pre_dispatch,
+    )
+    from torch._subclasses.functional_tensor import FunctionalTensorMode
+    from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode
+    from torch._subclasses.schema_check_mode import SchemaCheckMode
+
+    mode_len_pre_dispatch = _len_torch_dispatch_stack_pre_dispatch()
+    old_pre_dispatch_modes = [
+        _pop_mode_from_pre_dispatch() for _ in range(mode_len_pre_dispatch)
+    ]
+
+    has_proxy_mode_in_pre_dispatch = False
+    has_functional_mode_in_pre_dispatch = False
+    has_schema_check_mode_in_pre_dispatch = False
+
+    for i in old_pre_dispatch_modes:
+        if isinstance(i, ProxyTorchDispatchMode):
+            has_proxy_mode_in_pre_dispatch = True
+        if isinstance(i, FunctionalTensorMode):
+            has_functional_mode_in_pre_dispatch = True
+        if isinstance(i, SchemaCheckMode):
+            has_schema_check_mode_in_pre_dispatch = True
+
+    mode_len = _len_torch_dispatch_stack()
+    old_modes = [_pop_mode() for _ in range(mode_len)]
+
+    for old in old_modes:
+        if (
+            isinstance(old, FunctionalTensorMode)
+            and has_functional_mode_in_pre_dispatch
+        ):
+            raise AssertionError(
+                "Can't have FunctionalMode available both in PreDispatch and Python Key"
+            )
+        if isinstance(old, ProxyTorchDispatchMode) and has_proxy_mode_in_pre_dispatch:
+            raise AssertionError(
+                "Can't have ProxyTorchDispatchMode available both in PreDispatch and Python Key"
+            )
+        if (
+            isinstance(old, SchemaCheckMode)
+            and has_schema_check_mode_in_pre_dispatch
+        ):
+            raise AssertionError(
+                "Can't have SchemaCheckMode available both in PreDispatch and Python Key"
+            )
+
+    # Manually disable proxy and fake modes, if any are active
+    try:
+        yield old_pre_dispatch_modes + old_modes
+    finally:
+        for mode in reversed(old_modes):
+            _push_mode(mode)
+        for mode in reversed(old_pre_dispatch_modes):
+            _push_mode(mode)
+
+
+class BaseTorchDispatchMode(TorchDispatchMode):
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+        return func(*args, **kwargs)
+
+
+# Subtypes which have __tensor_flatten__ and __tensor_unflatten__.
+class TensorWithFlatten(Protocol):
+    def __tensor_flatten__(self) -> tuple[Sequence[str], object]:
+        ...
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors: int, flatten_spec: int, outer_size: int, outer_stride: int) -> torch.Tensor:
+        ...
+
+    # It would be really nice to be able to say that the return of
+    # is_traceable_wrapper_subclass() is Intersection[torch.Tensor,
+    # TensorWithFlatten] - but that doesn't exist.
+
+    shape: torch._C.Size
+
+    @overload
+    def stride(self, dim: None = None) -> tuple[int, ...]:
+        ...
+
+    @overload
+    def stride(self, dim: int) -> int:
+        ...
+
+    @overload
+    def size(self, dim: None = None) -> tuple[int, ...]:
+        ...
+
+    @overload
+    def size(self, dim: int) -> int:
+        ...
+
+    def storage_offset(self) -> int:
+        ...
+
+    def dim(self) -> int:
+        ...
+
+    @overload
+    def to(
+            self,
+            dtype: torch.types._dtype,
+            non_blocking: bool = False,
+            copy: bool = False,
+            *,
+            memory_format: Optional[torch.memory_format] = None
+    ) -> torch.Tensor:
+        ...
+
+    @overload
+    def to(
+            self,
+            device: Optional["torch._prims_common.DeviceLikeType"] = None,
+            dtype: Optional[torch.types._dtype] = None,
+            non_blocking: bool = False,
+            copy: bool = False,
+            *,
+            memory_format: Optional[torch.memory_format] = None
+    ) -> torch.Tensor:
+        ...
+
+    @overload
+    def to(
+            self,
+            other: torch.Tensor,
+            non_blocking: bool = False,
+            copy: bool = False,
+            *,
+            memory_format: Optional[torch.memory_format] = None
+    ) -> torch.Tensor:
+        ...
+
+
+
+
+def is_traceable_wrapper_subclass(t: object) -> TypeIs[TensorWithFlatten]:
+    """
+    Returns whether or not a tensor subclass that implements __torch_dispatch__
+    is 'traceable' with torch.compile.
+    In order for a tensor subclass to support TorchDispatchMode-style tracing in PT2,
+    It must implement two magic methods: __tensor_flatten__ and __tensor_unflatten__.
+    It is also expected to obey some restrictions around traceability and aliasing:
+        * The subclass's __torch_dispatch__() implementation should desugar into pytorch
+            dispatcher operations that can be traced into a graph.
+        * The subclass should use return_and_correct_aliasing(). This is needed today to make
+            sure that torch.compile does the right thing in a few cases around input mutation
+            and output aliasing.
+
+    Expected magic method signatures:
+        attrs, ctx = t.__tensor_flatten__()
+            attrs: list of attribute name strings for inner tensors
+            ctx: dict containing any other subclass-specific metadata needed for unflattening
+
+        t = MySubClass.__tensor_unflatten__(inner_tensors, ctx, outer_size, outer_stride)
+            inner_tensors: dict mapping attribute name -> tensor for each inner tensor
+            ctx: dict with subclass metadata in the form that __tensor_flatten__() produces
+            outer_size: expected (possibly symbolic) size that the returned subclass
+                instance should have. Note that this arg is useful for certain subclasses
+                that require the shape info to be constructed. In most cases, this arg can be
+                safely ignored.
+            outer_stride: expected (possibly symbolic) stride that the returned subclass
+                instance should have. Note that this arg is useful for certain subclasses
+                that require the stride info to be constructed. In most cases, this arg can be
+                safely ignored.
+    """
+    is_subclass = isinstance(t, torch.Tensor) and type(t) != torch.Tensor
+    return (
+        is_subclass
+        and hasattr(t, "__tensor_flatten__")
+        and hasattr(t, "__tensor_unflatten__")
+    )
+
+def is_traceable_wrapper_subclass_type(t: type) -> TypeIs[type[TensorWithFlatten]]:
+    """Same as above, but takes a type argument instead of an instance."""
+    return (issubclass(t, torch.Tensor) and t != torch.Tensor
+            and hasattr(t, "__tensor_flatten__") and hasattr(t, "__tensor_unflatten__"))
+
+
+def transform_subclass(t, callback, outer_size=None, outer_stride=None):
+    """
+    Given a traceable, wrapper tensor subclass ``t`` that implements
+    ``__torch_dispatch__`` and holds some inner tensors,
+    and a callback of type ``Callable[[str, torch.Tensor], torch.Tensor]``,
+    `transform_subclass` will construct a fresh instance of the wrapper tensor subclass.
+    It will do so by grabbing each inner tensor attribute from the wrapper,
+    passing them into ``callback`` to get a transformed tensor,
+    and putting each transformed tensor into the fresh tensor subclass instance.
+
+    Note: this function will not handle ensuring that the fresh subclass
+    gets the same (autograd, and aliasing) metadata as the original tensor.
+    This is generally handled in other subsystems like AOTAutograd.
+    """
+    outer_size = outer_size if outer_size is not None else t.size()
+    outer_stride = outer_stride if outer_stride is not None else t.stride()
+
+    attrs, ctx = t.__tensor_flatten__()
+    transformed_tensors_dict = {}
+    for attr in attrs:
+        transformed_tensors_dict[attr] = callback(attr, getattr(t, attr))
+    sub = type(t).__tensor_unflatten__(
+        transformed_tensors_dict, ctx, outer_size, outer_stride
+    )
+
+    # NB: Purposefully guard here to simplify the inner / outer symbols.
+    # Using sym_eq() for symbolic comparison can result in an expression that's too
+    # difficult to guard on, so we use == here.
+    assert sub.shape == outer_size, (
+        f"Expected return value from {type(t)}__tensor_unflatten__() to have "
+        f"shape equal to {outer_size}, but got: {sub.shape}"
+    )
+    assert sub.stride() == outer_stride, (
+        f"Expected return value from {type(t)}__tensor_unflatten__() to have "
+        f"stride equal to {outer_stride}, but got: {sub.stride()}"
+    )
+
+    return sub
+
+
+def _correct_storage_aliasing(func, schema_info, args, outs):
+    """
+    Given: an OpOverload, a SchemaInfo (cached information from torchgen about schema),
+    and the inputs/outputs to the OpOverload,
+    this function checks to see if func is a view operator
+    (by checking if any of the outputs in the op's schema
+     are immutable aliases of inputs).
+    If so, this function manually aliases the storage of the output tensor
+    with its corresponding input tensor alias.
+    It does this by unsafely overwriting the storage field of the output tensor
+    to be the same storage as the input.
+    """
+    assert isinstance(func, torch._ops.OpOverload)
+    assert isinstance(args, tuple)
+    assert isinstance(outs, (list, tuple))
+
+    def alias_non_inplace_storage(arg, ret):
+        # This is hopefully a reasonable assert:
+        # subclasses that rely on this API for output aliasing
+        # should always return wrapper tensor subclasses for us to manually alias.
+        # in theory if a subclass that needs this API wants to sometimes return
+        # plain tensors, we could remove the assert and just not perform the aliasing,
+        # but it seems safer to learn more about this case first.
+        if is_traceable_wrapper_subclass(arg) or is_traceable_wrapper_subclass(ret):
+            ret_list = ret if isinstance(ret, list) else [ret]
+            for r in ret_list:
+                assert type(arg) == type(
+                    r
+                ), f"""Called {str(func)} with input of type {type(arg)}
+and output of type {type(ret)}. But expected types to match."""
+        # Need to call a non-dispatcher helper, because we explicitly do **not**
+        # want our subclass to intercept the set_() call.
+        # instead, our subclass should directly have its storage swapped out.
+        # we **explicitly** don't want to reset the sizes on ret, if the storage implies a size change.
+        # Why?
+        # The purpose of this API is *not* to change the size/strides of our output- we assume it's already correct.
+        # We just want to "fix up" the storage aliasing, without modifying or output's metadata.
+        # Example: out = inp.expand(inp.shape[0], inp.shape[0])
+        #     This requires swapping the storage of out to be the same as inp,
+        #     but we do *not* want it to change the sizes/strides that were compute for out.
+
+        if isinstance(ret, list):
+            for r in ret:
+                torch._functionalize_unsafe_set(r, arg)
+        else:
+            assert isinstance(ret, torch.Tensor), f"type: {type(ret)}"
+            torch._functionalize_unsafe_set(ret, arg)
+
+    def is_read_only_alias_match(arg, ret):
+        shared_aliases = arg.alias_set & ret.alias_set
+        return len(shared_aliases) > 0 and not arg.is_write
+
+    num_args = len(func._schema.arguments)
+    num_returns = len(func._schema.returns)
+    for arg_idx in range(num_args):
+        for return_idx in range(num_returns):
+            if is_read_only_alias_match(
+                schema_info.args[arg_idx], schema_info.outs[return_idx]
+            ):
+                alias_non_inplace_storage(args[arg_idx], outs[return_idx])
+
+
+# This abstracts over the fact that in return_and_correct_aliasing,
+# we sometimes use torchgen schema parsing (for aten ops, since torchscript's schema parsing is sometimes buggy),
+# and sometimes use torchscript schema parsing (for custom ops, for which torchgen parsing is untested).
+@dataclass
+class AliasInfo:
+    alias_set: set[str]
+    is_write: bool
+    name: Optional[str]
+
+
+@dataclass
+class SchemaInfo:
+    args: list[AliasInfo]
+    outs: list[AliasInfo]
+
+
+# Can't import torch._ops.OpOverload due to circular reference
+parsed_schema_map: dict[Any, SchemaInfo] = {}
+
+
+# Given an OpOverload, returns schema information on it.
+# This is cached for efficiency, since it can involve running torchgen
+def get_alias_info(func) -> SchemaInfo:
+    if func in parsed_schema_map:
+        return parsed_schema_map[func]
+    # For ATen ops: use torchgen (since torchscript parser doesn't handle alias annotations
+    # properly for some ops that output tensorlists)
+    if func.namespace == "aten":
+        torchgen_schema_str = str(func._schema)
+        assert torchgen_schema_str.startswith("aten::")
+        # remove the aten:: namespace, which is added by the torchscript parser,
+        # and torchgen doesn't know how to handle
+        torchgen_schema_str = torchgen_schema_str[6:]
+        import re
+
+        # the torchscript parser ends up converting int[2]=1 into int[2]=[1, 1],
+        # which torchgen chokes on.
+        torchgen_schema_str = re.sub(r"=\[[0, ]+\]", "=0", torchgen_schema_str)
+        torchgen_schema_str = re.sub(r"=\[[1, ]+\]", "=1", torchgen_schema_str)
+        # for aten::rot90 / aten:fft_*
+        torchgen_schema_str = re.sub(r"=\[(-?[0-9]+), (-?[0-9]+)\]", r"=[\1,\2]", torchgen_schema_str)
+        torchgen_schema = torchgen.model.FunctionSchema.parse(torchgen_schema_str)
+        arg_schemas = [
+            AliasInfo(
+                alias_set=(
+                    set() if a.annotation is None else set(a.annotation.alias_set)
+                ),
+                is_write=a.annotation is not None and a.annotation.is_write,
+                name=a.name,
+            )
+            for a in torchgen_schema.arguments.flat_all
+        ]
+        out_schemas = [
+            AliasInfo(
+                alias_set=(
+                    set() if a.annotation is None else set(a.annotation.alias_set)
+                ),
+                is_write=a.annotation is not None and a.annotation.is_write,
+                name=a.name,
+            )
+            for a in torchgen_schema.returns
+        ]
+    else:
+        # For non-aten ops, torchgen is untested so we rely on torchscript schema parsing
+        arg_schemas = [
+            AliasInfo(
+                alias_set=(
+                    set() if a.alias_info is None else set(a.alias_info.before_set)
+                ),
+                is_write=a.alias_info is not None and a.alias_info.is_write,
+                name=a.name,
+            )
+            for a in func._schema.arguments
+        ]
+        out_schemas = [
+            AliasInfo(
+                alias_set=(
+                    set() if a.alias_info is None else set(a.alias_info.before_set)
+                ),
+                is_write=a.alias_info is not None and a.alias_info.is_write,
+                name=a.name,
+            )
+            for a in func._schema.returns
+        ]
+    schema_info = SchemaInfo(args=arg_schemas, outs=out_schemas)
+    parsed_schema_map[func] = schema_info
+    return schema_info
+
+
+def return_and_correct_aliasing(func, args, kwargs, out):
+    """
+    This function should be used by wrapper tensor ``__torch_dispatch__`` subclasses
+    that would like to work with torch.compile. It ensures that the subclass
+    properly implements the aliasing behavior of every op,
+    which is needed for correctness in AOTAutograd.
+    This function will handle:
+
+        * When we see a view op, we will alias the storages of any
+          input and output tensor subclasses
+
+        * When we see an inplace or out= op, we will directly
+          return the corresponding input tensor, instead of returning
+          a (potentially) fresh output tensor.
+    """
+
+    # Caching here because torchgen parsing is definitely not fast, and this function is called
+    # once for every op in the graph during functionalization.
+    schema_info = get_alias_info(func)
+
+    def get_write_alias(x):
+        if len(x.alias_set) == 0:
+            return None
+        alias_set = list(x.alias_set)
+        # torchscript allows for complicated alias sets, but our dispatcher ops only really involve simple aliasing
+        assert len(alias_set) == 1
+        if x.is_write:
+            return alias_set[0]
+        return None
+
+    def get_arg_from_alias(output_alias, schema_info, args, kwargs):
+        new_args, new_kwargs = torch.fx.operator_schemas.normalize_function(  # type: ignore[misc]
+            func, args=args, kwargs=kwargs
+        )
+
+        arg_indices = [
+            i for i, a in enumerate(schema_info.args) if output_alias in a.alias_set
+        ]
+        # For any dispatcher op with an output alias, we expect it to map to exactly one alias in the schema's input arguments.
+        assert len(arg_indices) == 1
+        idx = arg_indices[0]
+        arg_info = schema_info.args[idx]
+        if arg_info.name is not None and arg_info.name in new_kwargs:
+            return new_kwargs[arg_info.name]
+        return new_args[idx]
+
+    # Fix up the storages of any outs so that they point to the same storage as the input,
+    # if func is a view op.
+    _correct_storage_aliasing(
+        func, schema_info, args, (out,) if not isinstance(out, tuple) else out
+    )
+
+    # For inplace_view ops in particular, we'll try hard to make sure that the wrapper subclass's
+    # metadata is set correctly.
+    if torch.Tag.inplace_view in func.tags:
+        # no_dispatch() to make sure that we secretly change the metadata on the wrapper,
+        # but don't end up dispatching the op anywhere else.
+        mutated_args = [
+            x
+            for i, x in enumerate(args)
+            if get_write_alias(schema_info.args[i]) is not None
+        ]
+        # Assumption: we have a very small number of inplace_view ops that follow a strict schema:
+        # there is only a single argument that gets its metadata mutated.
+        assert len(mutated_args) == 1
+        # This check exists because we generally *do* want to update the metadata of any wrapper subclasses,
+        # but FunctionalTensor is special: it overrides all size/stride calls to plumb to the inner tensor.
+        # so we don't actually need to update the metadata (and attempting to do so causes errors)
+        from torch._subclasses.functional_tensor import FunctionalTensor
+
+        if not isinstance(mutated_args[0], FunctionalTensor):
+            with torch.utils._mode_utils.no_dispatch():
+                # See Note: [Fake Tensor Dispatch Keys]
+                # we're borrowing the way it modifies dispatch key TLS.
+                meta_in_tls = torch._C._meta_in_tls_dispatch_include()
+                torch._C._set_meta_in_tls_dispatch_include(True)
+                try:
+                    func(*args, **kwargs)
+                finally:
+                    torch._C._set_meta_in_tls_dispatch_include(meta_in_tls)
+
+    # Next: we need to make sure to return inputs directly, if the output is a mutable alias (e.g. add_()).
+
+    # simple case: none of our outputs have mutable aliases, so we can return the output as-is
+    if not any(get_write_alias(r) is not None for r in schema_info.outs):
+        return out
+
+    # simplifying assumption: we don't have **any** ops with return types like "-> (Tensor(a!), Tensor)"
+    if not all(get_write_alias(r) is not None for r in schema_info.outs):
+        raise RuntimeError("Unsupported schema: " + str(func._schema))
+
+    if len(func._schema.returns) == 1:
+        return get_arg_from_alias(
+            get_write_alias(schema_info.outs[0]), schema_info, args, kwargs
+        )
+
+    # In the multi-return case, all aten ops return a tuple / list, so cast accordingly.
+    outs_to_return = type(out)(
+        [
+            (
+                get_arg_from_alias(
+                    get_write_alias(schema_info.outs[i]), schema_info, args, kwargs
+                )
+                if get_write_alias(r) is not None
+                else o
+            )
+            for ((i, r), o) in zip(enumerate(schema_info.outs), out)
+        ]
+    )
+    return outs_to_return
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_pytree.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_pytree.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b8859e62829084ef97b19b1bf4a460094153262
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_pytree.py
@@ -0,0 +1,1842 @@
+"""
+Contains utility functions for working with nested python data structures.
+
+A *pytree* is Python nested data structure. It is a tree in the sense that
+nodes are Python collections (e.g., list, tuple, dict) and the leaves are
+Python values. Furthermore, a pytree should not contain reference cycles.
+
+pytrees are useful for working with nested collections of Tensors. For example,
+one can use `tree_map` to map a function over all Tensors inside some nested
+collection of Tensors and `tree_leaves` to get a flat list of all Tensors
+inside some nested collection. pytrees are helpful for implementing nested
+collection support for PyTorch APIs.
+
+This pytree implementation is not very performant due to Python overhead
+To improve the performance we can move parts of the implementation to C++.
+"""
+
+import dataclasses
+import functools
+import importlib
+import importlib.metadata
+import json
+import sys
+import threading
+import types
+import warnings
+from collections import defaultdict, deque, namedtuple, OrderedDict
+from collections.abc import Hashable, Iterable, Mapping, Sequence
+from enum import Enum
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Generic,
+    Optional,
+    overload,
+    Protocol,
+    TypeVar,
+    Union,
+)
+from typing_extensions import deprecated, NamedTuple
+
+
+__all__ = [
+    "PyTree",
+    "Context",
+    "FlattenFunc",
+    "UnflattenFunc",
+    "DumpableContext",
+    "ToDumpableContextFn",
+    "FromDumpableContextFn",
+    "TreeSpec",
+    "LeafSpec",
+    "keystr",
+    "key_get",
+    "register_pytree_node",
+    "tree_flatten",
+    "tree_flatten_with_path",
+    "tree_unflatten",
+    "tree_iter",
+    "tree_leaves",
+    "tree_leaves_with_path",
+    "tree_structure",
+    "tree_map",
+    "tree_map_with_path",
+    "tree_map_",
+    "tree_map_only",
+    "tree_map_only_",
+    "tree_all",
+    "tree_any",
+    "tree_all_only",
+    "tree_any_only",
+    "treespec_dumps",
+    "treespec_loads",
+    "treespec_pprint",
+]
+
+
+T = TypeVar("T")
+S = TypeVar("S")
+U = TypeVar("U")
+R = TypeVar("R")
+
+
+DEFAULT_TREESPEC_SERIALIZATION_PROTOCOL = 1
+NO_SERIALIZED_TYPE_NAME_FOUND = "NO_SERIALIZED_TYPE_NAME_FOUND"
+
+
+class KeyEntry(Protocol):
+    def __hash__(self) -> int:
+        ...
+
+    def __eq__(self, other: object) -> bool:
+        ...
+
+    def __str__(self) -> str:
+        ...
+
+    def get(self, parent: Any) -> Any:
+        ...
+
+
+class EnumEncoder(json.JSONEncoder):
+    def default(self, obj: object) -> str:
+        if isinstance(obj, Enum):
+            return obj.value  # type: ignore[no-any-return]
+        return super().default(obj)  # type: ignore[no-any-return]
+
+
+Context = Any
+PyTree = Any
+FlattenFunc = Callable[[PyTree], tuple[list[Any], Context]]
+UnflattenFunc = Callable[[Iterable[Any], Context], PyTree]
+DumpableContext = Any  # Any json dumpable text
+ToDumpableContextFn = Callable[[Context], DumpableContext]
+FromDumpableContextFn = Callable[[DumpableContext], Context]
+ToStrFunc = Callable[["TreeSpec", list[str]], str]
+MaybeFromStrFunc = Callable[[str], Optional[tuple[Any, Context, str]]]
+KeyPath = tuple[KeyEntry, ...]
+FlattenWithKeysFunc = Callable[[PyTree], tuple[list[tuple[KeyEntry, Any]], Any]]
+
+
+# A NodeDef holds two callables:
+# - flatten_fn should take the collection and return a flat list of values.
+#   It can also return some context that is used in reconstructing the
+#   collection.
+# - unflatten_fn should take a flat list of values and some context
+#   (returned by flatten_fn). It returns the collection by reconstructing
+#   it from the list and the context.
+# - flatten_with_keys_fn, which is a callable that takes a
+#   pytree and returns a list of (keypath, value) pairs and a context.
+class NodeDef(NamedTuple):
+    type: type[Any]
+    flatten_fn: FlattenFunc
+    unflatten_fn: UnflattenFunc
+    flatten_with_keys_fn: Optional[FlattenWithKeysFunc]
+
+
+_NODE_REGISTRY_LOCK = threading.RLock()
+SUPPORTED_NODES: dict[type[Any], NodeDef] = {}
+
+
+# _SerializeNodeDef holds the following:
+# - typ: the type of the node (e.g., "Dict", "List", etc)
+# - serialized_type_name: the fully qualified name of the type, e.g. "collections.OrderedDict"
+# - to_dumpable_context takes a TreeSpec, and returns a serialized string format of the
+#   context, and the version number
+# - from_dumpable_context takes in a string representation of the context, and the
+#   version, and returns the deserialized context
+class _SerializeNodeDef(NamedTuple):
+    typ: type[Any]
+    serialized_type_name: str
+    to_dumpable_context: Optional[ToDumpableContextFn]
+    from_dumpable_context: Optional[FromDumpableContextFn]
+
+
+SUPPORTED_SERIALIZED_TYPES: dict[type[Any], _SerializeNodeDef] = {}
+SERIALIZED_TYPE_TO_PYTHON_TYPE: dict[str, type[Any]] = {}
+
+# NB: we try really hard to not import _cxx_pytree (which depends on optree)
+# as much as possible. This is for isolation: a user who is not using C++ pytree
+# shouldn't pay for it, and it helps makes things like cpython upgrades easier.
+try:
+    _optree_version = importlib.metadata.version("optree")
+except importlib.metadata.PackageNotFoundError:
+    # No optree package found
+    _cxx_pytree_dynamo_traceable = _cxx_pytree_exists = False
+else:
+    from torch._vendor.packaging.version import Version
+
+    # Keep this in sync with torch.utils._cxx_pytree!
+    if Version(_optree_version) < Version("0.13.0"):
+        # optree package less than our required minimum version.
+        # Pretend the optree package doesn't exist.
+        # NB: We will raise ImportError if the user directly tries to
+        # `import torch.utils._cxx_pytree` (look in that file for the check).
+        _cxx_pytree_dynamo_traceable = _cxx_pytree_exists = False
+    else:
+        _cxx_pytree_dynamo_traceable = _cxx_pytree_exists = True
+
+_cxx_pytree_imported = False
+_cxx_pytree_pending_imports: list[Any] = []
+
+
+def register_pytree_node(
+    cls: type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+    flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None,
+) -> None:
+    """Register a container-like type as pytree node.
+
+    Args:
+        cls: the type to register
+        flatten_fn: A callable that takes a pytree and returns a flattened
+            representation of the pytree and additional context to represent the
+            flattened pytree.
+        unflatten_fn: A callable that takes a flattened version of the pytree,
+            additional context, and returns an unflattened pytree.
+        serialized_type_name: A keyword argument used to specify the fully qualified
+            name used when serializing the tree spec.
+        to_dumpable_context: An optional keyword argument to custom specify how
+            to convert the context of the pytree to a custom json dumpable
+            representation. This is used for json serialization, which is being
+            used in torch.export right now.
+        from_dumpable_context: An optional keyword argument to custom specify how
+            to convert the custom json dumpable representation of the context
+            back to the original context. This is used for json deserialization,
+            which is being used in torch.export right now.
+        flatten_with_keys_fn: An optional keyword argument to specify how to
+            access each pytree leaf's keypath when flattening and tree-mapping.
+            Like ``flatten_fn``, but in place of a List[leaf], it should return
+            a List[(keypath, leaf)].
+    """
+    with _NODE_REGISTRY_LOCK:
+        if cls in SUPPORTED_NODES:
+            raise ValueError(f"{cls} is already registered as pytree node.")
+
+    _private_register_pytree_node(
+        cls,
+        flatten_fn,
+        unflatten_fn,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=to_dumpable_context,
+        from_dumpable_context=from_dumpable_context,
+        flatten_with_keys_fn=flatten_with_keys_fn,
+    )
+
+    if not _cxx_pytree_exists:
+        return
+
+    if _cxx_pytree_imported:
+        from . import _cxx_pytree as cxx
+
+        cxx._private_register_pytree_node(
+            cls,
+            flatten_fn,
+            unflatten_fn,
+            serialized_type_name=serialized_type_name,
+            to_dumpable_context=to_dumpable_context,
+            from_dumpable_context=from_dumpable_context,
+        )
+    else:
+        args = (cls, flatten_fn, unflatten_fn)
+        kwargs = {
+            "serialized_type_name": serialized_type_name,
+            "to_dumpable_context": to_dumpable_context,
+            "from_dumpable_context": from_dumpable_context,
+        }
+        _cxx_pytree_pending_imports.append((args, kwargs))
+
+
+def register_dataclass(cls: type[Any]) -> None:
+    """Registers a ``dataclasses.dataclass`` type as a pytree node.
+
+    This is a simpler API than :func:`register_pytree_node` for registering
+    a dataclass.
+
+    Args:
+        cls: the dataclass type to register
+
+    Example:
+
+        >>> from torch import Tensor
+        >>> from dataclasses import dataclass
+        >>> import torch.utils._pytree as pytree
+        >>>
+        >>> @dataclass
+        >>> class Point:
+        >>>     x: Tensor
+        >>>     y: Tensor
+        >>>
+        >>> pytree.register_dataclass(Point)
+        >>>
+        >>> point = Point(torch.tensor(0), torch.tensor(1))
+        >>> point = pytree.tree_map(lambda x: x + 1, point)
+        >>> assert torch.allclose(point.x, torch.tensor(1))
+        >>> assert torch.allclose(point.y, torch.tensor(2))
+
+    """
+    import torch.export
+
+    # Eventually we should move the export code here. It is not specific to export,
+    # aside from the serialization pieces.
+    torch.export.register_dataclass(cls)
+
+
+CONSTANT_NODES: set[type] = set()
+
+
+def register_constant(cls: type[Any]) -> None:
+    """Registers a type as a pytree node with no leaves.
+
+    In a :func:`torch.compile` region, if instances of these types get passed to
+    :func:`torch._dynamo.nonstrict_trace`-ed function, they treated as a
+    constant (sometimes referred to as "static"):
+
+    1. if the instance object existed before the :func:`torch.compile` region,
+    we _assume_ no mutation will happen to it inside the :func:`torch.compile`
+    region, require that it has non-default `__eq__` and `__hash__` methods, and
+    we guard on the instance based on its `__eq__` method, i.e., if a new
+    instance fails to match any instances from the previous compilations,
+    :func:`torch.compile` will recompile the function using the new instance.
+
+    2. else if the instance object is created inside the :func:`torch.compile`
+    region, we currently don't support using it in a
+    :func:`torch._dynamo.nonstrict_trace`-ed function.
+
+    In general, if your class holds Tensors or dynamic int/float/bool (values that
+    may change from run-to-run of a function being compiled), then you probably
+    do not want to register it as a constant.
+
+    Otherwise if you want to pass instance of a class to a
+    :func:`torch._dynamo.nonstrict_trace`-ed function, but you either can't use
+    :func:`register_pytree_node` on the class, or the class is "constant" enough
+    that you don't want to bother using :func:`register_pytree_node`, you should
+    consider using this function.
+
+    Args:
+        cls: the type to register as a constant. This type must be hashable.
+
+    Example:
+
+        >>> from dataclasses import dataclass
+        >>> import torch.utils._pytree as pytree
+        >>>
+        >>> @dataclass(frozen=True)
+        >>> class Config:
+        >>>     norm: str
+        >>>
+        >>> pytree.register_constant(Config)
+        >>>
+        >>> config = Config("l2")
+        >>> values, spec = pytree.tree_flatten(config)
+        >>> assert len(values) == 0
+
+    """
+    if cls.__eq__ is object.__eq__:  # type: ignore[comparison-overlap]
+        raise TypeError(
+            "register_constant(cls) expects `cls` to have a non-default `__eq__` implementation."
+        )
+
+    # Class with a custom `__eq__` without `__hash__` won't inherit the default
+    # `__hash__` from object; see https://stackoverflow.com/a/1608907.
+    if cls.__hash__ is None:  # type: ignore[comparison-overlap]
+        raise TypeError(
+            "register_constant(cls) expects `cls` to have a non-default `__hash__` implementation."
+        )
+
+    def _flatten(x):  # type: ignore[no-untyped-def]
+        return [], ConstantNode(x)
+
+    def _unflatten(_, context):  # type: ignore[no-untyped-def]
+        return context.value
+
+    def _flatten_with_keys(x):  # type: ignore[no-untyped-def]
+        return [], ConstantNode(x)
+
+    with _NODE_REGISTRY_LOCK:
+        _private_register_pytree_node(
+            cls,
+            _flatten,
+            _unflatten,
+            flatten_with_keys_fn=_flatten_with_keys,
+        )
+        CONSTANT_NODES.add(cls)
+
+
+def is_constant_class(cls: type[Any]) -> bool:
+    return isinstance(cls, type) and cls in CONSTANT_NODES
+
+
+@dataclasses.dataclass(frozen=True)
+class ConstantNode:
+    value: Any
+
+
+def _is_constant_holder(spec: "TreeSpec") -> bool:
+    """Checks if the spec is from a pytree registered with register_constant"""
+    return isinstance(spec.context, ConstantNode)
+
+
+def _retrieve_constant(spec: "TreeSpec") -> Any:
+    """Given a spec from a pytree registered with register_constant, retrieves the constant"""
+    assert _is_constant_holder(spec)
+    return tree_unflatten([], spec)
+
+
+def _register_namedtuple(
+    cls: type[Any],
+    *,
+    serialized_type_name: str,
+) -> None:
+    """
+    Registers a namedtuple as a valid pytree node. By default namedtuples are
+    valid pytree nodes, but they are not serializable. This API provides the
+    argument `serialized_type_name` which allows these namedtuples to be
+    serialized.
+
+    Args:
+        cls: the dataclass type to register
+        serialized_type_name: The serialized name for the dataclass. This is
+        required if you want to serialize the pytree TreeSpec containing this
+        namedtuple.
+    """
+    _private_register_pytree_node(
+        cls,
+        _namedtuple_flatten,
+        _namedtuple_unflatten,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=_namedtuple_serialize,
+        from_dumpable_context=_namedtuple_deserialize,
+        flatten_with_keys_fn=_namedtuple_flatten_with_keys,
+    )
+
+
+@deprecated(
+    "`torch.utils._pytree._register_pytree_node` is deprecated. "
+    "Please use `torch.utils._pytree.register_pytree_node` instead.",
+    category=FutureWarning,
+)
+def _register_pytree_node(
+    cls: type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    to_str_fn: Optional[ToStrFunc] = None,  # deprecated
+    maybe_from_str_fn: Optional[MaybeFromStrFunc] = None,  # deprecated
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+    flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None,
+) -> None:
+    """Register a container-like type as pytree node for the Python pytree only.
+
+    Args:
+        cls: the type to register
+        flatten_fn: A callable that takes a pytree and returns a flattened
+            representation of the pytree and additional context to represent the
+            flattened pytree.
+        unflatten_fn: A callable that takes a flattened version of the pytree,
+            additional context, and returns an unflattened pytree.
+        serialized_type_name: A keyword argument used to specify the fully qualified
+            name used when serializing the tree spec.
+        to_dumpable_context: An optional keyword argument to custom specify how
+            to convert the context of the pytree to a custom json dumpable
+            representation. This is used for json serialization, which is being
+            used in torch.export right now.
+        from_dumpable_context: An optional keyword argument to custom specify how
+            to convert the custom json dumpable representation of the context
+            back to the original context. This is used for json deserialization,
+            which is being used in torch.export right now.
+        flatten_with_keys_fn: An optional keyword argument to specify how to
+            access each pytree leaf's keypath when flattening and tree-mapping.
+            Like ``flatten_fn``, but in place of a List[leaf], it should return
+            a List[(keypath, leaf)].
+    """
+    if to_str_fn is not None or maybe_from_str_fn is not None:
+        warnings.warn(
+            "`to_str_fn` and `maybe_from_str_fn` is deprecated. "
+            "Please use `to_dumpable_context` and `from_dumpable_context` instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+
+    _private_register_pytree_node(
+        cls,
+        flatten_fn,
+        unflatten_fn,
+        serialized_type_name=serialized_type_name,
+        to_dumpable_context=to_dumpable_context,
+        from_dumpable_context=from_dumpable_context,
+        flatten_with_keys_fn=flatten_with_keys_fn,
+    )
+
+
+def _deregister_pytree_node(
+    cls: type[Any],
+) -> None:
+    """This is an internal function that is used to deregister a pytree node type
+    for the Python pytree only. This should be only used inside PyTorch.
+    """
+    with _NODE_REGISTRY_LOCK:
+        del SUPPORTED_NODES[cls]
+        node_def = SUPPORTED_SERIALIZED_TYPES[cls]
+        del SERIALIZED_TYPE_TO_PYTHON_TYPE[node_def.serialized_type_name]
+        del SUPPORTED_SERIALIZED_TYPES[cls]
+        CONSTANT_NODES.discard(cls)
+
+
+def _private_register_pytree_node(
+    cls: type[Any],
+    flatten_fn: FlattenFunc,
+    unflatten_fn: UnflattenFunc,
+    *,
+    serialized_type_name: Optional[str] = None,
+    to_dumpable_context: Optional[ToDumpableContextFn] = None,
+    from_dumpable_context: Optional[FromDumpableContextFn] = None,
+    flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None,
+) -> None:
+    """This is an internal function that is used to register a pytree node type
+    for the Python pytree only. End-users should use :func:`register_pytree_node`
+    instead.
+    """
+    with _NODE_REGISTRY_LOCK:
+        if cls in SUPPORTED_NODES:
+            # TODO: change this warning to an error after OSS/internal stabilize
+            warnings.warn(
+                f"{cls} is already registered as pytree node. "
+                "Overwriting the previous registration.",
+            )
+
+        node_def = NodeDef(cls, flatten_fn, unflatten_fn, flatten_with_keys_fn)
+        SUPPORTED_NODES[cls] = node_def
+
+        if (to_dumpable_context is None) ^ (from_dumpable_context is None):
+            raise ValueError(
+                f"Both to_dumpable_context and from_dumpable_context for {cls} must "
+                "be None or registered."
+            )
+
+        if serialized_type_name is None:
+            serialized_type_name = NO_SERIALIZED_TYPE_NAME_FOUND
+
+        serialize_node_def = _SerializeNodeDef(
+            cls,
+            serialized_type_name,
+            to_dumpable_context,
+            from_dumpable_context,
+        )
+        SUPPORTED_SERIALIZED_TYPES[cls] = serialize_node_def
+        SERIALIZED_TYPE_TO_PYTHON_TYPE[serialized_type_name] = cls
+
+
+@dataclasses.dataclass(frozen=True)
+class SequenceKey(Generic[T]):
+    idx: int
+
+    def __str__(self) -> str:
+        return f"[{self.idx!r}]"
+
+    def get(self, sequence: Sequence[T]) -> T:
+        return sequence[self.idx]
+
+
+K = TypeVar("K", bound=Hashable)
+
+
+@dataclasses.dataclass(frozen=True)
+class MappingKey(Generic[K, T]):
+    key: K
+
+    def __str__(self) -> str:
+        return f"[{self.key!r}]"
+
+    def get(self, mapping: Mapping[K, T]) -> T:
+        return mapping[self.key]
+
+
+@dataclasses.dataclass(frozen=True)
+class GetAttrKey:
+    name: str
+
+    def __str__(self) -> str:
+        return f".{self.name}"
+
+    def get(self, obj: Any) -> Any:
+        return getattr(obj, self.name)
+
+
+def _tuple_flatten(d: tuple[T, ...]) -> tuple[list[T], Context]:
+    return list(d), None
+
+
+def _tuple_flatten_with_keys(
+    d: tuple[T, ...]
+) -> tuple[list[tuple[KeyEntry, T]], Context]:
+    values, context = _tuple_flatten(d)
+    return [(SequenceKey(i), v) for i, v in enumerate(values)], context
+
+
+def _tuple_unflatten(values: Iterable[T], context: Context) -> tuple[T, ...]:
+    return tuple(values)
+
+
+def _list_flatten(d: list[T]) -> tuple[list[T], Context]:
+    return d, None
+
+
+def _list_flatten_with_keys(d: list[T]) -> tuple[list[tuple[KeyEntry, T]], Context]:
+    values, context = _list_flatten(d)
+    return [(SequenceKey(i), v) for i, v in enumerate(values)], context
+
+
+def _list_unflatten(values: Iterable[T], context: Context) -> list[T]:
+    return list(values)
+
+
+def _dict_flatten(d: dict[Any, T]) -> tuple[list[T], Context]:
+    return list(d.values()), list(d.keys())
+
+
+def _dict_flatten_with_keys(
+    d: dict[Any, T]
+) -> tuple[list[tuple[KeyEntry, T]], Context]:
+    values, context = _dict_flatten(d)
+    return [(MappingKey(k), v) for k, v in zip(context, values)], context
+
+
+def _dict_unflatten(values: Iterable[T], context: Context) -> dict[Any, T]:
+    return dict(zip(context, values))
+
+
+def _namedtuple_flatten(d: NamedTuple) -> tuple[list[Any], Context]:
+    return list(d), type(d)
+
+
+def _namedtuple_flatten_with_keys(
+    d: NamedTuple,
+) -> tuple[list[tuple[KeyEntry, Any]], Context]:
+    values, context = _namedtuple_flatten(d)
+    return (
+        [(GetAttrKey(field), v) for field, v in zip(context._fields, values)],
+        context,
+    )
+
+
+def _namedtuple_unflatten(values: Iterable[T], context: Context) -> NamedTuple:
+    return cast(NamedTuple, context(*values))
+
+
+def _namedtuple_serialize(context: Context) -> DumpableContext:
+    if context not in SUPPORTED_SERIALIZED_TYPES:
+        raise NotImplementedError(
+            f"Can't serialize TreeSpec of namedtuple class {context} because we "
+            "didn't register a serializated_type_name. Please register using "
+            "`_register_namedtuple`."
+        )
+
+    serialize_node_def = SUPPORTED_SERIALIZED_TYPES[context]
+    serialized_type_name = serialize_node_def.serialized_type_name
+
+    if serialized_type_name == NO_SERIALIZED_TYPE_NAME_FOUND:
+        raise NotImplementedError(
+            f"Can't serialize TreeSpec of namedtuple class {context} because we "
+            "couldn't find a serializated_type_name. Please register using "
+            "`_register_namedtuple`."
+        )
+    return serialized_type_name
+
+
+def _namedtuple_deserialize(dumpable_context: DumpableContext) -> Context:
+    if dumpable_context not in SERIALIZED_TYPE_TO_PYTHON_TYPE:
+        raise NotImplementedError(
+            f"Can't deserialize TreeSpec of namedtuple class {dumpable_context} "
+            "because we couldn't find a serializated name."
+        )
+
+    typ = SERIALIZED_TYPE_TO_PYTHON_TYPE[dumpable_context]
+    return typ
+
+
+def _ordereddict_flatten(d: OrderedDict[Any, T]) -> tuple[list[T], Context]:
+    return list(d.values()), list(d.keys())
+
+
+def _ordereddict_flatten_with_keys(
+    d: OrderedDict[Any, T]
+) -> tuple[list[tuple[KeyEntry, T]], Context]:
+    values, context = _ordereddict_flatten(d)
+    return [(MappingKey(k), v) for k, v in zip(context, values)], context
+
+
+def _ordereddict_unflatten(
+    values: Iterable[T],
+    context: Context,
+) -> OrderedDict[Any, T]:
+    return OrderedDict((key, value) for key, value in zip(context, values))
+
+
+_odict_flatten = _ordereddict_flatten
+_odict_unflatten = _ordereddict_unflatten
+
+
+def _defaultdict_flatten(d: defaultdict[Any, T]) -> tuple[list[T], Context]:
+    values, dict_context = _dict_flatten(d)
+    return values, [d.default_factory, dict_context]
+
+
+def _defaultdict_flatten_with_keys(
+    d: defaultdict[Any, T]
+) -> tuple[list[tuple[KeyEntry, T]], Context]:
+    values, context = _defaultdict_flatten(d)
+    _, dict_context = context
+    return [(MappingKey(k), v) for k, v in zip(dict_context, values)], context
+
+
+def _defaultdict_unflatten(
+    values: Iterable[T],
+    context: Context,
+) -> defaultdict[Any, T]:
+    default_factory, dict_context = context
+    return defaultdict(default_factory, _dict_unflatten(values, dict_context))
+
+
+def _defaultdict_serialize(context: Context) -> DumpableContext:
+    default_factory, dict_context = context
+    json_defaultdict = {
+        "default_factory_module": default_factory.__module__,
+        "default_factory_name": default_factory.__qualname__,
+        "dict_context": dict_context,
+    }
+    return json_defaultdict
+
+
+def _defaultdict_deserialize(dumpable_context: DumpableContext) -> Context:
+    assert isinstance(dumpable_context, dict)
+    assert set(dumpable_context) == {
+        "default_factory_module",
+        "default_factory_name",
+        "dict_context",
+    }
+
+    default_factory_module = dumpable_context["default_factory_module"]
+    default_factory_name = dumpable_context["default_factory_name"]
+    assert isinstance(default_factory_module, str)
+    assert isinstance(default_factory_name, str)
+    module = importlib.import_module(default_factory_module)
+    default_factory = getattr(module, default_factory_name)
+
+    dict_context = dumpable_context["dict_context"]
+    return [default_factory, dict_context]
+
+
+def _deque_flatten(d: deque[T]) -> tuple[list[T], Context]:
+    return list(d), d.maxlen
+
+
+def _deque_flatten_with_keys(
+    d: deque[T],
+) -> tuple[list[tuple[KeyEntry, T]], Context]:
+    values, context = _deque_flatten(d)
+    return [(SequenceKey(i), v) for i, v in enumerate(values)], context
+
+
+def _deque_unflatten(values: Iterable[T], context: Context) -> deque[T]:
+    return deque(values, maxlen=context)
+
+
+_private_register_pytree_node(
+    tuple,
+    _tuple_flatten,
+    _tuple_unflatten,
+    serialized_type_name="builtins.tuple",
+    flatten_with_keys_fn=_tuple_flatten_with_keys,
+)
+_private_register_pytree_node(
+    list,
+    _list_flatten,
+    _list_unflatten,
+    serialized_type_name="builtins.list",
+    flatten_with_keys_fn=_list_flatten_with_keys,
+)
+_private_register_pytree_node(
+    dict,
+    _dict_flatten,
+    _dict_unflatten,
+    serialized_type_name="builtins.dict",
+    flatten_with_keys_fn=_dict_flatten_with_keys,
+)
+_private_register_pytree_node(
+    namedtuple,  # type: ignore[arg-type]
+    _namedtuple_flatten,
+    _namedtuple_unflatten,
+    serialized_type_name="collections.namedtuple",
+    to_dumpable_context=_namedtuple_serialize,
+    from_dumpable_context=_namedtuple_deserialize,
+    flatten_with_keys_fn=_namedtuple_flatten_with_keys,
+)
+_private_register_pytree_node(
+    OrderedDict,
+    _ordereddict_flatten,
+    _ordereddict_unflatten,
+    serialized_type_name="collections.OrderedDict",
+    flatten_with_keys_fn=_ordereddict_flatten_with_keys,
+)
+_private_register_pytree_node(
+    defaultdict,
+    _defaultdict_flatten,
+    _defaultdict_unflatten,
+    serialized_type_name="collections.defaultdict",
+    to_dumpable_context=_defaultdict_serialize,
+    from_dumpable_context=_defaultdict_deserialize,
+    flatten_with_keys_fn=_defaultdict_flatten_with_keys,
+)
+_private_register_pytree_node(
+    deque,
+    _deque_flatten,
+    _deque_unflatten,
+    serialized_type_name="collections.deque",
+    flatten_with_keys_fn=_deque_flatten_with_keys,
+)
+
+
+STANDARD_DICT_TYPES: frozenset[type] = frozenset(
+    {dict, OrderedDict, defaultdict},
+)
+BUILTIN_TYPES: frozenset[type] = frozenset(
+    {tuple, list, dict, namedtuple, OrderedDict, defaultdict, deque},  # type: ignore[arg-type]
+)
+
+
+# h/t https://stackoverflow.com/questions/2166818/how-to-check-if-an-object-is-an-instance-of-a-namedtuple
+def _is_namedtuple_instance(tree: Any) -> bool:
+    typ = type(tree)
+    bases = typ.__bases__
+    if len(bases) != 1 or bases[0] != tuple:
+        return False
+    fields = getattr(typ, "_fields", None)
+    if not isinstance(fields, tuple):
+        return False
+    return all(type(entry) == str for entry in fields)
+
+
+def _get_node_type(tree: Any) -> Any:
+    if _is_namedtuple_instance(tree):
+        return namedtuple
+    return type(tree)
+
+
+# A leaf is defined as anything that is not a Node.
+def _is_leaf(tree: PyTree, is_leaf: Optional[Callable[[PyTree], bool]] = None) -> bool:
+    return (is_leaf is not None and is_leaf(tree)) or _get_node_type(
+        tree
+    ) not in SUPPORTED_NODES
+
+
+# A TreeSpec represents the structure of a pytree. It holds:
+# "type": the type of root Node of the pytree
+# context: some context that is useful in unflattening the pytree
+# children_specs: specs for each child of the root Node
+# num_leaves: the number of leaves
+@dataclasses.dataclass(init=True, frozen=True, eq=True, repr=False)
+class TreeSpec:
+    type: Any
+    context: Context
+    children_specs: list["TreeSpec"]
+
+    num_nodes: int = dataclasses.field(init=False)
+    num_leaves: int = dataclasses.field(init=False)
+    num_children: int = dataclasses.field(init=False)
+
+    def __post_init__(self) -> None:
+        num_nodes = sum((spec.num_nodes for spec in self.children_specs), start=1)
+        num_leaves = sum(spec.num_leaves for spec in self.children_specs)
+        num_children = len(self.children_specs)
+        object.__setattr__(self, "num_nodes", num_nodes)
+        object.__setattr__(self, "num_leaves", num_leaves)
+        object.__setattr__(self, "num_children", num_children)
+
+    def __repr__(self, indent: int = 0) -> str:
+        repr_prefix: str = f"TreeSpec({self.type.__name__}, {self.context}, ["
+        children_specs_str: str = ""
+        if self.num_children > 0:
+            indent += 2
+            children_specs_str += self.children_specs[0].__repr__(indent)
+            children_specs_str += "," if self.num_children > 1 else ""
+            children_specs_str += ",".join(
+                [
+                    "\n" + " " * indent + child.__repr__(indent)
+                    for child in self.children_specs[1:]
+                ]
+            )
+        repr_suffix: str = f"{children_specs_str}])"
+        return repr_prefix + repr_suffix
+
+    def __eq__(self, other: PyTree) -> bool:
+        if self is other:
+            return True
+        elif other.__class__ is self.__class__:
+            if str(self.type) != str(other.type):
+                return False
+            if self.context != other.context:
+                return False
+            elif self.children_specs != other.children_specs:
+                return False
+            return True
+        return NotImplemented
+
+    def is_leaf(self) -> bool:
+        return self.num_nodes == 1 and self.num_leaves == 1
+
+    def flatten_up_to(self, tree: PyTree) -> list[PyTree]:
+        def helper(treespec: TreeSpec, tree: PyTree, subtrees: list[PyTree]) -> None:
+            if treespec.is_leaf():
+                subtrees.append(tree)
+                return
+
+            node_type = _get_node_type(tree)
+            if treespec.type not in BUILTIN_TYPES:
+                # Always require custom node types to match exactly
+                if node_type != treespec.type:
+                    raise ValueError(
+                        f"Type mismatch; "
+                        f"expected {treespec.type!r}, but got {node_type!r}.",
+                    )
+                flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+                children, context = flatten_fn(tree)
+                if len(children) != treespec.num_children:
+                    raise ValueError(
+                        f"Node arity mismatch; "
+                        f"expected {treespec.num_children}, but got {len(children)}.",
+                    )
+                if context != treespec.context:
+                    raise ValueError(
+                        f"Node context mismatch for custom node type {treespec.type!r}.",
+                    )
+            else:
+                # For builtin dictionary types, we allow some flexibility
+                # Otherwise, we require exact matches
+                both_standard_dict = (
+                    treespec.type in STANDARD_DICT_TYPES
+                    and node_type in STANDARD_DICT_TYPES
+                )
+                if not both_standard_dict and node_type != treespec.type:
+                    raise ValueError(
+                        f"Node type mismatch; "
+                        f"expected {treespec.type!r}, but got {node_type!r}.",
+                    )
+                if len(tree) != treespec.num_children:
+                    raise ValueError(
+                        f"Node arity mismatch; "
+                        f"expected {treespec.num_children}, but got {len(tree)}.",
+                    )
+
+                if both_standard_dict:
+                    # dictionary types are compatible with each other
+                    dict_context = (
+                        treespec.context
+                        if treespec.type is not defaultdict
+                        # ignore mismatch of `default_factory` for defaultdict
+                        else treespec.context[1]
+                    )
+                    expected_keys = dict_context
+                    got_key_set = set(tree)
+                    expected_key_set = set(expected_keys)
+                    if got_key_set != expected_key_set:
+                        missing_keys = expected_key_set.difference(got_key_set)
+                        extra_keys = got_key_set.difference(expected_key_set)
+                        message = ""
+                        if missing_keys:
+                            message += f"; missing key(s): {missing_keys}"
+                        if extra_keys:
+                            message += f"; extra key(s): {extra_keys}"
+                        raise ValueError(f"Node keys mismatch{message}.")
+                    children = [tree[key] for key in expected_keys]
+                else:
+                    # node_type is treespec.type
+                    flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+                    children, context = flatten_fn(tree)
+                    if (
+                        node_type is not deque  # ignore mismatch of `maxlen` for deque
+                    ) and context != treespec.context:
+                        raise ValueError(
+                            f"Node context mismatch for node type {treespec.type!r}; "
+                            f"expected {treespec.context!r}, but got {context!r}.",  # namedtuple type mismatch
+                        )
+
+            for subtree, subspec in zip(children, treespec.children_specs):
+                helper(subspec, subtree, subtrees)
+
+        subtrees: list[PyTree] = []
+        helper(self, tree, subtrees)
+        return subtrees
+
+    def unflatten(self, leaves: Iterable[Any]) -> PyTree:
+        if not isinstance(leaves, (list, tuple)):
+            leaves = list(leaves)
+        if len(leaves) != self.num_leaves:
+            raise ValueError(
+                f"treespec.unflatten(leaves): `leaves` has length {len(leaves)} "
+                f"but the spec refers to a pytree that holds {self.num_leaves} "
+                f"items ({self}).",
+            )
+        if self.is_leaf():
+            return leaves[0]
+
+        unflatten_fn = SUPPORTED_NODES[self.type].unflatten_fn
+
+        # Recursively unflatten the children
+        start = 0
+        end = 0
+        child_pytrees = []
+        for child_spec in self.children_specs:
+            end += child_spec.num_leaves
+            child_pytrees.append(child_spec.unflatten(leaves[start:end]))
+            start = end
+
+        return unflatten_fn(child_pytrees, self.context)
+
+
+# NOTE: subclassing a dataclass is subtle. In order to enable reasoning about
+# this class with `dataclasses.fields`, etc., while having a simplified
+# constructor that takes no argument, we wrap with `dataclass(init=True, ...)`
+# again, with fields that have `init=False`.
+@dataclasses.dataclass(init=True, frozen=True, eq=False, repr=False)
+class LeafSpec(TreeSpec):
+    type: Any = dataclasses.field(default=None, init=False)
+    context: Context = dataclasses.field(default=None, init=False)
+    children_specs: list["TreeSpec"] = dataclasses.field(
+        default_factory=list, init=False
+    )
+
+    def __post_init__(self) -> None:
+        # Override `__post_init__` for `num_leaves` derivation.
+        object.__setattr__(self, "num_nodes", 1)
+        object.__setattr__(self, "num_leaves", 1)
+        object.__setattr__(self, "num_children", 0)
+
+    def __repr__(self, indent: int = 0) -> str:
+        return "*"
+
+
+# All leaves are equivalent, so represent with a single object to save on
+# object construction time
+_LEAF_SPEC = LeafSpec()
+
+
+def tree_flatten(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> tuple[list[Any], TreeSpec]:
+    """Flattens a pytree into a list of values and a TreeSpec that can be used
+    to reconstruct the pytree.
+    """
+
+    def helper(node: PyTree, leaves: list[Any]) -> TreeSpec:
+        if _is_leaf(node, is_leaf=is_leaf):
+            leaves.append(node)
+            return _LEAF_SPEC
+
+        node_type = _get_node_type(node)
+        flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+        children, context = flatten_fn(node)
+
+        # Recursively flatten the children
+        subspecs = [helper(child, leaves) for child in children]
+        return TreeSpec(node_type, context, subspecs)
+
+    leaves: list[Any] = []
+    treespec = helper(tree, leaves)
+    return leaves, treespec
+
+
+def tree_unflatten(leaves: Iterable[Any], treespec: TreeSpec) -> PyTree:
+    """Given a list of values and a TreeSpec, builds a pytree.
+    This is the inverse operation of `tree_flatten`.
+    """
+    if not isinstance(treespec, TreeSpec):
+        raise TypeError(
+            f"tree_unflatten(leaves, treespec): Expected `treespec` to be "
+            f"instance of TreeSpec but got item of type {type(treespec)}.",
+        )
+    return treespec.unflatten(leaves)
+
+
+def tree_iter(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Iterable[Any]:
+    """Get an iterator over the leaves of a pytree."""
+    if _is_leaf(tree, is_leaf=is_leaf):
+        yield tree
+    else:
+        node_type = _get_node_type(tree)
+        flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+        child_pytrees, _ = flatten_fn(tree)
+
+        # Recursively flatten the children
+        for child in child_pytrees:
+            yield from tree_iter(child, is_leaf=is_leaf)
+
+
+def tree_leaves(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> list[Any]:
+    """Get a list of leaves of a pytree."""
+    return list(tree_iter(tree, is_leaf=is_leaf))
+
+
+def tree_structure(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> TreeSpec:
+    """Get the TreeSpec for a pytree."""
+    return tree_flatten(tree, is_leaf=is_leaf)[1]
+
+
+def tree_map(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Map a multi-input function over pytree args to produce a new pytree.
+
+    See also :func:`tree_map_`.
+
+    >>> tree_map(lambda x: x + 1, {'x': 7, 'y': (42, 64)})
+    {'x': 8, 'y': (43, 65)}
+    >>> tree_map(lambda x: x is None, {'x': 7, 'y': (42, 64), 'z': None})
+    {'x': False, 'y': (False, False), 'z': True}
+
+    If multiple inputs are given, the structure of the tree is taken from the first input;
+    subsequent inputs need only have ``tree`` as a prefix:
+
+    >>> tree_map(lambda x, y: [x] + y, [5, 6], [[7, 9], [1, 2]])
+    [[5, 7, 9], [6, 1, 2]]
+
+    Args:
+        func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees.
+        tree (pytree): A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        A new pytree with the same structure as ``tree`` but with the value at each leaf given by
+        ``func(x, *xs)`` where ``x`` is the value at the corresponding leaf in ``tree`` and ``xs``
+        is the tuple of values at corresponding nodes in ``rests``.
+    """
+    leaves, treespec = tree_flatten(tree, is_leaf=is_leaf)
+    flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests]
+    return treespec.unflatten(map(func, *flat_args))
+
+
+def tree_map_(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Like :func:`tree_map`, but do an inplace call on each leaf and return the original tree.
+
+    See also :func:`tree_map`.
+
+    Args:
+        func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees.
+        tree (pytree): A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf (callable, optional): An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns:
+        The original ``tree`` with the value at each leaf is given by the side-effect of function
+        ``func(x, *xs)`` (not the return value) where ``x`` is the value at the corresponding leaf
+        in ``tree`` and ``xs`` is the tuple of values at values at corresponding nodes in ``rests``.
+    """
+    leaves, treespec = tree_flatten(tree, is_leaf=is_leaf)
+    flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests]
+    deque(map(func, *flat_args), maxlen=0)  # consume and exhaust the iterable
+    return tree
+
+
+Type2 = tuple[type[T], type[S]]
+Type3 = tuple[type[T], type[S], type[U]]
+if sys.version_info >= (3, 10):
+    TypeAny = Union[type[Any], tuple[type[Any], ...], types.UnionType]
+else:
+    TypeAny = Union[type[Any], tuple[type[Any], ...]]
+
+Fn2 = Callable[[Union[T, S]], R]
+Fn3 = Callable[[Union[T, S, U]], R]
+Fn = Callable[[T], R]
+FnAny = Callable[[Any], R]
+
+MapOnlyFn = Callable[[T], Callable[[Any], Any]]
+
+
+# These specializations help with type inference on the lambda passed to this
+# function
+@overload
+def map_only(type_or_types_or_pred: type[T], /) -> MapOnlyFn[Fn[T, Any]]:
+    ...
+
+
+@overload
+def map_only(type_or_types_or_pred: Type2[T, S], /) -> MapOnlyFn[Fn2[T, S, Any]]:
+    ...
+
+
+@overload
+def map_only(type_or_types_or_pred: Type3[T, S, U], /) -> MapOnlyFn[Fn3[T, S, U, Any]]:
+    ...
+
+
+# This specialization is needed for the implementations below that call
+@overload
+def map_only(type_or_types_or_pred: TypeAny, /) -> MapOnlyFn[FnAny[Any]]:
+    ...
+
+
+@overload
+def map_only(type_or_types_or_pred: Callable[[Any], bool], /) -> MapOnlyFn[FnAny[Any]]:
+    ...
+
+
+def map_only(
+    type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]], /
+) -> MapOnlyFn[FnAny[Any]]:
+    """
+    Suppose you are writing a tree_map over tensors, leaving everything
+    else unchanged.  Ordinarily you would have to write:
+
+        def go(t):
+            if isinstance(t, Tensor):
+                return ...
+            else:
+                return t
+
+    With this function, you only need to write:
+
+        @map_only(Tensor)
+        def go(t):
+            return ...
+
+    You can also directly use 'tree_map_only'
+    """
+    if isinstance(type_or_types_or_pred, (type, tuple)) or (
+        sys.version_info >= (3, 10)
+        and isinstance(type_or_types_or_pred, types.UnionType)
+    ):
+
+        def pred(x: Any) -> bool:
+            return isinstance(x, type_or_types_or_pred)  # type: ignore[arg-type]
+
+    elif callable(type_or_types_or_pred):
+        pred = type_or_types_or_pred  # type: ignore[assignment]
+    else:
+        raise TypeError("Argument must be a type, a tuple of types, or a callable.")
+
+    def wrapper(func: Callable[[T], Any]) -> Callable[[Any], Any]:
+        @functools.wraps(func)
+        def wrapped(x: T) -> Any:
+            if pred(x):
+                return func(x)
+            return x
+
+        return wrapped
+
+    return wrapper
+
+
+@overload
+def tree_map_only(
+    type_or_types_or_pred: type[T],
+    /,
+    func: Fn[T, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    type_or_types_or_pred: Type2[T, S],
+    /,
+    func: Fn2[T, S, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    type_or_types_or_pred: Type3[T, S, U],
+    /,
+    func: Fn3[T, S, U, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    type_or_types_or_pred: TypeAny,
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only(
+    type_or_types_or_pred: Callable[[Any], bool],
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+def tree_map_only(
+    type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]],
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    return tree_map(map_only(type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
+
+
+@overload
+def tree_map_only_(
+    type_or_types_or_pred: type[T],
+    /,
+    func: Fn[T, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    type_or_types_or_pred: Type2[T, S],
+    /,
+    func: Fn2[T, S, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    type_or_types_or_pred: Type3[T, S, U],
+    /,
+    func: Fn3[T, S, U, Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    type_or_types_or_pred: TypeAny,
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+@overload
+def tree_map_only_(
+    type_or_types_or_pred: Callable[[Any], bool],
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    ...
+
+
+def tree_map_only_(
+    type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]],
+    /,
+    func: FnAny[Any],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    return tree_map_(map_only(type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
+
+
+def tree_all(
+    pred: Callable[[Any], bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return all(map(pred, flat_args))
+
+
+def tree_any(
+    pred: Callable[[Any], bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return any(map(pred, flat_args))
+
+
+@overload
+def tree_all_only(
+    type_or_types: type[T],
+    /,
+    pred: Fn[T, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_all_only(
+    type_or_types: Type2[T, S],
+    /,
+    pred: Fn2[T, S, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_all_only(
+    type_or_types: Type3[T, S, U],
+    /,
+    pred: Fn3[T, S, U, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+def tree_all_only(
+    type_or_types: TypeAny,
+    /,
+    pred: FnAny[bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return all(pred(x) for x in flat_args if isinstance(x, type_or_types))
+
+
+@overload
+def tree_any_only(
+    type_or_types: type[T],
+    /,
+    pred: Fn[T, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_any_only(
+    type_or_types: Type2[T, S],
+    /,
+    pred: Fn2[T, S, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+@overload
+def tree_any_only(
+    type_or_types: Type3[T, S, U],
+    /,
+    pred: Fn3[T, S, U, bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    ...
+
+
+def tree_any_only(
+    type_or_types: TypeAny,
+    /,
+    pred: FnAny[bool],
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> bool:
+    flat_args = tree_iter(tree, is_leaf=is_leaf)
+    return any(pred(x) for x in flat_args if isinstance(x, type_or_types))
+
+
+# Broadcasts a pytree to the provided TreeSpec and returns the flattened
+# values. If this is not possible, then this function returns None.
+#
+# For example, given pytree=0 and spec=TreeSpec(list, None, [LeafSpec(), LeafSpec()]),
+# would return [0, 0]. This is useful for part of the vmap implementation:
+# a user can pass in vmap(fn, in_dims)(*inputs). `in_dims` should be
+# broadcastable to the tree structure of `inputs` and we use
+# _broadcast_to_and_flatten to check this.
+def _broadcast_to_and_flatten(
+    tree: PyTree,
+    treespec: TreeSpec,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Optional[list[Any]]:
+    assert isinstance(treespec, TreeSpec)
+
+    if _is_leaf(tree, is_leaf=is_leaf):
+        return [tree] * treespec.num_leaves
+    if treespec.is_leaf():
+        return None
+    node_type = _get_node_type(tree)
+    if node_type != treespec.type:
+        return None
+
+    flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
+    child_pytrees, ctx = flatten_fn(tree)
+
+    # Check if the Node is different from the spec
+    if len(child_pytrees) != treespec.num_children or ctx != treespec.context:
+        return None
+
+    # Recursively flatten the children
+    result: list[Any] = []
+    for child, child_spec in zip(child_pytrees, treespec.children_specs):
+        flat = _broadcast_to_and_flatten(child, child_spec, is_leaf=is_leaf)
+        if flat is not None:
+            result += flat
+        else:
+            return None
+
+    return result
+
+
+@dataclasses.dataclass
+class _TreeSpecSchema:
+    """
+    _TreeSpecSchema is the schema used to serialize the TreeSpec
+    It contains the following fields:
+    - type: A string name of the type. null for the case of a LeafSpec.
+    - context: Any format which is json dumpable
+    - children_spec: A list of children serialized specs.
+    """
+
+    type: Optional[str]
+    context: DumpableContext
+    children_spec: list["_TreeSpecSchema"]
+
+
+class _ProtocolFn(NamedTuple):
+    treespec_to_json: Callable[[TreeSpec], DumpableContext]
+    json_to_treespec: Callable[[DumpableContext], TreeSpec]
+
+
+_SUPPORTED_PROTOCOLS: dict[int, _ProtocolFn] = {}
+
+
+def _treespec_to_json(treespec: TreeSpec) -> _TreeSpecSchema:
+    if treespec.is_leaf():
+        return _TreeSpecSchema(None, None, [])
+
+    if treespec.type not in SUPPORTED_SERIALIZED_TYPES:
+        raise NotImplementedError(
+            f"Serializing {treespec.type} in pytree is not registered.",
+        )
+
+    serialize_node_def = SUPPORTED_SERIALIZED_TYPES[treespec.type]
+
+    serialized_type_name = serialize_node_def.serialized_type_name
+
+    if serialized_type_name == NO_SERIALIZED_TYPE_NAME_FOUND:
+        raise NotImplementedError(
+            f"No registered serialization name for {treespec.type} found. "
+            "Please update your _register_pytree_node call with a `serialized_type_name` kwarg."
+        )
+
+    if serialize_node_def.to_dumpable_context is None:
+        try:
+            serialized_context = json.dumps(treespec.context, cls=EnumEncoder)
+        except TypeError as e:
+            raise TypeError(
+                "Unable to serialize context. "
+                "Please make the context json dump-able, or register a "
+                "custom serializer using _register_pytree_node."
+            ) from e
+    else:
+        serialized_context = serialize_node_def.to_dumpable_context(treespec.context)
+
+    child_schemas = [_treespec_to_json(child) for child in treespec.children_specs]
+
+    return _TreeSpecSchema(serialized_type_name, serialized_context, child_schemas)
+
+
+def _json_to_treespec(json_schema: DumpableContext) -> TreeSpec:
+    if (
+        json_schema["type"] is None
+        and json_schema["context"] is None
+        and len(json_schema["children_spec"]) == 0
+    ):
+        return _LEAF_SPEC
+
+    if json_schema["type"] not in SERIALIZED_TYPE_TO_PYTHON_TYPE:
+        raise NotImplementedError(
+            f'Deserializing {json_schema["type"]} in pytree is not registered.',
+        )
+
+    typ = SERIALIZED_TYPE_TO_PYTHON_TYPE[json_schema["type"]]
+    serialize_node_def = SUPPORTED_SERIALIZED_TYPES[typ]
+
+    if serialize_node_def.from_dumpable_context is None:
+        try:
+            context = json.loads(json_schema["context"])
+        except TypeError as ex:
+            raise TypeError(
+                "Unable to deserialize context. "
+                "Please make the context json load-able, or register a "
+                "custom serializer using _register_pytree_node.",
+            ) from ex
+    else:
+        context = serialize_node_def.from_dumpable_context(json_schema["context"])
+
+    children_specs = [
+        _json_to_treespec(child_string) for child_string in json_schema["children_spec"]
+    ]
+
+    return TreeSpec(typ, context, children_specs)
+
+
+_SUPPORTED_PROTOCOLS[1] = _ProtocolFn(_treespec_to_json, _json_to_treespec)
+
+
+def treespec_dumps(treespec: TreeSpec, protocol: Optional[int] = None) -> str:
+    if not isinstance(treespec, TreeSpec):
+        raise TypeError(
+            f"treespec_dumps(treespec, protocol): Expected `treespec` to be instance of "
+            f"TreeSpec but got item of type {type(treespec)}.",
+        )
+
+    if protocol is None:
+        protocol = DEFAULT_TREESPEC_SERIALIZATION_PROTOCOL
+
+    if protocol in _SUPPORTED_PROTOCOLS:
+        json_spec = _SUPPORTED_PROTOCOLS[protocol].treespec_to_json(treespec)
+    else:
+        raise ValueError(
+            f"Unknown protocol {protocol}. "
+            f"Available protocols: {list(_SUPPORTED_PROTOCOLS.keys())}",
+        )
+
+    str_spec = json.dumps((protocol, dataclasses.asdict(json_spec)), cls=EnumEncoder)
+    return str_spec
+
+
+@functools.lru_cache
+def treespec_loads(serialized: str) -> TreeSpec:
+    protocol, json_schema = json.loads(serialized)
+
+    if protocol in _SUPPORTED_PROTOCOLS:
+        return _SUPPORTED_PROTOCOLS[protocol].json_to_treespec(json_schema)
+    raise ValueError(
+        f"Unknown protocol {protocol}. "
+        f"Available protocols: {list(_SUPPORTED_PROTOCOLS.keys())}",
+    )
+
+
+class _DummyLeaf:
+    def __repr__(self) -> str:
+        return "*"
+
+
+def treespec_pprint(treespec: TreeSpec) -> str:
+    dummy_tree = tree_unflatten(
+        [_DummyLeaf() for _ in range(treespec.num_leaves)],
+        treespec,
+    )
+    return repr(dummy_tree)
+
+
+# TODO(angelayi): remove this function after OSS/internal stabilize
+@deprecated(
+    "`pytree_to_str` is deprecated. Please use `treespec_dumps` instead.",
+    category=FutureWarning,
+)
+def pytree_to_str(treespec: TreeSpec) -> str:
+    return treespec_dumps(treespec)
+
+
+# TODO(angelayi): remove this function after OSS/internal stabilize
+@deprecated(
+    "`str_to_pytree` is deprecated. Please use `treespec_loads` instead.",
+    category=FutureWarning,
+)
+def str_to_pytree(json: str) -> TreeSpec:
+    return treespec_loads(json)
+
+
+def arg_tree_leaves(*args: PyTree, **kwargs: PyTree) -> list[Any]:
+    """Get a flat list of arguments to this function
+
+    A slightly faster version of tree_leaves((args, kwargs))
+    """
+    leaves: list[Any] = []
+    for a in args:
+        leaves.extend(tree_iter(a))
+    for a in kwargs.values():
+        leaves.extend(tree_iter(a))
+    return leaves
+
+
+def tree_flatten_with_path(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> tuple[list[tuple[KeyPath, Any]], TreeSpec]:
+    """Flattens a pytree like :func:`tree_flatten`, but also returns each leaf's key path.
+
+    Args:
+        tree: a pytree to flatten. If it contains a custom type, that type must be
+            registered with an appropriate `tree_flatten_with_path_fn` when registered
+            with :func:`register_pytree_node`.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+    Returns:
+        A tuple where the first element is a list of (key path, leaf) pairs, and the
+        second element is a :class:`TreeSpec` representing the structure of the flattened
+        tree.
+    """
+    _, treespec = tree_flatten(tree, is_leaf)
+    return list(_generate_key_paths((), tree, is_leaf)), treespec
+
+
+def tree_leaves_with_path(
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> list[tuple[KeyPath, Any]]:
+    """Gets the leaves of a pytree like ``tree_leaves`` and returns each leaf's key path.
+
+    Args:
+        tree: a pytree. If it contains a custom type, that type must be
+            registered with an appropriate `tree_flatten_with_path_fn` when registered
+            with :func:`register_pytree_node`.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+    Returns:
+        A list of (key path, leaf) pairs.
+    """
+    return list(_generate_key_paths((), tree, is_leaf))
+
+
+def _generate_key_paths(
+    key_path: KeyPath,
+    tree: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> Iterable[tuple[KeyPath, Any]]:
+    if is_leaf and is_leaf(tree):
+        yield key_path, tree
+        return
+
+    node_type = _get_node_type(tree)
+    handler = SUPPORTED_NODES.get(node_type)
+    if not handler:
+        # This is a leaf
+        yield key_path, tree
+        return
+
+    flatten_with_keys = handler.flatten_with_keys_fn
+    if flatten_with_keys:
+        key_children, _ = flatten_with_keys(tree)
+        for k, c in key_children:
+            yield from _generate_key_paths((*key_path, k), c, is_leaf)
+    else:
+        # We registered this pytree but didn't add a flatten_with_keys_fn, complain.
+        raise ValueError(
+            f"Did not find a flatten_with_keys_fn for type: {node_type}. "
+            "Please pass a flatten_with_keys_fn argument to register_pytree_node."
+        )
+
+
+def tree_map_with_path(
+    func: Callable[..., Any],
+    tree: PyTree,
+    *rests: PyTree,
+    is_leaf: Optional[Callable[[PyTree], bool]] = None,
+) -> PyTree:
+    """Like :func:`tree_map`, but the provided callable takes an additional key path argument.
+
+    Args:
+        func: A function that takes ``2 + len(rests)`` arguments, to be applied at the
+            corresponding leaves of the pytrees. The first positional argument
+            to ``func`` is the key path of the leaf in question. The second
+            positional argument is the value of the leaf.
+        tree: A pytree to be mapped over, with each leaf providing the first positional
+            argument to function ``func``.
+        rests: A tuple of pytrees, each of which has the same structure as
+            ``tree`` or has ``tree`` as a prefix.
+        is_leaf: An extra leaf predicate function that will be called at each
+            flattening step. The function should have a single argument with signature
+            ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated
+            as a leaf. Otherwise, the default pytree registry will be used to determine a node is a
+            leaf or not. If the function is not specified, the default pytree registry will be used.
+
+    Returns
+        A new pytree with the same structure as ``tree`` but with the value at each leaf given by
+        ``func(keypath, x, *xs)`` where ``keypath`` is the key path at the
+        corresponding leaf in ``tree``, ``x`` is the value at that leaf, and
+        ``xs`` is the tuple of values at corresponding nodes in ``rests``.
+    """
+    keypath_leaves, treespec = tree_flatten_with_path(tree, is_leaf)
+    keypath_leaves = list(zip(*keypath_leaves))
+    all_keypath_leaves = keypath_leaves + [treespec.flatten_up_to(r) for r in rests]
+    return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))
+
+
+def keystr(kp: KeyPath) -> str:
+    """Given a key path, return a pretty-printed representation."""
+    return "".join([str(k) for k in kp])
+
+
+def key_get(obj: Any, kp: KeyPath) -> Any:
+    """Given an object and a key path, return the value at the key path."""
+    for k in kp:
+        obj = k.get(obj)
+    return obj
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_stats.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d9d48233ee036a15874ad74d56565823cf71c91
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_stats.py
@@ -0,0 +1,28 @@
+# NOTE! PLEASE KEEP THIS FILE *FREE* OF TORCH DEPS! IT SHOULD BE IMPORTABLE ANYWHERE.
+# IF YOU FEEL AN OVERWHELMING URGE TO ADD A TORCH DEP, MAKE A TRAMPOLINE FILE A LA torch._dynamo.utils
+# AND SCRUB AWAY TORCH NOTIONS THERE.
+import collections
+import functools
+from typing import Callable, TypeVar
+from collections import OrderedDict
+from typing_extensions import ParamSpec
+
+
+simple_call_counter: OrderedDict[str, int] = collections.OrderedDict()
+
+_P = ParamSpec("_P")
+_R = TypeVar("_R")
+
+
+def count_label(label: str) -> None:
+    prev = simple_call_counter.setdefault(label, 0)
+    simple_call_counter[label] = prev + 1
+
+def count(fn: Callable[_P, _R]) -> Callable[_P, _R]:
+    @functools.wraps(fn)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _R:
+        if fn.__qualname__ not in simple_call_counter:
+            simple_call_counter[fn.__qualname__] = 0
+        simple_call_counter[fn.__qualname__] = simple_call_counter[fn.__qualname__] + 1
+        return fn(*args, **kwargs)
+    return wrapper
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_strobelight/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_strobelight/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_strobelight/cli_function_profiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_strobelight/cli_function_profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2c1ee83bc1c3f58b00d85a511a8e8950df9f4d1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_strobelight/cli_function_profiler.py
@@ -0,0 +1,312 @@
+# mypy: disallow-untyped-defs
+
+import functools
+import logging
+import os
+import re
+import subprocess
+import time
+from collections.abc import Sequence
+from threading import Lock
+from typing import Any, Callable, Optional, TypeVar
+from typing_extensions import ParamSpec
+
+
+logger = logging.getLogger("strobelight_function_profiler")
+
+console_handler = logging.StreamHandler()
+formatter = logging.Formatter(
+    "%(name)s, line %(lineno)d, %(asctime)s, %(levelname)s: %(message)s"
+)
+console_handler.setFormatter(formatter)
+
+logger.addHandler(console_handler)
+logger.setLevel(logging.INFO)
+logger.propagate = False
+
+_P = ParamSpec("_P")
+_R = TypeVar("_R")
+
+
+class StrobelightCLIProfilerError(Exception):
+    """
+    Raised when an error happens during strobelight profiling
+    """
+
+
+def _pid_namespace_link(pid: Optional[int] = None) -> str:
+    """Returns the link to the process's namespace, example: pid:[4026531836]"""
+    PID_NAMESPACE_PATH = "/proc/{}/ns/pid"
+    pid = pid or os.getpid()
+    return os.readlink(PID_NAMESPACE_PATH.format(pid))
+
+
+def _pid_namespace(pid: Optional[int] = None) -> int:
+    """Returns the process's namespace id"""
+    pid = pid or os.getpid()
+    link = _pid_namespace_link(pid)
+    return int(link[link.find("[") + 1 : -1])
+
+
+def _command_to_string(command: Sequence[str]) -> str:
+    return " ".join(command)
+
+
+class StrobelightCLIFunctionProfiler:
+    """
+    Note: this is a meta only tool.
+
+    StrobelightCLIFunctionProfiler can be used to profile a python function and
+    generate a strobelight link with the results. It works on meta servers but
+    does not requries an fbcode target.
+    When stop_at_error is false(default), error during profiling does not prevent
+    the work function from running.
+
+    Check function_profiler_example.py for an example.
+    """
+
+    # This lock is used to make sure only one thread is running the profiler at any point.
+    _lock = Lock()
+
+    def __init__(
+        self,
+        *,
+        stop_at_error: bool = False,
+        max_profile_duration_sec: int = 60 * 10,
+        sample_each: float = 1e7,  # sample each sample_each cycles.
+        run_user_name: str = "pytorch-strobelight-ondemand",
+        timeout_wait_for_running_sec: int = 60,
+        timeout_wait_for_finished_sec: int = 60,
+        recorded_env_variables: Optional[list[str]] = None,
+        sample_tags: Optional[list[str]] = None,
+        stack_max_len: int = 127,
+        async_stack_max_len: int = 127,
+    ):
+        self.stop_at_error = stop_at_error
+        self.max_profile_duration_sec = max_profile_duration_sec
+        self.sample_each = sample_each
+        self.run_user_name = run_user_name
+        self.timeout_wait_for_running_sec = timeout_wait_for_running_sec
+        self.timeout_wait_for_finished_sec = timeout_wait_for_finished_sec
+        # Results of the most recent run.
+        # Tracks the strobelight run id of the most recent run
+        self.current_run_id: Optional[int] = None
+        self.sample_tags = sample_tags
+
+    def _run_async(self) -> None:
+        processId = os.getpid()
+        namespace = _pid_namespace(processId)
+        command = [
+            "strobeclient",
+            "run",
+            "--profiler",
+            "pyperf",
+            "--event",
+            "cycles",
+            "--async",
+            "--sample-interval",
+            f"{int(self.sample_each)}",
+            "--duration-ms",
+            f"{int(self.max_profile_duration_sec * 1000)}",
+            "--pid",
+            f"{namespace}:{processId}",
+        ]
+
+        if self.sample_tags:
+            command.append("--sample-tags")
+            command.append(",".join(self.sample_tags))
+
+        logger.debug("running command: %s", _command_to_string(command))
+        result = subprocess.run(command, capture_output=True)
+        output = result.stderr.decode("utf-8")
+        logger.debug("output:\n{%s}", output)
+
+        if result.returncode != 0:
+            raise StrobelightCLIProfilerError(
+                f"failed to start strobelight profiling, error in run_async:{output}"
+            )
+
+        if match := re.search(r"INFO Run Id: (-?\d+)", output):
+            self.current_run_id = int(match.group(1))
+            return
+
+        raise StrobelightCLIProfilerError(
+            f"failed to start strobelight profiling, unexpected result {output}"
+        )
+
+    def _wait_for_running(self, counter: int = 0) -> None:
+        if counter > 20:
+            raise StrobelightCLIProfilerError(
+                "wait_for_running called more than 20 times"
+            )
+
+        command = ["strobeclient", "getRunStatus", "--run-id", f"{self.current_run_id}"]
+        logger.debug("running command: %s", _command_to_string(command))
+        result = subprocess.run(command, capture_output=True)
+        output = result.stderr.decode("utf-8")
+        logger.debug("output:\n{%s}", output)
+
+        if result.returncode != 0:
+            raise StrobelightCLIProfilerError(
+                f"failed to start strobelight profiling, error in wait_for_running:{output}"
+            )
+
+        if match := re.search("Profile run status: (.*)", output):
+            current_status = match.group(1)
+            if current_status == "RUNNING":
+                return
+            elif current_status == "PREPARING":
+                time.sleep(10)
+                self._wait_for_running(counter + 1)
+                return
+            else:
+                raise StrobelightCLIProfilerError(f"unexpected {current_status} phase")
+
+        raise StrobelightCLIProfilerError(f"unexpected output\n: {output} ")
+
+    def _stop_run(self) -> None:
+        command = ["strobeclient", "stopRun", "--run-id", str(self.current_run_id)]
+        logger.debug("running command: %s", _command_to_string(command))
+        result = subprocess.run(command, capture_output=True)
+        output = result.stderr.decode("utf-8")
+        logger.debug("output:\n{%s}", output)
+
+        if result.returncode != 0:
+            raise StrobelightCLIProfilerError(
+                f"failed to stop strobelight profiling, return code is not 0 :{output}"
+            )
+
+        if match := re.search("INFO ::1:(.*)", output):
+            current_status = match.group(1)
+            if current_status.__contains__("Success!"):
+                return
+            else:
+                raise StrobelightCLIProfilerError(
+                    f"failed to stop strobelight profiling, got {current_status} result"
+                )
+
+        raise StrobelightCLIProfilerError(f"unexpected output\n: {output} ")
+
+    def _get_results(self) -> None:
+        command = ["strobeclient", "getRunStatus", "--run-id", str(self.current_run_id)]
+        logger.debug("running command: %s", _command_to_string(command))
+        result = subprocess.run(command, capture_output=True)
+        output = result.stderr.decode("utf-8")
+        logger.debug("output:\n{%s}", output)
+
+        if result.returncode != 0:
+            raise StrobelightCLIProfilerError(
+                f"failed to extract profiling results, return code is not 0 : {output}"
+            )
+
+        if match := re.search("INFO ::1:(.*)", output):
+            current_status = match.group(1)
+            if current_status.__contains__("Profile run status: PROCESSING"):
+                time.sleep(10)
+                self._get_results()
+                return
+            elif not current_status.__contains__("Profile run finished with SUCCESS"):
+                raise StrobelightCLIProfilerError(
+                    f"failed to extract profiling results, unexpected response {output}"
+                )
+
+        for item in re.findall(
+            r"(Total samples(.*)|GraphProfiler(.*)|Icicle view \(python stack\)(.*))",
+            output,
+        ):
+            logger.info(item[0])
+
+    def _stop_strobelight_no_throw(
+        self,
+        collect_results: bool,
+    ) -> None:
+        try:
+            # call stop run
+            self._stop_run()
+            logger.info("strobelight profiling stopped")
+
+            logger.debug("collection stopped")
+
+            if not collect_results:
+                return
+
+            self._get_results()
+        except Exception:
+            logger.warning("error during stop_strobelight", exc_info=True)
+
+    # Return true if strobelight started and is running. Never throw.
+    def _start_strobelight(self) -> bool:
+        strobelight_started = False
+        try:
+            self._run_async()
+            strobelight_started = True
+            logger.info("strobelight run id is: %s", self.current_run_id)
+            self._wait_for_running()
+            logger.info("strobelight profiling running")
+            return True
+
+        except Exception:
+            logger.warning("error during start_strobelight:", exc_info=True)
+            if strobelight_started:
+                self._stop_strobelight_no_throw(collect_results=False)
+            return False
+
+    def profile(
+        self, work_function: Callable[_P, _R], *args: _P.args, **kwargs: _P.kwargs
+    ) -> Optional[_R]:
+        self.current_run_id = None
+
+        if locked := StrobelightCLIFunctionProfiler._lock.acquire(False):
+            if not locked:
+                if self.stop_at_error:
+                    raise StrobelightCLIProfilerError("concurrent runs not supported")
+
+                logger.warning("concurrent runs not supported")
+                return work_function(*args, **kwargs)
+
+            started = self._start_strobelight()
+            if not started:
+                if self.stop_at_error:
+                    StrobelightCLIFunctionProfiler._lock.release()
+                    raise StrobelightCLIProfilerError(
+                        "failed to start strobelight profiling"
+                    )
+                result = work_function(*args, **kwargs)
+                StrobelightCLIFunctionProfiler._lock.release()
+                return result
+
+            try:
+                logger.debug("collection started")
+                result = work_function(*args, **kwargs)
+                self._stop_strobelight_no_throw(collect_results=True)
+                StrobelightCLIFunctionProfiler._lock.release()
+                return result
+            except Exception as error:
+                logger.warning("work function throw exception", exc_info=True)
+                self._stop_strobelight_no_throw(collect_results=False)
+                StrobelightCLIFunctionProfiler._lock.release()
+                raise error
+        return None
+
+
+# A function decorator that wraps profile, if no profiler is provided one with
+# default args is created. A function can be annotated as:
+# @strobelight()
+# @strobelight(profiler = StrobelightFunctionProfiler(stop_at_error=True,..))
+# @strobelight(stop_at_error=True,...)
+def strobelight(
+    profiler: Optional[StrobelightCLIFunctionProfiler] = None, **kwargs: Any
+) -> Callable[[Callable[_P, _R]], Callable[_P, Optional[_R]]]:
+    if not profiler:
+        profiler = StrobelightCLIFunctionProfiler(**kwargs)
+
+    def strobelight_inner(
+        work_function: Callable[_P, _R]
+    ) -> Callable[_P, Optional[_R]]:
+        @functools.wraps(work_function)
+        def wrapper_function(*args: _P.args, **kwargs: _P.kwargs) -> Optional[_R]:
+            return profiler.profile(work_function, *args, **kwargs)
+
+        return wrapper_function
+
+    return strobelight_inner
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@@ -0,0 +1,1392 @@
+# mypy: allow-untyped-defs
+import functools
+import math
+import operator
+import sys
+from typing import Callable, Optional, SupportsFloat, TYPE_CHECKING, TypeVar, Union
+from typing_extensions import TypeVarTuple, Unpack
+
+import sympy
+from sympy import S
+from sympy.core import sympify
+from sympy.core.expr import Expr
+from sympy.core.function import Application
+from sympy.core.logic import _torf, fuzzy_and, fuzzy_or
+from sympy.core.numbers import equal_valued
+from sympy.core.operations import LatticeOp, ShortCircuit
+from sympy.core.sorting import ordered
+from sympy.core.traversal import walk
+from sympy.printing.precedence import PRECEDENCE
+from sympy.utilities.iterables import sift
+
+from .numbers import int_oo
+
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+
+_T = TypeVar("_T", bound=SupportsFloat)
+_Ts = TypeVarTuple("_Ts")
+
+# Portions of this file are adapted from the Sympy codebase, which was
+# licensed as follows:
+#
+#   Copyright (c) 2006-2023 SymPy Development Team
+#
+#   All rights reserved.
+#
+#   Redistribution and use in source and binary forms, with or without
+#   modification, are permitted provided that the following conditions are met:
+#
+#     a. Redistributions of source code must retain the above copyright notice,
+#        this list of conditions and the following disclaimer.
+#     b. Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#     c. Neither the name of SymPy nor the names of its contributors
+#        may be used to endorse or promote products derived from this software
+#        without specific prior written permission.
+#
+#   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#   AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#   IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#   ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
+#   ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+#   DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+#   SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+#   CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+#   LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+#   OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
+#   DAMAGE.
+
+__all__ = [
+    "FloorDiv",
+    "ModularIndexing",
+    "Where",
+    "PythonMod",
+    "Mod",
+    "CleanDiv",
+    "CeilToInt",
+    "FloorToInt",
+    "CeilDiv",
+    "IntTrueDiv",
+    "FloatTrueDiv",
+    "LShift",
+    "RShift",
+    "IsNonOverlappingAndDenseIndicator",
+    "TruncToFloat",
+    "TruncToInt",
+    "RoundToInt",
+    "RoundDecimal",
+    "ToFloat",
+    "FloatPow",
+    "PowByNatural",
+    "Identity",
+]
+
+
+def _is_symbols_binary_summation(expr: sympy.Expr) -> bool:
+    # No need to check that two args are not the same, since expr is pr-optimized but we do it anyway.
+    return (
+        expr.is_Add
+        and len(expr._args) == 2
+        and expr._args[0].is_symbol
+        and expr._args[1].is_symbol
+        and expr._args[0] is not expr._args[1]
+    )
+
+
+def _keep_float(
+    f: Callable[[Unpack[_Ts]], _T]
+) -> Callable[[Unpack[_Ts]], Union[_T, sympy.Float]]:
+    @functools.wraps(f)
+    def inner(*args: Unpack[_Ts]) -> Union[_T, sympy.Float]:
+        r: Union[_T, sympy.Float] = f(*args)
+        if any(isinstance(a, sympy.Float) for a in args) and not isinstance(
+            r, sympy.Float
+        ):
+            r = sympy.Float(float(r))
+        return r
+
+    return inner
+
+
+def fuzzy_eq(x: Optional[bool], y: Optional[bool]) -> Optional[bool]:
+    if None in (x, y):
+        return None
+    return x == y
+
+
+def simple_floordiv_gcd(p: sympy.Basic, q: sympy.Basic) -> sympy.Basic:
+    """
+    Fast path for sympy.gcd, using a simple factoring strategy.
+
+    We try to rewrite p and q in the form n*e*p1 + n*e*p2 and n*e*q0,
+    where n is the greatest common integer factor and e is the largest
+    syntactic common factor (i.e., common sub-expression) in p and q.
+    Then the gcd returned is n*e, cancelling which we would be left with
+    p1 + p2 and q0.
+
+    Note that further factoring of p1 + p2 and q0 might be possible with
+    sympy.factor (which uses domain-specific theories). E.g., we are unable
+    to find that x*y + x + y + 1 is divisible by x + 1. More generally,
+    when q is of the form q1 + q2 (instead of being already factored) it
+    might be necessary to fall back on sympy.gcd.
+    """
+
+    def integer_coefficient(x: sympy.Basic) -> int:
+        integer_coefficients: list[int] = [
+            abs(int(arg))
+            for arg in sympy.Mul.make_args(x)
+            if isinstance(arg, (int, sympy.Integer))
+        ]
+        return math.prod(integer_coefficients)
+
+    def integer_factor(expr: sympy.Basic) -> int:
+        integer_factors: Iterable[int] = map(
+            integer_coefficient, sympy.Add.make_args(expr)
+        )
+        return functools.reduce(math.gcd, integer_factors)
+
+    gcd: int = math.gcd(integer_factor(p), integer_factor(q))
+    p, q = p / gcd, q / gcd  # type: ignore[operator, assignment]  # remove in py3.12
+
+    base_splits: list[tuple[sympy.Basic, ...]] = list(
+        map(sympy.Mul.make_args, sympy.Add.make_args(p))
+    )
+    divisor_split: tuple[sympy.Basic, ...] = sympy.Mul.make_args(q)
+    for x in divisor_split:
+        if all(x in base_split for base_split in base_splits):
+            gcd = gcd * x  # type: ignore[operator]  # remove in py3.12
+    return gcd  # type: ignore[return-value]  # remove in py3.12
+
+
+# It would be nice to have assertions on whether or not inputs is_integer
+# However, with bugs like https://github.com/sympy/sympy/issues/26620 sympy
+# sometimes inconsistently reports floats an integers.
+#
+# What we can assume from sympy is that if something is an int, it
+# definitely is is_integer, but if it is a float it may or may not
+# be is_integer.  So we are unable to do strong asserts that things
+# are NOT integers.
+
+
+# TODO: In Triton, // rounds to zero, but in Python, it is floor division.
+# When we can prove both arguments are non-negative, we should just have a
+# GenericFloorDiv (name pending) which can codegen efficiently in Python/C,
+# and then PythonFloorDiv and CIntDiv which have the appropriate rounding
+# semantics.
+#
+# Right now, FloorDiv de facto changes behavior if arguments are negative or
+# not, this can potentially cause correctness issues.
+class FloorDiv(sympy.Function):
+    """
+    We maintain this so that:
+    1. We can use divisibility guards to simplify FloorDiv(a, b) to a / b.
+    2. Printing out the expression is nicer (compared to say, representing a//b as (a - a % b) / b)
+
+    NB: This is Python-style floor division, round to -Inf
+    """
+
+    nargs: tuple[int, ...] = (2,)
+    precedence: int = 35  # lower precedence than add
+    is_integer: bool = True
+
+    @property
+    def base(self) -> sympy.Basic:
+        return self.args[0]
+
+    @property
+    def divisor(self) -> sympy.Basic:
+        return self.args[1]
+
+    def _sympystr(self, printer: sympy.printing.StrPrinter) -> str:
+        base = printer.parenthesize(self.base, PRECEDENCE["Atom"] - 0.5)
+        divisor = printer.parenthesize(self.divisor, PRECEDENCE["Atom"] - 0.5)
+        return f"({base}//{divisor})"
+
+    # Automatic evaluation.
+    # https://docs.sympy.org/latest/guides/custom-functions.html#best-practices-for-eval
+    @classmethod
+    def eval(
+        cls, base: sympy.Integer, divisor: sympy.Integer
+    ) -> Union[sympy.Basic, None]:
+        # python test/test_dynamic_shapes.py -k TestDimConstraints.test_dim_constraints_solve_full
+        # Assert triggered by inequality solver
+        # assert base.is_integer, base
+        # assert divisor.is_integer, divisor
+
+        # We don't provide the same error message as in Python because SymPy
+        # makes it difficult to check the types.
+        if divisor.is_zero:
+            raise ZeroDivisionError("division by zero")
+        if base in (int_oo, -int_oo, sympy.oo, -sympy.oo) and divisor in (
+            int_oo,
+            -int_oo,
+            sympy.oo,
+            -sympy.oo,
+        ):
+            return sympy.nan
+        if base is sympy.nan or divisor is sympy.nan:
+            return sympy.nan
+
+        if base.is_zero:
+            return sympy.S.Zero
+        if base.is_integer and equal_valued(divisor, 1):
+            return base
+        if base.is_integer and equal_valued(divisor, -1):
+            return sympy.Mul(base, -1)
+        if (
+            isinstance(base, sympy.Number)
+            and isinstance(divisor, sympy.Number)
+            and (
+                base in (int_oo, -int_oo, sympy.oo, -sympy.oo)
+                or divisor in (int_oo, -int_oo, sympy.oo, -sympy.oo)
+            )
+        ):
+            r = float(base) / float(divisor)
+            if r == math.inf:
+                return int_oo
+            elif r == -math.inf:
+                return -int_oo
+            elif math.isnan(r):
+                return sympy.nan
+            else:
+                return sympy.Integer(math.floor(r))
+        if isinstance(base, sympy.Integer) and isinstance(divisor, sympy.Integer):
+            return sympy.Integer(int(base) // int(divisor))
+        if isinstance(base, FloorDiv):
+            return FloorDiv(base.args[0], base.args[1] * divisor)
+
+        # Expands (x + y) // b into x // b + y // b.
+        # This only works if floor is an identity, i.e. x / b is an integer.
+        if isinstance(divisor, sympy.Integer):
+            quotients = 0
+            terms = []
+            for term in sympy.Add.make_args(base):
+                quotient = term / divisor
+
+                if quotient.is_integer:
+                    terms.append(term)
+                    quotients += quotient
+
+            if len(terms) != 0:
+                # Passing evaluate = False since expression will be optimized during the subtraction post its construction.
+                return (
+                    FloorDiv(base - sympy.Add(*terms, evaluate=False), divisor)
+                    + quotients
+                )
+
+        try:
+            gcd = simple_floordiv_gcd(base, divisor)
+            if equal_valued(gcd, 1) and isinstance(divisor, sympy.Add):
+                gcd = sympy.gcd(base, divisor)
+            if not equal_valued(gcd, 1):
+                return FloorDiv(
+                    sympy.simplify(base / gcd), sympy.simplify(divisor / gcd)
+                )
+        except sympy.PolynomialError:
+            pass  # https://github.com/pytorch/pytorch/issues/108276
+
+        return None
+
+    def _ccode(self, printer):
+        base = printer.parenthesize(self.base, PRECEDENCE["Atom"] - 0.5)
+        divisor = printer.parenthesize(self.divisor, PRECEDENCE["Atom"] - 0.5)
+        return f"floor({base}/{divisor})"
+
+
+class ModularIndexing(sympy.Function):
+    """
+    ModularIndexing(a, b, c) => (a // b) % c where % is the C modulus
+    """
+
+    nargs: tuple[int, ...] = (3,)
+    is_integer: bool = True
+    precedence: int = 35  # lower precedence than add
+
+    @classmethod
+    def eval(
+        cls, base: sympy.Integer, divisor: sympy.Integer, modulus: sympy.Integer
+    ) -> Optional[sympy.Basic]:
+        if base == 0 or modulus == 1:
+            return sympy.S.Zero
+
+        if (
+            isinstance(base, sympy.Integer)
+            and isinstance(divisor, sympy.Integer)
+            and isinstance(modulus, sympy.Integer)
+        ):
+            return (base // divisor) % modulus
+
+        try:
+            if divisor != 1:
+                gcd = sympy.gcd(base, divisor)
+                if gcd != 1:
+                    return ModularIndexing(
+                        sympy.simplify(base / gcd),
+                        sympy.simplify(divisor / gcd),
+                        modulus,
+                    )
+        except sympy.PolynomialError:
+            pass  # https://github.com/pytorch/pytorch/issues/108276
+
+        if isinstance(base, sympy.Add):
+            new_terms: list[sympy.Integer] = []
+            all_positive: bool = True
+            for term in base.args:
+                if sympy.gcd(term, modulus * divisor) != modulus * divisor:
+                    if (isinstance(term, sympy.Integer) and term < 0) or (
+                        isinstance(term, sympy.Mul)
+                        and isinstance(term.args[0], sympy.Integer)
+                        and term.args[0] < 0
+                    ):
+                        # workaround for https://github.com/openai/triton/issues/619,
+                        # if there are negative terms, // produces wrong result
+                        # TODO if https://github.com/openai/triton/issues/619 is fixed
+                        # this optimization would become valid
+                        all_positive = False
+                        break
+                    else:
+                        new_terms.append(term)
+
+            if len(new_terms) != len(base.args) and all_positive:
+                return ModularIndexing(sum(new_terms), divisor, modulus)
+
+        if isinstance(base, FloorDiv):
+            return ModularIndexing(base.args[0], base.args[1] * divisor, modulus)
+
+        return None
+
+    def _eval_is_nonnegative(self) -> Optional[bool]:
+        p, q = self.args[:2]
+        return fuzzy_eq(p.is_nonnegative, q.is_nonnegative)  # type: ignore[attr-defined]
+
+    def _eval_is_positive(self) -> Optional[bool]:
+        p, q = self.args[:2]
+        return fuzzy_eq(p.is_positive, q.is_positive)  # type: ignore[attr-defined]
+
+
+class Where(sympy.Function):
+    """
+    Good ol' ternary operator
+    """
+
+    nargs: tuple[int, ...] = (3,)
+    precedence: int = 35  # lower precedence than add
+
+    def _eval_is_integer(self) -> Optional[bool]:
+        return True if self.args[1].is_integer and self.args[2].is_integer else None  # type: ignore[attr-defined]
+
+    def _eval_is_nonnegative(self) -> Optional[bool]:
+        return (
+            True
+            if self.args[1].is_nonnegative and self.args[2].is_nonnegative  # type: ignore[attr-defined]
+            else None
+        )
+
+    def _eval_is_positive(self) -> Optional[bool]:
+        return True if self.args[1].is_positive and self.args[2].is_positive else None  # type: ignore[attr-defined]
+
+    @classmethod
+    def eval(
+        cls, c: sympy.Basic, p: sympy.Basic, q: sympy.Basic
+    ) -> Optional[sympy.Basic]:
+        if c == sympy.true:
+            return p
+        elif c == sympy.false:
+            return q
+        return None
+
+
+# Python-style modulus: take sign from RHS
+class PythonMod(sympy.Function):
+    nargs: tuple[int, ...] = (2,)
+
+    precedence: int = 35  # lower precedence than add
+    is_integer: bool = True
+
+    @classmethod
+    def eval(cls, p: sympy.Expr, q: sympy.Expr) -> Optional[sympy.Expr]:
+        # python test/dynamo/test_export.py -k ExportTests.test_trivial_constraint
+        # Triggered by sympy.solvers.inequalities.reduce_inequalities
+        # assert p.is_integer, p
+        # assert q.is_integer, q
+
+        if q.is_zero:
+            raise ZeroDivisionError("Modulo by zero")
+
+        # Three cases:
+        #   1. p == 0
+        #   2. p is either q or -q
+        #   3. p is integer and q == 1
+        if p is S.Zero or p in (q, -q) or q == 1:
+            return S.Zero
+
+        # Evaluate if they are both literals.
+        if q.is_Number and p.is_Number:
+            return p % q
+
+        # If q == 2, it's a matter of whether p is odd or even.
+        if q.is_Number and q == 2:
+            if p.is_even:
+                return S.Zero
+            if p.is_odd:
+                return S.One
+
+        # If p is a multiple of q.
+        r = p / q
+        if r.is_integer:
+            return S.Zero
+
+        # If p < q and its ratio is positive, then:
+        #   - floor(p / q) = 0
+        #   - p % q = p - floor(p / q) * q = p
+        less = p < q
+        if less.is_Boolean and bool(less) and r.is_positive:
+            return p
+
+        if sympy.Mod(p, q) == 0:
+            return S.Zero
+
+        return None
+
+    # NB: args[1] for PythonMod
+    def _eval_is_nonnegative(self) -> Optional[bool]:
+        return True if self.args[1].is_positive else None  # type: ignore[attr-defined]
+
+    def _eval_is_nonpositive(self) -> Optional[bool]:
+        return True if self.args[1].is_negative else None  # type: ignore[attr-defined]
+
+
+# Generic modulus: only defined on non-negative arguments
+class Mod(sympy.Function):
+    nargs = (2,)
+    precedence: int = 35  # lower precedence than add
+
+    is_integer = True
+    is_nonnegative = True
+
+    @classmethod
+    def eval(cls, p, q):
+        # This was adapted from: sympy/core/mod.py
+
+        # Triggered by
+        # python test/test_dynamic_shapes.py -k TestDimConstraints.test_dim_constraints_solve_full
+        # assert p.is_integer, p
+        # assert q.is_integer, q
+
+        if q.is_zero:
+            raise ZeroDivisionError("Modulo by zero")
+
+        # Three cases:
+        #   1. p == 0
+        #   2. p is either q or -q
+        #   3. p is integer and q == 1
+        if p is S.Zero or p in (q, -q) or q == 1:
+            return S.Zero
+
+        # Evaluate if they are both literals.
+        if q.is_Number and p.is_Number:
+            assert p >= 0, p
+            assert q >= 1, q
+            return p % q
+
+        # If q == 2, it's a matter of whether p is odd or even.
+        if q.is_Number and q == 2:
+            if p.is_even:
+                return S.Zero
+            if p.is_odd:
+                return S.One
+
+        # If p is a multiple of q.
+        r = p / q
+        if r.is_integer:
+            return S.Zero
+
+        # If p < q and its ratio is positive, then:
+        #   - floor(p / q) = 0
+        #   - p % q = p - floor(p / q) * q = p
+        less = p < q
+        if less.is_Boolean and bool(less) and r.is_positive:
+            return p
+
+
+class CleanDiv(FloorDiv):
+    """
+    Div where we can assume no rounding.
+    This is to enable future optimizations.
+    """
+
+
+# Don't use sympy ceiling/floor as they will attempt simplifications involving
+# frac
+class CeilToInt(sympy.Function):
+    is_integer = True
+
+    @classmethod
+    def eval(cls, number):
+        # assert number.is_integer is not True, number
+        if number in (sympy.oo, int_oo):
+            return int_oo
+        if number in (-sympy.oo, -int_oo):
+            return -int_oo
+        if isinstance(number, sympy.Number):
+            return sympy.Integer(math.ceil(float(number)))
+
+
+class FloorToInt(sympy.Function):
+    is_integer = True
+
+    @classmethod
+    def eval(cls, number):
+        if number in (sympy.oo, int_oo):
+            return int_oo
+        if number in (-sympy.oo, int_oo):
+            return -int_oo
+        if isinstance(number, sympy.Integer):
+            return number
+        if isinstance(number, sympy.Number):
+            return sympy.Integer(math.floor(float(number)))
+
+
+class CeilDiv(sympy.Function):
+    """
+    Div used in indexing that rounds up.
+    """
+
+    is_integer = True
+
+    def __new__(cls, base, divisor):
+        base = sympy.sympify(base)
+        divisor = sympy.sympify(divisor)
+        if sympy.gcd(base, divisor) == divisor:
+            return CleanDiv(base, divisor)
+        else:
+            return FloorDiv(base + (divisor - 1), divisor)
+
+
+class LShift(sympy.Function):
+    is_integer = True
+
+    @classmethod
+    def eval(cls, base, shift):
+        if shift < 0:
+            raise ValueError("negative shift count")
+        return base * 2**shift
+
+
+class RShift(sympy.Function):
+    is_integer = True
+
+    @classmethod
+    def eval(cls, base, shift):
+        if shift < 0:
+            raise ValueError("negative shift count")
+        return FloorDiv(base, 2**shift)
+
+
+class MinMaxBase(Expr, LatticeOp):  # type: ignore[misc]
+    def __new__(cls, *original_args, **assumptions):
+        from sympy.core.parameters import global_parameters
+
+        evaluate = assumptions.pop("evaluate", global_parameters.evaluate)
+        args = (sympify(arg) for arg in original_args)
+
+        # See the comment in _satisfy_unique_summations_symbols.
+        unique_summations_symbols = (
+            None
+            if not evaluate
+            else cls._satisfy_unique_summations_symbols(original_args)
+        )
+
+        if evaluate:
+            try:
+                # first standard filter, for cls.zero and cls.identity
+                # also reshape Max(a, Max(b, c)) to Max(a, b, c)
+                args = frozenset(cls._new_args_filter(args))  # type: ignore[assignment]
+            except ShortCircuit:
+                return cls.zero  # type: ignore[attr-defined]
+
+            # No need to run _collapse_arguments and _find_localzeros, see the comment
+            # in _satisfy_unique_summations_symbols.
+            if unique_summations_symbols is None:
+                # remove redundant args that are easily identified
+                args = cls._collapse_arguments(args, **assumptions)
+
+                # find local zeros
+                args = cls._find_localzeros(args, **assumptions)
+
+        args = frozenset(args)
+
+        if not args:
+            return cls.identity  # type: ignore[attr-defined]
+
+        if len(args) == 1:
+            return list(args).pop()
+
+        # base creation
+        obj = Expr.__new__(cls, *ordered(args), **assumptions)
+        obj._argset = args
+
+        obj.unique_summations_symbols = unique_summations_symbols
+        return obj
+
+    @classmethod
+    def _satisfy_unique_summations_symbols(
+        cls, args
+    ) -> Optional[set[sympy.core.symbol.Symbol]]:
+        """
+        One common case in some models is building expressions of the form
+        max(max(max(a+b...), c+d), e+f) which is simplified to max(a+b, c+d, e+f, ...).
+        For such expressions, we call the Max constructor X times (once for each nested
+        max) and the expression gets flattened.
+
+        An expensive cost in constructing those expressions is running _collapse_arguments
+        and _find_localzeros. However, those two optimizations are unnecessary when the args
+        to max are all of the form a+b, c+d, ..etc where each term uses a unique set of symbols.
+
+        This function is used to detect such properties of the expressions we are building
+        and if so inform that we do not need to run those optimizations. To detect those,
+        we store a property in the expression that tells that this expression is a min/max
+        operation over terms that use unique symbols "unique_summations_symbols". This property
+        also memoize the set of symbols used in all the terms to make it faster to detect this
+        property inductively.
+
+        When we apply max to add a new term, all we need to do is check if the new term uses
+        unique symbols (with respect to existing terms and itself).
+        Example:
+        t = Max(a+b, c+d) ==> satisfies the property
+        Max(t, h+j)       ==> h,j not in [a,b,c,d] => satisfy the property.
+
+        The function returns None if the new expression does not satisfy the unique_summations_symbols
+        property. Otherwise, it returns a new set of unique symbols.
+        """
+        if len(args) != 2:
+            return None
+
+        (lhs, rhs) = (
+            (args[1], args[0])
+            if isinstance(args[1], MinMaxBase)
+            else (args[0], args[1])
+        )
+
+        if not _is_symbols_binary_summation(rhs):
+            return None
+
+        # base case max(a+b, c+d) ==> satisfies the property if a+b and c+d use unique symbols.
+        if _is_symbols_binary_summation(lhs):
+            return cls._unique_symbols(args)
+
+        # inductive case max(t, h+j) ==> satisfies the property if h, j not in t.unique_summations_symbols
+        if isinstance(lhs, MinMaxBase):
+            lhs_unique_summations_symbols = getattr(
+                lhs, "unique_summations_symbols", None
+            )
+            if lhs_unique_summations_symbols is not None:
+                return cls._unique_symbols([rhs], lhs_unique_summations_symbols)
+
+        return None
+
+    @classmethod
+    def _unique_symbols(
+        cls, args, initial_set: Optional[set[sympy.core.symbol.Symbol]] = None
+    ) -> Optional[set[sympy.core.symbol.Symbol]]:
+        """
+        Return seen_symbols if all atoms in all args are all unique symbols,
+        else returns None. initial_set can be used to represent initial value for seen_symbols
+        """
+        seen_symbols = set() if initial_set is None else initial_set
+        for arg in args:
+            for element in arg.atoms():
+                if not isinstance(element, sympy.core.symbol.Symbol):
+                    return None
+                elif element in seen_symbols:
+                    return None
+                else:
+                    seen_symbols.add(element)
+        return seen_symbols
+
+    @classmethod
+    def _collapse_arguments(cls, args, **assumptions):
+        """Remove redundant args.
+
+        Examples
+        ========
+
+        >>> from sympy import Min, Max
+        >>> from sympy.abc import a, b, c, d, e
+
+        Any arg in parent that appears in any
+        parent-like function in any of the flat args
+        of parent can be removed from that sub-arg:
+
+        >>> Min(a, Max(b, Min(a, c, d)))
+        Min(a, Max(b, Min(c, d)))
+
+        If the arg of parent appears in an opposite-than parent
+        function in any of the flat args of parent that function
+        can be replaced with the arg:
+
+        >>> Min(a, Max(b, Min(c, d, Max(a, e))))
+        Min(a, Max(b, Min(a, c, d)))
+        """
+        if not args:
+            return args
+        args = list(ordered(args))
+        if cls is Min:
+            other = Max
+        else:
+            other = Min  # type: ignore[assignment]
+
+        # find global comparable max of Max and min of Min if a new
+        # value is being introduced in these args at position 0 of
+        # the ordered args
+        if args[0].is_number:
+            sifted = mins, maxs = [], []  # type: ignore[var-annotated]
+            for i in args:
+                for v in walk(i, Min, Max):
+                    if v.args[0].is_comparable:
+                        sifted[isinstance(v, Max)].append(v)
+            small = Min.identity
+            for i in mins:
+                v = i.args[0]
+                if v.is_number and (v < small) == True:  # noqa: E712
+                    small = v
+            big = Max.identity
+            for i in maxs:
+                v = i.args[0]
+                if v.is_number and (v > big) == True:  # noqa: E712
+                    big = v
+            # at the point when this function is called from __new__,
+            # there may be more than one numeric arg present since
+            # local zeros have not been handled yet, so look through
+            # more than the first arg
+            if cls is Min:
+                for arg in args:
+                    if not arg.is_number:
+                        break
+                    if (arg < small) == True:  # noqa: E712
+                        small = arg
+            elif cls == Max:
+                for arg in args:
+                    if not arg.is_number:
+                        break
+                    if (arg > big) == True:  # noqa: E712
+                        big = arg
+            T = None
+            if cls is Min:
+                if small != Min.identity:
+                    other = Max
+                    T = small
+            elif big != Max.identity:
+                other = Min  # type: ignore[assignment]
+                T = big
+            if T is not None:
+                # remove numerical redundancy
+                for i in range(len(args)):
+                    a = args[i]
+                    if isinstance(a, other):
+                        a0 = a.args[0]
+                        if (  # noqa: E712
+                            (a0 > T) if other == Max else (a0 < T)  # noqa: E712
+                        ) == True:  # noqa: E712
+                            args[i] = cls.identity  # type: ignore[attr-defined]
+
+        # remove redundant symbolic args
+        def do(ai, a):
+            if not isinstance(ai, (Min, Max)):
+                return ai
+            cond = a in ai.args
+            if not cond:
+                return ai.func(*[do(i, a) for i in ai.args], evaluate=False)
+            if isinstance(ai, cls):
+                return ai.func(*[do(i, a) for i in ai.args if i != a], evaluate=False)
+            return a
+
+        for i, a in enumerate(args):
+            args[i + 1 :] = [do(ai, a) for ai in args[i + 1 :]]
+
+        # factor out common elements as for
+        # Min(Max(x, y), Max(x, z)) -> Max(x, Min(y, z))
+        # and vice versa when swapping Min/Max -- do this only for the
+        # easy case where all functions contain something in common;
+        # trying to find some optimal subset of args to modify takes
+        # too long
+
+        def factor_minmax(args):
+            is_other = lambda arg: isinstance(arg, other)  # noqa: E731
+            other_args, remaining_args = sift(args, is_other, binary=True)
+            if not other_args:
+                return args
+
+            # Min(Max(x, y, z), Max(x, y, u, v)) -> {x,y}, ({z}, {u,v})
+            arg_sets = [set(arg.args) for arg in other_args]
+            common = set.intersection(*arg_sets)
+            if not common:
+                return args
+
+            new_other_args = list(common)
+            arg_sets_diff = [arg_set - common for arg_set in arg_sets]
+
+            # If any set is empty after removing common then all can be
+            # discarded e.g. Min(Max(a, b, c), Max(a, b)) -> Max(a, b)
+            if all(arg_sets_diff):
+                other_args_diff = [other(*s, evaluate=False) for s in arg_sets_diff]
+                new_other_args.append(cls(*other_args_diff, evaluate=False))
+
+            other_args_factored = other(*new_other_args, evaluate=False)
+            return remaining_args + [other_args_factored]
+
+        if len(args) > 1:
+            args = factor_minmax(args)
+
+        return args
+
+    @classmethod
+    def _new_args_filter(cls, arg_sequence):
+        """
+        Generator filtering args.
+
+        first standard filter, for cls.zero and cls.identity.
+        Also reshape ``Max(a, Max(b, c))`` to ``Max(a, b, c)``,
+        and check arguments for comparability
+        """
+        for arg in arg_sequence:
+            # pre-filter, checking comparability of arguments
+            if (
+                not isinstance(arg, Expr)
+                or arg.is_extended_real is False
+                or (arg.is_number and not arg.is_comparable)
+            ):
+                raise ValueError(f"The argument '{arg}' is not comparable.")
+
+            if arg == cls.zero:  # type: ignore[attr-defined]
+                raise ShortCircuit(arg)
+            elif arg == cls.identity:  # type: ignore[attr-defined]
+                continue
+            elif arg.func == cls:
+                yield from arg.args
+            else:
+                yield arg
+
+    @classmethod
+    def _find_localzeros(cls, values, **options):
+        """
+        Sequentially allocate values to localzeros.
+
+        When a value is identified as being more extreme than another member it
+        replaces that member; if this is never true, then the value is simply
+        appended to the localzeros.
+
+        Unlike the sympy implementation, we only look for zero and one, we don't
+        do generic is connected test pairwise which is slow
+        """
+
+        # First, collapse all numeric arguments
+        other_values = set()
+        num_value = None
+        for arg in values:
+            if arg.is_Number:
+                if num_value is None:
+                    num_value = arg
+                else:
+                    if cls is Max:
+                        num_value = max(num_value, arg)
+                    elif cls is Min:
+                        num_value = min(num_value, arg)
+                    else:
+                        raise AssertionError(f"impossible {cls}")
+            else:
+                other_values.add(arg)
+
+        # Special cases when there is only one symbolic value
+        if num_value is None:
+            return other_values
+
+        if len(other_values) == 0:
+            return {num_value}
+
+        if len(other_values) == 1:
+            other_value = next(iter(other_values))
+            if num_value in (0.0, 0) and other_value.is_nonnegative:
+                return other_values if cls is Max else {num_value}
+            if num_value == 1 and other_value.is_positive:
+                return other_values if cls is Max else {num_value}
+
+        other_values.add(num_value)
+        return other_values
+
+    _eval_is_algebraic = lambda s: _torf(i.is_algebraic for i in s.args)  # noqa: E731
+    _eval_is_antihermitian = lambda s: _torf(  # noqa: E731
+        i.is_antihermitian for i in s.args  # noqa: E731
+    )  # noqa: E731
+    _eval_is_commutative = lambda s: _torf(  # noqa: E731
+        i.is_commutative for i in s.args  # noqa: E731
+    )  # noqa: E731
+    _eval_is_complex = lambda s: _torf(i.is_complex for i in s.args)  # noqa: E731
+    _eval_is_composite = lambda s: _torf(i.is_composite for i in s.args)  # noqa: E731
+    _eval_is_even = lambda s: _torf(i.is_even for i in s.args)  # noqa: E731
+    _eval_is_finite = lambda s: _torf(i.is_finite for i in s.args)  # noqa: E731
+    _eval_is_hermitian = lambda s: _torf(i.is_hermitian for i in s.args)  # noqa: E731
+    _eval_is_imaginary = lambda s: _torf(i.is_imaginary for i in s.args)  # noqa: E731
+    _eval_is_infinite = lambda s: _torf(i.is_infinite for i in s.args)  # noqa: E731
+    _eval_is_integer = lambda s: _torf(i.is_integer for i in s.args)  # noqa: E731
+    _eval_is_irrational = lambda s: _torf(i.is_irrational for i in s.args)  # noqa: E731
+    _eval_is_negative = lambda s: _torf(i.is_negative for i in s.args)  # noqa: E731
+    _eval_is_noninteger = lambda s: _torf(i.is_noninteger for i in s.args)  # noqa: E731
+    _eval_is_nonnegative = lambda s: _torf(  # noqa: E731
+        i.is_nonnegative for i in s.args  # noqa: E731
+    )  # noqa: E731
+    _eval_is_nonpositive = lambda s: _torf(  # noqa: E731
+        i.is_nonpositive for i in s.args  # noqa: E731
+    )  # noqa: E731
+    _eval_is_nonzero = lambda s: _torf(i.is_nonzero for i in s.args)  # noqa: E731
+    _eval_is_odd = lambda s: _torf(i.is_odd for i in s.args)  # noqa: E731
+    _eval_is_polar = lambda s: _torf(i.is_polar for i in s.args)  # noqa: E731
+    _eval_is_positive = lambda s: _torf(i.is_positive for i in s.args)  # noqa: E731
+    _eval_is_prime = lambda s: _torf(i.is_prime for i in s.args)  # noqa: E731
+    _eval_is_rational = lambda s: _torf(i.is_rational for i in s.args)  # noqa: E731
+    _eval_is_real = lambda s: _torf(i.is_real for i in s.args)  # noqa: E731
+    _eval_is_extended_real = lambda s: _torf(  # noqa: E731
+        i.is_extended_real for i in s.args  # noqa: E731
+    )  # noqa: E731
+    _eval_is_transcendental = lambda s: _torf(  # noqa: E731
+        i.is_transcendental for i in s.args  # noqa: E731
+    )  # noqa: E731
+    _eval_is_zero = lambda s: _torf(i.is_zero for i in s.args)  # noqa: E731
+
+
+class Max(MinMaxBase, Application):  # type: ignore[misc]
+    r"""
+    Return, if possible, the maximum value of the list.
+    """
+
+    zero = S.Infinity
+    identity = S.NegativeInfinity
+
+    def _eval_is_positive(self):  # type:ignore[override]
+        return fuzzy_or(a.is_positive for a in self.args)  # type: ignore[attr-defined]
+
+    def _eval_is_nonnegative(self):  # type:ignore[override]
+        return fuzzy_or(a.is_nonnegative for a in self.args)  # type: ignore[attr-defined]
+
+    def _eval_is_negative(self):  # type:ignore[override]
+        return fuzzy_and(a.is_negative for a in self.args)
+
+
+class Min(MinMaxBase, Application):  # type: ignore[misc]
+    """
+    Return, if possible, the minimum value of the list.
+    """
+
+    zero = S.NegativeInfinity
+    identity = S.Infinity
+
+    def _eval_is_positive(self):  # type:ignore[override]
+        return fuzzy_and(a.is_positive for a in self.args)  # type: ignore[attr-defined]
+
+    def _eval_is_nonnegative(self):  # type:ignore[override]
+        return fuzzy_and(a.is_nonnegative for a in self.args)  # type: ignore[attr-defined]
+
+    def _eval_is_negative(self):  # type:ignore[override]
+        return fuzzy_or(a.is_negative for a in self.args)
+
+
+def safe_pow(base, exp):
+    sign = 1
+    if base < 0:
+        base = -base
+        sign = 1 if exp % 2 == 0 else -1
+    return sign * _safe_pow(base, exp)
+
+
+# Prevent people from overflowing pow
+def _safe_pow(base, exponent):
+    if exponent < 0:
+        raise ValueError("Exponent must be non-negative.")
+
+    if exponent == 0:
+        return 1
+
+    half_exp = safe_pow(base, exponent // 2)
+    if half_exp is int_oo:
+        return int_oo
+
+    # TODO: microoptimization is to avoid overflowing into arbitrary precision
+    # and detect overflow prior to doing operations
+
+    result = half_exp * half_exp
+    if result > sys.maxsize:
+        return int_oo
+
+    if exponent % 2 == 1:
+        result *= base
+        if result > sys.maxsize:
+            return int_oo
+
+    return result
+
+
+class PowByNatural(sympy.Function):
+    is_integer = True
+
+    precedence: int = 50  # precedence of mul
+
+    @classmethod
+    def eval(cls, base, exp):
+        if isinstance(base, sympy.Integer) and isinstance(exp, sympy.Integer):
+            r = safe_pow(base, exp)
+            if r in (-int_oo, int_oo):
+                return r
+            return sympy.Integer(r)
+        if isinstance(exp, sympy.Integer):
+            # Rely on regular sympy Pow for this (note that iterated
+            # multiplication turns into a Pow anyway, you can't escape!!)
+            return sympy.Pow(base, exp)
+        if exp in (int_oo, sympy.oo):
+            if base.is_nonnegative:
+                return int_oo
+            elif base.is_negative:
+                return sympy.zoo  # this is apparently what (-2)**sympy.oo does
+        # NB: do NOT translate into sympy.Pow, we will lose knowledge that exp
+        # is a natural number if we do
+
+
+# base is assumed to be nonnegative, thereby prevent complex numbers from
+# occuring
+class FloatPow(sympy.Function):
+    is_real = True
+
+    precedence: int = 60  # precedence of pow
+
+    @classmethod
+    def eval(cls, base, exp):
+        # NB: These test sympy.Number, not sympy.Float, because:
+        #   - Sometimes we may have sympy.oo or int_oo, and that's not a Float
+        #     (but coerces to math.Inf)
+        #   - Sometimes Float(0.0) will unpredictably decay to Integer(0),
+        #     but we should still accept it in floatey contexts
+        if isinstance(base, sympy.Number) and isinstance(exp, sympy.Number):
+            return sympy.Float(float(base) ** float(exp))
+        # NB: do not do any nontrivial reasoning
+
+
+# Overloaded to be compatible with regular Python.
+# https://github.com/pytorch/pytorch/issues/90900
+#
+# In particular, sympy division is willing to simplify x/x == 1
+# where 1 is an integer, but this must be a float if x was float.
+class FloatTrueDiv(sympy.Function):
+    is_real = True
+
+    precedence: int = 35  # lower precedence than add
+
+    @classmethod
+    def eval(cls, base, divisor):
+        # assert base.is_integer is not True, base
+        # assert divisor.is_integer is not True, divisor
+
+        if divisor.is_zero:
+            raise ZeroDivisionError("division by zero")
+
+        if isinstance(base, sympy.Number) and isinstance(divisor, sympy.Number):
+            return sympy.Float(float(base) / float(divisor))
+
+
+# Overloaded to be compatible with regular Python.  We distinguish this from
+# FloatTrueDiv, because the code generation has to be different for this case:
+# Python has a fancy algorithm for integer true division that isn't just
+# "promote both arguments to float and use float division", so you need to
+# codegen it differently.  While technically you can work it out from the
+# types of the input, this is often inconvenient to do in Inductor codegen,
+# so just have a different operator
+# NB: Right now, Inductor codegen doesn't implement this correctly lol
+class IntTrueDiv(sympy.Function):
+    is_real = True
+
+    precedence: int = 35  # lower precedence than add
+
+    @classmethod
+    def eval(cls, base, divisor):
+        if divisor.is_zero:
+            raise ZeroDivisionError("division by zero")
+
+        if (
+            isinstance(base, sympy.Number)
+            and isinstance(divisor, sympy.Number)
+            and (
+                base in (int_oo, -int_oo, sympy.oo, -sympy.oo)
+                or divisor in (int_oo, -int_oo, sympy.oo, -sympy.oo)
+            )
+        ):
+            # Don't have to worry about precision here, you're getting zero or
+            # inf from the division
+            return sympy.Float(float(base) / float(divisor))
+        if isinstance(base, sympy.Integer) and isinstance(divisor, sympy.Integer):
+            return sympy.Float(int(base) / int(divisor))
+
+
+# TODO: As an indicator, this != 0 implies == 1 (and vice versa).
+# Because we do not have the ability to guard on the stride permutation
+# at the moment, it is hard to make further inferences when this is true,
+# as although we know the tensor is contiguous in *some* layout, we don't
+# know which one (however, you could, for example, make the inference that
+# reshaping this to a 1D tensor can be guard-free.)
+class IsNonOverlappingAndDenseIndicator(sympy.Function):
+    is_integer = True
+
+    @classmethod
+    def eval(cls, *args):
+        assert len(args) % 2 == 0
+        dim = len(args) // 2
+        sizes = args[0:dim]
+        strides = args[dim:]
+
+        # sym_node imported in torch.__init__. Local import to avoid an import cycle
+        from torch.fx.experimental.symbolic_shapes import (
+            eval_is_non_overlapping_and_dense,
+        )
+
+        if all(isinstance(a, sympy.Integer) for a in args):
+            return eval_is_non_overlapping_and_dense(
+                [int(a) for a in sizes], [int(a) for a in strides]
+            )
+
+        if dim == 1:
+            # Manually implement the rank one short circuit
+            if strides[0].is_Number and strides[0] == 1:
+                return 1
+
+            if sizes[0].is_Number and sizes[0] < 2:
+                return 1
+
+            # return 0 case covered by case above
+
+            # TODO: Inability to access size-obliviousness sucks: if we have a
+            # size oblivious test on a size-like unbacked SymInt, we could
+            # confidently return zero when we have a size-like u0 stride
+            # and a size-like u1 size.  Maybe a fancy ValueRanges analysis for
+            # this function could help figure this out.
+
+        if all(isinstance(a, sympy.Integer) for a in strides):
+            assert dim != 0
+            # When all strides are integral, we can sort, and the size for the
+            # largest stride doesn't matter and can be arbitrarily symbolic
+            s_sizes, s_strides = zip(
+                *sorted(zip(sizes, strides), key=operator.itemgetter(1))
+            )
+            # Put something arbitrary in the max size spot, it'll be ignored
+            if all(isinstance(a, sympy.Integer) for a in s_sizes[:-1]):
+                s_sizes = s_sizes[:-1] + (42,)
+                # We can reuse the regular eval, because it is invariant to
+                # permutation of dimensions
+                return eval_is_non_overlapping_and_dense(
+                    [int(a) for a in s_sizes], [int(a) for a in s_strides]
+                )
+
+        return None
+
+
+# NB: this is inconsistent with math.trunc in Python
+class TruncToFloat(sympy.Function):
+    is_real = True
+
+    @classmethod
+    def eval(cls, number):
+        # assert number.is_integer is not True, number
+        if isinstance(number, sympy.Number):
+            # NB: It is safe to use truncation to integer, which is what
+            # math.trunc does, as Python integers are arbitrary precision and
+            # so we are guaranteed not to lose precision when we do this
+            return sympy.Float(math.trunc(float(number)))
+
+
+class TruncToInt(sympy.Function):
+    is_integer = True
+
+    @classmethod
+    def eval(cls, number):
+        # assert number.is_integer is not True, number
+        if number in (sympy.oo, int_oo):
+            return int_oo
+        if number in (-sympy.oo, -int_oo):
+            return -int_oo
+        if isinstance(number, sympy.Number):
+            return sympy.Integer(math.trunc(float(number)))
+
+
+# This is float -> int
+class RoundToInt(sympy.Function):
+    is_integer = True
+
+    @classmethod
+    def eval(cls, number):
+        # assert number.is_integer is not True, number
+
+        if number is sympy.oo:
+            return int_oo
+        if number is -sympy.oo:
+            return -int_oo
+        if isinstance(number, sympy.Number):
+            return sympy.Integer(round(float(number), 0))
+
+
+# To get float -> int, Python style round semantics.
+#
+#   x = PyFloat_AsDouble(self);
+#   if (o_ndigits == Py_None) {
+#       /* single-argument round or with None ndigits:
+#        * round to nearest integer */
+#       rounded = round(x);
+#       if (fabs(x-rounded) == 0.5)
+#           /* halfway case: round to even */
+#           rounded = 2.0*round(x/2.0);
+#       return PyLong_FromDouble(rounded);
+#   }
+
+
+# NB: Like Round, this only ever returns floats.  ndigits cannot be None
+class RoundDecimal(sympy.Function):
+    is_real = True
+
+    @classmethod
+    def eval(cls, number, ndigits):
+        # assert number.is_integer is not True, number
+
+        if isinstance(number, sympy.Number) and isinstance(ndigits, sympy.Integer):
+            return sympy.Float(round(float(number), int(ndigits)))
+
+
+class ToFloat(sympy.Function):
+    is_real = True
+
+    @classmethod
+    def eval(cls, number):
+        if number in [sympy.oo, -sympy.oo]:
+            return number
+
+        if isinstance(number, sympy.Integer):
+            return sympy.Float(int(number))
+        if number is int_oo:
+            return sympy.oo
+        if number is -int_oo:
+            return -sympy.oo
+
+
+class Identity(sympy.Function):
+    """
+    Prevents expansion and other optimizations
+    """
+
+    precedence = 10
+
+    def __repr__(self):  # type: ignore[override]
+        return f"Identity({self.args[0]})"
+
+    def _eval_is_real(self):
+        return self.args[0].is_real
+
+    def _eval_is_integer(self):
+        return self.args[0].is_integer  # type: ignore[attr-defined]
+
+    def _eval_expand_identity(self, **hints):
+        # Removes the identity op.
+        return self.args[0]
+
+
+def make_opaque_unary_fn(name):
+    class OpaqueUnaryFn(sympy.Function):
+        """
+        Unlike the builtin sympy functions on real numbers like sympy.sqrt,
+        these equivalents do not do any nontrivial reasoning besides
+        constant propagation.  This helps avoid performing transformations
+        that are valid for real numbers but are invalid for floating point;
+        in particular, while we are willing to make optimizations that change
+        numerics for Tensor compute, we are NOT willing to make optimziations
+        that change numerics for size compute.
+        """
+
+        _torch_handler_name = name
+
+        @classmethod
+        def eval(cls, a):
+            if isinstance(a, (sympy.Integer, sympy.Float)):
+                # Python converts to float64 before computing, c.f.
+                # >>> math.sin(2**53+1)
+                # -0.848925964814655
+                # >>> math.sin(float(2**53+1))
+                # -0.848925964814655
+                try:
+                    return sympy.Float(getattr(math, name)(float(a)))
+                # Just use sympy semantics for infinity/overflow, you might get some
+                # weird objects but ask silly questions, get silly answers
+                except OverflowError:
+                    return getattr(sympy, name)(a)
+            elif a in [sympy.oo, -sympy.oo, sympy.zoo, -sympy.zoo, int_oo, -int_oo]:
+                if a is int_oo:
+                    a = sympy.oo
+                if a is -int_oo:
+                    a = -sympy.oo
+                if name == "log2":
+                    return sympy.log(a, 2)
+                return getattr(sympy, name)(a)
+            return None
+
+    nm = "OpaqueUnaryFn_" + name
+    OpaqueUnaryFn.__name__ = nm
+    OpaqueUnaryFn.__qualname__ = nm
+
+    return OpaqueUnaryFn
+
+
+# Keep in sync with math_op_names in torch/fx/experimental/sym_node.py
+OpaqueUnaryFn_sqrt = make_opaque_unary_fn("sqrt")
+OpaqueUnaryFn_cos = make_opaque_unary_fn("cos")
+OpaqueUnaryFn_cosh = make_opaque_unary_fn("cosh")
+OpaqueUnaryFn_sin = make_opaque_unary_fn("sin")
+OpaqueUnaryFn_sinh = make_opaque_unary_fn("sinh")
+OpaqueUnaryFn_tan = make_opaque_unary_fn("tan")
+OpaqueUnaryFn_tanh = make_opaque_unary_fn("tanh")
+OpaqueUnaryFn_asin = make_opaque_unary_fn("asin")
+OpaqueUnaryFn_acos = make_opaque_unary_fn("acos")
+OpaqueUnaryFn_atan = make_opaque_unary_fn("atan")
+OpaqueUnaryFn_exp = make_opaque_unary_fn("exp")
+OpaqueUnaryFn_log = make_opaque_unary_fn("log")
+OpaqueUnaryFn_asinh = make_opaque_unary_fn("asinh")
+OpaqueUnaryFn_log2 = make_opaque_unary_fn("log2")
+
+
+def make_opaque_bitwise_fn(name, real_op_name):
+    if name == "bitwise_and":
+        prec = PRECEDENCE["BitwiseAnd"]
+    elif name == "bitwise_or":
+        prec = PRECEDENCE["BitwiseOr"]
+    else:
+        raise AssertionError(f"unrecognized {name}")
+
+    class BitwiseFn(sympy.Function):
+        _torch_handler_name = name
+        precedence: int = prec
+
+        @classmethod
+        def eval(cls, a, b):
+            if a.is_Boolean and b.is_Boolean:
+                return getattr(operator, real_op_name)(a, b)
+            if a.is_Boolean:
+                a = sympy.Integer(1 if a else 0)
+            if b.is_Boolean:
+                b = sympy.Integer(1 if b else 0)
+            if isinstance(a, (sympy.Integer, int)) and isinstance(
+                b, (sympy.Integer, int)
+            ):
+                return sympy.Integer(getattr(operator, real_op_name)(int(a), int(b)))
+            return None
+
+    BitwiseFn.__name__ = "BitwiseFn_" + name
+    return BitwiseFn
+
+
+BitwiseFn_bitwise_and = make_opaque_bitwise_fn("bitwise_and", "and_")
+BitwiseFn_bitwise_or = make_opaque_bitwise_fn("bitwise_or", "or_")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/interp.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/interp.py
new file mode 100644
index 0000000000000000000000000000000000000000..396d1d46d28998f6097be1c3225aec0e5d771d3e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/interp.py
@@ -0,0 +1,225 @@
+# mypy: allow-untyped-defs
+"""
+This is a simple interpreter for Sympy expressions that dispatches to
+classes following the torch._inductor.virtualized calling convention.
+For directness, the interpreter takes the handler directly rather than
+consulting the TLS.  It does not use most of the methods on the full
+handler; only those with corresponding Sympy expressions.  To see an example
+of a full handler, see torch.utils._sympy.value_ranges.ValueRangeAnalysis.
+"""
+
+import functools
+import logging
+from typing import Any, Union
+
+import sympy
+from sympy.logic.boolalg import Boolean as SympyBoolean, BooleanAtom
+
+import torch
+
+from .functions import (
+    BitwiseFn_bitwise_and,
+    BitwiseFn_bitwise_or,
+    CeilToInt,
+    CleanDiv,
+    FloatPow,
+    FloatTrueDiv,
+    FloorDiv,
+    FloorToInt,
+    Identity,
+    IntTrueDiv,
+    IsNonOverlappingAndDenseIndicator,
+    Max,
+    Min,
+    Mod,
+    ModularIndexing,
+    OpaqueUnaryFn_log2,
+    PowByNatural,
+    PythonMod,
+    RoundDecimal,
+    RoundToInt,
+    ToFloat,
+    TruncToFloat,
+    TruncToInt,
+    Where,
+)
+
+
+log = logging.getLogger(__name__)
+
+
+# TODO: Dedupe this with SYMPY_INTERP
+
+
+@functools.lru_cache(None)
+def handlers():
+    # TODO add CeilDiv (it doesn't appear in the index_expr)
+
+    # TODO default to some decompositions if the interpreter doesn't have them
+    # like decomposing ModularIndexing or implementing Le(a,b) as Ge(b, a)
+
+    HANDLERS = {
+        sympy.Or: "or_",
+        sympy.And: "and_",
+        sympy.Eq: "eq",
+        sympy.Ne: "ne",
+        sympy.Lt: "lt",
+        sympy.Gt: "gt",
+        sympy.Le: "le",
+        sympy.Ge: "ge",
+        sympy.Not: "not_",
+        IntTrueDiv: "int_truediv",
+        FloatTrueDiv: "truediv",
+        FloorDiv: "floordiv",
+        CleanDiv: "floordiv",  # TODO: hmm?
+        TruncToFloat: "trunc",
+        Where: "where",
+        sympy.Add: "add",
+        sympy.Mul: "mul",
+        FloatPow: "pow",
+        PowByNatural: "pow_by_natural",
+        # sympy simplifies x * x into Pow(x, 2), so we need to handle this.
+        # Do NOT use builtin Pow for floats
+        # TODO: There is a hazard here, if we have float * float it will
+        # also get turned into Pow(float, 2) but we don't want this because
+        # pow_by_natural is assumed to only be integers.  Probably the fix is
+        # to add a FloatMul to impede this optimization
+        sympy.Pow: "pow_by_natural",
+        Mod: "mod",
+        PythonMod: "mod",  # TODO: this is wrong
+        # TODO: Inductor can generate these, but it's ill-specified which
+        # semantics were intended here.  Needs to be cleaned up along with
+        # FloorDiv in a bigger cleanup
+        sympy.Mod: "mod",
+        sympy.Abs: "abs",
+        sympy.log: "log",
+        sympy.exp: "exp",
+        sympy.Min: "minimum",
+        sympy.Max: "maximum",
+        Min: "minimum",
+        Max: "maximum",
+        ModularIndexing: "modular_indexing",
+        sympy.functions.elementary.piecewise.ExprCondPair: "expr_cond_pair",
+        sympy.Piecewise: "piecewise",
+        Identity: "identity",
+        IsNonOverlappingAndDenseIndicator: "is_non_overlapping_and_dense_indicator",
+        RoundDecimal: "round_decimal",
+        # TODO: do the rest of the opaque unary functions...
+        OpaqueUnaryFn_log2: "log2",
+        BitwiseFn_bitwise_and: "bitwise_and",
+        BitwiseFn_bitwise_or: "bitwise_or",
+    }
+    # TODO: This is kind of pointless, we shouldn't be generating sympy.sin
+    # for these functions, they should be Opaque instead
+    for name in ["cos", "sin", "tan", "sinh", "cosh", "tanh", "asin", "acos", "atan"]:
+        HANDLERS[getattr(sympy, name)] = name
+
+    return HANDLERS
+
+
+ASSOCIATIVE_OPS = {"minimum", "maximum", "mul", "add", "and_", "or_"}
+
+
+def _run_sympy_handler(analysis, args, expr, index_dtype=torch.int64):
+    # Special cases
+    if isinstance(expr, sympy.Pow) and isinstance(
+        expr.args[1], sympy.core.numbers.Half
+    ):
+        return analysis.sqrt(args[0])
+    if isinstance(expr, ToFloat):
+        return analysis.to_dtype(args[0], torch.float64)
+
+    # These handlers are special because they take an extra dtype argument
+    # specifying what they should convert to, and we need to appropriately set
+    # this up when we convert from Sympy.  A reasonable default when you
+    # are translating is to conservatively do int64, and then narrow these
+    # arguments later when you discover you can narrow the index range.  But
+    # if you already know that 32-bit indexing is OK, you can directly do the
+    # sympy translation with index_dtype=torch.int32
+    INDEX_DTYPE_HANDLERS = {
+        TruncToInt: "trunc_to_int",
+        sympy.floor: "floor_to_int",
+        sympy.ceiling: "ceil_to_int",
+        FloorToInt: "floor_to_int",
+        CeilToInt: "ceil_to_int",
+        RoundToInt: "round_to_int",
+    }
+    if (handler_name := INDEX_DTYPE_HANDLERS.get(expr.func)) is not None:
+        return getattr(analysis, handler_name)(*args, index_dtype)
+
+    # Fastpath for n-ary integral addition
+    if expr.func is sympy.Add and expr.is_integer and hasattr(analysis, "sym_sum"):
+        r = analysis.sym_sum(args)
+        log.debug("sym_sum(%s) -> %s", args, r)
+        return r
+
+    if hasattr(expr.func, "_torch_handler_name"):
+        handler_name = expr.func._torch_handler_name
+    else:
+        handler_name = handlers()[expr.func]
+    handler = getattr(analysis, handler_name)
+    try:
+        if handler_name in ASSOCIATIVE_OPS:
+            assert len(args) > 1
+            acc = handler(args[0], args[1])
+            for i in range(2, len(args)):
+                acc = handler(acc, args[i])
+            log.debug("%s(%s) -> %s", handler_name, args, acc)
+            return acc
+        else:
+            r = handler(*args)
+            log.debug("%s(%s) -> %s", handler_name, args, r)
+            return r
+    except NotImplementedError:
+        raise
+    except Exception:
+        log.warning("failed while executing %s(%s)", handler_name, args)
+        raise
+
+
+_nil = object()
+
+
+def sympy_interp(
+    analysis,
+    env: dict[sympy.Symbol, Any],
+    expr: Union[sympy.Expr, SympyBoolean],
+    *,
+    index_dtype=torch.int64,
+    missing_handler=None,
+):
+    # Handle base cases
+    dtype = None
+    if isinstance(expr, BooleanAtom):
+        dtype = torch.bool
+    elif isinstance(expr, sympy.Integer):
+        dtype = torch.int64
+    elif isinstance(expr, sympy.Number):
+        dtype = torch.double
+
+    if dtype is not None:
+        return analysis.constant(expr, dtype)
+    elif isinstance(expr, sympy.Symbol):
+        if (r := env.get(expr, _nil)) is not _nil:
+            return r
+        elif missing_handler:
+            return missing_handler(expr)
+        else:
+            raise KeyError(expr)
+
+    # Recursive case
+    return _run_sympy_handler(
+        analysis,
+        [
+            sympy_interp(
+                analysis,
+                env,
+                arg,
+                index_dtype=index_dtype,
+                missing_handler=missing_handler,
+            )
+            for arg in expr.args
+        ],  # type: ignore[arg-type]
+        expr,
+        index_dtype=index_dtype,
+    )  # type: ignore[arg-type]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/numbers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/numbers.py
new file mode 100644
index 0000000000000000000000000000000000000000..d02b9879cad2373d46c9aa61e0476907a44e6be3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/numbers.py
@@ -0,0 +1,397 @@
+# mypy: allow-untyped-defs
+import mpmath.libmp as mlib  # type: ignore[import-untyped]
+import sympy
+from sympy import Expr
+from sympy.core.decorators import _sympifyit
+from sympy.core.expr import AtomicExpr
+from sympy.core.numbers import Number
+from sympy.core.parameters import global_parameters
+from sympy.core.singleton import S, Singleton
+
+
+class IntInfinity(Number, metaclass=Singleton):
+    r"""Positive integer infinite quantity.
+
+    Integer infinity is a value in an extended integers which
+    is greater than all other integers.  We distinguish it from
+    sympy's existing notion of infinity in that it reports that
+    it is_integer.
+
+    Infinity is a singleton, and can be accessed by ``S.IntInfinity``,
+    or can be imported as ``int_oo``.
+    """
+
+    # NB: We can't actually mark this as infinite, as integer and infinite are
+    # inconsistent assumptions in sympy.  We also report that we are complex,
+    # different from sympy.oo
+
+    is_integer = True
+    is_commutative = True
+    is_number = True
+    is_extended_real = True
+    is_comparable = True
+    is_extended_positive = True
+    is_prime = False
+
+    # Ensure we get dispatched to before plain numbers
+    _op_priority = 100.0
+
+    __slots__ = ()
+
+    def __new__(cls):
+        return AtomicExpr.__new__(cls)
+
+    def _sympystr(self, printer):
+        return "int_oo"
+
+    def _eval_subs(self, old, new):
+        if self == old:
+            return new
+
+    # We could do these, not sure about it
+    """
+    def _eval_evalf(self, prec=None):
+        return Float('inf')
+
+    def evalf(self, prec=None, **options):
+        return self._eval_evalf(prec)
+    """
+
+    @_sympifyit("other", NotImplemented)
+    def __add__(self, other):
+        if isinstance(other, Number) and global_parameters.evaluate:
+            if other in (S.Infinity, S.NegativeInfinity):
+                return other
+            if other in (S.NegativeIntInfinity, S.NaN):
+                return S.NaN
+            return self
+        return Number.__add__(self, other)
+
+    __radd__ = __add__
+
+    @_sympifyit("other", NotImplemented)
+    def __sub__(self, other):
+        if isinstance(other, Number) and global_parameters.evaluate:
+            if other is S.Infinity:
+                return S.NegativeInfinity
+            if other is S.NegativeInfinity:
+                return S.Infinity
+            if other in (S.IntInfinity, S.NaN):
+                return S.NaN
+            return self
+        return Number.__sub__(self, other)
+
+    @_sympifyit("other", NotImplemented)
+    def __rsub__(self, other):
+        return (-self).__add__(other)
+
+    @_sympifyit("other", NotImplemented)
+    def __mul__(self, other):
+        if isinstance(other, Number) and global_parameters.evaluate:
+            if other.is_zero or other is S.NaN:
+                return S.NaN
+            if other.is_extended_positive:
+                return self
+            return S.NegativeIntInfinity
+        return Number.__mul__(self, other)
+
+    __rmul__ = __mul__
+
+    @_sympifyit("other", NotImplemented)
+    def __truediv__(self, other):
+        if isinstance(other, Number) and global_parameters.evaluate:
+            if other in (
+                S.Infinity,
+                S.IntInfinity,
+                S.NegativeInfinity,
+                S.NegativeIntInfinity,
+                S.NaN,
+            ):
+                return S.NaN
+            if other.is_extended_nonnegative:
+                return S.Infinity  # truediv produces float
+            return S.NegativeInfinity  # truediv produces float
+        return Number.__truediv__(self, other)
+
+    def __abs__(self):
+        return S.IntInfinity
+
+    def __neg__(self):
+        return S.NegativeIntInfinity
+
+    def _eval_power(self, expt):
+        if expt.is_extended_positive:
+            return S.IntInfinity
+        if expt.is_extended_negative:
+            return S.Zero
+        if expt is S.NaN:
+            return S.NaN
+        if expt is S.ComplexInfinity:
+            return S.NaN
+        if expt.is_extended_real is False and expt.is_number:
+            from sympy.functions.elementary.complexes import re
+
+            expt_real = re(expt)
+            if expt_real.is_positive:
+                return S.ComplexInfinity
+            if expt_real.is_negative:
+                return S.Zero
+            if expt_real.is_zero:
+                return S.NaN
+
+            return self ** expt.evalf()
+
+    def _as_mpf_val(self, prec):
+        return mlib.finf
+
+    def __hash__(self):
+        return super().__hash__()
+
+    def __eq__(self, other):
+        return other is S.IntInfinity
+
+    def __ne__(self, other):
+        return other is not S.IntInfinity
+
+    def __gt__(self, other):
+        if other is S.Infinity:
+            return sympy.false  # sympy.oo > int_oo
+        elif other is S.IntInfinity:
+            return sympy.false  # consistency with sympy.oo
+        else:
+            return sympy.true
+
+    def __ge__(self, other):
+        if other is S.Infinity:
+            return sympy.false  # sympy.oo > int_oo
+        elif other is S.IntInfinity:
+            return sympy.true  # consistency with sympy.oo
+        else:
+            return sympy.true
+
+    def __lt__(self, other):
+        if other is S.Infinity:
+            return sympy.true  # sympy.oo > int_oo
+        elif other is S.IntInfinity:
+            return sympy.false  # consistency with sympy.oo
+        else:
+            return sympy.false
+
+    def __le__(self, other):
+        if other is S.Infinity:
+            return sympy.true  # sympy.oo > int_oo
+        elif other is S.IntInfinity:
+            return sympy.true  # consistency with sympy.oo
+        else:
+            return sympy.false
+
+    @_sympifyit("other", NotImplemented)
+    def __mod__(self, other):
+        if not isinstance(other, Expr):
+            return NotImplemented
+        return S.NaN
+
+    __rmod__ = __mod__
+
+    def floor(self):
+        return self
+
+    def ceiling(self):
+        return self
+
+
+int_oo = S.IntInfinity
+
+
+class NegativeIntInfinity(Number, metaclass=Singleton):
+    """Negative integer infinite quantity.
+
+    NegativeInfinity is a singleton, and can be accessed
+    by ``S.NegativeInfinity``.
+
+    See Also
+    ========
+
+    IntInfinity
+    """
+
+    # Ensure we get dispatched to before plain numbers
+    _op_priority = 100.0
+
+    is_integer = True
+    is_extended_real = True
+    is_commutative = True
+    is_comparable = True
+    is_extended_negative = True
+    is_number = True
+    is_prime = False
+
+    __slots__ = ()
+
+    def __new__(cls):
+        return AtomicExpr.__new__(cls)
+
+    def _eval_subs(self, old, new):
+        if self == old:
+            return new
+
+    def _sympystr(self, printer):
+        return "-int_oo"
+
+    """
+    def _eval_evalf(self, prec=None):
+        return Float('-inf')
+
+    def evalf(self, prec=None, **options):
+        return self._eval_evalf(prec)
+    """
+
+    @_sympifyit("other", NotImplemented)
+    def __add__(self, other):
+        if isinstance(other, Number) and global_parameters.evaluate:
+            if other is S.Infinity:
+                return S.Infinity
+            if other in (S.IntInfinity, S.NaN):
+                return S.NaN
+            return self
+        return Number.__add__(self, other)
+
+    __radd__ = __add__
+
+    @_sympifyit("other", NotImplemented)
+    def __sub__(self, other):
+        if isinstance(other, Number) and global_parameters.evaluate:
+            if other is S.NegativeInfinity:
+                return S.Infinity
+            if other in (S.NegativeIntInfinity, S.NaN):
+                return S.NaN
+            return self
+        return Number.__sub__(self, other)
+
+    @_sympifyit("other", NotImplemented)
+    def __rsub__(self, other):
+        return (-self).__add__(other)
+
+    @_sympifyit("other", NotImplemented)
+    def __mul__(self, other):
+        if isinstance(other, Number) and global_parameters.evaluate:
+            if other.is_zero or other is S.NaN:
+                return S.NaN
+            if other.is_extended_positive:
+                return self
+            return S.IntInfinity
+        return Number.__mul__(self, other)
+
+    __rmul__ = __mul__
+
+    @_sympifyit("other", NotImplemented)
+    def __truediv__(self, other):
+        if isinstance(other, Number) and global_parameters.evaluate:
+            if other in (
+                S.Infinity,
+                S.IntInfinity,
+                S.NegativeInfinity,
+                S.NegativeIntInfinity,
+                S.NaN,
+            ):
+                return S.NaN
+            if other.is_extended_nonnegative:
+                return self
+            return S.Infinity  # truediv returns float
+        return Number.__truediv__(self, other)
+
+    def __abs__(self):
+        return S.IntInfinity
+
+    def __neg__(self):
+        return S.IntInfinity
+
+    def _eval_power(self, expt):
+        if expt.is_number:
+            if expt in (
+                S.NaN,
+                S.Infinity,
+                S.NegativeInfinity,
+                S.IntInfinity,
+                S.NegativeIntInfinity,
+            ):
+                return S.NaN
+
+            if isinstance(expt, sympy.Integer) and expt.is_extended_positive:
+                if expt.is_odd:
+                    return S.NegativeIntInfinity
+                else:
+                    return S.IntInfinity
+
+            inf_part = S.IntInfinity**expt
+            s_part = S.NegativeOne**expt
+            if inf_part == 0 and s_part.is_finite:
+                return inf_part
+            if (
+                inf_part is S.ComplexInfinity
+                and s_part.is_finite
+                and not s_part.is_zero
+            ):
+                return S.ComplexInfinity
+            return s_part * inf_part
+
+    def _as_mpf_val(self, prec):
+        return mlib.fninf
+
+    def __hash__(self):
+        return super().__hash__()
+
+    def __eq__(self, other):
+        return other is S.NegativeIntInfinity
+
+    def __ne__(self, other):
+        return other is not S.NegativeIntInfinity
+
+    def __gt__(self, other):
+        if other is S.NegativeInfinity:
+            return sympy.true  # -sympy.oo < -int_oo
+        elif other is S.NegativeIntInfinity:
+            return sympy.false  # consistency with sympy.oo
+        else:
+            return sympy.false
+
+    def __ge__(self, other):
+        if other is S.NegativeInfinity:
+            return sympy.true  # -sympy.oo < -int_oo
+        elif other is S.NegativeIntInfinity:
+            return sympy.true  # consistency with sympy.oo
+        else:
+            return sympy.false
+
+    def __lt__(self, other):
+        if other is S.NegativeInfinity:
+            return sympy.false  # -sympy.oo < -int_oo
+        elif other is S.NegativeIntInfinity:
+            return sympy.false  # consistency with sympy.oo
+        else:
+            return sympy.true
+
+    def __le__(self, other):
+        if other is S.NegativeInfinity:
+            return sympy.false  # -sympy.oo < -int_oo
+        elif other is S.NegativeIntInfinity:
+            return sympy.true  # consistency with sympy.oo
+        else:
+            return sympy.true
+
+    @_sympifyit("other", NotImplemented)
+    def __mod__(self, other):
+        if not isinstance(other, Expr):
+            return NotImplemented
+        return S.NaN
+
+    __rmod__ = __mod__
+
+    def floor(self):
+        return self
+
+    def ceiling(self):
+        return self
+
+    def as_powers_dict(self):
+        return {S.NegativeOne: 1, S.IntInfinity: 1}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/printers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/printers.py
new file mode 100644
index 0000000000000000000000000000000000000000..996c68a8bb7a60824c3ad05f061f9226d8cdc733
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/printers.py
@@ -0,0 +1,477 @@
+import sys
+from typing import Optional
+
+import sympy
+from sympy.printing.precedence import PRECEDENCE, precedence
+from sympy.printing.str import StrPrinter
+
+
+INDEX_TYPE = "int64_t"
+
+
+# This printer contains rules that are supposed to be generic for both C/C++ and
+# Python
+class ExprPrinter(StrPrinter):
+    # override this so that _print_FloorDiv is used
+    printmethod = "_torch_sympystr"
+
+    def _print_Mul(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, "*", precedence(expr))
+
+    def _print_Add(self, expr: sympy.Expr, order: Optional[str] = None) -> str:
+        return self.stringify(expr.args, " + ", precedence(expr))
+
+    def _print_Relational(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, f" {expr.rel_op} ", precedence(expr))
+
+    def _print_BitwiseFn_bitwise_and(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, " & ", PRECEDENCE["BitwiseAnd"])
+
+    def _print_BitwiseFn_bitwise_or(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, " | ", PRECEDENCE["BitwiseOr"])
+
+    # NB: this is OK to put here, because Mod is only defined for positive
+    # numbers, and so across C/Python its behavior is consistent
+    def _print_Mod(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, " % ", PRECEDENCE["Atom"] - 0.5)
+
+    def _print_FloatTrueDiv(self, expr: sympy.Expr) -> str:
+        s = self.stringify(expr.args, " / ", PRECEDENCE["Atom"] - 0.5)
+        return f"({s})"
+
+    def _print_CleanDiv(self, expr: sympy.Expr) -> str:
+        return self._print_FloorDiv(expr)
+
+    def _print_Identity(self, expr: sympy.Expr) -> str:
+        return self._print(expr.args[0])
+
+    # This must be implemented because sympy will collect x * x into Pow(x, 2), without
+    # any explicit intervention.  We print it just like x * x, notably, we
+    # never generate sympy.Pow with floats.
+    #
+    # NB: this pow by natural, you should never have used builtin sympy.pow
+    # for FloatPow, and a symbolic exponent should be PowByNatural.  These
+    # means exp is guaranteed to be integer.
+    def _print_Pow(self, expr: sympy.Expr) -> str:
+        base, exp = expr.args
+        assert exp == int(exp), exp
+        exp = int(exp)
+        assert exp >= 0
+        if exp > 0:
+            return self.stringify([base] * exp, "*", PRECEDENCE["Mul"])
+        return "1"
+
+    # Explicit NotImplemented functions are to prevent default sympy printing
+    # behavior, which will just barf out ToFloat(...) to your IR.  The error
+    # message is better here because it tells you which printer class it needs
+    # to go in.
+
+    def _print_ToFloat(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(f"_print_ToFloat not implemented for {type(self)}")
+
+    def _print_Infinity(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(f"_print_Infinity not implemented for {type(self)}")
+
+    def _print_NegativeInfinity(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(
+            f"_print_NegativeInfinity not implemented for {type(self)}"
+        )
+
+    def _print_FloorDiv(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(f"_print_FloorDiv not implemented for {type(self)}")
+
+    def _print_PythonMod(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(f"_print_PythonMod not implemented for {type(self)}")
+
+    def _print_IntTrueDiv(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(f"_print_IntTrueDiv not implemented for {type(self)}")
+
+    def _print_PowByNatural(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(
+            f"_print_PowByNatural not implemented for {type(self)}"
+        )
+
+    def _print_FloatPow(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(f"_print_FloatPow not implemented for {type(self)}")
+
+    def _print_TruncToInt(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(f"_print_TruncToInt not implemented for {type(self)}")
+
+    def _print_RoundToInt(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(f"_print_RoundToInt not implemented for {type(self)}")
+
+    def _print_RoundDecimal(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(
+            f"_print_RoundDecimal not implemented for {type(self)}"
+        )
+
+    # NB: Some float operations are INTENTIONALLY not implemented for
+    # printers.  You can implement them as a quick unblock, but it is better
+    # to ask yourself why we haven't done this computation in the Tensor
+    # universe instead
+
+    def _print_TruncToFloat(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(
+            f"_print_TruncToFloat not implemented for {type(self)}"
+        )
+
+
+class PythonPrinter(ExprPrinter):
+    def _print_ToFloat(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        # NB: We use sym_float here because the printer is used for cache
+        # serialization, and cache guards get evaluated with SymInt to
+        # propagate guards to the parent ShapeEnv.  However, this comes at a
+        # runtime cost for guards involving float.  If this is unacceptable
+        # overhead, what you want to do is have two separate printers for
+        # SymInt, one for when the inputs are guaranteed to be int, and
+        # another for when they could be SymInt.
+        #
+        # NB: sym_min/sym_max also have this problem, but I chose not to fix
+        # those.
+        #
+        # See https://github.com/pytorch/pytorch/issues/142507 for more
+        # context.
+        return f"torch.sym_float({self._print(expr.args[0])})"
+
+    def _print_And(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, " and ", precedence(expr))
+
+    def _print_Or(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, " or ", precedence(expr))
+
+    def _print_ModularIndexing(self, expr: sympy.Expr) -> str:
+        x, div, mod = (
+            self.parenthesize(arg, PRECEDENCE["Atom"] - 0.5) for arg in expr.args
+        )
+        if div != "1":
+            x = f"({x} // {div})"
+        return f"({x} % {mod})"
+
+    def _print_Infinity(self, expr: sympy.Expr) -> str:
+        return "math.inf"
+
+    def _print_NegativeInfinity(self, expr: sympy.Expr) -> str:
+        return "-math.inf"
+
+    # WARNING: this is dangerous for Triton, which has C-style modulus
+    def _print_PythonMod(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, " % ", PRECEDENCE["Atom"] - 0.5)
+
+    # WARNING: this is dangerous for Triton, which has C-style modulus
+    def _print_FloorDiv(self, expr: sympy.Expr) -> str:
+        x, div = (self.parenthesize(arg, PRECEDENCE["Atom"] - 0.5) for arg in expr.args)
+        return f"{x} // {div}"
+
+    # WARNING: this is dangerous for Triton, when lhs, rhs > 2**53, Python
+    # does a special algorithm
+    def _print_IntTrueDiv(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, " / ", PRECEDENCE["Atom"] - 0.5)
+
+    def _helper_sqrt(self, expr: sympy.Expr) -> str:
+        return f"math.sqrt({self._print(expr)})"
+
+    def _print_OpaqueUnaryFn_sqrt(self, expr: sympy.Expr) -> str:
+        return self._helper_sqrt(expr.args[0])
+
+    def _print_FloatPow(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, " ** ", PRECEDENCE["Pow"])
+
+    # TODO: Not sure this works with Triton, even when base/exp are integral
+    def _print_PowByNatural(self, expr: sympy.Expr) -> str:
+        return self.stringify(expr.args, " ** ", PRECEDENCE["Pow"])
+
+    def _print_floor(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.floor({self._print(expr.args[0])})"
+
+    def _print_FloorToInt(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.floor({self._print(expr.args[0])})"
+
+    def _print_TruncToInt(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        # This also could have been int(), they'll do the same thing for float
+        return f"math.trunc({self._print(expr.args[0])})"
+
+    def _print_ceiling(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.ceil({self._print(expr.args[0])})"
+
+    def _print_CeilToInt(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.ceil({self._print(expr.args[0])})"
+
+    def _print_Abs(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"abs({self._print(expr.args[0])})"
+
+    # NB: It's expected that we've made explicit any promotion in the sympy
+    # expression, so it doesn't matter that Python max/min doesn't perform
+    # promotion
+    def _print_Max(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) >= 2
+        return f"max({', '.join(map(self._print, expr.args))})"
+
+    def _print_Min(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) >= 2
+        return f"min({', '.join(map(self._print, expr.args))})"
+
+    def _print_OpaqueUnaryFn_cos(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.cos({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_cosh(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.cosh({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_acos(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.acos({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_sin(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.sin({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_sinh(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.sinh({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_asin(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.asin({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_tan(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.tan({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_tanh(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.tanh({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_atan(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"math.atan({self._print(expr.args[0])})"
+
+    def _print_RoundToInt(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"round({self._print(expr.args[0])})"
+
+    def _print_RoundDecimal(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 2
+        number, ndigits = expr.args
+        assert isinstance(ndigits, sympy.Integer)
+        return f"round({self._print(number)}, {ndigits})"
+
+
+class CppPrinter(ExprPrinter):
+    def _print_Integer(self, expr: sympy.Expr) -> str:
+        return (
+            f"{int(expr)}LL" if sys.platform in ["darwin", "win32"] else f"{int(expr)}L"
+        )
+
+    def _print_Where(self, expr: sympy.Expr) -> str:
+        c, p, q = (
+            self.parenthesize(arg, PRECEDENCE["Atom"] - 0.5) for arg in expr.args
+        )
+        return f"{c} ? {p} : {q}"
+
+    def _print_ModularIndexing(self, expr: sympy.Expr) -> str:
+        x, div, mod = expr.args
+        x = self.doprint(x)
+        if div != 1:
+            div = self.doprint(div)
+            if expr.is_integer:
+                x = f"c10::div_floor_integer(static_cast({x}), static_cast({div}))"
+            else:
+                x = f"c10::div_floor_floating(static_cast({x}), static_cast({div}))"
+        mod = self.doprint(mod)
+        return f"(static_cast<{INDEX_TYPE}>({x}) % static_cast<{INDEX_TYPE}>({mod}))"
+
+    def _print_FloorDiv(self, expr: sympy.Expr) -> str:
+        x, div = expr.args
+        x = self.doprint(x)
+        div = self.doprint(div)
+        if expr.is_integer:
+            return f"c10::div_floor_integer(static_cast({x}), static_cast({div}))"
+        return f"c10::div_floor_floating(static_cast({x}), static_cast({div}))"
+
+    def _print_floor(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        r = f"std::floor({self._print(expr.args[0])})"
+        return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r
+
+    def _print_FloorToInt(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        r = f"std::floor({self._print(expr.args[0])})"
+        return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r
+
+    def _print_TruncToInt(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        r = f"std::trunc({self._print(expr.args[0])})"
+        return f"static_cast<{INDEX_TYPE}>({r})"
+
+    def _print_TruncToFloat(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::trunc({self._print(expr.args[0])})"
+
+    def _print_ToFloat(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"static_cast({self._print(expr.args[0])})"
+
+    def _print_PythonMod(self, expr: sympy.Expr) -> str:
+        x, div = expr.args
+        x = self.doprint(x)
+        div = self.doprint(div)
+        return f"c10::div_mod({x}, {div})"
+
+    def _print_IntTrueDiv(self, expr: sympy.Expr) -> str:
+        lhs, rhs = expr.args
+        # TODO: This is only accurate up to 2**53
+        return f"static_cast({self._print(lhs)}) / static_cast({self._print(rhs)})"
+
+    # TODO: PowByNatural: we need to implement our own int-int pow.  Do NOT
+    # use std::pow, that operates on floats
+    def _print_PowByNatural(self, expr: sympy.Expr) -> str:
+        raise NotImplementedError(
+            f"_print_PowByNatural not implemented for {type(self)}"
+        )
+
+    def _print_FloatPow(self, expr: sympy.Expr) -> str:
+        base, exp = expr.args
+        return f"std::pow({self._print(base)}, {self._print(exp)})"
+
+    def _print_Pow(self, expr: sympy.Expr) -> str:
+        # Uses float constants to perform FP div
+        base, exp = expr.args
+
+        if exp == 0.5 or exp == -0.5:
+            base = self._print(base)
+            return f"std::sqrt({base})" if exp == 0.5 else f"1.0/std::sqrt({base})"
+        if exp.is_integer:
+            exp = int(exp)
+            if exp > 0:
+                r = self.stringify([base] * exp, "*", PRECEDENCE["Mul"])
+            elif exp < -1:
+                r = (
+                    "1.0/("
+                    + self.stringify([base] * abs(exp), "*", PRECEDENCE["Mul"])
+                    + ")"
+                )
+            elif exp == -1:
+                r = "1.0/" + self._print(base)
+            else:  # exp == 0
+                r = "1.0"
+
+            return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r
+        else:
+            # TODO: float vs double
+            return f"std::pow({base}, {float(exp)})"
+
+    def _print_Rational(self, expr: sympy.Expr) -> str:
+        # Uses float constants to perform FP div
+        if expr.q == 1:
+            r = f"{expr.p}"
+        else:
+            r = f"{expr.p}.0/{expr.q}.0"
+        return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r
+
+    def _print_ceiling(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        r = f"std::ceil({self._print(expr.args[0])})"
+        return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r
+
+    def _print_CeilToInt(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        r = f"std::ceil({self._print(expr.args[0])})"
+        return f"static_cast<{INDEX_TYPE}>({r})" if expr.is_integer else r
+
+    def _print_Min(self, expr: sympy.Expr) -> str:
+        args = [self._print(a) for a in expr.args]
+        if len(args) == 2:
+            return f"std::min(static_cast<{INDEX_TYPE}>({args[0]}), static_cast<{INDEX_TYPE}>({args[1]}))"
+        else:
+            # Initializer list overload
+            il = "{" + ", ".join(args) + "}"
+            return f"std::min({il})"
+
+    def _print_Max(self, expr: sympy.Expr) -> str:
+        args = [self._print(a) for a in expr.args]
+        if len(args) == 2:
+            return f"std::max(static_cast<{INDEX_TYPE}>({args[0]}), static_cast<{INDEX_TYPE}>({args[1]}))"
+        else:
+            # Initializer list overload
+            il = "{" + ", ".join(args) + "}"
+            return f"std::max({il})"
+
+    def _print_Abs(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::abs({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_cos(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::cos({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_cosh(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::cosh({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_acos(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::acos({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_sin(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::sin({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_sinh(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::sinh({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_asin(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::asin({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_tan(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::tan({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_tanh(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::tanh({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_atan(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        return f"std::atan({self._print(expr.args[0])})"
+
+    def _print_OpaqueUnaryFn_sqrt(self, expr: sympy.Expr) -> str:
+        return f"std::sqrt({self._print(expr.args[0])})"
+
+    def _print_RoundToInt(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 1
+        # TODO: dispatch to llrint depending on index type
+        return f"std::lrint({self._print(expr.args[0])})"
+
+    def _print_RoundDecimal(self, expr: sympy.Expr) -> str:
+        assert len(expr.args) == 2
+        number, ndigits = expr.args
+        if number.is_integer:
+            # ndigits < 0 should have been filtered by the sympy function
+            assert ndigits < 0
+            raise ValueError(
+                f"For integer inputs, only non-negative ndigits are currently supported, but got {ndigits}."
+            )
+        number_str = self.parenthesize(number, PRECEDENCE["Mul"])
+        return f"static_cast(std::nearbyint(1e{ndigits} * {number_str}) * 1e{-ndigits})"
+
+    def _print_BooleanTrue(self, expr: sympy.Expr) -> str:
+        return "true"
+
+    def _print_BooleanFalse(self, expr: sympy.Expr) -> str:
+        return "false"
+
+    def _print_Infinity(self, expr: sympy.Expr) -> str:
+        return "std::numeric_limits::infinity()"
+
+    def _print_NegativeInfinity(self, expr: sympy.Expr) -> str:
+        return f"-{self._print_Infinity(expr)}"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/reference.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/reference.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c960e92f22310ecd8ebce22c6b6d3ec86095f50
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/reference.py
@@ -0,0 +1,581 @@
+# mypy: allow-untyped-defs
+import math
+import operator
+from typing import Union
+
+import sympy
+
+import torch
+from torch.utils._sympy.functions import (
+    _keep_float,
+    BitwiseFn_bitwise_and,
+    BitwiseFn_bitwise_or,
+    FloatPow,
+    FloatTrueDiv,
+    FloorDiv,
+    IntTrueDiv,
+    Max,
+    Min,
+    Mod,
+    OpaqueUnaryFn_exp,
+    OpaqueUnaryFn_log,
+    OpaqueUnaryFn_log2,
+    OpaqueUnaryFn_sqrt,
+    PowByNatural,
+    RoundDecimal,
+    RoundToInt,
+    ToFloat,
+    TruncToInt,
+)
+
+
+# The sympy interpretation of operators.  It will also sometimes work with
+# plain int/float, but if you do certain operations you will get out a
+# sympy.Basic in the end.  If you want the Python/FX traceable interpretation,
+# check PythonReferenceAnalysis.
+# NB: For magic methods this needs to use normal magic methods
+# so that test_magic_methods works
+class ReferenceAnalysis:
+    @staticmethod
+    def constant(c, dtype):
+        return sympy.sympify(c)
+
+    @staticmethod
+    def or_(a, b):
+        return a | b
+
+    @staticmethod
+    def and_(a, b):
+        return a & b
+
+    @staticmethod
+    def eq(a, b):
+        if isinstance(a, sympy.Expr) or isinstance(b, sympy.Expr):
+            return sympy.Eq(a, b)
+        return a == b
+
+    @classmethod
+    def ne(cls, a, b):
+        return cls.not_(cls.eq(a, b))
+
+    @staticmethod
+    def lt(a, b):
+        return a < b
+
+    @staticmethod
+    def gt(a, b):
+        return a > b
+
+    @staticmethod
+    def le(a, b):
+        return a <= b
+
+    @staticmethod
+    def ge(a, b):
+        return a >= b
+
+    @staticmethod
+    def not_(a):
+        assert not isinstance(a, bool)
+        return ~a
+
+    @staticmethod
+    def reciprocal(x):
+        return FloatTrueDiv(1.0, x)
+
+    @staticmethod
+    def square(x):
+        return PowByNatural(x, 2)
+
+    @staticmethod
+    def trunc_to_int(x, dtype):
+        return TruncToInt(x)
+
+    @staticmethod
+    def ceil_to_int(x, dtype):
+        return sympy.ceiling(x)
+
+    @staticmethod
+    def floor_to_int(x, dtype):
+        return sympy.floor(x)
+
+    @staticmethod
+    def floor(x):
+        return _keep_float(sympy.floor)(x)
+
+    @staticmethod
+    def ceil(x):
+        return _keep_float(sympy.ceiling)(x)
+
+    @staticmethod
+    def to_dtype(x, dtype):
+        if dtype == torch.float64:
+            return ToFloat(x)
+        raise NotImplementedError(f"to_dtype {dtype} NYI")
+
+    @staticmethod
+    def mod(x, y):
+        return Mod(x, y)
+
+    @staticmethod
+    def abs(x):
+        return abs(x)
+
+    @staticmethod
+    def neg(x):
+        return -x
+
+    @staticmethod
+    def truediv(a, b):
+        return FloatTrueDiv(a, b)
+
+    @staticmethod
+    def int_truediv(a, b):
+        return IntTrueDiv(a, b)
+
+    @staticmethod
+    def floordiv(a, b):
+        return FloorDiv(a, b)
+
+    @staticmethod
+    def truncdiv(a, b):
+        raise NotImplementedError("TODO: truncdiv")
+
+    @staticmethod
+    def add(a, b):
+        return _keep_float(operator.add)(a, b)
+
+    @classmethod
+    def sym_sum(cls, args):
+        return sympy.Add(*args)
+
+    @staticmethod
+    def mul(a, b):
+        return _keep_float(operator.mul)(a, b)
+
+    @staticmethod
+    def sub(a, b):
+        return _keep_float(operator.sub)(a, b)
+
+    @staticmethod
+    def exp(x):
+        return OpaqueUnaryFn_exp(x)
+
+    @staticmethod
+    def log(x):
+        return OpaqueUnaryFn_log(x)
+
+    @staticmethod
+    def log2(x):
+        return OpaqueUnaryFn_log2(x)
+
+    @staticmethod
+    def sqrt(x):
+        return OpaqueUnaryFn_sqrt(x)
+
+    @staticmethod
+    def pow(a, b):
+        return _keep_float(FloatPow)(a, b)
+
+    @staticmethod
+    def pow_by_natural(a, b):
+        return PowByNatural(a, b)
+
+    @staticmethod
+    def minimum(a, b):
+        return Min(a, b)
+
+    @staticmethod
+    def maximum(a, b):
+        return Max(a, b)
+
+    @staticmethod
+    def round_to_int(a, dtype):
+        return RoundToInt(a)
+
+    @staticmethod
+    def round_decimal(a, b):
+        return RoundDecimal(a, b)
+
+    @staticmethod
+    def bitwise_and(a, b):
+        return BitwiseFn_bitwise_and(a, b)
+
+    @staticmethod
+    def bitwise_or(a, b):
+        return BitwiseFn_bitwise_or(a, b)
+
+
+# Unlike ReferenceAnalysis, does NOT sympyify, instead, works with plain
+# Python types and is FX traceable.  Inheritance here is purely for code
+# sharing (TODO: considering splitting out a BaseReferenceAnalysis).
+class PythonReferenceAnalysis(ReferenceAnalysis):
+    @staticmethod
+    def constant(c, dtype):
+        if dtype is torch.int64:
+            return int(c)
+        elif dtype is torch.double:
+            return float(c)
+        elif dtype is torch.bool:
+            return bool(c)
+        else:
+            raise AssertionError(f"unrecognized dtype {dtype}")
+
+    @staticmethod
+    def not_(a):
+        return torch.sym_not(a)
+
+    @classmethod
+    def sym_sum(cls, args):
+        if len(args) == 0:
+            return 0
+        if len(args) == 1:
+            return args[0]
+        acc = cls.add(args[0], args[1])
+        for i in range(2, len(args)):
+            acc = cls.add(acc, args[i])
+        return acc
+
+    @staticmethod
+    def floordiv(a, b):
+        return a // b
+
+    @staticmethod
+    def mod(x, y):
+        return x % y
+
+    @staticmethod
+    def truncdiv(a, b):
+        return a / b
+
+    @staticmethod
+    def to_dtype(x, dtype):
+        if dtype == torch.float64:
+            return torch.sym_float(x)
+        raise NotImplementedError(f"to_dtype {dtype} NYI")
+
+    @staticmethod
+    def exp(x):
+        raise AssertionError("exp is not valid shape sympy expr")
+
+    @staticmethod
+    def log(x):
+        raise AssertionError("log is not valid shape sympy expr")
+
+    @staticmethod
+    def log2(x):
+        return torch._sym_log2(x)  # type: ignore[attr-defined]
+
+    @staticmethod
+    def sqrt(x):
+        return torch._sym_sqrt(x)  # type: ignore[attr-defined]
+
+    @staticmethod
+    def minimum(a, b):
+        return torch.sym_min(a, b)
+
+    @staticmethod
+    def maximum(a, b):
+        return torch.sym_max(a, b)
+
+    @staticmethod
+    def floor_to_int(x, dtype):
+        return math.floor(x)
+
+    @staticmethod
+    def ceil_to_int(x, dtype):
+        return math.ceil(x)
+
+    @staticmethod
+    def floor(x):
+        return float(math.floor(x))
+
+    @staticmethod
+    def ceil(x):
+        return float(math.ceil(x))
+
+    @staticmethod
+    def truediv(a, b):
+        return a / b
+
+    @staticmethod
+    def pow(a, b):
+        return a**b
+
+    @staticmethod
+    def pow_by_natural(a, b):
+        # Pray that safe_pow is not needed here lol.  In particular, this
+        # never participates in VR low/high ranges, so overflow should be
+        # unlikely
+        return a**b
+
+    @staticmethod
+    def round_to_int(a, dtype):
+        return round(a)
+
+    @staticmethod
+    def round_decimal(a, b):
+        return round(a, ndigits=b)
+
+    @staticmethod
+    def bitwise_and(a, b):
+        return a & b
+
+    @staticmethod
+    def bitwise_or(a, b):
+        return a | b
+
+
+# Like PythonReferenceAnalysis, but some export-unfriendly choices of
+# operators to make things faster
+class OptimizedPythonReferenceAnalysis(PythonReferenceAnalysis):
+    @staticmethod
+    def sym_sum(args):
+        return torch.sym_sum(args)
+
+
+def _to_dtype(x: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
+    return torch.ops.prims.convert_element_type.default(x, dtype)
+
+
+# Suppose we have some int/float arguments.  This diagram commutes:
+#
+#   int/float  -- PythonReferenceAnalysis.op -->  int/float
+#       |                                           |
+#       |                                           |
+#      torch.tensor(..., dtype=torch.int64/torch.float64)
+#       |                                           |
+#       V                                           V
+#    Tensor    -- TensorReferenceAnalysis.op -->  Tensor
+#
+# NB: int before and after must be representable in int64 (we will
+# insert guards accordingly.)
+#
+# This is guaranteed to be FX traceable with OpOverloads only.
+class TensorReferenceAnalysis:
+    # NB: This is actually dead, because with Proxy tracing the factory
+    # function isn't traced correctly.  Here for completeness.
+    @staticmethod
+    def constant(c, dtype):
+        d: Union[int, float, bool]
+        if dtype is torch.int64:
+            d = int(c)
+        elif dtype is torch.double:
+            d = float(c)
+        elif dtype is torch.bool:
+            d = bool(c)
+        else:
+            raise AssertionError(f"unrecognized dtype {dtype}")
+        return torch.ops.aten.scalar_tensor.default(d, dtype=dtype)
+
+    @staticmethod
+    def or_(a, b):
+        return torch.ops.aten.logical_or.default(a, b)
+
+    @staticmethod
+    def and_(a, b):
+        return torch.ops.aten.logical_and.default(a, b)
+
+    @staticmethod
+    def bitwise_and(a, b):
+        return torch.ops.aten.bitwise_and(a, b)
+
+    @staticmethod
+    def bitwise_or(a, b):
+        return torch.ops.aten.bitwise_or(a, b)
+
+    @staticmethod
+    def eq(a, b):
+        return torch.ops.aten.eq.Tensor(a, b)
+
+    @classmethod
+    def ne(cls, a, b):
+        return torch.ops.aten.ne.Tensor(a, b)
+
+    @staticmethod
+    def lt(a, b):
+        return torch.ops.aten.lt.Tensor(a, b)
+
+    @staticmethod
+    def gt(a, b):
+        return torch.ops.aten.gt.Tensor(a, b)
+
+    @staticmethod
+    def le(a, b):
+        return torch.ops.aten.le.Tensor(a, b)
+
+    @staticmethod
+    def ge(a, b):
+        return torch.ops.aten.ge.Tensor(a, b)
+
+    @staticmethod
+    def not_(a):
+        return torch.ops.aten.logical_not.default(a)
+
+    @staticmethod
+    def reciprocal(x):
+        return torch.ops.aten.reciprocal.default(x)
+
+    @staticmethod
+    def square(x):
+        # TODO: maybe composite implicit autograd doesn't work here?
+        return torch.ops.aten.square.default(x)
+
+    @staticmethod
+    def trunc_to_int(x, dtype):
+        return _to_dtype(torch.ops.aten.trunc.default(x), dtype)
+
+    @staticmethod
+    def ceil_to_int(x, dtype):
+        return _to_dtype(torch.ops.aten.ceil.default(x), dtype)
+
+    @staticmethod
+    def floor_to_int(x, dtype):
+        return _to_dtype(torch.ops.aten.floor.default(x), dtype)
+
+    @staticmethod
+    def floor(x):
+        return torch.ops.aten.floor.default(x)
+
+    @staticmethod
+    def ceil(x):
+        return torch.ops.aten.ceil.default(x)
+
+    @staticmethod
+    def to_dtype(x, dtype):
+        return _to_dtype(x, dtype)
+
+    @staticmethod
+    def mod(x, y):
+        # TODO: https://github.com/pytorch/pytorch/pull/133654
+        raise NotImplementedError(
+            "no C-style modulus operation available from frontend atm"
+        )
+
+    @staticmethod
+    def abs(x):
+        return torch.ops.aten.abs.default(x)
+
+    @staticmethod
+    def neg(x):
+        return torch.ops.aten.neg.default(x)
+
+    @staticmethod
+    def truediv(a, b):
+        return torch.ops.aten.true_divide.Tensor(a, b)
+
+    @staticmethod
+    def int_truediv(a, b):
+        raise NotImplementedError(
+            "Python int truediv difficult to implement in PyTorch atm"
+        )
+
+        # TODO: This is wrong, CPython has a custom implementation of true
+        # division that results in higher precision when the floats are
+        # sufficiently large.  Short term fix: add a guard here
+        return torch.ops.aten.true_divide.default(
+            _to_dtype(a, torch.float64), _to_dtype(b, torch.float64)
+        )
+
+    @staticmethod
+    def floordiv(a, b):
+        return torch.ops.aten.div.Tensor_mode(a, b, rounding_mode="floor")
+
+    @staticmethod
+    def truncdiv(a, b):
+        raise NotImplementedError(
+            "no C-style truncdiv operation available from frontend atm"
+        )
+
+    @staticmethod
+    def add(a, b):
+        return torch.ops.aten.add.Tensor(a, b)
+
+    @staticmethod
+    def mul(a, b):
+        return torch.ops.aten.mul.Tensor(a, b)
+
+    @staticmethod
+    def sub(a, b):
+        return torch.ops.aten.sub.Tensor(a, b)
+
+    @staticmethod
+    def exp(x):
+        return torch.ops.aten.exp.default(x)
+
+    @staticmethod
+    def log(x):
+        return torch.ops.aten.log.default(x)
+
+    @staticmethod
+    def log2(x):
+        return torch.ops.aten.log2.default(x)
+
+    @staticmethod
+    def sqrt(x):
+        return torch.ops.aten.sqrt.default(x)
+
+    @staticmethod
+    def sin(x):
+        return torch.ops.aten.sin.default(x)
+
+    @staticmethod
+    def cos(x):
+        return torch.ops.aten.cos.default(x)
+
+    @staticmethod
+    def tanh(x):
+        return torch.ops.aten.tanh.default(x)
+
+    @staticmethod
+    def sinh(x):
+        return torch.ops.aten.sinh.default(x)
+
+    @staticmethod
+    def cosh(x):
+        return torch.ops.aten.cosh.default(x)
+
+    @staticmethod
+    def tan(x):
+        return torch.ops.aten.tan.default(x)
+
+    @staticmethod
+    def acos(x):
+        return torch.ops.aten.acos.default(x)
+
+    @staticmethod
+    def atan(x):
+        return torch.ops.aten.atan.default(x)
+
+    @staticmethod
+    def asin(x):
+        return torch.ops.aten.asin.default(x)
+
+    @staticmethod
+    def pow(a, b):
+        return torch.ops.aten.pow.Tensor_Tensor(a, b)
+
+    @staticmethod
+    def pow_by_natural(a, b):
+        # NB: pow handles int x int fine
+        return torch.ops.aten.pow.Tensor_Tensor(a, b)
+
+    @staticmethod
+    def minimum(a, b):
+        return torch.ops.aten.minimum.default(a, b)
+
+    @staticmethod
+    def maximum(a, b):
+        return torch.ops.aten.maximum.default(a, b)
+
+    @staticmethod
+    def round_to_int(a, dtype):
+        return torch.ops.aten.round.default(a)
+
+    @staticmethod
+    def round_decimal(a, b):
+        raise NotImplementedError(
+            "round decimal doesn't support Tensor second argument atm"
+        )
+
+        # return torch.ops.aten.round.decimals(a, b)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/singleton_int.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/singleton_int.py
new file mode 100644
index 0000000000000000000000000000000000000000..0bac76121f8b65baf44d0a527f98e7300adf730c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/singleton_int.py
@@ -0,0 +1,96 @@
+# mypy: allow-untyped-defs
+import sympy
+from sympy.multipledispatch import dispatch
+
+
+__all__ = ["SingletonInt"]
+
+
+class SingletonInt(sympy.AtomicExpr):
+    # This is probably not super important unless we are in multiple dispatch
+    # situations with other more exotic Expr types.
+    _op_priority = 99999
+
+    def __new__(cls, *args, coeff=None, **kwargs):
+        instance = super().__new__(cls, *args, **kwargs)
+        return instance
+
+    # The semantics of this class should match that of NestedIntSymNodeImpl in
+    # c10/core/NestedIntSymNodeImpl.h
+    def __init__(self, val, *, coeff=1):
+        self._val = val
+        self._coeff = coeff
+        super().__init__()
+
+    # See NOTE [ Inequalities with nested int ]
+    def _eval_Eq(self, other):
+        if (
+            isinstance(other, SingletonInt)
+            and other._val == self._val
+            and self._coeff == other._coeff
+        ):
+            return sympy.true
+        else:
+            return sympy.false
+
+    # This is necessary so that calling expr.free_symbols on exprs that contain
+    # this Singleton does not error
+    @property
+    def free_symbols(self):
+        return set()
+
+    def __mul__(self, other):
+        if isinstance(other, SingletonInt):
+            raise ValueError(
+                "SingletonInt cannot be multiplied by another SingletonInt"
+            )
+        return SingletonInt(self._val, coeff=self._coeff * other)
+
+    def __rmul__(self, other):
+        if isinstance(other, SingletonInt):
+            raise ValueError(
+                "SingletonInt cannot be multiplied by another SingletonInt"
+            )
+        return SingletonInt(self._val, coeff=self._coeff * other)
+
+    # Make sure we promptly raise an error instead of falling back to building
+    # an expression tree. There are probably more ops, how can we be exhaustive?
+    def __add__(self, other):
+        raise NotImplementedError("NYI")
+
+    def __sub__(self, other):
+        raise NotImplementedError("NYI")
+
+    def __truediv__(self, other):
+        raise NotImplementedError("NYI")
+
+    def __floordiv__(self, other):
+        raise NotImplementedError("NYI")
+
+    def __mod__(self, other):
+        raise NotImplementedError("NYI")
+
+
+# See NOTE [ Inequalities with nested int ]
+@dispatch(sympy.Integer, SingletonInt)
+def _eval_is_ge(a, b):
+    if a < 2:
+        return sympy.false
+    raise ValueError("Symbolic SingletonInt: Relation is indeterminate")
+
+
+@dispatch(SingletonInt, sympy.Integer)  # type: ignore[no-redef]
+def _eval_is_ge(a, b):  # noqa: F811
+    if b <= 2:
+        return sympy.true
+    raise ValueError("Symbolic SingletonInt: Relation is indeterminate")
+
+
+@dispatch(SingletonInt, SingletonInt)  # type: ignore[no-redef]
+def _eval_is_ge(a, b):  # noqa: F811
+    if a._val == b._val:
+        if a._coeff >= b._coeff:
+            return sympy.true
+        else:
+            return sympy.false
+    raise ValueError("Symbolic SingletonInt: Relation is indeterminate")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/solve.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/solve.py
new file mode 100644
index 0000000000000000000000000000000000000000..334a023c0f36b883bcbd4ca71126b65e39916426
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/solve.py
@@ -0,0 +1,178 @@
+import logging
+from typing import Optional
+
+import sympy
+
+from torch.utils._sympy.functions import FloorDiv
+
+
+log = logging.getLogger(__name__)
+
+_MIRROR_REL_OP: dict[type[sympy.Basic], type[sympy.Rel]] = {
+    sympy.Eq: sympy.Eq,
+    sympy.Ne: sympy.Ne,
+    sympy.Ge: sympy.Le,
+    sympy.Gt: sympy.Lt,
+    sympy.Le: sympy.Ge,
+    sympy.Lt: sympy.Gt,
+}
+
+INEQUALITY_TYPES = (sympy.Gt, sympy.Ge, sympy.Lt, sympy.Le)
+
+
+def mirror_rel_op(type: type) -> Optional[type[sympy.Rel]]:
+    return _MIRROR_REL_OP.get(type, None)
+
+
+# Tries to simplify 'expr', so as to leave only 'thing' in the left-hand side.
+#
+# Returns a tuple of:
+#   1. The simplified expression
+#   2. The expression on the right-hand side
+#
+# Returns 'None' if it can't reach a state where the only thing in the left
+# hand side is 'thing'.
+#
+# 'trials': number of times 'try_solve' will try to isolate 'thing' to the
+# left-hand side.
+#
+# 'floordiv_inequality': flag to enable conversion of 'FloorDiv' into
+# inequalities.
+def try_solve(
+    expr: sympy.Basic,
+    thing: sympy.Basic,
+    trials: int = 5,
+    floordiv_inequality: bool = True,
+) -> Optional[tuple[sympy.Rel, sympy.Expr]]:
+    mirror = mirror_rel_op(type(expr))
+
+    # Ignore unsupported expressions:
+    #   - Those that are not relational operations
+    #   - Those that don't have a mirror (just avoiding unexpected classes)
+    if not isinstance(expr, sympy.Rel) or mirror is None:
+        log.debug("expression with unsupported type: %s", type(expr))
+        return None
+
+    lhs_has_thing = expr.lhs.has(thing)
+    rhs_has_thing = expr.rhs.has(thing)
+
+    # Give up when 'thing' appears on both sides of the relational expression.
+    # That is because, as is, we assume the thing we are trying to isolate is
+    # only on the right-hand side.
+    if lhs_has_thing and rhs_has_thing:
+        log.debug("thing (%s) found in both sides of expression: %s", thing, expr)
+        return None
+
+    # Try considering both LHS and RHS by mirroring the original expression:
+    # a < b ==> b > a
+    expressions = []
+
+    # Add each version of 'expr' if 'thing' is in its left-hand side.
+    if lhs_has_thing:
+        expressions.append(expr)
+    if rhs_has_thing:
+        expressions.append(mirror(expr.rhs, expr.lhs))
+
+    for e in expressions:
+        if e is None:
+            continue
+
+        assert isinstance(e, sympy.Rel)
+
+        for _ in range(trials):
+            trial = _try_isolate_lhs(e, thing, floordiv_inequality=floordiv_inequality)
+            # Stop if there was no change in this trial.
+            if trial == e:
+                break
+            e = trial  # type: ignore[assignment]
+
+        # Return if we were able to isolate 'thing' on the left-hand side.
+        if isinstance(e, sympy.Rel) and e.lhs == thing:
+            log.debug("solved: %s ---> %s", expr, e)
+            return e, e.rhs
+
+    return None
+
+
+def _try_isolate_lhs(
+    e: sympy.Basic, thing: sympy.Basic, floordiv_inequality: bool
+) -> sympy.Basic:
+    op = type(e)
+
+    if isinstance(e, sympy.Rel):
+        # Move any constants in the left-hand side to the right-hand side.
+        lhs_not_thing = (
+            sum(a for a in e.lhs.args if not a.has(thing))
+            if isinstance(e.lhs, sympy.Add)
+            else 0
+        )
+        e = op(e.lhs - lhs_not_thing, e.rhs - lhs_not_thing)  # type: ignore[attr-defined]
+
+    # Divide both sides by the factors that don't contain thing.
+    if isinstance(e, sympy.Rel) and isinstance(e.lhs, sympy.Mul):
+        lhs, rhs = e.args
+        other = sympy.Mul(*[a for a in lhs.args if not a.has(thing)])
+
+        # If we can't tell whether 'other' is negative or positive, we do nothing.
+        # That is because we don't know whether we have mirror the operation or not.
+        # We also divide only when we know 'rhs' is not zero.
+        if not (isinstance(e, INEQUALITY_TYPES) and other.is_negative is None) and not (
+            not isinstance(e, INEQUALITY_TYPES) and rhs.is_zero
+        ):
+            # Divide both sides by 'other'.
+            lhs = lhs / other
+            rhs = rhs / other
+
+            # If 'e' is an inequality and 'other' is negative, we have to
+            # mirror the expression.
+            if isinstance(e, INEQUALITY_TYPES) and other.is_negative:
+                op = mirror_rel_op(op)  # type: ignore[assignment]
+
+            assert op is not None
+            e = op(lhs, rhs)
+
+    ################################################################################
+    # left-hand side is FloorDiv
+    ################################################################################
+    #
+    # Given the expression: a // b op c
+    # where 'op' is a relational operation, these rules only work if:
+    #   - b > 0
+    #   - c is an integer
+    if (
+        floordiv_inequality
+        and isinstance(e, sympy.Rel)
+        and isinstance(e.lhs, FloorDiv)
+        and e.lhs.divisor.is_positive
+        and e.rhs.is_integer
+    ):
+        # a // b == expr
+        # => a >= (b * expr) and a < (b * (expr + 1))
+        if isinstance(e, sympy.Eq):
+            numerator, denominator = e.lhs.args
+            return sympy.And(
+                sympy.Ge(numerator, (e.rhs * denominator)),  # type: ignore[arg-type]
+                sympy.Lt(numerator, ((e.rhs + 1) * denominator)),  # type: ignore[arg-type]
+            )
+        # a // b != expr
+        # => a < (b * expr) or a >= (b * (expr + 1))
+        if isinstance(e, sympy.Ne):
+            numerator, denominator = e.lhs.args
+            return sympy.Or(
+                sympy.Lt(numerator, (e.rhs * denominator)),  # type: ignore[arg-type]
+                sympy.Ge(numerator, ((e.rhs + 1) * denominator)),  # type: ignore[arg-type]
+            )
+        # The transformations below only work if b is positive.
+        # Note: we only have this information for constants.
+        # a // b > expr  => a >= b * (expr + 1)
+        # a // b >= expr => a >= b * expr
+        if isinstance(e, (sympy.Gt, sympy.Ge)):
+            quotient = e.rhs if isinstance(e, sympy.Ge) else (e.rhs + 1)  # type: ignore[arg-type]
+            return sympy.Ge(e.lhs.args[0], (quotient * e.lhs.args[1]))  # type: ignore[arg-type]
+        # a // b < expr  => a < b * expr
+        # a // b <= expr => a < b * (expr + 1)
+        if isinstance(e, (sympy.Lt, sympy.Le)):
+            quotient = e.rhs if isinstance(e, sympy.Lt) else (e.rhs + 1)  # type: ignore[arg-type]
+            return sympy.Lt(e.lhs.args[0], (quotient * e.lhs.args[1]))  # type: ignore[arg-type]
+
+    return e
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/symbol.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/symbol.py
new file mode 100644
index 0000000000000000000000000000000000000000..de810498bbab5f46fbac6e070de510b5b620dcfd
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/symbol.py
@@ -0,0 +1,101 @@
+# mypy: allow-untyped-defs
+"""
+This file contains canonical definitions for our symbol naming conventions,
+across torch.fx.experimental.symbolic_shapes and torch._inductor.  The
+intention is:
+
+1. To make it easily greppable where all the sites we use a prefix are
+2. Make it possible to easily tell if we can introduce a new prefix without
+   introducing a conflict
+
+You can occasionally test if prefixes have been hardcoded by renaming prefixes
+in this file and seeing what breaks.
+"""
+
+from collections.abc import Iterable
+from enum import auto, Enum
+from typing import Union
+
+import sympy
+
+
+class SymT(Enum):
+    SIZE = auto()
+    FLOAT = auto()
+    UNBACKED_INT = auto()
+    UNBACKED_FLOAT = auto()
+    # Inductor: The intermediates in inner_fn tmp0, one generated per ops call.
+    # If one of these shows up in an indexing expression, that means an
+    # indirect load is happening.
+    TMP = auto()
+    # Inductor: Placeholder variable that is later replaced with TMP
+    INDIRECT = auto()
+    # Inductor: Some size expressions are replaced with a precomputed size ps0
+    # which is computed host side, and then directly reused in the kernel, so
+    # we don't repeatedly recompute it on device.
+    PRECOMPUTED_SIZE = auto()
+    # Inductor: An indexing variable i0 in loops IR which ranges over non-reduced
+    # dim in the loop
+    INDEX = auto()
+    # Inductor: A reduction indexing (r0, r1) variables in loops IR which ranges over
+    # reduced dim(s) in the loop
+    R0_INDEX = auto()
+    R1_INDEX = auto()
+    # Inductor: In templated kernels torch._inductor.kernel, we have a hook to
+    # store the final output and append epilogue fusions.  To do this, we must
+    # know what the indexes the outputs range over.  NB: These will also
+    # advertise as INDEX, this is... probably OK?
+    TEMPLATE_INDEX = auto()
+    # Inductor: iteration domain for blockIdx.x/blockIdx.y
+    XBLOCK = auto()
+    YBLOCK = auto()
+    ZBLOCK = auto()
+    # Inductor: this is used solely for dynamic_reshape_indexer
+    VIEW = auto()
+    # Alternate (non-modular) indexing used in halide kernels
+    HALIDE = auto()
+
+
+# Invariant: there must not be a prefix which is a prefix of another string,
+# as this introduces ambiguity
+prefix_str = {
+    SymT.SIZE: "s",  # integer
+    SymT.UNBACKED_INT: "u",  # integer
+    # Prefix z here is chosen to avoid false aliasing in symbol_is_type test
+    # DO NOT add a "z" type.  You also need to avoid conflicts on these
+    # prefixes but this is somewhat easier to manage
+    SymT.FLOAT: "zf",
+    SymT.UNBACKED_FLOAT: "zuf",
+    SymT.TMP: "tmp",
+    SymT.PRECOMPUTED_SIZE: "ps",
+    SymT.INDEX: "i",
+    SymT.R0_INDEX: "r0_",
+    SymT.R1_INDEX: "r1_",
+    SymT.TEMPLATE_INDEX: "idx",
+    SymT.XBLOCK: "x",
+    SymT.YBLOCK: "y",
+    SymT.ZBLOCK: "z",
+    SymT.INDIRECT: "indirect",  # false aliasing?
+    SymT.VIEW: "view",
+    SymT.HALIDE: "h",
+}
+
+
+def make_symbol(prefix: SymT, idx: int, **kwargs) -> sympy.Symbol:
+    # TODO: maybe put the assumptions here directly
+    return sympy.Symbol(f"{prefix_str[prefix]}{idx}", **kwargs)
+
+
+# This type is a little wider than it should be, because free_symbols says
+# that it contains Basic, rather than Symbol
+def symbol_is_type(sym: sympy.Basic, prefix: Union[SymT, Iterable[SymT]]) -> bool:
+    assert isinstance(sym, sympy.Symbol)
+    name_str = sym.name.lower()  # Match capitalized names like XBLOCK, RBLOCK
+    if isinstance(prefix, SymT):
+        return name_str.startswith(prefix_str[prefix])
+    else:
+        return name_str.startswith(tuple(prefix_str[p] for p in prefix))
+
+
+def free_symbol_is_type(e: sympy.Expr, prefix: Union[SymT, Iterable[SymT]]) -> bool:
+    return any(symbol_is_type(v, prefix) for v in e.free_symbols)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/value_ranges.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/value_ranges.py
new file mode 100644
index 0000000000000000000000000000000000000000..784f9e7ba05149f5aea812f4e3a856ff1cebbe30
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_sympy/value_ranges.py
@@ -0,0 +1,1052 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import dataclasses
+import functools
+import itertools
+import logging
+import math
+import operator
+from typing import (
+    Callable,
+    Generic,
+    Optional,
+    overload,
+    SupportsFloat,
+    TYPE_CHECKING,
+    TypeVar,
+    Union,
+)
+from typing_extensions import TypeGuard
+
+import sympy
+from sympy.logic.boolalg import Boolean as SympyBoolean, BooleanAtom
+
+import torch
+from torch._logging import LazyString
+from torch._prims_common import dtype_to_type
+
+from .functions import (
+    _keep_float,
+    FloatTrueDiv,
+    FloorDiv,
+    IntTrueDiv,
+    OpaqueUnaryFn_exp,
+    OpaqueUnaryFn_log,
+    OpaqueUnaryFn_log2,
+    OpaqueUnaryFn_sqrt,
+    PowByNatural,
+    RoundDecimal,
+    RoundToInt,
+    safe_pow,
+    ToFloat,
+    TruncToFloat,
+    TruncToInt,
+)
+from .interp import sympy_interp
+from .numbers import int_oo, IntInfinity, NegativeIntInfinity
+
+
+log = logging.getLogger(__name__)
+
+__all__ = ["ValueRanges", "bound_sympy"]
+
+_T = TypeVar("_T", sympy.Expr, SympyBoolean)
+
+
+class ValueRangeError(RuntimeError):
+    pass
+
+
+# Like sympify, but supports less stuff, and also ensures that direct
+# sympy expressions don't have free variables
+def simple_sympify(e):
+    if isinstance(e, bool):
+        return sympy.true if e else sympy.false
+    elif isinstance(e, int):
+        return sympy.Integer(e)
+    elif isinstance(e, float):
+        # infinity is special; we use it to bracket integers as well
+        if math.isinf(e):
+            return sympy.oo if e > 0 else -sympy.oo
+        return sympy.Float(e)
+    elif isinstance(e, sympy.Expr):
+        assert e.is_number, e
+        # NaNs can occur when doing things like 0 * sympy.oo, but it is better
+        # if the operator notices this and takes care of it, because sometimes
+        # the NaN is inappropriate (for example, for ints, the [-oo, oo] range
+        # should go to zero when multiplied with [0, 0])
+        assert e != sympy.nan
+        return e
+    elif isinstance(e, BooleanAtom):
+        return e
+    else:
+        raise AssertionError(f"not simple sympy type {type(e)}: {e}")
+
+
+# Sympy atomics only. Unlike <=, it also works on Sympy bools.
+def sympy_generic_le(lower, upper):
+    if isinstance(lower, sympy.Expr):
+        assert isinstance(upper, sympy.Expr)
+        # instead of lower <= upper, we do upper >= lower since upper is mostly int_oo
+        # and we have better code paths there.
+        return upper >= lower
+    else:
+        # only negative condition is True > False
+        assert isinstance(lower, SympyBoolean) and isinstance(upper, SympyBoolean), (
+            lower,
+            upper,
+        )
+        return not (lower and not upper)
+
+
+def vr_is_bool(vr: ValueRanges[_T]) -> TypeGuard[ValueRanges[SympyBoolean]]:
+    return vr.is_bool
+
+
+def vr_is_expr(vr: ValueRanges[_T]) -> TypeGuard[ValueRanges[sympy.Expr]]:
+    return not vr.is_bool
+
+
+ExprIn = Union[int, float, sympy.Expr]
+BoolIn = Union[bool, SympyBoolean]
+AllIn = Union[ExprIn, BoolIn]
+ExprFn = Callable[[sympy.Expr], sympy.Expr]
+ExprFn2 = Callable[[sympy.Expr, sympy.Expr], sympy.Expr]
+BoolFn = Callable[[SympyBoolean], SympyBoolean]
+BoolFn2 = Callable[[SympyBoolean, SympyBoolean], SympyBoolean]
+AllFn = Union[ExprFn, BoolFn]
+AllFn2 = Union[ExprFn2, BoolFn2]
+
+
+@dataclasses.dataclass(frozen=True)
+class ValueRanges(Generic[_T]):
+    if TYPE_CHECKING:
+        # ruff doesn't understand circular references but mypy does
+        ExprVR = ValueRanges[sympy.Expr]  # noqa: F821
+        BoolVR = ValueRanges[SympyBoolean]  # noqa: F821
+        AllVR = Union[ExprVR, BoolVR]
+
+    # Although the type signature here suggests you can pass any
+    # sympy expression, in practice the analysis here only works
+    # with constant sympy expressions
+    lower: _T
+    upper: _T
+    is_bool: bool
+    is_int: bool
+    is_float: bool
+
+    def __repr__(self) -> str:
+        return f"VR[{self.lower}, {self.upper}]"
+
+    @overload
+    def __init__(
+        self: ValueRanges[sympy.Expr],
+        lower: ExprIn,
+        upper: ExprIn,
+    ) -> None:
+        ...
+
+    @overload
+    def __init__(  # type: ignore[misc]
+        self: ValueRanges[SympyBoolean],
+        lower: BoolIn,
+        upper: BoolIn,
+    ) -> None:
+        ...
+
+    def __init__(self, lower: AllIn, upper: AllIn) -> None:
+        lower = simple_sympify(lower)
+        upper = simple_sympify(upper)
+        # TODO: when the bounds have free variables, this may be
+        # nontrivial to actually verify
+        try:
+            if not sympy_generic_le(lower, upper):
+                raise ValueRangeError(f"Invalid ranges [{lower}:{upper}]")
+        except TypeError as e:
+            raise TypeError(f"Could not compare {lower} <= {upper}") from e
+
+        is_bool_lower = isinstance(lower, SympyBoolean)
+        is_bool_upper = isinstance(upper, SympyBoolean)
+        assert is_bool_lower == is_bool_upper, (lower, upper)
+
+        # Warning: is_int/is_float is best effort.  We do pretty well in
+        # Dynamo, but in Inductor these attributes are often wrong because we
+        # are not very rigorous in dtype analysis.  This is also why we need
+        # the flexible analysis for is_int: sometimes a sympy.oo pops in for
+        # an integer bound. I would /like/ for us not to do this, but it's
+        # too hard to push the invariant through right now.
+        if isinstance(lower, sympy.Integer) and upper == sympy.oo:
+            upper = int_oo
+        if isinstance(upper, sympy.Integer) and lower == -sympy.oo:
+            lower = -int_oo
+        # NB: [-int_oo, -int_oo] and [int_oo, int_oo] are allowed
+        integer_types = (sympy.Integer, NegativeIntInfinity, IntInfinity)
+        is_int_lower = isinstance(lower, integer_types)
+        is_int_upper = isinstance(upper, integer_types)
+
+        # Because this is a frozen class
+        object.__setattr__(self, "lower", lower)
+        object.__setattr__(self, "upper", upper)
+        # Unlike bool/int in Python, we don't report bools are ints
+        #
+        # NB: is_bool_lower == is_bool_upper, so we only need to check one
+        object.__setattr__(self, "is_bool", is_bool_lower)
+        object.__setattr__(
+            self,
+            "is_int",
+            not self.is_bool and is_int_lower and is_int_upper,
+        )
+        """
+        # This assert is just impossible right now, too many sympy bugs
+        if self.is_int:
+            # NB: sympy will sometimes randomly lose the float-ness of zero,
+            # so we also need to account for that in the assertion here.
+            # See also https://github.com/sympy/sympy/issues/26620
+            assert isinstance(lower, sympy.Integer) or lower in [-sympy.oo, 0], (
+                lower,
+                upper,
+            )
+            assert isinstance(upper, sympy.Integer) or upper in [sympy.oo, 0], (lower, upper)
+        """
+        # NB: [-oo, oo] always advertises as float!
+        object.__setattr__(self, "is_float", not self.is_bool and not self.is_int)
+        assert self.is_bool or self.is_int or self.is_float, (lower, upper)
+
+    def boolify(self) -> ValueRanges[SympyBoolean]:
+        if vr_is_bool(self):
+            return self
+        elif self == ValueRanges.unknown():
+            return ValueRanges.unknown_bool()
+        else:
+            raise AssertionError(f"not bool like {self}")
+
+    def __contains__(self, x: AllIn) -> bool:
+        return ValueRanges.wrap(x).issubset(self)
+
+    def issubset(self, other):
+        if other is self.unknown_int():
+            return True
+        return sympy_generic_le(other.lower, self.lower) and sympy_generic_le(
+            self.upper, other.upper
+        )
+
+    def tighten(self, other) -> ValueRanges:
+        """Given two ValueRanges, returns their intersection"""
+        return self & other
+
+    # Intersection
+    @overload
+    def __and__(
+        self: ValueRanges[sympy.Expr],
+        other: ValueRanges[sympy.Expr],
+    ) -> ValueRanges[sympy.Expr]:
+        ...
+
+    @overload
+    def __and__(  # type: ignore[misc]
+        self: ValueRanges[SympyBoolean],
+        other: ValueRanges[SympyBoolean],
+    ) -> ValueRanges[SympyBoolean]:
+        ...
+
+    def __and__(self: AllVR, other: AllVR) -> AllVR:
+        if other in (ValueRanges.unknown(), ValueRanges.unknown_int()):
+            return self
+        if self in (ValueRanges.unknown(), ValueRanges.unknown_int()):
+            return other
+        assert self.is_bool == other.is_bool, (self, other)
+        assert self.is_int == other.is_int, (self, other)
+        assert self.is_float == other.is_float, (self, other)
+        if self.is_bool:
+            return ValueRanges(
+                sympy.Or(self.lower, other.lower), sympy.And(self.upper, other.upper)
+            )
+        else:
+            return ValueRanges(
+                sympy.Max(self.lower, other.lower), sympy.Min(self.upper, other.upper)
+            )
+
+    # Union
+    @overload
+    def __or__(
+        self: ValueRanges[sympy.Expr],
+        other: ValueRanges[sympy.Expr],
+    ) -> ValueRanges[sympy.Expr]:
+        ...
+
+    @overload
+    def __or__(  # type: ignore[misc]
+        self: ValueRanges[SympyBoolean],
+        other: ValueRanges[SympyBoolean],
+    ) -> ValueRanges[SympyBoolean]:
+        ...
+
+    def __or__(self: AllVR, other: AllVR) -> AllVR:
+        if ValueRanges.unknown() in (self, other):
+            return ValueRanges.unknown()
+        assert self.is_bool == other.is_bool, (self, other)
+        assert self.is_int == other.is_int, (self, other)
+        assert self.is_float == other.is_float, (self, other)
+        if self.is_bool:
+            return ValueRanges(
+                sympy.And(self.lower, other.lower), sympy.Or(self.upper, other.upper)
+            )
+        else:
+            return ValueRanges(
+                sympy.Min(self.lower, other.lower), sympy.Max(self.upper, other.upper)
+            )
+
+    def is_singleton(self) -> bool:
+        return self.lower == self.upper
+
+    @staticmethod
+    @functools.cache
+    def unknown() -> ValueRanges[sympy.Expr]:
+        return ValueRanges(-sympy.oo, sympy.oo)
+
+    @staticmethod
+    @functools.cache
+    def unknown_int() -> ValueRanges[sympy.Expr]:
+        return ValueRanges(-int_oo, int_oo)
+
+    @staticmethod
+    @functools.cache
+    def unknown_bool() -> ValueRanges[SympyBoolean]:
+        return ValueRanges(sympy.false, sympy.true)
+
+    @overload
+    @staticmethod
+    # work around the fact that bool and int overlap
+    def wrap(arg: Union[ExprIn, ExprVR]) -> ExprVR:  # type: ignore[overload-overlap]
+        ...
+
+    @overload
+    @staticmethod
+    def wrap(arg: Union[BoolIn, BoolVR]) -> BoolVR:  # type: ignore[misc]
+        ...
+
+    @staticmethod
+    def wrap(arg: Union[AllIn, AllVR]) -> AllVR:
+        if isinstance(arg, ValueRanges):
+            return arg
+        if isinstance(arg, float) and math.isnan(arg):
+            return ValueRanges.unknown()
+        # arg is either ExprIn or BoolIn, but we don't know it here
+        return ValueRanges(arg, arg)  # type: ignore[arg-type]
+
+    @staticmethod
+    def increasing_map(x: Union[ExprIn, ExprVR], fn: ExprFn) -> ExprVR:
+        """Increasing: x <= y => f(x) <= f(y)."""
+        x = ValueRanges.wrap(x)
+        return ValueRanges(fn(x.lower), fn(x.upper))
+
+    @overload
+    @staticmethod
+    def decreasing_map(x: Union[ExprIn, ExprVR], fn: ExprFn) -> ExprVR:
+        ...
+
+    @overload
+    @staticmethod
+    def decreasing_map(x: Union[BoolIn, BoolVR], fn: BoolFn) -> BoolVR:  # type: ignore[misc]
+        ...
+
+    @staticmethod
+    def decreasing_map(x: Union[AllIn, AllVR], fn: AllFn) -> AllVR:
+        """Decreasing: x <= y => f(x) >= f(y)."""
+        x = ValueRanges.wrap(x)
+        # consistently either Expr or Bool, but we don't know it here
+        return ValueRanges(fn(x.upper), fn(x.lower))  # type: ignore[arg-type]
+
+    @staticmethod
+    def monotone_map(x: Union[ExprIn, ExprVR], fn: ExprFn) -> ExprVR:
+        """It's increasing or decreasing."""
+        x = ValueRanges.wrap(x)
+        l = fn(x.lower)
+        u = fn(x.upper)
+        return ValueRanges(min(l, u), max(l, u))
+
+    @staticmethod
+    def convex_min_zero_map(x: Union[ExprIn, ExprVR], fn: ExprFn) -> ExprVR:
+        """Fn is convex and has a minimum at 0."""
+        x = ValueRanges.wrap(x)
+        if 0 in x:
+            upper = max(fn(x.lower), fn(x.upper))
+            upper = simple_sympify(upper)
+            if isinstance(upper, sympy.Float) or upper == sympy.oo:
+                return ValueRanges(0.0, upper)
+            return ValueRanges(0, upper)
+        return ValueRanges.monotone_map(x, fn)
+
+    @overload
+    @staticmethod
+    def coordinatewise_increasing_map(
+        x: Union[ExprIn, ExprVR],
+        y: Union[ExprIn, ExprVR],
+        fn: ExprFn2,
+    ) -> ExprVR:
+        ...
+
+    @overload
+    @staticmethod
+    def coordinatewise_increasing_map(  # type: ignore[misc]
+        x: Union[BoolIn, BoolVR],
+        y: Union[BoolIn, BoolVR],
+        fn: BoolFn2,
+    ) -> BoolVR:
+        ...
+
+    @staticmethod
+    def coordinatewise_increasing_map(
+        x: Union[AllIn, AllVR],
+        y: Union[AllIn, AllVR],
+        fn: AllFn2,
+    ) -> AllVR:
+        """
+        It's increasing on each coordinate.
+
+        Mathematically:
+        For every 1 <= i <= n and x_i <= y_i we have that
+        f(x1, .., xn) <= f(x1, , yi, ..., xn)
+        """
+        x, y = ValueRanges.wrap(x), ValueRanges.wrap(y)
+        return ValueRanges(
+            fn(x.lower, y.lower),  # type: ignore[arg-type]
+            fn(x.upper, y.upper),  # type: ignore[arg-type]
+        )
+
+    @classmethod
+    def coordinatewise_monotone_map(cls, x, y, fn):
+        """It's increasing or decreasing on each coordinate."""
+        x, y = cls.wrap(x), cls.wrap(y)
+        products = [
+            fn(a, b)
+            for a, b in itertools.product([x.lower, x.upper], [y.lower, y.upper])
+        ]
+        return ValueRanges(min(products), max(products))
+
+
+class SymPyValueRangeAnalysis:
+    """
+    It gives bounds on a SymPy operator given bounds on its arguments
+    See the function `bound_sympy` for a function that applies this logic to a full SymPy expression
+    """
+
+    @staticmethod
+    def constant(value, dtype):
+        if isinstance(value, ValueRanges):
+            assert value.is_singleton()
+            value = value.lower
+        # NB: value is NOT a sympy expression, it's a constant!
+        is_python = isinstance(value, (int, float, bool))
+        assert is_python or isinstance(
+            value, (BooleanAtom, sympy.Integer, sympy.Number)
+        )
+
+        # using nan makes subsequent computation throw, and for the purposes of optimization
+        # returning -math.inf - math.inf is equivalent to giving up
+        if isinstance(value, SupportsFloat) and math.isnan(value):
+            if dtype == torch.bool:
+                return ValueRanges.unknown_bool()
+            elif dtype.is_floating_point:
+                return ValueRanges.unknown()
+            else:
+                return ValueRanges.unknown_int()
+
+        if is_python:
+            type_ = dtype_to_type(dtype)
+            value = type_(value)
+        else:
+            # We do a type check on a best-effort basis
+            # We don't want to force a cast to sympy.Float if the value is Rational to avoid losing precision
+            if dtype == torch.bool:
+                assert isinstance(value, BooleanAtom)
+            elif dtype.is_floating_point:
+                assert not value.is_finite or value.is_real
+            else:
+                # dtype is intXX
+                assert value.is_integer
+
+        r = ValueRanges.wrap(value)
+        return r
+
+    @staticmethod
+    def to_dtype(a, dtype, src_dtype=None):
+        if dtype == torch.float64:
+            return ValueRanges.increasing_map(a, ToFloat)
+        elif dtype == torch.bool:
+            return ValueRanges.unknown_bool()
+        elif not dtype.is_floating_point:
+            return ValueRanges.unknown_int()
+        return ValueRanges.unknown()
+
+    @staticmethod
+    def trunc_to_int(a, dtype):
+        return ValueRanges.increasing_map(a, TruncToInt)
+
+    @staticmethod
+    def not_(a):
+        a = ValueRanges.wrap(a)
+        a = a.boolify()
+        assert a.is_bool
+        return ValueRanges.decreasing_map(a, sympy.Not)
+
+    @staticmethod
+    def or_(a, b):
+        return ValueRanges.coordinatewise_increasing_map(a, b, sympy.Or)
+
+    @staticmethod
+    def and_(a, b):
+        return ValueRanges.coordinatewise_increasing_map(a, b, sympy.And)
+
+    @staticmethod
+    def _bool_to_int(x):
+        if x.is_singleton():
+            return ValueRanges.wrap(sympy.Integer(1 if x.lower else 0))
+        else:
+            return ValueRanges(sympy.Integer(0), sympy.Integer(1))
+
+    @classmethod
+    def bitwise_and(cls, a, b):
+        a, b = ValueRanges.wrap(a), ValueRanges.wrap(b)
+        if a.is_bool and b.is_bool:
+            return cls.and_(a, b)
+        if a.is_bool:
+            a = cls._bool_to_int(a)
+        if b.is_bool:
+            b = cls._bool_to_int(b)
+        lower = min(a.lower, b.lower)
+        if lower < 0 and lower != -sympy.oo and lower != -int_oo:
+            # If both lower bounds are negative, then bits start like
+            # 1...10..., so the smallest possible value is 1...101...1.
+            # Thus, we need to find the next smallest power of 2 (inclusive).
+            try:
+                lower = -(1 << int(-lower - 1).bit_length())
+            except Exception:
+                lower = -int_oo
+        else:
+            lower = 0
+        return ValueRanges(lower, max(a.upper, b.upper))
+
+    @classmethod
+    def bitwise_or(cls, a, b):
+        a, b = ValueRanges.wrap(a), ValueRanges.wrap(b)
+        if a.is_bool and b.is_bool:
+            return cls.or_(a, b)
+        if a.is_bool:
+            a = cls._bool_to_int(a)
+        if b.is_bool:
+            b = cls._bool_to_int(b)
+        upper = max(a.upper, b.upper)
+        if upper == 0:
+            upper = 0
+        elif upper > 0 and upper != sympy.oo and upper != int_oo:
+            # If both upper bounds are positive, then the largest
+            # possible value is 01...1, so we need to find
+            # next largest power of 2 (exclusive), minus 1
+            try:
+                upper = (1 << int(upper).bit_length()) - 1
+            except Exception:
+                upper = int_oo
+        elif upper < 0:
+            upper = -1
+        return ValueRanges(min(a.lower, b.lower), upper)
+
+    @staticmethod
+    def eq(a, b):
+        a = ValueRanges.wrap(a)
+        b = ValueRanges.wrap(b)
+        if a.is_singleton() and b.is_singleton() and a.lower == b.lower:
+            return ValueRanges.wrap(sympy.true)
+        elif a.lower > b.upper or b.lower > a.upper:  # ranges disjoint
+            return ValueRanges.wrap(sympy.false)
+        return ValueRanges(sympy.false, sympy.true)
+
+    @classmethod
+    def ne(cls, a, b):
+        return cls.not_(cls.eq(a, b))
+
+    @classmethod
+    def identity(cls, a):
+        return ValueRanges.wrap(a)
+
+    @classmethod
+    def lt(cls, a, b):
+        a = ValueRanges.wrap(a)
+        b = ValueRanges.wrap(b)
+        assert a.is_bool == b.is_bool
+        if a.is_bool:
+            return cls.and_(cls.not_(a), b)
+        else:
+            if a.upper < b.lower:
+                return ValueRanges.wrap(sympy.true)
+            elif a.lower >= b.upper:
+                return ValueRanges.wrap(sympy.false)
+            return ValueRanges(sympy.false, sympy.true)
+
+    @classmethod
+    def gt(cls, a, b):
+        return cls.lt(b, a)
+
+    @classmethod
+    def le(cls, a, b):
+        return cls.not_(cls.gt(a, b))
+
+    @classmethod
+    def ge(cls, a, b):
+        return cls.not_(cls.lt(a, b))
+
+    @staticmethod
+    def add(a, b):
+        return ValueRanges.coordinatewise_increasing_map(
+            a, b, _keep_float(operator.add)
+        )
+
+    @classmethod
+    def mul(cls, a, b):
+        a = ValueRanges.wrap(a)
+        b = ValueRanges.wrap(b)
+
+        assert a.is_bool == b.is_bool
+        if a.is_bool:
+            return cls.and_(a, b)
+
+        def safe_mul(a, b):
+            # Make unknown() * wrap(0.0) == wrap(0.0)
+            if a == 0.0 or a == 0:
+                return a
+            elif b == 0.0 or b == 0:
+                return b
+            else:
+                return a * b
+
+        return ValueRanges.coordinatewise_monotone_map(a, b, _keep_float(safe_mul))
+
+    @staticmethod
+    def int_truediv(a, b):
+        a = ValueRanges.wrap(a)
+        b = ValueRanges.wrap(b)
+        if 0 in b or ((-int_oo in a or int_oo in a) and (-int_oo in b or int_oo in b)):
+            return ValueRanges.unknown()
+        else:
+            return ValueRanges.coordinatewise_monotone_map(
+                a, b, _keep_float(IntTrueDiv)
+            )
+
+    @staticmethod
+    def truediv(a, b):
+        a = ValueRanges.wrap(a)
+        b = ValueRanges.wrap(b)
+        if 0 in b or (
+            (-sympy.oo in a or sympy.oo in a) and (-sympy.oo in b or sympy.oo in b)
+        ):
+            return ValueRanges.unknown()
+        else:
+            return ValueRanges.coordinatewise_monotone_map(
+                a, b, _keep_float(FloatTrueDiv)
+            )
+
+    @staticmethod
+    def floordiv(a, b):
+        a = ValueRanges.wrap(a)
+        b = ValueRanges.wrap(b)
+        if 0 in b:
+            return ValueRanges.unknown_int()
+        products = []
+        for x, y in itertools.product([a.lower, a.upper], [b.lower, b.upper]):
+            r = FloorDiv(x, y)
+            if r is sympy.nan:
+                products.append((sympy.sign(x) * sympy.sign(y)) * int_oo)
+            else:
+                products.append(r)
+
+        return ValueRanges(min(products), max(products))
+
+    @classmethod
+    def mod(cls, x, y):
+        x = ValueRanges.wrap(x)
+        y = ValueRanges.wrap(y)
+        # nb. We implement C semantics
+
+        def c_mod(a, b):
+            ret = abs(a) % abs(b)
+            if a < 0:
+                ret *= -1
+            return ret
+
+        def c_div(a, b):
+            x = a / b
+            return sympy.Integer(x) if x.is_finite and x not in (int_oo, -int_oo) else x
+
+        if 0 in y:
+            return ValueRanges.unknown_int()
+        elif y.is_singleton():
+            y_val = abs(y.lower)
+            # If it wraps, we need to take the whole interval
+
+            # The function is locally linear if they are in the same class
+            if c_div(x.lower, y_val) == c_div(x.upper, y_val):
+                return ValueRanges.increasing_map(x, lambda u: c_mod(u, y_val))
+            if x.upper < 0:
+                # Negative case
+                return ValueRanges(-y_val + 1, 0)
+            elif x.lower > 0:
+                # Positive case
+                return ValueRanges(0, y_val - 1)
+            else:
+                # Mixed case
+                lower = max(-y_val + 1, x.lower)
+                upper = min(y_val - 1, x.upper)
+                return ValueRanges(lower, upper)
+        else:
+            # Too difficult, we bail out
+            upper = cls.abs(y).upper - 1
+            return ValueRanges(-upper, upper)
+
+    @classmethod
+    def modular_indexing(cls, a, b, c):
+        return cls.mod(cls.floordiv(a, b), c)
+
+    @classmethod
+    def is_non_overlapping_and_dense_indicator(cls, *args):
+        return ValueRanges.unknown_int()
+
+    @classmethod
+    def pow_by_natural(cls, a, b):
+        a = ValueRanges.wrap(a)
+        b = ValueRanges.wrap(b)
+        if a.is_singleton() and b.is_singleton():
+            return ValueRanges.wrap(safe_pow(a.lower, b.lower))
+        # NB: Exclude zero, because zero is special
+        elif a.lower >= 1:
+            # We should know that b >= 0 but we may have forgotten this fact due
+            # to replacements, so don't assert it, but DO clamp it to prevent
+            # degenerate problems
+            return ValueRanges.coordinatewise_increasing_map(
+                a, b & ValueRanges(0, int_oo), PowByNatural
+            )
+        elif b.is_singleton():
+            if b.lower % 2 == 0:
+                # x^n where n is even
+                return ValueRanges.convex_min_zero_map(
+                    a, lambda x: safe_pow(x, b.lower)
+                )
+            else:
+                # x^n where n is odd
+                return ValueRanges.increasing_map(a, lambda x: safe_pow(x, b.lower))
+        else:
+            # a is potentially negative, and we don't know if the exponent is
+            # even or odd.  So just conservatively set the upper and lower
+            # bound based on what the maximum absolute value could be, in both
+            # directions
+            max_base = max(a.upper, -a.lower)
+            return ValueRanges(
+                -(safe_pow(max_base, b.upper)), safe_pow(max_base, b.upper)
+            )
+
+    @classmethod
+    def pow(cls, a, b):
+        return ValueRanges.unknown()
+
+        # We could implement all this, but for floating point pow, is there
+        # really a point?
+        """
+        a = ValueRanges.wrap(a)
+        b = ValueRanges.wrap(b)
+
+        # Not implemented yet. It's a bit tricky
+        # If you want to implement it, compute the partial derivatives of a ** b
+        # and check the ranges where the function is increasing / decreasing
+        # Another non-tight way of doing this is defaulting to doing noting that for a > 0,  a ** b == exp(b * log(a))
+        # If this second option is implemented, by carefult about the types and possible infinities here and there.
+        if not b.is_singleton():
+            return ValueRanges.unknown()
+
+        b = b.lower
+        if a.is_singleton():
+            a = a.lower
+            r = a**b
+            if not r.is_finite:
+                return ValueRanges.unknown()
+            return ValueRanges.wrap(r)
+
+        if b == 0:
+            if not a.lower.is_finite:
+                return ValueRanges.unknown()
+            return ValueRanges.wrap(1.0)
+
+        if b < 0:
+            a = cls.reciprocal(a)
+            b = -b
+
+        if a == ValueRanges.unknown():
+            return ValueRanges.unknown()
+
+        # If the base is positive, then we're good, otherwise nothing's defined
+        if a.lower >= 0:
+            return ValueRanges.increasing_map(a, lambda x: x**b)
+        else:
+            return ValueRanges.unknown()
+        """
+
+    @staticmethod
+    def reciprocal(x):
+        """Needed as it's used in pow, but it won't appear on a SymPy expression"""
+        x = ValueRanges.wrap(x)
+        if 0 in x:
+            return ValueRanges.unknown()
+        else:
+            return ValueRanges.decreasing_map(x, lambda y: FloatTrueDiv(1.0, y))  # type: ignore[operator]
+
+    @staticmethod
+    def abs(x):
+        return ValueRanges.convex_min_zero_map(x, abs)
+
+    @staticmethod
+    def exp(x):
+        return ValueRanges.increasing_map(x, OpaqueUnaryFn_exp)
+
+    @staticmethod
+    def log(x):
+        x = ValueRanges.wrap(x)
+        if x.lower <= 0:
+            return ValueRanges.unknown()
+        return ValueRanges.increasing_map(x, OpaqueUnaryFn_log)
+
+    @staticmethod
+    def log2(x):
+        x = ValueRanges.wrap(x)
+        if x.lower <= 0:
+            return ValueRanges.unknown()
+        return ValueRanges.increasing_map(x, OpaqueUnaryFn_log2)
+
+    @classmethod
+    def minimum(cls, a, b):
+        return cls.min_or_max(a, b, sympy.Min)
+
+    @classmethod
+    def maximum(cls, a, b):
+        return cls.min_or_max(a, b, sympy.Max)
+
+    @staticmethod
+    def min_or_max(a, b, fn):
+        a = ValueRanges.wrap(a)
+        b = ValueRanges.wrap(b)
+        return ValueRanges.coordinatewise_increasing_map(a, b, fn)
+
+    @classmethod
+    def floor_to_int(cls, x, dtype):
+        return ValueRanges.increasing_map(x, sympy.functions.elementary.integers.floor)
+
+    @classmethod
+    def ceil_to_int(cls, x, dtype):
+        return ValueRanges.increasing_map(
+            x, sympy.functions.elementary.integers.ceiling
+        )
+
+    # I think these implementations are sound.  The hazard here is that sympy
+    # will carry out the floor/ceil at too high precision and then something
+    # bad will happen when we convert it to float.
+    #
+    # For truncation, the implementation is clearly sound, because the desired
+    # target float is always exactly representable, since you're just chopping
+    # off bits the mantissa.  But what about ceil/floor?
+    #
+    # The important constraint here is that we're not defining floor on
+    # arbitrary real numbers, only representable float numbers.  So we can
+    # take advantage of the fact that before we reach the first
+    # unrepresentable integer in floating point space, we have the range of
+    # numbers corresponding to exponent zero: all integers, with no fractional
+    # amounts.  floor/ceil is an identity operation in this case.  In the
+    # range below here, representable floating point numbers are spaced
+    # exactly 1/2 apart, and notably, both the floor/ceil are defined floating
+    # point numbers.  There is no "gap" as you step up to the next exponent.
+
+    @classmethod
+    def floor(cls, x):
+        return ValueRanges.increasing_map(
+            x, _keep_float(sympy.functions.elementary.integers.floor)
+        )
+
+    @classmethod
+    def ceil(cls, x):
+        return ValueRanges.increasing_map(
+            x, _keep_float(sympy.functions.elementary.integers.ceiling)
+        )
+
+    @classmethod
+    def round_decimal(cls, number, ndigits):
+        if not ndigits.is_singleton():
+            return ValueRanges.unknown()
+
+        ndigits = ndigits.lower
+        # We can't use functools.partial here since sympy doesn't support keyword arguments, but we have to bind
+        # the second parameter.
+        fn = lambda number: RoundDecimal(number, ndigits)  # type: ignore[misc, assignment]  # noqa: E731
+
+        return ValueRanges.increasing_map(number, fn)
+
+    @classmethod
+    def round_to_int(cls, number, dtype):
+        return ValueRanges.increasing_map(number, RoundToInt)
+
+    # It's used in some models on symints
+    @staticmethod
+    def sqrt(x):
+        x = ValueRanges.wrap(x)
+        if x.lower < 0:
+            return ValueRanges.unknown()
+        return ValueRanges.increasing_map(x, OpaqueUnaryFn_sqrt)
+
+    @staticmethod
+    def where(a, b, c):
+        b = ValueRanges.wrap(b)
+        c = ValueRanges.wrap(c)
+        a = a.boolify()
+        # We sometimes write unknown without specifying the type correctly
+        # In particular, we do that when initialising the bounds for loads in bounds.py
+        assert b.is_bool == c.is_bool or ValueRanges.unknown() in (b, c)
+        if b.is_bool:
+            return ValueRanges(sympy.And(b.lower, c.lower), sympy.Or(b.upper, c.upper))
+        else:
+            return ValueRanges(sympy.Min(b.lower, c.lower), sympy.Max(b.upper, c.upper))
+
+    # expr_cond_pair is used to represent a single (expr, condition) pair in piecewise.
+    # We just return the value range of the expression and its corresponding condition as a tuple
+    # and defer the analysis to piecewise
+    @staticmethod
+    def expr_cond_pair(a, b):
+        b = b.boolify()
+        return (a, b)
+
+    # piecewise function can be used to convert a SymBool to SymInt:
+    # int_expr = Piecewise((1, bool_expr), (0, True)), it evalutes to 1 when sym_bool is True and 0 otherwise.
+    #
+    # ranges is a sequence of (expr_range, condition_range) pairs. The range pair is constructed in expr_cond_pair.
+    # The ValueRange of Piecewise is just the union of all expr ranges whose condition expr can be True.
+    @staticmethod
+    def piecewise(*ranges):
+        init_range = None
+        for expr_range, cond_range in ranges:
+            if sympy.true in cond_range:
+                if init_range is None:
+                    init_range = expr_range
+                else:
+                    init_range = init_range | expr_range
+        return init_range
+
+    @staticmethod
+    def cos(x):
+        # TODO: We should tighten value ranges
+        # If input range span is pi + 2*pi*k, then output range is (-1, 1)
+        # otherwise the minimum of the value of the function on the extremes
+        return ValueRanges(-1.0, 1.0)
+
+    @staticmethod
+    def cosh(x):
+        return ValueRanges(0.0, sympy.oo)
+        """
+        x = ValueRanges.wrap(x)
+        if x.lower > 0:
+            return ValueRanges.increasing_map(x, OpaqueUnaryFn_cosh)
+        elif x.upper < 0:
+            return ValueRanges.decreasing_map(x, OpaqueUnaryFn_cosh)
+        return ValueRanges(0.0, sympy.oo)
+        """
+
+    @staticmethod
+    def sin(x):
+        # TODO: We should tighten value ranges
+        # See details on cos
+        return ValueRanges(-1.0, 1.0)
+
+    @staticmethod
+    def sinh(x):
+        # return ValueRanges.increasing_map(x, OpaqueUnaryFn_sinh)
+        return ValueRanges(-sympy.oo, sympy.oo)
+
+    @staticmethod
+    def tan(x):
+        return ValueRanges(-sympy.oo, sympy.oo)
+
+    @staticmethod
+    def tanh(x):
+        # return ValueRanges.increasing_map(x, OpaqueUnaryFn_tanh)
+        return ValueRanges(-sympy.oo, sympy.oo)
+
+    @staticmethod
+    def asin(x):
+        return ValueRanges(-sympy.oo, sympy.oo)
+        """
+        x = ValueRanges.wrap(x)
+        if -1 <= x.lower and x.upper <= 1:
+            return ValueRanges.increasing_map(x, OpaqueUnaryFn_asinh)
+        return ValueRanges.unknown()
+        """
+
+    @staticmethod
+    def acos(x):
+        return ValueRanges(-sympy.oo, sympy.oo)
+        """
+        x = ValueRanges.wrap(x)
+        if -1 <= x.lower and x.upper <= 1:
+            return ValueRanges.decreasing_map(x, OpaqueUnaryFn_acos)
+        return ValueRanges.unknown()
+        """
+
+    @staticmethod
+    def atan(x):
+        return ValueRanges(-sympy.oo, sympy.oo)
+        # return ValueRanges.increasing_map(x, OpaqueUnaryFn_atan)
+
+    @staticmethod
+    def trunc(x):
+        return ValueRanges.increasing_map(x, TruncToFloat)
+
+
+def bound_sympy(
+    expr: sympy.Expr, ranges: Optional[dict[sympy.Symbol, ValueRanges]] = None
+) -> ValueRanges:
+    log.debug(
+        "bound_sympy(%s)%s",
+        expr,
+        LazyString(
+            lambda: (
+                "\n"
+                + "\n".join(
+                    f"  {k}: {r}" for k, r in ranges.items() if k in expr.free_symbols
+                )
+                if ranges
+                else ""
+            )
+        ),
+    )
+    if isinstance(expr, sympy.Number):
+        return ValueRanges.wrap(expr)
+
+    ranges = ranges or {}
+
+    # If there's a tracing context, augment available constrained ranges.
+    context = torch._guards.TracingContext.try_get()
+    if context and context.fake_mode.shape_env:
+        if ranges:
+            ranges = {**context.fake_mode.shape_env.var_to_range, **ranges}
+        else:
+            ranges = context.fake_mode.shape_env.var_to_range
+
+    def missing_handler(s):
+        if s.is_integer:  # type: ignore[attr-defined]
+            if s.is_positive:  # type: ignore[attr-defined]
+                vr = ValueRanges(1, int_oo)
+            elif s.is_nonnegative:  # type: ignore[attr-defined]
+                vr = ValueRanges(0, int_oo)
+            else:
+                vr = ValueRanges.unknown_int()
+        else:
+            # Don't bother trying very hard here
+            vr = ValueRanges.unknown()
+        return vr
+
+    return sympy_interp(
+        SymPyValueRangeAnalysis, ranges, expr, missing_handler=missing_handler
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_thunk.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_thunk.py
new file mode 100644
index 0000000000000000000000000000000000000000..08cf6efc96fcffef154e8421905f570d1fdcda66
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_thunk.py
@@ -0,0 +1,28 @@
+from typing import Callable, Generic, Optional, TypeVar
+
+
+R = TypeVar("R")
+
+
+class Thunk(Generic[R]):
+    """
+    A simple lazy evaluation implementation that lets you delay
+    execution of a function.  It properly handles releasing the
+    function once it is forced.
+    """
+
+    f: Optional[Callable[[], R]]
+    r: Optional[R]
+
+    __slots__ = ["f", "r"]
+
+    def __init__(self, f: Callable[[], R]):
+        self.f = f
+        self.r = None
+
+    def force(self) -> R:
+        if self.f is None:
+            return self.r  # type: ignore[return-value]
+        self.r = self.f()
+        self.f = None
+        return self.r
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_traceback.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_traceback.py
new file mode 100644
index 0000000000000000000000000000000000000000..08aead4768182208a02fe215994434834d4a7450
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_traceback.py
@@ -0,0 +1,255 @@
+# mypy: allow-untyped-defs
+from types import TracebackType
+from typing import Optional
+import tempfile
+import traceback
+import contextlib
+import inspect
+import os.path
+
+# This file contains utilities for ensuring dynamically compile()'d
+# code fragments display their line numbers in backtraces.
+#
+# The constraints:
+#
+# - We don't have control over the user exception printer (in particular,
+#   we cannot assume the linecache trick will work, c.f.
+#   https://stackoverflow.com/q/50515651/23845 )
+#
+# - We don't want to create temporary files every time we compile()
+#   some code; file creation should happen lazily only at exception
+#   time.  Arguably, you *should* be willing to write out your
+#   generated Python code to file system, but in some situations
+#   (esp. library code) it would violate user expectation to write
+#   to the file system, so we try to avoid it.  In particular, we'd
+#   like to keep the files around, so users can open up the files
+#   mentioned in the trace; if the file is invisible, we want to
+#   avoid clogging up the filesystem.
+#
+#   If this is not a constraint for you, there is a substantially simpler
+#   way to implement the functionality in this PR: instead of using
+#   eval/exec directly, just always write a Python file to filesystem
+#   and compile that.
+#
+# - You have control over a context where the compiled code will get
+#   executed, so that we can interpose while the stack is unwinding
+#   (otherwise, we have no way to interpose on the exception printing
+#   process.)
+#
+# There are two things you have to do to make use of the utilities here:
+#
+# - When you compile your source code, you must save its string source
+#   in its f_globals under the magic name "__compile_source__"
+#
+# - Before running the compiled code, enter the
+#   report_compile_source_on_error() context manager.
+
+@contextlib.contextmanager
+def report_compile_source_on_error():
+    try:
+        yield
+    except Exception as exc:
+        tb = exc.__traceback__
+
+        # Walk the traceback, looking for frames that have
+        # source attached
+        stack = []
+        while tb is not None:
+            filename = tb.tb_frame.f_code.co_filename
+            source = tb.tb_frame.f_globals.get("__compile_source__")
+
+            if filename == "" and source is not None:
+                # What black magic are we doing here?  Intuitively, what
+                # we would like to do is overwrite the co_filename on any
+                # frames that were generated from exec/eval so that they
+                # point to a temporary file that has the actual line
+                # information, so Python's default error printer can print
+                # useful line information on it.
+                #
+                # Writing out the temporary file is easy.  But overwriting
+                # co_filename is not!  You can't modify the code object
+                # associated with a frame.  You can, however, reconstruct
+                # a traceback with entirely new frames from scratch, so that's
+                # what we do.  But there's another problem, which is how to
+                # make the frame?
+                #
+                # The black magic is we make a frankenstein frame and code
+                # object which resembles the original frame/code enough so
+                # that it will print properly under traceback and the default
+                # error printer, but IT IS NOT THE ORIGINAL FRAME (you
+                # couldn't, e.g., execute its code with different variables
+                # and expect it to work.)
+
+                # Don't delete the temporary file so the user can inspect it
+                # TODO: This creates a temporary file for every frame, but we
+                # technically only need one per distinct __compile_source__
+                with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix=".py") as f:
+                    f.write(source)
+                # Create a frame.  Python doesn't let you construct
+                # FrameType directly, so just make one with compile
+                frame = tb.tb_frame
+                code = compile('__inspect_currentframe()', f.name, 'eval')
+                code = code.replace(co_name=frame.f_code.co_name)
+                # Python 3.11 only
+                if hasattr(frame.f_code, 'co_linetable'):
+                    # We can't copy ALL of the metadata over, because you
+                    # can cause Python to segfault this way.  What exactly
+                    # do we need?  We need enough information for
+                    # traceback to be able to print the exception
+                    # correctly.  Code reading Lib/traceback.py reveals
+                    # that traceback calls code.co_positions() in order to
+                    # get the augmented line/col numbers.  Objects/codeobject.c,
+                    # specifically _PyCode_InitAddressRange, reveals that
+                    # this iterator is initialized from co_linetable and
+                    # co_firstfileno.  So copy these we must!
+                    code = code.replace(  # type: ignore[call-arg]
+                        co_linetable=frame.f_code.co_linetable,  # type: ignore[attr-defined]
+                        co_firstlineno=frame.f_code.co_firstlineno,  # type: ignore[attr-defined]
+                    )
+                fake_frame = eval(
+                    code,
+                    frame.f_globals,
+                    {
+                        **frame.f_locals,
+                        '__inspect_currentframe': inspect.currentframe
+                    }
+                )
+                fake_tb = TracebackType(
+                    None, fake_frame, tb.tb_lasti, tb.tb_lineno
+                )
+                stack.append(fake_tb)
+            else:
+                stack.append(tb)
+
+            tb = tb.tb_next
+
+        # Reconstruct the linked list
+        tb_next = None
+        for tb in reversed(stack):
+            tb.tb_next = tb_next
+            tb_next = tb
+
+        raise exc.with_traceback(tb_next)  # noqa: B904
+
+def shorten_filename(fn, *, base=None):
+    """Shorten a source filepath, with the assumption that torch/ subdirectories don't need to be shown to user."""
+    if base is None:
+        base = os.path.dirname(os.path.dirname(__file__))
+    # Truncate torch/foo.py to foo.py
+    try:
+        prefix = os.path.commonpath([fn, base])
+    except ValueError:
+        return fn
+    else:
+        return fn[len(prefix) + 1:]
+
+def format_frame(frame, *, base=None, line=False):
+    """
+    Format a FrameSummary in a short way, without printing full absolute path or code.
+
+    The idea is the result fits on a single line.
+    """
+    extra_line = ""
+    if line:
+        extra_line = f"{frame.line}  # "
+    return f"{extra_line}{shorten_filename(frame.filename, base=base)}:{frame.lineno} in {frame.name}"
+
+def format_traceback_short(tb):
+    """Format a TracebackType in a short way, printing only the inner-most frame."""
+    return format_frame(traceback.extract_tb(tb)[-1])
+
+class CapturedTraceback:
+    __slots__ = ['tb', 'skip']
+
+    def __init__(self, tb, skip=0):
+        self.tb = tb
+        self.skip = skip
+
+    def cleanup(self):
+        self.tb = None
+
+    def summary(self):
+        import torch._C._profiler
+
+        if self.tb is None:
+            # TODO: Maybe indicate that the traceback was elided?
+            return traceback.StackSummary()
+
+        return _extract_symbolized_tb(
+            torch._C._profiler.symbolize_tracebacks([self.tb])[0],
+            self.skip
+        )
+
+    def __getstate__(self):
+        return (None, {
+            'tb': None,  # TB is not pickleable
+            'skip': self.skip,
+        })
+
+    @staticmethod
+    def extract(*, script=False, cpp=False, skip=0):
+        """
+        Like traceback.extract_stack(), but faster (approximately 20x faster); it
+        is fast enough that you can unconditionally log stacks this way as part of
+        normal execution.  It returns a torch._C._profiler.CapturedTraceback
+        object that must be formatted specially with format_captured_tb.
+
+        By default, this only reports Python backtraces (like extract_stack).  You
+        can set the script/cpp kwargs to also turn on TorchScript/C++ trace
+        reporting.
+        """
+        import torch._C._profiler
+
+        if script or cpp:
+            assert skip == 0, "skip with script/cpp NYI"
+
+        return CapturedTraceback(
+            torch._C._profiler.gather_traceback(python=True, script=script, cpp=cpp),
+            # Elide extract() frame if we don't have script/cpp frames.  If
+            # we do have those frames, it doesn't work so force zero.
+            0 if script or cpp else skip + 1
+        )
+
+    def format(self):
+        """
+        Formats a single torch._C._profiler.CapturedTraceback into a list of
+        strings equivalent to the output of traceback.format_list.  Note that if
+        pass it CapturedTraceback with C++ traces,  it is better not to use this
+        function and use the batch formatting API format_captured_tbs to amortize
+        the cost of symbolization
+        """
+        return traceback.format_list(self.summary())
+
+    @staticmethod
+    def format_all(tbs):
+        """
+        Bulk version of CapturedTraceback.format.  Returns a list of list of strings.
+        """
+        import torch._C._profiler
+
+        # Directly populate tracebacks that already have cached summaries
+        rs: list[Optional[list[str]]] = []
+        delayed_idxs = []
+        for i, tb in enumerate(tbs):
+            if tb.tb is None:
+                rs.append([])
+            else:
+                rs.append(None)
+                delayed_idxs.append(i)
+
+        torch._C._profiler.symbolize_tracebacks([tbs[i].tb for i in delayed_idxs])
+        for i in delayed_idxs:
+            rs[i] = traceback.format_list(tbs[i].summary())
+
+        return rs
+
+
+def _extract_symbolized_tb(tb, skip):
+    """
+    Given a symbolized traceback from symbolize_tracebacks, return a StackSummary object of
+    pre-processed stack trace entries.
+    """
+    stack = traceback.StackSummary()
+    for f in reversed(tb[skip:]):
+        stack.append(traceback.FrameSummary(f['filename'], f['line'], f['name']))
+    return stack
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_triton.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_triton.py
new file mode 100644
index 0000000000000000000000000000000000000000..1609a3fe77c87085ef9b9917c5f4f6b70f03e600
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_triton.py
@@ -0,0 +1,135 @@
+# mypy: allow-untyped-defs
+import functools
+import hashlib
+
+
+@functools.lru_cache(None)
+def has_triton_package() -> bool:
+    try:
+        from triton.compiler.compiler import triton_key
+
+        return triton_key is not None
+    except ImportError:
+        return False
+    except RuntimeError:
+        return False
+
+
+@functools.lru_cache(None)
+def has_triton_tma():
+    if has_triton_package():
+        import torch
+
+        if (
+            torch.cuda.is_available()
+            and torch.cuda.get_device_capability() >= (9, 0)
+            and not torch.version.hip
+        ):
+            try:
+                from triton.tools.experimental_descriptor import (  # noqa: F401
+                    create_1d_tma_descriptor,
+                    create_2d_tma_descriptor,
+                )
+
+                return True
+            except ImportError:
+                pass
+
+    return False
+
+
+@functools.lru_cache(None)
+def has_triton_tma_device():
+    if has_triton_package():
+        import torch
+
+        if (
+            torch.cuda.is_available()
+            and torch.cuda.get_device_capability() >= (9, 0)
+            and not torch.version.hip
+        ):
+            try:
+                from triton.language.extra.cuda import (  # noqa: F401
+                    experimental_device_tensormap_create1d,
+                    experimental_device_tensormap_create2d,
+                )
+
+                return True
+            except ImportError:
+                pass
+
+    return False
+
+
+@functools.lru_cache(None)
+def has_triton() -> bool:
+    if not has_triton_package():
+        return False
+
+    from torch._dynamo.device_interface import get_interface_for_device
+
+    def cuda_extra_check(device_interface):
+        return device_interface.Worker.get_device_properties().major >= 7
+
+    def cpu_extra_check(device_interface):
+        import triton.backends
+
+        return "cpu" in triton.backends.backends
+
+    def _return_true(device_interface):
+        return True
+
+    triton_supported_devices = {
+        "cuda": cuda_extra_check,
+        "xpu": _return_true,
+        "cpu": cpu_extra_check,
+    }
+
+    def is_device_compatible_with_triton():
+        for device, extra_check in triton_supported_devices.items():
+            device_interface = get_interface_for_device(device)
+            if device_interface.is_available() and extra_check(device_interface):
+                return True
+        return False
+
+    return is_device_compatible_with_triton()
+
+
+@functools.lru_cache(None)
+def triton_backend():
+    from triton.compiler.compiler import make_backend
+    from triton.runtime.driver import driver
+
+    target = driver.active.get_current_target()
+    return make_backend(target)
+
+
+@functools.lru_cache(None)
+def triton_hash_with_backend():
+    from triton.compiler.compiler import triton_key
+
+    backend = triton_backend()
+    key = f"{triton_key()}-{backend.hash()}"
+
+    # Hash is upper case so that it can't contain any Python keywords.
+    return hashlib.sha256(key.encode("utf-8")).hexdigest().upper()
+
+
+def dtype_to_string(dtype):
+    if dtype.name.startswith("fp"):
+        suffix = "float" + dtype.name[2:]
+    elif dtype.name.startswith("bf"):
+        suffix = "bfloat" + dtype.name[2:]
+    else:
+        suffix = dtype.name
+    return "triton.language." + suffix
+
+
+def patch_triton_dtype_repr():
+    import triton
+
+    # Hack to get triton dtype repr to produce an evaluatable expression
+    # triton.language.float32 emits triton.language.fp32 which does not
+    # exist
+    # REMOVE when https://github.com/openai/triton/pull/3342 lands
+    triton.language.dtype.__repr__ = lambda self: dtype_to_string(self)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_typing_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_typing_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffb6b383e4e6b9506baa803ab5ac6613dfc9b387
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_typing_utils.py
@@ -0,0 +1,14 @@
+"""Miscellaneous utilities to aid with typing."""
+
+from typing import Optional, TypeVar
+
+
+# Helper to turn Optional[T] into T when we know None either isn't
+# possible or should trigger an exception.
+T = TypeVar("T")
+
+
+def not_none(obj: Optional[T]) -> T:
+    if obj is None:
+        raise TypeError("Invariant encountered: value was None when it should not be")
+    return obj
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_zip.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_zip.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7dd6445fabe98209d3294e933a3f1baca71bd64
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/_zip.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+import argparse
+import glob
+import os
+from pathlib import Path
+from zipfile import ZipFile
+
+# Exclude some standard library modules to:
+# 1. Slim down the final zipped file size
+# 2. Remove functionality we don't want to support.
+DENY_LIST = [
+    # Interface to unix databases
+    "dbm",
+    # ncurses bindings (terminal interfaces)
+    "curses",
+    # Tcl/Tk GUI
+    "tkinter",
+    "tkinter",
+    # Tests for the standard library
+    "test",
+    "tests",
+    "idle_test",
+    "__phello__.foo.py",
+    # importlib frozen modules. These are already baked into CPython.
+    "_bootstrap.py",
+    "_bootstrap_external.py",
+]
+
+strip_file_dir = ""
+
+
+def remove_prefix(text, prefix):
+    if text.startswith(prefix):
+        return text[len(prefix) :]
+    return text
+
+
+def write_to_zip(file_path, strip_file_path, zf, prepend_str=""):
+    stripped_file_path = prepend_str + remove_prefix(file_path, strip_file_dir + "/")
+    path = Path(stripped_file_path)
+    if path.name in DENY_LIST:
+        return
+    zf.write(file_path, stripped_file_path)
+
+
+def main() -> None:
+    global strip_file_dir
+    parser = argparse.ArgumentParser(description="Zip py source")
+    parser.add_argument("paths", nargs="*", help="Paths to zip.")
+    parser.add_argument(
+        "--install-dir", "--install_dir", help="Root directory for all output files"
+    )
+    parser.add_argument(
+        "--strip-dir",
+        "--strip_dir",
+        help="The absolute directory we want to remove from zip",
+    )
+    parser.add_argument(
+        "--prepend-str",
+        "--prepend_str",
+        help="A string to prepend onto all paths of a file in the zip",
+        default="",
+    )
+    parser.add_argument("--zip-name", "--zip_name", help="Output zip name")
+
+    args = parser.parse_args()
+
+    zip_file_name = args.install_dir + "/" + args.zip_name
+    strip_file_dir = args.strip_dir
+    prepend_str = args.prepend_str
+    zf = ZipFile(zip_file_name, mode="w")
+
+    for p in sorted(args.paths):
+        if os.path.isdir(p):
+            files = glob.glob(p + "/**/*.py", recursive=True)
+            for file_path in sorted(files):
+                # strip the absolute path
+                write_to_zip(
+                    file_path, strip_file_dir + "/", zf, prepend_str=prepend_str
+                )
+        else:
+            write_to_zip(p, strip_file_dir + "/", zf, prepend_str=prepend_str)
+
+
+if __name__ == "__main__":
+    main()  # pragma: no cover
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/backcompat/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/backcompat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a53076c90a6ed6cc90aa9d584be70d32b316360
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/backcompat/__init__.py
@@ -0,0 +1,22 @@
+# mypy: allow-untyped-defs
+from torch._C import _set_backcompat_broadcast_warn
+from torch._C import _get_backcompat_broadcast_warn
+from torch._C import _set_backcompat_keepdim_warn
+from torch._C import _get_backcompat_keepdim_warn
+
+
+class Warning:
+    def __init__(self, setter, getter):
+        self.setter = setter
+        self.getter = getter
+
+    def set_enabled(self, value):
+        self.setter(value)
+
+    def get_enabled(self):
+        return self.getter()
+
+    enabled = property(get_enabled, set_enabled)
+
+broadcast_warning = Warning(_set_backcompat_broadcast_warn, _get_backcompat_broadcast_warn)
+keepdim_warning = Warning(_set_backcompat_keepdim_warn, _get_backcompat_keepdim_warn)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/backcompat/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/backcompat/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a3bc424ed57fb1cacc8ae67fde02b70a21d51b08
Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/backcompat/__pycache__/__init__.cpython-310.pyc differ
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/backend_registration.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/backend_registration.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa687de37ea04b96cc09bfce6ba4d4c6ee775bb7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/backend_registration.py
@@ -0,0 +1,382 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.overrides import (
+    handle_torch_function,
+    has_torch_function_unary,
+)
+from torch._C import _rename_privateuse1_backend, _get_privateuse1_backend_name
+from typing import Optional, Union
+
+__all__ = ["rename_privateuse1_backend", "generate_methods_for_privateuse1_backend"]
+
+# TODO: Should use `torch._C._get_privateuse1_backend_name()` to get
+# renamed-backend name for `privateuse1`, but the func will cause an
+# error with torch.jit.script, so we use the global variable named
+# `_privateuse1_backend_name`.
+_privateuse1_backend_name = "privateuseone"
+
+def rename_privateuse1_backend(backend_name: str) -> None:
+    r"""
+    Rename the privateuse1 backend device to make it more convenient to use as a device name within PyTorch APIs.
+
+    The steps are:
+
+    (1) (In C++) implement kernels for various torch operations, and register them
+        to the PrivateUse1 dispatch key.
+    (2) (In python) call torch.utils.rename_privateuse1_backend("foo")
+
+    You can now use "foo" as an ordinary device string in python.
+
+    Note: this API can only be called once per process. Attempting to change
+    the external backend after it's already been set will result in an error.
+
+    Note(AMP): If you want to support AMP on your device, you can register a custom backend module.
+    The backend must register a custom backend module with ``torch._register_device_module("foo", BackendModule)``.
+    BackendModule needs to have the following API's:
+
+    (1) ``get_amp_supported_dtype() -> List[torch.dtype]``
+        get the supported dtypes on your "foo" device in AMP, maybe the "foo" device supports one more dtype.
+
+    Note(random): If you want to support to set seed for your device, BackendModule needs to have the following API's:
+
+    (1) ``_is_in_bad_fork() -> bool``
+        Return ``True`` if now it is in bad_fork, else return ``False``.
+
+    (2) ``manual_seed_all(seed int) -> None``
+        Sets the seed for generating random numbers for your devices.
+
+    (3) ``device_count() -> int``
+        Returns the number of "foo"s available.
+
+    (4) ``get_rng_state(device: Union[int, str, torch.device] = 'foo') -> Tensor``
+        Returns a list of ByteTensor representing the random number states of all devices.
+
+    (5) ``set_rng_state(new_state: Tensor, device: Union[int, str, torch.device] = 'foo') -> None``
+        Sets the random number generator state of the specified "foo" device.
+
+    And there are some common funcs:
+
+    (1) ``is_available() -> bool``
+        Returns a bool indicating if "foo" is currently available.
+
+    (2) ``current_device() -> int``
+        Returns the index of a currently selected device.
+
+    For more details, see https://pytorch.org/tutorials/advanced/extend_dispatcher.html#get-a-dispatch-key-for-your-backend
+    For an existing example, see https://github.com/bdhirsh/pytorch_open_registration_example
+
+    Example::
+
+        >>> # xdoctest: +SKIP("failing")
+        >>> torch.utils.rename_privateuse1_backend("foo")
+        # This will work, assuming that you've implemented the right C++ kernels
+        # to implement torch.ones.
+        >>> a = torch.ones(2, device="foo")
+
+    """
+    _rename_privateuse1_backend(backend_name)
+    global _privateuse1_backend_name
+    _privateuse1_backend_name = backend_name
+
+def _check_register_once(module, attr):
+    if hasattr(module, attr):
+        raise RuntimeError(f"The custom device module of {module} has already been registered with {attr}")
+
+
+def _normalization_device(custom_backend_name: str, device: Optional[Union[int, str, torch.device]] = None) -> int:
+    def _get_current_device_index():
+        _get_device_index = "current_device"
+        if hasattr(torch, custom_backend_name) and \
+                hasattr(getattr(torch, custom_backend_name), _get_device_index):
+            return getattr(getattr(torch, custom_backend_name), _get_device_index)()
+        else:
+            # The default device index is 0.
+            return 0
+
+    if device is None:
+        return _get_current_device_index()
+    # if isinstance(device, str), this means that the parameter passed in is in the string format "foo:0"
+    # convert str object to torch.device object, and then process it uniformly
+    elif isinstance(device, str):
+        device = torch.device(device)
+
+    # variable devcie can only be torch.device type or int type
+    if isinstance(device, torch.device):
+        if device.type != custom_backend_name:
+            raise RuntimeError(f"Invalid device, must be {custom_backend_name} device")
+        elif device.index is None:
+            device_idx = _get_current_device_index()
+        else:
+            device_idx = device.index
+    # if isinstance(device, int), we can take the index number directly
+    else:
+        device_idx = device
+    return device_idx
+
+
+def _generate_tensor_methods_for_privateuse1_backend(custom_backend_name: str) -> None:
+    @property  # type: ignore[misc]
+    def wrap_tensor_backend(self: torch.Tensor) -> bool:
+        if has_torch_function_unary(self):
+            # TODO mypy doesn't support @property, see: https://github.com/python/mypy/issues/6185
+            return handle_torch_function(wrap_tensor_backend.__get__, (self,), self)  # type: ignore[attr-defined]
+        return self.device.type == custom_backend_name
+
+    _check_register_once(torch.Tensor, f'is_{custom_backend_name}')
+    wrap_tensor_backend.fget.__name__ = f'is_{custom_backend_name}'  # type: ignore[attr-defined]
+    setattr(torch.Tensor, f'is_{custom_backend_name}', wrap_tensor_backend)
+
+    def wrap_tensor_to(self: torch.Tensor, device: Optional[Union[int, torch.device]] = None, non_blocking=False,
+                       **kwargs) -> torch.Tensor:
+        r"""Perform Tensor device conversion. Call the to operator implementation.
+
+        .. note::
+            If the ``self`` Tensor already
+            has the correct :class:`torch.device`, then ``self`` is returned.
+            Otherwise, the returned tensor is a copy of ``self`` with the desired :class:`torch.device`.
+
+        Args:
+            device (int, optional): if specified, all parameters will be copied to that device
+            non_blocking (bool): If ``True`` and the source is in pinned memory,
+                the copy will be asynchronous with respect to the host. Otherwise,
+                the argument has no effect.
+            **kwargs (dict): For compatibility, may contain the key ``memory_format`` argument.
+        """
+        if has_torch_function_unary(self):
+            return handle_torch_function(wrap_tensor_to, (self,), self, device=device, non_blocking=False, **kwargs)
+        device_idx = _normalization_device(custom_backend_name, device)
+        return self.to(device=torch.device(f'{custom_backend_name}:{device_idx}'), non_blocking=non_blocking, **kwargs)
+
+    _check_register_once(torch.Tensor, custom_backend_name)
+    wrap_tensor_to.__name__ = custom_backend_name
+    setattr(torch.Tensor, custom_backend_name, wrap_tensor_to)
+
+
+def _generate_module_methods_for_privateuse1_backend(custom_backend_name: str) -> None:
+    # Generate Module attributes and methods depends on Tensor methods,
+    # so we need to check whether Tensor methods is already registered.
+    if not hasattr(torch.Tensor, custom_backend_name):
+        raise RuntimeError(
+            f"Can not automatically generate {custom_backend_name}() method for torch.nn.Module."
+            f"Because torch.Tensor doesn't has the method {custom_backend_name}()."
+            f"For this error, you can try setting for_tensor=True.")
+
+    def wrap_module_to(self: torch.nn.modules.module.T,
+                       device: Optional[Union[int, torch.device]] = None) -> torch.nn.modules.module.T:
+        r"""Move all model parameters and buffers to the custom device.
+
+        This also makes associated parameters and buffers different objects. So
+        it should be called before constructing optimizer if the module will
+        live on device while being optimized.
+
+        .. note::
+            This method modifies the module in-place.
+
+        Args:
+            device (int, optional): if specified, all parameters will be copied to that device
+        """
+        return self._apply(lambda t: getattr(t, custom_backend_name)(device))
+
+    _check_register_once(torch.nn.Module, custom_backend_name)
+    setattr(torch.nn.Module, custom_backend_name, wrap_module_to)
+
+def _generate_packed_sequence_methods_for_privateuse1_backend(custom_backend_name: str) -> None:
+    # Generate PackedSequence Module attributes and methods depends on Tensor methods,
+    # so we need to check whether Tensor methods is already registered.
+    if not hasattr(torch.Tensor, f'is_{custom_backend_name}') or \
+       not hasattr(torch.Tensor, custom_backend_name):
+        raise RuntimeError(
+            f"Can not automatically generate is_{custom_backend_name}() or "
+            f"{custom_backend_name}() method for torch.nn.utils.rnn.PackedSequence."
+            f"Because torch.Tensor doesn't has the method is_{custom_backend_name}()"
+            f"or {custom_backend_name}()."
+            f"For this error, you can try setting for_tensor=True.")
+
+    @property  # type: ignore[misc]
+    def wrap_tensor_backend(self: torch.nn.utils.rnn.PackedSequence) -> bool:
+        return self.data.device.type == custom_backend_name
+
+    _check_register_once(torch.nn.utils.rnn.PackedSequence, f'is_{custom_backend_name}')
+    setattr(torch.nn.utils.rnn.PackedSequence, f'is_{custom_backend_name}', wrap_tensor_backend)
+
+    def wrap_module_to(self: torch.nn.utils.rnn.PackedSequence,
+                       *args, **kwargs) -> torch.nn.utils.rnn.PackedSequence:
+        r"""Move all model parameters and buffers to the custom device.
+
+        This also makes associated parameters and buffers different objects. So
+        it should be called before constructing optimizer if the module will
+        live on device while being optimized.
+
+        .. note::
+            This method modifies the module in-place.
+
+        Args:
+            device (int, optional): if specified, all parameters will be copied to that device
+        """
+        ex = torch.tensor((), dtype=self.data.dtype, device=self.data.device).to(*args, **kwargs)
+        if ex.device.type == custom_backend_name:
+            return self.to(*args, **kwargs)
+        kwargs.update({'device': custom_backend_name})
+        return self.to(*args, **kwargs)
+
+    _check_register_once(torch.nn.utils.rnn.PackedSequence, custom_backend_name)
+    setattr(torch.nn.utils.rnn.PackedSequence, custom_backend_name, wrap_module_to)
+
+def _generate_storage_methods_for_privateuse1_backend(custom_backend_name: str,
+                                                      unsupported_dtype: Optional[list[torch.dtype]] = None) -> None:
+    # Attribute is registered in the _StorageBase class
+    # and UntypedStorage obtains through inheritance.
+    @property  # type: ignore[misc]
+    def wrap_storage_backend(self: torch.storage._StorageBase) -> bool:
+        r"""Return the internal :class:`torch.UntypedStorage`."""
+        return self.device.type == custom_backend_name
+
+    _check_register_once(torch.storage._StorageBase, f'is_{custom_backend_name}')
+    setattr(torch.storage._StorageBase, f'is_{custom_backend_name}', wrap_storage_backend)
+
+    def wrap_storage_to(self, device=None, non_blocking=False):
+        r"""Return a copy of this object in custom device memory.
+
+        If this object is already in device memory and on the correct device, then
+        no copy is performed and the original object is returned.
+
+        Args:
+            device (int): The destination device id. Defaults to the current device.
+            non_blocking (bool): If ``True`` and the source is in pinned memory,
+            the copy will be asynchronous with respect to the host. Otherwise,
+            the argument has no effect.
+        """
+        # There should be a judgment related to storage device and a judgment related to storage type,
+        # but it depends on the extended function, so this part is temporarily omitted in the automatic generation.
+        device_idx = _normalization_device(custom_backend_name, device)
+
+        if getattr(self, f'is_{custom_backend_name}'):
+            # storage has already on expected device.
+            if self.get_device() == device_idx:
+                return self
+        # For sparse storage, custom need to extend the implementation by themselves.
+        if self.is_sparse:
+            raise RuntimeError(f"Can not support a sparse storage move to {custom_backend_name} backend")
+        # create untyped_storage and copy data
+        untyped_storage = torch.UntypedStorage(
+            self.size(), device=torch.device(f'{custom_backend_name}:{device_idx}')
+        )
+        untyped_storage.copy_(self, non_blocking)
+        return untyped_storage
+
+    _check_register_once(torch.storage._StorageBase, custom_backend_name)
+    setattr(torch.storage._StorageBase, custom_backend_name, wrap_storage_to)
+
+    # Register the corresponding attribute for the TypedStorage class.
+    # When the TypedStorage class is removed, the registration is also removed.
+
+    @property  # type: ignore[misc]
+    def wrap_typed_storage_backend(self: torch.storage.TypedStorage) -> bool:
+        torch.storage._warn_typed_storage_removal()
+        return self._untyped_storage.device.type == custom_backend_name
+
+    _check_register_once(torch.TypedStorage, f'is_{custom_backend_name}')
+    setattr(torch.storage.TypedStorage, f'is_{custom_backend_name}', wrap_typed_storage_backend)
+
+    def wrap_typed_storage_to(self: torch.storage.TypedStorage,
+                              device=None, non_blocking=False, **kwargs) -> torch.storage.TypedStorage:
+        torch.storage._warn_typed_storage_removal()
+        if unsupported_dtype and self.dtype in unsupported_dtype:
+            raise RuntimeError(f"Cannot create {custom_backend_name} storage "
+                               f"as {self.dtype} dtype is not supported by this backend")
+        custom_backend_storage: torch.UntypedStorage = getattr(
+            self._untyped_storage, custom_backend_name)(device, non_blocking, **kwargs)
+        return self._new_wrapped_storage(custom_backend_storage)
+
+    _check_register_once(torch.TypedStorage, custom_backend_name)
+    setattr(torch.TypedStorage, custom_backend_name, wrap_typed_storage_to)
+
+
+def generate_methods_for_privateuse1_backend(for_tensor: bool = True, for_module: bool = True,
+                                             for_packed_sequence: bool = True,
+                                             for_storage: bool = False,
+                                             unsupported_dtype: Optional[list[torch.dtype]] = None) -> None:
+    r"""
+    Automatically generate attributes and methods for the custom backend after rename privateuse1 backend.
+
+    In the default scenario, storage-related methods will not be generated automatically.
+
+    When you implement kernels for various torch operations, and register them to the PrivateUse1 dispatch key.
+    And call the function torch.rename_privateuse1_backend("foo") to rename your backend name.
+    At this point, you can easily register specific methods and attributes by calling this function.
+    Just like torch.Tensor.foo(), torch.Tensor.is_foo, torch.Storage.foo(), torch.Storage.is_foo.
+
+    Note: We recommend you use generic functions (check devices are equal or to(device=)).
+    We provide these methods for convenience only and they will be "monkey patched" onto the objects
+    and so will not be properly typed. For Storage methods generate, if you need to support sparse data storage,
+    you need to extend the implementation yourself.
+
+    Args:
+        for_tensor (bool): whether register related methods for torch.Tensor class.
+        for_module (bool): whether register related methods for torch.nn.Module class.
+        for_storage (bool): whether register related methods for torch.Storage class.
+        unsupported_dtype (List[torch.dtype]): takes effect only when the storage method needs to be generated,
+            indicating that the storage does not support the torch.dtype type.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("failing")
+        >>> torch.utils.rename_privateuse1_backend("foo")
+        >>> torch.utils.generate_methods_for_privateuse1_backend()
+        # Then automatically generate backend-related attributes and methods.
+        >>> a = torch.tensor(2).foo()
+        >>> a.is_foo
+        >>> hasattr(torch.nn.Module, 'foo')
+    """
+    custom_backend_name = _get_privateuse1_backend_name()
+
+    if for_tensor:
+        _generate_tensor_methods_for_privateuse1_backend(custom_backend_name)
+
+    if for_module:
+        _generate_module_methods_for_privateuse1_backend(custom_backend_name)
+
+    if for_storage:
+        _generate_storage_methods_for_privateuse1_backend(custom_backend_name, unsupported_dtype)
+
+    if for_packed_sequence:
+        _generate_packed_sequence_methods_for_privateuse1_backend(custom_backend_name)
+
+def _get_custom_mod_func(func_name: str):
+    r"""
+    Return the func named `func_name` defined in custom device module. If not defined,
+    return `None`. And the func is registered with `torch.utils.rename_privateuse1_backend('foo')`
+    and `torch._register_device_module('foo', BackendModule)`.
+    If the custom device module or the func is not defined, it will give warning or error message.
+    Args:
+        func_name (str): return the callable func named func_name defined in custom device module.
+    Example::
+        class DummyfooModule:
+            @staticmethod
+            def is_available():
+                return True
+            @staticmethod
+            def func_name(*args, **kwargs):
+                ....
+        torch.utils.rename_privateuse1_backend("foo")
+        torch._register_device_module("foo", DummyfooModule)
+        foo_is_available_func = torch.utils.backend_registration._get_custom_mod_func("is_available")
+        if foo_is_available_func:
+            foo_is_available = foo_is_available_func()
+        func_ = torch.utils.backend_registration._get_custom_mod_func("func_name")
+        if func_:
+            result = func_(*args, **kwargs)
+    Attention: This function is not meant to be used directly by users, which is why
+    it is marked as private. It is a convenience function for backend implementers to
+    more easily call the hooks into their backend extensions.
+    """
+    assert isinstance(func_name, str), f"func_name must be `str`, but got `{type(func_name)}`."
+    backend_name = _get_privateuse1_backend_name()
+    custom_device_mod = getattr(torch, backend_name, None)  # type: ignore[arg-type]
+    function = getattr(custom_device_mod, func_name, None)  # type: ignore[arg-type]
+    if custom_device_mod is None or function is None:
+        message = f'Try to call torch.{backend_name}.{func_name}. The backend must register a custom backend '
+        message += f"module with `torch._register_device_module('{backend_name}', BackendModule)`. And "
+        message += f"BackendModule needs to have the following API's:\n `{func_name}(*args, **kwargs)`. \n"
+        raise RuntimeError(message)
+    return function
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e814aaf4671ca35484c43bc38677849d02a81ec
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/__init__.py
@@ -0,0 +1,6 @@
+from torch.utils.benchmark.utils.common import *  # noqa: F403
+from torch.utils.benchmark.utils.timer import *  # noqa: F403
+from torch.utils.benchmark.utils.compare import *  # noqa: F403
+from torch.utils.benchmark.utils.fuzzer import *  # noqa: F403
+from torch.utils.benchmark.utils.valgrind_wrapper.timer_interface import *  # noqa: F403
+from torch.utils.benchmark.utils.sparse_fuzzer import *  # noqa: F403
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/blas_compare_setup.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/blas_compare_setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..1057037d169a44829adf257f1fb1e01b87ca3122
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/blas_compare_setup.py
@@ -0,0 +1,222 @@
+# mypy: allow-untyped-defs
+import collections
+import os
+import shutil
+import subprocess
+
+try:
+    # no type stub for conda command line interface
+    import conda.cli.python_api  # type: ignore[import]
+    from conda.cli.python_api import Commands as conda_commands
+except ImportError:
+    # blas_compare.py will fail to import these when it's inside a conda env,
+    # but that's fine as it only wants the constants.
+    pass
+
+
+WORKING_ROOT = "/tmp/pytorch_blas_compare_environments"
+MKL_2020_3 = "mkl_2020_3"
+MKL_2020_0 = "mkl_2020_0"
+OPEN_BLAS = "open_blas"
+EIGEN = "eigen"
+
+
+GENERIC_ENV_VARS = ("USE_CUDA=0", "USE_ROCM=0")
+BASE_PKG_DEPS = (
+    "cmake",
+    "hypothesis",
+    "ninja",
+    "numpy",
+    "pyyaml",
+    "setuptools",
+    "typing_extensions",
+)
+
+
+SubEnvSpec = collections.namedtuple(
+    "SubEnvSpec", (
+        "generic_installs",
+        "special_installs",
+        "environment_variables",
+
+        # Validate install.
+        "expected_blas_symbols",
+        "expected_mkl_version",
+    ))
+
+
+SUB_ENVS = {
+    MKL_2020_3: SubEnvSpec(
+        generic_installs=(),
+        special_installs=("intel", ("mkl=2020.3", "mkl-include=2020.3")),
+        environment_variables=("BLAS=MKL",) + GENERIC_ENV_VARS,
+        expected_blas_symbols=("mkl_blas_sgemm",),
+        expected_mkl_version="2020.0.3",
+    ),
+
+    MKL_2020_0: SubEnvSpec(
+        generic_installs=(),
+        special_installs=("intel", ("mkl=2020.0", "mkl-include=2020.0")),
+        environment_variables=("BLAS=MKL",) + GENERIC_ENV_VARS,
+        expected_blas_symbols=("mkl_blas_sgemm",),
+        expected_mkl_version="2020.0.0",
+    ),
+
+    OPEN_BLAS: SubEnvSpec(
+        generic_installs=("openblas",),
+        special_installs=(),
+        environment_variables=("BLAS=OpenBLAS",) + GENERIC_ENV_VARS,
+        expected_blas_symbols=("exec_blas",),
+        expected_mkl_version=None,
+    ),
+
+    # EIGEN: SubEnvSpec(
+    #     generic_installs=(),
+    #     special_installs=(),
+    #     environment_variables=("BLAS=Eigen",) + GENERIC_ENV_VARS,
+    #     expected_blas_symbols=(),
+    # ),
+}
+
+
+def conda_run(*args):
+    """Convenience method."""
+    stdout, stderr, retcode = conda.cli.python_api.run_command(*args)
+    if retcode:
+        raise OSError(f"conda error: {str(args)}  retcode: {retcode}\n{stderr}")
+
+    return stdout
+
+
+def main():
+    if os.path.exists(WORKING_ROOT):
+        print("Cleaning: removing old working root.")
+        shutil.rmtree(WORKING_ROOT)
+    os.makedirs(WORKING_ROOT)
+
+    git_root = subprocess.check_output(
+        "git rev-parse --show-toplevel",
+        shell=True,
+        cwd=os.path.dirname(os.path.realpath(__file__))
+    ).decode("utf-8").strip()
+
+    for env_name, env_spec in SUB_ENVS.items():
+        env_path = os.path.join(WORKING_ROOT, env_name)
+        print(f"Creating env: {env_name}: ({env_path})")
+        conda_run(
+            conda_commands.CREATE,
+            "--no-default-packages",
+            "--prefix", env_path,
+            "python=3",
+        )
+
+        print("Testing that env can be activated:")
+        base_source = subprocess.run(
+            f"source activate {env_path}",
+            shell=True,
+            capture_output=True,
+            check=False,
+        )
+        if base_source.returncode:
+            raise OSError(
+                "Failed to source base environment:\n"
+                f"  stdout: {base_source.stdout.decode('utf-8')}\n"
+                f"  stderr: {base_source.stderr.decode('utf-8')}"
+            )
+
+        print("Installing packages:")
+        conda_run(
+            conda_commands.INSTALL,
+            "--prefix", env_path,
+            *(BASE_PKG_DEPS + env_spec.generic_installs)
+        )
+
+        if env_spec.special_installs:
+            channel, channel_deps = env_spec.special_installs
+            print(f"Installing packages from channel: {channel}")
+            conda_run(
+                conda_commands.INSTALL,
+                "--prefix", env_path,
+                "-c", channel, *channel_deps
+            )
+
+        if env_spec.environment_variables:
+            print("Setting environment variables.")
+
+            # This does not appear to be possible using the python API.
+            env_set = subprocess.run(
+                f"source activate {env_path} && "
+                f"conda env config vars set {' '.join(env_spec.environment_variables)}",
+                shell=True,
+                capture_output=True,
+                check=False,
+            )
+            if env_set.returncode:
+                raise OSError(
+                    "Failed to set environment variables:\n"
+                    f"  stdout: {env_set.stdout.decode('utf-8')}\n"
+                    f"  stderr: {env_set.stderr.decode('utf-8')}"
+                )
+
+            # Check that they were actually set correctly.
+            actual_env_vars = subprocess.run(
+                f"source activate {env_path} && env",
+                shell=True,
+                capture_output=True,
+                check=True,
+            ).stdout.decode("utf-8").strip().splitlines()
+            for e in env_spec.environment_variables:
+                assert e in actual_env_vars, f"{e} not in envs"
+
+        print(f"Building PyTorch for env: `{env_name}`")
+        # We have to re-run during each build to pick up the new
+        # build config settings.
+        subprocess.run(
+            f"source activate {env_path} && "
+            f"cd {git_root} && "
+            "python setup.py install --cmake",
+            shell=True,
+            capture_output=True,
+            check=True,
+        )
+
+        print("Checking configuration:")
+        check_run = subprocess.run(
+            # Shameless abuse of `python -c ...`
+            f"source activate {env_path} && "
+            'python -c "'
+            "import torch;"
+            "from torch.utils.benchmark import Timer;"
+            "print(torch.__config__.show());"
+            "setup = 'x=torch.ones((128, 128));y=torch.ones((128, 128))';"
+            "counts = Timer('torch.mm(x, y)', setup).collect_callgrind(collect_baseline=False);"
+            "stats = counts.as_standardized().stats(inclusive=True);"
+            "print(stats.filter(lambda l: 'blas' in l.lower()))\"",
+            shell=True,
+            capture_output=True,
+            check=False,
+        )
+        if check_run.returncode:
+            raise OSError(
+                "Failed to set environment variables:\n"
+                f"  stdout: {check_run.stdout.decode('utf-8')}\n"
+                f"  stderr: {check_run.stderr.decode('utf-8')}"
+            )
+        check_run_stdout = check_run.stdout.decode('utf-8')
+        print(check_run_stdout)
+
+        for e in env_spec.environment_variables:
+            if "BLAS" in e:
+                assert e in check_run_stdout, f"PyTorch build did not respect `BLAS=...`: {e}"
+
+        for s in env_spec.expected_blas_symbols:
+            assert s in check_run_stdout
+
+        if env_spec.expected_mkl_version is not None:
+            assert f"- Intel(R) Math Kernel Library Version {env_spec.expected_mkl_version}" in check_run_stdout
+
+        print(f"Build complete: {env_name}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/compare.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/compare.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d797a5b0a2bfc7be3cecd13d4d1ad2ac4e52686
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/compare.py
@@ -0,0 +1,99 @@
+# mypy: allow-untyped-defs
+"""Example of Timer and Compare APIs:
+
+$ python -m examples.compare
+"""
+
+import pickle
+import sys
+import time
+
+import torch
+
+import torch.utils.benchmark as benchmark_utils
+
+
+class FauxTorch:
+    """Emulate different versions of pytorch.
+
+    In normal circumstances this would be done with multiple processes
+    writing serialized measurements, but this simplifies that model to
+    make the example clearer.
+    """
+    def __init__(self, real_torch, extra_ns_per_element):
+        self._real_torch = real_torch
+        self._extra_ns_per_element = extra_ns_per_element
+
+    def extra_overhead(self, result):
+        # time.sleep has a ~65 us overhead, so only fake a
+        # per-element overhead if numel is large enough.
+        numel = int(result.numel())
+        if numel > 5000:
+            time.sleep(numel * self._extra_ns_per_element * 1e-9)
+        return result
+
+    def add(self, *args, **kwargs):
+        return self.extra_overhead(self._real_torch.add(*args, **kwargs))
+
+    def mul(self, *args, **kwargs):
+        return self.extra_overhead(self._real_torch.mul(*args, **kwargs))
+
+    def cat(self, *args, **kwargs):
+        return self.extra_overhead(self._real_torch.cat(*args, **kwargs))
+
+    def matmul(self, *args, **kwargs):
+        return self.extra_overhead(self._real_torch.matmul(*args, **kwargs))
+
+
+def main():
+    tasks = [
+        ("add", "add", "torch.add(x, y)"),
+        ("add", "add (extra +0)", "torch.add(x, y + zero)"),
+    ]
+
+    serialized_results = []
+    repeats = 2
+    timers = [
+        benchmark_utils.Timer(
+            stmt=stmt,
+            globals={
+                "torch": torch if branch == "master" else FauxTorch(torch, overhead_ns),
+                "x": torch.ones((size, 4)),
+                "y": torch.ones((1, 4)),
+                "zero": torch.zeros(()),
+            },
+            label=label,
+            sub_label=sub_label,
+            description=f"size: {size}",
+            env=branch,
+            num_threads=num_threads,
+        )
+        for branch, overhead_ns in [("master", None), ("my_branch", 1), ("severe_regression", 5)]
+        for label, sub_label, stmt in tasks
+        for size in [1, 10, 100, 1000, 10000, 50000]
+        for num_threads in [1, 4]
+    ]
+
+    for i, timer in enumerate(timers * repeats):
+        serialized_results.append(pickle.dumps(
+            timer.blocked_autorange(min_run_time=0.05)
+        ))
+        print(f"\r{i + 1} / {len(timers) * repeats}", end="")
+        sys.stdout.flush()
+    print()
+
+    comparison = benchmark_utils.Compare([
+        pickle.loads(i) for i in serialized_results
+    ])
+
+    print("== Unformatted " + "=" * 80 + "\n" + "/" * 95 + "\n")
+    comparison.print()
+
+    print("== Formatted " + "=" * 80 + "\n" + "/" * 93 + "\n")
+    comparison.trim_significant_figures()
+    comparison.colorize()
+    comparison.print()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/fuzzer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/fuzzer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee2c9f9c04ed10b3dac5aa89d76b9d060421b664
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/fuzzer.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+"""Example of the Timer and Fuzzer APIs:
+
+$ python -m examples.fuzzer
+"""
+
+import sys
+
+import torch.utils.benchmark as benchmark_utils
+
+
+def main():
+    add_fuzzer = benchmark_utils.Fuzzer(
+        parameters=[
+            [
+                benchmark_utils.FuzzedParameter(
+                    name=f"k{i}",
+                    minval=16,
+                    maxval=16 * 1024,
+                    distribution="loguniform",
+                ) for i in range(3)
+            ],
+            benchmark_utils.FuzzedParameter(
+                name="d",
+                distribution={2: 0.6, 3: 0.4},
+            ),
+        ],
+        tensors=[
+            [
+                benchmark_utils.FuzzedTensor(
+                    name=name,
+                    size=("k0", "k1", "k2"),
+                    dim_parameter="d",
+                    probability_contiguous=0.75,
+                    min_elements=64 * 1024,
+                    max_elements=128 * 1024,
+                ) for name in ("x", "y")
+            ],
+        ],
+        seed=0,
+    )
+
+    n = 250
+    measurements = []
+    for i, (tensors, tensor_properties, _) in enumerate(add_fuzzer.take(n=n)):
+        x, x_order = tensors["x"], str(tensor_properties["x"]["order"])
+        y, y_order = tensors["y"], str(tensor_properties["y"]["order"])
+        shape = ", ".join(tuple(f'{i:>4}' for i in x.shape))
+
+        description = "".join([
+            f"{x.numel():>7} | {shape:<16} | ",
+            f"{'contiguous' if x.is_contiguous() else x_order:<12} | ",
+            f"{'contiguous' if y.is_contiguous() else y_order:<12} | ",
+        ])
+
+        timer = benchmark_utils.Timer(
+            stmt="x + y",
+            globals=tensors,
+            description=description,
+        )
+
+        measurements.append(timer.blocked_autorange(min_run_time=0.1))
+        measurements[-1].metadata = {"numel": x.numel()}
+        print(f"\r{i + 1} / {n}", end="")
+        sys.stdout.flush()
+    print()
+
+    # More string munging to make pretty output.
+    print(f"Average attempts per valid config: {1. / (1. - add_fuzzer.rejection_rate):.1f}")
+
+    def time_fn(m):
+        return m.median / m.metadata["numel"]
+    measurements.sort(key=time_fn)
+
+    template = f"{{:>6}}{' ' * 19}Size    Shape{' ' * 13}X order        Y order\n{'-' * 80}"
+    print(template.format("Best:"))
+    for m in measurements[:15]:
+        print(f"{time_fn(m) * 1e9:>4.1f} ns / element     {m.description}")
+
+    print("\n" + template.format("Worst:"))
+    for m in measurements[-15:]:
+        print(f"{time_fn(m) * 1e9:>4.1f} ns / element     {m.description}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/op_benchmark.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/op_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdf3a7853d73783b65f3bfe9885a919e83f91c8f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/op_benchmark.py
@@ -0,0 +1,105 @@
+# mypy: allow-untyped-defs
+"""Example use of Timer and op fuzzers to measure kernel performance.
+
+$ python -m examples.op_benchmark
+"""
+
+import numpy as np
+import torch
+
+from torch.utils.benchmark import Timer
+from torch.utils.benchmark.op_fuzzers.binary import BinaryOpFuzzer
+from torch.utils.benchmark.op_fuzzers.unary import UnaryOpFuzzer
+import operator
+
+
+_MEASURE_TIME = 1.0
+
+
+def assert_dicts_equal(dict_0, dict_1):
+    """Builtin dict comparison will not compare numpy arrays.
+    e.g.
+        x = {"a": np.ones((2, 1))}
+        x == x  # Raises ValueError
+    """
+    assert set(dict_0.keys()) == set(dict_0.keys())
+    assert all(np.all(v == dict_1[k]) for k, v in dict_0.items() if k != "dtype")
+
+
+def run(n, stmt, fuzzer_cls):
+    float_iter = fuzzer_cls(seed=0, dtype=torch.float32).take(n)
+    int_iter = fuzzer_cls(seed=0, dtype=torch.int32).take(n)
+    raw_results = []
+    for i, (float_values, int_values) in enumerate(zip(float_iter, int_iter)):
+        float_tensors, float_tensor_params, float_params = float_values
+        int_tensors, int_tensor_params, int_params = int_values
+
+        # This benchmark assumes that the two fuzzers generate identically
+        # sized and strided Tensors, since the same seed is used.
+        assert_dicts_equal(float_params, int_params)
+        assert_dicts_equal(float_tensor_params["x"], int_tensor_params["x"])
+
+        float_measurement, int_measurement = (
+            Timer(
+                stmt,
+                globals=tensors,
+            ).blocked_autorange(min_run_time=_MEASURE_TIME)
+            for tensors in (float_tensors, int_tensors)
+        )
+
+        descriptions = []
+        for name in float_tensors:
+            shape_str = "(" + ", ".join([
+                f"2 ** {int(np.log2(i))}"
+                if 2 ** int(np.log2(i)) == i and i > 1
+                else str(i)
+                for i in float_tensors[name].shape
+            ]) + ")"
+            order = float_tensor_params[name]["order"]
+            order_str = ("" if all(order == np.arange(len(order))) else str(tuple(order)))
+            steps = float_tensor_params[name]["steps"]
+            steps_str = str(steps) if sum(steps) > len(steps) else ""
+            descriptions.append((name, shape_str, order_str, steps_str))
+        raw_results.append((float_measurement, int_measurement, descriptions))
+
+        print(f"\r{i + 1} / {n}", end="")
+    print()
+
+    parsed_results, name_len, shape_len, order_len, steps_len = [], 0, 0, 0, 0
+    for float_measurement, int_measurement, descriptions in raw_results:
+        t_float = float_measurement.median * 1e6
+        t_int = int_measurement.median * 1e6
+        rel_diff = abs(t_float - t_int) / (t_float + t_int) * 2
+        parsed_results.append((t_float, t_int, rel_diff, descriptions))
+        for name, shape, order, steps in descriptions:
+            name_len = max(name_len, len(name))
+            shape_len = max(shape_len, len(shape))
+            order_len = max(order_len, len(order))
+            steps_len = max(steps_len, len(steps))
+
+    parsed_results.sort(key=operator.itemgetter(2))
+
+    print(f"stmt: {stmt}")
+    print(f" diff    faster{'':>17}{' ' * name_len} ", end="")
+    print(f"{'shape'.ljust(shape_len)}{'':>16}{'order'.ljust(order_len)}", end="")
+    print(f"          steps\n{'-' * 100}")
+    for results, spacer in [(parsed_results[:10], "..."), (parsed_results[-10:], "")]:
+        for t_float, t_int, rel_diff, descriptions in results:
+            time_str = [f"{rel_diff * 100:>4.1f}%    {'int' if t_int < t_float else 'float':<20}"]
+            time_str.extend(["".ljust(len(time_str[0])) for _ in descriptions[:-1]])
+            for t_str, (name, shape, order, steps) in zip(time_str, descriptions):
+                name = f"{name}:".ljust(name_len + 1)
+                shape = shape.ljust(shape_len + 10)
+                order = order.ljust(order_len)
+                print(f"{t_str} {name}  {shape}|     {order}      |   {steps}")
+        print(spacer)
+
+
+def main():
+    run(n=100, stmt="torch.median(x, dim=0)", fuzzer_cls=UnaryOpFuzzer)
+    run(n=100, stmt="torch.square(x)", fuzzer_cls=UnaryOpFuzzer)
+    run(n=100, stmt="x + y", fuzzer_cls=BinaryOpFuzzer)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/simple_timeit.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/simple_timeit.py
new file mode 100644
index 0000000000000000000000000000000000000000..8137d4d8791975b46b1314c2f3a05ed048dbdcd3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/simple_timeit.py
@@ -0,0 +1,25 @@
+"""Trivial use of Timer API:
+
+$ python -m examples.simple_timeit
+"""
+
+import torch
+
+import torch.utils.benchmark as benchmark_utils
+
+
+def main() -> None:
+    timer = benchmark_utils.Timer(
+        stmt="x + y",
+        globals={"x": torch.ones((4, 8)), "y": torch.ones((1, 8))},
+        label="Broadcasting add (4x8)",
+    )
+
+    for i in range(3):
+        print(f"Run: {i}\n{'-' * 40}")
+        print(f"timeit:\n{timer.timeit(10000)}\n")
+        print(f"autorange:\n{timer.blocked_autorange()}\n\n")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/spectral_ops_fuzz_test.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/spectral_ops_fuzz_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3c8cbe5b12c2d238e6ca580228e94045b11ae48
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/examples/spectral_ops_fuzz_test.py
@@ -0,0 +1,114 @@
+# mypy: allow-untyped-defs
+"""Microbenchmarks for the torch.fft module"""
+from argparse import ArgumentParser
+from collections import namedtuple
+from collections.abc import Iterable
+
+import torch
+import torch.fft
+from torch.utils import benchmark
+from torch.utils.benchmark.op_fuzzers.spectral import SpectralOpFuzzer
+
+
+def _dim_options(ndim):
+    if ndim == 1:
+        return [None]
+    elif ndim == 2:
+        return [0, 1, None]
+    elif ndim == 3:
+        return [0, 1, 2, (0, 1), (0, 2), None]
+    raise ValueError(f"Expected ndim in range 1-3, got {ndim}")
+
+
+def run_benchmark(name: str, function: object, dtype: torch.dtype, seed: int, device: str, samples: int,
+                  probability_regular: float):
+    cuda = device == 'cuda'
+    spectral_fuzzer = SpectralOpFuzzer(seed=seed, dtype=dtype, cuda=cuda,
+                                       probability_regular=probability_regular)
+    results = []
+    for tensors, tensor_params, params in spectral_fuzzer.take(samples):
+        shape = [params['k0'], params['k1'], params['k2']][:params['ndim']]
+        str_shape = ' x '.join([f"{s:<4}" for s in shape])
+        sub_label = f"{str_shape} {'' if tensor_params['x']['is_contiguous'] else '(discontiguous)'}"
+        for dim in _dim_options(params['ndim']):
+            for nthreads in (1, 4, 16) if not cuda else (1,):
+                measurement = benchmark.Timer(
+                    stmt='func(x, dim=dim)',
+                    globals={'func': function, 'x': tensors['x'], 'dim': dim},
+                    label=f"{name}_{device}",
+                    sub_label=sub_label,
+                    description=f"dim={dim}",
+                    num_threads=nthreads,
+                ).blocked_autorange(min_run_time=1)
+                measurement.metadata = {
+                    'name': name,
+                    'device': device,
+                    'dim': dim,
+                    'shape': shape,
+                }
+                measurement.metadata.update(tensor_params['x'])
+                results.append(measurement)
+    return results
+
+
+Benchmark = namedtuple('Benchmark', ['name', 'function', 'dtype'])
+BENCHMARKS = [
+    Benchmark('fft_real', torch.fft.fftn, torch.float32),
+    Benchmark('fft_complex', torch.fft.fftn, torch.complex64),
+    Benchmark('ifft', torch.fft.ifftn, torch.complex64),
+    Benchmark('rfft', torch.fft.rfftn, torch.float32),
+    Benchmark('irfft', torch.fft.irfftn, torch.complex64),
+]
+BENCHMARK_MAP = {b.name: b for b in BENCHMARKS}
+BENCHMARK_NAMES = [b.name for b in BENCHMARKS]
+DEVICE_NAMES = ['cpu', 'cuda']
+
+def _output_csv(file, results):
+    file.write('benchmark,device,num_threads,numel,shape,contiguous,dim,mean (us),median (us),iqr (us)\n')
+    for measurement in results:
+        metadata = measurement.metadata
+        device, dim, shape, name, numel, contiguous = (
+            metadata['device'], metadata['dim'], metadata['shape'],
+            metadata['name'], metadata['numel'], metadata['is_contiguous'])
+
+        if isinstance(dim, Iterable):
+            dim_str = '-'.join(str(d) for d in dim)
+        else:
+            dim_str = str(dim)
+            shape_str = 'x'.join(str(s) for s in shape)
+
+        print(name, device, measurement.task_spec.num_threads, numel, shape_str, contiguous, dim_str,  # type: ignore[possibly-undefined]
+              measurement.mean * 1e6, measurement.median * 1e6, measurement.iqr * 1e6,
+              sep=',', file=file)
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser(description=__doc__)
+    parser.add_argument('--device', type=str, choices=DEVICE_NAMES, nargs='+', default=DEVICE_NAMES)
+    parser.add_argument('--bench', type=str, choices=BENCHMARK_NAMES, nargs='+', default=BENCHMARK_NAMES)
+    parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--samples', type=int, default=10)
+    parser.add_argument('--probability-regular', '--probability_regular', type=float, default=1.0)
+    parser.add_argument('-o', '--output', type=str)
+    args = parser.parse_args()
+
+    num_benchmarks = len(args.device) * len(args.bench)
+    i = 0
+    results = []
+    for device in args.device:
+        for bench in (BENCHMARK_MAP[b] for b in args.bench):
+            results += run_benchmark(
+                name=bench.name, function=bench.function, dtype=bench.dtype,
+                seed=args.seed, device=device, samples=args.samples,
+                probability_regular=args.probability_regular)
+            i += 1
+            print(f'Completed {bench.name} benchmark on {device} ({i} of {num_benchmarks})')
+
+    if args.output is not None:
+        with open(args.output, 'w') as f:
+            _output_csv(f, results)
+
+    compare = benchmark.Compare(results)
+    compare.trim_significant_figures()
+    compare.colorize()
+    compare.print()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/binary.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/binary.py
new file mode 100644
index 0000000000000000000000000000000000000000..75f394179b3e09a90882057346ed1737e3b84367
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/binary.py
@@ -0,0 +1,107 @@
+# mypy: allow-untyped-defs
+import numpy as np
+import torch
+
+from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedTensor
+
+
+_MIN_DIM_SIZE = 16
+_MAX_DIM_SIZE = 16 * 1024 ** 2
+_POW_TWO_SIZES = tuple(2 ** i for i in range(
+    int(np.log2(_MIN_DIM_SIZE)),
+    int(np.log2(_MAX_DIM_SIZE)) + 1,
+))
+
+
+class BinaryOpFuzzer(Fuzzer):
+    def __init__(self, seed, dtype=torch.float32, cuda=False):
+        super().__init__(
+            parameters=[
+                # Dimensionality of x and y. (e.g. 1D, 2D, or 3D.)
+                FuzzedParameter("dim", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),
+
+                # Shapes for `x` and `y`.
+                #       It is important to test all shapes, however
+                #   powers of two are especially important and therefore
+                #   warrant special attention. This is done by generating
+                #   both a value drawn from all integers between the min and
+                #   max allowed values, and another from only the powers of two
+                #   (both distributions are loguniform) and then randomly
+                #   selecting between the two.
+                #       Moreover, `y` will occasionally have singleton
+                #   dimensions in order to test broadcasting.
+                [
+                    FuzzedParameter(
+                        name=f"k_any_{i}",
+                        minval=_MIN_DIM_SIZE,
+                        maxval=_MAX_DIM_SIZE,
+                        distribution="loguniform",
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"k_pow2_{i}",
+                        distribution={size: 1. / len(_POW_TWO_SIZES) for size in _POW_TWO_SIZES}
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"k{i}",
+                        distribution={
+                            ParameterAlias(f"k_any_{i}"): 0.8,
+                            ParameterAlias(f"k_pow2_{i}"): 0.2,
+                        },
+                        strict=True,
+                    ) for i in range(3)
+                ],
+
+                [
+                    FuzzedParameter(
+                        name=f"y_k{i}",
+                        distribution={
+                            ParameterAlias(f"k{i}"): 0.8,
+                            1: 0.2,
+                        },
+                        strict=True,
+                    ) for i in range(3)
+                ],
+
+                # Steps for `x` and `y`. (Benchmarks strided memory access.)
+                [
+                    FuzzedParameter(
+                        name=f"{name}_step_{i}",
+                        distribution={1: 0.8, 2: 0.06, 4: 0.06, 8: 0.04, 16: 0.04},
+                    )
+                    for i in range(3)
+                    for name in ("x", "y")
+                ],
+
+                # Repeatable entropy for downstream applications.
+                FuzzedParameter(name="random_value", minval=0, maxval=2 ** 32 - 1, distribution="uniform"),
+            ],
+            tensors=[
+                FuzzedTensor(
+                    name="x",
+                    size=("k0", "k1", "k2"),
+                    steps=("x_step_0", "x_step_1", "x_step_2"),
+                    probability_contiguous=0.75,
+                    min_elements=4 * 1024,
+                    max_elements=32 * 1024 ** 2,
+                    max_allocation_bytes=2 * 1024**3,  # 2 GB
+                    dim_parameter="dim",
+                    dtype=dtype,
+                    cuda=cuda,
+                ),
+                FuzzedTensor(
+                    name="y",
+                    size=("y_k0", "y_k1", "y_k2"),
+                    steps=("x_step_0", "x_step_1", "x_step_2"),
+                    probability_contiguous=0.75,
+                    max_allocation_bytes=2 * 1024**3,  # 2 GB
+                    dim_parameter="dim",
+                    dtype=dtype,
+                    cuda=cuda,
+                ),
+            ],
+            seed=seed,
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/sparse_binary.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/sparse_binary.py
new file mode 100644
index 0000000000000000000000000000000000000000..014361877dea148064fb71f1b504988d8eebbb17
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/sparse_binary.py
@@ -0,0 +1,107 @@
+# mypy: allow-untyped-defs
+import numpy as np
+import torch
+
+from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedSparseTensor
+
+
+_MIN_DIM_SIZE = 16
+_MAX_DIM_SIZE = 16 * 1024 ** 2
+_POW_TWO_SIZES = tuple(2 ** i for i in range(
+    int(np.log2(_MIN_DIM_SIZE)),
+    int(np.log2(_MAX_DIM_SIZE)) + 1,
+))
+
+
+class BinaryOpSparseFuzzer(Fuzzer):
+    def __init__(self, seed, dtype=torch.float32, cuda=False):
+        super().__init__(
+            parameters=[
+                # Dimensionality of x and y. (e.g. 1D, 2D, or 3D.)
+                FuzzedParameter("dim_parameter", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),
+                FuzzedParameter(
+                    name="sparse_dim",
+                    distribution={1: 0.4, 2: 0.4, 3: 0.2},
+                    strict=True
+                ),
+                # Shapes for `x` and `y`.
+                #       It is important to test all shapes, however
+                #   powers of two are especially important and therefore
+                #   warrant special attention. This is done by generating
+                #   both a value drawn from all integers between the min and
+                #   max allowed values, and another from only the powers of two
+                #   (both distributions are loguniform) and then randomly
+                #   selecting between the two.
+                #       Moreover, `y` will occasionally have singleton
+                #   dimensions in order to test broadcasting.
+                [
+                    FuzzedParameter(
+                        name=f"k_any_{i}",
+                        minval=_MIN_DIM_SIZE,
+                        maxval=_MAX_DIM_SIZE,
+                        distribution="loguniform",
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"k_pow2_{i}",
+                        distribution={size: 1. / len(_POW_TWO_SIZES) for size in _POW_TWO_SIZES}
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"k{i}",
+                        distribution={
+                            ParameterAlias(f"k_any_{i}"): 0.8,
+                            ParameterAlias(f"k_pow2_{i}"): 0.2,
+                        },
+                        strict=True,
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"y_k{i}",
+                        distribution={
+                            ParameterAlias(f"k{i}"): 1.0},
+                        strict=True,
+                    ) for i in range(3)
+                ],
+                FuzzedParameter(
+                    name="density",
+                    distribution={0.1: 0.4, 0.05: 0.3, 0.01: 0.3},
+                ),
+                FuzzedParameter(
+                    name="coalesced",
+                    distribution={True: 0.5, False: 0.5},
+                ),
+                # Repeatable entropy for downstream applications.
+                FuzzedParameter(name="random_value", minval=0, maxval=2 ** 32 - 1, distribution="uniform"),
+            ],
+            tensors=[
+                FuzzedSparseTensor(
+                    name="x",
+                    size=("k0", "k1", "k2"),
+                    dim_parameter="dim_parameter",
+                    sparse_dim="sparse_dim",
+                    density="density",
+                    coalesced="coalesced",
+                    min_elements=4 * 1024,
+                    max_elements=32 * 1024 ** 2,
+                    dtype=dtype,
+                    cuda=cuda,
+                ),
+                FuzzedSparseTensor(
+                    name="y",
+                    size=("y_k0", "y_k1", "y_k2"),
+                    dim_parameter="dim_parameter",
+                    sparse_dim="sparse_dim",
+                    density="density",
+                    coalesced="coalesced",
+                    min_elements=4 * 1024,
+                    max_elements=32 * 1024 ** 2,
+                    dtype=dtype,
+                    cuda=cuda,
+                ),
+            ],
+            seed=seed,
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/sparse_unary.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/sparse_unary.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6fe622183f68f02770507250d41f280e88a1d92
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/sparse_unary.py
@@ -0,0 +1,83 @@
+# mypy: allow-untyped-defs
+
+import numpy as np
+import torch
+from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedSparseTensor
+
+
+_MIN_DIM_SIZE = 16
+_MAX_DIM_SIZE = 16 * 1024 ** 2
+_POW_TWO_SIZES = tuple(2 ** i for i in range(
+    int(np.log2(_MIN_DIM_SIZE)),
+    int(np.log2(_MAX_DIM_SIZE)) + 1,
+))
+
+class UnaryOpSparseFuzzer(Fuzzer):
+    def __init__(self, seed, dtype=torch.float32, cuda=False):
+        super().__init__(
+            parameters=[
+                # Sparse dim parameter of x. (e.g. 1D, 2D, or 3D.)
+                FuzzedParameter("dim_parameter", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),
+                FuzzedParameter(
+                    name="sparse_dim",
+                    distribution={1: 0.4, 2: 0.4, 3: 0.2},
+                    strict=True
+                ),
+                # Shapes for `x`.
+                #   It is important to test all shapes, however
+                #   powers of two are especially important and therefore
+                #   warrant special attention. This is done by generating
+                #   both a value drawn from all integers between the min and
+                #   max allowed values, and another from only the powers of two
+                #   (both distributions are loguniform) and then randomly
+                #   selecting between the two.
+                [
+                    FuzzedParameter(
+                        name=f"k_any_{i}",
+                        minval=_MIN_DIM_SIZE,
+                        maxval=_MAX_DIM_SIZE,
+                        distribution="loguniform",
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"k_pow2_{i}",
+                        distribution={size: 1. / len(_POW_TWO_SIZES) for size in _POW_TWO_SIZES}
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"k{i}",
+                        distribution={
+                            ParameterAlias(f"k_any_{i}"): 0.8,
+                            ParameterAlias(f"k_pow2_{i}"): 0.2,
+                        },
+                        strict=True,
+                    ) for i in range(3)
+                ],
+                FuzzedParameter(
+                    name="density",
+                    distribution={0.1: 0.4, 0.05: 0.3, 0.01: 0.3},
+                ),
+                FuzzedParameter(
+                    name="coalesced",
+                    distribution={True: 0.5, False: 0.5},
+                ),
+                FuzzedParameter(name="random_value", minval=0, maxval=2 ** 32 - 1, distribution="uniform"),
+            ],
+            tensors=[
+                FuzzedSparseTensor(
+                    name="x",
+                    size=("k0", "k1", "k2"),
+                    dim_parameter="dim_parameter",
+                    sparse_dim="sparse_dim",
+                    min_elements=4 * 1024,
+                    max_elements=32 * 1024 ** 2,
+                    density="density",
+                    coalesced="coalesced",
+                    dtype=dtype,
+                    cuda=cuda,
+                ),
+            ],
+            seed=seed,
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/spectral.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/spectral.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b9e92d7a2c7b13a955d2dd9ef2a26ec8e574903
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/spectral.py
@@ -0,0 +1,94 @@
+# mypy: allow-untyped-defs
+import math
+
+import torch
+from torch.utils import benchmark
+from torch.utils.benchmark import FuzzedParameter, FuzzedTensor, ParameterAlias
+
+
+__all__ = ['SpectralOpFuzzer']
+
+MIN_DIM_SIZE = 16
+MAX_DIM_SIZE = 16 * 1024
+
+def power_range(upper_bound, base):
+    return (base ** i for i in range(int(math.log(upper_bound, base)) + 1))
+
+# List of regular numbers from MIN_DIM_SIZE to MAX_DIM_SIZE
+# These numbers factorize into multiples of prime factors 2, 3, and 5 only
+# and are usually the fastest in FFT implementations.
+REGULAR_SIZES = []
+for i in power_range(MAX_DIM_SIZE, 2):
+    for j in power_range(MAX_DIM_SIZE // i, 3):
+        ij = i * j
+        for k in power_range(MAX_DIM_SIZE // ij, 5):
+            ijk = ij * k
+            if ijk > MIN_DIM_SIZE:
+                REGULAR_SIZES.append(ijk)
+REGULAR_SIZES.sort()
+
+class SpectralOpFuzzer(benchmark.Fuzzer):
+    def __init__(self, *, seed: int, dtype=torch.float64,
+                 cuda: bool = False, probability_regular: float = 1.0):
+        super().__init__(
+            parameters=[
+                # Dimensionality of x. (e.g. 1D, 2D, or 3D.)
+                FuzzedParameter("ndim", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),
+
+                # Shapes for `x`.
+                #   It is important to test all shapes, however
+                #   regular sizes are especially important to the FFT and therefore
+                #   warrant special attention. This is done by generating
+                #   both a value drawn from all integers between the min and
+                #   max allowed values, and another from only the regular numbers
+                #   (both distributions are loguniform) and then randomly
+                #   selecting between the two.
+                [
+                    FuzzedParameter(
+                        name=f"k_any_{i}",
+                        minval=MIN_DIM_SIZE,
+                        maxval=MAX_DIM_SIZE,
+                        distribution="loguniform",
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"k_regular_{i}",
+                        distribution={size: 1. / len(REGULAR_SIZES) for size in REGULAR_SIZES}
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"k{i}",
+                        distribution={
+                            ParameterAlias(f"k_regular_{i}"): probability_regular,
+                            ParameterAlias(f"k_any_{i}"): 1 - probability_regular,
+                        },
+                        strict=True,
+                    ) for i in range(3)
+                ],
+
+                # Steps for `x`. (Benchmarks strided memory access.)
+                [
+                    FuzzedParameter(
+                        name=f"step_{i}",
+                        distribution={1: 0.8, 2: 0.06, 4: 0.06, 8: 0.04, 16: 0.04},
+                    ) for i in range(3)
+                ],
+            ],
+            tensors=[
+                FuzzedTensor(
+                    name="x",
+                    size=("k0", "k1", "k2"),
+                    steps=("step_0", "step_1", "step_2"),
+                    probability_contiguous=0.75,
+                    min_elements=4 * 1024,
+                    max_elements=32 * 1024 ** 2,
+                    max_allocation_bytes=2 * 1024**3,  # 2 GB
+                    dim_parameter="ndim",
+                    dtype=dtype,
+                    cuda=cuda,
+                ),
+            ],
+            seed=seed,
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/unary.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/unary.py
new file mode 100644
index 0000000000000000000000000000000000000000..e780b421f24c8c9a68d9196036dc925fa634eccb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/op_fuzzers/unary.py
@@ -0,0 +1,82 @@
+# mypy: allow-untyped-defs
+import numpy as np
+import torch
+
+from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedTensor
+
+
+_MIN_DIM_SIZE = 16
+_MAX_DIM_SIZE = 16 * 1024 ** 2
+_POW_TWO_SIZES = tuple(2 ** i for i in range(
+    int(np.log2(_MIN_DIM_SIZE)),
+    int(np.log2(_MAX_DIM_SIZE)) + 1,
+))
+
+
+class UnaryOpFuzzer(Fuzzer):
+    def __init__(self, seed, dtype=torch.float32, cuda=False):
+        super().__init__(
+            parameters=[
+                # Dimensionality of x. (e.g. 1D, 2D, or 3D.)
+                FuzzedParameter("dim", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),
+
+                # Shapes for `x`.
+                #   It is important to test all shapes, however
+                #   powers of two are especially important and therefore
+                #   warrant special attention. This is done by generating
+                #   both a value drawn from all integers between the min and
+                #   max allowed values, and another from only the powers of two
+                #   (both distributions are loguniform) and then randomly
+                #   selecting between the two.
+                [
+                    FuzzedParameter(
+                        name=f"k_any_{i}",
+                        minval=_MIN_DIM_SIZE,
+                        maxval=_MAX_DIM_SIZE,
+                        distribution="loguniform",
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"k_pow2_{i}",
+                        distribution={size: 1. / len(_POW_TWO_SIZES) for size in _POW_TWO_SIZES}
+                    ) for i in range(3)
+                ],
+                [
+                    FuzzedParameter(
+                        name=f"k{i}",
+                        distribution={
+                            ParameterAlias(f"k_any_{i}"): 0.8,
+                            ParameterAlias(f"k_pow2_{i}"): 0.2,
+                        },
+                        strict=True,
+                    ) for i in range(3)
+                ],
+
+                # Steps for `x`. (Benchmarks strided memory access.)
+                [
+                    FuzzedParameter(
+                        name=f"x_step_{i}",
+                        distribution={1: 0.8, 2: 0.06, 4: 0.06, 8: 0.04, 16: 0.04},
+                    ) for i in range(3)
+                ],
+
+                # Repeatable entropy for downstream applications.
+                FuzzedParameter(name="random_value", minval=0, maxval=2 ** 32 - 1, distribution="uniform"),
+            ],
+            tensors=[
+                FuzzedTensor(
+                    name="x",
+                    size=("k0", "k1", "k2"),
+                    steps=("x_step_0", "x_step_1", "x_step_2"),
+                    probability_contiguous=0.75,
+                    min_elements=4 * 1024,
+                    max_elements=32 * 1024 ** 2,
+                    max_allocation_bytes=2 * 1024**3,  # 2 GB
+                    dim_parameter="dim",
+                    dtype=dtype,
+                    cuda=cuda,
+                ),
+            ],
+            seed=seed,
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/_stubs.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/_stubs.py
new file mode 100644
index 0000000000000000000000000000000000000000..60861d1f412a0ebd310285f434d3d24bee38bbb3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/_stubs.py
@@ -0,0 +1,40 @@
+from typing import Any, Callable, Protocol, runtime_checkable
+
+
+class TimerClass(Protocol):
+    """This is the portion of the `timeit.Timer` API used by benchmark utils."""
+    def __init__(
+        self,
+        stmt: str,
+        setup: str,
+        timer: Callable[[], float],
+        globals: dict[str, Any],
+        **kwargs: Any,
+    ) -> None:
+        ...
+
+    def timeit(self, number: int) -> float:
+        ...
+
+
+@runtime_checkable
+class TimeitModuleType(Protocol):
+    """Modules generated from `timeit_template.cpp`."""
+    def timeit(self, number: int) -> float:
+        ...
+
+
+class CallgrindModuleType(Protocol):
+    """Replicates the valgrind endpoints in `torch._C`.
+
+    These bindings are used to collect Callgrind profiles on earlier versions
+    of PyTorch and will eventually be removed.
+    """
+    __file__: str
+    __name__: str
+
+    def _valgrind_supported_platform(self) -> bool:
+        ...
+
+    def _valgrind_toggle(self) -> None:
+        ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/common.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..e25909f6c85ebcec237127aa0f5497cbd0c97908
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/common.py
@@ -0,0 +1,356 @@
+"""Base shared classes and utilities."""
+
+import collections
+import contextlib
+import dataclasses
+import os
+import shutil
+import tempfile
+import textwrap
+import time
+from typing import cast, Any, Optional
+from collections.abc import Iterable, Iterator
+import uuid
+
+import torch
+
+
+__all__ = ["TaskSpec", "Measurement", "select_unit", "unit_to_english", "trim_sigfig", "ordered_unique", "set_torch_threads"]
+
+
+_MAX_SIGNIFICANT_FIGURES = 4
+_MIN_CONFIDENCE_INTERVAL = 25e-9  # 25 ns
+
+# Measurement will include a warning if the distribution is suspect. All
+# runs are expected to have some variation; these parameters set the
+# thresholds.
+_IQR_WARN_THRESHOLD = 0.1
+_IQR_GROSS_WARN_THRESHOLD = 0.25
+
+
+@dataclasses.dataclass(init=True, repr=False, eq=True, frozen=True)
+class TaskSpec:
+    """Container for information used to define a Timer. (except globals)"""
+    stmt: str
+    setup: str
+    global_setup: str = ""
+    label: Optional[str] = None
+    sub_label: Optional[str] = None
+    description: Optional[str] = None
+    env: Optional[str] = None
+    num_threads: int = 1
+
+    @property
+    def title(self) -> str:
+        """Best effort attempt at a string label for the measurement."""
+        if self.label is not None:
+            return self.label + (f": {self.sub_label}" if self.sub_label else "")
+        elif "\n" not in self.stmt:
+            return self.stmt + (f": {self.sub_label}" if self.sub_label else "")
+        return (
+            f"stmt:{f' ({self.sub_label})' if self.sub_label else ''}\n"
+            f"{textwrap.indent(self.stmt, '  ')}"
+        )
+
+    def setup_str(self) -> str:
+        return (
+            "" if (self.setup == "pass" or not self.setup)
+            else f"setup:\n{textwrap.indent(self.setup, '  ')}" if "\n" in self.setup
+            else f"setup: {self.setup}"
+        )
+
+    def summarize(self) -> str:
+        """Build TaskSpec portion of repr string for other containers."""
+        sections = [
+            self.title,
+            self.description or "",
+            self.setup_str(),
+        ]
+        return "\n".join([f"{i}\n" if "\n" in i else i for i in sections if i])
+
+_TASKSPEC_FIELDS = tuple(i.name for i in dataclasses.fields(TaskSpec))
+
+
+@dataclasses.dataclass(init=True, repr=False)
+class Measurement:
+    """The result of a Timer measurement.
+
+    This class stores one or more measurements of a given statement. It is
+    serializable and provides several convenience methods
+    (including a detailed __repr__) for downstream consumers.
+    """
+    number_per_run: int
+    raw_times: list[float]
+    task_spec: TaskSpec
+    metadata: Optional[dict[Any, Any]] = None  # Reserved for user payloads.
+
+    def __post_init__(self) -> None:
+        self._sorted_times: tuple[float, ...] = ()
+        self._warnings: tuple[str, ...] = ()
+        self._median: float = -1.0
+        self._mean: float = -1.0
+        self._p25: float = -1.0
+        self._p75: float = -1.0
+
+    def __getattr__(self, name: str) -> Any:
+        # Forward TaskSpec fields for convenience.
+        if name in _TASKSPEC_FIELDS:
+            return getattr(self.task_spec, name)
+        return super().__getattribute__(name)
+
+    # =========================================================================
+    # == Convenience methods for statistics ===================================
+    # =========================================================================
+    #
+    # These methods use raw time divided by number_per_run; this is an
+    # extrapolation and hides the fact that different number_per_run will
+    # result in different amortization of overheads, however if Timer has
+    # selected an appropriate number_per_run then this is a non-issue, and
+    # forcing users to handle that division would result in a poor experience.
+    @property
+    def times(self) -> list[float]:
+        return [t / self.number_per_run for t in self.raw_times]
+
+    @property
+    def median(self) -> float:
+        self._lazy_init()
+        return self._median
+
+    @property
+    def mean(self) -> float:
+        self._lazy_init()
+        return self._mean
+
+    @property
+    def iqr(self) -> float:
+        self._lazy_init()
+        return self._p75 - self._p25
+
+    @property
+    def significant_figures(self) -> int:
+        """Approximate significant figure estimate.
+
+        This property is intended to give a convenient way to estimate the
+        precision of a measurement. It only uses the interquartile region to
+        estimate statistics to try to mitigate skew from the tails, and
+        uses a static z value of 1.645 since it is not expected to be used
+        for small values of `n`, so z can approximate `t`.
+
+        The significant figure estimation used in conjunction with the
+        `trim_sigfig` method to provide a more human interpretable data
+        summary. __repr__ does not use this method; it simply displays raw
+        values. Significant figure estimation is intended for `Compare`.
+        """
+        self._lazy_init()
+        n_total = len(self._sorted_times)
+        lower_bound = int(n_total // 4)
+        upper_bound = int(torch.tensor(3 * n_total / 4).ceil())
+        interquartile_points: tuple[float, ...] = self._sorted_times[lower_bound:upper_bound]
+        std = torch.tensor(interquartile_points).std(unbiased=False).item()
+        sqrt_n = torch.tensor(len(interquartile_points)).sqrt().item()
+
+        # Rough estimates. These are by no means statistically rigorous.
+        confidence_interval = max(1.645 * std / sqrt_n, _MIN_CONFIDENCE_INTERVAL)
+        relative_ci = torch.tensor(self._median / confidence_interval).log10().item()
+        num_significant_figures = int(torch.tensor(relative_ci).floor())
+        return min(max(num_significant_figures, 1), _MAX_SIGNIFICANT_FIGURES)
+
+    @property
+    def has_warnings(self) -> bool:
+        self._lazy_init()
+        return bool(self._warnings)
+
+    def _lazy_init(self) -> None:
+        if self.raw_times and not self._sorted_times:
+            self._sorted_times = tuple(sorted(self.times))
+            _sorted_times = torch.tensor(self._sorted_times, dtype=torch.float64)
+            self._median = _sorted_times.quantile(.5).item()
+            self._mean = _sorted_times.mean().item()
+            self._p25 = _sorted_times.quantile(.25).item()
+            self._p75 = _sorted_times.quantile(.75).item()
+
+            def add_warning(msg: str) -> None:
+                rel_iqr = self.iqr / self.median * 100
+                self._warnings += (
+                    f"  WARNING: Interquartile range is {rel_iqr:.1f}% "
+                    f"of the median measurement.\n           {msg}",
+                )
+
+            if not self.meets_confidence(_IQR_GROSS_WARN_THRESHOLD):
+                add_warning("This suggests significant environmental influence.")
+            elif not self.meets_confidence(_IQR_WARN_THRESHOLD):
+                add_warning("This could indicate system fluctuation.")
+
+
+    def meets_confidence(self, threshold: float = _IQR_WARN_THRESHOLD) -> bool:
+        return self.iqr / self.median < threshold
+
+    @property
+    def title(self) -> str:
+        return self.task_spec.title
+
+    @property
+    def env(self) -> str:
+        return (
+            "Unspecified env" if self.taskspec.env is None
+            else cast(str, self.taskspec.env)
+        )
+
+    @property
+    def as_row_name(self) -> str:
+        return self.sub_label or self.stmt or "[Unknown]"
+
+    def __repr__(self) -> str:
+        """
+        Example repr:
+            
+              Broadcasting add (4x8)
+              Median: 5.73 us
+              IQR:    2.25 us (4.01 to 6.26)
+              372 measurements, 100 runs per measurement, 1 thread
+              WARNING: Interquartile range is 39.4% of the median measurement.
+                       This suggests significant environmental influence.
+        """
+        self._lazy_init()
+        skip_line, newline = "MEASUREMENT_REPR_SKIP_LINE", "\n"
+        n = len(self._sorted_times)
+        time_unit, time_scale = select_unit(self._median)
+        iqr_filter = '' if n >= 4 else skip_line
+
+        repr_str = f"""
+{super().__repr__()}
+{self.task_spec.summarize()}
+  {'Median: ' if n > 1 else ''}{self._median / time_scale:.2f} {time_unit}
+  {iqr_filter}IQR:    {self.iqr / time_scale:.2f} {time_unit} ({self._p25 / time_scale:.2f} to {self._p75 / time_scale:.2f})
+  {n} measurement{'s' if n > 1 else ''}, {self.number_per_run} runs {'per measurement,' if n > 1 else ','} {self.num_threads} thread{'s' if self.num_threads > 1 else ''}
+{newline.join(self._warnings)}""".strip()  # noqa: B950
+
+        return "\n".join(l for l in repr_str.splitlines(keepends=False) if skip_line not in l)
+
+    @staticmethod
+    def merge(measurements: Iterable["Measurement"]) -> list["Measurement"]:
+        """Convenience method for merging replicates.
+
+        Merge will extrapolate times to `number_per_run=1` and will not
+        transfer any metadata. (Since it might differ between replicates)
+        """
+        grouped_measurements: collections.defaultdict[TaskSpec, list[Measurement]] = collections.defaultdict(list)
+        for m in measurements:
+            grouped_measurements[m.task_spec].append(m)
+
+        def merge_group(task_spec: TaskSpec, group: list["Measurement"]) -> "Measurement":
+            times: list[float] = []
+            for m in group:
+                # Different measurements could have different `number_per_run`,
+                # so we call `.times` which normalizes the results.
+                times.extend(m.times)
+
+            return Measurement(
+                number_per_run=1,
+                raw_times=times,
+                task_spec=task_spec,
+                metadata=None,
+            )
+
+        return [merge_group(t, g) for t, g in grouped_measurements.items()]
+
+
+def select_unit(t: float) -> tuple[str, float]:
+    """Determine how to scale times for O(1) magnitude.
+
+    This utility is used to format numbers for human consumption.
+    """
+    time_unit = {-3: "ns", -2: "us", -1: "ms"}.get(int(torch.tensor(t).log10().item() // 3), "s")
+    time_scale = {"ns": 1e-9, "us": 1e-6, "ms": 1e-3, "s": 1}[time_unit]
+    return time_unit, time_scale
+
+
+def unit_to_english(u: str) -> str:
+    return {
+        "ns": "nanosecond",
+        "us": "microsecond",
+        "ms": "millisecond",
+        "s": "second",
+    }[u]
+
+
+def trim_sigfig(x: float, n: int) -> float:
+    """Trim `x` to `n` significant figures. (e.g. 3.14159, 2 -> 3.10000)"""
+    assert n == int(n)
+    magnitude = int(torch.tensor(x).abs().log10().ceil().item())
+    scale = 10 ** (magnitude - n)
+    return float(torch.tensor(x / scale).round() * scale)
+
+
+def ordered_unique(elements: Iterable[Any]) -> list[Any]:
+    return list(collections.OrderedDict(dict.fromkeys(elements)).keys())
+
+
+@contextlib.contextmanager
+def set_torch_threads(n: int) -> Iterator[None]:
+    prior_num_threads = torch.get_num_threads()
+    try:
+        torch.set_num_threads(n)
+        yield
+    finally:
+        torch.set_num_threads(prior_num_threads)
+
+
+def _make_temp_dir(prefix: Optional[str] = None, gc_dev_shm: bool = False) -> str:
+    """Create a temporary directory. The caller is responsible for cleanup.
+
+    This function is conceptually similar to `tempfile.mkdtemp`, but with
+    the key additional feature that it will use shared memory if the
+    `BENCHMARK_USE_DEV_SHM` environment variable is set. This is an
+    implementation detail, but an important one for cases where many Callgrind
+    measurements are collected at once. (Such as when collecting
+    microbenchmarks.)
+
+    This is an internal utility, and is exported solely so that microbenchmarks
+    can reuse the util.
+    """
+    use_dev_shm: bool = (os.getenv("BENCHMARK_USE_DEV_SHM") or "").lower() in ("1", "true")
+    if use_dev_shm:
+        root = "/dev/shm/pytorch_benchmark_utils"
+        assert os.name == "posix", f"tmpfs (/dev/shm) is POSIX only, current platform is {os.name}"
+        assert os.path.exists("/dev/shm"), "This system does not appear to support tmpfs (/dev/shm)."
+        os.makedirs(root, exist_ok=True)
+
+        # Because we're working in shared memory, it is more important than
+        # usual to clean up ALL intermediate files. However we don't want every
+        # worker to walk over all outstanding directories, so instead we only
+        # check when we are sure that it won't lead to contention.
+        if gc_dev_shm:
+            for i in os.listdir(root):
+                owner_file = os.path.join(root, i, "owner.pid")
+                if not os.path.exists(owner_file):
+                    continue
+
+                with open(owner_file) as f:
+                    owner_pid = int(f.read())
+
+                if owner_pid == os.getpid():
+                    continue
+
+                try:
+                    # https://stackoverflow.com/questions/568271/how-to-check-if-there-exists-a-process-with-a-given-pid-in-python
+                    os.kill(owner_pid, 0)
+
+                except OSError:
+                    print(f"Detected that {os.path.join(root, i)} was orphaned in shared memory. Cleaning up.")
+                    shutil.rmtree(os.path.join(root, i))
+
+    else:
+        root = tempfile.gettempdir()
+
+    # We include the time so names sort by creation time, and add a UUID
+    # to ensure we don't collide.
+    name = f"{prefix or tempfile.gettempprefix()}__{int(time.time())}__{uuid.uuid4()}"
+    path = os.path.join(root, name)
+    os.makedirs(path, exist_ok=False)
+
+    if use_dev_shm:
+        with open(os.path.join(path, "owner.pid"), "w") as f:
+            f.write(str(os.getpid()))
+
+    return path
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/compare.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/compare.py
new file mode 100644
index 0000000000000000000000000000000000000000..592ec66b4c04a2e7133ffe64ba7eedb23a0d5aed
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/compare.py
@@ -0,0 +1,345 @@
+# mypy: allow-untyped-defs
+"""Display class to aggregate and print the results of many measurements."""
+import collections
+import enum
+import itertools as it
+from typing import Optional
+
+from torch.utils.benchmark.utils import common
+from torch import tensor as _tensor
+import operator
+
+__all__ = ["Colorize", "Compare"]
+
+BEST = "\033[92m"
+GOOD = "\033[34m"
+BAD = "\033[2m\033[91m"
+VERY_BAD = "\033[31m"
+BOLD = "\033[1m"
+TERMINATE = "\033[0m"
+
+
+class Colorize(enum.Enum):
+    NONE = "none"
+    COLUMNWISE = "columnwise"
+    ROWWISE = "rowwise"
+
+
+# Classes to separate internal bookkeeping from what is rendered.
+class _Column:
+    def __init__(
+        self,
+        grouped_results: list[tuple[Optional[common.Measurement], ...]],
+        time_scale: float,
+        time_unit: str,
+        trim_significant_figures: bool,
+        highlight_warnings: bool,
+    ):
+        self._grouped_results = grouped_results
+        self._flat_results = [*it.chain.from_iterable(grouped_results)]
+        self._time_scale = time_scale
+        self._time_unit = time_unit
+        self._trim_significant_figures = trim_significant_figures
+        self._highlight_warnings = (
+            highlight_warnings
+            and any(r.has_warnings for r in self._flat_results if r)
+        )
+        leading_digits = [
+            int(_tensor(r.median / self._time_scale).log10().ceil()) if r else None
+            for r in self._flat_results
+        ]
+        unit_digits = max(d for d in leading_digits if d is not None)
+        decimal_digits = min(
+            max(m.significant_figures - digits, 0)
+            for digits, m in zip(leading_digits, self._flat_results)
+            if (m is not None) and (digits is not None)
+        ) if self._trim_significant_figures else 1
+        length = unit_digits + decimal_digits + (1 if decimal_digits else 0)
+        self._template = f"{{:>{length}.{decimal_digits}f}}{{:>{7 if self._highlight_warnings else 0}}}"
+
+    def get_results_for(self, group):
+        return self._grouped_results[group]
+
+    def num_to_str(self, value: Optional[float], estimated_sigfigs: int, spread: Optional[float]):
+        if value is None:
+            return " " * len(self.num_to_str(1, estimated_sigfigs, None))
+
+        if self._trim_significant_figures:
+            value = common.trim_sigfig(value, estimated_sigfigs)
+
+        return self._template.format(
+            value,
+            f" (! {spread * 100:.0f}%)" if self._highlight_warnings and spread is not None else "")
+
+
+def optional_min(seq):
+    l = list(seq)
+    return None if len(l) == 0 else min(l)
+
+
+class _Row:
+    def __init__(self, results, row_group, render_env, env_str_len,
+                 row_name_str_len, time_scale, colorize, num_threads=None):
+        super().__init__()
+        self._results = results
+        self._row_group = row_group
+        self._render_env = render_env
+        self._env_str_len = env_str_len
+        self._row_name_str_len = row_name_str_len
+        self._time_scale = time_scale
+        self._colorize = colorize
+        self._columns: tuple[_Column, ...] = ()
+        self._num_threads = num_threads
+
+    def register_columns(self, columns: tuple[_Column, ...]):
+        self._columns = columns
+
+    def as_column_strings(self):
+        concrete_results = [r for r in self._results if r is not None]
+        env = f"({concrete_results[0].env})" if self._render_env else ""
+        env = env.ljust(self._env_str_len + 4)
+        output = ["  " + env + concrete_results[0].as_row_name]
+        for m, col in zip(self._results, self._columns or ()):
+            if m is None:
+                output.append(col.num_to_str(None, 1, None))
+            else:
+                output.append(col.num_to_str(
+                    m.median / self._time_scale,
+                    m.significant_figures,
+                    m.iqr / m.median if m.has_warnings else None
+                ))
+        return output
+
+    @staticmethod
+    def color_segment(segment, value, best_value):
+        if value <= best_value * 1.01 or value <= best_value + 100e-9:
+            return BEST + BOLD + segment + TERMINATE * 2
+        if value <= best_value * 1.1:
+            return GOOD + BOLD + segment + TERMINATE * 2
+        if value >= best_value * 5:
+            return VERY_BAD + BOLD + segment + TERMINATE * 2
+        if value >= best_value * 2:
+            return BAD + segment + TERMINATE * 2
+
+        return segment
+
+    def row_separator(self, overall_width):
+        return (
+            [f"{self._num_threads} threads: ".ljust(overall_width, "-")]
+            if self._num_threads is not None else []
+        )
+
+    def finalize_column_strings(self, column_strings, col_widths):
+        best_values = [-1 for _ in column_strings]
+        if self._colorize == Colorize.ROWWISE:
+            row_min = min(r.median for r in self._results if r is not None)
+            best_values = [row_min for _ in column_strings]
+        elif self._colorize == Colorize.COLUMNWISE:
+            best_values = [
+                optional_min(r.median for r in column.get_results_for(self._row_group) if r is not None)
+                for column in (self._columns or ())
+            ]
+
+        row_contents = [column_strings[0].ljust(col_widths[0])]
+        for col_str, width, result, best_value in zip(column_strings[1:], col_widths[1:], self._results, best_values):
+            col_str = col_str.center(width)
+            if self._colorize != Colorize.NONE and result is not None and best_value is not None:
+                col_str = self.color_segment(col_str, result.median, best_value)
+            row_contents.append(col_str)
+        return row_contents
+
+
+class Table:
+    def __init__(
+            self,
+            results: list[common.Measurement],
+            colorize: Colorize,
+            trim_significant_figures: bool,
+            highlight_warnings: bool
+    ):
+        assert len({r.label for r in results}) == 1
+
+        self.results = results
+        self._colorize = colorize
+        self._trim_significant_figures = trim_significant_figures
+        self._highlight_warnings = highlight_warnings
+        self.label = results[0].label
+        self.time_unit, self.time_scale = common.select_unit(
+            min(r.median for r in results)
+        )
+
+        self.row_keys = common.ordered_unique([self.row_fn(i) for i in results])
+        self.row_keys.sort(key=operator.itemgetter(slice(2)))  # preserve stmt order
+        self.column_keys = common.ordered_unique([self.col_fn(i) for i in results])
+        self.rows, self.columns = self.populate_rows_and_columns()
+
+    @staticmethod
+    def row_fn(m: common.Measurement) -> tuple[int, Optional[str], str]:
+        return m.num_threads, m.env, m.as_row_name
+
+    @staticmethod
+    def col_fn(m: common.Measurement) -> Optional[str]:
+        return m.description
+
+    def populate_rows_and_columns(self) -> tuple[tuple[_Row, ...], tuple[_Column, ...]]:
+        rows: list[_Row] = []
+        columns: list[_Column] = []
+        ordered_results: list[list[Optional[common.Measurement]]] = [
+            [None for _ in self.column_keys]
+            for _ in self.row_keys
+        ]
+        row_position = {key: i for i, key in enumerate(self.row_keys)}
+        col_position = {key: i for i, key in enumerate(self.column_keys)}
+        for r in self.results:
+            i = row_position[self.row_fn(r)]
+            j = col_position[self.col_fn(r)]
+            ordered_results[i][j] = r
+
+        unique_envs = {r.env for r in self.results}
+        render_env = len(unique_envs) > 1
+        env_str_len = max(len(i) for i in unique_envs) if render_env else 0
+
+        row_name_str_len = max(len(r.as_row_name) for r in self.results)
+
+        prior_num_threads = -1
+        prior_env = ""
+        row_group = -1
+        rows_by_group: list[list[list[Optional[common.Measurement]]]] = []
+        for (num_threads, env, _), row in zip(self.row_keys, ordered_results):
+            thread_transition = (num_threads != prior_num_threads)
+            if thread_transition:
+                prior_num_threads = num_threads
+                prior_env = ""
+                row_group += 1
+                rows_by_group.append([])
+            rows.append(
+                _Row(
+                    results=row,
+                    row_group=row_group,
+                    render_env=(render_env and env != prior_env),
+                    env_str_len=env_str_len,
+                    row_name_str_len=row_name_str_len,
+                    time_scale=self.time_scale,
+                    colorize=self._colorize,
+                    num_threads=num_threads if thread_transition else None,
+                )
+            )
+            rows_by_group[-1].append(row)
+            prior_env = env
+
+        for i in range(len(self.column_keys)):
+            grouped_results = [tuple(row[i] for row in g) for g in rows_by_group]
+            column = _Column(
+                grouped_results=grouped_results,
+                time_scale=self.time_scale,
+                time_unit=self.time_unit,
+                trim_significant_figures=self._trim_significant_figures,
+                highlight_warnings=self._highlight_warnings,)
+            columns.append(column)
+
+        rows_tuple, columns_tuple = tuple(rows), tuple(columns)
+        for ri in rows_tuple:
+            ri.register_columns(columns_tuple)
+        return rows_tuple, columns_tuple
+
+    def render(self) -> str:
+        string_rows = [[""] + self.column_keys]
+        string_rows.extend(r.as_column_strings() for r in self.rows)
+        num_cols = max(len(i) for i in string_rows)
+        for sr in string_rows:
+            sr.extend(["" for _ in range(num_cols - len(sr))])
+
+        col_widths = [max(len(j) for j in i) for i in zip(*string_rows)]
+        finalized_columns = ["  |  ".join(i.center(w) for i, w in zip(string_rows[0], col_widths))]
+        overall_width = len(finalized_columns[0])
+        for string_row, row in zip(string_rows[1:], self.rows):
+            finalized_columns.extend(row.row_separator(overall_width))
+            finalized_columns.append("  |  ".join(row.finalize_column_strings(string_row, col_widths)))
+
+        newline = "\n"
+        has_warnings = self._highlight_warnings and any(ri.has_warnings for ri in self.results)
+        return f"""
+[{(' ' + (self.label or '') + ' ').center(overall_width - 2, '-')}]
+{newline.join(finalized_columns)}
+
+Times are in {common.unit_to_english(self.time_unit)}s ({self.time_unit}).
+{'(! XX%) Measurement has high variance, where XX is the IQR / median * 100.' + newline if has_warnings else ""}"""[1:]
+
+
+class Compare:
+    """Helper class for displaying the results of many measurements in a
+    formatted table.
+
+    The table format is based on the information fields provided in
+    :class:`torch.utils.benchmark.Timer` (`description`, `label`, `sub_label`,
+    `num_threads`, etc).
+
+    The table can be directly printed using :meth:`print` or casted as a `str`.
+
+    For a full tutorial on how to use this class, see:
+    https://pytorch.org/tutorials/recipes/recipes/benchmark.html
+
+    Args:
+        results: List of Measurment to display.
+    """
+    def __init__(self, results: list[common.Measurement]):
+        self._results: list[common.Measurement] = []
+        self.extend_results(results)
+        self._trim_significant_figures = False
+        self._colorize = Colorize.NONE
+        self._highlight_warnings = False
+
+    def __str__(self):
+        return "\n".join(self._render())
+
+    def extend_results(self, results):
+        """Append results to already stored ones.
+
+        All added results must be instances of ``Measurement``.
+        """
+        for r in results:
+            if not isinstance(r, common.Measurement):
+                raise ValueError(
+                    "Expected an instance of `Measurement`, " f"got {type(r)} instead."
+                )
+        self._results.extend(results)
+
+    def trim_significant_figures(self):
+        """Enables trimming of significant figures when building the formatted table."""
+        self._trim_significant_figures = True
+
+    def colorize(self, rowwise=False):
+        """Colorize formatted table.
+
+        Colorize columnwise by default.
+        """
+        self._colorize = Colorize.ROWWISE if rowwise else Colorize.COLUMNWISE
+
+    def highlight_warnings(self):
+        """Enables warning highlighting when building formatted table."""
+        self._highlight_warnings = True
+
+    def print(self):
+        """Print formatted table"""
+        print(str(self))
+
+    def _render(self):
+        results = common.Measurement.merge(self._results)
+        grouped_results = self._group_by_label(results)
+        output = [self._layout(group) for group in grouped_results.values()]
+        return output
+
+    def _group_by_label(self, results: list[common.Measurement]):
+        grouped_results: collections.defaultdict[str, list[common.Measurement]] = collections.defaultdict(list)
+        for r in results:
+            grouped_results[r.label].append(r)
+        return grouped_results
+
+    def _layout(self, results: list[common.Measurement]):
+        table = Table(
+            results,
+            self._colorize,
+            self._trim_significant_figures,
+            self._highlight_warnings
+        )
+        return table.render()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/compile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/compile.py
new file mode 100644
index 0000000000000000000000000000000000000000..23b74f946c000763e1e76b23631dc349448ba71b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/compile.py
@@ -0,0 +1,191 @@
+# mypy: allow-untyped-defs
+from typing import Any, Callable, cast, Optional, Union
+
+import torch
+import torch._dynamo
+from torch._dynamo.testing import CompileCounterWithBackend
+from torch.utils.benchmark import Timer
+
+
+__all__ = ["bench_all", "benchmark_compile"]
+
+
+_warned_tensor_cores = False
+_default_float_32_precision = torch.get_float32_matmul_precision()
+
+try:
+    from tabulate import tabulate
+
+    HAS_TABULATE = True
+except ModuleNotFoundError:
+    HAS_TABULATE = False
+    tabulate = None  # type: ignore[assignment]
+    print("tabulate is not installed, please pip install tabulate to use this utility")
+
+if HAS_TABULATE:
+    def _enable_tensor_cores():
+        global _warned_tensor_cores
+
+        if torch.cuda.is_available():
+            if torch.backends.cuda.matmul.allow_tf32 is False and torch.cuda.get_device_capability() >= (8, 0):
+                torch.set_float32_matmul_precision("high")
+                if not _warned_tensor_cores:
+                    print("Your GPU supports tensor cores")
+                    print("we will enable it automatically by setting `torch.set_float32_matmul_precision('high')`")
+                    _warned_tensor_cores = True
+
+    def _disable_tensor_cores():
+        torch.set_float32_matmul_precision(_default_float_32_precision)
+
+    def bench_loop(
+        model: Union[torch.nn.Module, Callable],
+        sample_input: Union[torch.Tensor, Any],
+        num_iters: int = 5,
+        optimizer: Optional[torch.optim.Optimizer] = None,
+        loss_fn: Optional[Callable] = None,
+    ):
+        # Define the statement and setup for the benchmark
+        if optimizer and loss_fn:
+            # Training mode
+            stmt = """
+    output = model(sample_input)
+    loss = loss_fn(output) if loss_fn else output.sum()
+    loss.backward()
+    optimizer.step()
+    optimizer.zero_grad()
+            """
+        else:
+            # Inference mode
+            stmt = "model(sample_input)"
+
+        # Create the Timer object
+        timer = Timer(
+            stmt=stmt,
+            globals={"model": model, "sample_input": sample_input, "optimizer": optimizer, "loss_fn": loss_fn},
+        )
+
+
+        result = timer.timeit(number=num_iters)
+
+        # Get the average time per iteration in milliseconds
+        avg_time = result.mean * 1000
+        return round(avg_time, 2)
+
+    def benchmark_compile(
+        model: Union[torch.nn.Module, Callable],
+        sample_input: Union[torch.Tensor, Any],
+        num_iters: int = 5,
+        backend: Optional[str] = None,
+        mode: Optional[str] = "default",
+        optimizer: Optional[torch.optim.Optimizer] = None,
+        loss_fn : Union[torch.nn.Module, Callable, None] = None,
+    ):
+        """
+        Use this utility to benchmark torch.compile
+        """
+        if backend:
+            try:
+                torch._dynamo.reset()
+                compile_counter_with_backend = CompileCounterWithBackend(backend)
+                opt_model = torch.compile(model, backend=compile_counter_with_backend, mode=mode)
+
+                # Compilation only happens after the first inference
+                compilation_time = bench_loop(opt_model, sample_input, 1, optimizer, loss_fn)
+
+                running_time = bench_loop(opt_model, sample_input, num_iters, optimizer, loss_fn)
+
+                if compile_counter_with_backend.frame_count == 0:
+                    raise RuntimeError("No compilation occurred during benchmarking.")
+
+                if compile_counter_with_backend.frame_count > 1:
+                    raise RuntimeError("Recompilation occurred during benchmarking.")
+
+            except Exception as e:
+                print(e)
+                print(f"Failed to compile {backend} with mode {mode}")
+                return None, None
+        else:
+            opt_model = model
+            compilation_time = None
+            running_time = bench_loop(opt_model, sample_input, num_iters, optimizer, loss_fn)
+
+        compilation_time = round(compilation_time, 2) if compilation_time else None
+        running_time = round(running_time, 2) if running_time else None
+
+
+        return compilation_time, running_time
+
+
+    def bench_all(
+        model : Union[torch.nn.Module, Callable],
+        sample_input: Union[torch.Tensor, Any],
+        num_iters : int = 5,
+        optimizer: Optional[torch.optim.Optimizer] = None,
+        loss_fn : Union[torch.nn.Module, Callable, None] = None,
+    ):
+        """
+        This is a simple utility that can be used to benchmark torch.compile
+        In particular it ensures that your GPU is setup to use tensor cores if it supports its
+        It also tries out all the main backends and prints a table of results so you can easily compare them all
+        Many of the backendds have their own optional dependencies so please pip install them seperately
+
+        You will get one table for inference and another for training
+        If you'd like to leverage this utility for training make sure to pass in a torch.optim.Optimizer
+
+        The important warnings are
+        Your GPU supports tensor cores
+        we will enable it automatically by setting `torch.set_float32_matmul_precision('high')`
+
+        If a compilation fails for any reason including the dependency not being included
+        then we will print Failed to compile {backend} with mode {mode}
+        """
+        field_names = ["Train/Inference", "Backend", "Mode", "Compilation Time", "Average Running Time"]
+        table = []
+
+
+        eager_time = None
+        torch._dynamo.reset()
+        _, eager_time = benchmark_compile(model, sample_input, num_iters, None, None, optimizer)
+        table.append(
+            [("Training" if optimizer else "Inference"), "Eager", "-", "-", f"{eager_time} ms"]
+        )
+
+        for backend in torch._dynamo.list_backends():
+
+            if backend == "inductor":
+                mode_options = cast(list[Optional[str]], list(torch._inductor.list_mode_options().keys())) + [None]
+                for mode in mode_options:
+                    if mode == "default":
+                        continue
+                    torch._dynamo.reset()
+                    try:
+                        if torch.cuda.is_available():
+                            _enable_tensor_cores()
+                        compilation_time, running_time = benchmark_compile(
+                            model, sample_input, num_iters, backend, mode, optimizer, loss_fn)
+                    finally:
+                        if torch.cuda.is_available():
+                            _disable_tensor_cores()
+                            table.append([
+                                ("Training" if optimizer else "Inference"),
+                                backend if backend else "-",
+                                mode if mode is not None else "-",
+                                f"{compilation_time} ms " if compilation_time else "-",
+                                f"{running_time} ms " if running_time else "-",
+                            ])
+
+            else:
+                torch._dynamo.reset()
+                compilation_time, running_time = benchmark_compile(
+                    model, sample_input, num_iters, backend, None, optimizer, loss_fn)
+
+                if running_time is not None:
+                    table.append([
+                        ("Training" if optimizer else "Inference"),
+                        backend, "-",
+                        f"{compilation_time} ms " or "-",
+                        f"{running_time} ms ",
+                    ])
+
+
+        return tabulate(table, headers=field_names, tablefmt="github")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/cpp_jit.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/cpp_jit.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7aec25f6a76ef1dfa55a991a3edda42a11a34cf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/cpp_jit.py
@@ -0,0 +1,172 @@
+"""JIT C++ strings into executables."""
+import atexit
+import os
+import re
+import shutil
+import textwrap
+import threading
+from typing import Any, Optional
+
+import torch
+from torch.utils.benchmark.utils._stubs import CallgrindModuleType, TimeitModuleType
+from torch.utils.benchmark.utils.common import _make_temp_dir
+from torch.utils import cpp_extension
+
+
+LOCK = threading.Lock()
+SOURCE_ROOT = os.path.split(os.path.abspath(__file__))[0]
+
+# We calculate uuid once at import time so that separate processes will have
+# separate build roots, but threads will share the same build root.
+# `cpp_extension` uses build root as part of the cache key, so per-invocation
+# uuid's (e.g. different build root per _compile_template call) would lead to
+# a 0% cache hit rate and spurious recompilation. Consider the following:
+#   ```
+#   setup = "auto x = torch::ones({1024, 1024});"
+#   stmt = "torch::mm(x, x);"
+#   for num_threads in [1, 2, 4, 8]:
+#     print(Timer(stmt, setup, num_threads=num_threads, language="c++").blocked_autorange())
+#   ````
+# `setup` and `stmt` do not change, so we can reuse the executable from the
+# first pass through the loop.
+_BUILD_ROOT: Optional[str] = None
+
+def _get_build_root() -> str:
+    global _BUILD_ROOT
+    if _BUILD_ROOT is None:
+        _BUILD_ROOT = _make_temp_dir(prefix="benchmark_utils_jit_build")
+        atexit.register(shutil.rmtree, _BUILD_ROOT)
+    return _BUILD_ROOT
+
+
+# BACK_TESTING_NOTE:
+#   There are two workflows where this code could be used. One is the obvious
+#   case where someone simply builds or installs PyTorch and uses Timer.
+#   The other is that the entire `torch/utils/benchmark` folder from a CURRENT
+#   PyTorch checkout is copy-pasted into a much OLDER version of the PyTorch
+#   source code. This is what we refer to here as "back testing". The rationale
+#   is that we might want to use current tooling to study some aspect of an
+#   earlier version of PyTorch. (e.g. a regression.)
+#
+#   The problem is that Timer relies on several aspects of core PyTorch, namely
+#   some binding functions for Valgrind symbols in `torch._C` and the
+#   `torch.__config__._cxx_flags()` method. If we were to naively copy code
+#   around this wouldn't work as the symbols of interest aren't present in
+#   earlier versions of PyTorch. In order to work around this, we must add back
+#   testing shims. These shims will never activate during normal use, but will
+#   allow Timer to function outside of the "correct" version of PyTorch by
+#   emulating functionality that was added later.
+#
+#   These shims are temporary, and as Timer becomes more integrated with
+#   PyTorch the cost and complexity of such shims will increase. Once back
+#   testing is no longer required (which is to say we have done enough historic
+#   analysis and the shims no longer justify their maintenance and code
+#   complexity costs) back testing paths will be removed.
+
+CXX_FLAGS: Optional[list[str]]
+if hasattr(torch.__config__, "_cxx_flags"):
+    try:
+        CXX_FLAGS = torch.__config__._cxx_flags().strip().split()
+        if CXX_FLAGS is not None and "-g" not in CXX_FLAGS:
+            CXX_FLAGS.append("-g")
+        # remove "-W" flags to allow build benchmarks
+        # with a relaxed constraint of compiler versions
+        if CXX_FLAGS is not None:
+            CXX_FLAGS = list(filter(lambda x: not x.startswith("-W"), CXX_FLAGS))
+
+    except RuntimeError:
+        # We are in FBCode.
+        CXX_FLAGS = None
+else:
+    # FIXME: Remove when back testing is no longer required.
+    CXX_FLAGS = ["-O2", "-fPIC", "-g"]
+
+EXTRA_INCLUDE_PATHS: list[str] = [os.path.join(SOURCE_ROOT, "valgrind_wrapper")]
+CONDA_PREFIX = os.getenv("CONDA_PREFIX")
+if CONDA_PREFIX is not None:
+    # Load will automatically search /usr/include, but not conda include.
+    EXTRA_INCLUDE_PATHS.append(os.path.join(CONDA_PREFIX, "include"))
+
+
+COMPAT_CALLGRIND_BINDINGS: Optional[CallgrindModuleType] = None
+def get_compat_bindings() -> CallgrindModuleType:
+    with LOCK:
+        global COMPAT_CALLGRIND_BINDINGS
+        if COMPAT_CALLGRIND_BINDINGS is None:
+            COMPAT_CALLGRIND_BINDINGS = cpp_extension.load(
+                name="callgrind_bindings",
+                sources=[os.path.join(
+                    SOURCE_ROOT,
+                    "valgrind_wrapper",
+                    "compat_bindings.cpp"
+                )],
+                extra_cflags=CXX_FLAGS,
+                extra_include_paths=EXTRA_INCLUDE_PATHS,
+            )
+    return COMPAT_CALLGRIND_BINDINGS
+
+
+def _compile_template(
+    *,
+    stmt: str,
+    setup: str,
+    global_setup: str,
+    src: str,
+    is_standalone: bool
+) -> Any:
+    for before, after, indentation in (
+        ("// GLOBAL_SETUP_TEMPLATE_LOCATION", global_setup, 0),
+        ("// SETUP_TEMPLATE_LOCATION", setup, 4),
+        ("// STMT_TEMPLATE_LOCATION", stmt, 8)
+    ):
+        # C++ doesn't care about indentation so this code isn't load
+        # bearing the way it is with Python, but this makes the source
+        # look nicer if a human has to look at it.
+        src = re.sub(
+            before,
+            textwrap.indent(after, " " * indentation)[indentation:],
+            src
+        )
+
+    # We want to isolate different Timers. However `cpp_extension` will
+    # cache builds which will significantly reduce the cost of repeated
+    # invocations.
+    with LOCK:
+        name = f"timer_cpp_{abs(hash(src))}"
+        build_dir = os.path.join(_get_build_root(), name)
+        os.makedirs(build_dir, exist_ok=True)
+
+        src_path = os.path.join(build_dir, "timer_src.cpp")
+        with open(src_path, "w") as f:
+            f.write(src)
+
+    # `cpp_extension` has its own locking scheme, so we don't need our lock.
+    return cpp_extension.load(
+        name=name,
+        sources=[src_path],
+        build_directory=build_dir,
+        extra_cflags=CXX_FLAGS,
+        extra_include_paths=EXTRA_INCLUDE_PATHS,
+        is_python_module=not is_standalone,
+        is_standalone=is_standalone,
+    )
+
+
+def compile_timeit_template(*, stmt: str, setup: str, global_setup: str) -> TimeitModuleType:
+    template_path: str = os.path.join(SOURCE_ROOT, "timeit_template.cpp")
+    with open(template_path) as f:
+        src: str = f.read()
+
+    module = _compile_template(stmt=stmt, setup=setup, global_setup=global_setup, src=src, is_standalone=False)
+    assert isinstance(module, TimeitModuleType)
+    return module
+
+
+def compile_callgrind_template(*, stmt: str, setup: str, global_setup: str) -> str:
+    template_path: str = os.path.join(SOURCE_ROOT, "valgrind_wrapper", "timer_callgrind_template.cpp")
+    with open(template_path) as f:
+        src: str = f.read()
+
+    target = _compile_template(stmt=stmt, setup=setup, global_setup=global_setup, src=src, is_standalone=True)
+    assert isinstance(target, str)
+    return target
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/fuzzer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/fuzzer.py
new file mode 100644
index 0000000000000000000000000000000000000000..31d5ea3b6cc77c36c9425e72348da2d10dae63e8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/fuzzer.py
@@ -0,0 +1,462 @@
+# mypy: allow-untyped-defs
+import functools
+import itertools as it
+from typing import Any, Callable, Optional, Union
+
+import torch
+
+
+__all__ = [
+    "Fuzzer",
+    "FuzzedParameter", "ParameterAlias",
+    "FuzzedTensor",
+]
+
+
+_DISTRIBUTIONS = (
+    "loguniform",
+    "uniform",
+)
+
+
+class FuzzedParameter:
+    """Specification for a parameter to be generated during fuzzing."""
+    def __init__(
+        self,
+        name: str,
+        minval: Optional[Union[int, float]] = None,
+        maxval: Optional[Union[int, float]] = None,
+        distribution: Optional[Union[str, dict[Any, float]]] = None,
+        strict: bool = False,
+    ):
+        """
+        Args:
+            name:
+                A string name with which to identify the parameter.
+                FuzzedTensors can reference this string in their
+                specifications.
+            minval:
+                The lower bound for the generated value. See the description
+                of `distribution` for type behavior.
+            maxval:
+                The upper bound for the generated value. Type behavior is
+                identical to `minval`.
+            distribution:
+                Specifies the distribution from which this parameter should
+                be drawn. There are three possibilities:
+                    - "loguniform"
+                        Samples between `minval` and `maxval` (inclusive) such
+                        that the probabilities are uniform in log space. As a
+                        concrete example, if minval=1 and maxval=100, a sample
+                        is as likely to fall in [1, 10) as it is [10, 100].
+                    - "uniform"
+                        Samples are chosen with uniform probability between
+                        `minval` and `maxval` (inclusive). If either `minval`
+                        or `maxval` is a float then the distribution is the
+                        continuous uniform distribution; otherwise samples
+                        are constrained to the integers.
+                    - dict:
+                        If a dict is passed, the keys are taken to be choices
+                        for the variables and the values are interpreted as
+                        probabilities. (And must sum to one.)
+                If a dict is passed, `minval` and `maxval` must not be set.
+                Otherwise, they must be set.
+            strict:
+                If a parameter is strict, it will not be included in the
+                iterative resampling process which Fuzzer uses to find a
+                valid parameter configuration. This allows an author to
+                prevent skew from resampling for a given parameter (for
+                instance, a low size limit could inadvertently bias towards
+                Tensors with fewer dimensions) at the cost of more iterations
+                when generating parameters.
+        """
+        self._name = name
+        self._minval = minval
+        self._maxval = maxval
+        self._distribution = self._check_distribution(distribution)
+        self.strict = strict
+
+    @property
+    def name(self):
+        return self._name
+
+    def sample(self, state):
+        if self._distribution == "loguniform":
+            return self._loguniform(state)
+
+        if self._distribution == "uniform":
+            return self._uniform(state)
+
+        if isinstance(self._distribution, dict):
+            return self._custom_distribution(state)
+
+    def _check_distribution(self, distribution):
+        if not isinstance(distribution, dict):
+            assert distribution in _DISTRIBUTIONS
+        else:
+            assert not any(i < 0 for i in distribution.values()), "Probabilities cannot be negative"
+            assert abs(sum(distribution.values()) - 1) <= 1e-5, "Distribution is not normalized"
+            assert self._minval is None
+            assert self._maxval is None
+
+        return distribution
+
+    def _loguniform(self, state):
+        import numpy as np
+        output = int(2 ** state.uniform(
+            low=np.log2(self._minval) if self._minval is not None else None,
+            high=np.log2(self._maxval) if self._maxval is not None else None,
+        ))
+        if self._minval is not None and output < self._minval:
+            return self._minval
+        if self._maxval is not None and output > self._maxval:
+            return self._maxval
+        return output
+
+    def _uniform(self, state):
+        if isinstance(self._minval, int) and isinstance(self._maxval, int):
+            return int(state.randint(low=self._minval, high=self._maxval + 1))
+        return state.uniform(low=self._minval, high=self._maxval)
+
+    def _custom_distribution(self, state):
+        import numpy as np
+        # If we directly pass the keys to `choice`, numpy will convert
+        # them to numpy dtypes.
+        index = state.choice(
+            np.arange(len(self._distribution)),
+            p=tuple(self._distribution.values()))
+        return list(self._distribution.keys())[index]
+
+
+class ParameterAlias:
+    """Indicates that a parameter should alias the value of another parameter.
+
+    When used in conjunction with a custom distribution, this allows fuzzed
+    tensors to represent a broader range of behaviors. For example, the
+    following sometimes produces Tensors which broadcast:
+
+    Fuzzer(
+        parameters=[
+            FuzzedParameter("x_len", 4, 1024, distribution="uniform"),
+
+            # `y` will either be size one, or match the size of `x`.
+            FuzzedParameter("y_len", distribution={
+                0.5: 1,
+                0.5: ParameterAlias("x_len")
+            }),
+        ],
+        tensors=[
+            FuzzedTensor("x", size=("x_len",)),
+            FuzzedTensor("y", size=("y_len",)),
+        ],
+    )
+
+    Chains of alias' are allowed, but may not contain cycles.
+    """
+    def __init__(self, alias_to):
+        self.alias_to = alias_to
+
+    def __repr__(self):
+        return f"ParameterAlias[alias_to: {self.alias_to}]"
+
+
+def dtype_size(dtype):
+    if dtype == torch.bool:
+        return 1
+    if dtype.is_floating_point or dtype.is_complex:
+        return int(torch.finfo(dtype).bits / 8)
+    return int(torch.iinfo(dtype).bits / 8)
+
+
+def prod(values, base=1):
+    """np.prod can overflow, so for sizes the product should be done in Python.
+
+    Even though np.prod type promotes to int64, it can still overflow in which
+    case the negative value will pass the size check and OOM when attempting to
+    actually allocate the Tensor.
+    """
+    return functools.reduce(lambda x, y: int(x) * int(y), values, base)
+
+
+class FuzzedTensor:
+    def __init__(
+        self,
+        name: str,
+        size: tuple[Union[str, int], ...],
+        steps: Optional[tuple[Union[str, int], ...]] = None,
+        probability_contiguous: float = 0.5,
+        min_elements: Optional[int] = None,
+        max_elements: Optional[int] = None,
+        max_allocation_bytes: Optional[int] = None,
+        dim_parameter: Optional[str] = None,
+        roll_parameter: Optional[str] = None,
+        dtype=torch.float32,
+        cuda=False,
+        tensor_constructor: Optional[Callable] = None
+    ):
+        """
+        Args:
+            name:
+                A string identifier for the generated Tensor.
+            size:
+                A tuple of integers or strings specifying the size of the generated
+                Tensor. String values will replaced with a concrete int during the
+                generation process, while ints are simply passed as literals.
+            steps:
+                An optional tuple with the same length as `size`. This indicates
+                that a larger Tensor should be allocated, and then sliced to
+                produce the generated Tensor. For instance, if size is (4, 8)
+                and steps is (1, 4), then a tensor `t` of size (4, 32) will be
+                created and then `t[:, ::4]` will be used. (Allowing one to test
+                Tensors with strided memory.)
+            probability_contiguous:
+                A number between zero and one representing the chance that the
+                generated Tensor has a contiguous memory layout. This is achieved by
+                randomly permuting the shape of a Tensor, calling `.contiguous()`,
+                and then permuting back. This is applied before `steps`, which can
+                also cause a Tensor to be non-contiguous.
+            min_elements:
+                The minimum number of parameters that this Tensor must have for a
+                set of parameters to be valid. (Otherwise they are resampled.)
+            max_elements:
+                Like `min_elements`, but setting an upper bound.
+            max_allocation_bytes:
+                Like `max_elements`, but for the size of Tensor that must be
+                allocated prior to slicing for `steps` (if applicable). For
+                example, a FloatTensor with size (1024, 1024) and steps (4, 4)
+                would have 1M elements, but would require a 64 MB allocation.
+            dim_parameter:
+                The length of `size` and `steps` will be truncated to this value.
+                This allows Tensors of varying dimensions to be generated by the
+                Fuzzer.
+            dtype:
+                The PyTorch dtype of the generated Tensor.
+            cuda:
+                Whether to place the Tensor on a GPU.
+            tensor_constructor:
+                Callable which will be used instead of the default Tensor
+                construction method. This allows the author to enforce properties
+                of the Tensor (e.g. it can only have certain values). The dtype and
+                concrete shape of the Tensor to be created will be passed, and
+                concrete values of all parameters will be passed as kwargs. Note
+                that transformations to the result (permuting, slicing) will be
+                performed by the Fuzzer; the tensor_constructor is only responsible
+                for creating an appropriately sized Tensor.
+        """
+        self._name = name
+        self._size = size
+        self._steps = steps
+        self._probability_contiguous = probability_contiguous
+        self._min_elements = min_elements
+        self._max_elements = max_elements
+        self._max_allocation_bytes = max_allocation_bytes
+        self._dim_parameter = dim_parameter
+        self._dtype = dtype
+        self._cuda = cuda
+        self._tensor_constructor = tensor_constructor
+
+    @property
+    def name(self):
+        return self._name
+
+    @staticmethod
+    def default_tensor_constructor(size, dtype, **kwargs):
+        if dtype.is_floating_point or dtype.is_complex:
+            return torch.rand(size=size, dtype=dtype, device="cpu")
+        else:
+            return torch.randint(1, 127, size=size, dtype=dtype, device="cpu")
+
+    def _make_tensor(self, params, state):
+        import numpy as np
+        size, steps, allocation_size = self._get_size_and_steps(params)
+        constructor = (
+            self._tensor_constructor or
+            self.default_tensor_constructor
+        )
+
+        raw_tensor = constructor(size=allocation_size, dtype=self._dtype, **params)
+        if self._cuda:
+            raw_tensor = raw_tensor.cuda()
+
+        # Randomly permute the Tensor and call `.contiguous()` to force re-ordering
+        # of the memory, and then permute it back to the original shape.
+        dim = len(size)
+        order = np.arange(dim)
+        if state.rand() > self._probability_contiguous:
+            while dim > 1 and np.all(order == np.arange(dim)):
+                order = state.permutation(raw_tensor.dim())
+
+            raw_tensor = raw_tensor.permute(tuple(order)).contiguous()
+            raw_tensor = raw_tensor.permute(tuple(np.argsort(order)))
+
+        slices = [slice(0, size * step, step) for size, step in zip(size, steps)]
+        tensor = raw_tensor[slices]
+
+        properties = {
+            "numel": int(tensor.numel()),
+            "order": order,
+            "steps": steps,
+            "is_contiguous": tensor.is_contiguous(),
+            "dtype": str(self._dtype),
+        }
+
+        return tensor, properties
+
+    def _get_size_and_steps(self, params):
+        dim = (
+            params[self._dim_parameter]
+            if self._dim_parameter is not None
+            else len(self._size)
+        )
+
+        def resolve(values, dim):
+            """Resolve values into concrete integers."""
+            values = tuple(params.get(i, i) for i in values)
+            if len(values) > dim:
+                values = values[:dim]
+            if len(values) < dim:
+                values = values + tuple(1 for _ in range(dim - len(values)))
+            return values
+
+        size = resolve(self._size, dim)
+        steps = resolve(self._steps or (), dim)
+        allocation_size = tuple(size_i * step_i for size_i, step_i in zip(size, steps))
+        return size, steps, allocation_size
+
+    def satisfies_constraints(self, params):
+        size, _, allocation_size = self._get_size_and_steps(params)
+        # Product is computed in Python to avoid integer overflow.
+        num_elements = prod(size)
+        assert num_elements >= 0
+
+        allocation_bytes = prod(allocation_size, base=dtype_size(self._dtype))
+
+        def nullable_greater(left, right):
+            if left is None or right is None:
+                return False
+            return left > right
+
+        return not any((
+            nullable_greater(num_elements, self._max_elements),
+            nullable_greater(self._min_elements, num_elements),
+            nullable_greater(allocation_bytes, self._max_allocation_bytes),
+        ))
+
+
+class Fuzzer:
+    def __init__(
+        self,
+        parameters: list[Union[FuzzedParameter, list[FuzzedParameter]]],
+        tensors: list[Union[FuzzedTensor, list[FuzzedTensor]]],
+        constraints: Optional[list[Callable]] = None,
+        seed: Optional[int] = None
+    ):
+        """
+        Args:
+            parameters:
+                List of FuzzedParameters which provide specifications
+                for generated parameters. Iterable elements will be
+                unpacked, though arbitrary nested structures will not.
+            tensors:
+                List of FuzzedTensors which define the Tensors which
+                will be created each step based on the parameters for
+                that step. Iterable elements will be unpacked, though
+                arbitrary nested structures will not.
+            constraints:
+                List of callables. They will be called with params
+                as kwargs, and if any of them return False the current
+                set of parameters will be rejected.
+            seed:
+                Seed for the RandomState used by the Fuzzer. This will
+                also be used to set the PyTorch random seed so that random
+                ops will create reproducible Tensors.
+        """
+        import numpy as np
+        if seed is None:
+            seed = int(np.random.RandomState().randint(0, 2 ** 32 - 1, dtype=np.int64))
+        self._seed = seed
+        self._parameters = Fuzzer._unpack(parameters, FuzzedParameter)
+        self._tensors = Fuzzer._unpack(tensors, FuzzedTensor)
+        self._constraints = constraints or ()
+
+        p_names = {p.name for p in self._parameters}
+        t_names = {t.name for t in self._tensors}
+        name_overlap = p_names.intersection(t_names)
+        if name_overlap:
+            raise ValueError(f"Duplicate names in parameters and tensors: {name_overlap}")
+
+        self._rejections = 0
+        self._total_generated = 0
+
+    @staticmethod
+    def _unpack(values, cls):
+        return tuple(it.chain.from_iterable(
+            [[i] if isinstance(i, cls) else i for i in values]
+        ))
+
+    def take(self, n):
+        import numpy as np
+        state = np.random.RandomState(self._seed)
+        torch.manual_seed(state.randint(low=0, high=2 ** 63, dtype=np.int64))
+        for _ in range(n):
+            params = self._generate(state)
+            tensors = {}
+            tensor_properties = {}
+            for t in self._tensors:
+                tensor, properties = t._make_tensor(params, state)
+                tensors[t.name] = tensor
+                tensor_properties[t.name] = properties
+            yield tensors, tensor_properties, params
+
+    @property
+    def rejection_rate(self):
+        if not self._total_generated:
+            return 0.
+        return self._rejections / self._total_generated
+
+    def _generate(self, state):
+        strict_params: dict[str, Union[float, int, ParameterAlias]] = {}
+        for _ in range(1000):
+            candidate_params: dict[str, Union[float, int, ParameterAlias]] = {}
+            for p in self._parameters:
+                if p.strict:
+                    if p.name in strict_params:
+                        candidate_params[p.name] = strict_params[p.name]
+                    else:
+                        candidate_params[p.name] = p.sample(state)
+                        strict_params[p.name] = candidate_params[p.name]
+                else:
+                    candidate_params[p.name] = p.sample(state)
+
+            candidate_params = self._resolve_aliases(candidate_params)
+
+            self._total_generated += 1
+            if not all(f(candidate_params) for f in self._constraints):
+                self._rejections += 1
+                continue
+
+            if not all(t.satisfies_constraints(candidate_params) for t in self._tensors):
+                self._rejections += 1
+                continue
+
+            return candidate_params
+        raise ValueError("Failed to generate a set of valid parameters.")
+
+    @staticmethod
+    def _resolve_aliases(params):
+        params = dict(params)
+        alias_count = sum(isinstance(v, ParameterAlias) for v in params.values())
+
+        keys = list(params.keys())
+        while alias_count:
+            for k in keys:
+                v = params[k]
+                if isinstance(v, ParameterAlias):
+                    params[k] = params[v.alias_to]
+            alias_count_new = sum(isinstance(v, ParameterAlias) for v in params.values())
+            if alias_count == alias_count_new:
+                raise ValueError(f"ParameterAlias cycle detected\n{params}")
+
+            alias_count = alias_count_new
+
+        return params
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/sparse_fuzzer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/sparse_fuzzer.py
new file mode 100644
index 0000000000000000000000000000000000000000..498f94ca26f11118c620214534f3bc6e91ed2f5d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/sparse_fuzzer.py
@@ -0,0 +1,121 @@
+# mypy: allow-untyped-defs
+from typing import Optional, Union
+from numbers import Number
+import torch
+from torch.utils.benchmark import FuzzedTensor
+import math
+
+class FuzzedSparseTensor(FuzzedTensor):
+    def __init__(
+        self,
+        name: str,
+        size: tuple[Union[str, int], ...],
+        min_elements: Optional[int] = None,
+        max_elements: Optional[int] = None,
+        dim_parameter: Optional[str] = None,
+        sparse_dim: Optional[str] = None,
+        nnz: Optional[str] = None,
+        density: Optional[str] = None,
+        coalesced: Optional[str] = None,
+        dtype=torch.float32,
+        cuda=False
+    ):
+        """
+        Args:
+            name:
+                A string identifier for the generated Tensor.
+            size:
+                A tuple of integers or strings specifying the size of the generated
+                Tensor. String values will replaced with a concrete int during the
+                generation process, while ints are simply passed as literals.
+            min_elements:
+                The minimum number of parameters that this Tensor must have for a
+                set of parameters to be valid. (Otherwise they are resampled.)
+            max_elements:
+                Like `min_elements`, but setting an upper bound.
+            dim_parameter:
+                The length of `size` will be truncated to this value.
+                This allows Tensors of varying dimensions to be generated by the
+                Fuzzer.
+            sparse_dim:
+                The number of sparse dimensions in a sparse tensor.
+            density:
+                This value allows tensors of varying sparsities to be generated by the Fuzzer.
+            coalesced:
+                The sparse tensor format permits uncoalesced sparse tensors,
+                where there may be duplicate coordinates in the indices.
+            dtype:
+                The PyTorch dtype of the generated Tensor.
+            cuda:
+                Whether to place the Tensor on a GPU.
+        """
+        super().__init__(name=name, size=size, min_elements=min_elements,
+                         max_elements=max_elements, dim_parameter=dim_parameter, dtype=dtype, cuda=cuda)
+        self._density = density
+        self._coalesced = coalesced
+        self._sparse_dim = sparse_dim
+
+    @staticmethod
+    def sparse_tensor_constructor(size, dtype, sparse_dim, nnz, is_coalesced):
+        """sparse_tensor_constructor creates a sparse tensor with coo format.
+
+        Note that when `is_coalesced` is False, the number of elements is doubled but the number of indices
+        represents the same amount of number of non zeros `nnz`, i.e, this is virtually the same tensor
+        with the same sparsity pattern. Moreover, most of the sparse operation will use coalesce() method
+        and what we want here is to get a sparse tensor with the same `nnz` even if this is coalesced or not.
+
+        In the other hand when `is_coalesced` is True the number of elements is reduced in the coalescing process
+        by an unclear amount however the probability to generate duplicates indices are low for most of the cases.
+        This decision was taken on purpose to maintain the construction cost as low as possible.
+        """
+        if isinstance(size, Number):
+            size = [size] * sparse_dim
+        assert all(size[d] > 0 for d in range(sparse_dim)) or nnz == 0, 'invalid arguments'
+        v_size = [nnz] + list(size[sparse_dim:])
+        if dtype.is_floating_point:
+            v = torch.rand(size=v_size, dtype=dtype, device="cpu")
+        else:
+            v = torch.randint(1, 127, size=v_size, dtype=dtype, device="cpu")
+
+        i = torch.rand(sparse_dim, nnz, device="cpu")
+        i.mul_(torch.tensor(size[:sparse_dim]).unsqueeze(1).to(i))
+        i = i.to(torch.long)
+
+        if not is_coalesced:
+            v = torch.cat([v, torch.randn_like(v)], 0)
+            i = torch.cat([i, i], 1)
+
+        x = torch.sparse_coo_tensor(i, v, torch.Size(size))
+        if is_coalesced:
+            x = x.coalesce()
+        return x
+
+    def _make_tensor(self, params, state):
+        size, _, _ = self._get_size_and_steps(params)
+        density = params['density']
+        nnz = math.ceil(sum(size) * density)
+        assert nnz <= sum(size)
+
+        is_coalesced = params['coalesced']
+        sparse_dim = params['sparse_dim'] if self._sparse_dim else len(size)
+        sparse_dim = min(sparse_dim, len(size))
+        tensor = self.sparse_tensor_constructor(size, self._dtype, sparse_dim, nnz, is_coalesced)
+
+        if self._cuda:
+            tensor = tensor.cuda()
+        sparse_dim = tensor.sparse_dim()
+        dense_dim = tensor.dense_dim()
+        is_hybrid = len(size[sparse_dim:]) > 0
+
+        properties = {
+            "numel": int(tensor.numel()),
+            "shape": tensor.size(),
+            "is_coalesced": tensor.is_coalesced(),
+            "density": density,
+            "sparsity": 1.0 - density,
+            "sparse_dim": sparse_dim,
+            "dense_dim": dense_dim,
+            "is_hybrid": is_hybrid,
+            "dtype": str(self._dtype),
+        }
+        return tensor, properties
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/timeit_template.cpp b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/timeit_template.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..30b6f79c0b5aebca676035ac0b7c08cfce639b23
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/timeit_template.cpp
@@ -0,0 +1,43 @@
+/* C++ template for Timer.timeit
+
+This template will be consumed by `cpp_jit.py`, and will replace:
+    `GLOBAL_SETUP_TEMPLATE_LOCATION`,
+    `SETUP_TEMPLATE_LOCATION`
+      and
+    `STMT_TEMPLATE_LOCATION`
+sections with user provided statements.
+*/
+#include 
+
+#include 
+#include 
+#include 
+#include 
+
+// Global setup. (e.g. #includes)
+// GLOBAL_SETUP_TEMPLATE_LOCATION
+
+double timeit(int n) {
+  pybind11::gil_scoped_release no_gil;
+
+  // Setup
+  // SETUP_TEMPLATE_LOCATION
+
+  {
+    // Warmup
+    // STMT_TEMPLATE_LOCATION
+  }
+
+  // Main loop
+  auto start_time = std::chrono::high_resolution_clock::now();
+  for (const auto loop_idx : c10::irange(n)) {
+    (void)loop_idx;
+    // STMT_TEMPLATE_LOCATION
+  }
+  auto end_time = std::chrono::high_resolution_clock::now();
+  return std::chrono::duration(end_time - start_time).count();
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("timeit", &timeit);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/timer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..731ac21359a47a224bb944199bf53f81488dba88
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/timer.py
@@ -0,0 +1,541 @@
+"""Timer class based on the timeit.Timer class, but torch aware."""
+import enum
+import timeit
+import textwrap
+from typing import overload, Any, Callable, NoReturn, Optional, Union
+
+import torch
+from torch.utils.benchmark.utils import common, cpp_jit
+from torch.utils.benchmark.utils._stubs import TimerClass, TimeitModuleType
+from torch.utils.benchmark.utils.valgrind_wrapper import timer_interface as valgrind_timer_interface
+
+
+__all__ = ["Timer", "timer", "Language"]
+
+
+if torch.backends.cuda.is_built() and torch.cuda.is_available():  # type: ignore[no-untyped-call]
+    def timer() -> float:
+        torch.cuda.synchronize()
+        return timeit.default_timer()
+elif torch.xpu.is_available():
+    def timer() -> float:
+        torch.xpu.synchronize()
+        return timeit.default_timer()
+elif torch._C._get_privateuse1_backend_name() != "privateuseone":
+    privateuse1_device_handler = getattr(torch, torch._C._get_privateuse1_backend_name(), None) \
+        if torch._C._get_privateuse1_backend_name() != "cpu" else None
+
+    def timer() -> float:
+        if privateuse1_device_handler:
+            privateuse1_device_handler.synchronize()
+        return timeit.default_timer()
+else:
+    timer = timeit.default_timer
+
+
+class Language(enum.Enum):
+    PYTHON = 0
+    CPP = 1
+
+
+class CPPTimer:
+    def __init__(
+        self,
+        stmt: str,
+        setup: str,
+        global_setup: str,
+        timer: Callable[[], float],
+        globals: dict[str, Any],
+    ) -> None:
+        if timer is not timeit.default_timer:
+            raise NotImplementedError(
+                "PyTorch was built with CUDA and a GPU is present; however "
+                "Timer does not yet support GPU measurements. If your "
+                "code is CPU only, pass `timer=timeit.default_timer` to the "
+                "Timer's constructor to indicate this. (Note that this will "
+                "produce incorrect results if the GPU is in fact used, as "
+                "Timer will not synchronize CUDA.)"
+            )
+
+        if globals:
+            raise ValueError("C++ timing does not support globals.")
+
+        self._stmt: str = textwrap.dedent(stmt)
+        self._setup: str = textwrap.dedent(setup)
+        self._global_setup: str = textwrap.dedent(global_setup)
+        self._timeit_module: Optional[TimeitModuleType] = None
+
+    def timeit(self, number: int) -> float:
+        if self._timeit_module is None:
+            self._timeit_module = cpp_jit.compile_timeit_template(
+                stmt=self._stmt,
+                setup=self._setup,
+                global_setup=self._global_setup,
+            )
+
+        return self._timeit_module.timeit(number)
+
+
+class Timer:
+    """Helper class for measuring execution time of PyTorch statements.
+
+    For a full tutorial on how to use this class, see:
+    https://pytorch.org/tutorials/recipes/recipes/benchmark.html
+
+    The PyTorch Timer is based on `timeit.Timer` (and in fact uses
+    `timeit.Timer` internally), but with several key differences:
+
+    1) Runtime aware:
+        Timer will perform warmups (important as some elements of PyTorch are
+        lazily initialized), set threadpool size so that comparisons are
+        apples-to-apples, and synchronize asynchronous CUDA functions when
+        necessary.
+
+    2) Focus on replicates:
+        When measuring code, and particularly complex kernels / models,
+        run-to-run variation is a significant confounding factor. It is
+        expected that all measurements should include replicates to quantify
+        noise and allow median computation, which is more robust than mean.
+        To that effect, this class deviates from the `timeit` API by
+        conceptually merging `timeit.Timer.repeat` and `timeit.Timer.autorange`.
+        (Exact algorithms are discussed in method docstrings.) The `timeit`
+        method is replicated for cases where an adaptive strategy is not
+        desired.
+
+    3) Optional metadata:
+        When defining a Timer, one can optionally specify `label`, `sub_label`,
+        `description`, and `env`. (Defined later) These fields are included in
+        the representation of result object and by the `Compare` class to group
+        and display results for comparison.
+
+    4) Instruction counts
+        In addition to wall times, Timer can run a statement under Callgrind
+        and report instructions executed.
+
+    Directly analogous to `timeit.Timer` constructor arguments:
+
+        `stmt`, `setup`, `timer`, `globals`
+
+    PyTorch Timer specific constructor arguments:
+
+        `label`, `sub_label`, `description`, `env`, `num_threads`
+
+    Args:
+        stmt: Code snippet to be run in a loop and timed.
+
+        setup: Optional setup code. Used to define variables used in `stmt`
+
+        global_setup: (C++ only)
+            Code which is placed at the top level of the file for things like
+            `#include` statements.
+
+        timer:
+            Callable which returns the current time. If PyTorch was built
+            without CUDA or there is no GPU present, this defaults to
+            `timeit.default_timer`; otherwise it will synchronize CUDA before
+            measuring the time.
+
+        globals:
+            A dict which defines the global variables when `stmt` is being
+            executed. This is the other method for providing variables which
+            `stmt` needs.
+
+        label:
+            String which summarizes `stmt`. For instance, if `stmt` is
+            "torch.nn.functional.relu(torch.add(x, 1, out=out))"
+            one might set label to "ReLU(x + 1)" to improve readability.
+
+        sub_label:
+            Provide supplemental information to disambiguate measurements
+            with identical stmt or label. For instance, in our example
+            above sub_label might be "float" or "int", so that it is easy
+            to differentiate:
+            "ReLU(x + 1): (float)"
+
+            "ReLU(x + 1): (int)"
+            when printing Measurements or summarizing using `Compare`.
+
+        description:
+            String to distinguish measurements with identical label and
+            sub_label. The principal use of `description` is to signal to
+            `Compare` the columns of data. For instance one might set it
+            based on the input size  to create a table of the form: ::
+
+                                        | n=1 | n=4 | ...
+                                        ------------- ...
+                ReLU(x + 1): (float)    | ... | ... | ...
+                ReLU(x + 1): (int)      | ... | ... | ...
+
+
+            using `Compare`. It is also included when printing a Measurement.
+
+        env:
+            This tag indicates that otherwise identical tasks were run in
+            different environments, and are therefore not equivalent, for
+            instance when A/B testing a change to a kernel. `Compare` will
+            treat Measurements with different `env` specification as distinct
+            when merging replicate runs.
+
+        num_threads:
+            The size of the PyTorch threadpool when executing `stmt`. Single
+            threaded performance is important as both a key inference workload
+            and a good indicator of intrinsic algorithmic efficiency, so the
+            default is set to one. This is in contrast to the default PyTorch
+            threadpool size which tries to utilize all cores.
+    """
+
+    _timer_cls: type[TimerClass] = timeit.Timer
+
+    def __init__(
+        self,
+        stmt: str = "pass",
+        setup: str = "pass",
+        global_setup: str = "",
+        timer: Callable[[], float] = timer,
+        globals: Optional[dict[str, Any]] = None,
+        label: Optional[str] = None,
+        sub_label: Optional[str] = None,
+        description: Optional[str] = None,
+        env: Optional[str] = None,
+        num_threads: int = 1,
+        language: Union[Language, str] = Language.PYTHON,
+    ):
+        if not isinstance(stmt, str):
+            raise ValueError("Currently only a `str` stmt is supported.")
+
+        # We copy `globals` to prevent mutations from leaking.
+        # (For instance, `eval` adds the `__builtins__` key)
+        self._globals = dict(globals or {})
+
+        timer_kwargs = {}
+        if language in (Language.PYTHON, "py", "python"):
+            # Include `torch` if not specified as a convenience feature.
+            self._globals.setdefault("torch", torch)
+            self._language: Language = Language.PYTHON
+            if global_setup:
+                raise ValueError(
+                    f"global_setup is C++ only, got `{global_setup}`. Most "
+                    "likely this code can simply be moved to `setup`."
+                )
+
+        elif language in (Language.CPP, "cpp", "c++"):
+            assert self._timer_cls is timeit.Timer, "_timer_cls has already been swapped."
+            self._timer_cls = CPPTimer
+            setup = ("" if setup == "pass" else setup)
+            self._language = Language.CPP
+            timer_kwargs["global_setup"] = global_setup
+
+        else:
+            raise ValueError(f"Invalid language `{language}`.")
+
+        # Convenience adjustment so that multi-line code snippets defined in
+        # functions do not IndentationError (Python) or look odd (C++). The
+        # leading newline removal is for the initial newline that appears when
+        # defining block strings. For instance:
+        #   textwrap.dedent("""
+        #     print("This is a stmt")
+        #   """)
+        # produces '\nprint("This is a stmt")\n'.
+        #
+        # Stripping this down to 'print("This is a stmt")' doesn't change
+        # what gets executed, but it makes __repr__'s nicer.
+        stmt = textwrap.dedent(stmt)
+        stmt = (stmt[1:] if stmt and stmt[0] == "\n" else stmt).rstrip()
+        setup = textwrap.dedent(setup)
+        setup = (setup[1:] if setup and setup[0] == "\n" else setup).rstrip()
+
+        self._timer = self._timer_cls(
+            stmt=stmt,
+            setup=setup,
+            timer=timer,
+            globals=valgrind_timer_interface.CopyIfCallgrind.unwrap_all(self._globals),
+            **timer_kwargs,
+        )
+        self._task_spec = common.TaskSpec(
+            stmt=stmt,
+            setup=setup,
+            global_setup=global_setup,
+            label=label,
+            sub_label=sub_label,
+            description=description,
+            env=env,
+            num_threads=num_threads,
+        )
+
+    def _timeit(self, number: int) -> float:
+        # Even calling a timer in C++ takes ~50 ns, so no real operation should
+        # take less than 1 ns. (And this prevents divide by zero errors.)
+        return max(self._timer.timeit(number), 1e-9)
+
+    def timeit(self, number: int = 1000000) -> common.Measurement:
+        """Mirrors the semantics of timeit.Timer.timeit().
+
+        Execute the main statement (`stmt`) `number` times.
+        https://docs.python.org/3/library/timeit.html#timeit.Timer.timeit
+        """
+        with common.set_torch_threads(self._task_spec.num_threads):
+            # Warmup
+            self._timeit(number=max(int(number // 100), 2))
+
+            return common.Measurement(
+                number_per_run=number,
+                raw_times=[self._timeit(number=number)],
+                task_spec=self._task_spec
+            )
+
+    def repeat(self, repeat: int = -1, number: int = -1) -> None:
+        raise NotImplementedError("See `Timer.blocked_autorange.`")
+
+    def autorange(self, callback: Optional[Callable[[int, float], NoReturn]] = None) -> None:
+        raise NotImplementedError("See `Timer.blocked_autorange.`")
+
+    def _threaded_measurement_loop(
+        self,
+        number: int,
+        time_hook: Callable[[], float],
+        stop_hook: Callable[[list[float]], bool],
+        min_run_time: float,
+        max_run_time: Optional[float] = None,
+        callback: Optional[Callable[[int, float], NoReturn]] = None
+    ) -> list[float]:
+        total_time = 0.0
+        can_stop = False
+        times: list[float] = []
+        with common.set_torch_threads(self._task_spec.num_threads):
+            while (total_time < min_run_time) or (not can_stop):
+                time_spent = time_hook()
+                times.append(time_spent)
+                total_time += time_spent
+                if callback:
+                    callback(number, time_spent)
+                can_stop = stop_hook(times)
+                if max_run_time and total_time > max_run_time:
+                    break
+        return times
+
+    def _estimate_block_size(self, min_run_time: float) -> int:
+        with common.set_torch_threads(self._task_spec.num_threads):
+            # Estimate the block size needed for measurement to be negligible
+            # compared to the inner loop. This also serves as a warmup.
+            overhead = torch.tensor([self._timeit(0) for _ in range(5)]).median().item()
+            number = 1
+            while True:
+                time_taken = self._timeit(number)
+                relative_overhead = overhead / time_taken
+                if relative_overhead <= 1e-4 and time_taken >= min_run_time / 1000:
+                    break
+                if time_taken > min_run_time:
+                    break
+                # Avoid overflow in C++ pybind11 interface
+                if number * 10 > 2147483647:
+                    break
+                number *= 10
+        return number
+
+    def blocked_autorange(
+        self,
+        callback: Optional[Callable[[int, float], NoReturn]] = None,
+        min_run_time: float = 0.2,
+    ) -> common.Measurement:
+        """Measure many replicates while keeping timer overhead to a minimum.
+
+        At a high level, blocked_autorange executes the following pseudo-code::
+
+            `setup`
+
+            total_time = 0
+            while total_time < min_run_time
+                start = timer()
+                for _ in range(block_size):
+                    `stmt`
+                total_time += (timer() - start)
+
+        Note the variable `block_size` in the inner loop. The choice of block
+        size is important to measurement quality, and must balance two
+        competing objectives:
+
+            1) A small block size results in more replicates and generally
+               better statistics.
+
+            2) A large block size better amortizes the cost of `timer`
+               invocation, and results in a less biased measurement. This is
+               important because CUDA synchronization time is non-trivial
+               (order single to low double digit microseconds) and would
+               otherwise bias the measurement.
+
+        blocked_autorange sets block_size by running a warmup period,
+        increasing block size until timer overhead is less than 0.1% of
+        the overall computation. This value is then used for the main
+        measurement loop.
+
+        Returns:
+            A `Measurement` object that contains measured runtimes and
+            repetition counts, and can be used to compute statistics.
+            (mean, median, etc.)
+        """
+        number = self._estimate_block_size(min_run_time)
+
+        def time_hook() -> float:
+            return self._timeit(number)
+
+        def stop_hook(times: list[float]) -> bool:
+            return True
+
+        times = self._threaded_measurement_loop(
+            number, time_hook, stop_hook,
+            min_run_time=min_run_time,
+            callback=callback)
+
+        return common.Measurement(
+            number_per_run=number,
+            raw_times=times,
+            task_spec=self._task_spec
+        )
+
+    def adaptive_autorange(
+            self,
+            threshold: float = 0.1,
+            *,
+            min_run_time: float = 0.01,
+            max_run_time: float = 10.0,
+            callback: Optional[Callable[[int, float], NoReturn]] = None,
+    ) -> common.Measurement:
+        """Similar to `blocked_autorange` but also checks for variablility in measurements
+        and repeats until iqr/median is smaller than `threshold` or `max_run_time` is reached.
+
+
+        At a high level, adaptive_autorange executes the following pseudo-code::
+
+            `setup`
+
+            times = []
+            while times.sum < max_run_time
+                start = timer()
+                for _ in range(block_size):
+                    `stmt`
+                times.append(timer() - start)
+
+                enough_data = len(times)>3 and times.sum > min_run_time
+                small_iqr=times.iqr/times.mean float:
+            return self._timeit(number)
+
+        def stop_hook(times: list[float]) -> bool:
+            if len(times) > 3:
+                return common.Measurement(
+                    number_per_run=number,
+                    raw_times=times,
+                    task_spec=self._task_spec
+                ).meets_confidence(threshold=threshold)
+            return False
+        times = self._threaded_measurement_loop(
+            number, time_hook, stop_hook, min_run_time, max_run_time, callback=callback)
+
+        return common.Measurement(
+            number_per_run=number,
+            raw_times=times,
+            task_spec=self._task_spec
+        )
+
+    @overload
+    def collect_callgrind(
+        self,
+        number: int,
+        *,
+        repeats: None,
+        collect_baseline: bool,
+        retain_out_file: bool,
+    ) -> valgrind_timer_interface.CallgrindStats:
+        ...
+
+    @overload
+    def collect_callgrind(
+        self,
+        number: int,
+        *,
+        repeats: int,
+        collect_baseline: bool,
+        retain_out_file: bool,
+    ) -> tuple[valgrind_timer_interface.CallgrindStats, ...]:
+        ...
+
+    def collect_callgrind(
+        self,
+        number: int = 100,
+        *,
+        repeats: Optional[int] = None,
+        collect_baseline: bool = True,
+        retain_out_file: bool = False,
+    ) -> Any:
+        """Collect instruction counts using Callgrind.
+
+        Unlike wall times, instruction counts are deterministic
+        (modulo non-determinism in the program itself and small amounts of
+        jitter from the Python interpreter.) This makes them ideal for detailed
+        performance analysis. This method runs `stmt` in a separate process
+        so that Valgrind can instrument the program. Performance is severely
+        degraded due to the instrumentation, however this is ameliorated by
+        the fact that a small number of iterations is generally sufficient to
+        obtain good measurements.
+
+        In order to to use this method `valgrind`, `callgrind_control`, and
+        `callgrind_annotate` must be installed.
+
+        Because there is a process boundary between the caller (this process)
+        and the `stmt` execution, `globals` cannot contain arbitrary in-memory
+        data structures. (Unlike timing methods) Instead, globals are
+        restricted to builtins, `nn.Modules`'s, and TorchScripted functions/modules
+        to reduce the surprise factor from serialization and subsequent
+        deserialization. The `GlobalsBridge` class provides more detail on this
+        subject. Take particular care with nn.Modules: they rely on pickle and
+        you may need to add an import to `setup` for them to transfer properly.
+
+        By default, a profile for an empty statement will be collected and
+        cached to indicate how many instructions are from the Python loop which
+        drives `stmt`.
+
+        Returns:
+            A `CallgrindStats` object which provides instruction counts and
+            some basic facilities for analyzing and manipulating results.
+        """
+        if not isinstance(self._task_spec.stmt, str):
+            raise ValueError("`collect_callgrind` currently only supports string `stmt`")
+
+        if repeats is not None and repeats < 1:
+            raise ValueError("If specified, `repeats` must be >= 1")
+
+        # Check that the statement is valid. It doesn't guarantee success, but it's much
+        # simpler and quicker to raise an exception for a faulty `stmt` or `setup` in
+        # the parent process rather than the valgrind subprocess.
+        self._timeit(1)
+        is_python = (self._language == Language.PYTHON)
+        assert is_python or not self._globals
+        result = valgrind_timer_interface.wrapper_singleton().collect_callgrind(
+            task_spec=self._task_spec,
+            globals=self._globals,
+            number=number,
+            repeats=repeats or 1,
+            collect_baseline=collect_baseline and is_python,
+            is_python=is_python,
+            retain_out_file=retain_out_file,
+        )
+
+        return (result[0] if repeats is None else result)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/callgrind.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/callgrind.h
new file mode 100644
index 0000000000000000000000000000000000000000..f078cc82b95daf94d2bea51f1e1b1a8c12daea23
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/callgrind.h
@@ -0,0 +1,129 @@
+
+/*
+   ----------------------------------------------------------------
+
+   Notice that the following BSD-style license applies to this one
+   file (callgrind.h) only.  The rest of Valgrind is licensed under the
+   terms of the GNU General Public License, version 2, unless
+   otherwise indicated.  See the COPYING file in the source
+   distribution for details.
+
+   ----------------------------------------------------------------
+
+   This file is part of callgrind, a valgrind tool for cache simulation
+   and call tree tracing.
+
+   Copyright (C) 2003-2017 Josef Weidendorfer.  All rights reserved.
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+   1. Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+
+   2. The origin of this software must not be misrepresented; you must
+      not claim that you wrote the original software.  If you use this
+      software in a product, an acknowledgment in the product
+      documentation would be appreciated but is not required.
+
+   3. Altered source versions must be plainly marked as such, and must
+      not be misrepresented as being the original software.
+
+   4. The name of the author may not be used to endorse or promote
+      products derived from this software without specific prior written
+      permission.
+
+   THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
+   OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+   WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+   ARE DISCLAIMED.  IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
+   DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+   DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+   GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+   INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+   WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+   ----------------------------------------------------------------
+
+   Notice that the above BSD-style license applies to this one file
+   (callgrind.h) only.  The entire rest of Valgrind is licensed under
+   the terms of the GNU General Public License, version 2.  See the
+   COPYING file in the source distribution for details.
+
+   ----------------------------------------------------------------
+*/
+
+#ifndef __CALLGRIND_H
+#define __CALLGRIND_H
+
+#include "valgrind.h"
+
+/* !! ABIWARNING !! ABIWARNING !! ABIWARNING !! ABIWARNING !!
+   This enum comprises an ABI exported by Valgrind to programs
+   which use client requests.  DO NOT CHANGE THE ORDER OF THESE
+   ENTRIES, NOR DELETE ANY -- add new ones at the end.
+
+   The identification ('C','T') for Callgrind has historical
+   reasons: it was called "Calltree" before. Besides, ('C','G') would
+   clash with cachegrind.
+ */
+
+typedef
+   enum {
+      VG_USERREQ__DUMP_STATS = VG_USERREQ_TOOL_BASE('C','T'),
+      VG_USERREQ__ZERO_STATS,
+      VG_USERREQ__TOGGLE_COLLECT,
+      VG_USERREQ__DUMP_STATS_AT,
+      VG_USERREQ__START_INSTRUMENTATION,
+      VG_USERREQ__STOP_INSTRUMENTATION
+   } Vg_CallgrindClientRequest;
+
+/* Dump current state of cost centers, and zero them afterwards */
+#define CALLGRIND_DUMP_STATS                                    \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__DUMP_STATS,       \
+                                  0, 0, 0, 0, 0)
+
+/* Dump current state of cost centers, and zero them afterwards.
+   The argument is appended to a string stating the reason which triggered
+   the dump. This string is written as a description field into the
+   profile data dump. */
+#define CALLGRIND_DUMP_STATS_AT(pos_str)                        \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__DUMP_STATS_AT,    \
+                                  pos_str, 0, 0, 0, 0)
+
+/* Zero cost centers */
+#define CALLGRIND_ZERO_STATS                                    \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__ZERO_STATS,       \
+                                  0, 0, 0, 0, 0)
+
+/* Toggles collection state.
+   The collection state specifies whether the happening of events
+   should be noted or if they are to be ignored. Events are noted
+   by increment of counters in a cost center */
+#define CALLGRIND_TOGGLE_COLLECT                                \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__TOGGLE_COLLECT,   \
+                                  0, 0, 0, 0, 0)
+
+/* Start full callgrind instrumentation if not already switched on.
+   When cache simulation is done, it will flush the simulated cache;
+   this will lead to an artificial cache warmup phase afterwards with
+   cache misses which would not have happened in reality. */
+#define CALLGRIND_START_INSTRUMENTATION                              \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__START_INSTRUMENTATION, \
+                                  0, 0, 0, 0, 0)
+
+/* Stop full callgrind instrumentation if not already switched off.
+   This flushes Valgrinds translation cache, and does no additional
+   instrumentation afterwards, which effectivly will run at the same
+   speed as the "none" tool (ie. at minimal slowdown).
+   Use this to bypass Callgrind aggregation for uninteresting code parts.
+   To start Callgrind in this mode to ignore the setup phase, use
+   the option "--instr-atstart=no". */
+#define CALLGRIND_STOP_INSTRUMENTATION                               \
+  VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__STOP_INSTRUMENTATION,  \
+                                  0, 0, 0, 0, 0)
+
+#endif /* __CALLGRIND_H */
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/compat_bindings.cpp b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/compat_bindings.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..cd41f0de092f0b1488c8945edf2af80c6f9b596c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/compat_bindings.cpp
@@ -0,0 +1,35 @@
+/* Used to collect profiles of old versions of PyTorch. */
+#include 
+#include 
+
+bool _valgrind_supported_platform() {
+#if defined(NVALGRIND)
+  return false;
+#else
+  return true;
+#endif
+}
+
+void _valgrind_toggle() {
+#if defined(NVALGRIND)
+  TORCH_CHECK(false, "Valgrind is not supported.");
+#else
+  CALLGRIND_TOGGLE_COLLECT;
+#endif
+}
+
+void _valgrind_toggle_and_dump_stats() {
+#if defined(NVALGRIND)
+  TORCH_CHECK(false, "Valgrind is not supported.");
+#else
+  // NB: See note in Module.cpp
+  CALLGRIND_TOGGLE_COLLECT;
+  CALLGRIND_DUMP_STATS;
+#endif
+}
+
+PYBIND11_MODULE(callgrind_bindings, m) {
+  m.def("_valgrind_supported_platform", &_valgrind_supported_platform);
+  m.def("_valgrind_toggle", &_valgrind_toggle);
+  m.def("_valgrind_toggle_and_dump_stats", &_valgrind_dump_stats);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/timer_callgrind_template.cpp b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/timer_callgrind_template.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..587685c7df7445b299c35462307f47cf6012a00d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/timer_callgrind_template.cpp
@@ -0,0 +1,68 @@
+/* C++ template for Timer.collect_callgrind
+
+This template will be consumed by `cpp_jit.py`, and will replace:
+    `GLOBAL_SETUP_TEMPLATE_LOCATION`,
+    `SETUP_TEMPLATE_LOCATION`
+      and
+    `STMT_TEMPLATE_LOCATION`
+sections with user provided statements.
+*/
+
+#include 
+#include 
+#include 
+
+#include 
+
+// Global setup. (e.g. #includes)
+// GLOBAL_SETUP_TEMPLATE_LOCATION
+
+#if defined(NVALGRIND)
+static_assert(false);
+#endif
+
+int main(int argc, char* argv[]) {
+  // This file should only be called inside of `Timer`, so we can adopt a
+  // very simple and rigid argument parsing scheme.
+  TORCH_CHECK(argc == 9);
+  TORCH_CHECK(std::string(argv[1]) == "--number");
+  auto number = std::stoi(argv[2]);
+
+  TORCH_CHECK(
+      std::string(argv[3]) == "--number-warmup" ||
+      std::string(argv[3]) == "--number_warmup");
+  auto number_warmup = std::stoi(argv[4]);
+
+  TORCH_CHECK(std::string(argv[5]) == "--repeats");
+  auto repeats = std::stoi(argv[6]);
+
+  TORCH_CHECK(
+      std::string(argv[7]) == "--number-threads" ||
+      std::string(argv[7]) == "--number_threads");
+  auto number_threads = std::stoi(argv[8]);
+  torch::set_num_threads(number_threads);
+
+  // Setup
+  // SETUP_TEMPLATE_LOCATION
+
+  // Warmup
+  for (const auto i : c10::irange(number_warmup)) {
+    (void)i;
+    // STMT_TEMPLATE_LOCATION
+  }
+
+  // Main loop
+  for (const auto repeat : c10::irange(repeats)) {
+    (void)repeat;
+    CALLGRIND_TOGGLE_COLLECT;
+
+    for (const auto i : c10::irange(number)) {
+      (void)i;
+      // STMT_TEMPLATE_LOCATION
+    }
+
+    // NB: See note in Module.cpp
+    CALLGRIND_TOGGLE_COLLECT;
+    CALLGRIND_DUMP_STATS;
+  }
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..900d8c3722a8a5b9c4890d49a35e59dbb19d7bb8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py
@@ -0,0 +1,910 @@
+"""Intermediate layer between `Timer` and `valgrind`."""
+import collections
+import enum
+import dataclasses
+import itertools as it
+import os
+import pickle
+import re
+import shutil
+import subprocess
+import sys
+import textwrap
+from typing import (
+    cast, Any, Callable, NamedTuple,
+    Optional, Union, TYPE_CHECKING)
+from collections.abc import Iterator
+
+import torch
+from torch.utils.benchmark.utils import common, cpp_jit
+from torch.utils.benchmark.utils._stubs import CallgrindModuleType
+import operator
+
+
+__all__ = ["FunctionCount", "FunctionCounts", "CallgrindStats", "CopyIfCallgrind"]
+
+
+if TYPE_CHECKING:
+    CompletedProcessType = subprocess.CompletedProcess[str]
+else:
+    CompletedProcessType = subprocess.CompletedProcess
+
+
+class FunctionCount(NamedTuple):
+    # TODO(#105471): Rename the count field
+    count: int  # type: ignore[assignment]
+    function: str
+
+
+@dataclasses.dataclass(repr=False, eq=False, frozen=True)
+class FunctionCounts:
+    """Container for manipulating Callgrind results.
+
+    It supports:
+        1) Addition and subtraction to combine or diff results.
+        2) Tuple-like indexing.
+        3) A `denoise` function which strips CPython calls which are known to
+           be non-deterministic and quite noisy.
+        4) Two higher order methods (`filter` and `transform`) for custom
+           manipulation.
+    """
+    _data: tuple[FunctionCount, ...]
+    inclusive: bool
+    truncate_rows: bool = True
+
+    # For normal use, torch._tensor_str.PRINT_OPTS.linewidth determines
+    # the print settings. This is simply to allow hermetic unit tests.
+    _linewidth: Optional[int] = None
+
+    def __iter__(self) -> Iterator[FunctionCount]:
+        yield from self._data
+
+    def __len__(self) -> int:
+        return len(self._data)
+
+    def __getitem__(self, item: Any) -> Union[FunctionCount, "FunctionCounts"]:
+        data: Union[FunctionCount, tuple[FunctionCount, ...]] = self._data[item]
+        return (
+            FunctionCounts(cast(tuple[FunctionCount, ...], data), self.inclusive, truncate_rows=False)
+            if isinstance(data, tuple) else data
+        )
+
+    def __repr__(self) -> str:
+        count_len = 0
+        for c, _ in self:
+            # Account for sign in string length.
+            count_len = max(count_len, len(str(c)) + int(c < 0))
+
+        lines = []
+        linewidth = self._linewidth or torch._tensor_str.PRINT_OPTS.linewidth
+        fn_str_len = max(linewidth - count_len - 4, 40)
+        for c, fn in self:
+            if len(fn) > fn_str_len:
+                left_len = int((fn_str_len - 5) // 2)
+                fn = fn[:left_len] + " ... " + fn[-(fn_str_len - left_len - 5):]
+            lines.append(f"  {c:>{count_len}}  {fn}")
+
+        if self.truncate_rows and len(lines) > 18:
+            lines = lines[:9] + ["...".rjust(count_len + 2)] + lines[-9:]
+
+        if not self.inclusive:
+            lines.extend(["", f"Total: {self.sum()}"])
+
+        return "\n".join([super().__repr__()] + lines)
+
+    def __add__(
+        self,
+        other: "FunctionCounts",
+    ) -> "FunctionCounts":
+        return self._merge(other, lambda c: c)
+
+    def __sub__(
+        self,
+        other: "FunctionCounts",
+    ) -> "FunctionCounts":
+        return self._merge(other, operator.neg)
+
+    def __mul__(self, other: Union[int, float]) -> "FunctionCounts":
+        return self._from_dict({
+            fn: int(c * other) for c, fn in self._data
+        }, self.inclusive)
+
+    def transform(self, map_fn: Callable[[str], str]) -> "FunctionCounts":
+        """Apply `map_fn` to all of the function names.
+
+        This can be used to regularize function names (e.g. stripping irrelevant
+        parts of the file path), coalesce entries by mapping multiple functions
+        to the same name (in which case the counts are added together), etc.
+        """
+        counts: collections.defaultdict[str, int] = collections.defaultdict(int)
+        for c, fn in self._data:
+            counts[map_fn(fn)] += c
+
+        return self._from_dict(counts, self.inclusive)
+
+    def filter(self, filter_fn: Callable[[str], bool]) -> "FunctionCounts":
+        """Keep only the elements where `filter_fn` applied to function name returns True."""
+        return FunctionCounts(tuple(i for i in self if filter_fn(i.function)), self.inclusive)
+
+    def sum(self) -> int:
+        return sum(c for c, _ in self)
+
+    def denoise(self) -> "FunctionCounts":
+        """Remove known noisy instructions.
+
+        Several instructions in the CPython interpreter are rather noisy. These
+        instructions involve unicode to dictionary lookups which Python uses to
+        map variable names. FunctionCounts is generally a content agnostic
+        container, however this is sufficiently important for obtaining
+        reliable results to warrant an exception."""
+        return self.filter(lambda fn: "dictobject.c:lookdict_unicode" not in fn)
+
+    def _merge(
+        self,
+        second: "FunctionCounts",
+        merge_fn: Callable[[int], int]
+    ) -> "FunctionCounts":
+        assert self.inclusive == second.inclusive, "Cannot merge inclusive and exclusive counts."
+        counts: collections.defaultdict[str, int] = collections.defaultdict(int)
+        for c, fn in self:
+            counts[fn] += c
+
+        for c, fn in second:
+            counts[fn] += merge_fn(c)
+
+        return self._from_dict(counts, self.inclusive)
+
+    @staticmethod
+    def _from_dict(counts: dict[str, int], inclusive: bool) -> "FunctionCounts":
+        flat_counts = (FunctionCount(c, fn) for fn, c in counts.items() if c)
+        return FunctionCounts(tuple(sorted(flat_counts, reverse=True)), inclusive)
+
+
+@dataclasses.dataclass(repr=False, eq=False, frozen=True)
+class CallgrindStats:
+    """Top level container for Callgrind results collected by Timer.
+
+    Manipulation is generally done using the FunctionCounts class, which is
+    obtained by calling `CallgrindStats.stats(...)`. Several convenience
+    methods are provided as well; the most significant is
+    `CallgrindStats.as_standardized()`.
+    """
+    task_spec: common.TaskSpec
+    number_per_run: int
+    built_with_debug_symbols: bool
+    baseline_inclusive_stats: FunctionCounts
+    baseline_exclusive_stats: FunctionCounts
+    stmt_inclusive_stats: FunctionCounts
+    stmt_exclusive_stats: FunctionCounts
+    stmt_callgrind_out: Optional[str]
+
+    def __repr__(self) -> str:
+        base_stats = self.baseline_exclusive_stats
+        output = f"""
+{super().__repr__()}
+{self.task_spec.summarize()}
+  {'':>25}All{'':>10}Noisy symbols removed
+    Instructions: {self.counts(denoise=False):>12}{'':>15}{self.counts(denoise=True):>12}
+    Baseline:     {base_stats.sum():>12}{'':>15}{base_stats.denoise().sum():>12}
+{self.number_per_run} runs per measurement, {self.task_spec.num_threads} thread{'s' if self.task_spec.num_threads > 1 else ''}
+""".strip()
+        if not self.built_with_debug_symbols:
+            output += textwrap.dedent("""
+            Warning: PyTorch was not built with debug symbols.
+                     Source information may be limited. Rebuild with
+                     REL_WITH_DEB_INFO=1 for more detailed results.""")
+        return output
+
+    def stats(self, inclusive: bool = False) -> FunctionCounts:
+        """Returns detailed function counts.
+
+        Conceptually, the FunctionCounts returned can be thought of as a tuple
+        of (count, path_and_function_name) tuples.
+
+        `inclusive` matches the semantics of callgrind. If True, the counts
+        include instructions executed by children. `inclusive=True` is useful
+        for identifying hot spots in code; `inclusive=False` is useful for
+        reducing noise when diffing counts from two different runs. (See
+        CallgrindStats.delta(...) for more details)
+        """
+        return self.stmt_inclusive_stats if inclusive else self.stmt_exclusive_stats
+
+    def counts(self, *, denoise: bool = False) -> int:
+        """Returns the total number of instructions executed.
+
+        See `FunctionCounts.denoise()` for an explanation of the `denoise` arg.
+        """
+        stats = self.stmt_exclusive_stats
+        return (stats.denoise() if denoise else stats).sum()
+
+    # FIXME: Once 3.7 is the minimum version, type annotate `other` per PEP 563
+    def delta(
+        self,
+        other: "CallgrindStats",
+        inclusive: bool = False,
+    ) -> FunctionCounts:
+        """Diff two sets of counts.
+
+        One common reason to collect instruction counts is to determine the
+        the effect that a particular change will have on the number of instructions
+        needed to perform some unit of work. If a change increases that number, the
+        next logical question is "why". This generally involves looking at what part
+        if the code increased in instruction count. This function automates that
+        process so that one can easily diff counts on both an inclusive and
+        exclusive basis.
+        """
+        return self.stats(inclusive=inclusive) - other.stats(inclusive=inclusive)
+
+    def as_standardized(self) -> "CallgrindStats":
+        """Strip library names and some prefixes from function strings.
+
+        When comparing two different sets of instruction counts, on stumbling
+        block can be path prefixes. Callgrind includes the full filepath
+        when reporting a function (as it should). However, this can cause
+        issues when diffing profiles. If a key component such as Python
+        or PyTorch was built in separate locations in the two profiles, which
+        can result in something resembling::
+
+            23234231 /tmp/first_build_dir/thing.c:foo(...)
+             9823794 /tmp/first_build_dir/thing.c:bar(...)
+              ...
+               53453 .../aten/src/Aten/...:function_that_actually_changed(...)
+              ...
+             -9823794 /tmp/second_build_dir/thing.c:bar(...)
+            -23234231 /tmp/second_build_dir/thing.c:foo(...)
+
+        Stripping prefixes can ameliorate this issue by regularizing the
+        strings and causing better cancellation of equivalent call sites
+        when diffing.
+        """
+        def strip(stats: FunctionCounts) -> FunctionCounts:
+            transforms = (
+                # PyTorch may have been built in different locations.
+                (r"^.+build/\.\./", "build/../"),
+                (r"^.+/" + re.escape("build/aten/"), "build/aten/"),
+
+                # "Python" and "Objects" come from CPython.
+                (r"^.+/" + re.escape("Python/"), "Python/"),
+                (r"^.+/" + re.escape("Objects/"), "Objects/"),
+
+                # Strip library name. e.g. `libtorch.so`
+                (r"\s\[.+\]$", ""),
+            )
+
+            for before, after in transforms:
+                stats = stats.transform(lambda fn: re.sub(before, after, fn))
+
+            return stats
+
+        return CallgrindStats(
+            task_spec=self.task_spec,
+            number_per_run=self.number_per_run,
+            built_with_debug_symbols=self.built_with_debug_symbols,
+            baseline_inclusive_stats=strip(self.baseline_inclusive_stats),
+            baseline_exclusive_stats=strip(self.baseline_exclusive_stats),
+            stmt_inclusive_stats=strip(self.stmt_inclusive_stats),
+            stmt_exclusive_stats=strip(self.stmt_exclusive_stats),
+
+            # `as_standardized` will change symbol names, so the contents will
+            # no longer map directly to `callgrind.out`
+            stmt_callgrind_out=None,
+        )
+
+
+class Serialization(enum.Enum):
+    PICKLE = 0
+    TORCH = 1
+    TORCH_JIT = 2
+
+
+_GLOBALS_ALLOWED_TYPES: dict[Serialization, tuple[Any, ...]] = {
+    Serialization.PICKLE: (str, bytes, bool, int, float, complex),
+    Serialization.TORCH_JIT: (torch.jit.ScriptFunction, torch.jit.ScriptModule),
+    Serialization.TORCH: (torch.nn.Module,),
+}
+
+
+class CopyIfCallgrind:
+    """Signal that a global may be replaced with a deserialized copy.
+
+    See `GlobalsBridge` for why this matters.
+    """
+    def __init__(self, value: Any, *, setup: Optional[str] = None):
+        for method, supported_types in _GLOBALS_ALLOWED_TYPES.items():
+            if any(isinstance(value, t) for t in supported_types):
+                self._value: Any = value
+                self._setup: Optional[str] = setup
+                self._serialization: Serialization = method
+                break
+        else:
+            supported_str = "\n".join([
+                getattr(t, "__name__", repr(t))
+                for t in it.chain(_GLOBALS_ALLOWED_TYPES.values())])
+
+            raise ValueError(
+                f"Unsupported type: {type(value)}\n"
+                f"`collect_callgrind` restricts globals to the following types:\n"
+                f"{textwrap.indent(supported_str, '  ')}"
+            )
+
+    @property
+    def value(self) -> Any:
+        return self._value
+
+    @property
+    def setup(self) -> Optional[str]:
+        return self._setup
+
+    @property
+    def serialization(self) -> Serialization:
+        return self._serialization
+
+    @staticmethod
+    def unwrap_all(globals: dict[str, Any]) -> dict[str, Any]:
+        return {
+            k: (v.value if isinstance(v, CopyIfCallgrind) else v)
+            for k, v in globals.items()
+        }
+
+
+class GlobalsBridge:
+    """Handle the transfer of (certain) globals when collecting Callgrind statistics.
+
+    Key takeaway: Any globals passed must be wrapped in `CopyIfCallgrind` to
+                  work with `Timer.collect_callgrind`.
+
+    Consider the following code snippet:
+    ```
+        import pickle
+        import timeit
+
+        class Counter:
+            value = 0
+
+            def __call__(self):
+                self.value += 1
+
+        counter = Counter()
+        timeit.Timer("counter()", globals={"counter": counter}).timeit(10)
+        print(counter.value)  # 10
+
+        timeit.Timer(
+            "counter()",
+            globals={"counter": pickle.loads(pickle.dumps(counter))}
+        ).timeit(20)
+        print(counter.value)  # Still 10
+    ```
+
+    In the first case, `stmt` is executed using the objects in `globals`;
+    however, the addition of serialization and deserialization changes the
+    semantics and may meaningfully change behavior.
+
+    This is a practical consideration when collecting Callgrind statistics.
+    Unlike `exec` based execution (which `timeit` uses under the hood) which
+    can share in-memory data structures with the caller, Callgrind collection
+    requires an entirely new process in order to run under Valgrind. This means
+    that any data structures used for statement execution will have to be
+    serialized and deserialized in the subprocess.
+
+    In order to avoid surprising semantics from (user invisible) process
+    boundaries, what can be passed through `globals` is severely restricted
+    for `Timer.collect_callgrind`. It is expected that most setup should be
+    achievable (albeit perhaps less ergonomically) by passing a `setup`
+    string.
+
+    There are, however, exceptions. One such class are TorchScripted functions.
+    Because they require a concrete file with source code it is not possible
+    to define them using a `setup` string. Another group are torch.nn.Modules,
+    whose construction can be complex and prohibitively cumbersome to coerce
+    into a `setup` string. Finally, most builtin types are sufficiently well
+    behaved and sufficiently common to warrant allowing as well. (e.g.
+    `globals={"n": 1}` is very convenient.)
+
+    Fortunately, all have well defined serialization semantics. This class
+    is responsible for enabling the Valgrind subprocess to use elements in
+    `globals` so long as they are an allowed type.
+
+    Caveats:
+        The user is required to acknowledge this serialization by wrapping
+        elements in `globals` with `CopyIfCallgrind`.
+
+        While ScriptFunction and ScriptModule are expected to save and load
+        quite robustly, it is up to the user to ensure that an nn.Module can
+        un-pickle successfully.
+
+        `torch.Tensor` and `np.ndarray` are deliberately excluded. The
+        serialization/deserialization process perturbs the representation of a
+        tensor in ways that could result in incorrect measurements. For example,
+        if a tensor lives in pinned CPU memory, this fact would not be preserved
+        by a dump, and that will in turn change the performance of certain CUDA
+        operations.
+    """
+
+    def __init__(self, globals: dict[str, Any], data_dir: str) -> None:
+        self._globals: dict[str, CopyIfCallgrind] = {}
+        self._data_dir = data_dir
+        if not os.path.exists(data_dir):
+            os.mkdir(data_dir)
+
+        if globals.get("torch", torch) is not torch:
+            raise ValueError("`collect_callgrind` does not support mocking out `torch`.")
+
+        for name, value in globals.items():
+            if name in ("torch", "__builtins__"):
+                # Torch will be imported by the collection script, and
+                # __builtins__ is added by Timer.
+                continue
+
+            if not isinstance(value, CopyIfCallgrind):
+                raise ValueError(
+                    "`collect_callgrind` requires that globals be wrapped in "
+                    "`CopyIfCallgrind` so that serialization is explicit."
+                )
+
+            self._globals[name] = value
+
+    def construct(self) -> str:
+        load_lines = []
+        for name, wrapped_value in self._globals.items():
+            if wrapped_value.setup is not None:
+                load_lines.append(textwrap.dedent(wrapped_value.setup))
+
+            if wrapped_value.serialization == Serialization.PICKLE:
+                path = os.path.join(self._data_dir, f"{name}.pkl")
+                load_lines.append(
+                    f"with open({repr(path)}, 'rb') as f:\n    {name} = pickle.load(f)")
+                with open(path, "wb") as f:
+                    pickle.dump(wrapped_value.value, f)
+
+            elif wrapped_value.serialization == Serialization.TORCH:
+                path = os.path.join(self._data_dir, f"{name}.pt")
+                # TODO: Figure out if we can use torch.serialization.add_safe_globals here
+                # Using weights_only=False after the change in
+                # https://dev-discuss.pytorch.org/t/bc-breaking-change-torch-load-is-being-flipped-to-use-weights-only-true-by-default-in-the-nightlies-after-137602/2573
+                load_lines.append(f"{name} = torch.load({repr(path)}, weights_only=False)")
+                torch.save(wrapped_value.value, path)
+
+            elif wrapped_value.serialization == Serialization.TORCH_JIT:
+                path = os.path.join(self._data_dir, f"{name}.pt")
+                load_lines.append(f"{name} = torch.jit.load({repr(path)})")
+                with open(path, "wb") as f:
+                    torch.jit.save(wrapped_value.value, f)  # type: ignore[no-untyped-call]
+
+            else:
+                raise NotImplementedError(
+                    f"Unknown serialization method: {wrapped_value.serialization}")
+
+        return "\n".join(load_lines)
+
+
+class _ValgrindWrapper:
+    def __init__(self) -> None:
+        self._bindings_module: Optional[CallgrindModuleType] = None
+        valgrind_symbols = (
+            "_valgrind_supported_platform",
+            "_valgrind_toggle",
+            "_valgrind_toggle_and_dump_stats",
+        )
+        if all(hasattr(torch._C, symbol) for symbol in valgrind_symbols):
+            self._supported_platform: bool = torch._C._valgrind_supported_platform()
+
+        else:
+            print("Callgrind bindings are not present in `torch._C`. JIT-ing bindings.")
+            self._bindings_module = cpp_jit.get_compat_bindings()
+            assert all(hasattr(self._bindings_module, symbol) for symbol in valgrind_symbols)
+            self._supported_platform = self._bindings_module._valgrind_supported_platform()
+
+        self._commands_available: dict[str, bool] = {}
+        if self._supported_platform:
+            # Only bother checking on supported platforms.
+            for cmd in ("valgrind", "callgrind_control", "callgrind_annotate"):
+                self._commands_available[cmd] = not subprocess.run(
+                    ["which", cmd],
+                    capture_output=True,
+                    check=False,
+                ).returncode
+
+        self._build_type: Optional[str] = None
+        build_search = re.search("BUILD_TYPE=(.+),", torch.__config__.show())  # type: ignore[no-untyped-call]
+        if build_search is not None:
+            self._build_type = build_search.groups()[0].split(",")[0]
+
+    def _validate(self) -> None:
+        if not self._supported_platform:
+            raise OSError("Valgrind is not supported on this platform.")
+
+        missing_cmds = [cmd for cmd, available in self._commands_available.items() if not available]
+        if missing_cmds:
+            raise OSError("Missing: " + ", ".join(missing_cmds))
+
+    def collect_callgrind(
+        self,
+        task_spec: common.TaskSpec,
+        globals: dict[str, Any],
+        *,
+        number: int,
+        repeats: int,
+        collect_baseline: bool,
+        is_python: bool,
+        retain_out_file: bool,
+    ) -> tuple[CallgrindStats, ...]:
+        """Collect stats, and attach a reference run which can be used to filter interpreter overhead."""
+        self._validate()
+        assert is_python or not collect_baseline
+
+        *task_stats, baseline_stats = self._invoke(
+            task_spec=task_spec,
+            globals=globals,
+            number=number,
+            repeats=repeats,
+            collect_baseline=collect_baseline,
+            is_python=is_python,
+            retain_out_file=retain_out_file,
+        )
+        assert len(task_stats) == repeats
+
+        return tuple(
+            CallgrindStats(
+                task_spec=task_spec,
+                number_per_run=number,
+                built_with_debug_symbols=self._build_type == "RelWithDebInfo",
+                baseline_inclusive_stats=baseline_stats[0],
+                baseline_exclusive_stats=baseline_stats[1],
+                stmt_inclusive_stats=stmt_inclusive_stats,
+                stmt_exclusive_stats=stmt_exclusive_stats,
+                stmt_callgrind_out=out_contents,
+            )
+            for stmt_inclusive_stats, stmt_exclusive_stats, out_contents in task_stats
+        )
+
+    def _invoke(
+        self,
+        *,
+        task_spec: common.TaskSpec,
+        globals: dict[str, Any],
+        number: int,
+        repeats: int,
+        collect_baseline: bool,
+        is_python: bool,
+        retain_out_file: bool,
+    ) -> tuple[tuple[FunctionCounts, FunctionCounts, Optional[str]], ...]:
+        """Core invocation method for Callgrind collection.
+
+        Valgrind operates by effectively replacing the CPU with an emulated
+        version which allows it to instrument any code at the cost of severe
+        performance degradation. This has the practical effect that in order
+        to collect Callgrind statistics, a new process has to be created
+        running under `valgrind`. The steps for this process are:
+
+        1) Create a scratch directory.
+        2) Codegen a run script. (_ValgrindWrapper._construct_script)
+            Inside the run script:
+                * Validate that Python and torch match the parent process
+                * Validate that it is indeed running under valgrind
+                * Execute `setup` and warm up `stmt`
+                * Begin collecting stats
+                * Run the `stmt` loop
+                * Stop collecting stats
+        3) Parse the run results.
+        4) Cleanup the scratch directory.
+        """
+        working_dir = common._make_temp_dir(prefix="callgrind")
+        data_dir = os.path.join(working_dir, "data")
+        script_file = os.path.join(working_dir, "timer_callgrind.py")
+        callgrind_out = os.path.join(working_dir, "callgrind.out")
+        error_log = os.path.join(working_dir, "error.txt")
+        stat_log = os.path.join(working_dir, "callgrind_stat.txt")
+        stdout_stderr_log = os.path.join(working_dir, "stdout_stderr.log")
+
+        def run(args: list[str], **kwargs: Any) -> tuple[CompletedProcessType, str]:
+            # https://thraxil.org/users/anders/posts/2008/03/13/Subprocess-Hanging-PIPE-is-your-enemy/
+            f_stdout_stderr = open(stdout_stderr_log, "wb")
+            try:
+                invocation = subprocess.run(
+                    args,
+                    stdout=f_stdout_stderr,
+                    stderr=subprocess.STDOUT,
+                    **kwargs,
+                )
+                with open(stdout_stderr_log) as f:
+                    return invocation, f.read()
+            finally:
+                f_stdout_stderr.close()
+
+        try:
+            if is_python:
+                if self._bindings_module is not None:
+                    shutil.copy(
+                        self._bindings_module.__file__,
+                        os.path.join(working_dir, os.path.split(self._bindings_module.__file__)[1])
+                    )
+
+                script_file = os.path.join(working_dir, "timer_callgrind.py")
+                with open(script_file, "w") as f:
+                    f.write(self._construct_script(
+                        task_spec,
+                        globals=GlobalsBridge(globals, data_dir),
+                        number=number,
+                        repeats=repeats,
+                        collect_baseline=collect_baseline,
+                        error_log=error_log,
+                        stat_log=stat_log,
+                        bindings=self._bindings_module))
+
+                run_loop_cmd = ["python", script_file]
+            else:
+                assert not collect_baseline
+                run_loop_exec = cpp_jit.compile_callgrind_template(
+                    stmt=task_spec.stmt,
+                    setup=task_spec.setup,
+                    global_setup=task_spec.global_setup,
+                )
+                run_loop_cmd = [
+                    run_loop_exec,
+                    "--number", str(number),
+                    "--number-warmup", str(min(number, 10)),
+                    "--repeats", str(repeats),
+                    "--number-threads", str(task_spec.num_threads),
+                ]
+
+            valgrind_invocation, valgrind_invocation_output = run([
+                "valgrind",
+                "--tool=callgrind",
+                f"--callgrind-out-file={callgrind_out}",
+                "--dump-line=yes",
+                "--dump-instr=yes",
+                "--instr-atstart=yes",
+                "--collect-atstart=no",
+            ] + run_loop_cmd)
+
+            if valgrind_invocation.returncode:
+                error_report = ""
+                if os.path.exists(error_log):
+                    with open(error_log) as f:
+                        error_report = f.read()
+                if not error_report:
+                    error_report = "Unknown error.\n" + valgrind_invocation_output
+
+                raise OSError(f"Failed to collect callgrind profile:\n{error_report}")
+
+            def parse_output(fpath: str, inclusive: bool) -> FunctionCounts:
+                _annotate_invocation, annotate_invocation_output = run([
+                    "callgrind_annotate",
+                    f"--inclusive={'yes' if inclusive else 'no'}",
+                    "--threshold=100",
+                    "--show-percs=no",
+                    fpath
+                ], check=True)
+
+                total_pattern = re.compile(r"^([0-9,]+)\s+PROGRAM TOTALS")
+                begin_pattern = re.compile(r"Ir\s+file:function")
+                function_pattern = re.compile(r"^\s*([0-9,]+)\s+(.+:.+)$")
+
+                class ScanState(enum.Enum):
+                    SCANNING_FOR_TOTAL = 0
+                    SCANNING_FOR_START = 1
+                    PARSING = 2
+
+                scan_state = ScanState.SCANNING_FOR_TOTAL
+                fn_counts = []
+                for l in annotate_invocation_output.splitlines(keepends=False):
+                    if scan_state == ScanState.SCANNING_FOR_TOTAL:
+                        total_match = total_pattern.match(l)
+                        if total_match:
+                            program_totals = int(total_match.groups()[0].replace(",", ""))
+                            scan_state = ScanState.SCANNING_FOR_START
+
+                    elif scan_state == ScanState.SCANNING_FOR_START:
+                        if begin_pattern.match(l):
+                            scan_state = ScanState.PARSING
+
+                    else:
+                        assert scan_state == ScanState.PARSING
+                        fn_match = function_pattern.match(l)
+                        if fn_match:
+                            ir_str, file_function = fn_match.groups()
+                            ir = int(ir_str.replace(",", ""))
+                            if ir == program_totals:  # type: ignore[possibly-undefined]
+                                # Callgrind includes some top level red herring symbols when
+                                # a program dumps multiple profiles.
+                                continue
+                            fn_counts.append(FunctionCount(ir, file_function))
+
+                        elif re.match(r"-+", l):
+                            # Ignore heading separator lines.
+                            continue
+
+                        else:
+                            break
+
+                assert scan_state == ScanState.PARSING, f"Failed to parse {fpath}"
+                return FunctionCounts(tuple(sorted(fn_counts, reverse=True)), inclusive=inclusive)
+
+            def read_results(i: int) -> tuple[FunctionCounts, FunctionCounts, Optional[str]]:
+                if i == repeats and not collect_baseline:
+                    # Null baseline.
+                    return (
+                        FunctionCounts((), inclusive=True),
+                        FunctionCounts((), inclusive=False),
+                        None,
+                    )
+
+                fpath = f"{callgrind_out}.{i + 1}"  # Callgrind one-indexes files.
+                callgrind_out_contents: Optional[str] = None
+                if retain_out_file:
+                    with open(fpath) as f:
+                        callgrind_out_contents = f.read()
+
+                return (
+                    parse_output(fpath, inclusive=True),
+                    parse_output(fpath, inclusive=False),
+                    callgrind_out_contents
+                )
+
+            return tuple(read_results(i) for i in range(repeats + 1))
+        finally:
+            shutil.rmtree(working_dir)
+
+    @staticmethod
+    def _construct_script(
+        task_spec: common.TaskSpec,
+        globals: GlobalsBridge,
+        *,
+        number: int,
+        repeats: int,
+        collect_baseline: bool,
+        error_log: str,
+        stat_log: str,
+        bindings: Optional[CallgrindModuleType],
+    ) -> str:
+        def block_stmt(stmt: str, indent: int = 0) -> str:
+            """Partially unroll benchmark loop.
+
+            The naive template looks something like:
+                "for _ in range({number}): {stmt}"
+
+            However a loop in Python is surprisingly expensive, and significantly
+            increases the number of background Python instructions. So instead we
+            partially unroll the loops, with a block size of 100 chosen to keep
+            the instruction overhead from `range` low while also not ballooning
+            the size of the generated file.
+            """
+            block_size = 100
+            loop_count = number // block_size
+            if loop_count == 1:
+                # There is no point in having `for _ in range(1): ...` rather
+                # than just `...`, and this lets us save shave a few background
+                # instructions.
+                loop_count = 0
+            remainder = number - block_size * loop_count
+            blocked_stmt = ""
+
+            if loop_count:
+                unrolled_stmts = textwrap.indent("\n".join([stmt] * block_size), " " * 4)
+                blocked_stmt += f"for _ in range({loop_count}):\n{unrolled_stmts}\n"
+
+            if remainder:
+                blocked_stmt += "\n".join([stmt] * remainder)
+
+            return textwrap.indent(blocked_stmt, " " * indent)
+
+        pass_baseline = (
+            "callgrind_bindings._valgrind_toggle()\n"
+            f"{block_stmt('pass')}\n"
+            "callgrind_bindings._valgrind_toggle_and_dump_stats()"
+        )
+
+        return textwrap.dedent(r"""
+            import gc
+            import os
+            import pickle
+            import subprocess
+            import sys
+            import time
+
+            # Mitigate https://github.com/pytorch/pytorch/issues/37377
+            # which can sometimes cause the subprocess call to fail.
+            import numpy as np
+
+            import torch
+            torch.set_num_threads({num_threads})
+
+            {bindings_import}
+
+            PID = os.getpid()
+
+            def log_failure(msg):
+                with open({error_log_repr}, "wt") as f:
+                    f.write(msg)
+                sys.exit(1)
+
+            def check_result(completed_process):
+                if completed_process.returncode:
+                    log_failure(f"Command failed: {{' '.join(completed_process.args)}}")
+                return completed_process
+
+            # =============================================================================
+            # == Check that subprocess matches parent =====================================
+            # =============================================================================
+            if os.path.realpath(sys.executable) != "{parent_interpreter}":
+                log_failure(
+                    "Interpreter mismatch:\n"
+                    f"  {{os.path.realpath(sys.executable)}}\n    vs.\n  {parent_interpreter}"
+                )
+
+            if torch.__file__ != "{torch_file}":
+                log_failure(
+                    "PyTorch does not match expected file:\n"
+                    f"  {{torch.__file__}}\n    vs.\n  {torch_file}"
+                )
+
+            # =============================================================================
+            # == User specified setup =====================================================
+            # =============================================================================
+            # Load serialized globals
+            {load_globals}
+
+            # User setup str
+            {setup}
+
+            for _ in range({warmup_number}):
+            {indented_stmt}
+
+            # =============================================================================
+            # == Callgrind management =====================================================
+            # =============================================================================
+            with open("{stat_log}", "wb") as stat_file:
+                # If many instances of callgrind are running at once, the output of
+                # `callgrind_control` may exceed 16kb which would cause `subprocess.PIPE`
+                # to deadlock. So instead we use a file.
+                callgrind_stat = check_result(subprocess.run(
+                    ["callgrind_control", "--stat"],
+                    stdout=stat_file,
+                    stderr=subprocess.STDOUT,
+                ))
+
+            with open("{stat_log}", "rt") as stat_file:
+                stat_lines = stat_file.read().splitlines()
+
+            if f"PID {{PID}}: python {{__file__}}" not in stat_lines:
+                log_failure("Process does not appear to be running callgrind.")
+
+            gc.collect()
+            time.sleep(0.01)
+
+            # =============================================================================
+            # == User code block ==========================================================
+            # =============================================================================
+            for _ in range({repeats}):
+                callgrind_bindings._valgrind_toggle()
+            {blocked_stmt}
+                callgrind_bindings._valgrind_toggle_and_dump_stats()
+                gc.collect()
+
+            {baseline}
+        """).strip().format(
+            indented_stmt=textwrap.indent(task_spec.stmt, " " * 4),
+            blocked_stmt=block_stmt(task_spec.stmt, indent=4),
+            baseline=(pass_baseline if collect_baseline else ""),
+            number=number,
+            repeats=repeats,
+            load_globals=globals.construct(),
+            setup=task_spec.setup,
+            warmup_number=min(number, 10),
+            num_threads=task_spec.num_threads,
+            error_log_repr=repr(error_log),
+            stat_log=stat_log,
+            parent_interpreter=os.path.realpath(sys.executable),
+            torch_file=torch.__file__,
+            bindings_import=(
+                "import torch._C as callgrind_bindings" if bindings is None
+                else f"import {bindings.__name__} as callgrind_bindings"),
+        )
+
+
+CALLGRIND_SINGLETON: Optional[_ValgrindWrapper] = None
+def wrapper_singleton() -> _ValgrindWrapper:
+    global CALLGRIND_SINGLETON
+    if CALLGRIND_SINGLETON is None:
+        CALLGRIND_SINGLETON = _ValgrindWrapper()
+    return CALLGRIND_SINGLETON
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/valgrind.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/valgrind.h
new file mode 100644
index 0000000000000000000000000000000000000000..d33dd30932aa86b8284cb93d0e29ec646e820197
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/benchmark/utils/valgrind_wrapper/valgrind.h
@@ -0,0 +1,7157 @@
+/* -*- c -*-
+   ----------------------------------------------------------------
+
+   Notice that the following BSD-style license applies to this one
+   file (valgrind.h) only.  The rest of Valgrind is licensed under the
+   terms of the GNU General Public License, version 2, unless
+   otherwise indicated.  See the COPYING file in the source
+   distribution for details.
+
+   ----------------------------------------------------------------
+
+   This file is part of Valgrind, a dynamic binary instrumentation
+   framework.
+
+   Copyright (C) 2000-2017 Julian Seward.  All rights reserved.
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+   1. Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+
+   2. The origin of this software must not be misrepresented; you must 
+      not claim that you wrote the original software.  If you use this 
+      software in a product, an acknowledgment in the product 
+      documentation would be appreciated but is not required.
+
+   3. Altered source versions must be plainly marked as such, and must
+      not be misrepresented as being the original software.
+
+   4. The name of the author may not be used to endorse or promote 
+      products derived from this software without specific prior written 
+      permission.
+
+   THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
+   OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+   WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+   ARE DISCLAIMED.  IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
+   DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+   DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+   GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+   INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+   WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+   ----------------------------------------------------------------
+
+   Notice that the above BSD-style license applies to this one file
+   (valgrind.h) only.  The entire rest of Valgrind is licensed under
+   the terms of the GNU General Public License, version 2.  See the
+   COPYING file in the source distribution for details.
+
+   ---------------------------------------------------------------- 
+*/
+
+
+/* This file is for inclusion into client (your!) code.
+
+   You can use these macros to manipulate and query Valgrind's 
+   execution inside your own programs.
+
+   The resulting executables will still run without Valgrind, just a
+   little bit more slowly than they otherwise would, but otherwise
+   unchanged.  When not running on valgrind, each client request
+   consumes very few (eg. 7) instructions, so the resulting performance
+   loss is negligible unless you plan to execute client requests
+   millions of times per second.  Nevertheless, if that is still a
+   problem, you can compile with the NVALGRIND symbol defined (gcc
+   -DNVALGRIND) so that client requests are not even compiled in.  */
+
+#ifndef __VALGRIND_H
+#define __VALGRIND_H
+
+
+/* ------------------------------------------------------------------ */
+/* VERSION NUMBER OF VALGRIND                                         */
+/* ------------------------------------------------------------------ */
+
+/* Specify Valgrind's version number, so that user code can
+   conditionally compile based on our version number.  Note that these
+   were introduced at version 3.6 and so do not exist in version 3.5
+   or earlier.  The recommended way to use them to check for "version
+   X.Y or later" is (eg)
+
+#if defined(__VALGRIND_MAJOR__) && defined(__VALGRIND_MINOR__)   \
+    && (__VALGRIND_MAJOR__ > 3                                   \
+        || (__VALGRIND_MAJOR__ == 3 && __VALGRIND_MINOR__ >= 6))
+*/
+#define __VALGRIND_MAJOR__    3
+#define __VALGRIND_MINOR__    17
+
+
+#include 
+
+/* Nb: this file might be included in a file compiled with -ansi.  So
+   we can't use C++ style "//" comments nor the "asm" keyword (instead
+   use "__asm__"). */
+
+/* Derive some tags indicating what the target platform is.  Note
+   that in this file we're using the compiler's CPP symbols for
+   identifying architectures, which are different to the ones we use
+   within the rest of Valgrind.  Note, __powerpc__ is active for both
+   32 and 64-bit PPC, whereas __powerpc64__ is only active for the
+   latter (on Linux, that is).
+
+   Misc note: how to find out what's predefined in gcc by default:
+   gcc -Wp,-dM somefile.c
+*/
+#undef PLAT_x86_darwin
+#undef PLAT_amd64_darwin
+#undef PLAT_x86_win32
+#undef PLAT_amd64_win64
+#undef PLAT_x86_linux
+#undef PLAT_amd64_linux
+#undef PLAT_ppc32_linux
+#undef PLAT_ppc64be_linux
+#undef PLAT_ppc64le_linux
+#undef PLAT_arm_linux
+#undef PLAT_arm64_linux
+#undef PLAT_s390x_linux
+#undef PLAT_mips32_linux
+#undef PLAT_mips64_linux
+#undef PLAT_nanomips_linux
+#undef PLAT_x86_solaris
+#undef PLAT_amd64_solaris
+
+
+#if defined(__APPLE__) && defined(__i386__)
+#  define PLAT_x86_darwin 1
+#elif defined(__APPLE__) && defined(__x86_64__)
+#  define PLAT_amd64_darwin 1
+#elif (defined(__MINGW32__) && defined(__i386__)) \
+      || defined(__CYGWIN32__) \
+      || (defined(_WIN32) && defined(_M_IX86))
+#  define PLAT_x86_win32 1
+#elif (defined(__MINGW32__) && defined(__x86_64__)) \
+      || (defined(_WIN32) && defined(_M_X64))
+/* __MINGW32__ and _WIN32 are defined in 64 bit mode as well. */
+#  define PLAT_amd64_win64 1
+#elif defined(__linux__) && defined(__i386__)
+#  define PLAT_x86_linux 1
+#elif defined(__linux__) && defined(__x86_64__) && !defined(__ILP32__)
+#  define PLAT_amd64_linux 1
+#elif defined(__linux__) && defined(__powerpc__) && !defined(__powerpc64__)
+#  define PLAT_ppc32_linux 1
+#elif defined(__linux__) && defined(__powerpc__) && defined(__powerpc64__) && _CALL_ELF != 2
+/* Big Endian uses ELF version 1 */
+#  define PLAT_ppc64be_linux 1
+#elif defined(__linux__) && defined(__powerpc__) && defined(__powerpc64__) && _CALL_ELF == 2
+/* Little Endian uses ELF version 2 */
+#  define PLAT_ppc64le_linux 1
+#elif defined(__linux__) && defined(__arm__) && !defined(__aarch64__)
+#  define PLAT_arm_linux 1
+#elif defined(__linux__) && defined(__aarch64__) && !defined(__arm__)
+#  define PLAT_arm64_linux 1
+#elif defined(__linux__) && defined(__s390__) && defined(__s390x__)
+#  define PLAT_s390x_linux 1
+#elif defined(__linux__) && defined(__mips__) && (__mips==64)
+#  define PLAT_mips64_linux 1
+#elif defined(__linux__) && defined(__mips__) && (__mips==32)
+#  define PLAT_mips32_linux 1
+#elif defined(__linux__) && defined(__nanomips__)
+#  define PLAT_nanomips_linux 1
+#elif defined(__sun) && defined(__i386__)
+#  define PLAT_x86_solaris 1
+#elif defined(__sun) && defined(__x86_64__)
+#  define PLAT_amd64_solaris 1
+#else
+/* If we're not compiling for our target platform, don't generate
+   any inline asms.  */
+#  if !defined(NVALGRIND)
+#    define NVALGRIND 1
+#  endif
+#endif
+
+
+/* ------------------------------------------------------------------ */
+/* ARCHITECTURE SPECIFICS for SPECIAL INSTRUCTIONS.  There is nothing */
+/* in here of use to end-users -- skip to the next section.           */
+/* ------------------------------------------------------------------ */
+
+/*
+ * VALGRIND_DO_CLIENT_REQUEST(): a statement that invokes a Valgrind client
+ * request. Accepts both pointers and integers as arguments.
+ *
+ * VALGRIND_DO_CLIENT_REQUEST_STMT(): a statement that invokes a Valgrind
+ * client request that does not return a value.
+
+ * VALGRIND_DO_CLIENT_REQUEST_EXPR(): a C expression that invokes a Valgrind
+ * client request and whose value equals the client request result.  Accepts
+ * both pointers and integers as arguments.  Note that such calls are not
+ * necessarily pure functions -- they may have side effects.
+ */
+
+#define VALGRIND_DO_CLIENT_REQUEST(_zzq_rlval, _zzq_default,            \
+                                   _zzq_request, _zzq_arg1, _zzq_arg2,  \
+                                   _zzq_arg3, _zzq_arg4, _zzq_arg5)     \
+  do { (_zzq_rlval) = VALGRIND_DO_CLIENT_REQUEST_EXPR((_zzq_default),   \
+                        (_zzq_request), (_zzq_arg1), (_zzq_arg2),       \
+                        (_zzq_arg3), (_zzq_arg4), (_zzq_arg5)); } while (0)
+
+#define VALGRIND_DO_CLIENT_REQUEST_STMT(_zzq_request, _zzq_arg1,        \
+                           _zzq_arg2,  _zzq_arg3, _zzq_arg4, _zzq_arg5) \
+  do { (void) VALGRIND_DO_CLIENT_REQUEST_EXPR(0,                        \
+                    (_zzq_request), (_zzq_arg1), (_zzq_arg2),           \
+                    (_zzq_arg3), (_zzq_arg4), (_zzq_arg5)); } while (0)
+
+#if defined(NVALGRIND)
+
+/* Define NVALGRIND to completely remove the Valgrind magic sequence
+   from the compiled code (analogous to NDEBUG's effects on
+   assert()) */
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+        _zzq_default, _zzq_request,                               \
+        _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)    \
+      (_zzq_default)
+
+#else  /* ! NVALGRIND */
+
+/* The following defines the magic code sequences which the JITter
+   spots and handles magically.  Don't look too closely at them as
+   they will rot your brain.
+
+   The assembly code sequences for all architectures is in this one
+   file.  This is because this file must be stand-alone, and we don't
+   want to have multiple files.
+
+   For VALGRIND_DO_CLIENT_REQUEST, we must ensure that the default
+   value gets put in the return slot, so that everything works when
+   this is executed not under Valgrind.  Args are passed in a memory
+   block, and so there's no intrinsic limit to the number that could
+   be passed, but it's currently five.
+   
+   The macro args are: 
+      _zzq_rlval    result lvalue
+      _zzq_default  default value (result returned when running on real CPU)
+      _zzq_request  request code
+      _zzq_arg1..5  request params
+
+   The other two macros are used to support function wrapping, and are
+   a lot simpler.  VALGRIND_GET_NR_CONTEXT returns the value of the
+   guest's NRADDR pseudo-register and whatever other information is
+   needed to safely run the call original from the wrapper: on
+   ppc64-linux, the R2 value at the divert point is also needed.  This
+   information is abstracted into a user-visible type, OrigFn.
+
+   VALGRIND_CALL_NOREDIR_* behaves the same as the following on the
+   guest, but guarantees that the branch instruction will not be
+   redirected: x86: call *%eax, amd64: call *%rax, ppc32/ppc64:
+   branch-and-link-to-r11.  VALGRIND_CALL_NOREDIR is just text, not a
+   complete inline asm, since it needs to be combined with more magic
+   inline asm stuff to be useful.
+*/
+
+/* ----------------- x86-{linux,darwin,solaris} ---------------- */
+
+#if defined(PLAT_x86_linux)  ||  defined(PLAT_x86_darwin)  \
+    ||  (defined(PLAT_x86_win32) && defined(__GNUC__)) \
+    ||  defined(PLAT_x86_solaris)
+
+typedef
+   struct { 
+      unsigned int nraddr; /* where's the code? */
+   }
+   OrigFn;
+
+#define __SPECIAL_INSTRUCTION_PREAMBLE                            \
+                     "roll $3,  %%edi ; roll $13, %%edi\n\t"      \
+                     "roll $29, %%edi ; roll $19, %%edi\n\t"
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+        _zzq_default, _zzq_request,                               \
+        _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)    \
+  __extension__                                                   \
+  ({volatile unsigned int _zzq_args[6];                           \
+    volatile unsigned int _zzq_result;                            \
+    _zzq_args[0] = (unsigned int)(_zzq_request);                  \
+    _zzq_args[1] = (unsigned int)(_zzq_arg1);                     \
+    _zzq_args[2] = (unsigned int)(_zzq_arg2);                     \
+    _zzq_args[3] = (unsigned int)(_zzq_arg3);                     \
+    _zzq_args[4] = (unsigned int)(_zzq_arg4);                     \
+    _zzq_args[5] = (unsigned int)(_zzq_arg5);                     \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %EDX = client_request ( %EAX ) */         \
+                     "xchgl %%ebx,%%ebx"                          \
+                     : "=d" (_zzq_result)                         \
+                     : "a" (&_zzq_args[0]), "0" (_zzq_default)    \
+                     : "cc", "memory"                             \
+                    );                                            \
+    _zzq_result;                                                  \
+  })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                       \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                   \
+    volatile unsigned int __addr;                                 \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %EAX = guest_NRADDR */                    \
+                     "xchgl %%ecx,%%ecx"                          \
+                     : "=a" (__addr)                              \
+                     :                                            \
+                     : "cc", "memory"                             \
+                    );                                            \
+    _zzq_orig->nraddr = __addr;                                   \
+  }
+
+#define VALGRIND_CALL_NOREDIR_EAX                                 \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* call-noredir *%EAX */                     \
+                     "xchgl %%edx,%%edx\n\t"
+
+#define VALGRIND_VEX_INJECT_IR()                                 \
+ do {                                                            \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE              \
+                     "xchgl %%edi,%%edi\n\t"                     \
+                     : : : "cc", "memory"                        \
+                    );                                           \
+ } while (0)
+
+#endif /* PLAT_x86_linux || PLAT_x86_darwin || (PLAT_x86_win32 && __GNUC__)
+          || PLAT_x86_solaris */
+
+/* ------------------------- x86-Win32 ------------------------- */
+
+#if defined(PLAT_x86_win32) && !defined(__GNUC__)
+
+typedef
+   struct { 
+      unsigned int nraddr; /* where's the code? */
+   }
+   OrigFn;
+
+#if defined(_MSC_VER)
+
+#define __SPECIAL_INSTRUCTION_PREAMBLE                            \
+                     __asm rol edi, 3  __asm rol edi, 13          \
+                     __asm rol edi, 29 __asm rol edi, 19
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+        _zzq_default, _zzq_request,                               \
+        _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)    \
+    valgrind_do_client_request_expr((uintptr_t)(_zzq_default),    \
+        (uintptr_t)(_zzq_request), (uintptr_t)(_zzq_arg1),        \
+        (uintptr_t)(_zzq_arg2), (uintptr_t)(_zzq_arg3),           \
+        (uintptr_t)(_zzq_arg4), (uintptr_t)(_zzq_arg5))
+
+static __inline uintptr_t
+valgrind_do_client_request_expr(uintptr_t _zzq_default, uintptr_t _zzq_request,
+                                uintptr_t _zzq_arg1, uintptr_t _zzq_arg2,
+                                uintptr_t _zzq_arg3, uintptr_t _zzq_arg4,
+                                uintptr_t _zzq_arg5)
+{
+    volatile uintptr_t _zzq_args[6];
+    volatile unsigned int _zzq_result;
+    _zzq_args[0] = (uintptr_t)(_zzq_request);
+    _zzq_args[1] = (uintptr_t)(_zzq_arg1);
+    _zzq_args[2] = (uintptr_t)(_zzq_arg2);
+    _zzq_args[3] = (uintptr_t)(_zzq_arg3);
+    _zzq_args[4] = (uintptr_t)(_zzq_arg4);
+    _zzq_args[5] = (uintptr_t)(_zzq_arg5);
+    __asm { __asm lea eax, _zzq_args __asm mov edx, _zzq_default
+            __SPECIAL_INSTRUCTION_PREAMBLE
+            /* %EDX = client_request ( %EAX ) */
+            __asm xchg ebx,ebx
+            __asm mov _zzq_result, edx
+    }
+    return _zzq_result;
+}
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                       \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                   \
+    volatile unsigned int __addr;                                 \
+    __asm { __SPECIAL_INSTRUCTION_PREAMBLE                        \
+            /* %EAX = guest_NRADDR */                             \
+            __asm xchg ecx,ecx                                    \
+            __asm mov __addr, eax                                 \
+    }                                                             \
+    _zzq_orig->nraddr = __addr;                                   \
+  }
+
+#define VALGRIND_CALL_NOREDIR_EAX ERROR
+
+#define VALGRIND_VEX_INJECT_IR()                                 \
+ do {                                                            \
+    __asm { __SPECIAL_INSTRUCTION_PREAMBLE                       \
+            __asm xchg edi,edi                                   \
+    }                                                            \
+ } while (0)
+
+#else
+#error Unsupported compiler.
+#endif
+
+#endif /* PLAT_x86_win32 */
+
+/* ----------------- amd64-{linux,darwin,solaris} --------------- */
+
+#if defined(PLAT_amd64_linux)  ||  defined(PLAT_amd64_darwin) \
+    ||  defined(PLAT_amd64_solaris) \
+    ||  (defined(PLAT_amd64_win64) && defined(__GNUC__))
+
+typedef
+   struct { 
+      unsigned long int nraddr; /* where's the code? */
+   }
+   OrigFn;
+
+#define __SPECIAL_INSTRUCTION_PREAMBLE                            \
+                     "rolq $3,  %%rdi ; rolq $13, %%rdi\n\t"      \
+                     "rolq $61, %%rdi ; rolq $51, %%rdi\n\t"
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+        _zzq_default, _zzq_request,                               \
+        _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)    \
+    __extension__                                                 \
+    ({ volatile unsigned long int _zzq_args[6];                   \
+    volatile unsigned long int _zzq_result;                       \
+    _zzq_args[0] = (unsigned long int)(_zzq_request);             \
+    _zzq_args[1] = (unsigned long int)(_zzq_arg1);                \
+    _zzq_args[2] = (unsigned long int)(_zzq_arg2);                \
+    _zzq_args[3] = (unsigned long int)(_zzq_arg3);                \
+    _zzq_args[4] = (unsigned long int)(_zzq_arg4);                \
+    _zzq_args[5] = (unsigned long int)(_zzq_arg5);                \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %RDX = client_request ( %RAX ) */         \
+                     "xchgq %%rbx,%%rbx"                          \
+                     : "=d" (_zzq_result)                         \
+                     : "a" (&_zzq_args[0]), "0" (_zzq_default)    \
+                     : "cc", "memory"                             \
+                    );                                            \
+    _zzq_result;                                                  \
+    })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                       \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                   \
+    volatile unsigned long int __addr;                            \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %RAX = guest_NRADDR */                    \
+                     "xchgq %%rcx,%%rcx"                          \
+                     : "=a" (__addr)                              \
+                     :                                            \
+                     : "cc", "memory"                             \
+                    );                                            \
+    _zzq_orig->nraddr = __addr;                                   \
+  }
+
+#define VALGRIND_CALL_NOREDIR_RAX                                 \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* call-noredir *%RAX */                     \
+                     "xchgq %%rdx,%%rdx\n\t"
+
+#define VALGRIND_VEX_INJECT_IR()                                 \
+ do {                                                            \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE              \
+                     "xchgq %%rdi,%%rdi\n\t"                     \
+                     : : : "cc", "memory"                        \
+                    );                                           \
+ } while (0)
+
+#endif /* PLAT_amd64_linux || PLAT_amd64_darwin || PLAT_amd64_solaris */
+
+/* ------------------------- amd64-Win64 ------------------------- */
+
+#if defined(PLAT_amd64_win64) && !defined(__GNUC__)
+
+#error Unsupported compiler.
+
+#endif /* PLAT_amd64_win64 */
+
+/* ------------------------ ppc32-linux ------------------------ */
+
+#if defined(PLAT_ppc32_linux)
+
+typedef
+   struct { 
+      unsigned int nraddr; /* where's the code? */
+   }
+   OrigFn;
+
+#define __SPECIAL_INSTRUCTION_PREAMBLE                            \
+                    "rlwinm 0,0,3,0,31  ; rlwinm 0,0,13,0,31\n\t" \
+                    "rlwinm 0,0,29,0,31 ; rlwinm 0,0,19,0,31\n\t"
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+        _zzq_default, _zzq_request,                               \
+        _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)    \
+                                                                  \
+    __extension__                                                 \
+  ({         unsigned int  _zzq_args[6];                          \
+             unsigned int  _zzq_result;                           \
+             unsigned int* _zzq_ptr;                              \
+    _zzq_args[0] = (unsigned int)(_zzq_request);                  \
+    _zzq_args[1] = (unsigned int)(_zzq_arg1);                     \
+    _zzq_args[2] = (unsigned int)(_zzq_arg2);                     \
+    _zzq_args[3] = (unsigned int)(_zzq_arg3);                     \
+    _zzq_args[4] = (unsigned int)(_zzq_arg4);                     \
+    _zzq_args[5] = (unsigned int)(_zzq_arg5);                     \
+    _zzq_ptr = _zzq_args;                                         \
+    __asm__ volatile("mr 3,%1\n\t" /*default*/                    \
+                     "mr 4,%2\n\t" /*ptr*/                        \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %R3 = client_request ( %R4 ) */           \
+                     "or 1,1,1\n\t"                               \
+                     "mr %0,3"     /*result*/                     \
+                     : "=b" (_zzq_result)                         \
+                     : "b" (_zzq_default), "b" (_zzq_ptr)         \
+                     : "cc", "memory", "r3", "r4");               \
+    _zzq_result;                                                  \
+    })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                       \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                   \
+    unsigned int __addr;                                          \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %R3 = guest_NRADDR */                     \
+                     "or 2,2,2\n\t"                               \
+                     "mr %0,3"                                    \
+                     : "=b" (__addr)                              \
+                     :                                            \
+                     : "cc", "memory", "r3"                       \
+                    );                                            \
+    _zzq_orig->nraddr = __addr;                                   \
+  }
+
+#define VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                   \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* branch-and-link-to-noredir *%R11 */       \
+                     "or 3,3,3\n\t"
+
+#define VALGRIND_VEX_INJECT_IR()                                 \
+ do {                                                            \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE              \
+                     "or 5,5,5\n\t"                              \
+                    );                                           \
+ } while (0)
+
+#endif /* PLAT_ppc32_linux */
+
+/* ------------------------ ppc64-linux ------------------------ */
+
+#if defined(PLAT_ppc64be_linux)
+
+typedef
+   struct { 
+      unsigned long int nraddr; /* where's the code? */
+      unsigned long int r2;  /* what tocptr do we need? */
+   }
+   OrigFn;
+
+#define __SPECIAL_INSTRUCTION_PREAMBLE                            \
+                     "rotldi 0,0,3  ; rotldi 0,0,13\n\t"          \
+                     "rotldi 0,0,61 ; rotldi 0,0,51\n\t"
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+        _zzq_default, _zzq_request,                               \
+        _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)    \
+                                                                  \
+  __extension__                                                   \
+  ({         unsigned long int  _zzq_args[6];                     \
+             unsigned long int  _zzq_result;                      \
+             unsigned long int* _zzq_ptr;                         \
+    _zzq_args[0] = (unsigned long int)(_zzq_request);             \
+    _zzq_args[1] = (unsigned long int)(_zzq_arg1);                \
+    _zzq_args[2] = (unsigned long int)(_zzq_arg2);                \
+    _zzq_args[3] = (unsigned long int)(_zzq_arg3);                \
+    _zzq_args[4] = (unsigned long int)(_zzq_arg4);                \
+    _zzq_args[5] = (unsigned long int)(_zzq_arg5);                \
+    _zzq_ptr = _zzq_args;                                         \
+    __asm__ volatile("mr 3,%1\n\t" /*default*/                    \
+                     "mr 4,%2\n\t" /*ptr*/                        \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %R3 = client_request ( %R4 ) */           \
+                     "or 1,1,1\n\t"                               \
+                     "mr %0,3"     /*result*/                     \
+                     : "=b" (_zzq_result)                         \
+                     : "b" (_zzq_default), "b" (_zzq_ptr)         \
+                     : "cc", "memory", "r3", "r4");               \
+    _zzq_result;                                                  \
+  })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                       \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                   \
+    unsigned long int __addr;                                     \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %R3 = guest_NRADDR */                     \
+                     "or 2,2,2\n\t"                               \
+                     "mr %0,3"                                    \
+                     : "=b" (__addr)                              \
+                     :                                            \
+                     : "cc", "memory", "r3"                       \
+                    );                                            \
+    _zzq_orig->nraddr = __addr;                                   \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %R3 = guest_NRADDR_GPR2 */                \
+                     "or 4,4,4\n\t"                               \
+                     "mr %0,3"                                    \
+                     : "=b" (__addr)                              \
+                     :                                            \
+                     : "cc", "memory", "r3"                       \
+                    );                                            \
+    _zzq_orig->r2 = __addr;                                       \
+  }
+
+#define VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                   \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* branch-and-link-to-noredir *%R11 */       \
+                     "or 3,3,3\n\t"
+
+#define VALGRIND_VEX_INJECT_IR()                                 \
+ do {                                                            \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE              \
+                     "or 5,5,5\n\t"                              \
+                    );                                           \
+ } while (0)
+
+#endif /* PLAT_ppc64be_linux */
+
+#if defined(PLAT_ppc64le_linux)
+
+typedef
+   struct {
+      unsigned long int nraddr; /* where's the code? */
+      unsigned long int r2;     /* what tocptr do we need? */
+   }
+   OrigFn;
+
+#define __SPECIAL_INSTRUCTION_PREAMBLE                            \
+                     "rotldi 0,0,3  ; rotldi 0,0,13\n\t"          \
+                     "rotldi 0,0,61 ; rotldi 0,0,51\n\t"
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+        _zzq_default, _zzq_request,                               \
+        _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)    \
+                                                                  \
+  __extension__                                                   \
+  ({         unsigned long int  _zzq_args[6];                     \
+             unsigned long int  _zzq_result;                      \
+             unsigned long int* _zzq_ptr;                         \
+    _zzq_args[0] = (unsigned long int)(_zzq_request);             \
+    _zzq_args[1] = (unsigned long int)(_zzq_arg1);                \
+    _zzq_args[2] = (unsigned long int)(_zzq_arg2);                \
+    _zzq_args[3] = (unsigned long int)(_zzq_arg3);                \
+    _zzq_args[4] = (unsigned long int)(_zzq_arg4);                \
+    _zzq_args[5] = (unsigned long int)(_zzq_arg5);                \
+    _zzq_ptr = _zzq_args;                                         \
+    __asm__ volatile("mr 3,%1\n\t" /*default*/                    \
+                     "mr 4,%2\n\t" /*ptr*/                        \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %R3 = client_request ( %R4 ) */           \
+                     "or 1,1,1\n\t"                               \
+                     "mr %0,3"     /*result*/                     \
+                     : "=b" (_zzq_result)                         \
+                     : "b" (_zzq_default), "b" (_zzq_ptr)         \
+                     : "cc", "memory", "r3", "r4");               \
+    _zzq_result;                                                  \
+  })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                       \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                   \
+    unsigned long int __addr;                                     \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %R3 = guest_NRADDR */                     \
+                     "or 2,2,2\n\t"                               \
+                     "mr %0,3"                                    \
+                     : "=b" (__addr)                              \
+                     :                                            \
+                     : "cc", "memory", "r3"                       \
+                    );                                            \
+    _zzq_orig->nraddr = __addr;                                   \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %R3 = guest_NRADDR_GPR2 */                \
+                     "or 4,4,4\n\t"                               \
+                     "mr %0,3"                                    \
+                     : "=b" (__addr)                              \
+                     :                                            \
+                     : "cc", "memory", "r3"                       \
+                    );                                            \
+    _zzq_orig->r2 = __addr;                                       \
+  }
+
+#define VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                   \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* branch-and-link-to-noredir *%R12 */       \
+                     "or 3,3,3\n\t"
+
+#define VALGRIND_VEX_INJECT_IR()                                 \
+ do {                                                            \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE              \
+                     "or 5,5,5\n\t"                              \
+                    );                                           \
+ } while (0)
+
+#endif /* PLAT_ppc64le_linux */
+
+/* ------------------------- arm-linux ------------------------- */
+
+#if defined(PLAT_arm_linux)
+
+typedef
+   struct { 
+      unsigned int nraddr; /* where's the code? */
+   }
+   OrigFn;
+
+#define __SPECIAL_INSTRUCTION_PREAMBLE                            \
+            "mov r12, r12, ror #3  ; mov r12, r12, ror #13 \n\t"  \
+            "mov r12, r12, ror #29 ; mov r12, r12, ror #19 \n\t"
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+        _zzq_default, _zzq_request,                               \
+        _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)    \
+                                                                  \
+  __extension__                                                   \
+  ({volatile unsigned int  _zzq_args[6];                          \
+    volatile unsigned int  _zzq_result;                           \
+    _zzq_args[0] = (unsigned int)(_zzq_request);                  \
+    _zzq_args[1] = (unsigned int)(_zzq_arg1);                     \
+    _zzq_args[2] = (unsigned int)(_zzq_arg2);                     \
+    _zzq_args[3] = (unsigned int)(_zzq_arg3);                     \
+    _zzq_args[4] = (unsigned int)(_zzq_arg4);                     \
+    _zzq_args[5] = (unsigned int)(_zzq_arg5);                     \
+    __asm__ volatile("mov r3, %1\n\t" /*default*/                 \
+                     "mov r4, %2\n\t" /*ptr*/                     \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* R3 = client_request ( R4 ) */             \
+                     "orr r10, r10, r10\n\t"                      \
+                     "mov %0, r3"     /*result*/                  \
+                     : "=r" (_zzq_result)                         \
+                     : "r" (_zzq_default), "r" (&_zzq_args[0])    \
+                     : "cc","memory", "r3", "r4");                \
+    _zzq_result;                                                  \
+  })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                       \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                   \
+    unsigned int __addr;                                          \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* R3 = guest_NRADDR */                      \
+                     "orr r11, r11, r11\n\t"                      \
+                     "mov %0, r3"                                 \
+                     : "=r" (__addr)                              \
+                     :                                            \
+                     : "cc", "memory", "r3"                       \
+                    );                                            \
+    _zzq_orig->nraddr = __addr;                                   \
+  }
+
+#define VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                    \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* branch-and-link-to-noredir *%R4 */        \
+                     "orr r12, r12, r12\n\t"
+
+#define VALGRIND_VEX_INJECT_IR()                                 \
+ do {                                                            \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE              \
+                     "orr r9, r9, r9\n\t"                        \
+                     : : : "cc", "memory"                        \
+                    );                                           \
+ } while (0)
+
+#endif /* PLAT_arm_linux */
+
+/* ------------------------ arm64-linux ------------------------- */
+
+#if defined(PLAT_arm64_linux)
+
+typedef
+   struct { 
+      unsigned long int nraddr; /* where's the code? */
+   }
+   OrigFn;
+
+#define __SPECIAL_INSTRUCTION_PREAMBLE                            \
+            "ror x12, x12, #3  ;  ror x12, x12, #13 \n\t"         \
+            "ror x12, x12, #51 ;  ror x12, x12, #61 \n\t"
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+        _zzq_default, _zzq_request,                               \
+        _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)    \
+                                                                  \
+  __extension__                                                   \
+  ({volatile unsigned long int  _zzq_args[6];                     \
+    volatile unsigned long int  _zzq_result;                      \
+    _zzq_args[0] = (unsigned long int)(_zzq_request);             \
+    _zzq_args[1] = (unsigned long int)(_zzq_arg1);                \
+    _zzq_args[2] = (unsigned long int)(_zzq_arg2);                \
+    _zzq_args[3] = (unsigned long int)(_zzq_arg3);                \
+    _zzq_args[4] = (unsigned long int)(_zzq_arg4);                \
+    _zzq_args[5] = (unsigned long int)(_zzq_arg5);                \
+    __asm__ volatile("mov x3, %1\n\t" /*default*/                 \
+                     "mov x4, %2\n\t" /*ptr*/                     \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* X3 = client_request ( X4 ) */             \
+                     "orr x10, x10, x10\n\t"                      \
+                     "mov %0, x3"     /*result*/                  \
+                     : "=r" (_zzq_result)                         \
+                     : "r" ((unsigned long int)(_zzq_default)),   \
+                       "r" (&_zzq_args[0])                        \
+                     : "cc","memory", "x3", "x4");                \
+    _zzq_result;                                                  \
+  })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                       \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                   \
+    unsigned long int __addr;                                     \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* X3 = guest_NRADDR */                      \
+                     "orr x11, x11, x11\n\t"                      \
+                     "mov %0, x3"                                 \
+                     : "=r" (__addr)                              \
+                     :                                            \
+                     : "cc", "memory", "x3"                       \
+                    );                                            \
+    _zzq_orig->nraddr = __addr;                                   \
+  }
+
+#define VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                    \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* branch-and-link-to-noredir X8 */          \
+                     "orr x12, x12, x12\n\t"
+
+#define VALGRIND_VEX_INJECT_IR()                                 \
+ do {                                                            \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE              \
+                     "orr x9, x9, x9\n\t"                        \
+                     : : : "cc", "memory"                        \
+                    );                                           \
+ } while (0)
+
+#endif /* PLAT_arm64_linux */
+
+/* ------------------------ s390x-linux ------------------------ */
+
+#if defined(PLAT_s390x_linux)
+
+typedef
+  struct {
+     unsigned long int nraddr; /* where's the code? */
+  }
+  OrigFn;
+
+/* __SPECIAL_INSTRUCTION_PREAMBLE will be used to identify Valgrind specific
+ * code. This detection is implemented in platform specific toIR.c
+ * (e.g. VEX/priv/guest_s390_decoder.c).
+ */
+#define __SPECIAL_INSTRUCTION_PREAMBLE                           \
+                     "lr 15,15\n\t"                              \
+                     "lr 1,1\n\t"                                \
+                     "lr 2,2\n\t"                                \
+                     "lr 3,3\n\t"
+
+#define __CLIENT_REQUEST_CODE "lr 2,2\n\t"
+#define __GET_NR_CONTEXT_CODE "lr 3,3\n\t"
+#define __CALL_NO_REDIR_CODE  "lr 4,4\n\t"
+#define __VEX_INJECT_IR_CODE  "lr 5,5\n\t"
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                         \
+       _zzq_default, _zzq_request,                               \
+       _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)    \
+  __extension__                                                  \
+ ({volatile unsigned long int _zzq_args[6];                      \
+   volatile unsigned long int _zzq_result;                       \
+   _zzq_args[0] = (unsigned long int)(_zzq_request);             \
+   _zzq_args[1] = (unsigned long int)(_zzq_arg1);                \
+   _zzq_args[2] = (unsigned long int)(_zzq_arg2);                \
+   _zzq_args[3] = (unsigned long int)(_zzq_arg3);                \
+   _zzq_args[4] = (unsigned long int)(_zzq_arg4);                \
+   _zzq_args[5] = (unsigned long int)(_zzq_arg5);                \
+   __asm__ volatile(/* r2 = args */                              \
+                    "lgr 2,%1\n\t"                               \
+                    /* r3 = default */                           \
+                    "lgr 3,%2\n\t"                               \
+                    __SPECIAL_INSTRUCTION_PREAMBLE               \
+                    __CLIENT_REQUEST_CODE                        \
+                    /* results = r3 */                           \
+                    "lgr %0, 3\n\t"                              \
+                    : "=d" (_zzq_result)                         \
+                    : "a" (&_zzq_args[0]), "0" (_zzq_default)    \
+                    : "cc", "2", "3", "memory"                   \
+                   );                                            \
+   _zzq_result;                                                  \
+ })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                      \
+ { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                   \
+   volatile unsigned long int __addr;                            \
+   __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                    __GET_NR_CONTEXT_CODE                        \
+                    "lgr %0, 3\n\t"                              \
+                    : "=a" (__addr)                              \
+                    :                                            \
+                    : "cc", "3", "memory"                        \
+                   );                                            \
+   _zzq_orig->nraddr = __addr;                                   \
+ }
+
+#define VALGRIND_CALL_NOREDIR_R1                                 \
+                    __SPECIAL_INSTRUCTION_PREAMBLE               \
+                    __CALL_NO_REDIR_CODE
+
+#define VALGRIND_VEX_INJECT_IR()                                 \
+ do {                                                            \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE              \
+                     __VEX_INJECT_IR_CODE);                      \
+ } while (0)
+
+#endif /* PLAT_s390x_linux */
+
+/* ------------------------- mips32-linux ---------------- */
+
+#if defined(PLAT_mips32_linux)
+
+typedef
+   struct { 
+      unsigned int nraddr; /* where's the code? */
+   }
+   OrigFn;
+
+/* .word  0x342
+ * .word  0x742
+ * .word  0xC2
+ * .word  0x4C2*/
+#define __SPECIAL_INSTRUCTION_PREAMBLE          \
+                     "srl $0, $0, 13\n\t"       \
+                     "srl $0, $0, 29\n\t"       \
+                     "srl $0, $0, 3\n\t"        \
+                     "srl $0, $0, 19\n\t"
+                    
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+       _zzq_default, _zzq_request,                                \
+       _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)     \
+  __extension__                                                   \
+  ({ volatile unsigned int _zzq_args[6];                          \
+    volatile unsigned int _zzq_result;                            \
+    _zzq_args[0] = (unsigned int)(_zzq_request);                  \
+    _zzq_args[1] = (unsigned int)(_zzq_arg1);                     \
+    _zzq_args[2] = (unsigned int)(_zzq_arg2);                     \
+    _zzq_args[3] = (unsigned int)(_zzq_arg3);                     \
+    _zzq_args[4] = (unsigned int)(_zzq_arg4);                     \
+    _zzq_args[5] = (unsigned int)(_zzq_arg5);                     \
+        __asm__ volatile("move $11, %1\n\t" /*default*/           \
+                     "move $12, %2\n\t" /*ptr*/                   \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* T3 = client_request ( T4 ) */             \
+                     "or $13, $13, $13\n\t"                       \
+                     "move %0, $11\n\t"     /*result*/            \
+                     : "=r" (_zzq_result)                         \
+                     : "r" (_zzq_default), "r" (&_zzq_args[0])    \
+                     : "$11", "$12", "memory");                   \
+    _zzq_result;                                                  \
+  })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                       \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                   \
+    volatile unsigned int __addr;                                 \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* %t9 = guest_NRADDR */                     \
+                     "or $14, $14, $14\n\t"                       \
+                     "move %0, $11"     /*result*/                \
+                     : "=r" (__addr)                              \
+                     :                                            \
+                     : "$11"                                      \
+                    );                                            \
+    _zzq_orig->nraddr = __addr;                                   \
+  }
+
+#define VALGRIND_CALL_NOREDIR_T9                                 \
+                     __SPECIAL_INSTRUCTION_PREAMBLE              \
+                     /* call-noredir *%t9 */                     \
+                     "or $15, $15, $15\n\t"
+
+#define VALGRIND_VEX_INJECT_IR()                                 \
+ do {                                                            \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE              \
+                     "or $11, $11, $11\n\t"                      \
+                    );                                           \
+ } while (0)
+
+
+#endif /* PLAT_mips32_linux */
+
+/* ------------------------- mips64-linux ---------------- */
+
+#if defined(PLAT_mips64_linux)
+
+typedef
+   struct {
+      unsigned long nraddr; /* where's the code? */
+   }
+   OrigFn;
+
+/* dsll $0,$0, 3
+ * dsll $0,$0, 13
+ * dsll $0,$0, 29
+ * dsll $0,$0, 19*/
+#define __SPECIAL_INSTRUCTION_PREAMBLE                              \
+                     "dsll $0,$0, 3 ; dsll $0,$0,13\n\t"            \
+                     "dsll $0,$0,29 ; dsll $0,$0,19\n\t"
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                            \
+       _zzq_default, _zzq_request,                                  \
+       _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)       \
+  __extension__                                                     \
+  ({ volatile unsigned long int _zzq_args[6];                       \
+    volatile unsigned long int _zzq_result;                         \
+    _zzq_args[0] = (unsigned long int)(_zzq_request);               \
+    _zzq_args[1] = (unsigned long int)(_zzq_arg1);                  \
+    _zzq_args[2] = (unsigned long int)(_zzq_arg2);                  \
+    _zzq_args[3] = (unsigned long int)(_zzq_arg3);                  \
+    _zzq_args[4] = (unsigned long int)(_zzq_arg4);                  \
+    _zzq_args[5] = (unsigned long int)(_zzq_arg5);                  \
+        __asm__ volatile("move $11, %1\n\t" /*default*/             \
+                         "move $12, %2\n\t" /*ptr*/                 \
+                         __SPECIAL_INSTRUCTION_PREAMBLE             \
+                         /* $11 = client_request ( $12 ) */         \
+                         "or $13, $13, $13\n\t"                     \
+                         "move %0, $11\n\t"     /*result*/          \
+                         : "=r" (_zzq_result)                       \
+                         : "r" (_zzq_default), "r" (&_zzq_args[0])  \
+                         : "$11", "$12", "memory");                 \
+    _zzq_result;                                                    \
+  })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                         \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                     \
+    volatile unsigned long int __addr;                              \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE                 \
+                     /* $11 = guest_NRADDR */                       \
+                     "or $14, $14, $14\n\t"                         \
+                     "move %0, $11"     /*result*/                  \
+                     : "=r" (__addr)                                \
+                     :                                              \
+                     : "$11");                                      \
+    _zzq_orig->nraddr = __addr;                                     \
+  }
+
+#define VALGRIND_CALL_NOREDIR_T9                                    \
+                     __SPECIAL_INSTRUCTION_PREAMBLE                 \
+                     /* call-noredir $25 */                         \
+                     "or $15, $15, $15\n\t"
+
+#define VALGRIND_VEX_INJECT_IR()                                    \
+ do {                                                               \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE                 \
+                     "or $11, $11, $11\n\t"                         \
+                    );                                              \
+ } while (0)
+
+#endif /* PLAT_mips64_linux */
+
+#if defined(PLAT_nanomips_linux)
+
+typedef
+   struct {
+      unsigned int nraddr; /* where's the code? */
+   }
+   OrigFn;
+/*
+   8000 c04d  srl  zero, zero, 13
+   8000 c05d  srl  zero, zero, 29
+   8000 c043  srl  zero, zero,  3
+   8000 c053  srl  zero, zero, 19
+*/
+
+#define __SPECIAL_INSTRUCTION_PREAMBLE "srl[32] $zero, $zero, 13 \n\t" \
+                                       "srl[32] $zero, $zero, 29 \n\t" \
+                                       "srl[32] $zero, $zero, 3  \n\t" \
+                                       "srl[32] $zero, $zero, 19 \n\t"
+
+#define VALGRIND_DO_CLIENT_REQUEST_EXPR(                          \
+       _zzq_default, _zzq_request,                                \
+       _zzq_arg1, _zzq_arg2, _zzq_arg3, _zzq_arg4, _zzq_arg5)     \
+  __extension__                                                   \
+  ({ volatile unsigned int _zzq_args[6];                          \
+    volatile unsigned int _zzq_result;                            \
+    _zzq_args[0] = (unsigned int)(_zzq_request);                  \
+    _zzq_args[1] = (unsigned int)(_zzq_arg1);                     \
+    _zzq_args[2] = (unsigned int)(_zzq_arg2);                     \
+    _zzq_args[3] = (unsigned int)(_zzq_arg3);                     \
+    _zzq_args[4] = (unsigned int)(_zzq_arg4);                     \
+    _zzq_args[5] = (unsigned int)(_zzq_arg5);                     \
+    __asm__ volatile("move $a7, %1\n\t" /* default */             \
+                     "move $t0, %2\n\t" /* ptr */                 \
+                     __SPECIAL_INSTRUCTION_PREAMBLE               \
+                     /* $a7 = client_request( $t0 ) */            \
+                     "or[32] $t0, $t0, $t0\n\t"                   \
+                     "move %0, $a7\n\t"     /* result */          \
+                     : "=r" (_zzq_result)                         \
+                     : "r" (_zzq_default), "r" (&_zzq_args[0])    \
+                     : "$a7", "$t0", "memory");                   \
+    _zzq_result;                                                  \
+  })
+
+#define VALGRIND_GET_NR_CONTEXT(_zzq_rlval)                         \
+  { volatile OrigFn* _zzq_orig = &(_zzq_rlval);                     \
+    volatile unsigned long int __addr;                              \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE                 \
+                     /* $a7 = guest_NRADDR */                       \
+                     "or[32] $t1, $t1, $t1\n\t"                     \
+                     "move %0, $a7"     /*result*/                  \
+                     : "=r" (__addr)                                \
+                     :                                              \
+                     : "$a7");                                      \
+    _zzq_orig->nraddr = __addr;                                     \
+  }
+
+#define VALGRIND_CALL_NOREDIR_T9                                    \
+                     __SPECIAL_INSTRUCTION_PREAMBLE                 \
+                     /* call-noredir $25 */                         \
+                     "or[32] $t2, $t2, $t2\n\t"
+
+#define VALGRIND_VEX_INJECT_IR()                                    \
+ do {                                                               \
+    __asm__ volatile(__SPECIAL_INSTRUCTION_PREAMBLE                 \
+                     "or[32] $t3, $t3, $t3\n\t"                     \
+                    );                                              \
+ } while (0)
+
+#endif
+/* Insert assembly code for other platforms here... */
+
+#endif /* NVALGRIND */
+
+
+/* ------------------------------------------------------------------ */
+/* PLATFORM SPECIFICS for FUNCTION WRAPPING.  This is all very        */
+/* ugly.  It's the least-worst tradeoff I can think of.               */
+/* ------------------------------------------------------------------ */
+
+/* This section defines magic (a.k.a appalling-hack) macros for doing
+   guaranteed-no-redirection macros, so as to get from function
+   wrappers to the functions they are wrapping.  The whole point is to
+   construct standard call sequences, but to do the call itself with a
+   special no-redirect call pseudo-instruction that the JIT
+   understands and handles specially.  This section is long and
+   repetitious, and I can't see a way to make it shorter.
+
+   The naming scheme is as follows:
+
+      CALL_FN_{W,v}_{v,W,WW,WWW,WWWW,5W,6W,7W,etc}
+
+   'W' stands for "word" and 'v' for "void".  Hence there are
+   different macros for calling arity 0, 1, 2, 3, 4, etc, functions,
+   and for each, the possibility of returning a word-typed result, or
+   no result.
+*/
+
+/* Use these to write the name of your wrapper.  NOTE: duplicates
+   VG_WRAP_FUNCTION_Z{U,Z} in pub_tool_redir.h.  NOTE also: inserts
+   the default behaviour equivalance class tag "0000" into the name.
+   See pub_tool_redir.h for details -- normally you don't need to
+   think about this, though. */
+
+/* Use an extra level of macroisation so as to ensure the soname/fnname
+   args are fully macro-expanded before pasting them together. */
+#define VG_CONCAT4(_aa,_bb,_cc,_dd) _aa##_bb##_cc##_dd
+
+#define I_WRAP_SONAME_FNNAME_ZU(soname,fnname)                    \
+   VG_CONCAT4(_vgw00000ZU_,soname,_,fnname)
+
+#define I_WRAP_SONAME_FNNAME_ZZ(soname,fnname)                    \
+   VG_CONCAT4(_vgw00000ZZ_,soname,_,fnname)
+
+/* Use this macro from within a wrapper function to collect the
+   context (address and possibly other info) of the original function.
+   Once you have that you can then use it in one of the CALL_FN_
+   macros.  The type of the argument _lval is OrigFn. */
+#define VALGRIND_GET_ORIG_FN(_lval)  VALGRIND_GET_NR_CONTEXT(_lval)
+
+/* Also provide end-user facilities for function replacement, rather
+   than wrapping.  A replacement function differs from a wrapper in
+   that it has no way to get hold of the original function being
+   called, and hence no way to call onwards to it.  In a replacement
+   function, VALGRIND_GET_ORIG_FN always returns zero. */
+
+#define I_REPLACE_SONAME_FNNAME_ZU(soname,fnname)                 \
+   VG_CONCAT4(_vgr00000ZU_,soname,_,fnname)
+
+#define I_REPLACE_SONAME_FNNAME_ZZ(soname,fnname)                 \
+   VG_CONCAT4(_vgr00000ZZ_,soname,_,fnname)
+
+/* Derivatives of the main macros below, for calling functions
+   returning void. */
+
+#define CALL_FN_v_v(fnptr)                                        \
+   do { volatile unsigned long _junk;                             \
+        CALL_FN_W_v(_junk,fnptr); } while (0)
+
+#define CALL_FN_v_W(fnptr, arg1)                                  \
+   do { volatile unsigned long _junk;                             \
+        CALL_FN_W_W(_junk,fnptr,arg1); } while (0)
+
+#define CALL_FN_v_WW(fnptr, arg1,arg2)                            \
+   do { volatile unsigned long _junk;                             \
+        CALL_FN_W_WW(_junk,fnptr,arg1,arg2); } while (0)
+
+#define CALL_FN_v_WWW(fnptr, arg1,arg2,arg3)                      \
+   do { volatile unsigned long _junk;                             \
+        CALL_FN_W_WWW(_junk,fnptr,arg1,arg2,arg3); } while (0)
+
+#define CALL_FN_v_WWWW(fnptr, arg1,arg2,arg3,arg4)                \
+   do { volatile unsigned long _junk;                             \
+        CALL_FN_W_WWWW(_junk,fnptr,arg1,arg2,arg3,arg4); } while (0)
+
+#define CALL_FN_v_5W(fnptr, arg1,arg2,arg3,arg4,arg5)             \
+   do { volatile unsigned long _junk;                             \
+        CALL_FN_W_5W(_junk,fnptr,arg1,arg2,arg3,arg4,arg5); } while (0)
+
+#define CALL_FN_v_6W(fnptr, arg1,arg2,arg3,arg4,arg5,arg6)        \
+   do { volatile unsigned long _junk;                             \
+        CALL_FN_W_6W(_junk,fnptr,arg1,arg2,arg3,arg4,arg5,arg6); } while (0)
+
+#define CALL_FN_v_7W(fnptr, arg1,arg2,arg3,arg4,arg5,arg6,arg7)   \
+   do { volatile unsigned long _junk;                             \
+        CALL_FN_W_7W(_junk,fnptr,arg1,arg2,arg3,arg4,arg5,arg6,arg7); } while (0)
+
+/* ----------------- x86-{linux,darwin,solaris} ---------------- */
+
+#if defined(PLAT_x86_linux)  ||  defined(PLAT_x86_darwin) \
+    ||  defined(PLAT_x86_solaris)
+
+/* These regs are trashed by the hidden call.  No need to mention eax
+   as gcc can already see that, plus causes gcc to bomb. */
+#define __CALLER_SAVED_REGS /*"eax"*/ "ecx", "edx"
+
+/* Macros to save and align the stack before making a function
+   call and restore it afterwards as gcc may not keep the stack
+   pointer aligned if it doesn't realise calls are being made
+   to other functions. */
+
+#define VALGRIND_ALIGN_STACK               \
+      "movl %%esp,%%edi\n\t"               \
+      "andl $0xfffffff0,%%esp\n\t"
+#define VALGRIND_RESTORE_STACK             \
+      "movl %%edi,%%esp\n\t"
+
+/* These CALL_FN_ macros assume that on x86-linux, sizeof(unsigned
+   long) == 4. */
+
+#define CALL_FN_W_v(lval, orig)                                   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[1];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_W(lval, orig, arg1)                             \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[2];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "subl $12, %%esp\n\t"                                    \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1,arg2)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "subl $8, %%esp\n\t"                                     \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWW(lval, orig, arg1,arg2,arg3)                 \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[4];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "subl $4, %%esp\n\t"                                     \
+         "pushl 12(%%eax)\n\t"                                    \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1,arg2,arg3,arg4)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[5];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "pushl 16(%%eax)\n\t"                                    \
+         "pushl 12(%%eax)\n\t"                                    \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1,arg2,arg3,arg4,arg5)        \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[6];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "subl $12, %%esp\n\t"                                    \
+         "pushl 20(%%eax)\n\t"                                    \
+         "pushl 16(%%eax)\n\t"                                    \
+         "pushl 12(%%eax)\n\t"                                    \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_6W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6)   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[7];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "subl $8, %%esp\n\t"                                     \
+         "pushl 24(%%eax)\n\t"                                    \
+         "pushl 20(%%eax)\n\t"                                    \
+         "pushl 16(%%eax)\n\t"                                    \
+         "pushl 12(%%eax)\n\t"                                    \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7)                            \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[8];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "subl $4, %%esp\n\t"                                     \
+         "pushl 28(%%eax)\n\t"                                    \
+         "pushl 24(%%eax)\n\t"                                    \
+         "pushl 20(%%eax)\n\t"                                    \
+         "pushl 16(%%eax)\n\t"                                    \
+         "pushl 12(%%eax)\n\t"                                    \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[9];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "pushl 32(%%eax)\n\t"                                    \
+         "pushl 28(%%eax)\n\t"                                    \
+         "pushl 24(%%eax)\n\t"                                    \
+         "pushl 20(%%eax)\n\t"                                    \
+         "pushl 16(%%eax)\n\t"                                    \
+         "pushl 12(%%eax)\n\t"                                    \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8,arg9)                  \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[10];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "subl $12, %%esp\n\t"                                    \
+         "pushl 36(%%eax)\n\t"                                    \
+         "pushl 32(%%eax)\n\t"                                    \
+         "pushl 28(%%eax)\n\t"                                    \
+         "pushl 24(%%eax)\n\t"                                    \
+         "pushl 20(%%eax)\n\t"                                    \
+         "pushl 16(%%eax)\n\t"                                    \
+         "pushl 12(%%eax)\n\t"                                    \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[11];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "subl $8, %%esp\n\t"                                     \
+         "pushl 40(%%eax)\n\t"                                    \
+         "pushl 36(%%eax)\n\t"                                    \
+         "pushl 32(%%eax)\n\t"                                    \
+         "pushl 28(%%eax)\n\t"                                    \
+         "pushl 24(%%eax)\n\t"                                    \
+         "pushl 20(%%eax)\n\t"                                    \
+         "pushl 16(%%eax)\n\t"                                    \
+         "pushl 12(%%eax)\n\t"                                    \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1,arg2,arg3,arg4,arg5,       \
+                                  arg6,arg7,arg8,arg9,arg10,      \
+                                  arg11)                          \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[12];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      _argvec[11] = (unsigned long)(arg11);                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "subl $4, %%esp\n\t"                                     \
+         "pushl 44(%%eax)\n\t"                                    \
+         "pushl 40(%%eax)\n\t"                                    \
+         "pushl 36(%%eax)\n\t"                                    \
+         "pushl 32(%%eax)\n\t"                                    \
+         "pushl 28(%%eax)\n\t"                                    \
+         "pushl 24(%%eax)\n\t"                                    \
+         "pushl 20(%%eax)\n\t"                                    \
+         "pushl 16(%%eax)\n\t"                                    \
+         "pushl 12(%%eax)\n\t"                                    \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1,arg2,arg3,arg4,arg5,       \
+                                  arg6,arg7,arg8,arg9,arg10,      \
+                                  arg11,arg12)                    \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[13];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      _argvec[11] = (unsigned long)(arg11);                       \
+      _argvec[12] = (unsigned long)(arg12);                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "pushl 48(%%eax)\n\t"                                    \
+         "pushl 44(%%eax)\n\t"                                    \
+         "pushl 40(%%eax)\n\t"                                    \
+         "pushl 36(%%eax)\n\t"                                    \
+         "pushl 32(%%eax)\n\t"                                    \
+         "pushl 28(%%eax)\n\t"                                    \
+         "pushl 24(%%eax)\n\t"                                    \
+         "pushl 20(%%eax)\n\t"                                    \
+         "pushl 16(%%eax)\n\t"                                    \
+         "pushl 12(%%eax)\n\t"                                    \
+         "pushl 8(%%eax)\n\t"                                     \
+         "pushl 4(%%eax)\n\t"                                     \
+         "movl (%%eax), %%eax\n\t"  /* target->%eax */            \
+         VALGRIND_CALL_NOREDIR_EAX                                \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=a" (_res)                                  \
+         : /*in*/    "a" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "edi"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#endif /* PLAT_x86_linux || PLAT_x86_darwin || PLAT_x86_solaris */
+
+/* ---------------- amd64-{linux,darwin,solaris} --------------- */
+
+#if defined(PLAT_amd64_linux)  ||  defined(PLAT_amd64_darwin) \
+    ||  defined(PLAT_amd64_solaris)
+
+/* ARGREGS: rdi rsi rdx rcx r8 r9 (the rest on stack in R-to-L order) */
+
+/* These regs are trashed by the hidden call. */
+#define __CALLER_SAVED_REGS /*"rax",*/ "rcx", "rdx", "rsi",       \
+                            "rdi", "r8", "r9", "r10", "r11"
+
+/* This is all pretty complex.  It's so as to make stack unwinding
+   work reliably.  See bug 243270.  The basic problem is the sub and
+   add of 128 of %rsp in all of the following macros.  If gcc believes
+   the CFA is in %rsp, then unwinding may fail, because what's at the
+   CFA is not what gcc "expected" when it constructs the CFIs for the
+   places where the macros are instantiated.
+
+   But we can't just add a CFI annotation to increase the CFA offset
+   by 128, to match the sub of 128 from %rsp, because we don't know
+   whether gcc has chosen %rsp as the CFA at that point, or whether it
+   has chosen some other register (eg, %rbp).  In the latter case,
+   adding a CFI annotation to change the CFA offset is simply wrong.
+
+   So the solution is to get hold of the CFA using
+   __builtin_dwarf_cfa(), put it in a known register, and add a
+   CFI annotation to say what the register is.  We choose %rbp for
+   this (perhaps perversely), because:
+
+   (1) %rbp is already subject to unwinding.  If a new register was
+       chosen then the unwinder would have to unwind it in all stack
+       traces, which is expensive, and
+
+   (2) %rbp is already subject to precise exception updates in the
+       JIT.  If a new register was chosen, we'd have to have precise
+       exceptions for it too, which reduces performance of the
+       generated code.
+
+   However .. one extra complication.  We can't just whack the result
+   of __builtin_dwarf_cfa() into %rbp and then add %rbp to the
+   list of trashed registers at the end of the inline assembly
+   fragments; gcc won't allow %rbp to appear in that list.  Hence
+   instead we need to stash %rbp in %r15 for the duration of the asm,
+   and say that %r15 is trashed instead.  gcc seems happy to go with
+   that.
+
+   Oh .. and this all needs to be conditionalised so that it is
+   unchanged from before this commit, when compiled with older gccs
+   that don't support __builtin_dwarf_cfa.  Furthermore, since
+   this header file is freestanding, it has to be independent of
+   config.h, and so the following conditionalisation cannot depend on
+   configure time checks.
+
+   Although it's not clear from
+   'defined(__GNUC__) && defined(__GCC_HAVE_DWARF2_CFI_ASM)',
+   this expression excludes Darwin.
+   .cfi directives in Darwin assembly appear to be completely
+   different and I haven't investigated how they work.
+
+   For even more entertainment value, note we have to use the
+   completely undocumented __builtin_dwarf_cfa(), which appears to
+   really compute the CFA, whereas __builtin_frame_address(0) claims
+   to but actually doesn't.  See
+   https://bugs.kde.org/show_bug.cgi?id=243270#c47
+*/
+#if defined(__GNUC__) && defined(__GCC_HAVE_DWARF2_CFI_ASM)
+#  define __FRAME_POINTER                                         \
+      ,"r"(__builtin_dwarf_cfa())
+#  define VALGRIND_CFI_PROLOGUE                                   \
+      "movq %%rbp, %%r15\n\t"                                     \
+      "movq %2, %%rbp\n\t"                                        \
+      ".cfi_remember_state\n\t"                                   \
+      ".cfi_def_cfa rbp, 0\n\t"
+#  define VALGRIND_CFI_EPILOGUE                                   \
+      "movq %%r15, %%rbp\n\t"                                     \
+      ".cfi_restore_state\n\t"
+#else
+#  define __FRAME_POINTER
+#  define VALGRIND_CFI_PROLOGUE
+#  define VALGRIND_CFI_EPILOGUE
+#endif
+
+/* Macros to save and align the stack before making a function
+   call and restore it afterwards as gcc may not keep the stack
+   pointer aligned if it doesn't realise calls are being made
+   to other functions. */
+
+#define VALGRIND_ALIGN_STACK               \
+      "movq %%rsp,%%r14\n\t"               \
+      "andq $0xfffffffffffffff0,%%rsp\n\t"
+#define VALGRIND_RESTORE_STACK             \
+      "movq %%r14,%%rsp\n\t"
+
+/* These CALL_FN_ macros assume that on amd64-linux, sizeof(unsigned
+   long) == 8. */
+
+/* NB 9 Sept 07.  There is a nasty kludge here in all these CALL_FN_
+   macros.  In order not to trash the stack redzone, we need to drop
+   %rsp by 128 before the hidden call, and restore afterwards.  The
+   nastyness is that it is only by luck that the stack still appears
+   to be unwindable during the hidden call - since then the behaviour
+   of any routine using this macro does not match what the CFI data
+   says.  Sigh.
+
+   Why is this important?  Imagine that a wrapper has a stack
+   allocated local, and passes to the hidden call, a pointer to it.
+   Because gcc does not know about the hidden call, it may allocate
+   that local in the redzone.  Unfortunately the hidden call may then
+   trash it before it comes to use it.  So we must step clear of the
+   redzone, for the duration of the hidden call, to make it safe.
+
+   Probably the same problem afflicts the other redzone-style ABIs too
+   (ppc64-linux); but for those, the stack is
+   self describing (none of this CFI nonsense) so at least messing
+   with the stack pointer doesn't give a danger of non-unwindable
+   stack. */
+
+#define CALL_FN_W_v(lval, orig)                                        \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[1];                               \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $128,%%rsp\n\t"                                         \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_W(lval, orig, arg1)                                  \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[2];                               \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $128,%%rsp\n\t"                                         \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1,arg2)                            \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[3];                               \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $128,%%rsp\n\t"                                         \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_WWW(lval, orig, arg1,arg2,arg3)                      \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[4];                               \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      _argvec[3] = (unsigned long)(arg3);                              \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $128,%%rsp\n\t"                                         \
+         "movq 24(%%rax), %%rdx\n\t"                                   \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1,arg2,arg3,arg4)                \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[5];                               \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      _argvec[3] = (unsigned long)(arg3);                              \
+      _argvec[4] = (unsigned long)(arg4);                              \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $128,%%rsp\n\t"                                         \
+         "movq 32(%%rax), %%rcx\n\t"                                   \
+         "movq 24(%%rax), %%rdx\n\t"                                   \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1,arg2,arg3,arg4,arg5)             \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[6];                               \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      _argvec[3] = (unsigned long)(arg3);                              \
+      _argvec[4] = (unsigned long)(arg4);                              \
+      _argvec[5] = (unsigned long)(arg5);                              \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $128,%%rsp\n\t"                                         \
+         "movq 40(%%rax), %%r8\n\t"                                    \
+         "movq 32(%%rax), %%rcx\n\t"                                   \
+         "movq 24(%%rax), %%rdx\n\t"                                   \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_6W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6)        \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[7];                               \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      _argvec[3] = (unsigned long)(arg3);                              \
+      _argvec[4] = (unsigned long)(arg4);                              \
+      _argvec[5] = (unsigned long)(arg5);                              \
+      _argvec[6] = (unsigned long)(arg6);                              \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $128,%%rsp\n\t"                                         \
+         "movq 48(%%rax), %%r9\n\t"                                    \
+         "movq 40(%%rax), %%r8\n\t"                                    \
+         "movq 32(%%rax), %%rcx\n\t"                                   \
+         "movq 24(%%rax), %%rdx\n\t"                                   \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,        \
+                                 arg7)                                 \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[8];                               \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      _argvec[3] = (unsigned long)(arg3);                              \
+      _argvec[4] = (unsigned long)(arg4);                              \
+      _argvec[5] = (unsigned long)(arg5);                              \
+      _argvec[6] = (unsigned long)(arg6);                              \
+      _argvec[7] = (unsigned long)(arg7);                              \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $136,%%rsp\n\t"                                         \
+         "pushq 56(%%rax)\n\t"                                         \
+         "movq 48(%%rax), %%r9\n\t"                                    \
+         "movq 40(%%rax), %%r8\n\t"                                    \
+         "movq 32(%%rax), %%rcx\n\t"                                   \
+         "movq 24(%%rax), %%rdx\n\t"                                   \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,        \
+                                 arg7,arg8)                            \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[9];                               \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      _argvec[3] = (unsigned long)(arg3);                              \
+      _argvec[4] = (unsigned long)(arg4);                              \
+      _argvec[5] = (unsigned long)(arg5);                              \
+      _argvec[6] = (unsigned long)(arg6);                              \
+      _argvec[7] = (unsigned long)(arg7);                              \
+      _argvec[8] = (unsigned long)(arg8);                              \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $128,%%rsp\n\t"                                         \
+         "pushq 64(%%rax)\n\t"                                         \
+         "pushq 56(%%rax)\n\t"                                         \
+         "movq 48(%%rax), %%r9\n\t"                                    \
+         "movq 40(%%rax), %%r8\n\t"                                    \
+         "movq 32(%%rax), %%rcx\n\t"                                   \
+         "movq 24(%%rax), %%rdx\n\t"                                   \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,        \
+                                 arg7,arg8,arg9)                       \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[10];                              \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      _argvec[3] = (unsigned long)(arg3);                              \
+      _argvec[4] = (unsigned long)(arg4);                              \
+      _argvec[5] = (unsigned long)(arg5);                              \
+      _argvec[6] = (unsigned long)(arg6);                              \
+      _argvec[7] = (unsigned long)(arg7);                              \
+      _argvec[8] = (unsigned long)(arg8);                              \
+      _argvec[9] = (unsigned long)(arg9);                              \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $136,%%rsp\n\t"                                         \
+         "pushq 72(%%rax)\n\t"                                         \
+         "pushq 64(%%rax)\n\t"                                         \
+         "pushq 56(%%rax)\n\t"                                         \
+         "movq 48(%%rax), %%r9\n\t"                                    \
+         "movq 40(%%rax), %%r8\n\t"                                    \
+         "movq 32(%%rax), %%rcx\n\t"                                   \
+         "movq 24(%%rax), %%rdx\n\t"                                   \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,       \
+                                  arg7,arg8,arg9,arg10)                \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[11];                              \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      _argvec[3] = (unsigned long)(arg3);                              \
+      _argvec[4] = (unsigned long)(arg4);                              \
+      _argvec[5] = (unsigned long)(arg5);                              \
+      _argvec[6] = (unsigned long)(arg6);                              \
+      _argvec[7] = (unsigned long)(arg7);                              \
+      _argvec[8] = (unsigned long)(arg8);                              \
+      _argvec[9] = (unsigned long)(arg9);                              \
+      _argvec[10] = (unsigned long)(arg10);                            \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $128,%%rsp\n\t"                                         \
+         "pushq 80(%%rax)\n\t"                                         \
+         "pushq 72(%%rax)\n\t"                                         \
+         "pushq 64(%%rax)\n\t"                                         \
+         "pushq 56(%%rax)\n\t"                                         \
+         "movq 48(%%rax), %%r9\n\t"                                    \
+         "movq 40(%%rax), %%r8\n\t"                                    \
+         "movq 32(%%rax), %%rcx\n\t"                                   \
+         "movq 24(%%rax), %%rdx\n\t"                                   \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,       \
+                                  arg7,arg8,arg9,arg10,arg11)          \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[12];                              \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      _argvec[3] = (unsigned long)(arg3);                              \
+      _argvec[4] = (unsigned long)(arg4);                              \
+      _argvec[5] = (unsigned long)(arg5);                              \
+      _argvec[6] = (unsigned long)(arg6);                              \
+      _argvec[7] = (unsigned long)(arg7);                              \
+      _argvec[8] = (unsigned long)(arg8);                              \
+      _argvec[9] = (unsigned long)(arg9);                              \
+      _argvec[10] = (unsigned long)(arg10);                            \
+      _argvec[11] = (unsigned long)(arg11);                            \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $136,%%rsp\n\t"                                         \
+         "pushq 88(%%rax)\n\t"                                         \
+         "pushq 80(%%rax)\n\t"                                         \
+         "pushq 72(%%rax)\n\t"                                         \
+         "pushq 64(%%rax)\n\t"                                         \
+         "pushq 56(%%rax)\n\t"                                         \
+         "movq 48(%%rax), %%r9\n\t"                                    \
+         "movq 40(%%rax), %%r8\n\t"                                    \
+         "movq 32(%%rax), %%rcx\n\t"                                   \
+         "movq 24(%%rax), %%rdx\n\t"                                   \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,       \
+                                arg7,arg8,arg9,arg10,arg11,arg12)      \
+   do {                                                                \
+      volatile OrigFn        _orig = (orig);                           \
+      volatile unsigned long _argvec[13];                              \
+      volatile unsigned long _res;                                     \
+      _argvec[0] = (unsigned long)_orig.nraddr;                        \
+      _argvec[1] = (unsigned long)(arg1);                              \
+      _argvec[2] = (unsigned long)(arg2);                              \
+      _argvec[3] = (unsigned long)(arg3);                              \
+      _argvec[4] = (unsigned long)(arg4);                              \
+      _argvec[5] = (unsigned long)(arg5);                              \
+      _argvec[6] = (unsigned long)(arg6);                              \
+      _argvec[7] = (unsigned long)(arg7);                              \
+      _argvec[8] = (unsigned long)(arg8);                              \
+      _argvec[9] = (unsigned long)(arg9);                              \
+      _argvec[10] = (unsigned long)(arg10);                            \
+      _argvec[11] = (unsigned long)(arg11);                            \
+      _argvec[12] = (unsigned long)(arg12);                            \
+      __asm__ volatile(                                                \
+         VALGRIND_CFI_PROLOGUE                                         \
+         VALGRIND_ALIGN_STACK                                          \
+         "subq $128,%%rsp\n\t"                                         \
+         "pushq 96(%%rax)\n\t"                                         \
+         "pushq 88(%%rax)\n\t"                                         \
+         "pushq 80(%%rax)\n\t"                                         \
+         "pushq 72(%%rax)\n\t"                                         \
+         "pushq 64(%%rax)\n\t"                                         \
+         "pushq 56(%%rax)\n\t"                                         \
+         "movq 48(%%rax), %%r9\n\t"                                    \
+         "movq 40(%%rax), %%r8\n\t"                                    \
+         "movq 32(%%rax), %%rcx\n\t"                                   \
+         "movq 24(%%rax), %%rdx\n\t"                                   \
+         "movq 16(%%rax), %%rsi\n\t"                                   \
+         "movq 8(%%rax), %%rdi\n\t"                                    \
+         "movq (%%rax), %%rax\n\t"  /* target->%rax */                 \
+         VALGRIND_CALL_NOREDIR_RAX                                     \
+         VALGRIND_RESTORE_STACK                                        \
+         VALGRIND_CFI_EPILOGUE                                         \
+         : /*out*/   "=a" (_res)                                       \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER                 \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r14", "r15" \
+      );                                                               \
+      lval = (__typeof__(lval)) _res;                                  \
+   } while (0)
+
+#endif /* PLAT_amd64_linux || PLAT_amd64_darwin || PLAT_amd64_solaris */
+
+/* ------------------------ ppc32-linux ------------------------ */
+
+#if defined(PLAT_ppc32_linux)
+
+/* This is useful for finding out about the on-stack stuff:
+
+   extern int f9  ( int,int,int,int,int,int,int,int,int );
+   extern int f10 ( int,int,int,int,int,int,int,int,int,int );
+   extern int f11 ( int,int,int,int,int,int,int,int,int,int,int );
+   extern int f12 ( int,int,int,int,int,int,int,int,int,int,int,int );
+
+   int g9 ( void ) {
+      return f9(11,22,33,44,55,66,77,88,99);
+   }
+   int g10 ( void ) {
+      return f10(11,22,33,44,55,66,77,88,99,110);
+   }
+   int g11 ( void ) {
+      return f11(11,22,33,44,55,66,77,88,99,110,121);
+   }
+   int g12 ( void ) {
+      return f12(11,22,33,44,55,66,77,88,99,110,121,132);
+   }
+*/
+
+/* ARGREGS: r3 r4 r5 r6 r7 r8 r9 r10 (the rest on stack somewhere) */
+
+/* These regs are trashed by the hidden call. */
+#define __CALLER_SAVED_REGS                                       \
+   "lr", "ctr", "xer",                                            \
+   "cr0", "cr1", "cr2", "cr3", "cr4", "cr5", "cr6", "cr7",        \
+   "r0", "r2", "r3", "r4", "r5", "r6", "r7", "r8", "r9", "r10",   \
+   "r11", "r12", "r13"
+
+/* Macros to save and align the stack before making a function
+   call and restore it afterwards as gcc may not keep the stack
+   pointer aligned if it doesn't realise calls are being made
+   to other functions. */
+
+#define VALGRIND_ALIGN_STACK               \
+      "mr 28,1\n\t"                        \
+      "rlwinm 1,1,0,0,27\n\t"
+#define VALGRIND_RESTORE_STACK             \
+      "mr 1,28\n\t"
+
+/* These CALL_FN_ macros assume that on ppc32-linux, 
+   sizeof(unsigned long) == 4. */
+
+#define CALL_FN_W_v(lval, orig)                                   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[1];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_W(lval, orig, arg1)                             \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[2];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1,arg2)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWW(lval, orig, arg1,arg2,arg3)                 \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[4];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      _argvec[3] = (unsigned long)arg3;                           \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 5,12(11)\n\t"                                       \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1,arg2,arg3,arg4)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[5];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      _argvec[3] = (unsigned long)arg3;                           \
+      _argvec[4] = (unsigned long)arg4;                           \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 5,12(11)\n\t"                                       \
+         "lwz 6,16(11)\n\t"  /* arg4->r6 */                       \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1,arg2,arg3,arg4,arg5)        \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[6];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      _argvec[3] = (unsigned long)arg3;                           \
+      _argvec[4] = (unsigned long)arg4;                           \
+      _argvec[5] = (unsigned long)arg5;                           \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 5,12(11)\n\t"                                       \
+         "lwz 6,16(11)\n\t"  /* arg4->r6 */                       \
+         "lwz 7,20(11)\n\t"                                       \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_6W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6)   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[7];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      _argvec[3] = (unsigned long)arg3;                           \
+      _argvec[4] = (unsigned long)arg4;                           \
+      _argvec[5] = (unsigned long)arg5;                           \
+      _argvec[6] = (unsigned long)arg6;                           \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 5,12(11)\n\t"                                       \
+         "lwz 6,16(11)\n\t"  /* arg4->r6 */                       \
+         "lwz 7,20(11)\n\t"                                       \
+         "lwz 8,24(11)\n\t"                                       \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7)                            \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[8];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      _argvec[3] = (unsigned long)arg3;                           \
+      _argvec[4] = (unsigned long)arg4;                           \
+      _argvec[5] = (unsigned long)arg5;                           \
+      _argvec[6] = (unsigned long)arg6;                           \
+      _argvec[7] = (unsigned long)arg7;                           \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 5,12(11)\n\t"                                       \
+         "lwz 6,16(11)\n\t"  /* arg4->r6 */                       \
+         "lwz 7,20(11)\n\t"                                       \
+         "lwz 8,24(11)\n\t"                                       \
+         "lwz 9,28(11)\n\t"                                       \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[9];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      _argvec[3] = (unsigned long)arg3;                           \
+      _argvec[4] = (unsigned long)arg4;                           \
+      _argvec[5] = (unsigned long)arg5;                           \
+      _argvec[6] = (unsigned long)arg6;                           \
+      _argvec[7] = (unsigned long)arg7;                           \
+      _argvec[8] = (unsigned long)arg8;                           \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 5,12(11)\n\t"                                       \
+         "lwz 6,16(11)\n\t"  /* arg4->r6 */                       \
+         "lwz 7,20(11)\n\t"                                       \
+         "lwz 8,24(11)\n\t"                                       \
+         "lwz 9,28(11)\n\t"                                       \
+         "lwz 10,32(11)\n\t" /* arg8->r10 */                      \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8,arg9)                  \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[10];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      _argvec[3] = (unsigned long)arg3;                           \
+      _argvec[4] = (unsigned long)arg4;                           \
+      _argvec[5] = (unsigned long)arg5;                           \
+      _argvec[6] = (unsigned long)arg6;                           \
+      _argvec[7] = (unsigned long)arg7;                           \
+      _argvec[8] = (unsigned long)arg8;                           \
+      _argvec[9] = (unsigned long)arg9;                           \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "addi 1,1,-16\n\t"                                       \
+         /* arg9 */                                               \
+         "lwz 3,36(11)\n\t"                                       \
+         "stw 3,8(1)\n\t"                                         \
+         /* args1-8 */                                            \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 5,12(11)\n\t"                                       \
+         "lwz 6,16(11)\n\t"  /* arg4->r6 */                       \
+         "lwz 7,20(11)\n\t"                                       \
+         "lwz 8,24(11)\n\t"                                       \
+         "lwz 9,28(11)\n\t"                                       \
+         "lwz 10,32(11)\n\t" /* arg8->r10 */                      \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[11];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      _argvec[3] = (unsigned long)arg3;                           \
+      _argvec[4] = (unsigned long)arg4;                           \
+      _argvec[5] = (unsigned long)arg5;                           \
+      _argvec[6] = (unsigned long)arg6;                           \
+      _argvec[7] = (unsigned long)arg7;                           \
+      _argvec[8] = (unsigned long)arg8;                           \
+      _argvec[9] = (unsigned long)arg9;                           \
+      _argvec[10] = (unsigned long)arg10;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "addi 1,1,-16\n\t"                                       \
+         /* arg10 */                                              \
+         "lwz 3,40(11)\n\t"                                       \
+         "stw 3,12(1)\n\t"                                        \
+         /* arg9 */                                               \
+         "lwz 3,36(11)\n\t"                                       \
+         "stw 3,8(1)\n\t"                                         \
+         /* args1-8 */                                            \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 5,12(11)\n\t"                                       \
+         "lwz 6,16(11)\n\t"  /* arg4->r6 */                       \
+         "lwz 7,20(11)\n\t"                                       \
+         "lwz 8,24(11)\n\t"                                       \
+         "lwz 9,28(11)\n\t"                                       \
+         "lwz 10,32(11)\n\t" /* arg8->r10 */                      \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10,arg11)     \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[12];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      _argvec[3] = (unsigned long)arg3;                           \
+      _argvec[4] = (unsigned long)arg4;                           \
+      _argvec[5] = (unsigned long)arg5;                           \
+      _argvec[6] = (unsigned long)arg6;                           \
+      _argvec[7] = (unsigned long)arg7;                           \
+      _argvec[8] = (unsigned long)arg8;                           \
+      _argvec[9] = (unsigned long)arg9;                           \
+      _argvec[10] = (unsigned long)arg10;                         \
+      _argvec[11] = (unsigned long)arg11;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "addi 1,1,-32\n\t"                                       \
+         /* arg11 */                                              \
+         "lwz 3,44(11)\n\t"                                       \
+         "stw 3,16(1)\n\t"                                        \
+         /* arg10 */                                              \
+         "lwz 3,40(11)\n\t"                                       \
+         "stw 3,12(1)\n\t"                                        \
+         /* arg9 */                                               \
+         "lwz 3,36(11)\n\t"                                       \
+         "stw 3,8(1)\n\t"                                         \
+         /* args1-8 */                                            \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 5,12(11)\n\t"                                       \
+         "lwz 6,16(11)\n\t"  /* arg4->r6 */                       \
+         "lwz 7,20(11)\n\t"                                       \
+         "lwz 8,24(11)\n\t"                                       \
+         "lwz 9,28(11)\n\t"                                       \
+         "lwz 10,32(11)\n\t" /* arg8->r10 */                      \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                arg7,arg8,arg9,arg10,arg11,arg12) \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[13];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)arg1;                           \
+      _argvec[2] = (unsigned long)arg2;                           \
+      _argvec[3] = (unsigned long)arg3;                           \
+      _argvec[4] = (unsigned long)arg4;                           \
+      _argvec[5] = (unsigned long)arg5;                           \
+      _argvec[6] = (unsigned long)arg6;                           \
+      _argvec[7] = (unsigned long)arg7;                           \
+      _argvec[8] = (unsigned long)arg8;                           \
+      _argvec[9] = (unsigned long)arg9;                           \
+      _argvec[10] = (unsigned long)arg10;                         \
+      _argvec[11] = (unsigned long)arg11;                         \
+      _argvec[12] = (unsigned long)arg12;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "addi 1,1,-32\n\t"                                       \
+         /* arg12 */                                              \
+         "lwz 3,48(11)\n\t"                                       \
+         "stw 3,20(1)\n\t"                                        \
+         /* arg11 */                                              \
+         "lwz 3,44(11)\n\t"                                       \
+         "stw 3,16(1)\n\t"                                        \
+         /* arg10 */                                              \
+         "lwz 3,40(11)\n\t"                                       \
+         "stw 3,12(1)\n\t"                                        \
+         /* arg9 */                                               \
+         "lwz 3,36(11)\n\t"                                       \
+         "stw 3,8(1)\n\t"                                         \
+         /* args1-8 */                                            \
+         "lwz 3,4(11)\n\t"   /* arg1->r3 */                       \
+         "lwz 4,8(11)\n\t"                                        \
+         "lwz 5,12(11)\n\t"                                       \
+         "lwz 6,16(11)\n\t"  /* arg4->r6 */                       \
+         "lwz 7,20(11)\n\t"                                       \
+         "lwz 8,24(11)\n\t"                                       \
+         "lwz 9,28(11)\n\t"                                       \
+         "lwz 10,32(11)\n\t" /* arg8->r10 */                      \
+         "lwz 11,0(11)\n\t"  /* target->r11 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         VALGRIND_RESTORE_STACK                                   \
+         "mr %0,3"                                                \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#endif /* PLAT_ppc32_linux */
+
+/* ------------------------ ppc64-linux ------------------------ */
+
+#if defined(PLAT_ppc64be_linux)
+
+/* ARGREGS: r3 r4 r5 r6 r7 r8 r9 r10 (the rest on stack somewhere) */
+
+/* These regs are trashed by the hidden call. */
+#define __CALLER_SAVED_REGS                                       \
+   "lr", "ctr", "xer",                                            \
+   "cr0", "cr1", "cr2", "cr3", "cr4", "cr5", "cr6", "cr7",        \
+   "r0", "r3", "r4", "r5", "r6", "r7", "r8", "r9", "r10",         \
+   "r11", "r12", "r13"
+
+/* Macros to save and align the stack before making a function
+   call and restore it afterwards as gcc may not keep the stack
+   pointer aligned if it doesn't realise calls are being made
+   to other functions. */
+
+#define VALGRIND_ALIGN_STACK               \
+      "mr 28,1\n\t"                        \
+      "rldicr 1,1,0,59\n\t"
+#define VALGRIND_RESTORE_STACK             \
+      "mr 1,28\n\t"
+
+/* These CALL_FN_ macros assume that on ppc64-linux, sizeof(unsigned
+   long) == 8. */
+
+#define CALL_FN_W_v(lval, orig)                                   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+0];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1] = (unsigned long)_orig.r2;                       \
+      _argvec[2] = (unsigned long)_orig.nraddr;                   \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_W(lval, orig, arg1)                             \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+1];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1,arg2)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+2];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWW(lval, orig, arg1,arg2,arg3)                 \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+3];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(11)\n\t" /* arg3->r5 */                      \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1,arg2,arg3,arg4)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+4];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(11)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(11)\n\t" /* arg4->r6 */                      \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1,arg2,arg3,arg4,arg5)        \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+5];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(11)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(11)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(11)\n\t" /* arg5->r7 */                      \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_6W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6)   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+6];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(11)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(11)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(11)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(11)\n\t" /* arg6->r8 */                      \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7)                            \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+7];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(11)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(11)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(11)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(11)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(11)\n\t" /* arg7->r9 */                      \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+8];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      _argvec[2+8] = (unsigned long)arg8;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(11)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(11)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(11)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(11)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(11)\n\t" /* arg7->r9 */                      \
+         "ld  10, 64(11)\n\t" /* arg8->r10 */                     \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8,arg9)                  \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+9];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      _argvec[2+8] = (unsigned long)arg8;                         \
+      _argvec[2+9] = (unsigned long)arg9;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "addi 1,1,-128\n\t"  /* expand stack frame */            \
+         /* arg9 */                                               \
+         "ld  3,72(11)\n\t"                                       \
+         "std 3,112(1)\n\t"                                       \
+         /* args1-8 */                                            \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(11)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(11)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(11)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(11)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(11)\n\t" /* arg7->r9 */                      \
+         "ld  10, 64(11)\n\t" /* arg8->r10 */                     \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+10];                       \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      _argvec[2+8] = (unsigned long)arg8;                         \
+      _argvec[2+9] = (unsigned long)arg9;                         \
+      _argvec[2+10] = (unsigned long)arg10;                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "addi 1,1,-128\n\t"  /* expand stack frame */            \
+         /* arg10 */                                              \
+         "ld  3,80(11)\n\t"                                       \
+         "std 3,120(1)\n\t"                                       \
+         /* arg9 */                                               \
+         "ld  3,72(11)\n\t"                                       \
+         "std 3,112(1)\n\t"                                       \
+         /* args1-8 */                                            \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(11)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(11)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(11)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(11)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(11)\n\t" /* arg7->r9 */                      \
+         "ld  10, 64(11)\n\t" /* arg8->r10 */                     \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10,arg11)     \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+11];                       \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      _argvec[2+8] = (unsigned long)arg8;                         \
+      _argvec[2+9] = (unsigned long)arg9;                         \
+      _argvec[2+10] = (unsigned long)arg10;                       \
+      _argvec[2+11] = (unsigned long)arg11;                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "addi 1,1,-144\n\t"  /* expand stack frame */            \
+         /* arg11 */                                              \
+         "ld  3,88(11)\n\t"                                       \
+         "std 3,128(1)\n\t"                                       \
+         /* arg10 */                                              \
+         "ld  3,80(11)\n\t"                                       \
+         "std 3,120(1)\n\t"                                       \
+         /* arg9 */                                               \
+         "ld  3,72(11)\n\t"                                       \
+         "std 3,112(1)\n\t"                                       \
+         /* args1-8 */                                            \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(11)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(11)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(11)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(11)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(11)\n\t" /* arg7->r9 */                      \
+         "ld  10, 64(11)\n\t" /* arg8->r10 */                     \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                arg7,arg8,arg9,arg10,arg11,arg12) \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+12];                       \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      _argvec[2+8] = (unsigned long)arg8;                         \
+      _argvec[2+9] = (unsigned long)arg9;                         \
+      _argvec[2+10] = (unsigned long)arg10;                       \
+      _argvec[2+11] = (unsigned long)arg11;                       \
+      _argvec[2+12] = (unsigned long)arg12;                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 11,%1\n\t"                                           \
+         "std 2,-16(11)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(11)\n\t"  /* use nraddr's tocptr */           \
+         "addi 1,1,-144\n\t"  /* expand stack frame */            \
+         /* arg12 */                                              \
+         "ld  3,96(11)\n\t"                                       \
+         "std 3,136(1)\n\t"                                       \
+         /* arg11 */                                              \
+         "ld  3,88(11)\n\t"                                       \
+         "std 3,128(1)\n\t"                                       \
+         /* arg10 */                                              \
+         "ld  3,80(11)\n\t"                                       \
+         "std 3,120(1)\n\t"                                       \
+         /* arg9 */                                               \
+         "ld  3,72(11)\n\t"                                       \
+         "std 3,112(1)\n\t"                                       \
+         /* args1-8 */                                            \
+         "ld   3, 8(11)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(11)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(11)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(11)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(11)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(11)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(11)\n\t" /* arg7->r9 */                      \
+         "ld  10, 64(11)\n\t" /* arg8->r10 */                     \
+         "ld  11, 0(11)\n\t"  /* target->r11 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R11                  \
+         "mr 11,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(11)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#endif /* PLAT_ppc64be_linux */
+
+/* ------------------------- ppc64le-linux ----------------------- */
+#if defined(PLAT_ppc64le_linux)
+
+/* ARGREGS: r3 r4 r5 r6 r7 r8 r9 r10 (the rest on stack somewhere) */
+
+/* These regs are trashed by the hidden call. */
+#define __CALLER_SAVED_REGS                                       \
+   "lr", "ctr", "xer",                                            \
+   "cr0", "cr1", "cr2", "cr3", "cr4", "cr5", "cr6", "cr7",        \
+   "r0", "r3", "r4", "r5", "r6", "r7", "r8", "r9", "r10",         \
+   "r11", "r12", "r13"
+
+/* Macros to save and align the stack before making a function
+   call and restore it afterwards as gcc may not keep the stack
+   pointer aligned if it doesn't realise calls are being made
+   to other functions. */
+
+#define VALGRIND_ALIGN_STACK               \
+      "mr 28,1\n\t"                        \
+      "rldicr 1,1,0,59\n\t"
+#define VALGRIND_RESTORE_STACK             \
+      "mr 1,28\n\t"
+
+/* These CALL_FN_ macros assume that on ppc64-linux, sizeof(unsigned
+   long) == 8. */
+
+#define CALL_FN_W_v(lval, orig)                                   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+0];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1] = (unsigned long)_orig.r2;                       \
+      _argvec[2] = (unsigned long)_orig.nraddr;                   \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_W(lval, orig, arg1)                             \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+1];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1,arg2)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+2];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWW(lval, orig, arg1,arg2,arg3)                 \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+3];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(12)\n\t" /* arg3->r5 */                      \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1,arg2,arg3,arg4)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+4];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(12)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(12)\n\t" /* arg4->r6 */                      \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1,arg2,arg3,arg4,arg5)        \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+5];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(12)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(12)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(12)\n\t" /* arg5->r7 */                      \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_6W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6)   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+6];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(12)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(12)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(12)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(12)\n\t" /* arg6->r8 */                      \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7)                            \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+7];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(12)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(12)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(12)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(12)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(12)\n\t" /* arg7->r9 */                      \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+8];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      _argvec[2+8] = (unsigned long)arg8;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(12)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(12)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(12)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(12)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(12)\n\t" /* arg7->r9 */                      \
+         "ld  10, 64(12)\n\t" /* arg8->r10 */                     \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8,arg9)                  \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+9];                        \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      _argvec[2+8] = (unsigned long)arg8;                         \
+      _argvec[2+9] = (unsigned long)arg9;                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "addi 1,1,-128\n\t"  /* expand stack frame */            \
+         /* arg9 */                                               \
+         "ld  3,72(12)\n\t"                                       \
+         "std 3,96(1)\n\t"                                        \
+         /* args1-8 */                                            \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(12)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(12)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(12)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(12)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(12)\n\t" /* arg7->r9 */                      \
+         "ld  10, 64(12)\n\t" /* arg8->r10 */                     \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+10];                       \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      _argvec[2+8] = (unsigned long)arg8;                         \
+      _argvec[2+9] = (unsigned long)arg9;                         \
+      _argvec[2+10] = (unsigned long)arg10;                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "addi 1,1,-128\n\t"  /* expand stack frame */            \
+         /* arg10 */                                              \
+         "ld  3,80(12)\n\t"                                       \
+         "std 3,104(1)\n\t"                                       \
+         /* arg9 */                                               \
+         "ld  3,72(12)\n\t"                                       \
+         "std 3,96(1)\n\t"                                        \
+         /* args1-8 */                                            \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(12)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(12)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(12)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(12)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(12)\n\t" /* arg7->r9 */                      \
+         "ld  10, 64(12)\n\t" /* arg8->r10 */                     \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10,arg11)     \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+11];                       \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      _argvec[2+8] = (unsigned long)arg8;                         \
+      _argvec[2+9] = (unsigned long)arg9;                         \
+      _argvec[2+10] = (unsigned long)arg10;                       \
+      _argvec[2+11] = (unsigned long)arg11;                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "addi 1,1,-144\n\t"  /* expand stack frame */            \
+         /* arg11 */                                              \
+         "ld  3,88(12)\n\t"                                       \
+         "std 3,112(1)\n\t"                                       \
+         /* arg10 */                                              \
+         "ld  3,80(12)\n\t"                                       \
+         "std 3,104(1)\n\t"                                       \
+         /* arg9 */                                               \
+         "ld  3,72(12)\n\t"                                       \
+         "std 3,96(1)\n\t"                                        \
+         /* args1-8 */                                            \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(12)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(12)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(12)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(12)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(12)\n\t" /* arg7->r9 */                      \
+         "ld  10, 64(12)\n\t" /* arg8->r10 */                     \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                arg7,arg8,arg9,arg10,arg11,arg12) \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3+12];                       \
+      volatile unsigned long _res;                                \
+      /* _argvec[0] holds current r2 across the call */           \
+      _argvec[1]   = (unsigned long)_orig.r2;                     \
+      _argvec[2]   = (unsigned long)_orig.nraddr;                 \
+      _argvec[2+1] = (unsigned long)arg1;                         \
+      _argvec[2+2] = (unsigned long)arg2;                         \
+      _argvec[2+3] = (unsigned long)arg3;                         \
+      _argvec[2+4] = (unsigned long)arg4;                         \
+      _argvec[2+5] = (unsigned long)arg5;                         \
+      _argvec[2+6] = (unsigned long)arg6;                         \
+      _argvec[2+7] = (unsigned long)arg7;                         \
+      _argvec[2+8] = (unsigned long)arg8;                         \
+      _argvec[2+9] = (unsigned long)arg9;                         \
+      _argvec[2+10] = (unsigned long)arg10;                       \
+      _argvec[2+11] = (unsigned long)arg11;                       \
+      _argvec[2+12] = (unsigned long)arg12;                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "mr 12,%1\n\t"                                           \
+         "std 2,-16(12)\n\t"  /* save tocptr */                   \
+         "ld   2,-8(12)\n\t"  /* use nraddr's tocptr */           \
+         "addi 1,1,-144\n\t"  /* expand stack frame */            \
+         /* arg12 */                                              \
+         "ld  3,96(12)\n\t"                                       \
+         "std 3,120(1)\n\t"                                       \
+         /* arg11 */                                              \
+         "ld  3,88(12)\n\t"                                       \
+         "std 3,112(1)\n\t"                                       \
+         /* arg10 */                                              \
+         "ld  3,80(12)\n\t"                                       \
+         "std 3,104(1)\n\t"                                       \
+         /* arg9 */                                               \
+         "ld  3,72(12)\n\t"                                       \
+         "std 3,96(1)\n\t"                                        \
+         /* args1-8 */                                            \
+         "ld   3, 8(12)\n\t"  /* arg1->r3 */                      \
+         "ld   4, 16(12)\n\t" /* arg2->r4 */                      \
+         "ld   5, 24(12)\n\t" /* arg3->r5 */                      \
+         "ld   6, 32(12)\n\t" /* arg4->r6 */                      \
+         "ld   7, 40(12)\n\t" /* arg5->r7 */                      \
+         "ld   8, 48(12)\n\t" /* arg6->r8 */                      \
+         "ld   9, 56(12)\n\t" /* arg7->r9 */                      \
+         "ld  10, 64(12)\n\t" /* arg8->r10 */                     \
+         "ld  12, 0(12)\n\t"  /* target->r12 */                   \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R12                  \
+         "mr 12,%1\n\t"                                           \
+         "mr %0,3\n\t"                                            \
+         "ld 2,-16(12)\n\t" /* restore tocptr */                  \
+         VALGRIND_RESTORE_STACK                                   \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[2])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r28"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#endif /* PLAT_ppc64le_linux */
+
+/* ------------------------- arm-linux ------------------------- */
+
+#if defined(PLAT_arm_linux)
+
+/* These regs are trashed by the hidden call. */
+#define __CALLER_SAVED_REGS "r0", "r1", "r2", "r3","r4", "r12", "r14"
+
+/* Macros to save and align the stack before making a function
+   call and restore it afterwards as gcc may not keep the stack
+   pointer aligned if it doesn't realise calls are being made
+   to other functions. */
+
+/* This is a bit tricky.  We store the original stack pointer in r10
+   as it is callee-saves.  gcc doesn't allow the use of r11 for some
+   reason.  Also, we can't directly "bic" the stack pointer in thumb
+   mode since r13 isn't an allowed register number in that context.
+   So use r4 as a temporary, since that is about to get trashed
+   anyway, just after each use of this macro.  Side effect is we need
+   to be very careful about any future changes, since
+   VALGRIND_ALIGN_STACK simply assumes r4 is usable. */
+#define VALGRIND_ALIGN_STACK               \
+      "mov r10, sp\n\t"                    \
+      "mov r4,  sp\n\t"                    \
+      "bic r4,  r4, #7\n\t"                \
+      "mov sp,  r4\n\t"
+#define VALGRIND_RESTORE_STACK             \
+      "mov sp,  r10\n\t"
+
+/* These CALL_FN_ macros assume that on arm-linux, sizeof(unsigned
+   long) == 4. */
+
+#define CALL_FN_W_v(lval, orig)                                   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[1];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0\n"                                           \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_W(lval, orig, arg1)                             \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[2];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0\n"                                           \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1,arg2)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0\n"                                           \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWW(lval, orig, arg1,arg2,arg3)                 \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[4];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r2, [%1, #12] \n\t"                                 \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0\n"                                           \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1,arg2,arg3,arg4)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[5];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r2, [%1, #12] \n\t"                                 \
+         "ldr r3, [%1, #16] \n\t"                                 \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1,arg2,arg3,arg4,arg5)        \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[6];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "sub sp, sp, #4 \n\t"                                    \
+         "ldr r0, [%1, #20] \n\t"                                 \
+         "push {r0} \n\t"                                         \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r2, [%1, #12] \n\t"                                 \
+         "ldr r3, [%1, #16] \n\t"                                 \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_6W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6)   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[7];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr r0, [%1, #20] \n\t"                                 \
+         "ldr r1, [%1, #24] \n\t"                                 \
+         "push {r0, r1} \n\t"                                     \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r2, [%1, #12] \n\t"                                 \
+         "ldr r3, [%1, #16] \n\t"                                 \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7)                            \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[8];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "sub sp, sp, #4 \n\t"                                    \
+         "ldr r0, [%1, #20] \n\t"                                 \
+         "ldr r1, [%1, #24] \n\t"                                 \
+         "ldr r2, [%1, #28] \n\t"                                 \
+         "push {r0, r1, r2} \n\t"                                 \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r2, [%1, #12] \n\t"                                 \
+         "ldr r3, [%1, #16] \n\t"                                 \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[9];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr r0, [%1, #20] \n\t"                                 \
+         "ldr r1, [%1, #24] \n\t"                                 \
+         "ldr r2, [%1, #28] \n\t"                                 \
+         "ldr r3, [%1, #32] \n\t"                                 \
+         "push {r0, r1, r2, r3} \n\t"                             \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r2, [%1, #12] \n\t"                                 \
+         "ldr r3, [%1, #16] \n\t"                                 \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8,arg9)                  \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[10];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "sub sp, sp, #4 \n\t"                                    \
+         "ldr r0, [%1, #20] \n\t"                                 \
+         "ldr r1, [%1, #24] \n\t"                                 \
+         "ldr r2, [%1, #28] \n\t"                                 \
+         "ldr r3, [%1, #32] \n\t"                                 \
+         "ldr r4, [%1, #36] \n\t"                                 \
+         "push {r0, r1, r2, r3, r4} \n\t"                         \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r2, [%1, #12] \n\t"                                 \
+         "ldr r3, [%1, #16] \n\t"                                 \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[11];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr r0, [%1, #40] \n\t"                                 \
+         "push {r0} \n\t"                                         \
+         "ldr r0, [%1, #20] \n\t"                                 \
+         "ldr r1, [%1, #24] \n\t"                                 \
+         "ldr r2, [%1, #28] \n\t"                                 \
+         "ldr r3, [%1, #32] \n\t"                                 \
+         "ldr r4, [%1, #36] \n\t"                                 \
+         "push {r0, r1, r2, r3, r4} \n\t"                         \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r2, [%1, #12] \n\t"                                 \
+         "ldr r3, [%1, #16] \n\t"                                 \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1,arg2,arg3,arg4,arg5,       \
+                                  arg6,arg7,arg8,arg9,arg10,      \
+                                  arg11)                          \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[12];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      _argvec[11] = (unsigned long)(arg11);                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "sub sp, sp, #4 \n\t"                                    \
+         "ldr r0, [%1, #40] \n\t"                                 \
+         "ldr r1, [%1, #44] \n\t"                                 \
+         "push {r0, r1} \n\t"                                     \
+         "ldr r0, [%1, #20] \n\t"                                 \
+         "ldr r1, [%1, #24] \n\t"                                 \
+         "ldr r2, [%1, #28] \n\t"                                 \
+         "ldr r3, [%1, #32] \n\t"                                 \
+         "ldr r4, [%1, #36] \n\t"                                 \
+         "push {r0, r1, r2, r3, r4} \n\t"                         \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r2, [%1, #12] \n\t"                                 \
+         "ldr r3, [%1, #16] \n\t"                                 \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1,arg2,arg3,arg4,arg5,       \
+                                  arg6,arg7,arg8,arg9,arg10,      \
+                                  arg11,arg12)                    \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[13];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      _argvec[11] = (unsigned long)(arg11);                       \
+      _argvec[12] = (unsigned long)(arg12);                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr r0, [%1, #40] \n\t"                                 \
+         "ldr r1, [%1, #44] \n\t"                                 \
+         "ldr r2, [%1, #48] \n\t"                                 \
+         "push {r0, r1, r2} \n\t"                                 \
+         "ldr r0, [%1, #20] \n\t"                                 \
+         "ldr r1, [%1, #24] \n\t"                                 \
+         "ldr r2, [%1, #28] \n\t"                                 \
+         "ldr r3, [%1, #32] \n\t"                                 \
+         "ldr r4, [%1, #36] \n\t"                                 \
+         "push {r0, r1, r2, r3, r4} \n\t"                         \
+         "ldr r0, [%1, #4] \n\t"                                  \
+         "ldr r1, [%1, #8] \n\t"                                  \
+         "ldr r2, [%1, #12] \n\t"                                 \
+         "ldr r3, [%1, #16] \n\t"                                 \
+         "ldr r4, [%1] \n\t"  /* target->r4 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_R4                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, r0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "r10"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#endif /* PLAT_arm_linux */
+
+/* ------------------------ arm64-linux ------------------------ */
+
+#if defined(PLAT_arm64_linux)
+
+/* These regs are trashed by the hidden call. */
+#define __CALLER_SAVED_REGS \
+     "x0", "x1", "x2", "x3","x4", "x5", "x6", "x7", "x8", "x9",   \
+     "x10", "x11", "x12", "x13", "x14", "x15", "x16", "x17",      \
+     "x18", "x19", "x20", "x30",                                  \
+     "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9",  \
+     "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17",      \
+     "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25",      \
+     "v26", "v27", "v28", "v29", "v30", "v31"
+
+/* x21 is callee-saved, so we can use it to save and restore SP around
+   the hidden call. */
+#define VALGRIND_ALIGN_STACK               \
+      "mov x21, sp\n\t"                    \
+      "bic sp, x21, #15\n\t"
+#define VALGRIND_RESTORE_STACK             \
+      "mov sp,  x21\n\t"
+
+/* These CALL_FN_ macros assume that on arm64-linux,
+   sizeof(unsigned long) == 8. */
+
+#define CALL_FN_W_v(lval, orig)                                   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[1];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0\n"                                           \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_W(lval, orig, arg1)                             \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[2];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0\n"                                           \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1,arg2)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0\n"                                           \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWW(lval, orig, arg1,arg2,arg3)                 \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[4];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x2, [%1, #24] \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0\n"                                           \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1,arg2,arg3,arg4)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[5];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x2, [%1, #24] \n\t"                                 \
+         "ldr x3, [%1, #32] \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1,arg2,arg3,arg4,arg5)        \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[6];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x2, [%1, #24] \n\t"                                 \
+         "ldr x3, [%1, #32] \n\t"                                 \
+         "ldr x4, [%1, #40] \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_6W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6)   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[7];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x2, [%1, #24] \n\t"                                 \
+         "ldr x3, [%1, #32] \n\t"                                 \
+         "ldr x4, [%1, #40] \n\t"                                 \
+         "ldr x5, [%1, #48] \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7)                            \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[8];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x2, [%1, #24] \n\t"                                 \
+         "ldr x3, [%1, #32] \n\t"                                 \
+         "ldr x4, [%1, #40] \n\t"                                 \
+         "ldr x5, [%1, #48] \n\t"                                 \
+         "ldr x6, [%1, #56] \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[9];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x2, [%1, #24] \n\t"                                 \
+         "ldr x3, [%1, #32] \n\t"                                 \
+         "ldr x4, [%1, #40] \n\t"                                 \
+         "ldr x5, [%1, #48] \n\t"                                 \
+         "ldr x6, [%1, #56] \n\t"                                 \
+         "ldr x7, [%1, #64] \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8,arg9)                  \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[10];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "sub sp, sp, #0x20 \n\t"                                 \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x2, [%1, #24] \n\t"                                 \
+         "ldr x3, [%1, #32] \n\t"                                 \
+         "ldr x4, [%1, #40] \n\t"                                 \
+         "ldr x5, [%1, #48] \n\t"                                 \
+         "ldr x6, [%1, #56] \n\t"                                 \
+         "ldr x7, [%1, #64] \n\t"                                 \
+         "ldr x8, [%1, #72] \n\t"                                 \
+         "str x8, [sp, #0]  \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[11];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "sub sp, sp, #0x20 \n\t"                                 \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x2, [%1, #24] \n\t"                                 \
+         "ldr x3, [%1, #32] \n\t"                                 \
+         "ldr x4, [%1, #40] \n\t"                                 \
+         "ldr x5, [%1, #48] \n\t"                                 \
+         "ldr x6, [%1, #56] \n\t"                                 \
+         "ldr x7, [%1, #64] \n\t"                                 \
+         "ldr x8, [%1, #72] \n\t"                                 \
+         "str x8, [sp, #0]  \n\t"                                 \
+         "ldr x8, [%1, #80] \n\t"                                 \
+         "str x8, [sp, #8]  \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10,arg11)     \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[12];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      _argvec[11] = (unsigned long)(arg11);                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "sub sp, sp, #0x30 \n\t"                                 \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x2, [%1, #24] \n\t"                                 \
+         "ldr x3, [%1, #32] \n\t"                                 \
+         "ldr x4, [%1, #40] \n\t"                                 \
+         "ldr x5, [%1, #48] \n\t"                                 \
+         "ldr x6, [%1, #56] \n\t"                                 \
+         "ldr x7, [%1, #64] \n\t"                                 \
+         "ldr x8, [%1, #72] \n\t"                                 \
+         "str x8, [sp, #0]  \n\t"                                 \
+         "ldr x8, [%1, #80] \n\t"                                 \
+         "str x8, [sp, #8]  \n\t"                                 \
+         "ldr x8, [%1, #88] \n\t"                                 \
+         "str x8, [sp, #16] \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10,arg11,     \
+                                  arg12)                          \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[13];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      _argvec[11] = (unsigned long)(arg11);                       \
+      _argvec[12] = (unsigned long)(arg12);                       \
+      __asm__ volatile(                                           \
+         VALGRIND_ALIGN_STACK                                     \
+         "sub sp, sp, #0x30 \n\t"                                 \
+         "ldr x0, [%1, #8] \n\t"                                  \
+         "ldr x1, [%1, #16] \n\t"                                 \
+         "ldr x2, [%1, #24] \n\t"                                 \
+         "ldr x3, [%1, #32] \n\t"                                 \
+         "ldr x4, [%1, #40] \n\t"                                 \
+         "ldr x5, [%1, #48] \n\t"                                 \
+         "ldr x6, [%1, #56] \n\t"                                 \
+         "ldr x7, [%1, #64] \n\t"                                 \
+         "ldr x8, [%1, #72] \n\t"                                 \
+         "str x8, [sp, #0]  \n\t"                                 \
+         "ldr x8, [%1, #80] \n\t"                                 \
+         "str x8, [sp, #8]  \n\t"                                 \
+         "ldr x8, [%1, #88] \n\t"                                 \
+         "str x8, [sp, #16] \n\t"                                 \
+         "ldr x8, [%1, #96] \n\t"                                 \
+         "str x8, [sp, #24] \n\t"                                 \
+         "ldr x8, [%1] \n\t"  /* target->x8 */                    \
+         VALGRIND_BRANCH_AND_LINK_TO_NOREDIR_X8                   \
+         VALGRIND_RESTORE_STACK                                   \
+         "mov %0, x0"                                             \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS, "x21"   \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#endif /* PLAT_arm64_linux */
+
+/* ------------------------- s390x-linux ------------------------- */
+
+#if defined(PLAT_s390x_linux)
+
+/* Similar workaround as amd64 (see above), but we use r11 as frame
+   pointer and save the old r11 in r7. r11 might be used for
+   argvec, therefore we copy argvec in r1 since r1 is clobbered
+   after the call anyway.  */
+#if defined(__GNUC__) && defined(__GCC_HAVE_DWARF2_CFI_ASM)
+#  define __FRAME_POINTER                                         \
+      ,"d"(__builtin_dwarf_cfa())
+#  define VALGRIND_CFI_PROLOGUE                                   \
+      ".cfi_remember_state\n\t"                                   \
+      "lgr 1,%1\n\t" /* copy the argvec pointer in r1 */          \
+      "lgr 7,11\n\t"                                              \
+      "lgr 11,%2\n\t"                                             \
+      ".cfi_def_cfa r11, 0\n\t"
+#  define VALGRIND_CFI_EPILOGUE                                   \
+      "lgr 11, 7\n\t"                                             \
+      ".cfi_restore_state\n\t"
+#else
+#  define __FRAME_POINTER
+#  define VALGRIND_CFI_PROLOGUE                                   \
+      "lgr 1,%1\n\t"
+#  define VALGRIND_CFI_EPILOGUE
+#endif
+
+/* Nb: On s390 the stack pointer is properly aligned *at all times*
+   according to the s390 GCC maintainer. (The ABI specification is not
+   precise in this regard.) Therefore, VALGRIND_ALIGN_STACK and
+   VALGRIND_RESTORE_STACK are not defined here. */
+
+/* These regs are trashed by the hidden call. Note that we overwrite
+   r14 in s390_irgen_noredir (VEX/priv/guest_s390_irgen.c) to give the
+   function a proper return address. All others are ABI defined call
+   clobbers. */
+#if defined(__VX__) || defined(__S390_VX__)
+#define __CALLER_SAVED_REGS "0", "1", "2", "3", "4", "5", "14",   \
+      "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7",             \
+      "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15",       \
+      "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23",     \
+      "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+#else
+#define __CALLER_SAVED_REGS "0", "1", "2", "3", "4", "5", "14",   \
+      "f0", "f1", "f2", "f3", "f4", "f5", "f6", "f7"
+#endif
+
+/* Nb: Although r11 is modified in the asm snippets below (inside 
+   VALGRIND_CFI_PROLOGUE) it is not listed in the clobber section, for
+   two reasons:
+   (1) r11 is restored in VALGRIND_CFI_EPILOGUE, so effectively it is not
+       modified
+   (2) GCC will complain that r11 cannot appear inside a clobber section,
+       when compiled with -O -fno-omit-frame-pointer
+ */
+
+#define CALL_FN_W_v(lval, orig)                                  \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long  _argvec[1];                        \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-160\n\t"                                      \
+         "lg 1, 0(1)\n\t"  /* target->r1 */                      \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,160\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "d" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"7"     \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+/* The call abi has the arguments in r2-r6 and stack */
+#define CALL_FN_W_W(lval, orig, arg1)                            \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[2];                         \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-160\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,160\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"7"     \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1, arg2)                     \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[3];                         \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-160\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,160\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"7"     \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_WWW(lval, orig, arg1, arg2, arg3)              \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[4];                         \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      _argvec[3] = (unsigned long)arg3;                          \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-160\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 4,24(1)\n\t"                                        \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,160\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"7"     \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1, arg2, arg3, arg4)       \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[5];                         \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      _argvec[3] = (unsigned long)arg3;                          \
+      _argvec[4] = (unsigned long)arg4;                          \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-160\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 4,24(1)\n\t"                                        \
+         "lg 5,32(1)\n\t"                                        \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,160\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"7"     \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1, arg2, arg3, arg4, arg5)   \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[6];                         \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      _argvec[3] = (unsigned long)arg3;                          \
+      _argvec[4] = (unsigned long)arg4;                          \
+      _argvec[5] = (unsigned long)arg5;                          \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-160\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 4,24(1)\n\t"                                        \
+         "lg 5,32(1)\n\t"                                        \
+         "lg 6,40(1)\n\t"                                        \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,160\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"6","7" \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_6W(lval, orig, arg1, arg2, arg3, arg4, arg5,   \
+                     arg6)                                       \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[7];                         \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      _argvec[3] = (unsigned long)arg3;                          \
+      _argvec[4] = (unsigned long)arg4;                          \
+      _argvec[5] = (unsigned long)arg5;                          \
+      _argvec[6] = (unsigned long)arg6;                          \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-168\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 4,24(1)\n\t"                                        \
+         "lg 5,32(1)\n\t"                                        \
+         "lg 6,40(1)\n\t"                                        \
+         "mvc 160(8,15), 48(1)\n\t"                              \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,168\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"6","7" \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1, arg2, arg3, arg4, arg5,   \
+                     arg6, arg7)                                 \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[8];                         \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      _argvec[3] = (unsigned long)arg3;                          \
+      _argvec[4] = (unsigned long)arg4;                          \
+      _argvec[5] = (unsigned long)arg5;                          \
+      _argvec[6] = (unsigned long)arg6;                          \
+      _argvec[7] = (unsigned long)arg7;                          \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-176\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 4,24(1)\n\t"                                        \
+         "lg 5,32(1)\n\t"                                        \
+         "lg 6,40(1)\n\t"                                        \
+         "mvc 160(8,15), 48(1)\n\t"                              \
+         "mvc 168(8,15), 56(1)\n\t"                              \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,176\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"6","7" \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1, arg2, arg3, arg4, arg5,   \
+                     arg6, arg7 ,arg8)                           \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[9];                         \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      _argvec[3] = (unsigned long)arg3;                          \
+      _argvec[4] = (unsigned long)arg4;                          \
+      _argvec[5] = (unsigned long)arg5;                          \
+      _argvec[6] = (unsigned long)arg6;                          \
+      _argvec[7] = (unsigned long)arg7;                          \
+      _argvec[8] = (unsigned long)arg8;                          \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-184\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 4,24(1)\n\t"                                        \
+         "lg 5,32(1)\n\t"                                        \
+         "lg 6,40(1)\n\t"                                        \
+         "mvc 160(8,15), 48(1)\n\t"                              \
+         "mvc 168(8,15), 56(1)\n\t"                              \
+         "mvc 176(8,15), 64(1)\n\t"                              \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,184\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"6","7" \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1, arg2, arg3, arg4, arg5,   \
+                     arg6, arg7 ,arg8, arg9)                     \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[10];                        \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      _argvec[3] = (unsigned long)arg3;                          \
+      _argvec[4] = (unsigned long)arg4;                          \
+      _argvec[5] = (unsigned long)arg5;                          \
+      _argvec[6] = (unsigned long)arg6;                          \
+      _argvec[7] = (unsigned long)arg7;                          \
+      _argvec[8] = (unsigned long)arg8;                          \
+      _argvec[9] = (unsigned long)arg9;                          \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-192\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 4,24(1)\n\t"                                        \
+         "lg 5,32(1)\n\t"                                        \
+         "lg 6,40(1)\n\t"                                        \
+         "mvc 160(8,15), 48(1)\n\t"                              \
+         "mvc 168(8,15), 56(1)\n\t"                              \
+         "mvc 176(8,15), 64(1)\n\t"                              \
+         "mvc 184(8,15), 72(1)\n\t"                              \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,192\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"6","7" \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1, arg2, arg3, arg4, arg5,  \
+                     arg6, arg7 ,arg8, arg9, arg10)              \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[11];                        \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      _argvec[3] = (unsigned long)arg3;                          \
+      _argvec[4] = (unsigned long)arg4;                          \
+      _argvec[5] = (unsigned long)arg5;                          \
+      _argvec[6] = (unsigned long)arg6;                          \
+      _argvec[7] = (unsigned long)arg7;                          \
+      _argvec[8] = (unsigned long)arg8;                          \
+      _argvec[9] = (unsigned long)arg9;                          \
+      _argvec[10] = (unsigned long)arg10;                        \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-200\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 4,24(1)\n\t"                                        \
+         "lg 5,32(1)\n\t"                                        \
+         "lg 6,40(1)\n\t"                                        \
+         "mvc 160(8,15), 48(1)\n\t"                              \
+         "mvc 168(8,15), 56(1)\n\t"                              \
+         "mvc 176(8,15), 64(1)\n\t"                              \
+         "mvc 184(8,15), 72(1)\n\t"                              \
+         "mvc 192(8,15), 80(1)\n\t"                              \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,200\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"6","7" \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1, arg2, arg3, arg4, arg5,  \
+                     arg6, arg7 ,arg8, arg9, arg10, arg11)       \
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[12];                        \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      _argvec[3] = (unsigned long)arg3;                          \
+      _argvec[4] = (unsigned long)arg4;                          \
+      _argvec[5] = (unsigned long)arg5;                          \
+      _argvec[6] = (unsigned long)arg6;                          \
+      _argvec[7] = (unsigned long)arg7;                          \
+      _argvec[8] = (unsigned long)arg8;                          \
+      _argvec[9] = (unsigned long)arg9;                          \
+      _argvec[10] = (unsigned long)arg10;                        \
+      _argvec[11] = (unsigned long)arg11;                        \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-208\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 4,24(1)\n\t"                                        \
+         "lg 5,32(1)\n\t"                                        \
+         "lg 6,40(1)\n\t"                                        \
+         "mvc 160(8,15), 48(1)\n\t"                              \
+         "mvc 168(8,15), 56(1)\n\t"                              \
+         "mvc 176(8,15), 64(1)\n\t"                              \
+         "mvc 184(8,15), 72(1)\n\t"                              \
+         "mvc 192(8,15), 80(1)\n\t"                              \
+         "mvc 200(8,15), 88(1)\n\t"                              \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,208\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"6","7" \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1, arg2, arg3, arg4, arg5,  \
+                     arg6, arg7 ,arg8, arg9, arg10, arg11, arg12)\
+   do {                                                          \
+      volatile OrigFn        _orig = (orig);                     \
+      volatile unsigned long _argvec[13];                        \
+      volatile unsigned long _res;                               \
+      _argvec[0] = (unsigned long)_orig.nraddr;                  \
+      _argvec[1] = (unsigned long)arg1;                          \
+      _argvec[2] = (unsigned long)arg2;                          \
+      _argvec[3] = (unsigned long)arg3;                          \
+      _argvec[4] = (unsigned long)arg4;                          \
+      _argvec[5] = (unsigned long)arg5;                          \
+      _argvec[6] = (unsigned long)arg6;                          \
+      _argvec[7] = (unsigned long)arg7;                          \
+      _argvec[8] = (unsigned long)arg8;                          \
+      _argvec[9] = (unsigned long)arg9;                          \
+      _argvec[10] = (unsigned long)arg10;                        \
+      _argvec[11] = (unsigned long)arg11;                        \
+      _argvec[12] = (unsigned long)arg12;                        \
+      __asm__ volatile(                                          \
+         VALGRIND_CFI_PROLOGUE                                   \
+         "aghi 15,-216\n\t"                                      \
+         "lg 2, 8(1)\n\t"                                        \
+         "lg 3,16(1)\n\t"                                        \
+         "lg 4,24(1)\n\t"                                        \
+         "lg 5,32(1)\n\t"                                        \
+         "lg 6,40(1)\n\t"                                        \
+         "mvc 160(8,15), 48(1)\n\t"                              \
+         "mvc 168(8,15), 56(1)\n\t"                              \
+         "mvc 176(8,15), 64(1)\n\t"                              \
+         "mvc 184(8,15), 72(1)\n\t"                              \
+         "mvc 192(8,15), 80(1)\n\t"                              \
+         "mvc 200(8,15), 88(1)\n\t"                              \
+         "mvc 208(8,15), 96(1)\n\t"                              \
+         "lg 1, 0(1)\n\t"                                        \
+         VALGRIND_CALL_NOREDIR_R1                                \
+         "aghi 15,216\n\t"                                       \
+         VALGRIND_CFI_EPILOGUE                                   \
+         "lgr %0, 2\n\t"                                         \
+         : /*out*/   "=d" (_res)                                 \
+         : /*in*/    "a" (&_argvec[0]) __FRAME_POINTER           \
+         : /*trash*/ "cc", "memory", __CALLER_SAVED_REGS,"6","7" \
+      );                                                         \
+      lval = (__typeof__(lval)) _res;                            \
+   } while (0)
+
+
+#endif /* PLAT_s390x_linux */
+
+/* ------------------------- mips32-linux ----------------------- */
+ 
+#if defined(PLAT_mips32_linux)
+
+/* These regs are trashed by the hidden call. */
+#define __CALLER_SAVED_REGS "$2", "$3", "$4", "$5", "$6",       \
+"$7", "$8", "$9", "$10", "$11", "$12", "$13", "$14", "$15", "$24", \
+"$25", "$31"
+
+/* These CALL_FN_ macros assume that on mips-linux, sizeof(unsigned
+   long) == 4. */
+
+#define CALL_FN_W_v(lval, orig)                                   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[1];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "subu $29, $29, 16 \n\t"                                 \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 16\n\t"                                  \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_W(lval, orig, arg1)                             \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+     volatile unsigned long _argvec[2];                           \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "subu $29, $29, 16 \n\t"                                 \
+         "lw $4, 4(%1) \n\t"   /* arg1*/                          \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 16 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory",  __CALLER_SAVED_REGS               \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1,arg2)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "subu $29, $29, 16 \n\t"                                 \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 16 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWW(lval, orig, arg1,arg2,arg3)                 \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[4];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "subu $29, $29, 16 \n\t"                                 \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $6, 12(%1) \n\t"                                     \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 16 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1,arg2,arg3,arg4)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[5];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "subu $29, $29, 16 \n\t"                                 \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $6, 12(%1) \n\t"                                     \
+         "lw $7, 16(%1) \n\t"                                     \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 16 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1,arg2,arg3,arg4,arg5)        \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[6];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "lw $4, 20(%1) \n\t"                                     \
+         "subu $29, $29, 24\n\t"                                  \
+         "sw $4, 16($29) \n\t"                                    \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $6, 12(%1) \n\t"                                     \
+         "lw $7, 16(%1) \n\t"                                     \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 24 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+#define CALL_FN_W_6W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6)   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[7];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "lw $4, 20(%1) \n\t"                                     \
+         "subu $29, $29, 32\n\t"                                  \
+         "sw $4, 16($29) \n\t"                                    \
+         "lw $4, 24(%1) \n\t"                                     \
+         "nop\n\t"                                                \
+         "sw $4, 20($29) \n\t"                                    \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $6, 12(%1) \n\t"                                     \
+         "lw $7, 16(%1) \n\t"                                     \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 32 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7)                            \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[8];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "lw $4, 20(%1) \n\t"                                     \
+         "subu $29, $29, 32\n\t"                                  \
+         "sw $4, 16($29) \n\t"                                    \
+         "lw $4, 24(%1) \n\t"                                     \
+         "sw $4, 20($29) \n\t"                                    \
+         "lw $4, 28(%1) \n\t"                                     \
+         "sw $4, 24($29) \n\t"                                    \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $6, 12(%1) \n\t"                                     \
+         "lw $7, 16(%1) \n\t"                                     \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 32 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[9];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "lw $4, 20(%1) \n\t"                                     \
+         "subu $29, $29, 40\n\t"                                  \
+         "sw $4, 16($29) \n\t"                                    \
+         "lw $4, 24(%1) \n\t"                                     \
+         "sw $4, 20($29) \n\t"                                    \
+         "lw $4, 28(%1) \n\t"                                     \
+         "sw $4, 24($29) \n\t"                                    \
+         "lw $4, 32(%1) \n\t"                                     \
+         "sw $4, 28($29) \n\t"                                    \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $6, 12(%1) \n\t"                                     \
+         "lw $7, 16(%1) \n\t"                                     \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 40 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8,arg9)                  \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[10];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "lw $4, 20(%1) \n\t"                                     \
+         "subu $29, $29, 40\n\t"                                  \
+         "sw $4, 16($29) \n\t"                                    \
+         "lw $4, 24(%1) \n\t"                                     \
+         "sw $4, 20($29) \n\t"                                    \
+         "lw $4, 28(%1) \n\t"                                     \
+         "sw $4, 24($29) \n\t"                                    \
+         "lw $4, 32(%1) \n\t"                                     \
+         "sw $4, 28($29) \n\t"                                    \
+         "lw $4, 36(%1) \n\t"                                     \
+         "sw $4, 32($29) \n\t"                                    \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $6, 12(%1) \n\t"                                     \
+         "lw $7, 16(%1) \n\t"                                     \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 40 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[11];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "lw $4, 20(%1) \n\t"                                     \
+         "subu $29, $29, 48\n\t"                                  \
+         "sw $4, 16($29) \n\t"                                    \
+         "lw $4, 24(%1) \n\t"                                     \
+         "sw $4, 20($29) \n\t"                                    \
+         "lw $4, 28(%1) \n\t"                                     \
+         "sw $4, 24($29) \n\t"                                    \
+         "lw $4, 32(%1) \n\t"                                     \
+         "sw $4, 28($29) \n\t"                                    \
+         "lw $4, 36(%1) \n\t"                                     \
+         "sw $4, 32($29) \n\t"                                    \
+         "lw $4, 40(%1) \n\t"                                     \
+         "sw $4, 36($29) \n\t"                                    \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $6, 12(%1) \n\t"                                     \
+         "lw $7, 16(%1) \n\t"                                     \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 48 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1,arg2,arg3,arg4,arg5,       \
+                                  arg6,arg7,arg8,arg9,arg10,      \
+                                  arg11)                          \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[12];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      _argvec[11] = (unsigned long)(arg11);                       \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "lw $4, 20(%1) \n\t"                                     \
+         "subu $29, $29, 48\n\t"                                  \
+         "sw $4, 16($29) \n\t"                                    \
+         "lw $4, 24(%1) \n\t"                                     \
+         "sw $4, 20($29) \n\t"                                    \
+         "lw $4, 28(%1) \n\t"                                     \
+         "sw $4, 24($29) \n\t"                                    \
+         "lw $4, 32(%1) \n\t"                                     \
+         "sw $4, 28($29) \n\t"                                    \
+         "lw $4, 36(%1) \n\t"                                     \
+         "sw $4, 32($29) \n\t"                                    \
+         "lw $4, 40(%1) \n\t"                                     \
+         "sw $4, 36($29) \n\t"                                    \
+         "lw $4, 44(%1) \n\t"                                     \
+         "sw $4, 40($29) \n\t"                                    \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $6, 12(%1) \n\t"                                     \
+         "lw $7, 16(%1) \n\t"                                     \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 48 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1,arg2,arg3,arg4,arg5,       \
+                                  arg6,arg7,arg8,arg9,arg10,      \
+                                  arg11,arg12)                    \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[13];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      _argvec[11] = (unsigned long)(arg11);                       \
+      _argvec[12] = (unsigned long)(arg12);                       \
+      __asm__ volatile(                                           \
+         "subu $29, $29, 8 \n\t"                                  \
+         "sw $28, 0($29) \n\t"                                    \
+         "sw $31, 4($29) \n\t"                                    \
+         "lw $4, 20(%1) \n\t"                                     \
+         "subu $29, $29, 56\n\t"                                  \
+         "sw $4, 16($29) \n\t"                                    \
+         "lw $4, 24(%1) \n\t"                                     \
+         "sw $4, 20($29) \n\t"                                    \
+         "lw $4, 28(%1) \n\t"                                     \
+         "sw $4, 24($29) \n\t"                                    \
+         "lw $4, 32(%1) \n\t"                                     \
+         "sw $4, 28($29) \n\t"                                    \
+         "lw $4, 36(%1) \n\t"                                     \
+         "sw $4, 32($29) \n\t"                                    \
+         "lw $4, 40(%1) \n\t"                                     \
+         "sw $4, 36($29) \n\t"                                    \
+         "lw $4, 44(%1) \n\t"                                     \
+         "sw $4, 40($29) \n\t"                                    \
+         "lw $4, 48(%1) \n\t"                                     \
+         "sw $4, 44($29) \n\t"                                    \
+         "lw $4, 4(%1) \n\t"                                      \
+         "lw $5, 8(%1) \n\t"                                      \
+         "lw $6, 12(%1) \n\t"                                     \
+         "lw $7, 16(%1) \n\t"                                     \
+         "lw $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "addu $29, $29, 56 \n\t"                                 \
+         "lw $28, 0($29) \n\t"                                    \
+         "lw $31, 4($29) \n\t"                                    \
+         "addu $29, $29, 8 \n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#endif /* PLAT_mips32_linux */
+
+/* ------------------------- nanomips-linux -------------------- */
+
+#if defined(PLAT_nanomips_linux)
+
+/* These regs are trashed by the hidden call. */
+#define __CALLER_SAVED_REGS "$t4", "$t5", "$a0", "$a1", "$a2",     \
+"$a3", "$a4", "$a5", "$a6", "$a7", "$t0", "$t1", "$t2", "$t3",     \
+"$t8","$t9", "$at"
+
+/* These CALL_FN_ macros assume that on mips-linux, sizeof(unsigned
+   long) == 4. */
+
+#define CALL_FN_W_v(lval, orig)                                   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[1];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      __asm__ volatile(                                           \
+         "lw $t9, 0(%1)\n\t"                                      \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0\n"                                         \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_W(lval, orig, arg1)                             \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[2];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      __asm__ volatile(                                           \
+         "lw $t9, 0(%1)\n\t"                                      \
+         "lw $a0, 4(%1)\n\t"                                      \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0\n"                                         \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1,arg2)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[3];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      __asm__ volatile(                                           \
+         "lw $t9, 0(%1)\n\t"                                      \
+         "lw $a0, 4(%1)\n\t"                                      \
+         "lw $a1, 8(%1)\n\t"                                      \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0\n"                                         \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWW(lval, orig, arg1,arg2,arg3)                 \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[4];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      __asm__ volatile(                                           \
+         "lw $t9, 0(%1)\n\t"                                      \
+         "lw $a0, 4(%1)\n\t"                                      \
+         "lw $a1, 8(%1)\n\t"                                      \
+         "lw $a2,12(%1)\n\t"                                      \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0\n"                                         \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1,arg2,arg3,arg4)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[5];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      __asm__ volatile(                                           \
+         "lw $t9, 0(%1)\n\t"                                      \
+         "lw $a0, 4(%1)\n\t"                                      \
+         "lw $a1, 8(%1)\n\t"                                      \
+         "lw $a2,12(%1)\n\t"                                      \
+         "lw $a3,16(%1)\n\t"                                      \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0\n"                                         \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1,arg2,arg3,arg4,arg5)        \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[6];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      __asm__ volatile(                                           \
+         "lw $t9, 0(%1)\n\t"                                      \
+         "lw $a0, 4(%1)\n\t"                                      \
+         "lw $a1, 8(%1)\n\t"                                      \
+         "lw $a2,12(%1)\n\t"                                      \
+         "lw $a3,16(%1)\n\t"                                      \
+         "lw $a4,20(%1)\n\t"                                      \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0\n"                                         \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+#define CALL_FN_W_6W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6)   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[7];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      __asm__ volatile(                                           \
+         "lw $t9, 0(%1)\n\t"                                      \
+         "lw $a0, 4(%1)\n\t"                                      \
+         "lw $a1, 8(%1)\n\t"                                      \
+         "lw $a2,12(%1)\n\t"                                      \
+         "lw $a3,16(%1)\n\t"                                      \
+         "lw $a4,20(%1)\n\t"                                      \
+         "lw $a5,24(%1)\n\t"                                      \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0\n"                                         \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7)                            \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[8];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      __asm__ volatile(                                           \
+         "lw $t9, 0(%1)\n\t"                                      \
+         "lw $a0, 4(%1)\n\t"                                      \
+         "lw $a1, 8(%1)\n\t"                                      \
+         "lw $a2,12(%1)\n\t"                                      \
+         "lw $a3,16(%1)\n\t"                                      \
+         "lw $a4,20(%1)\n\t"                                      \
+         "lw $a5,24(%1)\n\t"                                      \
+         "lw $a6,28(%1)\n\t"                                      \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0\n"                                         \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[9];                          \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      __asm__ volatile(                                           \
+         "lw $t9, 0(%1)\n\t"                                      \
+         "lw $a0, 4(%1)\n\t"                                      \
+         "lw $a1, 8(%1)\n\t"                                      \
+         "lw $a2,12(%1)\n\t"                                      \
+         "lw $a3,16(%1)\n\t"                                      \
+         "lw $a4,20(%1)\n\t"                                      \
+         "lw $a5,24(%1)\n\t"                                      \
+         "lw $a6,28(%1)\n\t"                                      \
+         "lw $a7,32(%1)\n\t"                                      \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0\n"                                         \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8,arg9)                  \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[10];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      __asm__ volatile(                                           \
+         "addiu $sp, $sp, -16  \n\t"                              \
+         "lw $t9,36(%1)        \n\t"                              \
+         "sw $t9, 0($sp)       \n\t"                              \
+         "lw $t9, 0(%1)        \n\t"                              \
+         "lw $a0, 4(%1)        \n\t"                              \
+         "lw $a1, 8(%1)        \n\t"                              \
+         "lw $a2,12(%1)        \n\t"                              \
+         "lw $a3,16(%1)        \n\t"                              \
+         "lw $a4,20(%1)        \n\t"                              \
+         "lw $a5,24(%1)        \n\t"                              \
+         "lw $a6,28(%1)        \n\t"                              \
+         "lw $a7,32(%1)        \n\t"                              \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0         \n\t"                              \
+         "addiu $sp, $sp, 16   \n\t"                              \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[11];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      __asm__ volatile(                                           \
+         "addiu $sp, $sp, -16  \n\t"                              \
+         "lw $t9,36(%1)        \n\t"                              \
+         "sw $t9, 0($sp)       \n\t"                              \
+         "lw $t9,40(%1)        \n\t"                              \
+         "sw $t9, 4($sp)       \n\t"                              \
+         "lw $t9, 0(%1)        \n\t"                              \
+         "lw $a0, 4(%1)        \n\t"                              \
+         "lw $a1, 8(%1)        \n\t"                              \
+         "lw $a2,12(%1)        \n\t"                              \
+         "lw $a3,16(%1)        \n\t"                              \
+         "lw $a4,20(%1)        \n\t"                              \
+         "lw $a5,24(%1)        \n\t"                              \
+         "lw $a6,28(%1)        \n\t"                              \
+         "lw $a7,32(%1)        \n\t"                              \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0         \n\t"                              \
+         "addiu $sp, $sp, 16   \n\t"                              \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1,arg2,arg3,arg4,arg5,       \
+                                  arg6,arg7,arg8,arg9,arg10,      \
+                                  arg11)                          \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[12];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      _argvec[11] = (unsigned long)(arg11);                       \
+      __asm__ volatile(                                           \
+         "addiu $sp, $sp, -16  \n\t"                              \
+         "lw $t9,36(%1)        \n\t"                              \
+         "sw $t9, 0($sp)       \n\t"                              \
+         "lw $t9,40(%1)        \n\t"                              \
+         "sw $t9, 4($sp)       \n\t"                              \
+         "lw $t9,44(%1)        \n\t"                              \
+         "sw $t9, 8($sp)       \n\t"                              \
+         "lw $t9, 0(%1)        \n\t"                              \
+         "lw $a0, 4(%1)        \n\t"                              \
+         "lw $a1, 8(%1)        \n\t"                              \
+         "lw $a2,12(%1)        \n\t"                              \
+         "lw $a3,16(%1)        \n\t"                              \
+         "lw $a4,20(%1)        \n\t"                              \
+         "lw $a5,24(%1)        \n\t"                              \
+         "lw $a6,28(%1)        \n\t"                              \
+         "lw $a7,32(%1)        \n\t"                              \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0         \n\t"                              \
+         "addiu $sp, $sp, 16   \n\t"                              \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1,arg2,arg3,arg4,arg5,       \
+                                  arg6,arg7,arg8,arg9,arg10,      \
+                                  arg11,arg12)                    \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long _argvec[13];                         \
+      volatile unsigned long _res;                                \
+      _argvec[0] = (unsigned long)_orig.nraddr;                   \
+      _argvec[1] = (unsigned long)(arg1);                         \
+      _argvec[2] = (unsigned long)(arg2);                         \
+      _argvec[3] = (unsigned long)(arg3);                         \
+      _argvec[4] = (unsigned long)(arg4);                         \
+      _argvec[5] = (unsigned long)(arg5);                         \
+      _argvec[6] = (unsigned long)(arg6);                         \
+      _argvec[7] = (unsigned long)(arg7);                         \
+      _argvec[8] = (unsigned long)(arg8);                         \
+      _argvec[9] = (unsigned long)(arg9);                         \
+      _argvec[10] = (unsigned long)(arg10);                       \
+      _argvec[11] = (unsigned long)(arg11);                       \
+      _argvec[12] = (unsigned long)(arg12);                       \
+      __asm__ volatile(                                           \
+         "addiu $sp, $sp, -16  \n\t"                              \
+         "lw $t9,36(%1)        \n\t"                              \
+         "sw $t9, 0($sp)       \n\t"                              \
+         "lw $t9,40(%1)        \n\t"                              \
+         "sw $t9, 4($sp)       \n\t"                              \
+         "lw $t9,44(%1)        \n\t"                              \
+         "sw $t9, 8($sp)       \n\t"                              \
+         "lw $t9,48(%1)        \n\t"                              \
+         "sw $t9,12($sp)       \n\t"                              \
+         "lw $t9, 0(%1)        \n\t"                              \
+         "lw $a0, 4(%1)        \n\t"                              \
+         "lw $a1, 8(%1)        \n\t"                              \
+         "lw $a2,12(%1)        \n\t"                              \
+         "lw $a3,16(%1)        \n\t"                              \
+         "lw $a4,20(%1)        \n\t"                              \
+         "lw $a5,24(%1)        \n\t"                              \
+         "lw $a6,28(%1)        \n\t"                              \
+         "lw $a7,32(%1)        \n\t"                              \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $a0         \n\t"                              \
+         "addiu $sp, $sp, 16   \n\t"                              \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) _res;                             \
+   } while (0)
+
+#endif /* PLAT_nanomips_linux */
+
+/* ------------------------- mips64-linux ------------------------- */
+
+#if defined(PLAT_mips64_linux)
+
+/* These regs are trashed by the hidden call. */
+#define __CALLER_SAVED_REGS "$2", "$3", "$4", "$5", "$6",       \
+"$7", "$8", "$9", "$10", "$11", "$12", "$13", "$14", "$15", "$24", \
+"$25", "$31"
+
+/* These CALL_FN_ macros assume that on mips64-linux,
+   sizeof(long long) == 8. */
+
+#define MIPS64_LONG2REG_CAST(x) ((long long)(long)x)
+
+#define CALL_FN_W_v(lval, orig)                                   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[1];                     \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      __asm__ volatile(                                           \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "0" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_W(lval, orig, arg1)                             \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[2];                     \
+      volatile unsigned long long  _res;                          \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      __asm__ volatile(                                           \
+         "ld $4, 8(%1)\n\t"   /* arg1*/                           \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_WW(lval, orig, arg1,arg2)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[3];                     \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = _orig.nraddr;                                  \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      __asm__ volatile(                                           \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+
+#define CALL_FN_W_WWW(lval, orig, arg1,arg2,arg3)                 \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[4];                     \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = _orig.nraddr;                                  \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      _argvec[3] = MIPS64_LONG2REG_CAST(arg3);                    \
+      __asm__ volatile(                                           \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $6, 24(%1)\n\t"                                      \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_WWWW(lval, orig, arg1,arg2,arg3,arg4)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[5];                     \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      _argvec[3] = MIPS64_LONG2REG_CAST(arg3);                    \
+      _argvec[4] = MIPS64_LONG2REG_CAST(arg4);                    \
+      __asm__ volatile(                                           \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $6, 24(%1)\n\t"                                      \
+         "ld $7, 32(%1)\n\t"                                      \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_5W(lval, orig, arg1,arg2,arg3,arg4,arg5)        \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[6];                     \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      _argvec[3] = MIPS64_LONG2REG_CAST(arg3);                    \
+      _argvec[4] = MIPS64_LONG2REG_CAST(arg4);                    \
+      _argvec[5] = MIPS64_LONG2REG_CAST(arg5);                    \
+      __asm__ volatile(                                           \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $6, 24(%1)\n\t"                                      \
+         "ld $7, 32(%1)\n\t"                                      \
+         "ld $8, 40(%1)\n\t"                                      \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_6W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6)   \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[7];                     \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      _argvec[3] = MIPS64_LONG2REG_CAST(arg3);                    \
+      _argvec[4] = MIPS64_LONG2REG_CAST(arg4);                    \
+      _argvec[5] = MIPS64_LONG2REG_CAST(arg5);                    \
+      _argvec[6] = MIPS64_LONG2REG_CAST(arg6);                    \
+      __asm__ volatile(                                           \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $6, 24(%1)\n\t"                                      \
+         "ld $7, 32(%1)\n\t"                                      \
+         "ld $8, 40(%1)\n\t"                                      \
+         "ld $9, 48(%1)\n\t"                                      \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_7W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7)                            \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[8];                     \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      _argvec[3] = MIPS64_LONG2REG_CAST(arg3);                    \
+      _argvec[4] = MIPS64_LONG2REG_CAST(arg4);                    \
+      _argvec[5] = MIPS64_LONG2REG_CAST(arg5);                    \
+      _argvec[6] = MIPS64_LONG2REG_CAST(arg6);                    \
+      _argvec[7] = MIPS64_LONG2REG_CAST(arg7);                    \
+      __asm__ volatile(                                           \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $6, 24(%1)\n\t"                                      \
+         "ld $7, 32(%1)\n\t"                                      \
+         "ld $8, 40(%1)\n\t"                                      \
+         "ld $9, 48(%1)\n\t"                                      \
+         "ld $10, 56(%1)\n\t"                                     \
+         "ld $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_8W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8)                       \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[9];                     \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      _argvec[3] = MIPS64_LONG2REG_CAST(arg3);                    \
+      _argvec[4] = MIPS64_LONG2REG_CAST(arg4);                    \
+      _argvec[5] = MIPS64_LONG2REG_CAST(arg5);                    \
+      _argvec[6] = MIPS64_LONG2REG_CAST(arg6);                    \
+      _argvec[7] = MIPS64_LONG2REG_CAST(arg7);                    \
+      _argvec[8] = MIPS64_LONG2REG_CAST(arg8);                    \
+      __asm__ volatile(                                           \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $6, 24(%1)\n\t"                                      \
+         "ld $7, 32(%1)\n\t"                                      \
+         "ld $8, 40(%1)\n\t"                                      \
+         "ld $9, 48(%1)\n\t"                                      \
+         "ld $10, 56(%1)\n\t"                                     \
+         "ld $11, 64(%1)\n\t"                                     \
+         "ld $25, 0(%1) \n\t"  /* target->t9 */                   \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_9W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,   \
+                                 arg7,arg8,arg9)                  \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[10];                    \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      _argvec[3] = MIPS64_LONG2REG_CAST(arg3);                    \
+      _argvec[4] = MIPS64_LONG2REG_CAST(arg4);                    \
+      _argvec[5] = MIPS64_LONG2REG_CAST(arg5);                    \
+      _argvec[6] = MIPS64_LONG2REG_CAST(arg6);                    \
+      _argvec[7] = MIPS64_LONG2REG_CAST(arg7);                    \
+      _argvec[8] = MIPS64_LONG2REG_CAST(arg8);                    \
+      _argvec[9] = MIPS64_LONG2REG_CAST(arg9);                    \
+      __asm__ volatile(                                           \
+         "dsubu $29, $29, 8\n\t"                                  \
+         "ld $4, 72(%1)\n\t"                                      \
+         "sd $4, 0($29)\n\t"                                      \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $6, 24(%1)\n\t"                                      \
+         "ld $7, 32(%1)\n\t"                                      \
+         "ld $8, 40(%1)\n\t"                                      \
+         "ld $9, 48(%1)\n\t"                                      \
+         "ld $10, 56(%1)\n\t"                                     \
+         "ld $11, 64(%1)\n\t"                                     \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "daddu $29, $29, 8\n\t"                                  \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_10W(lval, orig, arg1,arg2,arg3,arg4,arg5,arg6,  \
+                                  arg7,arg8,arg9,arg10)           \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[11];                    \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      _argvec[3] = MIPS64_LONG2REG_CAST(arg3);                    \
+      _argvec[4] = MIPS64_LONG2REG_CAST(arg4);                    \
+      _argvec[5] = MIPS64_LONG2REG_CAST(arg5);                    \
+      _argvec[6] = MIPS64_LONG2REG_CAST(arg6);                    \
+      _argvec[7] = MIPS64_LONG2REG_CAST(arg7);                    \
+      _argvec[8] = MIPS64_LONG2REG_CAST(arg8);                    \
+      _argvec[9] = MIPS64_LONG2REG_CAST(arg9);                    \
+      _argvec[10] = MIPS64_LONG2REG_CAST(arg10);                  \
+      __asm__ volatile(                                           \
+         "dsubu $29, $29, 16\n\t"                                 \
+         "ld $4, 72(%1)\n\t"                                      \
+         "sd $4, 0($29)\n\t"                                      \
+         "ld $4, 80(%1)\n\t"                                      \
+         "sd $4, 8($29)\n\t"                                      \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $6, 24(%1)\n\t"                                      \
+         "ld $7, 32(%1)\n\t"                                      \
+         "ld $8, 40(%1)\n\t"                                      \
+         "ld $9, 48(%1)\n\t"                                      \
+         "ld $10, 56(%1)\n\t"                                     \
+         "ld $11, 64(%1)\n\t"                                     \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "daddu $29, $29, 16\n\t"                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_11W(lval, orig, arg1,arg2,arg3,arg4,arg5,       \
+                                  arg6,arg7,arg8,arg9,arg10,      \
+                                  arg11)                          \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[12];                    \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      _argvec[3] = MIPS64_LONG2REG_CAST(arg3);                    \
+      _argvec[4] = MIPS64_LONG2REG_CAST(arg4);                    \
+      _argvec[5] = MIPS64_LONG2REG_CAST(arg5);                    \
+      _argvec[6] = MIPS64_LONG2REG_CAST(arg6);                    \
+      _argvec[7] = MIPS64_LONG2REG_CAST(arg7);                    \
+      _argvec[8] = MIPS64_LONG2REG_CAST(arg8);                    \
+      _argvec[9] = MIPS64_LONG2REG_CAST(arg9);                    \
+      _argvec[10] = MIPS64_LONG2REG_CAST(arg10);                  \
+      _argvec[11] = MIPS64_LONG2REG_CAST(arg11);                  \
+      __asm__ volatile(                                           \
+         "dsubu $29, $29, 24\n\t"                                 \
+         "ld $4, 72(%1)\n\t"                                      \
+         "sd $4, 0($29)\n\t"                                      \
+         "ld $4, 80(%1)\n\t"                                      \
+         "sd $4, 8($29)\n\t"                                      \
+         "ld $4, 88(%1)\n\t"                                      \
+         "sd $4, 16($29)\n\t"                                     \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $6, 24(%1)\n\t"                                      \
+         "ld $7, 32(%1)\n\t"                                      \
+         "ld $8, 40(%1)\n\t"                                      \
+         "ld $9, 48(%1)\n\t"                                      \
+         "ld $10, 56(%1)\n\t"                                     \
+         "ld $11, 64(%1)\n\t"                                     \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "daddu $29, $29, 24\n\t"                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#define CALL_FN_W_12W(lval, orig, arg1,arg2,arg3,arg4,arg5,       \
+                                  arg6,arg7,arg8,arg9,arg10,      \
+                                  arg11,arg12)                    \
+   do {                                                           \
+      volatile OrigFn        _orig = (orig);                      \
+      volatile unsigned long long _argvec[13];                    \
+      volatile unsigned long long _res;                           \
+      _argvec[0] = MIPS64_LONG2REG_CAST(_orig.nraddr);            \
+      _argvec[1] = MIPS64_LONG2REG_CAST(arg1);                    \
+      _argvec[2] = MIPS64_LONG2REG_CAST(arg2);                    \
+      _argvec[3] = MIPS64_LONG2REG_CAST(arg3);                    \
+      _argvec[4] = MIPS64_LONG2REG_CAST(arg4);                    \
+      _argvec[5] = MIPS64_LONG2REG_CAST(arg5);                    \
+      _argvec[6] = MIPS64_LONG2REG_CAST(arg6);                    \
+      _argvec[7] = MIPS64_LONG2REG_CAST(arg7);                    \
+      _argvec[8] = MIPS64_LONG2REG_CAST(arg8);                    \
+      _argvec[9] = MIPS64_LONG2REG_CAST(arg9);                    \
+      _argvec[10] = MIPS64_LONG2REG_CAST(arg10);                  \
+      _argvec[11] = MIPS64_LONG2REG_CAST(arg11);                  \
+      _argvec[12] = MIPS64_LONG2REG_CAST(arg12);                  \
+      __asm__ volatile(                                           \
+         "dsubu $29, $29, 32\n\t"                                 \
+         "ld $4, 72(%1)\n\t"                                      \
+         "sd $4, 0($29)\n\t"                                      \
+         "ld $4, 80(%1)\n\t"                                      \
+         "sd $4, 8($29)\n\t"                                      \
+         "ld $4, 88(%1)\n\t"                                      \
+         "sd $4, 16($29)\n\t"                                     \
+         "ld $4, 96(%1)\n\t"                                      \
+         "sd $4, 24($29)\n\t"                                     \
+         "ld $4, 8(%1)\n\t"                                       \
+         "ld $5, 16(%1)\n\t"                                      \
+         "ld $6, 24(%1)\n\t"                                      \
+         "ld $7, 32(%1)\n\t"                                      \
+         "ld $8, 40(%1)\n\t"                                      \
+         "ld $9, 48(%1)\n\t"                                      \
+         "ld $10, 56(%1)\n\t"                                     \
+         "ld $11, 64(%1)\n\t"                                     \
+         "ld $25, 0(%1)\n\t"  /* target->t9 */                    \
+         VALGRIND_CALL_NOREDIR_T9                                 \
+         "daddu $29, $29, 32\n\t"                                 \
+         "move %0, $2\n"                                          \
+         : /*out*/   "=r" (_res)                                  \
+         : /*in*/    "r" (&_argvec[0])                            \
+         : /*trash*/ "memory", __CALLER_SAVED_REGS                \
+      );                                                          \
+      lval = (__typeof__(lval)) (long)_res;                       \
+   } while (0)
+
+#endif /* PLAT_mips64_linux */
+
+/* ------------------------------------------------------------------ */
+/* ARCHITECTURE INDEPENDENT MACROS for CLIENT REQUESTS.               */
+/*                                                                    */
+/* ------------------------------------------------------------------ */
+
+/* Some request codes.  There are many more of these, but most are not
+   exposed to end-user view.  These are the public ones, all of the
+   form 0x1000 + small_number.
+
+   Core ones are in the range 0x00000000--0x0000ffff.  The non-public
+   ones start at 0x2000.
+*/
+
+/* These macros are used by tools -- they must be public, but don't
+   embed them into other programs. */
+#define VG_USERREQ_TOOL_BASE(a,b) \
+   ((unsigned int)(((a)&0xff) << 24 | ((b)&0xff) << 16))
+#define VG_IS_TOOL_USERREQ(a, b, v) \
+   (VG_USERREQ_TOOL_BASE(a,b) == ((v) & 0xffff0000))
+
+/* !! ABIWARNING !! ABIWARNING !! ABIWARNING !! ABIWARNING !! 
+   This enum comprises an ABI exported by Valgrind to programs
+   which use client requests.  DO NOT CHANGE THE NUMERIC VALUES OF THESE
+   ENTRIES, NOR DELETE ANY -- add new ones at the end of the most
+   relevant group. */
+typedef
+   enum { VG_USERREQ__RUNNING_ON_VALGRIND  = 0x1001,
+          VG_USERREQ__DISCARD_TRANSLATIONS = 0x1002,
+
+          /* These allow any function to be called from the simulated
+             CPU but run on the real CPU.  Nb: the first arg passed to
+             the function is always the ThreadId of the running
+             thread!  So CLIENT_CALL0 actually requires a 1 arg
+             function, etc. */
+          VG_USERREQ__CLIENT_CALL0 = 0x1101,
+          VG_USERREQ__CLIENT_CALL1 = 0x1102,
+          VG_USERREQ__CLIENT_CALL2 = 0x1103,
+          VG_USERREQ__CLIENT_CALL3 = 0x1104,
+
+          /* Can be useful in regression testing suites -- eg. can
+             send Valgrind's output to /dev/null and still count
+             errors. */
+          VG_USERREQ__COUNT_ERRORS = 0x1201,
+
+          /* Allows the client program and/or gdbserver to execute a monitor
+             command. */
+          VG_USERREQ__GDB_MONITOR_COMMAND = 0x1202,
+
+          /* Allows the client program to change a dynamic command line
+             option.  */
+          VG_USERREQ__CLO_CHANGE = 0x1203,
+
+          /* These are useful and can be interpreted by any tool that
+             tracks malloc() et al, by using vg_replace_malloc.c. */
+          VG_USERREQ__MALLOCLIKE_BLOCK = 0x1301,
+          VG_USERREQ__RESIZEINPLACE_BLOCK = 0x130b,
+          VG_USERREQ__FREELIKE_BLOCK   = 0x1302,
+          /* Memory pool support. */
+          VG_USERREQ__CREATE_MEMPOOL   = 0x1303,
+          VG_USERREQ__DESTROY_MEMPOOL  = 0x1304,
+          VG_USERREQ__MEMPOOL_ALLOC    = 0x1305,
+          VG_USERREQ__MEMPOOL_FREE     = 0x1306,
+          VG_USERREQ__MEMPOOL_TRIM     = 0x1307,
+          VG_USERREQ__MOVE_MEMPOOL     = 0x1308,
+          VG_USERREQ__MEMPOOL_CHANGE   = 0x1309,
+          VG_USERREQ__MEMPOOL_EXISTS   = 0x130a,
+
+          /* Allow printfs to valgrind log. */
+          /* The first two pass the va_list argument by value, which
+             assumes it is the same size as or smaller than a UWord,
+             which generally isn't the case.  Hence are deprecated.
+             The second two pass the vargs by reference and so are
+             immune to this problem. */
+          /* both :: char* fmt, va_list vargs (DEPRECATED) */
+          VG_USERREQ__PRINTF           = 0x1401,
+          VG_USERREQ__PRINTF_BACKTRACE = 0x1402,
+          /* both :: char* fmt, va_list* vargs */
+          VG_USERREQ__PRINTF_VALIST_BY_REF = 0x1403,
+          VG_USERREQ__PRINTF_BACKTRACE_VALIST_BY_REF = 0x1404,
+
+          /* Stack support. */
+          VG_USERREQ__STACK_REGISTER   = 0x1501,
+          VG_USERREQ__STACK_DEREGISTER = 0x1502,
+          VG_USERREQ__STACK_CHANGE     = 0x1503,
+
+          /* Wine support */
+          VG_USERREQ__LOAD_PDB_DEBUGINFO = 0x1601,
+
+          /* Querying of debug info. */
+          VG_USERREQ__MAP_IP_TO_SRCLOC = 0x1701,
+
+          /* Disable/enable error reporting level.  Takes a single
+             Word arg which is the delta to this thread's error
+             disablement indicator.  Hence 1 disables or further
+             disables errors, and -1 moves back towards enablement.
+             Other values are not allowed. */
+          VG_USERREQ__CHANGE_ERR_DISABLEMENT = 0x1801,
+
+          /* Some requests used for Valgrind internal, such as
+             self-test or self-hosting. */
+          /* Initialise IR injection */
+          VG_USERREQ__VEX_INIT_FOR_IRI = 0x1901,
+          /* Used by Inner Valgrind to inform Outer Valgrind where to
+             find the list of inner guest threads */
+          VG_USERREQ__INNER_THREADS    = 0x1902
+   } Vg_ClientRequest;
+
+#if !defined(__GNUC__)
+#  define __extension__ /* */
+#endif
+
+
+/* Returns the number of Valgrinds this code is running under.  That
+   is, 0 if running natively, 1 if running under Valgrind, 2 if
+   running under Valgrind which is running under another Valgrind,
+   etc. */
+#define RUNNING_ON_VALGRIND                                           \
+    (unsigned)VALGRIND_DO_CLIENT_REQUEST_EXPR(0 /* if not */,         \
+                                    VG_USERREQ__RUNNING_ON_VALGRIND,  \
+                                    0, 0, 0, 0, 0)                    \
+
+
+/* Discard translation of code in the range [_qzz_addr .. _qzz_addr +
+   _qzz_len - 1].  Useful if you are debugging a JITter or some such,
+   since it provides a way to make sure valgrind will retranslate the
+   invalidated area.  Returns no value. */
+#define VALGRIND_DISCARD_TRANSLATIONS(_qzz_addr,_qzz_len)              \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__DISCARD_TRANSLATIONS,  \
+                                    _qzz_addr, _qzz_len, 0, 0, 0)
+
+#define VALGRIND_INNER_THREADS(_qzz_addr)                               \
+   VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__INNER_THREADS,           \
+                                   _qzz_addr, 0, 0, 0, 0)
+
+
+/* These requests are for getting Valgrind itself to print something.
+   Possibly with a backtrace.  This is a really ugly hack.  The return value
+   is the number of characters printed, excluding the "**** " part at the
+   start and the backtrace (if present). */
+
+#if defined(__GNUC__) || defined(__INTEL_COMPILER) && !defined(_MSC_VER)
+/* Modern GCC will optimize the static routine out if unused,
+   and unused attribute will shut down warnings about it.  */
+static int VALGRIND_PRINTF(const char *format, ...)
+   __attribute__((format(__printf__, 1, 2), __unused__));
+#endif
+static int
+#if defined(_MSC_VER)
+__inline
+#endif
+VALGRIND_PRINTF(const char *format, ...)
+{
+#if defined(NVALGRIND)
+   (void)format;
+   return 0;
+#else /* NVALGRIND */
+#if defined(_MSC_VER) || defined(__MINGW64__)
+   uintptr_t _qzz_res;
+#else
+   unsigned long _qzz_res;
+#endif
+   va_list vargs;
+   va_start(vargs, format);
+#if defined(_MSC_VER) || defined(__MINGW64__)
+   _qzz_res = VALGRIND_DO_CLIENT_REQUEST_EXPR(0,
+                              VG_USERREQ__PRINTF_VALIST_BY_REF,
+                              (uintptr_t)format,
+                              (uintptr_t)&vargs,
+                              0, 0, 0);
+#else
+   _qzz_res = VALGRIND_DO_CLIENT_REQUEST_EXPR(0,
+                              VG_USERREQ__PRINTF_VALIST_BY_REF,
+                              (unsigned long)format,
+                              (unsigned long)&vargs, 
+                              0, 0, 0);
+#endif
+   va_end(vargs);
+   return (int)_qzz_res;
+#endif /* NVALGRIND */
+}
+
+#if defined(__GNUC__) || defined(__INTEL_COMPILER) && !defined(_MSC_VER)
+static int VALGRIND_PRINTF_BACKTRACE(const char *format, ...)
+   __attribute__((format(__printf__, 1, 2), __unused__));
+#endif
+static int
+#if defined(_MSC_VER)
+__inline
+#endif
+VALGRIND_PRINTF_BACKTRACE(const char *format, ...)
+{
+#if defined(NVALGRIND)
+   (void)format;
+   return 0;
+#else /* NVALGRIND */
+#if defined(_MSC_VER) || defined(__MINGW64__)
+   uintptr_t _qzz_res;
+#else
+   unsigned long _qzz_res;
+#endif
+   va_list vargs;
+   va_start(vargs, format);
+#if defined(_MSC_VER) || defined(__MINGW64__)
+   _qzz_res = VALGRIND_DO_CLIENT_REQUEST_EXPR(0,
+                              VG_USERREQ__PRINTF_BACKTRACE_VALIST_BY_REF,
+                              (uintptr_t)format,
+                              (uintptr_t)&vargs,
+                              0, 0, 0);
+#else
+   _qzz_res = VALGRIND_DO_CLIENT_REQUEST_EXPR(0,
+                              VG_USERREQ__PRINTF_BACKTRACE_VALIST_BY_REF,
+                              (unsigned long)format,
+                              (unsigned long)&vargs, 
+                              0, 0, 0);
+#endif
+   va_end(vargs);
+   return (int)_qzz_res;
+#endif /* NVALGRIND */
+}
+
+
+/* These requests allow control to move from the simulated CPU to the
+   real CPU, calling an arbitrary function.
+   
+   Note that the current ThreadId is inserted as the first argument.
+   So this call:
+
+     VALGRIND_NON_SIMD_CALL2(f, arg1, arg2)
+
+   requires f to have this signature:
+
+     Word f(Word tid, Word arg1, Word arg2)
+
+   where "Word" is a word-sized type.
+
+   Note that these client requests are not entirely reliable.  For example,
+   if you call a function with them that subsequently calls printf(),
+   there's a high chance Valgrind will crash.  Generally, your prospects of
+   these working are made higher if the called function does not refer to
+   any global variables, and does not refer to any libc or other functions
+   (printf et al).  Any kind of entanglement with libc or dynamic linking is
+   likely to have a bad outcome, for tricky reasons which we've grappled
+   with a lot in the past.
+*/
+#define VALGRIND_NON_SIMD_CALL0(_qyy_fn)                          \
+    VALGRIND_DO_CLIENT_REQUEST_EXPR(0 /* default return */,       \
+                                    VG_USERREQ__CLIENT_CALL0,     \
+                                    _qyy_fn,                      \
+                                    0, 0, 0, 0)
+
+#define VALGRIND_NON_SIMD_CALL1(_qyy_fn, _qyy_arg1)                    \
+    VALGRIND_DO_CLIENT_REQUEST_EXPR(0 /* default return */,            \
+                                    VG_USERREQ__CLIENT_CALL1,          \
+                                    _qyy_fn,                           \
+                                    _qyy_arg1, 0, 0, 0)
+
+#define VALGRIND_NON_SIMD_CALL2(_qyy_fn, _qyy_arg1, _qyy_arg2)         \
+    VALGRIND_DO_CLIENT_REQUEST_EXPR(0 /* default return */,            \
+                                    VG_USERREQ__CLIENT_CALL2,          \
+                                    _qyy_fn,                           \
+                                    _qyy_arg1, _qyy_arg2, 0, 0)
+
+#define VALGRIND_NON_SIMD_CALL3(_qyy_fn, _qyy_arg1, _qyy_arg2, _qyy_arg3) \
+    VALGRIND_DO_CLIENT_REQUEST_EXPR(0 /* default return */,             \
+                                    VG_USERREQ__CLIENT_CALL3,           \
+                                    _qyy_fn,                            \
+                                    _qyy_arg1, _qyy_arg2,               \
+                                    _qyy_arg3, 0)
+
+
+/* Counts the number of errors that have been recorded by a tool.  Nb:
+   the tool must record the errors with VG_(maybe_record_error)() or
+   VG_(unique_error)() for them to be counted. */
+#define VALGRIND_COUNT_ERRORS                                     \
+    (unsigned)VALGRIND_DO_CLIENT_REQUEST_EXPR(                    \
+                               0 /* default return */,            \
+                               VG_USERREQ__COUNT_ERRORS,          \
+                               0, 0, 0, 0, 0)
+
+/* Several Valgrind tools (Memcheck, Massif, Helgrind, DRD) rely on knowing
+   when heap blocks are allocated in order to give accurate results.  This
+   happens automatically for the standard allocator functions such as
+   malloc(), calloc(), realloc(), memalign(), new, new[], free(), delete,
+   delete[], etc.
+
+   But if your program uses a custom allocator, this doesn't automatically
+   happen, and Valgrind will not do as well.  For example, if you allocate
+   superblocks with mmap() and then allocates chunks of the superblocks, all
+   Valgrind's observations will be at the mmap() level and it won't know that
+   the chunks should be considered separate entities.  In Memcheck's case,
+   that means you probably won't get heap block overrun detection (because
+   there won't be redzones marked as unaddressable) and you definitely won't
+   get any leak detection.
+
+   The following client requests allow a custom allocator to be annotated so
+   that it can be handled accurately by Valgrind.
+
+   VALGRIND_MALLOCLIKE_BLOCK marks a region of memory as having been allocated
+   by a malloc()-like function.  For Memcheck (an illustrative case), this
+   does two things:
+
+   - It records that the block has been allocated.  This means any addresses
+     within the block mentioned in error messages will be
+     identified as belonging to the block.  It also means that if the block
+     isn't freed it will be detected by the leak checker.
+
+   - It marks the block as being addressable and undefined (if 'is_zeroed' is
+     not set), or addressable and defined (if 'is_zeroed' is set).  This
+     controls how accesses to the block by the program are handled.
+   
+   'addr' is the start of the usable block (ie. after any
+   redzone), 'sizeB' is its size.  'rzB' is the redzone size if the allocator
+   can apply redzones -- these are blocks of padding at the start and end of
+   each block.  Adding redzones is recommended as it makes it much more likely
+   Valgrind will spot block overruns.  `is_zeroed' indicates if the memory is
+   zeroed (or filled with another predictable value), as is the case for
+   calloc().
+   
+   VALGRIND_MALLOCLIKE_BLOCK should be put immediately after the point where a
+   heap block -- that will be used by the client program -- is allocated.
+   It's best to put it at the outermost level of the allocator if possible;
+   for example, if you have a function my_alloc() which calls
+   internal_alloc(), and the client request is put inside internal_alloc(),
+   stack traces relating to the heap block will contain entries for both
+   my_alloc() and internal_alloc(), which is probably not what you want.
+
+   For Memcheck users: if you use VALGRIND_MALLOCLIKE_BLOCK to carve out
+   custom blocks from within a heap block, B, that has been allocated with
+   malloc/calloc/new/etc, then block B will be *ignored* during leak-checking
+   -- the custom blocks will take precedence.
+
+   VALGRIND_FREELIKE_BLOCK is the partner to VALGRIND_MALLOCLIKE_BLOCK.  For
+   Memcheck, it does two things:
+
+   - It records that the block has been deallocated.  This assumes that the
+     block was annotated as having been allocated via
+     VALGRIND_MALLOCLIKE_BLOCK.  Otherwise, an error will be issued.
+
+   - It marks the block as being unaddressable.
+
+   VALGRIND_FREELIKE_BLOCK should be put immediately after the point where a
+   heap block is deallocated.
+
+   VALGRIND_RESIZEINPLACE_BLOCK informs a tool about reallocation. For
+   Memcheck, it does four things:
+
+   - It records that the size of a block has been changed.  This assumes that
+     the block was annotated as having been allocated via
+     VALGRIND_MALLOCLIKE_BLOCK.  Otherwise, an error will be issued.
+
+   - If the block shrunk, it marks the freed memory as being unaddressable.
+
+   - If the block grew, it marks the new area as undefined and defines a red
+     zone past the end of the new block.
+
+   - The V-bits of the overlap between the old and the new block are preserved.
+
+   VALGRIND_RESIZEINPLACE_BLOCK should be put after allocation of the new block
+   and before deallocation of the old block.
+
+   In many cases, these three client requests will not be enough to get your
+   allocator working well with Memcheck.  More specifically, if your allocator
+   writes to freed blocks in any way then a VALGRIND_MAKE_MEM_UNDEFINED call
+   will be necessary to mark the memory as addressable just before the zeroing
+   occurs, otherwise you'll get a lot of invalid write errors.  For example,
+   you'll need to do this if your allocator recycles freed blocks, but it
+   zeroes them before handing them back out (via VALGRIND_MALLOCLIKE_BLOCK).
+   Alternatively, if your allocator reuses freed blocks for allocator-internal
+   data structures, VALGRIND_MAKE_MEM_UNDEFINED calls will also be necessary.
+
+   Really, what's happening is a blurring of the lines between the client
+   program and the allocator... after VALGRIND_FREELIKE_BLOCK is called, the
+   memory should be considered unaddressable to the client program, but the
+   allocator knows more than the rest of the client program and so may be able
+   to safely access it.  Extra client requests are necessary for Valgrind to
+   understand the distinction between the allocator and the rest of the
+   program.
+
+   Ignored if addr == 0.
+*/
+#define VALGRIND_MALLOCLIKE_BLOCK(addr, sizeB, rzB, is_zeroed)          \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__MALLOCLIKE_BLOCK,       \
+                                    addr, sizeB, rzB, is_zeroed, 0)
+
+/* See the comment for VALGRIND_MALLOCLIKE_BLOCK for details.
+   Ignored if addr == 0.
+*/
+#define VALGRIND_RESIZEINPLACE_BLOCK(addr, oldSizeB, newSizeB, rzB)     \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__RESIZEINPLACE_BLOCK,    \
+                                    addr, oldSizeB, newSizeB, rzB, 0)
+
+/* See the comment for VALGRIND_MALLOCLIKE_BLOCK for details.
+   Ignored if addr == 0.
+*/
+#define VALGRIND_FREELIKE_BLOCK(addr, rzB)                              \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__FREELIKE_BLOCK,         \
+                                    addr, rzB, 0, 0, 0)
+
+/* Create a memory pool. */
+#define VALGRIND_CREATE_MEMPOOL(pool, rzB, is_zeroed)             \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__CREATE_MEMPOOL,   \
+                                    pool, rzB, is_zeroed, 0, 0)
+
+/* Create a memory pool with some flags specifying extended behaviour.
+   When flags is zero, the behaviour is identical to VALGRIND_CREATE_MEMPOOL.
+   
+   The flag VALGRIND_MEMPOOL_METAPOOL specifies that the pieces of memory 
+   associated with the pool using VALGRIND_MEMPOOL_ALLOC  will be used
+   by the application as superblocks to dole out MALLOC_LIKE blocks using
+   VALGRIND_MALLOCLIKE_BLOCK. In other words, a meta pool is a "2 levels"
+   pool : first level is the blocks described by VALGRIND_MEMPOOL_ALLOC.
+   The second level blocks are described using VALGRIND_MALLOCLIKE_BLOCK.
+   Note that the association between the pool and the second level blocks
+   is implicit : second level blocks will be located inside first level
+   blocks. It is necessary to use the VALGRIND_MEMPOOL_METAPOOL flag
+   for such 2 levels pools, as otherwise valgrind will detect overlapping
+   memory blocks, and will abort execution (e.g. during leak search).
+
+   Such a meta pool can also be marked as an 'auto free' pool using the flag
+   VALGRIND_MEMPOOL_AUTO_FREE, which must be OR-ed together with the
+   VALGRIND_MEMPOOL_METAPOOL. For an 'auto free' pool, VALGRIND_MEMPOOL_FREE
+   will automatically free the second level blocks that are contained
+   inside the first level block freed with VALGRIND_MEMPOOL_FREE.
+   In other words, calling VALGRIND_MEMPOOL_FREE will cause implicit calls
+   to VALGRIND_FREELIKE_BLOCK for all the second level blocks included
+   in the first level block.
+   Note: it is an error to use the VALGRIND_MEMPOOL_AUTO_FREE flag
+   without the VALGRIND_MEMPOOL_METAPOOL flag.
+*/
+#define VALGRIND_MEMPOOL_AUTO_FREE  1
+#define VALGRIND_MEMPOOL_METAPOOL   2
+#define VALGRIND_CREATE_MEMPOOL_EXT(pool, rzB, is_zeroed, flags)        \
+   VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__CREATE_MEMPOOL,          \
+                                   pool, rzB, is_zeroed, flags, 0)
+
+/* Destroy a memory pool. */
+#define VALGRIND_DESTROY_MEMPOOL(pool)                            \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__DESTROY_MEMPOOL,  \
+                                    pool, 0, 0, 0, 0)
+
+/* Associate a piece of memory with a memory pool. */
+#define VALGRIND_MEMPOOL_ALLOC(pool, addr, size)                  \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__MEMPOOL_ALLOC,    \
+                                    pool, addr, size, 0, 0)
+
+/* Disassociate a piece of memory from a memory pool. */
+#define VALGRIND_MEMPOOL_FREE(pool, addr)                         \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__MEMPOOL_FREE,     \
+                                    pool, addr, 0, 0, 0)
+
+/* Disassociate any pieces outside a particular range. */
+#define VALGRIND_MEMPOOL_TRIM(pool, addr, size)                   \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__MEMPOOL_TRIM,     \
+                                    pool, addr, size, 0, 0)
+
+/* Resize and/or move a piece associated with a memory pool. */
+#define VALGRIND_MOVE_MEMPOOL(poolA, poolB)                       \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__MOVE_MEMPOOL,     \
+                                    poolA, poolB, 0, 0, 0)
+
+/* Resize and/or move a piece associated with a memory pool. */
+#define VALGRIND_MEMPOOL_CHANGE(pool, addrA, addrB, size)         \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__MEMPOOL_CHANGE,   \
+                                    pool, addrA, addrB, size, 0)
+
+/* Return 1 if a mempool exists, else 0. */
+#define VALGRIND_MEMPOOL_EXISTS(pool)                             \
+    (unsigned)VALGRIND_DO_CLIENT_REQUEST_EXPR(0,                  \
+                               VG_USERREQ__MEMPOOL_EXISTS,        \
+                               pool, 0, 0, 0, 0)
+
+/* Mark a piece of memory as being a stack. Returns a stack id.
+   start is the lowest addressable stack byte, end is the highest
+   addressable stack byte. */
+#define VALGRIND_STACK_REGISTER(start, end)                       \
+    (unsigned)VALGRIND_DO_CLIENT_REQUEST_EXPR(0,                  \
+                               VG_USERREQ__STACK_REGISTER,        \
+                               start, end, 0, 0, 0)
+
+/* Unmark the piece of memory associated with a stack id as being a
+   stack. */
+#define VALGRIND_STACK_DEREGISTER(id)                             \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__STACK_DEREGISTER, \
+                                    id, 0, 0, 0, 0)
+
+/* Change the start and end address of the stack id.
+   start is the new lowest addressable stack byte, end is the new highest
+   addressable stack byte. */
+#define VALGRIND_STACK_CHANGE(id, start, end)                     \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__STACK_CHANGE,     \
+                                    id, start, end, 0, 0)
+
+/* Load PDB debug info for Wine PE image_map. */
+#define VALGRIND_LOAD_PDB_DEBUGINFO(fd, ptr, total_size, delta)     \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__LOAD_PDB_DEBUGINFO, \
+                                    fd, ptr, total_size, delta, 0)
+
+/* Map a code address to a source file name and line number.  buf64
+   must point to a 64-byte buffer in the caller's address space.  The
+   result will be dumped in there and is guaranteed to be zero
+   terminated.  If no info is found, the first byte is set to zero. */
+#define VALGRIND_MAP_IP_TO_SRCLOC(addr, buf64)                    \
+    (unsigned)VALGRIND_DO_CLIENT_REQUEST_EXPR(0,                  \
+                               VG_USERREQ__MAP_IP_TO_SRCLOC,      \
+                               addr, buf64, 0, 0, 0)
+
+/* Disable error reporting for this thread.  Behaves in a stack like
+   way, so you can safely call this multiple times provided that
+   VALGRIND_ENABLE_ERROR_REPORTING is called the same number of times
+   to re-enable reporting.  The first call of this macro disables
+   reporting.  Subsequent calls have no effect except to increase the
+   number of VALGRIND_ENABLE_ERROR_REPORTING calls needed to re-enable
+   reporting.  Child threads do not inherit this setting from their
+   parents -- they are always created with reporting enabled. */
+#define VALGRIND_DISABLE_ERROR_REPORTING                                \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__CHANGE_ERR_DISABLEMENT, \
+                                    1, 0, 0, 0, 0)
+
+/* Re-enable error reporting, as per comments on
+   VALGRIND_DISABLE_ERROR_REPORTING. */
+#define VALGRIND_ENABLE_ERROR_REPORTING                                 \
+    VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__CHANGE_ERR_DISABLEMENT, \
+                                    -1, 0, 0, 0, 0)
+
+/* Execute a monitor command from the client program.
+   If a connection is opened with GDB, the output will be sent
+   according to the output mode set for vgdb.
+   If no connection is opened, output will go to the log output.
+   Returns 1 if command not recognised, 0 otherwise. */
+#define VALGRIND_MONITOR_COMMAND(command)                               \
+   VALGRIND_DO_CLIENT_REQUEST_EXPR(0, VG_USERREQ__GDB_MONITOR_COMMAND, \
+                                   command, 0, 0, 0, 0)
+
+
+/* Change the value of a dynamic command line option.
+   Note that unknown or not dynamically changeable options
+   will cause a warning message to be output.  */
+#define VALGRIND_CLO_CHANGE(option)                           \
+   VALGRIND_DO_CLIENT_REQUEST_STMT(VG_USERREQ__CLO_CHANGE, \
+                                   option, 0, 0, 0, 0)
+
+
+#undef PLAT_x86_darwin
+#undef PLAT_amd64_darwin
+#undef PLAT_x86_win32
+#undef PLAT_amd64_win64
+#undef PLAT_x86_linux
+#undef PLAT_amd64_linux
+#undef PLAT_ppc32_linux
+#undef PLAT_ppc64be_linux
+#undef PLAT_ppc64le_linux
+#undef PLAT_arm_linux
+#undef PLAT_s390x_linux
+#undef PLAT_mips32_linux
+#undef PLAT_mips64_linux
+#undef PLAT_nanomips_linux
+#undef PLAT_x86_solaris
+#undef PLAT_amd64_solaris
+
+#endif   /* __VALGRIND_H */
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/bottleneck/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/bottleneck/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/bottleneck/__main__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/bottleneck/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8bc43be0e2bbb7aed97cda2c10e45895d6071b9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/bottleneck/__main__.py
@@ -0,0 +1,229 @@
+# mypy: allow-untyped-defs
+import argparse
+import cProfile
+import pstats
+import sys
+import os
+
+import torch
+from torch.autograd import profiler
+from torch.utils.collect_env import get_env_info
+
+
+def redirect_argv(new_argv):
+    sys.argv[:] = new_argv[:]
+
+
+def compiled_with_cuda(sysinfo):
+    if sysinfo.cuda_compiled_version:
+        return f'compiled w/ CUDA {sysinfo.cuda_compiled_version}'
+    return 'not compiled w/ CUDA'
+
+
+env_summary = """
+--------------------------------------------------------------------------------
+  Environment Summary
+--------------------------------------------------------------------------------
+PyTorch {pytorch_version}{debug_str} {cuda_compiled}
+Running with Python {py_version} and {cuda_runtime}
+
+`{pip_version} list` truncated output:
+{pip_list_output}
+""".strip()
+
+
+def run_env_analysis():
+    print('Running environment analysis...')
+    info = get_env_info()
+
+    result: dict[str, str] = {}
+
+    debug_str = ''
+    if info.is_debug_build:
+        debug_str = ' DEBUG'
+
+    cuda_avail = ''
+    if info.is_cuda_available:
+        cuda = info.cuda_runtime_version
+        if cuda is not None:
+            cuda_avail = 'CUDA ' + cuda
+    else:
+        cuda = 'CUDA unavailable'
+
+    pip_version = info.pip_version
+    pip_list_output = info.pip_packages
+    if pip_list_output is None:
+        pip_list_output = 'Unable to fetch'
+
+    result = {
+        'debug_str': debug_str,
+        'pytorch_version': info.torch_version,
+        'cuda_compiled': compiled_with_cuda(info),
+        'py_version': f'{sys.version_info[0]}.{sys.version_info[1]}',
+        'cuda_runtime': cuda_avail,
+        'pip_version': pip_version,
+        'pip_list_output': pip_list_output,
+    }
+
+    return env_summary.format(**result)
+
+
+def run_cprofile(code, globs, launch_blocking=False):
+    print('Running your script with cProfile')
+    prof = cProfile.Profile()
+    prof.enable()
+    exec(code, globs, None)
+    prof.disable()
+    return prof
+
+
+cprof_summary = """
+--------------------------------------------------------------------------------
+  cProfile output
+--------------------------------------------------------------------------------
+""".strip()
+
+
+def print_cprofile_summary(prof, sortby='tottime', topk=15):
+    print(cprof_summary)
+    cprofile_stats = pstats.Stats(prof).sort_stats(sortby)
+    cprofile_stats.print_stats(topk)
+
+
+def run_autograd_prof(code, globs):
+    def run_prof(use_cuda=False):
+        with profiler.profile(use_cuda=use_cuda) as prof:
+            exec(code, globs, None)
+        return prof
+
+    print('Running your script with the autograd profiler...')
+    result = [run_prof(use_cuda=False)]
+    if torch.cuda.is_available():
+        result.append(run_prof(use_cuda=True))
+    else:
+        result.append(None)
+
+    return result
+
+
+autograd_prof_summary = """
+--------------------------------------------------------------------------------
+  autograd profiler output ({mode} mode)
+--------------------------------------------------------------------------------
+        {description}
+{cuda_warning}
+{output}
+""".strip()
+
+
+def print_autograd_prof_summary(prof, mode, sortby='cpu_time', topk=15):
+    valid_sortby = ['cpu_time', 'cuda_time', 'cpu_time_total', 'cuda_time_total', 'count']
+    if sortby not in valid_sortby:
+        warn = ('WARNING: invalid sorting option for autograd profiler results: {}\n'
+                'Expected `cpu_time`, `cpu_time_total`, or `count`. '
+                'Defaulting to `cpu_time`.')
+        print(warn.format(sortby))
+        sortby = 'cpu_time'
+
+    if mode == 'CUDA':
+        cuda_warning = ('\n\tBecause the autograd profiler uses the CUDA event API,\n'
+                        '\tthe CUDA time column reports approximately max(cuda_time, cpu_time).\n'
+                        '\tPlease ignore this output if your code does not use CUDA.\n')
+    else:
+        cuda_warning = ''
+
+    sorted_events = sorted(prof.function_events,
+                           key=lambda x: getattr(x, sortby), reverse=True)
+    topk_events = sorted_events[:topk]
+
+    result = {
+        'mode': mode,
+        'description': f'top {topk} events sorted by {sortby}',
+        'output': torch.autograd.profiler_util._build_table(topk_events),
+        'cuda_warning': cuda_warning
+    }
+
+    print(autograd_prof_summary.format(**result))
+
+
+descript = """
+`bottleneck` is a tool that can be used as an initial step for debugging
+bottlenecks in your program.
+
+It summarizes runs of your script with the Python profiler and PyTorch\'s
+autograd profiler. Because your script will be profiled, please ensure that it
+exits in a finite amount of time.
+
+For more complicated uses of the profilers, please see
+https://docs.python.org/3/library/profile.html and
+https://pytorch.org/docs/main/autograd.html#profiler for more information.
+""".strip()
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description=descript)
+    parser.add_argument('scriptfile', type=str,
+                        help='Path to the script to be run. '
+                        'Usually run with `python path/to/script`.')
+    parser.add_argument('args', type=str, nargs=argparse.REMAINDER,
+                        help='Command-line arguments to be passed to the script.')
+    return parser.parse_args()
+
+
+def cpu_time_total(autograd_prof):
+    return sum(event.cpu_time_total for event in autograd_prof.function_events)
+
+
+def main():
+    args = parse_args()
+
+    # Customizable constants.
+    scriptfile = args.scriptfile
+    scriptargs = [] if args.args is None else args.args
+    scriptargs.insert(0, scriptfile)
+    cprofile_sortby = 'tottime'
+    cprofile_topk = 15
+    autograd_prof_sortby = 'cpu_time_total'
+    autograd_prof_topk = 15
+
+    redirect_argv(scriptargs)
+
+    sys.path.insert(0, os.path.dirname(scriptfile))
+    with open(scriptfile, 'rb') as stream:
+        code = compile(stream.read(), scriptfile, 'exec')
+    globs = {
+        '__file__': scriptfile,
+        '__name__': '__main__',
+        '__package__': None,
+        '__cached__': None,
+    }
+
+    print(descript)
+
+    env_summary = run_env_analysis()
+
+    if torch.cuda.is_available():
+        torch.cuda.init()
+    cprofile_prof = run_cprofile(code, globs)
+    autograd_prof_cpu, autograd_prof_cuda = run_autograd_prof(code, globs)
+
+    print(env_summary)
+    print_cprofile_summary(cprofile_prof, cprofile_sortby, cprofile_topk)
+
+    if not torch.cuda.is_available():
+        print_autograd_prof_summary(autograd_prof_cpu, 'CPU', autograd_prof_sortby, autograd_prof_topk)
+        return
+
+    # Print both the result of the CPU-mode and CUDA-mode autograd profilers
+    # if their execution times are very different.
+    cuda_prof_exec_time = cpu_time_total(autograd_prof_cuda)
+    if len(autograd_prof_cpu.function_events) > 0:
+        cpu_prof_exec_time = cpu_time_total(autograd_prof_cpu)
+        pct_diff = (cuda_prof_exec_time - cpu_prof_exec_time) / cuda_prof_exec_time
+        if abs(pct_diff) > 0.05:
+            print_autograd_prof_summary(autograd_prof_cpu, 'CPU', autograd_prof_sortby, autograd_prof_topk)
+
+    print_autograd_prof_summary(autograd_prof_cuda, 'CUDA', autograd_prof_sortby, autograd_prof_topk)
+
+if __name__ == '__main__':
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/bundled_inputs.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/bundled_inputs.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cc733abc50e4d0054b622d139a3656957984f60
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/bundled_inputs.py
@@ -0,0 +1,470 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+from typing import Any, TypeVar, Optional, NamedTuple, Union, Callable
+from collections.abc import Sequence
+import textwrap
+import torch
+from torch._C import TupleType, ListType
+from torch.jit._recursive import wrap_cpp_module
+
+
+T = TypeVar("T")
+
+MAX_RAW_TENSOR_SIZE = 16
+
+class InflatableArg(NamedTuple):
+    """Helper type for bundled inputs.
+
+    'value' is the compressed/deflated input that is stored in the model. Value
+    must be of the same type as the argument to the function that it is a deflated
+    input for.
+
+    'fmt' is a formatable code string that is executed to inflate the compressed data into
+    the appropriate input. It can use 'value' as an input to the format str. It must result
+    in a value of the same type as 'value'.
+
+    'fmt_fn' is a formatable function code string that is executed to inflate the compressed
+    data into the appropriate input. It must result in a value of the same type as 'value'.
+    The function name should be the formatable part of the string.
+
+    Note: Only top level InflatableArgs can be inflated. i.e. you cannot place
+    an inflatable arg inside of some other structure. You should instead create
+    an inflatable arg such that the fmt code string returns the full structure
+    of your input.
+    """
+
+    value: Any
+    fmt: str = "{}"
+    fmt_fn: str = ""
+
+
+def bundle_inputs(
+        model: torch.jit.ScriptModule,
+        inputs: Union[Optional[Sequence[tuple[Any, ...]]], dict[Callable, Optional[Sequence[tuple[Any, ...]]]]],
+        info: Optional[Union[list[str], dict[Callable, list[str]]]] = None,
+        *,
+        _receive_inflate_expr: Optional[list[str]] = None,
+) -> torch.jit.ScriptModule:
+    """Create and return a copy of the specified model with inputs attached.
+
+    The original model is not mutated or changed in any way.
+
+    Models with bundled inputs can be invoked in a uniform manner by
+    benchmarking and code coverage tools.
+
+    If inputs is passed in as a list then the inputs will be bundled for 'forward'.
+    If inputs is instead passed in as a map then all the methods specified in the map
+    will have their corresponding inputs bundled. Info should match watchever type is
+    chosen for the inputs.
+
+    The returned model will support the following methods:
+
+        `get_all_bundled_inputs_for_() -> List[Tuple[Any, ...]]`
+            Returns a list of tuples suitable for passing to the model like
+            `for inp in model.get_all_bundled_inputs_for_foo(): model.foo(*inp)`
+
+        `get_bundled_inputs_functions_and_info() -> Dict[str, Dict[str: List[str]]]`
+            Returns a dictionary mapping function names to a metadata dictionary.
+            This nested dictionary maps preset strings like:
+                'get_inputs_function_name' -> the name of a function attribute in this model that can be
+                    run to get back a list of inputs corresponding to that function.
+                'info' -> the user provided extra information about the bundled inputs
+
+    If forward has bundled inputs then these following functions will also be defined on the returned module:
+
+        `get_all_bundled_inputs() -> List[Tuple[Any, ...]]`
+            Returns a list of tuples suitable for passing to the model like
+            `for inp in model.get_all_bundled_inputs(): model(*inp)`
+
+        `get_num_bundled_inputs() -> int`
+            Equivalent to `len(model.get_all_bundled_inputs())`,
+            but slightly easier to call from C++.
+
+    Inputs can be specified in one of two ways:
+
+      - The model can define `_generate_bundled_inputs_for_`.
+        If the user chooses this method inputs[] should map to None
+
+      - The `inputs` argument to this function can be a dictionary mapping functions to a
+        list of inputs, of the same form that will be returned by get_all_bundled_inputs_for_.
+        Alternatively if only bundling inputs for forward the map can be omitted and a singular list of inputs
+        can be provided instead.
+
+        The type of the inputs is List[Tuple[Any, ...]]. The outer list corresponds with a
+        list of inputs, the inner tuple is the list of args that together make up one input.
+        For inputs of functions that take one arg, this will be a tuple of length one. The Any, ...
+        is the actual data that makes up the args, e.g. a tensor.
+
+    Info is an optional parameter that maps functions to a list of strings providing extra information about that
+    function's bundled inputs. Alternatively if only bundling inputs for forward the map can be omitted and
+    a singular list of information can be provided instead. This could be descriptions, expected outputs, etc.
+        - Ex: info={model.forward : ['man eating icecream', 'an airplane', 'a dog']}
+
+    This function will attempt to optimize arguments so that (e.g.)
+    arguments like `torch.zeros(1000)` will be represented compactly.
+    Only top-level arguments will be optimized.
+    Tensors in lists or tuples will not.
+    """
+    if not isinstance(model, torch.jit.ScriptModule):
+        raise Exception("Only ScriptModule is supported.")  # noqa: TRY002
+
+    ignored_methods, ignored_attrs = _get_bundled_inputs_attributes_and_methods(model)
+    clone = torch._C._hack_do_not_use_clone_module_with_class(  # type: ignore[attr-defined]
+        model._c,
+        ignored_methods,
+        ignored_attrs,
+    )
+
+    # The above cloning function returns a torch._C.scriptmodule and we need a torch.jit.scriptmodule.
+    # Fortunately theres a function in _recursive that does exactly that conversion.
+    cloned_module = wrap_cpp_module(clone)
+    if isinstance(inputs, dict):
+        assert isinstance(info, dict) or info is None
+        augment_many_model_functions_with_bundled_inputs(cloned_module, inputs, _receive_inflate_expr, info)
+    else:
+        assert isinstance(info, list) or info is None
+        augment_model_with_bundled_inputs(cloned_module, inputs, _receive_inflate_expr, info)
+    return cloned_module
+
+def augment_model_with_bundled_inputs(
+        model: torch.jit.ScriptModule,
+        inputs: Optional[Sequence[tuple[Any, ...]]] = None,
+        _receive_inflate_expr: Optional[list[str]] = None,  # For debugging.
+        info: Optional[list[str]] = None,  # Optional argument to provide info about forward or its inputs
+        skip_size_check=False,
+) -> None:
+    """Add bundled sample inputs to a model for the forward function.
+
+    Models with bundled inputs can be invoked in a uniform manner by
+    benchmarking and code coverage tools.
+
+    Augmented models will support the following methods:
+
+        `get_all_bundled_inputs() -> List[Tuple[Any, ...]]`
+            Returns a list of tuples suitable for passing to the model like
+            `for inp in model.get_all_bundled_inputs(): model(*inp)`
+
+        `get_num_bundled_inputs() -> int`
+            Equivalent to `len(model.get_all_bundled_inputs())`,
+            but slightly easier to call from C++.
+
+        `get_bundled_inputs_functions_and_info() -> Dict[str, Dict[str: List[str]]]`
+            Returns a dictionary mapping function names to a metadata dictionary.
+            This nested dictionary maps preset strings like:
+                'get_inputs_function_name' -> the name of a function attribute in this model that can be
+                    run to get back a list of inputs corresponding to that function.
+                'info' -> the user provided extra information about the bundled inputs
+
+    Inputs can be specified in one of two ways:
+
+      - The model can define `_generate_bundled_inputs_for_forward`.
+        If the user chooses this method inputs should be None
+
+      - `inputs` is a list of inputs of form List[Tuple[Any, ...]]. A list of tuples where the elements
+        of each tuple are the args that make up one input.
+    """
+    if not isinstance(model, torch.jit.ScriptModule):
+        raise Exception("Only ScriptModule is supported.")  # noqa: TRY002
+
+    forward: Callable = model.forward
+
+    # Sometimes forward won't have a name attached so just in case
+    if not hasattr(forward, "__name__"):
+        forward.__name__ = 'forward'
+    augment_many_model_functions_with_bundled_inputs(
+        model,
+        inputs={forward : inputs},
+        _receive_inflate_expr=_receive_inflate_expr,
+        info={forward : info} if info else None,
+        skip_size_check=skip_size_check,
+    )
+
+
+def augment_many_model_functions_with_bundled_inputs(
+        model: torch.jit.ScriptModule,
+        inputs: dict[Callable, Optional[Sequence[tuple[Any, ...]]]],
+        _receive_inflate_expr: Optional[list[str]] = None,  # For debugging.
+        info: Optional[dict[Callable, list[str]]] = None,  # Optional argument to provide info about the function or its inputs
+        skip_size_check=False,
+) -> None:
+    """Add bundled sample inputs to a model for an arbitrary list of public functions.
+
+    Models with bundled inputs can be invoked in a uniform manner by
+    benchmarking and code coverage tools.
+
+    Augmented models will support the following methods:
+
+        `get_all_bundled_inputs_for_() -> List[Tuple[Any, ...]]`
+            Returns a list of tuples suitable for passing to the model like
+            `for inp in model.get_all_bundled_inputs_for_foo(): model.foo(*inp)`
+
+        `get_bundled_inputs_functions_and_info() -> Dict[str, Dict[str: List[str]]]`
+            Returns a dictionary mapping function names to a metadata dictionary.
+            This nested dictionary maps preset strings like:
+                'get_inputs_function_name' -> the name of a function attribute in this model that can be
+                    run to get back a list of inputs corresponding to that function.
+                'info' -> the user provided extra information about the bundled inputs
+
+    If forward has bundled inputs then these following functions are also defined:
+
+        `get_all_bundled_inputs() -> List[Tuple[Any, ...]]`
+            Returns a list of tuples suitable for passing to the model like
+            `for inp in model.get_all_bundled_inputs(): model(*inp)`
+
+        `get_num_bundled_inputs() -> int`
+            Equivalent to `len(model.get_all_bundled_inputs())`,
+            but slightly easier to call from C++.
+
+    Inputs can be specified in one of two ways:
+
+      - The model can define `_generate_bundled_inputs_for_`.
+        If the user chooses this method inputs[] should map to None
+
+      - The `inputs` argument to this function can be a dictionary mapping functions to a
+        list of inputs, of the same form that will be returned by get_all_bundled_inputs_for_.
+        The type of the inputs is List[Tuple[Any, ...]]. The outer list corresponds with a
+        list of inputs, the inner tuple is the list of args that together make up one input.
+        For inputs of functions that take one arg, this will be a tuple of length one. The Any, ...
+        is the actual data that makes up the args, e.g. a tensor.
+
+    Info is an optional parameter that maps functions to a list of strings providing extra information about that
+    function's bundled inputs. This could be descriptions, expected outputs, etc.
+        - Ex: info={model.forward : ['man eating icecream', 'an airplane', 'a dog']}
+
+    This function will attempt to optimize arguments so that (e.g.)
+    arguments like `torch.zeros(1000)` will be represented compactly.
+    Only top-level arguments will be optimized.
+    Tensors in lists or tuples will not.
+    """
+    if not isinstance(model, torch.jit.ScriptModule):
+        raise Exception("Only ScriptModule is supported.")  # noqa: TRY002
+
+    if not inputs:
+        raise Exception("Please provide inputs for at least 1 function")  # noqa: TRY002
+
+    if hasattr(model, "get_all_bundled_inputs") or hasattr(model, "get_bundled_inputs_functions_and_info"):
+        raise Exception(  # noqa: TRY002
+            "Models can only be augmented with bundled inputs once. "
+            "This Model seems to have already been augmented with "
+            "bundled inputs. Please start afresh with one that "
+            "doesn't have bundled inputs.",
+        )
+
+    get_bundled_inputs_functions_and_info_template = ""
+
+    for function, input_list in inputs.items():
+        if hasattr(function, "__name__"):
+            function_name = function.__name__
+        else:
+            if hasattr(function, "name"):
+                function_name = function.name  # type: ignore[attr-defined]
+            else:
+                raise Exception(  # noqa: TRY002
+                    'At least one of your functions has no attribute name please ensure all have one. m.foo.name = "foo"')
+
+
+        if input_list is not None and not isinstance(input_list, Sequence):
+            raise TypeError(f"Error inputs for function {function_name} is not a Sequence")
+
+        function_arg_types = [arg.type for arg in function.schema.arguments[1:]]  # type: ignore[attr-defined]
+        deflated_inputs_type: ListType = ListType(TupleType(function_arg_types))
+        model._c._register_attribute(f"_bundled_inputs_deflated_{function_name}", deflated_inputs_type, [])
+
+        if hasattr(model, "_generate_bundled_inputs_for_" + function_name):
+            if input_list is not None:
+                raise Exception(  # noqa: TRY002
+                    f"inputs[{function_name}] is not None, but _generate_bundled_inputs_for_{function_name} is already defined"
+                )
+            # Model author already defined _generate_bundled_inputs_for_.
+        elif input_list is None or len(input_list) == 0:
+            raise Exception(  # noqa: TRY002
+                f"inputs for {function_name} must be specified if "
+                f"_generate_bundled_inputs_for_{function_name} is not already defined"
+            )
+        else:
+            # Iterate over the inputs and args in each input.
+            # Accumulate `deflated_inputs` as (possibly) compressed values
+            # and `parts` to be joined into the expression that unpacks them.
+            deflated_inputs = []
+            parts = []
+            for inp_idx, args in enumerate(input_list):
+                if not isinstance(args, tuple) and not isinstance(args, list):  # type: ignore[arg-type]
+                    raise TypeError(
+                        f"Error bundled input for function {function_name} idx: {inp_idx} is not a Tuple or a List"
+                    )
+                deflated_args = []
+                parts.append("(")
+                for arg_idx, arg in enumerate(args):
+                    inflate_helper_fn_name = _get_inflate_helper_fn_name(arg_idx, inp_idx, function_name)
+                    deflated, inflater, helper_definition = _inflate_expr(
+                        arg,
+                        f"deflated[{inp_idx}][{arg_idx}]",
+                        inflate_helper_fn_name,
+                        skip_size_check=skip_size_check,
+                    )
+                    deflated_args.append(deflated)
+                    parts.append(f"    {inflater},")
+                    if helper_definition:
+                        model.define(textwrap.dedent(helper_definition))
+                deflated_inputs.append(tuple(deflated_args))
+                parts.append("),")
+            parts.append("")
+            expr = "\n".join(parts)
+
+            # Back-channel return this expr for debugging.
+            if _receive_inflate_expr is not None:
+                _receive_inflate_expr.append(expr)
+            setattr(model, f"_bundled_inputs_deflated_{function_name}", deflated_inputs)
+            definition = textwrap.dedent("""
+                def _generate_bundled_inputs_for_{name}(self):
+                    deflated = self._bundled_inputs_deflated_{name}
+                    return [
+                {expr}
+                    ]
+                """).format(expr=expr, name=function_name)
+            model.define(definition)
+
+        # Define get_all_bundled_inputs_for_ that caches the generated inputs.
+        model.define(textwrap.dedent("""
+            def get_all_bundled_inputs_for_{name}(self):
+                all_inputs = self._generate_bundled_inputs_for_{name}()
+                assert all_inputs is not None
+                return all_inputs
+            """).format(name=function_name))
+
+        # Add to the high level helper methods
+        inputs_info = repr(info[function]) if info and function in info else '[]'
+        get_bundled_inputs_functions_and_info_template += f"""
+            temp_dict : Dict[str,List[str]] = {{}}
+            info: List[str] = {inputs_info}
+
+            temp_dict['info'] = info
+            temp_dict['get_inputs_function_name'] = ['get_all_bundled_inputs_for_{function_name}']
+            all_inputs['{function_name}'] = temp_dict
+            """
+
+        # To ensure backwards compatibility and a streamlined api for forward these wrappers are provided
+        if function_name == 'forward':
+            model.define(textwrap.dedent("""
+                def get_all_bundled_inputs(self):
+                    return self.get_all_bundled_inputs_for_forward()
+                """))
+            model.define(textwrap.dedent("""
+                def get_num_bundled_inputs(self):
+                    return len(self.get_all_bundled_inputs_for_forward())
+                """))
+
+    # Define some high level helper methods that act on all bundled inputs
+    model.define(textwrap.dedent(f"""
+        def get_bundled_inputs_functions_and_info(self):
+            all_inputs : Dict[str, Dict[str,List[str]]] = {{}}
+            {get_bundled_inputs_functions_and_info_template}
+            return all_inputs
+        """))
+
+def _inflate_expr(
+    arg: T, ref: str, inflate_helper_fn_name: str, skip_size_check: bool = False
+) -> tuple[Union[T, torch.Tensor], str, Optional[str]]:
+    # Allow custom inflation expressions any object.
+    # For example, calling custom image-decoding ops.
+    # Or just use "{}" as the format string to ignore size limits.
+    if isinstance(arg, InflatableArg):
+        if arg.fmt_fn:
+            if arg.fmt not in ["{}", ""]:
+                raise Exception(  # noqa: TRY002
+                    f"Bundled input argument at position '{ref}' has "
+                    f"both arg.fmt_fn => \n{arg.fmt_fn} "
+                    f"\n and arg.fmt  => {arg.fmt}. "
+                    "Please choose `arg.fmt` if the deflater is straightforward or "
+                    "`arg.fmt_fn` if you need a function."
+                )
+
+            helper_definition = arg.fmt_fn.format(inflate_helper_fn_name)
+            expr = f"self.{inflate_helper_fn_name}({ref})"
+
+            return arg.value, expr, helper_definition
+        else:
+            return arg.value, arg.fmt.format(ref), None
+
+    if isinstance(arg, torch.Tensor):
+        # Small-storage tensors can just be saved directly.
+        if arg._typed_storage().size() <= MAX_RAW_TENSOR_SIZE or skip_size_check:
+            return arg, ref, None
+        # Small contiguous tensors can be cloned to have small storage.
+        # TODO: Should we do this even for non-contiguous tensors?
+        if arg.is_contiguous() and arg.numel() <= MAX_RAW_TENSOR_SIZE:
+            return arg.clone(), ref, None
+        # Example inputs commonly come from torch.zeros, torch.ones, or torch.full.
+        # These can be represented compactly.
+        for fmt in [torch.contiguous_format, torch.channels_last]:
+            if arg.is_contiguous(memory_format=fmt) and (arg == arg.flatten()[0]).all().item():
+                return (arg.flatten()[0].clone().expand(*arg.size()),
+                        f"{ref}.contiguous(memory_format={fmt})", None)
+        # Prevent big tensors from being bundled by default.
+        # TODO: Provide more useful diagnostics.
+        raise Exception(  # noqa: TRY002
+            f"Bundled input argument at position '{ref}' is "
+            f"a tensor with storage size {arg._typed_storage().size()}. "
+            f"You probably don't want to bundle this as an input. "
+        )
+    else:
+        return arg, ref, None
+
+def _get_bundled_inputs_attributes_and_methods(script_module: torch.jit.ScriptModule) -> tuple[list[str], list[str]]:
+    methods: list[str] = []
+    attributes: list[str] = []
+
+    # Has bundled inputs for forward
+    if hasattr(script_module, 'get_all_bundled_inputs'):
+        methods.append('get_all_bundled_inputs')
+        methods.append('get_num_bundled_inputs')
+        methods.append('run_on_bundled_input')
+
+    if hasattr(script_module, 'get_bundled_inputs_functions_and_info'):
+        methods.append('get_bundled_inputs_functions_and_info')
+        all_info = script_module.get_bundled_inputs_functions_and_info()
+        for function_name in all_info:
+            methods.append("get_all_bundled_inputs_for_" + function_name)
+            methods.append("_generate_bundled_inputs_for_" + function_name)
+            attributes.append("_bundled_inputs_deflated_" + function_name)
+
+            bundled_inputs_fn = getattr(
+                script_module,
+                f"get_all_bundled_inputs_for_{function_name}"
+            )
+            num_bundled_inputs: int = len(bundled_inputs_fn())
+
+            # Check inflate helper functions for each function, argument and bundled input
+            func = getattr(script_module, function_name)
+            for arg_idx in range(len(func.schema.arguments) - 1):
+                for input_idx in range(num_bundled_inputs):
+                    helper_fn_name = _get_inflate_helper_fn_name(
+                        arg_idx=arg_idx,
+                        input_idx=input_idx,
+                        function_name=function_name
+                    )
+                    # if the arg has an InflatableArg with fmt_fn, add the helper function name
+                    if hasattr(script_module, helper_fn_name):
+                        methods.append(helper_fn_name)
+
+    return (methods, attributes)
+
+
+def _get_inflate_helper_fn_name(
+    arg_idx: int,
+    input_idx: int,
+    function_name: str,
+) -> str:
+    return f"_inflate_helper_for_{function_name}_input_{input_idx}_arg_{arg_idx}"
+
+
+
+def bundle_randn(*size, dtype=None):
+    """Generate a tensor that will be inflated with torch.randn."""
+    stub = torch.zeros(1, dtype=dtype).expand(*size)
+    return InflatableArg(value=stub, fmt="torch.randn_like({})")
+
+
+def bundle_large_tensor(t):
+    """Wrap a tensor to allow bundling regardless of size."""
+    return InflatableArg(value=t, fmt="{}")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/checkpoint.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ca576818da817571acbee44b140591c2135d73a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/checkpoint.py
@@ -0,0 +1,1548 @@
+# mypy: allow-untyped-defs
+import contextlib
+import platform
+import uuid
+import warnings
+import weakref
+from collections import defaultdict
+from typing import *  # noqa: F403
+import enum
+from weakref import ReferenceType
+
+import torch
+import torch.fx.traceback as fx_traceback
+from torch._functorch._aot_autograd.functional_utils import is_fun
+from torch.utils._pytree import tree_map
+from torch.testing._internal.logging_tensor import capture_logs, LoggingTensorMode
+from torch.utils._python_dispatch import TorchDispatchMode
+
+__all__ = [
+    "checkpoint",
+    "checkpoint_sequential",
+    "CheckpointError",
+    "CheckpointFunction",
+    "check_backward_validity",
+    "detach_variable",
+    "get_device_states",
+    "set_device_states",
+    "noop_context_fn",
+    "set_checkpoint_early_stop",
+    "DefaultDeviceType",
+    "set_checkpoint_debug_enabled",
+    "CheckpointPolicy",
+    "SelectiveCheckpointContext",
+    "create_selective_checkpoint_contexts",
+    "SAC_IGNORED_OPS",
+]
+
+_DEFAULT_DETERMINISM_MODE = "default"
+
+_checkpoint_debug_enabled: Optional[bool] = None
+
+
+@contextlib.contextmanager
+def set_checkpoint_debug_enabled(enabled: Optional[bool]):
+    """
+    Context manager that sets whether checkpoint should print additional debug
+    information when running. See the ``debug`` flag for
+    :func:`~torch.utils.checkpoint.checkpoint` for more information. Note that
+    when set, this context manager overrides the value of ``debug`` passed to
+    checkpoint. To defer to the local setting, pass ``None`` to this context.
+
+    Args:
+        enabled (bool): Whether checkpoint should print debug information.
+            Default is 'None'.
+    """
+    global _checkpoint_debug_enabled
+    try:
+        prev = _checkpoint_debug_enabled
+        _checkpoint_debug_enabled = enabled
+        yield
+    finally:
+        _checkpoint_debug_enabled = prev
+
+
+def detach_variable(inputs: Tuple[Any, ...]) -> Tuple[torch.Tensor, ...]:
+    if isinstance(inputs, tuple):
+        out = []
+        for inp in inputs:
+            if not isinstance(inp, torch.Tensor):
+                out.append(inp)
+                continue
+
+            x = inp.detach()
+            x.requires_grad = inp.requires_grad
+            out.append(x)
+        return tuple(out)
+    else:
+        raise RuntimeError(
+            "Only tuple of tensors is supported. Got Unsupported input type: ",
+            type(inputs).__name__,
+        )
+
+
+def check_backward_validity(inputs: Iterable[Any]) -> None:
+    if not any(inp.requires_grad for inp in inputs if isinstance(inp, torch.Tensor)):
+        warnings.warn(
+            "None of the inputs have requires_grad=True. Gradients will be None"
+        )
+
+
+def _get_device_module(device="cuda"):
+    if device == "meta":
+        return torch.device("meta")
+    device_module = getattr(torch, device)
+    return device_module
+
+
+class DefaultDeviceType:
+    r"""
+    A class that manages the default device type for checkpointing.
+
+    If no non-CPU tensors are present, the default device type will
+    be used. The default value is 'cuda'. The device type is used in
+    the checkpointing process when determining which device states
+    to save and restore for recomputation.
+    """
+
+    _default_device_type = "cuda"
+
+    @staticmethod
+    def set_device_type(device: str = "cuda"):
+        """
+        Set the default device type for checkpointing.
+
+        Args:
+            device (str): The device type to be set as default. Default is 'cuda'.
+        """
+        DefaultDeviceType._default_device_type = device
+
+    @staticmethod
+    def get_device_type() -> str:
+        """
+        Get the current default device type for checkpointing.
+
+        Returns:
+            str: The current default device type.
+        """
+        return DefaultDeviceType._default_device_type
+
+
+def _infer_device_type(*args):
+    device_types = []
+
+    def add_device_types(arg):
+        nonlocal device_types
+        if isinstance(arg, torch.Tensor) and not arg.device.type == "cpu":
+            device_types.append(arg.device.type)
+    tree_map(add_device_types, args)
+
+    device_types_set = set(device_types)
+    if len(device_types_set) > 1:
+        warnings.warn(
+            "Tensor arguments, excluding CPU tensors, are detected on at least two types of devices. "
+            "Device state will only be saved for devices of a single device type, and the remaining "
+            "devices will be ignored. Consequently, if any checkpointed functions involve randomness, "
+            "this may result in incorrect gradients. (Note that if CUDA devices are among the devices "
+            "detected, it will be prioritized; otherwise, the first device encountered will be selected.)"
+            f"\nDevice types: {sorted(device_types_set)} first device type: {device_types[0]}"
+        )
+    if len(device_types) == 0:
+        return DefaultDeviceType.get_device_type()
+    elif "cuda" in device_types_set:
+        return "cuda"
+    else:
+        return device_types[0]
+
+
+# We can't know if the run_fn will internally move some args to different devices,
+# which would require logic to preserve rng states for those devices as well.
+# We could paranoically stash and restore ALL the rng states for all visible devices,
+# but that seems very wasteful for most cases.  Compromise:  Stash the RNG state for
+# the device of all Tensor args.
+#
+# To consider:  maybe get_device_states and set_device_states should reside in torch/random.py?
+def get_device_states(*args) -> Tuple[List[int], List[torch.Tensor]]:
+    # This will not error out if "arg" is a CPU tensor or a non-tensor type because
+    # the conditionals short-circuit.
+    fwd_device_ids = []
+
+    def add_device_ids(arg):
+        nonlocal fwd_device_ids
+        if isinstance(arg, torch.Tensor) and arg.device.type not in {"cpu", "meta"}:
+            fwd_device_ids.append(arg.get_device())
+    tree_map(add_device_ids, args)
+
+    fwd_device_states = []
+    device_module = _get_device_module(_infer_device_type(*args))
+    for device_id in fwd_device_ids:
+        with device_module.device(device_id):
+            fwd_device_states.append(device_module.get_rng_state())
+
+    return fwd_device_ids, fwd_device_states
+
+
+def set_device_states(devices, states, *, device_type=None) -> None:
+    """Sets random number generator states for the specified devices.
+
+    Args:
+        devices: Device ids to set states for.
+        states: States to set.
+        device_type: ``device_type`` of the devices to set states for. Default
+            is the device returned by a call to ``DefaultDeviceType.get_device_type()``,
+            which is ``cuda`` if not changed by calling ``DefaultDeviceType::set_device_type()``.
+    """
+    if device_type is None:
+        device_type = DefaultDeviceType.get_device_type()
+    if device_type == "meta":
+        return
+    device_module = _get_device_module(device_type)
+    for device, state in zip(devices, states):
+        with device_module.device(device):
+            device_module.set_rng_state(state)
+
+
+def _get_autocast_kwargs(device_type="cuda"):
+    if torch.amp.is_autocast_available(device_type):
+        device_autocast_kwargs = {
+            "enabled": torch.is_autocast_enabled(device_type),
+            "dtype": torch.get_autocast_dtype(device_type),
+            "cache_enabled": torch.is_autocast_cache_enabled(),
+        }
+    else:
+        device_autocast_kwargs = None
+
+    cpu_autocast_kwargs = {
+        "enabled": torch.is_autocast_enabled('cpu'),
+        "dtype": torch.get_autocast_dtype('cpu'),
+        "cache_enabled": torch.is_autocast_cache_enabled(),
+    }
+
+    return device_autocast_kwargs, cpu_autocast_kwargs
+
+
+class CheckpointFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, run_function, preserve_rng_state, *args):
+        check_backward_validity(args)
+        ctx.run_function = run_function
+        ctx.preserve_rng_state = preserve_rng_state
+        # Accommodates the (remote) possibility that autocast is enabled for cpu AND gpu.
+        ctx.device_type = _infer_device_type(*args)
+        ctx.device_autocast_kwargs, ctx.cpu_autocast_kwargs = _get_autocast_kwargs(
+            ctx.device_type
+        )
+        if preserve_rng_state:
+            ctx.fwd_cpu_state = torch.get_rng_state()
+            # Don't eagerly initialize the cuda context by accident.
+            # (If the user intends that the context is initialized later, within their
+            # run_function, we SHOULD actually stash the cuda state here.  Unfortunately,
+            # we have no way to anticipate this will happen before we run the function.)
+            ctx.had_device_in_fwd = False
+            device_module = _get_device_module(ctx.device_type)
+            if getattr(device_module, "_initialized", False):
+                ctx.had_device_in_fwd = True
+                ctx.fwd_devices, ctx.fwd_device_states = get_device_states(*args)
+
+        # Save non-tensor inputs in ctx, keep a placeholder None for tensors
+        # to be filled out during the backward.
+        ctx.inputs = []
+        ctx.tensor_indices = []
+        tensor_inputs = []
+        for i, arg in enumerate(args):
+            if torch.is_tensor(arg):
+                tensor_inputs.append(arg)
+                ctx.tensor_indices.append(i)
+                ctx.inputs.append(None)
+            else:
+                ctx.inputs.append(arg)
+
+        ctx.save_for_backward(*tensor_inputs)
+
+        with torch.no_grad():
+            outputs = run_function(*args)
+        return outputs
+
+    @staticmethod
+    def backward(ctx, *args):
+        if not torch.autograd._is_checkpoint_valid():
+            raise RuntimeError(
+                "When use_reentrant=True, torch.utils.checkpoint is incompatible"
+                " with .grad() or passing an `inputs` parameter to .backward()."
+                " To resolve this error, you can either set use_reentrant=False,"
+                " or call .backward() without passing the `inputs` argument."
+            )
+        # Copy the list to avoid modifying original list.
+        inputs = list(ctx.inputs)
+        tensor_indices = ctx.tensor_indices
+        tensors = ctx.saved_tensors
+
+        # Fill in inputs with appropriate saved tensors.
+        for i, idx in enumerate(tensor_indices):
+            inputs[idx] = tensors[i]
+
+        # Stash the surrounding rng state, and mimic the state that was
+        # present at this time during forward.  Restore the surrounding state
+        # when we're done.
+        rng_devices = []
+        if ctx.preserve_rng_state and ctx.had_device_in_fwd:
+            rng_devices = ctx.fwd_devices
+        with torch.random.fork_rng(
+            devices=rng_devices, enabled=ctx.preserve_rng_state, device_type=ctx.device_type
+        ):
+            if ctx.preserve_rng_state:
+                torch.set_rng_state(ctx.fwd_cpu_state)
+                if ctx.had_device_in_fwd:
+                    set_device_states(ctx.fwd_devices, ctx.fwd_device_states, device_type=ctx.device_type)
+            detached_inputs = detach_variable(tuple(inputs))
+
+            device_autocast_ctx = torch.amp.autocast(
+                device_type=ctx.device_type, **ctx.device_autocast_kwargs
+            ) if torch.amp.is_autocast_available(ctx.device_type) else contextlib.nullcontext()
+            with torch.enable_grad(), device_autocast_ctx, torch.amp.autocast("cpu", **ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
+                outputs = ctx.run_function(*detached_inputs)
+
+        if isinstance(outputs, torch.Tensor):
+            outputs = (outputs,)
+
+        # run backward() with only tensor that requires grad
+        outputs_with_grad = []
+        args_with_grad = []
+        for i in range(len(outputs)):
+            if torch.is_tensor(outputs[i]) and outputs[i].requires_grad:
+                outputs_with_grad.append(outputs[i])
+                args_with_grad.append(args[i])
+        if len(outputs_with_grad) == 0:
+            raise RuntimeError(
+                "none of output has requires_grad=True,"
+                " this checkpoint() is not necessary"
+            )
+        torch.autograd.backward(outputs_with_grad, args_with_grad)
+        grads = tuple(
+            inp.grad if isinstance(inp, torch.Tensor) else None
+            for inp in detached_inputs
+        )
+
+        return (None, None) + grads
+
+
+def noop_context_fn():
+    return contextlib.nullcontext(), contextlib.nullcontext()
+
+# TorchDynamo does not step inside utils.checkpoint function.  The flow
+# looks likes this
+#  1) TorchDynamo tries to wrap utils.checkpoint in a HigherOrderOp by
+#     speculatively checking if the forward function is safe to trace.
+#  2) If yes, then Dynamo-generated Fx graph has the wrapped higher
+#     order op. As a result, TorchDynamo does not look inside utils.checkpoint.
+#  3) If not, then TorchDynamo falls back to eager by performing a graph
+#     break. And here, the following disable wrapper ensures that
+#     TorchDynamo does not trigger again on the frames created by
+#     utils.checkpoint innards.
+@torch._disable_dynamo
+def checkpoint(
+    function,
+    *args,
+    use_reentrant: Optional[bool] = None,
+    context_fn: Callable[[], Tuple[ContextManager, ContextManager]] = noop_context_fn,
+    determinism_check: str = _DEFAULT_DETERMINISM_MODE,
+    debug: bool = False,
+    **kwargs
+):
+    r"""Checkpoint a model or part of the model.
+
+    Activation checkpointing is a technique that trades compute for memory.
+    Instead of keeping tensors needed for backward alive until they are used in
+    gradient computation during backward, forward computation in checkpointed
+    regions omits saving tensors for backward and recomputes them during the
+    backward pass. Activation checkpointing can be applied to any part of a
+    model.
+
+    There are currently two checkpointing implementations available, determined
+    by the :attr:`use_reentrant` parameter. It is recommended that you use
+    ``use_reentrant=False``. Please refer the note below for a discussion of
+    their differences.
+
+    .. warning::
+
+        If the :attr:`function` invocation during the backward pass differs
+        from the forward pass, e.g., due to a global variable, the checkpointed
+        version may not be equivalent, potentially causing an
+        error being raised or leading to silently incorrect gradients.
+
+    .. warning::
+
+        The ``use_reentrant`` parameter should be passed explicitly. In version
+        2.4 we will raise an exception if ``use_reentrant`` is not passed.
+        If you are using the ``use_reentrant=True`` variant, please refer to the
+        note below for important considerations and potential limitations.
+
+    .. note::
+
+        The reentrant variant of checkpoint (``use_reentrant=True``) and
+        the non-reentrant variant of checkpoint (``use_reentrant=False``)
+        differ in the following ways:
+
+        * Non-reentrant checkpoint stops recomputation as soon as all needed
+          intermediate activations have been recomputed. This feature is enabled
+          by default, but can be disabled with :func:`set_checkpoint_early_stop`.
+          Reentrant checkpoint always recomputes :attr:`function` in its
+          entirety during the backward pass.
+
+        * The reentrant variant does not record the autograd graph during the
+          forward pass, as it runs with the forward pass under
+          :func:`torch.no_grad`. The non-reentrant version does record the
+          autograd graph, allowing one to perform backward on the graph within
+          checkpointed regions.
+
+        * The reentrant checkpoint only supports the
+          :func:`torch.autograd.backward` API for the backward pass without its
+          `inputs` argument, while the non-reentrant version supports all ways
+          of performing the backward pass.
+
+        * At least one input and output must have ``requires_grad=True`` for the
+          reentrant variant. If this condition is unmet, the checkpointed part
+          of the model will not have gradients. The non-reentrant version does
+          not have this requirement.
+
+        * The reentrant version does not consider tensors in nested structures
+          (e.g., custom objects, lists, dicts, etc) as participating in
+          autograd, while the non-reentrant version does.
+
+        * The reentrant checkpoint does not support checkpointed regions with
+          detached tensors from the computational graph, whereas the
+          non-reentrant version does. For the reentrant variant, if the
+          checkpointed segment contains tensors detached using ``detach()`` or
+          with :func:`torch.no_grad`, the backward pass will raise an error.
+          This is because ``checkpoint`` makes all the outputs require gradients
+          and this causes issues when a tensor is defined to have no gradient in
+          the model. To avoid this, detach the tensors outside of the
+          ``checkpoint`` function.
+
+    Args:
+        function: describes what to run in the forward pass of the model or
+            part of the model. It should also know how to handle the inputs
+            passed as the tuple. For example, in LSTM, if user passes
+            ``(activation, hidden)``, :attr:`function` should correctly use the
+            first input as ``activation`` and the second input as ``hidden``
+        preserve_rng_state(bool, optional):  Omit stashing and restoring
+            the RNG state during each checkpoint. Note that under torch.compile,
+            this flag doesn't take effect and we always preserve RNG state.
+            Default: ``True``
+        use_reentrant(bool):
+            specify whether to use the activation checkpoint variant that
+            requires reentrant autograd. This parameter should be passed
+            explicitly. In version 2.5 we will raise an exception if
+            ``use_reentrant`` is not passed. If ``use_reentrant=False``,
+            ``checkpoint`` will use an implementation that does not require
+            reentrant autograd. This allows ``checkpoint`` to support additional
+            functionality, such as working as expected with
+            ``torch.autograd.grad`` and support for keyword arguments input into
+            the checkpointed function.
+        context_fn(Callable, optional): A callable returning a tuple of two
+            context managers. The function and its recomputation will be run
+            under the first and second context managers respectively.
+            This argument is only supported if ``use_reentrant=False``.
+        determinism_check(str, optional): A string specifying the determinism
+            check to perform. By default it is set to ``"default"`` which
+            compares the shapes, dtypes, and devices of the recomputed tensors
+            against those the saved tensors. To turn off this check, specify
+            ``"none"``. Currently these are the only two supported values.
+            Please open an issue if you would like to see more determinism
+            checks. This argument is only supported if ``use_reentrant=False``,
+            if ``use_reentrant=True``, the determinism check is always disabled.
+        debug(bool, optional): If ``True``, error messages will also include
+            a trace of the operators ran during the original forward computation
+            as well as the recomputation. This argument is only supported if
+            ``use_reentrant=False``.
+        args: tuple containing inputs to the :attr:`function`
+
+    Returns:
+        Output of running :attr:`function` on :attr:`*args`
+    """
+    if use_reentrant is None:
+        warnings.warn(
+            "torch.utils.checkpoint: the use_reentrant parameter should be "
+            "passed explicitly. In version 2.5 we will raise an exception "
+            "if use_reentrant is not passed. use_reentrant=False is "
+            "recommended, but if you need to preserve the current default "
+            "behavior, you can pass use_reentrant=True. Refer to docs for more "
+            "details on the differences between the two variants.",
+            stacklevel=2
+        )
+        use_reentrant = True
+
+    # Hack to mix *args with **kwargs in a python 2.7-compliant way
+    preserve = kwargs.pop("preserve_rng_state", True)
+    if kwargs and use_reentrant:
+        raise ValueError(
+            "Unexpected keyword arguments: " + ",".join(arg for arg in kwargs)
+        )
+
+    if use_reentrant:
+        if context_fn is not noop_context_fn or debug is not False:
+            raise ValueError(
+                "Passing `context_fn` or `debug` is only supported when "
+                "use_reentrant=False."
+            )
+        return CheckpointFunction.apply(function, preserve, *args)
+    else:
+        gen = _checkpoint_without_reentrant_generator(
+            function, preserve, context_fn, determinism_check, debug, *args, **kwargs
+        )
+        # Runs pre-forward logic
+        next(gen)
+        ret = function(*args, **kwargs)
+        # Runs post-forward logic
+        try:
+            next(gen)
+        except StopIteration:
+            return ret
+
+
+def checkpoint_sequential(functions, segments, input, use_reentrant=None, **kwargs):
+    r"""Checkpoint a sequential model to save memory.
+
+    Sequential models execute a list of modules/functions in order
+    (sequentially). Therefore, we can divide such a model in various segments
+    and checkpoint each segment. All segments except the last will not store
+    the intermediate activations. The inputs of each checkpointed segment will
+    be saved for re-running the segment in the backward pass.
+
+    .. warning::
+        The ``use_reentrant`` parameter should be passed explicitly. In version
+        2.4 we will raise an exception if ``use_reentrant`` is not passed.
+        If you are using the ``use_reentrant=True` variant, please see
+        :func:`~torch.utils.checkpoint.checkpoint` for
+        the important considerations and limitations of this variant. It is
+        recommended that you use ``use_reentrant=False``.
+
+    .. warning:
+        Since PyTorch 1.4, it allows only one Tensor as the input and
+        intermediate outputs, just like :class:`torch.nn.Sequential`.
+
+    Args:
+        functions: A :class:`torch.nn.Sequential` or the list of modules or
+            functions (comprising the model) to run sequentially.
+        segments: Number of chunks to create in the model
+        input: A Tensor that is input to :attr:`functions`
+        preserve_rng_state(bool, optional):  Omit stashing and restoring
+            the RNG state during each checkpoint.
+            Default: ``True``
+        use_reentrant(bool):
+            specify whether to use the activation checkpoint variant that
+            requires reentrant autograd. This parameter should be passed
+            explicitly. In version 2.5 we will raise an exception if
+            ``use_reentrant`` is not passed. If ``use_reentrant=False``,
+            ``checkpoint`` will use an implementation that does not require
+            reentrant autograd. This allows ``checkpoint`` to support additional
+            functionality, such as working as expected with
+            ``torch.autograd.grad`` and support for keyword arguments input into
+            the checkpointed function.
+
+    Returns:
+        Output of running :attr:`functions` sequentially on :attr:`*inputs`
+
+    Example:
+        >>> # xdoctest: +SKIP("stub")
+        >>> model = nn.Sequential(...)
+        >>> input_var = checkpoint_sequential(model, chunks, input_var)
+    """
+    if use_reentrant is None:
+        warnings.warn(
+            "torch.utils.checkpoint.checkpoint_sequential: the use_reentrant "
+            "parameter should be passed explicitly. "
+            "In version 2.5 we will raise an exception if use_reentrant "
+            "is not passed. use_reentrant=False is "
+            "recommended, but if you need to preserve the current default "
+            "behavior, you can pass use_reentrant=True. Refer to docs for more "
+            "details on the differences between the two variants."
+        )
+        use_reentrant = True
+
+    # Hack for keyword-only parameter in a python 2.7-compliant way
+    preserve = kwargs.pop("preserve_rng_state", True)
+    if kwargs:
+        raise ValueError(
+            "Unexpected keyword arguments: " + ",".join(arg for arg in kwargs)
+        )
+
+    def run_function(start, end, functions):
+        def forward(input):
+            for j in range(start, end + 1):
+                input = functions[j](input)
+            return input
+
+        return forward
+
+    if isinstance(functions, torch.nn.Sequential):
+        functions = list(functions.children())
+
+    segment_size = len(functions) // segments
+    # the last chunk has to be non-volatile
+    end = -1
+    for start in range(0, segment_size * (segments - 1), segment_size):
+        end = start + segment_size - 1
+        input = checkpoint(
+            run_function(start, end, functions),
+            input,
+            use_reentrant=use_reentrant,
+            preserve_rng_state=preserve,
+        )
+    return run_function(end + 1, len(functions) - 1, functions)(input)
+
+
+def _internal_assert(cond):
+    if not cond:
+        raise AssertionError(
+            "Something went unexpectedly wrong in activation checkpoint. "
+            "Please report this bug by filing an issue to PyTorch."
+        )
+
+
+# NOTE [ Nestable Checkpoint ]
+#
+# The semantics of nested checkpoint can be defined by two basic rules.
+# Following the two rules leads to an important implication that is central
+# to motivating the design.
+#
+# Rule 1. Saved tensors are managed by inner-most checkpoint only and hidden
+#         from any outer layers of checkpoint.
+#
+# Rule 2. The inputs of inner checkpoints are treated as tensors saved to its
+#         parent checkpoint.
+#
+# Implication: To recompute any given saved tensor, we need to recompute all of
+#              the checkpoints wrapping it.
+#
+# Why is this implied? To unpack a saved tensor X during backward we need to
+# recompute the inner-most checkpoint (#1), and in order to recompute that
+# checkpoint I need to have its inputs, which are managed by that checkpoint's
+# parent (#2), which thus also needs to be recomputed first. Continue this line
+# of reasoning and we realize that in order to unpack X, all checkpoints that
+# were active at the time X was saved need to be recomputed. (unless we have
+# already done so in that backward for some other saved tensor).
+#
+# In practice, we use a noop autograd Function to save inputs as saved tensors.
+# During unpack calling ctx.saved_tensor triggers the parent checkpoint to
+# recompute.
+#
+# Rule 3. We should start recomputation as if there are no checkpoints currently
+#         active. Checkpoints encountered during recomputation are still
+#         respected.
+#
+# When we start recomputation, we push the saved variable hook meant for
+# recomputation on the stack. See examples in Rule 6 for more context.
+#
+#                                  * * * *
+#
+# Beyond the basic semantics specific to nested checkpoint, we impose several
+# more constraints that may apply to checkpointing in general.
+#
+# Rule 4. Lifetime of recomputed tensors
+#
+#         Recomputed tensors are considered specific to particular invocations
+#         of backward and are always cleared immediately as they are unpacked
+#         Particularly, we require this to happen even if retain_graph=True.
+#
+# [ Implementation details of Rule 4 ]
+#
+# If we were okay with recomputed tensors staying alive after backward is run
+# with retain_graph=True, we would store recomputed variables as the values of a
+# WeakKeyDictionary and pack strong references to the keys, so that as we
+# backward, those packed keys would be cleared as long as retain_graph=False.
+# Clearing the packed key clears the corresponding entry in the WKD.
+#
+# If we wish recomputed variables to be immediately cleared as we unpack them in
+# the retain_graph=True case, we cannot rely on the packed keys to be cleared by
+# backward automatically. Instead of packing the strong reference to the key
+# directly, we pack a container object, which we manually clear as we unpack.
+#
+# An important detail is that if a second backward happens, the second
+# recomputation needs to reset the container with a newly created key.
+#
+# Rule 5. Stop recomputation as soon as we've recomputed the saved tensors we
+#         know we need.
+#
+# [ Implementation details of Rule 5 ]
+#
+# During recomputation, raise an exception if the number of recomputed tensors
+# matches the number of tensors that we expected to recompute. We wrap the
+# recomputation call with a try-catch to catch this specific exception. See
+# Rule #6 below for some examples.
+#
+# Rule 6. We support doing backward inside checkpoint context
+#
+# [ retain_graph is True]
+#
+# def fn(x):
+#   y = x.sin()
+#   z = y.cos()
+#   gx, = torch.autograd.grad(z, x, retains_grad=True)
+#   return gx, z
+#
+# out = checkpoint(fn)(inp)
+# out.backward()
+#
+# Because z is saved by cos while checkpoint is enabled, it would not be
+# actually saved, and so the .grad() call inside must trigger a recomputation.
+#
+# During recomputation the "inner pack hook" has two responsibilities:
+#
+# 1) As usual, populating the WeakKeyDictionary storing recomputed tensors
+# 2) Pack the actual tensor (detached) so that one may perform backward on the
+#    recomputed graph. The tensors saved to this graph will live until the end
+#    of recomputation, or die earlier if someone performs backward with
+#    retain_graph=False.
+#
+# More generally performing backward on the recomputed graph occurs in the
+# following cases:
+# - If backward is performed inside forward,
+#   - During the original forward IF early-stop is disabled
+#   - During the original backward
+# - If there are multiple .grad()/.backward() calls, we would perform backward
+#   on the recomputed graph even if early-stop is enabled (see the example below)
+#
+# [ retain_graph is False ]
+#
+# The example below shows what happens if during recomputation we find that some
+# of the tensors we are trying to recompute have already been cleared.
+#
+# Spoiler: we don't do anything special, we just skip over them!
+#
+# def fn(x):
+#   y = x.sin()                           # (1)
+#   z = y.cos()                           # (2)
+#   gx, = torch.autograd.grad(z, x)       # (3)
+#   return x.cos() * gx                   # (4)
+#
+# out = checkpoint(fn)(inp)
+# out.backward()                          # (5)
+#
+# 1, 2. Don't save x and y since we are inside a checkpoint.
+# 3. Trigger a recompute of fn since x and y weren't saved.
+#    And depending on whether early stop is enabled, either stop at (2) or
+#    continue running the function.
+#    Because we are running backward with retain_graph=False, we clear x and y's
+#    holders.
+# 4. Don't save x since we are inside a checkpoint.
+# 5. Calling backward triggers another recompute of fn. During recompute, we see
+#    that x and y have already been cleared in the original graph as indicated
+#    by holder=None. We skip over them. We still save x at (4) (since its holder
+#    is still alive.)
+
+_enable_checkpoint_early_stop = True
+
+
+@contextlib.contextmanager
+def set_checkpoint_early_stop(enable: bool):
+    """Context manager that sets whether checkpoint should stop recomputation early.
+
+    By default, non-reentrant checkpoint stops recomputation as soon as it
+    has computed all needed Tensors. This context manager can be used to disable
+    that feature if it is problematic for your specific application.
+
+    This context manager only needs to be active when forward is run. It does
+    not need to be active during backward.
+
+    Example::
+
+    >>> # xdoctest: +SKIP(failing)
+    >>> message = "saved tensors default hooks are disabled"
+    >>> with set_checkpoint_early_stop(False):
+    ...     # Any checkpoint under this context manager will respect this
+    ...     # context manager, even if its backward is performed outside.
+    ...     out = checkpoint(fn, inputs)
+    ...
+    >>> out.backward()
+    """
+    global _enable_checkpoint_early_stop
+    try:
+        prev = _enable_checkpoint_early_stop
+        _enable_checkpoint_early_stop = enable
+        yield
+    finally:
+        _enable_checkpoint_early_stop = prev
+
+
+class _Handle:
+    pass
+
+
+class _Holder:
+    def __init__(self):
+        self.handles: Dict[int, Optional[_Handle]] = {}
+
+
+class _NoopSaveInputs(torch.autograd.Function):
+    @staticmethod
+    def forward(*args):
+        return torch.empty((0,))
+
+    @staticmethod
+    def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None:
+        # Only tensors can be saved with ctx.save_for_backward, everything else
+        # is captured by get_args, which is saved directly on ctx
+        tensor_indices, tensors = zip(
+            *[(i, o) for i, o in enumerate(inputs) if isinstance(o, torch.Tensor)]
+        )
+        idx2saved_idx = {b: a for a, b in enumerate(tensor_indices)}
+        # args but with tensors replaced with None as placeholders
+        args = [None if isinstance(o, torch.Tensor) else o for o in inputs]
+
+        def get_args(saved_tensors):
+            # restore the placeholders with the original tensors grabbed from
+            # ctx.saved_tensors (which may be saved on a parent checkpoint if
+            # this checkpoint is nested, and that would trigger a recursive
+            # unpack!)
+            ret = [
+                saved_tensors[idx2saved_idx[i]] if i in tensor_indices else o
+                for i, o in enumerate(args)
+            ]
+            # grab the tail since we also saved the dummy to avoid having to explicitly
+            # handle the case where there are no tensor inputs
+            return ret[1:]
+
+        ctx.get_args = get_args
+        ctx.save_for_backward(*tensors)
+
+    @staticmethod
+    def backward(ctx, *grad_outputs):
+        raise AssertionError("Did not expect to backward on this graph")
+
+
+class _CheckpointFrame:
+    def __init__(self, recompute_fn, early_stop, unpack_error_cb, metadata_fn):
+        self.recompute_fn = recompute_fn
+        self.input_saver = None
+        self.weak_holders: List[ReferenceType] = []
+        # We store this as a weakkeydictionary so that in the case of a partial
+        # backward, the entries in the dict are cleared alongside the Holder
+        # which will be removed when the SavedVariable is cleared.
+        self.recomputed: DefaultDict[
+            int, weakref.WeakKeyDictionary[_Handle, torch.Tensor]
+        ] = defaultdict(weakref.WeakKeyDictionary)
+        # We need both recomp_counter and recomputed since they can diverge
+        # https://github.com/pytorch/pytorch/pull/90105#discussion_r1135889885
+        self.recomp_counter: DefaultDict[int, int] = defaultdict(int)
+        self.is_recomputed: DefaultDict[int, bool] = defaultdict(bool)
+
+        # See Rule 5
+        self.early_stop = early_stop
+
+        # Debugging
+        self.metadata_fn = metadata_fn
+        self.unpack_error_cb = unpack_error_cb
+        self.x_metadatas = []
+        self.forward_completed = False
+        self.ignore_saved_mismatch = False
+
+    def check_recomputed_tensors_match(self, gid):
+        if self.ignore_saved_mismatch:
+            # TODO: we can probably make this check stricter by checking that
+            #       the metadata of the first tensors still match.
+            return
+        # NOTE [ Error handling for checkpoint ]
+        #
+        # At a high level, we need to check that the tensors saved
+        # during original forward matches tensors saved during recompute
+        # This means handling 3 cases:
+        #
+        # 1. During recompute, more tensors were saved.
+        #
+        #    Usually this is hidden due to the StopRecomputationError
+        #    but if early stop is not enabled, or we would have errored
+        #    anyway because there aren't enough weak_holders. But we
+        #    do want to have a nice error. See the _recomputation_hook
+        #    for details.
+        if not len(self.weak_holders) == self.recomp_counter[gid]:
+            # 2. During recompute, fewer tensors were saved
+            #
+            # We know that everytime we save something do original forward
+            # we append to weak_holder, and every time we save a tensor
+            # during recompute we increment recompute_counter.
+            raise CheckpointError(
+                "torch.utils.checkpoint: A different number of tensors was saved "
+                "during the original forward and recomputation.\n"
+                f"Number of tensors saved during forward: {len(self.weak_holders)}\n"
+                f"Number of tensors saved during recomputation: {self.recomp_counter[gid]}"
+            )
+
+        # 3. During recompute, the same tensors were saved, but they
+        #    have different metadata
+        nb_meta_different = []
+        for idx, weak_holder in enumerate(self.weak_holders):
+            holder = weak_holder()
+            if holder is None:
+                continue
+            # We've seen all holders since we iterate over them in order
+            # For every holder that is still alive now, it must've been
+            # alive when we saw it during recompute, therefore, the
+            # gid must be set.
+            _internal_assert(gid in holder.handles)
+            # We know this is the first unpack, so it couldn't have been set
+            # to None yet.
+            _internal_assert(holder.handles[gid] is not None)
+            # We always set these together in the recomputation hook
+            _internal_assert(holder.handles[gid] in self.recomputed[gid])
+            # see pack hook, x_metadata is 1:1 with weak_holders.
+            x_meta = self.x_metadatas[idx]
+            recomputed_x = self.recomputed[gid][holder.handles[gid]]
+            if x_meta != self.metadata_fn(recomputed_x):
+                nb_meta_different.append((idx, x_meta, self.metadata_fn(recomputed_x)))
+
+        if len(nb_meta_different) > 0:
+            mismatched_tensors = ""
+            for idx, x_meta, recomputed_meta in nb_meta_different:
+                mismatched_tensors += (
+                    f"tensor at position {idx}:\n"
+                    f"saved metadata: {x_meta}\n"
+                    f"recomputed metadata: {recomputed_meta}\n"
+                )
+            raise CheckpointError(
+                "torch.utils.checkpoint: Recomputed values for the following tensors "
+                "have different metadata than during the forward pass.\n"
+                f"{mismatched_tensors}"
+            )
+
+
+_checkpoint_error_template = """ \
+An error happened while unpacking tensors; dumping logs of latest computation
+because you passed `debug=True` to `torch.utils.checkpoint.checkpoint()`.
+Scroll all the way down for guidance on how to navigate these logs.
+
++~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+
+|        1. Stack traces of the operators that ran in the original forward     |
++------------------------------------------------------------------------------+
+
+{forward_traces}
++~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+
+|        2. Stack traces of the operators that ran during recomputation        |
++------------------------------------------------------------------------------+
+
+{recompute_traces}
++~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+
+|       3. Log of operators in the original forward and recomputation          |
++------------------------------------------------------------------------------+
+(Scroll up to correlate stack traces with each operation listed below. This
+ helps identify their source in the code.)
+
+IMPORTANT: Differences in "detach" calls between the original forward and the
+           recomputation are expected. They are introduced by the checkpointing
+           mechanism and can be ignored.
+
+Operations executed during the original forward:
+
+{forward_ops}
+
+Operations executed during recomputation:
+
+{recompute_ops}
+
++------------------------------------------------------------------------------+
+ ERROR: Detected non-determinism while running activation checkpointing
+
+ You are seeing this error because you passed `debug=True` to checkpoint and
+ tensors to be saved during the original forward and differ between those saved
+ during recomputation. This can happen if different operators were ran in the
+ original forward and in the recomputation.
+
+ To identify where the mismatch may be coming from, you can do the following:
+
+ 1) Compare the operators ran during original forward and recomputation to
+    see where they differ. These operators are printed above in the order they
+    were executed.
+
+ 2) Review the stack trace for each operator to locate its invocation source.
+    Each operator's stack trace is printed in their execution order.
+
+ Note that the logs can be quite long. Here's how they are structured:
+ (Tip: you can Ctrl-f for these headers)
+
+ 1. Stack traces of the operators that ran in the original forward
+ 2. Stack traces of the operators that ran during recomputation
+ 3. Log of operators in the original forward and recomputation
+ 4. Error message                                             <--- You are here
+--------------------------------------------------------------------------------
+"""
+
+class CheckpointError(RuntimeError):
+    pass
+
+
+def _get_debug_context_and_cb() -> Tuple[Callable[[], Any], Callable[[CheckpointError], None]]:
+    # This function returns the context_fn and error_cb to be used by the
+    # checkpointing mechanism. error_cb is invoked when an error is detected
+    # during unpack.
+
+    # record_context_cpp is not support on non-linux non-x86_64 platforms
+    cpp_tb = platform.machine() == 'x86_64' and platform.system() == 'Linux'
+
+    class CaptureLogs:
+        def __init__(self):
+            self.logs = None
+            self.tbs = None
+
+        def get_context_manager(self):
+            @contextlib.contextmanager
+            def logging_mode():
+                with LoggingTensorMode(), \
+                     capture_logs(True, python_tb=True, script_tb=True, cpp_tb=cpp_tb) as logs_and_tb:
+                    self.logs, self.tbs = logs_and_tb
+                    yield logs_and_tb
+            return logging_mode()
+
+    capture_logs_fwd = CaptureLogs()
+    capture_logs_recompute = CaptureLogs()
+
+    def unpack_error_cb(e: CheckpointError):
+        def get_str_tb(label, capture_logs):
+            out = ""
+            total_len = len(capture_logs.logs)
+            for i, (log, tb) in enumerate(zip(capture_logs.logs, capture_logs.tbs)):
+                out += f"{log}   ({i + 1} of {total_len} in {label})\n\n"
+                found_torch_dispatch = False
+                for line in tb:
+                    # Start printing stack trace only after __torch_dispatch__ is found
+                    is_torch_dispatch = line['name'] == '__torch_dispatch__'
+                    if not found_torch_dispatch and not is_torch_dispatch:
+                        continue
+                    elif is_torch_dispatch:
+                        found_torch_dispatch = True
+                        continue
+                    out += f"{line['filename']}:{line['line']}:{line['name']}\n"
+                out += "\n\n"
+            return out
+        assert capture_logs_fwd.logs is not None
+        assert capture_logs_recompute.logs is not None
+        raise CheckpointError(
+            _checkpoint_error_template.format(
+                forward_traces=get_str_tb("original", capture_logs_fwd),
+                recompute_traces=get_str_tb("recompute", capture_logs_recompute),
+                forward_ops="\n".join(capture_logs_fwd.logs),
+                recompute_ops="\n".join(capture_logs_recompute.logs)
+            )
+        ) from e
+
+    def context_fn():
+        return capture_logs_fwd.get_context_manager(), capture_logs_recompute.get_context_manager()
+
+    return context_fn, unpack_error_cb
+
+def _default_meta_extractor(x: torch.Tensor) -> Dict[str, Any]:
+    # These properties are fast to check, easy to understand
+    return {
+        "shape": x.shape,
+        "dtype": x.dtype,
+        "device": x.device
+    }
+
+_allowed_determinism_checks_to_fns: Dict[str, Callable[[torch.Tensor], Any]] = {
+    _DEFAULT_DETERMINISM_MODE: _default_meta_extractor,
+    "none": lambda _: None,
+}
+
+# See Rule 5
+class _StopRecomputationError(Exception):
+    pass
+
+
+class _recomputation_hook(torch.autograd.graph.saved_tensors_hooks):
+    def __init__(self, target_frame_ref: ReferenceType, gid: int):
+        def pack_hook(x):
+            x = x.detach() if x.requires_grad else x
+            target_frame = target_frame_ref()
+            assert target_frame is not None  # appease mypy
+            recomp_idx = target_frame.recomp_counter[gid]
+            target_frame.recomp_counter[gid] += 1
+
+            if recomp_idx >= len(target_frame.weak_holders):
+                assert not target_frame.early_stop
+                if not target_frame.forward_completed:
+                    # We run into this case when early stop is not enabled and do
+                    # grad within checkpoint.
+                    # We need to set this flag, so we don't error out later when
+                    # we check if the number of tensors saved during forward and
+                    # recomputation match.
+                    target_frame.ignore_saved_mismatch = True
+                    return x
+                raise CheckpointError(
+                    "torch.utils.checkpoint: trying to save more tensors during "
+                    "recomputation than during the original forward pass."
+                )
+
+            holder = target_frame.weak_holders[recomp_idx]()
+
+            # This holder may have been cleared because someone may have called
+            # backward within forward. If so, we don't need to save.
+            if holder is not None:
+                _internal_assert(holder.handles.get(gid, None) is None)
+                holder.handles[gid] = _Handle()
+                target_frame.recomputed[gid][holder.handles[gid]] = x
+
+            if target_frame.early_stop and target_frame.recomp_counter[gid] == len(
+                target_frame.weak_holders
+            ):
+                raise _StopRecomputationError
+            # See Rule 6: [ retain_graph is True ] above
+            return x
+
+        def unpack_hook(x):
+            # See Rule 6: [ retain_graph is True ] above for an example of when
+            # the graph created during recomputation could be backwarded.
+            return x
+
+        super().__init__(pack_hook, unpack_hook)
+
+
+class _checkpoint_hook(torch.autograd.graph.saved_tensors_hooks):
+    def __init__(self, frame):
+        def pack_hook(x):
+            # See Rule 4 above
+            holder = _Holder()
+            frame.weak_holders.append(weakref.ref(holder))
+            # Save metadata to detect non-determinism
+            if frame.metadata_fn is not None:
+                with torch.no_grad():
+                    frame.x_metadatas.append(frame.metadata_fn(x))
+            return holder
+
+        def unpack_hook(holder):
+            gid = torch._C._current_graph_task_id()
+            if gid == -1:
+                # generate a temporary id if we trigger unpack outside of a backward call
+                gid = int(uuid.uuid4())
+
+            if not frame.is_recomputed[gid]:
+                ctx = frame.input_saver.grad_fn
+                args = ctx.get_args(ctx.saved_tensors)
+
+                try:
+                    with _recomputation_hook(
+                        weakref.ref(frame), gid
+                    ), torch.autograd.enable_grad():
+                        frame.recompute_fn(*args)
+                except _StopRecomputationError:
+                    pass
+                frame.is_recomputed[gid] = True
+                frame.check_recomputed_tensors_match(gid)
+
+            _internal_assert(gid in holder.handles)
+
+            if holder.handles[gid] is None:
+                raise CheckpointError(
+                    "torch.utils.checkpoint: Unpack is being triggered for a tensor that was already "
+                    "unpacked once. If you are calling ctx.saved_tensors in backward, make sure to do "
+                    "so only once. Otherwise please open an issue with details on your use case."
+                )
+            _internal_assert(holder.handles[gid] in frame.recomputed[gid])
+            ret = frame.recomputed[gid][holder.handles[gid]]
+            holder.handles[gid] = None
+            return ret
+
+        if frame.unpack_error_cb is not None:
+            def unpack_hook_with_error_cb(holder):
+                try:
+                    return unpack_hook(holder)
+                except CheckpointError as e:
+                    frame.unpack_error_cb(e)
+            super().__init__(pack_hook, unpack_hook_with_error_cb)
+        else:
+            super().__init__(pack_hook, unpack_hook)
+
+
+def _is_compiling(func, args, kwargs):
+    # Check if we are under AOTAutograd tracing
+    # There should probably be a better way to do this...
+    # TODO: unify _is_compiling across all compile stacks
+    for arg in args:
+        if isinstance(arg, torch.Tensor) and is_fun(arg):
+            return True
+    return False
+
+
+class _VersionWrapper:
+    # Check that cached tensors are not mutated.
+    def __init__(self, val):
+        self.val: Union[torch.Tensor, Any] = val
+        self.version: Optional[int] = val._version if isinstance(val, torch.Tensor) else None
+
+    def get_val(self, allow_cache_entry_mutation):
+        if self.version is not None and not allow_cache_entry_mutation:
+            if self.val._version != self.version:
+                # Can we give user a stack trace of where the mutation happened?
+                raise RuntimeError(
+                    "Tensor cached during selective activation checkpoint has been mutated"
+                )
+        return self.val
+
+
+def _maybe_detach(x, any_ret_has_alias_info):
+    # We detach for two separate reasons:
+    # - For view ops, we need to ensure that when the tensor is returned from
+    #   CachedDispatchMode, as_view sees that the AutogradMeta is nullptr
+    # - Avoid reference cycles
+    # For case 1, it is not enough to check whether x has differentiable dtype
+    # because non-differentiable dtype can have non-nullptr AutogradMeta, e.g.
+    # when the tensor is a view.
+    if isinstance(x, torch.Tensor) and (x.is_floating_point() or x.is_complex() or any_ret_has_alias_info):
+        with torch._C._SetExcludeDispatchKeyGuard(torch._C.DispatchKey.ADInplaceOrView, False):
+            # Ensure that view performed beneath autograd properly propagates
+            # version counter. TODO: Use reentrant_dispatch instead of
+            # manually manipulating dispatch keys. Using reentrant_dispatch
+            # would respect inference_mode, though that is not relevant for
+            # this case.
+            x = x.detach()
+    return x
+
+
+class SelectiveCheckpointContext:
+    """
+    Context passed to policy function during selective checkpointing.
+
+    This class is used to pass relevant metadata to the policy function during
+    selective checkpointing. The metadata includes whether the current invocation
+    of the policy function is during recomputation or not.
+
+    Example:
+        >>> # xdoctest: +SKIP(stub)
+        >>>
+        >>> def policy_fn(ctx, op, *args, **kwargs):
+        >>>    print(ctx.is_recompute)
+        >>>
+        >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
+        >>>
+        >>> out = torch.utils.checkpoint.checkpoint(
+        >>>     fn, x, y,
+        >>>     use_reentrant=False,
+        >>>     context_fn=context_fn,
+        >>> )
+    """
+    def __init__(self, *, is_recompute):
+        self.is_recompute = is_recompute
+
+
+class CheckpointPolicy(enum.Enum):
+    """
+    Enum for specifying the policy for checkpointing during backpropagation.
+
+    The following policies are supported:
+
+    - ``{MUST,PREFER}_SAVE``: The operation's output will be saved during the forward
+      pass and will not be recomputed during the backward pass
+    - ``{MUST,PREFER}_RECOMPUTE``: The operation's output will not be saved during the
+      forward pass and will be recomputed during the backward pass
+
+    Use ``MUST_*`` over ``PREFER_*`` to indicate that the policy should not be overridden
+    by other subsystems like `torch.compile`.
+
+    .. note::
+        A policy function that always returns ``PREFER_RECOMPUTE`` is
+        equivalent to vanilla checkpointing.
+
+        A policy function that returns ``PREFER_SAVE`` every op is
+        NOT equivalent to not using checkpointing. Using such a policy would
+        save additional tensors not limited to ones that are actually needed for
+        gradient computation.
+    """
+    MUST_SAVE = 0
+    PREFER_SAVE = 1
+    MUST_RECOMPUTE = 2
+    PREFER_RECOMPUTE = 3
+
+
+def _policy_from_bool(b):
+    # For backward compatability
+    return CheckpointPolicy.MUST_SAVE if b else CheckpointPolicy.PREFER_RECOMPUTE
+
+
+SAC_IGNORED_OPS = {
+    # AC inserts different number of detach during forward and recompute.
+    torch.ops.aten.detach.default,
+    # AC's determinism check invokes additional metadata ops during forward.
+    # With subclasses involved, these metadata ops become dispatchable, this
+    # can result in incorrectness if these ops are selected cached.
+    torch.ops.prim.device.default,
+} | set(torch._subclasses.functional_tensor.FunctionalTensor.metadata_fns)
+
+
+class _CachingTorchDispatchMode(TorchDispatchMode):
+    # Used together with _CachedTorchDispatchMode to implement SAC.
+    def __init__(self, policy_fn, storage):
+        self.policy_fn = policy_fn
+        self.storage = storage
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if func in SAC_IGNORED_OPS:
+            return func(*args, **kwargs)
+
+        kwargs = {} if kwargs is None else kwargs
+        policy = self.policy_fn(SelectiveCheckpointContext(is_recompute=False),
+                                func, *args, **kwargs)
+        if isinstance(policy, bool):
+            policy = _policy_from_bool(policy)
+
+        is_compiling = _is_compiling(func, args, kwargs)
+
+        if is_compiling:
+            # Overwrite each node's "recompute" tag to add in the user annotation.
+            fx_traceback.current_meta["recompute"] = policy
+
+        out = func(*args, **kwargs)
+
+        any_ret_has_alias_info = any(ret.alias_info is not None for ret in func._schema.returns)
+
+        if policy in (CheckpointPolicy.MUST_SAVE, CheckpointPolicy.PREFER_SAVE) or is_compiling:
+            self.storage[func].append(tree_map(lambda x: _VersionWrapper(_maybe_detach(x, any_ret_has_alias_info)), out))
+        return out
+
+class _CachedTorchDispatchMode(TorchDispatchMode):
+    # Used together with _CachedTorchDispatchMode to implement SAC.
+    def __init__(self, policy_fn, storage, allow_cache_entry_mutation):
+        self.policy_fn = policy_fn
+        self.storage = storage
+        self.allow_cache_entry_mutation = allow_cache_entry_mutation
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if func in SAC_IGNORED_OPS:
+            return func(*args, **kwargs)
+
+        kwargs = {} if kwargs is None else kwargs
+        policy = self.policy_fn(SelectiveCheckpointContext(is_recompute=True),
+                                func, *args, **kwargs)
+        if isinstance(policy, bool):
+            policy = _policy_from_bool(policy)
+
+        is_compiling = _is_compiling(func, args, kwargs)
+
+        if policy in (CheckpointPolicy.MUST_SAVE, CheckpointPolicy.PREFER_SAVE) or is_compiling:
+            storage = self.storage.get(func)
+            if storage is None:
+                raise RuntimeError(f"{func} encountered during backward, but not found in storage")
+            if len(storage) == 0:
+                raise RuntimeError(
+                    "Trying to backward an extra time. You are only allowed to backward once "
+                    "on any region computed under selective activation checkpoint."
+                )
+            out = tree_map(lambda x: x.get_val(self.allow_cache_entry_mutation), storage.pop(0))
+        else:
+            out = func(*args, **kwargs)
+        return out
+
+
+def create_selective_checkpoint_contexts(policy_fn_or_list, allow_cache_entry_mutation=False):
+    """
+    Helper to avoid recomputing certain ops during activation checkpointing.
+
+    Use this with `torch.utils.checkpoint.checkpoint` to control which
+    operations are recomputed during the backward pass.
+
+    Args:
+        policy_fn_or_list (Callable or List):
+          - If a policy function is provided, it should accept a
+            :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and
+            kwargs to the op, and return a :class:`CheckpointPolicy` enum value
+            indicating whether the execution of the op should be recomputed or not.
+          - If a list of operations is provided, it is equivalent to a policy
+            returning `CheckpointPolicy.MUST_SAVE` for the specified
+            operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other
+            operations.
+        allow_cache_entry_mutation (bool, optional): By default, an error is
+            raised if any tensors cached by selective activation checkpoint are
+            mutated in order to ensure correctness. If set to `True`, this check
+            is disabled.
+    Returns:
+        A tuple of two context managers.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(LINUX)
+        >>> import functools
+        >>>
+        >>> x = torch.rand(10, 10, requires_grad=True)
+        >>> y = torch.rand(10, 10, requires_grad=True)
+        >>>
+        >>> ops_to_save = [
+        >>>    torch.ops.aten.mm.default,
+        >>> ]
+        >>>
+        >>> def policy_fn(ctx, op, *args, **kwargs):
+        >>>    if op in ops_to_save:
+        >>>        return CheckpointPolicy.MUST_SAVE
+        >>>    else:
+        >>>        return CheckpointPolicy.PREFER_RECOMPUTE
+        >>>
+        >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
+        >>>
+        >>> # or equivalently
+        >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
+        >>>
+        >>> def fn(x, y):
+        >>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
+        >>>
+        >>> out = torch.utils.checkpoint.checkpoint(
+        >>>     fn, x, y,
+        >>>     use_reentrant=False,
+        >>>     context_fn=context_fn,
+        >>> )
+    """
+    # NB: If grad_mode is disabled, checkpoint would not run forward under
+    #     context_fn anyway, so proceed as usual.
+    if isinstance(policy_fn_or_list, list):
+        for op in policy_fn_or_list:
+            if not isinstance(op, torch._ops.OpOverload):
+                _extra_msg = (
+                    "Please update the OpOverloadPacket to a specific OpOverload."
+                    "For example, if you have `torch.ops.aten.mm`, change it to `torch.ops.aten.mm.default`."
+                ) if isinstance(op, torch._ops.OpOverloadPacket) else ""
+                raise ValueError(
+                    f"Expected op in `op_list` to be an OpOverload but got: {op} "
+                    f"of type {type(op)}. {_extra_msg}"
+                )
+
+        def policy_fn(ctx, op, *args, **kwargs):
+            if op in policy_fn_or_list:
+                return CheckpointPolicy.MUST_SAVE
+            else:
+                return CheckpointPolicy.PREFER_RECOMPUTE
+    elif callable(policy_fn_or_list):
+        policy_fn = policy_fn_or_list
+    else:
+        raise TypeError("policy_fn_or_list must be either a function or a list of ops.")
+
+    storage: Dict[Any, List[Any]] = defaultdict(list)
+    return (
+        _CachingTorchDispatchMode(policy_fn, storage),
+        _CachedTorchDispatchMode(policy_fn, storage, allow_cache_entry_mutation),
+    )
+
+# NB: this helper wraps fn before calling checkpoint_impl. kwargs and
+#     saving/restoring of global state is handled here.
+
+def _checkpoint_without_reentrant_generator(
+    fn,
+    preserve_rng_state=True,
+    context_fn: Callable[[], Tuple[ContextManager, ContextManager]] = noop_context_fn,
+    determinism_check: str = _DEFAULT_DETERMINISM_MODE,
+    debug: bool = False,
+    *args,
+    **kwargs
+):
+    """Checkpointing without reentrant autograd.
+
+    Args:
+        fn: describes what to run in the forward pass of the model or
+            part of the model. It should also know how to handle the inputs
+            passed as the tuple. For example, in LSTM, if user passes
+            ``(activation, hidden)``, :attr:`function` should correctly use the
+            first input as ``activation`` and the second input as ``hidden``
+        preserve_rng_state(bool, optional):  Omit stashing and restoring
+            the RNG state during each checkpoint.
+            Default: ``True``
+        context_fn(Callable, optional): A callable returning a tuple of two
+            context managers. The function and its recomputation will be run
+            under the first and second context managers respectively.
+        determinism_check(str, optional): A string specifying the determinism
+            check to perform. By default it is set to ``"default"`` which
+            compares the shapes, dtypes, and devices of the recomputed tensors
+            against those the saved tensors. To turn off this check, specify
+            ``"none"``. Currently these are the only two supported values.
+            Please open an issue if you would like to see more determinism
+            checks.
+        debug(bool, optional): If ``True``, error messages will also include
+            a trace of the operators ran during the original forward computation
+            as well as the recomputation.
+        *args: Arguments to pass in to the given ``function``.
+        **kwargs: Keyword arguments to pass into the given ``function``.
+    """
+    unpack_error_cb = None
+
+    if _checkpoint_debug_enabled if _checkpoint_debug_enabled is not None else debug:
+        if context_fn != noop_context_fn:
+            raise ValueError(
+                "debug=True is incompatible with non-default context_fn"
+            )
+        context_fn, unpack_error_cb = _get_debug_context_and_cb()
+
+    if determinism_check in _allowed_determinism_checks_to_fns:
+        metadata_fn = _allowed_determinism_checks_to_fns[determinism_check]
+    else:
+        raise ValueError(
+            f"determinism_check should be one of {list(_allowed_determinism_checks_to_fns.keys())}, "
+            f"but got {determinism_check}"
+        )
+
+    device_type = _infer_device_type(*args)
+    device_module = _get_device_module(device_type)
+    forward_context, recompute_context = context_fn()
+    if _is_compiling(fn, args, kwargs) and context_fn != noop_context_fn:
+        assert (
+            isinstance(forward_context, TorchDispatchMode) and
+            isinstance(recompute_context, TorchDispatchMode)
+        ), \
+            "In torch.compile mode, `context_fn` arg passed to `torch.utils.checkpoint` " + \
+            "must generate a tuple of two `TorchDispatchMode`s."
+    # Accommodates the (remote) possibility that autocast is enabled for cpu AND gpu.
+    device_autocast_kwargs, cpu_autocast_kwargs = _get_autocast_kwargs(device_type=device_type)
+
+    if preserve_rng_state:
+        fwd_cpu_state = torch.get_rng_state()
+        # Don't eagerly initialize the cuda context by accident.
+        # (If the user intends that the context is initialized later, within their
+        # run_function, we SHOULD actually stash the cuda state here.  Unfortunately,
+        # we have no way to anticipate this will happen before we run the function.
+        # If they do so, we raise an error.)
+        had_device_in_fwd = False
+        if getattr(device_module, "_initialized", False):
+            had_device_in_fwd = True
+            fwd_devices, fwd_device_states = get_device_states(*args)
+
+    def recompute_fn(*inputs):
+        kwargs, *args = inputs
+        # This will be called later during recomputation. This wrapping enables
+        # the necessary global state to be captured.
+        rng_devices = []
+        if preserve_rng_state and had_device_in_fwd:
+            rng_devices = fwd_devices
+        with torch.random.fork_rng(
+            devices=rng_devices, enabled=preserve_rng_state, device_type=device_type
+        ):
+            if preserve_rng_state:
+                torch.set_rng_state(fwd_cpu_state)
+                if had_device_in_fwd:
+                    set_device_states(fwd_devices, fwd_device_states, device_type=device_type)
+
+            device_autocast_ctx = torch.amp.autocast(
+                device_type=device_type, **device_autocast_kwargs
+            ) if torch.amp.is_autocast_available(device_type) else contextlib.nullcontext()
+            with device_autocast_ctx, torch.amp.autocast("cpu", **cpu_autocast_kwargs), recompute_context:  # type: ignore[attr-defined]
+                fn(*args, **kwargs)
+
+    new_frame = _CheckpointFrame(
+        recompute_fn,
+        _enable_checkpoint_early_stop,
+        unpack_error_cb,
+        metadata_fn
+    )
+    dummy = torch.empty((0,), requires_grad=True)
+    new_frame.input_saver = _NoopSaveInputs.apply(dummy, kwargs, *args)
+
+    # When ambient grad_mode is False
+    if new_frame.input_saver.grad_fn is None:
+        yield
+        return
+
+    with _checkpoint_hook(new_frame), forward_context:
+        yield
+    new_frame.forward_completed = True
+
+    if getattr(device_module, "_initialized", False) and \
+       preserve_rng_state and not had_device_in_fwd:  # type: ignore[possibly-undefined]
+        # Device was not initialized before running the forward, so we didn't
+        # stash the device state.
+        raise RuntimeError(
+            "PyTorch's device state was initialized in the forward pass "
+            "of a Checkpoint, which is not allowed. Please open an issue "
+            "if you need this feature."
+        )
+
+    return
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/collect_env.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/collect_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce397d6090ead9d7d6051187c98dc4b3e76658c5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/collect_env.py
@@ -0,0 +1,692 @@
+# mypy: allow-untyped-defs
+
+# Unlike the rest of the PyTorch this file must be python2 compliant.
+# This script outputs relevant system environment info
+# Run it with `python collect_env.py` or `python -m torch.utils.collect_env`
+import datetime
+import json
+import locale
+import re
+import subprocess
+import sys
+import os
+from collections import namedtuple
+
+
+try:
+    import torch
+    TORCH_AVAILABLE = True
+except (ImportError, NameError, AttributeError, OSError):
+    TORCH_AVAILABLE = False
+
+# System Environment Information
+SystemEnv = namedtuple('SystemEnv', [
+    'torch_version',
+    'is_debug_build',
+    'cuda_compiled_version',
+    'gcc_version',
+    'clang_version',
+    'cmake_version',
+    'os',
+    'libc_version',
+    'python_version',
+    'python_platform',
+    'is_cuda_available',
+    'cuda_runtime_version',
+    'cuda_module_loading',
+    'nvidia_driver_version',
+    'nvidia_gpu_models',
+    'cudnn_version',
+    'pip_version',  # 'pip' or 'pip3'
+    'pip_packages',
+    'conda_packages',
+    'hip_compiled_version',
+    'hip_runtime_version',
+    'miopen_runtime_version',
+    'caching_allocator_config',
+    'is_xnnpack_available',
+    'cpu_info',
+])
+
+COMMON_PATTERNS = [
+    "torch",
+    "numpy",
+    "triton",
+    "optree",
+]
+
+NVIDIA_PATTERNS = [
+    "cuda-cudart",
+    "cuda-cupti",
+    "cuda-libraries",
+    "cuda-opencl",
+    "cuda-nvrtc",
+    "cuda-runtime",
+    "cublas",
+    "cudnn",
+    "cufft",
+    "curand",
+    "cusolver",
+    "cusparse",
+    "nccl",
+    "nvjitlink",
+    "nvtx",
+]
+
+CONDA_PATTERNS = [
+    "cudatoolkit",
+    "soumith",
+    "mkl",
+    "magma",
+]
+
+PIP_PATTERNS = [
+    "mypy",
+    "flake8",
+    "onnx",
+]
+
+
+def run(command):
+    """Return (return-code, stdout, stderr)."""
+    shell = True if type(command) is str else False
+    p = subprocess.Popen(command, stdout=subprocess.PIPE,
+                         stderr=subprocess.PIPE, shell=shell)
+    raw_output, raw_err = p.communicate()
+    rc = p.returncode
+    if get_platform() == 'win32':
+        enc = 'oem'
+    else:
+        enc = locale.getpreferredencoding()
+    output = raw_output.decode(enc)
+    err = raw_err.decode(enc)
+    return rc, output.strip(), err.strip()
+
+
+def run_and_read_all(run_lambda, command):
+    """Run command using run_lambda; reads and returns entire output if rc is 0."""
+    rc, out, _ = run_lambda(command)
+    if rc != 0:
+        return None
+    return out
+
+
+def run_and_parse_first_match(run_lambda, command, regex):
+    """Run command using run_lambda, returns the first regex match if it exists."""
+    rc, out, _ = run_lambda(command)
+    if rc != 0:
+        return None
+    match = re.search(regex, out)
+    if match is None:
+        return None
+    return match.group(1)
+
+def run_and_return_first_line(run_lambda, command):
+    """Run command using run_lambda and returns first line if output is not empty."""
+    rc, out, _ = run_lambda(command)
+    if rc != 0:
+        return None
+    return out.split('\n')[0]
+
+
+def get_conda_packages(run_lambda, patterns=None):
+    if patterns is None:
+        patterns = CONDA_PATTERNS + COMMON_PATTERNS + NVIDIA_PATTERNS
+    conda = os.environ.get('CONDA_EXE', 'conda')
+    out = run_and_read_all(run_lambda, "{} list".format(conda))
+    if out is None:
+        return out
+
+    return "\n".join(
+        line
+        for line in out.splitlines()
+        if not line.startswith("#")
+        and any(name in line for name in patterns)
+    )
+
+def get_gcc_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'gcc --version', r'gcc (.*)')
+
+def get_clang_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'clang --version', r'clang version (.*)')
+
+
+def get_cmake_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'cmake --version', r'cmake (.*)')
+
+
+def get_nvidia_driver_version(run_lambda):
+    if get_platform() == 'darwin':
+        cmd = 'kextstat | grep -i cuda'
+        return run_and_parse_first_match(run_lambda, cmd,
+                                         r'com[.]nvidia[.]CUDA [(](.*?)[)]')
+    smi = get_nvidia_smi()
+    return run_and_parse_first_match(run_lambda, smi, r'Driver Version: (.*?) ')
+
+
+def get_gpu_info(run_lambda):
+    if get_platform() == 'darwin' or (TORCH_AVAILABLE and hasattr(torch.version, 'hip') and torch.version.hip is not None):
+        if TORCH_AVAILABLE and torch.cuda.is_available():
+            if torch.version.hip is not None:
+                prop = torch.cuda.get_device_properties(0)
+                if hasattr(prop, "gcnArchName"):
+                    gcnArch = " ({})".format(prop.gcnArchName)
+                else:
+                    gcnArch = "NoGCNArchNameOnOldPyTorch"
+            else:
+                gcnArch = ""
+            return torch.cuda.get_device_name(None) + gcnArch
+        return None
+    smi = get_nvidia_smi()
+    uuid_regex = re.compile(r' \(UUID: .+?\)')
+    rc, out, _ = run_lambda(smi + ' -L')
+    if rc != 0:
+        return None
+    # Anonymize GPUs by removing their UUID
+    return re.sub(uuid_regex, '', out)
+
+
+def get_running_cuda_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'nvcc --version', r'release .+ V(.*)')
+
+
+def get_cudnn_version(run_lambda):
+    """Return a list of libcudnn.so; it's hard to tell which one is being used."""
+    if get_platform() == 'win32':
+        system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
+        cuda_path = os.environ.get('CUDA_PATH', "%CUDA_PATH%")
+        where_cmd = os.path.join(system_root, 'System32', 'where')
+        cudnn_cmd = '{} /R "{}\\bin" cudnn*.dll'.format(where_cmd, cuda_path)
+    elif get_platform() == 'darwin':
+        # CUDA libraries and drivers can be found in /usr/local/cuda/. See
+        # https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#install
+        # https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac
+        # Use CUDNN_LIBRARY when cudnn library is installed elsewhere.
+        cudnn_cmd = 'ls /usr/local/cuda/lib/libcudnn*'
+    else:
+        cudnn_cmd = 'ldconfig -p | grep libcudnn | rev | cut -d" " -f1 | rev'
+    rc, out, _ = run_lambda(cudnn_cmd)
+    # find will return 1 if there are permission errors or if not found
+    if len(out) == 0 or (rc != 1 and rc != 0):
+        l = os.environ.get('CUDNN_LIBRARY')
+        if l is not None and os.path.isfile(l):
+            return os.path.realpath(l)
+        return None
+    files_set = set()
+    for fn in out.split('\n'):
+        fn = os.path.realpath(fn)  # eliminate symbolic links
+        if os.path.isfile(fn):
+            files_set.add(fn)
+    if not files_set:
+        return None
+    # Alphabetize the result because the order is non-deterministic otherwise
+    files = sorted(files_set)
+    if len(files) == 1:
+        return files[0]
+    result = '\n'.join(files)
+    return 'Probably one of the following:\n{}'.format(result)
+
+
+def get_nvidia_smi():
+    # Note: nvidia-smi is currently available only on Windows and Linux
+    smi = 'nvidia-smi'
+    if get_platform() == 'win32':
+        system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
+        program_files_root = os.environ.get('PROGRAMFILES', 'C:\\Program Files')
+        legacy_path = os.path.join(program_files_root, 'NVIDIA Corporation', 'NVSMI', smi)
+        new_path = os.path.join(system_root, 'System32', smi)
+        smis = [new_path, legacy_path]
+        for candidate_smi in smis:
+            if os.path.exists(candidate_smi):
+                smi = '"{}"'.format(candidate_smi)
+                break
+    return smi
+
+
+# example outputs of CPU infos
+#  * linux
+#    Architecture:            x86_64
+#      CPU op-mode(s):        32-bit, 64-bit
+#      Address sizes:         46 bits physical, 48 bits virtual
+#      Byte Order:            Little Endian
+#    CPU(s):                  128
+#      On-line CPU(s) list:   0-127
+#    Vendor ID:               GenuineIntel
+#      Model name:            Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
+#        CPU family:          6
+#        Model:               106
+#        Thread(s) per core:  2
+#        Core(s) per socket:  32
+#        Socket(s):           2
+#        Stepping:            6
+#        BogoMIPS:            5799.78
+#        Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr
+#                             sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl
+#                             xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16
+#                             pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand
+#                             hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced
+#                             fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap
+#                             avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1
+#                             xsaves wbnoinvd ida arat avx512vbmi pku ospke avx512_vbmi2 gfni vaes vpclmulqdq
+#                             avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear flush_l1d arch_capabilities
+#    Virtualization features:
+#      Hypervisor vendor:     KVM
+#      Virtualization type:   full
+#    Caches (sum of all):
+#      L1d:                   3 MiB (64 instances)
+#      L1i:                   2 MiB (64 instances)
+#      L2:                    80 MiB (64 instances)
+#      L3:                    108 MiB (2 instances)
+#    NUMA:
+#      NUMA node(s):          2
+#      NUMA node0 CPU(s):     0-31,64-95
+#      NUMA node1 CPU(s):     32-63,96-127
+#    Vulnerabilities:
+#      Itlb multihit:         Not affected
+#      L1tf:                  Not affected
+#      Mds:                   Not affected
+#      Meltdown:              Not affected
+#      Mmio stale data:       Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
+#      Retbleed:              Not affected
+#      Spec store bypass:     Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+#      Spectre v1:            Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+#      Spectre v2:            Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
+#      Srbds:                 Not affected
+#      Tsx async abort:       Not affected
+#  * win32
+#    Architecture=9
+#    CurrentClockSpeed=2900
+#    DeviceID=CPU0
+#    Family=179
+#    L2CacheSize=40960
+#    L2CacheSpeed=
+#    Manufacturer=GenuineIntel
+#    MaxClockSpeed=2900
+#    Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
+#    ProcessorType=3
+#    Revision=27142
+#
+#    Architecture=9
+#    CurrentClockSpeed=2900
+#    DeviceID=CPU1
+#    Family=179
+#    L2CacheSize=40960
+#    L2CacheSpeed=
+#    Manufacturer=GenuineIntel
+#    MaxClockSpeed=2900
+#    Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
+#    ProcessorType=3
+#    Revision=27142
+
+def get_cpu_info(run_lambda):
+    rc, out, err = 0, '', ''
+    if get_platform() == 'linux':
+        rc, out, err = run_lambda('lscpu')
+    elif get_platform() == 'win32':
+        rc, out, err = run_lambda(
+            'powershell.exe "gwmi -Class Win32_Processor | Select-Object -Property Name,Manufacturer,Family,\
+            Architecture,ProcessorType,DeviceID,CurrentClockSpeed,MaxClockSpeed,L2CacheSize,L2CacheSpeed,Revision\
+            | ConvertTo-Json"'
+        )
+        if rc == 0:
+            lst = []
+            try:
+                obj = json.loads(out)
+                if type(obj) is list:
+                    for o in obj:
+                        lst.append("----------------------")
+                        lst.extend([f"{k}: {v}" for (k, v) in o.items()])
+                else:
+                    lst.extend([f"{k}: {v}" for (k, v) in obj.items()])
+            except ValueError as e:
+                lst.append(out)
+                lst.append(str(e))
+            out = "\n".join(lst)
+    elif get_platform() == 'darwin':
+        rc, out, err = run_lambda("sysctl -n machdep.cpu.brand_string")
+    cpu_info = 'None'
+    if rc == 0:
+        cpu_info = out
+    else:
+        cpu_info = err
+    return cpu_info
+
+
+def get_platform():
+    if sys.platform.startswith('linux'):
+        return 'linux'
+    elif sys.platform.startswith('win32'):
+        return 'win32'
+    elif sys.platform.startswith('cygwin'):
+        return 'cygwin'
+    elif sys.platform.startswith('darwin'):
+        return 'darwin'
+    else:
+        return sys.platform
+
+
+def get_mac_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'sw_vers -productVersion', r'(.*)')
+
+
+def get_windows_version(run_lambda):
+    ret = run_and_read_all(
+        run_lambda,
+        'powershell.exe "gwmi -Class Win32_OperatingSystem | Select-Object -Property Caption,\
+        OSArchitecture,Version | ConvertTo-Json"',
+    )
+    try:
+        obj = json.loads(ret)
+        ret = f'{obj["Caption"]} ({obj["Version"]} {obj["OSArchitecture"]})'
+    except ValueError as e:
+        ret += f"\n{str(e)}"
+    return ret
+
+
+def get_lsb_version(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'lsb_release -a', r'Description:\t(.*)')
+
+
+def check_release_file(run_lambda):
+    return run_and_parse_first_match(run_lambda, 'cat /etc/*-release',
+                                     r'PRETTY_NAME="(.*)"')
+
+
+def get_os(run_lambda):
+    from platform import machine
+    platform = get_platform()
+
+    if platform == 'win32' or platform == 'cygwin':
+        return get_windows_version(run_lambda)
+
+    if platform == 'darwin':
+        version = get_mac_version(run_lambda)
+        if version is None:
+            return None
+        return 'macOS {} ({})'.format(version, machine())
+
+    if platform == 'linux':
+        # Ubuntu/Debian based
+        desc = get_lsb_version(run_lambda)
+        if desc is not None:
+            return '{} ({})'.format(desc, machine())
+
+        # Try reading /etc/*-release
+        desc = check_release_file(run_lambda)
+        if desc is not None:
+            return '{} ({})'.format(desc, machine())
+
+        return '{} ({})'.format(platform, machine())
+
+    # Unknown platform
+    return platform
+
+
+def get_python_platform():
+    import platform
+    return platform.platform()
+
+
+def get_libc_version():
+    import platform
+    if get_platform() != 'linux':
+        return 'N/A'
+    return '-'.join(platform.libc_ver())
+
+
+def get_pip_packages(run_lambda, patterns=None):
+    """Return `pip list` output. Note: will also find conda-installed pytorch and numpy packages."""
+    if patterns is None:
+        patterns = PIP_PATTERNS + COMMON_PATTERNS + NVIDIA_PATTERNS
+
+    pip_version = 'pip3' if sys.version[0] == '3' else 'pip'
+
+    os.environ['PIP_DISABLE_PIP_VERSION_CHECK'] = '1'
+    # People generally have pip as `pip` or `pip3`
+    # But here it is invoked as `python -mpip`
+    out = run_and_read_all(run_lambda, [sys.executable, '-mpip', 'list', '--format=freeze'])
+    filtered_out = '\n'.join(
+        line
+        for line in out.splitlines()
+        if any(name in line for name in patterns)
+    )
+
+    return pip_version, filtered_out
+
+
+def get_cachingallocator_config():
+    ca_config = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', '')
+    return ca_config
+
+
+def get_cuda_module_loading_config():
+    if TORCH_AVAILABLE and torch.cuda.is_available():
+        torch.cuda.init()
+        config = os.environ.get('CUDA_MODULE_LOADING', '')
+        return config
+    else:
+        return "N/A"
+
+
+def is_xnnpack_available():
+    if TORCH_AVAILABLE:
+        import torch.backends.xnnpack
+        return str(torch.backends.xnnpack.enabled)  # type: ignore[attr-defined]
+    else:
+        return "N/A"
+
+def get_env_info():
+    """
+    Collects environment information to aid in debugging.
+
+    The returned environment information contains details on torch version, is debug build
+    or not, cuda compiled version, gcc version, clang version, cmake version, operating
+    system, libc version, python version, python platform, CUDA availability, CUDA
+    runtime version, CUDA module loading config, GPU model and configuration, Nvidia
+    driver version, cuDNN version, pip version and versions of relevant pip and
+    conda packages, HIP runtime version, MIOpen runtime version,
+    Caching allocator config, XNNPACK availability and CPU information.
+
+    Returns:
+        SystemEnv (namedtuple): A tuple containining various environment details
+            and system information.
+    """
+    run_lambda = run
+    pip_version, pip_list_output = get_pip_packages(run_lambda)
+
+    if TORCH_AVAILABLE:
+        version_str = torch.__version__
+        debug_mode_str = str(torch.version.debug)
+        cuda_available_str = str(torch.cuda.is_available())
+        cuda_version_str = torch.version.cuda
+        if not hasattr(torch.version, 'hip') or torch.version.hip is None:  # cuda version
+            hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'
+        else:  # HIP version
+            def get_version_or_na(cfg, prefix):
+                _lst = [s.rsplit(None, 1)[-1] for s in cfg if prefix in s]
+                return _lst[0] if _lst else 'N/A'
+
+            cfg = torch._C._show_config().split('\n')
+            hip_runtime_version = get_version_or_na(cfg, 'HIP Runtime')
+            miopen_runtime_version = get_version_or_na(cfg, 'MIOpen')
+            cuda_version_str = 'N/A'
+            hip_compiled_version = torch.version.hip
+    else:
+        version_str = debug_mode_str = cuda_available_str = cuda_version_str = 'N/A'
+        hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'
+
+    sys_version = sys.version.replace("\n", " ")
+
+    conda_packages = get_conda_packages(run_lambda)
+
+    return SystemEnv(
+        torch_version=version_str,
+        is_debug_build=debug_mode_str,
+        python_version='{} ({}-bit runtime)'.format(sys_version, sys.maxsize.bit_length() + 1),
+        python_platform=get_python_platform(),
+        is_cuda_available=cuda_available_str,
+        cuda_compiled_version=cuda_version_str,
+        cuda_runtime_version=get_running_cuda_version(run_lambda),
+        cuda_module_loading=get_cuda_module_loading_config(),
+        nvidia_gpu_models=get_gpu_info(run_lambda),
+        nvidia_driver_version=get_nvidia_driver_version(run_lambda),
+        cudnn_version=get_cudnn_version(run_lambda),
+        hip_compiled_version=hip_compiled_version,
+        hip_runtime_version=hip_runtime_version,
+        miopen_runtime_version=miopen_runtime_version,
+        pip_version=pip_version,
+        pip_packages=pip_list_output,
+        conda_packages=conda_packages,
+        os=get_os(run_lambda),
+        libc_version=get_libc_version(),
+        gcc_version=get_gcc_version(run_lambda),
+        clang_version=get_clang_version(run_lambda),
+        cmake_version=get_cmake_version(run_lambda),
+        caching_allocator_config=get_cachingallocator_config(),
+        is_xnnpack_available=is_xnnpack_available(),
+        cpu_info=get_cpu_info(run_lambda),
+    )
+
+env_info_fmt = """
+PyTorch version: {torch_version}
+Is debug build: {is_debug_build}
+CUDA used to build PyTorch: {cuda_compiled_version}
+ROCM used to build PyTorch: {hip_compiled_version}
+
+OS: {os}
+GCC version: {gcc_version}
+Clang version: {clang_version}
+CMake version: {cmake_version}
+Libc version: {libc_version}
+
+Python version: {python_version}
+Python platform: {python_platform}
+Is CUDA available: {is_cuda_available}
+CUDA runtime version: {cuda_runtime_version}
+CUDA_MODULE_LOADING set to: {cuda_module_loading}
+GPU models and configuration: {nvidia_gpu_models}
+Nvidia driver version: {nvidia_driver_version}
+cuDNN version: {cudnn_version}
+HIP runtime version: {hip_runtime_version}
+MIOpen runtime version: {miopen_runtime_version}
+Is XNNPACK available: {is_xnnpack_available}
+
+CPU:
+{cpu_info}
+
+Versions of relevant libraries:
+{pip_packages}
+{conda_packages}
+""".strip()
+
+
+def pretty_str(envinfo):
+    def replace_nones(dct, replacement='Could not collect'):
+        for key in dct.keys():
+            if dct[key] is not None:
+                continue
+            dct[key] = replacement
+        return dct
+
+    def replace_bools(dct, true='Yes', false='No'):
+        for key in dct.keys():
+            if dct[key] is True:
+                dct[key] = true
+            elif dct[key] is False:
+                dct[key] = false
+        return dct
+
+    def prepend(text, tag='[prepend]'):
+        lines = text.split('\n')
+        updated_lines = [tag + line for line in lines]
+        return '\n'.join(updated_lines)
+
+    def replace_if_empty(text, replacement='No relevant packages'):
+        if text is not None and len(text) == 0:
+            return replacement
+        return text
+
+    def maybe_start_on_next_line(string):
+        # If `string` is multiline, prepend a \n to it.
+        if string is not None and len(string.split('\n')) > 1:
+            return '\n{}\n'.format(string)
+        return string
+
+    mutable_dict = envinfo._asdict()
+
+    # If nvidia_gpu_models is multiline, start on the next line
+    mutable_dict['nvidia_gpu_models'] = \
+        maybe_start_on_next_line(envinfo.nvidia_gpu_models)
+
+    # If the machine doesn't have CUDA, report some fields as 'No CUDA'
+    dynamic_cuda_fields = [
+        'cuda_runtime_version',
+        'nvidia_gpu_models',
+        'nvidia_driver_version',
+    ]
+    all_cuda_fields = dynamic_cuda_fields + ['cudnn_version']
+    all_dynamic_cuda_fields_missing = all(
+        mutable_dict[field] is None for field in dynamic_cuda_fields)
+    if TORCH_AVAILABLE and not torch.cuda.is_available() and all_dynamic_cuda_fields_missing:
+        for field in all_cuda_fields:
+            mutable_dict[field] = 'No CUDA'
+        if envinfo.cuda_compiled_version is None:
+            mutable_dict['cuda_compiled_version'] = 'None'
+
+    # Replace True with Yes, False with No
+    mutable_dict = replace_bools(mutable_dict)
+
+    # Replace all None objects with 'Could not collect'
+    mutable_dict = replace_nones(mutable_dict)
+
+    # If either of these are '', replace with 'No relevant packages'
+    mutable_dict['pip_packages'] = replace_if_empty(mutable_dict['pip_packages'])
+    mutable_dict['conda_packages'] = replace_if_empty(mutable_dict['conda_packages'])
+
+    # Tag conda and pip packages with a prefix
+    # If they were previously None, they'll show up as ie '[conda] Could not collect'
+    if mutable_dict['pip_packages']:
+        mutable_dict['pip_packages'] = prepend(mutable_dict['pip_packages'],
+                                               '[{}] '.format(envinfo.pip_version))
+    if mutable_dict['conda_packages']:
+        mutable_dict['conda_packages'] = prepend(mutable_dict['conda_packages'],
+                                                 '[conda] ')
+    mutable_dict['cpu_info'] = envinfo.cpu_info
+    return env_info_fmt.format(**mutable_dict)
+
+
+def get_pretty_env_info():
+    """
+    Returns a pretty string of environment information.
+
+    This function retrieves environment information by calling the `get_env_info` function
+    and then formats the information into a human-readable string. The retrieved environment
+    information is listed in the document of `get_env_info`.
+    This function is used in `python collect_env.py` that should be executed when reporting a bug.
+
+    Returns:
+        str: A pretty string of the environment information.
+    """
+    return pretty_str(get_env_info())
+
+
+def main():
+    print("Collecting environment information...")
+    output = get_pretty_env_info()
+    print(output)
+
+    if TORCH_AVAILABLE and hasattr(torch, 'utils') and hasattr(torch.utils, '_crash_handler'):
+        minidump_dir = torch.utils._crash_handler.DEFAULT_MINIDUMP_DIR
+        if sys.platform == "linux" and os.path.exists(minidump_dir):
+            dumps = [os.path.join(minidump_dir, dump) for dump in os.listdir(minidump_dir)]
+            latest = max(dumps, key=os.path.getctime)
+            ctime = os.path.getctime(latest)
+            creation_time = datetime.datetime.fromtimestamp(ctime).strftime('%Y-%m-%d %H:%M:%S')
+            msg = "\n*** Detected a minidump at {} created on {}, ".format(latest, creation_time) + \
+                  "if this is related to your bug please include it when you file a report ***"
+            print(msg, file=sys.stderr)
+
+
+
+if __name__ == '__main__':
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/cpp_backtrace.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/cpp_backtrace.py
new file mode 100644
index 0000000000000000000000000000000000000000..af4a7fcb63e263038255359c946a6a0d4a21dbd0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/cpp_backtrace.py
@@ -0,0 +1,12 @@
+# mypy: allow-untyped-defs
+from torch._C import _get_cpp_backtrace
+
+def get_cpp_backtrace(frames_to_skip=0, maximum_number_of_frames=64) -> str:
+    r"""
+    Return a string containing the C++ stack trace of the current thread.
+
+    Args:
+        frames_to_skip (int): the number of frames to skip from the top of the stack
+        maximum_number_of_frames (int): the maximum number of frames to return
+    """
+    return _get_cpp_backtrace(frames_to_skip, maximum_number_of_frames)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..82c23c2c9fece8354a5426aa22a54d744772cc19
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py
@@ -0,0 +1,2902 @@
+# mypy: allow-untyped-defs
+import copy
+import glob
+import importlib
+import importlib.abc
+import os
+import re
+import shlex
+import shutil
+import setuptools
+import subprocess
+import sys
+import sysconfig
+import warnings
+import collections
+from pathlib import Path
+import errno
+
+import torch
+import torch._appdirs
+from .file_baton import FileBaton
+from ._cpp_extension_versioner import ExtensionVersioner
+from .hipify import hipify_python
+from .hipify.hipify_python import GeneratedFileCleaner
+from typing import Optional, Union
+from torch.torch_version import TorchVersion, Version
+
+from setuptools.command.build_ext import build_ext
+
+IS_WINDOWS = sys.platform == 'win32'
+IS_MACOS = sys.platform.startswith('darwin')
+IS_LINUX = sys.platform.startswith('linux')
+LIB_EXT = '.pyd' if IS_WINDOWS else '.so'
+EXEC_EXT = '.exe' if IS_WINDOWS else ''
+CLIB_PREFIX = '' if IS_WINDOWS else 'lib'
+CLIB_EXT = '.dll' if IS_WINDOWS else '.so'
+SHARED_FLAG = '/DLL' if IS_WINDOWS else '-shared'
+
+_HERE = os.path.abspath(__file__)
+_TORCH_PATH = os.path.dirname(os.path.dirname(_HERE))
+TORCH_LIB_PATH = os.path.join(_TORCH_PATH, 'lib')
+
+
+SUBPROCESS_DECODE_ARGS = ('oem',) if IS_WINDOWS else ()
+MINIMUM_GCC_VERSION = (5, 0, 0)
+MINIMUM_MSVC_VERSION = (19, 0, 24215)
+
+VersionRange = tuple[tuple[int, ...], tuple[int, ...]]
+VersionMap = dict[str, VersionRange]
+# The following values were taken from the following GitHub gist that
+# summarizes the minimum valid major versions of g++/clang++ for each supported
+# CUDA version: https://gist.github.com/ax3l/9489132
+# Or from include/crt/host_config.h in the CUDA SDK
+# The second value is the exclusive(!) upper bound, i.e. min <= version < max
+CUDA_GCC_VERSIONS: VersionMap = {
+    '11.0': (MINIMUM_GCC_VERSION, (10, 0)),
+    '11.1': (MINIMUM_GCC_VERSION, (11, 0)),
+    '11.2': (MINIMUM_GCC_VERSION, (11, 0)),
+    '11.3': (MINIMUM_GCC_VERSION, (11, 0)),
+    '11.4': ((6, 0, 0), (12, 0)),
+    '11.5': ((6, 0, 0), (12, 0)),
+    '11.6': ((6, 0, 0), (12, 0)),
+    '11.7': ((6, 0, 0), (12, 0)),
+}
+
+MINIMUM_CLANG_VERSION = (3, 3, 0)
+CUDA_CLANG_VERSIONS: VersionMap = {
+    '11.1': (MINIMUM_CLANG_VERSION, (11, 0)),
+    '11.2': (MINIMUM_CLANG_VERSION, (12, 0)),
+    '11.3': (MINIMUM_CLANG_VERSION, (12, 0)),
+    '11.4': (MINIMUM_CLANG_VERSION, (13, 0)),
+    '11.5': (MINIMUM_CLANG_VERSION, (13, 0)),
+    '11.6': (MINIMUM_CLANG_VERSION, (14, 0)),
+    '11.7': (MINIMUM_CLANG_VERSION, (14, 0)),
+}
+
+__all__ = ["get_default_build_root", "check_compiler_ok_for_platform", "get_compiler_abi_compatibility_and_version", "BuildExtension",
+           "CppExtension", "CUDAExtension", "SyclExtension", "include_paths", "library_paths", "load", "load_inline", "is_ninja_available",
+           "verify_ninja_availability", "remove_extension_h_precompiler_headers", "get_cxx_compiler", "check_compiler_is_gcc"]
+# Taken directly from python stdlib < 3.9
+# See https://github.com/pytorch/pytorch/issues/48617
+def _nt_quote_args(args: Optional[list[str]]) -> list[str]:
+    """Quote command-line arguments for DOS/Windows conventions.
+
+    Just wraps every argument which contains blanks in double quotes, and
+    returns a new argument list.
+    """
+    # Cover None-type
+    if not args:
+        return []
+    return [f'"{arg}"' if ' ' in arg else arg for arg in args]
+
+def _find_cuda_home() -> Optional[str]:
+    """Find the CUDA install path."""
+    # Guess #1
+    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
+    if cuda_home is None:
+        # Guess #2
+        nvcc_path = shutil.which("nvcc")
+        if nvcc_path is not None:
+            cuda_home = os.path.dirname(os.path.dirname(nvcc_path))
+        else:
+            # Guess #3
+            if IS_WINDOWS:
+                cuda_homes = glob.glob(
+                    'C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v*.*')
+                if len(cuda_homes) == 0:
+                    cuda_home = ''
+                else:
+                    cuda_home = cuda_homes[0]
+            else:
+                cuda_home = '/usr/local/cuda'
+            if not os.path.exists(cuda_home):
+                cuda_home = None
+    if cuda_home and not torch.cuda.is_available():
+        print(f"No CUDA runtime is found, using CUDA_HOME='{cuda_home}'",
+              file=sys.stderr)
+    return cuda_home
+
+def _find_rocm_home() -> Optional[str]:
+    """Find the ROCm install path."""
+    # Guess #1
+    rocm_home = os.environ.get('ROCM_HOME') or os.environ.get('ROCM_PATH')
+    if rocm_home is None:
+        # Guess #2
+        hipcc_path = shutil.which('hipcc')
+        if hipcc_path is not None:
+            rocm_home = os.path.dirname(os.path.dirname(
+                os.path.realpath(hipcc_path)))
+            # can be either /hip/bin/hipcc or /bin/hipcc
+            if os.path.basename(rocm_home) == 'hip':
+                rocm_home = os.path.dirname(rocm_home)
+        else:
+            # Guess #3
+            fallback_path = '/opt/rocm'
+            if os.path.exists(fallback_path):
+                rocm_home = fallback_path
+    if rocm_home and torch.version.hip is None:
+        print(f"No ROCm runtime is found, using ROCM_HOME='{rocm_home}'",
+              file=sys.stderr)
+    return rocm_home
+
+def _find_sycl_home() -> Optional[str]:
+    sycl_home = None
+    icpx_path = shutil.which('icpx')
+    # Guess 1: for source code build developer/user, we'll have icpx in PATH,
+    # which will tell us the SYCL_HOME location.
+    if icpx_path is not None:
+        sycl_home = os.path.dirname(os.path.dirname(
+            os.path.realpath(icpx_path)))
+
+    # Guess 2: for users install Pytorch with XPU support, the sycl runtime is
+    # inside intel-sycl-rt, which is automatically installed via pip dependency.
+    else:
+        try:
+            files = importlib.metadata.files('intel-sycl-rt') or []
+            for f in files:
+                if f.name == "libsycl.so":
+                    sycl_home = os.path.dirname(Path(f.locate()).parent.resolve())
+                    break
+        except importlib.metadata.PackageNotFoundError:
+            print("Trying to find SYCL_HOME from intel-sycl-rt package, but it is not installed.",
+                  file=sys.stderr)
+    return sycl_home
+
+def _join_rocm_home(*paths) -> str:
+    """
+    Join paths with ROCM_HOME, or raises an error if it ROCM_HOME is not set.
+
+    This is basically a lazy way of raising an error for missing $ROCM_HOME
+    only once we need to get any ROCm-specific path.
+    """
+    if ROCM_HOME is None:
+        raise OSError('ROCM_HOME environment variable is not set. '
+                      'Please set it to your ROCm install root.')
+    elif IS_WINDOWS:
+        raise OSError('Building PyTorch extensions using '
+                      'ROCm and Windows is not supported.')
+    return os.path.join(ROCM_HOME, *paths)
+
+def _join_sycl_home(*paths) -> str:
+    """
+    Join paths with SYCL_HOME, or raises an error if it SYCL_HOME is not found.
+
+    This is basically a lazy way of raising an error for missing SYCL_HOME
+    only once we need to get any SYCL-specific path.
+    """
+    if SYCL_HOME is None:
+        raise OSError('SYCL runtime is not dected. Please setup the pytorch '
+                      'prerequisites for Intel GPU following the instruction in '
+                      'https://github.com/pytorch/pytorch?tab=readme-ov-file#intel-gpu-support '
+                      'or install intel-sycl-rt via pip.')
+
+    return os.path.join(SYCL_HOME, *paths)
+
+
+
+ABI_INCOMPATIBILITY_WARNING = '''
+
+                               !! WARNING !!
+
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+Your compiler ({}) may be ABI-incompatible with PyTorch!
+Please use a compiler that is ABI-compatible with GCC 5.0 and above.
+See https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html.
+
+See https://gist.github.com/goldsborough/d466f43e8ffc948ff92de7486c5216d6
+for instructions on how to install GCC 5 or higher.
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
+                              !! WARNING !!
+'''
+WRONG_COMPILER_WARNING = '''
+
+                               !! WARNING !!
+
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+Your compiler ({user_compiler}) is not compatible with the compiler Pytorch was
+built with for this platform, which is {pytorch_compiler} on {platform}. Please
+use {pytorch_compiler} to to compile your extension. Alternatively, you may
+compile PyTorch from source using {user_compiler}, and then you can also use
+{user_compiler} to compile your extension.
+
+See https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md for help
+with compiling PyTorch from source.
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
+                              !! WARNING !!
+'''
+CUDA_MISMATCH_MESSAGE = '''
+The detected CUDA version ({0}) mismatches the version that was used to compile
+PyTorch ({1}). Please make sure to use the same CUDA versions.
+'''
+CUDA_MISMATCH_WARN = "The detected CUDA version ({0}) has a minor version mismatch with the version that was used to compile PyTorch ({1}). Most likely this shouldn't be a problem."
+CUDA_NOT_FOUND_MESSAGE = '''
+CUDA was not found on the system, please set the CUDA_HOME or the CUDA_PATH
+environment variable or add NVCC to your system PATH. The extension compilation will fail.
+'''
+ROCM_HOME = _find_rocm_home()
+HIP_HOME = _join_rocm_home('hip') if ROCM_HOME else None
+IS_HIP_EXTENSION = True if ((ROCM_HOME is not None) and (torch.version.hip is not None)) else False
+ROCM_VERSION = None
+if torch.version.hip is not None:
+    ROCM_VERSION = tuple(int(v) for v in torch.version.hip.split('.')[:2])
+
+CUDA_HOME = _find_cuda_home() if torch.cuda._is_compiled() else None
+CUDNN_HOME = os.environ.get('CUDNN_HOME') or os.environ.get('CUDNN_PATH')
+SYCL_HOME = _find_sycl_home() if torch.xpu._is_compiled() else None
+
+# PyTorch releases have the version pattern major.minor.patch, whereas when
+# PyTorch is built from source, we append the git commit hash, which gives
+# it the below pattern.
+BUILT_FROM_SOURCE_VERSION_PATTERN = re.compile(r'\d+\.\d+\.\d+\w+\+\w+')
+
+COMMON_MSVC_FLAGS = ['/MD', '/wd4819', '/wd4251', '/wd4244', '/wd4267', '/wd4275', '/wd4018', '/wd4190', '/wd4624', '/wd4067', '/wd4068', '/EHsc']
+
+MSVC_IGNORE_CUDAFE_WARNINGS = [
+    'base_class_has_different_dll_interface',
+    'field_without_dll_interface',
+    'dll_interface_conflict_none_assumed',
+    'dll_interface_conflict_dllexport_assumed'
+]
+
+COMMON_NVCC_FLAGS = [
+    '-D__CUDA_NO_HALF_OPERATORS__',
+    '-D__CUDA_NO_HALF_CONVERSIONS__',
+    '-D__CUDA_NO_BFLOAT16_CONVERSIONS__',
+    '-D__CUDA_NO_HALF2_OPERATORS__',
+    '--expt-relaxed-constexpr'
+]
+
+COMMON_HIP_FLAGS = [
+    '-fPIC',
+    '-D__HIP_PLATFORM_AMD__=1',
+    '-DUSE_ROCM=1',
+    '-DHIPBLAS_V2',
+]
+
+COMMON_HIPCC_FLAGS = [
+    '-DCUDA_HAS_FP16=1',
+    '-D__HIP_NO_HALF_OPERATORS__=1',
+    '-D__HIP_NO_HALF_CONVERSIONS__=1',
+]
+
+_COMMON_SYCL_FLAGS = [
+    '-fsycl',
+    '-fsycl-targets=spir64_gen,spir64',
+]
+
+def _get_sycl_arch_list():
+    if 'TORCH_XPU_ARCH_LIST' in os.environ:
+        return os.environ.get('TORCH_XPU_ARCH_LIST')
+    arch_list = torch.xpu.get_arch_list()
+    # Dropping dg2-* archs since they lack hardware support for fp64 and require
+    # special consideration from the user. If needed these platforms can
+    # be requested thru TORCH_XPU_ARCH_LIST environment variable.
+    arch_list = [x for x in arch_list if not x.startswith('dg2-')]
+    return ','.join(arch_list)
+
+_SYCL_DLINK_FLAGS = [
+    *_COMMON_SYCL_FLAGS,
+    '-fsycl-link',
+    '--offload-compress',
+    f'-Xs "-device {_get_sycl_arch_list()}"',
+]
+
+JIT_EXTENSION_VERSIONER = ExtensionVersioner()
+
+PLAT_TO_VCVARS = {
+    'win32' : 'x86',
+    'win-amd64' : 'x86_amd64',
+}
+
+min_supported_cpython = "0x03090000"  # Python 3.9 hexcode
+
+def get_cxx_compiler():
+    if IS_WINDOWS:
+        compiler = os.environ.get('CXX', 'cl')
+    else:
+        compiler = os.environ.get('CXX', 'c++')
+    return compiler
+
+def _is_binary_build() -> bool:
+    return not BUILT_FROM_SOURCE_VERSION_PATTERN.match(torch.version.__version__)
+
+
+def _accepted_compilers_for_platform() -> list[str]:
+    # gnu-c++ and gnu-cc are the conda gcc compilers
+    return ['clang++', 'clang'] if IS_MACOS else ['g++', 'gcc', 'gnu-c++', 'gnu-cc', 'clang++', 'clang']
+
+def _maybe_write(filename, new_content):
+    r'''
+    Equivalent to writing the content into the file but will not touch the file
+    if it already had the right content (to avoid triggering recompile).
+    '''
+    if os.path.exists(filename):
+        with open(filename) as f:
+            content = f.read()
+
+        if content == new_content:
+            # The file already contains the right thing!
+            return
+
+    with open(filename, 'w') as source_file:
+        source_file.write(new_content)
+
+def get_default_build_root() -> str:
+    """
+    Return the path to the root folder under which extensions will built.
+
+    For each extension module built, there will be one folder underneath the
+    folder returned by this function. For example, if ``p`` is the path
+    returned by this function and ``ext`` the name of an extension, the build
+    folder for the extension will be ``p/ext``.
+
+    This directory is **user-specific** so that multiple users on the same
+    machine won't meet permission issues.
+    """
+    return os.path.realpath(torch._appdirs.user_cache_dir(appname='torch_extensions'))
+
+
+def check_compiler_ok_for_platform(compiler: str) -> bool:
+    """
+    Verify that the compiler is the expected one for the current platform.
+
+    Args:
+        compiler (str): The compiler executable to check.
+
+    Returns:
+        True if the compiler is gcc/g++ on Linux or clang/clang++ on macOS,
+        and always True for Windows.
+    """
+    if IS_WINDOWS:
+        return True
+    compiler_path = shutil.which(compiler)
+    if compiler_path is None:
+        return False
+    # Use os.path.realpath to resolve any symlinks, in particular from 'c++' to e.g. 'g++'.
+    compiler_path = os.path.realpath(compiler_path)
+    # Check the compiler name
+    if any(name in compiler_path for name in _accepted_compilers_for_platform()):
+        return True
+    # If compiler wrapper is used try to infer the actual compiler by invoking it with -v flag
+    env = os.environ.copy()
+    env['LC_ALL'] = 'C'  # Don't localize output
+    version_string = subprocess.check_output([compiler, '-v'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS)
+    if IS_LINUX:
+        # Check for 'gcc' or 'g++' for sccache wrapper
+        pattern = re.compile("^COLLECT_GCC=(.*)$", re.MULTILINE)
+        results = re.findall(pattern, version_string)
+        if len(results) != 1:
+            # Clang is also a supported compiler on Linux
+            # Though on Ubuntu it's sometimes called "Ubuntu clang version"
+            return 'clang version' in version_string
+        compiler_path = os.path.realpath(results[0].strip())
+        # On RHEL/CentOS c++ is a gcc compiler wrapper
+        if os.path.basename(compiler_path) == 'c++' and 'gcc version' in version_string:
+            return True
+        return any(name in compiler_path for name in _accepted_compilers_for_platform())
+    if IS_MACOS:
+        # Check for 'clang' or 'clang++'
+        return version_string.startswith("Apple clang")
+    return False
+
+
+def get_compiler_abi_compatibility_and_version(compiler) -> tuple[bool, TorchVersion]:
+    """
+    Determine if the given compiler is ABI-compatible with PyTorch alongside its version.
+
+    Args:
+        compiler (str): The compiler executable name to check (e.g. ``g++``).
+            Must be executable in a shell process.
+
+    Returns:
+        A tuple that contains a boolean that defines if the compiler is (likely) ABI-incompatible with PyTorch,
+        followed by a `TorchVersion` string that contains the compiler version separated by dots.
+    """
+    if not _is_binary_build():
+        return (True, TorchVersion('0.0.0'))
+    if os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') in ['ON', '1', 'YES', 'TRUE', 'Y']:
+        return (True, TorchVersion('0.0.0'))
+
+    # First check if the compiler is one of the expected ones for the particular platform.
+    if not check_compiler_ok_for_platform(compiler):
+        warnings.warn(WRONG_COMPILER_WARNING.format(
+            user_compiler=compiler,
+            pytorch_compiler=_accepted_compilers_for_platform()[0],
+            platform=sys.platform))
+        return (False, TorchVersion('0.0.0'))
+
+    if IS_MACOS:
+        # There is no particular minimum version we need for clang, so we're good here.
+        return (True, TorchVersion('0.0.0'))
+    try:
+        if IS_LINUX:
+            minimum_required_version = MINIMUM_GCC_VERSION
+            versionstr = subprocess.check_output([compiler, '-dumpfullversion', '-dumpversion'])
+            version = versionstr.decode(*SUBPROCESS_DECODE_ARGS).strip().split('.')
+        else:
+            minimum_required_version = MINIMUM_MSVC_VERSION
+            compiler_info = subprocess.check_output(compiler, stderr=subprocess.STDOUT)
+            match = re.search(r'(\d+)\.(\d+)\.(\d+)', compiler_info.decode(*SUBPROCESS_DECODE_ARGS).strip())
+            version = ['0', '0', '0'] if match is None else list(match.groups())
+    except Exception:
+        _, error, _ = sys.exc_info()
+        warnings.warn(f'Error checking compiler version for {compiler}: {error}')
+        return (False, TorchVersion('0.0.0'))
+
+    if tuple(map(int, version)) >= minimum_required_version:
+        return (True, TorchVersion('.'.join(version)))
+
+    compiler = f'{compiler} {".".join(version)}'
+    warnings.warn(ABI_INCOMPATIBILITY_WARNING.format(compiler))
+
+    return (False, TorchVersion('.'.join(version)))
+
+
+def _check_cuda_version(compiler_name: str, compiler_version: TorchVersion) -> None:
+    if not CUDA_HOME:
+        raise RuntimeError(CUDA_NOT_FOUND_MESSAGE)
+
+    nvcc = os.path.join(CUDA_HOME, 'bin', 'nvcc')
+    cuda_version_str = subprocess.check_output([nvcc, '--version']).strip().decode(*SUBPROCESS_DECODE_ARGS)
+    cuda_version = re.search(r'release (\d+[.]\d+)', cuda_version_str)
+    if cuda_version is None:
+        return
+
+    cuda_str_version = cuda_version.group(1)
+    cuda_ver = Version(cuda_str_version)
+    if torch.version.cuda is None:
+        return
+
+    torch_cuda_version = Version(torch.version.cuda)
+    if cuda_ver != torch_cuda_version:
+        # major/minor attributes are only available in setuptools>=49.4.0
+        if getattr(cuda_ver, "major", None) is None:
+            raise ValueError("setuptools>=49.4.0 is required")
+        if cuda_ver.major != torch_cuda_version.major:
+            raise RuntimeError(CUDA_MISMATCH_MESSAGE.format(cuda_str_version, torch.version.cuda))
+        warnings.warn(CUDA_MISMATCH_WARN.format(cuda_str_version, torch.version.cuda))
+
+    if not (sys.platform.startswith('linux') and
+            os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') not in ['ON', '1', 'YES', 'TRUE', 'Y'] and
+            _is_binary_build()):
+        return
+
+    cuda_compiler_bounds: VersionMap = CUDA_CLANG_VERSIONS if compiler_name.startswith('clang') else CUDA_GCC_VERSIONS
+
+    if cuda_str_version not in cuda_compiler_bounds:
+        warnings.warn(f'There are no {compiler_name} version bounds defined for CUDA version {cuda_str_version}')
+    else:
+        min_compiler_version, max_excl_compiler_version = cuda_compiler_bounds[cuda_str_version]
+        # Special case for 11.4.0, which has lower compiler bounds than 11.4.1
+        if "V11.4.48" in cuda_version_str and cuda_compiler_bounds == CUDA_GCC_VERSIONS:
+            max_excl_compiler_version = (11, 0)
+        min_compiler_version_str = '.'.join(map(str, min_compiler_version))
+        max_excl_compiler_version_str = '.'.join(map(str, max_excl_compiler_version))
+
+        version_bound_str = f'>={min_compiler_version_str}, <{max_excl_compiler_version_str}'
+
+        if compiler_version < TorchVersion(min_compiler_version_str):
+            raise RuntimeError(
+                f'The current installed version of {compiler_name} ({compiler_version}) is less '
+                f'than the minimum required version by CUDA {cuda_str_version} ({min_compiler_version_str}). '
+                f'Please make sure to use an adequate version of {compiler_name} ({version_bound_str}).'
+            )
+        if compiler_version >= TorchVersion(max_excl_compiler_version_str):
+            raise RuntimeError(
+                f'The current installed version of {compiler_name} ({compiler_version}) is greater '
+                f'than the maximum required version by CUDA {cuda_str_version}. '
+                f'Please make sure to use an adequate version of {compiler_name} ({version_bound_str}).'
+            )
+
+
+def _append_sycl_std_if_no_std_present(cflags):
+    if not any(flag.startswith('-sycl-std=') for flag in cflags):
+        cflags.append('-sycl-std=2020')
+
+
+def _wrap_sycl_host_flags(cflags):
+    host_cxx = get_cxx_compiler()
+    host_cflags = [
+        f'-fsycl-host-compiler={host_cxx}',
+        shlex.quote(f'-fsycl-host-compiler-options={cflags}'),
+    ]
+    return host_cflags
+
+
+class BuildExtension(build_ext):
+    """
+    A custom :mod:`setuptools` build extension .
+
+    This :class:`setuptools.build_ext` subclass takes care of passing the
+    minimum required compiler flags (e.g. ``-std=c++17``) as well as mixed
+    C++/CUDA/SYCL compilation (and support for CUDA/SYCL files in general).
+
+    When using :class:`BuildExtension`, it is allowed to supply a dictionary
+    for ``extra_compile_args`` (rather than the usual list) that maps from
+    languages/compilers (the only expected values are ``cxx``, ``nvcc`` or
+    ``sycl``) to a list of additional compiler flags to supply to the compiler.
+    This makes it possible to supply different flags to the C++, CUDA and SYCL
+    compiler during mixed compilation.
+
+    ``use_ninja`` (bool): If ``use_ninja`` is ``True`` (default), then we
+    attempt to build using the Ninja backend. Ninja greatly speeds up
+    compilation compared to the standard ``setuptools.build_ext``.
+    Fallbacks to the standard distutils backend if Ninja is not available.
+
+    .. note::
+        By default, the Ninja backend uses #CPUS + 2 workers to build the
+        extension. This may use up too many resources on some systems. One
+        can control the number of workers by setting the `MAX_JOBS` environment
+        variable to a non-negative number.
+    """
+
+    @classmethod
+    def with_options(cls, **options):
+        """Return a subclass with alternative constructor that extends any original keyword arguments to the original constructor with the given options."""
+        class cls_with_options(cls):  # type: ignore[misc, valid-type]
+            def __init__(self, *args, **kwargs):
+                kwargs.update(options)
+                super().__init__(*args, **kwargs)
+
+        return cls_with_options
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.no_python_abi_suffix = kwargs.get("no_python_abi_suffix", False)
+
+        self.use_ninja = kwargs.get('use_ninja', True)
+        if self.use_ninja:
+            # Test if we can use ninja. Fallback otherwise.
+            msg = ('Attempted to use ninja as the BuildExtension backend but '
+                   '{}. Falling back to using the slow distutils backend.')
+            if not is_ninja_available():
+                warnings.warn(msg.format('we could not find ninja.'))
+                self.use_ninja = False
+
+    def finalize_options(self) -> None:
+        super().finalize_options()
+        if self.use_ninja:
+            self.force = True
+
+    def build_extensions(self) -> None:
+        compiler_name, compiler_version = self._check_abi()
+
+        cuda_ext = False
+        sycl_ext = False
+        extension_iter = iter(self.extensions)
+        extension = next(extension_iter, None)
+        while not (cuda_ext and sycl_ext) and extension:
+            for source in extension.sources:
+                _, ext = os.path.splitext(source)
+                if ext == '.cu':
+                    cuda_ext = True
+                elif ext == '.sycl':
+                    sycl_ext = True
+
+                # This check accounts on a case when cuda and sycl sources
+                # are mixed in the same extension. We can stop checking
+                # sources if both are found or there is no more sources.
+                if cuda_ext and sycl_ext:
+                    break
+
+            extension = next(extension_iter, None)
+
+        if sycl_ext:
+            assert self.use_ninja, "ninja is required to build sycl extensions."
+
+        if cuda_ext and not IS_HIP_EXTENSION:
+            _check_cuda_version(compiler_name, compiler_version)
+
+        for extension in self.extensions:
+            # Ensure at least an empty list of flags for 'cxx', 'nvcc' and 'sycl' when
+            # extra_compile_args is a dict. Otherwise, default torch flags do
+            # not get passed. Necessary when only one of 'cxx', 'nvcc' or 'sycl' is
+            # passed to extra_compile_args in CUDAExtension or SyclExtension, i.e.
+            #   CUDAExtension(..., extra_compile_args={'cxx': [...]})
+            # or
+            #   CUDAExtension(..., extra_compile_args={'nvcc': [...]})
+            if isinstance(extension.extra_compile_args, dict):
+                for ext in ['cxx', 'nvcc', 'sycl']:
+                    if ext not in extension.extra_compile_args:
+                        extension.extra_compile_args[ext] = []
+
+            self._add_compile_flag(extension, '-DTORCH_API_INCLUDE_EXTENSION_H')
+
+            if IS_HIP_EXTENSION:
+                self._hipify_compile_flags(extension)
+
+            if extension.py_limited_api:
+                # compile any extension that has passed in py_limited_api to the
+                # Extension constructor with the Py_LIMITED_API flag set to our
+                # min supported CPython version.
+                # See https://docs.python.org/3/c-api/stable.html#c.Py_LIMITED_API
+                self._add_compile_flag(extension, f'-DPy_LIMITED_API={min_supported_cpython}')
+            else:
+                # pybind11 is not CPython API stable so don't add these flags used when
+                # compiling pybind11 when pybind11 is not even used. otherwise, the build
+                # logs are confusing.
+                # See note [Pybind11 ABI constants]
+                for name in ["COMPILER_TYPE", "STDLIB", "BUILD_ABI"]:
+                    val = getattr(torch._C, f"_PYBIND11_{name}")
+                    if val is not None and not IS_WINDOWS:
+                        self._add_compile_flag(extension, f'-DPYBIND11_{name}="{val}"')
+            self._define_torch_extension_name(extension)
+            self._add_gnu_cpp_abi_flag(extension)
+
+            if 'nvcc_dlink' in extension.extra_compile_args:
+                assert self.use_ninja, f"With dlink=True, ninja is required to build cuda extension {extension.name}."
+
+        # Register .cu, .cuh, .hip, .mm and .sycl as valid source extensions.
+        # NOTE: At the moment .sycl is not a standard extension for SYCL supported
+        # by compiler. Here we introduce a torch level convention that SYCL sources
+        # should have .sycl file extension.
+        self.compiler.src_extensions += ['.cu', '.cuh', '.hip', '.sycl']
+        if torch.backends.mps.is_built():
+            self.compiler.src_extensions += ['.mm']
+        # Save the original _compile method for later.
+        if self.compiler.compiler_type == 'msvc':
+            self.compiler._cpp_extensions += ['.cu', '.cuh']
+            original_compile = self.compiler.compile
+            original_spawn = self.compiler.spawn
+        else:
+            original_compile = self.compiler._compile
+
+        def append_std17_if_no_std_present(cflags) -> None:
+            # NVCC does not allow multiple -std to be passed, so we avoid
+            # overriding the option if the user explicitly passed it.
+            cpp_format_prefix = '/{}:' if self.compiler.compiler_type == 'msvc' else '-{}='
+            cpp_flag_prefix = cpp_format_prefix.format('std')
+            cpp_flag = cpp_flag_prefix + 'c++17'
+            if not any(flag.startswith(cpp_flag_prefix) for flag in cflags):
+                cflags.append(cpp_flag)
+
+        def unix_cuda_flags(cflags):
+            cflags = (COMMON_NVCC_FLAGS +
+                      ['--compiler-options', "'-fPIC'"] +
+                      cflags + _get_cuda_arch_flags(cflags))
+
+            # NVCC does not allow multiple -ccbin/--compiler-bindir to be passed, so we avoid
+            # overriding the option if the user explicitly passed it.
+            _ccbin = os.getenv("CC")
+            if (
+                _ccbin is not None
+                and not any(flag.startswith(('-ccbin', '--compiler-bindir')) for flag in cflags)
+            ):
+                cflags.extend(['-ccbin', _ccbin])
+
+            return cflags
+
+        def convert_to_absolute_paths_inplace(paths):
+            # Helper function. See Note [Absolute include_dirs]
+            if paths is not None:
+                for i in range(len(paths)):
+                    if not os.path.isabs(paths[i]):
+                        paths[i] = os.path.abspath(paths[i])
+
+        def unix_wrap_single_compile(obj, src, ext, cc_args, extra_postargs, pp_opts) -> None:
+            # Copy before we make any modifications.
+            cflags = copy.deepcopy(extra_postargs)
+            try:
+                original_compiler = self.compiler.compiler_so
+                if _is_cuda_file(src):
+                    nvcc = [_join_rocm_home('bin', 'hipcc') if IS_HIP_EXTENSION else _join_cuda_home('bin', 'nvcc')]
+                    self.compiler.set_executable('compiler_so', nvcc)
+                    if isinstance(cflags, dict):
+                        cflags = cflags['nvcc']
+                    if IS_HIP_EXTENSION:
+                        cflags = COMMON_HIPCC_FLAGS + cflags + _get_rocm_arch_flags(cflags)
+                    else:
+                        cflags = unix_cuda_flags(cflags)
+                elif isinstance(cflags, dict):
+                    cflags = cflags['cxx']
+                if IS_HIP_EXTENSION:
+                    cflags = COMMON_HIP_FLAGS + cflags
+                append_std17_if_no_std_present(cflags)
+
+                original_compile(obj, src, ext, cc_args, cflags, pp_opts)
+            finally:
+                # Put the original compiler back in place.
+                self.compiler.set_executable('compiler_so', original_compiler)
+
+        def unix_wrap_ninja_compile(sources,
+                                    output_dir=None,
+                                    macros=None,
+                                    include_dirs=None,
+                                    debug=0,
+                                    extra_preargs=None,
+                                    extra_postargs=None,
+                                    depends=None):
+            r"""Compiles sources by outputting a ninja file and running it."""
+            # NB: I copied some lines from self.compiler (which is an instance
+            # of distutils.UnixCCompiler). See the following link.
+            # https://github.com/python/cpython/blob/f03a8f8d5001963ad5b5b28dbd95497e9cc15596/Lib/distutils/ccompiler.py#L564-L567
+            # This can be fragile, but a lot of other repos also do this
+            # (see https://github.com/search?q=_setup_compile&type=Code)
+            # so it is probably OK; we'll also get CI signal if/when
+            # we update our python version (which is when distutils can be
+            # upgraded)
+
+            # Use absolute path for output_dir so that the object file paths
+            # (`objects`) get generated with absolute paths.
+            output_dir = os.path.abspath(output_dir)
+
+            # See Note [Absolute include_dirs]
+            convert_to_absolute_paths_inplace(self.compiler.include_dirs)
+
+            _, objects, extra_postargs, pp_opts, _ = \
+                self.compiler._setup_compile(output_dir, macros,
+                                             include_dirs, sources,
+                                             depends, extra_postargs)
+            common_cflags = self.compiler._get_cc_args(pp_opts, debug, extra_preargs)
+            extra_cc_cflags = self.compiler.compiler_so[1:]
+            with_cuda = any(map(_is_cuda_file, sources))
+            with_sycl = any(map(_is_sycl_file, sources))
+
+            # extra_postargs can be either:
+            # - a dict mapping cxx/nvcc/sycl to extra flags
+            # - a list of extra flags.
+            if isinstance(extra_postargs, dict):
+                post_cflags = extra_postargs['cxx']
+            else:
+                post_cflags = list(extra_postargs)
+            if IS_HIP_EXTENSION:
+                post_cflags = COMMON_HIP_FLAGS + post_cflags
+            append_std17_if_no_std_present(post_cflags)
+
+            cuda_post_cflags = None
+            cuda_cflags = None
+            if with_cuda:
+                cuda_cflags = common_cflags
+                if isinstance(extra_postargs, dict):
+                    cuda_post_cflags = extra_postargs['nvcc']
+                else:
+                    cuda_post_cflags = list(extra_postargs)
+                if IS_HIP_EXTENSION:
+                    cuda_post_cflags = cuda_post_cflags + _get_rocm_arch_flags(cuda_post_cflags)
+                    cuda_post_cflags = COMMON_HIP_FLAGS + COMMON_HIPCC_FLAGS + cuda_post_cflags
+                else:
+                    cuda_post_cflags = unix_cuda_flags(cuda_post_cflags)
+                append_std17_if_no_std_present(cuda_post_cflags)
+                cuda_cflags = [shlex.quote(f) for f in cuda_cflags]
+                cuda_post_cflags = [shlex.quote(f) for f in cuda_post_cflags]
+
+            if isinstance(extra_postargs, dict) and 'nvcc_dlink' in extra_postargs:
+                cuda_dlink_post_cflags = unix_cuda_flags(extra_postargs['nvcc_dlink'])
+            else:
+                cuda_dlink_post_cflags = None
+
+            sycl_post_cflags = None
+            sycl_cflags = None
+            sycl_dlink_post_cflags = None
+            if with_sycl:
+                sycl_cflags = extra_cc_cflags + common_cflags + _COMMON_SYCL_FLAGS
+                if isinstance(extra_postargs, dict):
+                    sycl_post_cflags = extra_postargs['sycl']
+                else:
+                    sycl_post_cflags = list(extra_postargs)
+                append_std17_if_no_std_present(sycl_cflags)
+                _append_sycl_std_if_no_std_present(sycl_cflags)
+                host_cflags = extra_cc_cflags + common_cflags + post_cflags
+                append_std17_if_no_std_present(host_cflags)
+                # escaping quoted arguments to pass them thru SYCL compiler
+                host_cflags = [item.replace('"', '\\\\"') for item in host_cflags]
+                host_cflags = ' '.join(host_cflags)
+                # Note the order: shlex.quote sycl_flags first, _wrap_sycl_host_flags
+                # second. Reason is that sycl host flags are quoted, space containing
+                # strings passed to SYCL compiler.
+                sycl_cflags = [shlex.quote(f) for f in sycl_cflags]
+                sycl_cflags += _wrap_sycl_host_flags(host_cflags)
+                sycl_dlink_post_cflags = _SYCL_DLINK_FLAGS
+                sycl_post_cflags = [shlex.quote(f) for f in sycl_post_cflags]
+
+            _write_ninja_file_and_compile_objects(
+                sources=sources,
+                objects=objects,
+                cflags=[shlex.quote(f) for f in extra_cc_cflags + common_cflags],
+                post_cflags=[shlex.quote(f) for f in post_cflags],
+                cuda_cflags=cuda_cflags,
+                cuda_post_cflags=cuda_post_cflags,
+                cuda_dlink_post_cflags=cuda_dlink_post_cflags,
+                sycl_cflags=sycl_cflags,
+                sycl_post_cflags=sycl_post_cflags,
+                sycl_dlink_post_cflags=sycl_dlink_post_cflags,
+                build_directory=output_dir,
+                verbose=True,
+                with_cuda=with_cuda,
+                with_sycl=with_sycl)
+
+            # Return *all* object filenames, not just the ones we just built.
+            return objects
+
+        def win_cuda_flags(cflags):
+            return (COMMON_NVCC_FLAGS +
+                    cflags + _get_cuda_arch_flags(cflags))
+
+        def win_wrap_single_compile(sources,
+                                    output_dir=None,
+                                    macros=None,
+                                    include_dirs=None,
+                                    debug=0,
+                                    extra_preargs=None,
+                                    extra_postargs=None,
+                                    depends=None):
+
+            self.cflags = copy.deepcopy(extra_postargs)
+            extra_postargs = None
+
+            def spawn(cmd):
+                # Using regex to match src, obj and include files
+                src_regex = re.compile('/T(p|c)(.*)')
+                src_list = [
+                    m.group(2) for m in (src_regex.match(elem) for elem in cmd)
+                    if m
+                ]
+
+                obj_regex = re.compile('/Fo(.*)')
+                obj_list = [
+                    m.group(1) for m in (obj_regex.match(elem) for elem in cmd)
+                    if m
+                ]
+
+                include_regex = re.compile(r'((\-|\/)I.*)')
+                include_list = [
+                    m.group(1)
+                    for m in (include_regex.match(elem) for elem in cmd) if m
+                ]
+
+                if len(src_list) >= 1 and len(obj_list) >= 1:
+                    src = src_list[0]
+                    obj = obj_list[0]
+                    if _is_cuda_file(src):
+                        nvcc = _join_cuda_home('bin', 'nvcc')
+                        if isinstance(self.cflags, dict):
+                            cflags = self.cflags['nvcc']
+                        elif isinstance(self.cflags, list):
+                            cflags = self.cflags
+                        else:
+                            cflags = []
+
+                        cflags = win_cuda_flags(cflags) + ['-std=c++17', '--use-local-env']
+                        for flag in COMMON_MSVC_FLAGS:
+                            cflags = ['-Xcompiler', flag] + cflags
+                        for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS:
+                            cflags = ['-Xcudafe', '--diag_suppress=' + ignore_warning] + cflags
+                        cmd = [nvcc, '-c', src, '-o', obj] + include_list + cflags
+                    elif isinstance(self.cflags, dict):
+                        cflags = COMMON_MSVC_FLAGS + self.cflags['cxx']
+                        append_std17_if_no_std_present(cflags)
+                        cmd += cflags
+                    elif isinstance(self.cflags, list):
+                        cflags = COMMON_MSVC_FLAGS + self.cflags
+                        append_std17_if_no_std_present(cflags)
+                        cmd += cflags
+
+                return original_spawn(cmd)
+
+            try:
+                self.compiler.spawn = spawn
+                return original_compile(sources, output_dir, macros,
+                                        include_dirs, debug, extra_preargs,
+                                        extra_postargs, depends)
+            finally:
+                self.compiler.spawn = original_spawn
+
+        def win_wrap_ninja_compile(sources,
+                                   output_dir=None,
+                                   macros=None,
+                                   include_dirs=None,
+                                   debug=0,
+                                   extra_preargs=None,
+                                   extra_postargs=None,
+                                   depends=None):
+
+            if not self.compiler.initialized:
+                self.compiler.initialize()
+            output_dir = os.path.abspath(output_dir)
+
+            # Note [Absolute include_dirs]
+            # Convert relative path in self.compiler.include_dirs to absolute path if any.
+            # For ninja build, the build location is not local, but instead, the build happens
+            # in a script-created build folder. Thus, relative paths lose their correctness.
+            # To be consistent with jit extension, we allow user to enter relative include_dirs
+            # in setuptools.setup, and we convert the relative path to absolute path here.
+            convert_to_absolute_paths_inplace(self.compiler.include_dirs)
+
+            _, objects, extra_postargs, pp_opts, _ = \
+                self.compiler._setup_compile(output_dir, macros,
+                                             include_dirs, sources,
+                                             depends, extra_postargs)
+            common_cflags = extra_preargs or []
+            cflags = []
+            if debug:
+                cflags.extend(self.compiler.compile_options_debug)
+            else:
+                cflags.extend(self.compiler.compile_options)
+            common_cflags.extend(COMMON_MSVC_FLAGS)
+            cflags = cflags + common_cflags + pp_opts
+            with_cuda = any(map(_is_cuda_file, sources))
+
+            # extra_postargs can be either:
+            # - a dict mapping cxx/nvcc to extra flags
+            # - a list of extra flags.
+            if isinstance(extra_postargs, dict):
+                post_cflags = extra_postargs['cxx']
+            else:
+                post_cflags = list(extra_postargs)
+            append_std17_if_no_std_present(post_cflags)
+
+            cuda_post_cflags = None
+            cuda_cflags = None
+            if with_cuda:
+                cuda_cflags = ['-std=c++17', '--use-local-env']
+                for common_cflag in common_cflags:
+                    cuda_cflags.append('-Xcompiler')
+                    cuda_cflags.append(common_cflag)
+                for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS:
+                    cuda_cflags.append('-Xcudafe')
+                    cuda_cflags.append('--diag_suppress=' + ignore_warning)
+                cuda_cflags.extend(pp_opts)
+                if isinstance(extra_postargs, dict):
+                    cuda_post_cflags = extra_postargs['nvcc']
+                else:
+                    cuda_post_cflags = list(extra_postargs)
+                cuda_post_cflags = win_cuda_flags(cuda_post_cflags)
+
+            cflags = _nt_quote_args(cflags)
+            post_cflags = _nt_quote_args(post_cflags)
+            if with_cuda:
+                cuda_cflags = _nt_quote_args(cuda_cflags)
+                cuda_post_cflags = _nt_quote_args(cuda_post_cflags)
+            if isinstance(extra_postargs, dict) and 'nvcc_dlink' in extra_postargs:
+                cuda_dlink_post_cflags = win_cuda_flags(extra_postargs['nvcc_dlink'])
+            else:
+                cuda_dlink_post_cflags = None
+
+            _write_ninja_file_and_compile_objects(
+                sources=sources,
+                objects=objects,
+                cflags=cflags,
+                post_cflags=post_cflags,
+                cuda_cflags=cuda_cflags,
+                cuda_post_cflags=cuda_post_cflags,
+                cuda_dlink_post_cflags=cuda_dlink_post_cflags,
+                sycl_cflags=None,
+                sycl_post_cflags=None,
+                sycl_dlink_post_cflags=None,
+                build_directory=output_dir,
+                verbose=True,
+                with_cuda=with_cuda,
+                with_sycl=False)
+
+            # Return *all* object filenames, not just the ones we just built.
+            return objects
+
+        # Monkey-patch the _compile or compile method.
+        # https://github.com/python/cpython/blob/dc0284ee8f7a270b6005467f26d8e5773d76e959/Lib/distutils/ccompiler.py#L511
+        if self.compiler.compiler_type == 'msvc':
+            if self.use_ninja:
+                self.compiler.compile = win_wrap_ninja_compile
+            else:
+                self.compiler.compile = win_wrap_single_compile
+        else:
+            if self.use_ninja:
+                self.compiler.compile = unix_wrap_ninja_compile
+            else:
+                self.compiler._compile = unix_wrap_single_compile
+
+        build_ext.build_extensions(self)
+
+    def get_ext_filename(self, ext_name):
+        # Get the original shared library name. For Python 3, this name will be
+        # suffixed with ".so", where  will be something like
+        # cpython-37m-x86_64-linux-gnu.
+        ext_filename = super().get_ext_filename(ext_name)
+        # If `no_python_abi_suffix` is `True`, we omit the Python 3 ABI
+        # component. This makes building shared libraries with setuptools that
+        # aren't Python modules nicer.
+        if self.no_python_abi_suffix:
+            # The parts will be e.g. ["my_extension", "cpython-37m-x86_64-linux-gnu", "so"].
+            ext_filename_parts = ext_filename.split('.')
+            # Omit the second to last element.
+            without_abi = ext_filename_parts[:-2] + ext_filename_parts[-1:]
+            ext_filename = '.'.join(without_abi)
+        return ext_filename
+
+    def _check_abi(self) -> tuple[str, TorchVersion]:
+        # On some platforms, like Windows, compiler_cxx is not available.
+        if hasattr(self.compiler, 'compiler_cxx'):
+            compiler = self.compiler.compiler_cxx[0]
+        else:
+            compiler = get_cxx_compiler()
+        _, version = get_compiler_abi_compatibility_and_version(compiler)
+        # Warn user if VC env is activated but `DISTUILS_USE_SDK` is not set.
+        if IS_WINDOWS and 'VSCMD_ARG_TGT_ARCH' in os.environ and 'DISTUTILS_USE_SDK' not in os.environ:
+            msg = ('It seems that the VC environment is activated but DISTUTILS_USE_SDK is not set.'
+                   'This may lead to multiple activations of the VC env.'
+                   'Please set `DISTUTILS_USE_SDK=1` and try again.')
+            raise UserWarning(msg)
+        return compiler, version
+
+    def _add_compile_flag(self, extension, flag):
+        extension.extra_compile_args = copy.deepcopy(extension.extra_compile_args)
+        if isinstance(extension.extra_compile_args, dict):
+            for args in extension.extra_compile_args.values():
+                args.append(flag)
+        else:
+            extension.extra_compile_args.append(flag)
+
+    # Simple hipify, replace the first occurrence of CUDA with HIP
+    # in flags starting with "-" and containing "CUDA", but exclude -I flags
+    def _hipify_compile_flags(self, extension):
+        if isinstance(extension.extra_compile_args, dict) and 'nvcc' in extension.extra_compile_args:
+            modified_flags = []
+            for flag in extension.extra_compile_args['nvcc']:
+                if flag.startswith("-") and "CUDA" in flag and not flag.startswith("-I"):
+                    # check/split flag into flag and value
+                    parts = flag.split("=", 1)
+                    if len(parts) == 2:
+                        flag_part, value_part = parts
+                        # replace fist instance of "CUDA" with "HIP" only in the flag and not flag value
+                        modified_flag_part = flag_part.replace("CUDA", "HIP", 1)
+                        modified_flag = f"{modified_flag_part}={value_part}"
+                    else:
+                        # replace fist instance of "CUDA" with "HIP" in flag
+                        modified_flag = flag.replace("CUDA", "HIP", 1)
+                    modified_flags.append(modified_flag)
+                    print(f'Modified flag: {flag} -> {modified_flag}', file=sys.stderr)
+                else:
+                    modified_flags.append(flag)
+            extension.extra_compile_args['nvcc'] = modified_flags
+
+    def _define_torch_extension_name(self, extension):
+        # pybind11 doesn't support dots in the names
+        # so in order to support extensions in the packages
+        # like torch._C, we take the last part of the string
+        # as the library name
+        names = extension.name.split('.')
+        name = names[-1]
+        define = f'-DTORCH_EXTENSION_NAME={name}'
+        self._add_compile_flag(extension, define)
+
+    def _add_gnu_cpp_abi_flag(self, extension):
+        # use the same CXX ABI as what PyTorch was compiled with
+        self._add_compile_flag(extension, '-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI)))
+
+
+def CppExtension(name, sources, *args, **kwargs):
+    """
+    Create a :class:`setuptools.Extension` for C++.
+
+    Convenience method that creates a :class:`setuptools.Extension` with the
+    bare minimum (but often sufficient) arguments to build a C++ extension.
+
+    All arguments are forwarded to the :class:`setuptools.Extension`
+    constructor. Full list arguments can be found at
+    https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
+
+    .. warning::
+        The PyTorch python API (as provided in libtorch_python) cannot be built
+        with the flag ``py_limited_api=True``.  When this flag is passed, it is
+        the user's responsibility in their library to not use APIs from
+        libtorch_python (in particular pytorch/python bindings) and to only use
+        APIs from libtorch (aten objects, operators and the dispatcher). For
+        example, to give access to custom ops from python, the library should
+        register the ops through the dispatcher.
+
+        Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
+        as a compile flag when py_limited_api is specified as an option for
+        the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
+        -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
+        safety, and sanity in order to encourage best practices. To target a
+        different version, set min_supported_cpython to the hexcode of the
+        CPython version of choice.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
+        >>> from setuptools import setup
+        >>> from torch.utils.cpp_extension import BuildExtension, CppExtension
+        >>> setup(
+        ...     name='extension',
+        ...     ext_modules=[
+        ...         CppExtension(
+        ...             name='extension',
+        ...             sources=['extension.cpp'],
+        ...             extra_compile_args=['-g'],
+        ...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
+        ...     ],
+        ...     cmdclass={
+        ...         'build_ext': BuildExtension
+        ...     })
+    """
+    include_dirs = kwargs.get('include_dirs', [])
+    include_dirs += include_paths()
+    kwargs['include_dirs'] = include_dirs
+
+    library_dirs = kwargs.get('library_dirs', [])
+    library_dirs += library_paths()
+    kwargs['library_dirs'] = library_dirs
+
+    libraries = kwargs.get('libraries', [])
+    libraries.append('c10')
+    libraries.append('torch')
+    libraries.append('torch_cpu')
+    if not kwargs.get('py_limited_api', False):
+        # torch_python uses more than the python limited api
+        libraries.append('torch_python')
+    if IS_WINDOWS:
+        libraries.append("sleef")
+
+    kwargs['libraries'] = libraries
+
+    kwargs['language'] = 'c++'
+    return setuptools.Extension(name, sources, *args, **kwargs)
+
+
+def CUDAExtension(name, sources, *args, **kwargs):
+    """
+    Create a :class:`setuptools.Extension` for CUDA/C++.
+
+    Convenience method that creates a :class:`setuptools.Extension` with the
+    bare minimum (but often sufficient) arguments to build a CUDA/C++
+    extension. This includes the CUDA include path, library path and runtime
+    library.
+
+    All arguments are forwarded to the :class:`setuptools.Extension`
+    constructor. Full list arguments can be found at
+    https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
+
+    .. warning::
+        The PyTorch python API (as provided in libtorch_python) cannot be built
+        with the flag ``py_limited_api=True``.  When this flag is passed, it is
+        the user's responsibility in their library to not use APIs from
+        libtorch_python (in particular pytorch/python bindings) and to only use
+        APIs from libtorch (aten objects, operators and the dispatcher). For
+        example, to give access to custom ops from python, the library should
+        register the ops through the dispatcher.
+
+        Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
+        as a compile flag when py_limited_api is specified as an option for
+        the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
+        -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
+        safety, and sanity in order to encourage best practices. To target a
+        different version, set min_supported_cpython to the hexcode of the
+        CPython version of choice.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
+        >>> from setuptools import setup
+        >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+        >>> setup(
+        ...     name='cuda_extension',
+        ...     ext_modules=[
+        ...         CUDAExtension(
+        ...                 name='cuda_extension',
+        ...                 sources=['extension.cpp', 'extension_kernel.cu'],
+        ...                 extra_compile_args={'cxx': ['-g'],
+        ...                                     'nvcc': ['-O2']},
+        ...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
+        ...     ],
+        ...     cmdclass={
+        ...         'build_ext': BuildExtension
+        ...     })
+
+    Compute capabilities:
+
+    By default the extension will be compiled to run on all archs of the cards visible during the
+    building process of the extension, plus PTX. If down the road a new card is installed the
+    extension may need to be recompiled. If a visible card has a compute capability (CC) that's
+    newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch
+    will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
+    support (see below for details on PTX).
+
+    You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
+    CCs you want the extension to support:
+
+    ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
+    ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
+
+    The +PTX option causes extension kernel binaries to include PTX instructions for the specified
+    CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
+    the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
+    CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
+    provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
+    those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
+    off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
+    "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
+    "8.0 8.6" would be better.
+
+    Note that while it's possible to include all supported archs, the more archs get included the
+    slower the building process will be, as it will build a separate kernel image for each arch.
+
+    Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
+    To workaround the issue, move python binding logic to pure C++ file.
+
+    Example use:
+        #include 
+        at::Tensor SigmoidAlphaBlendForwardCuda(....)
+
+    Instead of:
+        #include 
+        torch::Tensor SigmoidAlphaBlendForwardCuda(...)
+
+    Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
+    Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
+
+    Relocatable device code linking:
+
+    If you want to reference device symbols across compilation units (across object files),
+    the object files need to be built with `relocatable device code` (-rdc=true or -dc).
+    An exception to this rule is "dynamic parallelism" (nested kernel launches)  which is not used a lot anymore.
+    `Relocatable device code` is less optimized so it needs to be used only on object files that need it.
+    Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
+    helps reduce the protentional perf degradation of `-rdc`.
+    Note that it needs to be used at both steps to be useful.
+
+    If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
+    There is also a case where `-dlink` is used without `-rdc`:
+    when an extension is linked against a static lib containing rdc-compiled objects
+    like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
+
+    Note: Ninja is required to build a CUDA Extension with RDC linking.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
+        >>> CUDAExtension(
+        ...        name='cuda_extension',
+        ...        sources=['extension.cpp', 'extension_kernel.cu'],
+        ...        dlink=True,
+        ...        dlink_libraries=["dlink_lib"],
+        ...        extra_compile_args={'cxx': ['-g'],
+        ...                            'nvcc': ['-O2', '-rdc=true']})
+    """
+    library_dirs = kwargs.get('library_dirs', [])
+    library_dirs += library_paths(device_type="cuda")
+    kwargs['library_dirs'] = library_dirs
+
+    libraries = kwargs.get('libraries', [])
+    libraries.append('c10')
+    libraries.append('torch')
+    libraries.append('torch_cpu')
+    if not kwargs.get('py_limited_api', False):
+        # torch_python uses more than the python limited api
+        libraries.append('torch_python')
+    if IS_HIP_EXTENSION:
+        libraries.append('amdhip64')
+        libraries.append('c10_hip')
+        libraries.append('torch_hip')
+    else:
+        libraries.append('cudart')
+        libraries.append('c10_cuda')
+        libraries.append('torch_cuda')
+    kwargs['libraries'] = libraries
+
+    include_dirs = kwargs.get('include_dirs', [])
+
+    if IS_HIP_EXTENSION:
+        build_dir = os.getcwd()
+        hipify_result = hipify_python.hipify(
+            project_directory=build_dir,
+            output_directory=build_dir,
+            header_include_dirs=include_dirs,
+            includes=[os.path.join(build_dir, '*')],  # limit scope to build_dir only
+            extra_files=[os.path.abspath(s) for s in sources],
+            show_detailed=True,
+            is_pytorch_extension=True,
+            hipify_extra_files_only=True,  # don't hipify everything in includes path
+        )
+
+        hipified_sources = set()
+        for source in sources:
+            s_abs = os.path.abspath(source)
+            hipified_s_abs = (hipify_result[s_abs].hipified_path if (s_abs in hipify_result and
+                              hipify_result[s_abs].hipified_path is not None) else s_abs)
+            # setup() arguments must *always* be /-separated paths relative to the setup.py directory,
+            # *never* absolute paths
+            hipified_sources.add(os.path.relpath(hipified_s_abs, build_dir))
+
+        sources = list(hipified_sources)
+
+    include_dirs += include_paths(device_type="cuda")
+    kwargs['include_dirs'] = include_dirs
+
+    kwargs['language'] = 'c++'
+
+    dlink_libraries = kwargs.get('dlink_libraries', [])
+    dlink = kwargs.get('dlink', False) or dlink_libraries
+    if dlink:
+        extra_compile_args = kwargs.get('extra_compile_args', {})
+
+        extra_compile_args_dlink = extra_compile_args.get('nvcc_dlink', [])
+        extra_compile_args_dlink += ['-dlink']
+        extra_compile_args_dlink += [f'-L{x}' for x in library_dirs]
+        extra_compile_args_dlink += [f'-l{x}' for x in dlink_libraries]
+
+        if (torch.version.cuda is not None) and TorchVersion(torch.version.cuda) >= '11.2':
+            extra_compile_args_dlink += ['-dlto']   # Device Link Time Optimization started from cuda 11.2
+
+        extra_compile_args['nvcc_dlink'] = extra_compile_args_dlink
+
+        kwargs['extra_compile_args'] = extra_compile_args
+
+    return setuptools.Extension(name, sources, *args, **kwargs)
+
+
+def SyclExtension(name, sources, *args, **kwargs):
+    r"""
+    Creates a :class:`setuptools.Extension` for SYCL/C++.
+
+    Convenience method that creates a :class:`setuptools.Extension` with the
+    bare minimum (but often sufficient) arguments to build a SYCL/C++
+    extension.
+
+    All arguments are forwarded to the :class:`setuptools.Extension`
+    constructor.
+
+    .. warning::
+        The PyTorch python API (as provided in libtorch_python) cannot be built
+        with the flag ``py_limited_api=True``.  When this flag is passed, it is
+        the user's responsibility in their library to not use APIs from
+        libtorch_python (in particular pytorch/python bindings) and to only use
+        APIs from libtorch (aten objects, operators and the dispatcher). For
+        example, to give access to custom ops from python, the library should
+        register the ops through the dispatcher.
+
+        Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
+        as a compile flag when py_limited_api is specified as an option for
+        the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
+        -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
+        safety, and sanity in order to encourage best practices. To target a
+        different version, set min_supported_cpython to the hexcode of the
+        CPython version of choice.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
+        >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension
+        >>> setup(
+        ...     name='xpu_extension',
+        ...     ext_modules=[
+        ...     SyclExtension(
+        ...                 name='xpu_extension',
+        ...                 sources=['extension.cpp', 'extension_kernel.cpp'],
+        ...                 extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']})
+        ...     ],
+        ...     cmdclass={
+        ...         'build_ext': BuildExtension
+        ...     })
+
+    By default the extension will be compiled to run on all archs of the cards visible during the
+    building process of the extension. If down the road a new card is installed the
+    extension may need to be recompiled. You can override the default behavior using
+    `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension
+    to support:
+
+    ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py``
+
+    Note that while it's possible to include all supported archs, the more archs get included the
+    slower the building process will be, as it will build a separate kernel image for each arch.
+
+    Note: Ninja is required to build SyclExtension.
+    """
+    library_dirs = kwargs.get("library_dirs", [])
+    library_dirs += library_paths()
+    kwargs["library_dirs"] = library_dirs
+
+    libraries = kwargs.get("libraries", [])
+    libraries.append("c10")
+    libraries.append("c10_xpu")
+    libraries.append("torch")
+    libraries.append("torch_cpu")
+    if not kwargs.get('py_limited_api', False):
+        # torch_python uses more than the python limited api
+        libraries.append("torch_python")
+    libraries.append("torch_xpu")
+    kwargs["libraries"] = libraries
+
+    include_dirs = kwargs.get("include_dirs", [])
+    include_dirs += include_paths()
+    kwargs["include_dirs"] = include_dirs
+
+    kwargs["language"] = "c++"
+
+    return setuptools.Extension(name, sources, *args, **kwargs)
+
+def include_paths(device_type: str = "cpu") -> list[str]:
+    """
+    Get the include paths required to build a C++ or CUDA or SYCL extension.
+
+    Args:
+        device_type: Defaults to "cpu".
+    Returns:
+        A list of include path strings.
+    """
+    lib_include = os.path.join(_TORCH_PATH, 'include')
+    paths = [
+        lib_include,
+        # Remove this once torch/torch.h is officially no longer supported for C++ extensions.
+        os.path.join(lib_include, 'torch', 'csrc', 'api', 'include'),
+    ]
+    if device_type == "cuda" and IS_HIP_EXTENSION:
+        paths.append(os.path.join(lib_include, 'THH'))
+        paths.append(_join_rocm_home('include'))
+    elif device_type == "cuda":
+        cuda_home_include = _join_cuda_home('include')
+        # if we have the Debian/Ubuntu packages for cuda, we get /usr as cuda home.
+        # but gcc doesn't like having /usr/include passed explicitly
+        if cuda_home_include != '/usr/include':
+            paths.append(cuda_home_include)
+
+        # Support CUDA_INC_PATH env variable supported by CMake files
+        if (cuda_inc_path := os.environ.get("CUDA_INC_PATH", None)) and \
+                cuda_inc_path != '/usr/include':
+            paths.append(cuda_inc_path)
+        if CUDNN_HOME is not None:
+            paths.append(os.path.join(CUDNN_HOME, 'include'))
+    elif device_type == "xpu":
+        paths.append(_join_sycl_home('include'))
+        paths.append(_join_sycl_home('include', 'sycl'))
+    return paths
+
+
+def library_paths(device_type: str = "cpu") -> list[str]:
+    """
+    Get the library paths required to build a C++ or CUDA extension.
+
+    Args:
+        device_type: Defaults to "cpu".
+
+    Returns:
+        A list of library path strings.
+    """
+    # We need to link against libtorch.so
+    paths = [TORCH_LIB_PATH]
+
+    if device_type == "cuda" and IS_HIP_EXTENSION:
+        lib_dir = 'lib'
+        paths.append(_join_rocm_home(lib_dir))
+        if HIP_HOME is not None:
+            paths.append(os.path.join(HIP_HOME, 'lib'))
+    elif device_type == "cuda":
+        if IS_WINDOWS:
+            lib_dir = os.path.join('lib', 'x64')
+        else:
+            lib_dir = 'lib64'
+            if (not os.path.exists(_join_cuda_home(lib_dir)) and
+                    os.path.exists(_join_cuda_home('lib'))):
+                # 64-bit CUDA may be installed in 'lib' (see e.g. gh-16955)
+                # Note that it's also possible both don't exist (see
+                # _find_cuda_home) - in that case we stay with 'lib64'.
+                lib_dir = 'lib'
+
+        paths.append(_join_cuda_home(lib_dir))
+        if CUDNN_HOME is not None:
+            paths.append(os.path.join(CUDNN_HOME, lib_dir))
+    elif device_type == "xpu":
+        if IS_WINDOWS:
+            lib_dir = os.path.join('lib', 'x64')
+        else:
+            lib_dir = 'lib64'
+            if (not os.path.exists(_join_sycl_home(lib_dir)) and
+                    os.path.exists(_join_sycl_home('lib'))):
+                lib_dir = 'lib'
+
+        paths.append(_join_sycl_home(lib_dir))
+
+    return paths
+
+
+def load(name,
+         sources: Union[str, list[str]],
+         extra_cflags=None,
+         extra_cuda_cflags=None,
+         extra_sycl_cflags=None,
+         extra_ldflags=None,
+         extra_include_paths=None,
+         build_directory=None,
+         verbose=False,
+         with_cuda: Optional[bool] = None,
+         with_sycl: Optional[bool] = None,
+         is_python_module=True,
+         is_standalone=False,
+         keep_intermediates=True):
+    """
+    Load a PyTorch C++ extension just-in-time (JIT).
+
+    To load an extension, a Ninja build file is emitted, which is used to
+    compile the given sources into a dynamic library. This library is
+    subsequently loaded into the current Python process as a module and
+    returned from this function, ready for use.
+
+    By default, the directory to which the build file is emitted and the
+    resulting library compiled to is ``/torch_extensions/``, where
+    ```` is the temporary folder on the current platform and ````
+    the name of the extension. This location can be overridden in two ways.
+    First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
+    replaces ``/torch_extensions`` and all extensions will be compiled
+    into subfolders of this directory. Second, if the ``build_directory``
+    argument to this function is supplied, it overrides the entire path, i.e.
+    the library will be compiled into that folder directly.
+
+    To compile the sources, the default system compiler (``c++``) is used,
+    which can be overridden by setting the ``CXX`` environment variable. To pass
+    additional arguments to the compilation process, ``extra_cflags`` or
+    ``extra_ldflags`` can be provided. For example, to compile your extension
+    with optimizations, pass ``extra_cflags=['-O3']``. You can also use
+    ``extra_cflags`` to pass further include directories.
+
+    CUDA support with mixed compilation is provided. Simply pass CUDA source
+    files (``.cu`` or ``.cuh``) along with other sources. Such files will be
+    detected and compiled with nvcc rather than the C++ compiler. This includes
+    passing the CUDA lib64 directory as a library directory, and linking
+    ``cudart``. You can pass additional flags to nvcc via
+    ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
+    heuristics for finding the CUDA install directory are used, which usually
+    work fine. If not, setting the ``CUDA_HOME`` environment variable is the
+    safest option.
+
+    SYCL support with mixed compilation is provided. Simply pass SYCL source
+    files (``.sycl``) along with other sources. Such files will be detected
+    and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather
+    than the C++ compiler. You can pass additional flags to SYCL compiler
+    via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++.
+    SYCL compiler is expected to be found via system PATH environment
+    variable.
+
+    Args:
+        name: The name of the extension to build. This MUST be the same as the
+            name of the pybind11 module!
+        sources: A list of relative or absolute paths to C++ source files.
+        extra_cflags: optional list of compiler flags to forward to the build.
+        extra_cuda_cflags: optional list of compiler flags to forward to nvcc
+            when building CUDA sources.
+        extra_sycl_cflags: optional list of compiler flags to forward to SYCL
+            compiler when building SYCL sources.
+        extra_ldflags: optional list of linker flags to forward to the build.
+        extra_include_paths: optional list of include directories to forward
+            to the build.
+        build_directory: optional path to use as build workspace.
+        verbose: If ``True``, turns on verbose logging of load steps.
+        with_cuda: Determines whether CUDA headers and libraries are added to
+            the build. If set to ``None`` (default), this value is
+            automatically determined based on the existence of ``.cu`` or
+            ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
+            and libraries to be included.
+        with_sycl: Determines whether SYCL headers and libraries are added to
+            the build. If set to ``None`` (default), this value is
+            automatically determined based on the existence of ``.sycl`` in
+            ``sources``. Set it to `True`` to force SYCL headers and
+            libraries to be included.
+        is_python_module: If ``True`` (default), imports the produced shared
+            library as a Python module. If ``False``, behavior depends on
+            ``is_standalone``.
+        is_standalone: If ``False`` (default) loads the constructed extension
+            into the process as a plain dynamic library. If ``True``, build a
+            standalone executable.
+
+    Returns:
+        If ``is_python_module`` is ``True``:
+            Returns the loaded PyTorch extension as a Python module.
+
+        If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
+            Returns nothing. (The shared library is loaded into the process as
+            a side effect.)
+
+        If ``is_standalone`` is ``True``.
+            Return the path to the executable. (On Windows, TORCH_LIB_PATH is
+            added to the PATH environment variable as a side effect.)
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torch.utils.cpp_extension import load
+        >>> module = load(
+        ...     name='extension',
+        ...     sources=['extension.cpp', 'extension_kernel.cu'],
+        ...     extra_cflags=['-O2'],
+        ...     verbose=True)
+    """
+    return _jit_compile(
+        name,
+        [sources] if isinstance(sources, str) else sources,
+        extra_cflags,
+        extra_cuda_cflags,
+        extra_sycl_cflags,
+        extra_ldflags,
+        extra_include_paths,
+        build_directory or _get_build_directory(name, verbose),
+        verbose,
+        with_cuda,
+        with_sycl,
+        is_python_module,
+        is_standalone,
+        keep_intermediates=keep_intermediates)
+
+def _get_pybind11_abi_build_flags():
+    # Note [Pybind11 ABI constants]
+    #
+    # Pybind11 before 2.4 used to build an ABI strings using the following pattern:
+    # f"__pybind11_internals_v{PYBIND11_INTERNALS_VERSION}{PYBIND11_INTERNALS_KIND}{PYBIND11_BUILD_TYPE}__"
+    # Since 2.4 compier type, stdlib and build abi parameters are also encoded like this:
+    # f"__pybind11_internals_v{PYBIND11_INTERNALS_VERSION}{PYBIND11_INTERNALS_KIND}{PYBIND11_COMPILER_TYPE}{PYBIND11_STDLIB}{PYBIND11_BUILD_ABI}{PYBIND11_BUILD_TYPE}__"
+    #
+    # This was done in order to further narrow down the chances of compiler ABI incompatibility
+    # that can cause a hard to debug segfaults.
+    # For PyTorch extensions we want to relax those restrictions and pass compiler, stdlib and abi properties
+    # captured during PyTorch native library compilation in torch/csrc/Module.cpp
+
+    abi_cflags = []
+    for pname in ["COMPILER_TYPE", "STDLIB", "BUILD_ABI"]:
+        pval = getattr(torch._C, f"_PYBIND11_{pname}")
+        if pval is not None and not IS_WINDOWS:
+            abi_cflags.append(f'-DPYBIND11_{pname}=\\"{pval}\\"')
+    return abi_cflags
+
+def _get_glibcxx_abi_build_flags():
+    glibcxx_abi_cflags = ['-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI))]
+    return glibcxx_abi_cflags
+
+def check_compiler_is_gcc(compiler):
+    if not IS_LINUX:
+        return False
+
+    env = os.environ.copy()
+    env['LC_ALL'] = 'C'  # Don't localize output
+    try:
+        version_string = subprocess.check_output([compiler, '-v'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS)
+    except Exception:
+        try:
+            version_string = subprocess.check_output([compiler, '--version'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS)
+        except Exception:
+            return False
+    # Check for 'gcc' or 'g++' for sccache wrapper
+    pattern = re.compile("^COLLECT_GCC=(.*)$", re.MULTILINE)
+    results = re.findall(pattern, version_string)
+    if len(results) != 1:
+        return False
+    compiler_path = os.path.realpath(results[0].strip())
+    # On RHEL/CentOS c++ is a gcc compiler wrapper
+    if os.path.basename(compiler_path) == 'c++' and 'gcc version' in version_string:
+        return True
+    return False
+
+def _check_and_build_extension_h_precompiler_headers(
+        extra_cflags,
+        extra_include_paths,
+        is_standalone=False):
+    r'''
+    Precompiled Headers(PCH) can pre-build the same headers and reduce build time for pytorch load_inline modules.
+    GCC offical manual: https://gcc.gnu.org/onlinedocs/gcc-4.0.4/gcc/Precompiled-Headers.html
+    PCH only works when built pch file(header.h.gch) and build target have the same build parameters. So, We need
+    add a signature file to record PCH file parameters. If the build parameters(signature) changed, it should rebuild
+    PCH file.
+
+    Note:
+    1. Windows and MacOS have different PCH mechanism. We only support Linux currently.
+    2. It only works on GCC/G++.
+    '''
+    if not IS_LINUX:
+        return
+
+    compiler = get_cxx_compiler()
+
+    b_is_gcc = check_compiler_is_gcc(compiler)
+    if b_is_gcc is False:
+        return
+
+    head_file = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h')
+    head_file_pch = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.gch')
+    head_file_signature = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.sign')
+
+    def listToString(s):
+        # initialize an empty string
+        string = ""
+        if s is None:
+            return string
+
+        # traverse in the string
+        for element in s:
+            string += (element + ' ')
+        # return string
+        return string
+
+    def format_precompiler_header_cmd(compiler, head_file, head_file_pch, common_cflags, torch_include_dirs, extra_cflags, extra_include_paths):
+        return re.sub(
+            r"[ \n]+",
+            " ",
+            f"""
+                {compiler} -x c++-header {head_file} -o {head_file_pch} {torch_include_dirs} {extra_include_paths} {extra_cflags} {common_cflags}
+            """,
+        ).strip()
+
+    def command_to_signature(cmd):
+        signature = cmd.replace(' ', '_')
+        return signature
+
+    def check_pch_signature_in_file(file_path, signature):
+        b_exist = os.path.isfile(file_path)
+        if b_exist is False:
+            return False
+
+        with open(file_path) as file:
+            # read all content of a file
+            content = file.read()
+            # check if string present in a file
+            return signature == content
+
+    def _create_if_not_exist(path_dir):
+        if not os.path.exists(path_dir):
+            try:
+                Path(path_dir).mkdir(parents=True, exist_ok=True)
+            except OSError as exc:  # Guard against race condition
+                if exc.errno != errno.EEXIST:
+                    raise RuntimeError(f"Fail to create path {path_dir}") from exc
+
+    def write_pch_signature_to_file(file_path, pch_sign):
+        _create_if_not_exist(os.path.dirname(file_path))
+        with open(file_path, "w") as f:
+            f.write(pch_sign)
+            f.close()
+
+    def build_precompile_header(pch_cmd):
+        try:
+            subprocess.check_output(pch_cmd, shell=True, stderr=subprocess.STDOUT)
+        except subprocess.CalledProcessError as e:
+            raise RuntimeError(f"Compile PreCompile Header fail, command: {pch_cmd}") from e
+
+    extra_cflags_str = listToString(extra_cflags)
+    extra_include_paths_str = " ".join(
+        [f"-I{include}" for include in extra_include_paths] if extra_include_paths else []
+    )
+
+    lib_include = os.path.join(_TORCH_PATH, 'include')
+    torch_include_dirs = [
+        f"-I {lib_include}",
+        # Python.h
+        "-I {}".format(sysconfig.get_path("include")),
+        # torch/all.h
+        "-I {}".format(os.path.join(lib_include, 'torch', 'csrc', 'api', 'include')),
+    ]
+
+    torch_include_dirs_str = listToString(torch_include_dirs)
+
+    common_cflags = []
+    if not is_standalone:
+        common_cflags += ['-DTORCH_API_INCLUDE_EXTENSION_H']
+
+    common_cflags += ['-std=c++17', '-fPIC']
+    common_cflags += [f"{x}" for x in _get_pybind11_abi_build_flags()]
+    common_cflags += [f"{x}" for x in _get_glibcxx_abi_build_flags()]
+    common_cflags_str = listToString(common_cflags)
+
+    pch_cmd = format_precompiler_header_cmd(compiler, head_file, head_file_pch, common_cflags_str, torch_include_dirs_str, extra_cflags_str, extra_include_paths_str)
+    pch_sign = command_to_signature(pch_cmd)
+
+    if os.path.isfile(head_file_pch) is not True:
+        build_precompile_header(pch_cmd)
+        write_pch_signature_to_file(head_file_signature, pch_sign)
+    else:
+        b_same_sign = check_pch_signature_in_file(head_file_signature, pch_sign)
+        if b_same_sign is False:
+            build_precompile_header(pch_cmd)
+            write_pch_signature_to_file(head_file_signature, pch_sign)
+
+def remove_extension_h_precompiler_headers():
+    def _remove_if_file_exists(path_file):
+        if os.path.exists(path_file):
+            os.remove(path_file)
+
+    head_file_pch = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.gch')
+    head_file_signature = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.sign')
+
+    _remove_if_file_exists(head_file_pch)
+    _remove_if_file_exists(head_file_signature)
+
+def load_inline(name,
+                cpp_sources,
+                cuda_sources=None,
+                sycl_sources=None,
+                functions=None,
+                extra_cflags=None,
+                extra_cuda_cflags=None,
+                extra_sycl_cflags=None,
+                extra_ldflags=None,
+                extra_include_paths=None,
+                build_directory=None,
+                verbose=False,
+                with_cuda=None,
+                with_sycl=None,
+                is_python_module=True,
+                with_pytorch_error_handling=True,
+                keep_intermediates=True,
+                use_pch=False):
+    r'''
+    Load a PyTorch C++ extension just-in-time (JIT) from string sources.
+
+    This function behaves exactly like :func:`load`, but takes its sources as
+    strings rather than filenames. These strings are stored to files in the
+    build directory, after which the behavior of :func:`load_inline` is
+    identical to :func:`load`.
+
+    See `the
+    tests `_
+    for good examples of using this function.
+
+    Sources may omit two required parts of a typical non-inline C++ extension:
+    the necessary header includes, as well as the (pybind11) binding code. More
+    precisely, strings passed to ``cpp_sources`` are first concatenated into a
+    single ``.cpp`` file. This file is then prepended with ``#include
+    ``.
+
+    Furthermore, if the ``functions`` argument is supplied, bindings will be
+    automatically generated for each function specified. ``functions`` can
+    either be a list of function names, or a dictionary mapping from function
+    names to docstrings. If a list is given, the name of each function is used
+    as its docstring.
+
+    The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
+    file and  prepended with ``torch/types.h``, ``cuda.h`` and
+    ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
+    separately, but ultimately linked into a single library. Note that no
+    bindings are generated for functions in ``cuda_sources`` per se. To bind
+    to a CUDA kernel, you must create a C++ function that calls it, and either
+    declare or define this C++ function in one of the ``cpp_sources`` (and
+    include its name in ``functions``).
+
+    The sources in ``sycl_sources`` are concatenated into a separate ``.sycl``
+    file and  prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes.
+    The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately
+    linked into a single library. Note that no bindings are generated for
+    functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must
+    create a C++ function that calls it, and either declare or define this
+    C++ function in one of the ``cpp_sources`` (and include its name
+    in ``functions``).
+
+    See :func:`load` for a description of arguments omitted below.
+
+    Args:
+        cpp_sources: A string, or list of strings, containing C++ source code.
+        cuda_sources: A string, or list of strings, containing CUDA source code.
+        sycl_sources: A string, or list of strings, containing SYCL source code.
+        functions: A list of function names for which to generate function
+            bindings. If a dictionary is given, it should map function names to
+            docstrings (which are otherwise just the function names).
+        with_cuda: Determines whether CUDA headers and libraries are added to
+            the build. If set to ``None`` (default), this value is
+            automatically determined based on whether ``cuda_sources`` is
+            provided. Set it to ``True`` to force CUDA headers
+            and libraries to be included.
+        with_sycl: Determines whether SYCL headers and libraries are added to
+            the build. If set to ``None`` (default), this value is
+            automatically determined based on whether ``sycl_sources`` is
+            provided. Set it to ``True`` to force SYCL headers
+            and libraries to be included.
+        with_pytorch_error_handling: Determines whether pytorch error and
+            warning macros are handled by pytorch instead of pybind. To do
+            this, each function ``foo`` is called via an intermediary ``_safe_foo``
+            function. This redirection might cause issues in obscure cases
+            of cpp. This flag should be set to ``False`` when this redirect
+            causes issues.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
+        >>> from torch.utils.cpp_extension import load_inline
+        >>> source = """
+        at::Tensor sin_add(at::Tensor x, at::Tensor y) {
+          return x.sin() + y.sin();
+        }
+        """
+        >>> module = load_inline(name='inline_extension',
+        ...                      cpp_sources=[source],
+        ...                      functions=['sin_add'])
+
+    .. note::
+        Since load_inline will just-in-time compile the source code, please ensure
+        that you have the right toolchains installed in the runtime. For example,
+        when loading C++, make sure a C++ compiler is available. If you're loading
+        a CUDA extension, you will need to additionally install the corresponding CUDA
+        toolkit (nvcc and any other dependencies your code has). Compiling toolchains
+        are not included when you install torch and must be additionally installed.
+
+        During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build
+        the extension. This may use up too many resources on some systems. One
+        can control the number of workers by setting the `MAX_JOBS` environment
+        variable to a non-negative number.
+    '''
+    build_directory = build_directory or _get_build_directory(name, verbose)
+
+    if isinstance(cpp_sources, str):
+        cpp_sources = [cpp_sources]
+    cuda_sources = cuda_sources or []
+    if isinstance(cuda_sources, str):
+        cuda_sources = [cuda_sources]
+    sycl_sources = sycl_sources or []
+    if isinstance(sycl_sources, str):
+        sycl_sources = [sycl_sources]
+
+    cpp_sources.insert(0, '#include ')
+
+    if use_pch is True:
+        # Using PreCompile Header('torch/extension.h') to reduce compile time.
+        _check_and_build_extension_h_precompiler_headers(extra_cflags, extra_include_paths)
+    else:
+        remove_extension_h_precompiler_headers()
+
+    # If `functions` is supplied, we create the pybind11 bindings for the user.
+    # Here, `functions` is (or becomes, after some processing) a map from
+    # function names to function docstrings.
+    if functions is not None:
+        module_def = []
+        module_def.append('PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {')
+        if isinstance(functions, str):
+            functions = [functions]
+        if isinstance(functions, list):
+            # Make the function docstring the same as the function name.
+            functions = {f: f for f in functions}
+        elif not isinstance(functions, dict):
+            raise ValueError(f"Expected 'functions' to be a list or dict, but was {type(functions)}")
+        for function_name, docstring in functions.items():
+            if with_pytorch_error_handling:
+                module_def.append(f'm.def("{function_name}", torch::wrap_pybind_function({function_name}), "{docstring}");')
+            else:
+                module_def.append(f'm.def("{function_name}", {function_name}, "{docstring}");')
+        module_def.append('}')
+        cpp_sources += module_def
+
+    cpp_source_path = os.path.join(build_directory, 'main.cpp')
+    _maybe_write(cpp_source_path, "\n".join(cpp_sources))
+
+    sources = [cpp_source_path]
+
+    if cuda_sources:
+        cuda_sources.insert(0, '#include ')
+        cuda_sources.insert(1, '#include ')
+        cuda_sources.insert(2, '#include ')
+
+        cuda_source_path = os.path.join(build_directory, 'cuda.cu')
+        _maybe_write(cuda_source_path, "\n".join(cuda_sources))
+
+        sources.append(cuda_source_path)
+
+    if sycl_sources:
+        sycl_sources.insert(0, '#include ')
+        sycl_sources.insert(1, '#include ')
+
+        sycl_source_path = os.path.join(build_directory, 'sycl.sycl')
+        _maybe_write(sycl_source_path, "\n".join(sycl_sources))
+
+        sources.append(sycl_source_path)
+
+    return _jit_compile(
+        name,
+        sources,
+        extra_cflags,
+        extra_cuda_cflags,
+        extra_sycl_cflags,
+        extra_ldflags,
+        extra_include_paths,
+        build_directory,
+        verbose,
+        with_cuda,
+        with_sycl,
+        is_python_module,
+        is_standalone=False,
+        keep_intermediates=keep_intermediates)
+
+
+def _jit_compile(name,
+                 sources,
+                 extra_cflags,
+                 extra_cuda_cflags,
+                 extra_sycl_cflags,
+                 extra_ldflags,
+                 extra_include_paths,
+                 build_directory: str,
+                 verbose: bool,
+                 with_cuda: Optional[bool],
+                 with_sycl: Optional[bool],
+                 is_python_module,
+                 is_standalone,
+                 keep_intermediates=True) -> None:
+    if is_python_module and is_standalone:
+        raise ValueError("`is_python_module` and `is_standalone` are mutually exclusive.")
+
+    if with_cuda is None:
+        with_cuda = any(map(_is_cuda_file, sources))
+    with_cudnn = any('cudnn' in f for f in extra_ldflags or [])
+    if with_sycl is None:
+        with_sycl = any(map(_is_sycl_file, sources))
+    old_version = JIT_EXTENSION_VERSIONER.get_version(name)
+    version = JIT_EXTENSION_VERSIONER.bump_version_if_changed(
+        name,
+        sources,
+        build_arguments=[extra_cflags, extra_cuda_cflags, extra_ldflags, extra_include_paths],
+        build_directory=build_directory,
+        with_cuda=with_cuda,
+        with_sycl=with_sycl,
+        is_python_module=is_python_module,
+        is_standalone=is_standalone,
+    )
+    if version > 0:
+        if version != old_version and verbose:
+            print(f'The input conditions for extension module {name} have changed. ' +
+                  f'Bumping to version {version} and re-building as {name}_v{version}...',
+                  file=sys.stderr)
+        name = f'{name}_v{version}'
+
+    baton = FileBaton(os.path.join(build_directory, 'lock'))
+    if baton.try_acquire():
+        try:
+            if version != old_version:
+                with GeneratedFileCleaner(keep_intermediates=keep_intermediates) as clean_ctx:
+                    if IS_HIP_EXTENSION and (with_cuda or with_cudnn):
+                        hipify_result = hipify_python.hipify(
+                            project_directory=build_directory,
+                            output_directory=build_directory,
+                            header_include_dirs=(extra_include_paths if extra_include_paths is not None else []),
+                            extra_files=[os.path.abspath(s) for s in sources],
+                            ignores=[_join_rocm_home('*'), os.path.join(_TORCH_PATH, '*')],  # no need to hipify ROCm or PyTorch headers
+                            show_detailed=verbose,
+                            show_progress=verbose,
+                            is_pytorch_extension=True,
+                            clean_ctx=clean_ctx
+                        )
+
+                        hipified_sources = set()
+                        for source in sources:
+                            s_abs = os.path.abspath(source)
+                            hipified_sources.add(hipify_result[s_abs].hipified_path if s_abs in hipify_result else s_abs)
+
+                        sources = list(hipified_sources)
+
+                    _write_ninja_file_and_build_library(
+                        name=name,
+                        sources=sources,
+                        extra_cflags=extra_cflags or [],
+                        extra_cuda_cflags=extra_cuda_cflags or [],
+                        extra_sycl_cflags=extra_sycl_cflags or [],
+                        extra_ldflags=extra_ldflags or [],
+                        extra_include_paths=extra_include_paths or [],
+                        build_directory=build_directory,
+                        verbose=verbose,
+                        with_cuda=with_cuda,
+                        with_sycl=with_sycl,
+                        is_standalone=is_standalone)
+            elif verbose:
+                print('No modifications detected for re-loaded extension '
+                      f'module {name}, skipping build step...', file=sys.stderr)
+        finally:
+            baton.release()
+    else:
+        baton.wait()
+
+    if verbose:
+        print(f'Loading extension module {name}...', file=sys.stderr)
+
+    if is_standalone:
+        return _get_exec_path(name, build_directory)
+
+    return _import_module_from_library(name, build_directory, is_python_module)
+
+
+def _write_ninja_file_and_compile_objects(
+        sources: list[str],
+        objects,
+        cflags,
+        post_cflags,
+        cuda_cflags,
+        cuda_post_cflags,
+        cuda_dlink_post_cflags,
+        sycl_cflags,
+        sycl_post_cflags,
+        sycl_dlink_post_cflags,
+        build_directory: str,
+        verbose: bool,
+        with_cuda: Optional[bool],
+        with_sycl: Optional[bool]) -> None:
+    verify_ninja_availability()
+
+    compiler = get_cxx_compiler()
+
+    get_compiler_abi_compatibility_and_version(compiler)
+    if with_cuda is None:
+        with_cuda = any(map(_is_cuda_file, sources))
+    if with_sycl is None:
+        with_sycl = any(map(_is_sycl_file, sources))
+    build_file_path = os.path.join(build_directory, 'build.ninja')
+    if verbose:
+        print(f'Emitting ninja build file {build_file_path}...', file=sys.stderr)
+
+    # Create build_directory if it does not exist
+    if not os.path.exists(build_directory):
+        if verbose:
+            print(f'Creating directory {build_directory}...', file=sys.stderr)
+        # This is like mkdir -p, i.e. will also create parent directories.
+        os.makedirs(build_directory, exist_ok=True)
+
+    _write_ninja_file(
+        path=build_file_path,
+        cflags=cflags,
+        post_cflags=post_cflags,
+        cuda_cflags=cuda_cflags,
+        cuda_post_cflags=cuda_post_cflags,
+        cuda_dlink_post_cflags=cuda_dlink_post_cflags,
+        sycl_cflags=sycl_cflags,
+        sycl_post_cflags=sycl_post_cflags,
+        sycl_dlink_post_cflags=sycl_dlink_post_cflags,
+        sources=sources,
+        objects=objects,
+        ldflags=None,
+        library_target=None,
+        with_cuda=with_cuda,
+        with_sycl=with_sycl)
+    if verbose:
+        print('Compiling objects...', file=sys.stderr)
+    _run_ninja_build(
+        build_directory,
+        verbose,
+        # It would be better if we could tell users the name of the extension
+        # that failed to build but there isn't a good way to get it here.
+        error_prefix='Error compiling objects for extension')
+
+
+def _write_ninja_file_and_build_library(
+        name,
+        sources: list[str],
+        extra_cflags,
+        extra_cuda_cflags,
+        extra_sycl_cflags,
+        extra_ldflags,
+        extra_include_paths,
+        build_directory: str,
+        verbose: bool,
+        with_cuda: Optional[bool],
+        with_sycl: Optional[bool],
+        is_standalone: bool = False) -> None:
+    verify_ninja_availability()
+
+    compiler = get_cxx_compiler()
+
+    get_compiler_abi_compatibility_and_version(compiler)
+    if with_cuda is None:
+        with_cuda = any(map(_is_cuda_file, sources))
+    if with_sycl is None:
+        with_sycl = any(map(_is_sycl_file, sources))
+    extra_ldflags = _prepare_ldflags(
+        extra_ldflags or [],
+        with_cuda,
+        verbose,
+        is_standalone)
+    build_file_path = os.path.join(build_directory, 'build.ninja')
+    if verbose:
+        print(f'Emitting ninja build file {build_file_path}...', file=sys.stderr)
+
+    # Create build_directory if it does not exist
+    if not os.path.exists(build_directory):
+        if verbose:
+            print(f'Creating directory {build_directory}...', file=sys.stderr)
+        # This is like mkdir -p, i.e. will also create parent directories.
+        os.makedirs(build_directory, exist_ok=True)
+
+    # NOTE: Emitting a new ninja build file does not cause re-compilation if
+    # the sources did not change, so it's ok to re-emit (and it's fast).
+    _write_ninja_file_to_build_library(
+        path=build_file_path,
+        name=name,
+        sources=sources,
+        extra_cflags=extra_cflags or [],
+        extra_cuda_cflags=extra_cuda_cflags or [],
+        extra_sycl_cflags=extra_sycl_cflags or [],
+        extra_ldflags=extra_ldflags or [],
+        extra_include_paths=extra_include_paths or [],
+        with_cuda=with_cuda,
+        with_sycl=with_sycl,
+        is_standalone=is_standalone)
+
+    if verbose:
+        print(f'Building extension module {name}...', file=sys.stderr)
+    _run_ninja_build(
+        build_directory,
+        verbose,
+        error_prefix=f"Error building extension '{name}'")
+
+
+def is_ninja_available():
+    """Return ``True`` if the `ninja `_ build system is available on the system, ``False`` otherwise."""
+    try:
+        subprocess.check_output('ninja --version'.split())
+    except Exception:
+        return False
+    else:
+        return True
+
+
+def verify_ninja_availability():
+    """Raise ``RuntimeError`` if `ninja `_ build system is not available on the system, does nothing otherwise."""
+    if not is_ninja_available():
+        raise RuntimeError("Ninja is required to load C++ extensions")
+
+
+def _prepare_ldflags(extra_ldflags, with_cuda, verbose, is_standalone):
+    if IS_WINDOWS:
+        python_lib_path = os.path.join(sys.base_exec_prefix, 'libs')
+
+        extra_ldflags.append('c10.lib')
+        if with_cuda:
+            extra_ldflags.append('c10_cuda.lib')
+        extra_ldflags.append('torch_cpu.lib')
+        if with_cuda:
+            extra_ldflags.append('torch_cuda.lib')
+            # /INCLUDE is used to ensure torch_cuda is linked against in a project that relies on it.
+            # Related issue: https://github.com/pytorch/pytorch/issues/31611
+            extra_ldflags.append('-INCLUDE:?warp_size@cuda@at@@YAHXZ')
+        extra_ldflags.append('torch.lib')
+        extra_ldflags.append(f'/LIBPATH:{TORCH_LIB_PATH}')
+        if not is_standalone:
+            extra_ldflags.append('torch_python.lib')
+            extra_ldflags.append(f'/LIBPATH:{python_lib_path}')
+
+    else:
+        extra_ldflags.append(f'-L{TORCH_LIB_PATH}')
+        extra_ldflags.append('-lc10')
+        if with_cuda:
+            extra_ldflags.append('-lc10_hip' if IS_HIP_EXTENSION else '-lc10_cuda')
+        extra_ldflags.append('-ltorch_cpu')
+        if with_cuda:
+            extra_ldflags.append('-ltorch_hip' if IS_HIP_EXTENSION else '-ltorch_cuda')
+        extra_ldflags.append('-ltorch')
+        if not is_standalone:
+            extra_ldflags.append('-ltorch_python')
+
+        if is_standalone:
+            extra_ldflags.append(f"-Wl,-rpath,{TORCH_LIB_PATH}")
+
+    if with_cuda:
+        if verbose:
+            print('Detected CUDA files, patching ldflags', file=sys.stderr)
+        if IS_WINDOWS:
+            extra_ldflags.append(f'/LIBPATH:{_join_cuda_home("lib", "x64")}')
+            extra_ldflags.append('cudart.lib')
+            if CUDNN_HOME is not None:
+                extra_ldflags.append(f'/LIBPATH:{os.path.join(CUDNN_HOME, "lib", "x64")}')
+        elif not IS_HIP_EXTENSION:
+            extra_lib_dir = "lib64"
+            if (not os.path.exists(_join_cuda_home(extra_lib_dir)) and
+                    os.path.exists(_join_cuda_home("lib"))):
+                # 64-bit CUDA may be installed in "lib"
+                # Note that it's also possible both don't exist (see _find_cuda_home) - in that case we stay with "lib64"
+                extra_lib_dir = "lib"
+            extra_ldflags.append(f'-L{_join_cuda_home(extra_lib_dir)}')
+            extra_ldflags.append('-lcudart')
+            if CUDNN_HOME is not None:
+                extra_ldflags.append(f'-L{os.path.join(CUDNN_HOME, "lib64")}')
+        elif IS_HIP_EXTENSION:
+            extra_ldflags.append(f'-L{_join_rocm_home("lib")}')
+            extra_ldflags.append('-lamdhip64')
+    return extra_ldflags
+
+
+def _get_cuda_arch_flags(cflags: Optional[list[str]] = None) -> list[str]:
+    """
+    Determine CUDA arch flags to use.
+
+    For an arch, say "6.1", the added compile flag will be
+    ``-gencode=arch=compute_61,code=sm_61``.
+    For an added "+PTX", an additional
+    ``-gencode=arch=compute_xx,code=compute_xx`` is added.
+
+    See select_compute_arch.cmake for corresponding named and supported arches
+    when building with CMake.
+    """
+    # If cflags is given, there may already be user-provided arch flags in it
+    # (from `extra_compile_args`)
+    if cflags is not None:
+        for flag in cflags:
+            if 'TORCH_EXTENSION_NAME' in flag:
+                continue
+            if 'arch' in flag:
+                return []
+
+    # Note: keep combined names ("arch1+arch2") above single names, otherwise
+    # string replacement may not do the right thing
+    named_arches = collections.OrderedDict([
+        ('Kepler+Tesla', '3.7'),
+        ('Kepler', '3.5+PTX'),
+        ('Maxwell+Tegra', '5.3'),
+        ('Maxwell', '5.0;5.2+PTX'),
+        ('Pascal', '6.0;6.1+PTX'),
+        ('Volta+Tegra', '7.2'),
+        ('Volta', '7.0+PTX'),
+        ('Turing', '7.5+PTX'),
+        ('Ampere+Tegra', '8.7'),
+        ('Ampere', '8.0;8.6+PTX'),
+        ('Ada', '8.9+PTX'),
+        ('Hopper', '9.0+PTX'),
+        ('Blackwell+Tegra', '10.1'),
+        ('Blackwell', '10.0;12.0+PTX'),
+    ])
+
+    supported_arches = ['3.5', '3.7', '5.0', '5.2', '5.3', '6.0', '6.1', '6.2',
+                        '7.0', '7.2', '7.5', '8.0', '8.6', '8.7', '8.9', '9.0', '9.0a',
+                        '10.0', '10.0a', '10.1', '10.1a', '12.0', '12.0a']
+    valid_arch_strings = supported_arches + [s + "+PTX" for s in supported_arches]
+
+    # The default is sm_30 for CUDA 9.x and 10.x
+    # First check for an env var (same as used by the main setup.py)
+    # Can be one or more architectures, e.g. "6.1" or "3.5;5.2;6.0;6.1;7.0+PTX"
+    # See cmake/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake
+    _arch_list = os.environ.get('TORCH_CUDA_ARCH_LIST', None)
+
+    # If not given, determine what's best for the GPU / CUDA version that can be found
+    if not _arch_list:
+        warnings.warn(
+            "TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. \n"
+            "If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].")
+        arch_list = []
+        # the assumption is that the extension should run on any of the currently visible cards,
+        # which could be of different types - therefore all archs for visible cards should be included
+        for i in range(torch.cuda.device_count()):
+            capability = torch.cuda.get_device_capability(i)
+            supported_sm = [int("".join(re.findall(r"\d+", arch.split('_')[1])))
+                            for arch in torch.cuda.get_arch_list() if 'sm_' in arch]
+            max_supported_sm = max((sm // 10, sm % 10) for sm in supported_sm)
+            # Capability of the device may be higher than what's supported by the user's
+            # NVCC, causing compilation error. User's NVCC is expected to match the one
+            # used to build pytorch, so we use the maximum supported capability of pytorch
+            # to clamp the capability.
+            capability = min(max_supported_sm, capability)
+            arch = f'{capability[0]}.{capability[1]}'
+            if arch not in arch_list:
+                arch_list.append(arch)
+        arch_list = sorted(arch_list)
+        arch_list[-1] += '+PTX'
+    else:
+        # Deal with lists that are ' ' separated (only deal with ';' after)
+        _arch_list = _arch_list.replace(' ', ';')
+        # Expand named arches
+        for named_arch, archval in named_arches.items():
+            _arch_list = _arch_list.replace(named_arch, archval)
+
+        arch_list = _arch_list.split(';')
+
+    flags = []
+    for arch in arch_list:
+        if arch not in valid_arch_strings:
+            raise ValueError(f"Unknown CUDA arch ({arch}) or GPU not supported")
+        else:
+            # Handle both single and double-digit architecture versions
+            version = arch.split('+')[0]  # Remove "+PTX" if present
+            major, minor = version.split('.')
+            num = f"{major}{minor}"
+            flags.append(f'-gencode=arch=compute_{num},code=sm_{num}')
+            if arch.endswith('+PTX'):
+                flags.append(f'-gencode=arch=compute_{num},code=compute_{num}')
+
+    return sorted(set(flags))
+
+
+def _get_rocm_arch_flags(cflags: Optional[list[str]] = None) -> list[str]:
+    # If cflags is given, there may already be user-provided arch flags in it
+    # (from `extra_compile_args`)
+    if cflags is not None:
+        for flag in cflags:
+            if 'amdgpu-target' in flag or 'offload-arch' in flag:
+                return ['-fno-gpu-rdc']
+    # Use same defaults as used for building PyTorch
+    # Allow env var to override, just like during initial cmake build.
+    _archs = os.environ.get('PYTORCH_ROCM_ARCH', None)
+    if not _archs:
+        archFlags = torch._C._cuda_getArchFlags()
+        if archFlags:
+            archs = archFlags.split()
+        else:
+            archs = []
+    else:
+        archs = _archs.replace(' ', ';').split(';')
+    flags = [f'--offload-arch={arch}' for arch in archs]
+    flags += ['-fno-gpu-rdc']
+    return flags
+
+def _get_build_directory(name: str, verbose: bool) -> str:
+    root_extensions_directory = os.environ.get('TORCH_EXTENSIONS_DIR')
+    if root_extensions_directory is None:
+        root_extensions_directory = get_default_build_root()
+        cu_str = ('cpu' if torch.version.cuda is None else
+                  f'cu{torch.version.cuda.replace(".", "")}')
+        python_version = f'py{sys.version_info.major}{sys.version_info.minor}{getattr(sys, "abiflags", "")}'
+        build_folder = f'{python_version}_{cu_str}'
+
+        root_extensions_directory = os.path.join(
+            root_extensions_directory, build_folder)
+
+    if verbose:
+        print(f'Using {root_extensions_directory} as PyTorch extensions root...', file=sys.stderr)
+
+    build_directory = os.path.join(root_extensions_directory, name)
+    if not os.path.exists(build_directory):
+        if verbose:
+            print(f'Creating extension directory {build_directory}...', file=sys.stderr)
+        # This is like mkdir -p, i.e. will also create parent directories.
+        os.makedirs(build_directory, exist_ok=True)
+
+    return build_directory
+
+
+def _get_num_workers(verbose: bool) -> Optional[int]:
+    max_jobs = os.environ.get('MAX_JOBS')
+    if max_jobs is not None and max_jobs.isdigit():
+        if verbose:
+            print(f'Using envvar MAX_JOBS ({max_jobs}) as the number of workers...',
+                  file=sys.stderr)
+        return int(max_jobs)
+    if verbose:
+        print('Allowing ninja to set a default number of workers... '
+              '(overridable by setting the environment variable MAX_JOBS=N)',
+              file=sys.stderr)
+    return None
+
+
+def _get_vc_env(vc_arch: str) -> dict[str, str]:
+    try:
+        from setuptools import distutils
+        return distutils._msvccompiler._get_vc_env(vc_arch)
+    except AttributeError:
+        from setuptools._distutils import _msvccompiler
+        return _msvccompiler._get_vc_env(vc_arch)
+
+
+def _run_ninja_build(build_directory: str, verbose: bool, error_prefix: str) -> None:
+    command = ['ninja', '-v']
+    num_workers = _get_num_workers(verbose)
+    if num_workers is not None:
+        command.extend(['-j', str(num_workers)])
+    env = os.environ.copy()
+    # Try to activate the vc env for the users
+    if IS_WINDOWS and 'VSCMD_ARG_TGT_ARCH' not in env:
+        from setuptools import distutils
+
+        plat_name = distutils.util.get_platform()
+        plat_spec = PLAT_TO_VCVARS[plat_name]
+        vc_env = {k.upper(): v for k, v in _get_vc_env(plat_spec).items()}
+        for k, v in env.items():
+            uk = k.upper()
+            if uk not in vc_env:
+                vc_env[uk] = v
+        env = vc_env
+    try:
+        sys.stdout.flush()
+        sys.stderr.flush()
+        # Warning: don't pass stdout=None to subprocess.run to get output.
+        # subprocess.run assumes that sys.__stdout__ has not been modified and
+        # attempts to write to it by default.  However, when we call _run_ninja_build
+        # from ahead-of-time cpp extensions, the following happens:
+        # 1) If the stdout encoding is not utf-8, setuptools detachs __stdout__.
+        #    https://github.com/pypa/setuptools/blob/7e97def47723303fafabe48b22168bbc11bb4821/setuptools/dist.py#L1110
+        #    (it probably shouldn't do this)
+        # 2) subprocess.run (on POSIX, with no stdout override) relies on
+        #    __stdout__ not being detached:
+        #    https://github.com/python/cpython/blob/c352e6c7446c894b13643f538db312092b351789/Lib/subprocess.py#L1214
+        # To work around this, we pass in the fileno directly and hope that
+        # it is valid.
+        stdout_fileno = 1
+        subprocess.run(
+            command,
+            stdout=stdout_fileno if verbose else subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            cwd=build_directory,
+            check=True,
+            env=env)
+    except subprocess.CalledProcessError as e:
+        # Python 2 and 3 compatible way of getting the error object.
+        _, error, _ = sys.exc_info()
+        # error.output contains the stdout and stderr of the build attempt.
+        message = error_prefix
+        # `error` is a CalledProcessError (which has an `output`) attribute, but
+        # mypy thinks it's Optional[BaseException] and doesn't narrow
+        if hasattr(error, 'output') and error.output:  # type: ignore[union-attr]
+            message += f": {error.output.decode(*SUBPROCESS_DECODE_ARGS)}"  # type: ignore[union-attr]
+        raise RuntimeError(message) from e
+
+
+def _get_exec_path(module_name, path):
+    if IS_WINDOWS and TORCH_LIB_PATH not in os.getenv('PATH', '').split(';'):
+        torch_lib_in_path = any(
+            os.path.exists(p) and os.path.samefile(p, TORCH_LIB_PATH)
+            for p in os.getenv('PATH', '').split(';')
+        )
+        if not torch_lib_in_path:
+            os.environ['PATH'] = f"{TORCH_LIB_PATH};{os.getenv('PATH', '')}"
+    return os.path.join(path, f'{module_name}{EXEC_EXT}')
+
+
+def _import_module_from_library(module_name, path, is_python_module):
+    filepath = os.path.join(path, f"{module_name}{LIB_EXT}")
+    if is_python_module:
+        # https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
+        spec = importlib.util.spec_from_file_location(module_name, filepath)
+        assert spec is not None
+        module = importlib.util.module_from_spec(spec)
+        assert isinstance(spec.loader, importlib.abc.Loader)
+        spec.loader.exec_module(module)
+        return module
+    else:
+        torch.ops.load_library(filepath)
+        return filepath
+
+
+def _write_ninja_file_to_build_library(path,
+                                       name,
+                                       sources,
+                                       extra_cflags,
+                                       extra_cuda_cflags,
+                                       extra_sycl_cflags,
+                                       extra_ldflags,
+                                       extra_include_paths,
+                                       with_cuda,
+                                       with_sycl,
+                                       is_standalone) -> None:
+    extra_cflags = [flag.strip() for flag in extra_cflags]
+    extra_cuda_cflags = [flag.strip() for flag in extra_cuda_cflags]
+    extra_sycl_cflags = [flag.strip() for flag in extra_sycl_cflags]
+    extra_ldflags = [flag.strip() for flag in extra_ldflags]
+    extra_include_paths = [flag.strip() for flag in extra_include_paths]
+
+    # Turn into absolute paths so we can emit them into the ninja build
+    # file wherever it is.
+    user_includes = [os.path.abspath(file) for file in extra_include_paths]
+
+    # include_paths() gives us the location of torch/extension.h
+    # TODO generalize with_cuda as specific device type.
+    if with_cuda:
+        system_includes = include_paths("cuda")
+    else:
+        system_includes = include_paths("cpu")
+    # sysconfig.get_path('include') gives us the location of Python.h
+    # Explicitly specify 'posix_prefix' scheme on non-Windows platforms to workaround error on some MacOS
+    # installations where default `get_path` points to non-existing `/Library/Python/M.m/include` folder
+    python_include_path = sysconfig.get_path('include', scheme='nt' if IS_WINDOWS else 'posix_prefix')
+    if python_include_path is not None:
+        system_includes.append(python_include_path)
+
+    common_cflags = []
+    if not is_standalone:
+        common_cflags.append(f'-DTORCH_EXTENSION_NAME={name}')
+        common_cflags.append('-DTORCH_API_INCLUDE_EXTENSION_H')
+
+    common_cflags += [f"{x}" for x in _get_pybind11_abi_build_flags()]
+
+    # Windows does not understand `-isystem` and quotes flags later.
+    if IS_WINDOWS:
+        common_cflags += [f'-I{include}' for include in user_includes + system_includes]
+    else:
+        common_cflags += [f'-I{shlex.quote(include)}' for include in user_includes]
+        common_cflags += [f'-isystem {shlex.quote(include)}' for include in system_includes]
+
+    common_cflags += [f"{x}" for x in _get_glibcxx_abi_build_flags()]
+
+    if IS_WINDOWS:
+        cflags = common_cflags + COMMON_MSVC_FLAGS + ['/std:c++17'] + extra_cflags
+        cflags = _nt_quote_args(cflags)
+    else:
+        cflags = common_cflags + ['-fPIC', '-std=c++17'] + extra_cflags
+
+    if with_cuda and IS_HIP_EXTENSION:
+        cuda_flags = ['-DWITH_HIP'] + cflags + COMMON_HIP_FLAGS + COMMON_HIPCC_FLAGS
+        cuda_flags += extra_cuda_cflags
+        cuda_flags += _get_rocm_arch_flags(cuda_flags)
+    elif with_cuda:
+        cuda_flags = common_cflags + COMMON_NVCC_FLAGS + _get_cuda_arch_flags()
+        if IS_WINDOWS:
+            for flag in COMMON_MSVC_FLAGS:
+                cuda_flags = ['-Xcompiler', flag] + cuda_flags
+            for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS:
+                cuda_flags = ['-Xcudafe', '--diag_suppress=' + ignore_warning] + cuda_flags
+            cuda_flags = cuda_flags + ['-std=c++17']
+            cuda_flags = _nt_quote_args(cuda_flags)
+            cuda_flags += _nt_quote_args(extra_cuda_cflags)
+        else:
+            cuda_flags += ['--compiler-options', "'-fPIC'"]
+            cuda_flags += extra_cuda_cflags
+            if not any(flag.startswith('-std=') for flag in cuda_flags):
+                cuda_flags.append('-std=c++17')
+            cc_env = os.getenv("CC")
+            if cc_env is not None:
+                cuda_flags = ['-ccbin', cc_env] + cuda_flags
+    else:
+        cuda_flags = None
+
+    if with_sycl:
+        sycl_cflags = cflags + _COMMON_SYCL_FLAGS
+        sycl_cflags += extra_sycl_cflags
+        _append_sycl_std_if_no_std_present(sycl_cflags)
+        host_cflags = cflags
+        # escaping quoted arguments to pass them thru SYCL compiler
+        host_cflags = [item.replace('\\"', '\\\\"') for item in host_cflags]
+        host_cflags = ' '.join(host_cflags)
+        sycl_cflags += _wrap_sycl_host_flags(host_cflags)
+        sycl_dlink_post_cflags = _SYCL_DLINK_FLAGS
+    else:
+        sycl_cflags = None
+        sycl_dlink_post_cflags = None
+
+    def object_file_path(source_file: str) -> str:
+        # '/path/to/file.cpp' -> 'file'
+        file_name = os.path.splitext(os.path.basename(source_file))[0]
+        if _is_cuda_file(source_file) and with_cuda:
+            # Use a different object filename in case a C++ and CUDA file have
+            # the same filename but different extension (.cpp vs. .cu).
+            target = f'{file_name}.cuda.o'
+        elif _is_sycl_file(source_file) and with_sycl:
+            target = f'{file_name}.sycl.o'
+        else:
+            target = f'{file_name}.o'
+        return target
+
+    objects = [object_file_path(src) for src in sources]
+    ldflags = ([] if is_standalone else [SHARED_FLAG]) + extra_ldflags
+
+    # The darwin linker needs explicit consent to ignore unresolved symbols.
+    if IS_MACOS:
+        ldflags.append('-undefined dynamic_lookup')
+    elif IS_WINDOWS:
+        ldflags = _nt_quote_args(ldflags)
+
+    ext = EXEC_EXT if is_standalone else LIB_EXT
+    library_target = f'{name}{ext}'
+
+    _write_ninja_file(
+        path=path,
+        cflags=cflags,
+        post_cflags=None,
+        cuda_cflags=cuda_flags,
+        cuda_post_cflags=None,
+        cuda_dlink_post_cflags=None,
+        sycl_cflags=sycl_cflags,
+        sycl_post_cflags=[],
+        sycl_dlink_post_cflags=sycl_dlink_post_cflags,
+        sources=sources,
+        objects=objects,
+        ldflags=ldflags,
+        library_target=library_target,
+        with_cuda=with_cuda,
+        with_sycl=with_sycl)
+
+
+def _write_ninja_file(path,
+                      cflags,
+                      post_cflags,
+                      cuda_cflags,
+                      cuda_post_cflags,
+                      cuda_dlink_post_cflags,
+                      sycl_cflags,
+                      sycl_post_cflags,
+                      sycl_dlink_post_cflags,
+                      sources,
+                      objects,
+                      ldflags,
+                      library_target,
+                      with_cuda,
+                      with_sycl) -> None:
+    r"""Write a ninja file that does the desired compiling and linking.
+
+    `path`: Where to write this file
+    `cflags`: list of flags to pass to $cxx. Can be None.
+    `post_cflags`: list of flags to append to the $cxx invocation. Can be None.
+    `cuda_cflags`: list of flags to pass to $nvcc. Can be None.
+    `cuda_post_cflags`: list of flags to append to the $nvcc invocation. Can be None.
+    `cuda_dlink_post_cflags`: list of flags to append to the $nvcc device code link invocation. Can be None.
+    `sycl_cflags`: list of flags to pass to SYCL compiler. Can be None.
+    `sycl_post_cflags`: list of flags to append to the SYCL compiler invocation. Can be None.
+    `sycl_dlink_post_cflags`: list of flags to append to the SYCL compiler device code link invocation. Can be None.
+e.
+    `sources`: list of paths to source files
+    `objects`: list of desired paths to objects, one per source.
+    `ldflags`: list of flags to pass to linker. Can be None.
+    `library_target`: Name of the output library. Can be None; in that case,
+                      we do no linking.
+    `with_cuda`: If we should be compiling with CUDA.
+    """
+    def sanitize_flags(flags):
+        if flags is None:
+            return []
+        else:
+            return [flag.strip() for flag in flags]
+
+    cflags = sanitize_flags(cflags)
+    post_cflags = sanitize_flags(post_cflags)
+    cuda_cflags = sanitize_flags(cuda_cflags)
+    cuda_post_cflags = sanitize_flags(cuda_post_cflags)
+    cuda_dlink_post_cflags = sanitize_flags(cuda_dlink_post_cflags)
+    sycl_cflags = sanitize_flags(sycl_cflags)
+    sycl_post_cflags = sanitize_flags(sycl_post_cflags)
+    sycl_dlink_post_cflags = sanitize_flags(sycl_dlink_post_cflags)
+    ldflags = sanitize_flags(ldflags)
+
+    # Sanity checks...
+    assert len(sources) == len(objects)
+    assert len(sources) > 0
+
+    compiler = get_cxx_compiler()
+
+    # Version 1.3 is required for the `deps` directive.
+    config = ['ninja_required_version = 1.3']
+    config.append(f'cxx = {compiler}')
+    if with_cuda or cuda_dlink_post_cflags:
+        if "PYTORCH_NVCC" in os.environ:
+            nvcc = os.getenv("PYTORCH_NVCC")    # user can set nvcc compiler with ccache using the environment variable here
+        else:
+            if IS_HIP_EXTENSION:
+                nvcc = _join_rocm_home('bin', 'hipcc')
+            else:
+                nvcc = _join_cuda_home('bin', 'nvcc')
+        config.append(f'nvcc = {nvcc}')
+    if with_sycl or sycl_dlink_post_cflags:
+        sycl = 'icx' if IS_WINDOWS else 'icpx'
+        config.append(f'sycl = {sycl}')
+
+    if IS_HIP_EXTENSION:
+        post_cflags = COMMON_HIP_FLAGS + post_cflags
+    flags = [f'cflags = {" ".join(cflags)}']
+    flags.append(f'post_cflags = {" ".join(post_cflags)}')
+    if with_cuda:
+        flags.append(f'cuda_cflags = {" ".join(cuda_cflags)}')
+        flags.append(f'cuda_post_cflags = {" ".join(cuda_post_cflags)}')
+    flags.append(f'cuda_dlink_post_cflags = {" ".join(cuda_dlink_post_cflags)}')
+    if with_sycl:
+        flags.append(f'sycl_cflags = {" ".join(sycl_cflags)}')
+        flags.append(f'sycl_post_cflags = {" ".join(sycl_post_cflags)}')
+    flags.append(f'sycl_dlink_post_cflags = {" ".join(sycl_dlink_post_cflags)}')
+    flags.append(f'ldflags = {" ".join(ldflags)}')
+
+    # Turn into absolute paths so we can emit them into the ninja build
+    # file wherever it is.
+    sources = [os.path.abspath(file) for file in sources]
+
+    # See https://ninja-build.org/build.ninja.html for reference.
+    compile_rule = ['rule compile']
+    if IS_WINDOWS:
+        compile_rule.append(
+            '  command = cl /showIncludes $cflags -c $in /Fo$out $post_cflags')
+        compile_rule.append('  deps = msvc')
+    else:
+        compile_rule.append(
+            '  command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags')
+        compile_rule.append('  depfile = $out.d')
+        compile_rule.append('  deps = gcc')
+
+    if with_cuda:
+        cuda_compile_rule = ['rule cuda_compile']
+        nvcc_gendeps = ''
+        # --generate-dependencies-with-compile is not supported by ROCm
+        # Nvcc flag `--generate-dependencies-with-compile` is not supported by sccache, which may increase build time.
+        if torch.version.cuda is not None and os.getenv('TORCH_EXTENSION_SKIP_NVCC_GEN_DEPENDENCIES', '0') != '1':
+            cuda_compile_rule.append('  depfile = $out.d')
+            cuda_compile_rule.append('  deps = gcc')
+            # Note: non-system deps with nvcc are only supported
+            # on Linux so use --generate-dependencies-with-compile
+            # to make this work on Windows too.
+            nvcc_gendeps = '--generate-dependencies-with-compile --dependency-output $out.d'
+        cuda_compile_rule.append(
+            f'  command = $nvcc {nvcc_gendeps} $cuda_cflags -c $in -o $out $cuda_post_cflags')
+
+    if with_sycl:
+        sycl_compile_rule = ['rule sycl_compile']
+        # SYCL compiler does not recognize .sycl extension automatically,
+        # so we pass '-x c++' explicitly notifying compiler of file format
+        sycl_compile_rule.append(
+            '  command = $sycl $sycl_cflags -c -x c++ $in -o $out $sycl_post_cflags')
+
+
+    # Emit one build rule per source to enable incremental build.
+    build = []
+    for source_file, object_file in zip(sources, objects):
+        is_cuda_source = _is_cuda_file(source_file) and with_cuda
+        is_sycl_source = _is_sycl_file(source_file) and with_sycl
+        if is_cuda_source:
+            rule = 'cuda_compile'
+        elif is_sycl_source:
+            rule = 'sycl_compile'
+        else:
+            rule = 'compile'
+        if IS_WINDOWS:
+            source_file = source_file.replace(':', '$:')
+            object_file = object_file.replace(':', '$:')
+        source_file = source_file.replace(" ", "$ ")
+        object_file = object_file.replace(" ", "$ ")
+        build.append(f'build {object_file}: {rule} {source_file}')
+
+    if cuda_dlink_post_cflags:
+        cuda_devlink_out = os.path.join(os.path.dirname(objects[0]), 'dlink.o')
+        cuda_devlink_rule = ['rule cuda_devlink']
+        cuda_devlink_rule.append('  command = $nvcc $in -o $out $cuda_dlink_post_cflags')
+        cuda_devlink = [f'build {cuda_devlink_out}: cuda_devlink {" ".join(objects)}']
+        objects += [cuda_devlink_out]
+    else:
+        cuda_devlink_rule, cuda_devlink = [], []
+
+    if sycl_dlink_post_cflags:
+        sycl_devlink_out = os.path.join(os.path.dirname(objects[0]), 'sycl_dlink.o')
+        sycl_devlink_rule = ['rule sycl_devlink']
+        sycl_devlink_rule.append('  command = $sycl $in -o $out $sycl_dlink_post_cflags')
+        sycl_devlink = [f'build {sycl_devlink_out}: sycl_devlink {" ".join(objects)}']
+        objects += [sycl_devlink_out]
+    else:
+        sycl_devlink_rule, sycl_devlink = [], []
+
+    if library_target is not None:
+        link_rule = ['rule link']
+        if IS_WINDOWS:
+            cl_paths = subprocess.check_output(['where',
+                                                'cl']).decode(*SUBPROCESS_DECODE_ARGS).split('\r\n')
+            if len(cl_paths) >= 1:
+                cl_path = os.path.dirname(cl_paths[0]).replace(':', '$:')
+            else:
+                raise RuntimeError("MSVC is required to load C++ extensions")
+            link_rule.append(f'  command = "{cl_path}/link.exe" $in /nologo $ldflags /out:$out')
+        else:
+            link_rule.append('  command = $cxx $in $ldflags -o $out')
+
+        link = [f'build {library_target}: link {" ".join(objects)}']
+
+        default = [f'default {library_target}']
+    else:
+        link_rule, link, default = [], [], []
+
+    # 'Blocks' should be separated by newlines, for visual benefit.
+    blocks = [config, flags, compile_rule]
+    if with_cuda:
+        blocks.append(cuda_compile_rule)  # type: ignore[possibly-undefined]
+    if with_sycl:
+        blocks.append(sycl_compile_rule)  # type: ignore[possibly-undefined]
+    blocks += [cuda_devlink_rule, sycl_devlink_rule, link_rule, build, cuda_devlink, sycl_devlink, link, default]
+    content = "\n\n".join("\n".join(b) for b in blocks)
+    # Ninja requires a new lines at the end of the .ninja file
+    content += "\n"
+    _maybe_write(path, content)
+
+def _join_cuda_home(*paths) -> str:
+    """
+    Join paths with CUDA_HOME, or raises an error if it CUDA_HOME is not set.
+
+    This is basically a lazy way of raising an error for missing $CUDA_HOME
+    only once we need to get any CUDA-specific path.
+    """
+    if CUDA_HOME is None:
+        raise OSError('CUDA_HOME environment variable is not set. '
+                      'Please set it to your CUDA install root.')
+    return os.path.join(CUDA_HOME, *paths)
+
+
+def _is_cuda_file(path: str) -> bool:
+    valid_ext = ['.cu', '.cuh']
+    if IS_HIP_EXTENSION:
+        valid_ext.append('.hip')
+    return os.path.splitext(path)[1] in valid_ext
+
+def _is_sycl_file(path: str) -> bool:
+    valid_ext = ['.sycl']
+    return os.path.splitext(path)[1] in valid_ext
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4feeda1e59fb9a5089f7df871d1c8b29a2cd3835
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/__init__.py
@@ -0,0 +1,77 @@
+from torch.utils.data.dataloader import (
+    _DatasetKind,
+    DataLoader,
+    default_collate,
+    default_convert,
+    get_worker_info,
+)
+from torch.utils.data.datapipes._decorator import (
+    argument_validation,
+    functional_datapipe,
+    guaranteed_datapipes_determinism,
+    non_deterministic,
+    runtime_validation,
+    runtime_validation_disabled,
+)
+from torch.utils.data.datapipes.datapipe import (
+    DataChunk,
+    DFIterDataPipe,
+    IterDataPipe,
+    MapDataPipe,
+)
+from torch.utils.data.dataset import (
+    ChainDataset,
+    ConcatDataset,
+    Dataset,
+    IterableDataset,
+    random_split,
+    StackDataset,
+    Subset,
+    TensorDataset,
+)
+from torch.utils.data.distributed import DistributedSampler
+from torch.utils.data.sampler import (
+    BatchSampler,
+    RandomSampler,
+    Sampler,
+    SequentialSampler,
+    SubsetRandomSampler,
+    WeightedRandomSampler,
+)
+
+
+__all__ = [
+    "BatchSampler",
+    "ChainDataset",
+    "ConcatDataset",
+    "DFIterDataPipe",
+    "DataChunk",
+    "DataLoader",
+    "Dataset",
+    "DistributedSampler",
+    "IterDataPipe",
+    "IterableDataset",
+    "MapDataPipe",
+    "RandomSampler",
+    "Sampler",
+    "SequentialSampler",
+    "StackDataset",
+    "Subset",
+    "SubsetRandomSampler",
+    "TensorDataset",
+    "WeightedRandomSampler",
+    "_DatasetKind",
+    "argument_validation",
+    "default_collate",
+    "default_convert",
+    "functional_datapipe",
+    "get_worker_info",
+    "guaranteed_datapipes_determinism",
+    "non_deterministic",
+    "random_split",
+    "runtime_validation",
+    "runtime_validation_disabled",
+]
+
+# Please keep this list sorted
+assert __all__ == sorted(__all__)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef03e053211b67a0ed109bc69f4a6b058783d4cf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/__init__.py
@@ -0,0 +1,54 @@
+# mypy: allow-untyped-defs
+r"""Utility classes & functions for data loading. Code in this folder is mostly used by ../dataloder.py.
+
+A lot of multiprocessing is used in data loading, which only supports running
+functions defined in global environment (py2 can't serialize static methods).
+Therefore, for code tidiness we put these functions into different files in this
+folder.
+"""
+
+import atexit
+import sys
+
+# old private location of the ExceptionWrapper that some users rely on:
+from torch._utils import ExceptionWrapper
+
+
+IS_WINDOWS = sys.platform == "win32"
+
+
+MP_STATUS_CHECK_INTERVAL = 5.0
+r"""Interval (in seconds) to check status of processes to avoid hanging in
+    multiprocessing data loading. This is mainly used in getting data from
+    another process, in which case we need to periodically check whether the
+    sender is alive to prevent hanging."""
+
+
+python_exit_status = False
+r"""Whether Python is shutting down. This flag is guaranteed to be set before
+the Python core library resources are freed, but Python may already be exiting
+for some time when this is set.
+
+Hook to set this flag is `_set_python_exit_flag`, and is inspired by a similar
+hook in Python 3.7 multiprocessing library:
+https://github.com/python/cpython/blob/d4d60134b29290049e28df54f23493de4f1824b6/Lib/multiprocessing/util.py#L277-L327
+"""
+
+
+try:
+    import numpy
+
+    HAS_NUMPY = True
+except ModuleNotFoundError:
+    HAS_NUMPY = False
+
+
+def _set_python_exit_flag():
+    global python_exit_status
+    python_exit_status = True
+
+
+atexit.register(_set_python_exit_flag)
+
+
+from . import collate, fetch, pin_memory, signal_handling, worker
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py
new file mode 100644
index 0000000000000000000000000000000000000000..68a4da0731c0e29234bf4461c065398da36f9b00
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py
@@ -0,0 +1,398 @@
+# mypy: allow-untyped-defs
+r"""Contains definitions of the methods used by the _BaseDataLoaderIter workers.
+
+These methods are used to collate samples fetched from dataset into Tensor(s).
+These **needs** to be in global scope since Py2 doesn't support serializing
+static methods.
+
+`default_collate` and `default_convert` are exposed to users via 'dataloader.py'.
+"""
+
+import collections
+import contextlib
+import copy
+import re
+from typing import Callable, Optional, Union
+
+import torch
+
+
+np_str_obj_array_pattern = re.compile(r"[SaUO]")
+
+
+def default_convert(data):
+    r"""
+    Convert each NumPy array element into a :class:`torch.Tensor`.
+
+    If the input is a `Sequence`, `Collection`, or `Mapping`, it tries to convert each element inside to a :class:`torch.Tensor`.
+    If the input is not an NumPy array, it is left unchanged.
+    This is used as the default function for collation when both `batch_sampler` and `batch_size`
+    are NOT defined in :class:`~torch.utils.data.DataLoader`.
+
+    The general input type to output type mapping is similar to that
+    of :func:`~torch.utils.data.default_collate`. See the description there for more details.
+
+    Args:
+        data: a single data point to be converted
+
+    Examples:
+        >>> # xdoctest: +SKIP
+        >>> # Example with `int`
+        >>> default_convert(0)
+        0
+        >>> # Example with NumPy array
+        >>> default_convert(np.array([0, 1]))
+        tensor([0, 1])
+        >>> # Example with NamedTuple
+        >>> Point = namedtuple('Point', ['x', 'y'])
+        >>> default_convert(Point(0, 0))
+        Point(x=0, y=0)
+        >>> default_convert(Point(np.array(0), np.array(0)))
+        Point(x=tensor(0), y=tensor(0))
+        >>> # Example with List
+        >>> default_convert([np.array([0, 1]), np.array([2, 3])])
+        [tensor([0, 1]), tensor([2, 3])]
+    """
+    elem_type = type(data)
+    if isinstance(data, torch.Tensor):
+        return data
+    elif (
+        elem_type.__module__ == "numpy"
+        and elem_type.__name__ != "str_"
+        and elem_type.__name__ != "string_"
+    ):
+        # array of string classes and object
+        if (
+            elem_type.__name__ == "ndarray"
+            and np_str_obj_array_pattern.search(data.dtype.str) is not None
+        ):
+            return data
+        return torch.as_tensor(data)
+    elif isinstance(data, collections.abc.Mapping):
+        try:
+            if isinstance(data, collections.abc.MutableMapping):
+                # The mapping type may have extra properties, so we can't just
+                # use `type(data)(...)` to create the new mapping.
+                # Create a clone and update it if the mapping type is mutable.
+                clone = copy.copy(data)
+                clone.update({key: default_convert(data[key]) for key in data})
+                return clone
+            else:
+                return elem_type({key: default_convert(data[key]) for key in data})
+        except TypeError:
+            # The mapping type may not support `copy()` / `update(mapping)`
+            # or `__init__(iterable)`.
+            return {key: default_convert(data[key]) for key in data}
+    elif isinstance(data, tuple) and hasattr(data, "_fields"):  # namedtuple
+        return elem_type(*(default_convert(d) for d in data))
+    elif isinstance(data, tuple):
+        return [default_convert(d) for d in data]  # Backwards compatibility.
+    elif isinstance(data, collections.abc.Sequence) and not isinstance(
+        data, (str, bytes)
+    ):
+        try:
+            if isinstance(data, collections.abc.MutableSequence):
+                # The sequence type may have extra properties, so we can't just
+                # use `type(data)(...)` to create the new sequence.
+                # Create a clone and update it if the sequence type is mutable.
+                clone = copy.copy(data)  # type: ignore[arg-type]
+                for i, d in enumerate(data):
+                    clone[i] = default_convert(d)
+                return clone
+            else:
+                return elem_type([default_convert(d) for d in data])
+        except TypeError:
+            # The sequence type may not support `copy()` / `__setitem__(index, item)`
+            # or `__init__(iterable)` (e.g., `range`).
+            return [default_convert(d) for d in data]
+    else:
+        return data
+
+
+default_collate_err_msg_format = (
+    "default_collate: batch must contain tensors, numpy arrays, numbers, "
+    "dicts or lists; found {}"
+)
+
+
+def collate(
+    batch,
+    *,
+    collate_fn_map: Optional[dict[Union[type, tuple[type, ...]], Callable]] = None,
+):
+    r"""
+    General collate function that handles collection type of element within each batch.
+
+    The function also opens function registry to deal with specific element types. `default_collate_fn_map`
+    provides default collate functions for tensors, numpy arrays, numbers and strings.
+
+    Args:
+        batch: a single batch to be collated
+        collate_fn_map: Optional dictionary mapping from element type to the corresponding collate function.
+            If the element type isn't present in this dictionary,
+            this function will go through each key of the dictionary in the insertion order to
+            invoke the corresponding collate function if the element type is a subclass of the key.
+
+    Examples:
+        >>> def collate_tensor_fn(batch, *, collate_fn_map):
+        ...     # Extend this function to handle batch of tensors
+        ...     return torch.stack(batch, 0)
+        >>> def custom_collate(batch):
+        ...     collate_map = {torch.Tensor: collate_tensor_fn}
+        ...     return collate(batch, collate_fn_map=collate_map)
+        >>> # Extend `default_collate` by in-place modifying `default_collate_fn_map`
+        >>> default_collate_fn_map.update({torch.Tensor: collate_tensor_fn})
+
+    Note:
+        Each collate function requires a positional argument for batch and a keyword argument
+        for the dictionary of collate functions as `collate_fn_map`.
+    """
+    elem = batch[0]
+    elem_type = type(elem)
+
+    if collate_fn_map is not None:
+        if elem_type in collate_fn_map:
+            return collate_fn_map[elem_type](batch, collate_fn_map=collate_fn_map)
+
+        for collate_type in collate_fn_map:
+            if isinstance(elem, collate_type):
+                return collate_fn_map[collate_type](
+                    batch, collate_fn_map=collate_fn_map
+                )
+
+    if isinstance(elem, collections.abc.Mapping):
+        try:
+            if isinstance(elem, collections.abc.MutableMapping):
+                # The mapping type may have extra properties, so we can't just
+                # use `type(data)(...)` to create the new mapping.
+                # Create a clone and update it if the mapping type is mutable.
+                clone = copy.copy(elem)
+                clone.update(
+                    {
+                        key: collate(
+                            [d[key] for d in batch], collate_fn_map=collate_fn_map
+                        )
+                        for key in elem
+                    }
+                )
+                return clone
+            else:
+                return elem_type(
+                    {
+                        key: collate(
+                            [d[key] for d in batch], collate_fn_map=collate_fn_map
+                        )
+                        for key in elem
+                    }
+                )
+        except TypeError:
+            # The mapping type may not support `copy()` / `update(mapping)`
+            # or `__init__(iterable)`.
+            return {
+                key: collate([d[key] for d in batch], collate_fn_map=collate_fn_map)
+                for key in elem
+            }
+    elif isinstance(elem, tuple) and hasattr(elem, "_fields"):  # namedtuple
+        return elem_type(
+            *(
+                collate(samples, collate_fn_map=collate_fn_map)
+                for samples in zip(*batch)
+            )
+        )
+    elif isinstance(elem, collections.abc.Sequence):
+        # check to make sure that the elements in batch have consistent size
+        it = iter(batch)
+        elem_size = len(next(it))
+        if not all(len(elem) == elem_size for elem in it):
+            raise RuntimeError("each element in list of batch should be of equal size")
+        transposed = list(zip(*batch))  # It may be accessed twice, so we use a list.
+
+        if isinstance(elem, tuple):
+            return [
+                collate(samples, collate_fn_map=collate_fn_map)
+                for samples in transposed
+            ]  # Backwards compatibility.
+        else:
+            try:
+                if isinstance(elem, collections.abc.MutableSequence):
+                    # The sequence type may have extra properties, so we can't just
+                    # use `type(data)(...)` to create the new sequence.
+                    # Create a clone and update it if the sequence type is mutable.
+                    clone = copy.copy(elem)  # type: ignore[arg-type]
+                    for i, samples in enumerate(transposed):
+                        clone[i] = collate(samples, collate_fn_map=collate_fn_map)
+                    return clone
+                else:
+                    return elem_type(
+                        [
+                            collate(samples, collate_fn_map=collate_fn_map)
+                            for samples in transposed
+                        ]
+                    )
+            except TypeError:
+                # The sequence type may not support `copy()` / `__setitem__(index, item)`
+                # or `__init__(iterable)` (e.g., `range`).
+                return [
+                    collate(samples, collate_fn_map=collate_fn_map)
+                    for samples in transposed
+                ]
+
+    raise TypeError(default_collate_err_msg_format.format(elem_type))
+
+
+def collate_tensor_fn(
+    batch,
+    *,
+    collate_fn_map: Optional[dict[Union[type, tuple[type, ...]], Callable]] = None,
+):
+    elem = batch[0]
+    out = None
+    if elem.is_nested:
+        raise RuntimeError(
+            "Batches of nested tensors are not currently supported by the default collate_fn; "
+            "please provide a custom collate_fn to handle them appropriately."
+        )
+    if elem.layout in {
+        torch.sparse_coo,
+        torch.sparse_csr,
+        torch.sparse_bsr,
+        torch.sparse_csc,
+        torch.sparse_bsc,
+    }:
+        raise RuntimeError(
+            "Batches of sparse tensors are not currently supported by the default collate_fn; "
+            "please provide a custom collate_fn to handle them appropriately."
+        )
+    if torch.utils.data.get_worker_info() is not None:
+        # If we're in a background process, concatenate directly into a
+        # shared memory tensor to avoid an extra copy
+        numel = sum(x.numel() for x in batch)
+        storage = elem._typed_storage()._new_shared(numel, device=elem.device)
+        out = elem.new(storage).resize_(len(batch), *list(elem.size()))
+    return torch.stack(batch, 0, out=out)
+
+
+def collate_numpy_array_fn(
+    batch,
+    *,
+    collate_fn_map: Optional[dict[Union[type, tuple[type, ...]], Callable]] = None,
+):
+    elem = batch[0]
+    # array of string classes and object
+    if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
+        raise TypeError(default_collate_err_msg_format.format(elem.dtype))
+
+    return collate([torch.as_tensor(b) for b in batch], collate_fn_map=collate_fn_map)
+
+
+def collate_numpy_scalar_fn(
+    batch,
+    *,
+    collate_fn_map: Optional[dict[Union[type, tuple[type, ...]], Callable]] = None,
+):
+    return torch.as_tensor(batch)
+
+
+def collate_float_fn(
+    batch,
+    *,
+    collate_fn_map: Optional[dict[Union[type, tuple[type, ...]], Callable]] = None,
+):
+    return torch.tensor(batch, dtype=torch.float64)
+
+
+def collate_int_fn(
+    batch,
+    *,
+    collate_fn_map: Optional[dict[Union[type, tuple[type, ...]], Callable]] = None,
+):
+    return torch.tensor(batch)
+
+
+def collate_str_fn(
+    batch,
+    *,
+    collate_fn_map: Optional[dict[Union[type, tuple[type, ...]], Callable]] = None,
+):
+    return batch
+
+
+default_collate_fn_map: dict[Union[type, tuple[type, ...]], Callable] = {
+    torch.Tensor: collate_tensor_fn
+}
+with contextlib.suppress(ImportError):
+    import numpy as np
+
+    # For both ndarray and memmap (subclass of ndarray)
+    default_collate_fn_map[np.ndarray] = collate_numpy_array_fn
+    # See scalars hierarchy: https://numpy.org/doc/stable/reference/arrays.scalars.html
+    # Skip string scalars
+    default_collate_fn_map[(np.bool_, np.number, np.object_)] = collate_numpy_scalar_fn
+default_collate_fn_map[float] = collate_float_fn
+default_collate_fn_map[int] = collate_int_fn
+default_collate_fn_map[str] = collate_str_fn
+default_collate_fn_map[bytes] = collate_str_fn
+
+
+def default_collate(batch):
+    r"""
+    Take in a batch of data and put the elements within the batch into a tensor with an additional outer dimension - batch size.
+
+    The exact output type can be a :class:`torch.Tensor`, a `Sequence` of :class:`torch.Tensor`, a
+    Collection of :class:`torch.Tensor`, or left unchanged, depending on the input type.
+    This is used as the default function for collation when
+    `batch_size` or `batch_sampler` is defined in :class:`~torch.utils.data.DataLoader`.
+
+    Here is the general input type (based on the type of the element within the batch) to output type mapping:
+
+        * :class:`torch.Tensor` -> :class:`torch.Tensor` (with an added outer dimension batch size)
+        * NumPy Arrays -> :class:`torch.Tensor`
+        * `float` -> :class:`torch.Tensor`
+        * `int` -> :class:`torch.Tensor`
+        * `str` -> `str` (unchanged)
+        * `bytes` -> `bytes` (unchanged)
+        * `Mapping[K, V_i]` -> `Mapping[K, default_collate([V_1, V_2, ...])]`
+        * `NamedTuple[V1_i, V2_i, ...]` -> `NamedTuple[default_collate([V1_1, V1_2, ...]),
+          default_collate([V2_1, V2_2, ...]), ...]`
+        * `Sequence[V1_i, V2_i, ...]` -> `Sequence[default_collate([V1_1, V1_2, ...]),
+          default_collate([V2_1, V2_2, ...]), ...]`
+
+    Args:
+        batch: a single batch to be collated
+
+    Examples:
+        >>> # xdoctest: +SKIP
+        >>> # Example with a batch of `int`s:
+        >>> default_collate([0, 1, 2, 3])
+        tensor([0, 1, 2, 3])
+        >>> # Example with a batch of `str`s:
+        >>> default_collate(['a', 'b', 'c'])
+        ['a', 'b', 'c']
+        >>> # Example with `Map` inside the batch:
+        >>> default_collate([{'A': 0, 'B': 1}, {'A': 100, 'B': 100}])
+        {'A': tensor([  0, 100]), 'B': tensor([  1, 100])}
+        >>> # Example with `NamedTuple` inside the batch:
+        >>> Point = namedtuple('Point', ['x', 'y'])
+        >>> default_collate([Point(0, 0), Point(1, 1)])
+        Point(x=tensor([0, 1]), y=tensor([0, 1]))
+        >>> # Example with `Tuple` inside the batch:
+        >>> default_collate([(0, 1), (2, 3)])
+        [tensor([0, 2]), tensor([1, 3])]
+        >>> # Example with `List` inside the batch:
+        >>> default_collate([[0, 1], [2, 3]])
+        [tensor([0, 2]), tensor([1, 3])]
+        >>> # Two options to extend `default_collate` to handle specific type
+        >>> # Option 1: Write custom collate function and invoke `default_collate`
+        >>> def custom_collate(batch):
+        ...     elem = batch[0]
+        ...     if isinstance(elem, CustomType):  # Some custom condition
+        ...         return ...
+        ...     else:  # Fall back to `default_collate`
+        ...         return default_collate(batch)
+        >>> # Option 2: In-place modify `default_collate_fn_map`
+        >>> def collate_customtype_fn(batch, *, collate_fn_map=None):
+        ...     return ...
+        >>> default_collate_fn_map.update(CustomType, collate_customtype_fn)
+        >>> default_collate(batch)  # Handle `CustomType` automatically
+    """
+    return collate(batch, collate_fn_map=default_collate_fn_map)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fa6c49404f676ad3811080ff9631e49fb275513
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py
@@ -0,0 +1,55 @@
+# mypy: allow-untyped-defs
+r"""Contains definitions of the methods used by the _BaseDataLoaderIter to fetch data from an iterable-style or map-style dataset.
+
+This logic is shared in both single- and multi-processing data loading.
+"""
+
+
+class _BaseDatasetFetcher:
+    def __init__(self, dataset, auto_collation, collate_fn, drop_last):
+        self.dataset = dataset
+        self.auto_collation = auto_collation
+        self.collate_fn = collate_fn
+        self.drop_last = drop_last
+
+    def fetch(self, possibly_batched_index):
+        raise NotImplementedError
+
+
+class _IterableDatasetFetcher(_BaseDatasetFetcher):
+    def __init__(self, dataset, auto_collation, collate_fn, drop_last):
+        super().__init__(dataset, auto_collation, collate_fn, drop_last)
+        self.dataset_iter = iter(dataset)
+        self.ended = False
+
+    def fetch(self, possibly_batched_index):
+        if self.ended:
+            raise StopIteration
+
+        if self.auto_collation:
+            data = []
+            for _ in possibly_batched_index:
+                try:
+                    data.append(next(self.dataset_iter))
+                except StopIteration:
+                    self.ended = True
+                    break
+            if len(data) == 0 or (
+                self.drop_last and len(data) < len(possibly_batched_index)
+            ):
+                raise StopIteration
+        else:
+            data = next(self.dataset_iter)
+        return self.collate_fn(data)
+
+
+class _MapDatasetFetcher(_BaseDatasetFetcher):
+    def fetch(self, possibly_batched_index):
+        if self.auto_collation:
+            if hasattr(self.dataset, "__getitems__") and self.dataset.__getitems__:
+                data = self.dataset.__getitems__(possibly_batched_index)
+            else:
+                data = [self.dataset[idx] for idx in possibly_batched_index]
+        else:
+            data = self.dataset[possibly_batched_index]
+        return self.collate_fn(data)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..0efe39854fb0af6b10c76a264db329e647b0e801
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py
@@ -0,0 +1,110 @@
+# mypy: allow-untyped-defs
+r"""Contains definitions of the methods used by the _BaseDataLoaderIter to put fetched tensors into pinned memory.
+
+These **needs** to be in global scope since Py2 doesn't support serializing
+static methods.
+"""
+
+import collections
+import copy
+import queue
+
+import torch
+from torch._utils import ExceptionWrapper
+
+from . import MP_STATUS_CHECK_INTERVAL
+
+
+def _pin_memory_loop(in_queue, out_queue, device_id, done_event, device):
+    # This setting is thread local, and prevents the copy in pin_memory from
+    # consuming all CPU cores.
+    torch.set_num_threads(1)
+
+    torch.multiprocessing._set_thread_name("pt_data_pin")
+
+    if device == "cuda":
+        torch.cuda.set_device(device_id)
+    elif device == "xpu":
+        torch.xpu.set_device(device_id)  # type: ignore[attr-defined]
+    elif device == torch._C._get_privateuse1_backend_name():
+        custom_device_mod = getattr(torch, torch._C._get_privateuse1_backend_name())
+        custom_device_mod.set_device(device_id)
+    elif device is None:
+        torch.accelerator.set_device_index(device_id)
+
+    def do_one_step():
+        try:
+            r = in_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
+        except queue.Empty:
+            return
+        idx, data = r
+        if not done_event.is_set() and not isinstance(data, ExceptionWrapper):
+            try:
+                data = pin_memory(data, device)
+            except Exception:
+                data = ExceptionWrapper(
+                    where=f"in pin memory thread for device {device_id}"
+                )
+            r = (idx, data)
+        while not done_event.is_set():
+            try:
+                out_queue.put(r, timeout=MP_STATUS_CHECK_INTERVAL)
+                break
+            except queue.Full:
+                continue
+
+    # See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on the
+    # logic of this function.
+    while not done_event.is_set():
+        # Make sure that we don't preserve any object from one iteration
+        # to the next
+        do_one_step()
+
+
+def pin_memory(data, device=None):
+    if isinstance(data, torch.Tensor):
+        return data.pin_memory(device)
+    elif isinstance(data, (str, bytes)):
+        return data
+    elif isinstance(data, collections.abc.Mapping):
+        try:
+            if isinstance(data, collections.abc.MutableMapping):
+                # The sequence type may have extra properties, so we can't just
+                # use `type(data)(...)` to create the new sequence.
+                # Create a clone and update it if the sequence type is mutable.
+                clone = copy.copy(data)
+                clone.update(
+                    {k: pin_memory(sample, device) for k, sample in data.items()}
+                )
+                return clone
+            else:
+                return type(data)({k: pin_memory(sample, device) for k, sample in data.items()})  # type: ignore[call-arg]
+        except TypeError:
+            # The mapping type may not support `copy()` / `update(mapping)`
+            # or `__init__(iterable)`.
+            return {k: pin_memory(sample, device) for k, sample in data.items()}
+    elif isinstance(data, tuple) and hasattr(data, "_fields"):  # namedtuple
+        return type(data)(*(pin_memory(sample, device) for sample in data))
+    elif isinstance(data, tuple):
+        return [
+            pin_memory(sample, device) for sample in data
+        ]  # Backwards compatibility.
+    elif isinstance(data, collections.abc.Sequence):
+        try:
+            if isinstance(data, collections.abc.MutableSequence):
+                # The sequence type may have extra properties, so we can't just
+                # use `type(data)(...)` to create the new sequence.
+                # Create a clone and update it if the sequence type is mutable.
+                clone = copy.copy(data)  # type: ignore[arg-type]
+                for i, item in enumerate(data):
+                    clone[i] = pin_memory(item, device)
+                return clone
+            return type(data)([pin_memory(sample, device) for sample in data])  # type: ignore[call-arg]
+        except TypeError:
+            # The sequence type may not support `copy()` / `__setitem__(index, item)`
+            # or `__init__(iterable)` (e.g., `range`).
+            return [pin_memory(sample, device) for sample in data]
+    elif hasattr(data, "pin_memory"):
+        return data.pin_memory()
+    else:
+        return data
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1d54f05e360e11e679345334988c8d416e58104
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py
@@ -0,0 +1,79 @@
+# mypy: allow-untyped-defs
+r"""Signal handling for multiprocessing data loading.
+
+NOTE [ Signal handling in multiprocessing data loading ]
+
+In cases like DataLoader, if a worker process dies due to bus error/segfault
+or just hang, the main process will hang waiting for data. This is difficult
+to avoid on PyTorch side as it can be caused by limited shm, or other
+libraries users call in the workers. In this file and `DataLoader.cpp`, we make
+our best effort to provide some error message to users when such unfortunate
+events happen.
+
+When a _BaseDataLoaderIter starts worker processes, their pids are registered in a
+defined in `DataLoader.cpp`: id(_BaseDataLoaderIter) => Collection[ Worker pids ]
+via `_set_worker_pids`.
+
+When an error happens in a worker process, the main process received a SIGCHLD,
+and Python will eventually call the handler registered below
+(in `_set_SIGCHLD_handler`). In the handler, the `_error_if_any_worker_fails`
+call checks all registered worker pids and raise proper error message to
+prevent main process from hanging waiting for data from worker.
+
+Additionally, at the beginning of each worker's `_utils.worker._worker_loop`,
+`_set_worker_signal_handlers` is called to register critical signal handlers
+(e.g., for SIGSEGV, SIGBUS, SIGFPE, SIGTERM) in C, which just prints an error
+message to stderr before triggering the default handler. So a message will also
+be printed from the worker process when it is killed by such signals.
+
+See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for the reasoning of
+this signal handling design and other mechanism we implement to make our
+multiprocessing data loading robust to errors.
+"""
+
+import signal
+import threading
+
+# Some of the following imported functions are not used in this file, but are to
+# be used `_utils.signal_handling.XXXXX`.
+from torch._C import (  # noqa: F401
+    _error_if_any_worker_fails,
+    _remove_worker_pids,
+    _set_worker_pids,
+    _set_worker_signal_handlers,
+)
+
+from . import IS_WINDOWS
+
+
+_SIGCHLD_handler_set = False
+r"""Whether SIGCHLD handler is set for DataLoader worker failures. Only one
+handler needs to be set for all DataLoaders in a process."""
+
+
+def _set_SIGCHLD_handler():
+    # Windows doesn't support SIGCHLD handler
+    if IS_WINDOWS:
+        return
+    # can't set signal in child threads
+    if not isinstance(threading.current_thread(), threading._MainThread):  # type: ignore[attr-defined]
+        return
+    global _SIGCHLD_handler_set
+    if _SIGCHLD_handler_set:
+        return
+    previous_handler = signal.getsignal(signal.SIGCHLD)
+    if not callable(previous_handler):
+        # This doesn't catch default handler, but SIGCHLD default handler is a
+        # no-op.
+        previous_handler = None
+
+    def handler(signum, frame):
+        # This following call uses `waitid` with WNOHANG from C side. Therefore,
+        # Python can still get and update the process status successfully.
+        _error_if_any_worker_fails()
+        if previous_handler is not None:
+            assert callable(previous_handler)
+            previous_handler(signum, frame)
+
+    signal.signal(signal.SIGCHLD, handler)
+    _SIGCHLD_handler_set = True
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py
new file mode 100644
index 0000000000000000000000000000000000000000..a275e2e86b6fff601bc5799f5f301faccf933c8f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py
@@ -0,0 +1,374 @@
+# mypy: allow-untyped-defs
+r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers.
+
+These **needs** to be in global scope since Py2 doesn't support serializing
+static methods.
+"""
+
+import os
+import queue
+import random
+from dataclasses import dataclass
+from typing import Optional, TYPE_CHECKING, Union
+
+import torch
+from torch._utils import ExceptionWrapper
+
+from . import HAS_NUMPY, IS_WINDOWS, MP_STATUS_CHECK_INTERVAL, signal_handling
+
+
+if TYPE_CHECKING:
+    from torch.utils.data import Dataset
+
+if IS_WINDOWS:
+    import ctypes
+    from ctypes.wintypes import BOOL, DWORD, HANDLE
+
+    # On Windows, the parent ID of the worker process remains unchanged when the manager process
+    # is gone, and the only way to check it through OS is to let the worker have a process handle
+    # of the manager and ask if the process status has changed.
+    class ManagerWatchdog:
+        def __init__(self) -> None:
+            self.manager_pid = os.getppid()
+
+            # mypy cannot detect this code is windows only
+            self.kernel32 = ctypes.WinDLL("kernel32", use_last_error=True)  # type: ignore[attr-defined]
+            self.kernel32.OpenProcess.argtypes = (DWORD, BOOL, DWORD)
+            self.kernel32.OpenProcess.restype = HANDLE
+            self.kernel32.WaitForSingleObject.argtypes = (HANDLE, DWORD)
+            self.kernel32.WaitForSingleObject.restype = DWORD
+
+            # Value obtained from https://msdn.microsoft.com/en-us/library/ms684880.aspx
+            SYNCHRONIZE = 0x00100000
+            self.manager_handle = self.kernel32.OpenProcess(
+                SYNCHRONIZE, 0, self.manager_pid
+            )
+
+            if not self.manager_handle:
+                raise ctypes.WinError(ctypes.get_last_error())  # type: ignore[attr-defined]
+
+            self.manager_dead = False
+
+        def is_alive(self):
+            if not self.manager_dead:
+                # Value obtained from https://msdn.microsoft.com/en-us/library/windows/desktop/ms687032.aspx
+                self.manager_dead = (
+                    self.kernel32.WaitForSingleObject(self.manager_handle, 0) == 0
+                )
+            return not self.manager_dead
+
+else:
+
+    class ManagerWatchdog:  # type: ignore[no-redef]
+        def __init__(self) -> None:
+            self.manager_pid = os.getppid()
+            self.manager_dead = False
+
+        def is_alive(self):
+            if not self.manager_dead:
+                self.manager_dead = os.getppid() != self.manager_pid
+            return not self.manager_dead
+
+
+_worker_info: Optional["WorkerInfo"] = None
+
+
+class WorkerInfo:
+    id: int
+    num_workers: int
+    seed: int
+    dataset: "Dataset"
+    __initialized = False
+
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+        self.__keys = tuple(kwargs.keys())
+        self.__initialized = True
+
+    def __setattr__(self, key, val):
+        if self.__initialized:
+            raise RuntimeError(
+                f"Cannot assign attributes to {self.__class__.__name__} objects"
+            )
+        return super().__setattr__(key, val)
+
+    def __repr__(self):
+        items = [f"{k}={getattr(self, k)}" for k in self.__keys]
+        return f"{self.__class__.__name__}({', '.join(items)})"
+
+
+def get_worker_info() -> Optional[WorkerInfo]:
+    r"""Returns the information about the current
+    :class:`~torch.utils.data.DataLoader` iterator worker process.
+
+    When called in a worker, this returns an object guaranteed to have the
+    following attributes:
+
+    * :attr:`id`: the current worker id.
+    * :attr:`num_workers`: the total number of workers.
+    * :attr:`seed`: the random seed set for the current worker. This value is
+      determined by main process RNG and the worker id. See
+      :class:`~torch.utils.data.DataLoader`'s documentation for more details.
+    * :attr:`dataset`: the copy of the dataset object in **this** process. Note
+      that this will be a different object in a different process than the one
+      in the main process.
+
+    When called in the main process, this returns ``None``.
+
+    .. note::
+       When used in a :attr:`worker_init_fn` passed over to
+       :class:`~torch.utils.data.DataLoader`, this method can be useful to
+       set up each worker process differently, for instance, using ``worker_id``
+       to configure the ``dataset`` object to only read a specific fraction of a
+       sharded dataset, or use ``seed`` to seed other libraries used in dataset
+       code.
+    """
+    return _worker_info
+
+
+r"""Dummy class used to signal the end of an IterableDataset"""
+
+
+@dataclass(frozen=True)
+class _IterableDatasetStopIteration:
+    worker_id: int
+
+
+r"""Dummy class used to resume the fetching when worker reuse is enabled"""
+
+
+@dataclass(frozen=True)
+class _ResumeIteration:
+    seed: Optional[int] = None
+
+
+# The function `_generate_state` is adapted from `numpy.random.SeedSequence`
+# from https://github.com/numpy/numpy/blob/main/numpy/random/bit_generator.pyx
+# It's MIT licensed, here is the copyright:
+
+# Copyright (c) 2015 Melissa E. O'Neill
+# Copyright (c) 2019 NumPy Developers
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+
+# This function generates an array of int32 as the seed for
+# `numpy.random`, in order to prevent state collision due to same
+# seed and algorithm for `numpy.random` and `random` modules.
+# TODO: Implement `SeedSequence` like object for `torch.random`
+def _generate_state(base_seed, worker_id):
+    INIT_A = 0x43B0D7E5
+    MULT_A = 0x931E8875
+    INIT_B = 0x8B51F9DD
+    MULT_B = 0x58F38DED
+    MIX_MULT_L = 0xCA01F9DD
+    MIX_MULT_R = 0x4973F715
+    XSHIFT = 4 * 8 // 2
+    MASK32 = 0xFFFFFFFF
+
+    entropy = [worker_id, base_seed & MASK32, base_seed >> 32, 0]
+    pool = [0] * 4
+
+    hash_const_A = INIT_A
+
+    def hash(value):
+        nonlocal hash_const_A
+        value = (value ^ hash_const_A) & MASK32
+        hash_const_A = (hash_const_A * MULT_A) & MASK32
+        value = (value * hash_const_A) & MASK32
+        value = (value ^ (value >> XSHIFT)) & MASK32
+        return value
+
+    def mix(x, y):
+        result_x = (MIX_MULT_L * x) & MASK32
+        result_y = (MIX_MULT_R * y) & MASK32
+        result = (result_x - result_y) & MASK32
+        result = (result ^ (result >> XSHIFT)) & MASK32
+        return result
+
+    # Add in the entropy to the pool.
+    for i in range(len(pool)):
+        pool[i] = hash(entropy[i])
+
+    # Mix all bits together so late bits can affect earlier bits.
+    for i_src in range(len(pool)):
+        for i_dst in range(len(pool)):
+            if i_src != i_dst:
+                pool[i_dst] = mix(pool[i_dst], hash(pool[i_src]))
+
+    hash_const_B = INIT_B
+    state = []
+    for i_dst in range(4):
+        data_val = pool[i_dst]
+        data_val = (data_val ^ hash_const_B) & MASK32
+        hash_const_B = (hash_const_B * MULT_B) & MASK32
+        data_val = (data_val * hash_const_B) & MASK32
+        data_val = (data_val ^ (data_val >> XSHIFT)) & MASK32
+        state.append(data_val)
+    return state
+
+
+def _worker_loop(
+    dataset_kind,
+    dataset,
+    index_queue,
+    data_queue,
+    done_event,
+    auto_collation,
+    collate_fn,
+    drop_last,
+    base_seed,
+    init_fn,
+    worker_id,
+    num_workers,
+    persistent_workers,
+    shared_seed,
+):
+    # See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on the
+    # logic of this function.
+
+    try:
+        # Initialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
+        # module's handlers are executed after Python returns from C low-level
+        # handlers, likely when the same fatal signal had already happened
+        # again.
+        # https://docs.python.org/3/library/signal.html#execution-of-python-signal-handlers
+        signal_handling._set_worker_signal_handlers()
+
+        torch.multiprocessing._set_thread_name("pt_data_worker")
+
+        torch.set_num_threads(1)
+        seed = base_seed + worker_id
+        random.seed(seed)
+        torch.manual_seed(seed)
+        if HAS_NUMPY:
+            np_seed = _generate_state(base_seed, worker_id)
+            import numpy as np
+
+            np.random.seed(np_seed)
+
+        from torch.utils.data import IterDataPipe
+        from torch.utils.data.graph_settings import apply_random_seed
+
+        shared_rng = torch.Generator()
+        if isinstance(dataset, IterDataPipe):
+            assert shared_seed is not None
+            shared_rng.manual_seed(shared_seed)
+            dataset = apply_random_seed(dataset, shared_rng)
+
+        global _worker_info
+        _worker_info = WorkerInfo(
+            id=worker_id, num_workers=num_workers, seed=seed, dataset=dataset
+        )
+
+        from torch.utils.data import _DatasetKind
+
+        init_exception = None
+
+        try:
+            if init_fn is not None:
+                init_fn(worker_id)
+
+            fetcher = _DatasetKind.create_fetcher(
+                dataset_kind, dataset, auto_collation, collate_fn, drop_last
+            )
+        except Exception:
+            init_exception = ExceptionWrapper(
+                where=f"in DataLoader worker process {worker_id}"
+            )
+
+        # When using Iterable mode, some worker can exit earlier than others due
+        # to the IterableDataset behaving differently for different workers.
+        # When such things happen, an `_IterableDatasetStopIteration` object is
+        # sent over to the main process with the ID of this worker, so that the
+        # main process won't send more tasks to this worker, and will send
+        # `None` to this worker to properly exit it.
+        #
+        # Note that we cannot set `done_event` from a worker as it is shared
+        # among all processes. Instead, we set the `iteration_end` flag to
+        # signify that the iterator is exhausted. When either `done_event` or
+        # `iteration_end` is set, we skip all processing step and just wait for
+        # `None`.
+        iteration_end = False
+
+        watchdog = ManagerWatchdog()
+
+        while watchdog.is_alive():
+            try:
+                r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
+            except queue.Empty:
+                continue
+            if isinstance(r, _ResumeIteration):
+                # Acknowledge the main process
+                data_queue.put((r, None))
+                iteration_end = False
+
+                if isinstance(dataset, IterDataPipe):
+                    assert r.seed is not None
+                    shared_rng.manual_seed(r.seed)
+                    dataset = apply_random_seed(dataset, shared_rng)
+
+                # Recreate the fetcher for worker-reuse policy
+                fetcher = _DatasetKind.create_fetcher(
+                    dataset_kind, dataset, auto_collation, collate_fn, drop_last
+                )
+                continue
+            elif r is None:
+                # Received the final signal
+                assert done_event.is_set() or iteration_end
+                break
+            elif done_event.is_set() or iteration_end:
+                # `done_event` is set. But I haven't received the final signal
+                # (None) yet. I will keep continuing until get it, and skip the
+                # processing steps.
+                continue
+            idx, index = r
+            data: Union[_IterableDatasetStopIteration, ExceptionWrapper]
+            if init_exception is not None:
+                data = init_exception
+                init_exception = None
+            else:
+                try:
+                    data = fetcher.fetch(index)  # type: ignore[possibly-undefined]
+                except Exception as e:
+                    if (
+                        isinstance(e, StopIteration)
+                        and dataset_kind == _DatasetKind.Iterable
+                    ):
+                        data = _IterableDatasetStopIteration(worker_id)
+                        # Set `iteration_end`
+                        #   (1) to save future `next(...)` calls, and
+                        #   (2) to avoid sending multiple `_IterableDatasetStopIteration`s.
+                        iteration_end = True
+                    else:
+                        # It is important that we don't store exc_info in a variable.
+                        # `ExceptionWrapper` does the correct thing.
+                        # See NOTE [ Python Traceback Reference Cycle Problem ]
+                        data = ExceptionWrapper(
+                            where=f"in DataLoader worker process {worker_id}"
+                        )
+            data_queue.put((idx, data))
+            del data, idx, index, r  # save memory
+    except KeyboardInterrupt:
+        # Main process will raise KeyboardInterrupt anyways.
+        pass
+    if done_event.is_set():
+        data_queue.cancel_join_thread()
+        data_queue.close()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/backward_compatibility.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/backward_compatibility.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8f1c4e30ef720f676cf6581333cf3d48733e640
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/backward_compatibility.py
@@ -0,0 +1,11 @@
+# mypy: allow-untyped-defs
+from typing_extensions import deprecated as _deprecated
+
+
+@_deprecated(
+    "Usage of `backward_compatibility.worker_init_fn` is deprecated "
+    "as `DataLoader` automatically applies sharding in every worker",
+    category=FutureWarning,
+)
+def worker_init_fn(worker_id):
+    pass
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..66a371085b39674abed8ef8efd72e3d1512f3ff3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py
@@ -0,0 +1,1663 @@
+# mypy: allow-untyped-defs
+r"""Definition of the DataLoader and associated iterators that subclass _BaseDataLoaderIter.
+
+To support these two classes, in `./_utils` we define many utility methods and
+functions to be run in multiprocessing. E.g., the data loading worker loop is
+in `./_utils/worker.py`.
+"""
+
+import functools
+import itertools
+import logging
+import multiprocessing as python_multiprocessing
+import os
+import queue
+import threading
+import warnings
+from collections.abc import Iterable
+from typing import Any, Callable, Generic, Optional, TypeVar, Union
+
+import torch
+import torch.distributed as dist
+import torch.utils.data.graph_settings
+from torch._utils import ExceptionWrapper
+from torch.utils.data import _utils
+from torch.utils.data.datapipes.datapipe import (
+    _IterDataPipeSerializationWrapper,
+    _MapDataPipeSerializationWrapper,
+    IterDataPipe,
+    MapDataPipe,
+)
+from torch.utils.data.dataset import Dataset, IterableDataset
+from torch.utils.data.sampler import (
+    BatchSampler,
+    RandomSampler,
+    Sampler,
+    SequentialSampler,
+)
+
+
+__all__ = [
+    "DataLoader",
+    "get_worker_info",
+    "default_collate",
+    "default_convert",
+]
+
+
+_T = TypeVar("_T")
+_T_co = TypeVar("_T_co", covariant=True)
+_worker_init_fn_t = Callable[[int], None]
+
+# Ideally we would parameterize `DataLoader` by the return type of `collate_fn`, but there is currently no way to have that
+# type parameter set to a default value if the user doesn't pass in a custom 'collate_fn'.
+# See https://github.com/python/mypy/issues/3737.
+_collate_fn_t = Callable[[list[_T]], Any]
+
+
+# These functions used to be defined in this file. However, it was moved to
+# _utils/collate.py. Although it is rather hard to access this from user land
+# (one has to explicitly directly `import torch.utils.data.dataloader`), there
+# probably is user code out there using it. This aliasing maintains BC in this
+# aspect.
+default_collate: _collate_fn_t = _utils.collate.default_collate
+default_convert = _utils.collate.default_convert
+
+get_worker_info = _utils.worker.get_worker_info
+
+logger = logging.getLogger(__name__)
+
+
+class _DatasetKind:
+    Map = 0
+    Iterable = 1
+
+    @staticmethod
+    def create_fetcher(kind, dataset, auto_collation, collate_fn, drop_last):
+        if kind == _DatasetKind.Map:
+            return _utils.fetch._MapDatasetFetcher(
+                dataset, auto_collation, collate_fn, drop_last
+            )
+        else:
+            return _utils.fetch._IterableDatasetFetcher(
+                dataset, auto_collation, collate_fn, drop_last
+            )
+
+
+class _InfiniteConstantSampler(Sampler):
+    r"""Analogous to ``itertools.repeat(None, None)``.
+
+    Used as sampler for :class:`~torch.utils.data.IterableDataset`.
+    """
+
+    def __iter__(self):
+        while True:
+            yield None
+
+
+def _get_distributed_settings():
+    if dist.is_available() and dist.is_initialized():
+        return dist.get_world_size(), dist.get_rank()
+    else:
+        return 1, 0
+
+
+def _sharding_worker_init_fn(worker_init_fn, world_size, rank_id, worker_id):
+    global_worker_id = worker_id
+    info = torch.utils.data.get_worker_info()
+    assert info is not None
+    total_workers = info.num_workers
+    datapipe = info.dataset
+    assert isinstance(datapipe, (IterDataPipe, MapDataPipe))
+    # To distribute elements across distributed process evenly, we should shard data on distributed
+    # processes first then shard on worker processes
+    total_workers *= world_size
+    global_worker_id = global_worker_id * world_size + rank_id
+    # For BC, use default SHARDING_PRIORITIES
+    torch.utils.data.graph_settings.apply_sharding(
+        datapipe, total_workers, global_worker_id
+    )
+    if worker_init_fn is not None:
+        worker_init_fn(worker_id)
+
+
+def _share_dist_seed(generator, pg):
+    _shared_seed = torch.empty((), dtype=torch.int64).random_(generator=generator)
+    if isinstance(pg, dist.ProcessGroup):
+        dist.broadcast(_shared_seed, src=0, group=pg)
+    return _shared_seed.item()
+
+
+class DataLoader(Generic[_T_co]):
+    r"""
+    Data loader combines a dataset and a sampler, and provides an iterable over the given dataset.
+
+    The :class:`~torch.utils.data.DataLoader` supports both map-style and
+    iterable-style datasets with single- or multi-process loading, customizing
+    loading order and optional automatic batching (collation) and memory pinning.
+
+    See :py:mod:`torch.utils.data` documentation page for more details.
+
+    Args:
+        dataset (Dataset): dataset from which to load the data.
+        batch_size (int, optional): how many samples per batch to load
+            (default: ``1``).
+        shuffle (bool, optional): set to ``True`` to have the data reshuffled
+            at every epoch (default: ``False``).
+        sampler (Sampler or Iterable, optional): defines the strategy to draw
+            samples from the dataset. Can be any ``Iterable`` with ``__len__``
+            implemented. If specified, :attr:`shuffle` must not be specified.
+        batch_sampler (Sampler or Iterable, optional): like :attr:`sampler`, but
+            returns a batch of indices at a time. Mutually exclusive with
+            :attr:`batch_size`, :attr:`shuffle`, :attr:`sampler`,
+            and :attr:`drop_last`.
+        num_workers (int, optional): how many subprocesses to use for data
+            loading. ``0`` means that the data will be loaded in the main process.
+            (default: ``0``)
+        collate_fn (Callable, optional): merges a list of samples to form a
+            mini-batch of Tensor(s).  Used when using batched loading from a
+            map-style dataset.
+        pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
+            into device/CUDA pinned memory before returning them.  If your data elements
+            are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
+            see the example below.
+        drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
+            if the dataset size is not divisible by the batch size. If ``False`` and
+            the size of dataset is not divisible by the batch size, then the last batch
+            will be smaller. (default: ``False``)
+        timeout (numeric, optional): if positive, the timeout value for collecting a batch
+            from workers. Should always be non-negative. (default: ``0``)
+        worker_init_fn (Callable, optional): If not ``None``, this will be called on each
+            worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
+            input, after seeding and before data loading. (default: ``None``)
+        multiprocessing_context (str or multiprocessing.context.BaseContext, optional): If
+            ``None``, the default `multiprocessing context`_ of your operating system will
+            be used. (default: ``None``)
+        generator (torch.Generator, optional): If not ``None``, this RNG will be used
+            by RandomSampler to generate random indexes and multiprocessing to generate
+            ``base_seed`` for workers. (default: ``None``)
+        prefetch_factor (int, optional, keyword-only arg): Number of batches loaded
+            in advance by each worker. ``2`` means there will be a total of
+            2 * num_workers batches prefetched across all workers. (default value depends
+            on the set value for num_workers. If value of num_workers=0 default is ``None``.
+            Otherwise, if value of ``num_workers > 0`` default is ``2``).
+        persistent_workers (bool, optional): If ``True``, the data loader will not shut down
+            the worker processes after a dataset has been consumed once. This allows to
+            maintain the workers `Dataset` instances alive. (default: ``False``)
+        pin_memory_device (str, optional): the device to :attr:`pin_memory` on if ``pin_memory`` is
+            ``True``. If not given, the current :ref:`accelerator` will be the
+            default. This argument is discouraged and subject to deprecated.
+        in_order (bool, optional): If ``False``, the data loader will not enforce that batches
+            are returned in a first-in, first-out order. Only applies when ``num_workers > 0``. (default: ``True``)
+
+
+    .. warning:: If the ``spawn`` start method is used, :attr:`worker_init_fn`
+                 cannot be an unpicklable object, e.g., a lambda function. See
+                 :ref:`multiprocessing-best-practices` on more details related
+                 to multiprocessing in PyTorch.
+
+    .. warning:: ``len(dataloader)`` heuristic is based on the length of the sampler used.
+                 When :attr:`dataset` is an :class:`~torch.utils.data.IterableDataset`,
+                 it instead returns an estimate based on ``len(dataset) / batch_size``, with proper
+                 rounding depending on :attr:`drop_last`, regardless of multi-process loading
+                 configurations. This represents the best guess PyTorch can make because PyTorch
+                 trusts user :attr:`dataset` code in correctly handling multi-process
+                 loading to avoid duplicate data.
+
+                 However, if sharding results in multiple workers having incomplete last batches,
+                 this estimate can still be inaccurate, because (1) an otherwise complete batch can
+                 be broken into multiple ones and (2) more than one batch worth of samples can be
+                 dropped when :attr:`drop_last` is set. Unfortunately, PyTorch can not detect such
+                 cases in general.
+
+                 See `Dataset Types`_ for more details on these two types of datasets and how
+                 :class:`~torch.utils.data.IterableDataset` interacts with
+                 `Multi-process data loading`_.
+
+    .. warning:: See :ref:`reproducibility`, and :ref:`dataloader-workers-random-seed`, and
+                 :ref:`data-loading-randomness` notes for random seed related questions.
+
+    .. warning:: Setting `in_order` to `False` can harm reproducibility and may lead to a skewed data
+                 distribution being fed to the trainer in cases with imbalanced data.
+
+    .. _multiprocessing context:
+        https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
+    """
+
+    dataset: Dataset[_T_co]
+    batch_size: Optional[int]
+    num_workers: int
+    pin_memory: bool
+    drop_last: bool
+    timeout: float
+    sampler: Union[Sampler, Iterable]
+    pin_memory_device: str
+    prefetch_factor: Optional[int]
+    _iterator: Optional["_BaseDataLoaderIter"]
+    __initialized = False
+
+    def __init__(
+        self,
+        dataset: Dataset[_T_co],
+        batch_size: Optional[int] = 1,
+        shuffle: Optional[bool] = None,
+        sampler: Union[Sampler, Iterable, None] = None,
+        batch_sampler: Union[Sampler[list], Iterable[list], None] = None,
+        num_workers: int = 0,
+        collate_fn: Optional[_collate_fn_t] = None,
+        pin_memory: bool = False,
+        drop_last: bool = False,
+        timeout: float = 0,
+        worker_init_fn: Optional[_worker_init_fn_t] = None,
+        multiprocessing_context=None,
+        generator=None,
+        *,
+        prefetch_factor: Optional[int] = None,
+        persistent_workers: bool = False,
+        pin_memory_device: str = "",
+        in_order: bool = True,
+    ):
+        torch._C._log_api_usage_once("python.data_loader")
+
+        if num_workers < 0:
+            raise ValueError(
+                "num_workers option should be non-negative; "
+                "use num_workers=0 to disable multiprocessing."
+            )
+
+        if timeout < 0:
+            raise ValueError("timeout option should be non-negative")
+
+        if num_workers == 0 and prefetch_factor is not None:
+            raise ValueError(
+                "prefetch_factor option could only be specified in multiprocessing."
+                "let num_workers > 0 to enable multiprocessing, otherwise set prefetch_factor to None."
+            )
+        elif num_workers > 0 and prefetch_factor is None:
+            prefetch_factor = 2
+        elif prefetch_factor is not None and prefetch_factor < 0:
+            raise ValueError("prefetch_factor option should be non-negative")
+
+        if persistent_workers and num_workers == 0:
+            raise ValueError("persistent_workers option needs num_workers > 0")
+
+        self.dataset = dataset
+        self.num_workers = num_workers
+        self.prefetch_factor = prefetch_factor
+        self.pin_memory = pin_memory
+        self.pin_memory_device = pin_memory_device
+        self.timeout = timeout
+        self.worker_init_fn = worker_init_fn
+        self.multiprocessing_context = multiprocessing_context
+        self.in_order = in_order
+
+        # Adds forward compatibilities so classic DataLoader can work with DataPipes:
+        #   _DataPipeSerializationWrapper container makes it easier to serialize without redefining pickler
+        if isinstance(self.dataset, IterDataPipe):
+            self.dataset = _IterDataPipeSerializationWrapper(self.dataset)
+        elif isinstance(self.dataset, MapDataPipe):
+            self.dataset = _MapDataPipeSerializationWrapper(self.dataset)
+
+        # Arg-check dataset related before checking samplers because we want to
+        # tell users that iterable-style datasets are incompatible with custom
+        # samplers first, so that they don't learn that this combo doesn't work
+        # after spending time fixing the custom sampler errors.
+        if isinstance(dataset, IterableDataset):
+            self._dataset_kind = _DatasetKind.Iterable
+            # NOTE [ Custom Samplers and IterableDataset ]
+            #
+            # `IterableDataset` does not support custom `batch_sampler` or
+            # `sampler` since the key is irrelevant (unless we support
+            # generator-style dataset one day...).
+            #
+            # For `sampler`, we always create a dummy sampler. This is an
+            # infinite sampler even when the dataset may have an implemented
+            # finite `__len__` because in multi-process data loading, naive
+            # settings will return duplicated data (which may be desired), and
+            # thus using a sampler with length matching that of dataset will
+            # cause data lost (you may have duplicates of the first couple
+            # batches, but never see anything afterwards). Therefore,
+            # `Iterabledataset` always uses an infinite sampler, an instance of
+            # `_InfiniteConstantSampler` defined above.
+            #
+            # A custom `batch_sampler` essentially only controls the batch size.
+            # However, it is unclear how useful it would be since an iterable-style
+            # dataset can handle that within itself. Moreover, it is pointless
+            # in multi-process data loading as the assignment order of batches
+            # to workers is an implementation detail so users can not control
+            # how to batchify each worker's iterable. Thus, we disable this
+            # option. If this turns out to be useful in future, we can re-enable
+            # this, and support custom samplers that specify the assignments to
+            # specific workers.
+            if isinstance(dataset, IterDataPipe):
+                if shuffle is not None:
+                    dataset = torch.utils.data.graph_settings.apply_shuffle_settings(
+                        dataset, shuffle=shuffle
+                    )
+            # We cannot check `shuffle is not None` here, since previously `shuffle=False` was the default.
+            elif shuffle not in {False, None}:
+                raise ValueError(
+                    f"DataLoader with IterableDataset: expected unspecified shuffle option, but got shuffle={shuffle}"
+                )
+
+            if sampler is not None:
+                # See NOTE [ Custom Samplers and IterableDataset ]
+                raise ValueError(
+                    f"DataLoader with IterableDataset: expected unspecified sampler option, but got sampler={sampler}"
+                )
+            elif batch_sampler is not None:
+                # See NOTE [ Custom Samplers and IterableDataset ]
+                raise ValueError(
+                    "DataLoader with IterableDataset: expected unspecified "
+                    f"batch_sampler option, but got batch_sampler={batch_sampler}"
+                )
+        else:
+            shuffle = bool(shuffle)
+            self._dataset_kind = _DatasetKind.Map
+
+        if sampler is not None and shuffle:
+            raise ValueError("sampler option is mutually exclusive with shuffle")
+
+        if batch_sampler is not None:
+            # auto_collation with custom batch_sampler
+            if batch_size != 1 or shuffle or sampler is not None or drop_last:
+                raise ValueError(
+                    "batch_sampler option is mutually exclusive "
+                    "with batch_size, shuffle, sampler, and "
+                    "drop_last"
+                )
+            batch_size = None
+            drop_last = False
+        elif batch_size is None:
+            # no auto_collation
+            if drop_last:
+                raise ValueError(
+                    "batch_size=None option disables auto-batching "
+                    "and is mutually exclusive with drop_last"
+                )
+
+        if sampler is None:  # give default samplers
+            if self._dataset_kind == _DatasetKind.Iterable:
+                # See NOTE [ Custom Samplers and IterableDataset ]
+                sampler = _InfiniteConstantSampler()
+            else:  # map-style
+                if shuffle:
+                    sampler = RandomSampler(dataset, generator=generator)  # type: ignore[arg-type]
+                else:
+                    sampler = SequentialSampler(dataset)  # type: ignore[arg-type]
+
+        if batch_size is not None and batch_sampler is None:
+            # auto_collation without custom batch_sampler
+            batch_sampler = BatchSampler(sampler, batch_size, drop_last)
+
+        self.batch_size = batch_size
+        self.drop_last = drop_last
+        self.sampler = sampler
+        self.batch_sampler = batch_sampler
+        self.generator = generator
+
+        if collate_fn is None:
+            if self._auto_collation:
+                collate_fn = _utils.collate.default_collate
+            else:
+                collate_fn = _utils.collate.default_convert
+
+        self.collate_fn = collate_fn
+        self.persistent_workers = persistent_workers
+
+        self.__initialized = True
+        self._IterableDataset_len_called = (
+            None  # See NOTE [ IterableDataset and __len__ ]
+        )
+
+        self._iterator = None
+
+        self.check_worker_number_rationality()
+
+        torch.set_vital("Dataloader", "enabled", "True")  # type: ignore[attr-defined]
+
+    def _get_iterator(self) -> "_BaseDataLoaderIter":
+        if self.num_workers == 0:
+            return _SingleProcessDataLoaderIter(self)
+        else:
+            self.check_worker_number_rationality()
+            return _MultiProcessingDataLoaderIter(self)
+
+    @property
+    def multiprocessing_context(self):
+        return self.__multiprocessing_context
+
+    @multiprocessing_context.setter
+    def multiprocessing_context(self, multiprocessing_context):
+        if multiprocessing_context is not None:
+            if self.num_workers > 0:
+                if isinstance(multiprocessing_context, str):
+                    valid_start_methods = torch.multiprocessing.get_all_start_methods()
+                    if multiprocessing_context not in valid_start_methods:
+                        raise ValueError(
+                            "multiprocessing_context option "
+                            f"should specify a valid start method in {valid_start_methods!r}, but got "
+                            f"multiprocessing_context={multiprocessing_context!r}"
+                        )
+                    multiprocessing_context = torch.multiprocessing.get_context(
+                        multiprocessing_context
+                    )
+
+                if not isinstance(
+                    multiprocessing_context, python_multiprocessing.context.BaseContext
+                ):
+                    raise TypeError(
+                        "multiprocessing_context option should be a valid context "
+                        "object or a string specifying the start method, but got "
+                        f"multiprocessing_context={multiprocessing_context}"
+                    )
+            else:
+                raise ValueError(
+                    "multiprocessing_context can only be used with "
+                    "multi-process loading (num_workers > 0), but got "
+                    f"num_workers={self.num_workers}"
+                )
+
+        self.__multiprocessing_context = multiprocessing_context
+
+    def __setattr__(self, attr, val):
+        if self.__initialized and attr in (
+            "batch_size",
+            "batch_sampler",
+            "sampler",
+            "drop_last",
+            "dataset",
+            "persistent_workers",
+        ):
+            raise ValueError(
+                f"{attr} attribute should not be set after {self.__class__.__name__} is initialized"
+            )
+
+        super().__setattr__(attr, val)
+
+    # We quote '_BaseDataLoaderIter' since it isn't defined yet and the definition can't be moved up
+    # since '_BaseDataLoaderIter' references 'DataLoader'.
+    def __iter__(self) -> "_BaseDataLoaderIter":
+        # When using a single worker the returned iterator should be
+        # created everytime to avoid resetting its state
+        # However, in the case of a multiple workers iterator
+        # the iterator is only created once in the lifetime of the
+        # DataLoader object so that workers can be reused
+        if self.persistent_workers and self.num_workers > 0:
+            if self._iterator is None:
+                self._iterator = self._get_iterator()
+            else:
+                self._iterator._reset(self)
+            return self._iterator
+        else:
+            return self._get_iterator()
+
+    @property
+    def _auto_collation(self):
+        return self.batch_sampler is not None
+
+    @property
+    def _index_sampler(self):
+        # The actual sampler used for generating indices for `_DatasetFetcher`
+        # (see _utils/fetch.py) to read data at each time. This would be
+        # `.batch_sampler` if in auto-collation mode, and `.sampler` otherwise.
+        # We can't change `.sampler` and `.batch_sampler` attributes for BC
+        # reasons.
+        if self._auto_collation:
+            return self.batch_sampler
+        else:
+            return self.sampler
+
+    def __len__(self) -> int:
+        if self._dataset_kind == _DatasetKind.Iterable:
+            # NOTE [ IterableDataset and __len__ ]
+            #
+            # For `IterableDataset`, `__len__` could be inaccurate when one naively
+            # does multi-processing data loading, since the samples will be duplicated.
+            # However, no real use case should be actually using that behavior, so
+            # it should count as a user error. We should generally trust user
+            # code to do the proper thing (e.g., configure each replica differently
+            # in `__iter__`), and give us the correct `__len__` if they choose to
+            # implement it (this will still throw if the dataset does not implement
+            # a `__len__`).
+            #
+            # To provide a further warning, we track if `__len__` was called on the
+            # `DataLoader`, save the returned value in `self._len_called`, and warn
+            # if the iterator ends up yielding more than this number of samples.
+
+            # Cannot statically verify that dataset is Sized
+            length = self._IterableDataset_len_called = len(self.dataset)  # type: ignore[assignment, arg-type]
+            if (
+                self.batch_size is not None
+            ):  # IterableDataset doesn't allow custom sampler or batch_sampler
+                from math import ceil
+
+                if self.drop_last:
+                    length = length // self.batch_size
+                else:
+                    length = ceil(length / self.batch_size)
+            return length
+        else:
+            return len(self._index_sampler)
+
+    def check_worker_number_rationality(self):
+        # This function check whether the dataloader's worker number is rational based on
+        # current system's resource. Current rule is that if the number of workers this
+        # Dataloader will create is bigger than the number of logical cpus that is allowed to
+        # use, than we will pop up a warning to let user pay attention.
+        #
+        # eg. If current system has 2 physical CPUs with 16 cores each. And each core support 2
+        #     threads, then the total logical cpus here is 2 * 16 * 2 = 64. Let's say current
+        #     DataLoader process can use half of them which is 32, then the rational max number of
+        #     worker that initiated from this process is 32.
+        #     Now, let's say the created DataLoader has num_works = 40, which is bigger than 32.
+        #     So the warning message is triggered to notify the user to lower the worker number if
+        #     necessary.
+        #
+        #
+        # [Note] Please note that this function repects `cpuset` only when os.sched_getaffinity is
+        #        available (available in most of Linux system, but not OSX and Windows).
+        #        When os.sched_getaffinity is not available, os.cpu_count() is called instead, but
+        #        it doesn't repect cpuset.
+        #        We don't take threading into account since each worker process is single threaded
+        #        at this time.
+        #
+        #        We don't set any threading flags (eg. OMP_NUM_THREADS, MKL_NUM_THREADS, etc)
+        #        other than `torch.set_num_threads` to 1 in the worker process, if the passing
+        #        in functions use 3rd party modules that rely on those threading flags to determine
+        #        how many thread to create (eg. numpy, etc), then it is caller's responsibility to
+        #        set those flags correctly.
+        def _create_warning_msg(num_worker_suggest, num_worker_created, cpuset_checked):
+            suggested_max_worker_msg = (
+                (
+                    (
+                        "Our suggested max number of worker in current system is {}{}, which is smaller "
+                        "than what this DataLoader is going to create."
+                    ).format(
+                        num_worker_suggest,
+                        (
+                            ""
+                            if cpuset_checked
+                            else " (`cpuset` is not taken into account)"
+                        ),
+                    )
+                )
+                if num_worker_suggest is not None
+                else (
+                    "DataLoader is not able to compute a suggested max number of worker in current system."
+                )
+            )
+
+            warn_msg = (
+                f"This DataLoader will create {num_worker_created} worker processes in total. {suggested_max_worker_msg} "
+                "Please be aware that excessive worker creation might get DataLoader running slow or even freeze, "
+                "lower the worker number to avoid potential slowness/freeze if necessary."
+            )
+            return warn_msg
+
+        if not self.num_workers or self.num_workers == 0:
+            return
+
+        # try to compute a suggested max number of worker based on system's resource
+        max_num_worker_suggest = None
+        cpuset_checked = False
+        if hasattr(os, "sched_getaffinity"):
+            try:
+                max_num_worker_suggest = len(os.sched_getaffinity(0))
+                cpuset_checked = True
+            except Exception:
+                pass
+        if max_num_worker_suggest is None:
+            # os.cpu_count() could return Optional[int]
+            # get cpu count first and check None in order to satisfy mypy check
+            cpu_count = os.cpu_count()
+            if cpu_count is not None:
+                max_num_worker_suggest = cpu_count
+
+        if max_num_worker_suggest is None:
+            warnings.warn(
+                _create_warning_msg(
+                    max_num_worker_suggest, self.num_workers, cpuset_checked
+                )
+            )
+            return
+
+        if self.num_workers > max_num_worker_suggest:
+            warnings.warn(
+                _create_warning_msg(
+                    max_num_worker_suggest, self.num_workers, cpuset_checked
+                )
+            )
+
+
+class _BaseDataLoaderIter:
+    def __init__(self, loader: DataLoader) -> None:
+        self._dataset = loader.dataset
+        self._shared_seed = None
+        self._pg = None
+        if isinstance(self._dataset, IterDataPipe):
+            if dist.is_available() and dist.is_initialized():
+                self._pg = dist.new_group(backend="gloo")
+            self._shared_seed = _share_dist_seed(loader.generator, self._pg)
+            shared_rng = torch.Generator()
+            shared_rng.manual_seed(self._shared_seed)
+            self._dataset = torch.utils.data.graph_settings.apply_random_seed(
+                self._dataset, shared_rng
+            )
+        self._dataset_kind = loader._dataset_kind
+        self._IterableDataset_len_called = loader._IterableDataset_len_called
+        self._auto_collation = loader._auto_collation
+        self._drop_last = loader.drop_last
+        self._index_sampler = loader._index_sampler
+        self._num_workers = loader.num_workers
+        ws, rank = _get_distributed_settings()
+        self._world_size = ws
+        self._rank = rank
+        # If pin_memory_device not set, default behaviour is current accelerator.
+        # If pin_memory_device is set but pin_memory is not set, the default
+        # behaviour false.
+        if len(loader.pin_memory_device) == 0:
+            if loader.pin_memory and not torch.accelerator.is_available():
+                warn_msg = (
+                    "'pin_memory' argument is set as true but no accelerator is found, "
+                    "then device pinned memory won't be used."
+                )
+                warnings.warn(warn_msg)
+
+            self._pin_memory = loader.pin_memory and torch.accelerator.is_available()
+            self._pin_memory_device = None
+            # Currently, pin_memory would raise error on the MPS backend (see
+            # https://github.com/pytorch/pytorch/issues/86060), so forcibly
+            # disable pin_memory on MPS. Remove this restriction once pinned
+            # memory allocation for MPS is fixed.
+            if (
+                self._pin_memory
+                and (acc := torch.accelerator.current_accelerator()) is not None
+                and acc.type == "mps"
+            ):
+                self._pin_memory = False
+                warn_msg = (
+                    "'pin_memory' argument is set as true but not supported on MPS now, "
+                    "then device pinned memory won't be used."
+                )
+                warnings.warn(warn_msg)
+        else:
+            if not loader.pin_memory:
+                warn_msg = (
+                    "'pin_memory_device' is set but 'pin_memory' argument is not set, "
+                    "then device pinned memory won't be used."
+                    "please set 'pin_memory' to true, if you need to use the device pin memory"
+                )
+                warnings.warn(warn_msg)
+
+            self._pin_memory = loader.pin_memory
+            self._pin_memory_device = loader.pin_memory_device
+        self._timeout = loader.timeout
+        self._collate_fn = loader.collate_fn
+        self._sampler_iter = iter(self._index_sampler)
+        self._base_seed = (
+            torch.empty((), dtype=torch.int64)
+            .random_(generator=loader.generator)
+            .item()
+        )
+        self._persistent_workers = loader.persistent_workers
+        self._num_yielded = 0
+        self._profile_name = f"enumerate(DataLoader)#{self.__class__.__name__}.__next__"
+
+    def __iter__(self) -> "_BaseDataLoaderIter":
+        return self
+
+    def _reset(self, loader, first_iter=False):
+        self._sampler_iter = iter(self._index_sampler)
+        self._num_yielded = 0
+        self._IterableDataset_len_called = loader._IterableDataset_len_called
+        if isinstance(self._dataset, IterDataPipe):
+            self._shared_seed = _share_dist_seed(loader.generator, self._pg)
+            shared_rng = torch.Generator()
+            shared_rng.manual_seed(self._shared_seed)
+            self._dataset = torch.utils.data.graph_settings.apply_random_seed(
+                self._dataset, shared_rng
+            )
+
+    def _next_index(self):
+        return next(self._sampler_iter)  # may raise StopIteration
+
+    def _next_data(self):
+        raise NotImplementedError
+
+    def __next__(self) -> Any:
+        with torch.autograd.profiler.record_function(self._profile_name):
+            if self._sampler_iter is None:
+                # TODO(https://github.com/pytorch/pytorch/issues/76750)
+                self._reset()  # type: ignore[call-arg]
+            data = self._next_data()
+            self._num_yielded += 1
+            if (
+                self._dataset_kind == _DatasetKind.Iterable
+                and self._IterableDataset_len_called is not None
+                and self._num_yielded > self._IterableDataset_len_called
+            ):
+                warn_msg = (
+                    f"Length of IterableDataset {self._dataset} was reported to be {self._IterableDataset_len_called}"
+                    f"(when accessing len(dataloader)), but {self._num_yielded} samples have been fetched. "
+                )
+                if self._num_workers > 0:
+                    warn_msg += (
+                        "For multiprocessing data-loading, this could be caused by not properly configuring the "
+                        "IterableDataset replica at each worker. Please see "
+                        "https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset for examples."
+                    )
+                warnings.warn(warn_msg)
+            return data
+
+    def __len__(self) -> int:
+        return len(self._index_sampler)
+
+    def __getstate__(self):
+        # TODO: add limited pickling support for sharing an iterator
+        # across multiple threads for HOGWILD.
+        # Probably the best way to do this is by moving the sample pushing
+        # to a separate thread and then just sharing the data queue
+        # but signalling the end is tricky without a non-blocking API
+        raise NotImplementedError("{} cannot be pickled", self.__class__.__name__)
+
+
+class _SingleProcessDataLoaderIter(_BaseDataLoaderIter):
+    def __init__(self, loader):
+        super().__init__(loader)
+        assert self._timeout == 0
+        assert self._num_workers == 0
+
+        # Adds forward compatibilities so classic DataLoader can work with DataPipes:
+        #   Taking care of distributed sharding
+        if isinstance(self._dataset, (IterDataPipe, MapDataPipe)):
+            # For BC, use default SHARDING_PRIORITIES
+            torch.utils.data.graph_settings.apply_sharding(
+                self._dataset, self._world_size, self._rank
+            )
+
+        self._dataset_fetcher = _DatasetKind.create_fetcher(
+            self._dataset_kind,
+            self._dataset,
+            self._auto_collation,
+            self._collate_fn,
+            self._drop_last,
+        )
+
+    def _next_data(self):
+        index = self._next_index()  # may raise StopIteration
+        data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
+        if self._pin_memory:
+            data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)
+        return data
+
+
+class _MultiProcessingDataLoaderIter(_BaseDataLoaderIter):
+    r"""Iterates once over the DataLoader's dataset, as specified by the sampler."""
+
+    # NOTE [ Data Loader Multiprocessing Shutdown Logic ]
+    #
+    # Preliminary:
+    #
+    # Our data model looks like this (queues are indicated with curly brackets):
+    #
+    #                main process                              ||
+    #                     |                                    ||
+    #               {index_queue}                              ||
+    #                     |                                    ||
+    #              worker processes                            ||     DATA
+    #                     |                                    ||
+    #            {worker_result_queue}                         ||     FLOW
+    #                     |                                    ||
+    #      pin_memory_thread of main process                   ||   DIRECTION
+    #                     |                                    ||
+    #               {data_queue}                               ||
+    #                     |                                    ||
+    #                data output                               \/
+    #
+    # P.S. `worker_result_queue` and `pin_memory_thread` part may be omitted if
+    #      `pin_memory=False`.
+    #
+    #
+    # Terminating multiprocessing logic requires very careful design. In
+    # particular, we need to make sure that
+    #
+    #   1. The iterator gracefully exits the workers when its last reference is
+    #      gone or it is depleted.
+    #
+    #      In this case, the workers should be gracefully exited because the
+    #      main process may still need to continue to run, and we want cleaning
+    #      up code in the workers to be executed (e.g., releasing GPU memory).
+    #      Naturally, we implement the shutdown logic in `__del__` of
+    #      DataLoaderIterator.
+    #
+    #      We delay the discussion on the logic in this case until later.
+    #
+    #   2. The iterator exits the workers when the loader process and/or worker
+    #      processes exits normally or with error.
+    #
+    #      We set all workers and `pin_memory_thread` to have `daemon=True`.
+    #
+    #      You may ask, why can't we make the workers non-daemonic, and
+    #      gracefully exit using the same logic as we have in `__del__` when the
+    #      iterator gets deleted (see 1 above)?
+    #
+    #      First of all, `__del__` is **not** guaranteed to be called when
+    #      interpreter exits. Even if it is called, by the time it executes,
+    #      many Python core library resources may already be freed, and even
+    #      simple things like acquiring an internal lock of a queue may hang.
+    #      Therefore, in this case, we actually need to prevent `__del__` from
+    #      being executed, and rely on the automatic termination of daemonic
+    #      children.
+    #
+    #      Thus, we register an `atexit` hook that sets a global flag
+    #      `_utils.python_exit_status`. Since `atexit` hooks are executed in the
+    #      reverse order of registration, we are guaranteed that this flag is
+    #      set before library resources we use are freed (which, at least in
+    #      CPython, is done via an `atexit` handler defined in
+    #      `multiprocessing/util.py`
+    #      https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/util.py#L320-L362
+    #      registered when an object requiring this mechanism is first
+    #      created, e.g., `mp.Queue`
+    #      https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/context.py#L100-L103
+    #      https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/queues.py#L29
+    #      )
+    #
+    #      So in `__del__`, we check if `_utils.python_exit_status` is set or
+    #      `None` (freed), and perform no-op if so.
+    #
+    #      However, simply letting library clean-up codes run can also be bad,
+    #      because such codes (i.e., `multiprocessing.util._exit_function()`)
+    #      include join putting threads for `mp.Queue`, which can be blocking.
+    #      Hence, the main process putting threads are called with
+    #      `cancel_join_thread` at creation.  See later section
+    #      [ 3b. A process won't hang when putting into a queue; ]
+    #      for more details.
+    #
+    #      Here are two example cases where library clean-up codes can run
+    #      before `__del__` is called:
+    #
+    #        1. If we hold onto a reference to the iterator, it more often
+    #           than not tries to do `multiprocessing` library cleaning before
+    #           clearing the alive referenced objects (https://github.com/pytorch/pytorch/issues/48666)
+    #           and thus prevents our cleaning-up code to run first.
+    #
+    #        2. A similar issue araises when a `DataLoader` is used in a subprocess.
+    #           When a process ends, it shuts the all its daemonic children
+    #           down with a SIGTERM (instead of joining them without a timeout).
+    #           Simiarly for threads, but by a different mechanism. This fact,
+    #           together with a few implementation details of multiprocessing, forces
+    #           us to make workers daemonic. All of our problems arise when a
+    #           DataLoader is used in a subprocess, and are caused by multiprocessing
+    #           code which looks more or less like this:
+    #
+    #               try:
+    #                   your_function_using_a_dataloader()
+    #               finally:
+    #                   multiprocessing.util._exit_function()
+    #
+    #           The joining/termination mentioned above happens inside
+    #           `_exit_function()`. Now, if `your_function_using_a_dataloader()`
+    #           throws, the stack trace stored in the exception will prevent the
+    #           frame which uses `DataLoaderIter` to be freed. If the frame has any
+    #           reference to the `DataLoaderIter` (e.g., in a method of the iter),
+    #           its  `__del__`, which starts the shutdown procedure, will not be
+    #           called. That, in turn, means that workers aren't notified. Attempting
+    #           to join in `_exit_function` will then result in a hang.
+    #
+    #           For context, `_exit_function` is also registered as an `atexit` call.
+    #           So it is unclear to me (@ssnl) why this is needed in a finally block.
+    #           The code dates back to 2008 and there is no comment on the original
+    #           PEP 371 or patch https://bugs.python.org/issue3050 (containing both
+    #           the finally block and the `atexit` registration) that explains this.
+    #
+    #
+    #      Finally, another choice is to just shutdown workers with logic in 1
+    #      above whenever we see an error in `next`. This isn't ideal because
+    #        a. It prevents users from using try-catch to resume data loading.
+    #        b. It doesn't prevent hanging if users have references to the
+    #           iterator.
+    #
+    #   3. All processes exit if any of them die unexpectedly by fatal signals.
+    #
+    #      As shown above, the workers are set as daemonic children of the main
+    #      process. However, automatic cleaning-up of such child processes only
+    #      happens if the parent process exits gracefully (e.g., not via fatal
+    #      signals like SIGKILL). So we must ensure that each process will exit
+    #      even the process that should send/receive data to/from it were
+    #      killed, i.e.,
+    #
+    #        a. A process won't hang when getting from a queue.
+    #
+    #           Even with carefully designed data dependencies (i.e., a `put()`
+    #           always corresponding to a `get()`), hanging on `get()` can still
+    #           happen when data in queue is corrupted (e.g., due to
+    #           `cancel_join_thread` or unexpected exit).
+    #
+    #           For child exit, we set a timeout whenever we try to get data
+    #           from `data_queue`, and check the workers' status on each timeout
+    #           and error.
+    #           See `_DataLoaderiter._get_batch()` and
+    #           `_DataLoaderiter._try_get_data()` for details.
+    #
+    #           Additionally, for child exit on non-Windows platforms, we also
+    #           register a SIGCHLD handler (which is supported on Windows) on
+    #           the main process, which checks if any of the workers fail in the
+    #           (Python) handler. This is more efficient and faster in detecting
+    #           worker failures, compared to only using the above mechanism.
+    #           See `DataLoader.cpp` and `_utils/signal_handling.py` for details.
+    #
+    #           For `.get()` calls where the sender(s) is not the workers, we
+    #           guard them with timeouts, and check the status of the sender
+    #           when timeout happens:
+    #             + in the workers, the `_utils.worker.ManagerWatchdog` class
+    #               checks the status of the main process.
+    #             + if `pin_memory=True`, when getting from `pin_memory_thread`,
+    #               check `pin_memory_thread` status periodically until `.get()`
+    #               returns or see that `pin_memory_thread` died.
+    #
+    #        b. A process won't hang when putting into a queue;
+    #
+    #           We use `mp.Queue` which has a separate background thread to put
+    #           objects from an unbounded buffer array. The background thread is
+    #           daemonic and usually automatically joined when the process
+    #           *exits*.
+    #
+    #           In case that the receiver has ended abruptly while
+    #           reading from the pipe, the join will hang forever.  The usual
+    #           solution for this in Python is calling  `q.cancel_join_thread`,
+    #           which prevents automatically joining it when finalizing
+    #           (exiting).
+    #
+    #           Nonetheless, `cancel_join_thread` must only be called when the
+    #           queue is **not** going to be read from or write into by another
+    #           process, because it may hold onto a lock or leave corrupted data
+    #           in the queue, leading other readers/writers to hang.
+    #
+    #           Hence,
+    #             + For worker processes, we only do so (for their output
+    #               queues, i.e., `worker_result_queue`) before exiting.
+    #             + For `pin_memory_thread`, its output queue `data_queue` is a
+    #               `queue.Queue` that does blocking `put` if the queue is full.
+    #               So there is no above problem, but as a result, in
+    #               `_pin_memory_loop`, we do need to  wrap the `put` in a loop
+    #               that breaks not only upon success, but also when the main
+    #               process stops reading, i.e., is shutting down.
+    #             + For loader process, we `cancel_join_thread()` for all
+    #               `_index_queues` because the whole purpose of workers and
+    #               `pin_memory_thread` is to serve the loader process.  If
+    #               loader process is already exiting, we don't really care if
+    #               the queues are corrupted.
+    #
+    #
+    # Now let's get back to 1:
+    #   how we gracefully exit the workers when the last reference to the
+    #   iterator is gone.
+    #
+    # To achieve this, we implement the following logic along with the design
+    # choices mentioned above:
+    #
+    # `workers_done_event`:
+    #   A `multiprocessing.Event` shared among the main process and all worker
+    #   processes. This is used to signal the workers that the iterator is
+    #   shutting down. After it is set, they will not send processed data to
+    #   queues anymore, and only wait for the final `None` before exiting.
+    #   `done_event` isn't strictly needed. I.e., we can just check for `None`
+    #   from the input queue, but it allows us to skip wasting resources
+    #   processing data if we are already shutting down.
+    #
+    # `pin_memory_thread_done_event`:
+    #   A `threading.Event` for a similar purpose to that of
+    #   `workers_done_event`, but is for the `pin_memory_thread`. The reason
+    #   that separate events are needed is that `pin_memory_thread` reads from
+    #   the output queue of the workers. But the workers, upon seeing that
+    #   `workers_done_event` is set, only wants to see the final `None`, and is
+    #   not required to flush all data in the output queue (e.g., it may call
+    #   `cancel_join_thread` on that queue if its `IterableDataset` iterator
+    #   happens to exhaust coincidentally, which is out of the control of the
+    #   main process). Thus, since we will exit `pin_memory_thread` before the
+    #   workers (see below), two separete events are used.
+    #
+    # NOTE: In short, the protocol is that the main process will set these
+    #       `done_event`s and then the corresponding processes/threads a `None`,
+    #       and that they may exit at any time after receiving the `None`.
+    #
+    # NOTE: Using `None` as the final signal is valid, since normal data will
+    #       always be a 2-tuple with the 1st element being the index of the data
+    #       transferred (different from dataset index/key), and the 2nd being
+    #       either the dataset key or the data sample (depending on which part
+    #       of the data model the queue is at).
+    #
+    # [ worker processes ]
+    #   While loader process is alive:
+    #     Get from `index_queue`.
+    #       If get anything else,
+    #          Check `workers_done_event`.
+    #            If set, continue to next iteration
+    #                    i.e., keep getting until see the `None`, then exit.
+    #            Otherwise, process data:
+    #                If is fetching from an `IterableDataset` and the iterator
+    #                    is exhausted, send an `_IterableDatasetStopIteration`
+    #                    object to signal iteration end. The main process, upon
+    #                    receiving such an object, will send `None` to this
+    #                    worker and not use the corresponding `index_queue`
+    #                    anymore.
+    #       If timed out,
+    #          No matter `workers_done_event` is set (still need to see `None`)
+    #          or not, must continue to next iteration.
+    #   (outside loop)
+    #   If `workers_done_event` is set,  (this can be False with `IterableDataset`)
+    #     `data_queue.cancel_join_thread()`.  (Everything is ending here:
+    #                                          main process won't read from it;
+    #                                          other workers will also call
+    #                                          `cancel_join_thread`.)
+    #
+    # [ pin_memory_thread ]
+    #   # No need to check main thread. If this thread is alive, the main loader
+    #   # thread must be alive, because this thread is set as daemonic.
+    #   While `pin_memory_thread_done_event` is not set:
+    #     Get from `worker_result_queue`.
+    #       If timed out, continue to get in the next iteration.
+    #       Otherwise, process data.
+    #       While `pin_memory_thread_done_event` is not set:
+    #         Put processed data to `data_queue` (a `queue.Queue` with blocking put)
+    #         If timed out, continue to put in the next iteration.
+    #         Otherwise, break, i.e., continuing to the out loop.
+    #
+    #   NOTE: we don't check the status of the main thread because
+    #           1. if the process is killed by fatal signal, `pin_memory_thread`
+    #              ends.
+    #           2. in other cases, either the cleaning-up in __del__ or the
+    #              automatic exit of daemonic thread will take care of it.
+    #              This won't busy-wait either because `.get(timeout)` does not
+    #              busy-wait.
+    #
+    # [ main process ]
+    #   In the DataLoader Iter's `__del__`
+    #     b. Exit `pin_memory_thread`
+    #          i.   Set `pin_memory_thread_done_event`.
+    #          ii   Put `None` in `worker_result_queue`.
+    #          iii. Join the `pin_memory_thread`.
+    #          iv.  `worker_result_queue.cancel_join_thread()`.
+    #
+    #     c. Exit the workers.
+    #          i.   Set `workers_done_event`.
+    #          ii.  Put `None` in each worker's `index_queue`.
+    #          iii. Join the workers.
+    #          iv.  Call `.cancel_join_thread()` on each worker's `index_queue`.
+    #
+    #        NOTE: (c) is better placed after (b) because it may leave corrupted
+    #              data in `worker_result_queue`, which `pin_memory_thread`
+    #              reads from, in which case the `pin_memory_thread` can only
+    #              happen at timing out, which is slow. Nonetheless, same thing
+    #              happens if a worker is killed by signal at unfortunate times,
+    #              but in other cases, we are better off having a non-corrupted
+    #              `worker_result_queue` for `pin_memory_thread`.
+    #
+    #   NOTE: If `pin_memory=False`, there is no `pin_memory_thread` and (b)
+    #         can be omitted
+    #
+    # NB: `done_event`s isn't strictly needed. E.g., we can just check for
+    #     `None` from `index_queue`, but it allows us to skip wasting resources
+    #     processing indices already in `index_queue` if we are already shutting
+    #     down.
+
+    def __init__(self, loader):
+        super().__init__(loader)
+
+        self._prefetch_factor = loader.prefetch_factor
+        self._in_order = loader.in_order
+
+        assert self._num_workers > 0
+        assert self._prefetch_factor > 0
+
+        if loader.multiprocessing_context is None:
+            multiprocessing_context = torch.multiprocessing
+        else:
+            multiprocessing_context = loader.multiprocessing_context
+
+        self._worker_init_fn = loader.worker_init_fn
+
+        # Adds forward compatibilities so classic DataLoader can work with DataPipes:
+        #   Additional worker init function will take care of sharding in MP and Distributed
+        if isinstance(self._dataset, (IterDataPipe, MapDataPipe)):
+            self._worker_init_fn = functools.partial(
+                _sharding_worker_init_fn,
+                self._worker_init_fn,
+                self._world_size,
+                self._rank,
+            )
+
+        # No certainty which module multiprocessing_context is
+        self._worker_result_queue = multiprocessing_context.Queue()  # type: ignore[var-annotated]
+        self._worker_pids_set = False
+        self._shutdown = False
+        self._workers_done_event = multiprocessing_context.Event()
+
+        self._index_queues = []
+        self._workers = []
+        for i in range(self._num_workers):
+            # No certainty which module multiprocessing_context is
+            index_queue = multiprocessing_context.Queue()  # type: ignore[var-annotated]
+            # Need to `cancel_join_thread` here!
+            # See sections (2) and (3b) above.
+            index_queue.cancel_join_thread()
+            w = multiprocessing_context.Process(
+                target=_utils.worker._worker_loop,
+                args=(
+                    self._dataset_kind,
+                    self._dataset,
+                    index_queue,
+                    self._worker_result_queue,
+                    self._workers_done_event,
+                    self._auto_collation,
+                    self._collate_fn,
+                    self._drop_last,
+                    self._base_seed,
+                    self._worker_init_fn,
+                    i,
+                    self._num_workers,
+                    self._persistent_workers,
+                    self._shared_seed,
+                ),
+            )
+            w.daemon = True
+            # NB: Process.start() actually take some time as it needs to
+            #     start a process and pass the arguments over via a pipe.
+            #     Therefore, we only add a worker to self._workers list after
+            #     it started, so that we do not call .join() if program dies
+            #     before it starts, and __del__ tries to join but will get:
+            #     AssertionError: can only join a started process.
+            w.start()
+            self._index_queues.append(index_queue)
+            self._workers.append(w)
+
+        if self._pin_memory:
+            self._pin_memory_thread_done_event = threading.Event()
+
+            # Queue is not type-annotated
+            self._data_queue = queue.Queue()  # type: ignore[var-annotated]
+            current_device = -1
+            if self._pin_memory_device == "cuda":
+                current_device = torch.cuda.current_device()
+            elif self._pin_memory_device == "xpu":
+                current_device = torch.xpu.current_device()
+            elif self._pin_memory_device == torch._C._get_privateuse1_backend_name():
+                custom_device_mod = getattr(
+                    torch, torch._C._get_privateuse1_backend_name()
+                )
+                current_device = custom_device_mod.current_device()
+            elif self._pin_memory_device is None:
+                current_device = torch.accelerator.current_device_index()
+            pin_memory_thread = threading.Thread(
+                target=_utils.pin_memory._pin_memory_loop,
+                args=(
+                    self._worker_result_queue,
+                    self._data_queue,
+                    current_device,
+                    self._pin_memory_thread_done_event,
+                    self._pin_memory_device,
+                ),
+            )
+            pin_memory_thread.daemon = True
+            pin_memory_thread.start()
+            # Similar to workers (see comment above), we only register
+            # pin_memory_thread once it is started.
+            self._pin_memory_thread = pin_memory_thread
+        else:
+            self._data_queue = self._worker_result_queue  # type: ignore[assignment]
+
+        # In some rare cases, persistent workers (daemonic processes)
+        # would be terminated before `__del__` of iterator is invoked
+        # when main process exits
+        # It would cause failure when pin_memory_thread tries to read
+        # corrupted data from worker_result_queue
+        # atexit is used to shutdown thread and child processes in the
+        # right sequence before main process exits
+        if self._persistent_workers and self._pin_memory:
+            import atexit
+
+            for w in self._workers:
+                atexit.register(_MultiProcessingDataLoaderIter._clean_up_worker, w)
+
+        # .pid can be None only before process is spawned (not the case, so ignore)
+        _utils.signal_handling._set_worker_pids(id(self), tuple(w.pid for w in self._workers))  # type: ignore[misc]
+        _utils.signal_handling._set_SIGCHLD_handler()
+        self._worker_pids_set = True
+        self._reset(loader, first_iter=True)
+
+    def _reset(self, loader, first_iter=False):
+        super()._reset(loader, first_iter)
+        self._send_idx = 0  # idx of the next task to be sent to workers
+        self._rcvd_idx = 0  # idx of the next task to be returned in __next__
+        # information about data not yet yielded, i.e., tasks w/ indices in range [rcvd_idx, send_idx).
+        # map: task idx => - (worker_id,)        if data isn't fetched (outstanding)
+        #                  \ (worker_id, data)   if data is already fetched (out-of-order)
+        self._task_info = {}
+        self._tasks_outstanding = (
+            0  # always equal to count(v for v in task_info.values() if len(v) == 1)
+        )
+        # A list of booleans representing whether each worker still has work to
+        # do, i.e., not having exhausted its iterable dataset object. It always
+        # contains all `True`s if not using an iterable-style dataset
+        # (i.e., if kind != Iterable).
+        # Not that this indicates that a worker still has work to do *for this epoch*.
+        # It does not mean that a worker is dead. In case of `_persistent_workers`,
+        # the worker will be reset to available in the next epoch.
+        self._workers_status = [True for i in range(self._num_workers)]
+        # A list of integers representing how many tasks are outstanding for each worker
+        # Incremented when a task is dispatched to the worker
+        # Decremented when that data has been given to the main thread
+        # Each worker should have at most self._prefetch_factor tasks outstanding
+        self._workers_num_tasks = [0 for i in range(self._num_workers)]
+        # Reset the worker queue cycle so it resumes next epoch at worker 0
+        self._worker_queue_idx_cycle = itertools.cycle(range(self._num_workers))
+        # We resume the prefetching in case it was enabled
+        if not first_iter:
+            for idx in range(self._num_workers):
+                self._index_queues[idx].put(
+                    _utils.worker._ResumeIteration(self._shared_seed)
+                )
+            resume_iteration_cnt = self._num_workers
+            while resume_iteration_cnt > 0:
+                return_idx, return_data = self._get_data()
+                if isinstance(return_idx, _utils.worker._ResumeIteration):
+                    assert return_data is None
+                    resume_iteration_cnt -= 1
+        # prime the prefetch loop
+        for _ in range(self._prefetch_factor * self._num_workers):
+            self._try_put_index()
+
+    def _try_get_data(self, timeout=_utils.MP_STATUS_CHECK_INTERVAL):
+        # Tries to fetch data from `self._data_queue` once for a given timeout.
+        # This can also be used as inner loop of fetching without timeout, with
+        # the sender status as the loop condition.
+        #
+        # This raises a `RuntimeError` if any worker died expectedly. This error
+        # can come from either the SIGCHLD handler in `_utils/signal_handling.py`
+        # (only for non-Windows platforms), or the manual check below on errors
+        # and timeouts.
+        #
+        # Returns a 2-tuple:
+        #   (bool: whether successfully get data, any: data if successful else None)
+        try:
+            data = self._data_queue.get(timeout=timeout)
+            return (True, data)
+        except Exception as e:
+            # At timeout and error, we manually check whether any worker has
+            # failed. Note that this is the only mechanism for Windows to detect
+            # worker failures.
+            failed_workers = []
+            for worker_id, w in enumerate(self._workers):
+                if self._workers_status[worker_id] and not w.is_alive():
+                    failed_workers.append(w)
+                    self._mark_worker_as_unavailable(worker_id)
+            if len(failed_workers) > 0:
+                pids_str = ", ".join(str(w.pid) for w in failed_workers)
+                raise RuntimeError(
+                    f"DataLoader worker (pid(s) {pids_str}) exited unexpectedly"
+                ) from e
+            if isinstance(e, queue.Empty):
+                return (False, None)
+
+            import errno
+            import tempfile
+
+            try:
+                # Raise an exception if we are this close to the FDs limit.
+                # Apparently, trying to open only one file is not a sufficient
+                # test.
+                # See NOTE [ DataLoader on Linux and open files limit ]
+                fds_limit_margin = 10
+                [tempfile.NamedTemporaryFile() for i in range(fds_limit_margin)]
+            except OSError as e:
+                if e.errno == errno.EMFILE:
+                    raise RuntimeError(
+                        "Too many open files. Communication with the"
+                        " workers is no longer possible. Please increase the"
+                        " limit using `ulimit -n` in the shell or change the"
+                        " sharing strategy by calling"
+                        " `torch.multiprocessing.set_sharing_strategy('file_system')`"
+                        " at the beginning of your code"
+                    ) from None
+            raise
+
+    # NOTE [ DataLoader on Linux and open files limit ]
+    #
+    # On Linux when DataLoader is used with multiprocessing we pass the data between
+    # the root process and the workers through SHM files. We remove those files from
+    # the filesystem as soon as they are created and keep them alive by
+    # passing around their file descriptors through AF_UNIX sockets. (See
+    # docs/source/multiprocessing.rst and 'Multiprocessing Technical Notes` in
+    # the wiki (https://github.com/pytorch/pytorch/wiki).)
+    #
+    # This sometimes leads us to exceeding the open files limit. When that happens,
+    # and the offending file descriptor is coming over a socket, the `socket` Python
+    # package silently strips the file descriptor from the message, setting only the
+    # `MSG_CTRUNC` flag (which might be a bit misleading since the manpage says that
+    # it _indicates that some control data were discarded due to lack of space in
+    # the buffer for ancillary data_). This might reflect the C implementation of
+    # AF_UNIX sockets.
+    #
+    # This behaviour can be reproduced with the script and instructions at the
+    # bottom of this note.
+    #
+    # When that happens, the standard Python `multiprocessing` (and not
+    # `torch.multiprocessing`) raises a `RuntimeError: received 0 items of ancdata`
+    #
+    # Sometimes, instead of the FD being stripped, you may get an `OSError:
+    # Too many open files`, both in the script below and in DataLoader. However,
+    # this is rare and seems to be nondeterministic.
+    #
+    #
+    #   #!/usr/bin/env python3
+    #   import sys
+    #   import socket
+    #   import os
+    #   import array
+    #   import shutil
+    #   import socket
+    #
+    #
+    #   if len(sys.argv) != 4:
+    #       print("Usage: ", sys.argv[0], " tmp_dirname iteration (send|recv)")
+    #       sys.exit(1)
+    #
+    #   if __name__ == '__main__':
+    #       dirname = sys.argv[1]
+    #       sock_path = dirname + "/sock"
+    #       iterations = int(sys.argv[2])
+    #       def dummy_path(i):
+    #           return dirname + "/" + str(i) + ".dummy"
+    #
+    #
+    #       if sys.argv[3] == 'send':
+    #           while not os.path.exists(sock_path):
+    #               pass
+    #           client = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)
+    #           client.connect(sock_path)
+    #           for i in range(iterations):
+    #               fd = os.open(dummy_path(i), os.O_WRONLY | os.O_CREAT)
+    #               ancdata = array.array('i', [fd])
+    #               msg = bytes([i % 256])
+    #               print("Sending fd ", fd, " (iteration #", i, ")")
+    #               client.sendmsg([msg], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, ancdata)])
+    #
+    #
+    #       else:
+    #           assert sys.argv[3] == 'recv'
+    #
+    #           if os.path.exists(dirname):
+    #               raise Exception("Directory exists")
+    #
+    #           os.mkdir(dirname)
+    #
+    #           print("Opening socket...")
+    #           server = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)
+    #           server.bind(sock_path)
+    #
+    #           print("Listening...")
+    #           for i in range(iterations):
+    #               a = array.array('i')
+    #               msg, ancdata, flags, addr = server.recvmsg(1, socket.CMSG_SPACE(a.itemsize))
+    #               assert(len(ancdata) == 1)
+    #               cmsg_level, cmsg_type, cmsg_data = ancdata[0]
+    #               a.frombytes(cmsg_data)
+    #               print("Received fd ", a[0], " (iteration #", i, ")")
+    #
+    #           shutil.rmtree(dirname)
+    #
+    # Steps to reproduce:
+    #
+    # 1. Run two shells and set lower file descriptor limit in the receiving one:
+    # (shell1) ulimit -n 1020
+    # (shell2) ulimit -n 1022
+    #
+    # 2. Run the script above with the `recv` option in the first shell
+    # (shell1) ./test_socket.py sock_tmp 1017 recv
+    #
+    # 3. Run the script with the `send` option in the second shell:
+    # (shell2) ./test_socket.py sock_tmp 1017 send
+
+    def _get_data(self):
+        # Fetches data from `self._data_queue`.
+        #
+        # We check workers' status every `MP_STATUS_CHECK_INTERVAL` seconds,
+        # which we achieve by running `self._try_get_data(timeout=MP_STATUS_CHECK_INTERVAL)`
+        # in a loop. This is the only mechanism to detect worker failures for
+        # Windows. For other platforms, a SIGCHLD handler is also used for
+        # worker failure detection.
+        #
+        # If `pin_memory=True`, we also need check if `pin_memory_thread` had
+        # died at timeouts.
+        if self._timeout > 0:
+            success, data = self._try_get_data(self._timeout)
+            if success:
+                return data
+            else:
+                raise RuntimeError(
+                    f"DataLoader timed out after {self._timeout} seconds"
+                )
+        elif self._pin_memory:
+            while self._pin_memory_thread.is_alive():
+                success, data = self._try_get_data()
+                if success:
+                    return data
+            else:
+                # while condition is false, i.e., pin_memory_thread died.
+                raise RuntimeError("Pin memory thread exited unexpectedly")
+            # In this case, `self._data_queue` is a `queue.Queue`,. But we don't
+            # need to call `.task_done()` because we don't use `.join()`.
+        else:
+            while True:
+                success, data = self._try_get_data()
+                if success:
+                    return data
+
+    def _next_data(self):
+        while True:
+            # If the worker responsible for `self._rcvd_idx` has already ended
+            # and was unable to fulfill this task (due to exhausting an `IterableDataset`),
+            # we try to advance `self._rcvd_idx` to find the next valid index.
+            #
+            # This part needs to run in the loop because both the `self._get_data()`
+            # call and `_IterableDatasetStopIteration` check below can mark
+            # extra worker(s) as dead.
+            while self._rcvd_idx < self._send_idx:
+                info = self._task_info.get(self._rcvd_idx, None)
+                if info:
+                    worker_id = info[0]
+                    if (
+                        len(info) == 2 or self._workers_status[worker_id]
+                    ):  # has data or is still active
+                        break
+                    del self._task_info[self._rcvd_idx]
+                self._rcvd_idx += 1
+            else:
+                # no valid `self._rcvd_idx` is found (i.e., didn't break)
+                if not self._persistent_workers:
+                    self._shutdown_workers()
+                raise StopIteration
+
+            # Now `self._rcvd_idx` is the batch index we want to fetch
+
+            # Check if the next sample has already been generated
+            if len(self._task_info[self._rcvd_idx]) == 2:
+                worker_id, data = self._task_info.pop(self._rcvd_idx)
+                self._rcvd_idx += 1
+                return self._process_data(data, worker_id)
+
+            assert not self._shutdown and self._tasks_outstanding > 0
+            idx, data = self._get_data()
+            self._tasks_outstanding -= 1
+            if self._dataset_kind == _DatasetKind.Iterable:
+                # Check for _IterableDatasetStopIteration
+                if isinstance(data, _utils.worker._IterableDatasetStopIteration):
+                    if self._persistent_workers:
+                        self._workers_status[data.worker_id] = False
+                    else:
+                        self._mark_worker_as_unavailable(data.worker_id)
+                    self._try_put_index()
+                    continue
+
+            if idx != self._rcvd_idx:
+                if not self._in_order:
+                    # don't store it for later, process now
+                    # delete from self._task_info immediately
+                    # this keeps the object size manageable
+                    worker_id = self._task_info.pop(idx)[0]
+                    return self._process_data(data, worker_id)
+                # store out-of-order samples
+                self._task_info[idx] += (data,)
+            else:
+                worker_id = self._task_info.pop(idx)[0]
+                self._rcvd_idx += 1
+                return self._process_data(data, worker_id)
+
+    def _try_put_index(self):
+        max_tasks = self._prefetch_factor * self._num_workers
+        assert self._tasks_outstanding < max_tasks
+
+        try:
+            index = self._next_index()
+        except StopIteration:
+            return
+        for _ in range(self._num_workers):  # find the next active worker, if any
+            worker_queue_idx = next(self._worker_queue_idx_cycle)
+            if self._workers_status[worker_queue_idx]:
+                if self._in_order:
+                    break
+                elif self._workers_num_tasks[worker_queue_idx] < max_tasks // sum(
+                    self._workers_status
+                ):
+                    # when self._in_order is False, distribute work to a worker if it has capacity
+                    # _workers_status is updated only in this thread, so the sum is guaranteed > 0
+                    break
+        else:
+            # not found (i.e., didn't break)
+            return
+
+        self._index_queues[worker_queue_idx].put((self._send_idx, index))  # type: ignore[possibly-undefined]
+        self._task_info[self._send_idx] = (worker_queue_idx,)
+        self._workers_num_tasks[worker_queue_idx] += 1
+        self._tasks_outstanding += 1
+        self._send_idx += 1
+
+    def _process_data(self, data, worker_idx):
+        self._workers_num_tasks[worker_idx] -= 1
+        self._try_put_index()
+        if isinstance(data, ExceptionWrapper):
+            data.reraise()
+        return data
+
+    def _mark_worker_as_unavailable(self, worker_id, shutdown=False):
+        # Mark a worker as having finished its work e.g., due to
+        # exhausting an `IterableDataset`. This should be used only when this
+        # `_MultiProcessingDataLoaderIter` is going to continue running.
+
+        assert self._workers_status[worker_id] or (
+            self._persistent_workers and shutdown
+        )
+
+        # Signal termination to that specific worker.
+        q = self._index_queues[worker_id]
+        # Indicate that no more data will be put on this queue by the current
+        # process.
+        q.put(None)
+
+        # Note that we don't actually join the worker here, nor do we remove the
+        # worker's pid from C side struct because (1) joining may be slow, and
+        # (2) since we don't join, the worker may still raise error, and we
+        # prefer capturing those, rather than ignoring them, even though they
+        # are raised after the worker has finished its job.
+        # Joinning is deferred to `_shutdown_workers`, which it is called when
+        # all workers finish their jobs (e.g., `IterableDataset` replicas) or
+        # when this iterator is garbage collected.
+
+        self._workers_status[worker_id] = False
+
+        assert self._workers_done_event.is_set() == shutdown
+
+    def _shutdown_workers(self):
+        # Called when shutting down this `_MultiProcessingDataLoaderIter`.
+        # See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on
+        # the logic of this function.
+        if (
+            _utils is None
+            or _utils.python_exit_status is True
+            or _utils.python_exit_status is None
+        ):
+            # See (2) of the note. If Python is shutting down, do no-op.
+            return
+        # Normal exit when last reference is gone / iterator is depleted.
+        # See (1) and the second half of the note.
+        if not self._shutdown:
+            self._shutdown = True
+            try:
+                # Normal exit when last reference is gone / iterator is depleted.
+                # See (1) and the second half of the note.
+
+                # Exit `pin_memory_thread` first because exiting workers may leave
+                # corrupted data in `worker_result_queue` which `pin_memory_thread`
+                # reads from.
+                if hasattr(self, "_pin_memory_thread"):
+                    # Use hasattr in case error happens before we set the attribute.
+                    self._pin_memory_thread_done_event.set()
+                    # Send something to pin_memory_thread in case it is waiting
+                    # so that it can wake up and check `pin_memory_thread_done_event`
+                    self._worker_result_queue.put((None, None))
+                    self._pin_memory_thread.join()
+                    self._worker_result_queue.cancel_join_thread()
+                    self._worker_result_queue.close()
+
+                # Exit workers now.
+                self._workers_done_event.set()
+                for worker_id in range(len(self._workers)):
+                    # Get number of workers from `len(self._workers)` instead of
+                    # `self._num_workers` in case we error before starting all
+                    # workers.
+                    # If we are using workers_status with persistent_workers
+                    # we have to shut it down because the worker is paused
+                    if self._persistent_workers or self._workers_status[worker_id]:
+                        self._mark_worker_as_unavailable(worker_id, shutdown=True)
+                for w in self._workers:
+                    # We should be able to join here, but in case anything went
+                    # wrong, we set a timeout and if the workers fail to join,
+                    # they are killed in the `finally` block.
+                    w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
+                for q in self._index_queues:
+                    q.cancel_join_thread()
+                    q.close()
+            finally:
+                # Even though all this function does is putting into queues that
+                # we have called `cancel_join_thread` on, weird things can
+                # happen when a worker is killed by a signal, e.g., hanging in
+                # `Event.set()`. So we need to guard this with SIGCHLD handler,
+                # and remove pids from the C side data structure only at the
+                # end.
+                #
+                # FIXME: Unfortunately, for Windows, we are missing a worker
+                #        error detection mechanism here in this function, as it
+                #        doesn't provide a SIGCHLD handler.
+                if self._worker_pids_set:
+                    _utils.signal_handling._remove_worker_pids(id(self))
+                    self._worker_pids_set = False
+                for w in self._workers:
+                    if w.is_alive():
+                        # Existing mechanisms try to make the workers exit
+                        # peacefully, but in case that we unfortunately reach
+                        # here, which we shouldn't, (e.g., pytorch/pytorch#39570),
+                        # we kill the worker.
+                        w.terminate()
+
+    # staticmethod is used to remove reference to `_MultiProcessingDataLoaderIter`
+    @staticmethod
+    def _clean_up_worker(w):
+        try:
+            w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
+        finally:
+            if w.is_alive():
+                w.terminate()
+
+    def __del__(self):
+        self._shutdown_workers()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac93de335b2d7379246de9cee658dd9eafe1d303
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/__init__.py
@@ -0,0 +1 @@
+from torch.utils.data.datapipes import dataframe as dataframe, iter as iter, map as map
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/__pycache__/__init__.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/_decorator.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/_decorator.py
new file mode 100644
index 0000000000000000000000000000000000000000..13e28a19d626643f62ae7988e15f5cce20720ce9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/_decorator.py
@@ -0,0 +1,213 @@
+# mypy: allow-untyped-defs
+import inspect
+from functools import wraps
+from typing import Any, Callable, get_type_hints, Optional, Union
+
+from torch.utils.data.datapipes._typing import _DataPipeMeta
+from torch.utils.data.datapipes.datapipe import IterDataPipe, MapDataPipe
+
+
+######################################################
+# Functional API
+######################################################
+class functional_datapipe:
+    name: str
+
+    def __init__(self, name: str, enable_df_api_tracing=False) -> None:
+        """
+        Define a functional datapipe.
+
+        Args:
+            enable_df_api_tracing - if set, any returned DataPipe would accept
+            DataFrames API in tracing mode.
+        """
+        self.name = name
+        self.enable_df_api_tracing = enable_df_api_tracing
+
+    def __call__(self, cls):
+        if issubclass(cls, IterDataPipe):
+            if isinstance(cls, type):  # type: ignore[arg-type]
+                if not isinstance(cls, _DataPipeMeta):
+                    raise TypeError(
+                        "`functional_datapipe` can only decorate IterDataPipe"
+                    )
+            # with non_deterministic decorator
+            else:
+                if not isinstance(cls, non_deterministic) and not (
+                    hasattr(cls, "__self__")
+                    and isinstance(cls.__self__, non_deterministic)
+                ):
+                    raise TypeError(
+                        "`functional_datapipe` can only decorate IterDataPipe"
+                    )
+            IterDataPipe.register_datapipe_as_function(
+                self.name, cls, enable_df_api_tracing=self.enable_df_api_tracing
+            )
+        elif issubclass(cls, MapDataPipe):
+            MapDataPipe.register_datapipe_as_function(self.name, cls)
+
+        return cls
+
+
+######################################################
+# Determinism
+######################################################
+_determinism: bool = False
+
+
+class guaranteed_datapipes_determinism:
+    prev: bool
+
+    def __init__(self) -> None:
+        global _determinism
+        self.prev = _determinism
+        _determinism = True
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        global _determinism
+        _determinism = self.prev
+
+
+class non_deterministic:
+    cls: Optional[type[IterDataPipe]] = None
+    # TODO: Lambda for picking
+    deterministic_fn: Callable[[], bool]
+
+    def __init__(self, arg: Union[type[IterDataPipe], Callable[[], bool]]) -> None:
+        # 1. Decorator doesn't have any argument
+        if isinstance(arg, type):  # type: ignore[arg-type]
+            if not issubclass(arg, IterDataPipe):  # type: ignore[arg-type]
+                raise TypeError(
+                    "Only `IterDataPipe` can be decorated with `non_deterministic`"
+                    f", but {arg.__name__} is found"
+                )
+            self.cls = arg  # type: ignore[assignment]
+        # 2. Decorator has an argument of a function
+        #    This class should behave differently given different inputs. Use this
+        #    function to verify the determinism for each instance.
+        #    When the function returns True, the instance is non-deterministic. Otherwise,
+        #    the instance is a deterministic DataPipe.
+        elif isinstance(arg, Callable):  # type:ignore[arg-type]
+            self.deterministic_fn = arg  # type: ignore[assignment, misc]
+        else:
+            raise TypeError(f"{arg} can not be decorated by non_deterministic")
+
+    def __call__(self, *args, **kwargs):
+        global _determinism
+        #  Decorate IterDataPipe
+        if self.cls is not None:
+            if _determinism:
+                raise TypeError(
+                    f"{self.cls.__name__} is non-deterministic, but you set 'guaranteed_datapipes_determinism'. "
+                    "You can turn off determinism for this DataPipe if that is acceptable "
+                    "for your application"
+                )
+            return self.cls(*args, **kwargs)  # type: ignore[call-arg]
+
+        # Decorate with a functional argument
+        if not (
+            isinstance(args[0], type)
+            and issubclass(args[0], IterDataPipe)  # type: ignore[arg-type]
+        ):
+            raise TypeError(
+                f"Only `IterDataPipe` can be decorated, but {args[0].__name__} is found"
+            )
+        self.cls = args[0]
+        return self.deterministic_wrapper_fn
+
+    def deterministic_wrapper_fn(self, *args, **kwargs) -> IterDataPipe:
+        res = self.deterministic_fn(*args, **kwargs)  # type: ignore[call-arg, misc]
+        if not isinstance(res, bool):
+            raise TypeError(
+                "deterministic_fn of `non_deterministic` decorator is required "
+                f"to return a boolean value, but {type(res)} is found"
+            )
+        global _determinism
+        if _determinism and res:
+            raise TypeError(
+                f"{self.cls.__name__} is non-deterministic with the inputs, but you set "  # type: ignore[union-attr]
+                "'guaranteed_datapipes_determinism'. You can turn off determinism "
+                "for this DataPipe if that is acceptable for your application"
+            )
+        return self.cls(*args, **kwargs)  # type: ignore[call-arg, misc]
+
+
+######################################################
+# Type validation
+######################################################
+# Validate each argument of DataPipe with hint as a subtype of the hint.
+def argument_validation(f):
+    signature = inspect.signature(f)
+    hints = get_type_hints(f)
+
+    @wraps(f)
+    def wrapper(*args, **kwargs):
+        bound = signature.bind(*args, **kwargs)
+        for argument_name, value in bound.arguments.items():
+            if argument_name in hints and isinstance(
+                hints[argument_name], _DataPipeMeta
+            ):
+                hint = hints[argument_name]
+                if not isinstance(value, IterDataPipe):
+                    raise TypeError(
+                        f"Expected argument '{argument_name}' as a IterDataPipe, but found {type(value)}"
+                    )
+                if not value.type.issubtype(hint.type):
+                    raise TypeError(
+                        f"Expected type of argument '{argument_name}' as a subtype of "
+                        f"hint {hint.type}, but found {value.type}"
+                    )
+
+        return f(*args, **kwargs)
+
+    return wrapper
+
+
+# Default value is True
+_runtime_validation_enabled: bool = True
+
+
+class runtime_validation_disabled:
+    prev: bool
+
+    def __init__(self) -> None:
+        global _runtime_validation_enabled
+        self.prev = _runtime_validation_enabled
+        _runtime_validation_enabled = False
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        global _runtime_validation_enabled
+        _runtime_validation_enabled = self.prev
+
+
+# Runtime checking
+# Validate output data is subtype of return hint
+def runtime_validation(f):
+    # TODO:
+    # Can be extended to validate '__getitem__' and nonblocking
+    if f.__name__ != "__iter__":
+        raise TypeError(
+            f"Can not decorate function {f.__name__} with 'runtime_validation'"
+        )
+
+    @wraps(f)
+    def wrapper(self):
+        global _runtime_validation_enabled
+        if not _runtime_validation_enabled:
+            yield from f(self)
+        else:
+            it = f(self)
+            for d in it:
+                if not self.type.issubtype_of_instance(d):
+                    raise RuntimeError(
+                        f"Expected an instance as subtype of {self.type}, but found {d}({type(d)})"
+                    )
+                yield d
+
+    return wrapper
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae42f75885c1da320e28fdfc87767a87d61e4271
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py
@@ -0,0 +1,279 @@
+# mypy: allow-untyped-defs
+import functools
+import inspect
+from enum import Enum
+
+import torch
+
+
+class _SnapshotState(Enum):
+    r"""
+    These are the snapshotting-related states that IterDataPipes can be in.
+
+    `NotStarted` - allows you to restore a snapshot and create an iterator with reset
+    `Restored` - cannot restore again, allows you to create an iterator without resetting the DataPipe
+    `Iterating` - can restore, will reset if you create a new iterator
+    """
+
+    NotStarted = 0
+    Restored = 1
+    Iterating = 2
+
+
+def _simplify_obj_name(obj) -> str:
+    """Simplify the display strings of objects for the purpose of rendering within DataPipe error messages."""
+    if inspect.isfunction(obj):
+        return obj.__name__
+    else:
+        return repr(obj)
+
+
+def _strip_datapipe_from_name(name: str) -> str:
+    return name.replace("IterDataPipe", "").replace("MapDataPipe", "")
+
+
+def _generate_input_args_string(obj):
+    """Generate a string for the input arguments of an object."""
+    signature = inspect.signature(obj.__class__)
+    input_param_names = set(signature.parameters.keys())
+    result = []
+    for name, value in inspect.getmembers(obj):
+        if name in input_param_names:
+            result.append((name, _simplify_obj_name(value)))
+    return ", ".join([f"{name}={value}" for name, value in result])
+
+
+def _generate_iterdatapipe_msg(datapipe, simplify_dp_name: bool = False):
+    output_string = (
+        f"{datapipe.__class__.__name__}({_generate_input_args_string(datapipe)})"
+    )
+    if simplify_dp_name:
+        output_string = _strip_datapipe_from_name(output_string)
+    return output_string
+
+
+def _gen_invalid_iterdatapipe_msg(datapipe):
+    return (
+        "This iterator has been invalidated because another iterator has been created "
+        f"from the same IterDataPipe: {_generate_iterdatapipe_msg(datapipe)}\n"
+        "This may be caused multiple references to the same IterDataPipe. We recommend "
+        "using `.fork()` if that is necessary."
+    )
+
+
+_feedback_msg = (
+    "\nFor feedback regarding this single iterator per IterDataPipe constraint, feel free "
+    "to comment on this issue: https://github.com/pytorch/data/issues/45."
+)
+
+
+def _check_iterator_valid(datapipe, iterator_id, next_method_exists=False) -> None:
+    r"""
+    Given an instance of a DataPipe and an iterator ID, check if the IDs match, and if not, raises an exception.
+
+    In the case of ChildDataPipe, the ID gets compared to the one stored in `main_datapipe` as well.
+    """
+    if next_method_exists:
+        # This is the case where `IterDataPipe` has both `__iter__` and `__next__`.
+        # The `_valid_iterator_id` should either be never set (`None`), or set by at most one
+        # iterator (`0`). Otherwise, it means there are multiple iterators.
+        if datapipe._valid_iterator_id is not None and datapipe._valid_iterator_id != 0:
+            extra_msg = "\nNote that this exception is raised inside your IterDataPipe's a `__next__` method"
+            raise RuntimeError(
+                _gen_invalid_iterdatapipe_msg(datapipe) + extra_msg + _feedback_msg
+            )
+    elif (
+        hasattr(datapipe, "_is_child_datapipe") and datapipe._is_child_datapipe is True
+    ):
+        if hasattr(datapipe, "_check_valid_iterator_id"):
+            if not datapipe._check_valid_iterator_id(iterator_id):
+                raise RuntimeError(
+                    "This iterator has been invalidated, because a new iterator has been created "
+                    f"from one of the ChildDataPipes of "
+                    f"{_generate_iterdatapipe_msg(datapipe.main_datapipe)}."
+                    + _feedback_msg
+                )
+        else:
+            raise RuntimeError(
+                "ChildDataPipe must have method `_check_valid_iterator_id`."
+            )
+    elif datapipe._valid_iterator_id != iterator_id:
+        raise RuntimeError(_gen_invalid_iterdatapipe_msg(datapipe) + _feedback_msg)
+
+
+def _set_datapipe_valid_iterator_id(datapipe):
+    """Given a DataPipe, updates its valid iterator ID and reset the DataPipe."""
+    if hasattr(datapipe, "_is_child_datapipe") and datapipe._is_child_datapipe is True:
+        if hasattr(datapipe, "_set_main_datapipe_valid_iterator_id"):
+            datapipe._set_main_datapipe_valid_iterator_id()  # reset() is called within this method when appropriate
+        else:
+            raise RuntimeError(
+                "ChildDataPipe must have method `_set_main_datapipe_valid_iterator_id`."
+            )
+    else:
+        if datapipe._valid_iterator_id is None:
+            datapipe._valid_iterator_id = 0
+        else:
+            datapipe._valid_iterator_id += 1
+        datapipe.reset()
+    return datapipe._valid_iterator_id
+
+
+def hook_iterator(namespace):
+    r"""
+    Define a hook that is applied to all `__iter__` of metaclass `_DataPipeMeta`.
+
+    This is done for the purpose of profiling and checking if an iterator is still valid.
+    """
+
+    def profiler_record_fn_context(datapipe):
+        if not hasattr(datapipe, "_profile_name"):
+            datapipe._profile_name = _generate_iterdatapipe_msg(
+                datapipe, simplify_dp_name=True
+            )
+        return torch.autograd.profiler.record_function(datapipe._profile_name)
+
+    class IteratorDecorator:
+        r"""
+        Wrap the iterator and modifying its `__next__` method.
+
+        This decorator is applied to DataPipes of which `__iter__` method is NOT a generator function.
+        Those `__iter__` method commonly returns `self` but not necessarily.
+        """
+
+        def __init__(self, iterator, datapipe, iterator_id, has_next_method):
+            self.iterator = iterator
+            self.datapipe = datapipe
+            self.iterator_id = iterator_id
+            self._profiler_enabled = torch.autograd._profiler_enabled()
+            # Check if `__iter__` returns `self` and `DataPipe` has `__next__`
+            self.self_and_has_next_method = (
+                self.iterator is self.datapipe and has_next_method
+            )
+
+        def __iter__(self):
+            return self
+
+        def _get_next(self):
+            """Return next with logic related to iterator validity, profiler, and incrementation of samples yielded."""
+            _check_iterator_valid(self.datapipe, self.iterator_id)
+            result = next(self.iterator)
+            if not self.self_and_has_next_method:
+                self.datapipe._number_of_samples_yielded += 1
+            return result
+
+        def __next__(self):
+            # TODO: Add try-except to in-place reduce traceback from the Exception
+            # See: https://github.com/pytorch/data/issues/284
+            if self._profiler_enabled:
+                with profiler_record_fn_context(self.datapipe):
+                    return self._get_next()
+            else:  # Decided against using `contextlib.nullcontext` for performance reasons
+                return self._get_next()
+
+        def __getattr__(self, name):
+            return getattr(self.iterator, name)
+
+    func = namespace["__iter__"]
+
+    # ``__iter__`` of IterDataPipe is a generator function
+    if inspect.isgeneratorfunction(func):
+
+        @functools.wraps(func)
+        def wrap_generator(*args, **kwargs):
+            gen = func(*args, **kwargs)
+            datapipe = args[0]
+            if datapipe._fast_forward_iterator:
+                it = datapipe._fast_forward_iterator
+                datapipe._fast_forward_iterator = None
+                datapipe._snapshot_state = _SnapshotState.Iterating
+                while True:
+                    try:
+                        yield next(it)
+                    except StopIteration:
+                        return
+            iterator_id = _set_datapipe_valid_iterator_id(
+                datapipe
+            )  # This ID is tied to each created iterator
+            _profiler_enabled = torch.autograd._profiler_enabled()
+            try:
+                if _profiler_enabled:
+                    with profiler_record_fn_context(datapipe):
+                        response = gen.send(None)
+                else:
+                    response = gen.send(None)
+
+                while True:
+                    datapipe._number_of_samples_yielded += 1
+                    request = yield response
+                    # Pass through here every time `__next__` is called
+                    if _profiler_enabled:
+                        with profiler_record_fn_context(datapipe):
+                            _check_iterator_valid(datapipe, iterator_id)
+                            response = gen.send(request)
+                    else:  # Decided against using `contextlib.nullcontext` for performance reasons
+                        _check_iterator_valid(datapipe, iterator_id)
+                        response = gen.send(request)
+            except StopIteration:
+                return
+            except Exception as e:
+                # TODO: Simplify the traceback message to skip over `response = gen.send(None)`
+                #       Part of https://github.com/pytorch/data/issues/284
+                datapipe = args[0]
+                msg = "thrown by __iter__ of"
+                single_iterator_msg = "single iterator per IterDataPipe constraint"
+                if hasattr(e.args, "__len__"):
+                    full_msg = f"{msg} {datapipe.__class__.__name__}({_generate_input_args_string(datapipe)})"
+                    if len(e.args) == 0 or not isinstance(
+                        e.args[0], str
+                    ):  # If an exception message doesn't exist
+                        e.args = (f"\nThis exception is {full_msg}",)
+                    elif msg not in e.args[0] and single_iterator_msg not in e.args[0]:
+                        e.args = (
+                            e.args[0] + f"\nThis exception is {full_msg}",
+                        ) + e.args[1:]
+                raise
+
+        namespace["__iter__"] = wrap_generator
+    else:  # ``__iter__`` of IterDataPipe is NOT a generator function
+        # IterDataPipe is an iterator with both ``__iter__`` and ``__next__``
+        # And ``__iter__`` may or may not return `self`
+        if "__next__" in namespace:  # If `__next__` exists, put a wrapper around it
+            next_func = namespace["__next__"]
+
+            @functools.wraps(next_func)
+            def wrap_next(*args, **kwargs):
+                datapipe = args[0]
+                if torch.autograd._profiler_enabled():
+                    with profiler_record_fn_context(datapipe):
+                        result = next_func(*args, **kwargs)
+                else:
+                    result = next_func(*args, **kwargs)
+                datapipe._number_of_samples_yielded += 1
+                return result
+
+            namespace["__next__"] = wrap_next
+
+            # Note that if the `__next__` and `__iter__` do something completely unrelated. It may cause issue but
+            # the user will be violating the iterator protocol. Potential issue:
+            # 1. Valid iterator ID may not update or checked properly
+            # 2. The number of samples yielded will be miscounted
+
+        # Regardless if `__next__` exists or not, `__iter__` needs a wrapper to track the number of valid iterators
+        @functools.wraps(func)
+        def wrap_iter(*args, **kwargs):
+            iter_ret = func(*args, **kwargs)
+            datapipe = args[0]
+            datapipe._snapshot_state = _SnapshotState.Iterating
+            if datapipe._fast_forward_iterator:
+                iter_ret = datapipe._fast_forward_iterator
+                datapipe._fast_forward_iterator = None
+                return iter_ret
+            iterator_id = _set_datapipe_valid_iterator_id(
+                datapipe
+            )  # This ID is tied to each created iterator
+            return IteratorDecorator(
+                iter_ret, datapipe, iterator_id, "__next__" in namespace
+            )
+
+        namespace["__iter__"] = wrap_iter
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/_typing.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..0df815358bd0e402ead283cba54157ca5d0aa383
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/_typing.py
@@ -0,0 +1,482 @@
+# mypy: allow-untyped-defs
+# Taking reference from official Python typing
+# https://github.com/python/cpython/blob/master/Lib/typing.py
+
+import collections
+import functools
+import numbers
+import sys
+
+# Please check [Note: TypeMeta and TypeAlias]
+# In case of metaclass conflict due to ABCMeta or _ProtocolMeta
+# For Python 3.9, only Protocol in typing uses metaclass
+from abc import ABCMeta
+from collections.abc import Iterator
+
+# TODO: Use TypeAlias when Python 3.6 is deprecated
+from typing import (  # type: ignore[attr-defined]
+    _eval_type,
+    _GenericAlias,
+    _tp_cache,
+    _type_check,
+    _type_repr,
+    Any,
+    ForwardRef,
+    Generic,
+    get_type_hints,
+    TypeVar,
+    Union,
+)
+
+from torch.utils.data.datapipes._hook_iterator import _SnapshotState, hook_iterator
+
+
+class GenericMeta(ABCMeta):  # type: ignore[no-redef]
+    pass
+
+
+class Integer(numbers.Integral):
+    pass
+
+
+class Boolean(numbers.Integral):
+    pass
+
+
+# Python 'type' object is not subscriptable
+# Tuple[int, List, dict] -> valid
+# tuple[int, list, dict] -> invalid
+# Map Python 'type' to abstract base class
+TYPE2ABC = {
+    bool: Boolean,
+    int: Integer,
+    float: numbers.Real,
+    complex: numbers.Complex,
+    dict: dict,
+    list: list,
+    set: set,
+    tuple: tuple,
+    None: type(None),
+}
+
+
+def issubtype(left, right, recursive=True):
+    r"""
+    Check if the left-side type is a subtype of the right-side type.
+
+    If any of type is a composite type like `Union` and `TypeVar` with
+    bounds, it would be expanded into a list of types and check all
+    of left-side types are subtypes of either one from right-side types.
+    """
+    left = TYPE2ABC.get(left, left)
+    right = TYPE2ABC.get(right, right)
+
+    if right is Any or left == right:
+        return True
+
+    if isinstance(right, _GenericAlias):
+        if getattr(right, "__origin__", None) is Generic:
+            return True
+
+    if right == type(None):
+        return False
+
+    # Right-side type
+    constraints = _decompose_type(right)
+
+    if len(constraints) == 0 or Any in constraints:
+        return True
+
+    if left is Any:
+        return False
+
+    # Left-side type
+    variants = _decompose_type(left)
+
+    # all() will return True for empty variants
+    if len(variants) == 0:
+        return False
+
+    return all(
+        _issubtype_with_constraints(variant, constraints, recursive)
+        for variant in variants
+    )
+
+
+def _decompose_type(t, to_list=True):
+    if isinstance(t, TypeVar):
+        if t.__bound__ is not None:
+            ts = [t.__bound__]
+        else:
+            # For T_co, __constraints__ is ()
+            ts = list(t.__constraints__)
+    elif hasattr(t, "__origin__") and t.__origin__ == Union:
+        ts = t.__args__
+    else:
+        if not to_list:
+            return None
+        ts = [t]
+    # Ignored: Generator has incompatible item type "object"; expected "Type[Any]"
+    ts = [TYPE2ABC.get(_t, _t) for _t in ts]  # type: ignore[misc]
+    return ts
+
+
+def _issubtype_with_constraints(variant, constraints, recursive=True):
+    r"""
+    Check if the variant is a subtype of either one from constraints.
+
+    For composite types like `Union` and `TypeVar` with bounds, they
+    would be expanded for testing.
+    """
+    if variant in constraints:
+        return True
+
+    # [Note: Subtype for Union and TypeVar]
+    # Python typing is able to flatten Union[Union[...]] or Union[TypeVar].
+    # But it couldn't flatten the following scenarios:
+    #   - Union[int, TypeVar[Union[...]]]
+    #   - TypeVar[TypeVar[...]]
+    # So, variant and each constraint may be a TypeVar or a Union.
+    # In these cases, all of inner types from the variant are required to be
+    # extraced and verified as a subtype of any constraint. And, all of
+    # inner types from any constraint being a TypeVar or a Union are
+    # also required to be extracted and verified if the variant belongs to
+    # any of them.
+
+    # Variant
+    vs = _decompose_type(variant, to_list=False)
+
+    # Variant is TypeVar or Union
+    if vs is not None:
+        return all(_issubtype_with_constraints(v, constraints, recursive) for v in vs)
+
+    # Variant is not TypeVar or Union
+    if hasattr(variant, "__origin__") and variant.__origin__ is not None:
+        v_origin = variant.__origin__
+        # In Python-3.9 typing library untyped generics do not have args
+        v_args = getattr(variant, "__args__", None)
+    else:
+        v_origin = variant
+        v_args = None
+
+    # Constraints
+    for constraint in constraints:
+        cs = _decompose_type(constraint, to_list=False)
+
+        # Constraint is TypeVar or Union
+        if cs is not None:
+            if _issubtype_with_constraints(variant, cs, recursive):
+                return True
+        # Constraint is not TypeVar or Union
+        else:
+            # __origin__ can be None for plain list, tuple, ... in Python 3.6
+            if hasattr(constraint, "__origin__") and constraint.__origin__ is not None:
+                c_origin = constraint.__origin__
+                if v_origin == c_origin:
+                    if not recursive:
+                        return True
+                    # In Python-3.9 typing library untyped generics do not have args
+                    c_args = getattr(constraint, "__args__", None)
+                    if c_args is None or len(c_args) == 0:
+                        return True
+                    if (
+                        v_args is not None
+                        and len(v_args) == len(c_args)
+                        and all(
+                            issubtype(v_arg, c_arg)
+                            for v_arg, c_arg in zip(v_args, c_args)
+                        )
+                    ):
+                        return True
+            # Tuple[int] -> Tuple
+            else:
+                if v_origin == constraint:
+                    return True
+
+    return False
+
+
+def issubinstance(data, data_type):
+    if not issubtype(type(data), data_type, recursive=False):
+        return False
+
+    # In Python-3.9 typing library __args__ attribute is not defined for untyped generics
+    dt_args = getattr(data_type, "__args__", None)
+    if isinstance(data, tuple):
+        if dt_args is None or len(dt_args) == 0:
+            return True
+        if len(dt_args) != len(data):
+            return False
+        return all(issubinstance(d, t) for d, t in zip(data, dt_args))
+    elif isinstance(data, (list, set)):
+        if dt_args is None or len(dt_args) == 0:
+            return True
+        t = dt_args[0]
+        return all(issubinstance(d, t) for d in data)
+    elif isinstance(data, dict):
+        if dt_args is None or len(dt_args) == 0:
+            return True
+        kt, vt = dt_args
+        return all(
+            issubinstance(k, kt) and issubinstance(v, vt) for k, v in data.items()
+        )
+
+    return True
+
+
+# [Note: TypeMeta and TypeAlias]
+# In order to keep compatibility for Python 3.6, use Meta for the typing.
+# TODO: When PyTorch drops the support for Python 3.6, it can be converted
+# into the Alias system and using `__class_getitem__` for DataPipe. The
+# typing system will gain benefit of performance and resolving metaclass
+# conflicts as elaborated in https://www.python.org/dev/peps/pep-0560/
+
+
+class _DataPipeType:
+    r"""Save type annotation in `param`."""
+
+    def __init__(self, param):
+        self.param = param
+
+    def __repr__(self):
+        return _type_repr(self.param)
+
+    def __eq__(self, other):
+        if isinstance(other, _DataPipeType):
+            return self.param == other.param
+        return NotImplemented
+
+    def __hash__(self):
+        return hash(self.param)
+
+    def issubtype(self, other):
+        if isinstance(other.param, _GenericAlias):
+            if getattr(other.param, "__origin__", None) is Generic:
+                return True
+        if isinstance(other, _DataPipeType):
+            return issubtype(self.param, other.param)
+        if isinstance(other, type):
+            return issubtype(self.param, other)
+        raise TypeError(f"Expected '_DataPipeType' or 'type', but found {type(other)}")
+
+    def issubtype_of_instance(self, other):
+        return issubinstance(other, self.param)
+
+
+# Default type for DataPipe without annotation
+_T_co = TypeVar("_T_co", covariant=True)
+_DEFAULT_TYPE = _DataPipeType(Generic[_T_co])
+
+
+class _DataPipeMeta(GenericMeta):
+    r"""
+    Metaclass for `DataPipe`.
+
+    Add `type` attribute and `__init_subclass__` based on the type, and validate the return hint of `__iter__`.
+
+    Note that there is subclass `_IterDataPipeMeta` specifically for `IterDataPipe`.
+    """
+
+    type: _DataPipeType
+
+    def __new__(cls, name, bases, namespace, **kwargs):
+        return super().__new__(cls, name, bases, namespace, **kwargs)  # type: ignore[call-overload]
+
+        # TODO: the statements below are not reachable by design as there is a bug and typing is low priority for now.
+        cls.__origin__ = None
+        if "type" in namespace:
+            return super().__new__(cls, name, bases, namespace, **kwargs)  # type: ignore[call-overload]
+
+        namespace["__type_class__"] = False
+        #  For plain derived class without annotation
+        for base in bases:
+            if isinstance(base, _DataPipeMeta):
+                return super().__new__(cls, name, bases, namespace, **kwargs)  # type: ignore[call-overload]
+
+        namespace.update(
+            {"type": _DEFAULT_TYPE, "__init_subclass__": _dp_init_subclass}
+        )
+        return super().__new__(cls, name, bases, namespace, **kwargs)  # type: ignore[call-overload]
+
+    def __init__(self, name, bases, namespace, **kwargs):
+        super().__init__(name, bases, namespace, **kwargs)  # type: ignore[call-overload]
+
+    # TODO: Fix isinstance bug
+    @_tp_cache
+    def _getitem_(self, params):
+        if params is None:
+            raise TypeError(f"{self.__name__}[t]: t can not be None")
+        if isinstance(params, str):
+            params = ForwardRef(params)
+        if not isinstance(params, tuple):
+            params = (params,)
+
+        msg = f"{self.__name__}[t]: t must be a type"
+        params = tuple(_type_check(p, msg) for p in params)
+
+        if isinstance(self.type.param, _GenericAlias):
+            orig = getattr(self.type.param, "__origin__", None)
+            if isinstance(orig, type) and orig is not Generic:
+                p = self.type.param[params]  # type: ignore[index]
+                t = _DataPipeType(p)
+                l = len(str(self.type)) + 2
+                name = self.__name__[:-l]
+                name = name + "[" + str(t) + "]"
+                bases = (self,) + self.__bases__
+                return self.__class__(
+                    name,
+                    bases,
+                    {
+                        "__init_subclass__": _dp_init_subclass,
+                        "type": t,
+                        "__type_class__": True,
+                    },
+                )
+
+        if len(params) > 1:
+            raise TypeError(
+                f"Too many parameters for {self} actual {len(params)}, expected 1"
+            )
+
+        t = _DataPipeType(params[0])
+
+        if not t.issubtype(self.type):
+            raise TypeError(
+                f"Can not subclass a DataPipe[{t}] from DataPipe[{self.type}]"
+            )
+
+        # Types are equal, fast path for inheritance
+        if self.type == t:
+            return self
+
+        name = self.__name__ + "[" + str(t) + "]"
+        bases = (self,) + self.__bases__
+
+        return self.__class__(
+            name,
+            bases,
+            {"__init_subclass__": _dp_init_subclass, "__type_class__": True, "type": t},
+        )
+
+    # TODO: Fix isinstance bug
+    def _eq_(self, other):
+        if not isinstance(other, _DataPipeMeta):
+            return NotImplemented
+        if self.__origin__ is None or other.__origin__ is None:  # type: ignore[has-type]
+            return self is other
+        return (
+            self.__origin__ == other.__origin__  # type: ignore[has-type]
+            and self.type == other.type
+        )
+
+    # TODO: Fix isinstance bug
+    def _hash_(self):
+        return hash((self.__name__, self.type))
+
+
+class _IterDataPipeMeta(_DataPipeMeta):
+    r"""
+    Metaclass for `IterDataPipe` and inherits from `_DataPipeMeta`.
+
+    Add various functions for behaviors specific to `IterDataPipe`.
+    """
+
+    def __new__(cls, name, bases, namespace, **kwargs):
+        if "reset" in namespace:
+            reset_func = namespace["reset"]
+
+            @functools.wraps(reset_func)
+            def conditional_reset(*args, **kwargs):
+                r"""
+                Only execute DataPipe's `reset()` method if `_SnapshotState` is `Iterating` or `NotStarted`.
+
+                This allows recently restored DataPipe to preserve its restored state during the initial `__iter__` call.
+                """
+                datapipe = args[0]
+                if datapipe._snapshot_state in (
+                    _SnapshotState.Iterating,
+                    _SnapshotState.NotStarted,
+                ):
+                    # Reset `NotStarted` is necessary because the `source_datapipe` of a DataPipe might have
+                    # already begun iterating.
+                    datapipe._number_of_samples_yielded = 0
+                    datapipe._fast_forward_iterator = None
+                    reset_func(*args, **kwargs)
+                datapipe._snapshot_state = _SnapshotState.Iterating
+
+            namespace["reset"] = conditional_reset
+
+        if "__iter__" in namespace:
+            hook_iterator(namespace)
+        return super().__new__(cls, name, bases, namespace, **kwargs)  # type: ignore[call-overload]
+
+
+def _dp_init_subclass(sub_cls, *args, **kwargs):
+    # Add function for datapipe instance to reinforce the type
+    sub_cls.reinforce_type = reinforce_type
+
+    # TODO:
+    # - add global switch for type checking at compile-time
+
+    # Ignore internal type class
+    if getattr(sub_cls, "__type_class__", False):
+        return
+
+    # Check if the string type is valid
+    if isinstance(sub_cls.type.param, ForwardRef):
+        base_globals = sys.modules[sub_cls.__module__].__dict__
+        try:
+            param = _eval_type(sub_cls.type.param, base_globals, locals())
+            sub_cls.type.param = param
+        except TypeError as e:
+            raise TypeError(
+                f"{sub_cls.type.param.__forward_arg__} is not supported by Python typing"
+            ) from e
+
+    if "__iter__" in sub_cls.__dict__:
+        iter_fn = sub_cls.__dict__["__iter__"]
+        hints = get_type_hints(iter_fn)
+        if "return" in hints:
+            return_hint = hints["return"]
+            # Plain Return Hint for Python 3.6
+            if return_hint == Iterator:
+                return
+            if not (
+                hasattr(return_hint, "__origin__")
+                and (
+                    return_hint.__origin__ == Iterator
+                    or return_hint.__origin__ == collections.abc.Iterator
+                )
+            ):
+                raise TypeError(
+                    "Expected 'Iterator' as the return annotation for `__iter__` of {}"
+                    ", but found {}".format(
+                        sub_cls.__name__, _type_repr(hints["return"])
+                    )
+                )
+            data_type = return_hint.__args__[0]
+            if not issubtype(data_type, sub_cls.type.param):
+                raise TypeError(
+                    f"Expected return type of '__iter__' as a subtype of {sub_cls.type},"
+                    f" but found {_type_repr(data_type)} for {sub_cls.__name__}"
+                )
+
+
+def reinforce_type(self, expected_type):
+    r"""
+    Reinforce the type for DataPipe instance.
+
+    And the 'expected_type' is required to be a subtype of the original type
+    hint to restrict the type requirement of DataPipe instance.
+    """
+    if isinstance(expected_type, tuple):
+        expected_type = tuple[expected_type]  # type: ignore[valid-type]
+    _type_check(expected_type, msg="'expected_type' must be a type")
+
+    if not issubtype(expected_type, self.type.param):
+        raise TypeError(
+            f"Expected 'expected_type' as subtype of {self.type}, but found {_type_repr(expected_type)}"
+        )
+
+    self.type = _DataPipeType(expected_type)
+    return self
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9feb5f113c0f37f820da9f5abeea14e6bed96c44
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/__init__.py
@@ -0,0 +1,11 @@
+from torch.utils.data.datapipes.dataframe.dataframes import (
+    CaptureDataFrame,
+    DFIterDataPipe,
+)
+from torch.utils.data.datapipes.dataframe.datapipes import DataFramesAsTuplesPipe
+
+
+__all__ = ["CaptureDataFrame", "DFIterDataPipe", "DataFramesAsTuplesPipe"]
+
+# Please keep this list sorted
+assert __all__ == sorted(__all__)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/dataframe_wrapper.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/dataframe_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bbd2505b4b5fe3720d8a5eec9c0da65aa5a5cab
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/dataframe_wrapper.py
@@ -0,0 +1,128 @@
+# mypy: allow-untyped-defs
+from typing import Any, Optional
+
+
+_pandas: Any = None
+_WITH_PANDAS: Optional[bool] = None
+
+
+def _try_import_pandas() -> bool:
+    try:
+        import pandas  # type: ignore[import]
+
+        global _pandas
+        _pandas = pandas
+        return True
+    except ImportError:
+        return False
+
+
+# pandas used only for prototyping, will be shortly replaced with TorchArrow
+def _with_pandas() -> bool:
+    global _WITH_PANDAS
+    if _WITH_PANDAS is None:
+        _WITH_PANDAS = _try_import_pandas()
+    return _WITH_PANDAS
+
+
+class PandasWrapper:
+    @classmethod
+    def create_dataframe(cls, data, columns):
+        if not _with_pandas():
+            raise RuntimeError("DataFrames prototype requires pandas to function")
+        return _pandas.DataFrame(data, columns=columns)  # type: ignore[union-attr]
+
+    @classmethod
+    def is_dataframe(cls, data):
+        if not _with_pandas():
+            return False
+        return isinstance(data, _pandas.core.frame.DataFrame)  # type: ignore[union-attr]
+
+    @classmethod
+    def is_column(cls, data):
+        if not _with_pandas():
+            return False
+        return isinstance(data, _pandas.core.series.Series)  # type: ignore[union-attr]
+
+    @classmethod
+    def iterate(cls, data):
+        if not _with_pandas():
+            raise RuntimeError("DataFrames prototype requires pandas to function")
+        yield from data.itertuples(index=False)
+
+    @classmethod
+    def concat(cls, buffer):
+        if not _with_pandas():
+            raise RuntimeError("DataFrames prototype requires pandas to function")
+        return _pandas.concat(buffer)  # type: ignore[union-attr]
+
+    @classmethod
+    def get_item(cls, data, idx):
+        if not _with_pandas():
+            raise RuntimeError("DataFrames prototype requires pandas to function")
+        return data[idx : idx + 1]
+
+    @classmethod
+    def get_len(cls, df):
+        if not _with_pandas():
+            raise RuntimeError("DataFrames prototype requires pandas to function")
+        return len(df.index)
+
+    @classmethod
+    def get_columns(cls, df):
+        if not _with_pandas():
+            raise RuntimeError("DataFrames prototype requires pandas to function")
+        return list(df.columns.values.tolist())
+
+
+# When you build own implementation just override it with dataframe_wrapper.set_df_wrapper(new_wrapper_class)
+default_wrapper = PandasWrapper
+
+
+def get_df_wrapper():
+    return default_wrapper
+
+
+def set_df_wrapper(wrapper):
+    global default_wrapper
+    default_wrapper = wrapper
+
+
+def create_dataframe(data, columns=None):
+    wrapper = get_df_wrapper()
+    return wrapper.create_dataframe(data, columns)
+
+
+def is_dataframe(data):
+    wrapper = get_df_wrapper()
+    return wrapper.is_dataframe(data)
+
+
+def get_columns(data):
+    wrapper = get_df_wrapper()
+    return wrapper.get_columns(data)
+
+
+def is_column(data):
+    wrapper = get_df_wrapper()
+    return wrapper.is_column(data)
+
+
+def concat(buffer):
+    wrapper = get_df_wrapper()
+    return wrapper.concat(buffer)
+
+
+def iterate(data):
+    wrapper = get_df_wrapper()
+    return wrapper.iterate(data)
+
+
+def get_item(data, idx):
+    wrapper = get_df_wrapper()
+    return wrapper.get_item(data, idx)
+
+
+def get_len(df):
+    wrapper = get_df_wrapper()
+    return wrapper.get_len(df)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/dataframes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/dataframes.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3958ec0c793f64a32c7144248e89b792f70a7c4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/dataframes.py
@@ -0,0 +1,457 @@
+# mypy: allow-untyped-defs
+from typing import Any, Optional
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.dataframe.structures import DataChunkDF
+from torch.utils.data.datapipes.datapipe import DFIterDataPipe, IterDataPipe
+
+
+# TODO(VitalyFedyunin): Add error when two different traces get combined
+
+__all__ = [
+    "Capture",
+    "CaptureA",
+    "CaptureAdd",
+    "CaptureCall",
+    "CaptureControl",
+    "CaptureDataFrame",
+    "CaptureDataFrameWithDataPipeOps",
+    "CaptureF",
+    "CaptureGetAttr",
+    "CaptureGetItem",
+    "CaptureInitial",
+    "CaptureLikeMock",
+    "CaptureMul",
+    "CaptureSetItem",
+    "CaptureSub",
+    "CaptureVariable",
+    "CaptureVariableAssign",
+    "DataFrameTracer",
+    "DataFrameTracedOps",
+    "disable_capture",
+    "get_val",
+]
+
+
+def disable_capture():
+    CaptureControl.disabled = True
+
+
+class CaptureControl:
+    disabled = False
+
+
+class DataFrameTracedOps(DFIterDataPipe):
+    def __init__(self, source_datapipe, output_var):
+        self.source_datapipe = source_datapipe
+        self.output_var = output_var
+
+    def __iter__(self):
+        for item in self.source_datapipe:
+            yield self.output_var.apply_ops(item)
+
+
+#  TODO(VitalyFedyunin): Extract this list from the DFIterDataPipe registred functions
+DATAPIPES_OPS = [
+    "_dataframes_as_tuples",
+    "groupby",
+    "_dataframes_filter",
+    "map",
+    "to_datapipe",
+    "shuffle",
+    "concat",
+    "batch",
+    "_dataframes_per_row",
+    "_dataframes_concat",
+    "_dataframes_shuffle",
+]
+
+UNIMPLEMENTED_ATTR = ["__deepcopy__", "__setstate__", "is_shardable", "apply_sharding"]
+
+
+class Capture:
+    # TODO: All operations are shared across entire InitialCapture, need to figure out what if we join two captures
+
+    def __init__(self, schema_df=None):
+        self.ctx = {"operations": [], "variables": [], "schema_df": schema_df}
+
+    def __str__(self):
+        return self._ops_str()
+
+    def _ops_str(self):
+        res = ""
+        for op in self.ctx["operations"]:
+            if len(res) > 0:
+                res += "\n"
+            res += str(op)
+        return res
+
+    def __getstate__(self):
+        # TODO(VitalyFedyunin): Currently can't pickle (why?)
+        self.ctx["schema_df"] = None
+        for var in self.ctx["variables"]:
+            var.calculated_value = None
+        state = {}
+        for item in self.__dict__:
+            state[item] = getattr(self, item)
+        return state
+
+    def __setstate__(self, state):
+        for k, v in state.items():
+            setattr(self, k, v)
+
+    def __getattr__(self, attrname):
+        if attrname == "kwarg" or attrname == "kwargs":
+            raise RuntimeError("no kwargs!")
+        if attrname in ["__deepcopy__"]:
+            raise AttributeError
+        result = CaptureGetAttr(self, attrname, ctx=self.ctx)
+        return result
+
+    def __getitem__(self, key):
+        return CaptureGetItem(self, key, ctx=self.ctx)
+
+    def __setitem__(self, key, value):
+        self.ctx["operations"].append(CaptureSetItem(self, key, value, ctx=self.ctx))
+
+    def __add__(self, add_val):
+        res = CaptureAdd(self, add_val, ctx=self.ctx)
+        var = CaptureVariable(res, ctx=self.ctx)
+        self.ctx["operations"].append(
+            CaptureVariableAssign(variable=var, value=res, ctx=self.ctx)
+        )
+        return var
+
+    def __sub__(self, add_val):
+        res = CaptureSub(self, add_val, ctx=self.ctx)
+        var = CaptureVariable(res, ctx=self.ctx)
+        self.ctx["operations"].append(
+            CaptureVariableAssign(variable=var, value=res, ctx=self.ctx)
+        )
+        return var
+
+    def __mul__(self, add_val):
+        res = CaptureMul(self, add_val, ctx=self.ctx)
+        var = CaptureVariable(res, ctx=self.ctx)
+        t = CaptureVariableAssign(variable=var, value=res, ctx=self.ctx)
+        self.ctx["operations"].append(t)
+        return var
+
+    def _is_context_empty(self):
+        return len(self.ctx["operations"]) == 0 and len(self.ctx["variables"]) == 0
+
+    def apply_ops_2(self, dataframe):
+        # TODO(VitalyFedyunin): Make this calculation thread safe (as currently it updates pointer)
+        self.ctx["variables"][0].calculated_value = dataframe
+        for op in self.ctx["operations"]:
+            op.execute()
+
+    @property
+    def columns(self):
+        self.apply_ops_2(self.ctx["schema_df"])
+        value = self.execute()
+        return value.columns
+
+    # TODO(VitalyFedyunin): Add tests
+    # TODO(VitalyFedyunin): Need to join context if one of them are empty because we used capture
+
+    def __call__(self, *args, **kwargs):
+        # TODO: Check if args or kwargs have more than one different context
+        if self._is_context_empty():
+            # TODO: Allow CaptureA to take context from mock
+            for arg in args:
+                if isinstance(arg, Capture) and not arg._is_context_empty():
+                    self.ctx = arg.ctx
+                    break
+            if self._is_context_empty():
+                for k, v in kwargs.items():
+                    if isinstance(k, Capture) and not k._is_context_empty():
+                        self.ctx = k.ctx
+                        break
+                    if isinstance(v, Capture) and not v._is_context_empty():
+                        self.ctx = v.ctx
+                        break
+
+        res = CaptureCall(self, ctx=self.ctx, args=args, kwargs=kwargs)
+        var = CaptureVariable(None, ctx=self.ctx)
+        t = CaptureVariableAssign(ctx=self.ctx, variable=var, value=res)
+        self.ctx["operations"].append(t)
+        return var
+
+
+class CaptureF(Capture):
+    def __init__(self, ctx=None, **kwargs):
+        if ctx is None:
+            self.ctx = {"operations": [], "variables": []}
+        else:
+            self.ctx = ctx
+        self.kwargs = kwargs
+
+
+class CaptureA(CaptureF):
+    def __str__(self):
+        return f"{self.kwargs['name']}"
+
+    def execute(self):
+        value = self.kwargs["real_attribute"]
+        return value
+
+
+class CaptureLikeMock:
+    def __init__(self, name):
+        import unittest.mock as mock
+
+        # TODO(VitalyFedyunin): Do not use provate function here, copy own implementation instead.
+        get_target, attribute = mock._get_target(name)  # type: ignore[attr-defined]
+        self.get_target = get_target
+        self.attribute = attribute
+        self.name = name
+
+    def __enter__(self):
+        self.save = getattr(self.get_target(), self.attribute)
+        capt = CaptureA(name=self.name, real_attribute=self.save)
+        setattr(self.get_target(), self.attribute, capt)
+
+    def __exit__(self, *exc_info):
+        setattr(self.get_target(), self.attribute, self.save)
+
+
+class CaptureCall(Capture):
+    def __init__(self, callable, ctx=None, **kwargs):
+        if ctx is None:
+            self.ctx = {"operations": [], "variables": []}
+        else:
+            self.ctx = ctx
+        self.kwargs = kwargs
+        self.callable = callable
+
+    def __str__(self):
+        return "{callable}({args},{kwargs})".format(
+            callable=self.callable, **self.kwargs
+        )
+
+    def execute(self):
+        # TODO: VitalyFedyunin execute kwargs and maybe nested structures
+        executed_args = []
+        for arg in self.kwargs["args"]:
+            if isinstance(arg, Capture):
+                executed_args.append(arg.execute())
+            else:
+                executed_args.append(arg)
+        left = get_val(self.callable)
+        return left(*executed_args, **self.kwargs["kwargs"])
+
+
+class CaptureVariableAssign(CaptureF):
+    def __str__(self):
+        variable = self.kwargs["variable"]
+        value = self.kwargs["value"]
+        return f"{variable} = {value}"
+
+    def execute(self):
+        self.kwargs["variable"].calculated_value = self.kwargs["value"].execute()
+
+
+class CaptureVariable(Capture):
+    # TODO(VitalyFedyunin): This should be atomic and thread safe
+    names_idx = 0
+
+    def __init__(self, value, ctx):
+        if CaptureControl.disabled:
+            raise RuntimeError("Attempting to create capture variable with capture off")
+        self.ctx = ctx
+        self.value = value
+        self.name = f"var_{CaptureVariable.names_idx}"
+        CaptureVariable.names_idx += 1
+        self.ctx["variables"].append(self)
+
+    def __str__(self):
+        return self.name
+
+    def execute(self):
+        return self.calculated_value
+
+    def apply_ops(self, dataframe):
+        # TODO(VitalyFedyunin): Make this calculation thread safe (as currently it updates pointer)
+        self.ctx["variables"][0].calculated_value = dataframe
+        for op in self.ctx["operations"]:
+            op.execute()
+        return self.calculated_value
+
+
+class CaptureGetItem(Capture):
+    def __init__(self, left, key, ctx):
+        self.ctx = ctx
+        self.left = left
+        self.key = key
+
+    def __str__(self):
+        return f"{self.left}[{get_val(self.key)}]"
+
+    def execute(self):
+        left = self.left.execute()
+        return left[self.key]
+
+
+class CaptureSetItem(Capture):
+    def __init__(self, left, key, value, ctx):
+        self.ctx = ctx
+        self.left = left
+        self.key = key
+        self.value = value
+
+    def __str__(self):
+        return f"{self.left}[{get_val(self.key)}] = {self.value}"
+
+    def execute(self):
+        left = self.left.execute()
+        value = self.value.execute()
+        left[self.key] = value
+
+
+class CaptureAdd(Capture):
+    def __init__(self, left, right, ctx):
+        self.ctx = ctx
+        self.left = left
+        self.right = right
+
+    def __str__(self):
+        return f"{self.left} + {self.right}"
+
+    def execute(self):
+        return get_val(self.left) + get_val(self.right)
+
+
+class CaptureMul(Capture):
+    def __init__(self, left, right, ctx):
+        self.ctx = ctx
+        self.left = left
+        self.right = right
+
+    def __str__(self):
+        return f"{self.left} * {self.right}"
+
+    def execute(self):
+        return get_val(self.left) * get_val(self.right)
+
+
+class CaptureSub(Capture):
+    def __init__(self, left, right, ctx):
+        self.ctx = ctx
+        self.left = left
+        self.right = right
+
+    def __str__(self):
+        return f"{self.left} - {self.right}"
+
+    def execute(self):
+        return get_val(self.left) - get_val(self.right)
+
+
+class CaptureGetAttr(Capture):
+    def __init__(self, src, name, ctx):
+        self.ctx = ctx
+        self.src = src
+        self.name = name
+
+    def __str__(self):
+        return f"{self.src}.{self.name}"
+
+    def execute(self):
+        val = get_val(self.src)
+        return getattr(val, self.name)
+
+
+def get_val(capture):
+    if isinstance(capture, Capture):
+        return capture.execute()
+    elif isinstance(capture, str):
+        return f'"{capture}"'
+    else:
+        return capture
+
+
+class CaptureInitial(CaptureVariable):
+    def __init__(self, schema_df=None):
+        new_ctx: dict[str, list[Any]] = {
+            "operations": [],
+            "variables": [],
+            "schema_df": schema_df,
+        }
+        super().__init__(None, new_ctx)
+        self.name = f"input_{self.name}"
+
+
+class CaptureDataFrame(CaptureInitial):
+    pass
+
+
+class CaptureDataFrameWithDataPipeOps(CaptureDataFrame):
+    def as_datapipe(self):
+        return DataFrameTracedOps(self.ctx["variables"][0].source_datapipe, self)
+
+    def raw_iterator(self):
+        return self.as_datapipe().__iter__()
+
+    def __iter__(self):
+        return iter(self._dataframes_as_tuples())
+
+    def batch(self, batch_size=10, drop_last: bool = False, wrapper_class=DataChunkDF):
+        dp = self._dataframes_per_row()._dataframes_concat(batch_size)
+        dp = dp.as_datapipe().batch(1, drop_last=drop_last, wrapper_class=wrapper_class)
+        dp._dp_contains_dataframe = True
+        return dp
+
+    def groupby(
+        self,
+        group_key_fn,
+        *,
+        buffer_size=10000,
+        group_size=None,
+        guaranteed_group_size=None,
+        drop_remaining=False,
+    ):
+        dp = self._dataframes_per_row()
+        dp = dp.as_datapipe().groupby(
+            group_key_fn,
+            buffer_size=buffer_size,
+            group_size=group_size,
+            guaranteed_group_size=guaranteed_group_size,
+            drop_remaining=drop_remaining,
+        )
+        return dp
+
+    def shuffle(self, *args, **kwargs):
+        return self._dataframes_shuffle(*args, **kwargs)
+
+    def filter(self, *args, **kwargs):
+        return self._dataframes_filter(*args, **kwargs)
+
+    def collate(self, *args, **kwargs):
+        raise RuntimeError("Can't collate unbatched DataFrames stream")
+
+    def __getattr__(self, attrname):  # ?
+        if attrname in UNIMPLEMENTED_ATTR:
+            raise AttributeError("Attempting to get ", attrname)
+        if attrname in DATAPIPES_OPS:
+            return (self.as_datapipe()).__getattr__(attrname)
+        return super().__getattr__(attrname)
+
+
+@functional_datapipe("trace_as_dataframe")
+class DataFrameTracer(CaptureDataFrameWithDataPipeOps, IterDataPipe):  # type: ignore[misc]
+    source_datapipe: Optional[Any] = None
+
+    # TODO(VitalyFedyunin): Must implement all special functions of datapipes
+
+    def set_shuffle_settings(self, *args, **kwargs):
+        pass
+
+    def is_shardable(self):
+        return False
+
+    def __init__(self, source_datapipe, schema_df=None):
+        self.source_datapipe = source_datapipe
+        if schema_df is None:
+            schema_df = next(iter(self.source_datapipe))
+        super().__init__(schema_df=schema_df)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/datapipes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/datapipes.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9b89d6437aab6aff00998ce2af88b308f7f78bc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/datapipes.py
@@ -0,0 +1,136 @@
+# mypy: allow-untyped-defs
+import random
+from typing import Any
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
+from torch.utils.data.datapipes.datapipe import DFIterDataPipe, IterDataPipe
+
+
+__all__ = [
+    "ConcatDataFramesPipe",
+    "DataFramesAsTuplesPipe",
+    "ExampleAggregateAsDataFrames",
+    "FilterDataFramesPipe",
+    "PerRowDataFramesPipe",
+    "ShuffleDataFramesPipe",
+]
+
+
+@functional_datapipe("_dataframes_as_tuples")
+class DataFramesAsTuplesPipe(IterDataPipe):
+    def __init__(self, source_datapipe):
+        self.source_datapipe = source_datapipe
+
+    def __iter__(self):
+        for df in self.source_datapipe:
+            # for record in df.to_records(index=False):
+            yield from df_wrapper.iterate(df)
+
+
+@functional_datapipe("_dataframes_per_row", enable_df_api_tracing=True)
+class PerRowDataFramesPipe(DFIterDataPipe):
+    def __init__(self, source_datapipe):
+        self.source_datapipe = source_datapipe
+
+    def __iter__(self):
+        for df in self.source_datapipe:
+            # TODO(VitalyFedyunin): Replacing with TorchArrow only API, as we are dropping pandas as followup
+            for i in range(len(df)):
+                yield df[i : i + 1]
+
+
+@functional_datapipe("_dataframes_concat", enable_df_api_tracing=True)
+class ConcatDataFramesPipe(DFIterDataPipe):
+    def __init__(self, source_datapipe, batch=3):
+        self.source_datapipe = source_datapipe
+        self.n_batch = batch
+
+    def __iter__(self):
+        buffer = []
+        for df in self.source_datapipe:
+            buffer.append(df)
+            if len(buffer) == self.n_batch:
+                yield df_wrapper.concat(buffer)
+                buffer = []
+        if len(buffer):
+            yield df_wrapper.concat(buffer)
+
+
+@functional_datapipe("_dataframes_shuffle", enable_df_api_tracing=True)
+class ShuffleDataFramesPipe(DFIterDataPipe):
+    def __init__(self, source_datapipe):
+        self.source_datapipe = source_datapipe
+
+    def __iter__(self):
+        size = None
+        all_buffer: list[Any] = []
+        for df in self.source_datapipe:
+            if size is None:
+                size = df_wrapper.get_len(df)
+            all_buffer.extend(
+                df_wrapper.get_item(df, i) for i in range(df_wrapper.get_len(df))
+            )
+        random.shuffle(all_buffer)
+        buffer = []
+        for df in all_buffer:
+            buffer.append(df)
+            if len(buffer) == size:
+                yield df_wrapper.concat(buffer)
+                buffer = []
+        if len(buffer):
+            yield df_wrapper.concat(buffer)
+
+
+@functional_datapipe("_dataframes_filter", enable_df_api_tracing=True)
+class FilterDataFramesPipe(DFIterDataPipe):
+    def __init__(self, source_datapipe, filter_fn):
+        self.source_datapipe = source_datapipe
+        self.filter_fn = filter_fn
+
+    def __iter__(self):
+        size = None
+        all_buffer = []
+        filter_res = []
+        for df in self.source_datapipe:
+            if size is None:
+                size = len(df.index)
+            for i in range(len(df.index)):
+                all_buffer.append(df[i : i + 1])
+                filter_res.append(self.filter_fn(df.iloc[i]))
+
+        buffer = []
+        for df, res in zip(all_buffer, filter_res):
+            if res:
+                buffer.append(df)
+                if len(buffer) == size:
+                    yield df_wrapper.concat(buffer)
+                    buffer = []
+        if len(buffer):
+            yield df_wrapper.concat(buffer)
+
+
+@functional_datapipe("_to_dataframes_pipe", enable_df_api_tracing=True)
+class ExampleAggregateAsDataFrames(DFIterDataPipe):
+    def __init__(self, source_datapipe, dataframe_size=10, columns=None):
+        self.source_datapipe = source_datapipe
+        self.columns = columns
+        self.dataframe_size = dataframe_size
+
+    def _as_list(self, item):
+        try:
+            return list(item)
+        except (
+            Exception
+        ):  # TODO(VitalyFedyunin): Replace with better iterable exception
+            return [item]
+
+    def __iter__(self):
+        aggregate = []
+        for item in self.source_datapipe:
+            aggregate.append(self._as_list(item))
+            if len(aggregate) == self.dataframe_size:
+                yield df_wrapper.create_dataframe(aggregate, columns=self.columns)
+                aggregate = []
+        if len(aggregate) > 0:
+            yield df_wrapper.create_dataframe(aggregate, columns=self.columns)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/structures.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/structures.py
new file mode 100644
index 0000000000000000000000000000000000000000..26b4c33db03cc584f223444c07730ef67f4495e7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/dataframe/structures.py
@@ -0,0 +1,22 @@
+from collections.abc import Iterator
+from typing import Any
+
+from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
+from torch.utils.data.datapipes.datapipe import DataChunk
+
+
+__all__ = ["DataChunkDF"]
+
+
+class DataChunkDF(DataChunk):
+    """DataChunkDF iterating over individual items inside of DataFrame containers, to access DataFrames user `raw_iterator`."""
+
+    def __iter__(self) -> Iterator[Any]:
+        for df in self.items:
+            yield from df_wrapper.iterate(df)
+
+    def __len__(self) -> int:
+        total_len = 0
+        for df in self.items:
+            total_len += df_wrapper.get_len(df)
+        return total_len
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3eeee0ebfdd539ea4351e57711034fc25084046
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.py
@@ -0,0 +1,416 @@
+import functools
+import pickle
+from collections.abc import Iterable, Iterator
+from typing import Callable, Optional, TypeVar
+
+from torch.utils._import_utils import import_dill
+from torch.utils.data.datapipes._hook_iterator import _SnapshotState
+from torch.utils.data.datapipes._typing import _DataPipeMeta, _IterDataPipeMeta
+from torch.utils.data.datapipes.utils.common import (
+    _deprecation_warning,
+    _iter_deprecated_functional_names,
+    _map_deprecated_functional_names,
+)
+from torch.utils.data.dataset import Dataset, IterableDataset
+
+
+dill = import_dill()
+HAS_DILL = dill is not None
+
+__all__ = [
+    "DataChunk",
+    "DFIterDataPipe",
+    "IterDataPipe",
+    "MapDataPipe",
+]
+
+
+_T = TypeVar("_T")
+_T_co = TypeVar("_T_co", covariant=True)
+
+UNTRACABLE_DATAFRAME_PIPES = [
+    "batch",  # As it returns DataChunks
+    "groupby",  # As it returns DataChunks
+    "_dataframes_as_tuples",  # As it unpacks DF
+    "trace_as_dataframe",  # As it used to mark DF for tracing
+]
+
+
+class DataChunk(list[_T]):
+    def __init__(self, items: Iterable[_T]) -> None:
+        items = list(items)
+        super().__init__(items)
+        self.items = items
+
+    def as_str(self, indent: str = "") -> str:
+        return indent + "[" + ", ".join(str(i) for i in iter(self)) + "]"
+
+    def __iter__(self) -> Iterator[_T]:
+        yield from super().__iter__()
+
+    def raw_iterator(self) -> Iterator[_T]:
+        yield from self.items
+
+
+class IterDataPipe(IterableDataset[_T_co], metaclass=_IterDataPipeMeta):
+    r"""
+    Iterable-style DataPipe.
+
+    All DataPipes that represent an iterable of data samples should subclass this.
+    This style of DataPipes is particularly useful when data come from a stream, or
+    when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its
+    elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``.
+
+    All subclasses should overwrite :meth:`__iter__`, which would return an
+    iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its
+    method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should
+    override ``reset()`` if necessary. The common usages include resetting buffers, pointers,
+    and various state variables within the custom ``IterDataPipe``.
+
+    Note:
+        Only `one` iterator can be valid for each ``IterDataPipe`` at a time,
+        and the creation a second iterator will invalidate the first one. This constraint is necessary because
+        some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators.
+        The code example below presents details on how this constraint looks in practice.
+        If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_.
+
+    These DataPipes can be invoked in two ways, using the class constructor or applying their
+    functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes).
+    You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple
+    operations in succession.
+
+    .. _GitHub IterDataPipe Single Iterator Issue:
+        https://github.com/pytorch/data/issues/45
+
+    Note:
+        When a subclass is used with :class:`~torch.utils.data.DataLoader`, each
+        item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader`
+        iterator. When :attr:`num_workers > 0`, each worker process will have a
+        different copy of the DataPipe object, so it is often desired to configure
+        each copy independently to avoid having duplicate data returned from the
+        workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker
+        process, returns information about the worker. It can be used in either the
+        dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's
+        :attr:`worker_init_fn` option to modify each copy's behavior.
+
+    Examples:
+        General Usage:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.iter import IterableWrapper, Mapper
+            >>> dp = IterableWrapper(range(10))
+            >>> map_dp_1 = Mapper(dp, lambda x: x + 1)  # Using class constructor
+            >>> map_dp_2 = dp.map(lambda x: x + 1)  # Using functional form (recommended)
+            >>> list(map_dp_1)
+            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+            >>> list(map_dp_2)
+            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+            >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0)
+            >>> list(filter_dp)
+            [2, 4, 6, 8, 10]
+        Single Iterator Constraint Example:
+            >>> from torchdata.datapipes.iter import IterableWrapper, Mapper
+            >>> source_dp = IterableWrapper(range(10))
+            >>> it1 = iter(source_dp)
+            >>> list(it1)
+            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+            >>> it1 = iter(source_dp)
+            >>> it2 = iter(source_dp)  # The creation of a new iterator invalidates `it1`
+            >>> next(it2)
+            0
+            >>> next(it1)  # Further usage of `it1` will raise a `RunTimeError`
+    """
+
+    functions: dict[str, Callable] = {}
+    reduce_ex_hook: Optional[Callable] = None
+    getstate_hook: Optional[Callable] = None
+    str_hook: Optional[Callable] = None
+    repr_hook: Optional[Callable] = None
+    _valid_iterator_id: Optional[int] = None
+    _number_of_samples_yielded: int = 0
+    _snapshot_state: _SnapshotState = _SnapshotState.NotStarted
+    _fast_forward_iterator: Optional[Iterator] = None
+
+    def __iter__(self) -> Iterator[_T_co]:
+        return self
+
+    def __getattr__(self, attribute_name):
+        if attribute_name in IterDataPipe.functions:
+            if attribute_name in _iter_deprecated_functional_names:
+                kwargs = _iter_deprecated_functional_names[attribute_name]
+                _deprecation_warning(**kwargs)
+            f = IterDataPipe.functions[attribute_name]
+            function = functools.partial(f, self)
+            functools.update_wrapper(wrapper=function, wrapped=f, assigned=("__doc__",))
+            return function
+        else:
+            raise AttributeError(
+                f"'{self.__class__.__name__}' object has no attribute '{attribute_name}"
+            )
+
+    @classmethod
+    def register_function(cls, function_name, function):
+        cls.functions[function_name] = function
+
+    @classmethod
+    def register_datapipe_as_function(
+        cls, function_name, cls_to_register, enable_df_api_tracing=False
+    ):
+        if function_name in cls.functions:
+            raise Exception(  # noqa: TRY002
+                f"Unable to add DataPipe function name {function_name} as it is already taken"
+            )
+
+        def class_function(cls, enable_df_api_tracing, source_dp, *args, **kwargs):
+            result_pipe = cls(source_dp, *args, **kwargs)
+            if isinstance(result_pipe, IterDataPipe):
+                if enable_df_api_tracing or isinstance(source_dp, DFIterDataPipe):
+                    if function_name not in UNTRACABLE_DATAFRAME_PIPES:
+                        result_pipe = result_pipe.trace_as_dataframe()
+
+            return result_pipe
+
+        function = functools.partial(
+            class_function, cls_to_register, enable_df_api_tracing
+        )
+        functools.update_wrapper(
+            wrapper=function, wrapped=cls_to_register, assigned=("__doc__",)
+        )
+        cls.functions[function_name] = function
+
+    def __getstate__(self):
+        """
+        Serialize `lambda` functions when `dill` is available.
+
+        If this doesn't cover your custom DataPipe's use case, consider writing custom methods for
+        `__getstate__` and `__setstate__`, or use `pickle.dumps` for serialization.
+        """
+        state = self.__dict__
+        if IterDataPipe.getstate_hook is not None:
+            return IterDataPipe.getstate_hook(state)
+        return state
+
+    def __reduce_ex__(self, *args, **kwargs):
+        if IterDataPipe.reduce_ex_hook is not None:
+            try:
+                return IterDataPipe.reduce_ex_hook(self)
+            except NotImplementedError:
+                pass
+        return super().__reduce_ex__(*args, **kwargs)
+
+    @classmethod
+    def set_getstate_hook(cls, hook_fn):
+        if IterDataPipe.getstate_hook is not None and hook_fn is not None:
+            raise RuntimeError("Attempt to override existing getstate_hook")
+        IterDataPipe.getstate_hook = hook_fn
+
+    @classmethod
+    def set_reduce_ex_hook(cls, hook_fn):
+        if IterDataPipe.reduce_ex_hook is not None and hook_fn is not None:
+            raise RuntimeError("Attempt to override existing reduce_ex_hook")
+        IterDataPipe.reduce_ex_hook = hook_fn
+
+    def __repr__(self):
+        if self.repr_hook is not None:
+            return self.repr_hook(self)
+        # Instead of showing , return the class name
+        return str(self.__class__.__qualname__)
+
+    def __str__(self):
+        if self.str_hook is not None:
+            return self.str_hook(self)
+        # Instead of showing , return the class name
+        return str(self.__class__.__qualname__)
+
+    def __dir__(self):
+        # for auto-completion in a REPL (e.g. Jupyter notebook)
+        return list(super().__dir__()) + list(self.functions.keys())
+
+    def reset(self) -> None:
+        r"""
+        Reset the `IterDataPipe` to the initial state.
+
+        By default, no-op. For subclasses of `IterDataPipe`, depending on their functionalities,
+        they may want to override this method with implementations that
+        may clear the buffers and reset pointers of the DataPipe.
+        The `reset` method is always called when `__iter__` is called as part of `hook_iterator`.
+        """
+
+
+class DFIterDataPipe(IterDataPipe):
+    def _is_dfpipe(self):
+        return True
+
+
+class MapDataPipe(Dataset[_T_co], metaclass=_DataPipeMeta):
+    r"""
+    Map-style DataPipe.
+
+    All datasets that represent a map from keys to data samples should subclass this.
+    Subclasses should overwrite :meth:`__getitem__`, supporting fetching a
+    data sample for a given, unique key. Subclasses can also optionally overwrite
+    :meth:`__len__`, which is expected to return the size of the dataset by many
+    :class:`~torch.utils.data.Sampler` implementations and the default options
+    of :class:`~torch.utils.data.DataLoader`.
+
+    These DataPipes can be invoked in two ways, using the class constructor or applying their
+    functional form onto an existing `MapDataPipe` (recommend, available to most but not all DataPipes).
+
+    Note:
+        :class:`~torch.utils.data.DataLoader` by default constructs an index
+        sampler that yields integral indices. To make it work with a map-style
+        DataPipe with non-integral indices/keys, a custom sampler must be provided.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.map import SequenceWrapper, Mapper
+        >>> dp = SequenceWrapper(range(10))
+        >>> map_dp_1 = dp.map(lambda x: x + 1)  # Using functional form (recommended)
+        >>> list(map_dp_1)
+        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        >>> map_dp_2 = Mapper(dp, lambda x: x + 1)  # Using class constructor
+        >>> list(map_dp_2)
+        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        >>> batch_dp = map_dp_1.batch(batch_size=2)
+        >>> list(batch_dp)
+        [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
+    """
+
+    functions: dict[str, Callable] = {}
+    reduce_ex_hook: Optional[Callable] = None
+    getstate_hook: Optional[Callable] = None
+    str_hook: Optional[Callable] = None
+    repr_hook: Optional[Callable] = None
+
+    def __getattr__(self, attribute_name):
+        if attribute_name in MapDataPipe.functions:
+            if attribute_name in _map_deprecated_functional_names:
+                kwargs = _map_deprecated_functional_names[attribute_name]
+                _deprecation_warning(**kwargs)
+            f = MapDataPipe.functions[attribute_name]
+            function = functools.partial(f, self)
+            functools.update_wrapper(wrapper=function, wrapped=f, assigned=("__doc__",))
+            return function
+        else:
+            raise AttributeError(
+                f"'{self.__class__.__name__}' object has no attribute '{attribute_name}"
+            )
+
+    @classmethod
+    def register_function(cls, function_name, function):
+        cls.functions[function_name] = function
+
+    @classmethod
+    def register_datapipe_as_function(cls, function_name, cls_to_register):
+        if function_name in cls.functions:
+            raise Exception(  # noqa: TRY002
+                f"Unable to add DataPipe function name {function_name} as it is already taken"
+            )
+
+        def class_function(cls, source_dp, *args, **kwargs):
+            result_pipe = cls(source_dp, *args, **kwargs)
+            return result_pipe
+
+        function = functools.partial(class_function, cls_to_register)
+        functools.update_wrapper(
+            wrapper=function, wrapped=cls_to_register, assigned=("__doc__",)
+        )
+        cls.functions[function_name] = function
+
+    def __getstate__(self):
+        """
+        Serialize `lambda` functions when `dill` is available.
+
+        If this doesn't cover your custom DataPipe's use case, consider writing custom methods for
+        `__getstate__` and `__setstate__`, or use `pickle.dumps` for serialization.
+        """
+        state = self.__dict__
+        if MapDataPipe.getstate_hook is not None:
+            return MapDataPipe.getstate_hook(state)
+        return state
+
+    def __reduce_ex__(self, *args, **kwargs):
+        if MapDataPipe.reduce_ex_hook is not None:
+            try:
+                return MapDataPipe.reduce_ex_hook(self)
+            except NotImplementedError:
+                pass
+        return super().__reduce_ex__(*args, **kwargs)
+
+    @classmethod
+    def set_getstate_hook(cls, hook_fn):
+        if MapDataPipe.getstate_hook is not None and hook_fn is not None:
+            raise RuntimeError("Attempt to override existing getstate_hook")
+        MapDataPipe.getstate_hook = hook_fn
+
+    @classmethod
+    def set_reduce_ex_hook(cls, hook_fn):
+        if MapDataPipe.reduce_ex_hook is not None and hook_fn is not None:
+            raise RuntimeError("Attempt to override existing reduce_ex_hook")
+        MapDataPipe.reduce_ex_hook = hook_fn
+
+    def __repr__(self):
+        if self.repr_hook is not None:
+            return self.repr_hook(self)
+        # Instead of showing , return the class name
+        return str(self.__class__.__qualname__)
+
+    def __str__(self):
+        if self.str_hook is not None:
+            return self.str_hook(self)
+        # Instead of showing , return the class name
+        return str(self.__class__.__qualname__)
+
+    def __dir__(self):
+        # for auto-completion in a REPL (e.g. Jupyter notebook)
+        return list(super().__dir__()) + list(self.functions.keys())
+
+
+class _DataPipeSerializationWrapper:
+    def __init__(self, datapipe):
+        self._datapipe = datapipe
+
+    def __getstate__(self):
+        use_dill = False
+        try:
+            value = pickle.dumps(self._datapipe)
+        except Exception:
+            if HAS_DILL:
+                value = dill.dumps(self._datapipe)
+                use_dill = True
+            else:
+                raise
+        return (value, use_dill)
+
+    def __setstate__(self, state):
+        value, use_dill = state
+        if use_dill:
+            self._datapipe = dill.loads(value)
+        else:
+            self._datapipe = pickle.loads(value)
+
+    def __len__(self):
+        try:
+            return len(self._datapipe)
+        except Exception as e:
+            raise TypeError(
+                f"{type(self).__name__} instance doesn't have valid length"
+            ) from e
+
+
+class _IterDataPipeSerializationWrapper(_DataPipeSerializationWrapper, IterDataPipe):
+    def __init__(self, datapipe: IterDataPipe[_T_co]):
+        super().__init__(datapipe)
+        self._datapipe_iter: Optional[Iterator[_T_co]] = None
+
+    def __iter__(self) -> "_IterDataPipeSerializationWrapper":
+        self._datapipe_iter = iter(self._datapipe)
+        return self
+
+    def __next__(self) -> _T_co:  # type: ignore[type-var]
+        assert self._datapipe_iter is not None
+        return next(self._datapipe_iter)
+
+
+class _MapDataPipeSerializationWrapper(_DataPipeSerializationWrapper, MapDataPipe):
+    def __getitem__(self, idx):
+        return self._datapipe[idx]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d0e27fb8ec4c5df1b95564c6ba42b08d88b6c247
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/datapipe.pyi
@@ -0,0 +1,697 @@
+# mypy: allow-untyped-defs
+# This base template ("datapipe.pyi.in") is generated from mypy stubgen with minimal editing for code injection
+# The output file will be "datapipe.pyi". This is executed as part of torch/CMakeLists.txt
+# Note that, for mypy, .pyi file takes precedent over .py file, such that we must define the interface for other
+# classes/objects here, even though we are not injecting extra code into them at the moment.
+
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    Iterator,
+    List,
+    Literal,
+    Optional,
+    Type,
+    TypeVar,
+    Union,
+)
+
+from torch.utils.data import Dataset, default_collate, IterableDataset
+from torch.utils.data.datapipes._hook_iterator import _SnapshotState
+from torch.utils.data.datapipes._typing import _DataPipeMeta, _IterDataPipeMeta
+
+_T = TypeVar("_T")
+_T_co = TypeVar("_T_co", covariant=True)
+UNTRACABLE_DATAFRAME_PIPES: Any
+
+class DataChunk(List[_T]):
+    items: List[_T]
+    def __init__(self, items: Iterable[_T]) -> None: ...
+    def as_str(self, indent: str = "") -> str: ...
+    def __iter__(self) -> Iterator[_T]: ...
+    def raw_iterator(self) -> Iterator[_T]: ...
+
+class MapDataPipe(Dataset[_T_co], metaclass=_DataPipeMeta):
+    functions: Dict[str, Callable] = ...
+    reduce_ex_hook: Optional[Callable] = ...
+    getstate_hook: Optional[Callable] = ...
+    str_hook: Optional[Callable] = ...
+    repr_hook: Optional[Callable] = ...
+    def __getattr__(self, attribute_name: Any): ...
+    @classmethod
+    def register_function(cls, function_name: Any, function: Any) -> None: ...
+    @classmethod
+    def register_datapipe_as_function(
+        cls,
+        function_name: Any,
+        cls_to_register: Any,
+    ): ...
+    def __getstate__(self): ...
+    def __reduce_ex__(self, *args: Any, **kwargs: Any): ...
+    @classmethod
+    def set_getstate_hook(cls, hook_fn: Any) -> None: ...
+    @classmethod
+    def set_reduce_ex_hook(cls, hook_fn: Any) -> None: ...
+    # Functional form of 'BatcherMapDataPipe'
+    def batch(self, batch_size: int, drop_last: bool = False, wrapper_class: type[DataChunk] = DataChunk) -> MapDataPipe:
+        r"""
+        Create mini-batches of data (functional name: ``batch``).
+    
+        An outer dimension will be added as ``batch_size`` if ``drop_last`` is set to ``True``,
+        or ``length % batch_size`` for the last batch if ``drop_last`` is set to ``False``.
+    
+        Args:
+            datapipe: Iterable DataPipe being batched
+            batch_size: The size of each batch
+            drop_last: Option to drop the last batch if it's not full
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.map import SequenceWrapper
+            >>> dp = SequenceWrapper(range(10))
+            >>> batch_dp = dp.batch(batch_size=2)
+            >>> list(batch_dp)
+            [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]
+        """
+    
+    # Functional form of 'ConcaterMapDataPipe'
+    def concat(self, *datapipes: MapDataPipe) -> MapDataPipe:
+        r"""
+        Concatenate multiple Map DataPipes (functional name: ``concat``).
+    
+        The new index of is the cumulative sum of source DataPipes.
+        For example, if there are 2 source DataPipes both with length 5,
+        index 0 to 4 of the resulting `ConcatMapDataPipe` would refer to
+        elements of the first DataPipe, and 5 to 9 would refer to elements
+        of the second DataPipe.
+    
+        Args:
+            datapipes: Map DataPipes being concatenated
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.map import SequenceWrapper
+            >>> dp1 = SequenceWrapper(range(3))
+            >>> dp2 = SequenceWrapper(range(3))
+            >>> concat_dp = dp1.concat(dp2)
+            >>> list(concat_dp)
+            [0, 1, 2, 0, 1, 2]
+        """
+    
+    # Functional form of 'MapperMapDataPipe'
+    def map(self, fn: Callable= ...) -> MapDataPipe:
+        r"""
+        Apply the input function over each item from the source DataPipe (functional name: ``map``).
+    
+        The function can be any regular Python function or partial object. Lambda
+        function is not recommended as it is not supported by pickle.
+    
+        Args:
+            datapipe: Source MapDataPipe
+            fn: Function being applied to each item
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.map import SequenceWrapper, Mapper
+            >>> def add_one(x):
+            ...     return x + 1
+            >>> dp = SequenceWrapper(range(10))
+            >>> map_dp_1 = dp.map(add_one)
+            >>> list(map_dp_1)
+            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+            >>> map_dp_2 = Mapper(dp, lambda x: x + 1)
+            >>> list(map_dp_2)
+            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        """
+    
+    # Functional form of 'ShufflerIterDataPipe'
+    def shuffle(self, *, indices: Optional[list] = None) -> IterDataPipe:
+        r"""
+        Shuffle the input MapDataPipe via its indices (functional name: ``shuffle``).
+    
+        When it is used with :class:`~torch.utils.data.DataLoader`, the methods to
+        set up random seed are different based on :attr:`num_workers`.
+    
+        For single-process mode (:attr:`num_workers == 0`), the random seed is set before
+        the :class:`~torch.utils.data.DataLoader` in the main process. For multi-process
+        mode (:attr:`num_worker > 0`), ``worker_init_fn`` is used to set up a random seed
+        for each worker process.
+    
+        Args:
+            datapipe: MapDataPipe being shuffled
+            indices: a list of indices of the MapDataPipe. If not provided, we assume it uses 0-based indexing
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.map import SequenceWrapper
+            >>> dp = SequenceWrapper(range(10))
+            >>> shuffle_dp = dp.shuffle().set_seed(0)
+            >>> list(shuffle_dp)
+            [7, 8, 1, 5, 3, 4, 2, 0, 9, 6]
+            >>> list(shuffle_dp)
+            [6, 1, 9, 5, 2, 4, 7, 3, 8, 0]
+            >>> # Reset seed for Shuffler
+            >>> shuffle_dp = shuffle_dp.set_seed(0)
+            >>> list(shuffle_dp)
+            [7, 8, 1, 5, 3, 4, 2, 0, 9, 6]
+    
+        Note:
+            Even thought this ``shuffle`` operation takes a ``MapDataPipe`` as the input, it would return an
+            ``IterDataPipe`` rather than a ``MapDataPipe``, because ``MapDataPipe`` should be non-sensitive to
+            the order of data order for the sake of random reads, but ``IterDataPipe`` depends on the order
+            of data during data-processing.
+        """
+    
+    # Functional form of 'ZipperMapDataPipe'
+    def zip(self, *datapipes: MapDataPipe[_T_co]) -> MapDataPipe:
+        r"""
+        Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``).
+    
+        This MataPipe is out of bound as soon as the shortest input DataPipe is exhausted.
+    
+        Args:
+            *datapipes: Map DataPipes being aggregated
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.map import SequenceWrapper
+            >>> dp1 = SequenceWrapper(range(3))
+            >>> dp2 = SequenceWrapper(range(10, 13))
+            >>> zip_dp = dp1.zip(dp2)
+            >>> list(zip_dp)
+            [(0, 10), (1, 11), (2, 12)]
+        """
+    
+
+class IterDataPipe(IterableDataset[_T_co], metaclass=_IterDataPipeMeta):
+    functions: Dict[str, Callable] = ...
+    reduce_ex_hook: Optional[Callable] = ...
+    getstate_hook: Optional[Callable] = ...
+    str_hook: Optional[Callable] = ...
+    repr_hook: Optional[Callable] = ...
+    _number_of_samples_yielded: int = ...
+    _snapshot_state: _SnapshotState = _SnapshotState.Iterating  # noqa: PYI015
+    _fast_forward_iterator: Optional[Iterator] = ...
+    def __getattr__(self, attribute_name: Any): ...
+    @classmethod
+    def register_function(cls, function_name: Any, function: Any) -> None: ...
+    @classmethod
+    def register_datapipe_as_function(
+        cls,
+        function_name: Any,
+        cls_to_register: Any,
+        enable_df_api_tracing: bool = ...,
+    ): ...
+    def __getstate__(self): ...
+    def __reduce_ex__(self, *args: Any, **kwargs: Any): ...
+    @classmethod
+    def set_getstate_hook(cls, hook_fn: Any) -> None: ...
+    @classmethod
+    def set_reduce_ex_hook(cls, hook_fn: Any) -> None: ...
+    # Functional form of 'BatcherIterDataPipe'
+    def batch(self, batch_size: int, drop_last: bool = False, wrapper_class: type[DataChunk] = DataChunk) -> IterDataPipe:
+        r"""
+        Creates mini-batches of data (functional name: ``batch``).
+    
+        An outer dimension will be added as ``batch_size`` if ``drop_last`` is set to ``True``, or ``length % batch_size`` for the
+        last batch if ``drop_last`` is set to ``False``.
+    
+        Args:
+            datapipe: Iterable DataPipe being batched
+            batch_size: The size of each batch
+            drop_last: Option to drop the last batch if it's not full
+            wrapper_class: wrapper to apply onto each batch (type ``List``) before yielding,
+                defaults to ``DataChunk``
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.iter import IterableWrapper
+            >>> dp = IterableWrapper(range(10))
+            >>> dp = dp.batch(batch_size=3, drop_last=True)
+            >>> list(dp)
+            [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+        """
+    
+    # Functional form of 'CollatorIterDataPipe'
+    def collate(self, conversion: Union[Callable[..., Any], dict[Union[str, Any], Union[Callable, Any]], None] = default_collate, collate_fn: Optional[Callable] = None) -> IterDataPipe:
+        r"""
+        Collates samples from DataPipe to Tensor(s) by a custom collate function (functional name: ``collate``).
+    
+        By default, it uses :func:`torch.utils.data.default_collate`.
+    
+        .. note::
+            While writing a custom collate function, you can import :func:`torch.utils.data.default_collate` for the
+            default behavior and `functools.partial` to specify any additional arguments.
+    
+        Args:
+            datapipe: Iterable DataPipe being collated
+            collate_fn: Customized collate function to collect and combine data or a batch of data.
+                Default function collates to Tensor(s) based on data type.
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> # Convert integer data to float Tensor
+            >>> class MyIterDataPipe(torch.utils.data.IterDataPipe):
+            ...     def __init__(self, start, end):
+            ...         super(MyIterDataPipe).__init__()
+            ...         assert end > start, "this example code only works with end >= start"
+            ...         self.start = start
+            ...         self.end = end
+            ...
+            ...     def __iter__(self):
+            ...         return iter(range(self.start, self.end))
+            ...
+            ...     def __len__(self):
+            ...         return self.end - self.start
+            ...
+            >>> ds = MyIterDataPipe(start=3, end=7)
+            >>> print(list(ds))
+            [3, 4, 5, 6]
+            >>> def collate_fn(batch):
+            ...     return torch.tensor(batch, dtype=torch.float)
+            ...
+            >>> collated_ds = CollateIterDataPipe(ds, collate_fn=collate_fn)
+            >>> print(list(collated_ds))
+            [tensor(3.), tensor(4.), tensor(5.), tensor(6.)]
+        """
+    
+    # Functional form of 'ConcaterIterDataPipe'
+    def concat(self, *datapipes: IterDataPipe) -> IterDataPipe:
+        r"""
+        Concatenates multiple Iterable DataPipes (functional name: ``concat``).
+    
+        The resulting DataPipe will yield all the elements from the first input DataPipe, before yielding from the subsequent ones.
+    
+        Args:
+            datapipes: Iterable DataPipes being concatenated
+    
+        Example:
+            >>> # xdoctest: +REQUIRES(module:torchdata)
+            >>> import random
+            >>> from torchdata.datapipes.iter import IterableWrapper
+            >>> dp1 = IterableWrapper(range(3))
+            >>> dp2 = IterableWrapper(range(5))
+            >>> list(dp1.concat(dp2))
+            [0, 1, 2, 0, 1, 2, 3, 4]
+        """
+    
+    # Functional form of 'DemultiplexerIterDataPipe'
+    def demux(self, num_instances: int, classifier_fn: Callable[[_T_co], Optional[int]], drop_none: bool = False, buffer_size: int = 1000) -> List[IterDataPipe]:
+        r"""
+        Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``).
+    
+        A list of the child DataPipes is returned from this operation.
+    
+        Args:
+            datapipe: Iterable DataPipe being filtered
+            num_instances: number of instances of the DataPipe to create
+            classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None``
+            drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None``
+            buffer_size: this defines the maximum number of inputs that the buffer can hold across all child
+                DataPipes while waiting for their values to be yielded.
+                Defaults to ``1000``. Use ``-1`` for the unlimited buffer.
+    
+        Examples:
+            >>> # xdoctest: +REQUIRES(module:torchdata)
+            >>> from torchdata.datapipes.iter import IterableWrapper
+            >>> def odd_or_even(n):
+            ...     return n % 2
+            >>> source_dp = IterableWrapper(range(5))
+            >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even)
+            >>> list(dp1)
+            [0, 2, 4]
+            >>> list(dp2)
+            [1, 3]
+            >>> # It can also filter out any element that gets `None` from the `classifier_fn`
+            >>> def odd_or_even_no_zero(n):
+            ...     return n % 2 if n != 0 else None
+            >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True)
+            >>> list(dp1)
+            [2, 4]
+            >>> list(dp2)
+            [1, 3]
+        """
+    
+    # Functional form of 'FilterIterDataPipe'
+    def filter(self, filter_fn: Callable, input_col=None) -> IterDataPipe:
+        r"""
+        Filters out elements from the source datapipe according to input ``filter_fn`` (functional name: ``filter``).
+    
+        Args:
+            datapipe: Iterable DataPipe being filtered
+            filter_fn: Customized function mapping an element to a boolean.
+            input_col: Index or indices of data which ``filter_fn`` is applied, such as:
+    
+                - ``None`` as default to apply ``filter_fn`` to the data directly.
+                - Integer(s) is used for list/tuple.
+                - Key(s) is used for dict.
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.iter import IterableWrapper
+            >>> def is_even(n):
+            ...     return n % 2 == 0
+            >>> dp = IterableWrapper(range(5))
+            >>> filter_dp = dp.filter(filter_fn=is_even)
+            >>> list(filter_dp)
+            [0, 2, 4]
+        """
+    
+    # Functional form of 'ForkerIterDataPipe'
+    def fork(self, num_instances: int, buffer_size: int = 1000, copy: Optional[Literal["shallow", "deep"]] = None) -> List[IterDataPipe]:
+        r"""
+        Creates multiple instances of the same Iterable DataPipe (functional name: ``fork``).
+    
+        Args:
+            datapipe: Iterable DataPipe being copied
+            num_instances: number of instances of the datapipe to create
+            buffer_size: this restricts how far ahead the leading child DataPipe
+               can read relative to the slowest child DataPipe.
+               Defaults to ``1000``. Use ``-1`` for the unlimited buffer.
+            copy: copy strategy to use for items yielded by each branch. Supported
+                options are ``None`` for no copying, ``"shallow"`` for shallow object
+                copies, and ``"deep"`` for deep object copies. Defaults to ``None``.
+    
+        Note:
+            All branches of the forked pipeline return the identical object unless
+            the copy parameter is supplied. If the object is mutable or contains
+            mutable objects, changing them in one branch will affect all others.
+    
+        Example:
+            >>> # xdoctest: +REQUIRES(module:torchdata)
+            >>> from torchdata.datapipes.iter import IterableWrapper
+            >>> source_dp = IterableWrapper(range(5))
+            >>> dp1, dp2 = source_dp.fork(num_instances=2)
+            >>> list(dp1)
+            [0, 1, 2, 3, 4]
+            >>> list(dp2)
+            [0, 1, 2, 3, 4]
+        """
+    
+    # Functional form of 'GrouperIterDataPipe'
+    def groupby(self, group_key_fn: Callable[[_T_co], Any], *, keep_key: bool = False, buffer_size: int = 10000, group_size: Optional[int] = None, guaranteed_group_size: Optional[int] = None, drop_remaining: bool = False) -> IterDataPipe:
+        r"""
+        Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``.
+    
+        (functional name: ``groupby``).
+    
+        The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group
+        will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full,
+        the DataPipe will yield the largest batch with the same key, provided that its size is larger
+        than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``.
+    
+        After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity
+        will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``.
+    
+        Args:
+            datapipe: Iterable datapipe to be grouped
+            group_key_fn: Function used to generate group key from the data of the source datapipe
+            keep_key: Option to yield the matching key along with the items in a tuple,
+                resulting in `(key, [items])` otherwise returning [items]
+            buffer_size: The size of buffer for ungrouped data
+            group_size: The max size of each group, a batch is yielded as soon as it reaches this size
+            guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full
+            drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer
+                when the buffer is full
+    
+        Example:
+            >>> import os
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.iter import IterableWrapper
+            >>> def group_fn(file):
+            ...     return os.path.basename(file).split(".")[0]
+            >>> source_dp = IterableWrapper(["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"])
+            >>> dp0 = source_dp.groupby(group_key_fn=group_fn)
+            >>> list(dp0)
+            [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']]
+            >>> # A group is yielded as soon as its size equals to `group_size`
+            >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2)
+            >>> list(dp1)
+            [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']]
+            >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size`
+            >>> dp2 = source_dp.groupby(group_key_fn=group_fn, buffer_size=3, group_size=3, guaranteed_group_size=2)
+            >>> list(dp2)
+            [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']]
+        """
+    
+    # Functional form of 'FileListerIterDataPipe'
+    def list_files(self, masks: Union[str, list[str]] = "", *, recursive: bool = False, abspath: bool = False, non_deterministic: bool = False, length: int = -1) -> IterDataPipe:
+        r"""
+        Given path(s) to the root directory, yields file pathname(s) (path + filename) of files within the root directory.
+    
+        Multiple root directories can be provided (functional name: ``list_files``).
+    
+        Args:
+            root: Root directory or a sequence of root directories
+            masks: Unix style filter string or string list for filtering file name(s)
+            recursive: Whether to return pathname from nested directories or not
+            abspath: Whether to return relative pathname or absolute pathname
+            non_deterministic: Whether to return pathname in sorted order or not.
+                If ``False``, the results yielded from each root directory will be sorted
+            length: Nominal length of the datapipe
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.iter import FileLister
+            >>> dp = FileLister(root=".", recursive=True)
+            >>> list(dp)
+            ['example.py', './data/data.tar']
+        """
+    
+    # Functional form of 'MapperIterDataPipe'
+    def map(self, fn: Callable, input_col=None, output_col=None) -> IterDataPipe:
+        r"""
+        Applies a function over each item from the source DataPipe (functional name: ``map``).
+    
+        The function can be any regular Python function or partial object. Lambda
+        function is not recommended as it is not supported by pickle.
+    
+        Args:
+            datapipe: Source Iterable DataPipe
+            fn: Function being applied over each item
+            input_col: Index or indices of data which ``fn`` is applied, such as:
+    
+                - ``None`` as default to apply ``fn`` to the data directly.
+                - Integer(s) is used for list/tuple.
+                - Key(s) is used for dict.
+    
+            output_col: Index of data where result of ``fn`` is placed. ``output_col`` can be specified
+                only when ``input_col`` is not ``None``
+    
+                - ``None`` as default to replace the index that ``input_col`` specified; For ``input_col`` with
+                  multiple indices, the left-most one is used, and other indices will be removed.
+                - Integer is used for list/tuple. ``-1`` represents to append result at the end.
+                - Key is used for dict. New key is acceptable.
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.iter import IterableWrapper, Mapper
+            >>> def add_one(x):
+            ...     return x + 1
+            >>> dp = IterableWrapper(range(10))
+            >>> map_dp_1 = dp.map(add_one)  # Invocation via functional form is preferred
+            >>> list(map_dp_1)
+            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+            >>> # We discourage the usage of `lambda` functions as they are not serializable with `pickle`
+            >>> # Use `functools.partial` or explicitly define the function instead
+            >>> map_dp_2 = Mapper(dp, lambda x: x + 1)
+            >>> list(map_dp_2)
+            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        """
+    
+    # Functional form of 'MultiplexerIterDataPipe'
+    def mux(self, *datapipes) -> IterDataPipe:
+        r"""
+        Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``).
+    
+        As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration,
+        and so on. It ends when the shortest input DataPipe is exhausted.
+    
+        Args:
+            datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted
+    
+        Example:
+            >>> # xdoctest: +REQUIRES(module:torchdata)
+            >>> from torchdata.datapipes.iter import IterableWrapper
+            >>> dp1, dp2, dp3 = IterableWrapper(range(3)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25))
+            >>> list(dp1.mux(dp2, dp3))
+            [0, 10, 20, 1, 11, 21, 2, 12, 22]
+        """
+    
+    # Functional form of 'FileOpenerIterDataPipe'
+    def open_files(self, mode: str = "r", encoding: Optional[str] = None, length: int = -1) -> IterDataPipe:
+        r"""
+        Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``).
+    
+        Args:
+            datapipe: Iterable datapipe that provides pathnames
+            mode: An optional string that specifies the mode in which
+                the file is opened by ``open()``. It defaults to ``r``, other options are
+                ``b`` for reading in binary mode and ``t`` for text mode.
+            encoding: An optional string that specifies the encoding of the
+                underlying file. It defaults to ``None`` to match the default encoding of ``open``.
+            length: Nominal length of the datapipe
+    
+        Note:
+            The opened file handles will be closed by Python's GC periodically. Users can choose
+            to close them explicitly.
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.iter import FileLister, FileOpener, StreamReader
+            >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith('.txt'))
+            >>> dp = FileOpener(dp)
+            >>> dp = StreamReader(dp)
+            >>> list(dp)
+            [('./abc.txt', 'abc')]
+        """
+    
+    # Functional form of 'StreamReaderIterDataPipe'
+    def read_from_stream(self, chunk: Optional[int] = None) -> IterDataPipe:
+        r"""
+        Given IO streams and their label names, yield bytes with label name as tuple.
+    
+        (functional name: ``read_from_stream``).
+    
+        Args:
+            datapipe: Iterable DataPipe provides label/URL and byte stream
+            chunk: Number of bytes to be read from stream per iteration.
+                If ``None``, all bytes will be read until the EOF.
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.iter import IterableWrapper, StreamReader
+            >>> from io import StringIO
+            >>> dp = IterableWrapper([("alphabet", StringIO("abcde"))])
+            >>> list(StreamReader(dp, chunk=1))
+            [('alphabet', 'a'), ('alphabet', 'b'), ('alphabet', 'c'), ('alphabet', 'd'), ('alphabet', 'e')]
+        """
+    
+    # Functional form of 'RoutedDecoderIterDataPipe'
+    def routed_decode(self, *handlers: Callable, key_fn: Callable= ...) -> IterDataPipe:
+        r"""
+        Decodes binary streams from input DataPipe, yields pathname and decoded data in a tuple.
+    
+        (functional name: ``routed_decode``)
+    
+        Args:
+            datapipe: Iterable datapipe that provides pathname and binary stream in tuples
+            handlers: Optional user defined decoder handlers. If ``None``, basic and image decoder
+                handlers will be set as default. If multiple handles are provided, the priority
+                order follows the order of handlers (the first handler has the top priority)
+            key_fn: Function for decoder to extract key from pathname to dispatch handlers.
+                Default is set to extract file extension from pathname
+    
+        Note:
+            When ``key_fn`` is specified returning anything other than extension, the default
+            handler will not work and users need to specify custom handler. Custom handler
+            could use regex to determine the eligibility to handle data.
+        """
+    
+    # Functional form of 'ShardingFilterIterDataPipe'
+    def sharding_filter(self, sharding_group_filter=None) -> IterDataPipe:
+        r"""
+        Wrapper that allows DataPipe to be sharded (functional name: ``sharding_filter``).
+    
+        After ``apply_sharding`` is called, each instance of the DataPipe (on different workers) will have every `n`-th element of the
+        original DataPipe, where `n` equals to the number of instances.
+    
+        Args:
+            source_datapipe: Iterable DataPipe that will be sharded
+        """
+    
+    # Functional form of 'ShufflerIterDataPipe'
+    def shuffle(self, *, buffer_size: int = 10000, unbatch_level: int = 0) -> IterDataPipe:
+        r"""
+        Shuffle the input DataPipe with a buffer (functional name: ``shuffle``).
+    
+        The buffer with ``buffer_size`` is filled with elements from the datapipe first. Then,
+        each item will be yielded from the buffer by reservoir sampling via iterator.
+    
+        ``buffer_size`` is required to be larger than ``0``. For ``buffer_size == 1``, the
+        datapipe is not shuffled. In order to fully shuffle all elements from datapipe,
+        ``buffer_size`` is required to be greater than or equal to the size of datapipe.
+    
+        When it is used with :class:`torch.utils.data.DataLoader`, the methods to
+        set up random seed are different based on :attr:`num_workers`.
+    
+        For single-process mode (:attr:`num_workers == 0`), the random seed is set before
+        the :class:`~torch.utils.data.DataLoader` in the main process. For multi-process
+        mode (:attr:`num_worker > 0`), `worker_init_fn` is used to set up a random seed
+        for each worker process.
+    
+        Args:
+            datapipe: The IterDataPipe being shuffled
+            buffer_size: The buffer size for shuffling (default to ``10000``)
+            unbatch_level: Specifies if it is necessary to unbatch source data before
+                applying the shuffle
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.iter import IterableWrapper
+            >>> dp = IterableWrapper(range(10))
+            >>> shuffle_dp = dp.shuffle()
+            >>> list(shuffle_dp)
+            [0, 4, 1, 6, 3, 2, 9, 5, 7, 8]
+        """
+    
+    # Functional form of 'UnBatcherIterDataPipe'
+    def unbatch(self, unbatch_level: int = 1) -> IterDataPipe:
+        r"""
+        Undos batching of data (functional name: ``unbatch``).
+    
+        In other words, it flattens the data up to the specified level within a batched DataPipe.
+    
+        Args:
+            datapipe: Iterable DataPipe being un-batched
+            unbatch_level: Defaults to ``1`` (only flattening the top level). If set to ``2``,
+                it will flatten the top two levels, and ``-1`` will flatten the entire DataPipe.
+    
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> from torchdata.datapipes.iter import IterableWrapper
+            >>> source_dp = IterableWrapper([[[0, 1], [2]], [[3, 4], [5]], [[6]]])
+            >>> dp1 = source_dp.unbatch()
+            >>> list(dp1)
+            [[0, 1], [2], [3, 4], [5], [6]]
+            >>> dp2 = source_dp.unbatch(unbatch_level=2)
+            >>> list(dp2)
+            [0, 1, 2, 3, 4, 5, 6]
+        """
+    
+    # Functional form of 'ZipperIterDataPipe'
+    def zip(self, *datapipes: IterDataPipe) -> IterDataPipe:
+        r"""
+        Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``).
+    
+        The output is stopped as soon as the shortest input DataPipe is exhausted.
+    
+        Args:
+            *datapipes: Iterable DataPipes being aggregated
+    
+        Example:
+            >>> # xdoctest: +REQUIRES(module:torchdata)
+            >>> from torchdata.datapipes.iter import IterableWrapper
+            >>> dp1, dp2, dp3 = IterableWrapper(range(5)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25))
+            >>> list(dp1.zip(dp2, dp3))
+            [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)]
+        """
+    
+
+class DFIterDataPipe(IterDataPipe):
+    def _is_dfpipe(self): ...
+    def __iter__(self): ...
+
+class _DataPipeSerializationWrapper:
+    def __init__(self, datapipe): ...
+    def __getstate__(self): ...
+    def __setstate__(self, state): ...
+    def __len__(self): ...
+
+class _IterDataPipeSerializationWrapper(_DataPipeSerializationWrapper, IterDataPipe):
+    def __iter__(self): ...
+
+class _MapDataPipeSerializationWrapper(_DataPipeSerializationWrapper, MapDataPipe):
+    def __getitem__(self, idx): ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/gen_pyi.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/gen_pyi.py
new file mode 100644
index 0000000000000000000000000000000000000000..59d81d28b2af7f1d8109ed5d3c20e278c7f566c9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/gen_pyi.py
@@ -0,0 +1,305 @@
+# mypy: allow-untyped-defs
+import os
+import pathlib
+from collections import defaultdict
+from typing import Any, Union
+
+
+def materialize_lines(lines: list[str], indentation: int) -> str:
+    output = ""
+    new_line_with_indent = "\n" + " " * indentation
+    for i, line in enumerate(lines):
+        if i != 0:
+            output += new_line_with_indent
+        output += line.replace("\n", new_line_with_indent)
+    return output
+
+
+def gen_from_template(
+    dir: str,
+    template_name: str,
+    output_name: str,
+    replacements: list[tuple[str, Any, int]],
+):
+    template_path = os.path.join(dir, template_name)
+    output_path = os.path.join(dir, output_name)
+
+    with open(template_path) as f:
+        content = f.read()
+    for placeholder, lines, indentation in replacements:
+        with open(output_path, "w") as f:
+            content = content.replace(
+                placeholder, materialize_lines(lines, indentation)
+            )
+            f.write(content)
+
+
+def find_file_paths(dir_paths: list[str], files_to_exclude: set[str]) -> set[str]:
+    """
+    When given a path to a directory, returns the paths to the relevant files within it.
+
+    This function does NOT recursive traverse to subdirectories.
+    """
+    paths: set[str] = set()
+    for dir_path in dir_paths:
+        all_files = os.listdir(dir_path)
+        python_files = {fname for fname in all_files if ".py" == fname[-3:]}
+        filter_files = {
+            fname for fname in python_files if fname not in files_to_exclude
+        }
+        paths.update({os.path.join(dir_path, fname) for fname in filter_files})
+    return paths
+
+
+def extract_method_name(line: str) -> str:
+    """Extract method name from decorator in the form of "@functional_datapipe({method_name})"."""
+    if '("' in line:
+        start_token, end_token = '("', '")'
+    elif "('" in line:
+        start_token, end_token = "('", "')"
+    else:
+        raise RuntimeError(
+            f"Unable to find appropriate method name within line:\n{line}"
+        )
+    start, end = line.find(start_token) + len(start_token), line.find(end_token)
+    return line[start:end]
+
+
+def extract_class_name(line: str) -> str:
+    """Extract class name from class definition in the form of "class {CLASS_NAME}({Type}):"."""
+    start_token = "class "
+    end_token = "("
+    start, end = line.find(start_token) + len(start_token), line.find(end_token)
+    return line[start:end]
+
+
+def parse_datapipe_file(
+    file_path: str,
+) -> tuple[dict[str, str], dict[str, str], set[str], dict[str, list[str]]]:
+    """Given a path to file, parses the file and returns a dictionary of method names to function signatures."""
+    method_to_signature, method_to_class_name, special_output_type = {}, {}, set()
+    doc_string_dict = defaultdict(list)
+    with open(file_path) as f:
+        open_paren_count = 0
+        method_name, class_name, signature = "", "", ""
+        skip = False
+        for line in f:
+            if line.count('"""') % 2 == 1:
+                skip = not skip
+            if skip or '"""' in line:  # Saving docstrings
+                doc_string_dict[method_name].append(line)
+                continue
+            if "@functional_datapipe" in line:
+                method_name = extract_method_name(line)
+                doc_string_dict[method_name] = []
+                continue
+            if method_name and "class " in line:
+                class_name = extract_class_name(line)
+                continue
+            if method_name and ("def __init__(" in line or "def __new__(" in line):
+                if "def __new__(" in line:
+                    special_output_type.add(method_name)
+                open_paren_count += 1
+                start = line.find("(") + len("(")
+                line = line[start:]
+            if open_paren_count > 0:
+                open_paren_count += line.count("(")
+                open_paren_count -= line.count(")")
+                if open_paren_count == 0:
+                    end = line.rfind(")")
+                    signature += line[:end]
+                    method_to_signature[method_name] = process_signature(signature)
+                    method_to_class_name[method_name] = class_name
+                    method_name, class_name, signature = "", "", ""
+                elif open_paren_count < 0:
+                    raise RuntimeError(
+                        "open parenthesis count < 0. This shouldn't be possible."
+                    )
+                else:
+                    signature += line.strip("\n").strip(" ")
+    return (
+        method_to_signature,
+        method_to_class_name,
+        special_output_type,
+        doc_string_dict,
+    )
+
+
+def parse_datapipe_files(
+    file_paths: set[str],
+) -> tuple[dict[str, str], dict[str, str], set[str], dict[str, list[str]]]:
+    (
+        methods_and_signatures,
+        methods_and_class_names,
+        methods_with_special_output_types,
+    ) = ({}, {}, set())
+    methods_and_doc_strings = {}
+    for path in file_paths:
+        (
+            method_to_signature,
+            method_to_class_name,
+            methods_needing_special_output_types,
+            doc_string_dict,
+        ) = parse_datapipe_file(path)
+        methods_and_signatures.update(method_to_signature)
+        methods_and_class_names.update(method_to_class_name)
+        methods_with_special_output_types.update(methods_needing_special_output_types)
+        methods_and_doc_strings.update(doc_string_dict)
+    return (
+        methods_and_signatures,
+        methods_and_class_names,
+        methods_with_special_output_types,
+        methods_and_doc_strings,
+    )
+
+
+def split_outside_bracket(line: str, delimiter: str = ",") -> list[str]:
+    """Given a line of text, split it on comma unless the comma is within a bracket '[]'."""
+    bracket_count = 0
+    curr_token = ""
+    res = []
+    for char in line:
+        if char == "[":
+            bracket_count += 1
+        elif char == "]":
+            bracket_count -= 1
+        elif char == delimiter and bracket_count == 0:
+            res.append(curr_token)
+            curr_token = ""
+            continue
+        curr_token += char
+    res.append(curr_token)
+    return res
+
+
+def process_signature(line: str) -> str:
+    """
+    Clean up a given raw function signature.
+
+    This includes removing the self-referential datapipe argument, default
+    arguments of input functions, newlines, and spaces.
+    """
+    tokens: list[str] = split_outside_bracket(line)
+    for i, token in enumerate(tokens):
+        tokens[i] = token.strip(" ")
+        if token == "cls":
+            tokens[i] = "self"
+        elif i > 0 and ("self" == tokens[i - 1]) and (tokens[i][0] != "*"):
+            # Remove the datapipe after 'self' or 'cls' unless it has '*'
+            tokens[i] = ""
+        elif "Callable =" in token:  # Remove default argument if it is a function
+            head, _default_arg = token.rsplit("=", 2)
+            tokens[i] = head.strip(" ") + "= ..."
+    tokens = [t for t in tokens if t != ""]
+    line = ", ".join(tokens)
+    return line
+
+
+def get_method_definitions(
+    file_path: Union[str, list[str]],
+    files_to_exclude: set[str],
+    deprecated_files: set[str],
+    default_output_type: str,
+    method_to_special_output_type: dict[str, str],
+    root: str = "",
+) -> list[str]:
+    """
+    #.pyi generation for functional DataPipes Process.
+
+    # 1. Find files that we want to process (exclude the ones who don't)
+    # 2. Parse method name and signature
+    # 3. Remove first argument after self (unless it is "*datapipes"), default args, and spaces
+    """
+    if root == "":
+        root = str(pathlib.Path(__file__).parent.resolve())
+    file_path = [file_path] if isinstance(file_path, str) else file_path
+    file_path = [os.path.join(root, path) for path in file_path]
+    file_paths = find_file_paths(
+        file_path, files_to_exclude=files_to_exclude.union(deprecated_files)
+    )
+    (
+        methods_and_signatures,
+        methods_and_class_names,
+        methods_w_special_output_types,
+        methods_and_doc_strings,
+    ) = parse_datapipe_files(file_paths)
+
+    for fn_name in method_to_special_output_type:
+        if fn_name not in methods_w_special_output_types:
+            methods_w_special_output_types.add(fn_name)
+
+    method_definitions = []
+    for method_name, arguments in methods_and_signatures.items():
+        class_name = methods_and_class_names[method_name]
+        if method_name in methods_w_special_output_types:
+            output_type = method_to_special_output_type[method_name]
+        else:
+            output_type = default_output_type
+        doc_string = "".join(methods_and_doc_strings[method_name])
+        if doc_string == "":
+            doc_string = "    ...\n"
+        method_definitions.append(
+            f"# Functional form of '{class_name}'\n"
+            f"def {method_name}({arguments}) -> {output_type}:\n"
+            f"{doc_string}"
+        )
+    method_definitions.sort(
+        key=lambda s: s.split("\n")[1]
+    )  # sorting based on method_name
+
+    return method_definitions
+
+
+# Defined outside of main() so they can be imported by TorchData
+iterDP_file_path: str = "iter"
+iterDP_files_to_exclude: set[str] = {"__init__.py", "utils.py"}
+iterDP_deprecated_files: set[str] = set()
+iterDP_method_to_special_output_type: dict[str, str] = {
+    "demux": "List[IterDataPipe]",
+    "fork": "List[IterDataPipe]",
+}
+
+mapDP_file_path: str = "map"
+mapDP_files_to_exclude: set[str] = {"__init__.py", "utils.py"}
+mapDP_deprecated_files: set[str] = set()
+mapDP_method_to_special_output_type: dict[str, str] = {"shuffle": "IterDataPipe"}
+
+
+def main() -> None:
+    """
+    # Inject file into template datapipe.pyi.in.
+
+    TODO: The current implementation of this script only generates interfaces for built-in methods. To generate
+          interface for user-defined DataPipes, consider changing `IterDataPipe.register_datapipe_as_function`.
+    """
+    iter_method_definitions = get_method_definitions(
+        iterDP_file_path,
+        iterDP_files_to_exclude,
+        iterDP_deprecated_files,
+        "IterDataPipe",
+        iterDP_method_to_special_output_type,
+    )
+
+    map_method_definitions = get_method_definitions(
+        mapDP_file_path,
+        mapDP_files_to_exclude,
+        mapDP_deprecated_files,
+        "MapDataPipe",
+        mapDP_method_to_special_output_type,
+    )
+
+    path = pathlib.Path(__file__).parent.resolve()
+    replacements = [
+        ("${IterDataPipeMethods}", iter_method_definitions, 4),
+        ("${MapDataPipeMethods}", map_method_definitions, 4),
+    ]
+    gen_from_template(
+        dir=str(path),
+        template_name="datapipe.pyi.in",
+        output_name="datapipe.pyi",
+        replacements=replacements,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..37d1664753b151f34d6d0461bb597803f3ffd40e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/__init__.py
@@ -0,0 +1,65 @@
+from torch.utils.data.datapipes.iter.callable import (
+    CollatorIterDataPipe as Collator,
+    MapperIterDataPipe as Mapper,
+)
+from torch.utils.data.datapipes.iter.combinatorics import (
+    SamplerIterDataPipe as Sampler,
+    ShufflerIterDataPipe as Shuffler,
+)
+from torch.utils.data.datapipes.iter.combining import (
+    ConcaterIterDataPipe as Concater,
+    DemultiplexerIterDataPipe as Demultiplexer,
+    ForkerIterDataPipe as Forker,
+    MultiplexerIterDataPipe as Multiplexer,
+    ZipperIterDataPipe as Zipper,
+)
+from torch.utils.data.datapipes.iter.filelister import (
+    FileListerIterDataPipe as FileLister,
+)
+from torch.utils.data.datapipes.iter.fileopener import (
+    FileOpenerIterDataPipe as FileOpener,
+)
+from torch.utils.data.datapipes.iter.grouping import (
+    BatcherIterDataPipe as Batcher,
+    GrouperIterDataPipe as Grouper,
+    UnBatcherIterDataPipe as UnBatcher,
+)
+from torch.utils.data.datapipes.iter.routeddecoder import (
+    RoutedDecoderIterDataPipe as RoutedDecoder,
+)
+from torch.utils.data.datapipes.iter.selecting import FilterIterDataPipe as Filter
+from torch.utils.data.datapipes.iter.sharding import (
+    ShardingFilterIterDataPipe as ShardingFilter,
+)
+from torch.utils.data.datapipes.iter.streamreader import (
+    StreamReaderIterDataPipe as StreamReader,
+)
+from torch.utils.data.datapipes.iter.utils import (
+    IterableWrapperIterDataPipe as IterableWrapper,
+)
+
+
+__all__ = [
+    "Batcher",
+    "Collator",
+    "Concater",
+    "Demultiplexer",
+    "FileLister",
+    "FileOpener",
+    "Filter",
+    "Forker",
+    "Grouper",
+    "IterableWrapper",
+    "Mapper",
+    "Multiplexer",
+    "RoutedDecoder",
+    "Sampler",
+    "ShardingFilter",
+    "Shuffler",
+    "StreamReader",
+    "UnBatcher",
+    "Zipper",
+]
+
+# Please keep this list sorted
+assert __all__ == sorted(__all__)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0e4191fd20a2d0e67903f92517daeb5c56f568b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py
@@ -0,0 +1,242 @@
+# mypy: allow-untyped-defs
+import functools
+from collections import namedtuple
+from collections.abc import Iterator, Sized
+from typing import Any, Callable, Optional, TypeVar, Union
+
+from torch.utils.data._utils.collate import default_collate
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+from torch.utils.data.datapipes.utils.common import (
+    _check_unpickable_fn,
+    validate_input_col,
+)
+
+
+__all__ = [
+    "CollatorIterDataPipe",
+    "MapperIterDataPipe",
+]
+
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+@functional_datapipe("map")
+class MapperIterDataPipe(IterDataPipe[_T_co]):
+    r"""
+    Applies a function over each item from the source DataPipe (functional name: ``map``).
+
+    The function can be any regular Python function or partial object. Lambda
+    function is not recommended as it is not supported by pickle.
+
+    Args:
+        datapipe: Source Iterable DataPipe
+        fn: Function being applied over each item
+        input_col: Index or indices of data which ``fn`` is applied, such as:
+
+            - ``None`` as default to apply ``fn`` to the data directly.
+            - Integer(s) is used for list/tuple.
+            - Key(s) is used for dict.
+
+        output_col: Index of data where result of ``fn`` is placed. ``output_col`` can be specified
+            only when ``input_col`` is not ``None``
+
+            - ``None`` as default to replace the index that ``input_col`` specified; For ``input_col`` with
+              multiple indices, the left-most one is used, and other indices will be removed.
+            - Integer is used for list/tuple. ``-1`` represents to append result at the end.
+            - Key is used for dict. New key is acceptable.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.iter import IterableWrapper, Mapper
+        >>> def add_one(x):
+        ...     return x + 1
+        >>> dp = IterableWrapper(range(10))
+        >>> map_dp_1 = dp.map(add_one)  # Invocation via functional form is preferred
+        >>> list(map_dp_1)
+        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        >>> # We discourage the usage of `lambda` functions as they are not serializable with `pickle`
+        >>> # Use `functools.partial` or explicitly define the function instead
+        >>> map_dp_2 = Mapper(dp, lambda x: x + 1)
+        >>> list(map_dp_2)
+        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+    """
+
+    datapipe: IterDataPipe
+    fn: Callable
+
+    def __init__(
+        self,
+        datapipe: IterDataPipe,
+        fn: Callable,
+        input_col=None,
+        output_col=None,
+    ) -> None:
+        super().__init__()
+        self.datapipe = datapipe
+
+        _check_unpickable_fn(fn)
+        self.fn = fn  # type: ignore[assignment]
+
+        self.input_col = input_col
+        if input_col is None and output_col is not None:
+            raise ValueError("`output_col` must be None when `input_col` is None.")
+        if isinstance(output_col, (list, tuple)):
+            if len(output_col) > 1:
+                raise ValueError("`output_col` must be a single-element list or tuple")
+            output_col = output_col[0]
+        self.output_col = output_col
+        validate_input_col(fn, input_col)
+
+    def _apply_fn(self, data):
+        if self.input_col is None and self.output_col is None:
+            return self.fn(data)
+
+        if self.input_col is None:
+            res = self.fn(data)
+        elif isinstance(self.input_col, (list, tuple)):
+            args = tuple(data[col] for col in self.input_col)
+            res = self.fn(*args)
+        else:
+            res = self.fn(data[self.input_col])
+
+        # Copy tuple to list and run in-place modification because tuple is immutable.
+        if isinstance(data, tuple):
+            t_flag = True
+            data = list(data)
+        else:
+            t_flag = False
+
+        if self.output_col is None:
+            if isinstance(self.input_col, (list, tuple)):
+                data[self.input_col[0]] = res
+                for idx in sorted(self.input_col[1:], reverse=True):
+                    del data[idx]
+            else:
+                data[self.input_col] = res
+        else:
+            if self.output_col == -1:
+                data.append(res)
+            else:
+                data[self.output_col] = res
+
+        # Convert list back to tuple
+        return tuple(data) if t_flag else data
+
+    def __iter__(self) -> Iterator[_T_co]:
+        for data in self.datapipe:
+            yield self._apply_fn(data)
+
+    def __len__(self) -> int:
+        if isinstance(self.datapipe, Sized):
+            return len(self.datapipe)
+        raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
+
+
+def _collate_helper(conversion, item):
+    # TODO(VitalyFedyunin): Verify that item is any sort of batch
+    if len(item.items) > 1:
+        # TODO(VitalyFedyunin): Compact all batch dataframes into one
+        raise RuntimeError("Only supports one DataFrame per batch")
+    df = item[0]
+    columns_name = df_wrapper.get_columns(df)
+    tuple_names: list = []
+    tuple_values: list = []
+
+    for name in conversion.keys():
+        if name not in columns_name:
+            raise RuntimeError("Conversion keys missmatch")
+
+    for name in columns_name:
+        if name in conversion:
+            if not callable(conversion[name]):
+                raise RuntimeError(
+                    "Collate (DF)DataPipe requires callable as dict values"
+                )
+            collation_fn = conversion[name]
+        else:
+            # TODO(VitalyFedyunin): Add default collation into df_wrapper
+            try:
+                import torcharrow.pytorch as tap  # type: ignore[import]
+
+                collation_fn = tap.rec.Default()
+            except Exception as e:
+                raise RuntimeError(
+                    "unable to import default collation function from the TorchArrow"
+                ) from e
+
+        tuple_names.append(str(name))
+        value = collation_fn(df[name])
+        tuple_values.append(value)
+
+    # TODO(VitalyFedyunin): We can dynamically extract types from the tuple_values here
+    # TODO(VitalyFedyunin): Instead of ignoring mypy error, make sure tuple_names is not empty
+    tpl_cls = namedtuple("CollateResult", tuple_names)  # type: ignore[misc]
+    tuple = tpl_cls(*tuple_values)
+    return tuple
+
+
+@functional_datapipe("collate")
+class CollatorIterDataPipe(MapperIterDataPipe):
+    r"""
+    Collates samples from DataPipe to Tensor(s) by a custom collate function (functional name: ``collate``).
+
+    By default, it uses :func:`torch.utils.data.default_collate`.
+
+    .. note::
+        While writing a custom collate function, you can import :func:`torch.utils.data.default_collate` for the
+        default behavior and `functools.partial` to specify any additional arguments.
+
+    Args:
+        datapipe: Iterable DataPipe being collated
+        collate_fn: Customized collate function to collect and combine data or a batch of data.
+            Default function collates to Tensor(s) based on data type.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # Convert integer data to float Tensor
+        >>> class MyIterDataPipe(torch.utils.data.IterDataPipe):
+        ...     def __init__(self, start, end):
+        ...         super(MyIterDataPipe).__init__()
+        ...         assert end > start, "this example code only works with end >= start"
+        ...         self.start = start
+        ...         self.end = end
+        ...
+        ...     def __iter__(self):
+        ...         return iter(range(self.start, self.end))
+        ...
+        ...     def __len__(self):
+        ...         return self.end - self.start
+        ...
+        >>> ds = MyIterDataPipe(start=3, end=7)
+        >>> print(list(ds))
+        [3, 4, 5, 6]
+        >>> def collate_fn(batch):
+        ...     return torch.tensor(batch, dtype=torch.float)
+        ...
+        >>> collated_ds = CollateIterDataPipe(ds, collate_fn=collate_fn)
+        >>> print(list(collated_ds))
+        [tensor(3.), tensor(4.), tensor(5.), tensor(6.)]
+    """
+
+    def __init__(
+        self,
+        datapipe: IterDataPipe,
+        conversion: Union[
+            Callable[..., Any], dict[Union[str, Any], Union[Callable, Any]], None
+        ] = default_collate,
+        collate_fn: Optional[Callable] = None,
+    ) -> None:
+        # TODO(VitalyFedyunin): Replace `Callable[..., Any]` with `Callable[[IColumn], Any]`
+        # TODO(VitalyFedyunin): Replace with `Dict[Union[str, IColumn], Union[Callable, Enum]]`
+        if collate_fn is not None:
+            super().__init__(datapipe, fn=collate_fn)
+        else:
+            if callable(conversion):
+                super().__init__(datapipe, fn=conversion)
+            else:
+                # TODO(VitalyFedyunin): Validate passed dictionary
+                collate_fn = functools.partial(_collate_helper, conversion)
+                super().__init__(datapipe, fn=collate_fn)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combinatorics.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combinatorics.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c602ce4eeda0cf54643556f798c9ec9cd03a14d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combinatorics.py
@@ -0,0 +1,190 @@
+# mypy: allow-untyped-defs
+import random
+from collections.abc import Iterator, Sized
+from typing import Optional, TypeVar
+
+import torch
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+from torch.utils.data.sampler import Sampler, SequentialSampler
+
+
+__all__ = [
+    "SamplerIterDataPipe",
+    "ShufflerIterDataPipe",
+]
+
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+class SamplerIterDataPipe(IterDataPipe[_T_co]):
+    r"""
+    Generate sample elements using the provided ``Sampler`` (defaults to :class:`SequentialSampler`).
+
+    Args:
+        datapipe: IterDataPipe to sample from
+        sampler: Sampler class to generate sample elements from input DataPipe.
+            Default is :class:`SequentialSampler` for IterDataPipe
+    """
+
+    datapipe: IterDataPipe
+    sampler: Sampler
+
+    def __init__(
+        self,
+        datapipe: IterDataPipe,
+        sampler: type[Sampler] = SequentialSampler,
+        sampler_args: Optional[tuple] = None,
+        sampler_kwargs: Optional[dict] = None,
+    ) -> None:
+        assert isinstance(
+            datapipe, Sized
+        ), "Sampler class requires input datapipe implemented `__len__`"
+        super().__init__()
+        self.datapipe = datapipe
+        self.sampler_args = () if sampler_args is None else sampler_args
+        self.sampler_kwargs = {} if sampler_kwargs is None else sampler_kwargs
+        # https://github.com/python/mypy/pull/9629 will solve
+        self.sampler = sampler(*self.sampler_args, data_source=self.datapipe, **self.sampler_kwargs)  # type: ignore[misc]
+
+    def __iter__(self) -> Iterator[_T_co]:
+        return iter(self.sampler)
+
+    def __len__(self) -> int:
+        # Dataset has been tested as `Sized`
+        if isinstance(self.sampler, Sized):
+            return len(self.sampler)
+        raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
+
+
+@functional_datapipe("shuffle")
+class ShufflerIterDataPipe(IterDataPipe[_T_co]):
+    r"""
+    Shuffle the input DataPipe with a buffer (functional name: ``shuffle``).
+
+    The buffer with ``buffer_size`` is filled with elements from the datapipe first. Then,
+    each item will be yielded from the buffer by reservoir sampling via iterator.
+
+    ``buffer_size`` is required to be larger than ``0``. For ``buffer_size == 1``, the
+    datapipe is not shuffled. In order to fully shuffle all elements from datapipe,
+    ``buffer_size`` is required to be greater than or equal to the size of datapipe.
+
+    When it is used with :class:`torch.utils.data.DataLoader`, the methods to
+    set up random seed are different based on :attr:`num_workers`.
+
+    For single-process mode (:attr:`num_workers == 0`), the random seed is set before
+    the :class:`~torch.utils.data.DataLoader` in the main process. For multi-process
+    mode (:attr:`num_worker > 0`), `worker_init_fn` is used to set up a random seed
+    for each worker process.
+
+    Args:
+        datapipe: The IterDataPipe being shuffled
+        buffer_size: The buffer size for shuffling (default to ``10000``)
+        unbatch_level: Specifies if it is necessary to unbatch source data before
+            applying the shuffle
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> dp = IterableWrapper(range(10))
+        >>> shuffle_dp = dp.shuffle()
+        >>> list(shuffle_dp)
+        [0, 4, 1, 6, 3, 2, 9, 5, 7, 8]
+    """
+
+    datapipe: IterDataPipe[_T_co]
+    buffer_size: int
+    _buffer: list[_T_co]
+    _enabled: bool
+    _seed: Optional[int]
+    _rng: random.Random
+
+    def __init__(
+        self,
+        datapipe: IterDataPipe[_T_co],
+        *,
+        buffer_size: int = 10000,
+        unbatch_level: int = 0,
+    ) -> None:
+        super().__init__()
+        # TODO: Performance optimization
+        #       buffer can be a fixed size and remove expensive `append()` and `len()` operations
+        self._buffer: list[_T_co] = []
+        assert buffer_size > 0, "buffer_size should be larger than 0"
+        if unbatch_level == 0:
+            self.datapipe = datapipe
+        else:
+            self.datapipe = datapipe.unbatch(unbatch_level=unbatch_level)
+        self.buffer_size = buffer_size
+        self._enabled = True
+        self._seed = None
+        self._rng = random.Random()
+
+    def set_shuffle(self, shuffle=True):
+        self._enabled = shuffle
+        return self
+
+    def set_seed(self, seed: int):
+        self._seed = seed
+        return self
+
+    def __iter__(self) -> Iterator[_T_co]:
+        if not self._enabled:
+            yield from self.datapipe
+        else:
+            for x in self.datapipe:
+                if len(self._buffer) == self.buffer_size:
+                    idx = self._rng.randint(0, len(self._buffer) - 1)
+                    val, self._buffer[idx] = self._buffer[idx], x
+                    yield val
+                else:
+                    self._buffer.append(x)
+            while self._buffer:
+                idx = self._rng.randint(0, len(self._buffer) - 1)
+                yield self._buffer.pop(idx)
+
+    def __len__(self) -> int:
+        if isinstance(self.datapipe, Sized):
+            return len(self.datapipe)
+        raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
+
+    def reset(self) -> None:
+        self._buffer = []
+        if self._enabled:
+            if self._seed is None:
+                self._seed = int(torch.empty((), dtype=torch.int64).random_().item())
+            self._rng.seed(self._seed)
+            self._seed = None
+
+    def __getstate__(self):
+        state = (
+            self.datapipe,
+            self.buffer_size,
+            self._enabled,
+            self._seed,
+            self._buffer,
+            self._rng.getstate(),
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        )
+        if IterDataPipe.getstate_hook is not None:
+            return IterDataPipe.getstate_hook(state)
+        return state
+
+    def __setstate__(self, state):
+        (
+            self.datapipe,
+            self.buffer_size,
+            self._enabled,
+            self._seed,
+            self._buffer,
+            rng_state,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        ) = state
+        self._rng = random.Random()
+        self._rng.setstate(rng_state)
+
+    def __del__(self):
+        self._buffer.clear()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py
new file mode 100644
index 0000000000000000000000000000000000000000..deaca079c68c0fc138505e839e7cff2c78c38dcc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/combining.py
@@ -0,0 +1,696 @@
+# mypy: allow-untyped-defs
+import copy as copymodule
+import warnings
+from abc import ABC, abstractmethod
+from collections import deque
+from collections.abc import Iterator, Sized
+from typing import Any, Callable, Literal, Optional, TypeVar
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes._hook_iterator import _SnapshotState
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+from torch.utils.data.datapipes.utils.common import _check_unpickable_fn, StreamWrapper
+
+
+__all__ = [
+    "ConcaterIterDataPipe",
+    "DemultiplexerIterDataPipe",
+    "ForkerIterDataPipe",
+    "MultiplexerIterDataPipe",
+    "ZipperIterDataPipe",
+]
+
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+@functional_datapipe("concat")
+class ConcaterIterDataPipe(IterDataPipe):
+    r"""
+    Concatenates multiple Iterable DataPipes (functional name: ``concat``).
+
+    The resulting DataPipe will yield all the elements from the first input DataPipe, before yielding from the subsequent ones.
+
+    Args:
+        datapipes: Iterable DataPipes being concatenated
+
+    Example:
+        >>> # xdoctest: +REQUIRES(module:torchdata)
+        >>> import random
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> dp1 = IterableWrapper(range(3))
+        >>> dp2 = IterableWrapper(range(5))
+        >>> list(dp1.concat(dp2))
+        [0, 1, 2, 0, 1, 2, 3, 4]
+    """
+
+    datapipes: tuple[IterDataPipe]
+
+    def __init__(self, *datapipes: IterDataPipe):
+        if len(datapipes) == 0:
+            raise ValueError("Expected at least one DataPipe, but got nothing")
+        if not all(isinstance(dp, IterDataPipe) for dp in datapipes):
+            raise TypeError("Expected all inputs to be `IterDataPipe`")
+        self.datapipes = datapipes  # type: ignore[assignment]
+
+    def __iter__(self) -> Iterator:
+        for dp in self.datapipes:
+            yield from dp
+
+    def __len__(self) -> int:
+        if all(isinstance(dp, Sized) for dp in self.datapipes):
+            return sum(len(dp) for dp in self.datapipes)
+        else:
+            raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
+
+
+@functional_datapipe("fork")
+class ForkerIterDataPipe(IterDataPipe):
+    r"""
+    Creates multiple instances of the same Iterable DataPipe (functional name: ``fork``).
+
+    Args:
+        datapipe: Iterable DataPipe being copied
+        num_instances: number of instances of the datapipe to create
+        buffer_size: this restricts how far ahead the leading child DataPipe
+           can read relative to the slowest child DataPipe.
+           Defaults to ``1000``. Use ``-1`` for the unlimited buffer.
+        copy: copy strategy to use for items yielded by each branch. Supported
+            options are ``None`` for no copying, ``"shallow"`` for shallow object
+            copies, and ``"deep"`` for deep object copies. Defaults to ``None``.
+
+    Note:
+        All branches of the forked pipeline return the identical object unless
+        the copy parameter is supplied. If the object is mutable or contains
+        mutable objects, changing them in one branch will affect all others.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(module:torchdata)
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> source_dp = IterableWrapper(range(5))
+        >>> dp1, dp2 = source_dp.fork(num_instances=2)
+        >>> list(dp1)
+        [0, 1, 2, 3, 4]
+        >>> list(dp2)
+        [0, 1, 2, 3, 4]
+    """
+
+    def __new__(
+        cls,
+        datapipe: IterDataPipe,
+        num_instances: int,
+        buffer_size: int = 1000,
+        copy: Optional[Literal["shallow", "deep"]] = None,
+    ):
+        if num_instances < 1:
+            raise ValueError(
+                f"Expected `num_instances` larger than 0, but {num_instances} is found"
+            )
+        if num_instances == 1:
+            return datapipe
+        container = _ForkerIterDataPipe(datapipe, num_instances, buffer_size, copy)  # type: ignore[abstract]
+        return [_ChildDataPipe(container, i) for i in range(num_instances)]
+
+
+class _ContainerTemplate(ABC):
+    r"""Abstract class for container ``DataPipes``. The followings are three required methods."""
+
+    @abstractmethod
+    def get_next_element_by_instance(self, instance_id: int):
+        ...
+
+    @abstractmethod
+    def is_every_instance_exhausted(self) -> bool:
+        ...
+
+    @abstractmethod
+    def reset(self) -> None:
+        ...
+
+    @abstractmethod
+    def get_length_by_instance(self, instance_id: int):
+        r"""Raise TypeError if it's not supposed to be implemented to support `list(datapipe)`."""
+
+
+def _no_op(x):
+    return x
+
+
+class _ForkerIterDataPipe(IterDataPipe, _ContainerTemplate):
+    r"""
+    Container to hold instance-specific information on behalf of ForkerIterDataPipe.
+
+    It tracks the state of its child DataPipes, maintains the buffer, and yields the next value
+    as requested by the child DataPipes.
+    """
+
+    def __init__(
+        self,
+        datapipe: IterDataPipe,
+        num_instances: int,
+        buffer_size: int = 1000,
+        copy: Optional[Literal["shallow", "deep"]] = None,
+    ):
+        self.main_datapipe = datapipe
+        self._datapipe_iterator: Optional[Iterator[Any]] = None
+        self.num_instances = num_instances
+        self.buffer: deque = deque()
+        self.buffer_size = buffer_size
+        if self.buffer_size < 0:
+            warnings.warn(
+                "Unlimited buffer size is set for `fork`, "
+                "please be aware of OOM at random places",
+                UserWarning,
+            )
+        if copy is None:
+            self.copy_fn = _no_op
+        elif copy == "shallow":
+            self.copy_fn = copymodule.copy
+        elif copy == "deep":
+            self.copy_fn = copymodule.deepcopy
+        else:
+            raise ValueError(
+                f"Unknown copy method `{copy}` requested, choose one of None, `shallow` or `deep`."
+            )
+
+        self.child_pointers: list[int] = [
+            0
+        ] * num_instances  # Indicate the indices of the next element to get
+        self.slowest_ptr = 0  # The index to read by the slowest child
+        self.leading_ptr = 0  # The index to read by the fastest child
+        self.end_ptr: Optional[int] = None  # The index to stop child
+        self._child_stop: list[bool] = [True for _ in range(num_instances)]
+
+    def __len__(self):
+        return len(self.main_datapipe)
+
+    def get_next_element_by_instance(self, instance_id: int):
+        if self._datapipe_iterator is None and self._child_stop[instance_id]:
+            self._datapipe_iterator = iter(self.main_datapipe)
+            self._snapshot_state = _SnapshotState.Iterating
+            for i in range(self.num_instances):
+                self._child_stop[i] = False
+        try:
+            while not self._child_stop[instance_id]:
+                self.child_pointers[instance_id] += 1
+                if (
+                    self.end_ptr is not None
+                    and self.child_pointers[instance_id] == self.end_ptr
+                ):
+                    self._child_stop[instance_id] = True
+                    break
+                # Use buffer
+                if self.buffer and self.child_pointers[instance_id] <= self.leading_ptr:
+                    idx = self.child_pointers[instance_id] - self.slowest_ptr - 1
+                    return_val = self.buffer[idx]
+                else:  # Retrieve one element from main datapipe
+                    self.leading_ptr = self.child_pointers[instance_id]
+                    try:
+                        return_val = next(self._datapipe_iterator)  # type: ignore[arg-type]
+                        self.buffer.append(return_val)
+                    except StopIteration:
+                        self._child_stop[instance_id] = True
+                        self._datapipe_iterator = None
+                        self.end_ptr = self.leading_ptr
+                        continue
+                if self.child_pointers[instance_id] == self.slowest_ptr + 1:
+                    new_min = min(
+                        self.child_pointers
+                    )  # Can optimize by avoiding the call to min()
+                    if self.slowest_ptr < new_min:
+                        self.slowest_ptr = new_min
+                        self.buffer.popleft()
+                if (
+                    self.buffer_size >= 0
+                    and self.leading_ptr > self.buffer_size + self.slowest_ptr
+                ):
+                    raise BufferError(
+                        "ForkerIterDataPipe buffer overflow,"
+                        + f"buffer size {self.buffer_size} is insufficient."
+                    )
+
+                yield self.copy_fn(return_val)  # type: ignore[possibly-undefined]
+        finally:
+            self._child_stop[instance_id] = True
+            # Cleanup _datapipe_iterator for the case that fork exits earlier
+            if all(self._child_stop):
+                self._datapipe_iterator = None
+                self._cleanup()
+
+    def is_every_instance_exhausted(self) -> bool:
+        return self.end_ptr is not None and all(self._child_stop)
+
+    def get_length_by_instance(self, instance_id: int) -> int:
+        return len(self.main_datapipe)
+
+    def reset(self) -> None:
+        self._datapipe_iterator = None
+        self.buffer = deque()
+        self.child_pointers = [0] * self.num_instances
+        self.slowest_ptr = 0
+        self.leading_ptr = 0
+        self.end_ptr = None
+        self._child_stop = [True for _ in range(self.num_instances)]
+
+    def __getstate__(self):
+        state = (
+            self.main_datapipe,
+            self.num_instances,
+            self.buffer_size,
+            self.copy_fn,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        )
+        if IterDataPipe.getstate_hook is not None:
+            return IterDataPipe.getstate_hook(state)
+        return state
+
+    def __setstate__(self, state):
+        (
+            self.main_datapipe,
+            self.num_instances,
+            self.buffer_size,
+            self.copy_fn,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        ) = state
+        self._datapipe_iterator = None
+        self.buffer = deque()
+        self.child_pointers = [0] * self.num_instances
+        self.slowest_ptr = 0
+        self.leading_ptr = 0
+        self.end_ptr = None
+        self._child_stop = [True for _ in range(self.num_instances)]
+
+    def _cleanup(self):
+        while self.buffer:
+            d = self.buffer.popleft()
+            StreamWrapper.close_streams(d)
+
+    def __del__(self):
+        self._cleanup()
+
+
+class _ChildDataPipe(IterDataPipe):
+    r"""
+    Iterable Datapipe that is a child of a main DataPipe.
+
+    The instance of this class will pass its instance_id to get the next value from its main DataPipe.
+
+    Note:
+        ChildDataPipe, like all other IterDataPipe, follows the single iterator per IterDataPipe constraint.
+        Since ChildDataPipes share a common buffer, when an iterator is created for one of the ChildDataPipes,
+        the previous iterators  for all ChildDataPipes must be invalidated, with the exception when a ChildDataPipe
+        hasn't had an iterator created from it since the last invalidation. See the example below.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(module:torchdata)
+        >>> # Singler Iterator per IteraDataPipe Invalidation
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> source_dp = IterableWrapper(range(10))
+        >>> cdp1, cdp2 = source_dp.fork(num_instances=2)
+        >>> it1, it2 = iter(cdp1), iter(cdp2)
+        >>> it3 = iter(cdp1)
+        >>> # The line above invalidates `it1` and `it2`, and resets `ForkerIterDataPipe`.
+        >>> it4 = iter(cdp2)
+        >>> # The line above doesn't invalidate `it3`, because an iterator for `cdp2` hasn't been created since
+        >>> # the last invalidation.
+
+    Args:
+        main_datapipe: Main DataPipe with a method 'get_next_element_by_instance(instance_id)'
+        instance_id: integer identifier of this instance
+    """
+
+    _is_child_datapipe: bool = True
+
+    def __init__(self, main_datapipe: IterDataPipe, instance_id: int):
+        assert isinstance(main_datapipe, _ContainerTemplate)
+
+        self.main_datapipe: IterDataPipe = main_datapipe
+        self.instance_id = instance_id
+
+    def __iter__(self):
+        # Note that the logic behind setting iterator ID and `reset` are handled within `hook_iterator`
+        # We want to separate the code for reset and yield, so that 'reset' executes before __next__ is called
+        return self.main_datapipe.get_next_element_by_instance(self.instance_id)
+
+    def __len__(self):
+        return self.main_datapipe.get_length_by_instance(self.instance_id)
+
+    # This method is called by `hook_iterator` in `_typing.py`.
+    def _set_main_datapipe_valid_iterator_id(self) -> int:
+        r"""
+        Update the valid iterator ID for both this DataPipe object and `main_datapipe`.
+
+        `main_datapipe.reset()` is called when the ID is incremented to a new generation.
+        """
+        # 1. First time any child iterator is created
+        if self.main_datapipe._valid_iterator_id is None:
+            self.main_datapipe._valid_iterator_id = 0  # type: ignore[attr-defined]
+        # 2. This instance was already in the same generation as `main_datapipe`,
+        #    we need to increment the ID further by 1
+        elif self.main_datapipe._valid_iterator_id == self._valid_iterator_id:  # type: ignore[has-type]
+            self.main_datapipe._valid_iterator_id += 1  # type: ignore[attr-defined]
+            # Whenever a new generation of iterator is created, the `main_datapipe` must reset
+            if not self.main_datapipe.is_every_instance_exhausted():
+                warnings.warn(
+                    "Some child DataPipes are not exhausted when __iter__ is called. We are resetting "
+                    "the buffer and each child DataPipe will read from the start again.",
+                    UserWarning,
+                )
+            self.main_datapipe.reset()
+        # 3. Otherwise, the iterator is behind the others, so it will just need to catch up by setting
+        #    the instance's iterator to match that of `main_datapipe`
+        self._valid_iterator_id = self.main_datapipe._valid_iterator_id
+        return self._valid_iterator_id
+
+    # This method is called by `hook_iterator` in `_typing.py`.
+    def _check_valid_iterator_id(self, iterator_id) -> bool:
+        r"""Check the valid iterator ID against that of DataPipe object and that of `main_datapipe`."""
+        return (
+            iterator_id == self._valid_iterator_id
+            and iterator_id == self.main_datapipe._valid_iterator_id
+        )
+
+
+@functional_datapipe("demux")
+class DemultiplexerIterDataPipe(IterDataPipe):
+    r"""
+    Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``).
+
+    A list of the child DataPipes is returned from this operation.
+
+    Args:
+        datapipe: Iterable DataPipe being filtered
+        num_instances: number of instances of the DataPipe to create
+        classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None``
+        drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None``
+        buffer_size: this defines the maximum number of inputs that the buffer can hold across all child
+            DataPipes while waiting for their values to be yielded.
+            Defaults to ``1000``. Use ``-1`` for the unlimited buffer.
+
+    Examples:
+        >>> # xdoctest: +REQUIRES(module:torchdata)
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> def odd_or_even(n):
+        ...     return n % 2
+        >>> source_dp = IterableWrapper(range(5))
+        >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even)
+        >>> list(dp1)
+        [0, 2, 4]
+        >>> list(dp2)
+        [1, 3]
+        >>> # It can also filter out any element that gets `None` from the `classifier_fn`
+        >>> def odd_or_even_no_zero(n):
+        ...     return n % 2 if n != 0 else None
+        >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True)
+        >>> list(dp1)
+        [2, 4]
+        >>> list(dp2)
+        [1, 3]
+    """
+
+    def __new__(
+        cls,
+        datapipe: IterDataPipe,
+        num_instances: int,
+        classifier_fn: Callable[[_T_co], Optional[int]],
+        drop_none: bool = False,
+        buffer_size: int = 1000,
+    ):
+        if num_instances < 1:
+            raise ValueError(
+                f"Expected `num_instances` larger than 0, but {num_instances} is found"
+            )
+
+        _check_unpickable_fn(classifier_fn)
+
+        # When num_instances == 1, demux can be replaced by filter,
+        # but keep it as Demultiplexer for the sake of consistency
+        # like throwing Error when classification result is out of o range
+        container = _DemultiplexerIterDataPipe(datapipe, num_instances, classifier_fn, drop_none, buffer_size)  # type: ignore[abstract]
+        return [_ChildDataPipe(container, i) for i in range(num_instances)]
+
+
+class _DemultiplexerIterDataPipe(IterDataPipe, _ContainerTemplate):
+    r"""
+    Container to hold instance-specific information on behalf of DemultiplexerIterDataPipe.
+
+    It tracks the state of its child DataPipes, maintains the buffer, classifies and yields the next correct value
+    as requested by the child DataPipes.
+    """
+
+    def __init__(
+        self,
+        datapipe: IterDataPipe[_T_co],
+        num_instances: int,
+        classifier_fn: Callable[[_T_co], Optional[int]],
+        drop_none: bool,
+        buffer_size: int,
+    ):
+        self.main_datapipe = datapipe
+        self._datapipe_iterator: Optional[Iterator[Any]] = None
+        self.num_instances = num_instances
+        self.buffer_size = buffer_size
+        if self.buffer_size < 0:
+            warnings.warn(
+                "Unlimited buffer size is set for `demux`, "
+                "please be aware of OOM at random places",
+                UserWarning,
+            )
+        self.current_buffer_usage = 0
+        self.child_buffers: list[deque[_T_co]] = [deque() for _ in range(num_instances)]
+        self.classifier_fn = classifier_fn
+        self.drop_none = drop_none
+        self.main_datapipe_exhausted = False
+        self._child_stop: list[bool] = [True for _ in range(num_instances)]
+
+    def _find_next(self, instance_id: int) -> _T_co:  # type: ignore[type-var]
+        while True:
+            if self.main_datapipe_exhausted or self._child_stop[instance_id]:
+                raise StopIteration
+            if self._datapipe_iterator is None:
+                raise ValueError(
+                    "_datapipe_iterator has not been set, likely because this private method is called directly "
+                    "without invoking get_next_element_by_instance() first."
+                )
+            value = next(self._datapipe_iterator)
+            classification = self.classifier_fn(value)
+            if classification is None and self.drop_none:
+                StreamWrapper.close_streams(value)
+                continue
+            if (
+                classification is None
+                or classification >= self.num_instances
+                or classification < 0
+            ):
+                raise ValueError(
+                    f"Output of the classification fn should be between 0 and {self.num_instances - 1}. "
+                    + f"{classification} is returned."
+                )
+            if classification == instance_id:
+                return value
+            self.child_buffers[classification].append(value)
+            self.current_buffer_usage += 1
+            if self.buffer_size >= 0 and self.current_buffer_usage > self.buffer_size:
+                raise BufferError(
+                    f"DemultiplexerIterDataPipe buffer overflow, buffer size {self.buffer_size} is insufficient."
+                )
+
+    def get_next_element_by_instance(self, instance_id: int):
+        if self._datapipe_iterator is None and self._child_stop[instance_id]:
+            self._datapipe_iterator = iter(self.main_datapipe)
+            self._snapshot_state = (
+                _SnapshotState.Iterating
+            )  # This is necessary for the DataPipe to reset properly.
+            self.main_datapipe_exhausted = False
+            for i in range(self.num_instances):
+                self._child_stop[i] = False
+
+        try:
+            while not self._child_stop[instance_id]:
+                if self.child_buffers[instance_id]:
+                    self.current_buffer_usage -= 1
+                    yield self.child_buffers[instance_id].popleft()
+                else:
+                    try:
+                        yield self._find_next(instance_id)
+                    except StopIteration:
+                        self._child_stop[instance_id] = True
+                        self.main_datapipe_exhausted = True
+                        self._datapipe_iterator = None
+        finally:
+            self._child_stop[instance_id] = True
+            # Cleanup _datapipe_iterator for the case that demux exits earlier
+            if all(self._child_stop):
+                self._datapipe_iterator = None
+            if self.child_buffers[instance_id]:
+                self._cleanup(instance_id)
+
+    def is_every_instance_exhausted(self) -> bool:
+        return self.main_datapipe_exhausted and all(self._child_stop)
+
+    def get_length_by_instance(self, instance_id: int) -> int:
+        raise TypeError
+
+    def reset(self) -> None:
+        self._datapipe_iterator = None
+        self.current_buffer_usage = 0
+        self.child_buffers = [deque() for _ in range(self.num_instances)]
+        self._child_stop = [True for _ in range(self.num_instances)]
+        self.main_datapipe_exhausted = False
+
+    def __getstate__(self):
+        state = (
+            self.main_datapipe,
+            self.num_instances,
+            self.buffer_size,
+            self.classifier_fn,
+            self.drop_none,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        )
+        if IterDataPipe.getstate_hook is not None:
+            return IterDataPipe.getstate_hook(state)
+        return state
+
+    def __setstate__(self, state):
+        (
+            self.main_datapipe,
+            self.num_instances,
+            self.buffer_size,
+            self.classifier_fn,
+            self.drop_none,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        ) = state
+        self._datapipe_iterator = None
+        self.current_buffer_usage = 0
+        self.child_buffers = [deque() for _ in range(self.num_instances)]
+        self._child_stop = [True for _ in range(self.num_instances)]
+        self.main_datapipe_exhausted = False
+
+    def _cleanup(self, instance_id: Optional[int] = None):
+        ids = (
+            range(self.num_instances)
+            if instance_id is None
+            else [
+                instance_id,
+            ]
+        )
+        for i in ids:
+            q = self.child_buffers[i]
+            while q:
+                d = q.popleft()
+                StreamWrapper.close_streams(d)
+
+    def __del__(self):
+        self._cleanup()
+
+
+@functional_datapipe("mux")
+class MultiplexerIterDataPipe(IterDataPipe):
+    r"""
+    Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``).
+
+    As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration,
+    and so on. It ends when the shortest input DataPipe is exhausted.
+
+    Args:
+        datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted
+
+    Example:
+        >>> # xdoctest: +REQUIRES(module:torchdata)
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> dp1, dp2, dp3 = IterableWrapper(range(3)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25))
+        >>> list(dp1.mux(dp2, dp3))
+        [0, 10, 20, 1, 11, 21, 2, 12, 22]
+    """
+
+    def __init__(self, *datapipes):
+        self.datapipes = datapipes
+        self.buffer: list = (
+            []
+        )  # Store values to be yielded only when every iterator provides one
+
+    def __iter__(self):
+        iterators = [iter(x) for x in self.datapipes]
+        while len(iterators):
+            for it in iterators:
+                try:
+                    value = next(it)
+                    self.buffer.append(value)
+                except StopIteration:
+                    self.buffer.clear()
+                    return
+            yield from self.buffer
+            self.buffer.clear()
+
+    def __len__(self):
+        if all(isinstance(dp, Sized) for dp in self.datapipes):
+            return min(len(dp) for dp in self.datapipes) * len(self.datapipes)
+        else:
+            raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
+
+    def reset(self) -> None:
+        self.buffer = []
+
+    def __getstate__(self):
+        state = (
+            self.datapipes,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        )
+        if IterDataPipe.getstate_hook is not None:
+            return IterDataPipe.getstate_hook(state)
+        return state
+
+    def __setstate__(self, state):
+        (
+            self.datapipes,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        ) = state
+        self.buffer = []
+
+    def __del__(self):
+        self.buffer.clear()
+
+
+@functional_datapipe("zip")
+class ZipperIterDataPipe(IterDataPipe[tuple[_T_co]]):
+    r"""
+    Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``).
+
+    The output is stopped as soon as the shortest input DataPipe is exhausted.
+
+    Args:
+        *datapipes: Iterable DataPipes being aggregated
+
+    Example:
+        >>> # xdoctest: +REQUIRES(module:torchdata)
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> dp1, dp2, dp3 = IterableWrapper(range(5)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25))
+        >>> list(dp1.zip(dp2, dp3))
+        [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)]
+    """
+
+    datapipes: tuple[IterDataPipe]
+
+    def __init__(self, *datapipes: IterDataPipe):
+        if not all(isinstance(dp, IterDataPipe) for dp in datapipes):
+            raise TypeError(
+                "All inputs are required to be `IterDataPipe` for `ZipIterDataPipe`."
+            )
+        super().__init__()
+        self.datapipes = datapipes  # type: ignore[assignment]
+
+    def __iter__(self) -> Iterator[tuple[_T_co]]:
+        iterators = [iter(datapipe) for datapipe in self.datapipes]
+        yield from zip(*iterators)
+
+    def __len__(self) -> int:
+        if all(isinstance(dp, Sized) for dp in self.datapipes):
+            return min(len(dp) for dp in self.datapipes)
+        else:
+            raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/filelister.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/filelister.py
new file mode 100644
index 0000000000000000000000000000000000000000..9de99cf9b4a2f3085b9e4f14fb5e1472d5deb998
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/filelister.py
@@ -0,0 +1,69 @@
+# mypy: allow-untyped-defs
+from collections.abc import Iterator, Sequence
+from typing import Union
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+from torch.utils.data.datapipes.iter.utils import IterableWrapperIterDataPipe
+from torch.utils.data.datapipes.utils.common import get_file_pathnames_from_root
+
+
+__all__ = ["FileListerIterDataPipe"]
+
+
+@functional_datapipe("list_files")
+class FileListerIterDataPipe(IterDataPipe[str]):
+    r"""
+    Given path(s) to the root directory, yields file pathname(s) (path + filename) of files within the root directory.
+
+    Multiple root directories can be provided (functional name: ``list_files``).
+
+    Args:
+        root: Root directory or a sequence of root directories
+        masks: Unix style filter string or string list for filtering file name(s)
+        recursive: Whether to return pathname from nested directories or not
+        abspath: Whether to return relative pathname or absolute pathname
+        non_deterministic: Whether to return pathname in sorted order or not.
+            If ``False``, the results yielded from each root directory will be sorted
+        length: Nominal length of the datapipe
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.iter import FileLister
+        >>> dp = FileLister(root=".", recursive=True)
+        >>> list(dp)
+        ['example.py', './data/data.tar']
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Sequence[str], IterDataPipe] = ".",
+        masks: Union[str, list[str]] = "",
+        *,
+        recursive: bool = False,
+        abspath: bool = False,
+        non_deterministic: bool = False,
+        length: int = -1,
+    ) -> None:
+        super().__init__()
+        if isinstance(root, str):
+            root = [root]
+        if not isinstance(root, IterDataPipe):
+            root = IterableWrapperIterDataPipe(root)
+        self.datapipe: IterDataPipe = root
+        self.masks: Union[str, list[str]] = masks
+        self.recursive: bool = recursive
+        self.abspath: bool = abspath
+        self.non_deterministic: bool = non_deterministic
+        self.length: int = length
+
+    def __iter__(self) -> Iterator[str]:
+        for path in self.datapipe:
+            yield from get_file_pathnames_from_root(
+                path, self.masks, self.recursive, self.abspath, self.non_deterministic
+            )
+
+    def __len__(self):
+        if self.length == -1:
+            raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
+        return self.length
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/fileopener.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/fileopener.py
new file mode 100644
index 0000000000000000000000000000000000000000..2542c89773bdd956f32af325a297cf62a88cce6b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/fileopener.py
@@ -0,0 +1,77 @@
+# mypy: allow-untyped-defs
+from collections.abc import Iterable
+from io import IOBase
+from typing import Optional
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+from torch.utils.data.datapipes.utils.common import get_file_binaries_from_pathnames
+
+
+__all__ = [
+    "FileOpenerIterDataPipe",
+]
+
+
+@functional_datapipe("open_files")
+class FileOpenerIterDataPipe(IterDataPipe[tuple[str, IOBase]]):
+    r"""
+    Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``).
+
+    Args:
+        datapipe: Iterable datapipe that provides pathnames
+        mode: An optional string that specifies the mode in which
+            the file is opened by ``open()``. It defaults to ``r``, other options are
+            ``b`` for reading in binary mode and ``t`` for text mode.
+        encoding: An optional string that specifies the encoding of the
+            underlying file. It defaults to ``None`` to match the default encoding of ``open``.
+        length: Nominal length of the datapipe
+
+    Note:
+        The opened file handles will be closed by Python's GC periodically. Users can choose
+        to close them explicitly.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.iter import FileLister, FileOpener, StreamReader
+        >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith('.txt'))
+        >>> dp = FileOpener(dp)
+        >>> dp = StreamReader(dp)
+        >>> list(dp)
+        [('./abc.txt', 'abc')]
+    """
+
+    def __init__(
+        self,
+        datapipe: Iterable[str],
+        mode: str = "r",
+        encoding: Optional[str] = None,
+        length: int = -1,
+    ):
+        super().__init__()
+        self.datapipe: Iterable = datapipe
+        self.mode: str = mode
+        self.encoding: Optional[str] = encoding
+
+        if self.mode not in ("b", "t", "rb", "rt", "r"):
+            raise ValueError(f"Invalid mode {mode}")
+        # TODO: enforce typing for each instance based on mode, otherwise
+        #       `argument_validation` with this DataPipe may be potentially broken
+
+        if "b" in mode and encoding is not None:
+            raise ValueError("binary mode doesn't take an encoding argument")
+
+        self.length: int = length
+
+    # Remove annotation due to 'IOBase' is a general type and true type
+    # is determined at runtime based on mode. Some `DataPipe` requiring
+    # a subtype would cause mypy error.
+    def __iter__(self):
+        yield from get_file_binaries_from_pathnames(
+            self.datapipe, self.mode, self.encoding
+        )
+
+    def __len__(self):
+        if self.length == -1:
+            raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
+        return self.length
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py
new file mode 100644
index 0000000000000000000000000000000000000000..08d124fdc6087e277a274578527ad65d8142da0a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/grouping.py
@@ -0,0 +1,322 @@
+# mypy: allow-untyped-defs
+import warnings
+from collections import defaultdict
+from collections.abc import Iterator, Sized
+from typing import Any, Callable, Optional, TypeVar
+
+import torch.utils.data.datapipes.iter.sharding
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import DataChunk, IterDataPipe
+from torch.utils.data.datapipes.utils.common import _check_unpickable_fn
+
+
+__all__ = [
+    "BatcherIterDataPipe",
+    "GrouperIterDataPipe",
+    "UnBatcherIterDataPipe",
+]
+
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+def __getattr__(name: str):
+    if name in ["SHARDING_PRIORITIES", "ShardingFilterIterDataPipe"]:
+        warnings.warn(
+            f"`{name}` from `torch.utils.data.datapipes.iter.grouping` is going to be removed in PyTorch 2.1"
+            f"Please use `{name}` from the `torch.utils.data.datapipes.iter.sharding`",
+            category=FutureWarning,
+            stacklevel=2,
+        )
+
+        return getattr(torch.utils.data.datapipes.iter.sharding, name)
+
+    raise AttributeError(f"module {__name__} has no attribute {name}")
+
+
+@functional_datapipe("batch")
+class BatcherIterDataPipe(IterDataPipe[DataChunk]):
+    r"""
+    Creates mini-batches of data (functional name: ``batch``).
+
+    An outer dimension will be added as ``batch_size`` if ``drop_last`` is set to ``True``, or ``length % batch_size`` for the
+    last batch if ``drop_last`` is set to ``False``.
+
+    Args:
+        datapipe: Iterable DataPipe being batched
+        batch_size: The size of each batch
+        drop_last: Option to drop the last batch if it's not full
+        wrapper_class: wrapper to apply onto each batch (type ``List``) before yielding,
+            defaults to ``DataChunk``
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> dp = IterableWrapper(range(10))
+        >>> dp = dp.batch(batch_size=3, drop_last=True)
+        >>> list(dp)
+        [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+    """
+
+    datapipe: IterDataPipe
+    batch_size: int
+    drop_last: bool
+
+    def __init__(
+        self,
+        datapipe: IterDataPipe,
+        batch_size: int,
+        drop_last: bool = False,
+        wrapper_class: type[DataChunk] = DataChunk,
+    ) -> None:
+        assert batch_size > 0, "Batch size is required to be larger than 0!"
+        super().__init__()
+        self.datapipe = datapipe
+        self.batch_size = batch_size
+        self.drop_last = drop_last
+        self.wrapper_class = wrapper_class
+
+    def __iter__(self) -> Iterator[DataChunk]:
+        batch: list = []
+        for x in self.datapipe:
+            batch.append(x)
+            if len(batch) == self.batch_size:
+                yield self.wrapper_class(batch)
+                batch = []
+        if len(batch) > 0:
+            if not self.drop_last:
+                yield self.wrapper_class(batch)
+
+    def __len__(self) -> int:
+        if isinstance(self.datapipe, Sized):
+            if self.drop_last:
+                return len(self.datapipe) // self.batch_size
+            else:
+                return (len(self.datapipe) + self.batch_size - 1) // self.batch_size
+        else:
+            raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
+
+
+@functional_datapipe("unbatch")
+class UnBatcherIterDataPipe(IterDataPipe):
+    r"""
+    Undos batching of data (functional name: ``unbatch``).
+
+    In other words, it flattens the data up to the specified level within a batched DataPipe.
+
+    Args:
+        datapipe: Iterable DataPipe being un-batched
+        unbatch_level: Defaults to ``1`` (only flattening the top level). If set to ``2``,
+            it will flatten the top two levels, and ``-1`` will flatten the entire DataPipe.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> source_dp = IterableWrapper([[[0, 1], [2]], [[3, 4], [5]], [[6]]])
+        >>> dp1 = source_dp.unbatch()
+        >>> list(dp1)
+        [[0, 1], [2], [3, 4], [5], [6]]
+        >>> dp2 = source_dp.unbatch(unbatch_level=2)
+        >>> list(dp2)
+        [0, 1, 2, 3, 4, 5, 6]
+    """
+
+    def __init__(self, datapipe: IterDataPipe, unbatch_level: int = 1):
+        self.datapipe = datapipe
+        self.unbatch_level = unbatch_level
+
+    def __iter__(self):
+        for element in self.datapipe:
+            yield from self._dive(element, unbatch_level=self.unbatch_level)
+
+    def _dive(self, element, unbatch_level):
+        if unbatch_level < -1:
+            raise ValueError("unbatch_level must be -1 or >= 0")
+        if unbatch_level == -1:
+            if isinstance(element, (list, DataChunk)):
+                for item in element:
+                    yield from self._dive(item, unbatch_level=-1)
+            else:
+                yield element
+        elif unbatch_level == 0:
+            yield element
+        else:
+            if isinstance(element, (list, DataChunk)):
+                for item in element:
+                    yield from self._dive(item, unbatch_level=unbatch_level - 1)
+            else:
+                raise IndexError(
+                    f"unbatch_level {self.unbatch_level} exceeds the depth of the DataPipe"
+                )
+
+
+@functional_datapipe("groupby")
+class GrouperIterDataPipe(IterDataPipe[DataChunk]):
+    r"""
+    Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``.
+
+    (functional name: ``groupby``).
+
+    The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group
+    will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full,
+    the DataPipe will yield the largest batch with the same key, provided that its size is larger
+    than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``.
+
+    After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity
+    will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``.
+
+    Args:
+        datapipe: Iterable datapipe to be grouped
+        group_key_fn: Function used to generate group key from the data of the source datapipe
+        keep_key: Option to yield the matching key along with the items in a tuple,
+            resulting in `(key, [items])` otherwise returning [items]
+        buffer_size: The size of buffer for ungrouped data
+        group_size: The max size of each group, a batch is yielded as soon as it reaches this size
+        guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full
+        drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer
+            when the buffer is full
+
+    Example:
+        >>> import os
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> def group_fn(file):
+        ...     return os.path.basename(file).split(".")[0]
+        >>> source_dp = IterableWrapper(["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"])
+        >>> dp0 = source_dp.groupby(group_key_fn=group_fn)
+        >>> list(dp0)
+        [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']]
+        >>> # A group is yielded as soon as its size equals to `group_size`
+        >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2)
+        >>> list(dp1)
+        [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']]
+        >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size`
+        >>> dp2 = source_dp.groupby(group_key_fn=group_fn, buffer_size=3, group_size=3, guaranteed_group_size=2)
+        >>> list(dp2)
+        [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']]
+    """
+
+    def __init__(
+        self,
+        datapipe: IterDataPipe[_T_co],
+        group_key_fn: Callable[[_T_co], Any],
+        *,
+        keep_key: bool = False,
+        buffer_size: int = 10000,
+        group_size: Optional[int] = None,
+        guaranteed_group_size: Optional[int] = None,
+        drop_remaining: bool = False,
+    ):
+        _check_unpickable_fn(group_key_fn)
+        self.datapipe = datapipe
+        self.group_key_fn = group_key_fn
+
+        self.keep_key = keep_key
+        self.max_buffer_size = buffer_size
+        self.buffer_elements: defaultdict[Any, list] = defaultdict(list)
+        self.curr_buffer_size = 0
+        self.group_size = group_size
+        self.guaranteed_group_size = None
+        if group_size is not None and buffer_size is not None:
+            assert 0 < group_size <= buffer_size
+            self.guaranteed_group_size = group_size
+        if guaranteed_group_size is not None:
+            assert group_size is not None and 0 < guaranteed_group_size <= group_size
+            self.guaranteed_group_size = guaranteed_group_size
+        self.drop_remaining = drop_remaining
+        self.wrapper_class = DataChunk
+
+    def _remove_biggest_key(self):
+        biggest_key = None
+        biggest_size = 0
+        result_to_yield = None
+        for findkey in self.buffer_elements.keys():
+            if len(self.buffer_elements[findkey]) > biggest_size:
+                biggest_size = len(self.buffer_elements[findkey])
+                biggest_key = findkey
+
+        if (
+            self.guaranteed_group_size is not None
+            and biggest_size < self.guaranteed_group_size
+            and not self.drop_remaining
+        ):
+            raise RuntimeError(
+                "Failed to group items", str(self.buffer_elements[biggest_key])
+            )
+
+        if (
+            self.guaranteed_group_size is None
+            or biggest_size >= self.guaranteed_group_size
+        ):
+            result_to_yield = self.buffer_elements[biggest_key]
+
+        self.curr_buffer_size -= biggest_size
+        del self.buffer_elements[biggest_key]
+
+        return result_to_yield
+
+    def __iter__(self):
+        for x in self.datapipe:
+            key = self.group_key_fn(x)
+
+            self.buffer_elements[key].append(x)
+            self.curr_buffer_size += 1
+
+            if self.group_size is not None and self.group_size == len(
+                self.buffer_elements[key]
+            ):
+                result: DataChunk[Any] = self.wrapper_class(self.buffer_elements[key])
+                yield (key, result) if self.keep_key else result
+                self.curr_buffer_size -= len(self.buffer_elements[key])
+                del self.buffer_elements[key]
+
+            if self.curr_buffer_size == self.max_buffer_size:
+                result_to_yield = self._remove_biggest_key()
+                if result_to_yield is not None:
+                    result = self.wrapper_class(result_to_yield)
+                    yield (key, result) if self.keep_key else result
+
+        for key in tuple(self.buffer_elements.keys()):
+            result = self.wrapper_class(self.buffer_elements.pop(key))
+            self.curr_buffer_size -= len(result)
+            yield (key, result) if self.keep_key else result
+
+    def reset(self) -> None:
+        self.curr_buffer_size = 0
+        self.buffer_elements = defaultdict(list)
+
+    def __getstate__(self):
+        state = (
+            self.datapipe,
+            self.group_key_fn,
+            self.keep_key,
+            self.max_buffer_size,
+            self.group_size,
+            self.guaranteed_group_size,
+            self.drop_remaining,
+            self.wrapper_class,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        )
+        if IterDataPipe.getstate_hook is not None:
+            return IterDataPipe.getstate_hook(state)
+        return state
+
+    def __setstate__(self, state):
+        (
+            self.datapipe,
+            self.group_key_fn,
+            self.keep_key,
+            self.max_buffer_size,
+            self.group_size,
+            self.guaranteed_group_size,
+            self.drop_remaining,
+            self.wrapper_class,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        ) = state
+        self.curr_buffer_size = 0
+        self.buffer_elements = defaultdict(list)
+
+    def __del__(self):
+        self.buffer_elements.clear()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/routeddecoder.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/routeddecoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..611b4870a493a2bde5ba4233a8e5d78a1c21ba79
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/routeddecoder.py
@@ -0,0 +1,70 @@
+from collections.abc import Iterable, Iterator, Sized
+from io import BufferedIOBase
+from typing import Any, Callable
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+from torch.utils.data.datapipes.utils.common import _deprecation_warning
+from torch.utils.data.datapipes.utils.decoder import (
+    basichandlers as decoder_basichandlers,
+    Decoder,
+    extension_extract_fn,
+    imagehandler as decoder_imagehandler,
+)
+
+
+__all__ = ["RoutedDecoderIterDataPipe"]
+
+
+@functional_datapipe("routed_decode")
+class RoutedDecoderIterDataPipe(IterDataPipe[tuple[str, Any]]):
+    r"""
+    Decodes binary streams from input DataPipe, yields pathname and decoded data in a tuple.
+
+    (functional name: ``routed_decode``)
+
+    Args:
+        datapipe: Iterable datapipe that provides pathname and binary stream in tuples
+        handlers: Optional user defined decoder handlers. If ``None``, basic and image decoder
+            handlers will be set as default. If multiple handles are provided, the priority
+            order follows the order of handlers (the first handler has the top priority)
+        key_fn: Function for decoder to extract key from pathname to dispatch handlers.
+            Default is set to extract file extension from pathname
+
+    Note:
+        When ``key_fn`` is specified returning anything other than extension, the default
+        handler will not work and users need to specify custom handler. Custom handler
+        could use regex to determine the eligibility to handle data.
+    """
+
+    def __init__(
+        self,
+        datapipe: Iterable[tuple[str, BufferedIOBase]],
+        *handlers: Callable,
+        key_fn: Callable = extension_extract_fn,
+    ) -> None:
+        super().__init__()
+        self.datapipe: Iterable[tuple[str, BufferedIOBase]] = datapipe
+        if not handlers:
+            handlers = (decoder_basichandlers, decoder_imagehandler("torch"))
+        self.decoder = Decoder(*handlers, key_fn=key_fn)
+        _deprecation_warning(
+            type(self).__name__,
+            deprecation_version="1.12",
+            removal_version="1.13",
+            old_functional_name="routed_decode",
+        )
+
+    def add_handler(self, *handler: Callable) -> None:
+        self.decoder.add_handler(*handler)
+
+    def __iter__(self) -> Iterator[tuple[str, Any]]:
+        for data in self.datapipe:
+            pathname = data[0]
+            result = self.decoder(data)
+            yield (pathname, result[pathname])
+
+    def __len__(self) -> int:
+        if isinstance(self.datapipe, Sized):
+            return len(self.datapipe)
+        raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/selecting.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/selecting.py
new file mode 100644
index 0000000000000000000000000000000000000000..97dcb2d6c49105205ab00159538b3a0c4f1ed01b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/selecting.py
@@ -0,0 +1,102 @@
+# mypy: allow-untyped-defs
+from collections.abc import Iterator
+from typing import Callable, TypeVar
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+from torch.utils.data.datapipes.utils.common import (
+    _check_unpickable_fn,
+    StreamWrapper,
+    validate_input_col,
+)
+
+
+__all__ = ["FilterIterDataPipe"]
+
+
+_T = TypeVar("_T")
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+@functional_datapipe("filter")
+class FilterIterDataPipe(IterDataPipe[_T_co]):
+    r"""
+    Filters out elements from the source datapipe according to input ``filter_fn`` (functional name: ``filter``).
+
+    Args:
+        datapipe: Iterable DataPipe being filtered
+        filter_fn: Customized function mapping an element to a boolean.
+        input_col: Index or indices of data which ``filter_fn`` is applied, such as:
+
+            - ``None`` as default to apply ``filter_fn`` to the data directly.
+            - Integer(s) is used for list/tuple.
+            - Key(s) is used for dict.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> def is_even(n):
+        ...     return n % 2 == 0
+        >>> dp = IterableWrapper(range(5))
+        >>> filter_dp = dp.filter(filter_fn=is_even)
+        >>> list(filter_dp)
+        [0, 2, 4]
+    """
+
+    datapipe: IterDataPipe[_T_co]
+    filter_fn: Callable
+
+    def __init__(
+        self,
+        datapipe: IterDataPipe[_T_co],
+        filter_fn: Callable,
+        input_col=None,
+    ) -> None:
+        super().__init__()
+        self.datapipe = datapipe
+
+        _check_unpickable_fn(filter_fn)
+        self.filter_fn = filter_fn  # type: ignore[assignment]
+
+        self.input_col = input_col
+        validate_input_col(filter_fn, input_col)
+
+    def _apply_filter_fn(self, data) -> bool:
+        if self.input_col is None:
+            return self.filter_fn(data)
+        elif isinstance(self.input_col, (list, tuple)):
+            args = tuple(data[col] for col in self.input_col)
+            return self.filter_fn(*args)
+        else:
+            return self.filter_fn(data[self.input_col])
+
+    def __iter__(self) -> Iterator[_T_co]:
+        for data in self.datapipe:
+            condition, filtered = self._returnIfTrue(data)
+            if condition:
+                yield filtered
+            else:
+                StreamWrapper.close_streams(data)
+
+    def _returnIfTrue(self, data: _T) -> tuple[bool, _T]:
+        condition = self._apply_filter_fn(data)
+
+        if df_wrapper.is_column(condition):
+            # We are operating on DataFrames filter here
+            result = []
+            for idx, mask in enumerate(df_wrapper.iterate(condition)):
+                if mask:
+                    result.append(df_wrapper.get_item(data, idx))
+            if len(result):
+                return True, df_wrapper.concat(result)
+            else:
+                return False, None  # type: ignore[return-value]
+
+        if not isinstance(condition, bool):
+            raise ValueError(
+                "Boolean output is required for `filter_fn` of FilterIterDataPipe, got",
+                type(condition),
+            )
+
+        return condition, data
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/sharding.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/sharding.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e381c87a4a5826cd0e26a8c3054e4bb5c0b5798
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/sharding.py
@@ -0,0 +1,101 @@
+# mypy: allow-untyped-defs
+from collections.abc import Sized
+from enum import IntEnum
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+
+
+__all__ = [
+    "SHARDING_PRIORITIES",
+    "ShardingFilterIterDataPipe",
+]
+
+
+class SHARDING_PRIORITIES(IntEnum):
+    DEFAULT = 1
+    DISTRIBUTED = 2
+    MULTIPROCESSING = 3
+
+
+class _ShardingIterDataPipe(IterDataPipe):
+    def apply_sharding(
+        self,
+        num_of_instances: int,
+        instance_id: int,
+        sharding_group: SHARDING_PRIORITIES,
+    ):
+        raise NotImplementedError
+
+
+@functional_datapipe("sharding_filter")
+class ShardingFilterIterDataPipe(_ShardingIterDataPipe):
+    r"""
+    Wrapper that allows DataPipe to be sharded (functional name: ``sharding_filter``).
+
+    After ``apply_sharding`` is called, each instance of the DataPipe (on different workers) will have every `n`-th element of the
+    original DataPipe, where `n` equals to the number of instances.
+
+    Args:
+        source_datapipe: Iterable DataPipe that will be sharded
+    """
+
+    def __init__(self, source_datapipe: IterDataPipe, sharding_group_filter=None):
+        self.source_datapipe = source_datapipe
+        self.sharding_group_filter = sharding_group_filter
+        self.groups: dict[int, tuple[int, int]] = {}
+        self.num_of_instances = 1
+        self.instance_id = 0
+        self._update_num_of_instances()
+
+    def apply_sharding(
+        self, num_of_instances, instance_id, sharding_group=SHARDING_PRIORITIES.DEFAULT
+    ):
+        if instance_id >= num_of_instances:
+            raise ValueError(
+                f"instance_id({instance_id}) should be smaller than num_of_instances({num_of_instances})"
+            )
+        if sharding_group == SHARDING_PRIORITIES.DEFAULT:
+            if len(self.groups) and SHARDING_PRIORITIES.DEFAULT not in self.groups:
+                raise RuntimeError(
+                    "ShardingFilter cannot mix DEFAULT and non DEFAULT groups"
+                )
+        else:
+            if SHARDING_PRIORITIES.DEFAULT in self.groups:
+                raise RuntimeError(
+                    "ShardingFilter cannot mix DEFAULT and non DEFAULT groups"
+                )
+        self.groups[sharding_group] = (num_of_instances, instance_id)
+        self._update_num_of_instances()
+
+    def _update_num_of_instances(self):
+        sorted_sharding_groups = [
+            self.groups[key]
+            for key in sorted(self.groups.keys())
+            if self.sharding_group_filter is None or key == self.sharding_group_filter
+        ]
+
+        sorted_sharding_groups.reverse()
+
+        self.num_of_instances = 1
+        self.instance_id = 0
+
+        for group_num_of_instances, group_instance_id in sorted_sharding_groups:
+            self.instance_id += self.num_of_instances * group_instance_id
+            self.num_of_instances *= group_num_of_instances
+
+    def __iter__(self):
+        for i, item in enumerate(self.source_datapipe):
+            if i % self.num_of_instances == self.instance_id:
+                yield item
+
+    def __len__(self):
+        if isinstance(self.source_datapipe, Sized):
+            return len(self.source_datapipe) // self.num_of_instances + (
+                1
+                if (
+                    self.instance_id < len(self.source_datapipe) % self.num_of_instances
+                )
+                else 0
+            )
+        raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/streamreader.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/streamreader.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c3af4f12a81f87eee10738ca63c01ead8b06c72
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/streamreader.py
@@ -0,0 +1,46 @@
+from collections.abc import Iterator
+from io import IOBase
+from typing import Optional
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+
+
+__all__ = ["StreamReaderIterDataPipe"]
+
+
+@functional_datapipe("read_from_stream")
+class StreamReaderIterDataPipe(IterDataPipe[tuple[str, bytes]]):
+    r"""
+    Given IO streams and their label names, yield bytes with label name as tuple.
+
+    (functional name: ``read_from_stream``).
+
+    Args:
+        datapipe: Iterable DataPipe provides label/URL and byte stream
+        chunk: Number of bytes to be read from stream per iteration.
+            If ``None``, all bytes will be read until the EOF.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.iter import IterableWrapper, StreamReader
+        >>> from io import StringIO
+        >>> dp = IterableWrapper([("alphabet", StringIO("abcde"))])
+        >>> list(StreamReader(dp, chunk=1))
+        [('alphabet', 'a'), ('alphabet', 'b'), ('alphabet', 'c'), ('alphabet', 'd'), ('alphabet', 'e')]
+    """
+
+    def __init__(
+        self, datapipe: IterDataPipe[tuple[str, IOBase]], chunk: Optional[int] = None
+    ):
+        self.datapipe = datapipe
+        self.chunk = chunk
+
+    def __iter__(self) -> Iterator[tuple[str, bytes]]:
+        for furl, stream in self.datapipe:
+            while True:
+                d = stream.read(self.chunk)
+                if not d:
+                    stream.close()
+                    break
+                yield (furl, d)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0728746af23ddd7e628dbcdd0218af44aceb1ec9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/utils.py
@@ -0,0 +1,54 @@
+# mypy: allow-untyped-defs
+import copy
+import warnings
+
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+
+
+__all__ = ["IterableWrapperIterDataPipe"]
+
+
+class IterableWrapperIterDataPipe(IterDataPipe):
+    r"""
+    Wraps an iterable object to create an IterDataPipe.
+
+    Args:
+        iterable: Iterable object to be wrapped into an IterDataPipe
+        deepcopy: Option to deepcopy input iterable object for each
+            iterator. The copy is made when the first element is read in ``iter()``.
+
+    .. note::
+        If ``deepcopy`` is explicitly set to ``False``, users should ensure
+        that the data pipeline doesn't contain any in-place operations over
+        the iterable instance to prevent data inconsistency across iterations.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.iter import IterableWrapper
+        >>> dp = IterableWrapper(range(10))
+        >>> list(dp)
+        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+    """
+
+    def __init__(self, iterable, deepcopy=True):
+        self.iterable = iterable
+        self.deepcopy = deepcopy
+
+    def __iter__(self):
+        source_data = self.iterable
+        if self.deepcopy:
+            try:
+                source_data = copy.deepcopy(self.iterable)
+            # For the case that data cannot be deep-copied,
+            # all in-place operations will affect iterable variable.
+            # When this DataPipe is iterated second time, it will
+            # yield modified items.
+            except TypeError:
+                warnings.warn(
+                    "The input iterable can not be deepcopied, "
+                    "please be aware of in-place modification would affect source data."
+                )
+        yield from source_data
+
+    def __len__(self):
+        return len(self.iterable)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fa8932dd6fcafa2d807c591755852e74d7fdc51
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/__init__.py
@@ -0,0 +1,19 @@
+# Functional DataPipe
+from torch.utils.data.datapipes.map.callable import MapperMapDataPipe as Mapper
+from torch.utils.data.datapipes.map.combinatorics import (
+    ShufflerIterDataPipe as Shuffler,
+)
+from torch.utils.data.datapipes.map.combining import (
+    ConcaterMapDataPipe as Concater,
+    ZipperMapDataPipe as Zipper,
+)
+from torch.utils.data.datapipes.map.grouping import BatcherMapDataPipe as Batcher
+from torch.utils.data.datapipes.map.utils import (
+    SequenceWrapperMapDataPipe as SequenceWrapper,
+)
+
+
+__all__ = ["Batcher", "Concater", "Mapper", "SequenceWrapper", "Shuffler", "Zipper"]
+
+# Please keep this list sorted
+assert __all__ == sorted(__all__)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/callable.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/callable.py
new file mode 100644
index 0000000000000000000000000000000000000000..cee08b7a8c8d1578befd1fb5c75647293a9c3db6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/callable.py
@@ -0,0 +1,65 @@
+# mypy: allow-untyped-defs
+from typing import Callable, TypeVar
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import MapDataPipe
+from torch.utils.data.datapipes.utils.common import _check_unpickable_fn
+
+
+__all__ = ["MapperMapDataPipe", "default_fn"]
+
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+# Default function to return each item directly
+# In order to keep datapipe picklable, eliminates the usage
+# of python lambda function
+def default_fn(data):
+    return data
+
+
+@functional_datapipe("map")
+class MapperMapDataPipe(MapDataPipe[_T_co]):
+    r"""
+    Apply the input function over each item from the source DataPipe (functional name: ``map``).
+
+    The function can be any regular Python function or partial object. Lambda
+    function is not recommended as it is not supported by pickle.
+
+    Args:
+        datapipe: Source MapDataPipe
+        fn: Function being applied to each item
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.map import SequenceWrapper, Mapper
+        >>> def add_one(x):
+        ...     return x + 1
+        >>> dp = SequenceWrapper(range(10))
+        >>> map_dp_1 = dp.map(add_one)
+        >>> list(map_dp_1)
+        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        >>> map_dp_2 = Mapper(dp, lambda x: x + 1)
+        >>> list(map_dp_2)
+        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+    """
+
+    datapipe: MapDataPipe
+    fn: Callable
+
+    def __init__(
+        self,
+        datapipe: MapDataPipe,
+        fn: Callable = default_fn,
+    ) -> None:
+        super().__init__()
+        self.datapipe = datapipe
+        _check_unpickable_fn(fn)
+        self.fn = fn  # type: ignore[assignment]
+
+    def __len__(self) -> int:
+        return len(self.datapipe)
+
+    def __getitem__(self, index) -> _T_co:
+        return self.fn(self.datapipe[index])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combinatorics.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combinatorics.py
new file mode 100644
index 0000000000000000000000000000000000000000..619d0e5c7a0e8a13f40f16f3a219e56ce7adfd31
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combinatorics.py
@@ -0,0 +1,130 @@
+# mypy: allow-untyped-defs
+import random
+from collections.abc import Iterator
+from typing import Optional, TypeVar
+
+import torch
+from torch.utils.data.datapipes.datapipe import IterDataPipe, MapDataPipe
+
+
+__all__ = ["ShufflerIterDataPipe"]
+
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+# @functional_datapipe('shuffle')
+class ShufflerIterDataPipe(IterDataPipe[_T_co]):
+    r"""
+    Shuffle the input MapDataPipe via its indices (functional name: ``shuffle``).
+
+    When it is used with :class:`~torch.utils.data.DataLoader`, the methods to
+    set up random seed are different based on :attr:`num_workers`.
+
+    For single-process mode (:attr:`num_workers == 0`), the random seed is set before
+    the :class:`~torch.utils.data.DataLoader` in the main process. For multi-process
+    mode (:attr:`num_worker > 0`), ``worker_init_fn`` is used to set up a random seed
+    for each worker process.
+
+    Args:
+        datapipe: MapDataPipe being shuffled
+        indices: a list of indices of the MapDataPipe. If not provided, we assume it uses 0-based indexing
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.map import SequenceWrapper
+        >>> dp = SequenceWrapper(range(10))
+        >>> shuffle_dp = dp.shuffle().set_seed(0)
+        >>> list(shuffle_dp)
+        [7, 8, 1, 5, 3, 4, 2, 0, 9, 6]
+        >>> list(shuffle_dp)
+        [6, 1, 9, 5, 2, 4, 7, 3, 8, 0]
+        >>> # Reset seed for Shuffler
+        >>> shuffle_dp = shuffle_dp.set_seed(0)
+        >>> list(shuffle_dp)
+        [7, 8, 1, 5, 3, 4, 2, 0, 9, 6]
+
+    Note:
+        Even thought this ``shuffle`` operation takes a ``MapDataPipe`` as the input, it would return an
+        ``IterDataPipe`` rather than a ``MapDataPipe``, because ``MapDataPipe`` should be non-sensitive to
+        the order of data order for the sake of random reads, but ``IterDataPipe`` depends on the order
+        of data during data-processing.
+    """
+
+    datapipe: MapDataPipe[_T_co]
+    _enabled: bool
+    _seed: Optional[int]
+    _rng: random.Random
+
+    def __init__(
+        self,
+        datapipe: MapDataPipe[_T_co],
+        *,
+        indices: Optional[list] = None,
+    ) -> None:
+        super().__init__()
+        self.datapipe = datapipe
+        self.indices = list(range(len(datapipe))) if indices is None else indices
+        self._enabled = True
+        self._seed = None
+        self._rng = random.Random()
+        self._shuffled_indices: list = self.indices
+
+    def set_shuffle(self, shuffle=True):
+        self._enabled = shuffle
+        return self
+
+    def set_seed(self, seed: int):
+        self._seed = seed
+        return self
+
+    def __iter__(self) -> Iterator[_T_co]:
+        if not self._enabled:
+            for idx in self.indices:
+                yield self.datapipe[idx]
+        else:
+            while self._shuffled_indices:
+                idx = self._shuffled_indices.pop()
+                yield self.datapipe[idx]
+
+    def reset(self) -> None:
+        if self._enabled and self._seed is None:
+            self._seed = int(torch.empty((), dtype=torch.int64).random_().item())
+        self._rng.seed(self._seed)
+        self._seed = None
+        self._shuffled_indices = self._rng.sample(self.indices, len(self.indices))
+
+    def __len__(self) -> int:
+        return len(self.datapipe)
+
+    def __getstate__(self):
+        state = (
+            self.datapipe,
+            self.indices,
+            self._enabled,
+            self._seed,
+            self._rng.getstate(),
+            self._shuffled_indices,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        )
+        if IterDataPipe.getstate_hook is not None:
+            return IterDataPipe.getstate_hook(state)
+        return state
+
+    def __setstate__(self, state):
+        (
+            self.datapipe,
+            self.indices,
+            self._enabled,
+            self._seed,
+            rng_state,
+            self._shuffled_indices,
+            self._valid_iterator_id,
+            self._number_of_samples_yielded,
+        ) = state
+        self._rng = random.Random()
+        self._rng.setstate(rng_state)
+
+
+MapDataPipe.register_datapipe_as_function("shuffle", ShufflerIterDataPipe)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py
new file mode 100644
index 0000000000000000000000000000000000000000..97f9ef142a7c2822d24145cc93fbb75f7ef5a71d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/combining.py
@@ -0,0 +1,105 @@
+# mypy: allow-untyped-defs
+from collections.abc import Sized
+from typing import TypeVar
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import MapDataPipe
+
+
+__all__ = ["ConcaterMapDataPipe", "ZipperMapDataPipe"]
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+@functional_datapipe("concat")
+class ConcaterMapDataPipe(MapDataPipe):
+    r"""
+    Concatenate multiple Map DataPipes (functional name: ``concat``).
+
+    The new index of is the cumulative sum of source DataPipes.
+    For example, if there are 2 source DataPipes both with length 5,
+    index 0 to 4 of the resulting `ConcatMapDataPipe` would refer to
+    elements of the first DataPipe, and 5 to 9 would refer to elements
+    of the second DataPipe.
+
+    Args:
+        datapipes: Map DataPipes being concatenated
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.map import SequenceWrapper
+        >>> dp1 = SequenceWrapper(range(3))
+        >>> dp2 = SequenceWrapper(range(3))
+        >>> concat_dp = dp1.concat(dp2)
+        >>> list(concat_dp)
+        [0, 1, 2, 0, 1, 2]
+    """
+
+    datapipes: tuple[MapDataPipe]
+
+    def __init__(self, *datapipes: MapDataPipe):
+        if len(datapipes) == 0:
+            raise ValueError("Expected at least one DataPipe, but got nothing")
+        if not all(isinstance(dp, MapDataPipe) for dp in datapipes):
+            raise TypeError("Expected all inputs to be `MapDataPipe`")
+        if not all(isinstance(dp, Sized) for dp in datapipes):
+            raise TypeError("Expected all inputs to be `Sized`")
+        self.datapipes = datapipes  # type: ignore[assignment]
+
+    def __getitem__(self, index) -> _T_co:  # type: ignore[type-var]
+        offset = 0
+        for dp in self.datapipes:
+            if index - offset < len(dp):
+                return dp[index - offset]
+            else:
+                offset += len(dp)
+        raise IndexError(f"Index {index} is out of range.")
+
+    def __len__(self) -> int:
+        return sum(len(dp) for dp in self.datapipes)
+
+
+@functional_datapipe("zip")
+class ZipperMapDataPipe(MapDataPipe[tuple[_T_co, ...]]):
+    r"""
+    Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``).
+
+    This MataPipe is out of bound as soon as the shortest input DataPipe is exhausted.
+
+    Args:
+        *datapipes: Map DataPipes being aggregated
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.map import SequenceWrapper
+        >>> dp1 = SequenceWrapper(range(3))
+        >>> dp2 = SequenceWrapper(range(10, 13))
+        >>> zip_dp = dp1.zip(dp2)
+        >>> list(zip_dp)
+        [(0, 10), (1, 11), (2, 12)]
+    """
+
+    datapipes: tuple[MapDataPipe[_T_co], ...]
+
+    def __init__(self, *datapipes: MapDataPipe[_T_co]) -> None:
+        if len(datapipes) == 0:
+            raise ValueError("Expected at least one DataPipe, but got nothing")
+        if not all(isinstance(dp, MapDataPipe) for dp in datapipes):
+            raise TypeError("Expected all inputs to be `MapDataPipe`")
+        if not all(isinstance(dp, Sized) for dp in datapipes):
+            raise TypeError("Expected all inputs to be `Sized`")
+        self.datapipes = datapipes
+
+    def __getitem__(self, index) -> tuple[_T_co, ...]:
+        res = []
+        for dp in self.datapipes:
+            try:
+                res.append(dp[index])
+            except IndexError as e:
+                raise IndexError(
+                    f"Index {index} is out of range for one of the input MapDataPipes {dp}."
+                ) from e
+        return tuple(res)
+
+    def __len__(self) -> int:
+        return min(len(dp) for dp in self.datapipes)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/grouping.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/grouping.py
new file mode 100644
index 0000000000000000000000000000000000000000..e77f96730e5adb521466c782ce49deba05316c59
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/grouping.py
@@ -0,0 +1,74 @@
+# mypy: allow-untyped-defs
+from collections.abc import Sized
+from typing import TypeVar
+
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import DataChunk, MapDataPipe
+
+
+__all__ = ["BatcherMapDataPipe"]
+
+
+_T = TypeVar("_T")
+
+
+@functional_datapipe("batch")
+class BatcherMapDataPipe(MapDataPipe[DataChunk]):
+    r"""
+    Create mini-batches of data (functional name: ``batch``).
+
+    An outer dimension will be added as ``batch_size`` if ``drop_last`` is set to ``True``,
+    or ``length % batch_size`` for the last batch if ``drop_last`` is set to ``False``.
+
+    Args:
+        datapipe: Iterable DataPipe being batched
+        batch_size: The size of each batch
+        drop_last: Option to drop the last batch if it's not full
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.map import SequenceWrapper
+        >>> dp = SequenceWrapper(range(10))
+        >>> batch_dp = dp.batch(batch_size=2)
+        >>> list(batch_dp)
+        [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]
+    """
+
+    datapipe: MapDataPipe
+    batch_size: int
+    drop_last: bool
+
+    def __init__(
+        self,
+        datapipe: MapDataPipe[_T],
+        batch_size: int,
+        drop_last: bool = False,
+        wrapper_class: type[DataChunk] = DataChunk,
+    ) -> None:
+        assert batch_size > 0, "Batch size is required to be larger than 0!"
+        super().__init__()
+        self.datapipe = datapipe
+        self.batch_size = batch_size
+        self.drop_last = drop_last
+        self.wrapper_class = wrapper_class
+
+    def __getitem__(self, index) -> DataChunk:
+        batch: list = []
+        indices = range(index * self.batch_size, (index + 1) * self.batch_size)
+        try:
+            batch.extend(self.datapipe[i] for i in indices)
+            return self.wrapper_class(batch)
+        except IndexError as e:
+            if not self.drop_last and len(batch) > 0:
+                return self.wrapper_class(batch)
+            else:
+                raise IndexError(f"Index {index} is out of bound.") from e
+
+    def __len__(self) -> int:
+        if isinstance(self.datapipe, Sized):
+            if self.drop_last:
+                return len(self.datapipe) // self.batch_size
+            else:
+                return (len(self.datapipe) + self.batch_size - 1) // self.batch_size
+        else:
+            raise TypeError(f"{type(self).__name__} instance doesn't have valid length")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..38e8b8ff56fe440f341f1b1d402f5aaf5446c445
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/map/utils.py
@@ -0,0 +1,53 @@
+# mypy: allow-untyped-defs
+import copy
+import warnings
+
+from torch.utils.data.datapipes.datapipe import MapDataPipe
+
+
+__all__ = ["SequenceWrapperMapDataPipe"]
+
+
+class SequenceWrapperMapDataPipe(MapDataPipe):
+    r"""
+    Wraps a sequence object into a MapDataPipe.
+
+    Args:
+        sequence: Sequence object to be wrapped into an MapDataPipe
+        deepcopy: Option to deepcopy input sequence object
+
+    .. note::
+      If ``deepcopy`` is set to False explicitly, users should ensure
+      that data pipeline doesn't contain any in-place operations over
+      the iterable instance, in order to prevent data inconsistency
+      across iterations.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> from torchdata.datapipes.map import SequenceWrapper
+        >>> dp = SequenceWrapper(range(10))
+        >>> list(dp)
+        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+        >>> dp = SequenceWrapper({'a': 100, 'b': 200, 'c': 300, 'd': 400})
+        >>> dp['a']
+        100
+    """
+
+    def __init__(self, sequence, deepcopy=True):
+        if deepcopy:
+            try:
+                self.sequence = copy.deepcopy(sequence)
+            except TypeError:
+                warnings.warn(
+                    "The input sequence can not be deepcopied, "
+                    "please be aware of in-place modification would affect source data"
+                )
+                self.sequence = sequence
+        else:
+            self.sequence = sequence
+
+    def __getitem__(self, index):
+        return self.sequence[index]
+
+    def __len__(self):
+        return len(self.sequence)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/__init__.py
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py
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index 0000000000000000000000000000000000000000..ddf3eecdd949fac30eb9f2cecb4a22f39ce005df
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/common.py
@@ -0,0 +1,412 @@
+# mypy: allow-untyped-defs
+import fnmatch
+import functools
+import inspect
+import os
+import warnings
+from collections.abc import Iterable
+from io import IOBase
+from typing import Any, Callable, Optional, Union
+
+from torch.utils._import_utils import dill_available
+
+
+__all__ = [
+    "validate_input_col",
+    "StreamWrapper",
+    "get_file_binaries_from_pathnames",
+    "get_file_pathnames_from_root",
+    "match_masks",
+    "validate_pathname_binary_tuple",
+]
+
+
+# BC for torchdata
+DILL_AVAILABLE = dill_available()
+
+
+def validate_input_col(fn: Callable, input_col: Optional[Union[int, tuple, list]]):
+    """
+    Check that function used in a callable datapipe works with the input column.
+
+    This simply ensures that the number of positional arguments matches the size
+    of the input column. The function must not contain any non-default
+    keyword-only arguments.
+
+    Examples:
+        >>> # xdoctest: +SKIP("Failing on some CI machines")
+        >>> def f(a, b, *, c=1):
+        >>>     return a + b + c
+        >>> def f_def(a, b=1, *, c=1):
+        >>>     return a + b + c
+        >>> assert validate_input_col(f, [1, 2])
+        >>> assert validate_input_col(f_def, 1)
+        >>> assert validate_input_col(f_def, [1, 2])
+
+    Notes:
+        If the function contains variable positional (`inspect.VAR_POSITIONAL`) arguments,
+        for example, f(a, *args), the validator will accept any size of input column
+        greater than or equal to the number of positional arguments.
+        (in this case, 1).
+
+    Args:
+        fn: The function to check.
+        input_col: The input column to check.
+
+    Raises:
+        ValueError: If the function is not compatible with the input column.
+    """
+    try:
+        sig = inspect.signature(fn)
+    except (
+        ValueError
+    ):  # Signature cannot be inspected, likely it is a built-in fn or written in C
+        return
+    if isinstance(input_col, (list, tuple)):
+        input_col_size = len(input_col)
+    else:
+        input_col_size = 1
+
+    pos = []
+    var_positional = False
+    non_default_kw_only = []
+
+    for p in sig.parameters.values():
+        if p.kind in (
+            inspect.Parameter.POSITIONAL_ONLY,
+            inspect.Parameter.POSITIONAL_OR_KEYWORD,
+        ):
+            pos.append(p)
+        elif p.kind is inspect.Parameter.VAR_POSITIONAL:
+            var_positional = True
+        elif p.kind is inspect.Parameter.KEYWORD_ONLY:
+            if p.default is p.empty:
+                non_default_kw_only.append(p)
+        else:
+            continue
+
+    if isinstance(fn, functools.partial):
+        fn_name = getattr(fn.func, "__name__", repr(fn.func))
+    else:
+        fn_name = getattr(fn, "__name__", repr(fn))
+
+    if len(non_default_kw_only) > 0:
+        raise ValueError(
+            f"The function {fn_name} takes {len(non_default_kw_only)} "
+            f"non-default keyword-only parameters, which is not allowed."
+        )
+
+    if len(sig.parameters) < input_col_size:
+        if not var_positional:
+            raise ValueError(
+                f"The function {fn_name} takes {len(sig.parameters)} "
+                f"parameters, but {input_col_size} are required."
+            )
+    else:
+        if len(pos) > input_col_size:
+            if any(p.default is p.empty for p in pos[input_col_size:]):
+                raise ValueError(
+                    f"The function {fn_name} takes {len(pos)} "
+                    f"positional parameters, but {input_col_size} are required."
+                )
+        elif len(pos) < input_col_size:
+            if not var_positional:
+                raise ValueError(
+                    f"The function {fn_name} takes {len(pos)} "
+                    f"positional parameters, but {input_col_size} are required."
+                )
+
+
+def _is_local_fn(fn):
+    # Functions or Methods
+    if hasattr(fn, "__code__"):
+        return fn.__code__.co_flags & inspect.CO_NESTED
+    # Callable Objects
+    else:
+        if hasattr(fn, "__qualname__"):
+            return "" in fn.__qualname__
+        fn_type = type(fn)
+        if hasattr(fn_type, "__qualname__"):
+            return "" in fn_type.__qualname__
+    return False
+
+
+def _check_unpickable_fn(fn: Callable):
+    """
+    Check function is pickable or not.
+
+    If it is a lambda or local function, a UserWarning will be raised. If it's not a callable function, a TypeError will be raised.
+    """
+    if not callable(fn):
+        raise TypeError(f"A callable function is expected, but {type(fn)} is provided.")
+
+    # Extract function from partial object
+    # Nested partial function is automatically expanded as a single partial object
+    if isinstance(fn, functools.partial):
+        fn = fn.func
+
+    # Local function
+    if _is_local_fn(fn) and not dill_available():
+        warnings.warn(
+            "Local function is not supported by pickle, please use "
+            "regular python function or functools.partial instead."
+        )
+        return
+
+    # Lambda function
+    if hasattr(fn, "__name__") and fn.__name__ == "" and not dill_available():
+        warnings.warn(
+            "Lambda function is not supported by pickle, please use "
+            "regular python function or functools.partial instead."
+        )
+        return
+
+
+def match_masks(name: str, masks: Union[str, list[str]]) -> bool:
+    # empty mask matches any input name
+    if not masks:
+        return True
+
+    if isinstance(masks, str):
+        return fnmatch.fnmatch(name, masks)
+
+    for mask in masks:
+        if fnmatch.fnmatch(name, mask):
+            return True
+    return False
+
+
+def get_file_pathnames_from_root(
+    root: str,
+    masks: Union[str, list[str]],
+    recursive: bool = False,
+    abspath: bool = False,
+    non_deterministic: bool = False,
+) -> Iterable[str]:
+    # print out an error message and raise the error out
+    def onerror(err: OSError):
+        warnings.warn(err.filename + " : " + err.strerror)
+        raise err
+
+    if os.path.isfile(root):
+        path = root
+        if abspath:
+            path = os.path.abspath(path)
+        fname = os.path.basename(path)
+        if match_masks(fname, masks):
+            yield path
+    else:
+        for path, dirs, files in os.walk(root, onerror=onerror):
+            if abspath:
+                path = os.path.abspath(path)
+            if not non_deterministic:
+                files.sort()
+            for f in files:
+                if match_masks(f, masks):
+                    yield os.path.join(path, f)
+            if not recursive:
+                break
+            if not non_deterministic:
+                # Note that this is in-place modifying the internal list from `os.walk`
+                # This only works because `os.walk` doesn't shallow copy before turn
+                # https://github.com/python/cpython/blob/f4c03484da59049eb62a9bf7777b963e2267d187/Lib/os.py#L407
+                dirs.sort()
+
+
+def get_file_binaries_from_pathnames(
+    pathnames: Iterable, mode: str, encoding: Optional[str] = None
+):
+    if not isinstance(pathnames, Iterable):
+        pathnames = [
+            pathnames,
+        ]
+
+    if mode in ("b", "t"):
+        mode = "r" + mode
+
+    for pathname in pathnames:
+        if not isinstance(pathname, str):
+            raise TypeError(
+                f"Expected string type for pathname, but got {type(pathname)}"
+            )
+        yield pathname, StreamWrapper(open(pathname, mode, encoding=encoding))
+
+
+def validate_pathname_binary_tuple(data: tuple[str, IOBase]):
+    if not isinstance(data, tuple):
+        raise TypeError(
+            f"pathname binary data should be tuple type, but it is type {type(data)}"
+        )
+    if len(data) != 2:
+        raise TypeError(
+            f"pathname binary stream tuple length should be 2, but got {len(data)}"
+        )
+    if not isinstance(data[0], str):
+        raise TypeError(
+            f"pathname within the tuple should have string type pathname, but it is type {type(data[0])}"
+        )
+    if not isinstance(data[1], IOBase) and not isinstance(data[1], StreamWrapper):
+        raise TypeError(
+            f"binary stream within the tuple should have IOBase or"
+            f"its subclasses as type, but it is type {type(data[1])}"
+        )
+
+
+# Deprecated function names and its corresponding DataPipe type and kwargs for the `_deprecation_warning` function
+_iter_deprecated_functional_names: dict[str, dict] = {}
+_map_deprecated_functional_names: dict[str, dict] = {}
+
+
+def _deprecation_warning(
+    old_class_name: str,
+    *,
+    deprecation_version: str,
+    removal_version: str,
+    old_functional_name: str = "",
+    old_argument_name: str = "",
+    new_class_name: str = "",
+    new_functional_name: str = "",
+    new_argument_name: str = "",
+    deprecate_functional_name_only: bool = False,
+) -> None:
+    if new_functional_name and not old_functional_name:
+        raise ValueError(
+            "Old functional API needs to be specified for the deprecation warning."
+        )
+    if new_argument_name and not old_argument_name:
+        raise ValueError(
+            "Old argument name needs to be specified for the deprecation warning."
+        )
+
+    if old_functional_name and old_argument_name:
+        raise ValueError(
+            "Deprecating warning for functional API and argument should be separated."
+        )
+
+    msg = f"`{old_class_name}()`"
+    if deprecate_functional_name_only and old_functional_name:
+        msg = f"{msg}'s functional API `.{old_functional_name}()` is"
+    elif old_functional_name:
+        msg = f"{msg} and its functional API `.{old_functional_name}()` are"
+    elif old_argument_name:
+        msg = f"The argument `{old_argument_name}` of {msg} is"
+    else:
+        msg = f"{msg} is"
+    msg = (
+        f"{msg} deprecated since {deprecation_version} and will be removed in {removal_version}."
+        f"\nSee https://github.com/pytorch/data/issues/163 for details."
+    )
+
+    if new_class_name or new_functional_name:
+        msg = f"{msg}\nPlease use"
+        if new_class_name:
+            msg = f"{msg} `{new_class_name}()`"
+        if new_class_name and new_functional_name:
+            msg = f"{msg} or"
+        if new_functional_name:
+            msg = f"{msg} `.{new_functional_name}()`"
+        msg = f"{msg} instead."
+
+    if new_argument_name:
+        msg = f"{msg}\nPlease use `{old_class_name}({new_argument_name}=)` instead."
+
+    warnings.warn(msg, FutureWarning)
+
+
+class StreamWrapper:
+    """
+    StreamWrapper is introduced to wrap file handler generated by DataPipe operation like `FileOpener`.
+
+    StreamWrapper would guarantee the wrapped file handler is closed when it's out of scope.
+    """
+
+    session_streams: dict[Any, int] = {}
+    debug_unclosed_streams: bool = False
+
+    def __init__(self, file_obj, parent_stream=None, name=None):
+        self.file_obj = file_obj
+        self.child_counter = 0
+        self.parent_stream = parent_stream
+        self.close_on_last_child = False
+        self.name = name
+        self.closed = False
+        if parent_stream is not None:
+            if not isinstance(parent_stream, StreamWrapper):
+                raise RuntimeError(
+                    f"Parent stream should be StreamWrapper, {type(parent_stream)} was given"
+                )
+            parent_stream.child_counter += 1
+            self.parent_stream = parent_stream
+        if StreamWrapper.debug_unclosed_streams:
+            StreamWrapper.session_streams[self] = 1
+
+    @classmethod
+    def close_streams(cls, v, depth=0):
+        """Traverse structure and attempts to close all found StreamWrappers on best effort basis."""
+        if depth > 10:
+            return
+        if isinstance(v, StreamWrapper):
+            v.close()
+        else:
+            # Traverse only simple structures
+            if isinstance(v, dict):
+                for vv in v.values():
+                    cls.close_streams(vv, depth=depth + 1)
+            elif isinstance(v, (list, tuple)):
+                for vv in v:
+                    cls.close_streams(vv, depth=depth + 1)
+
+    def __getattr__(self, name):
+        file_obj = self.__dict__["file_obj"]
+        return getattr(file_obj, name)
+
+    def close(self, *args, **kwargs):
+        if self.closed:
+            return
+        if StreamWrapper.debug_unclosed_streams:
+            del StreamWrapper.session_streams[self]
+        if hasattr(self, "parent_stream") and self.parent_stream is not None:
+            self.parent_stream.child_counter -= 1
+            if (
+                not self.parent_stream.child_counter
+                and self.parent_stream.close_on_last_child
+            ):
+                self.parent_stream.close()
+        try:
+            self.file_obj.close(*args, **kwargs)
+        except AttributeError:
+            pass
+        self.closed = True
+
+    def autoclose(self):
+        """Automatically close stream when all child streams are closed or if there are none."""
+        self.close_on_last_child = True
+        if self.child_counter == 0:
+            self.close()
+
+    def __dir__(self):
+        attrs = list(self.__dict__.keys()) + list(StreamWrapper.__dict__.keys())
+        attrs += dir(self.file_obj)
+        return list(set(attrs))
+
+    def __del__(self):
+        if not self.closed:
+            self.close()
+
+    def __iter__(self):
+        yield from self.file_obj
+
+    def __next__(self):
+        return next(self.file_obj)
+
+    def __repr__(self):
+        if self.name is None:
+            return f"StreamWrapper<{self.file_obj!r}>"
+        else:
+            return f"StreamWrapper<{self.name},{self.file_obj!r}>"
+
+    def __getstate__(self):
+        return self.file_obj
+
+    def __setstate__(self, obj):
+        self.file_obj = obj
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/decoder.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc32ae6dc223f726300aac25b65404181e4a95f6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/decoder.py
@@ -0,0 +1,378 @@
+# mypy: allow-untyped-defs
+# This file takes partial of the implementation from NVIDIA's webdataset at here:
+# https://github.com/tmbdev/webdataset/blob/master/webdataset/autodecode.py
+
+import io
+import json
+import os.path
+import pickle
+import tempfile
+
+import torch
+from torch.utils.data.datapipes.utils.common import StreamWrapper
+
+
+__all__ = [
+    "Decoder",
+    "ImageHandler",
+    "MatHandler",
+    "audiohandler",
+    "basichandlers",
+    "extension_extract_fn",
+    "handle_extension",
+    "imagehandler",
+    "mathandler",
+    "videohandler",
+]
+
+
+################################################################
+# handle basic datatypes
+################################################################
+def basichandlers(extension: str, data):
+    """Transforms raw data (byte stream) into python objects.
+
+    Looks at the extension and loads the data into a python object supporting
+    the corresponding extension.
+
+    Args:
+        extension (str): The file extension
+        data (byte stream): Data to load into a python object.
+
+    Returns:
+        object: The data loaded into a corresponding python object
+            supporting the extension.
+
+    Example:
+        >>> import pickle
+        >>> data = pickle.dumps('some data')
+        >>> new_data = basichandlers('pickle', data)
+        >>> new_data
+        some data
+
+    The transformation of data for extensions are:
+        - txt, text, transcript: utf-8 decoded data of str format
+        - cls, cls2, class, count, index, inx, id: int
+        - json, jsn: json loaded data
+        - pickle, pyd: pickle loaded data
+        - pt: torch loaded data
+    """
+
+    if extension in "txt text transcript":
+        return data.decode("utf-8")
+
+    if extension in "cls cls2 class count index inx id".split():
+        try:
+            return int(data)
+        except ValueError:
+            return None
+
+    if extension in "json jsn":
+        return json.loads(data)
+
+    if extension in "pyd pickle".split():
+        return pickle.loads(data)
+
+    if extension in "pt".split():
+        stream = io.BytesIO(data)
+        return torch.load(stream)
+
+    # if extension in "ten tb".split():
+    #     from . import tenbin
+    #     return tenbin.decode_buffer(data)
+
+    # if extension in "mp msgpack msg".split():
+    #     import msgpack
+    #     return msgpack.unpackb(data)
+
+    return None
+
+
+################################################################
+# handle images
+################################################################
+imagespecs = {
+    "l8": ("numpy", "uint8", "l"),
+    "rgb8": ("numpy", "uint8", "rgb"),
+    "rgba8": ("numpy", "uint8", "rgba"),
+    "l": ("numpy", "float", "l"),
+    "rgb": ("numpy", "float", "rgb"),
+    "rgba": ("numpy", "float", "rgba"),
+    "torchl8": ("torch", "uint8", "l"),
+    "torchrgb8": ("torch", "uint8", "rgb"),
+    "torchrgba8": ("torch", "uint8", "rgba"),
+    "torchl": ("torch", "float", "l"),
+    "torchrgb": ("torch", "float", "rgb"),
+    "torch": ("torch", "float", "rgb"),
+    "torchrgba": ("torch", "float", "rgba"),
+    "pill": ("pil", None, "l"),
+    "pil": ("pil", None, "rgb"),
+    "pilrgb": ("pil", None, "rgb"),
+    "pilrgba": ("pil", None, "rgba"),
+}
+
+
+def handle_extension(extensions, f):
+    """
+    Return a decoder handler function for the list of extensions.
+
+    Extensions can be a space separated list of extensions.
+    Extensions can contain dots, in which case the corresponding number
+    of extension components must be present in the key given to f.
+    Comparisons are case insensitive.
+    Examples:
+    handle_extension("jpg jpeg", my_decode_jpg)  # invoked for any file.jpg
+    handle_extension("seg.jpg", special_case_jpg)  # invoked only for file.seg.jpg
+    """
+    extensions = extensions.lower().split()
+
+    def g(key, data):
+        extension = key.lower().split(".")
+
+        for target in extensions:
+            target = target.split(".")
+            if len(target) > len(extension):
+                continue
+
+            if extension[-len(target) :] == target:
+                return f(data)
+            return None
+
+    return g
+
+
+class ImageHandler:
+    """
+    Decode image data using the given `imagespec`.
+
+    The `imagespec` specifies whether the image is decoded
+    to numpy/torch/pi, decoded to uint8/float, and decoded
+    to l/rgb/rgba:
+
+    - l8: numpy uint8 l
+    - rgb8: numpy uint8 rgb
+    - rgba8: numpy uint8 rgba
+    - l: numpy float l
+    - rgb: numpy float rgb
+    - rgba: numpy float rgba
+    - torchl8: torch uint8 l
+    - torchrgb8: torch uint8 rgb
+    - torchrgba8: torch uint8 rgba
+    - torchl: torch float l
+    - torchrgb: torch float rgb
+    - torch: torch float rgb
+    - torchrgba: torch float rgba
+    - pill: pil None l
+    - pil: pil None rgb
+    - pilrgb: pil None rgb
+    - pilrgba: pil None rgba
+    """
+
+    def __init__(self, imagespec):
+        assert imagespec in list(
+            imagespecs.keys()
+        ), f"unknown image specification: {imagespec}"
+        self.imagespec = imagespec.lower()
+
+    def __call__(self, extension, data):
+        if extension.lower() not in "jpg jpeg png ppm pgm pbm pnm".split():
+            return None
+
+        try:
+            import numpy as np
+        except ModuleNotFoundError as e:
+            raise ModuleNotFoundError(
+                "Package `numpy` is required to be installed for default image decoder."
+                "Please use `pip install numpy` to install the package"
+            ) from e
+
+        try:
+            import PIL.Image
+        except ModuleNotFoundError as e:
+            raise ModuleNotFoundError(
+                "Package `PIL` is required to be installed for default image decoder."
+                "Please use `pip install Pillow` to install the package"
+            ) from e
+
+        imagespec = self.imagespec
+        atype, etype, mode = imagespecs[imagespec]
+
+        with io.BytesIO(data) as stream:
+            img = PIL.Image.open(stream)
+            img.load()
+            img = img.convert(mode.upper())
+            if atype == "pil":
+                return img
+            elif atype == "numpy":
+                result = np.asarray(img)
+                assert (
+                    result.dtype == np.uint8
+                ), f"numpy image array should be type uint8, but got {result.dtype}"
+                if etype == "uint8":
+                    return result
+                else:
+                    return result.astype("f") / 255.0
+            elif atype == "torch":
+                result = np.asarray(img)
+                assert (
+                    result.dtype == np.uint8
+                ), f"numpy image array should be type uint8, but got {result.dtype}"
+
+                if etype == "uint8":
+                    result = np.array(result.transpose(2, 0, 1))
+                    return torch.tensor(result)
+                else:
+                    result = np.array(result.transpose(2, 0, 1))
+                    return torch.tensor(result) / 255.0
+            return None
+
+
+def imagehandler(imagespec):
+    return ImageHandler(imagespec)
+
+
+################################################################
+# torch video
+################################################################
+def videohandler(extension, data):
+    if extension not in "mp4 ogv mjpeg avi mov h264 mpg webm wmv".split():
+        return None
+
+    try:
+        import torchvision.io
+    except ImportError as e:
+        raise ModuleNotFoundError(
+            "Package `torchvision` is required to be installed for default video file loader."
+            "Please use `pip install torchvision` or `conda install torchvision -c pytorch`"
+            "to install the package"
+        ) from e
+
+    with tempfile.TemporaryDirectory() as dirname:
+        fname = os.path.join(dirname, f"file.{extension}")
+        with open(fname, "wb") as stream:
+            stream.write(data)
+            return torchvision.io.read_video(fname)
+
+
+################################################################
+# torchaudio
+################################################################
+def audiohandler(extension, data):
+    if extension not in ["flac", "mp3", "sox", "wav", "m4a", "ogg", "wma"]:
+        return None
+
+    try:
+        import torchaudio  # type: ignore[import]
+    except ImportError as e:
+        raise ModuleNotFoundError(
+            "Package `torchaudio` is required to be installed for default audio file loader."
+            "Please use `pip install torchaudio` or `conda install torchaudio -c pytorch`"
+            "to install the package"
+        ) from e
+
+    with tempfile.TemporaryDirectory() as dirname:
+        fname = os.path.join(dirname, f"file.{extension}")
+        with open(fname, "wb") as stream:
+            stream.write(data)
+            return torchaudio.load(fname)
+
+
+################################################################
+# mat
+################################################################
+class MatHandler:
+    def __init__(self, **loadmat_kwargs) -> None:
+        try:
+            import scipy.io as sio
+        except ImportError as e:
+            raise ModuleNotFoundError(
+                "Package `scipy` is required to be installed for mat file."
+                "Please use `pip install scipy` or `conda install scipy`"
+                "to install the package"
+            ) from e
+        self.sio = sio
+        self.loadmat_kwargs = loadmat_kwargs
+
+    def __call__(self, extension, data):
+        if extension != "mat":
+            return None
+        with io.BytesIO(data) as stream:
+            return self.sio.loadmat(stream, **self.loadmat_kwargs)
+
+
+def mathandler(**loadmat_kwargs):
+    return MatHandler(**loadmat_kwargs)
+
+
+################################################################
+# a sample decoder
+################################################################
+# Extract extension from pathname
+def extension_extract_fn(pathname):
+    ext = os.path.splitext(pathname)[1]
+    # Remove dot
+    if ext:
+        ext = ext[1:]
+    return ext
+
+
+class Decoder:
+    """
+    Decode key/data sets using a list of handlers.
+
+    For each key/data item, this iterates through the list of
+    handlers until some handler returns something other than None.
+    """
+
+    def __init__(self, *handler, key_fn=extension_extract_fn):
+        self.handlers = list(handler) if handler else []
+        self.key_fn = key_fn
+
+    # Insert new handler from the beginning of handlers list to make sure the new
+    # handler having the highest priority
+    def add_handler(self, *handler):
+        if not handler:
+            return
+        self.handlers = list(handler) + self.handlers
+
+    @staticmethod
+    def _is_stream_handle(data):
+        obj_to_check = data.file_obj if isinstance(data, StreamWrapper) else data
+        return isinstance(obj_to_check, (io.BufferedIOBase, io.RawIOBase))
+
+    def decode1(self, key, data):
+        if not data:
+            return data
+
+        # if data is a stream handle, we need to read all the content before decoding
+        if Decoder._is_stream_handle(data):
+            ds = data
+            # The behavior of .read can differ between streams (e.g. HTTPResponse), hence this is used instead
+            data = b"".join(data)
+            ds.close()
+
+        for f in self.handlers:
+            result = f(key, data)
+            if result is not None:
+                return result
+        return data
+
+    def decode(self, data):
+        result = {}
+        # single data tuple(pathname, data stream)
+        if isinstance(data, tuple):
+            data = [data]
+
+        if data is not None:
+            for k, v in data:
+                # TODO: xinyu, figure out why Nvidia do this?
+                if k[0] == "_":
+                    if isinstance(v, bytes):
+                        v = v.decode("utf-8")
+                        result[k] = v
+                        continue
+                result[k] = self.decode1(self.key_fn(k), v)
+        return result
+
+    def __call__(self, data):
+        return self.decode(data)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/snapshot.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/snapshot.py
new file mode 100644
index 0000000000000000000000000000000000000000..d120025a934ec2456306c59dd70a6f5d348e16b4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/datapipes/utils/snapshot.py
@@ -0,0 +1,64 @@
+# mypy: allow-untyped-defs
+from torch.utils.data.datapipes._hook_iterator import _SnapshotState
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+from torch.utils.data.graph_settings import apply_random_seed
+
+
+# TODO: Caveats
+#   1. Caller (either the ReadingService or DataLoader) must pass in the initial RNG
+#   2. `in_batch_shuffle` and `bucketbatch` are not compatible with this because they currently
+#      lack the option to `set_seed`.
+def _simple_graph_snapshot_restoration(
+    datapipe: IterDataPipe, n_iterations: int, rng=None
+) -> None:
+    r"""
+    Fast-forward the given DataPipe and its parents by ``n_iterations``, re-doing computations to restore a snapshot.
+
+    For instance, applying this function to the final DataPipe of a graph will restore the snapshot
+    (via fast-forward) every DataPipe within the graph.
+
+    After you deserialize a DataPipe, you can use its `_number_of_samples_yielded` attribute as the input
+    to this function to forward the DataPipe.
+
+    A DataPipe cannot be restored twice in a row unless there is an iteration started between the restoration
+    attempts.
+
+    Note:
+        This is the simplest but least efficient way to fast-forward a DataPipe. Usage of other fast-forwarding
+        methods (custom ones if necessary) are recommended.
+
+    Args:
+        datapipe: IterDataPipe to be fast-forwarded
+        n_iterations: number of iterations to fast-forward
+        rng: ``Optional[torch.Generator]``. If not ``None``, this RNG will be used for shuffling. The generator
+            should be in its `initial` state as it was first passed into ``DataLoader`` or ``ReadingService``.
+    """
+    if datapipe._snapshot_state == _SnapshotState.Restored:
+        raise RuntimeError(
+            "Snapshot restoration cannot be applied. You can only restore simple snapshot to the graph "
+            "if your graph has not been restored."
+        )
+
+    # For this snapshot restoration function, we want the DataPipe to be at its initial state prior to
+    # simple fast-forwarding. Therefore, we need to call `reset` twice, because if `SnapshotState` is `Restored`,
+    # the first reset will not actually reset.
+    datapipe.reset()  # This ensures `SnapshotState` is `Iterating` by this point, even if it was `Restored`.
+    apply_random_seed(datapipe, rng)
+
+    remainder = n_iterations
+    it = iter(datapipe)  # This always reset the DataPipe if it hasn't already.
+    while remainder > 0:
+        try:
+            next(it)
+            remainder -= 1
+        except StopIteration as e:
+            raise RuntimeError(
+                f"Fast-forward {datapipe} by {n_iterations} iterations "
+                "exceeds the number of samples available."
+            ) from e
+    datapipe._fast_forward_iterator = it
+    # While the DataPipe has `_fast_forward_iterator`, `next()` will get result from there instead of elsewhere.
+
+    # This will prevent the DataPipe from resetting in the `iter()` call
+    # If another DataPipe is consuming it, it won't have to start over again
+    datapipe._snapshot_state = _SnapshotState.Restored
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/dataset.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0234c553ce683741a03b4a5a73e226f492d2e44
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/dataset.py
@@ -0,0 +1,485 @@
+# mypy: allow-untyped-defs
+import bisect
+import itertools
+import math
+import warnings
+from collections.abc import Sequence
+
+# UP006 wants 'Iterable' to be imported from collections.abc but it needs to
+# stay from typing for now due to BC concerns. In particular several internal
+# targets fail to typecheck with:
+#     TypeError: Cannot create a consistent method resolution order (MRO) for
+#     bases Iterable, Generic
+from typing import cast, Generic, Iterable, Optional, TypeVar, Union  # noqa: UP035
+from typing_extensions import deprecated
+
+# No 'default_generator' in torch/__init__.pyi
+from torch import default_generator, Generator, randperm, Tensor
+
+
+__all__ = [
+    "Dataset",
+    "IterableDataset",
+    "TensorDataset",
+    "StackDataset",
+    "ConcatDataset",
+    "ChainDataset",
+    "Subset",
+    "random_split",
+]
+
+
+_T = TypeVar("_T")
+_T_co = TypeVar("_T_co", covariant=True)
+_T_dict = dict[str, _T_co]
+_T_tuple = tuple[_T_co, ...]
+_T_stack = TypeVar("_T_stack", _T_tuple, _T_dict)
+
+
+class Dataset(Generic[_T_co]):
+    r"""An abstract class representing a :class:`Dataset`.
+
+    All datasets that represent a map from keys to data samples should subclass
+    it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
+    data sample for a given key. Subclasses could also optionally overwrite
+    :meth:`__len__`, which is expected to return the size of the dataset by many
+    :class:`~torch.utils.data.Sampler` implementations and the default options
+    of :class:`~torch.utils.data.DataLoader`. Subclasses could also
+    optionally implement :meth:`__getitems__`, for speedup batched samples
+    loading. This method accepts list of indices of samples of batch and returns
+    list of samples.
+
+    .. note::
+      :class:`~torch.utils.data.DataLoader` by default constructs an index
+      sampler that yields integral indices.  To make it work with a map-style
+      dataset with non-integral indices/keys, a custom sampler must be provided.
+    """
+
+    def __getitem__(self, index) -> _T_co:
+        raise NotImplementedError("Subclasses of Dataset should implement __getitem__.")
+
+    # def __getitems__(self, indices: List) -> List[_T_co]:
+    # Not implemented to prevent false-positives in fetcher check in
+    # torch.utils.data._utils.fetch._MapDatasetFetcher
+
+    def __add__(self, other: "Dataset[_T_co]") -> "ConcatDataset[_T_co]":
+        return ConcatDataset([self, other])
+
+    # No `def __len__(self)` default?
+    # See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
+    # in pytorch/torch/utils/data/sampler.py
+
+
+class IterableDataset(Dataset[_T_co], Iterable[_T_co]):
+    r"""An iterable Dataset.
+
+    All datasets that represent an iterable of data samples should subclass it.
+    Such form of datasets is particularly useful when data come from a stream.
+
+    All subclasses should overwrite :meth:`__iter__`, which would return an
+    iterator of samples in this dataset.
+
+    When a subclass is used with :class:`~torch.utils.data.DataLoader`, each
+    item in the dataset will be yielded from the :class:`~torch.utils.data.DataLoader`
+    iterator. When :attr:`num_workers > 0`, each worker process will have a
+    different copy of the dataset object, so it is often desired to configure
+    each copy independently to avoid having duplicate data returned from the
+    workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker
+    process, returns information about the worker. It can be used in either the
+    dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's
+    :attr:`worker_init_fn` option to modify each copy's behavior.
+
+    Example 1: splitting workload across all workers in :meth:`__iter__`::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER)
+        >>> # xdoctest: +SKIP("Fails on MacOS12")
+        >>> class MyIterableDataset(torch.utils.data.IterableDataset):
+        ...     def __init__(self, start, end):
+        ...         super(MyIterableDataset).__init__()
+        ...         assert end > start, "this example code only works with end >= start"
+        ...         self.start = start
+        ...         self.end = end
+        ...
+        ...     def __iter__(self):
+        ...         worker_info = torch.utils.data.get_worker_info()
+        ...         if worker_info is None:  # single-process data loading, return the full iterator
+        ...             iter_start = self.start
+        ...             iter_end = self.end
+        ...         else:  # in a worker process
+        ...             # split workload
+        ...             per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))
+        ...             worker_id = worker_info.id
+        ...             iter_start = self.start + worker_id * per_worker
+        ...             iter_end = min(iter_start + per_worker, self.end)
+        ...         return iter(range(iter_start, iter_end))
+        ...
+        >>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
+        >>> ds = MyIterableDataset(start=3, end=7)
+
+        >>> # Single-process loading
+        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
+        [tensor([3]), tensor([4]), tensor([5]), tensor([6])]
+
+        >>> # xdoctest: +REQUIRES(POSIX)
+        >>> # Multi-process loading with two worker processes
+        >>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
+        >>> # xdoctest: +IGNORE_WANT("non deterministic")
+        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
+        [tensor([3]), tensor([5]), tensor([4]), tensor([6])]
+
+        >>> # With even more workers
+        >>> # xdoctest: +IGNORE_WANT("non deterministic")
+        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=12)))
+        [tensor([3]), tensor([5]), tensor([4]), tensor([6])]
+
+    Example 2: splitting workload across all workers using :attr:`worker_init_fn`::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER)
+        >>> class MyIterableDataset(torch.utils.data.IterableDataset):
+        ...     def __init__(self, start, end):
+        ...         super(MyIterableDataset).__init__()
+        ...         assert end > start, "this example code only works with end >= start"
+        ...         self.start = start
+        ...         self.end = end
+        ...
+        ...     def __iter__(self):
+        ...         return iter(range(self.start, self.end))
+        ...
+        >>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
+        >>> ds = MyIterableDataset(start=3, end=7)
+
+        >>> # Single-process loading
+        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
+        [3, 4, 5, 6]
+        >>>
+        >>> # Directly doing multi-process loading yields duplicate data
+        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
+        [3, 3, 4, 4, 5, 5, 6, 6]
+
+        >>> # Define a `worker_init_fn` that configures each dataset copy differently
+        >>> def worker_init_fn(worker_id):
+        ...     worker_info = torch.utils.data.get_worker_info()
+        ...     dataset = worker_info.dataset  # the dataset copy in this worker process
+        ...     overall_start = dataset.start
+        ...     overall_end = dataset.end
+        ...     # configure the dataset to only process the split workload
+        ...     per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))
+        ...     worker_id = worker_info.id
+        ...     dataset.start = overall_start + worker_id * per_worker
+        ...     dataset.end = min(dataset.start + per_worker, overall_end)
+        ...
+
+        >>> # Mult-process loading with the custom `worker_init_fn`
+        >>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
+        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))
+        [3, 5, 4, 6]
+
+        >>> # With even more workers
+        >>> print(list(torch.utils.data.DataLoader(ds, num_workers=12, worker_init_fn=worker_init_fn)))
+        [3, 4, 5, 6]
+    """
+
+    def __add__(self, other: Dataset[_T_co]):
+        return ChainDataset([self, other])
+
+    # No `def __len__(self)` default? Subclasses raise `TypeError` when needed.
+    # See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
+
+
+class TensorDataset(Dataset[tuple[Tensor, ...]]):
+    r"""Dataset wrapping tensors.
+
+    Each sample will be retrieved by indexing tensors along the first dimension.
+
+    Args:
+        *tensors (Tensor): tensors that have the same size of the first dimension.
+    """
+
+    tensors: tuple[Tensor, ...]
+
+    def __init__(self, *tensors: Tensor) -> None:
+        assert all(
+            tensors[0].size(0) == tensor.size(0) for tensor in tensors
+        ), "Size mismatch between tensors"
+        self.tensors = tensors
+
+    def __getitem__(self, index):
+        return tuple(tensor[index] for tensor in self.tensors)
+
+    def __len__(self):
+        return self.tensors[0].size(0)
+
+
+class StackDataset(Dataset[_T_stack]):
+    r"""Dataset as a stacking of multiple datasets.
+
+    This class is useful to assemble different parts of complex input data, given as datasets.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> images = ImageDataset()
+        >>> texts = TextDataset()
+        >>> tuple_stack = StackDataset(images, texts)
+        >>> tuple_stack[0] == (images[0], texts[0])
+        >>> dict_stack = StackDataset(image=images, text=texts)
+        >>> dict_stack[0] == {'image': images[0], 'text': texts[0]}
+
+    Args:
+        *args (Dataset): Datasets for stacking returned as tuple.
+        **kwargs (Dataset): Datasets for stacking returned as dict.
+    """
+
+    datasets: Union[tuple, dict]
+
+    def __init__(self, *args: Dataset[_T_co], **kwargs: Dataset[_T_co]) -> None:
+        if args:
+            if kwargs:
+                raise ValueError(
+                    "Supported either ``tuple``- (via ``args``) or"
+                    "``dict``- (via ``kwargs``) like input/output, but both types are given."
+                )
+            self._length = len(args[0])  # type: ignore[arg-type]
+            if any(self._length != len(dataset) for dataset in args):  # type: ignore[arg-type]
+                raise ValueError("Size mismatch between datasets")
+            self.datasets = args
+        elif kwargs:
+            tmp = list(kwargs.values())
+            self._length = len(tmp[0])  # type: ignore[arg-type]
+            if any(self._length != len(dataset) for dataset in tmp):  # type: ignore[arg-type]
+                raise ValueError("Size mismatch between datasets")
+            self.datasets = kwargs
+        else:
+            raise ValueError("At least one dataset should be passed")
+
+    def __getitem__(self, index):
+        if isinstance(self.datasets, dict):
+            return {k: dataset[index] for k, dataset in self.datasets.items()}
+        return tuple(dataset[index] for dataset in self.datasets)
+
+    def __getitems__(self, indices: list):
+        # add batched sampling support when parent datasets supports it.
+        if isinstance(self.datasets, dict):
+            dict_batch: list[_T_dict] = [{} for _ in indices]
+            for k, dataset in self.datasets.items():
+                if callable(getattr(dataset, "__getitems__", None)):
+                    items = dataset.__getitems__(indices)  # type: ignore[attr-defined]
+                    if len(items) != len(indices):
+                        raise ValueError(
+                            "Nested dataset's output size mismatch."
+                            f" Expected {len(indices)}, got {len(items)}"
+                        )
+                    for data, d_sample in zip(items, dict_batch):
+                        d_sample[k] = data
+                else:
+                    for idx, d_sample in zip(indices, dict_batch):
+                        d_sample[k] = dataset[idx]
+            return dict_batch
+
+        # tuple data
+        list_batch: list[list] = [[] for _ in indices]
+        for dataset in self.datasets:
+            if callable(getattr(dataset, "__getitems__", None)):
+                items = dataset.__getitems__(indices)  # type: ignore[attr-defined]
+                if len(items) != len(indices):
+                    raise ValueError(
+                        "Nested dataset's output size mismatch."
+                        f" Expected {len(indices)}, got {len(items)}"
+                    )
+                for data, t_sample in zip(items, list_batch):
+                    t_sample.append(data)
+            else:
+                for idx, t_sample in zip(indices, list_batch):
+                    t_sample.append(dataset[idx])
+        tuple_batch: list[_T_tuple] = [tuple(sample) for sample in list_batch]
+        return tuple_batch
+
+    def __len__(self):
+        return self._length
+
+
+class ConcatDataset(Dataset[_T_co]):
+    r"""Dataset as a concatenation of multiple datasets.
+
+    This class is useful to assemble different existing datasets.
+
+    Args:
+        datasets (sequence): List of datasets to be concatenated
+    """
+
+    datasets: list[Dataset[_T_co]]
+    cumulative_sizes: list[int]
+
+    @staticmethod
+    def cumsum(sequence):
+        r, s = [], 0
+        for e in sequence:
+            l = len(e)
+            r.append(l + s)
+            s += l
+        return r
+
+    def __init__(self, datasets: Iterable[Dataset]) -> None:
+        super().__init__()
+        self.datasets = list(datasets)
+        assert len(self.datasets) > 0, "datasets should not be an empty iterable"  # type: ignore[arg-type]
+        for d in self.datasets:
+            assert not isinstance(
+                d, IterableDataset
+            ), "ConcatDataset does not support IterableDataset"
+        self.cumulative_sizes = self.cumsum(self.datasets)
+
+    def __len__(self):
+        return self.cumulative_sizes[-1]
+
+    def __getitem__(self, idx):
+        if idx < 0:
+            if -idx > len(self):
+                raise ValueError(
+                    "absolute value of index should not exceed dataset length"
+                )
+            idx = len(self) + idx
+        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
+        if dataset_idx == 0:
+            sample_idx = idx
+        else:
+            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
+        return self.datasets[dataset_idx][sample_idx]
+
+    @property
+    @deprecated(
+        "`cummulative_sizes` attribute is renamed to `cumulative_sizes`",
+        category=FutureWarning,
+    )
+    def cummulative_sizes(self):
+        return self.cumulative_sizes
+
+
+class ChainDataset(IterableDataset):
+    r"""Dataset for chaining multiple :class:`IterableDataset` s.
+
+    This class is useful to assemble different existing dataset streams. The
+    chaining operation is done on-the-fly, so concatenating large-scale
+    datasets with this class will be efficient.
+
+    Args:
+        datasets (iterable of IterableDataset): datasets to be chained together
+    """
+
+    def __init__(self, datasets: Iterable[Dataset]) -> None:
+        super().__init__()
+        self.datasets = datasets
+
+    def __iter__(self):
+        for d in self.datasets:
+            assert isinstance(
+                d, IterableDataset
+            ), "ChainDataset only supports IterableDataset"
+            yield from d
+
+    def __len__(self):
+        total = 0
+        for d in self.datasets:
+            assert isinstance(
+                d, IterableDataset
+            ), "ChainDataset only supports IterableDataset"
+            total += len(d)  # type: ignore[arg-type]
+        return total
+
+
+class Subset(Dataset[_T_co]):
+    r"""
+    Subset of a dataset at specified indices.
+
+    Args:
+        dataset (Dataset): The whole Dataset
+        indices (sequence): Indices in the whole set selected for subset
+    """
+
+    dataset: Dataset[_T_co]
+    indices: Sequence[int]
+
+    def __init__(self, dataset: Dataset[_T_co], indices: Sequence[int]) -> None:
+        self.dataset = dataset
+        self.indices = indices
+
+    def __getitem__(self, idx):
+        if isinstance(idx, list):
+            return self.dataset[[self.indices[i] for i in idx]]
+        return self.dataset[self.indices[idx]]
+
+    def __getitems__(self, indices: list[int]) -> list[_T_co]:
+        # add batched sampling support when parent dataset supports it.
+        # see torch.utils.data._utils.fetch._MapDatasetFetcher
+        if callable(getattr(self.dataset, "__getitems__", None)):
+            return self.dataset.__getitems__([self.indices[idx] for idx in indices])  # type: ignore[attr-defined]
+        else:
+            return [self.dataset[self.indices[idx]] for idx in indices]
+
+    def __len__(self):
+        return len(self.indices)
+
+
+def random_split(
+    dataset: Dataset[_T],
+    lengths: Sequence[Union[int, float]],
+    generator: Optional[Generator] = default_generator,
+) -> list[Subset[_T]]:
+    r"""
+    Randomly split a dataset into non-overlapping new datasets of given lengths.
+
+    If a list of fractions that sum up to 1 is given,
+    the lengths will be computed automatically as
+    floor(frac * len(dataset)) for each fraction provided.
+
+    After computing the lengths, if there are any remainders, 1 count will be
+    distributed in round-robin fashion to the lengths
+    until there are no remainders left.
+
+    Optionally fix the generator for reproducible results, e.g.:
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> generator1 = torch.Generator().manual_seed(42)
+        >>> generator2 = torch.Generator().manual_seed(42)
+        >>> random_split(range(10), [3, 7], generator=generator1)
+        >>> random_split(range(30), [0.3, 0.3, 0.4], generator=generator2)
+
+    Args:
+        dataset (Dataset): Dataset to be split
+        lengths (sequence): lengths or fractions of splits to be produced
+        generator (Generator): Generator used for the random permutation.
+    """
+    if math.isclose(sum(lengths), 1) and sum(lengths) <= 1:
+        subset_lengths: list[int] = []
+        for i, frac in enumerate(lengths):
+            if frac < 0 or frac > 1:
+                raise ValueError(f"Fraction at index {i} is not between 0 and 1")
+            n_items_in_split = int(
+                math.floor(len(dataset) * frac)  # type: ignore[arg-type]
+            )
+            subset_lengths.append(n_items_in_split)
+        remainder = len(dataset) - sum(subset_lengths)  # type: ignore[arg-type]
+        # add 1 to all the lengths in round-robin fashion until the remainder is 0
+        for i in range(remainder):
+            idx_to_add_at = i % len(subset_lengths)
+            subset_lengths[idx_to_add_at] += 1
+        lengths = subset_lengths
+        for i, length in enumerate(lengths):
+            if length == 0:
+                warnings.warn(
+                    f"Length of split at index {i} is 0. "
+                    f"This might result in an empty dataset."
+                )
+
+    # Cannot verify that dataset is Sized
+    if sum(lengths) != len(dataset):  # type: ignore[arg-type]
+        raise ValueError(
+            "Sum of input lengths does not equal the length of the input dataset!"
+        )
+
+    indices = randperm(sum(lengths), generator=generator).tolist()  # type: ignore[arg-type, call-overload]
+    lengths = cast(Sequence[int], lengths)
+    return [
+        Subset(dataset, indices[offset - length : offset])
+        for offset, length in zip(itertools.accumulate(lengths), lengths)
+    ]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/distributed.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..949e3e0c23b409690ceb0dfa21f16b54c8493320
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/distributed.py
@@ -0,0 +1,150 @@
+import math
+from collections.abc import Iterator
+from typing import Optional, TypeVar
+
+import torch
+import torch.distributed as dist
+from torch.utils.data.dataset import Dataset
+from torch.utils.data.sampler import Sampler
+
+
+__all__ = ["DistributedSampler"]
+
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+class DistributedSampler(Sampler[_T_co]):
+    r"""Sampler that restricts data loading to a subset of the dataset.
+
+    It is especially useful in conjunction with
+    :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
+    process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
+    :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
+    original dataset that is exclusive to it.
+
+    .. note::
+        Dataset is assumed to be of constant size and that any instance of it always
+        returns the same elements in the same order.
+
+    Args:
+        dataset: Dataset used for sampling.
+        num_replicas (int, optional): Number of processes participating in
+            distributed training. By default, :attr:`world_size` is retrieved from the
+            current distributed group.
+        rank (int, optional): Rank of the current process within :attr:`num_replicas`.
+            By default, :attr:`rank` is retrieved from the current distributed
+            group.
+        shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
+            indices.
+        seed (int, optional): random seed used to shuffle the sampler if
+            :attr:`shuffle=True`. This number should be identical across all
+            processes in the distributed group. Default: ``0``.
+        drop_last (bool, optional): if ``True``, then the sampler will drop the
+            tail of the data to make it evenly divisible across the number of
+            replicas. If ``False``, the sampler will add extra indices to make
+            the data evenly divisible across the replicas. Default: ``False``.
+
+    .. warning::
+        In distributed mode, calling the :meth:`set_epoch` method at
+        the beginning of each epoch **before** creating the :class:`DataLoader` iterator
+        is necessary to make shuffling work properly across multiple epochs. Otherwise,
+        the same ordering will be always used.
+
+    Example::
+
+        >>> # xdoctest: +SKIP
+        >>> sampler = DistributedSampler(dataset) if is_distributed else None
+        >>> loader = DataLoader(dataset, shuffle=(sampler is None),
+        ...                     sampler=sampler)
+        >>> for epoch in range(start_epoch, n_epochs):
+        ...     if is_distributed:
+        ...         sampler.set_epoch(epoch)
+        ...     train(loader)
+    """
+
+    def __init__(
+        self,
+        dataset: Dataset,
+        num_replicas: Optional[int] = None,
+        rank: Optional[int] = None,
+        shuffle: bool = True,
+        seed: int = 0,
+        drop_last: bool = False,
+    ) -> None:
+        if num_replicas is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            num_replicas = dist.get_world_size()
+        if rank is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            rank = dist.get_rank()
+        if rank >= num_replicas or rank < 0:
+            raise ValueError(
+                f"Invalid rank {rank}, rank should be in the interval [0, {num_replicas - 1}]"
+            )
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.drop_last = drop_last
+        # If the dataset length is evenly divisible by # of replicas, then there
+        # is no need to drop any data, since the dataset will be split equally.
+        if self.drop_last and len(self.dataset) % self.num_replicas != 0:  # type: ignore[arg-type]
+            # Split to nearest available length that is evenly divisible.
+            # This is to ensure each rank receives the same amount of data when
+            # using this Sampler.
+            self.num_samples = math.ceil(
+                (len(self.dataset) - self.num_replicas) / self.num_replicas  # type: ignore[arg-type]
+            )
+        else:
+            self.num_samples = math.ceil(len(self.dataset) / self.num_replicas)  # type: ignore[arg-type]
+        self.total_size = self.num_samples * self.num_replicas
+        self.shuffle = shuffle
+        self.seed = seed
+
+    def __iter__(self) -> Iterator[_T_co]:
+        if self.shuffle:
+            # deterministically shuffle based on epoch and seed
+            g = torch.Generator()
+            g.manual_seed(self.seed + self.epoch)
+            indices = torch.randperm(len(self.dataset), generator=g).tolist()  # type: ignore[arg-type]
+        else:
+            indices = list(range(len(self.dataset)))  # type: ignore[arg-type]
+
+        if not self.drop_last:
+            # add extra samples to make it evenly divisible
+            padding_size = self.total_size - len(indices)
+            if padding_size <= len(indices):
+                indices += indices[:padding_size]
+            else:
+                indices += (indices * math.ceil(padding_size / len(indices)))[
+                    :padding_size
+                ]
+        else:
+            # remove tail of data to make it evenly divisible.
+            indices = indices[: self.total_size]
+        assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.rank : self.total_size : self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self) -> int:
+        return self.num_samples
+
+    def set_epoch(self, epoch: int) -> None:
+        r"""
+        Set the epoch for this sampler.
+
+        When :attr:`shuffle=True`, this ensures all replicas
+        use a different random ordering for each epoch. Otherwise, the next iteration of this
+        sampler will yield the same ordering.
+
+        Args:
+            epoch (int): Epoch number.
+        """
+        self.epoch = epoch
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/graph.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..26a4eae6d18c32954d784310b5e87834332662ae
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/graph.py
@@ -0,0 +1,161 @@
+# mypy: allow-untyped-defs
+import io
+import pickle
+import warnings
+from collections.abc import Collection
+from typing import Optional, Union
+
+from torch.utils._import_utils import dill_available
+from torch.utils.data.datapipes.datapipe import IterDataPipe, MapDataPipe
+
+
+__all__ = ["traverse", "traverse_dps"]
+
+DataPipe = Union[IterDataPipe, MapDataPipe]
+DataPipeGraph = dict[int, tuple[DataPipe, "DataPipeGraph"]]
+
+
+def _stub_unpickler():
+    return "STUB"
+
+
+# TODO(VitalyFedyunin): Make sure it works without dill module installed
+def _list_connected_datapipes(
+    scan_obj: DataPipe, only_datapipe: bool, cache: set[int]
+) -> list[DataPipe]:
+    f = io.BytesIO()
+    p = pickle.Pickler(
+        f
+    )  # Not going to work for lambdas, but dill infinite loops on typing and can't be used as is
+    if dill_available():
+        from dill import Pickler as dill_Pickler
+
+        d = dill_Pickler(f)
+    else:
+        d = None
+
+    captured_connections = []
+
+    def getstate_hook(ori_state):
+        state = None
+        if isinstance(ori_state, dict):
+            state = {}
+            for k, v in ori_state.items():
+                if isinstance(v, (IterDataPipe, MapDataPipe, Collection)):
+                    state[k] = v
+        elif isinstance(ori_state, (tuple, list)):
+            state = []  # type: ignore[assignment]
+            for v in ori_state:
+                if isinstance(v, (IterDataPipe, MapDataPipe, Collection)):
+                    state.append(v)  # type: ignore[attr-defined]
+        elif isinstance(ori_state, (IterDataPipe, MapDataPipe, Collection)):
+            state = ori_state  # type: ignore[assignment]
+        return state
+
+    def reduce_hook(obj):
+        if obj == scan_obj or id(obj) in cache:
+            raise NotImplementedError
+        else:
+            captured_connections.append(obj)
+            # Adding id to remove duplicate DataPipe serialized at the same level
+            cache.add(id(obj))
+            return _stub_unpickler, ()
+
+    datapipe_classes: tuple[type[DataPipe]] = (IterDataPipe, MapDataPipe)  # type: ignore[assignment]
+
+    try:
+        for cls in datapipe_classes:
+            cls.set_reduce_ex_hook(reduce_hook)
+            if only_datapipe:
+                cls.set_getstate_hook(getstate_hook)
+        try:
+            p.dump(scan_obj)
+        except (pickle.PickleError, AttributeError, TypeError):
+            if dill_available():
+                d.dump(scan_obj)
+            else:
+                raise
+    finally:
+        for cls in datapipe_classes:
+            cls.set_reduce_ex_hook(None)
+            if only_datapipe:
+                cls.set_getstate_hook(None)
+        if dill_available():
+            from dill import extend as dill_extend
+
+            dill_extend(False)  # Undo change to dispatch table
+    return captured_connections
+
+
+def traverse_dps(datapipe: DataPipe) -> DataPipeGraph:
+    r"""
+    Traverse the DataPipes and their attributes to extract the DataPipe graph.
+
+    This only looks into the attribute from each DataPipe that is either a
+    DataPipe and a Python collection object such as ``list``, ``tuple``,
+    ``set`` and ``dict``.
+
+    Args:
+        datapipe: the end DataPipe of the graph
+    Returns:
+        A graph represented as a nested dictionary, where keys are ids of DataPipe instances
+        and values are tuples of DataPipe instance and the sub-graph
+    """
+    cache: set[int] = set()
+    return _traverse_helper(datapipe, only_datapipe=True, cache=cache)
+
+
+def traverse(datapipe: DataPipe, only_datapipe: Optional[bool] = None) -> DataPipeGraph:
+    r"""
+    Traverse the DataPipes and their attributes to extract the DataPipe graph.
+
+    [Deprecated]
+    When ``only_dataPipe`` is specified as ``True``, it would only look into the
+    attribute from each DataPipe that is either a DataPipe and a Python collection object
+    such as ``list``, ``tuple``, ``set`` and ``dict``.
+
+    Note:
+        This function is deprecated. Please use `traverse_dps` instead.
+
+    Args:
+        datapipe: the end DataPipe of the graph
+        only_datapipe: If ``False`` (default), all attributes of each DataPipe are traversed.
+          This argument is deprecating and will be removed after the next release.
+    Returns:
+        A graph represented as a nested dictionary, where keys are ids of DataPipe instances
+        and values are tuples of DataPipe instance and the sub-graph
+    """
+    msg = (
+        "`traverse` function and will be removed after 1.13. "
+        "Please use `traverse_dps` instead."
+    )
+    if not only_datapipe:
+        msg += " And, the behavior will be changed to the equivalent of `only_datapipe=True`."
+    warnings.warn(msg, FutureWarning)
+    if only_datapipe is None:
+        only_datapipe = False
+    cache: set[int] = set()
+    return _traverse_helper(datapipe, only_datapipe, cache)
+
+
+# Add cache here to prevent infinite recursion on DataPipe
+def _traverse_helper(
+    datapipe: DataPipe, only_datapipe: bool, cache: set[int]
+) -> DataPipeGraph:
+    if not isinstance(datapipe, (IterDataPipe, MapDataPipe)):
+        raise RuntimeError(
+            f"Expected `IterDataPipe` or `MapDataPipe`, but {type(datapipe)} is found"
+        )
+
+    dp_id = id(datapipe)
+    if dp_id in cache:
+        return {}
+    cache.add(dp_id)
+    # Using cache.copy() here is to prevent the same DataPipe pollutes the cache on different paths
+    items = _list_connected_datapipes(datapipe, only_datapipe, cache.copy())
+    d: DataPipeGraph = {dp_id: (datapipe, {})}
+    for item in items:
+        # Using cache.copy() here is to prevent recursion on a single path rather than global graph
+        # Single DataPipe can present multiple times in different paths in graph
+        d[dp_id][1].update(_traverse_helper(item, only_datapipe, cache.copy()))
+    return d
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/graph_settings.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/graph_settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cc16c86b0f3d27ab05dae069bc3ed7a87b297ab
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/graph_settings.py
@@ -0,0 +1,174 @@
+# mypy: allow-untyped-defs
+import inspect
+import warnings
+from typing import Any, Optional
+from typing_extensions import deprecated
+
+import torch
+from torch.utils.data.datapipes.iter.sharding import (
+    _ShardingIterDataPipe,
+    SHARDING_PRIORITIES,
+)
+from torch.utils.data.graph import DataPipe, DataPipeGraph, traverse_dps
+
+
+__all__ = [
+    "apply_random_seed",
+    "apply_sharding",
+    "apply_shuffle_seed",
+    "apply_shuffle_settings",
+    "get_all_graph_pipes",
+]
+
+
+def get_all_graph_pipes(graph: DataPipeGraph) -> list[DataPipe]:
+    return _get_all_graph_pipes_helper(graph, set())
+
+
+def _get_all_graph_pipes_helper(
+    graph: DataPipeGraph, id_cache: set[int]
+) -> list[DataPipe]:
+    results: list[DataPipe] = []
+    for dp_id, (datapipe, sub_graph) in graph.items():
+        if dp_id in id_cache:
+            continue
+        id_cache.add(dp_id)
+        results.append(datapipe)
+        results.extend(_get_all_graph_pipes_helper(sub_graph, id_cache))
+    return results
+
+
+def _is_sharding_datapipe(datapipe: DataPipe) -> bool:
+    return isinstance(datapipe, _ShardingIterDataPipe) or (
+        hasattr(datapipe, "apply_sharding")
+        and inspect.ismethod(datapipe.apply_sharding)
+    )
+
+
+def apply_sharding(
+    datapipe: DataPipe,
+    num_of_instances: int,
+    instance_id: int,
+    sharding_group=SHARDING_PRIORITIES.DEFAULT,
+) -> DataPipe:
+    r"""
+    Apply dynamic sharding over the ``sharding_filter`` DataPipe that has a method ``apply_sharding``.
+
+    RuntimeError will be raised when multiple ``sharding_filter`` are presented in the same branch.
+    """
+    graph = traverse_dps(datapipe)
+
+    def _helper(graph, prev_applied=None):
+        for dp, sub_graph in graph.values():
+            applied = None
+            if _is_sharding_datapipe(dp):
+                if prev_applied is not None:
+                    raise RuntimeError(
+                        "Sharding twice on a single pipeline is likely unintended and will cause data loss. "
+                        f"Sharding already applied to {prev_applied} while trying to apply to {dp}"
+                    )
+                # For BC, only provide sharding_group if accepted
+                sig = inspect.signature(dp.apply_sharding)
+                if len(sig.parameters) < 3:
+                    dp.apply_sharding(num_of_instances, instance_id)
+                else:
+                    dp.apply_sharding(
+                        num_of_instances, instance_id, sharding_group=sharding_group
+                    )
+                applied = dp
+            if applied is None:
+                applied = prev_applied
+            _helper(sub_graph, applied)
+
+    _helper(graph)
+
+    return datapipe
+
+
+def _is_shuffle_datapipe(datapipe: DataPipe) -> bool:
+    return (
+        hasattr(datapipe, "set_shuffle")
+        and hasattr(datapipe, "set_seed")
+        and inspect.ismethod(datapipe.set_shuffle)
+        and inspect.ismethod(datapipe.set_seed)
+    )
+
+
+def apply_shuffle_settings(
+    datapipe: DataPipe, shuffle: Optional[bool] = None
+) -> DataPipe:
+    r"""
+    Traverse the graph of ``DataPipes`` to find and set shuffle attribute.
+
+    Apply the method to each `DataPipe` that has APIs of ``set_shuffle``
+    and ``set_seed``.
+
+    Args:
+        datapipe: DataPipe that needs to set shuffle attribute
+        shuffle: Shuffle option (default: ``None`` and no-op to the graph)
+    """
+    if shuffle is None:
+        return datapipe
+
+    graph = traverse_dps(datapipe)
+    all_pipes = get_all_graph_pipes(graph)
+    shufflers = [pipe for pipe in all_pipes if _is_shuffle_datapipe(pipe)]
+    if not shufflers and shuffle:
+        warnings.warn(
+            "`shuffle=True` was set, but the datapipe does not contain a `Shuffler`. Adding one at the end. "
+            "Be aware that the default buffer size might not be sufficient for your task."
+        )
+        datapipe = datapipe.shuffle()
+        shufflers = [
+            datapipe,
+        ]
+
+    for shuffler in shufflers:
+        shuffler.set_shuffle(shuffle)
+
+    return datapipe
+
+
+@deprecated(
+    "`apply_shuffle_seed` is deprecated since 1.12 and will be removed in the future releases. "
+    "Please use `apply_random_seed` instead.",
+    category=FutureWarning,
+)
+def apply_shuffle_seed(datapipe: DataPipe, rng: Any) -> DataPipe:
+    return apply_random_seed(datapipe, rng)
+
+
+def _is_random_datapipe(datapipe: DataPipe) -> bool:
+    return hasattr(datapipe, "set_seed") and inspect.ismethod(datapipe.set_seed)
+
+
+def apply_random_seed(datapipe: DataPipe, rng: torch.Generator) -> DataPipe:
+    r"""
+    Traverse the graph of ``DataPipes`` to find random ``DataPipe`` with an API of ``set_seed``.
+
+    Then set the random seed based on the provided RNG to those ``DataPipe``.
+
+    Args:
+        datapipe: DataPipe that needs to set randomness
+        rng: Random number generator to generate random seeds
+    """
+    graph = traverse_dps(datapipe)
+    all_pipes = get_all_graph_pipes(graph)
+    # Using a set to track id of DataPipe to prevent setting randomness per DataPipe more than once.
+    # And, `id` is used in case of unhashable DataPipe
+    cache = set()
+    random_datapipes = []
+    for pipe in all_pipes:
+        if id(pipe) in cache:
+            continue
+        if _is_random_datapipe(pipe):
+            random_datapipes.append(pipe)
+            cache.add(id(pipe))
+
+    for pipe in random_datapipes:
+        random_seed = int(
+            torch.empty((), dtype=torch.int64).random_(generator=rng).item()
+        )
+        pipe.set_seed(random_seed)
+
+    return datapipe
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/sampler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce5c8a09734c94de6037427fb39f6fd9c78bfc74
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/data/sampler.py
@@ -0,0 +1,348 @@
+# mypy: allow-untyped-defs
+import itertools
+from collections.abc import Iterable, Iterator, Sequence, Sized
+from typing import Generic, Optional, TypeVar, Union
+
+import torch
+
+
+__all__ = [
+    "BatchSampler",
+    "RandomSampler",
+    "Sampler",
+    "SequentialSampler",
+    "SubsetRandomSampler",
+    "WeightedRandomSampler",
+]
+
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+class Sampler(Generic[_T_co]):
+    r"""Base class for all Samplers.
+
+    Every Sampler subclass has to provide an :meth:`__iter__` method, providing a
+    way to iterate over indices or lists of indices (batches) of dataset elements,
+    and may provide a :meth:`__len__` method that returns the length of the returned iterators.
+
+    Args:
+        data_source (Dataset): This argument is not used and will be removed in 2.2.0.
+            You may still have custom implementation that utilizes it.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> class AccedingSequenceLengthSampler(Sampler[int]):
+        >>>     def __init__(self, data: List[str]) -> None:
+        >>>         self.data = data
+        >>>
+        >>>     def __len__(self) -> int:
+        >>>         return len(self.data)
+        >>>
+        >>>     def __iter__(self) -> Iterator[int]:
+        >>>         sizes = torch.tensor([len(x) for x in self.data])
+        >>>         yield from torch.argsort(sizes).tolist()
+        >>>
+        >>> class AccedingSequenceLengthBatchSampler(Sampler[List[int]]):
+        >>>     def __init__(self, data: List[str], batch_size: int) -> None:
+        >>>         self.data = data
+        >>>         self.batch_size = batch_size
+        >>>
+        >>>     def __len__(self) -> int:
+        >>>         return (len(self.data) + self.batch_size - 1) // self.batch_size
+        >>>
+        >>>     def __iter__(self) -> Iterator[List[int]]:
+        >>>         sizes = torch.tensor([len(x) for x in self.data])
+        >>>         for batch in torch.chunk(torch.argsort(sizes), len(self)):
+        >>>             yield batch.tolist()
+
+    .. note:: The :meth:`__len__` method isn't strictly required by
+              :class:`~torch.utils.data.DataLoader`, but is expected in any
+              calculation involving the length of a :class:`~torch.utils.data.DataLoader`.
+    """
+
+    def __init__(self, data_source: Optional[Sized] = None) -> None:
+        if data_source is not None:
+            import warnings
+
+            warnings.warn(
+                "`data_source` argument is not used and will be removed in 2.2.0."
+                "You may still have custom implementation that utilizes it."
+            )
+
+    def __iter__(self) -> Iterator[_T_co]:
+        raise NotImplementedError
+
+    # NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
+    #
+    # Many times we have an abstract class representing a collection/iterable of
+    # data, e.g., `torch.utils.data.Sampler`, with its subclasses optionally
+    # implementing a `__len__` method. In such cases, we must make sure to not
+    # provide a default implementation, because both straightforward default
+    # implementations have their issues:
+    #
+    #   + `return NotImplemented`:
+    #     Calling `len(subclass_instance)` raises:
+    #       TypeError: 'NotImplementedType' object cannot be interpreted as an integer
+    #
+    #   + `raise NotImplementedError`:
+    #     This prevents triggering some fallback behavior. E.g., the built-in
+    #     `list(X)` tries to call `len(X)` first, and executes a different code
+    #     path if the method is not found or `NotImplemented` is returned, while
+    #     raising a `NotImplementedError` will propagate and make the call fail
+    #     where it could have used `__iter__` to complete the call.
+    #
+    # Thus, the only two sensible things to do are
+    #
+    #   + **not** provide a default `__len__`.
+    #
+    #   + raise a `TypeError` instead, which is what Python uses when users call
+    #     a method that is not defined on an object.
+    #     (@ssnl verifies that this works on at least Python 3.7.)
+
+
+class SequentialSampler(Sampler[int]):
+    r"""Samples elements sequentially, always in the same order.
+
+    Args:
+        data_source (Dataset): dataset to sample from
+    """
+
+    data_source: Sized
+
+    def __init__(self, data_source: Sized) -> None:
+        self.data_source = data_source
+
+    def __iter__(self) -> Iterator[int]:
+        return iter(range(len(self.data_source)))
+
+    def __len__(self) -> int:
+        return len(self.data_source)
+
+
+class RandomSampler(Sampler[int]):
+    r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset.
+
+    If with replacement, then user can specify :attr:`num_samples` to draw.
+
+    Args:
+        data_source (Dataset): dataset to sample from
+        replacement (bool): samples are drawn on-demand with replacement if ``True``, default=``False``
+        num_samples (int): number of samples to draw, default=`len(dataset)`.
+        generator (Generator): Generator used in sampling.
+    """
+
+    data_source: Sized
+    replacement: bool
+
+    def __init__(
+        self,
+        data_source: Sized,
+        replacement: bool = False,
+        num_samples: Optional[int] = None,
+        generator=None,
+    ) -> None:
+        self.data_source = data_source
+        self.replacement = replacement
+        self._num_samples = num_samples
+        self.generator = generator
+
+        if not isinstance(self.replacement, bool):
+            raise TypeError(
+                f"replacement should be a boolean value, but got replacement={self.replacement}"
+            )
+
+        if not isinstance(self.num_samples, int) or self.num_samples <= 0:
+            raise ValueError(
+                f"num_samples should be a positive integer value, but got num_samples={self.num_samples}"
+            )
+
+    @property
+    def num_samples(self) -> int:
+        # dataset size might change at runtime
+        if self._num_samples is None:
+            return len(self.data_source)
+        return self._num_samples
+
+    def __iter__(self) -> Iterator[int]:
+        n = len(self.data_source)
+        if self.generator is None:
+            seed = int(torch.empty((), dtype=torch.int64).random_().item())
+            generator = torch.Generator()
+            generator.manual_seed(seed)
+        else:
+            generator = self.generator
+
+        if self.replacement:
+            for _ in range(self.num_samples // 32):
+                yield from torch.randint(
+                    high=n, size=(32,), dtype=torch.int64, generator=generator
+                ).tolist()
+            yield from torch.randint(
+                high=n,
+                size=(self.num_samples % 32,),
+                dtype=torch.int64,
+                generator=generator,
+            ).tolist()
+        else:
+            for _ in range(self.num_samples // n):
+                yield from torch.randperm(n, generator=generator).tolist()
+            yield from torch.randperm(n, generator=generator).tolist()[
+                : self.num_samples % n
+            ]
+
+    def __len__(self) -> int:
+        return self.num_samples
+
+
+class SubsetRandomSampler(Sampler[int]):
+    r"""Samples elements randomly from a given list of indices, without replacement.
+
+    Args:
+        indices (sequence): a sequence of indices
+        generator (Generator): Generator used in sampling.
+    """
+
+    indices: Sequence[int]
+
+    def __init__(self, indices: Sequence[int], generator=None) -> None:
+        self.indices = indices
+        self.generator = generator
+
+    def __iter__(self) -> Iterator[int]:
+        for i in torch.randperm(len(self.indices), generator=self.generator):
+            yield self.indices[i]
+
+    def __len__(self) -> int:
+        return len(self.indices)
+
+
+class WeightedRandomSampler(Sampler[int]):
+    r"""Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights).
+
+    Args:
+        weights (sequence)   : a sequence of weights, not necessary summing up to one
+        num_samples (int): number of samples to draw
+        replacement (bool): if ``True``, samples are drawn with replacement.
+            If not, they are drawn without replacement, which means that when a
+            sample index is drawn for a row, it cannot be drawn again for that row.
+        generator (Generator): Generator used in sampling.
+
+    Example:
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> list(WeightedRandomSampler([0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True))
+        [4, 4, 1, 4, 5]
+        >>> list(WeightedRandomSampler([0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False))
+        [0, 1, 4, 3, 2]
+    """
+
+    weights: torch.Tensor
+    num_samples: int
+    replacement: bool
+
+    def __init__(
+        self,
+        weights: Sequence[float],
+        num_samples: int,
+        replacement: bool = True,
+        generator=None,
+    ) -> None:
+        if (
+            not isinstance(num_samples, int)
+            or isinstance(num_samples, bool)
+            or num_samples <= 0
+        ):
+            raise ValueError(
+                f"num_samples should be a positive integer value, but got num_samples={num_samples}"
+            )
+        if not isinstance(replacement, bool):
+            raise ValueError(
+                f"replacement should be a boolean value, but got replacement={replacement}"
+            )
+
+        weights_tensor = torch.as_tensor(weights, dtype=torch.double)
+        if len(weights_tensor.shape) != 1:
+            raise ValueError(
+                "weights should be a 1d sequence but given "
+                f"weights have shape {tuple(weights_tensor.shape)}"
+            )
+
+        self.weights = weights_tensor
+        self.num_samples = num_samples
+        self.replacement = replacement
+        self.generator = generator
+
+    def __iter__(self) -> Iterator[int]:
+        rand_tensor = torch.multinomial(
+            self.weights, self.num_samples, self.replacement, generator=self.generator
+        )
+        yield from iter(rand_tensor.tolist())
+
+    def __len__(self) -> int:
+        return self.num_samples
+
+
+class BatchSampler(Sampler[list[int]]):
+    r"""Wraps another sampler to yield a mini-batch of indices.
+
+    Args:
+        sampler (Sampler or Iterable): Base sampler. Can be any iterable object
+        batch_size (int): Size of mini-batch.
+        drop_last (bool): If ``True``, the sampler will drop the last batch if
+            its size would be less than ``batch_size``
+
+    Example:
+        >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=False))
+        [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
+        >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True))
+        [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+    """
+
+    def __init__(
+        self,
+        sampler: Union[Sampler[int], Iterable[int]],
+        batch_size: int,
+        drop_last: bool,
+    ) -> None:
+        # Since collections.abc.Iterable does not check for `__getitem__`, which
+        # is one way for an object to be an iterable, we don't do an `isinstance`
+        # check here.
+        if (
+            not isinstance(batch_size, int)
+            or isinstance(batch_size, bool)
+            or batch_size <= 0
+        ):
+            raise ValueError(
+                f"batch_size should be a positive integer value, but got batch_size={batch_size}"
+            )
+        if not isinstance(drop_last, bool):
+            raise ValueError(
+                f"drop_last should be a boolean value, but got drop_last={drop_last}"
+            )
+        self.sampler = sampler
+        self.batch_size = batch_size
+        self.drop_last = drop_last
+
+    def __iter__(self) -> Iterator[list[int]]:
+        # Implemented based on the benchmarking in https://github.com/pytorch/pytorch/pull/76951
+        sampler_iter = iter(self.sampler)
+        if self.drop_last:
+            # Create multiple references to the same iterator
+            args = [sampler_iter] * self.batch_size
+            for batch_droplast in zip(*args):
+                yield [*batch_droplast]
+        else:
+            batch = [*itertools.islice(sampler_iter, self.batch_size)]
+            while batch:
+                yield batch
+                batch = [*itertools.islice(sampler_iter, self.batch_size)]
+
+    def __len__(self) -> int:
+        # Can only be called if self.sampler has __len__ implemented
+        # We cannot enforce this condition, so we turn off typechecking for the
+        # implementation below.
+        # Somewhat related: see NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
+        if self.drop_last:
+            return len(self.sampler) // self.batch_size  # type: ignore[arg-type]
+        else:
+            return (len(self.sampler) + self.batch_size - 1) // self.batch_size  # type: ignore[arg-type]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/deterministic.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/deterministic.py
new file mode 100644
index 0000000000000000000000000000000000000000..a055c43be531a5c65c4f29f6c8165104e98e5ca0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/deterministic.py
@@ -0,0 +1,22 @@
+# mypy: allow-untyped-defs
+import sys
+import types
+
+import torch
+
+
+class _Deterministic(types.ModuleType):
+    @property
+    def fill_uninitialized_memory(self):
+        """
+        Whether to fill uninitialized memory with a known value when
+        :meth:`torch.use_deterministic_algorithms()` is set to ``True``.
+        """
+        return torch._C._get_deterministic_fill_uninitialized_memory()
+
+    @fill_uninitialized_memory.setter
+    def fill_uninitialized_memory(self, mode):
+        return torch._C._set_deterministic_fill_uninitialized_memory(mode)
+
+
+sys.modules[__name__].__class__ = _Deterministic
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/dlpack.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/dlpack.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bfa4b9f85bd6fc8bb8524926210b1e931e2bd50
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/dlpack.py
@@ -0,0 +1,121 @@
+from typing import Any
+
+import torch
+import enum
+
+from torch._C import _from_dlpack
+from torch._C import _to_dlpack as to_dlpack
+
+
+class DLDeviceType(enum.IntEnum):
+    # Enums as in DLPack specification (aten/src/ATen/dlpack.h)
+    kDLCPU = 1,
+    kDLGPU = 2,
+    kDLCPUPinned = 3,
+    kDLOpenCL = 4,
+    kDLVulkan = 7,
+    kDLMetal = 8,
+    kDLVPI = 9,
+    kDLROCM = 10,
+    kDLExtDev = 12,
+    kDLOneAPI = 14,
+
+
+torch._C._add_docstr(to_dlpack, r"""to_dlpack(tensor) -> PyCapsule
+
+Returns an opaque object (a "DLPack capsule") representing the tensor.
+
+.. note::
+  ``to_dlpack`` is a legacy DLPack interface. The capsule it returns
+  cannot be used for anything in Python other than use it as input to
+  ``from_dlpack``. The more idiomatic use of DLPack is to call
+  ``from_dlpack`` directly on the tensor object - this works when that
+  object has a ``__dlpack__`` method, which PyTorch and most other
+  libraries indeed have now.
+
+.. warning::
+  Only call ``from_dlpack`` once per capsule produced with ``to_dlpack``.
+  Behavior when a capsule is consumed multiple times is undefined.
+
+Args:
+    tensor: a tensor to be exported
+
+The DLPack capsule shares the tensor's memory.
+""")
+
+
+# TODO: add a typing.Protocol to be able to tell Mypy that only objects with
+# __dlpack__ and __dlpack_device__ methods are accepted.
+def from_dlpack(ext_tensor: Any) -> 'torch.Tensor':
+    """from_dlpack(ext_tensor) -> Tensor
+
+    Converts a tensor from an external library into a ``torch.Tensor``.
+
+    The returned PyTorch tensor will share the memory with the input tensor
+    (which may have come from another library). Note that in-place operations
+    will therefore also affect the data of the input tensor. This may lead to
+    unexpected issues (e.g., other libraries may have read-only flags or
+    immutable data structures), so the user should only do this if they know
+    for sure that this is fine.
+
+    Args:
+        ext_tensor (object with ``__dlpack__`` attribute, or a DLPack capsule):
+            The tensor or DLPack capsule to convert.
+
+            If ``ext_tensor`` is a tensor (or ndarray) object, it must support
+            the ``__dlpack__`` protocol (i.e., have a ``ext_tensor.__dlpack__``
+            method). Otherwise ``ext_tensor`` may be a DLPack capsule, which is
+            an opaque ``PyCapsule`` instance, typically produced by a
+            ``to_dlpack`` function or method.
+
+    Examples::
+
+        >>> import torch.utils.dlpack
+        >>> t = torch.arange(4)
+
+        # Convert a tensor directly (supported in PyTorch >= 1.10)
+        >>> t2 = torch.from_dlpack(t)
+        >>> t2[:2] = -1  # show that memory is shared
+        >>> t2
+        tensor([-1, -1,  2,  3])
+        >>> t
+        tensor([-1, -1,  2,  3])
+
+        # The old-style DLPack usage, with an intermediate capsule object
+        >>> capsule = torch.utils.dlpack.to_dlpack(t)
+        >>> capsule
+        
+        >>> t3 = torch.from_dlpack(capsule)
+        >>> t3
+        tensor([-1, -1,  2,  3])
+        >>> t3[0] = -9  # now we're sharing memory between 3 tensors
+        >>> t3
+        tensor([-9, -1,  2,  3])
+        >>> t2
+        tensor([-9, -1,  2,  3])
+        >>> t
+        tensor([-9, -1,  2,  3])
+
+    """
+    if hasattr(ext_tensor, '__dlpack__'):
+        device = ext_tensor.__dlpack_device__()
+        # device is either CUDA or ROCm, we need to pass the current
+        # stream
+        if device[0] in (DLDeviceType.kDLGPU, DLDeviceType.kDLROCM):
+            stream = torch.cuda.current_stream(f'cuda:{device[1]}')
+            # cuda_stream is the pointer to the stream and it is a public
+            # attribute, but it is not documented
+            # The array API specify that the default legacy stream must be passed
+            # with a value of 1 for CUDA
+            # https://data-apis.org/array-api/latest/API_specification/array_object.html?dlpack-self-stream-none#dlpack-self-stream-none
+            is_cuda = device[0] == DLDeviceType.kDLGPU
+            # Since pytorch is not using PTDS by default, lets directly pass
+            # the legacy stream
+            stream_ptr = 1 if is_cuda and stream.cuda_stream == 0 else stream.cuda_stream
+            dlpack = ext_tensor.__dlpack__(stream=stream_ptr)
+        else:
+            dlpack = ext_tensor.__dlpack__()
+    else:
+        # Old versions just call the converter
+        dlpack = ext_tensor
+    return _from_dlpack(dlpack)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/file_baton.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/file_baton.py
new file mode 100644
index 0000000000000000000000000000000000000000..77ee5091b3f727a8ec56bf6dde10f7fbc2e3352f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/file_baton.py
@@ -0,0 +1,50 @@
+# mypy: allow-untyped-defs
+import os
+import time
+
+
+class FileBaton:
+    """A primitive, file-based synchronization utility."""
+
+    def __init__(self, lock_file_path, wait_seconds=0.1):
+        """
+        Create a new :class:`FileBaton`.
+
+        Args:
+            lock_file_path: The path to the file used for locking.
+            wait_seconds: The seconds to periodically sleep (spin) when
+                calling ``wait()``.
+        """
+        self.lock_file_path = lock_file_path
+        self.wait_seconds = wait_seconds
+        self.fd = None
+
+    def try_acquire(self):
+        """
+        Try to atomically create a file under exclusive access.
+
+        Returns:
+            True if the file could be created, else False.
+        """
+        try:
+            self.fd = os.open(self.lock_file_path, os.O_CREAT | os.O_EXCL)
+            return True
+        except FileExistsError:
+            return False
+
+    def wait(self):
+        """
+        Periodically sleeps for a certain amount until the baton is released.
+
+        The amount of time slept depends on the ``wait_seconds`` parameter
+        passed to the constructor.
+        """
+        while os.path.exists(self.lock_file_path):
+            time.sleep(self.wait_seconds)
+
+    def release(self):
+        """Release the baton and removes its file."""
+        if self.fd is not None:
+            os.close(self.fd)
+
+        os.remove(self.lock_file_path)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/flop_counter.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/flop_counter.py
new file mode 100644
index 0000000000000000000000000000000000000000..255978dd6de077460a8603e278c47bf3cd2e58ac
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/flop_counter.py
@@ -0,0 +1,792 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten
+from .module_tracker import ModuleTracker
+from typing import Any, Optional, Union, TypeVar, Callable
+from collections.abc import Iterator
+from typing_extensions import ParamSpec
+from collections import defaultdict
+from torch.utils._python_dispatch import TorchDispatchMode
+from math import prod
+from functools import wraps
+import warnings
+
+__all__ = ["FlopCounterMode", "register_flop_formula"]
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+aten = torch.ops.aten
+
+def get_shape(i):
+    if isinstance(i, torch.Tensor):
+        return i.shape
+    return i
+
+flop_registry: dict[Any, Any] = {}
+
+def shape_wrapper(f):
+    @wraps(f)
+    def nf(*args, out_val=None, **kwargs):
+        args, kwargs, out_shape = tree_map(get_shape, (args, kwargs, out_val))
+        return f(*args, out_shape=out_shape, **kwargs)
+    return nf
+
+def register_flop_formula(targets, get_raw=False) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
+    def register_fun(flop_formula: Callable[_P, _T]) -> Callable[_P, _T]:
+        if not get_raw:
+            flop_formula = shape_wrapper(flop_formula)
+
+        def register(target):
+            if not isinstance(target, torch._ops.OpOverloadPacket):
+                raise ValueError(
+                    f"register_flop_formula(targets): expected each target to be "
+                    f"OpOverloadPacket (i.e. torch.ops.mylib.foo), got "
+                    f"{target} which is of type {type(target)}")
+            if target in flop_registry:
+                raise RuntimeError(f"duplicate registrations for {target}")
+            flop_registry[target] = flop_formula
+
+        # To handle allowing multiple aten_ops at once
+        torch.utils._pytree.tree_map_(register, targets)
+
+        return flop_formula
+
+    return register_fun
+
+@register_flop_formula(aten.mm)
+def mm_flop(a_shape, b_shape, *args, out_shape=None, **kwargs) -> int:
+    """Count flops for matmul."""
+    # Inputs should be a list of length 2.
+    # Inputs contains the shapes of two matrices.
+    m, k = a_shape
+    k2, n = b_shape
+    assert k == k2
+    # NB(chilli): Should be 2 * k - 1 technically for FLOPs.
+    return m * n * 2 * k
+
+@register_flop_formula(aten.addmm)
+def addmm_flop(self_shape, a_shape, b_shape, out_shape=None, **kwargs) -> int:
+    """Count flops for addmm."""
+    return mm_flop(a_shape, b_shape)
+
+@register_flop_formula(aten.bmm)
+def bmm_flop(a_shape, b_shape, out_shape=None, **kwargs) -> int:
+    """Count flops for the bmm operation."""
+    # Inputs should be a list of length 2.
+    # Inputs contains the shapes of two tensor.
+    b, m, k = a_shape
+    b2, k2, n = b_shape
+    assert b == b2
+    assert k == k2
+    # NB(chilli): Should be 2 * k - 1 technically for FLOPs.
+    flop = b * m * n * 2 * k
+    return flop
+
+@register_flop_formula(aten.baddbmm)
+def baddbmm_flop(self_shape, a_shape, b_shape, out_shape=None, **kwargs) -> int:
+    """Count flops for the baddbmm operation."""
+    # Inputs should be a list of length 3.
+    # Inputs contains the shapes of three tensors.
+    return bmm_flop(a_shape, b_shape)
+
+@register_flop_formula(aten._scaled_mm)
+def _scaled_mm_flop(
+    a_shape,
+    b_shape,
+    scale_a_shape,
+    scale_b_shape,
+    bias_shape=None,
+    scale_result_shape=None,
+    out_dtype=None,
+    use_fast_accum=False,
+    out_shape=None,
+    **kwargs,
+) -> int:
+    """Count flops for _scaled_mm."""
+    return mm_flop(a_shape, b_shape)
+
+
+def conv_flop_count(
+    x_shape: list[int],
+    w_shape: list[int],
+    out_shape: list[int],
+    transposed: bool = False,
+) -> int:
+    """Count flops for convolution.
+
+    Note only multiplication is
+    counted. Computation for bias are ignored.
+    Flops for a transposed convolution are calculated as
+    flops = (x_shape[2:] * prod(w_shape) * batch_size).
+    Args:
+        x_shape (list(int)): The input shape before convolution.
+        w_shape (list(int)): The filter shape.
+        out_shape (list(int)): The output shape after convolution.
+        transposed (bool): is the convolution transposed
+    Returns:
+        int: the number of flops
+    """
+
+    batch_size = x_shape[0]
+    conv_shape = (x_shape if transposed else out_shape)[2:]
+    c_out, c_in, *filter_size = w_shape
+
+    """
+    General idea here is that for a regular conv, for each point in the output
+    spatial dimension we convolve the filter with something (hence
+    `prod(conv_shape) * prod(filter_size)` ops). Then, this gets multiplied by
+    1. batch_size, 2. the cross product of input and weight channels.
+
+    For the transpose, it's not each point in the *output* spatial dimension but
+    each point in the *input* spatial dimension.
+    """
+    # NB(chilli): I don't think this properly accounts for padding :think:
+    # NB(chilli): Should be 2 * c_in - 1 technically for FLOPs.
+    flop = prod(conv_shape) * prod(filter_size) * batch_size * c_out * c_in * 2
+    return flop
+
+@register_flop_formula([aten.convolution, aten._convolution])
+def conv_flop(x_shape, w_shape, _bias, _stride, _padding, _dilation, transposed, *args, out_shape=None, **kwargs) -> int:
+    """Count flops for convolution."""
+    return conv_flop_count(x_shape, w_shape, out_shape, transposed=transposed)
+
+
+@register_flop_formula(aten.convolution_backward)
+def conv_backward_flop(
+        grad_out_shape,
+        x_shape,
+        w_shape,
+        _bias,
+        _stride,
+        _padding,
+        _dilation,
+        transposed,
+        _output_padding,
+        _groups,
+        output_mask,
+        out_shape) -> int:
+
+    def t(shape):
+        return [shape[1], shape[0]] + list(shape[2:])
+    flop_count = 0
+
+    """
+    Let's say we have a regular 1D conv
+    {A, B, C} [inp]
+    {i, j} [weight]
+    => (conv)
+    {Ai + Bj, Bi + Cj} [out]
+
+    And as a reminder, the transposed conv of the above is
+    => {Ai, Aj + Bi, Bj + Ci, Cj} [transposed conv out]
+
+    For the backwards of conv, we now have
+    {D, E} [grad_out]
+    {A, B, C} [inp]
+    {i, j} [weight]
+
+    # grad_inp as conv_transpose(grad_out, weight)
+    Let's first compute grad_inp. To do so, we can simply look at all the
+    multiplications that each element of inp is involved in. For example, A is
+    only involved in the first element of the output (and thus only depends upon
+    D in grad_out), and C is only involved in the last element of the output
+    (and thus only depends upon E in grad_out)
+
+    {Di, Dj + Ei, Ej} [grad_inp]
+
+    Note that this corresponds to the below conv_transpose. This gives us the
+    output_mask[0] branch, which is grad_inp.
+
+    {D, E} [inp (grad_out)]
+    {i, j} [weight]
+    => (conv_transpose)
+    {Di, Dj + Ei, Ej} [out (grad_inp)]
+
+    I leave the fact that grad_inp for a transposed conv is just conv(grad_out,
+    weight) as an exercise for the reader.
+
+    # grad_weight as conv(inp, grad_out)
+    To compute grad_weight, we again look at the terms in the output, which as
+    a reminder is:
+    => {Ai + Bj, Bi + Cj} [out]
+    => {D, E} [grad_out]
+    If we manually compute the gradient for the weights, we see it's
+    {AD + BE, BD + CE} [grad_weight]
+
+    This corresponds to the below conv
+    {A, B, C} [inp]
+    {D, E} [weight (grad_out)]
+    => (conv)
+    {AD + BE, BD + CE} [out (grad_weight)]
+
+    # grad_weight of transposed conv as conv(grad_out, inp)
+    As a reminder, the terms of the output of a transposed conv are:
+    => {Ai, Aj + Bi, Bj + Ci, Cj} [transposed conv out]
+    => {D, E, F, G} [grad_out]
+
+    Manually computing the gradient for the weights, we see it's
+    {AD + BE + CF, AE + BF + CG} [grad_weight]
+
+    This corresponds to the below conv
+    {D, E, F, G} [inp (grad_out)]
+    {A, B, C} [weight (inp)]
+    => (conv)
+    {AD + BE + CF, AE + BF + CG} [out (grad_weight)]
+
+    For the full backwards formula, there are also some details involving
+    transpose of the batch/channel dimensions and groups, but I skip those for
+    the sake of brevity (and they're pretty similar to matmul backwards)
+
+    Check [conv backwards decomposition as conv forwards]
+    """
+    # grad_inp as conv_transpose(grad_out, weight)
+    if output_mask[0]:
+        grad_input_shape = get_shape(out_shape[0])
+        flop_count += conv_flop_count(grad_out_shape, w_shape, grad_input_shape, not transposed)
+
+    if output_mask[1]:
+        grad_weight_shape = get_shape(out_shape[1])
+        if transposed:
+            # grad_weight of transposed conv as conv(grad_out, inp)
+            flop_count += conv_flop_count(t(grad_out_shape), t(x_shape), t(grad_weight_shape), transposed=False)
+        else:
+            # grad_weight as conv(inp, grad_out)
+            flop_count += conv_flop_count(t(x_shape), t(grad_out_shape), t(grad_weight_shape), transposed=False)
+
+    return flop_count
+
+def sdpa_flop_count(query_shape, key_shape, value_shape):
+    """
+    Count flops for self-attention.
+
+    NB: We can assume that value_shape == key_shape
+    """
+    b, h, s_q, d_q = query_shape
+    _b2, _h2, s_k, _d2 = key_shape
+    _b3, _h3, _s3, d_v = value_shape
+    assert b == _b2 == _b3 and h == _h2 == _h3 and d_q == _d2 and s_k == _s3 and d_q == _d2
+    total_flops = 0
+    # q: [b, h, s_q, d_q] @ k: [b, h, d_q, s_k] -> scores: [b, h, s_q, s_k]
+    total_flops += bmm_flop((b * h, s_q, d_q), (b * h, d_q, s_k))
+    # scores: [b, h, s_q, s_k] @ v: [b, h, s_k, d_v] -> out: [b, h, s_q, d_v]
+    total_flops += bmm_flop((b * h, s_q, s_k), (b * h, s_k, d_v))
+    return total_flops
+
+
+@register_flop_formula([aten._scaled_dot_product_efficient_attention,
+                        aten._scaled_dot_product_flash_attention,
+                        aten._scaled_dot_product_cudnn_attention])
+def sdpa_flop(query_shape, key_shape, value_shape, *args, out_shape=None, **kwargs) -> int:
+    """Count flops for self-attention."""
+    # NB: We aren't accounting for causal attention here
+    return sdpa_flop_count(query_shape, key_shape, value_shape)
+
+
+def _offsets_to_lengths(offsets, max_len):
+    """
+    If the offsets tensor is fake, then we don't know the actual lengths.
+    In that case, we can just assume the worst case; each batch has max length.
+    """
+    from torch._subclasses.fake_tensor import FakeTensor
+    from torch._subclasses.functional_tensor import FunctionalTensor
+    if not isinstance(offsets, (FakeTensor, FunctionalTensor)) and offsets.device.type != "meta":
+        return offsets.diff().tolist()
+    return [max_len] * (offsets.size(0) - 1)
+
+
+def _unpack_flash_attention_nested_shapes(
+    *,
+    query,
+    key,
+    value,
+    grad_out=None,
+    cum_seq_q,
+    cum_seq_k,
+    max_q,
+    max_k,
+) -> Iterator[tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], Optional[tuple[int, ...]]]]:
+    """
+    Given inputs to a flash_attention_(forward|backward) kernel, this will handle behavior for
+    NestedTensor inputs by effectively unbinding the NestedTensor and yielding the shapes for
+    each batch element.
+
+    In the case that this isn't a NestedTensor kernel, then it just yields the original shapes.
+    """
+    if cum_seq_q is not None:
+        # This means we should be dealing with a Nested Jagged Tensor query.
+        # The inputs will have shape                  (sum(sequence len), heads, dimension)
+        # In comparison, non-Nested inputs have shape (batch, heads, sequence len, dimension)
+        # To deal with this, we convert to a shape of (batch, heads, max_seq_len, dimension)
+        # So the flops calculation in this case is an overestimate of the actual flops.
+        assert len(key.shape) == 3
+        assert len(value.shape) == 3
+        assert grad_out is None or grad_out.shape == query.shape
+        _, h_q, d_q = query.shape
+        _, h_k, d_k = key.shape
+        _, h_v, d_v = value.shape
+        assert cum_seq_q is not None
+        assert cum_seq_k is not None
+        assert cum_seq_q.shape == cum_seq_k.shape
+        seq_q_lengths = _offsets_to_lengths(cum_seq_q, max_q)
+        seq_k_lengths = _offsets_to_lengths(cum_seq_k, max_k)
+        for (seq_q_len, seq_k_len) in zip(seq_q_lengths, seq_k_lengths):
+            new_query_shape = (1, h_q, seq_q_len, d_q)
+            new_key_shape = (1, h_k, seq_k_len, d_k)
+            new_value_shape = (1, h_v, seq_k_len, d_v)
+            new_grad_out_shape = new_query_shape if grad_out is not None else None
+            yield new_query_shape, new_key_shape, new_value_shape, new_grad_out_shape
+        return
+
+    yield query.shape, key.shape, value.shape, grad_out.shape if grad_out is not None else None
+
+
+def _unpack_efficient_attention_nested_shapes(
+    *,
+    query,
+    key,
+    value,
+    grad_out=None,
+    cu_seqlens_q,
+    cu_seqlens_k,
+    max_seqlen_q,
+    max_seqlen_k,
+) -> Iterator[tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], Optional[tuple[int, ...]]]]:
+    """
+    Given inputs to a efficient_attention_(forward|backward) kernel, this will handle behavior for
+    NestedTensor inputs by effectively unbinding the NestedTensor and yielding the shapes for
+    each batch element.
+
+    In the case that this isn't a NestedTensor kernel, then it just yields the original shapes.
+    """
+    if cu_seqlens_q is not None:
+        # Unlike flash_attention_forward, we get a 4D tensor instead of a 3D tensor for efficient attention.
+        #
+        # This means we should be dealing with a Nested Jagged Tensor query.
+        # The inputs will have shape                  (sum(sequence len), heads, dimension)
+        # In comparison, non-Nested inputs have shape (batch, heads, sequence len, dimension)
+        # To deal with this, we convert to a shape of (batch, heads, max_seq_len, dimension)
+        # So the flops calculation in this case is an overestimate of the actual flops.
+        assert len(key.shape) == 4
+        assert len(value.shape) == 4
+        assert grad_out is None or grad_out.shape == query.shape
+        _, _, h_q, d_q = query.shape
+        _, _, h_k, d_k = key.shape
+        _, _, h_v, d_v = value.shape
+        assert cu_seqlens_q is not None
+        assert cu_seqlens_k is not None
+        assert cu_seqlens_q.shape == cu_seqlens_k.shape
+        seqlens_q = _offsets_to_lengths(cu_seqlens_q, max_seqlen_q)
+        seqlens_k = _offsets_to_lengths(cu_seqlens_k, max_seqlen_k)
+        for len_q, len_k in zip(seqlens_q, seqlens_k):
+            new_query_shape = (1, h_q, len_q, d_q)
+            new_key_shape = (1, h_k, len_k, d_k)
+            new_value_shape = (1, h_v, len_k, d_v)
+            new_grad_out_shape = new_query_shape if grad_out is not None else None
+            yield new_query_shape, new_key_shape, new_value_shape, new_grad_out_shape
+        return
+
+    yield query.shape, key.shape, value.shape, grad_out.shape if grad_out is not None else None
+
+
+@register_flop_formula(aten._flash_attention_forward, get_raw=True)
+def _flash_attention_forward_flop(
+    query,
+    key,
+    value,
+    cum_seq_q,
+    cum_seq_k,
+    max_q,
+    max_k,
+    *args,
+    out_shape=None,
+    **kwargs
+) -> int:
+    """Count flops for self-attention."""
+    # NB: We aren't accounting for causal attention here
+    # in case this is a nested tensor, we unpack the individual batch elements
+    # and then sum the flops per batch element
+    sizes = _unpack_flash_attention_nested_shapes(
+        query=query,
+        key=key,
+        value=value,
+        cum_seq_q=cum_seq_q,
+        cum_seq_k=cum_seq_k,
+        max_q=max_q,
+        max_k=max_k,
+    )
+    return sum(
+        sdpa_flop_count(query_shape, key_shape, value_shape)
+        for query_shape, key_shape, value_shape, _ in sizes
+    )
+
+
+@register_flop_formula(aten._efficient_attention_forward, get_raw=True)
+def _efficient_attention_forward_flop(
+    query,
+    key,
+    value,
+    bias,
+    cu_seqlens_q,
+    cu_seqlens_k,
+    max_seqlen_q,
+    max_seqlen_k,
+    *args,
+    **kwargs
+) -> int:
+    """Count flops for self-attention."""
+    # NB: We aren't accounting for causal attention here
+    # in case this is a nested tensor, we unpack the individual batch elements
+    # and then sum the flops per batch element
+    sizes = _unpack_efficient_attention_nested_shapes(
+        query=query,
+        key=key,
+        value=value,
+        cu_seqlens_q=cu_seqlens_q,
+        cu_seqlens_k=cu_seqlens_k,
+        max_seqlen_q=max_seqlen_q,
+        max_seqlen_k=max_seqlen_k,
+    )
+    return sum(
+        sdpa_flop_count(query_shape, key_shape, value_shape)
+        for query_shape, key_shape, value_shape, _ in sizes
+    )
+
+
+def sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape):
+    total_flops = 0
+    b, h, s_q, d_q = query_shape
+    _b2, _h2, s_k, _d2 = key_shape
+    _b3, _h3, _s3, d_v = value_shape
+    _b4, _h4, _s4, _d4 = grad_out_shape
+    assert b == _b2 == _b3 == _b4 and h == _h2 == _h3 == _h4 and d_q == _d2
+    assert d_v == _d4 and s_k == _s3 and s_q == _s4
+    total_flops = 0
+    # Step 1: We recompute the scores matrix.
+    # q: [b, h, s_q, d_q] @ k: [b, h, d_q, s_k] -> scores: [b, h, s_q, s_k]
+    total_flops += bmm_flop((b * h, s_q, d_q), (b * h, d_q, s_k))
+
+    # Step 2: We propagate the gradients through the score @ v operation.
+    # gradOut: [b, h, s_q, d_v] @ v: [b, h, d_v, s_k] -> gradScores: [b, h, s_q, s_k]
+    total_flops += bmm_flop((b * h, s_q, d_v), (b * h, d_v, s_k))
+    # scores: [b, h, s_k, s_q] @ gradOut: [b, h, s_q, d_v] -> gradV: [b, h, s_k, d_v]
+    total_flops += bmm_flop((b * h, s_k, s_q), (b * h, s_q, d_v))
+
+    # Step 3: We propagate th gradients through the k @ v operation
+    # gradScores: [b, h, s_q, s_k] @ k: [b, h, s_k, d_q] -> gradQ: [b, h, s_q, d_q]
+    total_flops += bmm_flop((b * h, s_q, s_k), (b * h, s_k, d_q))
+    # q: [b, h, d_q, s_q] @ gradScores: [b, h, s_q, s_k] -> gradK: [b, h, d_q, s_k]
+    total_flops += bmm_flop((b * h, d_q, s_q), (b * h, s_q, s_k))
+    return total_flops
+
+
+@register_flop_formula([aten._scaled_dot_product_efficient_attention_backward,
+                        aten._scaled_dot_product_flash_attention_backward,
+                        aten._scaled_dot_product_cudnn_attention_backward])
+def sdpa_backward_flop(grad_out_shape, query_shape, key_shape, value_shape, *args, out_shape=None, **kwargs) -> int:
+    """Count flops for self-attention backward."""
+    return sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape)
+
+@register_flop_formula(aten._flash_attention_backward, get_raw=True)
+def _flash_attention_backward_flop(
+    grad_out,
+    query,
+    key,
+    value,
+    out,  # named _out_shape to avoid kwarg collision with out_shape created in wrapper
+    logsumexp,
+    cum_seq_q,
+    cum_seq_k,
+    max_q,
+    max_k,
+    *args,
+    **kwargs,
+) -> int:
+    # in case this is a nested tensor, we unpack the individual batch elements
+    # and then sum the flops per batch element
+    shapes = _unpack_flash_attention_nested_shapes(
+        query=query,
+        key=key,
+        value=value,
+        grad_out=grad_out,
+        cum_seq_q=cum_seq_q,
+        cum_seq_k=cum_seq_k,
+        max_q=max_q,
+        max_k=max_k,
+    )
+    return sum(
+        sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape)
+        for query_shape, key_shape, value_shape, grad_out_shape in shapes
+    )
+
+
+@register_flop_formula(aten._efficient_attention_backward, get_raw=True)
+def _efficient_attention_backward_flop(
+    grad_out,
+    query,
+    key,
+    value,
+    bias,
+    out,  # named _out to avoid kwarg collision with out created in wrapper
+    cu_seqlens_q,
+    cu_seqlens_k,
+    max_seqlen_q,
+    max_seqlen_k,
+    *args,
+    **kwargs,
+) -> int:
+    # in case this is a nested tensor, we unpack the individual batch elements
+    # and then sum the flops per batch element
+    shapes = _unpack_efficient_attention_nested_shapes(
+        query=query,
+        key=key,
+        value=value,
+        grad_out=grad_out,
+        cu_seqlens_q=cu_seqlens_q,
+        cu_seqlens_k=cu_seqlens_k,
+        max_seqlen_q=max_seqlen_q,
+        max_seqlen_k=max_seqlen_k,
+    )
+    return sum(
+        sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape)
+        for query_shape, key_shape, value_shape, grad_out_shape in shapes
+    )
+
+
+flop_registry = {
+    aten.mm: mm_flop,
+    aten.addmm: addmm_flop,
+    aten.bmm: bmm_flop,
+    aten.baddbmm: baddbmm_flop,
+    aten._scaled_mm: _scaled_mm_flop,
+    aten.convolution: conv_flop,
+    aten._convolution: conv_flop,
+    aten.convolution_backward: conv_backward_flop,
+    aten._scaled_dot_product_efficient_attention: sdpa_flop,
+    aten._scaled_dot_product_flash_attention: sdpa_flop,
+    aten._scaled_dot_product_cudnn_attention: sdpa_flop,
+    aten._scaled_dot_product_efficient_attention_backward: sdpa_backward_flop,
+    aten._scaled_dot_product_flash_attention_backward: sdpa_backward_flop,
+    aten._scaled_dot_product_cudnn_attention_backward: sdpa_backward_flop,
+    aten._flash_attention_forward: _flash_attention_forward_flop,
+    aten._efficient_attention_forward: _efficient_attention_forward_flop,
+    aten._flash_attention_backward: _flash_attention_backward_flop,
+    aten._efficient_attention_backward: _efficient_attention_backward_flop,
+}
+
+def normalize_tuple(x):
+    if not isinstance(x, tuple):
+        return (x,)
+    return x
+
+
+# Define the suffixes for different orders of magnitude
+suffixes = ["", "K", "M", "B", "T"]
+# Thanks BingChat!
+def get_suffix_str(number):
+    # Find the index of the appropriate suffix based on the number of digits
+    # with some additional overflow.
+    # i.e. 1.01B should be displayed as 1001M, not 1.001B
+    index = max(0, min(len(suffixes) - 1, (len(str(number)) - 2) // 3))
+    return suffixes[index]
+
+def convert_num_with_suffix(number, suffix):
+    index = suffixes.index(suffix)
+    # Divide the number by 1000^index and format it to two decimal places
+    value = f"{number / 1000 ** index:.3f}"
+    # Return the value and the suffix as a string
+    return value + suffixes[index]
+
+def convert_to_percent_str(num, denom):
+    if denom == 0:
+        return "0%"
+    return f"{num / denom:.2%}"
+
+def _pytreeify_preserve_structure(f):
+    @wraps(f)
+    def nf(args):
+        flat_args, spec = tree_flatten(args)
+        out = f(*flat_args)
+        return tree_unflatten(out, spec)
+
+    return nf
+
+
+class FlopCounterMode:
+    """
+    ``FlopCounterMode`` is a context manager that counts the number of flops within its context.
+
+    It does this using a ``TorchDispatchMode``.
+
+    It also supports hierarchical output by passing a module (or list of
+    modules) to FlopCounterMode on construction. If you do not need hierarchical
+    output, you do not need to use it with a module.
+
+    Example usage
+
+    .. code-block:: python
+
+        mod = ...
+        with FlopCounterMode(mod) as flop_counter:
+            mod.sum().backward()
+
+    """
+
+    def __init__(
+            self,
+            mods: Optional[Union[torch.nn.Module, list[torch.nn.Module]]] = None,
+            depth: int = 2,
+            display: bool = True,
+            custom_mapping: Optional[dict[Any, Any]] = None):
+        super().__init__()
+        self.flop_counts: dict[str, dict[Any, int]] = defaultdict(lambda: defaultdict(int))
+        self.depth = depth
+        self.display = display
+        self.mode: Optional[_FlopCounterMode] = None
+        if custom_mapping is None:
+            custom_mapping = {}
+        if mods is not None:
+            warnings.warn("mods argument is not needed anymore, you can stop passing it", stacklevel=2)
+        self.flop_registry = {
+            **flop_registry,
+            **{k: v if getattr(v, "_get_raw", False) else shape_wrapper(v) for k, v in custom_mapping.items()}
+        }
+        self.mod_tracker = ModuleTracker()
+
+    def get_total_flops(self) -> int:
+        return sum(self.flop_counts['Global'].values())
+
+    def get_flop_counts(self) -> dict[str, dict[Any, int]]:
+        """Return the flop counts as a dictionary of dictionaries.
+
+        The outer
+        dictionary is keyed by module name, and the inner dictionary is keyed by
+        operation name.
+
+        Returns:
+            Dict[str, Dict[Any, int]]: The flop counts as a dictionary.
+        """
+        return {k: dict(v) for k, v in self.flop_counts.items()}
+
+    def get_table(self, depth=None):
+        if depth is None:
+            depth = self.depth
+        if depth is None:
+            depth = 999999
+
+        import tabulate
+        tabulate.PRESERVE_WHITESPACE = True
+        header = ["Module", "FLOP", "% Total"]
+        values = []
+        global_flops = self.get_total_flops()
+        global_suffix = get_suffix_str(global_flops)
+        is_global_subsumed = False
+
+        def process_mod(mod_name, depth):
+            nonlocal is_global_subsumed
+
+            total_flops = sum(self.flop_counts[mod_name].values())
+
+            is_global_subsumed |= total_flops >= global_flops
+
+            padding = " " * depth
+            values = []
+            values.append([
+                padding + mod_name,
+                convert_num_with_suffix(total_flops, global_suffix),
+                convert_to_percent_str(total_flops, global_flops)
+            ])
+            for k, v in self.flop_counts[mod_name].items():
+                values.append([
+                    padding + " - " + str(k),
+                    convert_num_with_suffix(v, global_suffix),
+                    convert_to_percent_str(v, global_flops)
+                ])
+            return values
+
+        for mod in sorted(self.flop_counts.keys()):
+            if mod == 'Global':
+                continue
+            mod_depth = mod.count(".") + 1
+            if mod_depth > depth:
+                continue
+
+            cur_values = process_mod(mod, mod_depth - 1)
+            values.extend(cur_values)
+
+        # We do a bit of messing around here to only output the "Global" value
+        # if there are any FLOPs in there that aren't already fully contained by
+        # a module.
+        if 'Global' in self.flop_counts and not is_global_subsumed:
+            for value in values:
+                value[0] = " " + value[0]
+
+            values = process_mod('Global', 0) + values
+
+        if len(values) == 0:
+            values = [["Global", "0", "0%"]]
+
+        return tabulate.tabulate(values, headers=header, colalign=("left", "right", "right"))
+
+    # NB: This context manager is NOT reentrant
+    def __enter__(self):
+        self.flop_counts.clear()
+        self.mod_tracker.__enter__()
+        self.mode = _FlopCounterMode(self)
+        self.mode.__enter__()
+        return self
+
+    def __exit__(self, *args):
+        assert self.mode is not None
+        b = self.mode.__exit__(*args)
+        self.mode = None  # break cycles
+        self.mod_tracker.__exit__()
+        if self.display:
+            print(self.get_table(self.depth))
+        return b
+
+    def _count_flops(self, func_packet, out, args, kwargs):
+        if func_packet in self.flop_registry:
+            flop_count_func = self.flop_registry[func_packet]
+            flop_count = flop_count_func(*args, **kwargs, out_val=out)  # type: ignore[operator]
+            for par in set(self.mod_tracker.parents):
+                self.flop_counts[par][func_packet] += flop_count
+
+        return out
+
+
+class _FlopCounterMode(TorchDispatchMode):
+    def __init__(self, counter: FlopCounterMode):
+        self.counter = counter
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        kwargs = kwargs if kwargs else {}
+
+        # Skip ops from non-standard dispatch_sizes_strides_policy such as NJT
+        if func in {torch.ops.aten.is_contiguous.default,
+                    torch.ops.aten.is_contiguous.memory_format,
+                    torch.ops.aten.is_strides_like_format.default,
+                    torch.ops.aten.is_non_overlapping_and_dense.default,
+                    torch.ops.aten.size.default,
+                    torch.ops.aten.sym_size.default,
+                    torch.ops.aten.stride.default,
+                    torch.ops.aten.sym_stride.default,
+                    torch.ops.aten.storage_offset.default,
+                    torch.ops.aten.sym_storage_offset.default,
+                    torch.ops.aten.numel.default,
+                    torch.ops.aten.sym_numel.default,
+                    torch.ops.aten.dim.default,
+                    torch.ops.prim.layout.default}:
+
+            return NotImplemented
+
+        # If we don't have func in flop_registry, see if it can decompose
+        if func not in self.counter.flop_registry and func is not torch.ops.prim.device.default:
+            with self:
+                r = func.decompose(*args, **kwargs)
+                if r is not NotImplemented:
+                    return r
+
+        # no further decomposition; execute & count flops
+        out = func(*args, **kwargs)
+        return self.counter._count_flops(func._overloadpacket, out, args, kwargs)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..58f3ace6c03d093337c9fa417ccbe8bc267b6c69
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/__init__.py
@@ -0,0 +1 @@
+from .version import __version__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/constants.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9053b261ad44d1ef8b8cbdf3a27da0306d92f36
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/constants.py
@@ -0,0 +1,62 @@
+"""Constants for annotations in the mapping.
+
+The constants defined here are used to annotate the mapping tuples in cuda_to_hip_mappings.py.
+They are based on
+https://github.com/ROCm/HIPIFY/blob/master/src/Statistics.h
+and fall in three categories: 1) type of mapping, 2) API of mapping, 3) unsupported
+mapping.
+"""
+
+CONV_VERSION = 0,
+CONV_INIT = 1
+CONV_DEVICE = 2
+CONV_MEM = 3
+CONV_KERN = 4
+CONV_COORD_FUNC = 5
+CONV_MATH_FUNC = 6
+CONV_DEVICE_FUNC = 7
+CONV_SPECIAL_FUNC = 8
+CONV_STREAM = 9
+CONV_EVENT = 10
+CONV_OCCUPANCY = 11
+CONV_CONTEXT = 12
+CONV_PEER = 13
+CONV_MODULE = 14
+CONV_CACHE = 15
+CONV_EXEC = 16
+CONV_ERROR = 17
+CONV_DEF = 18
+CONV_TEX = 19
+CONV_GL = 20
+CONV_GRAPHICS = 21
+CONV_SURFACE = 22
+CONV_JIT = 23
+CONV_D3D9 = 24
+CONV_D3D10 = 25
+CONV_D3D11 = 26
+CONV_VDPAU = 27
+CONV_EGL = 28
+CONV_THREAD = 29
+CONV_OTHER = 30
+CONV_INCLUDE = 31
+CONV_INCLUDE_CUDA_MAIN_H = 32
+CONV_TYPE = 33
+CONV_LITERAL = 34
+CONV_NUMERIC_LITERAL = 35
+CONV_LAST = 36
+
+API_DRIVER = 37
+API_RUNTIME = 38
+API_BLAS = 39
+API_SPECIAL = 40
+API_RAND = 41
+API_LAST = 42
+API_FFT = 43
+API_RTC = 44
+API_ROCTX = 45
+
+HIP_UNSUPPORTED = 46
+API_PYTORCH = 1337
+API_CAFFE2 = 1338
+API_C10 = 1339
+API_ROCMSMI = 1340
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/cuda_to_hip_mappings.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/cuda_to_hip_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c9f90346331c294af06627f7372090aeab4af72
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/cuda_to_hip_mappings.py
@@ -0,0 +1,8746 @@
+import collections
+import os
+
+from .constants import (API_BLAS, API_C10, API_CAFFE2, API_DRIVER, API_FFT,
+                        API_PYTORCH, API_RAND, API_ROCTX, API_RTC, API_RUNTIME,
+                        API_SPECIAL, API_ROCMSMI, CONV_CACHE, CONV_CONTEXT, CONV_D3D9,
+                        CONV_D3D10, CONV_D3D11, CONV_DEF, CONV_DEVICE,
+                        CONV_DEVICE_FUNC, CONV_EGL, CONV_ERROR, CONV_EVENT,
+                        CONV_EXEC, CONV_GL, CONV_GRAPHICS, CONV_INCLUDE,
+                        CONV_INCLUDE_CUDA_MAIN_H, CONV_INIT, CONV_JIT,
+                        CONV_MATH_FUNC, CONV_MEM, CONV_MODULE,
+                        CONV_NUMERIC_LITERAL, CONV_OCCUPANCY, CONV_OTHER,
+                        CONV_PEER, CONV_SPECIAL_FUNC, CONV_STREAM,
+                        CONV_SURFACE, CONV_TEX, CONV_THREAD, CONV_TYPE,
+                        CONV_VDPAU, CONV_VERSION, HIP_UNSUPPORTED)
+
+""" Mapping of CUDA functions, include files, constants, and types to ROCm/HIP equivalents
+This closely follows the implementation in hipify-clang
+https://github.com/ROCm-Developer-Tools/HIP/blob/master/hipify-clang/src/CUDA2HipMap.cpp
+and its structure.
+There are different maps for fundamental names, include files, identifies, sparse, and
+PyTorch specific translations.
+Each of the entries in these maps translates a CUDA string to a tuple containing the
+ROCm/HIP string, a type and API annotation and - optionally - an annotation if it is not
+supported in ROCm/HIP yet.
+"""
+
+_IS_FBCODE = os.environ.get("IS_FBCODE", "0") == "1"
+
+# FBCODE compiles against rccl sources instead of an installed rccl package.
+# The header location is src/rccl.h versus rccl/rccl.h, respectively.
+_RCCL_HEADER = "" if _IS_FBCODE else ""
+
+# List of math functions that should be replaced inside device code only.
+MATH_TRANSPILATIONS = collections.OrderedDict(
+    [
+        ("std::max", ("::max")),
+        ("std::min", ("::min")),
+        ("std::ceil", ("::ceil")),
+        ("std::floor", ("::floor")),
+        ("std::exp", ("::exp")),
+        ("std::log", ("::log")),
+        ("std::pow", ("::pow")),
+        ("std::fabs", ("::fabs")),
+        ("std::fmod", ("::fmod")),
+        ("std::remainder", ("::remainder")),
+        ("std::frexp", ("::frexp")),
+    ]
+)
+
+CUDA_TYPE_NAME_MAP = collections.OrderedDict(
+    [
+        ("CUresult", ("hipError_t", CONV_TYPE, API_DRIVER)),
+        ("cudaError_t", ("hipError_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaError", ("hipError_t", CONV_TYPE, API_RUNTIME)),
+        (
+            "CUDA_ARRAY3D_DESCRIPTOR",
+            ("HIP_ARRAY3D_DESCRIPTOR", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUDA_ARRAY_DESCRIPTOR", ("HIP_ARRAY_DESCRIPTOR", CONV_TYPE, API_DRIVER)),
+        ("CUDA_MEMCPY2D", ("hip_Memcpy2D", CONV_TYPE, API_DRIVER)),
+        ("CUDA_MEMCPY3D", ("HIP_MEMCPY3D", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUDA_MEMCPY3D_PEER",
+            ("HIP_MEMCPY3D_PEER", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_POINTER_ATTRIBUTE_P2P_TOKENS",
+            (
+                "HIP_POINTER_ATTRIBUTE_P2P_TOKENS",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CUDA_RESOURCE_DESC",
+            ("HIP_RESOURCE_DESC", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_RESOURCE_VIEW_DESC",
+            ("HIP_RESOURCE_VIEW_DESC", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUipcEventHandle",
+            ("hipIpcEventHandle", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUipcMemHandle", ("hipIpcMemHandle", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        ("CUaddress_mode", ("hipAddress_mode", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUarray_cubemap_face",
+            ("hipArray_cubemap_face", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUarray_format", ("hipArray_format", CONV_TYPE, API_DRIVER)),
+        ("CUcomputemode", ("hipComputemode", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        ("CUmem_advise", ("hipMemAdvise", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUmem_range_attribute",
+            ("hipMemRangeAttribute", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUctx_flags", ("hipCctx_flags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        ("CUdevice", ("hipDevice_t", CONV_TYPE, API_DRIVER)),
+        ("CUdevice_attribute_enum", ("hipDeviceAttribute_t", CONV_TYPE, API_DRIVER)),
+        ("CUdevice_attribute", ("hipDeviceAttribute_t", CONV_TYPE, API_DRIVER)),
+        ("CUpointer_attribute", ("hipPointer_attribute", CONV_TYPE, API_DRIVER)),
+        ("CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL", ("HIP_POINTER_ATTRIBUTE_DEVICE_ORDINAL", CONV_TYPE, API_DRIVER)),
+        ("CU_POINTER_ATTRIBUTE_BUFFER_ID", ("HIP_POINTER_ATTRIBUTE_BUFFER_ID", CONV_TYPE, API_DRIVER)),
+        ("CUdeviceptr", ("hipDeviceptr_t", CONV_TYPE, API_DRIVER)),
+        ("CUarray_st", ("hipArray", CONV_TYPE, API_DRIVER)),
+        ("CUarray", ("hipArray *", CONV_TYPE, API_DRIVER)),
+        ("CUdevprop_st", ("hipDeviceProp_t", CONV_TYPE, API_DRIVER)),
+        ("CUdevprop", ("hipDeviceProp_t", CONV_TYPE, API_DRIVER)),
+        ("CUfunction", ("hipFunction_t", CONV_TYPE, API_DRIVER)),
+        (
+            "CUgraphicsResource",
+            ("hipGraphicsResource_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUmipmappedArray",
+            ("hipMipmappedArray_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUfunction_attribute",
+            ("hipFuncAttribute_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUfunction_attribute_enum",
+            ("hipFuncAttribute_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUgraphicsMapResourceFlags",
+            ("hipGraphicsMapFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUgraphicsMapResourceFlags_enum",
+            ("hipGraphicsMapFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUgraphicsRegisterFlags",
+            ("hipGraphicsRegisterFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUgraphicsRegisterFlags_enum",
+            ("hipGraphicsRegisterFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUoccupancy_flags",
+            ("hipOccupancyFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUoccupancy_flags_enum",
+            ("hipOccupancyFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUfunc_cache_enum", ("hipFuncCache", CONV_TYPE, API_DRIVER)),
+        ("CUfunc_cache", ("hipFuncCache", CONV_TYPE, API_DRIVER)),
+        ("CUipcMem_flags", ("hipIpcMemFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUipcMem_flags_enum",
+            ("hipIpcMemFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUjit_cacheMode", ("hipJitCacheMode", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUjit_cacheMode_enum",
+            ("hipJitCacheMode", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUjit_fallback", ("hipJitFallback", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUjit_fallback_enum",
+            ("hipJitFallback", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUjit_option", ("hipJitOption", CONV_JIT, API_DRIVER)),
+        ("CUjit_option_enum", ("hipJitOption", CONV_JIT, API_DRIVER)),
+        ("CUjit_target", ("hipJitTarget", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED)),
+        ("CUjit_target_enum", ("hipJitTarget", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED)),
+        ("CUjitInputType", ("hipJitInputType", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUjitInputType_enum",
+            ("hipJitInputType", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUlimit", ("hipLimit_t", CONV_TYPE, API_DRIVER)),
+        ("CUlimit_enum", ("hipLimit_t", CONV_TYPE, API_DRIVER)),
+        (
+            "CUmemAttach_flags",
+            ("hipMemAttachFlags_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUmemAttach_flags_enum",
+            ("hipMemAttachFlags_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUmemorytype", ("hipMemType_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        ("CUmemorytype_enum", ("hipMemType_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        ("CUresourcetype", ("hipResourceType", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUresourcetype_enum",
+            ("hipResourceType", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUresourceViewFormat", ("hipResourceViewFormat", CONV_TEX, API_DRIVER)),
+        ("CUresourceViewFormat_enum", ("hipResourceViewFormat", CONV_TEX, API_DRIVER)),
+        ("CUsharedconfig", ("hipSharedMemConfig", CONV_TYPE, API_DRIVER)),
+        ("CUsharedconfig_enum", ("hipSharedMemConfig", CONV_TYPE, API_DRIVER)),
+        ("CUcontext", ("hipCtx_t", CONV_TYPE, API_DRIVER)),
+        ("CUmodule", ("hipModule_t", CONV_TYPE, API_DRIVER)),
+        ("CUstream", ("hipStream_t", CONV_TYPE, API_DRIVER)),
+        ("CUstream_st", ("ihipStream_t", CONV_TYPE, API_DRIVER)),
+        ("CUstreamCallback", ("hipStreamCallback_t", CONV_TYPE, API_DRIVER)),
+        ("CUsurfObject", ("hipSurfaceObject", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUsurfref",
+            ("hipSurfaceReference_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUtexObject", ("hipTextureObject_t", CONV_TYPE, API_DRIVER)),
+        ("CUtexref", ("textureReference", CONV_TYPE, API_DRIVER)),
+        ("CUstream_flags", ("hipStreamFlags", CONV_TYPE, API_DRIVER)),
+        (
+            "CUstreamWaitValue_flags",
+            ("hipStreamWaitValueFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUstreamWriteValue_flags",
+            ("hipStreamWriteValueFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUstreamBatchMemOpType",
+            ("hipStreamBatchMemOpType", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUdevice_P2PAttribute",
+            ("hipDeviceP2PAttribute", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUevent", ("hipEvent_t", CONV_TYPE, API_DRIVER)),
+        ("CUevent_st", ("ihipEvent_t", CONV_TYPE, API_DRIVER)),
+        ("CUevent_flags", ("hipEventFlags", CONV_EVENT, API_DRIVER, HIP_UNSUPPORTED)),
+        ("CUfilter_mode", ("hipTextureFilterMode", CONV_TEX, API_DRIVER)),
+        ("CUGLDeviceList", ("hipGLDeviceList", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)),
+        ("CUGLmap_flags", ("hipGLMapFlags", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUd3d9DeviceList",
+            ("hipD3D9DeviceList", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUd3d9map_flags",
+            ("hipD3D9MapFlags", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUd3d9register_flags",
+            ("hipD3D9RegisterFlags", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUd3d10DeviceList",
+            ("hipd3d10DeviceList", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUd3d10map_flags",
+            ("hipD3D10MapFlags", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUd3d10register_flags",
+            ("hipD3D10RegisterFlags", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUd3d11DeviceList",
+            ("hipd3d11DeviceList", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUeglStreamConnection_st",
+            ("hipEglStreamConnection", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUeglStreamConnection",
+            ("hipEglStreamConnection", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "libraryPropertyType_t",
+            ("hipLibraryPropertyType_t", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "libraryPropertyType",
+            ("hipLibraryPropertyType_t", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaStreamCallback_t", ("hipStreamCallback_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaArray", ("hipArray", CONV_MEM, API_RUNTIME)),
+        ("cudaArray_t", ("hipArray_t", CONV_MEM, API_RUNTIME)),
+        ("cudaArray_const_t", ("hipArray_const_t", CONV_MEM, API_RUNTIME)),
+        ("cudaMipmappedArray_t", ("hipMipmappedArray_t", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaMipmappedArray_const_t",
+            ("hipMipmappedArray_const_t", CONV_MEM, API_RUNTIME),
+        ),
+        ("cudaArrayDefault", ("hipArrayDefault", CONV_MEM, API_RUNTIME)),
+        ("cudaArrayLayered", ("hipArrayLayered", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaArraySurfaceLoadStore",
+            ("hipArraySurfaceLoadStore", CONV_MEM, API_RUNTIME),
+        ),
+        ("cudaArrayCubemap", ("hipArrayCubemap", CONV_MEM, API_RUNTIME)),
+        ("cudaArrayTextureGather", ("hipArrayTextureGather", CONV_MEM, API_RUNTIME)),
+        ("cudaMemoryAdvise", ("hipMemoryAdvise", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED)),
+        (
+            "cudaMemRangeAttribute",
+            ("hipMemRangeAttribute", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaMemcpyKind", ("hipMemcpyKind", CONV_MEM, API_RUNTIME)),
+        ("cudaMemoryType", ("hipMemoryType", CONV_MEM, API_RUNTIME)),
+        ("cudaExtent", ("hipExtent", CONV_MEM, API_RUNTIME)),
+        ("cudaPitchedPtr", ("hipPitchedPtr", CONV_MEM, API_RUNTIME)),
+        ("cudaPos", ("hipPos", CONV_MEM, API_RUNTIME)),
+        ("cudaEvent_t", ("hipEvent_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaStream_t", ("hipStream_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaPointerAttributes", ("hipPointerAttribute_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaDeviceAttr", ("hipDeviceAttribute_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaDeviceProp", ("hipDeviceProp_t", CONV_TYPE, API_RUNTIME)),
+        (
+            "cudaDeviceP2PAttr",
+            ("hipDeviceP2PAttribute", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaComputeMode",
+            ("hipComputeMode", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaFuncCache", ("hipFuncCache_t", CONV_CACHE, API_RUNTIME)),
+        (
+            "cudaFuncAttributes",
+            ("hipFuncAttributes", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaSharedMemConfig", ("hipSharedMemConfig", CONV_TYPE, API_RUNTIME)),
+        ("cudaLimit", ("hipLimit_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaOutputMode", ("hipOutputMode", CONV_OTHER, API_RUNTIME, HIP_UNSUPPORTED)),
+        ("cudaTextureReadMode", ("hipTextureReadMode", CONV_TEX, API_RUNTIME)),
+        ("cudaTextureFilterMode", ("hipTextureFilterMode", CONV_TEX, API_RUNTIME)),
+        ("cudaChannelFormatKind", ("hipChannelFormatKind", CONV_TEX, API_RUNTIME)),
+        ("cudaChannelFormatDesc", ("hipChannelFormatDesc", CONV_TEX, API_RUNTIME)),
+        ("cudaResourceDesc", ("hipResourceDesc", CONV_TEX, API_RUNTIME)),
+        ("cudaResourceViewDesc", ("hipResourceViewDesc", CONV_TEX, API_RUNTIME)),
+        ("cudaTextureDesc", ("hipTextureDesc", CONV_TEX, API_RUNTIME)),
+        (
+            "surfaceReference",
+            ("hipSurfaceReference", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaTextureObject_t", ("hipTextureObject_t", CONV_TEX, API_RUNTIME)),
+        ("cudaResourceType", ("hipResourceType", CONV_TEX, API_RUNTIME)),
+        ("cudaResourceViewFormat", ("hipResourceViewFormat", CONV_TEX, API_RUNTIME)),
+        ("cudaTextureAddressMode", ("hipTextureAddressMode", CONV_TEX, API_RUNTIME)),
+        (
+            "cudaSurfaceBoundaryMode",
+            ("hipSurfaceBoundaryMode", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaSurfaceFormatMode",
+            ("hipSurfaceFormatMode", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaTextureType1D", ("hipTextureType1D", CONV_TEX, API_RUNTIME)),
+        ("cudaTextureType2D", ("hipTextureType2D", CONV_TEX, API_RUNTIME)),
+        ("cudaTextureType3D", ("hipTextureType3D", CONV_TEX, API_RUNTIME)),
+        ("cudaTextureTypeCubemap", ("hipTextureTypeCubemap", CONV_TEX, API_RUNTIME)),
+        (
+            "cudaTextureType1DLayered",
+            ("hipTextureType1DLayered", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaTextureType2DLayered",
+            ("hipTextureType2DLayered", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaTextureTypeCubemapLayered",
+            ("hipTextureTypeCubemapLayered", CONV_TEX, API_RUNTIME),
+        ),
+        ("cudaIpcEventHandle_t", ("hipIpcEventHandle_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaIpcEventHandle_st", ("hipIpcEventHandle_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaIpcMemHandle_t", ("hipIpcMemHandle_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaIpcMemHandle_st", ("hipIpcMemHandle_t", CONV_TYPE, API_RUNTIME)),
+        (
+            "cudaGraphicsCubeFace",
+            ("hipGraphicsCubeFace", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsMapFlags",
+            ("hipGraphicsMapFlags", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsRegisterFlags",
+            ("hipGraphicsRegisterFlags", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLDeviceList",
+            ("hipGLDeviceList", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaGLMapFlags", ("hipGLMapFlags", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED)),
+        (
+            "cudaD3D9DeviceList",
+            ("hipD3D9DeviceList", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9MapFlags",
+            ("hipD3D9MapFlags", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9RegisterFlags",
+            ("hipD3D9RegisterFlags", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10DeviceList",
+            ("hipd3d10DeviceList", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10MapFlags",
+            ("hipD3D10MapFlags", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10RegisterFlags",
+            ("hipD3D10RegisterFlags", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D11DeviceList",
+            ("hipd3d11DeviceList", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaEglStreamConnection",
+            ("hipEglStreamConnection", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cublasHandle_t", ("hipblasHandle_t", CONV_TYPE, API_BLAS)),
+        ("cublasOperation_t", ("hipblasOperation_t", CONV_TYPE, API_BLAS)),
+        ("cublasStatus_t", ("hipblasStatus_t", CONV_TYPE, API_BLAS)),
+        ("cublasFillMode_t", ("hipblasFillMode_t", CONV_TYPE, API_BLAS)),
+        ("cublasDiagType_t", ("hipblasDiagType_t", CONV_TYPE, API_BLAS)),
+        ("cublasSideMode_t", ("hipblasSideMode_t", CONV_TYPE, API_BLAS)),
+        ("cublasPointerMode_t", ("hipblasPointerMode_t", CONV_TYPE, API_BLAS)),
+        ("cublasGemmAlgo_t", ("hipblasGemmAlgo_t", CONV_TYPE, API_BLAS)),
+        (
+            "cublasAtomicsMode_t",
+            ("hipblasAtomicsMode_t", CONV_TYPE, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDataType_t",
+            ("hipblasDatatype_t", CONV_TYPE, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("curandStatus", ("hiprandStatus_t", CONV_TYPE, API_RAND)),
+        ("curandStatus_t", ("hiprandStatus_t", CONV_TYPE, API_RAND)),
+        ("curandRngType", ("hiprandRngType_t", CONV_TYPE, API_RAND)),
+        ("curandRngType_t", ("hiprandRngType_t", CONV_TYPE, API_RAND)),
+        ("curandGenerator_st", ("hiprandGenerator_st", CONV_TYPE, API_RAND)),
+        ("curandGenerator_t", ("hiprandGenerator_t", CONV_TYPE, API_RAND)),
+        (
+            "curandDirectionVectorSet",
+            ("hiprandDirectionVectorSet_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandDirectionVectorSet_t",
+            ("hiprandDirectionVectorSet_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        ("curandOrdering", ("hiprandOrdering_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED)),
+        (
+            "curandOrdering_t",
+            ("hiprandOrdering_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandDistribution_st",
+            ("hiprandDistribution_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandHistogramM2V_st",
+            ("hiprandDistribution_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandDistribution_t",
+            ("hiprandDistribution_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandHistogramM2V_t",
+            ("hiprandDistribution_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandDistributionShift_st",
+            ("hiprandDistributionShift_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandDistributionShift_t",
+            ("hiprandDistributionShift_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandDistributionM2Shift_st",
+            ("hiprandDistributionM2Shift_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandDistributionM2Shift_t",
+            ("hiprandDistributionM2Shift_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandHistogramM2_st",
+            ("hiprandHistogramM2_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandHistogramM2_t",
+            ("hiprandHistogramM2_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandHistogramM2K_st",
+            ("hiprandHistogramM2K_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandHistogramM2K_t",
+            ("hiprandHistogramM2K_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandDiscreteDistribution_st",
+            ("hiprandDiscreteDistribution_st", CONV_TYPE, API_RAND),
+        ),
+        (
+            "curandDiscreteDistribution_t",
+            ("hiprandDiscreteDistribution_t", CONV_TYPE, API_RAND),
+        ),
+        ("curandMethod", ("hiprandMethod_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED)),
+        ("curandMethod_t", ("hiprandMethod_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED)),
+        (
+            "curandDirectionVectors32_t",
+            ("hiprandDirectionVectors32_t", CONV_TYPE, API_RAND),
+        ),
+        (
+            "curandDirectionVectors64_t",
+            ("hiprandDirectionVectors64_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        ("curandStateMtgp32_t", ("hiprandStateMtgp32_t", CONV_TYPE, API_RAND)),
+        ("curandStateMtgp32", ("hiprandStateMtgp32_t", CONV_TYPE, API_RAND)),
+        (
+            "curandStateScrambledSobol64_t",
+            ("hiprandStateScrambledSobol64_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandStateSobol64_t",
+            ("hiprandStateSobol64_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandStateScrambledSobol32_t",
+            ("hiprandStateScrambledSobol32_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED),
+        ),
+        ("curandStateSobol32_t", ("hiprandStateSobol32_t", CONV_TYPE, API_RAND)),
+        ("curandStateMRG32k3a_t", ("hiprandStateMRG32k3a_t", CONV_TYPE, API_RAND)),
+        (
+            "curandStatePhilox4_32_10_t",
+            ("hiprandStatePhilox4_32_10_t", CONV_TYPE, API_RAND),
+        ),
+        ("curandStateXORWOW_t", ("hiprandStateXORWOW_t", CONV_TYPE, API_RAND)),
+        ("curandState_t", ("hiprandState_t", CONV_TYPE, API_RAND)),
+        ("curandState", ("hiprandState_t", CONV_TYPE, API_RAND)),
+        ("CUuuid", ("hipUUID", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraph_t", ("hipGraph_t", CONV_TYPE, API_RAND)),
+        ("cudaGraphExec_t", ("hipGraphExec_t", CONV_TYPE, API_RAND)),
+        ("__nv_bfloat16", ("__hip_bfloat16", CONV_TYPE, API_RUNTIME)),
+        ("__nv_bfloat162", ("__hip_bfloat162", CONV_TYPE, API_RUNTIME)),
+    ]
+)
+
+CUDA_INCLUDE_MAP = collections.OrderedDict(
+    [
+        # since pytorch uses "\b{pattern}\b" as the actual re pattern,
+        # patterns listed here have to begin and end with alnum chars
+        (
+            "include " to differentiate
+        ("", (_RCCL_HEADER, CONV_INCLUDE, API_RUNTIME)),
+        ("nvrtc.h", ("hip/hiprtc.h", CONV_INCLUDE, API_RTC)),
+        ("thrust/system/cuda", ("thrust/system/hip", CONV_INCLUDE, API_BLAS)),
+        ("cub/util_allocator.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/block/block_reduce.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/block/block_raking_layout.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/cub.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/config.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/util_ptx.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/util_type.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/device/device_run_length_encode.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/block/block_load.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/block/block_store.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/block/block_scan.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/device/device_radix_sort.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/device/device_reduce.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/device/device_scan.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("cub/device/device_select.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)),
+        ("nvtx3/nvtx3.hpp", ("roctracer/roctx.h", CONV_INCLUDE, API_ROCTX)),
+        ("nvToolsExt.h", ("roctracer/roctx.h", CONV_INCLUDE, API_ROCTX)),
+        ("nvml.h", ("rocm_smi/rocm_smi.h", CONV_INCLUDE, API_ROCMSMI)),
+    ]
+)
+
+CUDA_IDENTIFIER_MAP = collections.OrderedDict(
+    [
+        ("__CUDACC__", ("__HIPCC__", CONV_DEF, API_RUNTIME)),
+        (
+            "CUDA_ERROR_INVALID_CONTEXT",
+            ("hipErrorInvalidContext", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CUDA_ERROR_CONTEXT_ALREADY_CURRENT",
+            ("hipErrorContextAlreadyCurrent", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CUDA_ERROR_ARRAY_IS_MAPPED",
+            ("hipErrorArrayIsMapped", CONV_TYPE, API_DRIVER),
+        ),
+        ("CUDA_ERROR_ALREADY_MAPPED", ("hipErrorAlreadyMapped", CONV_TYPE, API_DRIVER)),
+        (
+            "CUDA_ERROR_ALREADY_ACQUIRED",
+            ("hipErrorAlreadyAcquired", CONV_TYPE, API_DRIVER),
+        ),
+        ("CUDA_ERROR_NOT_MAPPED", ("hipErrorNotMapped", CONV_TYPE, API_DRIVER)),
+        (
+            "CUDA_ERROR_NOT_MAPPED_AS_ARRAY",
+            ("hipErrorNotMappedAsArray", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CUDA_ERROR_NOT_MAPPED_AS_POINTER",
+            ("hipErrorNotMappedAsPointer", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CUDA_ERROR_CONTEXT_ALREADY_IN_USE",
+            ("hipErrorContextAlreadyInUse", CONV_TYPE, API_DRIVER),
+        ),
+        ("CUDA_ERROR_INVALID_SOURCE", ("hipErrorInvalidSource", CONV_TYPE, API_DRIVER)),
+        ("CUDA_ERROR_FILE_NOT_FOUND", ("hipErrorFileNotFound", CONV_TYPE, API_DRIVER)),
+        ("CUDA_ERROR_NOT_FOUND", ("hipErrorNotFound", CONV_TYPE, API_DRIVER)),
+        (
+            "CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING",
+            (
+                "hipErrorLaunchIncompatibleTexturing",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE",
+            ("hipErrorPrimaryContextActive", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_CONTEXT_IS_DESTROYED",
+            ("hipErrorContextIsDestroyed", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_NOT_PERMITTED",
+            ("hipErrorNotPermitted", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_NOT_SUPPORTED",
+            ("hipErrorNotSupported", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorMissingConfiguration",
+            ("hipErrorMissingConfiguration", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorPriorLaunchFailure",
+            ("hipErrorPriorLaunchFailure", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidDeviceFunction",
+            ("hipErrorInvalidDeviceFunction", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidConfiguration",
+            ("hipErrorInvalidConfiguration", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidPitchValue",
+            ("hipErrorInvalidPitchValue", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidSymbol",
+            ("hipErrorInvalidSymbol", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidHostPointer",
+            ("hipErrorInvalidHostPointer", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidDevicePointer",
+            ("hipErrorInvalidDevicePointer", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaErrorInvalidTexture",
+            ("hipErrorInvalidTexture", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidTextureBinding",
+            ("hipErrorInvalidTextureBinding", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidChannelDescriptor",
+            (
+                "hipErrorInvalidChannelDescriptor",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaErrorInvalidMemcpyDirection",
+            ("hipErrorInvalidMemcpyDirection", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorAddressOfConstant",
+            ("hipErrorAddressOfConstant", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorTextureFetchFailed",
+            ("hipErrorTextureFetchFailed", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorTextureNotBound",
+            ("hipErrorTextureNotBound", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorSynchronizationError",
+            ("hipErrorSynchronizationError", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidFilterSetting",
+            ("hipErrorInvalidFilterSetting", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidNormSetting",
+            ("hipErrorInvalidNormSetting", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorMixedDeviceExecution",
+            ("hipErrorMixedDeviceExecution", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorNotYetImplemented",
+            ("hipErrorNotYetImplemented", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorMemoryValueTooLarge",
+            ("hipErrorMemoryValueTooLarge", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInsufficientDriver",
+            ("hipErrorInsufficientDriver", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorSetOnActiveProcess",
+            ("hipErrorSetOnActiveProcess", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidSurface",
+            ("hipErrorInvalidSurface", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorDuplicateVariableName",
+            ("hipErrorDuplicateVariableName", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorDuplicateTextureName",
+            ("hipErrorDuplicateTextureName", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorDuplicateSurfaceName",
+            ("hipErrorDuplicateSurfaceName", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorDevicesUnavailable",
+            ("hipErrorDevicesUnavailable", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorIncompatibleDriverContext",
+            (
+                "hipErrorIncompatibleDriverContext",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaErrorDeviceAlreadyInUse",
+            ("hipErrorDeviceAlreadyInUse", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorLaunchMaxDepthExceeded",
+            ("hipErrorLaunchMaxDepthExceeded", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorLaunchFileScopedTex",
+            ("hipErrorLaunchFileScopedTex", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorLaunchFileScopedSurf",
+            ("hipErrorLaunchFileScopedSurf", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorSyncDepthExceeded",
+            ("hipErrorSyncDepthExceeded", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorLaunchPendingCountExceeded",
+            (
+                "hipErrorLaunchPendingCountExceeded",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaErrorNotPermitted",
+            ("hipErrorNotPermitted", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorNotSupported",
+            ("hipErrorNotSupported", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorStartupFailure",
+            ("hipErrorStartupFailure", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorApiFailureBase",
+            ("hipErrorApiFailureBase", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("CUDA_SUCCESS", ("hipSuccess", CONV_TYPE, API_DRIVER)),
+        ("cudaSuccess", ("hipSuccess", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_ERROR_INVALID_VALUE", ("hipErrorInvalidValue", CONV_TYPE, API_DRIVER)),
+        ("cudaErrorInvalidValue", ("hipErrorInvalidValue", CONV_TYPE, API_RUNTIME)),
+        (
+            "CUDA_ERROR_OUT_OF_MEMORY",
+            ("hipErrorMemoryAllocation", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorMemoryAllocation",
+            ("hipErrorMemoryAllocation", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "CUDA_ERROR_NOT_INITIALIZED",
+            ("hipErrorNotInitialized", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorInitializationError",
+            ("hipErrorInitializationError", CONV_TYPE, API_RUNTIME),
+        ),
+        ("CUDA_ERROR_DEINITIALIZED", ("hipErrorDeinitialized", CONV_TYPE, API_DRIVER)),
+        (
+            "cudaErrorCudartUnloading",
+            ("hipErrorDeinitialized", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_PROFILER_DISABLED",
+            ("hipErrorProfilerDisabled", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorProfilerDisabled",
+            ("hipErrorProfilerDisabled", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_PROFILER_NOT_INITIALIZED",
+            ("hipErrorProfilerNotInitialized", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorProfilerNotInitialized",
+            ("hipErrorProfilerNotInitialized", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_PROFILER_ALREADY_STARTED",
+            ("hipErrorProfilerAlreadyStarted", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorProfilerAlreadyStarted",
+            ("hipErrorProfilerAlreadyStarted", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_PROFILER_ALREADY_STOPPED",
+            ("hipErrorProfilerAlreadyStopped", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorProfilerAlreadyStopped",
+            ("hipErrorProfilerAlreadyStopped", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("CUDA_ERROR_NO_DEVICE", ("hipErrorNoDevice", CONV_TYPE, API_DRIVER)),
+        ("cudaErrorNoDevice", ("hipErrorNoDevice", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_ERROR_INVALID_DEVICE", ("hipErrorInvalidDevice", CONV_TYPE, API_DRIVER)),
+        ("cudaErrorInvalidDevice", ("hipErrorInvalidDevice", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_ERROR_INVALID_IMAGE", ("hipErrorInvalidImage", CONV_TYPE, API_DRIVER)),
+        (
+            "cudaErrorInvalidKernelImage",
+            ("hipErrorInvalidImage", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("CUDA_ERROR_MAP_FAILED", ("hipErrorMapFailed", CONV_TYPE, API_DRIVER)),
+        (
+            "cudaErrorMapBufferObjectFailed",
+            ("hipErrorMapFailed", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("CUDA_ERROR_UNMAP_FAILED", ("hipErrorUnmapFailed", CONV_TYPE, API_DRIVER)),
+        (
+            "cudaErrorUnmapBufferObjectFailed",
+            ("hipErrorUnmapFailed", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_NO_BINARY_FOR_GPU",
+            ("hipErrorNoBinaryForGpu", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorNoKernelImageForDevice",
+            ("hipErrorNoBinaryForGpu", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_ECC_UNCORRECTABLE",
+            ("hipErrorECCNotCorrectable", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorECCUncorrectable",
+            ("hipErrorECCNotCorrectable", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_UNSUPPORTED_LIMIT",
+            ("hipErrorUnsupportedLimit", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorUnsupportedLimit",
+            ("hipErrorUnsupportedLimit", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_PEER_ACCESS_UNSUPPORTED",
+            ("hipErrorPeerAccessUnsupported", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorPeerAccessUnsupported",
+            ("hipErrorPeerAccessUnsupported", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_INVALID_PTX",
+            ("hipErrorInvalidKernelFile", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorInvalidPtx",
+            ("hipErrorInvalidKernelFile", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_INVALID_GRAPHICS_CONTEXT",
+            ("hipErrorInvalidGraphicsContext", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorInvalidGraphicsContext",
+            ("hipErrorInvalidGraphicsContext", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_NVLINK_UNCORRECTABLE",
+            ("hipErrorNvlinkUncorrectable", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorNvlinkUncorrectable",
+            ("hipErrorNvlinkUncorrectable", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND",
+            ("hipErrorSharedObjectSymbolNotFound", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorSharedObjectSymbolNotFound",
+            (
+                "hipErrorSharedObjectSymbolNotFound",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CUDA_ERROR_SHARED_OBJECT_INIT_FAILED",
+            ("hipErrorSharedObjectInitFailed", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorSharedObjectInitFailed",
+            ("hipErrorSharedObjectInitFailed", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_OPERATING_SYSTEM",
+            ("hipErrorOperatingSystem", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorOperatingSystem",
+            ("hipErrorOperatingSystem", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_INVALID_HANDLE",
+            ("hipErrorInvalidResourceHandle", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorInvalidResourceHandle",
+            ("hipErrorInvalidResourceHandle", CONV_TYPE, API_RUNTIME),
+        ),
+        ("CUDA_ERROR_NOT_READY", ("hipErrorNotReady", CONV_TYPE, API_DRIVER)),
+        ("cudaErrorNotReady", ("hipErrorNotReady", CONV_TYPE, API_RUNTIME)),
+        (
+            "CUDA_ERROR_ILLEGAL_ADDRESS",
+            ("hipErrorIllegalAddress", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorIllegalAddress",
+            ("hipErrorIllegalAddress", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES",
+            ("hipErrorLaunchOutOfResources", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorLaunchOutOfResources",
+            ("hipErrorLaunchOutOfResources", CONV_TYPE, API_RUNTIME),
+        ),
+        ("CUDA_ERROR_LAUNCH_TIMEOUT", ("hipErrorLaunchTimeOut", CONV_TYPE, API_DRIVER)),
+        (
+            "cudaErrorLaunchTimeout",
+            ("hipErrorLaunchTimeOut", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED",
+            ("hipErrorPeerAccessAlreadyEnabled", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorPeerAccessAlreadyEnabled",
+            ("hipErrorPeerAccessAlreadyEnabled", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "CUDA_ERROR_PEER_ACCESS_NOT_ENABLED",
+            ("hipErrorPeerAccessNotEnabled", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorPeerAccessNotEnabled",
+            ("hipErrorPeerAccessNotEnabled", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "CUDA_ERROR_ASSERT",
+            ("hipErrorAssert", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorAssert",
+            ("hipErrorAssert", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_TOO_MANY_PEERS",
+            ("hipErrorTooManyPeers", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorTooManyPeers",
+            ("hipErrorTooManyPeers", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED",
+            ("hipErrorHostMemoryAlreadyRegistered", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorHostMemoryAlreadyRegistered",
+            ("hipErrorHostMemoryAlreadyRegistered", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED",
+            ("hipErrorHostMemoryNotRegistered", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "cudaErrorHostMemoryNotRegistered",
+            ("hipErrorHostMemoryNotRegistered", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "CUDA_ERROR_HARDWARE_STACK_ERROR",
+            ("hipErrorHardwareStackError", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorHardwareStackError",
+            ("hipErrorHardwareStackError", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_ILLEGAL_INSTRUCTION",
+            ("hipErrorIllegalInstruction", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorIllegalInstruction",
+            ("hipErrorIllegalInstruction", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_MISALIGNED_ADDRESS",
+            ("hipErrorMisalignedAddress", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorMisalignedAddress",
+            ("hipErrorMisalignedAddress", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_INVALID_ADDRESS_SPACE",
+            ("hipErrorInvalidAddressSpace", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidAddressSpace",
+            ("hipErrorInvalidAddressSpace", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_INVALID_PC",
+            ("hipErrorInvalidPc", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorInvalidPc",
+            ("hipErrorInvalidPc", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_LAUNCH_FAILED",
+            ("hipErrorLaunchFailure", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaErrorLaunchFailure",
+            ("hipErrorLaunchFailure", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ERROR_UNKNOWN",
+            ("hipErrorUnknown", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cudaErrorUnknown", ("hipErrorUnknown", CONV_TYPE, API_RUNTIME)),
+        (
+            "CU_TR_ADDRESS_MODE_WRAP",
+            ("HIP_TR_ADDRESS_MODE_WRAP", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TR_ADDRESS_MODE_CLAMP",
+            ("HIP_TR_ADDRESS_MODE_CLAMP", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TR_ADDRESS_MODE_MIRROR",
+            ("HIP_TR_ADDRESS_MODE_MIRROR", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TR_ADDRESS_MODE_BORDER",
+            ("HIP_TR_ADDRESS_MODE_BORDER", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CUBEMAP_FACE_POSITIVE_X",
+            ("HIP_CUBEMAP_FACE_POSITIVE_X", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CUBEMAP_FACE_NEGATIVE_X",
+            ("HIP_CUBEMAP_FACE_NEGATIVE_X", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CUBEMAP_FACE_POSITIVE_Y",
+            ("HIP_CUBEMAP_FACE_POSITIVE_Y", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CUBEMAP_FACE_NEGATIVE_Y",
+            ("HIP_CUBEMAP_FACE_NEGATIVE_Y", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CUBEMAP_FACE_POSITIVE_Z",
+            ("HIP_CUBEMAP_FACE_POSITIVE_Z", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CUBEMAP_FACE_NEGATIVE_Z",
+            ("HIP_CUBEMAP_FACE_NEGATIVE_Z", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_AD_FORMAT_UNSIGNED_INT8",
+            ("HIP_AD_FORMAT_UNSIGNED_INT8", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_AD_FORMAT_UNSIGNED_INT16",
+            ("HIP_AD_FORMAT_UNSIGNED_INT16", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_AD_FORMAT_UNSIGNED_INT32",
+            ("HIP_AD_FORMAT_UNSIGNED_INT32", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_AD_FORMAT_SIGNED_INT8",
+            ("HIP_AD_FORMAT_SIGNED_INT8", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_AD_FORMAT_SIGNED_INT16",
+            ("HIP_AD_FORMAT_SIGNED_INT16", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_AD_FORMAT_SIGNED_INT32",
+            ("HIP_AD_FORMAT_SIGNED_INT32", CONV_TYPE, API_DRIVER),
+        ),
+        ("CU_AD_FORMAT_HALF", ("HIP_AD_FORMAT_HALF", CONV_TYPE, API_DRIVER)),
+        ("CU_AD_FORMAT_FLOAT", ("HIP_AD_FORMAT_FLOAT", CONV_TYPE, API_DRIVER)),
+        (
+            "CU_COMPUTEMODE_DEFAULT",
+            ("hipComputeModeDefault", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_COMPUTEMODE_EXCLUSIVE",
+            ("hipComputeModeExclusive", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_COMPUTEMODE_PROHIBITED",
+            ("hipComputeModeProhibited", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_COMPUTEMODE_EXCLUSIVE_PROCESS",
+            ("hipComputeModeExclusiveProcess", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEM_ADVISE_SET_READ_MOSTLY",
+            ("hipMemAdviseSetReadMostly", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEM_ADVISE_UNSET_READ_MOSTLY",
+            ("hipMemAdviseUnsetReadMostly", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEM_ADVISE_SET_PREFERRED_LOCATION",
+            (
+                "hipMemAdviseSetPreferredLocation",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION",
+            (
+                "hipMemAdviseUnsetPreferredLocation",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_MEM_ADVISE_SET_ACCESSED_BY",
+            ("hipMemAdviseSetAccessedBy", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEM_ADVISE_UNSET_ACCESSED_BY",
+            ("hipMemAdviseUnsetAccessedBy", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY",
+            ("hipMemRangeAttributeReadMostly", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION",
+            (
+                "hipMemRangeAttributePreferredLocation",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY",
+            ("hipMemRangeAttributeAccessedBy", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION",
+            (
+                "hipMemRangeAttributeLastPrefetchLocation",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_CTX_SCHED_AUTO",
+            ("HIP_CTX_SCHED_AUTO", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CTX_SCHED_SPIN",
+            ("HIP_CTX_SCHED_SPIN", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CTX_SCHED_YIELD",
+            ("HIP_CTX_SCHED_YIELD", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CTX_SCHED_BLOCKING_SYNC",
+            ("HIP_CTX_SCHED_BLOCKING_SYNC", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CTX_BLOCKING_SYNC",
+            ("HIP_CTX_BLOCKING_SYNC", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CTX_SCHED_MASK",
+            ("HIP_CTX_SCHED_MASK", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CTX_MAP_HOST",
+            ("HIP_CTX_MAP_HOST", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CTX_LMEM_RESIZE_TO_MAX",
+            ("HIP_CTX_LMEM_RESIZE_TO_MAX", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_CTX_FLAGS_MASK",
+            ("HIP_CTX_FLAGS_MASK", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_LAUNCH_PARAM_BUFFER_POINTER",
+            ("HIP_LAUNCH_PARAM_BUFFER_POINTER", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_LAUNCH_PARAM_BUFFER_SIZE",
+            ("HIP_LAUNCH_PARAM_BUFFER_SIZE", CONV_TYPE, API_DRIVER),
+        ),
+        ("CU_LAUNCH_PARAM_END", ("HIP_LAUNCH_PARAM_END", CONV_TYPE, API_DRIVER)),
+        (
+            "CU_IPC_HANDLE_SIZE",
+            ("HIP_IPC_HANDLE_SIZE", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEMHOSTALLOC_DEVICEMAP",
+            ("HIP_MEMHOSTALLOC_DEVICEMAP", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEMHOSTALLOC_PORTABLE",
+            ("HIP_MEMHOSTALLOC_PORTABLE", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEMHOSTALLOC_WRITECOMBINED",
+            ("HIP_MEMHOSTALLOC_WRITECOMBINED", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEMHOSTREGISTER_DEVICEMAP",
+            ("HIP_MEMHOSTREGISTER_DEVICEMAP", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEMHOSTREGISTER_IOMEMORY",
+            ("HIP_MEMHOSTREGISTER_IOMEMORY", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEMHOSTREGISTER_PORTABLE",
+            ("HIP_MEMHOSTREGISTER_PORTABLE", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_PARAM_TR_DEFAULT",
+            ("HIP_PARAM_TR_DEFAULT", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_STREAM_LEGACY",
+            ("HIP_STREAM_LEGACY", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_STREAM_PER_THREAD",
+            ("HIP_STREAM_PER_THREAD", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TRSA_OVERRIDE_FORMAT",
+            ("HIP_TRSA_OVERRIDE_FORMAT", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TRSF_NORMALIZED_COORDINATES",
+            ("HIP_TRSF_NORMALIZED_COORDINATES", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TRSF_READ_AS_INTEGER",
+            ("HIP_TRSF_READ_AS_INTEGER", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CU_TRSF_SRGB", ("HIP_TRSF_SRGB", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CUDA_ARRAY3D_2DARRAY",
+            ("HIP_ARRAY3D_LAYERED", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ARRAY3D_CUBEMAP",
+            ("HIP_ARRAY3D_CUBEMAP", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ARRAY3D_DEPTH_TEXTURE",
+            ("HIP_ARRAY3D_DEPTH_TEXTURE", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ARRAY3D_LAYERED",
+            ("HIP_ARRAY3D_LAYERED", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ARRAY3D_SURFACE_LDST",
+            ("HIP_ARRAY3D_SURFACE_LDST", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUDA_ARRAY3D_TEXTURE_GATHER",
+            ("HIP_ARRAY3D_TEXTURE_GATHER", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK",
+            (
+                "hipDeviceAttributeMaxThreadsPerBlock",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X",
+            ("hipDeviceAttributeMaxBlockDimX", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y",
+            ("hipDeviceAttributeMaxBlockDimY", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z",
+            ("hipDeviceAttributeMaxBlockDimZ", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X",
+            ("hipDeviceAttributeMaxGridDimX", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y",
+            ("hipDeviceAttributeMaxGridDimY", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z",
+            ("hipDeviceAttributeMaxGridDimZ", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK",
+            (
+                "hipDeviceAttributeMaxSharedMemoryPerBlock",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK",
+            (
+                "hipDeviceAttributeMaxSharedMemoryPerBlock",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY",
+            (
+                "hipDeviceAttributeTotalConstantMemory",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_WARP_SIZE",
+            ("hipDeviceAttributeWarpSize", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_PITCH",
+            ("hipDeviceAttributeMaxPitch", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK",
+            (
+                "hipDeviceAttributeMaxRegistersPerBlock",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK",
+            (
+                "hipDeviceAttributeMaxRegistersPerBlock",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_CLOCK_RATE",
+            ("hipDeviceAttributeClockRate", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT",
+            (
+                "hipDeviceAttributeTextureAlignment",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_GPU_OVERLAP",
+            (
+                "hipDeviceAttributeAsyncEngineCount",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT",
+            (
+                "hipDeviceAttributeMultiprocessorCount",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT",
+            (
+                "hipDeviceAttributeKernelExecTimeout",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_INTEGRATED",
+            ("hipDeviceAttributeIntegrated", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY",
+            (
+                "hipDeviceAttributeCanMapHostMemory",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_COMPUTE_MODE",
+            ("hipDeviceAttributeComputeMode", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture1DWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture2DWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT",
+            (
+                "hipDeviceAttributeMaxTexture2DHeight",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture3DWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT",
+            (
+                "hipDeviceAttributeMaxTexture3DHeight",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH",
+            (
+                "hipDeviceAttributeMaxTexture3DDepth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture2DLayeredWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT",
+            (
+                "hipDeviceAttributeMaxTexture2DLayeredHeight",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS",
+            (
+                "hipDeviceAttributeMaxTexture2DLayeredLayers",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture2DLayeredWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT",
+            (
+                "hipDeviceAttributeMaxTexture2DLayeredHeight",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES",
+            (
+                "hipDeviceAttributeMaxTexture2DLayeredLayers",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT",
+            (
+                "hipDeviceAttributeSurfaceAlignment",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS",
+            ("hipDeviceAttributeConcurrentKernels", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_ECC_ENABLED",
+            ("hipDeviceAttributeEccEnabled", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_PCI_BUS_ID",
+            ("hipDeviceAttributePciBusId", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID",
+            ("hipDeviceAttributePciDeviceId", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_TCC_DRIVER",
+            ("hipDeviceAttributeTccDriver", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE",
+            (
+                "hipDeviceAttributeMemoryClockRate",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH",
+            ("hipDeviceAttributeMemoryBusWidth", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE",
+            ("hipDeviceAttributeL2CacheSize", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR",
+            ("hipDeviceAttributeMaxThreadsPerMultiProcessor", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT",
+            (
+                "hipDeviceAttributeAsyncEngineCount",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING",
+            (
+                "hipDeviceAttributeUnifiedAddressing",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture1DLayeredWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS",
+            (
+                "hipDeviceAttributeMaxTexture1DLayeredLayers",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER",
+            (
+                "hipDeviceAttributeCanTex2DGather",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture2DGatherWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT",
+            (
+                "hipDeviceAttributeMaxTexture2DGatherHeight",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE",
+            (
+                "hipDeviceAttributeMaxTexture3DWidthAlternate",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE",
+            (
+                "hipDeviceAttributeMaxTexture3DHeightAlternate",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE",
+            (
+                "hipDeviceAttributeMaxTexture3DDepthAlternate",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID",
+            ("hipDeviceAttributePciDomainId", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT",
+            (
+                "hipDeviceAttributeTexturePitchAlignment",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH",
+            (
+                "hipDeviceAttributeMaxTextureCubemapWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH",
+            (
+                "hipDeviceAttributeMaxTextureCubemapLayeredWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS",
+            (
+                "hipDeviceAttributeMaxTextureCubemapLayeredLayers",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH",
+            (
+                "hipDeviceAttributeMaxSurface1DWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH",
+            (
+                "hipDeviceAttributeMaxSurface2DWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT",
+            (
+                "hipDeviceAttributeMaxSurface2DHeight",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH",
+            (
+                "hipDeviceAttributeMaxSurface3DWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT",
+            (
+                "hipDeviceAttributeMaxSurface3DHeight",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH",
+            (
+                "hipDeviceAttributeMaxSurface3DDepth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH",
+            (
+                "hipDeviceAttributeMaxSurface1DLayeredWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS",
+            (
+                "hipDeviceAttributeMaxSurface1DLayeredLayers",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH",
+            (
+                "hipDeviceAttributeMaxSurface2DLayeredWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT",
+            (
+                "hipDeviceAttributeMaxSurface2DLayeredHeight",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS",
+            (
+                "hipDeviceAttributeMaxSurface2DLayeredLayers",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH",
+            (
+                "hipDeviceAttributeMaxSurfaceCubemapWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH",
+            (
+                "hipDeviceAttributeMaxSurfaceCubemapLayeredWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS",
+            (
+                "hipDeviceAttributeMaxSurfaceCubemapLayeredLayers",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture1DLinearWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture2DLinearWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT",
+            (
+                "hipDeviceAttributeMaxTexture2DLinearHeight",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH",
+            (
+                "hipDeviceAttributeMaxTexture2DLinearPitch",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture2DMipmappedWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT",
+            (
+                "hipDeviceAttributeMaxTexture2DMipmappedHeight",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR",
+            ("hipDeviceAttributeComputeCapabilityMajor", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR",
+            ("hipDeviceAttributeComputeCapabilityMinor", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH",
+            (
+                "hipDeviceAttributeMaxTexture1DMipmappedWidth",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED",
+            (
+                "hipDeviceAttributeStreamPrioritiesSupported",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED",
+            (
+                "hipDeviceAttributeGlobalL1CacheSupported",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED",
+            (
+                "hipDeviceAttributeLocalL1CacheSupported",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR",
+            (
+                "hipDeviceAttributeMaxSharedMemoryPerMultiprocessor",
+                CONV_TYPE,
+                API_DRIVER,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR",
+            (
+                "hipDeviceAttributeMaxRegistersPerMultiprocessor",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY",
+            ("hipDeviceAttributeManagedMemory", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD",
+            ("hipDeviceAttributeIsMultiGpuBoard", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID",
+            (
+                "hipDeviceAttributeMultiGpuBoardGroupId",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED",
+            (
+                "hipDeviceAttributeHostNativeAtomicSupported",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO",
+            (
+                "hipDeviceAttributeSingleToDoublePrecisionPerfRatio",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS",
+            (
+                "hipDeviceAttributePageableMemoryAccess",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS",
+            (
+                "hipDeviceAttributeConcurrentManagedAccess",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED",
+            (
+                "hipDeviceAttributeComputePreemptionSupported",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM",
+            (
+                "hipDeviceAttributeCanUseHostPointerForRegisteredMem",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_ATTRIBUTE_MAX",
+            ("hipDeviceAttributeMax", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_POINTER_ATTRIBUTE_CONTEXT",
+            ("hipPointerAttributeContext", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_POINTER_ATTRIBUTE_MEMORY_TYPE",
+            ("hipPointerAttributeMemoryType", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_POINTER_ATTRIBUTE_DEVICE_POINTER",
+            (
+                "hipPointerAttributeDevicePointer",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_POINTER_ATTRIBUTE_HOST_POINTER",
+            ("hipPointerAttributeHostPointer", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_POINTER_ATTRIBUTE_P2P_TOKENS",
+            ("hipPointerAttributeP2pTokens", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_POINTER_ATTRIBUTE_SYNC_MEMOPS",
+            ("hipPointerAttributeSyncMemops", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_POINTER_ATTRIBUTE_BUFFER_ID",
+            ("hipPointerAttributeBufferId", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_POINTER_ATTRIBUTE_IS_MANAGED",
+            ("hipPointerAttributeIsManaged", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK",
+            (
+                "hipFuncAttributeMaxThreadsPerBlocks",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES",
+            ("hipFuncAttributeSharedSizeBytes", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES",
+            ("hipFuncAttributeMaxDynamicSharedMemorySize", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES",
+            ("hipFuncAttributeConstSizeBytes", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES",
+            ("hipFuncAttributeLocalSizeBytes", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_FUNC_ATTRIBUTE_NUM_REGS",
+            ("hipFuncAttributeNumRegs", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_FUNC_ATTRIBUTE_PTX_VERSION",
+            ("hipFuncAttributePtxVersion", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_FUNC_ATTRIBUTE_BINARY_VERSION",
+            ("hipFuncAttributeBinaryVersion", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_FUNC_ATTRIBUTE_CACHE_MODE_CA",
+            ("hipFuncAttributeCacheModeCA", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_FUNC_ATTRIBUTE_MAX",
+            ("hipFuncAttributeMax", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE",
+            ("hipGraphicsMapFlagsNone", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY",
+            ("hipGraphicsMapFlagsReadOnly", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD",
+            ("hipGraphicsMapFlagsWriteDiscard", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_GRAPHICS_REGISTER_FLAGS_NONE",
+            ("hipGraphicsRegisterFlagsNone", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY",
+            (
+                "hipGraphicsRegisterFlagsReadOnly",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD",
+            (
+                "hipGraphicsRegisterFlagsWriteDiscard",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST",
+            (
+                "hipGraphicsRegisterFlagsSurfaceLoadStore",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER",
+            (
+                "hipGraphicsRegisterFlagsTextureGather",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_OCCUPANCY_DEFAULT",
+            ("hipOccupancyDefault", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE",
+            (
+                "hipOccupancyDisableCachingOverride",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_FUNC_CACHE_PREFER_NONE",
+            ("hipFuncCachePreferNone", CONV_CACHE, API_DRIVER),
+        ),
+        (
+            "CU_FUNC_CACHE_PREFER_SHARED",
+            ("hipFuncCachePreferShared", CONV_CACHE, API_DRIVER),
+        ),
+        ("CU_FUNC_CACHE_PREFER_L1", ("hipFuncCachePreferL1", CONV_CACHE, API_DRIVER)),
+        (
+            "CU_FUNC_CACHE_PREFER_EQUAL",
+            ("hipFuncCachePreferEqual", CONV_CACHE, API_DRIVER),
+        ),
+        (
+            "CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS",
+            ("hipIpcMemLazyEnablePeerAccess", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CUDA_IPC_HANDLE_SIZE", ("HIP_IPC_HANDLE_SIZE", CONV_TYPE, API_DRIVER)),
+        (
+            "CU_JIT_CACHE_OPTION_NONE",
+            ("hipJitCacheModeOptionNone", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_JIT_CACHE_OPTION_CG",
+            ("hipJitCacheModeOptionCG", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_JIT_CACHE_OPTION_CA",
+            ("hipJitCacheModeOptionCA", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_PREFER_PTX",
+            ("hipJitFallbackPreferPtx", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_PREFER_BINARY",
+            ("hipJitFallbackPreferBinary", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CU_JIT_MAX_REGISTERS", ("hipJitOptionMaxRegisters", CONV_JIT, API_DRIVER)),
+        (
+            "CU_JIT_THREADS_PER_BLOCK",
+            ("hipJitOptionThreadsPerBlock", CONV_JIT, API_DRIVER),
+        ),
+        ("CU_JIT_WALL_TIME", ("hipJitOptionWallTime", CONV_JIT, API_DRIVER)),
+        ("CU_JIT_INFO_LOG_BUFFER", ("hipJitOptionInfoLogBuffer", CONV_JIT, API_DRIVER)),
+        (
+            "CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES",
+            ("hipJitOptionInfoLogBufferSizeBytes", CONV_JIT, API_DRIVER),
+        ),
+        (
+            "CU_JIT_ERROR_LOG_BUFFER",
+            ("hipJitOptionErrorLogBuffer", CONV_JIT, API_DRIVER),
+        ),
+        (
+            "CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES",
+            ("hipJitOptionErrorLogBufferSizeBytes", CONV_JIT, API_DRIVER),
+        ),
+        (
+            "CU_JIT_OPTIMIZATION_LEVEL",
+            ("hipJitOptionOptimizationLevel", CONV_JIT, API_DRIVER),
+        ),
+        (
+            "CU_JIT_TARGET_FROM_CUCONTEXT",
+            ("hipJitOptionTargetFromContext", CONV_JIT, API_DRIVER),
+        ),
+        ("CU_JIT_TARGET", ("hipJitOptionTarget", CONV_JIT, API_DRIVER)),
+        (
+            "CU_JIT_FALLBACK_STRATEGY",
+            ("hipJitOptionFallbackStrategy", CONV_JIT, API_DRIVER),
+        ),
+        (
+            "CU_JIT_GENERATE_DEBUG_INFO",
+            ("hipJitOptionGenerateDebugInfo", CONV_JIT, API_DRIVER),
+        ),
+        ("CU_JIT_LOG_VERBOSE", ("hipJitOptionLogVerbose", CONV_JIT, API_DRIVER)),
+        (
+            "CU_JIT_GENERATE_LINE_INFO",
+            ("hipJitOptionGenerateLineInfo", CONV_JIT, API_DRIVER),
+        ),
+        ("CU_JIT_CACHE_MODE", ("hipJitOptionCacheMode", CONV_JIT, API_DRIVER)),
+        ("CU_JIT_NEW_SM3X_OPT", ("hipJitOptionSm3xOpt", CONV_JIT, API_DRIVER)),
+        ("CU_JIT_FAST_COMPILE", ("hipJitOptionFastCompile", CONV_JIT, API_DRIVER)),
+        ("CU_JIT_NUM_OPTIONS", ("hipJitOptionNumOptions", CONV_JIT, API_DRIVER)),
+        (
+            "CU_TARGET_COMPUTE_10",
+            ("hipJitTargetCompute10", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_11",
+            ("hipJitTargetCompute11", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_12",
+            ("hipJitTargetCompute12", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_13",
+            ("hipJitTargetCompute13", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_20",
+            ("hipJitTargetCompute20", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_21",
+            ("hipJitTargetCompute21", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_30",
+            ("hipJitTargetCompute30", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_32",
+            ("hipJitTargetCompute32", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_35",
+            ("hipJitTargetCompute35", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_37",
+            ("hipJitTargetCompute37", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_50",
+            ("hipJitTargetCompute50", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_52",
+            ("hipJitTargetCompute52", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_53",
+            ("hipJitTargetCompute53", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_60",
+            ("hipJitTargetCompute60", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_61",
+            ("hipJitTargetCompute61", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_TARGET_COMPUTE_62",
+            ("hipJitTargetCompute62", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_JIT_INPUT_CUBIN",
+            ("hipJitInputTypeBin", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_JIT_INPUT_PTX",
+            ("hipJitInputTypePtx", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_JIT_INPUT_FATBINARY",
+            ("hipJitInputTypeFatBinary", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_JIT_INPUT_OBJECT",
+            ("hipJitInputTypeObject", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_JIT_INPUT_LIBRARY",
+            ("hipJitInputTypeLibrary", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_JIT_NUM_INPUT_TYPES",
+            ("hipJitInputTypeNumInputTypes", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_LIMIT_STACK_SIZE",
+            ("hipLimitStackSize", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_LIMIT_PRINTF_FIFO_SIZE",
+            ("hipLimitPrintfFifoSize", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_LIMIT_MALLOC_HEAP_SIZE",
+            ("hipLimitMallocHeapSize", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH",
+            ("hipLimitDevRuntimeSyncDepth", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT",
+            (
+                "hipLimitDevRuntimePendingLaunchCount",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_LIMIT_STACK_SIZE",
+            ("hipLimitStackSize", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEM_ATTACH_GLOBAL",
+            ("hipMemAttachGlobal", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEM_ATTACH_HOST",
+            ("hipMemAttachHost", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEM_ATTACH_SINGLE",
+            ("hipMemAttachSingle", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEMORYTYPE_HOST",
+            ("hipMemTypeHost", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEMORYTYPE_DEVICE",
+            ("hipMemTypeDevice", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEMORYTYPE_ARRAY",
+            ("hipMemTypeArray", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_MEMORYTYPE_UNIFIED",
+            ("hipMemTypeUnified", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_RESOURCE_TYPE_ARRAY",
+            ("hipResourceTypeArray", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_RESOURCE_TYPE_MIPMAPPED_ARRAY",
+            ("hipResourceTypeMipmappedArray", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_RESOURCE_TYPE_LINEAR",
+            ("hipResourceTypeLinear", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_RESOURCE_TYPE_PITCH2D",
+            ("hipResourceTypePitch2D", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CU_RES_VIEW_FORMAT_NONE", ("hipResViewFormatNone", CONV_TEX, API_DRIVER)),
+        (
+            "CU_RES_VIEW_FORMAT_UINT_1X8",
+            ("hipResViewFormatUnsignedChar1", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UINT_2X8",
+            ("hipResViewFormatUnsignedChar2", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UINT_4X8",
+            ("hipResViewFormatUnsignedChar4", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SINT_1X8",
+            ("hipResViewFormatSignedChar1", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SINT_2X8",
+            ("hipResViewFormatSignedChar2", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SINT_4X8",
+            ("hipResViewFormatSignedChar4", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UINT_1X16",
+            ("hipResViewFormatUnsignedShort1", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UINT_2X16",
+            ("hipResViewFormatUnsignedShort2", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UINT_4X16",
+            ("hipResViewFormatUnsignedShort4", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SINT_1X16",
+            ("hipResViewFormatSignedShort1", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SINT_2X16",
+            ("hipResViewFormatSignedShort2", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SINT_4X16",
+            ("hipResViewFormatSignedShort4", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UINT_1X32",
+            ("hipResViewFormatUnsignedInt1", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UINT_2X32",
+            ("hipResViewFormatUnsignedInt2", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UINT_4X32",
+            ("hipResViewFormatUnsignedInt4", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SINT_1X32",
+            ("hipResViewFormatSignedInt1", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SINT_2X32",
+            ("hipResViewFormatSignedInt2", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SINT_4X32",
+            ("hipResViewFormatSignedInt4", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_FLOAT_1X16",
+            ("hipResViewFormatHalf1", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_FLOAT_2X16",
+            ("hipResViewFormatHalf2", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_FLOAT_4X16",
+            ("hipResViewFormatHalf4", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_FLOAT_1X32",
+            ("hipResViewFormatFloat1", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_FLOAT_2X32",
+            ("hipResViewFormatFloat2", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_FLOAT_4X32",
+            ("hipResViewFormatFloat4", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UNSIGNED_BC1",
+            ("hipResViewFormatUnsignedBlockCompressed1", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UNSIGNED_BC2",
+            ("hipResViewFormatUnsignedBlockCompressed2", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UNSIGNED_BC3",
+            ("hipResViewFormatUnsignedBlockCompressed3", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UNSIGNED_BC4",
+            ("hipResViewFormatUnsignedBlockCompressed4", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SIGNED_BC4",
+            ("hipResViewFormatSignedBlockCompressed4", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UNSIGNED_BC5",
+            ("hipResViewFormatUnsignedBlockCompressed5", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SIGNED_BC5",
+            ("hipResViewFormatSignedBlockCompressed5", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UNSIGNED_BC6H",
+            ("hipResViewFormatUnsignedBlockCompressed6H", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_SIGNED_BC6H",
+            ("hipResViewFormatSignedBlockCompressed6H", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_RES_VIEW_FORMAT_UNSIGNED_BC7",
+            ("hipResViewFormatUnsignedBlockCompressed7", CONV_TEX, API_DRIVER),
+        ),
+        (
+            "CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE",
+            ("hipSharedMemBankSizeDefault", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE",
+            ("hipSharedMemBankSizeFourByte", CONV_TYPE, API_DRIVER),
+        ),
+        (
+            "CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE",
+            ("hipSharedMemBankSizeEightByte", CONV_TYPE, API_DRIVER),
+        ),
+        ("CU_STREAM_DEFAULT", ("hipStreamDefault", CONV_TYPE, API_DRIVER)),
+        ("CU_STREAM_NON_BLOCKING", ("hipStreamNonBlocking", CONV_TYPE, API_DRIVER)),
+        (
+            "CU_STREAM_WAIT_VALUE_GEQ",
+            ("hipStreamWaitValueGeq", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_STREAM_WAIT_VALUE_EQ",
+            ("hipStreamWaitValueEq", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_STREAM_WAIT_VALUE_AND",
+            ("hipStreamWaitValueAnd", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_STREAM_WAIT_VALUE_FLUSH",
+            ("hipStreamWaitValueFlush", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_STREAM_WRITE_VALUE_DEFAULT",
+            ("hipStreamWriteValueDefault", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_STREAM_WRITE_VALUE_NO_MEMORY_BARRIER",
+            (
+                "hipStreamWriteValueNoMemoryBarrier",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_STREAM_MEM_OP_WAIT_VALUE_32",
+            ("hipStreamBatchMemOpWaitValue32", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_STREAM_MEM_OP_WRITE_VALUE_32",
+            ("hipStreamBatchMemOpWriteValue32", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES",
+            (
+                "hipStreamBatchMemOpFlushRemoteWrites",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuGetErrorName",
+            ("hipGetErrorName", CONV_ERROR, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGetErrorString",
+            ("hipDrvGetErrorString", CONV_ERROR, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuInit", ("hipInit", CONV_INIT, API_DRIVER)),
+        ("cuDriverGetVersion", ("hipDriverGetVersion", CONV_VERSION, API_DRIVER)),
+        ("cuCtxCreate", ("hipCtxCreate", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxCreate_v2", ("hipCtxCreate", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxDestroy", ("hipCtxDestroy", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxDestroy_v2", ("hipCtxDestroy", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxGetApiVersion", ("hipCtxGetApiVersion", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxGetCacheConfig", ("hipCtxGetCacheConfig", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxGetCurrent", ("hipCtxGetCurrent", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxGetDevice", ("hipCtxGetDevice", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxGetFlags", ("hipCtxGetFlags", CONV_CONTEXT, API_DRIVER)),
+        ("cuDeviceGetUuid", ("hipDeviceGetUuid", CONV_CONTEXT, API_DRIVER)),
+        (
+            "cuCtxGetLimit",
+            ("hipCtxGetLimit", CONV_CONTEXT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuCtxGetSharedMemConfig",
+            ("hipCtxGetSharedMemConfig", CONV_CONTEXT, API_DRIVER),
+        ),
+        (
+            "cuCtxGetStreamPriorityRange",
+            ("hipCtxGetStreamPriorityRange", CONV_CONTEXT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuCtxPopCurrent_v2", ("hipCtxPopCurrent", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxPushCurrent_v2", ("hipCtxPushCurrent", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxSetCacheConfig", ("hipCtxSetCacheConfig", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxSetCurrent", ("hipCtxSetCurrent", CONV_CONTEXT, API_DRIVER)),
+        (
+            "cuCtxSetLimit",
+            ("hipCtxSetLimit", CONV_CONTEXT, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuCtxSetSharedMemConfig",
+            ("hipCtxSetSharedMemConfig", CONV_CONTEXT, API_DRIVER),
+        ),
+        ("cuCtxSynchronize", ("hipCtxSynchronize", CONV_CONTEXT, API_DRIVER)),
+        ("cuCtxAttach", ("hipCtxAttach", CONV_CONTEXT, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuCtxDetach", ("hipCtxDetach", CONV_CONTEXT, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuCtxEnablePeerAccess", ("hipCtxEnablePeerAccess", CONV_PEER, API_DRIVER)),
+        ("cuCtxDisablePeerAccess", ("hipCtxDisablePeerAccess", CONV_PEER, API_DRIVER)),
+        ("cuDeviceCanAccessPeer", ("hipDeviceCanAccessPeer", CONV_PEER, API_DRIVER)),
+        (
+            "cuDeviceGetP2PAttribute",
+            ("hipDeviceGetP2PAttribute", CONV_PEER, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuDevicePrimaryCtxGetState",
+            ("hipDevicePrimaryCtxGetState", CONV_CONTEXT, API_DRIVER),
+        ),
+        (
+            "cuDevicePrimaryCtxRelease",
+            ("hipDevicePrimaryCtxRelease", CONV_CONTEXT, API_DRIVER),
+        ),
+        (
+            "cuDevicePrimaryCtxReset",
+            ("hipDevicePrimaryCtxReset", CONV_CONTEXT, API_DRIVER),
+        ),
+        (
+            "cuDevicePrimaryCtxRetain",
+            ("hipDevicePrimaryCtxRetain", CONV_CONTEXT, API_DRIVER),
+        ),
+        (
+            "cuDevicePrimaryCtxSetFlags",
+            ("hipDevicePrimaryCtxSetFlags", CONV_CONTEXT, API_DRIVER),
+        ),
+        ("cuDeviceGet", ("hipDeviceGet", CONV_DEVICE, API_DRIVER)),
+        ("cuDeviceGetName", ("hipDeviceGetName", CONV_DEVICE, API_DRIVER)),
+        ("cuDeviceGetCount", ("hipGetDeviceCount", CONV_DEVICE, API_DRIVER)),
+        ("cuDeviceGetAttribute", ("hipDeviceGetAttribute", CONV_DEVICE, API_DRIVER)),
+        ("cuDeviceGetPCIBusId", ("hipDeviceGetPCIBusId", CONV_DEVICE, API_DRIVER)),
+        ("cuDeviceGetByPCIBusId", ("hipDeviceGetByPCIBusId", CONV_DEVICE, API_DRIVER)),
+        ("cuDeviceTotalMem_v2", ("hipDeviceTotalMem", CONV_DEVICE, API_DRIVER)),
+        (
+            "cuDeviceComputeCapability",
+            ("hipDeviceComputeCapability", CONV_DEVICE, API_DRIVER),
+        ),
+        ("cuDeviceGetProperties", ("hipGetDeviceProperties", CONV_DEVICE, API_DRIVER)),
+        ("cuLinkAddData", ("hipLinkAddData", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuLinkAddFile", ("hipLinkAddFile", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuLinkComplete",
+            ("hipLinkComplete", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuLinkCreate", ("hipLinkCreate", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuLinkDestroy", ("hipLinkDestroy", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuModuleGetFunction", ("hipModuleGetFunction", CONV_MODULE, API_DRIVER)),
+        ("cuModuleGetGlobal_v2", ("hipModuleGetGlobal", CONV_MODULE, API_DRIVER)),
+        (
+            "cuModuleGetSurfRef",
+            ("hipModuleGetSurfRef", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuModuleGetTexRef", ("hipModuleGetTexRef", CONV_MODULE, API_DRIVER)),
+        ("cuModuleLoad", ("hipModuleLoad", CONV_MODULE, API_DRIVER)),
+        ("cuModuleLoadData", ("hipModuleLoadData", CONV_MODULE, API_DRIVER)),
+        ("cuModuleLoadDataEx", ("hipModuleLoadDataEx", CONV_MODULE, API_DRIVER)),
+        (
+            "cuModuleLoadFatBinary",
+            ("hipModuleLoadFatBinary", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuModuleUnload", ("hipModuleUnload", CONV_MODULE, API_DRIVER)),
+        (
+            "CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK",
+            (
+                "hipDeviceP2PAttributePerformanceRank",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED",
+            (
+                "hipDeviceP2PAttributeAccessSupported",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED",
+            (
+                "hipDeviceP2PAttributeNativeAtomicSupported",
+                CONV_TYPE,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("CU_EVENT_DEFAULT", ("hipEventDefault", CONV_EVENT, API_DRIVER)),
+        ("CU_EVENT_BLOCKING_SYNC", ("hipEventBlockingSync", CONV_EVENT, API_DRIVER)),
+        ("CU_EVENT_DISABLE_TIMING", ("hipEventDisableTiming", CONV_EVENT, API_DRIVER)),
+        ("CU_EVENT_INTERPROCESS", ("hipEventInterprocess", CONV_EVENT, API_DRIVER)),
+        ("cuEventCreate", ("hipEventCreate", CONV_EVENT, API_DRIVER)),
+        ("cuEventDestroy", ("hipEventDestroy", CONV_EVENT, API_DRIVER)),
+        ("cuEventDestroy_v2", ("hipEventDestroy", CONV_EVENT, API_DRIVER)),
+        ("cuEventElapsedTime", ("hipEventElapsedTime", CONV_EVENT, API_DRIVER)),
+        ("cuEventQuery", ("hipEventQuery", CONV_EVENT, API_DRIVER)),
+        ("cuEventRecord", ("hipEventRecord", CONV_EVENT, API_DRIVER)),
+        ("cuEventSynchronize", ("hipEventSynchronize", CONV_EVENT, API_DRIVER)),
+        ("cuFuncSetAttribute", ("hipFuncSetAttribute", CONV_EVENT, API_DRIVER)),
+        (
+            "cuFuncGetAttribute",
+            ("hipFuncGetAttribute", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuFuncSetCacheConfig", ("hipFuncSetCacheConfig", CONV_MODULE, API_DRIVER)),
+        (
+            "cuFuncSetSharedMemConfig",
+            ("hipFuncSetSharedMemConfig", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuLaunchKernel", ("hipModuleLaunchKernel", CONV_MODULE, API_DRIVER)),
+        (
+            "cuFuncSetBlockShape",
+            ("hipFuncSetBlockShape", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuFuncSetSharedSize",
+            ("hipFuncSetSharedSize", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuLaunch", ("hipLaunch", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuLaunchGrid", ("hipLaunchGrid", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuLaunchGridAsync",
+            ("hipLaunchGridAsync", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuParamSetf", ("hipParamSetf", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuParamSeti", ("hipParamSeti", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuParamSetSize",
+            ("hipParamSetSize", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuParamSetSize",
+            ("hipParamSetSize", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuParamSetv", ("hipParamSetv", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuOccupancyMaxActiveBlocksPerMultiprocessor",
+            (
+                "hipModuleOccupancyMaxActiveBlocksPerMultiprocessor",
+                CONV_OCCUPANCY,
+                API_DRIVER,
+            ),
+        ),
+        (
+            "cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags",
+            (
+                "hipModuleOccupancyMaxActiveBlocksPerMultiprocessorWithFlags",
+                CONV_OCCUPANCY,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuOccupancyMaxPotentialBlockSize",
+            ("hipModuleOccupancyMaxPotentialBlockSize", CONV_OCCUPANCY, API_DRIVER),
+        ),
+        (
+            "cuOccupancyMaxPotentialBlockSizeWithFlags",
+            (
+                "hipModuleOccupancyMaxPotentialBlockSizeWithFlags",
+                CONV_OCCUPANCY,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cuStreamAddCallback", ("hipStreamAddCallback", CONV_STREAM, API_DRIVER)),
+        (
+            "cuStreamAttachMemAsync",
+            ("hipStreamAttachMemAsync", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuStreamCreate",
+            ("hipStreamCreate__", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuStreamCreateWithPriority",
+            ("hipStreamCreateWithPriority", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuStreamDestroy", ("hipStreamDestroy", CONV_STREAM, API_DRIVER)),
+        ("cuStreamDestroy_v2", ("hipStreamDestroy", CONV_STREAM, API_DRIVER)),
+        ("cuStreamGetFlags", ("hipStreamGetFlags", CONV_STREAM, API_DRIVER)),
+        (
+            "cuStreamGetPriority",
+            ("hipStreamGetPriority", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuStreamQuery", ("hipStreamQuery", CONV_STREAM, API_DRIVER)),
+        ("cuStreamSynchronize", ("hipStreamSynchronize", CONV_STREAM, API_DRIVER)),
+        ("cuStreamWaitEvent", ("hipStreamWaitEvent", CONV_STREAM, API_DRIVER)),
+        (
+            "cuStreamWaitValue32",
+            ("hipStreamWaitValue32", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuStreamWriteValue32",
+            ("hipStreamWriteValue32", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuStreamBatchMemOp",
+            ("hipStreamBatchMemOp", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuArray3DCreate", ("hipArray3DCreate", CONV_MEM, API_DRIVER)),
+        (
+            "cuArray3DGetDescriptor",
+            ("hipArray3DGetDescriptor", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuArrayCreate", ("hipArrayCreate", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuArrayDestroy", ("hipArrayDestroy", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuArrayGetDescriptor",
+            ("hipArrayGetDescriptor", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuIpcCloseMemHandle",
+            ("hipIpcCloseMemHandle", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuIpcGetEventHandle",
+            ("hipIpcGetEventHandle", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuIpcGetMemHandle",
+            ("hipIpcGetMemHandle", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuIpcOpenEventHandle",
+            ("hipIpcOpenEventHandle", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuIpcOpenMemHandle",
+            ("hipIpcOpenMemHandle", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemAlloc_v2", ("hipMalloc", CONV_MEM, API_DRIVER)),
+        ("cuMemAllocHost", ("hipMemAllocHost", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemAllocManaged",
+            ("hipMemAllocManaged", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMemAllocPitch",
+            ("hipMemAllocPitch__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemcpy", ("hipMemcpy__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuMemcpy2D", ("hipMemcpy2D__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemcpy2DAsync",
+            ("hipMemcpy2DAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMemcpy2DUnaligned",
+            ("hipMemcpy2DUnaligned", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemcpy3D", ("hipMemcpy3D__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemcpy3DAsync",
+            ("hipMemcpy3DAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMemcpy3DPeer",
+            ("hipMemcpy3DPeer__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMemcpy3DPeerAsync",
+            ("hipMemcpy3DPeerAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemcpyAsync", ("hipMemcpyAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuMemcpyAtoA", ("hipMemcpyAtoA", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuMemcpyAtoD", ("hipMemcpyAtoD", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuMemcpyAtoH", ("hipMemcpyAtoH", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemcpyAtoHAsync",
+            ("hipMemcpyAtoHAsync", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemcpyDtoA", ("hipMemcpyDtoA", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuMemcpyDtoD_v2", ("hipMemcpyDtoD", CONV_MEM, API_DRIVER)),
+        ("cuMemcpyDtoDAsync_v2", ("hipMemcpyDtoDAsync", CONV_MEM, API_DRIVER)),
+        ("cuMemcpyDtoH_v2", ("hipMemcpyDtoH", CONV_MEM, API_DRIVER)),
+        ("cuMemcpyDtoHAsync_v2", ("hipMemcpyDtoHAsync", CONV_MEM, API_DRIVER)),
+        ("cuMemcpyHtoA", ("hipMemcpyHtoA", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemcpyHtoAAsync",
+            ("hipMemcpyHtoAAsync", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemcpyHtoD_v2", ("hipMemcpyHtoD", CONV_MEM, API_DRIVER)),
+        ("cuMemcpyHtoDAsync_v2", ("hipMemcpyHtoDAsync", CONV_MEM, API_DRIVER)),
+        (
+            "cuMemcpyPeerAsync",
+            ("hipMemcpyPeerAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemcpyPeer", ("hipMemcpyPeer__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuMemFree", ("hipFree", CONV_MEM, API_DRIVER)),
+        ("cuMemFree_v2", ("hipFree", CONV_MEM, API_DRIVER)),
+        ("cuMemFreeHost", ("hipHostFree", CONV_MEM, API_DRIVER)),
+        (
+            "cuMemGetAddressRange",
+            ("hipMemGetAddressRange", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemGetInfo_v2", ("hipMemGetInfo", CONV_MEM, API_DRIVER)),
+        ("cuMemHostAlloc", ("hipHostMalloc", CONV_MEM, API_DRIVER)),
+        (
+            "cuMemHostGetDevicePointer",
+            ("hipMemHostGetDevicePointer", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMemHostGetFlags",
+            ("hipMemHostGetFlags", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemHostRegister_v2", ("hipHostRegister", CONV_MEM, API_DRIVER)),
+        ("cuMemHostUnregister", ("hipHostUnregister", CONV_MEM, API_DRIVER)),
+        ("cuMemsetD16_v2", ("hipMemsetD16", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemsetD16Async",
+            ("hipMemsetD16Async", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemsetD2D16_v2", ("hipMemsetD2D16", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemsetD2D16Async",
+            ("hipMemsetD2D16Async", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemsetD2D32_v2", ("hipMemsetD2D32", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemsetD2D32Async",
+            ("hipMemsetD2D32Async", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemsetD2D8_v2", ("hipMemsetD2D8", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemsetD2D8Async",
+            ("hipMemsetD2D8Async", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemsetD32_v2", ("hipMemset", CONV_MEM, API_DRIVER)),
+        ("cuMemsetD32Async", ("hipMemsetAsync", CONV_MEM, API_DRIVER)),
+        ("cuMemsetD8_v2", ("hipMemsetD8", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemsetD8Async",
+            ("hipMemsetD8Async", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMipmappedArrayCreate",
+            ("hipMipmappedArrayCreate", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMipmappedArrayDestroy",
+            ("hipMipmappedArrayDestroy", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMipmappedArrayGetLevel",
+            ("hipMipmappedArrayGetLevel", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMemPrefetchAsync",
+            ("hipMemPrefetchAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuMemAdvise", ("hipMemAdvise", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuMemRangeGetAttribute",
+            ("hipMemRangeGetAttribute", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMemRangeGetAttributes",
+            ("hipMemRangeGetAttributes", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuPointerGetAttribute",
+            ("hipPointerGetAttribute", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuMemGetAddressRange_v2",
+            ("hipMemGetAddressRange", CONV_MEM, API_DRIVER),
+        ),
+        (
+            "cuPointerGetAttributes",
+            ("hipPointerGetAttributes", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuPointerSetAttribute",
+            ("hipPointerSetAttribute", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("CU_TR_FILTER_MODE_POINT", ("hipFilterModePoint", CONV_TEX, API_DRIVER)),
+        (
+            "CU_TR_FILTER_MODE_LINEAR",
+            ("hipFilterModeLinear", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetAddress",
+            ("hipTexRefGetAddress", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetAddressMode",
+            ("hipTexRefGetAddressMode", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetArray",
+            ("hipTexRefGetArray", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetBorderColor",
+            ("hipTexRefGetBorderColor", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetFilterMode",
+            ("hipTexRefGetFilterMode", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetFlags",
+            ("hipTexRefGetFlags", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetFormat",
+            ("hipTexRefGetFormat", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetMaxAnisotropy",
+            ("hipTexRefGetMaxAnisotropy", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetMipmapFilterMode",
+            ("hipTexRefGetMipmapFilterMode", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetMipmapLevelBias",
+            ("hipTexRefGetMipmapLevelBias", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetMipmapLevelClamp",
+            ("hipTexRefGetMipmapLevelClamp", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefGetMipmappedArray",
+            ("hipTexRefGetMipmappedArray", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefSetAddress",
+            ("hipTexRefSetAddress", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefSetAddress2D",
+            ("hipTexRefSetAddress2D", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuTexRefSetAddressMode", ("hipTexRefSetAddressMode", CONV_TEX, API_DRIVER)),
+        ("cuTexRefSetArray", ("hipTexRefSetArray", CONV_TEX, API_DRIVER)),
+        (
+            "cuTexRefSetBorderColor",
+            ("hipTexRefSetBorderColor", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuTexRefSetFilterMode", ("hipTexRefSetFilterMode", CONV_TEX, API_DRIVER)),
+        ("cuTexRefSetFlags", ("hipTexRefSetFlags", CONV_TEX, API_DRIVER)),
+        ("cuTexRefSetFormat", ("hipTexRefSetFormat", CONV_TEX, API_DRIVER)),
+        (
+            "cuTexRefSetMaxAnisotropy",
+            ("hipTexRefSetMaxAnisotropy", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefSetMipmapFilterMode",
+            ("hipTexRefSetMipmapFilterMode", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefSetMipmapLevelBias",
+            ("hipTexRefSetMipmapLevelBias", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefSetMipmapLevelClamp",
+            ("hipTexRefSetMipmapLevelClamp", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexRefSetMipmappedArray",
+            ("hipTexRefSetMipmappedArray", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuTexRefCreate", ("hipTexRefCreate", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuTexRefDestroy",
+            ("hipTexRefDestroy", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuSurfRefGetArray",
+            ("hipSurfRefGetArray", CONV_SURFACE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuSurfRefSetArray",
+            ("hipSurfRefSetArray", CONV_SURFACE, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexObjectCreate",
+            ("hipTexObjectCreate", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexObjectDestroy",
+            ("hipTexObjectDestroy", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexObjectGetResourceDesc",
+            ("hipTexObjectGetResourceDesc", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexObjectGetResourceViewDesc",
+            ("hipTexObjectGetResourceViewDesc", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuTexObjectGetTextureDesc",
+            ("hipTexObjectGetTextureDesc", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuSurfObjectCreate",
+            ("hipSurfObjectCreate", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuSurfObjectDestroy",
+            ("hipSurfObjectDestroy", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuSurfObjectGetResourceDesc",
+            ("hipSurfObjectGetResourceDesc", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGraphicsMapResources",
+            ("hipGraphicsMapResources", CONV_GRAPHICS, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGraphicsResourceGetMappedMipmappedArray",
+            (
+                "hipGraphicsResourceGetMappedMipmappedArray",
+                CONV_GRAPHICS,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuGraphicsResourceGetMappedPointer",
+            (
+                "hipGraphicsResourceGetMappedPointer",
+                CONV_GRAPHICS,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuGraphicsResourceSetMapFlags",
+            (
+                "hipGraphicsResourceSetMapFlags",
+                CONV_GRAPHICS,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuGraphicsSubResourceGetMappedArray",
+            (
+                "hipGraphicsSubResourceGetMappedArray",
+                CONV_GRAPHICS,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuGraphicsUnmapResources",
+            ("hipGraphicsUnmapResources", CONV_GRAPHICS, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGraphicsUnregisterResource",
+            (
+                "hipGraphicsUnregisterResource",
+                CONV_GRAPHICS,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuProfilerInitialize",
+            ("hipProfilerInitialize", CONV_OTHER, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuProfilerStart", ("hipProfilerStart", CONV_OTHER, API_DRIVER)),
+        ("cuProfilerStop", ("hipProfilerStop", CONV_OTHER, API_DRIVER)),
+        (
+            "CU_GL_DEVICE_LIST_ALL",
+            ("HIP_GL_DEVICE_LIST_ALL", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_GL_DEVICE_LIST_CURRENT_FRAME",
+            ("HIP_GL_DEVICE_LIST_CURRENT_FRAME", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_GL_DEVICE_LIST_NEXT_FRAME",
+            ("HIP_GL_DEVICE_LIST_NEXT_FRAME", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuGLGetDevices", ("hipGLGetDevices", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuGraphicsGLRegisterBuffer",
+            ("hipGraphicsGLRegisterBuffer", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGraphicsGLRegisterImage",
+            ("hipGraphicsGLRegisterImage", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        ("cuWGLGetDevice", ("hipWGLGetDevice", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "CU_GL_MAP_RESOURCE_FLAGS_NONE",
+            ("HIP_GL_MAP_RESOURCE_FLAGS_NONE", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_GL_MAP_RESOURCE_FLAGS_READ_ONLY",
+            (
+                "HIP_GL_MAP_RESOURCE_FLAGS_READ_ONLY",
+                CONV_GL,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_GL_MAP_RESOURCE_FLAGS_WRITE_DISCARD",
+            (
+                "HIP_GL_MAP_RESOURCE_FLAGS_WRITE_DISCARD",
+                CONV_GL,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cuGLCtxCreate", ("hipGLCtxCreate", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)),
+        ("cuGLInit", ("hipGLInit", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)),
+        (
+            "cuGLMapBufferObject",
+            ("hipGLMapBufferObject", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGLMapBufferObjectAsync",
+            ("hipGLMapBufferObjectAsync", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGLRegisterBufferObject",
+            ("hipGLRegisterBufferObject", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGLSetBufferObjectMapFlags",
+            ("hipGLSetBufferObjectMapFlags", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGLUnmapBufferObject",
+            ("hipGLUnmapBufferObject", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGLUnmapBufferObjectAsync",
+            ("hipGLUnmapBufferObjectAsync", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGLUnregisterBufferObject",
+            ("hipGLUnregisterBufferObject", CONV_GL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_D3D9_DEVICE_LIST_ALL",
+            ("HIP_D3D9_DEVICE_LIST_ALL", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_D3D9_DEVICE_LIST_CURRENT_FRAME",
+            (
+                "HIP_D3D9_DEVICE_LIST_CURRENT_FRAME",
+                CONV_D3D9,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_D3D9_DEVICE_LIST_NEXT_FRAME",
+            ("HIP_D3D9_DEVICE_LIST_NEXT_FRAME", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9CtxCreate",
+            ("hipD3D9CtxCreate", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9CtxCreateOnDevice",
+            ("hipD3D9CtxCreateOnDevice", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9GetDevice",
+            ("hipD3D9GetDevice", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9GetDevices",
+            ("hipD3D9GetDevices", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9GetDirect3DDevice",
+            ("hipD3D9GetDirect3DDevice", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGraphicsD3D9RegisterResource",
+            ("hipGraphicsD3D9RegisterResource", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_D3D9_MAPRESOURCE_FLAGS_NONE",
+            ("HIP_D3D9_MAPRESOURCE_FLAGS_NONE", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_D3D9_MAPRESOURCE_FLAGS_READONLY",
+            (
+                "HIP_D3D9_MAPRESOURCE_FLAGS_READONLY",
+                CONV_D3D9,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_D3D9_MAPRESOURCE_FLAGS_WRITEDISCARD",
+            (
+                "HIP_D3D9_MAPRESOURCE_FLAGS_WRITEDISCARD",
+                CONV_D3D9,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_D3D9_REGISTER_FLAGS_NONE",
+            ("HIP_D3D9_REGISTER_FLAGS_NONE", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_D3D9_REGISTER_FLAGS_ARRAY",
+            ("HIP_D3D9_REGISTER_FLAGS_ARRAY", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9MapResources",
+            ("hipD3D9MapResources", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9RegisterResource",
+            ("hipD3D9RegisterResource", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9ResourceGetMappedArray",
+            ("hipD3D9ResourceGetMappedArray", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9ResourceGetMappedPitch",
+            ("hipD3D9ResourceGetMappedPitch", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9ResourceGetMappedPointer",
+            ("hipD3D9ResourceGetMappedPointer", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9ResourceGetMappedSize",
+            ("hipD3D9ResourceGetMappedSize", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9ResourceGetSurfaceDimensions",
+            (
+                "hipD3D9ResourceGetSurfaceDimensions",
+                CONV_D3D9,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuD3D9ResourceSetMapFlags",
+            ("hipD3D9ResourceSetMapFlags", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9UnmapResources",
+            ("hipD3D9UnmapResources", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D9UnregisterResource",
+            ("hipD3D9UnregisterResource", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_D3D10_DEVICE_LIST_ALL",
+            ("HIP_D3D10_DEVICE_LIST_ALL", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_D3D10_DEVICE_LIST_CURRENT_FRAME",
+            (
+                "HIP_D3D10_DEVICE_LIST_CURRENT_FRAME",
+                CONV_D3D10,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_D3D10_DEVICE_LIST_NEXT_FRAME",
+            (
+                "HIP_D3D10_DEVICE_LIST_NEXT_FRAME",
+                CONV_D3D10,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuD3D10GetDevice",
+            ("hipD3D10GetDevice", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10GetDevices",
+            ("hipD3D10GetDevices", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGraphicsD3D10RegisterResource",
+            (
+                "hipGraphicsD3D10RegisterResource",
+                CONV_D3D10,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_D3D10_MAPRESOURCE_FLAGS_NONE",
+            (
+                "HIP_D3D10_MAPRESOURCE_FLAGS_NONE",
+                CONV_D3D10,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_D3D10_MAPRESOURCE_FLAGS_READONLY",
+            (
+                "HIP_D3D10_MAPRESOURCE_FLAGS_READONLY",
+                CONV_D3D10,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_D3D10_MAPRESOURCE_FLAGS_WRITEDISCARD",
+            (
+                "HIP_D3D10_MAPRESOURCE_FLAGS_WRITEDISCARD",
+                CONV_D3D10,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_D3D10_REGISTER_FLAGS_NONE",
+            ("HIP_D3D10_REGISTER_FLAGS_NONE", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_D3D10_REGISTER_FLAGS_ARRAY",
+            ("HIP_D3D10_REGISTER_FLAGS_ARRAY", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10CtxCreate",
+            ("hipD3D10CtxCreate", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10CtxCreateOnDevice",
+            ("hipD3D10CtxCreateOnDevice", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10GetDirect3DDevice",
+            ("hipD3D10GetDirect3DDevice", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10MapResources",
+            ("hipD3D10MapResources", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10RegisterResource",
+            ("hipD3D10RegisterResource", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10ResourceGetMappedArray",
+            ("hipD3D10ResourceGetMappedArray", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10ResourceGetMappedPitch",
+            ("hipD3D10ResourceGetMappedPitch", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10ResourceGetMappedPointer",
+            (
+                "hipD3D10ResourceGetMappedPointer",
+                CONV_D3D10,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuD3D10ResourceGetMappedSize",
+            ("hipD3D10ResourceGetMappedSize", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10ResourceGetSurfaceDimensions",
+            (
+                "hipD3D10ResourceGetSurfaceDimensions",
+                CONV_D3D10,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuD310ResourceSetMapFlags",
+            ("hipD3D10ResourceSetMapFlags", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10UnmapResources",
+            ("hipD3D10UnmapResources", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D10UnregisterResource",
+            ("hipD3D10UnregisterResource", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_D3D11_DEVICE_LIST_ALL",
+            ("HIP_D3D11_DEVICE_LIST_ALL", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "CU_D3D11_DEVICE_LIST_CURRENT_FRAME",
+            (
+                "HIP_D3D11_DEVICE_LIST_CURRENT_FRAME",
+                CONV_D3D11,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CU_D3D11_DEVICE_LIST_NEXT_FRAME",
+            (
+                "HIP_D3D11_DEVICE_LIST_NEXT_FRAME",
+                CONV_D3D11,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuD3D11GetDevice",
+            ("hipD3D11GetDevice", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D11GetDevices",
+            ("hipD3D11GetDevices", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGraphicsD3D11RegisterResource",
+            (
+                "hipGraphicsD3D11RegisterResource",
+                CONV_D3D11,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuD3D11CtxCreate",
+            ("hipD3D11CtxCreate", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D11CtxCreateOnDevice",
+            ("hipD3D11CtxCreateOnDevice", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuD3D11GetDirect3DDevice",
+            ("hipD3D11GetDirect3DDevice", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGraphicsVDPAURegisterOutputSurface",
+            (
+                "hipGraphicsVDPAURegisterOutputSurface",
+                CONV_VDPAU,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuGraphicsVDPAURegisterVideoSurface",
+            (
+                "hipGraphicsVDPAURegisterVideoSurface",
+                CONV_VDPAU,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuVDPAUGetDevice",
+            ("hipVDPAUGetDevice", CONV_VDPAU, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuVDPAUCtxCreate",
+            ("hipVDPAUCtxCreate", CONV_VDPAU, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuEGLStreamConsumerAcquireFrame",
+            ("hipEGLStreamConsumerAcquireFrame", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuEGLStreamConsumerConnect",
+            ("hipEGLStreamConsumerConnect", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuEGLStreamConsumerConnectWithFlags",
+            (
+                "hipEGLStreamConsumerConnectWithFlags",
+                CONV_EGL,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cuEGLStreamConsumerDisconnect",
+            ("hipEGLStreamConsumerDisconnect", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuEGLStreamConsumerReleaseFrame",
+            ("hipEGLStreamConsumerReleaseFrame", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuEGLStreamProducerConnect",
+            ("hipEGLStreamProducerConnect", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuEGLStreamProducerDisconnect",
+            ("hipEGLStreamProducerDisconnect", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuEGLStreamProducerPresentFrame",
+            ("hipEGLStreamProducerPresentFrame", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuEGLStreamProducerReturnFrame",
+            ("hipEGLStreamProducerReturnFrame", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGraphicsEGLRegisterImage",
+            ("hipGraphicsEGLRegisterImage", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED),
+        ),
+        (
+            "cuGraphicsResourceGetMappedEglFrame",
+            (
+                "hipGraphicsResourceGetMappedEglFrame",
+                CONV_EGL,
+                API_DRIVER,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cudaDataType_t", ("hipDataType", CONV_TYPE, API_RUNTIME)),
+        ("cudaDataType", ("hipDataType", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_32F", ("HIP_R_32F", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_64F", ("HIP_R_64F", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_16F", ("HIP_R_16F", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_8I", ("HIP_R_8I", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_32F", ("HIP_C_32F", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_64F", ("HIP_C_64F", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_16F", ("HIP_C_16F", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_8I", ("HIP_C_8I", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_8U", ("HIP_R_8U", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_8U", ("HIP_C_8U", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_32I", ("HIP_R_32I", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_32I", ("HIP_C_32I", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_32U", ("HIP_R_32U", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_32U", ("HIP_C_32U", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_16BF", ("HIP_R_16BF", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_16BF", ("HIP_C_16BF", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_4I", ("HIP_R_4I", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_4I", ("HIP_C_4I", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_4U", ("HIP_R_4U", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_4U", ("HIP_C_4U", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_16I", ("HIP_R_16I", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_16I", ("HIP_C_16I", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_16U", ("HIP_R_16U", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_16U", ("HIP_C_16U", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_64I", ("HIP_R_64I", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_64I", ("HIP_C_64I", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_64U", ("HIP_R_64U", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_C_64U", ("HIP_C_64U", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_8F_E4M3", ("HIP_R_8F_E4M3", CONV_TYPE, API_RUNTIME)),
+        ("CUDA_R_8F_E5M2", ("HIP_R_8F_E5M2", CONV_TYPE, API_RUNTIME)),
+        (
+            "MAJOR_VERSION",
+            ("hipLibraryMajorVersion", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "MINOR_VERSION",
+            ("hipLibraryMinorVersion", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "PATCH_LEVEL",
+            ("hipLibraryPatchVersion", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemAttachGlobal",
+            ("hipMemAttachGlobal", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemAttachHost",
+            ("hipMemAttachHost", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemAttachSingle",
+            ("hipMemAttachSingle", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaOccupancyDefault",
+            ("hipOccupancyDefault", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaOccupancyDisableCachingOverride",
+            (
+                "hipOccupancyDisableCachingOverride",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cudaGetLastError", ("hipGetLastError", CONV_ERROR, API_RUNTIME)),
+        ("cudaPeekAtLastError", ("hipPeekAtLastError", CONV_ERROR, API_RUNTIME)),
+        ("cudaGetErrorName", ("hipGetErrorName", CONV_ERROR, API_RUNTIME)),
+        ("cudaGetErrorString", ("hipGetErrorString", CONV_ERROR, API_RUNTIME)),
+        ("cudaMemcpy3DParms", ("hipMemcpy3DParms", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaMemcpy3DPeerParms",
+            ("hipMemcpy3DPeerParms", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaMemcpy", ("hipMemcpy", CONV_MEM, API_RUNTIME)),
+        ("cudaMemcpyToArray", ("hipMemcpyToArray", CONV_MEM, API_RUNTIME)),
+        ("cudaMemcpyToSymbol", ("hipMemcpyToSymbol", CONV_MEM, API_RUNTIME)),
+        ("cudaMemcpyToSymbolAsync", ("hipMemcpyToSymbolAsync", CONV_MEM, API_RUNTIME)),
+        ("cudaMemcpyAsync", ("hipMemcpyAsync", CONV_MEM, API_RUNTIME)),
+        ("cudaMemcpy2D", ("hipMemcpy2D", CONV_MEM, API_RUNTIME)),
+        ("cudaMemcpy2DAsync", ("hipMemcpy2DAsync", CONV_MEM, API_RUNTIME)),
+        ("cudaMemcpy2DToArray", ("hipMemcpy2DToArray", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaMemcpy2DArrayToArray",
+            ("hipMemcpy2DArrayToArray", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemcpy2DFromArray",
+            ("hipMemcpy2DFromArray", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemcpy2DFromArrayAsync",
+            ("hipMemcpy2DFromArrayAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemcpy2DToArrayAsync",
+            ("hipMemcpy2DToArrayAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaMemcpy3D", ("hipMemcpy3D", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaMemcpy3DAsync",
+            ("hipMemcpy3DAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemcpy3DPeer",
+            ("hipMemcpy3DPeer", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemcpy3DPeerAsync",
+            ("hipMemcpy3DPeerAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemcpyArrayToArray",
+            ("hipMemcpyArrayToArray", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemcpyFromArrayAsync",
+            ("hipMemcpyFromArrayAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaMemcpyFromSymbol", ("hipMemcpyFromSymbol", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaMemcpyFromSymbolAsync",
+            ("hipMemcpyFromSymbolAsync", CONV_MEM, API_RUNTIME),
+        ),
+        ("cudaMemAdvise", ("hipMemAdvise", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED)),
+        (
+            "cudaMemRangeGetAttribute",
+            ("hipMemRangeGetAttribute", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemRangeGetAttributes",
+            ("hipMemRangeGetAttributes", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemAdviseSetReadMostly",
+            ("hipMemAdviseSetReadMostly", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemAdviseUnsetReadMostly",
+            ("hipMemAdviseUnsetReadMostly", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemAdviseSetPreferredLocation",
+            (
+                "hipMemAdviseSetPreferredLocation",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaMemAdviseUnsetPreferredLocation",
+            (
+                "hipMemAdviseUnsetPreferredLocation",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaMemAdviseSetAccessedBy",
+            ("hipMemAdviseSetAccessedBy", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemAdviseUnsetAccessedBy",
+            ("hipMemAdviseUnsetAccessedBy", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemRangeAttributeReadMostly",
+            ("hipMemRangeAttributeReadMostly", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemRangeAttributePreferredLocation",
+            (
+                "hipMemRangeAttributePreferredLocation",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaMemRangeAttributeAccessedBy",
+            ("hipMemRangeAttributeAccessedBy", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemRangeAttributeLastPrefetchLocation",
+            (
+                "hipMemRangeAttributeLastPrefetchLocation",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cudaMemcpyHostToHost", ("hipMemcpyHostToHost", CONV_MEM, API_RUNTIME)),
+        ("cudaMemcpyHostToDevice", ("hipMemcpyHostToDevice", CONV_MEM, API_RUNTIME)),
+        ("cudaMemcpyDeviceToHost", ("hipMemcpyDeviceToHost", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaMemcpyDeviceToDevice",
+            ("hipMemcpyDeviceToDevice", CONV_MEM, API_RUNTIME),
+        ),
+        ("cudaMemcpyDefault", ("hipMemcpyDefault", CONV_MEM, API_RUNTIME)),
+        ("cudaMemset", ("hipMemset", CONV_MEM, API_RUNTIME)),
+        ("cudaMemsetAsync", ("hipMemsetAsync", CONV_MEM, API_RUNTIME)),
+        ("cudaMemset2D", ("hipMemset2D", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaMemset2DAsync",
+            ("hipMemset2DAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaMemset3D", ("hipMemset3D", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED)),
+        (
+            "cudaMemset3DAsync",
+            ("hipMemset3DAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaMemGetInfo", ("hipMemGetInfo", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaArrayGetInfo",
+            ("hipArrayGetInfo", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaFreeMipmappedArray",
+            ("hipFreeMipmappedArray", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGetMipmappedArrayLevel",
+            ("hipGetMipmappedArrayLevel", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGetSymbolAddress",
+            ("hipGetSymbolAddress", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGetSymbolSize",
+            ("hipGetSymbolSize", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMemPrefetchAsync",
+            ("hipMemPrefetchAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaMallocHost", ("hipHostMalloc", CONV_MEM, API_RUNTIME)),
+        ("cudaMallocArray", ("hipMallocArray", CONV_MEM, API_RUNTIME)),
+        ("cudaMalloc", ("hipMalloc", CONV_MEM, API_RUNTIME)),
+        ("cudaMalloc3D", ("hipMalloc3D", CONV_MEM, API_RUNTIME)),
+        ("cudaMalloc3DArray", ("hipMalloc3DArray", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaMallocManaged",
+            ("hipMallocManaged", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaMallocMipmappedArray",
+            ("hipMallocMipmappedArray", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaMallocPitch", ("hipMallocPitch", CONV_MEM, API_RUNTIME)),
+        ("cudaFreeHost", ("hipHostFree", CONV_MEM, API_RUNTIME)),
+        ("cudaFreeArray", ("hipFreeArray", CONV_MEM, API_RUNTIME)),
+        ("cudaFree", ("hipFree", CONV_MEM, API_RUNTIME)),
+        ("cudaHostRegister", ("hipHostRegister", CONV_MEM, API_RUNTIME)),
+        ("cudaHostUnregister", ("hipHostUnregister", CONV_MEM, API_RUNTIME)),
+        ("cudaHostAlloc", ("hipHostMalloc", CONV_MEM, API_RUNTIME)),
+        ("cudaMemoryTypeHost", ("hipMemoryTypeHost", CONV_MEM, API_RUNTIME)),
+        ("cudaMemoryTypeDevice", ("hipMemoryTypeDevice", CONV_MEM, API_RUNTIME)),
+        ("make_cudaExtent", ("make_hipExtent", CONV_MEM, API_RUNTIME)),
+        ("make_cudaPitchedPtr", ("make_hipPitchedPtr", CONV_MEM, API_RUNTIME)),
+        ("make_cudaPos", ("make_hipPos", CONV_MEM, API_RUNTIME)),
+        ("cudaHostAllocDefault", ("hipHostMallocDefault", CONV_MEM, API_RUNTIME)),
+        ("cudaHostAllocPortable", ("hipHostMallocPortable", CONV_MEM, API_RUNTIME)),
+        ("cudaHostAllocMapped", ("hipHostMallocMapped", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaHostAllocWriteCombined",
+            ("hipHostMallocWriteCombined", CONV_MEM, API_RUNTIME),
+        ),
+        ("cudaHostGetFlags", ("hipHostGetFlags", CONV_MEM, API_RUNTIME)),
+        ("cudaHostRegisterDefault", ("hipHostRegisterDefault", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaHostRegisterPortable",
+            ("hipHostRegisterPortable", CONV_MEM, API_RUNTIME),
+        ),
+        ("cudaHostRegisterMapped", ("hipHostRegisterMapped", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaHostRegisterIoMemory",
+            ("hipHostRegisterIoMemory", CONV_MEM, API_RUNTIME),
+        ),
+        # ("warpSize", ("hipWarpSize", CONV_SPECIAL_FUNC, API_RUNTIME), (HIP actually uses warpSize...)),
+        ("cudaEventCreate", ("hipEventCreate", CONV_EVENT, API_RUNTIME)),
+        (
+            "cudaEventCreateWithFlags",
+            ("hipEventCreateWithFlags", CONV_EVENT, API_RUNTIME),
+        ),
+        ("cudaEventDestroy", ("hipEventDestroy", CONV_EVENT, API_RUNTIME)),
+        ("cudaEventRecord", ("hipEventRecord", CONV_EVENT, API_RUNTIME)),
+        ("cudaEventElapsedTime", ("hipEventElapsedTime", CONV_EVENT, API_RUNTIME)),
+        ("cudaEventSynchronize", ("hipEventSynchronize", CONV_EVENT, API_RUNTIME)),
+        ("cudaEventQuery", ("hipEventQuery", CONV_EVENT, API_RUNTIME)),
+        ("cudaEventDefault", ("hipEventDefault", CONV_EVENT, API_RUNTIME)),
+        ("cudaEventBlockingSync", ("hipEventBlockingSync", CONV_EVENT, API_RUNTIME)),
+        ("cudaEventDisableTiming", ("hipEventDisableTiming", CONV_EVENT, API_RUNTIME)),
+        ("cudaEventInterprocess", ("hipEventInterprocess", CONV_EVENT, API_RUNTIME)),
+        ("cudaStreamCreate", ("hipStreamCreate", CONV_STREAM, API_RUNTIME)),
+        (
+            "cudaStreamCreateWithFlags",
+            ("hipStreamCreateWithFlags", CONV_STREAM, API_RUNTIME),
+        ),
+        (
+            "cudaStreamCreateWithPriority",
+            ("hipStreamCreateWithPriority", CONV_STREAM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaStreamDestroy", ("hipStreamDestroy", CONV_STREAM, API_RUNTIME)),
+        ("cudaStreamWaitEvent", ("hipStreamWaitEvent", CONV_STREAM, API_RUNTIME)),
+        ("cudaStreamSynchronize", ("hipStreamSynchronize", CONV_STREAM, API_RUNTIME)),
+        ("cudaStreamGetFlags", ("hipStreamGetFlags", CONV_STREAM, API_RUNTIME)),
+        ("cudaStreamQuery", ("hipStreamQuery", CONV_STREAM, API_RUNTIME)),
+        ("cudaStreamAddCallback", ("hipStreamAddCallback", CONV_STREAM, API_RUNTIME)),
+        (
+            "cudaStreamAttachMemAsync",
+            ("hipStreamAttachMemAsync", CONV_STREAM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaStreamGetPriority",
+            ("hipStreamGetPriority", CONV_STREAM, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaCpuDeviceId", ("hipCpuDeviceId", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamDefault", ("hipStreamDefault", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamNonBlocking", ("hipStreamNonBlocking", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamGetCaptureInfo", ("hipStreamGetCaptureInfo", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamGetCaptureInfo_v2", ("hipStreamGetCaptureInfo_v2", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamCaptureStatus", ("hipStreamCaptureStatus", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamCaptureStatusActive", ("hipStreamCaptureStatusActive", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamCaptureMode", ("hipStreamCaptureMode", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamCaptureModeGlobal", ("hipStreamCaptureModeGlobal", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamCaptureModeRelaxed", ("hipStreamCaptureModeRelaxed", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamCaptureModeThreadLocal", ("hipStreamCaptureModeThreadLocal", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamBeginCapture", ("hipStreamBeginCapture", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamEndCapture", ("hipStreamEndCapture", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraphInstantiate", ("hipGraphInstantiate", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraphInstantiateWithFlags", ("hipGraphInstantiateWithFlags", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraphInstantiateFlagAutoFreeOnLaunch", ("hipGraphInstantiateFlagAutoFreeOnLaunch", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraphDestroy", ("hipGraphDestroy", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraphExecDestroy", ("hipGraphExecDestroy", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraphLaunch", ("hipGraphLaunch", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraphGetNodes", ("hipGraphGetNodes", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraphDebugDotPrint", ("hipGraphDebugDotPrint", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraphDebugDotFlagsVerbose", ("hipGraphDebugDotFlagsVerbose", CONV_NUMERIC_LITERAL, API_RUNTIME)),
+        ("cudaGraphRetainUserObject", ("hipGraphRetainUserObject", CONV_TYPE, API_RUNTIME)),
+        ("cudaGraphUserObjectMove", ("hipGraphUserObjectMove", CONV_TYPE, API_RUNTIME)),
+        ("cudaUserObject_t", ("hipUserObject_t", CONV_TYPE, API_RUNTIME)),
+        ("cudaUserObjectCreate", ("hipUserObjectCreate", CONV_TYPE, API_RUNTIME)),
+        ("cudaUserObjectNoDestructorSync", ("hipUserObjectNoDestructorSync", CONV_TYPE, API_RUNTIME)),
+        ("cudaThreadExchangeStreamCaptureMode", ("hipThreadExchangeStreamCaptureMode", CONV_TYPE, API_RUNTIME)),
+        ("cudaStreamIsCapturing", ("hipStreamIsCapturing", CONV_TYPE, API_RUNTIME)),
+        ("cudaDeviceSynchronize", ("hipDeviceSynchronize", CONV_DEVICE, API_RUNTIME)),
+        ("cudaDeviceReset", ("hipDeviceReset", CONV_DEVICE, API_RUNTIME)),
+        ("cudaSetDevice", ("hipSetDevice", CONV_DEVICE, API_RUNTIME)),
+        ("cudaGetDevice", ("hipGetDevice", CONV_DEVICE, API_RUNTIME)),
+        ("cudaGetDeviceCount", ("hipGetDeviceCount", CONV_DEVICE, API_RUNTIME)),
+        ("cudaChooseDevice", ("hipChooseDevice", CONV_DEVICE, API_RUNTIME)),
+        ("cudaThreadExit", ("hipDeviceReset", CONV_THREAD, API_RUNTIME)),
+        (
+            "cudaThreadGetCacheConfig",
+            ("hipDeviceGetCacheConfig", CONV_THREAD, API_RUNTIME),
+        ),
+        (
+            "cudaThreadGetLimit",
+            ("hipThreadGetLimit", CONV_THREAD, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaThreadSetCacheConfig",
+            ("hipDeviceSetCacheConfig", CONV_THREAD, API_RUNTIME),
+        ),
+        (
+            "cudaThreadSetLimit",
+            ("hipThreadSetLimit", CONV_THREAD, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaThreadSynchronize", ("hipDeviceSynchronize", CONV_THREAD, API_RUNTIME)),
+        ("cudaDeviceGetAttribute", ("hipDeviceGetAttribute", CONV_DEVICE, API_RUNTIME)),
+        (
+            "cudaDevAttrMaxThreadsPerBlock",
+            ("hipDeviceAttributeMaxThreadsPerBlock", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxBlockDimX",
+            ("hipDeviceAttributeMaxBlockDimX", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxBlockDimY",
+            ("hipDeviceAttributeMaxBlockDimY", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxBlockDimZ",
+            ("hipDeviceAttributeMaxBlockDimZ", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxGridDimX",
+            ("hipDeviceAttributeMaxGridDimX", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxGridDimY",
+            ("hipDeviceAttributeMaxGridDimY", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxGridDimZ",
+            ("hipDeviceAttributeMaxGridDimZ", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxSharedMemoryPerBlock",
+            ("hipDeviceAttributeMaxSharedMemoryPerBlock", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxSharedMemoryPerBlockOptin",
+            ("hipDeviceAttributeMaxSharedMemoryPerBlock", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrTotalConstantMemory",
+            ("hipDeviceAttributeTotalConstantMemory", CONV_TYPE, API_RUNTIME),
+        ),
+        ("cudaDevAttrWarpSize", ("hipDeviceAttributeWarpSize", CONV_TYPE, API_RUNTIME)),
+        (
+            "cudaDevAttrMaxPitch",
+            ("hipDeviceAttributeMaxPitch", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaDevAttrMaxRegistersPerBlock",
+            ("hipDeviceAttributeMaxRegistersPerBlock", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrClockRate",
+            ("hipDeviceAttributeClockRate", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrTextureAlignment",
+            (
+                "hipDeviceAttributeTextureAlignment",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrGpuOverlap",
+            ("hipDeviceAttributeGpuOverlap", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaDevAttrMultiProcessorCount",
+            ("hipDeviceAttributeMultiprocessorCount", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrKernelExecTimeout",
+            (
+                "hipDeviceAttributeKernelExecTimeout",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrIntegrated",
+            ("hipDeviceAttributeIntegrated", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaDevAttrCanMapHostMemory",
+            (
+                "hipDeviceAttributeCanMapHostMemory",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrComputeMode",
+            ("hipDeviceAttributeComputeMode", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxTexture1DWidth",
+            (
+                "hipDeviceAttributeMaxTexture1DWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DWidth",
+            (
+                "hipDeviceAttributeMaxTexture2DWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DHeight",
+            (
+                "hipDeviceAttributeMaxTexture2DHeight",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture3DWidth",
+            (
+                "hipDeviceAttributeMaxTexture3DWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture3DHeight",
+            (
+                "hipDeviceAttributeMaxTexture3DHeight",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture3DDepth",
+            (
+                "hipDeviceAttributeMaxTexture3DDepth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DLayeredWidth",
+            (
+                "hipDeviceAttributeMaxTexture2DLayeredWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DLayeredHeight",
+            (
+                "hipDeviceAttributeMaxTexture2DLayeredHeight",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DLayeredLayers",
+            (
+                "hipDeviceAttributeMaxTexture2DLayeredLayers",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrSurfaceAlignment",
+            (
+                "hipDeviceAttributeSurfaceAlignment",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrConcurrentKernels",
+            ("hipDeviceAttributeConcurrentKernels", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrEccEnabled",
+            ("hipDeviceAttributeEccEnabled", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaDevAttrPciBusId", ("hipDeviceAttributePciBusId", CONV_TYPE, API_RUNTIME)),
+        (
+            "cudaDevAttrPciDeviceId",
+            ("hipDeviceAttributePciDeviceId", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrTccDriver",
+            ("hipDeviceAttributeTccDriver", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaDevAttrMemoryClockRate",
+            ("hipDeviceAttributeMemoryClockRate", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrGlobalMemoryBusWidth",
+            ("hipDeviceAttributeMemoryBusWidth", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrL2CacheSize",
+            ("hipDeviceAttributeL2CacheSize", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxThreadsPerMultiProcessor",
+            ("hipDeviceAttributeMaxThreadsPerMultiProcessor", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrAsyncEngineCount",
+            (
+                "hipDeviceAttributeAsyncEngineCount",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrUnifiedAddressing",
+            (
+                "hipDeviceAttributeUnifiedAddressing",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture1DLayeredWidth",
+            (
+                "hipDeviceAttributeMaxTexture1DLayeredWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture1DLayeredLayers",
+            (
+                "hipDeviceAttributeMaxTexture1DLayeredLayers",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DGatherWidth",
+            (
+                "hipDeviceAttributeMaxTexture2DGatherWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DGatherHeight",
+            (
+                "hipDeviceAttributeMaxTexture2DGatherHeight",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture3DWidthAlt",
+            (
+                "hipDeviceAttributeMaxTexture3DWidthAlternate",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture3DHeightAlt",
+            (
+                "hipDeviceAttributeMaxTexture3DHeightAlternate",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture3DDepthAlt",
+            (
+                "hipDeviceAttributeMaxTexture3DDepthAlternate",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrPciDomainId",
+            ("hipDeviceAttributePciDomainId", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaDevAttrTexturePitchAlignment",
+            (
+                "hipDeviceAttributeTexturePitchAlignment",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTextureCubemapWidth",
+            (
+                "hipDeviceAttributeMaxTextureCubemapWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTextureCubemapLayeredWidth",
+            (
+                "hipDeviceAttributeMaxTextureCubemapLayeredWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTextureCubemapLayeredLayers",
+            (
+                "hipDeviceAttributeMaxTextureCubemapLayeredLayers",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface1DWidth",
+            (
+                "hipDeviceAttributeMaxSurface1DWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface2DWidth",
+            (
+                "hipDeviceAttributeMaxSurface2DWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface2DHeight",
+            (
+                "hipDeviceAttributeMaxSurface2DHeight",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface3DWidth",
+            (
+                "hipDeviceAttributeMaxSurface3DWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface3DHeight",
+            (
+                "hipDeviceAttributeMaxSurface3DHeight",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface3DDepth",
+            (
+                "hipDeviceAttributeMaxSurface3DDepth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface1DLayeredWidth",
+            (
+                "hipDeviceAttributeMaxSurface1DLayeredWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface1DLayeredLayers",
+            (
+                "hipDeviceAttributeMaxSurface1DLayeredLayers",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface2DLayeredWidth",
+            (
+                "hipDeviceAttributeMaxSurface2DLayeredWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface2DLayeredHeight",
+            (
+                "hipDeviceAttributeMaxSurface2DLayeredHeight",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurface2DLayeredLayers",
+            (
+                "hipDeviceAttributeMaxSurface2DLayeredLayers",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurfaceCubemapWidth",
+            (
+                "hipDeviceAttributeMaxSurfaceCubemapWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurfaceCubemapLayeredWidth",
+            (
+                "hipDeviceAttributeMaxSurfaceCubemapLayeredWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSurfaceCubemapLayeredLayers",
+            (
+                "hipDeviceAttributeMaxSurfaceCubemapLayeredLayers",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture1DLinearWidth",
+            (
+                "hipDeviceAttributeMaxTexture1DLinearWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DLinearWidth",
+            (
+                "hipDeviceAttributeMaxTexture2DLinearWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DLinearHeight",
+            (
+                "hipDeviceAttributeMaxTexture2DLinearHeight",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DLinearPitch",
+            (
+                "hipDeviceAttributeMaxTexture2DLinearPitch",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DMipmappedWidth",
+            (
+                "hipDeviceAttributeMaxTexture2DMipmappedWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxTexture2DMipmappedHeight",
+            (
+                "hipDeviceAttributeMaxTexture2DMipmappedHeight",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrComputeCapabilityMajor",
+            ("hipDeviceAttributeComputeCapabilityMajor", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrComputeCapabilityMinor",
+            ("hipDeviceAttributeComputeCapabilityMinor", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMaxTexture1DMipmappedWidth",
+            (
+                "hipDeviceAttributeMaxTexture1DMipmappedWidth",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrStreamPrioritiesSupported",
+            (
+                "hipDeviceAttributeStreamPrioritiesSupported",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrGlobalL1CacheSupported",
+            (
+                "hipDeviceAttributeGlobalL1CacheSupported",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrLocalL1CacheSupported",
+            (
+                "hipDeviceAttributeLocalL1CacheSupported",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxSharedMemoryPerMultiprocessor",
+            (
+                "hipDeviceAttributeMaxSharedMemoryPerMultiprocessor",
+                CONV_TYPE,
+                API_RUNTIME,
+            ),
+        ),
+        (
+            "cudaDevAttrMaxRegistersPerMultiprocessor",
+            (
+                "hipDeviceAttributeMaxRegistersPerMultiprocessor",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrManagedMemory",
+            (
+                "hipDeviceAttributeManagedMemory",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrIsMultiGpuBoard",
+            ("hipDeviceAttributeIsMultiGpuBoard", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDevAttrMultiGpuBoardGroupID",
+            (
+                "hipDeviceAttributeMultiGpuBoardGroupID",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrHostNativeAtomicSupported",
+            (
+                "hipDeviceAttributeHostNativeAtomicSupported",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrSingleToDoublePrecisionPerfRatio",
+            (
+                "hipDeviceAttributeSingleToDoublePrecisionPerfRatio",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrPageableMemoryAccess",
+            (
+                "hipDeviceAttributePageableMemoryAccess",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrConcurrentManagedAccess",
+            (
+                "hipDeviceAttributeConcurrentManagedAccess",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrComputePreemptionSupported",
+            (
+                "hipDeviceAttributeComputePreemptionSupported",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevAttrCanUseHostPointerForRegisteredMem",
+            (
+                "hipDeviceAttributeCanUseHostPointerForRegisteredMem",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaPointerGetAttributes",
+            ("hipPointerGetAttributes", CONV_MEM, API_RUNTIME),
+        ),
+        (
+            "cudaHostGetDevicePointer",
+            ("hipHostGetDevicePointer", CONV_MEM, API_RUNTIME),
+        ),
+        (
+            "cudaGetDeviceProperties",
+            ("hipGetDeviceProperties", CONV_DEVICE, API_RUNTIME),
+        ),
+        ("cudaDeviceGetPCIBusId", ("hipDeviceGetPCIBusId", CONV_DEVICE, API_RUNTIME)),
+        (
+            "cudaDeviceGetByPCIBusId",
+            ("hipDeviceGetByPCIBusId", CONV_DEVICE, API_RUNTIME),
+        ),
+        (
+            "cudaDeviceGetStreamPriorityRange",
+            (
+                "hipDeviceGetStreamPriorityRange",
+                CONV_DEVICE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaSetValidDevices",
+            ("hipSetValidDevices", CONV_DEVICE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaDevP2PAttrPerformanceRank",
+            (
+                "hipDeviceP2PAttributePerformanceRank",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevP2PAttrAccessSupported",
+            (
+                "hipDeviceP2PAttributeAccessSupported",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDevP2PAttrNativeAtomicSupported",
+            (
+                "hipDeviceP2PAttributeNativeAtomicSupported",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaDeviceGetP2PAttribute",
+            ("hipDeviceGetP2PAttribute", CONV_DEVICE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaComputeModeDefault",
+            ("hipComputeModeDefault", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaComputeModeExclusive",
+            ("hipComputeModeExclusive", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaComputeModeProhibited",
+            ("hipComputeModeProhibited", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaComputeModeExclusiveProcess",
+            ("hipComputeModeExclusiveProcess", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGetDeviceFlags",
+            ("hipGetDeviceFlags", CONV_DEVICE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaSetDeviceFlags", ("hipSetDeviceFlags", CONV_DEVICE, API_RUNTIME)),
+        ("cudaDeviceScheduleAuto", ("hipDeviceScheduleAuto", CONV_TYPE, API_RUNTIME)),
+        ("cudaDeviceScheduleSpin", ("hipDeviceScheduleSpin", CONV_TYPE, API_RUNTIME)),
+        ("cudaDeviceScheduleYield", ("hipDeviceScheduleYield", CONV_TYPE, API_RUNTIME)),
+        (
+            "cudaDeviceBlockingSync",
+            ("hipDeviceScheduleBlockingSync", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDeviceScheduleBlockingSync",
+            ("hipDeviceScheduleBlockingSync", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDeviceScheduleMask",
+            ("hipDeviceScheduleMask", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaDeviceMapHost", ("hipDeviceMapHost", CONV_TYPE, API_RUNTIME)),
+        (
+            "cudaDeviceLmemResizeToMax",
+            ("hipDeviceLmemResizeToMax", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaDeviceMask", ("hipDeviceMask", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED)),
+        (
+            "cudaDeviceSetCacheConfig",
+            ("hipDeviceSetCacheConfig", CONV_CACHE, API_RUNTIME),
+        ),
+        (
+            "cudaDeviceGetCacheConfig",
+            ("hipDeviceGetCacheConfig", CONV_CACHE, API_RUNTIME),
+        ),
+        (
+            "cudaFuncAttributes",
+            ("hipFuncAttributes", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaFuncAttributeMaxDynamicSharedMemorySize",
+            ("hipFuncAttributeMaxDynamicSharedMemorySize", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaFuncAttributePreferredSharedMemoryCarveout",
+            ("hipFuncAttributePreferredSharedMemoryCarveout", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaFuncSetAttribute",
+            ("hipFuncSetAttribute", CONV_EXEC, API_RUNTIME),
+        ),
+        ("cudaFuncSetCacheConfig", ("hipFuncSetCacheConfig", CONV_CACHE, API_RUNTIME)),
+        (
+            "cudaFuncCachePreferNone",
+            ("hipFuncCachePreferNone", CONV_CACHE, API_RUNTIME),
+        ),
+        (
+            "cudaFuncCachePreferShared",
+            ("hipFuncCachePreferShared", CONV_CACHE, API_RUNTIME),
+        ),
+        ("cudaFuncCachePreferL1", ("hipFuncCachePreferL1", CONV_CACHE, API_RUNTIME)),
+        (
+            "cudaFuncCachePreferEqual",
+            ("hipFuncCachePreferEqual", CONV_CACHE, API_RUNTIME),
+        ),
+        (
+            "cudaFuncGetAttributes",
+            ("hipFuncGetAttributes", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaFuncSetSharedMemConfig",
+            ("hipFuncSetSharedMemConfig", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGetParameterBuffer",
+            ("hipGetParameterBuffer", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaSetDoubleForDevice",
+            ("hipSetDoubleForDevice", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaSetDoubleForHost",
+            ("hipSetDoubleForHost", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaConfigureCall",
+            ("hipConfigureCall", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaLaunch", ("hipLaunch", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED)),
+        (
+            "cudaLaunchCooperativeKernel",
+            ("hipLaunchCooperativeKernel", CONV_EXEC, API_RUNTIME),
+        ),
+        ("cudaLaunchHostFunc", ("hipLaunchHostFunc", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED)),
+        (
+            "cudaSetupArgument",
+            ("hipSetupArgument", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaDriverGetVersion", ("hipDriverGetVersion", CONV_VERSION, API_RUNTIME)),
+        (
+            "cudaRuntimeGetVersion",
+            ("hipRuntimeGetVersion", CONV_VERSION, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaOccupancyMaxPotentialBlockSize",
+            ("hipOccupancyMaxPotentialBlockSize", CONV_OCCUPANCY, API_RUNTIME),
+        ),
+        (
+            "cudaOccupancyMaxPotentialBlockSizeWithFlags",
+            (
+                "hipOccupancyMaxPotentialBlockSizeWithFlags",
+                CONV_OCCUPANCY,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaOccupancyMaxActiveBlocksPerMultiprocessor",
+            (
+                "hipOccupancyMaxActiveBlocksPerMultiprocessor",
+                CONV_OCCUPANCY,
+                API_RUNTIME,
+            ),
+        ),
+        (
+            "cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags",
+            (
+                "hipOccupancyMaxActiveBlocksPerMultiprocessorWithFlags",
+                CONV_OCCUPANCY,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaOccupancyMaxPotentialBlockSizeVariableSMem",
+            (
+                "hipOccupancyMaxPotentialBlockSizeVariableSMem",
+                CONV_OCCUPANCY,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags",
+            (
+                "hipOccupancyMaxPotentialBlockSizeVariableSMemWithFlags",
+                CONV_OCCUPANCY,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cudaDeviceCanAccessPeer", ("hipDeviceCanAccessPeer", CONV_PEER, API_RUNTIME)),
+        (
+            "cudaDeviceDisablePeerAccess",
+            ("hipDeviceDisablePeerAccess", CONV_PEER, API_RUNTIME),
+        ),
+        (
+            "cudaDeviceEnablePeerAccess",
+            ("hipDeviceEnablePeerAccess", CONV_PEER, API_RUNTIME),
+        ),
+        ("cudaMemcpyPeerAsync", ("hipMemcpyPeerAsync", CONV_MEM, API_RUNTIME)),
+        ("cudaMemcpyPeer", ("hipMemcpyPeer", CONV_MEM, API_RUNTIME)),
+        (
+            "cudaIpcMemLazyEnablePeerAccess",
+            ("hipIpcMemLazyEnablePeerAccess", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaDeviceSetSharedMemConfig",
+            ("hipDeviceSetSharedMemConfig", CONV_DEVICE, API_RUNTIME),
+        ),
+        (
+            "cudaDeviceGetSharedMemConfig",
+            ("hipDeviceGetSharedMemConfig", CONV_DEVICE, API_RUNTIME),
+        ),
+        (
+            "cudaSharedMemBankSizeDefault",
+            ("hipSharedMemBankSizeDefault", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaSharedMemBankSizeFourByte",
+            ("hipSharedMemBankSizeFourByte", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaSharedMemBankSizeEightByte",
+            ("hipSharedMemBankSizeEightByte", CONV_TYPE, API_RUNTIME),
+        ),
+        (
+            "cudaLimitStackSize",
+            ("hipLimitStackSize", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaLimitPrintfFifoSize",
+            ("hipLimitPrintfFifoSize", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaLimitMallocHeapSize", ("hipLimitMallocHeapSize", CONV_TYPE, API_RUNTIME)),
+        (
+            "cudaLimitDevRuntimeSyncDepth",
+            ("hipLimitDevRuntimeSyncDepth", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaLimitDevRuntimePendingLaunchCount",
+            (
+                "hipLimitDevRuntimePendingLaunchCount",
+                CONV_TYPE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cudaDeviceGetLimit", ("hipDeviceGetLimit", CONV_DEVICE, API_RUNTIME)),
+        (
+            "cudaProfilerInitialize",
+            ("hipProfilerInitialize", CONV_OTHER, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaProfilerStart", ("hipProfilerStart", CONV_OTHER, API_RUNTIME)),
+        ("cudaProfilerStop", ("hipProfilerStop", CONV_OTHER, API_RUNTIME)),
+        (
+            "cudaKeyValuePair",
+            ("hipKeyValuePair", CONV_OTHER, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        ("cudaCSV", ("hipCSV", CONV_OTHER, API_RUNTIME, HIP_UNSUPPORTED)),
+        ("cudaReadModeElementType", ("hipReadModeElementType", CONV_TEX, API_RUNTIME)),
+        (
+            "cudaReadModeNormalizedFloat",
+            ("hipReadModeNormalizedFloat", CONV_TEX, API_RUNTIME),
+        ),
+        ("cudaFilterModePoint", ("hipFilterModePoint", CONV_TEX, API_RUNTIME)),
+        ("cudaFilterModeLinear", ("hipFilterModeLinear", CONV_TEX, API_RUNTIME)),
+        ("cudaBindTexture", ("hipBindTexture", CONV_TEX, API_RUNTIME)),
+        ("cudaUnbindTexture", ("hipUnbindTexture", CONV_TEX, API_RUNTIME)),
+        ("cudaBindTexture2D", ("hipBindTexture2D", CONV_TEX, API_RUNTIME)),
+        ("cudaBindTextureToArray", ("hipBindTextureToArray", CONV_TEX, API_RUNTIME)),
+        (
+            "cudaBindTextureToMipmappedArray",
+            ("hipBindTextureToMipmappedArray", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaGetTextureAlignmentOffset",
+            ("hipGetTextureAlignmentOffset", CONV_TEX, API_RUNTIME),
+        ),
+        ("cudaGetTextureReference", ("hipGetTextureReference", CONV_TEX, API_RUNTIME)),
+        (
+            "cudaChannelFormatKindSigned",
+            ("hipChannelFormatKindSigned", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaChannelFormatKindUnsigned",
+            ("hipChannelFormatKindUnsigned", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaChannelFormatKindFloat",
+            ("hipChannelFormatKindFloat", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaChannelFormatKindNone",
+            ("hipChannelFormatKindNone", CONV_TEX, API_RUNTIME),
+        ),
+        ("cudaCreateChannelDesc", ("hipCreateChannelDesc", CONV_TEX, API_RUNTIME)),
+        ("cudaGetChannelDesc", ("hipGetChannelDesc", CONV_TEX, API_RUNTIME)),
+        ("cudaResourceTypeArray", ("hipResourceTypeArray", CONV_TEX, API_RUNTIME)),
+        (
+            "cudaResourceTypeMipmappedArray",
+            ("hipResourceTypeMipmappedArray", CONV_TEX, API_RUNTIME),
+        ),
+        ("cudaResourceTypeLinear", ("hipResourceTypeLinear", CONV_TEX, API_RUNTIME)),
+        ("cudaResourceTypePitch2D", ("hipResourceTypePitch2D", CONV_TEX, API_RUNTIME)),
+        ("cudaResViewFormatNone", ("hipResViewFormatNone", CONV_TEX, API_RUNTIME)),
+        (
+            "cudaResViewFormatUnsignedChar1",
+            ("hipResViewFormatUnsignedChar1", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedChar2",
+            ("hipResViewFormatUnsignedChar2", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedChar4",
+            ("hipResViewFormatUnsignedChar4", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedChar1",
+            ("hipResViewFormatSignedChar1", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedChar2",
+            ("hipResViewFormatSignedChar2", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedChar4",
+            ("hipResViewFormatSignedChar4", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedShort1",
+            ("hipResViewFormatUnsignedShort1", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedShort2",
+            ("hipResViewFormatUnsignedShort2", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedShort4",
+            ("hipResViewFormatUnsignedShort4", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedShort1",
+            ("hipResViewFormatSignedShort1", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedShort2",
+            ("hipResViewFormatSignedShort2", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedShort4",
+            ("hipResViewFormatSignedShort4", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedInt1",
+            ("hipResViewFormatUnsignedInt1", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedInt2",
+            ("hipResViewFormatUnsignedInt2", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedInt4",
+            ("hipResViewFormatUnsignedInt4", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedInt1",
+            ("hipResViewFormatSignedInt1", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedInt2",
+            ("hipResViewFormatSignedInt2", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedInt4",
+            ("hipResViewFormatSignedInt4", CONV_TEX, API_RUNTIME),
+        ),
+        ("cudaResViewFormatHalf1", ("hipResViewFormatHalf1", CONV_TEX, API_RUNTIME)),
+        ("cudaResViewFormatHalf2", ("hipResViewFormatHalf2", CONV_TEX, API_RUNTIME)),
+        ("cudaResViewFormatHalf4", ("hipResViewFormatHalf4", CONV_TEX, API_RUNTIME)),
+        ("cudaResViewFormatFloat1", ("hipResViewFormatFloat1", CONV_TEX, API_RUNTIME)),
+        ("cudaResViewFormatFloat2", ("hipResViewFormatFloat2", CONV_TEX, API_RUNTIME)),
+        ("cudaResViewFormatFloat4", ("hipResViewFormatFloat4", CONV_TEX, API_RUNTIME)),
+        (
+            "cudaResViewFormatUnsignedBlockCompressed1",
+            ("hipResViewFormatUnsignedBlockCompressed1", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedBlockCompressed2",
+            ("hipResViewFormatUnsignedBlockCompressed2", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedBlockCompressed3",
+            ("hipResViewFormatUnsignedBlockCompressed3", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedBlockCompressed4",
+            ("hipResViewFormatUnsignedBlockCompressed4", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedBlockCompressed4",
+            ("hipResViewFormatSignedBlockCompressed4", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedBlockCompressed5",
+            ("hipResViewFormatUnsignedBlockCompressed5", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedBlockCompressed5",
+            ("hipResViewFormatSignedBlockCompressed5", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedBlockCompressed6H",
+            ("hipResViewFormatUnsignedBlockCompressed6H", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatSignedBlockCompressed6H",
+            ("hipResViewFormatSignedBlockCompressed6H", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaResViewFormatUnsignedBlockCompressed7",
+            ("hipResViewFormatUnsignedBlockCompressed7", CONV_TEX, API_RUNTIME),
+        ),
+        ("cudaAddressModeWrap", ("hipAddressModeWrap", CONV_TEX, API_RUNTIME)),
+        ("cudaAddressModeClamp", ("hipAddressModeClamp", CONV_TEX, API_RUNTIME)),
+        ("cudaAddressModeMirror", ("hipAddressModeMirror", CONV_TEX, API_RUNTIME)),
+        ("cudaAddressModeBorder", ("hipAddressModeBorder", CONV_TEX, API_RUNTIME)),
+        ("cudaCreateTextureObject", ("hipCreateTextureObject", CONV_TEX, API_RUNTIME)),
+        (
+            "cudaDestroyTextureObject",
+            ("hipDestroyTextureObject", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaGetTextureObjectResourceDesc",
+            ("hipGetTextureObjectResourceDesc", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaGetTextureObjectResourceViewDesc",
+            ("hipGetTextureObjectResourceViewDesc", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaGetTextureObjectTextureDesc",
+            ("hipGetTextureObjectTextureDesc", CONV_TEX, API_RUNTIME),
+        ),
+        (
+            "cudaBindSurfaceToArray",
+            ("hipBindSurfaceToArray", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGetSurfaceReference",
+            ("hipGetSurfaceReference", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaBoundaryModeZero",
+            ("hipBoundaryModeZero", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaBoundaryModeClamp",
+            ("hipBoundaryModeClamp", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaBoundaryModeTrap",
+            ("hipBoundaryModeTrap", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaFormatModeForced",
+            ("hipFormatModeForced", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaFormatModeAuto",
+            ("hipFormatModeAuto", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaCreateSurfaceObject",
+            ("hipCreateSurfaceObject", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaDestroySurfaceObject",
+            ("hipDestroySurfaceObject", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGetSurfaceObjectResourceDesc",
+            (
+                "hipGetSurfaceObjectResourceDesc",
+                CONV_SURFACE,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cudaIpcCloseMemHandle", ("hipIpcCloseMemHandle", CONV_DEVICE, API_RUNTIME)),
+        ("cudaIpcGetEventHandle", ("hipIpcGetEventHandle", CONV_DEVICE, API_RUNTIME)),
+        ("cudaIpcGetMemHandle", ("hipIpcGetMemHandle", CONV_DEVICE, API_RUNTIME)),
+        ("cudaIpcOpenEventHandle", ("hipIpcOpenEventHandle", CONV_DEVICE, API_RUNTIME)),
+        ("cudaIpcOpenMemHandle", ("hipIpcOpenMemHandle", CONV_DEVICE, API_RUNTIME)),
+        (
+            "cudaGLGetDevices",
+            ("hipGLGetDevices", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsGLRegisterBuffer",
+            ("hipGraphicsGLRegisterBuffer", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsGLRegisterImage",
+            ("hipGraphicsGLRegisterImage", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaWGLGetDevice",
+            ("hipWGLGetDevice", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsMapResources",
+            ("hipGraphicsMapResources", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsResourceGetMappedMipmappedArray",
+            (
+                "hipGraphicsResourceGetMappedMipmappedArray",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsResourceGetMappedPointer",
+            (
+                "hipGraphicsResourceGetMappedPointer",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsResourceSetMapFlags",
+            (
+                "hipGraphicsResourceSetMapFlags",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsSubResourceGetMappedArray",
+            (
+                "hipGraphicsSubResourceGetMappedArray",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsUnmapResources",
+            ("hipGraphicsUnmapResources", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsUnregisterResource",
+            (
+                "hipGraphicsUnregisterResource",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsCubeFacePositiveX",
+            (
+                "hipGraphicsCubeFacePositiveX",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsCubeFaceNegativeX",
+            (
+                "hipGraphicsCubeFaceNegativeX",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsCubeFacePositiveY",
+            (
+                "hipGraphicsCubeFacePositiveY",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsCubeFaceNegativeY",
+            (
+                "hipGraphicsCubeFaceNegativeY",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsCubeFacePositiveZ",
+            (
+                "hipGraphicsCubeFacePositiveZ",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsCubeFaceNegativeZ",
+            (
+                "hipGraphicsCubeFaceNegativeZ",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsMapFlagsNone",
+            ("hipGraphicsMapFlagsNone", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsMapFlagsReadOnly",
+            (
+                "hipGraphicsMapFlagsReadOnly",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsMapFlagsWriteDiscard",
+            (
+                "hipGraphicsMapFlagsWriteDiscard",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsRegisterFlagsNone",
+            (
+                "hipGraphicsRegisterFlagsNone",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsRegisterFlagsReadOnly",
+            (
+                "hipGraphicsRegisterFlagsReadOnly",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsRegisterFlagsWriteDiscard",
+            (
+                "hipGraphicsRegisterFlagsWriteDiscard",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsRegisterFlagsSurfaceLoadStore",
+            (
+                "hipGraphicsRegisterFlagsSurfaceLoadStore",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsRegisterFlagsTextureGather",
+            (
+                "hipGraphicsRegisterFlagsTextureGather",
+                CONV_GRAPHICS,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGLDeviceListAll",
+            ("HIP_GL_DEVICE_LIST_ALL", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLDeviceListCurrentFrame",
+            ("HIP_GL_DEVICE_LIST_CURRENT_FRAME", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLDeviceListNextFrame",
+            ("HIP_GL_DEVICE_LIST_NEXT_FRAME", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLGetDevices",
+            ("hipGLGetDevices", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsGLRegisterBuffer",
+            ("hipGraphicsGLRegisterBuffer", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsGLRegisterImage",
+            ("hipGraphicsGLRegisterImage", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaWGLGetDevice",
+            ("hipWGLGetDevice", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLMapFlagsNone",
+            ("HIP_GL_MAP_RESOURCE_FLAGS_NONE", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLMapFlagsReadOnly",
+            (
+                "HIP_GL_MAP_RESOURCE_FLAGS_READ_ONLY",
+                CONV_GL,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGLMapFlagsWriteDiscard",
+            (
+                "HIP_GL_MAP_RESOURCE_FLAGS_WRITE_DISCARD",
+                CONV_GL,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGLMapBufferObject",
+            ("hipGLMapBufferObject__", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLMapBufferObjectAsync",
+            ("hipGLMapBufferObjectAsync__", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLRegisterBufferObject",
+            ("hipGLRegisterBufferObject", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLSetBufferObjectMapFlags",
+            ("hipGLSetBufferObjectMapFlags", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLSetGLDevice",
+            ("hipGLSetGLDevice", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLUnmapBufferObject",
+            ("hipGLUnmapBufferObject", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLUnmapBufferObjectAsync",
+            ("hipGLUnmapBufferObjectAsync", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGLUnregisterBufferObject",
+            ("hipGLUnregisterBufferObject", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9DeviceListAll",
+            ("HIP_D3D9_DEVICE_LIST_ALL", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9DeviceListCurrentFrame",
+            (
+                "HIP_D3D9_DEVICE_LIST_CURRENT_FRAME",
+                CONV_D3D9,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D9DeviceListNextFrame",
+            (
+                "HIP_D3D9_DEVICE_LIST_NEXT_FRAME",
+                CONV_D3D9,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D9GetDevice",
+            ("hipD3D9GetDevice", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9GetDevices",
+            ("hipD3D9GetDevices", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9GetDirect3DDevice",
+            ("hipD3D9GetDirect3DDevice", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9SetDirect3DDevice",
+            ("hipD3D9SetDirect3DDevice", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsD3D9RegisterResource",
+            (
+                "hipGraphicsD3D9RegisterResource",
+                CONV_D3D9,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D9MapFlags",
+            ("hipD3D9MapFlags", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9MapFlagsNone",
+            (
+                "HIP_D3D9_MAPRESOURCE_FLAGS_NONE",
+                CONV_D3D9,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D9MapFlagsReadOnly",
+            (
+                "HIP_D3D9_MAPRESOURCE_FLAGS_READONLY",
+                CONV_D3D9,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D9MapFlagsWriteDiscard",
+            (
+                "HIP_D3D9_MAPRESOURCE_FLAGS_WRITEDISCARD",
+                CONV_D3D9,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D9RegisterFlagsNone",
+            ("HIP_D3D9_REGISTER_FLAGS_NONE", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9RegisterFlagsArray",
+            ("HIP_D3D9_REGISTER_FLAGS_ARRAY", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9MapResources",
+            ("hipD3D9MapResources", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9RegisterResource",
+            ("hipD3D9RegisterResource", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9ResourceGetMappedArray",
+            ("hipD3D9ResourceGetMappedArray", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9ResourceGetMappedPitch",
+            ("hipD3D9ResourceGetMappedPitch", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9ResourceGetMappedPointer",
+            (
+                "hipD3D9ResourceGetMappedPointer",
+                CONV_D3D9,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D9ResourceGetMappedSize",
+            ("hipD3D9ResourceGetMappedSize", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9ResourceGetSurfaceDimensions",
+            (
+                "hipD3D9ResourceGetSurfaceDimensions",
+                CONV_D3D9,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D9ResourceSetMapFlags",
+            ("hipD3D9ResourceSetMapFlags", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9UnmapResources",
+            ("hipD3D9UnmapResources", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D9UnregisterResource",
+            ("hipD3D9UnregisterResource", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10DeviceListAll",
+            ("HIP_D3D10_DEVICE_LIST_ALL", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10DeviceListCurrentFrame",
+            (
+                "HIP_D3D10_DEVICE_LIST_CURRENT_FRAME",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10DeviceListNextFrame",
+            (
+                "HIP_D3D10_DEVICE_LIST_NEXT_FRAME",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10GetDevice",
+            ("hipD3D10GetDevice", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10GetDevices",
+            ("hipD3D10GetDevices", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsD3D10RegisterResource",
+            (
+                "hipGraphicsD3D10RegisterResource",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10MapFlagsNone",
+            (
+                "HIP_D3D10_MAPRESOURCE_FLAGS_NONE",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10MapFlagsReadOnly",
+            (
+                "HIP_D3D10_MAPRESOURCE_FLAGS_READONLY",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10MapFlagsWriteDiscard",
+            (
+                "HIP_D3D10_MAPRESOURCE_FLAGS_WRITEDISCARD",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10RegisterFlagsNone",
+            ("HIP_D3D10_REGISTER_FLAGS_NONE", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10RegisterFlagsArray",
+            (
+                "HIP_D3D10_REGISTER_FLAGS_ARRAY",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10GetDirect3DDevice",
+            ("hipD3D10GetDirect3DDevice", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10MapResources",
+            ("hipD3D10MapResources", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10RegisterResource",
+            ("hipD3D10RegisterResource", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10ResourceGetMappedArray",
+            (
+                "hipD3D10ResourceGetMappedArray",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10ResourceGetMappedPitch",
+            (
+                "hipD3D10ResourceGetMappedPitch",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10ResourceGetMappedPointer",
+            (
+                "hipD3D10ResourceGetMappedPointer",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10ResourceGetMappedSize",
+            ("hipD3D10ResourceGetMappedSize", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10ResourceGetSurfaceDimensions",
+            (
+                "hipD3D10ResourceGetSurfaceDimensions",
+                CONV_D3D10,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D10ResourceSetMapFlags",
+            ("hipD3D10ResourceSetMapFlags", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10SetDirect3DDevice",
+            ("hipD3D10SetDirect3DDevice", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10UnmapResources",
+            ("hipD3D10UnmapResources", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D10UnregisterResource",
+            ("hipD3D10UnregisterResource", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D11DeviceListAll",
+            ("HIP_D3D11_DEVICE_LIST_ALL", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D11DeviceListCurrentFrame",
+            (
+                "HIP_D3D11_DEVICE_LIST_CURRENT_FRAME",
+                CONV_D3D11,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D11DeviceListNextFrame",
+            (
+                "HIP_D3D11_DEVICE_LIST_NEXT_FRAME",
+                CONV_D3D11,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D11GetDevice",
+            ("hipD3D11GetDevice", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D11GetDevices",
+            ("hipD3D11GetDevices", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsD3D11RegisterResource",
+            (
+                "hipGraphicsD3D11RegisterResource",
+                CONV_D3D11,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaD3D11GetDevice",
+            ("hipD3D11GetDevice", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaD3D11GetDevices",
+            ("hipD3D11GetDevices", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsD3D11RegisterResource",
+            (
+                "hipGraphicsD3D11RegisterResource",
+                CONV_D3D11,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsVDPAURegisterOutputSurface",
+            (
+                "hipGraphicsVDPAURegisterOutputSurface",
+                CONV_VDPAU,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaGraphicsVDPAURegisterVideoSurface",
+            (
+                "hipGraphicsVDPAURegisterVideoSurface",
+                CONV_VDPAU,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaVDPAUGetDevice",
+            ("hipVDPAUGetDevice", CONV_VDPAU, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaVDPAUSetVDPAUDevice",
+            ("hipVDPAUSetDevice", CONV_VDPAU, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaEGLStreamConsumerAcquireFrame",
+            (
+                "hipEGLStreamConsumerAcquireFrame",
+                CONV_EGL,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaEGLStreamConsumerConnect",
+            ("hipEGLStreamConsumerConnect", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaEGLStreamConsumerConnectWithFlags",
+            (
+                "hipEGLStreamConsumerConnectWithFlags",
+                CONV_EGL,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaEGLStreamConsumerReleaseFrame",
+            (
+                "hipEGLStreamConsumerReleaseFrame",
+                CONV_EGL,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaEGLStreamProducerConnect",
+            ("hipEGLStreamProducerConnect", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaEGLStreamProducerDisconnect",
+            ("hipEGLStreamProducerDisconnect", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaEGLStreamProducerPresentFrame",
+            (
+                "hipEGLStreamProducerPresentFrame",
+                CONV_EGL,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cudaEGLStreamProducerReturnFrame",
+            ("hipEGLStreamProducerReturnFrame", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsEGLRegisterImage",
+            ("hipGraphicsEGLRegisterImage", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        (
+            "cudaGraphicsResourceGetMappedEglFrame",
+            (
+                "hipGraphicsResourceGetMappedEglFrame",
+                CONV_EGL,
+                API_RUNTIME,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cublasInit", ("hipblasInit", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        (
+            "cublasShutdown",
+            ("hipblasShutdown", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasGetVersion",
+            ("hipblasGetVersion", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasGetError",
+            ("hipblasGetError", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasAlloc", ("hipblasAlloc", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasFree", ("hipblasFree", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        (
+            "cublasSetKernelStream",
+            ("hipblasSetKernelStream", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasGetAtomicsMode",
+            ("hipblasGetAtomicsMode", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSetAtomicsMode",
+            ("hipblasSetAtomicsMode", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasGetMathMode",
+            ("hipblasGetMathMode", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSetMathMode",
+            ("hipblasSetMathMode", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("CUBLAS_OP_N", ("HIPBLAS_OP_N", CONV_NUMERIC_LITERAL, API_BLAS)),
+        (
+            "CUBLAS_OP_T",
+            ("HIPBLAS_OP_T", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_OP_C",
+            ("HIPBLAS_OP_C", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_STATUS_SUCCESS",
+            ("HIPBLAS_STATUS_SUCCESS", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_STATUS_NOT_INITIALIZED",
+            ("HIPBLAS_STATUS_NOT_INITIALIZED", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_STATUS_ALLOC_FAILED",
+            ("HIPBLAS_STATUS_ALLOC_FAILED", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_STATUS_INVALID_VALUE",
+            ("HIPBLAS_STATUS_INVALID_VALUE", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_STATUS_MAPPING_ERROR",
+            ("HIPBLAS_STATUS_MAPPING_ERROR", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_STATUS_EXECUTION_FAILED",
+            ("HIPBLAS_STATUS_EXECUTION_FAILED", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_STATUS_INTERNAL_ERROR",
+            ("HIPBLAS_STATUS_INTERNAL_ERROR", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_STATUS_NOT_SUPPORTED",
+            ("HIPBLAS_STATUS_NOT_SUPPORTED", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_STATUS_ARCH_MISMATCH",
+            ("HIPBLAS_STATUS_ARCH_MISMATCH", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_FILL_MODE_LOWER",
+            ("HIPBLAS_FILL_MODE_LOWER", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_FILL_MODE_UPPER",
+            ("HIPBLAS_FILL_MODE_UPPER", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_DIAG_NON_UNIT",
+            ("HIPBLAS_DIAG_NON_UNIT", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        ("CUBLAS_DIAG_UNIT", ("HIPBLAS_DIAG_UNIT", CONV_NUMERIC_LITERAL, API_BLAS)),
+        ("CUBLAS_SIDE_LEFT", ("HIPBLAS_SIDE_LEFT", CONV_NUMERIC_LITERAL, API_BLAS)),
+        ("CUBLAS_SIDE_RIGHT", ("HIPBLAS_SIDE_RIGHT", CONV_NUMERIC_LITERAL, API_BLAS)),
+        (
+            "CUBLAS_POINTER_MODE_HOST",
+            ("HIPBLAS_POINTER_MODE_HOST", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_POINTER_MODE_DEVICE",
+            ("HIPBLAS_POINTER_MODE_DEVICE", CONV_NUMERIC_LITERAL, API_BLAS),
+        ),
+        (
+            "CUBLAS_ATOMICS_NOT_ALLOWED",
+            (
+                "HIPBLAS_ATOMICS_NOT_ALLOWED",
+                CONV_NUMERIC_LITERAL,
+                API_BLAS,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CUBLAS_ATOMICS_ALLOWED",
+            (
+                "HIPBLAS_ATOMICS_ALLOWED",
+                CONV_NUMERIC_LITERAL,
+                API_BLAS,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CUBLAS_DATA_FLOAT",
+            (
+                "HIPBLAS_DATA_FLOAT",
+                CONV_NUMERIC_LITERAL,
+                API_BLAS,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CUBLAS_DATA_DOUBLE",
+            (
+                "HIPBLAS_DATA_DOUBLE",
+                CONV_NUMERIC_LITERAL,
+                API_BLAS,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "CUBLAS_DATA_HALF",
+            ("HIPBLAS_DATA_HALF", CONV_NUMERIC_LITERAL, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUBLAS_DATA_INT8",
+            ("HIPBLAS_DATA_INT8", CONV_NUMERIC_LITERAL, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("CUBLAS_GEMM_DEFAULT", ("HIPBLAS_GEMM_DEFAULT", CONV_NUMERIC_LITERAL, API_BLAS)),
+        ("CUBLAS_GEMM_DEFAULT_TENSOR_OP", ("HIPBLAS_GEMM_DEFAULT", CONV_NUMERIC_LITERAL, API_BLAS)),
+        ("cublasCreate", ("hipblasCreate", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDestroy", ("hipblasDestroy", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasSetVector", ("hipblasSetVector", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasGetVector", ("hipblasGetVector", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasSetVectorAsync",
+            ("hipblasSetVectorAsync", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasGetVectorAsync",
+            ("hipblasGetVectorAsync", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSetMatrix", ("hipblasSetMatrix", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasGetMatrix", ("hipblasGetMatrix", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasGetMatrixAsync",
+            ("hipblasGetMatrixAsync", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSetMatrixAsync",
+            ("hipblasSetMatrixAsync", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasXerbla", ("hipblasXerbla", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSnrm2", ("hipblasSnrm2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDnrm2", ("hipblasDnrm2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasScnrm2", ("hipblasScnrm2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDznrm2", ("hipblasDznrm2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        (
+            "cublasNrm2Ex",
+            ("hipblasNrm2Ex", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSdot", ("hipblasSdot", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasSdotBatched",
+            ("hipblasSdotBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasDdot", ("hipblasDdot", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasDdotBatched",
+            ("hipblasDdotBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasCdotu", ("hipblasCdotu", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasCdotc", ("hipblasCdotc", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasZdotu", ("hipblasZdotu", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasZdotc", ("hipblasZdotc", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasSscal", ("hipblasSscal", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasSscalBatched",
+            ("hipblasSscalBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasDscal", ("hipblasDscal", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasDscalBatched",
+            ("hipblasDscalBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasCscal", ("hipblasCscal", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCsscal", ("hipblasCsscal", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZscal", ("hipblasZscal", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZdscal", ("hipblasZdscal", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSaxpy", ("hipblasSaxpy", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasSaxpyBatched",
+            ("hipblasSaxpyBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasDaxpy", ("hipblasDaxpy", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasCaxpy", ("hipblasCaxpy", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZaxpy", ("hipblasZaxpy", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasScopy", ("hipblasScopy", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasScopyBatched",
+            ("hipblasScopyBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasDcopy", ("hipblasDcopy", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasDcopyBatched",
+            ("hipblasDcopyBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasCcopy", ("hipblasCcopy", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZcopy", ("hipblasZcopy", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSswap", ("hipblasSswap", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDswap", ("hipblasDswap", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasCswap", ("hipblasCswap", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZswap", ("hipblasZswap", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasIsamax", ("hipblasIsamax", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasIdamax", ("hipblasIdamax", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasIcamax", ("hipblasIcamax", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasIzamax", ("hipblasIzamax", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasIsamin", ("hipblasIsamin", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasIdamin", ("hipblasIdamin", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasIcamin", ("hipblasIcamin", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasIzamin", ("hipblasIzamin", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSasum", ("hipblasSasum", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasSasumBatched",
+            ("hipblasSasumBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasDasum", ("hipblasDasum", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasDasumBatched",
+            ("hipblasDasumBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasScasum", ("hipblasScasum", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDzasum", ("hipblasDzasum", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSrot", ("hipblasSrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDrot", ("hipblasDrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCrot", ("hipblasCrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCsrot", ("hipblasCsrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZrot", ("hipblasZrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZdrot", ("hipblasZdrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSrotg", ("hipblasSrotg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDrotg", ("hipblasDrotg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCrotg", ("hipblasCrotg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZrotg", ("hipblasZrotg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSrotm", ("hipblasSrotm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDrotm", ("hipblasDrotm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSrotmg", ("hipblasSrotmg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDrotmg", ("hipblasDrotmg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSgemv", ("hipblasSgemv", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasSgemvBatched",
+            ("hipblasSgemvBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasDgemv", ("hipblasDgemv", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasCgemv", ("hipblasCgemv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZgemv", ("hipblasZgemv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSgbmv", ("hipblasSgbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDgbmv", ("hipblasDgbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCgbmv", ("hipblasCgbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZgbmv", ("hipblasZgbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasStrmv", ("hipblasStrmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDtrmv", ("hipblasDtrmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCtrmv", ("hipblasCtrmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZtrmv", ("hipblasZtrmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasStbmv", ("hipblasStbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDtbmv", ("hipblasDtbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCtbmv", ("hipblasCtbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZtbmv", ("hipblasZtbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasStpmv", ("hipblasStpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDtpmv", ("hipblasDtpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCtpmv", ("hipblasCtpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZtpmv", ("hipblasZtpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasStrsv", ("hipblasStrsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDtrsv", ("hipblasDtrsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCtrsv", ("hipblasCtrsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZtrsv", ("hipblasZtrsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasStpsv", ("hipblasStpsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDtpsv", ("hipblasDtpsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCtpsv", ("hipblasCtpsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZtpsv", ("hipblasZtpsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasStbsv", ("hipblasStbsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDtbsv", ("hipblasDtbsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCtbsv", ("hipblasCtbsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZtbsv", ("hipblasZtbsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSsymv", ("hipblasSsymv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDsymv", ("hipblasDsymv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCsymv", ("hipblasCsymv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZsymv", ("hipblasZsymv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasChemv", ("hipblasChemv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZhemv", ("hipblasZhemv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSsbmv", ("hipblasSsbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDsbmv", ("hipblasDsbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasChbmv", ("hipblasChbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZhbmv", ("hipblasZhbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSspmv", ("hipblasSspmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDspmv", ("hipblasDspmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasChpmv", ("hipblasChpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZhpmv", ("hipblasZhpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSger", ("hipblasSger", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDger", ("hipblasDger", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasCgeru", ("hipblasCgeru", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCgerc", ("hipblasCgerc", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZgeru", ("hipblasZgeru", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZgerc", ("hipblasZgerc", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSsyr", ("hipblasSsyr", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDsyr", ("hipblasDsyr", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasCher", ("hipblasCher", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZher", ("hipblasZher", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSspr", ("hipblasSspr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDspr", ("hipblasDspr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasChpr", ("hipblasChpr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZhpr", ("hipblasZhpr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSsyr2", ("hipblasSsyr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDsyr2", ("hipblasDsyr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCher2", ("hipblasCher2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZher2", ("hipblasZher2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSspr2", ("hipblasSspr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDspr2", ("hipblasDspr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasChpr2", ("hipblasChpr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZhpr2", ("hipblasZhpr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        (
+            "cublasSgemmBatched",
+            ("hipblasSgemmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDgemmBatched",
+            ("hipblasDgemmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasHgemmBatched",
+            ("hipblasHgemmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSgemmStridedBatched",
+            ("hipblasSgemmStridedBatched", CONV_MATH_FUNC, API_BLAS),
+        ),
+        (
+            "cublasDgemmStridedBatched",
+            ("hipblasDgemmStridedBatched", CONV_MATH_FUNC, API_BLAS),
+        ),
+        (
+            "cublasHgemmStridedBatched",
+            ("hipblasHgemmStridedBatched", CONV_MATH_FUNC, API_BLAS),
+        ),
+        (
+            "cublasCgemmBatched",
+            ("hipblasCgemmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgemm3mBatched",
+            ("hipblasCgemm3mBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgemmBatched",
+            ("hipblasZgemmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgemmStridedBatched",
+            (
+                "hipblasCgemmStridedBatched",
+                CONV_MATH_FUNC,
+                API_BLAS,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cublasCgemm3mStridedBatched",
+            (
+                "hipblasCgemm3mStridedBatched",
+                CONV_MATH_FUNC,
+                API_BLAS,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cublasZgemmStridedBatched",
+            (
+                "hipblasZgemmStridedBatched",
+                CONV_MATH_FUNC,
+                API_BLAS,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "cublasHgemmStridedBatched",
+            (
+                "hipblasHgemmStridedBatched",
+                CONV_MATH_FUNC,
+                API_BLAS,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cublasSgemm", ("hipblasSgemm", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDgemm", ("hipblasDgemm", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasCgemm", ("hipblasCgemm", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasZgemm", ("hipblasZgemm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasHgemm", ("hipblasHgemm", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasSsyrk", ("hipblasSsyrk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDsyrk", ("hipblasDsyrk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCsyrk", ("hipblasCsyrk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZsyrk", ("hipblasZsyrk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCherk", ("hipblasCherk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZherk", ("hipblasZherk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSsyr2k", ("hipblasSsyr2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDsyr2k", ("hipblasDsyr2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCsyr2k", ("hipblasCsyr2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZsyr2k", ("hipblasZyr2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSsyrkx", ("hipblasSsyrkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDsyrkx", ("hipblasDsyrkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCsyrkx", ("hipblasCsyrkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZsyrkx", ("hipblasZsyrkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCher2k", ("hipblasCher2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZher2k", ("hipblasZher2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCherkx", ("hipblasCherkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZherkx", ("hipblasZherkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSsymm", ("hipblasSsymm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDsymm", ("hipblasDsymm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCsymm", ("hipblasCsymm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZsymm", ("hipblasZsymm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasChemm", ("hipblasChemm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZhemm", ("hipblasZhemm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasStrsm", ("hipblasStrsm", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDtrsm", ("hipblasDtrsm", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasCtrsm", ("hipblasCtrsm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZtrsm", ("hipblasZtrsm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        (
+            "cublasStrsmBatched",
+            ("hipblasStrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDtrsmBatched",
+            ("hipblasDtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCtrsmBatched",
+            ("hipblasCtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZtrsmBatched",
+            ("hipblasZtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasStrmm", ("hipblasStrmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDtrmm", ("hipblasDtrmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCtrmm", ("hipblasCtrmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZtrmm", ("hipblasZtrmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSgeam", ("hipblasSgeam", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDgeam", ("hipblasDgeam", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasCgeam", ("hipblasCgeam", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZgeam", ("hipblasZgeam", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        (
+            "cublasSgetrfBatched",
+            ("hipblasSgetrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDgetrfBatched",
+            ("hipblasDgetrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgetrfBatched",
+            ("hipblasCgetrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgetrfBatched",
+            ("hipblasZgetrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSgetriBatched",
+            ("hipblasSgetriBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDgetriBatched",
+            ("hipblasDgetriBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgetriBatched",
+            ("hipblasCgetriBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgetriBatched",
+            ("hipblasZgetriBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSgetrsBatched",
+            ("hipblasSgetrsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDgetrsBatched",
+            ("hipblasDgetrsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgetrsBatched",
+            ("hipblasCgetrsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgetrsBatched",
+            ("hipblasZgetrsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasStrsmBatched",
+            ("hipblasStrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDtrsmBatched",
+            ("hipblasDtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCtrsmBatched",
+            ("hipblasCtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZtrsmBatched",
+            ("hipblasZtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSmatinvBatched",
+            ("hipblasSmatinvBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDmatinvBatched",
+            ("hipblasDmatinvBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCmatinvBatched",
+            ("hipblasCmatinvBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZmatinvBatched",
+            ("hipblasZmatinvBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSgeqrfBatched",
+            ("hipblasSgeqrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDgeqrfBatched",
+            ("hipblasDgeqrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgeqrfBatched",
+            ("hipblasCgeqrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgeqrfBatched",
+            ("hipblasZgeqrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSgelsBatched",
+            ("hipblasSgelsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDgelsBatched",
+            ("hipblasDgelsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgelsBatched",
+            ("hipblasCgelsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgelsBatched",
+            ("hipblasZgelsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSdgmm", ("hipblasSdgmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDdgmm", ("hipblasDdgmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCdgmm", ("hipblasCdgmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZdgmm", ("hipblasZdgmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasStpttr", ("hipblasStpttr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDtpttr", ("hipblasDtpttr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCtpttr", ("hipblasCtpttr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZtpttr", ("hipblasZtpttr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasStrttp", ("hipblasStrttp", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDtrttp", ("hipblasDtrttp", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCtrttp", ("hipblasCtrttp", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZtrttp", ("hipblasZtrttp", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCreate_v2", ("hipblasCreate_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDestroy_v2", ("hipblasDestroy_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasGetVersion_v2",
+            ("hipblasGetVersion_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSetWorkspace", ("hipblasSetWorkspace", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasSetStream", ("hipblasSetStream", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasGetStream", ("hipblasGetStream", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasSetStream_v2", ("hipblasSetStream_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasGetStream_v2", ("hipblasGetStream_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasGetPointerMode",
+            ("hipblasGetPointerMode", CONV_MATH_FUNC, API_BLAS),
+        ),
+        (
+            "cublasSetPointerMode",
+            ("hipblasSetPointerMode", CONV_MATH_FUNC, API_BLAS),
+        ),
+        (
+            "cublasGetPointerMode_v2",
+            ("hipblasGetPointerMode_v2", CONV_MATH_FUNC, API_BLAS),
+        ),
+        (
+            "cublasSetPointerMode_v2",
+            ("hipblasSetPointerMode_v2", CONV_MATH_FUNC, API_BLAS),
+        ),
+        ("cublasSgemv_v2", ("hipblasSgemv_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDgemv_v2", ("hipblasDgemv_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasCgemv_v2",
+            ("hipblasCgemv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgemv_v2",
+            ("hipblasZgemv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSgbmv_v2",
+            ("hipblasSgbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDgbmv_v2",
+            ("hipblasDgbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgbmv_v2",
+            ("hipblasCgbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgbmv_v2",
+            ("hipblasZgbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasStrmv_v2",
+            ("hipblasStrmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDtrmv_v2",
+            ("hipblasDtrmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCtrmv_v2",
+            ("hipblasCtrmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZtrmv_v2",
+            ("hipblasZtrmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasStbmv_v2",
+            ("hipblasStbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDtbmv_v2",
+            ("hipblasDtbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCtbmv_v2",
+            ("hipblasCtbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZtbmv_v2",
+            ("hipblasZtbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasStpmv_v2",
+            ("hipblasStpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDtpmv_v2",
+            ("hipblasDtpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCtpmv_v2",
+            ("hipblasCtpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZtpmv_v2",
+            ("hipblasZtpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasStrsv_v2",
+            ("hipblasStrsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDtrsv_v2",
+            ("hipblasDtrsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCtrsv_v2",
+            ("hipblasCtrsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZtrsv_v2",
+            ("hipblasZtrsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasStpsv_v2",
+            ("hipblasStpsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDtpsv_v2",
+            ("hipblasDtpsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCtpsv_v2",
+            ("hipblasCtpsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZtpsv_v2",
+            ("hipblasZtpsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasStbsv_v2",
+            ("hipblasStbsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDtbsv_v2",
+            ("hipblasDtbsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCtbsv_v2",
+            ("hipblasCtbsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZtbsv_v2",
+            ("hipblasZtbsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSsymv_v2",
+            ("hipblasSsymv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDsymv_v2",
+            ("hipblasDsymv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCsymv_v2",
+            ("hipblasCsymv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZsymv_v2",
+            ("hipblasZsymv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasChemv_v2",
+            ("hipblasChemv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZhemv_v2",
+            ("hipblasZhemv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSsbmv_v2",
+            ("hipblasSsbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDsbmv_v2",
+            ("hipblasDsbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasChbmv_v2",
+            ("hipblasChbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZhbmv_v2",
+            ("hipblasZhbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSspmv_v2",
+            ("hipblasSspmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDspmv_v2",
+            ("hipblasDspmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasChpmv_v2",
+            ("hipblasChpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZhpmv_v2",
+            ("hipblasZhpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSger_v2", ("hipblasSger_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDger_v2", ("hipblasDger_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasCgeru_v2",
+            ("hipblasCgeru_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgerc_v2",
+            ("hipblasCergc_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgeru_v2",
+            ("hipblasZgeru_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgerc_v2",
+            ("hipblasZgerc_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSsyr_v2", ("hipblasSsyr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDsyr_v2", ("hipblasDsyr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCsyr_v2", ("hipblasCsyr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZsyr_v2", ("hipblasZsyr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCher_v2", ("hipblasCher_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZher_v2", ("hipblasZher_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSspr_v2", ("hipblasSspr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDspr_v2", ("hipblasDspr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasChpr_v2", ("hipblasChpr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasZhpr_v2", ("hipblasZhpr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        (
+            "cublasSsyr2_v2",
+            ("hipblasSsyr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDsyr2_v2",
+            ("hipblasDsyr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCsyr2_v2",
+            ("hipblasCsyr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZsyr2_v2",
+            ("hipblasZsyr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCher2_v2",
+            ("hipblasCher2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZher2_v2",
+            ("hipblasZher2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSspr2_v2",
+            ("hipblasSspr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDspr2_v2",
+            ("hipblasDspr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasChpr2_v2",
+            ("hipblasChpr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZhpr2_v2",
+            ("hipblasZhpr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSgemm_v2", ("hipblasSgemm_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDgemm_v2", ("hipblasDgemm_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasCgemm_v2",
+            ("hipblasCgemm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgemm3m",
+            ("hipblasCgemm3m", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgemm3mEx",
+            ("hipblasCgemm3mEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgemm_v2",
+            ("hipblasZgemm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZgemm3m",
+            ("hipblasZgemm3m", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSgemmEx",
+            ("hipblasSgemmEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasGemmEx", ("hipblasGemmEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        (
+            "cublasGemmBatchedEx",
+            ("hipblasGemmBatchedEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasGemmStridedBatchedEx",
+            ("hipblasGemmStridedBatchedEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCgemmEx",
+            ("hipblasCgemmEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasUint8gemmBias",
+            ("hipblasUint8gemmBias", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSsyrk_v2",
+            ("hipblasSsyrk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDsyrk_v2",
+            ("hipblasDsyrk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCsyrk_v2",
+            ("hipblasCsyrk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZsyrk_v2",
+            ("hipblasZsyrk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCsyrkEx",
+            ("hipblasCsyrkEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCsyrk3mEx",
+            ("hipblasCsyrk3mEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCherk_v2",
+            ("hipblasCherk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCherkEx",
+            ("hipblasCherkEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCherk3mEx",
+            ("hipblasCherk3mEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZherk_v2",
+            ("hipblasZherk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSsyr2k_v2",
+            ("hipblasSsyr2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDsyr2k_v2",
+            ("hipblasDsyr2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCsyr2k_v2",
+            ("hipblasCsyr2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZsyr2k_v2",
+            ("hipblasZsyr2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCher2k_v2",
+            ("hipblasCher2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZher2k_v2",
+            ("hipblasZher2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSsymm_v2",
+            ("hipblasSsymm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDsymm_v2",
+            ("hipblasDsymm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCsymm_v2",
+            ("hipblasCsymm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZsymm_v2",
+            ("hipblasZsymm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasChemm_v2",
+            ("hipblasChemm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZhemm_v2",
+            ("hipblasZhemm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasStrsm_v2",
+            ("hipblasStrsm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDtrsm_v2",
+            ("hipblasDtrsm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCtrsm_v2",
+            ("hipblasCtrsm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZtrsm_v2",
+            ("hipblasZtrsm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasStrmm_v2",
+            ("hipblasStrmm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDtrmm_v2",
+            ("hipblasDtrmm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCtrmm_v2",
+            ("hipblasCtrmm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZtrmm_v2",
+            ("hipblasZtrmm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSnrm2_v2", ("hipblasSnrm2_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDnrm2_v2", ("hipblasDnrm2_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasScnrm2_v2",
+            ("hipblasScnrm2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDznrm2_v2",
+            ("hipblasDznrm2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasDotEx", ("hipblasDotEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDotcEx", ("hipblasDotcEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSdot_v2", ("hipblasSdot_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDdot_v2", ("hipblasDdot_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasCdotu_v2",
+            ("hipblasCdotu_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCdotc_v2",
+            ("hipblasCdotc_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZdotu_v2",
+            ("hipblasZdotu_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZdotc_v2",
+            ("hipblasZdotc_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasScalEx", ("hipblasScalEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSscal_v2", ("hipblasSscal_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDscal_v2", ("hipblasDscal_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasCscal_v2",
+            ("hipblasCscal_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCsscal_v2",
+            ("hipblasCsscal_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZscal_v2",
+            ("hipblasZcsal_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZdscal_v2",
+            ("hipblasZdscal_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasAxpyEx", ("hipblasAxpyEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasSaxpy_v2", ("hipblasSaxpy_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDaxpy_v2", ("hipblasDaxpy_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasCaxpy_v2",
+            ("hipblasCaxpy_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZaxpy_v2",
+            ("hipblasZaxpy_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasScopy_v2", ("hipblasScopy_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDcopy_v2", ("hipblasDcopy_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasCcopy_v2",
+            ("hipblasCcopy_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZcopy_v2",
+            ("hipblasZcopy_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSswap_v2", ("hipblasSswap_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDswap_v2", ("hipblasDswap_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasCswap_v2",
+            ("hipblasCswap_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZswap_v2",
+            ("hipblasZswap_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasIsamax_v2", ("hipblasIsamax_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasIdamax_v2", ("hipblasIdamax_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasIcamax_v2",
+            ("hipblasIcamax_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasIzamax_v2",
+            ("hipblasIzamax_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasIsamin_v2", ("hipblasIsamin_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasIdamin_v2", ("hipblasIdamin_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasIcamin_v2",
+            ("hipblasIcamin_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasIzamin_v2",
+            ("hipblasIzamin_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSasum_v2", ("hipblasSasum_v2", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasDasum_v2", ("hipblasDasum_v2", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cublasScasum_v2",
+            ("hipblasScasum_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDzasum_v2",
+            ("hipblasDzasum_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasSrot_v2", ("hipblasSrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasDrot_v2", ("hipblasDrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        ("cublasCrot_v2", ("hipblasCrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        (
+            "cublasCsrot_v2",
+            ("hipblasCsrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        ("cublasZrot_v2", ("hipblasZrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)),
+        (
+            "cublasZdrot_v2",
+            ("hipblasZdrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSrotg_v2",
+            ("hipblasSrotg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDrotg_v2",
+            ("hipblasDrotg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasCrotg_v2",
+            ("hipblasCrotg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasZrotg_v2",
+            ("hipblasZrotg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSrotm_v2",
+            ("hipblasSrotm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDrotm_v2",
+            ("hipblasDrotm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasSrotmg_v2",
+            ("hipblasSrotmg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasDrotmg_v2",
+            ("hipblasDrotmg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED),
+        ),
+        (
+            "cublasComputeType_t",
+            ("hipblasComputeType_t", CONV_MATH_FUNC, API_BLAS)
+        ),
+        (
+            "CUBLAS_COMPUTE_32I",
+            ("HIPBLAS_COMPUTE_32I", CONV_MATH_FUNC, API_BLAS)
+        ),
+        (
+            "CUBLAS_COMPUTE_32F",
+            ("HIPBLAS_COMPUTE_32F", CONV_MATH_FUNC, API_BLAS)
+        ),
+        (
+            "CUBLAS_COMPUTE_32F_FAST_TF32",
+            ("HIPBLAS_COMPUTE_32F_FAST_TF32", CONV_MATH_FUNC, API_BLAS)
+        ),
+        (
+            "CUBLAS_COMPUTE_64F",
+            ("HIPBLAS_COMPUTE_64F", CONV_MATH_FUNC, API_BLAS)
+        ),
+        ("cublasLtEpilogue_t", ("hipblasLtEpilogue_t", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_EPILOGUE_DEFAULT", ("HIPBLASLT_EPILOGUE_DEFAULT", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_EPILOGUE_RELU", ("HIPBLASLT_EPILOGUE_RELU", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_EPILOGUE_BIAS", ("HIPBLASLT_EPILOGUE_BIAS", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_EPILOGUE_RELU_BIAS", ("HIPBLASLT_EPILOGUE_RELU_BIAS", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_EPILOGUE_GELU", ("HIPBLASLT_EPILOGUE_GELU", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_EPILOGUE_GELU_BIAS", ("HIPBLASLT_EPILOGUE_GELU_BIAS", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtHandle_t", ("hipblasLtHandle_t", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulDesc_t", ("hipblasLtMatmulDesc_t", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulDescOpaque_t", ("hipblasLtMatmulDescOpaque_t", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulDescAttributes_t", ("hipblasLtMatmulDescAttributes_t", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_DESC_TRANSA", ("HIPBLASLT_MATMUL_DESC_TRANSA", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_DESC_TRANSB", ("HIPBLASLT_MATMUL_DESC_TRANSB", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_DESC_EPILOGUE", ("HIPBLASLT_MATMUL_DESC_EPILOGUE", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_DESC_BIAS_POINTER", ("HIPBLASLT_MATMUL_DESC_BIAS_POINTER", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_DESC_A_SCALE_POINTER", ("HIPBLASLT_MATMUL_DESC_A_SCALE_POINTER", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_DESC_B_SCALE_POINTER", ("HIPBLASLT_MATMUL_DESC_B_SCALE_POINTER", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_DESC_D_SCALE_POINTER", ("HIPBLASLT_MATMUL_DESC_D_SCALE_POINTER", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_DESC_AMAX_D_POINTER", ("HIPBLASLT_MATMUL_DESC_AMAX_D_POINTER", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_DESC_BIAS_DATA_TYPE", ("HIPBLASLT_MATMUL_DESC_BIAS_DATA_TYPE", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatrixLayout_t", ("hipblasLtMatrixLayout_t", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatrixLayoutOpaque_t", ("hipblasLtMatrixLayoutOpaque_t", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatrixLayoutAttribute_t", ("hipblasLtMatrixLayoutAttribute_t", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatrixLayoutCreate", ("hipblasLtMatrixLayoutCreate", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatrixLayoutDestroy", ("hipblasLtMatrixLayoutDestroy", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatrixLayoutSetAttribute", ("hipblasLtMatrixLayoutSetAttribute", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT", ("HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET", ("HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulPreference_t", ("hipblasLtMatmulPreference_t", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulPreferenceOpaque_t", ("hipblasLtMatmulPreferenceOpaque_t", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulPreferenceAttributes_t", ("hipblasLtMatmulPreferenceAttributes_t", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_PREF_SEARCH_MODE", ("HIPBLASLT_MATMUL_PREF_SEARCH_MODE", CONV_MATH_FUNC, API_BLAS)),
+        ("CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES", ("HIPBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulAlgo_t", ("hipblasLtMatmulAlgo_t", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulHeuristicResult_t", ("hipblasLtMatmulHeuristicResult_t", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtCreate", ("hipblasLtCreate", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtDestroy", ("hipblasLtDestroy", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulDescCreate", ("hipblasLtMatmulDescCreate", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulDescDestroy", ("hipblasLtMatmulDescDestroy", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulDescSetAttribute", ("hipblasLtMatmulDescSetAttribute", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulPreferenceCreate", ("hipblasLtMatmulPreferenceCreate", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulPreferenceDestroy", ("hipblasLtMatmulPreferenceDestroy", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulPreferenceSetAttribute", ("hipblasLtMatmulPreferenceSetAttribute", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmulAlgoGetHeuristic", ("hipblasLtMatmulAlgoGetHeuristic", CONV_MATH_FUNC, API_BLAS)),
+        ("cublasLtMatmul", ("hipblasLtMatmul", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "CURAND_STATUS_SUCCESS",
+            ("HIPRAND_STATUS_SUCCESS", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_VERSION_MISMATCH",
+            ("HIPRAND_STATUS_VERSION_MISMATCH", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_NOT_INITIALIZED",
+            ("HIPRAND_STATUS_NOT_INITIALIZED", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_ALLOCATION_FAILED",
+            ("HIPRAND_STATUS_ALLOCATION_FAILED", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_TYPE_ERROR",
+            ("HIPRAND_STATUS_TYPE_ERROR", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_OUT_OF_RANGE",
+            ("HIPRAND_STATUS_OUT_OF_RANGE", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_LENGTH_NOT_MULTIPLE",
+            ("HIPRAND_STATUS_LENGTH_NOT_MULTIPLE", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_DOUBLE_PRECISION_REQUIRED",
+            (
+                "HIPRAND_STATUS_DOUBLE_PRECISION_REQUIRED",
+                CONV_NUMERIC_LITERAL,
+                API_RAND,
+            ),
+        ),
+        (
+            "CURAND_STATUS_LAUNCH_FAILURE",
+            ("HIPRAND_STATUS_LAUNCH_FAILURE", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_PREEXISTING_FAILURE",
+            ("HIPRAND_STATUS_PREEXISTING_FAILURE", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_INITIALIZATION_FAILED",
+            ("HIPRAND_STATUS_INITIALIZATION_FAILED", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_ARCH_MISMATCH",
+            ("HIPRAND_STATUS_ARCH_MISMATCH", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_STATUS_INTERNAL_ERROR",
+            ("HIPRAND_STATUS_INTERNAL_ERROR", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        ("CURAND_RNG_TEST", ("HIPRAND_RNG_TEST", CONV_NUMERIC_LITERAL, API_RAND)),
+        (
+            "mtgp32dc_params_fast_11213",
+            ("mtgp32dc_params_fast_11213", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_PSEUDO_DEFAULT",
+            ("HIPRAND_RNG_PSEUDO_DEFAULT", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_PSEUDO_XORWOW",
+            ("HIPRAND_RNG_PSEUDO_XORWOW", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_PSEUDO_MRG32K3A",
+            ("HIPRAND_RNG_PSEUDO_MRG32K3A", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_PSEUDO_MTGP32",
+            ("HIPRAND_RNG_PSEUDO_MTGP32", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_PSEUDO_MT19937",
+            ("HIPRAND_RNG_PSEUDO_MT19937", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_PSEUDO_PHILOX4_32_10",
+            ("HIPRAND_RNG_PSEUDO_PHILOX4_32_10", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_QUASI_DEFAULT",
+            ("HIPRAND_RNG_QUASI_DEFAULT", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_QUASI_SOBOL32",
+            ("HIPRAND_RNG_QUASI_SOBOL32", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_QUASI_SCRAMBLED_SOBOL32",
+            ("HIPRAND_RNG_QUASI_SCRAMBLED_SOBOL32", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_QUASI_SOBOL64",
+            ("HIPRAND_RNG_QUASI_SOBOL64", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "CURAND_RNG_QUASI_SCRAMBLED_SOBOL64",
+            ("HIPRAND_RNG_QUASI_SCRAMBLED_SOBOL64", CONV_NUMERIC_LITERAL, API_RAND),
+        ),
+        (
+            "curand_ORDERING_PSEUDO_BEST",
+            (
+                "HIPRAND_ORDERING_PSEUDO_BEST",
+                CONV_NUMERIC_LITERAL,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "curand_ORDERING_PSEUDO_DEFAULT",
+            (
+                "HIPRAND_ORDERING_PSEUDO_DEFAULT",
+                CONV_NUMERIC_LITERAL,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "curand_ORDERING_PSEUDO_SEEDED",
+            (
+                "HIPRAND_ORDERING_PSEUDO_SEEDED",
+                CONV_NUMERIC_LITERAL,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "curand_ORDERING_QUASI_DEFAULT",
+            (
+                "HIPRAND_ORDERING_QUASI_DEFAULT",
+                CONV_NUMERIC_LITERAL,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "curand_DIRECTION_VECTORS_32_JOEKUO6",
+            (
+                "HIPRAND_DIRECTION_VECTORS_32_JOEKUO6",
+                CONV_NUMERIC_LITERAL,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "curand_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6",
+            (
+                "HIPRAND_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6",
+                CONV_NUMERIC_LITERAL,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "curand_DIRECTION_VECTORS_64_JOEKUO6",
+            (
+                "HIPRAND_DIRECTION_VECTORS_64_JOEKUO6",
+                CONV_NUMERIC_LITERAL,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "curand_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6",
+            (
+                "HIPRAND_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6",
+                CONV_NUMERIC_LITERAL,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "curand_CHOOSE_BEST",
+            ("HIPRAND_CHOOSE_BEST", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_ITR",
+            ("HIPRAND_ITR", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_KNUTH",
+            ("HIPRAND_KNUTH", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_HITR",
+            ("HIPRAND_HITR", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        ("curand_M1", ("HIPRAND_M1", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED)),
+        ("curand_M2", ("HIPRAND_M2", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED)),
+        (
+            "curand_BINARY_SEARCH",
+            ("HIPRAND_BINARY_SEARCH", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_DISCRETE_GAUSS",
+            ("HIPRAND_DISCRETE_GAUSS", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_REJECTION",
+            ("HIPRAND_REJECTION", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_DEVICE_API",
+            ("HIPRAND_DEVICE_API", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_FAST_REJECTION",
+            ("HIPRAND_FAST_REJECTION", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_3RD",
+            ("HIPRAND_3RD", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_DEFINITION",
+            ("HIPRAND_DEFINITION", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_POISSON",
+            ("HIPRAND_POISSON", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED),
+        ),
+        ("curandCreateGenerator", ("hiprandCreateGenerator", CONV_MATH_FUNC, API_RAND)),
+        (
+            "curandCreateGeneratorHost",
+            ("hiprandCreateGeneratorHost", CONV_MATH_FUNC, API_RAND),
+        ),
+        (
+            "curandCreatePoissonDistribution",
+            ("hiprandCreatePoissonDistribution", CONV_MATH_FUNC, API_RAND),
+        ),
+        (
+            "curandDestroyDistribution",
+            ("hiprandDestroyDistribution", CONV_MATH_FUNC, API_RAND),
+        ),
+        (
+            "curandDestroyGenerator",
+            ("hiprandDestroyGenerator", CONV_MATH_FUNC, API_RAND),
+        ),
+        ("curandGenerate", ("hiprandGenerate", CONV_MATH_FUNC, API_RAND)),
+        (
+            "curandGenerateLogNormal",
+            ("hiprandGenerateLogNormal", CONV_MATH_FUNC, API_RAND),
+        ),
+        (
+            "curandGenerateLogNormalDouble",
+            ("hiprandGenerateLogNormalDouble", CONV_MATH_FUNC, API_RAND),
+        ),
+        (
+            "curandGenerateLongLong",
+            ("hiprandGenerateLongLong", CONV_MATH_FUNC, API_RAND, HIP_UNSUPPORTED),
+        ),
+        ("curandGenerateNormal", ("hiprandGenerateNormal", CONV_MATH_FUNC, API_RAND)),
+        (
+            "curandGenerateNormalDouble",
+            ("hiprandGenerateNormalDouble", CONV_MATH_FUNC, API_RAND),
+        ),
+        ("curandGeneratePoisson", ("hiprandGeneratePoisson", CONV_MATH_FUNC, API_RAND)),
+        ("curandGenerateSeeds", ("hiprandGenerateSeeds", CONV_MATH_FUNC, API_RAND)),
+        ("curandGenerateUniform", ("hiprandGenerateUniform", CONV_MATH_FUNC, API_RAND)),
+        (
+            "curandGenerateUniformDouble",
+            ("hiprandGenerateUniformDouble", CONV_MATH_FUNC, API_RAND),
+        ),
+        (
+            "curandGetDirectionVectors32",
+            ("hiprandGetDirectionVectors32", CONV_MATH_FUNC, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandGetDirectionVectors64",
+            ("hiprandGetDirectionVectors64", CONV_MATH_FUNC, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandGetProperty",
+            ("hiprandGetProperty", CONV_MATH_FUNC, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandGetScrambleConstants32",
+            (
+                "hiprandGetScrambleConstants32",
+                CONV_MATH_FUNC,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "curandGetScrambleConstants64",
+            (
+                "hiprandGetScrambleConstants64",
+                CONV_MATH_FUNC,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("curandGetVersion", ("hiprandGetVersion", CONV_MATH_FUNC, API_RAND)),
+        (
+            "curandSetGeneratorOffset",
+            ("hiprandSetGeneratorOffset", CONV_MATH_FUNC, API_RAND),
+        ),
+        (
+            "curandSetGeneratorOrdering",
+            ("hiprandSetGeneratorOrdering", CONV_MATH_FUNC, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curandSetPseudoRandomGeneratorSeed",
+            ("hiprandSetPseudoRandomGeneratorSeed", CONV_MATH_FUNC, API_RAND),
+        ),
+        (
+            "curandSetQuasiRandomGeneratorDimensions",
+            ("hiprandSetQuasiRandomGeneratorDimensions", CONV_MATH_FUNC, API_RAND),
+        ),
+        ("curandSetStream", ("hiprandSetStream", CONV_MATH_FUNC, API_RAND)),
+        ("curand", ("hiprand", CONV_DEVICE_FUNC, API_RAND)),
+        ("curand4", ("hiprand4", CONV_DEVICE_FUNC, API_RAND)),
+        ("curand_init", ("hiprand_init", CONV_DEVICE_FUNC, API_RAND)),
+        ("curand_log_normal", ("hiprand_log_normal", CONV_DEVICE_FUNC, API_RAND)),
+        (
+            "curand_log_normal_double",
+            ("hiprand_log_normal_double", CONV_DEVICE_FUNC, API_RAND),
+        ),
+        ("curand_log_normal2", ("hiprand_log_normal2", CONV_DEVICE_FUNC, API_RAND)),
+        (
+            "curand_log_normal2_double",
+            ("hiprand_log_normal2_double", CONV_DEVICE_FUNC, API_RAND),
+        ),
+        ("curand_log_normal4", ("hiprand_log_normal4", CONV_DEVICE_FUNC, API_RAND)),
+        (
+            "curand_log_normal4_double",
+            ("hiprand_log_normal4_double", CONV_DEVICE_FUNC, API_RAND),
+        ),
+        (
+            "curand_mtgp32_single",
+            ("hiprand_mtgp32_single", CONV_DEVICE_FUNC, API_RAND, HIP_UNSUPPORTED),
+        ),
+        (
+            "curand_mtgp32_single_specific",
+            (
+                "hiprand_mtgp32_single_specific",
+                CONV_DEVICE_FUNC,
+                API_RAND,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        (
+            "curand_mtgp32_specific",
+            ("hiprand_mtgp32_specific", CONV_DEVICE_FUNC, API_RAND, HIP_UNSUPPORTED),
+        ),
+        ("curand_normal", ("hiprand_normal", CONV_DEVICE_FUNC, API_RAND)),
+        (
+            "curandMakeMTGP32Constants",
+            ("hiprandMakeMTGP32Constants", CONV_DEVICE_FUNC, API_RAND),
+        ),
+        (
+            "curandMakeMTGP32KernelState",
+            ("hiprandMakeMTGP32KernelState", CONV_DEVICE_FUNC, API_RAND),
+        ),
+        ("curand_normal_double", ("hiprand_normal_double", CONV_DEVICE_FUNC, API_RAND)),
+        ("curand_normal2", ("hiprand_normal2", CONV_DEVICE_FUNC, API_RAND)),
+        (
+            "curand_normal2_double",
+            ("hiprand_normal2_double", CONV_DEVICE_FUNC, API_RAND),
+        ),
+        ("curand_normal4", ("hiprand_normal4", CONV_DEVICE_FUNC, API_RAND)),
+        (
+            "curand_normal4_double",
+            ("hiprand_normal4_double", CONV_DEVICE_FUNC, API_RAND),
+        ),
+        ("curand_uniform", ("hiprand_uniform", CONV_DEVICE_FUNC, API_RAND)),
+        (
+            "curand_uniform_double",
+            ("hiprand_uniform_double", CONV_DEVICE_FUNC, API_RAND),
+        ),
+        (
+            "curand_uniform2_double",
+            ("hiprand_uniform2_double", CONV_DEVICE_FUNC, API_RAND),
+        ),
+        ("curand_uniform4", ("hiprand_uniform4", CONV_DEVICE_FUNC, API_RAND)),
+        (
+            "curand_uniform4_double",
+            ("hiprand_uniform4_double", CONV_DEVICE_FUNC, API_RAND),
+        ),
+        ("curand_discrete", ("hiprand_discrete", CONV_DEVICE_FUNC, API_RAND)),
+        ("curand_discrete4", ("hiprand_discrete4", CONV_DEVICE_FUNC, API_RAND)),
+        ("curand_poisson", ("hiprand_poisson", CONV_DEVICE_FUNC, API_RAND)),
+        ("curand_poisson4", ("hiprand_poisson4", CONV_DEVICE_FUNC, API_RAND)),
+        (
+            "curand_Philox4x32_10",
+            ("hiprand_Philox4x32_10", CONV_DEVICE_FUNC, API_RAND, HIP_UNSUPPORTED),
+        ),
+        ("mtgp32_kernel_params", ("mtgp32_kernel_params_t", CONV_MATH_FUNC, API_RAND)),
+        ("CUFFT_FORWARD", ("HIPFFT_FORWARD", CONV_NUMERIC_LITERAL, API_BLAS)),
+        ("CUFFT_INVERSE", ("HIPFFT_BACKWARD", CONV_NUMERIC_LITERAL, API_BLAS)),
+        (
+            "CUFFT_COMPATIBILITY_DEFAULT",
+            (
+                "HIPFFT_COMPATIBILITY_DEFAULT",
+                CONV_NUMERIC_LITERAL,
+                API_BLAS,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cuComplex", ("hipComplex", CONV_TYPE, API_BLAS)),
+        ("cuDoubleComplex", ("hipDoubleComplex", CONV_TYPE, API_BLAS)),
+        ("cufftResult_t", ("hipfftResult_t", CONV_TYPE, API_FFT)),
+        ("cufftResult", ("hipfftResult", CONV_TYPE, API_FFT)),
+        ("CUFFT_SUCCESS", ("HIPFFT_SUCCESS", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_INVALID_PLAN", ("HIPFFT_INVALID_PLAN", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_ALLOC_FAILED", ("HIPFFT_ALLOC_FAILED", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_INVALID_TYPE", ("HIPFFT_INVALID_TYPE", CONV_NUMERIC_LITERAL, API_FFT)),
+        (
+            "CUFFT_INVALID_VALUE",
+            ("HIPFFT_INVALID_VALUE", CONV_NUMERIC_LITERAL, API_FFT),
+        ),
+        (
+            "CUFFT_INTERNAL_ERROR",
+            ("HIPFFT_INTERNAL_ERROR", CONV_NUMERIC_LITERAL, API_FFT),
+        ),
+        ("CUFFT_EXEC_FAILED", ("HIPFFT_EXEC_FAILED", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_SETUP_FAILED", ("HIPFFT_SETUP_FAILED", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_INVALID_SIZE", ("HIPFFT_INVALID_SIZE", CONV_NUMERIC_LITERAL, API_FFT)),
+        (
+            "CUFFT_UNALIGNED_DATA",
+            ("HIPFFT_UNALIGNED_DATA", CONV_NUMERIC_LITERAL, API_FFT),
+        ),
+        (
+            "CUFFT_INCOMPLETE_PARAMETER_LIST",
+            ("HIPFFT_INCOMPLETE_PARAMETER_LIST", CONV_NUMERIC_LITERAL, API_FFT),
+        ),
+        (
+            "CUFFT_INVALID_DEVICE",
+            ("HIPFFT_INVALID_DEVICE", CONV_NUMERIC_LITERAL, API_FFT),
+        ),
+        ("CUFFT_PARSE_ERROR", ("HIPFFT_PARSE_ERROR", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_NO_WORKSPACE", ("HIPFFT_NO_WORKSPACE", CONV_NUMERIC_LITERAL, API_FFT)),
+        (
+            "CUFFT_NOT_IMPLEMENTED",
+            ("HIPFFT_NOT_IMPLEMENTED", CONV_NUMERIC_LITERAL, API_FFT),
+        ),
+        (
+            "CUFFT_LICENSE_ERROR",
+            ("HIPFFT_LICENSE_ERROR", CONV_NUMERIC_LITERAL, API_FFT, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUFFT_NOT_SUPPORTED",
+            ("HIPFFT_NOT_SUPPORTED", CONV_NUMERIC_LITERAL, API_FFT),
+        ),
+        ("cufftType_t", ("hipfftType_t", CONV_TYPE, API_FFT)),
+        ("cufftType", ("hipfftType", CONV_TYPE, API_FFT)),
+        ("CUFFT_R2C", ("HIPFFT_R2C", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_C2R", ("HIPFFT_C2R", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_C2C", ("HIPFFT_C2C", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_D2Z", ("HIPFFT_D2Z", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_Z2D", ("HIPFFT_Z2D", CONV_NUMERIC_LITERAL, API_FFT)),
+        ("CUFFT_Z2Z", ("HIPFFT_Z2Z", CONV_NUMERIC_LITERAL, API_FFT)),
+        (
+            "cufftCompatibility_t",
+            ("hipfftCompatibility_t", CONV_TYPE, API_FFT, HIP_UNSUPPORTED),
+        ),
+        (
+            "cufftCompatibility",
+            ("hipfftCompatibility", CONV_TYPE, API_FFT, HIP_UNSUPPORTED),
+        ),
+        (
+            "CUFFT_COMPATIBILITY_FFTW_PADDING",
+            (
+                "HIPFFT_COMPATIBILITY_FFTW_PADDING",
+                CONV_NUMERIC_LITERAL,
+                API_FFT,
+                HIP_UNSUPPORTED,
+            ),
+        ),
+        ("cufftReal", ("hipfftReal", CONV_TYPE, API_FFT)),
+        ("cufftDoubleReal", ("hipfftDoubleReal", CONV_TYPE, API_FFT)),
+        ("cufftComplex", ("hipfftComplex", CONV_TYPE, API_FFT)),
+        ("cufftDoubleComplex", ("hipfftDoubleComplex", CONV_TYPE, API_FFT)),
+        ("cufftHandle", ("hipfftHandle", CONV_TYPE, API_FFT)),
+        ("cufftPlan1d", ("hipfftPlan1d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftPlan2d", ("hipfftPlan2d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftPlan3d", ("hipfftPlan3d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftPlanMany", ("hipfftPlanMany", CONV_MATH_FUNC, API_FFT)),
+        ("cufftMakePlan1d", ("hipfftMakePlan1d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftMakePlan2d", ("hipfftMakePlan2d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftMakePlan3d", ("hipfftMakePlan3d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftMakePlanMany", ("hipfftMakePlanMany", CONV_MATH_FUNC, API_FFT)),
+        ("cufftMakePlanMany64", ("hipfftMakePlanMany64", CONV_MATH_FUNC, API_FFT)),
+        ("cufftGetSizeMany64", ("hipfftGetSizeMany64", CONV_MATH_FUNC, API_FFT)),
+        ("cufftEstimate1d", ("hipfftEstimate1d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftEstimate2d", ("hipfftEstimate2d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftEstimate3d", ("hipfftEstimate3d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftEstimateMany", ("hipfftEstimateMany", CONV_MATH_FUNC, API_FFT)),
+        ("cufftCreate", ("hipfftCreate", CONV_MATH_FUNC, API_FFT)),
+        ("cufftGetSize1d", ("hipfftGetSize1d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftGetSize2d", ("hipfftGetSize2d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftGetSize3d", ("hipfftGetSize3d", CONV_MATH_FUNC, API_FFT)),
+        ("cufftGetSizeMany", ("hipfftGetSizeMany", CONV_MATH_FUNC, API_FFT)),
+        ("cufftGetSize", ("hipfftGetSize", CONV_MATH_FUNC, API_FFT)),
+        ("cufftSetWorkArea", ("hipfftSetWorkArea", CONV_MATH_FUNC, API_FFT)),
+        (
+            "cufftSetAutoAllocation",
+            ("hipfftSetAutoAllocation", CONV_MATH_FUNC, API_FFT),
+        ),
+        ("cufftXtExec", ("hipfftXtExec", CONV_MATH_FUNC, API_FFT)),
+        ("cufftXtMakePlanMany", ("hipfftXtMakePlanMany", CONV_MATH_FUNC, API_FFT)),
+        ("cufftExecC2C", ("hipfftExecC2C", CONV_MATH_FUNC, API_FFT)),
+        ("cufftExecR2C", ("hipfftExecR2C", CONV_MATH_FUNC, API_FFT)),
+        ("cufftExecC2R", ("hipfftExecC2R", CONV_MATH_FUNC, API_FFT)),
+        ("cufftExecZ2Z", ("hipfftExecZ2Z", CONV_MATH_FUNC, API_FFT)),
+        ("cufftExecD2Z", ("hipfftExecD2Z", CONV_MATH_FUNC, API_FFT)),
+        ("cufftExecZ2D", ("hipfftExecZ2D", CONV_MATH_FUNC, API_FFT)),
+        ("cufftSetStream", ("hipfftSetStream", CONV_MATH_FUNC, API_FFT)),
+        ("cufftDestroy", ("hipfftDestroy", CONV_MATH_FUNC, API_FFT)),
+        ("cufftGetVersion", ("hipfftGetVersion", CONV_MATH_FUNC, API_FFT)),
+        (
+            "cufftGetProperty",
+            ("hipfftGetProperty", CONV_MATH_FUNC, API_FFT, HIP_UNSUPPORTED),
+        ),
+        ("nvrtcResult", ("hiprtcResult", CONV_TYPE, API_RTC)),
+        ("NVRTC_SUCCESS", ("HIPRTC_SUCCESS", CONV_TYPE, API_RTC)),
+        (
+            "NVRTC_ERROR_OUT_OF_MEMORY",
+            ("HIPRTC_ERROR_OUT_OF_MEMORY", CONV_TYPE, API_RTC),
+        ),
+        (
+            "NVRTC_ERROR_PROGRAM_CREATION_FAILURE",
+            ("HIPRTC_ERROR_PROGRAM_CREATION_FAILURE", CONV_TYPE, API_RTC),
+        ),
+        (
+            "NVRTC_ERROR_INVALID_INPUT",
+            ("HIPRTC_ERROR_INVALID_INPUT", CONV_TYPE, API_RTC),
+        ),
+        (
+            "NVRTC_ERROR_INVALID_PROGRAM",
+            ("HIPRTC_ERROR_INVALID_PROGRAM", CONV_TYPE, API_RTC),
+        ),
+        ("NVRTC_ERROR_COMPILATION", ("HIPRTC_ERROR_COMPILATION", CONV_TYPE, API_RTC)),
+        (
+            "NVRTC_ERROR_BUILTIN_OPERATION_FAILURE",
+            ("HIPRTC_ERROR_BUILTIN_OPERATION_FAILURE", CONV_TYPE, API_RTC),
+        ),
+        (
+            "NVRTC_ERROR_NO_NAME_EXPRESSIONS_AFTER_COMPILATION",
+            ("HIPRTC_ERROR_NO_NAME_EXPRESSIONS_AFTER_COMPILATION", CONV_TYPE, API_RTC),
+        ),
+        (
+            "NVRTC_ERROR_NAME_EXPRESSION_NOT_VALID",
+            ("HIPRTC_ERROR_NAME_EXPRESSION_NOT_VALID", CONV_TYPE, API_RTC),
+        ),
+        (
+            "NVRTC_ERROR_INTERNAL_ERROR",
+            ("HIPRTC_ERROR_INTERNAL_ERROR", CONV_TYPE, API_RTC),
+        ),
+        ("nvrtcGetErrorString", ("hiprtcGetErrorString", CONV_JIT, API_RTC)),
+        ("nvrtcVersion", ("hiprtcVersion", CONV_JIT, API_RTC)),
+        ("nvrtcProgram", ("hiprtcProgram", CONV_TYPE, API_RTC)),
+        ("nvrtcAddNameExpression", ("hiprtcAddNameExpression", CONV_JIT, API_RTC)),
+        ("nvrtcCompileProgram", ("hiprtcCompileProgram", CONV_JIT, API_RTC)),
+        ("nvrtcCreateProgram", ("hiprtcCreateProgram", CONV_JIT, API_RTC)),
+        ("nvrtcDestroyProgram", ("hiprtcDestroyProgram", CONV_JIT, API_RTC)),
+        ("nvrtcGetLoweredName", ("hiprtcGetLoweredName", CONV_JIT, API_RTC)),
+        ("nvrtcGetProgramLog", ("hiprtcGetProgramLog", CONV_JIT, API_RTC)),
+        ("nvrtcGetProgramLogSize", ("hiprtcGetProgramLogSize", CONV_JIT, API_RTC)),
+        ("nvrtcGetPTX", ("hiprtcGetCode", CONV_JIT, API_RTC)),
+        ("nvrtcGetPTXSize", ("hiprtcGetCodeSize", CONV_JIT, API_RTC)),
+        ("thrust::cuda", ("thrust::hip", CONV_MATH_FUNC, API_BLAS)),
+        (
+            "cudaCpuDeviceId",
+            ("hipCpuDeviceId", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED),
+        ),
+        # The caffe2 directory does a string match; pytorch does a word-boundary match.
+        # Patterns such as 'cub::' will not match for pytorch.
+        # We list all current uses of cub symbols for this reason.
+        ("cub::", ("hipcub::", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::ArgMax", ("hipcub::ArgMax", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::ArgMin", ("hipcub::ArgMin", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::BLOCK_SCAN_WARP_SCANS", ("hipcub::BLOCK_SCAN_WARP_SCANS", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::BLOCK_REDUCE_WARP_REDUCTIONS", ("hipcub::BLOCK_REDUCE_WARP_REDUCTIONS", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::BLOCK_STORE_WARP_TRANSPOSE", ("hipcub::BLOCK_STORE_WARP_TRANSPOSE", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::BLOCK_LOAD_DIRECT", ("hipcub::BLOCK_LOAD_DIRECT", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::BLOCK_STORE_DIRECT", ("hipcub::BLOCK_STORE_DIRECT", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        (
+            "cub::BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY",
+            ("hipcub::BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY", CONV_SPECIAL_FUNC, API_RUNTIME)
+        ),
+        ("cub::BlockReduce", ("hipcub::BlockReduce", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::BlockScan", ("hipcub::BlockScan", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::BlockLoad", ("hipcub::BlockLoad", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::BlockStore", ("hipcub::BlockStore", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::BlockRakingLayout", ("hipcub::BlockRakingLayout", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::BlockRadixSort", ("hipcub::BlockRadixSort", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::Uninitialized", ("hipcub::Uninitialized", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::RowMajorTid", ("hipcub::RowMajorTid", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::CachingDeviceAllocator", ("hipcub::CachingDeviceAllocator", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::CountingInputIterator", ("hipcub::CountingInputIterator", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::DeviceRadixSort", ("hipcub::DeviceRadixSort", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::DeviceReduce", ("hipcub::DeviceReduce", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::DeviceRunLengthEncode", ("hipcub::DeviceRunLengthEncode", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::DeviceScan", ("hipcub::DeviceScan", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::DeviceSegmentedRadixSort", ("hipcub::DeviceSegmentedRadixSort", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::DeviceSegmentedReduce", ("hipcub::DeviceSegmentedReduce", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::DeviceSelect", ("hipcub::DeviceSelect", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::FpLimits", ("hipcub::FpLimits", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::KeyValuePair", ("hipcub::KeyValuePair", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::Max", ("hipcub::Max", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::Min", ("hipcub::Min", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::Sum", ("hipcub::Sum", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::Log2", ("hipcub::Log2", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::LaneId", ("hipcub::LaneId", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::WarpMask", ("hipcub::WarpMask", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::ShuffleIndex", ("hipcub::ShuffleIndex", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::ShuffleDown", ("hipcub::ShuffleDown", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::ArgIndexInputIterator", ("hipcub::ArgIndexInputIterator", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::TransformInputIterator", ("hipcub::TransformInputIterator", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::WarpReduce", ("hipcub::WarpReduce", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("cub::CTA_SYNC", ("hipcub::CTA_SYNC", CONV_SPECIAL_FUNC, API_RUNTIME)),
+        ("nvtxMark", ("roctxMark", CONV_OTHER, API_ROCTX)),
+        ("nvtxMarkA", ("roctxMarkA", CONV_OTHER, API_ROCTX)),
+        ("nvtxRangePushA", ("roctxRangePushA", CONV_OTHER, API_ROCTX)),
+        ("nvtxRangePop", ("roctxRangePop", CONV_OTHER, API_ROCTX)),
+        ("nvtxRangeStartA", ("roctxRangeStartA", CONV_OTHER, API_ROCTX)),
+        ("nvtxRangeEnd", ("roctxRangeStop", CONV_OTHER, API_ROCTX)),
+        ("nvtxRangeId_t", ("int", CONV_OTHER, API_ROCTX)),
+        ("nvmlReturn_t", ("rsmi_status_t", CONV_OTHER, API_ROCMSMI)),
+        ("NVML_SUCCESS", ("RSMI_STATUS_SUCCESS", CONV_OTHER, API_ROCMSMI)),
+        ("NVML_P2P_CAPS_INDEX_READ", ("RSMI_STATUS_SUCCESS", CONV_OTHER, API_ROCMSMI)),
+        ("NVML_P2P_STATUS_OK", ("RSMI_STATUS_SUCCESS", CONV_OTHER, API_ROCMSMI)),
+        ("NVML_ERROR_INSUFFICIENT_SIZE", ("RSMI_STATUS_INSUFFICIENT_SIZE", CONV_OTHER, API_ROCMSMI)),
+        ("nvmlDevice_t", ("uint32_t", CONV_OTHER, API_ROCMSMI)),
+        ("nvmlGpuP2PStatus_t", ("bool", CONV_OTHER, API_ROCMSMI)),
+        ("nvmlProcessInfo_t", ("rsmi_process_info_t", CONV_OTHER, API_ROCMSMI)),
+        ("nvmlGpuP2PCapsIndex_t", ("uint32_t", CONV_OTHER, API_ROCMSMI)),
+    ]
+)
+
+CUDA_SPECIAL_MAP = collections.OrderedDict(
+    [
+        # SPARSE
+        ("cusparseStatus_t", ("hipsparseStatus_t", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseHandle_t", ("hipsparseHandle_t", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cuComplex", ("hipComplex", CONV_TYPE, API_SPECIAL)),
+        ("cuDoubleComplex", ("hipDoubleComplex", CONV_TYPE, API_SPECIAL)),
+        (
+            "CUSPARSE_POINTER_MODE_HOST",
+            ("HIPSPARSE_POINTER_MODE_HOST", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        ("cusparseOperation_t", ("hipsparseOperation_t", CONV_TYPE, API_SPECIAL)),
+        (
+            "cusparseCreateMatDescr",
+            ("hipsparseCreateMatDescr", CONV_MATH_FUNC, API_SPECIAL),
+        ),
+        ("cusparseCreate", ("hipsparseCreate", CONV_MATH_FUNC, API_SPECIAL)),
+        (
+            "cusparseDestroyMatDescr",
+            ("hipsparseDestroyMatDescr", CONV_MATH_FUNC, API_SPECIAL),
+        ),
+        ("cusparseDestroy", ("hipsparseDestroy", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseXcoo2csr", ("hipsparseXcoo2csr", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseMatDescr_t", ("hipsparseMatDescr_t", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDiagType_t", ("hipsparseDiagType_t", CONV_TYPE, API_SPECIAL)),
+        ("CUSPARSE_DIAG_TYPE_UNIT", ("HIPSPARSE_DIAG_TYPE_UNIT", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_DIAG_TYPE_NON_UNIT", ("HIPSPARSE_DIAG_TYPE_NON_UNIT", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("cusparseSetMatDiagType", ("hipsparseSetMatDiagType", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseFillMode_t", ("hipsparseFillMode_t", CONV_TYPE, API_SPECIAL)),
+        ("CUSPARSE_FILL_MODE_UPPER", ("HIPSPARSE_FILL_MODE_UPPER", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_FILL_MODE_LOWER", ("HIPSPARSE_FILL_MODE_LOWER", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("cusparseSetMatFillMode", ("hipsparseSetMatFillMode", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDirection_t", ("hipsparseDirection_t", CONV_TYPE, API_SPECIAL)),
+        ("CUSPARSE_DIRECTION_ROW", ("HIPSPARSE_DIRECTION_ROW", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_DIRECTION_COLUMN", ("HIPSPARSE_DIRECTION_COLUMN", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("cusparseSolvePolicy_t", ("hipsparseSolvePolicy_t", CONV_TYPE, API_SPECIAL)),
+        ("CUSPARSE_SOLVE_POLICY_NO_LEVEL", ("HIPSPARSE_SOLVE_POLICY_NO_LEVEL", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_SOLVE_POLICY_USE_LEVEL", ("HIPSPARSE_SOLVE_POLICY_USE_LEVEL", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("cusparseCreateBsrsv2Info", ("hipsparseCreateBsrsv2Info", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCreateBsrsm2Info", ("hipsparseCreateBsrsm2Info", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDestroyBsrsv2Info", ("hipsparseDestroyBsrsv2Info", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDestroyBsrsm2Info", ("hipsparseDestroyBsrsm2Info", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSbsrmm", ("hipsparseSbsrmm", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDbsrmm", ("hipsparseDbsrmm", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCbsrmm", ("hipsparseCbsrmm", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZbsrmm", ("hipsparseZbsrmm", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSbsrmv", ("hipsparseSbsrmv", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDbsrmv", ("hipsparseDbsrmv", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCbsrmv", ("hipsparseCbsrmv", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZbsrmv", ("hipsparseZbsrmv", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSbsrsv2_bufferSize", ("hipsparseSbsrsv2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDbsrsv2_bufferSize", ("hipsparseDbsrsv2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCbsrsv2_bufferSize", ("hipsparseCbsrsv2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZbsrsv2_bufferSize", ("hipsparseZbsrsv2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSbsrsv2_analysis", ("hipsparseSbsrsv2_analysis", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDbsrsv2_analysis", ("hipsparseDbsrsv2_analysis", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCbsrsv2_analysis", ("hipsparseCbsrsv2_analysis", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZbsrsv2_analysis", ("hipsparseZbsrsv2_analysis", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSbsrsv2_solve", ("hipsparseSbsrsv2_solve", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDbsrsv2_solve", ("hipsparseDbsrsv2_solve", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCbsrsv2_solve", ("hipsparseCbsrsv2_solve", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZbsrsv2_solve", ("hipsparseZbsrsv2_solve", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSbsrsm2_bufferSize", ("hipsparseSbsrsm2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDbsrsm2_bufferSize", ("hipsparseDbsrsm2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCbsrsm2_bufferSize", ("hipsparseCbsrsm2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZbsrsm2_bufferSize", ("hipsparseZbsrsm2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSbsrsm2_analysis", ("hipsparseSbsrsm2_analysis", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDbsrsm2_analysis", ("hipsparseDbsrsm2_analysis", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCbsrsm2_analysis", ("hipsparseCbsrsm2_analysis", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZbsrsm2_analysis", ("hipsparseZbsrsm2_analysis", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSbsrsm2_solve", ("hipsparseSbsrsm2_solve", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDbsrsm2_solve", ("hipsparseDbsrsm2_solve", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCbsrsm2_solve", ("hipsparseCbsrsm2_solve", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZbsrsm2_solve", ("hipsparseZbsrsm2_solve", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseScsrmm2", ("hipsparseScsrmm2", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDcsrmm2", ("hipsparseDcsrmm2", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCcsrmm2", ("hipsparseCcsrmm2", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZcsrmm2", ("hipsparseZcsrmm2", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseScsrmm", ("hipsparseScsrmm", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDcsrmm", ("hipsparseDcsrmm", CONV_MATH_FUNC, API_SPECIAL)),
+        (
+            "cusparseXcsrsort_bufferSizeExt",
+            ("hipsparseXcsrsort_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL),
+        ),
+        ("cusparseCreateCsrgemm2Info", ("hipsparseCreateCsrgemm2Info", CONV_MATH_FUNC, API_SPECIAL)),
+        (
+            "cusparseDestroyCsrgemm2Info",
+            ("hipsparseDestroyCsrgemm2Info", CONV_MATH_FUNC, API_SPECIAL),
+        ),
+        ("cusparseXcsrgemm2Nnz", ("hipsparseXcsrgemm2Nnz", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDcsrgemm2_bufferSizeExt", ("hipsparseDcsrgemm2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseScsrgemm2_bufferSizeExt", ("hipsparseScsrgemm2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDcsrgemm2", ("hipsparseDcsrgemm2", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseScsrgemm2", ("hipsparseScsrgemm2", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSetPointerMode", ("hipsparseSetPointerMode", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseXcsrgeam2Nnz", ("hipsparseXcsrgeam2Nnz", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseScsrgeam2_bufferSizeExt", ("hipsparseScsrgeam2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDcsrgeam2_bufferSizeExt", ("hipsparseDcsrgeam2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCcsrgeam2_bufferSizeExt", ("hipsparseCcsrgeam2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZcsrgeam2_bufferSizeExt", ("hipsparseZcsrgeam2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseScsrgeam2", ("hipsparseScsrgeam2", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDcsrgeam2", ("hipsparseDcsrgeam2", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCcsrgeam2", ("hipsparseCcsrgeam2", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseZcsrgeam2", ("hipsparseZcsrgeam2", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseXcsrsort", ("hipsparseXcsrsort", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseXbsrsm2_zeroPivot", ("hipsparseXbsrsm2_zeroPivot", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseXbsrsv2_zeroPivot", ("hipsparseXbsrsv2_zeroPivot", CONV_MATH_FUNC, API_SPECIAL)),
+        (
+            "cusparseXcoosort_bufferSizeExt",
+            ("hipsparseXcoosort_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL),
+        ),
+        (
+            "cusparseXcoosortByRow",
+            ("hipsparseXcoosortByRow", CONV_MATH_FUNC, API_SPECIAL),
+        ),
+        ("cusparseSetStream", ("hipsparseSetStream", CONV_MATH_FUNC, API_SPECIAL)),
+        (
+            "cusparseCreateIdentityPermutation",
+            ("hipsparseCreateIdentityPermutation", CONV_MATH_FUNC, API_SPECIAL),
+        ),
+        (
+            "cusparseSetMatIndexBase",
+            ("hipsparseSetMatIndexBase", CONV_MATH_FUNC, API_SPECIAL),
+        ),
+        ("cusparseSetMatType", ("hipsparseSetMatType", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpMV", ("hipsparseSpMV", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpMV_bufferSize", ("hipsparseSpMV_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpMM", ("hipsparseSpMM", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpMM_bufferSize", ("hipsparseSpMM_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCreateDnMat", ("hipsparseCreateDnMat", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDnMatSetStridedBatch", ("hipsparseDnMatSetStridedBatch", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCsrSetStridedBatch", ("hipsparseCsrSetStridedBatch", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCreateDnVec", ("hipsparseCreateDnVec", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCreateCsr", ("hipsparseCreateCsr", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDestroyDnMat", ("hipsparseDestroyDnMat", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDestroyDnVec", ("hipsparseDestroyDnVec", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDestroySpMat", ("hipsparseDestroySpMat", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpGEMM_destroyDescr", ("hipsparseSpGEMM_destroyDescr", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCreateCoo", ("hipsparseCreateCoo", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCreateCsr", ("hipsparseCreateCsr", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpGEMM_createDescr", ("hipsparseSpGEMM_createDescr", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseDnMatSetStridedBatch", ("hipsparseDnMatSetStridedBatch", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpGEMM_copy", ("hipsparseSpGEMM_copy", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSDDMM_bufferSize", ("hipsparseSDDMM_bufferSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSDDMM_preprocess", ("hipsparseSDDMM_preprocess", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSDDMM", ("hipsparseSDDMM", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpGEMM_compute", ("hipsparseSpGEMM_compute", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpGEMM_workEstimation", ("hipsparseSpGEMM_workEstimation", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpMatGetSize", ("hipsparseSpMatGetSize", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseCsrSetPointers", ("hipsparseCsrSetPointers", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusparseSpMVAlg_t", ("hipsparseSpMVAlg_t", CONV_TYPE, API_SPECIAL)),
+        ("cusparseSpMMAlg_t", ("hipsparseSpMMAlg_t", CONV_TYPE, API_SPECIAL)),
+        ("cusparseIndexType_t", ("hipsparseIndexType_t", CONV_TYPE, API_SPECIAL)),
+        # Unsupported ("cusparseMatDescr", ("hipsparseMatDescr", CONV_TYPE, API_SPECIAL)),
+        # Unsupported ("cusparseDnMatDescr", ("hipsparseDnMatDescr", CONV_TYPE, API_SPECIAL)),
+        # Unsupported ("cusparseDnVecDescr", ("hipsparseDnVecDescr", CONV_TYPE, API_SPECIAL)),
+        # Unsupported ("cusparseSpMatDescr", ("hipsparseSpMatDescr", CONV_TYPE, API_SPECIAL)),
+        # Unsupported ("cusparseSpGEMMDescr", ("hipsparseSpGEMMDescr", CONV_TYPE, API_SPECIAL)),
+        ("cusparseDnMatDescr_t", ("hipsparseDnMatDescr_t", CONV_TYPE, API_SPECIAL)),
+        ("cusparseDnVecDescr_t", ("hipsparseDnVecDescr_t", CONV_TYPE, API_SPECIAL)),
+        ("cusparseSpMatDescr_t", ("hipsparseSpMatDescr_t", CONV_TYPE, API_SPECIAL)),
+        ("cusparseSpGEMMDescr_t", ("hipsparseSpGEMMDescr_t", CONV_TYPE, API_SPECIAL)),
+        ("CUSPARSE_INDEX_32I", ("HIPSPARSE_INDEX_32I", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_INDEX_64I", ("HIPSPARSE_INDEX_64I", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_ORDER_COL", ("HIPSPARSE_ORDER_COLUMN", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_MV_ALG_DEFAULT", ("HIPSPARSE_MV_ALG_DEFAULT", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_MM_ALG_DEFAULT", ("HIPSPARSE_MM_ALG_DEFAULT", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_SPMM_COO_ALG1", ("HIPSPARSE_SPMM_COO_ALG1", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_SPMM_COO_ALG2", ("HIPSPARSE_SPMM_COO_ALG2", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_COOMV_ALG", ("HIPSPARSE_COOMV_ALG", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_SPMM_CSR_ALG1", ("HIPSPARSE_CSRMM_ALG1", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_SPGEMM_DEFAULT", ("HIPSPARSE_SPGEMM_DEFAULT", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSPARSE_SDDMM_ALG_DEFAULT", ("HIPSPARSE_SDDMM_ALG_DEFAULT", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        (
+            "CUSPARSE_STATUS_SUCCESS",
+            ("HIPSPARSE_STATUS_SUCCESS", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_STATUS_NOT_INITIALIZED",
+            ("HIPSPARSE_STATUS_NOT_INITIALIZED", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_STATUS_ALLOC_FAILED",
+            ("HIPSPARSE_STATUS_ALLOC_FAILED", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_STATUS_INVALID_VALUE",
+            ("HIPSPARSE_STATUS_INVALID_VALUE", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_STATUS_MAPPING_ERROR",
+            ("HIPSPARSE_STATUS_MAPPING_ERROR", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_STATUS_EXECUTION_FAILED",
+            ("HIPSPARSE_STATUS_EXECUTION_FAILED", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_STATUS_INTERNAL_ERROR",
+            ("HIPSPARSE_STATUS_INTERNAL_ERROR", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED",
+            (
+                "HIPSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED",
+                CONV_NUMERIC_LITERAL,
+                API_SPECIAL,
+            ),
+        ),
+        (
+            "CUSPARSE_STATUS_ARCH_MISMATCH",
+            ("HIPSPARSE_STATUS_ARCH_MISMATCH", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_STATUS_ZERO_PIVOT",
+            ("HIPSPARSE_STATUS_ZERO_PIVOT", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_OPERATION_TRANSPOSE",
+            ("HIPSPARSE_OPERATION_TRANSPOSE", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_OPERATION_NON_TRANSPOSE",
+            ("HIPSPARSE_OPERATION_NON_TRANSPOSE", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE",
+            (
+                "HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE",
+                CONV_NUMERIC_LITERAL,
+                API_SPECIAL,
+            ),
+        ),
+        (
+            "CUSPARSE_INDEX_BASE_ZERO",
+            ("HIPSPARSE_INDEX_BASE_ZERO", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_INDEX_BASE_ONE",
+            ("HIPSPARSE_INDEX_BASE_ONE", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUSPARSE_MATRIX_TYPE_GENERAL",
+            ("HIPSPARSE_MATRIX_TYPE_GENERAL", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        # SOLVER
+        ("cublasOperation_t", ("hipsolverOperation_t", CONV_TYPE, API_SPECIAL)),
+        ("CUBLAS_OP_N", ("HIPSOLVER_OP_N", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        (
+            "CUBLAS_OP_T",
+            ("HIPSOLVER_OP_T", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUBLAS_OP_C",
+            ("HIPSOLVER_OP_C", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        ("cublasFillMode_t", ("hipsolverFillMode_t", CONV_TYPE, API_SPECIAL)),
+        (
+            "CUBLAS_FILL_MODE_LOWER",
+            ("HIPSOLVER_FILL_MODE_LOWER", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        (
+            "CUBLAS_FILL_MODE_UPPER",
+            ("HIPSOLVER_FILL_MODE_UPPER", CONV_NUMERIC_LITERAL, API_SPECIAL),
+        ),
+        ("cublasSideMode_t", ("hipsolverSideMode_t", CONV_TYPE, API_SPECIAL)),
+        ("CUBLAS_SIDE_LEFT", ("HIPSOLVER_SIDE_LEFT", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUBLAS_SIDE_RIGHT", ("HIPSOLVER_SIDE_RIGHT", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+
+        ("cusolverEigMode_t", ("hipsolverEigMode_t", CONV_TYPE, API_SPECIAL)),
+        ("CUSOLVER_EIG_MODE_VECTOR", ("HIPSOLVER_EIG_MODE_VECTOR", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+        ("CUSOLVER_EIG_MODE_NOVECTOR", ("HIPSOLVER_EIG_MODE_NOVECTOR", CONV_NUMERIC_LITERAL, API_SPECIAL)),
+
+        ("syevjInfo_t", ("hipsolverSyevjInfo_t", CONV_TYPE, API_SPECIAL)),
+        ("cusolverDnCreateSyevjInfo", ("hipsolverDnCreateSyevjInfo", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusolverDnXsyevjSetSortEig", ("hipsolverDnXsyevjSetSortEig", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusolverDnDestroySyevjInfo", ("hipsolverDnDestroySyevjInfo", CONV_MATH_FUNC, API_SPECIAL)),
+
+        ("gesvdjInfo_t", ("hipsolverGesvdjInfo_t", CONV_TYPE, API_SPECIAL)),
+        ("cusolverDnCreateGesvdjInfo", ("hipsolverDnCreateGesvdjInfo", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusolverDnXgesvdjSetSortEig", ("hipsolverDnXgesvdjSetSortEig", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusolverDnDestroyGesvdjInfo", ("hipsolverDnDestroyGesvdjInfo", CONV_MATH_FUNC, API_SPECIAL)),
+
+        ("cusolverDnHandle_t", ("hipsolverDnHandle_t", CONV_TYPE, API_SPECIAL)),
+        ("cusolverDnCreate", ("hipsolverDnCreate", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusolverDnSetStream", ("hipsolverDnSetStream", CONV_MATH_FUNC, API_SPECIAL)),
+        ("cusolverDnDestroy", ("hipsolverDnDestroy", CONV_MATH_FUNC, API_SPECIAL)),
+
+        # from aten/src/ATen/native/hip/linalg/HIPSolver.cpp
+        ('cusolverDnParams_t', ('hipsolverDnParams_t', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgeqrf', ('hipsolverDnCgeqrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgeqrf_bufferSize', ('hipsolverDnCgeqrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgesvd', ('hipsolverDnCgesvd', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgesvd_bufferSize', ('hipsolverDnCgesvd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgesvdj', ('hipsolverDnCgesvdj', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgesvdjBatched', ('hipsolverDnCgesvdjBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgesvdjBatched_bufferSize', ('hipsolverDnCgesvdjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgesvdj_bufferSize', ('hipsolverDnCgesvdj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgetrf', ('hipsolverDnCgetrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgetrf_bufferSize', ('hipsolverDnCgetrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgetrs', ('hipsolverDnCgetrs', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCheevd', ('hipsolverDnCheevd', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCheevd_bufferSize', ('hipsolverDnCheevd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCheevj', ('hipsolverDnCheevj', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCheevjBatched', ('hipsolverDnCheevjBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCheevjBatched_bufferSize', ('hipsolverDnCheevjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCheevj_bufferSize', ('hipsolverDnCheevj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCpotrf', ('hipsolverDnCpotrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCpotrfBatched', ('hipsolverDnCpotrfBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCpotrf_bufferSize', ('hipsolverDnCpotrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCpotrs', ('hipsolverDnCpotrs', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCpotrsBatched', ('hipsolverDnCpotrsBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCungqr', ('hipsolverDnCungqr', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCungqr_bufferSize', ('hipsolverDnCungqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCunmqr', ('hipsolverDnCunmqr', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCunmqr_bufferSize', ('hipsolverDnCunmqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgeqrf', ('hipsolverDnDgeqrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgeqrf_bufferSize', ('hipsolverDnDgeqrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgesvd', ('hipsolverDnDgesvd', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgesvd_bufferSize', ('hipsolverDnDgesvd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgesvdj', ('hipsolverDnDgesvdj', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgesvdjBatched', ('hipsolverDnDgesvdjBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgesvdjBatched_bufferSize', ('hipsolverDnDgesvdjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgesvdj_bufferSize', ('hipsolverDnDgesvdj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgetrf', ('hipsolverDnDgetrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgetrf_bufferSize', ('hipsolverDnDgetrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgetrs', ('hipsolverDnDgetrs', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDorgqr', ('hipsolverDnDorgqr', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDorgqr_bufferSize', ('hipsolverDnDorgqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDormqr', ('hipsolverDnDormqr', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDormqr_bufferSize', ('hipsolverDnDormqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDpotrf', ('hipsolverDnDpotrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDpotrfBatched', ('hipsolverDnDpotrfBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDpotrf_bufferSize', ('hipsolverDnDpotrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDpotrs', ('hipsolverDnDpotrs', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDpotrsBatched', ('hipsolverDnDpotrsBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDsyevd', ('hipsolverDnDsyevd', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDsyevd_bufferSize', ('hipsolverDnDsyevd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDsyevj', ('hipsolverDnDsyevj', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDsyevjBatched', ('hipsolverDnDsyevjBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDsyevjBatched_bufferSize', ('hipsolverDnDsyevjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDsyevj_bufferSize', ('hipsolverDnDsyevj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgeqrf', ('hipsolverDnSgeqrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgeqrf_bufferSize', ('hipsolverDnSgeqrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgesvd', ('hipsolverDnSgesvd', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgesvd_bufferSize', ('hipsolverDnSgesvd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgesvdj', ('hipsolverDnSgesvdj', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgesvdjBatched', ('hipsolverDnSgesvdjBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgesvdjBatched_bufferSize', ('hipsolverDnSgesvdjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgesvdj_bufferSize', ('hipsolverDnSgesvdj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgetrf', ('hipsolverDnSgetrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgetrf_bufferSize', ('hipsolverDnSgetrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSgetrs', ('hipsolverDnSgetrs', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSorgqr', ('hipsolverDnSorgqr', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSorgqr_bufferSize', ('hipsolverDnSorgqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSormqr', ('hipsolverDnSormqr', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSormqr_bufferSize', ('hipsolverDnSormqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSpotrf', ('hipsolverDnSpotrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSpotrfBatched', ('hipsolverDnSpotrfBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSpotrf_bufferSize', ('hipsolverDnSpotrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSpotrs', ('hipsolverDnSpotrs', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSpotrsBatched', ('hipsolverDnSpotrsBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSsyevd', ('hipsolverDnSsyevd', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSsyevd_bufferSize', ('hipsolverDnSsyevd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSsyevj', ('hipsolverDnSsyevj', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSsyevjBatched', ('hipsolverDnSsyevjBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSsyevjBatched_bufferSize', ('hipsolverDnSsyevjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSsyevj_bufferSize', ('hipsolverDnSsyevj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnXgeqrf', ('hipsolverDnXgeqrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnXgeqrf_bufferSize', ('hipsolverDnXgeqrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnXpotrf', ('hipsolverDnXpotrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnXpotrf_bufferSize', ('hipsolverDnXpotrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnXpotrs', ('hipsolverDnXpotrs', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnXsyevd', ('hipsolverDnXsyevd', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnXsyevd_bufferSize', ('hipsolverDnXsyevd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgeqrf', ('hipsolverDnZgeqrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgeqrf_bufferSize', ('hipsolverDnZgeqrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgesvd', ('hipsolverDnZgesvd', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgesvd_bufferSize', ('hipsolverDnZgesvd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgesvdj', ('hipsolverDnZgesvdj', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgesvdjBatched', ('hipsolverDnZgesvdjBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgesvdjBatched_bufferSize', ('hipsolverDnZgesvdjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgesvdj_bufferSize', ('hipsolverDnZgesvdj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgetrf', ('hipsolverDnZgetrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgetrf_bufferSize', ('hipsolverDnZgetrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgetrs', ('hipsolverDnZgetrs', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZheevd', ('hipsolverDnZheevd', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZheevd_bufferSize', ('hipsolverDnZheevd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZheevj', ('hipsolverDnZheevj', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZheevjBatched', ('hipsolverDnZheevjBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZheevjBatched_bufferSize', ('hipsolverDnZheevjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZheevj_bufferSize', ('hipsolverDnZheevj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZpotrf', ('hipsolverDnZpotrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZpotrfBatched', ('hipsolverDnZpotrfBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZpotrf_bufferSize', ('hipsolverDnZpotrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZpotrs', ('hipsolverDnZpotrs', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZpotrsBatched', ('hipsolverDnZpotrsBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZungqr', ('hipsolverDnZungqr', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZungqr_bufferSize', ('hipsolverDnZungqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZunmqr', ('hipsolverDnZunmqr', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZunmqr_bufferSize', ('hipsolverDnZunmqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+
+        # sytrf
+        ('cusolverDnDsytrf_bufferSize', ('hipsolverDnDsytrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSsytrf_bufferSize', ('hipsolverDnSsytrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZsytrf_bufferSize', ('hipsolverDnZsytrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCsytrf_bufferSize', ('hipsolverDnCsytrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDsytrf', ('hipsolverDnDsytrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnSsytrf', ('hipsolverDnSsytrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZsytrf', ('hipsolverDnZsytrf', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCsytrf', ('hipsolverDnCsytrf', CONV_MATH_FUNC, API_SPECIAL)),
+
+        # gesdva strided
+        (
+            'cusolverDnSgesvdaStridedBatched_bufferSize',
+            ('hipsolverDnSgesvdaStridedBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)
+        ),
+        (
+            'cusolverDnDgesvdaStridedBatched_bufferSize',
+            ('hipsolverDnDgesvdaStridedBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)
+        ),
+        (
+            'cusolverDnCgesvdaStridedBatched_bufferSize',
+            ('hipsolverDnCgesvdaStridedBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)
+        ),
+        (
+            'cusolverDnZgesvdaStridedBatched_bufferSize',
+            ('hipsolverDnZgesvdaStridedBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)
+        ),
+        ('cusolverDnSgesvdaStridedBatched', ('hipsolverDnSgesvdaStridedBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnDgesvdaStridedBatched', ('hipsolverDnDgesvdaStridedBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnCgesvdaStridedBatched', ('hipsolverDnCgesvdaStridedBatched', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnZgesvdaStridedBatched', ('hipsolverDnZgesvdaStridedBatched', CONV_MATH_FUNC, API_SPECIAL)),
+
+        # gesvdj SetXXX
+        ('cusolverDnXgesvdjSetTolerance', ('hipsolverDnXgesvdjSetTolerance', CONV_MATH_FUNC, API_SPECIAL)),
+        ('cusolverDnXgesvdjSetMaxSweeps', ('hipsolverDnXgesvdjSetMaxSweeps', CONV_MATH_FUNC, API_SPECIAL)),
+    ]
+)
+
+PYTORCH_SPECIFIC_MAPPINGS = collections.OrderedDict(
+    [
+        ("USE_CUDA", ("USE_ROCM", API_PYTORCH)),
+        ("TORCH_CUDA_CPP_API", ("TORCH_HIP_CPP_API", API_PYTORCH)),
+        ("TORCH_CUDA_CU_API", ("TORCH_HIP_API", API_PYTORCH)),
+        ("CUDA_VERSION", ("TORCH_HIP_VERSION", API_PYTORCH)),
+        ("cudaHostAllocator", ("hipHostAllocator", API_PYTORCH)),
+        ("cudaDeviceAllocator", ("hipDeviceAllocator", API_PYTORCH)),
+        ("define MAX_NUM_BLOCKS 200", ("define MAX_NUM_BLOCKS 64", API_PYTORCH)),
+        ("cuda::CUDAGuard", ("hip::HIPGuardMasqueradingAsCUDA", API_PYTORCH)),
+        ("CUDAGuard", ("HIPGuardMasqueradingAsCUDA", API_PYTORCH)),
+        (
+            "cuda::OptionalCUDAGuard",
+            ("hip::OptionalHIPGuardMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        ("OptionalCUDAGuard", ("OptionalHIPGuardMasqueradingAsCUDA", API_PYTORCH)),
+        (
+            "cuda::CUDAStreamGuard",
+            ("hip::HIPStreamGuardMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        ("CUDAStreamGuard", ("HIPStreamGuardMasqueradingAsCUDA", API_PYTORCH)),
+        (
+            "cuda::OptionalCUDAStreamGuard",
+            ("hip::OptionalHIPStreamGuardMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        (
+            "OptionalCUDAStreamGuard",
+            ("OptionalHIPStreamGuardMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        (
+            "cuda::CUDAMultiStreamGuard",
+            ("hip::HIPMultiStreamGuardMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        (
+            "CUDAMultiStreamGuard",
+            ("HIPMultiStreamGuardMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        # Only get needs to be transformed this way; all the other ones can go
+        # straight to the normal versions hip::HIPCachingAllocator
+        (
+            "cuda::CUDACachingAllocator::get",
+            ("hip::HIPCachingAllocatorMasqueradingAsCUDA::get", API_PYTORCH),
+        ),
+        (
+            "CUDACachingAllocator::get",
+            ("HIPCachingAllocatorMasqueradingAsCUDA::get", API_PYTORCH),
+        ),
+        (
+            "cuda::CUDACachingAllocator::recordStream",
+            (
+                "hip::HIPCachingAllocatorMasqueradingAsCUDA::recordStreamMasqueradingAsCUDA",
+                API_PYTORCH,
+            ),
+        ),
+        (
+            "CUDACachingAllocator::recordStream",
+            (
+                "HIPCachingAllocatorMasqueradingAsCUDA::recordStreamMasqueradingAsCUDA",
+                API_PYTORCH,
+            ),
+        ),
+        (
+            "cuda::CUDAAllocator::recordStream",
+            (
+                "hip::HIPCachingAllocatorMasqueradingAsCUDA::recordStreamMasqueradingAsCUDA",
+                API_PYTORCH,
+            ),
+        ),
+        (
+            "CUDAAllocator::recordStream",
+            (
+                "HIPCachingAllocatorMasqueradingAsCUDA::recordStreamMasqueradingAsCUDA",
+                API_PYTORCH,
+            ),
+        ),
+        ("cuda::CUDAStream", ("hip::HIPStreamMasqueradingAsCUDA", API_PYTORCH)),
+        ("CUDAStream", ("HIPStreamMasqueradingAsCUDA", API_PYTORCH)),
+        (
+            "cuda::getStreamFromPool",
+            ("hip::getStreamFromPoolMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        ("getStreamFromPool", ("getStreamFromPoolMasqueradingAsCUDA", API_PYTORCH)),
+        (
+            "cuda::getDefaultCUDAStream",
+            ("hip::getDefaultHIPStreamMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        (
+            "cuda::getStreamFromExternal",
+            ("hip::getStreamFromExternalMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        ("getStreamFromExternal", ("getStreamFromExternalMasqueradingAsCUDA", API_PYTORCH)),
+        (
+            "cuda::getDefaultCUDAStream",
+            ("hip::getDefaultHIPStreamMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        (
+            "getDefaultCUDAStream",
+            ("getDefaultHIPStreamMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        (
+            "cuda::getCurrentCUDAStream",
+            ("hip::getCurrentHIPStreamMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        (
+            "getCurrentCUDAStream",
+            ("getCurrentHIPStreamMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        (
+            "cuda::setCurrentCUDAStream",
+            ("hip::setCurrentHIPStreamMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        (
+            "setCurrentCUDAStream",
+            ("setCurrentHIPStreamMasqueradingAsCUDA", API_PYTORCH),
+        ),
+        (
+            "ATen/cudnn/Handle.h",
+            ("ATen/miopen/Handle.h", API_PYTORCH),
+        ),
+        # TODO: Undo this special-case; see the header for motivation behind this
+        # hack.  It's VERY important this is only applied to PyTorch HIPify.
+        (
+            "c10/cuda/CUDAGuard.h",
+            ("ATen/hip/impl/HIPGuardImplMasqueradingAsCUDA.h", API_PYTORCH),
+        ),
+        (
+            "c10/cuda/CUDACachingAllocator.h",
+            ("ATen/hip/impl/HIPCachingAllocatorMasqueradingAsCUDA.h", API_PYTORCH),
+        ),
+        (
+            "c10/cuda/CUDAStream.h",
+            ("ATen/hip/impl/HIPStreamMasqueradingAsCUDA.h", API_PYTORCH),
+        ),
+        ("gloo/cuda.h", ("gloo/hip.h", API_PYTORCH)),
+        (
+            "gloo/cuda_allreduce_halving_doubling.h",
+            ("gloo/hip_allreduce_halving_doubling.h", API_PYTORCH),
+        ),
+        (
+            "gloo/cuda_allreduce_halving_doubling_pipelined.h",
+            ("gloo/hip_allreduce_halving_doubling_pipelined.h", API_PYTORCH),
+        ),
+        ("gloo/cuda_allreduce_ring.h", ("gloo/hip_allreduce_ring.h", API_PYTORCH)),
+        (
+            "gloo/cuda_broadcast_one_to_all.h",
+            ("gloo/hip_broadcast_one_to_all.h", API_PYTORCH),
+        ),
+        (
+            "gloo::CudaAllreduceHalvingDoublingPipelined",
+            ("gloo::HipAllreduceHalvingDoublingPipelined", API_PYTORCH),
+        ),
+        ("gloo::CudaBroadcastOneToAll", ("gloo::HipBroadcastOneToAll", API_PYTORCH)),
+        ("gloo::CudaHostWorkspace", ("gloo::HipHostWorkspace", API_PYTORCH)),
+        ("gloo::CudaDeviceWorkspace", ("gloo::HipDeviceWorkspace", API_PYTORCH)),
+        ("CUDNN_RNN_RELU", ("miopenRNNRELU", API_PYTORCH)),
+        ("CUDNN_RNN_TANH", ("miopenRNNTANH", API_PYTORCH)),
+        ("CUDNN_LSTM", ("miopenLSTM", API_PYTORCH)),
+        ("CUDNN_GRU", ("miopenGRU", API_PYTORCH)),
+        ("cudnnRNNMode_t", ("miopenRNNMode_t", API_PYTORCH)),
+        ("magma_queue_create_from_cuda", ("magma_queue_create_from_hip", API_PYTORCH)),
+    ]
+)
+
+CAFFE2_SPECIFIC_MAPPINGS = collections.OrderedDict(
+    [
+        ("PYTORCH_NO_CUDA_MEMORY_CACHING", ("PYTORCH_NO_CUDA_MEMORY_CACHING", API_CAFFE2)),
+        ("cuda_stream", ("hip_stream", API_CAFFE2)),
+        # if the header is a native hip folder (under hip directory),
+        # there is no need to add a hip path to it; the trie in hipify script
+        # takes this mapping order to forbid further replacement
+        ("/hip/", ("/hip/", API_CAFFE2)),
+        ("/context_gpu", ("/hip/context_gpu", API_CAFFE2)),
+        ("/common_gpu", ("/hip/common_gpu", API_CAFFE2)),
+        ("/cuda_nccl_gpu", ("/hip/hip_nccl_gpu", API_CAFFE2)),
+        ("/mixed_utils", ("/hip/mixed_utils", API_CAFFE2)),
+        ("/operator_fallback_gpu", ("/hip/operator_fallback_gpu", API_CAFFE2)),
+        (
+            "/spatial_batch_norm_op_impl",
+            ("/hip/spatial_batch_norm_op_impl", API_CAFFE2),
+        ),
+        (
+            "/recurrent_network_executor_gpu",
+            ("/hip/recurrent_network_executor_gpu", API_CAFFE2),
+        ),
+        (
+            "/generate_proposals_op_util_nms_gpu",
+            ("/hip/generate_proposals_op_util_nms_gpu", API_CAFFE2),
+        ),
+        ("/max_pool_with_index_gpu", ("/hip/max_pool_with_index_gpu", API_CAFFE2)),
+        ("/THCCachingAllocator_gpu", ("/hip/THCCachingAllocator_gpu", API_CAFFE2)),
+        ("/top_k_heap_selection", ("/hip/top_k_heap_selection", API_CAFFE2)),
+        ("/top_k_radix_selection", ("/hip/top_k_radix_selection", API_CAFFE2)),
+        ("/GpuAtomics", ("/hip/GpuAtomics", API_CAFFE2)),
+        ("/GpuDefs", ("/hip/GpuDefs", API_CAFFE2)),
+        ("/GpuScanUtils", ("/hip/GpuScanUtils", API_CAFFE2)),
+        ("/GpuBitonicSort", ("/hip/GpuBitonicSort", API_CAFFE2)),
+        ("/math/reduce.cuh", ("/math/hip/reduce.cuh", API_CAFFE2)),
+        ("/sgd/adagrad_fused_op_gpu.cuh", ("/sgd/hip/adagrad_fused_op_gpu.cuh", API_CAFFE2)),
+        ("/operators/segment_reduction_op_gpu.cuh", ("/operators/hip/segment_reduction_op_gpu.cuh", API_CAFFE2)),
+        ("/gather_op.cuh", ("/hip/gather_op.cuh", API_CAFFE2)),
+        ("caffe2/core/common_cudnn.h", ("caffe2/core/hip/common_miopen.h", API_CAFFE2)),
+        ("REGISTER_CUDA_OPERATOR", ("REGISTER_HIP_OPERATOR", API_CAFFE2)),
+        ("CUDA_1D_KERNEL_LOOP", ("HIP_1D_KERNEL_LOOP", API_CAFFE2)),
+        ("CUDAContext", ("HIPContext", API_CAFFE2)),
+        ("CAFFE_CUDA_NUM_THREADS", ("CAFFE_HIP_NUM_THREADS", API_CAFFE2)),
+        ("HasCudaGPU", ("HasHipGPU", API_CAFFE2)),
+        ("__expf", ("expf", API_CAFFE2)),
+        ("CUBLAS_ENFORCE", ("HIPBLAS_ENFORCE", API_CAFFE2)),
+        ("CUBLAS_CHECK", ("HIPBLAS_CHECK", API_CAFFE2)),
+        ("cublas_handle", ("hipblas_handle", API_CAFFE2)),
+        ("CURAND_ENFORCE", ("HIPRAND_ENFORCE", API_CAFFE2)),
+        ("CURAND_CHECK", ("HIPRAND_CHECK", API_CAFFE2)),
+        ("curandGenerateUniform", ("hiprandGenerateUniform", API_CAFFE2)),
+        ("curand_generator", ("hiprand_generator", API_CAFFE2)),
+        ("CaffeCudaGetDevice", ("CaffeHipGetDevice", API_CAFFE2)),
+        # do not rename CUDA_KERNEL_ASSERT, lazyInitCUDA in caffe2 sources
+        # the ordered dict guarantees this pattern will match first, before "CUDA"
+        ("CUDA_KERNEL_ASSERT", ("CUDA_KERNEL_ASSERT", API_CAFFE2)),
+        ("lazyInitCUDA", ("lazyInitCUDA", API_CAFFE2)),
+        ("CUDA_VERSION", ("TORCH_HIP_VERSION", API_CAFFE2)),
+        ("CUDA", ("HIP", API_CAFFE2)),
+        ("Cuda", ("Hip", API_CAFFE2)),
+        ("cuda_", ("hip_", API_CAFFE2)),
+        ("_cuda", ("_hip", API_CAFFE2)),
+        ("CUDNN", ("MIOPEN", API_CAFFE2)),
+        ("CuDNN", ("MIOPEN", API_CAFFE2)),
+        ("cudnn", ("miopen", API_CAFFE2)),
+        ("namespace cuda", ("namespace hip", API_CAFFE2)),
+        ("cuda::CUDAGuard", ("hip::HIPGuard", API_CAFFE2)),
+        ("cuda::OptionalCUDAGuard", ("hip::OptionalHIPGuard", API_CAFFE2)),
+        ("cuda::CUDAStreamGuard", ("hip::HIPStreamGuard", API_CAFFE2)),
+        ("cuda::OptionalCUDAStreamGuard", ("hip::OptionalHIPStreamGuard", API_CAFFE2)),
+        ("c10/cuda/CUDAGuard.h", ("c10/hip/HIPGuard.h", API_CAFFE2)),
+        ("gloo/cuda", ("gloo/hip", API_CAFFE2)),
+    ]
+)
+
+# We must tread very carefully here.  Blanket conversions like are done
+# in CAFFE2_SPECIFIC_MAPPINGS are not presently supported on PyTorch,
+# because a regex for CUDA will also match a filename like CUDAGuard.h,
+# but the HIPIFY script doesn't presently move the file and so the substitution
+# will be invalid.  Instead, we specifically list out every identifier
+# and file from c10/cuda which may be used externally, and do substitutions this
+# way.
+#
+# NB: if you want a transformation to ONLY apply to the c10/ directory,
+# put it as API_CAFFE2
+C10_MAPPINGS = collections.OrderedDict(
+    [
+        ("CUDA_VERSION", ("TORCH_HIP_VERSION", API_PYTORCH)),
+        ("CUDA_LAUNCH_BLOCKING=1", ("AMD_SERIALIZE_KERNEL=3", API_C10)),
+        ("CUDA_LAUNCH_BLOCKING", ("AMD_SERIALIZE_KERNEL", API_C10)),
+        ("cuda::compat::", ("hip::compat::", API_C10)),
+        ("c10/cuda/CUDAAlgorithm.h", ("c10/hip/HIPAlgorithm.h", API_C10)),
+        ("c10/cuda/CUDADeviceAssertion.h", ("c10/hip/HIPDeviceAssertion.h", API_C10)),
+        ("c10/cuda/CUDADeviceAssertionHost.h", ("c10/hip/HIPDeviceAssertionHost.h", API_C10)),
+        ("c10/cuda/CUDAException.h", ("c10/hip/HIPException.h", API_C10)),
+        ("c10/cuda/CUDAMacros.h", ("c10/hip/HIPMacros.h", API_C10)),
+        ("c10/cuda/CUDAMathCompat.h", ("c10/hip/HIPMathCompat.h", API_C10)),
+        ("c10/cuda/CUDAFunctions.h", ("c10/hip/HIPFunctions.h", API_C10)),
+        ("c10/cuda/CUDAMiscFunctions.h", ("c10/hip/HIPMiscFunctions.h", API_C10)),
+        ("c10/cuda/CUDAStream.h", ("c10/hip/HIPStream.h", API_C10)),
+        ("c10/cuda/CUDAGraphsC10Utils.h", ("c10/hip/HIPGraphsC10Utils.h", API_C10)),
+        ("c10/cuda/CUDAAllocatorConfig.h", ("c10/hip/HIPAllocatorConfig.h", API_C10)),
+        ("c10/cuda/CUDACachingAllocator.h", ("c10/hip/HIPCachingAllocator.h", API_C10)),
+        ("c10/cuda/impl/CUDATest.h", ("c10/hip/impl/HIPTest.h", API_C10)),
+        ("c10/cuda/impl/CUDAGuardImpl.h", ("c10/hip/impl/HIPGuardImpl.h", API_C10)),
+        (
+            "c10/cuda/impl/cuda_cmake_macros.h",
+            ("c10/hip/impl/hip_cmake_macros.h", API_C10),
+        ),
+        ("C10_CUDA_CHECK", ("C10_HIP_CHECK", API_C10)),
+        ("C10_CUDA_CHECK_WARN", ("C10_HIP_CHECK_WARN", API_C10)),
+        ("C10_CUDA_ERROR_HANDLED", ("C10_HIP_ERROR_HANDLED", API_C10)),
+        ("C10_CUDA_IGNORE_ERROR", ("C10_HIP_IGNORE_ERROR", API_C10)),
+        ("C10_CUDA_CLEAR_ERROR", ("C10_HIP_CLEAR_ERROR", API_C10)),
+        ("c10::cuda", ("c10::hip", API_C10)),
+        ("cuda::CUDAStream", ("hip::HIPStream", API_C10)),
+        ("CUDAStream", ("HIPStream", API_C10)),
+        # This substitution is not permissible, because there's another copy of this
+        # function in torch/cuda.h
+        # ("cuda::device_count", ("hip::device_count", API_C10)),
+        ("cuda::current_device", ("hip::current_device", API_C10)),
+        ("cuda::set_device", ("hip::set_device", API_C10)),
+        ("cuda::device_synchronize", ("hip::device_synchronize", API_C10)),
+        ("cuda::getStreamFromPool", ("hip::getStreamFromPool", API_C10)),
+        ("getStreamFromPool", ("getStreamFromPool", API_C10)),
+        ("cuda::getDefaultCUDAStream", ("hip::getDefaultHIPStream", API_C10)),
+        ("getDefaultCUDAStream", ("getDefaultHIPStream", API_C10)),
+        ("cuda::getCurrentCUDAStream", ("hip::getCurrentHIPStream", API_C10)),
+        ("getCurrentCUDAStream", ("getCurrentHIPStream", API_C10)),
+        ("cuda::get_cuda_check_prefix", ("hip::get_cuda_check_prefix", API_C10)),
+        ("cuda::setCurrentCUDAStream", ("hip::setCurrentHIPStream", API_C10)),
+        ("setCurrentCUDAStream", ("setCurrentHIPStream", API_C10)),
+        ("cuda::CUDACachingAllocator", ("hip::HIPCachingAllocator", API_C10)),
+        ("CUDACachingAllocator", ("HIPCachingAllocator", API_C10)),
+        ("cuda::CUDAAllocatorConfig", ("hip::HIPAllocatorConfig", API_C10)),
+        ("CUDAAllocatorConfig", ("HIPAllocatorConfig", API_C10)),
+        ("pinned_use_cuda_host_register", ("pinned_use_hip_host_register", API_C10)),
+        ("c10::cuda::CUDAAllocator", ("c10::hip::HIPAllocator", API_C10)),
+        ("cuda::CUDAAllocator", ("hip::HIPAllocator", API_C10)),
+        ("CUDAStreamCaptureModeGuard", ("HIPStreamCaptureModeGuard", API_C10)),
+        ("cuda::CUDAStreamCaptureModeGuard", ("cuda::HIPStreamCaptureModeGuard", API_C10)),
+        ("CUDAAllocator", ("HIPAllocator", API_C10)),
+        ("C10_CUDA_KERNEL_LAUNCH_CHECK", ("C10_HIP_KERNEL_LAUNCH_CHECK", API_C10))
+    ]
+)
+
+# NB: C10 mappings are more specific than Caffe2 mappings, so run them
+# first
+CUDA_TO_HIP_MAPPINGS = [
+    CUDA_IDENTIFIER_MAP,
+    CUDA_TYPE_NAME_MAP,
+    CUDA_INCLUDE_MAP,
+    CUDA_SPECIAL_MAP,
+    C10_MAPPINGS,
+    PYTORCH_SPECIFIC_MAPPINGS,
+    CAFFE2_SPECIFIC_MAPPINGS,
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cbdf6c7ecc04df5f0b8ef39f012c9d9588ad67e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py
@@ -0,0 +1,1176 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+""" The Python Hipify script.
+##
+# Copyright (c) 2015-2016 Advanced Micro Devices, Inc. All rights reserved.
+#               2017-2018 Advanced Micro Devices, Inc. and
+#                         Facebook Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+"""
+import argparse
+import fnmatch
+import re
+import shutil
+import sys
+import os
+
+from . import constants
+from .cuda_to_hip_mappings import CUDA_TO_HIP_MAPPINGS
+from .cuda_to_hip_mappings import MATH_TRANSPILATIONS
+
+from typing import Optional
+from collections.abc import Iterator
+from collections.abc import Mapping, Iterable
+from enum import Enum
+import functools
+import hashlib
+
+class CurrentState(Enum):
+    INITIALIZED = 1
+    DONE = 2
+
+class HipifyResult:
+    def __init__(self, current_state, hipified_path):
+        self.current_state = current_state
+        self.hipified_path = hipified_path
+        self.status = ""
+
+    def __str__(self):
+        return (f"HipifyResult:: current_state: {self.current_state}, hipified_path : {self.hipified_path}, status: {self.status}")
+
+HipifyFinalResult = dict[str, HipifyResult]
+HIPIFY_C_BREADCRUMB = "// !!! This is a file automatically generated by hipify!!!\n"
+HIPIFY_FINAL_RESULT: HipifyFinalResult = {}
+
+# Hardcode the PyTorch template map
+"""This dictionary provides the mapping from PyTorch kernel template types
+to their actual types."""
+PYTORCH_TEMPLATE_MAP = {"Dtype": "scalar_t", "T": "scalar_t"}
+
+__all__ = ['InputError', 'openf', 'bcolors', 'GeneratedFileCleaner', 'match_extensions', 'matched_files_iter',
+           'preprocess_file_and_save_result', 'compute_stats', 'add_dim3', 'processKernelLaunches', 'find_closure_group',
+           'find_bracket_group', 'find_parentheses_group', 'replace_math_functions', 'hip_header_magic', 'replace_extern_shared',
+           'get_hip_file_path', 'is_out_of_place', 'is_pytorch_file', 'is_cusparse_file', 'is_special_file', 'is_caffe2_gpu_file',
+           'is_caffe2_gpu_file', 'Trie', 'preprocessor', 'file_specific_replacement', 'file_add_header',
+           'fix_static_global_kernels', 'extract_arguments', 'str2bool', 'CurrentState', 'HipifyResult', 'hipify']
+
+
+class InputError(Exception):
+    # Exception raised for errors in the input.
+
+    def __init__(self, message):
+        super().__init__(message)
+        self.message = message
+
+    def __str__(self):
+        return f"Input error: {self.message}"
+
+
+def openf(filename, mode):
+    return open(filename, mode, errors='ignore')
+
+
+# Color coding for printing
+class bcolors:
+    HEADER = '\033[95m'
+    OKBLUE = '\033[94m'
+    OKGREEN = '\033[92m'
+    WARNING = '\033[93m'
+    FAIL = '\033[91m'
+    ENDC = '\033[0m'
+    BOLD = '\033[1m'
+    UNDERLINE = '\033[4m'
+
+
+# To the programmer, the output of hipify most likely are intermediates.
+# This class allows users of hipify to ask for a cleanup by running the
+# hipify and compilation in a with instantiating this context manager class
+# with keep_intermediates=False.
+# The main usecase is the cpp_extensions, specifically the load method.
+# It is a good idea to keep intermediates (in case of errors or to
+# not recompile unchanged files), but in cases where you don't want to
+# keep them (e.g. in the CI), this can be used to remove files.
+class GeneratedFileCleaner:
+    """Context Manager to clean up generated files"""
+    def __init__(self, keep_intermediates=False):
+        self.keep_intermediates = keep_intermediates
+        self.files_to_clean = set()
+        self.dirs_to_clean = []
+
+    def __enter__(self):
+        return self
+
+    def open(self, fn, *args, **kwargs):
+        if not os.path.exists(fn):
+            self.files_to_clean.add(os.path.abspath(fn))
+        return open(fn, *args, **kwargs)
+
+    def makedirs(self, dn, exist_ok=False):
+        parent, n = os.path.split(dn)
+        if not n:
+            parent, n = os.path.split(parent)
+        if parent and n and not os.path.exists(parent):
+            self.makedirs(parent, exist_ok=True)
+        if not os.path.isdir(dn) or not exist_ok:
+            os.mkdir(dn)
+            self.dirs_to_clean.append(os.path.abspath(dn))
+
+    def __exit__(self, type, value, traceback):
+        if not self.keep_intermediates:
+            for f in self.files_to_clean:
+                os.unlink(f)
+            for d in self.dirs_to_clean[::-1]:
+                os.rmdir(d)
+
+
+# Follow UNIX convention for paths to use '/' instead of '\\' on Windows
+def _to_unix_path(path: str) -> str:
+    return path.replace(os.sep, '/')
+
+def match_extensions(filename: str, extensions: Iterable) -> bool:
+    """Helper method to see if filename ends with certain extension"""
+    return any(filename.endswith(e) for e in extensions)
+
+
+def _fnmatch(filepath, patterns):
+    return any(fnmatch.fnmatch(filepath, pattern) for pattern in patterns)
+
+
+def matched_files_iter(
+        root_path: str,
+        includes: Iterable = (),
+        ignores: Iterable = (),
+        extensions: Iterable = (),
+        out_of_place_only: bool = False,
+        is_pytorch_extension: bool = False) -> Iterator[str]:
+
+    exact_matches = set(includes)
+
+    # This is a very rough heuristic; really, we want to avoid scanning
+    # any file which is not checked into source control, but this script
+    # needs to work even if you're in a Git or Hg checkout, so easier to
+    # just block the biggest time sinks that won't matter in the
+    # end.
+    for (abs_dirpath, dirs, filenames) in os.walk(root_path, topdown=True):
+        rel_dirpath = os.path.relpath(abs_dirpath, root_path)
+        if rel_dirpath == '.':
+            # Blah blah blah O(n) blah blah
+            if ".git" in dirs:
+                dirs.remove(".git")
+            if "build" in dirs:
+                dirs.remove("build")
+            if "third_party" in dirs:
+                dirs.remove("third_party")
+                dirs.append("third_party/nvfuser")
+        for filename in filenames:
+            filepath = _to_unix_path(os.path.join(abs_dirpath, filename))
+            rel_filepath = _to_unix_path(os.path.join(rel_dirpath, filename))
+            # We respect extensions, UNLESS you wrote the entire
+            # filename verbatim, in which case we always accept it
+            if (
+                _fnmatch(filepath, includes)
+                and (not _fnmatch(filepath, ignores))
+                and (match_extensions(filepath, extensions) or filepath in exact_matches)
+            ):
+                if not is_pytorch_extension:  # for pytorch extensions, consider all files
+                    if not is_pytorch_file(rel_filepath) and not is_caffe2_gpu_file(rel_filepath):
+                        continue
+                    if out_of_place_only and not is_out_of_place(rel_filepath):
+                        continue
+                yield filepath
+
+
+def preprocess_file_and_save_result(
+        output_directory: str,
+        filepath: str,
+        all_files: Iterable,
+        header_include_dirs: Iterable,
+        stats: dict[str, list],
+        hip_clang_launch: bool,
+        is_pytorch_extension: bool,
+        clean_ctx: GeneratedFileCleaner,
+        show_progress: bool) -> None:
+    fin_path = os.path.abspath(os.path.join(output_directory, filepath))
+    hipify_result = HipifyResult(current_state=CurrentState.INITIALIZED, hipified_path=fin_path)
+    HIPIFY_FINAL_RESULT[fin_path] = hipify_result
+    result = preprocessor(output_directory, filepath, all_files, header_include_dirs, stats,
+                          hip_clang_launch, is_pytorch_extension, clean_ctx, show_progress)
+
+    # Show what happened
+    if show_progress and "ignored" not in result.status:
+        print(
+            fin_path, "->",
+            result.hipified_path, result.status, flush=True)
+
+    HIPIFY_FINAL_RESULT[fin_path] = result
+
+
+def compute_stats(stats):
+    unsupported_calls = {cuda_call for (cuda_call, _filepath) in stats["unsupported_calls"]}
+
+    # Print the number of unsupported calls
+    print(f"Total number of unsupported CUDA function calls: {len(unsupported_calls):d}")
+
+    # Print the list of unsupported calls
+    print(", ".join(unsupported_calls))
+
+    # Print the number of kernel launches
+    print(f"\nTotal number of replaced kernel launches: {len(stats['kernel_launches']):d}")
+
+
+def add_dim3(kernel_string, cuda_kernel):
+    '''adds dim3() to the second and third arguments in the kernel launch'''
+    count = 0
+    closure = 0
+    kernel_string = kernel_string.replace("<<<", "").replace(">>>", "")
+    arg_locs: list[dict[str, int]] = [{} for _ in range(2)]
+    arg_locs[count]['start'] = 0
+    for ind, c in enumerate(kernel_string):
+        if count > 1:
+            break
+        if c == "(":
+            closure += 1
+        elif c == ")":
+            closure -= 1
+        if (c == "," or ind == len(kernel_string) - 1) and closure == 0:
+            arg_locs[count]['end'] = ind + (c != ",")
+            count += 1
+            if count < 2:
+                arg_locs[count]['start'] = ind + 1
+
+    first_arg_raw = kernel_string[arg_locs[0]['start']:arg_locs[0]['end'] + 1]
+    second_arg_raw = kernel_string[arg_locs[1]['start']:arg_locs[1]['end']]
+
+    first_arg_clean = kernel_string[arg_locs[0]['start']:arg_locs[0]['end']].replace("\n", "").strip(" ")
+    second_arg_clean = kernel_string[arg_locs[1]['start']:arg_locs[1]['end']].replace("\n", "").strip(" ")
+
+    first_arg_dim3 = f"dim3({first_arg_clean})"
+    second_arg_dim3 = f"dim3({second_arg_clean})"
+
+    first_arg_raw_dim3 = first_arg_raw.replace(first_arg_clean, first_arg_dim3)
+    second_arg_raw_dim3 = second_arg_raw.replace(second_arg_clean, second_arg_dim3)
+    cuda_kernel = cuda_kernel.replace(first_arg_raw + second_arg_raw, first_arg_raw_dim3 + second_arg_raw_dim3)
+    return cuda_kernel
+
+
+RE_KERNEL_LAUNCH = re.compile(r'([ ]+)(detail?)::[ ]+\\\n[ ]+')
+
+
+def processKernelLaunches(string, stats):
+    """ Replace the CUDA style Kernel launches with the HIP style kernel launches."""
+    # Concat the namespace with the kernel names. (Find cleaner way of doing this later).
+    string = RE_KERNEL_LAUNCH.sub(lambda inp: f"{inp.group(1)}{inp.group(2)}::", string)
+
+    def grab_method_and_template(in_kernel):
+        # The positions for relevant kernel components.
+        pos = {
+            "kernel_launch": {"start": in_kernel["start"], "end": in_kernel["end"]},
+            "kernel_name": {"start": -1, "end": -1},
+            "template": {"start": -1, "end": -1}
+        }
+
+        # Count for balancing template
+        count = {"<>": 0}
+
+        # Status for whether we are parsing a certain item.
+        START = 0
+        AT_TEMPLATE = 1
+        AFTER_TEMPLATE = 2
+        AT_KERNEL_NAME = 3
+
+        status = START
+
+        # Parse the string character by character
+        for i in range(pos["kernel_launch"]["start"] - 1, -1, -1):
+            char = string[i]
+
+            # Handle Templating Arguments
+            if status in (START, AT_TEMPLATE):
+                if char == ">":
+                    if status == START:
+                        status = AT_TEMPLATE
+                        pos["template"]["end"] = i
+                    count["<>"] += 1
+
+                if char == "<":
+                    count["<>"] -= 1
+                    if count["<>"] == 0 and (status == AT_TEMPLATE):
+                        pos["template"]["start"] = i
+                        status = AFTER_TEMPLATE
+
+            # Handle Kernel Name
+            if status != AT_TEMPLATE:
+                if string[i].isalnum() or string[i] in {'(', ')', '_', ':', '#'}:
+                    if status != AT_KERNEL_NAME:
+                        status = AT_KERNEL_NAME
+                        pos["kernel_name"]["end"] = i
+
+                    # Case: Kernel name starts the string.
+                    if i == 0:
+                        pos["kernel_name"]["start"] = 0
+
+                        # Finished
+                        return [(pos["kernel_name"]), (pos["template"]), (pos["kernel_launch"])]
+
+                else:
+                    # Potential ending point if we're already traversing a kernel's name.
+                    if status == AT_KERNEL_NAME:
+                        pos["kernel_name"]["start"] = i
+
+                        # Finished
+                        return [(pos["kernel_name"]), (pos["template"]), (pos["kernel_launch"])]
+
+    def find_kernel_bounds(string):
+        """Finds the starting and ending points for all kernel launches in the string."""
+        kernel_end = 0
+        kernel_positions = []
+
+        # Continue until we cannot find any more kernels anymore.
+        while string.find("<<<", kernel_end) != -1:
+            # Get kernel starting position (starting from the previous ending point)
+            kernel_start = string.find("<<<", kernel_end)
+
+            # Get kernel ending position (adjust end point past the >>>)
+            kernel_end = string.find(">>>", kernel_start) + 3
+            if kernel_end <= 0:
+                raise InputError("no kernel end found")
+
+            # Add to list of traversed kernels
+            kernel_positions.append({"start": kernel_start, "end": kernel_end,
+                                     "group": string[kernel_start: kernel_end]})
+
+        return kernel_positions
+
+    # Replace comments and string literals from the code so that find_kernel_bounds does not
+    # wrongly capture kernels in comments and string literals.
+    # This function replaces them with "x" to keep positions.
+    def mask_comments(string):
+        in_comment = ''
+        prev_c = ''
+        new_string = ''
+        for c in string:
+            if in_comment == '':
+                # Outside comments
+                if c == '/' and prev_c == '/':
+                    in_comment = '//'
+                elif c == '*' and prev_c == '/':
+                    in_comment = '/*'
+                elif c == '"' and prev_c != '\\' and prev_c != "'":
+                    in_comment = '"'
+            elif in_comment == '//':
+                # In // xxx
+                if c == '\r' or c == '\n':
+                    in_comment = ''
+            elif in_comment == '/*':
+                # In /* xxx */
+                if c == '/' and prev_c == '*':
+                    in_comment = ''
+            elif in_comment == '"':
+                # In ""
+                if c == '"' and prev_c != '\\':
+                    in_comment = ''
+            prev_c = c
+            if in_comment == '':
+                new_string += c
+            else:
+                new_string += 'x'
+        return new_string
+
+    # Grab positional ranges of all kernel launches
+    get_kernel_positions = list(find_kernel_bounds(mask_comments(string)))
+    output_string = string
+
+    # Replace each CUDA kernel with a HIP kernel.
+    for kernel in get_kernel_positions:
+        # Get kernel components
+        params = grab_method_and_template(kernel)
+
+        # Find parenthesis after kernel launch
+        parenthesis = string.find("(", kernel["end"])
+
+        # Extract cuda kernel
+        cuda_kernel = string[params[0]["start"]:parenthesis + 1]
+        kernel_string = string[kernel['start']:kernel['end']]
+        end_param_index = 0 if params[1]['end'] == -1 else 1
+        kernel_name_with_template = string[params[0]['start']:params[end_param_index]['end'] + 1]
+        cuda_kernel_dim3 = add_dim3(kernel_string, cuda_kernel)
+        # Keep number of kernel launch params consistent (grid dims, group dims, stream, dynamic shared size)
+        num_klp = len(extract_arguments(0, kernel["group"].replace("<<<", "(").replace(">>>", ")")))
+
+        hip_kernel = "hipLaunchKernelGGL(" + cuda_kernel_dim3[0:-1].replace(
+            ">>>", ", 0" * (4 - num_klp) + ">>>").replace("<<<", ", ").replace(
+            ">>>", ", ").replace(kernel_name_with_template, "(" + kernel_name_with_template + ")")
+
+        # Replace cuda kernel with hip kernel
+        output_string = output_string.replace(cuda_kernel, hip_kernel)
+
+        # Update the statistics
+        stats["kernel_launches"].append(hip_kernel)
+
+    return output_string
+
+
+def find_closure_group(input_string, start, group):
+    """Generalization for finding a balancing closure group
+
+         if group = ["(", ")"], then finds the first balanced parentheses.
+         if group = ["{", "}"], then finds the first balanced bracket.
+
+    Given an input string, a starting position in the input string, and the group type,
+    find_closure_group returns the positions of group[0] and group[1] as a tuple.
+
+    Example:
+        >>> find_closure_group("(hi)", 0, ["(", ")"])
+        (0, 3)
+    """
+
+    inside_parenthesis = False
+    parens = 0
+    pos = start
+    p_start, p_end = -1, -1
+
+    while pos < len(input_string):
+        if input_string[pos] == group[0]:
+            if inside_parenthesis is False:
+                inside_parenthesis = True
+                parens = 1
+                p_start = pos
+            else:
+                parens += 1
+        elif input_string[pos] == group[1] and inside_parenthesis:
+            parens -= 1
+
+            if parens == 0:
+                p_end = pos
+                return p_start, p_end
+
+        pos += 1
+    return None, None
+
+
+def find_bracket_group(input_string, start):
+    """Finds the first balanced parantheses."""
+    return find_closure_group(input_string, start, group=["{", "}"])
+
+
+def find_parentheses_group(input_string, start):
+    """Finds the first balanced bracket."""
+    return find_closure_group(input_string, start, group=["(", ")"])
+
+
+RE_ASSERT = re.compile(r"\bassert[ ]*\(")
+
+
+def replace_math_functions(input_string):
+    """FIXME: Temporarily replace std:: invocations of math functions
+        with non-std:: versions to prevent linker errors NOTE: This
+        can lead to correctness issues when running tests, since the
+        correct version of the math function (exp/expf) might not get
+        called.  Plan is to remove this function once HIP supports
+        std:: math function calls inside device code
+
+    """
+    output_string = input_string
+    for func in MATH_TRANSPILATIONS:
+        output_string = output_string.replace(fr'{func}(', f'{MATH_TRANSPILATIONS[func]}(')
+
+    return output_string
+
+
+RE_SYNCTHREADS = re.compile(r":?:?\b(__syncthreads)\b(\w*\()")
+
+
+def hip_header_magic(input_string):
+    """If the file makes kernel builtin calls and does not include the cuda_runtime.h header,
+    then automatically add an #include to match the "magic" includes provided by NVCC.
+    TODO:
+        Update logic to ignore cases where the cuda_runtime.h is included by another file.
+    """
+
+    # Copy the input.
+    output_string = input_string
+
+    # Check if one of the following headers is already included.
+    headers = ["hip/hip_runtime.h", "hip/hip_runtime_api.h"]
+    if any(re.search(fr'#include ("{ext}"|<{ext}>)', output_string) for ext in headers):
+        return output_string
+
+    # Rough logic to detect if we're inside device code
+    hasDeviceLogic: int
+    hasDeviceLogic = "hipLaunchKernelGGL" in output_string
+    hasDeviceLogic += "__global__" in output_string
+    hasDeviceLogic += "__shared__" in output_string
+    hasDeviceLogic += RE_SYNCTHREADS.search(output_string) is not None
+
+    # If device logic found, provide the necessary header.
+    if hasDeviceLogic:
+        output_string = '#include "hip/hip_runtime.h"\n' + input_string
+
+    return output_string
+
+
+RE_EXTERN_SHARED = re.compile(r"extern\s+([\w\(\)]+)?\s*__shared__\s+([\w:<>\s]+)\s+(\w+)\s*\[\s*\]\s*;")
+
+
+def replace_extern_shared(input_string):
+    """Match extern __shared__ type foo[]; syntax and use HIP_DYNAMIC_SHARED() MACRO instead.
+       https://github.com/ROCm-Developer-Tools/HIP/blob/master/docs/markdown/hip_kernel_language.md#__shared__
+    Example:
+        "extern __shared__ char smemChar[];" => "HIP_DYNAMIC_SHARED( char, smemChar)"
+        "extern __shared__ unsigned char smem[];" => "HIP_DYNAMIC_SHARED( unsigned char, my_smem)"
+    """
+    output_string = input_string
+    output_string = RE_EXTERN_SHARED.sub(
+        lambda inp: f"HIP_DYNAMIC_SHARED({inp.group(1) or ''} {inp.group(2)}, {inp.group(3)})", output_string)
+
+    return output_string
+
+
+def get_hip_file_path(rel_filepath, is_pytorch_extension=False):
+    """
+    Returns the new name of the hipified file
+    """
+    # At the moment, some PyTorch source files are HIPified in place.  The predicate
+    # is_out_of_place tells us if this is the case or not.
+    assert not os.path.isabs(rel_filepath)
+    if not is_pytorch_extension and not is_out_of_place(rel_filepath):
+        return rel_filepath
+
+    dirpath, filename = os.path.split(rel_filepath)
+    root, ext = os.path.splitext(filename)
+
+    # Here's the plan:
+    #
+    # In general, we need to disambiguate the HIPified filename so that
+    # it gets a different name from the original filename, so
+    # that we don't overwrite the original file
+    #
+    # There's a lot of different naming conventions across PyTorch
+    # and Caffe2, but the general recipe is to convert occurrences
+    # of cuda/gpu to hip, and add hip if there are no occurrences
+    # of cuda/gpu anywhere.
+    #
+    # Concretely, we do the following:
+    #
+    #   - If there is a directory component named "cuda", replace
+    #     it with "hip", AND
+    #
+    #   - If the file name contains "CUDA", replace it with "HIP", AND
+    #
+    #   - ALWAYS replace '.cu' with '.hip', because those files
+    #     contain CUDA kernels that needs to be hipified and processed with
+    #     hip compiler
+    #
+    #   - If we are not hipifying a PyTorch extension, and the parent
+    #     directory name did not change as a result of the above
+    #     transformations, insert "hip" in the file path
+    #     as the direct parent folder of the file
+    #
+    #   - If we are hipifying a PyTorch extension, and the parent directory
+    #     name as well as the filename (incl. extension) did not change as
+    #     a result of the above transformations, insert "_hip" in the filename
+    #
+    # This isn't set in stone; we might adjust this to support other
+    # naming conventions.
+
+    if ext == '.cu':
+        ext = '.hip'
+
+    orig_filename = filename
+    orig_dirpath = dirpath
+
+    dirpath = dirpath.replace('cuda', 'hip')
+    dirpath = dirpath.replace('CUDA', 'HIP')
+    dirpath = dirpath.replace('THC', 'THH')
+
+    root = root.replace('cuda', 'hip')
+    root = root.replace('CUDA', 'HIP')
+    # Special case to handle caffe2/core/THCCachingAllocator
+    if dirpath != "caffe2/core":
+        root = root.replace('THC', 'THH')
+
+    if not is_pytorch_extension and dirpath == orig_dirpath:
+        dirpath = os.path.join(dirpath, 'hip')
+
+    if is_pytorch_extension and dirpath == orig_dirpath and (root + ext) == orig_filename:
+        root = root + "_hip"
+
+    return os.path.join(dirpath, root + ext)
+
+
+def is_out_of_place(rel_filepath):
+    assert not os.path.isabs(rel_filepath)
+    if rel_filepath.startswith("torch/"):
+        return False
+    if rel_filepath.startswith("third_party/nvfuser/"):
+        return False
+    if rel_filepath.startswith("tools/autograd/templates/"):
+        return False
+    return True
+
+
+# Keep this synchronized with includes/ignores in build_amd.py
+def is_pytorch_file(rel_filepath):
+    assert not os.path.isabs(rel_filepath)
+    if rel_filepath.startswith("aten/"):
+        if rel_filepath.startswith("aten/src/ATen/core/"):
+            return False
+        return True
+    if rel_filepath.startswith("torch/"):
+        return True
+    if rel_filepath.startswith("third_party/nvfuser/"):
+        return True
+    if rel_filepath.startswith("tools/autograd/templates/"):
+        return True
+    return False
+
+
+def is_cusparse_file(rel_filepath):
+    if is_pytorch_file(rel_filepath):
+        return "sparse" in rel_filepath.lower()
+    return False
+
+
+def is_special_file(rel_filepath):
+    if is_pytorch_file(rel_filepath):
+        if "sparse" in rel_filepath.lower():
+            return True
+        elif "linalg" in rel_filepath.lower():
+            if "batchlinearalgebralibblas" in rel_filepath.lower():
+                return False  # don't use "special" mappings for this specific linalg cublas file
+            return True
+    return False
+
+def is_caffe2_gpu_file(rel_filepath):
+    assert not os.path.isabs(rel_filepath)
+    if rel_filepath.startswith("c10/cuda"):
+        return True
+    filename = os.path.basename(rel_filepath)
+    _, ext = os.path.splitext(filename)
+    return ('gpu' in filename or ext in ['.cu', '.cuh']) and ('cudnn' not in filename)
+
+class TrieNode:
+    """A Trie node whose children are represented as a directory of char: TrieNode.
+       A special char '' represents end of word
+    """
+
+    def __init__(self):
+        self.children = {}
+
+class Trie:
+    """Creates a Trie out of a list of words. The trie can be exported to a Regex pattern.
+    The corresponding Regex should match much faster than a simple Regex union."""
+
+    def __init__(self):
+        """Initialize the trie with an empty root node."""
+        self.root = TrieNode()
+        self._hash = hashlib.md5(usedforsecurity=False)
+        self._digest = self._hash.digest()
+
+    def add(self, word):
+        """Add a word to the Trie. """
+        self._hash.update(word.encode())
+        self._digest = self._hash.digest()
+        node = self.root
+
+        for char in word:
+            node.children.setdefault(char, TrieNode())
+            node = node.children[char]
+        node.children[''] = True    # Mark the end of the word
+
+    def dump(self):
+        """Return the root node of Trie. """
+        return self.root
+
+    def quote(self, char):
+        """ Escape a char for regex. """
+        return re.escape(char)
+
+    def search(self, word):
+        """Search whether word is present in the Trie.
+        Returns True if yes, else return False"""
+        node = self.root
+        for char in word:
+            if char in node.children:
+                node = node.children[char]
+            else:
+                return False
+
+        # make sure to check the end-of-word marker present
+        return '' in node.children
+
+    @functools.lru_cache  # noqa: B019
+    def _pattern(self, root, digest):
+        """Convert a Trie into a regular expression pattern
+
+        Memoized on the hash digest of the trie, which is built incrementally
+        during add().
+        """
+        node = root
+
+        if "" in node.children and len(node.children.keys()) == 1:
+            return None
+
+        alt = []    # store alternative patterns
+        cc = []     # store char to char classes
+        q = 0       # for node representing the end of word
+        for char in sorted(node.children.keys()):
+            if isinstance(node.children[char], TrieNode):
+                try:
+                    recurse = self._pattern(node.children[char], self._digest)
+                    alt.append(self.quote(char) + recurse)
+                except Exception:
+                    cc.append(self.quote(char))
+            else:
+                q = 1
+        cconly = not len(alt) > 0
+
+        if len(cc) > 0:
+            if len(cc) == 1:
+                alt.append(cc[0])
+            else:
+                alt.append('[' + ''.join(cc) + ']')
+
+        if len(alt) == 1:
+            result = alt[0]
+        else:
+            result = "(?:" + "|".join(alt) + ")"
+
+        if q:
+            if cconly:
+                result += "?"
+            else:
+                result = f"(?:{result})?"
+        return result
+
+    def pattern(self):
+        """Export the Trie to a regex pattern."""
+        return self._pattern(self.root, self._digest)
+
+    def export_to_regex(self):
+        """Export the Trie to a regex pattern."""
+        return self._pattern(self.root, self._digest)
+
+CAFFE2_TRIE = Trie()
+CAFFE2_MAP = {}
+PYTORCH_TRIE = Trie()
+PYTORCH_MAP: dict[str, object] = {}
+
+# In PyTorch, we map cuBLAS->rocBLAS and cuSPARSE->hipSPARSE. Note the prefix, roc versus hip.
+# The 'hip' APIs offer a more direct CUDA-friendly mapping, but calling rocBLAS directly has better performance.
+# Unfortunately, the roc* types and hip* types differ, i.e., rocblas_float_complex versus hipComplex.
+# In the case of SPARSE, we must use the hip types for complex instead of the roc types,
+# but the pytorch mappings assume roc. Therefore, we create a new SPARSE mapping that has a higher priority.
+# Its mappings will trigger first, and only when a miss occurs will the lower-priority pytorch mapping take place.
+# When a file contains "sparse" in the filename, a mapping marked with API_SPARSE is preferred over other choices.
+# Similarly, "linalg" files require rocBLAS -> hipSOLVER so they also need special handling.
+PYTORCH_SPECIAL_MAP = {}
+
+for mapping in CUDA_TO_HIP_MAPPINGS:
+    assert isinstance(mapping, Mapping)
+    for src, value in mapping.items():
+        dst = value[0]
+        meta_data = value[1:]
+        if constants.API_CAFFE2 not in meta_data:
+            PYTORCH_TRIE.add(src)
+            # if src is already in PYTORCH_MAP and dst belongs to API_SPECIAL
+            # do not overwrite PYTORCH_MAP, store dst separately
+            if constants.API_SPECIAL in meta_data and PYTORCH_MAP.get(src, ""):
+                PYTORCH_SPECIAL_MAP[src] = dst
+            else:
+                PYTORCH_MAP[src] = dst
+        if constants.API_PYTORCH not in meta_data and constants.API_SPECIAL not in meta_data:
+            CAFFE2_TRIE.add(src)
+            CAFFE2_MAP[src] = dst
+RE_CAFFE2_PREPROCESSOR = re.compile(CAFFE2_TRIE.export_to_regex())
+RE_PYTORCH_PREPROCESSOR = re.compile(fr'(?<=\W)({PYTORCH_TRIE.export_to_regex()})(?=\W)')
+
+RE_QUOTE_HEADER = re.compile(r'#include "([^"]+)"')
+RE_ANGLE_HEADER = re.compile(r'#include <([^>]+)>')
+RE_THC_GENERIC_FILE = re.compile(r'#define THC_GENERIC_FILE "([^"]+)"')
+RE_CU_SUFFIX = re.compile(r'\.cu\b')  # be careful not to pick up .cuh
+
+"""
+Returns a HipifyResult object with the following details:
+    "hipified_path" : absolute path of hipified source file
+    "status"        : "ok"      if hipified file was written out
+                      "skipped" if an identical hipified file already existed or hipified file couldn't be written out
+                      "ignored" if the source file was a hipified file itself or not meant to be hipified
+    "current_state" : CurrentState.INITIALIZED if source file is first ready to be hipified
+                      CurrentState.DONE if source file is done with hipification process
+"""
+
+
+def preprocessor(
+        output_directory: str,
+        filepath: str,
+        all_files: Iterable,
+        header_include_dirs: Iterable,
+        stats: dict[str, list],
+        hip_clang_launch: bool,
+        is_pytorch_extension: bool,
+        clean_ctx: GeneratedFileCleaner,
+        show_progress: bool) -> HipifyResult:
+    """ Executes the CUDA -> HIP conversion on the specified file. """
+    fin_path = os.path.abspath(os.path.join(output_directory, filepath))
+    hipify_result = HIPIFY_FINAL_RESULT[fin_path]
+    if filepath not in all_files:
+        hipify_result.hipified_path = None
+        hipify_result.status = "[ignored, not to be hipified]"
+        hipify_result.current_state = CurrentState.DONE
+        return hipify_result
+
+    rel_filepath = _to_unix_path(os.path.relpath(filepath, output_directory))
+
+    with open(fin_path, encoding='utf-8') as fin:
+        if fin.readline() == HIPIFY_C_BREADCRUMB:
+            hipify_result.hipified_path = None
+            hipify_result.status = "[ignored, input is hipified output]"
+            hipify_result.current_state = CurrentState.DONE
+            return hipify_result
+        fin.seek(0)
+        output_source = fin.read()
+
+    orig_output_source = output_source
+
+    # get_hip_file_path needs a relative path to work correctly
+    fout_path = os.path.abspath(os.path.join(output_directory, get_hip_file_path(rel_filepath, is_pytorch_extension)))
+    if not os.path.exists(os.path.dirname(fout_path)):
+        clean_ctx.makedirs(os.path.dirname(fout_path))
+
+    # unsupported_calls statistics reporting is broken atm
+    def pt_repl(m):
+        return PYTORCH_MAP[m.group(0)]
+
+    def pt_special_repl(m):
+        # checks SPECIAL map first, and if a miss occurs, falls back to pytorch mappings
+        return PYTORCH_SPECIAL_MAP.get(m.group(0), pt_repl(m))
+
+
+    if is_pytorch_extension:
+        output_source = RE_PYTORCH_PREPROCESSOR.sub(pt_repl, output_source)
+    else:
+        if is_special_file(rel_filepath):
+            output_source = RE_PYTORCH_PREPROCESSOR.sub(pt_special_repl, output_source)
+        elif is_pytorch_file(rel_filepath):
+            output_source = RE_PYTORCH_PREPROCESSOR.sub(pt_repl, output_source)
+        else:
+            def c2_repl(m):
+                return CAFFE2_MAP[m.group(0)]
+            output_source = RE_CAFFE2_PREPROCESSOR.sub(c2_repl, output_source)
+
+    # Header rewrites
+    def mk_repl(templ, include_current_dir=True):
+        def repl(m):
+            f = m.group(1)
+            filename = os.path.basename(f)
+            if (
+                f.startswith(("ATen/cuda",
+                              "ATen/native/cuda",
+                              "ATen/native/nested/cuda",
+                              "ATen/native/quantized/cuda",
+                              "ATen/native/sparse/cuda",
+                              "ATen/native/transformers/cuda",
+                              "THC/")) or
+                (f.startswith("THC") and not f.startswith("THCP"))
+            ):
+                return templ.format(get_hip_file_path(m.group(1), is_pytorch_extension))
+            # if filename is one of the files being hipified for this extension
+            if (is_pytorch_extension and any(s.endswith(filename) for s in all_files)):
+                header_dir = None
+                header_filepath = None
+                # If include_current_dir True, look first in same dir as the including source file
+                if include_current_dir:
+                    header_dir_to_check = os.path.dirname(fin_path)
+                    header_path_to_check = os.path.abspath(os.path.join(header_dir_to_check, f))
+                    if os.path.exists(header_path_to_check):
+                        header_dir = header_dir_to_check
+                        header_filepath = header_path_to_check
+                # If not found, look in include dirs one by one and first match wins
+                if header_filepath is None:
+                    for header_include_dir in header_include_dirs:
+                        header_dir_to_check = os.path.join(output_directory, header_include_dir)
+                        header_path_to_check = os.path.abspath(os.path.join(header_dir_to_check, f))
+                        if os.path.exists(header_path_to_check):
+                            header_dir = header_dir_to_check
+                            header_filepath = header_path_to_check
+                # If header file not found, keep as is
+                if header_filepath is None:
+                    return m.group(0)
+                # Hipify header file first if needed
+                if header_filepath not in HIPIFY_FINAL_RESULT:
+                    preprocess_file_and_save_result(output_directory,
+                                                    header_filepath,
+                                                    all_files, header_include_dirs, stats, hip_clang_launch,
+                                                    is_pytorch_extension, clean_ctx, show_progress)
+                elif header_filepath in HIPIFY_FINAL_RESULT:
+                    header_result = HIPIFY_FINAL_RESULT[header_filepath]
+                    if header_result.current_state == CurrentState.INITIALIZED:
+                        # get_hip_file_path needs a relative path to work correctly
+                        header_rel_path = os.path.relpath(header_filepath, output_directory)
+                        header_fout_path = os.path.abspath(os.path.join(output_directory,
+                                                                        get_hip_file_path(header_rel_path, is_pytorch_extension)))
+                        header_result.hipified_path = header_fout_path
+                        HIPIFY_FINAL_RESULT[header_filepath] = header_result
+                        return templ.format(os.path.relpath(header_fout_path if header_fout_path is not None
+                                                            else header_filepath, header_dir))
+                hipified_header_filepath = HIPIFY_FINAL_RESULT[header_filepath].hipified_path
+                return templ.format(os.path.relpath(hipified_header_filepath if hipified_header_filepath is not None
+                                                    else header_filepath, header_dir))
+
+            return m.group(0)
+        return repl
+    output_source = RE_QUOTE_HEADER.sub(mk_repl('#include "{0}"', True), output_source)
+    output_source = RE_ANGLE_HEADER.sub(mk_repl('#include <{0}>', False), output_source)
+    output_source = RE_THC_GENERIC_FILE.sub(mk_repl('#define THC_GENERIC_FILE "{0}"'), output_source)
+
+    # CMakeLists.txt rewrites
+    if filepath.endswith('CMakeLists.txt'):
+        output_source = output_source.replace('CUDA', 'HIP')
+        output_source = output_source.replace('THC', 'THH')
+        output_source = RE_CU_SUFFIX.sub('.hip', output_source)
+
+    # Perform Kernel Launch Replacements
+    if not hip_clang_launch:
+        output_source = processKernelLaunches(output_source, stats)
+
+    # Replace std:: with non-std:: versions
+    if (filepath.endswith((".cu", ".cuh"))) and "PowKernel" not in filepath:
+        output_source = replace_math_functions(output_source)
+
+    # Include header if device code is contained.
+    output_source = hip_header_magic(output_source)
+
+    # Replace the extern __shared__
+    # NOTE: No longer needed after transition from hcc to hipclang.
+    # output_source = replace_extern_shared(output_source)
+
+    # Don't write out identical hipified files for extensions if dirpath has not changed
+    if (
+        is_pytorch_extension
+        and orig_output_source == output_source
+        and os.path.dirname(fin_path) == os.path.dirname(fout_path)
+    ):
+        hipify_result.hipified_path = fin_path
+        hipify_result.status = "[skipped, no changes]"
+        hipify_result.current_state = CurrentState.DONE
+        return hipify_result
+
+    # Add hipify breadcrumb for C-style files to avoid re-hipification
+    if fin_path != fout_path and match_extensions(fin_path, (".cu", ".cuh", ".c", ".cc", ".cpp", ".h", ".hpp")):
+        output_source = HIPIFY_C_BREADCRUMB + output_source
+
+    do_write = True
+    if os.path.exists(fout_path):
+        with open(fout_path, encoding='utf-8') as fout_old:
+            do_write = fout_old.read() != output_source
+    if do_write:
+        try:
+            with clean_ctx.open(fout_path, 'w', encoding='utf-8') as fout:
+                fout.write(output_source)
+            hipify_result.hipified_path = fout_path
+            hipify_result.status = "[ok]"
+            hipify_result.current_state = CurrentState.DONE
+            return hipify_result
+        except PermissionError as e:
+            print(f'{bcolors.WARNING}Failed to save {fout_path} with "{e.strerror}", leaving {fin_path} unchanged.{bcolors.ENDC}',
+                  file=sys.stderr)
+            hipify_result.hipified_path = fin_path
+            hipify_result.status = "[skipped, no permissions]"
+            hipify_result.current_state = CurrentState.DONE
+            return hipify_result
+    else:
+        hipify_result.hipified_path = fout_path
+        hipify_result.status = "[skipped, already hipified]"
+        hipify_result.current_state = CurrentState.DONE
+        return hipify_result
+
+def file_specific_replacement(filepath, search_string, replace_string, strict=False):
+    with openf(filepath, "r+") as f:
+        contents = f.read()
+        if strict:
+            contents = re.sub(fr'\b({re.escape(search_string)})\b', lambda x: replace_string, contents)
+        else:
+            contents = contents.replace(search_string, replace_string)
+        f.seek(0)
+        f.write(contents)
+        f.truncate()
+
+
+def file_add_header(filepath, header):
+    with openf(filepath, "r+") as f:
+        contents = f.read()
+        if header[0] != "<" and header[-1] != ">":
+            header = f'"{header}"'
+        contents = (f'#include {header} \n') + contents
+        f.seek(0)
+        f.write(contents)
+        f.truncate()
+
+
+def fix_static_global_kernels(in_txt):
+    """Static global kernels in HIP results in a compilation error."""
+    in_txt = in_txt.replace(" __global__ static", "__global__")
+    return in_txt
+
+
+RE_INCLUDE = re.compile(r"#include .*\n")
+
+
+def extract_arguments(start, string):
+    """ Return the list of arguments in the upcoming function parameter closure.
+        Example:
+        string (input): '(blocks, threads, 0, THCState_getCurrentStream(state))'
+        arguments (output):
+            '[{'start': 1, 'end': 7},
+            {'start': 8, 'end': 16},
+            {'start': 17, 'end': 19},
+            {'start': 20, 'end': 53}]'
+    """
+
+    arguments = []
+    closures = {
+        "<": 0,
+        "(": 0
+    }
+    current_position = start
+    argument_start_pos = current_position + 1
+
+    # Search for final parenthesis
+    while current_position < len(string):
+        if string[current_position] == "(":
+            closures["("] += 1
+        elif string[current_position] == ")":
+            closures["("] -= 1
+        elif string[current_position] == "<":
+            closures["<"] += 1
+        elif string[current_position] == ">" and string[current_position - 1] != "-" and closures["<"] > 0:
+            closures["<"] -= 1
+
+        # Finished all arguments
+        if closures["("] == 0 and closures["<"] == 0:
+            # Add final argument
+            arguments.append({"start": argument_start_pos, "end": current_position})
+            break
+
+        # Finished current argument
+        if closures["("] == 1 and closures["<"] == 0 and string[current_position] == ",":
+            arguments.append({"start": argument_start_pos, "end": current_position})
+            argument_start_pos = current_position + 1
+
+        current_position += 1
+
+    return arguments
+
+
+def str2bool(v):
+    """ArgumentParser doesn't support type=bool. Thus, this helper method will convert
+    from possible string types to True / False."""
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+
+def hipify(
+    project_directory: str,
+    show_detailed: bool = False,
+    extensions: Iterable = (".cu", ".cuh", ".c", ".cc", ".cpp", ".h", ".in", ".hpp"),
+    header_extensions: Iterable = (".cuh", ".h", ".hpp"),
+    output_directory: str = "",
+    header_include_dirs: Iterable = (),
+    includes: Iterable = ('*',),
+    extra_files: Iterable = (),
+    out_of_place_only: bool = False,
+    ignores: Iterable = (),
+    show_progress: bool = True,
+    hip_clang_launch: bool = False,
+    is_pytorch_extension: bool = False,
+    hipify_extra_files_only: bool = False,
+    clean_ctx: Optional[GeneratedFileCleaner] = None
+) -> HipifyFinalResult:
+    if project_directory == "":
+        project_directory = os.getcwd()
+
+    # Verify the project directory exists.
+    if not os.path.exists(project_directory):
+        print("The project folder specified does not exist.")
+        sys.exit(1)
+
+    # If no output directory, provide a default one.
+    if not output_directory:
+        project_directory.rstrip("/")
+        output_directory = project_directory + "_amd"
+
+    if project_directory != output_directory:
+        includes = [include.replace(project_directory, output_directory) for include in includes]
+        ignores = [ignore.replace(project_directory, output_directory) for ignore in ignores]
+
+    # Copy from project directory to output directory if not done already.
+    if not os.path.exists(output_directory):
+        shutil.copytree(project_directory, output_directory)
+
+    includes = list(map(_to_unix_path, includes))
+    ignores = list(map(_to_unix_path, ignores))
+
+    all_files = list(matched_files_iter(output_directory, includes=includes,
+                                        ignores=ignores, extensions=extensions,
+                                        out_of_place_only=out_of_place_only,
+                                        is_pytorch_extension=is_pytorch_extension))
+    all_files_set = set(all_files)
+    for f in extra_files:
+        if not os.path.isabs(f):
+            f = os.path.join(output_directory, f)
+        if f not in all_files_set:
+            all_files.append(f)
+
+    # List all files in header_include_paths to ensure they are hipified
+    from pathlib import Path
+    for header_include_dir in header_include_dirs:
+        if os.path.isabs(header_include_dir):
+            header_include_dir_path = Path(header_include_dir)
+        else:
+            header_include_dir_path = Path(os.path.join(output_directory, header_include_dir))
+        all_files.extend(
+            str(path) for path in header_include_dir_path.rglob('*') if path.is_file()
+            and _fnmatch(str(path), includes)
+            and (not _fnmatch(str(path), ignores))
+            and match_extensions(path.name, header_extensions)
+        )
+
+    if clean_ctx is None:
+        clean_ctx = GeneratedFileCleaner(keep_intermediates=True)
+
+    # Preprocessing statistics.
+    stats: dict[str, list] = {"unsupported_calls": [], "kernel_launches": []}
+
+    for filepath in (all_files if not hipify_extra_files_only else extra_files):
+        preprocess_file_and_save_result(output_directory, filepath, all_files, header_include_dirs,
+                                        stats, hip_clang_launch, is_pytorch_extension, clean_ctx, show_progress)
+
+    print(bcolors.OKGREEN + "Successfully preprocessed all matching files." + bcolors.ENDC, file=sys.stderr)
+
+    # Show detailed summary
+    if show_detailed:
+        compute_stats(stats)
+
+    return HIPIFY_FINAL_RESULT
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/version.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f356cc57bfa00a3b251402604c54702fb414c96
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hipify/version.py
@@ -0,0 +1 @@
+__version__ = '1.0.0'
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hooks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..c41add0fcbefd0e87233194371b5fcf20f8b1da8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/hooks.py
@@ -0,0 +1,251 @@
+# mypy: allow-untyped-defs
+import torch
+from collections import OrderedDict
+import weakref
+import warnings
+from typing import Any
+
+__all__ = ["RemovableHandle", "unserializable_hook", "warn_if_has_hooks", "BackwardHook"]
+
+class RemovableHandle:
+    r"""
+    A handle which provides the capability to remove a hook.
+
+    Args:
+        hooks_dict (dict): A dictionary of hooks, indexed by hook ``id``.
+        extra_dict (Union[dict, List[dict]]): An additional dictionary or list of
+            dictionaries whose keys will be deleted when the same keys are
+            removed from ``hooks_dict``.
+    """
+
+    id: int
+    next_id: int = 0
+
+    def __init__(self, hooks_dict: Any, *, extra_dict: Any = None) -> None:
+        self.hooks_dict_ref = weakref.ref(hooks_dict)
+        self.id = RemovableHandle.next_id
+        RemovableHandle.next_id += 1
+
+        self.extra_dict_ref: tuple = ()
+        if isinstance(extra_dict, dict):
+            self.extra_dict_ref = (weakref.ref(extra_dict),)
+        elif isinstance(extra_dict, list):
+            self.extra_dict_ref = tuple(weakref.ref(d) for d in extra_dict)
+
+    def remove(self) -> None:
+        hooks_dict = self.hooks_dict_ref()
+        if hooks_dict is not None and self.id in hooks_dict:
+            del hooks_dict[self.id]
+
+        for ref in self.extra_dict_ref:
+            extra_dict = ref()
+            if extra_dict is not None and self.id in extra_dict:
+                del extra_dict[self.id]
+
+    def __getstate__(self):
+        if self.extra_dict_ref is None:
+            return (self.hooks_dict_ref(), self.id)
+        else:
+            return (self.hooks_dict_ref(), self.id, tuple(ref() for ref in self.extra_dict_ref))
+
+    def __setstate__(self, state) -> None:
+        if state[0] is None:
+            # create a dead reference
+            self.hooks_dict_ref = weakref.ref(OrderedDict())
+        else:
+            self.hooks_dict_ref = weakref.ref(state[0])
+        self.id = state[1]
+        RemovableHandle.next_id = max(RemovableHandle.next_id, self.id + 1)
+
+        if len(state) < 3 or state[2] is None:
+            self.extra_dict_ref = ()
+        else:
+            self.extra_dict_ref = tuple(weakref.ref(d) for d in state[2])
+
+    def __enter__(self) -> "RemovableHandle":
+        return self
+
+    def __exit__(self, type: Any, value: Any, tb: Any) -> None:
+        self.remove()
+
+
+def unserializable_hook(f):
+    """
+    Mark a function as an unserializable hook with this decorator.
+
+    This suppresses warnings that would otherwise arise if you attempt
+    to serialize a tensor that has a hook.
+    """
+    f.__torch_unserializable__ = True
+    return f
+
+
+def warn_if_has_hooks(tensor):
+    if tensor._backward_hooks:
+        for k in tensor._backward_hooks:
+            hook = tensor._backward_hooks[k]
+            if not hasattr(hook, "__torch_unserializable__"):
+                warnings.warn(f"backward hook {repr(hook)} on tensor will not be "
+                              "serialized.  If this is expected, you can "
+                              "decorate the function with @torch.utils.hooks.unserializable_hook "
+                              "to suppress this warning")
+
+class BackwardHook:
+    """
+    A wrapper class to implement nn.Module backward hooks.
+
+    It handles:
+      - Ignoring non-Tensor inputs and replacing them by None before calling the user hook
+      - Generating the proper Node to capture a set of Tensor's gradients
+      - Linking the gradients captures for the outputs with the gradients captured for the input
+      - Calling the user hook once both output and input gradients are available
+    """
+
+    def __init__(self, module, user_hooks, user_pre_hooks):
+        self.user_hooks = user_hooks
+        self.user_pre_hooks = user_pre_hooks
+        self.module = module
+
+        self.grad_outputs = None
+        self.n_outputs = -1
+        self.output_tensors_index = None
+        self.n_inputs = -1
+        self.input_tensors_index = None
+
+    def _pack_with_none(self, indices, values, size):
+        res = [None] * size
+        for idx, val in zip(indices, values):
+            res[idx] = val
+
+        return tuple(res)
+
+    def _unpack_none(self, indices, values):
+        res = [values[idx] for idx in indices]
+
+        return tuple(res)
+
+    def _set_user_hook(self, grad_fn):
+        def hook(grad_input, _):
+            if self.grad_outputs is None:
+                # This happens because the gradient in your nn.Module flows to
+                # the Module's input without " passing through the Module's
+                # output, e.g. when you're doing double backward.
+                return
+            res = self._pack_with_none(self.input_tensors_index, grad_input, self.n_inputs)
+
+            for hook in self.user_hooks:
+                out = hook(self.module, res, self.grad_outputs)
+
+                if out is None:
+                    continue
+
+                if len(out) != len(res):
+                    raise RuntimeError("Backward hook returned an invalid number of grad_input, "
+                                       f"got {len(out)}, but expected {len(res)}")
+
+                res = out
+
+            self.grad_outputs = None
+
+            return self._unpack_none(self.input_tensors_index, res)
+
+        grad_fn.register_hook(hook)
+
+    def _apply_on_tensors(self, fn, args):
+        # Can be used to apply the given function to the tensors contained in the
+        # args. Will return updated args and the tensors indices
+        tensors_idx = []
+        tensors = []
+
+        requires_grad = False
+        for i, arg in enumerate(args):
+            if isinstance(arg, torch.Tensor):
+                tensors_idx.append(i)
+                tensors.append(arg)
+                requires_grad |= arg.requires_grad
+
+        if not (requires_grad and torch.is_grad_enabled()):
+            return args, None
+
+        new_tensors = torch.nn.modules._functions.BackwardHookFunction.apply(*tensors)
+        if len(new_tensors) == 0:
+            raise RuntimeError("Cannot set Module backward hook for a Module with no input Tensors.")
+
+        grad_fns = [t.grad_fn for t in new_tensors if t.grad_fn is not None and t.grad_fn.name() == "BackwardHookFunctionBackward"]
+        if len(grad_fns) == 0:
+            raise RuntimeError("Error while setting up backward hooks. Please open "
+                               "an issue with a code sample to reproduce this.")
+
+        fn(grad_fns[0])
+
+        arg_list = list(args)
+        for idx, val in zip(tensors_idx, new_tensors):
+            arg_list[idx] = val
+
+        if type(args) is tuple:
+            out = tuple(arg_list)
+        else:
+            out = type(args)(*arg_list)
+        return out, tensors_idx
+
+    def setup_input_hook(self, args):
+        def fn(grad_fn):
+            self._set_user_hook(grad_fn)
+
+        res, input_idx = self._apply_on_tensors(fn, args)
+        self.n_inputs = len(args)
+        self.input_tensors_index = input_idx
+        return res
+
+    def setup_output_hook(self, args):
+        def fn(grad_fn):
+            def hook(_, grad_output):
+                self.grad_outputs = self._pack_with_none(self.output_tensors_index,
+                                                         grad_output,
+                                                         self.n_outputs)
+
+                if self.user_pre_hooks:
+                    expected_len = len(self.grad_outputs)
+                    for user_pre_hook in self.user_pre_hooks:
+                        hook_grad_outputs = user_pre_hook(self.module, self.grad_outputs)
+                        if hook_grad_outputs is None:
+                            continue
+
+                        actual_len = len(hook_grad_outputs)
+                        if actual_len != expected_len:
+                            raise RuntimeError("Backward pre hook returned an invalid number of grad_output, "
+                                               f"got {actual_len}, but expected {expected_len}")
+                        self.grad_outputs = hook_grad_outputs
+
+                # We need to be able to clear self.grad_outputs but also return it
+                local_grad_outputs = self.grad_outputs
+
+                # Special case if no input required gradients, this hook should call the user
+                # hook directly
+                if self.input_tensors_index is None:
+                    grad_inputs = self._pack_with_none([], [], self.n_inputs)
+                    for user_hook in self.user_hooks:
+                        res = user_hook(self.module, grad_inputs, self.grad_outputs)
+                        if res is not None and not (isinstance(res, tuple) and all(el is None for el in res)):
+                            raise RuntimeError("Backward hook for Modules where no input requires "
+                                               "gradient should always return None or None for all gradients.")
+                    self.grad_outputs = None
+
+                if local_grad_outputs is not None:
+                    assert self.output_tensors_index is not None  # mypy
+                    return tuple(local_grad_outputs[i] for i in self.output_tensors_index)
+
+            grad_fn.register_hook(hook)
+
+        is_tuple = True
+        if not isinstance(args, tuple):
+            args = (args,)
+            is_tuple = False
+
+        res, output_idx = self._apply_on_tensors(fn, args)
+        self.n_outputs = len(args)
+        self.output_tensors_index = output_idx
+
+        if not is_tuple:
+            res = res[0]
+        return res
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/jit/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/jit/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/jit/log_extract.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/jit/log_extract.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5804e710bae53b3a1e7d0dd94785d00de41ab78
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/jit/log_extract.py
@@ -0,0 +1,113 @@
+# mypy: allow-untyped-defs
+from contextlib import contextmanager
+from typing import Any, cast
+import random
+import torch
+import time
+from torch.utils.benchmark import Timer
+
+def extract_ir(filename: str) -> list[str]:
+    BEGIN = ""
+    END = ""
+    pfx = None
+    graphs = []
+    with open(filename) as f:
+        split_strs = f.read().split(BEGIN)
+        for i, split_str in enumerate(split_strs):
+            if i == 0:
+                continue
+            end_loc = split_str.find(END)
+            if end_loc == -1:
+                continue
+            s = split_str[:end_loc]
+            pfx = split_strs[i - 1].splitlines()[-1]
+            lines = [x[len(pfx):] for x in s.splitlines(keepends=True)]
+            graphs.append(''.join(lines))
+
+    return graphs
+
+
+def make_tensor_from_type(inp_type: torch._C.TensorType):
+    size = inp_type.sizes()
+    stride = inp_type.strides()
+    device = inp_type.device()
+    dtype = inp_type.dtype()
+    assert size is not None
+    assert stride is not None
+    assert device is not None
+    assert dtype is not None
+    return torch.empty_strided(size=size, stride=stride, device=device, dtype=dtype)
+
+def load_graph_and_inputs(ir: str) -> tuple[Any, list[Any]]:
+    graph = torch._C.parse_ir(ir, parse_tensor_constants=True)
+    graph.makeMultiOutputIntoTuple()
+    inputs = []
+    for inp in graph.inputs():
+        if isinstance(inp.type(), torch._C.FloatType):
+            inputs.append(random.uniform(.1, 100))
+        elif isinstance(inp.type(), torch._C.IntType):
+            inputs.append(random.randint(1, 100))
+        elif isinstance(inp.type(), torch._C.TensorType):
+            tensorType = cast(torch._C.TensorType, inp.type())
+            inputs.append(make_tensor_from_type(tensorType))
+        elif isinstance(inp.type(), torch._C.BoolType):
+            inputs.append(random.randint(0, 1) == 1)
+        else:
+            raise NotImplementedError(f"A default value is not implemented for type {inp.type()}")
+
+    func = torch._C._create_function_from_graph("forward", graph)
+    torch._C._jit_pass_erase_shape_information(func.graph)
+    return (func, inputs)
+
+def time_cuda(fn, inputs, test_runs):
+    t = Timer(stmt="fn(*inputs)", globals={"fn": fn, "inputs" : inputs})
+    times = t.blocked_autorange()
+    return times.median * 1000  # time in ms
+
+def time_cpu(fn, inputs, test_runs):
+    s = time.perf_counter()
+    for _ in range(test_runs):
+        fn(*inputs)
+    e = time.perf_counter()
+    return (e - s) / test_runs * 1000  # time in ms
+
+def run_test(ir, inputs, *, warmup_runs=10, test_runs=20) -> float:
+    graph, _ = load_graph_and_inputs(ir)
+    for _ in range(warmup_runs):
+        graph(*inputs)
+
+    is_cpu = None
+    for input in inputs:
+        if isinstance(input, torch.Tensor):
+            is_cpu = input.device.type == "cpu"
+            break
+    assert is_cpu is not None
+
+    out = time_cpu(graph, inputs, test_runs) if is_cpu else time_cuda(graph, inputs, test_runs)
+    return out
+
+@contextmanager
+def no_fuser(*args, **kwargs):
+    old_optimize = torch._C._get_graph_executor_optimize(False)
+    try:
+        yield
+    finally:
+        torch._C._get_graph_executor_optimize(old_optimize)
+
+def run_baseline_no_fusion(ir, inputs) -> float:
+    with no_fuser():
+        return run_test(ir, inputs)
+
+
+def run_nnc(ir, inputs, dynamic) -> float:
+    try:
+        strat = [("DYNAMIC", 10)] if dynamic else [("STATIC", 10)]
+        old_strat = torch.jit.set_fusion_strategy(strat)
+        with torch.jit.fuser("fuser1"):
+            return run_test(ir, inputs)
+    finally:
+        torch.jit.set_fusion_strategy(old_strat)
+
+def run_nvfuser(ir, inputs) -> float:
+    with torch.jit.fuser("fuser2"):
+        return run_test(ir, inputs)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/mkldnn.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/mkldnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..06ca96d2de9a9729b8250d5c333d74d2352af861
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/mkldnn.py
@@ -0,0 +1,234 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+class MkldnnLinear(torch.jit.ScriptModule):
+    def __init__(self, dense_module, dtype):
+        super().__init__()
+        self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype))
+        if dense_module.bias is not None:
+            # Bias can be fp32 or bf16 for OneDNN bf16 path, but for good accuracy,
+            # we use fp32 dtype.
+            self.register_buffer('bias', dense_module.bias.to_mkldnn())
+        else:
+            # TODO: Remove this once ScriptModule supports registering None buffer
+            self.register_buffer(
+                'bias',
+                torch.zeros([dense_module.weight.size(0)], dtype=torch.float).to_mkldnn())
+
+    @torch.jit.script_method
+    def __getstate__(self):
+        return (self.weight.to_dense(), self.bias.to_dense(), self.training)
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = state[0].to_mkldnn()
+        self.bias = state[1].to_mkldnn()
+        self.training = state[2]
+
+    @torch.jit.script_method
+    def forward(self, x):
+        x_mkldnn = x if x.is_mkldnn else x.to_mkldnn()
+        y_mkldnn = torch._C._nn.mkldnn_linear(x_mkldnn, self.weight, self.bias)
+        y = y_mkldnn if x.is_mkldnn else y_mkldnn.to_dense()
+        return y
+
+
+class _MkldnnConvNd(torch.jit.ScriptModule):
+    """Common base of MkldnnConv1d and MkldnnConv2d."""
+
+    __constants__ = ['stride', 'padding', 'dilation', 'groups']
+
+    def __init__(self, dense_module):
+        super().__init__()
+
+        self.stride = dense_module.stride
+        self.padding = dense_module.padding
+        self.dilation = dense_module.dilation
+        self.groups = dense_module.groups
+
+        if dense_module.bias is not None:
+            self.register_buffer('bias', dense_module.bias.to_mkldnn())
+        else:
+            # Bias can be fp32 or bf16 for OneDNN bf16 path, but for good accuracy,
+            # we use fp32 dtype.
+            # TODO: Remove this once ScriptModule supports registering None buffer
+            self.register_buffer(
+                'bias',
+                torch.zeros([dense_module.weight.size(0)], dtype=torch.float).to_mkldnn())
+
+    @torch.jit.script_method
+    def __getstate__(self):
+        return (self.weight.to_dense(), self.bias.to_dense(), self.training)
+
+    @torch.jit.script_method
+    def forward(self, x):
+        return torch.mkldnn_convolution(
+            x,
+            self.weight,
+            self.bias,
+            self.padding,
+            self.stride,
+            self.dilation,
+            self.groups)
+
+
+class MkldnnConv1d(_MkldnnConvNd):
+    def __init__(self, dense_module, dtype):
+        super().__init__(dense_module)
+
+        self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype))
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = state[0].to_mkldnn()
+        self.bias = state[1].to_mkldnn()
+        self.training = state[2]
+
+
+class MkldnnConv2d(_MkldnnConvNd):
+    def __init__(self, dense_module, dtype):
+        super().__init__(dense_module)
+
+        self.register_buffer('weight', torch._C._nn.mkldnn_reorder_conv2d_weight(
+            dense_module.weight.to_mkldnn(dtype),
+            self.padding,
+            self.stride,
+            self.dilation,
+            self.groups))
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = torch._C._nn.mkldnn_reorder_conv2d_weight(
+            state[0].to_mkldnn(),
+            self.padding,
+            self.stride,
+            self.dilation,
+            self.groups)
+        self.bias = state[1].to_mkldnn()
+        self.training = state[2]
+
+class MkldnnConv3d(_MkldnnConvNd):
+    def __init__(self, dense_module, dtype):
+        super().__init__(dense_module)
+
+        self.register_buffer('weight', torch._C._nn.mkldnn_reorder_conv3d_weight(
+            dense_module.weight.to_mkldnn(dtype),
+            self.padding,
+            self.stride,
+            self.dilation,
+            self.groups))
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = torch._C._nn.mkldnn_reorder_conv3d_weight(
+            state[0].to_mkldnn(),
+            self.padding,
+            self.stride,
+            self.dilation,
+            self.groups)
+        self.bias = state[1].to_mkldnn()
+        self.training = state[2]
+
+
+class MkldnnBatchNorm(torch.jit.ScriptModule):
+    __constants__ = ['exponential_average_factor', 'eps']
+
+    def __init__(self, dense_module):
+        super().__init__()
+
+        assert not dense_module.training
+        assert dense_module.track_running_stats
+        assert dense_module.affine
+
+        if dense_module.momentum is None:
+            self.exponential_average_factor = 0.0
+        else:
+            self.exponential_average_factor = dense_module.momentum
+        self.eps = dense_module.eps
+
+        self.register_buffer('weight', dense_module.weight.to_mkldnn())
+        self.register_buffer('bias', dense_module.bias.to_mkldnn())
+        self.register_buffer('running_mean', dense_module.running_mean.to_mkldnn())
+        self.register_buffer('running_var', dense_module.running_var.to_mkldnn())
+
+    @torch.jit.script_method
+    def __getstate__(self):
+        weight = self.weight.to_dense()
+        bias = self.bias.to_dense()
+        running_mean = self.running_mean.to_dense()
+        running_var = self.running_var.to_dense()
+        return (weight, bias, running_mean, running_var, self.training)
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = state[0].to_mkldnn()
+        self.bias = state[1].to_mkldnn()
+        self.running_mean = state[2].to_mkldnn()
+        self.running_var = state[3].to_mkldnn()
+        self.training = state[4]
+
+    @torch.jit.script_method
+    def forward(self, x):
+        return torch.batch_norm(
+            x,
+            self.weight,
+            self.bias,
+            self.running_mean,
+            self.running_var,
+            False,  # training
+            self.exponential_average_factor,
+            self.eps,
+            False,  # cuda_enabled
+        )
+
+class MkldnnPrelu(torch.jit.ScriptModule):
+    def __init__(self, dense_module, dtype):
+        super().__init__()
+        self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype))
+
+    @torch.jit.script_method
+    def __getstate__(self):
+        return (self.weight.to_dense(), self.training)
+
+    @torch.jit.script_method
+    def __setstate__(self, state):
+        self.weight = state[0].to_mkldnn()
+        self.training = state[1]
+
+    @torch.jit.script_method
+    def forward(self, x):
+        x_mkldnn = x if x.is_mkldnn else x.to_mkldnn()
+        y_mkldnn = torch.prelu(x_mkldnn, self.weight)
+        y = y_mkldnn if x.is_mkldnn else y_mkldnn.to_dense()
+        return y
+
+def to_mkldnn(module, dtype=torch.float):
+    assert dtype in [torch.float, torch.bfloat16, torch.half], \
+        "MKLDNN only support float, bfloat16, and half path now"
+
+    def m_fn(m, d):
+        if isinstance(m, torch.nn.Linear):
+            return MkldnnLinear(m, d)
+        elif isinstance(m, torch.nn.Conv1d):
+            return MkldnnConv1d(m, d)
+        elif isinstance(m, torch.nn.Conv2d):
+            return MkldnnConv2d(m, d)
+        elif isinstance(m, torch.nn.Conv3d):
+            return MkldnnConv3d(m, d)
+        elif isinstance(m, (torch.nn.BatchNorm2d, torch.nn.BatchNorm3d)):
+            # For batchnorm bf16 path, OneDNN requires weight and bias need fp32 dtype.
+            # so it doesn't need dtype argument.
+            return MkldnnBatchNorm(m)
+        elif isinstance(m, torch.nn.PReLU):
+            return MkldnnPrelu(m, d)
+        else:
+            return m
+
+    def m_fn_rec(m, d):
+        new_m = m_fn(m, d)
+        for name, sub_m in m.named_children():
+            setattr(new_m, name, m_fn_rec(sub_m, d))
+        return new_m
+
+    return m_fn_rec(module, dtype)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/mobile_optimizer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/mobile_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..819f19d5b71ea5a236c587ce6be97056f6d7aebb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/mobile_optimizer.py
@@ -0,0 +1,135 @@
+# mypy: allow-untyped-defs
+"""This module contains utility method for mobile model optimization and lint."""
+
+import torch
+from enum import Enum
+from torch._C import _MobileOptimizerType as MobileOptimizerType
+from typing import Optional, AnyStr
+
+class LintCode(Enum):
+    BUNDLED_INPUT = 1
+    REQUIRES_GRAD = 2
+    DROPOUT = 3
+    BATCHNORM = 4
+
+def optimize_for_mobile(
+        script_module: torch.jit.ScriptModule,
+        optimization_blocklist: Optional[set[MobileOptimizerType]] = None,
+        preserved_methods: Optional[list[AnyStr]] = None,
+        backend: str = 'CPU') -> torch.jit.RecursiveScriptModule:
+    """
+    Optimize a torch script module for mobile deployment.
+
+    Args:
+        script_module: An instance of torch script module with type of ScriptModule.
+        optimization_blocklist: A set with type of MobileOptimizerType. When set is not passed,
+            optimization method will run all the optimizer pass; otherwise, optimizer
+            method will run the optimization pass that is not included inside optimization_blocklist.
+        preserved_methods: A list of methods that needed to be preserved when freeze_module pass is invoked
+        backend: Device type to use for running the result model ('CPU'(default), 'Vulkan' or 'Metal').
+    Returns:
+        A new optimized torch script module
+    """
+    if not isinstance(script_module, torch.jit.ScriptModule):
+        raise TypeError(
+            f'Got {type(script_module)}, but ScriptModule is expected.')
+
+    if optimization_blocklist is None:
+        optimization_blocklist = set()
+
+    if preserved_methods is None:
+        preserved_methods = []
+
+    # Convert potential byte arrays into strings (if there is any) to pass type checking
+    # Here we use a new name as assigning it back to preserved_methods will invoke
+    # mypy errors (i.e. List[AnyStr] = List[str])
+    preserved_methods_str: list[str] = [str(method) for method in preserved_methods]
+
+    bundled_inputs_attributes = _get_bundled_inputs_preserved_attributes(script_module, preserved_methods_str)
+    if all(hasattr(script_module, method) for method in bundled_inputs_attributes):
+        preserved_methods_str = list(set(preserved_methods_str + bundled_inputs_attributes))
+
+    non_exist_methods = [method for method in preserved_methods_str if not hasattr(script_module, method)]
+    if non_exist_methods:
+        raise AttributeError(
+            f"The following methods to preserve do not exist in script_module: {', '.join(non_exist_methods)}")
+
+    backend = backend.lower()
+    if backend == 'cpu':
+        optimized_cpp_module = torch._C._jit_pass_optimize_for_mobile(
+            script_module._c,
+            optimization_blocklist,
+            preserved_methods_str)
+    elif backend == 'vulkan':
+        optimized_cpp_module = torch._C._jit_pass_vulkan_optimize_for_mobile(
+            script_module._c,
+            optimization_blocklist,
+            preserved_methods_str)
+    elif backend == 'metal':
+        optimized_cpp_module = torch._C._jit_pass_metal_optimize_for_mobile(script_module._c, preserved_methods_str)
+    else:
+        raise TypeError("Unknown backend, must be one of 'CPU', 'Vulkan' or 'Metal'")
+
+    return torch.jit._recursive.wrap_cpp_module(optimized_cpp_module)
+
+
+def generate_mobile_module_lints(script_module: torch.jit.ScriptModule):
+    """
+    Generate a list of lints for a given torch script module.
+
+    Args:
+        script_module: An instance of torch script module with type of ScriptModule.
+
+    Returns:
+        lint_map: A list of dictionary that contains modules lints
+    """
+    if not isinstance(script_module, torch.jit.ScriptModule):
+        raise TypeError(
+            f'Got {type(script_module)}, but ScriptModule is expected.')
+
+    lint_list = []
+
+    if not hasattr(script_module, "_generate_bundled_inputs_for_forward"):
+        lint_list.append({"name": LintCode.BUNDLED_INPUT.name, "message": "No bundled input for forward, please add bundled inputs "
+                          "before saving the module using torch.utils.bundled_inputs.augment_model_with_bundled_inputs."})
+
+    for name, param in script_module.named_parameters():
+        if param.requires_grad:
+            lint_list.append({"name": LintCode.REQUIRES_GRAD.name, "message": f"Param {name} requires grad, "
+                             "please set torch.no_grad() to reduce memory usage and improve computation speed during "
+                              "inference phase."})
+
+    op_names = torch.jit.export_opnames(script_module)
+    for op_name in op_names:
+        if "dropout" in op_name:
+            lint_list.append({"name": LintCode.DROPOUT.name,
+                              "message": f"Operator {op_name} exists, remember to call eval() before "
+                              "saving the module.and call torch.utils.mobile_optimizer.optimize_for_mobile to drop dropout "
+                              "operator."})
+        if "batch_norm" in op_name:
+            lint_list.append({"name": LintCode.BATCHNORM.name,
+                              "message": f"Operator {op_name} exists, remember to call eval() before "
+                              "saving the module and call torch.utils.mobile_optimizer.optimize_for_mobile to drop batch_norm "
+                              "operator."})
+
+    return lint_list
+
+def _get_bundled_inputs_preserved_attributes(script_module: torch.jit.ScriptModule, preserved_methods: list[str]) -> list[str]:
+
+    bundled_inputs_attributes = []
+    # Has bundled inputs for forward
+    if hasattr(script_module, 'get_all_bundled_inputs'):
+        bundled_inputs_attributes.append('get_all_bundled_inputs')
+        bundled_inputs_attributes.append('get_num_bundled_inputs')
+
+    # Bundled inputs in module after the change that introduced bundled inputs for multiple functions
+    if hasattr(script_module, 'get_bundled_inputs_functions_and_info'):
+        bundled_inputs_attributes.append('get_bundled_inputs_functions_and_info')
+        all_info = script_module.get_bundled_inputs_functions_and_info()
+        for function_name in all_info:
+            if function_name not in preserved_methods:
+                bundled_inputs_attributes.append(function_name)
+            bundled_inputs_attributes.append("get_all_bundled_inputs_for_" + function_name)
+            bundled_inputs_attributes.append("_bundled_inputs_deflated_" + function_name)
+
+    return bundled_inputs_attributes
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ab8fd9a35e11fa0571f04adf25181c1cd14d42b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/__init__.py
@@ -0,0 +1,410 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+"""
+model_dump: a one-stop shop for TorchScript model inspection.
+
+The goal of this tool is to provide a simple way to extract lots of
+useful information from a TorchScript model and make it easy for humans
+to consume.  It (mostly) replaces zipinfo, common uses of show_pickle,
+and various ad-hoc analysis notebooks.
+
+The tool extracts information from the model and serializes it as JSON.
+That JSON can then be rendered by an HTML+JS page, either by
+loading the JSON over HTTP or producing a fully self-contained page
+with all of the code and data burned-in.
+"""
+
+# Maintainer notes follow.
+"""
+The implementation strategy has tension between 3 goals:
+- Small file size.
+- Fully self-contained.
+- Easy, modern JS environment.
+Using Preact and HTM achieves 1 and 2 with a decent result for 3.
+However, the models I tested with result in ~1MB JSON output,
+so even using something heavier like full React might be tolerable
+if the build process can be worked out.
+
+One principle I have followed that I think is very beneficial
+is to keep the JSON data as close as possible to the model
+and do most of the rendering logic on the client.
+This makes for easier development (just refresh, usually),
+allows for more laziness and dynamism, and lets us add more
+views of the same data without bloating the HTML file.
+
+Currently, this code doesn't actually load the model or even
+depend on any part of PyTorch.  I don't know if that's an important
+feature to maintain, but it's probably worth preserving the ability
+to run at least basic analysis on models that cannot be loaded.
+
+I think the easiest way to develop this code is to cd into model_dump and
+run "python -m http.server", then load http://localhost:8000/skeleton.html
+in the browser.  In another terminal, run
+"python -m torch.utils.model_dump --style=json FILE > \
+    torch/utils/model_dump/model_info.json"
+every time you update the Python code or model.
+When you update JS, just refresh.
+
+Possible improvements:
+    - Fix various TODO comments in this file and the JS.
+    - Make the HTML much less janky, especially the auxiliary data panel.
+    - Make the auxiliary data panel start small, expand when
+      data is available, and have a button to clear/contract.
+    - Clean up the JS.  There's a lot of copypasta because
+      I don't really know how to use Preact.
+    - Make the HTML render and work nicely inside a Jupyter notebook.
+    - Add the ability for JS to choose the URL to load the JSON based
+      on the page URL (query or hash).  That way we could publish the
+      inlined skeleton once and have it load various JSON blobs.
+    - Add a button to expand all expandable sections so ctrl-F works well.
+    - Add hyperlinking from data to code, and code to code.
+    - Add hyperlinking from debug info to Diffusion.
+    - Make small tensor contents available.
+    - Do something nice for quantized models
+      (they probably don't work at all right now).
+"""
+
+import argparse
+import io
+import json
+import os
+import pickle
+import pprint
+import re
+import sys
+import urllib.parse
+import zipfile
+from pathlib import Path
+import warnings
+
+import torch.utils.show_pickle
+
+
+DEFAULT_EXTRA_FILE_SIZE_LIMIT = 16 * 1024
+
+__all__ = ['get_storage_info', 'hierarchical_pickle', 'get_model_info', 'get_inline_skeleton',
+           'burn_in_info', 'get_info_and_burn_skeleton']
+
+def get_storage_info(storage):
+    assert isinstance(storage, torch.utils.show_pickle.FakeObject)
+    assert storage.module == "pers"
+    assert storage.name == "obj"
+    assert storage.state is None
+    assert isinstance(storage.args, tuple)
+    assert len(storage.args) == 1
+    sa = storage.args[0]
+    assert isinstance(sa, tuple)
+    assert len(sa) == 5
+    assert sa[0] == "storage"
+    assert isinstance(sa[1], torch.utils.show_pickle.FakeClass)
+    assert sa[1].module == "torch"
+    assert sa[1].name.endswith("Storage")
+    storage_info = [sa[1].name.replace("Storage", "")] + list(sa[2:])
+    return storage_info
+
+
+def hierarchical_pickle(data):
+    if isinstance(data, (bool, int, float, str, type(None))):
+        return data
+    if isinstance(data, list):
+        return [hierarchical_pickle(d) for d in data]
+    if isinstance(data, tuple):
+        return {
+            "__tuple_values__": hierarchical_pickle(list(data)),
+        }
+    if isinstance(data, dict):
+        return {
+            "__is_dict__": True,
+            "keys": hierarchical_pickle(list(data.keys())),
+            "values": hierarchical_pickle(list(data.values())),
+        }
+    if isinstance(data, torch.utils.show_pickle.FakeObject):
+        typename = f"{data.module}.{data.name}"
+        if (
+            typename.startswith(('__torch__.', 'torch.jit.LoweredWrapper.', 'torch.jit.LoweredModule.'))
+        ):
+            assert data.args == ()
+            return {
+                "__module_type__": typename,
+                "state": hierarchical_pickle(data.state),
+            }
+        if typename == "torch._utils._rebuild_tensor_v2":
+            assert data.state is None
+            storage, offset, size, stride, requires_grad, *_ = data.args
+            storage_info = get_storage_info(storage)
+            return {"__tensor_v2__": [storage_info, offset, size, stride, requires_grad]}
+        if typename == "torch._utils._rebuild_qtensor":
+            assert data.state is None
+            storage, offset, size, stride, quantizer, requires_grad, *_ = data.args
+            storage_info = get_storage_info(storage)
+            assert isinstance(quantizer, tuple)
+            assert isinstance(quantizer[0], torch.utils.show_pickle.FakeClass)
+            assert quantizer[0].module == "torch"
+            if quantizer[0].name == "per_tensor_affine":
+                assert len(quantizer) == 3
+                assert isinstance(quantizer[1], float)
+                assert isinstance(quantizer[2], int)
+                quantizer_extra = list(quantizer[1:3])
+            else:
+                quantizer_extra = []
+            quantizer_json = [quantizer[0].name] + quantizer_extra
+            return {"__qtensor__": [storage_info, offset, size, stride, quantizer_json, requires_grad]}
+        if typename == "torch.jit._pickle.restore_type_tag":
+            assert data.state is None
+            obj, typ = data.args
+            assert isinstance(typ, str)
+            return hierarchical_pickle(obj)
+        if re.fullmatch(r"torch\.jit\._pickle\.build_[a-z]+list", typename):
+            assert data.state is None
+            ls, = data.args
+            assert isinstance(ls, list)
+            return hierarchical_pickle(ls)
+        if typename == "torch.device":
+            assert data.state is None
+            name, = data.args
+            assert isinstance(name, str)
+            # Just forget that it was a device and return the name.
+            return name
+        if typename == "builtin.UnicodeDecodeError":
+            assert data.state is None
+            msg, = data.args
+            assert isinstance(msg, str)
+            # Hack: Pretend this is a module so we don't need custom serialization.
+            # Hack: Wrap the message in a tuple so it looks like a nice state object.
+            # TODO: Undo at least that second hack.  We should support string states.
+            return {
+                "__module_type__": typename,
+                "state": hierarchical_pickle((msg,)),
+            }
+        raise Exception(f"Can't prepare fake object of type for JS: {typename}")  # noqa: TRY002
+    raise Exception(f"Can't prepare data of type for JS: {type(data)}")  # noqa: TRY002
+
+
+def get_model_info(
+        path_or_file,
+        title=None,
+        extra_file_size_limit=DEFAULT_EXTRA_FILE_SIZE_LIMIT):
+    """Get JSON-friendly information about a model.
+
+    The result is suitable for being saved as model_info.json,
+    or passed to burn_in_info.
+    """
+
+    if isinstance(path_or_file, os.PathLike):
+        default_title = os.fspath(path_or_file)
+        file_size = path_or_file.stat().st_size  # type: ignore[attr-defined]
+    elif isinstance(path_or_file, str):
+        default_title = path_or_file
+        file_size = Path(path_or_file).stat().st_size
+    else:
+        default_title = "buffer"
+        path_or_file.seek(0, io.SEEK_END)
+        file_size = path_or_file.tell()
+        path_or_file.seek(0)
+
+    title = title or default_title
+
+    with zipfile.ZipFile(path_or_file) as zf:
+        path_prefix = None
+        zip_files = []
+        for zi in zf.infolist():
+            prefix = re.sub("/.*", "", zi.filename)
+            if path_prefix is None:
+                path_prefix = prefix
+            elif prefix != path_prefix:
+                raise Exception(f"Mismatched prefixes: {path_prefix} != {prefix}")  # noqa: TRY002
+            zip_files.append(dict(
+                filename=zi.filename,
+                compression=zi.compress_type,
+                compressed_size=zi.compress_size,
+                file_size=zi.file_size,
+            ))
+
+        assert path_prefix is not None
+        version = zf.read(path_prefix + "/version").decode("utf-8").strip()
+
+        def get_pickle(name):
+            assert path_prefix is not None
+            with zf.open(path_prefix + f"/{name}.pkl") as handle:
+                raw = torch.utils.show_pickle.DumpUnpickler(handle, catch_invalid_utf8=True).load()
+                return hierarchical_pickle(raw)
+
+        model_data = get_pickle("data")
+        constants = get_pickle("constants")
+
+        # Intern strings that are likely to be re-used.
+        # Pickle automatically detects shared structure,
+        # so re-used strings are stored efficiently.
+        # However, JSON has no way of representing this,
+        # so we have to do it manually.
+        interned_strings : dict[str, int] = {}
+
+        def ist(s):
+            if s not in interned_strings:
+                interned_strings[s] = len(interned_strings)
+            return interned_strings[s]
+
+        code_files = {}
+        for zi in zf.infolist():
+            if not zi.filename.endswith(".py"):
+                continue
+            with zf.open(zi) as handle:
+                raw_code = handle.read()
+            with zf.open(zi.filename + ".debug_pkl") as handle:
+                raw_debug = handle.read()
+
+            # Parse debug info and add begin/end markers if not present
+            # to ensure that we cover the entire source code.
+            debug_info_t = pickle.loads(raw_debug)
+            text_table = None
+
+            if (len(debug_info_t) == 3 and
+                    isinstance(debug_info_t[0], str) and
+                    debug_info_t[0] == 'FORMAT_WITH_STRING_TABLE'):
+                _, text_table, content = debug_info_t
+
+                def parse_new_format(line):
+                    # (0, (('', '', 0), 0, 0))
+                    num, ((text_indexes, fname_idx, offset), start, end), tag = line
+                    text = ''.join(text_table[x] for x in text_indexes)  # type: ignore[index]
+                    fname = text_table[fname_idx]  # type: ignore[index]
+                    return num, ((text, fname, offset), start, end), tag
+
+                debug_info_t = map(parse_new_format, content)
+
+            debug_info = list(debug_info_t)
+            if not debug_info:
+                debug_info.append((0, (('', '', 0), 0, 0)))
+            if debug_info[-1][0] != len(raw_code):
+                debug_info.append((len(raw_code), (('', '', 0), 0, 0)))
+
+            code_parts = []
+            for di, di_next in zip(debug_info, debug_info[1:]):
+                start, source_range, *_ = di
+                end = di_next[0]
+                assert end > start
+                source, s_start, s_end = source_range
+                s_text, s_file, s_line = source
+                # TODO: Handle this case better.  TorchScript ranges are in bytes,
+                # but JS doesn't really handle byte strings.
+                # if bytes and chars are not equivalent for this string,
+                # zero out the ranges so we don't highlight the wrong thing.
+                if len(s_text) != len(s_text.encode("utf-8")):
+                    s_start = 0
+                    s_end = 0
+                text = raw_code[start:end]
+                code_parts.append([text.decode("utf-8"), ist(s_file), s_line, ist(s_text), s_start, s_end])
+            code_files[zi.filename] = code_parts
+
+        extra_files_json_pattern = re.compile(re.escape(path_prefix) + "/extra/.*\\.json")
+        extra_files_jsons = {}
+        for zi in zf.infolist():
+            if not extra_files_json_pattern.fullmatch(zi.filename):
+                continue
+            if zi.file_size > extra_file_size_limit:
+                continue
+            with zf.open(zi) as handle:
+                try:
+                    json_content = json.load(handle)
+                    extra_files_jsons[zi.filename] = json_content
+                except json.JSONDecodeError:
+                    extra_files_jsons[zi.filename] = "INVALID JSON"
+
+        always_render_pickles = {
+            "bytecode.pkl",
+        }
+        extra_pickles = {}
+        for zi in zf.infolist():
+            if not zi.filename.endswith(".pkl"):
+                continue
+            with zf.open(zi) as handle:
+                # TODO: handle errors here and just ignore the file?
+                # NOTE: For a lot of these files (like bytecode),
+                # we could get away with just unpickling, but this should be safer.
+                obj = torch.utils.show_pickle.DumpUnpickler(handle, catch_invalid_utf8=True).load()
+            buf = io.StringIO()
+            pprint.pprint(obj, buf)
+            contents = buf.getvalue()
+            # Checked the rendered length instead of the file size
+            # because pickles with shared structure can explode in size during rendering.
+            if os.path.basename(zi.filename) not in always_render_pickles and \
+                    len(contents) > extra_file_size_limit:
+                continue
+            extra_pickles[zi.filename] = contents
+
+    return {"model": dict(
+        title=title,
+        file_size=file_size,
+        version=version,
+        zip_files=zip_files,
+        interned_strings=list(interned_strings),
+        code_files=code_files,
+        model_data=model_data,
+        constants=constants,
+        extra_files_jsons=extra_files_jsons,
+        extra_pickles=extra_pickles,
+    )}
+
+
+def get_inline_skeleton():
+    """Get a fully-inlined skeleton of the frontend.
+
+    The returned HTML page has no external network dependencies for code.
+    It can load model_info.json over HTTP, or be passed to burn_in_info.
+    """
+
+    import importlib.resources
+
+    skeleton = importlib.resources.read_text(__package__, "skeleton.html")
+    js_code = importlib.resources.read_text(__package__, "code.js")
+    for js_module in ["preact", "htm"]:
+        js_lib = importlib.resources.read_binary(__package__, f"{js_module}.mjs")
+        js_url = "data:application/javascript," + urllib.parse.quote(js_lib)
+        js_code = js_code.replace(f"https://unpkg.com/{js_module}?module", js_url)
+    skeleton = skeleton.replace(' src="./code.js">', ">\n" + js_code)
+    return skeleton
+
+
+def burn_in_info(skeleton, info):
+    """Burn model info into the HTML skeleton.
+
+    The result will render the hard-coded model info and
+    have no external network dependencies for code or data.
+    """
+
+    # Note that Python's json serializer does not escape slashes in strings.
+    # Since we're inlining this JSON directly into a script tag, a string
+    # containing "" would end the script prematurely and
+    # mess up our page.  Unconditionally escape fixes that.
+    return skeleton.replace(
+        "BURNED_IN_MODEL_INFO = null",
+        "BURNED_IN_MODEL_INFO = " + json.dumps(info, sort_keys=True).replace("/", "\\/"))
+
+
+def get_info_and_burn_skeleton(path_or_bytesio, **kwargs):
+    model_info = get_model_info(path_or_bytesio, **kwargs)
+    skeleton = get_inline_skeleton()
+    page = burn_in_info(skeleton, model_info)
+    return page
+
+
+def main(argv, *, stdout=None):
+    warnings.warn("torch.utils.model_dump is deprecated and will be removed in a future PyTorch release.")
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--style", choices=["json", "html"])
+    parser.add_argument("--title")
+    parser.add_argument("model")
+    args = parser.parse_args(argv[1:])
+
+    info = get_model_info(args.model, title=args.title)
+
+    output = stdout or sys.stdout
+
+    if args.style == "json":
+        output.write(json.dumps(info, sort_keys=True) + "\n")
+    elif args.style == "html":
+        skeleton = get_inline_skeleton()
+        page = burn_in_info(skeleton, info)
+        output.write(page)
+    else:
+        raise Exception("Invalid style")  # noqa: TRY002
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/__main__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d4bdac389bb1f270d74efb6c876258d46077110
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/__main__.py
@@ -0,0 +1,5 @@
+#!/usr/bin/env python3
+import sys
+from . import main
+
+sys.exit(main(sys.argv))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/code.js b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/code.js
new file mode 100644
index 0000000000000000000000000000000000000000..173ddfb639d847159ee4fdf46691404bf1bbb7a3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/code.js
@@ -0,0 +1,689 @@
+import { h, Component, render } from 'https://unpkg.com/preact?module';
+import htm from 'https://unpkg.com/htm?module';
+
+const html = htm.bind(h);
+
+const BURNED_IN_MODEL_INFO = null;
+
+// https://stackoverflow.com/a/20732091
+function humanFileSize(size) {
+  if (size == 0) { return "0 B"; }
+  var i = Math.floor( Math.log(size) / Math.log(1024) );
+  return (size / Math.pow(1024, i)).toFixed(2) * 1 + ' ' + ['B', 'kB', 'MB', 'GB', 'TB'][i];
+}
+
+function caret(down) {
+  return down ? "\u25BE" : "\u25B8";
+}
+
+class Blamer {
+  constructor() {
+    this.blame_on_click = false;
+    this.aux_content_pane = null;
+  }
+
+  setAuxContentPane(pane) {
+    this.aux_content_pane = pane;
+  }
+
+  readyBlame() {
+    this.blame_on_click = true;
+  }
+
+  maybeBlame(arg) {
+    if (!this.blame_on_click) {
+      return;
+    }
+    this.blame_on_click = false;
+    if (!this.aux_content_pane) {
+      return;
+    }
+    this.aux_content_pane.doBlame(arg);
+  }
+}
+
+let blame = new Blamer();
+
+class Hider extends Component {
+  constructor() {
+    super();
+    this.state = { shown: null };
+  }
+
+  componentDidMount() {
+    this.setState({ shown: this.props.shown === "true" });
+  }
+
+  render({name, children}, {shown}) {
+    let my_caret = html` this.click()} >${caret(shown)}`;
+    return html`

+      
${my_caret} ${name}

+      
${shown ? this.props.children : []}
`;
+  }
+
+  click() {
+    this.setState({shown: !this.state.shown});
+  }
+}
+
+function ModelSizeSection({model: {file_size, zip_files}}) {
+  let store_size = 0;
+  let compr_size = 0;
+  for (const zi of zip_files) {
+    if (zi.compression === 0) {
+      // TODO: Maybe check that compressed_size === file_size.
+      store_size += zi.compressed_size;
+    } else {
+      compr_size += zi.compressed_size;
+    }
+  }
+  let zip_overhead = file_size - store_size - compr_size;
+  // TODO: Better formatting.  Right-align this.
+  return html`
+    <${Hider} name="Model Size" shown=true>
+    
.
+      Model size: ${file_size} (${humanFileSize(file_size)})
+      Stored files: ${store_size} (${humanFileSize(store_size)})
+      Compressed files: ${compr_size} (${humanFileSize(compr_size)})
+      Zip overhead: ${zip_overhead} (${humanFileSize(zip_overhead)})
+    
`;
+}
+
+function StructuredDataSection({name, data, shown}) {
+  return html`
+    <${Hider} name=${name} shown=${shown}>
+    

+      <${StructuredData} data=${data} indent="" prefix=""/>
+    
`;
+}
+
+class StructuredData extends Component {
+  constructor() {
+    super();
+    this.state = { shown: false };
+
+    this.INLINE_TYPES = new Set(["boolean", "number", "string"])
+    this.IGNORED_STATE_KEYS = new Set(["training", "_is_full_backward_hook"])
+  }
+
+  click() {
+    this.setState({shown: !this.state.shown});
+  }
+
+  expando(data) {
+    if (data === null || this.INLINE_TYPES.has(typeof(data))) {
+      return false;
+    }
+    if (typeof(data) != "object") {
+      throw new Error("Not an object");
+    }
+    if (Array.isArray(data)) {
+      // TODO: Maybe show simple lists and tuples on one line.
+      return true;
+    }
+    if (data.__tuple_values__) {
+      // TODO: Maybe show simple lists and tuples on one line.
+      return true;
+    }
+    if (data.__is_dict__) {
+      // TODO: Maybe show simple (empty?) dicts on one line.
+      return true;
+    }
+    if (data.__module_type__) {
+      return true;
+    }
+    if (data.__tensor_v2__) {
+      return false;
+    }
+    if (data.__qtensor__) {
+      return false;
+    }
+    throw new Error("Can't handle data type.", data);
+  }
+
+  renderHeadline(data) {
+    if (data === null) {
+      return "None";
+    }
+    if (typeof(data) == "boolean") {
+      const sd = String(data);
+      return sd.charAt(0).toUpperCase() + sd.slice(1);
+    }
+    if (typeof(data) == "number") {
+      return JSON.stringify(data);
+    }
+    if (typeof(data) == "string") {
+      return JSON.stringify(data);
+    }
+    if (typeof(data) != "object") {
+      throw new Error("Not an object");
+    }
+    if (Array.isArray(data)) {
+      return "list([";
+    }
+    if (data.__tuple_values__) {
+      return "tuple((";
+    }
+    if (data.__is_dict__) {
+      return "dict({";
+    }
+    if (data.__module_type__) {
+      return data.__module_type__ + "()";
+    }
+    if (data.__tensor_v2__) {
+      const [storage, offset, size, stride, grad] = data.__tensor_v2__;
+      const [dtype, key, device, numel] = storage;
+      return this.renderTensor(
+        "tensor", dtype, key, device, numel, offset, size, stride, grad, []);
+    }
+    if (data.__qtensor__) {
+      const [storage, offset, size, stride, quantizer, grad] = data.__qtensor__;
+      const [dtype, key, device, numel] = storage;
+      let extra_parts = [];
+      if (quantizer[0] == "per_tensor_affine") {
+        extra_parts.push(`scale=${quantizer[1]}`);
+        extra_parts.push(`zero_point=${quantizer[2]}`);
+      } else {
+        extra_parts.push(`quantizer=${quantizer[0]}`);
+      }
+      return this.renderTensor(
+        "qtensor", dtype, key, device, numel, offset, size, stride, grad, extra_parts);
+    }
+    throw new Error("Can't handle data type.", data);
+  }
+
+  renderTensor(
+      prefix,
+      dtype,
+      storage_key,
+      device,
+      storage_numel,
+      offset,
+      size,
+      stride,
+      grad,
+      extra_parts) {
+    let parts = [
+      "(" + size.join(",") + ")",
+      dtype,
+    ];
+    parts.push(...extra_parts);
+    if (device != "cpu") {
+      parts.push(device);
+    }
+    if (grad) {
+      parts.push("grad");
+    }
+    // TODO: Check stride and indicate if the tensor is channels-last or non-contiguous
+    // TODO: Check size, stride, offset, and numel and indicate if
+    // the tensor doesn't use all data in storage.
+    // TODO: Maybe show key?
+    void(offset);
+    void(stride);
+    void(storage_key);
+    void(storage_numel);
+    return prefix + "(" + parts.join(", ") + ")";
+  }
+
+  renderBody(indent, data) {
+    if (data === null || this.INLINE_TYPES.has(typeof(data))) {
+      throw "Should not reach here."
+    }
+    if (typeof(data) != "object") {
+      throw new Error("Not an object");
+    }
+    if (Array.isArray(data)) {
+      let new_indent = indent + "\u00A0\u00A0";
+      let parts = [];
+      for (let idx = 0; idx < data.length; idx++) {
+        // Does it make sense to put explicit index numbers here?
+        parts.push(html`
<${StructuredData} prefix=${idx + ": "} indent=${new_indent} data=${data[idx]} />`);
+      }
+      return parts;
+    }
+    if (data.__tuple_values__) {
+      // Handled the same as lists.
+      return this.renderBody(indent, data.__tuple_values__);
+    }
+    if (data.__is_dict__) {
+      let new_indent = indent + "\u00A0\u00A0";
+      let parts = [];
+      for (let idx = 0; idx < data.keys.length; idx++) {
+        if (typeof(data.keys[idx]) != "string") {
+          parts.push(html`
${new_indent}Non-string key`);
+        } else {
+          parts.push(html`
<${StructuredData} prefix=${data.keys[idx] + ": "} indent=${new_indent} data=${data.values[idx]} />`);
+        }
+      }
+      return parts;
+    }
+    if (data.__module_type__) {
+      const mstate = data.state;
+      if (mstate === null || typeof(mstate) != "object") {
+        throw new Error("Bad module state");
+      }
+      let new_indent = indent + "\u00A0\u00A0";
+      let parts = [];
+      if (mstate.__is_dict__) {
+        // TODO: Less copy/paste between this and normal dicts.
+        for (let idx = 0; idx < mstate.keys.length; idx++) {
+          if (typeof(mstate.keys[idx]) != "string") {
+            parts.push(html`
${new_indent}Non-string key`);
+          } else if (this.IGNORED_STATE_KEYS.has(mstate.keys[idx])) {
+            // Do nothing.
+          } else {
+            parts.push(html`
<${StructuredData} prefix=${mstate.keys[idx] + ": "} indent=${new_indent} data=${mstate.values[idx]} />`);
+          }
+        }
+      } else if (mstate.__tuple_values__) {
+        parts.push(html`
<${StructuredData} prefix="" indent=${new_indent} data=${mstate} />`);
+      } else if (mstate.__module_type__) {
+        // We normally wouldn't have the state of a module be another module,
+        // but we use "modules" to encode special values (like Unicode decode
+        // errors) that might be valid states.  Just go with it.
+        parts.push(html`
<${StructuredData} prefix="" indent=${new_indent} data=${mstate} />`);
+      } else {
+        throw new Error("Bad module state");
+      }
+      return parts;
+    }
+    if (data.__tensor_v2__) {
+      throw "Should not reach here."
+    }
+    if (data.__qtensor__) {
+      throw "Should not reach here."
+    }
+    throw new Error("Can't handle data type.", data);
+  }
+
+  render({data, indent, prefix}, {shown}) {
+    const exp = this.expando(data) ? html` this.click()} >${caret(shown)} ` : "";
+    const headline = this.renderHeadline(data);
+    const body = shown ? this.renderBody(indent, data) : "";
+    return html`${indent}${exp}${prefix}${headline}${body}`;
+  }
+}
+
+function ZipContentsSection({model: {zip_files}}) {
+  // TODO: Add human-readable sizes?
+  // TODO: Add sorting options?
+  // TODO: Add hierarchical collapsible tree?
+  return html`
+    <${Hider} name="Zip Contents" shown=false>
+    
+      
+        
+          
+          
+          
+          
+        
+      
+      
+        ${zip_files.map(zf => html`
+          
+          
+          
+          
+        `)}
+      
+    
ModeSizeCompressedName
${{0: "store", 8: "deflate"}[zf.compression] || zf.compression}${zf.file_size}${zf.compressed_size}${zf.filename}
`;
+}
+
+function CodeSection({model: {code_files}}) {
+  return html`
+    <${Hider} name="Code" shown=false>
+    

+      ${Object.entries(code_files).map(([fn, code]) => html`<${OneCodeSection}
+          filename=${fn} code=${code} />`)}
+    
`;
+}
+
+class OneCodeSection extends Component {
+  constructor() {
+    super();
+    this.state = { shown: false };
+  }
+
+  click() {
+    const shown = !this.state.shown;
+    this.setState({shown: shown});
+  }
+
+  render({filename, code}, {shown}) {
+    const header = html`
+        

+         this.click()} >${caret(shown)} 
+        ${filename}

+        `;
+    if (!shown) {
+      return header;
+    }
+    return html`
+      ${header}
+      
${code.map(c => this.renderBlock(c))}

+      `;
+  }
+
+  renderBlock([text, ist_file, line, ist_s_text, s_start, s_end]) {
+    return html` blame.maybeBlame({ist_file, line, ist_s_text, s_start, s_end})}
+      >${text}`;
+  }
+}
+
+function ExtraJsonSection({files}) {
+  return html`
+    <${Hider} name="Extra files (JSON)" shown=false>
+    

+      
Use "Log Raw Model Info" for hierarchical view in browser console.

+      ${Object.entries(files).map(([fn, json]) => html`<${OneJsonSection}
+          filename=${fn} json=${json} />`)}
+    
`;
+}
+
+class OneJsonSection extends Component {
+  constructor() {
+    super();
+    this.state = { shown: false };
+  }
+
+  click() {
+    const shown = !this.state.shown;
+    this.setState({shown: shown});
+  }
+
+  render({filename, json}, {shown}) {
+    const header = html`
+        

+         this.click()} >${caret(shown)} 
+        ${filename}

+        `;
+    if (!shown) {
+      return header;
+    }
+    return html`
+      ${header}
+      
${JSON.stringify(json, null, 2)}

+      `;
+  }
+}
+
+function ExtraPicklesSection({files}) {
+  return html`
+    <${Hider} name="Extra Pickles" shown=false>
+    

+      ${Object.entries(files).map(([fn, content]) => html`<${OnePickleSection}
+          filename=${fn} content=${content} />`)}
+    
`;
+}
+
+class OnePickleSection extends Component {
+  constructor() {
+    super();
+    this.state = { shown: false };
+  }
+
+  click() {
+    const shown = !this.state.shown;
+    this.setState({shown: shown});
+  }
+
+  render({filename, content}, {shown}) {
+    const header = html`
+        

+         this.click()} >${caret(shown)} 
+        ${filename}

+        `;
+    if (!shown) {
+      return header;
+    }
+    return html`
+      ${header}
+      
${content}

+      `;
+  }
+}
+
+function assertStorageAreEqual(key, lhs, rhs) {
+  if (lhs.length !== rhs.length ||
+    !lhs.every((val, idx) => val === rhs[idx])) {
+    throw new Error("Storage mismatch for key '" + key + "'");
+  }
+}
+
+function computeTensorMemory(numel, dtype) {
+  const sizes = {
+    "Byte": 1,
+    "Char": 1,
+    "Short": 2,
+    "Int": 4,
+    "Long": 8,
+    "Half": 2,
+    "Float": 4,
+    "Double": 8,
+    "ComplexHalf": 4,
+    "ComplexFloat": 8,
+    "ComplexDouble": 16,
+    "Bool": 1,
+    "QInt8": 1,
+    "QUInt8": 1,
+    "QInt32": 4,
+    "BFloat16": 2,
+  };
+  let dtsize = sizes[dtype];
+  if (!dtsize) {
+    throw new Error("Unrecognized dtype: " + dtype);
+  }
+  return numel * dtsize;
+}
+
+// TODO: Maybe track by dtype as well.
+// TODO: Maybe distinguish between visible size and storage size.
+function getTensorStorages(data) {
+  if (data === null) {
+    return new Map();
+  }
+  if (typeof(data) == "boolean") {
+    return new Map();
+  }
+  if (typeof(data) == "number") {
+    return new Map();
+  }
+  if (typeof(data) == "string") {
+    return new Map();
+  }
+  if (typeof(data) != "object") {
+    throw new Error("Not an object");
+  }
+  if (Array.isArray(data)) {
+    let result = new Map();
+    for (const item of data) {
+      const tensors = getTensorStorages(item);
+      for (const [key, storage] of tensors.entries()) {
+        if (!result.has(key)) {
+          result.set(key, storage);
+        } else {
+          const old_storage = result.get(key);
+          assertStorageAreEqual(key, old_storage, storage);
+        }
+      }
+    }
+    return result;
+  }
+  if (data.__tuple_values__) {
+    return getTensorStorages(data.__tuple_values__);
+  }
+  if (data.__is_dict__) {
+    return getTensorStorages(data.values);
+  }
+  if (data.__module_type__) {
+    return getTensorStorages(data.state);
+  }
+  if (data.__tensor_v2__) {
+    const [storage, offset, size, stride, grad] = data.__tensor_v2__;
+    const [dtype, key, device, numel] = storage;
+    return new Map([[key, storage]]);
+  }
+  if (data.__qtensor__) {
+    const [storage, offset, size, stride, quantizer, grad] = data.__qtensor__;
+    const [dtype, key, device, numel] = storage;
+    return new Map([[key, storage]]);
+  }
+  throw new Error("Can't handle data type.", data);
+}
+
+function getTensorMemoryByDevice(pickles) {
+  let all_tensors = [];
+  for (const [name, pickle] of pickles) {
+    const tensors = getTensorStorages(pickle);
+    all_tensors.push(...tensors.values());
+  }
+  let result = {};
+  for (const storage of all_tensors.values()) {
+    const [dtype, key, device, numel] = storage;
+    const size = computeTensorMemory(numel, dtype);
+    result[device] = (result[device] || 0) + size;
+  }
+  return result;
+}
+
+// Make this a separate component so it is rendered lazily.
+class OpenTensorMemorySection extends Component {
+  render({model: {model_data, constants}}) {
+    let sizes = getTensorMemoryByDevice(new Map([
+      ["data", model_data],
+      ["constants", constants],
+    ]));
+    return html`
+      
+        
+          
+            
+            
+            
+          
+        
+        
+          ${Object.entries(sizes).map(([dev, size]) => html`
+            
+            
+            
+          `)}
+        
+      
DeviceBytesHuman
${dev}${size}${humanFileSize(size)}
`;
+  }
+}
+
+function TensorMemorySection({model}) {
+  return html`
+    <${Hider} name="Tensor Memory" shown=false>
+    <${OpenTensorMemorySection} model=${model} />`;
+}
+
+class AuxContentPane extends Component {
+  constructor() {
+    super();
+    this.state = {
+      blame_info: null,
+    };
+  }
+
+  doBlame(arg) {
+    this.setState({...this.state, blame_info: arg});
+  }
+
+  render({model: {interned_strings}}, {blame_info}) {
+    let blame_content = "";
+    if (blame_info) {
+      const {ist_file, line, ist_s_text, s_start, s_end} = blame_info;
+      let s_text = interned_strings[ist_s_text];
+      if (s_start != 0 || s_end != s_text.length) {
+        let prefix = s_text.slice(0, s_start);
+        let main = s_text.slice(s_start, s_end);
+        let suffix = s_text.slice(s_end);
+        s_text = html`${prefix}${main}${suffix}`;
+      }
+      blame_content = html`
+        
${interned_strings[ist_file]}:${line}

+        
${s_start}:${s_end}

+        
${s_text}


+        `;
+    }
+    return html`
+      
+      

+      ${blame_content}
+      `;
+  }
+}
+
+class App extends Component {
+  constructor() {
+    super();
+    this.state = {
+      err: false,
+      model: null,
+    };
+  }
+
+  componentDidMount() {
+    const app = this;
+    if (BURNED_IN_MODEL_INFO !== null) {
+      app.setState({model: BURNED_IN_MODEL_INFO});
+    } else {
+      fetch("./model_info.json").then(function(response) {
+        if (!response.ok) {
+          throw new Error("Response not ok.");
+        }
+        return response.json();
+      }).then(function(body) {
+        app.setState({model: body});
+      }).catch(function(error) {
+        console.log("Top-level error: ", error);
+      });
+    }
+  }
+
+  componentDidCatch(error) {
+    void(error);
+    this.setState({...this.state, err: true});
+  }
+
+  render(_, {err}) {
+    if (this.state.model === null) {
+      return html`
Loading...
`;
+    }
+
+    const model = this.state.model.model;
+
+    let error_msg = "";
+    if (err) {
+      error_msg = html`
An error occurred.  Check console
`;
+    }
+
+    return html`
+      ${error_msg}
+      

+        
TorchScript Model (version ${model.version}): ${model.title}

+        
+        <${ModelSizeSection} model=${model}/>
+        <${StructuredDataSection} name="Model Data" data=${model.model_data} shown=true/>
+        <${StructuredDataSection} name="Constants" data=${model.constants} shown=false/>
+        <${ZipContentsSection} model=${model}/>
+        <${CodeSection} model=${model}/>
+        <${ExtraJsonSection} files=${model.extra_files_jsons}/>
+        <${ExtraPicklesSection} files=${model.extra_pickles}/>
+        <${TensorMemorySection} model=${model}/>
+      

+      

+        <${AuxContentPane}
+          err=${this.state.error}
+          model=${model}
+          ref=${(p) => blame.setAuxContentPane(p)}/>
+      

+      `;
+  }
+}
+
+render(h(App), document.body);
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/htm.mjs b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/htm.mjs
new file mode 100644
index 0000000000000000000000000000000000000000..06f25a13d8021ff4f43de442bbf0279f24735d6c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/htm.mjs
@@ -0,0 +1,2 @@
+// HTM, Apache License
+var n=function(t,s,r,e){var u;s[0]=0;for(var h=1;h=5&&((e||!n&&5===r)&&(h.push(r,0,e,s),r=6),n&&(h.push(r,n,0,s),r=6)),e=""},a=0;a"===t?(r=1,e=""):e=t+e[0]:u?t===u?u="":e+=t:'"'===t||"'"===t?u=t:">"===t?(p(),r=1):r&&("="===t?(r=5,s=e,e=""):"/"===t&&(r<5||">"===n[a][l+1])?(p(),3===r&&(h=h[0]),r=h,(h=h[0]).push(2,0,r),r=0):" "===t||"\t"===t||"\n"===t||"\r"===t?(p(),r=2):e+=t),3===r&&"!--"===e&&(r=4,h=h[0])}return p(),h}(s)),r),arguments,[])).length>1?r:r[0]}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/preact.mjs b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/preact.mjs
new file mode 100644
index 0000000000000000000000000000000000000000..8c85bd948c6772ca8d40fc8d6fab6a220d55a1ef
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_dump/preact.mjs
@@ -0,0 +1,2 @@
+// Preact, MIT License
+var n,l,u,i,t,o,r={},f=[],e=/acit|ex(?:s|g|n|p|$)|rph|grid|ows|mnc|ntw|ine[ch]|zoo|^ord|itera/i;function c(e,n){for(var t in n)e[t]=n[t];return e}function s(e){var n=e.parentNode;n&&n.removeChild(e)}function a(e,n,t){var _,l,o,r=arguments,i={};for(o in n)"key"==o?_=n[o]:"ref"==o?l=n[o]:i[o]=n[o];if(arguments.length>3)for(t=[t],o=3;o0?v(m.type,m.props,m.key,null,m.__v):m)){if(m.__=t,m.__b=t.__b+1,null===(h=P[p])||h&&m.key==h.key&&m.type===h.type)P[p]=void 0;else for(a=0;a3)for(t=[t],o=3;o
+
+  
+    TorchScript Model
+    
+    
+    
+  
+
+  
+  
+
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_zoo.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_zoo.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0c6004e23ea806a2c83e12cd2998e0279e0b16f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/model_zoo.py
@@ -0,0 +1,2 @@
+# torchvision imports tqdm from here.
+from torch.hub import tqdm, load_state_dict_from_url as load_url  # noqa: F401
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/module_tracker.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/module_tracker.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ac97f2e2e826961d5f74b5153ba5b4905a57919
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/module_tracker.py
@@ -0,0 +1,159 @@
+# mypy: allow-untyped-defs
+import logging
+import weakref
+from typing import TYPE_CHECKING
+
+import torch
+from torch.autograd.graph import register_multi_grad_hook
+from torch.nn.modules.module import (
+    register_module_forward_hook,
+    register_module_forward_pre_hook,
+)
+from torch.utils._pytree import tree_flatten
+
+
+if TYPE_CHECKING:
+    from torch.utils.hooks import RemovableHandle
+
+
+logger = logging.getLogger(__name__)
+
+
+__all__ = ["ModuleTracker"]
+
+
+class ModuleTracker:
+    """
+    ``ModuleTracker`` is a context manager that tracks the nn.Module hierarchy during execution
+    so that other system can query which Module is currently being executed (or its backward is being
+    executed).
+
+    You can access the ``parents`` attribute on this context manager to get the set of all the
+    Modules currently being executed via their fqn (fully qualified name, also used as the key within
+    the state_dict).
+    You can access the ``is_bw`` attribute to know if you are currently running in backward or not.
+
+    Note that ``parents`` is never empty and always contains the "Global" key. The ``is_bw`` flag
+    will remain ``True`` after the forward until another Module is executed. If you need it to be
+    more accurate, please submit an issue requesting this. Adding a map from fqn to the module instance
+    is possible but not done yet, please submit an issue requesting this if you need it.
+
+    Example usage
+
+    .. code-block:: python
+
+        mod = torch.nn.Linear(2, 2)
+
+        with ModuleTracker() as tracker:
+            # Access anything during the forward pass
+            def my_linear(m1, m2, bias):
+                print(f"Current modules: {tracker.parents}")
+                return torch.mm(m1, m2.t()) + bias
+            torch.nn.functional.linear = my_linear
+
+            mod(torch.rand(2, 2))
+
+    """
+
+    parents: set[str]
+    """
+    A Set containing the fqn for each module currently running their forward
+    """
+
+    def __init__(self) -> None:
+        self.parents = {"Global"}
+        self._known_modules: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
+        self._seen_modules: weakref.WeakSet = weakref.WeakSet()
+        self._has_callback = False
+        self._hooks: list[RemovableHandle] = []
+
+    def _maybe_set_engine_callback(self):
+        # This assumes no concurrent calls to backward
+        if self._has_callback:
+            return
+
+        def callback():
+            self.parents = {"Global"}
+            self._has_callback = False
+
+        torch.autograd.Variable._execution_engine.queue_callback(callback)
+        self._has_callback = True
+
+    @property
+    def is_bw(self):
+        """
+        A boolean marking if this is currently running during the backward pass or not
+        """
+        return torch._C._current_graph_task_id() != -1
+
+    def _get_mod_name(self, mod):
+        if mod not in self._known_modules:
+            self._known_modules[mod] = type(mod).__name__
+        mod_name = self._known_modules[mod]
+        if mod not in self._seen_modules:
+            for name, submod in mod.named_children():
+                self._known_modules[submod] = f"{mod_name}.{name}"
+                self._get_mod_name(submod)
+            self._seen_modules.add(mod)
+        return mod_name
+
+    def _get_append_fn(self, name, is_bw):
+        def fn(*args):
+            if is_bw:
+                self._maybe_set_engine_callback()
+            if name in self.parents:
+                logger.info(
+                    "The module hierarchy tracking seems to be broken as this Module was already entered. %s during %s",
+                    name,
+                    "backward" if is_bw else "forward",
+                )
+            self.parents.add(name)
+
+        return fn
+
+    def _get_pop_fn(self, name, is_bw):
+        def fn(*args):
+            if name in self.parents:
+                self.parents.remove(name)
+            else:
+                logger.info(
+                    "The Module hierarchy tracking is confused as we're exiting a Module that was never entered. %s during %s",
+                    name,
+                    "backward" if is_bw else "forward",
+                )
+
+        return fn
+
+    def _fw_pre_hook(self, mod, input):
+        name = self._get_mod_name(mod)
+        self._get_append_fn(name, False)()
+
+        args, _ = tree_flatten(input)
+        tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad]
+        if tensors:
+            self._hooks.append(
+                register_multi_grad_hook(tensors, self._get_pop_fn(name, True))
+            )
+
+    def _fw_post_hook(self, mod, input, output):
+        name = self._get_mod_name(mod)
+        self._get_pop_fn(name, False)()
+
+        args, _ = tree_flatten(output)
+        tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad]
+        if tensors:
+            self._hooks.append(
+                register_multi_grad_hook(tensors, self._get_append_fn(name, True))
+            )
+
+    def __enter__(self):
+        self._fw_pre_handle = register_module_forward_pre_hook(self._fw_pre_hook)
+        self._fw_post_handle = register_module_forward_hook(self._fw_post_hook)
+        return self
+
+    def __exit__(self, *args):
+        self._fw_pre_handle.remove()
+        self._fw_post_handle.remove()
+        for hook in self._hooks:
+            hook.remove()
+        self._hooks.clear()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d63bc18b69b138a026622de599aed656cc868c8e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/__init__.py
@@ -0,0 +1 @@
+from . import config
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..53d126bf7df6c63405b4cbc4bbf4a19d40903f59
Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/__pycache__/__init__.cpython-310.pyc differ
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/__pycache__/config.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/__pycache__/config.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..062e15a1dd975feb9c25dbf40d69c5b9ee07a3c1
Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/__pycache__/config.cpython-310.pyc differ
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/config.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a3fba9f5b82f88f362cd8361656d7820e0216a1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/serialization/config.py
@@ -0,0 +1,25 @@
+import sys
+from typing import Optional as _Optional, TYPE_CHECKING as _TYPE_CHECKING
+
+
+if _TYPE_CHECKING:
+    from torch.serialization import LoadEndianness as _LoadEndianess
+
+from torch.utils._config_module import install_config_module as _install_config_module
+
+
+class load:
+    mmap: bool = False
+    endianness: _Optional["_LoadEndianess"] = None
+    # MAP_PRIVATE = 2
+    mmap_flags: _Optional[int] = None if sys.platform == "win32" else 2
+    calculate_storage_offsets: bool = False
+
+
+class save:
+    compute_crc32: bool = True
+    use_pinned_memory_for_d2h: bool = False
+    storage_alignment: int = 64
+
+
+_install_config_module(sys.modules[__name__])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/show_pickle.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/show_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd8b6c2b8ab9cabd9d41da709f3b3a6ab3d9a0e4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/show_pickle.py
@@ -0,0 +1,151 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+import sys
+import pickle
+import struct
+import pprint
+import zipfile
+import fnmatch
+from typing import Any, IO
+
+__all__ = ["FakeObject", "FakeClass", "DumpUnpickler", "main"]
+
+class FakeObject:
+    def __init__(self, module, name, args):
+        self.module = module
+        self.name = name
+        self.args = args
+        # NOTE: We don't distinguish between state never set and state set to None.
+        self.state = None
+
+    def __repr__(self):
+        state_str = "" if self.state is None else f"(state={self.state!r})"
+        return f"{self.module}.{self.name}{self.args!r}{state_str}"
+
+    def __setstate__(self, state):
+        self.state = state
+
+    @staticmethod
+    def pp_format(printer, obj, stream, indent, allowance, context, level):
+        if not obj.args and obj.state is None:
+            stream.write(repr(obj))
+            return
+        if obj.state is None:
+            stream.write(f"{obj.module}.{obj.name}")
+            printer._format(obj.args, stream, indent + 1, allowance + 1, context, level)
+            return
+        if not obj.args:
+            stream.write(f"{obj.module}.{obj.name}()(state=\n")
+            indent += printer._indent_per_level
+            stream.write(" " * indent)
+            printer._format(obj.state, stream, indent, allowance + 1, context, level + 1)
+            stream.write(")")
+            return
+        raise Exception("Need to implement")  # noqa: TRY002
+
+
+class FakeClass:
+    def __init__(self, module, name):
+        self.module = module
+        self.name = name
+        self.__new__ = self.fake_new  # type: ignore[assignment]
+
+    def __repr__(self):
+        return f"{self.module}.{self.name}"
+
+    def __call__(self, *args):
+        return FakeObject(self.module, self.name, args)
+
+    def fake_new(self, *args):
+        return FakeObject(self.module, self.name, args[1:])
+
+
+class DumpUnpickler(pickle._Unpickler):  # type: ignore[name-defined]
+    def __init__(
+            self,
+            file,
+            *,
+            catch_invalid_utf8=False,
+            **kwargs):
+        super().__init__(file, **kwargs)
+        self.catch_invalid_utf8 = catch_invalid_utf8
+
+    def find_class(self, module, name):
+        return FakeClass(module, name)
+
+    def persistent_load(self, pid):
+        return FakeObject("pers", "obj", (pid,))
+
+    dispatch = dict(pickle._Unpickler.dispatch)  # type: ignore[attr-defined]
+
+    # Custom objects in TorchScript are able to return invalid UTF-8 strings
+    # from their pickle (__getstate__) functions.  Install a custom loader
+    # for strings that catches the decode exception and replaces it with
+    # a sentinel object.
+    def load_binunicode(self):
+        strlen, = struct.unpack(" sys.maxsize:
+            raise Exception("String too long.")  # noqa: TRY002
+        str_bytes = self.read(strlen)  # type: ignore[attr-defined]
+        obj: Any
+        try:
+            obj = str(str_bytes, "utf-8", "surrogatepass")
+        except UnicodeDecodeError as exn:
+            if not self.catch_invalid_utf8:
+                raise
+            obj = FakeObject("builtin", "UnicodeDecodeError", (str(exn),))
+        self.append(obj)  # type: ignore[attr-defined]
+    dispatch[pickle.BINUNICODE[0]] = load_binunicode  # type: ignore[assignment]
+
+    @classmethod
+    def dump(cls, in_stream, out_stream):
+        value = cls(in_stream).load()
+        pprint.pprint(value, stream=out_stream)
+        return value
+
+
+def main(argv, output_stream=None):
+    if len(argv) != 2:
+        # Don't spam stderr if not using stdout.
+        if output_stream is not None:
+            raise Exception("Pass argv of length 2.")  # noqa: TRY002
+        sys.stderr.write("usage: show_pickle PICKLE_FILE\n")
+        sys.stderr.write("  PICKLE_FILE can be any of:\n")
+        sys.stderr.write("    path to a pickle file\n")
+        sys.stderr.write("    file.zip@member.pkl\n")
+        sys.stderr.write("    file.zip@*/pattern.*\n")
+        sys.stderr.write("      (shell glob pattern for members)\n")
+        sys.stderr.write("      (only first match will be shown)\n")
+        return 2
+
+    fname = argv[1]
+    handle: IO[bytes]
+    if "@" not in fname:
+        with open(fname, "rb") as handle:
+            DumpUnpickler.dump(handle, output_stream)
+    else:
+        zfname, mname = fname.split("@", 1)
+        with zipfile.ZipFile(zfname) as zf:
+            if "*" not in mname:
+                with zf.open(mname) as handle:
+                    DumpUnpickler.dump(handle, output_stream)
+            else:
+                found = False
+                for info in zf.infolist():
+                    if fnmatch.fnmatch(info.filename, mname):
+                        with zf.open(info) as handle:
+                            DumpUnpickler.dump(handle, output_stream)
+                        found = True
+                        break
+                if not found:
+                    raise Exception(f"Could not find member matching {mname} in {zfname}")  # noqa: TRY002
+
+
+if __name__ == "__main__":
+    # This hack works on every version of Python I've tested.
+    # I've tested on the following versions:
+    #   3.7.4
+    if True:
+        pprint.PrettyPrinter._dispatch[FakeObject.__repr__] = FakeObject.pp_format  # type: ignore[attr-defined]
+
+    sys.exit(main(sys.argv))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2ac5edd05e16ef51e75f2ca68864b65da5d58
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py
@@ -0,0 +1,19 @@
+import tensorboard
+from torch._vendor.packaging.version import Version
+
+if not hasattr(tensorboard, "__version__") or Version(
+    tensorboard.__version__
+) < Version("1.15"):
+    raise ImportError("TensorBoard logging requires TensorBoard version 1.15 or above")
+
+del Version
+del tensorboard
+
+from .writer import FileWriter, SummaryWriter
+from tensorboard.summary.writer.record_writer import RecordWriter
+
+__all__ = [
+    "FileWriter",
+    "RecordWriter",
+    "SummaryWriter",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_convert_np.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_convert_np.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e20ec6337c3662fdd74836d280cedae2678b66a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_convert_np.py
@@ -0,0 +1,33 @@
+"""This module converts objects into numpy array."""
+
+import numpy as np
+
+import torch
+
+
+def make_np(x: torch.Tensor) -> np.ndarray:
+    """
+    Convert an object into numpy array.
+
+    Args:
+      x: An instance of torch tensor
+
+    Returns:
+        numpy.array: Numpy array
+    """
+    if isinstance(x, np.ndarray):
+        return x
+    if np.isscalar(x):
+        return np.array([x])
+    if isinstance(x, torch.Tensor):
+        return _prepare_pytorch(x)
+    raise NotImplementedError(
+        f"Got {type(x)}, but numpy array or torch tensor are expected."
+    )
+
+
+def _prepare_pytorch(x: torch.Tensor) -> np.ndarray:
+    if x.dtype == torch.bfloat16:
+        x = x.to(torch.float16)
+    x = x.detach().cpu().numpy()
+    return x
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_embedding.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..44cb6c41b017f170c432f307907949e37de497c2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_embedding.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+import math
+import numpy as np
+from ._convert_np import make_np
+from ._utils import make_grid
+from tensorboard.compat import tf
+from tensorboard.plugins.projector.projector_config_pb2 import EmbeddingInfo
+
+
+_HAS_GFILE_JOIN = hasattr(tf.io.gfile, "join")
+
+
+def _gfile_join(a, b):
+    # The join API is different between tensorboard's TF stub and TF:
+    # https://github.com/tensorflow/tensorboard/issues/6080
+    # We need to try both because `tf` may point to either the stub or the real TF.
+    if _HAS_GFILE_JOIN:
+        return tf.io.gfile.join(a, b)
+    else:
+        fs = tf.io.gfile.get_filesystem(a)
+        return fs.join(a, b)
+
+
+def make_tsv(metadata, save_path, metadata_header=None):
+    if not metadata_header:
+        metadata = [str(x) for x in metadata]
+    else:
+        assert len(metadata_header) == len(
+            metadata[0]
+        ), "len of header must be equal to the number of columns in metadata"
+        metadata = ["\t".join(str(e) for e in l) for l in [metadata_header] + metadata]
+
+    metadata_bytes = tf.compat.as_bytes("\n".join(metadata) + "\n")
+    with tf.io.gfile.GFile(_gfile_join(save_path, "metadata.tsv"), "wb") as f:
+        f.write(metadata_bytes)
+
+
+# https://github.com/tensorflow/tensorboard/issues/44 image label will be squared
+def make_sprite(label_img, save_path):
+    from PIL import Image
+    from io import BytesIO
+
+    # this ensures the sprite image has correct dimension as described in
+    # https://www.tensorflow.org/get_started/embedding_viz
+    nrow = int(math.ceil((label_img.size(0)) ** 0.5))
+    arranged_img_CHW = make_grid(make_np(label_img), ncols=nrow)
+
+    # augment images so that #images equals nrow*nrow
+    arranged_augment_square_HWC = np.zeros(
+        (arranged_img_CHW.shape[2], arranged_img_CHW.shape[2], 3)
+    )
+    arranged_img_HWC = arranged_img_CHW.transpose(1, 2, 0)  # chw -> hwc
+    arranged_augment_square_HWC[: arranged_img_HWC.shape[0], :, :] = arranged_img_HWC
+    im = Image.fromarray(np.uint8((arranged_augment_square_HWC * 255).clip(0, 255)))
+
+    with BytesIO() as buf:
+        im.save(buf, format="PNG")
+        im_bytes = buf.getvalue()
+
+    with tf.io.gfile.GFile(_gfile_join(save_path, "sprite.png"), "wb") as f:
+        f.write(im_bytes)
+
+
+def get_embedding_info(metadata, label_img, subdir, global_step, tag):
+    info = EmbeddingInfo()
+    info.tensor_name = f"{tag}:{str(global_step).zfill(5)}"
+    info.tensor_path = _gfile_join(subdir, "tensors.tsv")
+    if metadata is not None:
+        info.metadata_path = _gfile_join(subdir, "metadata.tsv")
+    if label_img is not None:
+        info.sprite.image_path = _gfile_join(subdir, "sprite.png")
+        info.sprite.single_image_dim.extend([label_img.size(3), label_img.size(2)])
+    return info
+
+
+def write_pbtxt(save_path, contents):
+    config_path = _gfile_join(save_path, "projector_config.pbtxt")
+    with tf.io.gfile.GFile(config_path, "wb") as f:
+        f.write(tf.compat.as_bytes(contents))
+
+
+def make_mat(matlist, save_path):
+    with tf.io.gfile.GFile(_gfile_join(save_path, "tensors.tsv"), "wb") as f:
+        for x in matlist:
+            x = [str(i.item()) for i in x]
+            f.write(tf.compat.as_bytes("\t".join(x) + "\n"))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_onnx_graph.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_onnx_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b7381737b3e77a34b32f28a48833a1fedd61104
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_onnx_graph.py
@@ -0,0 +1,61 @@
+# mypy: allow-untyped-defs
+from tensorboard.compat.proto.graph_pb2 import GraphDef
+from tensorboard.compat.proto.node_def_pb2 import NodeDef
+from tensorboard.compat.proto.versions_pb2 import VersionDef
+from tensorboard.compat.proto.attr_value_pb2 import AttrValue
+from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto
+
+
+def load_onnx_graph(fname):
+    import onnx
+
+    m = onnx.load(fname)  # type: ignore[attr-defined]
+    g = m.graph
+    return parse(g)
+
+
+def parse(graph):
+    nodes = []
+    import itertools
+
+    nodes_proto = list(itertools.chain(graph.input, graph.output))
+
+    for node in nodes_proto:
+        print(node.name)
+        shapeproto = TensorShapeProto(
+            dim=[
+                TensorShapeProto.Dim(size=d.dim_value)
+                for d in node.type.tensor_type.shape.dim
+            ]
+        )
+        nodes.append(
+            NodeDef(
+                name=node.name.encode(encoding="utf_8"),
+                op="Variable",
+                input=[],
+                attr={
+                    "dtype": AttrValue(type=node.type.tensor_type.elem_type),
+                    "shape": AttrValue(shape=shapeproto),
+                },
+            )
+        )
+
+    for node in graph.node:
+        _attr = [" = ".join([str(f[1]) for f in s.ListFields()]) for s in node.attribute]
+        attr = ", ".join(_attr).encode(encoding="utf_8")
+        print(node.output[0])
+        nodes.append(
+            NodeDef(
+                name=node.output[0].encode(encoding="utf_8"),
+                op=node.op_type,
+                input=node.input,
+                attr={"parameters": AttrValue(s=attr)},
+            )
+        )
+
+    # two pass token replacement, appends opname to object id
+    mapping = {}
+    for node in nodes:
+        mapping[node.name] = node.op + "_" + node.name
+
+    return GraphDef(node=nodes, versions=VersionDef(producer=22))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_proto_graph.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_proto_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..30140a22cff673458b0e2b2fb5c43416d318a5ca
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_proto_graph.py
@@ -0,0 +1,54 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+from tensorboard.compat.proto.node_def_pb2 import NodeDef
+from tensorboard.compat.proto.attr_value_pb2 import AttrValue
+from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto
+
+
+def attr_value_proto(dtype, shape, s):
+    """Create a dict of objects matching a NodeDef's attr field.
+
+    Follows https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/attr_value.proto
+    specifically designed for a NodeDef. The values have been reverse engineered from
+    standard TensorBoard logged data.
+    """
+    attr = {}
+    if s is not None:
+        attr["attr"] = AttrValue(s=s.encode(encoding="utf_8"))
+    if shape is not None:
+        shapeproto = tensor_shape_proto(shape)
+        attr["_output_shapes"] = AttrValue(list=AttrValue.ListValue(shape=[shapeproto]))
+    return attr
+
+
+def tensor_shape_proto(outputsize):
+    """Create an object matching a tensor_shape field.
+
+    Follows https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/tensor_shape.proto .
+    """
+    return TensorShapeProto(dim=[TensorShapeProto.Dim(size=d) for d in outputsize])
+
+
+def node_proto(
+    name,
+    op="UnSpecified",
+    input=None,
+    dtype=None,
+    shape: Optional[tuple] = None,
+    outputsize=None,
+    attributes="",
+):
+    """Create an object matching a NodeDef.
+
+    Follows https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/node_def.proto .
+    """
+    if input is None:
+        input = []
+    if not isinstance(input, list):
+        input = [input]
+    return NodeDef(
+        name=name.encode(encoding="utf_8"),
+        op=op,
+        input=input,
+        attr=attr_value_proto(dtype, outputsize, attributes),
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_pytorch_graph.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_pytorch_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e9e453183dde426fadeb3dc0405a985833082be
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_pytorch_graph.py
@@ -0,0 +1,376 @@
+# mypy: allow-untyped-defs
+from collections import OrderedDict
+import contextlib
+from typing import Any
+
+from tensorboard.compat.proto.config_pb2 import RunMetadata
+from tensorboard.compat.proto.graph_pb2 import GraphDef
+from tensorboard.compat.proto.step_stats_pb2 import StepStats, DeviceStepStats
+from tensorboard.compat.proto.versions_pb2 import VersionDef
+
+import torch
+from ._proto_graph import node_proto
+
+methods_OP = [
+    "attributeNames",
+    "hasMultipleOutputs",
+    "hasUses",
+    "inputs",
+    "kind",
+    "outputs",
+    "outputsSize",
+    "scopeName",
+]
+# Some additional methods to explure for methods_IO are
+#
+#   'unique' (type int)
+#   'type' (type >)
+#
+# But the below are sufficient for now.
+methods_IO = ["node", "offset", "debugName"]
+
+GETATTR_KIND = "prim::GetAttr"
+CLASSTYPE_KIND = "ClassType"
+
+
+class NodeBase:
+    def __init__(
+        self,
+        debugName=None,
+        inputs=None,
+        scope=None,
+        tensor_size=None,
+        op_type="UnSpecified",
+        attributes="",
+    ):
+        # TODO; Specify a __slots__ for this class or potentially
+        # used namedtuple instead
+        self.debugName = debugName
+        self.inputs = inputs
+        self.tensor_size = tensor_size
+        self.kind = op_type
+        self.attributes = attributes
+        self.scope = scope
+
+    def __repr__(self):
+        repr = []
+        repr.append(str(type(self)))
+        repr.extend(
+            m + ": " + str(getattr(self, m)) + str(type(getattr(self, m)))
+            for m in dir(self)
+            if "__" not in m
+        )
+        return "\n".join(repr) + "\n\n"
+
+
+class NodePy(NodeBase):
+    def __init__(self, node_cpp, valid_methods):
+        super().__init__(node_cpp)
+        valid_methods = valid_methods[:]
+        self.inputs = []
+
+        for m in valid_methods:
+            if m == "inputs" or m == "outputs":
+                list_of_node = list(getattr(node_cpp, m)())
+                io_unique_names = []
+                io_tensor_sizes = []
+                for n in list_of_node:
+                    io_unique_names.append(n.debugName())
+                    if n.isCompleteTensor():
+                        io_tensor_sizes.append(n.type().sizes())
+                    else:
+                        io_tensor_sizes.append(None)
+
+                setattr(self, m, io_unique_names)
+                setattr(self, m + "tensor_size", io_tensor_sizes)
+
+            else:
+                setattr(self, m, getattr(node_cpp, m)())
+
+
+class NodePyIO(NodePy):
+    def __init__(self, node_cpp, input_or_output=None):
+        super().__init__(node_cpp, methods_IO)
+        try:
+            tensor_size = node_cpp.type().sizes()
+        except RuntimeError:
+            tensor_size = [
+                1,
+            ]  # fail when constant model is used.
+        self.tensor_size = tensor_size
+        # Kind attribute string is purely descriptive and will be shown
+        # in detailed information for the node in TensorBoard's graph plugin.
+        #
+        # NodePyOP nodes get this from their kind() method.
+        self.kind = "Parameter"
+        if input_or_output:
+            self.input_or_output = input_or_output
+            self.kind = "IO Node"
+
+
+class NodePyOP(NodePy):
+    def __init__(self, node_cpp):
+        super().__init__(node_cpp, methods_OP)
+        # Replace single quote which causes strange behavior in TensorBoard
+        # TODO: See if we can remove this in the future
+        self.attributes = str(
+            {k: _node_get(node_cpp, k) for k in node_cpp.attributeNames()}
+        ).replace("'", " ")
+        self.kind = node_cpp.kind()
+
+
+class GraphPy:
+    """Helper class to convert torch.nn.Module to GraphDef proto and visualization with TensorBoard.
+
+    GraphDef generation operates in two passes:
+
+    In the first pass, all nodes are read and saved to two lists.
+    One list is for input/output nodes (nodes_io), which only have inbound
+    or outbound connections, but not both. Another list is for internal
+    operator nodes (nodes_op). The first pass also saves all scope name
+    appeared in the nodes in scope_name_appeared list for later processing.
+
+    In the second pass, scope names are fully applied to all nodes.
+    debugNameToScopedName is a mapping from a node's ID to its fully qualified
+    scope name. e.g. Net1/Linear[0]/1. Unfortunately torch.jit doesn't have
+    totally correct scope output, so this is nontrivial. The function
+    populate_namespace_from_OP_to_IO and find_common_root are used to
+    assign scope name to a node based on the connection between nodes
+    in a heuristic kind of way. Bookkeeping is done with shallowest_scope_name
+    and scope_name_appeared.
+    """
+
+    def __init__(self):
+        self.nodes_op = []
+        self.nodes_io = OrderedDict()
+        self.unique_name_to_scoped_name = {}
+        self.shallowest_scope_name = "default"
+        self.scope_name_appeared = []
+
+    def append(self, x):
+        if isinstance(x, NodePyIO):
+            self.nodes_io[x.debugName] = x
+        if isinstance(x, NodePyOP):
+            self.nodes_op.append(x)
+
+    def printall(self):
+        print("all nodes")
+        for node in self.nodes_op:
+            print(node)
+        for key in self.nodes_io:
+            print(self.nodes_io[key])
+
+    def find_common_root(self):
+        for fullscope in self.scope_name_appeared:
+            if fullscope:
+                self.shallowest_scope_name = fullscope.split("/")[0]
+
+    def populate_namespace_from_OP_to_IO(self):
+        for node in self.nodes_op:
+            for node_output, outputSize in zip(node.outputs, node.outputstensor_size):
+                self.scope_name_appeared.append(node.scopeName)
+                self.nodes_io[node_output] = NodeBase(
+                    node_output,
+                    node.inputs,
+                    node.scopeName,
+                    outputSize,
+                    op_type=node.kind,
+                    attributes=node.attributes,
+                )
+
+        self.find_common_root()
+
+        for node in self.nodes_op:
+            for input_node_id in node.inputs:
+                self.unique_name_to_scoped_name[input_node_id] = (
+                    node.scopeName + "/" + input_node_id
+                )
+
+        for key, node in self.nodes_io.items():
+            if type(node) == NodeBase:
+                self.unique_name_to_scoped_name[key] = node.scope + "/" + node.debugName
+            if hasattr(node, "input_or_output"):
+                self.unique_name_to_scoped_name[key] = (
+                    node.input_or_output + "/" + node.debugName
+                )
+
+            if hasattr(node, "scope") and node.scope is not None:
+                self.unique_name_to_scoped_name[key] = node.scope + "/" + node.debugName
+                if node.scope == "" and self.shallowest_scope_name:
+                    self.unique_name_to_scoped_name[node.debugName] = (
+                        self.shallowest_scope_name + "/" + node.debugName
+                    )
+
+        # replace name
+        for key, node in self.nodes_io.items():
+            self.nodes_io[key].inputs = [
+                self.unique_name_to_scoped_name[node_input_id]
+                for node_input_id in node.inputs
+            ]
+            if node.debugName in self.unique_name_to_scoped_name:
+                self.nodes_io[key].debugName = self.unique_name_to_scoped_name[
+                    node.debugName
+                ]
+
+    def to_proto(self):
+        """Convert graph representation of GraphPy object to TensorBoard required format."""
+        # TODO: compute correct memory usage and CPU time once
+        # PyTorch supports it
+        nodes = [
+            node_proto(
+                v.debugName,
+                input=v.inputs,
+                outputsize=v.tensor_size,
+                op=v.kind,
+                attributes=v.attributes,
+            )
+            for v in self.nodes_io.values()
+        ]
+        return nodes
+
+
+def parse(graph, trace, args=None, omit_useless_nodes=True):
+    """Parse an optimized PyTorch model graph and produces a list of nodes and node stats.
+
+    Useful for eventual conversion to TensorBoard protobuf format.
+
+    Args:
+      graph (PyTorch module): The model graph to be parsed.
+      trace (PyTorch JIT TracedModule): The model trace to be parsed.
+      args (tuple): input tensor[s] for the model.
+      omit_useless_nodes (boolean): Whether to remove nodes from the graph.
+    """
+    nodes_py = GraphPy()
+    for node in graph.inputs():
+        if omit_useless_nodes:
+            if (
+                len(node.uses()) == 0
+            ):  # number of user of the node (= number of outputs/ fanout)
+                continue
+
+        if node.type().kind() != CLASSTYPE_KIND:
+            nodes_py.append(NodePyIO(node, "input"))
+
+    attr_to_scope: dict[Any, str] = {}
+    for node in graph.nodes():
+        if node.kind() == GETATTR_KIND:
+            attr_name = node.s("name")
+            attr_key = node.output().debugName()
+            parent = node.input().node()
+            if (
+                parent.kind() == GETATTR_KIND
+            ):  # If the parent node is not the top-level "self" node
+                parent_attr_key = parent.output().debugName()
+                parent_scope = attr_to_scope[parent_attr_key]
+                attr_scope = parent_scope.split("/")[-1]
+                attr_to_scope[attr_key] = f"{parent_scope}/{attr_scope}.{attr_name}"
+            else:
+                attr_to_scope[attr_key] = f"__module.{attr_name}"
+            # We don't need classtype nodes; scope will provide this information
+            if node.output().type().kind() != CLASSTYPE_KIND:
+                node_py = NodePyOP(node)
+                node_py.scopeName = attr_to_scope[attr_key]  # type: ignore[attr-defined]
+                nodes_py.append(node_py)
+        else:
+            nodes_py.append(NodePyOP(node))
+
+    for i, node in enumerate(graph.outputs()):  # Create sink nodes for output ops
+        node_pyio = NodePyIO(node, "output")
+        node_pyio.debugName = f"output.{i + 1}"
+        node_pyio.inputs = [node.debugName()]
+        nodes_py.append(node_pyio)
+
+    def parse_traced_name(module):
+        if isinstance(module, torch.jit.TracedModule):
+            module_name = module._name
+        else:
+            module_name = getattr(module, "original_name", "Module")
+        return module_name
+
+    alias_to_name = {}
+    base_name = parse_traced_name(trace)
+    for name, module in trace.named_modules(prefix="__module"):
+        mod_name = parse_traced_name(module)
+        attr_name = name.split(".")[-1]
+        alias_to_name[name] = f"{mod_name}[{attr_name}]"
+
+    for node in nodes_py.nodes_op:
+        module_aliases = node.scopeName.split("/")
+        replacements = [
+            alias_to_name[alias] if alias in alias_to_name else alias.split(".")[-1]
+            for alias in module_aliases
+        ]
+        node.scopeName = base_name
+        if any(replacements):
+            node.scopeName += "/" + "/".join(replacements)
+
+    nodes_py.populate_namespace_from_OP_to_IO()
+    return nodes_py.to_proto()
+
+
+def graph(model, args, verbose=False, use_strict_trace=True):
+    """
+    Process a PyTorch model and produces a `GraphDef` proto that can be logged to TensorBoard.
+
+    Args:
+      model (PyTorch module): The model to be parsed.
+      args (tuple): input tensor[s] for the model.
+      verbose (bool): Whether to print out verbose information while
+        processing.
+      use_strict_trace (bool): Whether to pass keyword argument `strict` to
+        `torch.jit.trace`. Pass False when you want the tracer to
+        record your mutable container types (list, dict)
+    """
+    with _set_model_to_eval(model):
+        try:
+            trace = torch.jit.trace(model, args, strict=use_strict_trace)
+            graph = trace.graph
+            torch._C._jit_pass_inline(graph)
+        except RuntimeError as e:
+            print(e)
+            print("Error occurs, No graph saved")
+            raise e
+
+    if verbose:
+        print(graph)
+    list_of_nodes = parse(graph, trace, args)
+    # We are hardcoding that this was run on CPU even though it might have actually
+    # run on GPU. Note this is what is shown in TensorBoard and has no bearing
+    # on actual execution.
+    # TODO: See if we can extract GPU vs CPU information from the PyTorch model
+    # and pass it correctly to TensorBoard.
+    #
+    # Definition of StepStats and DeviceStepStats can be found at
+    # https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/graph/tf_graph_common/test/graph-test.ts
+    # and
+    # https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/step_stats.proto
+    stepstats = RunMetadata(
+        step_stats=StepStats(dev_stats=[DeviceStepStats(device="/device:CPU:0")])
+    )
+    return GraphDef(node=list_of_nodes, versions=VersionDef(producer=22)), stepstats
+    # The producer version has been reverse engineered from standard
+    # TensorBoard logged data.
+
+
+@contextlib.contextmanager
+def _set_model_to_eval(model):
+    """Context manager to temporarily set the training mode of ``model`` to eval."""
+    if not isinstance(model, torch.jit.ScriptFunction):
+        originally_training = model.training
+        model.train(False)
+        try:
+            yield
+        finally:
+            model.train(originally_training)
+    else:
+        # Do nothing for ScriptFunction
+        try:
+            yield
+        finally:
+            pass
+
+
+def _node_get(node: torch._C.Node, key: str):
+    """Get attributes of a node which is polymorphic over return type."""
+    sel = node.kindOf(key)
+    return getattr(node, sel)(key)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8acaf1696cb1f253fb4307e91e1fa088278334f0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/_utils.py
@@ -0,0 +1,127 @@
+# mypy: allow-untyped-defs
+import numpy as np
+import numpy.typing as npt
+
+
+# Functions for converting
+def figure_to_image(figures, close=True):
+    """Render matplotlib figure to numpy format.
+
+    Note that this requires the ``matplotlib`` package.
+
+    Args:
+        figures (matplotlib.pyplot.figure or list of figures): figure or a list of figures
+        close (bool): Flag to automatically close the figure
+
+    Returns:
+        numpy.array: image in [CHW] order
+    """
+    import matplotlib.pyplot as plt
+    import matplotlib.backends.backend_agg as plt_backend_agg
+
+    def render_to_rgb(figure):
+        canvas = plt_backend_agg.FigureCanvasAgg(figure)
+        canvas.draw()
+        data: npt.NDArray = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8)
+        w, h = figure.canvas.get_width_height()
+        image_hwc = data.reshape([h, w, 4])[:, :, 0:3]
+        image_chw = np.moveaxis(image_hwc, source=2, destination=0)
+        if close:
+            plt.close(figure)
+        return image_chw
+
+    if isinstance(figures, list):
+        images = [render_to_rgb(figure) for figure in figures]
+        return np.stack(images)
+    else:
+        image = render_to_rgb(figures)
+        return image
+
+
+def _prepare_video(V):
+    """
+    Convert a 5D tensor into 4D tensor.
+
+    Convesrion is done from [batchsize, time(frame), channel(color), height, width]  (5D tensor)
+    to [time(frame), new_width, new_height, channel] (4D tensor).
+
+    A batch of images are spreaded to a grid, which forms a frame.
+    e.g. Video with batchsize 16 will have a 4x4 grid.
+    """
+    b, t, c, h, w = V.shape
+
+    if V.dtype == np.uint8:
+        V = np.float32(V) / 255.0
+
+    def is_power2(num):
+        return num != 0 and ((num & (num - 1)) == 0)
+
+    # pad to nearest power of 2, all at once
+    if not is_power2(V.shape[0]):
+        len_addition = int(2 ** V.shape[0].bit_length() - V.shape[0])
+        V = np.concatenate((V, np.zeros(shape=(len_addition, t, c, h, w))), axis=0)
+
+    n_rows = 2 ** ((b.bit_length() - 1) // 2)
+    n_cols = V.shape[0] // n_rows
+
+    V = np.reshape(V, newshape=(n_rows, n_cols, t, c, h, w))
+    V = np.transpose(V, axes=(2, 0, 4, 1, 5, 3))
+    V = np.reshape(V, newshape=(t, n_rows * h, n_cols * w, c))
+
+    return V
+
+
+def make_grid(I, ncols=8):
+    # I: N1HW or N3HW
+    assert isinstance(I, np.ndarray), "plugin error, should pass numpy array here"
+    if I.shape[1] == 1:
+        I = np.concatenate([I, I, I], 1)
+    assert I.ndim == 4 and I.shape[1] == 3
+    nimg = I.shape[0]
+    H = I.shape[2]
+    W = I.shape[3]
+    ncols = min(nimg, ncols)
+    nrows = int(np.ceil(float(nimg) / ncols))
+    canvas = np.zeros((3, H * nrows, W * ncols), dtype=I.dtype)
+    i = 0
+    for y in range(nrows):
+        for x in range(ncols):
+            if i >= nimg:
+                break
+            canvas[:, y * H : (y + 1) * H, x * W : (x + 1) * W] = I[i]
+            i = i + 1
+    return canvas
+
+    # if modality == 'IMG':
+    #     if x.dtype == np.uint8:
+    #         x = x.astype(np.float32) / 255.0
+
+
+def convert_to_HWC(tensor, input_format):  # tensor: numpy array
+    assert len(set(input_format)) == len(
+        input_format
+    ), f"You can not use the same dimension shordhand twice.         input_format: {input_format}"
+    assert len(tensor.shape) == len(
+        input_format
+    ), f"size of input tensor and input format are different. \
+        tensor shape: {tensor.shape}, input_format: {input_format}"
+    input_format = input_format.upper()
+
+    if len(input_format) == 4:
+        index = [input_format.find(c) for c in "NCHW"]
+        tensor_NCHW = tensor.transpose(index)
+        tensor_CHW = make_grid(tensor_NCHW)
+        return tensor_CHW.transpose(1, 2, 0)
+
+    if len(input_format) == 3:
+        index = [input_format.find(c) for c in "HWC"]
+        tensor_HWC = tensor.transpose(index)
+        if tensor_HWC.shape[2] == 1:
+            tensor_HWC = np.concatenate([tensor_HWC, tensor_HWC, tensor_HWC], 2)
+        return tensor_HWC
+
+    if len(input_format) == 2:
+        index = [input_format.find(c) for c in "HW"]
+        tensor = tensor.transpose(index)
+        tensor = np.stack([tensor, tensor, tensor], 2)
+        return tensor
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/summary.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/summary.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fca4d9b7e66c7f6a3802e79a1980ecc2e77097c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/summary.py
@@ -0,0 +1,982 @@
+# mypy: allow-untyped-defs
+import json
+import logging
+import os
+import struct
+
+from typing import Any, Optional
+
+import torch
+import numpy as np
+
+from google.protobuf import struct_pb2
+
+from tensorboard.compat.proto.summary_pb2 import (
+    HistogramProto,
+    Summary,
+    SummaryMetadata,
+)
+from tensorboard.compat.proto.tensor_pb2 import TensorProto
+from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto
+from tensorboard.plugins.custom_scalar import layout_pb2
+from tensorboard.plugins.pr_curve.plugin_data_pb2 import PrCurvePluginData
+from tensorboard.plugins.text.plugin_data_pb2 import TextPluginData
+
+from ._convert_np import make_np
+from ._utils import _prepare_video, convert_to_HWC
+
+__all__ = [
+    "half_to_int",
+    "int_to_half",
+    "hparams",
+    "scalar",
+    "histogram_raw",
+    "histogram",
+    "make_histogram",
+    "image",
+    "image_boxes",
+    "draw_boxes",
+    "make_image",
+    "video",
+    "make_video",
+    "audio",
+    "custom_scalars",
+    "text",
+    "tensor_proto",
+    "pr_curve_raw",
+    "pr_curve",
+    "compute_curve",
+    "mesh",
+]
+
+logger = logging.getLogger(__name__)
+
+def half_to_int(f: float) -> int:
+    """Casts a half-precision float value into an integer.
+
+    Converts a half precision floating point value, such as `torch.half` or
+    `torch.bfloat16`, into an integer value which can be written into the
+    half_val field of a TensorProto for storage.
+
+    To undo the effects of this conversion, use int_to_half().
+
+    """
+    buf = struct.pack("f", f)
+    return struct.unpack("i", buf)[0]
+
+def int_to_half(i: int) -> float:
+    """Casts an integer value to a half-precision float.
+
+    Converts an integer value obtained from half_to_int back into a floating
+    point value.
+
+    """
+    buf = struct.pack("i", i)
+    return struct.unpack("f", buf)[0]
+
+def _tensor_to_half_val(t: torch.Tensor) -> list[int]:
+    return [half_to_int(x) for x in t.flatten().tolist()]
+
+def _tensor_to_complex_val(t: torch.Tensor) -> list[float]:
+    return torch.view_as_real(t).flatten().tolist()
+
+def _tensor_to_list(t: torch.Tensor) -> list[Any]:
+    return t.flatten().tolist()
+
+# type maps: torch.Tensor type -> (protobuf type, protobuf val field)
+_TENSOR_TYPE_MAP = {
+    torch.half: ("DT_HALF", "half_val", _tensor_to_half_val),
+    torch.float16: ("DT_HALF", "half_val", _tensor_to_half_val),
+    torch.bfloat16: ("DT_BFLOAT16", "half_val", _tensor_to_half_val),
+    torch.float32: ("DT_FLOAT", "float_val", _tensor_to_list),
+    torch.float: ("DT_FLOAT", "float_val", _tensor_to_list),
+    torch.float64: ("DT_DOUBLE", "double_val", _tensor_to_list),
+    torch.double: ("DT_DOUBLE", "double_val", _tensor_to_list),
+    torch.int8: ("DT_INT8", "int_val", _tensor_to_list),
+    torch.uint8: ("DT_UINT8", "int_val", _tensor_to_list),
+    torch.qint8: ("DT_UINT8", "int_val", _tensor_to_list),
+    torch.int16: ("DT_INT16", "int_val", _tensor_to_list),
+    torch.short: ("DT_INT16", "int_val", _tensor_to_list),
+    torch.int: ("DT_INT32", "int_val", _tensor_to_list),
+    torch.int32: ("DT_INT32", "int_val", _tensor_to_list),
+    torch.qint32: ("DT_INT32", "int_val", _tensor_to_list),
+    torch.int64: ("DT_INT64", "int64_val", _tensor_to_list),
+    torch.complex32: ("DT_COMPLEX32", "scomplex_val", _tensor_to_complex_val),
+    torch.chalf: ("DT_COMPLEX32", "scomplex_val", _tensor_to_complex_val),
+    torch.complex64: ("DT_COMPLEX64", "scomplex_val", _tensor_to_complex_val),
+    torch.cfloat: ("DT_COMPLEX64", "scomplex_val", _tensor_to_complex_val),
+    torch.bool: ("DT_BOOL", "bool_val", _tensor_to_list),
+    torch.complex128: ("DT_COMPLEX128", "dcomplex_val", _tensor_to_complex_val),
+    torch.cdouble: ("DT_COMPLEX128", "dcomplex_val", _tensor_to_complex_val),
+    torch.uint8: ("DT_UINT8", "uint32_val", _tensor_to_list),
+    torch.quint8: ("DT_UINT8", "uint32_val", _tensor_to_list),
+    torch.quint4x2: ("DT_UINT8", "uint32_val", _tensor_to_list),
+}
+
+
+def _calc_scale_factor(tensor):
+    converted = tensor.numpy() if not isinstance(tensor, np.ndarray) else tensor
+    return 1 if converted.dtype == np.uint8 else 255
+
+
+def _draw_single_box(
+    image,
+    xmin,
+    ymin,
+    xmax,
+    ymax,
+    display_str,
+    color="black",
+    color_text="black",
+    thickness=2,
+):
+    from PIL import ImageDraw, ImageFont
+
+    font = ImageFont.load_default()
+    draw = ImageDraw.Draw(image)
+    (left, right, top, bottom) = (xmin, xmax, ymin, ymax)
+    draw.line(
+        [(left, top), (left, bottom), (right, bottom), (right, top), (left, top)],
+        width=thickness,
+        fill=color,
+    )
+    if display_str:
+        text_bottom = bottom
+        # Reverse list and print from bottom to top.
+        _left, _top, _right, _bottom = font.getbbox(display_str)
+        text_width, text_height = _right - _left, _bottom - _top
+        margin = np.ceil(0.05 * text_height)
+        draw.rectangle(
+            [
+                (left, text_bottom - text_height - 2 * margin),
+                (left + text_width, text_bottom),
+            ],
+            fill=color,
+        )
+        draw.text(
+            (left + margin, text_bottom - text_height - margin),
+            display_str,
+            fill=color_text,
+            font=font,
+        )
+    return image
+
+
+def hparams(hparam_dict=None, metric_dict=None, hparam_domain_discrete=None):
+    """Output three `Summary` protocol buffers needed by hparams plugin.
+
+    `Experiment` keeps the metadata of an experiment, such as the name of the
+      hyperparameters and the name of the metrics.
+    `SessionStartInfo` keeps key-value pairs of the hyperparameters
+    `SessionEndInfo` describes status of the experiment e.g. STATUS_SUCCESS
+
+    Args:
+      hparam_dict: A dictionary that contains names of the hyperparameters
+        and their values.
+      metric_dict: A dictionary that contains names of the metrics
+        and their values.
+      hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that
+        contains names of the hyperparameters and all discrete values they can hold
+
+    Returns:
+      The `Summary` protobufs for Experiment, SessionStartInfo and
+        SessionEndInfo
+    """
+    import torch
+    from tensorboard.plugins.hparams.api_pb2 import (
+        DataType,
+        Experiment,
+        HParamInfo,
+        MetricInfo,
+        MetricName,
+        Status,
+    )
+    from tensorboard.plugins.hparams.metadata import (
+        EXPERIMENT_TAG,
+        PLUGIN_DATA_VERSION,
+        PLUGIN_NAME,
+        SESSION_END_INFO_TAG,
+        SESSION_START_INFO_TAG,
+    )
+    from tensorboard.plugins.hparams.plugin_data_pb2 import (
+        HParamsPluginData,
+        SessionEndInfo,
+        SessionStartInfo,
+    )
+
+    # TODO: expose other parameters in the future.
+    # hp = HParamInfo(name='lr',display_name='learning rate',
+    # type=DataType.DATA_TYPE_FLOAT64, domain_interval=Interval(min_value=10,
+    # max_value=100))
+    # mt = MetricInfo(name=MetricName(tag='accuracy'), display_name='accuracy',
+    # description='', dataset_type=DatasetType.DATASET_VALIDATION)
+    # exp = Experiment(name='123', description='456', time_created_secs=100.0,
+    # hparam_infos=[hp], metric_infos=[mt], user='tw')
+
+    if not isinstance(hparam_dict, dict):
+        logger.warning("parameter: hparam_dict should be a dictionary, nothing logged.")
+        raise TypeError(
+            "parameter: hparam_dict should be a dictionary, nothing logged."
+        )
+    if not isinstance(metric_dict, dict):
+        logger.warning("parameter: metric_dict should be a dictionary, nothing logged.")
+        raise TypeError(
+            "parameter: metric_dict should be a dictionary, nothing logged."
+        )
+
+    hparam_domain_discrete = hparam_domain_discrete or {}
+    if not isinstance(hparam_domain_discrete, dict):
+        raise TypeError(
+            "parameter: hparam_domain_discrete should be a dictionary, nothing logged."
+        )
+    for k, v in hparam_domain_discrete.items():
+        if (
+            k not in hparam_dict
+            or not isinstance(v, list)
+            or not all(isinstance(d, type(hparam_dict[k])) for d in v)
+        ):
+            raise TypeError(
+                f"parameter: hparam_domain_discrete[{k}] should be a list of same type as hparam_dict[{k}]."
+            )
+    hps = []
+
+    ssi = SessionStartInfo()
+    for k, v in hparam_dict.items():
+        if v is None:
+            continue
+        if isinstance(v, (int, float)):
+            ssi.hparams[k].number_value = v
+
+            if k in hparam_domain_discrete:
+                domain_discrete: Optional[struct_pb2.ListValue] = struct_pb2.ListValue(
+                    values=[
+                        struct_pb2.Value(number_value=d)
+                        for d in hparam_domain_discrete[k]
+                    ]
+                )
+            else:
+                domain_discrete = None
+
+            hps.append(
+                HParamInfo(
+                    name=k,
+                    type=DataType.Value("DATA_TYPE_FLOAT64"),
+                    domain_discrete=domain_discrete,
+                )
+            )
+            continue
+
+        if isinstance(v, str):
+            ssi.hparams[k].string_value = v
+
+            if k in hparam_domain_discrete:
+                domain_discrete = struct_pb2.ListValue(
+                    values=[
+                        struct_pb2.Value(string_value=d)
+                        for d in hparam_domain_discrete[k]
+                    ]
+                )
+            else:
+                domain_discrete = None
+
+            hps.append(
+                HParamInfo(
+                    name=k,
+                    type=DataType.Value("DATA_TYPE_STRING"),
+                    domain_discrete=domain_discrete,
+                )
+            )
+            continue
+
+        if isinstance(v, bool):
+            ssi.hparams[k].bool_value = v
+
+            if k in hparam_domain_discrete:
+                domain_discrete = struct_pb2.ListValue(
+                    values=[
+                        struct_pb2.Value(bool_value=d)
+                        for d in hparam_domain_discrete[k]
+                    ]
+                )
+            else:
+                domain_discrete = None
+
+            hps.append(
+                HParamInfo(
+                    name=k,
+                    type=DataType.Value("DATA_TYPE_BOOL"),
+                    domain_discrete=domain_discrete,
+                )
+            )
+            continue
+
+        if isinstance(v, torch.Tensor):
+            v = make_np(v)[0]
+            ssi.hparams[k].number_value = v
+            hps.append(HParamInfo(name=k, type=DataType.Value("DATA_TYPE_FLOAT64")))
+            continue
+        raise ValueError(
+            "value should be one of int, float, str, bool, or torch.Tensor"
+        )
+
+    content = HParamsPluginData(session_start_info=ssi, version=PLUGIN_DATA_VERSION)
+    smd = SummaryMetadata(
+        plugin_data=SummaryMetadata.PluginData(
+            plugin_name=PLUGIN_NAME, content=content.SerializeToString()
+        )
+    )
+    ssi = Summary(value=[Summary.Value(tag=SESSION_START_INFO_TAG, metadata=smd)])
+
+    mts = [MetricInfo(name=MetricName(tag=k)) for k in metric_dict.keys()]
+
+    exp = Experiment(hparam_infos=hps, metric_infos=mts)
+
+    content = HParamsPluginData(experiment=exp, version=PLUGIN_DATA_VERSION)
+    smd = SummaryMetadata(
+        plugin_data=SummaryMetadata.PluginData(
+            plugin_name=PLUGIN_NAME, content=content.SerializeToString()
+        )
+    )
+    exp = Summary(value=[Summary.Value(tag=EXPERIMENT_TAG, metadata=smd)])
+
+    sei = SessionEndInfo(status=Status.Value("STATUS_SUCCESS"))
+    content = HParamsPluginData(session_end_info=sei, version=PLUGIN_DATA_VERSION)
+    smd = SummaryMetadata(
+        plugin_data=SummaryMetadata.PluginData(
+            plugin_name=PLUGIN_NAME, content=content.SerializeToString()
+        )
+    )
+    sei = Summary(value=[Summary.Value(tag=SESSION_END_INFO_TAG, metadata=smd)])
+
+    return exp, ssi, sei
+
+
+def scalar(name, tensor, collections=None, new_style=False, double_precision=False):
+    """Output a `Summary` protocol buffer containing a single scalar value.
+
+    The generated Summary has a Tensor.proto containing the input Tensor.
+    Args:
+      name: A name for the generated node. Will also serve as the series name in
+        TensorBoard.
+      tensor: A real numeric Tensor containing a single value.
+      collections: Optional list of graph collections keys. The new summary op is
+        added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
+      new_style: Whether to use new style (tensor field) or old style (simple_value
+        field). New style could lead to faster data loading.
+    Returns:
+      A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
+    Raises:
+      ValueError: If tensor has the wrong shape or type.
+    """
+    tensor = make_np(tensor).squeeze()
+    assert (
+        tensor.ndim == 0
+    ), f"Tensor should contain one element (0 dimensions). Was given size: {tensor.size} and {tensor.ndim} dimensions."
+    # python float is double precision in numpy
+    scalar = float(tensor)
+    if new_style:
+        tensor_proto = TensorProto(float_val=[scalar], dtype="DT_FLOAT")
+        if double_precision:
+            tensor_proto = TensorProto(double_val=[scalar], dtype="DT_DOUBLE")
+
+        plugin_data = SummaryMetadata.PluginData(plugin_name="scalars")
+        smd = SummaryMetadata(plugin_data=plugin_data)
+        return Summary(
+            value=[
+                Summary.Value(
+                    tag=name,
+                    tensor=tensor_proto,
+                    metadata=smd,
+                )
+            ]
+        )
+    else:
+        return Summary(value=[Summary.Value(tag=name, simple_value=scalar)])
+
+
+def tensor_proto(tag, tensor):
+    """Outputs a `Summary` protocol buffer containing the full tensor.
+    The generated Summary has a Tensor.proto containing the input Tensor.
+    Args:
+      tag: A name for the generated node. Will also serve as the series name in
+        TensorBoard.
+      tensor: Tensor to be converted to protobuf
+    Returns:
+      A tensor protobuf in a `Summary` protobuf.
+    Raises:
+      ValueError: If tensor is too big to be converted to protobuf, or
+                     tensor data type is not supported
+    """
+    if tensor.numel() * tensor.itemsize >= (1 << 31):
+        raise ValueError(
+            "tensor is bigger than protocol buffer's hard limit of 2GB in size"
+        )
+
+    if tensor.dtype in _TENSOR_TYPE_MAP:
+        dtype, field_name, conversion_fn = _TENSOR_TYPE_MAP[tensor.dtype]
+        tensor_proto = TensorProto(
+            **{
+                "dtype": dtype,
+                "tensor_shape": TensorShapeProto(
+                    dim=[TensorShapeProto.Dim(size=x) for x in tensor.shape]
+                ),
+                field_name: conversion_fn(tensor),
+            },
+        )
+    else:
+        raise ValueError(f"{tag} has unsupported tensor dtype {tensor.dtype}")
+
+    plugin_data = SummaryMetadata.PluginData(plugin_name="tensor")
+    smd = SummaryMetadata(plugin_data=plugin_data)
+    return Summary(value=[Summary.Value(tag=tag, metadata=smd, tensor=tensor_proto)])
+
+
+def histogram_raw(name, min, max, num, sum, sum_squares, bucket_limits, bucket_counts):
+    # pylint: disable=line-too-long
+    """Output a `Summary` protocol buffer with a histogram.
+
+    The generated
+    [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
+    has one summary value containing a histogram for `values`.
+    Args:
+      name: A name for the generated node. Will also serve as a series name in
+        TensorBoard.
+      min: A float or int min value
+      max: A float or int max value
+      num: Int number of values
+      sum: Float or int sum of all values
+      sum_squares: Float or int sum of squares for all values
+      bucket_limits: A numeric `Tensor` with upper value per bucket
+      bucket_counts: A numeric `Tensor` with number of values per bucket
+    Returns:
+      A scalar `Tensor` of type `string`. The serialized `Summary` protocol
+      buffer.
+    """
+    hist = HistogramProto(
+        min=min,
+        max=max,
+        num=num,
+        sum=sum,
+        sum_squares=sum_squares,
+        bucket_limit=bucket_limits,
+        bucket=bucket_counts,
+    )
+    return Summary(value=[Summary.Value(tag=name, histo=hist)])
+
+
+def histogram(name, values, bins, max_bins=None):
+    # pylint: disable=line-too-long
+    """Output a `Summary` protocol buffer with a histogram.
+
+    The generated
+    [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
+    has one summary value containing a histogram for `values`.
+    This op reports an `InvalidArgument` error if any value is not finite.
+    Args:
+      name: A name for the generated node. Will also serve as a series name in
+        TensorBoard.
+      values: A real numeric `Tensor`. Any shape. Values to use to
+        build the histogram.
+    Returns:
+      A scalar `Tensor` of type `string`. The serialized `Summary` protocol
+      buffer.
+    """
+    values = make_np(values)
+    hist = make_histogram(values.astype(float), bins, max_bins)
+    return Summary(value=[Summary.Value(tag=name, histo=hist)])
+
+
+def make_histogram(values, bins, max_bins=None):
+    """Convert values into a histogram proto using logic from histogram.cc."""
+    if values.size == 0:
+        raise ValueError("The input has no element.")
+    values = values.reshape(-1)
+    counts, limits = np.histogram(values, bins=bins)
+    num_bins = len(counts)
+    if max_bins is not None and num_bins > max_bins:
+        subsampling = num_bins // max_bins
+        subsampling_remainder = num_bins % subsampling
+        if subsampling_remainder != 0:
+            counts = np.pad(
+                counts,
+                pad_width=[[0, subsampling - subsampling_remainder]],
+                mode="constant",
+                constant_values=0,
+            )
+        counts = counts.reshape(-1, subsampling).sum(axis=-1)
+        new_limits = np.empty((counts.size + 1,), limits.dtype)
+        new_limits[:-1] = limits[:-1:subsampling]
+        new_limits[-1] = limits[-1]
+        limits = new_limits
+
+    # Find the first and the last bin defining the support of the histogram:
+
+    cum_counts = np.cumsum(np.greater(counts, 0))
+    start, end = np.searchsorted(cum_counts, [0, cum_counts[-1] - 1], side="right")
+    start = int(start)
+    end = int(end) + 1
+    del cum_counts
+
+    # TensorBoard only includes the right bin limits. To still have the leftmost limit
+    # included, we include an empty bin left.
+    # If start == 0, we need to add an empty one left, otherwise we can just include the bin left to the
+    # first nonzero-count bin:
+    counts = (
+        counts[start - 1 : end] if start > 0 else np.concatenate([[0], counts[:end]])
+    )
+    limits = limits[start : end + 1]
+
+    if counts.size == 0 or limits.size == 0:
+        raise ValueError("The histogram is empty, please file a bug report.")
+
+    sum_sq = values.dot(values)
+    return HistogramProto(
+        min=values.min(),
+        max=values.max(),
+        num=len(values),
+        sum=values.sum(),
+        sum_squares=sum_sq,
+        bucket_limit=limits.tolist(),
+        bucket=counts.tolist(),
+    )
+
+
+def image(tag, tensor, rescale=1, dataformats="NCHW"):
+    """Output a `Summary` protocol buffer with images.
+
+    The summary has up to `max_images` summary values containing images. The
+    images are built from `tensor` which must be 3-D with shape `[height, width,
+    channels]` and where `channels` can be:
+    *  1: `tensor` is interpreted as Grayscale.
+    *  3: `tensor` is interpreted as RGB.
+    *  4: `tensor` is interpreted as RGBA.
+    The `name` in the outputted Summary.Value protobufs is generated based on the
+    name, with a suffix depending on the max_outputs setting:
+    *  If `max_outputs` is 1, the summary value tag is '*name*/image'.
+    *  If `max_outputs` is greater than 1, the summary value tags are
+       generated sequentially as '*name*/image/0', '*name*/image/1', etc.
+    Args:
+      tag: A name for the generated node. Will also serve as a series name in
+        TensorBoard.
+      tensor: A 3-D `uint8` or `float32` `Tensor` of shape `[height, width,
+        channels]` where `channels` is 1, 3, or 4.
+        'tensor' can either have values in [0, 1] (float32) or [0, 255] (uint8).
+        The image() function will scale the image values to [0, 255] by applying
+        a scale factor of either 1 (uint8) or 255 (float32). Out-of-range values
+        will be clipped.
+    Returns:
+      A scalar `Tensor` of type `string`. The serialized `Summary` protocol
+      buffer.
+    """
+    tensor = make_np(tensor)
+    tensor = convert_to_HWC(tensor, dataformats)
+    # Do not assume that user passes in values in [0, 255], use data type to detect
+    scale_factor = _calc_scale_factor(tensor)
+    tensor = tensor.astype(np.float32)
+    tensor = (tensor * scale_factor).clip(0, 255).astype(np.uint8)
+    image = make_image(tensor, rescale=rescale)
+    return Summary(value=[Summary.Value(tag=tag, image=image)])
+
+
+def image_boxes(
+    tag, tensor_image, tensor_boxes, rescale=1, dataformats="CHW", labels=None
+):
+    """Output a `Summary` protocol buffer with images."""
+    tensor_image = make_np(tensor_image)
+    tensor_image = convert_to_HWC(tensor_image, dataformats)
+    tensor_boxes = make_np(tensor_boxes)
+    tensor_image = tensor_image.astype(np.float32) * _calc_scale_factor(tensor_image)
+    image = make_image(
+        tensor_image.clip(0, 255).astype(np.uint8),
+        rescale=rescale,
+        rois=tensor_boxes,
+        labels=labels,
+    )
+    return Summary(value=[Summary.Value(tag=tag, image=image)])
+
+
+def draw_boxes(disp_image, boxes, labels=None):
+    # xyxy format
+    num_boxes = boxes.shape[0]
+    list_gt = range(num_boxes)
+    for i in list_gt:
+        disp_image = _draw_single_box(
+            disp_image,
+            boxes[i, 0],
+            boxes[i, 1],
+            boxes[i, 2],
+            boxes[i, 3],
+            display_str=None if labels is None else labels[i],
+            color="Red",
+        )
+    return disp_image
+
+
+def make_image(tensor, rescale=1, rois=None, labels=None):
+    """Convert a numpy representation of an image to Image protobuf."""
+    from PIL import Image
+
+    height, width, channel = tensor.shape
+    scaled_height = int(height * rescale)
+    scaled_width = int(width * rescale)
+    image = Image.fromarray(tensor)
+    if rois is not None:
+        image = draw_boxes(image, rois, labels=labels)
+    ANTIALIAS = Image.Resampling.LANCZOS
+    image = image.resize((scaled_width, scaled_height), ANTIALIAS)
+    import io
+
+    output = io.BytesIO()
+    image.save(output, format="PNG")
+    image_string = output.getvalue()
+    output.close()
+    return Summary.Image(
+        height=height,
+        width=width,
+        colorspace=channel,
+        encoded_image_string=image_string,
+    )
+
+
+def video(tag, tensor, fps=4):
+    tensor = make_np(tensor)
+    tensor = _prepare_video(tensor)
+    # If user passes in uint8, then we don't need to rescale by 255
+    scale_factor = _calc_scale_factor(tensor)
+    tensor = tensor.astype(np.float32)
+    tensor = (tensor * scale_factor).clip(0, 255).astype(np.uint8)
+    video = make_video(tensor, fps)
+    return Summary(value=[Summary.Value(tag=tag, image=video)])
+
+
+def make_video(tensor, fps):
+    try:
+        import moviepy  # noqa: F401
+    except ImportError:
+        print("add_video needs package moviepy")
+        return
+    try:
+        from moviepy import editor as mpy
+    except ImportError:
+        print(
+            "moviepy is installed, but can't import moviepy.editor.",
+            "Some packages could be missing [imageio, requests]",
+        )
+        return
+    import tempfile
+
+    _t, h, w, c = tensor.shape
+
+    # encode sequence of images into gif string
+    clip = mpy.ImageSequenceClip(list(tensor), fps=fps)
+
+    filename = tempfile.NamedTemporaryFile(suffix=".gif", delete=False).name
+    try:  # newer version of moviepy use logger instead of progress_bar argument.
+        clip.write_gif(filename, verbose=False, logger=None)
+    except TypeError:
+        try:  # older version of moviepy does not support progress_bar argument.
+            clip.write_gif(filename, verbose=False, progress_bar=False)
+        except TypeError:
+            clip.write_gif(filename, verbose=False)
+
+    with open(filename, "rb") as f:
+        tensor_string = f.read()
+
+    try:
+        os.remove(filename)
+    except OSError:
+        logger.warning("The temporary file used by moviepy cannot be deleted.")
+
+    return Summary.Image(
+        height=h, width=w, colorspace=c, encoded_image_string=tensor_string
+    )
+
+
+def audio(tag, tensor, sample_rate=44100):
+    array = make_np(tensor)
+    array = array.squeeze()
+    if abs(array).max() > 1:
+        print("warning: audio amplitude out of range, auto clipped.")
+        array = array.clip(-1, 1)
+    assert array.ndim == 1, "input tensor should be 1 dimensional."
+    array = (array * np.iinfo(np.int16).max).astype(" 127:  # weird, value > 127 breaks protobuf
+        num_thresholds = 127
+    data = np.stack((tp, fp, tn, fn, precision, recall))
+    pr_curve_plugin_data = PrCurvePluginData(
+        version=0, num_thresholds=num_thresholds
+    ).SerializeToString()
+    plugin_data = SummaryMetadata.PluginData(
+        plugin_name="pr_curves", content=pr_curve_plugin_data
+    )
+    smd = SummaryMetadata(plugin_data=plugin_data)
+    tensor = TensorProto(
+        dtype="DT_FLOAT",
+        float_val=data.reshape(-1).tolist(),
+        tensor_shape=TensorShapeProto(
+            dim=[
+                TensorShapeProto.Dim(size=data.shape[0]),
+                TensorShapeProto.Dim(size=data.shape[1]),
+            ]
+        ),
+    )
+    return Summary(value=[Summary.Value(tag=tag, metadata=smd, tensor=tensor)])
+
+
+def pr_curve(tag, labels, predictions, num_thresholds=127, weights=None):
+    # weird, value > 127 breaks protobuf
+    num_thresholds = min(num_thresholds, 127)
+    data = compute_curve(
+        labels, predictions, num_thresholds=num_thresholds, weights=weights
+    )
+    pr_curve_plugin_data = PrCurvePluginData(
+        version=0, num_thresholds=num_thresholds
+    ).SerializeToString()
+    plugin_data = SummaryMetadata.PluginData(
+        plugin_name="pr_curves", content=pr_curve_plugin_data
+    )
+    smd = SummaryMetadata(plugin_data=plugin_data)
+    tensor = TensorProto(
+        dtype="DT_FLOAT",
+        float_val=data.reshape(-1).tolist(),
+        tensor_shape=TensorShapeProto(
+            dim=[
+                TensorShapeProto.Dim(size=data.shape[0]),
+                TensorShapeProto.Dim(size=data.shape[1]),
+            ]
+        ),
+    )
+    return Summary(value=[Summary.Value(tag=tag, metadata=smd, tensor=tensor)])
+
+
+# https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/summary.py
+def compute_curve(labels, predictions, num_thresholds=None, weights=None):
+    _MINIMUM_COUNT = 1e-7
+
+    if weights is None:
+        weights = 1.0
+
+    # Compute bins of true positives and false positives.
+    bucket_indices = np.int32(np.floor(predictions * (num_thresholds - 1)))
+    float_labels = labels.astype(np.float64)
+    histogram_range = (0, num_thresholds - 1)
+    tp_buckets, _ = np.histogram(
+        bucket_indices,
+        bins=num_thresholds,
+        range=histogram_range,
+        weights=float_labels * weights,
+    )
+    fp_buckets, _ = np.histogram(
+        bucket_indices,
+        bins=num_thresholds,
+        range=histogram_range,
+        weights=(1.0 - float_labels) * weights,
+    )
+
+    # Obtain the reverse cumulative sum.
+    tp = np.cumsum(tp_buckets[::-1])[::-1]
+    fp = np.cumsum(fp_buckets[::-1])[::-1]
+    tn = fp[0] - fp
+    fn = tp[0] - tp
+    precision = tp / np.maximum(_MINIMUM_COUNT, tp + fp)
+    recall = tp / np.maximum(_MINIMUM_COUNT, tp + fn)
+    return np.stack((tp, fp, tn, fn, precision, recall))
+
+
+def _get_tensor_summary(
+    name, display_name, description, tensor, content_type, components, json_config
+):
+    """Create a tensor summary with summary metadata.
+
+    Args:
+      name: Uniquely identifiable name of the summary op. Could be replaced by
+        combination of name and type to make it unique even outside of this
+        summary.
+      display_name: Will be used as the display name in TensorBoard.
+        Defaults to `name`.
+      description: A longform readable description of the summary data. Markdown
+        is supported.
+      tensor: Tensor to display in summary.
+      content_type: Type of content inside the Tensor.
+      components: Bitmask representing present parts (vertices, colors, etc.) that
+        belong to the summary.
+      json_config: A string, JSON-serialized dictionary of ThreeJS classes
+        configuration.
+
+    Returns:
+      Tensor summary with metadata.
+    """
+    import torch
+    from tensorboard.plugins.mesh import metadata
+
+    tensor = torch.as_tensor(tensor)
+
+    tensor_metadata = metadata.create_summary_metadata(
+        name,
+        display_name,
+        content_type,
+        components,
+        tensor.shape,
+        description,
+        json_config=json_config,
+    )
+
+    tensor = TensorProto(
+        dtype="DT_FLOAT",
+        float_val=tensor.reshape(-1).tolist(),
+        tensor_shape=TensorShapeProto(
+            dim=[
+                TensorShapeProto.Dim(size=tensor.shape[0]),
+                TensorShapeProto.Dim(size=tensor.shape[1]),
+                TensorShapeProto.Dim(size=tensor.shape[2]),
+            ]
+        ),
+    )
+
+    tensor_summary = Summary.Value(
+        tag=metadata.get_instance_name(name, content_type),
+        tensor=tensor,
+        metadata=tensor_metadata,
+    )
+
+    return tensor_summary
+
+
+def _get_json_config(config_dict):
+    """Parse and returns JSON string from python dictionary."""
+    json_config = "{}"
+    if config_dict is not None:
+        json_config = json.dumps(config_dict, sort_keys=True)
+    return json_config
+
+
+# https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/mesh/summary.py
+def mesh(
+    tag, vertices, colors, faces, config_dict, display_name=None, description=None
+):
+    """Output a merged `Summary` protocol buffer with a mesh/point cloud.
+
+    Args:
+      tag: A name for this summary operation.
+      vertices: Tensor of shape `[dim_1, ..., dim_n, 3]` representing the 3D
+        coordinates of vertices.
+      faces: Tensor of shape `[dim_1, ..., dim_n, 3]` containing indices of
+        vertices within each triangle.
+      colors: Tensor of shape `[dim_1, ..., dim_n, 3]` containing colors for each
+        vertex.
+      display_name: If set, will be used as the display name in TensorBoard.
+        Defaults to `name`.
+      description: A longform readable description of the summary data. Markdown
+        is supported.
+      config_dict: Dictionary with ThreeJS classes names and configuration.
+
+    Returns:
+      Merged summary for mesh/point cloud representation.
+    """
+    from tensorboard.plugins.mesh import metadata
+    from tensorboard.plugins.mesh.plugin_data_pb2 import MeshPluginData
+
+    json_config = _get_json_config(config_dict)
+
+    summaries = []
+    tensors = [
+        (vertices, MeshPluginData.VERTEX),
+        (faces, MeshPluginData.FACE),
+        (colors, MeshPluginData.COLOR),
+    ]
+    tensors = [tensor for tensor in tensors if tensor[0] is not None]
+    components = metadata.get_components_bitmask(
+        [content_type for (tensor, content_type) in tensors]
+    )
+
+    for tensor, content_type in tensors:
+        summaries.append(
+            _get_tensor_summary(
+                tag,
+                display_name,
+                description,
+                tensor,
+                content_type,
+                components,
+                json_config,
+            )
+        )
+
+    return Summary(value=summaries)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6792c5b8ab0f447d23194597a2084283da32526
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/tensorboard/writer.py
@@ -0,0 +1,1208 @@
+# mypy: allow-untyped-defs
+"""Provide an API for writing protocol buffers to event files to be consumed by TensorBoard for visualization."""
+
+import os
+import time
+from typing import Optional, TYPE_CHECKING, Union
+
+import torch
+
+if TYPE_CHECKING:
+    from matplotlib.figure import Figure
+from tensorboard.compat import tf
+from tensorboard.compat.proto import event_pb2
+from tensorboard.compat.proto.event_pb2 import Event, SessionLog
+from tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig
+from tensorboard.summary.writer.event_file_writer import EventFileWriter
+
+from ._convert_np import make_np
+from ._embedding import get_embedding_info, make_mat, make_sprite, make_tsv, write_pbtxt
+from ._onnx_graph import load_onnx_graph
+from ._pytorch_graph import graph
+from ._utils import figure_to_image
+from .summary import (
+    audio,
+    custom_scalars,
+    histogram,
+    histogram_raw,
+    hparams,
+    image,
+    image_boxes,
+    mesh,
+    pr_curve,
+    pr_curve_raw,
+    scalar,
+    tensor_proto,
+    text,
+    video,
+)
+
+__all__ = ["FileWriter", "SummaryWriter"]
+
+
+class FileWriter:
+    """Writes protocol buffers to event files to be consumed by TensorBoard.
+
+    The `FileWriter` class provides a mechanism to create an event file in a
+    given directory and add summaries and events to it. The class updates the
+    file contents asynchronously. This allows a training program to call methods
+    to add data to the file directly from the training loop, without slowing down
+    training.
+    """
+
+    def __init__(self, log_dir, max_queue=10, flush_secs=120, filename_suffix=""):
+        """Create a `FileWriter` and an event file.
+
+        On construction the writer creates a new event file in `log_dir`.
+        The other arguments to the constructor control the asynchronous writes to
+        the event file.
+
+        Args:
+          log_dir: A string. Directory where event file will be written.
+          max_queue: Integer. Size of the queue for pending events and
+            summaries before one of the 'add' calls forces a flush to disk.
+            Default is ten items.
+          flush_secs: Number. How often, in seconds, to flush the
+            pending events and summaries to disk. Default is every two minutes.
+          filename_suffix: A string. Suffix added to all event filenames
+            in the log_dir directory. More details on filename construction in
+            tensorboard.summary.writer.event_file_writer.EventFileWriter.
+        """
+        # Sometimes PosixPath is passed in and we need to coerce it to
+        # a string in all cases
+        # TODO: See if we can remove this in the future if we are
+        # actually the ones passing in a PosixPath
+        log_dir = str(log_dir)
+        self.event_writer = EventFileWriter(
+            log_dir, max_queue, flush_secs, filename_suffix
+        )
+
+    def get_logdir(self):
+        """Return the directory where event file will be written."""
+        return self.event_writer.get_logdir()
+
+    def add_event(self, event, step=None, walltime=None):
+        """Add an event to the event file.
+
+        Args:
+          event: An `Event` protocol buffer.
+          step: Number. Optional global step value for training process
+            to record with the event.
+          walltime: float. Optional walltime to override the default (current)
+            walltime (from time.time()) seconds after epoch
+        """
+        event.wall_time = time.time() if walltime is None else walltime
+        if step is not None:
+            # Make sure step is converted from numpy or other formats
+            # since protobuf might not convert depending on version
+            event.step = int(step)
+        self.event_writer.add_event(event)
+
+    def add_summary(self, summary, global_step=None, walltime=None):
+        """Add a `Summary` protocol buffer to the event file.
+
+        This method wraps the provided summary in an `Event` protocol buffer
+        and adds it to the event file.
+
+        Args:
+          summary: A `Summary` protocol buffer.
+          global_step: Number. Optional global step value for training process
+            to record with the summary.
+          walltime: float. Optional walltime to override the default (current)
+            walltime (from time.time()) seconds after epoch
+        """
+        event = event_pb2.Event(summary=summary)
+        self.add_event(event, global_step, walltime)
+
+    def add_graph(self, graph_profile, walltime=None):
+        """Add a `Graph` and step stats protocol buffer to the event file.
+
+        Args:
+          graph_profile: A `Graph` and step stats protocol buffer.
+          walltime: float. Optional walltime to override the default (current)
+            walltime (from time.time()) seconds after epoch
+        """
+        graph = graph_profile[0]
+        stepstats = graph_profile[1]
+        event = event_pb2.Event(graph_def=graph.SerializeToString())
+        self.add_event(event, None, walltime)
+
+        trm = event_pb2.TaggedRunMetadata(
+            tag="step1", run_metadata=stepstats.SerializeToString()
+        )
+        event = event_pb2.Event(tagged_run_metadata=trm)
+        self.add_event(event, None, walltime)
+
+    def add_onnx_graph(self, graph, walltime=None):
+        """Add a `Graph` protocol buffer to the event file.
+
+        Args:
+          graph: A `Graph` protocol buffer.
+          walltime: float. Optional walltime to override the default (current)
+            _get_file_writerfrom time.time())
+        """
+        event = event_pb2.Event(graph_def=graph.SerializeToString())
+        self.add_event(event, None, walltime)
+
+    def flush(self):
+        """Flushes the event file to disk.
+
+        Call this method to make sure that all pending events have been written to
+        disk.
+        """
+        self.event_writer.flush()
+
+    def close(self):
+        """Flushes the event file to disk and close the file.
+
+        Call this method when you do not need the summary writer anymore.
+        """
+        self.event_writer.close()
+
+    def reopen(self):
+        """Reopens the EventFileWriter.
+
+        Can be called after `close()` to add more events in the same directory.
+        The events will go into a new events file.
+        Does nothing if the EventFileWriter was not closed.
+        """
+        self.event_writer.reopen()
+
+
+class SummaryWriter:
+    """Writes entries directly to event files in the log_dir to be consumed by TensorBoard.
+
+    The `SummaryWriter` class provides a high-level API to create an event file
+    in a given directory and add summaries and events to it. The class updates the
+    file contents asynchronously. This allows a training program to call methods
+    to add data to the file directly from the training loop, without slowing down
+    training.
+    """
+
+    def __init__(
+        self,
+        log_dir=None,
+        comment="",
+        purge_step=None,
+        max_queue=10,
+        flush_secs=120,
+        filename_suffix="",
+    ):
+        """Create a `SummaryWriter` that will write out events and summaries to the event file.
+
+        Args:
+            log_dir (str): Save directory location. Default is
+              runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each run.
+              Use hierarchical folder structure to compare
+              between runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc.
+              for each new experiment to compare across them.
+            comment (str): Comment log_dir suffix appended to the default
+              ``log_dir``. If ``log_dir`` is assigned, this argument has no effect.
+            purge_step (int):
+              When logging crashes at step :math:`T+X` and restarts at step :math:`T`,
+              any events whose global_step larger or equal to :math:`T` will be
+              purged and hidden from TensorBoard.
+              Note that crashed and resumed experiments should have the same ``log_dir``.
+            max_queue (int): Size of the queue for pending events and
+              summaries before one of the 'add' calls forces a flush to disk.
+              Default is ten items.
+            flush_secs (int): How often, in seconds, to flush the
+              pending events and summaries to disk. Default is every two minutes.
+            filename_suffix (str): Suffix added to all event filenames in
+              the log_dir directory. More details on filename construction in
+              tensorboard.summary.writer.event_file_writer.EventFileWriter.
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+
+            # create a summary writer with automatically generated folder name.
+            writer = SummaryWriter()
+            # folder location: runs/May04_22-14-54_s-MacBook-Pro.local/
+
+            # create a summary writer using the specified folder name.
+            writer = SummaryWriter("my_experiment")
+            # folder location: my_experiment
+
+            # create a summary writer with comment appended.
+            writer = SummaryWriter(comment="LR_0.1_BATCH_16")
+            # folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/
+
+        """
+        torch._C._log_api_usage_once("tensorboard.create.summarywriter")
+        if not log_dir:
+            import socket
+            from datetime import datetime
+
+            current_time = datetime.now().strftime("%b%d_%H-%M-%S")
+            log_dir = os.path.join(
+                "runs", current_time + "_" + socket.gethostname() + comment
+            )
+        self.log_dir = log_dir
+        self.purge_step = purge_step
+        self.max_queue = max_queue
+        self.flush_secs = flush_secs
+        self.filename_suffix = filename_suffix
+
+        # Initialize the file writers, but they can be cleared out on close
+        # and recreated later as needed.
+        self.file_writer = self.all_writers = None
+        self._get_file_writer()
+
+        # Create default bins for histograms, see generate_testdata.py in tensorflow/tensorboard
+        v = 1e-12
+        buckets = []
+        neg_buckets = []
+        while v < 1e20:
+            buckets.append(v)
+            neg_buckets.append(-v)
+            v *= 1.1
+        self.default_bins = neg_buckets[::-1] + [0] + buckets
+
+    def _get_file_writer(self):
+        """Return the default FileWriter instance. Recreates it if closed."""
+        if self.all_writers is None or self.file_writer is None:
+            self.file_writer = FileWriter(
+                self.log_dir, self.max_queue, self.flush_secs, self.filename_suffix
+            )
+            self.all_writers = {self.file_writer.get_logdir(): self.file_writer}
+            if self.purge_step is not None:
+                most_recent_step = self.purge_step
+                self.file_writer.add_event(
+                    Event(step=most_recent_step, file_version="brain.Event:2")
+                )
+                self.file_writer.add_event(
+                    Event(
+                        step=most_recent_step,
+                        session_log=SessionLog(status=SessionLog.START),
+                    )
+                )
+                self.purge_step = None
+        return self.file_writer
+
+    def get_logdir(self):
+        """Return the directory where event files will be written."""
+        return self.log_dir
+
+    def add_hparams(
+        self,
+        hparam_dict,
+        metric_dict,
+        hparam_domain_discrete=None,
+        run_name=None,
+        global_step=None,
+    ):
+        """Add a set of hyperparameters to be compared in TensorBoard.
+
+        Args:
+            hparam_dict (dict): Each key-value pair in the dictionary is the
+              name of the hyper parameter and it's corresponding value.
+              The type of the value can be one of `bool`, `string`, `float`,
+              `int`, or `None`.
+            metric_dict (dict): Each key-value pair in the dictionary is the
+              name of the metric and it's corresponding value. Note that the key used
+              here should be unique in the tensorboard record. Otherwise the value
+              you added by ``add_scalar`` will be displayed in hparam plugin. In most
+              cases, this is unwanted.
+            hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that
+              contains names of the hyperparameters and all discrete values they can hold
+            run_name (str): Name of the run, to be included as part of the logdir.
+              If unspecified, will use current timestamp.
+            global_step (int): Global step value to record
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            with SummaryWriter() as w:
+                for i in range(5):
+                    w.add_hparams({'lr': 0.1*i, 'bsize': i},
+                                  {'hparam/accuracy': 10*i, 'hparam/loss': 10*i})
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_hparam.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_hparams")
+        if type(hparam_dict) is not dict or type(metric_dict) is not dict:
+            raise TypeError("hparam_dict and metric_dict should be dictionary.")
+        exp, ssi, sei = hparams(hparam_dict, metric_dict, hparam_domain_discrete)
+
+        if not run_name:
+            run_name = str(time.time())
+        logdir = os.path.join(self._get_file_writer().get_logdir(), run_name)
+        with SummaryWriter(log_dir=logdir) as w_hp:
+            w_hp.file_writer.add_summary(exp, global_step)
+            w_hp.file_writer.add_summary(ssi, global_step)
+            w_hp.file_writer.add_summary(sei, global_step)
+            for k, v in metric_dict.items():
+                w_hp.add_scalar(k, v, global_step)
+
+    def add_scalar(
+        self,
+        tag,
+        scalar_value,
+        global_step=None,
+        walltime=None,
+        new_style=False,
+        double_precision=False,
+    ):
+        """Add scalar data to summary.
+
+        Args:
+            tag (str): Data identifier
+            scalar_value (float or string/blobname): Value to save
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              with seconds after epoch of event
+            new_style (boolean): Whether to use new style (tensor field) or old
+              style (simple_value field). New style could lead to faster data loading.
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            writer = SummaryWriter()
+            x = range(100)
+            for i in x:
+                writer.add_scalar('y=2x', i * 2, i)
+            writer.close()
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_scalar.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_scalar")
+
+        summary = scalar(
+            tag, scalar_value, new_style=new_style, double_precision=double_precision
+        )
+        self._get_file_writer().add_summary(summary, global_step, walltime)
+
+    def add_scalars(self, main_tag, tag_scalar_dict, global_step=None, walltime=None):
+        """Add many scalar data to summary.
+
+        Args:
+            main_tag (str): The parent name for the tags
+            tag_scalar_dict (dict): Key-value pair storing the tag and corresponding values
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            writer = SummaryWriter()
+            r = 5
+            for i in range(100):
+                writer.add_scalars('run_14h', {'xsinx':i*np.sin(i/r),
+                                                'xcosx':i*np.cos(i/r),
+                                                'tanx': np.tan(i/r)}, i)
+            writer.close()
+            # This call adds three values to the same scalar plot with the tag
+            # 'run_14h' in TensorBoard's scalar section.
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_scalars.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_scalars")
+        walltime = time.time() if walltime is None else walltime
+        fw_logdir = self._get_file_writer().get_logdir()
+        for tag, scalar_value in tag_scalar_dict.items():
+            fw_tag = fw_logdir + "/" + main_tag.replace("/", "_") + "_" + tag
+            assert self.all_writers is not None
+            if fw_tag in self.all_writers.keys():
+                fw = self.all_writers[fw_tag]
+            else:
+                fw = FileWriter(
+                    fw_tag, self.max_queue, self.flush_secs, self.filename_suffix
+                )
+                self.all_writers[fw_tag] = fw
+            fw.add_summary(scalar(main_tag, scalar_value), global_step, walltime)
+
+    def add_tensor(
+        self,
+        tag,
+        tensor,
+        global_step=None,
+        walltime=None,
+    ):
+        """Add tensor data to summary.
+
+        Args:
+            tag (str): Data identifier
+            tensor (torch.Tensor): tensor to save
+            global_step (int): Global step value to record
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            writer = SummaryWriter()
+            x = torch.tensor([1,2,3])
+            writer.add_scalar('x', x)
+            writer.close()
+
+        Expected result:
+            Summary::tensor::float_val [1,2,3]
+                   ::tensor::shape [3]
+                   ::tag 'x'
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_tensor")
+
+        summary = tensor_proto(tag, tensor)
+        self._get_file_writer().add_summary(summary, global_step, walltime)
+
+    def add_histogram(
+        self,
+        tag,
+        values,
+        global_step=None,
+        bins="tensorflow",
+        walltime=None,
+        max_bins=None,
+    ):
+        """Add histogram to summary.
+
+        Args:
+            tag (str): Data identifier
+            values (torch.Tensor, numpy.ndarray, or string/blobname): Values to build histogram
+            global_step (int): Global step value to record
+            bins (str): One of {'tensorflow','auto', 'fd', ...}. This determines how the bins are made. You can find
+              other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            import numpy as np
+            writer = SummaryWriter()
+            for i in range(10):
+                x = np.random.random(1000)
+                writer.add_histogram('distribution centers', x + i, i)
+            writer.close()
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_histogram.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_histogram")
+        if isinstance(bins, str) and bins == "tensorflow":
+            bins = self.default_bins
+        self._get_file_writer().add_summary(
+            histogram(tag, values, bins, max_bins=max_bins), global_step, walltime
+        )
+
+    def add_histogram_raw(
+        self,
+        tag,
+        min,
+        max,
+        num,
+        sum,
+        sum_squares,
+        bucket_limits,
+        bucket_counts,
+        global_step=None,
+        walltime=None,
+    ):
+        """Add histogram with raw data.
+
+        Args:
+            tag (str): Data identifier
+            min (float or int): Min value
+            max (float or int): Max value
+            num (int): Number of values
+            sum (float or int): Sum of all values
+            sum_squares (float or int): Sum of squares for all values
+            bucket_limits (torch.Tensor, numpy.ndarray): Upper value per bucket.
+              The number of elements of it should be the same as `bucket_counts`.
+            bucket_counts (torch.Tensor, numpy.ndarray): Number of values per bucket
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+            see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/README.md
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            import numpy as np
+            writer = SummaryWriter()
+            dummy_data = []
+            for idx, value in enumerate(range(50)):
+                dummy_data += [idx + 0.001] * value
+
+            bins = list(range(50+2))
+            bins = np.array(bins)
+            values = np.array(dummy_data).astype(float).reshape(-1)
+            counts, limits = np.histogram(values, bins=bins)
+            sum_sq = values.dot(values)
+            writer.add_histogram_raw(
+                tag='histogram_with_raw_data',
+                min=values.min(),
+                max=values.max(),
+                num=len(values),
+                sum=values.sum(),
+                sum_squares=sum_sq,
+                bucket_limits=limits[1:].tolist(),
+                bucket_counts=counts.tolist(),
+                global_step=0)
+            writer.close()
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_histogram_raw.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_histogram_raw")
+        if len(bucket_limits) != len(bucket_counts):
+            raise ValueError(
+                "len(bucket_limits) != len(bucket_counts), see the document."
+            )
+        self._get_file_writer().add_summary(
+            histogram_raw(
+                tag, min, max, num, sum, sum_squares, bucket_limits, bucket_counts
+            ),
+            global_step,
+            walltime,
+        )
+
+    def add_image(
+        self, tag, img_tensor, global_step=None, walltime=None, dataformats="CHW"
+    ):
+        """Add image data to summary.
+
+        Note that this requires the ``pillow`` package.
+
+        Args:
+            tag (str): Data identifier
+            img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+            dataformats (str): Image data format specification of the form
+              CHW, HWC, HW, WH, etc.
+        Shape:
+            img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to
+            convert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job.
+            Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitable as long as
+            corresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``.
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            import numpy as np
+            img = np.zeros((3, 100, 100))
+            img[0] = np.arange(0, 10000).reshape(100, 100) / 10000
+            img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
+
+            img_HWC = np.zeros((100, 100, 3))
+            img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000
+            img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
+
+            writer = SummaryWriter()
+            writer.add_image('my_image', img, 0)
+
+            # If you have non-default dimension setting, set the dataformats argument.
+            writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC')
+            writer.close()
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_image.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_image")
+        self._get_file_writer().add_summary(
+            image(tag, img_tensor, dataformats=dataformats), global_step, walltime
+        )
+
+    def add_images(
+        self, tag, img_tensor, global_step=None, walltime=None, dataformats="NCHW"
+    ):
+        """Add batched image data to summary.
+
+        Note that this requires the ``pillow`` package.
+
+        Args:
+            tag (str): Data identifier
+            img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+            dataformats (str): Image data format specification of the form
+              NCHW, NHWC, CHW, HWC, HW, WH, etc.
+        Shape:
+            img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will be
+            accepted. e.g. NCHW or NHWC.
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            import numpy as np
+
+            img_batch = np.zeros((16, 3, 100, 100))
+            for i in range(16):
+                img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i
+                img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i
+
+            writer = SummaryWriter()
+            writer.add_images('my_image_batch', img_batch, 0)
+            writer.close()
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_images.png
+           :scale: 30 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_images")
+        self._get_file_writer().add_summary(
+            image(tag, img_tensor, dataformats=dataformats), global_step, walltime
+        )
+
+    def add_image_with_boxes(
+        self,
+        tag,
+        img_tensor,
+        box_tensor,
+        global_step=None,
+        walltime=None,
+        rescale=1,
+        dataformats="CHW",
+        labels=None,
+    ):
+        """Add image and draw bounding boxes on the image.
+
+        Args:
+            tag (str): Data identifier
+            img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data
+            box_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Box data (for detected objects)
+              box should be represented as [x1, y1, x2, y2].
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+            rescale (float): Optional scale override
+            dataformats (str): Image data format specification of the form
+              NCHW, NHWC, CHW, HWC, HW, WH, etc.
+            labels (list of string): The label to be shown for each bounding box.
+        Shape:
+            img_tensor: Default is :math:`(3, H, W)`. It can be specified with ``dataformats`` argument.
+            e.g. CHW or HWC
+
+            box_tensor: (torch.Tensor, numpy.ndarray, or string/blobname): NX4,  where N is the number of
+            boxes and each 4 elements in a row represents (xmin, ymin, xmax, ymax).
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_image_with_boxes")
+        if labels is not None:
+            if isinstance(labels, str):
+                labels = [labels]
+            if len(labels) != box_tensor.shape[0]:
+                labels = None
+        self._get_file_writer().add_summary(
+            image_boxes(
+                tag,
+                img_tensor,
+                box_tensor,
+                rescale=rescale,
+                dataformats=dataformats,
+                labels=labels,
+            ),
+            global_step,
+            walltime,
+        )
+
+    def add_figure(
+        self,
+        tag: str,
+        figure: Union["Figure", list["Figure"]],
+        global_step: Optional[int] = None,
+        close: bool = True,
+        walltime: Optional[float] = None,
+    ) -> None:
+        """Render matplotlib figure into an image and add it to summary.
+
+        Note that this requires the ``matplotlib`` package.
+
+        Args:
+            tag: Data identifier
+            figure: Figure or a list of figures
+            global_step: Global step value to record
+            close: Flag to automatically close the figure
+            walltime: Optional override default walltime (time.time())
+              seconds after epoch of event
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_figure")
+        if isinstance(figure, list):
+            self.add_image(
+                tag,
+                figure_to_image(figure, close),
+                global_step,
+                walltime,
+                dataformats="NCHW",
+            )
+        else:
+            self.add_image(
+                tag,
+                figure_to_image(figure, close),
+                global_step,
+                walltime,
+                dataformats="CHW",
+            )
+
+    def add_video(self, tag, vid_tensor, global_step=None, fps=4, walltime=None):
+        """Add video data to summary.
+
+        Note that this requires the ``moviepy`` package.
+
+        Args:
+            tag (str): Data identifier
+            vid_tensor (torch.Tensor): Video data
+            global_step (int): Global step value to record
+            fps (float or int): Frames per second
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+        Shape:
+            vid_tensor: :math:`(N, T, C, H, W)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_video")
+        self._get_file_writer().add_summary(
+            video(tag, vid_tensor, fps), global_step, walltime
+        )
+
+    def add_audio(
+        self, tag, snd_tensor, global_step=None, sample_rate=44100, walltime=None
+    ):
+        """Add audio data to summary.
+
+        Args:
+            tag (str): Data identifier
+            snd_tensor (torch.Tensor): Sound data
+            global_step (int): Global step value to record
+            sample_rate (int): sample rate in Hz
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+        Shape:
+            snd_tensor: :math:`(1, L)`. The values should lie between [-1, 1].
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_audio")
+        self._get_file_writer().add_summary(
+            audio(tag, snd_tensor, sample_rate=sample_rate), global_step, walltime
+        )
+
+    def add_text(self, tag, text_string, global_step=None, walltime=None):
+        """Add text data to summary.
+
+        Args:
+            tag (str): Data identifier
+            text_string (str): String to save
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+        Examples::
+
+            writer.add_text('lstm', 'This is an lstm', 0)
+            writer.add_text('rnn', 'This is an rnn', 10)
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_text")
+        self._get_file_writer().add_summary(
+            text(tag, text_string), global_step, walltime
+        )
+
+    def add_onnx_graph(self, prototxt):
+        torch._C._log_api_usage_once("tensorboard.logging.add_onnx_graph")
+        self._get_file_writer().add_onnx_graph(load_onnx_graph(prototxt))
+
+    def add_graph(
+        self, model, input_to_model=None, verbose=False, use_strict_trace=True
+    ):
+        """Add graph data to summary.
+
+        Args:
+            model (torch.nn.Module): Model to draw.
+            input_to_model (torch.Tensor or list of torch.Tensor): A variable or a tuple of
+                variables to be fed.
+            verbose (bool): Whether to print graph structure in console.
+            use_strict_trace (bool): Whether to pass keyword argument `strict` to
+                `torch.jit.trace`. Pass False when you want the tracer to
+                record your mutable container types (list, dict)
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_graph")
+        # A valid PyTorch model should have a 'forward' method
+        self._get_file_writer().add_graph(
+            graph(model, input_to_model, verbose, use_strict_trace)
+        )
+
+    @staticmethod
+    def _encode(rawstr):
+        # I'd use urllib but, I'm unsure about the differences from python3 to python2, etc.
+        retval = rawstr
+        retval = retval.replace("%", f"%{ord('%'):02x}")
+        retval = retval.replace("/", f"%{ord('/'):02x}")
+        retval = retval.replace("\\", "%%%02x" % (ord("\\")))  # noqa: UP031
+        return retval
+
+    def add_embedding(
+        self,
+        mat,
+        metadata=None,
+        label_img=None,
+        global_step=None,
+        tag="default",
+        metadata_header=None,
+    ):
+        """Add embedding projector data to summary.
+
+        Args:
+            mat (torch.Tensor or numpy.ndarray): A matrix which each row is the feature vector of the data point
+            metadata (list): A list of labels, each element will be converted to string
+            label_img (torch.Tensor): Images correspond to each data point
+            global_step (int): Global step value to record
+            tag (str): Name for the embedding
+            metadata_header (list): A list of headers for multi-column metadata. If given, each metadata must be
+                a list with values corresponding to headers.
+        Shape:
+            mat: :math:`(N, D)`, where N is number of data and D is feature dimension
+
+            label_img: :math:`(N, C, H, W)`
+
+        Examples::
+
+            import keyword
+            import torch
+            meta = []
+            while len(meta)<100:
+                meta = meta+keyword.kwlist # get some strings
+            meta = meta[:100]
+
+            for i, v in enumerate(meta):
+                meta[i] = v+str(i)
+
+            label_img = torch.rand(100, 3, 10, 32)
+            for i in range(100):
+                label_img[i]*=i/100.0
+
+            writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)
+            writer.add_embedding(torch.randn(100, 5), label_img=label_img)
+            writer.add_embedding(torch.randn(100, 5), metadata=meta)
+
+        .. note::
+            Categorical (i.e. non-numeric) metadata cannot have more than 50 unique values if they are to be used for
+            coloring in the embedding projector.
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_embedding")
+        mat = make_np(mat)
+        if global_step is None:
+            global_step = 0
+            # clear pbtxt?
+
+        # Maybe we should encode the tag so slashes don't trip us up?
+        # I don't think this will mess us up, but better safe than sorry.
+        subdir = f"{str(global_step).zfill(5)}/{self._encode(tag)}"
+        save_path = os.path.join(self._get_file_writer().get_logdir(), subdir)
+
+        fs = tf.io.gfile
+        if fs.exists(save_path):
+            if fs.isdir(save_path):
+                print(
+                    "warning: Embedding dir exists, did you set global_step for add_embedding()?"
+                )
+            else:
+                raise NotADirectoryError(
+                    f"Path: `{save_path}` exists, but is a file. Cannot proceed."
+                )
+        else:
+            fs.makedirs(save_path)
+
+        if metadata is not None:
+            assert mat.shape[0] == len(
+                metadata
+            ), "#labels should equal with #data points"
+            make_tsv(metadata, save_path, metadata_header=metadata_header)
+
+        if label_img is not None:
+            assert (
+                mat.shape[0] == label_img.shape[0]
+            ), "#images should equal with #data points"
+            make_sprite(label_img, save_path)
+
+        assert (
+            mat.ndim == 2
+        ), "mat should be 2D, where mat.size(0) is the number of data points"
+        make_mat(mat, save_path)
+
+        # Filesystem doesn't necessarily have append semantics, so we store an
+        # internal buffer to append to and re-write whole file after each
+        # embedding is added
+        if not hasattr(self, "_projector_config"):
+            self._projector_config = ProjectorConfig()
+        embedding_info = get_embedding_info(
+            metadata, label_img, subdir, global_step, tag
+        )
+        self._projector_config.embeddings.extend([embedding_info])
+
+        from google.protobuf import text_format
+
+        config_pbtxt = text_format.MessageToString(self._projector_config)
+        write_pbtxt(self._get_file_writer().get_logdir(), config_pbtxt)
+
+    def add_pr_curve(
+        self,
+        tag,
+        labels,
+        predictions,
+        global_step=None,
+        num_thresholds=127,
+        weights=None,
+        walltime=None,
+    ):
+        """Add precision recall curve.
+
+        Plotting a precision-recall curve lets you understand your model's
+        performance under different threshold settings. With this function,
+        you provide the ground truth labeling (T/F) and prediction confidence
+        (usually the output of your model) for each target. The TensorBoard UI
+        will let you choose the threshold interactively.
+
+        Args:
+            tag (str): Data identifier
+            labels (torch.Tensor, numpy.ndarray, or string/blobname):
+              Ground truth data. Binary label for each element.
+            predictions (torch.Tensor, numpy.ndarray, or string/blobname):
+              The probability that an element be classified as true.
+              Value should be in [0, 1]
+            global_step (int): Global step value to record
+            num_thresholds (int): Number of thresholds used to draw the curve.
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            import numpy as np
+            labels = np.random.randint(2, size=100)  # binary label
+            predictions = np.random.rand(100)
+            writer = SummaryWriter()
+            writer.add_pr_curve('pr_curve', labels, predictions, 0)
+            writer.close()
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve")
+        labels, predictions = make_np(labels), make_np(predictions)
+        self._get_file_writer().add_summary(
+            pr_curve(tag, labels, predictions, num_thresholds, weights),
+            global_step,
+            walltime,
+        )
+
+    def add_pr_curve_raw(
+        self,
+        tag,
+        true_positive_counts,
+        false_positive_counts,
+        true_negative_counts,
+        false_negative_counts,
+        precision,
+        recall,
+        global_step=None,
+        num_thresholds=127,
+        weights=None,
+        walltime=None,
+    ):
+        """Add precision recall curve with raw data.
+
+        Args:
+            tag (str): Data identifier
+            true_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): true positive counts
+            false_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): false positive counts
+            true_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): true negative counts
+            false_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): false negative counts
+            precision (torch.Tensor, numpy.ndarray, or string/blobname): precision
+            recall (torch.Tensor, numpy.ndarray, or string/blobname): recall
+            global_step (int): Global step value to record
+            num_thresholds (int): Number of thresholds used to draw the curve.
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+            see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve_raw")
+        self._get_file_writer().add_summary(
+            pr_curve_raw(
+                tag,
+                true_positive_counts,
+                false_positive_counts,
+                true_negative_counts,
+                false_negative_counts,
+                precision,
+                recall,
+                num_thresholds,
+                weights,
+            ),
+            global_step,
+            walltime,
+        )
+
+    def add_custom_scalars_multilinechart(
+        self, tags, category="default", title="untitled"
+    ):
+        """Shorthand for creating multilinechart. Similar to ``add_custom_scalars()``, but the only necessary argument is *tags*.
+
+        Args:
+            tags (list): list of tags that have been used in ``add_scalar()``
+
+        Examples::
+
+            writer.add_custom_scalars_multilinechart(['twse/0050', 'twse/2330'])
+        """
+        torch._C._log_api_usage_once(
+            "tensorboard.logging.add_custom_scalars_multilinechart"
+        )
+        layout = {category: {title: ["Multiline", tags]}}
+        self._get_file_writer().add_summary(custom_scalars(layout))
+
+    def add_custom_scalars_marginchart(
+        self, tags, category="default", title="untitled"
+    ):
+        """Shorthand for creating marginchart.
+
+        Similar to ``add_custom_scalars()``, but the only necessary argument is *tags*,
+        which should have exactly 3 elements.
+
+        Args:
+            tags (list): list of tags that have been used in ``add_scalar()``
+
+        Examples::
+
+            writer.add_custom_scalars_marginchart(['twse/0050', 'twse/2330', 'twse/2006'])
+        """
+        torch._C._log_api_usage_once(
+            "tensorboard.logging.add_custom_scalars_marginchart"
+        )
+        assert len(tags) == 3
+        layout = {category: {title: ["Margin", tags]}}
+        self._get_file_writer().add_summary(custom_scalars(layout))
+
+    def add_custom_scalars(self, layout):
+        """Create special chart by collecting charts tags in 'scalars'.
+
+        NOTE: This function can only be called once for each SummaryWriter() object.
+
+        Because it only provides metadata to tensorboard, the function can be called before or after the training loop.
+
+        Args:
+            layout (dict): {categoryName: *charts*}, where *charts* is also a dictionary
+              {chartName: *ListOfProperties*}. The first element in *ListOfProperties* is the chart's type
+              (one of **Multiline** or **Margin**) and the second element should be a list containing the tags
+              you have used in add_scalar function, which will be collected into the new chart.
+
+        Examples::
+
+            layout = {'Taiwan':{'twse':['Multiline',['twse/0050', 'twse/2330']]},
+                         'USA':{ 'dow':['Margin',   ['dow/aaa', 'dow/bbb', 'dow/ccc']],
+                              'nasdaq':['Margin',   ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}}
+
+            writer.add_custom_scalars(layout)
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_custom_scalars")
+        self._get_file_writer().add_summary(custom_scalars(layout))
+
+    def add_mesh(
+        self,
+        tag,
+        vertices,
+        colors=None,
+        faces=None,
+        config_dict=None,
+        global_step=None,
+        walltime=None,
+    ):
+        """Add meshes or 3D point clouds to TensorBoard.
+
+        The visualization is based on Three.js,
+        so it allows users to interact with the rendered object. Besides the basic definitions
+        such as vertices, faces, users can further provide camera parameter, lighting condition, etc.
+        Please check https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene for
+        advanced usage.
+
+        Args:
+            tag (str): Data identifier
+            vertices (torch.Tensor): List of the 3D coordinates of vertices.
+            colors (torch.Tensor): Colors for each vertex
+            faces (torch.Tensor): Indices of vertices within each triangle. (Optional)
+            config_dict: Dictionary with ThreeJS classes names and configuration.
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+
+        Shape:
+            vertices: :math:`(B, N, 3)`. (batch, number_of_vertices, channels)
+
+            colors: :math:`(B, N, 3)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.
+
+            faces: :math:`(B, N, 3)`. The values should lie in [0, number_of_vertices] for type `uint8`.
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            vertices_tensor = torch.as_tensor([
+                [1, 1, 1],
+                [-1, -1, 1],
+                [1, -1, -1],
+                [-1, 1, -1],
+            ], dtype=torch.float).unsqueeze(0)
+            colors_tensor = torch.as_tensor([
+                [255, 0, 0],
+                [0, 255, 0],
+                [0, 0, 255],
+                [255, 0, 255],
+            ], dtype=torch.int).unsqueeze(0)
+            faces_tensor = torch.as_tensor([
+                [0, 2, 3],
+                [0, 3, 1],
+                [0, 1, 2],
+                [1, 3, 2],
+            ], dtype=torch.int).unsqueeze(0)
+
+            writer = SummaryWriter()
+            writer.add_mesh('my_mesh', vertices=vertices_tensor, colors=colors_tensor, faces=faces_tensor)
+
+            writer.close()
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_mesh")
+        self._get_file_writer().add_summary(
+            mesh(tag, vertices, colors, faces, config_dict), global_step, walltime
+        )
+
+    def flush(self):
+        """Flushes the event file to disk.
+
+        Call this method to make sure that all pending events have been written to
+        disk.
+        """
+        if self.all_writers is None:
+            return
+        for writer in self.all_writers.values():
+            writer.flush()
+
+    def close(self):
+        if self.all_writers is None:
+            return  # ignore double close
+        for writer in self.all_writers.values():
+            writer.flush()
+            writer.close()
+        self.file_writer = self.all_writers = None
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/throughput_benchmark.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/throughput_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..2778b37b5a78dfc51d0b6211d7eff320678ee752
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/throughput_benchmark.py
@@ -0,0 +1,160 @@
+# mypy: allow-untyped-defs
+
+import torch._C
+
+
+def format_time(time_us=None, time_ms=None, time_s=None):
+    """Define time formatting."""
+    assert sum([time_us is not None, time_ms is not None, time_s is not None]) == 1
+
+    US_IN_SECOND = 1e6
+    US_IN_MS = 1e3
+
+    if time_us is None:
+        if time_ms is not None:
+            time_us = time_ms * US_IN_MS
+        elif time_s is not None:
+            time_us = time_s * US_IN_SECOND
+        else:
+            raise AssertionError("Shouldn't reach here :)")
+
+    if time_us >= US_IN_SECOND:
+        return f'{time_us / US_IN_SECOND:.3f}s'
+    if time_us >= US_IN_MS:
+        return f'{time_us / US_IN_MS:.3f}ms'
+    return f'{time_us:.3f}us'
+
+
+class ExecutionStats:
+    def __init__(self, c_stats, benchmark_config):
+        self._c_stats = c_stats
+        self.benchmark_config = benchmark_config
+
+    @property
+    def latency_avg_ms(self):
+        return self._c_stats.latency_avg_ms
+
+    @property
+    def num_iters(self):
+        return self._c_stats.num_iters
+
+    @property
+    def iters_per_second(self):
+        """Return total number of iterations per second across all calling threads."""
+        return self.num_iters / self.total_time_seconds
+
+    @property
+    def total_time_seconds(self):
+        return self.num_iters * (
+            self.latency_avg_ms / 1000.0) / self.benchmark_config.num_calling_threads
+
+    def __str__(self):
+        return '\n'.join([
+            "Average latency per example: " + format_time(time_ms=self.latency_avg_ms),
+            f"Total number of iterations: {self.num_iters}",
+            f"Total number of iterations per second (across all threads): {self.iters_per_second:.2f}",
+            "Total time: " + format_time(time_s=self.total_time_seconds)
+        ])
+
+
+class ThroughputBenchmark:
+    """
+    This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
+
+    This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
+    for executing a PyTorch module (nn.Module or ScriptModule) under an inference
+    server like load. It can emulate multiple calling threads to a single module
+    provided. In the future we plan to enhance this component to support inter and
+    intra-op parallelism as well as multiple models running in a single process.
+
+    Please note that even though nn.Module is supported, it might incur an overhead
+    from the need to hold GIL every time we execute Python code or pass around
+    inputs as Python objects. As soon as you have a ScriptModule version of your
+    model for inference deployment it is better to switch to using it in this
+    benchmark.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined vars")
+        >>> from torch.utils import ThroughputBenchmark
+        >>> bench = ThroughputBenchmark(my_module)
+        >>> # Pre-populate benchmark's data set with the inputs
+        >>> for input in inputs:
+        ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
+        ...     bench.add_input(input[0], x2=input[1])
+        >>> # Inputs supplied above are randomly used during the execution
+        >>> stats = bench.benchmark(
+        ...     num_calling_threads=4,
+        ...     num_warmup_iters = 100,
+        ...     num_iters = 1000,
+        ... )
+        >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
+        >>> print("Number of iterations: {}".format(stats.num_iters))
+    """
+
+    def __init__(self, module):
+        if isinstance(module, torch.jit.ScriptModule):
+            self._benchmark = torch._C.ThroughputBenchmark(module._c)
+        else:
+            self._benchmark = torch._C.ThroughputBenchmark(module)
+
+    def run_once(self, *args, **kwargs):
+        """
+        Given input id (input_idx) run benchmark once and return prediction.
+
+        This is useful for testing that benchmark actually runs the module you
+        want it to run. input_idx here is an index into inputs array populated
+        by calling add_input() method.
+        """
+        return self._benchmark.run_once(*args, **kwargs)
+
+    def add_input(self, *args, **kwargs):
+        """
+        Store a single input to a module into the benchmark memory and keep it there.
+
+        During the benchmark execution every thread is going to pick up a
+        random input from the all the inputs ever supplied to the benchmark via
+        this function.
+        """
+        self._benchmark.add_input(*args, **kwargs)
+
+    def benchmark(
+            self,
+            num_calling_threads=1,
+            num_warmup_iters=10,
+            num_iters=100,
+            profiler_output_path=""):
+        """
+        Run a benchmark on the module.
+
+        Args:
+            num_warmup_iters (int): Warmup iters are used to make sure we run a module
+                a few times before actually measuring things. This way we avoid cold
+                caches and any other similar problems. This is the number of warmup
+                iterations for each of the thread in separate
+
+            num_iters (int): Number of iterations the benchmark should run with.
+                This number is separate from the warmup iterations. Also the number is
+                shared across all the threads. Once the num_iters iterations across all
+                the threads is reached, we will stop execution. Though total number of
+                iterations might be slightly larger. Which is reported as
+                stats.num_iters where stats is the result of this function
+
+            profiler_output_path (str): Location to save Autograd Profiler trace.
+                If not empty, Autograd Profiler will be enabled for the main benchmark
+                execution (but not the warmup phase). The full trace will be saved
+                into the file path provided by this argument
+
+
+        This function returns BenchmarkExecutionStats object which is defined via pybind11.
+        It currently has two fields:
+            - num_iters - number of actual iterations the benchmark have made
+            - avg_latency_ms - average time it took to infer on one input example in milliseconds
+        """
+        config = torch._C.BenchmarkConfig()
+        config.num_calling_threads = num_calling_threads
+        config.num_warmup_iters = num_warmup_iters
+        config.num_iters = num_iters
+        config.profiler_output_path = profiler_output_path
+        c_stats = self._benchmark.benchmark(config)
+        return ExecutionStats(c_stats, config)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/viz/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/viz/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/viz/_cycles.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/viz/_cycles.py
new file mode 100644
index 0000000000000000000000000000000000000000..455810310817346fc747ed3488a7284695880c83
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/viz/_cycles.py
@@ -0,0 +1,499 @@
+# mypy: allow-untyped-defs
+import gc
+import sys
+from typing import Any, NamedTuple, Optional
+import types
+import weakref
+import json
+from tempfile import NamedTemporaryFile
+import torch
+from torch.cuda._memory_viz import _frames_fmt, _block_extra
+import atexit
+import logging
+logger = logging.getLogger(__name__)
+
+def observe_garbage(observer):
+    enabled = True
+
+    def disable():
+        # when GC runs during exit, things like `sys` will already be unloaded
+        # so we have to disable the callback to avoid hitting errors.
+        nonlocal enabled
+        enabled = False
+    atexit.register(disable)
+
+    def gc_callback(phase, info):
+        nonlocal enabled
+        if not enabled:
+            return
+        if phase == "start":
+            gc.set_debug(gc.DEBUG_SAVEALL)
+        elif phase == "stop":
+            orig_trace = sys.getprofile()
+            self_return = [False]
+
+            def do_collect(*args, **kwargs):
+                nonlocal enabled
+                if not self_return[0]:
+                    self_return[0] = True
+                else:
+                    sys.setprofile(orig_trace)
+                    enabled = False
+                    try:
+                        # things in gc.garbage have survived a collection
+                        # so to free them we have to collect a generation greater than them
+                        # but that might _also_ free other stuff and we don't want to miss
+                        # that stuff. So we have to now force gc at the highest level here,
+                        # report all of what we found, _then_ we can free it up.
+                        if info['generation'] != 2:
+                            gc.collect()
+                        observer(gc.garbage)
+                        gc.garbage.clear()
+                        # we have to re-run GC to clean up the cycles
+                        # we saved from before.
+                        gc.set_debug(0)
+                        before = torch.cuda.memory_allocated()
+                        gc.collect()
+                        after = torch.cuda.memory_allocated()
+                        if before != after:
+                            logger.warning("CUDA Memory changed during GC, %d bytes freed.", before - after)
+                    finally:
+                        enabled = True
+                if orig_trace is not None:
+                    return orig_trace(*args, **kwargs)
+            sys.setprofile(do_collect)
+
+    gc.callbacks.append(gc_callback)
+
+    # provide a way to disarm the callback
+    def remove():
+        gc.callbacks.remove(gc_callback)
+    return remove
+
+# Function to visualize cycles adapated from refcycle:
+# Copyright 2013 Mark Dickinson
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+def _get_cell_type():
+    def f(x=None):
+        return lambda: x
+    return type(f().__closure__[0])
+
+CellType = _get_cell_type()
+
+def annotated_references(obj):
+    """
+    Return known information about references held by the given object.
+
+    Returns a mapping from referents to lists of descriptions.  Note that there
+    may be more than one edge leading to any particular referent; hence the
+    need for a list.  Descriptions are currently strings.
+
+    """
+    references: dict[int, list[str]] = {}
+
+    def add_reference(name, obj):
+        references.setdefault(id(obj), []).append(name)
+
+    def add_attrs(*attrs):
+        for attr in attrs:
+            if hasattr(obj, attr):
+                add_reference(attr, getattr(obj, attr))
+
+    def add_cell_references():
+        try:
+            add_attrs("cell_contents")
+        except ValueError:
+            # if cell_contents is empty,
+            # accessing it raises ValueError
+            # in this case there is no object to
+            # annotate
+            pass
+
+    def add_function_references():
+        add_attrs("__defaults__",
+                  "__closure__",
+                  "__globals__",
+                  "__code__",
+                  "__name__",
+                  "__module__",
+                  "__doc__"
+                  "__qualname__",
+                  "__annotations__",
+                  "__kwdefaults__")
+
+
+    def add_sequence_references():
+        for position, item in enumerate(obj):
+            add_reference(f"[{position}]", item)
+
+    def add_dict_references():
+        for key, value in obj.items():
+            add_reference("key", key)
+            add_reference(f"[{repr(key)}]", value)
+
+    def add_set_references():
+        for elt in obj:
+            add_reference("element", elt)
+
+    def add_bound_method_references():
+        add_attrs("__self__", "__func__", "im_class")
+
+    def add_weakref_references():
+        # For subclasses of weakref, we can't reliably distinguish the
+        # callback (if any) from other attributes.
+        if type(obj) is weakref.ref:
+            referents = gc.get_referents(obj)
+            if len(referents) == 1:
+                target = referents[0]
+                add_reference("__callback__", target)
+
+
+    def add_frame_references():
+        f_locals = obj.f_locals
+        add_attrs("f_back", "f_code", "f_builtins", "f_globals", "f_trace", "f_locals")
+        # Some badly-behaved code replaces the f_locals dict with
+        # something that doesn't support the full dict interface.  So we
+        # only continue with the annotation if f_locals is a Python dict.
+        if type(f_locals) is dict:
+            for name, local in obj.f_locals.items():
+                add_reference(f"local {name}", local)
+
+    def add_getset_descriptor_references():
+        add_attrs("__objclass__", "__name__", "__doc__")
+
+    type_based_references = {
+        tuple: add_sequence_references,
+        list: add_sequence_references,
+        dict: add_dict_references,
+        set: add_set_references,
+        frozenset: add_set_references,
+        types.FunctionType: add_function_references,
+        types.FrameType: add_frame_references,
+        CellType: add_cell_references,
+        types.MethodType: add_bound_method_references,
+        weakref.ref: add_weakref_references,
+        types.GetSetDescriptorType: add_getset_descriptor_references,
+    }
+
+    for type_ in type(obj).__mro__:
+        if type_ in type_based_references:
+            type_based_references[type_]()
+
+    add_attrs("__dict__", "__class__")
+    if isinstance(obj, type):
+        add_attrs("__mro__")
+
+    return references
+
+###############################################################################
+# Object annotations.
+
+
+BASE_TYPES = (int, float, complex, type(None), str, bytes)
+FRAME_FILENAME_LIMIT = 32
+
+def object_annotation(obj):
+    """
+    Return a string to be used for Graphviz nodes.
+
+    The string should be short but as informative as possible.
+    """
+
+    def format_sequence(obj):
+        body = ','.join(repr(x) if isinstance(x, BASE_TYPES) else type(x).__name__ for i, x in zip(range(8), obj))
+        if len(obj) > 8:
+            body = f'{body}, ...{len(obj) - 8}'
+        return body
+
+    # For basic types, use the repr.
+    if isinstance(obj, BASE_TYPES):
+        return repr(obj)
+    if type(obj).__name__ == 'function':
+        return f"function\n{obj.__name__}"
+    elif isinstance(obj, types.MethodType):
+        try:
+            func_name = obj.__func__.__qualname__
+        except AttributeError:
+            func_name = ""
+        return f"instancemethod\n{func_name}"
+    elif isinstance(obj, list):
+        return f"[{format_sequence(obj)}]"
+    elif isinstance(obj, tuple):
+        return f"({format_sequence(obj)})"
+    elif isinstance(obj, dict):
+        return f"dict[{len(obj)}]"
+    elif isinstance(obj, types.ModuleType):
+        return f"module\n{obj.__name__}"
+    elif isinstance(obj, type):
+        return f"type\n{obj.__name__}"
+    elif isinstance(obj, weakref.ref):
+        referent = obj()
+        if referent is None:
+            return "weakref (dead referent)"
+        else:
+            return f"weakref to id 0x{id(referent):x}"
+    elif isinstance(obj, types.FrameType):
+        filename = obj.f_code.co_filename
+        if len(filename) > FRAME_FILENAME_LIMIT:
+            filename = "..." + filename[-(FRAME_FILENAME_LIMIT - 3):]
+        return f"frame\n{filename}:{obj.f_lineno}"
+    else:
+        return f"object\n{type(obj).__module__}.{type(obj).__name__}"
+
+
+
+class Node(NamedTuple):
+    label: str
+    context: Optional[str]
+    root: bool
+    referrents: list[tuple[str, int]]
+
+def create_graph(objects, *, context=None, filter=None):
+    if context is None:
+        context = cuda_allocation_context()
+    if filter is None:
+        filter = is_cuda_tensor
+
+    objects = [obj for obj in objects if not isinstance(obj, weakref.ProxyTypes)]
+    nodes = [Node(object_annotation(obj), context(obj), filter(obj), []) for obj in objects]
+    node_referrers: list[list[int]] = [[] for obj in objects]
+
+    id_to_node = {id(obj): i for i, obj in enumerate(objects)}
+    for obj in objects:
+        fidx = id_to_node[id(obj)]
+        f = nodes[fidx]
+        references = annotated_references(obj)
+        for referrent in gc.get_referents(obj):
+            rid = id(referrent)
+            tidx = id_to_node.get(rid, None)
+            if tidx is None:
+                continue
+            labels = references.get(rid, ["?"])
+            node_referrers[tidx].append(fidx)
+            for label in labels:
+                f.referrents.append((label, tidx))
+
+    to_search = [i for i, n in enumerate(nodes) if n.root]
+    to_keep = set()
+    while to_search:
+        idx = to_search.pop()
+        if idx in to_keep:
+            continue
+        to_keep.add(idx)
+        referrers = node_referrers[idx]
+        to_search.extend(referrers)
+    id_to_filtered_id: dict[int, int] = {}
+    filtered: list[Any] = []
+    for i, n in enumerate(nodes):
+        if i in to_keep:
+            id_to_filtered_id[i] = len(id_to_filtered_id)
+            filtered.append(n)
+    for n in filtered:
+        n.referrents[:] = [(label, id_to_filtered_id[idx])
+                           for (label, idx) in n.referrents
+                           if idx in id_to_filtered_id]
+    return filtered
+
+def escape(n):
+    return json.dumps(n)
+
+
+def is_cuda_tensor(obj):
+    return isinstance(obj, torch.Tensor) and obj.is_cuda and not isinstance(obj, torch._subclasses.FakeTensor)
+
+def cuda_allocation_context():
+    snapshot = torch.cuda.memory._snapshot()
+    addr_to_frame = {}
+    for seg in snapshot['segments']:
+        addr = seg['address']
+        for blk in seg['blocks']:
+            if blk['state'] == 'active_allocated':
+                frames, _real_size = _block_extra(blk)
+                addr_to_frame[addr] = frames
+            addr += blk['size']
+
+    def object_context(obj):
+        if is_cuda_tensor(obj):
+            addr = obj.untyped_storage().data_ptr()
+            frames = addr_to_frame.get(addr)
+            if frames is not None:
+                return '\n'.join(_frames_fmt(frames, full_filename=True))
+        return None
+    return object_context
+
+def to_dot(nodes):
+    lines = ["digraph GraphName {", "node [shape=rect];", 'rankdir=LR;']
+    for i, n in enumerate(nodes):
+        lines.append(f'{i} [label={escape(n.label)}, color={ "red" if n.root else "black"}];')
+
+    for i, f in enumerate(nodes):
+        for label, j in f.referrents:
+            lines.append(f'{i} -> {j} [label = {escape(label)}]')
+    lines.append("}\n")
+    return '\n'.join(lines)
+
+_template = """
+
+
+
+  
+
+
+  

+    

+    

+    

+      
Mouse over tensor objects to see where they were allocated.

+    

+  

+
+
+
+
+"""
+_listener_template = """
+document.getElementById('node{id}').addEventListener('mouseover', function(event) {{
+  document.getElementById("stacktrace").textContent = {stack}
+}})
+"""
+def to_html(nodes):
+    listeners = []
+    for i, n in enumerate(nodes):
+        if n.context is None:
+            continue
+        s = _listener_template.format(id=str(i + 1), stack=escape(f'{n.label}:\n{n.context}'))
+        listeners.append(s)
+    dot = to_dot(nodes)
+    return _template.replace('$DOT', repr(dot)).replace('$LISTENERS', '\n'.join(listeners))
+
+def observe_tensor_cycles(callback):
+    torch.cuda.memory._record_memory_history(max_entries=100000)
+
+    def observer(garbage):
+        if garbage:
+            if not any(is_cuda_tensor(obj) for obj in garbage):
+                logger.info("No CUDA Tensors found in garbage")
+                return
+            callback(to_html(create_graph(garbage)))
+    return observe_garbage(observer)
+
+
+def warn_tensor_cycles():
+    """
+    Install a warning that reports whenever a cycle that is holding CUDA memory is observed.
+
+    The warning produces an .html file that visualizes the cycle,
+    and links it to the stack frame that allocted the CUDA tensor.
+
+    Reference cycles are freed by the cycle collector rather than being cleaned up
+    when the objects in the cycle first become unreachable. If a cycle points to a tensor,
+    the CUDA memory for that tensor will not be freed until garbage collection runs.
+    Accumulation of CUDA allocations can lead to out of memory errors (OOMs), as well as
+    non-deterministic allocation behavior which is harder to debug.
+    """
+    logger.info("Watching Python reference cycles for CUDA Tensors.")
+
+    def write_and_log(html):
+        with NamedTemporaryFile('w', suffix='.html', delete=False) as f:
+            f.write(html)
+            logger.warning('Reference cycle includes a CUDA Tensor see visualization of cycle %s', f.name)
+    return observe_tensor_cycles(write_and_log)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/weak.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/weak.py
new file mode 100644
index 0000000000000000000000000000000000000000..f729ff06489ffed372b3641f00b35255a5ca6f5d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/utils/weak.py
@@ -0,0 +1,322 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import weakref
+from weakref import ref
+from _weakrefset import _IterationGuard  # type: ignore[attr-defined]
+from collections.abc import MutableMapping, Mapping
+from torch import Tensor
+import collections.abc as _collections_abc
+
+
+WeakRef = ref
+
+
+__all__ = ['TensorWeakRef', 'WeakIdRef', 'WeakIdKeyDictionary', 'WeakTensorKeyDictionary']
+
+
+# This file defines a variant of WeakKeyDictionary that overrides the hashing
+# behavior of the key to use object identity, rather than the builtin
+# __eq__/__hash__ functions.  This is useful for Tensor weak keys, as their
+# __eq__ implementation return a Tensor (elementwise equality), which means
+# you can't use them directly with the WeakKeyDictionary in standard library.
+#
+# Our implementation strategy is to create a wrapper weak key object, which we
+# use as a key in a stock Python dictionary.  This is similar to how weakref
+# implements WeakKeyDictionary, but instead of using weakref.ref as the
+# wrapper, we use a custom wrapper that has different __eq__ and __hash__
+# behavior.  Note that we subsequently store this weak key directly in an
+# ORDINARY dictionary, since the newly constructed WeakIdKey's only use would
+# be a dictionary so it would have no strong references.  Ensuring that
+# only live WeakIdKeys are in the map is handled by putting finalizers on the
+# original key object.
+
+
+# It is simpler to implement this with composition, but if we want to
+# directly reuse the callback mechanism on weakref, we need the weakref
+# and the key to be exactly the same object.  Reusing the callback mechanism
+# minimizes the divergence between our implementation and Lib/weakref.py
+#
+# NB: Prefer using this when working with weakrefs of Tensors; e.g., do
+# WeakIdRef(tensor) rather than weakref.ref(tensor); it handles a number of
+# easy to get wrong cases transparently for you.
+class WeakIdRef(weakref.ref):
+    __slots__ = ['_id']
+
+    def __init__(self, key, callback=None):
+        # Unlike stock weakref, which preserves hash semantics of the
+        # original object but lazily defers hash calls until the first
+        # time the user attempts to hash the weakref, we can eagerly
+        # cache the id of the key as we know this is definitely the hash
+        # method
+        self._id = id(key)
+        super().__init__(key, callback)  # type: ignore[call-arg]
+
+    def __call__(self):
+        r = super().__call__()
+        # Special logic for Tensor PyObject resurrection
+        if hasattr(r, '_fix_weakref'):
+            r._fix_weakref()  # type: ignore[union-attr]
+        return r
+
+    def __hash__(self):
+        return self._id
+
+    def __eq__(self, other):
+        # An attractive but wrong alternate implementation is to only test if
+        # the stored _ids match.  This can lead to an ABA problem if you have:
+        #
+        #   a1 = A()
+        #   w1 = WeakIdRef(a1)
+        #   del a1
+        #   a2 = A()  # suppose it gets the same ID as a1
+        #   w2 = WeakIdRef(a2)
+        #   print(w1 == w2)
+        #
+        # This should be False, as a1 and a2 are unrelated (and a1 is
+        # dead anyway)
+        a = self()
+        b = other()
+        if a is not None and b is not None:
+            return a is b
+        return self is other
+
+# This is the same as WeakIdRef but equality is checked using hash() rather than id.
+# This will be equivalent to the one above except for classes where hash is not their id.
+class _WeakHashRef(weakref.ref):
+    __slots__ = ['_id']
+
+    def __init__(self, key, callback=None):
+        # Unlike stock weakref, which preserves hash semantics of the
+        # original object but lazily defers hash calls until the first
+        # time the user attempts to hash the weakref, we can eagerly
+        # cache the id of the key as we know this is definitely the hash
+        # method
+        self._id = hash(key)
+        super().__init__(key, callback)  # type: ignore[call-arg]
+
+    def __call__(self):
+        r = super().__call__()
+        # Special logic for Tensor PyObject resurrection
+        if hasattr(r, '_fix_weakref'):
+            r._fix_weakref()  # type: ignore[union-attr]
+        return r
+
+    def __hash__(self):
+        return self._id
+
+    def __eq__(self, other):
+        # Use hash equality to determine ref equality.
+        # ScriptObject implements __hash__ to return the wrapped IValue's id, so
+        # this is equivalent to doing an identity comparison.
+        a = self()
+        b = other()
+        if a is not None and b is not None:
+            return hash(a) == hash(b)
+        return self is other
+
+# This is directly adapted from cpython/Lib/weakref.py
+class WeakIdKeyDictionary(MutableMapping):
+    def __init__(self, dict=None, ref_type=WeakIdRef):  # CHANGED
+        self.data = {}
+
+        self.ref_type = ref_type  # CHANGED
+
+        def remove(k, selfref=ref(self)):
+            self = selfref()
+            if self is not None:
+                if self._iterating:
+                    self._pending_removals.append(k)
+                else:
+                    try:
+                        del self.data[k]
+                    except KeyError:
+                        pass
+        self._remove = remove
+        # A list of dead weakrefs (keys to be removed)
+        self._pending_removals = []
+        self._iterating = set()
+        self._dirty_len = False
+        if dict is not None:
+            self.update(dict)
+
+    def _commit_removals(self):
+        # NOTE: We don't need to call this method before mutating the dict,
+        # because a dead weakref never compares equal to a live weakref,
+        # even if they happened to refer to equal objects.
+        # However, it means keys may already have been removed.
+        pop = self._pending_removals.pop
+        d = self.data
+        while True:
+            try:
+                key = pop()
+            except IndexError:
+                return
+
+            try:
+                del d[key]
+            except KeyError:
+                pass
+
+    def _scrub_removals(self):
+        d = self.data
+        self._pending_removals = [k for k in self._pending_removals if k in d]
+        self._dirty_len = False
+
+    def __delitem__(self, key):
+        self._dirty_len = True
+        del self.data[self.ref_type(key)]  # CHANGED
+
+    def __getitem__(self, key):
+        return self.data[self.ref_type(key)]  # CHANGED
+
+    def __len__(self):
+        if self._dirty_len and self._pending_removals:
+            # self._pending_removals may still contain keys which were
+            # explicitly removed, we have to scrub them (see issue #21173).
+            self._scrub_removals()
+        return len(self.data) - len(self._pending_removals)
+
+    def __repr__(self):
+        return f"<{self.__class__.__name__} at {id(self):#x}>"
+
+    def __setitem__(self, key, value):
+        self.data[self.ref_type(key, self._remove)] = value  # CHANGED
+
+    def copy(self):
+        new = WeakIdKeyDictionary()
+        with _IterationGuard(self):
+            for key, value in self.data.items():
+                o = key()
+                if o is not None:
+                    new[o] = value
+        return new
+
+    __copy__ = copy
+
+    def __deepcopy__(self, memo):
+        from copy import deepcopy
+        new = self.__class__()
+        with _IterationGuard(self):
+            for key, value in self.data.items():
+                o = key()
+                if o is not None:
+                    new[o] = deepcopy(value, memo)
+        return new
+
+    def get(self, key, default=None):
+        return self.data.get(self.ref_type(key), default)  # CHANGED
+
+    def __contains__(self, key):
+        try:
+            wr = self.ref_type(key)  # CHANGED
+        except TypeError:
+            return False
+        return wr in self.data
+
+    def items(self):
+        with _IterationGuard(self):
+            for wr, value in self.data.items():
+                key = wr()
+                if key is not None:
+                    yield key, value
+
+    def keys(self):
+        with _IterationGuard(self):
+            for wr in self.data:
+                obj = wr()
+                if obj is not None:
+                    yield obj
+
+    __iter__ = keys
+
+    def values(self):
+        with _IterationGuard(self):
+            for wr, value in self.data.items():
+                if wr() is not None:
+                    yield value
+
+    def keyrefs(self):
+        """Return a list of weak references to the keys.
+
+        The references are not guaranteed to be 'live' at the time
+        they are used, so the result of calling the references needs
+        to be checked before being used.  This can be used to avoid
+        creating references that will cause the garbage collector to
+        keep the keys around longer than needed.
+
+        """
+        return list(self.data)
+
+    def popitem(self):
+        self._dirty_len = True
+        while True:
+            key, value = self.data.popitem()
+            o = key()
+            if o is not None:
+                return o, value
+
+    def pop(self, key, *args):
+        self._dirty_len = True
+        return self.data.pop(self.ref_type(key), *args)  # CHANGED
+
+    def setdefault(self, key, default=None):
+        return self.data.setdefault(self.ref_type(key, self._remove), default)  # CHANGED
+
+    def update(self, dict=None, **kwargs):  # type: ignore[override]
+        d = self.data
+        if dict is not None:
+            if not hasattr(dict, "items"):
+                dict = type({})(dict)
+            for key, value in dict.items():
+                d[self.ref_type(key, self._remove)] = value  # CHANGED
+        if len(kwargs):
+            self.update(kwargs)
+
+    def __ior__(self, other):
+        self.update(other)
+        return self
+
+    def __or__(self, other):
+        if isinstance(other, _collections_abc.Mapping):
+            c = self.copy()
+            c.update(other)
+            return c
+        return NotImplemented
+
+    def __ror__(self, other):
+        if isinstance(other, _collections_abc.Mapping):
+            c = self.__class__()
+            c.update(other)
+            c.update(self)
+            return c
+        return NotImplemented
+
+    # Default Mapping equality will tests keys for equality, but
+    # we want to test ids for equality
+    def __eq__(self, other):
+        if not isinstance(other, Mapping):
+            return NotImplemented
+        return {id(k): v for k, v in self.items()} == {id(k): v for k, v in other.items()}
+
+# Convenience alias
+WeakTensorKeyDictionary = WeakIdKeyDictionary
+
+
+class TensorWeakRef:
+    """Wrapper around a weak ref of a Tensor that handles the _fix_weakref() call required when unwrapping a Tensor weakref."""
+
+    ref: WeakRef[Tensor]
+
+    def __init__(self, tensor: Tensor):
+        assert isinstance(tensor, Tensor)
+        self.ref = weakref.ref(tensor)
+
+    def __call__(self):
+        out = self.ref()
+        if out is None:
+            return out
+        assert isinstance(out, Tensor)
+        # TODO, add _fix_weakref type binding
+        out._fix_weakref()  # type: ignore[attr-defined]
+        return out
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/xpu/streams.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/xpu/streams.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd381cf8341987876afb3722ef1c2ce42528d3ec
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/torch/xpu/streams.py
@@ -0,0 +1,173 @@
+# mypy: allow-untyped-defs
+import ctypes
+
+import torch
+from torch._utils import _dummy_type
+
+
+if not hasattr(torch._C, "_XpuStreamBase"):
+    # Define dummy base classes
+    torch._C.__dict__["_XpuStreamBase"] = _dummy_type("_XpuStreamBase")
+    torch._C.__dict__["_XpuEventBase"] = _dummy_type("_XpuEventBase")
+
+
+class Stream(torch._C._XpuStreamBase):
+    r"""Wrapper around a XPU stream.
+
+    A XPU stream is a linear sequence of execution that belongs to a specific
+    device, independent from other streams. It supports with statement as a
+    context manager to ensure the operators within the with block are running
+    on the corresponding stream.
+
+    Args:
+        device(torch.device or int, optional): a device on which to allocate
+            the stream. If :attr:`device` is ``None`` (default) or a negative
+            integer, this will use the current device.
+        priority(int, optional): priority of the stream, which can be positive, 0, or negative.
+            A lower number indicates a higher priority. By default, the priority is set to 0.
+            If the value falls outside of the allowed priority range, it will automatically be
+            mapped to the nearest valid priority (lowest for large positive numbers or
+            highest for large negative numbers).
+    """
+
+    def __new__(cls, device=None, priority=0, **kwargs):
+        # setting device manager is expensive, so we avoid it unless necessary
+        if device is None or ("stream_id" in kwargs and "device_index" in kwargs):
+            return super().__new__(cls, priority=priority, **kwargs)
+        else:
+            with torch.xpu.device(device):
+                return super().__new__(cls, priority=priority, **kwargs)
+
+    def wait_event(self, event) -> None:
+        r"""Make all future work submitted to the stream wait for an event.
+
+        Args:
+            event (torch.xpu.Event): an event to wait for.
+        """
+        event.wait(self)
+
+    def wait_stream(self, stream) -> None:
+        r"""Synchronize with another stream.
+
+        All future work submitted to this stream will wait until all kernels
+        submitted to a given stream at the time of call complete.
+
+        Args:
+            stream (Stream): a stream to synchronize.
+        """
+        self.wait_event(stream.record_event())
+
+    def record_event(self, event=None):
+        r"""Record an event.
+
+        Args:
+            event (torch.xpu.Event, optional): event to record. If not given, a new one
+                will be allocated.
+
+        Returns:
+            Recorded event.
+        """
+        if event is None:
+            event = Event()
+        event.record(self)
+        return event
+
+    def query(self) -> bool:
+        r"""Check if all the work submitted has been completed.
+
+        Returns:
+            A boolean indicating if all kernels in this stream are completed.
+        """
+        return super().query()
+
+    def synchronize(self) -> None:
+        r"""Wait for all the kernels in this stream to complete."""
+        super().synchronize()
+
+    @property
+    def _as_parameter_(self):
+        return ctypes.c_void_p(self.sycl_queue)
+
+    def __eq__(self, o):
+        if isinstance(o, Stream):
+            return super().__eq__(o)
+        return False
+
+    def __hash__(self):
+        return hash((self.sycl_queue, self.device))
+
+    def __repr__(self):
+        return f"torch.xpu.Stream(device={self.device} sycl_queue={self.sycl_queue:#x})"
+
+
+class Event(torch._C._XpuEventBase):
+    r"""Wrapper around a XPU event.
+
+    XPU events are synchronization markers that can be used to monitor the
+    device's progress, and to synchronize XPU streams.
+
+    The underlying XPU events are lazily initialized when the event is first
+    recorded. After creation, only streams on the same device may record the
+    event. However, streams on any device can wait on the event.
+
+    Args:
+        enable_timing (bool, optional): indicates if the event should measure time
+            (default: ``False``)
+    """
+
+    def __new__(cls, enable_timing=False):
+        return super().__new__(cls, enable_timing=enable_timing)
+
+    def record(self, stream=None) -> None:
+        r"""Record the event in a given stream.
+
+        Uses ``torch.xpu.current_stream()`` if no stream is specified. The
+        stream's device must match the event's device.
+        """
+        if stream is None:
+            stream = torch.xpu.current_stream()
+        super().record(stream)
+
+    def wait(self, stream=None) -> None:
+        r"""Make all future work submitted to the given stream wait for this event.
+
+        Use ``torch.xpu.current_stream()`` if no stream is specified.
+        """
+        if stream is None:
+            stream = torch.xpu.current_stream()
+        super().wait(stream)
+
+    def query(self) -> bool:
+        r"""Check if all work currently captured by event has completed.
+
+        Returns:
+            A boolean indicating if all work currently captured by event has
+            completed.
+        """
+        return super().query()
+
+    def elapsed_time(self, end_event):
+        r"""Return the time elapsed.
+
+        Time reported in milliseconds after the event was recorded and
+        before the end_event was recorded.
+        """
+        return super().elapsed_time(end_event)
+
+    def synchronize(self) -> None:
+        r"""Wait for the event to complete.
+
+        Waits until the completion of all work currently captured in this event.
+        This prevents the CPU thread from proceeding until the event completes.
+        """
+        super().synchronize()
+
+    @property
+    def _as_parameter_(self):
+        return ctypes.c_void_p(self.sycl_event)
+
+    def __repr__(self):
+        if self.sycl_event:
+            return f"torch.xpu.Event(sycl_event={self.sycl_event:#x})"
+        else:
+            return "torch.xpu.Event(uninitialized)"